diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md index a7f4f0a902..aa15123d8e 100644 --- a/.github/ISSUE_TEMPLATE/bug_report.md +++ b/.github/ISSUE_TEMPLATE/bug_report.md @@ -19,7 +19,7 @@ Steps to reproduce the behavior (**always include the command you ran**): #### Code sample - ### Expected behavior @@ -28,7 +28,7 @@ Minimal means having the shortest code but still preserving the bug. --> ### Environment - - fairseq Version (e.g., 1.0 or master): + - fairseq Version (e.g., 1.0 or main): - PyTorch Version (e.g., 1.0) - OS (e.g., Linux): - How you installed fairseq (`pip`, source): diff --git a/.github/ISSUE_TEMPLATE/how-to-question.md b/.github/ISSUE_TEMPLATE/how-to-question.md index 4beb180dbf..04f3f15d3e 100644 --- a/.github/ISSUE_TEMPLATE/how-to-question.md +++ b/.github/ISSUE_TEMPLATE/how-to-question.md @@ -6,9 +6,9 @@ labels: 'question, needs triage' ## ❓ Questions and Help -### Before asking: -1. search the issues. -2. search the docs. +### Before asking: +1. search the issues. +2. search the docs. @@ -16,13 +16,13 @@ labels: 'question, needs triage' #### Code - + #### What have you tried? #### What's your environment? - - fairseq Version (e.g., 1.0 or master): + - fairseq Version (e.g., 1.0 or main): - PyTorch Version (e.g., 1.0) - OS (e.g., Linux): - How you installed fairseq (`pip`, source): diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md index b28ff98e7b..d005e2df4f 100644 --- a/.github/PULL_REQUEST_TEMPLATE.md +++ b/.github/PULL_REQUEST_TEMPLATE.md @@ -1,15 +1,15 @@ # Before submitting - [ ] Was this discussed/approved via a Github issue? (no need for typos, doc improvements) -- [ ] Did you read the [contributor guideline](https://github.com/pytorch/fairseq/blob/master/CONTRIBUTING.md)? -- [ ] Did you make sure to update the docs? -- [ ] Did you write any new necessary tests? +- [ ] Did you read the [contributor guideline](https://github.com/pytorch/fairseq/blob/main/CONTRIBUTING.md)? +- [ ] Did you make sure to update the docs? +- [ ] Did you write any new necessary tests? ## What does this PR do? Fixes # (issue). -## PR review -Anyone in the community is free to review the PR once the tests have passed. +## PR review +Anyone in the community is free to review the PR once the tests have passed. If we didn't discuss your PR in Github issues there's a high chance it will not be merged. ## Did you have fun? diff --git a/.github/stale.yml b/.github/stale.yml new file mode 100644 index 0000000000..b12867dab0 --- /dev/null +++ b/.github/stale.yml @@ -0,0 +1,30 @@ +# Configuration for probot-stale - https://github.com/probot/stale +# Mostly copied from github.com/facebook/react/blob/master/.github/stale.yml +# Number of days of inactivity before an issue becomes stale +daysUntilStale: 90 +# Number of days of inactivity before a stale issue is closed +daysUntilClose: 7 +# Issues with these labels will never be considered stale +exemptLabels: + - bug +# Label to use when marking an issue as stale +staleLabel: stale +issues: + # Comment to post when marking an issue as stale. + markComment: > + This issue has been automatically marked as stale. + **If this issue is still affecting you, please leave any comment** (for example, "bump"), and we'll keep it open. + We are sorry that we haven't been able to prioritize it yet. If you have any new additional information, please include it with your comment! + # Comment to post when closing a stale issue. + closeComment: > + Closing this issue after a prolonged period of inactivity. If this issue is still present in the latest release, please create a new issue with up-to-date information. Thank you! +pulls: + # Comment to post when marking a pull request as stale. + markComment: > + This pull request has been automatically marked as stale. + **If this pull request is still relevant, please leave any comment** (for example, "bump"), and we'll keep it open. + We are sorry that we haven't been able to prioritize reviewing it yet. Your contribution is very much appreciated. + # Comment to post when closing a stale pull request. + closeComment: > + Closing this pull request after a prolonged period of inactivity. If this issue is still present in the latest release, please ask for this pull request to be reopened. Thank you! + diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 6ae8093a8a..f493f91f0d 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -1,10 +1,10 @@ name: build on: - # Trigger the workflow on push to master or any pull request + # Trigger the workflow on push to main or any pull request push: branches: - - master + - main pull_request: jobs: @@ -19,26 +19,37 @@ jobs: runs-on: ${{ matrix.platform }} steps: - - uses: actions/checkout@v1 + - uses: actions/checkout@v2 + - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v1 + uses: actions/setup-python@v2 with: python-version: ${{ matrix.python-version }} + - name: Conditionally install pytorch if: matrix.platform == 'windows-latest' run: pip3 install torch -f https://download.pytorch.org/whl/torch_stable.html + - name: Install locally run: | python -m pip install --upgrade pip + git submodule update --init --recursive python setup.py build_ext --inplace python -m pip install --editable . + + - name: Install optional test requirements + run: | + python -m pip install iopath transformers pyarrow + python -m pip install git+https://github.com/facebookresearch/fairscale.git@master + - name: Lint with flake8 run: | pip install flake8 # stop the build if there are Python syntax errors or undefined names - flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics + flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics --extend-exclude fairseq/model_parallel/megatron # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide - flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics + flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics --extend-exclude fairseq/model_parallel/megatron + - name: Run tests run: | python setup.py test diff --git a/.github/workflows/build_wheels.yml b/.github/workflows/build_wheels.yml new file mode 100644 index 0000000000..7261708596 --- /dev/null +++ b/.github/workflows/build_wheels.yml @@ -0,0 +1,41 @@ +name: build_wheels + +on: + push: + branches: + - v[0-9]+.[0-9]+.[x0-9]+ + tags: + - v* + +jobs: + build_wheels: + name: Build wheels on ${{ matrix.os }} + runs-on: ${{ matrix.os }} + strategy: + matrix: + os: [ubuntu-latest, macos-latest] + + steps: + - uses: actions/checkout@v2 + + - name: Install Python + uses: actions/setup-python@v2 + with: + python-version: '3.7' + + - name: Install cibuildwheel + run: | + python -m pip install cibuildwheel + + - name: Build wheels for CPython + run: | + python -m cibuildwheel --output-dir dist + env: + CIBW_BUILD: "cp36-*64 cp37-*64 cp38-*64" + CIBW_MANYLINUX_X86_64_IMAGE: manylinux1 + CIBW_BEFORE_BUILD: git submodule update --init --recursive && pip install . + + - uses: actions/upload-artifact@v2 + with: + name: wheels + path: ./dist/*.whl diff --git a/.github/workflows/build_windows.yml b/.github/workflows/build_windows.yml deleted file mode 100644 index 3161fd09c7..0000000000 --- a/.github/workflows/build_windows.yml +++ /dev/null @@ -1,44 +0,0 @@ -name: build_windows - -on: - # Trigger the workflow on push to master or any pull request - push: - branches: - - master - pull_request: - -jobs: - build: - - strategy: - max-parallel: 4 - matrix: - platform: [windows-latest] - python-version: [3.6, 3.7] - - runs-on: ${{ matrix.platform }} - - steps: - - uses: actions/checkout@v1 - - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v1 - with: - python-version: ${{ matrix.python-version }} - - name: Conditionally install pytorch - if: matrix.platform == 'windows-latest' - run: pip3 install torch -f https://download.pytorch.org/whl/torch_stable.html - - name: Install locally - run: | - python -m pip install --upgrade pip - python setup.py build_ext --inplace - python -m pip install --editable . - - name: Lint with flake8 - run: | - pip install flake8 - # stop the build if there are Python syntax errors or undefined names - flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics - # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide - flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics - - name: Run tests - run: | - python setup.py test diff --git a/.gitignore b/.gitignore index a7c4577149..4112804793 100644 --- a/.gitignore +++ b/.gitignore @@ -113,6 +113,7 @@ ENV/ /fairseq/temporal_convolution_tbc /fairseq/modules/*_layer/*_forward.cu /fairseq/modules/*_layer/*_backward.cu +/fairseq/version.py # data data-bin/ @@ -130,3 +131,6 @@ data-bin/ # Experimental Folder experimental/* + +# Weights and Biases logs +wandb/ diff --git a/.gitmodules b/.gitmodules index df0d3d3071..07a55d45d4 100644 --- a/.gitmodules +++ b/.gitmodules @@ -1,7 +1,3 @@ -[submodule "fairseq/models/huggingface/transformers"] - path = fairseq/models/huggingface/transformers - url = https://github.com/myleott/transformers.git - branch = fairseq [submodule "fairseq/model_parallel/megatron"] path = fairseq/model_parallel/megatron url = https://github.com/ngoyal2707/Megatron-LM diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 4d7ca6a98e..3930c46196 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -5,7 +5,7 @@ possible. ## Pull Requests We actively welcome your pull requests. -1. Fork the repo and create your branch from `master`. +1. Fork the repo and create your branch from `main`. 2. If you've added code that should be tested, add tests. 3. If you've changed APIs, update the documentation. 4. Ensure the test suite passes. diff --git a/README.md b/README.md index 8542791c2f..dd68717480 100644 --- a/README.md +++ b/README.md @@ -1,8 +1,8 @@

- +

- MIT License + MIT License Latest Release Build Status Documentation Status @@ -14,114 +14,159 @@ Fairseq(-py) is a sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language modeling and other text generation tasks. -### What's New: +We provide reference implementations of various sequence modeling papers: + +

List of implemented papers

+ +* **Convolutional Neural Networks (CNN)** + + [Language Modeling with Gated Convolutional Networks (Dauphin et al., 2017)](examples/language_model/conv_lm/README.md) + + [Convolutional Sequence to Sequence Learning (Gehring et al., 2017)](examples/conv_seq2seq/README.md) + + [Classical Structured Prediction Losses for Sequence to Sequence Learning (Edunov et al., 2018)](https://github.com/pytorch/fairseq/tree/classic_seqlevel) + + [Hierarchical Neural Story Generation (Fan et al., 2018)](examples/stories/README.md) + + [wav2vec: Unsupervised Pre-training for Speech Recognition (Schneider et al., 2019)](examples/wav2vec/README.md) +* **LightConv and DynamicConv models** + + [Pay Less Attention with Lightweight and Dynamic Convolutions (Wu et al., 2019)](examples/pay_less_attention_paper/README.md) +* **Long Short-Term Memory (LSTM) networks** + + Effective Approaches to Attention-based Neural Machine Translation (Luong et al., 2015) +* **Transformer (self-attention) networks** + + Attention Is All You Need (Vaswani et al., 2017) + + [Scaling Neural Machine Translation (Ott et al., 2018)](examples/scaling_nmt/README.md) + + [Understanding Back-Translation at Scale (Edunov et al., 2018)](examples/backtranslation/README.md) + + [Adaptive Input Representations for Neural Language Modeling (Baevski and Auli, 2018)](examples/language_model/README.adaptive_inputs.md) + + [Lexically constrained decoding with dynamic beam allocation (Post & Vilar, 2018)](examples/constrained_decoding/README.md) + + [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context (Dai et al., 2019)](examples/truncated_bptt/README.md) + + [Adaptive Attention Span in Transformers (Sukhbaatar et al., 2019)](examples/adaptive_span/README.md) + + [Mixture Models for Diverse Machine Translation: Tricks of the Trade (Shen et al., 2019)](examples/translation_moe/README.md) + + [RoBERTa: A Robustly Optimized BERT Pretraining Approach (Liu et al., 2019)](examples/roberta/README.md) + + [Facebook FAIR's WMT19 News Translation Task Submission (Ng et al., 2019)](examples/wmt19/README.md) + + [Jointly Learning to Align and Translate with Transformer Models (Garg et al., 2019)](examples/joint_alignment_translation/README.md ) + + [Multilingual Denoising Pre-training for Neural Machine Translation (Liu et at., 2020)](examples/mbart/README.md) + + [Neural Machine Translation with Byte-Level Subwords (Wang et al., 2020)](examples/byte_level_bpe/README.md) + + [Unsupervised Quality Estimation for Neural Machine Translation (Fomicheva et al., 2020)](examples/unsupervised_quality_estimation/README.md) + + [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations (Baevski et al., 2020)](examples/wav2vec/README.md) + + [Generating Medical Reports from Patient-Doctor Conversations Using Sequence-to-Sequence Models (Enarvi et al., 2020)](examples/pointer_generator/README.md) + + [Linformer: Self-Attention with Linear Complexity (Wang et al., 2020)](examples/linformer/README.md) + + [Cross-lingual Retrieval for Iterative Self-Supervised Training (Tran et al., 2020)](examples/criss/README.md) + + [Deep Transformers with Latent Depth (Li et al., 2020)](examples/latent_depth/README.md) + + [Unsupervised Cross-lingual Representation Learning for Speech Recognition (Conneau et al., 2020)](https://arxiv.org/abs/2006.13979) + + [Robust wav2vec 2.0: Analyzing Domain Shift in Self-Supervised Pre-Training (Hsu, et al., 2021)](https://arxiv.org/abs/2104.01027) + + [Unsupervised Speech Recognition (Baevski, et al., 2021)](https://arxiv.org/abs/2105.11084) +* **Non-autoregressive Transformers** + + Non-Autoregressive Neural Machine Translation (Gu et al., 2017) + + Deterministic Non-Autoregressive Neural Sequence Modeling by Iterative Refinement (Lee et al. 2018) + + Insertion Transformer: Flexible Sequence Generation via Insertion Operations (Stern et al. 2019) + + Mask-Predict: Parallel Decoding of Conditional Masked Language Models (Ghazvininejad et al., 2019) + + [Levenshtein Transformer (Gu et al., 2019)](examples/nonautoregressive_translation/README.md) +* **Finetuning** + + [Better Fine-Tuning by Reducing Representational Collapse (Aghajanyan et al. 2020)](examples/rxf/README.md) + +

+### What's New: -- April 2020: [Monotonic Multihead Attention code released](examples/simultaneous_translation/README.md) -- April 2020: [Quant-Noise code released](examples/quant_noise/README.md) -- April 2020: [Initial model parallel support and 11B parameters unidirectional LM released](examples/megatron_11b/README.md) -- March 2020: [Byte-level BPE code released](examples/byte_level_bpe/README.md) -- February 2020: [mBART model and code released](examples/mbart/README.md) -- February 2020: [Added tutorial for back-translation](https://github.com/pytorch/fairseq/tree/master/examples/backtranslation#training-your-own-model-wmt18-english-german) -- December 2019: [fairseq 0.9.0 released](https://github.com/pytorch/fairseq/releases/tag/v0.9.0) -- November 2019: [VizSeq released (a visual analysis toolkit for evaluating fairseq models)](https://facebookresearch.github.io/vizseq/docs/getting_started/fairseq_example) -- November 2019: [CamemBERT model and code released](examples/camembert/README.md) -- November 2019: [BART model and code released](examples/bart/README.md) -- November 2019: [XLM-R models and code released](examples/xlmr/README.md) -- September 2019: [Nonautoregressive translation code released](examples/nonautoregressive_translation/README.md) -- August 2019: [WMT'19 models released](examples/wmt19/README.md) -- July 2019: fairseq relicensed under MIT license -- July 2019: [RoBERTa models and code released](examples/roberta/README.md) -- June 2019: [wav2vec models and code released](examples/wav2vec/README.md) +* September 2021 [`master` branch renamed to `main`](https://github.com/github/renaming). +* July 2021 [Released DrNMT code](examples/discriminative_reranking_nmt/README.md) +* July 2021 [Released Robust wav2vec 2.0 model](examples/wav2vec/README.md) +* June 2021 [Released XLMR-XL and XLMR-XXL models](examples/xlmr/README.md) +* May 2021 [Released Unsupervised Speech Recognition code](examples/wav2vec/unsupervised/README.md) +* March 2021 [Added full parameter and optimizer state sharding + CPU offloading](examples/fully_sharded_data_parallel/README.md) +* February 2021 [Added LASER training code](examples/laser/README.md) +* December 2020: [Added Adaptive Attention Span code](examples/adaptive_span/README.md) +* December 2020: [GottBERT model and code released](examples/gottbert/README.md) +* November 2020: Adopted the [Hydra](https://github.com/facebookresearch/hydra) configuration framework + * [see documentation explaining how to use it for new and existing projects](docs/hydra_integration.md) +* November 2020: [fairseq 0.10.0 released](https://github.com/pytorch/fairseq/releases/tag/v0.10.0) +* October 2020: [Added R3F/R4F (Better Fine-Tuning) code](examples/rxf/README.md) +* October 2020: [Deep Transformer with Latent Depth code released](examples/latent_depth/README.md) +* October 2020: [Added CRISS models and code](examples/criss/README.md) + +
Previous updates

+ +* September 2020: [Added Linformer code](examples/linformer/README.md) +* September 2020: [Added pointer-generator networks](examples/pointer_generator/README.md) +* August 2020: [Added lexically constrained decoding](examples/constrained_decoding/README.md) +* August 2020: [wav2vec2 models and code released](examples/wav2vec/README.md) +* July 2020: [Unsupervised Quality Estimation code released](examples/unsupervised_quality_estimation/README.md) +* May 2020: [Follow fairseq on Twitter](https://twitter.com/fairseq) +* April 2020: [Monotonic Multihead Attention code released](examples/simultaneous_translation/README.md) +* April 2020: [Quant-Noise code released](examples/quant_noise/README.md) +* April 2020: [Initial model parallel support and 11B parameters unidirectional LM released](examples/megatron_11b/README.md) +* March 2020: [Byte-level BPE code released](examples/byte_level_bpe/README.md) +* February 2020: [mBART model and code released](examples/mbart/README.md) +* February 2020: [Added tutorial for back-translation](https://github.com/pytorch/fairseq/tree/main/examples/backtranslation#training-your-own-model-wmt18-english-german) +* December 2019: [fairseq 0.9.0 released](https://github.com/pytorch/fairseq/releases/tag/v0.9.0) +* November 2019: [VizSeq released (a visual analysis toolkit for evaluating fairseq models)](https://facebookresearch.github.io/vizseq/docs/getting_started/fairseq_example) +* November 2019: [CamemBERT model and code released](examples/camembert/README.md) +* November 2019: [BART model and code released](examples/bart/README.md) +* November 2019: [XLM-R models and code released](examples/xlmr/README.md) +* September 2019: [Nonautoregressive translation code released](examples/nonautoregressive_translation/README.md) +* August 2019: [WMT'19 models released](examples/wmt19/README.md) +* July 2019: fairseq relicensed under MIT license +* July 2019: [RoBERTa models and code released](examples/roberta/README.md) +* June 2019: [wav2vec models and code released](examples/wav2vec/README.md) + +

### Features: -Fairseq provides reference implementations of various sequence-to-sequence models, including: -- **Convolutional Neural Networks (CNN)** - - [Language Modeling with Gated Convolutional Networks (Dauphin et al., 2017)](examples/language_model/conv_lm/README.md) - - [Convolutional Sequence to Sequence Learning (Gehring et al., 2017)](examples/conv_seq2seq/README.md) - - [Classical Structured Prediction Losses for Sequence to Sequence Learning (Edunov et al., 2018)](https://github.com/pytorch/fairseq/tree/classic_seqlevel) - - [Hierarchical Neural Story Generation (Fan et al., 2018)](examples/stories/README.md) - - [wav2vec: Unsupervised Pre-training for Speech Recognition (Schneider et al., 2019)](examples/wav2vec/README.md) -- **LightConv and DynamicConv models** - - [Pay Less Attention with Lightweight and Dynamic Convolutions (Wu et al., 2019)](examples/pay_less_attention_paper/README.md) -- **Long Short-Term Memory (LSTM) networks** - - Effective Approaches to Attention-based Neural Machine Translation (Luong et al., 2015) -- **Transformer (self-attention) networks** - - Attention Is All You Need (Vaswani et al., 2017) - - [Scaling Neural Machine Translation (Ott et al., 2018)](examples/scaling_nmt/README.md) - - [Understanding Back-Translation at Scale (Edunov et al., 2018)](examples/backtranslation/README.md) - - [Adaptive Input Representations for Neural Language Modeling (Baevski and Auli, 2018)](examples/language_model/transformer_lm/README.md) - - [Mixture Models for Diverse Machine Translation: Tricks of the Trade (Shen et al., 2019)](examples/translation_moe/README.md) - - [RoBERTa: A Robustly Optimized BERT Pretraining Approach (Liu et al., 2019)](examples/roberta/README.md) - - [Facebook FAIR's WMT19 News Translation Task Submission (Ng et al., 2019)](examples/wmt19/README.md) - - [Jointly Learning to Align and Translate with Transformer Models (Garg et al., 2019)](examples/joint_alignment_translation/README.md ) - - [Multilingual Denoising Pre-training for Neural Machine Translation (Liu et at., 2020)](examples/mbart/README.md) - - [Neural Machine Translation with Byte-Level Subwords (Wang et al., 2020)](examples/byte_level_bpe/README.md) -- **Non-autoregressive Transformers** - - Non-Autoregressive Neural Machine Translation (Gu et al., 2017) - - Deterministic Non-Autoregressive Neural Sequence Modeling by Iterative Refinement (Lee et al. 2018) - - Insertion Transformer: Flexible Sequence Generation via Insertion Operations (Stern et al. 2019) - - Mask-Predict: Parallel Decoding of Conditional Masked Language Models (Ghazvininejad et al., 2019) - - [Levenshtein Transformer (Gu et al., 2019)](examples/nonautoregressive_translation/README.md) - - -**Additionally:** -- multi-GPU (distributed) training on one machine or across multiple machines -- fast generation on both CPU and GPU with multiple search algorithms implemented: - - beam search - - Diverse Beam Search ([Vijayakumar et al., 2016](https://arxiv.org/abs/1610.02424)) - - sampling (unconstrained, top-k and top-p/nucleus) -- large mini-batch training even on a single GPU via delayed updates -- mixed precision training (trains faster with less GPU memory on [NVIDIA tensor cores](https://developer.nvidia.com/tensor-cores)) -- extensible: easily register new models, criterions, tasks, optimizers and learning rate schedulers +* multi-GPU training on one machine or across multiple machines (data and model parallel) +* fast generation on both CPU and GPU with multiple search algorithms implemented: + + beam search + + Diverse Beam Search ([Vijayakumar et al., 2016](https://arxiv.org/abs/1610.02424)) + + sampling (unconstrained, top-k and top-p/nucleus) + + [lexically constrained decoding](examples/constrained_decoding/README.md) (Post & Vilar, 2018) +* [gradient accumulation](https://fairseq.readthedocs.io/en/latest/getting_started.html#large-mini-batch-training-with-delayed-updates) enables training with large mini-batches even on a single GPU +* [mixed precision training](https://fairseq.readthedocs.io/en/latest/getting_started.html#training-with-half-precision-floating-point-fp16) (trains faster with less GPU memory on [NVIDIA tensor cores](https://developer.nvidia.com/tensor-cores)) +* [extensible](https://fairseq.readthedocs.io/en/latest/overview.html): easily register new models, criterions, tasks, optimizers and learning rate schedulers +* [flexible configuration](docs/hydra_integration.md) based on [Hydra](https://github.com/facebookresearch/hydra) allowing a combination of code, command-line and file based configuration +* [full parameter and optimizer state sharding](examples/fully_sharded_data_parallel/README.md) +* [offloading parameters to CPU](examples/fully_sharded_data_parallel/README.md) We also provide [pre-trained models for translation and language modeling](#pre-trained-models-and-examples) with a convenient `torch.hub` interface: -```python + +``` python en2de = torch.hub.load('pytorch/fairseq', 'transformer.wmt19.en-de.single_model') en2de.translate('Hello world', beam=5) # 'Hallo Welt' ``` + See the PyTorch Hub tutorials for [translation](https://pytorch.org/hub/pytorch_fairseq_translation/) and [RoBERTa](https://pytorch.org/hub/pytorch_fairseq_roberta/) for more examples. -![Model](fairseq.gif) - # Requirements and Installation -* [PyTorch](http://pytorch.org/) version >= 1.4.0 +* [PyTorch](http://pytorch.org/) version >= 1.5.0 * Python version >= 3.6 * For training new models, you'll also need an NVIDIA GPU and [NCCL](https://github.com/NVIDIA/nccl) -* **For faster training** install NVIDIA's [apex](https://github.com/NVIDIA/apex) library: -```bash -git clone https://github.com/NVIDIA/apex -cd apex -pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" --global-option="--deprecated_fused_adam" --global-option="--xentropy" --global-option="--fast_multihead_attn" ./ -``` +* **To install fairseq** and develop locally: -To install fairseq: -```bash -pip install fairseq -``` +``` bash +git clone https://github.com/pytorch/fairseq +cd fairseq +pip install --editable ./ -On MacOS: -```bash -CFLAGS="-stdlib=libc++" pip install fairseq -``` +# on MacOS: +# CFLAGS="-stdlib=libc++" pip install --editable ./ -If you use Docker make sure to increase the shared memory size either with -`--ipc=host` or `--shm-size` as command line options to `nvidia-docker run`. +# to install the latest stable release (0.10.x) +# pip install fairseq +``` -**Installing from source** +* **For faster training** install NVIDIA's [apex](https://github.com/NVIDIA/apex) library: -To install fairseq from source and develop locally: -```bash -git clone https://github.com/pytorch/fairseq -cd fairseq -pip install --editable . +``` bash +git clone https://github.com/NVIDIA/apex +cd apex +pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" \ + --global-option="--deprecated_fused_adam" --global-option="--xentropy" \ + --global-option="--fast_multihead_attn" ./ ``` +* **For large datasets** install [PyArrow](https://arrow.apache.org/docs/python/install.html#using-pip): `pip install pyarrow` +* If you use Docker make sure to increase the shared memory size either with `--ipc=host` or `--shm-size` + as command line options to `nvidia-docker run` . + # Getting Started The [full documentation](https://fairseq.readthedocs.io/) contains instructions @@ -133,33 +178,40 @@ types and tasks. We provide pre-trained models and pre-processed, binarized test sets for several tasks listed below, as well as example training and evaluation commands. -- [Translation](examples/translation/README.md): convolutional and transformer models are available -- [Language Modeling](examples/language_model/README.md): convolutional and transformer models are available -- [wav2vec](examples/wav2vec/README.md): wav2vec large model is available +* [Translation](examples/translation/README.md): convolutional and transformer models are available +* [Language Modeling](examples/language_model/README.md): convolutional and transformer models are available We also have more detailed READMEs to reproduce results from specific papers: -- [Training with Quantization Noise for Extreme Model Compression](examples/quant_noise/README.md) -- [Neural Machine Translation with Byte-Level Subwords (Wang et al., 2020)](examples/byte_level_bpe/README.md) -- [Jointly Learning to Align and Translate with Transformer Models (Garg et al., 2019)](examples/joint_alignment_translation/README.md ) -- [Levenshtein Transformer (Gu et al., 2019)](examples/nonautoregressive_translation/README.md) -- [Facebook FAIR's WMT19 News Translation Task Submission (Ng et al., 2019)](examples/wmt19/README.md) -- [RoBERTa: A Robustly Optimized BERT Pretraining Approach (Liu et al., 2019)](examples/roberta/README.md) -- [wav2vec: Unsupervised Pre-training for Speech Recognition (Schneider et al., 2019)](examples/wav2vec/README.md) -- [Mixture Models for Diverse Machine Translation: Tricks of the Trade (Shen et al., 2019)](examples/translation_moe/README.md) -- [Pay Less Attention with Lightweight and Dynamic Convolutions (Wu et al., 2019)](examples/pay_less_attention_paper/README.md) -- [Understanding Back-Translation at Scale (Edunov et al., 2018)](examples/backtranslation/README.md) -- [Classical Structured Prediction Losses for Sequence to Sequence Learning (Edunov et al., 2018)](https://github.com/pytorch/fairseq/tree/classic_seqlevel) -- [Hierarchical Neural Story Generation (Fan et al., 2018)](examples/stories/README.md) -- [Scaling Neural Machine Translation (Ott et al., 2018)](examples/scaling_nmt/README.md) -- [Convolutional Sequence to Sequence Learning (Gehring et al., 2017)](examples/conv_seq2seq/README.md) -- [Language Modeling with Gated Convolutional Networks (Dauphin et al., 2017)](examples/language_model/conv_lm/README.md) + +* [Cross-lingual Retrieval for Iterative Self-Supervised Training (Tran et al., 2020)](examples/criss/README.md) +* [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations (Baevski et al., 2020)](examples/wav2vec/README.md) +* [Unsupervised Quality Estimation for Neural Machine Translation (Fomicheva et al., 2020)](examples/unsupervised_quality_estimation/README.md) +* [Training with Quantization Noise for Extreme Model Compression ({Fan*, Stock*} et al., 2020)](examples/quant_noise/README.md) +* [Neural Machine Translation with Byte-Level Subwords (Wang et al., 2020)](examples/byte_level_bpe/README.md) +* [Multilingual Denoising Pre-training for Neural Machine Translation (Liu et at., 2020)](examples/mbart/README.md) +* [Reducing Transformer Depth on Demand with Structured Dropout (Fan et al., 2019)](examples/layerdrop/README.md) +* [Jointly Learning to Align and Translate with Transformer Models (Garg et al., 2019)](examples/joint_alignment_translation/README.md) +* [Levenshtein Transformer (Gu et al., 2019)](examples/nonautoregressive_translation/README.md) +* [Facebook FAIR's WMT19 News Translation Task Submission (Ng et al., 2019)](examples/wmt19/README.md) +* [RoBERTa: A Robustly Optimized BERT Pretraining Approach (Liu et al., 2019)](examples/roberta/README.md) +* [wav2vec: Unsupervised Pre-training for Speech Recognition (Schneider et al., 2019)](examples/wav2vec/README.md) +* [Mixture Models for Diverse Machine Translation: Tricks of the Trade (Shen et al., 2019)](examples/translation_moe/README.md) +* [Pay Less Attention with Lightweight and Dynamic Convolutions (Wu et al., 2019)](examples/pay_less_attention_paper/README.md) +* [Understanding Back-Translation at Scale (Edunov et al., 2018)](examples/backtranslation/README.md) +* [Classical Structured Prediction Losses for Sequence to Sequence Learning (Edunov et al., 2018)](https://github.com/pytorch/fairseq/tree/classic_seqlevel) +* [Hierarchical Neural Story Generation (Fan et al., 2018)](examples/stories/README.md) +* [Scaling Neural Machine Translation (Ott et al., 2018)](examples/scaling_nmt/README.md) +* [Convolutional Sequence to Sequence Learning (Gehring et al., 2017)](examples/conv_seq2seq/README.md) +* [Language Modeling with Gated Convolutional Networks (Dauphin et al., 2017)](examples/language_model/README.conv.md) # Join the fairseq community +* Twitter: https://twitter.com/fairseq * Facebook page: https://www.facebook.com/groups/fairseq.users * Google group: https://groups.google.com/forum/#!forum/fairseq-users # License + fairseq(-py) is MIT-licensed. The license applies to the pre-trained models as well. @@ -167,7 +219,7 @@ The license applies to the pre-trained models as well. Please cite as: -```bibtex +``` bibtex @inproceedings{ott2019fairseq, title = {fairseq: A Fast, Extensible Toolkit for Sequence Modeling}, author = {Myle Ott and Sergey Edunov and Alexei Baevski and Angela Fan and Sam Gross and Nathan Ng and David Grangier and Michael Auli}, diff --git a/docs/command_line_tools.rst b/docs/command_line_tools.rst index 28e011a4ef..c16300ff5c 100644 --- a/docs/command_line_tools.rst +++ b/docs/command_line_tools.rst @@ -17,7 +17,7 @@ Fairseq provides several command-line tools for training and evaluating models: fairseq-preprocess ~~~~~~~~~~~~~~~~~~ -.. automodule:: preprocess +.. automodule:: fairseq_cli.preprocess .. argparse:: :module: fairseq.options @@ -29,7 +29,7 @@ fairseq-preprocess fairseq-train ~~~~~~~~~~~~~ -.. automodule:: train +.. automodule:: fairseq_cli.train .. argparse:: :module: fairseq.options @@ -41,7 +41,7 @@ fairseq-train fairseq-generate ~~~~~~~~~~~~~~~~ -.. automodule:: generate +.. automodule:: fairseq_cli.generate .. argparse:: :module: fairseq.options @@ -53,7 +53,7 @@ fairseq-generate fairseq-interactive ~~~~~~~~~~~~~~~~~~~ -.. automodule:: interactive +.. automodule:: fairseq_cli.interactive .. argparse:: :module: fairseq.options @@ -65,7 +65,7 @@ fairseq-interactive fairseq-score ~~~~~~~~~~~~~ -.. automodule:: score +.. automodule:: fairseq_cli.score .. argparse:: :module: fairseq_cli.score @@ -77,7 +77,7 @@ fairseq-score fairseq-eval-lm ~~~~~~~~~~~~~~~ -.. automodule:: eval_lm +.. automodule:: fairseq_cli.eval_lm .. argparse:: :module: fairseq.options diff --git a/docs/conf.py b/docs/conf.py index d6d150c1f0..87b0db98c7 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -19,11 +19,13 @@ import os import sys +from fairseq import __version__ + # source code directory, relative to this file, for sphinx-autobuild -sys.path.insert(0, os.path.abspath('..')) +sys.path.insert(0, os.path.abspath("..")) -source_suffix = ['.rst'] +source_suffix = [".rst"] # -- General configuration ------------------------------------------------ @@ -35,34 +37,34 @@ # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ - 'sphinx.ext.autodoc', - 'sphinx.ext.intersphinx', - 'sphinx.ext.viewcode', - 'sphinx.ext.napoleon', - 'sphinxarg.ext', + "sphinx.ext.autodoc", + "sphinx.ext.intersphinx", + "sphinx.ext.viewcode", + "sphinx.ext.napoleon", + "sphinxarg.ext", ] # Add any paths that contain templates here, relative to this directory. -templates_path = ['_templates'] +templates_path = ["_templates"] # The master toctree document. -master_doc = 'index' +master_doc = "index" # General information about the project. -project = 'fairseq' -copyright = '2019, Facebook AI Research (FAIR)' -author = 'Facebook AI Research (FAIR)' +project = "fairseq" +copyright = "Facebook AI Research (FAIR)" +author = "Facebook AI Research (FAIR)" -github_doc_root = 'https://github.com/pytorch/fairseq/tree/master/docs/' +github_doc_root = "https://github.com/pytorch/fairseq/tree/main/docs/" # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. # # The short X.Y version. -version = '0.9.0' +version = __version__ # The full version, including alpha/beta/rc tags. -release = '0.9.0' +release = __version__ # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. @@ -74,11 +76,11 @@ # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This patterns also effect to html_static_path and html_extra_path -exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] +exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"] # The name of the Pygments (syntax highlighting) style to use. -pygments_style = 'sphinx' -highlight_language = 'python' +pygments_style = "sphinx" +highlight_language = "python" # If true, `todo` and `todoList` produce output, else they produce nothing. todo_include_todos = False @@ -89,7 +91,7 @@ # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. # -html_theme = 'sphinx_rtd_theme' +html_theme = "sphinx_rtd_theme" # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the @@ -100,11 +102,11 @@ # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". -html_static_path = ['_static'] +html_static_path = ["_static"] html_context = { - 'css_files': [ - '_static/theme_overrides.css', # override wide tables in RTD theme + "css_files": [ + "_static/theme_overrides.css", # override wide tables in RTD theme ], } @@ -113,7 +115,7 @@ # # This is required for the alabaster theme # refs: http://alabaster.readthedocs.io/en/latest/installation.html#sidebars -#html_sidebars = { +# html_sidebars = { # '**': [ # 'about.html', # 'navigation.html', @@ -121,12 +123,12 @@ # 'searchbox.html', # 'donate.html', # ] -#} +# } # Example configuration for intersphinx: refer to the Python standard library. intersphinx_mapping = { - 'numpy': ('http://docs.scipy.org/doc/numpy/', None), - 'python': ('https://docs.python.org/', None), - 'torch': ('https://pytorch.org/docs/master/', None), + "numpy": ("http://docs.scipy.org/doc/numpy/", None), + "python": ("https://docs.python.org/", None), + "torch": ("https://pytorch.org/docs/master/", None), } diff --git a/fairseq.gif b/docs/fairseq.gif similarity index 100% rename from fairseq.gif rename to docs/fairseq.gif diff --git a/fairseq_logo.png b/docs/fairseq_logo.png similarity index 100% rename from fairseq_logo.png rename to docs/fairseq_logo.png diff --git a/docs/getting_started.rst b/docs/getting_started.rst index c76534163a..745ad7763c 100644 --- a/docs/getting_started.rst +++ b/docs/getting_started.rst @@ -47,6 +47,9 @@ hypothesis along with an average log-likelihood; and *P* is the positional score per token position, including the end-of-sentence marker which is omitted from the text. +Other types of output lines you might see are *D*, the detokenized hypothesis, +*T*, the reference target, *A*, alignment info, *E* the history of generation steps. + See the `README `__ for a full list of pre-trained models available. @@ -87,7 +90,7 @@ well for the IWSLT 2014 dataset: > mkdir -p checkpoints/fconv > CUDA_VISIBLE_DEVICES=0 fairseq-train data-bin/iwslt14.tokenized.de-en \ - --lr 0.25 --clip-norm 0.1 --dropout 0.2 --max-tokens 4000 \ + --optimizer nag --lr 0.25 --clip-norm 0.1 --dropout 0.2 --max-tokens 4000 \ --arch fconv_iwslt_de_en --save-dir checkpoints/fconv By default, :ref:`fairseq-train` will use all available GPUs on your machine. Use the @@ -167,18 +170,47 @@ The easiest way to launch jobs is with the `torch.distributed.launch For example, to train a large English-German Transformer model on 2 nodes each with 8 GPUs (in total 16 GPUs), run the following command on each node, -replacing ``node_rank=0`` with ``node_rank=1`` on the second node: +replacing ``node_rank=0`` with ``node_rank=1`` on the second node and making +sure to update ``--master_addr`` to the IP address of the first node: .. code-block:: console > python -m torch.distributed.launch --nproc_per_node=8 \ --nnodes=2 --node_rank=0 --master_addr="192.168.1.1" \ - --master_port=1234 \ + --master_port=12345 \ $(which fairseq-train) data-bin/wmt16_en_de_bpe32k \ --arch transformer_vaswani_wmt_en_de_big --share-all-embeddings \ --optimizer adam --adam-betas '(0.9, 0.98)' --clip-norm 0.0 \ --lr-scheduler inverse_sqrt --warmup-init-lr 1e-07 --warmup-updates 4000 \ - --lr 0.0005 --min-lr 1e-09 \ + --lr 0.0005 \ --dropout 0.3 --weight-decay 0.0 --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \ --max-tokens 3584 \ - --fp16 --distributed-no-spawn + --max-epoch 70 \ + --fp16 + +On SLURM clusters, fairseq will automatically detect the number of nodes and +GPUs, but a port number must be provided: + +.. code-block:: console + + > salloc --gpus=16 --nodes 2 (...) + > srun fairseq-train --distributed-port 12345 (...). + +Sharding very large datasets +---------------------------- + +It can be challenging to train over very large datasets, particularly if your +machine does not have much system RAM. Most tasks in fairseq support training +over "sharded" datasets, in which the original dataset has been preprocessed +into non-overlapping chunks (or "shards"). + +For example, instead of preprocessing all your data into a single "data-bin" +directory, you can split the data and create "data-bin1", "data-bin2", etc. +Then you can adapt your training command like so: + +.. code-block:: console + + > fairseq-train data-bin1:data-bin2:data-bin3 (...) + +Training will now iterate over each shard, one by one, with each shard +corresponding to an "epoch", thus reducing system memory usage. diff --git a/docs/hydra_integration.md b/docs/hydra_integration.md new file mode 100644 index 0000000000..6a15298382 --- /dev/null +++ b/docs/hydra_integration.md @@ -0,0 +1,284 @@ +## Hydra + +[Hydra](https://github.com/facebookresearch/hydra) is an open-source Python +framework that simplifies the development of research and other complex +applications. The key feature is the ability to dynamically create a +hierarchical configuration by composition and override it through config files +and the command line. The name Hydra comes from its ability to run multiple +similar jobs - much like a Hydra with multiple heads. + +## Motivation + +Until recently, all components in fairseq were configured through a shared +`args` namespace that was created at application startup. Components declared +their own `add_args` method to update the argparse parser, hoping that the names +would not clash with arguments from other components. While this model works for +smaller applications, as fairseq grew and became integrated into other +applications, this became problematic. In order to determine how to configure +each component, one needed to a) examine what args were added by this component, +and b) read the code to figure out what shared arguments it is using that were +added in other places. Reproducing models involved sharing commands that often +contained dozens of command line switches. + +The model described above is still supported by fairseq for backward +compatibility, but will be deprecated some time in the future. + +New components in fairseq should now create a dataclass that encapsulates all +parameters required to configure this component. The dataclass is registered +along with the component, and fairseq takes care of constructing and providing +this configuration object to the component's constructor. Note that sharing +parameters can optionally still work, but one has to explicitly point to the +"source of truth" (see inheritance example below). These changes make components +in fairseq more independent and re-usable by other applications: all that is +needed to create a component is to initialize its dataclass and overwrite some +of the defaults. + +While configuring fairseq through command line (using either the legacy argparse +based or the new Hydra based entry points) is still fully supported, you can now +take advantage of configuring fairseq completely or piece-by-piece through +hierarchical YAML configuration files. These files can also be shipped as +examples that others can use to run an identically configured job. + +Additionally, Hydra has a rich and growing [library of +plugins](https://github.com/facebookresearch/hydra/tree/master/plugins) that +provide functionality such as hyperparameter sweeping (including using bayesian +optimization through the [Ax](https://github.com/facebook/Ax) library), job +launching across various platforms, and more. + +## Creating or migrating components + +In general, each new (or updated) component should provide a companion +[dataclass](https://www.python.org/dev/peps/pep-0557/). These dataclass are +typically located in the same file as the component and are passed as arguments +to the `register_*()` functions. Top-level configs that should be present in +every fairseq application are placed in the +[global](fairseq/dataclass/configs.py) config file and added to the +`FairseqConfig` object. + +Each dataclass is a plain-old-data object, similar to a `NamedTuple`. These +classes are decorated with a `@dataclass` decorator, and typically inherit from +`FairseqDataclass` (which adds some functionality for backward compatibility). +Each field must have a type, and generally has metadata (such as a help string) +and a default value. Only primitive types or other config objects are allowed as +data types for each field. + +#### Example: + +```python +from dataclasses import dataclass, field +from fairseq.dataclass import FairseqDataclass + +@dataclass +class InteractiveConfig(FairseqDataclass): + buffer_size: int = field( + default=0, + metadata={ + "help": "read this many sentences into a buffer before processing them" + }, + ) + input: str = field( + default="-", + metadata={"help": "file to read from; use - for stdin"}, + ) +``` + +### Inherting values + +Some components require sharing a value. For example, a learning rate scheduler +and an optimizer may both need to know the initial learning rate value. One can +declare a field that, by default, will inherit its value from another config +node in the same hierarchy: + +```python +@dataclass +FairseqAdamConfig(FairseqDataclass): + ... + lr: List[float] = II("optimization.lr") + ... +``` + +`II("optimization.lr")` is syntactic sugar for `"${optimization.lr}"`, which is +the value one can use in a YAML config file or through command line to achieve +the same effect. Note that this assumes that there is an "optimization" config +object in the root config and it has a field called "lr". + +### Tasks and Models + +Creating Tasks and Models works same as before, except that legacy +implementations now inherit from `LegacyFairseq*` base classes, while new +components inherit from `FairseqTask` and `FairseqModel` and provide a dataclass +to the `register_*()` functions. + +#### Task example: + +```python +@dataclass +class LanguageModelingConfig(FairseqDataclass): + data: Optional[str] = field( + default=None, metadata={"help": "path to data directory"} + ) + ... + +@register_task("language_modeling", dataclass=LanguageModelingConfig) +class LanguageModelingTask(FairseqTask): + ... + @classmethod + def setup_task(cls, cfg: LanguageModelingConfig): + ... +``` + +#### Model example: + +```python +@dataclass +class TransformerLanguageModelConfig(FairseqDataclass): + activation_fn: ChoiceEnum(utils.get_available_activation_fns()) = field( + default="relu", metadata={"help": "activation function to use"} + ) + dropout: float = field(default=0.1, metadata={"help": "dropout probability"}) + ... + +@register_model("transformer_lm", dataclass=TransformerLanguageModelConfig) +class TransformerLanguageModel(FairseqLanguageModel): + ... + @classmethod + def build_model(cls, cfg: TransformerLanguageModelConfig, task: FairseqTask): + ... +``` + +### Other components + +Other components work as before, but they now take their configuration dataclass +as the only constructor argument: + +```python +@dataclass +class MosesTokenizerConfig(FairseqDataclass): + source_lang: str = field(default="en", metadata={"help": "source language"}) + ... + +@register_tokenizer("moses", dataclass=MosesTokenizerConfig) +class MosesTokenizer(object): + def __init__(self, cfg: MosesTokenizerConfig): + ... +``` + +Note that if you are adding a new registry for a new set of components, you need +to add it to the `FairseqConfig` object in `fairseq/dataclass/configs.py`: + +```python +@dataclass +class FairseqConfig(object): + ... + my_new_registry: Any = None +``` + +## Training with `fairseq-hydra-train` + +To fully take advantage of configuration flexibility offered by Hydra, you may +want to train new models using the `fairseq-hydra-train` entry point. Legacy CLI +tools such as `fairseq-train` will remain supported for the foreseeable future +but will be deprecated eventually. + +On startup, Hydra will create a configuration object that contains a hierarchy +of all the necessary dataclasses populated with their default values in the +code. The default values are overwritten by values found in YAML files in +`fairseq/config` directory (which currently sets minimal defaults) and then +further overwritten by values provided through command line arguments. + +Some of the most common use cases are shown below: + +### 1. Override default values through command line: + +```shell script +$ fairseq-hydra-train \ + distributed_training.distributed_world_size=1 \ + dataset.batch_size=2 \ + task.data=data-bin \ + model=transformer_lm/transformer_lm_gpt \ + task=language_modeling \ + optimization.max_update=5000 +``` + +Note that along with explicitly providing values for parameters such as +`dataset.batch_size`, this also tells Hydra to overlay configuration found in +`fairseq/config/model/transformer_lm/transformer_lm_gpt.yaml` over the default +values in the dataclass. If you want to train a model without specifying a +particular architecture you can simply specify `model=transformer_lm`. This only +works for migrated tasks and models. + +### 2. Replace bundled configs with an external config: + +```shell script +$ fairseq-hydra-train \ + --config-dir /path/to/external/configs \ + --config-name wiki103 +``` + +where `/path/to/external/configs/wiki103.yaml` contains: + +```yaml +# @package _group_ + +model: + _name: transformer_lm +distributed_training: + distributed_world_size: 1 +dataset: + batch_size: 2 +task: + _name: language_modeling + data: /path/to/data + add_bos_token: false + max_target_positions: 1024 +optimization: + max_update: 50000 + lr: [ 0.25 ] +criterion: cross_entropy +optimizer: adam +lr_scheduler: + _name: cosine +``` + +Note that here bundled configs from `fairseq/config` directory are not used, +however the defaults from each dataclass will still be used (unless overwritten +by your external config). + +Additionally you can choose to break up your configs by creating a directory +structure in the same location as your main config file, with the names of the +top-level fields (such as "model", "dataset", etc), and placing config files +with meaningful names that would populate that specific section of your +top-level config file (for example, you might have +`model/small_transformer_lm.yaml`, `model/big_transformer_lm.yaml`, etc). You +can then specify the correct configuration via command line, defaults in the +main config, or even launch all of them as a sweep (see Hydra documentation on +how to do this). + +### 3. Add an external config directory to Hydra search path: + +This allows combining default configuration (including using any bundled config +files), while specifying your own config files for some parts of the +configuration. + +```shell script +$ fairseq-hydra-train \ + distributed_training.distributed_world_size=1 \ + dataset.batch_size=2 \ + task.data=/path/to/data/ \ + model=transformer_lm/2_layers \ + task=language_modeling \ + optimization.max_update=5000 \ + --config-dir /path/to/external/configs +``` + +where `/path/to/external/configs` has the following structure: +``` +. ++-- model +| +-- transformer_lm +| | +-- 2_layers.yaml +``` + +and `2_layers.yaml` contains a copy of `transformer_lm_gpt.yaml` but with +`decoder_layers` set to 2. You can add other configs to configure other +components as well. diff --git a/docs/tutorial_classifying_names.rst b/docs/tutorial_classifying_names.rst index b420d850bc..b02fec0489 100644 --- a/docs/tutorial_classifying_names.rst +++ b/docs/tutorial_classifying_names.rst @@ -212,7 +212,7 @@ following contents:: @register_task('simple_classification') - class SimpleClassificationTask(FairseqTask): + class SimpleClassificationTask(LegacyFairseqTask): @staticmethod def add_args(parser): @@ -282,8 +282,6 @@ following contents:: tgt_sizes=torch.ones(len(labels)), # targets have length 1 tgt_dict=self.label_vocab, left_pad_source=False, - max_source_positions=self.args.max_positions, - max_target_positions=1, # Since our target is a single class label, there's no need for # teacher forcing. If we set this to ``True`` then our Model's # ``forward()`` method would receive an additional argument called @@ -315,7 +313,8 @@ following contents:: # def get_batch_iterator( # self, dataset, max_tokens=None, max_sentences=None, max_positions=None, # ignore_invalid_inputs=False, required_batch_size_multiple=1, - # seed=1, num_shards=1, shard_id=0, + # seed=1, num_shards=1, shard_id=0, num_workers=0, epoch=1, + # data_buffer_size=0, disable_iterator_cache=False, # ): # (...) diff --git a/docs/tutorial_simple_lstm.rst b/docs/tutorial_simple_lstm.rst index 30bdc7213e..f52988507c 100644 --- a/docs/tutorial_simple_lstm.rst +++ b/docs/tutorial_simple_lstm.rst @@ -61,6 +61,7 @@ save the following in a new file named :file:`fairseq/models/simple_lstm.py`:: hidden_size=hidden_dim, num_layers=1, bidirectional=False, + batch_first=True, ) def forward(self, src_tokens, src_lengths): diff --git a/examples/__init__.py b/examples/__init__.py index 9369be1b77..44bb24ae61 100644 --- a/examples/__init__.py +++ b/examples/__init__.py @@ -3,6 +3,7 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. -__version__ = '0.9.0' - -import examples.noisychannel # noqa +try: + from fairseq.version import __version__ # noqa +except ImportError: + pass diff --git a/examples/adaptive_span/README.md b/examples/adaptive_span/README.md new file mode 100644 index 0000000000..d5224fb289 --- /dev/null +++ b/examples/adaptive_span/README.md @@ -0,0 +1,90 @@ +# Adaptive Span + +Adaptive Span is a novel self-attention mechanism that can learn its optimal +attention span. This allows us to extend significantly the maximum context size +used in Transformer, while maintaining control over their memory footprint +and computational time. It uses the Truncated BPTT technique for training, +as in [transformerXL](https://github.com/pytorch/fairseq/blob/main/examples/truncated_bptt/README.md). + +Adaptive Span was introduced by paper: +[Adaptive Attention Span in Transformers](https://arxiv.org/abs/1905.07799), +which achieved state-of-the-art language modeling results at the time of publication. + +We manage to reproduce their result in fairseq and keep most of the +[original implementation](https://github.com/facebookresearch/adaptive-span) untouched. +You can refer to the their sweep file as well if any combination of hyperparameter is not clear. + +##### 0. Setup + +First you need to process the Enwik8 dataset, we use the pre-tokenized dataset +from [adaptive span paper](https://github.com/facebookresearch/adaptive-span/blob/master/get_data.sh). +You can download the dataset, and then run: +```bash +fairseq-preprocess --only-source --trainpref ~/data/enwik8/train.txt \ + --validpref ~/data/enwik8/valid.txt --testpref ~/data/enwik8/test.txt \ + --destdir ~/data/enwik8/data-bin/ --joined-dictionary --workers 20 +``` + +##### 1. Train a Adaptive Span model on Enwik8 + +We will train a 12-layer Adaptive Span model following the [hyperparameters +used in the original +paper](https://github.com/facebookresearch/adaptive-span/blob/master/experiments/enwik8.sh). + +The following command assumes 4 GPUs, so that the total batch size is 64 +sequences (4 x 16). Training should take 2-3 days on 4 V100 GPUs: +```bash +CUDA_VISIBLE_DEVICES=0,1,2,3 fairseq-train \ + --user-dir examples/adaptive_span \ + --data ~/data/enwik8/data-bin/ \ + --fp16 --fp16-no-flatten-grads --max-update 600000 \ + --task truncated_bptt_lm --tokens-per-sample 512 --arch adaptive_span \ + --n-layer 12 --d-model 512 --n-head 8 --d-inner 2048 --dropout 0.3 \ + --attn-span 8192 --optimizer adagrad_with_grad_clip --adagrad-clip 0.03 \ + --validate-interval-updates 1000 \ + --lr-scheduler fixed --warmup-updates 32000 --batch-size-valid 32 \ + --lr 0.07 --criterion adaptive_span_loss --batch-size 16 --update-freq 1 \ + --seed 2 --log-format json --log-interval 25 --aux-loss-scaler 5e-07 +``` +This should land around 1.05 on validation, 1.03 on test. You can lower the +--aux-loss-scaler for better performance (longer span). It gives ~0.03 bpc +improvement to the transformerXL baseline here. +If training on a single GPU, set `--update-freq=4` to accumulate 4x gradients +and simulate training on 4 GPUs. +You can also reproduce the transformerXL result on enwik8 using this code base. +It should land around 1.06 on test,matching the [original paper](https://github.com/kimiyoung/transformer-xl/blob/master/pytorch/run_enwik8_base.sh). +You can try by +```bash +CUDA_VISIBLE_DEVICES=0,1,2,3 fairseq-train \ + --user-dir examples/truncated_bptt \ + ~/data/enwik8/data-bin/ \ + --task truncated_bptt_lm --fp16 --max-update 400000 \ + --tokens-per-sample 512 --arch transformer_xl --n-layer 12 \ + --d-model 512 --n-head 8 --d-head 64 --d-inner 2048 --dropout 0.1 \ + --dropatt 0.0 --mem-len 512 --optimizer adam --clip-norm 0.25 \ + --lr-scheduler cosine --warmup-updates 0 \ + --lr 0.0 --lr 0.00025 --batch-size 15 \ + --update-freq 1 --seed 2 --log-format json --log-interval 25 \ + --fp16 +``` + +##### 2. Evaluate +For Adaptive Span: +```bash +fairseq-eval-lm ~/data/enwik8/data-bin/ --path model/checkpoint_best.pt \ + --user-dir examples/adaptive_span \ + --task truncated_bptt_lm --batch-size 8 --tokens-per-sample 512 --gen-subset test +``` +For Transformer-XL evaluation: +```bash +fairseq-eval-lm ~/data/enwik8/data-bin/ --path model/checkpoint_best.pt \ + --user-dir examples/truncated_bptt/ --task truncated_bptt_lm --batch-size 8 \ + --tokens-per-sample 80 \ + --model-overrides '{"mem_len":2100,"clamp_len":820,"same_length":True}' \ + --gen-subset valid +``` + +*Note:* During training the model saw 512 tokens of context +(``--tokens-per-sample=512``), with batch size 8. These settings match the evaluation +settings from [the original +paper](https://github.com/facebookresearch/adaptive-span/blob/master/experiments/enwik8.sh). diff --git a/examples/adaptive_span/__init__.py b/examples/adaptive_span/__init__.py new file mode 100644 index 0000000000..e0a142a769 --- /dev/null +++ b/examples/adaptive_span/__init__.py @@ -0,0 +1,19 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import importlib +import os + +# automatically import any Python files in the current directory +cur_dir = os.path.dirname(__file__) +for file in os.listdir(cur_dir): + path = os.path.join(cur_dir, file) + if ( + not file.startswith("_") + and not file.startswith(".") + and (file.endswith(".py") or os.path.isdir(path)) + ): + mod_name = file[: file.find(".py")] if file.endswith(".py") else file + module = importlib.import_module(__name__ + "." + mod_name) diff --git a/examples/adaptive_span/adagrad_with_grad_clip.py b/examples/adaptive_span/adagrad_with_grad_clip.py new file mode 100644 index 0000000000..585ce184ab --- /dev/null +++ b/examples/adaptive_span/adagrad_with_grad_clip.py @@ -0,0 +1,128 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from torch.optim import Adagrad + +from fairseq.optim import LegacyFairseqOptimizer, register_optimizer + + +@register_optimizer("adagrad_with_grad_clip") +class FairseqAdagradWithGradClip(LegacyFairseqOptimizer): + def __init__(self, args, params): + super().__init__(args) + self._optimizer = AdagradWithGradClip(params, **self.optimizer_config) + + @staticmethod + def add_args(parser): + """Add optimizer-specific arguments to the parser.""" + # fmt: off + parser.add_argument('--weight-decay', '--wd', default=0.0, type=float, metavar='WD', + help='weight decay') + parser.add_argument('--adagrad-clip', default=0.0, type=float, metavar='D', + help='internal grad clip') + # fmt: on + + @property + def optimizer_config(self): + """ + Return a kwarg dictionary that will be used to override optimizer + args stored in checkpoints. This allows us to load a checkpoint and + resume training using a different set of optimizer args, e.g., with a + different learning rate. + """ + return { + "lr": self.args.lr[0], + "weight_decay": self.args.weight_decay, + "grad_clip": self.args.adagrad_clip, + } + + @property + def supports_flat_params(self): + return False + + +def _clip_grad(clr, grad, group_grad_clip): + if group_grad_clip > 0: + norm = grad.norm(2).item() + if norm > group_grad_clip: + clr *= group_grad_clip / (norm + 1e-10) + return clr + + +class AdagradWithGradClip(Adagrad): + """Adagrad algorithm with custom gradient clipping""" + + def __init__( + self, + params, + lr=1e-2, + lr_decay=0, + weight_decay=0, + initial_accumulator_value=0, + grad_clip=0, + ): + Adagrad.__init__( + self, + params, + lr=lr, + lr_decay=lr_decay, + weight_decay=weight_decay, + initial_accumulator_value=initial_accumulator_value, + ) + self.defaults["grad_clip"] = grad_clip + self.param_groups[0].setdefault("grad_clip", grad_clip) + + def step(self, closure=None): + loss = None + if closure is not None: + loss = closure() + + for group in self.param_groups: + for p in group["params"]: + if p.grad is None: + continue + + grad = p.grad.data + state = self.state[p] + + state["step"] += 1 + + if group["weight_decay"] != 0: + if p.grad.data.is_sparse: + raise RuntimeError( + "weight_decay option is " + "not compatible with sparse " + "gradients" + ) + grad = grad.add(group["weight_decay"], p.data) + + clr = group["lr"] / (1 + (state["step"] - 1) * group["lr_decay"]) + + # clip + clr = _clip_grad(clr=clr, grad=grad, group_grad_clip=group["grad_clip"]) + + if grad.is_sparse: + # the update is non-linear so indices must be unique + grad = grad.coalesce() + grad_indices = grad._indices() + grad_values = grad._values() + size = grad.size() + + def make_sparse(values): + constructor = grad.new + if grad_indices.dim() == 0 or values.dim() == 0: + return constructor().resize_as_(grad) + return constructor(grad_indices, values, size) + + state["sum"].add_(make_sparse(grad_values.pow(2))) + std = state["sum"]._sparse_mask(grad) + std_values = std._values().sqrt_().add_(1e-10) + p.data.add_(-clr, make_sparse(grad_values / std_values)) + else: + state["sum"].addcmul_(1, grad, grad) + std = state["sum"].sqrt().add_(1e-10) + p.data.addcdiv_(-clr, grad, std) + + return loss diff --git a/examples/adaptive_span/adaptive_span_attention.py b/examples/adaptive_span/adaptive_span_attention.py new file mode 100644 index 0000000000..07f757bb8e --- /dev/null +++ b/examples/adaptive_span/adaptive_span_attention.py @@ -0,0 +1,160 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class AdaptiveMask(nn.Module): + """Soft masking function for adaptive size. + It masks out the last K values of an input. The masking value + goes from 1 to 0 gradually, so K can be learned with + back-propagation. + Args: + max_size: maximum size (i.e. input dimension) + ramp_size: size of the ramp going from 0 to 1 + init_val: initial size proportion not to be masked out + shape: learn multiple sizes independent of each other + """ + + def __init__(self, max_size, ramp_size, init_val=0, shape=(1,)): + nn.Module.__init__(self) + self._max_size = max_size + self._ramp_size = ramp_size + self.current_val = nn.Parameter(torch.zeros(*shape) + init_val) + mask_template = torch.linspace(1 - max_size, 0, steps=max_size) + self.register_buffer("mask_template", mask_template) + + def forward(self, x): + mask = self.mask_template.float() + self.current_val.float() * self._max_size + mask = mask / self._ramp_size + 1 + mask = mask.clamp(0, 1) + if x.size(-1) < self._max_size: + # the input could have been trimmed beforehand to save computation + mask = mask.narrow(-1, self._max_size - x.size(-1), x.size(-1)) + x = (x * mask).type_as(x) + return x + + def get_current_max_size(self, include_ramp=True): + current_size = math.ceil(self.current_val.max().item() * self._max_size) + if include_ramp: + current_size += self._ramp_size + current_size = max(0, min(self._max_size, current_size)) + return current_size + + def get_current_avg_size(self, include_ramp=True): + current_size = math.ceil( + self.current_val.float().mean().item() * self._max_size + ) + if include_ramp: + current_size += self._ramp_size + current_size = max(0, min(self._max_size, current_size)) + return current_size + + def clamp_param(self): + """this need to be called after each update""" + self.current_val.data.clamp_(0, 1) + + +class AdaptiveSpan(nn.Module): + """Adaptive attention span for Transformerself. + This module learns an attention span length from data for each + self-attention head. + Args: + attn_span: maximum attention span + adapt_span_loss: loss coefficient for the span length + adapt_span_ramp: length of the masking ramp + adapt_span_init: initial size ratio + adapt_span_cache: adapt cache size to reduce memory usage + """ + + def __init__( + self, + attn_span, + adapt_span_ramp, + adapt_span_init, + n_head, + adapt_span_layer, + **kargs + ): + nn.Module.__init__(self) + self._max_span = attn_span + self._n_head = n_head + self._adapt_span_layer = adapt_span_layer + if self._adapt_span_layer: + self._mask = AdaptiveMask( + max_size=self._max_span, + ramp_size=adapt_span_ramp, + init_val=adapt_span_init, + ) + else: + self._mask = AdaptiveMask( + max_size=self._max_span, + ramp_size=adapt_span_ramp, + init_val=adapt_span_init, + shape=(n_head, 1, 1), + ) + + def forward(self, attn, normalize=True): + """mask attention with the right span""" + # batch and head dimensions are merged together, so separate them first + self.clamp_param() + if self._adapt_span_layer: + attn = self._mask(attn) + else: + B = attn.size(0) # batch size + M = attn.size(1) # block size + attn = attn.reshape(B // self._n_head, self._n_head, M, -1) + attn = self._mask(attn) + attn = attn.view(B, M, -1) + return attn + + def get_trim_len(self): + """how much of memory can be trimmed to reduce computation""" + L = self._max_span + trim_len = min(L - 1, L - self._mask.get_current_max_size()) + # too fine granularity might be bad for the memory management + trim_len = math.floor(trim_len / 64) * 64 + return trim_len + + def trim_memory(self, query, key, value, key_pe): + """trim out unnecessary memory beforehand to reduce computation""" + trim_len = self.get_trim_len() + cache_size = key.size(1) - query.size(1) + trim_len_cache = trim_len - (self._max_span - cache_size) + if trim_len_cache > 0: + key = key[:, trim_len_cache:, :] + value = value[:, trim_len_cache:, :] + elif trim_len_cache < 0: + # cache is too short! this happens when validation resumes + # after a lot of updates. + key = F.pad(key, [0, 0, -trim_len_cache, 0]) + value = F.pad(value, [0, 0, -trim_len_cache, 0]) + if trim_len > 0: + if key_pe is not None: + key_pe = key_pe[:, :, trim_len:] + return key, value, key_pe + + def get_cache_size(self): + """determine how long the cache should be""" + trim_len = self.get_trim_len() + # give a buffer of 64 steps since a span might increase + # in future updates + return min(self._max_span, self._max_span - trim_len + 64) + + def get_loss(self): + """a loss term for regularizing the span length""" + return self._max_span * self._mask.current_val.float().mean() + + def get_current_max_span(self): + return self._mask.get_current_max_size() + + def get_current_avg_span(self): + return self._mask.get_current_avg_size() + + def clamp_param(self): + self._mask.clamp_param() diff --git a/examples/adaptive_span/adaptive_span_loss.py b/examples/adaptive_span/adaptive_span_loss.py new file mode 100644 index 0000000000..056245807e --- /dev/null +++ b/examples/adaptive_span/adaptive_span_loss.py @@ -0,0 +1,106 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math +from dataclasses import dataclass + +import torch.nn.functional as F +from fairseq import metrics, utils +from fairseq.criterions import register_criterion +from fairseq.criterions.cross_entropy import CrossEntropyCriterion +from fairseq.dataclass import FairseqDataclass +from omegaconf import II + + +@dataclass +class AdaptiveSpanCriterionConfig(FairseqDataclass): + sentence_avg: bool = II("optimization.sentence_avg") + + +@register_criterion("adaptive_span_loss", dataclass=AdaptiveSpanCriterionConfig) +class AdaptiveSpanCriterion(CrossEntropyCriterion): + def __init__(self, task, sentence_avg): + super().__init__(task, sentence_avg) + + def forward(self, model, sample, reduce=True): + """Compute the loss for the given sample. + + Returns a tuple with three elements: + 1) the loss here is summed, different from the adaptive span code + 2) the sample size, which is used as the denominator for the gradient + 3) logging outputs to display while training + """ + net_output = model(**sample["net_input"]) + loss, aux_loss, avg_span, max_span = self.compute_loss( + model, net_output, sample, reduce=reduce + ) + sample_size = ( + sample["target"].size(0) if self.sentence_avg else sample["ntokens"] + ) + loss /= sample_size + total_loss = loss + aux_loss + sample_size = 1 + + logging_output = { + "loss": loss.data, + "ntokens": sample["ntokens"], + "nsentences": sample["target"].size(0), + "sample_size": sample_size, + "total_loss": total_loss.data, + "avg_span": avg_span * sample_size, + "max_span": max_span * sample_size, + } + return total_loss, sample_size, logging_output + + def compute_loss(self, model, net_output, sample, reduce=True): + loss, _ = super().compute_loss(model, net_output, sample, reduce) + aux_loss = model.get_aux_loss() + avg_span = model.get_current_avg_span() + max_span = model.get_current_max_span() + return loss, aux_loss, avg_span, max_span + + @staticmethod + def reduce_metrics(logging_outputs) -> None: + """Aggregate logging outputs from data parallel training.""" + loss_sum = sum(log.get("loss", 0) for log in logging_outputs) + ntokens = sum(log.get("ntokens", 0) for log in logging_outputs) + sample_size = sum(log.get("sample_size", 0) for log in logging_outputs) + total_loss_sum = sum(log.get("total_loss", 0) for log in logging_outputs) + avg_span_sum = sum(log.get("avg_span", 0) for log in logging_outputs) + max_span_sum = sum(log.get("max_span", 0) for log in logging_outputs) + + # we divide by log(2) to convert the loss from base e to base 2 + metrics.log_scalar( + "loss", loss_sum / sample_size / math.log(2), sample_size, round=3 + ) + metrics.log_scalar("avg_span", avg_span_sum / sample_size, sample_size, round=3) + metrics.log_scalar("max_span", max_span_sum / sample_size, sample_size, round=3) + # total loss contains the L1 norm on adaptive-span + metrics.log_scalar( + "total_loss", + total_loss_sum / sample_size / math.log(2), + sample_size, + round=3, + ) + if sample_size != ntokens: + metrics.log_scalar( + "nll_loss", loss_sum / ntokens / math.log(2), ntokens, round=3 + ) + metrics.log_derived( + "ppl", lambda meters: utils.get_perplexity(meters["nll_loss"].avg) + ) + else: + metrics.log_derived( + "ppl", lambda meters: utils.get_perplexity(meters["loss"].avg) + ) + + @staticmethod + def logging_outputs_can_be_summed() -> bool: + """ + Whether the logging outputs returned by `forward` can be summed + across workers prior to calling `reduce_metrics`. Setting this + to True will improves distributed training speed. + """ + return True diff --git a/examples/adaptive_span/adaptive_span_model.py b/examples/adaptive_span/adaptive_span_model.py new file mode 100644 index 0000000000..d96c95b85d --- /dev/null +++ b/examples/adaptive_span/adaptive_span_model.py @@ -0,0 +1,263 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from fairseq.modules.layer_norm import LayerNorm + +from .adaptive_span_attention import AdaptiveSpan + +# Size notations: +# B = batch_size, H = d_model, M = block_size, L = attn_span + + +def _skew(X, pad_value): + """shift every row 1 step to right""" + # X = B x M x L + B, M, L = X.size() + X = F.pad(X, (0, M + 1), value=pad_value) # B x M x (L+M+1) + X = X.view(B, -1) # B x ML+MM+M + X = X[:, :-M] # B x ML+MM + X = X.view(B, M, M + L) # B x M x L+M + return X + + +def _unskew(X): + """reverse _skew operation""" + # X = B x M x L+M + B, M, L = X.size() + L -= M + X = X.view(B, -1) # B x ML+MM + X = F.pad(X, (0, M)) # B x ML+MM+M + X = X.view(B, M, M + L + 1) # B x M x L+M+1 + X = X[:, :, :L] # B x M x L + return X + + +class SeqAttention(nn.Module): + """Sequential self-attention layer. + Each token will attend to its previous fixed number of steps. + Note that attention doesn't include the current step itself. + """ + + def __init__(self, d_model, n_head, attn_span, dropout, adapt_span_layer, **kargs): + nn.Module.__init__(self) + self.dropout = nn.Dropout(dropout) + self.d_model = d_model # size of a single head + self.attn_span = attn_span + self.adaptive_span = AdaptiveSpan( + attn_span=attn_span, + n_head=n_head, + adapt_span_layer=adapt_span_layer, + **kargs + ) + + def forward(self, query, key, value, key_pe): + # query size = B x M x H + # key, value sizes = B x (M+L) x H + + key, value, key_pe = self.adaptive_span.trim_memory(query, key, value, key_pe) + + # compute attention from context + # B x M (dest) x (M+L) (src) + attn_cont = torch.matmul(query, key.transpose(-1, -2)) + attn_cont = _unskew(attn_cont) # B x M x L + + # compute the effect of position embedding + attn_pos = torch.matmul(query, key_pe) # B x M x L_pos + attn = attn_cont + attn_pos + + attn = attn / math.sqrt(self.d_model) # B x M X L_pos + + attn = F.softmax(attn.float(), dim=-1).type_as(attn) + + # trim attention lengths according to the learned span + attn = self.adaptive_span(attn) + + attn = self.dropout(attn) # B x M X L_pos + + attn_cont = _skew(attn, 0) # B x M X (L+M) + out = torch.matmul(attn_cont, value) # B x M x H + return out + + def get_cache_size(self): + return self.adaptive_span.get_cache_size() + + +class MultiHeadSeqAttention(nn.Module): + def __init__(self, d_model, n_head, **kargs): + nn.Module.__init__(self) + assert d_model % n_head == 0 + self.n_head = n_head + self.head_dim = d_model // n_head + self.attn = SeqAttention(d_model=self.head_dim, n_head=n_head, **kargs) + self.proj_query = nn.Linear(d_model, d_model, bias=False) + nn.init.xavier_normal_(self.proj_query.weight) + self.proj_out = nn.Linear(d_model, d_model, bias=False) + nn.init.xavier_normal_(self.proj_out.weight) + self.proj_val = nn.Linear(d_model, d_model, bias=False) + nn.init.xavier_normal_(self.proj_val.weight) + self.proj_key = nn.Linear(d_model, d_model, bias=False) + nn.init.xavier_normal_(self.proj_key.weight) + + def head_reshape(self, x): + K = self.n_head + D = self.head_dim + x = x.view(x.size()[:-1] + (K, D)) # B x (M+L) x K x D + x = x.transpose(1, 2).contiguous() # B x K x (M+L) x D + x = x.view(-1, x.size(-2), x.size(-1)) # B_K x (M+L) x D + return x + + def forward(self, query, key, value, key_pe): + B = query.size(0) + K = self.n_head + D = self.head_dim + M = query.size(1) + + query = self.proj_query(query) + query = self.head_reshape(query) + value = self.proj_val(value) + value = self.head_reshape(value) + key = self.proj_key(key) + key = self.head_reshape(key) + + out = self.attn(query, key, value, key_pe) # B_K x M x D + out = out.view(B, K, M, D) # B x K x M x D + out = out.transpose(1, 2).contiguous() # B x M x K x D + out = out.view(B, M, -1) # B x M x K_D + out = self.proj_out(out) + return out + + +class FeedForwardLayer(nn.Module): + def __init__(self, d_model, d_inner, dropout, **kargs): + nn.Module.__init__(self) + self.fc1 = nn.Linear(d_model, d_inner) + self.fc2 = nn.Linear(d_inner, d_model) + nn.init.xavier_uniform_(self.fc1.weight) + nn.init.xavier_uniform_(self.fc2.weight) + self.dropout = nn.Dropout(dropout) + + def forward(self, h): + h1 = F.relu(self.fc1(h)) + h1 = self.dropout(h1) + h2 = self.fc2(h1) + return h2 + + +class TransformerSeqLayer(nn.Module): + def __init__(self, d_model, **kargs): + nn.Module.__init__(self) + self.attn = MultiHeadSeqAttention(d_model=d_model, **kargs) + self.norm1 = LayerNorm(d_model) + self.ff = FeedForwardLayer(d_model=d_model, **kargs) + self.norm2 = LayerNorm(d_model) + + def forward(self, h, h_cache, key_pe): + # h = B x M x H + # h_cache = B x L x H + h_all = torch.cat([h_cache, h], dim=1) # B x (M+L) x H + attn_out = self.attn(h, h_all, h_all, key_pe) + h = self.norm1(h + attn_out) # B x M x H + if self.ff is not None: + ff_out = self.ff(h) + out = self.norm2(h + ff_out) # B x M x H + else: + out = h + return out + + def get_cache_size(self): + return self.attn.attn.get_cache_size() + + +class TransformerSeq(nn.Module): + def __init__( + self, + vocab_size, + d_model, + n_head, + n_layer, + attn_span, + emb_dropout, + aux_loss_scaler, + adapt_span_layer, + **kargs + ): + nn.Module.__init__(self) + # token embeddings + self.in_emb = nn.Embedding(vocab_size, d_model) + nn.init.normal_(self.in_emb.weight, mean=0, std=d_model ** -0.5) + self.out_emb = nn.Linear(d_model, vocab_size) + self.aux_loss_scaler = aux_loss_scaler + if emb_dropout > 0: + self.emb_dropout = nn.Dropout(emb_dropout) + else: + self.emb_dropout = None + # position embeddings + self.key_pe = nn.Parameter(torch.randn(1, d_model // n_head, attn_span)) + + self.layers = nn.ModuleList() + self.layers.extend( + TransformerSeqLayer( + d_model=d_model, + n_head=n_head, + attn_span=attn_span, + adapt_span_layer=adapt_span_layer, + **kargs + ) + for _ in range(n_layer) + ) + + def forward(self, x, h_cache, target=None): + # x size = B x M + block_size = x.size(1) + h = self.in_emb(x) # B x M x H + if self.emb_dropout is not None: + h = self.emb_dropout(h) + + h_cache_next = [] + for l, layer in enumerate(self.layers): + cache_size = layer.attn.attn.get_cache_size() + if cache_size > block_size: + h_cache_next_l = torch.cat( + [h_cache[l][:, -cache_size + block_size :, :], h], dim=1 + ).detach() + else: + h_cache_next_l = h[:, -cache_size:, :].detach() + h_cache_next.append(h_cache_next_l) + h = layer(h, h_cache[l], self.key_pe) # B x M x H + + if self.emb_dropout is not None: + h = self.emb_dropout(h) + + out = F.log_softmax(self.out_emb(h).float(), dim=-1).type_as(h) + dummy_loss = None + + return out, h_cache_next, dummy_loss + + def get_aux_loss(self): + loss = 0.0 + for layer in self.layers: + loss += layer.attn.attn.adaptive_span.get_loss() + return self.aux_loss_scaler * loss + + def get_current_max_span(self): + max_span = 0.0 + for layer in self.layers: + max_span = max( + max_span, layer.attn.attn.adaptive_span.get_current_max_span() + ) + return max_span + + def get_current_avg_span(self): + avg_span = 0.0 + for layer in self.layers: + avg_span += layer.attn.attn.adaptive_span.get_current_avg_span() + return avg_span / len(self.layers) diff --git a/examples/adaptive_span/adaptive_span_model_wrapper.py b/examples/adaptive_span/adaptive_span_model_wrapper.py new file mode 100644 index 0000000000..5b147fe11f --- /dev/null +++ b/examples/adaptive_span/adaptive_span_model_wrapper.py @@ -0,0 +1,145 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +from dataclasses import dataclass +from typing import Dict, List, Optional + +import torch +from fairseq.dataclass import FairseqDataclass +from fairseq.models import ( + FairseqIncrementalDecoder, + FairseqLanguageModel, + register_model, +) +from .adaptive_span_model import TransformerSeq as AdaptiveSpanTransformerModel + + +logger = logging.getLogger(__name__) + + +@dataclass +class AdaptiveSpanSmallConfig(FairseqDataclass): + # defaults come from https://github.com/facebookresearch/adaptive-span/blob/master/experiments/enwik8_small.sh + vocab_size: int = 50 + d_model: int = 256 + n_head: int = 4 + d_inner: int = 1024 + n_layer: int = 8 + attn_span: int = 1024 + dropout: float = 0.0 + emb_dropout: float = 0.0 + adapt_span_ramp: int = 32 + adapt_span_init: float = 0.0 + aux_loss_scaler: float = 0.000002 + adapt_span_layer: bool = False + + +@register_model("adaptive_span", dataclass=AdaptiveSpanSmallConfig) +class AdaptiveSpanTransformer(FairseqLanguageModel): + @classmethod + def build_model(cls, cfg: AdaptiveSpanSmallConfig, task): + return cls(AdaptiveSpanDecoder(cfg, task)) + + def get_aux_loss(self): + return self.decoder.get_aux_loss() + + def get_current_max_span(self): + return self.decoder.get_current_max_span() + + def get_current_avg_span(self): + return self.decoder.get_current_avg_span() + + +class AdaptiveSpanDecoder(FairseqIncrementalDecoder): + def __init__(self, cfg, task): + + super().__init__(task.target_dictionary) + + self.config = cfg + config = AdaptiveSpanSmallConfig( + vocab_size=len(task.target_dictionary), + d_model=cfg.d_model, + n_head=cfg.n_head, + d_inner=cfg.d_inner, + n_layer=cfg.n_layer, + attn_span=cfg.attn_span, + dropout=cfg.dropout, + emb_dropout=cfg.emb_dropout, + adapt_span_ramp=cfg.adapt_span_ramp, + adapt_span_init=cfg.adapt_span_init, + aux_loss_scaler=cfg.aux_loss_scaler, + adapt_span_layer=cfg.adapt_span_layer, + ) + logger.info(config) + self.model = AdaptiveSpanTransformerModel(**config.__dict__) + + self._mems = None + + def forward( + self, + src_tokens, + incremental_state: Optional[Dict[str, List[torch.Tensor]]] = None, + encoder_out=None, + ): + bsz = src_tokens.size(0) + if incremental_state is not None: # used during inference + mems = self.get_incremental_state("mems") + src_tokens = src_tokens[:, -1:] # only keep the most recent token + else: + mems = self._mems + + if mems is None: + # first time init + mems = self.init_hid_cache(bsz) + output = self.model(x=src_tokens, h_cache=mems,) + if incremental_state is not None: + self.set_incremental_state(incremental_state, "mems", output[1]) + else: + self._mems = output[1] + return (output[0],) + + def max_positions(self): + return self.config.attn_span + + def init_hid_cache(self, batch_sz): + hid = [] + for layer in self.model.layers: + param = next(self.model.parameters()) + h = torch.zeros( + batch_sz, + layer.get_cache_size(), + self.config.d_model, + dtype=param.dtype, + device=param.device, + ) + hid.append(h) + return hid + + def get_aux_loss(self): + return self.model.get_aux_loss() + + def get_current_max_span(self): + return self.model.get_current_max_span() + + def get_current_avg_span(self): + return self.model.get_current_avg_span() + + def reorder_incremental_state( + self, + incremental_state: Dict[str, Dict[str, Optional[torch.Tensor]]], + new_order: torch.Tensor, + ): + """Reorder incremental state. + + This will be called when the order of the input has changed from the + previous time step. A typical use case is beam search, where the input + order changes between time steps based on the selection of beams. + """ + raise NotImplementedError("This is required for generation/beam search") + # mems = self.get_incremental_state(incremental_state, "mems") + # if mems is not None: + # new_mems = [mems_i.index_select(1, new_order) for mems_i in mems] + # self.set_incremental_state(incremental_state, "mems", new_mems) diff --git a/examples/adaptive_span/truncated_bptt_lm_task.py b/examples/adaptive_span/truncated_bptt_lm_task.py new file mode 120000 index 0000000000..a92da3a298 --- /dev/null +++ b/examples/adaptive_span/truncated_bptt_lm_task.py @@ -0,0 +1 @@ +../truncated_bptt/truncated_bptt_lm_task.py \ No newline at end of file diff --git a/examples/backtranslation/deduplicate_lines.py b/examples/backtranslation/deduplicate_lines.py index 35a407e556..50e458328c 100644 --- a/examples/backtranslation/deduplicate_lines.py +++ b/examples/backtranslation/deduplicate_lines.py @@ -7,8 +7,8 @@ import argparse import fileinput import hashlib -from multiprocessing import Pool import sys +from multiprocessing import Pool def get_hashes_and_lines(raw_line): @@ -18,12 +18,12 @@ def get_hashes_and_lines(raw_line): def main(): parser = argparse.ArgumentParser() - parser.add_argument('--workers', type=int, default=10) - parser.add_argument('files', nargs='*', help='input files') + parser.add_argument("--workers", type=int, default=10) + parser.add_argument("files", nargs="*", help="input files") args = parser.parse_args() seen = set() - with fileinput.input(args.files, mode='rb') as h: + with fileinput.input(args.files, mode="rb") as h: pool = Pool(args.workers) results = pool.imap_unordered(get_hashes_and_lines, h, 1000) for i, (hash, raw_line) in enumerate(results): @@ -37,5 +37,5 @@ def main(): print(file=sys.stderr, flush=True) -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/examples/backtranslation/extract_bt_data.py b/examples/backtranslation/extract_bt_data.py index 26a46942c8..e766391e87 100644 --- a/examples/backtranslation/extract_bt_data.py +++ b/examples/backtranslation/extract_bt_data.py @@ -11,26 +11,38 @@ def main(): - parser = argparse.ArgumentParser(description=( - 'Extract back-translations from the stdout of fairseq-generate. ' - 'If there are multiply hypotheses for a source, we only keep the first one. ' - )) - parser.add_argument('--output', required=True, help='output prefix') - parser.add_argument('--srclang', required=True, help='source language (extracted from H-* lines)') - parser.add_argument('--tgtlang', required=True, help='target language (extracted from S-* lines)') - parser.add_argument('--minlen', type=int, help='min length filter') - parser.add_argument('--maxlen', type=int, help='max length filter') - parser.add_argument('--ratio', type=float, help='ratio filter') - parser.add_argument('files', nargs='*', help='input files') + parser = argparse.ArgumentParser( + description=( + "Extract back-translations from the stdout of fairseq-generate. " + "If there are multiply hypotheses for a source, we only keep the first one. " + ) + ) + parser.add_argument("--output", required=True, help="output prefix") + parser.add_argument( + "--srclang", required=True, help="source language (extracted from H-* lines)" + ) + parser.add_argument( + "--tgtlang", required=True, help="target language (extracted from S-* lines)" + ) + parser.add_argument("--minlen", type=int, help="min length filter") + parser.add_argument("--maxlen", type=int, help="max length filter") + parser.add_argument("--ratio", type=float, help="ratio filter") + parser.add_argument("files", nargs="*", help="input files") args = parser.parse_args() def validate(src, tgt): - srclen = len(src.split(' ')) if src != '' else 0 - tgtlen = len(tgt.split(' ')) if tgt != '' else 0 + srclen = len(src.split(" ")) if src != "" else 0 + tgtlen = len(tgt.split(" ")) if tgt != "" else 0 if ( (args.minlen is not None and (srclen < args.minlen or tgtlen < args.minlen)) - or (args.maxlen is not None and (srclen > args.maxlen or tgtlen > args.maxlen)) - or (args.ratio is not None and (max(srclen, tgtlen) / float(min(srclen, tgtlen)) > args.ratio)) + or ( + args.maxlen is not None + and (srclen > args.maxlen or tgtlen > args.maxlen) + ) + or ( + args.ratio is not None + and (max(srclen, tgtlen) / float(min(srclen, tgtlen)) > args.ratio) + ) ): return False return True @@ -41,19 +53,20 @@ def safe_index(toks, index, default): except IndexError: return default - with open(args.output + '.' + args.srclang, 'w') as src_h, \ - open(args.output + '.' + args.tgtlang, 'w') as tgt_h: + with open(args.output + "." + args.srclang, "w") as src_h, open( + args.output + "." + args.tgtlang, "w" + ) as tgt_h: for line in tqdm(fileinput.input(args.files)): - if line.startswith('S-'): - tgt = safe_index(line.rstrip().split('\t'), 1, '') - elif line.startswith('H-'): + if line.startswith("S-"): + tgt = safe_index(line.rstrip().split("\t"), 1, "") + elif line.startswith("H-"): if tgt is not None: - src = safe_index(line.rstrip().split('\t'), 2, '') + src = safe_index(line.rstrip().split("\t"), 2, "") if validate(src, tgt): print(src, file=src_h) print(tgt, file=tgt_h) tgt = None -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/examples/backtranslation/tokenized_bleu.sh b/examples/backtranslation/tokenized_bleu.sh index 1589da334a..c6d6aaa193 100644 --- a/examples/backtranslation/tokenized_bleu.sh +++ b/examples/backtranslation/tokenized_bleu.sh @@ -37,10 +37,10 @@ sacrebleu -t $DATASET -l $LANGPAIR --echo src -q \ | sacremoses normalize -l $SRCLANG -q \ | sacremoses tokenize -a -l $SRCLANG -q \ | python $BPEROOT/apply_bpe.py -c $BPECODE \ -| python interactive.py $DATABIN --path $MODEL \ +| fairseq-interactive $DATABIN --path $MODEL \ -s $SRCLANG -t $TGTLANG \ --beam 5 --remove-bpe --buffer-size 1024 --max-tokens 8000 \ | grep ^H- | cut -f 3- \ -| python score.py --ref $TMP_REF +| fairseq-score --ref $TMP_REF rm -f $TMP_REF diff --git a/examples/bart/README.glue.md b/examples/bart/README.glue.md index 797fdee31f..a010934e1e 100644 --- a/examples/bart/README.glue.md +++ b/examples/bart/README.glue.md @@ -24,9 +24,9 @@ NUM_CLASSES=2 MAX_SENTENCES=16 # Batch size. BART_PATH=/path/to/bart/model.pt -CUDA_VISIBLE_DEVICES=0,1 python train.py RTE-bin/ \ +CUDA_VISIBLE_DEVICES=0,1 fairseq-train RTE-bin/ \ --restore-file $BART_PATH \ - --max-sentences $MAX_SENTENCES \ + --batch-size $MAX_SENTENCES \ --max-tokens 4400 \ --task sentence_prediction \ --add-prev-output-tokens \ @@ -63,9 +63,9 @@ For `STS-B` additionally add `--regression-target --best-checkpoint-metric loss` **Note:** -a) `--total-num-updates` is used by `--polynomial_decay` scheduler and is calculated for `--max-epoch=10` and `--max-sentences=32/64/128` depending on the task. +a) `--total-num-updates` is used by `--polynomial_decay` scheduler and is calculated for `--max-epoch=10` and `--batch-size=32/64/128` depending on the task. -b) Above cmd-args and hyperparams are tested on Nvidia `V100` GPU with `32gb` of memory for each task. Depending on the GPU memory resources available to you, you can use increase `--update-freq` and reduce `--max-sentences`. +b) Above cmd-args and hyperparams are tested on Nvidia `V100` GPU with `32gb` of memory for each task. Depending on the GPU memory resources available to you, you can use increase `--update-freq` and reduce `--batch-size`. ### Inference on GLUE task After training the model as mentioned in previous step, you can perform inference with checkpoints in `checkpoints/` directory using following python code snippet: diff --git a/examples/bart/README.md b/examples/bart/README.md index 9ca210a401..4050a724ee 100644 --- a/examples/bart/README.md +++ b/examples/bart/README.md @@ -1,15 +1,16 @@ # BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension -[https://arxiv.org/pdf/1910.13461.pdf] +[https://arxiv.org/abs/1910.13461](https://arxiv.org/abs/1910.13461) ## Introduction -BART is sequence-to-sequence model trained with denoising as pretraining objective. We show that this pretraining objective is more generic and show that we can match [RoBERTa](../roberta) Results on SQuAD and GLUE and gain state-of-the-art results on summarization (XSum, CNN dataset), long form generative question answering (ELI5) and dialog response genration (ConvAI2). See the associated paper for more details. +BART is sequence-to-sequence model trained with denoising as pretraining objective. We show that this pretraining objective is more generic and show that we can match [RoBERTa](../roberta) results on SQuAD and GLUE and gain state-of-the-art results on summarization (XSum, CNN dataset), long form generative question answering (ELI5) and dialog response genration (ConvAI2). See the associated paper for more details. ## Pre-trained models Model | Description | # params | Download ---|---|---|--- +`bart.base` | BART model with 6 encoder and decoder layers | 140M | [bart.base.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/bart.base.tar.gz) `bart.large` | BART model with 12 encoder and decoder layers | 400M | [bart.large.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/bart.large.tar.gz) `bart.large.mnli` | `bart.large` finetuned on `MNLI` | 400M | [bart.large.mnli.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/bart.large.mnli.tar.gz) `bart.large.cnn` | `bart.large` finetuned on `CNN-DM` | 400M | [bart.large.cnn.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/bart.large.cnn.tar.gz) @@ -99,7 +100,7 @@ bart.predict('mnli', tokens).argmax() # 2: entailment ##### Register a new (randomly initialized) classification head: ```python bart.register_classification_head('new_task', num_classes=3) -logprobs = bart.predict('new_task', tokens) +logprobs = bart.predict('new_task', tokens) ``` ##### Batched prediction: @@ -130,6 +131,31 @@ bart.cuda() bart.predict('new_task', tokens) ``` +#### Filling masks: + +BART can be used to fill multiple `` tokens in the input. +```python +bart = torch.hub.load('pytorch/fairseq', 'bart.base') +bart.eval() +bart.fill_mask(['The cat on the .'], topk=3, beam=10) +# [[('The cat was on the ground.', tensor(-0.6183)), ('The cat was on the floor.', tensor(-0.6798)), ('The cat sleeps on the couch.', tensor(-0.6830))]] +``` + +Note that by default we enforce the output length to match the input length. +This can be disabled by setting ``match_source_len=False``: +``` +bart.fill_mask(['The cat on the .'], topk=3, beam=10, match_source_len=False) +# [[('The cat was on the ground.', tensor(-0.6185)), ('The cat was asleep on the couch.', tensor(-0.6276)), ('The cat was on the floor.', tensor(-0.6800))]] +``` + +Example code to fill masks for a batch of sentences using GPU +``` +bart.cuda() +bart.fill_mask(['The cat on the .', 'The dog on the .'], topk=3, beam=10) +# [[('The cat was on the ground.', tensor(-0.6183)), ('The cat was on the floor.', tensor(-0.6798)), ('The cat sleeps on the couch.', tensor(-0.6830))], [('The dog was on the ground.', tensor(-0.6190)), ('The dog lay on the ground.', tensor(-0.6711)), +('The dog was asleep on the couch', tensor(-0.6796))]] +``` + #### Evaluating the `bart.large.mnli` model: Example python code snippet to evaluate accuracy on the MNLI `dev_matched` set. @@ -153,38 +179,23 @@ with open('glue_data/MNLI/dev_matched.tsv') as fin: ``` #### Evaluating the `bart.large.cnn` model: -Follow instructions [here](https://github.com/abisee/cnn-dailymail) to download and process into data-files such that `test.source` and `test.target` has one line for each non-tokenized sample. +- Follow instructions [here](https://github.com/abisee/cnn-dailymail) to download and process into data-files such that `test.source` and `test.target` has one line for each non-tokenized sample. +- For simpler preprocessing, you can also `wget https://cdn-datasets.huggingface.co/summarization/cnn_dm_v2.tgz`, although there is no guarantee of identical scores +- `huggingface/transformers` has a simpler interface that supports [single-gpu](https://github.com/huggingface/transformers/blob/master/examples/legacy/seq2seq/run_eval.py) and [multi-gpu](https://github.com/huggingface/transformers/blob/master/examples/legacy/seq2seq/run_distributed_eval.py) beam search. + In `huggingface/transformers`, the BART models' paths are `facebook/bart-large-cnn` and `facebook/bart-large-xsum`. -```python -bart = torch.hub.load('pytorch/fairseq', 'bart.large.cnn') -bart.cuda() -bart.eval() -bart.half() -count = 1 -bsz = 32 -with open('test.source') as source, open('test.hypo', 'w') as fout: - sline = source.readline().strip() - slines = [sline] - for sline in source: - if count % bsz == 0: - with torch.no_grad(): - hypotheses_batch = bart.sample(slines, beam=4, lenpen=2.0, max_len_b=140, min_len=55, no_repeat_ngram_size=3) - - for hypothesis in hypotheses_batch: - fout.write(hypothesis + '\n') - fout.flush() - slines = [] - - slines.append(sline.strip()) - count += 1 - if slines != []: - hypotheses_batch = bart.sample(slines, beam=4, lenpen=2.0, max_len_b=140, min_len=55, no_repeat_ngram_size=3) - for hypothesis in hypotheses_batch: - fout.write(hypothesis + '\n') - fout.flush() -``` - -Install `files2rouge` from [here](https://github.com/pltrdy/files2rouge). +In `fairseq`, summaries can be generated using: + +```bash +cp data-bin/cnn_dm/dict.source.txt checkpoints/ +python examples/bart/summarize.py \ + --model-dir pytorch/fairseq \ + --model-file bart.large.cnn \ + --src cnn_dm/test.source \ + --out cnn_dm/test.hypo +``` + +For calculating rouge, install `files2rouge` from [here](https://github.com/pltrdy/files2rouge). ```bash export CLASSPATH=/path/to/stanford-corenlp-full-2016-10-31/stanford-corenlp-3.7.0.jar diff --git a/examples/bart/README.summarization.md b/examples/bart/README.summarization.md index 4af7ab8d6a..8727584f2b 100644 --- a/examples/bart/README.summarization.md +++ b/examples/bart/README.summarization.md @@ -52,7 +52,7 @@ MAX_TOKENS=2048 UPDATE_FREQ=4 BART_PATH=/path/to/bart/model.pt -CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python train.py cnn_dm-bin \ +CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 fairseq-train cnn_dm-bin \ --restore-file $BART_PATH \ --max-tokens $MAX_TOKENS \ --task translation \ @@ -80,42 +80,23 @@ Expected training time is about `5 hours`. Training time can be reduced with dis Use TOTAL_NUM_UPDATES=15000 UPDATE_FREQ=2 for Xsum task ### Inference for CNN-DM test data using above trained checkpoint. -After training the model as mentioned in previous step, you can perform inference with checkpoints in `checkpoints/` directory using following python code snippet: +After training the model as mentioned in previous step, you can perform inference with checkpoints in `checkpoints/` directory using `eval_cnn.py`, for example -```python -import torch -from fairseq.models.bart import BARTModel - -bart = BARTModel.from_pretrained( - 'checkpoints/', - checkpoint_file='checkpoint_best.pt', - data_name_or_path='cnn_dm-bin' -) - -bart.cuda() -bart.eval() -bart.half() -count = 1 -bsz = 32 -with open('cnn_dm/test.source') as source, open('cnn_dm/test.hypo', 'w') as fout: - sline = source.readline().strip() - slines = [sline] - for sline in source: - if count % bsz == 0: - with torch.no_grad(): - hypotheses_batch = bart.sample(slines, beam=4, lenpen=2.0, max_len_b=140, min_len=55, no_repeat_ngram_size=3) - - for hypothesis in hypotheses_batch: - fout.write(hypothesis + '\n') - fout.flush() - slines = [] - - slines.append(sline.strip()) - count += 1 - if slines != []: - hypotheses_batch = bart.sample(slines, beam=4, lenpen=2.0, max_len_b=140, min_len=55, no_repeat_ngram_size=3) - for hypothesis in hypotheses_batch: - fout.write(hypothesis + '\n') - fout.flush() +```bash +cp data-bin/cnn_dm/dict.source.txt checkpoints/ +python examples/bart/summarize.py \ + --model-dir checkpoints \ + --model-file checkpoint_best.pt \ + --src cnn_dm/test.source \ + --out cnn_dm/test.hypo +``` +For XSUM, which uses beam=6, lenpen=1.0, max_len_b=60, min_len=10: +```bash +cp data-bin/cnn_dm/dict.source.txt checkpoints/ +python examples/bart/summarize.py \ + --model-dir checkpoints \ + --model-file checkpoint_best.pt \ + --src cnn_dm/test.source \ + --out cnn_dm/test.hypo \ + --xsum-kwargs ``` -Use beam=6, lenpen=1.0, max_len_b=60, min_len=10 for Xsum Generation diff --git a/examples/bart/summarize.py b/examples/bart/summarize.py new file mode 100644 index 0000000000..04435f80e3 --- /dev/null +++ b/examples/bart/summarize.py @@ -0,0 +1,100 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +from fairseq.models.bart import BARTModel +import argparse + +XSUM_KWARGS = dict(beam=6, lenpen=1.0, max_len_b=60, min_len=10, no_repeat_ngram_size=3) +CNN_KWARGS = dict(beam=4, lenpen=2.0, max_len_b=140, min_len=55, no_repeat_ngram_size=3) + + +@torch.no_grad() +def generate(bart, infile, outfile="bart_hypo.txt", bsz=32, n_obs=None, **eval_kwargs): + count = 1 + + # if n_obs is not None: bsz = min(bsz, n_obs) + + with open(infile) as source, open(outfile, "w") as fout: + sline = source.readline().strip() + slines = [sline] + for sline in source: + if n_obs is not None and count > n_obs: + break + if count % bsz == 0: + hypotheses_batch = bart.sample(slines, **eval_kwargs) + for hypothesis in hypotheses_batch: + fout.write(hypothesis + "\n") + fout.flush() + slines = [] + + slines.append(sline.strip()) + count += 1 + + if slines != []: + hypotheses_batch = bart.sample(slines, **eval_kwargs) + for hypothesis in hypotheses_batch: + fout.write(hypothesis + "\n") + fout.flush() + + +def main(): + """ + Usage:: + + python examples/bart/summarize.py \ + --model-dir $HOME/bart.large.cnn \ + --model-file model.pt \ + --src $HOME/data-bin/cnn_dm/test.source + """ + parser = argparse.ArgumentParser() + parser.add_argument( + "--model-dir", + required=True, + type=str, + default="bart.large.cnn/", + help="path containing model file and src_dict.txt", + ) + parser.add_argument( + "--model-file", + default="checkpoint_best.pt", + help="where in model_dir are weights saved", + ) + parser.add_argument( + "--src", default="test.source", help="text to summarize", type=str + ) + parser.add_argument( + "--out", default="test.hypo", help="where to save summaries", type=str + ) + parser.add_argument("--bsz", default=32, help="where to save summaries", type=int) + parser.add_argument( + "--n", default=None, help="how many examples to summarize", type=int + ) + parser.add_argument( + "--xsum-kwargs", + action="store_true", + default=False, + help="if true use XSUM_KWARGS else CNN_KWARGS", + ) + args = parser.parse_args() + eval_kwargs = XSUM_KWARGS if args.xsum_kwargs else CNN_KWARGS + if args.model_dir == "pytorch/fairseq": + bart = torch.hub.load("pytorch/fairseq", args.model_file) + else: + bart = BARTModel.from_pretrained( + args.model_dir, + checkpoint_file=args.model_file, + data_name_or_path=args.model_dir, + ) + bart = bart.eval() + if torch.cuda.is_available(): + bart = bart.cuda().half() + generate( + bart, args.src, bsz=args.bsz, n_obs=args.n, outfile=args.out, **eval_kwargs + ) + + +if __name__ == "__main__": + main() diff --git a/examples/byte_level_bpe/README.md b/examples/byte_level_bpe/README.md index fb44a87e50..657092660e 100644 --- a/examples/byte_level_bpe/README.md +++ b/examples/byte_level_bpe/README.md @@ -29,7 +29,7 @@ fairseq-train "data/bin_${VOCAB}" --task translation --user-dir examples/byte_le --lr 5e-4 --lr-scheduler inverse_sqrt --warmup-updates 4000 \ --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \ --log-format 'simple' --log-interval 100 --save-dir "checkpoints/${VOCAB}" \ - --max-sentences 100 --max-update 100000 --update-freq 2 + --batch-size 100 --max-update 100000 --update-freq 2 ``` ## Generation @@ -38,10 +38,10 @@ fairseq-train "data/bin_${VOCAB}" --task translation --user-dir examples/byte_le # BPE=--bpe bytes # BPE=--bpe characters BPE=--bpe byte_bpe --sentencepiece-model-path data/spm_bbpe2048.model -# BPE=--bpe sentencepiece --sentencepiece-vocab data/spm_bpe2048.model +# BPE=--bpe sentencepiece --sentencepiece-model data/spm_bpe2048.model # BPE=--bpe byte_bpe --sentencepiece-model-path data/spm_bbpe4096.model -# BPE=--bpe sentencepiece --sentencepiece-vocab data/spm_bpe4096.model -# BPE=--bpe sentencepiece --sentencepiece-vocab data/spm_bpe16384.model +# BPE=--bpe sentencepiece --sentencepiece-model data/spm_bpe4096.model +# BPE=--bpe sentencepiece --sentencepiece-model data/spm_bpe16384.model ``` ```bash diff --git a/examples/byte_level_bpe/get_bitext.py b/examples/byte_level_bpe/get_bitext.py index a114391143..6ac1eeec1e 100644 --- a/examples/byte_level_bpe/get_bitext.py +++ b/examples/byte_level_bpe/get_bitext.py @@ -4,203 +4,251 @@ # LICENSE file in the root directory of this source tree. -import os.path as op import argparse import os -from multiprocessing import cpu_count +import os.path as op from collections import namedtuple -from typing import Optional, List +from multiprocessing import cpu_count +from typing import List, Optional import sentencepiece as sp - -from fairseq.data.encoders.moses_tokenizer import MosesTokenizer -from fairseq.data.encoders.byte_utils import byte_encode -from fairseq.data.encoders.sentencepiece_bpe import SentencepieceBPE -from fairseq.data.encoders.characters import Characters from fairseq.data.encoders.byte_bpe import ByteBPE +from fairseq.data.encoders.byte_utils import byte_encode from fairseq.data.encoders.bytes import Bytes +from fairseq.data.encoders.characters import Characters +from fairseq.data.encoders.moses_tokenizer import MosesTokenizer +from fairseq.data.encoders.sentencepiece_bpe import SentencepieceBPE -SPLITS = ['train', 'valid', 'test'] +SPLITS = ["train", "valid", "test"] def _convert_xml(in_path: str, out_path: str): - with open(in_path) as f, open(out_path, 'w') as f_o: + with open(in_path) as f, open(out_path, "w") as f_o: for s in f: ss = s.strip() - if not ss.startswith('', '').split('">') + ss = ss.replace("", "").split('">') assert len(ss) == 2 - f_o.write(ss[1].strip() + '\n') + f_o.write(ss[1].strip() + "\n") def _convert_train(in_path: str, out_path: str): - with open(in_path) as f, open(out_path, 'w') as f_o: + with open(in_path) as f, open(out_path, "w") as f_o: for s in f: ss = s.strip() - if ss.startswith('<'): + if ss.startswith("<"): continue - f_o.write(ss.strip() + '\n') + f_o.write(ss.strip() + "\n") def _get_bytes(in_path: str, out_path: str): - with open(in_path) as f, open(out_path, 'w') as f_o: + with open(in_path) as f, open(out_path, "w") as f_o: for s in f: - f_o.write(Bytes.encode(s.strip()) + '\n') + f_o.write(Bytes.encode(s.strip()) + "\n") def _get_chars(in_path: str, out_path: str): - with open(in_path) as f, open(out_path, 'w') as f_o: + with open(in_path) as f, open(out_path, "w") as f_o: for s in f: - f_o.write(Characters.encode(s.strip()) + '\n') + f_o.write(Characters.encode(s.strip()) + "\n") def pretokenize(in_path: str, out_path: str, src: str, tgt: str): - Args = namedtuple('Args', ['moses_source_lang', 'moses_target_lang', - 'moses_no_dash_splits', 'moses_no_escape']) - args = Args(moses_source_lang=src, moses_target_lang=tgt, - moses_no_dash_splits=False, moses_no_escape=False) + Args = namedtuple( + "Args", + [ + "moses_source_lang", + "moses_target_lang", + "moses_no_dash_splits", + "moses_no_escape", + ], + ) + args = Args( + moses_source_lang=src, + moses_target_lang=tgt, + moses_no_dash_splits=False, + moses_no_escape=False, + ) pretokenizer = MosesTokenizer(args) - with open(in_path) as f, open(out_path, 'w') as f_o: + with open(in_path) as f, open(out_path, "w") as f_o: for s in f: - f_o.write(pretokenizer.encode(s.strip()) + '\n') + f_o.write(pretokenizer.encode(s.strip()) + "\n") def _convert_to_bchar(in_path_prefix: str, src: str, tgt: str, out_path: str): - with open(out_path, 'w') as f_o: + with open(out_path, "w") as f_o: for lang in [src, tgt]: - with open(f'{in_path_prefix}.{lang}') as f: + with open(f"{in_path_prefix}.{lang}") as f: for s in f: - f_o.write(byte_encode(s.strip()) + '\n') + f_o.write(byte_encode(s.strip()) + "\n") def _get_bpe(in_path: str, model_prefix: str, vocab_size: int): arguments = [ - f'--input={in_path}', f'--model_prefix={model_prefix}', - f'--model_type=bpe', f'--vocab_size={vocab_size}', - '--character_coverage=1.0', '--normalization_rule_name=identity', - f'--num_threads={cpu_count()}' + f"--input={in_path}", + f"--model_prefix={model_prefix}", + f"--model_type=bpe", + f"--vocab_size={vocab_size}", + "--character_coverage=1.0", + "--normalization_rule_name=identity", + f"--num_threads={cpu_count()}", ] - sp.SentencePieceTrainer.Train(' '.join(arguments)) + sp.SentencePieceTrainer.Train(" ".join(arguments)) def _apply_bbpe(model_path: str, in_path: str, out_path: str): - Args = namedtuple('Args', ['sentencepiece_model_path']) + Args = namedtuple("Args", ["sentencepiece_model_path"]) args = Args(sentencepiece_model_path=model_path) tokenizer = ByteBPE(args) - with open(in_path) as f, open(out_path, 'w') as f_o: + with open(in_path) as f, open(out_path, "w") as f_o: for s in f: - f_o.write(tokenizer.encode(s.strip()) + '\n') + f_o.write(tokenizer.encode(s.strip()) + "\n") def _apply_bpe(model_path: str, in_path: str, out_path: str): - Args = namedtuple('Args', ['sentencepiece_vocab']) - args = Args(sentencepiece_vocab=model_path) + Args = namedtuple("Args", ["sentencepiece_model"]) + args = Args(sentencepiece_model=model_path) tokenizer = SentencepieceBPE(args) - with open(in_path) as f, open(out_path, 'w') as f_o: + with open(in_path) as f, open(out_path, "w") as f_o: for s in f: - f_o.write(tokenizer.encode(s.strip()) + '\n') + f_o.write(tokenizer.encode(s.strip()) + "\n") def _concat_files(in_paths: List[str], out_path: str): - with open(out_path, 'w') as f_o: + with open(out_path, "w") as f_o: for p in in_paths: with open(p) as f: for r in f: f_o.write(r) -def preprocess_iwslt17(root: str, src: str, tgt: str, bpe_size: Optional[int], - need_chars: bool, bbpe_size: Optional[int], - need_bytes: bool): +def preprocess_iwslt17( + root: str, + src: str, + tgt: str, + bpe_size: Optional[int], + need_chars: bool, + bbpe_size: Optional[int], + need_bytes: bool, +): # extract bitext - in_root = op.join(root, f'{src}-{tgt}') + in_root = op.join(root, f"{src}-{tgt}") for lang in [src, tgt]: _convert_train( - op.join(in_root, f'train.tags.{src}-{tgt}.{lang}'), - op.join(root, f'train.{lang}') + op.join(in_root, f"train.tags.{src}-{tgt}.{lang}"), + op.join(root, f"train.{lang}"), ) _convert_xml( - op.join(in_root, f'IWSLT17.TED.dev2010.{src}-{tgt}.{lang}.xml'), - op.join(root, f'valid.{lang}') + op.join(in_root, f"IWSLT17.TED.dev2010.{src}-{tgt}.{lang}.xml"), + op.join(root, f"valid.{lang}"), ) _convert_xml( - op.join(in_root, f'IWSLT17.TED.tst2015.{src}-{tgt}.{lang}.xml'), - op.join(root, f'test.{lang}') + op.join(in_root, f"IWSLT17.TED.tst2015.{src}-{tgt}.{lang}.xml"), + op.join(root, f"test.{lang}"), ) # pre-tokenize for lang in [src, tgt]: for split in SPLITS: - pretokenize(op.join(root, f'{split}.{lang}'), - op.join(root, f'{split}.moses.{lang}'), src, tgt) + pretokenize( + op.join(root, f"{split}.{lang}"), + op.join(root, f"{split}.moses.{lang}"), + src, + tgt, + ) # tokenize with BPE vocabulary if bpe_size is not None: # learn vocabulary - concated_train_path = op.join(root, 'train.all') + concated_train_path = op.join(root, "train.all") _concat_files( - [op.join(root, 'train.moses.fr'), op.join(root, 'train.moses.en')], - concated_train_path + [op.join(root, "train.moses.fr"), op.join(root, "train.moses.en")], + concated_train_path, ) - bpe_model_prefix = op.join(root, f'spm_bpe{bpe_size}') + bpe_model_prefix = op.join(root, f"spm_bpe{bpe_size}") _get_bpe(concated_train_path, bpe_model_prefix, bpe_size) os.remove(concated_train_path) # apply for lang in [src, tgt]: for split in SPLITS: _apply_bpe( - bpe_model_prefix + '.model', - op.join(root, f'{split}.moses.{lang}'), - op.join(root, f'{split}.moses.bpe{bpe_size}.{lang}') + bpe_model_prefix + ".model", + op.join(root, f"{split}.moses.{lang}"), + op.join(root, f"{split}.moses.bpe{bpe_size}.{lang}"), ) # tokenize with bytes vocabulary if need_bytes: for lang in [src, tgt]: for split in SPLITS: - _get_bytes(op.join(root, f'{split}.moses.{lang}'), - op.join(root, f'{split}.moses.bytes.{lang}')) + _get_bytes( + op.join(root, f"{split}.moses.{lang}"), + op.join(root, f"{split}.moses.bytes.{lang}"), + ) # tokenize with characters vocabulary if need_chars: for lang in [src, tgt]: for split in SPLITS: - _get_chars(op.join(root, f'{split}.moses.{lang}'), - op.join(root, f'{split}.moses.chars.{lang}')) + _get_chars( + op.join(root, f"{split}.moses.{lang}"), + op.join(root, f"{split}.moses.chars.{lang}"), + ) # tokenize with byte-level BPE vocabulary if bbpe_size is not None: # learn vocabulary - bchar_path = op.join(root, 'train.bchar') - _convert_to_bchar(op.join(root, 'train.moses'), src, tgt, bchar_path) - bbpe_model_prefix = op.join(root, f'spm_bbpe{bbpe_size}') + bchar_path = op.join(root, "train.bchar") + _convert_to_bchar(op.join(root, "train.moses"), src, tgt, bchar_path) + bbpe_model_prefix = op.join(root, f"spm_bbpe{bbpe_size}") _get_bpe(bchar_path, bbpe_model_prefix, bbpe_size) os.remove(bchar_path) # apply for lang in [src, tgt]: for split in SPLITS: _apply_bbpe( - bbpe_model_prefix + '.model', - op.join(root, f'{split}.moses.{lang}'), - op.join(root, f'{split}.moses.bbpe{bbpe_size}.{lang}') + bbpe_model_prefix + ".model", + op.join(root, f"{split}.moses.{lang}"), + op.join(root, f"{split}.moses.bbpe{bbpe_size}.{lang}"), ) def main(): parser = argparse.ArgumentParser() - parser.add_argument('--root', type=str, default='data') - parser.add_argument('--bpe-vocab', default=None, type=int, - help='Generate tokenized bitext with BPE of size K.' - 'Default to None (disabled).') - parser.add_argument('--bbpe-vocab', default=None, type=int, - help='Generate tokenized bitext with BBPE of size K.' - 'Default to None (disabled).') - parser.add_argument('--byte-vocab', action='store_true', - help='Generate tokenized bitext with bytes vocabulary') - parser.add_argument('--char-vocab', action='store_true', - help='Generate tokenized bitext with chars vocabulary') + parser.add_argument("--root", type=str, default="data") + parser.add_argument( + "--bpe-vocab", + default=None, + type=int, + help="Generate tokenized bitext with BPE of size K." + "Default to None (disabled).", + ) + parser.add_argument( + "--bbpe-vocab", + default=None, + type=int, + help="Generate tokenized bitext with BBPE of size K." + "Default to None (disabled).", + ) + parser.add_argument( + "--byte-vocab", + action="store_true", + help="Generate tokenized bitext with bytes vocabulary", + ) + parser.add_argument( + "--char-vocab", + action="store_true", + help="Generate tokenized bitext with chars vocabulary", + ) args = parser.parse_args() - preprocess_iwslt17(args.root, 'fr', 'en', args.bpe_vocab, args.char_vocab, - args.bbpe_vocab, args.byte_vocab) + preprocess_iwslt17( + args.root, + "fr", + "en", + args.bpe_vocab, + args.char_vocab, + args.bbpe_vocab, + args.byte_vocab, + ) -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/examples/byte_level_bpe/gru_transformer.py b/examples/byte_level_bpe/gru_transformer.py index 79ebbda1fc..d4efa93a4d 100644 --- a/examples/byte_level_bpe/gru_transformer.py +++ b/examples/byte_level_bpe/gru_transformer.py @@ -11,7 +11,7 @@ import torch.nn as nn import torch.nn.functional as F from fairseq.models import register_model, register_model_architecture -from fairseq.models.transformer import TransformerModel, TransformerEncoder +from fairseq.models.transformer import TransformerEncoder, TransformerModel @register_model("gru_transformer") @@ -24,9 +24,12 @@ def build_encoder(cls, args, src_dict, embed_tokens): class GRUTransformerEncoder(TransformerEncoder): def __init__(self, args, dictionary, embed_tokens): super().__init__(args, dictionary, embed_tokens) - self.emb_ctx = nn.GRU(input_size=embed_tokens.embedding_dim, - hidden_size=embed_tokens.embedding_dim // 2, - num_layers=1, bidirectional=True) + self.emb_ctx = nn.GRU( + input_size=embed_tokens.embedding_dim, + hidden_size=embed_tokens.embedding_dim // 2, + num_layers=1, + bidirectional=True, + ) def forward_embedding(self, src_tokens): # embed tokens and positions @@ -36,13 +39,13 @@ def forward_embedding(self, src_tokens): # contextualize embeddings x = x.transpose(0, 1) - x = F.dropout(x, p=self.dropout, training=self.training) + x = self.dropout_module(x) x, _ = self.emb_ctx.forward(x) x = x.transpose(0, 1) if self.layernorm_embedding is not None: x = self.layernorm_embedding(x) - x = F.dropout(x, p=self.dropout, training=self.training) + x = self.dropout_module(x) return x, embed diff --git a/examples/camembert/README.md b/examples/camembert/README.md index 9e41c57823..5ef4fe3f15 100644 --- a/examples/camembert/README.md +++ b/examples/camembert/README.md @@ -30,7 +30,7 @@ camembert.eval() # disable dropout (or leave in train mode to finetune) ##### Load CamemBERT (for PyTorch 1.0 or custom models): ```python # Download camembert model -wget https://dl.fbaipublicfiles.com/fairseq/models/camembert.tar.gz +wget https://dl.fbaipublicfiles.com/fairseq/models/camembert-base.tar.gz tar -xzvf camembert.tar.gz # Load the model in fairseq diff --git a/examples/constrained_decoding/README.md b/examples/constrained_decoding/README.md new file mode 100644 index 0000000000..e04b8b6a01 --- /dev/null +++ b/examples/constrained_decoding/README.md @@ -0,0 +1,123 @@ +# (Vectorized) Lexically constrained decoding with dynamic beam allocation + +This page provides instructions for how to use lexically constrained decoding in Fairseq. +Fairseq implements the code described in the following papers: + +* [Fast Lexically Constrained Decoding With Dynamic Beam Allocation](https://www.aclweb.org/anthology/N18-1119/) (Post & Vilar, 2018) +* [Improved Lexically Constrained Decoding for Translation and Monolingual Rewriting](https://www.aclweb.org/anthology/N19-1090/) (Hu et al., 2019) + +## Quick start + +Constrained search is enabled by adding the command-line argument `--constraints` to `fairseq-interactive`. +Constraints are appended to each line of input, separated by tabs. Each constraint (one or more tokens) +is a separate field. + +The following command, using [Fairseq's WMT19 German--English model](https://github.com/pytorch/fairseq/blob/main/examples/wmt19/README.md), +translates the sentence *Die maschinelle Übersetzung ist schwer zu kontrollieren.* with the constraints +"hard" and "to influence". + + echo -e "Die maschinelle Übersetzung ist schwer zu kontrollieren.\thard\ttoinfluence" \ + | normalize.py | tok.py \ + | fairseq-interactive /path/to/model \ + --path /path/to/model/model1.pt \ + --bpe fastbpe \ + --bpe-codes /path/to/model/bpecodes \ + --constraints \ + -s de -t en \ + --beam 10 + +(tok.py and normalize.py can be found in the same directory as this README; they are just shortcuts around Fairseq's WMT19 preprocessing). +This will generate the following output: + + [snip] + S-0 Die masch@@ in@@ elle Über@@ setzung ist schwer zu kontrollieren . + W-0 1.844 seconds + C-0 hard + C-0 influence + H-0 -1.5333266258239746 Mach@@ ine trans@@ lation is hard to influence . + D-0 -1.5333266258239746 Machine translation is hard to influence . + P-0 -0.5434 -0.1423 -0.1930 -0.1415 -0.2346 -1.8031 -0.1701 -11.7727 -0.1815 -0.1511 + +By default, constraints are generated in the order supplied, with any number (zero or more) of tokens generated +between constraints. If you wish for the decoder to order the constraints, then use `--constraints unordered`. +Note that you may want to use a larger beam. + +## Implementation details + +The heart of the implementation is in `fairseq/search.py`, which adds a `LexicallyConstrainedBeamSearch` instance. +This instance of beam search tracks the progress of each hypothesis in the beam through the set of constraints +provided for each input sentence. It does this using one of two classes, both found in `fairseq/token_generation_contstraints.py`: + +* OrderedConstraintState: assumes the `C` input constraints will be generated in the provided order +* UnorderedConstraintState: tries to apply `C` (phrasal) constraints in all `C!` orders + +## Differences from Sockeye + +There are a number of [differences from Sockeye's implementation](https://awslabs.github.io/sockeye/inference.html#lexical-constraints). + +* Generating constraints in the order supplied (the default option here) is not available in Sockeye. +* Due to an improved beam allocation method, there is no need to prune the beam. +* Again due to better allocation, beam sizes as low as 10 or even 5 are often sufficient. +* [The vector extensions described in Hu et al.](https://github.com/edwardjhu/sockeye/tree/trie_constraints) (NAACL 2019) were never merged + into the main Sockeye branch. + +## Citation + +The paper first describing lexical constraints for seq2seq decoding is: + +```bibtex +@inproceedings{hokamp-liu-2017-lexically, + title = "Lexically Constrained Decoding for Sequence Generation Using Grid Beam Search", + author = "Hokamp, Chris and + Liu, Qun", + booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", + month = jul, + year = "2017", + address = "Vancouver, Canada", + publisher = "Association for Computational Linguistics", + url = "https://www.aclweb.org/anthology/P17-1141", + doi = "10.18653/v1/P17-1141", + pages = "1535--1546", +} +``` + +The fairseq implementation uses the extensions described in + +```bibtex +@inproceedings{post-vilar-2018-fast, + title = "Fast Lexically Constrained Decoding with Dynamic Beam Allocation for Neural Machine Translation", + author = "Post, Matt and + Vilar, David", + booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)", + month = jun, + year = "2018", + address = "New Orleans, Louisiana", + publisher = "Association for Computational Linguistics", + url = "https://www.aclweb.org/anthology/N18-1119", + doi = "10.18653/v1/N18-1119", + pages = "1314--1324", +} +``` + +and + +```bibtex +@inproceedings{hu-etal-2019-improved, + title = "Improved Lexically Constrained Decoding for Translation and Monolingual Rewriting", + author = "Hu, J. Edward and + Khayrallah, Huda and + Culkin, Ryan and + Xia, Patrick and + Chen, Tongfei and + Post, Matt and + Van Durme, Benjamin", + booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)", + month = jun, + year = "2019", + address = "Minneapolis, Minnesota", + publisher = "Association for Computational Linguistics", + url = "https://www.aclweb.org/anthology/N19-1090", + doi = "10.18653/v1/N19-1090", + pages = "839--850", +} +``` diff --git a/examples/constrained_decoding/normalize.py b/examples/constrained_decoding/normalize.py new file mode 100755 index 0000000000..4ae2b5111b --- /dev/null +++ b/examples/constrained_decoding/normalize.py @@ -0,0 +1,27 @@ +#!/usr/bin/env python3 +# +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import sys + +from sacremoses.normalize import MosesPunctNormalizer + + +def main(args): + normalizer = MosesPunctNormalizer(lang=args.lang, penn=args.penn) + for line in sys.stdin: + print(normalizer.normalize(line.rstrip()), flush=True) + + +if __name__ == "__main__": + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument("--lang", "-l", default="en") + parser.add_argument("--penn", "-p", action="store_true") + args = parser.parse_args() + + main(args) diff --git a/examples/constrained_decoding/tok.py b/examples/constrained_decoding/tok.py new file mode 100755 index 0000000000..b1f888a8c0 --- /dev/null +++ b/examples/constrained_decoding/tok.py @@ -0,0 +1,34 @@ +#!/usr/bin/env python3 +# +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import sys + +import sacremoses + + +def main(args): + """Tokenizes, preserving tabs""" + mt = sacremoses.MosesTokenizer(lang=args.lang) + + def tok(s): + return mt.tokenize(s, return_str=True) + + for line in sys.stdin: + parts = list(map(tok, line.split("\t"))) + print(*parts, sep="\t", flush=True) + + +if __name__ == "__main__": + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument("--lang", "-l", default="en") + parser.add_argument("--penn", "-p", action="store_true") + parser.add_argument("--fields", "-f", help="fields to tokenize") + args = parser.parse_args() + + main(args) diff --git a/examples/criss/README.md b/examples/criss/README.md new file mode 100644 index 0000000000..4689ed7c10 --- /dev/null +++ b/examples/criss/README.md @@ -0,0 +1,61 @@ +# Cross-lingual Retrieval for Iterative Self-Supervised Training + +https://arxiv.org/pdf/2006.09526.pdf + +## Introduction + +CRISS is a multilingual sequence-to-sequnce pretraining method where mining and training processes are applied iteratively, improving cross-lingual alignment and translation ability at the same time. + +## Requirements: + +* faiss: https://github.com/facebookresearch/faiss +* mosesdecoder: https://github.com/moses-smt/mosesdecoder +* flores: https://github.com/facebookresearch/flores +* LASER: https://github.com/facebookresearch/LASER + +## Unsupervised Machine Translation +##### 1. Download and decompress CRISS checkpoints +``` +cd examples/criss +wget https://dl.fbaipublicfiles.com/criss/criss_3rd_checkpoints.tar.gz +tar -xf criss_checkpoints.tar.gz +``` +##### 2. Download and preprocess Flores test dataset +Make sure to run all scripts from examples/criss directory +``` +bash download_and_preprocess_flores_test.sh +``` + +##### 3. Run Evaluation on Sinhala-English +``` +bash unsupervised_mt/eval.sh +``` + +## Sentence Retrieval +##### 1. Download and preprocess Tatoeba dataset +``` +bash download_and_preprocess_tatoeba.sh +``` + +##### 2. Run Sentence Retrieval on Tatoeba Kazakh-English +``` +bash sentence_retrieval/sentence_retrieval_tatoeba.sh +``` + +## Mining +##### 1. Install faiss +Follow instructions on https://github.com/facebookresearch/faiss/blob/master/INSTALL.md +##### 2. Mine pseudo-parallel data between Kazakh and English +``` +bash mining/mine_example.sh +``` + +## Citation +```bibtex +@article{tran2020cross, + title={Cross-lingual retrieval for iterative self-supervised training}, + author={Tran, Chau and Tang, Yuqing and Li, Xian and Gu, Jiatao}, + journal={arXiv preprint arXiv:2006.09526}, + year={2020} +} +``` diff --git a/examples/criss/download_and_preprocess_flores_test.sh b/examples/criss/download_and_preprocess_flores_test.sh new file mode 100644 index 0000000000..ed4b390fbd --- /dev/null +++ b/examples/criss/download_and_preprocess_flores_test.sh @@ -0,0 +1,64 @@ +#!/bin/bash +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +SPM_ENCODE=flores/scripts/spm_encode.py +DATA=data_tmp +SPM_MODEL=criss_checkpoints/sentence.bpe.model +DICT=criss_checkpoints/dict.txt + +download_data() { + CORPORA=$1 + URL=$2 + + if [ -f $CORPORA ]; then + echo "$CORPORA already exists, skipping download" + else + echo "Downloading $URL" + wget $URL -O $CORPORA --no-check-certificate || rm -f $CORPORA + if [ -f $CORPORA ]; then + echo "$URL successfully downloaded." + else + echo "$URL not successfully downloaded." + rm -f $CORPORA + fi + fi +} + +if [[ -f flores ]]; then + echo "flores already cloned" +else + git clone https://github.com/facebookresearch/flores +fi + +mkdir -p $DATA +download_data $DATA/wikipedia_en_ne_si_test_sets.tgz "https://github.com/facebookresearch/flores/raw/master/data/wikipedia_en_ne_si_test_sets.tgz" +pushd $DATA +pwd +tar -vxf wikipedia_en_ne_si_test_sets.tgz +popd + + +for lang in ne_NP si_LK; do + datadir=$DATA/${lang}-en_XX-flores + rm -rf $datadir + mkdir -p $datadir + TEST_PREFIX=$DATA/wikipedia_en_ne_si_test_sets/wikipedia.test + python $SPM_ENCODE \ + --model ${SPM_MODEL} \ + --output_format=piece \ + --inputs ${TEST_PREFIX}.${lang:0:2}-en.${lang:0:2} ${TEST_PREFIX}.${lang:0:2}-en.en \ + --outputs $datadir/test.bpe.${lang}-en_XX.${lang} $datadir/test.bpe.${lang}-en_XX.en_XX + + # binarize data + fairseq-preprocess \ + --source-lang ${lang} --target-lang en_XX \ + --testpref $datadir/test.bpe.${lang}-en_XX \ + --destdir $datadir \ + --srcdict ${DICT} \ + --joined-dictionary \ + --workers 4 +done diff --git a/examples/criss/download_and_preprocess_tatoeba.sh b/examples/criss/download_and_preprocess_tatoeba.sh new file mode 100644 index 0000000000..7ed64f017d --- /dev/null +++ b/examples/criss/download_and_preprocess_tatoeba.sh @@ -0,0 +1,46 @@ +#!/bin/bash +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +SPM_ENCODE=flores/scripts/spm_encode.py +DATA=data_tmp +SPM_MODEL=criss_checkpoints/sentence.bpe.model +DICT=criss_checkpoints/dict.txt + +if [[ -f flores ]]; then + echo "flores already cloned" +else + git clone https://github.com/facebookresearch/flores +fi +if [[ -f LASER ]]; then + echo "LASER already cloned" +else + git clone https://github.com/facebookresearch/LASER +fi +mkdir -p data_tmp +declare -A lang_tatoeba_map=( ["ar_AR"]="ara" ["de_DE"]="deu" ["es_XX"]="spa" ["et_EE"]="est" ["fi_FI"]="fin" ["fr_XX"]="fra" ["hi_IN"]="hin" ["it_IT"]="ita" ["ja_XX"]="jpn" ["ko_KR"]="kor" ["kk_KZ"]="kaz" ["nl_XX"]="nld" ["ru_RU"]="rus" ["tr_TR"]="tur" ["vi_VN"]="vie" ["zh_CN"]="cmn") +for lang in ar_AR de_DE es_XX et_EE fi_FI fr_XX hi_IN it_IT ja_XX kk_KZ ko_KR nl_XX ru_RU tr_TR vi_VN zh_CN; do + lang_tatoeba=${lang_tatoeba_map[$lang]} + echo $lang_tatoeba + datadir=$DATA/${lang}-en_XX-tatoeba + rm -rf $datadir + mkdir -p $datadir + TEST_PREFIX=LASER/data/tatoeba/v1/tatoeba + python $SPM_ENCODE \ + --model ${SPM_MODEL} \ + --output_format=piece \ + --inputs ${TEST_PREFIX}.${lang_tatoeba}-eng.${lang_tatoeba} ${TEST_PREFIX}.${lang_tatoeba}-eng.eng \ + --outputs $datadir/test.bpe.${lang}-en_XX.${lang} $datadir/test.bpe.${lang}-en_XX.en_XX + + # binarize data + fairseq-preprocess \ + --source-lang ${lang} --target-lang en_XX \ + --testpref $datadir/test.bpe.${lang}-en_XX \ + --destdir $datadir \ + --srcdict ${DICT} \ + --joined-dictionary \ + --workers 4 +done diff --git a/examples/criss/mining/mine.py b/examples/criss/mining/mine.py new file mode 100644 index 0000000000..c872da196f --- /dev/null +++ b/examples/criss/mining/mine.py @@ -0,0 +1,240 @@ +#!/usr/bin/env python3 -u +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +import argparse +import glob +from subprocess import check_call + +try: + import faiss + + has_faiss = True +except ImportError: + has_faiss = False +import numpy as np + + +GB = 1024 * 1024 * 1024 + + +def call(cmd): + print(cmd) + check_call(cmd, shell=True) + + +def get_batches(directory, lang, prefix="all_avg_pool"): + print(f"Finding in {directory}/{prefix}.{lang}*") + files = glob.glob(f"{directory}/{prefix}.{lang}*") + emb_files = [] + txt_files = [] + for emb_fi in files: + emb_files.append(emb_fi) + txt_fi = emb_fi.replace(prefix, "sentences") + txt_files.append(txt_fi) + return emb_files, txt_files + + +def load_batch(emb_file, dim): + embeddings = np.fromfile(emb_file, dtype=np.float32) + num_rows = int(embeddings.shape[0] / dim) + embeddings = embeddings.reshape((num_rows, dim)) + faiss.normalize_L2(embeddings) + return embeddings + + +def knnGPU_sharded(x_batches_f, y_batches_f, dim, k, direction="x2y"): + if not has_faiss: + raise ImportError("Please install Faiss") + sims = [] + inds = [] + xfrom = 0 + xto = 0 + for x_batch_f in x_batches_f: + yfrom = 0 + yto = 0 + x_batch = load_batch(x_batch_f, dim) + xto = xfrom + x_batch.shape[0] + bsims, binds = [], [] + for y_batch_f in y_batches_f: + y_batch = load_batch(y_batch_f, dim) + neighbor_size = min(k, y_batch.shape[0]) + yto = yfrom + y_batch.shape[0] + print("{}-{} -> {}-{}".format(xfrom, xto, yfrom, yto)) + idx = faiss.IndexFlatIP(dim) + idx = faiss.index_cpu_to_all_gpus(idx) + idx.add(y_batch) + bsim, bind = idx.search(x_batch, neighbor_size) + + bsims.append(bsim) + binds.append(bind + yfrom) + yfrom += y_batch.shape[0] + del idx + del y_batch + bsims = np.concatenate(bsims, axis=1) + binds = np.concatenate(binds, axis=1) + aux = np.argsort(-bsims, axis=1) + sim_batch = np.zeros((x_batch.shape[0], k), dtype=np.float32) + ind_batch = np.zeros((x_batch.shape[0], k), dtype=np.int64) + for i in range(x_batch.shape[0]): + for j in range(k): + sim_batch[i, j] = bsims[i, aux[i, j]] + ind_batch[i, j] = binds[i, aux[i, j]] + sims.append(sim_batch) + inds.append(ind_batch) + xfrom += x_batch.shape[0] + del x_batch + sim = np.concatenate(sims, axis=0) + ind = np.concatenate(inds, axis=0) + return sim, ind + + +def score(sim, fwd_mean, bwd_mean, margin): + return margin(sim, (fwd_mean + bwd_mean) / 2) + + +def score_candidates( + sim_mat, candidate_inds, fwd_mean, bwd_mean, margin, verbose=False +): + print(" - scoring {:d} candidates".format(sim_mat.shape[0])) + scores = np.zeros(candidate_inds.shape) + for i in range(scores.shape[0]): + for j in range(scores.shape[1]): + k = int(candidate_inds[i, j]) + scores[i, j] = score(sim_mat[i, j], fwd_mean[i], bwd_mean[k], margin) + return scores + + +def load_text(files): + all_sentences = [] + for fi in files: + with open(fi) as sentence_fi: + for line in sentence_fi: + all_sentences.append(line.strip()) + print(f"Read {len(all_sentences)} sentences") + return all_sentences + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Mine bitext") + parser.add_argument("--src-lang", help="Source language") + parser.add_argument("--tgt-lang", help="Target language") + parser.add_argument( + "--dict-path", help="Path to dictionary file", default="dict.txt" + ) + parser.add_argument( + "--spm-path", help="Path to SPM model file", default="sentence.bpe.model" + ) + parser.add_argument("--dim", type=int, default=1024, help="Embedding dimension") + parser.add_argument("--mem", type=int, default=5, help="Memory in GB") + parser.add_argument("--src-dir", help="Source directory") + parser.add_argument("--tgt-dir", help="Target directory") + parser.add_argument("--output", help="Output path") + parser.add_argument( + "--neighborhood", type=int, default=4, help="Embedding dimension" + ) + parser.add_argument( + "--threshold", type=float, default=1.06, help="Threshold on mined bitext" + ) + parser.add_argument( + "--valid-size", + type=int, + default=2000, + help="Number of sentences used for validation set", + ) + parser.add_argument( + "--min-count", + type=int, + default=50000, + help="Min num sentences used for each language", + ) + args = parser.parse_args() + + x_batches_f, x_sents_f = get_batches(args.src_dir, args.src_lang) + y_batches_f, y_sents_f = get_batches(args.tgt_dir, args.tgt_lang) + margin = lambda a, b: a / b + y2x_sim, y2x_ind = knnGPU_sharded( + y_batches_f, x_batches_f, args.dim, args.neighborhood, direction="y2x" + ) + x2y_sim, x2y_ind = knnGPU_sharded( + x_batches_f, y_batches_f, args.dim, args.neighborhood, direction="x2y" + ) + + x2y_mean = x2y_sim.mean(axis=1) + y2x_mean = y2x_sim.mean(axis=1) + fwd_scores = score_candidates(x2y_sim, x2y_ind, x2y_mean, y2x_mean, margin) + bwd_scores = score_candidates(y2x_sim, y2x_ind, y2x_mean, x2y_mean, margin) + fwd_best = x2y_ind[np.arange(x2y_sim.shape[0]), fwd_scores.argmax(axis=1)] + bwd_best = y2x_ind[np.arange(y2x_sim.shape[0]), bwd_scores.argmax(axis=1)] + indices = np.stack( + ( + np.concatenate((np.arange(x2y_ind.shape[0]), bwd_best)), + np.concatenate((fwd_best, np.arange(y2x_ind.shape[0]))), + ), + axis=1, + ) + scores = np.concatenate((fwd_scores.max(axis=1), bwd_scores.max(axis=1))) + + x_sentences = load_text(x_sents_f) + y_sentences = load_text(y_sents_f) + + threshold = args.threshold + min_count = args.min_count + seen_src, seen_trg = set(), set() + directory = args.output + call(f"mkdir -p {directory}") + src_out = open( + f"{directory}/all.{args.src_lang}", + mode="w", + encoding="utf-8", + errors="surrogateescape", + ) + tgt_out = open( + f"{directory}/all.{args.tgt_lang}", + mode="w", + encoding="utf-8", + errors="surrogateescape", + ) + scores_out = open( + f"{directory}/all.scores", mode="w", encoding="utf-8", errors="surrogateescape" + ) + count = 0 + for i in np.argsort(-scores): + src_ind, trg_ind = indices[i] + if src_ind not in seen_src and trg_ind not in seen_trg: + seen_src.add(src_ind) + seen_trg.add(trg_ind) + if scores[i] > threshold or count < min_count: + if x_sentences[src_ind]: + print(scores[i], file=scores_out) + print(x_sentences[src_ind], file=src_out) + print(y_sentences[trg_ind], file=tgt_out) + count += 1 + else: + print(f"Ignoring sentence: {x_sentences[src_ind]}") + src_out.close() + tgt_out.close() + scores_out.close() + + print(f"Found {count} pairs for threshold={threshold}") + with open(f"{directory}/all.{args.src_lang}") as all_s, open( + f"{directory}/all.{args.tgt_lang}" + ) as all_t, open(f"{directory}/valid.{args.src_lang}", "w") as valid_s, open( + f"{directory}/valid.{args.tgt_lang}", "w" + ) as valid_t, open( + f"{directory}/train.{args.src_lang}", "w" + ) as train_s, open( + f"{directory}/train.{args.tgt_lang}", "w" + ) as train_t: + count = 0 + for s_line, t_line in zip(all_s, all_t): + s_line = s_line.split("\t")[1] + t_line = t_line.split("\t")[1] + if count >= args.valid_size: + train_s.write(s_line) + train_t.write(t_line) + else: + valid_s.write(s_line) + valid_t.write(t_line) + count += 1 diff --git a/examples/criss/mining/mine_example.sh b/examples/criss/mining/mine_example.sh new file mode 100644 index 0000000000..ace995ac44 --- /dev/null +++ b/examples/criss/mining/mine_example.sh @@ -0,0 +1,103 @@ +#!/bin/bash +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. +# +source_lang=kk_KZ +target_lang=en_XX +MODEL=criss_checkpoints/criss.3rd.pt +SPM=criss_checkpoints/sentence.bpe.model +SPLIT=test +LANG_DICT=criss_checkpoints/lang_dict.txt +SPM_ENCODE=flores/scripts/spm_encode.py +SAVE_ENCODER=save_encoder.py +ENCODER_SAVE_ROOT=sentence_embeddings/$MODEL +DICT=criss_checkpoints/dict.txt +THRESHOLD=1.02 +MIN_COUNT=500 + +DATA_DIR=data_tmp +SAVE_DIR=mining/${source_lang}_${target_lang}_mined +ENCODER_SAVE_DIR=${ENCODER_SAVE_ROOT}/${source_lang}-${target_lang} +INPUT_DIR=$DATA_DIR/${source_lang}-${target_lang}-tatoeba + +mkdir -p $ENCODER_SAVE_DIR/${target_lang} +mkdir -p $ENCODER_SAVE_DIR/${source_lang} +mkdir -p $SAVE_DIR + +## Save encoder outputs + +# Save encoder outputs for source sentences +python $SAVE_ENCODER \ + ${INPUT_DIR} \ + --path ${MODEL} \ + --task translation_multi_simple_epoch \ + --lang-pairs ${source_lang}-${target_lang} \ + --lang-dict ${LANG_DICT} \ + --gen-subset ${SPLIT} \ + --bpe 'sentencepiece' \ + -s ${source_lang} -t ${target_lang} \ + --sentencepiece-model ${SPM} \ + --remove-bpe 'sentencepiece' \ + --beam 1 \ + --lang-tok-style mbart \ + --encoder-save-dir ${ENCODER_SAVE_DIR}/${source_lang} + +## Save encoder outputs for target sentences +python $SAVE_ENCODER \ + ${INPUT_DIR} \ + --path ${MODEL} \ + --lang-pairs ${source_lang}-${target_lang} \ + --lang-dict ${LANG_DICT} \ + --task translation_multi_simple_epoch \ + --gen-subset ${SPLIT} \ + --bpe 'sentencepiece' \ + -t ${source_lang} -s ${target_lang} \ + --sentencepiece-model ${SPM} \ + --remove-bpe 'sentencepiece' \ + --beam 1 \ + --lang-tok-style mbart \ + --encoder-save-dir ${ENCODER_SAVE_DIR}/${target_lang} + +## Mining +python mining/mine.py \ + --src-lang ${source_lang} \ + --tgt-lang ${target_lang} \ + --dim 1024 \ + --mem 10 \ + --neighborhood 4 \ + --src-dir ${ENCODER_SAVE_DIR}/${source_lang} \ + --tgt-dir ${ENCODER_SAVE_DIR}/${target_lang} \ + --output $SAVE_DIR \ + --threshold ${THRESHOLD} \ + --min-count ${MIN_COUNT} \ + --valid-size 100 \ + --dict-path ${DICT} \ + --spm-path ${SPM} \ + + +## Process and binarize mined data +python $SPM_ENCODE \ + --model ${SPM} \ + --output_format=piece \ + --inputs mining/${source_lang}_${target_lang}_mined/train.${source_lang} mining/${source_lang}_${target_lang}_mined/train.${target_lang} \ + --outputs mining/${source_lang}_${target_lang}_mined/train.bpe.${source_lang} mining/${source_lang}_${target_lang}_mined/train.bpe.${target_lang} + +python $SPM_ENCODE \ + --model ${SPM} \ + --output_format=piece \ + --inputs mining/${source_lang}_${target_lang}_mined/valid.${source_lang} mining/${source_lang}_${target_lang}_mined/valid.${target_lang} \ + --outputs mining/${source_lang}_${target_lang}_mined/valid.bpe.${source_lang} mining/${source_lang}_${target_lang}_mined/valid.bpe.${target_lang} + + +fairseq-preprocess \ + --source-lang ${source_lang} \ + --target-lang ${target_lang} \ + --trainpref mining/${source_lang}_${target_lang}_mined/train.bpe \ + --validpref mining/${source_lang}_${target_lang}_mined/valid.bpe \ + --destdir mining/${source_lang}_${target_lang}_mined \ + --srcdict ${DICT} \ + --joined-dictionary \ + --workers 8 diff --git a/examples/criss/save_encoder.py b/examples/criss/save_encoder.py new file mode 100644 index 0000000000..d911d066e3 --- /dev/null +++ b/examples/criss/save_encoder.py @@ -0,0 +1,213 @@ +#!/usr/bin/env python3 -u +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +""" +Translate pre-processed data with a trained model. +""" + +import numpy as np +import torch +from fairseq import checkpoint_utils, options, progress_bar, tasks, utils +from fairseq.sequence_generator import EnsembleModel + + +def get_avg_pool( + models, sample, prefix_tokens, src_dict, remove_bpe, has_langtok=False +): + model = EnsembleModel(models) + + # model.forward normally channels prev_output_tokens into the decoder + # separately, but SequenceGenerator directly calls model.encoder + encoder_input = { + k: v for k, v in sample["net_input"].items() if k != "prev_output_tokens" + } + + # compute the encoder output for each beam + encoder_outs = model.forward_encoder(encoder_input) + np_encoder_outs = encoder_outs[0].encoder_out.cpu().numpy().astype(np.float32) + encoder_mask = 1 - encoder_outs[0].encoder_padding_mask.cpu().numpy().astype( + np.float32 + ) + encoder_mask = np.expand_dims(encoder_mask.T, axis=2) + if has_langtok: + encoder_mask = encoder_mask[1:, :, :] + np_encoder_outs = np_encoder_outs[1, :, :] + masked_encoder_outs = encoder_mask * np_encoder_outs + avg_pool = (masked_encoder_outs / encoder_mask.sum(axis=0)).sum(axis=0) + return avg_pool + + +def main(args): + assert args.path is not None, "--path required for generation!" + assert ( + not args.sampling or args.nbest == args.beam + ), "--sampling requires --nbest to be equal to --beam" + assert ( + args.replace_unk is None or args.raw_text + ), "--replace-unk requires a raw text dataset (--raw-text)" + + args.beam = 1 + utils.import_user_module(args) + + if args.max_tokens is None: + args.max_tokens = 12000 + print(args) + use_cuda = torch.cuda.is_available() and not args.cpu + + # Load dataset splits + task = tasks.setup_task(args) + task.load_dataset(args.gen_subset) + + # Set dictionaries + try: + src_dict = getattr(task, "source_dictionary", None) + except NotImplementedError: + src_dict = None + tgt_dict = task.target_dictionary + + # Load ensemble + print("| loading model(s) from {}".format(args.path)) + models, _model_args = checkpoint_utils.load_model_ensemble( + args.path.split(":"), + arg_overrides=eval(args.model_overrides), + task=task, + ) + + # Optimize ensemble for generation + for model in models: + model.make_generation_fast_( + beamable_mm_beam_size=None if args.no_beamable_mm else args.beam, + need_attn=args.print_alignment, + ) + if args.fp16: + model.half() + if use_cuda: + model.cuda() + + # Load alignment dictionary for unknown word replacement + # (None if no unknown word replacement, empty if no path to align dictionary) + align_dict = utils.load_align_dict(args.replace_unk) + + # Load dataset (possibly sharded) + itr = task.get_batch_iterator( + dataset=task.dataset(args.gen_subset), + max_tokens=args.max_tokens, + max_positions=utils.resolve_max_positions( + task.max_positions(), + ), + ignore_invalid_inputs=args.skip_invalid_size_inputs_valid_test, + required_batch_size_multiple=args.required_batch_size_multiple, + num_shards=args.num_shards, + shard_id=args.shard_id, + num_workers=args.num_workers, + ).next_epoch_itr(shuffle=False) + + num_sentences = 0 + source_sentences = [] + shard_id = 0 + all_avg_pool = None + encoder_has_langtok = ( + hasattr(task.args, "encoder_langtok") + and task.args.encoder_langtok is not None + and hasattr(task.args, "lang_tok_replacing_bos_eos") + and not task.args.lang_tok_replacing_bos_eos + ) + with progress_bar.build_progress_bar(args, itr) as t: + for sample in t: + if sample is None: + print("Skipping None") + continue + sample = utils.move_to_cuda(sample) if use_cuda else sample + if "net_input" not in sample: + continue + + prefix_tokens = None + if args.prefix_size > 0: + prefix_tokens = sample["target"][:, : args.prefix_size] + + with torch.no_grad(): + avg_pool = get_avg_pool( + models, + sample, + prefix_tokens, + src_dict, + args.post_process, + has_langtok=encoder_has_langtok, + ) + if all_avg_pool is not None: + all_avg_pool = np.concatenate((all_avg_pool, avg_pool)) + else: + all_avg_pool = avg_pool + + if not isinstance(sample["id"], list): + sample_ids = sample["id"].tolist() + else: + sample_ids = sample["id"] + for i, sample_id in enumerate(sample_ids): + # Remove padding + src_tokens = utils.strip_pad( + sample["net_input"]["src_tokens"][i, :], tgt_dict.pad() + ) + + # Either retrieve the original sentences or regenerate them from tokens. + if align_dict is not None: + src_str = task.dataset(args.gen_subset).src.get_original_text( + sample_id + ) + else: + if src_dict is not None: + src_str = src_dict.string(src_tokens, args.post_process) + else: + src_str = "" + + if not args.quiet: + if src_dict is not None: + print("S-{}\t{}".format(sample_id, src_str)) + + source_sentences.append(f"{sample_id}\t{src_str}") + + num_sentences += sample["nsentences"] + if all_avg_pool.shape[0] >= 1000000: + with open( + f"{args.encoder_save_dir}/all_avg_pool.{args.source_lang}.{shard_id}", + "w", + ) as avg_pool_file: + all_avg_pool.tofile(avg_pool_file) + with open( + f"{args.encoder_save_dir}/sentences.{args.source_lang}.{shard_id}", + "w", + ) as sentence_file: + sentence_file.writelines(f"{line}\n" for line in source_sentences) + all_avg_pool = None + source_sentences = [] + shard_id += 1 + + if all_avg_pool is not None: + with open( + f"{args.encoder_save_dir}/all_avg_pool.{args.source_lang}.{shard_id}", "w" + ) as avg_pool_file: + all_avg_pool.tofile(avg_pool_file) + with open( + f"{args.encoder_save_dir}/sentences.{args.source_lang}.{shard_id}", "w" + ) as sentence_file: + sentence_file.writelines(f"{line}\n" for line in source_sentences) + return None + + +def cli_main(): + parser = options.get_generation_parser() + parser.add_argument( + "--encoder-save-dir", + default="", + type=str, + metavar="N", + help="directory to save encoder outputs", + ) + args = options.parse_args_and_arch(parser) + main(args) + + +if __name__ == "__main__": + cli_main() diff --git a/examples/criss/sentence_retrieval/encoder_analysis.py b/examples/criss/sentence_retrieval/encoder_analysis.py new file mode 100644 index 0000000000..b41bfbe387 --- /dev/null +++ b/examples/criss/sentence_retrieval/encoder_analysis.py @@ -0,0 +1,92 @@ +#!/usr/bin/env python3 -u +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +import argparse +import glob + +import numpy as np + + +DIM = 1024 + + +def compute_dist(source_embs, target_embs, k=5, return_sim_mat=False): + target_ids = [tid for tid in target_embs] + source_mat = np.stack(source_embs.values(), axis=0) + normalized_source_mat = source_mat / np.linalg.norm( + source_mat, axis=1, keepdims=True + ) + target_mat = np.stack(target_embs.values(), axis=0) + normalized_target_mat = target_mat / np.linalg.norm( + target_mat, axis=1, keepdims=True + ) + sim_mat = normalized_source_mat.dot(normalized_target_mat.T) + if return_sim_mat: + return sim_mat + neighbors_map = {} + for i, sentence_id in enumerate(source_embs): + idx = np.argsort(sim_mat[i, :])[::-1][:k] + neighbors_map[sentence_id] = [target_ids[tid] for tid in idx] + return neighbors_map + + +def load_embeddings(directory, LANGS): + sentence_embeddings = {} + sentence_texts = {} + for lang in LANGS: + sentence_embeddings[lang] = {} + sentence_texts[lang] = {} + lang_dir = f"{directory}/{lang}" + embedding_files = glob.glob(f"{lang_dir}/all_avg_pool.{lang}.*") + for embed_file in embedding_files: + shard_id = embed_file.split(".")[-1] + embeddings = np.fromfile(embed_file, dtype=np.float32) + num_rows = embeddings.shape[0] // DIM + embeddings = embeddings.reshape((num_rows, DIM)) + + with open(f"{lang_dir}/sentences.{lang}.{shard_id}") as sentence_file: + for idx, line in enumerate(sentence_file): + sentence_id, sentence = line.strip().split("\t") + sentence_texts[lang][sentence_id] = sentence + sentence_embeddings[lang][sentence_id] = embeddings[idx, :] + + return sentence_embeddings, sentence_texts + + +def compute_accuracy(directory, LANGS): + sentence_embeddings, sentence_texts = load_embeddings(directory, LANGS) + + top_1_accuracy = {} + + top1_str = " ".join(LANGS) + "\n" + for source_lang in LANGS: + top_1_accuracy[source_lang] = {} + top1_str += f"{source_lang} " + for target_lang in LANGS: + top1 = 0 + top5 = 0 + neighbors_map = compute_dist( + sentence_embeddings[source_lang], sentence_embeddings[target_lang] + ) + for sentence_id, neighbors in neighbors_map.items(): + if sentence_id == neighbors[0]: + top1 += 1 + if sentence_id in neighbors[:5]: + top5 += 1 + n = len(sentence_embeddings[target_lang]) + top1_str += f"{top1/n} " + top1_str += "\n" + + print(top1_str) + print(top1_str, file=open(f"{directory}/accuracy", "w")) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Analyze encoder outputs") + parser.add_argument("directory", help="Source language corpus") + parser.add_argument("--langs", help="List of langs") + args = parser.parse_args() + langs = args.langs.split(",") + compute_accuracy(args.directory, langs) diff --git a/examples/criss/sentence_retrieval/sentence_retrieval_tatoeba.sh b/examples/criss/sentence_retrieval/sentence_retrieval_tatoeba.sh new file mode 100644 index 0000000000..0428d8bef9 --- /dev/null +++ b/examples/criss/sentence_retrieval/sentence_retrieval_tatoeba.sh @@ -0,0 +1,59 @@ +#!/bin/bash +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. +# +source_lang=kk_KZ +target_lang=en_XX +MODEL=criss_checkpoints/criss.3rd.pt +SPM=criss_checkpoints/sentence.bpe.model +SPLIT=test +LANG_DICT=criss_checkpoints/lang_dict.txt +ENCODER_ANALYSIS=sentence_retrieval/encoder_analysis.py +SAVE_ENCODER=save_encoder.py +ENCODER_SAVE_ROOT=sentence_embeddings/$MODEL + + + +DATA_DIR=data_tmp +INPUT_DIR=$DATA_DIR/${source_lang}-${target_lang}-tatoeba +ENCODER_SAVE_DIR=${ENCODER_SAVE_ROOT}/${source_lang}-${target_lang} +mkdir -p $ENCODER_SAVE_DIR/${target_lang} +mkdir -p $ENCODER_SAVE_DIR/${source_lang} + +# Save encoder outputs for source sentences +python $SAVE_ENCODER \ + ${INPUT_DIR} \ + --path ${MODEL} \ + --task translation_multi_simple_epoch \ + --lang-dict ${LANG_DICT} \ + --gen-subset ${SPLIT} \ + --bpe 'sentencepiece' \ + --lang-pairs ${source_lang}-${target_lang} \ + -s ${source_lang} -t ${target_lang} \ + --sentencepiece-model ${SPM} \ + --remove-bpe 'sentencepiece' \ + --beam 1 \ + --lang-tok-style mbart \ + --encoder-save-dir ${ENCODER_SAVE_DIR}/${source_lang} + +# Save encoder outputs for target sentences +python $SAVE_ENCODER \ + ${INPUT_DIR} \ + --path ${MODEL} \ + --lang-dict ${LANG_DICT} \ + --task translation_multi_simple_epoch \ + --gen-subset ${SPLIT} \ + --bpe 'sentencepiece' \ + --lang-pairs ${target_lang}-${source_lang} \ + -t ${source_lang} -s ${target_lang} \ + --sentencepiece-model ${SPM} \ + --remove-bpe 'sentencepiece' \ + --beam 1 \ + --lang-tok-style mbart \ + --encoder-save-dir ${ENCODER_SAVE_DIR}/${target_lang} + +# Analyze sentence retrieval accuracy +python $ENCODER_ANALYSIS --langs "${source_lang},${target_lang}" ${ENCODER_SAVE_DIR} diff --git a/examples/criss/unsupervised_mt/eval.sh b/examples/criss/unsupervised_mt/eval.sh new file mode 100644 index 0000000000..03b773ed5a --- /dev/null +++ b/examples/criss/unsupervised_mt/eval.sh @@ -0,0 +1,37 @@ +#!/bin/bash +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. +# +SRC=si_LK +TGT=en_XX +MODEL=criss_checkpoints/criss.3rd.pt + +MULTIBLEU=mosesdecoder/scripts/generic/multi-bleu.perl +MOSES=mosesdecoder +REPLACE_UNICODE_PUNCT=$MOSES/scripts/tokenizer/replace-unicode-punctuation.perl +NORM_PUNC=$MOSES/scripts/tokenizer/normalize-punctuation.perl +REM_NON_PRINT_CHAR=$MOSES/scripts/tokenizer/remove-non-printing-char.perl +TOKENIZER=$MOSES/scripts/tokenizer/tokenizer.perl +GEN_TMP_DIR=gen_tmp +LANG_DICT=criss_checkpoints/lang_dict.txt + +if [ ! -d "mosesdecoder" ]; then + git clone https://github.com/moses-smt/mosesdecoder +fi +mkdir -p $GEN_TMP_DIR +fairseq-generate data_tmp/${SRC}-${TGT}-flores \ + --task translation_multi_simple_epoch \ + --max-tokens 2000 \ + --path ${MODEL} \ + --skip-invalid-size-inputs-valid-test \ + --beam 5 --lenpen 1.0 --gen-subset test \ + --remove-bpe=sentencepiece \ + --source-lang ${SRC} --target-lang ${TGT} \ + --decoder-langtok --lang-pairs 'en_XX-ar_AR,en_XX-de_DE,en_XX-es_XX,en_XX-fr_XX,en_XX-hi_IN,en_XX-it_IT,en_XX-ja_XX,en_XX-ko_KR,en_XX-nl_XX,en_XX-ru_RU,en_XX-zh_CN,en_XX-tr_TR,en_XX-vi_VN,en_XX-ro_RO,en_XX-my_MM,en_XX-ne_NP,en_XX-si_LK,en_XX-cs_CZ,en_XX-lt_LT,en_XX-kk_KZ,en_XX-gu_IN,en_XX-fi_FI,en_XX-et_EE,en_XX-lv_LV,ar_AR-en_XX,cs_CZ-en_XX,de_DE-en_XX,es_XX-en_XX,et_EE-en_XX,fi_FI-en_XX,fr_XX-en_XX,gu_IN-en_XX,hi_IN-en_XX,it_IT-en_XX,ja_XX-en_XX,kk_KZ-en_XX,ko_KR-en_XX,lt_LT-en_XX,lv_LV-en_XX,my_MM-en_XX,ne_NP-en_XX,nl_XX-en_XX,ro_RO-en_XX,ru_RU-en_XX,si_LK-en_XX,tr_TR-en_XX,vi_VN-en_XX,zh_CN-en_XX,ar_AR-es_XX,es_XX-ar_AR,ar_AR-hi_IN,hi_IN-ar_AR,ar_AR-zh_CN,zh_CN-ar_AR,cs_CZ-es_XX,es_XX-cs_CZ,cs_CZ-hi_IN,hi_IN-cs_CZ,cs_CZ-zh_CN,zh_CN-cs_CZ,de_DE-es_XX,es_XX-de_DE,de_DE-hi_IN,hi_IN-de_DE,de_DE-zh_CN,zh_CN-de_DE,es_XX-hi_IN,hi_IN-es_XX,es_XX-zh_CN,zh_CN-es_XX,et_EE-es_XX,es_XX-et_EE,et_EE-hi_IN,hi_IN-et_EE,et_EE-zh_CN,zh_CN-et_EE,fi_FI-es_XX,es_XX-fi_FI,fi_FI-hi_IN,hi_IN-fi_FI,fi_FI-zh_CN,zh_CN-fi_FI,fr_XX-es_XX,es_XX-fr_XX,fr_XX-hi_IN,hi_IN-fr_XX,fr_XX-zh_CN,zh_CN-fr_XX,gu_IN-es_XX,es_XX-gu_IN,gu_IN-hi_IN,hi_IN-gu_IN,gu_IN-zh_CN,zh_CN-gu_IN,hi_IN-zh_CN,zh_CN-hi_IN,it_IT-es_XX,es_XX-it_IT,it_IT-hi_IN,hi_IN-it_IT,it_IT-zh_CN,zh_CN-it_IT,ja_XX-es_XX,es_XX-ja_XX,ja_XX-hi_IN,hi_IN-ja_XX,ja_XX-zh_CN,zh_CN-ja_XX,kk_KZ-es_XX,es_XX-kk_KZ,kk_KZ-hi_IN,hi_IN-kk_KZ,kk_KZ-zh_CN,zh_CN-kk_KZ,ko_KR-es_XX,es_XX-ko_KR,ko_KR-hi_IN,hi_IN-ko_KR,ko_KR-zh_CN,zh_CN-ko_KR,lt_LT-es_XX,es_XX-lt_LT,lt_LT-hi_IN,hi_IN-lt_LT,lt_LT-zh_CN,zh_CN-lt_LT,lv_LV-es_XX,es_XX-lv_LV,lv_LV-hi_IN,hi_IN-lv_LV,lv_LV-zh_CN,zh_CN-lv_LV,my_MM-es_XX,es_XX-my_MM,my_MM-hi_IN,hi_IN-my_MM,my_MM-zh_CN,zh_CN-my_MM,ne_NP-es_XX,es_XX-ne_NP,ne_NP-hi_IN,hi_IN-ne_NP,ne_NP-zh_CN,zh_CN-ne_NP,nl_XX-es_XX,es_XX-nl_XX,nl_XX-hi_IN,hi_IN-nl_XX,nl_XX-zh_CN,zh_CN-nl_XX,ro_RO-es_XX,es_XX-ro_RO,ro_RO-hi_IN,hi_IN-ro_RO,ro_RO-zh_CN,zh_CN-ro_RO,ru_RU-es_XX,es_XX-ru_RU,ru_RU-hi_IN,hi_IN-ru_RU,ru_RU-zh_CN,zh_CN-ru_RU,si_LK-es_XX,es_XX-si_LK,si_LK-hi_IN,hi_IN-si_LK,si_LK-zh_CN,zh_CN-si_LK,tr_TR-es_XX,es_XX-tr_TR,tr_TR-hi_IN,hi_IN-tr_TR,tr_TR-zh_CN,zh_CN-tr_TR,vi_VN-es_XX,es_XX-vi_VN,vi_VN-hi_IN,hi_IN-vi_VN,vi_VN-zh_CN,zh_CN-vi_VN' \ + --lang-dict ${LANG_DICT} --lang-tok-style 'mbart' --sampling-method 'temperature' --sampling-temperature '1.0' > $GEN_TMP_DIR/${SRC}_${TGT}.gen +cat $GEN_TMP_DIR/${SRC}_${TGT}.gen | grep -P "^T-" | cut -f2 | $REPLACE_UNICODE_PUNCT | $NORM_PUNC -l ${TGT:0:2} | $REM_NON_PRINT_CHAR | $TOKENIZER -no-escape ${TGT:0:2} > $GEN_TMP_DIR/${SRC}_${TGT}.hyp +cat $GEN_TMP_DIR/${SRC}_${TGT}.gen | grep -P "^H-" | cut -f3 | $REPLACE_UNICODE_PUNCT | $NORM_PUNC -l ${TGT:0:2} | $REM_NON_PRINT_CHAR | $TOKENIZER -no-escape ${TGT:0:2} > $GEN_TMP_DIR/${SRC}_${TGT}.ref +${MULTIBLEU} $GEN_TMP_DIR/${SRC}_${TGT}.ref < $GEN_TMP_DIR/${SRC}_${TGT}.hyp diff --git a/examples/cross_lingual_language_model/README.md b/examples/cross_lingual_language_model/README.md index a78f86d8da..af9128e39e 100644 --- a/examples/cross_lingual_language_model/README.md +++ b/examples/cross_lingual_language_model/README.md @@ -61,14 +61,14 @@ fairseq-train \ --max-update 2400000 --save-interval 1 --no-epoch-checkpoints \ --arch xlm_base \ --optimizer adam --lr-scheduler reduce_lr_on_plateau \ ---lr-shrink 0.5 --lr 0.0001 --min-lr 1e-09 \ +--lr-shrink 0.5 --lr 0.0001 --stop-min-lr 1e-09 \ --dropout 0.1 \ --criterion legacy_masked_lm_loss \ --max-tokens 2048 --tokens-per-sample 256 --attention-dropout 0.1 \ --dataset-impl lazy --seed 0 \ --masked-lm-only \ --monolingual-langs 'ar,de,en,hi,fr' --num-segment 5 \ ---ddp-backend=no_c10d +--ddp-backend=legacy_ddp ``` Some Notes: diff --git a/examples/discriminative_reranking_nmt/README.md b/examples/discriminative_reranking_nmt/README.md new file mode 100644 index 0000000000..b155e855f2 --- /dev/null +++ b/examples/discriminative_reranking_nmt/README.md @@ -0,0 +1,202 @@ +# Discriminative Reranking for Neural Machine Translation +https://aclanthology.org/2021.acl-long.563/ + +This folder contains source code for training DrNMT, a discriminatively trained reranker for neural machine translation. + +## Data preparation +1. Follow the instructions under `examples/translation` to build a base MT model. Prepare three files, one with source sentences, one with ground truth target sentences, and one with hypotheses generated from the base MT model. Each line in the file contains one sentence in raw text (i.e. no sentencepiece, etc.). Below is an example of the files with _N_ hypotheses for each source sentence. + +``` +# Example of the source sentence file: (The file should contain L lines.) + +source_sentence_1 +source_sentence_2 +source_sentence_3 +... +source_sentence_L + +# Example of the target sentence file: (The file should contain L lines.) + +target_sentence_1 +target_sentence_2 +target_sentence_3 +... +target_sentence_L + +# Example of the hypotheses file: (The file should contain L*N lines.) + +source_sentence_1_hypo_1 +source_sentence_1_hypo_2 +... +source_sentence_1_hypo_N +source_sentence_2_hypo_1 +... +source_sentence_2_hypo_N +... +source_sentence_L_hypo_1 +... +source_sentence_L_hypo_N +``` + +2. Download the [XLMR model](https://github.com/fairinternal/fairseq-py/tree/main/examples/xlmr#pre-trained-models). +``` +wget https://dl.fbaipublicfiles.com/fairseq/models/xlmr.base.tar.gz +tar zxvf xlmr.base.tar.gz + +# The folder should contain dict.txt, model.pt and sentencepiece.bpe.model. +``` + +3. Prepare scores and BPE data. +* `N`: Number of hypotheses per each source sentence. We use 50 in the paper. +* `SPLIT`: Name of the data split, i.e. train, valid, test. Use split_name, split_name1, split_name2, ..., if there are multiple datasets for a split, e.g. train, train1, valid, valid1. +* `NUM_SHARDS`: Number of shards. Set this to 1 for non-train splits. +* `METRIC`: The metric for DrNMT to optimize for. We support either `bleu` or `ter`. +``` +# For each data split, e.g. train, valid, test, etc., run the following: + +SOURCE_FILE=/path/to/source_sentence_file +TARGET_FILE=/path/to/target_sentence_file +HYPO_FILE=/path/to/hypo_file +XLMR_DIR=/path/to/xlmr +OUTPUT_DIR=/path/to/output + +python scripts/prep_data.py \ + --input-source ${SOURCE_FILE} \ + --input-target ${TARGET_FILE} \ + --input-hypo ${HYPO_FILE} \ + --output-dir ${OUTPUT_DIR} \ + --split $SPLIT + --beam $N \ + --sentencepiece-model ${XLMR_DIR}/sentencepiece.bpe.model \ + --metric $METRIC \ + --num-shards ${NUM_SHARDS} + +# The script will create ${OUTPUT_DIR}/$METRIC with ${NUM_SHARDS} splits. +# Under split*/input_src, split*/input_tgt and split*/$METRIC, there will be $SPLIT.bpe and $SPLIT.$METRIC files, respectively. + +``` + +4. Pre-process the data into fairseq format. +``` +# use comma to separate if there are more than one train or valid set +for suffix in src tgt ; do + fairseq-preprocess --only-source \ + --trainpref ${OUTPUT_DIR}/$METRIC/split1/input_${suffix}/train.bpe \ + --validpref ${OUTPUT_DIR}/$METRIC/split1/input_${suffix}/valid.bpe \ + --destdir ${OUTPUT_DIR}/$METRIC/split1/input_${suffix} \ + --workers 60 \ + --srcdict ${XLMR_DIR}/dict.txt +done + +for i in `seq 2 ${NUM_SHARDS}`; do + for suffix in src tgt ; do + fairseq-preprocess --only-source \ + --trainpref ${OUTPUT_DIR}/$METRIC/split${i}/input_${suffix}/train.bpe \ + --destdir ${OUTPUT_DIR}/$METRIC/split${i}/input_${suffix} \ + --workers 60 \ + --srcdict ${XLMR_DIR}/dict.txt + + ln -s ${OUTPUT_DIR}/$METRIC/split1/input_${suffix}/valid* ${OUTPUT_DIR}/$METRIC/split${i}/input_${suffix}/. + done + + ln -s ${OUTPUT_DIR}/$METRIC/split1/$METRIC/valid* ${OUTPUT_DIR}/$METRIC/split${i}/$METRIC/. +done +``` + +## Training + +``` +EXP_DIR=/path/to/exp + +# An example of training the model with the config for De-En experiment in the paper. +# The config uses 16 GPUs and 50 hypotheses. +# For training with fewer number of GPUs, set +# distributed_training.distributed_world_size=k +optimization.update_freq='[x]' where x = 16/k +# For training with fewer number of hypotheses, set +# task.mt_beam=N dataset.batch_size=N dataset.required_batch_size_multiple=N + +fairseq-hydra-train -m \ + --config-dir config/ --config-name deen \ + task.data=${OUTPUT_DIR}/$METRIC/split1/ \ + task.num_data_splits=${NUM_SHARDS} \ + model.pretrained_model=${XLMR_DIR}/model.pt \ + common.user_dir=${FAIRSEQ_ROOT}/examples/discriminative_reranking_nmt \ + checkpoint.save_dir=${EXP_DIR} + +``` + +## Inference & scoring +Perform DrNMT reranking (fw + reranker score) +1. Tune weights on valid sets. +``` +# genrate N hypotheses with the base MT model (fw score) +VALID_SOURCE_FILE=/path/to/source_sentences # one sentence per line, converted to the sentencepiece used by the base MT model +VALID_TARGET_FILE=/path/to/target_sentences # one sentence per line in raw text, i.e. no sentencepiece and tokenization +MT_MODEL=/path/to/mt_model +MT_DATA_PATH=/path/to/mt_data + +cat ${VALID_SOURCE_FILE} | \ + fairseq-interactive ${MT_DATA_PATH} \ + --max-tokens 4000 --buffer-size 16 \ + --num-workers 32 --path ${MT_MODEL} \ + --beam $N --nbest $N \ + --post-process sentencepiece &> valid-hypo.out + +# replace "bleu" with "ter" to optimize for TER +python drnmt_rerank.py \ + ${OUTPUT_DIR}/$METRIC/split1/ \ + --path ${EXP_DIR}/checkpoint_best.pt \ + --in-text valid-hypo.out \ + --results-path ${EXP_DIR} \ + --gen-subset valid \ + --target-text ${VALID_TARGET_FILE} \ + --user-dir ${FAIRSEQ_ROOT}/examples/discriminative_reranking_nmt \ + --bpe sentencepiece \ + --sentencepiece-model ${XLMR_DIR}/sentencepiece.bpe.model \ + --beam $N \ + --batch-size $N \ + --metric bleu \ + --tune + +``` + +2. Apply best weights on test sets +``` +# genrate N hypotheses with the base MT model (fw score) +TEST_SOURCE_FILE=/path/to/source_sentences # one sentence per line, converted to the sentencepiece used by the base MT model + +cat ${TEST_SOURCE_FILE} | \ + fairseq-interactive ${MT_DATA_PATH} \ + --max-tokens 4000 --buffer-size 16 \ + --num-workers 32 --path ${MT_MODEL} \ + --beam $N --nbest $N \ + --post-process sentencepiece &> test-hypo.out + +# replace "bleu" with "ter" to evaluate TER +# Add --target-text for evaluating BLEU/TER, +# otherwise the script will only generate the hypotheses with the highest scores only. +python drnmt_rerank.py \ + ${OUTPUT_DIR}/$METRIC/split1/ \ + --path ${EXP_DIR}/checkpoint_best.pt \ + --in-text test-hypo.out \ + --results-path ${EXP_DIR} \ + --gen-subset test \ + --user-dir ${FAIRSEQ_ROOT}/examples/discriminative_reranking_nmt \ + --bpe sentencepiece \ + --sentencepiece-model ${XLMR_DIR}/sentencepiece.bpe.model \ + --beam $N \ + --batch-size $N \ + --metric bleu \ + --fw-weight ${BEST_FW_WEIGHT} \ + --lenpen ${BEST_LENPEN} +``` + +## Citation +```bibtex +@inproceedings{lee2021discriminative, + title={Discriminative Reranking for Neural Machine Translation}, + author={Lee, Ann and Auli, Michael and Ranzato, Marc'Aurelio}, + booktitle={ACL}, + year={2021} +} +``` diff --git a/examples/discriminative_reranking_nmt/__init__.py b/examples/discriminative_reranking_nmt/__init__.py new file mode 100644 index 0000000000..0278f6a273 --- /dev/null +++ b/examples/discriminative_reranking_nmt/__init__.py @@ -0,0 +1 @@ +from . import criterions, models, tasks # noqa diff --git a/examples/discriminative_reranking_nmt/config/deen.yaml b/examples/discriminative_reranking_nmt/config/deen.yaml new file mode 100644 index 0000000000..3fc2d5fcf5 --- /dev/null +++ b/examples/discriminative_reranking_nmt/config/deen.yaml @@ -0,0 +1,56 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 50 + seed: 2 + +checkpoint: + no_epoch_checkpoints: true + best_checkpoint_metric: bleu + maximize_best_checkpoint_metric: true + +task: + _name: discriminative_reranking_nmt + data: ??? + num_data_splits: ??? + include_src: true + mt_beam: 50 + eval_target_metric: true + target_metric: bleu + +dataset: + batch_size: 50 + num_workers: 6 + required_batch_size_multiple: 50 + valid_subset: ??? + +criterion: + _name: kl_divergence_rereanking + target_dist_norm: minmax + temperature: 0.5 + +optimization: + max_epoch: 200 + lr: [0.00005] + update_freq: [32] + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-06 + +lr_scheduler: + _name: polynomial_decay + warmup_updates: 8000 + total_num_update: 320000 + +model: + _name: discriminative_nmt_reranker + pretrained_model: ??? + classifier_dropout: 0.2 + +distributed_training: + ddp_backend: no_c10d + distributed_world_size: 16 diff --git a/examples/discriminative_reranking_nmt/criterions/__init__.py b/examples/discriminative_reranking_nmt/criterions/__init__.py new file mode 100644 index 0000000000..7c257c2700 --- /dev/null +++ b/examples/discriminative_reranking_nmt/criterions/__init__.py @@ -0,0 +1,6 @@ +from .discriminative_reranking_criterion import KLDivergenceRerankingCriterion + + +__all__ = [ + "KLDivergenceRerankingCriterion", +] diff --git a/examples/discriminative_reranking_nmt/criterions/discriminative_reranking_criterion.py b/examples/discriminative_reranking_nmt/criterions/discriminative_reranking_criterion.py new file mode 100644 index 0000000000..0b02ce1877 --- /dev/null +++ b/examples/discriminative_reranking_nmt/criterions/discriminative_reranking_criterion.py @@ -0,0 +1,138 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math +from dataclasses import dataclass, field + +import torch +import torch.nn.functional as F + +from fairseq import metrics, utils +from fairseq.criterions import FairseqCriterion, register_criterion +from fairseq.dataclass import ChoiceEnum, FairseqDataclass + + +_EPSILON = torch.finfo(torch.float32).eps +TARGET_DIST_NORM_CHOICES = ChoiceEnum(["none", "minmax"]) + + +@dataclass +class KLDivergenceRerankingCriterionConfig(FairseqDataclass): + target_dist_norm: TARGET_DIST_NORM_CHOICES = field( + default="none", + metadata={"help": "method to normalize the range of target scores"}, + ) + temperature: float = field( + default=1.0, + metadata={"help": "temperature in softmax for target distributions"}, + ) + forward_batch_size: int = field( + default=32, + metadata={ + "help": "number of hypotheses per batch for model forward (set a value smaller than --mt-beam to avoid OOM when training with a large beam size)" + }, + ) + + +@register_criterion( + "kl_divergence_rereanking", dataclass=KLDivergenceRerankingCriterionConfig +) +class KLDivergenceRerankingCriterion(FairseqCriterion): + def __init__( + self, task, target_dist_norm, temperature, forward_batch_size, + ): + super().__init__(task) + self.target_dist_norm = target_dist_norm + self.temperature = temperature + self.forward_batch_size = forward_batch_size + + def forward(self, model, sample, reduce=True): + """Compute the loss for the given sample. + + Returns a tuple with three elements: + 1) the loss + 2) the sample size, which is used as the denominator for the gradient + 3) logging outputs to display while training + """ + + sample_size = sample["id"].numel() + assert sample_size % self.task.cfg.mt_beam == 0, ( + f"sample_size ({sample_size}) cannot be divided by beam size ({self.task.cfg.mt_beam})." + f"Please set --required-batch-size-multiple={self.task.cfg.mt_beam}." + ) + + # split into smaller batches for model forward + batch_out = [] + for i in range(0, sample_size, self.forward_batch_size): + j = min(i + self.forward_batch_size, sample_size) + + out = model( + src_tokens=sample["net_input"]["src_tokens"][i:j, :], + src_lengths=sample["net_input"]["src_lengths"][i:j], + ) + + batch_out.append( + model.sentence_forward(out, sample["net_input"]["src_tokens"][i:j, :]) + ) + + batch_out = torch.cat(batch_out, dim=0).view( + self.task.cfg.mt_beam, sample_size // self.task.cfg.mt_beam, -1 + ) # T x B x C + if model.joint_classification == "sent": + batch_out = model.joint_forward(batch_out) + scores = model.classification_forward(batch_out.view(sample_size, 1, -1)).view( + -1, self.task.cfg.mt_beam + ) # input: B x T x C + + loss = self.compute_kl_loss( + scores, sample["target"][:, 0].view(-1, self.task.cfg.mt_beam) + ) + + sample_size = sample_size // self.task.cfg.mt_beam + + logging_output = { + "loss": loss.detach(), + "ntokens": sample["ntokens"], + "nsentences": sample_size * self.task.cfg.mt_beam, + "sample_size": sample_size, + "scores": scores.detach(), + } + + return loss, sample_size, logging_output + + def compute_kl_loss(self, logits, target): + norm_target = target + if self.target_dist_norm == "minmax": + min_v = torch.min(target, 1, keepdim=True).values + max_v = torch.max(target, 1, keepdim=True).values + norm_target = (target - min_v) / (max_v - min_v + _EPSILON) + + target_dist = F.softmax( + norm_target / self.temperature, dim=-1, dtype=torch.float32 + ) + model_dist = F.log_softmax(logits, dim=-1, dtype=torch.float32) + loss = -(target_dist * model_dist - target_dist * target_dist.log()).sum() + return loss + + @staticmethod + def reduce_metrics(logging_outputs) -> None: + """Aggregate logging outputs from data parallel training.""" + loss_sum = utils.item(sum(log.get("loss", 0) for log in logging_outputs)) + + sample_size = utils.item( + sum(log.get("sample_size", 0) for log in logging_outputs) + ) + + loss = loss_sum / sample_size / math.log(2) + metrics.log_scalar("loss", loss, sample_size, round=3) + + @staticmethod + def logging_outputs_can_be_summed() -> bool: + """ + Whether the logging outputs returned by `forward` can be summed + across workers prior to calling `reduce_metrics`. Setting this + to True will improves distributed training speed. + """ + return True diff --git a/examples/discriminative_reranking_nmt/drnmt_rerank.py b/examples/discriminative_reranking_nmt/drnmt_rerank.py new file mode 100644 index 0000000000..2e0fc2bd29 --- /dev/null +++ b/examples/discriminative_reranking_nmt/drnmt_rerank.py @@ -0,0 +1,364 @@ +#!/usr/bin/env python3 -u +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +""" +Score raw text with a trained model. +""" + +from collections import namedtuple +import logging +from multiprocessing import Pool +import sys +import os +import random + +import numpy as np +import sacrebleu +import torch + +from fairseq import checkpoint_utils, options, utils + + +logger = logging.getLogger("fairseq_cli.drnmt_rerank") +logger.setLevel(logging.INFO) + +Batch = namedtuple("Batch", "ids src_tokens src_lengths") + + +pool_init_variables = {} + + +def init_loaded_scores(mt_scores, model_scores, hyp, ref): + global pool_init_variables + pool_init_variables["mt_scores"] = mt_scores + pool_init_variables["model_scores"] = model_scores + pool_init_variables["hyp"] = hyp + pool_init_variables["ref"] = ref + + +def parse_fairseq_gen(filename, task): + source = {} + hypos = {} + scores = {} + with open(filename, "r", encoding="utf-8") as f: + for line in f: + line = line.strip() + if line.startswith("S-"): # source + uid, text = line.split("\t", 1) + uid = int(uid[2:]) + source[uid] = text + elif line.startswith("D-"): # hypo + uid, score, text = line.split("\t", 2) + uid = int(uid[2:]) + if uid not in hypos: + hypos[uid] = [] + scores[uid] = [] + hypos[uid].append(text) + scores[uid].append(float(score)) + else: + continue + + source_out = [source[i] for i in range(len(hypos))] + hypos_out = [h for i in range(len(hypos)) for h in hypos[i]] + scores_out = [s for i in range(len(scores)) for s in scores[i]] + + return source_out, hypos_out, scores_out + + +def read_target(filename): + with open(filename, "r", encoding="utf-8") as f: + output = [line.strip() for line in f] + return output + + +def make_batches(args, src, hyp, task, max_positions, encode_fn): + assert len(src) * args.beam == len( + hyp + ), f"Expect {len(src) * args.beam} hypotheses for {len(src)} source sentences with beam size {args.beam}. Got {len(hyp)} hypotheses intead." + hyp_encode = [ + task.source_dictionary.encode_line(encode_fn(h), add_if_not_exist=False).long() + for h in hyp + ] + if task.cfg.include_src: + src_encode = [ + task.source_dictionary.encode_line( + encode_fn(s), add_if_not_exist=False + ).long() + for s in src + ] + tokens = [(src_encode[i // args.beam], h) for i, h in enumerate(hyp_encode)] + lengths = [(t1.numel(), t2.numel()) for t1, t2 in tokens] + else: + tokens = [(h,) for h in hyp_encode] + lengths = [(h.numel(),) for h in hyp_encode] + + itr = task.get_batch_iterator( + dataset=task.build_dataset_for_inference(tokens, lengths), + max_tokens=args.max_tokens, + max_sentences=args.batch_size, + max_positions=max_positions, + ignore_invalid_inputs=args.skip_invalid_size_inputs_valid_test, + ).next_epoch_itr(shuffle=False) + + for batch in itr: + yield Batch( + ids=batch["id"], + src_tokens=batch["net_input"]["src_tokens"], + src_lengths=batch["net_input"]["src_lengths"], + ) + + +def decode_rerank_scores(args): + if args.max_tokens is None and args.batch_size is None: + args.batch_size = 1 + + logger.info(args) + + use_cuda = torch.cuda.is_available() and not args.cpu + + # Load ensemble + logger.info("loading model(s) from {}".format(args.path)) + models, _model_args, task = checkpoint_utils.load_model_ensemble_and_task( + [args.path], arg_overrides=eval(args.model_overrides), + ) + + for model in models: + if args.fp16: + model.half() + if use_cuda: + model.cuda() + + # Initialize generator + generator = task.build_generator(args) + + # Handle tokenization and BPE + tokenizer = task.build_tokenizer(args) + bpe = task.build_bpe(args) + + def encode_fn(x): + if tokenizer is not None: + x = tokenizer.encode(x) + if bpe is not None: + x = bpe.encode(x) + return x + + max_positions = utils.resolve_max_positions( + task.max_positions(), *[model.max_positions() for model in models] + ) + + src, hyp, mt_scores = parse_fairseq_gen(args.in_text, task) + model_scores = {} + logger.info("decode reranker score") + for batch in make_batches(args, src, hyp, task, max_positions, encode_fn): + src_tokens = batch.src_tokens + src_lengths = batch.src_lengths + if use_cuda: + src_tokens = src_tokens.cuda() + src_lengths = src_lengths.cuda() + + sample = { + "net_input": {"src_tokens": src_tokens, "src_lengths": src_lengths}, + } + scores = task.inference_step(generator, models, sample) + + for id, sc in zip(batch.ids.tolist(), scores.tolist()): + model_scores[id] = sc[0] + + model_scores = [model_scores[i] for i in range(len(model_scores))] + + return src, hyp, mt_scores, model_scores + + +def get_score(mt_s, md_s, w1, lp, tgt_len): + return mt_s / (tgt_len ** lp) * w1 + md_s + + +def get_best_hyps(mt_scores, md_scores, hypos, fw_weight, lenpen, beam): + assert len(mt_scores) == len(md_scores) and len(mt_scores) == len(hypos) + hypo_scores = [] + best_hypos = [] + best_scores = [] + offset = 0 + for i in range(len(hypos)): + tgt_len = len(hypos[i].split()) + hypo_scores.append( + get_score(mt_scores[i], md_scores[i], fw_weight, lenpen, tgt_len) + ) + + if (i + 1) % beam == 0: + max_i = np.argmax(hypo_scores) + best_hypos.append(hypos[offset + max_i]) + best_scores.append(hypo_scores[max_i]) + hypo_scores = [] + offset += beam + return best_hypos, best_scores + + +def eval_metric(args, hypos, ref): + if args.metric == "bleu": + score = sacrebleu.corpus_bleu(hypos, [ref]).score + else: + score = sacrebleu.corpus_ter(hypos, [ref]).score + + return score + + +def score_target_hypo(args, fw_weight, lp): + mt_scores = pool_init_variables["mt_scores"] + model_scores = pool_init_variables["model_scores"] + hyp = pool_init_variables["hyp"] + ref = pool_init_variables["ref"] + best_hypos, _ = get_best_hyps( + mt_scores, model_scores, hyp, fw_weight, lp, args.beam + ) + rerank_eval = None + if ref: + rerank_eval = eval_metric(args, best_hypos, ref) + print(f"fw_weight {fw_weight}, lenpen {lp}, eval {rerank_eval}") + + return rerank_eval + + +def print_result(best_scores, best_hypos, output_file): + for i, (s, h) in enumerate(zip(best_scores, best_hypos)): + print(f"{i}\t{s}\t{h}", file=output_file) + + +def main(args): + utils.import_user_module(args) + + src, hyp, mt_scores, model_scores = decode_rerank_scores(args) + + assert ( + not args.tune or args.target_text is not None + ), "--target-text has to be set when tuning weights" + if args.target_text: + ref = read_target(args.target_text) + assert len(src) == len( + ref + ), f"different numbers of source and target sentences ({len(src)} vs. {len(ref)})" + + orig_best_hypos = [hyp[i] for i in range(0, len(hyp), args.beam)] + orig_eval = eval_metric(args, orig_best_hypos, ref) + + if args.tune: + logger.info("tune weights for reranking") + + random_params = np.array( + [ + [ + random.uniform( + args.lower_bound_fw_weight, args.upper_bound_fw_weight + ), + random.uniform(args.lower_bound_lenpen, args.upper_bound_lenpen), + ] + for k in range(args.num_trials) + ] + ) + + logger.info("launching pool") + with Pool( + 32, + initializer=init_loaded_scores, + initargs=(mt_scores, model_scores, hyp, ref), + ) as p: + rerank_scores = p.starmap( + score_target_hypo, + [ + (args, random_params[i][0], random_params[i][1],) + for i in range(args.num_trials) + ], + ) + if args.metric == "bleu": + best_index = np.argmax(rerank_scores) + else: + best_index = np.argmin(rerank_scores) + best_fw_weight = random_params[best_index][0] + best_lenpen = random_params[best_index][1] + else: + assert ( + args.lenpen is not None and args.fw_weight is not None + ), "--lenpen and --fw-weight should be set" + best_fw_weight, best_lenpen = args.fw_weight, args.lenpen + + best_hypos, best_scores = get_best_hyps( + mt_scores, model_scores, hyp, best_fw_weight, best_lenpen, args.beam + ) + + if args.results_path is not None: + os.makedirs(args.results_path, exist_ok=True) + output_path = os.path.join( + args.results_path, "generate-{}.txt".format(args.gen_subset), + ) + with open(output_path, "w", buffering=1, encoding="utf-8") as o: + print_result(best_scores, best_hypos, o) + else: + print_result(best_scores, best_hypos, sys.stdout) + + if args.target_text: + rerank_eval = eval_metric(args, best_hypos, ref) + print(f"before reranking, {args.metric.upper()}:", orig_eval) + print( + f"after reranking with fw_weight={best_fw_weight}, lenpen={best_lenpen}, {args.metric.upper()}:", + rerank_eval, + ) + + +def cli_main(): + parser = options.get_generation_parser(interactive=True) + + parser.add_argument( + "--in-text", + default=None, + required=True, + help="text from fairseq-interactive output, containing source sentences and hypotheses", + ) + parser.add_argument("--target-text", default=None, help="reference text") + parser.add_argument("--metric", type=str, choices=["bleu", "ter"], default="bleu") + parser.add_argument( + "--tune", + action="store_true", + help="if set, tune weights on fw scores and lenpen instead of applying fixed weights for reranking", + ) + parser.add_argument( + "--lower-bound-fw-weight", + default=0.0, + type=float, + help="lower bound of search space", + ) + parser.add_argument( + "--upper-bound-fw-weight", + default=3, + type=float, + help="upper bound of search space", + ) + parser.add_argument( + "--lower-bound-lenpen", + default=0.0, + type=float, + help="lower bound of search space", + ) + parser.add_argument( + "--upper-bound-lenpen", + default=3, + type=float, + help="upper bound of search space", + ) + parser.add_argument( + "--fw-weight", type=float, default=None, help="weight on the fw model score" + ) + parser.add_argument( + "--num-trials", + default=1000, + type=int, + help="number of trials to do for random search", + ) + + args = options.parse_args_and_arch(parser) + main(args) + + +if __name__ == "__main__": + cli_main() diff --git a/examples/discriminative_reranking_nmt/models/__init__.py b/examples/discriminative_reranking_nmt/models/__init__.py new file mode 100644 index 0000000000..c593ea5f18 --- /dev/null +++ b/examples/discriminative_reranking_nmt/models/__init__.py @@ -0,0 +1,6 @@ +from .discriminative_reranking_model import DiscriminativeNMTReranker + + +__all__ = [ + "DiscriminativeNMTReranker", +] diff --git a/examples/discriminative_reranking_nmt/models/discriminative_reranking_model.py b/examples/discriminative_reranking_nmt/models/discriminative_reranking_model.py new file mode 100644 index 0000000000..e4b5887f82 --- /dev/null +++ b/examples/discriminative_reranking_nmt/models/discriminative_reranking_model.py @@ -0,0 +1,365 @@ +from dataclasses import dataclass, field +import os + +import torch +import torch.nn as nn + +from fairseq import utils +from fairseq.dataclass import ChoiceEnum, FairseqDataclass +from fairseq.models import ( + BaseFairseqModel, + register_model, +) + +from fairseq.models.roberta.model import RobertaClassificationHead + +from fairseq.modules import ( + LayerNorm, + TransformerSentenceEncoder, + TransformerSentenceEncoderLayer, +) + + +ACTIVATION_FN_CHOICES = ChoiceEnum(utils.get_available_activation_fns()) +JOINT_CLASSIFICATION_CHOICES = ChoiceEnum(["none", "sent"]) +SENTENCE_REP_CHOICES = ChoiceEnum(["head", "meanpool", "maxpool"]) + + +def update_init_roberta_model_state(state): + """ + update the state_dict of a Roberta model for initializing + weights of the BertRanker + """ + for k in list(state.keys()): + if ".lm_head." in k or "version" in k: + del state[k] + continue + # remove 'encoder/decoder.sentence_encoder.' from the key + assert k.startswith("encoder.sentence_encoder.") or k.startswith( + "decoder.sentence_encoder." + ), f"Cannot recognize parameter name {k}" + if "layernorm_embedding" in k: + new_k = k.replace(".layernorm_embedding.", ".emb_layer_norm.") + state[new_k[25:]] = state[k] + else: + state[k[25:]] = state[k] + del state[k] + + +class BaseRanker(nn.Module): + def __init__(self, args, task): + super().__init__() + + self.separator_token = task.dictionary.eos() + self.padding_idx = task.dictionary.pad() + + def forward(self, src_tokens): + raise NotImplementedError + + def get_segment_labels(self, src_tokens): + segment_boundary = (src_tokens == self.separator_token).long() + segment_labels = ( + segment_boundary.cumsum(dim=1) + - segment_boundary + - (src_tokens == self.padding_idx).long() + ) + + return segment_labels + + def get_positions(self, src_tokens, segment_labels): + segment_positions = ( + torch.arange(src_tokens.shape[1]) + .to(src_tokens.device) + .repeat(src_tokens.shape[0], 1) + ) + segment_boundary = (src_tokens == self.separator_token).long() + _, col_idx = (segment_positions * segment_boundary).nonzero(as_tuple=True) + col_idx = torch.cat([torch.zeros(1).type_as(col_idx), col_idx]) + offset = torch.cat( + [ + torch.zeros(1).type_as(segment_boundary), + segment_boundary.sum(dim=1).cumsum(dim=0)[:-1], + ] + ) + segment_positions -= col_idx[segment_labels + offset.unsqueeze(1)] * ( + segment_labels != 0 + ) + + padding_mask = src_tokens.ne(self.padding_idx) + segment_positions = (segment_positions + 1) * padding_mask.type_as( + segment_positions + ) + self.padding_idx + + return segment_positions + + +class BertRanker(BaseRanker): + def __init__(self, args, task): + super(BertRanker, self).__init__(args, task) + + init_model = getattr(args, "pretrained_model", "") + self.joint_layers = nn.ModuleList() + if os.path.isfile(init_model): + print(f"initialize weight from {init_model}") + + from fairseq import hub_utils + + x = hub_utils.from_pretrained( + os.path.dirname(init_model), + checkpoint_file=os.path.basename(init_model), + ) + + in_state_dict = x["models"][0].state_dict() + init_args = x["args"].model + + num_positional_emb = init_args.max_positions + task.dictionary.pad() + 1 + + # follow the setup in roberta + self.model = TransformerSentenceEncoder( + padding_idx=task.dictionary.pad(), + vocab_size=len(task.dictionary), + num_encoder_layers=getattr( + args, "encoder_layers", init_args.encoder_layers + ), + embedding_dim=init_args.encoder_embed_dim, + ffn_embedding_dim=init_args.encoder_ffn_embed_dim, + num_attention_heads=init_args.encoder_attention_heads, + dropout=init_args.dropout, + attention_dropout=init_args.attention_dropout, + activation_dropout=init_args.activation_dropout, + num_segments=2, # add language embeddings + max_seq_len=num_positional_emb, + offset_positions_by_padding=False, + encoder_normalize_before=True, + apply_bert_init=True, + activation_fn=init_args.activation_fn, + freeze_embeddings=args.freeze_embeddings, + n_trans_layers_to_freeze=args.n_trans_layers_to_freeze, + ) + + # still need to learn segment embeddings as we added a second language embedding + if args.freeze_embeddings: + for p in self.model.segment_embeddings.parameters(): + p.requires_grad = False + + update_init_roberta_model_state(in_state_dict) + print("loading weights from the pretrained model") + self.model.load_state_dict( + in_state_dict, strict=False + ) # ignore mismatch in language embeddings + + ffn_embedding_dim = init_args.encoder_ffn_embed_dim + num_attention_heads = init_args.encoder_attention_heads + dropout = init_args.dropout + attention_dropout = init_args.attention_dropout + activation_dropout = init_args.activation_dropout + activation_fn = init_args.activation_fn + + classifier_embed_dim = getattr( + args, "embed_dim", init_args.encoder_embed_dim + ) + if classifier_embed_dim != init_args.encoder_embed_dim: + self.transform_layer = nn.Linear( + init_args.encoder_embed_dim, classifier_embed_dim + ) + else: + self.model = TransformerSentenceEncoder( + padding_idx=task.dictionary.pad(), + vocab_size=len(task.dictionary), + num_encoder_layers=args.encoder_layers, + embedding_dim=args.embed_dim, + ffn_embedding_dim=args.ffn_embed_dim, + num_attention_heads=args.attention_heads, + dropout=args.dropout, + attention_dropout=args.attention_dropout, + activation_dropout=args.activation_dropout, + max_seq_len=task.max_positions() + if task.max_positions() + else args.tokens_per_sample, + num_segments=2, + offset_positions_by_padding=False, + encoder_normalize_before=args.encoder_normalize_before, + apply_bert_init=args.apply_bert_init, + activation_fn=args.activation_fn, + ) + + classifier_embed_dim = args.embed_dim + ffn_embedding_dim = args.ffn_embed_dim + num_attention_heads = args.attention_heads + dropout = args.dropout + attention_dropout = args.attention_dropout + activation_dropout = args.activation_dropout + activation_fn = args.activation_fn + + self.joint_classification = args.joint_classification + if args.joint_classification == "sent": + if args.joint_normalize_before: + self.joint_layer_norm = LayerNorm(classifier_embed_dim) + else: + self.joint_layer_norm = None + + self.joint_layers = nn.ModuleList( + [ + TransformerSentenceEncoderLayer( + embedding_dim=classifier_embed_dim, + ffn_embedding_dim=ffn_embedding_dim, + num_attention_heads=num_attention_heads, + dropout=dropout, + attention_dropout=attention_dropout, + activation_dropout=activation_dropout, + activation_fn=activation_fn, + ) + for _ in range(args.num_joint_layers) + ] + ) + + self.classifier = RobertaClassificationHead( + classifier_embed_dim, + classifier_embed_dim, + 1, # num_classes + "tanh", + args.classifier_dropout, + ) + + def forward(self, src_tokens, src_lengths): + segment_labels = self.get_segment_labels(src_tokens) + positions = self.get_positions(src_tokens, segment_labels) + + inner_states, _ = self.model( + tokens=src_tokens, + segment_labels=segment_labels, + last_state_only=True, + positions=positions, + ) + + return inner_states[-1].transpose(0, 1) # T x B x C -> B x T x C + + def sentence_forward(self, encoder_out, src_tokens=None, sentence_rep="head"): + # encoder_out: B x T x C + if sentence_rep == "head": + x = encoder_out[:, :1, :] + else: # 'meanpool', 'maxpool' + assert src_tokens is not None, "meanpool requires src_tokens input" + segment_labels = self.get_segment_labels(src_tokens) + padding_mask = src_tokens.ne(self.padding_idx) + encoder_mask = segment_labels * padding_mask.type_as(segment_labels) + + if sentence_rep == "meanpool": + ntokens = torch.sum(encoder_mask, dim=1, keepdim=True) + x = torch.sum( + encoder_out * encoder_mask.unsqueeze(2), dim=1, keepdim=True + ) / ntokens.unsqueeze(2).type_as(encoder_out) + else: # 'maxpool' + encoder_out[ + (encoder_mask == 0).unsqueeze(2).repeat(1, 1, encoder_out.shape[-1]) + ] = -float("inf") + x, _ = torch.max(encoder_out, dim=1, keepdim=True) + + if hasattr(self, "transform_layer"): + x = self.transform_layer(x) + + return x # B x 1 x C + + def joint_forward(self, x): + # x: T x B x C + if self.joint_layer_norm: + x = self.joint_layer_norm(x.transpose(0, 1)) + x = x.transpose(0, 1) + + for layer in self.joint_layers: + x, _ = layer(x, self_attn_padding_mask=None) + return x + + def classification_forward(self, x): + # x: B x T x C + return self.classifier(x) + + +@dataclass +class DiscriminativeNMTRerankerConfig(FairseqDataclass): + pretrained_model: str = field( + default="", metadata={"help": "pretrained model to load"} + ) + sentence_rep: SENTENCE_REP_CHOICES = field( + default="head", + metadata={ + "help": "method to transform the output of the transformer stack to a sentence-level representation" + }, + ) + + dropout: float = field(default=0.1, metadata={"help": "dropout probability"}) + attention_dropout: float = field( + default=0.0, metadata={"help": "dropout probability for attention weights"} + ) + activation_dropout: float = field( + default=0.0, metadata={"help": "dropout probability after activation in FFN"} + ) + classifier_dropout: float = field( + default=0.0, metadata={"help": "classifier dropout probability"} + ) + embed_dim: int = field(default=768, metadata={"help": "embedding dimension"}) + ffn_embed_dim: int = field( + default=2048, metadata={"help": "embedding dimension for FFN"} + ) + encoder_layers: int = field(default=12, metadata={"help": "num encoder layers"}) + attention_heads: int = field(default=8, metadata={"help": "num attention heads"}) + encoder_normalize_before: bool = field( + default=False, metadata={"help": "apply layernorm before each encoder block"} + ) + apply_bert_init: bool = field( + default=False, metadata={"help": "use custom param initialization for BERT"} + ) + activation_fn: ACTIVATION_FN_CHOICES = field( + default="relu", metadata={"help": "activation function to use"} + ) + freeze_embeddings: bool = field( + default=False, metadata={"help": "freeze embeddings in the pretrained model"} + ) + n_trans_layers_to_freeze: int = field( + default=0, + metadata={ + "help": "number of layers to freeze in the pretrained transformer model" + }, + ) + + # joint classfication + joint_classification: JOINT_CLASSIFICATION_CHOICES = field( + default="none", + metadata={"help": "method to compute joint features for classification"}, + ) + num_joint_layers: int = field( + default=1, metadata={"help": "number of joint layers"} + ) + joint_normalize_before: bool = field( + default=False, + metadata={"help": "apply layer norm on the input to the joint layer"}, + ) + + +@register_model( + "discriminative_nmt_reranker", dataclass=DiscriminativeNMTRerankerConfig +) +class DiscriminativeNMTReranker(BaseFairseqModel): + @classmethod + def build_model(cls, args, task): + model = BertRanker(args, task) + return DiscriminativeNMTReranker(args, model) + + def __init__(self, args, model): + super().__init__() + + self.model = model + self.sentence_rep = args.sentence_rep + self.joint_classification = args.joint_classification + + def forward(self, src_tokens, src_lengths, **kwargs): + return self.model(src_tokens, src_lengths) + + def sentence_forward(self, encoder_out, src_tokens): + return self.model.sentence_forward(encoder_out, src_tokens, self.sentence_rep) + + def joint_forward(self, x): + return self.model.joint_forward(x) + + def classification_forward(self, x): + return self.model.classification_forward(x) diff --git a/examples/discriminative_reranking_nmt/scripts/prep_data.py b/examples/discriminative_reranking_nmt/scripts/prep_data.py new file mode 100755 index 0000000000..7aa7d37edc --- /dev/null +++ b/examples/discriminative_reranking_nmt/scripts/prep_data.py @@ -0,0 +1,136 @@ +#!/usr/bin/env python + +import argparse +from multiprocessing import Pool +from pathlib import Path + +import sacrebleu +import sentencepiece as spm + + +def read_text_file(filename): + with open(filename, "r") as f: + output = [line.strip() for line in f] + + return output + + +def get_bleu(in_sent, target_sent): + bleu = sacrebleu.corpus_bleu([in_sent], [[target_sent]]) + out = " ".join( + map(str, [bleu.score, bleu.sys_len, bleu.ref_len] + bleu.counts + bleu.totals) + ) + return out + + +def get_ter(in_sent, target_sent): + ter = sacrebleu.corpus_ter([in_sent], [[target_sent]]) + out = " ".join(map(str, [ter.score, ter.num_edits, ter.ref_length])) + return out + + +def init(sp_model): + global sp + sp = spm.SentencePieceProcessor() + sp.Load(sp_model) + + +def process(source_sent, target_sent, hypo_sent, metric): + source_bpe = " ".join(sp.EncodeAsPieces(source_sent)) + hypo_bpe = [" ".join(sp.EncodeAsPieces(h)) for h in hypo_sent] + + if metric == "bleu": + score_str = [get_bleu(h, target_sent) for h in hypo_sent] + else: # ter + score_str = [get_ter(h, target_sent) for h in hypo_sent] + + return source_bpe, hypo_bpe, score_str + + +def main(args): + assert ( + args.split.startswith("train") or args.num_shards == 1 + ), "--num-shards should be set to 1 for valid and test sets" + assert ( + args.split.startswith("train") + or args.split.startswith("valid") + or args.split.startswith("test") + ), "--split should be set to train[n]/valid[n]/test[n]" + + source_sents = read_text_file(args.input_source) + target_sents = read_text_file(args.input_target) + + num_sents = len(source_sents) + assert num_sents == len( + target_sents + ), f"{args.input_source} and {args.input_target} should have the same number of sentences." + + hypo_sents = read_text_file(args.input_hypo) + assert ( + len(hypo_sents) % args.beam == 0 + ), f"Number of hypotheses ({len(hypo_sents)}) cannot be divided by beam size ({args.beam})." + + hypo_sents = [ + hypo_sents[i : i + args.beam] for i in range(0, len(hypo_sents), args.beam) + ] + assert num_sents == len( + hypo_sents + ), f"{args.input_hypo} should contain {num_sents * args.beam} hypotheses but only has {len(hypo_sents) * args.beam}. (--beam={args.beam})" + + output_dir = args.output_dir / args.metric + for ns in range(args.num_shards): + print(f"processing shard {ns+1}/{args.num_shards}") + shard_output_dir = output_dir / f"split{ns+1}" + source_output_dir = shard_output_dir / "input_src" + hypo_output_dir = shard_output_dir / "input_tgt" + metric_output_dir = shard_output_dir / args.metric + + source_output_dir.mkdir(parents=True, exist_ok=True) + hypo_output_dir.mkdir(parents=True, exist_ok=True) + metric_output_dir.mkdir(parents=True, exist_ok=True) + + if args.n_proc > 1: + with Pool( + args.n_proc, initializer=init, initargs=(args.sentencepiece_model,) + ) as p: + output = p.starmap( + process, + [ + (source_sents[i], target_sents[i], hypo_sents[i], args.metric) + for i in range(ns, num_sents, args.num_shards) + ], + ) + else: + init(args.sentencepiece_model) + output = [ + process(source_sents[i], target_sents[i], hypo_sents[i], args.metric) + for i in range(ns, num_sents, args.num_shards) + ] + + with open(source_output_dir / f"{args.split}.bpe", "w") as s_o, open( + hypo_output_dir / f"{args.split}.bpe", "w" + ) as h_o, open(metric_output_dir / f"{args.split}.{args.metric}", "w") as m_o: + for source_bpe, hypo_bpe, score_str in output: + assert len(hypo_bpe) == len(score_str) + for h, m in zip(hypo_bpe, score_str): + s_o.write(f"{source_bpe}\n") + h_o.write(f"{h}\n") + m_o.write(f"{m}\n") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--input-source", type=Path, required=True) + parser.add_argument("--input-target", type=Path, required=True) + parser.add_argument("--input-hypo", type=Path, required=True) + parser.add_argument("--output-dir", type=Path, required=True) + parser.add_argument("--split", type=str, required=True) + parser.add_argument("--beam", type=int, required=True) + parser.add_argument("--sentencepiece-model", type=str, required=True) + parser.add_argument("--metric", type=str, choices=["bleu", "ter"], default="bleu") + parser.add_argument("--num-shards", type=int, default=1) + parser.add_argument("--n-proc", type=int, default=8) + + args = parser.parse_args() + + main(args) diff --git a/examples/discriminative_reranking_nmt/tasks/__init__.py b/examples/discriminative_reranking_nmt/tasks/__init__.py new file mode 100644 index 0000000000..2d78ca9870 --- /dev/null +++ b/examples/discriminative_reranking_nmt/tasks/__init__.py @@ -0,0 +1,6 @@ +from .discriminative_reranking_task import DiscriminativeRerankingNMTTask + + +__all__ = [ + "DiscriminativeRerankingNMTTask", +] diff --git a/examples/discriminative_reranking_nmt/tasks/discriminative_reranking_task.py b/examples/discriminative_reranking_nmt/tasks/discriminative_reranking_task.py new file mode 100644 index 0000000000..0e7fbba888 --- /dev/null +++ b/examples/discriminative_reranking_nmt/tasks/discriminative_reranking_task.py @@ -0,0 +1,475 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from dataclasses import dataclass, field + +import itertools +import logging +import os + +import numpy as np +import torch + +from fairseq import metrics +from fairseq.data import ( + ConcatDataset, + ConcatSentencesDataset, + data_utils, + Dictionary, + IdDataset, + indexed_dataset, + NestedDictionaryDataset, + NumSamplesDataset, + NumelDataset, + PrependTokenDataset, + RawLabelDataset, + RightPadDataset, + SortDataset, + TruncateDataset, + TokenBlockDataset, +) +from fairseq.dataclass import ChoiceEnum, FairseqDataclass +from fairseq.tasks import FairseqTask, register_task +from omegaconf import II, MISSING + + +EVAL_BLEU_ORDER = 4 +TARGET_METRIC_CHOICES = ChoiceEnum(["bleu", "ter"]) + +logger = logging.getLogger(__name__) + + +@dataclass +class DiscriminativeRerankingNMTConfig(FairseqDataclass): + data: str = field(default=MISSING, metadata={"help": "path to data directory"}) + num_data_splits: int = field( + default=1, metadata={"help": "total number of data splits"} + ) + no_shuffle: bool = field( + default=False, metadata={"help": "do not shuffle training data"} + ) + max_positions: int = field( + default=512, metadata={"help": "number of positional embeddings to learn"} + ) + include_src: bool = field( + default=False, metadata={"help": "include source sentence"} + ) + mt_beam: int = field(default=50, metadata={"help": "beam size of input hypotheses"}) + eval_target_metric: bool = field( + default=False, + metadata={"help": "evaluation with the target metric during validation"}, + ) + target_metric: TARGET_METRIC_CHOICES = field( + default="bleu", metadata={"help": "name of the target metric to optimize for"} + ) + train_subset: str = field( + default=II("dataset.train_subset"), + metadata={"help": "data subset to use for training (e.g. train, valid, test)"}, + ) + seed: int = field( + default=II("common.seed"), + metadata={"help": "pseudo random number generator seed"}, + ) + + +class RerankerScorer(object): + """Scores the target for a given (source (optional), target) input.""" + + def __init__(self, args, mt_beam): + self.mt_beam = mt_beam + + @torch.no_grad() + def generate(self, models, sample, **kwargs): + """Score a batch of translations.""" + net_input = sample["net_input"] + + assert len(models) == 1, "does not support model ensemble" + model = models[0] + + bs = net_input["src_tokens"].shape[0] + assert ( + model.joint_classification == "none" or bs % self.mt_beam == 0 + ), f"invalid batch size ({bs}) for joint classification with beam size ({self.mt_beam})" + + model.eval() + logits = model(**net_input) + + batch_out = model.sentence_forward(logits, net_input["src_tokens"]) + if model.joint_classification == "sent": + batch_out = model.joint_forward( + batch_out.view(self.mt_beam, bs // self.mt_beam, -1) + ) + scores = model.classification_forward( + batch_out.view(bs, 1, -1) + ) # input: B x T x C + + return scores + + +@register_task( + "discriminative_reranking_nmt", dataclass=DiscriminativeRerankingNMTConfig +) +class DiscriminativeRerankingNMTTask(FairseqTask): + """ + Translation rerank task. + The input can be either (src, tgt) sentence pairs or tgt sentence only. + """ + + cfg: DiscriminativeRerankingNMTConfig + + def __init__(self, cfg: DiscriminativeRerankingNMTConfig, data_dictionary=None): + super().__init__(cfg) + self.dictionary = data_dictionary + self._max_positions = cfg.max_positions + # args.tokens_per_sample = self._max_positions + # self.num_classes = 1 # for model + + @classmethod + def load_dictionary(cls, cfg, filename): + """Load the dictionary from the filename""" + dictionary = Dictionary.load(filename) + dictionary.add_symbol("") # for loading pretrained XLMR model + + return dictionary + + @classmethod + def setup_task(cls, cfg: DiscriminativeRerankingNMTConfig, **kwargs): + # load data dictionary (assume joint dictionary) + data_path = cfg.data + data_dict = cls.load_dictionary( + cfg, os.path.join(data_path, "input_src/dict.txt") + ) + + logger.info("[input] src dictionary: {} types".format(len(data_dict))) + + return DiscriminativeRerankingNMTTask(cfg, data_dict) + + def load_dataset(self, split, epoch=0, combine=False, **kwargs): + """Load a given dataset split (e.g., train, valid, test).""" + if self.cfg.data.endswith("1"): + data_shard = (epoch - 1) % self.cfg.num_data_splits + 1 + data_path = self.cfg.data[:-1] + str(data_shard) + else: + data_path = self.cfg.data + + def get_path(type, data_split): + return os.path.join(data_path, str(type), data_split) + + def make_dataset(type, dictionary, data_split, combine): + split_path = get_path(type, data_split) + + dataset = data_utils.load_indexed_dataset( + split_path, dictionary, combine=combine, + ) + return dataset + + def load_split(data_split, metric): + input_src = None + if self.cfg.include_src: + input_src = make_dataset( + "input_src", self.dictionary, data_split, combine=False + ) + assert input_src is not None, "could not find dataset: {}".format( + get_path("input_src", data_split) + ) + + input_tgt = make_dataset( + "input_tgt", self.dictionary, data_split, combine=False + ) + assert input_tgt is not None, "could not find dataset: {}".format( + get_path("input_tgt", data_split) + ) + + label_path = f"{get_path(metric, data_split)}.{metric}" + assert os.path.exists(label_path), f"could not find dataset: {label_path}" + + np_labels = np.loadtxt(label_path) + if self.cfg.target_metric == "ter": + np_labels = -np_labels + label = RawLabelDataset(np_labels) + + return input_src, input_tgt, label + + src_datasets = [] + tgt_datasets = [] + label_datasets = [] + + if split == self.cfg.train_subset: + for k in itertools.count(): + split_k = "train" + (str(k) if k > 0 else "") + prefix = os.path.join(data_path, "input_tgt", split_k) + if not indexed_dataset.dataset_exists(prefix, impl=None): + if k > 0: + break + else: + raise FileNotFoundError(f"Dataset not found: {prefix}") + input_src, input_tgt, label = load_split( + split_k, self.cfg.target_metric + ) + src_datasets.append(input_src) + tgt_datasets.append(input_tgt) + label_datasets.append(label) + else: + input_src, input_tgt, label = load_split(split, self.cfg.target_metric) + src_datasets.append(input_src) + tgt_datasets.append(input_tgt) + label_datasets.append(label) + + if len(tgt_datasets) == 1: + input_tgt, label = tgt_datasets[0], label_datasets[0] + if self.cfg.include_src: + input_src = src_datasets[0] + else: + input_tgt = ConcatDataset(tgt_datasets) + label = ConcatDataset(label_datasets) + if self.cfg.include_src: + input_src = ConcatDataset(src_datasets) + + input_tgt = TruncateDataset(input_tgt, self.cfg.max_positions) + if self.cfg.include_src: + input_src = PrependTokenDataset(input_src, self.dictionary.bos()) + input_src = TruncateDataset(input_src, self.cfg.max_positions) + src_lengths = NumelDataset(input_src, reduce=False) + src_tokens = ConcatSentencesDataset(input_src, input_tgt) + else: + src_tokens = PrependTokenDataset(input_tgt, self.dictionary.bos()) + src_lengths = NumelDataset(src_tokens, reduce=False) + + dataset = { + "id": IdDataset(), + "net_input": { + "src_tokens": RightPadDataset( + src_tokens, pad_idx=self.source_dictionary.pad(), + ), + "src_lengths": src_lengths, + }, + "nsentences": NumSamplesDataset(), + "ntokens": NumelDataset(src_tokens, reduce=True), + "target": label, + } + + dataset = NestedDictionaryDataset(dataset, sizes=[src_tokens.sizes],) + + assert len(dataset) % self.cfg.mt_beam == 0, ( + "dataset size (%d) is not a multiple of beam size (%d)" + % (len(dataset), self.cfg.mt_beam) + ) + + # no need to shuffle valid/test sets + if not self.cfg.no_shuffle and split == self.cfg.train_subset: + + # need to keep all hypothese together + start_idx = np.arange(0, len(dataset), self.cfg.mt_beam) + with data_utils.numpy_seed(self.cfg.seed + epoch): + np.random.shuffle(start_idx) + + idx = np.arange(0, self.cfg.mt_beam) + shuffle = np.tile(idx, (len(start_idx), 1)).reshape(-1) + np.tile( + start_idx, (self.cfg.mt_beam, 1) + ).transpose().reshape(-1) + + dataset = SortDataset(dataset, sort_order=[shuffle],) + + logger.info(f"Loaded {split} with #samples: {len(dataset)}") + + self.datasets[split] = dataset + return self.datasets[split] + + def build_dataset_for_inference(self, src_tokens, src_lengths, **kwargs): + assert not self.cfg.include_src or len(src_tokens[0]) == 2 + input_src = None + if self.cfg.include_src: + input_src = TokenBlockDataset( + [t[0] for t in src_tokens], + [l[0] for l in src_lengths], + block_size=None, # ignored for "eos" break mode + pad=self.source_dictionary.pad(), + eos=self.source_dictionary.eos(), + break_mode="eos", + ) + input_src = PrependTokenDataset(input_src, self.dictionary.bos()) + input_src = TruncateDataset(input_src, self.cfg.max_positions) + + input_tgt = TokenBlockDataset( + [t[-1] for t in src_tokens], + [l[-1] for l in src_lengths], + block_size=None, # ignored for "eos" break mode + pad=self.source_dictionary.pad(), + eos=self.source_dictionary.eos(), + break_mode="eos", + ) + input_tgt = TruncateDataset(input_tgt, self.cfg.max_positions) + if self.cfg.include_src: + src_tokens = ConcatSentencesDataset(input_src, input_tgt) + src_lengths = NumelDataset(input_src, reduce=False) + else: + input_tgt = PrependTokenDataset(input_tgt, self.dictionary.bos()) + src_tokens = input_tgt + src_lengths = NumelDataset(src_tokens, reduce=False) + + dataset = { + "id": IdDataset(), + "net_input": { + "src_tokens": RightPadDataset( + src_tokens, pad_idx=self.source_dictionary.pad(), + ), + "src_lengths": src_lengths, + }, + "nsentences": NumSamplesDataset(), + "ntokens": NumelDataset(src_tokens, reduce=True), + } + + return NestedDictionaryDataset(dataset, sizes=[src_tokens.sizes],) + + def build_model(self, cfg: FairseqDataclass): + return super().build_model(cfg) + + def build_generator(self, args): + return RerankerScorer(args, mt_beam=self.cfg.mt_beam) + + def max_positions(self): + return self._max_positions + + @property + def source_dictionary(self): + return self.dictionary + + @property + def target_dictionary(self): + return self.dictionary + + def create_dummy_batch(self, device): + dummy_target = ( + torch.zeros(self.cfg.mt_beam, EVAL_BLEU_ORDER * 2 + 3).long().to(device) + if not self.cfg.eval_ter + else torch.zeros(self.cfg.mt_beam, 3).long().to(device) + ) + + return { + "id": torch.zeros(self.cfg.mt_beam, 1).long().to(device), + "net_input": { + "src_tokens": torch.zeros(self.cfg.mt_beam, 4).long().to(device), + "src_lengths": torch.ones(self.cfg.mt_beam, 1).long().to(device), + }, + "nsentences": 0, + "ntokens": 0, + "target": dummy_target, + } + + def train_step( + self, sample, model, criterion, optimizer, update_num, ignore_grad=False + ): + if ignore_grad and sample is None: + sample = self.create_dummy_batch(model.device) + + return super().train_step( + sample, model, criterion, optimizer, update_num, ignore_grad + ) + + def valid_step(self, sample, model, criterion): + if sample is None: + sample = self.create_dummy_batch(model.device) + + loss, sample_size, logging_output = super().valid_step(sample, model, criterion) + + if not self.cfg.eval_target_metric: + return loss, sample_size, logging_output + + scores = logging_output["scores"] + + if self.cfg.target_metric == "bleu": + assert sample["target"].shape[1] == EVAL_BLEU_ORDER * 2 + 3, ( + "target does not contain enough information (" + + str(sample["target"].shape[1]) + + "for evaluating BLEU" + ) + + max_id = torch.argmax(scores, dim=1) + select_id = max_id + torch.arange( + 0, sample_size * self.cfg.mt_beam, self.cfg.mt_beam + ).to(max_id.device) + bleu_data = sample["target"][select_id, 1:].sum(0).data + + logging_output["_bleu_sys_len"] = bleu_data[0] + logging_output["_bleu_ref_len"] = bleu_data[1] + + for i in range(EVAL_BLEU_ORDER): + logging_output["_bleu_counts_" + str(i)] = bleu_data[2 + i] + logging_output["_bleu_totals_" + str(i)] = bleu_data[ + 2 + EVAL_BLEU_ORDER + i + ] + + elif self.cfg.target_metric == "ter": + assert sample["target"].shape[1] == 3, ( + "target does not contain enough information (" + + str(sample["target"].shape[1]) + + "for evaluating TER" + ) + + max_id = torch.argmax(scores, dim=1) + select_id = max_id + torch.arange( + 0, sample_size * self.cfg.mt_beam, self.cfg.mt_beam + ).to(max_id.device) + ter_data = sample["target"][select_id, 1:].sum(0).data + + logging_output["_ter_num_edits"] = -ter_data[0] + logging_output["_ter_ref_len"] = -ter_data[1] + + return loss, sample_size, logging_output + + def reduce_metrics(self, logging_outputs, criterion): + super().reduce_metrics(logging_outputs, criterion) + + if not self.cfg.eval_target_metric: + return + + def sum_logs(key): + return sum(log.get(key, 0) for log in logging_outputs) + + if self.cfg.target_metric == "bleu": + counts, totals = [], [] + for i in range(EVAL_BLEU_ORDER): + counts.append(sum_logs("_bleu_counts_" + str(i))) + totals.append(sum_logs("_bleu_totals_" + str(i))) + + if max(totals) > 0: + # log counts as numpy arrays -- log_scalar will sum them correctly + metrics.log_scalar("_bleu_counts", np.array(counts)) + metrics.log_scalar("_bleu_totals", np.array(totals)) + metrics.log_scalar("_bleu_sys_len", sum_logs("_bleu_sys_len")) + metrics.log_scalar("_bleu_ref_len", sum_logs("_bleu_ref_len")) + + def compute_bleu(meters): + import inspect + import sacrebleu + + fn_sig = inspect.getfullargspec(sacrebleu.compute_bleu)[0] + if "smooth_method" in fn_sig: + smooth = {"smooth_method": "exp"} + else: + smooth = {"smooth": "exp"} + bleu = sacrebleu.compute_bleu( + correct=meters["_bleu_counts"].sum, + total=meters["_bleu_totals"].sum, + sys_len=meters["_bleu_sys_len"].sum, + ref_len=meters["_bleu_ref_len"].sum, + **smooth, + ) + return round(bleu.score, 2) + + metrics.log_derived("bleu", compute_bleu) + elif self.cfg.target_metric == "ter": + num_edits = sum_logs("_ter_num_edits") + ref_len = sum_logs("_ter_ref_len") + + if ref_len > 0: + metrics.log_scalar("_ter_num_edits", num_edits) + metrics.log_scalar("_ter_ref_len", ref_len) + + def compute_ter(meters): + score = meters["_ter_num_edits"].sum / meters["_ter_ref_len"].sum + return round(score.item(), 2) + + metrics.log_derived("ter", compute_ter) diff --git a/examples/fast_noisy_channel/README.md b/examples/fast_noisy_channel/README.md new file mode 100644 index 0000000000..f2631a8c34 --- /dev/null +++ b/examples/fast_noisy_channel/README.md @@ -0,0 +1,345 @@ +# Language Models not just for Pre-training: Fast Online Neural Noisy Channel Modeling + +## Introduction +- [Yee et al. (2019)](https://www.aclweb.org/anthology/D19-1571.pdf) introduce a simple and effective noisy channel modeling approach for neural machine translation. However, the noisy channel online decoding approach introduced in this paper is too slow to be practical. +- To address this, [Bhosale et al. (2020)](http://www.statmt.org/wmt20/pdf/2020.wmt-1.68.pdf) introduces 3 simple approximations to make this approach very fast and practical without much loss in accuracy. +- This README provides intructions on how to run online decoding or generation with the noisy channel modeling approach, including ways to make it very fast without much loss in accuracy. + +## Noisy Channel Modeling + +[Yee et al. (2019)](https://www.aclweb.org/anthology/D19-1571.pdf) applies the Bayes Rule to predict `P(y|x)`, the probability of the target `y` given the source `x`. +```P(y|x) = P(x|y) * P(y) / P(x)``` +- `P(x|y)` predicts the source `x` given the target `y` and is referred to as the **channel model** +- `P(y)` is a **language model** over the target `y` +- `P(x)` is generally not modeled since it is constant for all `y`. + +We use Transformer models to parameterize the direct model `P(y|x)`, the channel model `P(x|y)` and the language model `P(y)`. + +During online decoding with beam search, we generate the top `K2` candidates per beam and score them with the following linear combination of the channel model, the language model as well as the direct model scores. + +```(1 / t) * log(P(y|x) + (1 / s) * ( λ1 * log(P(x|y)) + λ2 * log(P(y) ) )``` +- `t` - Target Prefix Length +- `s` - Source Length +- `λ1` - Channel Model Weight +- `λ2` - Language Model Weight + +The top `beam_size` candidates based on the above combined scores are chosen to continue the beams in beam search. In beam search with a direct model alone, the scores from the direct model `P(y|x)` are used to choose the top candidates in beam search. + +This framework provides a great way to utlize strong target language models trained on large amounts of unlabeled data. Language models can prefer targets unrelated to the source, so we also need a channel model whose role is to ensure that the target preferred by the language model also translates back to the source. + +### Training Translation Models and Language Models + +For training Transformer models in fairseq for machine translation, refer to instructions [here](https://github.com/pytorch/fairseq/tree/main/examples/translation) + +For training Transformer models in fairseq for language modeling, refer to instructions [here](https://github.com/pytorch/fairseq/tree/main/examples/language_model) + +### Generation with Language Model for German-English translation with fairseq + +Here are instructions to generate using a direct model and a target-side language model. + +Note: +- Download and install fairseq as per instructions [here](https://github.com/pytorch/fairseq) +- Preprocess and binarize the dataset as per instructions in section [Test Data Preprocessing](#test-data-preprocessing) + +```sh +binarized_data=data_dir/binarized +direct_model=de_en_seed4.pt +lm_model=en_lm.pt +lm_data=lm_data +wget https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/direct_models/seed4.pt -O ${direct_model} +wget https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/lm_model/transformer_lm.pt -O ${lm_model} +mkdir -p ${lm_data} +wget https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/lm_model/lm_dict/dict.txt -O ${lm_data}/dict.txt + +k2=10 +lenpen=0.16 +lm_wt=0.14 +fairseq-generate ${binarized_data} \ + --user-dir examples/fast_noisy_channel \ + --beam 5 \ + --path ${direct_model} \ + --lm-model ${lm_model} \ + --lm-data ${lm_data} \ + --k2 ${k2} \ + --combine-method lm_only \ + --task noisy_channel_translation \ + --lenpen ${lenpen} \ + --lm-wt ${lm_wt} \ + --gen-subset valid \ + --remove-bpe \ + --fp16 \ + --batch-size 10 +``` +### Noisy Channel Generation for German-English translation with fairseq + +Here are instructions for noisy channel generation with a direct model, channel model and language model as explained in section [Noisy Channel Modeling](#noisy-channel-modeling). + +Note: +- Download and install fairseq as per instructions [here](https://github.com/pytorch/fairseq) +- Preprocess and binarize the dataset as per instructions in section [Test Data Preprocessing](#test-data-preprocessing) + +```sh +binarized_data=data_dir/binarized +direct_model=de_en_seed4.pt +lm_model=en_lm.pt +lm_data=lm_data +ch_model=en_de.big.seed4.pt +wget https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/direct_models/seed4.pt -O ${direct_model} +wget https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/lm_model/transformer_lm.pt -O ${lm_model} +mkdir -p ${lm_data} +wget https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/lm_model/lm_dict/dict.txt -O ${lm_data}/dict.txt +wget https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/big.seed4.pt -O ${ch_model} + +k2=10 +lenpen=0.21 +lm_wt=0.50 +bw_wt=0.30 +fairseq-generate ${binarized_data} \ + --user-dir examples/fast_noisy_channel \ + --beam 5 \ + --path ${direct_model} \ + --lm-model ${lm_model} \ + --lm-data ${lm_data} \ + --channel-model ${ch_model} \ + --k2 ${k2} \ + --combine-method noisy_channel \ + --task noisy_channel_translation \ + --lenpen ${lenpen} \ + --lm-wt ${lm_wt} \ + --ch-wt ${bw_wt} \ + --gen-subset test \ + --remove-bpe \ + --fp16 \ + --batch-size 1 +``` +## Fast Noisy Channel Modeling + +[Bhosale et al. (2020)](http://www.statmt.org/wmt20/pdf/2020.wmt-1.68.pdf) introduces 3 approximations that speed up online noisy channel decoding - +- Smaller channel models (`Tranformer Base` with 1 encoder and decoder layer each vs. `Transformer Big`) + - This involves training a channel model that is possibly smaller and less accurate in terms of BLEU than a channel model of the same size as the direct model. + - Since the role of the channel model is mainly to assign low scores to generations from the language model if they don't translate back to the source, we may not need the most accurate channel model for this purpose. +- Smaller output vocabulary size for the channel model (~30,000 -> ~1000) + - The channel model doesn't need to score the full output vocabulary, it just needs to score the source tokens, which are completely known. + - This is specified using the arguments `--channel-scoring-type src_vocab --top-k-vocab 500` + - This means that the output vocabulary for the channel model will be the source tokens for all examples in the batch and the top-K most frequent tokens in the vocabulary + - This reduces the memory consumption needed to store channel model scores significantly +- Smaller number of candidates (`k2`) scored per beam + - This is specified by reducing the argument `--k2` + + +### Fast Noisy Channel Generation for German-English translation with fairseq + +Here are instructions for **fast** noisy channel generation with a direct model, channel model and language model as explained in section [Fast Noisy Channel Modeling](#fast-noisy-channel-modeling). The main differences are that we use a smaller channel model, reduce `--k2`, set `--channel-scoring-type src_vocab --top-k-vocab 500` and increase the `--batch-size`. + +Note: +- Download and install fairseq as per instructions [here](https://github.com/pytorch/fairseq) +- Preprocess and binarize the dataset as per instructions in section [Test Data Preprocessing](#test-data-preprocessing) + +```sh +binarized_data=data_dir/binarized +direct_model=de_en_seed4.pt +lm_model=en_lm.pt +lm_data=lm_data +small_ch_model=en_de.base_1_1.seed4.pt +wget https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/direct_models/seed4.pt -O ${direct_model} +wget https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/lm_model/transformer_lm.pt -O ${lm_model} +mkdir -p ${lm_data} +wget https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/lm_model/lm_dict/dict.txt -O ${lm_data}/dict.txt +wget https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/base_1_1.seed4.pt -O ${small_ch_model} + +k2=3 +lenpen=0.23 +lm_wt=0.58 +bw_wt=0.26 +fairseq-generate ${binarized_data} \ + --user-dir examples/fast_noisy_channel \ + --beam 5 \ + --path ${direct_model} \ + --lm-model ${lm_model} \ + --lm-data ${lm_data} \ + --channel-model ${small_ch_model} \ + --k2 ${k2} \ + --combine-method noisy_channel \ + --task noisy_channel_translation \ + --lenpen ${lenpen} \ + --lm-wt ${lm_wt} \ + --ch-wt ${bw_wt} \ + --gen-subset test \ + --remove-bpe \ + --fp16 \ + --batch-size 50 \ + --channel-scoring-type src_vocab --top-k-vocab 500 +``` + +## Test Data Preprocessing + +For preprocessing and binarizing the test sets for Romanian-English and German-English translation, we use the following script - + +```sh +FAIRSEQ=/path/to/fairseq +cd $FAIRSEQ +SCRIPTS=$FAIRSEQ/mosesdecoder/scripts +if [ ! -d "${SCRIPTS}" ]; then + echo 'Cloning Moses github repository (for tokenization scripts)...' + git clone https://github.com/moses-smt/mosesdecoder.git +fi +TOKENIZER=$SCRIPTS/tokenizer/tokenizer.perl +NORMALIZE=$SCRIPTS/tokenizer/normalize-punctuation.perl + +s=de +t=en +test=wmt18 + +mkdir -p data_dir + +# Tokenization +if [ $s == "ro" ] ; then + # Note: Get normalise-romanian.py and remove-diacritics.py from + # https://github.com/rsennrich/wmt16-scripts/tree/master/preprocess + sacrebleu -t $test -l $s-$t --echo src | \ + $NORMALIZE -l $s | \ + python normalise-romanian.py | \ + python remove-diacritics.py | \ + $TOKENIZER -l $s -a -q > data_dir/$test.$s-$t.$s +else + sacrebleu -t $test -l $s-$t --echo src | perl $NORMALIZE -l $s | perl $TOKENIZER -threads 8 -a -l $s > data_dir/$test.$s-$t.$s +fi + +sacrebleu -t $test -l $s-$t --echo ref | perl $NORMALIZE -l $t | perl $TOKENIZER -threads 8 -a -l $t > data_dir/$test.$s-$t.$t + + +# Applying BPE +src_bpe_code=/path/to/source/language/bpe/code +tgt_bpe_code=/path/to/target/language/bpe/code +src_dict=/path/to/source/language/dict +tgt_dict=/path/to/target/language/dict + +FASTBPE=$FAIRSEQ/fastBPE +if [ ! -d "${FASTBPE}" ] ; then + git clone https://github.com/glample/fastBPE.git + # Follow compilation instructions at https://github.com/glample/fastBPE + g++ -std=c++11 -pthread -O3 fastBPE/main.cc -IfastBPE -o fast +fi + +${FASTBPE}/fast applybpe data_dir/bpe.$test.$s-$t.$s data_dir/$test.$s-$t.$s ${src_bpe_code} +${FASTBPE}/fast applybpe data_dir/bpe.$test.$s-$t.$s data_dir/$test.$s-$t.$s ${tgt_bpe_code} + +fairseq-preprocess -s $s -t $t \ + --testpref data_dir/bpe.$test.$s-$t \ + --destdir data_dir/binarized \ + --srcdict ${src_dict} \ + --tgtdict ${tgt_dict} +``` + +## Calculating BLEU + +```sh +DETOKENIZER=$SCRIPTS/tokenizer/detokenizer.perl +cat ${generation_output} | grep -P "^H" | sort -V | cut -f 3- | $DETOKENIZER -l $t -q -a | sacrebleu -t $test -l $s-$t +``` + + +## Romanian-English Translation + +The direct and channel models are trained using bitext data (WMT16) combined with backtranslated data (The monolingual data used for backtranslation comes from http://data.statmt.org/rsennrich/wmt16_backtranslations/ (Sennrich et al., 2016c)) + +The backtranslated data is generated using an ensemble of 3 English-Romanian models trained on bitext training data (WMT16) with unrestricted sampling. + +### BPE Codes and Dictionary + +We learn a joint BPE vocabulary of 18K types on the bitext training data which is used for both the source and target. +||Path| +|----------|------| +| BPE Code | [joint_bpe_18k](https://dl.fbaipublicfiles.com/fast_noisy_channel/ro_en/bpe_18k) | +| Dictionary | [dict](https://dl.fbaipublicfiles.com/fast_noisy_channel/ro_en/dict) | + +### Direct Models +For Ro-En with backtranslation, the direct and channel models use a Transformer-Big architecture. + +| Seed | Model | +|----|----| +| 2 | [ro_en_seed2.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/ro_en/direct_models/seed2.pt) +| 4 | [ro_en_seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/ro_en/direct_models/seed4.pt) +| 6 | [ro_en_seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/ro_en/direct_models/seed6.pt) + +### Channel Models +For channel models, we follow the same steps as for the direct models. But backtranslated data is generated in the opposite direction using [this Romanian monolingual data](http://data.statmt.org/rsennrich/wmt16_backtranslations/). +The best lenpen, LM weight and CH weight are obtained by sweeping over the validation set (wmt16/dev) using beam 5. +| Model Size | Lenpen | LM Weight | CH Weight | Seed 2 | Seed 4 | Seed 6 | +|----|----|----|----|----|----|----| +| `big` | 0.84 | 0.64 | 0.56 | [big.seed2.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/ro_en/channel_models/big.seed2.pt) | [big.seed2.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/ro_en/channel_models/big.seed2.pt) | [big.seed2.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/ro_en/channel_models/big.seed2.pt) | +| `base_1_1` | 0.63 | 0.40 | 0.37 | [base_1_1.seed2.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/ro_en/channel_models/base_1_1.seed2.pt) | [base_1_1.seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/ro_en/channel_models/base_1_1.seed4.pt) | [base_1_1.seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/ro_en/channel_models/base_1_1.seed6.pt) | + +### Language Model +The model is trained on de-duplicated English Newscrawl data from 2007-2018 comprising 186 million sentences or 4.5B words after normalization and tokenization. +| | Path | +|----|----| +| `--lm-model` | [transformer_en_lm](https://dl.fbaipublicfiles.com/fast_noisy_channel/ro_en/lm_model/transformer_lm.pt) | +| `--lm-data` | [lm_data](https://dl.fbaipublicfiles.com/fast_noisy_channel/ro_en/lm_model/lm_dict) + +## German-English Translation + +### BPE Codes and Dictionaries + +| | Path| +|----------|------| +| Source BPE Code | [de_bpe_code_24K](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/de_bpe_code_24K) | +| Target BPE Code | [en_bpe_code_24K](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/en_bpe_code_24K) +| Source Dictionary | [de_dict](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/de_dict) | +| Target Dictionary | [en_dict](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/en_dict) | + +### Direct Models +We train on WMT’19 training data. Following [Ng et al., 2019](http://statmt.org/wmt19/pdf/53/WMT33.pdf), we apply language identification filtering and remove sentences longer than 250 tokens as well as sentence pairs with a source/target length ratio exceeding 1.5. This results in 26.8M sentence pairs. +We use the Transformer-Big architecture for the direct model. + +| Seed | Model | +|:----:|----| +| 4 | [de_en_seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/direct_models/seed4.pt) +| 5 | [de_en_seed5.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/direct_models/seed5.pt) +| 6 | [de_en_seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/direct_models/seed6.pt) + +### Channel Models + +We train on WMT’19 training data. Following [Ng et al., 2019](http://statmt.org/wmt19/pdf/53/WMT33.pdf), we apply language identification filtering and remove sentences longer than 250 tokens as well as sentence pairs with a source/target length ratio exceeding 1.5. This results in 26.8M sentence pairs. + +| Model Size | Seed 4 | Seed 5 | Seed 6 | +|----|----|----|----| +| `big` | [big.seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/big.seed4.pt) | [big.seed5.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/big.seed5.pt) | [big.seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/big.seed6.pt) | +| `big_1_1` | [big_1_1.seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/big_1_1.seed4.pt) | [big_1_1.seed5.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/big_1_1.seed5.pt) | [big_1_1.seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/big_1_1.seed6.pt) | +| `base` | [base.seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/base.seed4.pt) | [base.seed5.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/base.seed5.pt) | [base.seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/base.seed6.pt) | +| `base_1_1` | [base_1_1.seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/base_1_1.seed4.pt) | [base_1_1.seed5.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/base_1_1.seed5.pt) | [base_1_1.seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/base_1_1.seed6.pt) | +| `half` | [half.seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/half.seed4.pt) | [half.seed5.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/half.seed5.pt) | [half.seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/half.seed6.pt) | +| `half_1_1` | [half_1_1.seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/half_1_1.seed4.pt) | [half_1_1.seed5.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/half_1_1.seed5.pt) | [half_1_1.seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/half_1_1.seed6.pt) | +| `quarter` | [quarter.seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/quarter.seed4.pt) | [quarter.seed5.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/quarter.seed5.pt) | [quarter.seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/quarter.seed6.pt) | +| `quarter_1_1` | [quarter_1_1.seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/quarter_1_1.seed4.pt) | [quarter_1_1.seed5.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/quarter_1_1.seed5.pt) | [quarter_1_1.seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/quarter_1_1.seed6.pt) | +| `8th` | [8th.seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/8th.seed4.pt) | [8th.seed5.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/8th.seed5.pt) | [8th.seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/8th.seed6.pt) | +| `8th_1_1` | [8th_1_1.seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/8th_1_1.seed4.pt) | [8th_1_1.seed5.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/8th_1_1.seed5.pt) | [8th_1_1.seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/8th_1_1.seed6.pt) | +| `16th` | [16th.seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/16th.seed4.pt) | [16th.seed5.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/16th.seed5.pt) | [16th.seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/16th.seed6.pt) | +| `16th_1_1` | [16th_1_1.seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/16th_1_1.seed4.pt) | [16th_1_1.seed5.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/16th_1_1.seed5.pt) | [16th_1_1.seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/16th_1_1.seed6.pt) | + +### Language Model +The model is trained on de-duplicated English Newscrawl data from 2007-2018 comprising 186 million sentences or 4.5B words after normalization and tokenization. +| | Path | +|----|----| +| `--lm-model` | [transformer_en_lm](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/lm_model/transformer_lm.pt) | +| `--lm-data` | [lm_data](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/lm_model/lm_dict/) + + +## Citation + +```bibtex +@inproceedings{bhosale2020language, + title={Language Models not just for Pre-training: Fast Online Neural Noisy Channel Modeling}, + author={Shruti Bhosale and Kyra Yee and Sergey Edunov and Michael Auli}, + booktitle={Proceedings of the Fifth Conference on Machine Translation (WMT)}, + year={2020}, +} + +@inproceedings{yee2019simple, + title={Simple and Effective Noisy Channel Modeling for Neural Machine Translation}, + author={Yee, Kyra and Dauphin, Yann and Auli, Michael}, + booktitle={Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)}, + pages={5700--5705}, + year={2019} +} +``` diff --git a/examples/fast_noisy_channel/__init__.py b/examples/fast_noisy_channel/__init__.py new file mode 100644 index 0000000000..9b248c3a24 --- /dev/null +++ b/examples/fast_noisy_channel/__init__.py @@ -0,0 +1,8 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from . import noisy_channel_translation # noqa +from . import noisy_channel_sequence_generator # noqa +from . import noisy_channel_beam_search # noqa diff --git a/examples/fast_noisy_channel/noisy_channel_beam_search.py b/examples/fast_noisy_channel/noisy_channel_beam_search.py new file mode 100644 index 0000000000..23869ebcd0 --- /dev/null +++ b/examples/fast_noisy_channel/noisy_channel_beam_search.py @@ -0,0 +1,71 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +from fairseq.search import Search + + +class NoisyChannelBeamSearch(Search): + + def __init__(self, tgt_dict): + super().__init__(tgt_dict) + self.fw_scores_buf = None + self.lm_scores_buf = None + + def _init_buffers(self, t): + # super()._init_buffers(t) + if self.fw_scores_buf is None: + self.scores_buf = t.new() + self.indices_buf = torch.LongTensor().to(device=t.device) + self.beams_buf = torch.LongTensor().to(device=t.device) + self.fw_scores_buf = t.new() + self.lm_scores_buf = t.new() + + def combine_fw_bw(self, combine_method, fw_cum, bw, step): + if combine_method == "noisy_channel": + fw_norm = fw_cum.div(step + 1) + lprobs = bw + fw_norm + elif combine_method == "lm_only": + lprobs = bw + fw_cum + + return lprobs + + def step(self, step, fw_lprobs, scores, bw_lprobs, lm_lprobs, combine_method): + self._init_buffers(fw_lprobs) + bsz, beam_size, vocab_size = fw_lprobs.size() + + if step == 0: + # at the first step all hypotheses are equally likely, so use + # only the first beam + fw_lprobs = fw_lprobs[:, ::beam_size, :].contiguous() + bw_lprobs = bw_lprobs[:, ::beam_size, :].contiguous() + # nothing to add since we are at the first step + fw_lprobs_cum = fw_lprobs + + else: + # make probs contain cumulative scores for each hypothesis + raw_scores = (scores[:, :, step - 1].unsqueeze(-1)) + fw_lprobs_cum = (fw_lprobs.add(raw_scores)) + + combined_lprobs = self.combine_fw_bw(combine_method, fw_lprobs_cum, bw_lprobs, step) + + # choose the top k according to the combined noisy channel model score + torch.topk( + combined_lprobs.view(bsz, -1), + k=min( + # Take the best 2 x beam_size predictions. We'll choose the first + # beam_size of these which don't predict eos to continue with. + beam_size * 2, + combined_lprobs.view(bsz, -1).size(1) - 1, # -1 so we never select pad + ), + out=(self.scores_buf, self.indices_buf), + ) + # save corresponding fw and lm scores + self.fw_scores_buf = torch.gather(fw_lprobs_cum.view(bsz, -1), 1, self.indices_buf) + self.lm_scores_buf = torch.gather(lm_lprobs.view(bsz, -1), 1, self.indices_buf) + # Project back into relative indices and beams + self.beams_buf = self.indices_buf // vocab_size + self.indices_buf.fmod_(vocab_size) + return self.scores_buf, self.fw_scores_buf, self.lm_scores_buf, self.indices_buf, self.beams_buf diff --git a/examples/fast_noisy_channel/noisy_channel_sequence_generator.py b/examples/fast_noisy_channel/noisy_channel_sequence_generator.py new file mode 100644 index 0000000000..ea8fae98e8 --- /dev/null +++ b/examples/fast_noisy_channel/noisy_channel_sequence_generator.py @@ -0,0 +1,842 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from typing import Dict, List, Optional + +import math +import numpy as np + +import torch +import torch.nn.functional as F +from torch import Tensor + +from .noisy_channel_beam_search import NoisyChannelBeamSearch +from fairseq.sequence_generator import EnsembleModel + + +class NoisyChannelSequenceGenerator(object): + def __init__( + self, + combine_method, + tgt_dict, + src_dict=None, + beam_size=1, + max_len_a=0, + max_len_b=200, + min_len=1, + len_penalty=1.0, + unk_penalty=0.0, + retain_dropout=False, + temperature=1.0, + match_source_len=False, + no_repeat_ngram_size=0, + normalize_scores=True, + channel_models=None, + k2=10, + ch_weight=1.0, + channel_scoring_type='log_norm', + top_k_vocab=0, + lm_models=None, + lm_dict=None, + lm_weight=1.0, + normalize_lm_scores_by_tgt_len=False, + ): + """Generates translations of a given source sentence, + using beam search with noisy channel decoding. + + Args: + combine_method (string, optional): Method to combine direct, LM and + channel model scores (default: None) + tgt_dict (~fairseq.data.Dictionary): target dictionary + src_dict (~fairseq.data.Dictionary): source dictionary + beam_size (int, optional): beam width (default: 1) + max_len_a/b (int, optional): generate sequences of maximum length + ax + b, where x is the source length + min_len (int, optional): the minimum length of the generated output + (not including end-of-sentence) + len_penalty (float, optional): length penalty, where <1.0 favors + shorter, >1.0 favors longer sentences (default: 1.0) + unk_penalty (float, optional): unknown word penalty, where <0 + produces more unks, >0 produces fewer (default: 0.0) + retain_dropout (bool, optional): use dropout when generating + (default: False) + temperature (float, optional): temperature, where values + >1.0 produce more uniform samples and values <1.0 produce + sharper samples (default: 1.0) + match_source_len (bool, optional): outputs should match the source + length (default: False) + no_repeat_ngram_size (int, optional): Size of n-grams that we avoid + repeating in the generation (default: 0) + normalize_scores (bool, optional): normalize scores by the length + of the output (default: True) + channel_models (List[~fairseq.models.FairseqModel]): ensemble of models + translating from the target to the source + k2 (int, optional): Top K2 candidates to score per beam at each step (default:10) + ch_weight (int, optional): Weight associated with the channel model score + assuming that the direct model score has weight 1.0 (default: 1.0) + channel_scoring_type (str, optional): String specifying how to score + the channel model (default: 'log_norm') + top_k_vocab (int, optional): If `channel_scoring_type` is `'src_vocab'` or + `'src_vocab_batched'`, then this parameter specifies the number of + most frequent tokens to include in the channel model output vocabulary, + in addition to the source tokens in the input batch (default: 0) + lm_models (List[~fairseq.models.FairseqModel]): ensemble of models + generating text in the target language + lm_dict (~fairseq.data.Dictionary): LM Model dictionary + lm_weight (int, optional): Weight associated with the LM model score + assuming that the direct model score has weight 1.0 (default: 1.0) + normalize_lm_scores_by_tgt_len (bool, optional): Should we normalize LM scores + by the target length? By default, we normalize the combination of + LM and channel model scores by the source length + """ + self.pad = tgt_dict.pad() + self.unk = tgt_dict.unk() + self.eos = tgt_dict.eos() + self.vocab_size = len(tgt_dict) + self.beam_size = beam_size + # the max beam size is the dictionary size - 1, since we never select pad + self.beam_size = min(beam_size, self.vocab_size - 1) + self.max_len_a = max_len_a + self.max_len_b = max_len_b + self.min_len = min_len + self.normalize_scores = normalize_scores + self.len_penalty = len_penalty + self.unk_penalty = unk_penalty + self.retain_dropout = retain_dropout + self.temperature = temperature + self.match_source_len = match_source_len + self.no_repeat_ngram_size = no_repeat_ngram_size + self.channel_models = channel_models + self.src_dict = src_dict + self.tgt_dict = tgt_dict + self.combine_method = combine_method + self.k2 = k2 + self.ch_weight = ch_weight + self.channel_scoring_type = channel_scoring_type + self.top_k_vocab = top_k_vocab + self.lm_models = lm_models + self.lm_dict = lm_dict + self.lm_weight = lm_weight + self.log_softmax_fn = torch.nn.LogSoftmax(dim=1) + self.normalize_lm_scores_by_tgt_len = normalize_lm_scores_by_tgt_len + + self.share_tgt_dict = (self.lm_dict == self.tgt_dict) + self.tgt_to_lm = make_dict2dict(tgt_dict, lm_dict) + + self.ch_scoring_bsz = 3072 + + assert temperature > 0, '--temperature must be greater than 0' + + self.search = NoisyChannelBeamSearch(tgt_dict) + + @torch.no_grad() + def generate( + self, + models, + sample, + prefix_tokens=None, + bos_token=None, + **kwargs + ): + """Generate a batch of translations. + Args: + models (List[~fairseq.models.FairseqModel]): ensemble of models + sample (dict): batch + prefix_tokens (torch.LongTensor, optional): force decoder to begin + with these tokens + """ + model = EnsembleModel(models) + incremental_states = torch.jit.annotate( + List[Dict[str, Dict[str, Optional[Tensor]]]], + [ + torch.jit.annotate(Dict[str, Dict[str, Optional[Tensor]]], {}) + for i in range(model.models_size) + ], + ) + if not self.retain_dropout: + model.eval() + + # model.forward normally channels prev_output_tokens into the decoder + # separately, but SequenceGenerator directly calls model.encoder + encoder_input = { + k: v for k, v in sample['net_input'].items() + if k != 'prev_output_tokens' + } + src_tokens = encoder_input['src_tokens'] + src_lengths_no_eos = (src_tokens.ne(self.eos) & src_tokens.ne(self.pad)).long().sum(dim=1) + input_size = src_tokens.size() + # batch dimension goes first followed by source lengths + bsz = input_size[0] + src_len = input_size[1] + beam_size = self.beam_size + + if self.match_source_len: + max_len = src_lengths_no_eos.max().item() + else: + max_len = min( + int(self.max_len_a * src_len + self.max_len_b), + # exclude the EOS marker + model.max_decoder_positions() - 1, + ) + + # compute the encoder output for each beam + encoder_outs = model.forward_encoder(encoder_input) + new_order = torch.arange(bsz).view(-1, 1).repeat(1, beam_size).view(-1) + new_order = new_order.to(src_tokens.device).long() + encoder_outs = model.reorder_encoder_out(encoder_outs, new_order) + + src_lengths = encoder_input['src_lengths'] + # initialize buffers + scores = src_tokens.new(bsz * beam_size, max_len + 1).float().fill_(0) + lm_prefix_scores = src_tokens.new(bsz * beam_size).float().fill_(0) + + scores_buf = scores.clone() + tokens = src_tokens.new(bsz * beam_size, max_len + 2).long().fill_(self.pad) + tokens_buf = tokens.clone() + tokens[:, 0] = self.eos if bos_token is None else bos_token + + # reorder source tokens so they may be used as a reference in generating P(S|T) + src_tokens = reorder_all_tokens(src_tokens, src_lengths, self.src_dict.eos_index) + + src_tokens = src_tokens.repeat(1, beam_size).view(-1, src_len) + src_lengths = src_lengths.view(bsz, -1).repeat(1, beam_size).view(bsz*beam_size, -1) + + attn, attn_buf = None, None + nonpad_idxs = None + + # The cands_to_ignore indicates candidates that should be ignored. + # For example, suppose we're sampling and have already finalized 2/5 + # samples. Then the cands_to_ignore would mark 2 positions as being ignored, + # so that we only finalize the remaining 3 samples. + cands_to_ignore = src_tokens.new_zeros(bsz, beam_size).eq(-1) # forward and backward-compatible False mask + + # list of completed sentences + finalized = [[] for i in range(bsz)] + finished = [False for i in range(bsz)] + num_remaining_sent = bsz + + # number of candidate hypos per step + cand_size = 2 * beam_size # 2 x beam size in case half are EOS + + # offset arrays for converting between different indexing schemes + bbsz_offsets = (torch.arange(0, bsz) * beam_size).unsqueeze(1).type_as(tokens) + cand_offsets = torch.arange(0, cand_size).type_as(tokens) + + # helper function for allocating buffers on the fly + buffers = {} + + def buffer(name, type_of=tokens): # noqa + if name not in buffers: + buffers[name] = type_of.new() + return buffers[name] + + def is_finished(sent, step, unfin_idx): + """ + Check whether we've finished generation for a given sentence, by + comparing the worst score among finalized hypotheses to the best + possible score among unfinalized hypotheses. + """ + assert len(finalized[sent]) <= beam_size + if len(finalized[sent]) == beam_size: + return True + return False + + def finalize_hypos(step, bbsz_idx, eos_scores, combined_noisy_channel_eos_scores): + """ + Finalize the given hypotheses at this step, while keeping the total + number of finalized hypotheses per sentence <= beam_size. + + Note: the input must be in the desired finalization order, so that + hypotheses that appear earlier in the input are preferred to those + that appear later. + + Args: + step: current time step + bbsz_idx: A vector of indices in the range [0, bsz*beam_size), + indicating which hypotheses to finalize + eos_scores: A vector of the same size as bbsz_idx containing + fw scores for each hypothesis + combined_noisy_channel_eos_scores: A vector of the same size as bbsz_idx containing + combined noisy channel scores for each hypothesis + """ + assert bbsz_idx.numel() == eos_scores.numel() + + # clone relevant token and attention tensors + tokens_clone = tokens.index_select(0, bbsz_idx) + tokens_clone = tokens_clone[:, 1:step + 2] # skip the first index, which is EOS + assert not tokens_clone.eq(self.eos).any() + tokens_clone[:, step] = self.eos + attn_clone = attn.index_select(0, bbsz_idx)[:, :, 1:step+2] if attn is not None else None + + # compute scores per token position + pos_scores = scores.index_select(0, bbsz_idx)[:, :step+1] + pos_scores[:, step] = eos_scores + # convert from cumulative to per-position scores + pos_scores[:, 1:] = pos_scores[:, 1:] - pos_scores[:, :-1] + + # normalize sentence-level scores + if self.normalize_scores: + combined_noisy_channel_eos_scores /= (step + 1) ** self.len_penalty + + cum_unfin = [] + prev = 0 + for f in finished: + if f: + prev += 1 + else: + cum_unfin.append(prev) + + sents_seen = set() + for i, (idx, score) in enumerate(zip(bbsz_idx.tolist(), combined_noisy_channel_eos_scores.tolist())): + unfin_idx = idx // beam_size + sent = unfin_idx + cum_unfin[unfin_idx] + + sents_seen.add((sent, unfin_idx)) + + if self.match_source_len and step > src_lengths_no_eos[unfin_idx]: + score = -math.inf + + def get_hypo(): + + if attn_clone is not None: + # remove padding tokens from attn scores + hypo_attn = attn_clone[i][nonpad_idxs[sent]] + _, alignment = hypo_attn.max(dim=0) + else: + hypo_attn = None + alignment = None + + return { + 'tokens': tokens_clone[i], + 'score': score, + 'attention': hypo_attn, # src_len x tgt_len + 'alignment': alignment, + 'positional_scores': pos_scores[i], + } + + if len(finalized[sent]) < beam_size: + finalized[sent].append(get_hypo()) + + newly_finished = [] + for sent, unfin_idx in sents_seen: + # check termination conditions for this sentence + if not finished[sent] and is_finished(sent, step, unfin_idx): + finished[sent] = True + newly_finished.append(unfin_idx) + return newly_finished + + def noisy_channel_rescoring(lprobs, beam_size, bsz, src_tokens, tokens, k): + """Rescore the top k hypothesis from each beam using noisy channel modeling + Returns: + new_fw_lprobs: the direct model probabilities after pruning the top k + new_ch_lm_lprobs: the combined channel and language model probabilities + new_lm_lprobs: the language model probabilities after pruning the top k + """ + with torch.no_grad(): + lprobs_size = lprobs.size() + if prefix_tokens is not None and step < prefix_tokens.size(1): + probs_slice = lprobs.view(bsz, -1, lprobs.size(-1))[:, 0, :] + cand_scores = torch.gather( + probs_slice, dim=1, + index=prefix_tokens[:, step].view(-1, 1).data + ).expand(-1, beam_size).contiguous().view(bsz*beam_size, 1) + cand_indices = prefix_tokens[:, step].view(-1, 1).expand(bsz, beam_size).data.contiguous().view(bsz*beam_size, 1) + + # need to calculate and save fw and lm probs for prefix tokens + fw_top_k = cand_scores + fw_top_k_idx = cand_indices + k = 1 + else: + # take the top k best words for every sentence in batch*beam + fw_top_k, fw_top_k_idx = torch.topk(lprobs.view(beam_size*bsz, -1), k=k) + eos_idx = torch.nonzero(fw_top_k_idx.view(bsz*beam_size*k, -1) == self.eos)[:, 0] + ch_scores = fw_top_k.new_full((beam_size*bsz*k, ), 0) + src_size = torch.sum(src_tokens[:, :] != self.src_dict.pad_index, dim=1, keepdim=True, dtype=fw_top_k.dtype) + + if self.combine_method != "lm_only": + temp_src_tokens_full = src_tokens[:, :].repeat(1, k).view(bsz*beam_size*k, -1) + not_padding = temp_src_tokens_full[:, 1:] != self.src_dict.pad_index + cur_tgt_size = step+2 + + # add eos to all candidate sentences except those that already end in eos + eos_tokens = tokens[:, 0].repeat(1, k).view(-1, 1) + eos_tokens[eos_idx] = self.tgt_dict.pad_index + + if step == 0: + channel_input = torch.cat((fw_top_k_idx.view(-1, 1), eos_tokens), 1) + else: + # move eos from beginning to end of target sentence + channel_input = torch.cat((tokens[:, 1:step + 1].repeat(1, k).view(-1, step), fw_top_k_idx.view(-1, 1), eos_tokens), 1) + + ch_input_lengths = torch.tensor(np.full(channel_input.size(0), cur_tgt_size)) + ch_input_lengths[eos_idx] = cur_tgt_size-1 + if self.channel_scoring_type == "unnormalized": + ch_encoder_output = channel_model.encoder(channel_input, src_lengths=ch_input_lengths) + ch_decoder_output, _ = channel_model.decoder(temp_src_tokens_full, encoder_out=ch_encoder_output, features_only=True) + del ch_encoder_output + ch_intermed_scores = channel_model.decoder.unnormalized_scores_given_target(ch_decoder_output, target_ids=temp_src_tokens_full[:, 1:]) + ch_intermed_scores = ch_intermed_scores.float() + ch_intermed_scores *= not_padding.float() + ch_scores = torch.sum(ch_intermed_scores, dim=1) + elif self.channel_scoring_type == "k2_separate": + for k_idx in range(k): + k_eos_tokens = eos_tokens[k_idx::k, :] + if step == 0: + k_ch_input = torch.cat((fw_top_k_idx[:, k_idx:k_idx+1], k_eos_tokens), 1) + else: + # move eos from beginning to end of target sentence + k_ch_input = torch.cat((tokens[:, 1:step + 1], fw_top_k_idx[:, k_idx:k_idx+1], k_eos_tokens), 1) + k_ch_input_lengths = ch_input_lengths[k_idx::k] + k_ch_output = channel_model(k_ch_input, k_ch_input_lengths, src_tokens) + k_ch_lprobs = channel_model.get_normalized_probs(k_ch_output, log_probs=True) + k_ch_intermed_scores = torch.gather(k_ch_lprobs[:, :-1, :], 2, src_tokens[:, 1:].unsqueeze(2)).squeeze(2) + k_ch_intermed_scores *= not_padding.float() + ch_scores[k_idx::k] = torch.sum(k_ch_intermed_scores, dim=1) + elif self.channel_scoring_type == "src_vocab": + ch_encoder_output = channel_model.encoder(channel_input, src_lengths=ch_input_lengths) + ch_decoder_output, _ = channel_model.decoder(temp_src_tokens_full, encoder_out=ch_encoder_output, features_only=True) + + del ch_encoder_output + ch_lprobs = normalized_scores_with_batch_vocab( + channel_model.decoder, + ch_decoder_output, src_tokens, k, bsz, beam_size, + self.src_dict.pad_index, top_k=self.top_k_vocab) + ch_scores = torch.sum(ch_lprobs, dim=1) + elif self.channel_scoring_type == "src_vocab_batched": + ch_bsz_size = temp_src_tokens_full.shape[0] + ch_lprobs_list = [None] * len(range(0, ch_bsz_size, self.ch_scoring_bsz)) + for i, start_idx in enumerate(range(0, ch_bsz_size, self.ch_scoring_bsz)): + end_idx = min(start_idx + self.ch_scoring_bsz, ch_bsz_size) + temp_src_tokens_full_batch = temp_src_tokens_full[start_idx:end_idx, :] + channel_input_batch = channel_input[start_idx:end_idx, :] + ch_input_lengths_batch = ch_input_lengths[start_idx:end_idx] + ch_encoder_output_batch = channel_model.encoder(channel_input_batch, src_lengths=ch_input_lengths_batch) + ch_decoder_output_batch, _ = channel_model.decoder(temp_src_tokens_full_batch, encoder_out=ch_encoder_output_batch, features_only=True) + ch_lprobs_list[i] = normalized_scores_with_batch_vocab( + channel_model.decoder, + ch_decoder_output_batch, src_tokens, k, bsz, beam_size, + self.src_dict.pad_index, top_k=self.top_k_vocab, + start_idx=start_idx, end_idx=end_idx) + ch_lprobs = torch.cat(ch_lprobs_list, dim=0) + ch_scores = torch.sum(ch_lprobs, dim=1) + else: + ch_output = channel_model(channel_input, ch_input_lengths, temp_src_tokens_full) + ch_lprobs = channel_model.get_normalized_probs(ch_output, log_probs=True) + ch_intermed_scores = torch.gather(ch_lprobs[:, :-1, :], 2, temp_src_tokens_full[:, 1:].unsqueeze(2)).squeeze().view(bsz*beam_size*k, -1) + ch_intermed_scores *= not_padding.float() + ch_scores = torch.sum(ch_intermed_scores, dim=1) + + else: + cur_tgt_size = 0 + ch_scores = ch_scores.view(bsz*beam_size, k) + expanded_lm_prefix_scores = lm_prefix_scores.unsqueeze(1).expand(-1, k).flatten() + + if self.share_tgt_dict: + lm_scores = get_lm_scores(lm, tokens[:, :step + 1].view(-1, step+1), lm_incremental_states, fw_top_k_idx.view(-1, 1), torch.tensor(np.full(tokens.size(0), step+1)), k) + else: + new_lm_input = dict2dict(tokens[:, :step + 1].view(-1, step+1), self.tgt_to_lm) + new_cands = dict2dict(fw_top_k_idx.view(-1, 1), self.tgt_to_lm) + lm_scores = get_lm_scores(lm, new_lm_input, lm_incremental_states, new_cands, torch.tensor(np.full(tokens.size(0), step+1)), k) + + lm_scores.add_(expanded_lm_prefix_scores) + ch_lm_scores = combine_ch_lm(self.combine_method, ch_scores, lm_scores, src_size, cur_tgt_size) + # initialize all as min value + new_fw_lprobs = ch_scores.new(lprobs_size).fill_(-1e17).view(bsz*beam_size, -1) + new_ch_lm_lprobs = ch_scores.new(lprobs_size).fill_(-1e17).view(bsz*beam_size, -1) + new_lm_lprobs = ch_scores.new(lprobs_size).fill_(-1e17).view(bsz*beam_size, -1) + new_fw_lprobs[:, self.pad] = -math.inf + new_ch_lm_lprobs[:, self.pad] = -math.inf + new_lm_lprobs[:, self.pad] = -math.inf + + new_fw_lprobs.scatter_(1, fw_top_k_idx, fw_top_k) + new_ch_lm_lprobs.scatter_(1, fw_top_k_idx, ch_lm_scores) + new_lm_lprobs.scatter_(1, fw_top_k_idx, lm_scores.view(-1, k)) + return new_fw_lprobs, new_ch_lm_lprobs, new_lm_lprobs + + def combine_ch_lm(combine_type, ch_scores, lm_scores1, src_size, tgt_size): + if self.channel_scoring_type == "unnormalized": + ch_scores = self.log_softmax_fn( + ch_scores.view(-1, self.beam_size * self.k2) + ).view(ch_scores.shape) + ch_scores = ch_scores * self.ch_weight + lm_scores1 = lm_scores1 * self.lm_weight + + if combine_type == "lm_only": + # log P(T|S) + log P(T) + ch_scores = lm_scores1.view(ch_scores.size()) + elif combine_type == "noisy_channel": + # 1/t log P(T|S) + 1/s log P(S|T) + 1/t log P(T) + if self.normalize_lm_scores_by_tgt_len: + ch_scores.div_(src_size) + lm_scores_norm = lm_scores1.view(ch_scores.size()).div(tgt_size) + ch_scores.add_(lm_scores_norm) + # 1/t log P(T|S) + 1/s log P(S|T) + 1/s log P(T) + else: + ch_scores.add_(lm_scores1.view(ch_scores.size())) + ch_scores.div_(src_size) + + return ch_scores + + if self.channel_models is not None: + channel_model = self.channel_models[0] # assume only one channel_model model + else: + channel_model = None + + lm = EnsembleModel(self.lm_models) + lm_incremental_states = torch.jit.annotate( + List[Dict[str, Dict[str, Optional[Tensor]]]], + [ + torch.jit.annotate(Dict[str, Dict[str, Optional[Tensor]]], {}) + for i in range(lm.models_size) + ], + ) + + reorder_state = None + batch_idxs = None + for step in range(max_len + 1): # one extra step for EOS marker + # reorder decoder internal states based on the prev choice of beams + if reorder_state is not None: + if batch_idxs is not None: + # update beam indices to take into account removed sentences + corr = batch_idxs - torch.arange(batch_idxs.numel()).type_as(batch_idxs) + reorder_state.view(-1, beam_size).add_(corr.unsqueeze(-1) * beam_size) + model.reorder_incremental_state(incremental_states, reorder_state) + encoder_outs = model.reorder_encoder_out(encoder_outs, reorder_state) + + lm.reorder_incremental_state(lm_incremental_states, reorder_state) + + fw_lprobs, avg_attn_scores = model.forward_decoder( + tokens[:, :step + 1], encoder_outs, incremental_states, temperature=self.temperature, + ) + + fw_lprobs[:, self.pad] = -math.inf # never select pad + fw_lprobs[:, self.unk] -= self.unk_penalty # apply unk penalty + fw_lprobs, ch_lm_lprobs, lm_lprobs = noisy_channel_rescoring(fw_lprobs, beam_size, bsz, src_tokens, tokens, self.k2) + + # handle min and max length constraints + if step >= max_len: + fw_lprobs[:, :self.eos] = -math.inf + fw_lprobs[:, self.eos + 1:] = -math.inf + elif step < self.min_len: + fw_lprobs[:, self.eos] = -math.inf + + # handle prefix tokens (possibly with different lengths) + if prefix_tokens is not None and step < prefix_tokens.size(1): + prefix_toks = prefix_tokens[:, step].unsqueeze(-1).repeat(1, beam_size).view(-1) + prefix_mask = prefix_toks.ne(self.pad) + + prefix_fw_lprobs = fw_lprobs.gather(-1, prefix_toks.unsqueeze(-1)) + fw_lprobs[prefix_mask] = -math.inf + fw_lprobs[prefix_mask] = fw_lprobs[prefix_mask].scatter_( + -1, prefix_toks[prefix_mask].unsqueeze(-1), prefix_fw_lprobs + ) + + prefix_ch_lm_lprobs = ch_lm_lprobs.gather(-1, prefix_toks.unsqueeze(-1)) + ch_lm_lprobs[prefix_mask] = -math.inf + ch_lm_lprobs[prefix_mask] = ch_lm_lprobs[prefix_mask].scatter_( + -1, prefix_toks[prefix_mask].unsqueeze(-1), prefix_ch_lm_lprobs + ) + + prefix_lm_lprobs = lm_lprobs.gather(-1, prefix_toks.unsqueeze(-1)) + lm_lprobs[prefix_mask] = -math.inf + lm_lprobs[prefix_mask] = lm_lprobs[prefix_mask].scatter_( + -1, prefix_toks[prefix_mask].unsqueeze(-1), prefix_lm_lprobs + ) + + # if prefix includes eos, then we should make sure tokens and + # scores are the same across all beams + eos_mask = prefix_toks.eq(self.eos) + if eos_mask.any(): + # validate that the first beam matches the prefix + first_beam = tokens[eos_mask].view(-1, beam_size, tokens.size(-1))[:, 0, 1:step + 1] + eos_mask_batch_dim = eos_mask.view(-1, beam_size)[:, 0] + target_prefix = prefix_tokens[eos_mask_batch_dim][:, :step] + assert (first_beam == target_prefix).all() + + def replicate_first_beam(tensor, mask): + tensor = tensor.view(-1, beam_size, tensor.size(-1)) + tensor[mask] = tensor[mask][:, :1, :] + return tensor.view(-1, tensor.size(-1)) + + # copy tokens, scores and lprobs from the first beam to all beams + tokens = replicate_first_beam(tokens, eos_mask_batch_dim) + scores = replicate_first_beam(scores, eos_mask_batch_dim) + + fw_lprobs = replicate_first_beam(fw_lprobs, eos_mask_batch_dim) + ch_lm_lprobs = replicate_first_beam(ch_lm_lprobs, eos_mask_batch_dim) + lm_lprobs = replicate_first_beam(lm_lprobs, eos_mask_batch_dim) + + if self.no_repeat_ngram_size > 0: + # for each beam and batch sentence, generate a list of previous ngrams + gen_ngrams = [{} for bbsz_idx in range(bsz * beam_size)] + for bbsz_idx in range(bsz * beam_size): + gen_tokens = tokens[bbsz_idx].tolist() + for ngram in zip(*[gen_tokens[i:] for i in range(self.no_repeat_ngram_size)]): + gen_ngrams[bbsz_idx][tuple(ngram[:-1])] = \ + gen_ngrams[bbsz_idx].get(tuple(ngram[:-1]), []) + [ngram[-1]] + + # Record attention scores + if avg_attn_scores is not None: + if attn is None: + attn = scores.new(bsz * beam_size, src_tokens.size(1), max_len + 2) + attn_buf = attn.clone() + nonpad_idxs = src_tokens.ne(self.pad) + attn[:, :, step + 1].copy_(avg_attn_scores) + + scores = scores.type_as(fw_lprobs) + scores_buf = scores_buf.type_as(fw_lprobs) + + self.search.set_src_lengths(src_lengths_no_eos) + + if self.no_repeat_ngram_size > 0: + def calculate_banned_tokens(bbsz_idx): + # before decoding the next token, prevent decoding of ngrams that have already appeared + ngram_index = tuple(tokens[bbsz_idx, step + 2 - self.no_repeat_ngram_size:step + 1].tolist()) + return gen_ngrams[bbsz_idx].get(ngram_index, []) + + if step + 2 - self.no_repeat_ngram_size >= 0: + # no banned tokens if we haven't generated no_repeat_ngram_size tokens yet + banned_tokens = [calculate_banned_tokens(bbsz_idx) for bbsz_idx in range(bsz * beam_size)] + else: + banned_tokens = [[] for bbsz_idx in range(bsz * beam_size)] + + for bbsz_idx in range(bsz * beam_size): + fw_lprobs[bbsz_idx, banned_tokens[bbsz_idx]] = -math.inf + + combined_noisy_channel_scores, fw_lprobs_top_k, lm_lprobs_top_k, cand_indices, cand_beams = self.search.step( + step, + fw_lprobs.view(bsz, -1, self.vocab_size), + scores.view(bsz, beam_size, -1)[:, :, :step], ch_lm_lprobs.view(bsz, -1, self.vocab_size), + lm_lprobs.view(bsz, -1, self.vocab_size), self.combine_method + ) + + # cand_bbsz_idx contains beam indices for the top candidate + # hypotheses, with a range of values: [0, bsz*beam_size), + # and dimensions: [bsz, cand_size] + cand_bbsz_idx = cand_beams.add(bbsz_offsets) + + # finalize hypotheses that end in eos (except for candidates to be ignored) + eos_mask = cand_indices.eq(self.eos) + eos_mask[:, :beam_size] &= ~cands_to_ignore + + # only consider eos when it's among the top beam_size indices + eos_bbsz_idx = torch.masked_select( + cand_bbsz_idx[:, :beam_size], mask=eos_mask[:, :beam_size] + ) + + finalized_sents = set() + if eos_bbsz_idx.numel() > 0: + eos_scores = torch.masked_select( + fw_lprobs_top_k[:, :beam_size], mask=eos_mask[:, :beam_size] + ) + combined_noisy_channel_eos_scores = torch.masked_select( + combined_noisy_channel_scores[:, :beam_size], + mask=eos_mask[:, :beam_size], + ) + + # finalize hypo using channel model score + finalized_sents = finalize_hypos( + step, eos_bbsz_idx, eos_scores, combined_noisy_channel_eos_scores) + + num_remaining_sent -= len(finalized_sents) + + assert num_remaining_sent >= 0 + if num_remaining_sent == 0: + break + + if len(finalized_sents) > 0: + new_bsz = bsz - len(finalized_sents) + + # construct batch_idxs which holds indices of batches to keep for the next pass + batch_mask = cand_indices.new_ones(bsz) + batch_mask[cand_indices.new(finalized_sents)] = 0 + batch_idxs = torch.nonzero(batch_mask).squeeze(-1) + + eos_mask = eos_mask[batch_idxs] + cand_beams = cand_beams[batch_idxs] + bbsz_offsets.resize_(new_bsz, 1) + cand_bbsz_idx = cand_beams.add(bbsz_offsets) + + lm_lprobs_top_k = lm_lprobs_top_k[batch_idxs] + + fw_lprobs_top_k = fw_lprobs_top_k[batch_idxs] + cand_indices = cand_indices[batch_idxs] + if prefix_tokens is not None: + prefix_tokens = prefix_tokens[batch_idxs] + src_lengths_no_eos = src_lengths_no_eos[batch_idxs] + cands_to_ignore = cands_to_ignore[batch_idxs] + + scores = scores.view(bsz, -1)[batch_idxs].view(new_bsz * beam_size, -1) + scores_buf.resize_as_(scores) + tokens = tokens.view(bsz, -1)[batch_idxs].view(new_bsz * beam_size, -1) + tokens_buf.resize_as_(tokens) + src_tokens = src_tokens.view(bsz, -1)[batch_idxs].view(new_bsz * beam_size, -1) + src_lengths = src_lengths.view(bsz, -1)[batch_idxs].view(new_bsz * beam_size, -1) + lm_prefix_scores = lm_prefix_scores.view(bsz, -1)[batch_idxs].view(new_bsz * beam_size, -1).squeeze() + + if attn is not None: + attn = attn.view(bsz, -1)[batch_idxs].view(new_bsz * beam_size, attn.size(1), -1) + attn_buf.resize_as_(attn) + bsz = new_bsz + else: + batch_idxs = None + + # Set active_mask so that values > cand_size indicate eos or + # ignored hypos and values < cand_size indicate candidate + # active hypos. After this, the min values per row are the top + # candidate active hypos. + eos_mask[:, :beam_size] |= cands_to_ignore + active_mask = torch.add( + eos_mask.type_as(cand_offsets) * cand_size, + cand_offsets[: eos_mask.size(1)], + ) + + # get the top beam_size active hypotheses, which are just the hypos + # with the smallest values in active_mask + active_hypos, new_cands_to_ignore = buffer('active_hypos'), buffer('new_cands_to_ignore') + torch.topk( + active_mask, k=beam_size, dim=1, largest=False, + out=(new_cands_to_ignore, active_hypos) + ) + + # update cands_to_ignore to ignore any finalized hypos + cands_to_ignore = new_cands_to_ignore.ge(cand_size)[:, :beam_size] + assert (~cands_to_ignore).any(dim=1).all() + + active_bbsz_idx = buffer('active_bbsz_idx') + torch.gather( + cand_bbsz_idx, dim=1, index=active_hypos, + out=active_bbsz_idx, + ) + active_scores = torch.gather( + fw_lprobs_top_k, dim=1, index=active_hypos, + out=scores[:, step].view(bsz, beam_size), + ) + + active_bbsz_idx = active_bbsz_idx.view(-1) + active_scores = active_scores.view(-1) + + # copy tokens and scores for active hypotheses + torch.index_select( + tokens[:, :step + 1], dim=0, index=active_bbsz_idx, + out=tokens_buf[:, :step + 1], + ) + torch.gather( + cand_indices, dim=1, index=active_hypos, + out=tokens_buf.view(bsz, beam_size, -1)[:, :, step + 1], + ) + if step > 0: + torch.index_select( + scores[:, :step], dim=0, index=active_bbsz_idx, + out=scores_buf[:, :step], + ) + torch.gather( + fw_lprobs_top_k, dim=1, index=active_hypos, + out=scores_buf.view(bsz, beam_size, -1)[:, :, step], + ) + torch.gather( + lm_lprobs_top_k, dim=1, index=active_hypos, + out=lm_prefix_scores.view(bsz, beam_size) + ) + + # copy attention for active hypotheses + if attn is not None: + torch.index_select( + attn[:, :, :step + 2], dim=0, index=active_bbsz_idx, + out=attn_buf[:, :, :step + 2], + ) + + # swap buffers + tokens, tokens_buf = tokens_buf, tokens + scores, scores_buf = scores_buf, scores + if attn is not None: + attn, attn_buf = attn_buf, attn + + # reorder incremental state in decoder + reorder_state = active_bbsz_idx + + # sort by score descending + for sent in range(len(finalized)): + finalized[sent] = sorted(finalized[sent], key=lambda r: r['score'], reverse=True) + + return finalized + + +def get_lm_scores(model, input_tokens, incremental_states, cand_tokens, input_len, k): + with torch.no_grad(): + lm_lprobs, avg_attn_scores = model.forward_decoder( + input_tokens, encoder_outs=None, incremental_states=incremental_states, + ) + + lm_lprobs_size = lm_lprobs.size(0) + probs_next_wrd = torch.gather(lm_lprobs.repeat(1, k).view(lm_lprobs_size*k, -1), 1, cand_tokens).squeeze().view(-1) + + return probs_next_wrd + + +def make_dict2dict(old_dict, new_dict): + dict2dict_map = {} + for sym in old_dict.symbols: + dict2dict_map[old_dict.index(sym)] = new_dict.index(sym) + return dict2dict_map + + +def dict2dict(tokens, dict2dict_map): + if tokens.device == torch.device('cpu'): + tokens_tmp = tokens + else: + tokens_tmp = tokens.cpu() + return tokens_tmp.map_( + tokens_tmp, + lambda _, val, dict2dict_map=dict2dict_map : dict2dict_map[float(val)] + ).to(tokens.device) + + +def reorder_tokens(tokens, lengths, eos): + # reorder source tokens so they may be used as reference for P(S|T) + return torch.cat((tokens.new([eos]), tokens[-lengths:-1], tokens[:-lengths]), 0) + + +def reorder_all_tokens(tokens, lengths, eos): + # used to reorder src tokens from [ .. ] to [ ...] + # so source tokens can be used to predict P(S|T) + return torch.stack([reorder_tokens(token, length, eos) for token, length in zip(tokens, lengths)]) + + +def normalized_scores_with_batch_vocab( + model_decoder, features, target_ids, k, bsz, beam_size, + pad_idx, top_k=0, vocab_size_meter=None, start_idx=None, + end_idx=None, **kwargs): + """ + Get normalized probabilities (or log probs) from a net's output + w.r.t. vocab consisting of target IDs in the batch + """ + if model_decoder.adaptive_softmax is None: + weight = model_decoder.output_projection.weight + vocab_ids = torch.unique( + torch.cat( + (torch.unique(target_ids), torch.arange(top_k, device=target_ids.device)) + ) + ) + id_map = dict(zip(vocab_ids.tolist(), range(len(vocab_ids)))) + mapped_target_ids = target_ids.cpu().apply_( + lambda x, id_map=id_map: id_map[x] + ).to(target_ids.device) + expanded_target_ids = mapped_target_ids[:, :].repeat(1, k).view(bsz*beam_size*k, -1) + if start_idx is not None and end_idx is not None: + expanded_target_ids = expanded_target_ids[start_idx:end_idx, :] + logits = F.linear(features, weight[vocab_ids, :]) + log_softmax = F.log_softmax(logits, dim=-1, dtype=torch.float32) + intermed_scores = torch.gather( + log_softmax[:, :-1, :], + 2, + expanded_target_ids[:, 1:].unsqueeze(2), + ).squeeze() + not_padding = expanded_target_ids[:, 1:] != pad_idx + intermed_scores *= not_padding.float() + return intermed_scores + else: + raise ValueError("adaptive softmax doesn't work with " + + "`normalized_scores_with_batch_vocab()`") diff --git a/examples/fast_noisy_channel/noisy_channel_translation.py b/examples/fast_noisy_channel/noisy_channel_translation.py new file mode 100644 index 0000000000..b74bdfd456 --- /dev/null +++ b/examples/fast_noisy_channel/noisy_channel_translation.py @@ -0,0 +1,127 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from fairseq.tasks.translation import TranslationTask +from fairseq.tasks.language_modeling import LanguageModelingTask +from fairseq import checkpoint_utils +import argparse +from fairseq.tasks import register_task +import torch + + +@register_task("noisy_channel_translation") +class NoisyChannelTranslation(TranslationTask): + """ + Rescore the top k candidates from each beam using noisy channel modeling + """ + + @staticmethod + def add_args(parser): + """Add task-specific arguments to the parser.""" + TranslationTask.add_args(parser) + # fmt: off + parser.add_argument('--channel-model', metavar='FILE', + help='path to P(S|T) model. P(S|T) and P(T|S) must share source and target dictionaries.') + parser.add_argument('--combine-method', default='lm_only', + choices=['lm_only', 'noisy_channel'], + help="""method for combining direct and channel model scores. + lm_only: decode with P(T|S)P(T) + noisy_channel: decode with 1/t P(T|S) + 1/s(P(S|T)P(T))""") + parser.add_argument('--normalize-lm-scores-by-tgt-len', action='store_true', default=False, + help='normalize lm score by target length instead of source length') + parser.add_argument('--channel-scoring-type', default='log_norm', choices=['unnormalized', 'log_norm', 'k2_separate', 'src_vocab', 'src_vocab_batched'], + help="Normalize bw scores with log softmax or return bw scores without log softmax") + parser.add_argument('--top-k-vocab', default=0, type=int, + help='top k vocab IDs to use with `src_vocab` in channel model scoring') + parser.add_argument('--k2', default=50, type=int, + help='the top k2 candidates to rescore with the noisy channel model for each beam') + parser.add_argument('--ch-wt', default=1, type=float, + help='weight for the channel model') + parser.add_argument('--lm-model', metavar='FILE', + help='path to lm model file, to model P(T). P(T) must share the same vocab as the direct model on the target side') + parser.add_argument('--lm-data', metavar='FILE', + help='path to lm model training data for target language, used to properly load LM with correct dictionary') + parser.add_argument('--lm-wt', default=1, type=float, + help='the weight of the lm in joint decoding') + # fmt: on + + def build_generator( + self, models, args, seq_gen_cls=None, extra_gen_cls_kwargs=None + ): + if getattr(args, "score_reference", False): + raise NotImplementedError() + else: + from .noisy_channel_sequence_generator import NoisyChannelSequenceGenerator + use_cuda = torch.cuda.is_available() and not self.args.cpu + assert self.args.lm_model is not None, '--lm-model required for noisy channel generation!' + assert self.args.lm_data is not None, '--lm-data required for noisy channel generation to map between LM and bitext vocabs' + if self.args.channel_model is not None: + import copy + ch_args_task = copy.deepcopy(self.args) + tmp = ch_args_task.source_lang + ch_args_task.source_lang = ch_args_task.target_lang + ch_args_task.target_lang = tmp + ch_args_task._name = 'translation' + channel_task = TranslationTask.setup_task(ch_args_task) + + arg_dict = {} + arg_dict['task'] = 'language_modeling' + arg_dict['sample_break_mode'] = 'eos' + arg_dict['data'] = self.args.lm_data + arg_dict['output_dictionary_size'] = -1 + lm_args = argparse.Namespace(**arg_dict) + lm_task = LanguageModelingTask.setup_task(lm_args) + lm_dict = lm_task.output_dictionary + + if self.args.channel_model is not None: + channel_models, _ = checkpoint_utils.load_model_ensemble(self.args.channel_model.split(':'), task=channel_task) + + for model in channel_models: + model.make_generation_fast_( + beamable_mm_beam_size=None if args.no_beamable_mm else args.beam, + need_attn=args.print_alignment, + ) + if self.args.fp16: + model.half() + if use_cuda: + model.cuda() + else: + channel_models = None + + lm_models, _ = checkpoint_utils.load_model_ensemble(self.args.lm_model.split(':'), task=lm_task) + + for model in lm_models: + model.make_generation_fast_( + beamable_mm_beam_size=None if args.no_beamable_mm else args.beam, + need_attn=args.print_alignment, + ) + if self.args.fp16: + model.half() + if use_cuda: + model.cuda() + return NoisyChannelSequenceGenerator( + combine_method=self.args.combine_method, + tgt_dict=self.target_dictionary, + src_dict=self.source_dictionary, + beam_size=getattr(args, 'beam', 5), + max_len_a=getattr(args, 'max_len_a', 0), + max_len_b=getattr(args, 'max_len_b', 200), + min_len=getattr(args, 'min_len', 1), + len_penalty=getattr(args, 'lenpen', 1), + unk_penalty=getattr(args, 'unkpen', 0), + temperature=getattr(args, 'temperature', 1.), + match_source_len=getattr(args, 'match_source_len', False), + no_repeat_ngram_size=getattr(args, 'no_repeat_ngram_size', 0), + normalize_scores=(not getattr(args, 'unnormalized', False)), + channel_models=channel_models, + k2=getattr(self.args, 'k2', 50), + ch_weight=getattr(self.args, 'ch_wt', 1), + channel_scoring_type=self.args.channel_scoring_type, + top_k_vocab=self.args.top_k_vocab, + lm_models=lm_models, + lm_dict=lm_dict, + lm_weight=getattr(self.args, 'lm_wt', 1), + normalize_lm_scores_by_tgt_len=getattr(self.args, 'normalize_lm_scores_by_tgt_len', False), + ) diff --git a/examples/flores101/README.md b/examples/flores101/README.md new file mode 100644 index 0000000000..635c13f40b --- /dev/null +++ b/examples/flores101/README.md @@ -0,0 +1,223 @@ +

+ +

+ +# Flores101: Large-Scale Multilingual Machine Translation + +## Introduction + +Baseline pretrained models for small and large tracks of WMT 21 Large-Scale Multilingual Machine Translation competition. + +Flores Task at WMT 21: http://www.statmt.org/wmt21/large-scale-multilingual-translation-task.html + +Flores announement blog post: https://ai.facebook.com/blog/flores-researchers-kick-off-multilingual-translation-challenge-at-wmt-and-call-for-compute-grants/ + + + +## Pretrained models + +Model | Num layers | Embed dimension | FFN dimension| Vocab Size | #params | Download +---|---|---|---|---|---|--- +`flores101_mm100_615M` | 12 | 1024 | 4096 | 256,000 | 615M | https://dl.fbaipublicfiles.com/flores101/pretrained_models/flores101_mm100_615M.tar.gz +`flores101_mm100_175M` | 6 | 512 | 2048 | 256,000 | 175M | https://dl.fbaipublicfiles.com/flores101/pretrained_models/flores101_mm100_175M.tar.gz + + +These models are trained similar to [M2M-100](https://arxiv.org/abs/2010.11125) with additional support for the languages that are part of the WMT Large-Scale Multilingual Machine Translation track. Full list of languages can be found at the bottom. + + +## Example Generation code + +### Download model, sentencepiece vocab + +```bash +fairseq=/path/to/fairseq +cd $fairseq + +# Download 615M param model. +wget https://dl.fbaipublicfiles.com/flores101/pretrained_models/flores101_mm100_615M.tar.gz + +# Extract +tar -xvzf flores101_mm100_615M.tar.gz +``` + +### Encode using our SentencePiece Model +Note: Install SentencePiece from [here](https://github.com/google/sentencepiece) + + +```bash +fairseq=/path/to/fairseq +cd $fairseq + +# Download example dataset From German to French +sacrebleu --echo src -l de-fr -t wmt19 | head -n 20 > raw_input.de-fr.de +sacrebleu --echo ref -l de-fr -t wmt19 | head -n 20 > raw_input.de-fr.fr + +for lang in de fr ; do + python scripts/spm_encode.py \ + --model flores101_mm100_615M/sentencepiece.bpe.model \ + --output_format=piece \ + --inputs=raw_input.de-fr.${lang} \ + --outputs=spm.de-fr.${lang} +done +``` + +### Binarization + +```bash +fairseq-preprocess \ + --source-lang de --target-lang fr \ + --testpref spm.de-fr \ + --thresholdsrc 0 --thresholdtgt 0 \ + --destdir data_bin \ + --srcdict flores101_mm100_615M/dict.txt --tgtdict flores101_mm100_615M/dict.txt +``` + +### Generation + + +```bash +fairseq-generate \ + data_bin \ + --batch-size 1 \ + --path flores101_mm100_615M/model.pt \ + --fixed-dictionary flores101_mm100_615M/dict.txt \ + -s de -t fr \ + --remove-bpe 'sentencepiece' \ + --beam 5 \ + --task translation_multi_simple_epoch \ + --lang-pairs flores101_mm100_615M/language_pairs.txt \ + --decoder-langtok --encoder-langtok src \ + --gen-subset test \ + --fp16 \ + --dataset-impl mmap \ + --distributed-world-size 1 --distributed-no-spawn +``` + +### Supported Languages and lang code + +Language | lang code +---|--- +Akrikaans | af +Amharic | am +Arabic | ar +Assamese | as +Asturian | ast +Aymara | ay +Azerbaijani | az +Bashkir | ba +Belarusian | be +Bulgarian | bg +Bengali | bn +Breton | br +Bosnian | bs +Catalan | ca +Cebuano | ceb +Chokwe | cjk +Czech | cs +Welsh | cy +Danish | da +German | de +Dyula| dyu +Greek | el +English | en +Spanish | es +Estonian | et +Persian | fa +Fulah | ff +Finnish | fi +French | fr +Western Frisian | fy +Irish | ga +Scottish Gaelic | gd +Galician | gl +Gujarati | gu +Hausa | ha +Hebrew | he +Hindi | hi +Croatian | hr +Haitian Creole | ht +Hungarian | hu +Armenian | hy +Indonesian | id +Igbo | ig +Iloko | ilo +Icelandic | is +Italian | it +Japanese | ja +Javanese | jv +Georgian | ka +Kachin | kac +Kamba | kam +Kabuverdianu | kea +Kongo | kg +Kazakh | kk +Central Khmer | km +Kimbundu | kmb +Northern Kurdish | kmr +Kannada | kn +Korean | ko +Kurdish | ku +Kyrgyz | ky +Luxembourgish | lb +Ganda | lg +Lingala | ln +Lao | lo +Lithuanian | lt +Luo | luo +Latvian | lv +Malagasy | mg +Maori | mi +Macedonian | mk +Malayalam | ml +Mongolian | mn +Marathi | mr +Malay | ms +Maltese | mt +Burmese | my +Nepali | ne +Dutch | nl +Norwegian | no +Northern Sotho | ns +Nyanja | ny +Occitan | oc +Oromo | om +Oriya | or +Punjabi | pa +Polish | pl +Pashto | ps +Portuguese | pt +Quechua | qu +Romanian | ro +Russian | ru +Sindhi | sd +Shan | shn +Sinhala | si +Slovak | sk +Slovenian | sl +Shona | sn +Somali | so +Albanian | sq +Serbian | sr +Swati | ss +Sundanese | su +Swedish | sv +Swahili | sw +Tamil | ta +Telugu | te +Tajik | tg +Thai | th +Tigrinya | ti +Tagalog | tl +Tswana | tn +Turkish | tr +Ukrainian | uk +Umbundu | umb +Urdu | ur +Uzbek | uz +Vietnamese | vi +Wolof | wo +Xhosa | xh +Yiddish | yi +Yoruba | yo +Chinese| zh +Zulu | zu diff --git a/examples/flores101/flores_logo.png b/examples/flores101/flores_logo.png new file mode 100644 index 0000000000..d4d1455c6e Binary files /dev/null and b/examples/flores101/flores_logo.png differ diff --git a/examples/fully_sharded_data_parallel/README.md b/examples/fully_sharded_data_parallel/README.md new file mode 100644 index 0000000000..b9e44fef48 --- /dev/null +++ b/examples/fully_sharded_data_parallel/README.md @@ -0,0 +1,177 @@ +# Fully Sharded Data Parallel (FSDP) + +## Overview +Recent work by [Microsoft](https://arxiv.org/abs/1910.02054) and +[Google](https://arxiv.org/abs/2004.13336) has shown that data parallel +training can be made significantly more efficient by sharding the model +parameters and optimizer state across data parallel workers. These ideas are +encapsulated in the new **`FullyShardedDataParallel` (FSDP)** wrapper provided +by [fairscale](https://github.com/facebookresearch/fairscale/). + +Compared to PyTorch DDP: +* FSDP produces identical results as PyTorch DDP (it's still synchronous data parallel training) +* FSDP shards parameters (FP16 + FP32) and optimizer state across data parallel GPUs +* FSDP is faster than PyTorch DDP because the optimizer step is sharded, and the communication can be overlapped with the forward pass +* FSDP enables training 13B parameter models on 8 GPUs and 175B parameter models on 128 GPUs + +FSDP is fully supported in fairseq via the following new arguments: +* `--ddp-backend=fully_sharded`: enables full sharding via FSDP +* `--cpu-offload`: offloads the optimizer state and FP32 model copy to CPU (combine with `--optimizer=cpu_adam`) +* `--no-reshard-after-forward`: increases training speed for large models (1B+ params) and is similar to ZeRO stage 2 +* other popular options (`--fp16`, `--update-freq`, `--checkpoint-activations`, `--offload-activations`, etc.) continue to work as normal + +
Limitations

+ +FSDP currently has several limitations compared to fairseq's default DDP backend (PyTorch DDP): +* while FSDP is full compatible with pointwise Optimizers (e.g., Adam, AdamW, Adadelta, Adamax, SGD, etc.), it is not currently compatible with non-pointwise Optimizers (e.g., Adagrad, Adafactor, LAMB, etc.) +* FSDP depends on flattening the parameters, so models that currently require `--fp16-no-flatten-grads` may not be supported + +See the [fairscale docs](https://fairscale.readthedocs.io/en/latest/api/nn/fsdp_tips.html) for a more detailed +explanation of these and other limitations. + +

+ +
How it works

+ +Fully Sharded Data Parallel + +See the [fairscale docs](https://fairscale.readthedocs.io/en/latest/api/nn/fsdp_tips.html) for a more detailed +explanation of how FSDP works. + +

+ +## Example usage + +The following examples illustrate how to train a very large language model with +13 billion parameters on 1 GPU by offloading parameters and optimizer states to +CPU, or on 8 GPUs by fully sharding the params and optimizer states across GPUs. + +These examples use the WikiText-103 dataset for demonstration purposes, but +in practice a much larger dataset will be needed to achieve good results. +Follow the [instructions here](https://github.com/pytorch/fairseq/blob/main/examples/roberta/README.pretraining.md#1-preprocess-the-data) +to preprocess the WikiText-103 dataset using the GPT-2/RoBERTa vocabulary. + +### 13B params on 1 V100 GPU (with CPU offloading) + +The following command trains a 13B parameter GPT-3 model on a single V100 GPU +using the `--cpu-offload` feature to offload parameters and optimizer states to +CPU. In this setting, the optimizer step (Adam) happens on CPU. We also use the +`--checkpoint-activations` feature (sometimes called [gradient checkpointing](https://pytorch.org/docs/stable/checkpoint.html)), +which further saves memory in exchange for a small increase in computation. + +**Requirements:** +- Install the latest master version of fairscale: `pip install git+https://github.com/facebookresearch/fairscale.git@master` +- You'll need 32GB of GPU memory and ~256GB of system memory to train the 13B param model. +- If you have less system memory, the 6.7B param model can be trained with ~128GB of system memory, just set `--arch transformer_lm_gpt3_6_7` +- We use the CPU Adam optimizer from [DeepSpeed](https://github.com/microsoft/DeepSpeed), so you'll need to `pip install deepspeed` before running the command. + +**Notes:** +- The command will take ~5 minutes to start training, during which time it will appear to be hung, since randomly initializing 13B weights can be slow. +- The `--cpu-offload` feature requires training in mixed precision (`--fp16`). +- Tune the `OMP_NUM_THREADS` env variable for best performance with CPU offloading. +- The example command below stops training after 10 steps (`--max-update 10`) and does not save checkpoints (`--no-save`). + +```bash +OMP_NUM_THREADS=20 CUDA_VISIBLE_DEVICES=0 \ + fairseq-train data-bin/wikitext-103-roberta-bpe-bin \ + --ddp-backend fully_sharded --fp16 --fp16-init-scale 4 \ + --cpu-offload --checkpoint-activations \ + --task language_modeling --tokens-per-sample 2048 --batch-size 8 \ + --arch transformer_lm_gpt3_13 \ + --optimizer cpu_adam --adam-betas "(0.9,0.98)" \ + --lr 0.0001 --lr-scheduler polynomial_decay --warmup-updates 5 --total-num-update 10 \ + --max-update 10 --no-save --log-format json --log-interval 1 +``` + +
Example output

+ +``` +(...) +2021-03-08 12:29:51 | INFO | fairseq_cli.train | num. model params: 13,110,865,920 (num. trained: 13,110,865,920) +(...) +2021-03-08 12:29:51 | INFO | fairseq_cli.train | training on 1 devices (GPUs/TPUs) +2021-03-08 12:29:51 | INFO | fairseq_cli.train | max tokens per GPU = None and batch size per GPU = 8 +(...) +Adam Optimizer #0 is created with AVX2 arithmetic capability. +Config: alpha=0.000100, betas=(0.900000, 0.980000), weight_decay=0.000000, adam_w=1 +(...) +2021-03-08 12:31:36 | INFO | train_inner | {"epoch": 1, "update": 0.0, "loss": "16.475", "ppl": "91120.8", "wps": "0", "ups": "0", "wpb": "16384", "bsz": "8", "num_updates": "1", "lr": "2e-05", "gnorm": "20.751", "loss_scale": "4", "train_wall": "99", "gb_free": "9.3", "wall": "105"} +2021-03-08 12:32:33 | INFO | train_inner | {"epoch": 1, "update": 0.0, "loss": "16.446", "ppl": "89281.6", "wps": "288.7", "ups": "0.02", "wpb": "16384", "bsz": "8", "num_updates": "2", "lr": "4e-05", "gnorm": "19.777", "loss_scale": "4", "train_wall": "57", "gb_free": "9.3", "wall": "161"} +2021-03-08 12:33:12 | INFO | fairseq.trainer | NOTE: gradient overflow detected, ignoring gradient, setting loss scale to: 2.0 +2021-03-08 12:33:51 | INFO | fairseq.trainer | NOTE: gradient overflow detected, ignoring gradient, setting loss scale to: 1.0 +2021-03-08 12:34:45 | INFO | train_inner | {"epoch": 1, "update": 0.001, "loss": "25.22", "ppl": "3.90691e+07", "wps": "123.4", "ups": "0.01", "wpb": "16384", "bsz": "8", "num_updates": "3", "lr": "6e-05", "gnorm": "131.281", "loss_scale": "1", "train_wall": "133", "gb_free": "9.3", "wall": "294"} +2021-03-08 12:35:43 | INFO | train_inner | {"epoch": 1, "update": 0.001, "loss": "18.079", "ppl": "276809", "wps": "285.5", "ups": "0.02", "wpb": "16384", "bsz": "8", "num_updates": "4", "lr": "8e-05", "gnorm": "13.776", "loss_scale": "1", "train_wall": "57", "gb_free": "9.3", "wall": "351"} +2021-03-08 12:36:35 | INFO | train_inner | {"epoch": 1, "update": 0.001, "loss": "23.729", "ppl": "1.39088e+07", "wps": "316.7", "ups": "0.02", "wpb": "16384", "bsz": "8", "num_updates": "5", "lr": "0.0001", "gnorm": "72.774", "loss_scale": "1", "train_wall": "52", "gb_free": "9.3", "wall": "403"} +2021-03-08 12:37:28 | INFO | train_inner | {"epoch": 1, "update": 0.001, "loss": "20.429", "ppl": "1.41203e+06", "wps": "307.6", "ups": "0.02", "wpb": "16384", "bsz": "8", "num_updates": "6", "lr": "8e-05", "gnorm": "60.846", "loss_scale": "1", "train_wall": "53", "gb_free": "9.3", "wall": "456"} +2021-03-08 12:38:27 | INFO | train_inner | {"epoch": 1, "update": 0.001, "loss": "18.965", "ppl": "511684", "wps": "279.4", "ups": "0.02", "wpb": "16384", "bsz": "8", "num_updates": "7", "lr": "6e-05", "gnorm": "22.687", "loss_scale": "1", "train_wall": "59", "gb_free": "9.3", "wall": "515"} +2021-03-08 12:39:18 | INFO | train_inner | {"epoch": 1, "update": 0.001, "loss": "18.345", "ppl": "332887", "wps": "319.1", "ups": "0.02", "wpb": "16384", "bsz": "8", "num_updates": "8", "lr": "4e-05", "gnorm": "8.451", "loss_scale": "1", "train_wall": "51", "gb_free": "9.3", "wall": "566"} +2021-03-08 12:40:11 | INFO | train_inner | {"epoch": 1, "update": 0.002, "loss": "18.262", "ppl": "314336", "wps": "305.9", "ups": "0.02", "wpb": "16384", "bsz": "8", "num_updates": "9", "lr": "2e-05", "gnorm": "6.457", "loss_scale": "1", "train_wall": "54", "gb_free": "9.3", "wall": "620"} +2021-03-08 12:41:04 | INFO | train_inner | {"epoch": 1, "update": 0.002, "loss": "17.556", "ppl": "192686", "wps": "311.8", "ups": "0.02", "wpb": "16384", "bsz": "8", "num_updates": "10", "lr": "0", "gnorm": "5.796", "loss_scale": "1", "train_wall": "53", "gb_free": "9.3", "wall": "673"} +2021-03-08 12:41:04 | INFO | fairseq_cli.train | Stopping training due to num_updates: 10 >= max_update: 10 +2021-03-08 12:41:04 | INFO | fairseq_cli.train | begin validation on "valid" subset +2021-03-08 12:43:15 | INFO | valid | {"epoch": 1, "valid_loss": "17.953", "valid_ppl": "253807", "valid_wps": "1868.4", "valid_wpb": "15400.2", "valid_bsz": "7.6", "valid_num_updates": "10"} +2021-03-08 12:43:15 | INFO | fairseq_cli.train | end of epoch 1 (average epoch stats below) +2021-03-08 12:43:15 | INFO | train | {"epoch": 1, "train_loss": "19.351", "train_ppl": "668509", "train_wps": "210.9", "train_ups": "0.01", "train_wpb": "16384", "train_bsz": "8", "train_num_updates": "10", "train_lr": "0", "train_gnorm": "36.26", "train_loss_scale": "1", "train_train_wall": "667", "train_gb_free": "9.3", "train_wall": "804"} +2021-03-08 12:43:15 | INFO | fairseq_cli.train | done training in 798.6 seconds +``` + +

+ +### 13B params on 8 V100 GPUs (with full parameter + optimizer state sharding) + +FSDP can also shard the parameters and optimizer states across multiple GPUs, +reducing memory requirements significantly. On 8 x 32GB GPUs, sharding enables +training the same 13B parameter model *without offloading the parameters to +CPU*. However, without CPU offloading we'd only be able to fit a batch size of +1 per GPU, which would cause training speed to suffer. + +We obtain the best performance on 8 GPUs by combining full sharding and CPU +offloading. The following command trains the same 13B parameter GPT-3 model as +before on 8 x 32GB V100 GPUs; training speed increases superlinearly from ~310 +words per second to ~3200 words per second. + +```bash +OMP_NUM_THREADS=20 CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \ + fairseq-train data-bin/wikitext-103-roberta-bpe-bin \ + --ddp-backend fully_sharded --fp16 --fp16-init-scale 4 \ + --cpu-offload --checkpoint-activations \ + --task language_modeling --tokens-per-sample 2048 --batch-size 8 \ + --arch transformer_lm_gpt3_13 \ + --optimizer cpu_adam --adam-betas "(0.9,0.98)" \ + --lr 0.0001 --lr-scheduler polynomial_decay --warmup-updates 5 --total-num-update 10 \ + --max-update 10 --no-save --log-format json --log-interval 1 +``` + +
Example output

+ +``` +(...) +2021-03-08 18:04:09 | INFO | fairseq_cli.train | num. model params: 13,110,865,920 (num. trained: 13,110,865,920) +(...) +2021-03-08 18:04:09 | INFO | fairseq_cli.train | training on 8 devices (GPUs/TPUs) +2021-03-08 18:04:09 | INFO | fairseq_cli.train | max tokens per GPU = None and batch size per GPU = 8 +(...) +Adam Optimizer #0 is created with AVX2 arithmetic capability. +Config: alpha=0.000100, betas=(0.900000, 0.980000), weight_decay=0.000000, adam_w=1 +(...) +2021-03-08 18:05:06 | INFO | train_inner | {"epoch": 1, "update": 0.001, "loss": "16.408", "ppl": "86945.6", "wps": "0", "ups": "0", "wpb": "131072", "bsz": "64", "num_updates": "1", "lr": "2e-05", "gnorm": "18.27", "loss_scale": "4", "train_wall": "47", "gb_free": "9.3", "wall": "56"} +2021-03-08 18:05:45 | INFO | train_inner | {"epoch": 1, "update": 0.002, "loss": "16.352", "ppl": "83644.3", "wps": "3283.4", "ups": "0.03", "wpb": "131072", "bsz": "64", "num_updates": "2", "lr": "4e-05", "gnorm": "18.411", "loss_scale": "4", "train_wall": "40", "gb_free": "9.3", "wall": "96"} +2021-03-08 18:06:21 | INFO | fairseq.trainer | NOTE: gradient overflow detected, ignoring gradient, setting loss scale to: 2.0 +2021-03-08 18:06:56 | INFO | fairseq.trainer | NOTE: gradient overflow detected, ignoring gradient, setting loss scale to: 1.0 +2021-03-08 18:07:37 | INFO | train_inner | {"epoch": 1, "update": 0.006, "loss": "23.682", "ppl": "1.34537e+07", "wps": "1176.6", "ups": "0.01", "wpb": "131072", "bsz": "64", "num_updates": "3", "lr": "6e-05", "gnorm": "119.682", "loss_scale": "1", "train_wall": "111", "gb_free": "9.3", "wall": "208"} +2021-03-08 18:08:18 | INFO | train_inner | {"epoch": 1, "update": 0.007, "loss": "18.988", "ppl": "519921", "wps": "3189.1", "ups": "0.02", "wpb": "131072", "bsz": "64", "num_updates": "4", "lr": "8e-05", "gnorm": "14.934", "loss_scale": "1", "train_wall": "41", "gb_free": "9.3", "wall": "249"} +2021-03-08 18:08:59 | INFO | train_inner | {"epoch": 1, "update": 0.008, "loss": "20.08", "ppl": "1.10798e+06", "wps": "3223.1", "ups": "0.02", "wpb": "131072", "bsz": "64", "num_updates": "5", "lr": "0.0001", "gnorm": "59.92", "loss_scale": "1", "train_wall": "41", "gb_free": "9.3", "wall": "289"} +2021-03-08 18:09:39 | INFO | train_inner | {"epoch": 1, "update": 0.009, "loss": "18.323", "ppl": "327980", "wps": "3256.6", "ups": "0.02", "wpb": "131072", "bsz": "64", "num_updates": "6", "lr": "8e-05", "gnorm": "37.425", "loss_scale": "1", "train_wall": "40", "gb_free": "9.3", "wall": "330"} +2021-03-08 18:10:20 | INFO | train_inner | {"epoch": 1, "update": 0.01, "loss": "17.264", "ppl": "157354", "wps": "3188.7", "ups": "0.02", "wpb": "131072", "bsz": "64", "num_updates": "7", "lr": "6e-05", "gnorm": "10.824", "loss_scale": "1", "train_wall": "41", "gb_free": "9.3", "wall": "371"} +2021-03-08 18:11:01 | INFO | train_inner | {"epoch": 1, "update": 0.011, "loss": "16.794", "ppl": "113647", "wps": "3230", "ups": "0.02", "wpb": "131072", "bsz": "64", "num_updates": "8", "lr": "4e-05", "gnorm": "5.616", "loss_scale": "1", "train_wall": "41", "gb_free": "9.3", "wall": "411"} +2021-03-08 18:11:39 | INFO | train_inner | {"epoch": 1, "update": 0.012, "loss": "16.706", "ppl": "106938", "wps": "3384", "ups": "0.03", "wpb": "131072", "bsz": "64", "num_updates": "9", "lr": "2e-05", "gnorm": "5.318", "loss_scale": "1", "train_wall": "39", "gb_free": "9.3", "wall": "450"} +2021-03-08 18:12:19 | INFO | train_inner | {"epoch": 1, "update": 0.013, "loss": "16.548", "ppl": "95796.2", "wps": "3274.4", "ups": "0.02", "wpb": "131072", "bsz": "64", "num_updates": "10", "lr": "0", "gnorm": "5.22", "loss_scale": "1", "train_wall": "40", "gb_free": "9.3", "wall": "490"} +2021-03-08 18:12:19 | INFO | fairseq_cli.train | Stopping training due to num_updates: 10 >= max_update: 10 +2021-03-08 18:12:19 | INFO | fairseq_cli.train | begin validation on "valid" subset +2021-03-08 18:12:45 | INFO | valid | {"epoch": 1, "valid_loss": "16.624", "valid_ppl": "101000", "valid_wps": "10855.9", "valid_wpb": "123202", "valid_bsz": "60.5", "valid_num_updates": "10"} +2021-03-08 18:12:45 | INFO | fairseq_cli.train | end of epoch 1 (average epoch stats below) +2021-03-08 18:12:45 | INFO | train | {"epoch": 1, "train_loss": "18.114", "train_ppl": "283776", "train_wps": "2567.8", "train_ups": "0.02", "train_wpb": "131072", "train_bsz": "64", "train_num_updates": "10", "train_lr": "0", "train_gnorm": "29.562", "train_loss_scale": "1", "train_train_wall": "480", "train_gb_free": "9.3", "train_wall": "516"} +2021-03-08 18:12:45 | INFO | fairseq_cli.train | done training in 509.9 seconds +``` + +

diff --git a/examples/gottbert/README.md b/examples/gottbert/README.md new file mode 100644 index 0000000000..1d58feb279 --- /dev/null +++ b/examples/gottbert/README.md @@ -0,0 +1,64 @@ +# GottBERT: a pure German language model + +## Introduction + +[GottBERT](http://arxiv.org/abs/2012.02110) is a pretrained language model trained on 145GB of German text based on RoBERTa. + +## Example usage + +### fairseq +##### Load GottBERT from torch.hub (PyTorch >= 1.1): +```python +import torch +gottbert = torch.hub.load('pytorch/fairseq', 'gottbert-base') +gottbert.eval() # disable dropout (or leave in train mode to finetune) +``` + +##### Load GottBERT (for PyTorch 1.0 or custom models): +```python +# Download gottbert model +wget https://dl.gottbert.de/fairseq/models/gottbert-base.tar.gz +tar -xzvf gottbert.tar.gz + +# Load the model in fairseq +from fairseq.models.roberta import GottbertModel +gottbert = GottbertModel.from_pretrained('/path/to/gottbert') +gottbert.eval() # disable dropout (or leave in train mode to finetune) +``` + +##### Filling masks: +```python +masked_line = 'Gott ist ! :)' +gottbert.fill_mask(masked_line, topk=3) +# [('Gott ist gut ! :)', 0.3642110526561737, ' gut'), +# ('Gott ist überall ! :)', 0.06009674072265625, ' überall'), +# ('Gott ist großartig ! :)', 0.0370681993663311, ' großartig')] +``` + +##### Extract features from GottBERT + +```python +# Extract the last layer's features +line = "Der erste Schluck aus dem Becher der Naturwissenschaft macht atheistisch , aber auf dem Grunde des Bechers wartet Gott !" +tokens = gottbert.encode(line) +last_layer_features = gottbert.extract_features(tokens) +assert last_layer_features.size() == torch.Size([1, 27, 768]) + +# Extract all layer's features (layer 0 is the embedding layer) +all_layers = gottbert.extract_features(tokens, return_all_hiddens=True) +assert len(all_layers) == 13 +assert torch.all(all_layers[-1] == last_layer_features) +``` +## Citation +If you use our work, please cite: + +```bibtex +@misc{scheible2020gottbert, + title={GottBERT: a pure German Language Model}, + author={Raphael Scheible and Fabian Thomczyk and Patric Tippmann and Victor Jaravine and Martin Boeker}, + year={2020}, + eprint={2012.02110}, + archivePrefix={arXiv}, + primaryClass={cs.CL} +} +``` diff --git a/examples/hubert/README.md b/examples/hubert/README.md new file mode 100644 index 0000000000..b501a6eb2a --- /dev/null +++ b/examples/hubert/README.md @@ -0,0 +1,115 @@ +# HuBERT + +## Pre-trained and fine-tuned (ASR) models +Model | Pretraining Data | Finetuning Dataset | Model +|---|---|---|--- +HuBERT Base (~95M params) | [Librispeech](http://www.openslr.org/12) 960 hr | No finetuning (Pretrained Model) | [download](https://dl.fbaipublicfiles.com/hubert/hubert_base_ls960.pt) +HuBERT Large (~316M params) | [Libri-Light](https://github.com/facebookresearch/libri-light) 60k hr | No finetuning (Pretrained Model) | [download](https://dl.fbaipublicfiles.com/hubert/hubert_large_ll60k.pt) +HuBERT Extra Large (~1B params) | [Libri-Light](https://github.com/facebookresearch/libri-light) 60k hr | No finetuning (Pretrained Model) | [download](https://dl.fbaipublicfiles.com/hubert/hubert_xtralarge_ll60k.pt) +HuBERT Large | [Libri-Light](https://github.com/facebookresearch/libri-light) 60k hr | [Librispeech](http://www.openslr.org/12) 960 hr | [download](https://dl.fbaipublicfiles.com/hubert/hubert_large_ll60k_finetune_ls960.pt) +HuBERT Extra Large | [Libri-Light](https://github.com/facebookresearch/libri-light) 60k hr | [Librispeech](http://www.openslr.org/12) 960 hr | [download](https://dl.fbaipublicfiles.com/hubert/hubert_xtralarge_ll60k_finetune_ls960.pt) + +## Load a model +``` +ckpt_path = "/path/to/the/checkpoint.pt" +models, cfg, task = fairseq.checkpoint_utils.load_model_ensemble_and_task([ckpt_path]) +model = models[0] +``` + +## Train a new model + +### Data preparation + +Follow the steps in `./simple_kmeans` to create: +- `{train,valid}.tsv` waveform list files +- `{train,valid}.km` frame-aligned pseudo label files. +The `label_rate` is the same as the feature frame rate used for clustering, +which is 100Hz for MFCC features and 50Hz for HuBERT features by default. + +### Pre-train a HuBERT model + +Suppose `{train,valid}.tsv` are saved at `/path/to/data`, `{train,valid}.km` +are saved at `/path/to/labels`, and the label rate is 100Hz. + +To train a base model (12 layer transformer), run: +```sh +$ python fairseq_cli/hydra_train.py \ + --config-dir /path/to/fairseq-py/examples/hubert/config/pretrain \ + --config-name hubert_base_librispeech \ + task.data=/path/to/data task.label_dir=/path/to/labels model.label_rate=100 +``` + +### Fine-tune a HuBERT model with a CTC loss + +Suppose `{train,valid}.tsv` are saved at `/path/to/data`, and their +corresponding character transcripts `{train,valid}.ltr` are saved at +`/path/to/trans`. + +To fine-tune a pre-trained HuBERT model at `/path/to/checkpoint`, run +```sh +$ python fairseq_cli/hydra_train.py \ + --config-dir /path/to/fairseq-py/examples/hubert/config/finetune \ + --config-name base_10h \ + task.data=/path/to/data task.label_dir=/path/to/trans \ + model.w2v_path=/path/to/checkpoint +``` + +### Decode a HuBERT model + +Suppose the `test.tsv` and `test.ltr` are the waveform list and transcripts of +the split to be decoded, saved at `/path/to/data`, and the fine-tuned model is +saved at `/path/to/checkpoint`. We support three decoding modes: +- Viterbi decoding: greedy decoding without a language model +- KenLM decoding: decoding with an arpa-format KenLM n-gram language model +- Fairseq-LM deocding: decoding with a Fairseq neural language model + + +#### Viterbi decoding + +`task.normalize` needs to be consistent with the value used during fine-tuning. +Decoding results will be saved at +`/path/to/experiment/directory/decode/viterbi/test`. + +```sh +$ python examples/speech_recognition/new/infer.py \ + --config-dir /path/to/fairseq-py/examples/hubert/config/decode \ + --config-name infer_viterbi \ + task.data=/path/to/data \ + task.normalize=[true|false] \ + decoding.exp_dir=/path/to/experiment/directory \ + common_eval.path=/path/to/checkpoint + dataset.gen_subset=test \ +``` + +#### KenLM / Fairseq-LM decoding + +Suppose the pronunciation lexicon and the n-gram LM are saved at +`/path/to/lexicon` and `/path/to/arpa`, respectively. Decoding results will be +saved at `/path/to/experiment/directory/decode/kenlm/test`. + +```sh +$ python examples/speech_recognition/new/infer.py \ + --config-dir /path/to/fairseq-py/examples/hubert/config/decode \ + --config-name infer_kenlm \ + task.data=/path/to/data \ + task.normalize=[true|false] \ + decoding.exp_dir=/path/to/experiment/directory \ + common_eval.path=/path/to/checkpoint + dataset.gen_subset=test \ + decoding.decoder.lexicon=/path/to/lexicon \ + decoding.decoder.lmpath=/path/to/arpa +``` + +The command above uses the default decoding hyperparameter, which can be found +in `examples/speech_recognition/hydra/decoder.py`. These parameters can be +configured from the command line. For example, to search with a beam size of +500, we can append the command above with `decoding.decoder.beam=500`. +Important parameters include: +- decoding.decoder.beam +- decoding.decoder.beamthreshold +- decoding.decoder.lmweight +- decoding.decoder.wordscore +- decoding.decoder.silweight + +To decode with a Fairseq LM, use `--config-name infer_fsqlm` instead, and +change the path of lexicon and LM accordingly. diff --git a/examples/hubert/config/decode/ax_sweep/ngram.yaml b/examples/hubert/config/decode/ax_sweep/ngram.yaml new file mode 100644 index 0000000000..5a02df1f7d --- /dev/null +++ b/examples/hubert/config/decode/ax_sweep/ngram.yaml @@ -0,0 +1,33 @@ +# @package _global_ + +common_eval: + results_path: ${decoding.exp_dir}/decode/${decoding.decoder.name}_ax/${dataset.gen_subset} + +hydra: + sweeper: + ax_config: + max_trials: 60 + early_stop: + minimize: true + max_epochs_without_improvement: 10 + epsilon: 0.025 + experiment: + name: ${dataset.gen_subset} + objective_name: wer + minimize: true + parameter_constraints: null + outcome_constraints: null + status_quo: null + client: + verbose_logging: false + random_seed: null + params: + decoding.decoder.lmweight: + type: range + bounds: [0.0, 8.0] + decoding.decoder.wordscore: + type: range + bounds: [-5.0, 5.0] + decoding.decoder.silweight: + type: range + bounds: [-10.0, 0.0] diff --git a/examples/hubert/config/decode/ax_sweep/transformer.yaml b/examples/hubert/config/decode/ax_sweep/transformer.yaml new file mode 100644 index 0000000000..85ed3bd1a5 --- /dev/null +++ b/examples/hubert/config/decode/ax_sweep/transformer.yaml @@ -0,0 +1,33 @@ +# @package _global_ + +common_eval: + results_path: ${decoding.exp_dir}/decode/${decoding.decoder.name}_ax/${dataset.gen_subset} + +hydra: + sweeper: + ax_config: + max_trials: 60 + early_stop: + minimize: true + max_epochs_without_improvement: 10 + epsilon: 0.025 + experiment: + name: ${dataset.gen_subset} + objective_name: wer + minimize: true + parameter_constraints: null + outcome_constraints: null + status_quo: null + client: + verbose_logging: false + random_seed: null + params: + decoding.decoder.lmweight: + type: range + bounds: [0.0, 4.0] + decoding.decoder.wordscore: + type: range + bounds: [-5.0, 5.0] + decoding.decoder.silweight: + type: range + bounds: [-8.0, 0.0] diff --git a/examples/hubert/config/decode/infer_fsqlm.yaml b/examples/hubert/config/decode/infer_fsqlm.yaml new file mode 100644 index 0000000000..026ad8db89 --- /dev/null +++ b/examples/hubert/config/decode/infer_fsqlm.yaml @@ -0,0 +1,36 @@ +# @package _group_ + +defaults: + - model: null + +hydra: + run: + dir: ${common_eval.results_path}/beam${decoding.beam}_th${decoding.beamthreshold}_lmw${decoding.lmweight}_wrd${decoding.wordscore}_sil${decoding.silweight} + sweep: + dir: ${common_eval.results_path} + subdir: beam${decoding.beam}_th${decoding.beamthreshold}_lmw${decoding.lmweight}_wrd${decoding.wordscore}_sil${decoding.silweight} + +task: + _name: hubert_pretraining + single_target: true + fine_tuning: true + data: ??? + normalize: ??? + +decoding: + type: fairseqlm + lexicon: ??? + lmpath: ??? + beamthreshold: 25 + beam: 500 + lmweight: 2 + wordscore: -1 + silweight: 0 + unique_wer_file: true +common_eval: + results_path: ??? + path: ??? + post_process: letter +dataset: + max_tokens: 1100000 + gen_subset: ??? diff --git a/examples/hubert/config/decode/infer_kenlm.yaml b/examples/hubert/config/decode/infer_kenlm.yaml new file mode 100644 index 0000000000..04642aeb65 --- /dev/null +++ b/examples/hubert/config/decode/infer_kenlm.yaml @@ -0,0 +1,36 @@ +# @package _group_ + +defaults: + - model: null + +hydra: + run: + dir: ${common_eval.results_path}/beam${decoding.beam}_th${decoding.beamthreshold}_lmw${decoding.lmweight}_wrd${decoding.wordscore}_sil${decoding.silweight} + sweep: + dir: ${common_eval.results_path} + subdir: beam${decoding.beam}_th${decoding.beamthreshold}_lmw${decoding.lmweight}_wrd${decoding.wordscore}_sil${decoding.silweight} + +task: + _name: hubert_pretraining + single_target: true + fine_tuning: true + data: ??? + normalize: ??? + +decoding: + type: kenlm + lexicon: ??? + lmpath: ??? + beamthreshold: 100 + beam: 500 + lmweight: 2 + wordscore: -1 + silweight: 0 + unique_wer_file: true +common_eval: + results_path: ??? + path: ??? + post_process: letter +dataset: + max_tokens: 1100000 + gen_subset: ??? diff --git a/examples/hubert/config/decode/infer_viterbi.yaml b/examples/hubert/config/decode/infer_viterbi.yaml new file mode 100644 index 0000000000..4afc74c18c --- /dev/null +++ b/examples/hubert/config/decode/infer_viterbi.yaml @@ -0,0 +1,29 @@ +# @package _group_ + +defaults: + - model: null + +hydra: + run: + dir: ${common_eval.results_path}/viterbi + sweep: + dir: ${common_eval.results_path} + subdir: viterbi + +task: + _name: hubert_pretraining + single_target: true + fine_tuning: true + data: ??? + normalize: ??? + +decoding: + type: viterbi + unique_wer_file: true +common_eval: + results_path: ??? + path: ??? + post_process: letter +dataset: + max_tokens: 1100000 + gen_subset: ??? diff --git a/examples/hubert/config/decode/run/submitit_slurm.yaml b/examples/hubert/config/decode/run/submitit_slurm.yaml new file mode 100644 index 0000000000..0b8065832e --- /dev/null +++ b/examples/hubert/config/decode/run/submitit_slurm.yaml @@ -0,0 +1,17 @@ +# @package _global_ +hydra: + launcher: + cpus_per_task: ${distributed_training.distributed_world_size} + gpus_per_node: ${distributed_training.distributed_world_size} + tasks_per_node: ${hydra.launcher.gpus_per_node} + nodes: 1 + mem_gb: 200 + timeout_min: 4320 + max_num_timeout: 50 + name: ${hydra.job.config_name} + submitit_folder: ${hydra.sweep.dir}/submitit + +distributed_training: + distributed_world_size: 1 + distributed_no_spawn: true + distributed_port: 29761 diff --git a/examples/hubert/config/decode/run/submitit_slurm_8gpu.yaml b/examples/hubert/config/decode/run/submitit_slurm_8gpu.yaml new file mode 100644 index 0000000000..2f669f3763 --- /dev/null +++ b/examples/hubert/config/decode/run/submitit_slurm_8gpu.yaml @@ -0,0 +1,17 @@ +# @package _global_ +hydra: + launcher: + cpus_per_task: ${distributed_training.distributed_world_size} + gpus_per_node: ${distributed_training.distributed_world_size} + tasks_per_node: ${hydra.launcher.gpus_per_node} + nodes: 1 + mem_gb: 200 + timeout_min: 4320 + max_num_timeout: 50 + name: ${hydra.job.config_name} + submitit_folder: ${hydra.sweep.dir}/submitit + +distributed_training: + distributed_world_size: 8 + distributed_no_spawn: true + distributed_port: 29761 diff --git a/examples/hubert/config/finetune/base_10h.yaml b/examples/hubert/config/finetune/base_10h.yaml new file mode 100644 index 0000000000..a22c7c0347 --- /dev/null +++ b/examples/hubert/config/finetune/base_10h.yaml @@ -0,0 +1,100 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + tensorboard_logdir: tblog + seed: 1337 + +checkpoint: + save_interval: 5 + keep_interval_updates: 1 + no_epoch_checkpoints: true + best_checkpoint_metric: wer + +distributed_training: + ddp_backend: c10d + find_unused_parameters: true + distributed_world_size: 1 + distributed_port: 29671 + nprocs_per_node: 8 + +task: + _name: hubert_pretraining + data: ??? + fine_tuning: true + label_dir: ??? + normalize: false # must be consistent with pre-training + labels: ["ltr"] + single_target: true + +dataset: + num_workers: 0 + max_tokens: 3200000 + validate_after_updates: ${model.freeze_finetune_updates} + validate_interval: 5 + train_subset: train + valid_subset: valid + +criterion: + _name: ctc + zero_infinity: true + +optimization: + max_update: 25000 + lr: [2e-5] + sentence_avg: true + update_freq: [1] + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-08 + +lr_scheduler: + _name: tri_stage + warmup_steps: 8000 + hold_steps: 0 + decay_steps: 72000 + final_lr_scale: 0.05 + +model: + _name: hubert_ctc + w2v_path: ??? + apply_mask: true + mask_selection: static + mask_length: 10 + mask_other: 0 + mask_prob: 0.75 + mask_channel_selection: static + mask_channel_length: 64 + mask_channel_other: 0 + mask_channel_prob: 0.5 + layerdrop: 0.1 + dropout: 0.0 + activation_dropout: 0.1 + attention_dropout: 0.0 + feature_grad_mult: 0.0 + freeze_finetune_updates: 10000 + +hydra: + job: + config: + override_dirname: + kv_sep: '-' + item_sep: '__' + exclude_keys: + - run + - task.data + - task.label_dir + - model.w2v_path + - dataset.train_subset + - dataset.valid_subset + - criterion.wer_kenlm_model + - criterion.wer_lexicon + run: + dir: ??? + sweep: + dir: ??? + subdir: ${hydra.job.config_name}__${hydra.job.override_dirname} diff --git a/examples/hubert/config/finetune/ckpt/it1.yaml b/examples/hubert/config/finetune/ckpt/it1.yaml new file mode 100644 index 0000000000..2af96b3f72 --- /dev/null +++ b/examples/hubert/config/finetune/ckpt/it1.yaml @@ -0,0 +1,7 @@ +# @package _global_ + +task: + normalize: false + +model: + w2v_path: /checkpoint/wnhsu/w2v/hubert_final/iter1/hubert.km.randcrop.pmw1_0.puw0_0.grpnorm.ml10.mp0_8.untie.mxsz250000.ufreq1.maxtok1400000.MU400k.s1337.ngpu32/checkpoint_last.pt diff --git a/examples/hubert/config/finetune/lm/ls_4gram.yaml b/examples/hubert/config/finetune/lm/ls_4gram.yaml new file mode 100644 index 0000000000..8c7728ad29 --- /dev/null +++ b/examples/hubert/config/finetune/lm/ls_4gram.yaml @@ -0,0 +1,7 @@ +# @package _global_ + +criterion: + wer_kenlm_model: /checkpoint/abdo/old_checkpoint02/datasets/librispeech/4-gram.bin + wer_lexicon: /checkpoint/abdo/old_checkpoint02/datasets/librispeech/10h/raw/lexicon_ltr.lst + wer_lm_weight: 2.0 + wer_word_score: -1.0 diff --git a/examples/hubert/config/finetune/run/submitit_reg.yaml b/examples/hubert/config/finetune/run/submitit_reg.yaml new file mode 100644 index 0000000000..27509503e7 --- /dev/null +++ b/examples/hubert/config/finetune/run/submitit_reg.yaml @@ -0,0 +1,20 @@ +# @package _global_ + +hydra: + launcher: + cpus_per_task: 8 + gpus_per_node: 8 + tasks_per_node: ${hydra.launcher.gpus_per_node} + nodes: 1 + comment: null + mem_gb: 384 + timeout_min: 4320 + max_num_timeout: 100 + constraint: volta32gb + name: ${hydra.job.config_name}/${hydra.job.override_dirname} + submitit_folder: ${hydra.sweep.dir}/submitit/%j + +distributed_training: + distributed_world_size: 8 + distributed_port: 29671 + nprocs_per_node: 8 diff --git a/examples/hubert/config/pretrain/data/iter1.yaml b/examples/hubert/config/pretrain/data/iter1.yaml new file mode 100644 index 0000000000..0a1b65d802 --- /dev/null +++ b/examples/hubert/config/pretrain/data/iter1.yaml @@ -0,0 +1,8 @@ +# @package _global_ + +task: + label_dir: ??? + labels: ["km"] + +model: + label_rate: 100 diff --git a/examples/hubert/config/pretrain/data/iter2.yaml b/examples/hubert/config/pretrain/data/iter2.yaml new file mode 100644 index 0000000000..2d4bfe61cc --- /dev/null +++ b/examples/hubert/config/pretrain/data/iter2.yaml @@ -0,0 +1,8 @@ +# @package _global_ + +task: + label_dir: ??? + labels: ["km"] + +model: + label_rate: 50 diff --git a/examples/hubert/config/pretrain/hubert_base_librispeech.yaml b/examples/hubert/config/pretrain/hubert_base_librispeech.yaml new file mode 100644 index 0000000000..bd84461a16 --- /dev/null +++ b/examples/hubert/config/pretrain/hubert_base_librispeech.yaml @@ -0,0 +1,97 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + seed: 1337 + tensorboard_logdir: tblog + +checkpoint: + save_interval_updates: 25000 + keep_interval_updates: 1 + no_epoch_checkpoints: true + + +distributed_training: + ddp_backend: no_c10d + distributed_backend: 'nccl' + distributed_world_size: 32 + distributed_port: 29671 + nprocs_per_node: 8 + find_unused_parameters: true + +task: + _name: hubert_pretraining + data: ??? + label_dir: ??? + labels: ??? + label_rate: ${model.label_rate} + sample_rate: 16000 + max_sample_size: 250000 + min_sample_size: 32000 + pad_audio: false + random_crop: true + normalize: false # must be consistent with extractor + +dataset: + num_workers: 6 + max_tokens: 1400000 + skip_invalid_size_inputs_valid_test: true + validate_interval: 5 + validate_interval_updates: 10000 + +criterion: + _name: hubert + pred_masked_weight: 1.0 + pred_nomask_weight: 0.0 + loss_weights: [10,] + +optimization: + max_update: 400000 + lr: [0.0005] + clip_norm: 10.0 + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-06 + weight_decay: 0.01 + +lr_scheduler: + _name: polynomial_decay + warmup_updates: 32000 + +model: + _name: hubert + label_rate: ??? + skip_masked: false + skip_nomask: false + mask_prob: 0.80 + extractor_mode: default + conv_feature_layers: '[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2' + final_dim: 256 + encoder_layerdrop: 0.05 + dropout_input: 0.1 + dropout_features: 0.1 + dropout: 0.1 + attention_dropout: 0.1 + feature_grad_mult: 0.1 + untie_final_proj: true + activation_dropout: 0.0 + +hydra: + job: + config: + override_dirname: + kv_sep: '-' + item_sep: '__' + exclude_keys: + - run + - task.data + - task.label_dir + run: + dir: ??? + sweep: + dir: ??? + subdir: ${hydra.job.config_name}__${hydra.job.override_dirname} diff --git a/examples/hubert/config/pretrain/hubert_large_librivox.yaml b/examples/hubert/config/pretrain/hubert_large_librivox.yaml new file mode 100644 index 0000000000..a5192b5f29 --- /dev/null +++ b/examples/hubert/config/pretrain/hubert_large_librivox.yaml @@ -0,0 +1,101 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + seed: 1337 + tensorboard_logdir: tblog + +checkpoint: + save_interval_updates: 25000 + keep_interval_updates: 1 + no_epoch_checkpoints: true + + +distributed_training: + ddp_backend: no_c10d + distributed_backend: 'nccl' + distributed_world_size: 128 + distributed_port: 29671 + nprocs_per_node: 8 + find_unused_parameters: true + +task: + _name: hubert_pretraining + data: ??? + label_dir: ??? + labels: ??? + label_rate: ${model.label_rate} + sample_rate: 16000 + max_sample_size: 250000 + min_sample_size: 32000 + pad_audio: false + random_crop: true + normalize: true # must be consistent with extractor + +dataset: + num_workers: 6 + max_tokens: 900000 + skip_invalid_size_inputs_valid_test: true + validate_interval: 5 + validate_interval_updates: 10000 + +criterion: + _name: hubert + pred_masked_weight: 1.0 + pred_nomask_weight: 0.0 + loss_weights: [10,] + +optimization: + max_update: 400000 + lr: [0.0015] + clip_norm: 1.0 + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-06 + weight_decay: 0.01 + +lr_scheduler: + _name: polynomial_decay + warmup_updates: 32000 + +model: + _name: hubert + label_rate: ??? + encoder_layers: 24 + encoder_embed_dim: 1024 + encoder_ffn_embed_dim: 4096 + encoder_attention_heads: 16 + final_dim: 768 + skip_masked: false + skip_nomask: false + mask_prob: 0.80 + extractor_mode: layer_norm + conv_feature_layers: '[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2' + encoder_layerdrop: 0.0 + dropout_input: 0.0 + dropout_features: 0.0 + dropout: 0.0 + attention_dropout: 0.0 + layer_norm_first: true + feature_grad_mult: 1.0 + untie_final_proj: true + activation_dropout: 0.0 + +hydra: + job: + config: + override_dirname: + kv_sep: '-' + item_sep: '__' + exclude_keys: + - run + - task.data + run: + dir: /checkpoint/wnhsu/w2v/hubert_final/hydra_pt + sweep: + dir: /checkpoint/wnhsu/w2v/hubert_final/hydra_pt + subdir: ${hydra.job.config_name}__${hydra.job.override_dirname} diff --git a/examples/hubert/config/pretrain/hubert_xlarge_librivox.yaml b/examples/hubert/config/pretrain/hubert_xlarge_librivox.yaml new file mode 100644 index 0000000000..34e8f2bfb9 --- /dev/null +++ b/examples/hubert/config/pretrain/hubert_xlarge_librivox.yaml @@ -0,0 +1,101 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + seed: 1337 + tensorboard_logdir: tblog + +checkpoint: + save_interval_updates: 25000 + keep_interval_updates: 1 + no_epoch_checkpoints: true + + +distributed_training: + ddp_backend: no_c10d + distributed_backend: 'nccl' + distributed_world_size: 256 + distributed_port: 29671 + nprocs_per_node: 8 + find_unused_parameters: true + +task: + _name: hubert_pretraining + data: ??? + label_dir: ??? + labels: ??? + label_rate: ${model.label_rate} + sample_rate: 16000 + max_sample_size: 250000 + min_sample_size: 32000 + pad_audio: false + random_crop: true + normalize: true # must be consistent with extractor + +dataset: + num_workers: 6 + max_tokens: 360000 + skip_invalid_size_inputs_valid_test: true + validate_interval: 5 + validate_interval_updates: 10000 + +criterion: + _name: hubert + pred_masked_weight: 1.0 + pred_nomask_weight: 0.0 + loss_weights: [10,] + +optimization: + max_update: 400000 + lr: [0.003] + clip_norm: 1.0 + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-06 + weight_decay: 0.01 + +lr_scheduler: + _name: polynomial_decay + warmup_updates: 32000 + +model: + _name: hubert + label_rate: ??? + encoder_layers: 48 + encoder_embed_dim: 1280 + encoder_ffn_embed_dim: 5120 + encoder_attention_heads: 16 + final_dim: 1024 + skip_masked: false + skip_nomask: false + mask_prob: 0.80 + extractor_mode: layer_norm + conv_feature_layers: '[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2' + encoder_layerdrop: 0.0 + dropout_input: 0.0 + dropout_features: 0.0 + dropout: 0.0 + attention_dropout: 0.0 + layer_norm_first: true + feature_grad_mult: 1.0 + untie_final_proj: true + activation_dropout: 0.0 + +hydra: + job: + config: + override_dirname: + kv_sep: '-' + item_sep: '__' + exclude_keys: + - run + - task.data + run: + dir: /checkpoint/wnhsu/w2v/hubert_final/hydra_pt + sweep: + dir: /checkpoint/wnhsu/w2v/hubert_final/hydra_pt + subdir: ${hydra.job.config_name}__${hydra.job.override_dirname} diff --git a/examples/hubert/config/pretrain/run/submitit_reg.yaml b/examples/hubert/config/pretrain/run/submitit_reg.yaml new file mode 100644 index 0000000000..46c979cd28 --- /dev/null +++ b/examples/hubert/config/pretrain/run/submitit_reg.yaml @@ -0,0 +1,20 @@ +# @package _global_ + +hydra: + launcher: + cpus_per_task: 8 + gpus_per_node: 8 + tasks_per_node: ${hydra.launcher.gpus_per_node} + nodes: 4 + comment: null + mem_gb: 384 + timeout_min: 4320 + max_num_timeout: 100 + constraint: volta32gb + name: ${hydra.job.config_name}/${hydra.job.override_dirname} + submitit_folder: ${hydra.sweep.dir}/submitit/%j + +distributed_training: + distributed_world_size: 32 + distributed_port: 29671 + nprocs_per_node: 8 diff --git a/examples/hubert/measure_teacher_quality.py b/examples/hubert/measure_teacher_quality.py new file mode 100644 index 0000000000..92279b2214 --- /dev/null +++ b/examples/hubert/measure_teacher_quality.py @@ -0,0 +1,241 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import numpy as np +import os.path as op +import re +from tabulate import tabulate +from collections import Counter + + +def comp_purity(p_xy, axis): + max_p = p_xy.max(axis=axis) + marg_p = p_xy.sum(axis=axis) + indv_pur = max_p / marg_p + aggr_pur = max_p.sum() + return indv_pur, aggr_pur + + +def comp_entropy(p): + return (-p * np.log(p + 1e-8)).sum() + + +def comp_norm_mutual_info(p_xy): + p_x = p_xy.sum(axis=1, keepdims=True) + p_y = p_xy.sum(axis=0, keepdims=True) + pmi = np.log(p_xy / np.matmul(p_x, p_y) + 1e-8) + mi = (p_xy * pmi).sum() + h_x = comp_entropy(p_x) + h_y = comp_entropy(p_y) + return mi, mi / h_x, mi / h_y, h_x, h_y + + +def pad(labs, n): + if n == 0: + return np.array(labs) + return np.concatenate([[labs[0]] * n, labs, [labs[-1]] * n]) + + +def comp_avg_seg_dur(labs_list): + n_frms = 0 + n_segs = 0 + for labs in labs_list: + labs = np.array(labs) + edges = np.zeros(len(labs)).astype(bool) + edges[0] = True + edges[1:] = labs[1:] != labs[:-1] + n_frms += len(edges) + n_segs += edges.astype(int).sum() + return n_frms / n_segs + + +def comp_joint_prob(uid2refs, uid2hyps): + """ + Args: + pad: padding for spliced-feature derived labels + """ + cnts = Counter() + skipped = [] + abs_frmdiff = 0 + for uid in uid2refs: + if uid not in uid2hyps: + skipped.append(uid) + continue + refs = uid2refs[uid] + hyps = uid2hyps[uid] + abs_frmdiff += abs(len(refs) - len(hyps)) + min_len = min(len(refs), len(hyps)) + refs = refs[:min_len] + hyps = hyps[:min_len] + cnts.update(zip(refs, hyps)) + tot = sum(cnts.values()) + + ref_set = sorted({ref for ref, _ in cnts.keys()}) + hyp_set = sorted({hyp for _, hyp in cnts.keys()}) + ref2pid = dict(zip(ref_set, range(len(ref_set)))) + hyp2lid = dict(zip(hyp_set, range(len(hyp_set)))) + # print(hyp_set) + p_xy = np.zeros((len(ref2pid), len(hyp2lid)), dtype=float) + for (ref, hyp), cnt in cnts.items(): + p_xy[ref2pid[ref], hyp2lid[hyp]] = cnt + p_xy /= p_xy.sum() + return p_xy, ref2pid, hyp2lid, tot, abs_frmdiff, skipped + + +def read_phn(tsv_path, rm_stress=True): + uid2phns = {} + with open(tsv_path) as f: + for line in f: + uid, phns = line.rstrip().split("\t") + phns = phns.split(",") + if rm_stress: + phns = [re.sub("[0-9]", "", phn) for phn in phns] + uid2phns[uid] = phns + return uid2phns + + +def read_lab(tsv_path, lab_path, pad_len=0, upsample=1): + """ + tsv is needed to retrieve the uids for the labels + """ + with open(tsv_path) as f: + f.readline() + uids = [op.splitext(op.basename(line.rstrip().split()[0]))[0] for line in f] + with open(lab_path) as f: + labs_list = [pad(line.rstrip().split(), pad_len).repeat(upsample) for line in f] + assert len(uids) == len(labs_list) + return dict(zip(uids, labs_list)) + + +def main_lab_lab( + tsv_dir, + lab_dir, + lab_name, + lab_sets, + ref_dir, + ref_name, + pad_len=0, + upsample=1, + verbose=False, +): + # assume tsv_dir is the same for both the reference and the hypotheses + tsv_dir = lab_dir if tsv_dir is None else tsv_dir + + uid2refs = {} + for s in lab_sets: + uid2refs.update(read_lab(f"{tsv_dir}/{s}.tsv", f"{ref_dir}/{s}.{ref_name}")) + + uid2hyps = {} + for s in lab_sets: + uid2hyps.update( + read_lab( + f"{tsv_dir}/{s}.tsv", f"{lab_dir}/{s}.{lab_name}", pad_len, upsample + ) + ) + _main(uid2refs, uid2hyps, verbose) + + +def main_phn_lab( + tsv_dir, + lab_dir, + lab_name, + lab_sets, + phn_dir, + phn_sets, + pad_len=0, + upsample=1, + verbose=False, +): + uid2refs = {} + for s in phn_sets: + uid2refs.update(read_phn(f"{phn_dir}/{s}.tsv")) + + uid2hyps = {} + tsv_dir = lab_dir if tsv_dir is None else tsv_dir + for s in lab_sets: + uid2hyps.update( + read_lab( + f"{tsv_dir}/{s}.tsv", f"{lab_dir}/{s}.{lab_name}", pad_len, upsample + ) + ) + _main(uid2refs, uid2hyps, verbose) + + +def _main(uid2refs, uid2hyps, verbose): + (p_xy, ref2pid, hyp2lid, tot, frmdiff, skipped) = comp_joint_prob( + uid2refs, uid2hyps + ) + ref_pur_by_hyp, ref_pur = comp_purity(p_xy, axis=0) + hyp_pur_by_ref, hyp_pur = comp_purity(p_xy, axis=1) + (mi, mi_norm_by_ref, mi_norm_by_hyp, h_ref, h_hyp) = comp_norm_mutual_info(p_xy) + outputs = { + "ref pur": ref_pur, + "hyp pur": hyp_pur, + "H(ref)": h_ref, + "H(hyp)": h_hyp, + "MI": mi, + "MI/H(ref)": mi_norm_by_ref, + "ref segL": comp_avg_seg_dur(uid2refs.values()), + "hyp segL": comp_avg_seg_dur(uid2hyps.values()), + "p_xy shape": p_xy.shape, + "frm tot": tot, + "frm diff": frmdiff, + "utt tot": len(uid2refs), + "utt miss": len(skipped), + } + print(tabulate([outputs.values()], outputs.keys(), floatfmt=".4f")) + + +if __name__ == "__main__": + """ + compute quality of labels with respect to phone or another labels if set + """ + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument("tsv_dir") + parser.add_argument("lab_dir") + parser.add_argument("lab_name") + parser.add_argument("--lab_sets", default=["valid"], type=str, nargs="+") + parser.add_argument( + "--phn_dir", + default="/checkpoint/wnhsu/data/librispeech/960h/fa/raw_phn/phone_frame_align_v1", + ) + parser.add_argument( + "--phn_sets", default=["dev-clean", "dev-other"], type=str, nargs="+" + ) + parser.add_argument("--pad_len", default=0, type=int, help="padding for hypotheses") + parser.add_argument( + "--upsample", default=1, type=int, help="upsample factor for hypotheses" + ) + parser.add_argument("--ref_lab_dir", default="") + parser.add_argument("--ref_lab_name", default="") + parser.add_argument("--verbose", action="store_true") + args = parser.parse_args() + + if args.ref_lab_dir and args.ref_lab_name: + main_lab_lab( + args.tsv_dir, + args.lab_dir, + args.lab_name, + args.lab_sets, + args.ref_lab_dir, + args.ref_lab_name, + args.pad_len, + args.upsample, + args.verbose, + ) + else: + main_phn_lab( + args.tsv_dir, + args.lab_dir, + args.lab_name, + args.lab_sets, + args.phn_dir, + args.phn_sets, + args.pad_len, + args.upsample, + args.verbose, + ) diff --git a/examples/hubert/simple_kmeans/README.md b/examples/hubert/simple_kmeans/README.md new file mode 100644 index 0000000000..cd17da3b3e --- /dev/null +++ b/examples/hubert/simple_kmeans/README.md @@ -0,0 +1,71 @@ +# Sharded Feature Extraction and K-means Application + +This folder contains scripts for preparing HUBERT labels from tsv files, the +steps are: +1. feature extraction +2. k-means clustering +3. k-means application + + +## Data preparation + +`*.tsv` files contains a list of audio, where each line is the root, and +following lines are the subpath for each audio: +``` + + + +... +``` + + +## Feature extraction + +### MFCC feature +Suppose the tsv file is at `${tsv_dir}/${split}.tsv`. To extract 39-D +mfcc+delta+ddelta features for the 1st iteration HUBERT training, run: +```sh +python dump_mfcc_feature.py ${tsv_dir} ${split} ${nshard} ${rank} ${feat_dir} +``` +This would shard the tsv file into `${nshard}` and extract features for the +`${rank}`-th shard, where rank is an integer in `[0, nshard-1]`. Features would +be saved at `${feat_dir}/${split}_${rank}_${nshard}.{npy,len}`. + + +### HUBERT feature +To extract features from the `${layer}`-th transformer layer of a trained +HUBERT model saved at `${ckpt_path}`, run: +```sh +python dump_hubert_feature.py ${tsv_dir} ${split} ${ckpt_path} ${layer} ${nshard} ${rank} ${feat_dir} +``` +Features would also be saved at `${feat_dir}/${split}_${rank}_${nshard}.{npy,len}`. + +- if out-of-memory, decrease the chunk size with `--max_chunk` + + +## K-means clustering +To fit a k-means model with `${n_clusters}` clusters on 10% of the `${split}` data, run +```sh +python learn_kmeans.py ${feat_dir} ${split} ${nshard} ${km_path} ${n_cluster} --percent 0.1 +``` +This saves the k-means model to `${km_path}`. + +- set `--precent -1` to use all data +- more kmeans options can be found with `-h` flag + + +## K-means application +To apply a trained k-means model `${km_path}` to obtain labels for `${split}`, run +```sh +python dump_km_label.py ${feat_dir} ${split} ${km_path} ${nshard} ${rank} ${lab_dir} +``` +This would extract labels for the `${rank}`-th shard out of `${nshard}` shards +and dump them to `${lab_dir}/${split}_${rank}_${shard}.km` + + +Finally, merge shards for `${split}` by running +```sh +for rank in $(seq 0 $((nshard - 1))); do + cat $lab_dir/${split}_${rank}_${nshard}.km +done > $lab_dir/${split}.km +``` diff --git a/examples/hubert/simple_kmeans/dump_hubert_feature.py b/examples/hubert/simple_kmeans/dump_hubert_feature.py new file mode 100644 index 0000000000..5c7b67f8b1 --- /dev/null +++ b/examples/hubert/simple_kmeans/dump_hubert_feature.py @@ -0,0 +1,93 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +import os +import sys + +import fairseq +import soundfile as sf +import torch +import torch.nn.functional as F + +from feature_utils import get_path_iterator, dump_feature + + +logging.basicConfig( + format="%(asctime)s | %(levelname)s | %(name)s | %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", + level=os.environ.get("LOGLEVEL", "INFO").upper(), + stream=sys.stdout, +) +logger = logging.getLogger("dump_hubert_feature") + + +class HubertFeatureReader(object): + def __init__(self, ckpt_path, layer, max_chunk=1600000): + ( + model, + cfg, + task, + ) = fairseq.checkpoint_utils.load_model_ensemble_and_task([ckpt_path]) + self.model = model[0].eval().cuda() + self.task = task + self.layer = layer + self.max_chunk = max_chunk + logger.info(f"TASK CONFIG:\n{self.task.cfg}") + logger.info(f" max_chunk = {self.max_chunk}") + + def read_audio(self, path, ref_len=None): + wav, sr = sf.read(path) + assert sr == self.task.cfg.sample_rate, sr + if wav.ndim == 2: + wav = wav.mean(-1) + assert wav.ndim == 1, wav.ndim + if ref_len is not None and abs(ref_len - len(wav)) > 160: + logging.warning(f"ref {ref_len} != read {len(wav)} ({path})") + return wav + + def get_feats(self, path, ref_len=None): + x = self.read_audio(path, ref_len) + with torch.no_grad(): + x = torch.from_numpy(x).float().cuda() + if self.task.cfg.normalize: + x = F.layer_norm(x, x.shape) + x = x.view(1, -1) + + feat = [] + for start in range(0, x.size(1), self.max_chunk): + x_chunk = x[:, start: start + self.max_chunk] + feat_chunk, _ = self.model.extract_features( + source=x_chunk, + padding_mask=None, + mask=False, + output_layer=self.layer, + ) + feat.append(feat_chunk) + return torch.cat(feat, 1).squeeze(0) + + +def main(tsv_dir, split, ckpt_path, layer, nshard, rank, feat_dir, max_chunk): + reader = HubertFeatureReader(ckpt_path, layer, max_chunk) + generator, num = get_path_iterator(f"{tsv_dir}/{split}.tsv", nshard, rank) + dump_feature(reader, generator, num, split, nshard, rank, feat_dir) + + +if __name__ == "__main__": + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument("tsv_dir") + parser.add_argument("split") + parser.add_argument("ckpt_path") + parser.add_argument("layer", type=int) + parser.add_argument("nshard", type=int) + parser.add_argument("rank", type=int) + parser.add_argument("feat_dir") + parser.add_argument("--max_chunk", type=int, default=1600000) + args = parser.parse_args() + logger.info(args) + + main(**vars(args)) diff --git a/examples/hubert/simple_kmeans/dump_hubert_feature_s2t.py b/examples/hubert/simple_kmeans/dump_hubert_feature_s2t.py new file mode 100644 index 0000000000..6fff4faf44 --- /dev/null +++ b/examples/hubert/simple_kmeans/dump_hubert_feature_s2t.py @@ -0,0 +1,92 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import csv +import io +import logging +import os +import os.path as op +import sys + +from dump_hubert_feature import HubertFeatureReader +from feature_utils import get_shard_range, dump_feature +from fairseq.data.audio.audio_utils import get_waveform +from fairseq.data.audio.speech_to_text_dataset import ( + read_from_uncompressed_zip, +) + + +logging.basicConfig( + format="%(asctime)s | %(levelname)s | %(name)s | %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", + level=os.environ.get("LOGLEVEL", "INFO").upper(), + stream=sys.stdout, +) +logger = logging.getLogger("dump_hubert_feature_s2t") + + +class HubertFeatureReaderS2T(HubertFeatureReader): + def read_audio(self, path, ref_len=None): + path, *extra = path.split(":") + assert len(extra) == 2 + assert path.endswith(".zip") + + data = read_from_uncompressed_zip(path, int(extra[0]), int(extra[1])) + f = io.BytesIO(data) + wav, sr = get_waveform(f) + assert sr == self.task.cfg.sample_rate, sr + if wav.ndim == 2: + wav = wav.mean(-1) + assert wav.ndim == 1, wav.ndim + if ref_len is not None and abs(ref_len - len(wav)) > 160: + logging.warning(f"ref {ref_len} != read {len(wav)} ({path})") + return wav + + +def get_path_iterator(root, tsv, nshard, rank): + with open(tsv) as f: + reader = csv.DictReader( + f, + delimiter="\t", + quotechar=None, + doublequote=False, + lineterminator="\n", + quoting=csv.QUOTE_NONE, + ) + subpaths = [op.join(root, e["audio"]) for e in reader] + start, end = get_shard_range(len(subpaths), nshard, rank) + subpaths = subpaths[start:end] + def iterate(): + for subpath in subpaths: + yield op.join(root, subpath), None + return iterate, len(subpaths) + + +def main( + root, tsv_path, ckpt_path, layer, nshard, rank, feat_dir, split, max_chunk +): + reader = HubertFeatureReaderS2T(ckpt_path, layer, max_chunk) + generator, num = get_path_iterator(root, tsv_path, nshard, rank) + dump_feature(reader, generator, num, split, nshard, rank, feat_dir) + + + +if __name__ == "__main__": + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument("root") + parser.add_argument("tsv_path") + parser.add_argument("ckpt_path") + parser.add_argument("layer", type=int) + parser.add_argument("nshard", type=int) + parser.add_argument("rank", type=int) + parser.add_argument("feat_dir") + parser.add_argument("split") + parser.add_argument("--max_chunk", type=int, default=1600000) + args = parser.parse_args() + logger.info(args) + + main(**vars(args)) diff --git a/examples/hubert/simple_kmeans/dump_km_label.py b/examples/hubert/simple_kmeans/dump_km_label.py new file mode 100644 index 0000000000..8871307804 --- /dev/null +++ b/examples/hubert/simple_kmeans/dump_km_label.py @@ -0,0 +1,98 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +import os +import sys + +import numpy as np + +import joblib +import torch +import tqdm + +logging.basicConfig( + format="%(asctime)s | %(levelname)s | %(name)s | %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", + level=os.environ.get("LOGLEVEL", "INFO").upper(), + stream=sys.stdout, +) +logger = logging.getLogger("dump_km_label") + + +class ApplyKmeans(object): + def __init__(self, km_path): + self.km_model = joblib.load(km_path) + self.C_np = self.km_model.cluster_centers_.transpose() + self.Cnorm_np = (self.C_np ** 2).sum(0, keepdims=True) + + self.C = torch.from_numpy(self.C_np) + self.Cnorm = torch.from_numpy(self.Cnorm_np) + if torch.cuda.is_available(): + self.C = self.C.cuda() + self.Cnorm = self.Cnorm.cuda() + + def __call__(self, x): + if isinstance(x, torch.Tensor): + dist = ( + x.pow(2).sum(1, keepdim=True) + - 2 * torch.matmul(x, self.C) + + self.Cnorm + ) + return dist.argmin(dim=1).cpu().numpy() + else: + dist = ( + (x ** 2).sum(1, keepdims=True) + - 2 * np.matmul(x, self.C_np) + + self.Cnorm_np + ) + return np.argmin(dist, axis=1) + + +def get_feat_iterator(feat_dir, split, nshard, rank): + feat_path = f"{feat_dir}/{split}_{rank}_{nshard}.npy" + leng_path = f"{feat_dir}/{split}_{rank}_{nshard}.len" + with open(leng_path, "r") as f: + lengs = [int(line.rstrip()) for line in f] + offsets = [0] + np.cumsum(lengs[:-1]).tolist() + + def iterate(): + feat = np.load(feat_path, mmap_mode="r") + assert feat.shape[0] == (offsets[-1] + lengs[-1]) + for offset, leng in zip(offsets, lengs): + yield feat[offset: offset + leng] + + return iterate, len(lengs) + + +def dump_label(feat_dir, split, km_path, nshard, rank, lab_dir): + apply_kmeans = ApplyKmeans(km_path) + generator, num = get_feat_iterator(feat_dir, split, nshard, rank) + iterator = generator() + + lab_path = f"{lab_dir}/{split}_{rank}_{nshard}.km" + os.makedirs(lab_dir, exist_ok=True) + with open(lab_path, "w") as f: + for feat in tqdm.tqdm(iterator, total=num): + # feat = torch.from_numpy(feat).cuda() + lab = apply_kmeans(feat).tolist() + f.write(" ".join(map(str, lab)) + "\n") + logger.info("finished successfully") + + +if __name__ == "__main__": + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument("feat_dir") + parser.add_argument("split") + parser.add_argument("km_path") + parser.add_argument("nshard", type=int) + parser.add_argument("rank", type=int) + parser.add_argument("lab_dir") + args = parser.parse_args() + logging.info(str(args)) + + dump_label(**vars(args)) diff --git a/examples/hubert/simple_kmeans/dump_mfcc_feature.py b/examples/hubert/simple_kmeans/dump_mfcc_feature.py new file mode 100644 index 0000000000..70d0016663 --- /dev/null +++ b/examples/hubert/simple_kmeans/dump_mfcc_feature.py @@ -0,0 +1,78 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +import os +import sys + +import soundfile as sf +import torch +import torchaudio + +from feature_utils import get_path_iterator, dump_feature + +logging.basicConfig( + format="%(asctime)s | %(levelname)s | %(name)s | %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", + level=os.environ.get("LOGLEVEL", "INFO").upper(), + stream=sys.stdout, +) +logger = logging.getLogger("dump_mfcc_feature") + + +class MfccFeatureReader(object): + def __init__(self, sample_rate): + self.sample_rate = sample_rate + + def read_audio(self, path, ref_len=None): + wav, sr = sf.read(path) + assert sr == self.sample_rate, sr + if wav.ndim == 2: + wav = wav.mean(-1) + assert wav.ndim == 1, wav.ndim + if ref_len is not None and abs(ref_len - len(wav)) > 160: + logging.warning(f"ref {ref_len} != read {len(wav)} ({path})") + return wav + + def get_feats(self, path, ref_len=None): + x = self.read_audio(path, ref_len) + with torch.no_grad(): + x = torch.from_numpy(x).float() + x = x.view(1, -1) + + mfccs = torchaudio.compliance.kaldi.mfcc( + waveform=x, + sample_frequency=self.sample_rate, + use_energy=False, + ) # (time, freq) + mfccs = mfccs.transpose(0, 1) # (freq, time) + deltas = torchaudio.functional.compute_deltas(mfccs) + ddeltas = torchaudio.functional.compute_deltas(deltas) + concat = torch.cat([mfccs, deltas, ddeltas], dim=0) + concat = concat.transpose(0, 1).contiguous() # (freq, time) + return concat + + +def main(tsv_dir, split, nshard, rank, feat_dir, sample_rate): + reader = MfccFeatureReader(sample_rate) + generator, num = get_path_iterator(f"{tsv_dir}/{split}.tsv", nshard, rank) + dump_feature(reader, generator, num, split, nshard, rank, feat_dir) + + + +if __name__ == "__main__": + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument("tsv_dir") + parser.add_argument("split") + parser.add_argument("nshard", type=int) + parser.add_argument("rank", type=int) + parser.add_argument("feat_dir") + parser.add_argument("--sample_rate", type=int, default=16000) + args = parser.parse_args() + logger.info(args) + + main(**vars(args)) diff --git a/examples/hubert/simple_kmeans/dump_w2v2_feature.py b/examples/hubert/simple_kmeans/dump_w2v2_feature.py new file mode 100644 index 0000000000..a1f0d902ac --- /dev/null +++ b/examples/hubert/simple_kmeans/dump_w2v2_feature.py @@ -0,0 +1,95 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +import os +import sys + +import fairseq +import soundfile as sf +import torch +import torch.nn.functional as F + +from feature_utils import get_path_iterator, dump_feature + + +logging.basicConfig( + format="%(asctime)s | %(levelname)s | %(name)s | %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", + level=os.environ.get("LOGLEVEL", "INFO").upper(), + stream=sys.stdout, +) +logger = logging.getLogger("dump_w2v2_feature") + + +class Wav2Vec2FeatureReader(object): + def __init__(self, ckpt_path, layer, max_chunk=1600000): + ( + model, + cfg, + task, + ) = fairseq.checkpoint_utils.load_model_ensemble_and_task([ckpt_path]) + self.model = model[0].eval().cuda() + self.task = task + self.layer = layer # assume this is 1-based like HuBERT + self.max_chunk = max_chunk + logger.info(f"TASK CONFIG:\n{self.task.cfg}") + logger.info(f" max_chunk = {self.max_chunk}") + logger.info(f" model:\n{self.model}") + + def read_audio(self, path, ref_len=None): + wav, sr = sf.read(path) + assert sr == self.task.cfg.sample_rate, sr + if wav.ndim == 2: + wav = wav.mean(-1) + assert wav.ndim == 1, wav.ndim + if ref_len is not None and abs(ref_len - len(wav)) > 160: + logging.warning(f"ref {ref_len} != read {len(wav)} ({path})") + return wav + + def get_feats(self, path, ref_len=None): + x = self.read_audio(path, ref_len) + with torch.no_grad(): + x = torch.from_numpy(x).float().cuda() + if self.task.cfg.normalize: + x = F.layer_norm(x, x.shape) + x = x.view(1, -1) + + feat = [] + for start in range(0, x.size(1), self.max_chunk): + x_chunk = x[:, start: start + self.max_chunk] + res = self.model.extract_features( + source=x_chunk, + padding_mask=None, + mask=False, + layer=self.layer - 1, + ) + feat_chunk = res["x"] + feat.append(feat_chunk) + return torch.cat(feat, 1).squeeze(0) + + +def main(tsv_dir, split, ckpt_path, layer, nshard, rank, feat_dir, max_chunk): + reader = Wav2Vec2FeatureReader(ckpt_path, layer, max_chunk) + generator, num = get_path_iterator(f"{tsv_dir}/{split}.tsv", nshard, rank) + dump_feature(reader, generator, num, split, nshard, rank, feat_dir) + + +if __name__ == "__main__": + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument("tsv_dir") + parser.add_argument("split") + parser.add_argument("ckpt_path") + parser.add_argument("layer", type=int) + parser.add_argument("nshard", type=int) + parser.add_argument("rank", type=int) + parser.add_argument("feat_dir") + parser.add_argument("--max_chunk", type=int, default=1600000) + args = parser.parse_args() + logger.info(args) + + main(**vars(args)) diff --git a/examples/hubert/simple_kmeans/feature_utils.py b/examples/hubert/simple_kmeans/feature_utils.py new file mode 100644 index 0000000000..f80bc45697 --- /dev/null +++ b/examples/hubert/simple_kmeans/feature_utils.py @@ -0,0 +1,66 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +import os +import sys + +import tqdm +from npy_append_array import NpyAppendArray + + +logging.basicConfig( + format="%(asctime)s | %(levelname)s | %(name)s | %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", + level=os.environ.get("LOGLEVEL", "INFO").upper(), + stream=sys.stdout, +) +logger = logging.getLogger("feature_utils") + + +def get_shard_range(tot, nshard, rank): + assert rank < nshard and rank >= 0, f"invaid rank/nshard {rank}/{nshard}" + start = round(tot / nshard * rank) + end = round(tot / nshard * (rank + 1)) + assert start < end, f"start={start}, end={end}" + logger.info( + f"rank {rank} of {nshard}, process {end-start} " + f"({start}-{end}) out of {tot}" + ) + return start, end + + +def get_path_iterator(tsv, nshard, rank): + with open(tsv, "r") as f: + root = f.readline().rstrip() + lines = [line.rstrip() for line in f] + start, end = get_shard_range(len(lines), nshard, rank) + lines = lines[start:end] + def iterate(): + for line in lines: + subpath, nsample = line.split("\t") + yield f"{root}/{subpath}", int(nsample) + return iterate, len(lines) + + +def dump_feature(reader, generator, num, split, nshard, rank, feat_dir): + iterator = generator() + + feat_path = f"{feat_dir}/{split}_{rank}_{nshard}.npy" + leng_path = f"{feat_dir}/{split}_{rank}_{nshard}.len" + + os.makedirs(feat_dir, exist_ok=True) + if os.path.exists(feat_path): + os.remove(feat_path) + + feat_f = NpyAppendArray(feat_path) + with open(leng_path, "w") as leng_f: + for path, nsample in tqdm.tqdm(iterator, total=num): + feat = reader.get_feats(path, nsample) + feat_f.append(feat.cpu().numpy()) + leng_f.write(f"{len(feat)}\n") + logger.info("finished successfully") + + diff --git a/examples/hubert/simple_kmeans/learn_kmeans.py b/examples/hubert/simple_kmeans/learn_kmeans.py new file mode 100644 index 0000000000..113ac655b8 --- /dev/null +++ b/examples/hubert/simple_kmeans/learn_kmeans.py @@ -0,0 +1,146 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +import os +import sys + +import numpy as np +from sklearn.cluster import MiniBatchKMeans + +import joblib + +logging.basicConfig( + format="%(asctime)s | %(levelname)s | %(name)s | %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", + level=os.environ.get("LOGLEVEL", "INFO").upper(), + stream=sys.stdout, +) +logger = logging.getLogger("learn_kmeans") + + +def get_km_model( + n_clusters, + init, + max_iter, + batch_size, + tol, + max_no_improvement, + n_init, + reassignment_ratio, +): + return MiniBatchKMeans( + n_clusters=n_clusters, + init=init, + max_iter=max_iter, + batch_size=batch_size, + verbose=1, + compute_labels=False, + tol=tol, + max_no_improvement=max_no_improvement, + init_size=None, + n_init=n_init, + reassignment_ratio=reassignment_ratio, + ) + + +def load_feature_shard(feat_dir, split, nshard, rank, percent): + feat_path = f"{feat_dir}/{split}_{rank}_{nshard}.npy" + leng_path = f"{feat_dir}/{split}_{rank}_{nshard}.len" + with open(leng_path, "r") as f: + lengs = [int(line.rstrip()) for line in f] + offsets = [0] + np.cumsum(lengs[:-1]).tolist() + + if percent < 0: + return np.load(feat_path, mmap_mode="r") + else: + nsample = int(np.ceil(len(lengs) * percent)) + indices = np.random.choice(len(lengs), nsample, replace=False) + feat = np.load(feat_path, mmap_mode="r") + sampled_feat = np.concatenate( + [feat[offsets[i]: offsets[i] + lengs[i]] for i in indices], axis=0 + ) + logger.info( + ( + f"sampled {nsample} utterances, {len(sampled_feat)} frames " + f"from shard {rank}/{nshard}" + ) + ) + return sampled_feat + + +def load_feature(feat_dir, split, nshard, seed, percent): + assert percent <= 1.0 + feat = np.concatenate( + [ + load_feature_shard(feat_dir, split, nshard, r, percent) + for r in range(nshard) + ], + axis=0, + ) + logging.info(f"loaded feature with dimension {feat.shape}") + return feat + + +def learn_kmeans( + feat_dir, + split, + nshard, + km_path, + n_clusters, + seed, + percent, + init, + max_iter, + batch_size, + tol, + n_init, + reassignment_ratio, + max_no_improvement, +): + np.random.seed(seed) + feat = load_feature(feat_dir, split, nshard, seed, percent) + km_model = get_km_model( + n_clusters, + init, + max_iter, + batch_size, + tol, + max_no_improvement, + n_init, + reassignment_ratio, + ) + km_model.fit(feat) + joblib.dump(km_model, km_path) + + inertia = -km_model.score(feat) / len(feat) + logger.info("total intertia: %.5f", inertia) + logger.info("finished successfully") + + +if __name__ == "__main__": + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument("feat_dir", type=str) + parser.add_argument("split", type=str) + parser.add_argument("nshard", type=int) + parser.add_argument("km_path", type=str) + parser.add_argument("n_clusters", type=int) + parser.add_argument("--seed", default=0, type=int) + parser.add_argument( + "--percent", default=-1, type=float, help="sample a subset; -1 for all" + ) + parser.add_argument("--init", default="k-means++") + parser.add_argument("--max_iter", default=100, type=int) + parser.add_argument("--batch_size", default=10000, type=int) + parser.add_argument("--tol", default=0.0, type=float) + parser.add_argument("--max_no_improvement", default=100, type=int) + parser.add_argument("--n_init", default=20, type=int) + parser.add_argument("--reassignment_ratio", default=0.0, type=float) + args = parser.parse_args() + logging.info(str(args)) + + learn_kmeans(**vars(args)) diff --git a/examples/hubert/update_ckpt.py b/examples/hubert/update_ckpt.py new file mode 100644 index 0000000000..53c9e74ea6 --- /dev/null +++ b/examples/hubert/update_ckpt.py @@ -0,0 +1,22 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch + +src_ckpt = "/checkpoint/wnhsu/w2v/archived/hubert_base_ls960_it2.pt" +ref_ckpt = "/checkpoint/wnhsu/w2v/hubert_icassp_oss_v3/iter2_km100-400k-grp-L6/oss.km500_p0_1_s334.pmw1_0.puw0_0.grpnorm.ml10.mp0_8.untie.mxsz250000.ufreq1.maxtok1400000.MU100k.s1337.ngpu32/checkpoint_last.pt" +new_ckpt = "/checkpoint/wnhsu/w2v/archived/hubert_base_ls960_it2_updated.pt" + + +def update_state(state): + state["model"]["label_embs_concat"] = state["model"].pop("label_embs") + state["args"].task = "hubert_pretraining" + state["args"].labels = f"['{state['args'].labels}']" + return state + + +src_state = torch.load(src_ckpt) +src_state = update_state(src_state) +torch.save(src_state, new_ckpt) diff --git a/examples/language_model/transformer_lm/README.md b/examples/language_model/README.adaptive_inputs.md similarity index 77% rename from examples/language_model/transformer_lm/README.md rename to examples/language_model/README.adaptive_inputs.md index 0ca6482afc..6650d58f37 100644 --- a/examples/language_model/transformer_lm/README.md +++ b/examples/language_model/README.adaptive_inputs.md @@ -4,13 +4,13 @@ Description | Parameters | Dataset | Model and Test set(s) ---|---:|---|--- -Adaptive Inputs
([Baevski and Auli, 2018](https://arxiv.org/abs/1809.10853)) | 1026M | [Google Billion Words](https://github.com/ciprian-chelba/1-billion-word-language-modeling-benchmark) | [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/lm/adaptive_lm_gbw_huge.bz2) -Adaptive Inputs
([Baevski and Auli, 2018](https://arxiv.org/abs/1809.10853)) | 247M | [WikiText-103](https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/) | [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/lm/adaptive_lm_wiki103.bz2) +Adaptive Inputs
([Baevski and Auli, 2018](https://arxiv.org/abs/1809.10853)) | 1026M | [Google Billion Words](https://github.com/ciprian-chelba/1-billion-word-language-modeling-benchmark) | [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/lm/adaptive_lm_gbw_huge.tar.bz2) +Adaptive Inputs
([Baevski and Auli, 2018](https://arxiv.org/abs/1809.10853)) | 247M | [WikiText-103](https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/) | [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/lm/adaptive_lm_wiki103.v2.tar.bz2) ## Training an LM with adaptive inputs -First, see the general [language modeling README](../README.md) for instructions -on preprocessing the WikiText-103 data. +First, see the general [language modeling README](README.md) for instructions on +preprocessing the WikiText-103 data. Then use the following training command to train a model with adaptive inputs using the `transformer_lm_wiki103` model architecture: @@ -19,10 +19,10 @@ fairseq-train --task language_modeling \ data-bin/wikitext-103 \ --save-dir checkpoints/transformer_wikitext-103 \ --arch transformer_lm_wiki103 \ - --max-update 286000 --max-lr 1.0 --t-mult 2 --lr-period-updates 270000 --lr-scheduler cosine --lr-shrink 0.75 \ - --warmup-updates 16000 --warmup-init-lr 1e-07 --min-lr 1e-09 --optimizer nag --lr 0.0001 --clip-norm 0.1 \ + --max-update 286000 --lr 1.0 --t-mult 2 --lr-period-updates 270000 --lr-scheduler cosine --lr-shrink 0.75 \ + --warmup-updates 16000 --warmup-init-lr 1e-07 --stop-min-lr 1e-09 --optimizer nag --min-lr 0.0001 --clip-norm 0.1 \ --criterion adaptive_loss --max-tokens 3072 --update-freq 3 --tokens-per-sample 3072 --seed 1 \ - --sample-break-mode none --skip-invalid-size-inputs-valid-test --ddp-backend=no_c10d + --sample-break-mode none --skip-invalid-size-inputs-valid-test --ddp-backend=legacy_ddp ``` ## Citation diff --git a/examples/language_model/conv_lm/README.md b/examples/language_model/README.conv.md similarity index 76% rename from examples/language_model/conv_lm/README.md rename to examples/language_model/README.conv.md index 83ac0b454b..1ff8635906 100644 --- a/examples/language_model/conv_lm/README.md +++ b/examples/language_model/README.conv.md @@ -2,8 +2,7 @@ ## Example usage -First download and preprocess the data following the main [language modeling -README](../README.md). +First download and preprocess the data following the main [language modeling README](README.md). Then to train a convolutional LM using the `fconv_lm_dauphin_wikitext103` architecture: @@ -12,11 +11,14 @@ fairseq-train --task language_modeling \ data-bin/wikitext-103 \ --save-dir checkpoints/fconv_wikitext-103 \ --arch fconv_lm_dauphin_wikitext103 \ - --max-epoch 35 \ --optimizer nag \ + --adaptive-softmax-cutoff 10000,20000,200000 \ + --dropout 0.2 \ + --criterion adaptive_loss \ + --optimizer nag --clip-norm 0.1 --weight-decay 5e-06 \ --lr 1.0 --lr-scheduler reduce_lr_on_plateau --lr-shrink 0.5 \ - --clip-norm 0.1 --dropout 0.2 --weight-decay 5e-06 --criterion adaptive_loss \ - --adaptive-softmax-cutoff 10000,20000,200000 --max-tokens 1024 --tokens-per-sample 1024 \ - --ddp-backend=no_c10d + --max-tokens 1024 --tokens-per-sample 1024 \ + --ddp-backend legacy_ddp \ + --max-epoch 35 ``` And evaluate with: diff --git a/examples/language_model/README.md b/examples/language_model/README.md index 43f3381a1f..e78ea48e08 100644 --- a/examples/language_model/README.md +++ b/examples/language_model/README.md @@ -5,7 +5,7 @@ Model | Description | Dataset | Download ---|---|---|--- `transformer_lm.gbw.adaptive_huge` | Adaptive Inputs
([Baevski and Auli, 2018](https://arxiv.org/abs/1809.10853))
1026M params | [Google Billion Words](https://github.com/ciprian-chelba/1-billion-word-language-modeling-benchmark) | [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/lm/adaptive_lm_gbw_huge.tar.bz2) -`transformer_lm.wiki103.adaptive` | Adaptive Inputs
([Baevski and Auli, 2018](https://arxiv.org/abs/1809.10853))
247M params | [WikiText-103](https://einstein.ai/research/the-wikitext-long-term-dependency-language-modeling-dataset) | [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/lm/adaptive_lm_wiki103.tar.bz2) +`transformer_lm.wiki103.adaptive` | Adaptive Inputs
([Baevski and Auli, 2018](https://arxiv.org/abs/1809.10853))
247M params | [WikiText-103](https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset) | [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/lm/adaptive_lm_wiki103.v2.tar.bz2) `transformer_lm.wmt19.en` | English LM
([Ng et al., 2019](https://arxiv.org/abs/1907.06616)) | [WMT News Crawl](http://data.statmt.org/news-crawl/) | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt19.en.tar.gz) `transformer_lm.wmt19.de` | German LM
([Ng et al., 2019](https://arxiv.org/abs/1907.06616)) | [WMT News Crawl](http://data.statmt.org/news-crawl/) | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt19.de.tar.gz) `transformer_lm.wmt19.ru` | Russian LM
([Ng et al., 2019](https://arxiv.org/abs/1907.06616)) | [WMT News Crawl](http://data.statmt.org/news-crawl/) | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt19.ru.tar.gz) @@ -72,8 +72,7 @@ fairseq-preprocess \ ### 2) Train a language model Next we'll train a basic transformer language model on wikitext-103. For more -advanced examples (e.g., using [adaptive inputs](https://arxiv.org/abs/1809.10853)), -please see the [Transformer LM README](transformer_lm/README.md). +advanced usage, see the [adaptive inputs README](README.adaptive_inputs.md). To train a basic LM (assumes 2 GPUs): ``` @@ -100,7 +99,7 @@ number of GPUs. ```bash fairseq-eval-lm data-bin/wikitext-103 \ --path checkpoints/transformer_wiki103/checkpoint_best.pt \ - --max-sentences 2 \ + --batch-size 2 \ --tokens-per-sample 512 \ --context-window 400 # | Evaluated 245569 tokens in 56.1s (4379.02 tokens/s) @@ -120,5 +119,5 @@ dataset, but results in better (lower) perplexity. ## Convolutional language models -Please see the [convolutional LM README](conv_lm/README.md) for instructions to -train convolutional language models. +Please see the [convolutional LM README](README.conv.md) for instructions on +training convolutional language models. diff --git a/examples/laser/README.md b/examples/laser/README.md new file mode 100644 index 0000000000..66acada04f --- /dev/null +++ b/examples/laser/README.md @@ -0,0 +1,144 @@ +# LASER Language-Agnostic SEntence Representations + +LASER is a library to calculate and use multilingual sentence embeddings. + +You can find more information about LASER and how to use it on the official [LASER repository](https://github.com/facebookresearch/LASER). + +This folder contains source code for training LASER embeddings. + + +## Prepare data and configuration file + +Binarize your data with fairseq, as described [here](https://fairseq.readthedocs.io/en/latest/getting_started.html#data-pre-processing). + +Create a json config file with this format: +``` +{ + "src_vocab": "/path/to/spm.src.cvocab", + "tgt_vocab": "/path/to/spm.tgt.cvocab", + "train": [ + { + "type": "translation", + "id": 0, + "src": "/path/to/srclang1-tgtlang0/train.srclang1", + "tgt": "/path/to/srclang1-tgtlang0/train.tgtlang0" + }, + { + "type": "translation", + "id": 1, + "src": "/path/to/srclang1-tgtlang1/train.srclang1", + "tgt": "/path/to/srclang1-tgtlang1/train.tgtlang1" + }, + { + "type": "translation", + "id": 0, + "src": "/path/to/srclang2-tgtlang0/train.srclang2", + "tgt": "/path/to/srclang2-tgtlang0/train.tgtlang0" + }, + { + "type": "translation", + "id": 1, + "src": "/path/to/srclang2-tgtlang1/train.srclang2", + "tgt": "/path/to/srclang2-tgtlang1/train.tgtlang1" + }, + ... + ], + "valid": [ + { + "type": "translation", + "id": 0, + "src": "/unused", + "tgt": "/unused" + } + ] +} +``` +where paths are paths to binarized indexed fairseq dataset files. +`id` represents the target language id. + + +## Training Command Line Example + +``` +fairseq-train \ + /path/to/configfile_described_above.json \ + --user-dir examples/laser/laser_src \ + --log-interval 100 --log-format simple \ + --task laser --arch laser_lstm \ + --save-dir . \ + --optimizer adam \ + --lr 0.001 \ + --lr-scheduler inverse_sqrt \ + --clip-norm 5 \ + --warmup-updates 90000 \ + --update-freq 2 \ + --dropout 0.0 \ + --encoder-dropout-out 0.1 \ + --max-tokens 2000 \ + --max-epoch 50 \ + --encoder-bidirectional \ + --encoder-layers 5 \ + --encoder-hidden-size 512 \ + --decoder-layers 1 \ + --decoder-hidden-size 2048 \ + --encoder-embed-dim 320 \ + --decoder-embed-dim 320 \ + --decoder-lang-embed-dim 32 \ + --warmup-init-lr 0.001 \ + --disable-validation +``` + + +## Applications + +We showcase several applications of multilingual sentence embeddings +with code to reproduce our results (in the directory "tasks"). + +* [**Cross-lingual document classification**](https://github.com/facebookresearch/LASER/tree/master/tasks/mldoc) using the + [*MLDoc*](https://github.com/facebookresearch/MLDoc) corpus [2,6] +* [**WikiMatrix**](https://github.com/facebookresearch/LASER/tree/master/tasks/WikiMatrix) + Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia [7] +* [**Bitext mining**](https://github.com/facebookresearch/LASER/tree/master/tasks/bucc) using the + [*BUCC*](https://comparable.limsi.fr/bucc2018/bucc2018-task.html) corpus [3,5] +* [**Cross-lingual NLI**](https://github.com/facebookresearch/LASER/tree/master/tasks/xnli) + using the [*XNLI*](https://www.nyu.edu/projects/bowman/xnli/) corpus [4,5,6] +* [**Multilingual similarity search**](https://github.com/facebookresearch/LASER/tree/master/tasks/similarity) [1,6] +* [**Sentence embedding of text files**](https://github.com/facebookresearch/LASER/tree/master/tasks/embed) + example how to calculate sentence embeddings for arbitrary text files in any of the supported language. + +**For all tasks, we use exactly the same multilingual encoder, without any task specific optimization or fine-tuning.** + + + +## References + +[1] Holger Schwenk and Matthijs Douze, + [*Learning Joint Multilingual Sentence Representations with Neural Machine Translation*](https://aclanthology.info/papers/W17-2619/w17-2619), + ACL workshop on Representation Learning for NLP, 2017 + +[2] Holger Schwenk and Xian Li, + [*A Corpus for Multilingual Document Classification in Eight Languages*](http://www.lrec-conf.org/proceedings/lrec2018/pdf/658.pdf), + LREC, pages 3548-3551, 2018. + +[3] Holger Schwenk, + [*Filtering and Mining Parallel Data in a Joint Multilingual Space*](http://aclweb.org/anthology/P18-2037) + ACL, July 2018 + +[4] Alexis Conneau, Guillaume Lample, Ruty Rinott, Adina Williams, Samuel R. Bowman, Holger Schwenk and Veselin Stoyanov, + [*XNLI: Cross-lingual Sentence Understanding through Inference*](https://aclweb.org/anthology/D18-1269), + EMNLP, 2018. + +[5] Mikel Artetxe and Holger Schwenk, + [*Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings*](https://arxiv.org/abs/1811.01136) + arXiv, Nov 3 2018. + +[6] Mikel Artetxe and Holger Schwenk, + [*Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond*](https://arxiv.org/abs/1812.10464) + arXiv, Dec 26 2018. + +[7] Holger Schwenk, Vishrav Chaudhary, Shuo Sun, Hongyu Gong and Paco Guzman, + [*WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia*](https://arxiv.org/abs/1907.05791) + arXiv, July 11 2019. + +[8] Holger Schwenk, Guillaume Wenzek, Sergey Edunov, Edouard Grave and Armand Joulin + [*CCMatrix: Mining Billions of High-Quality Parallel Sentences on the WEB*](https://arxiv.org/abs/1911.04944) diff --git a/validate.py b/examples/laser/laser_src/__init__.py similarity index 61% rename from validate.py rename to examples/laser/laser_src/__init__.py index 9c1c66bba5..9ffbd656d8 100644 --- a/validate.py +++ b/examples/laser/laser_src/__init__.py @@ -1,11 +1,8 @@ -#!/usr/bin/env python3 -u # Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. -from fairseq_cli.validate import cli_main - - -if __name__ == '__main__': - cli_main() +from .laser_task import * # noqa +from .laser_lstm import * # noqa +from .laser_transformer import * # noqa diff --git a/examples/laser/laser_src/laser_lstm.py b/examples/laser/laser_src/laser_lstm.py new file mode 100644 index 0000000000..10df90e002 --- /dev/null +++ b/examples/laser/laser_src/laser_lstm.py @@ -0,0 +1,585 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from fairseq import options, utils + +from fairseq.models import ( + FairseqEncoder, + FairseqIncrementalDecoder, + FairseqEncoderDecoderModel, + register_model, + register_model_architecture, +) + + +@register_model("laser_lstm") +class LSTMModel(FairseqEncoderDecoderModel): + def __init__(self, encoder, decoder): + super().__init__(encoder, decoder) + + def forward( + self, + src_tokens, + src_lengths, + prev_output_tokens=None, + tgt_tokens=None, + tgt_lengths=None, + target_language_id=None, + dataset_name="", + ): + assert target_language_id is not None + + src_encoder_out = self.encoder(src_tokens, src_lengths, dataset_name) + return self.decoder( + prev_output_tokens, src_encoder_out, lang_id=target_language_id + ) + + @staticmethod + def add_args(parser): + """Add model-specific arguments to the parser.""" + parser.add_argument( + "--dropout", + default=0.1, + type=float, + metavar="D", + help="dropout probability", + ) + parser.add_argument( + "--encoder-embed-dim", + type=int, + metavar="N", + help="encoder embedding dimension", + ) + parser.add_argument( + "--encoder-embed-path", + default=None, + type=str, + metavar="STR", + help="path to pre-trained encoder embedding", + ) + parser.add_argument( + "--encoder-hidden-size", type=int, metavar="N", help="encoder hidden size" + ) + parser.add_argument( + "--encoder-layers", type=int, metavar="N", help="number of encoder layers" + ) + parser.add_argument( + "--encoder-bidirectional", + action="store_true", + help="make all layers of encoder bidirectional", + ) + parser.add_argument( + "--decoder-embed-dim", + type=int, + metavar="N", + help="decoder embedding dimension", + ) + parser.add_argument( + "--decoder-embed-path", + default=None, + type=str, + metavar="STR", + help="path to pre-trained decoder embedding", + ) + parser.add_argument( + "--decoder-hidden-size", type=int, metavar="N", help="decoder hidden size" + ) + parser.add_argument( + "--decoder-layers", type=int, metavar="N", help="number of decoder layers" + ) + parser.add_argument( + "--decoder-out-embed-dim", + type=int, + metavar="N", + help="decoder output embedding dimension", + ) + parser.add_argument( + "--decoder-zero-init", + type=str, + metavar="BOOL", + help="initialize the decoder hidden/cell state to zero", + ) + parser.add_argument( + "--decoder-lang-embed-dim", + type=int, + metavar="N", + help="decoder language embedding dimension", + ) + parser.add_argument( + "--fixed-embeddings", + action="store_true", + help="keep embeddings fixed (ENCODER ONLY)", + ) # TODO Also apply to decoder embeddings? + + # Granular dropout settings (if not specified these default to --dropout) + parser.add_argument( + "--encoder-dropout-in", + type=float, + metavar="D", + help="dropout probability for encoder input embedding", + ) + parser.add_argument( + "--encoder-dropout-out", + type=float, + metavar="D", + help="dropout probability for encoder output", + ) + parser.add_argument( + "--decoder-dropout-in", + type=float, + metavar="D", + help="dropout probability for decoder input embedding", + ) + parser.add_argument( + "--decoder-dropout-out", + type=float, + metavar="D", + help="dropout probability for decoder output", + ) + + @classmethod + def build_model(cls, args, task): + """Build a new model instance.""" + # make sure that all args are properly defaulted (in case there are any new ones) + base_architecture(args) + + def load_pretrained_embedding_from_file(embed_path, dictionary, embed_dim): + num_embeddings = len(dictionary) + padding_idx = dictionary.pad() + embed_tokens = Embedding(num_embeddings, embed_dim, padding_idx) + embed_dict = utils.parse_embedding(embed_path) + utils.print_embed_overlap(embed_dict, dictionary) + return utils.load_embedding(embed_dict, dictionary, embed_tokens) + + pretrained_encoder_embed = None + if args.encoder_embed_path: + pretrained_encoder_embed = load_pretrained_embedding_from_file( + args.encoder_embed_path, task.source_dictionary, args.encoder_embed_dim + ) + pretrained_decoder_embed = None + if args.decoder_embed_path: + pretrained_decoder_embed = load_pretrained_embedding_from_file( + args.decoder_embed_path, task.target_dictionary, args.decoder_embed_dim + ) + + num_langs = task.num_tasks if hasattr(task, "num_tasks") else 0 + + encoder = LSTMEncoder( + dictionary=task.source_dictionary, + embed_dim=args.encoder_embed_dim, + hidden_size=args.encoder_hidden_size, + num_layers=args.encoder_layers, + dropout_in=args.encoder_dropout_in, + dropout_out=args.encoder_dropout_out, + bidirectional=args.encoder_bidirectional, + pretrained_embed=pretrained_encoder_embed, + fixed_embeddings=args.fixed_embeddings, + ) + decoder = LSTMDecoder( + dictionary=task.target_dictionary, + embed_dim=args.decoder_embed_dim, + hidden_size=args.decoder_hidden_size, + out_embed_dim=args.decoder_out_embed_dim, + num_layers=args.decoder_layers, + dropout_in=args.decoder_dropout_in, + dropout_out=args.decoder_dropout_out, + zero_init=options.eval_bool(args.decoder_zero_init), + encoder_embed_dim=args.encoder_embed_dim, + encoder_output_units=encoder.output_units, + pretrained_embed=pretrained_decoder_embed, + num_langs=num_langs, + lang_embed_dim=args.decoder_lang_embed_dim, + ) + return cls(encoder, decoder) + + +class LSTMEncoder(FairseqEncoder): + """LSTM encoder.""" + + def __init__( + self, + dictionary, + embed_dim=512, + hidden_size=512, + num_layers=1, + dropout_in=0.1, + dropout_out=0.1, + bidirectional=False, + left_pad=True, + pretrained_embed=None, + padding_value=0.0, + fixed_embeddings=False, + ): + super().__init__(dictionary) + self.num_layers = num_layers + self.dropout_in = dropout_in + self.dropout_out = dropout_out + self.bidirectional = bidirectional + self.hidden_size = hidden_size + + num_embeddings = len(dictionary) + self.padding_idx = dictionary.pad() + if pretrained_embed is None: + self.embed_tokens = Embedding(num_embeddings, embed_dim, self.padding_idx) + else: + self.embed_tokens = pretrained_embed + if fixed_embeddings: + self.embed_tokens.weight.requires_grad = False + + self.lstm = LSTM( + input_size=embed_dim, + hidden_size=hidden_size, + num_layers=num_layers, + dropout=self.dropout_out if num_layers > 1 else 0.0, + bidirectional=bidirectional, + ) + self.left_pad = left_pad + self.padding_value = padding_value + + self.output_units = hidden_size + if bidirectional: + self.output_units *= 2 + + def forward(self, src_tokens, src_lengths, dataset_name): + if self.left_pad: + # convert left-padding to right-padding + src_tokens = utils.convert_padding_direction( + src_tokens, + self.padding_idx, + left_to_right=True, + ) + + bsz, seqlen = src_tokens.size() + + # embed tokens + x = self.embed_tokens(src_tokens) + x = F.dropout(x, p=self.dropout_in, training=self.training) + + # B x T x C -> T x B x C + x = x.transpose(0, 1) + + # pack embedded source tokens into a PackedSequence + try: + packed_x = nn.utils.rnn.pack_padded_sequence(x, src_lengths.data.tolist()) + except BaseException: + raise Exception(f"Packing failed in dataset {dataset_name}") + + # apply LSTM + if self.bidirectional: + state_size = 2 * self.num_layers, bsz, self.hidden_size + else: + state_size = self.num_layers, bsz, self.hidden_size + h0 = x.data.new(*state_size).zero_() + c0 = x.data.new(*state_size).zero_() + packed_outs, (final_hiddens, final_cells) = self.lstm(packed_x, (h0, c0)) + + # unpack outputs and apply dropout + x, _ = nn.utils.rnn.pad_packed_sequence( + packed_outs, padding_value=self.padding_value + ) + x = F.dropout(x, p=self.dropout_out, training=self.training) + assert list(x.size()) == [seqlen, bsz, self.output_units] + + if self.bidirectional: + + def combine_bidir(outs): + return torch.cat( + [ + torch.cat([outs[2 * i], outs[2 * i + 1]], dim=0).view( + 1, bsz, self.output_units + ) + for i in range(self.num_layers) + ], + dim=0, + ) + + final_hiddens = combine_bidir(final_hiddens) + final_cells = combine_bidir(final_cells) + + encoder_padding_mask = src_tokens.eq(self.padding_idx).t() + + # Set padded outputs to -inf so they are not selected by max-pooling + padding_mask = src_tokens.eq(self.padding_idx).t().unsqueeze(-1) + if padding_mask.any(): + x = x.float().masked_fill_(padding_mask, float("-inf")).type_as(x) + + # Build the sentence embedding by max-pooling over the encoder outputs + sentemb = x.max(dim=0)[0] + + return { + "sentemb": sentemb, + "encoder_out": (x, final_hiddens, final_cells), + "encoder_padding_mask": encoder_padding_mask + if encoder_padding_mask.any() + else None, + } + + def reorder_encoder_out(self, encoder_out_dict, new_order): + encoder_out_dict["sentemb"] = encoder_out_dict["sentemb"].index_select( + 0, new_order + ) + encoder_out_dict["encoder_out"] = tuple( + eo.index_select(1, new_order) for eo in encoder_out_dict["encoder_out"] + ) + if encoder_out_dict["encoder_padding_mask"] is not None: + encoder_out_dict["encoder_padding_mask"] = encoder_out_dict[ + "encoder_padding_mask" + ].index_select(1, new_order) + return encoder_out_dict + + def max_positions(self): + """Maximum input length supported by the encoder.""" + return int(1e5) # an arbitrary large number + + +class LSTMDecoder(FairseqIncrementalDecoder): + """LSTM decoder.""" + + def __init__( + self, + dictionary, + embed_dim=512, + hidden_size=512, + out_embed_dim=512, + num_layers=1, + dropout_in=0.1, + dropout_out=0.1, + zero_init=False, + encoder_embed_dim=512, + encoder_output_units=512, + pretrained_embed=None, + num_langs=1, + lang_embed_dim=0, + ): + super().__init__(dictionary) + self.dropout_in = dropout_in + self.dropout_out = dropout_out + self.hidden_size = hidden_size + + num_embeddings = len(dictionary) + padding_idx = dictionary.pad() + if pretrained_embed is None: + self.embed_tokens = Embedding(num_embeddings, embed_dim, padding_idx) + else: + self.embed_tokens = pretrained_embed + + self.layers = nn.ModuleList( + [ + LSTMCell( + input_size=encoder_output_units + embed_dim + lang_embed_dim + if layer == 0 + else hidden_size, + hidden_size=hidden_size, + ) + for layer in range(num_layers) + ] + ) + if hidden_size != out_embed_dim: + self.additional_fc = Linear(hidden_size, out_embed_dim) + self.fc_out = Linear(out_embed_dim, num_embeddings, dropout=dropout_out) + + if zero_init: + self.sentemb2init = None + else: + self.sentemb2init = Linear( + encoder_output_units, 2 * num_layers * hidden_size + ) + + if lang_embed_dim == 0: + self.embed_lang = None + else: + self.embed_lang = nn.Embedding(num_langs, lang_embed_dim) + nn.init.uniform_(self.embed_lang.weight, -0.1, 0.1) + + def forward( + self, prev_output_tokens, encoder_out_dict, incremental_state=None, lang_id=0 + ): + sentemb = encoder_out_dict["sentemb"] + encoder_out = encoder_out_dict["encoder_out"] + + if incremental_state is not None: + prev_output_tokens = prev_output_tokens[:, -1:] + bsz, seqlen = prev_output_tokens.size() + + # get outputs from encoder + encoder_outs, _, _ = encoder_out[:3] + srclen = encoder_outs.size(0) + + # embed tokens + x = self.embed_tokens(prev_output_tokens) + x = F.dropout(x, p=self.dropout_in, training=self.training) + + # embed language identifier + if self.embed_lang is not None: + lang_ids = prev_output_tokens.data.new_full((bsz,), lang_id) + langemb = self.embed_lang(lang_ids) + # TODO Should we dropout here??? + + # B x T x C -> T x B x C + x = x.transpose(0, 1) + + # initialize previous states (or get from cache during incremental generation) + cached_state = utils.get_incremental_state( + self, incremental_state, "cached_state" + ) + if cached_state is not None: + prev_hiddens, prev_cells, input_feed = cached_state + else: + num_layers = len(self.layers) + if self.sentemb2init is None: + prev_hiddens = [ + x.data.new(bsz, self.hidden_size).zero_() for i in range(num_layers) + ] + prev_cells = [ + x.data.new(bsz, self.hidden_size).zero_() for i in range(num_layers) + ] + else: + init = self.sentemb2init(sentemb) + prev_hiddens = [ + init[:, (2 * i) * self.hidden_size : (2 * i + 1) * self.hidden_size] + for i in range(num_layers) + ] + prev_cells = [ + init[ + :, + (2 * i + 1) * self.hidden_size : (2 * i + 2) * self.hidden_size, + ] + for i in range(num_layers) + ] + input_feed = x.data.new(bsz, self.hidden_size).zero_() + + attn_scores = x.data.new(srclen, seqlen, bsz).zero_() + outs = [] + for j in range(seqlen): + if self.embed_lang is None: + input = torch.cat((x[j, :, :], sentemb), dim=1) + else: + input = torch.cat((x[j, :, :], sentemb, langemb), dim=1) + + for i, rnn in enumerate(self.layers): + # recurrent cell + hidden, cell = rnn(input, (prev_hiddens[i], prev_cells[i])) + + # hidden state becomes the input to the next layer + input = F.dropout(hidden, p=self.dropout_out, training=self.training) + + # save state for next time step + prev_hiddens[i] = hidden + prev_cells[i] = cell + + out = hidden + out = F.dropout(out, p=self.dropout_out, training=self.training) + + # input feeding + input_feed = out + + # save final output + outs.append(out) + + # cache previous states (no-op except during incremental generation) + utils.set_incremental_state( + self, + incremental_state, + "cached_state", + (prev_hiddens, prev_cells, input_feed), + ) + + # collect outputs across time steps + x = torch.cat(outs, dim=0).view(seqlen, bsz, self.hidden_size) + + # T x B x C -> B x T x C + x = x.transpose(1, 0) + + # srclen x tgtlen x bsz -> bsz x tgtlen x srclen + attn_scores = attn_scores.transpose(0, 2) + + # project back to size of vocabulary + if hasattr(self, "additional_fc"): + x = self.additional_fc(x) + x = F.dropout(x, p=self.dropout_out, training=self.training) + x = self.fc_out(x) + + return x, attn_scores + + def reorder_incremental_state(self, incremental_state, new_order): + super().reorder_incremental_state(incremental_state, new_order) + cached_state = utils.get_incremental_state( + self, incremental_state, "cached_state" + ) + if cached_state is None: + return + + def reorder_state(state): + if isinstance(state, list): + return [reorder_state(state_i) for state_i in state] + return state.index_select(0, new_order) + + new_state = tuple(map(reorder_state, cached_state)) + utils.set_incremental_state(self, incremental_state, "cached_state", new_state) + + def max_positions(self): + """Maximum output length supported by the decoder.""" + return int(1e5) # an arbitrary large number + + +def Embedding(num_embeddings, embedding_dim, padding_idx): + m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx) + nn.init.uniform_(m.weight, -0.1, 0.1) + nn.init.constant_(m.weight[padding_idx], 0) + return m + + +def LSTM(input_size, hidden_size, **kwargs): + m = nn.LSTM(input_size, hidden_size, **kwargs) + for name, param in m.named_parameters(): + if "weight" in name or "bias" in name: + param.data.uniform_(-0.1, 0.1) + return m + + +def LSTMCell(input_size, hidden_size, **kwargs): + m = nn.LSTMCell(input_size, hidden_size, **kwargs) + for name, param in m.named_parameters(): + if "weight" in name or "bias" in name: + param.data.uniform_(-0.1, 0.1) + return m + + +def Linear(in_features, out_features, bias=True, dropout=0): + """Weight-normalized Linear layer (input: N x T x C)""" + m = nn.Linear(in_features, out_features, bias=bias) + m.weight.data.uniform_(-0.1, 0.1) + if bias: + m.bias.data.uniform_(-0.1, 0.1) + return m + + +@register_model_architecture("laser_lstm", "laser_lstm") +def base_architecture(args): + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) + args.encoder_embed_path = getattr(args, "encoder_embed_path", None) + args.encoder_hidden_size = getattr( + args, "encoder_hidden_size", args.encoder_embed_dim + ) + args.encoder_layers = getattr(args, "encoder_layers", 1) + args.encoder_bidirectional = getattr(args, "encoder_bidirectional", False) + args.encoder_dropout_in = getattr(args, "encoder_dropout_in", args.dropout) + args.encoder_dropout_out = getattr(args, "encoder_dropout_out", args.dropout) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512) + args.decoder_embed_path = getattr(args, "decoder_embed_path", None) + args.decoder_hidden_size = getattr( + args, "decoder_hidden_size", args.decoder_embed_dim + ) + args.decoder_layers = getattr(args, "decoder_layers", 1) + args.decoder_out_embed_dim = getattr(args, "decoder_out_embed_dim", 512) + args.decoder_dropout_in = getattr(args, "decoder_dropout_in", args.dropout) + args.decoder_dropout_out = getattr(args, "decoder_dropout_out", args.dropout) + args.decoder_zero_init = getattr(args, "decoder_zero_init", "0") + args.decoder_lang_embed_dim = getattr(args, "decoder_lang_embed_dim", 0) + args.fixed_embeddings = getattr(args, "fixed_embeddings", False) diff --git a/examples/laser/laser_src/laser_task.py b/examples/laser/laser_src/laser_task.py new file mode 100644 index 0000000000..e4152fde68 --- /dev/null +++ b/examples/laser/laser_src/laser_task.py @@ -0,0 +1,331 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +from collections import OrderedDict, defaultdict +import json +import os +import logging +from argparse import ArgumentError + +from fairseq import options, models +from fairseq.data import ( + data_utils, + Dictionary, + LanguagePairDataset, + IndexedDataset, + FairseqDataset, +) +from .multitask_data_utils import ( + MultitaskDatasetWrapper, + MultidatasetEpochBatchIterator, +) + + +from fairseq.tasks import LegacyFairseqTask, register_task + +logger = logging.getLogger(__name__) + + +@register_task("laser") +class LaserTask(LegacyFairseqTask): + @staticmethod + def add_args(parser): + """Add task-specific arguments to the parser.""" + parser.add_argument( + "configfile", metavar="PATH", help="dataset configuration file in json" + ) + parser.add_argument( + "--weighting-alpha", + type=float, + default=None, + help="alpha for automatic weighting", + ) + parser.add_argument( + "--raw-text", action="store_true", help="load raw text dataset" + ) + parser.add_argument( + "--left-pad-source", + default="True", + type=str, + metavar="BOOL", + help="pad the source on the left (default: True)", + ) + parser.add_argument( + "--left-pad-target", + default="False", + type=str, + metavar="BOOL", + help="pad the target on the left (default: False)", + ) + try: + parser.add_argument( + "--max-source-positions", + default=1024, + type=int, + metavar="N", + help="max number of tokens in the source sequence", + ) + parser.add_argument( + "--max-target-positions", + default=1024, + type=int, + metavar="N", + help="max number of tokens in the target sequence", + ) + except ArgumentError: + # this might have already been defined. Once we transition this to hydra it should be fine to add it here. + pass + + def __init__(self, args, config, src_dictionary, tgt_dictionary, num_tasks): + super().__init__(args) + self.config = config + self.src_dictionary = src_dictionary + self.tgt_dictionary = tgt_dictionary + self.num_tasks = num_tasks + + @classmethod + def setup_task(cls, args, **kwargs): + with open(args.configfile, "r") as f: + config = json.load(f) + num_tasks = max(dataset["id"] for dataset in config["train"]) + 1 + + args.left_pad_source = options.eval_bool(args.left_pad_source) + args.left_pad_target = options.eval_bool(args.left_pad_target) + + src_dictionary = Dictionary.load(config["src_vocab"]) + tgt_dictionary = Dictionary.load(config["tgt_vocab"]) + + logger.info( + "| src Dictionary {} : {} types".format( + config["src_vocab"], len(src_dictionary) + ) + ) + logger.info( + "| tgt Dictionary {} : {} types".format( + config["tgt_vocab"], len(tgt_dictionary) + ) + ) + + return cls(args, config, src_dictionary, tgt_dictionary, num_tasks) + + # Experimental overriding for backtranslation + def build_model(self, args): + model = models.build_model(args, self) + return model + + def dataset(self, split): + if split not in self.datasets: + raise KeyError("Dataset not loaded: " + split) + return self.datasets[split] + + def load_dataset(self, split, epoch=1, **kwargs): + """Load a dataset split.""" + + def indexed_dataset(path, dictionary): + if self.args.raw_text: + raise Exception("Unable to handle raw text.") + dataset = IndexedDataset(path, fix_lua_indexing=True) + + return dataset + + pair_datasets = OrderedDict() + + if split == "valid": + self.datasets[split] = pair_datasets + return + + if split not in self.config: + raise FileNotFoundError( + "Dataset not found in config file: {}".format(split) + ) + + size_by_corpus = defaultdict(int) + size_sum = 0 + size_sum_with_subsampling = 0 + init_pair_datasets = {} + + for dataset_config in self.config[split]: + src_path = os.path.dirname(dataset_config["src"]) + corpus_name = src_path.split("/")[-2] + language_pair_name = src_path.split("/")[-1] + pair_datasets_key = corpus_name + "-" + language_pair_name + + logger.info(f"loading... {pair_datasets_key}") + if "src" in dataset_config: + src_dataset = indexed_dataset( + dataset_config["src"], self.src_dictionary + ) + else: + src_dataset = None + + if "tgt" in dataset_config: + tgt_dataset = indexed_dataset( + dataset_config["tgt"], self.tgt_dictionary + ) + else: + tgt_dataset = None + + dataset = LanguagePairDataset( + src_dataset, + src_dataset.sizes, + self.src_dictionary, + tgt_dataset, + tgt_dataset.sizes, + self.tgt_dictionary, + left_pad_source=self.args.left_pad_source, + left_pad_target=self.args.left_pad_target, + ) + + if pair_datasets_key in init_pair_datasets: + logger.warning( + f"Ignoring already added {pair_datasets_key}. " + f"Consider using `sample` key in order to upsample." + ) + else: + init_pair_datasets[pair_datasets_key] = { + "dataset": dataset, + "sample": dataset_config.get("sample", None), + "id": dataset_config.get("id", None), + "len": len(dataset), + } + + length_sum = 0 + weighted_freqs_sum = 0 + freq_per_dataset = {} + vmax = 0 + vmin = 1 + weighted_freq_per_dataset = {} + + if self.args.weighting_alpha: + for key in init_pair_datasets: + if init_pair_datasets[key]["sample"] is None: + length_sum += len(init_pair_datasets[key]["dataset"]) + + for key in init_pair_datasets: + if init_pair_datasets[key]["sample"] is None: + val = float(init_pair_datasets[key]["len"]) / length_sum + freq_per_dataset[key] = val + weighted_freqs_sum += val ** self.args.weighting_alpha + + for key in freq_per_dataset: + val = ( + freq_per_dataset[key] ** self.args.weighting_alpha + / weighted_freqs_sum + ) + vmin = min(vmin, val) + vmax = max(vmax, val) + weighted_freq_per_dataset[key] = val + + for pair_datasets_key in init_pair_datasets: + dataset_config = init_pair_datasets[pair_datasets_key] + dataset = dataset_config["dataset"] + sample = dataset_config["sample"] + if sample is None: + sample = 1.0 + + if pair_datasets_key in weighted_freq_per_dataset: + w = vmax / weighted_freq_per_dataset[pair_datasets_key] + sample = w + + sample = round(sample) + + initial_sample = sample + initial_pair_datasets_key = pair_datasets_key + + while sample >= 1.0: + assert ( + pair_datasets_key not in pair_datasets + ), f"{pair_datasets_key} already in" + size_sum_with_subsampling += len(dataset) + pair_datasets[pair_datasets_key] = MultitaskDatasetWrapper( + dataset, dataset_config.get("id", 0), 1.0, name=pair_datasets_key + ) + size_sum += len(dataset) + sample -= 1.0 + pair_datasets_key += "-up" + + assert sample < 1e-6, f"sample remains > 0 {pair_datasets_key}" + + logger.info( + f"added pair {initial_pair_datasets_key} length {len(dataset)} new_length = {len(dataset)*initial_sample}" + ) + size_by_corpus[corpus_name] += len(dataset) + + self.datasets[split] = pair_datasets + logger.info( + f"Datasets number = {len(self.datasets[split])} size = {size_sum} size_sum_with_subsampling = {size_sum_with_subsampling}" + ) + + @property + def source_dictionary(self): + return self.src_dictionary + + @property + def target_dictionary(self): + return self.tgt_dictionary + + def get_batch_iterator( + self, + dataset, + max_tokens=None, + max_sentences=None, + max_positions=None, + ignore_invalid_inputs=False, + required_batch_size_multiple=1, + seed=1, + num_shards=1, + shard_id=0, + num_workers=0, + epoch=1, + data_buffer_size=0, + disable_iterator_cache=False, + ): + + assert isinstance(dataset, OrderedDict) + assert len(dataset) + assert isinstance(dataset[next(iter(dataset))], FairseqDataset) + + # initialize the dataset with the correct starting epoch + for _, dt in dataset.items(): + dt.set_epoch(epoch) + + indices = OrderedDict() + batch_sampler = OrderedDict() + + with data_utils.numpy_seed(seed + epoch): + for key, dt in dataset.items(): + logger.info(f"\t ordered_indices {key}") + indices[key] = dt.ordered_indices() + + # filter examples that are too large + if max_positions is not None: + for key, dt in dataset.items(): + logger.info(f"\t filter_by_size {key}") + indices[key], ignored = dt.filter_indices_by_size( + indices[key], max_positions + ) + + for key, dt in dataset.items(): + logger.info(f"\t batch_by_size {key}") + batch_sampler[key] = data_utils.batch_by_size( + indices[key], + dt.num_tokens, + max_tokens=max_tokens, + max_sentences=max_sentences, + required_batch_size_multiple=required_batch_size_multiple, + ) + + epoch_iter = MultidatasetEpochBatchIterator( + dataset=dataset, + batch_sampler=batch_sampler, + seed=seed, + num_shards=num_shards, + shard_id=shard_id, + num_workers=num_workers, + epoch=epoch, + ) + + return epoch_iter diff --git a/examples/laser/laser_src/laser_transformer.py b/examples/laser/laser_src/laser_transformer.py new file mode 100644 index 0000000000..0be030994f --- /dev/null +++ b/examples/laser/laser_src/laser_transformer.py @@ -0,0 +1,354 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging + +from typing import Any, Dict, List, Optional +from torch import Tensor + +import torch +import torch.nn as nn + +from fairseq.models import ( + FairseqEncoderDecoderModel, + register_model, + register_model_architecture, +) +from fairseq.models.transformer import ( + base_architecture, + Embedding, + TransformerModel, + TransformerEncoder, + TransformerDecoder, +) +from fairseq.modules import ( + TransformerDecoderLayer, +) + +logger = logging.getLogger(__name__) + + +@register_model("laser_transformer") +class LaserTransformerModel(FairseqEncoderDecoderModel): + """Train Transformer for LASER task + + Requires --task laser + """ + + def __init__(self, encoder, decoder): + super().__init__(encoder, decoder) + + def forward( + self, + src_tokens, + src_lengths, + prev_output_tokens=None, + tgt_tokens=None, + tgt_lengths=None, + target_language_id=-1, + dataset_name="", + ): + laser_encoder_out = self.encoder(src_tokens, src_lengths) + return self.decoder( + prev_output_tokens, laser_encoder_out, lang_id=target_language_id + ) + + @staticmethod + def add_args(parser): + """Add model-specific arguments to the parser.""" + TransformerModel.add_args(parser) + parser.add_argument( + "--decoder-lang-embed-dim", + type=int, + metavar="N", + help="decoder language embedding dimension", + ) + + @classmethod + def build_model(cls, args, task): + base_laser_transformer_architecture(args) + + num_langs = task.num_tasks if hasattr(task, "num_tasks") else 0 + + def load_embed_tokens(dictionary, embed_dim): + num_embeddings = len(dictionary) + padding_idx = dictionary.pad() + + return Embedding(num_embeddings, embed_dim, padding_idx) + + encoder_embed_tokens = load_embed_tokens( + task.source_dictionary, args.encoder_embed_dim + ) + decoder_embed_tokens = load_embed_tokens( + task.target_dictionary, args.decoder_embed_dim + ) + num_langs = task.num_tasks if hasattr(task, "num_tasks") else 0 + + encoder = LaserTransformerEncoder( + args, task.source_dictionary, encoder_embed_tokens + ) + + decoder = LaserTransformerDecoder( + args, + task.target_dictionary, + decoder_embed_tokens, + num_langs=num_langs, + lang_embed_dim=args.decoder_lang_embed_dim, + ) + + return cls(encoder, decoder) + + +class LaserTransformerEncoder(TransformerEncoder): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def forward(self, src_tokens, *args, **kwargs): + encoder_out = super().forward(src_tokens, *args, **kwargs) + + x = encoder_out["encoder_out"][0] # T x B x C + padding_mask = src_tokens.eq(self.padding_idx).t().unsqueeze(-1) + + if padding_mask.any(): + x = x.float().masked_fill_(padding_mask, float("-inf")).type_as(x) + + # Build the sentence embedding by max-pooling over the encoder outputs + sentemb = x.max(dim=0)[0] + + # The Pytorch Mobile lite interpreter does not supports returning NamedTuple in + # `foward` so we use a dictionary instead. + # TorchScript does not support mixed values so the values are all lists. + # The empty list is equivalent to None. + return {"sentemb": [sentemb]} # B x C + + @torch.jit.export + def reorder_encoder_out(self, encoder_out: Dict[str, List[Tensor]], new_order): + """ + Same as the one in transformer.py, with new_sentemb + """ + if len(encoder_out["sentemb"]) == 0: + new_sentemb = [] + else: + new_sentemb = [encoder_out["sentemb"][0].index_select(0, new_order)] + + return { + "sentemb": new_sentemb, # B x C + } + + +class LaserTransformerDecoder(TransformerDecoder): + def __init__(self, args, dictionary, *kargs, **kwargs): + self.num_langs = kwargs.get("num_langs", 1) + self.lang_embed_dim = kwargs.get("lang_embed_dim", 0) + kwargs.pop("num_langs", None) + kwargs.pop("lang_embed_dim", None) + + super().__init__(args, dictionary, *kargs, **kwargs, no_encoder_attn=True) + + if self.lang_embed_dim == 0: + self.embed_lang = None + else: + self.embed_lang = nn.Embedding(self.num_langs, self.lang_embed_dim) + nn.init.uniform_(self.embed_lang.weight, -0.1, 0.1) + + if self.output_projection is not None: + laser_output_embed_dim = ( + self.output_embed_dim + self.lang_embed_dim + args.encoder_embed_dim + ) + self.output_projection = nn.Linear( + laser_output_embed_dim, len(dictionary), bias=False + ) + nn.init.normal_( + self.output_projection.weight, + mean=0, + std=laser_output_embed_dim ** -0.5, + ) + + def build_decoder_layer(self, args, no_encoder_attn=False): + decoder_embed_dim = args.decoder_embed_dim + args.decoder_embed_dim = ( + decoder_embed_dim + self.lang_embed_dim + args.encoder_embed_dim + ) + res = TransformerDecoderLayer(args, no_encoder_attn=True) + args.decoder_embed_dim = decoder_embed_dim + + return res + + def extract_features( + self, + prev_output_tokens, + encoder_out: Optional[Dict[str, List[Tensor]]], + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, + full_context_alignment: bool = False, + alignment_layer: Optional[int] = None, + alignment_heads: Optional[int] = None, + lang_id: Optional[int] = None, + ): + """ + Similar to *forward* but only return features. + + Includes several features from "Jointly Learning to Align and + Translate with Transformer Models" (Garg et al., EMNLP 2019). + + Args: + full_context_alignment (bool, optional): don't apply + auto-regressive mask to self-attention (default: False). + alignment_layer (int, optional): return mean alignment over + heads at this layer (default: last layer). + alignment_heads (int, optional): only average alignment over + this many heads (default: all heads). + + Returns: + tuple: + - the decoder's features of shape `(batch, tgt_len, embed_dim)` + - a dictionary with any model-specific outputs + """ + if alignment_layer is None: + alignment_layer = self.num_layers - 1 + + # embed positions + positions = ( + self.embed_positions( + prev_output_tokens, incremental_state=incremental_state + ) + if self.embed_positions is not None + else None + ) + + if incremental_state is not None: + prev_output_tokens = prev_output_tokens[:, -1:] + if positions is not None: + positions = positions[:, -1:] + + bsz, seqlen = prev_output_tokens.size() + + # embed tokens and positions + x = self.embed_scale * self.embed_tokens(prev_output_tokens) + + if self.quant_noise is not None: + x = self.quant_noise(x) + + if self.project_in_dim is not None: + x = self.project_in_dim(x) + + if positions is not None: + x += positions + + if self.layernorm_embedding is not None: + x = self.layernorm_embedding(x) + + x = self.dropout_module(x) + + # B x T x C -> T x B x C + x = x.transpose(0, 1) + + if self.embed_lang is not None: + lang_ids = prev_output_tokens.data.new_full((bsz,), lang_id) + langemb = self.embed_lang(lang_ids) + langemb = langemb.unsqueeze(0) + repeat_vals = [x.shape[0] // langemb.shape[0]] + [-1] * ( + len(langemb.shape) - 1 + ) + x = torch.cat((x, langemb.expand(*repeat_vals)), dim=-1) + + sentemb = encoder_out["sentemb"][0] + sentemb = sentemb.unsqueeze(0) + + repeat_vals = [x.shape[0] // sentemb.shape[0]] + [-1] * (len(sentemb.shape) - 1) + x = torch.cat((x, sentemb.expand(*repeat_vals)), dim=-1) + + self_attn_padding_mask: Optional[Tensor] = None + if self.cross_self_attention or prev_output_tokens.eq(self.padding_idx).any(): + self_attn_padding_mask = prev_output_tokens.eq(self.padding_idx) + + # decoder layers + attn: Optional[Tensor] = None + inner_states: List[Optional[Tensor]] = [x] + for idx, layer in enumerate(self.layers): + if incremental_state is None and not full_context_alignment: + self_attn_mask = self.buffered_future_mask(x) + else: + self_attn_mask = None + + x, layer_attn, _ = layer( + x, + None, + None, + incremental_state, + self_attn_mask=self_attn_mask, + self_attn_padding_mask=self_attn_padding_mask, + need_attn=bool((idx == alignment_layer)), + need_head_weights=bool((idx == alignment_layer)), + ) + inner_states.append(x) + if layer_attn is not None and idx == alignment_layer: + attn = layer_attn.float().to(x) + + if attn is not None: + if alignment_heads is not None: + attn = attn[:alignment_heads] + + # average probabilities over heads + attn = attn.mean(dim=0) + + if self.layer_norm is not None: + x = self.layer_norm(x) + + # T x B x C -> B x T x C + x = x.transpose(0, 1) + + if self.project_out_dim is not None: + x = self.project_out_dim(x) + + return x, {"attn": [attn], "inner_states": inner_states} + + def forward( + self, + prev_output_tokens, + encoder_out: Optional[Dict[str, List[Tensor]]] = None, + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, + features_only: bool = False, + alignment_layer: Optional[int] = None, + alignment_heads: Optional[int] = None, + src_lengths: Optional[Any] = None, + return_all_hiddens: bool = False, + lang_id: Optional[int] = None, + ): + """ + Args: + prev_output_tokens (LongTensor): previous decoder outputs of shape + `(batch, tgt_len)`, for teacher forcing + encoder_out (optional): output from the encoder, used for + encoder-side attention + incremental_state (dict): dictionary used for storing state during + :ref:`Incremental decoding` + features_only (bool, optional): only return features without + applying output layer (default: False). + + Returns: + tuple: + - the decoder's output of shape `(batch, tgt_len, vocab)` + - a dictionary with any model-specific outputs + """ + + assert lang_id is not None + + x, extra = self.extract_features( + prev_output_tokens, + encoder_out=encoder_out, + incremental_state=incremental_state, + alignment_layer=alignment_layer, + alignment_heads=alignment_heads, + lang_id=lang_id, + ) + if not features_only: + x = self.output_layer(x) + return x, extra + + +@register_model_architecture("laser_transformer", "laser_transformer") +def base_laser_transformer_architecture(args): + base_architecture(args) + args.decoder_lang_embed_dim = getattr(args, "decoder_lang_embed_dim", 0) diff --git a/examples/laser/laser_src/multitask_data_utils.py b/examples/laser/laser_src/multitask_data_utils.py new file mode 100644 index 0000000000..b05caea267 --- /dev/null +++ b/examples/laser/laser_src/multitask_data_utils.py @@ -0,0 +1,143 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from collections import OrderedDict + +import numpy as np + +from fairseq.data import BaseWrapperDataset, FairseqDataset, iterators + + +class MultiItr(object): + def __init__(self, itr): + self.itr = itr + self._counts = [0 for x in itr] + + def __len__(self): + return sum(len(itr) for itr in self.itr) + + def __iter__(self): + return self + + def __next__(self): + ratios = [count / len(itr) for count, itr in zip(self._counts, self.itr)] + idx = ratios.index(min(ratios)) + self._counts[idx] += 1 + return next(self.itr[idx]) + + +class MultidatasetEpochBatchIterator(iterators.EpochBatchIterating): + """A wrapper around multiple epoch batch iterators.""" + + def __init__( + self, + dataset, + batch_sampler, + seed=1, + num_shards=1, + shard_id=0, + num_workers=0, + epoch=1, + ): + + assert isinstance(dataset, OrderedDict) + assert len(dataset) + assert isinstance(dataset[next(iter(dataset))], FairseqDataset) + + self.iterators = [] + + self.epoch = epoch + for key, dt in dataset.items(): + epoch_iter = iterators.EpochBatchIterator( + dataset=dt, + collate_fn=dt.collater, + batch_sampler=batch_sampler[key], + seed=seed, + num_shards=num_shards, + shard_id=shard_id, + num_workers=0, + epoch=epoch, + ) + self.iterators.append(epoch_iter) + + def __len__(self): + return sum(len(itr) for itr in self.iterators) + + def next_epoch_itr(self, shuffle=True, fix_batches_to_gpus=False): + # `self.epoch += 1` should be handled by underlying `EpochBatchIterator`s. + return MultiItr( + [ + itr.next_epoch_itr( + shuffle=shuffle, fix_batches_to_gpus=fix_batches_to_gpus + ) + for itr in self.iterators + ] + ) + + def end_of_epoch(self): + return all(itr.end_of_epoch() for itr in self.iterators) + + @property + def next_epoch_idx(self): + """Return the epoch index after *next_epoch_itr* is called.""" + + epochs = [itr.next_epoch_idx for itr in self.iterators] + self.epoch = epochs[0] + assert all(epoch == self.epoch for epoch in epochs) + + return self.epoch + + @property + def iterations_in_epoch(self): + return sum(itr.iterations_in_epoch for itr in self.iterators) + + def state_dict(self): + return { + "iterators": [it.state_dict() for it in self.iterators], + "epoch": self.epoch, + } + + def load_state_dict(self, state_dict): + self.epoch = state_dict["epoch"] + for it, d in zip(self.iterators, state_dict["iterators"]): + it.load_state_dict(d) + + +class MultitaskDatasetWrapper(BaseWrapperDataset): + """A wrapper for a multitask dataset.""" + + def __init__(self, dataset, target_language_id, sample=1.0, name=""): + super().__init__(dataset) + self.target_language_id = target_language_id + self.sample = sample + self.name = name + + def collater(self, *args, **kwargs): + ans = self.dataset.collater(*args, **kwargs) + if "net_input" in ans: + ans["net_input"]["target_language_id"] = self.target_language_id + ans["net_input"]["dataset_name"] = self.name + return ans + + def num_tokens(self, *args, **kwargs): + return self.dataset.num_tokens(*args, **kwargs) + + def ordered_indices(self, *args, **kwargs): + indices = self.dataset.ordered_indices(*args, **kwargs) + # Hacky solution for sampling + size = int(self.sample * indices.shape[0]) + + return indices.take(np.sort(np.random.permutation(indices.shape[0])[:size])) + + def size(self, index: int): + return self.dataset.size(index) + + @property + def supports_prefetch(self): + """Whether this dataset supports prefetching.""" + return getattr(self.dataset, "supports_prefetch", False) + + def prefetch(self, indices): + return self.dataset.prefetch(indices) diff --git a/examples/latent_depth/README.md b/examples/latent_depth/README.md new file mode 100644 index 0000000000..7774c33305 --- /dev/null +++ b/examples/latent_depth/README.md @@ -0,0 +1,77 @@ +# Deep Transformers with Latent Depth (Li et al., 2020) + +[https://arxiv.org/abs/2009.13102](https://arxiv.org/abs/2009.13102). + +## Introduction + +We present a probabilistic framework to automatically learn which layer(s) to use by learning the posterior distributions of layer selection. As an extension of this framework, we propose a novel method to train one shared Transformer network for multilingual machine translation with different layer selection posteriors for each language pair. + +## Training a multilingual model with latent depth + +Below is an example of training with latent depth in decoder for one-to-many (O2M) related languages. We use the same preprocessed (numberized and binarized) TED8 dataset as in [Balancing Training for Multilingual Neural Machine Translation (Wang et al., 2020)](https://github.com/cindyxinyiwang/multiDDS), which could be generated by [the script](https://github.com/cindyxinyiwang/multiDDS/blob/multiDDS/util_scripts/prepare_multilingual_data.sh) the author provided. +```bash +lang_pairs_str="eng-aze,eng-bel,eng-ces,eng-glg,eng-por,eng-rus,eng-slk,eng-tur" +databin_dir= + +fairseq-train ${databin_dir} \ + --user-dir examples/latent_depth/latent_depth_src \ + --lang-pairs "${lang_pairs_str}" \ + --arch multilingual_transformer_iwslt_de_en \ + --task multilingual_translation_latent_depth \ + --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \ + --share-encoders \ + --share-decoders \ + --decoder-langtok \ + --share-decoder-input-output-embed \ + --dropout 0.3 --attention-dropout 0.3 \ + --optimizer adam --adam-eps 1e-06 --adam-betas '(0.9, 0.98)' \ + --lr-scheduler inverse_sqrt --stop-min-lr 1e-9 --warmup-init-lr 1e-7 --warmup-updates 8000 \ + --max-tokens 4096 --update-freq 1 \ + --lr 0.0015 \ + --clip-norm 1.0 \ + --seed 2 \ + --ddp-backend=legacy_ddp \ + --encoder-layers 12 \ + --decoder-layers 24 \ + --decoder-latent-layer \ + --sparsity-weight 0.1 \ + --anneal-updates 5000 \ + --soft-update 500 \ + --target-layers 12 \ + --share-weight 0.1 +``` +## Inference command + +```bash +lang_pairs_str="eng-aze,eng-bel,eng-ces,eng-glg,eng-por,eng-rus,eng-slk,eng-tur" +databin_dir= +model_path= +src_lang= +tgt_lang= +gen_data= + +fairseq-generate ${databin_dir} \ + --path ${model_path} \ + --task multilingual_translation_latent_depth \ + --decoder-latent-layer \ + --lang-pairs "${lang_pairs_str}" \ + -s ${src_lang} -t ${tgt_lang} \ + --gen-subset $gen_data \ + --scoring sacrebleu \ + --remove-bpe 'sentencepiece' \ + --lenpen 1.0 \ + --beam 5 \ + --decoder-langtok \ + --max-tokens 4096 +``` + + +## Citation +```bibtex +@article{li2020deep, + title={Deep Transformers with Latent Depth}, + author={Li, Xian and Stickland, Asa Cooper and Tang, Yuqing and Kong, Xiang}, + journal={arXiv preprint arXiv:2009.13102}, + year={2020} +} +``` diff --git a/examples/latent_depth/latent_depth_src/__init__.py b/examples/latent_depth/latent_depth_src/__init__.py new file mode 100644 index 0000000000..c5fa76039f --- /dev/null +++ b/examples/latent_depth/latent_depth_src/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from . import multilingual_translation_latent_depth # noqa +from .loss import latent_depth # noqa +from .models import latent_multilingual_transformer # noqa +from .modules import latent_layers # noqa diff --git a/examples/latent_depth/latent_depth_src/loss/__init__.py b/examples/latent_depth/latent_depth_src/loss/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/examples/latent_depth/latent_depth_src/loss/latent_depth.py b/examples/latent_depth/latent_depth_src/loss/latent_depth.py new file mode 100644 index 0000000000..a3b9535eca --- /dev/null +++ b/examples/latent_depth/latent_depth_src/loss/latent_depth.py @@ -0,0 +1,99 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math + +import torch +from torch.nn.modules.loss import _Loss + + +class LatentLayersKLLoss(_Loss): + def __init__(self, args): + super().__init__() + self.args = args + + def forward(self, layer_samples, lang_idx, update_num, sample_size): + prior = self.args.prior + samples = layer_samples[lang_idx] + eps = 1e-7 + if prior == "uniform": + # uniform prior + kl_loss = (samples * (torch.log(samples + eps) - math.log(0.5))).sum(-1) + elif prior == "agged_posterior": + # aggregated posterior + y_t = torch.stack([x.detach() for x in layer_samples], dim=0) + agged_q = torch.sum(y_t, dim=0) + row_norm = agged_q.sum(-1) + normed_agg_q = agged_q / row_norm + kl_loss = ( + samples * (torch.log(samples + eps) - torch.log(normed_agg_q + eps)) + ).sum(-1) + else: + raise NotImplementedError("The specified prior is not implemented.") + + # normalized by number of layers + kl_loss /= layer_samples[0].size()[0] + kl_weight = min( + self.args.sparsity_weight, + (update_num - self.args.soft_update) + * self.args.sparsity_weight + / self.args.anneal_updates, + ) + kl_loss *= kl_weight * sample_size + return kl_loss + + +class LatentLayersSparsityLoss(_Loss): + def __init__(self, args): + super().__init__() + self.args = args + + def is_valid(self, update_num): + if self.args.target_layers <= 0: + return False + return update_num > (self.args.soft_update + self.args.anneal_updates) + + def forward(self, layer_samples_list, update_num, sample_size): + batch_loss = 0 + share_loss = 0 + global_sparsity_loss = 0 + layer_samples = torch.stack(layer_samples_list, dim=0) + if ( + self.args.target_layers > 0 or self.args.share_weight > 0 + ) and update_num > (self.args.soft_update + self.args.anneal_updates): + # anneal sparsity weight + if update_num < (self.args.anneal_updates + self.args.soft_update): + weight_anneal = 0 + elif update_num < (2 * self.args.anneal_updates + self.args.soft_update): + weight_anneal = ( + (update_num - self.args.soft_update - self.args.anneal_updates) + * self.args.share_weight + / self.args.anneal_updates + ) + else: + weight_anneal = 1 + # compute ratio among languages + layer_utilization = torch.sum(layer_samples, dim=0) + layer_utilization /= layer_samples.size()[0] + if self.args.share_weight > 0: + # encouraging sharing across languages + share_loss = sum( + -1.0 * v * math.log(v) for v in layer_utilization if v > 0 + ) + batch_loss += ( + weight_anneal * self.args.share_weight * sample_size * share_loss + ) + if self.args.target_layers > 0: + # computed expected number of layers selected + expeted_layers = sum(layer_utilization) + # compute l2 loss wrt target number of layers + global_sparsity_loss = (expeted_layers - self.args.target_layers) ** 2 + batch_loss += ( + weight_anneal + * self.args.share_weight + * sample_size + * global_sparsity_loss + ) + return batch_loss diff --git a/examples/latent_depth/latent_depth_src/models/__init__.py b/examples/latent_depth/latent_depth_src/models/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/examples/latent_depth/latent_depth_src/models/latent_multilingual_transformer.py b/examples/latent_depth/latent_depth_src/models/latent_multilingual_transformer.py new file mode 100644 index 0000000000..12b7e67d03 --- /dev/null +++ b/examples/latent_depth/latent_depth_src/models/latent_multilingual_transformer.py @@ -0,0 +1,75 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from fairseq.models import register_model, register_model_architecture +from fairseq.models.multilingual_transformer import MultilingualTransformerModel +from fairseq.models.transformer import ( + TransformerDecoder, + TransformerEncoder, + base_architecture, +) + +from .latent_transformer import LatentTransformerDecoder, LatentTransformerEncoder + + +@register_model("latent_multilingual_transformer") +class LatentMultilingualTransformerModel(MultilingualTransformerModel): + """A variant of standard multilingual Transformer models which encoder and/or + decoders supports latent depth, as is in "Deep Transformer with Latent Depth" + (https://arxiv.org/abs/2009.13102). + """ + + @staticmethod + def add_args(parser): + """Add model-specific arguments to the parser.""" + MultilingualTransformerModel.add_args(parser) + parser.add_argument( + '--soft-select', + action='store_true', + help='use soft samples in training an inference', + ) + parser.add_argument( + '--sampling-tau', + type=float, + default=5., + help='sampling temperature', + ) + + @classmethod + def _get_module_class(cls, is_encoder, args, lang_dict, embed_tokens, langs): + if is_encoder: + if hasattr(args, "encoder_latent_layer") and args.encoder_latent_layer: + return LatentTransformerEncoder( + args, lang_dict, embed_tokens, num_logits=len(langs) + ) + else: + return TransformerEncoder(args, lang_dict, embed_tokens) + else: + if hasattr(args, "decoder_latent_layer") and args.decoder_latent_layer: + return LatentTransformerDecoder( + args, lang_dict, embed_tokens, num_logits=len(langs) + ) + else: + return TransformerDecoder(args, lang_dict, embed_tokens) + + +@register_model_architecture( + "latent_multilingual_transformer", "latent_multilingual_transformer" +) +def latent_multilingual_architecture(args): + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 1024) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 4) + args.encoder_layers = getattr(args, "encoder_layers", 12) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512) + args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 1024) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 4) + args.decoder_layers = getattr(args, "decoder_layers", 24) + args.share_encoders = getattr(args, "share_encoders", True) + args.share_decoders = getattr(args, "share_decoders", True) + args.share_encoder_embeddings = getattr(args, "share_encoder_embeddings", True) + args.share_decoder_embeddings = getattr(args, "share_decoder_embeddings", True) + + base_architecture(args) diff --git a/examples/latent_depth/latent_depth_src/models/latent_transformer.py b/examples/latent_depth/latent_depth_src/models/latent_transformer.py new file mode 100644 index 0000000000..6a825301a4 --- /dev/null +++ b/examples/latent_depth/latent_depth_src/models/latent_transformer.py @@ -0,0 +1,156 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from typing import Any, Dict, Optional + +import torch.nn as nn +from fairseq.models.fairseq_encoder import EncoderOut +from fairseq.models.transformer import TransformerDecoder, TransformerEncoder +from fairseq.modules import TransformerDecoderLayer, TransformerEncoderLayer +from torch import Tensor + +from ..modules.latent_layers import LayerSelect + + +class LatentTransformerEncoder(TransformerEncoder): + """Latent depth (https://arxiv.org/abs/2009.13102) implemented in + TransformerEncoder. + """ + + def __init__(self, args, dictionary, embed_tokens, num_logits=1): + self.num_logits = num_logits + self.num_layers = args.encoder_layers + super().__init__(args, dictionary, embed_tokens) + self.layer_select = LayerSelect( + num_layers=self.num_layers, + num_logits=self.num_logits, + soft_select=getattr(args, "soft_select", False), + sampling_tau=getattr(args, "sampling_tau", 5.), + ) + self.lang_idx = None + self.layers = nn.ModuleList( + [self._build_encoder_layer(args, idx) for idx in range(args.encoder_layers)] + ) + + def set_lang_idx(self, lang_idx): + self.lang_idx = lang_idx + + def _build_encoder_layer(self, args, idx=None): + return LatentTransformerEncoderLayer(args, idx, layer_select=self.layer_select) + + def forward(self, src_tokens, src_lengths, return_all_hiddens: bool = False): + self.layer_select.sample(self.lang_idx) + return super().forward(src_tokens, src_lengths, return_all_hiddens) + + +class LatentTransformerEncoderLayer(TransformerEncoderLayer): + """Encoder layer with each (non_residual) block weighted by samples of Bernouli + or Gumbel Signmoid samples. + + Args: + args (argparse.Namespace): parsed command-line arguments from standard + TransformerEncoderLayer. + idx (int): layer index (used to retrieve samples). + layer_select (LayerSelect, optional): instance of LayerSelect module with logits + parameters and sampling method. + """ + + def __init__(self, args, idx, layer_select=None): + super().__init__(args) + self.idx = idx + self.layer_select = layer_select + + def residual_connection(self, x, residual): + return residual + x * self.layer_select(self.idx) + + +class LatentTransformerDecoder(TransformerDecoder): + """Latent depth (https://arxiv.org/abs/2009.13102) implemented in + TransformerDecoder. + """ + + def __init__( + self, args, dictionary, embed_tokens, no_encoder_attn=False, num_logits=1 + ): + self.num_logits = num_logits + self.num_layers = args.decoder_layers + super().__init__( + args, dictionary, embed_tokens, no_encoder_attn=no_encoder_attn + ) + self.layer_select = LayerSelect( + num_layers=self.num_layers, + num_logits=self.num_logits, + soft_select=getattr(args, "soft_select", False), + sampling_tau=getattr(args, "sampling_tau", 5.), + ) + self.lang_idx = None + self.layers = nn.ModuleList( + [ + self._build_decoder_layer(args, no_encoder_attn, idx) + for idx in range(args.decoder_layers) + ] + ) + + def set_lang_idx(self, lang_idx): + self.lang_idx = lang_idx + + def _build_decoder_layer(self, args, no_encoder_attn=False, idx=None): + return LatentTransformerDecoderLayer( + args, idx, layer_select=self.layer_select, no_encoder_attn=no_encoder_attn + ) + + def forward( + self, + prev_output_tokens, + encoder_out: Optional[EncoderOut] = None, + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, + features_only: bool = False, + alignment_layer: Optional[int] = None, + alignment_heads: Optional[int] = None, + src_lengths: Optional[Any] = None, + return_all_hiddens: bool = False, + ): + self.layer_select.sample(self.lang_idx) + return super().forward( + prev_output_tokens=prev_output_tokens, + encoder_out=encoder_out, + incremental_state=incremental_state, + features_only=features_only, + alignment_layer=alignment_layer, + src_lengths=src_lengths, + return_all_hiddens=return_all_hiddens, + ) + + +class LatentTransformerDecoderLayer(TransformerDecoderLayer): + """Decoder layer with each (non_residual) block weighted by samples of Bernouli + or Gumbel Signmoid samples. + + Args: + args (argparse.Namespace): parsed command-line arguments from standard + TransformerDecoderLayer. + idx (int): layer index (used to retrieve samples). + layer_select (LayerSelect, optional): instance of LayerSelect module with logits + parameters and sampling method. + no_encoder_attn (bool, optional): whether to attend to encoder outputs + (default: False). + + """ + + def __init__( + self, + args, + idx, + layer_select=None, + no_encoder_attn=False, + add_bias_kv=False, + add_zero_attn=False, + ): + super().__init__(args, no_encoder_attn, add_bias_kv, add_zero_attn) + self.idx = idx + self.layer_select = layer_select + + def residual_connection(self, x, residual): + return residual + x * self.layer_select(self.idx) diff --git a/examples/latent_depth/latent_depth_src/modules/__init__.py b/examples/latent_depth/latent_depth_src/modules/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/examples/latent_depth/latent_depth_src/modules/latent_layers.py b/examples/latent_depth/latent_depth_src/modules/latent_layers.py new file mode 100644 index 0000000000..2be05d5535 --- /dev/null +++ b/examples/latent_depth/latent_depth_src/modules/latent_layers.py @@ -0,0 +1,75 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn as nn + + +class LayerSelect(nn.Module): + """Compute samples (from a Gumbel-Sigmoid distribution) which is used as + either (soft) weighting or (hard) selection of residual connection. + https://arxiv.org/abs/2009.13102 + """ + def __init__(self, num_layers, num_logits, soft_select=False, sampling_tau=5.): + super(LayerSelect, self).__init__() + self.layer_logits = torch.nn.Parameter( + torch.Tensor(num_logits, num_layers), + requires_grad=True, + ) + self.hard_select = not soft_select + self.tau = sampling_tau + self.detach_grad = False + self.layer_samples = [None] * num_logits + + def sample(self, logit_idx): + """To leverage the efficiency of distributed training, samples for all + layers are computed at once for each logit_idx. Logits are parameters + learnt independent of each other. + + Args: + logit_idx: The index of logit parameters used for sampling. + """ + assert logit_idx is not None + self.samples = self._gumbel_sigmoid( + self.layer_logits[logit_idx, :].detach() + if self.detach_grad + else self.layer_logits[logit_idx, :], + dim=-1, + tau=self.tau, + hard=self.hard_select, + ) + self.layer_samples[logit_idx] = self.samples + + def forward(self, i): + sample = self.samples[i] + return sample + + def _gumbel_sigmoid( + self, logits, tau=1, hard=False, eps=1e-10, dim=-1, threshold=0.5 + ): + # ~Gumbel(0,1) + gumbels1 = ( + -torch.empty_like(logits, memory_format=torch.legacy_contiguous_format) + .exponential_() + .log() + ) + gumbels2 = ( + -torch.empty_like(logits, memory_format=torch.legacy_contiguous_format) + .exponential_() + .log() + ) + # Difference of two gumbels because we apply a sigmoid + gumbels1 = (logits + gumbels1 - gumbels2) / tau + y_soft = gumbels1.sigmoid() + if hard: + # Straight through. + y_hard = torch.zeros_like( + logits, memory_format=torch.legacy_contiguous_format + ).masked_fill(y_soft > threshold, 1.0) + ret = y_hard - y_soft.detach() + y_soft + else: + # Reparametrization trick. + ret = y_soft + return ret diff --git a/examples/latent_depth/latent_depth_src/multilingual_translation_latent_depth.py b/examples/latent_depth/latent_depth_src/multilingual_translation_latent_depth.py new file mode 100644 index 0000000000..b5cd51a470 --- /dev/null +++ b/examples/latent_depth/latent_depth_src/multilingual_translation_latent_depth.py @@ -0,0 +1,194 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from fairseq.tasks import register_task +from fairseq.tasks.multilingual_translation import MultilingualTranslationTask + +from .loss.latent_depth import LatentLayersKLLoss, LatentLayersSparsityLoss + + +@register_task("multilingual_translation_latent_depth") +class MultilingualTranslationTaskLatentDepth(MultilingualTranslationTask): + """A task for multiple translation with latent depth. + + See `"Deep Transformer with Latent Depth" + (Li et al., 2020) `_. + """ + + @staticmethod + def add_args(parser): + """Add task-specific arguments to the parser.""" + # fmt: off + MultilingualTranslationTask.add_args(parser) + parser.add_argument('--encoder-latent-layer', action='store_true', help='latent layer selection in encoder') + parser.add_argument('--decoder-latent-layer', action='store_true', help='latent layer selection in decoder') + parser.add_argument('--target-layers', default=-1, type=int, + help='number of effective layers to learn; -1 means no constraint') + parser.add_argument('--sparsity-weight', default=0.0, type=float, + help='weight for sparsity loss') + parser.add_argument('--share-weight', default=0.0, type=float, + help='weight for sharing loss') + parser.add_argument('--soft-update', default=1, type=int, + help='number of updates with soft sampling') + parser.add_argument('--anneal-updates', default=1, type=int, + help='number of updates to anneal the KL loss weight') + parser.add_argument('--prior', default="uniform", type=str, + help='prior used for computing KL loss') + # fmt: on + + def __init__(self, args, dicts, training): + super().__init__(args, dicts, training) + self.src_langs, self.tgt_langs = zip( + *[(lang.split("-")[0], lang.split("-")[1]) for lang in args.lang_pairs] + ) + if self.training and self.encoder_latent_layer: + assert self.args.share_encoders + if self.training and self.decoder_latent_layer: + assert self.args.share_decoders + if training or self.encoder_latent_layer or self.decoder_latent_layer: + self.lang_pairs = args.lang_pairs + else: + self.lang_pairs = ["{}-{}".format(args.source_lang, args.target_lang)] + self.eval_lang_pairs = self.lang_pairs + self.model_lang_pairs = self.lang_pairs + if self.training and (self.encoder_latent_layer or self.decoder_latent_layer): + self.kl_loss = LatentLayersKLLoss(self.args) + self.sparsity_loss = LatentLayersSparsityLoss(self.args) + + def _per_lang_pair_train_loss( + self, lang_pair, model, update_num, criterion, sample, optimizer, ignore_grad + ): + src, tgt = lang_pair.split("-") + if self.encoder_latent_layer: + src_lang_idx = self.src_lang_idx_dict[src] + model.models[lang_pair].encoder.set_lang_idx(src_lang_idx) + model.models[lang_pair].encoder.layer_select.hard_select = ( + update_num > self.args.soft_update + ) + if self.decoder_latent_layer: + tgt_lang_idx = self.tgt_lang_idx_dict[tgt] + model.models[lang_pair].decoder.set_lang_idx(tgt_lang_idx) + model.models[lang_pair].decoder.layer_select.hard_select = ( + update_num > self.args.soft_update + ) + + loss, sample_size, logging_output = criterion( + model.models[lang_pair], sample[lang_pair] + ) + if self.encoder_latent_layer: + none_samples = sum( + 1 if x is None else 0 + for x in model.models[lang_pair].encoder.layer_select.layer_samples + ) + if none_samples == 0 or self.args.prior != "agged_posterior": + loss += self.kl_loss( + model.models[lang_pair].encoder.layer_select.layer_samples, + src_lang_idx, + update_num, + sample_size, + ) + if self.decoder_latent_layer: + none_samples = sum( + 1 if x is None else 0 + for x in model.models[lang_pair].decoder.layer_select.layer_samples + ) + if none_samples == 0 or self.args.prior != "agged_posterior": + loss += self.kl_loss( + model.models[lang_pair].decoder.layer_select.layer_samples, + tgt_lang_idx, + update_num, + sample_size, + ) + if ignore_grad: + loss *= 0 + + if hasattr(self, "sparsity_loss") and self.sparsity_loss.is_valid(update_num): + # need to retain the graph if sparsity loss needs to be added + loss.backward(retain_graph=True) + else: + optimizer.backward(loss) + + return loss, sample_size, logging_output + + def train_step( + self, sample, model, criterion, optimizer, update_num, ignore_grad=False + ): + agg_loss, agg_sample_size, agg_logging_output = super().train_step( + sample, model, criterion, optimizer, update_num, ignore_grad + ) + # compute auxiliary loss from layere sparsity, based on all samples from all languages + if hasattr(self, "sparsity_loss") and self.sparsity_loss.is_valid(update_num): + sparsity_loss = 0 + if self.encoder_latent_layer: + sparsity_loss += self.sparsity_loss( + next( + iter(model.models.values()) + ).encoder.layer_select.layer_samples, + update_num, + agg_sample_size, + ) + if self.decoder_latent_layer: + sparsity_loss += self.sparsity_loss( + next( + iter(model.models.values()) + ).decoder.layer_select.layer_samples, + update_num, + agg_sample_size, + ) + if sparsity_loss > 0: + optimizer.backward(sparsity_loss) + return agg_loss, agg_sample_size, agg_logging_output + + def _per_lang_pair_valid_loss(self, lang_pair, model, criterion, sample): + src, tgt = lang_pair.split("-") + if self.encoder_latent_layer: + src_lang_idx = self.src_lang_idx_dict[src] + model.models[lang_pair].encoder.set_lang_idx(src_lang_idx) + if self.decoder_latent_layer: + tgt_lang_idx = self.tgt_lang_idx_dict[tgt] + model.models[lang_pair].decoder.set_lang_idx(tgt_lang_idx) + loss, sample_size, logging_output = criterion( + model.models[lang_pair], sample[lang_pair] + ) + return loss, sample_size, logging_output + + def inference_step( + self, generator, models, sample, prefix_tokens=None, constraints=None + ): + if self.encoder_latent_layer or self.decoder_latent_layer: + for model in models: + if self.encoder_latent_layer: + assert model.encoder.layer_select is not None + src_lang_idx = self.src_lang_idx_dict[self.args.source_lang] + model.encoder.set_lang_idx(src_lang_idx) + if self.decoder_latent_layer: + assert model.decoder.layer_select is not None + tgt_lang_idx = self.tgt_lang_idx_dict[self.args.target_lang] + model.decoder.set_lang_idx(tgt_lang_idx) + return super().inference_step( + generator, models, sample, prefix_tokens, constraints + ) + + @property + def encoder_latent_layer(self): + return ( + hasattr(self.args, "encoder_latent_layer") + and self.args.encoder_latent_layer + ) + + @property + def decoder_latent_layer(self): + return ( + hasattr(self.args, "decoder_latent_layer") + and self.args.decoder_latent_layer + ) + + @property + def src_lang_idx_dict(self): + return {lang: lang_idx for lang_idx, lang in enumerate(self.src_langs)} + + @property + def tgt_lang_idx_dict(self): + return {lang: lang_idx for lang_idx, lang in enumerate(self.tgt_langs)} diff --git a/examples/layerdrop/README.md b/examples/layerdrop/README.md index d7ede13642..4d48ee9615 100644 --- a/examples/layerdrop/README.md +++ b/examples/layerdrop/README.md @@ -26,7 +26,11 @@ Model | Description | Download Evaluate performance of these pre-trained models: ```bash # Example for Machine Translation -python generate.py /path/to/bped/wmt/data --path nmt_checkpoint.pt --lenpen 0.4 --batch-size 64 --remove-bpe --beam 8 --gen-subset test > wmt16_gen.txt +fairseq-generate /path/to/bped/wmt/data --path nmt_checkpoint.pt \ + --beam 8 --lenpen 0.4 \ + --batch-size 64 \ + --remove-bpe \ + --gen-subset test > wmt16_gen.txt bash scripts/compound_split_bleu.sh wmt16_gen.txt # prints BLEU4 = 30.17 ``` @@ -111,8 +115,10 @@ num. model params: 146163712 ``` If you would like to pick up training with a model that has been pruned, simply adding these flags is sufficient. If you would like to use a script that only does evaluation (no training), you may need to pass an override command. A specific example would be for language modeling: -``` -python eval_lm.py /path/to/wikitext-103 --path '/path/to/model/checkpoint' --model-overrides "{'decoder_layers_to_keep':'0,2,4,6,8,10,12,14'}" +```bash +fairseq-eval-lm /path/to/wikitext-103 \ + --path /path/to/model/checkpoint.pt \ + --model-overrides "{'decoder_layers_to_keep':'0,2,4,6,8,10,12,14'}" ``` This model override command overrides the training parameters and updates the model arguments so that the pruned model is run instead of the full model. @@ -120,9 +126,9 @@ This model override command overrides the training parameters and updates the mo Looking to reproduce the results in the paper? -1. For Translation on WMT16 en-de, we followed this setting [here](https://github.com/pytorch/fairseq/blob/master/examples/scaling_nmt/README.md) -2. To train RoBERTa, we followed this setting [here](https://github.com/pytorch/fairseq/tree/master/examples/roberta) -3. To train Language Models on Wikitext-103, we followed this setting [here](https://github.com/pytorch/fairseq/tree/master/examples/language_model) +1. For Translation on WMT16 en-de, we followed this setting [here](https://github.com/pytorch/fairseq/blob/main/examples/scaling_nmt/README.md) +2. To train RoBERTa, we followed this setting [here](https://github.com/pytorch/fairseq/tree/main/examples/roberta) +3. To train Language Models on Wikitext-103, we followed this setting [here](https://github.com/pytorch/fairseq/tree/main/examples/language_model) ## Tips diff --git a/examples/linformer/README.md b/examples/linformer/README.md new file mode 100644 index 0000000000..f8b36bc691 --- /dev/null +++ b/examples/linformer/README.md @@ -0,0 +1,22 @@ +# Linformer: Self-Attention with Linear Complexity (Wang et al., 2020) + +This example contains code to train Linformer models as described in our paper +[Linformer: Self-Attention with Linear Complexity](https://arxiv.org/abs/2006.04768). + +## Training a new Linformer RoBERTa model + +You can mostly follow the [RoBERTa pretraining README](/examples/roberta/README.pretraining.md), +updating your training command with `--user-dir examples/linformer/linformer_src --arch linformer_roberta_base`. + +## Citation + +If you use our work, please cite: + +```bibtex +@article{wang2020linformer, + title={Linformer: Self-Attention with Linear Complexity}, + author={Wang, Sinong and Li, Belinda and Khabsa, Madian and Fang, Han and Ma, Hao}, + journal={arXiv preprint arXiv:2006.04768}, + year={2020} +} +``` diff --git a/generate.py b/examples/linformer/linformer_src/__init__.py similarity index 61% rename from generate.py rename to examples/linformer/linformer_src/__init__.py index 67109c51b7..1c52f135ea 100644 --- a/generate.py +++ b/examples/linformer/linformer_src/__init__.py @@ -1,11 +1,6 @@ -#!/usr/bin/env python3 -u # Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. -from fairseq_cli.generate import cli_main - - -if __name__ == '__main__': - cli_main() +from .models import linformer_roberta # noqa diff --git a/examples/linformer/linformer_src/models/__init__.py b/examples/linformer/linformer_src/models/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/examples/linformer/linformer_src/models/linformer_roberta.py b/examples/linformer/linformer_src/models/linformer_roberta.py new file mode 100644 index 0000000000..18ad44f079 --- /dev/null +++ b/examples/linformer/linformer_src/models/linformer_roberta.py @@ -0,0 +1,119 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +""" +Linformer: Self-Attention with Linear Complexity +""" + +import logging + +import torch +from fairseq import utils +from fairseq.models import register_model, register_model_architecture +from fairseq.models.roberta import ( + init_bert_params, + roberta_base_architecture, + roberta_large_architecture, + RobertaEncoder, + RobertaModel, +) + +from ..modules.linformer_sentence_encoder import LinformerTransformerEncoder + + +logger = logging.getLogger(__name__) + + +@register_model("linformer_roberta") +class LinformerModel(RobertaModel): + @staticmethod + def add_args(parser): + RobertaModel.add_args(parser) + + # add args for Linformer + parser.add_argument( + "--compressed", type=int, help="compressed ratio of sequence length" + ) + parser.add_argument( + "--shared-kv-compressed", + type=int, + help="share compressed matrix between k and v, in each layer", + ) + parser.add_argument( + "--shared-layer-kv-compressed", + type=int, + help="share compressed matrix between k and v and across all layers", + ) + parser.add_argument( + "--freeze-compress", + type=int, + help="freeze the parameters in compressed layer", + ) + + @classmethod + def build_model(cls, args, task): + """Build a new model instance.""" + + # make sure all arguments are present + base_architecture(args) + + if not hasattr(args, "max_positions"): + args.max_positions = args.tokens_per_sample + + encoder = LinformerEncoder(args, task.source_dictionary) + return cls(args, encoder) + + +class LinformerEncoder(RobertaEncoder): + """Linformer encoder.""" + + def __init__(self, args, dictionary): + super().__init__(args, dictionary) + self.register_buffer("version", torch.tensor(2)) + + def build_encoder(self, args, dictionary, embed_tokens): + encoder = LinformerTransformerEncoder(args, dictionary, embed_tokens) + encoder.apply(init_bert_params) + return encoder + + def upgrade_state_dict_named(self, state_dict, name): + super().upgrade_state_dict_named(state_dict, name) + prefix = name + "." if name != "" else "" + + # some old checkpoints had weight sharing implemented incorrectly + # (note: this was correct in the original paper code) + if utils.item(state_dict.get(f"{prefix}version", torch.tensor(1))) < 2: + state_dict[f"{prefix}version"] = torch.tensor(1) + # check if input embeddings and output embeddings were tied + if not torch.allclose( + state_dict[f"{prefix}sentence_encoder.embed_tokens.weight"], + state_dict[f"{prefix}lm_head.weight"], + ): + # they weren't tied, re-init the LM head without weight sharing + self.lm_head = self.build_lm_head( + embed_dim=self.args.encoder_embed_dim, + output_dim=len(self.dictionary), + activation_fn=self.args.activation_fn, + weight=None, # don't share weights + ) + + +@register_model_architecture("linformer_roberta", "linformer_roberta") +def base_architecture(args): + args.compressed = getattr(args, "compressed", 4) + args.shared_kv_compressed = getattr(args, "shared_kv_compressed", 0) + args.shared_layer_kv_compressed = getattr(args, "shared_layer_kv_compressed", 0) + args.freeze_compress = getattr(args, "freeze_compress", 0) + roberta_base_architecture(args) + + +@register_model_architecture("linformer_roberta", "linformer_roberta_base") +def linformer_roberta_base_architecture(args): + base_architecture(args) + + +@register_model_architecture("linformer_roberta", "linformer_roberta_large") +def linformer_roberta_large_architecture(args): + roberta_large_architecture(args) + base_architecture(args) diff --git a/examples/linformer/linformer_src/modules/__init__.py b/examples/linformer/linformer_src/modules/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/examples/linformer/linformer_src/modules/linformer_sentence_encoder.py b/examples/linformer/linformer_src/modules/linformer_sentence_encoder.py new file mode 100644 index 0000000000..44f7989bd8 --- /dev/null +++ b/examples/linformer/linformer_src/modules/linformer_sentence_encoder.py @@ -0,0 +1,54 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math + +import torch.nn as nn +from fairseq.models.transformer import TransformerEncoder + +from .linformer_sentence_encoder_layer import LinformerTransformerEncoderLayer + + +class LinformerTransformerEncoder(TransformerEncoder): + """ + Implementation for a Bi-directional Linformer based Sentence Encoder used + in BERT/XLM style pre-trained models. + + This first computes the token embedding using the token embedding matrix, + position embeddings (if specified) and segment embeddings + (if specified). After applying the specified number of + LinformerEncoderLayers, it outputs all the internal states of the + encoder as well as the final representation associated with the first + token (usually CLS token). + + Input: + - tokens: B x T matrix representing sentences + - segment_labels: B x T matrix representing segment label for tokens + + Output: + - a tuple of the following: + - a list of internal model states used to compute the + predictions where each tensor has shape T x B x C + - sentence representation associated with first input token + in format B x C. + """ + + def __init__(self, args, dictionary, embed_tokens): + self.compress_layer = None + super().__init__(args, dictionary, embed_tokens) + + def build_encoder_layer(self, args): + if self.args.shared_layer_kv_compressed == 1 and self.compress_layer is None: + compress_layer = nn.Linear( + self.args.max_positions, + self.args.max_positions // self.args.compressed, + ) + # intialize parameters for compressed layer + nn.init.xavier_uniform_(compress_layer.weight, gain=1 / math.sqrt(2)) + if self.args.freeze_compress == 1: + compress_layer.weight.requires_grad = False + self.compress_layer = compress_layer + + return LinformerTransformerEncoderLayer(args, self.compress_layer) diff --git a/examples/linformer/linformer_src/modules/linformer_sentence_encoder_layer.py b/examples/linformer/linformer_src/modules/linformer_sentence_encoder_layer.py new file mode 100644 index 0000000000..7e2caa0340 --- /dev/null +++ b/examples/linformer/linformer_src/modules/linformer_sentence_encoder_layer.py @@ -0,0 +1,65 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +from fairseq import utils +from fairseq.modules import TransformerEncoderLayer + +from .multihead_linear_attention import MultiheadLinearAttention + + +class LinformerTransformerEncoderLayer(TransformerEncoderLayer): + """ + Implements a Linformer Encoder Layer used in BERT/XLM style pre-trained + models. + """ + + def __init__(self, args, shared_compress_layer): + # wrap in a list so it's not automatically registered by PyTorch + self.shared_compress_layer = [shared_compress_layer] + + super().__init__(args) + + self.register_buffer("version", torch.tensor(2)) + + def build_self_attention(self, embed_dim, args): + return MultiheadLinearAttention( + embed_dim, + args.encoder_attention_heads, + dropout=args.dropout, + self_attention=True, + q_noise=args.quant_noise_pq, + qn_block_size=args.quant_noise_pq_block_size, + compressed=args.compressed, + max_seq_len=args.max_positions, + shared_kv_compressed=args.shared_kv_compressed, + shared_compress_layer=self.shared_compress_layer[0], + freeze_compress=args.freeze_compress, + ) + + def upgrade_state_dict_named(self, state_dict, name): + super().upgrade_state_dict_named(state_dict, name) + prefix = name + "." if name != "" else "" + + # some old checkpoints had weight sharing implemented incorrectly + # (note: this was correct in the original paper code) + if utils.item(state_dict.get(f"{prefix}version", torch.tensor(1))) < 2: + state_dict[f"{prefix}version"] = torch.tensor(1) + # check compression layer sharing + if f"{prefix}shared_compress_layer.weight" in state_dict: + # reinitialize block without sharing compression layer to match + # old behavior + self.shared_compress_layer = [ + torch.nn.Linear( + self.shared_compress_layer[0].weight.size(1), + self.shared_compress_layer[0].weight.size(0), + ) + ] + self.self_attn = self.build_self_attention(self.embed_dim, self.args) + # delete shared_compress_layer, since it's already copied to + # self_attn.compress_k.weight + del state_dict[f"{prefix}shared_compress_layer.weight"] + if f"{prefix}shared_compress_layer.bias" in state_dict: + del state_dict[f"{prefix}shared_compress_layer.bias"] diff --git a/examples/linformer/linformer_src/modules/multihead_linear_attention.py b/examples/linformer/linformer_src/modules/multihead_linear_attention.py new file mode 100644 index 0000000000..6be1007279 --- /dev/null +++ b/examples/linformer/linformer_src/modules/multihead_linear_attention.py @@ -0,0 +1,481 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math +from typing import Dict, Optional, Tuple + +import torch +import torch.nn.functional as F +from fairseq import utils +from fairseq.incremental_decoding_utils import with_incremental_state +from fairseq.modules.quant_noise import quant_noise +from torch import Tensor, nn +from torch.nn import Parameter + + +@with_incremental_state +class MultiheadLinearAttention(nn.Module): + """Multi-headed linformer attention. + + Projects the key and values down to the compressed dimension, before computing self-attention. + + See "Linformer: Self-Attention with Linear Complexity" for more details. + """ + + def __init__( + self, + embed_dim, + num_heads, + kdim=None, + vdim=None, + dropout=0.0, + bias=True, + add_bias_kv=False, + add_zero_attn=False, + self_attention=False, + encoder_decoder_attention=False, + q_noise=0.0, + qn_block_size=8, + compressed=1, + max_seq_len=256, + shared_kv_compressed=0, + shared_compress_layer=None, + freeze_compress=0, + ): + super().__init__() + self.embed_dim = embed_dim + self.kdim = kdim if kdim is not None else embed_dim + self.vdim = vdim if vdim is not None else embed_dim + self.qkv_same_dim = self.kdim == embed_dim and self.vdim == embed_dim + + self.num_heads = num_heads + self.dropout = dropout + self.head_dim = embed_dim // num_heads + assert ( + self.head_dim * num_heads == self.embed_dim + ), "embed_dim must be divisible by num_heads" + self.scaling = self.head_dim ** -0.5 + + self.self_attention = self_attention + self.encoder_decoder_attention = encoder_decoder_attention + + assert not self.self_attention or self.qkv_same_dim, ( + "Self-attention requires query, key and " "value to be of the same size" + ) + + self.k_proj = quant_noise( + nn.Linear(self.kdim, embed_dim, bias=bias), q_noise, qn_block_size + ) + self.v_proj = quant_noise( + nn.Linear(self.vdim, embed_dim, bias=bias), q_noise, qn_block_size + ) + self.q_proj = quant_noise( + nn.Linear(embed_dim, embed_dim, bias=bias), q_noise, qn_block_size + ) + + # used for compress sequence to subsequence + if shared_compress_layer is None: + self.compress_seq_len = max_seq_len // compressed + self.compress_k = nn.Linear(max_seq_len, self.compress_seq_len, bias=False) + if shared_kv_compressed == 0: + self.compress_v = nn.Linear( + max_seq_len, self.compress_seq_len, bias=False + ) + self.layerwise_sharing = False + else: + self.compress_k = shared_compress_layer + if shared_kv_compressed == 0: + self.compress_v = shared_compress_layer + self.layerwise_sharing = True + self.shared_kv_compressed = shared_kv_compressed + + self.out_proj = quant_noise( + nn.Linear(embed_dim, embed_dim, bias=bias), q_noise, qn_block_size + ) + + if add_bias_kv: + self.bias_k = Parameter(torch.Tensor(1, 1, embed_dim)) + self.bias_v = Parameter(torch.Tensor(1, 1, embed_dim)) + else: + self.bias_k = self.bias_v = None + + self.add_zero_attn = add_zero_attn + + self.reset_parameters() + + if freeze_compress == 1: + self.compress_k.weight.requires_grad = False + if shared_kv_compressed == 0: + self.compress_v.weight.requires_grad = False + + self.onnx_trace = False + + def prepare_for_onnx_export_(self): + self.onnx_trace = True + + def reset_parameters(self): + if self.qkv_same_dim: + # Empirically observed the convergence to be much better with + # the scaled initialization + nn.init.xavier_uniform_(self.k_proj.weight, gain=1 / math.sqrt(2)) + nn.init.xavier_uniform_(self.v_proj.weight, gain=1 / math.sqrt(2)) + nn.init.xavier_uniform_(self.q_proj.weight, gain=1 / math.sqrt(2)) + if ( + not self.layerwise_sharing + ): # otherwise, we already initialize the parameters + nn.init.xavier_uniform_(self.compress_k.weight, gain=1 / math.sqrt(2)) + if self.shared_kv_compressed == 0: + nn.init.xavier_uniform_( + self.compress_v.weight, gain=1 / math.sqrt(2) + ) + else: + nn.init.xavier_uniform_(self.k_proj.weight) + nn.init.xavier_uniform_(self.v_proj.weight) + nn.init.xavier_uniform_(self.q_proj.weight) + if ( + not self.layerwise_sharing + ): # otherwise, we already initialize the parameters + nn.init.xavier_uniform_(self.compress_k.weight) + if self.shared_kv_compressed == 0: + nn.init.xavier_uniform_(self.compress_v.weight) + + nn.init.xavier_uniform_(self.out_proj.weight) + if self.out_proj.bias is not None: + nn.init.constant_(self.out_proj.bias, 0.0) + if self.bias_k is not None: + nn.init.xavier_normal_(self.bias_k) + if self.bias_v is not None: + nn.init.xavier_normal_(self.bias_v) + + def forward( + self, + query, + key: Optional[Tensor], + value: Optional[Tensor], + key_padding_mask: Optional[Tensor] = None, + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, + need_weights: bool = True, + static_kv: bool = False, + attn_mask: Optional[Tensor] = None, + before_softmax: bool = False, + need_head_weights: bool = False, + ) -> Tuple[Tensor, Optional[Tensor]]: + """Input shape: Time x Batch x Channel + + Args: + key_padding_mask (ByteTensor, optional): mask to exclude + keys that are pads, of shape `(batch, src_len)`, where + padding elements are indicated by 1s. + need_weights (bool, optional): return the attention weights, + averaged over heads (default: False). + attn_mask (ByteTensor, optional): typically used to + implement causal attention, where the mask prevents the + attention from looking forward in time (default: None). + before_softmax (bool, optional): return the raw attention + weights and values before the attention softmax. + need_head_weights (bool, optional): return the attention + weights for each head. Implies *need_weights*. Default: + return the average attention weights over all heads. + """ + if need_head_weights: + need_weights = True + + tgt_len, bsz, embed_dim = query.size() + assert embed_dim == self.embed_dim + assert list(query.size()) == [tgt_len, bsz, embed_dim] + + if incremental_state is not None: + saved_state = self._get_input_buffer(incremental_state) + if saved_state is not None and "prev_key" in saved_state: + # previous time steps are cached - no need to recompute + # key and value if they are static + if static_kv: + assert self.encoder_decoder_attention and not self.self_attention + key = value = None + else: + saved_state = None + + if self.self_attention: + q = self.q_proj(query) + + k_input = query.permute(1, 2, 0).contiguous() # B * C * T + k_input = ( + F.linear(k_input, self.compress_k.weight[:, 0:tgt_len]) + .permute(2, 0, 1) + .contiguous() + ) + k = self.k_proj(k_input) + + v_input = query.permute(1, 2, 0).contiguous() # B * C * T + if self.shared_kv_compressed == 0: + v_input = ( + F.linear(v_input, self.compress_v.weight[:, 0:tgt_len]) + .permute(2, 0, 1) + .contiguous() + ) + if self.shared_kv_compressed == 1: # use shared kv compressed linear layer + v_input = ( + F.linear(v_input, self.compress_k.weight[:, 0:tgt_len]) + .permute(2, 0, 1) + .contiguous() + ) + v = self.v_proj(v_input) + elif self.encoder_decoder_attention: + # encoder-decoder attention + q = self.q_proj(query) + if key is None: + assert value is None + k = v = None + else: + k = self.k_proj(key) + v = self.v_proj(key) + + else: + assert key is not None and value is not None + q = self.q_proj(query) + k = self.k_proj(key) + v = self.v_proj(value) + q *= self.scaling + + if self.bias_k is not None: + assert self.bias_v is not None + k = torch.cat([k, self.bias_k.repeat(1, bsz, 1)]) + v = torch.cat([v, self.bias_v.repeat(1, bsz, 1)]) + if attn_mask is not None: + attn_mask = torch.cat( + [attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1 + ) + if key_padding_mask is not None: + key_padding_mask = torch.cat( + [ + key_padding_mask, + key_padding_mask.new_zeros(key_padding_mask.size(0), 1), + ], + dim=1, + ) + + q = ( + q.contiguous() + .view(tgt_len, bsz * self.num_heads, self.head_dim) + .transpose(0, 1) + ) + if k is not None: + k = ( + k.contiguous() + .view(-1, bsz * self.num_heads, self.head_dim) + .transpose(0, 1) + ) + if v is not None: + v = ( + v.contiguous() + .view(-1, bsz * self.num_heads, self.head_dim) + .transpose(0, 1) + ) + + if saved_state is not None: + # saved states are stored with shape (bsz, num_heads, seq_len, head_dim) + if "prev_key" in saved_state: + _prev_key = saved_state["prev_key"] + assert _prev_key is not None + prev_key = _prev_key.view(bsz * self.num_heads, -1, self.head_dim) + if static_kv: + k = prev_key + else: + assert k is not None + k = torch.cat([prev_key, k], dim=1) + if "prev_value" in saved_state: + _prev_value = saved_state["prev_value"] + assert _prev_value is not None + prev_value = _prev_value.view(bsz * self.num_heads, -1, self.head_dim) + if static_kv: + v = prev_value + else: + assert v is not None + v = torch.cat([prev_value, v], dim=1) + prev_key_padding_mask: Optional[Tensor] = None + if "prev_key_padding_mask" in saved_state: + prev_key_padding_mask = saved_state["prev_key_padding_mask"] + assert k is not None and v is not None + key_padding_mask = MultiheadLinearAttention._append_prev_key_padding_mask( + key_padding_mask=key_padding_mask, + prev_key_padding_mask=prev_key_padding_mask, + batch_size=bsz, + src_len=k.size(1), + static_kv=static_kv, + ) + + saved_state["prev_key"] = k.view(bsz, self.num_heads, -1, self.head_dim) + saved_state["prev_value"] = v.view(bsz, self.num_heads, -1, self.head_dim) + saved_state["prev_key_padding_mask"] = key_padding_mask + # In this branch incremental_state is never None + assert incremental_state is not None + incremental_state = self._set_input_buffer(incremental_state, saved_state) + assert k is not None + src_len = k.size(1) + + if self.add_zero_attn: + assert v is not None + src_len += 1 + k = torch.cat([k, k.new_zeros((k.size(0), 1) + k.size()[2:])], dim=1) + v = torch.cat([v, v.new_zeros((v.size(0), 1) + v.size()[2:])], dim=1) + if attn_mask is not None: + attn_mask = torch.cat( + [attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1 + ) + + attn_weights = torch.bmm(q, k.transpose(1, 2)) + attn_weights = MultiheadLinearAttention.apply_sparse_mask( + attn_weights, tgt_len, src_len, bsz + ) + + assert list(attn_weights.size()) == [bsz * self.num_heads, tgt_len, src_len] + + if attn_mask is not None: + attn_mask = attn_mask.unsqueeze(0) + if self.onnx_trace: + attn_mask = attn_mask.repeat(attn_weights.size(0), 1, 1) + attn_weights += attn_mask + + if before_softmax: + return attn_weights, v + + attn_weights_float = utils.softmax( + attn_weights, dim=-1, onnx_trace=self.onnx_trace + ) + attn_weights = attn_weights_float.type_as(attn_weights) + attn_probs = F.dropout( + attn_weights, + p=self.dropout, + training=self.training, + ) + assert v is not None + attn = torch.bmm(attn_probs, v) + assert list(attn.size()) == [bsz * self.num_heads, tgt_len, self.head_dim] + if self.onnx_trace and attn.size(1) == 1: + # when ONNX tracing a single decoder step (sequence length == 1) + # the transpose is a no-op copy before view, thus unnecessary + attn = attn.contiguous().view(tgt_len, bsz, embed_dim) + else: + attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim) + attn = self.out_proj(attn) + attn_weights: Optional[Tensor] = None + if need_weights: + attn_weights = attn_weights_float.view( + bsz, self.num_heads, tgt_len, src_len + ).transpose(1, 0) + if not need_head_weights: + # average attention weights over heads + attn_weights = attn_weights.mean(dim=0) + + return attn, attn_weights + + @staticmethod + def _append_prev_key_padding_mask( + key_padding_mask: Optional[Tensor], + prev_key_padding_mask: Optional[Tensor], + batch_size: int, + src_len: int, + static_kv: bool, + ) -> Optional[Tensor]: + # saved key padding masks have shape (bsz, seq_len) + if prev_key_padding_mask is not None and static_kv: + new_key_padding_mask = prev_key_padding_mask + elif prev_key_padding_mask is not None and key_padding_mask is not None: + new_key_padding_mask = torch.cat( + [prev_key_padding_mask.float(), key_padding_mask.float()], dim=1 + ) + # During incremental decoding, as the padding token enters and + # leaves the frame, there will be a time when prev or current + # is None + elif prev_key_padding_mask is not None: + filler = torch.zeros( + (batch_size, src_len - prev_key_padding_mask.size(1)), + device=prev_key_padding_mask.device, + ) + new_key_padding_mask = torch.cat( + [prev_key_padding_mask.float(), filler.float()], dim=1 + ) + elif key_padding_mask is not None: + filler = torch.zeros( + (batch_size, src_len - key_padding_mask.size(1)), + device=key_padding_mask.device, + ) + new_key_padding_mask = torch.cat( + [filler.float(), key_padding_mask.float()], dim=1 + ) + else: + new_key_padding_mask = prev_key_padding_mask + return new_key_padding_mask + + @torch.jit.export + def reorder_incremental_state( + self, + incremental_state: Dict[str, Dict[str, Optional[Tensor]]], + new_order: Tensor, + ): + """Reorder buffered internal state (for incremental generation).""" + input_buffer = self._get_input_buffer(incremental_state) + if input_buffer is not None: + for k in input_buffer.keys(): + input_buffer_k = input_buffer[k] + if input_buffer_k is not None: + if self.encoder_decoder_attention and input_buffer_k.size( + 0 + ) == new_order.size(0): + break + input_buffer[k] = input_buffer_k.index_select(0, new_order) + incremental_state = self._set_input_buffer(incremental_state, input_buffer) + return incremental_state + + def _get_input_buffer( + self, incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] + ) -> Dict[str, Optional[Tensor]]: + result = self.get_incremental_state(incremental_state, "attn_state") + if result is not None: + return result + else: + empty_result: Dict[str, Optional[Tensor]] = {} + return empty_result + + def _set_input_buffer( + self, + incremental_state: Dict[str, Dict[str, Optional[Tensor]]], + buffer: Dict[str, Optional[Tensor]], + ): + return self.set_incremental_state(incremental_state, "attn_state", buffer) + + def apply_sparse_mask(attn_weights, tgt_len: int, src_len: int, bsz: int): + return attn_weights + + def upgrade_state_dict_named(self, state_dict, name): + prefix = name + "." if name != "" else "" + items_to_add = {} + keys_to_remove = [] + for k in state_dict.keys(): + if k.endswith(prefix + "in_proj_weight"): + # in_proj_weight used to be q + k + v with same dimensions + dim = int(state_dict[k].shape[0] / 3) + items_to_add[prefix + "q_proj.weight"] = state_dict[k][:dim] + items_to_add[prefix + "k_proj.weight"] = state_dict[k][dim : 2 * dim] + items_to_add[prefix + "v_proj.weight"] = state_dict[k][2 * dim :] + + keys_to_remove.append(k) + + k_bias = prefix + "in_proj_bias" + if k_bias in state_dict.keys(): + dim = int(state_dict[k].shape[0] / 3) + items_to_add[prefix + "q_proj.bias"] = state_dict[k_bias][:dim] + items_to_add[prefix + "k_proj.bias"] = state_dict[k_bias][ + dim : 2 * dim + ] + items_to_add[prefix + "v_proj.bias"] = state_dict[k_bias][2 * dim :] + + keys_to_remove.append(prefix + "in_proj_bias") + + for k in keys_to_remove: + del state_dict[k] + + for key, value in items_to_add.items(): + state_dict[key] = value diff --git a/examples/m2m_100/README.md b/examples/m2m_100/README.md new file mode 100644 index 0000000000..02a68a5f09 --- /dev/null +++ b/examples/m2m_100/README.md @@ -0,0 +1,241 @@ +# Beyond English-Centric Multilingual Machine Translation + +## Introduction +In this work, we create a true Many-to-Many multilingual translation model that can translate directly between any pair of 100 languages. Our focus on non-English-Centric models brings gains of more than 10 BLEU when directly translating between non-English directions while performing competitively with the best single systems of WMT. + +If you are new to using fairseq, read the following walkthrough. Otherwise, skip to the sections below. + +0. **Generation Data** + +To download the generation data, follow the below commands. Note that all datasets need to be detokenized *before* applying SPM in the data preprocessing step. If you use these evaluation datasets, please cite their associated papers. +```bash +# WMT - use sacrebleu, example here: +sacrebleu -t wmt14 -l fr-en --echo src > wmt.test.fr-en.fr +sacrebleu -t wmt14 -l fr-en --echo ref > wmt.test.fr-en.en + +# WAT +wget http://lotus.kuee.kyoto-u.ac.jp/WAT/my-en-data/wat2020.my-en.zip +unzip wat2020.my-en.zip + +# FLORES +# download from: https://github.com/facebookresearch/flores + +# TED - need to detokenize with Moses! +# from: https://github.com/neulab/word-embeddings-for-nmt +wget http://phontron.com/data/ted_talks.tar.gz + +# Autshumato +# request to download: https://repo.sadilar.org/handle/20.500.12185/397 + +# Tatoeba Challenge +# available here: https://github.com/Helsinki-NLP/Tatoeba-Challenge +``` + +1. **Training Data** + +To produce the training data, we use a combination of [CCMatrix](https://arxiv.org/abs/1911.04944) and [CCAligned](https://arxiv.org/abs/1911.06154). Check out the instructions [here](https://github.com/facebookresearch/LASER/tree/master/tasks/CCMatrix) to download the raw data. + +2. **Preprocess Data** + +After downloading raw data, you will need to postprocess the data, then apply SPM, then binarize. Note that it is very important you run the postprocessing script, because this removes any instance of the evaluation data in the mined training data. + +```bash +# preprocess data + +# remove sentences with more than 50% punctuation +python /path/to/fairseq/examples/m2m_100/process_data/remove_too_much_punc.py + +# deduplicate training data +paste /path/to/datadir/train.$src /path/to/datadir/train.$tgt | awk '!x[$0]++' > /path/to/datadir/train.dedup +echo "keeping $(wc -l /path/to/datadir/train.dedup) bitext out of $(wc -l /path/to/datadir/train.$src)" +cut -f1 /path/to/datadir/train.dedup > /path/to/datadir/train.$src +cut -f2 /path/to/datadir/train.dedup > /path/to/datadir/train.$tgt + +# remove all instances of evaluation data from the training data +python /path/to/fairseq/examples/m2m_100/process_data/dedup_data.py + +# frequency cleaning +wget https://dl.fbaipublicfiles.com/m2m_100/histograms.tar.gz +tar -xvzf histograms.tar.gz +python /path/to/fairseq/examples/m2m_100/process_data/clean_histogram.py --src $src --tgt $tgt --src-file /path/to/source/file --tgt-file /path/to/output/file --src-output-file source_output.$src --tgt-output-file target_output.$tgt --histograms /path/to/histograms + +# apply SPM +wget https://dl.fbaipublicfiles.com/m2m_100/spm.128k.model +python /path/to/fairseq/scripts/spm_encode.py \ + --model spm.128k.model \ + --output_format=piece \ + --inputs=/path/to/input/file/here \ + --outputs=/path/to/output/file/here + +# length ratio cleaning +perl mosesdecoder/scripts/training/clean-corpus-n.perl --ratio 3 /path/to/training/data/train.spm.$src-$tgt $src $tgt /path/to/output/directory/train.spm.$src-$tgt 1 250 + +# binarize data +wget https://dl.fbaipublicfiles.com/m2m_100/data_dict.128k.txt +fairseq-preprocess \ + --source-lang $src --target-lang $tgt \ + --testpref spm.$src.$tgt \ + --thresholdsrc 0 --thresholdtgt 0 \ + --destdir data_bin \ + --srcdict data_dict.128k.txt --tgtdict data_dict.128k.txt +``` + +3. **Training Scripts** + +To reproduce the training of our models, we train with fairseq-py's multilingual translation [task](https://github.com/pytorch/fairseq/tree/main/examples/multilingual). If you are interested in model parallel training, also check out [fairscale](https://github.com/facebookresearch/fairscale). + +4. **Generation** + +To generate from our models, follow the the commands in the generation section below. + + +If you use any of the resources listed here, please cite: +```bibtex +@article{fan2020beyond, + title={Beyond English-Centric Multilingual Machine Translation}, + author={Fan, Angela and Bhosale, Shruti and Schwenk, Holger and Ma, Zhiyi and El-Kishky, Ahmed and Goyal, Siddharth and Baines, Mandeep and Celebi, Onur and Wenzek, Guillaume and Chaudhary, Vishrav and Goyal, Naman and Birch, Tom and Liptchinsky, Vitaliy and Edunov, Sergey and Grave, Edouard and Auli, Michael and Joulin, Armand}, + journal={arXiv preprint}, + year={2020} +} + +@article{schwenk2019ccmatrix, + title={Ccmatrix: Mining billions of high-quality parallel sentences on the web}, + author={Schwenk, Holger and Wenzek, Guillaume and Edunov, Sergey and Grave, Edouard and Joulin, Armand}, + journal={arXiv preprint arXiv:1911.04944}, + year={2019} +} + +@article{el2019massive, + title={A Massive Collection of Cross-Lingual Web-Document Pairs}, + author={El-Kishky, Ahmed and Chaudhary, Vishrav and Guzman, Francisco and Koehn, Philipp}, + journal={arXiv preprint arXiv:1911.06154}, + year={2019} +} +``` + + +## Trained Models + +### 418M and 1.2B Model +We include the last checkpoint for both of these models. + +```bash +wget https://dl.fbaipublicfiles.com/m2m_100/model_dict.128k.txt +wget https://dl.fbaipublicfiles.com/m2m_100/language_pairs_small_models.txt + +# 418M parameter model +wget https://dl.fbaipublicfiles.com/m2m_100/418M_last_checkpoint.pt + +# 1.2B parameter model +wget https://dl.fbaipublicfiles.com/m2m_100/1.2B_last_checkpoint.pt + +# Generation: +fairseq-generate $binarized_data_path --batch-size 32 --path $path_to_model --fixed-dictionary model_dict.128k.txt -s en -t fr --remove-bpe 'sentencepiece' --beam 5 --task translation_multi_simple_epoch --lang-pairs language_pairs_small_models.txt --decoder-langtok --encoder-langtok src --gen-subset test > gen_out +``` + +### 12B Model +12B parameter model trained on many-to-many training data for 100 languages. We include the last checkpoint, average of last 5 checkpoints, average of last 10 checkpoints. There isn't a universally best choice out of these three, but all three versions are pretty close in accuracy. You can either sweep over the 3 checkpoints on a dev test and use the best performing checkpoint for final testing. Or the last checkpoint can be a good default choice. + +**Model Download Links** +Configuration | 2 32GB GPUs | 4 16GB GPUs | 6 12GB GPUs | 8 8GB GPUs +:--|:--|:--|:--|:-- +Last Checkpoint | [12b_last_chk_2_gpus.pt](https://dl.fbaipublicfiles.com/m2m_100/12b_last_chk_2_gpus.pt) | [12b_last_chk_4_gpus.pt](https://dl.fbaipublicfiles.com/m2m_100/12b_last_chk_4_gpus.pt) | [12b_last_chk_6_gpus.pt](https://dl.fbaipublicfiles.com/m2m_100/12b_last_chk_6_gpus.pt) | [12b_last_chk_8_gpus.pt](https://dl.fbaipublicfiles.com/m2m_100/12b_last_chk_8_gpus.pt) +Average of last 5 checkpoints | [12b_avg5_chk_2_gpus.pt](https://dl.fbaipublicfiles.com/m2m_100/12b_avg5_chk_2_gpus.pt) | [12b_avg5_chk_4_gpus.pt](https://dl.fbaipublicfiles.com/m2m_100/12b_avg5_chk_4_gpus.pt) | [12b_avg5_chk_6_gpus.pt](https://dl.fbaipublicfiles.com/m2m_100/12b_avg5_chk_6_gpus.pt) | [12b_avg5_chk_8_gpus.pt](https://dl.fbaipublicfiles.com/m2m_100/12b_avg5_chk_8_gpus.pt) +Average of last 10 checkpoints | [12b_avg10_chk_2_gpus.pt](https://dl.fbaipublicfiles.com/m2m_100/12b_avg10_chk_2_gpus.pt) | [12b_avg10_chk_4_gpus.pt](https://dl.fbaipublicfiles.com/m2m_100/12b_avg10_chk_4_gpus.pt) | [12b_avg10_chk_6_gpus.pt](https://dl.fbaipublicfiles.com/m2m_100/12b_avg10_chk_6_gpus.pt) | [12b_avg10_chk_8_gpus.pt](https://dl.fbaipublicfiles.com/m2m_100/12b_avg10_chk_8_gpus.pt) + +**Generation Arguments** +Configuration | 2 32GB GPUs | 4 16GB GPUs | 6 12GB GPUs | 8 8GB GPUs +:--|:--|:--|:--|:-- +`--pipeline-encoder-balance` | `[26]` | `[1,15,10]` | `[1,9,9,7]` | `[1,6,6,6,7]` +`--pipeline-encoder-devices` | `[0]` | `[0,1,0]` | `[0,1,2,0]` | `[0,4,5,1,0]` +`--pipeline-decoder-balance` | `[3,22,1]` | `[3,11,11,1]` | `[3,7,7,8,1]` | `[1,6,6,6,6,1]` +`--pipeline-decoder-devices` | `[0,1,0]` | `[0,2,3,0]` | `[0,3,4,5,0]` | `[0,2,6,7,3,0]` + + +## SentencePiece Model + +```bash +wget https://dl.fbaipublicfiles.com/m2m_100/spm.128k.model +``` + +## Generation with M2M-100 + +### Encode using our SentencePiece Model + +Note: Install SentencePiece from [here](https://github.com/google/sentencepiece) + +```bash +fairseq=/path/to/fairseq +cd $fairseq +sacrebleu --echo src -l de-fr -t wmt19 | head -n 20 > raw_input.de-fr.de +sacrebleu --echo ref -l de-fr -t wmt19 | head -n 20 > raw_input.de-fr.fr +wget https://dl.fbaipublicfiles.com/m2m_100/spm.128k.model +for lang in de fr ; do + python scripts/spm_encode.py \ + --model spm.128k.model \ + --output_format=piece \ + --inputs=raw_input.de-fr.${lang} \ + --outputs=spm.de-fr.${lang} +done +``` + +### Binarization + +```bash +wget https://dl.fbaipublicfiles.com/m2m_100/data_dict.128k.txt +fairseq-preprocess \ + --source-lang de --target-lang fr \ + --testpref spm.de-fr \ + --thresholdsrc 0 --thresholdtgt 0 \ + --destdir data_bin \ + --srcdict data_dict.128k.txt --tgtdict data_dict.128k.txt +``` + +### Generation for the 12B model + +Note that generation can currently be run using 2 32GB / 4 16GB / 6 12GB / 8 8GB GPUs, and the corresponding model checkpoints and pipeline arguments can be found in the [12B Model Section](#12b-model). +Generation on CPUs will be added in the future. + +```bash +wget https://dl.fbaipublicfiles.com/m2m_100/model_dict.128k.txt +wget https://dl.fbaipublicfiles.com/m2m_100/language_pairs.txt +wget https://dl.fbaipublicfiles.com/m2m_100/12b_last_chk_4_gpus.pt +fairseq-generate \ + data_bin \ + --batch-size 1 \ + --path 12b_last_chk_4_gpus.pt \ + --fixed-dictionary model_dict.128k.txt \ + -s de -t fr \ + --remove-bpe 'sentencepiece' \ + --beam 5 \ + --task translation_multi_simple_epoch \ + --lang-pairs language_pairs.txt \ + --decoder-langtok --encoder-langtok src \ + --gen-subset test \ + --fp16 \ + --dataset-impl mmap \ + --distributed-world-size 1 --distributed-no-spawn \ + --pipeline-model-parallel \ + --pipeline-chunks 1 \ + --pipeline-encoder-balance '[1,15,10]' \ + --pipeline-encoder-devices '[0,1,0]' \ + --pipeline-decoder-balance '[3,11,11,1]' \ + --pipeline-decoder-devices '[0,2,3,0]' > gen_out +``` +## Evaluation with M2M-100 + +### Tokenization + +Note: Refer to tokenizers/README.md for more details on tokenization. + +```bash +cd ${fairseq}/examples/m2m_100 +cat ${fairseq}/gen_out | grep -P "^H" | sort -V | cut -f 3- | sh tok.sh fr > hyp +cat ${fairseq}/raw_input.de-fr.fr | sh tok.sh fr > ref +``` + +### BLEU + +```bash +sacrebleu -tok 'none' ref < hyp +``` diff --git a/examples/m2m_100/install_dependecies.sh b/examples/m2m_100/install_dependecies.sh new file mode 100755 index 0000000000..82a1054745 --- /dev/null +++ b/examples/m2m_100/install_dependecies.sh @@ -0,0 +1,78 @@ +#!/usr/bin/env bash +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +CWD=`pwd` +INSTALL_PATH=$CWD/tokenizers/thirdparty + +MOSES=$INSTALL_PATH/mosesdecoder +if [ ! -d $MOSES ]; then + echo 'Cloning Moses github repository (for tokenization scripts)...' + git clone https://github.com/moses-smt/mosesdecoder.git $MOSES + cd $MOSES + # To deal with differences in handling ' vs " + git checkout 03578921cc1a03402 + cd - +fi + +WMT16_SCRIPTS=$INSTALL_PATH/wmt16-scripts +if [ ! -d $WMT16_SCRIPTS ]; then + echo 'Cloning Romanian tokenization scripts' + git clone https://github.com/rsennrich/wmt16-scripts.git $WMT16_SCRIPTS +fi + +KYTEA=$INSTALL_PATH/kytea +if [ ! -f $KYTEA/bin/kytea ]; then + git clone https://github.com/neubig/kytea.git $KYTEA + cd $KYTEA + autoreconf -i + ./configure --prefix=`pwd` + make + make install + cd .. +fi + +export MECAB=$INSTALL_PATH/mecab-0.996-ko-0.9.2 +if [ ! -f $MECAB/bin/mecab ]; then + cd $INSTALL_PATH + curl -LO https://bitbucket.org/eunjeon/mecab-ko/downloads/mecab-0.996-ko-0.9.2.tar.gz + tar zxfv mecab-0.996-ko-0.9.2.tar.gz + cd mecab-0.996-ko-0.9.2/ + ./configure --prefix=`pwd` + make + make install + + cd .. + curl -LO https://bitbucket.org/eunjeon/mecab-ko-dic/downloads/mecab-ko-dic-2.1.1-20180720.tar.gz + tar zxfv mecab-ko-dic-2.1.1-20180720.tar.gz + cd mecab-ko-dic-2.1.1-20180720/ + ./autogen.sh + ./configure --prefix=`pwd` --with-dicdir=$MECAB/lib/mecab/dic/mecab-ko-dic --with-mecab-config=$MECAB/bin/mecab-config + make + sh -c 'echo "dicdir=$MECAB/lib/mecab/dic/mecab-ko-dic" > $MECAB/etc/mecabrc' + make install + cd $CWD +fi + +INDIC_RESOURCES_PATH=$INSTALL_PATH/indic_nlp_resources +if [ ! -d $INDIC_RESOURCES_PATH ]; then + echo 'Cloning indic_nlp_resources' + git clone https://github.com/anoopkunchukuttan/indic_nlp_resources.git $INDIC_RESOURCES_PATH +fi + + +if [ ! -f $INSTALL_PATH/seg_my.py ]; then + cd $INSTALL_PATH + wget http://lotus.kuee.kyoto-u.ac.jp/WAT/my-en-data/wat2020.my-en.zip + unzip wat2020.my-en.zip + # switch to python3 + cat wat2020.my-en/myseg.py |sed 's/^sys.std/###sys.std/g' | sed 's/### sys/sys/g' | sed 's/unichr/chr/g' > seg_my.py + cd $CWD +fi + + +pip install pythainlp sacrebleu indic-nlp-library + diff --git a/examples/m2m_100/process_data/clean_histogram.py b/examples/m2m_100/process_data/clean_histogram.py new file mode 100644 index 0000000000..e24e073dc0 --- /dev/null +++ b/examples/m2m_100/process_data/clean_histogram.py @@ -0,0 +1,52 @@ +import argparse + +parser = argparse.ArgumentParser() +parser.add_argument('--src', type=str, help='Source language') +parser.add_argument('--tgt', type=str, help='Target language') +parser.add_argument('--src-file', type=str, help='Input source file') +parser.add_argument('--tgt-file', type=str, help='Input target file') +parser.add_argument('--src-output-file', type=str, help='Output source file') +parser.add_argument('--tgt-output-file', type=str, help='Output target file') +parser.add_argument('--threshold', type=float, default=0.5, help='Threshold') +parser.add_argument('--threshold-character', type=str, default=']', help='Threshold character') +parser.add_argument('--histograms', type=str, help='Path to histograms') + +args = parser.parse_args() + + +def read_hist(f): + ch = [] + for line in f: + c = line[0] + if c == args.threshold_character: + break + ch.append(c) + return ch + + +with(open("{}/{}".format(args.histograms, args.src), 'r', encoding='utf8')) as f: + ch1 = read_hist(f) + +with(open("{}/{}".format(args.histograms, args.tgt), 'r', encoding='utf8')) as f: + ch2 = read_hist(f) + +print("Accepted characters for {}: {}".format(args.src, ch1)) +print("Accepted characters for {}: {}".format(args.tgt, ch2)) + +with open(args.src_file, 'r', encoding='utf8') as fs1, open(args.tgt_file, 'r', encoding='utf8') as fs2, open(args.src_output_file, 'w', encoding='utf8') as fos1, open(args.tgt_output_file, 'w', encoding='utf8') as fos2: + ls1 = fs1.readline() + ls2 = fs2.readline() + + while ls1 or ls2: + cnt1 = len([c for c in ls1.strip() if c in ch1]) + cnt2 = len([c for c in ls2.strip() if c in ch2]) + + if cnt1 / len(ls1) > args.threshold and cnt2 / len(ls2) > args.threshold: + fos1.write(ls1) + fos2.write(ls2) + else: + print("{} {} {} \n{} {} {}".format(args.src, cnt1 / len(ls1), ls1.strip(), args.tgt, cnt2 / len(ls2), ls2.strip())) + + ls1 = fs1.readline() + ls2 = fs2.readline() + \ No newline at end of file diff --git a/examples/m2m_100/process_data/dedup_data.py b/examples/m2m_100/process_data/dedup_data.py new file mode 100644 index 0000000000..58d9ed1cd1 --- /dev/null +++ b/examples/m2m_100/process_data/dedup_data.py @@ -0,0 +1,91 @@ +import argparse +from collections import namedtuple +import os + +DATADIR = "/path/to/train_data" +DEDUP_FROM_DIR = "/path/to/eval/data" +OUTPUT_DIR = "/path/to/output/data" + + +def main(args): + languages = set() + for language_directory in os.listdir(DATADIR): + if "_" in language_directory: + src, tgt = language_directory.split("_") + languages.add(LanguagePair(src=src, tgt=tgt)) + + data = existing_data() + train_languages = sorted(languages) + for language_pair in train_languages[args.start_index:args.start_index + args.size]: + print(language_pair) + dedup(language_pair, data) + + +LanguagePair = namedtuple("LanguagePair", ["src", "tgt"]) + + +def existing_data(): + data = set() + for file in os.listdir(DEDUP_FROM_DIR): + with open(os.path.join(DEDUP_FROM_DIR, file)) as f: + data |= set(f.readlines()) + return data + +def dedup(language_pair, data, verbose=True, output=True): + train_filenames = LanguagePair( + src=f"{DATADIR}/{language_pair.src}_{language_pair.tgt}/train.{language_pair.src}", + tgt=f"{DATADIR}/{language_pair.src}_{language_pair.tgt}/train.{language_pair.tgt}", + ) + + output_filenames = LanguagePair( + src=f"{OUTPUT_DIR}/train.dedup.{language_pair.src}-{language_pair.tgt}.{language_pair.src}", + tgt=f"{OUTPUT_DIR}/train.dedup.{language_pair.src}-{language_pair.tgt}.{language_pair.tgt}" + ) + + # If output exists, skip this pair. It has already been done. + if (os.path.exists(output_filenames.src) and + os.path.exists(output_filenames.tgt)): + if verbose: + print(f"{language_pair.src}-{language_pair.tgt} already done.") + return + + if verbose: + print(f"{language_pair.src}-{language_pair.tgt} ready, will check dups.") + + # If there is no output, no need to actually do the loop. + if not output: + return + + if os.path.exists(train_filenames.src) and os.path.exists(train_filenames.tgt): + with open(train_filenames.src) as f: + train_source = f.readlines() + + with open(train_filenames.tgt) as f: + train_target = f.readlines() + + # do dedup + new_train_source = [] + new_train_target = [] + for i, train_line in enumerate(train_source): + if train_line not in data and train_target[i] not in data: + new_train_source.append(train_line) + new_train_target.append(train_target[i]) + + assert len(train_source) == len(train_target) + assert len(new_train_source) == len(new_train_target) + assert len(new_train_source) <= len(train_source) + + with open(output_filenames.src, "w") as o: + for line in new_train_source: + o.write(line) + + with open(output_filenames.tgt, "w") as o: + for line in new_train_target: + o.write(line) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument("-s", "--start-index", required=True, type=int) + parser.add_argument("-n", "--size", required=True, type=int) + main(parser.parse_args()) diff --git a/examples/m2m_100/process_data/remove_too_much_punc.py b/examples/m2m_100/process_data/remove_too_much_punc.py new file mode 100644 index 0000000000..6c280de240 --- /dev/null +++ b/examples/m2m_100/process_data/remove_too_much_punc.py @@ -0,0 +1,36 @@ +import gzip +import argparse +from string import punctuation + +def len_no_punc(s, punc): + return len([ch for ch in s if ch in punc]) + +def filter_overpunc(len_npunc, len_sen): + return len_npunc < 0.5*len_sen + +def main(args): + punc = punctuation + "—|–" + print('Processing file {}'.format(args.input)) + with gzip.open(args.input, 'rt', encoding=args.encoding) as tsv: + with open(args.bitext + '.' + args.src_lang, 'wt', encoding=args.encoding) as fsrc: + with open(args.bitext + '.' + args.tgt_lang, 'wt', encoding=args.encoding) as ftgt: + line = tsv.readline() + fields = line.split('\t') + + src, tgt = fields[1], fields[2] + + nchar_npunc_src = len_no_punc(src, punc) + nchar_npunc_tgt = len_no_punc(tgt, punc) + + if filter_overpunc(nchar_npunc_src, len(src)) and filter_overpunc(nchar_npunc_tgt, len(tgt)): + fsrc.write(src.strip() + '\n') + ftgt.write(tgt.strip() + '\n') + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument("--input", required=True, type=str) + parser.add_argument('--encoding', default='utf-8', help='character encoding for input/output') + parser.add_argument('--bitext', type=str, required=True, help='language direction') + parser.add_argument('--src-lang', type=str, required=True, help='Source language') + parser.add_argument('--tgt-lang', type=str, required=True, help='Target language') + main(parser.parse_args()) diff --git a/examples/m2m_100/tok.sh b/examples/m2m_100/tok.sh new file mode 100755 index 0000000000..ba2ec5a2f3 --- /dev/null +++ b/examples/m2m_100/tok.sh @@ -0,0 +1,83 @@ +#!/usr/bin/env bash +# Copyright (c) 2019-present, Facebook, Inc. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. +# + +set -e + +TOKENIZERS_SCRIPTS=tokenizers +INSTALL_PATH=$TOKENIZERS_SCRIPTS/thirdparty + +N_THREADS=8 + +lg=$1 + +MOSES=$INSTALL_PATH/mosesdecoder +REPLACE_UNICODE_PUNCT=$MOSES/scripts/tokenizer/replace-unicode-punctuation.perl +NORM_PUNC=$MOSES/scripts/tokenizer/normalize-punctuation.perl +REM_NON_PRINT_CHAR=$MOSES/scripts/tokenizer/remove-non-printing-char.perl +TOKENIZER=$MOSES/scripts/tokenizer/tokenizer.perl + +# special tokenization for Romanian +WMT16_SCRIPTS=$INSTALL_PATH/wmt16-scripts + +NORMALIZE_ROMANIAN=$WMT16_SCRIPTS/preprocess/normalise-romanian.py +REMOVE_DIACRITICS=$WMT16_SCRIPTS/preprocess/remove-diacritics.py + +# Burmese +MY_SEGMENT=$INSTALL_PATH/seg_my.py + +# Arabic +AR_TOKENIZER=$TOKENIZERS_SCRIPTS/tokenizer_ar.sh + +# Korean +KO_SEGMENT=$TOKENIZERS_SCRIPTS/seg_ko.sh + +# Japanese +JA_SEGMENT=$TOKENIZERS_SCRIPTS/seg_ja.sh + +# Indic +IN_TOKENIZER=$TOKENIZERS_SCRIPTS/tokenize_indic.py +INDIC_RESOURCES_PATH=$INSTALL_PATH/indic_nlp_resources + +# Thai +THAI_TOKENIZER=$TOKENIZERS_SCRIPTS/tokenize_thai.py + +# Chinese +CHINESE_TOKENIZER=$TOKENIZERS_SCRIPTS/tokenize_zh.py + +# Chinese +if [ "$lg" = "zh" ]; then + cat - | $REPLACE_UNICODE_PUNCT | $NORM_PUNC -l $lg | $REM_NON_PRINT_CHAR | python $CHINESE_TOKENIZER +# Thai +elif [ "$lg" = "th" ]; then + cat - | python $THAI_TOKENIZER +# Japanese +elif [ "$lg" = "ja" ]; then + cat - | $REPLACE_UNICODE_PUNCT | $NORM_PUNC -l $lg | $REM_NON_PRINT_CHAR | ${JA_SEGMENT} +# Korean +elif [ "$lg" = "ko" ]; then + cat - | $REM_NON_PRINT_CHAR | ${KO_SEGMENT} +# Romanian +elif [ "$lg" = "ro" ]; then + cat - | $REPLACE_UNICODE_PUNCT | $NORM_PUNC -l $lg | $REM_NON_PRINT_CHAR | $NORMALIZE_ROMANIAN | $REMOVE_DIACRITICS | $TOKENIZER -no-escape -threads $N_THREADS -l $lg +# Burmese +elif [ "$lg" = "my" ]; then + cat - | python ${MY_SEGMENT} +# Arabic +elif [ "$lg" = "ar" ]; then + cat - | ${AR_TOKENIZER} +# Indic +elif [ "$lg" = "ne" ]; then + cat - | python ${IN_TOKENIZER} $lg +elif [ "$lg" = "si" ]; then + cat - | python ${IN_TOKENIZER} $lg +elif [ "$lg" = "hi" ]; then + cat - | python ${IN_TOKENIZER} $lg +# other languages +else + cat - | $REPLACE_UNICODE_PUNCT | $NORM_PUNC -l $lg | $REM_NON_PRINT_CHAR | $TOKENIZER -no-escape -threads $N_THREADS -l $lg +fi diff --git a/examples/m2m_100/tokenizers/README.md b/examples/m2m_100/tokenizers/README.md new file mode 100644 index 0000000000..e116932bc8 --- /dev/null +++ b/examples/m2m_100/tokenizers/README.md @@ -0,0 +1,18 @@ +# M2M-100 Tokenization + +We apply different tokenization strategies for different languages following the existing literature. Here we provide tok.sh a tokenizer that can be used to reproduce our results. + +To reproduce the results, follow these steps: + +``` +tgt_lang=... +reference_translation=... +cat generation_output | grep -P "^H" | sort -V | cut -f 3- | sh tok.sh $tgt_lang > hyp +cat $reference_translation |sh tok.sh $tgt_lang > ref +sacrebleu -tok 'none' ref < hyp +``` + +## Installation + +Tools needed for all the languages except Arabic can be installed by running install_dependencies.sh +If you want to evaluate Arabic models, please follow the instructions provided here: http://alt.qcri.org/tools/arabic-normalizer/ to install diff --git a/examples/m2m_100/tokenizers/seg_ja.sh b/examples/m2m_100/tokenizers/seg_ja.sh new file mode 100755 index 0000000000..be6f5ca5fe --- /dev/null +++ b/examples/m2m_100/tokenizers/seg_ja.sh @@ -0,0 +1,11 @@ +#!/usr/bin/env bash +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +SCRIPT=`realpath $0` +KYTEA=`dirname $SCRIPT`/thirdparty/kytea +export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$KYTEA/lib:/usr/local/lib +export PATH=$PATH:"$KYTEA/bin" + +cat - | tr -d "[:blank:]" | kytea -notags diff --git a/examples/m2m_100/tokenizers/seg_ko.sh b/examples/m2m_100/tokenizers/seg_ko.sh new file mode 100755 index 0000000000..c523d92634 --- /dev/null +++ b/examples/m2m_100/tokenizers/seg_ko.sh @@ -0,0 +1,12 @@ +#!/usr/bin/env bash +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +SCRIPT=`realpath $0` +MECAB=`dirname $SCRIPT`/thirdparty/mecab-0.996-ko-0.9.2 + +export PATH=$PATH:"$MECAB/bin":"$MECAB/lib" +export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:"$MECAB/lib" + +cat - | mecab -O wakati diff --git a/examples/m2m_100/tokenizers/thirdparty/.gitignore b/examples/m2m_100/tokenizers/thirdparty/.gitignore new file mode 100644 index 0000000000..19eb6a9dd7 --- /dev/null +++ b/examples/m2m_100/tokenizers/thirdparty/.gitignore @@ -0,0 +1,12 @@ +seg_my.py +indic_nlp_library/ +indic_nlp_resources/ +kytea/ +mecab-0.996-ko-0.9.2.tar.gz +mecab-0.996-ko-0.9.2/ +mosesdecoder/ +wat2020.my-en.zip +wat2020.my-en/ +wmt16-scripts/ +mecab-ko-dic-2.1.1-20180720/ +mecab-ko-dic-2.1.1-20180720.tar.gz \ No newline at end of file diff --git a/examples/m2m_100/tokenizers/tokenize_indic.py b/examples/m2m_100/tokenizers/tokenize_indic.py new file mode 100644 index 0000000000..a44fad07f7 --- /dev/null +++ b/examples/m2m_100/tokenizers/tokenize_indic.py @@ -0,0 +1,23 @@ +#!/usr/bin/env python3 +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +# Use: echo {text} | python tokenize_indic.py {language} + +import sys + +from indicnlp.normalize.indic_normalize import IndicNormalizerFactory +from indicnlp.tokenize.indic_tokenize import trivial_tokenize + + +factory = IndicNormalizerFactory() +normalizer = factory.get_normalizer( + sys.argv[1], remove_nuktas=False, nasals_mode="do_nothing" +) + +for line in sys.stdin: + normalized_line = normalizer.normalize(line.strip()) + tokenized_line = " ".join(trivial_tokenize(normalized_line, sys.argv[1])) + print(tokenized_line) diff --git a/examples/m2m_100/tokenizers/tokenize_thai.py b/examples/m2m_100/tokenizers/tokenize_thai.py new file mode 100644 index 0000000000..9c72cb8905 --- /dev/null +++ b/examples/m2m_100/tokenizers/tokenize_thai.py @@ -0,0 +1,13 @@ +#!/usr/bin/env python3 +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import sys + +from pythainlp import word_tokenize + + +for line in sys.stdin: + print(" ".join(word_tokenize(line.strip()))) diff --git a/examples/m2m_100/tokenizers/tokenize_zh.py b/examples/m2m_100/tokenizers/tokenize_zh.py new file mode 100644 index 0000000000..674b5849cb --- /dev/null +++ b/examples/m2m_100/tokenizers/tokenize_zh.py @@ -0,0 +1,14 @@ +#!/usr/bin/env python3 +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +import fileinput + +import sacrebleu + + +for line in fileinput.input(): + print(sacrebleu.tokenize_zh(line)) diff --git a/examples/m2m_100/tokenizers/tokenizer_ar.sh b/examples/m2m_100/tokenizers/tokenizer_ar.sh new file mode 100755 index 0000000000..ad35d7adf2 --- /dev/null +++ b/examples/m2m_100/tokenizers/tokenizer_ar.sh @@ -0,0 +1,27 @@ +#!/usr/bin/env sh +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +# +# Please follow the instructions here http://alt.qcri.org/tools/arabic-normalizer/ +# to install tools needed for Arabic + +echo "Please install Arabic tools: http://alt.qcri.org/tools/arabic-normalizer/" +echo "Then update environment variables in tokenizer_ar.sh" +exit 1 + +SVMTOOL=... +GOMOSESGO=... +QCRI_ARABIC_NORMALIZER=... + +export PERL5LIB="$SVMTOOL/lib":"$GOMOSESGO/bin/MADA-3.2":$PERL5LIB + + +tempfile=$(mktemp) +cat - > $tempfile + +cd $QCRI_ARABIC_NORMALIZER + +bash qcri_normalizer_mada3.2_aramorph1.2.1.sh $tempfile +cat $tempfile.mada_norm-aramorph.europarl_tok diff --git a/examples/mbart/README.md b/examples/mbart/README.md index 13e54c435d..a45e37243c 100644 --- a/examples/mbart/README.md +++ b/examples/mbart/README.md @@ -9,7 +9,7 @@ MBART is a sequence-to-sequence denoising auto-encoder pre-trained on large-scal Model | Description | # params | Download ---|---|---|--- -`mbart.CC25` | mBART model with 12 encoder and decoder layers trained on 25 languages' monolingual corpus | 610M | [mbart.CC25.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/mbart/mbart.CC25.tar.gz) +`mbart.CC25` | mBART model with 12 encoder and decoder layers trained on 25 languages' monolingual corpus | 610M | [mbart.CC25.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/mbart/mbart.cc25.v2.tar.gz) `mbart.ft.ro_en` | finetune mBART cc25 model on ro-en language pairs | 610M | [mbart.cc25.ft.enro.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/mbart/mbart.cc25.ft.enro.tar.gz) ## Results @@ -26,7 +26,7 @@ Model | en-ro | ro-en ## BPE data # download model -wget https://dl.fbaipublicfiles.com/fairseq/models/mbart/mbart.CC25.tar.gz +wget https://dl.fbaipublicfiles.com/fairseq/models/mbart/mbart.cc25.v2.tar.gz tar -xzvf mbart.CC25.tar.gz # bpe data install SPM [here](https://github.com/google/sentencepiece) @@ -45,39 +45,64 @@ ${SPM} --model=${MODEL} < ${DATA}/${TEST}.${TGT} > ${DATA}/${TEST}.spm.${TGT} & ```bash DICT=dict.txt -python preprocess.py \ ---source-lang ${SRC} \ ---target-lang ${TGT} \ ---trainpref ${DATA}/${TRAIN}.spm \ ---validpref ${DATA}/${VALID}.spm \ ---testpref ${DATA}/${TEST}.spm \ ---destdir ${DEST}/${NAME} \ ---thresholdtgt 0 \ ---thresholdsrc 0 \ ---srcdict ${DICT} \ ---tgtdict ${DICT} \ ---workers 70 +fairseq-preprocess \ + --source-lang ${SRC} \ + --target-lang ${TGT} \ + --trainpref ${DATA}/${TRAIN}.spm \ + --validpref ${DATA}/${VALID}.spm \ + --testpref ${DATA}/${TEST}.spm \ + --destdir ${DEST}/${NAME} \ + --thresholdtgt 0 \ + --thresholdsrc 0 \ + --srcdict ${DICT} \ + --tgtdict ${DICT} \ + --workers 70 ``` ## Finetune on EN-RO Finetune on mbart CC25 ```bash -PRETRAIN=/path/to/model/mbart.cc25 +PRETRAIN=mbart.cc25 # fix if you moved the downloaded checkpoint langs=ar_AR,cs_CZ,de_DE,en_XX,es_XX,et_EE,fi_FI,fr_XX,gu_IN,hi_IN,it_IT,ja_XX,kk_KZ,ko_KR,lt_LT,lv_LV,my_MM,ne_NP,nl_XX,ro_RO,ru_RU,si_LK,tr_TR,vi_VN,zh_CN -python train.py path_2_data --encoder-normalize-before --decoder-normalize-before --arch mbart_large --task translation_from_pretrained_bart --source-lang en_XX --target-lang ro_RO --criterion label_smoothed_cross_entropy --label-smoothing 0.2 --dataset-impl mmap --optimizer adam --adam-eps 1e-06 --adam-betas '(0.9, 0.98)' --lr-scheduler polynomial_decay --lr 3e-05 --min-lr -1 --warmup-updates 2500 --total-num-update 40000 --dropout 0.3 --attention-dropout 0.1 --weight-decay 0.0 --max-tokens 1024 --update-freq 2 --save-interval 1 --save-interval-updates 5000 --keep-interval-updates 10 --no-epoch-checkpoints --seed 222 --log-format simple --log-interval 2 --reset-optimizer --reset-meters --reset-dataloader --reset-lr-scheduler --restore-file $PRETRAIN --langs $langs --layernorm-embedding --ddp-backend no_c10d +fairseq-train path_2_data \ + --encoder-normalize-before --decoder-normalize-before \ + --arch mbart_large --layernorm-embedding \ + --task translation_from_pretrained_bart \ + --source-lang en_XX --target-lang ro_RO \ + --criterion label_smoothed_cross_entropy --label-smoothing 0.2 \ + --optimizer adam --adam-eps 1e-06 --adam-betas '(0.9, 0.98)' \ + --lr-scheduler polynomial_decay --lr 3e-05 --warmup-updates 2500 --total-num-update 40000 \ + --dropout 0.3 --attention-dropout 0.1 --weight-decay 0.0 \ + --max-tokens 1024 --update-freq 2 \ + --save-interval 1 --save-interval-updates 5000 --keep-interval-updates 10 --no-epoch-checkpoints \ + --seed 222 --log-format simple --log-interval 2 \ + --restore-file $PRETRAIN \ + --reset-optimizer --reset-meters --reset-dataloader --reset-lr-scheduler \ + --langs $langs \ + --ddp-backend legacy_ddp ``` ## Generate on EN-RO Get sacrebleu on finetuned en-ro model -get tokenizer [here](https://github.com/rsennrich/wmt16-scripts) +get tokenizer [here](https://github.com/rsennrich/wmt16-scripts) +```bash wget https://dl.fbaipublicfiles.com/fairseq/models/mbart/mbart.cc25.ft.enro.tar.gz tar -xzvf mbart.cc25.ft.enro.tar.gz +``` ```bash -model=model.pt -python generate.py path_2_data --path $model --task translation_from_pretrained_bart --gen-subset test -t ro_RO -s en_XX --bpe 'sentencepiece' --sentencepiece-vocab sentence.bpe.model --sacrebleu --remove-bpe 'sentencepiece' --max-sentences 32 --langs $langs > en_ro +model_dir=MBART_finetuned_enro # fix if you moved the checkpoint + +fairseq-generate path_2_data \ + --path $model_dir/model.pt \ + --task translation_from_pretrained_bart \ + --gen-subset test \ + -t ro_RO -s en_XX \ + --bpe 'sentencepiece' --sentencepiece-model $model_dir/sentence.bpe.model \ + --sacrebleu --remove-bpe 'sentencepiece' \ + --batch-size 32 --langs $langs > en_ro cat en_ro | grep -P "^H" |sort -V |cut -f 3- | sed 's/\[ro_RO\]//g' |$TOKENIZER ro > en_ro.hyp cat en_ro | grep -P "^T" |sort -V |cut -f 2- | sed 's/\[ro_RO\]//g' |$TOKENIZER ro > en_ro.ref diff --git a/examples/megatron_11b/README.md b/examples/megatron_11b/README.md index 3cf7aa3acd..945c96c91e 100644 --- a/examples/megatron_11b/README.md +++ b/examples/megatron_11b/README.md @@ -64,7 +64,7 @@ fairseq-train \ --lr-scheduler inverse_sqrt --lr 0.00015 \ --warmup-updates 3000 --weight-decay 0.01 \ --dropout 0.1 --attention-dropout 0.1 \ - --max-sentences 2 \ + --batch-size 2 \ --max-update 300000; ``` @@ -74,7 +74,7 @@ Note: Above was tested on `DGX-1` box, with `8xV100-32Gb` GPUs. **[Wikitext103](https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/)** -Model | vValid perplexity | Test perplexity +Model | Valid perplexity | Test perplexity ---|---|--- `megatron_11b` | 10.64 | 10.54 @@ -139,7 +139,7 @@ fairseq-eval-lm \ --path megatron_11b/model.pt \ --task language_modeling \ --gen-subset test \ - --max-sentences 8 \ + --batch-size 8 \ --criterion cross_entropy \ --context-window 992 \ --distributed-world-size 8 \ diff --git a/examples/megatron_11b/detok.py b/examples/megatron_11b/detok.py index a77a0b4960..49921b28a1 100644 --- a/examples/megatron_11b/detok.py +++ b/examples/megatron_11b/detok.py @@ -6,19 +6,27 @@ import argparse import fileinput + import sacremoses def main(): - parser = argparse.ArgumentParser(description='') - parser.add_argument('files', nargs='*', help='input files') + parser = argparse.ArgumentParser(description="") + parser.add_argument("files", nargs="*", help="input files") args = parser.parse_args() detok = sacremoses.MosesDetokenizer() for line in fileinput.input(args.files, openhook=fileinput.hook_compressed): - print(detok.detokenize(line.strip().split(' ')).replace(' @', '').replace('@ ', '').replace(' =', '=').replace('= ', '=').replace(' – ', '–')) + print( + detok.detokenize(line.strip().split(" ")) + .replace(" @", "") + .replace("@ ", "") + .replace(" =", "=") + .replace("= ", "=") + .replace(" – ", "–") + ) -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/examples/multilingual/ML50_langs.txt b/examples/multilingual/ML50_langs.txt new file mode 100644 index 0000000000..558abbc785 --- /dev/null +++ b/examples/multilingual/ML50_langs.txt @@ -0,0 +1,52 @@ +ar_AR +cs_CZ +de_DE +en_XX +es_XX +et_EE +fi_FI +fr_XX +gu_IN +hi_IN +it_IT +ja_XX +kk_KZ +ko_KR +lt_LT +lv_LV +my_MM +ne_NP +nl_XX +ro_RO +ru_RU +si_LK +tr_TR +vi_VN +zh_CN +af_ZA +az_AZ +bn_IN +fa_IR +he_IL +hr_HR +id_ID +ka_GE +km_KH +mk_MK +ml_IN +mn_MN +mr_IN +pl_PL +ps_AF +pt_XX +sv_SE +sw_KE +ta_IN +te_IN +th_TH +tl_XX +uk_UA +ur_PK +xh_ZA +gl_ES +sl_SI \ No newline at end of file diff --git a/examples/multilingual/README.md b/examples/multilingual/README.md new file mode 100644 index 0000000000..46ff9c351b --- /dev/null +++ b/examples/multilingual/README.md @@ -0,0 +1,158 @@ +# Multilingual Translation + +[[Multilingual Translation with Extensible Multilingual Pretraining and Finetuning, https://arxiv.org/abs/2008.00401]](https://arxiv.org/abs/2008.00401) + +## Introduction + +This work is for training multilingual translation models with multiple bitext datasets. This multilingual translation framework supports (see [[training section]](#Training) and [[finetuning section]](#Finetuning) for examples) + +* temperature based sampling over unbalancing datasets of different translation directions + - --sampling-method' with + choices=['uniform', 'temperature', 'concat'] + - --sampling-temperature +* configurable to automatically add source and/or target language tokens to source/target sentences using data which are prepared in the same way as bilignual training + - --encoder-langtok with choices=['src', 'tgt', None] to specify whether to add source or target language tokens to the source sentences + - --decoder-langtok (binary option) to specify whether to add target language tokens to the target sentences or not +* finetuning mBART pretrained models for multilingual translation + - --finetune-from-model to specify the path from which to load the pretrained model + +## Preprocessing data +Multilingual training requires a joint BPE vocab. Please follow [mBART's preprocessing steps](https://github.com/pytorch/fairseq/tree/main/examples/mbart#bpe-data) to reuse our pretrained sentence-piece model. + +You can also train a joint BPE model on your own dataset and then follow the steps in [[link]](https://github.com/pytorch/fairseq/tree/main/examples/translation#multilingual-translation). + +## Training + + +```bash +lang_pairs= +path_2_data= +lang_list= + +fairseq-train $path_2_data \ + --encoder-normalize-before --decoder-normalize-before \ + --arch transformer --layernorm-embedding \ + --task translation_multi_simple_epoch \ + --sampling-method "temperature" \ + --sampling-temperature 1.5 \ + --encoder-langtok "src" \ + --decoder-langtok \ + --lang-dict "$lang_list" \ + --lang-pairs "$lang_pairs" \ + --criterion label_smoothed_cross_entropy --label-smoothing 0.2 \ + --optimizer adam --adam-eps 1e-06 --adam-betas '(0.9, 0.98)' \ + --lr-scheduler inverse_sqrt --lr 3e-05 --warmup-updates 2500 --max-update 40000 \ + --dropout 0.3 --attention-dropout 0.1 --weight-decay 0.0 \ + --max-tokens 1024 --update-freq 2 \ + --save-interval 1 --save-interval-updates 5000 --keep-interval-updates 10 --no-epoch-checkpoints \ + --seed 222 --log-format simple --log-interval 2 +``` + +## Finetuning +We can also finetune multilingual models from a monolingual pretrained models, e.g. [mMBART](https://github.com/pytorch/fairseq/tree/main/examples/mbart). +```bash +lang_pairs= +path_2_data= +lang_list= +pretrained_model= + +fairseq-train $path_2_data \ + --finetune-from-model $pretrained_model \ + --encoder-normalize-before --decoder-normalize-before \ + --arch transformer --layernorm-embedding \ + --task translation_multi_simple_epoch \ + --sampling-method "temperature" \ + --sampling-temperature 1.5 \ + --encoder-langtok "src" \ + --decoder-langtok \ + --lang-dict "$lang_list" \ + --lang-pairs "$lang_pairs" \ + --criterion label_smoothed_cross_entropy --label-smoothing 0.2 \ + --optimizer adam --adam-eps 1e-06 --adam-betas '(0.9, 0.98)' \ + --lr-scheduler inverse_sqrt --lr 3e-05 --warmup-updates 2500 --max-update 40000 \ + --dropout 0.3 --attention-dropout 0.1 --weight-decay 0.0 \ + --max-tokens 1024 --update-freq 2 \ + --save-interval 1 --save-interval-updates 5000 --keep-interval-updates 10 --no-epoch-checkpoints \ + --seed 222 --log-format simple --log-interval 2 +``` +## Generate +The following command uses the multilingual task (translation_multi_simple_epoch) to generate translation from $source_lang to $target_lang on the test dataset. During generaton, the source language tokens are added to source sentences and the target language tokens are added as the starting token to decode target sentences. Options --lang-dict and --lang-pairs are needed to tell the generation process the ordered list of languages and translation directions that the trained model are awared of; they will need to be consistent with the training. + +```bash +model= +source_lang= +target_lang= + +fairseq-generate $path_2_data \ + --path $model \ + --task translation_multi_simple_epoch \ + --gen-subset test \ + --source-lang $source_lang \ + --target-lang $target_lang + --sacrebleu --remove-bpe 'sentencepiece'\ + --batch-size 32 \ + --encoder-langtok "src" \ + --decoder-langtok \ + --lang-dict "$lang_list" \ + --lang-pairs "$lang_pairs" > ${source_lang}_${target_lang}.txt +``` +Fairseq will generate translation into a file {source_lang}_${target_lang}.txt with sacreblue at the end. + +You can also use costomized tokenizer to compare the performance with the literature. For example, you get a tokenizer [here](https://github.com/rsennrich/wmt16-scripts) and do the following: +```bash +TOKENIZER= +TOK_CMD=<"$TOKENIZER $target_lang" or cat for sacrebleu> + +cat {source_lang}_${target_lang}.txt | grep -P "^H" |sort -V |cut -f 3- |$TOK_CMD > ${source_lang}_${target_lang}.hyp +cat {source_lang}_${target_lang}.txt | grep -P "^T" |sort -V |cut -f 2- |$TOK_CMD > ${source_lang}_${target_lang}.ref +sacrebleu -tok 'none' -s 'none' ${source_lang}_${target_lang}.ref < ${source_lang}_${target_lang}.hyp +``` + +# mBART50 models + +* [mMBART 50 pretrained model](https://dl.fbaipublicfiles.com/fairseq/models/mbart50/mbart50.pretrained.tar.gz). +* [mMBART 50 finetuned many-to-one](https://dl.fbaipublicfiles.com/fairseq/models/mbart50/mbart50.ft.n1.tar.gz). +* [mMBART 50 finetuned one-to-many](https://dl.fbaipublicfiles.com/fairseq/models/mbart50/mbart50.ft.1n.tar.gz). +* [mMBART 50 finetuned many-to-many](https://dl.fbaipublicfiles.com/fairseq/models/mbart50/mbart50.ft.nn.tar.gz). + +Please download and extract from the above tarballs. Each tarball contains +* The fairseq model checkpoint: model.pt +* The list of supported languages: ML50_langs.txt +* Sentence piece model: sentence.bpe.model +* Fairseq dictionary of each language: dict.{lang}.txt (please replace lang with a language specified in ML50_langs.txt) + +To use the trained models, +* use the tool [binarize.py](./data_scripts/binarize.py) to binarize your data using sentence.bpe.model and dict.{lang}.txt, and copy the dictionaries to your data path +* then run the generation command: +```bash +path_2_data= +model=/model.pt +lang_list=/ML50_langs.txt +source_lang= +target_lang= + +fairseq-generate $path_2_data \ + --path $model \ + --task translation_multi_simple_epoch \ + --gen-subset test \ + --source-lang $source_lang \ + --target-lang $target_lang + --sacrebleu --remove-bpe 'sentencepiece'\ + --batch-size 32 \ + --encoder-langtok "src" \ + --decoder-langtok \ + --lang-dict "$lang_list" +``` + +## Citation + +```bibtex +@article{tang2020multilingual, + title={Multilingual Translation with Extensible Multilingual Pretraining and Finetuning}, + author={Yuqing Tang and Chau Tran and Xian Li and Peng-Jen Chen and Naman Goyal and Vishrav Chaudhary and Jiatao Gu and Angela Fan}, + year={2020}, + eprint={2008.00401}, + archivePrefix={arXiv}, + primaryClass={cs.CL} +} +``` diff --git a/examples/multilingual/data_scripts/README.md b/examples/multilingual/data_scripts/README.md new file mode 100644 index 0000000000..cc610c0c9e --- /dev/null +++ b/examples/multilingual/data_scripts/README.md @@ -0,0 +1,24 @@ + +# Install dependency +```bash +pip install -r requirement.txt +``` + +# Download the data set +```bash +export WORKDIR_ROOT= + +``` +The downloaded data will be at $WORKDIR_ROOT/ML50 + +# preprocess the data +Install SPM [here](https://github.com/google/sentencepiece) +```bash +export WORKDIR_ROOT= +export SPM_PATH= +``` +* $WORKDIR_ROOT/ML50/raw: extracted raw data +* $WORKDIR_ROOT/ML50/dedup: dedup data +* $WORKDIR_ROOT/ML50/clean: data with valid and test sentences removed from the dedup data + + diff --git a/examples/multilingual/data_scripts/binarize.py b/examples/multilingual/data_scripts/binarize.py new file mode 100755 index 0000000000..ee54c6aabf --- /dev/null +++ b/examples/multilingual/data_scripts/binarize.py @@ -0,0 +1,200 @@ +import shutil +import os, sys +from subprocess import check_call, check_output +import glob +import argparse +import shutil +import pathlib +import itertools + +def call_output(cmd): + print(f"Executing: {cmd}") + ret = check_output(cmd, shell=True) + print(ret) + return ret + +def call(cmd): + print(cmd) + check_call(cmd, shell=True) + + +WORKDIR_ROOT = os.environ.get('WORKDIR_ROOT', None) + +if WORKDIR_ROOT is None or not WORKDIR_ROOT.strip(): + print('please specify your working directory root in OS environment variable WORKDIR_ROOT. Exitting..."') + sys.exit(-1) + +SPM_PATH = os.environ.get('SPM_PATH', None) + +if SPM_PATH is None or not SPM_PATH.strip(): + print("Please install sentence piecence from https://github.com/google/sentencepiece and set SPM_PATH pointing to the installed spm_encode.py. Exitting...") + sys.exit(-1) + + +SPM_MODEL = f'{WORKDIR_ROOT}/sentence.bpe.model' +SPM_VOCAB = f'{WORKDIR_ROOT}/dict_250k.txt' + +SPM_ENCODE = f'{SPM_PATH}' + +if not os.path.exists(SPM_MODEL): + call(f"wget https://dl.fbaipublicfiles.com/fairseq/models/mbart50/sentence.bpe.model -O {SPM_MODEL}") + + +if not os.path.exists(SPM_VOCAB): + call(f"wget https://dl.fbaipublicfiles.com/fairseq/models/mbart50/dict_250k.txt -O {SPM_VOCAB}") + + + +def get_data_size(raw): + cmd = f'wc -l {raw}' + ret = call_output(cmd) + return int(ret.split()[0]) + +def encode_spm(model, direction, prefix='', splits=['train', 'test', 'valid'], pairs_per_shard=None): + src, tgt = direction.split('-') + + for split in splits: + src_raw, tgt_raw = f'{RAW_DIR}/{split}{prefix}.{direction}.{src}', f'{RAW_DIR}/{split}{prefix}.{direction}.{tgt}' + if os.path.exists(src_raw) and os.path.exists(tgt_raw): + cmd = f"""python {SPM_ENCODE} \ + --model {model}\ + --output_format=piece \ + --inputs {src_raw} {tgt_raw} \ + --outputs {BPE_DIR}/{direction}{prefix}/{split}.bpe.{src} {BPE_DIR}/{direction}{prefix}/{split}.bpe.{tgt} """ + print(cmd) + call(cmd) + + +def binarize_( + bpe_dir, + databin_dir, + direction, spm_vocab=SPM_VOCAB, + splits=['train', 'test', 'valid'], +): + src, tgt = direction.split('-') + + try: + shutil.rmtree(f'{databin_dir}', ignore_errors=True) + os.mkdir(f'{databin_dir}') + except OSError as error: + print(error) + cmds = [ + "fairseq-preprocess", + f"--source-lang {src} --target-lang {tgt}", + f"--destdir {databin_dir}/", + f"--workers 8", + ] + if isinstance(spm_vocab, tuple): + src_vocab, tgt_vocab = spm_vocab + cmds.extend( + [ + f"--srcdict {src_vocab}", + f"--tgtdict {tgt_vocab}", + ] + ) + else: + cmds.extend( + [ + f"--joined-dictionary", + f"--srcdict {spm_vocab}", + ] + ) + input_options = [] + if 'train' in splits and glob.glob(f"{bpe_dir}/train.bpe*"): + input_options.append( + f"--trainpref {bpe_dir}/train.bpe", + ) + if 'valid' in splits and glob.glob(f"{bpe_dir}/valid.bpe*"): + input_options.append(f"--validpref {bpe_dir}/valid.bpe") + if 'test' in splits and glob.glob(f"{bpe_dir}/test.bpe*"): + input_options.append(f"--testpref {bpe_dir}/test.bpe") + if len(input_options) > 0: + cmd = " ".join(cmds + input_options) + print(cmd) + call(cmd) + + +def binarize( + databin_dir, + direction, spm_vocab=SPM_VOCAB, prefix='', + splits=['train', 'test', 'valid'], + pairs_per_shard=None, +): + def move_databin_files(from_folder, to_folder): + for bin_file in glob.glob(f"{from_folder}/*.bin") \ + + glob.glob(f"{from_folder}/*.idx") \ + + glob.glob(f"{from_folder}/dict*"): + try: + shutil.move(bin_file, to_folder) + except OSError as error: + print(error) + bpe_databin_dir = f"{BPE_DIR}/{direction}{prefix}_databin" + bpe_dir = f"{BPE_DIR}/{direction}{prefix}" + if pairs_per_shard is None: + binarize_(bpe_dir, bpe_databin_dir, direction, spm_vocab=spm_vocab, splits=splits) + move_databin_files(bpe_databin_dir, databin_dir) + else: + # binarize valid and test which will not be sharded + binarize_( + bpe_dir, bpe_databin_dir, direction, + spm_vocab=spm_vocab, splits=[s for s in splits if s != "train"]) + for shard_bpe_dir in glob.glob(f"{bpe_dir}/shard*"): + path_strs = os.path.split(shard_bpe_dir) + shard_str = path_strs[-1] + shard_folder = f"{bpe_databin_dir}/{shard_str}" + databin_shard_folder = f"{databin_dir}/{shard_str}" + print(f'working from {shard_folder} to {databin_shard_folder}') + os.makedirs(databin_shard_folder, exist_ok=True) + binarize_( + shard_bpe_dir, shard_folder, direction, + spm_vocab=spm_vocab, splits=["train"]) + + for test_data in glob.glob(f"{bpe_databin_dir}/valid.*") + glob.glob(f"{bpe_databin_dir}/test.*"): + filename = os.path.split(test_data)[-1] + try: + os.symlink(test_data, f"{databin_shard_folder}/{filename}") + except OSError as error: + print(error) + move_databin_files(shard_folder, databin_shard_folder) + + +def load_langs(path): + with open(path) as fr: + langs = [l.strip() for l in fr] + return langs + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument("--data_root", default=f"{WORKDIR_ROOT}/ML50") + parser.add_argument("--raw-folder", default='raw') + parser.add_argument("--bpe-folder", default='bpe') + parser.add_argument("--databin-folder", default='databin') + + args = parser.parse_args() + + DATA_PATH = args.data_root #'/private/home/yuqtang/public_data/ML50' + RAW_DIR = f'{DATA_PATH}/{args.raw_folder}' + BPE_DIR = f'{DATA_PATH}/{args.bpe_folder}' + DATABIN_DIR = f'{DATA_PATH}/{args.databin_folder}' + os.makedirs(BPE_DIR, exist_ok=True) + + raw_files = itertools.chain( + glob.glob(f'{RAW_DIR}/train*'), + glob.glob(f'{RAW_DIR}/valid*'), + glob.glob(f'{RAW_DIR}/test*'), + ) + + directions = [os.path.split(file_path)[-1].split('.')[1] for file_path in raw_files] + + for direction in directions: + prefix = "" + splits = ['train', 'valid', 'test'] + try: + shutil.rmtree(f'{BPE_DIR}/{direction}{prefix}', ignore_errors=True) + os.mkdir(f'{BPE_DIR}/{direction}{prefix}') + os.makedirs(DATABIN_DIR, exist_ok=True) + except OSError as error: + print(error) + spm_model, spm_vocab = SPM_MODEL, SPM_VOCAB + encode_spm(spm_model, direction=direction, splits=splits) + binarize(DATABIN_DIR, direction, spm_vocab=spm_vocab, splits=splits) diff --git a/examples/multilingual/data_scripts/check_iswlt_test_data.py b/examples/multilingual/data_scripts/check_iswlt_test_data.py new file mode 100644 index 0000000000..f8e2eb0f15 --- /dev/null +++ b/examples/multilingual/data_scripts/check_iswlt_test_data.py @@ -0,0 +1,67 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +import os, sys +import subprocess +import re +from subprocess import check_call, check_output + +WORKDIR_ROOT = os.environ.get('WORKDIR_ROOT', None) + +if WORKDIR_ROOT is None or not WORKDIR_ROOT.strip(): + print('please specify your working directory root in OS environment variable WORKDIR_ROOT. Exitting..."') + sys.exit(-1) + + +BLEU_REGEX = re.compile("^BLEU\\S* = (\\S+) ") +def run_eval_bleu(cmd): + output = check_output(cmd, shell=True, stderr=subprocess.STDOUT).decode("utf-8").strip() + print(output) + bleu = -1.0 + for line in output.strip().split('\n'): + m = BLEU_REGEX.search(line) + if m is not None: + bleu = m.groups()[0] + bleu = float(bleu) + break + return bleu + +def check_data_test_bleu(raw_folder, data_lang_pairs): + not_matchings = [] + for sacrebleu_set, src_tgts in data_lang_pairs: + for src_tgt in src_tgts: + print(f'checking test bleus for: {src_tgt} at {sacrebleu_set}') + src, tgt = src_tgt.split('-') + ssrc, stgt = src[:2], tgt[:2] + if os.path.exists(f'{raw_folder}/test.{tgt}-{src}.{src}'): + # reversed direction may have different test set + test_src = f'{raw_folder}/test.{tgt}-{src}.{src}' + else: + test_src = f'{raw_folder}/test.{src}-{tgt}.{src}' + cmd1 = f'cat {test_src} | sacrebleu -t "{sacrebleu_set}" -l {stgt}-{ssrc}; [ $? -eq 0 ] || echo ""' + test_tgt = f'{raw_folder}/test.{src}-{tgt}.{tgt}' + cmd2 = f'cat {test_tgt} | sacrebleu -t "{sacrebleu_set}" -l {ssrc}-{stgt}; [ $? -eq 0 ] || echo ""' + bleu1 = run_eval_bleu(cmd1) + if bleu1 != 100.0: + not_matchings.append(f'{sacrebleu_set}:{src_tgt} source side not matching: {test_src}') + bleu2 = run_eval_bleu(cmd2) + if bleu2 != 100.0: + not_matchings.append(f'{sacrebleu_set}:{src_tgt} target side not matching: {test_tgt}') + return not_matchings + +if __name__ == "__main__": + to_data_path = f'{WORKDIR_ROOT}/iwsltv2' + not_matching = check_data_test_bleu( + f'{to_data_path}/raw', + [ + ('iwslt17', ['en_XX-ar_AR', 'en_XX-ko_KR', 'ar_AR-en_XX', 'ko_KR-en_XX']), + ('iwslt17', ['en_XX-it_IT', 'en_XX-nl_XX', 'it_IT-en_XX', 'nl_XX-en_XX']), + ('iwslt17/tst2015', ['en_XX-vi_VN', "vi_VN-en_XX"]), + ] + ) + if len(not_matching) > 0: + print('the following datasets do not have matching test datasets:\n\t', '\n\t'.join(not_matching)) + diff --git a/examples/multilingual/data_scripts/check_self_overlaps.py b/examples/multilingual/data_scripts/check_self_overlaps.py new file mode 100644 index 0000000000..07b338dcfd --- /dev/null +++ b/examples/multilingual/data_scripts/check_self_overlaps.py @@ -0,0 +1,103 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +import os +import glob +import argparse +from utils.dedup import deup +import sys + +WORKDIR_ROOT = os.environ.get('WORKDIR_ROOT', None) + +if WORKDIR_ROOT is None or not WORKDIR_ROOT.strip(): + print('please specify your working directory root in OS environment variable WORKDIR_ROOT. Exitting..."') + sys.exit(-1) + +def get_directions(folder): + raw_files = glob.glob(f'{folder}/train*') + directions = [os.path.split(file_path)[-1].split('.')[1] for file_path in raw_files] + return directions + +def diff_list(lhs, rhs): + return set(lhs).difference(set(rhs)) + +def check_diff( + from_src_file, from_tgt_file, + to_src_file, to_tgt_file, +): + seen_in_from = set() + seen_src_in_from = set() + seen_tgt_in_from = set() + from_count = 0 + with open(from_src_file, encoding='utf-8') as fsrc, \ + open(from_tgt_file, encoding='utf-8') as ftgt: + for s, t in zip(fsrc, ftgt): + seen_in_from.add((s, t)) + seen_src_in_from.add(s) + seen_tgt_in_from.add(t) + from_count += 1 + common = 0 + common_src = 0 + common_tgt = 0 + to_count = 0 + seen = set() + + with open(to_src_file, encoding='utf-8') as fsrc, \ + open(to_tgt_file, encoding='utf-8') as ftgt: + for s, t in zip(fsrc, ftgt): + to_count += 1 + if (s, t) not in seen: + if (s, t) in seen_in_from: + common += 1 + if s in seen_src_in_from: + common_src += 1 + seen_src_in_from.remove(s) + if t in seen_tgt_in_from: + common_tgt += 1 + seen_tgt_in_from.remove(t) + seen.add((s, t)) + return common, common_src, common_tgt, from_count, to_count + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--folder", type=str, required=True, + help="the data folder ") + parser.add_argument("--split", type=str, default='test', + help="split (valid, test) to check against training data") + parser.add_argument('--directions', type=str, default=None, required=False) + + args = parser.parse_args() + + if args.directions is None: + directions = set(get_directions(args.folder)) + directions = sorted(directions) + else: + directions = args.directions.split(',') + directions = sorted(set(directions)) + + results = [] + print(f'checking where {args.split} split data are in training') + print(f'direction\tcommon_count\tsrc common\ttgt common\tfrom_size\tto_size') + + for direction in directions: + src, tgt = direction.split('-') + from_src_file = f'{args.folder}/{args.split}.{src}-{tgt}.{src}' + from_tgt_file = f'{args.folder}/{args.split}.{src}-{tgt}.{tgt}' + if not os.path.exists(from_src_file): + # some test/valid data might in reverse directinos: + from_src_file = f'{args.folder}/{args.split}.{tgt}-{src}.{src}' + from_tgt_file = f'{args.folder}/{args.split}.{tgt}-{src}.{tgt}' + to_src_file = f'{args.folder}/train.{src}-{tgt}.{src}' + to_tgt_file = f'{args.folder}/train.{src}-{tgt}.{tgt}' + if not os.path.exists(to_src_file) or not os.path.exists(from_src_file): + continue + r = check_diff(from_src_file, from_tgt_file, to_src_file, to_tgt_file) + results.append(r) + print(f'{direction}\t', '\t'.join(map(str, r))) + + +if __name__ == "__main__": + main() diff --git a/examples/multilingual/data_scripts/check_valid_test_overlaps.py b/examples/multilingual/data_scripts/check_valid_test_overlaps.py new file mode 100644 index 0000000000..40fa9aecdf --- /dev/null +++ b/examples/multilingual/data_scripts/check_valid_test_overlaps.py @@ -0,0 +1,124 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +import os +import argparse +import pandas as pd +import sys + + +WORKDIR_ROOT = os.environ.get('WORKDIR_ROOT', None) + +if WORKDIR_ROOT is None or not WORKDIR_ROOT.strip(): + print('please specify your working directory root in OS environment variable WORKDIR_ROOT. Exitting..."') + sys.exit(-1) + +def load_langs(path): + with open(path) as fr: + langs = [l.strip() for l in fr] + return langs + + + +def load_sentences(raw_data, split, direction): + src, tgt = direction.split('-') + src_path = f"{raw_data}/{split}.{direction}.{src}" + tgt_path = f"{raw_data}/{split}.{direction}.{tgt}" + if os.path.exists(src_path) and os.path.exists(tgt_path): + return [(src, open(src_path).read().splitlines()), (tgt, open(tgt_path).read().splitlines())] + else: + return [] + +def swap_direction(d): + src, tgt = d.split('-') + return f'{tgt}-{src}' + +def get_all_test_data(raw_data, directions, split='test'): + test_data = [ + x + for dd in directions + for d in [dd, swap_direction(dd)] + for x in load_sentences(raw_data, split, d) + ] + # all_test_data = {s for _, d in test_data for s in d} + all_test_data = {} + for lang, d in test_data: + for s in d: + s = s.strip() + lgs = all_test_data.get(s, set()) + lgs.add(lang) + all_test_data[s] = lgs + return all_test_data, test_data + + +def check_train_sentences(src_path, tgt_path, direction, all_test_data, mess_up_train={}): + # src, tgt = direction.split('-') + print(f'check training data for {direction} in {src_path} and {tgt_path}') + size = 0 + overlapped_size_counted_dup = 0 + if not os.path.exists(tgt_path) or not os.path.exists(src_path): + return mess_up_train, size, overlapped_size_counted_dup + + with open(src_path) as f, open(tgt_path) as g: + for src_line, tgt_line in zip(f, g): + s = src_line.strip() + t = tgt_line.strip() + size += 1 + if s in all_test_data: + langs = mess_up_train.get(s, set()) + langs.add(direction) + mess_up_train[s] = langs + overlapped_size_counted_dup += 1 + if t in all_test_data: + langs = mess_up_train.get(t, set()) + langs.add(direction) + mess_up_train[t] = langs + overlapped_size_counted_dup += 1 + print(f'{direction}: size={size}, overlapped={overlapped_size_counted_dup}') + return mess_up_train, size, overlapped_size_counted_dup + +def check_train_all(raw_data, directions, all_test_data): + mess_up_train = {} + data_sizes = {} + # raw_data = '~chau/data-bin/MineBART/multilingual_mined_100M/en_XX/et_EE-en_XX/all.{en_XX, et_EE}' + print(f'checking training data againsts # {len(all_test_data)} sentences') + print(f'example test data: ', [s for i, s in enumerate(all_test_data.keys()) if i < 10]) + for direction in directions: + src, tgt = direction.split('-') + path = f'{raw_data}/en_XX/{direction}/all' + src_path = f'{path}.{src}' + tgt_path = f'{path}.{tgt}' + print(f'checking {src_path} {tgt_path}') + _, size, overlapped_size_counted_dup = check_train_sentences(src_path, tgt_path, direction, all_test_data, mess_up_train) + data_sizes[direction] = (size, overlapped_size_counted_dup) + return mess_up_train, data_sizes + + + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--folder", type=str, required=True, + help="the data folder ") + parser.add_argument("--test-data", type=str, required=True, + help="the test data folder ") + parser.add_argument('--directions', type=str, default=None, required=False) + + args = parser.parse_args() + directions = args.directions.split(',') + directions = sorted(set(directions)) + + results = [] + # print(f'checking where {args.split} split data are in training') + # print(f'direction\tcommon_count\tsrc common\ttgt common\tfrom_size\tto_size') + raw_data = args.folder + all_test_data, test_data = get_all_test_data(args.test_data, directions, split='test') + mess_up_train, data_sizes = check_train_all(raw_data, directions, all_test_data) + print(data_sizes) + + +if __name__ == "__main__": + main() diff --git a/examples/multilingual/data_scripts/dedup_all.py b/examples/multilingual/data_scripts/dedup_all.py new file mode 100644 index 0000000000..ef39c05ee6 --- /dev/null +++ b/examples/multilingual/data_scripts/dedup_all.py @@ -0,0 +1,52 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + + +import os +import glob +import argparse +from utils.dedup import deup + +import sys +WORKDIR_ROOT = os.environ.get('WORKDIR_ROOT', None) + +if WORKDIR_ROOT is None or not WORKDIR_ROOT.strip(): + print('please specify your working directory root in OS environment variable WORKDIR_ROOT. Exitting..."') + sys.exit(-1) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--from-folder", type=str, required=True, + help="the data folder to be dedup") + parser.add_argument("--to-folder", type=str, required=True, + help="the data folder to save deduped data") + parser.add_argument('--directions', type=str, default=None, required=False) + + args = parser.parse_args() + + if args.directions is None: + raw_files = glob.glob(f'{args.from_folder}/train*') + + directions = [os.path.split(file_path)[-1].split('.')[1] for file_path in raw_files] + else: + directions = args.directions.split(',') + directions = sorted(set(directions)) + + for direction in directions: + src, tgt = direction.split('-') + src_file = f'{args.from_folder}/train.{src}-{tgt}.{src}' + tgt_file = f'{args.from_folder}/train.{src}-{tgt}.{tgt}' + src_file_out = f'{args.to_folder}/train.{src}-{tgt}.{src}' + tgt_file_out = f'{args.to_folder}/train.{src}-{tgt}.{tgt}' + assert src_file != src_file_out + assert tgt_file != tgt_file_out + print(f'deduping {src_file}, {tgt_file}') + deup(src_file, tgt_file, src_file_out, tgt_file_out) + + +if __name__ == "__main__": + main() diff --git a/examples/multilingual/data_scripts/download_ML50_v1.sh b/examples/multilingual/data_scripts/download_ML50_v1.sh new file mode 100644 index 0000000000..99fbc75920 --- /dev/null +++ b/examples/multilingual/data_scripts/download_ML50_v1.sh @@ -0,0 +1,30 @@ +#!/bin/bash +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +if [ -z $WORKDIR_ROOT ] ; +then + echo "please specify your working directory root in environment variable WORKDIR_ROOT. Exitting..." + exit +fi + +# first run download_wmt20.sh; it will install a few useful tools for other scripts +# TODO: need to print out instructions on downloading a few files which requires manually authentication from the websites +bash ./download_wmt20.sh + +python ./download_wmt19_and_before.py +bash ./download_wat19_my.sh +python ./download_ted_and_extract.py +bash ./download_lotus.sh +bash ./download_iitb.sh +bash ./download_af_xh.sh + + +# IWSLT downloading URLs have changed in between; TODO: fix them: +bash ./download_iwslt_and_extract.sh + +# TODO: globalvoices URLs changed; need to be fixed +bash ./download_flores_data.sh diff --git a/examples/multilingual/data_scripts/download_af_xh.sh b/examples/multilingual/data_scripts/download_af_xh.sh new file mode 100644 index 0000000000..a78fbbbbcc --- /dev/null +++ b/examples/multilingual/data_scripts/download_af_xh.sh @@ -0,0 +1,164 @@ +#!/bin/bash +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +# set -x -e + +if [ -z $WORKDIR_ROOT ] ; +then + echo "please specify your working directory root in environment variable WORKDIR_ROOT. Exitting..." + exit +fi + + +# put intermediate files +TMP_DIR=$WORKDIR_ROOT/temp/af_xhv2 +# output {train,valid,test} files to dest +DEST=${WORKDIR_ROOT}/ML50/raw + + + +ROOT=${WORKDIR_ROOT} +UTILS=$PWD/utils +TMX2CORPUS="${UTILS}/tmx2corpus" +TMX_TOOL="python ${TMX2CORPUS}/tmx2corpus.py" + +mkdir -p $TMP_DIR +mkdir -p $DEST +mkdir -p $UTILS + +function download_opus(){ + src=$1 + tgt=$2 + subset=$3 + ulr=$4 + + mkdir extract_$subset.$src-$tgt + pushd extract_$subset.$src-$tgt + if [ ! -f "$subset.$src-$tgt.tmx.gz" ]; then + wget $url -O "$subset.$src-$tgt.tmx.gz" + gzip -d "$subset.$src-$tgt.tmx.gz" + f=$subset.$src-$tgt.tmx + $TMX_TOOL $f + mv bitext.$src ../$subset.$src-$tgt.$src + mv bitext.$tgt ../$subset.$src-$tgt.$tgt + fi + popd +} + +function concat_subsets(){ + src=$1 + tgt=$2 + subsets=$3 + src_train=raw_train.$src-$tgt.$src + tgt_train=raw_train.$src-$tgt.$tgt + > $src_train + > $tgt_train + for subset in $subsets; do + cat $subset.$src-$tgt.$src >> $src_train + cat $subset.$src-$tgt.$tgt >> $tgt_train + done +} + + + +function get_seeded_random() +{ + seed="$1" + openssl enc -aes-256-ctr -pass pass:"$seed" -nosalt \ + /dev/null +} + +function split_train_valid(){ + src=$1 + tgt=$2 + raw_src_train=raw_train.$src-$tgt.$src + raw_tgt_train=raw_train.$src-$tgt.$tgt + + shuf --random-source=<(get_seeded_random 43) $raw_src_train > shuffled.$src-$tgt.$src + shuf --random-source=<(get_seeded_random 43) $raw_tgt_train > shuffled.$src-$tgt.$tgt + + head -n 1500 shuffled.$src-$tgt.$src > valid.$src-$tgt.$src + head -n 1500 shuffled.$src-$tgt.$tgt > valid.$src-$tgt.$tgt + + tail +1501 shuffled.$src-$tgt.$src > train.$src-$tgt.$src + tail +1501 shuffled.$src-$tgt.$tgt > train.$src-$tgt.$tgt +} + +function copy2dst(){ + lsrc=$1 + ltgt=$2 + src=${lsrc:0:2} + tgt=${ltgt:0:2} + + + cp valid.$src-$tgt.$src $DEST/valid.$lsrc-$ltgt.$lsrc + cp valid.$src-$tgt.$tgt $DEST/valid.$lsrc-$ltgt.$ltgt + + cp train.$src-$tgt.$src $DEST/train.$lsrc-$ltgt.$lsrc + cp train.$src-$tgt.$tgt $DEST/train.$lsrc-$ltgt.$ltgt +} + + + + +#for xh-en +declare -A xh_en_urls +xh_en_urls=( + [Tatoeba]=https://object.pouta.csc.fi/OPUS-Tatoeba/v20190709/tmx/en-xh.tmx.gz + [wikimedia]=https://object.pouta.csc.fi/OPUS-wikimedia/v20190628/tmx/en-xh.tmx.gz + [memat]=https://object.pouta.csc.fi/OPUS-memat/v1/tmx/en-xh.tmx.gz + [uedin]=https://object.pouta.csc.fi/OPUS-bible-uedin/v1/tmx/en-xh.tmx.gz + [GNOME]=https://object.pouta.csc.fi/OPUS-GNOME/v1/tmx/en-xh.tmx.gz + [XhosaNavy]=https://object.pouta.csc.fi/OPUS-XhosaNavy/v1/tmx/en-xh.tmx.gz + [KDE4]=https://object.pouta.csc.fi/OPUS-KDE4/v2/tmx/en-xh.tmx.gz + [Ubuntu]=https://object.pouta.csc.fi/OPUS-Ubuntu/v14.10/tmx/en-xh.tmx.gz +) + +mkdir $TMP_DIR/xh-en +pushd $TMP_DIR/xh-en +for k in "${!xh_en_urls[@]}" +do + name=$k + url=${xh_en_urls[$k]} + echo "$name: $url" + download_opus xh en $name $ulr +done +concat_subsets xh en "${!xh_en_urls[@]}" +split_train_valid xh en +copy2dst xh_ZA en_XX +popd + + +## +#for af-en +declare -A af_en_urls +af_en_urls=( + [Tatoeba]=https://object.pouta.csc.fi/OPUS-Tatoeba/v20190709/tmx/af-en.tmx.gz + [uedin]=https://object.pouta.csc.fi/OPUS-bible-uedin/v1/tmx/af-en.tmx.gz + [GNOME]=https://object.pouta.csc.fi/OPUS-GNOME/v1/tmx/af-en.tmx.gz + [QED]=https://object.pouta.csc.fi/OPUS-QED/v2.0a/tmx/af-en.tmx.gz + [KDE4]=https://object.pouta.csc.fi/OPUS-KDE4/v2/tmx/af-en.tmx.gz + [OpenSubtitles]=https://object.pouta.csc.fi/OPUS-OpenSubtitles/v2018/tmx/af-en.tmx.gz + [SPC]=https://object.pouta.csc.fi/OPUS-SPC/v1/tmx/af-en.tmx.gz + [Ubuntu]=https://object.pouta.csc.fi/OPUS-Ubuntu/v14.10/tmx/af-en.tmx.gz +) + +mkdir $TMP_DIR/af-en +pushd $TMP_DIR/af-en +for k in "${!af_en_urls[@]}" +do + name=$k + url=${af_en_urls[$k]} + echo "$name: $url" + download_opus af en $name $ulr +done +concat_subsets af en "${!af_en_urls[@]}" +split_train_valid af en +copy2dst af_ZA en_XX +popd + + diff --git a/examples/multilingual/data_scripts/download_flores_data.sh b/examples/multilingual/data_scripts/download_flores_data.sh new file mode 100644 index 0000000000..e6175ce0c3 --- /dev/null +++ b/examples/multilingual/data_scripts/download_flores_data.sh @@ -0,0 +1,246 @@ +#!/bin/bash + +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. +# + +if [ -z $WORKDIR_ROOT ] ; +then + echo "please specify your working directory root in environment variable WORKDIR_ROOT. Exitting..." + exit +fi + + +set -e +set -o pipefail + +SRC=en +SI_TGT=si +NE_TGT=ne + +DESTDIR=${WORKDIR_ROOT}/ML50/raw/ + +ROOT=${WORKDIR_ROOT}/tmp +mkdir -p $ROOT +DATA=$ROOT/data +NE_ROOT=$DATA/all-clean-ne +SI_ROOT=$DATA/all-clean-si + +mkdir -p $DATA $NE_ROOT $SI_ROOT + +SI_OPUS_DATASETS=( + "$SI_ROOT/GNOME.en-si" + "$SI_ROOT/Ubuntu.en-si" + "$SI_ROOT/KDE4.en-si" + "$SI_ROOT/OpenSubtitles.en-si" +) + +SI_OPUS_URLS=( + "https://object.pouta.csc.fi/OPUS-GNOME/v1/moses/en-si.txt.zip" + "https://object.pouta.csc.fi/OPUS-Ubuntu/v14.10/moses/en-si.txt.zip" + "https://object.pouta.csc.fi/OPUS-KDE4/v2/moses/en-si.txt.zip" + "https://object.pouta.csc.fi/OPUS-OpenSubtitles/v2018/moses/en-si.txt.zip" +) + +NE_OPUS_DATASETS=( + "$NE_ROOT/GNOME.en-ne" + "$NE_ROOT/Ubuntu.en-ne" + "$NE_ROOT/KDE4.en-ne" +) + +NE_OPUS_URLS=( + "https://object.pouta.csc.fi/OPUS-GNOME/v1/moses/en-ne.txt.zip" + "https://object.pouta.csc.fi/OPUS-Ubuntu/v14.10/moses/en-ne.txt.zip" + "https://object.pouta.csc.fi/OPUS-KDE4/v2/moses/en-ne.txt.zip" +) + +REMOVE_FILE_PATHS=() + +# Download data +download_data() { + CORPORA=$1 + URL=$2 + + if [ -f $CORPORA ]; then + echo "$CORPORA already exists, skipping download" + else + echo "Downloading $URL" + wget $URL -O $CORPORA --no-check-certificate || rm -f $CORPORA + if [ -f $CORPORA ]; then + echo "$URL successfully downloaded." + else + echo "$URL not successfully downloaded." + rm -f $CORPORA + exit -1 + fi + fi +} + +# Example: download_opus_data $LANG_ROOT $TGT +download_opus_data() { + LANG_ROOT=$1 + TGT=$2 + + if [ "$TGT" = "si" ]; then + URLS=("${SI_OPUS_URLS[@]}") + DATASETS=("${SI_OPUS_DATASETS[@]}") + else + URLS=("${NE_OPUS_URLS[@]}") + DATASETS=("${NE_OPUS_DATASETS[@]}") + fi + + # Download and extract data + for ((i=0;i<${#URLS[@]};++i)); do + URL=${URLS[i]} + CORPORA=${DATASETS[i]} + + download_data $CORPORA $URL + unzip -o $CORPORA -d $LANG_ROOT + REMOVE_FILE_PATHS+=( $CORPORA $CORPORA.xml $CORPORA.ids $LANG_ROOT/README $LANG_ROOT/LICENSE ) + done + + cat ${DATASETS[0]}.$SRC ${DATASETS[1]}.$SRC ${DATASETS[2]}.$SRC > $LANG_ROOT/GNOMEKDEUbuntu.$SRC-$TGT.$SRC + cat ${DATASETS[0]}.$TGT ${DATASETS[1]}.$TGT ${DATASETS[2]}.$TGT > $LANG_ROOT/GNOMEKDEUbuntu.$SRC-$TGT.$TGT + + REMOVE_FILE_PATHS+=( ${DATASETS[0]}.$SRC ${DATASETS[1]}.$SRC ${DATASETS[2]}.$SRC ) + REMOVE_FILE_PATHS+=( ${DATASETS[0]}.$TGT ${DATASETS[1]}.$TGT ${DATASETS[2]}.$TGT ) +} + +download_opus_data $SI_ROOT $SI_TGT +cp ${SI_OPUS_DATASETS[3]}.$SRC $SI_ROOT/OpenSubtitles2018.$SRC-$SI_TGT.$SRC +cp ${SI_OPUS_DATASETS[3]}.$SI_TGT $SI_ROOT/OpenSubtitles2018.$SRC-$SI_TGT.$SI_TGT +REMOVE_FILE_PATHS+=( ${SI_OPUS_DATASETS[3]}.$SRC ${SI_OPUS_DATASETS[3]}.$SI_TGT ) + +download_opus_data $NE_ROOT $NE_TGT + + +# Download and extract Global Voices data +GLOBAL_VOICES="$NE_ROOT/globalvoices.2018q4.ne-en" +GLOBAL_VOICES_URL="http://www.casmacat.eu/corpus/global-voices/globalvoices.ne-en.xliff.gz" + +download_data $GLOBAL_VOICES.gz $GLOBAL_VOICES_URL +gunzip -Nf $GLOBAL_VOICES.gz + +sed -ne 's?.*\(.*\).*?\1?p' $GLOBAL_VOICES > $GLOBAL_VOICES.$NE_TGT +sed -ne 's?.*]*>\(.*\).*?\1?p' $GLOBAL_VOICES > $GLOBAL_VOICES.$SRC + +REMOVE_FILE_PATHS+=( $GLOBAL_VOICES ) + +# Download and extract the bible dataset +BIBLE_TOOLS=bible-corpus-tools +XML_BIBLES=XML_Bibles +XML_BIBLES_DUP=XML_Bibles_dup + +if [ ! -e $BIBLE_TOOLS ]; then + echo "Cloning bible-corpus-tools repository..." + git clone https://github.com/christos-c/bible-corpus-tools.git +fi + +mkdir -p $BIBLE_TOOLS/bin $XML_BIBLES $XML_BIBLES_DUP +javac -cp "$BIBLE_TOOLS/lib/*" -d $BIBLE_TOOLS/bin $BIBLE_TOOLS/src/bible/readers/*.java $BIBLE_TOOLS/src/bible/*.java + +download_data bible.tar.gz "https://github.com/christos-c/bible-corpus/archive/v1.2.1.tar.gz" +tar xvzf bible.tar.gz + +cp bible-corpus-1.2.1/bibles/{Greek.xml,English.xml,Nepali.xml} $XML_BIBLES/ +cp bible-corpus-1.2.1/bibles/{Greek.xml,English-WEB.xml,Nepali.xml} $XML_BIBLES_DUP/ + +java -cp $BIBLE_TOOLS/lib/*:$BIBLE_TOOLS/bin bible.CreateMLBooks $XML_BIBLES +java -cp $BIBLE_TOOLS/lib/*:$BIBLE_TOOLS/bin bible.CreateMLBooks $XML_BIBLES_DUP +java -cp $BIBLE_TOOLS/lib/*:$BIBLE_TOOLS/bin bible.CreateVerseAlignedBooks $XML_BIBLES +java -cp $BIBLE_TOOLS/lib/*:$BIBLE_TOOLS/bin bible.CreateVerseAlignedBooks $XML_BIBLES_DUP + +cat $XML_BIBLES/aligned/*/English.txt > $NE_ROOT/bible.$SRC-$NE_TGT.$SRC +cat $XML_BIBLES/aligned/*/Nepali.txt > $NE_ROOT/bible.$SRC-$NE_TGT.$NE_TGT +cat $XML_BIBLES_DUP/aligned/*/English-WEB.txt > $NE_ROOT/bible_dup.$SRC-$NE_TGT.$SRC +cat $XML_BIBLES_DUP/aligned/*/Nepali.txt > $NE_ROOT/bible_dup.$SRC-$NE_TGT.$NE_TGT +REMOVE_FILE_PATHS+=( bible-corpus-1.2.1 bible.tar.gz $BIBLE_TOOLS $XML_BIBLES $XML_BIBLES_DUP ) + +# Download and extract the Penn Treebank dataset +NE_TAGGED=$ROOT/new_submissions_parallel_corpus_project_Nepal +NE_TAGGED_URL="http://www.cle.org.pk/Downloads/ling_resources/parallelcorpus/NepaliTaggedCorpus.zip" +EN_TAGGED_PATCH_URL="https://dl.fbaipublicfiles.com/fairseq/data/nepali-penn-treebank.en.patch" +NE_TAGGED_PATCH_URL="https://dl.fbaipublicfiles.com/fairseq/data/nepali-penn-treebank.ne.patch" +MOSES=mosesdecoder +MOSES_TOK=$MOSES/scripts/tokenizer +EN_PATCH_REGEX="{s:\\\/:\/:g;s/\*\T\*\-\n+//g;s/\-LCB\-/\{/g;s/\-RCB\-/\}/g; s/\-LSB\-/\[/g; s/\-RSB\-/\]/g;s/\-LRB\-/\(/g; s/\-RRB\-/\)/g; s/\'\'/\"/g; s/\`\`/\"/g; s/\ +\'s\ +/\'s /g; s/\ +\'re\ +/\'re /g; s/\"\ +/\"/g; s/\ +\"/\"/g; s/\ n't([\ \.\"])/n't\1/g; s/\r+(.)/\1/g;}" +NE_PATCH_REGEX="{s:\p{Cf}::g;s:\\\/:\/:g;s/\*\T\*\-\n+//g;s/\-LCB\-/\{/g;s/\-RCB\-/\}/g; s/\-LSB\-/\[/g; s/\-RSB\-/\]/g;s/\-LRB\-/\(/g; s/\-RRB\-/\)/g; s/\'\'/\"/g; s/\`\`/\"/g; s/\ +\'s\ +/\'s /g; s/\ +\'re\ +/\'re /g; s/\"\ +/\"/g; s/\ +\"/\"/g; s/\ n't([\ \.\"])/n't\1/g; s/\r+(.)/\1/g;}" + +download_data $DATA/nepali-penn-treebank.$SRC.patch $EN_TAGGED_PATCH_URL +download_data $DATA/nepali-penn-treebank.$NE_TGT.patch $NE_TAGGED_PATCH_URL +download_data original.zip $NE_TAGGED_URL +unzip -o original.zip -d $ROOT + +cat $NE_TAGGED/00.txt $NE_TAGGED/01.txt $NE_TAGGED/02.txt > $NE_TAGGED/nepali-penn-treebank.$SRC +cat $NE_TAGGED/00ne_revised.txt $NE_TAGGED/01ne_revised.txt $NE_TAGGED/02ne_revised.txt > $NE_TAGGED/nepali-penn-treebank.$NE_TGT + +patch $NE_TAGGED/nepali-penn-treebank.$SRC -i $DATA/nepali-penn-treebank.$SRC.patch -o $NE_TAGGED/nepali-penn-treebank-patched.$SRC +patch $NE_TAGGED/nepali-penn-treebank.$NE_TGT -i $DATA/nepali-penn-treebank.$NE_TGT.patch -o $NE_TAGGED/nepali-penn-treebank-patched.$NE_TGT + +if [ ! -e $MOSES ]; then + echo "Cloning moses repository..." + git clone https://github.com/moses-smt/mosesdecoder.git +fi + +cat $NE_TAGGED/nepali-penn-treebank-patched.$SRC | \ + perl -anpe "$EN_PATCH_REGEX" | \ + $MOSES_TOK/tokenizer.perl -l $SRC | \ + $MOSES_TOK/detokenizer.perl -l $SRC > $NE_ROOT/nepali-penn-treebank.$SRC + +cat $NE_TAGGED/nepali-penn-treebank-patched.$NE_TGT | \ + perl -CIO -anpe "$NE_PATCH_REGEX" | \ + $MOSES_TOK/detokenizer.perl -l $SRC > $NE_ROOT/nepali-penn-treebank.$NE_TGT + + +# Download nepali dictionary data +NE_DICT=$NE_ROOT/dictionaries +download_data $NE_DICT "http://www.seas.upenn.edu/~nlp/resources/TACL-data-release/dictionaries.tar.gz" +tar xvzf $NE_DICT +cp dictionaries/dict.ne $NE_ROOT/dictionary.$NE_TGT-$SRC +REMOVE_FILE_PATHS+=( $NE_DICT dictionaries ) + +REMOVE_FILE_PATHS+=( $MOSES $NE_TAGGED original.zip $DATA/nepali-penn-treebank.$SRC.patch $DATA/nepali-penn-treebank.$NE_TGT.patch ) + + +# Remove the temporary files +for ((i=0;i<${#REMOVE_FILE_PATHS[@]};++i)); do + rm -rf ${REMOVE_FILE_PATHS[i]} +done + +# Copy the training data +si=si_LK +ne=ne_NP +en=en_XX +cat $SI_ROOT/GNOMEKDEUbuntu.en-si.si $SI_ROOT/OpenSubtitles2018.en-si.si > $DESTDIR/train.$si-$en.$si +cat $SI_ROOT/GNOMEKDEUbuntu.en-si.en $SI_ROOT/OpenSubtitles2018.en-si.en > $DESTDIR/train.$si-$en.$en + +cat $NE_ROOT/bible_dup.en-ne.ne $NE_ROOT/bible.en-ne.ne $NE_ROOT/globalvoices.2018q4.ne-en.ne $NE_ROOT/GNOMEKDEUbuntu.en-ne.ne $NE_ROOT/nepali-penn-treebank.ne > $DESTDIR/train.$ne-$en.$ne +cat $NE_ROOT/bible_dup.en-ne.en $NE_ROOT/bible.en-ne.en $NE_ROOT/globalvoices.2018q4.ne-en.en $NE_ROOT/GNOMEKDEUbuntu.en-ne.en $NE_ROOT/nepali-penn-treebank.en > $DESTDIR/train.$ne-$en.$en + + +#Download the test sets +wget https://github.com/facebookresearch/flores/raw/master/data/wikipedia_en_ne_si_test_sets.tgz +tar -xvzf wikipedia_en_ne_si_test_sets.tgz + +cp wikipedia_en_ne_si_test_sets/wikipedia.dev.ne-en.ne $DESTDIR/valid.$ne-$en.$ne +cp wikipedia_en_ne_si_test_sets/wikipedia.dev.ne-en.en $DESTDIR/valid.$ne-$en.$en + +cp wikipedia_en_ne_si_test_sets/wikipedia.dev.si-en.si $DESTDIR/valid.$si-$en.$si +cp wikipedia_en_ne_si_test_sets/wikipedia.dev.si-en.en $DESTDIR/valid.$si-$en.$en + +cp wikipedia_en_ne_si_test_sets/wikipedia.devtest.ne-en.ne $DESTDIR/devtest.$ne-$en.$ne +cp wikipedia_en_ne_si_test_sets/wikipedia.devtest.ne-en.en $DESTDIR/devtest.$ne-$en.$en + +cp wikipedia_en_ne_si_test_sets/wikipedia.devtest.si-en.si $DESTDIR/devtest.$si-$en.$si +cp wikipedia_en_ne_si_test_sets/wikipedia.devtest.si-en.en $DESTDIR/devtest.$si-$en.$en + +cp wikipedia_en_ne_si_test_sets/wikipedia.test.ne-en.ne $DESTDIR/test.$ne-$en.$ne +cp wikipedia_en_ne_si_test_sets/wikipedia.test.ne-en.en $DESTDIR/test.$ne-$en.$en + +cp wikipedia_en_ne_si_test_sets/wikipedia.test.si-en.si $DESTDIR/test.$si-$en.$si +cp wikipedia_en_ne_si_test_sets/wikipedia.test.si-en.en $DESTDIR/test.$si-$en.$en + +rm -rf wikipedia_en_ne_si_test_sets.tgz wikipedia_en_ne_si_test_sets diff --git a/examples/multilingual/data_scripts/download_iitb.sh b/examples/multilingual/data_scripts/download_iitb.sh new file mode 100644 index 0000000000..a884e20839 --- /dev/null +++ b/examples/multilingual/data_scripts/download_iitb.sh @@ -0,0 +1,35 @@ +#!/bin/bash +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + + +if [ -z $WORKDIR_ROOT ] ; +then + echo "please specify your working directory root in environment variable WORKDIR_ROOT. Exitting..." + exit +fi + +IITB=$WORKDIR_ROOT/IITB +mkdir -p $IITB +pushd $IITB + +wget http://www.cfilt.iitb.ac.in/~moses/iitb_en_hi_parallel/iitb_corpus_download/parallel.tgz +tar -xvzf parallel.tgz + +wget http://www.cfilt.iitb.ac.in/~moses/iitb_en_hi_parallel/iitb_corpus_download/dev_test.tgz +tar -xvzf dev_test.tgz + +DESTDIR=${WORKDIR_ROOT}/ML50/raw/ + +cp parallel/IITB.en-hi.en $DESTDIR/train.hi_IN-en_XX.en_XX +cp parallel/IITB.en-hi.hi $DESTDIR/train.hi_IN-en_XX.hi_IN + +cp dev_test/dev.en $DESTDIR/valid.hi_IN-en_XX.en_XX +cp dev_test/dev.hi $DESTDIR/valid.hi_IN-en_XX.hi_IN + +cp dev_test/test.en $DESTDIR/test.hi_IN-en_XX.en_XX +cp dev_test/test.hi $DESTDIR/test.hi_IN-en_XX.hi_IN +popd \ No newline at end of file diff --git a/examples/multilingual/data_scripts/download_iwslt_and_extract.sh b/examples/multilingual/data_scripts/download_iwslt_and_extract.sh new file mode 100644 index 0000000000..ca3591b3db --- /dev/null +++ b/examples/multilingual/data_scripts/download_iwslt_and_extract.sh @@ -0,0 +1,225 @@ +#!/bin/bash +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +#echo 'Cloning Moses github repository (for tokenization scripts)...' +#git clone https://github.com/moses-smt/mosesdecoder.git + +if [ -z $WORKDIR_ROOT ] ; +then + echo "please specify your working directory root in environment variable WORKDIR_ROOT. Exitting..." + exit +fi + + + +data_root=${WORKDIR_ROOT}/iwsltv2 +DESTDIR=${WORKDIR_ROOT}/ML50/raw + + +langs="ar_AR it_IT nl_XX ko_KR vi_VN" +echo "data_root: $data_root" + +download_path=${data_root}/downloads +raw=${DESTDIR} +tmp=${data_root}/tmp +orig=${data_root}/orig + +mkdir -p $download_path $orig $raw $tmp +####################### +download_iwslt(){ + iwslt_key=$1 + src=$2 + tgt=$3 + save_prefix=$4 + pushd ${download_path} + if [[ ! -f ${save_prefix}$src-$tgt.tgz ]]; then + wget https://wit3.fbk.eu/archive/${iwslt_key}/texts/$src/$tgt/$src-$tgt.tgz -O ${save_prefix}$src-$tgt.tgz + [ $? -eq 0 ] && return 0 + fi + popd +} + +extract_iwslt(){ + src=$1 + tgt=$2 + prefix=$3 + pushd $orig + tar zxvf ${download_path}/${prefix}$src-${tgt}.tgz + popd +} + +generate_train(){ + lsrc=$1 + ltgt=$2 + src=${lsrc:0:2} + tgt=${ltgt:0:2} + for ll in $lsrc $ltgt; do + l=${ll:0:2} + f="$orig/*/train.tags.$src-$tgt.$l" + f_raw=$raw/train.$lsrc-$ltgt.$ll + cat $f \ + | grep -v '' \ + | grep -v '' \ + | grep -v '' \ + | grep -v '' \ + | grep -v '' \ + | sed -e 's///g' \ + | sed -e 's/<\/title>//g' \ + | sed -e 's/<description>//g' \ + | sed -e 's/<\/description>//g' \ + | sed 's/^\s*//g' \ + | sed 's/\s*$//g' \ + > $f_raw + [ $? -eq 0 ] && echo "extracted $f to $f_raw" + done + return 0 +} + +convert_valid_test(){ + src=$1 + tgt=$2 + for l in $src $tgt; do + echo "lang: ${l}" + for o in `ls $orig/*/IWSLT*.TED*.$src-$tgt.$l.xml`; do + fname=${o##*/} + f=$tmp/${fname%.*} + echo "$o => $f" + grep '<seg id' $o \ + | sed -e 's/<seg id="[0-9]*">\s*//g' \ + | sed -e 's/\s*<\/seg>\s*//g' \ + | sed -e "s/\’/\'/g" \ + > $f + echo "" + done + done +} + +generate_subset(){ + lsrc=$1 + ltgt=$2 + src=${lsrc:0:2} + tgt=${ltgt:0:2} + subset=$3 + prefix=$4 + for ll in $lsrc $ltgt; do + l=${ll:0:2} + f=$tmp/$prefix.${src}-${tgt}.$l + if [[ -f $f ]]; then + cp $f $raw/$subset.${lsrc}-$ltgt.${ll} + fi + done +} +################# + +echo "downloading iwslt training and dev data" +# using multilingual for it, nl +download_iwslt "2017-01-trnmted" DeEnItNlRo DeEnItNlRo +download_iwslt "2017-01-trnted" ar en +download_iwslt "2017-01-trnted" en ar +download_iwslt "2017-01-trnted" ko en +download_iwslt "2017-01-trnted" en ko +download_iwslt "2015-01" vi en +download_iwslt "2015-01" en vi + +echo "donwloading iwslt test data" +download_iwslt "2017-01-mted-test" it en "test." +download_iwslt "2017-01-mted-test" en it "test." +download_iwslt "2017-01-mted-test" nl en "test." +download_iwslt "2017-01-mted-test" en nl "test." + +download_iwslt "2017-01-ted-test" ar en "test." +download_iwslt "2017-01-ted-test" en ar "test." +download_iwslt "2017-01-ted-test" ko en "test." +download_iwslt "2017-01-ted-test" en ko "test." +download_iwslt "2015-01-test" vi en "test." +download_iwslt "2015-01-test" en vi "test." + +echo "extract training data tar balls" +extract_iwslt DeEnItNlRo DeEnItNlRo +extract_iwslt ar en +extract_iwslt en ar +extract_iwslt ko en +extract_iwslt en ko +extract_iwslt vi en +extract_iwslt en vi + + +echo "extracting iwslt test data" +for lang in $langs; do + l=${lang:0:2} + extract_iwslt $l en "test." + extract_iwslt en $l "test." +done + +echo "convert dev and test data" +for lang in $langs; do + s_lang=${lang:0:2} + convert_valid_test $s_lang en + convert_valid_test en $s_lang +done + + + +echo "creating training data into $raw" +for lang in $langs; do + generate_train $lang en_XX + generate_train en_XX $lang +done + +echo "creating iwslt dev data into raw" +generate_subset en_XX vi_VN valid "IWSLT15.TED.tst2013" +generate_subset vi_VN en_XX valid "IWSLT15.TED.tst2013" + +generate_subset en_XX ar_AR valid "IWSLT17.TED.tst2016" +generate_subset ar_AR en_XX valid "IWSLT17.TED.tst2016" +generate_subset en_XX ko_KR valid "IWSLT17.TED.tst2016" +generate_subset ko_KR en_XX valid "IWSLT17.TED.tst2016" + + +generate_subset en_XX it_IT valid "IWSLT17.TED.tst2010" +generate_subset it_IT en_XX valid "IWSLT17.TED.tst2010" +generate_subset en_XX nl_XX valid "IWSLT17.TED.tst2010" +generate_subset nl_XX en_XX valid "IWSLT17.TED.tst2010" + +echo "creating iswslt test data into raw" +generate_subset en_XX vi_VN test "IWSLT15.TED.tst2015" +generate_subset vi_VN en_XX test "IWSLT15.TED.tst2015" + +generate_subset en_XX ar_AR test "IWSLT17.TED.tst2017" +generate_subset ar_AR en_XX test "IWSLT17.TED.tst2017" +generate_subset en_XX ko_KR test "IWSLT17.TED.tst2017" +generate_subset ko_KR en_XX test "IWSLT17.TED.tst2017" + +generate_subset en_XX it_IT test "IWSLT17.TED.tst2017.mltlng" +generate_subset it_IT en_XX test "IWSLT17.TED.tst2017.mltlng" +generate_subset en_XX nl_XX test "IWSLT17.TED.tst2017.mltlng" +generate_subset nl_XX en_XX test "IWSLT17.TED.tst2017.mltlng" + +# normalze iwslt directions into x-en +pushd $raw +for lang in $langs; do + for split in test valid; do + x_en_f1=$split.$lang-en_XX.en_XX + x_en_f2=$split.$lang-en_XX.${lang} + + en_x_f1=$split.en_XX-$lang.en_XX + en_x_f2=$split.en_XX-$lang.${lang} + + if [ -f $en_x_f1 ] && [ ! -f $x_en_f1 ]; then + echo "cp $en_x_f1 $x_en_f1" + cp $en_x_f1 $x_en_f1 + fi + if [ -f $x_en_f2 ] && [ ! -f $x_en_f2 ]; then + echo "cp $en_x_f2 $x_en_f2" + cp $en_x_f2 $x_en_f2 + fi + done +done +popd \ No newline at end of file diff --git a/examples/multilingual/data_scripts/download_lotus.sh b/examples/multilingual/data_scripts/download_lotus.sh new file mode 100644 index 0000000000..c08c701314 --- /dev/null +++ b/examples/multilingual/data_scripts/download_lotus.sh @@ -0,0 +1,46 @@ +#!/bin/bash +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + + +if [ -z $WORKDIR_ROOT ] ; +then + echo "please specify your working directory root in environment variable WORKDIR_ROOT. Exitting..." + exit +fi + + +SRCDIR=$WORKDIR_ROOT/indic_languages_corpus +DESTDIR=${WORKDIR_ROOT}/ML50/raw/ +mkdir -p $SRCDIR +mkdir -p $DESTDIR + +cd $SRCDIR +wget http://lotus.kuee.kyoto-u.ac.jp/WAT/indic-multilingual/indic_languages_corpus.tar.gz +tar -xvzf indic_languages_corpus.tar.gz + +SRC_EXTRACT_DIR=$SRCDIR/indic_languages_corpus/bilingual + +cp $SRC_EXTRACT_DIR/ml-en/train.ml $DESTDIR/train.ml_IN-en_XX.ml_IN +cp $SRC_EXTRACT_DIR/ml-en/train.en $DESTDIR/train.ml_IN-en_XX.en_XX +cp $SRC_EXTRACT_DIR/ml-en/dev.ml $DESTDIR/valid.ml_IN-en_XX.ml_IN +cp $SRC_EXTRACT_DIR/ml-en/dev.en $DESTDIR/valid.ml_IN-en_XX.en_XX +cp $SRC_EXTRACT_DIR/ml-en/test.ml $DESTDIR/test.ml_IN-en_XX.ml_IN +cp $SRC_EXTRACT_DIR/ml-en/test.en $DESTDIR/test.ml_IN-en_XX.en_XX + +cp $SRC_EXTRACT_DIR/ur-en/train.ur $DESTDIR/train.ur_PK-en_XX.ur_PK +cp $SRC_EXTRACT_DIR/ur-en/train.en $DESTDIR/train.ur_PK-en_XX.en_XX +cp $SRC_EXTRACT_DIR/ur-en/dev.ur $DESTDIR/valid.ur_PK-en_XX.ur_PK +cp $SRC_EXTRACT_DIR/ur-en/dev.en $DESTDIR/valid.ur_PK-en_XX.en_XX +cp $SRC_EXTRACT_DIR/ur-en/test.ur $DESTDIR/test.ur_PK-en_XX.ur_PK +cp $SRC_EXTRACT_DIR/ur-en/test.en $DESTDIR/test.ur_PK-en_XX.en_XX + +cp $SRC_EXTRACT_DIR/te-en/train.te $DESTDIR/train.te_IN-en_XX.te_IN +cp $SRC_EXTRACT_DIR/te-en/train.en $DESTDIR/train.te_IN-en_XX.en_XX +cp $SRC_EXTRACT_DIR/te-en/dev.te $DESTDIR/valid.te_IN-en_XX.te_IN +cp $SRC_EXTRACT_DIR/te-en/dev.en $DESTDIR/valid.te_IN-en_XX.en_XX +cp $SRC_EXTRACT_DIR/te-en/test.te $DESTDIR/test.te_IN-en_XX.te_IN +cp $SRC_EXTRACT_DIR/te-en/test.en $DESTDIR/test.te_IN-en_XX.en_XX diff --git a/examples/multilingual/data_scripts/download_ted_and_extract.py b/examples/multilingual/data_scripts/download_ted_and_extract.py new file mode 100644 index 0000000000..eb756680fa --- /dev/null +++ b/examples/multilingual/data_scripts/download_ted_and_extract.py @@ -0,0 +1,338 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +import itertools +import os +import csv +from collections import defaultdict +from six.moves import zip +import io +import wget +import sys + +from subprocess import check_call, check_output + +# scripts and data locations +CWD = os.getcwd() +UTILS = f"{CWD}/utils" + +MOSES = f"{UTILS}/mosesdecoder" + +WORKDIR_ROOT = os.environ.get('WORKDIR_ROOT', None) + +if WORKDIR_ROOT is None or not WORKDIR_ROOT.strip(): + print('please specify your working directory root in OS environment variable WORKDIR_ROOT. Exitting..."') + sys.exit(-1) + + +# please donwload mosesdecoder here: +detok_cmd = f'{MOSES}/scripts/tokenizer/detokenizer.perl' + + +def call(cmd): + print(f"Executing: {cmd}") + check_call(cmd, shell=True) + +class MultiLingualAlignedCorpusReader(object): + """A class to read TED talk dataset + """ + + def __init__(self, corpus_path, delimiter='\t', + target_token=True, bilingual=True, corpus_type='file', + lang_dict={'source': ['fr'], 'target': ['en']}, + eval_lang_dict=None, zero_shot=False, + detok=True, + ): + + self.empty_line_flag = 'NULL' + self.corpus_path = corpus_path + self.delimiter = delimiter + self.bilingual = bilingual + self.lang_dict = lang_dict + self.lang_set = set() + self.target_token = target_token + self.zero_shot = zero_shot + self.eval_lang_dict = eval_lang_dict + self.corpus_type = corpus_type + self.detok = detok + + for list_ in self.lang_dict.values(): + for lang in list_: + self.lang_set.add(lang) + + self.data = dict() + self.data['train'] = self.read_aligned_corpus(split_type='train') + self.data['test'] = self.read_aligned_corpus(split_type='test') + self.data['dev'] = self.read_aligned_corpus(split_type='dev') + + def read_data(self, file_loc_): + data_list = list() + with io.open(file_loc_, 'r', encoding='utf8') as fp: + for line in fp: + try: + text = line.strip() + except IndexError: + text = self.empty_line_flag + data_list.append(text) + return data_list + + def filter_text(self, dict_): + if self.target_token: + field_index = 1 + else: + field_index = 0 + data_dict = defaultdict(list) + list1 = dict_['source'] + list2 = dict_['target'] + for sent1, sent2 in zip(list1, list2): + try: + src_sent = ' '.join(sent1.split()[field_index: ]) + except IndexError: + src_sent = 'NULL' + + if src_sent.find(self.empty_line_flag) != -1 or len(src_sent) == 0: + continue + + elif sent2.find(self.empty_line_flag) != -1 or len(sent2) == 0: + continue + + else: + data_dict['source'].append(sent1) + data_dict['target'].append(sent2) + return data_dict + + def read_file(self, split_type, data_type): + return self.data[split_type][data_type] + + def save_file(self, path_, split_type, data_type, lang): + tok_file = tok_file_name(path_, lang) + with io.open(tok_file, 'w', encoding='utf8') as fp: + for line in self.data[split_type][data_type]: + fp.write(line + '\n') + if self.detok: + de_tok(tok_file, lang) + + def add_target_token(self, list_, lang_id): + new_list = list() + token = '__' + lang_id + '__' + for sent in list_: + new_list.append(token + ' ' + sent) + return new_list + + def read_from_single_file(self, path_, s_lang, t_lang): + data_dict = defaultdict(list) + with io.open(path_, 'r', encoding='utf8') as fp: + reader = csv.DictReader(fp, delimiter='\t', quoting=csv.QUOTE_NONE) + for row in reader: + data_dict['source'].append(row[s_lang]) + data_dict['target'].append(row[t_lang]) + + if self.target_token: + text = self.add_target_token(data_dict['source'], t_lang) + data_dict['source'] = text + + return data_dict['source'], data_dict['target'] + + def read_aligned_corpus(self, split_type='train'): + data_dict = defaultdict(list) + iterable = [] + s_list = [] + t_list = [] + + if self.zero_shot: + if split_type == "train": + iterable = zip(self.lang_dict['source'], self.lang_dict['target']) + else: + iterable = zip(self.eval_lang_dict['source'], self.eval_lang_dict['target']) + + elif self.bilingual: + iterable = itertools.product(self.lang_dict['source'], self.lang_dict['target']) + + for s_lang, t_lang in iterable: + if s_lang == t_lang: + continue + if self.corpus_type == 'file': + split_type_file_path = os.path.join(self.corpus_path, + "all_talks_{}.tsv".format(split_type)) + s_list, t_list = self.read_from_single_file(split_type_file_path, + s_lang=s_lang, + t_lang=t_lang) + data_dict['source'] += s_list + data_dict['target'] += t_list + new_data_dict = self.filter_text(data_dict) + return new_data_dict + + +def read_langs(corpus_path): + split_type_file_path = os.path.join(corpus_path, 'extracted', + "all_talks_dev.tsv") + with io.open(split_type_file_path, 'r', encoding='utf8') as fp: + reader = csv.DictReader(fp, delimiter='\t', quoting=csv.QUOTE_NONE) + header = next(reader) + return [k for k in header.keys() if k != 'talk_name'] + +def extra_english(corpus_path, split): + split_type_file_path = os.path.join(corpus_path, + f"all_talks_{split}.tsv") + output_split_type_file_path = os.path.join(corpus_path, + f"all_talks_{split}.en") + with io.open(split_type_file_path, 'r', encoding='utf8') as fp, io.open(output_split_type_file_path, 'w', encoding='utf8') as fw: + reader = csv.DictReader(fp, delimiter='\t', quoting=csv.QUOTE_NONE) + for row in reader: + line = row['en'] + fw.write(line + '\n') + de_tok(output_split_type_file_path, 'en') + + + +def tok_file_name(filename, lang): + seps = filename.split('.') + seps.insert(-1, 'tok') + tok_file = '.'.join(seps) + return tok_file + +def de_tok(tok_file, lang): + # seps = tok_file.split('.') + # seps.insert(-1, 'detok') + # de_tok_file = '.'.join(seps) + de_tok_file = tok_file.replace('.tok.', '.') + cmd = 'perl {detok_cmd} -l {lang} < {tok_file} > {de_tok_file}'.format( + detok_cmd=detok_cmd, tok_file=tok_file, + de_tok_file=de_tok_file, lang=lang[:2]) + call(cmd) + +def extra_bitex( + ted_data_path, + lsrc_lang, + ltrg_lang, + target_token, + output_data_path, +): + def get_ted_lang(lang): + long_langs = ['pt-br', 'zh-cn', 'zh-tw', 'fr-ca'] + if lang[:5] in long_langs: + return lang[:5] + elif lang[:4] =='calv': + return lang[:5] + elif lang in ['pt_BR', 'zh_CN', 'zh_TW', 'fr_CA']: + return lang.lower().replace('_', '-') + return lang[:2] + src_lang = get_ted_lang(lsrc_lang) + trg_lang = get_ted_lang(ltrg_lang) + train_lang_dict={'source': [src_lang], 'target': [trg_lang]} + eval_lang_dict = {'source': [src_lang], 'target': [trg_lang]} + + obj = MultiLingualAlignedCorpusReader(corpus_path=ted_data_path, + lang_dict=train_lang_dict, + target_token=target_token, + corpus_type='file', + eval_lang_dict=eval_lang_dict, + zero_shot=False, + bilingual=True) + + os.makedirs(output_data_path, exist_ok=True) + lsrc_lang = lsrc_lang.replace('-', '_') + ltrg_lang = ltrg_lang.replace('-', '_') + obj.save_file(output_data_path + f"/train.{lsrc_lang}-{ltrg_lang}.{lsrc_lang}", + split_type='train', data_type='source', lang=src_lang) + obj.save_file(output_data_path + f"/train.{lsrc_lang}-{ltrg_lang}.{ltrg_lang}", + split_type='train', data_type='target', lang=trg_lang) + + obj.save_file(output_data_path + f"/test.{lsrc_lang}-{ltrg_lang}.{lsrc_lang}", + split_type='test', data_type='source', lang=src_lang) + obj.save_file(output_data_path + f"/test.{lsrc_lang}-{ltrg_lang}.{ltrg_lang}", + split_type='test', data_type='target', lang=trg_lang) + + obj.save_file(output_data_path + f"/valid.{lsrc_lang}-{ltrg_lang}.{lsrc_lang}", + split_type='dev', data_type='source', lang=src_lang) + obj.save_file(output_data_path + f"/valid.{lsrc_lang}-{ltrg_lang}.{ltrg_lang}", + split_type='dev', data_type='target', lang=trg_lang) + + +def bar_custom(current, total, width=80): + print("Downloading: %d%% [%d / %d] Ks" % (current / total * 100, current / 1000, total / 1000), end='\r') + + +def download_and_extract(download_to, extract_to): + url = 'http://phontron.com/data/ted_talks.tar.gz' + filename = f"{download_to}/ted_talks.tar.gz" + if os.path.exists(filename): + print(f'{filename} has already been downloaded so skip') + else: + filename = wget.download(url, filename, bar=bar_custom) + if os.path.exists(f'{extract_to}/all_talks_train.tsv'): + print(f'Already extracted so skip') + else: + extract_cmd = f'tar xzfv "{filename}" -C "{extract_to}"' + call(extract_cmd) + + +if __name__ == "__main__": + import argparse + parser = argparse.ArgumentParser() + parser.add_argument('--ted_data_path', type=str, default=WORKDIR_ROOT, required=False) + parser.add_argument( + '--direction-list', + type=str, + # default=None, + #for ML50 + default=( + "bn_IN-en_XX,he_IL-en_XX,fa_IR-en_XX,id_ID-en_XX,sv_SE-en_XX,pt_XX-en_XX,ka_GE-en_XX,ka_GE-en_XX,th_TH-en_XX," + "mr_IN-en_XX,hr_HR-en_XX,uk_UA-en_XX,az_AZ-en_XX,mk_MK-en_XX,gl_ES-en_XX,sl_SI-en_XX,mn_MN-en_XX," + #non-english directions + # "fr_XX-de_DE," # replaced with wmt20 + # "ja_XX-ko_KR,es_XX-pt_XX,ru_RU-sv_SE,hi_IN-bn_IN,id_ID-ar_AR,cs_CZ-pl_PL,ar_AR-tr_TR" + ), + required=False) + parser.add_argument('--target-token', action='store_true', default=False) + parser.add_argument('--extract-all-english', action='store_true', default=False) + + args = parser.parse_args() + + import sys + import json + + # TED Talks data directory + ted_data_path = args.ted_data_path + + download_to = f'{ted_data_path}/downloads' + extract_to = f'{ted_data_path}/extracted' + + #DESTDIR=${WORKDIR_ROOT}/ML50/raw/ + output_path = f'{ted_data_path}/ML50/raw' + os.makedirs(download_to, exist_ok=True) + os.makedirs(extract_to, exist_ok=True) + os.makedirs(output_path, exist_ok=True) + download_and_extract(download_to, extract_to) + + + if args.extract_all_english: + for split in ['train', 'dev', 'test']: + extra_english(ted_data_path, split) + exit(0) + if args.direction_list is not None: + directions = args.direction_list.strip().split(',') + directions = [tuple(d.strip().split('-', 1)) for d in directions if d] + else: + langs = read_langs(ted_data_path) + # directions = [ + # '{}.{}'.format(src, tgt) + # for src in langs + # for tgt in langs + # if src < tgt + # ] + directions = [('en', tgt) for tgt in langs if tgt != 'en'] + print(f'num directions={len(directions)}: {directions}') + + for src_lang, trg_lang in directions: + print('--working on {}-{}'.format(src_lang, trg_lang)) + extra_bitex( + extract_to, + src_lang, + trg_lang, + target_token=args.target_token, + output_data_path=output_path + ) diff --git a/examples/multilingual/data_scripts/download_wat19_my.sh b/examples/multilingual/data_scripts/download_wat19_my.sh new file mode 100644 index 0000000000..c1e2d47287 --- /dev/null +++ b/examples/multilingual/data_scripts/download_wat19_my.sh @@ -0,0 +1,36 @@ +#!/bin/bash +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + + +if [ -z $WORKDIR_ROOT ] ; +then + echo "please specify your working directory root in environment variable WORKDIR_ROOT. Exitting..." + exit +fi + + +SRCDIR=$WORKDIR_ROOT/indic_languages_corpus +DESTDIR=$WORKDIR_ROOT/ML50/raw +mkdir -p $SRCDIR +mkdir -p $DESTDIR + +WAT_MY_EN=wat2020.my-en.zip +cd $SRCDIR +# please refer to http://lotus.kuee.kyoto-u.ac.jp/WAT/my-en-data/ for latest URL if the following url expired +#- The data used for WAT2020 are identical to those used in WAT2019. +wget http://lotus.kuee.kyoto-u.ac.jp/WAT/my-en-data/$WAT_MY_EN +unzip $WAT_MY_EN + + +SRC_EXTRACT_DIR=$SRCDIR/wat2020.my-en/alt + +cp $SRC_EXTRACT_DIR/train.alt.en $DESTDIR/train.my_MM-en_XX.en_XX +cp $SRC_EXTRACT_DIR/train.alt.my $DESTDIR/train.my_MM-en_XX.my_MM +cp $SRC_EXTRACT_DIR/dev.alt.en $DESTDIR/valid.my_MM-en_XX.en_XX +cp $SRC_EXTRACT_DIR/dev.alt.my $DESTDIR/valid.my_MM-en_XX.my_MM +cp $SRC_EXTRACT_DIR/test.alt.en $DESTDIR/test.my_MM-en_XX.en_XX +cp $SRC_EXTRACT_DIR/test.alt.my $DESTDIR/test.my_MM-en_XX.my_MM diff --git a/examples/multilingual/data_scripts/download_wmt19_and_before.py b/examples/multilingual/data_scripts/download_wmt19_and_before.py new file mode 100644 index 0000000000..3465731eb3 --- /dev/null +++ b/examples/multilingual/data_scripts/download_wmt19_and_before.py @@ -0,0 +1,899 @@ +from typing import NamedTuple, List +from urllib.parse import urlparse +import os, sys +import subprocess +from subprocess import check_call, check_output +import glob +import wget +import re +import multiprocessing as mp +from functools import partial +import pathlib +from collections import OrderedDict + +WORKDIR_ROOT = os.environ.get('WORKDIR_ROOT', None) + +if WORKDIR_ROOT is None or not WORKDIR_ROOT.strip(): + print('please specify your working directory root in OS environment variable WORKDIR_ROOT. Exitting..."') + sys.exit(-1) + +# scripts and data locations +CWD = os.getcwd() +UTILS = f"{CWD}/utils" + +MOSES = f"{UTILS}/mosesdecoder" +SGM_TOOL = f'{MOSES}/scripts/ems/support/input-from-sgm.perl' + +TMX2CORPUS = f"{UTILS}/tmx2corpus" +TMX_TOOL = f'python {TMX2CORPUS}/tmx2corpus.py' + +to_data_path = f'{WORKDIR_ROOT}/wmt' +download_to = f'{to_data_path}/downloads' +manually_downloads = f'{to_data_path}/downloads' +extract_to = f'{to_data_path}/extracted' +#DESTDIR=${WORKDIR_ROOT}/ML50/raw/ +raw_data = f'{WORKDIR_ROOT}/ML50/raw' +#### + +class DLDataset(NamedTuple): + name: str + train_urls: List[str] + valid_urls: List[str] + test_urls: List[str] + train_files_patterns: List[str] = [] + valid_files_patterns: List[str] = [] + test_files_patterns: List[str] = [] + + + +def bar_custom(current, total, width=80): + print("Downloading: %d%% [%d / %d] Ks" % (current / total * 100, current / 1000, total / 1000), end='\r') + +def get_downloaded_file(dl_folder, url): + if isinstance(url, tuple): + url, f = url + else: + url_f = urlparse(url) + # f = os.path.split(url_f.path)[-1] + f = '_'.join(url_f.path.split('/')[1:]) + return url, f"{dl_folder}/{f}" + +def download_parts_and_combine(dl_folder, urls, filename): + parts = [] + for url_record in urls: + url, part_file = get_downloaded_file(dl_folder, url_record) + if os.path.exists(part_file): + print(f'{part_file} has already been downloaded so skip') + else: + part_file = wget.download(url, part_file, bar=bar_custom) + parts.append(part_file) + + def get_combine_cmd(parts): + #default as tar.gz.?? + return f'cat {" ".join(parts)} > {filename}' + + combine_cmd = get_combine_cmd(parts) + call(combine_cmd, debug=True) + return filename + +def download_a_url(dl_folder, url): + url, filename = get_downloaded_file(dl_folder, url) + if os.path.exists(filename): + print(f'{filename} has already been downloaded so skip') + return filename + + print(f'downloading {url} to {filename}') + if isinstance(url, list) or isinstance(url, tuple): + download_parts_and_combine(dl_folder, url, filename) + else: + wget.download(url, filename, bar=bar_custom) + print(f'dowloaded: {filename}') + return filename + +def download_files(dl_folder, urls, completed_urls={}): + for url_record in urls: + url, _ = get_downloaded_file(dl_folder, url_record) + filename = download_a_url(dl_folder, url_record) + completed_urls[str(url)] = filename + return completed_urls + +def check_need_manual_downalod(dl_folder, to_manually_download_urls): + to_be_manually_dowloaded = [] + manually_completed_urls = {} + for url_record, instruction in to_manually_download_urls: + url, filename = get_downloaded_file(dl_folder, url_record) + if not os.path.exists(filename): + print(f'{url} need to be download manually, please download it manually following {instruction}; and copy it to {filename}') + to_be_manually_dowloaded.append((url, filename)) + else: + manually_completed_urls[url] = filename + # if len(to_be_manually_dowloaded) > 0: + # raise ValueError('Missing files that need to be downloaded manually; stop the process now.') + return to_be_manually_dowloaded + +def download_dataset(to_folder, dl_dataset, completed_urls={}): + download_files(to_folder, dl_dataset.train_urls, completed_urls) + download_files(to_folder, dl_dataset.valid_urls, completed_urls) + download_files(to_folder, dl_dataset.test_urls, completed_urls) + print('completed downloading') + return completed_urls + +def call(cmd, debug=False): + if debug: + print(cmd) + check_call(cmd, shell=True) + + +def get_extract_name(file_path): + path = os.path.split(file_path) + return path[-1] + '_extract' #.split('.')[0] + +def extract_file(downloaded_file, extract_folder, get_extract_name=get_extract_name, debug=False): + extract_name = get_extract_name(downloaded_file) + extract_to = f'{extract_folder}/{extract_name}' + os.makedirs(extract_to, exist_ok=True) + if os.path.exists(f'{extract_to}/DONE'): + print(f'{downloaded_file} has already been extracted to {extract_to} so skip') + return extract_to + def get_extract_cmd(filename): + if filename.endswith('.tgz') or filename.endswith('tar.gz'): + return f'tar xzfv {filename} -C {extract_to}' + elif filename.endswith('.gz.tar'): + return f'tar xfv {filename} -C {extract_to}; (cd {extract_to}; gzip -d *.gz; [ $? -eq 0 ] || gzip -d */*.gz)' + elif filename.endswith('.tar'): + return f'tar xfv {filename} -C {extract_to}' + elif filename.endswith('.gz'): + return f'cp {filename} {extract_to}; (cd {extract_to}; gzip -d *.gz)' + elif filename.endswith('.zip'): + return f'unzip {filename} -d {extract_to}' + extract_cmd = get_extract_cmd(downloaded_file) + print(f'extracting {downloaded_file}') + if isinstance(extract_cmd, list): + for c in extract_cmd: + call(c, debug=debug) + else: + call(extract_cmd, debug=debug) + call(f'echo DONE > {extract_to}/DONE') + return extract_to + + +def extract_all_files( + completed_urls, extract_folder, + get_extract_name=get_extract_name, + completed_extraction={}, + debug=False): + extracted_folders = OrderedDict() + for url, downloaded_file in set(completed_urls.items()): + if downloaded_file in completed_extraction: + print(f'{downloaded_file} is already extracted; so skip') + continue + folder = extract_file(downloaded_file, extract_folder, get_extract_name, debug) + extracted_folders[url] = folder + return extracted_folders + + +def my_glob(folder): + for p in [f'{folder}/*', f'{folder}/*/*', f'{folder}/*/*/*']: + for f in glob.glob(p): + yield f + + +def sgm2raw(sgm, debug): + to_file = sgm[0:len(sgm) - len('.sgm')] + if os.path.exists(to_file): + debug and print(f'{sgm} already converted to {to_file}; so skip') + return to_file + cmd = f'{SGM_TOOL} < {sgm} > {to_file}' + call(cmd, debug) + return to_file + +def tmx2raw(tmx, debug): + to_file = tmx[0:len(tmx) - len('.tmx')] + to_folder = os.path.join(*os.path.split(tmx)[:-1]) + if os.path.exists(f'{to_folder}/bitext.en'): + debug and print(f'{tmx} already extracted to {to_file}; so skip') + return to_file + cmd = f'(cd {to_folder}; {TMX_TOOL} {tmx})' + call(cmd, debug) + return to_file + +CZENG16_REGEX = re.compile(r'.*?data.plaintext-format/0[0-9]train$') +WMT19_WIKITITLES_REGEX = re.compile(r'.*?wikititles-v1.(\w\w)-en.tsv.gz') +TSV_REGEX = re.compile(r'.*?(\w\w)-(\w\w).tsv$') + + + +def cut_wikitles(wiki_file, debug): + # different languages have different file names: + if wiki_file.endswith('wiki/fi-en/titles.fi-en'): + to_file1 = f'{wiki_file}.fi' + to_file2 = f'{wiki_file}.en' + BACKSLASH = '\\' + cmd1 = f"cat {wiki_file} | sed 's/|||/{BACKSLASH}t/g' |cut -f1 |awk '{{$1=$1}};1' > {to_file1}" + cmd2 = f"cat {wiki_file} | sed 's/|||/{BACKSLASH}t/g' |cut -f2 |awk '{{$1=$1}};1' > {to_file2}" +# elif WMT19_WIKITITLES_REGEX.match(wiki_file): +# src = WMT19_WIKITITLES_REGEX.match(wiki_file).groups()[0] +# to_file1 = f'{wiki_file}.{src}' +# to_file2 = f'{wiki_file}.en' +# cmd1 = f"cat {wiki_file} | cut -f1 |awk '{{$1=$1}};1' > {to_file1}" +# cmd2 = f"cat {wiki_file} | cut -f2 |awk '{{$1=$1}};1' > {to_file2}" + else: + return None + if os.path.exists(to_file1) and os.path.exists(to_file2): + debug and print(f'{wiki_file} already processed to {to_file1} and {to_file2}; so skip') + return wiki_file + + call(cmd1, debug=debug) + call(cmd2, debug=debug) + return wiki_file + +def cut_tsv(file, debug): + m = TSV_REGEX.match(file) + if m is None: + raise ValueError(f'{file} is not matching tsv pattern') + src = m.groups()[0] + tgt = m.groups()[1] + + to_file1 = f'{file}.{src}' + to_file2 = f'{file}.{tgt}' + cmd1 = f"cat {file} | cut -f1 |awk '{{$1=$1}};1' > {to_file1}" + cmd2 = f"cat {file} | cut -f2 |awk '{{$1=$1}};1' > {to_file2}" + if os.path.exists(to_file1) and os.path.exists(to_file2): + debug and print(f'{file} already processed to {to_file1} and {to_file2}; so skip') + return file + + call(cmd1, debug=debug) + call(cmd2, debug=debug) + return file + + +def convert_file_if_needed(file, debug): + if file.endswith('.sgm'): + return sgm2raw(file, debug) + elif file.endswith('.tmx'): + return tmx2raw(file, debug) + elif file.endswith('wiki/fi-en/titles.fi-en'): + return cut_wikitles(file, debug) +# elif WMT19_WIKITITLES_REGEX.match(file): +# return cut_wikitles(file, debug) + elif file.endswith('.tsv'): + return cut_tsv(file, debug) + elif CZENG16_REGEX.match(file): + return convert2czeng17(file, debug) + else: + return file + + +def convert_files_if_needed(extracted_foldrs, my_glob=my_glob, debug=False): + return { + url: list(sorted(set(convert_file_if_needed(f, debug)) for f in sorted(set(my_glob(folder))))) + for url, folder in extracted_foldrs.items() + } + +def match_patt(file_path, file_pattern, src, tgt, lang): + return file_pattern.format(src=src, tgt=tgt, lang=lang) in file_path + +def match_patts(file_path, file_patterns, src, tgt, lang): + for file_pattern in file_patterns: + params = { k: v for k, v in [('src', src), ('tgt', tgt), ('lang', lang)] if k in file_pattern} + matching = file_pattern.format(**params) + + if isinstance(file_pattern, tuple): + pattern, directions = file_pattern + if f'{src}-{tgt}' in directions and matching in file_path: + return True + else: + if matching in file_path: + return True + return False + +def extracted_glob(extracted_folder, file_patterns, src, tgt, lang): + def get_matching_pattern(file_pattern): + params = { + k: v + for k, v in [('src', src), ('tgt', tgt), ('lang', lang)] + if '{' + k + '}' in file_pattern + } + file_pattern = re.sub(r'{src:(.*?)}', r'\1' if lang == src else '', file_pattern) + file_pattern = re.sub(r'{tgt:(.*?)}', r'\1' if lang == tgt else '', file_pattern) + file_pattern = file_pattern.format(**params) + return file_pattern + for file_pattern in file_patterns: + if isinstance(file_pattern, tuple): + file_pattern, lang_pairs = file_pattern + if f'{src}-{tgt}' not in lang_pairs: + continue +# print('working on pattern: ', file_pattern, lang_pairs ) + matching_pattern = get_matching_pattern(file_pattern) + if matching_pattern is None: + continue + glob_patterns = f'{extracted_folder}/{matching_pattern}' +# print('glob_patterns: ', glob_patterns) + for f in glob.glob(glob_patterns): + yield f + +# for debug usage +def all_extracted_files(split, src, tgt, extracted_folders, split_urls): + def get_url(url): + if isinstance(url, tuple): + url, downloaded_file = url + return url + return [ + f + for url in split_urls + for f in my_glob(extracted_folders[str(get_url(url))]) + ] + +def concat_files(split, src, tgt, extracted_folders, split_urls, path_patterns, to_folder, debug=False): +# if debug: +# print('extracted files to be filtered by patterns: ', +# '\n\t'.join(sorted(all_extracted_files(split, src, tgt, extracted_folders, split_urls)))) + for lang in [src, tgt]: + to_file = f'{to_folder}/{split}.{src}-{tgt}.{lang}' + s_src, s_tgt, s_lang = src.split('_')[0], tgt.split('_')[0], lang.split('_')[0] + files = [] + for url in split_urls: + if isinstance(url, tuple): + url, downloaded_file = url + if str(url) not in extracted_folders: + print(f'warning: {url} not in extracted files') + for extracted_file in set( + extracted_glob( + extracted_folders[str(url)], path_patterns, + s_src, s_tgt, s_lang)): + files.append(extracted_file) + if len(files) == 0: + print('warning: ', f'No files found for split {to_file}') + continue + files = sorted(set(files)) + print(f'concating {len(files)} files into {to_file}') + cmd = ['cat'] + [f'"{f}"' for f in files] + [f'>{to_file}'] + cmd = " ".join(cmd) + call(cmd, debug=debug) + +UTILS = os.path.join(pathlib.Path(__file__).parent, 'utils') +LID_MODEL = f'{download_to}/lid.176.bin' +LID_MULTI = f'{UTILS}/fasttext_multi_filter.py' + +def lid_filter(split, src, tgt, from_folder, to_folder, debug=False): + if not os.path.exists(LID_MODEL): + call(f'wget -nc https://dl.fbaipublicfiles.com/fasttext/supervised-models/lid.176.bin -O {LID_MODEL}') + from_prefix = f'{from_folder}/{split}.{src}-{tgt}' + to_prefix = f'{to_folder}/{split}.{src}-{tgt}' + if os.path.exists(f'{from_prefix}.{src}') and os.path.exists(f'{from_prefix}.{tgt}'): + s_src, s_tgt = src.split('_')[0], tgt.split('_')[0] + cmd = ( + f'python {LID_MULTI} --model {LID_MODEL} --inputs {from_prefix}.{src} {from_prefix}.{tgt} ' + f'--langs {s_src} {s_tgt} --outputs {to_prefix}.{src} {to_prefix}.{tgt}' + ) + print(f'filtering {from_prefix}') + call(cmd, debug=debug) + +def concat_into_splits(dl_dataset, src, tgt, extracted_folders, to_folder, debug): + to_folder_tmp = f"{to_folder}_tmp" + os.makedirs(to_folder_tmp, exist_ok=True) + concat_files('train', src, tgt, + extracted_folders, + split_urls=dl_dataset.train_urls, + path_patterns=dl_dataset.train_files_patterns, + to_folder=to_folder_tmp, debug=debug) + lid_filter('train', src, tgt, to_folder_tmp, to_folder, debug) + + concat_files('valid', src, tgt, + extracted_folders, + split_urls=dl_dataset.valid_urls, + path_patterns=dl_dataset.valid_files_patterns, + to_folder=to_folder, debug=debug) + concat_files('test', src, tgt, + extracted_folders, + split_urls=dl_dataset.test_urls, + path_patterns=dl_dataset.test_files_patterns, + to_folder=to_folder, debug=debug) + + +def download_multi(dl_folder, extract_folder, urls, num_processes=8, debug=False): + pool = mp.Pool(processes=num_processes) + download_f = partial(download_a_url, dl_folder) + downloaded_files = pool.imap_unordered(download_f, urls) + pool.close() + pool.join() + +BLEU_REGEX = re.compile("^BLEU\\S* = (\\S+) ") +def run_eval_bleu(cmd): + output = check_output(cmd, shell=True, stderr=subprocess.STDOUT).decode("utf-8").strip() + print(output) + bleu = -1.0 + for line in output.strip().split('\n'): + m = BLEU_REGEX.search(line) + if m is not None: + bleu = m.groups()[0] + bleu = float(bleu) + break + return bleu + +def check_wmt_test_bleu(raw_folder, wmt_lang_pairs): + not_matchings = [] + for wmt, src_tgts in wmt_lang_pairs: + for src_tgt in src_tgts: + print(f'checking test bleus for: {src_tgt} at {wmt}') + src, tgt = src_tgt.split('-') + ssrc, stgt = src[:2], tgt[:2] + if os.path.exists(f'{raw_folder}/test.{tgt}-{src}.{src}'): + # reversed direction may have different test set + test_src = f'{raw_folder}/test.{tgt}-{src}.{src}' + else: + test_src = f'{raw_folder}/test.{src}-{tgt}.{src}' + cmd1 = f'cat {test_src} | sacrebleu -t "{wmt}" -l {stgt}-{ssrc}; [ $? -eq 0 ] || echo ""' + test_tgt = f'{raw_folder}/test.{src}-{tgt}.{tgt}' + cmd2 = f'cat {test_tgt} | sacrebleu -t "{wmt}" -l {ssrc}-{stgt}; [ $? -eq 0 ] || echo ""' + bleu1 = run_eval_bleu(cmd1) + if bleu1 != 100.0: + not_matchings.append(f'{wmt}:{src_tgt} source side not matching: {test_src}') + bleu2 = run_eval_bleu(cmd2) + if bleu2 != 100.0: + not_matchings.append(f'{wmt}:{src_tgt} target side not matching: {test_tgt}') + return not_matchings + +def download_and_extract( + to_folder, lang_pairs, dl_dataset, + to_manually_download_urls, + completed_urls={}, completed_extraction={}, + debug=False): + + dl_folder = f'{to_folder}/downloads' + extract_folder = f'{to_folder}/extracted' + raw_folder = f'{to_folder}/raw' + lid_filtered = f'{to_folder}/lid_filtered' + + os.makedirs(extract_folder, exist_ok=True) + os.makedirs(raw_folder, exist_ok=True) + os.makedirs(lid_filtered, exist_ok=True) + + + to_be_manually_dowloaded = check_need_manual_downalod(dl_folder, to_manually_download_urls) + + completed_urls = download_dataset( + dl_folder, dl_dataset, completed_urls) + if debug: + print('completed urls: ', completed_urls) + + + extracted_folders = extract_all_files( + completed_urls, + extract_folder=extract_folder, + completed_extraction=completed_extraction, + debug=debug) + if debug: + print('download files have been extracted to folders: ', extracted_folders) + + converted_files = convert_files_if_needed(extracted_folders, debug=False) + for src_tgt in lang_pairs: + print(f'working on {dl_dataset.name}: {src_tgt}') + src, tgt = src_tgt.split('-') + concat_into_splits(dl_dataset, + src=src, tgt=tgt, + extracted_folders=extracted_folders, + to_folder=raw_folder, debug=debug) + print('completed data into: ', raw_folder) + +def download_czang16(download_to, username=None): + wgets = [ + f'wget --user={username} --password=czeng -P {download_to} http://ufallab.ms.mff.cuni.cz/~bojar/czeng16-data/data-plaintext-format.{i}.tar' + for i in range(10)] + cmds = [] + for i, cmd in enumerate(wgets): + filename = f'{download_to}/data-plaintext-format.{i}.tar' + if os.path.exists(filename): + print(f'{filename} has already been downloaded; so skip') + continue + cmds.append(cmd) + if cmds and username is None: + raise ValueError('No czeng username is given; please register at http://ufal.mff.cuni.cz/czeng/czeng16 to obtain username to download') + for cmd in cmds: + call(cmd) + print('done with downloading czeng1.6') + +def download_czeng17_script(download_to, extract_folder, debug=False): + url = 'http://ufal.mff.cuni.cz/czeng/download.php?f=convert_czeng16_to_17.pl.zip' + filename = f'{download_to}/convert_czeng16_to_17.pl.zip' + extract_to = f'{extract_folder}/{get_extract_name(filename)}' + script_path = f'{extract_to}/convert_czeng16_to_17.pl' + + if not os.path.exists(script_path): + wget.download(url, filename, bar=bar_custom) + extract_to = extract_file(f'{download_to}/convert_czeng16_to_17.pl.zip', extract_folder, get_extract_name=get_extract_name, debug=debug) + return script_path + +czeng17_script_path = "" +def convert2czeng17(file, debug): + en_file = f'{file}.en' + cs_file = f'{file}.cs' + + if not os.path.exists(en_file) or not os.path.exists(cs_file): + cs_cmd = f'cat {file} | perl {czeng17_script_path} | cut -f3 > {cs_file}' + en_cmd = f'cat {file} | perl {czeng17_script_path} | cut -f4 > {en_file}' + call(cs_cmd, debug) + call(en_cmd, debug) + else: + print(f'already extracted: {en_file} and {cs_file}') + return file + +def extract_czeng17(extract_folder, debug=False): + url = 'http://ufal.mff.cuni.cz/czeng/download.php?f=convert_czeng16_to_17.pl.zip' + filename = f'{download_to}/convert_czeng16_to_17.pl.zip' + extract_to = f'{extract_folder}/{get_extract_name(filename)}' + script_path = f'{extract_to}/convert_czeng16_to_17.pl' + + if not os.path.exists(script_path): + wget.download(url, filename, bar=bar_custom) + extract_to = extract_file(f'{download_to}/convert_czeng16_to_17.pl.zip', extract_folder, get_extract_name=get_extract_name, debug=debug) + return script_path + +######### +# definitions of wmt data sources +# for es-en +# Punctuation in the official test sets will be encoded with ASCII characters (not complex Unicode characters) as much as possible. You may want to normalize your system's output before submission. You are able able to use a rawer version of the test sets that does not have this normalization. +# script to normalize punctuation: http://www.statmt.org/wmt11/normalize-punctuation.perl +wmt13_es_en = DLDataset( + name='wmt13_es-en', + train_urls=[ + 'http://www.statmt.org/wmt13/training-parallel-europarl-v7.tgz', + 'http://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz', + 'http://www.statmt.org/wmt13/training-parallel-un.tgz', + 'http://www.statmt.org/wmt13/training-parallel-nc-v8.tgz', + ], + valid_urls=[ + ('http://www.statmt.org/wmt13/dev.tgz', 'wmt13_dev.tgz') + ], + test_urls=[ + ('http://www.statmt.org/wmt13/test.tgz', 'wmt13_test.tgz') + ], + train_files_patterns=[ + ('*/europarl-v7.{src}-{tgt}.{lang}', ['es-en']), + ('*commoncrawl.{src}-{tgt}.{lang}', ['es-en']), + ('*/news-commentary-v8.{src}-{tgt}.{lang}', ['es-en']), + ('un/*undoc.2000.{src}-{tgt}.{lang}', ['es-en']), + ] , + valid_files_patterns=[ + ('dev/newstest2012.{lang}', ['es-en']) + ], + test_files_patterns=[ + ('test/newstest*.{lang}', ['es-en']) + ], +) + +wmt14_de_fr_en = DLDataset( + name='wmt14_de_fr_en', + train_urls=[ + 'http://www.statmt.org/wmt13/training-parallel-europarl-v7.tgz', + 'http://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz', + 'http://www.statmt.org/wmt13/training-parallel-un.tgz', + 'http://www.statmt.org/wmt14/training-parallel-nc-v9.tgz', + ('http://www.statmt.org/wmt10/training-giga-fren.tar', 'training-giga-fren.gz.tar'), #it is actuall a gz.tar + ], + valid_urls=[ + ('http://www.statmt.org/wmt14/dev.tgz', 'wmt14_dev.tgz'), + ], + test_urls=[ + ('http://www.statmt.org/wmt14/test-full.tgz', 'wmt14_test_full.tgz'), # cleaned test sets + ], + train_files_patterns=[ + ('*/europarl-v7.{src}-{tgt}.{lang}', ['fr-en', 'de-en']), + ('*commoncrawl.{src}-{tgt}.{lang}', ['fr-en', 'de-en']), + ('*/*news-commentary-v9.{src}-{tgt}.{lang}', ['fr-en', 'de-en']), + ('un/undoc.2000.{src}-{tgt}.{lang}', ['fr-en']), + ('*giga-{src}{tgt}*{lang}', ['fr-en']) + ], + valid_files_patterns=[ + ('dev/newstest2013.{lang}', ['fr-en', 'de-en']) + ], + test_files_patterns=[ + ('test-full/newstest*{src}{tgt}-{src:src}{tgt:ref}.{lang}', ['en-de', 'de-en', 'fr-en', 'en-fr']), + ], +) + +# pip install git+https://github.com/amake/tmx2corpus.git +wmt16_ro_en = DLDataset( + name='wmt16_ro-en', + train_urls=[ + ('http://data.statmt.org/wmt16/translation-task/training-parallel-ep-v8.tgz', 'wmt16_training-parallel-ep-v8.tgz'), + ('http://opus.nlpl.eu/download.php?f=SETIMES/v2/tmx/en-ro.tmx.gz', 'en-ro.tmx.gz'), + ], + valid_urls=[ + ('http://data.statmt.org/wmt16/translation-task/dev-romanian-updated.tgz', 'wmt16_dev.tgz') + ], + test_urls=[ + ('http://data.statmt.org/wmt16/translation-task/test.tgz', 'wmt16_test.tgz') + ], + train_files_patterns=[ + ('*/*europarl-v8.{src}-{tgt}.{lang}', ['ro-en']), + ('bitext.{lang}', ['ro-en']) #setimes from tmux + ] , + valid_files_patterns=[ + ('dev/newsdev2016*{src}{tgt}*.{lang}', ['ro-en', 'ro-en']) + ], + test_files_patterns=[ + ('test/newstest*{src}{tgt}*.{lang}', ['ro-en', 'en-ro']) + ], +) + +cwmt_wmt_instruction = 'cwmt download instruction at: http://nlp.nju.edu.cn/cwmt-wmt' +wmt17_fi_lv_tr_zh_en_manual_downloads = [ + # fake urls to have unique keys for the data + ( ('http://nlp.nju.edu.cn/cwmt-wmt/CASIA2015.zip', 'CASIA2015.zip'), cwmt_wmt_instruction), + ( ('http://nlp.nju.edu.cn/cwmt-wmt/CASICT2011.zip', 'CASICT2011.zip'), cwmt_wmt_instruction), + ( ('http://nlp.nju.edu.cn/cwmt-wmt/CASICT2015.zip', 'CASICT2015.zip'), cwmt_wmt_instruction), + ( ('http://nlp.nju.edu.cn/cwmt-wmt/Datum2015.zip', 'Datum2015.zip'), cwmt_wmt_instruction), + ( ('http://nlp.nju.edu.cn/cwmt-wmt/Datum2017.zip', 'Datum2017.zip'), cwmt_wmt_instruction), + ( ('http://nlp.nju.edu.cn/cwmt-wmt/NEU2017.zip', 'NEU2017.zip'), cwmt_wmt_instruction), +] +wmt17_fi_lv_tr_zh_en = DLDataset( + name='wmt17_fi_lv_tr_zh_en', + train_urls=[ + ('http://data.statmt.org/wmt17/translation-task/training-parallel-ep-v8.tgz', 'wmt17_training-parallel-ep-v8.tgz'), + 'http://data.statmt.org/wmt17/translation-task/training-parallel-nc-v12.tgz', + 'http://www.statmt.org/wmt15/wiki-titles.tgz', + ('http://opus.nlpl.eu/download.php?f=SETIMES/v2/tmx/en-tr.tmx.gz', 'en-tr.tmx.gz'), + ('http://data.statmt.org/wmt17/translation-task/rapid2016.tgz', 'wmt17_rapid2016.tgz'), + 'http://data.statmt.org/wmt17/translation-task/leta.v1.tgz', + 'http://data.statmt.org/wmt17/translation-task/dcep.lv-en.v1.tgz', + 'http://data.statmt.org/wmt17/translation-task/books.lv-en.v1.tgz', + (('https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-zh.tar.gz.00', + 'https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-zh.tar.gz.01',), 'UNv1.0.en-zh.tar.gz'), + #manually download files: + ('http://nlp.nju.edu.cn/cwmt-wmt/CASIA2015.zip', 'CASIA2015.zip'), + ('http://nlp.nju.edu.cn/cwmt-wmt/CASICT2011.zip', 'CASICT2011.zip'), + ('http://nlp.nju.edu.cn/cwmt-wmt/CASICT2015.zip', 'CASICT2015.zip'), + ('http://nlp.nju.edu.cn/cwmt-wmt/Datum2015.zip', 'Datum2015.zip'), + ('http://nlp.nju.edu.cn/cwmt-wmt/Datum2017.zip', 'Datum2017.zip'), + ('http://nlp.nju.edu.cn/cwmt-wmt/NEU2017.zip', 'NEU2017.zip'), + ], + valid_urls=[ + ('http://data.statmt.org/wmt17/translation-task/dev.tgz', 'wmt17_dev.tgz'), + ], + test_urls=[ + #NEW: Improved translations for zh test sets + ('http://data.statmt.org/wmt17/translation-task/test-update-1.tgz', 'wmt17_test_zh_en.tgz'), + ('http://data.statmt.org/wmt17/translation-task/test.tgz', 'wmt17_test_others.tgz') + ], + train_files_patterns=[ + ('casict*/cas*{src:ch}{tgt:en}.txt', ['zh-en', 'zh-en'] ), + ('casia*/cas*{src:ch}{tgt:en}.txt', ['zh-en', 'zh-en'] ), + ('dataum*/Book*{src:cn}{tgt:en}.txt', ['zh-en', 'zh-en']), + ('neu*/NEU*{src:cn}{tgt:en}.txt', ['zh-en', 'zh-en'] ), + ('*/*UNv1.0.en-zh.{src:zh}{tgt:en}', ['zh-en']), + ('training/*news-commentary-v12.{src}-{tgt}.{lang}', ['zh-en', ]), + + ('*/*europarl-v8.{src}-{tgt}.{lang}', ['fi-en', 'lv-en']), + ('wiki/fi-en/titles.{src}-{tgt}.{lang}', ['fi-en', ]), + ('rapid2016.{tgt}-{src}.{lang}', ['fi-en', 'lv-en']), + ('*/leta.{lang}', ['lv-en']), + ('*/dcep.{lang}', ['lv-en']), + ('*/farewell.{lang}', ['lv-en']), + ('bitext.{lang}', ['tr-en']), + ] , + valid_files_patterns=[ + ('dev/newsdev2017*{src}{tgt}-{src:src}{tgt:ref}.{lang}', + [ + 'fi-en', 'lv-en', 'tr-en', 'zh-en', + 'en-fi', 'en-lv', 'en-tr', 'en-zh' + ]), + ('dev/newstest2016*{src}{tgt}-{src:src}{tgt:ref}.{lang}', + [ + 'fi-en', 'tr-en', + 'en-fi', 'en-tr', + ]), + ], + test_files_patterns=[ + ('test/newstest2017-{src}{tgt}-{src:src}{tgt:ref}.{lang}', + [ + 'fi-en', 'lv-en', 'tr-en', + 'en-fi', 'en-lv', 'en-tr', + ]), + ('newstest2017-{src}{tgt}-{src:src}{tgt:ref}.{lang}', + [ + 'zh-en', + 'en-zh' + ]), + ], +) + +czeng_instruction = 'download instruction at: http://ufal.mff.cuni.cz/czeng/czeng16' +#alternative: use the prepared data but detokenize it? +wmt18_cs_et_en_manual_downloads = [ +#for cs, need to register and download; Register and download CzEng 1.6. +#Better results can be obtained by using a subset of sentences, released under a new version name CzEng 1.7. + # ((f'http://ufallab.ms.mff.cuni.cz/~bojar/czeng16-data/data-plaintext-format.{i}.tar', + # f'data-plaintext-format.{i}.tar'), czeng_instruction) + # for i in range(10) +] + +wmt18_cs_et_en = DLDataset( + name='wmt18_cs_et_en', + train_urls=[ + 'http://www.statmt.org/wmt13/training-parallel-europarl-v7.tgz', + 'http://data.statmt.org/wmt18/translation-task/training-parallel-ep-v8.tgz', + 'https://s3.amazonaws.com/web-language-models/paracrawl/release1/paracrawl-release1.en-cs.zipporah0-dedup-clean.tgz', + 'https://s3.amazonaws.com/web-language-models/paracrawl/release1/paracrawl-release1.en-et.zipporah0-dedup-clean.tgz', + 'http://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz', + 'http://data.statmt.org/wmt18/translation-task/training-parallel-nc-v13.tgz', + ('http://data.statmt.org/wmt18/translation-task/rapid2016.tgz', 'wmt18_rapid2016.tgz'), + # (tuple( + # (f'http://ufallab.ms.mff.cuni.cz/~bojar/czeng16-data/data-plaintext-format.{i}.tar', + # f'data-plaintext-format.{i}.tar') + # for i in range(10) + # ), + # 'czeng16_data_plaintext.gz.tar'), + ], + valid_urls=[ + ('http://data.statmt.org/wmt18/translation-task/dev.tgz', 'wmt18_dev.tgz'), + ], + test_urls=[ + ('http://data.statmt.org/wmt18/translation-task/test.tgz', 'wmt18_test.tgz'), + ], + train_files_patterns=[ + # ('*/*europarl-v7.{src}-{tgt}.{lang}', ['cs-en']), + ('*/*europarl-v8.{src}-{tgt}.{lang}', ['et-en']), + # ('*paracrawl-release1.{tgt}-{src}.zipporah0-dedup-clean.{lang}', ['cs-en', 'et-en']), + ('*paracrawl-release1.{tgt}-{src}.zipporah0-dedup-clean.{lang}', ['et-en']), + # ('*commoncrawl.{src}-{tgt}.{lang}', ['cs-en']), + # ('*/news-commentary-v13.{src}-{tgt}.{lang}', ['cs-en']), + # ('data.plaintext-format/*train.{lang}', ['cs-en']), + ('rapid2016.{tgt}-{src}.{lang}', ['et-en']), + ] , + valid_files_patterns=[ + ('dev/newsdev2018*{src}{tgt}-{src:src}{tgt:ref}.{lang}', ['et-en']), + # ('dev/newstest2017*{src}{tgt}-{src:src}{tgt:ref}.{lang}', ['cs-en']) + ], + test_files_patterns=[ + ('test/newstest2018-{src}{tgt}-{src:src}{tgt:ref}.{lang}', + # ['cs-en', 'et-en']), + ['et-en']), + ] +) + +ru_en_yandex_instruction = 'Yandex Corpus download instruction at: https://translate.yandex.ru/corpus?lang=en' +wmt19_ru_gu_kk_lt_manual_downloads = [ + (('https://translate.yandex.ru/corpus?lang=en', 'wmt19_1mcorpus.zip'), ru_en_yandex_instruction) +] +wmt19_ru_gu_kk_lt = DLDataset( + name='wmt19_ru_gu_kk_lt', + train_urls=[ + 'http://www.statmt.org/europarl/v9/training/europarl-v9.lt-en.tsv.gz', + 'https://s3.amazonaws.com/web-language-models/paracrawl/release3/en-lt.bicleaner07.tmx.gz', + 'https://s3.amazonaws.com/web-language-models/paracrawl/release1/paracrawl-release1.en-ru.zipporah0-dedup-clean.tgz', + 'http://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz', + 'http://data.statmt.org/news-commentary/v14/training/news-commentary-v14-wmt19.en-kk.tsv.gz', + 'http://data.statmt.org/news-commentary/v14/training/news-commentary-v14.en-ru.tsv.gz', + 'http://data.statmt.org/wikititles/v1/wikititles-v1.kk-en.tsv.gz', + 'http://data.statmt.org/wikititles/v1/wikititles-v1.ru-en.tsv.gz', + 'http://data.statmt.org/wikititles/v1/wikititles-v1.kk-en.tsv.gz', + 'http://data.statmt.org/wikititles/v1/wikititles-v1.lt-en.tsv.gz', + 'http://data.statmt.org/wikititles/v1/wikititles-v1.gu-en.tsv.gz', + (('https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-ru.tar.gz.00', + 'https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-ru.tar.gz.01', + 'https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-ru.tar.gz.02',), + 'wmt19_UNv1.0.en-ru.tar.gz'), + 'https://tilde-model.s3-eu-west-1.amazonaws.com/rapid2016.en-lt.tmx.zip', + ('https://translate.yandex.ru/corpus?lang=en', 'wmt19_1mcorpus.zip'), + ], + valid_urls=[ + ('http://data.statmt.org/wmt19/translation-task/dev.tgz', 'wmt19_dev.tgz'), + ], + test_urls=[ + ('http://data.statmt.org/wmt19/translation-task/test.tgz', 'wmt19_test.tgz'), + ], + train_files_patterns=[ + ('*europarl-v9.{src}-{tgt}.tsv.{lang}', ['lt-en']), + #paracrawl + ('*paracrawl-release1.{tgt}-{src}.zipporah0-dedup-clean.{lang}', ['ru-en']), + ('bitext.{lang}', ['lt-en',]), + ('*commoncrawl.{src}-{tgt}.{lang}', ['ru-en',]), + ('*news-commentary-v14-wmt19.{tgt}-{src}.tsv.{lang}', ['kk-en', ]), + ('*news-commentary-v14.{tgt}-{src}.tsv.{lang}', ['ru-en']), + #yandex + ('corpus.{tgt}_{src}.1m.{lang}', ['ru-en']), + ('wikititles_v1_wikititles-v1.{src}-{tgt}.tsv.{lang}', ['ru-en', 'kk-en', 'lt-en', 'gu-en']), + ('*/UNv1.0.{tgt}-{src}.{lang}', ['ru-en']), + #rapid + ('bitext.{lang}', ['lt-en']) + ], + valid_files_patterns=[ + ('dev/newsdev2019*{src}{tgt}-{src:src}{tgt:ref}.{lang}', ['gu-en', 'kk-en', 'lt-en']), + ('dev/newstest2018*{src}{tgt}-{src:src}{tgt:ref}.{lang}', ['ru-en']), + ], + test_files_patterns=[ + ('sgm/newstest2019-{src}{tgt}-{src:src}{tgt:ref}.{lang}', + ['ru-en', 'gu-en', 'kk-en', 'lt-en', 'en-ru', 'en-gu', 'en-kk', 'en-lt']), + ] +) + + +######### + +if __name__ == "__main__": + # speed up the downloads with multiple processing + dl_folder = f'{to_data_path}/downloads' + extract_folder = f'{to_data_path}/extracted' + + urls = [ + url + for dataset in [wmt13_es_en, wmt14_de_fr_en, wmt16_ro_en, wmt18_cs_et_en, wmt19_ru_gu_kk_lt] + for urls in [dataset.train_urls, dataset.valid_urls, dataset.test_urls] + for url in urls + ] + urls = set(urls) + download_multi(dl_folder, extract_folder, urls, num_processes=8, debug=True) + + # check manually downlaods + to_manually_download_urls = ( + wmt17_fi_lv_tr_zh_en_manual_downloads + wmt18_cs_et_en_manual_downloads + wmt19_ru_gu_kk_lt_manual_downloads + ) + to_be_manually_dowloaded = check_need_manual_downalod(dl_folder, to_manually_download_urls) + if len(to_be_manually_dowloaded) > 0: + print('Missing files that need to be downloaded manually; stop the process now.') + exit(-1) + + completed_urls = {} + completed_extraction = {} + def work_on_wmt(directions, wmt_data): + download_and_extract( + to_data_path, + directions, + wmt_data, + to_manually_download_urls=to_manually_download_urls, + completed_urls=completed_urls, completed_extraction=completed_extraction, debug=True) + + work_on_wmt( + ['es_XX-en_XX'], + wmt13_es_en,) + work_on_wmt( + [ + 'fr_XX-en_XX', 'en_XX-fr_XX', + # 'en_XX-de_DE', 'de_DE-en_XX', + ], + wmt14_de_fr_en,) + work_on_wmt( + ['ro_RO-en_XX', 'en_XX-ro_XX'], + wmt16_ro_en,) + work_on_wmt( + [ + # 'zh_CN-en_XX', + 'lv_LV-en_XX', 'fi_FI-en_XX', 'tr_TR-en_XX', + #in case the reversed directions have different train/valid/test data + # 'en_XX-zh_CN', + 'en_XX-lv_LV', 'en_XX-fi_FI', 'en_XX-tr_TR', + ], + wmt17_fi_lv_tr_zh_en, ) + # czeng17_script_path = download_czeng17_script(download_to, extract_to, debug=False) + # cz_username = None + work_on_wmt( + [ + # 'cs_CZ-en_XX', + 'et_EE-en_XX'], + wmt18_cs_et_en,) + work_on_wmt( + [ + # 'ru_RU-en_XX', 'en_XX-ru_RU', + 'gu_IN-en_XX', 'kk_KZ-en_XX', 'lt_LT-en_XX', + #in case the reversed directions have different train/valid/test data + 'en_XX-gu_IN', 'en_XX-kk_KZ', 'en_XX-lt_LT' + ], + wmt19_ru_gu_kk_lt,) + + not_matching = check_wmt_test_bleu( + f'{to_data_path}/raw', + [ + ('wmt13', ['es_XX-en_XX']), + ('wmt14/full', ['fr_XX-en_XX',]), + ('wmt16', ['ro_RO-en_XX',]), + # ('wmt17/improved', ['zh_CN-en_XX']), + ('wmt17', [ 'lv_LV-en_XX', 'fi_FI-en_XX', 'tr_TR-en_XX']), + ('wmt18', ['cs_CZ-en_XX', 'et_EE-en_XX']), + ('wmt19', ['gu_IN-en_XX', 'kk_KZ-en_XX', 'lt_LT-en_XX']), + #'ru_RU-en_XX', + ] + ) + if len(not_matching) > 0: + print('the following datasets do not have matching test datasets:\n\t', '\n\t'.join(not_matching)) + diff --git a/examples/multilingual/data_scripts/download_wmt20.sh b/examples/multilingual/data_scripts/download_wmt20.sh new file mode 100644 index 0000000000..31cd5c76b7 --- /dev/null +++ b/examples/multilingual/data_scripts/download_wmt20.sh @@ -0,0 +1,547 @@ +#!/bin/bash +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +if [ -z $WORKDIR_ROOT ] ; +then + echo "please specify your working directory root in environment variable WORKDIR_ROOT. Exitting..." + exit +fi + + + +set -x -e + +# TODO update the workdir and dest dir name +# put fasttext model +WORKDIR=$WORKDIR_ROOT +# put intermediate files +TMP_DIR=$WORKDIR_ROOT/tmp/tmp_wmt20_lowres_download +# output {train,valid,test} files to dest +DEST=$WORKDIR_ROOT/ML50/raw + +UTILS=$PWD/utils + +# per dataset locations +COMMONCRAWL_DIR=$TMP_DIR/commoncrawl +YANDEX_CORPUS=$WORKDIR_ROOT/wmt20/official/ru/yandex/1mcorpus.zip +# unzipped +CZENG_CORPUS=$WORKDIR_ROOT/wmt20/official/cs/czeng/czeng20-train +CCMT_DIR=$WORKDIR_ROOT/wmt20/official/zh/ccmt/parallel + +download_and_select() { + SUBFOLDER=$1 + URL=$2 + UNCOMPRESS_CMD=$3 + LANG=$4 + INPUT_FILEPATH=$5 + if [[ $# -gt 5 ]]; then + LANG_COL=$6 + EN_COL=$7 + fi + + mkdir -p $SUBFOLDER + cd $SUBFOLDER + wget -nc --content-disposition $URL + $UNCOMPRESS_CMD + + if [[ $# -gt 5 ]]; then + cut -f$LANG_COL $INPUT_FILEPATH > $INPUT_FILEPATH.$LANG + cut -f$EN_COL $INPUT_FILEPATH > $INPUT_FILEPATH.en + fi + cd .. + + ln -sf $SUBFOLDER/$INPUT_FILEPATH.$LANG $SUBFOLDER.$LANG + ln -sf $SUBFOLDER/$INPUT_FILEPATH.en $SUBFOLDER.en +} + +prepare_lid() { + pip install fasttext + + # TODO specify global workdir + MODEL=$WORKDIR/fasttext/lid.176.bin + LID_MULTI=$UTILS/fasttext_multi_filter.py + + if [ ! -f "$MODEL" ]; then + echo "downloading fasttext lid model..." + mkdir -p $WORKDIR/fasttext + wget -nc https://dl.fbaipublicfiles.com/fasttext/supervised-models/lid.176.bin -O $MODEL + fi +} + +prepare_moses() { + pushd $UTILS + echo 'Cloning Moses github repository (for tokenization scripts)...' + git clone https://github.com/moses-smt/mosesdecoder.git + popd +} + +lid_filter() { + # TODO specify global workdir + MODEL=$WORKDIR/fasttext/lid.176.bin + LID_MULTI=$UTILS/fasttext_multi_filter.py + + prepare_lid + + SRC=$1 + SRC_FILE=$2 + SRC_OUTPUT=$3 + TGT=$4 + TGT_FILE=$5 + TGT_OUTPUT=$6 + python $LID_MULTI --model $MODEL --inputs $SRC_FILE $TGT_FILE --langs $SRC $TGT --outputs $SRC_OUTPUT $TGT_OUTPUT +} + +prepare_ja_ted() { + mkdir -p ted + cd ted + + wget -nc https://wit3.fbk.eu/archive/2017-01-trnted//texts/en/ja/en-ja.tgz + tar -zxvf en-ja.tgz + cat en-ja/train.tags.en-ja.en | grep -v -P "^[ ]*\<" | sed 's/^[ \t]*//g' | sed 's/[ \t]*$//g' > en-ja/train.en-ja.en + cat en-ja/train.tags.en-ja.ja | grep -v -P "^[ ]*\<" | sed 's/^[ \t]*//g' | sed 's/[ \t]*$//g' > en-ja/train.en-ja.ja + + cd .. + ln -sf ted/en-ja/train.en-ja.ja ted.ja + ln -sf ted/en-ja/train.en-ja.en ted.en +} + +prepare_ja() { + OUTPUT_DIR=$TMP_DIR/ja + mkdir -p $OUTPUT_DIR + cd $OUTPUT_DIR + + download_and_select paracrawl "http://www.kecl.ntt.co.jp/icl/lirg/jparacrawl/release/2.0/bitext/en-ja.tar.gz" "tar -zxvf en-ja.tar.gz" ja en-ja/en-ja.bicleaner05.txt 4 3 & + download_and_select newscommentary "http://data.statmt.org/news-commentary/v15/training/news-commentary-v15.en-ja.tsv.gz" "gunzip -f news-commentary-v15.en-ja.tsv.gz" ja news-commentary-v15.en-ja.tsv 2 1 & + download_and_select wikititles "http://data.statmt.org/wikititles/v2/wikititles-v2.ja-en.tsv.gz" "gunzip -f wikititles-v2.ja-en.tsv.gz" ja wikititles-v2.ja-en.tsv 1 2 & + download_and_select wikimatrix "http://data.statmt.org/wmt20/translation-task/WikiMatrix/WikiMatrix.v1.en-ja.langid.tsv.gz" "gunzip -f WikiMatrix.v1.en-ja.langid.tsv.gz" ja WikiMatrix.v1.en-ja.langid.tsv 3 2 & + download_and_select subtitle "https://nlp.stanford.edu/projects/jesc/data/split.tar.gz" "tar -zxvf split.tar.gz" ja split/train 2 1 & + download_and_select kftt "http://www.phontron.com/kftt/download/kftt-data-1.0.tar.gz" "tar -zxvf kftt-data-1.0.tar.gz" ja kftt-data-1.0/data/orig/kyoto-train & + + prepare_ja_ted & + + # ted data needs to + + wait + + # remove previous results + rm -f all.?? + find ./ -maxdepth 1 -name "*.ja" | sort -V | xargs cat > all.ja + find ./ -maxdepth 1 -name "*.en" | sort -V | xargs cat > all.en + lid_filter ja all.ja $DEST/train.ja_XX-en_XX.ja_XX en all.en $DEST/train.ja_XX-en_XX.en_XX +} + +prepare_ta() { + OUTPUT_DIR=$TMP_DIR/ta + mkdir -p $OUTPUT_DIR + cd $OUTPUT_DIR + + download_and_select wikititles "http://data.statmt.org/wikititles/v2/wikititles-v2.ta-en.tsv.gz" "gunzip -f wikititles-v2.ta-en.tsv.gz" ta wikititles-v2.ta-en.tsv 1 2 & + download_and_select wikimatrix "http://data.statmt.org/wmt20/translation-task/WikiMatrix/WikiMatrix.v1.en-ta.langid.tsv.gz" "gunzip -f WikiMatrix.v1.en-ta.langid.tsv.gz" ta WikiMatrix.v1.en-ta.langid.tsv 3 2 & + download_and_select pmindia "http://data.statmt.org/pmindia/v1/parallel/pmindia.v1.ta-en.tsv" "" ta pmindia.v1.ta-en.tsv 2 1 & + download_and_select tanzil "https://object.pouta.csc.fi/OPUS-Tanzil/v1/moses/en-ta.txt.zip" "unzip en-ta.txt.zip" ta Tanzil.en-ta & + download_and_select pib "http://preon.iiit.ac.in/~jerin/resources/datasets/pib-v0.tar" "tar -xvf pib-v0.tar" ta pib/en-ta/train & + download_and_select mkb "http://preon.iiit.ac.in/~jerin/resources/datasets/mkb-v0.tar" "tar -xvf mkb-v0.tar" ta mkb/en-ta/mkb & + download_and_select ufal "http://ufal.mff.cuni.cz/~ramasamy/parallel/data/v2/en-ta-parallel-v2.tar.gz" "tar -zxvf en-ta-parallel-v2.tar.gz" ta en-ta-parallel-v2/corpus.bcn.train & + + wait + + # need special handling for nlpc + mkdir -p nlpc + cd nlpc + wget -nc https://raw.githubusercontent.com/nlpc-uom/English-Tamil-Parallel-Corpus/master/En-Ta%20Corpus/En-Ta%20English.txt + wget -nc https://github.com/nlpc-uom/English-Tamil-Parallel-Corpus/raw/master/En-Ta%20Corpus/En-Ta%20Tamil.txt + tail -n +4 "En-Ta English.txt" > en-ta.en + tail -n +4 "En-Ta Tamil.txt" > en-ta.ta + cd .. + ln -sf nlpc/en-ta.en nlpc.en + ln -sf nlpc/en-ta.ta nlpc.ta + + # remove previous results + rm -f all.?? + find ./ -maxdepth 1 -name "*.ta" | sort -V | xargs cat > all.ta + find ./ -maxdepth 1 -name "*.en" | sort -V | xargs cat > all.en + lid_filter ta all.ta $DEST/train.ta_IN-en_XX.ta_IN en all.en $DEST/train.ta_IN-en_XX.en_XX +} + +prepare_iu() { + OUTPUT_DIR=$TMP_DIR/iu + mkdir -p $OUTPUT_DIR + cd $OUTPUT_DIR + + download_and_select nh "https://nrc-digital-repository.canada.ca/eng/view/dataset/?id=c7e34fa7-7629-43c2-bd6d-19b32bf64f60" "tar -zxvf Nunavut-Hansard-Inuktitut-English-Parallel-Corpus-3.0.1.tgz" iu Nunavut-Hansard-Inuktitut-English-Parallel-Corpus-3.0/NunavutHansard > /dev/null & + download_and_select wikititles "http://data.statmt.org/wikititles/v2/wikititles-v2.iu-en.tsv.gz" "gunzip -f wikititles-v2.iu-en.tsv.gz" iu wikititles-v2.iu-en.tsv 1 2 & + + wait + + # remove previous results + rm -f all.?? + find ./ -maxdepth 1 -name "*.iu" | sort -V | xargs cat | nh/Nunavut-Hansard-Inuktitut-English-Parallel-Corpus-3.0/scripts/normalize-iu-spelling.pl > all.iu + find ./ -maxdepth 1 -name "*.en" | sort -V | xargs cat > all.en + paste all.iu all.en | awk -F $'\t' '$1!=""&&$2!=""' > all.iuen + cut -f1 all.iuen > $DEST/train.iu_CA-en_XX.iu_CA + cut -f2 all.iuen > $DEST/train.iu_CA-en_XX.en_XX +} + +prepare_km() { + OUTPUT_DIR=$TMP_DIR/km + mkdir -p $OUTPUT_DIR + cd $OUTPUT_DIR + + download_and_select paracrawl "http://data.statmt.org/wmt20/translation-task/ps-km/wmt20-sent.en-km.xz" "unxz wmt20-sent.en-km.zx" km wmt20-sent.en-km 2 1 & + + # km-parallel has multiple sets, concat all of them together + mkdir -p opus + cd opus + wget -nc "http://data.statmt.org/wmt20/translation-task/ps-km/km-parallel.tgz" + tar -zxvf km-parallel.tgz + find ./km-parallel -maxdepth 1 -name "*.km" | sort -V | xargs cat > opus.km + find ./km-parallel -maxdepth 1 -name "*.en" | sort -V | xargs cat > opus.en + cd .. + ln -sf opus/opus.km . + ln -sf opus/opus.en . + + wait + + # remove previous results + rm -f all.?? + find ./ -maxdepth 1 -name "*.km" | sort -V | xargs cat > all.km + find ./ -maxdepth 1 -name "*.en" | sort -V | xargs cat > all.en + lid_filter km all.km $DEST/train.km_KH-en_XX.km_KH en all.en $DEST/train.km_KH-en_XX.en_XX +} + +prepare_ps() { + OUTPUT_DIR=$TMP_DIR/ps + mkdir -p $OUTPUT_DIR + cd $OUTPUT_DIR + + download_and_select paracrawl "http://data.statmt.org/wmt20/translation-task/ps-km/wmt20-sent.en-ps.xz" "unxz wmt20-sent.en-ps.xz" ps wmt20-sent.en-ps 2 1 & + download_and_select wikititles "http://data.statmt.org/wikititles/v2/wikititles-v2.ps-en.tsv.gz" "gunzip -f wikititles-v2.ps-en.tsv.gz" ps wikititles-v2.ps-en.tsv 1 2 & + # ps-parallel has multiple sets, concat all of them together + mkdir -p opus + cd opus + wget -nc "http://data.statmt.org/wmt20/translation-task/ps-km/ps-parallel.tgz" + tar -zxvf ps-parallel.tgz + find ./ps-parallel -maxdepth 1 -name "*.ps" | sort -V | xargs cat > opus.ps + find ./ps-parallel -maxdepth 1 -name "*.en" | sort -V | xargs cat > opus.en + cd .. + ln -sf opus/opus.ps opus.ps + ln -sf opus/opus.en opus.en + + wait + + # remove previous results + rm -f all.?? + find ./ -maxdepth 1 -name "*.ps" | sort -V | xargs cat > all.ps + find ./ -maxdepth 1 -name "*.en" | sort -V | xargs cat > all.en + lid_filter ps all.ps $DEST/train.ps_AF-en_XX.ps_AF en all.en $DEST/train.ps_AF-en_XX.en_XX +} + +download_commoncrawl() { + mkdir -p $COMMONCRAWL_DIR + cd $COMMONCRAWL_DIR + + wget -nc "http://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz" + tar -zxvf training-parallel-commoncrawl.tgz +} +link_commoncrawl() { + LANG=$1 + ln -sf $COMMONCRAWL_DIR/commoncrawl.$LANG-en.en commoncrawl.en + ln -sf $COMMONCRAWL_DIR/commoncrawl.$LANG-en.$LANG commoncrawl.$LANG +} + +strip_xlf() { + INPUT_FILE=$1 + SRC=$2 + TGT=$3 + grep '<source xml:lang=' $INPUT_FILE | sed 's/^<[^<>]*>//g' | sed 's/<[^<>]*>$//g' > $INPUT_FILE.$SRC + grep '<target xml:lang=' $INPUT_FILE | sed 's/^<[^<>]*>//g' | sed 's/<[^<>]*>$//g' > $INPUT_FILE.$TGT +} + +download_and_process_tilde() { + URL=$1 + UNCOMPRESS_CMD=$2 + FILENAME=$3 + LANG=$4 + PROCESS_CMD=$5 + + mkdir -p tilde + cd tilde + wget -nc $URL + $UNCOMPRESS_CMD + echo "executing cmd" + echo $PROCESS_CMD + $PROCESS_CMD + cd .. + ln -sf tilde/$FILENAME.$LANG tilde.$LANG + ln -sf tilde/$FILENAME.en tilde.en +} + +prepare_cs() { + OUTPUT_DIR=$TMP_DIR/cs + mkdir -p $OUTPUT_DIR + cd $OUTPUT_DIR + + #download_and_select europarl "http://www.statmt.org/europarl/v10/training/europarl-v10.cs-en.tsv.gz" "gunzip europarl-v10.cs-en.tsv.gz" cs europarl-v10.cs-en.tsv 1 2 & + #download_and_select paracrawl "https://s3.amazonaws.com/web-language-models/paracrawl/release5.1/en-cs.txt.gz" "gunzip en-cs.txt.gz" cs en-cs.txt 2 1 & + #link_commoncrawl cs + #download_and_select newscommentary "http://data.statmt.org/news-commentary/v15/training/news-commentary-v15.cs-en.tsv.gz" "gunzip news-commentary-v15.cs-en.tsv.gz" cs news-commentary-v15.cs-en.tsv 1 2 & + #download_and_select wikititles "http://data.statmt.org/wikititles/v2/wikititles-v2.cs-en.tsv.gz" "gunzip wikititles-v2.cs-en.tsv.gz" cs wikititles-v2.cs-en.tsv 1 2 & + #download_and_process_tilde "http://data.statmt.org/wmt20/translation-task/rapid/RAPID_2019.cs-en.xlf.gz" "gunzip RAPID_2019.cs-en.xlf.gz" RAPID_2019.cs-en.xlf cs "strip_xlf RAPID_2019.cs-en.xlf cs en" & + #download_and_select wikimatrix "http://data.statmt.org/wmt20/translation-task/WikiMatrix/WikiMatrix.v1.cs-en.langid.tsv.gz" "gunzip WikiMatrix.v1.cs-en.langid.tsv.gz" cs WikiMatrix.v1.cs-en.langid.tsv 2 3 & + + #wait + + # remove previous results + #rm -f all.?? + #find ./ -maxdepth 1 -name "*.cs" | sort -V | xargs cat > all.cs + #find ./ -maxdepth 1 -name "*.en" | sort -V | xargs cat > all.en + if [ -z $CZENG_CORPUS ] ; + then + echo "Please download CZENG_CORPUS manually and place them at $CZENG_CORPUS. Exitting..." + exit + fi + cat $CZENG_CORPUS | sed '/^$/d' | cut -f5 > all.cs + cat $CZENG_CORPUS | sed '/^$/d' | cut -f6 > all.en + + lid_filter cs all.cs $DEST/train.cs_CZ-en_XX.cs_CZ en all.en $DEST/train.cs_CZ-en_XX.en_XX +} + +prepare_de() { + OUTPUT_DIR=$TMP_DIR/de + mkdir -p $OUTPUT_DIR + cd $OUTPUT_DIR + + download_and_select europarl "http://www.statmt.org/europarl/v10/training/europarl-v10.de-en.tsv.gz" "gunzip europarl-v10.de-en.tsv.gz" de europarl-v10.de-en.tsv 1 2 & + download_and_select paracrawl "https://s3.amazonaws.com/web-language-models/paracrawl/release5.1/en-de.txt.gz" "gunzip en-de.txt.gz" de en-de.txt 2 1 & + link_commoncrawl de + download_and_select newscommentary "http://data.statmt.org/news-commentary/v15/training/news-commentary-v15.de-en.tsv.gz" "gunzip news-commentary-v15.de-en.tsv.gz" de news-commentary-v15.de-en.tsv 1 2 & + download_and_select wikititles "http://data.statmt.org/wikititles/v2/wikititles-v2.de-en.tsv.gz" "gunzip wikititles-v2.de-en.tsv.gz" de wikititles-v2.de-en.tsv 1 2 & + download_and_process_tilde "http://data.statmt.org/wmt20/translation-task/rapid/RAPID_2019.de-en.xlf.gz" "gunzip RAPID_2019.de-en.xlf.gz" RAPID_2019.de-en.xlf de "strip_xlf RAPID_2019.de-en.xlf de en" & + download_and_select wikimatrix "http://data.statmt.org/wmt20/translation-task/WikiMatrix/WikiMatrix.v1.de-en.langid.tsv.gz" "gunzip WikiMatrix.v1.de-en.langid.tsv.gz" de WikiMatrix.v1.de-en.langid.tsv 2 3 & + + wait + + # remove previous results + rm -f all.?? + find ./ -maxdepth 1 -name "*.de" | sort -V | xargs cat > all.de + find ./ -maxdepth 1 -name "*.en" | sort -V | xargs cat > all.en + lid_filter de all.de $DEST/train.de_DE-en_XX.de_DE en all.en $DEST/train.de_DE-en_XX.en_XX +} + +prepare_tmx() { + TMX_FILE=$1 + git clone https://github.com/amake/TMX2Corpus $UTILS/tmx2corpus + pip install tinysegmenter + + python $UTILS/tmx2corpus/tmx2corpus.py $TMX_FILE +} + +prepare_pl() { + OUTPUT_DIR=$TMP_DIR/pl + mkdir -p $OUTPUT_DIR + cd $OUTPUT_DIR + + # download_and_select europarl "http://www.statmt.org/europarl/v10/training/europarl-v10.pl-en.tsv.gz" "gunzip europarl-v10.pl-en.tsv.gz" pl europarl-v10.pl-en.tsv 1 2 & + # download_and_select paracrawl "https://s3.amazonaws.com/web-language-models/paracrawl/release5.1/en-pl.txt.gz" "gunzip en-pl.txt.gz" pl en-pl.txt 2 1 & + # download_and_select wikititles "http://data.statmt.org/wikititles/v2/wikititles-v2.pl-en.tsv.gz" "gunzip wikititles-v2.pl-en.tsv.gz" pl wikititles-v2.pl-en.tsv 1 2 & + download_and_select tilde "https://tilde-model.s3-eu-west-1.amazonaws.com/rapid2019.en-pl.tmx.zip" "gunzip rapid2019.en-pl.tmx.zip" bitext pl "prepare_tmx RAPID_2019.UNIQUE.en-pl.tmx" & + # download_and_select wikimatrix "http://data.statmt.org/wmt20/translation-task/WikiMatrix/WikiMatrix.v1.en-pl.langid.tsv.gz" "gunzip WikiMatrix.v1.en-pl.langid.tsv.gz" pl WikiMatrix.v1.en-pl.langid.tsv 3 2 & + + wait + + # remove previous results + rm -f all.?? + find ./ -maxdepth 1 -name "*.pl" | sort -V | xargs cat > all.pl + find ./ -maxdepth 1 -name "*.en" | sort -V | xargs cat > all.en + lid_filter pl all.pl $DEST/train.pl_PL-en_XX.pl_PL en all.en $DEST/train.pl_PL-en_XX.en_XX +} + +prepare_uncorpus() { + $URLS=$1 + $FILES=$2 + + mkdir -p uncorpus + cd uncorpus + + for URL in $URLS; do + wget -nc $URL + done + cat $FILES > uncorpus.tar.gz + tar -zxvf uncorpus.tar.gz + + cd .. + ln -sf uncorpus/en-$LANG/UNv1.0.en-$LANG.$LANG uncorpus.$LANG + ln -sf uncorpus/en-$LANG/UNv1.0.en-$LANG.en uncorpus.en +} + +prepare_yandex() { + mkdir -p yandex + cd yandex + unzip $YANDEX_CORPUS ./ + cd .. + ln -s yandex/corpus.en_ru.1m.en yandex.en + ln -s yandex/corpus.en_ru.1m.ru yandex.ru +} + +prepare_ru() { + OUTPUT_DIR=$TMP_DIR/ru + mkdir -p $OUTPUT_DIR + cd $OUTPUT_DIR + + download_and_select paracrawl "https://s3.amazonaws.com/web-language-models/paracrawl/release1/paracrawl-release1.en-ru.zipporah0-dedup-clean.tgz" "tar -zxvf paracrawl-release1.en-ru.zipporah0-dedup-clean.tgz" ru paracrawl-release1.en-ru.zipporah0-dedup-clean & + link_commoncrawl ru + download_and_select newscommentary "http://data.statmt.org/news-commentary/v15/training/news-commentary-v15.en-ru.tsv.gz" "gunzip news-commentary-v15.en-ru.tsv.gz" ru news-commentary-v15.en-ru.tsv 2 1 & + prepare_yandex & + download_and_select wikititles "http://data.statmt.org/wikititles/v2/wikititles-v2.ru-en.tsv.gz" "gunzip wikititles-v2.ru-en.tsv.gz" ru wikititles-v2.ru-en.tsv 1 2 & + prepare_uncorpus "https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-ru.tar.gz.00 https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-ru.tar.gz.01 https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-ru.tar.gz.02" "UNv1.0.en-ru.tar.gz.00 UNv1.0.en-ru.tar.gz.01 UNv1.0.en-ru.tar.gz.02" & + download_and_select wikimatrix "http://data.statmt.org/wmt20/translation-task/WikiMatrix/WikiMatrix.v1.en-ru.langid.tsv.gz" "gunzip WikiMatrix.v1.en-ru.langid.tsv.gz" ru WikiMatrix.v1.en-ru.langid.tsv 3 2 & + + wait + + # remove previous results + rm -f all.?? + find ./ -maxdepth 1 -name "*.ru" | sort -V | xargs cat > all.ru + find ./ -maxdepth 1 -name "*.en" | sort -V | xargs cat > all.en + lid_filter ru all.ru $DEST/train.ru_RU-en_XX.ru_RU en all.en $DEST/train.ru_RU-en_XX.en_XX +} + +prepare_ccmt() { + mkdir -p ccmt + cd ccmt + # assume ccmt data is already unzipped under CCMT_DIR folder + cat $CCMT_DIR/datum2017/Book*_cn.txt | sed 's/ //g' > datum2017.detok.zh + cat $CCMT_DIR/datum2017/Book*_en.txt > datum2017.detok.en + cat $CCMT_DIR/casict2011/casict-A_ch.txt $CCMT_DIR/casict2011/casict-B_ch.txt $CCMT_DIR/casict2015/casict2015_ch.txt $CCMT_DIR/datum2015/datum_ch.txt $CCMT_DIR/neu2017/NEU_cn.txt datum2017.detok.zh > ccmt.zh + cat $CCMT_DIR/casict2011/casict-A_en.txt $CCMT_DIR/casict2011/casict-B_en.txt $CCMT_DIR/casict2015/casict2015_en.txt $CCMT_DIR/datum2015/datum_en.txt $CCMT_DIR/neu2017/NEU_en.txt datum2017.detok.en > ccmt.en + cd .. + ln -sf ccmt/ccmt.zh ccmt.zh + ln -sf ccmt/ccmt.en ccmt.en +} + +prepare_zh() { + OUTPUT_DIR=$TMP_DIR/zh + mkdir -p $OUTPUT_DIR + cd $OUTPUT_DIR + + download_and_select newscommentary "http://data.statmt.org/news-commentary/v15/training/news-commentary-v15.en-zh.tsv.gz" "gunzip news-commentary-v15.en-zh.tsv.gz" zh news-commentary-v15.en-zh.tsv 2 1 & + download_and_select wikititles "http://data.statmt.org/wikititles/v2/wikititles-v2.zh-en.tsv.gz" "gunzip wikititles-v2.zh-en.tsv.gz" zh wikititles-v2.zh-en.tsv 1 2 & + prepare_uncorpus "https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-zh.tar.gz.00 https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-zh.tar.gz.01" "UNv1.0.en-zh.tar.gz.00 UNv1.0.en-zh.tar.gz.01" & + prepare_ccmt & + download_and_select wikimatrix "http://data.statmt.org/wmt20/translation-task/WikiMatrix/WikiMatrix.v1.en-zh.langid.tsv.gz" "gunzip WikiMatrix.v1.en-zh.langid.tsv.gz" zh WikiMatrix.v1.en-zh.langid.tsv 3 2 & + + wait + + # remove previous results + rm -f all.?? + find ./ -maxdepth 1 -name "*.zh" | sort -V | xargs cat > all.zh + find ./ -maxdepth 1 -name "*.en" | sort -V | xargs cat > all.en + lid_filter zh all.zh $DEST/train.zh_CN-en_XX.zh_CN en all.en $DEST/train.zh_CN-en_XX.en_XX +} + +prepare_tests() { + OUTPUT_DIR=$TMP_DIR + mkdir -p $OUTPUT_DIR + cd $OUTPUT_DIR + wget -nc http://data.statmt.org/wmt20/translation-task/dev.tgz + tar -zxvf dev.tgz + cd dev + + cat newsdev2020-jaen-src.ja.sgm | $UTILS/strip_sgm.sh > newsdev2020-jaen.ja + cat newsdev2020-jaen-ref.en.sgm | $UTILS/strip_sgm.sh > newsdev2020-jaen.en + split newsdev2020-jaen.ja -a 0 -n r/1/2 > $DEST/valid.ja_XX-en_XX.ja_XX + split newsdev2020-jaen.en -a 0 -n r/1/2 > $DEST/valid.ja_XX-en_XX.en_XX + split newsdev2020-jaen.ja -a 0 -n r/2/2 > $DEST/test.ja_XX-en_XX.ja_XX + split newsdev2020-jaen.en -a 0 -n r/2/2 > $DEST/test.ja_XX-en_XX.en_XX + + cat newsdev2020-iuen-src.iu.sgm | strip_sgm.sh > newsdev2020-iuen.iu + cat newsdev2020-iuen-ref.en.sgm | strip_sgm.sh > newsdev2020-iuen.en + split newsdev2020-iuen.iu -a 0 -n r/1/2 > $DEST/valid.iu_CA-en_XX.iu_CA + split newsdev2020-iuen.en -a 0 -n r/1/2 > $DEST/valid.iu_CA-en_XX.en_XX + split newsdev2020-iuen.iu -a 0 -n r/2/2 > $DEST/test.iu_CA-en_XX.iu_CA + split newsdev2020-iuen.en -a 0 -n r/2/2 > $DEST/test.iu_CA-en_XX.en_XX + + cat newsdev2020-taen-src.ta.sgm | strip_sgm.sh > newsdev2020-taen.ta + cat newsdev2020-taen-ref.en.sgm | strip_sgm.sh > newsdev2020-taen.en + split newsdev2020-taen.ta -a 0 -n r/1/2 > $DEST/valid.ta_IN-en_XX.ta_IN + split newsdev2020-taen.en -a 0 -n r/1/2 > $DEST/valid.ta_IN-en_XX.en_XX + split newsdev2020-taen.ta -a 0 -n r/2/2 > $DEST/test.ta_IN-en_XX.ta_IN + split newsdev2020-taen.en -a 0 -n r/2/2 > $DEST/test.ta_IN-en_XX.en_XX + + cp wikipedia.dev.km-en.km $DEST/valid.km_KH-en_XX.km_KH + cp wikipedia.dev.km-en.en $DEST/valid.km_KH-en_XX.en_XX + cp wikipedia.devtest.km-en.km $DEST/test.km_KH-en_XX.km_KH + cp wikipedia.devtest.km-en.en $DEST/test.km_KH-en_XX.en_XX + + cp wikipedia.dev.ps-en.ps $DEST/valid.ps_AF-en_XX.ps_AF + cp wikipedia.dev.ps-en.en $DEST/valid.ps_AF-en_XX.en_XX + cp wikipedia.devtest.ps-en.ps $DEST/test.ps_AF-en_XX.ps_AF + cp wikipedia.devtest.ps-en.en $DEST/test.ps_AF-en_XX.en_XX + + cat newsdev2020-plen-src.pl.sgm | strip_sgm.sh > newsdev2020-plen.pl + cat newsdev2020-plen-ref.en.sgm | strip_sgm.sh > newsdev2020-plen.en + split newsdev2020-plen.pl -a 0 -n r/1/2 > $DEST/valid.pl_PL-en_XX.pl_PL + split newsdev2020-plen.en -a 0 -n r/1/2 > $DEST/valid.pl_PL-en_XX.en_XX + split newsdev2020-plen.pl -a 0 -n r/2/2 > $DEST/test.pl_PL-en_XX.pl_PL + split newsdev2020-plen.en -a 0 -n r/2/2 > $DEST/test.pl_PL-en_XX.en_XX + + cat newstest2018-encs-src.en.sgm | strip_sgm.sh > $DEST/valid.en_XX-cs_CZ.en_XX + cat newstest2018-encs-ref.cs.sgm | strip_sgm.sh > $DEST/valid.en_XX-cs_CZ.cs_CZ + cat newstest2019-encs-src.en.sgm | strip_sgm.sh > $DEST/test.en_XX-cs_CZ.en_XX + cat newstest2019-encs-ref.cs.sgm | strip_sgm.sh > $DEST/test.en_XX-cs_CZ.cs_CZ + + cat newstest2018-deen-src.de.sgm | strip_sgm.sh > $DEST/valid.de_DE-en_XX.de_DE + cat newstest2018-deen-ref.en.sgm | strip_sgm.sh > $DEST/valid.de_DE-en_XX.en_XX + cat newstest2018-ende-src.en.sgm | strip_sgm.sh > $DEST/valid.en_XX-de_DE.en_XX + cat newstest2018-ende-ref.de.sgm | strip_sgm.sh > $DEST/valid.en_XX-de_DE.de_DE + cat newstest2019-deen-src.de.sgm | strip_sgm.sh > $DEST/test.de_DE-en_XX.de_DE + cat newstest2019-deen-ref.en.sgm | strip_sgm.sh > $DEST/test.de_DE-en_XX.en_XX + cat newstest2019-ende-src.en.sgm | strip_sgm.sh > $DEST/test.en_XX-de_DE.en_XX + cat newstest2019-ende-ref.de.sgm | strip_sgm.sh > $DEST/test.en_XX-de_DE.de_DE + + cat newstest2018-ruen-src.ru.sgm | strip_sgm.sh > $DEST/valid.ru_RU-en_XX.ru_RU + cat newstest2018-ruen-ref.en.sgm | strip_sgm.sh > $DEST/valid.ru_RU-en_XX.en_XX + cat newstest2018-enru-src.en.sgm | strip_sgm.sh > $DEST/valid.en_XX-ru_RU.en_XX + cat newstest2018-enru-ref.ru.sgm | strip_sgm.sh > $DEST/valid.en_XX-ru_RU.ru_RU + cat newstest2019-ruen-src.ru.sgm | strip_sgm.sh > $DEST/test.ru_RU-en_XX.ru_RU + cat newstest2019-ruen-ref.en.sgm | strip_sgm.sh > $DEST/test.ru_RU-en_XX.en_XX + cat newstest2019-enru-src.en.sgm | strip_sgm.sh > $DEST/test.en_XX-ru_RU.en_XX + cat newstest2019-enru-ref.ru.sgm | strip_sgm.sh > $DEST/test.en_XX-ru_RU.ru_RU + + cat newstest2018-zhen-src.zh.sgm | strip_sgm.sh > $DEST/valid.zh_CN-en_XX.zh_CN + cat newstest2018-zhen-ref.en.sgm | strip_sgm.sh > $DEST/valid.zh_CN-en_XX.en_XX + cat newstest2018-enzh-src.en.sgm | strip_sgm.sh > $DEST/valid.en_XX-zh_CN.en_XX + cat newstest2018-enzh-ref.zh.sgm | strip_sgm.sh > $DEST/valid.en_XX-zh_CN.zh_CN + cat newstest2019-zhen-src.zh.sgm | strip_sgm.sh > $DEST/test.zh_CN-en_XX.zh_CN + cat newstest2019-zhen-ref.en.sgm | strip_sgm.sh > $DEST/test.zh_CN-en_XX.en_XX + cat newstest2019-enzh-src.en.sgm | strip_sgm.sh > $DEST/test.en_XX-zh_CN.en_XX + cat newstest2019-enzh-ref.zh.sgm | strip_sgm.sh > $DEST/test.en_XX-zh_CN.zh_CN +} + +mkdir -p $DEST + +prepare_lid +prepare_moses +download_commoncrawl + +prepare_ja & +prepare_ta & +prepare_km & +prepare_ps & +prepare_iu & +prepare_cs & +prepare_de & +prepare_pl & +prepare_ru & +prepare_zh & + +# prepare valid/test set +prepare_tests & + +# wait + +# TODO remove intermediate files +# rm -rf $TMP_DIR diff --git a/examples/multilingual/data_scripts/preprocess_ML50_v1.sh b/examples/multilingual/data_scripts/preprocess_ML50_v1.sh new file mode 100644 index 0000000000..4655936149 --- /dev/null +++ b/examples/multilingual/data_scripts/preprocess_ML50_v1.sh @@ -0,0 +1,27 @@ +#!/bin/bash +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +if [ -z $WORKDIR_ROOT ] ; +then + echo "please specify your working directory root in environment variable WORKDIR_ROOT. Exitting..." + exit +fi + +if [ -z $SPM_PATH ] ; +then + echo "Please install sentence piecence from https://github.com/google/sentencepiece and set SPM_PATH pointing to the installed spm_encode.py. Exitting..." + exit +fi + +ML50=${WORKDIR_ROOT}/ML50 + +mkdir -p $ML50/dedup +mkdir -p $ML50/cleaned_dedup + +python ./dedup_all.py --from-folder $ML50/raw --to-folder $ML50/dedup +python ./remove_valid_test_in_train.py --from-folder $ML50/dedup --to-folder $ML50/clean +python ./binarize.py --raw-folder $ML50/clean \ No newline at end of file diff --git a/examples/multilingual/data_scripts/remove_valid_test_in_train.py b/examples/multilingual/data_scripts/remove_valid_test_in_train.py new file mode 100755 index 0000000000..ef618adef7 --- /dev/null +++ b/examples/multilingual/data_scripts/remove_valid_test_in_train.py @@ -0,0 +1,290 @@ +import os, sys +import glob, itertools +import pandas as pd + +WORKDIR_ROOT = os.environ.get('WORKDIR_ROOT', None) + +if WORKDIR_ROOT is None or not WORKDIR_ROOT.strip(): + print('please specify your working directory root in OS environment variable WORKDIR_ROOT. Exitting..."') + sys.exit(-1) + + +def load_langs(path): + with open(path) as fr: + langs = [l.strip() for l in fr] + return langs + + + +def load_sentences(raw_data, split, direction): + src, tgt = direction.split('-') + src_path = f"{raw_data}/{split}.{direction}.{src}" + tgt_path = f"{raw_data}/{split}.{direction}.{tgt}" + if os.path.exists(src_path) and os.path.exists(tgt_path): + return [(src, open(src_path).read().splitlines()), (tgt, open(tgt_path).read().splitlines())] + else: + return [] + +def swap_direction(d): + src, tgt = d.split('-') + return f'{tgt}-{src}' + +def get_all_test_data(raw_data, directions, split='test'): + test_data = [ + x + for dd in directions + for d in [dd, swap_direction(dd)] + for x in load_sentences(raw_data, split, d) + ] + # all_test_data = {s for _, d in test_data for s in d} + all_test_data = {} + for lang, d in test_data: + for s in d: + s = s.strip() + lgs = all_test_data.get(s, set()) + lgs.add(lang) + all_test_data[s] = lgs + return all_test_data, test_data + +def check_train_sentences(raw_data, direction, all_test_data, mess_up_train={}): + src, tgt = direction.split('-') + tgt_path = f"{raw_data}/train.{direction}.{tgt}" + src_path = f"{raw_data}/train.{direction}.{src}" + print(f'check training data in {raw_data}/train.{direction}') + size = 0 + if not os.path.exists(tgt_path) or not os.path.exists(src_path): + return mess_up_train, size + with open(src_path) as f, open(tgt_path) as g: + for src_line, tgt_line in zip(f, g): + s = src_line.strip() + t = tgt_line.strip() + size += 1 + if s in all_test_data: + langs = mess_up_train.get(s, set()) + langs.add(direction) + mess_up_train[s] = langs + if t in all_test_data: + langs = mess_up_train.get(t, set()) + langs.add(direction) + mess_up_train[t] = langs + return mess_up_train, size + +def check_train_all(raw_data, directions, all_test_data): + mess_up_train = {} + data_sizes = {} + for direction in directions: + _, size = check_train_sentences(raw_data, direction, all_test_data, mess_up_train) + data_sizes[direction] = size + return mess_up_train, data_sizes + +def count_train_in_other_set(mess_up_train): + train_in_others = [(direction, s) for s, directions in mess_up_train.items() for direction in directions] + counts = {} + for direction, s in train_in_others: + counts[direction] = counts.get(direction, 0) + 1 + return counts + +def train_size_if_remove_in_otherset(data_sizes, mess_up_train): + counts_in_other = count_train_in_other_set(mess_up_train) + remain_sizes = [] + for direction, count in counts_in_other.items(): + remain_sizes.append((direction, data_sizes[direction] - count, data_sizes[direction], count, 100 * count / data_sizes[direction] )) + return remain_sizes + + +def remove_messed_up_sentences(raw_data, direction, mess_up_train, mess_up_train_pairs, corrected_langs): + split = 'train' + src_lang, tgt_lang = direction.split('-') + + tgt = f"{raw_data}/{split}.{direction}.{tgt_lang}" + src = f"{raw_data}/{split}.{direction}.{src_lang}" + print(f'working on {direction}: ', src, tgt) + if not os.path.exists(tgt) or not os.path.exists(src) : + return + + corrected_tgt = f"{to_folder}/{split}.{direction}.{tgt_lang}" + corrected_src = f"{to_folder}/{split}.{direction}.{src_lang}" + line_num = 0 + keep_num = 0 + with open(src, encoding='utf8',) as fsrc, \ + open(tgt, encoding='utf8',) as ftgt, \ + open(corrected_src, 'w', encoding='utf8') as fsrc_corrected, \ + open(corrected_tgt, 'w', encoding='utf8') as ftgt_corrected: + for s, t in zip(fsrc, ftgt): + s = s.strip() + t = t.strip() + if t not in mess_up_train \ + and s not in mess_up_train \ + and (s, t) not in mess_up_train_pairs \ + and (t, s) not in mess_up_train_pairs: + corrected_langs.add(direction) + print(s, file=fsrc_corrected) + print(t, file=ftgt_corrected) + keep_num += 1 + line_num += 1 + if line_num % 1000 == 0: + print(f'completed {line_num} lines', end='\r') + return line_num, keep_num + +########## + + +def merge_valid_test_messup(mess_up_train_valid, mess_up_train_test): + merged_mess = [] + for s in set(list(mess_up_train_valid.keys()) + list(mess_up_train_test.keys())): + if not s: + continue + valid = mess_up_train_valid.get(s, set()) + test = mess_up_train_test.get(s, set()) + merged_mess.append((s, valid | test)) + return dict(merged_mess) + + + +######### +def check_train_pairs(raw_data, direction, all_test_data, mess_up_train={}): + src, tgt = direction.split('-') + #a hack; TODO: check the reversed directions + path1 = f"{raw_data}/train.{src}-{tgt}.{src}" + path2 = f"{raw_data}/train.{src}-{tgt}.{tgt}" + if not os.path.exists(path1) or not os.path.exists(path2) : + return + + with open(path1) as f1, open(path2) as f2: + for src_line, tgt_line in zip(f1, f2): + s = src_line.strip() + t = tgt_line.strip() + if (s, t) in all_test_data or (t, s) in all_test_data: + langs = mess_up_train.get( (s, t), set()) + langs.add(src) + langs.add(tgt) + mess_up_train[(s, t)] = langs + + +def load_pairs(raw_data, split, direction): + src, tgt = direction.split('-') + src_f = f"{raw_data}/{split}.{direction}.{src}" + tgt_f = f"{raw_data}/{split}.{direction}.{tgt}" + if tgt != 'en_XX': + src_f, tgt_f = tgt_f, src_f + if os.path.exists(src_f) and os.path.exists(tgt_f): + return list(zip(open(src_f).read().splitlines(), + open(tgt_f).read().splitlines(), + )) + else: + return [] + +# skip_langs = ['cs_CZ', 'en_XX', 'tl_XX', 'tr_TR'] +def get_messed_up_test_pairs(split, directions): + test_pairs = [ + (d, load_pairs(raw_data, split, d)) + for d in directions + ] + # all_test_data = {s for _, d in test_data for s in d} + all_test_pairs = {} + for direction, d in test_pairs: + src, tgt = direction.split('-') + for s in d: + langs = all_test_pairs.get(s, set()) + langs.add(src) + langs.add(tgt) + all_test_pairs[s] = langs + mess_up_train_pairs = {} + for direction in directions: + check_train_pairs(raw_data, direction, all_test_pairs, mess_up_train_pairs) + return all_test_pairs, mess_up_train_pairs + + + +if __name__ == "__main__": + ####### + import argparse + parser = argparse.ArgumentParser() + parser.add_argument( + '--from-folder', + required=True, + type=str) + parser.add_argument( + '--to-folder', + required=True, + type=str) + parser.add_argument( + '--directions', + default=None, + type=str) + + + args = parser.parse_args() + raw_data = args.from_folder + to_folder = args.to_folder + os.makedirs(to_folder, exist_ok=True) + + if args.directions: + directions = args.directions.split(',') + else: + raw_files = itertools.chain( + glob.glob(f'{raw_data}/train*'), + glob.glob(f'{raw_data}/valid*'), + glob.glob(f'{raw_data}/test*'), + ) + directions = [os.path.split(file_path)[-1].split('.')[1] for file_path in raw_files] + print('working on directions: ', directions) + + ########## + + + + all_test_data, test_data = get_all_test_data(raw_data, directions, 'test') + print('==loaded test data==') + all_valid_data, valid_data = get_all_test_data(raw_data, directions, 'valid') + print('==loaded valid data==') + all_valid_test_data = merge_valid_test_messup(all_test_data, all_valid_data) + mess_up_train, data_sizes = check_train_all(raw_data, directions, all_valid_test_data) + print('training messing up with valid, test data:', len(mess_up_train)) + data_situation = train_size_if_remove_in_otherset(data_sizes, mess_up_train) + df = pd.DataFrame(data_situation, columns=['direction', 'train_size_after_remove', 'orig_size', 'num_to_remove', 'remove_percent']) + df.sort_values('remove_percent', ascending=False) + df.to_csv(f'{raw_data}/clean_summary.tsv', sep='\t') + print(f'projected data clean summary in: {raw_data}/clean_summary.tsv') + + # correct the dataset: + all_test_pairs, mess_up_test_train_pairs = get_messed_up_test_pairs('test', directions) + all_valid_pairs, mess_up_valid_train_pairs = get_messed_up_test_pairs('valid', directions) + + all_messed_pairs = set(mess_up_test_train_pairs.keys()).union(set(mess_up_valid_train_pairs.keys())) + corrected_directions = set() + + real_data_situation = [] + for direction in directions: + org_size, new_size = remove_messed_up_sentences(raw_data, direction, mess_up_train, all_messed_pairs, corrected_directions) + if org_size == 0: + print(f"{direction} has size 0") + continue + real_data_situation.append( + (direction, new_size, org_size, org_size - new_size, (org_size - new_size) / org_size * 100) + ) + print('corrected directions: ', corrected_directions) + df = pd.DataFrame(real_data_situation, columns=['direction', 'train_size_after_remove', 'orig_size', 'num_to_remove', 'remove_percent']) + df.sort_values('remove_percent', ascending=False) + df.to_csv(f'{raw_data}/actual_clean_summary.tsv', sep='\t') + print(f'actual data clean summary (which can be different from the projected one because of duplications) in: {raw_data}/actual_clean_summary.tsv') + + import shutil + for direction in directions: + src_lang, tgt_lang = direction.split('-') + for split in ['train', 'valid', 'test']: + # copying valid, test and uncorrected train + if direction in corrected_directions and split == 'train': + continue + tgt = f"{raw_data}/{split}.{direction}.{tgt_lang}" + src = f"{raw_data}/{split}.{direction}.{src_lang}" + if not (os.path.exists(src) and os.path.exists(tgt)): + continue + corrected_tgt = f"{to_folder}/{split}.{direction}.{tgt_lang}" + corrected_src = f"{to_folder}/{split}.{direction}.{src_lang}" + print(f'copying {src} to {corrected_src}') + shutil.copyfile(src, corrected_src) + print(f'copying {tgt} to {corrected_tgt}') + shutil.copyfile(tgt, corrected_tgt) + + print('completed') \ No newline at end of file diff --git a/examples/multilingual/data_scripts/requirement.txt b/examples/multilingual/data_scripts/requirement.txt new file mode 100644 index 0000000000..e85d7d540e --- /dev/null +++ b/examples/multilingual/data_scripts/requirement.txt @@ -0,0 +1,2 @@ +wget +pandas \ No newline at end of file diff --git a/examples/multilingual/data_scripts/utils/dedup.py b/examples/multilingual/data_scripts/utils/dedup.py new file mode 100644 index 0000000000..d6fed8c695 --- /dev/null +++ b/examples/multilingual/data_scripts/utils/dedup.py @@ -0,0 +1,41 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +import argparse + +def deup(src_file, tgt_file, src_file_out, tgt_file_out): + seen = set() + dup_count = 0 + with open(src_file, encoding='utf-8') as fsrc, \ + open(tgt_file, encoding='utf-8') as ftgt, \ + open(src_file_out, 'w', encoding='utf-8') as fsrc_out, \ + open(tgt_file_out, 'w', encoding='utf-8') as ftgt_out: + for s, t in zip(fsrc, ftgt): + if (s, t) not in seen: + fsrc_out.write(s) + ftgt_out.write(t) + seen.add((s, t)) + else: + dup_count += 1 + print(f'number of duplication: {dup_count}') + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--src-file", type=str, required=True, + help="src file") + parser.add_argument("--tgt-file", type=str, required=True, + help="tgt file") + parser.add_argument("--src-file-out", type=str, required=True, + help="src ouptut file") + parser.add_argument("--tgt-file-out", type=str, required=True, + help="tgt ouput file") + args = parser.parse_args() + deup(args.src_file, args.tgt_file, args.src_file_out, args.tgt_file_out) + + +if __name__ == "__main__": + main() diff --git a/examples/multilingual/data_scripts/utils/fasttext_multi_filter.py b/examples/multilingual/data_scripts/utils/fasttext_multi_filter.py new file mode 100644 index 0000000000..41b38ba5be --- /dev/null +++ b/examples/multilingual/data_scripts/utils/fasttext_multi_filter.py @@ -0,0 +1,63 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +#!/bin/python + +import fasttext +from multiprocessing import Pool +import contextlib +import sys +import argparse +from functools import partial +import io + +model = None +def init(model_path): + global model + model = fasttext.load_model(model_path) + +def pred(lines): + return lines, [model.predict(line.strip())[0][0][9:] for line in lines] + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--model", type=str, required=True, + help="model to load") + parser.add_argument("--inputs", nargs="+", default=['-'], + help="input files to filter") + parser.add_argument("--langs", nargs="+", required=True, + help="lang ids of each input file") + parser.add_argument("--outputs", nargs="+", default=['-'], + help="path to save lid filtered outputs") + parser.add_argument("--num-workers", type=int, metavar="N", default=10, + help="number of processes in parallel") + args = parser.parse_args() + + assert len(args.inputs) == len(args.langs) and len(args.inputs) == len(args.outputs) + + with contextlib.ExitStack() as stack: + inputs = [ + stack.enter_context(open(input, "r", encoding="utf-8", newline="\n", errors="replace")) + if input != "-" else io.TextIOWrapper(sys.stdin.buffer, encoding='utf-8', errors="replace") + for input in args.inputs + ] + outputs = [ + stack.enter_context(open(output, "w", encoding="utf-8", newline="\n")) + if output != "-" else sys.stdout + for output in args.outputs + ] + with Pool(args.num_workers, initializer=partial(init, args.model)) as p: + skip_cnt = 0 + for lines, preds in p.imap(pred, list(zip(*inputs)), chunksize=500): + if not all(a == b for a, b in zip(preds, args.langs)): + skip_cnt += 1 + continue + for line, output_h in zip(lines, outputs): + print(line.strip(), file=output_h) + print(f"Skipped {skip_cnt} lines.") + +if __name__ == "__main__": + main() diff --git a/examples/multilingual/data_scripts/utils/strip_sgm.sh b/examples/multilingual/data_scripts/utils/strip_sgm.sh new file mode 100755 index 0000000000..7f4f61d7b1 --- /dev/null +++ b/examples/multilingual/data_scripts/utils/strip_sgm.sh @@ -0,0 +1 @@ +grep "seg id" | sed 's/<seg id="[0-9]\+">//g' | sed 's/<\/seg>//g' diff --git a/examples/multilingual/finetune_multilingual_model.sh b/examples/multilingual/finetune_multilingual_model.sh new file mode 100644 index 0000000000..25960c5dc8 --- /dev/null +++ b/examples/multilingual/finetune_multilingual_model.sh @@ -0,0 +1,32 @@ +#!/bin/bash +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +path_2_data=$1 # <path to data> which contains binarized data for each directions +lang_list=$2 # <path to a file which contains a list of languages separted by new lines> +lang_pairs=$3 #a list language pairs to train multilingual models, e.g. "en-fr,en-cs,fr-en,cs-en" +# pretrained can be an mBART pretrained model as well +pretrained_model=$4 #<path to a pretrained model> + + +fairseq-train "$path_2_data" \ + --encoder-normalize-before --decoder-normalize-before \ + --arch transformer --layernorm-embedding \ + --task translation_multi_simple_epoch \ + --finetune-from-model "$pretrained_model" \ + --sampling-method "temperature" \ + --sampling-temperature "1.5" \ + --encoder-langtok "src" \ + --decoder-langtok \ + --lang-dict "$lang_list" \ + --lang-pairs "$lang_pairs" \ + --criterion label_smoothed_cross_entropy --label-smoothing 0.2 \ + --optimizer adam --adam-eps 1e-06 --adam-betas '(0.9, 0.98)' \ + --lr-scheduler inverse_sqrt --lr 3e-05 --warmup-updates 2500 --max-update 40000 \ + --dropout 0.3 --attention-dropout 0.1 --weight-decay 0.0 \ + --max-tokens 1024 --update-freq 2 \ + --save-interval 1 --save-interval-updates 5000 --keep-interval-updates 10 --no-epoch-checkpoints \ + --seed 222 --log-format simple --log-interval 2 diff --git a/examples/multilingual/multilingual_fairseq_gen.sh b/examples/multilingual/multilingual_fairseq_gen.sh new file mode 100644 index 0000000000..65aa322d7d --- /dev/null +++ b/examples/multilingual/multilingual_fairseq_gen.sh @@ -0,0 +1,26 @@ +#!/bin/bash +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +lang_pairs="en-fr,en-cs,fr-en,cs-en" +path_2_data=$1 # <path to data> +lang_list=$2 # <path to a file which contains list of languages separted by new lines> +model=$3 # <path to a trained model> +source_lang=cs +target_lang=en + +fairseq-generate "$path_2_data" \ + --path "$model" \ + --task translation_multi_simple_epoch \ + --gen-subset test \ + --source-lang "$source_lang" \ + --target-lang "$target_lang" \ + --sacrebleu --remove-bpe 'sentencepiece'\ + --batch-size 32 \ + --encoder-langtok "src" \ + --decoder-langtok \ + --lang-dict "$lang_list" \ + --lang-pairs "$lang_pairs" diff --git a/examples/multilingual/train_multilingual_model.sh b/examples/multilingual/train_multilingual_model.sh new file mode 100644 index 0000000000..cc050bd3f0 --- /dev/null +++ b/examples/multilingual/train_multilingual_model.sh @@ -0,0 +1,28 @@ +#!/bin/bash +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +path_2_data=$1 # <path to data> which contains binarized data for each directions +lang_list=$2 # <path to a file which contains a list of languages separted by new lines> +lang_pairs=$3 #a list language pairs to train multilingual models, e.g. "en-fr,en-cs,fr-en,cs-en" + +fairseq-train "$path_2_data" \ + --encoder-normalize-before --decoder-normalize-before \ + --arch transformer --layernorm-embedding \ + --task translation_multi_simple_epoch \ + --sampling-method "temperature" \ + --sampling-temperature 1.5 \ + --encoder-langtok "src" \ + --decoder-langtok \ + --lang-dict "$lang_list" \ + --lang-pairs "$lang_pairs" \ + --criterion label_smoothed_cross_entropy --label-smoothing 0.2 \ + --optimizer adam --adam-eps 1e-06 --adam-betas '(0.9, 0.98)' \ + --lr-scheduler inverse_sqrt --lr 3e-05 --warmup-updates 2500 --max-update 40000 \ + --dropout 0.3 --attention-dropout 0.1 --weight-decay 0.0 \ + --max-tokens 1024 --update-freq 2 \ + --save-interval 1 --save-interval-updates 5000 --keep-interval-updates 10 --no-epoch-checkpoints \ + --seed 222 --log-format simple --log-interval 2 diff --git a/examples/noisychannel/rerank.py b/examples/noisychannel/rerank.py index fd2aef36d6..bb80d11a67 100644 --- a/examples/noisychannel/rerank.py +++ b/examples/noisychannel/rerank.py @@ -7,25 +7,33 @@ from multiprocessing import Pool import numpy as np - -from fairseq import bleu, options +from fairseq import options from fairseq.data import dictionary +from fairseq.scoring import bleu -from . import ( +from examples.noisychannel import ( rerank_generate, + rerank_options, rerank_score_bw, rerank_score_lm, - rerank_options, rerank_utils, ) -def score_target_hypo(args, a, b, c, lenpen, target_outfile, hypo_outfile, write_hypos, normalize): +def score_target_hypo( + args, a, b, c, lenpen, target_outfile, hypo_outfile, write_hypos, normalize +): print("lenpen", lenpen, "weight1", a, "weight2", b, "weight3", c) gen_output_lst, bitext1_lst, bitext2_lst, lm_res_lst = load_score_files(args) dict = dictionary.Dictionary() - scorer = bleu.Scorer(dict.pad(), dict.eos(), dict.unk()) + scorer = scorer = bleu.Scorer( + bleu.BleuConfig( + pad=dict.pad(), + eos=dict.eos(), + unk=dict.unk(), + ) + ) ordered_hypos = {} ordered_targets = {} @@ -60,11 +68,21 @@ def score_target_hypo(args, a, b, c, lenpen, target_outfile, hypo_outfile, write bitext2_score = None bitext2_backwards = None - score = rerank_utils.get_score(a, b, c, target_len, - bitext1.rescore_score[i], bitext2_score, lm_score=lm_score, - lenpen=lenpen, src_len=bitext1.source_lengths[i], - tgt_len=bitext1.target_lengths[i], bitext1_backwards=bitext1.backwards, - bitext2_backwards=bitext2_backwards, normalize=normalize) + score = rerank_utils.get_score( + a, + b, + c, + target_len, + bitext1.rescore_score[i], + bitext2_score, + lm_score=lm_score, + lenpen=lenpen, + src_len=bitext1.source_lengths[i], + tgt_len=bitext1.target_lengths[i], + bitext1_backwards=bitext1.backwards, + bitext2_backwards=bitext2_backwards, + normalize=normalize, + ) if score > best_score: best_score = score @@ -87,8 +105,11 @@ def score_target_hypo(args, a, b, c, lenpen, target_outfile, hypo_outfile, write for key in range(len(gen_keys)): if args.prefix_len is None: assert hypo_lst[key] in gen_output.no_bpe_hypo[gen_keys[key]], ( - "pred and rescore hypo mismatch: i: " + str(key) + ", " - + str(hypo_lst[key]) + str(gen_keys[key]) + "pred and rescore hypo mismatch: i: " + + str(key) + + ", " + + str(hypo_lst[key]) + + str(gen_keys[key]) + str(gen_output.no_bpe_hypo[key]) ) sys_tok = dict.encode_line(hypo_lst[key]) @@ -96,7 +117,9 @@ def score_target_hypo(args, a, b, c, lenpen, target_outfile, hypo_outfile, write scorer.add(ref_tok, sys_tok) else: - full_hypo = rerank_utils.get_full_from_prefix(hypo_lst[key], gen_output.no_bpe_hypo[gen_keys[key]]) + full_hypo = rerank_utils.get_full_from_prefix( + hypo_lst[key], gen_output.no_bpe_hypo[gen_keys[key]] + ) sys_tok = dict.encode_line(full_hypo) ref_tok = dict.encode_line(gen_output.no_bpe_target[gen_keys[key]]) scorer.add(ref_tok, sys_tok) @@ -106,20 +129,31 @@ def score_target_hypo(args, a, b, c, lenpen, target_outfile, hypo_outfile, write # recover the orinal ids from n best list generation for key in range(len(gen_output.no_bpe_target)): if args.prefix_len is None: - assert hypo_lst[key] in gen_output.no_bpe_hypo[gen_keys[key]], \ - "pred and rescore hypo mismatch:"+"i:"+str(key)+str(hypo_lst[key]) + str(gen_output.no_bpe_hypo[key]) + assert hypo_lst[key] in gen_output.no_bpe_hypo[gen_keys[key]], ( + "pred and rescore hypo mismatch:" + + "i:" + + str(key) + + str(hypo_lst[key]) + + str(gen_output.no_bpe_hypo[key]) + ) ordered_hypos[gen_keys[key]] = hypo_lst[key] - ordered_targets[gen_keys[key]] = gen_output.no_bpe_target[gen_keys[key]] + ordered_targets[gen_keys[key]] = gen_output.no_bpe_target[ + gen_keys[key] + ] else: - full_hypo = rerank_utils.get_full_from_prefix(hypo_lst[key], gen_output.no_bpe_hypo[gen_keys[key]]) + full_hypo = rerank_utils.get_full_from_prefix( + hypo_lst[key], gen_output.no_bpe_hypo[gen_keys[key]] + ) ordered_hypos[gen_keys[key]] = full_hypo - ordered_targets[gen_keys[key]] = gen_output.no_bpe_target[gen_keys[key]] + ordered_targets[gen_keys[key]] = gen_output.no_bpe_target[ + gen_keys[key] + ] # write the hypos in the original order from nbest list generation if args.num_shards == (len(bitext1_lst)): - with open(target_outfile, 'w') as t: - with open(hypo_outfile, 'w') as h: + with open(target_outfile, "w") as t: + with open(hypo_outfile, "w") as h: for key in range(len(ordered_hypos)): t.write(ordered_targets[key]) h.write(ordered_hypos[key]) @@ -134,17 +168,38 @@ def score_target_hypo(args, a, b, c, lenpen, target_outfile, hypo_outfile, write def match_target_hypo(args, target_outfile, hypo_outfile): """combine scores from the LM and bitext models, and write the top scoring hypothesis to a file""" if len(args.weight1) == 1: - res = score_target_hypo(args, args.weight1[0], args.weight2[0], - args.weight3[0], args.lenpen[0], target_outfile, - hypo_outfile, True, args.normalize) + res = score_target_hypo( + args, + args.weight1[0], + args.weight2[0], + args.weight3[0], + args.lenpen[0], + target_outfile, + hypo_outfile, + True, + args.normalize, + ) rerank_scores = [res] else: print("launching pool") with Pool(32) as p: - rerank_scores = p.starmap(score_target_hypo, - [(args, args.weight1[i], args.weight2[i], args.weight3[i], - args.lenpen[i], target_outfile, hypo_outfile, - False, args.normalize) for i in range(len(args.weight1))]) + rerank_scores = p.starmap( + score_target_hypo, + [ + ( + args, + args.weight1[i], + args.weight2[i], + args.weight3[i], + args.lenpen[i], + target_outfile, + hypo_outfile, + False, + args.normalize, + ) + for i in range(len(args.weight1)) + ], + ) if len(rerank_scores) > 1: best_index = np.argmax(rerank_scores) @@ -154,11 +209,22 @@ def match_target_hypo(args, target_outfile, hypo_outfile): print("best weight1", args.weight1[best_index]) print("best weight2", args.weight2[best_index]) print("best weight3", args.weight3[best_index]) - return args.lenpen[best_index], args.weight1[best_index], \ - args.weight2[best_index], args.weight3[best_index], best_score + return ( + args.lenpen[best_index], + args.weight1[best_index], + args.weight2[best_index], + args.weight3[best_index], + best_score, + ) else: - return args.lenpen[0], args.weight1[0], args.weight2[0], args.weight3[0], rerank_scores[0] + return ( + args.lenpen[0], + args.weight1[0], + args.weight2[0], + args.weight3[0], + rerank_scores[0], + ) def load_score_files(args): @@ -174,55 +240,100 @@ def load_score_files(args): for shard_id in shard_ids: using_nbest = args.nbest_list is not None - pre_gen, left_to_right_preprocessed_dir, right_to_left_preprocessed_dir, \ - backwards_preprocessed_dir, lm_preprocessed_dir = \ - rerank_utils.get_directories(args.data_dir_name, args.num_rescore, args.gen_subset, - args.gen_model_name, shard_id, args.num_shards, args.sampling, - args.prefix_len, args.target_prefix_frac, args.source_prefix_frac) - - rerank1_is_gen = args.gen_model == args.score_model1 and args.source_prefix_frac is None - rerank2_is_gen = args.gen_model == args.score_model2 and args.source_prefix_frac is None - - score1_file = rerank_utils.rescore_file_name(pre_gen, args.prefix_len, args.model1_name, - target_prefix_frac=args.target_prefix_frac, - source_prefix_frac=args.source_prefix_frac, - backwards=args.backwards1) + ( + pre_gen, + left_to_right_preprocessed_dir, + right_to_left_preprocessed_dir, + backwards_preprocessed_dir, + lm_preprocessed_dir, + ) = rerank_utils.get_directories( + args.data_dir_name, + args.num_rescore, + args.gen_subset, + args.gen_model_name, + shard_id, + args.num_shards, + args.sampling, + args.prefix_len, + args.target_prefix_frac, + args.source_prefix_frac, + ) + + rerank1_is_gen = ( + args.gen_model == args.score_model1 and args.source_prefix_frac is None + ) + rerank2_is_gen = ( + args.gen_model == args.score_model2 and args.source_prefix_frac is None + ) + + score1_file = rerank_utils.rescore_file_name( + pre_gen, + args.prefix_len, + args.model1_name, + target_prefix_frac=args.target_prefix_frac, + source_prefix_frac=args.source_prefix_frac, + backwards=args.backwards1, + ) if args.score_model2 is not None: - score2_file = rerank_utils.rescore_file_name(pre_gen, args.prefix_len, args.model2_name, - target_prefix_frac=args.target_prefix_frac, - source_prefix_frac=args.source_prefix_frac, - backwards=args.backwards2) + score2_file = rerank_utils.rescore_file_name( + pre_gen, + args.prefix_len, + args.model2_name, + target_prefix_frac=args.target_prefix_frac, + source_prefix_frac=args.source_prefix_frac, + backwards=args.backwards2, + ) if args.language_model is not None: - lm_score_file = rerank_utils.rescore_file_name(pre_gen, args.prefix_len, args.lm_name, lm_file=True) + lm_score_file = rerank_utils.rescore_file_name( + pre_gen, args.prefix_len, args.lm_name, lm_file=True + ) # get gen output - predictions_bpe_file = pre_gen+"/generate_output_bpe.txt" + predictions_bpe_file = pre_gen + "/generate_output_bpe.txt" if using_nbest: print("Using predefined n-best list from interactive.py") predictions_bpe_file = args.nbest_list - gen_output = rerank_utils.BitextOutputFromGen(predictions_bpe_file, bpe_symbol=args.remove_bpe, - nbest=using_nbest, prefix_len=args.prefix_len, - target_prefix_frac=args.target_prefix_frac) + gen_output = rerank_utils.BitextOutputFromGen( + predictions_bpe_file, + bpe_symbol=args.post_process, + nbest=using_nbest, + prefix_len=args.prefix_len, + target_prefix_frac=args.target_prefix_frac, + ) if rerank1_is_gen: bitext1 = gen_output else: - bitext1 = rerank_utils.BitextOutput(score1_file, args.backwards1, args.right_to_left1, - args.remove_bpe, args.prefix_len, args.target_prefix_frac, - args.source_prefix_frac) + bitext1 = rerank_utils.BitextOutput( + score1_file, + args.backwards1, + args.right_to_left1, + args.post_process, + args.prefix_len, + args.target_prefix_frac, + args.source_prefix_frac, + ) if args.score_model2 is not None or args.nbest_list is not None: if rerank2_is_gen: bitext2 = gen_output else: - bitext2 = rerank_utils.BitextOutput(score2_file, args.backwards2, args.right_to_left2, - args.remove_bpe, args.prefix_len, args.target_prefix_frac, - args.source_prefix_frac) - - assert bitext2.source_lengths == bitext1.source_lengths, \ - "source lengths for rescoring models do not match" - assert bitext2.target_lengths == bitext1.target_lengths, \ - "target lengths for rescoring models do not match" + bitext2 = rerank_utils.BitextOutput( + score2_file, + args.backwards2, + args.right_to_left2, + args.post_process, + args.prefix_len, + args.target_prefix_frac, + args.source_prefix_frac, + ) + + assert ( + bitext2.source_lengths == bitext1.source_lengths + ), "source lengths for rescoring models do not match" + assert ( + bitext2.target_lengths == bitext1.target_lengths + ), "target lengths for rescoring models do not match" else: if args.diff_bpe: assert args.score_model2 is None @@ -231,8 +342,13 @@ def load_score_files(args): bitext2 = None if args.language_model is not None: - lm_res1 = rerank_utils.LMOutput(lm_score_file, args.lm_dict, args.prefix_len, - args.remove_bpe, args.target_prefix_frac) + lm_res1 = rerank_utils.LMOutput( + lm_score_file, + args.lm_dict, + args.prefix_len, + args.post_process, + args.target_prefix_frac, + ) else: lm_res1 = None @@ -258,28 +374,46 @@ def rerank(args): shard_ids = [args.shard_id] for shard_id in shard_ids: - pre_gen, left_to_right_preprocessed_dir, right_to_left_preprocessed_dir, \ - backwards_preprocessed_dir, lm_preprocessed_dir = \ - rerank_utils.get_directories(args.data_dir_name, args.num_rescore, args.gen_subset, - args.gen_model_name, shard_id, args.num_shards, args.sampling, - args.prefix_len, args.target_prefix_frac, args.source_prefix_frac) + ( + pre_gen, + left_to_right_preprocessed_dir, + right_to_left_preprocessed_dir, + backwards_preprocessed_dir, + lm_preprocessed_dir, + ) = rerank_utils.get_directories( + args.data_dir_name, + args.num_rescore, + args.gen_subset, + args.gen_model_name, + shard_id, + args.num_shards, + args.sampling, + args.prefix_len, + args.target_prefix_frac, + args.source_prefix_frac, + ) rerank_generate.gen_and_reprocess_nbest(args) rerank_score_bw.score_bw(args) rerank_score_lm.score_lm(args) if args.write_hypos is None: - write_targets = pre_gen+"/matched_targets" - write_hypos = pre_gen+"/matched_hypos" + write_targets = pre_gen + "/matched_targets" + write_hypos = pre_gen + "/matched_hypos" else: - write_targets = args.write_hypos+"_targets" + args.gen_subset - write_hypos = args.write_hypos+"_hypos" + args.gen_subset + write_targets = args.write_hypos + "_targets" + args.gen_subset + write_hypos = args.write_hypos + "_hypos" + args.gen_subset if args.all_shards: write_targets += "_all_shards" write_hypos += "_all_shards" - best_lenpen, best_weight1, best_weight2, best_weight3, best_score = \ - match_target_hypo(args, write_targets, write_hypos) + ( + best_lenpen, + best_weight1, + best_weight2, + best_weight3, + best_score, + ) = match_target_hypo(args, write_targets, write_hypos) return best_lenpen, best_weight1, best_weight2, best_weight3, best_score @@ -290,5 +424,5 @@ def cli_main(): rerank(args) -if __name__ == '__main__': +if __name__ == "__main__": cli_main() diff --git a/examples/noisychannel/rerank_generate.py b/examples/noisychannel/rerank_generate.py index 48f954d360..daeeae059a 100644 --- a/examples/noisychannel/rerank_generate.py +++ b/examples/noisychannel/rerank_generate.py @@ -8,22 +8,26 @@ Generate n-best translations using a trained model. """ -from contextlib import redirect_stdout import os import subprocess +from contextlib import redirect_stdout from fairseq import options from fairseq_cli import generate, preprocess -from . import rerank_options, rerank_utils +from examples.noisychannel import rerank_options, rerank_utils def gen_and_reprocess_nbest(args): if args.score_dict_dir is None: args.score_dict_dir = args.data if args.prefix_len is not None: - assert args.right_to_left1 is False, "prefix length not compatible with right to left models" - assert args.right_to_left2 is False, "prefix length not compatible with right to left models" + assert ( + args.right_to_left1 is False + ), "prefix length not compatible with right to left models" + assert ( + args.right_to_left2 is False + ), "prefix length not compatible with right to left models" if args.nbest_list is not None: assert args.score_model2 is None @@ -35,27 +39,50 @@ def gen_and_reprocess_nbest(args): scorer1_src = args.source_lang scorer1_tgt = args.target_lang - store_data = os.path.join(os.path.dirname(__file__))+"/rerank_data/"+args.data_dir_name + store_data = ( + os.path.join(os.path.dirname(__file__)) + "/rerank_data/" + args.data_dir_name + ) if not os.path.exists(store_data): os.makedirs(store_data) - pre_gen, left_to_right_preprocessed_dir, right_to_left_preprocessed_dir, \ - backwards_preprocessed_dir, lm_preprocessed_dir = \ - rerank_utils.get_directories(args.data_dir_name, args.num_rescore, args.gen_subset, - args.gen_model_name, args.shard_id, args.num_shards, - args.sampling, args.prefix_len, args.target_prefix_frac, - args.source_prefix_frac) - assert not (args.right_to_left1 and args.backwards1), "backwards right to left not supported" - assert not (args.right_to_left2 and args.backwards2), "backwards right to left not supported" - assert not (args.prefix_len is not None and args.target_prefix_frac is not None), \ - "target prefix frac and target prefix len incompatible" + ( + pre_gen, + left_to_right_preprocessed_dir, + right_to_left_preprocessed_dir, + backwards_preprocessed_dir, + lm_preprocessed_dir, + ) = rerank_utils.get_directories( + args.data_dir_name, + args.num_rescore, + args.gen_subset, + args.gen_model_name, + args.shard_id, + args.num_shards, + args.sampling, + args.prefix_len, + args.target_prefix_frac, + args.source_prefix_frac, + ) + assert not ( + args.right_to_left1 and args.backwards1 + ), "backwards right to left not supported" + assert not ( + args.right_to_left2 and args.backwards2 + ), "backwards right to left not supported" + assert not ( + args.prefix_len is not None and args.target_prefix_frac is not None + ), "target prefix frac and target prefix len incompatible" # make directory to store generation results if not os.path.exists(pre_gen): os.makedirs(pre_gen) - rerank1_is_gen = args.gen_model == args.score_model1 and args.source_prefix_frac is None - rerank2_is_gen = args.gen_model == args.score_model2 and args.source_prefix_frac is None + rerank1_is_gen = ( + args.gen_model == args.score_model1 and args.source_prefix_frac is None + ) + rerank2_is_gen = ( + args.gen_model == args.score_model2 and args.source_prefix_frac is None + ) if args.nbest_list is not None: rerank2_is_gen = True @@ -70,17 +97,25 @@ def gen_and_reprocess_nbest(args): if not os.path.exists(backwards_preprocessed_dir): os.makedirs(backwards_preprocessed_dir) - score1_file = rerank_utils.rescore_file_name(pre_gen, args.prefix_len, args.model1_name, - target_prefix_frac=args.target_prefix_frac, - source_prefix_frac=args.source_prefix_frac, - backwards=args.backwards1) + score1_file = rerank_utils.rescore_file_name( + pre_gen, + args.prefix_len, + args.model1_name, + target_prefix_frac=args.target_prefix_frac, + source_prefix_frac=args.source_prefix_frac, + backwards=args.backwards1, + ) if args.score_model2 is not None: - score2_file = rerank_utils.rescore_file_name(pre_gen, args.prefix_len, args.model2_name, - target_prefix_frac=args.target_prefix_frac, - source_prefix_frac=args.source_prefix_frac, - backwards=args.backwards2) + score2_file = rerank_utils.rescore_file_name( + pre_gen, + args.prefix_len, + args.model2_name, + target_prefix_frac=args.target_prefix_frac, + source_prefix_frac=args.source_prefix_frac, + backwards=args.backwards2, + ) - predictions_bpe_file = pre_gen+"/generate_output_bpe.txt" + predictions_bpe_file = pre_gen + "/generate_output_bpe.txt" using_nbest = args.nbest_list is not None @@ -92,17 +127,29 @@ def gen_and_reprocess_nbest(args): if not os.path.isfile(predictions_bpe_file): print("STEP 1: generate predictions using the p(T|S) model with bpe") print(args.data) - param1 = [args.data, - "--path", args.gen_model, - "--shard-id", str(args.shard_id), - "--num-shards", str(args.num_shards), - "--nbest", str(args.num_rescore), - "--batch-size", str(args.batch_size), - "--beam", str(args.num_rescore), - "--max-sentences", str(args.num_rescore), - "--gen-subset", args.gen_subset, - "--source-lang", args.source_lang, - "--target-lang", args.target_lang] + param1 = [ + args.data, + "--path", + args.gen_model, + "--shard-id", + str(args.shard_id), + "--num-shards", + str(args.num_shards), + "--nbest", + str(args.num_rescore), + "--batch-size", + str(args.batch_size), + "--beam", + str(args.num_rescore), + "--batch-size", + str(args.num_rescore), + "--gen-subset", + args.gen_subset, + "--source-lang", + args.source_lang, + "--target-lang", + args.target_lang, + ] if args.sampling: param1 += ["--sampling"] @@ -110,124 +157,229 @@ def gen_and_reprocess_nbest(args): input_args = options.parse_args_and_arch(gen_parser, param1) print(input_args) - with open(predictions_bpe_file, 'w') as f: + with open(predictions_bpe_file, "w") as f: with redirect_stdout(f): generate.main(input_args) - gen_output = rerank_utils.BitextOutputFromGen(predictions_bpe_file, bpe_symbol=args.remove_bpe, - nbest=using_nbest, prefix_len=args.prefix_len, - target_prefix_frac=args.target_prefix_frac) + gen_output = rerank_utils.BitextOutputFromGen( + predictions_bpe_file, + bpe_symbol=args.post_process, + nbest=using_nbest, + prefix_len=args.prefix_len, + target_prefix_frac=args.target_prefix_frac, + ) if args.diff_bpe: - rerank_utils.write_reprocessed(gen_output.no_bpe_source, gen_output.no_bpe_hypo, - gen_output.no_bpe_target, pre_gen+"/source_gen_bpe."+args.source_lang, - pre_gen+"/target_gen_bpe."+args.target_lang, - pre_gen+"/reference_gen_bpe."+args.target_lang) + rerank_utils.write_reprocessed( + gen_output.no_bpe_source, + gen_output.no_bpe_hypo, + gen_output.no_bpe_target, + pre_gen + "/source_gen_bpe." + args.source_lang, + pre_gen + "/target_gen_bpe." + args.target_lang, + pre_gen + "/reference_gen_bpe." + args.target_lang, + ) bitext_bpe = args.rescore_bpe_code - bpe_src_param = ["-c", bitext_bpe, - "--input", pre_gen+"/source_gen_bpe."+args.source_lang, - "--output", pre_gen+"/rescore_data."+args.source_lang] - bpe_tgt_param = ["-c", bitext_bpe, - "--input", pre_gen+"/target_gen_bpe."+args.target_lang, - "--output", pre_gen+"/rescore_data."+args.target_lang] - - subprocess.call(["python", - os.path.join(os.path.dirname(__file__), - "subword-nmt/subword_nmt/apply_bpe.py")] + bpe_src_param, - shell=False) - - subprocess.call(["python", - os.path.join(os.path.dirname(__file__), - "subword-nmt/subword_nmt/apply_bpe.py")] + bpe_tgt_param, - shell=False) - - if (not os.path.isfile(score1_file) and not rerank1_is_gen) or \ - (args.score_model2 is not None and not os.path.isfile(score2_file) and not rerank2_is_gen): - print("STEP 2: process the output of generate.py so we have clean text files with the translations") + bpe_src_param = [ + "-c", + bitext_bpe, + "--input", + pre_gen + "/source_gen_bpe." + args.source_lang, + "--output", + pre_gen + "/rescore_data." + args.source_lang, + ] + bpe_tgt_param = [ + "-c", + bitext_bpe, + "--input", + pre_gen + "/target_gen_bpe." + args.target_lang, + "--output", + pre_gen + "/rescore_data." + args.target_lang, + ] + + subprocess.call( + [ + "python", + os.path.join( + os.path.dirname(__file__), "subword-nmt/subword_nmt/apply_bpe.py" + ), + ] + + bpe_src_param, + shell=False, + ) + + subprocess.call( + [ + "python", + os.path.join( + os.path.dirname(__file__), "subword-nmt/subword_nmt/apply_bpe.py" + ), + ] + + bpe_tgt_param, + shell=False, + ) + + if (not os.path.isfile(score1_file) and not rerank1_is_gen) or ( + args.score_model2 is not None + and not os.path.isfile(score2_file) + and not rerank2_is_gen + ): + print( + "STEP 2: process the output of generate.py so we have clean text files with the translations" + ) rescore_file = "/rescore_data" if args.prefix_len is not None: - prefix_len_rescore_file = rescore_file + "prefix"+str(args.prefix_len) + prefix_len_rescore_file = rescore_file + "prefix" + str(args.prefix_len) if args.target_prefix_frac is not None: - target_prefix_frac_rescore_file = rescore_file + "target_prefix_frac"+str(args.target_prefix_frac) + target_prefix_frac_rescore_file = ( + rescore_file + "target_prefix_frac" + str(args.target_prefix_frac) + ) if args.source_prefix_frac is not None: - source_prefix_frac_rescore_file = rescore_file + "source_prefix_frac"+str(args.source_prefix_frac) + source_prefix_frac_rescore_file = ( + rescore_file + "source_prefix_frac" + str(args.source_prefix_frac) + ) if not args.right_to_left1 or not args.right_to_left2: if not args.diff_bpe: - rerank_utils.write_reprocessed(gen_output.source, gen_output.hypo, gen_output.target, - pre_gen+rescore_file+"."+args.source_lang, - pre_gen+rescore_file+"."+args.target_lang, - pre_gen+"/reference_file", bpe_symbol=args.remove_bpe) + rerank_utils.write_reprocessed( + gen_output.source, + gen_output.hypo, + gen_output.target, + pre_gen + rescore_file + "." + args.source_lang, + pre_gen + rescore_file + "." + args.target_lang, + pre_gen + "/reference_file", + bpe_symbol=args.post_process, + ) if args.prefix_len is not None: bw_rescore_file = prefix_len_rescore_file - rerank_utils.write_reprocessed(gen_output.source, gen_output.hypo, gen_output.target, - pre_gen+prefix_len_rescore_file+"."+args.source_lang, - pre_gen+prefix_len_rescore_file+"."+args.target_lang, - pre_gen+"/reference_file", prefix_len=args.prefix_len, - bpe_symbol=args.remove_bpe) + rerank_utils.write_reprocessed( + gen_output.source, + gen_output.hypo, + gen_output.target, + pre_gen + prefix_len_rescore_file + "." + args.source_lang, + pre_gen + prefix_len_rescore_file + "." + args.target_lang, + pre_gen + "/reference_file", + prefix_len=args.prefix_len, + bpe_symbol=args.post_process, + ) elif args.target_prefix_frac is not None: bw_rescore_file = target_prefix_frac_rescore_file - rerank_utils.write_reprocessed(gen_output.source, gen_output.hypo, gen_output.target, - pre_gen+target_prefix_frac_rescore_file+"."+args.source_lang, - pre_gen+target_prefix_frac_rescore_file+"."+args.target_lang, - pre_gen+"/reference_file", bpe_symbol=args.remove_bpe, - target_prefix_frac=args.target_prefix_frac) + rerank_utils.write_reprocessed( + gen_output.source, + gen_output.hypo, + gen_output.target, + pre_gen + + target_prefix_frac_rescore_file + + "." + + args.source_lang, + pre_gen + + target_prefix_frac_rescore_file + + "." + + args.target_lang, + pre_gen + "/reference_file", + bpe_symbol=args.post_process, + target_prefix_frac=args.target_prefix_frac, + ) else: bw_rescore_file = rescore_file if args.source_prefix_frac is not None: fw_rescore_file = source_prefix_frac_rescore_file - rerank_utils.write_reprocessed(gen_output.source, gen_output.hypo, gen_output.target, - pre_gen+source_prefix_frac_rescore_file+"."+args.source_lang, - pre_gen+source_prefix_frac_rescore_file+"."+args.target_lang, - pre_gen+"/reference_file", bpe_symbol=args.remove_bpe, - source_prefix_frac=args.source_prefix_frac) + rerank_utils.write_reprocessed( + gen_output.source, + gen_output.hypo, + gen_output.target, + pre_gen + + source_prefix_frac_rescore_file + + "." + + args.source_lang, + pre_gen + + source_prefix_frac_rescore_file + + "." + + args.target_lang, + pre_gen + "/reference_file", + bpe_symbol=args.post_process, + source_prefix_frac=args.source_prefix_frac, + ) else: fw_rescore_file = rescore_file if args.right_to_left1 or args.right_to_left2: - rerank_utils.write_reprocessed(gen_output.source, gen_output.hypo, gen_output.target, - pre_gen+"/right_to_left_rescore_data."+args.source_lang, - pre_gen+"/right_to_left_rescore_data."+args.target_lang, - pre_gen+"/right_to_left_reference_file", - right_to_left=True, bpe_symbol=args.remove_bpe) + rerank_utils.write_reprocessed( + gen_output.source, + gen_output.hypo, + gen_output.target, + pre_gen + "/right_to_left_rescore_data." + args.source_lang, + pre_gen + "/right_to_left_rescore_data." + args.target_lang, + pre_gen + "/right_to_left_reference_file", + right_to_left=True, + bpe_symbol=args.post_process, + ) print("STEP 3: binarize the translations") - if not args.right_to_left1 or args.score_model2 is not None and not args.right_to_left2 or not rerank1_is_gen: + if ( + not args.right_to_left1 + or args.score_model2 is not None + and not args.right_to_left2 + or not rerank1_is_gen + ): if args.backwards1 or args.backwards2: if args.backwards_score_dict_dir is not None: bw_dict = args.backwards_score_dict_dir else: bw_dict = args.score_dict_dir - bw_preprocess_param = ["--source-lang", scorer1_src, - "--target-lang", scorer1_tgt, - "--trainpref", pre_gen+bw_rescore_file, - "--srcdict", bw_dict + "/dict." + scorer1_src + ".txt", - "--tgtdict", bw_dict + "/dict." + scorer1_tgt + ".txt", - "--destdir", backwards_preprocessed_dir] + bw_preprocess_param = [ + "--source-lang", + scorer1_src, + "--target-lang", + scorer1_tgt, + "--trainpref", + pre_gen + bw_rescore_file, + "--srcdict", + bw_dict + "/dict." + scorer1_src + ".txt", + "--tgtdict", + bw_dict + "/dict." + scorer1_tgt + ".txt", + "--destdir", + backwards_preprocessed_dir, + ] preprocess_parser = options.get_preprocessing_parser() input_args = preprocess_parser.parse_args(bw_preprocess_param) preprocess.main(input_args) - preprocess_param = ["--source-lang", scorer1_src, - "--target-lang", scorer1_tgt, - "--trainpref", pre_gen+fw_rescore_file, - "--srcdict", args.score_dict_dir+"/dict."+scorer1_src+".txt", - "--tgtdict", args.score_dict_dir+"/dict."+scorer1_tgt+".txt", - "--destdir", left_to_right_preprocessed_dir] + preprocess_param = [ + "--source-lang", + scorer1_src, + "--target-lang", + scorer1_tgt, + "--trainpref", + pre_gen + fw_rescore_file, + "--srcdict", + args.score_dict_dir + "/dict." + scorer1_src + ".txt", + "--tgtdict", + args.score_dict_dir + "/dict." + scorer1_tgt + ".txt", + "--destdir", + left_to_right_preprocessed_dir, + ] preprocess_parser = options.get_preprocessing_parser() input_args = preprocess_parser.parse_args(preprocess_param) preprocess.main(input_args) if args.right_to_left1 or args.right_to_left2: - preprocess_param = ["--source-lang", scorer1_src, - "--target-lang", scorer1_tgt, - "--trainpref", pre_gen+"/right_to_left_rescore_data", - "--srcdict", args.score_dict_dir+"/dict."+scorer1_src+".txt", - "--tgtdict", args.score_dict_dir+"/dict."+scorer1_tgt+".txt", - "--destdir", right_to_left_preprocessed_dir] + preprocess_param = [ + "--source-lang", + scorer1_src, + "--target-lang", + scorer1_tgt, + "--trainpref", + pre_gen + "/right_to_left_rescore_data", + "--srcdict", + args.score_dict_dir + "/dict." + scorer1_src + ".txt", + "--tgtdict", + args.score_dict_dir + "/dict." + scorer1_tgt + ".txt", + "--destdir", + right_to_left_preprocessed_dir, + ] preprocess_parser = options.get_preprocessing_parser() input_args = preprocess_parser.parse_args(preprocess_param) preprocess.main(input_args) @@ -241,5 +393,5 @@ def cli_main(): gen_and_reprocess_nbest(args) -if __name__ == '__main__': +if __name__ == "__main__": cli_main() diff --git a/examples/noisychannel/rerank_options.py b/examples/noisychannel/rerank_options.py index 41a80d88d1..de91939e66 100644 --- a/examples/noisychannel/rerank_options.py +++ b/examples/noisychannel/rerank_options.py @@ -6,14 +6,14 @@ from fairseq import options -def get_reranking_parser(default_task='translation'): - parser = options.get_parser('Generation and reranking', default_task) +def get_reranking_parser(default_task="translation"): + parser = options.get_parser("Generation and reranking", default_task) add_reranking_args(parser) return parser -def get_tuning_parser(default_task='translation'): - parser = options.get_parser('Reranking tuning', default_task) +def get_tuning_parser(default_task="translation"): + parser = options.get_parser("Reranking tuning", default_task) add_reranking_args(parser) add_tuning_args(parser) return parser @@ -64,7 +64,7 @@ def add_reranking_args(parser): help='whether the first model group is a right to left model') group.add_argument('--right-to-left2', action='store_true', help='whether the second model group is a right to left model') - group.add_argument('--remove-bpe', default='@@ ', + group.add_argument('--post-process', '--remove-bpe', default='@@ ', help='the bpe symbol, used for the bitext and LM') group.add_argument('--prefix-len', default=None, type=int, help='the length of the target prefix to use in rescoring (in terms of words wo bpe)') @@ -103,24 +103,47 @@ def add_reranking_args(parser): help='the fraction of the source prefix to use in rescoring (in terms of words wo bpe)') group.add_argument('--normalize', action='store_true', help='whether to normalize by src and target len') - + # fmt: on return group def add_tuning_args(parser): group = parser.add_argument_group("Tuning") - group.add_argument('--lower-bound', default=[-0.7], nargs='+', type=float, - help='lower bound of search space') - group.add_argument('--upper-bound', default=[3], nargs='+', type=float, - help='upper bound of search space') - group.add_argument('--tune-param', default=['lenpen'], nargs='+', - choices=['lenpen', 'weight1', 'weight2', 'weight3'], - help='the parameter(s) to tune') - group.add_argument('--tune-subset', default='valid', choices=['valid', 'test', 'train'], - help='the subset to tune on ') - group.add_argument('--num-trials', default=1000, type=int, - help='number of trials to do for random search') - group.add_argument('--share-weights', action='store_true', - help='share weight2 and weight 3') + group.add_argument( + "--lower-bound", + default=[-0.7], + nargs="+", + type=float, + help="lower bound of search space", + ) + group.add_argument( + "--upper-bound", + default=[3], + nargs="+", + type=float, + help="upper bound of search space", + ) + group.add_argument( + "--tune-param", + default=["lenpen"], + nargs="+", + choices=["lenpen", "weight1", "weight2", "weight3"], + help="the parameter(s) to tune", + ) + group.add_argument( + "--tune-subset", + default="valid", + choices=["valid", "test", "train"], + help="the subset to tune on ", + ) + group.add_argument( + "--num-trials", + default=1000, + type=int, + help="number of trials to do for random search", + ) + group.add_argument( + "--share-weights", action="store_true", help="share weight2 and weight 3" + ) return group diff --git a/examples/noisychannel/rerank_score_bw.py b/examples/noisychannel/rerank_score_bw.py index 6a875e9fe3..b0bc913651 100644 --- a/examples/noisychannel/rerank_score_bw.py +++ b/examples/noisychannel/rerank_score_bw.py @@ -3,92 +3,134 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. -from contextlib import redirect_stdout import os +from contextlib import redirect_stdout from fairseq import options from fairseq_cli import generate -from . import rerank_options, rerank_utils +from examples.noisychannel import rerank_options, rerank_utils def score_bw(args): - if args.backwards1: - scorer1_src = args.target_lang - scorer1_tgt = args.source_lang + if args.backwards1: + scorer1_src = args.target_lang + scorer1_tgt = args.source_lang + else: + scorer1_src = args.source_lang + scorer1_tgt = args.target_lang + + if args.score_model2 is not None: + if args.backwards2: + scorer2_src = args.target_lang + scorer2_tgt = args.source_lang else: - scorer1_src = args.source_lang - scorer1_tgt = args.target_lang - - if args.score_model2 is not None: - if args.backwards2: - scorer2_src = args.target_lang - scorer2_tgt = args.source_lang - else: - scorer2_src = args.source_lang - scorer2_tgt = args.target_lang - - rerank1_is_gen = args.gen_model == args.score_model1 and args.source_prefix_frac is None - rerank2_is_gen = args.gen_model == args.score_model2 and args.source_prefix_frac is None - - pre_gen, left_to_right_preprocessed_dir, right_to_left_preprocessed_dir, \ - backwards_preprocessed_dir, lm_preprocessed_dir = \ - rerank_utils.get_directories(args.data_dir_name, args.num_rescore, args.gen_subset, - args.gen_model_name, args.shard_id, args.num_shards, - args.sampling, args.prefix_len, args.target_prefix_frac, - args.source_prefix_frac) - - score1_file = rerank_utils.rescore_file_name(pre_gen, args.prefix_len, args.model1_name, - target_prefix_frac=args.target_prefix_frac, - source_prefix_frac=args.source_prefix_frac, - backwards=args.backwards1) - - if args.score_model2 is not None: - score2_file = rerank_utils.rescore_file_name(pre_gen, args.prefix_len, args.model2_name, - target_prefix_frac=args.target_prefix_frac, - source_prefix_frac=args.source_prefix_frac, - backwards=args.backwards2) - - if args.right_to_left1: - rerank_data1 = right_to_left_preprocessed_dir - elif args.backwards1: - rerank_data1 = backwards_preprocessed_dir + scorer2_src = args.source_lang + scorer2_tgt = args.target_lang + + rerank1_is_gen = ( + args.gen_model == args.score_model1 and args.source_prefix_frac is None + ) + rerank2_is_gen = ( + args.gen_model == args.score_model2 and args.source_prefix_frac is None + ) + + ( + pre_gen, + left_to_right_preprocessed_dir, + right_to_left_preprocessed_dir, + backwards_preprocessed_dir, + lm_preprocessed_dir, + ) = rerank_utils.get_directories( + args.data_dir_name, + args.num_rescore, + args.gen_subset, + args.gen_model_name, + args.shard_id, + args.num_shards, + args.sampling, + args.prefix_len, + args.target_prefix_frac, + args.source_prefix_frac, + ) + + score1_file = rerank_utils.rescore_file_name( + pre_gen, + args.prefix_len, + args.model1_name, + target_prefix_frac=args.target_prefix_frac, + source_prefix_frac=args.source_prefix_frac, + backwards=args.backwards1, + ) + + if args.score_model2 is not None: + score2_file = rerank_utils.rescore_file_name( + pre_gen, + args.prefix_len, + args.model2_name, + target_prefix_frac=args.target_prefix_frac, + source_prefix_frac=args.source_prefix_frac, + backwards=args.backwards2, + ) + + if args.right_to_left1: + rerank_data1 = right_to_left_preprocessed_dir + elif args.backwards1: + rerank_data1 = backwards_preprocessed_dir + else: + rerank_data1 = left_to_right_preprocessed_dir + + gen_param = ["--batch-size", str(128), "--score-reference", "--gen-subset", "train"] + if not rerank1_is_gen and not os.path.isfile(score1_file): + print("STEP 4: score the translations for model 1") + + model_param1 = [ + "--path", + args.score_model1, + "--source-lang", + scorer1_src, + "--target-lang", + scorer1_tgt, + ] + gen_model1_param = [rerank_data1] + gen_param + model_param1 + + gen_parser = options.get_generation_parser() + input_args = options.parse_args_and_arch(gen_parser, gen_model1_param) + + with open(score1_file, "w") as f: + with redirect_stdout(f): + generate.main(input_args) + + if ( + args.score_model2 is not None + and not os.path.isfile(score2_file) + and not rerank2_is_gen + ): + print("STEP 4: score the translations for model 2") + + if args.right_to_left2: + rerank_data2 = right_to_left_preprocessed_dir + elif args.backwards2: + rerank_data2 = backwards_preprocessed_dir else: - rerank_data1 = left_to_right_preprocessed_dir - - gen_param = ["--batch-size", str(128), "--score-reference", "--gen-subset", "train"] - if not rerank1_is_gen and not os.path.isfile(score1_file): - print("STEP 4: score the translations for model 1") - - model_param1 = ["--path", args.score_model1, "--source-lang", scorer1_src, "--target-lang", scorer1_tgt] - gen_model1_param = [rerank_data1] + gen_param + model_param1 - - gen_parser = options.get_generation_parser() - input_args = options.parse_args_and_arch(gen_parser, gen_model1_param) - - with open(score1_file, 'w') as f: - with redirect_stdout(f): - generate.main(input_args) - - if args.score_model2 is not None and not os.path.isfile(score2_file) and not rerank2_is_gen: - print("STEP 4: score the translations for model 2") - - if args.right_to_left2: - rerank_data2 = right_to_left_preprocessed_dir - elif args.backwards2: - rerank_data2 = backwards_preprocessed_dir - else: - rerank_data2 = left_to_right_preprocessed_dir + rerank_data2 = left_to_right_preprocessed_dir - model_param2 = ["--path", args.score_model2, "--source-lang", scorer2_src, "--target-lang", scorer2_tgt] - gen_model2_param = [rerank_data2] + gen_param + model_param2 + model_param2 = [ + "--path", + args.score_model2, + "--source-lang", + scorer2_src, + "--target-lang", + scorer2_tgt, + ] + gen_model2_param = [rerank_data2] + gen_param + model_param2 - gen_parser = options.get_generation_parser() - input_args = options.parse_args_and_arch(gen_parser, gen_model2_param) + gen_parser = options.get_generation_parser() + input_args = options.parse_args_and_arch(gen_parser, gen_model2_param) - with open(score2_file, 'w') as f: - with redirect_stdout(f): - generate.main(input_args) + with open(score2_file, "w") as f: + with redirect_stdout(f): + generate.main(input_args) def cli_main(): @@ -97,5 +139,5 @@ def cli_main(): score_bw(args) -if __name__ == '__main__': +if __name__ == "__main__": cli_main() diff --git a/examples/noisychannel/rerank_score_lm.py b/examples/noisychannel/rerank_score_lm.py index 74b858e3c8..e80948d78b 100644 --- a/examples/noisychannel/rerank_score_lm.py +++ b/examples/noisychannel/rerank_score_lm.py @@ -7,27 +7,43 @@ from fairseq import options -from . import rerank_options, rerank_utils +from examples.noisychannel import rerank_options, rerank_utils def score_lm(args): using_nbest = args.nbest_list is not None - pre_gen, left_to_right_preprocessed_dir, right_to_left_preprocessed_dir, \ - backwards_preprocessed_dir, lm_preprocessed_dir = \ - rerank_utils.get_directories(args.data_dir_name, args.num_rescore, args.gen_subset, - args.gen_model_name, args.shard_id, args.num_shards, - args.sampling, args.prefix_len, args.target_prefix_frac, - args.source_prefix_frac) - - predictions_bpe_file = pre_gen+"/generate_output_bpe.txt" + ( + pre_gen, + left_to_right_preprocessed_dir, + right_to_left_preprocessed_dir, + backwards_preprocessed_dir, + lm_preprocessed_dir, + ) = rerank_utils.get_directories( + args.data_dir_name, + args.num_rescore, + args.gen_subset, + args.gen_model_name, + args.shard_id, + args.num_shards, + args.sampling, + args.prefix_len, + args.target_prefix_frac, + args.source_prefix_frac, + ) + + predictions_bpe_file = pre_gen + "/generate_output_bpe.txt" if using_nbest: print("Using predefined n-best list from interactive.py") predictions_bpe_file = args.nbest_list - gen_output = rerank_utils.BitextOutputFromGen(predictions_bpe_file, bpe_symbol=args.remove_bpe, nbest=using_nbest) + gen_output = rerank_utils.BitextOutputFromGen( + predictions_bpe_file, bpe_symbol=args.post_process, nbest=using_nbest + ) if args.language_model is not None: - lm_score_file = rerank_utils.rescore_file_name(pre_gen, args.prefix_len, args.lm_name, lm_file=True) + lm_score_file = rerank_utils.rescore_file_name( + pre_gen, args.prefix_len, args.lm_name, lm_file=True + ) if args.language_model is not None and not os.path.isfile(lm_score_file): print("STEP 4.5: language modeling for P(T)") @@ -38,10 +54,21 @@ def score_lm(args): else: bpe_status = "different" - rerank_utils.lm_scoring(lm_preprocessed_dir, bpe_status, gen_output, pre_gen, - args.lm_dict, args.lm_name, args.language_model, - args.lm_bpe_code, 128, lm_score_file, args.target_lang, - args.source_lang, prefix_len=args.prefix_len) + rerank_utils.lm_scoring( + lm_preprocessed_dir, + bpe_status, + gen_output, + pre_gen, + args.lm_dict, + args.lm_name, + args.language_model, + args.lm_bpe_code, + 128, + lm_score_file, + args.target_lang, + args.source_lang, + prefix_len=args.prefix_len, + ) def cli_main(): @@ -50,5 +77,5 @@ def cli_main(): score_lm(args) -if __name__ == '__main__': +if __name__ == "__main__": cli_main() diff --git a/examples/noisychannel/rerank_tune.py b/examples/noisychannel/rerank_tune.py index 789096b3fa..b2e8b7594a 100644 --- a/examples/noisychannel/rerank_tune.py +++ b/examples/noisychannel/rerank_tune.py @@ -5,16 +5,16 @@ import argparse import random -import numpy as np +import numpy as np from fairseq import options -from . import rerank, rerank_options +from examples.noisychannel import rerank, rerank_options def random_search(args): param_values = [] - tuneable_parameters = ['lenpen', 'weight1', 'weight2', 'weight3'] + tuneable_parameters = ["lenpen", "weight1", "weight2", "weight3"] initial_params = [args.lenpen, args.weight1, args.weight2, args.weight3] for i, elem in enumerate(initial_params): if type(elem) is not list: @@ -33,51 +33,60 @@ def random_search(args): param_values += initial_params random.seed(args.seed) - random_params = np.array([ - [random.uniform(args.lower_bound[i], args.upper_bound[i]) for i in range(len(args.tune_param))] - for k in range(args.num_trials) - ]) - set_params = np.array([ - [initial_params[i][0] for i in range(len(tuneable_parameters))] - for k in range(args.num_trials) - ]) + random_params = np.array( + [ + [ + random.uniform(args.lower_bound[i], args.upper_bound[i]) + for i in range(len(args.tune_param)) + ] + for k in range(args.num_trials) + ] + ) + set_params = np.array( + [ + [initial_params[i][0] for i in range(len(tuneable_parameters))] + for k in range(args.num_trials) + ] + ) random_params = np.concatenate((random_params, set_params), 1) rerank_args = vars(args).copy() if args.nbest_list: - rerank_args['gen_subset'] = 'test' + rerank_args["gen_subset"] = "test" else: - rerank_args['gen_subset'] = args.tune_subset + rerank_args["gen_subset"] = args.tune_subset for k in range(len(tune_parameters)): rerank_args[tune_parameters[k]] = list(random_params[:, k]) if args.share_weights: - k = tune_parameters.index('weight2') - rerank_args['weight3'] = list(random_params[:, k]) + k = tune_parameters.index("weight2") + rerank_args["weight3"] = list(random_params[:, k]) rerank_args = argparse.Namespace(**rerank_args) - best_lenpen, best_weight1, best_weight2, best_weight3, best_score = rerank.rerank(rerank_args) + best_lenpen, best_weight1, best_weight2, best_weight3, best_score = rerank.rerank( + rerank_args + ) rerank_args = vars(args).copy() - rerank_args['lenpen'] = [best_lenpen] - rerank_args['weight1'] = [best_weight1] - rerank_args['weight2'] = [best_weight2] - rerank_args['weight3'] = [best_weight3] + rerank_args["lenpen"] = [best_lenpen] + rerank_args["weight1"] = [best_weight1] + rerank_args["weight2"] = [best_weight2] + rerank_args["weight3"] = [best_weight3] # write the hypothesis from the valid set from the best trial if args.gen_subset != "valid": - rerank_args['gen_subset'] = "valid" + rerank_args["gen_subset"] = "valid" rerank_args = argparse.Namespace(**rerank_args) rerank.rerank(rerank_args) # test with the best hyperparameters on gen subset rerank_args = vars(args).copy() - rerank_args['gen_subset'] = args.gen_subset - rerank_args['lenpen'] = [best_lenpen] - rerank_args['weight1'] = [best_weight1] - rerank_args['weight2'] = [best_weight2] - rerank_args['weight3'] = [best_weight3] + rerank_args["gen_subset"] = args.gen_subset + rerank_args["lenpen"] = [best_lenpen] + rerank_args["weight1"] = [best_weight1] + rerank_args["weight2"] = [best_weight2] + rerank_args["weight3"] = [best_weight3] rerank_args = argparse.Namespace(**rerank_args) rerank.rerank(rerank_args) @@ -89,5 +98,5 @@ def cli_main(): random_search(args) -if __name__ == '__main__': +if __name__ == "__main__": cli_main() diff --git a/examples/noisychannel/rerank_utils.py b/examples/noisychannel/rerank_utils.py index e1fcf918c5..2c6bf1b1af 100644 --- a/examples/noisychannel/rerank_utils.py +++ b/examples/noisychannel/rerank_utils.py @@ -3,11 +3,11 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. -from contextlib import redirect_stdout import math import os import re import subprocess +from contextlib import redirect_stdout from fairseq import options from fairseq_cli import eval_lm, preprocess @@ -20,7 +20,7 @@ def reprocess(fle): # per source, so the values for hypothesis_dict are lists. # parses output of generate.py - with open(fle, 'r') as f: + with open(fle, "r") as f: txt = f.read() """reprocess generate.py output""" @@ -45,7 +45,9 @@ def reprocess(fle): if line_type == "H": h_txt = line[j:] hypo = re.search(hp, h_txt) - assert hypo is not None, ("regular expression failed to find the hypothesis scoring") + assert ( + hypo is not None + ), "regular expression failed to find the hypothesis scoring" _, i = hypo.span() score = hypo.group() if id_num in hypothesis_dict: @@ -56,9 +58,9 @@ def reprocess(fle): score_dict[id_num] = [float(score)] elif line_type == "S": - source_dict[id_num] = (line[j:]) + source_dict[id_num] = line[j:] elif line_type == "T": - target_dict[id_num] = (line[j:]) + target_dict[id_num] = line[j:] elif line_type == "P": pos_scores = (line[j:]).split() pos_scores = [float(x) for x in pos_scores] @@ -72,7 +74,7 @@ def reprocess(fle): def reprocess_nbest(fle): """reprocess interactive.py output""" - with open(fle, 'r') as f: + with open(fle, "r") as f: txt = f.read() source_dict = {} @@ -82,7 +84,7 @@ def reprocess_nbest(fle): pos_score_dict = {} lines = txt.split("\n") - hp = re.compile(r'[-]?\d+[.]?\d+') + hp = re.compile(r"[-]?\d+[.]?\d+") j = -1 for _i, line in enumerate(lines): @@ -119,59 +121,76 @@ def reprocess_nbest(fle): return source_dict, hypothesis_dict, score_dict, target_dict, pos_score_dict -def write_reprocessed(sources, hypos, targets, source_outfile, - hypo_outfile, target_outfile, right_to_left=False, - prefix_len=None, bpe_symbol=None, - target_prefix_frac=None, source_prefix_frac=None): +def write_reprocessed( + sources, + hypos, + targets, + source_outfile, + hypo_outfile, + target_outfile, + right_to_left=False, + prefix_len=None, + bpe_symbol=None, + target_prefix_frac=None, + source_prefix_frac=None, +): """writes nbest hypothesis for rescoring""" - assert not (prefix_len is not None and target_prefix_frac is not None), \ - "in writing reprocessed, only one type of prefix may be used" - assert not (prefix_len is not None and source_prefix_frac is not None), \ - "in writing reprocessed, only one type of prefix may be used" - assert not (target_prefix_frac is not None and source_prefix_frac is not None), \ - "in writing reprocessed, only one type of prefix may be used" - - with open(source_outfile, 'w') as source_file, \ - open(hypo_outfile, 'w') as hypo_file, \ - open(target_outfile, 'w') as target_file: + assert not ( + prefix_len is not None and target_prefix_frac is not None + ), "in writing reprocessed, only one type of prefix may be used" + assert not ( + prefix_len is not None and source_prefix_frac is not None + ), "in writing reprocessed, only one type of prefix may be used" + assert not ( + target_prefix_frac is not None and source_prefix_frac is not None + ), "in writing reprocessed, only one type of prefix may be used" + + with open(source_outfile, "w") as source_file, open( + hypo_outfile, "w" + ) as hypo_file, open(target_outfile, "w") as target_file: assert len(sources) == len(hypos), "sources and hypos list length mismatch" if right_to_left: for i in range(len(sources)): - for j in range(len(hypos[i])): - if prefix_len is None: - hypo_file.write(make_right_to_left(hypos[i][j])+"\n") - else: - raise NotImplementedError() - source_file.write(make_right_to_left(sources[i])+"\n") - target_file.write(make_right_to_left(targets[i])+"\n") + for j in range(len(hypos[i])): + if prefix_len is None: + hypo_file.write(make_right_to_left(hypos[i][j]) + "\n") + else: + raise NotImplementedError() + source_file.write(make_right_to_left(sources[i]) + "\n") + target_file.write(make_right_to_left(targets[i]) + "\n") else: for i in sorted(sources.keys()): - for j in range(len(hypos[i])): - if prefix_len is not None: - shortened = get_prefix_no_bpe(hypos[i][j], bpe_symbol, prefix_len)+"\n" - hypo_file.write(shortened) - source_file.write(sources[i]) - target_file.write(targets[i]) - elif target_prefix_frac is not None: - num_words, shortened, num_bpe_tokens = \ - calc_length_from_frac(hypos[i][j], target_prefix_frac, bpe_symbol) - shortened += "\n" - hypo_file.write(shortened) - source_file.write(sources[i]) - target_file.write(targets[i]) - elif source_prefix_frac is not None: - num_words, shortened, num_bpe_tokensn = \ - calc_length_from_frac(sources[i], source_prefix_frac, bpe_symbol) - shortened += "\n" - hypo_file.write(hypos[i][j]) - source_file.write(shortened) - target_file.write(targets[i]) - else: - hypo_file.write(hypos[i][j]) - source_file.write(sources[i]) - target_file.write(targets[i]) + for j in range(len(hypos[i])): + if prefix_len is not None: + shortened = ( + get_prefix_no_bpe(hypos[i][j], bpe_symbol, prefix_len) + + "\n" + ) + hypo_file.write(shortened) + source_file.write(sources[i]) + target_file.write(targets[i]) + elif target_prefix_frac is not None: + num_words, shortened, num_bpe_tokens = calc_length_from_frac( + hypos[i][j], target_prefix_frac, bpe_symbol + ) + shortened += "\n" + hypo_file.write(shortened) + source_file.write(sources[i]) + target_file.write(targets[i]) + elif source_prefix_frac is not None: + num_words, shortened, num_bpe_tokensn = calc_length_from_frac( + sources[i], source_prefix_frac, bpe_symbol + ) + shortened += "\n" + hypo_file.write(hypos[i][j]) + source_file.write(shortened) + target_file.write(targets[i]) + else: + hypo_file.write(hypos[i][j]) + source_file.write(sources[i]) + target_file.write(targets[i]) def calc_length_from_frac(bpe_sentence, prefix_frac, bpe_symbol): @@ -207,7 +226,9 @@ def get_prefix_from_len(sentence, bpe_symbol, prefix_len): if bpe_count == 0: return sentence[:prefix_len] else: - return sentence[:prefix_len]+get_prefix_from_len(sentence[prefix_len:], bpe_symbol, bpe_count) + return sentence[:prefix_len] + get_prefix_from_len( + sentence[prefix_len:], bpe_symbol, bpe_count + ) def get_num_bpe_tokens_from_len(sentence, bpe_symbol, prefix_len): @@ -225,9 +246,9 @@ def make_right_to_left(line): def remove_bpe(line, bpe_symbol): - line = line.replace("\n", '') - line = (line + ' ').replace(bpe_symbol, '').rstrip() - return line+("\n") + line = line.replace("\n", "") + line = (line + " ").replace(bpe_symbol, "").rstrip() + return line + ("\n") def remove_bpe_dict(pred_dict, bpe_symbol): @@ -242,7 +263,7 @@ def remove_bpe_dict(pred_dict, bpe_symbol): def parse_bleu_scoring(line): - p = re.compile(r'(BLEU4 = )\d+[.]\d+') + p = re.compile(r"(BLEU4 = )\d+[.]\d+") res = re.search(p, line) assert res is not None, line return float(res.group()[8:]) @@ -259,9 +280,21 @@ def get_full_from_prefix(hypo_prefix, hypos): raise Exception() -def get_score(a, b, c, target_len, bitext_score1, bitext_score2=None, lm_score=None, - lenpen=None, src_len=None, tgt_len=None, bitext1_backwards=False, - bitext2_backwards=False, normalize=False): +def get_score( + a, + b, + c, + target_len, + bitext_score1, + bitext_score2=None, + lm_score=None, + lenpen=None, + src_len=None, + tgt_len=None, + bitext1_backwards=False, + bitext2_backwards=False, + normalize=False, +): if bitext1_backwards: bitext1_norm = src_len else: @@ -275,9 +308,13 @@ def get_score(a, b, c, target_len, bitext_score1, bitext_score2=None, lm_score=N bitext2_norm = 1 bitext_score2 = 0 if normalize: - score = a*bitext_score1/bitext1_norm + b*bitext_score2/bitext2_norm+c*lm_score/src_len + score = ( + a * bitext_score1 / bitext1_norm + + b * bitext_score2 / bitext2_norm + + c * lm_score / src_len + ) else: - score = a*bitext_score1 + b*bitext_score2+c*lm_score + score = a * bitext_score1 + b * bitext_score2 + c * lm_score if lenpen is not None: score /= (target_len) ** float(lenpen) @@ -286,8 +323,16 @@ def get_score(a, b, c, target_len, bitext_score1, bitext_score2=None, lm_score=N class BitextOutput(object): - def __init__(self, output_file, backwards, right_to_left, bpe_symbol, - prefix_len=None, target_prefix_frac=None, source_prefix_frac=None): + def __init__( + self, + output_file, + backwards, + right_to_left, + bpe_symbol, + prefix_len=None, + target_prefix_frac=None, + source_prefix_frac=None, + ): """process output from rescoring""" source, hypo, score, target, pos_score = reprocess(output_file) if backwards: @@ -296,7 +341,9 @@ def __init__(self, output_file, backwards, right_to_left, bpe_symbol, self.hypo_fracs = target_prefix_frac # remove length penalty so we can use raw scores - score, num_bpe_tokens = get_score_from_pos(pos_score, prefix_len, hypo, bpe_symbol, self.hypo_fracs, backwards) + score, num_bpe_tokens = get_score_from_pos( + pos_score, prefix_len, hypo, bpe_symbol, self.hypo_fracs, backwards + ) source_lengths = {} target_lengths = {} @@ -341,7 +388,9 @@ def __init__(self, output_file, backwards, right_to_left, bpe_symbol, score[i] = float(score[i][0]) pos_score[i] = pos_score[i][0] else: - assert len(hypo[i]) == 1, "expected only one hypothesis per source sentence" + assert ( + len(hypo[i]) == 1 + ), "expected only one hypothesis per source sentence" source[i] = remove_bpe(source[i], bpe_symbol) target[i] = remove_bpe(target[i], bpe_symbol) hypo[i] = remove_bpe(hypo[i][0], bpe_symbol) @@ -360,11 +409,26 @@ def __init__(self, output_file, backwards, right_to_left, bpe_symbol, class BitextOutputFromGen(object): - def __init__(self, predictions_bpe_file, bpe_symbol=None, nbest=False, prefix_len=None, target_prefix_frac=None): + def __init__( + self, + predictions_bpe_file, + bpe_symbol=None, + nbest=False, + prefix_len=None, + target_prefix_frac=None, + ): if nbest: - pred_source, pred_hypo, pred_score, pred_target, pred_pos_score = reprocess_nbest(predictions_bpe_file) + ( + pred_source, + pred_hypo, + pred_score, + pred_target, + pred_pos_score, + ) = reprocess_nbest(predictions_bpe_file) else: - pred_source, pred_hypo, pred_score, pred_target, pred_pos_score = reprocess(predictions_bpe_file) + pred_source, pred_hypo, pred_score, pred_target, pred_pos_score = reprocess( + predictions_bpe_file + ) assert len(pred_source) == len(pred_hypo) assert len(pred_source) == len(pred_score) @@ -372,8 +436,9 @@ def __init__(self, predictions_bpe_file, bpe_symbol=None, nbest=False, prefix_le assert len(pred_source) == len(pred_pos_score) # remove length penalty so we can use raw scores - pred_score, num_bpe_tokens = get_score_from_pos(pred_pos_score, prefix_len, pred_hypo, - bpe_symbol, target_prefix_frac, False) + pred_score, num_bpe_tokens = get_score_from_pos( + pred_pos_score, prefix_len, pred_hypo, bpe_symbol, target_prefix_frac, False + ) self.source = pred_source self.target = pred_target @@ -414,7 +479,9 @@ def __init__(self, predictions_bpe_file, bpe_symbol=None, nbest=False, prefix_le index += 1 -def get_score_from_pos(pos_score_dict, prefix_len, hypo_dict, bpe_symbol, hypo_frac, backwards): +def get_score_from_pos( + pos_score_dict, prefix_len, hypo_dict, bpe_symbol, hypo_frac, backwards +): score_dict = {} num_bpe_tokens_dict = {} assert prefix_len is None or hypo_frac is None @@ -423,11 +490,15 @@ def get_score_from_pos(pos_score_dict, prefix_len, hypo_dict, bpe_symbol, hypo_f num_bpe_tokens_dict[key] = [] for i in range(len(pos_score_dict[key])): if prefix_len is not None and not backwards: - num_bpe_tokens = get_num_bpe_tokens_from_len(hypo_dict[key][i], bpe_symbol, prefix_len) + num_bpe_tokens = get_num_bpe_tokens_from_len( + hypo_dict[key][i], bpe_symbol, prefix_len + ) score_dict[key].append(sum(pos_score_dict[key][i][:num_bpe_tokens])) num_bpe_tokens_dict[key].append(num_bpe_tokens) elif hypo_frac is not None: - num_words, shortened, hypo_prefix_len = calc_length_from_frac(hypo_dict[key][i], hypo_frac, bpe_symbol) + num_words, shortened, hypo_prefix_len = calc_length_from_frac( + hypo_dict[key][i], hypo_frac, bpe_symbol + ) score_dict[key].append(sum(pos_score_dict[key][i][:hypo_prefix_len])) num_bpe_tokens_dict[key].append(hypo_prefix_len) else: @@ -437,10 +508,26 @@ def get_score_from_pos(pos_score_dict, prefix_len, hypo_dict, bpe_symbol, hypo_f class LMOutput(object): - def __init__(self, lm_score_file, lm_dict=None, prefix_len=None, bpe_symbol=None, target_prefix_frac=None): - lm_sentences, lm_sen_scores, lm_sen_pos_scores, lm_no_bpe_sentences, lm_bpe_tokens = \ - parse_lm(lm_score_file, prefix_len=prefix_len, - bpe_symbol=bpe_symbol, target_prefix_frac=target_prefix_frac) + def __init__( + self, + lm_score_file, + lm_dict=None, + prefix_len=None, + bpe_symbol=None, + target_prefix_frac=None, + ): + ( + lm_sentences, + lm_sen_scores, + lm_sen_pos_scores, + lm_no_bpe_sentences, + lm_bpe_tokens, + ) = parse_lm( + lm_score_file, + prefix_len=prefix_len, + bpe_symbol=bpe_symbol, + target_prefix_frac=target_prefix_frac, + ) self.sentences = lm_sentences self.score = lm_sen_scores @@ -452,7 +539,7 @@ def __init__(self, lm_score_file, lm_dict=None, prefix_len=None, bpe_symbol=None def parse_lm(input_file, prefix_len=None, bpe_symbol=None, target_prefix_frac=None): """parse output of eval_lm""" - with open(input_file, 'r') as f: + with open(input_file, "r") as f: text = f.readlines() text = text[7:] cleaned_text = text[:-2] @@ -467,20 +554,23 @@ def parse_lm(input_file, prefix_len=None, bpe_symbol=None, target_prefix_frac=No if tokens[0].isdigit(): line_id = int(tokens[0]) scores = [float(x[1:-1]) for x in tokens[2::2]] - sentences[line_id] = " ".join(tokens[1::2][:-1])+"\n" + sentences[line_id] = " ".join(tokens[1::2][:-1]) + "\n" if bpe_symbol is not None: # exclude <eos> symbol to match output from generate.py - bpe_sen = " ".join(tokens[1::2][:-1])+"\n" + bpe_sen = " ".join(tokens[1::2][:-1]) + "\n" no_bpe_sen = remove_bpe(bpe_sen, bpe_symbol) no_bpe_sentences[line_id] = no_bpe_sen if prefix_len is not None: - num_bpe_tokens = get_num_bpe_tokens_from_len(bpe_sen, bpe_symbol, prefix_len) + num_bpe_tokens = get_num_bpe_tokens_from_len( + bpe_sen, bpe_symbol, prefix_len + ) sen_scores[line_id] = sum(scores[:num_bpe_tokens]) num_bpe_tokens_dict[line_id] = num_bpe_tokens elif target_prefix_frac is not None: - num_words, shortened, target_prefix_len = calc_length_from_frac(bpe_sen, target_prefix_frac, - bpe_symbol) + num_words, shortened, target_prefix_len = calc_length_from_frac( + bpe_sen, target_prefix_frac, bpe_symbol + ) sen_scores[line_id] = sum(scores[:target_prefix_len]) num_bpe_tokens_dict[line_id] = target_prefix_len else: @@ -492,160 +582,269 @@ def parse_lm(input_file, prefix_len=None, bpe_symbol=None, target_prefix_frac=No return sentences, sen_scores, sen_pos_scores, no_bpe_sentences, num_bpe_tokens_dict -def get_directories(data_dir_name, num_rescore, gen_subset, - fw_name, shard_id, num_shards, - sampling=False, prefix_len=None, - target_prefix_frac=None, source_prefix_frac=None): - nbest_file_id = "nbest_" + str(num_rescore) + \ - "_subset_" + gen_subset + \ - "_fw_name_" + fw_name + \ - "_shard_" + str(shard_id) + \ - "_of_" + str(num_shards) +def get_directories( + data_dir_name, + num_rescore, + gen_subset, + fw_name, + shard_id, + num_shards, + sampling=False, + prefix_len=None, + target_prefix_frac=None, + source_prefix_frac=None, +): + nbest_file_id = ( + "nbest_" + + str(num_rescore) + + "_subset_" + + gen_subset + + "_fw_name_" + + fw_name + + "_shard_" + + str(shard_id) + + "_of_" + + str(num_shards) + ) if sampling: nbest_file_id += "_sampling" # the directory containing all information for this nbest list - pre_gen = os.path.join(os.path.dirname(__file__))+"/rerank_data/"+data_dir_name+"/"+nbest_file_id + pre_gen = ( + os.path.join(os.path.dirname(__file__)) + + "/rerank_data/" + + data_dir_name + + "/" + + nbest_file_id + ) # the directory to store the preprocessed nbest list, for left to right rescoring - left_to_right_preprocessed_dir = pre_gen+"/left_to_right_preprocessed" + left_to_right_preprocessed_dir = pre_gen + "/left_to_right_preprocessed" if source_prefix_frac is not None: - left_to_right_preprocessed_dir = left_to_right_preprocessed_dir + "/prefix_frac" + str(source_prefix_frac) + left_to_right_preprocessed_dir = ( + left_to_right_preprocessed_dir + "/prefix_frac" + str(source_prefix_frac) + ) # the directory to store the preprocessed nbest list, for right to left rescoring - right_to_left_preprocessed_dir = pre_gen+"/right_to_left_preprocessed" + right_to_left_preprocessed_dir = pre_gen + "/right_to_left_preprocessed" # the directory to store the preprocessed nbest list, for backwards rescoring - backwards_preprocessed_dir = pre_gen+"/backwards" + backwards_preprocessed_dir = pre_gen + "/backwards" if target_prefix_frac is not None: - backwards_preprocessed_dir = backwards_preprocessed_dir+"/prefix_frac"+str(target_prefix_frac) + backwards_preprocessed_dir = ( + backwards_preprocessed_dir + "/prefix_frac" + str(target_prefix_frac) + ) elif prefix_len is not None: - backwards_preprocessed_dir = backwards_preprocessed_dir+"/prefix_"+str(prefix_len) + backwards_preprocessed_dir = ( + backwards_preprocessed_dir + "/prefix_" + str(prefix_len) + ) # the directory to store the preprocessed nbest list, for rescoring with P(T) - lm_preprocessed_dir = pre_gen+"/lm_preprocessed" - - return pre_gen, left_to_right_preprocessed_dir, right_to_left_preprocessed_dir, \ - backwards_preprocessed_dir, lm_preprocessed_dir - - -def lm_scoring(preprocess_directory, bpe_status, gen_output, pre_gen, - cur_lm_dict, cur_lm_name, cur_language_model, cur_lm_bpe_code, - batch_size, lm_score_file, target_lang, source_lang, prefix_len=None): + lm_preprocessed_dir = pre_gen + "/lm_preprocessed" + + return ( + pre_gen, + left_to_right_preprocessed_dir, + right_to_left_preprocessed_dir, + backwards_preprocessed_dir, + lm_preprocessed_dir, + ) + + +def lm_scoring( + preprocess_directory, + bpe_status, + gen_output, + pre_gen, + cur_lm_dict, + cur_lm_name, + cur_language_model, + cur_lm_bpe_code, + batch_size, + lm_score_file, + target_lang, + source_lang, + prefix_len=None, +): if prefix_len is not None: - assert bpe_status == "different", "bpe status must be different to use prefix len" + assert ( + bpe_status == "different" + ), "bpe status must be different to use prefix len" if bpe_status == "no bpe": # run lm on output without bpe - write_reprocessed(gen_output.no_bpe_source, gen_output.no_bpe_hypo, - gen_output.no_bpe_target, pre_gen+"/rescore_data_no_bpe.de", - pre_gen+"/rescore_data_no_bpe.en", pre_gen+"/reference_file_no_bpe") - - preprocess_lm_param = ["--only-source", - "--trainpref", pre_gen+"/rescore_data_no_bpe."+target_lang, - "--srcdict", cur_lm_dict, - "--destdir", preprocess_directory] + write_reprocessed( + gen_output.no_bpe_source, + gen_output.no_bpe_hypo, + gen_output.no_bpe_target, + pre_gen + "/rescore_data_no_bpe.de", + pre_gen + "/rescore_data_no_bpe.en", + pre_gen + "/reference_file_no_bpe", + ) + + preprocess_lm_param = [ + "--only-source", + "--trainpref", + pre_gen + "/rescore_data_no_bpe." + target_lang, + "--srcdict", + cur_lm_dict, + "--destdir", + preprocess_directory, + ] preprocess_parser = options.get_preprocessing_parser() input_args = preprocess_parser.parse_args(preprocess_lm_param) preprocess.main(input_args) - eval_lm_param = [preprocess_directory, - "--path", cur_language_model, - "--output-word-probs", - "--batch-size", str(batch_size), - "--max-tokens", "1024", - "--sample-break-mode", "eos", - "--gen-subset", "train"] + eval_lm_param = [ + preprocess_directory, + "--path", + cur_language_model, + "--output-word-probs", + "--batch-size", + str(batch_size), + "--max-tokens", + "1024", + "--sample-break-mode", + "eos", + "--gen-subset", + "train", + ] eval_lm_parser = options.get_eval_lm_parser() input_args = options.parse_args_and_arch(eval_lm_parser, eval_lm_param) - with open(lm_score_file, 'w') as f: + with open(lm_score_file, "w") as f: with redirect_stdout(f): eval_lm.main(input_args) elif bpe_status == "shared": - preprocess_lm_param = ["--only-source", - "--trainpref", pre_gen+"/rescore_data."+target_lang, - "--srcdict", cur_lm_dict, - "--destdir", preprocess_directory] - preprocess_parser = options.get_preprocessing_parser() - input_args = preprocess_parser.parse_args(preprocess_lm_param) - preprocess.main(input_args) - - eval_lm_param = [preprocess_directory, - "--path", cur_language_model, - "--output-word-probs", - "--batch-size", str(batch_size), - "--sample-break-mode", "eos", - "--gen-subset", "train"] - - eval_lm_parser = options.get_eval_lm_parser() - input_args = options.parse_args_and_arch(eval_lm_parser, eval_lm_param) - - with open(lm_score_file, 'w') as f: - with redirect_stdout(f): - eval_lm.main(input_args) + preprocess_lm_param = [ + "--only-source", + "--trainpref", + pre_gen + "/rescore_data." + target_lang, + "--srcdict", + cur_lm_dict, + "--destdir", + preprocess_directory, + ] + preprocess_parser = options.get_preprocessing_parser() + input_args = preprocess_parser.parse_args(preprocess_lm_param) + preprocess.main(input_args) + + eval_lm_param = [ + preprocess_directory, + "--path", + cur_language_model, + "--output-word-probs", + "--batch-size", + str(batch_size), + "--sample-break-mode", + "eos", + "--gen-subset", + "train", + ] + + eval_lm_parser = options.get_eval_lm_parser() + input_args = options.parse_args_and_arch(eval_lm_parser, eval_lm_param) + + with open(lm_score_file, "w") as f: + with redirect_stdout(f): + eval_lm.main(input_args) elif bpe_status == "different": - rescore_file = pre_gen+"/rescore_data_no_bpe" - rescore_bpe = pre_gen+"/rescore_data_new_bpe" + rescore_file = pre_gen + "/rescore_data_no_bpe" + rescore_bpe = pre_gen + "/rescore_data_new_bpe" rescore_file += "." rescore_bpe += "." - write_reprocessed(gen_output.no_bpe_source, gen_output.no_bpe_hypo, - gen_output.no_bpe_target, rescore_file+source_lang, - rescore_file+target_lang, pre_gen+"/reference_file_no_bpe", - bpe_symbol=None) + write_reprocessed( + gen_output.no_bpe_source, + gen_output.no_bpe_hypo, + gen_output.no_bpe_target, + rescore_file + source_lang, + rescore_file + target_lang, + pre_gen + "/reference_file_no_bpe", + bpe_symbol=None, + ) # apply LM bpe to nbest list - bpe_src_param = ["-c", cur_lm_bpe_code, - "--input", rescore_file+target_lang, - "--output", rescore_bpe+target_lang] - subprocess.call(["python", - os.path.join(os.path.dirname(__file__), - "subword-nmt/subword_nmt/apply_bpe.py")] + bpe_src_param, - shell=False) + bpe_src_param = [ + "-c", + cur_lm_bpe_code, + "--input", + rescore_file + target_lang, + "--output", + rescore_bpe + target_lang, + ] + subprocess.call( + [ + "python", + os.path.join( + os.path.dirname(__file__), "subword-nmt/subword_nmt/apply_bpe.py" + ), + ] + + bpe_src_param, + shell=False, + ) # uncomment to use fastbpe instead of subword-nmt bpe # bpe_src_param = [rescore_bpe+target_lang, rescore_file+target_lang, cur_lm_bpe_code] # subprocess.call(["/private/home/edunov/fastBPE/fast", "applybpe"] + bpe_src_param, shell=False) preprocess_dir = preprocess_directory - preprocess_lm_param = ["--only-source", - "--trainpref", rescore_bpe+target_lang, - "--srcdict", cur_lm_dict, - "--destdir", preprocess_dir] + preprocess_lm_param = [ + "--only-source", + "--trainpref", + rescore_bpe + target_lang, + "--srcdict", + cur_lm_dict, + "--destdir", + preprocess_dir, + ] preprocess_parser = options.get_preprocessing_parser() input_args = preprocess_parser.parse_args(preprocess_lm_param) preprocess.main(input_args) - eval_lm_param = [preprocess_dir, - "--path", cur_language_model, - "--output-word-probs", - "--batch-size", str(batch_size), - "--max-tokens", "1024", - "--sample-break-mode", "eos", - "--gen-subset", "train"] + eval_lm_param = [ + preprocess_dir, + "--path", + cur_language_model, + "--output-word-probs", + "--batch-size", + str(batch_size), + "--max-tokens", + "1024", + "--sample-break-mode", + "eos", + "--gen-subset", + "train", + ] eval_lm_parser = options.get_eval_lm_parser() input_args = options.parse_args_and_arch(eval_lm_parser, eval_lm_param) - with open(lm_score_file, 'w') as f: + with open(lm_score_file, "w") as f: with redirect_stdout(f): eval_lm.main(input_args) -def rescore_file_name(nbest_dir, prefix_len, scorer_name, lm_file=False, - target_prefix_frac=None, source_prefix_frac=None, backwards=None): +def rescore_file_name( + nbest_dir, + prefix_len, + scorer_name, + lm_file=False, + target_prefix_frac=None, + source_prefix_frac=None, + backwards=None, +): if lm_file: - score_file = nbest_dir+"/lm_score_translations_model_"+scorer_name+".txt" + score_file = nbest_dir + "/lm_score_translations_model_" + scorer_name + ".txt" else: - score_file = nbest_dir+"/"+scorer_name+"_score_translations.txt" + score_file = nbest_dir + "/" + scorer_name + "_score_translations.txt" if backwards: if prefix_len is not None: - score_file += "prefix_len"+str(prefix_len) + score_file += "prefix_len" + str(prefix_len) elif target_prefix_frac is not None: - score_file += "target_prefix_frac"+str(target_prefix_frac) + score_file += "target_prefix_frac" + str(target_prefix_frac) else: if source_prefix_frac is not None: - score_file += "source_prefix_frac"+str(source_prefix_frac) + score_file += "source_prefix_frac" + str(source_prefix_frac) return score_file diff --git a/examples/nonautoregressive_translation/README.md b/examples/nonautoregressive_translation/README.md index bb0f4b8b06..8793e225c9 100644 --- a/examples/nonautoregressive_translation/README.md +++ b/examples/nonautoregressive_translation/README.md @@ -13,7 +13,7 @@ We also provided our own implementations for several popular non-autoregressive- ## Dataset -First, follow the [instructions to download and preprocess the WMT'14 En-De dataset](../translation#prepare-wmt14en2desh). +First, follow the [instructions to download and preprocess the WMT'14 En-De dataset](../translation#wmt14-english-to-german-convolutional). Make sure to learn a joint vocabulary by passing the `--joined-dictionary` option to `fairseq-preprocess`. ### Knowledge Distillation @@ -36,7 +36,7 @@ The following command will train a *Levenshtein Transformer* on the binarized da fairseq-train \ data-bin/wmt14_en_de_distill \ --save-dir checkpoints \ - --ddp-backend=no_c10d \ + --ddp-backend=legacy_ddp \ --task translation_lev \ --criterion nat_loss \ --arch levenshtein_transformer \ @@ -44,7 +44,7 @@ fairseq-train \ --share-all-embeddings \ --optimizer adam --adam-betas '(0.9,0.98)' \ --lr 0.0005 --lr-scheduler inverse_sqrt \ - --min-lr '1e-09' --warmup-updates 10000 \ + --stop-min-lr '1e-09' --warmup-updates 10000 \ --warmup-init-lr '1e-07' --label-smoothing 0.1 \ --dropout 0.3 --weight-decay 0.01 \ --decoder-learned-pos \ @@ -143,4 +143,4 @@ Note that we need to make sure the autoregressive model shares the same vocabula journal={arXiv preprint arXiv:1911.02727}, year={2019} } -``` \ No newline at end of file +``` diff --git a/examples/nonautoregressive_translation/scripts.md b/examples/nonautoregressive_translation/scripts.md index 63b945c1d3..9d3d7b67dc 100644 --- a/examples/nonautoregressive_translation/scripts.md +++ b/examples/nonautoregressive_translation/scripts.md @@ -6,7 +6,7 @@ Note that we need to have an additional module to perform "length prediction" (` fairseq-train \ data-bin/wmt14_en_de_distill \ --save-dir checkpoints \ - --ddp-backend=no_c10d \ + --ddp-backend=legacy_ddp \ --task translation_lev \ --criterion nat_loss \ --arch nonautoregressive_transformer \ @@ -14,7 +14,7 @@ fairseq-train \ --share-all-embeddings \ --optimizer adam --adam-betas '(0.9,0.98)' \ --lr 0.0005 --lr-scheduler inverse_sqrt \ - --min-lr '1e-09' --warmup-updates 10000 \ + --stop-min-lr '1e-09' --warmup-updates 10000 \ --warmup-init-lr '1e-07' --label-smoothing 0.1 \ --dropout 0.3 --weight-decay 0.01 \ --decoder-learned-pos \ @@ -35,7 +35,7 @@ Note that we implemented a low-rank appromixated CRF model by setting `--crf-low fairseq-train \ data-bin/wmt14_en_de_distill \ --save-dir checkpoints \ - --ddp-backend=no_c10d \ + --ddp-backend=legacy_ddp \ --task translation_lev \ --criterion nat_loss \ --arch nacrf_transformer \ @@ -43,7 +43,7 @@ fairseq-train \ --share-all-embeddings \ --optimizer adam --adam-betas '(0.9,0.98)' \ --lr 0.0005 --lr-scheduler inverse_sqrt \ - --min-lr '1e-09' --warmup-updates 10000 \ + --stop-min-lr '1e-09' --warmup-updates 10000 \ --warmup-init-lr '1e-07' --label-smoothing 0.1 \ --dropout 0.3 --weight-decay 0.01 \ --decoder-learned-pos \ @@ -68,7 +68,7 @@ Note that `--train-step` means how many iterations of refinement we used during fairseq-train \ data-bin/wmt14_en_de_distill \ --save-dir checkpoints \ - --ddp-backend=no_c10d \ + --ddp-backend=legacy_ddp \ --task translation_lev \ --criterion nat_loss \ --arch iterative_nonautoregressive_transformer \ @@ -76,7 +76,7 @@ fairseq-train \ --share-all-embeddings \ --optimizer adam --adam-betas '(0.9,0.98)' \ --lr 0.0005 --lr-scheduler inverse_sqrt \ - --min-lr '1e-09' --warmup-updates 10000 \ + --stop-min-lr '1e-09' --warmup-updates 10000 \ --warmup-init-lr '1e-07' --label-smoothing 0.1 \ --dropout 0.3 --weight-decay 0.01 \ --decoder-learned-pos \ @@ -101,7 +101,7 @@ Note that we need to specify the "slot-loss" (uniform or balanced tree) describe fairseq-train \ data-bin/wmt14_en_de_distill \ --save-dir checkpoints \ - --ddp-backend=no_c10d \ + --ddp-backend=legacy_ddp \ --task translation_lev \ --criterion nat_loss \ --arch insertion_transformer \ @@ -109,7 +109,7 @@ fairseq-train \ --share-all-embeddings \ --optimizer adam --adam-betas '(0.9,0.98)' \ --lr 0.0005 --lr-scheduler inverse_sqrt \ - --min-lr '1e-09' --warmup-updates 10000 \ + --stop-min-lr '1e-09' --warmup-updates 10000 \ --warmup-init-lr '1e-07' --label-smoothing 0.1 \ --dropout 0.3 --weight-decay 0.01 \ --decoder-learned-pos \ @@ -128,7 +128,7 @@ fairseq-train \ fairseq-train \ data-bin/wmt14_en_de_distill \ --save-dir checkpoints \ - --ddp-backend=no_c10d \ + --ddp-backend=legacy_ddp \ --task translation_lev \ --criterion nat_loss \ --arch cmlm_transformer \ @@ -136,7 +136,7 @@ fairseq-train \ --share-all-embeddings \ --optimizer adam --adam-betas '(0.9,0.98)' \ --lr 0.0005 --lr-scheduler inverse_sqrt \ - --min-lr '1e-09' --warmup-updates 10000 \ + --stop-min-lr '1e-09' --warmup-updates 10000 \ --warmup-init-lr '1e-07' --label-smoothing 0.1 \ --dropout 0.3 --weight-decay 0.01 \ --decoder-learned-pos \ @@ -157,7 +157,7 @@ fairseq-train \ fairseq-train \ data-bin/wmt14_en_de_distill \ --save-dir checkpoints \ - --ddp-backend=no_c10d \ + --ddp-backend=legacy_ddp \ --task translation_lev \ --criterion nat_loss \ --arch levenshtein_transformer \ @@ -165,7 +165,7 @@ fairseq-train \ --share-all-embeddings \ --optimizer adam --adam-betas '(0.9,0.98)' \ --lr 0.0005 --lr-scheduler inverse_sqrt \ - --min-lr '1e-09' --warmup-updates 10000 \ + --stop-min-lr '1e-09' --warmup-updates 10000 \ --warmup-init-lr '1e-07' --label-smoothing 0.1 \ --dropout 0.3 --weight-decay 0.01 \ --decoder-learned-pos \ diff --git a/examples/paraphraser/paraphrase.py b/examples/paraphraser/paraphrase.py index 405df296b3..d3422fb3db 100644 --- a/examples/paraphraser/paraphrase.py +++ b/examples/paraphraser/paraphrase.py @@ -13,57 +13,66 @@ def main(): - parser = argparse.ArgumentParser(description='') - parser.add_argument('--en2fr', required=True, - help='path to en2fr model') - parser.add_argument('--fr2en', required=True, - help='path to fr2en mixture of experts model') - parser.add_argument('--user-dir', - help='path to fairseq examples/translation_moe/src directory') - parser.add_argument('--num-experts', type=int, default=10, - help='(keep at 10 unless using a different model)') - parser.add_argument('files', nargs='*', default=['-'], - help='input files to paraphrase; "-" for stdin') + parser = argparse.ArgumentParser(description="") + parser.add_argument("--en2fr", required=True, help="path to en2fr model") + parser.add_argument( + "--fr2en", required=True, help="path to fr2en mixture of experts model" + ) + parser.add_argument( + "--user-dir", help="path to fairseq examples/translation_moe/src directory" + ) + parser.add_argument( + "--num-experts", + type=int, + default=10, + help="(keep at 10 unless using a different model)", + ) + parser.add_argument( + "files", + nargs="*", + default=["-"], + help='input files to paraphrase; "-" for stdin', + ) args = parser.parse_args() if args.user_dir is None: args.user_dir = os.path.join( os.path.dirname(os.path.dirname(os.path.abspath(__file__))), # examples/ - 'translation_moe', - 'src', + "translation_moe", + "src", ) if os.path.exists(args.user_dir): - logging.info('found user_dir:' + args.user_dir) + logging.info("found user_dir:" + args.user_dir) else: raise RuntimeError( - 'cannot find fairseq examples/translation_moe/src ' - '(tried looking here: {})'.format(args.user_dir) + "cannot find fairseq examples/translation_moe/src " + "(tried looking here: {})".format(args.user_dir) ) - logging.info('loading en2fr model from:' + args.en2fr) + logging.info("loading en2fr model from:" + args.en2fr) en2fr = TransformerModel.from_pretrained( model_name_or_path=args.en2fr, - tokenizer='moses', - bpe='sentencepiece', + tokenizer="moses", + bpe="sentencepiece", ).eval() - logging.info('loading fr2en model from:' + args.fr2en) + logging.info("loading fr2en model from:" + args.fr2en) fr2en = TransformerModel.from_pretrained( model_name_or_path=args.fr2en, - tokenizer='moses', - bpe='sentencepiece', + tokenizer="moses", + bpe="sentencepiece", user_dir=args.user_dir, - task='translation_moe', + task="translation_moe", ).eval() def gen_paraphrases(en): fr = en2fr.translate(en) return [ - fr2en.translate(fr, inference_step_args={'expert': i}) + fr2en.translate(fr, inference_step_args={"expert": i}) for i in range(args.num_experts) ] - logging.info('Type the input sentence and press return:') + logging.info("Type the input sentence and press return:") for line in fileinput.input(args.files): line = line.strip() if len(line) == 0: @@ -72,5 +81,5 @@ def gen_paraphrases(en): print(paraphrase) -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/examples/pay_less_attention_paper/README.md b/examples/pay_less_attention_paper/README.md index 3fb93b23d1..5adab11f4d 100644 --- a/examples/pay_less_attention_paper/README.md +++ b/examples/pay_less_attention_paper/README.md @@ -110,10 +110,10 @@ mkdir -p $SAVE CUDA_VISIBLE_DEVICES=0 $(which fairseq-train) data-bin/iwslt14.tokenized.de-en \ --clip-norm 0 --optimizer adam --lr 0.0005 \ --source-lang de --target-lang en --max-tokens 4000 --no-progress-bar \ - --log-interval 100 --min-lr '1e-09' --weight-decay 0.0001 \ + --log-interval 100 --stop-min-lr '1e-09' --weight-decay 0.0001 \ --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \ --lr-scheduler inverse_sqrt \ - --ddp-backend=no_c10d \ + --ddp-backend=legacy_ddp \ --max-update 50000 --warmup-updates 4000 --warmup-init-lr '1e-07' \ --adam-betas '(0.9, 0.98)' --keep-last-epochs 10 \ -a lightconv_iwslt_de_en --save-dir $SAVE \ @@ -137,10 +137,10 @@ python -m torch.distributed.launch --nproc_per_node 8 $(which fairseq-train) \ --max-update 30000 --share-all-embeddings --optimizer adam \ --adam-betas '(0.9, 0.98)' --clip-norm 0.0 --weight-decay 0.0 \ --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \ - --min-lr 1e-09 --update-freq 16 --attention-dropout 0.1 --keep-last-epochs 10 \ - --ddp-backend=no_c10d --max-tokens 3584 \ + --stop-min-lr 1e-09 --update-freq 16 --attention-dropout 0.1 --keep-last-epochs 10 \ + --ddp-backend=legacy_ddp --max-tokens 3584 \ --lr-scheduler cosine --warmup-init-lr 1e-7 --warmup-updates 10000 \ - --lr-shrink 1 --max-lr 0.001 --lr 1e-7 --min-lr 1e-9 --warmup-init-lr 1e-07 \ + --lr-shrink 1 --lr 0.001 --min-lr 1e-7 --warmup-init-lr 1e-07 \ --t-mult 1 --lr-period-updates 20000 \ --arch lightconv_wmt_en_de_big --save-dir $SAVE \ --dropout 0.3 --attention-dropout 0.1 --weight-dropout 0.1 \ @@ -162,10 +162,10 @@ python -m torch.distributed.launch --nproc_per_node 8 $(which fairseq-train) \ --max-update 30000 --share-all-embeddings --optimizer adam \ --adam-betas '(0.9, 0.98)' --clip-norm 0.0 --weight-decay 0.0 \ --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \ - --min-lr 1e-09 --update-freq 16 --attention-dropout 0.1 --keep-last-epochs 10 \ - --ddp-backend=no_c10d --max-tokens 3584 \ + --stop-min-lr 1e-09 --update-freq 16 --attention-dropout 0.1 --keep-last-epochs 10 \ + --ddp-backend=legacy_ddp --max-tokens 3584 \ --lr-scheduler cosine --warmup-init-lr 1e-7 --warmup-updates 10000 \ - --lr-shrink 1 --max-lr 0.001 --lr 1e-7 --min-lr 1e-9 --warmup-init-lr 1e-07 \ + --lr-shrink 1 --lr 0.001 --min-lr 1e-7 --warmup-init-lr 1e-07 \ --t-mult 1 --lr-period-updates 70000 \ --arch lightconv_wmt_en_fr_big --save-dir $SAVE \ --dropout 0.1 --attention-dropout 0.1 --weight-dropout 0.1 \ diff --git a/examples/pointer_generator/README.md b/examples/pointer_generator/README.md new file mode 100644 index 0000000000..6096570825 --- /dev/null +++ b/examples/pointer_generator/README.md @@ -0,0 +1,82 @@ +# Transformer with Pointer-Generator Network + +This page describes the `transformer_pointer_generator` model that incorporates +a pointing mechanism in the Transformer model that facilitates copying of input +words to the output. This architecture is described in [Enarvi et al. (2020)](https://www.aclweb.org/anthology/2020.nlpmc-1.4/). + +## Background + +The pointer-generator network was introduced in [See et al. (2017)](https://arxiv.org/abs/1704.04368) +for RNN encoder-decoder attention models. A similar mechanism can be +incorporated in a Transformer model by reusing one of the many attention +distributions for pointing. The attention distribution over the input words is +interpolated with the normal output distribution over the vocabulary words. This +allows the model to generate words that appear in the input, even if they don't +appear in the vocabulary, helping especially with small vocabularies. + +## Implementation + +The mechanism for copying out-of-vocabulary words from the input has been +implemented differently to See et al. In their [implementation](https://github.com/abisee/pointer-generator) +they convey the word identities through the model in order to be able to produce +words that appear in the input sequence but not in the vocabulary. A different +approach was taken in the Fairseq implementation to keep it self-contained in +the model file, avoiding any changes to the rest of the code base. Copying +out-of-vocabulary words is possible by pre-processing the input and +post-processing the output. This is described in detail in the next section. + +## Usage + +The training and evaluation procedure is outlined below. You can also find a +more detailed example for the XSum dataset on [this page](README.xsum.md). + +##### 1. Create a vocabulary and extend it with source position markers + +The pointing mechanism is especially helpful with small vocabularies, if we are +able to recover the identities of any out-of-vocabulary words that are copied +from the input. For this purpose, the model allows extending the vocabulary with +special tokens that can be used in place of `<unk>` tokens to identify different +input positions. For example, the user may add `<unk-0>`, `<unk-1>`, `<unk-2>`, +etc. to the end of the vocabulary, after the normal words. Below is an example +of how to create a vocabulary of 10000 most common words and add 1000 input +position markers. + +```bash +vocab_size=10000 +position_markers=1000 +export LC_ALL=C +cat train.src train.tgt | + tr -s '[:space:]' '\n' | + sort | + uniq -c | + sort -k1,1bnr -k2 | + head -n "$((vocab_size - 4))" | + awk '{ print $2 " " $1 }' >dict.pg.txt +python3 -c "[print('<unk-{}> 0'.format(n)) for n in range($position_markers)]" >>dict.pg.txt +``` + +##### 2. Preprocess the text data + +The idea is that any `<unk>` tokens in the text are replaced with `<unk-0>` if +it appears in the first input position, `<unk-1>` if it appears in the second +input position, and so on. This can be achieved using the `preprocess.py` script +that is provided in this directory. + +##### 3. Train a model + +The number of these special tokens is given to the model with the +`--source-position-markers` argument—the model simply maps all of these to the +same word embedding as `<unk>`. + +The attention distribution that is used for pointing is selected using the +`--alignment-heads` and `--alignment-layer` command-line arguments in the same +way as with the `transformer_align` model. + +##### 4. Generate text and postprocess it + +When using the model to generate text, you want to preprocess the input text in +the same way that training data was processed, replacing out-of-vocabulary words +with `<unk-N>` tokens. If any of these tokens are copied to the output, the +actual words can be retrieved from the unprocessed input text. Any `<unk-N>` +token should be replaced with the word at position N in the original input +sequence. This can be achieved using the `postprocess.py` script. diff --git a/examples/pointer_generator/README.xsum.md b/examples/pointer_generator/README.xsum.md new file mode 100644 index 0000000000..ac3a8c3ddc --- /dev/null +++ b/examples/pointer_generator/README.xsum.md @@ -0,0 +1,180 @@ +## Training a pointer-generator model on the Extreme Summarization dataset + +##### 1. Download the Extreme Summarization data and preprocess it + +Follow the instructions [here](https://github.com/EdinburghNLP/XSum) to obtain +the original Extreme Summarization dataset. You should have six files, +{train,validation,test}.{document,summary}. + +##### 2. Create a vocabulary and extend it with source position markers + +```bash +vocab_size=10000 +position_markers=1000 +export LC_ALL=C +cat train.document train.summary | + tr -s '[:space:]' '\n' | + sort | + uniq -c | + sort -k1,1bnr -k2 | + head -n "$((vocab_size - 4))" | + awk '{ print $2 " " $1 }' >dict.pg.txt +python3 -c "[print('<unk-{}> 0'.format(n)) for n in range($position_markers)]" >>dict.pg.txt +``` + +This creates the file dict.pg.txt that contains the 10k most frequent words, +followed by 1k source position markers: + +``` +the 4954867 +. 4157552 +, 3439668 +to 2212159 +a 1916857 +of 1916820 +and 1823350 +... +<unk-0> 0 +<unk-1> 0 +<unk-2> 0 +<unk-3> 0 +<unk-4> 0 +... +``` + +##### 2. Preprocess the text data + +```bash +./preprocess.py --source train.document --target train.summary --vocab <(cut -d' ' -f1 dict.pg.txt) --source-out train.pg.src --target-out train.pg.tgt +./preprocess.py --source validation.document --target validation.summary --vocab <(cut -d' ' -f1 dict.pg.txt) --source-out valid.pg.src --target-out valid.pg.tgt +./preprocess.py --source test.document --vocab <(cut -d' ' -f1 dict.pg.txt) --source-out test.pg.src +``` + +The data should now contain `<unk-N>` tokens in place of out-of-vocabulary words. + +##### 3. Binarize the dataset: + +```bash +fairseq-preprocess \ + --source-lang src \ + --target-lang tgt \ + --trainpref train.pg \ + --validpref valid.pg \ + --destdir bin \ + --workers 60 \ + --srcdict dict.pg.txt \ + --joined-dictionary +``` + +##### 3. Train a model + +```bash +total_updates=20000 +warmup_updates=500 +lr=0.001 +max_tokens=4096 +update_freq=4 +pointer_layer=-2 + +CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 fairseq-train bin \ + --user-dir examples/pointer_generator/pointer_generator_src \ + --max-tokens "$max_tokens" \ + --task translation \ + --source-lang src --target-lang tgt \ + --truncate-source \ + --layernorm-embedding \ + --share-all-embeddings \ + --encoder-normalize-before \ + --decoder-normalize-before \ + --required-batch-size-multiple 1 \ + --arch transformer_pointer_generator \ + --alignment-layer "$pointer_layer" \ + --alignment-heads 1 \ + --source-position-markers 1000 \ + --criterion label_smoothed_cross_entropy \ + --label-smoothing 0.1 \ + --dropout 0.1 --attention-dropout 0.1 \ + --weight-decay 0.01 --optimizer adam --adam-betas "(0.9, 0.999)" --adam-eps 1e-08 \ + --clip-norm 0.1 \ + --lr-scheduler inverse_sqrt --lr "$lr" --max-update "$total_updates" --warmup-updates "$warmup_updates" \ + --update-freq "$update_freq" \ + --skip-invalid-size-inputs-valid-test +``` + +Above we specify that our dictionary contains 1000 source position markers, and +that we want to use one attention head from the penultimate decoder layer for +pointing. It should run in 5.5 hours on one node with eight 32GB V100 GPUs. The +logged messages confirm that dictionary indices above 10000 will be mapped to +the `<unk>` embedding: + +``` +2020-09-24 20:43:53 | INFO | fairseq.tasks.translation | [src] dictionary: 11000 types +2020-09-24 20:43:53 | INFO | fairseq.tasks.translation | [tgt] dictionary: 11000 types +2020-09-24 20:43:53 | INFO | fairseq.data.data_utils | loaded 11332 examples from: bin/valid.src-tgt.src +2020-09-24 20:43:53 | INFO | fairseq.data.data_utils | loaded 11332 examples from: bin/valid.src-tgt.tgt +2020-09-24 20:43:53 | INFO | fairseq.tasks.translation | bin valid src-tgt 11332 examples +2020-09-24 20:43:53 | INFO | fairseq.models.transformer_pg | dictionary indices from 10000 to 10999 will be mapped to 3 +``` + +##### 4. Summarize the test sequences + +```bash +batch_size=32 +beam_size=6 +max_length=60 +length_penalty=1.0 + +fairseq-interactive bin \ + --user-dir examples/pointer_generator/pointer_generator_src \ + --batch-size "$batch_size" \ + --task translation \ + --source-lang src --target-lang tgt \ + --path checkpoints/checkpoint_last.pt \ + --input test.pg.src \ + --buffer-size 200 \ + --max-len-a 0 \ + --max-len-b "$max_length" \ + --lenpen "$length_penalty" \ + --beam "$beam_size" \ + --skip-invalid-size-inputs-valid-test | + tee generate.out +grep ^H generate.out | cut -f 3- >generate.hyp +``` + +Now you should have the generated sequences in `generate.hyp`. They contain +`<unk-N>` tokens that the model has copied from the source sequence. In order to +retrieve the original words, we need the unprocessed source sequences from +`test.document`. + +##### 5. Process the generated output + +Since we skipped too long inputs when producing `generate.hyp`, we also have to +skip too long sequences now that we read `test.document`. + +```bash +./postprocess.py \ + --source <(awk 'NF<1024' test.document) \ + --target generate.hyp \ + --target-out generate.hyp.processed +``` + +Now you'll find the final sequences from `generate.hyp.processed`, with +`<unk-N>` replaced with the original word from the source sequence. + +##### An example of a summarized sequence + +The original source document in `test.document`: + +> de roon moved to teesside in june 2016 for an initial # 8.8 m fee and played 33 premier league games last term . the netherlands international , 26 , scored five goals in 36 league and cup games during his spell at boro . meanwhile , manager garry monk confirmed the championship club 's interest in signing chelsea midfielder lewis baker . `` he 's a target and one of many that we 've had throughout the summer months , '' said monk . find all the latest football transfers on our dedicated page . + +The preprocessed source document in `test.src.pg`: + +> de \<unk-1> moved to \<unk-4> in june 2016 for an initial # \<unk-12> m fee and played 33 premier league games last term . the netherlands international , 26 , scored five goals in 36 league and cup games during his spell at boro . meanwhile , manager garry monk confirmed the championship club 's interest in signing chelsea midfielder lewis baker . `` he 's a target and one of many that we 've had throughout the summer months , '' said monk . find all the latest football transfers on our dedicated page . + +The generated summary in `generate.hyp`: + +> middlesbrough striker \<unk> de \<unk-1> has joined spanish side \<unk> on a season-long loan . + +The generated summary after postprocessing in `generate.hyp.processed`: + +> middlesbrough striker \<unk> de roon has joined spanish side \<unk> on a season-long loan . diff --git a/examples/pointer_generator/pointer_generator_src/__init__.py b/examples/pointer_generator/pointer_generator_src/__init__.py new file mode 100644 index 0000000000..c361ff6bd6 --- /dev/null +++ b/examples/pointer_generator/pointer_generator_src/__init__.py @@ -0,0 +1,6 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from . import transformer_pg # noqa diff --git a/examples/pointer_generator/pointer_generator_src/transformer_pg.py b/examples/pointer_generator/pointer_generator_src/transformer_pg.py new file mode 100644 index 0000000000..4ccf30f4eb --- /dev/null +++ b/examples/pointer_generator/pointer_generator_src/transformer_pg.py @@ -0,0 +1,518 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +from typing import Any, Dict, Optional, List, Tuple + +import torch +import torch.nn as nn +from fairseq import utils +from fairseq.models import register_model, register_model_architecture +from fairseq.models.transformer import ( + DEFAULT_MAX_SOURCE_POSITIONS, + DEFAULT_MAX_TARGET_POSITIONS, + TransformerDecoder, + TransformerEncoder, + TransformerModel, + base_architecture, +) +from torch import Tensor + + +logger = logging.getLogger(__name__) + + +@register_model("transformer_pointer_generator") +class TransformerPointerGeneratorModel(TransformerModel): + """ + Transformer model from `"Attention Is All You Need" (Vaswani et al, 2017) + <https://arxiv.org/abs/1706.03762>`_, augmented with a pointer-generator + network from `"Get To The Point: Summarization with Pointer-Generator + Networks" (See et al, 2017) <https://arxiv.org/abs/1704.04368>`_. + + Args: + encoder (TransformerPointerGeneratorEncoder): the encoder + decoder (TransformerPointerGeneratorDecoder): the decoder + + The Transformer pointer-generator model provides the following named + architectures and command-line arguments: + + .. argparse:: + :ref: fairseq.models.transformer_pointer_generator_parser + :prog: + """ + + @staticmethod + def add_args(parser): + """Add model-specific arguments to the parser.""" + # fmt: off + TransformerModel.add_args(parser) + parser.add_argument('--alignment-heads', type=int, metavar='N', + help='number of attention heads to be used for ' + 'pointing') + parser.add_argument('--alignment-layer', type=int, metavar='I', + help='layer number to be used for pointing (0 ' + 'corresponding to the bottommost layer)') + parser.add_argument('--source-position-markers', type=int, metavar='N', + help='dictionary includes N additional items that ' + 'represent an OOV token at a particular input ' + 'position') + parser.add_argument('--force-generation', type=float, metavar='P', + default=None, + help='set the vocabulary distribution weight to P, ' + 'instead of predicting it from the input (1.0 ' + 'corresponding to generation, 0.0 to pointing)') + # fmt: on + + @classmethod + def build_model(cls, args, task): + """Build a new model instance.""" + + # make sure all arguments are present in older models + base_architecture(args) + + if args.encoder_layers_to_keep: + args.encoder_layers = len(args.encoder_layers_to_keep.split(",")) + if args.decoder_layers_to_keep: + args.decoder_layers = len(args.decoder_layers_to_keep.split(",")) + + if getattr(args, "max_source_positions", None) is None: + args.max_source_positions = DEFAULT_MAX_SOURCE_POSITIONS + if getattr(args, "max_target_positions", None) is None: + args.max_target_positions = DEFAULT_MAX_TARGET_POSITIONS + if getattr(args, "source_position_markers", None) is None: + args.source_position_markers = args.max_source_positions + + src_dict, tgt_dict = task.source_dictionary, task.target_dictionary + if src_dict != tgt_dict: + raise ValueError("Pointer-generator requires a joined dictionary") + + def build_embedding(dictionary, embed_dim, path=None): + # The dictionary may include additional items that can be used in + # place of the normal OOV token and that all map to the same + # embedding. Using a different token for each input position allows + # one to restore the word identities from the original source text. + num_embeddings = len(dictionary) - args.source_position_markers + padding_idx = dictionary.pad() + unk_idx = dictionary.unk() + logger.info( + "dictionary indices from {0} to {1} will be mapped to {2}".format( + num_embeddings, len(dictionary) - 1, unk_idx + ) + ) + emb = Embedding(num_embeddings, embed_dim, padding_idx, unk_idx) + # if provided, load from preloaded dictionaries + if path: + embed_dict = utils.parse_embedding(path) + utils.load_embedding(embed_dict, dictionary, emb) + return emb + + if args.share_all_embeddings: + if args.encoder_embed_dim != args.decoder_embed_dim: + raise ValueError( + "--share-all-embeddings requires --encoder-embed-dim to match --decoder-embed-dim" + ) + if args.decoder_embed_path and ( + args.decoder_embed_path != args.encoder_embed_path + ): + raise ValueError( + "--share-all-embeddings not compatible with --decoder-embed-path" + ) + encoder_embed_tokens = build_embedding( + src_dict, args.encoder_embed_dim, args.encoder_embed_path + ) + decoder_embed_tokens = encoder_embed_tokens + args.share_decoder_input_output_embed = True + else: + encoder_embed_tokens = build_embedding( + src_dict, args.encoder_embed_dim, args.encoder_embed_path + ) + decoder_embed_tokens = build_embedding( + tgt_dict, args.decoder_embed_dim, args.decoder_embed_path + ) + + encoder = cls.build_encoder(args, src_dict, encoder_embed_tokens) + decoder = cls.build_decoder(args, tgt_dict, decoder_embed_tokens) + return cls(args, encoder, decoder) + + @classmethod + def build_encoder(cls, args, src_dict, embed_tokens): + return TransformerPointerGeneratorEncoder(args, src_dict, embed_tokens) + + @classmethod + def build_decoder(cls, args, tgt_dict, embed_tokens): + return TransformerPointerGeneratorDecoder(args, tgt_dict, embed_tokens) + + +class TransformerPointerGeneratorEncoder(TransformerEncoder): + """ + Transformer encoder consisting of *args.encoder_layers* layers. Each layer + is a :class:`TransformerEncoderLayer`. The pointer-generator variant adds + the source tokens to the encoder output as these are otherwise not passed + to the decoder. + """ + + def forward( + self, + src_tokens, + src_lengths: Optional[Tensor] = None, + return_all_hiddens: bool = False, + token_embeddings: Optional[Tensor] = None + ): + """ + Runs the `forward()` method of the parent Transformer class. Then adds + the source tokens into the encoder output tuple. + + While it might be more elegant that the model would pass the source + tokens to the `forward()` method of the decoder too, this would require + changes to `SequenceGenerator`. + + Args: + src_tokens (torch.LongTensor): tokens in the source language of + shape `(batch, src_len)` + src_lengths (torch.LongTensor): lengths of each source sentence of + shape `(batch)` + return_all_hiddens (bool, optional): also return all of the + intermediate hidden states (default: False). + token_embeddings (torch.Tensor, optional): precomputed embeddings + default `None` will recompute embeddings + + Returns: + namedtuple: + - **encoder_out** (Tensor): the last encoder layer's output of + shape `(src_len, batch, embed_dim)` + - **encoder_padding_mask** (ByteTensor): the positions of + padding elements of shape `(batch, src_len)` + - **encoder_embedding** (Tensor): the (scaled) embedding lookup + of shape `(batch, src_len, embed_dim)` + - **encoder_states** (List[Tensor]): all intermediate + hidden states of shape `(src_len, batch, embed_dim)`. + Only populated if *return_all_hiddens* is True. + - **src_tokens** (Tensor): input token ids of shape + `(batch, src_len)` + """ + encoder_out = self.forward_scriptable(src_tokens, + src_lengths, + return_all_hiddens, + token_embeddings) + + # The Pytorch Mobile lite interpreter does not supports returning NamedTuple in + # `forward` so we use a dictionary instead. + # TorchScript does not support mixed values so the values are all lists. + # The empty list is equivalent to None. + return { + "encoder_out": encoder_out["encoder_out"], # T x B x C + "encoder_padding_mask": encoder_out["encoder_padding_mask"], # B x T + "encoder_embedding": encoder_out["encoder_embedding"], # B x T x C + "encoder_states": encoder_out["encoder_states"], # List[T x B x C] + "src_tokens": [src_tokens], # B x T + "src_lengths": [], + } + + +class TransformerPointerGeneratorDecoder(TransformerDecoder): + """ + Transformer decoder consisting of *args.decoder_layers* layers. Each layer + is a :class:`TransformerDecoderLayer`. The pointer-generator variant mixes + the output probabilities with an attention distribution in the output layer. + + Args: + args (argparse.Namespace): parsed command-line arguments + dictionary (~fairseq.data.Dictionary): decoding dictionary + embed_tokens (torch.nn.Embedding): output embedding + """ + + def __init__(self, args, dictionary, embed_tokens): + super().__init__(args, dictionary, embed_tokens, no_encoder_attn=False) + + # In the pointer-generator model these arguments define the decoder + # layer and the number of attention heads that will be averaged to + # create the alignment for pointing. + self.alignment_heads = args.alignment_heads + self.alignment_layer = args.alignment_layer + + input_embed_dim = embed_tokens.embedding_dim + + # Generation probabilities / interpolation coefficients are predicted + # from the current decoder input embedding and the decoder output, which + # is the size of output_embed_dim. + p_gen_input_size = input_embed_dim + self.output_embed_dim + self.project_p_gens = nn.Linear(p_gen_input_size, 1) + nn.init.zeros_(self.project_p_gens.bias) + + # The dictionary may include a separate entry for an OOV token in each + # input position, so that their identity can be restored from the + # original source text. + self.num_types = len(dictionary) + self.num_oov_types = args.source_position_markers + self.num_embeddings = self.num_types - self.num_oov_types + self.force_p_gen = args.force_generation + + def forward( + self, + prev_output_tokens, + encoder_out: Optional[Dict[str, List[Tensor]]] = None, + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, + features_only: bool = False, + alignment_layer: Optional[int] = 0, + alignment_heads: Optional[int] = 1, + src_lengths: Optional[Any] = None, + return_all_hiddens: bool = False, + ): + """ + Args: + prev_output_tokens (LongTensor): previous decoder outputs of shape + `(batch, tgt_len)`, for teacher forcing + encoder_out (optional): output from the encoder, used for + encoder-side attention + incremental_state (dict, optional): dictionary used for storing + state during :ref:`Incremental decoding` + features_only (bool, optional): only return features without + applying output layer (default: False) + alignment_layer (int, optional): 0-based index of the layer to be + used for pointing (default: 0) + alignment_heads (int, optional): number of attention heads to be + used for pointing (default: 1) + + Returns: + tuple: + - the decoder's output of shape `(batch, tgt_len, vocab)` + - a dictionary with any model-specific outputs + """ + # The normal Transformer model doesn't pass the alignment_layer and + # alignment_heads parameters correctly. We use our local variables. + x, extra = self.extract_features( + prev_output_tokens, + encoder_out=encoder_out, + incremental_state=incremental_state, + alignment_layer=self.alignment_layer, + alignment_heads=self.alignment_heads, + ) + if not features_only: + # Embedding the tokens again for generation probability prediction, + # so that we don't have to reimplement the whole extract_features() + # method. + if incremental_state is not None: + prev_output_tokens = prev_output_tokens[:, -1:] + prev_output_embed = self.embed_tokens(prev_output_tokens) + prev_output_embed *= self.embed_scale + predictors = torch.cat((prev_output_embed, x), 2) + p_gens = self.project_p_gens(predictors) + p_gens = torch.sigmoid(p_gens.float()) + # Torchscript complains if encoder_out or attn are None because + # `output_layer()` signature expects tensors instead + attn: Optional[Tensor] = extra["attn"][0] + assert encoder_out is not None + assert attn is not None + x = self.output_layer(x, attn, encoder_out["src_tokens"][0], p_gens) + return x, extra + + def output_layer( + self, + features: Tensor, + attn: Tensor, + src_tokens: Tensor, + p_gens: Tensor + ) -> Tensor: + """ + Project features to the vocabulary size and mix with the attention + distributions. + """ + if self.force_p_gen is not None: + p_gens = self.force_p_gen + + # project back to size of vocabulary + if self.adaptive_softmax is None: + logits = self.output_projection(features) + else: + logits = features + + batch_size = logits.shape[0] + output_length = logits.shape[1] + assert logits.shape[2] == self.num_embeddings + assert src_tokens.shape[0] == batch_size + src_length = src_tokens.shape[1] + + # The final output distribution will be a mixture of the normal output + # distribution (softmax of logits) and attention weights. + gen_dists = self.get_normalized_probs_scriptable( + (logits, None), log_probs=False, sample=None + ) + gen_dists = torch.mul(gen_dists, p_gens) + padding_size = (batch_size, output_length, self.num_oov_types) + padding = gen_dists.new_zeros(padding_size) + gen_dists = torch.cat((gen_dists, padding), 2) + assert gen_dists.shape[2] == self.num_types + + # Scatter attention distributions to distributions over the extended + # vocabulary in a tensor of shape [batch_size, output_length, + # vocab_size]. Each attention weight will be written into a location + # that is for other dimensions the same as in the index tensor, but for + # the third dimension it's the value of the index tensor (the token ID). + attn = torch.mul(attn.float(), 1 - p_gens) + index = src_tokens[:, None, :] + index = index.expand(batch_size, output_length, src_length) + attn_dists_size = (batch_size, output_length, self.num_types) + attn_dists = attn.new_zeros(attn_dists_size) + attn_dists.scatter_add_(2, index, attn.float()) + + # Final distributions, [batch_size, output_length, num_types]. + return gen_dists + attn_dists + + def get_normalized_probs( + self, + net_output: Tuple[Tensor, Optional[Dict[str, List[Optional[Tensor]]]]], + log_probs: bool, + sample: Optional[Dict[str, Tensor]] = None, + ): + """ + Get normalized probabilities (or log probs) from a net's output. + Pointer-generator network output is already normalized. + """ + probs = net_output[0] + # Make sure the probabilities are greater than zero when returning log + # probabilities. + return probs.clamp(1e-10, 1.0).log() if log_probs else probs + + +class Embedding(nn.Embedding): + r"""A simple lookup table that stores embeddings of a fixed dictionary and size. + This module is often used to store word embeddings and retrieve them using indices. + The input to the module is a list of indices, and the output is the corresponding + word embeddings. This subclass differs from the standard PyTorch Embedding class by + allowing additional vocabulary entries that will be mapped to the unknown token + embedding. + Args: + num_embeddings (int): size of the dictionary of embeddings + embedding_dim (int): the size of each embedding vector + padding_idx (int): Pads the output with the embedding vector at :attr:`padding_idx` + (initialized to zeros) whenever it encounters the index. + unk_idx (int): Maps all token indices that are greater than or equal to + num_embeddings to this index. + Attributes: + weight (Tensor): the learnable weights of the module of shape (num_embeddings, embedding_dim) + initialized from :math:`\mathcal{N}(0, 1)` + Shape: + - Input: :math:`(*)`, LongTensor of arbitrary shape containing the indices to extract + - Output: :math:`(*, H)`, where `*` is the input shape and :math:`H=\text{embedding\_dim}` + .. note:: + Keep in mind that only a limited number of optimizers support + sparse gradients: currently it's :class:`optim.SGD` (`CUDA` and `CPU`), + :class:`optim.SparseAdam` (`CUDA` and `CPU`) and :class:`optim.Adagrad` (`CPU`) + .. note:: + With :attr:`padding_idx` set, the embedding vector at + :attr:`padding_idx` is initialized to all zeros. However, note that this + vector can be modified afterwards, e.g., using a customized + initialization method, and thus changing the vector used to pad the + output. The gradient for this vector from :class:`~torch.nn.Embedding` + is always zero. + """ + __constants__ = ["unk_idx"] + + # Torchscript: Inheriting from Embedding class produces an error when exporting to Torchscript + # -> RuntimeError: Unable to cast Python instance to C++ type (compile in debug mode for details + # It's happening because max_norm attribute from nn.Embedding is None by default and it cannot be + # cast to a C++ type + def __init__( + self, + num_embeddings: int, + embedding_dim: int, + padding_idx: Optional[int], + unk_idx: int, + max_norm: Optional[float] = float("inf"), + ): + super().__init__(num_embeddings, embedding_dim, padding_idx=padding_idx, max_norm=max_norm) + self.unk_idx = unk_idx + nn.init.normal_(self.weight, mean=0, std=embedding_dim ** -0.5) + nn.init.constant_(self.weight[padding_idx], 0) + + def forward(self, input): + input = torch.where( + input >= self.num_embeddings, torch.ones_like(input) * self.unk_idx, input + ) + return nn.functional.embedding( + input, self.weight, self.padding_idx, self.max_norm, + self.norm_type, self.scale_grad_by_freq, self.sparse + ) + + +@register_model_architecture( + "transformer_pointer_generator", "transformer_pointer_generator" +) +def transformer_pointer_generator(args): + args.alignment_heads = getattr(args, "alignment_heads", 1) + args.alignment_layer = getattr(args, "alignment_layer", -1) + base_architecture(args) + if args.alignment_layer < 0: + args.alignment_layer = args.decoder_layers + args.alignment_layer + + +@register_model_architecture( + "transformer_pointer_generator", "transformer_pointer_generator_iwslt_de_en" +) +def transformer_pointer_generator_iwslt_de_en(args): + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 1024) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 4) + args.encoder_layers = getattr(args, "encoder_layers", 6) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512) + args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 1024) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 4) + args.decoder_layers = getattr(args, "decoder_layers", 6) + transformer_pointer_generator(args) + + +@register_model_architecture( + "transformer_pointer_generator", "transformer_pointer_generator_wmt_en_de" +) +def transformer_pointer_generator_wmt_en_de(args): + transformer_pointer_generator(args) + + +# Transformer pointer-generator with the base Transformer parameters as used in +# the "Attention Is All You Need" paper (Vaswani et al., 2017) +@register_model_architecture( + "transformer_pointer_generator", + "transformer_pointer_generator_vaswani_wmt_en_de_big", +) +def transformer_pointer_generator_vaswani_wmt_en_de_big(args): + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4096) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 16) + args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 1024) + args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 4096) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 16) + args.dropout = getattr(args, "dropout", 0.3) + transformer_pointer_generator(args) + + +@register_model_architecture( + "transformer_pointer_generator", + "transformer_pointer_generator_vaswani_wmt_en_fr_big", +) +def transformer_pointer_generator_vaswani_wmt_en_fr_big(args): + args.dropout = getattr(args, "dropout", 0.1) + transformer_pointer_generator_vaswani_wmt_en_de_big(args) + + +@register_model_architecture( + "transformer_pointer_generator", "transformer_pointer_generator_wmt_en_de_big" +) +def transformer_pointer_generator_wmt_en_de_big(args): + args.attention_dropout = getattr(args, "attention_dropout", 0.1) + transformer_pointer_generator_vaswani_wmt_en_de_big(args) + + +# default parameters used in tensor2tensor implementation +@register_model_architecture( + "transformer_pointer_generator", "transformer_pointer_generator_wmt_en_de_big_t2t" +) +def transformer_pointer_generator_wmt_en_de_big_t2t(args): + args.encoder_normalize_before = getattr(args, "encoder_normalize_before", True) + args.decoder_normalize_before = getattr(args, "decoder_normalize_before", True) + args.attention_dropout = getattr(args, "attention_dropout", 0.1) + args.activation_dropout = getattr(args, "activation_dropout", 0.1) + transformer_pointer_generator_vaswani_wmt_en_de_big(args) diff --git a/examples/pointer_generator/postprocess.py b/examples/pointer_generator/postprocess.py new file mode 100755 index 0000000000..b213aed80f --- /dev/null +++ b/examples/pointer_generator/postprocess.py @@ -0,0 +1,96 @@ +#!/usr/bin/env python3 +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import re +import sys + + +class OOVIndexError(IndexError): + def __init__(self, pos, source_seq, target_seq): + super(OOVIndexError, self).__init__( + "A <unk-N> tag in the target sequence refers to a position that is " + "outside the source sequence. Most likely there was a mismatch in " + "provided source and target sequences. Otherwise this would mean that " + "the pointing mechanism somehow attended to a position that is past " + "the actual sequence end." + ) + self.source_pos = pos + self.source_seq = source_seq + self.target_seq = target_seq + + +def replace_oovs(source_in, target_in, target_out): + """Replaces <unk-N> tokens in the target text with the corresponding word in + the source text. + """ + + oov_re = re.compile("^<unk-([0-9]+)>$") + + for source_seq, target_seq in zip(source_in, target_in): + target_seq_out = [] + + pos_to_word = source_seq.strip().split() + for token in target_seq.strip().split(): + m = oov_re.match(token) + if m: + pos = int(m.group(1)) + if pos >= len(pos_to_word): + raise OOVIndexError(pos, source_seq, target_seq) + token_out = pos_to_word[pos] + else: + token_out = token + target_seq_out.append(token_out) + target_out.write(" ".join(target_seq_out) + "\n") + + +def main(): + parser = argparse.ArgumentParser( + description="Replaces <unk-N> tokens in target sequences with words from " + "the corresponding position in the source sequence." + ) + parser.add_argument( + "--source", type=str, help="text file with source sequences", required=True + ) + parser.add_argument( + "--target", type=str, help="text file with target sequences", required=True + ) + parser.add_argument( + "--target-out", + type=str, + help="where to write target sequences without <unk-N> " "entries", + required=True, + ) + args = parser.parse_args() + + target_in = ( + open(args.target, "r", encoding="utf-8") if args.target is not None else None + ) + target_out = ( + open(args.target_out, "w", encoding="utf-8") + if args.target_out is not None + else None + ) + with open(args.source, "r", encoding="utf-8") as source_in, open( + args.target, "r", encoding="utf-8" + ) as target_in, open(args.target_out, "w", encoding="utf-8") as target_out: + replace_oovs(source_in, target_in, target_out) + + +if __name__ == "__main__": + try: + main() + except OOVIndexError as e: + print(e, file=sys.stderr) + print("Source sequence:", e.source_seq.strip(), file=sys.stderr) + print("Target sequence:", e.target_seq.strip(), file=sys.stderr) + print( + "Source sequence length:", + len(e.source_seq.strip().split()), + file=sys.stderr, + ) + print("The offending tag points to:", e.source_pos) + sys.exit(2) diff --git a/examples/pointer_generator/preprocess.py b/examples/pointer_generator/preprocess.py new file mode 100755 index 0000000000..f72ca7d3d9 --- /dev/null +++ b/examples/pointer_generator/preprocess.py @@ -0,0 +1,102 @@ +#!/usr/bin/env python3 +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +from itertools import zip_longest + + +def replace_oovs(source_in, target_in, vocabulary, source_out, target_out): + """Replaces out-of-vocabulary words in source and target text with <unk-N>, + where N in is the position of the word in the source sequence. + """ + + def format_unk(pos): + return "<unk-{}>".format(pos) + + if target_in is None: + target_in = [] + + for seq_num, (source_seq, target_seq) in enumerate( + zip_longest(source_in, target_in) + ): + source_seq_out = [] + target_seq_out = [] + + word_to_pos = dict() + for position, token in enumerate(source_seq.strip().split()): + if token in vocabulary: + token_out = token + else: + if token in word_to_pos: + oov_pos = word_to_pos[token] + else: + word_to_pos[token] = position + oov_pos = position + token_out = format_unk(oov_pos) + source_seq_out.append(token_out) + source_out.write(" ".join(source_seq_out) + "\n") + + if target_seq is not None: + for token in target_seq.strip().split(): + if token in word_to_pos: + token_out = format_unk(word_to_pos[token]) + else: + token_out = token + target_seq_out.append(token_out) + if target_out is not None: + target_out.write(" ".join(target_seq_out) + "\n") + + +def main(): + parser = argparse.ArgumentParser( + description="Replaces out-of-vocabulary words in both source and target " + "sequences with tokens that indicate the position of the word " + "in the source sequence." + ) + parser.add_argument( + "--source", type=str, help="text file with source sequences", required=True + ) + parser.add_argument( + "--target", type=str, help="text file with target sequences", default=None + ) + parser.add_argument("--vocab", type=str, help="vocabulary file", required=True) + parser.add_argument( + "--source-out", + type=str, + help="where to write source sequences with <unk-N> entries", + required=True, + ) + parser.add_argument( + "--target-out", + type=str, + help="where to write target sequences with <unk-N> entries", + default=None, + ) + args = parser.parse_args() + + with open(args.vocab, encoding="utf-8") as vocab: + vocabulary = vocab.read().splitlines() + + target_in = ( + open(args.target, "r", encoding="utf-8") if args.target is not None else None + ) + target_out = ( + open(args.target_out, "w", encoding="utf-8") + if args.target_out is not None + else None + ) + with open(args.source, "r", encoding="utf-8") as source_in, open( + args.source_out, "w", encoding="utf-8" + ) as source_out: + replace_oovs(source_in, target_in, vocabulary, source_out, target_out) + if target_in is not None: + target_in.close() + if target_out is not None: + target_out.close() + + +if __name__ == "__main__": + main() diff --git a/examples/quant_noise/README.md b/examples/quant_noise/README.md index 3341b98886..a04d7e4e8a 100644 --- a/examples/quant_noise/README.md +++ b/examples/quant_noise/README.md @@ -1,4 +1,4 @@ -# Training with Quantization Noise for Extreme Model Compression ({Fan\*, Stock\*} *et al.*, 2019) +# Training with Quantization Noise for Extreme Model Compression ({Fan\*, Stock\*} *et al.*, 2020) This page contains information for how to train and quantize models with Quantization Noise, for both scalar quantization like `int8` and Iterative Product Quantization. Check out our paper [here](https://arxiv.org/abs/2004.07320). @@ -33,7 +33,7 @@ Unlike the section [Iterative Product Quantization](#iterative-product-quantizat #### Training -Scalar quantization with Quant-Noise consists in randomly quantizing a proportion `p` of the weights during training. Scalar quantization is implemented [here](https://github.com/pytorch/fairseq/tree/master/fairseq/modules/quantization/scalar) under the form of Fake Quantization, meaning that we emulate int8 on GPU by quantizing and de-quantizing both the weights and the activations. We rely on PyTorch's [quantization primitives](https://github.com/pytorch/pytorch/tree/master/torch/quantization). +Scalar quantization with Quant-Noise consists in randomly quantizing a proportion `p` of the weights during training. Scalar quantization is implemented [here](https://github.com/pytorch/fairseq/tree/main/fairseq/modules/quantization/scalar) under the form of Fake Quantization, meaning that we emulate int8 on GPU by quantizing and de-quantizing both the weights and the activations. We rely on PyTorch's [quantization primitives](https://github.com/pytorch/pytorch/tree/master/torch/quantization). To train a model with Quant-Noise, add the following flag: ``` @@ -49,7 +49,7 @@ When evaluating a network, all quantized modules and activation hooks automatica #### Integration with your own code Looking to quantize your own models with Quant-Noise + Scalar Quantization? -- Use the function `quantize_model_` implemented [here](https://github.com/pytorch/fairseq/tree/master/fairseq/modules/quantization/scalar/utils) to (1) replace all your modules by their quantized counterparts and (2) add hooks to those modules to quantize the activations. +- Use the function `quantize_model_` implemented [here](https://github.com/pytorch/fairseq/tree/main/fairseq/modules/quantization/scalar/utils.py) to (1) replace all your modules by their quantized counterparts and (2) add hooks to those modules to quantize the activations. - Then, perform your training as usual. Note that in `eval()` mode, the network is always fully quantized (weights and activations) by default (`p=1`). @@ -66,12 +66,12 @@ To train a model with Quant-Noise, add the following flags: --quant-noise-pq 0.1 --quant-noise-pq-block-size 8 ``` `quant-noise-pq` controls how much dropout is applied to the blocks of the weight matrix. `quant-noise-pq-block-size` controls the size of the weight matrix blocks. -We recommend training with 0.05 to 0.2 Quant-Noise, a value that worked well in our experiments. For the block-size, we recommend training with block-size of 8. Note that the block size must be a multiple of `input_features`, see the size checks [here](https://github.com/pytorch/fairseq/tree/master/fairseq/modules/quant_noise.py). Large block sizes result in higher compression ratio but may induce a loss in accuracy. +We recommend training with 0.05 to 0.2 Quant-Noise, a value that worked well in our experiments. For the block-size, we recommend training with block-size of 8. Note that the block size must be a multiple of `input_features`, see the size checks [here](https://github.com/pytorch/fairseq/tree/main/fairseq/modules/quant_noise.py). Large block sizes result in higher compression ratio but may induce a loss in accuracy. -We currently support training Transformer based models, such as sequence-to-sequence, language models, and BERT architectures. The `quant_noise` function [here](https://github.com/pytorch/fairseq/tree/master/fairseq/modules/quant_noise.py) wraps a module. It splits a weight matrix into blocks and applies random dropout to these blocks. +We currently support training Transformer based models, such as sequence-to-sequence, language models, and BERT architectures. The `quant_noise` function [here](https://github.com/pytorch/fairseq/tree/main/fairseq/modules/quant_noise.py) wraps a module. It splits a weight matrix into blocks and applies random dropout to these blocks. In the Transformer architectures, quant-noise is applied to the input and output embeddings, the attention, and the FFN. -Quant-Noise can also be combined with **LayerDrop** (see [here](https://github.com/pytorch/fairseq/tree/master/examples/layerdrop)) to add its pruning effect to the quantized model and make the model even smaller. We recommend training with LayerDrop 0.1 or 0.2. +Quant-Noise can also be combined with **LayerDrop** (see [here](https://github.com/pytorch/fairseq/tree/main/examples/layerdrop)) to add its pruning effect to the quantized model and make the model even smaller. We recommend training with LayerDrop 0.1 or 0.2. #### Quantization @@ -84,8 +84,8 @@ For the particular case of PQ, quantization is made sequentially. We recommend f #### Integration with your own code Looking to quantize your own models with Quant-Noise + iPQ? -- First wrap your modules with the `quant_noise` function [here](https://github.com/pytorch/fairseq/tree/master/fairseq/modules/quant_noise.py), which is module-agnostic and train your favorite model. -- Then, quantize your trained model using the code [here](https://github.com/pytorch/fairseq/tree/master/fairseq/modules/quantization/pq). This can be done *without any changes to your training loop*. Below is an example code for integration. +- First wrap your modules with the `quant_noise` function [here](https://github.com/pytorch/fairseq/tree/main/fairseq/modules/quant_noise.py), which is module-agnostic and train your favorite model. +- Then, quantize your trained model using the code [here](https://github.com/pytorch/fairseq/tree/main/fairseq/modules/quantization/pq). This can be done *without any changes to your training loop*. Below is an example code for integration. Note that we tried our approach only on Transformers and various Convolutional Models such as EfficientNets. ```python @@ -128,7 +128,7 @@ We detail below how to reproduce the state-of-the-art results in reported in the ### Training with Quant-Noise -To **train** RoBERTa + QuantNoise, we followed this setting [here](https://github.com/pytorch/fairseq/tree/master/examples/roberta). +To **train** RoBERTa + QuantNoise, we followed this setting [here](https://github.com/pytorch/fairseq/tree/main/examples/roberta). The following command can be used to train a RoBERTa Base + QuantNoise model: ```bash @@ -151,14 +151,14 @@ fairseq-train $DATA_DIR \ --warmup-updates $WARMUP_UPDATES --total-num-update $TOTAL_UPDATES \ --dropout 0.1 --attention-dropout 0.1 \ --weight-decay 0.01 \ - --max-sentences $MAX_SENTENCES \ + --batch-size $MAX_SENTENCES \ --update-freq $UPDATE_FREQ --max-update $TOTAL_UPDATES \ --save-dir checkpoint/roberta \ - --ddp-backend no_c10d --encoder-layerdrop 0.2 \ + --ddp-backend legacy_ddp --encoder-layerdrop 0.2 \ --quant-noise-pq 0.2 --quant-noise-pq-block-size 8 --untie-weights-roberta ``` -To **finetune** RoBERTa + QuantNoise, we followed this setting [here](https://github.com/pytorch/fairseq/blob/master/examples/roberta/README.glue.md). +To **finetune** RoBERTa + QuantNoise, we followed this setting [here](https://github.com/pytorch/fairseq/blob/main/examples/roberta/README.glue.md). The following command can be used to finetune a RoBERTa Base + QuantNoise model on the RTE dataset: ```bash @@ -172,7 +172,7 @@ ROBERTA_PATH=/path/to/roberta_quantnoise/model.pt fairseq-train /path/to/rte/data/ \ --restore-file $ROBERTA_PATH \ --max-positions 512 \ - --max-sentences $MAX_SENTENCES \ + --batch-size $MAX_SENTENCES \ --max-tokens 4400 \ --task sentence_prediction \ --reset-optimizer --reset-dataloader --reset-meters \ @@ -189,11 +189,11 @@ fairseq-train /path/to/rte/data/ \ --max-epoch 10 \ --find-unused-parameters \ --best-checkpoint-metric accuracy --maximize-best-checkpoint-metric \ - --ddp-backend no_c10d \ + --ddp-backend legacy_ddp \ --quant-noise-pq 0.2 --quant-noise-pq-block-size 8 ``` -To **train** Language Models on Wikitext-103, we followed this setting [here](https://github.com/pytorch/fairseq/tree/master/examples/language_model). +To **train** Language Models on Wikitext-103, we followed this setting [here](https://github.com/pytorch/fairseq/tree/main/examples/language_model). The following command can be used to train a Transformer + QuantNoise model on Wikitext-103: ```bash @@ -205,21 +205,21 @@ fairseq-train --task language_modeling /path/to/wikitext-103/data \ --arch transformer_lm_gbw \ --attention-dropout 0.1 --dropout 0.2 --relu-dropout 0.1 \ --clip-norm 0.1 --criterion adaptive_loss \ - --ddp-backend no_c10d \ + --ddp-backend legacy_ddp \ --decoder-attention-heads 8 --decoder-embed-dim 1024 --decoder-ffn-embed-dim 4096 --decoder-input-dim 1024 \ --decoder-layers 16 --decoder-normalize-before --decoder-output-dim 1024 \ - --lr 0.0001 --lr-period-updates 270000 --lr-scheduler cosine --lr-shrink 0.75 --max-lr 1.0 --t-mult 2.0 \ + --min-lr 0.0001 --lr-period-updates 270000 --lr-scheduler cosine --lr-shrink 0.75 --lr 1.0 --t-mult 2.0 \ --max-tokens 3072 --tokens-per-sample 3072 --momentum 0.99 --optimizer nag \ --sample-break-mode none --update-freq 3 \ --warmup-init-lr 1e-07 --warmup-updates 16000 \ - --weight-decay 0 --seed 1 --min-lr 1e-09 \ + --weight-decay 0 --seed 1 --stop-min-lr 1e-09 \ --quant-noise-pq 0.05 --quant-noise-pq-block-size 8 ``` To **evaluate** this model, note you need to use the `eval.py` script. The following command can be used to evaluate: ```bash -python eval_lm.py /path/to/wikitext-103/data --path /path/to/model/checkpoint \ +fairseq-eval-lm /path/to/wikitext-103/data --path /path/to/model/checkpoint \ --sample-break-mode complete \ --max-tokens 3072 \ --context-window 2560 \ @@ -242,7 +242,7 @@ fairseq-train --task sentence_prediction /path/to/data/ \ --restore-file $ROBERTA_PATH \ --save-dir checkpoints/roberta_finetuned \ --max-positions 512 \ - --max-sentences $MAX_SENTENCES \ + --batch-size $MAX_SENTENCES \ --max-tokens 4400 \ --init-token 0 --separator-token 2 \ --arch roberta_large \ @@ -252,7 +252,7 @@ fairseq-train --task sentence_prediction /path/to/data/ \ --weight-decay 0.1 --optimizer adam --adam-betas "(0.9, 0.98)" --adam-eps 1e-06 \ --clip-norm 0.0 --lr-scheduler polynomial_decay \ --fp16 --fp16-init-scale 4 --threshold-loss-scale 1 --fp16-scale-window 128 \ - --no-progress-bar --skip-invalid-size-inputs-valid-test --ddp-backend no_c10d \ + --no-progress-bar --skip-invalid-size-inputs-valid-test --ddp-backend legacy_ddp \ --quantization-config-path /path/to/config/yaml ``` @@ -266,10 +266,10 @@ fairseq-train --task language_modeling /path/to/wikitext-103/data \ --attention-dropout 0.1 --dropout 0.2 --relu-dropout 0.1 \ --bucket-cap-mb 25 --char-embedder-highway-layers 2 --character-embedding-dim 4 \ --clip-norm 0.1 --criterion adaptive_loss \ - --ddp-backend no_c10d \ + --ddp-backend legacy_ddp \ --decoder-attention-heads 8 --decoder-embed-dim 1024 --decoder-ffn-embed-dim 4096 --decoder-input-dim 1024 --decoder-layers 16 --decoder-normalize-before --decoder-output-dim 1024 \ --fp16 --keep-last-epochs -1 \ - --lr 0.0001 --lr-period-updates 270000 --lr-scheduler cosine --lr-shrink 0.75 --max-lr 0.05 --min-lr 1e-09 \ + --min-lr 0.0001 --lr-period-updates 270000 --lr-scheduler cosine --lr-shrink 0.75 --lr 0.05 --stop-min-lr 1e-09 \ --max-tokens 2944 --tokens-per-sample 2944\ --momentum 0.99 --no-epoch-checkpoints --no-progress-bar --optimizer nag --required-batch-size-multiple 8 \ --sample-break-mode none --t-mult 2.0 --skip-invalid-size-inputs-valid-test \ diff --git a/examples/roberta/README.custom_classification.md b/examples/roberta/README.custom_classification.md index 1e53ce4c18..7254bb7d17 100644 --- a/examples/roberta/README.custom_classification.md +++ b/examples/roberta/README.custom_classification.md @@ -103,10 +103,10 @@ NUM_CLASSES=2 # Number of classes for the classification task. MAX_SENTENCES=8 # Batch size. ROBERTA_PATH=/path/to/roberta.large/model.pt -CUDA_VISIBLE_DEVICES=0 python train.py IMDB-bin/ \ +CUDA_VISIBLE_DEVICES=0 fairseq-train IMDB-bin/ \ --restore-file $ROBERTA_PATH \ --max-positions 512 \ - --max-sentences $MAX_SENTENCES \ + --batch-size $MAX_SENTENCES \ --max-tokens 4400 \ --task sentence_prediction \ --reset-optimizer --reset-dataloader --reset-meters \ @@ -123,16 +123,16 @@ CUDA_VISIBLE_DEVICES=0 python train.py IMDB-bin/ \ --fp16 --fp16-init-scale 4 --threshold-loss-scale 1 --fp16-scale-window 128 \ --max-epoch 10 \ --best-checkpoint-metric accuracy --maximize-best-checkpoint-metric \ - --truncate-sequence \ + --shorten-method "truncate" \ --find-unused-parameters \ --update-freq 4 ``` The above command will finetune RoBERTa-large with an effective batch-size of 32 -sentences (`--max-sentences=8 --update-freq=4`). The expected +sentences (`--batch-size=8 --update-freq=4`). The expected `best-validation-accuracy` after 10 epochs is ~96.5%. -If you run out of GPU memory, try decreasing `--max-sentences` and increase +If you run out of GPU memory, try decreasing `--batch-size` and increase `--update-freq` to compensate. diff --git a/examples/roberta/README.glue.md b/examples/roberta/README.glue.md index d0a266b868..4f596d55af 100644 --- a/examples/roberta/README.glue.md +++ b/examples/roberta/README.glue.md @@ -17,54 +17,19 @@ Use `ALL` for preprocessing all the glue tasks. ### 3) Fine-tuning on GLUE task: Example fine-tuning cmd for `RTE` task ```bash -TOTAL_NUM_UPDATES=2036 # 10 epochs through RTE for bsz 16 -WARMUP_UPDATES=122 # 6 percent of the number of updates -LR=2e-05 # Peak LR for polynomial LR scheduler. -NUM_CLASSES=2 -MAX_SENTENCES=16 # Batch size. ROBERTA_PATH=/path/to/roberta/model.pt -CUDA_VISIBLE_DEVICES=0 python train.py RTE-bin/ \ - --restore-file $ROBERTA_PATH \ - --max-positions 512 \ - --max-sentences $MAX_SENTENCES \ - --max-tokens 4400 \ - --task sentence_prediction \ - --reset-optimizer --reset-dataloader --reset-meters \ - --required-batch-size-multiple 1 \ - --init-token 0 --separator-token 2 \ - --arch roberta_large \ - --criterion sentence_prediction \ - --num-classes $NUM_CLASSES \ - --dropout 0.1 --attention-dropout 0.1 \ - --weight-decay 0.1 --optimizer adam --adam-betas "(0.9, 0.98)" --adam-eps 1e-06 \ - --clip-norm 0.0 \ - --lr-scheduler polynomial_decay --lr $LR --total-num-update $TOTAL_NUM_UPDATES --warmup-updates $WARMUP_UPDATES \ - --fp16 --fp16-init-scale 4 --threshold-loss-scale 1 --fp16-scale-window 128 \ - --max-epoch 10 \ - --find-unused-parameters \ - --best-checkpoint-metric accuracy --maximize-best-checkpoint-metric; +CUDA_VISIBLE_DEVICES=0 fairseq-hydra-train -config-dir examples/roberta/config/finetuning --config-name rte \ +task.data=RTE-bin checkpoint.restore_file=$ROBERTA_PATH ``` -For each of the GLUE task, you will need to use following cmd-line arguments: - -Model | MNLI | QNLI | QQP | RTE | SST-2 | MRPC | CoLA | STS-B ----|---|---|---|---|---|---|---|--- -`--num-classes` | 3 | 2 | 2 | 2 | 2 | 2 | 2 | 1 -`--lr` | 1e-5 | 1e-5 | 1e-5 | 2e-5 | 1e-5 | 1e-5 | 1e-5 | 2e-5 -`--max-sentences` | 32 | 32 | 32 | 16 | 32 | 16 | 16 | 16 -`--total-num-update` | 123873 | 33112 | 113272 | 2036 | 20935 | 2296 | 5336 | 3598 -`--warmup-updates` | 7432 | 1986 | 28318 | 122 | 1256 | 137 | 320 | 214 - -For `STS-B` additionally add `--regression-target --best-checkpoint-metric loss` and remove `--maximize-best-checkpoint-metric`. +There are additional config files for each of the GLUE tasks in the examples/roberta/config/finetuning directory. **Note:** -a) `--total-num-updates` is used by `--polynomial_decay` scheduler and is calculated for `--max-epoch=10` and `--max-sentences=16/32` depending on the task. - -b) Above cmd-args and hyperparams are tested on one Nvidia `V100` GPU with `32gb` of memory for each task. Depending on the GPU memory resources available to you, you can use increase `--update-freq` and reduce `--max-sentences`. +a) Above cmd-args and hyperparams are tested on one Nvidia `V100` GPU with `32gb` of memory for each task. Depending on the GPU memory resources available to you, you can use increase `--update-freq` and reduce `--batch-size`. -c) All the settings in above table are suggested settings based on our hyperparam search within a fixed search space (for careful comparison across models). You might be able to find better metrics with wider hyperparam search. +b) All the settings in above table are suggested settings based on our hyperparam search within a fixed search space (for careful comparison across models). You might be able to find better metrics with wider hyperparam search. ### Inference on GLUE task After training the model as mentioned in previous step, you can perform inference with checkpoints in `checkpoints/` directory using following python code snippet: diff --git a/examples/roberta/README.md b/examples/roberta/README.md index fdddd5b8d2..ed4d5df52c 100644 --- a/examples/roberta/README.md +++ b/examples/roberta/README.md @@ -8,12 +8,13 @@ RoBERTa iterates on BERT's pretraining procedure, including training the model l ### What's New: +- December 2020: German model (GottBERT) is available: [GottBERT](https://github.com/pytorch/fairseq/tree/main/examples/gottbert). - January 2020: Italian model (UmBERTo) is available from Musixmatch Research: [UmBERTo](https://github.com/musixmatchresearch/umberto). -- November 2019: French model (CamemBERT) is available: [CamemBERT](https://github.com/pytorch/fairseq/tree/master/examples/camembert). -- November 2019: Multilingual encoder (XLM-RoBERTa) is available: [XLM-R](https://github.com/pytorch/fairseq/tree/master/examples/xlmr). +- November 2019: French model (CamemBERT) is available: [CamemBERT](https://github.com/pytorch/fairseq/tree/main/examples/camembert). +- November 2019: Multilingual encoder (XLM-RoBERTa) is available: [XLM-R](https://github.com/pytorch/fairseq/tree/main/examples/xlmr). - September 2019: TensorFlow and TPU support via the [transformers library](https://github.com/huggingface/transformers). - August 2019: RoBERTa is now supported in the [pytorch-transformers library](https://github.com/huggingface/pytorch-transformers). -- August 2019: Added [tutorial for finetuning on WinoGrande](https://github.com/pytorch/fairseq/tree/master/examples/roberta/wsc#roberta-training-on-winogrande-dataset). +- August 2019: Added [tutorial for finetuning on WinoGrande](https://github.com/pytorch/fairseq/tree/main/examples/roberta/wsc#roberta-training-on-winogrande-dataset). - August 2019: Added [tutorial for pretraining RoBERTa using your own data](README.pretraining.md). ## Pre-trained models @@ -276,7 +277,6 @@ print('| Accuracy: ', float(ncorrect)/float(nsamples)) - [Finetuning on custom classification tasks (e.g., IMDB)](README.custom_classification.md) - [Finetuning on Winograd Schema Challenge (WSC)](wsc/README.md) - [Finetuning on Commonsense QA (CQA)](commonsense_qa/README.md) -- Finetuning on SQuAD: coming soon ## Pretraining using your own data diff --git a/examples/roberta/README.pretraining.md b/examples/roberta/README.pretraining.md index b841631d3e..a4e7453529 100644 --- a/examples/roberta/README.pretraining.md +++ b/examples/roberta/README.pretraining.md @@ -48,35 +48,21 @@ fairseq-preprocess \ ### 2) Train RoBERTa base ```bash -TOTAL_UPDATES=125000 # Total number of training steps -WARMUP_UPDATES=10000 # Warmup the learning rate over this many updates -PEAK_LR=0.0005 # Peak learning rate, adjust as needed -TOKENS_PER_SAMPLE=512 # Max sequence length -MAX_POSITIONS=512 # Num. positional embeddings (usually same as above) -MAX_SENTENCES=16 # Number of sequences per batch (batch size) -UPDATE_FREQ=16 # Increase the batch size 16x - DATA_DIR=data-bin/wikitext-103 -fairseq-train --fp16 $DATA_DIR \ - --task masked_lm --criterion masked_lm \ - --arch roberta_base --sample-break-mode complete --tokens-per-sample $TOKENS_PER_SAMPLE \ - --optimizer adam --adam-betas '(0.9,0.98)' --adam-eps 1e-6 --clip-norm 0.0 \ - --lr-scheduler polynomial_decay --lr $PEAK_LR --warmup-updates $WARMUP_UPDATES --total-num-update $TOTAL_UPDATES \ - --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 \ - --max-sentences $MAX_SENTENCES --update-freq $UPDATE_FREQ \ - --max-update $TOTAL_UPDATES --log-format simple --log-interval 1 +fairseq-hydra-train -m --config-dir examples/roberta/config/pretraining \ +--config-name base task.data=$DATA_DIR ``` **Note:** You can optionally resume training the released RoBERTa base model by -adding `--restore-file /path/to/roberta.base/model.pt`. +adding `checkpoint.restore_file=/path/to/roberta.base/model.pt`. **Note:** The above command assumes training on 8x32GB V100 GPUs. Each GPU uses -a batch size of 16 sequences (`$MAX_SENTENCES`) and accumulates gradients to -further increase the batch size by 16x (`$UPDATE_FREQ`), for a total batch size +a batch size of 16 sequences (`dataset.batch_size`) and accumulates gradients to +further increase the batch size by 16x (`optimization.update_freq`), for a total batch size of 2048 sequences. If you have fewer GPUs or GPUs with less memory you may need -to reduce `$MAX_SENTENCES` and increase `$UPDATE_FREQ` to compensate. -Alternatively if you have more GPUs you can decrease `$UPDATE_FREQ` accordingly +to reduce `dataset.batch_size` and increase dataset.update_freq to compensate. +Alternatively if you have more GPUs you can decrease `dataset.update_freq` accordingly to increase training speed. **Note:** The learning rate and batch size are tightly connected and need to be diff --git a/examples/roberta/README.race.md b/examples/roberta/README.race.md index 365bcf248b..13c917e8ec 100644 --- a/examples/roberta/README.race.md +++ b/examples/roberta/README.race.md @@ -19,7 +19,7 @@ UPDATE_FREQ=8 # Accumulate gradients to simulate training on 8 GPUs. DATA_DIR=/path/to/race-output-dir ROBERTA_PATH=/path/to/roberta/model.pt -CUDA_VISIBLE_DEVICES=0,1 fairseq-train $DATA_DIR --ddp-backend=no_c10d \ +CUDA_VISIBLE_DEVICES=0,1 fairseq-train $DATA_DIR --ddp-backend=legacy_ddp \ --restore-file $ROBERTA_PATH \ --reset-optimizer --reset-dataloader --reset-meters \ --best-checkpoint-metric accuracy --maximize-best-checkpoint-metric \ @@ -28,7 +28,7 @@ CUDA_VISIBLE_DEVICES=0,1 fairseq-train $DATA_DIR --ddp-backend=no_c10d \ --init-token 0 --separator-token 2 \ --max-option-length 128 \ --max-positions 512 \ - --truncate-sequence \ + --shorten-method "truncate" \ --arch roberta_large \ --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 \ --criterion sentence_ranking \ @@ -36,7 +36,7 @@ CUDA_VISIBLE_DEVICES=0,1 fairseq-train $DATA_DIR --ddp-backend=no_c10d \ --clip-norm 0.0 \ --lr-scheduler fixed --lr $LR \ --fp16 --fp16-init-scale 4 --threshold-loss-scale 1 --fp16-scale-window 128 \ - --max-sentences $MAX_SENTENCES \ + --batch-size $MAX_SENTENCES \ --required-batch-size-multiple 1 \ --update-freq $UPDATE_FREQ \ --max-epoch $MAX_EPOCH @@ -46,7 +46,7 @@ CUDA_VISIBLE_DEVICES=0,1 fairseq-train $DATA_DIR --ddp-backend=no_c10d \ a) As contexts in RACE are relatively long, we are using smaller batch size per GPU while increasing update-freq to achieve larger effective batch size. -b) Above cmd-args and hyperparams are tested on one Nvidia `V100` GPU with `32gb` of memory for each task. Depending on the GPU memory resources available to you, you can use increase `--update-freq` and reduce `--max-sentences`. +b) Above cmd-args and hyperparams are tested on one Nvidia `V100` GPU with `32gb` of memory for each task. Depending on the GPU memory resources available to you, you can use increase `--update-freq` and reduce `--batch-size`. c) The setting in above command is based on our hyperparam search within a fixed search space (for careful comparison across models). You might be able to find better metrics with wider hyperparam search. @@ -61,7 +61,7 @@ fairseq-validate \ $DATA_DIR \ --valid-subset $TEST_SPLIT \ --path $MODEL_PATH \ - --max-sentences 1 \ + --batch-size 1 \ --task sentence_ranking \ --criterion sentence_ranking \ --save-predictions $PREDS_OUT diff --git a/examples/roberta/commonsense_qa/README.md b/examples/roberta/commonsense_qa/README.md index 7302794805..7f386decd8 100644 --- a/examples/roberta/commonsense_qa/README.md +++ b/examples/roberta/commonsense_qa/README.md @@ -39,7 +39,7 @@ DATA_DIR=data/CommonsenseQA FAIRSEQ_PATH=/path/to/fairseq FAIRSEQ_USER_DIR=${FAIRSEQ_PATH}/examples/roberta/commonsense_qa -CUDA_VISIBLE_DEVICES=0 fairseq-train --fp16 --ddp-backend=no_c10d \ +CUDA_VISIBLE_DEVICES=0 fairseq-train --fp16 --ddp-backend=legacy_ddp \ $DATA_DIR \ --user-dir $FAIRSEQ_USER_DIR \ --restore-file $ROBERTA_PATH \ @@ -53,14 +53,14 @@ CUDA_VISIBLE_DEVICES=0 fairseq-train --fp16 --ddp-backend=no_c10d \ --optimizer adam --adam-betas '(0.9, 0.98)' --adam-eps 1e-06 --clip-norm 0.0 \ --lr-scheduler polynomial_decay --lr $LR \ --warmup-updates $WARMUP_UPDATES --total-num-update $MAX_UPDATES \ - --max-sentences $MAX_SENTENCES \ + --batch-size $MAX_SENTENCES \ --max-update $MAX_UPDATES \ --log-format simple --log-interval 25 \ --seed $SEED ``` The above command assumes training on 1 GPU with 32GB of RAM. For GPUs with -less memory, decrease `--max-sentences` and increase `--update-freq` +less memory, decrease `--batch-size` and increase `--update-freq` accordingly to compensate. ### 3) Evaluate @@ -96,4 +96,4 @@ print('Accuracy: ' + str(ncorrect / float(nsamples))) ``` The above snippet is not batched, which makes it quite slow. See [instructions -for batched prediction with RoBERTa](https://github.com/pytorch/fairseq/tree/master/examples/roberta#batched-prediction). +for batched prediction with RoBERTa](https://github.com/pytorch/fairseq/tree/main/examples/roberta#batched-prediction). diff --git a/examples/roberta/commonsense_qa/commonsense_qa_task.py b/examples/roberta/commonsense_qa/commonsense_qa_task.py index 274e8d39aa..216093f708 100644 --- a/examples/roberta/commonsense_qa/commonsense_qa_task.py +++ b/examples/roberta/commonsense_qa/commonsense_qa_task.py @@ -8,40 +8,44 @@ import numpy as np import torch - from fairseq.data import ( - data_utils, Dictionary, - encoders, IdDataset, ListDataset, NestedDictionaryDataset, - NumSamplesDataset, NumelDataset, + NumSamplesDataset, RawLabelDataset, RightPadDataset, SortDataset, + data_utils, + encoders, ) -from fairseq.tasks import FairseqTask, register_task +from fairseq.tasks import LegacyFairseqTask, register_task -@register_task('commonsense_qa') -class CommonsenseQATask(FairseqTask): +@register_task("commonsense_qa") +class CommonsenseQATask(LegacyFairseqTask): """Task to finetune RoBERTa for Commonsense QA.""" @staticmethod def add_args(parser): """Add task-specific arguments to the parser.""" - parser.add_argument('data', metavar='DIR', - help='path to data directory; we load <split>.jsonl') - parser.add_argument('--init-token', type=int, default=None, - help='add token at the beginning of each batch item') - parser.add_argument('--num-classes', type=int, default=5) + parser.add_argument( + "data", metavar="DIR", help="path to data directory; we load <split>.jsonl" + ) + parser.add_argument( + "--init-token", + type=int, + default=None, + help="add token at the beginning of each batch item", + ) + parser.add_argument("--num-classes", type=int, default=5) def __init__(self, args, vocab): super().__init__(args) self.vocab = vocab - self.mask = vocab.add_symbol('<mask>') + self.mask = vocab.add_symbol("<mask>") self.bpe = encoders.build_bpe(args) @@ -53,20 +57,24 @@ def load_dictionary(cls, filename): filename (str): the filename """ dictionary = Dictionary.load(filename) - dictionary.add_symbol('<mask>') + dictionary.add_symbol("<mask>") return dictionary @classmethod def setup_task(cls, args, **kwargs): - assert args.criterion == 'sentence_ranking', 'Must set --criterion=sentence_ranking' + assert ( + args.criterion == "sentence_ranking" + ), "Must set --criterion=sentence_ranking" # load data and label dictionaries - vocab = cls.load_dictionary(os.path.join(args.data, 'dict.txt')) - print('| dictionary: {} types'.format(len(vocab))) + vocab = cls.load_dictionary(os.path.join(args.data, "dict.txt")) + print("| dictionary: {} types".format(len(vocab))) return cls(args, vocab) - def load_dataset(self, split, epoch=1, combine=False, data_path=None, return_only=False, **kwargs): + def load_dataset( + self, split, epoch=1, combine=False, data_path=None, return_only=False, **kwargs + ): """Load a given dataset split. Args: @@ -77,16 +85,18 @@ def binarize(s, append_bos=False): if self.bpe is not None: s = self.bpe.encode(s) tokens = self.vocab.encode_line( - s, append_eos=True, add_if_not_exist=False, + s, + append_eos=True, + add_if_not_exist=False, ).long() if append_bos and self.args.init_token is not None: tokens = torch.cat([tokens.new([self.args.init_token]), tokens]) return tokens if data_path is None: - data_path = os.path.join(self.args.data, split + '.jsonl') + data_path = os.path.join(self.args.data, split + ".jsonl") if not os.path.exists(data_path): - raise FileNotFoundError('Cannot find data: {}'.format(data_path)) + raise FileNotFoundError("Cannot find data: {}".format(data_path)) src_tokens = [[] for i in range(self.args.num_classes)] src_lengths = [[] for i in range(self.args.num_classes)] @@ -95,20 +105,23 @@ def binarize(s, append_bos=False): with open(data_path) as h: for line in h: example = json.loads(line.strip()) - if 'answerKey' in example: - label = ord(example['answerKey']) - ord('A') + if "answerKey" in example: + label = ord(example["answerKey"]) - ord("A") labels.append(label) - question = example['question']['stem'] - assert len(example['question']['choices']) == self.args.num_classes + question = example["question"]["stem"] + assert len(example["question"]["choices"]) == self.args.num_classes # format: `<s> Q: Where would I not want a fox? </s> A: hen house </s>` - question = 'Q: ' + question + question = "Q: " + question question_toks = binarize(question, append_bos=True) - for i, choice in enumerate(example['question']['choices']): - src = 'A: ' + choice['text'] + for i, choice in enumerate(example["question"]["choices"]): + src = "A: " + choice["text"] src_bin = torch.cat([question_toks, binarize(src)]) src_tokens[i].append(src_bin) src_lengths[i].append(len(src_bin)) - assert all(len(src_tokens[0]) == len(src_tokens[i]) for i in range(self.args.num_classes)) + assert all( + len(src_tokens[0]) == len(src_tokens[i]) + for i in range(self.args.num_classes) + ) assert len(src_tokens[0]) == len(src_lengths[0]) assert len(labels) == 0 or len(labels) == len(src_tokens[0]) @@ -118,24 +131,26 @@ def binarize(s, append_bos=False): src_lengths[i] = ListDataset(src_lengths[i]) dataset = { - 'id': IdDataset(), - 'nsentences': NumSamplesDataset(), - 'ntokens': NumelDataset(src_tokens[0], reduce=True), + "id": IdDataset(), + "nsentences": NumSamplesDataset(), + "ntokens": NumelDataset(src_tokens[0], reduce=True), } for i in range(self.args.num_classes): - dataset.update({ - 'net_input{}'.format(i + 1): { - 'src_tokens': RightPadDataset( - src_tokens[i], - pad_idx=self.source_dictionary.pad(), - ), - 'src_lengths': src_lengths[i], + dataset.update( + { + "net_input{}".format(i + 1): { + "src_tokens": RightPadDataset( + src_tokens[i], + pad_idx=self.source_dictionary.pad(), + ), + "src_lengths": src_lengths[i], + } } - }) + ) if len(labels) > 0: - dataset.update({'target': RawLabelDataset(labels)}) + dataset.update({"target": RawLabelDataset(labels)}) dataset = NestedDictionaryDataset( dataset, @@ -149,17 +164,18 @@ def binarize(s, append_bos=False): sort_order=[np.random.permutation(len(dataset))], ) - print('| Loaded {} with {} samples'.format(split, len(dataset))) + print("| Loaded {} with {} samples".format(split, len(dataset))) self.datasets[split] = dataset return self.datasets[split] def build_model(self, args): from fairseq import models + model = models.build_model(args, self) model.register_classification_head( - 'sentence_classification_head', + "sentence_classification_head", num_classes=1, ) diff --git a/examples/roberta/config/finetuning/cola.yaml b/examples/roberta/config/finetuning/cola.yaml new file mode 100644 index 0000000000..ac76611201 --- /dev/null +++ b/examples/roberta/config/finetuning/cola.yaml @@ -0,0 +1,59 @@ +# @package _group_ + +common: + fp16: true + fp16_init_scale: 4 + threshold_loss_scale: 1 + fp16_scale_window: 128 + log_format: json + log_interval: 200 + +task: + _name: sentence_prediction + data: ??? + init_token: 0 + separator_token: 2 + num_classes: 2 + max_positions: 512 + +checkpoint: + restore_file: ??? + reset_optimizer: true + reset_dataloader: true + reset_meters: true + best_checkpoint_metric: accuracy + maximize_best_checkpoint_metric: true + no_epoch_checkpoints: true + +distributed_training: + find_unused_parameters: true + distributed_world_size: 1 + +criterion: + _name: sentence_prediction + +dataset: + batch_size: 16 + required_batch_size_multiple: 1 + max_tokens: 4400 + +optimizer: + _name: adam + weight_decay: 0.1 + adam_betas: (0.9,0.98) + adam_eps: 1e-06 + +lr_scheduler: + _name: polynomial_decay + warmup_updates: 320 + +optimization: + clip_norm: 0.0 + lr: [1e-05] + max_update: 5336 + max_epoch: 10 + +model: + _name: roberta + dropout: 0.1 + attention_dropout: 0.1 diff --git a/examples/roberta/config/finetuning/mnli.yaml b/examples/roberta/config/finetuning/mnli.yaml new file mode 100644 index 0000000000..5be10c362f --- /dev/null +++ b/examples/roberta/config/finetuning/mnli.yaml @@ -0,0 +1,59 @@ +# @package _group_ + +common: + fp16: true + fp16_init_scale: 4 + threshold_loss_scale: 1 + fp16_scale_window: 128 + log_format: json + log_interval: 200 + +task: + _name: sentence_prediction + data: ??? + init_token: 0 + separator_token: 2 + num_classes: 3 + max_positions: 512 + +checkpoint: + restore_file: ??? + reset_optimizer: true + reset_dataloader: true + reset_meters: true + best_checkpoint_metric: accuracy + maximize_best_checkpoint_metric: true + no_epoch_checkpoints: true + +distributed_training: + find_unused_parameters: true + distributed_world_size: 1 + +criterion: + _name: sentence_prediction + +dataset: + batch_size: 32 + required_batch_size_multiple: 1 + max_tokens: 4400 + +optimizer: + _name: adam + weight_decay: 0.1 + adam_betas: (0.9,0.98) + adam_eps: 1e-06 + +lr_scheduler: + _name: polynomial_decay + warmup_updates: 7432 + +optimization: + clip_norm: 0.0 + lr: [1e-05] + max_update: 123873 + max_epoch: 10 + +model: + _name: roberta + dropout: 0.1 + attention_dropout: 0.1 diff --git a/examples/roberta/config/finetuning/mrpc.yaml b/examples/roberta/config/finetuning/mrpc.yaml new file mode 100644 index 0000000000..aa8b7db393 --- /dev/null +++ b/examples/roberta/config/finetuning/mrpc.yaml @@ -0,0 +1,59 @@ +# @package _group_ + +common: + fp16: true + fp16_init_scale: 4 + threshold_loss_scale: 1 + fp16_scale_window: 128 + log_format: json + log_interval: 200 + +task: + _name: sentence_prediction + data: ??? + init_token: 0 + separator_token: 2 + num_classes: 2 + max_positions: 512 + +checkpoint: + restore_file: ??? + reset_optimizer: true + reset_dataloader: true + reset_meters: true + best_checkpoint_metric: accuracy + maximize_best_checkpoint_metric: true + no_epoch_checkpoints: true + +distributed_training: + find_unused_parameters: true + distributed_world_size: 1 + +criterion: + _name: sentence_prediction + +dataset: + batch_size: 16 + required_batch_size_multiple: 1 + max_tokens: 4400 + +optimizer: + _name: adam + weight_decay: 0.1 + adam_betas: (0.9,0.98) + adam_eps: 1e-06 + +lr_scheduler: + _name: polynomial_decay + warmup_updates: 137 + +optimization: + clip_norm: 0.0 + lr: [1e-05] + max_update: 2296 + max_epoch: 10 + +model: + _name: roberta + dropout: 0.1 + attention_dropout: 0.1 diff --git a/examples/roberta/config/finetuning/qnli.yaml b/examples/roberta/config/finetuning/qnli.yaml new file mode 100644 index 0000000000..b4595b090e --- /dev/null +++ b/examples/roberta/config/finetuning/qnli.yaml @@ -0,0 +1,59 @@ +# @package _group_ + +common: + fp16: true + fp16_init_scale: 4 + threshold_loss_scale: 1 + fp16_scale_window: 128 + log_format: json + log_interval: 200 + +task: + _name: sentence_prediction + data: ??? + init_token: 0 + separator_token: 2 + num_classes: 2 + max_positions: 512 + +checkpoint: + restore_file: ??? + reset_optimizer: true + reset_dataloader: true + reset_meters: true + best_checkpoint_metric: accuracy + maximize_best_checkpoint_metric: true + no_epoch_checkpoints: true + +distributed_training: + find_unused_parameters: true + distributed_world_size: 1 + +criterion: + _name: sentence_prediction + +dataset: + batch_size: 32 + required_batch_size_multiple: 1 + max_tokens: 4400 + +optimizer: + _name: adam + weight_decay: 0.1 + adam_betas: (0.9,0.98) + adam_eps: 1e-06 + +lr_scheduler: + _name: polynomial_decay + warmup_updates: 1986 + +optimization: + clip_norm: 0.0 + lr: [1e-05] + max_update: 33112 + max_epoch: 10 + +model: + _name: roberta + dropout: 0.1 + attention_dropout: 0.1 diff --git a/examples/roberta/config/finetuning/qqp.yaml b/examples/roberta/config/finetuning/qqp.yaml new file mode 100644 index 0000000000..5a2b2ed743 --- /dev/null +++ b/examples/roberta/config/finetuning/qqp.yaml @@ -0,0 +1,59 @@ +# @package _group_ + +common: + fp16: true + fp16_init_scale: 4 + threshold_loss_scale: 1 + fp16_scale_window: 128 + log_format: json + log_interval: 200 + +task: + _name: sentence_prediction + data: ??? + init_token: 0 + separator_token: 2 + num_classes: 2 + max_positions: 512 + +checkpoint: + restore_file: ??? + reset_optimizer: true + reset_dataloader: true + reset_meters: true + best_checkpoint_metric: accuracy + maximize_best_checkpoint_metric: true + no_epoch_checkpoints: true + +distributed_training: + find_unused_parameters: true + distributed_world_size: 1 + +criterion: + _name: sentence_prediction + +dataset: + batch_size: 32 + required_batch_size_multiple: 1 + max_tokens: 4400 + +optimizer: + _name: adam + weight_decay: 0.1 + adam_betas: (0.9,0.98) + adam_eps: 1e-06 + +lr_scheduler: + _name: polynomial_decay + warmup_updates: 28318 + +optimization: + clip_norm: 0.0 + lr: [1e-05] + max_update: 113272 + max_epoch: 10 + +model: + _name: roberta + dropout: 0.1 + attention_dropout: 0.1 diff --git a/examples/roberta/config/finetuning/rte.yaml b/examples/roberta/config/finetuning/rte.yaml new file mode 100644 index 0000000000..7318465011 --- /dev/null +++ b/examples/roberta/config/finetuning/rte.yaml @@ -0,0 +1,59 @@ +# @package _group_ + +common: + fp16: true + fp16_init_scale: 4 + threshold_loss_scale: 1 + fp16_scale_window: 128 + log_format: json + log_interval: 200 + +task: + _name: sentence_prediction + data: ??? + init_token: 0 + separator_token: 2 + num_classes: 2 + max_positions: 512 + +checkpoint: + restore_file: ??? + reset_optimizer: true + reset_dataloader: true + reset_meters: true + best_checkpoint_metric: accuracy + maximize_best_checkpoint_metric: true + no_epoch_checkpoints: true + +distributed_training: + find_unused_parameters: true + distributed_world_size: 1 + +criterion: + _name: sentence_prediction + +dataset: + batch_size: 16 + required_batch_size_multiple: 1 + max_tokens: 4400 + +optimizer: + _name: adam + weight_decay: 0.1 + adam_betas: (0.9,0.98) + adam_eps: 1e-06 + +lr_scheduler: + _name: polynomial_decay + warmup_updates: 122 + +optimization: + clip_norm: 0.0 + lr: [2e-05] + max_update: 2036 + max_epoch: 10 + +model: + _name: roberta + dropout: 0.1 + attention_dropout: 0.1 diff --git a/examples/roberta/config/finetuning/sst_2.yaml b/examples/roberta/config/finetuning/sst_2.yaml new file mode 100644 index 0000000000..a93ad2f22c --- /dev/null +++ b/examples/roberta/config/finetuning/sst_2.yaml @@ -0,0 +1,59 @@ +# @package _group_ + +common: + fp16: true + fp16_init_scale: 4 + threshold_loss_scale: 1 + fp16_scale_window: 128 + log_format: json + log_interval: 200 + +task: + _name: sentence_prediction + data: ??? + init_token: 0 + separator_token: 2 + num_classes: 2 + max_positions: 512 + +checkpoint: + restore_file: ??? + reset_optimizer: true + reset_dataloader: true + reset_meters: true + best_checkpoint_metric: accuracy + maximize_best_checkpoint_metric: true + no_epoch_checkpoints: true + +distributed_training: + find_unused_parameters: true + distributed_world_size: 1 + +criterion: + _name: sentence_prediction + +dataset: + batch_size: 32 + required_batch_size_multiple: 1 + max_tokens: 4400 + +optimizer: + _name: adam + weight_decay: 0.1 + adam_betas: (0.9,0.98) + adam_eps: 1e-06 + +lr_scheduler: + _name: polynomial_decay + warmup_updates: 1256 + +optimization: + clip_norm: 0.0 + lr: [1e-05] + max_update: 20935 + max_epoch: 10 + +model: + _name: roberta + dropout: 0.1 + attention_dropout: 0.1 diff --git a/examples/roberta/config/finetuning/sts_b.yaml b/examples/roberta/config/finetuning/sts_b.yaml new file mode 100644 index 0000000000..2d495221ad --- /dev/null +++ b/examples/roberta/config/finetuning/sts_b.yaml @@ -0,0 +1,58 @@ +# @package _group_ + +common: + fp16: true + fp16_init_scale: 4 + threshold_loss_scale: 1 + fp16_scale_window: 128 + log_format: json + log_interval: 200 + +task: + _name: sentence_prediction + data: ??? + init_token: 0 + separator_token: 2 + num_classes: 1 + max_positions: 512 + +checkpoint: + restore_file: ??? + reset_optimizer: true + reset_dataloader: true + reset_meters: true + no_epoch_checkpoints: true + +distributed_training: + find_unused_parameters: true + distributed_world_size: 1 + +criterion: + _name: sentence_prediction + regression_target: true + +dataset: + batch_size: 16 + required_batch_size_multiple: 1 + max_tokens: 4400 + +optimizer: + _name: adam + weight_decay: 0.1 + adam_betas: (0.9,0.98) + adam_eps: 1e-06 + +lr_scheduler: + _name: polynomial_decay + warmup_updates: 214 + +optimization: + clip_norm: 0.0 + lr: [2e-05] + max_update: 3598 + max_epoch: 10 + +model: + _name: roberta + dropout: 0.1 + attention_dropout: 0.1 diff --git a/examples/roberta/config/pretraining/base.yaml b/examples/roberta/config/pretraining/base.yaml new file mode 100644 index 0000000000..97829908f7 --- /dev/null +++ b/examples/roberta/config/pretraining/base.yaml @@ -0,0 +1,42 @@ +# @package _group_ +common: + fp16: true + log_format: json + log_interval: 200 + +checkpoint: + no_epoch_checkpoints: true + +task: + _name: masked_lm + data: ??? + sample_break_mode: complete + tokens_per_sample: 512 + +criterion: masked_lm + +dataset: + batch_size: 16 + ignore_unused_valid_subsets: true + +optimizer: + _name: adam + weight_decay: 0.01 + adam_betas: (0.9,0.98) + adam_eps: 1e-06 + +lr_scheduler: + _name: polynomial_decay + warmup_updates: 10000 + +optimization: + clip_norm: 0 + lr: [0.0005] + max_update: 125000 + update_freq: [16] + +model: + _name: roberta + max_positions: 512 + dropout: 0.1 + attention_dropout: 0.1 diff --git a/examples/roberta/multiprocessing_bpe_encoder.py b/examples/roberta/multiprocessing_bpe_encoder.py index f0240c210f..43fe0451bf 100644 --- a/examples/roberta/multiprocessing_bpe_encoder.py +++ b/examples/roberta/multiprocessing_bpe_encoder.py @@ -8,7 +8,6 @@ import argparse import contextlib import sys - from collections import Counter from multiprocessing import Pool @@ -26,23 +25,23 @@ def main(): parser = argparse.ArgumentParser() parser.add_argument( "--encoder-json", - help='path to encoder.json', + help="path to encoder.json", ) parser.add_argument( "--vocab-bpe", type=str, - help='path to vocab.bpe', + help="path to vocab.bpe", ) parser.add_argument( "--inputs", nargs="+", - default=['-'], + default=["-"], help="input files to filter/encode", ) parser.add_argument( "--outputs", nargs="+", - default=['-'], + default=["-"], help="path to save encoded outputs", ) parser.add_argument( @@ -53,18 +52,21 @@ def main(): parser.add_argument("--workers", type=int, default=20) args = parser.parse_args() - assert len(args.inputs) == len(args.outputs), \ - "number of input and output paths should match" + assert len(args.inputs) == len( + args.outputs + ), "number of input and output paths should match" with contextlib.ExitStack() as stack: inputs = [ stack.enter_context(open(input, "r", encoding="utf-8")) - if input != "-" else sys.stdin + if input != "-" + else sys.stdin for input in args.inputs ] outputs = [ stack.enter_context(open(output, "w", encoding="utf-8")) - if output != "-" else sys.stdout + if output != "-" + else sys.stdout for output in args.outputs ] @@ -87,7 +89,6 @@ def main(): class MultiprocessingEncoder(object): - def __init__(self, args): self.args = args diff --git a/examples/roberta/preprocess_RACE.py b/examples/roberta/preprocess_RACE.py index f6f606a389..cdd6607271 100644 --- a/examples/roberta/preprocess_RACE.py +++ b/examples/roberta/preprocess_RACE.py @@ -25,7 +25,7 @@ def get_examples(data_dir, set_type): examples = [] levels = ["middle", "high"] - set_type_c = set_type.split('-') + set_type_c = set_type.split("-") if len(set_type_c) == 2: levels = [set_type_c[1]] set_type = set_type_c[0] @@ -33,13 +33,13 @@ def get_examples(data_dir, set_type): cur_dir = os.path.join(data_dir, set_type, level) for filename in os.listdir(cur_dir): cur_path = os.path.join(cur_dir, filename) - with open(cur_path, 'r') as f: + with open(cur_path, "r") as f: cur_data = json.load(f) answers = cur_data["answers"] options = cur_data["options"] questions = cur_data["questions"] context = cur_data["article"].replace("\n", " ") - context = re.sub(r'\s+', ' ', context) + context = re.sub(r"\s+", " ", context) for i in range(len(answers)): label = ord(answers[i]) - ord("A") qa_list = [] @@ -50,7 +50,7 @@ def get_examples(data_dir, set_type): qa_cat = question.replace("_", option) else: qa_cat = " ".join([question, option]) - qa_cat = re.sub(r'\s+', ' ', qa_cat) + qa_cat = re.sub(r"\s+", " ", qa_cat) qa_list.append(qa_cat) examples.append(InputExample(context, qa_list, label)) @@ -64,11 +64,11 @@ def main(): parser = argparse.ArgumentParser() parser.add_argument( "--input-dir", - help='input directory for downloaded RACE dataset', + help="input directory for downloaded RACE dataset", ) parser.add_argument( "--output-dir", - help='output directory for extracted data', + help="output directory for extracted data", ) args = parser.parse_args() @@ -77,17 +77,20 @@ def main(): for set_type in ["train", "dev", "test-middle", "test-high"]: examples = get_examples(args.input_dir, set_type) - qa_file_paths = [os.path.join(args.output_dir, set_type + ".input" + str(i + 1)) for i in range(4)] - qa_files = [open(qa_file_path, 'w') for qa_file_path in qa_file_paths] + qa_file_paths = [ + os.path.join(args.output_dir, set_type + ".input" + str(i + 1)) + for i in range(4) + ] + qa_files = [open(qa_file_path, "w") for qa_file_path in qa_file_paths] outf_context_path = os.path.join(args.output_dir, set_type + ".input0") outf_label_path = os.path.join(args.output_dir, set_type + ".label") - outf_context = open(outf_context_path, 'w') - outf_label = open(outf_label_path, 'w') + outf_context = open(outf_context_path, "w") + outf_label = open(outf_label_path, "w") for example in examples: - outf_context.write(example.paragraph + '\n') + outf_context.write(example.paragraph + "\n") for i in range(4): - qa_files[i].write(example.qa_list[i] + '\n') - outf_label.write(str(example.label) + '\n') + qa_files[i].write(example.qa_list[i] + "\n") + outf_label.write(str(example.label) + "\n") for f in qa_files: f.close() @@ -95,5 +98,5 @@ def main(): outf_context.close() -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/examples/roberta/wsc/README.md b/examples/roberta/wsc/README.md index 0d3f62a07f..21a045d999 100644 --- a/examples/roberta/wsc/README.md +++ b/examples/roberta/wsc/README.md @@ -51,7 +51,7 @@ CUDA_VISIBLE_DEVICES=0,1,2,3 fairseq-train WSC/ \ --no-epoch-checkpoints --no-last-checkpoints --no-save-optimizer-state \ --best-checkpoint-metric accuracy --maximize-best-checkpoint-metric \ --valid-subset val \ - --fp16 --ddp-backend no_c10d \ + --fp16 --ddp-backend legacy_ddp \ --user-dir $FAIRSEQ_USER_DIR \ --task wsc --criterion wsc --wsc-cross-entropy \ --arch roberta_large --bpe gpt2 --max-positions 512 \ @@ -59,7 +59,7 @@ CUDA_VISIBLE_DEVICES=0,1,2,3 fairseq-train WSC/ \ --optimizer adam --adam-betas '(0.9, 0.98)' --adam-eps 1e-06 \ --lr-scheduler polynomial_decay --lr $LR \ --warmup-updates $WARMUP_UPDATES --total-num-update $TOTAL_NUM_UPDATES \ - --max-sentences $MAX_SENTENCES \ + --batch-size $MAX_SENTENCES \ --max-update $TOTAL_NUM_UPDATES \ --log-format simple --log-interval 100 \ --seed $SEED @@ -110,7 +110,7 @@ CUDA_VISIBLE_DEVICES=0 fairseq-train winogrande_1.0/ \ --no-epoch-checkpoints --no-last-checkpoints --no-save-optimizer-state \ --best-checkpoint-metric accuracy --maximize-best-checkpoint-metric \ --valid-subset val \ - --fp16 --ddp-backend no_c10d \ + --fp16 --ddp-backend legacy_ddp \ --user-dir $FAIRSEQ_USER_DIR \ --task winogrande --criterion winogrande \ --wsc-margin-alpha 5.0 --wsc-margin-beta 0.4 \ @@ -119,7 +119,7 @@ CUDA_VISIBLE_DEVICES=0 fairseq-train winogrande_1.0/ \ --optimizer adam --adam-betas '(0.9, 0.98)' --adam-eps 1e-06 \ --lr-scheduler polynomial_decay --lr $LR \ --warmup-updates $WARMUP_UPDATES --total-num-update $TOTAL_NUM_UPDATES \ - --max-sentences $MAX_SENTENCES \ + --batch-size $MAX_SENTENCES \ --max-update $TOTAL_NUM_UPDATES \ --log-format simple --log-interval 100 ``` diff --git a/examples/roberta/wsc/wsc_criterion.py b/examples/roberta/wsc/wsc_criterion.py index dd909ab20c..ed0251fdec 100644 --- a/examples/roberta/wsc/wsc_criterion.py +++ b/examples/roberta/wsc/wsc_criterion.py @@ -7,23 +7,21 @@ import torch import torch.nn.functional as F - from fairseq import utils -from fairseq.data import encoders from fairseq.criterions import LegacyFairseqCriterion, register_criterion +from fairseq.data import encoders -@register_criterion('wsc') +@register_criterion("wsc") class WSCCriterion(LegacyFairseqCriterion): - def __init__(self, args, task): super().__init__(args, task) if self.args.save_predictions is not None: - self.prediction_h = open(self.args.save_predictions, 'w') + self.prediction_h = open(self.args.save_predictions, "w") else: self.prediction_h = None - self.bpe = encoders.build_bpe(args) - self.tokenizer = encoders.build_tokenizer(args) + self.bpe = encoders.build_bpe(args.bpe) + self.tokenizer = encoders.build_tokenizer(args.tokenizer) def __del__(self): if self.prediction_h is not None: @@ -32,12 +30,16 @@ def __del__(self): @staticmethod def add_args(parser): """Add criterion-specific arguments to the parser.""" - parser.add_argument('--wsc-margin-alpha', type=float, metavar='A', default=1.0) - parser.add_argument('--wsc-margin-beta', type=float, metavar='B', default=0.0) - parser.add_argument('--wsc-cross-entropy', action='store_true', - help='use cross entropy formulation instead of margin loss') - parser.add_argument('--save-predictions', metavar='FILE', - help='file to save predictions to') + parser.add_argument("--wsc-margin-alpha", type=float, metavar="A", default=1.0) + parser.add_argument("--wsc-margin-beta", type=float, metavar="B", default=0.0) + parser.add_argument( + "--wsc-cross-entropy", + action="store_true", + help="use cross entropy formulation instead of margin loss", + ) + parser.add_argument( + "--save-predictions", metavar="FILE", help="file to save predictions to" + ) def get_masked_input(self, tokens, mask): masked_tokens = tokens.clone() @@ -60,27 +62,26 @@ def get_loss(self, query_lprobs, cand_lprobs): ) else: return ( - - query_lprobs - + self.args.wsc_margin_alpha * ( - cand_lprobs - query_lprobs + self.args.wsc_margin_beta - ).clamp(min=0) + -query_lprobs + + self.args.wsc_margin_alpha + * (cand_lprobs - query_lprobs + self.args.wsc_margin_beta).clamp(min=0) ).sum() def forward(self, model, sample, reduce=True): # compute loss and accuracy - loss, nloss = 0., 0 + loss, nloss = 0.0, 0 ncorrect, nqueries = 0, 0 - for i, label in enumerate(sample['labels']): + for i, label in enumerate(sample["labels"]): query_lprobs = self.get_lprobs( model, - sample['query_tokens'][i].unsqueeze(0), - sample['query_masks'][i].unsqueeze(0), + sample["query_tokens"][i].unsqueeze(0), + sample["query_masks"][i].unsqueeze(0), ) cand_lprobs = self.get_lprobs( model, - sample['candidate_tokens'][i], - sample['candidate_masks'][i], + sample["candidate_tokens"][i], + sample["candidate_masks"][i], ) pred = (query_lprobs >= cand_lprobs).all().item() @@ -95,72 +96,72 @@ def forward(self, model, sample, reduce=True): nloss += 1 loss += self.get_loss(query_lprobs, cand_lprobs) - id = sample['id'][i].item() + id = sample["id"][i].item() if self.prediction_h is not None: - print('{}\t{}\t{}'.format(id, pred, label), file=self.prediction_h) + print("{}\t{}\t{}".format(id, pred, label), file=self.prediction_h) if nloss == 0: loss = torch.tensor(0.0, requires_grad=True) sample_size = nqueries if nqueries > 0 else 1 logging_output = { - 'loss': utils.item(loss.data) if reduce else loss.data, - 'ntokens': sample['ntokens'], - 'nsentences': sample['nsentences'], - 'sample_size': sample_size, - 'ncorrect': ncorrect, - 'nqueries': nqueries, + "loss": utils.item(loss.data) if reduce else loss.data, + "ntokens": sample["ntokens"], + "nsentences": sample["nsentences"], + "sample_size": sample_size, + "ncorrect": ncorrect, + "nqueries": nqueries, } return loss, sample_size, logging_output @staticmethod def aggregate_logging_outputs(logging_outputs): """Aggregate logging outputs from data parallel training.""" - loss_sum = sum(log.get('loss', 0) for log in logging_outputs) - ntokens = sum(log.get('ntokens', 0) for log in logging_outputs) - nsentences = sum(log.get('nsentences', 0) for log in logging_outputs) - sample_size = sum(log.get('sample_size', 0) for log in logging_outputs) + loss_sum = sum(log.get("loss", 0) for log in logging_outputs) + ntokens = sum(log.get("ntokens", 0) for log in logging_outputs) + nsentences = sum(log.get("nsentences", 0) for log in logging_outputs) + sample_size = sum(log.get("sample_size", 0) for log in logging_outputs) agg_output = { - 'loss': loss_sum / sample_size / math.log(2), - 'ntokens': ntokens, - 'nsentences': nsentences, - 'sample_size': sample_size, + "loss": loss_sum / sample_size / math.log(2), + "ntokens": ntokens, + "nsentences": nsentences, + "sample_size": sample_size, } - ncorrect = sum(log.get('ncorrect', 0) for log in logging_outputs) - nqueries = sum(log.get('nqueries', 0) for log in logging_outputs) + ncorrect = sum(log.get("ncorrect", 0) for log in logging_outputs) + nqueries = sum(log.get("nqueries", 0) for log in logging_outputs) if nqueries > 0: - agg_output['accuracy'] = ncorrect / float(nqueries) + agg_output["accuracy"] = ncorrect / float(nqueries) return agg_output -@register_criterion('winogrande') +@register_criterion("winogrande") class WinograndeCriterion(WSCCriterion): def forward(self, model, sample, reduce=True): # compute loss and accuracy query_lprobs = self.get_lprobs( model, - sample['query_tokens'], - sample['query_masks'], + sample["query_tokens"], + sample["query_masks"], ) cand_lprobs = self.get_lprobs( model, - sample['candidate_tokens'], - sample['candidate_masks'], + sample["candidate_tokens"], + sample["candidate_masks"], ) pred = query_lprobs >= cand_lprobs loss = self.get_loss(query_lprobs, cand_lprobs) - sample_size = sample['query_tokens'].size(0) + sample_size = sample["query_tokens"].size(0) ncorrect = pred.sum().item() logging_output = { - 'loss': utils.item(loss.data) if reduce else loss.data, - 'ntokens': sample['ntokens'], - 'nsentences': sample['nsentences'], - 'sample_size': sample_size, - 'ncorrect': ncorrect, - 'nqueries': sample_size, + "loss": utils.item(loss.data) if reduce else loss.data, + "ntokens": sample["ntokens"], + "nsentences": sample["nsentences"], + "sample_size": sample_size, + "ncorrect": ncorrect, + "nqueries": sample_size, } return loss, sample_size, logging_output diff --git a/examples/roberta/wsc/wsc_task.py b/examples/roberta/wsc/wsc_task.py index fbba0d8964..602ea737ed 100644 --- a/examples/roberta/wsc/wsc_task.py +++ b/examples/roberta/wsc/wsc_task.py @@ -10,47 +10,51 @@ import numpy as np import torch import torch.nn.functional as F - from fairseq import utils from fairseq.data import ( - data_utils, Dictionary, - encoders, IdDataset, ListDataset, NestedDictionaryDataset, - NumSamplesDataset, NumelDataset, + NumSamplesDataset, PadDataset, SortDataset, + data_utils, + encoders, ) -from fairseq.tasks import FairseqTask, register_task +from fairseq.tasks import LegacyFairseqTask, register_task from . import wsc_utils -@register_task('wsc') -class WSCTask(FairseqTask): +@register_task("wsc") +class WSCTask(LegacyFairseqTask): """Task to finetune RoBERTa for Winograd Schemas.""" @staticmethod def add_args(parser): """Add task-specific arguments to the parser.""" - parser.add_argument('data', metavar='DIR', - help='path to data directory; we load <split>.jsonl') - parser.add_argument('--init-token', type=int, default=None, - help='add token at the beginning of each batch item') + parser.add_argument( + "data", metavar="DIR", help="path to data directory; we load <split>.jsonl" + ) + parser.add_argument( + "--init-token", + type=int, + default=None, + help="add token at the beginning of each batch item", + ) def __init__(self, args, vocab): super().__init__(args) self.vocab = vocab - self.mask = vocab.add_symbol('<mask>') + self.mask = vocab.add_symbol("<mask>") self.bpe = encoders.build_bpe(args) self.tokenizer = encoders.build_tokenizer(args) # hack to handle GPT-2 BPE, which includes leading spaces - if args.bpe == 'gpt2': + if args.bpe == "gpt2": self.leading_space = True self.trailing_space = False else: @@ -65,16 +69,16 @@ def load_dictionary(cls, filename): filename (str): the filename """ dictionary = Dictionary.load(filename) - dictionary.add_symbol('<mask>') + dictionary.add_symbol("<mask>") return dictionary @classmethod def setup_task(cls, args, **kwargs): - assert args.criterion == 'wsc', 'Must set --criterion=wsc' + assert args.criterion == "wsc", "Must set --criterion=wsc" # load data and label dictionaries - vocab = cls.load_dictionary(os.path.join(args.data, 'dict.txt')) - print('| dictionary: {} types'.format(len(vocab))) + vocab = cls.load_dictionary(os.path.join(args.data, "dict.txt")) + print("| dictionary: {} types".format(len(vocab))) return cls(args, vocab) @@ -84,7 +88,9 @@ def binarize(self, s: str, append_eos: bool = False): if self.bpe is not None: s = self.bpe.encode(s) tokens = self.vocab.encode_line( - s, append_eos=append_eos, add_if_not_exist=False, + s, + append_eos=append_eos, + add_if_not_exist=False, ).long() if self.args.init_token is not None: tokens = torch.cat([tokens.new([self.args.init_token]), tokens]) @@ -98,19 +104,21 @@ def binarize_with_mask(self, txt, prefix, suffix, leading_space, trailing_space) mask = torch.zeros_like(toks, dtype=torch.bool) mask_start = len(self.binarize(prefix)) mask_size = len(self.binarize(leading_space + txt)) - mask[mask_start:mask_start + mask_size] = 1 + mask[mask_start : mask_start + mask_size] = 1 return toks, mask - def load_dataset(self, split, epoch=1, combine=False, data_path=None, return_only=False, **kwargs): + def load_dataset( + self, split, epoch=1, combine=False, data_path=None, return_only=False, **kwargs + ): """Load a given dataset split. Args: split (str): name of the split (e.g., train, valid, test) """ if data_path is None: - data_path = os.path.join(self.args.data, split + '.jsonl') + data_path = os.path.join(self.args.data, split + ".jsonl") if not os.path.exists(data_path): - raise FileNotFoundError('Cannot find data: {}'.format(data_path)) + raise FileNotFoundError("Cannot find data: {}".format(data_path)) query_tokens = [] query_masks = [] @@ -121,13 +129,15 @@ def load_dataset(self, split, epoch=1, combine=False, data_path=None, return_onl labels = [] for sentence, pronoun_span, query, label in wsc_utils.jsonl_iterator(data_path): - prefix = sentence[:pronoun_span.start].text - suffix = sentence[pronoun_span.end:].text_with_ws + prefix = sentence[: pronoun_span.start].text + suffix = sentence[pronoun_span.end :].text_with_ws # spaCy spans include trailing spaces, but we need to know about # leading spaces for the GPT-2 BPE - leading_space = ' ' if sentence[:pronoun_span.start].text_with_ws.endswith(' ') else '' - trailing_space = ' ' if pronoun_span.text_with_ws.endswith(' ') else '' + leading_space = ( + " " if sentence[: pronoun_span.start].text_with_ws.endswith(" ") else "" + ) + trailing_space = " " if pronoun_span.text_with_ws.endswith(" ") else "" # get noun phrases, excluding pronouns and anything overlapping with the query cand_spans = wsc_utils.filter_noun_chunks( @@ -152,7 +162,11 @@ def load_dataset(self, split, epoch=1, combine=False, data_path=None, return_onl cand_toks, cand_masks = [], [] for cand_span in cand_spans: toks, mask = self.binarize_with_mask( - cand_span.text, prefix, suffix, leading_space, trailing_space, + cand_span.text, + prefix, + suffix, + leading_space, + trailing_space, ) cand_toks.append(toks) cand_masks.append(mask) @@ -176,17 +190,17 @@ def load_dataset(self, split, epoch=1, combine=False, data_path=None, return_onl candidate_tokens = ListDataset(candidate_tokens, candidate_lengths) candidate_masks = ListDataset(candidate_masks, candidate_lengths) - labels = ListDataset(labels, [1]*len(labels)) + labels = ListDataset(labels, [1] * len(labels)) dataset = { - 'id': IdDataset(), - 'query_tokens': query_tokens, - 'query_masks': query_masks, - 'candidate_tokens': candidate_tokens, - 'candidate_masks': candidate_masks, - 'labels': labels, - 'nsentences': NumSamplesDataset(), - 'ntokens': NumelDataset(query_tokens, reduce=True), + "id": IdDataset(), + "query_tokens": query_tokens, + "query_masks": query_masks, + "candidate_tokens": candidate_tokens, + "candidate_masks": candidate_masks, + "labels": labels, + "nsentences": NumSamplesDataset(), + "ntokens": NumelDataset(query_tokens, reduce=True), } nested_dataset = NestedDictionaryDataset( @@ -210,9 +224,9 @@ def load_dataset(self, split, epoch=1, combine=False, data_path=None, return_onl def build_dataset_for_inference(self, sample_json): with tempfile.NamedTemporaryFile(buffering=0) as h: - h.write((json.dumps(sample_json) + '\n').encode('utf-8')) + h.write((json.dumps(sample_json) + "\n").encode("utf-8")) dataset = self.load_dataset( - 'disambiguate_pronoun', + "disambiguate_pronoun", data_path=h.name, return_only=True, ) @@ -239,19 +253,19 @@ def get_lprobs(tokens, mask): return scores cand_lprobs = get_lprobs( - sample['candidate_tokens'][0], - sample['candidate_masks'][0], + sample["candidate_tokens"][0], + sample["candidate_masks"][0], ) - if sample['query_tokens'][0] is not None: + if sample["query_tokens"][0] is not None: query_lprobs = get_lprobs( - sample['query_tokens'][0].unsqueeze(0), - sample['query_masks'][0].unsqueeze(0), + sample["query_tokens"][0].unsqueeze(0), + sample["query_masks"][0].unsqueeze(0), ) return (query_lprobs >= cand_lprobs).all().item() == 1 else: best_idx = cand_lprobs.argmax().item() - full_cand = sample['candidate_tokens'][0][best_idx] - mask = sample['candidate_masks'][0][best_idx] + full_cand = sample["candidate_tokens"][0][best_idx] + mask = sample["candidate_masks"][0][best_idx] toks = full_cand[mask.bool()] return self.bpe.decode(self.source_dictionary.string(toks)).strip() @@ -264,7 +278,7 @@ def target_dictionary(self): return self.vocab -@register_task('winogrande') +@register_task("winogrande") class WinograndeTask(WSCTask): """ Task for WinoGrande dataset. Efficient implementation for Winograd schema @@ -273,24 +287,26 @@ class WinograndeTask(WSCTask): @classmethod def setup_task(cls, args, **kwargs): - assert args.criterion == 'winogrande', 'Must set --criterion=winogrande' + assert args.criterion == "winogrande", "Must set --criterion=winogrande" # load data and label dictionaries - vocab = cls.load_dictionary(os.path.join(args.data, 'dict.txt')) - print('| dictionary: {} types'.format(len(vocab))) + vocab = cls.load_dictionary(os.path.join(args.data, "dict.txt")) + print("| dictionary: {} types".format(len(vocab))) return cls(args, vocab) - def load_dataset(self, split, epoch=1, combine=False, data_path=None, return_only=False, **kwargs): + def load_dataset( + self, split, epoch=1, combine=False, data_path=None, return_only=False, **kwargs + ): """Load a given dataset split. Args: split (str): name of the split (e.g., train, valid, test) """ if data_path is None: - data_path = os.path.join(self.args.data, split + '.jsonl') + data_path = os.path.join(self.args.data, split + ".jsonl") if not os.path.exists(data_path): - raise FileNotFoundError('Cannot find data: {}'.format(data_path)) + raise FileNotFoundError("Cannot find data: {}".format(data_path)) query_tokens = [] query_masks = [] @@ -299,19 +315,23 @@ def load_dataset(self, split, epoch=1, combine=False, data_path=None, return_onl candidate_masks = [] candidate_lengths = [] - itr = wsc_utils.winogrande_jsonl_iterator(data_path, eval=(split == 'test')) + itr = wsc_utils.winogrande_jsonl_iterator(data_path, eval=(split == "test")) for sample in itr: sentence, pronoun_span, query, cand_text = sample - prefix = sentence[:pronoun_span[0]].rstrip() - suffix = sentence[pronoun_span[1]:] + prefix = sentence[: pronoun_span[0]].rstrip() + suffix = sentence[pronoun_span[1] :] - leading_space = ' ' if sentence[:pronoun_span[0]].endswith(' ') else '' - trailing_space = '' + leading_space = " " if sentence[: pronoun_span[0]].endswith(" ") else "" + trailing_space = "" if query is not None: query_toks, query_mask = self.binarize_with_mask( - query, prefix, suffix, leading_space, trailing_space, + query, + prefix, + suffix, + leading_space, + trailing_space, ) query_len = len(query_toks) else: @@ -322,7 +342,11 @@ def load_dataset(self, split, epoch=1, combine=False, data_path=None, return_onl query_lengths.append(query_len) cand_toks, cand_mask = self.binarize_with_mask( - cand_text, prefix, suffix, leading_space, trailing_space, + cand_text, + prefix, + suffix, + leading_space, + trailing_space, ) candidate_tokens.append(cand_toks) @@ -342,17 +366,19 @@ def get_pad_dataset_fn(tokens, length, pad_idx): query_masks = get_pad_dataset_fn(query_masks, query_lengths, 0) candidate_lengths = np.array(candidate_lengths) - candidate_tokens = get_pad_dataset_fn(candidate_tokens, candidate_lengths, self.vocab.pad()) + candidate_tokens = get_pad_dataset_fn( + candidate_tokens, candidate_lengths, self.vocab.pad() + ) candidate_masks = get_pad_dataset_fn(candidate_masks, candidate_lengths, 0) dataset = { - 'id': IdDataset(), - 'query_tokens': query_tokens, - 'query_masks': query_masks, - 'candidate_tokens': candidate_tokens, - 'candidate_masks': candidate_masks, - 'nsentences': NumSamplesDataset(), - 'ntokens': NumelDataset(query_tokens, reduce=True), + "id": IdDataset(), + "query_tokens": query_tokens, + "query_masks": query_masks, + "candidate_tokens": candidate_tokens, + "candidate_masks": candidate_masks, + "nsentences": NumSamplesDataset(), + "ntokens": NumelDataset(query_tokens, reduce=True), } nested_dataset = NestedDictionaryDataset( diff --git a/examples/roberta/wsc/wsc_utils.py b/examples/roberta/wsc/wsc_utils.py index 2d4822479e..da6ba74383 100644 --- a/examples/roberta/wsc/wsc_utils.py +++ b/examples/roberta/wsc/wsc_utils.py @@ -3,48 +3,48 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. -from functools import lru_cache import json +from functools import lru_cache def convert_sentence_to_json(sentence): - if '_' in sentence: - prefix, rest = sentence.split('_', 1) - query, rest = rest.split('_', 1) - query_index = len(prefix.rstrip().split(' ')) + if "_" in sentence: + prefix, rest = sentence.split("_", 1) + query, rest = rest.split("_", 1) + query_index = len(prefix.rstrip().split(" ")) else: query, query_index = None, None - prefix, rest = sentence.split('[', 1) - pronoun, rest = rest.split(']', 1) - pronoun_index = len(prefix.rstrip().split(' ')) + prefix, rest = sentence.split("[", 1) + pronoun, rest = rest.split("]", 1) + pronoun_index = len(prefix.rstrip().split(" ")) - sentence = sentence.replace('_', '').replace('[', '').replace(']', '') + sentence = sentence.replace("_", "").replace("[", "").replace("]", "") return { - 'idx': 0, - 'text': sentence, - 'target': { - 'span1_index': query_index, - 'span1_text': query, - 'span2_index': pronoun_index, - 'span2_text': pronoun, + "idx": 0, + "text": sentence, + "target": { + "span1_index": query_index, + "span1_text": query, + "span2_index": pronoun_index, + "span2_text": pronoun, }, } def extended_noun_chunks(sentence): noun_chunks = {(np.start, np.end) for np in sentence.noun_chunks} - np_start, cur_np = 0, 'NONE' + np_start, cur_np = 0, "NONE" for i, token in enumerate(sentence): - np_type = token.pos_ if token.pos_ in {'NOUN', 'PROPN'} else 'NONE' + np_type = token.pos_ if token.pos_ in {"NOUN", "PROPN"} else "NONE" if np_type != cur_np: - if cur_np != 'NONE': + if cur_np != "NONE": noun_chunks.add((np_start, i)) - if np_type != 'NONE': + if np_type != "NONE": np_start = i cur_np = np_type - if cur_np != 'NONE': + if cur_np != "NONE": noun_chunks.add((np_start, len(sentence))) return [sentence[s:e] for (s, e) in sorted(noun_chunks)] @@ -61,14 +61,14 @@ def find_token(sentence, start_pos): def find_span(sentence, search_text, start=0): search_text = search_text.lower() for tok in sentence[start:]: - remainder = sentence[tok.i:].text.lower() + remainder = sentence[tok.i :].text.lower() if remainder.startswith(search_text): len_to_consume = len(search_text) start_idx = tok.idx - for next_tok in sentence[tok.i:]: + for next_tok in sentence[tok.i :]: end_idx = next_tok.idx + len(next_tok.text) if end_idx - start_idx == len_to_consume: - span = sentence[tok.i:next_tok.i + 1] + span = sentence[tok.i : next_tok.i + 1] return span return None @@ -76,13 +76,15 @@ def find_span(sentence, search_text, start=0): @lru_cache(maxsize=1) def get_detokenizer(): from sacremoses import MosesDetokenizer - detok = MosesDetokenizer(lang='en') + + detok = MosesDetokenizer(lang="en") return detok @lru_cache(maxsize=1) def get_spacy_nlp(): import en_core_web_lg + nlp = en_core_web_lg.load() return nlp @@ -95,45 +97,45 @@ def jsonl_iterator(input_fname, positive_only=False, ngram_order=3, eval=False): for line in fin: sample = json.loads(line.strip()) - if positive_only and 'label' in sample and not sample['label']: + if positive_only and "label" in sample and not sample["label"]: # only consider examples where the query is correct continue - target = sample['target'] + target = sample["target"] # clean up the query - query = target['span1_text'] + query = target["span1_text"] if query is not None: - if '\n' in query: + if "\n" in query: continue - if query.endswith('.') or query.endswith(','): + if query.endswith(".") or query.endswith(","): query = query[:-1] # split tokens - tokens = sample['text'].split(' ') + tokens = sample["text"].split(" ") def strip_pronoun(x): return x.rstrip('.,"') # find the pronoun - pronoun_idx = target['span2_index'] - pronoun = strip_pronoun(target['span2_text']) + pronoun_idx = target["span2_index"] + pronoun = strip_pronoun(target["span2_text"]) if strip_pronoun(tokens[pronoun_idx]) != pronoun: # hack: sometimes the index is misaligned if strip_pronoun(tokens[pronoun_idx + 1]) == pronoun: pronoun_idx += 1 else: - raise Exception('Misaligned pronoun!') + raise Exception("Misaligned pronoun!") assert strip_pronoun(tokens[pronoun_idx]) == pronoun # split tokens before and after the pronoun before = tokens[:pronoun_idx] - after = tokens[pronoun_idx + 1:] + after = tokens[pronoun_idx + 1 :] # the GPT BPE attaches leading spaces to tokens, so we keep track # of whether we need spaces before or after the pronoun - leading_space = ' ' if pronoun_idx > 0 else '' - trailing_space = ' ' if len(after) > 0 else '' + leading_space = " " if pronoun_idx > 0 else "" + trailing_space = " " if len(after) > 0 else "" # detokenize before = detok.detokenize(before, return_str=True) @@ -142,14 +144,14 @@ def strip_pronoun(x): # hack: when the pronoun ends in a period (or comma), move the # punctuation to the "after" part - if pronoun.endswith('.') or pronoun.endswith(','): + if pronoun.endswith(".") or pronoun.endswith(","): after = pronoun[-1] + trailing_space + after pronoun = pronoun[:-1] # hack: when the "after" part begins with a comma or period, remove # the trailing space - if after.startswith('.') or after.startswith(','): - trailing_space = '' + if after.startswith(".") or after.startswith(","): + trailing_space = "" # parse sentence with spacy sentence = nlp(before + leading_space + pronoun + trailing_space + after) @@ -164,13 +166,13 @@ def strip_pronoun(x): # convert to format where pronoun is surrounded by "[]" and # query is surrounded by "_" query_span = find_span(sentence, query) - query_with_ws = '_{}_{}'.format( + query_with_ws = "_{}_{}".format( query_span.text, - (' ' if query_span.text_with_ws.endswith(' ') else '') + (" " if query_span.text_with_ws.endswith(" ") else ""), ) - pronoun_with_ws = '[{}]{}'.format( + pronoun_with_ws = "[{}]{}".format( pronoun_span.text, - (' ' if pronoun_span.text_with_ws.endswith(' ') else '') + (" " if pronoun_span.text_with_ws.endswith(" ") else ""), ) if query_span.start < pronoun_span.start: first = (query_span, query_with_ws) @@ -179,41 +181,45 @@ def strip_pronoun(x): first = (pronoun_span, pronoun_with_ws) second = (query_span, query_with_ws) sentence = ( - sentence[:first[0].start].text_with_ws + sentence[: first[0].start].text_with_ws + first[1] - + sentence[first[0].end:second[0].start].text_with_ws + + sentence[first[0].end : second[0].start].text_with_ws + second[1] - + sentence[second[0].end:].text + + sentence[second[0].end :].text ) - yield sentence, sample.get('label', None) + yield sentence, sample.get("label", None) else: - yield sentence, pronoun_span, query, sample.get('label', None) + yield sentence, pronoun_span, query, sample.get("label", None) def winogrande_jsonl_iterator(input_fname, eval=False): with open(input_fname) as fin: for line in fin: sample = json.loads(line.strip()) - sentence, option1, option2 = sample['sentence'], sample['option1'],\ - sample['option2'] + sentence, option1, option2 = ( + sample["sentence"], + sample["option1"], + sample["option2"], + ) - pronoun_span = (sentence.index('_'), sentence.index('_') + 1) + pronoun_span = (sentence.index("_"), sentence.index("_") + 1) if eval: query, cand = option1, option2 else: - query = option1 if sample['answer'] == '1' else option2 - cand = option2 if sample['answer'] == '1' else option1 + query = option1 if sample["answer"] == "1" else option2 + cand = option2 if sample["answer"] == "1" else option1 yield sentence, pronoun_span, query, cand -def filter_noun_chunks(chunks, exclude_pronouns=False, exclude_query=None, exact_match=False): +def filter_noun_chunks( + chunks, exclude_pronouns=False, exclude_query=None, exact_match=False +): if exclude_pronouns: chunks = [ - np for np in chunks if ( - np.lemma_ != '-PRON-' - and not all(tok.pos_ == 'PRON' for tok in np) - ) + np + for np in chunks + if (np.lemma_ != "-PRON-" and not all(tok.pos_ == "PRON" for tok in np)) ] if exclude_query is not None: @@ -224,9 +230,8 @@ def filter_noun_chunks(chunks, exclude_pronouns=False, exclude_query=None, exact found = False for excl in excl_txt: if ( - (not exact_match and (lower_chunk in excl or excl in lower_chunk)) - or lower_chunk == excl - ): + not exact_match and (lower_chunk in excl or excl in lower_chunk) + ) or lower_chunk == excl: found = True break if not found: diff --git a/examples/rxf/README.md b/examples/rxf/README.md new file mode 100644 index 0000000000..22a1cc47df --- /dev/null +++ b/examples/rxf/README.md @@ -0,0 +1,52 @@ +[Better Fine-Tuning by Reducing Representational Collapse](https://arxiv.org/abs/2008.03156) +===================== +This repo contains the code to replicate all experiments from the _Better Fine-Tuning by Reducing Representational Collapse_ paper excluding the probing results. + +The R3F sentence prediction criterion is registered as `sentence_prediction_r3f` while the label smoothing version of it is implemented as `label_smoothed_cross_entropy_r3f`. The R4F version of the sentence prediction criterion can be achieved by applying spectral norm to the classification head via the `--spectral-norm-classification-head` parameter. + +## Hyper-parameters +Our methods introduce 3 new hyper-parameters; `--eps` which sets the standard deviation or range of the distribution we're sampling from, `--r3f-lambda` which controls the combining of logistic loss and noisy KL loss and `--noise-type` which controls which parametric distribution we use ('normal', 'uniform'). + +For example to run R3F on RTE from GLUE + +``` +TOTAL_NUM_UPDATES=3120 +WARMUP_UPDATES=187 +LR=1e-05 +NUM_CLASSES=2 +MAX_SENTENCES=8 # Batch size. +ROBERTA_PATH=/path/to/roberta/model.pt + +CUDA_VISIBLE_DEVICES=0 fairseq-train RTE-bin \ + --restore-file $ROBERTA_PATH \ + --max-positions 512 \ + --max-sentences $MAX_SENTENCES \ + --max-tokens 4400 \ + --task sentence_prediction \ + --reset-optimizer --reset-dataloader --reset-meters \ + --required-batch-size-multiple 1 \ + --init-token 0 --separator-token 2 \ + --arch roberta_large \ + --criterion sentence_prediction_r3f \ + --num-classes $NUM_CLASSES \ + --dropout 0.1 --attention-dropout 0.1 \ + --weight-decay 0.1 --optimizer adam --adam-betas "(0.9, 0.98)" --adam-eps 1e-06 \ + --clip-norm 0.0 \ + --lr-scheduler polynomial_decay --lr $LR --total-num-update $TOTAL_NUM_UPDATES --warmup-updates $WARMUP_UPDATES \ + --fp16 --fp16-init-scale 4 --threshold-loss-scale 1 --fp16-scale-window 128 \ + --max-epoch 10 \ + --find-unused-parameters \ + --best-checkpoint-metric accuracy --maximize-best-checkpoint-metric \ + --noise-type uniform --r3f-lambda 0.7 \ + --user-dir examples/rxf/rxf_src +``` + +## Citation +```bibtex +@article{aghajanyan2020better, + title={Better Fine-Tuning by Reducing Representational Collapse}, + author={Aghajanyan, Armen and Shrivastava, Akshat and Gupta, Anchit and Goyal, Naman and Zettlemoyer, Luke and Gupta, Sonal}, + journal={arXiv preprint arXiv:2008.03156}, + year={2020} +} +``` diff --git a/examples/rxf/__init__.py b/examples/rxf/__init__.py new file mode 100644 index 0000000000..b24cb6b797 --- /dev/null +++ b/examples/rxf/__init__.py @@ -0,0 +1,6 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from . import rxf_src # noqa diff --git a/examples/rxf/rxf_src/__init__.py b/examples/rxf/rxf_src/__init__.py new file mode 100644 index 0000000000..306e232d6f --- /dev/null +++ b/examples/rxf/rxf_src/__init__.py @@ -0,0 +1,6 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from . import label_smoothed_cross_entropy_r3f, sentence_prediction_r3f # noqa diff --git a/examples/rxf/rxf_src/label_smoothed_cross_entropy_r3f.py b/examples/rxf/rxf_src/label_smoothed_cross_entropy_r3f.py new file mode 100644 index 0000000000..079db13e61 --- /dev/null +++ b/examples/rxf/rxf_src/label_smoothed_cross_entropy_r3f.py @@ -0,0 +1,157 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math + +import torch +import torch.nn.functional as F +from fairseq import metrics, utils +from fairseq.criterions import FairseqCriterion, register_criterion +from fairseq.criterions.label_smoothed_cross_entropy import label_smoothed_nll_loss + + +@register_criterion("label_smoothed_cross_entropy_r3f") +class LabelSmoothedCrossEntropyR3FCriterion(FairseqCriterion): + def __init__( + self, task, sentence_avg, label_smoothing, eps, r3f_lambda, noise_type + ): + super().__init__(task) + self.sentence_avg = sentence_avg + self.label_smoothing = label_smoothing + self.eps = eps + self.r3f_lambda = r3f_lambda + self.noise_type = noise_type + if self.noise_type in {"normal"}: + self.noise_sampler = torch.distributions.normal.Normal( + loc=0.0, scale=self.eps + ) + elif self.noise_type == "uniform": + self.noise_sampler = torch.distributions.uniform.Uniform( + low=-self.eps, high=self.eps + ) + else: + raise Exception(f"unrecognized noise type {self.noise_type}") + + @staticmethod + def add_args(parser): + """Add criterion-specific arguments to the parser.""" + # fmt: off + parser.add_argument('--label-smoothing', default=0., type=float, metavar='D', + help='epsilon for label smoothing, 0 means no label smoothing') + parser.add_argument('--eps', type=float, default=1e-5, + help='noise eps') + parser.add_argument('--r3f-lambda', type=float, default=1.0, + help='lambda for combining logistic loss and noisy KL loss') + parser.add_argument('--noise-type', type=str, default='normal', + choices=['normal', 'uniform'], + help='type of noises') + # fmt: on + + def _get_symm_kl(self, noised_logits, input_logits): + return ( + F.kl_div( + F.log_softmax(noised_logits, dim=-1, dtype=torch.float32), + F.softmax(input_logits, dim=-1, dtype=torch.float32), + None, + None, + "sum", + ) + + F.kl_div( + F.log_softmax(input_logits, dim=-1, dtype=torch.float32), + F.softmax(noised_logits, dim=-1, dtype=torch.float32), + None, + None, + "sum", + ) + ) / noised_logits.size(0) + + def forward(self, model, sample, reduce=True): + """Compute the loss for the given sample. + + Returns a tuple with three elements: + 1) the loss + 2) the sample size, which is used as the denominator for the gradient + 3) logging outputs to display while training + """ + token_embeddings = model.encoder.embed_tokens(sample["net_input"]["src_tokens"]) + input_logits, extra = model(**sample["net_input"]) + loss, nll_loss = self.compute_loss( + model, (input_logits, extra), sample, reduce=reduce + ) + sample_size = ( + sample["target"].size(0) if self.sentence_avg else sample["ntokens"] + ) + + if model.training: + noise = self.noise_sampler.sample(sample_shape=token_embeddings.shape).to( + token_embeddings + ) + noised_embeddings = token_embeddings.clone() + noise + + noised_logits, _ = model( + **sample["net_input"], token_embeddings=noised_embeddings + ) + symm_kl = self._get_symm_kl(noised_logits, input_logits) + + if model.training: + symm_kl = symm_kl * sample_size + loss = loss + self.r3f_lambda * symm_kl + + logging_output = { + "loss": loss.data, + "nll_loss": nll_loss.data, + "ntokens": sample["ntokens"], + "nsentences": sample["target"].size(0), + "sample_size": sample_size, + } + + if model.training: + logging_output.update( + symm_kl=utils.item(symm_kl.data) if reduce else symm_kl.data + ) + + return loss, sample_size, logging_output + + def compute_loss(self, model, net_output, sample, reduce=True): + lprobs = model.get_normalized_probs(net_output, log_probs=True) + lprobs = lprobs.view(-1, lprobs.size(-1)) + target = model.get_targets(sample, net_output).view(-1, 1) + loss, nll_loss = label_smoothed_nll_loss( + lprobs, + target, + self.label_smoothing, + ignore_index=self.padding_idx, + reduce=reduce, + ) + return loss, nll_loss + + @staticmethod + def reduce_metrics(logging_outputs) -> None: + """Aggregate logging outputs from data parallel training.""" + loss_sum = sum(log.get("loss", 0) for log in logging_outputs) + nll_loss_sum = sum(log.get("nll_loss", 0) for log in logging_outputs) + ntokens = sum(log.get("ntokens", 0) for log in logging_outputs) + sample_size = sum(log.get("sample_size", 0) for log in logging_outputs) + symm_kl_sum = sum(log.get("symm_kl", 0) for log in logging_outputs) + + metrics.log_scalar("symm_kl", symm_kl_sum / sample_size, sample_size, round=3) + metrics.log_scalar( + "loss", loss_sum / sample_size / math.log(2), sample_size, round=3 + ) + metrics.log_scalar( + "nll_loss", nll_loss_sum / ntokens / math.log(2), ntokens, round=3 + ) + metrics.log_derived( + "ppl", lambda meters: utils.get_perplexity(meters["nll_loss"].avg) + ) + + @staticmethod + def logging_outputs_can_be_summed() -> bool: + """ + Whether the logging outputs returned by `forward` can be summed + across workers prior to calling `reduce_metrics`. Setting this + to True will improves distributed training speed. + """ + return True diff --git a/examples/rxf/rxf_src/sentence_prediction_r3f.py b/examples/rxf/rxf_src/sentence_prediction_r3f.py new file mode 100644 index 0000000000..6ecffd6b14 --- /dev/null +++ b/examples/rxf/rxf_src/sentence_prediction_r3f.py @@ -0,0 +1,171 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math + +import torch +import torch.nn.functional as F +from fairseq import utils +from fairseq.criterions import FairseqCriterion, register_criterion + + +@register_criterion("sentence_prediction_r3f") +class SentencePredictionR3F(FairseqCriterion): + def __init__( + self, + task, + eps, + r3f_lambda, + noise_type, + classification_head_name, + regression_target, + ): + super().__init__(task) + self.eps = eps + self.r3f_lambda = r3f_lambda + self.noise_type = noise_type + self.classification_head_name = classification_head_name + self.regression_target = regression_target + if self.noise_type in {"normal"}: + self.noise_sampler = torch.distributions.normal.Normal( + loc=0.0, scale=self.eps + ) + elif self.noise_type == "uniform": + self.noise_sampler = torch.distributions.uniform.Uniform( + low=-self.eps, high=self.eps + ) + else: + raise Exception(f"unrecognized noise type {self.noise_type}") + + @staticmethod + def add_args(parser): + # fmt: off + parser.add_argument('--eps', type=float, default=1e-5, + help='noise eps') + parser.add_argument('--r3f-lambda', type=float, default=1.0, + help='lambda for combining logistic loss and noisy KL loss') + parser.add_argument('--noise-type', type=str, default='uniform', + choices=['normal', 'uniform'], + help='type of noises for RXF methods') + parser.add_argument('--classification-head-name', + default='sentence_classification_head', + help='name of the classification head to use') + parser.add_argument('--regression-target', action='store_true') + # fmt: on + + def _get_symm_kl(self, noised_logits, input_logits): + return ( + F.kl_div( + F.log_softmax(noised_logits, dim=-1, dtype=torch.float32), + F.softmax(input_logits, dim=-1, dtype=torch.float32), + None, + None, + "sum", + ) + + F.kl_div( + F.log_softmax(input_logits, dim=-1, dtype=torch.float32), + F.softmax(noised_logits, dim=-1, dtype=torch.float32), + None, + None, + "sum", + ) + ) / noised_logits.size(0) + + def forward(self, model, sample, reduce=True): + """Compute the loss for the given sample. + + Returns a tuple with three elements: + 1) the loss + 2) the sample size, which is used as the denominator for the gradient + 3) logging outputs to display while training + """ + assert ( + hasattr(model, "classification_heads") + and self.classification_head_name in model.classification_heads + ), "model must provide sentence classification head for --criterion=sentence_prediction" + + token_embeddings = model.encoder.sentence_encoder.embed_tokens( + sample["net_input"]["src_tokens"] + ) + input_logits, _ = model( + **sample["net_input"], + features_only=True, + classification_head_name=self.classification_head_name, + token_embeddings=token_embeddings, + ) + if model.training and self.noise_sampler: + noise = self.noise_sampler.sample(sample_shape=token_embeddings.shape).to( + token_embeddings + ) + noised_embeddings = token_embeddings.detach().clone() + noise + + noised_logits, _ = model( + **sample["net_input"], + features_only=True, + classification_head_name=self.classification_head_name, + token_embeddings=noised_embeddings, + ) + symm_kl = self._get_symm_kl(noised_logits, input_logits) + else: + symm_kl = 0 + + targets = model.get_targets(sample, [input_logits]).view(-1) + sample_size = targets.numel() + + if not self.regression_target: + loss = F.nll_loss( + F.log_softmax(input_logits, dim=-1, dtype=torch.float32), + targets, + reduction="sum", + ) + if model.training: + symm_kl = symm_kl * sample_size + loss = loss + self.r3f_lambda * symm_kl + else: + logits = input_logits.squeeze().float() + targets = targets.float() + loss = F.mse_loss(logits, targets, reduction="sum") + + logging_output = { + "loss": utils.item(loss.data) if reduce else loss.data, + "ntokens": sample["ntokens"], + "nsentences": sample_size, + "sample_size": sample_size, + } + + if not self.regression_target: + preds = input_logits.max(dim=1)[1] + logging_output.update(ncorrect=(preds == targets).sum().item()) + + if model.training and self.noise_sampler: + logging_output.update( + symm_kl=utils.item(symm_kl.data) if reduce else symm_kl.data + ) + return loss, sample_size, logging_output + + @staticmethod + def aggregate_logging_outputs(logging_outputs): + """Aggregate logging outputs from data parallel training.""" + loss_sum = sum(log.get("loss", 0) for log in logging_outputs) + symm_kl_sum = sum(log.get("symm_kl", 0) for log in logging_outputs) + ntokens = sum(log.get("ntokens", 0) for log in logging_outputs) + nsentences = sum(log.get("nsentences", 0) for log in logging_outputs) + sample_size = sum(log.get("sample_size", 0) for log in logging_outputs) + + agg_output = { + "loss": loss_sum / sample_size / math.log(2), + "symm_kl": symm_kl_sum / sample_size, + "ntokens": ntokens, + "nsentences": nsentences, + "sample_size": sample_size, + } + + if len(logging_outputs) > 0 and "ncorrect" in logging_outputs[0]: + ncorrect = sum(log.get("ncorrect", 0) for log in logging_outputs) + agg_output.update(accuracy=ncorrect / nsentences) + + if sample_size != ntokens: + agg_output["nll_loss"] = loss_sum / ntokens / math.log(2) + return agg_output diff --git a/examples/scaling_nmt/README.md b/examples/scaling_nmt/README.md index 0782ef07ba..0cc3360c3b 100644 --- a/examples/scaling_nmt/README.md +++ b/examples/scaling_nmt/README.md @@ -70,16 +70,36 @@ good, but you may need to adjust this depending on how long you've trained: ```bash python scripts/average_checkpoints \ --inputs /path/to/checkpoints \ - --num-epoch-checkpoints 5 \ - --output checkpoint.avg5.pt + --num-epoch-checkpoints 10 \ + --output checkpoint.avg10.pt ``` Next, generate translations using a beam width of 4 and length penalty of 0.6: ```bash fairseq-generate \ data-bin/wmt16_en_de_bpe32k \ - --path checkpoint.avg5.pt \ - --beam 4 --lenpen 0.6 --remove-bpe + --path checkpoint.avg10.pt \ + --beam 4 --lenpen 0.6 --remove-bpe > gen.out +``` + +Finally, we apply the ["compound splitting" script](/scripts/compound_split_bleu.sh) to +add spaces around dashes. For example "Café-Liebhaber" would become three tokens: +"Café - Liebhaber". This typically results in larger BLEU scores, but it is not +appropriate to compare these inflated scores to work which does not include this trick. +This trick was used in the [original AIAYN code](https://github.com/tensorflow/tensor2tensor/blob/fc9335c0203685cbbfe2b30c92db4352d8f60779/tensor2tensor/utils/get_ende_bleu.sh), +so we used it in the Scaling NMT paper as well. That said, it's strongly advised to +report [sacrebleu](https://github.com/mjpost/sacrebleu) scores instead. + +To compute "compound split" tokenized BLEU (not recommended!): +```bash +bash scripts/compound_split_bleu.sh gen.out +# BLEU4 = 29.29, 60.3/35.0/22.8/15.3 (BP=1.000, ratio=1.004, syslen=64763, reflen=64496) +``` + +To compute detokenized BLEU with sacrebleu (preferred): +```bash +bash scripts/sacrebleu.sh wmt14/full en de gen.out +# BLEU+case.mixed+lang.en-de+numrefs.1+smooth.exp+test.wmt14/full+tok.13a+version.1.4.3 = 28.6 59.3/34.3/22.1/14.9 (BP = 1.000 ratio = 1.016 hyp_len = 63666 ref_len = 62688) ``` ## Citation diff --git a/examples/shuffled_word_order/README.finetuning.md b/examples/shuffled_word_order/README.finetuning.md new file mode 100644 index 0000000000..ecbcb65884 --- /dev/null +++ b/examples/shuffled_word_order/README.finetuning.md @@ -0,0 +1,135 @@ +# Fine-tuning details + +For each task (GLUE and PAWS), we perform hyperparam search for each model, and report the mean and standard deviation across 5 seeds of the best model. First, get the datasets following the instructions in [RoBERTa fine-tuning README](../roberta/README.glue.md). Alternatively, you can use [huggingface datasets](https://huggingface.co/docs/datasets/) to get the task data: + +```python +from datasets import load_dataset +import pandas as pd +from pathlib import Path + +key2file = { +"paws": { + "loc": "paws_data", + "columns": ["id", "sentence1", "sentence2", "label"], + "train": "train.tsv", + "validation": "dev.tsv", + "test": "test.tsv" + } +} + +task_data = load_dataset("paws", "labeled_final") +task_config = key2file["paws"] +save_path = Path(task_config["loc"]) +save_path.mkdir(exist_ok=True, parents=True) +for key, fl in task_config.items(): + if key in ["loc", "columns"]: + continue + print(f"Reading {key}") + columns = task_config["columns"] + df = pd.DataFrame(task_data[key]) + print(df.columns) + df = df[columns] + print(f"Got {len(df)} records") + save_loc = save_path / fl + print(f"Saving to : {save_loc}") + df.to_csv(save_loc, sep="\t", header=None, index=None) + +``` + +- Preprocess using RoBERTa GLUE preprocessing script, while keeping in mind the column numbers for `sentence1`, `sentence2` and `label` (which is 0,1,2 if you save the data according to the above example.) +- Then, fine-tuning is performed similarly to RoBERTa (for example, in case of RTE): + +```bash +TOTAL_NUM_UPDATES=30875 # 10 epochs through RTE for bsz 16 +WARMUP_UPDATES=1852 # 6 percent of the number of updates +LR=2e-05 # Peak LR for polynomial LR scheduler. +NUM_CLASSES=2 +MAX_SENTENCES=16 # Batch size. +SHUFFLED_ROBERTA_PATH=/path/to/shuffled_roberta/model.pt + +CUDA_VISIBLE_DEVICES=0 fairseq-train RTE-bin/ \ + --restore-file $SHUFFLED_ROBERTA_PATH \ + --max-positions 512 \ + --batch-size $MAX_SENTENCES \ + --max-tokens 4400 \ + --task sentence_prediction \ + --reset-optimizer --reset-dataloader --reset-meters \ + --required-batch-size-multiple 1 \ + --init-token 0 --separator-token 2 \ + --arch roberta_large \ + --criterion sentence_prediction \ + --num-classes $NUM_CLASSES \ + --dropout 0.1 --attention-dropout 0.1 \ + --weight-decay 0.1 --optimizer adam --adam-betas "(0.9, 0.98)" --adam-eps 1e-06 \ + --clip-norm 0.0 \ + --lr-scheduler polynomial_decay --lr $LR --total-num-update $TOTAL_NUM_UPDATES --warmup-updates $WARMUP_UPDATES \ + --fp16 --fp16-init-scale 4 --threshold-loss-scale 1 --fp16-scale-window 128 \ + --max-epoch 10 \ + --find-unused-parameters \ + --best-checkpoint-metric accuracy --maximize-best-checkpoint-metric; +``` + +- `TOTAL_NUM_UPDATES` is computed based on the `--batch_size` value and the dataset size. +- `WARMUP_UPDATES` is computed as 6% of `TOTAL_NUM_UPDATES` +- Best hyperparam of `--lr` and `--batch_size` is reported below: + +## `--lr` + +| | name | RTE | MRPC | SST-2 | CoLA | QQP | QNLI | MNLI | PAWS | +| --: | :----------- | ----: | ----: | ----: | ----: | ----: | ----: | ----: | ----: | +| 0 | original | 2e-05 | 2e-05 | 1e-05 | 2e-05 | 1e-05 | 1e-05 | 1e-05 | 2e-05 | +| 1 | n_1 | 2e-05 | 1e-05 | 1e-05 | 1e-05 | 3e-05 | 1e-05 | 2e-05 | 2e-05 | +| 2 | n_2 | 2e-05 | 2e-05 | 1e-05 | 1e-05 | 2e-05 | 1e-05 | 1e-05 | 3e-05 | +| 3 | n_3 | 3e-05 | 1e-05 | 2e-05 | 2e-05 | 3e-05 | 1e-05 | 1e-05 | 2e-05 | +| 4 | n_4 | 3e-05 | 1e-05 | 2e-05 | 2e-05 | 2e-05 | 1e-05 | 1e-05 | 2e-05 | +| 5 | r512 | 1e-05 | 3e-05 | 2e-05 | 2e-05 | 3e-05 | 2e-05 | 3e-05 | 2e-05 | +| 6 | rand_corpus | 2e-05 | 1e-05 | 3e-05 | 1e-05 | 3e-05 | 3e-05 | 3e-05 | 2e-05 | +| 7 | rand_uniform | 2e-05 | 1e-05 | 3e-05 | 2e-05 | 3e-05 | 3e-05 | 3e-05 | 1e-05 | +| 8 | rand_init | 1e-05 | 1e-05 | 3e-05 | 1e-05 | 1e-05 | 1e-05 | 2e-05 | 1e-05 | +| 9 | no_pos | 1e-05 | 3e-05 | 2e-05 | 1e-05 | 1e-05 | 1e-05 | 1e-05 | 1e-05 | + +## `--batch_size` + +| | name | RTE | MRPC | SST-2 | CoLA | QQP | QNLI | MNLI | PAWS | +| --: | :----------- | --: | ---: | ----: | ---: | --: | ---: | ---: | ---: | +| 0 | orig | 16 | 16 | 32 | 16 | 16 | 32 | 32 | 16 | +| 1 | n_1 | 32 | 32 | 16 | 32 | 32 | 16 | 32 | 16 | +| 2 | n_2 | 32 | 16 | 32 | 16 | 32 | 32 | 16 | 32 | +| 3 | n_3 | 32 | 32 | 16 | 32 | 32 | 16 | 32 | 32 | +| 4 | n_4 | 32 | 16 | 32 | 16 | 32 | 32 | 32 | 32 | +| 5 | r512 | 32 | 16 | 16 | 32 | 32 | 16 | 16 | 16 | +| 6 | rand_corpus | 16 | 16 | 16 | 16 | 32 | 16 | 16 | 32 | +| 7 | rand_uniform | 16 | 32 | 16 | 16 | 32 | 16 | 16 | 16 | +| 8 | rand_init | 16 | 16 | 32 | 16 | 16 | 16 | 32 | 16 | +| 9 | no_pos | 16 | 32 | 16 | 16 | 32 | 16 | 16 | 16 | + +- Perform inference similar to RoBERTa as well: + +```python +from fairseq.models.roberta import RobertaModel + +roberta = RobertaModel.from_pretrained( + 'checkpoints/', + checkpoint_file='checkpoint_best.pt', + data_name_or_path='PAWS-bin' +) + +label_fn = lambda label: roberta.task.label_dictionary.string( + [label + roberta.task.label_dictionary.nspecial] +) +ncorrect, nsamples = 0, 0 +roberta.cuda() +roberta.eval() +with open('paws_data/dev.tsv') as fin: + fin.readline() + for index, line in enumerate(fin): + tokens = line.strip().split('\t') + sent1, sent2, target = tokens[0], tokens[1], tokens[2] + tokens = roberta.encode(sent1, sent2) + prediction = roberta.predict('sentence_classification_head', tokens).argmax().item() + prediction_label = label_fn(prediction) + ncorrect += int(prediction_label == target) + nsamples += 1 +print('| Accuracy: ', float(ncorrect)/float(nsamples)) + +``` diff --git a/examples/shuffled_word_order/README.md b/examples/shuffled_word_order/README.md new file mode 100644 index 0000000000..f20483849a --- /dev/null +++ b/examples/shuffled_word_order/README.md @@ -0,0 +1,84 @@ +# Masked Language Modeling and the Distributional Hypothesis: Order Word Matters Pre-training for Little + +[https://arxiv.org/abs/2104.06644](https://arxiv.org/abs/2104.06644) + +## Introduction + +In this work, we pre-train [RoBERTa](../roberta) base on various word shuffled variants of BookWiki corpus (16GB). We observe that a word shuffled pre-trained model achieves surprisingly good scores on GLUE, PAWS and several parametric probing tasks. Please read our paper for more details on the experiments. + +## Pre-trained models + +| Model | Description | Download | +| ------------------------------------- | -------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------- | +| `roberta.base.orig` | RoBERTa (base) trained on natural corpus | [roberta.base.orig.tar.gz](https://dl.fbaipublicfiles.com/unnatural_pretraining/roberta.base.orig.tar.gz) | +| `roberta.base.shuffle.n1` | RoBERTa (base) trained on n=1 gram sentence word shuffled data | [roberta.base.shuffle.n1.tar.gz](https://dl.fbaipublicfiles.com/unnatural_pretraining/roberta.base.shuffle.n1.tar.gz) | +| `roberta.base.shuffle.n2` | RoBERTa (base) trained on n=2 gram sentence word shuffled data | [roberta.base.shuffle.n2.tar.gz](https://dl.fbaipublicfiles.com/unnatural_pretraining/roberta.base.shuffle.n2.tar.gz) | +| `roberta.base.shuffle.n3` | RoBERTa (base) trained on n=3 gram sentence word shuffled data | [roberta.base.shuffle.n3.tar.gz](https://dl.fbaipublicfiles.com/unnatural_pretraining/roberta.base.shuffle.n3.tar.gz) | +| `roberta.base.shuffle.n4` | RoBERTa (base) trained on n=4 gram sentence word shuffled data | [roberta.base.shuffle.n4.tar.gz](https://dl.fbaipublicfiles.com/unnatural_pretraining/roberta.base.shuffle.n4.tar.gz) | +| `roberta.base.shuffle.512` | RoBERTa (base) trained on unigram 512 word block shuffled data | [roberta.base.shuffle.512.tar.gz](https://dl.fbaipublicfiles.com/unnatural_pretraining/roberta.base.shuffle.512.tar.gz) | +| `roberta.base.shuffle.corpus` | RoBERTa (base) trained on unigram corpus word shuffled data | [roberta.base.shuffle.corpus.tar.gz](https://dl.fbaipublicfiles.com/unnatural_pretraining/roberta.base.shuffle.corpus.tar.gz) | +| `roberta.base.shuffle.corpus_uniform` | RoBERTa (base) trained on unigram corpus word shuffled data, where all words are uniformly sampled | [roberta.base.shuffle.corpus_uniform.tar.gz](https://dl.fbaipublicfiles.com/unnatural_pretraining/roberta.base.shuffle.corpus_uniform.tar.gz) | +| `roberta.base.nopos` | RoBERTa (base) without positional embeddings, trained on natural corpus | [roberta.base.nopos.tar.gz](https://dl.fbaipublicfiles.com/unnatural_pretraining/roberta.base.nopos.tar.gz) | + +## Results + +[GLUE (Wang et al, 2019)](https://gluebenchmark.com/) & [PAWS (Zhang et al, 2019)](https://github.com/google-research-datasets/paws) _(dev set, single model, single-task fine-tuning, median of 5 seeds)_ + +| name | CoLA | MNLI | MRPC | PAWS | QNLI | QQP | RTE | SST-2 | +| :----------------------------------- | ----: | ----: | ----: | ----: | ----: | ----: | ----: | ----: | +| `roberta.base.orig` | 61.4 | 86.11 | 89.19 | 94.46 | 92.53 | 91.26 | 74.64 | 93.92 | +| `roberta.base.shuffle.n1` | 35.15 | 82.64 | 86 | 89.97 | 89.02 | 91.01 | 69.02 | 90.47 | +| `roberta.base.shuffle.n2` | 54.37 | 83.43 | 86.24 | 93.46 | 90.44 | 91.36 | 70.83 | 91.79 | +| `roberta.base.shuffle.n3` | 48.72 | 83.85 | 86.36 | 94.05 | 91.69 | 91.24 | 70.65 | 92.02 | +| `roberta.base.shuffle.n4` | 58.64 | 83.77 | 86.98 | 94.32 | 91.69 | 91.4 | 70.83 | 92.48 | +| `roberta.base.shuffle.512` | 12.76 | 77.52 | 79.61 | 84.77 | 85.19 | 90.2 | 56.52 | 86.34 | +| `roberta.base.shuffle.corpus` | 0 | 71.9 | 70.52 | 58.52 | 71.11 | 85.52 | 53.99 | 83.35 | +| `roberta.base.shuffle.corpus_random` | 9.19 | 72.33 | 70.76 | 58.42 | 77.76 | 85.93 | 53.99 | 84.04 | +| `roberta.base.nopos` | 0 | 63.5 | 72.73 | 57.08 | 77.72 | 87.87 | 54.35 | 83.24 | + +For more results on probing tasks, please refer to [our paper](https://arxiv.org/abs/2104.06644). + +## Example Usage + +Follow the same usage as in [RoBERTa](https://github.com/pytorch/fairseq/tree/main/examples/roberta) to load and test your models: + +```python +# Download roberta.base.shuffle.n1 model +wget https://dl.fbaipublicfiles.com/unnatural_pretraining/roberta.base.shuffle.n1.tar.gz +tar -xzvf roberta.base.shuffle.n1.tar.gz + +# Load the model in fairseq +from fairseq.models.roberta import RoBERTaModel +roberta = RoBERTaModel.from_pretrained('/path/to/roberta.base.shuffle.n1', checkpoint_file='model.pt') +roberta.eval() # disable dropout (or leave in train mode to finetune) +``` + +**Note**: The model trained without positional embeddings (`roberta.base.nopos`) is a modified `RoBERTa` model, where the positional embeddings are not used. Thus, the typical `from_pretrained` method on fairseq version of RoBERTa will not be able to load the above model weights. To do so, construct a new `RoBERTaModel` object by setting the flag `use_positional_embeddings` to `False` (or [in the latest code](https://github.com/pytorch/fairseq/blob/main/fairseq/models/roberta/model.py#L543), set `no_token_positional_embeddings` to `True`), and then load the individual weights. + +## Fine-tuning Evaluation + +We provide the trained fine-tuned models on MNLI here for each model above for quick evaluation (1 seed for each model). Please refer to [finetuning details](README.finetuning.md) for the parameters of these models. Follow [RoBERTa](https://github.com/pytorch/fairseq/tree/main/examples/roberta) instructions to evaluate these models. + +| Model | MNLI M Dev Accuracy | Link | +| :----------------------------------------- | :------------------ | :--------------------------------------------------------------------------------------------------------------- | +| `roberta.base.orig.mnli` | 86.14 | [Download](https://dl.fbaipublicfiles.com/unnatural_pretraining/roberta.base.orig.mnli.tar.gz) | +| `roberta.base.shuffle.n1.mnli` | 82.55 | [Download](https://dl.fbaipublicfiles.com/unnatural_pretraining/roberta.base.shuffle.n1.mnli.tar.gz) | +| `roberta.base.shuffle.n2.mnli` | 83.21 | [Download](https://dl.fbaipublicfiles.com/unnatural_pretraining/roberta.base.shuffle.n2.mnli.tar.gz) | +| `roberta.base.shuffle.n3.mnli` | 83.89 | [Download](https://dl.fbaipublicfiles.com/unnatural_pretraining/roberta.base.shuffle.n3.mnli.tar.gz) | +| `roberta.base.shuffle.n4.mnli` | 84.00 | [Download](https://dl.fbaipublicfiles.com/unnatural_pretraining/roberta.base.shuffle.n4.mnli.tar.gz) | +| `roberta.base.shuffle.512.mnli` | 77.22 | [Download](https://dl.fbaipublicfiles.com/unnatural_pretraining/roberta.base.shuffle.512.mnli.tar.gz) | +| `roberta.base.shuffle.corpus.mnli` | 71.88 | [Download](https://dl.fbaipublicfiles.com/unnatural_pretraining/roberta.base.shuffle.corpus.mnli.tar.gz) | +| `roberta.base.shuffle.corpus_uniform.mnli` | 72.46 | [Download](https://dl.fbaipublicfiles.com/unnatural_pretraining/roberta.base.shuffle.corpus_uniform.mnli.tar.gz) | + +## Citation + +```bibtex +@misc{sinha2021masked, + title={Masked Language Modeling and the Distributional Hypothesis: Order Word Matters Pre-training for Little}, + author={Koustuv Sinha and Robin Jia and Dieuwke Hupkes and Joelle Pineau and Adina Williams and Douwe Kiela}, + year={2021}, + eprint={2104.06644}, + archivePrefix={arXiv}, + primaryClass={cs.CL} +} +``` diff --git a/examples/simultaneous_translation/README.md b/examples/simultaneous_translation/README.md index e27b65280e..62a005e0ec 100644 --- a/examples/simultaneous_translation/README.md +++ b/examples/simultaneous_translation/README.md @@ -1,106 +1,5 @@ -# Simultaneous Machine Translation - -This directory contains the code for the paper [Monotonic Multihead Attention](https://openreview.net/forum?id=Hyg96gBKPS) - -## Prepare Data - -[Please follow the instructions to download and preprocess the WMT'15 En-De dataset.](https://github.com/pytorch/fairseq/tree/simulastsharedtask/examples/translation#prepare-wmt14en2desh) - -## Training - -- MMA-IL - -```shell -fairseq-train \ - data-bin/wmt15_en_de_32k \ - --simul-type infinite_lookback \ - --user-dir $FAIRSEQ/example/simultaneous_translation \ - --mass-preservation \ - --criterion latency_augmented_label_smoothed_cross_entropy \ - --latency-weight-avg 0.1 \ - --max-update 50000 \ - --arch transformer_monotonic_iwslt_de_en save_dir_key=lambda \ - --optimizer adam --adam-betas '(0.9, 0.98)' \ - --lr-scheduler 'inverse_sqrt' \ - --warmup-init-lr 1e-7 --warmup-updates 4000 \ - --lr 5e-4 --min-lr 1e-9 --clip-norm 0.0 --weight-decay 0.0001\ - --dropout 0.3 \ - --label-smoothing 0.1\ - --max-tokens 3584 -``` - -- MMA-H - -```shell -fairseq-train \ - data-bin/wmt15_en_de_32k \ - --simul-type hard_aligned \ - --user-dir $FAIRSEQ/example/simultaneous_translation \ - --mass-preservation \ - --criterion latency_augmented_label_smoothed_cross_entropy \ - --latency-weight-var 0.1 \ - --max-update 50000 \ - --arch transformer_monotonic_iwslt_de_en save_dir_key=lambda \ - --optimizer adam --adam-betas '(0.9, 0.98)' \ - --lr-scheduler 'inverse_sqrt' \ - --warmup-init-lr 1e-7 --warmup-updates 4000 \ - --lr 5e-4 --min-lr 1e-9 --clip-norm 0.0 --weight-decay 0.0001\ - --dropout 0.3 \ - --label-smoothing 0.1\ - --max-tokens 3584 -``` - -- wait-k - -```shell -fairseq-train \ - data-bin/wmt15_en_de_32k \ - --simul-type wait-k \ - --waitk-lagging 3 \ - --user-dir $FAIRSEQ/example/simultaneous_translation \ - --mass-preservation \ - --criterion latency_augmented_label_smoothed_cross_entropy \ - --max-update 50000 \ - --arch transformer_monotonic_iwslt_de_en save_dir_key=lambda \ - --optimizer adam --adam-betas '(0.9, 0.98)' \ - --lr-scheduler 'inverse_sqrt' \ - --warmup-init-lr 1e-7 --warmup-updates 4000 \ - --lr 5e-4 --min-lr 1e-9 --clip-norm 0.0 --weight-decay 0.0001\ - --dropout 0.3 \ - --label-smoothing 0.1\ - --max-tokens 3584 -``` - - -## Evaluation - -More details on evaluation can be found [here](https://github.com/pytorch/fairseq/blob/simulastsharedtask/examples/simultaneous_translation/docs/evaluation.md) - -### Start the server - -```shell -python ./eval/server.py \ - --src-file $SRC_FILE \ - --ref-file $TGT_FILE -``` - -### Run the client - -```shell -python ./evaluate.py \ - --data-bin data-bin/wmt15_en_de_32k \ - --model-path ./checkpoints/checkpoint_best.pt - --scores --output $RESULT_DIR -``` - -### Run evaluation locally without server - -```shell -python ./eval/evaluate.py - --local \ - --src-file $SRC_FILE \ - --tgt-file $TGT_FILE \ - --data-bin data-bin/wmt15_en_de_32k \ - --model-path ./checkpoints/checkpoint_best.pt \ - --scores --output $RESULT_DIR -``` +# Simultaneous Translation +Examples of simultaneous translation in fairseq +- [English-to-Japanese text-to-text wait-k model](docs/enja-waitk.md) +- [English-to-Germen text-to-text monotonic multihead attention model](docs/ende-mma.md) +- [English-to-Germen speech-to-text simultaneous translation model](../speech_to_text/docs/simulst_mustc_example.md) diff --git a/examples/simultaneous_translation/__init__.py b/examples/simultaneous_translation/__init__.py index e6963d6d1b..5835316ba9 100644 --- a/examples/simultaneous_translation/__init__.py +++ b/examples/simultaneous_translation/__init__.py @@ -3,4 +3,4 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. -from . import criterions, models, eval # noqa +from . import models # noqa diff --git a/examples/simultaneous_translation/criterions/label_smoothed_cross_entropy_latency_augmented.py b/examples/simultaneous_translation/criterions/label_smoothed_cross_entropy_latency_augmented.py deleted file mode 100644 index d4d544ec5f..0000000000 --- a/examples/simultaneous_translation/criterions/label_smoothed_cross_entropy_latency_augmented.py +++ /dev/null @@ -1,76 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from fairseq.criterions import register_criterion -from fairseq.criterions.label_smoothed_cross_entropy import ( - LabelSmoothedCrossEntropyCriterion -) - -from examples.simultaneous_translation.utils.latency import ( - LatencyTraining -) - - -@register_criterion('latency_augmented_label_smoothed_cross_entropy') -class LatencyAugmentedLabelSmoothedCrossEntropyCriterion( - LabelSmoothedCrossEntropyCriterion -): - - def __init__(self, args, task): - super().__init__(args, task) - self.eps = args.label_smoothing - self.latency_weight_avg = args.latency_weight_avg - self.latency_weight_avg_type = args.latency_weight_avg_type - self.latency_weight_var = args.latency_weight_var - self.latency_weight_var_type = args.latency_weight_var_type - self.mass_preservation = args.mass_preservation - self.average_method = args.average_method - self.latency_train = LatencyTraining( - self.latency_weight_avg, - self.latency_weight_var, - self.latency_weight_avg_type, - self.latency_weight_var_type, - self.mass_preservation, - self.average_method, - ) - - @staticmethod - def add_args(parser): - super( - LatencyAugmentedLabelSmoothedCrossEntropyCriterion, - LatencyAugmentedLabelSmoothedCrossEntropyCriterion - ).add_args(parser) - """Add criterion-specific arguments to the parser.""" - # fmt: off - parser.add_argument("--latency-weight-avg", default=0., type=float, metavar='D', - help="Average loss weight") - parser.add_argument("--latency-weight-var", default=0., type=float, metavar='D', - help="Variance loss weight") - parser.add_argument("--latency-weight-avg-type", default="differentiable_average_lagging", - help="Statistics for Average loss type") - parser.add_argument("--latency-weight-var-type", default="variance_delay", - help="Statistics for variance loss type") - parser.add_argument("--average-method", default="weighted_average", - help="Average loss type") - # fmt: on - - def compute_loss(self, model, net_output, sample, reduce=True): - # Compute cross entropy loss first - loss, nll_loss = super().compute_loss(model, net_output, sample, reduce) - - # Obtain the expected alignment - attn_list = [item["alpha"] for item in net_output[-1]["attn_list"]] - - target_padding_mask = model.get_targets(sample, net_output).eq(self.padding_idx) - - source_padding_mask = net_output[-1].get("encoder_padding_mask", None) - - # Get latency loss - latency_loss = self.latency_train.loss( - attn_list, source_padding_mask, target_padding_mask) - - loss += latency_loss - - return loss, nll_loss diff --git a/examples/simultaneous_translation/docs/baseline.md b/examples/simultaneous_translation/docs/baseline.md deleted file mode 100644 index 9aeb44a364..0000000000 --- a/examples/simultaneous_translation/docs/baseline.md +++ /dev/null @@ -1,178 +0,0 @@ -# **Baseline Simultaneous Translation** ---- - -This is an instruction of training and evaluating a *wait-k* simultanoes LSTM model on MUST-C English-Gernam Dataset. - -[STACL: Simultaneous Translation with Implicit Anticipation and Controllable Latency using Prefix-to-Prefix Framework](https://https://www.aclweb.org/anthology/P19-1289/) - - -## **Requirements** -Install fairseq (make sure to use the correct branch): -``` -git clone --branch simulastsharedtask git@github.com:pytorch/fairseq.git -cd fairseq -pip install -e . -``` - -Assuming that fairseq is installed in a directory called `FAIRSEQ`. - -Install SentencePiece. One easy way is to use anaconda: - -``` -conda install -c powerai sentencepiece -``` - -Download the MuST-C data for English-German available at https://ict.fbk.eu/must-c/. -We will assume that the data is downloaded in a directory called `DATA_ROOT`. - - -## **Text-to-text Model** ---- -### Data Preparation -Train a SentencePiece model: -```shell -for lang in en de; do - python $FAIRSEQ/examples/simultaneous_translation/data/train_spm.py \ - --data-path $DATA_ROOT/data \ - --vocab-size 10000 \ - --max-frame 3000 \ - --model-type unigram \ - --lang $lang \ - --out-path . -``` - -Process the data with the SentencePiece model: -```shell -proc_dir=proc -mkdir -p $proc_dir -for split in train dev tst-COMMON tst-HE; do - for lang in en de; do - spm_encode \ - --model unigram-$lang-10000-3000/spm.model \ - < $DATA_ROOT/data/$split/txt/$split.$lang \ - > $proc_dir/$split.spm.$lang - done -done -``` - -Binarize the data: - -```shell -proc_dir=proc -fairseq-preprocess \ - --source-lang en --target-lang de \ - --trainpref $proc_dir/train.spm \ - --validpref $proc_dir/dev.spm \ - --testpref $proc_dir/tst-COMMON.spm \ - --thresholdtgt 0 \ - --thresholdsrc 0 \ - --workers 20 \ - --destdir ./data-bin/mustc_en_de \ -``` - -### Training - - -```shell -mkdir -p checkpoints -CUDA_VISIBLE_DEVICES=1 python $FAIRSEQ/train.py data-bin/mustc_en_de \ - --save-dir checkpoints \ - --arch berard_simul_text_iwslt \ - --simul-type waitk \ - --waitk-lagging 2 \ - --optimizer adam \ - --max-epoch 100 \ - --lr 0.001 \ - --clip-norm 5.0 \ - --max-sentences 128 \ - --log-format json \ - --log-interval 10 \ - --criterion cross_entropy_acc \ - --user-dir $FAIRSEQ/examples/simultaneous_translation -``` - -## **Speech-to-text Model** ---- -### Data Preparation -First, segment wav files. -```shell -python $FAIRSEQ/examples/simultaneous_translation/data/segment_wav.py \ - --datapath $DATA_ROOT -``` -Similar to text-to-text model, train a Sentencepiecemodel, but only train on German -```Shell -python $FAIRSEQ/examples/simultaneous_translation/data/train_spm.py \ - --data-path $DATA_ROOT/data \ - --vocab-size 10000 \ - --max-frame 3000 \ - --model-type unigram \ - --lang $lang \ - --out-path . -``` -## Training -```shell -mkdir -p checkpoints -CUDA_VISIBLE_DEVICES=1 python $FAIRSEQ/train.py data-bin/mustc_en_de \ - --save-dir checkpoints \ - --arch berard_simul_text_iwslt \ - --waitk-lagging 2 \ - --waitk-stride 10 \ - --input-feat-per-channel 40 \ - --encoder-hidden-size 512 \ - --output-layer-dim 128 \ - --decoder-num-layers 3 \ - --task speech_translation \ - --user-dir $FAIRSEQ/examples/simultaneous_translation - --optimizer adam \ - --max-epoch 100 \ - --lr 0.001 \ - --clip-norm 5.0 \ - --max-sentences 128 \ - --log-format json \ - --log-interval 10 \ - --criterion cross_entropy_acc \ - --user-dir $FAIRSEQ/examples/simultaneous_translation -``` - -## Evaluation ---- -### Evaluation Server -For text translation models, the server is set up as follow give input file and reference file. - -``` shell -python ./eval/server.py \ - --hostname localhost \ - --port 12321 \ - --src-file $DATA_ROOT/data/dev/txt/dev.en \ - --ref-file $DATA_ROOT/data/dev/txt/dev.de -``` -For speech translation models, the input is the data direcrory. -``` shell -python ./eval/server.py \ - --hostname localhost \ - --port 12321 \ - --ref-file $DATA_ROOT \ - --data-type speech -``` - -### Decode and Evaluate with Client -Once the server is set up, run client to evaluate translation quality and latency. -```shell -# TEXT -python $fairseq_dir/examples/simultaneous_translation/evaluate.py \ - data-bin/mustc_en_de \ - --user-dir $FAIRSEQ/examples/simultaneous_translation \ - --src-spm unigram-en-10000-3000/spm.model\ - --tgt-spm unigram-de-10000-3000/spm.model\ - -s en -t de \ - --path checkpoints/checkpoint_best.pt - -# SPEECH -python $fairseq_dir/examples/simultaneous_translation/evaluate.py \ - data-bin/mustc_en_de \ - --user-dir $FAIRSEQ/examples/simultaneous_translation \ - --data-type speech \ - --tgt-spm unigram-de-10000-3000/spm.model\ - -s en -t de \ - --path checkpoints/checkpoint_best.pt -``` diff --git a/examples/simultaneous_translation/docs/ende-mma.md b/examples/simultaneous_translation/docs/ende-mma.md new file mode 100644 index 0000000000..241d604a3b --- /dev/null +++ b/examples/simultaneous_translation/docs/ende-mma.md @@ -0,0 +1,74 @@ +# Simultaneous Machine Translation + +This directory contains the code for the paper [Monotonic Multihead Attention](https://openreview.net/forum?id=Hyg96gBKPS) + +## Prepare Data + +[Please follow the instructions to download and preprocess the WMT'15 En-De dataset.](https://github.com/pytorch/fairseq/tree/simulastsharedtask/examples/translation#prepare-wmt14en2desh) + +Another example of training an English to Japanese model can be found [here](docs/enja.md) + +## Training + +- MMA-IL + +```shell +fairseq-train \ + data-bin/wmt15_en_de_32k \ + --simul-type infinite_lookback \ + --user-dir $FAIRSEQ/example/simultaneous_translation \ + --mass-preservation \ + --criterion latency_augmented_label_smoothed_cross_entropy \ + --latency-weight-avg 0.1 \ + --max-update 50000 \ + --arch transformer_monotonic_iwslt_de_en save_dir_key=lambda \ + --optimizer adam --adam-betas '(0.9, 0.98)' \ + --lr-scheduler 'inverse_sqrt' \ + --warmup-init-lr 1e-7 --warmup-updates 4000 \ + --lr 5e-4 --stop-min-lr 1e-9 --clip-norm 0.0 --weight-decay 0.0001\ + --dropout 0.3 \ + --label-smoothing 0.1\ + --max-tokens 3584 +``` + +- MMA-H + +```shell +fairseq-train \ + data-bin/wmt15_en_de_32k \ + --simul-type hard_aligned \ + --user-dir $FAIRSEQ/example/simultaneous_translation \ + --mass-preservation \ + --criterion latency_augmented_label_smoothed_cross_entropy \ + --latency-weight-var 0.1 \ + --max-update 50000 \ + --arch transformer_monotonic_iwslt_de_en save_dir_key=lambda \ + --optimizer adam --adam-betas '(0.9, 0.98)' \ + --lr-scheduler 'inverse_sqrt' \ + --warmup-init-lr 1e-7 --warmup-updates 4000 \ + --lr 5e-4 --stop-min-lr 1e-9 --clip-norm 0.0 --weight-decay 0.0001\ + --dropout 0.3 \ + --label-smoothing 0.1\ + --max-tokens 3584 +``` + +- wait-k + +```shell +fairseq-train \ + data-bin/wmt15_en_de_32k \ + --simul-type wait-k \ + --waitk-lagging 3 \ + --user-dir $FAIRSEQ/example/simultaneous_translation \ + --mass-preservation \ + --criterion latency_augmented_label_smoothed_cross_entropy \ + --max-update 50000 \ + --arch transformer_monotonic_iwslt_de_en save_dir_key=lambda \ + --optimizer adam --adam-betas '(0.9, 0.98)' \ + --lr-scheduler 'inverse_sqrt' \ + --warmup-init-lr 1e-7 --warmup-updates 4000 \ + --lr 5e-4 --stop-min-lr 1e-9 --clip-norm 0.0 --weight-decay 0.0001\ + --dropout 0.3 \ + --label-smoothing 0.1\ + --max-tokens 3584 +``` diff --git a/examples/simultaneous_translation/docs/enja-waitk.md b/examples/simultaneous_translation/docs/enja-waitk.md new file mode 100644 index 0000000000..fb9d82576f --- /dev/null +++ b/examples/simultaneous_translation/docs/enja-waitk.md @@ -0,0 +1,106 @@ +# An example of English to Japaneses Simultaneous Translation System + +This is an example of training and evaluating a transformer *wait-k* English to Japanese simultaneous text-to-text translation model. + +## Data Preparation +This section introduces the data preparation for training and evaluation. +If you only want to evaluate the model, please jump to [Inference & Evaluation](#inference-&-evaluation) + +For illustration, we only use the following subsets of the available data from [WMT20 news translation task](http://www.statmt.org/wmt20/translation-task.html), which results in 7,815,391 sentence pairs. +- News Commentary v16 +- Wiki Titles v3 +- WikiMatrix V1 +- Japanese-English Subtitle Corpus +- The Kyoto Free Translation Task Corpus + +We use WMT20 development data as development set. Training `transformer_vaswani_wmt_en_de_big` model on such amount of data will result in 17.3 BLEU with greedy search and 19.7 with beam (10) search. Notice that a better performance can be achieved with the full WMT training data. + +We use [sentencepiece](https://github.com/google/sentencepiece) toolkit to tokenize the data with a vocabulary size of 32000. +Additionally, we filtered out the sentences longer than 200 words after tokenization. +Assuming the tokenized text data is saved at `${DATA_DIR}`, +we prepare the data binary with the following command. + +```bash +fairseq-preprocess \ + --source-lang en --target-lang ja \ + --trainpref ${DATA_DIR}/train \ + --validpref ${DATA_DIR}/dev \ + --testpref ${DATA_DIR}/test \ + --destdir ${WMT20_ENJA_DATA_BIN} \ + --nwordstgt 32000 --nwordssrc 32000 \ + --workers 20 +``` + +## Simultaneous Translation Model Training +To train a wait-k `(k=10)` model. +```bash +fairseq-train ${WMT20_ENJA_DATA_BIN} \ + --save-dir ${SAVEDIR} + --simul-type waitk \ + --waitk-lagging 10 \ + --max-epoch 70 \ + --arch transformer_monotonic_vaswani_wmt_en_de_big \ + --optimizer adam \ + --adam-betas '(0.9, 0.98)' \ + --lr-scheduler inverse_sqrt \ + --warmup-init-lr 1e-07 \ + --warmup-updates 4000 \ + --lr 0.0005 \ + --stop-min-lr 1e-09 \ + --clip-norm 10.0 \ + --dropout 0.3 \ + --weight-decay 0.0 \ + --criterion label_smoothed_cross_entropy \ + --label-smoothing 0.1 \ + --max-tokens 3584 +``` +This command is for training on 8 GPUs. Equivalently, the model can be trained on one GPU with `--update-freq 8`. + +## Inference & Evaluation +First of all, install [SimulEval](https://github.com/facebookresearch/SimulEval) for evaluation. + +```bash +git clone https://github.com/facebookresearch/SimulEval.git +cd SimulEval +pip install -e . +``` + +The following command is for the evaluation. +Assuming the source and reference files are `${SRC_FILE}` and `${REF_FILE}`, the sentencepiece model file for English is saved at `${SRC_SPM_PATH}` + + +```bash +simuleval \ + --source ${SRC_FILE} \ + --target ${TGT_FILE} \ + --data-bin ${WMT20_ENJA_DATA_BIN} \ + --sacrebleu-tokenizer ja-mecab \ + --eval-latency-unit char \ + --no-space \ + --src-splitter-type sentencepiecemodel \ + --src-splitter-path ${SRC_SPM_PATH} \ + --agent ${FAIRSEQ}/examples/simultaneous_translation/agents/simul_trans_text_agent_enja.py \ + --model-path ${SAVE_DIR}/${CHECKPOINT_FILENAME} \ + --output ${OUTPUT} \ + --scores +``` + +The `--data-bin` should be the same in previous sections if you prepare the data from the scratch. +If only for evaluation, a prepared data directory can be found [here](https://dl.fbaipublicfiles.com/simultaneous_translation/wmt20_enja_medium_databin.tgz) and a pretrained checkpoint (wait-k=10 model) can be downloaded from [here](https://dl.fbaipublicfiles.com/simultaneous_translation/wmt20_enja_medium_wait10_ckpt.pt). + +The output should look like this: +```bash +{ + "Quality": { + "BLEU": 11.442253287568398 + }, + "Latency": { + "AL": 8.6587861866951, + "AP": 0.7863304776251316, + "DAL": 9.477850951194764 + } +} +``` +The latency is evaluated by characters (`--eval-latency-unit`) on the target side. The latency is evaluated with `sacrebleu` with `MeCab` tokenizer `--sacrebleu-tokenizer ja-mecab`. `--no-space` indicates that do not add space when merging the predicted words. + +If `--output ${OUTPUT}` option is used, the detailed log and scores will be stored under the `${OUTPUT}` directory. diff --git a/examples/simultaneous_translation/docs/evaluation.md b/examples/simultaneous_translation/docs/evaluation.md deleted file mode 100644 index c53407354e..0000000000 --- a/examples/simultaneous_translation/docs/evaluation.md +++ /dev/null @@ -1,115 +0,0 @@ -# Introduction to evaluation interface -The simultaneous translation models from sharedtask participents are evaluated under a server-client protocol. The participents are requisted to plug in their own model API in the protocol, and submit a docker file. - -## Server-Client Protocol -An server-client protocol that will be used in evaluation. For example, when a *wait-k* model (k=3) translate the English sentence "Alice and Bob are good friends" to Genman sentence "Alice und Bob sind gute Freunde." , the evaluation process is shown as following figure. - -While every time client needs to read a new state (word or speech utterence), a "GET" request is supposed to sent over to server. Whenever a new token is generated, a "SEND" request with the word predicted (untokenized word) will be sent to server immediately. The server can hence calculate both latency and BLEU score of the sentence. - -### Server -The server code is provided and can be set up directly locally for development purpose. For example, to evaluate a text simultaneous test set, - -```shell - - python fairseq/examples/simultaneous_translation/eval/server.py \ - --hostname local_host \ - --port 1234 \ - --src-file SRC_FILE \ - --ref-file REF_FILE \ - --data-type text \ -``` -The state that server sent to client is has the following format -```json -{ - 'sent_id': Int, - 'segment_id': Int, - 'segment': String -} -``` - -### Client -The client will handle the evaluation process mentioned above. It should be out-of-box as well. The client's protocol is as following table - -|Action|Content| -|:---:|:---:| -|Request new word / utterence| ```{key: "Get", value: None}```| -|Predict word "W"| ```{key: "SEND", value: "W"}```| - - - -The core of the client module is the agent, which needs to be modified to different models accordingly. The abstract class of agent is as follow, the evaluation process happens in the `decode()` function. -```python -class Agent(object): - "an agent needs to follow this pattern" - def __init__(self, *args, **kwargs): - ... - - def init_states(self): - # Initializing states - ... - - def update_states(self, states, new_state): - # Update states with given new state from server - # TODO (describe the states) - ... - - def finish_eval(self, states, new_state): - # Check if evaluation is finished - ... - - def policy(self, state: list) -> dict: - # Provide a action given current states - # The action can only be either - # {key: "GET", value: NONE} - # or - # {key: "SEND", value: W} - ... - - def reset(self): - # Reset agent - ... - - def decode(self, session): - - states = self.init_states() - self.reset() - - # Evaluataion protocol happens here - while True: - # Get action from the current states according to self.policy() - action = self.policy(states) - - if action['key'] == GET: - # Read a new state from server - new_state = session.get_src() - states = self.update_states(states, new_state) - - if self.finish_eval(states, new_state): - # End of document - break - - elif action['key'] == SEND: - # Send a new prediction to server - session.send_hypo(action['value']) - - # Clean the history, wait for next sentence - if action['value'] == DEFAULT_EOS: - states = self.init_states() - self.reset() - else: - raise NotImplementedError - - -``` -Here an implementation of agent of text [*wait-k* model](somelink). Notice that the tokenization is not considered. - -## Quality -The quality is measured by detokenized BLEU. So make sure that the predicted words sent to server are detokenized. An implementation is can be find [here](some link) - -## Latency -The latency metrics are -* Average Proportion -* Average Lagging -* Differentiable Average Lagging -Again Thery will also be evaluated on detokenized text. - diff --git a/examples/simultaneous_translation/eval/agents/__init__.py b/examples/simultaneous_translation/eval/agents/__init__.py deleted file mode 100644 index d49426344c..0000000000 --- a/examples/simultaneous_translation/eval/agents/__init__.py +++ /dev/null @@ -1,20 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import importlib -import os -from fairseq import registry - -build_agent, register_agent, MONOTONIC_AGENT = registry.setup_registry('--agent-type') - - -DEFAULT_EOS = '</s>' -GET = 0 -SEND = 1 - -for file in os.listdir(os.path.dirname(__file__)): - if file.endswith('.py') and not file.startswith('_'): - module = file[:file.find('.py')] - importlib.import_module('agents.' + module) diff --git a/examples/simultaneous_translation/eval/agents/agent.py b/examples/simultaneous_translation/eval/agents/agent.py deleted file mode 100644 index 1977a24dd9..0000000000 --- a/examples/simultaneous_translation/eval/agents/agent.py +++ /dev/null @@ -1,65 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from . import GET, SEND, DEFAULT_EOS -import time -from multiprocessing.pool import ThreadPool as Pool -from functools import partial - - -class Agent(object): - "an agent needs to follow this pattern" - def __init__(self, *args, **kwargs): - pass - - def init_states(self, *args, **kwargs): - raise NotImplementedError - - def update_states(self, states, new_state): - raise NotImplementedError - - def finish_eval(self, states, new_state): - raise NotImplementedError - - def policy(self, state): - raise NotImplementedError - - def reset(self): - raise NotImplementedError - - def decode(self, session, low=0, high=100000, num_thread=10): - corpus_info = session.corpus_info() - high = min(corpus_info["num_sentences"] - 1, high) - if low >= high: - return - - t0 = time.time() - if num_thread > 1: - with Pool(10) as p: - p.map( - partial(self._decode_one, session), - [sent_id for sent_id in range(low, high + 1)] - ) - else: - for sent_id in range(low, high + 1): - self._decode_one(session, sent_id) - - print(f'Finished {low} to {high} in {time.time() - t0}s') - - def _decode_one(self, session, sent_id): - action = {} - self.reset() - states = self.init_states() - while action.get('value', None) != DEFAULT_EOS: - # take an action - action = self.policy(states) - - if action['key'] == GET: - new_states = session.get_src(sent_id, action["value"]) - states = self.update_states(states, new_states) - - elif action['key'] == SEND: - session.send_hypo(sent_id, action['value']) - print(" ".join(states["tokens"]["tgt"])) diff --git a/examples/simultaneous_translation/eval/agents/simul_t2t_enja.py b/examples/simultaneous_translation/eval/agents/simul_t2t_enja.py new file mode 100644 index 0000000000..8f3c8703ca --- /dev/null +++ b/examples/simultaneous_translation/eval/agents/simul_t2t_enja.py @@ -0,0 +1,226 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os + +from fairseq import checkpoint_utils, tasks +import sentencepiece as spm +import torch + +try: + from simuleval import READ_ACTION, WRITE_ACTION, DEFAULT_EOS + from simuleval.agents import TextAgent +except ImportError: + print("Please install simuleval 'pip install simuleval'") + + +BOS_PREFIX = "\u2581" + + +class SimulTransTextAgentJA(TextAgent): + """ + Simultaneous Translation + Text agent for Japanese + """ + def __init__(self, args): + + # Whether use gpu + self.gpu = getattr(args, "gpu", False) + + # Max len + self.max_len = args.max_len + + # Load Model + self.load_model_vocab(args) + + # build word splitter + self.build_word_splitter(args) + + self.eos = DEFAULT_EOS + + def initialize_states(self, states): + states.incremental_states = dict() + states.incremental_states["online"] = dict() + + def to_device(self, tensor): + if self.gpu: + return tensor.cuda() + else: + return tensor.cpu() + + def load_model_vocab(self, args): + + filename = args.model_path + if not os.path.exists(filename): + raise IOError("Model file not found: {}".format(filename)) + + state = checkpoint_utils.load_checkpoint_to_cpu(filename) + + task_args = state["cfg"]["task"] + task_args.data = args.data_bin + + task = tasks.setup_task(task_args) + + # build model for ensemble + state["cfg"]["model"].load_pretrained_encoder_from = None + state["cfg"]["model"].load_pretrained_decoder_from = None + + self.model = task.build_model(state["cfg"]["model"]) + self.model.load_state_dict(state["model"], strict=True) + self.model.eval() + self.model.share_memory() + + if self.gpu: + self.model.cuda() + + # Set dictionary + self.dict = {} + self.dict["tgt"] = task.target_dictionary + self.dict["src"] = task.source_dictionary + + @staticmethod + def add_args(parser): + # fmt: off + parser.add_argument('--model-path', type=str, required=True, + help='path to your pretrained model.') + parser.add_argument("--data-bin", type=str, required=True, + help="Path of data binary") + parser.add_argument("--max-len", type=int, default=100, + help="Max length of translation") + parser.add_argument("--tgt-splitter-type", type=str, default="SentencePiece", + help="Subword splitter type for target text.") + parser.add_argument("--tgt-splitter-path", type=str, default=None, + help="Subword splitter model path for target text.") + parser.add_argument("--src-splitter-type", type=str, default="SentencePiece", + help="Subword splitter type for source text.") + parser.add_argument("--src-splitter-path", type=str, default=None, + help="Subword splitter model path for source text.") + # fmt: on + return parser + + def build_word_splitter(self, args): + self.spm = {} + for lang in ['src', 'tgt']: + if getattr(args, f'{lang}_splitter_type', None): + path = getattr(args, f'{lang}_splitter_path', None) + if path: + self.spm[lang] = spm.SentencePieceProcessor() + self.spm[lang].Load(path) + + def segment_to_units(self, segment, states): + # Split a full word (segment) into subwords (units) + return self.spm['src'].EncodeAsPieces(segment) + + def update_model_encoder(self, states): + if len(states.units.source) == 0: + return + + src_indices = [ + self.dict['src'].index(x) + for x in states.units.source.value + ] + + if states.finish_read(): + # Append the eos index when the prediction is over + src_indices += [self.dict["tgt"].eos_index] + + src_indices = self.to_device( + torch.LongTensor(src_indices).unsqueeze(0) + ) + src_lengths = self.to_device( + torch.LongTensor([src_indices.size(1)]) + ) + + states.encoder_states = self.model.encoder(src_indices, src_lengths) + + torch.cuda.empty_cache() + + def update_states_read(self, states): + # Happens after a read action. + self.update_model_encoder(states) + + def units_to_segment(self, units, states): + # Merge sub words (units) to full word (segment). + # For Japanese, we can directly send + # the untokenized token to server except the BOS token + # with following option + # --sacrebleu-tokenizer MeCab + # --eval-latency-unit char + # --no-space + token = units.value.pop() + + if ( + token == self.dict["tgt"].eos_word + or len(states.segments.target) > self.max_len + ): + return DEFAULT_EOS + + if BOS_PREFIX == token: + return None + if token[0] == BOS_PREFIX: + return token[1:] + else: + return token + + def policy(self, states): + + if not getattr(states, "encoder_states", None): + # No encoder states, read a token first + return READ_ACTION + + # encode previous predicted target tokens + tgt_indices = self.to_device( + torch.LongTensor( + [self.model.decoder.dictionary.eos()] + + [ + self.dict['tgt'].index(x) + for x in states.units.target.value + if x is not None + ] + ).unsqueeze(0) + ) + + # Current steps + states.incremental_states["steps"] = { + "src": states.encoder_states["encoder_out"][0].size(0), + "tgt": 1 + len(states.units.target), + } + + # Online only means the reading is not finished + states.incremental_states["online"]["only"] = ( + torch.BoolTensor([not states.finish_read()]) + ) + + x, outputs = self.model.decoder.forward( + prev_output_tokens=tgt_indices, + encoder_out=states.encoder_states, + incremental_state=states.incremental_states, + ) + + states.decoder_out = x + + torch.cuda.empty_cache() + + if outputs.action == 0: + return READ_ACTION + else: + return WRITE_ACTION + + def predict(self, states): + # Predict target token from decoder states + decoder_states = states.decoder_out + + lprobs = self.model.get_normalized_probs( + [decoder_states[:, -1:]], log_probs=True + ) + + index = lprobs.argmax(dim=-1)[0, 0].item() + + if index != self.dict['tgt'].eos_index: + token = self.dict['tgt'].string([index]) + else: + token = self.dict['tgt'].eos_word + + return token diff --git a/examples/simultaneous_translation/eval/agents/simul_trans_agent.py b/examples/simultaneous_translation/eval/agents/simul_trans_agent.py deleted file mode 100644 index 1b6960c5fa..0000000000 --- a/examples/simultaneous_translation/eval/agents/simul_trans_agent.py +++ /dev/null @@ -1,163 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from . agent import Agent -from . import DEFAULT_EOS, GET, SEND -from fairseq import checkpoint_utils, utils, tasks -import os -import json - - -class SimulTransAgent(Agent): - def __init__(self, args): - # Load Model - self.load_model(args) - - # build word spliter - self.build_word_splitter(args) - - self.max_len = args.max_len - - self.eos = DEFAULT_EOS - - @staticmethod - def add_args(parser): - # fmt: off - parser.add_argument('--model-path', type=str, required=True, - help='path to your pretrained model.') - parser.add_argument("--data-bin", type=str, required=True, - help="Path of data binary") - parser.add_argument("--user-dir", type=str, default="example/simultaneous_translation", - help="User directory for simultaneous translation") - parser.add_argument("--src-splitter-type", type=str, default=None, - help="Subword splitter type for source text") - parser.add_argument("--tgt-splitter-type", type=str, default=None, - help="Subword splitter type for target text") - parser.add_argument("--src-splitter-path", type=str, default=None, - help="Subword splitter model path for source text") - parser.add_argument("--tgt-splitter-path", type=str, default=None, - help="Subword splitter model path for target text") - parser.add_argument("--max-len", type=int, default=150, - help="Maximum length difference between source and target prediction") - parser.add_argument('--model-overrides', default="{}", type=str, metavar='DICT', - help='A dictionary used to override model args at generation ' - 'that were used during model training') - # fmt: on - return parser - - def load_dictionary(self, task): - raise NotImplementedError - - def load_model(self, args): - args.user_dir = os.path.join(os.path.dirname(__file__), '..', '..') - utils.import_user_module(args) - filename = args.model_path - if not os.path.exists(filename): - raise IOError("Model file not found: {}".format(filename)) - - state = checkpoint_utils.load_checkpoint_to_cpu(filename, json.loads(args.model_overrides)) - - saved_args = state["args"] - saved_args.data = args.data_bin - - task = tasks.setup_task(saved_args) - - # build model for ensemble - self.model = task.build_model(saved_args) - self.model.load_state_dict(state["model"], strict=True) - - # Set dictionary - self.load_dictionary(task) - - def init_states(self): - return { - "indices": {"src": [], "tgt": []}, - "tokens": {"src": [], "tgt": []}, - "segments": {"src": [], "tgt": []}, - "steps": {"src": 0, "tgt": 0}, - "finished": False, - "finish_read": False, - "model_states": {} - } - - def update_states(self, states, new_state): - raise NotImplementedError - - def policy(self, states): - # Read and Write policy - action = None - - while action is None: - if states["finished"]: - # Finish the hypo by sending eos to server - return self.finish_action() - - # Model make decision given current states - decision = self.model.decision_from_states(states) - - if decision == 0 and not self.finish_read(states): - # READ - action = self.read_action(states) - else: - # WRITE - action = self.write_action(states) - - # None means we make decision again but not sending server anything - # This happened when read a bufffered token - # Or predict a subword - return action - - def finish_read(self, states): - raise NotImplementedError - - def write_action(self, states): - token, index = self.model.predict_from_states(states) - - if index == self.dict["tgt"].eos() or len(states["tokens"]["tgt"]) > self.max_len: - # Finish this sentence is predict EOS - states["finished"] = True - end_idx_last_full_word = self._target_length(states) - - else: - states["tokens"]["tgt"] += [token] - end_idx_last_full_word = ( - self.word_splitter["tgt"] - .end_idx_last_full_word(states["tokens"]["tgt"]) - ) - self._append_indices(states, [index], "tgt") - - if end_idx_last_full_word > states["steps"]["tgt"]: - # Only sent detokenized full words to the server - word = self.word_splitter["tgt"].merge( - states["tokens"]["tgt"][ - states["steps"]["tgt"]: end_idx_last_full_word - ] - ) - states["steps"]["tgt"] = end_idx_last_full_word - states["segments"]["tgt"] += [word] - - return {'key': SEND, 'value': word} - else: - return None - - def read_action(self, states): - return {'key': GET, 'value': None} - - def finish_action(self): - return {'key': SEND, 'value': DEFAULT_EOS} - - def reset(self): - pass - - def finish_eval(self, states, new_state): - if len(new_state) == 0 and len(states["indices"]["src"]) == 0: - return True - return False - - def _append_indices(self, states, new_indices, key): - states["indices"][key] += new_indices - - def _target_length(self, states): - return len(states["tokens"]['tgt']) diff --git a/examples/simultaneous_translation/eval/agents/simul_trans_text_agent.py b/examples/simultaneous_translation/eval/agents/simul_trans_text_agent.py deleted file mode 100644 index 65f7cbd313..0000000000 --- a/examples/simultaneous_translation/eval/agents/simul_trans_text_agent.py +++ /dev/null @@ -1,78 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from . simul_trans_agent import SimulTransAgent -from . import DEFAULT_EOS, GET -from . import register_agent -from . word_splitter import SPLITTER_DICT - - -@register_agent("simul_trans_text") -class SimulTransTextAgent(SimulTransAgent): - def build_word_splitter(self, args): - self.word_splitter = {} - - self.word_splitter["src"] = SPLITTER_DICT[args.src_splitter_type]( - getattr(args, f"src_splitter_path") - ) - self.word_splitter["tgt"] = SPLITTER_DICT[args.tgt_splitter_type]( - getattr(args, f"tgt_splitter_path") - ) - - def load_dictionary(self, task): - self.dict = {} - self.dict["tgt"] = task.target_dictionary - self.dict["src"] = task.source_dictionary - - def update_states(self, states, new_state): - if states["finish_read"]: - return states - - new_word = new_state["segment"] - - # Split words and index the token - if new_word not in [DEFAULT_EOS]: - tokens = self.word_splitter["src"].split(new_word) - # Get indices from dictionary - # You can change to you own dictionary - indices = self.dict["src"].encode_line( - tokens, - line_tokenizer=lambda x: x, - add_if_not_exist=False, - append_eos=False - ).tolist() - else: - tokens = [new_word] - indices = [self.dict["src"].eos()] - states["finish_read"] = True - - # Update states - states["segments"]["src"] += [new_word] - states["tokens"]["src"] += tokens - self._append_indices(states, indices, "src") - - return states - - def read_action(self, states): - # Increase source step by one - states["steps"]["src"] += 1 - - # At leat one word is read - if len(states["tokens"]["src"]) == 0: - return {'key': GET, 'value': None} - - # Only request new word if there is no buffered tokens - if len(states["tokens"]["src"]) <= states["steps"]["src"]: - return {'key': GET, 'value': None} - - return None - - def finish_read(self, states): - # The first means all segments (full words) has been read from server - # The second means all tokens (subwords) has been read locally - return ( - states["finish_read"] - and len(states["tokens"]["src"]) == states["steps"]["src"] - ) diff --git a/examples/simultaneous_translation/eval/agents/word_splitter.py b/examples/simultaneous_translation/eval/agents/word_splitter.py deleted file mode 100644 index ea564f21ee..0000000000 --- a/examples/simultaneous_translation/eval/agents/word_splitter.py +++ /dev/null @@ -1,89 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - - -class SubwordSplitter(object): - def process_line(self, string): - raise NotImplementedError - - def split(self, string): - raise NotImplementedError - - -class NoneWordSplitter(object): - def __init__(self, model): - pass - - def split(self, string): - return [string] - - def process_line(self, string): - return [string] - - def finished_word(self, string): - return True - - def merge(self, list_of_string): - return "".join(list_of_string) - - def last_full_word_step(self, tokens, step): - return len(tokens) - - def end_idx_last_full_word(self, tokens): - return len(tokens) - - -class BPEWordSplitter(object): - # TODO: lock back here - def __init__(self, model_path): - super().__init__() - from subword_nmt.apply_bpe import BPE - with open(model_path) as f: - self.model = BPE(f) - - def split(self, string): - return self.model.process_line(string).split() - - def end_idx_last_full_word(self, tokens): - # Begin of word indices - bow_indices = [0] + [i + 1 for i, t in enumerate(tokens[1:]) if t[-2:] != '@@'] - - if len(bow_indices) < 2: - return 0 - else: - return bow_indices[-1] - - def merge(self, list_of_string): - return " ".join([item.replace("@@", "") for item in list_of_string]) - - -class SentencePieceModelWordSplitter(object): - def __init__(self, model_path): - super().__init__() - import sentencepiece as spm - self.model = spm.SentencePieceProcessor() - self.model.Load(model_path) - - def split(self, string): - return self.model.EncodeAsPieces(string) - - def end_idx_last_full_word(self, tokens): - # Begin of word indices - bow_indices = [i for i, t in enumerate(tokens) if t[0] == '\u2581'] - - if len(bow_indices) < 2: - return 0 - else: - return bow_indices[-1] - - def merge(self, list_of_string): - return self.model.DecodePieces(list_of_string) - - -SPLITTER_DICT = { - None: NoneWordSplitter, - "BPE": BPEWordSplitter, - "SentencePieceModel": SentencePieceModelWordSplitter, -} diff --git a/examples/simultaneous_translation/eval/client.py b/examples/simultaneous_translation/eval/client.py deleted file mode 100644 index 5cbaa71d31..0000000000 --- a/examples/simultaneous_translation/eval/client.py +++ /dev/null @@ -1,102 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import requests -from typing import Optional -from scorers import build_scorer - - -class SimulSTEvaluationService(object): - DEFAULT_HOSTNAME = 'localhost' - DEFAULT_PORT = 12321 - - def __init__(self, hostname=DEFAULT_HOSTNAME, port=DEFAULT_PORT): - self.hostname = hostname - self.port = port - self.base_url = f'http://{self.hostname}:{self.port}' - - def __enter__(self): - self.new_session() - - def __exit__(self, exc_type, exc_val, exc_tb): - pass - - def new_session(self): - # start eval session - url = f'{self.base_url}' - - try: - _ = requests.post(url) - except Exception as e: - print(f'Failed to start an evaluation session: {e}') - - print('Evaluation session started.') - return self - - def get_scores(self): - # end eval session - url = f'{self.base_url}/result' - try: - r = requests.get(url) - print('Scores: {}'.format(r.json())) - print('Evaluation session finished.') - except Exception as e: - print(f'Failed to end an evaluation session: {e}') - - def get_src(self, sent_id: int, extra_params: Optional[dict] = None) -> str: - url = f'{self.base_url}/src' - params = {"sent_id": sent_id} - if extra_params is not None: - for key in extra_params.keys(): - params[key] = extra_params[key] - try: - r = requests.get( - url, - params=params - ) - except Exception as e: - print(f'Failed to request a source segment: {e}') - return r.json() - - def send_hypo(self, sent_id: int, hypo: str) -> None: - url = f'{self.base_url}/hypo' - params = {"sent_id": sent_id} - - try: - requests.put(url, params=params, data=hypo.encode("utf-8")) - except Exception as e: - print(f'Failed to send a translated segment: {e}') - - def corpus_info(self): - url = f'{self.base_url}' - try: - r = requests.get(url) - except Exception as e: - print(f'Failed to request corpus information: {e}') - - return r.json() - - -class SimulSTLocalEvaluationService(object): - def __init__(self, args): - self.scorer = build_scorer(args) - - def get_scores(self): - return self.scorer.score() - - def get_src(self, sent_id: int, extra_params: Optional[dict] = None) -> str: - if extra_params is not None: - segment_size = extra_params.get("segment_size", None) - else: - segment_size = None - - return self.scorer.send_src(int(sent_id), segment_size) - - def send_hypo(self, sent_id: int, hypo: str) -> None: - list_of_tokens = hypo.strip().split() - self.scorer.recv_hyp(sent_id, list_of_tokens) - - def corpus_info(self): - return self.scorer.get_info() diff --git a/examples/simultaneous_translation/eval/eval_latency.py b/examples/simultaneous_translation/eval/eval_latency.py deleted file mode 100644 index 12cfaa4ed1..0000000000 --- a/examples/simultaneous_translation/eval/eval_latency.py +++ /dev/null @@ -1,80 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from examples.simultaneous_translation.utils.latency import LatencyInference -import argparse -import torch -import json - - -LATENCY_METRICS = [ - 'differentiable_average_lagging', - 'average_lagging', - 'average_proportion', -] - - -class LatencyScorer(): - def __init__(self, start_from_zero=True): - self.recorder = [] - self.scores = {} - self.scorer = LatencyInference() - self.start_from_zero = start_from_zero - - def update_reorder(self, list_of_dict): - self.recorder = [] - for info in list_of_dict: - delays = [ - int(x) - int(not self.start_from_zero) - for x in info["delays"] - ] - delays = torch.LongTensor(delays).unsqueeze(0) - src_len = torch.LongTensor([info["src_len"]]).unsqueeze(0) - - self.recorder.append(self.scorer(delays, src_len)) - - def cal_latency(self): - self.scores = {} - for metric in LATENCY_METRICS: - self.scores[metric] = sum( - [x[metric][0, 0].item() for x in self.recorder] - ) / len(self.recorder) - return self.scores - - @classmethod - def score(cls, list_of_dict, start_from_zero=True): - scorer_to_return = cls(start_from_zero) - scorer_to_return.update_reorder(list_of_dict) - scorer_to_return.cal_latency() - return scorer_to_return.scores - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument("--input", required=True) - parser.add_argument("--start-from-zero", action="store_true") - args = parser.parse_args() - - scorer = LatencyInference() - recorder = [] - with open(args.input, 'r') as f: - for line in f: - info = json.loads(line) - - delays = [int(x) - int(not args.start_from_zero) for x in info["delays"]] - - delays = torch.LongTensor(delays).unsqueeze(0) - - src_len = torch.LongTensor([info["src_len"]]).unsqueeze(0) - - recorder.append(scorer(delays, src_len)) - - average_results = {} - - for metric in LATENCY_METRICS: - average_results[metric] = sum( - [x[metric][0, 0].item() for x in recorder] - ) / len(recorder) - print(f"{metric}: {average_results[metric]}") diff --git a/examples/simultaneous_translation/eval/evaluate.py b/examples/simultaneous_translation/eval/evaluate.py deleted file mode 100644 index 07f93e7fb0..0000000000 --- a/examples/simultaneous_translation/eval/evaluate.py +++ /dev/null @@ -1,70 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import argparse - -from client import SimulSTEvaluationService, SimulSTLocalEvaluationService -from fairseq.registry import REGISTRIES -from agents import build_agent - -DEFAULT_HOSTNAME = 'localhost' -DEFAULT_PORT = 12321 - - -def get_args(): - parser = argparse.ArgumentParser() - - parser.add_argument('--hostname', type=str, default=DEFAULT_HOSTNAME, - help='server hostname') - parser.add_argument('--port', type=int, default=DEFAULT_PORT, - help='server port number') - parser.add_argument('--agent-type', default='simul_trans_text', - help='Agent type') - parser.add_argument('--scorer-type', default='text', - help='Scorer type') - parser.add_argument('--start-idx', type=int, default=0, - help='Start index of the sentence to evaluate') - parser.add_argument('--end-idx', type=int, default=float('inf'), - help='End index of the sentence to evaluate') - parser.add_argument('--scores', action="store_true", - help='Request scores from server') - parser.add_argument('--reset-server', action="store_true", - help='Reset the server') - parser.add_argument('--num-threads', type=int, default=10, - help='Number of threads used by agent') - parser.add_argument('--local', action="store_true", default=False, - help='Local evaluation') - - args, _ = parser.parse_known_args() - - for registry_name, REGISTRY in REGISTRIES.items(): - choice = getattr(args, registry_name, None) - if choice is not None: - cls = REGISTRY["registry"][choice] - if hasattr(cls, "add_args"): - cls.add_args(parser) - args = parser.parse_args() - - return args - - -if __name__ == "__main__": - args = get_args() - - if args.local: - session = SimulSTLocalEvaluationService(args) - else: - session = SimulSTEvaluationService(args.hostname, args.port) - - if args.reset_server: - session.new_session() - - if args.agent_type is not None: - agent = build_agent(args) - agent.decode(session, args.start_idx, args.end_idx, args.num_threads) - - if args.scores: - session.get_scores() - print(session.get_scores()) diff --git a/examples/simultaneous_translation/eval/scorers/__init__.py b/examples/simultaneous_translation/eval/scorers/__init__.py deleted file mode 100644 index 6d4ab3acc7..0000000000 --- a/examples/simultaneous_translation/eval/scorers/__init__.py +++ /dev/null @@ -1,18 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import importlib -import os -from fairseq import registry -( - build_scorer, - register_scorer, - SCORER_REGISTRIES -) = registry.setup_registry('--scorer-type') - -for file in os.listdir(os.path.dirname(__file__)): - if file.endswith('.py') and not file.startswith('_'): - module = file[:file.find('.py')] - importlib.import_module('scorers.' + module) diff --git a/examples/simultaneous_translation/eval/scorers/scorer.py b/examples/simultaneous_translation/eval/scorers/scorer.py deleted file mode 100644 index d16f130e75..0000000000 --- a/examples/simultaneous_translation/eval/scorers/scorer.py +++ /dev/null @@ -1,184 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from vizseq.scorers.bleu import BLEUScorer -from vizseq.scorers.ter import TERScorer -from vizseq.scorers.meteor import METEORScorer -from examples.simultaneous_translation.eval.eval_latency import LatencyScorer -from collections import defaultdict -import json -import os - - -DEFAULT_EOS = '</s>' - - -class SimulScorer(object): - def __init__(self, args): - self.tokenizer = args.tokenizer - self.output_dir = args.output - if args.output is not None: - self.output_files = { - "text": os.path.join(args.output, "text"), - "delay": os.path.join(args.output, "delay"), - "scores": os.path.join(args.output, "scores") - } - else: - self.output_files = None - self.eos = DEFAULT_EOS - self.data = {"tgt": []} - self.reset() - - def get_info(self): - return {"num_sentences": len(self)} - - @staticmethod - def add_args(parser): - # fmt: off - parser.add_argument('--src-file', type=str, required=True, - help='Source input file') - parser.add_argument('--tgt-file', type=str, required=True, - help='Target reference file') - parser.add_argument('--tokenizer', default="13a", choices=["none", "13a"], - help='Tokenizer used for sacrebleu') - parser.add_argument('--output', type=str, default=None, - help='Path for output directory') - # fmt: on - - def send_src(self, sent_id, *args): - raise NotImplementedError - - def recv_hyp(self, sent_id, list_of_tokens): - for token in list_of_tokens: - self.translations[ - sent_id - ].append( - ( - token, - self.steps[sent_id] - ) - ) - - def reset(self): - self.steps = defaultdict(int) - self.translations = defaultdict(list) - - def src_lengths(self): - raise NotImplementedError - - def score(self): - translations = [] - delays = [] - for i in range(1 + max(self.translations.keys())): - translations += [" ".join(t[0] for t in self.translations[i][:-1])] - delays += [[t[1] for t in self.translations[i]]] - - bleu_score = BLEUScorer( - sent_level=False, corpus_level=True, - extra_args={'bleu_tokenizer': self.tokenizer} - ).score(translations, [self.data["tgt"]]) - - ter_score = TERScorer(sent_level=False, corpus_level=True).score( - translations, [self.data["tgt"]] - ) - meteor_score = METEORScorer(sent_level=False, corpus_level=True).score( - translations, [self.data["tgt"]] - ) - - latency_score = LatencyScorer().score( - [ - {"src_len": src_len, "delays": delay} - for src_len, delay in zip(self.src_lengths(), delays) - ], - start_from_zero=False - ) - - scores = { - 'BLEU': bleu_score[0], - 'TER': ter_score[0], - 'METEOR': meteor_score[0], - 'DAL': latency_score['differentiable_average_lagging'], - 'AL': latency_score['average_lagging'], - 'AP': latency_score['average_proportion'], - } - - if self.output_files is not None: - try: - os.makedirs(self.output_dir, exist_ok=True) - self.write_results_to_file(translations, delays, scores) - except BaseException as be: - print(f'Failed to write results to {self.output_dir}.') - print(be) - print('Skip writing predictions') - - return scores - - def write_results_to_file(self, translations, delays, scores): - if self.output_files["text"] is not None: - with open(self.output_files["text"], "w") as f: - for line in translations: - f.write(line + "\n") - - if self.output_files["delay"] is not None: - with open(self.output_files["delay"], "w") as f: - for i, delay in enumerate(delays): - f.write( - json.dumps( - { - "src_len": self.src_lengths()[i], - "delays": delay - } - ) + "\n" - ) - - with open(self.output_files["scores"], "w") as f: - for key, value in scores.items(): - f.write(f"{key}, {value}\n") - - @classmethod - def _load_text_file(cls, file, split=False): - with open(file) as f: - if split: - return [r.strip().split() for r in f] - else: - return [r.strip() for r in f] - - @classmethod - def _load_text_from_json(cls, file): - list_to_return = [] - with open(file) as f: - content = json.load(f) - for item in content["utts"].values(): - list_to_return.append(item["output"]["text"].strip()) - return list_to_return - - @classmethod - def _load_wav_info_from_json(cls, file): - list_to_return = [] - with open(file) as f: - content = json.load(f) - for item in content["utts"].values(): - list_to_return.append( - { - "path": item["input"]["path"].strip(), - "length": item["input"]["length_ms"] - } - ) - return list_to_return - - @classmethod - def _load_wav_info_from_list(cls, file): - list_to_return = [] - with open(file) as f: - for line in f: - list_to_return.append( - { - "path": line.strip(), - } - ) - return list_to_return - - def __len__(self): - return len(self.data["tgt"]) diff --git a/examples/simultaneous_translation/eval/scorers/text_scorer.py b/examples/simultaneous_translation/eval/scorers/text_scorer.py deleted file mode 100644 index 4a5daaff21..0000000000 --- a/examples/simultaneous_translation/eval/scorers/text_scorer.py +++ /dev/null @@ -1,41 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from . scorer import SimulScorer -from . import register_scorer - - -@register_scorer("text") -class SimulTextScorer(SimulScorer): - def __init__(self, args): - super().__init__(args) - self.data = { - "src": self._load_text_file(args.src_file, split=True), - "tgt": self._load_text_file(args.tgt_file, split=False) - } - - def send_src(self, sent_id, *args): - if self.steps[sent_id] >= len(self.data["src"][sent_id]): - dict_to_return = { - "sent_id": sent_id, - "segment_id": self.steps[sent_id], - "segment": self.eos - } - # Consider EOS - self.steps[sent_id] = len(self.data["src"][sent_id]) + 1 - else: - dict_to_return = { - "sent_id": sent_id, - "segment_id": self.steps[sent_id], - "segment": self.data["src"][sent_id][self.steps[sent_id]] - } - - self.steps[sent_id] += 1 - - return dict_to_return - - def src_lengths(self): - # +1 for eos - return [len(sent) + 1 for sent in self.data["src"]] diff --git a/examples/simultaneous_translation/eval/server.py b/examples/simultaneous_translation/eval/server.py deleted file mode 100644 index a108881e38..0000000000 --- a/examples/simultaneous_translation/eval/server.py +++ /dev/null @@ -1,84 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. -import argparse -import sys -import json -from tornado import web, ioloop -from scorers import build_scorer - -DEFAULT_HOSTNAME = 'localhost' -DEFAULT_PORT = 12321 - - -class ScorerHandler(web.RequestHandler): - def initialize(self, scorer): - self.scorer = scorer - - -class EvalSessionHandler(ScorerHandler): - def post(self): - self.scorer.reset() - - def get(self): - r = json.dumps(self.scorer.get_info()) - self.write(r) - - -class ResultHandler(ScorerHandler): - def get(self): - r = json.dumps(self.scorer.score()) - self.write(r) - - -class SourceHandler(ScorerHandler): - def get(self): - sent_id = int(self.get_argument('sent_id')) - segment_size = None - if "segment_size" in self.request.arguments: - string = self.get_argument('segment_size') - if len(string) > 0: - segment_size = int(string) - - r = json.dumps(self.scorer.send_src(int(sent_id), segment_size)) - - self.write(r) - - -class HypothesisHandler(ScorerHandler): - def put(self): - sent_id = int(self.get_argument('sent_id')) - list_of_tokens = self.request.body.decode('utf-8').strip().split() - self.scorer.recv_hyp(sent_id, list_of_tokens) - - -def add_args(): - parser = argparse.ArgumentParser() - # fmt: off - parser.add_argument('--hostname', type=str, default=DEFAULT_HOSTNAME, - help='Server hostname') - parser.add_argument('--port', type=int, default=DEFAULT_PORT, - help='Server port number') - - args, _ = parser.parse_known_args() - # fmt: on - return args - - -def start_server(scorer, hostname=DEFAULT_HOSTNAME, port=DEFAULT_PORT, debug=False): - app = web.Application([ - (r'/result', ResultHandler, dict(scorer=scorer)), - (r'/src', SourceHandler, dict(scorer=scorer)), - (r'/hypo', HypothesisHandler, dict(scorer=scorer)), - (r'/', EvalSessionHandler, dict(scorer=scorer)), - ], debug=debug) - app.listen(port, max_buffer_size=1024 ** 3) - sys.stdout.write(f"Evaluation Server Started. Listening to port {port}\n") - ioloop.IOLoop.current().start() - - -if __name__ == '__main__': - args = add_args() - scorer = build_scorer(args) - start_server(scorer, args.hostname, args.port, args.debug) diff --git a/examples/simultaneous_translation/models/__init__.py b/examples/simultaneous_translation/models/__init__.py index 138006ed8c..257a96593f 100644 --- a/examples/simultaneous_translation/models/__init__.py +++ b/examples/simultaneous_translation/models/__init__.py @@ -6,7 +6,10 @@ import importlib import os -for file in os.listdir(os.path.dirname(__file__)): - if file.endswith('.py') and not file.startswith('_'): - model_name = file[:file.find('.py')] - importlib.import_module('examples.simultaneous_translation.models.' + model_name) + +for file in sorted(os.listdir(os.path.dirname(__file__))): + if file.endswith(".py") and not file.startswith("_"): + model_name = file[: file.find(".py")] + importlib.import_module( + "examples.simultaneous_translation.models." + model_name + ) diff --git a/examples/simultaneous_translation/models/convtransformer_simul_trans.py b/examples/simultaneous_translation/models/convtransformer_simul_trans.py new file mode 100644 index 0000000000..4a26422f65 --- /dev/null +++ b/examples/simultaneous_translation/models/convtransformer_simul_trans.py @@ -0,0 +1,204 @@ +# Copyright (c) 2017-present, Facebook, Inc. +# All rights reserved. +# +# This source code is licensed under the license found in the LICENSE file in +# the root directory of this source tree. An additional grant of patent rights +# can be found in the PATENTS file in the same directory. + +from fairseq import checkpoint_utils +from fairseq.models import ( + register_model, + register_model_architecture, +) +from fairseq.models.speech_to_text import ( + ConvTransformerModel, + convtransformer_espnet, + ConvTransformerEncoder, +) +from fairseq.models.speech_to_text.modules.augmented_memory_attention import ( + augmented_memory, + SequenceEncoder, + AugmentedMemoryConvTransformerEncoder, +) + +from torch import nn, Tensor +from typing import Dict, List +from fairseq.models.speech_to_text.modules.emformer import NoSegAugmentedMemoryTransformerEncoderLayer + +@register_model("convtransformer_simul_trans") +class SimulConvTransformerModel(ConvTransformerModel): + """ + Implementation of the paper: + + SimulMT to SimulST: Adapting Simultaneous Text Translation to + End-to-End Simultaneous Speech Translation + + https://www.aclweb.org/anthology/2020.aacl-main.58.pdf + """ + + @staticmethod + def add_args(parser): + super(SimulConvTransformerModel, SimulConvTransformerModel).add_args(parser) + parser.add_argument( + "--train-monotonic-only", + action="store_true", + default=False, + help="Only train monotonic attention", + ) + + @classmethod + def build_decoder(cls, args, task, embed_tokens): + tgt_dict = task.tgt_dict + + from examples.simultaneous_translation.models.transformer_monotonic_attention import ( + TransformerMonotonicDecoder, + ) + + decoder = TransformerMonotonicDecoder(args, tgt_dict, embed_tokens) + + if getattr(args, "load_pretrained_decoder_from", None): + decoder = checkpoint_utils.load_pretrained_component_from_model( + component=decoder, checkpoint=args.load_pretrained_decoder_from + ) + return decoder + + +@register_model_architecture( + "convtransformer_simul_trans", "convtransformer_simul_trans_espnet" +) +def convtransformer_simul_trans_espnet(args): + convtransformer_espnet(args) + + +@register_model("convtransformer_augmented_memory") +@augmented_memory +class AugmentedMemoryConvTransformerModel(SimulConvTransformerModel): + @classmethod + def build_encoder(cls, args): + encoder = SequenceEncoder(args, AugmentedMemoryConvTransformerEncoder(args)) + + if getattr(args, "load_pretrained_encoder_from", None) is not None: + encoder = checkpoint_utils.load_pretrained_component_from_model( + component=encoder, checkpoint=args.load_pretrained_encoder_from + ) + + return encoder + + +@register_model_architecture( + "convtransformer_augmented_memory", "convtransformer_augmented_memory" +) +def augmented_memory_convtransformer_espnet(args): + convtransformer_espnet(args) + + +# ============================================================================ # +# Convtransformer +# with monotonic attention decoder +# with emformer encoder +# ============================================================================ # + + +class ConvTransformerEmformerEncoder(ConvTransformerEncoder): + def __init__(self, args): + super().__init__(args) + stride = self.conv_layer_stride(args) + trf_left_context = args.segment_left_context // stride + trf_right_context = args.segment_right_context // stride + context_config = [trf_left_context, trf_right_context] + self.transformer_layers = nn.ModuleList( + [ + NoSegAugmentedMemoryTransformerEncoderLayer( + input_dim=args.encoder_embed_dim, + num_heads=args.encoder_attention_heads, + ffn_dim=args.encoder_ffn_embed_dim, + num_layers=args.encoder_layers, + dropout_in_attn=args.dropout, + dropout_on_attn=args.dropout, + dropout_on_fc1=args.dropout, + dropout_on_fc2=args.dropout, + activation_fn=args.activation_fn, + context_config=context_config, + segment_size=args.segment_length, + max_memory_size=args.max_memory_size, + scaled_init=True, # TODO: use constant for now. + tanh_on_mem=args.amtrf_tanh_on_mem, + ) + ] + ) + self.conv_transformer_encoder = ConvTransformerEncoder(args) + + def forward(self, src_tokens, src_lengths): + encoder_out: Dict[str, List[Tensor]] = self.conv_transformer_encoder(src_tokens, src_lengths.to(src_tokens.device)) + output = encoder_out["encoder_out"][0] + encoder_padding_masks = encoder_out["encoder_padding_mask"] + + return { + "encoder_out": [output], + # This is because that in the original implementation + # the output didn't consider the last segment as right context. + "encoder_padding_mask": [encoder_padding_masks[0][:, : output.size(0)]] if len(encoder_padding_masks) > 0 + else [], + "encoder_embedding": [], + "encoder_states": [], + "src_tokens": [], + "src_lengths": [], + } + + @staticmethod + def conv_layer_stride(args): + # TODO: make it configurable from the args + return 4 + + +@register_model("convtransformer_emformer") +class ConvtransformerEmformer(SimulConvTransformerModel): + @staticmethod + def add_args(parser): + super(ConvtransformerEmformer, ConvtransformerEmformer).add_args(parser) + + parser.add_argument( + "--segment-length", + type=int, + metavar="N", + help="length of each segment (not including left context / right context)", + ) + parser.add_argument( + "--segment-left-context", + type=int, + help="length of left context in a segment", + ) + parser.add_argument( + "--segment-right-context", + type=int, + help="length of right context in a segment", + ) + parser.add_argument( + "--max-memory-size", + type=int, + default=-1, + help="Right context for the segment.", + ) + parser.add_argument( + "--amtrf-tanh-on-mem", + default=False, + action="store_true", + help="whether to use tanh on memory vector", + ) + + @classmethod + def build_encoder(cls, args): + encoder = ConvTransformerEmformerEncoder(args) + if getattr(args, "load_pretrained_encoder_from", None): + encoder = checkpoint_utils.load_pretrained_component_from_model( + component=encoder, checkpoint=args.load_pretrained_encoder_from + ) + return encoder + + +@register_model_architecture( + "convtransformer_emformer", + "convtransformer_emformer", +) +def convtransformer_emformer_base(args): + convtransformer_espnet(args) diff --git a/examples/simultaneous_translation/models/transformer_monotonic_attention.py b/examples/simultaneous_translation/models/transformer_monotonic_attention.py index 24c3ba5353..7b9414b0eb 100644 --- a/examples/simultaneous_translation/models/transformer_monotonic_attention.py +++ b/examples/simultaneous_translation/models/transformer_monotonic_attention.py @@ -3,16 +3,18 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. +from typing import Dict, List, NamedTuple, Optional + import torch import torch.nn as nn -import torch.nn.functional as F - +from examples.simultaneous_translation.modules.monotonic_transformer_layer import ( + TransformerMonotonicDecoderLayer, + TransformerMonotonicEncoderLayer, +) from fairseq.models import ( register_model, register_model_architecture, ) - - from fairseq.models.transformer import ( TransformerModel, TransformerEncoder, @@ -20,28 +22,36 @@ base_architecture, transformer_iwslt_de_en, transformer_vaswani_wmt_en_de_big, + tiny_architecture ) - - -from examples.simultaneous_translation.modules.monotonic_transformer_layer import ( - TransformerMonotonicDecoderLayer, - TransformerMonotonicEncoderLayer -) +from torch import Tensor DEFAULT_MAX_SOURCE_POSITIONS = 1024 DEFAULT_MAX_TARGET_POSITIONS = 1024 +READ_ACTION = 0 +WRITE_ACTION = 1 + +TransformerMonotonicDecoderOut = NamedTuple( + "TransformerMonotonicDecoderOut", + [ + ("action", int), + ("p_choose", Optional[Tensor]), + ("attn_list", Optional[List[Optional[Dict[str, Tensor]]]]), + ("encoder_out", Optional[Dict[str, List[Tensor]]]), + ("encoder_padding_mask", Optional[Tensor]), + ], +) -@register_model('transformer_unidirectional') +@register_model("transformer_unidirectional") class TransformerUnidirectionalModel(TransformerModel): @classmethod def build_encoder(cls, args, src_dict, embed_tokens): return TransformerMonotonicEncoder(args, src_dict, embed_tokens) -@register_model('transformer_monotonic') -class TransformerMonotonicModel(TransformerModel): - +@register_model("transformer_monotonic") +class TransformerModelSimulTrans(TransformerModel): @classmethod def build_encoder(cls, args, src_dict, embed_tokens): return TransformerMonotonicEncoder(args, src_dict, embed_tokens) @@ -50,102 +60,19 @@ def build_encoder(cls, args, src_dict, embed_tokens): def build_decoder(cls, args, tgt_dict, embed_tokens): return TransformerMonotonicDecoder(args, tgt_dict, embed_tokens) - def _indices_from_states(self, states): - if type(states["indices"]["src"]) == list: - if next(self.parameters()).is_cuda: - tensor = torch.cuda.LongTensor - else: - tensor = torch.LongTensor - - src_indices = tensor( - [states["indices"]["src"][: 1 + states["steps"]["src"]]] - ) - - tgt_indices = tensor( - [ - [self.decoder.dictionary.eos()] - + states["indices"]["tgt"] - ] - ) - else: - src_indices = states["indices"]["src"][: 1 + - states["steps"]["src"]] - tgt_indices = states["indices"]["tgt"] - - return src_indices, None, tgt_indices - - def predict_from_states(self, states): - decoder_states = self.decoder.output_layer( - states["decoder_features"] - ) - lprobs = self.get_normalized_probs( - [decoder_states[:, -1:]], - log_probs=True - ) - - index = lprobs.argmax(dim=-1) - - token = self.decoder.dictionary.string(index) - - return token, index[0, 0].item() - - def decision_from_states(self, states): - ''' - This funcion take states dictionary as input, and gives the agent - a decision of whether read a token from server. Moreover, the decoder - states are also calculated here so we can directly generate a target - token without recompute every thing - ''' - - self.eval() - - if len(states["tokens"]["src"]) == 0: - return 0 - - src_indices, src_lengths, tgt_indices = self._indices_from_states( - states) - - # Update encoder states if needed - if ( - "encoder_states" not in states or - states["encoder_states"][0].size(1) <= states["steps"]["src"] - ): - encoder_out_dict = self.encoder(src_indices, src_lengths) - states["encoder_states"] = encoder_out_dict - else: - encoder_out_dict = states["encoder_states"] - - # online means we still need tokens to feed the model - states["model_states"]["online"] = not ( - states["finish_read"] - and len(states["tokens"]["src"]) == states["steps"]["src"] - ) - - states["model_states"]["steps"] = states["steps"] - - x, outputs = self.decoder.forward( - prev_output_tokens=tgt_indices, - encoder_out=encoder_out_dict, - incremental_state=states["model_states"], - features_only=True, - ) - - states["decoder_features"] = x - - return outputs["action"] - class TransformerMonotonicEncoder(TransformerEncoder): - def __init__(self, args, dictionary, embed_tokens): super().__init__(args, dictionary, embed_tokens) self.dictionary = dictionary self.layers = nn.ModuleList([]) - self.layers.extend([ - TransformerMonotonicEncoderLayer(args) - for i in range(args.encoder_layers) - ]) + self.layers.extend( + [ + TransformerMonotonicEncoderLayer(args) + for i in range(args.encoder_layers) + ] + ) class TransformerMonotonicDecoder(TransformerDecoder): @@ -166,25 +93,37 @@ def __init__(self, args, dictionary, embed_tokens, no_encoder_attn=False): self.dictionary = dictionary self.layers = nn.ModuleList([]) - self.layers.extend([ - TransformerMonotonicDecoderLayer(args, no_encoder_attn) - for _ in range(args.decoder_layers) - ]) + self.layers.extend( + [ + TransformerMonotonicDecoderLayer(args) + for _ in range(args.decoder_layers) + ] + ) + self.policy_criterion = getattr(args, "policy_criterion", "any") + self.num_updates = None + + def set_num_updates(self, num_updates): + self.num_updates = num_updates def pre_attention( - self, prev_output_tokens, encoder_out_dict, - incremental_state=None + self, + prev_output_tokens, + encoder_out_dict: Dict[str, List[Tensor]], + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, ): - positions = self.embed_positions( - prev_output_tokens, - incremental_state=incremental_state, - ) if self.embed_positions is not None else None + positions = ( + self.embed_positions( + prev_output_tokens, + incremental_state=incremental_state, + ) + if self.embed_positions is not None + else None + ) if incremental_state is not None: prev_output_tokens = prev_output_tokens[:, -1:] if positions is not None: positions = positions[:, -1:] - # embed tokens and positions x = self.embed_scale * self.embed_tokens(prev_output_tokens) @@ -193,18 +132,28 @@ def pre_attention( if positions is not None: x += positions - x = F.dropout(x, p=self.dropout, training=self.training) + + x = self.dropout_module(x) # B x T x C -> T x B x C x = x.transpose(0, 1) - encoder_out = encoder_out_dict.encoder_out - encoder_padding_mask = encoder_out_dict.encoder_padding_mask + encoder_out = encoder_out_dict["encoder_out"][0] + + if "encoder_padding_mask" in encoder_out_dict: + encoder_padding_mask = ( + encoder_out_dict["encoder_padding_mask"][0] + if encoder_out_dict["encoder_padding_mask"] + and len(encoder_out_dict["encoder_padding_mask"]) > 0 + else None + ) + else: + encoder_padding_mask = None return x, encoder_out, encoder_padding_mask def post_attention(self, x): - if self.layer_norm: + if self.layer_norm is not None: x = self.layer_norm(x) # T x B x C -> B x T x C @@ -215,9 +164,32 @@ def post_attention(self, x): return x + def clean_cache( + self, + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]], + end_id: Optional[int] = None, + ): + """ + Clean cache in the monotonic layers. + The cache is generated because of a forward pass of decoder has run but no prediction, + so that the self attention key value in decoder is written in the incremental state. + end_id is the last idx of the layers + """ + if end_id is None: + end_id = len(self.layers) + + for index, layer in enumerate(self.layers): + if index < end_id: + layer.prune_incremental_state(incremental_state) + def extract_features( - self, prev_output_tokens, encoder_out, - incremental_state=None, **unused + self, + prev_output_tokens, + encoder_out: Optional[Dict[str, List[Tensor]]], + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, + full_context_alignment: bool = False, # unused + alignment_layer: Optional[int] = None, # unused + alignment_heads: Optional[int] = None, # unsed ): """ Similar to *forward* but only return features. @@ -228,19 +200,15 @@ def extract_features( - a dictionary with any model-specific outputs """ # incremental_state = None - ( - x, - encoder_outs, - encoder_padding_mask - ) = self.pre_attention( - prev_output_tokens, - encoder_out, - incremental_state + assert encoder_out is not None + (x, encoder_outs, encoder_padding_mask) = self.pre_attention( + prev_output_tokens, encoder_out, incremental_state ) attn = None inner_states = [x] - attn_list = [] - step_list = [] + attn_list: List[Optional[Dict[str, Tensor]]] = [] + + p_choose = torch.tensor([1.0]) for i, layer in enumerate(self.layers): @@ -250,111 +218,85 @@ def extract_features( encoder_padding_mask=encoder_padding_mask, incremental_state=incremental_state, self_attn_mask=self.buffered_future_mask(x) - if incremental_state is None else None, + if incremental_state is None + else None, ) inner_states.append(x) attn_list.append(attn) if incremental_state is not None: - curr_steps = layer.get_steps(incremental_state) - step_list.append(curr_steps) - - if incremental_state.get("online", False): - p_choose = attn["p_choose"].squeeze(0).squeeze(1).gather(1, curr_steps.t()) - - new_steps = ( - curr_steps - + (p_choose < 0.5).t().type_as(curr_steps) - ) - - if (new_steps >= incremental_state["steps"]["src"]).any(): - # We need to prune the last self_attn saved_state - # if model decide not to read - # otherwise there will be duplicated saved_state - for j in range(i + 1): - self.layers[j].prune_incremental_state( - incremental_state) - - return x, {"action": 0} - - if ( - incremental_state is not None - and not incremental_state.get("online", False) - ): - # Here is for fast evaluation - fastest_step = torch.max( - torch.cat(step_list, dim=1), - dim=1, - keepdim=True - )[0] + 1 - - if "fastest_step" in incremental_state: - incremental_state["fastest_step"] = torch.cat( - [incremental_state["fastest_step"], fastest_step], - dim=1 - ) - else: - incremental_state["fastest_step"] = fastest_step + if_online = incremental_state["online"]["only"] + assert if_online is not None + if if_online.to(torch.bool): + # Online indicates that the encoder states are still changing + assert attn is not None + if self.policy_criterion == "any": + # Any head decide to read than read + head_read = layer.encoder_attn._get_monotonic_buffer(incremental_state)["head_read"] + assert head_read is not None + if head_read.any(): + # We need to prune the last self_attn saved_state + # if model decide not to read + # otherwise there will be duplicated saved_state + self.clean_cache(incremental_state, i + 1) + + return x, TransformerMonotonicDecoderOut( + action=0, + p_choose=p_choose, + attn_list=None, + encoder_out=None, + encoder_padding_mask=None, + ) x = self.post_attention(x) - return x, { - "action": 1, - "attn_list": attn_list, - "step_list": step_list, - "encoder_out": encoder_out, - "encoder_padding_mask": encoder_padding_mask, - } - - def reorder_incremental_state(self, incremental_state, new_order): - super().reorder_incremental_state(incremental_state, new_order) - if "fastest_step" in incremental_state: - incremental_state["fastest_step"] = ( - incremental_state["fastest_step"] - .index_select(0, new_order) - ) + return x, TransformerMonotonicDecoderOut( + action=1, + p_choose=p_choose, + attn_list=attn_list, + encoder_out=encoder_out, + encoder_padding_mask=encoder_padding_mask, + ) -@register_model_architecture( - 'transformer_monotonic', - 'transformer_monotonic' -) -def base_monotonic_rchitecture(args): +@register_model_architecture("transformer_monotonic", "transformer_monotonic") +def base_monotonic_architecture(args): base_architecture(args) - args.encoder_unidirectional = getattr( - args, 'encoder_unidirectional', False) + args.encoder_unidirectional = getattr(args, "encoder_unidirectional", False) @register_model_architecture( - 'transformer_monotonic', - 'transformer_monotonic_iwslt_de_en' + "transformer_monotonic", "transformer_monotonic_iwslt_de_en" ) def transformer_monotonic_iwslt_de_en(args): transformer_iwslt_de_en(args) - base_monotonic_rchitecture(args) + base_monotonic_architecture(args) # parameters used in the "Attention Is All You Need" paper (Vaswani et al., 2017) @register_model_architecture( - 'transformer_monotonic', - 'transformer_monotonic_vaswani_wmt_en_de_big' + "transformer_monotonic", "transformer_monotonic_vaswani_wmt_en_de_big" ) def transformer_monotonic_vaswani_wmt_en_de_big(args): transformer_vaswani_wmt_en_de_big(args) @register_model_architecture( - 'transformer_monotonic', - 'transformer_monotonic_vaswani_wmt_en_fr_big' + "transformer_monotonic", "transformer_monotonic_vaswani_wmt_en_fr_big" ) def transformer_monotonic_vaswani_wmt_en_fr_big(args): transformer_monotonic_vaswani_wmt_en_fr_big(args) @register_model_architecture( - 'transformer_unidirectional', - 'transformer_unidirectional_iwslt_de_en' + "transformer_unidirectional", "transformer_unidirectional_iwslt_de_en" ) def transformer_unidirectional_iwslt_de_en(args): transformer_iwslt_de_en(args) + + +@register_model_architecture("transformer_monotonic", "transformer_monotonic_tiny") +def monotonic_tiny_architecture(args): + tiny_architecture(args) + base_monotonic_architecture(args) diff --git a/examples/simultaneous_translation/modules/__init__.py b/examples/simultaneous_translation/modules/__init__.py index 4f311b1ee6..f5ea180f9b 100644 --- a/examples/simultaneous_translation/modules/__init__.py +++ b/examples/simultaneous_translation/modules/__init__.py @@ -3,17 +3,21 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. -import importlib -import os +import os +import importlib from fairseq import registry + ( build_monotonic_attention, register_monotonic_attention, - MONOTONIC_ATTENTION_REGISTRY -) = registry.setup_registry('--simul-type') + MONOTONIC_ATTENTION_REGISTRY, + _, +) = registry.setup_registry("--simul-type") -for file in os.listdir(os.path.dirname(__file__)): - if file.endswith('.py') and not file.startswith('_'): - model_name = file[:file.find('.py')] - importlib.import_module('examples.simultaneous_translation.modules.' + model_name) +for file in sorted(os.listdir(os.path.dirname(__file__))): + if file.endswith(".py") and not file.startswith("_"): + model_name = file[: file.find(".py")] + importlib.import_module( + "examples.simultaneous_translation.modules." + model_name + ) diff --git a/examples/simultaneous_translation/modules/fixed_pre_decision.py b/examples/simultaneous_translation/modules/fixed_pre_decision.py new file mode 100644 index 0000000000..3991414aed --- /dev/null +++ b/examples/simultaneous_translation/modules/fixed_pre_decision.py @@ -0,0 +1,190 @@ +from functools import partial + +import torch +from torch import Tensor +import math +import torch.nn.functional as F + +from . import register_monotonic_attention +from .monotonic_multihead_attention import ( + MonotonicAttention, + MonotonicInfiniteLookbackAttention, + WaitKAttention +) +from typing import Dict, Optional + + +def fixed_pooling_monotonic_attention(monotonic_attention): + def create_model(monotonic_attention, klass): + class FixedStrideMonotonicAttention(monotonic_attention): + def __init__(self, args): + self.waitk_lagging = 0 + self.num_heads = 0 + self.noise_mean = 0.0 + self.noise_var = 0.0 + super().__init__(args) + self.pre_decision_type = args.fixed_pre_decision_type + self.pre_decision_ratio = args.fixed_pre_decision_ratio + self.pre_decision_pad_threshold = args.fixed_pre_decision_pad_threshold + assert self.pre_decision_ratio > 1 + + if args.fixed_pre_decision_type == "average": + self.pooling_layer = torch.nn.AvgPool1d( + kernel_size=self.pre_decision_ratio, + stride=self.pre_decision_ratio, + ceil_mode=True, + ) + elif args.fixed_pre_decision_type == "last": + + def last(key): + if key.size(2) < self.pre_decision_ratio: + return key + else: + k = key[ + :, + :, + self.pre_decision_ratio - 1:: self.pre_decision_ratio, + ].contiguous() + if key.size(-1) % self.pre_decision_ratio != 0: + k = torch.cat([k, key[:, :, -1:]], dim=-1).contiguous() + return k + + self.pooling_layer = last + else: + raise NotImplementedError + + @staticmethod + def add_args(parser): + super( + FixedStrideMonotonicAttention, FixedStrideMonotonicAttention + ).add_args(parser) + parser.add_argument( + "--fixed-pre-decision-ratio", + type=int, + required=True, + help=( + "Ratio for the fixed pre-decision," + "indicating how many encoder steps will start" + "simultaneous decision making process." + ), + ) + parser.add_argument( + "--fixed-pre-decision-type", + default="average", + choices=["average", "last"], + help="Pooling type", + ) + parser.add_argument( + "--fixed-pre-decision-pad-threshold", + type=float, + default=0.3, + help="If a part of the sequence has pad" + ",the threshold the pooled part is a pad.", + ) + + def insert_zeros(self, x): + bsz_num_heads, tgt_len, src_len = x.size() + stride = self.pre_decision_ratio + weight = F.pad(torch.ones(1, 1, 1).to(x), (stride - 1, 0)) + x_upsample = F.conv_transpose1d( + x.view(-1, src_len).unsqueeze(1), + weight, + stride=stride, + padding=0, + ) + return x_upsample.squeeze(1).view(bsz_num_heads, tgt_len, -1) + + def p_choose( + self, + query: Optional[Tensor], + key: Optional[Tensor], + key_padding_mask: Optional[Tensor] = None, + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, + ): + assert key is not None + assert query is not None + src_len = key.size(0) + tgt_len = query.size(0) + batch_size = query.size(1) + + key_pool = self.pooling_layer(key.transpose(0, 2)).transpose(0, 2) + + if key_padding_mask is not None: + key_padding_mask_pool = ( + self.pooling_layer(key_padding_mask.unsqueeze(0).float()) + .squeeze(0) + .gt(self.pre_decision_pad_threshold) + ) + # Make sure at least one element is not pad + key_padding_mask_pool[:, 0] = 0 + else: + key_padding_mask_pool = None + + if incremental_state is not None: + # The floor instead of ceil is used for inference + # But make sure the length key_pool at least 1 + if ( + max(1, math.floor(key.size(0) / self.pre_decision_ratio)) + ) < key_pool.size(0): + key_pool = key_pool[:-1] + if key_padding_mask_pool is not None: + key_padding_mask_pool = key_padding_mask_pool[:-1] + + p_choose_pooled = self.p_choose_from_qk( + query, + key_pool, + key_padding_mask_pool, + incremental_state=incremental_state, + ) + + # Upsample, interpolate zeros + p_choose = self.insert_zeros(p_choose_pooled) + + if p_choose.size(-1) < src_len: + # Append zeros if the upsampled p_choose is shorter than src_len + p_choose = torch.cat( + [ + p_choose, + torch.zeros( + p_choose.size(0), + tgt_len, + src_len - p_choose.size(-1) + ).to(p_choose) + ], + dim=2 + ) + else: + # can be larger than src_len because we used ceil before + p_choose = p_choose[:, :, :src_len] + p_choose[:, :, -1] = p_choose_pooled[:, :, -1] + + assert list(p_choose.size()) == [ + batch_size * self.num_heads, + tgt_len, + src_len, + ] + + return p_choose + + FixedStrideMonotonicAttention.__name__ = klass.__name__ + return FixedStrideMonotonicAttention + + return partial(create_model, monotonic_attention) + + +@register_monotonic_attention("waitk_fixed_pre_decision") +@fixed_pooling_monotonic_attention(WaitKAttention) +class WaitKAttentionFixedStride: + pass + + +@register_monotonic_attention("hard_aligned_fixed_pre_decision") +@fixed_pooling_monotonic_attention(MonotonicAttention) +class MonotonicAttentionFixedStride: + pass + + +@register_monotonic_attention("infinite_lookback_fixed_pre_decision") +@fixed_pooling_monotonic_attention(MonotonicInfiniteLookbackAttention) +class MonotonicInfiniteLookbackAttentionFixedStride: + pass diff --git a/examples/simultaneous_translation/modules/monotonic_multihead_attention.py b/examples/simultaneous_translation/modules/monotonic_multihead_attention.py index cfa7fdcd16..11ef60c945 100644 --- a/examples/simultaneous_translation/modules/monotonic_multihead_attention.py +++ b/examples/simultaneous_translation/modules/monotonic_multihead_attention.py @@ -4,470 +4,426 @@ # LICENSE file in the root directory of this source tree. import math + import torch -import torch.nn.functional as F +from torch import Tensor import torch.nn as nn -from fairseq import utils - -from fairseq.modules import MultiheadAttention - -from examples.simultaneous_translation.utils.functions import ( - exclusive_cumprod, - lengths_to_mask +from examples.simultaneous_translation.utils.p_choose_strategy import ( + learnable_p_choose, + waitk_p_choose ) +from examples.simultaneous_translation.utils.monotonic_attention import ( + expected_alignment_from_p_choose, + expected_soft_attention, + mass_preservation, +) +from fairseq.modules import MultiheadAttention -from fairseq.incremental_decoding_utils import with_incremental_state -from fairseq.utils import convert_padding_direction from . import register_monotonic_attention +from typing import Dict, Optional -@with_incremental_state -class MonotonicAttention(nn.Module): +@register_monotonic_attention("hard_aligned") +class MonotonicAttention(MultiheadAttention): """ Abstract class of monotonic attentions """ + k_in_proj: Dict[str, nn.Linear] + q_in_proj: Dict[str, nn.Linear] + def __init__(self, args): - self.eps = args.attention_eps - self.mass_preservation = args.mass_preservation + super().__init__( + embed_dim=args.decoder_embed_dim, + num_heads=args.decoder_attention_heads, + kdim=getattr(args, "encoder_embed_dim", None), + vdim=getattr(args, "encoder_embed_dim", None), + dropout=args.attention_dropout, + encoder_decoder_attention=True, + ) + self.soft_attention = False + + self.eps = getattr(args, "attention_eps", True) + self.mass_preservation = getattr(args, "mass_preservation", True) + + self.noise_type = args.noise_type self.noise_mean = args.noise_mean self.noise_var = args.noise_var self.energy_bias_init = args.energy_bias_init self.energy_bias = ( nn.Parameter(self.energy_bias_init * torch.ones([1])) - if args.energy_bias is True else 0 + if args.energy_bias is True + else 0 ) + self.k_in_proj = {"monotonic": self.k_proj} + self.q_in_proj = {"monotonic": self.q_proj} + self.chunk_size = None + @staticmethod def add_args(parser): # fmt: off - parser.add_argument('--no-mass-preservation', action="store_false", dest="mass_preservation", + parser.add_argument('--no-mass-preservation', action="store_false", + dest="mass_preservation", help='Do not stay on the last token when decoding') - parser.add_argument('--mass-preservation', action="store_true", dest="mass_preservation", + parser.add_argument('--mass-preservation', action="store_true", + dest="mass_preservation", help='Stay on the last token when decoding') parser.set_defaults(mass_preservation=True) - parser.add_argument('--noise-var', type=float, default=1.0, help='Variance of discretness noise') parser.add_argument('--noise-mean', type=float, default=0.0, help='Mean of discretness noise') - parser.add_argument('--energy-bias', action="store_true", default=False, + parser.add_argument('--noise-type', type=str, default="flat", + help='Type of discretness noise') + parser.add_argument('--energy-bias', action="store_true", + default=False, help='Bias for energy') parser.add_argument('--energy-bias-init', type=float, default=-2.0, help='Initial value of the bias for energy') parser.add_argument('--attention-eps', type=float, default=1e-6, help='Epsilon when calculating expected attention') - # fmt: on - def p_choose(self, *args): - raise NotImplementedError - - def input_projections(self, *args): - raise NotImplementedError - - def attn_energy(self, q_proj, k_proj, key_padding_mask=None): + def energy_from_qk( + self, + query: Tensor, + key: Tensor, + energy_type: str, + key_padding_mask: Optional[Tensor] = None, + bias: int = 0 + ): """ - Calculating monotonic energies - - ============================================================ - Expected input size - q_proj: bsz * num_heads, tgt_len, self.head_dim - k_proj: bsz * num_heads, src_len, self.head_dim - key_padding_mask: bsz, src_len - attn_mask: tgt_len, src_len + Compute energy from query and key + q_func_value is a tuple looks like + (q_proj_func, q_tensor) + q_tensor size: bsz, tgt_len, emb_dim + k_tensor size: bsz, src_len, emb_dim + key_padding_mask size: bsz, src_len + attn_mask: bsz, src_len """ - bsz, tgt_len, embed_dim = q_proj.size() - bsz = bsz // self.num_heads - src_len = k_proj.size(1) - attn_energy = torch.bmm(q_proj, k_proj.transpose(1, 2)) + self.energy_bias + length, bsz, _ = query.size() + q = self.q_in_proj[energy_type].forward(query) + q = ( + q.contiguous() + .view(length, bsz * self.num_heads, self.head_dim) + .transpose(0, 1) + ) + q = q * self.scaling + length, bsz, _ = key.size() + k = self.k_in_proj[energy_type].forward(key) + k = ( + k.contiguous() + .view(length, bsz * self.num_heads, self.head_dim) + .transpose(0, 1) + ) - attn_energy = attn_energy.view(bsz, self.num_heads, tgt_len, src_len) + energy = torch.bmm(q, k.transpose(1, 2)) + bias if key_padding_mask is not None: - attn_energy = attn_energy.masked_fill( - key_padding_mask.unsqueeze(1).unsqueeze(2).bool(), - float('-inf'), + energy = energy.masked_fill( + key_padding_mask.unsqueeze(1).to(torch.bool), + - float("inf") ) - return attn_energy - - def expected_alignment_train(self, p_choose, key_padding_mask): - """ - Calculating expected alignment for MMA - Mask is not need because p_choose will be 0 if masked - - q_ij = (1 − p_{ij−1})q_{ij−1} + a+{i−1j} - a_ij = p_ij q_ij - - parellel solution: - ai = p_i * cumprod(1 − pi) * cumsum(a_i / cumprod(1 − pi)) - - ============================================================ - Expected input size - p_choose: bsz * num_heads, tgt_len, src_len - """ - - # p_choose: bsz * num_heads, tgt_len, src_len - bsz_num_heads, tgt_len, src_len = p_choose.size() - - # cumprod_1mp : bsz * num_heads, tgt_len, src_len - cumprod_1mp = exclusive_cumprod(1 - p_choose, dim=2, eps=self.eps) - cumprod_1mp_clamp = torch.clamp(cumprod_1mp, self.eps, 1.0) - - init_attention = p_choose.new_zeros([bsz_num_heads, 1, src_len]) - init_attention[:, :, 0] = 1.0 - - previous_attn = [init_attention] - - for i in range(tgt_len): - # p_choose: bsz * num_heads, tgt_len, src_len - # cumprod_1mp_clamp : bsz * num_heads, tgt_len, src_len - # previous_attn[i]: bsz * num_heads, 1, src_len - # alpha_i: bsz * num_heads, src_len - alpha_i = ( - p_choose[:, i] - * cumprod_1mp[:, i] - * torch.cumsum( - previous_attn[i][:, 0] / cumprod_1mp_clamp[:, i], - dim=1 - ) - ).clamp(0, 1.0) - previous_attn.append(alpha_i.unsqueeze(1)) - - # alpha: bsz * num_heads, tgt_len, src_len - alpha = torch.cat(previous_attn[1:], dim=1) + return energy - if self.mass_preservation: - # Last token has the residual probabilities - alpha[:, :, -1] = 1 - alpha[:, :, :-1].sum(dim=-1).clamp(0.0, 1.0) + def p_choose_from_qk(self, query, key, key_padding_mask, incremental_states=None): + monotonic_energy = self.energy_from_qk( + query, + key, + "monotonic", + key_padding_mask=key_padding_mask, + bias=self.energy_bias, + ) - assert not torch.isnan(alpha).any(), "NaN detected in alpha." + p_choose = learnable_p_choose( + monotonic_energy, + self.noise_mean, + self.noise_var, + self.training + ) + return p_choose - return alpha + def p_choose(self, query, key, key_padding_mask, incremental_states=None): + return self.p_choose_from_qk(self, query, key, key_padding_mask) - def expected_alignment_infer(self, p_choose, key_padding_mask, incremental_state): + def monotonic_attention_process_infer( + self, + query: Optional[Tensor], + key: Optional[Tensor], + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]], + ): """ - Calculating mo alignment for MMA during inference time - - ============================================================ - Expected input size - p_choose: bsz * num_heads, tgt_len, src_len - key_padding_mask: bsz * src_len - incremental_state: dict + Monotonic attention at inference time + Notice that this function is designed for simuleval not sequence_generator """ - # p_choose: bsz * self.num_heads, src_len - bsz_num_heads, tgt_len, src_len = p_choose.size() - # One token at a time - assert tgt_len == 1 - p_choose = p_choose[:, 0, :] + assert query is not None + assert key is not None + if query.size(1) != 1: + raise RuntimeError( + "Simultaneous translation models don't support batch decoding." + ) + # 1. compute stepwise probability + p_choose = self.p_choose( + query, key, None, incremental_state + ).squeeze(1) + + # 2. Compute the alpha + src_len = key.size(0) + # Maximum steps allows in this iteration + max_steps = src_len - 1 if self.mass_preservation else src_len monotonic_cache = self._get_monotonic_buffer(incremental_state) - - # prev_monotonic_step: bsz, num_heads - bsz = bsz_num_heads // self.num_heads - prev_monotonic_step = monotonic_cache.get( - "step", - p_choose.new_zeros([bsz, self.num_heads]).long() - ) - bsz, num_heads = prev_monotonic_step.size() - assert num_heads == self.num_heads - assert bsz * num_heads == bsz_num_heads - - # p_choose: bsz, num_heads, src_len - p_choose = p_choose.view(bsz, num_heads, src_len) - - if key_padding_mask is not None: - src_lengths = src_len - \ - key_padding_mask.sum(dim=1, keepdim=True).long() - else: - src_lengths = prev_monotonic_step.new_ones(bsz, 1) * src_len - - # src_lengths: bsz, num_heads - src_lengths = src_lengths.expand_as(prev_monotonic_step) - # new_monotonic_step: bsz, num_heads - new_monotonic_step = prev_monotonic_step - - step_offset = 0 - if key_padding_mask is not None: - if key_padding_mask[:, 0].any(): - # left_pad_source = True: - step_offset = key_padding_mask.sum(dim=-1, keepdim=True) - - max_steps = ( - src_lengths - 1 if self.mass_preservation - else src_lengths + # Step for each head + monotonic_step = monotonic_cache.get( + 'head_step', + p_choose.new_zeros(1, self.num_heads).long() ) + assert monotonic_step is not None + finish_read = monotonic_step.eq(max_steps) + p_choose_i = torch.tensor(1) - # finish_read: bsz, num_heads - finish_read = new_monotonic_step.eq(max_steps) - - while finish_read.sum().item() < bsz * self.num_heads: - # p_choose: bsz * self.num_heads, src_len + while finish_read.sum().item() < self.num_heads: + # p_choose: self.num_heads, src_len # only choose the p at monotonic steps - # p_choose_i: bsz , self.num_heads + # p_choose_i: 1, self.num_heads p_choose_i = ( - p_choose - .gather( - 2, - (step_offset + new_monotonic_step).unsqueeze(2) - .clamp(0, src_len - 1) + p_choose.gather( + 1, + monotonic_step + .clamp(0, src_len - 1), ) - ).squeeze(2) + ) - action = ( + read_one_step = ( (p_choose_i < 0.5) - .type_as(prev_monotonic_step) + .type_as(monotonic_step) .masked_fill(finish_read, 0) ) # 1 x bsz # sample actions on unfinished seq - # 1 means stay, finish reading - # 0 means leave, continue reading - # dist = torch.distributions.bernoulli.Bernoulli(p_choose) - # action = dist.sample().type_as(finish_read) * (1 - finish_read) + # 0 means stay, finish reading + # 1 means leave, continue reading - new_monotonic_step += action + monotonic_step += read_one_step - finish_read = new_monotonic_step.eq(max_steps) | (action == 0) - # finish_read = (~ (finish_read.sum(dim=1, keepdim=True) < self.num_heads / 2)) | finish_read + finish_read = monotonic_step.eq(max_steps) | (read_one_step == 0) - monotonic_cache["step"] = new_monotonic_step + # p_choose at last steps + p_choose_i = ( + p_choose.gather( + 1, + monotonic_step + .clamp(0, src_len - 1), + ) + ) - # alpha: bsz * num_heads, 1, src_len - # new_monotonic_step: bsz, num_heads + monotonic_cache["head_step"] = monotonic_step + # Whether a head is looking for new input + monotonic_cache["head_read"] = ( + monotonic_step.eq(max_steps) & (p_choose_i < 0.5) + ) + self._set_monotonic_buffer(incremental_state, monotonic_cache) + + # 2. Update alpha alpha = ( p_choose - .new_zeros([bsz * self.num_heads, src_len]) + .new_zeros([self.num_heads, src_len]) .scatter( 1, - (step_offset + new_monotonic_step).view(bsz * - self.num_heads, 1).clamp(0, src_len - 1), + (monotonic_step) + .view(self.num_heads, 1).clamp(0, src_len - 1), 1 ) ) if not self.mass_preservation: alpha = alpha.masked_fill( - (new_monotonic_step == max_steps).view(bsz * self.num_heads, 1), + (monotonic_step == max_steps) + .view(self.num_heads, 1), 0 ) - alpha = alpha.unsqueeze(1) + # 4. Compute Beta + if self.soft_attention: + monotonic_step = monotonic_step.t() + beta_mask = torch.arange(src_len).expand_as(alpha).gt(monotonic_step).unsqueeze(1) + # If it's soft attention just do softmax on current context + soft_energy = self.energy_from_qk( + query, + key, + "soft" + ) + beta = torch.nn.functional.softmax( + soft_energy.masked_fill(beta_mask, -float("inf")), dim=-1 + ) + # It could happen that a head doesn't move at all + beta = beta.masked_fill(monotonic_step.eq(0).unsqueeze(1), 0) + else: + # If it's hard attention just select the last state + beta = alpha - self._set_monotonic_buffer(incremental_state, monotonic_cache) + return p_choose, alpha, beta + + def monotonic_attention_process_train( + self, + query: Optional[Tensor], + key: Optional[Tensor], + key_padding_mask: Optional[Tensor] = None, + ): + """ + Calculating monotonic attention process for training + Including: + stepwise probability: p_choose + expected hard alignment: alpha + expected soft attention: beta + """ + assert query is not None + assert key is not None + + # 1. compute stepwise probability + p_choose = self.p_choose_from_qk(query, key, key_padding_mask) - return alpha + # 2. compute expected_alignment + alpha = expected_alignment_from_p_choose( + p_choose, + key_padding_mask, + eps=self.eps, + ) + + if self.mass_preservation: + alpha = mass_preservation( + alpha, key_padding_mask + ) + + # 3. compute expected soft attention (soft aligned model only) + if self.soft_attention: + soft_energy = self.energy_from_qk( + query, + key, + "soft", + key_padding_mask=None, + ) - def v_proj_output(self, value): - raise NotImplementedError + beta = expected_soft_attention( + alpha, + soft_energy, + padding_mask=key_padding_mask, + chunk_size=self.chunk_size, + eps=self.eps, + ) + else: + beta = alpha + soft_energy = alpha + + return p_choose, alpha, beta, soft_energy def forward( - self, query, key, value, - key_padding_mask=None, incremental_state=None, *args, **kwargs, + self, + query: Optional[Tensor], + key: Optional[Tensor], + value: Optional[Tensor], + key_padding_mask: Optional[Tensor] = None, + attn_mask: Optional[Tensor] = None, + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, + need_weights: bool = True, static_kv: bool = False, need_head_weights: bool = False, ): + """ + query: tgt_len, bsz, embed_dim + key: src_len, bsz, embed_dim + value: src_len, bsz, embed_dim + """ + + assert attn_mask is None + assert query is not None + assert key is not None + assert value is not None tgt_len, bsz, embed_dim = query.size() src_len = value.size(0) - # stepwise prob - # p_choose: bsz * self.num_heads, tgt_len, src_len - p_choose = self.p_choose(query, key, key_padding_mask) + if key_padding_mask is not None: + assert not key_padding_mask[:, 0].any(), ( + "Only right padding is supported." + ) + key_padding_mask = ( + key_padding_mask + .unsqueeze(1) + .expand([bsz, self.num_heads, src_len]) + .contiguous() + .view(-1, src_len) + ) - # expected alignment alpha - # bsz * self.num_heads, tgt_len, src_len if incremental_state is not None: - alpha = self.expected_alignment_infer(p_choose, key_padding_mask, incremental_state) + # Inference + ( + p_choose, alpha, beta + ) = self.monotonic_attention_process_infer( + query, key, incremental_state + ) + soft_energy = beta else: - alpha = self.expected_alignment_train(p_choose, key_padding_mask) - - # expected attention beta - # bsz * self.num_heads, tgt_len, src_len - beta = self.expected_attention(alpha, query, key, value, key_padding_mask, incremental_state) - - attn_weights = beta - - v_proj = self.v_proj_output(value) - attn = torch.bmm(attn_weights.type_as(v_proj), v_proj) + # Train + ( + p_choose, alpha, beta, soft_energy + ) = self.monotonic_attention_process_train( + query, key, key_padding_mask + ) - attn = ( - attn + v = self.v_proj(value) + length, bsz, _ = v.size() + v = ( + v.contiguous() + .view(length, bsz * self.num_heads, self.head_dim) .transpose(0, 1) - .contiguous() - .view(tgt_len, bsz, embed_dim) ) - attn = self.out_proj(attn) + attn = torch.bmm(beta.type_as(v), v) - beta = beta.view(bsz, self.num_heads, tgt_len, src_len) - alpha = alpha.view(bsz, self.num_heads, tgt_len, src_len) - p_choose = p_choose.view(bsz, self.num_heads, tgt_len, src_len) + attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim) - return attn, {"alpha": alpha, "beta": beta, "p_choose": p_choose} + attn = self.out_proj(attn) - def reorder_incremental_state(self, incremental_state, new_order): - """Reorder buffered internal state (for incremental generation).""" - super().reorder_incremental_state(incremental_state, new_order) - input_buffer = self._get_monotonic_buffer(incremental_state) - if input_buffer is not None: - for k in input_buffer.keys(): - input_buffer[k] = input_buffer[k].index_select(0, new_order) - self._set_monotonic_buffer(incremental_state, input_buffer) + p_choose = p_choose.view(bsz, self.num_heads, tgt_len, src_len) + alpha = alpha.view(bsz, self.num_heads, tgt_len, src_len) + beta = beta.view(bsz, self.num_heads, tgt_len, src_len) - def _get_monotonic_buffer(self, incremental_state): - return utils.get_incremental_state( - self, - incremental_state, - 'monotonic', - ) or {} + return attn, { + "p_choose": p_choose, + "alpha": alpha, + "beta": beta, + } - def _set_monotonic_buffer(self, incremental_state, buffer): - utils.set_incremental_state( - self, + def _get_monotonic_buffer(self, incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]]): + maybe_incremental_state = self.get_incremental_state( incremental_state, 'monotonic', - buffer, ) - - def get_pointer(self, incremental_state): - return utils.get_incremental_state( - self, - incremental_state, - 'monotonic', - ) or {} - - def get_fastest_pointer(self, incremental_state): - return self.get_pointer(incremental_state)["step"].max(0)[0] - - def set_pointer(self, incremental_state, p_choose): - curr_pointer = self.get_pointer(incremental_state) - if len(curr_pointer) == 0: - buffer = torch.zeros_like(p_choose) + if maybe_incremental_state is None: + typed_empty_dict: Dict[str, Optional[Tensor]] = {} + return typed_empty_dict else: - buffer = self.get_pointer(incremental_state)["step"] - - buffer += (p_choose < 0.5).type_as(buffer) + return maybe_incremental_state - utils.set_incremental_state( - self, + def _set_monotonic_buffer(self, incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]], buffer: Dict[str, Optional[Tensor]]): + self.set_incremental_state( incremental_state, 'monotonic', - {"step": buffer}, - ) - - -@register_monotonic_attention("hard_aligned") -class MonotonicMultiheadAttentionHard(MonotonicAttention, MultiheadAttention): - - def __init__(self, args): - MultiheadAttention.__init__( - self, - embed_dim=args.decoder_embed_dim, - num_heads=args.decoder_attention_heads, - kdim=getattr(args, 'encoder_embed_dim', None), - vdim=getattr(args, 'encoder_embed_dim', None), - dropout=args.attention_dropout, - encoder_decoder_attention=True + buffer, ) - MonotonicAttention.__init__(self, args) - - self.k_in_proj = {"monotonic": self.k_proj} - self.q_in_proj = {"monotonic": self.q_proj} - self.v_in_proj = {"output": self.v_proj} - - def input_projections(self, query, key, value, name): - """ - Prepare inputs for multihead attention - - ============================================================ - Expected input size - query: tgt_len, bsz, embed_dim - key: src_len, bsz, embed_dim - value: src_len, bsz, embed_dim - name: monotonic or soft - """ - - if query is not None: - bsz = query.size(1) - q = self.q_in_proj[name](query) - q *= self.scaling - q = q.contiguous().view(-1, bsz * self.num_heads, self.head_dim).transpose(0, 1) - else: - q = None - - if key is not None: - bsz = key.size(1) - k = self.k_in_proj[name](key) - k = k.contiguous().view(-1, bsz * self.num_heads, self.head_dim).transpose(0, 1) - else: - k = None - - if value is not None: - bsz = value.size(1) - v = self.v_in_proj[name](value) - v = v.contiguous().view(-1, bsz * self.num_heads, self.head_dim).transpose(0, 1) - else: - v = None - - return q, k, v - - def p_choose(self, query, key, key_padding_mask=None): - """ - Calculating step wise prob for reading and writing - 1 to read, 0 to write - - ============================================================ - Expected input size - query: bsz, tgt_len, embed_dim - key: bsz, src_len, embed_dim - value: bsz, src_len, embed_dim - key_padding_mask: bsz, src_len - attn_mask: bsz, src_len - query: bsz, tgt_len, embed_dim - """ - - # prepare inputs - q_proj, k_proj, _ = self.input_projections(query, key, None, "monotonic") - - # attention energy - attn_energy = self.attn_energy(q_proj, k_proj, key_padding_mask) - - noise = 0 - - if self.training: - # add noise here to encourage discretness - noise = ( - torch - .normal(self.noise_mean, self.noise_var, attn_energy.size()) - .type_as(attn_energy) - .to(attn_energy.device) - ) - - p_choose = torch.sigmoid(attn_energy + noise) - _, _, tgt_len, src_len = p_choose.size() - - # p_choose: bsz * self.num_heads, tgt_len, src_len - return p_choose.view(-1, tgt_len, src_len) - - def expected_attention(self, alpha, *args): - ''' - For MMA-H, beta = alpha - ''' - return alpha - - def v_proj_output(self, value): - _, _, v_proj = self.input_projections(None, None, value, "output") - return v_proj - @register_monotonic_attention("infinite_lookback") -class MonotonicMultiheadAttentionInfiniteLookback(MonotonicMultiheadAttentionHard): +class MonotonicInfiniteLookbackAttention( + MonotonicAttention +): def __init__(self, args): super().__init__(args) + self.soft_attention = True self.init_soft_attention() def init_soft_attention(self): @@ -479,112 +435,85 @@ def init_soft_attention(self): if self.qkv_same_dim: # Empirically observed the convergence to be much better with # the scaled initialization - nn.init.xavier_uniform_(self.k_in_proj["soft"].weight, gain=1 / math.sqrt(2)) - nn.init.xavier_uniform_(self.q_in_proj["soft"].weight, gain=1 / math.sqrt(2)) + nn.init.xavier_uniform_( + self.k_in_proj["soft"].weight, gain=1 / math.sqrt(2) + ) + nn.init.xavier_uniform_( + self.q_in_proj["soft"].weight, gain=1 / math.sqrt(2) + ) else: nn.init.xavier_uniform_(self.k_in_proj["soft"].weight) nn.init.xavier_uniform_(self.q_in_proj["soft"].weight) - def expected_attention(self, alpha, query, key, value, key_padding_mask, incremental_state): - # monotonic attention, we will calculate milk here - bsz_x_num_heads, tgt_len, src_len = alpha.size() - bsz = int(bsz_x_num_heads / self.num_heads) - - q, k, _ = self.input_projections(query, key, None, "soft") - soft_energy = self.attn_energy(q, k, key_padding_mask) - assert list(soft_energy.size()) == [bsz, self.num_heads, tgt_len, src_len] - - soft_energy = soft_energy.view(bsz * self.num_heads, tgt_len, src_len) - - if incremental_state is not None: - monotonic_cache = self._get_monotonic_buffer(incremental_state) - monotonic_step = monotonic_cache["step"] + 1 - step_offset = 0 - if key_padding_mask is not None: - if key_padding_mask[:, 0].any(): - # left_pad_source = True: - step_offset = key_padding_mask.sum(dim=-1, keepdim=True) - monotonic_step += step_offset - mask = lengths_to_mask( - monotonic_step.view(-1), soft_energy.size(2), 1).unsqueeze(1) - - soft_energy = soft_energy.masked_fill(~ mask.bool(), float('-inf')) - soft_energy = soft_energy - soft_energy.max(dim=2, keepdim=True)[0] - exp_soft_energy = torch.exp(soft_energy) - exp_soft_energy_sum = exp_soft_energy.sum(dim=2) - beta = exp_soft_energy / exp_soft_energy_sum.unsqueeze(2) - - else: - # bsz * num_heads, tgt_len, src_len - soft_energy = soft_energy - soft_energy.max(dim=2, keepdim=True)[0] - exp_soft_energy = torch.exp(soft_energy) - exp_soft_energy_cumsum = torch.cumsum(exp_soft_energy, dim=2) - - if key_padding_mask is not None: - if key_padding_mask.any(): - exp_soft_energy_cumsum = ( - exp_soft_energy_cumsum.view(-1, self.num_heads, tgt_len, src_len) - .masked_fill(key_padding_mask.unsqueeze(1).unsqueeze(1), self.eps) - .view(-1, tgt_len, src_len) - ) +@register_monotonic_attention("waitk") +class WaitKAttention( + MonotonicInfiniteLookbackAttention +): + """ + STACL: Simultaneous Translation with Implicit Anticipation and + Controllable Latency using Prefix-to-Prefix Framework + https://www.aclweb.org/anthology/P19-1289/ + """ + def __init__(self, args): + super().__init__(args) + self.q_in_proj["soft"] = self.q_in_proj["monotonic"] + self.k_in_proj["soft"] = self.k_in_proj["monotonic"] - inner_items = alpha / exp_soft_energy_cumsum + self.waitk_lagging = args.waitk_lagging + assert self.waitk_lagging > 0, ( + f"Lagging has to been larger than 0, get {self.waitk_lagging}." + ) - beta = exp_soft_energy * torch.cumsum(inner_items.flip(dims=[2]), dim=2).flip(dims=[2]) + @staticmethod + def add_args(parser): + super( + MonotonicInfiniteLookbackAttention, + MonotonicInfiniteLookbackAttention + ).add_args(parser) - beta = F.dropout(beta, p=self.dropout, training=self.training) + parser.add_argument( + "--waitk-lagging", type=int, required=True, help="Wait K lagging" + ) - assert not torch.isnan(beta).any(), "NaN detected in beta." + def p_choose_from_qk( + self, + query: Optional[Tensor], + key: Optional[Tensor], + key_padding_mask: Optional[Tensor] = None, + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, + ): + assert query is not None + assert key is not None + + p_choose = waitk_p_choose( + tgt_len=query.size(0), + src_len=key.size(0), + bsz=query.size(1) * self.num_heads, + waitk_lagging=self.waitk_lagging, + key_padding_mask=key_padding_mask, + incremental_state=incremental_state, + ) - return beta + return p_choose.to(query) -@register_monotonic_attention("waitk") -class MonotonicMultiheadAttentionWaitk(MonotonicMultiheadAttentionInfiniteLookback): +@register_monotonic_attention("chunkwise") +class ChunkwiseAttention( + MonotonicInfiniteLookbackAttention +): def __init__(self, args): super().__init__(args) - self.q_in_proj["soft"] = self.q_in_proj["monotonic"] - self.k_in_proj["soft"] = self.k_in_proj["monotonic"] - self.waitk_lagging = args.waitk_lagging - assert self.waitk_lagging > 0, f"Lagging has to been larger than 0, get {self.waitk_lagging}." + self.chunk_size = args.mocha_chunk_size + assert self.chunk_size > 1 @staticmethod def add_args(parser): super( - MonotonicMultiheadAttentionWaitk, - MonotonicMultiheadAttentionWaitk, + MonotonicInfiniteLookbackAttention ).add_args(parser) - parser.add_argument('--waitk-lagging', type=int, required=True, - help='Wait k lagging') - - def p_choose(self, query, key, key_padding_mask=None, attn_mask=None, incremental_state=None): - """ - query: bsz, tgt_len - key: bsz, src_len - key_padding_mask: bsz, src_len - """ - src_len, bsz, _ = key.size() - tgt_len, bsz, _ = query.size() - p_choose = query.new_ones(bsz, tgt_len, src_len) - p_choose = torch.tril(p_choose, diagonal=self.waitk_lagging - 1) - p_choose = torch.triu(p_choose, diagonal=self.waitk_lagging - 1) - - if key_padding_mask is not None and key_padding_mask[:, 0].eq(1).any(): - # Left pad source - # add -1 to the end - p_choose = p_choose.masked_fill(key_padding_mask.float().flip(1).unsqueeze(1).bool(), -1) - p_choose = convert_padding_direction(p_choose.view(-1, src_len).long(), padding_idx=-1, right_to_left=True) - p_choose = p_choose.view(bsz, tgt_len, src_len).type_as(query) - # remove -1 - p_choose[p_choose.eq(-1)] = 0 - - # Extend to each head - p_choose = ( - p_choose.contiguous().unsqueeze(1) - .expand(-1, self.num_heads, -1, -1).contiguous() - .view(-1, tgt_len, src_len) + parser.add_argument( + "--mocha-chunk-size", type=int, + required=True, help="Mocha chunk size" ) - - return p_choose diff --git a/examples/simultaneous_translation/modules/monotonic_transformer_layer.py b/examples/simultaneous_translation/modules/monotonic_transformer_layer.py index a9545b2540..94bd71fb9c 100644 --- a/examples/simultaneous_translation/modules/monotonic_transformer_layer.py +++ b/examples/simultaneous_translation/modules/monotonic_transformer_layer.py @@ -3,55 +3,180 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. -from fairseq.modules import ( - LayerNorm, - TransformerEncoderLayer, - TransformerDecoderLayer -) +from fairseq.modules import TransformerDecoderLayer, TransformerEncoderLayer from . import build_monotonic_attention +from typing import Dict, Optional, List + +from torch import Tensor +import torch -class TransformerMonotonicEncoderLayer(TransformerEncoderLayer): +class TransformerMonotonicEncoderLayer(TransformerEncoderLayer): def forward(self, x, encoder_padding_mask): seq_len, _, _ = x.size() attn_mask = x.new_ones([seq_len, seq_len]).triu(1) - attn_mask = attn_mask.masked_fill(attn_mask.bool(), float('-inf')) + attn_mask = attn_mask.masked_fill(attn_mask.bool(), float("-inf")) return super().forward(x, encoder_padding_mask, attn_mask) class TransformerMonotonicDecoderLayer(TransformerDecoderLayer): + def __init__(self, args): + super().__init__(args) - def __init__(self, args, no_encoder_attn=False, add_bias_kv=False, add_zero_attn=False): - super().__init__( - args, - no_encoder_attn=True, - add_bias_kv=add_bias_kv, - add_zero_attn=add_zero_attn - ) + assert args.simul_type is not None, "A --simul-type is needed." self.encoder_attn = build_monotonic_attention(args) - self.encoder_attn_layer_norm = LayerNorm( - self.embed_dim, - export=getattr(args, 'char_inputs', False) + + def prune_incremental_state( + self, + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] + ): + input_buffer = self.self_attn._get_input_buffer(incremental_state) + for key in ["prev_key", "prev_value"]: + input_buffer_key = input_buffer[key] + assert input_buffer_key is not None + if input_buffer_key.size(2) > 1: + input_buffer[key] = input_buffer_key[:, :, :-1, :] + else: + typed_empty_dict: Dict[str, Optional[Tensor]] = {} + input_buffer = typed_empty_dict + break + assert incremental_state is not None + self.self_attn._set_input_buffer(incremental_state, input_buffer) + + def forward( + self, + x, + encoder_out: Optional[Tensor] = None, + encoder_padding_mask: Optional[Tensor] = None, + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, + prev_self_attn_state: Optional[List[Tensor]] = None, + prev_attn_state: Optional[List[Tensor]] = None, + self_attn_mask: Optional[Tensor] = None, + self_attn_padding_mask: Optional[Tensor] = None, + need_attn: bool = False, + need_head_weights: bool = False, + ): + """ + Args: + x (Tensor): input to the layer of shape `(seq_len, batch, embed_dim)` + encoder_padding_mask (ByteTensor, optional): binary + ByteTensor of shape `(batch, src_len)` where padding + elements are indicated by ``1``. + need_attn (bool, optional): return attention weights + need_head_weights (bool, optional): return attention weights + for each head (default: return average over heads). + + Returns: + encoded output of shape `(seq_len, batch, embed_dim)` + """ + if need_head_weights: + need_attn = True + + residual = x + if self.normalize_before: + x = self.self_attn_layer_norm(x) + if prev_self_attn_state is not None: + prev_key, prev_value = prev_self_attn_state[:2] + saved_state: Dict[str, Optional[Tensor]] = { + "prev_key": prev_key, + "prev_value": prev_value, + } + if len(prev_self_attn_state) >= 3: + saved_state["prev_key_padding_mask"] = prev_self_attn_state[2] + assert incremental_state is not None + self.self_attn._set_input_buffer(incremental_state, saved_state) + _self_attn_input_buffer = self.self_attn._get_input_buffer(incremental_state) + if self.cross_self_attention and not ( + incremental_state is not None + and _self_attn_input_buffer is not None + and "prev_key" in _self_attn_input_buffer + ): + if self_attn_mask is not None: + assert encoder_out is not None + self_attn_mask = torch.cat( + (x.new_zeros(x.size(0), encoder_out.size(0)), self_attn_mask), dim=1 + ) + if self_attn_padding_mask is not None: + if encoder_padding_mask is None: + assert encoder_out is not None + encoder_padding_mask = self_attn_padding_mask.new_zeros( + encoder_out.size(1), encoder_out.size(0) + ) + self_attn_padding_mask = torch.cat( + (encoder_padding_mask, self_attn_padding_mask), dim=1 + ) + assert encoder_out is not None + y = torch.cat((encoder_out, x), dim=0) + else: + y = x + + x, attn = self.self_attn( + query=x, + key=y, + value=y, + key_padding_mask=self_attn_padding_mask, + incremental_state=incremental_state, + need_weights=False, + attn_mask=self_attn_mask, ) + x = self.dropout_module(x) + x = self.residual_connection(x, residual) + if not self.normalize_before: + x = self.self_attn_layer_norm(x) - def prune_incremental_state(self, incremental_state): - def prune(module): - input_buffer = module._get_input_buffer(incremental_state) - for key in ["prev_key", "prev_value"]: - if input_buffer[key].size(2) > 1: - input_buffer[key] = input_buffer[key][:, :, :-1, :] - else: - input_buffer = {} - break - module._set_input_buffer(incremental_state, input_buffer) - prune(self.self_attn) - - def get_steps(self, incremental_state): - return ( - self.encoder_attn - ._get_monotonic_buffer( - incremental_state - ).get("step", 0) + assert self.encoder_attn is not None + residual = x + if self.normalize_before: + x = self.encoder_attn_layer_norm(x) + if prev_attn_state is not None: + prev_key, prev_value = prev_attn_state[:2] + saved_state: Dict[str, Optional[Tensor]] = { + "prev_key": prev_key, + "prev_value": prev_value, + } + if len(prev_attn_state) >= 3: + saved_state["prev_key_padding_mask"] = prev_attn_state[2] + assert incremental_state is not None + self.encoder_attn._set_input_buffer(incremental_state, saved_state) + + x, attn = self.encoder_attn( + query=x, + key=encoder_out, + value=encoder_out, + key_padding_mask=encoder_padding_mask, + incremental_state=incremental_state, + static_kv=True, + need_weights=need_attn or (not self.training and self.need_attn), + need_head_weights=need_head_weights, ) + x = self.dropout_module(x) + x = self.residual_connection(x, residual) + if not self.normalize_before: + x = self.encoder_attn_layer_norm(x) + + residual = x + if self.normalize_before: + x = self.final_layer_norm(x) + + x = self.activation_fn(self.fc1(x)) + x = self.activation_dropout_module(x) + x = self.fc2(x) + x = self.dropout_module(x) + x = self.residual_connection(x, residual) + if not self.normalize_before: + x = self.final_layer_norm(x) + if self.onnx_trace and incremental_state is not None: + saved_state = self.self_attn._get_input_buffer(incremental_state) + assert saved_state is not None + if self_attn_padding_mask is not None: + self_attn_state = [ + saved_state["prev_key"], + saved_state["prev_value"], + saved_state["prev_key_padding_mask"], + ] + else: + self_attn_state = [saved_state["prev_key"], saved_state["prev_value"]] + return x, attn, self_attn_state + return x, attn, None diff --git a/examples/simultaneous_translation/tests/test_text_models.py b/examples/simultaneous_translation/tests/test_text_models.py new file mode 100644 index 0000000000..127adfa633 --- /dev/null +++ b/examples/simultaneous_translation/tests/test_text_models.py @@ -0,0 +1,407 @@ +import argparse +import unittest +from typing import Any, Dict + +import torch +from examples.simultaneous_translation.models import ( + transformer_monotonic_attention +) + + +from tests.test_roberta import FakeTask + + +DEFAULT_CONFIG = { + "attention_eps": 1e-6, + "mass_preservation": True, + "noise_type": "flat", + "noise_mean": 0.0, + "noise_var": 1.0, + "energy_bias_init": -2, + "energy_bias": True +} + + +PAD_INDEX = 1 + + +def generate_config(overrides_kv): + new_dict = {key: value for key, value in DEFAULT_CONFIG.items()} + for key, value in overrides_kv.items(): + new_dict[key] = value + return new_dict + + +def make_sample_with_padding(longer_src=False) -> Dict[str, Any]: + tokens_1 = torch.LongTensor( + [ + [2, 10, 11, 12, 13, 14, 15, 10, 11, 12, 13, 14, 15, 2], + [ + 2, 11, 12, 14, 15, 10, 11, 12, 13, 14, 15, 2, + PAD_INDEX, PAD_INDEX + ], + ] + ) + tokens_2 = torch.LongTensor( + [ + [2, 11, 12, 13, 14, 2, PAD_INDEX, PAD_INDEX], + [2, 11, 22, 33, 2, PAD_INDEX, PAD_INDEX, PAD_INDEX] + ] + ) + if longer_src: + src_tokens = tokens_1[:, 1:] + prev_output_tokens = tokens_2 + else: + src_tokens = tokens_2[:, 1:8] + prev_output_tokens = tokens_1 + + src_lengths = src_tokens.ne(PAD_INDEX).sum(dim=1).long() + + sample = { + "net_input": { + "src_tokens": src_tokens, + "prev_output_tokens": prev_output_tokens, + "src_lengths": src_lengths, + }, + "target": prev_output_tokens[:, 1:], + } + return sample + + +def build_transformer_monotonic_attention(**extra_args: Any): + overrides = { + # Use characteristics dimensions + "encoder_embed_dim": 12, + "encoder_ffn_embed_dim": 14, + "decoder_embed_dim": 12, + "decoder_ffn_embed_dim": 14, + # Disable dropout so we have comparable tests. + "dropout": 0, + "attention_dropout": 0, + "activation_dropout": 0, + "encoder_layerdrop": 0, + } + overrides.update(extra_args) + # Overrides the defaults from the parser + args = argparse.Namespace(**overrides) + transformer_monotonic_attention.monotonic_tiny_architecture(args) + + torch.manual_seed(0) + task = FakeTask(args) + return ( + transformer_monotonic_attention + .TransformerModelSimulTrans + .build_model(args, task) + ) + + +def expected_alignment_formula( + p_choose, + mass_perservation=True, + padding_mask=None +): + # Online and Linear-Time Attention by Enforcing Monotonic Alignments + # https://arxiv.org/pdf/1704.00784.pdf + # Eq 18, 19 + bsz, tgt_len, src_len = p_choose.size() + alpha = torch.zeros_like(p_choose) + + if padding_mask is not None: + bsz_pad = padding_mask.size(0) + num_heads = int(bsz / bsz_pad) + padding_mask = ( + padding_mask + .unsqueeze(1) + .expand([bsz_pad, num_heads, src_len]) + .contiguous() + .view(-1, src_len) + ) + + p_choose = p_choose.masked_fill(padding_mask.unsqueeze(1), 0) + + for bsz_i in range(bsz): + for i in range(tgt_len): + for j in range(src_len): + if i == 0: + if j == 0: + # First source token + alpha[bsz_i, i, j] = p_choose[bsz_i, i, j] + else: + # First target token + alpha[bsz_i, i, j] = ( + p_choose[bsz_i, i, j] + * torch.prod( + 1 - p_choose[bsz_i, i, :j] + ) + ) + else: + alpha[bsz_i, i, j] = alpha[bsz_i, i - 1, j] + for k in range(j): + alpha[bsz_i, i, j] += ( + alpha[bsz_i, i - 1, k] + * torch.prod( + 1 - p_choose[bsz_i, i, k:j] + ) + ) + alpha[bsz_i, i, j] *= p_choose[bsz_i, i, j] + + alpha = alpha.masked_fill(padding_mask.unsqueeze(1), 0) + + if mass_perservation: + alpha = mass_perservation_formula(alpha, False, padding_mask) + + return alpha + + +def mass_perservation_formula(alpha, left_padding=False, padding_mask=None): + if padding_mask is None or alpha.size(-1) == 1: + if alpha.size(-1) > 1: + alpha[:, :, -1] = 1 - alpha[:, :, :-1].sum(dim=-1) + return alpha + + src_lens = (padding_mask.logical_not()).sum(dim=1).long() + + bsz, tgt_len, src_len = alpha.size() + + assert ( + not left_padding + or (left_padding and (not padding_mask[:, 0].any())) + ) + + alpha = alpha.masked_fill(padding_mask.unsqueeze(1), 0) + + for bsz_i in range(bsz): + if left_padding: + alpha[bsz_i, :, -1] = ( + 1 - alpha[bsz_i, :, :-1].sum(dim=-1) + ) + else: + alpha[bsz_i, :, src_lens[bsz_i] - 1] = ( + 1 - alpha[bsz_i, :, :src_lens[bsz_i] - 1].sum(dim=-1) + ) + + return alpha + + +def expected_soft_attention_formula( + alpha, + soft_energy, + padding_mask=None, + chunksize=1e10, +): + # Monotonic Infinite Lookback Attention for Simultaneous Machine Translation + # https://arxiv.org/pdf/1906.05218.pdf + # Eq 14 + + # Monotonic Chunkwise Attention + # https://arxiv.org/abs/1712.05382 + # Eq 17 + bsz, tgt_len, src_len = alpha.size() + beta = torch.zeros_like(alpha) + + if padding_mask is not None: + bsz_pad = padding_mask.size(0) + num_heads = int(bsz / bsz_pad) + # Expanding for potential head dimension + padding_mask = ( + padding_mask + .unsqueeze(1) + .expand([bsz_pad, num_heads, src_len]) + .contiguous() + .view(-1, src_len) + ) + soft_energy = soft_energy.masked_fill(padding_mask.unsqueeze(1), float('-inf')) + + for bsz_i in range(bsz): + for i in range(tgt_len): + for j in range(src_len): + for k in range(j, min([src_len, j + chunksize])): + if not padding_mask[bsz_i, j]: + beta[bsz_i, i, j] += ( + alpha[bsz_i, i, k] * torch.exp(soft_energy[bsz_i, i, j]) + / torch.sum(torch.exp(soft_energy[bsz_i, i, max([0, k - chunksize + 1]):k + 1])) + ) + return beta + + +class MonotonicAttentionTestAbstractClass(object): + def test_forward(self): + sample = make_sample_with_padding() + out, _ = self.model.forward(**sample["net_input"]) + loss = out.sum() + loss.backward() + + def test_p_choose(self): + sample = make_sample_with_padding() + _, extra_out = self.model.forward(**sample["net_input"]) + for item in extra_out.attn_list: + p_choose = item["p_choose"] + self.assertTrue(p_choose.le(1.0).all()) + self.assertTrue(p_choose.ge(0.0).all()) + + def test_expected_alignment(self): + for longer_src in [True, False]: + sample = make_sample_with_padding(longer_src) + _, extra_out = self.model.forward(**sample["net_input"]) + for item in extra_out.attn_list: + p_choose = item["p_choose"] + alpha_system = item["alpha"] + self.assertTrue(p_choose.size() == alpha_system.size()) + bsz, num_head, tgt_len, src_len = alpha_system.size() + alpha_system = alpha_system.view(-1, tgt_len, src_len) + p_choose = p_choose.view(-1, tgt_len, src_len) + + alpha_real = expected_alignment_formula( + p_choose, + self.model.decoder.layers[0].encoder_attn.mass_preservation, + sample["net_input"]["src_tokens"].eq(PAD_INDEX) + ) + + self.assertTrue( + torch.abs(alpha_system - alpha_real).le(5e-5).all(), + ) + + +class HardMonotonicAttentionTestCase( + unittest.TestCase, + MonotonicAttentionTestAbstractClass +): + def setUp(self): + self.model = build_transformer_monotonic_attention( + **generate_config({"simul_type": "hard_aligned"}) + ) + + +class InfiniteLookbackTestCase( + unittest.TestCase, + MonotonicAttentionTestAbstractClass +): + def setUp(self): + self.model = build_transformer_monotonic_attention( + **generate_config( + { + "simul_type": "infinite_lookback" + } + ) + ) + self.model.train() + + def test_fp16_for_long_input(self): + sample = { + "net_input": { + "src_tokens": torch.LongTensor([7] * 1000 + [2]).cuda().unsqueeze(0), + "prev_output_tokens": torch.LongTensor([7] * 1000 + [2]).cuda().unsqueeze(0), + "src_lengths": torch.LongTensor([1000]).cuda(), + }, + "target": torch.LongTensor([2] + [7] * 1000).unsqueeze(0).cuda() + } + self.model.cuda().half() + _, extra_out = self.model.forward(**sample["net_input"]) + for item in extra_out.attn_list: + for key in ["p_choose", "alpha", "beta", "soft_energy"]: + self.assertFalse(torch.isnan(item[key]).any()) + + def test_expected_attention(self): + for longer_src in [True, False]: + sample = make_sample_with_padding(longer_src) + _, extra_out = self.model.forward(**sample["net_input"]) + for item in extra_out.attn_list: + p_choose = item["p_choose"] + alpha_system = item["alpha"] + beta_system = item["beta"] + soft_energy_system = item["soft_energy"] + self.assertTrue(beta_system.size() == alpha_system.size()) + self.assertTrue(p_choose.size() == alpha_system.size()) + + bsz, num_head, tgt_len, src_len = alpha_system.size() + + alpha_system = alpha_system.view(-1, tgt_len, src_len) + beta_system = beta_system.view(-1, tgt_len, src_len) + p_choose = p_choose.view(-1, tgt_len, src_len) + soft_energy_system = soft_energy_system.view(-1, tgt_len, src_len) + + alpha_real = expected_alignment_formula( + p_choose, + self.model.decoder.layers[0].encoder_attn.mass_preservation, + sample["net_input"]["src_tokens"].eq(PAD_INDEX) + ) + + beta_real = expected_soft_attention_formula( + alpha_real, + soft_energy_system, + sample["net_input"]["src_tokens"].eq(PAD_INDEX), + chunksize=getattr( + self.model.decoder.layers[0].encoder_attn, + "chunk_size", + int(1e10) + ) + ) + + self.assertTrue( + torch.abs(beta_system - beta_real).le(1e-5).all(), + ) + + +class ChunkwiswTestCase( + InfiniteLookbackTestCase +): + def setUp(self): + self.model = build_transformer_monotonic_attention( + **generate_config( + { + "simul_type": "chunkwise", + "mocha_chunk_size": 3 + } + ) + ) + + +class WaitkTestCase(InfiniteLookbackTestCase): + def setUp(self): + self.model = build_transformer_monotonic_attention( + **generate_config( + { + "simul_type": "waitk", + "waitk_lagging": 3, + } + ) + ) + + def check_waitk(self, p_choose, lagging, padding_mask): + bsz, tgt_len, src_len = p_choose.size() + for bsz_i in range(bsz): + for i in range(tgt_len): + for j in range(src_len): + if not padding_mask[bsz_i, j]: + if j - i == lagging - 1: + self.assertTrue(p_choose[bsz_i, i, j] == 1) + else: + self.assertTrue(p_choose[bsz_i, i, j] == 0) + + def test_waitk_p_choose(self): + for longer_src in [True, False]: + for k in [1, 3, 10, 20, 100]: + sample = make_sample_with_padding(longer_src) + model = build_transformer_monotonic_attention( + **generate_config( + { + "simul_type": "waitk", + "waitk_lagging": k, + } + ) + ) + model.train() + _, extra_out = model.forward(**sample["net_input"]) + for item in extra_out.attn_list: + p_choose = item["p_choose"] + bsz, num_heads, tgt_len, src_len = p_choose.size() + padding_mask = sample["net_input"]["src_tokens"].eq(PAD_INDEX) + padding_mask = ( + padding_mask + .unsqueeze(1) + .expand([bsz, num_heads, src_len]) + .contiguous() + .view(-1, src_len) + ) + p_choose = p_choose.view(bsz * num_heads, tgt_len, src_len) + self.check_waitk(p_choose, k, padding_mask) diff --git a/examples/simultaneous_translation/utils/__init__.py b/examples/simultaneous_translation/utils/__init__.py index 8e5886008f..1e9ce844f5 100644 --- a/examples/simultaneous_translation/utils/__init__.py +++ b/examples/simultaneous_translation/utils/__init__.py @@ -8,7 +8,7 @@ # automatically import any Python files in the criterions/ directory -for file in os.listdir(os.path.dirname(__file__)): - if file.endswith('.py') and not file.startswith('_'): - module = file[:file.find('.py')] - importlib.import_module('examples.simultaneous_translation.utils.' + module) +for file in sorted(os.listdir(os.path.dirname(__file__))): + if file.endswith(".py") and not file.startswith("_"): + module = file[: file.find(".py")] + importlib.import_module("examples.simultaneous_translation.utils." + module) diff --git a/examples/simultaneous_translation/utils/functions.py b/examples/simultaneous_translation/utils/functions.py index 620dd1d866..590a6c11ce 100644 --- a/examples/simultaneous_translation/utils/functions.py +++ b/examples/simultaneous_translation/utils/functions.py @@ -6,17 +6,31 @@ import torch +def prob_check(tensor, eps=1e-10): + assert not torch.isnan(tensor).any(), ( + "Nan in a probability tensor." + ) + # Add the eps here to prevent errors introduced by precision + assert tensor.le(1.0 + eps).all() and tensor.ge(0.0 - eps).all(), ( + "Incorrect values in a probability tensor" + ", 0.0 <= tensor <= 1.0" + ) + + def exclusive_cumprod(tensor, dim: int, eps: float = 1e-10): """ Implementing exclusive cumprod. There is cumprod in pytorch, however there is no exclusive mode. cumprod(x) = [x1, x1x2, x2x3x4, ..., prod_{i=1}^n x_i] - exclusive means cumprod(x) = [1, x1, x1x2, x1x2x3, ..., prod_{i=1}^{n-1} x_i] + exclusive means + cumprod(x) = [1, x1, x1x2, x1x2x3, ..., prod_{i=1}^{n-1} x_i] """ tensor_size = list(tensor.size()) tensor_size[dim] = 1 return_tensor = safe_cumprod( - torch.cat([torch.ones(tensor_size).type_as(tensor), tensor], dim=dim), dim=dim, eps=eps + torch.cat([torch.ones(tensor_size).type_as(tensor), tensor], dim=dim), + dim=dim, + eps=eps, ) if dim == 0: @@ -26,7 +40,9 @@ def exclusive_cumprod(tensor, dim: int, eps: float = 1e-10): elif dim == 2: return return_tensor[:, :, :-1] else: - raise RuntimeError("Cumprod on dimension 3 and more is not implemented") + raise RuntimeError( + "Cumprod on dimension 3 and more is not implemented" + ) def safe_cumprod(tensor, dim: int, eps: float = 1e-10): @@ -50,42 +66,6 @@ def safe_cumprod(tensor, dim: int, eps: float = 1e-10): return exp_cumsum_log_tensor -def lengths_to_mask(lengths, max_len: int, dim: int = 0, negative_mask: bool = False): - """ - Convert a tensor of lengths to mask - For example, lengths = [[2, 3, 4]], max_len = 5 - mask = - [[1, 1, 1], - [1, 1, 1], - [0, 1, 1], - [0, 0, 1], - [0, 0, 0]] - """ - assert len(lengths.size()) <= 2 - if len(lengths) == 2: - if dim == 1: - lengths = lengths.t() - lengths = lengths - else: - lengths = lengths.unsqueeze(1) - - # lengths : batch_size, 1 - lengths = lengths.view(-1, 1) - - batch_size = lengths.size(0) - # batch_size, max_len - mask = torch.arange(max_len).expand(batch_size, max_len).type_as(lengths) < lengths - - if negative_mask: - mask = ~mask - - if dim == 0: - # max_len, batch_size - mask = mask.t() - - return mask - - def moving_sum(x, start_idx: int, end_idx: int): """ From MONOTONIC CHUNKWISE ATTENTION @@ -124,22 +104,22 @@ def moving_sum(x, start_idx: int, end_idx: int): [ 7, 17, 27], [ 4, 9, 14]] """ + # TODO: Make dimension configurable assert start_idx > 0 and end_idx > 0 - assert len(x.size()) == 2 - src_len, batch_size = x.size() - # batch_size, 1, src_len - x = x.t().unsqueeze(1) + batch_size, tgt_len, src_len = x.size() + x = x.view(-1, src_len).unsqueeze(1) # batch_size, 1, src_len - moving_sum_weight = x.new_ones([1, 1, end_idx + start_idx - 1]) + moving_sum_weight = torch.ones([1, 1, end_idx + start_idx - 1]).type_as(x) moving_sum = torch.nn.functional.conv1d( - x, - moving_sum_weight, - padding=start_idx + end_idx - 1 - ).squeeze(1).t() - moving_sum = moving_sum[end_idx: -start_idx] - - assert src_len == moving_sum.size(0) - assert batch_size == moving_sum.size(1) + x, moving_sum_weight, padding=start_idx + end_idx - 1 + ).squeeze(1) + + moving_sum = moving_sum[:, end_idx:-start_idx] + + assert src_len == moving_sum.size(1) + assert batch_size * tgt_len == moving_sum.size(0) + + moving_sum = moving_sum.view(batch_size, tgt_len, src_len) return moving_sum diff --git a/examples/simultaneous_translation/utils/latency.py b/examples/simultaneous_translation/utils/latency.py deleted file mode 100644 index 9d09584176..0000000000 --- a/examples/simultaneous_translation/utils/latency.py +++ /dev/null @@ -1,436 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch - - -class LatencyMetric(object): - @staticmethod - def length_from_padding_mask(padding_mask, batch_first: bool = False): - dim = 1 if batch_first else 0 - return padding_mask.size(dim) - padding_mask.sum(dim=dim, keepdim=True) - - def prepare_latency_metric( - self, - delays, - src_lens, - target_padding_mask=None, - batch_first: bool = False, - start_from_zero: bool = True - ): - assert len(delays.size()) == 2 - assert len(src_lens.size()) == 2 - - if start_from_zero: - delays = delays + 1 - - if batch_first: - # convert to batch_last - delays = delays.t() - src_lens = src_lens.t() - tgt_len, bsz = delays.size() - _, bsz_1 = src_lens.size() - - if target_padding_mask is not None: - target_padding_mask = target_padding_mask.t() - tgt_len_1, bsz_2 = target_padding_mask.size() - assert tgt_len == tgt_len_1 - assert bsz == bsz_2 - - assert bsz == bsz_1 - - if target_padding_mask is None: - tgt_lens = tgt_len * delays.new_ones([1, bsz]).float() - else: - # 1, batch_size - tgt_lens = self.length_from_padding_mask(target_padding_mask, False).float() - delays = delays.masked_fill(target_padding_mask, 0) - - return delays, src_lens, tgt_lens, target_padding_mask - - def __call__( - self, - delays, - src_lens, - target_padding_mask=None, - batch_first: bool = False, - start_from_zero: bool = True, - ): - delays, src_lens, tgt_lens, target_padding_mask = self.prepare_latency_metric( - delays, - src_lens, - target_padding_mask, - batch_first, - start_from_zero - ) - return self.cal_metric(delays, src_lens, tgt_lens, target_padding_mask) - - @staticmethod - def cal_metric(delays, src_lens, tgt_lens, target_padding_mask): - """ - Expected sizes: - delays: tgt_len, batch_size - src_lens: 1, batch_size - target_padding_mask: tgt_len, batch_size - """ - raise NotImplementedError - - -class AverageProportion(LatencyMetric): - """ - Function to calculate Average Proportion from - Can neural machine translation do simultaneous translation? - (https://arxiv.org/abs/1606.02012) - - Delays are monotonic steps, range from 1 to src_len. - Give src x tgt y, AP is calculated as: - - AP = 1 / (|x||y]) sum_i^|Y| deleys_i - """ - @staticmethod - def cal_metric(delays, src_lens, tgt_lens, target_padding_mask): - if target_padding_mask is not None: - AP = torch.sum(delays.masked_fill(target_padding_mask, 0), dim=0, keepdim=True) - else: - AP = torch.sum(delays, dim=0, keepdim=True) - - AP = AP / (src_lens * tgt_lens) - return AP - - -class AverageLagging(LatencyMetric): - """ - Function to calculate Average Lagging from - STACL: Simultaneous Translation with Implicit Anticipation - and Controllable Latency using Prefix-to-Prefix Framework - (https://arxiv.org/abs/1810.08398) - - Delays are monotonic steps, range from 1 to src_len. - Give src x tgt y, AP is calculated as: - - AL = 1 / tau sum_i^tau delays_i - (i - 1) / gamma - - Where - gamma = |y| / |x| - tau = argmin_i(delays_i = |x|) - """ - @staticmethod - def cal_metric(delays, src_lens, tgt_lens, target_padding_mask): - # tau = argmin_i(delays_i = |x|) - tgt_len, bsz = delays.size() - lagging_padding_mask = delays >= src_lens - lagging_padding_mask = torch.nn.functional.pad(lagging_padding_mask.t(), (1, 0)).t()[:-1, :] - gamma = tgt_lens / src_lens - lagging = delays - torch.arange(delays.size(0)).unsqueeze(1).type_as(delays).expand_as(delays) / gamma - lagging.masked_fill_(lagging_padding_mask, 0) - tau = (1 - lagging_padding_mask.type_as(lagging)).sum(dim=0, keepdim=True) - AL = lagging.sum(dim=0, keepdim=True) / tau - - return AL - - -class DifferentiableAverageLagging(LatencyMetric): - """ - Function to calculate Differentiable Average Lagging from - Monotonic Infinite Lookback Attention for Simultaneous Machine Translation - (https://arxiv.org/abs/1906.05218) - - Delays are monotonic steps, range from 0 to src_len-1. - (In the original paper thery are from 1 to src_len) - Give src x tgt y, AP is calculated as: - - DAL = 1 / |Y| sum_i^|Y| delays'_i - (i - 1) / gamma - - Where - delays'_i = - 1. delays_i if i == 1 - 2. max(delays_i, delays'_{i-1} + 1 / gamma) - - """ - @staticmethod - def cal_metric(delays, src_lens, tgt_lens, target_padding_mask): - tgt_len, bsz = delays.size() - - gamma = tgt_lens / src_lens - new_delays = torch.zeros_like(delays) - - for i in range(delays.size(0)): - if i == 0: - new_delays[i] = delays[i] - else: - new_delays[i] = torch.cat( - [ - new_delays[i - 1].unsqueeze(0) + 1 / gamma, - delays[i].unsqueeze(0) - ], - dim=0 - ).max(dim=0)[0] - - DAL = ( - new_delays - torch.arange(delays.size(0)).unsqueeze(1).type_as(delays).expand_as(delays) / gamma - ) - if target_padding_mask is not None: - DAL = DAL.masked_fill(target_padding_mask, 0) - - DAL = DAL.sum(dim=0, keepdim=True) / tgt_lens - - return DAL - - -class LatencyMetricVariance(LatencyMetric): - def prepare_latency_metric( - self, - delays, - src_lens, - target_padding_mask=None, - batch_first: bool = True, - start_from_zero: bool = True - ): - assert batch_first - assert len(delays.size()) == 3 - assert len(src_lens.size()) == 2 - - if start_from_zero: - delays = delays + 1 - - # convert to batch_last - bsz, num_heads_x_layers, tgt_len = delays.size() - bsz_1, _ = src_lens.size() - assert bsz == bsz_1 - - if target_padding_mask is not None: - bsz_2, tgt_len_1 = target_padding_mask.size() - assert tgt_len == tgt_len_1 - assert bsz == bsz_2 - - if target_padding_mask is None: - tgt_lens = tgt_len * delays.new_ones([bsz, tgt_len]).float() - else: - # batch_size, 1 - tgt_lens = self.length_from_padding_mask(target_padding_mask, True).float() - delays = delays.masked_fill(target_padding_mask.unsqueeze(1), 0) - - return delays, src_lens, tgt_lens, target_padding_mask - - -class VarianceDelay(LatencyMetricVariance): - @staticmethod - def cal_metric(delays, src_lens, tgt_lens, target_padding_mask): - """ - delays : bsz, num_heads_x_layers, tgt_len - src_lens : bsz, 1 - target_lens : bsz, 1 - target_padding_mask: bsz, tgt_len or None - """ - if delays.size(1) == 1: - return delays.new_zeros([1]) - - variance_delays = delays.var(dim=1) - - if target_padding_mask is not None: - variance_delays.masked_fill_(target_padding_mask, 0) - - return variance_delays.sum(dim=1, keepdim=True) / tgt_lens - - -class LatencyInference(object): - def __init__(self, start_from_zero=True): - self.metric_calculator = { - "differentiable_average_lagging": DifferentiableAverageLagging(), - "average_lagging": AverageLagging(), - "average_proportion": AverageProportion(), - } - - self.start_from_zero = start_from_zero - - def __call__(self, monotonic_step, src_lens): - """ - monotonic_step range from 0 to src_len. src_len means eos - delays: bsz, tgt_len - src_lens: bsz, 1 - """ - if not self.start_from_zero: - monotonic_step -= 1 - - src_lens = src_lens - - delays = ( - monotonic_step - .view(monotonic_step.size(0), -1, monotonic_step.size(-1)) - .max(dim=1)[0] - ) - - delays = ( - delays.masked_fill(delays >= src_lens, 0) - + (src_lens - 1) - .expand_as(delays) - .masked_fill(delays < src_lens, 0) - ) - return_dict = {} - for key, func in self.metric_calculator.items(): - return_dict[key] = func( - delays.float(), src_lens.float(), - target_padding_mask=None, - batch_first=True, - start_from_zero=True - ).t() - - return return_dict - - -class LatencyTraining(object): - def __init__( - self, avg_weight, var_weight, avg_type, var_type, - stay_on_last_token, average_method, - ): - self.avg_weight = avg_weight - self.var_weight = var_weight - self.avg_type = avg_type - self.var_type = var_type - self.stay_on_last_token = stay_on_last_token - self.average_method = average_method - - self.metric_calculator = { - "differentiable_average_lagging": DifferentiableAverageLagging(), - "average_lagging": AverageLagging(), - "average_proportion": AverageProportion(), - } - - self.variance_calculator = { - "variance_delay": VarianceDelay(), - } - - def expected_delays_from_attention( - self, attention, source_padding_mask=None, target_padding_mask=None - ): - if type(attention) == list: - # bsz, num_heads, tgt_len, src_len - bsz, num_heads, tgt_len, src_len = attention[0].size() - attention = torch.cat(attention, dim=1) - bsz, num_heads_x_layers, tgt_len, src_len = attention.size() - # bsz * num_heads * num_layers, tgt_len, src_len - attention = attention.view(-1, tgt_len, src_len) - else: - # bsz * num_heads * num_layers, tgt_len, src_len - bsz, tgt_len, src_len = attention.size() - num_heads_x_layers = 1 - attention = attention.view(-1, tgt_len, src_len) - - if not self.stay_on_last_token: - residual_attention = \ - 1 - attention[:, :, :-1].sum(dim=2, keepdim=True) - attention = torch.cat( - [attention[:, :, :-1], residual_attention], - dim=2 - ) - - # bsz * num_heads_x_num_layers, tgt_len, src_len for MMA - steps = ( - torch - .arange(1, 1 + src_len) - .unsqueeze(0) - .unsqueeze(1) - .expand_as(attention) - .type_as(attention) - ) - - if source_padding_mask is not None: - src_offset = ( - source_padding_mask.type_as(attention) - .sum(dim=1, keepdim=True) - .expand(bsz, num_heads_x_layers) - .contiguous() - .view(-1, 1) - ) - src_lens = src_len - src_offset - if source_padding_mask[:, 0].any(): - # Pad left - src_offset = src_offset.view(-1, 1, 1) - steps = steps - src_offset - steps = steps.masked_fill(steps <= 0, 0) - else: - src_lens = attention.new_ones([bsz, num_heads_x_layers]) * src_len - src_lens = src_lens.view(-1, 1) - - # bsz * num_heads_num_layers, tgt_len, src_len - expected_delays = (steps * attention).sum(dim=2).view( - bsz, num_heads_x_layers, tgt_len - ) - - if target_padding_mask is not None: - expected_delays.masked_fill_( - target_padding_mask.unsqueeze(1), - 0 - ) - - return expected_delays, src_lens - - def avg_loss(self, expected_delays, src_lens, target_padding_mask): - - bsz, num_heads_x_layers, tgt_len = expected_delays.size() - target_padding_mask = ( - target_padding_mask - .unsqueeze(1) - .expand_as(expected_delays) - .contiguous() - .view(-1, tgt_len) - ) - - if self.average_method == "average": - # bsz * tgt_len - expected_delays = expected_delays.mean(dim=1) - elif self.average_method == "weighted_average": - weights = torch.nn.functional.softmax(expected_delays, dim=1) - expected_delays = torch.sum(expected_delays * weights, dim=1) - elif self.average_method == "max": - # bsz * num_heads_x_num_layers, tgt_len - expected_delays = expected_delays.max(dim=1)[0] - else: - raise RuntimeError(f"{self.average_method} is not supported") - - src_lens = src_lens.view(bsz, -1)[:, :1] - target_padding_mask = target_padding_mask.view(bsz, -1, tgt_len)[:, 0] - - if self.avg_weight > 0.0: - if self.avg_type in self.metric_calculator: - average_delays = self.metric_calculator[self.avg_type]( - expected_delays, src_lens, target_padding_mask, - batch_first=True, start_from_zero=False - ) - else: - raise RuntimeError(f"{self.avg_type} is not supported.") - - # bsz * num_heads_x_num_layers, 1 - return self.avg_weight * average_delays.sum() - else: - return 0.0 - - def var_loss(self, expected_delays, src_lens, target_padding_mask): - src_lens = src_lens.view(expected_delays.size(0), expected_delays.size(1))[:, :1] - if self.var_weight > 0.0: - if self.var_type in self.variance_calculator: - variance_delays = self.variance_calculator[self.var_type]( - expected_delays, src_lens, target_padding_mask, - batch_first=True, start_from_zero=False - ) - else: - raise RuntimeError(f"{self.var_type} is not supported.") - - return self.var_weight * variance_delays.sum() - else: - return 0.0 - - def loss(self, attention, source_padding_mask=None, target_padding_mask=None): - expected_delays, src_lens = self.expected_delays_from_attention( - attention, source_padding_mask, target_padding_mask - ) - - latency_loss = 0 - - latency_loss += self.avg_loss(expected_delays, src_lens, target_padding_mask) - - latency_loss += self.var_loss(expected_delays, src_lens, target_padding_mask) - - return latency_loss diff --git a/examples/simultaneous_translation/utils/monotonic_attention.py b/examples/simultaneous_translation/utils/monotonic_attention.py new file mode 100644 index 0000000000..61dbb112bf --- /dev/null +++ b/examples/simultaneous_translation/utils/monotonic_attention.py @@ -0,0 +1,198 @@ +from typing import Optional +import torch +from torch import Tensor + +from examples.simultaneous_translation.utils.functions import ( + exclusive_cumprod, + prob_check, + moving_sum, +) + + +def expected_alignment_from_p_choose( + p_choose: Tensor, + padding_mask: Optional[Tensor] = None, + eps: float = 1e-6 +): + """ + Calculating expected alignment for from stepwise probability + + Reference: + Online and Linear-Time Attention by Enforcing Monotonic Alignments + https://arxiv.org/pdf/1704.00784.pdf + + q_ij = (1 − p_{ij−1})q_{ij−1} + a+{i−1j} + a_ij = p_ij q_ij + + Parallel solution: + ai = p_i * cumprod(1 − pi) * cumsum(a_i / cumprod(1 − pi)) + + ============================================================ + Expected input size + p_choose: bsz, tgt_len, src_len + """ + prob_check(p_choose) + + # p_choose: bsz, tgt_len, src_len + bsz, tgt_len, src_len = p_choose.size() + dtype = p_choose.dtype + + p_choose = p_choose.float() + + if padding_mask is not None: + p_choose = p_choose.masked_fill(padding_mask.unsqueeze(1), 0.0) + + # cumprod_1mp : bsz, tgt_len, src_len + cumprod_1mp = exclusive_cumprod(1 - p_choose, dim=2, eps=eps) + cumprod_1mp_clamp = torch.clamp(cumprod_1mp, eps, 1.0) + + alpha_0 = p_choose.new_zeros([bsz, 1, src_len]) + alpha_0[:, :, 0] = 1.0 + + previous_alpha = [alpha_0] + + for i in range(tgt_len): + # p_choose: bsz , tgt_len, src_len + # cumprod_1mp_clamp : bsz, tgt_len, src_len + # previous_alpha[i]: bsz, 1, src_len + # alpha_i: bsz, src_len + alpha_i = ( + p_choose[:, i] + * cumprod_1mp[:, i] + * torch.cumsum( + previous_alpha[i][:, 0] / cumprod_1mp_clamp[:, i], dim=1 + ) + ).clamp(0, 1.0) + + previous_alpha.append(alpha_i.unsqueeze(1)) + + # alpha: bsz * num_heads, tgt_len, src_len + alpha = torch.cat(previous_alpha[1:], dim=1) + + # Mix precision to prevent overflow for fp16 + alpha = alpha.type(dtype) + + prob_check(alpha) + + return alpha + + +def expected_soft_attention( + alpha: Tensor, + soft_energy: Tensor, + padding_mask: Optional[Tensor] = None, + chunk_size: Optional[int] = None, + eps: float = 1e-10 +): + """ + Function to compute expected soft attention for + monotonic infinite lookback attention from + expected alignment and soft energy. + + Reference: + Monotonic Chunkwise Attention + https://arxiv.org/abs/1712.05382 + + Monotonic Infinite Lookback Attention for Simultaneous Machine Translation + https://arxiv.org/abs/1906.05218 + + alpha: bsz, tgt_len, src_len + soft_energy: bsz, tgt_len, src_len + padding_mask: bsz, src_len + left_padding: bool + """ + if padding_mask is not None: + alpha = alpha.masked_fill(padding_mask.unsqueeze(1), 0.0) + soft_energy = soft_energy.masked_fill( + padding_mask.unsqueeze(1), -float("inf") + ) + + prob_check(alpha) + + dtype = alpha.dtype + + alpha = alpha.float() + soft_energy = soft_energy.float() + + soft_energy = soft_energy - soft_energy.max(dim=2, keepdim=True)[0] + exp_soft_energy = torch.exp(soft_energy) + eps + + if chunk_size is not None: + # Chunkwise + beta = ( + exp_soft_energy + * moving_sum( + alpha / (eps + moving_sum(exp_soft_energy, chunk_size, 1)), + 1, chunk_size + ) + ) + else: + # Infinite lookback + # Notice that infinite lookback is a special case of chunkwise + # where chunksize = inf + inner_items = alpha / (eps + torch.cumsum(exp_soft_energy, dim=2)) + + beta = ( + exp_soft_energy + * torch.cumsum(inner_items.flip(dims=[2]), dim=2) + .flip(dims=[2]) + ) + + if padding_mask is not None: + beta = beta.masked_fill( + padding_mask.unsqueeze(1).to(torch.bool), 0.0) + + # Mix precision to prevent overflow for fp16 + beta = beta.type(dtype) + + beta = beta.clamp(0, 1) + + prob_check(beta) + + return beta + + +def mass_preservation( + alpha: Tensor, + padding_mask: Optional[Tensor] = None, + left_padding: bool = False +): + """ + Function to compute the mass perservation for alpha. + This means that the residual weights of alpha will be assigned + to the last token. + + Reference: + Monotonic Infinite Lookback Attention for Simultaneous Machine Translation + https://arxiv.org/abs/1906.05218 + + alpha: bsz, tgt_len, src_len + padding_mask: bsz, src_len + left_padding: bool + """ + + prob_check(alpha) + + if padding_mask is not None: + if not left_padding: + assert not padding_mask[:, 0].any(), ( + "Find padding on the beginning of the sequence." + ) + alpha = alpha.masked_fill(padding_mask.unsqueeze(1), 0.0) + + if left_padding or padding_mask is None: + residuals = 1 - alpha[:, :, :-1].sum(dim=-1).clamp(0, 1) + alpha[:, :, -1] = residuals + else: + # right padding + _, tgt_len, src_len = alpha.size() + residuals = 1 - alpha.sum(dim=-1, keepdim=True).clamp(0, 1) + src_lens = src_len - padding_mask.sum(dim=1, keepdim=True) + src_lens = src_lens.expand(-1, tgt_len).contiguous() + # add back the last value + residuals += alpha.gather(2, src_lens.unsqueeze(2) - 1) + alpha = alpha.scatter(2, src_lens.unsqueeze(2) - 1, residuals) + + prob_check(alpha) + + return alpha diff --git a/examples/simultaneous_translation/utils/p_choose_strategy.py b/examples/simultaneous_translation/utils/p_choose_strategy.py new file mode 100644 index 0000000000..724c6912a6 --- /dev/null +++ b/examples/simultaneous_translation/utils/p_choose_strategy.py @@ -0,0 +1,126 @@ +from typing import Optional, Dict +from torch import Tensor +import torch + + +def waitk_p_choose( + tgt_len: int, + src_len: int, + bsz: int, + waitk_lagging: int, + key_padding_mask: Optional[Tensor] = None, + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None +): + + max_src_len = src_len + if incremental_state is not None: + # Retrieve target length from incremental states + # For inference the length of query is always 1 + max_tgt_len = incremental_state["steps"]["tgt"] + assert max_tgt_len is not None + max_tgt_len = int(max_tgt_len) + else: + max_tgt_len = tgt_len + + if max_src_len < waitk_lagging: + if incremental_state is not None: + max_tgt_len = 1 + return torch.zeros( + bsz, max_tgt_len, max_src_len + ) + + # Assuming the p_choose looks like this for wait k=3 + # src_len = 6, max_tgt_len = 5 + # [0, 0, 1, 0, 0, 0, 0] + # [0, 0, 0, 1, 0, 0, 0] + # [0, 0, 0, 0, 1, 0, 0] + # [0, 0, 0, 0, 0, 1, 0] + # [0, 0, 0, 0, 0, 0, 1] + # linearize the p_choose matrix: + # [0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0...] + # The indices of linearized matrix that equals 1 is + # 2 + 6 * 0 + # 3 + 6 * 1 + # ... + # n + src_len * n + k - 1 = n * (src_len + 1) + k - 1 + # n from 0 to max_tgt_len - 1 + # + # First, generate the indices (activate_indices_offset: bsz, max_tgt_len) + # Second, scatter a zeros tensor (bsz, max_tgt_len * src_len) + # with activate_indices_offset + # Third, resize the tensor to (bsz, max_tgt_len, src_len) + + activate_indices_offset = ( + ( + torch.arange(max_tgt_len) * (max_src_len + 1) + + waitk_lagging - 1 + ) + .unsqueeze(0) + .expand(bsz, max_tgt_len) + .long() + ) + + if key_padding_mask is not None: + if key_padding_mask[:, 0].any(): + # Left padding + activate_indices_offset += ( + key_padding_mask.sum(dim=1, keepdim=True) + ) + + # Need to clamp the indices that are too large + activate_indices_offset = ( + activate_indices_offset + .clamp( + 0, + min( + [ + max_tgt_len, + max_src_len - waitk_lagging + 1 + ] + ) * max_src_len - 1 + ) + ) + + p_choose = torch.zeros(bsz, max_tgt_len * max_src_len) + + p_choose = p_choose.scatter( + 1, + activate_indices_offset, + 1.0 + ).view(bsz, max_tgt_len, max_src_len) + + if key_padding_mask is not None: + p_choose = p_choose.to(key_padding_mask) + p_choose = p_choose.masked_fill(key_padding_mask.unsqueeze(1), 0) + + if incremental_state is not None: + p_choose = p_choose[:, -1:] + + return p_choose.float() + + +def learnable_p_choose( + energy, + noise_mean: float = 0.0, + noise_var: float = 0.0, + training: bool = True +): + """ + Calculating step wise prob for reading and writing + 1 to read, 0 to write + energy: bsz, tgt_len, src_len + """ + + noise = 0 + if training: + # add noise here to encourage discretness + noise = ( + torch.normal(noise_mean, noise_var, energy.size()) + .type_as(energy) + .to(energy.device) + ) + + p_choose = torch.sigmoid(energy + noise) + + # p_choose: bsz * self.num_heads, tgt_len, src_len + return p_choose diff --git a/examples/speech_recognition/README.md b/examples/speech_recognition/README.md index 19f7cc563e..17030bf0fd 100644 --- a/examples/speech_recognition/README.md +++ b/examples/speech_recognition/README.md @@ -1,3 +1,5 @@ +### 2021 Update: We are merging this example into the [S2T framework](../speech_to_text), which supports more generic speech-to-text tasks (e.g. speech translation) and more flexible data processing pipelines. Please stay tuned. + # Speech Recognition `examples/speech_recognition` is implementing ASR task in Fairseq, along with needed features, datasets, models and loss functions to train and infer model described in [Transformers with convolutional context for ASR (Abdelrahman Mohamed et al., 2019)](https://arxiv.org/abs/1904.11660). @@ -32,41 +34,16 @@ sclite -r ${RES_DIR}/ref.word-checkpoint_last.pt-${SET}.txt -h ${RES_DIR}/hypo.w ``` `Sum/Avg` row from first table of the report has WER -## Using wav2letter components -[wav2letter](https://github.com/facebookresearch/wav2letter) now has integration with fairseq. Currently this includes: +## Using flashlight (previously called [wav2letter](https://github.com/facebookresearch/wav2letter)) components +[flashlight](https://github.com/facebookresearch/flashlight) now has integration with fairseq. Currently this includes: * AutoSegmentationCriterion (ASG) -* wav2letter-style Conv/GLU model -* wav2letter's beam search decoder - -To use these, follow the instructions on [this page](https://github.com/facebookresearch/wav2letter/tree/master/bindings/python) to install python bindings. Please note that python bindings are for a *subset* of wav2letter and don't require its full dependencies (notably, `flashlight` and `ArrayFire` are *not* required). - -To quickly summarize the instructions: first, install [CUDA](https://developer.nvidia.com/cuda-downloads). Then follow these steps: -``` -# additional prerequisites - use equivalents for your distro -sudo apt-get install build-essential cmake libatlas-base-dev libfftw3-dev liblzma-dev libbz2-dev libzstd-dev -# install KenLM from source -git clone https://github.com/kpu/kenlm.git -cd kenlm -mkdir -p build && cd build -cmake .. -DCMAKE_BUILD_TYPE=Release -DCMAKE_POSITION_INDEPENDENT_CODE=ON -make -j16 -cd .. -export KENLM_ROOT_DIR=$(pwd) -cd .. -# install wav2letter python bindings -git clone https://github.com/facebookresearch/wav2letter.git -cd wav2letter/bindings/python -# make sure your python environment is active at this point -pip install torch packaging -pip install -e . -# try some examples to verify installation succeeded -python ./examples/criterion_example.py -python ./examples/decoder_example.py ../../src/decoder/test -python ./examples/feature_example.py ../../src/feature/test/data -``` - -## Training librispeech data (wav2letter style, Conv/GLU + ASG loss) +* flashlight-style Conv/GLU model +* flashlight's beam search decoder + +To use these, follow the instructions on [this page](https://github.com/facebookresearch/flashlight/tree/master/bindings/python) to install python bindings. + +## Training librispeech data (flashlight style, Conv/GLU + ASG loss) Training command: ``` python train.py $DIR_FOR_PREPROCESSED_DATA --save-dir $MODEL_PATH --max-epoch 100 --task speech_recognition --arch w2l_conv_glu_enc --batch-size 4 --optimizer sgd --lr 0.3,0.8 --momentum 0.8 --clip-norm 0.2 --max-tokens 50000 --log-format json --log-interval 100 --num-workers 0 --sentence-avg --criterion asg_loss --asg-transitions-init 5 --max-replabel 2 --linseg-updates 8789 --user-dir examples/speech_recognition @@ -74,13 +51,13 @@ python train.py $DIR_FOR_PREPROCESSED_DATA --save-dir $MODEL_PATH --max-epoch 10 Note that ASG loss currently doesn't do well with word-pieces. You should prepare a dataset with character targets by setting `nbpe=31` in `prepare-librispeech.sh`. -## Inference for librispeech (wav2letter decoder, n-gram LM) +## Inference for librispeech (flashlight decoder, n-gram LM) Inference command: ``` python examples/speech_recognition/infer.py $DIR_FOR_PREPROCESSED_DATA --task speech_recognition --seed 1 --nbest 1 --path $MODEL_PATH/checkpoint_last.pt --gen-subset $SET --results-path $RES_DIR --w2l-decoder kenlm --kenlm-model $KENLM_MODEL_PATH --lexicon $LEXICON_PATH --beam 200 --beam-threshold 15 --lm-weight 1.5 --word-score 1.5 --sil-weight -0.3 --criterion asg_loss --max-replabel 2 --user-dir examples/speech_recognition ``` -`$KENLM_MODEL_PATH` should be a standard n-gram language model file. `$LEXICON_PATH` should be a wav2letter-style lexicon (list of known words and their spellings). For ASG inference, a lexicon line should look like this (note the repetition labels): +`$KENLM_MODEL_PATH` should be a standard n-gram language model file. `$LEXICON_PATH` should be a flashlight-style lexicon (list of known words and their spellings). For ASG inference, a lexicon line should look like this (note the repetition labels): ``` doorbell D O 1 R B E L 1 ▁ ``` @@ -99,7 +76,7 @@ doorbell ▁DO OR BE L L ``` Lowercase vs. uppercase matters: the *word* should match the case of the n-gram language model (i.e. `$KENLM_MODEL_PATH`), while the *spelling* should match the case of the token dictionary (i.e. `$DIR_FOR_PREPROCESSED_DATA/dict.txt`). -## Inference for librispeech (wav2letter decoder, viterbi only) +## Inference for librispeech (flashlight decoder, viterbi only) Inference command: ``` python examples/speech_recognition/infer.py $DIR_FOR_PREPROCESSED_DATA --task speech_recognition --seed 1 --nbest 1 --path $MODEL_PATH/checkpoint_last.pt --gen-subset $SET --results-path $RES_DIR --w2l-decoder viterbi --criterion asg_loss --max-replabel 2 --user-dir examples/speech_recognition diff --git a/examples/speech_recognition/__init__.py b/examples/speech_recognition/__init__.py index cd780902e3..0278f6a273 100644 --- a/examples/speech_recognition/__init__.py +++ b/examples/speech_recognition/__init__.py @@ -1 +1 @@ -from . import tasks, criterions, models # noqa +from . import criterions, models, tasks # noqa diff --git a/examples/speech_recognition/criterions/ASG_loss.py b/examples/speech_recognition/criterions/ASG_loss.py index 39962baaf5..41f50bbd70 100644 --- a/examples/speech_recognition/criterions/ASG_loss.py +++ b/examples/speech_recognition/criterions/ASG_loss.py @@ -6,9 +6,9 @@ # LICENSE file in the root directory of this source tree. import torch +from examples.speech_recognition.data.replabels import pack_replabels from fairseq import utils from fairseq.criterions import FairseqCriterion, register_criterion -from examples.speech_recognition.data.replabels import pack_replabels @register_criterion("asg_loss") @@ -46,7 +46,7 @@ def __init__( linseg_updates, hide_linseg_messages, ): - from wav2letter.criterion import ASGLoss, CriterionScaleMode + from flashlight.lib.sequence.criterion import ASGLoss, CriterionScaleMode super().__init__(task) self.tgt_dict = task.target_dictionary @@ -70,7 +70,7 @@ def __init__( self.linseg_maximum = linseg_updates self.linseg_message_state = "none" if hide_linseg_messages else "start" - @staticmethod + @classmethod def build_criterion(cls, args, task): return cls( task, diff --git a/examples/speech_recognition/criterions/CTC_loss.py b/examples/speech_recognition/criterions/CTC_loss.py deleted file mode 100644 index 33ed0fb135..0000000000 --- a/examples/speech_recognition/criterions/CTC_loss.py +++ /dev/null @@ -1,197 +0,0 @@ -#!/usr/bin/env python3 - -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import logging -import math -from itertools import groupby - -import torch -import torch.nn.functional as F -from fairseq import utils -from fairseq.criterions import FairseqCriterion, register_criterion -from examples.speech_recognition.data.data_utils import encoder_padding_mask_to_lengths -from examples.speech_recognition.utils.wer_utils import Code, EditDistance, Token - - -logger = logging.getLogger(__name__) -logger.setLevel(logging.DEBUG) - - -def arr_to_toks(arr): - toks = [] - for a in arr: - toks.append(Token(str(a), 0.0, 0.0)) - return toks - - -def compute_ctc_uer(logprobs, targets, input_lengths, target_lengths, blank_idx): - """ - Computes utterance error rate for CTC outputs - - Args: - logprobs: (Torch.tensor) N, T1, D tensor of log probabilities out - of the encoder - targets: (Torch.tensor) N, T2 tensor of targets - input_lengths: (Torch.tensor) lengths of inputs for each sample - target_lengths: (Torch.tensor) lengths of targets for each sample - blank_idx: (integer) id of blank symbol in target dictionary - - Returns: - batch_errors: (float) errors in the batch - batch_total: (float) total number of valid samples in batch - """ - batch_errors = 0.0 - batch_total = 0.0 - for b in range(logprobs.shape[0]): - predicted = logprobs[b][: input_lengths[b]].argmax(1).tolist() - target = targets[b][: target_lengths[b]].tolist() - # dedup predictions - predicted = [p[0] for p in groupby(predicted)] - # remove blanks - nonblanks = [] - for p in predicted: - if p != blank_idx: - nonblanks.append(p) - predicted = nonblanks - - # compute the alignment based on EditDistance - alignment = EditDistance(False).align( - arr_to_toks(predicted), arr_to_toks(target) - ) - - # compute the number of errors - # note that alignment.codes can also be used for computing - # deletion, insersion and substitution error breakdowns in future - for a in alignment.codes: - if a != Code.match: - batch_errors += 1 - batch_total += len(target) - - return batch_errors, batch_total - - -@register_criterion("ctc_loss") -class CTCCriterion(FairseqCriterion): - def __init__(self, task): - assert hasattr(task, "target_dictionary") - super().__init__(task) - self.blank_idx = task.target_dictionary.index("<ctc_blank>") - - @staticmethod - def build_criterion(cls, args, task): - return cls(task) - - @staticmethod - def add_args(parser): - parser.add_argument( - "--use-source-side-sample-size", - action="store_true", - default=False, - help=( - "when compute average loss, using number of source tokens " - + "as denominator. " - + "This argument will be no-op if sentence-avg is used." - ), - ) - - def forward(self, model, sample, reduce=True, log_probs=True): - """Compute the loss for the given sample. - - Returns a tuple with three elements: - 1) the loss - 2) the sample size, which is used as the denominator for the gradient - 3) logging outputs to display while training - """ - net_output = model(**sample["net_input"]) - lprobs = model.get_normalized_probs(net_output, log_probs=log_probs) - if not hasattr(lprobs, "batch_first"): - logging.warning( - "ERROR: we need to know whether " - "batch first for the encoder output; " - "you need to set batch_first attribute for the return value of " - "model.get_normalized_probs. Now, we assume this is true, but " - "in the future, we will raise exception instead. " - ) - - batch_first = getattr(lprobs, "batch_first", True) - - if not batch_first: - max_seq_len = lprobs.size(0) - bsz = lprobs.size(1) - else: - max_seq_len = lprobs.size(1) - bsz = lprobs.size(0) - device = net_output["encoder_out"].device - - input_lengths = encoder_padding_mask_to_lengths( - net_output["encoder_padding_mask"], max_seq_len, bsz, device - ) - target_lengths = sample["target_lengths"] - targets = sample["target"] - - if batch_first: - # N T D -> T N D (F.ctc_loss expects this) - lprobs = lprobs.transpose(0, 1) - - pad_mask = sample["target"] != self.padding_idx - targets_flat = targets.masked_select(pad_mask) - - loss = F.ctc_loss( - lprobs, - targets_flat, - input_lengths, - target_lengths, - blank=self.blank_idx, - reduction="sum", - zero_infinity=True, - ) - - lprobs = lprobs.transpose(0, 1) # T N D -> N T D - errors, total = compute_ctc_uer( - lprobs, targets, input_lengths, target_lengths, self.blank_idx - ) - - if self.args.sentence_avg: - sample_size = sample["target"].size(0) - else: - if self.args.use_source_side_sample_size: - sample_size = torch.sum(input_lengths).item() - else: - sample_size = sample["ntokens"] - - logging_output = { - "loss": utils.item(loss.data) if reduce else loss.data, - "ntokens": sample["ntokens"], - "nsentences": sample["target"].size(0), - "sample_size": sample_size, - "errors": errors, - "total": total, - "nframes": torch.sum(sample["net_input"]["src_lengths"]).item(), - } - return loss, sample_size, logging_output - - @staticmethod - def aggregate_logging_outputs(logging_outputs): - """Aggregate logging outputs from data parallel training.""" - loss_sum = sum(log.get("loss", 0) for log in logging_outputs) - ntokens = sum(log.get("ntokens", 0) for log in logging_outputs) - nsentences = sum(log.get("nsentences", 0) for log in logging_outputs) - sample_size = sum(log.get("sample_size", 0) for log in logging_outputs) - errors = sum(log.get("errors", 0) for log in logging_outputs) - total = sum(log.get("total", 0) for log in logging_outputs) - nframes = sum(log.get("nframes", 0) for log in logging_outputs) - agg_output = { - "loss": loss_sum / sample_size / math.log(2), - "ntokens": ntokens, - "nsentences": nsentences, - "nframes": nframes, - "sample_size": sample_size, - "acc": 100.0 - min(errors * 100.0 / total, 100.0), - } - if sample_size != ntokens: - agg_output["nll_loss"] = loss_sum / ntokens / math.log(2) - return agg_output diff --git a/examples/speech_recognition/criterions/__init__.py b/examples/speech_recognition/criterions/__init__.py index e3a348afa4..579abd2ace 100644 --- a/examples/speech_recognition/criterions/__init__.py +++ b/examples/speech_recognition/criterions/__init__.py @@ -2,15 +2,15 @@ import os -# ASG loss requires wav2letter -blacklist = set() +# ASG loss requires flashlight bindings +files_to_skip = set() try: - import wav2letter + import flashlight.lib.sequence.criterion except ImportError: - blacklist.add("ASG_loss.py") + files_to_skip.add("ASG_loss.py") -for file in os.listdir(os.path.dirname(__file__)): - if file.endswith(".py") and not file.startswith("_") and file not in blacklist: +for file in sorted(os.listdir(os.path.dirname(__file__))): + if file.endswith(".py") and not file.startswith("_") and file not in files_to_skip: criterion_name = file[: file.find(".py")] importlib.import_module( "examples.speech_recognition.criterions." + criterion_name diff --git a/examples/speech_recognition/data/__init__.py b/examples/speech_recognition/data/__init__.py index 737a22ec3a..47bb6e24dd 100644 --- a/examples/speech_recognition/data/__init__.py +++ b/examples/speech_recognition/data/__init__.py @@ -5,6 +5,7 @@ from .asr_dataset import AsrDataset + __all__ = [ - 'AsrDataset', + "AsrDataset", ] diff --git a/examples/speech_recognition/data/asr_dataset.py b/examples/speech_recognition/data/asr_dataset.py index 47969a2853..63a6fcac85 100644 --- a/examples/speech_recognition/data/asr_dataset.py +++ b/examples/speech_recognition/data/asr_dataset.py @@ -4,6 +4,7 @@ # LICENSE file in the root directory of this source tree. import os + import numpy as np from fairseq.data import FairseqDataset @@ -30,16 +31,22 @@ class AsrDataset(FairseqDataset): """ def __init__( - self, aud_paths, aud_durations_ms, tgt, - tgt_dict, ids, speakers, - num_mel_bins=80, frame_length=25.0, frame_shift=10.0 + self, + aud_paths, + aud_durations_ms, + tgt, + tgt_dict, + ids, + speakers, + num_mel_bins=80, + frame_length=25.0, + frame_shift=10.0, ): assert frame_length > 0 assert frame_shift > 0 assert all(x > frame_length for x in aud_durations_ms) self.frame_sizes = [ - int(1 + (d - frame_length) / frame_shift) - for d in aud_durations_ms + int(1 + (d - frame_length) / frame_shift) for d in aud_durations_ms ] assert len(aud_paths) > 0 @@ -57,13 +64,17 @@ def __init__( self.frame_shift = frame_shift self.s2s_collater = Seq2SeqCollater( - 0, 1, pad_index=self.tgt_dict.pad(), - eos_index=self.tgt_dict.eos(), move_eos_to_beginning=True + 0, + 1, + pad_index=self.tgt_dict.pad(), + eos_index=self.tgt_dict.eos(), + move_eos_to_beginning=True, ) def __getitem__(self, index): import torchaudio import torchaudio.compliance.kaldi as kaldi + tgt_item = self.tgt[index] if self.tgt is not None else None path = self.aud_paths[index] @@ -74,7 +85,7 @@ def __getitem__(self, index): sound, num_mel_bins=self.num_mel_bins, frame_length=self.frame_length, - frame_shift=self.frame_shift + frame_shift=self.frame_shift, ) output_cmvn = data_utils.apply_mv_norm(output) diff --git a/examples/speech_recognition/data/collaters.py b/examples/speech_recognition/data/collaters.py index 14740d48b7..6acfec876b 100644 --- a/examples/speech_recognition/data/collaters.py +++ b/examples/speech_recognition/data/collaters.py @@ -12,18 +12,18 @@ from __future__ import absolute_import, division, print_function, unicode_literals -import numpy as np +import numpy as np import torch from fairseq.data import data_utils as fairseq_data_utils class Seq2SeqCollater(object): """ - Implements collate function mainly for seq2seq tasks - This expects each sample to contain feature (src_tokens) and - targets. - This collator is also used for aligned training task. + Implements collate function mainly for seq2seq tasks + This expects each sample to contain feature (src_tokens) and + targets. + This collator is also used for aligned training task. """ def __init__( diff --git a/examples/speech_recognition/data/data_utils.py b/examples/speech_recognition/data/data_utils.py index 03c41f47d9..cc4729e63c 100644 --- a/examples/speech_recognition/data/data_utils.py +++ b/examples/speech_recognition/data/data_utils.py @@ -19,6 +19,10 @@ def calc_mean_invstddev(feature): def apply_mv_norm(features): + # If there is less than 2 spectrograms, the variance cannot be computed (is NaN) + # and normalization is not possible, so return the item as it is + if features.size(0) < 2: + return features mean, invstddev = calc_mean_invstddev(features) res = (features - mean) * invstddev return res diff --git a/examples/speech_recognition/data/replabels.py b/examples/speech_recognition/data/replabels.py index d76bda7aef..441f1bd432 100644 --- a/examples/speech_recognition/data/replabels.py +++ b/examples/speech_recognition/data/replabels.py @@ -6,13 +6,13 @@ # LICENSE file in the root directory of this source tree. """ -Replabel transforms for use with wav2letter's ASG criterion. +Replabel transforms for use with flashlight's ASG criterion. """ def replabel_symbol(i): """ - Replabel symbols used in wav2letter, currently just "1", "2", ... + Replabel symbols used in flashlight, currently just "1", "2", ... This prevents training with numeral tokens, so this might change in the future """ return str(i) diff --git a/examples/speech_recognition/datasets/asr_prep_json.py b/examples/speech_recognition/datasets/asr_prep_json.py index 2bab825b89..b8db8ff166 100644 --- a/examples/speech_recognition/datasets/asr_prep_json.py +++ b/examples/speech_recognition/datasets/asr_prep_json.py @@ -6,52 +6,74 @@ from __future__ import absolute_import, division, print_function, unicode_literals -from collections import namedtuple -import concurrent.futures -from itertools import chain import argparse -import os +import concurrent.futures import json -import sentencepiece as spm import multiprocessing +import os +from collections import namedtuple +from itertools import chain +import sentencepiece as spm from fairseq.data import Dictionary + MILLISECONDS_TO_SECONDS = 0.001 def process_sample(aud_path, lable, utt_id, sp, tgt_dict): import torchaudio + input = {} output = {} si, ei = torchaudio.info(aud_path) - input["length_ms"] = int(si.length / si.channels / si.rate / MILLISECONDS_TO_SECONDS) + input["length_ms"] = int( + si.length / si.channels / si.rate / MILLISECONDS_TO_SECONDS + ) input["path"] = aud_path token = " ".join(sp.EncodeAsPieces(lable)) ids = tgt_dict.encode_line(token, append_eos=False) output["text"] = lable output["token"] = token - output["tokenid"] = ', '.join(map(str, [t.tolist() for t in ids])) + output["tokenid"] = ", ".join(map(str, [t.tolist() for t in ids])) return {utt_id: {"input": input, "output": output}} def main(): parser = argparse.ArgumentParser() - parser.add_argument("--audio-dirs", nargs="+", default=['-'], required=True, - help="input directories with audio files") - parser.add_argument("--labels", required=True, - help="aggregated input labels with format <ID LABEL> per line", - type=argparse.FileType('r', encoding='UTF-8')) - parser.add_argument("--spm-model", required=True, - help="sentencepiece model to use for encoding", - type=argparse.FileType('r', encoding='UTF-8')) - parser.add_argument("--dictionary", required=True, - help="file to load fairseq dictionary from", - type=argparse.FileType('r', encoding='UTF-8')) + parser.add_argument( + "--audio-dirs", + nargs="+", + default=["-"], + required=True, + help="input directories with audio files", + ) + parser.add_argument( + "--labels", + required=True, + help="aggregated input labels with format <ID LABEL> per line", + type=argparse.FileType("r", encoding="UTF-8"), + ) + parser.add_argument( + "--spm-model", + required=True, + help="sentencepiece model to use for encoding", + type=argparse.FileType("r", encoding="UTF-8"), + ) + parser.add_argument( + "--dictionary", + required=True, + help="file to load fairseq dictionary from", + type=argparse.FileType("r", encoding="UTF-8"), + ) parser.add_argument("--audio-format", choices=["flac", "wav"], default="wav") - parser.add_argument("--output", required=True, type=argparse.FileType('w'), - help="path to save json output") + parser.add_argument( + "--output", + required=True, + type=argparse.FileType("w"), + help="path to save json output", + ) args = parser.parse_args() sp = spm.SentencePieceProcessor() @@ -64,15 +86,17 @@ def main(): (utt_id, label) = line.split(" ", 1) labels[utt_id] = label if len(labels) == 0: - raise Exception('No labels found in ', args.labels_path) + raise Exception("No labels found in ", args.labels_path) - Sample = namedtuple('Sample', 'aud_path utt_id') + Sample = namedtuple("Sample", "aud_path utt_id") samples = [] - for path, _, files in chain.from_iterable(os.walk(path) for path in args.audio_dirs): + for path, _, files in chain.from_iterable( + os.walk(path) for path in args.audio_dirs + ): for f in files: if f.endswith(args.audio_format): if len(os.path.splitext(f)) != 2: - raise Exception('Expect <utt_id.extension> file name. Got: ', f) + raise Exception("Expect <utt_id.extension> file name. Got: ", f) utt_id = os.path.splitext(f)[0] if utt_id not in labels: continue @@ -81,12 +105,17 @@ def main(): utts = {} num_cpu = multiprocessing.cpu_count() with concurrent.futures.ThreadPoolExecutor(max_workers=num_cpu) as executor: - future_to_sample = {executor.submit(process_sample, s.aud_path, labels[s.utt_id], s.utt_id, sp, tgt_dict): s for s in samples} + future_to_sample = { + executor.submit( + process_sample, s.aud_path, labels[s.utt_id], s.utt_id, sp, tgt_dict + ): s + for s in samples + } for future in concurrent.futures.as_completed(future_to_sample): try: data = future.result() except Exception as exc: - print('generated an exception: ', exc) + print("generated an exception: ", exc) else: utts.update(data) json.dump({"utts": utts}, args.output, indent=4) diff --git a/examples/speech_recognition/infer.py b/examples/speech_recognition/infer.py index ffa0f1e753..6e9a878af4 100644 --- a/examples/speech_recognition/infer.py +++ b/examples/speech_recognition/infer.py @@ -8,19 +8,24 @@ Run inference for pre-processed data with a trained model. """ +import ast import logging import math import os +import sys -import sentencepiece as spm +import editdistance +import numpy as np import torch -from fairseq import checkpoint_utils, options, utils, tasks -from fairseq.logging import meters, progress_bar -from fairseq.utils import import_user_module +from fairseq import checkpoint_utils, options, progress_bar, tasks, utils +from fairseq.data.data_utils import post_process +from fairseq.logging.meters import StopwatchMeter, TimeMeter +logging.basicConfig() +logging.root.setLevel(logging.INFO) +logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) -logger.setLevel(logging.INFO) def add_asr_eval_argument(parser): @@ -34,50 +39,76 @@ def add_asr_eval_argument(parser): help="wfstlm on dictonary\ output units", ) - parser.add_argument( - "--lm-weight", - "--lm_weight", - type=float, - default=0.2, - help="weight for lm while interpolating with neural score", - ) + try: + parser.add_argument( + "--lm-weight", + "--lm_weight", + type=float, + default=0.2, + help="weight for lm while interpolating with neural score", + ) + except: + pass parser.add_argument( "--rnnt_len_penalty", default=-0.5, help="rnnt length penalty on word level" ) parser.add_argument( - "--w2l-decoder", choices=["viterbi", "kenlm"], help="use a w2l decoder" + "--w2l-decoder", + choices=["viterbi", "kenlm", "fairseqlm"], + help="use a w2l decoder", ) parser.add_argument("--lexicon", help="lexicon for w2l decoder") - parser.add_argument("--kenlm-model", help="kenlm model for w2l decoder") + parser.add_argument("--unit-lm", action="store_true", help="if using a unit lm") + parser.add_argument("--kenlm-model", "--lm-model", help="lm model for w2l decoder") parser.add_argument("--beam-threshold", type=float, default=25.0) + parser.add_argument("--beam-size-token", type=float, default=100) parser.add_argument("--word-score", type=float, default=1.0) parser.add_argument("--unk-weight", type=float, default=-math.inf) parser.add_argument("--sil-weight", type=float, default=0.0) + parser.add_argument( + "--dump-emissions", + type=str, + default=None, + help="if present, dumps emissions into this file and exits", + ) + parser.add_argument( + "--dump-features", + type=str, + default=None, + help="if present, dumps features into this file and exits", + ) + parser.add_argument( + "--load-emissions", + type=str, + default=None, + help="if present, loads emissions from this file", + ) return parser def check_args(args): - assert args.path is not None, "--path required for generation!" - assert args.results_path is not None, "--results_path required for generation!" + # assert args.path is not None, "--path required for generation!" + # assert args.results_path is not None, "--results_path required for generation!" assert ( not args.sampling or args.nbest == args.beam ), "--sampling requires --nbest to be equal to --beam" assert ( - args.replace_unk is None or args.dataset_impl == "raw" - ), "--replace-unk requires a raw text dataset (--dataset-impl=raw)" + args.replace_unk is None or args.raw_text + ), "--replace-unk requires a raw text dataset (--raw-text)" -def get_dataset_itr(args, task): +def get_dataset_itr(args, task, models): return task.get_batch_iterator( dataset=task.dataset(args.gen_subset), max_tokens=args.max_tokens, - max_sentences=args.max_sentences, - max_positions=(1000000.0, 1000000.0), + max_sentences=args.batch_size, + max_positions=(sys.maxsize, sys.maxsize), ignore_invalid_inputs=args.skip_invalid_size_inputs_valid_test, required_batch_size_multiple=args.required_batch_size_multiple, num_shards=args.num_shards, shard_id=args.shard_id, num_workers=args.num_workers, + data_buffer_size=args.data_buffer_size, ).next_epoch_itr(shuffle=False) @@ -86,25 +117,48 @@ def process_predictions( ): for hypo in hypos[: min(len(hypos), args.nbest)]: hyp_pieces = tgt_dict.string(hypo["tokens"].int().cpu()) - hyp_words = sp.DecodePieces(hyp_pieces.split()) - print( - "{} ({}-{})".format(hyp_pieces, speaker, id), file=res_files["hypo.units"] - ) - print("{} ({}-{})".format(hyp_words, speaker, id), file=res_files["hypo.words"]) + + if "words" in hypo: + hyp_words = " ".join(hypo["words"]) + else: + hyp_words = post_process(hyp_pieces, args.post_process) + + if res_files is not None: + print( + "{} ({}-{})".format(hyp_pieces, speaker, id), + file=res_files["hypo.units"], + ) + print( + "{} ({}-{})".format(hyp_words, speaker, id), + file=res_files["hypo.words"], + ) tgt_pieces = tgt_dict.string(target_tokens) - tgt_words = sp.DecodePieces(tgt_pieces.split()) - print("{} ({}-{})".format(tgt_pieces, speaker, id), file=res_files["ref.units"]) - print("{} ({}-{})".format(tgt_words, speaker, id), file=res_files["ref.words"]) - # only score top hypothesis + tgt_words = post_process(tgt_pieces, args.post_process) + + if res_files is not None: + print( + "{} ({}-{})".format(tgt_pieces, speaker, id), + file=res_files["ref.units"], + ) + print( + "{} ({}-{})".format(tgt_words, speaker, id), file=res_files["ref.words"] + ) + if not args.quiet: - logger.debug("HYPO:" + hyp_words) - logger.debug("TARGET:" + tgt_words) - logger.debug("___________________") + logger.info("HYPO:" + hyp_words) + logger.info("TARGET:" + tgt_words) + logger.info("___________________") + + hyp_words = hyp_words.split() + tgt_words = tgt_words.split() + return editdistance.eval(hyp_words, tgt_words), len(tgt_words) def prepare_result_files(args): def get_res_file(file_prefix): + if args.num_shards > 1: + file_prefix = f"{args.shard_id}_{file_prefix}" path = os.path.join( args.results_path, "{}-{}-{}.txt".format( @@ -113,6 +167,9 @@ def get_res_file(file_prefix): ) return open(path, "w", buffering=1) + if not args.results_path: + return None + return { "hypo.words": get_res_file("hypo.word"), "hypo.units": get_res_file("hypo.units"), @@ -121,33 +178,8 @@ def get_res_file(file_prefix): } -def load_models_and_criterions(filenames, arg_overrides=None, task=None): - models = [] - criterions = [] - for filename in filenames: - if not os.path.exists(filename): - raise IOError("Model file not found: {}".format(filename)) - state = checkpoint_utils.load_checkpoint_to_cpu(filename, arg_overrides) - - args = state["args"] - if task is None: - task = tasks.setup_task(args) - - # build model for ensemble - model = task.build_model(args) - model.load_state_dict(state["model"], strict=True) - models.append(model) - - criterion = task.build_criterion(args) - if "criterion" in state: - criterion.load_state_dict(state["criterion"], strict=True) - criterions.append(criterion) - return models, criterions, args - - def optimize_models(args, use_cuda, models): - """Optimize ensemble for generation - """ + """Optimize ensemble for generation""" for model in models: model.make_generation_fast_( beamable_mm_beam_size=None if args.no_beamable_mm else args.beam, @@ -159,112 +191,234 @@ def optimize_models(args, use_cuda, models): model.cuda() -def main(args): +class ExistingEmissionsDecoder(object): + def __init__(self, decoder, emissions): + self.decoder = decoder + self.emissions = emissions + + def generate(self, models, sample, **unused): + ids = sample["id"].cpu().numpy() + try: + emissions = np.stack(self.emissions[ids]) + except: + print([x.shape for x in self.emissions[ids]]) + raise Exception("invalid sizes") + emissions = torch.from_numpy(emissions) + return self.decoder.decode(emissions) + + +def main(args, task=None, model_state=None): check_args(args) - import_user_module(args) - if args.max_tokens is None and args.max_sentences is None: - args.max_tokens = 30000 + if args.max_tokens is None and args.batch_size is None: + args.max_tokens = 4000000 logger.info(args) use_cuda = torch.cuda.is_available() and not args.cpu - # Load dataset splits + logger.info("| decoding with criterion {}".format(args.criterion)) + task = tasks.setup_task(args) - task.load_dataset(args.gen_subset) - logger.info( - "| {} {} {} examples".format( - args.data, args.gen_subset, len(task.dataset(args.gen_subset)) + + # Load ensemble + if args.load_emissions: + models, criterions = [], [] + task.load_dataset(args.gen_subset) + else: + logger.info("| loading model(s) from {}".format(args.path)) + models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task( + utils.split_paths(args.path, separator="\\"), + arg_overrides=ast.literal_eval(args.model_overrides), + task=task, + suffix=args.checkpoint_suffix, + strict=(args.checkpoint_shard_count == 1), + num_shards=args.checkpoint_shard_count, + state=model_state, ) - ) + optimize_models(args, use_cuda, models) + task.load_dataset(args.gen_subset, task_cfg=saved_cfg.task) + # Set dictionary tgt_dict = task.target_dictionary - logger.info("| decoding with criterion {}".format(args.criterion)) - - # Load ensemble - logger.info("| loading model(s) from {}".format(args.path)) - models, criterions, _model_args = load_models_and_criterions( - args.path.split(os.pathsep), - arg_overrides=eval(args.model_overrides), # noqa - task=task, + logger.info( + "| {} {} {} examples".format( + args.data, args.gen_subset, len(task.dataset(args.gen_subset)) + ) ) - optimize_models(args, use_cuda, models) # hack to pass transitions to W2lDecoder if args.criterion == "asg_loss": - trans = criterions[0].asg.trans.data - args.asg_transitions = torch.flatten(trans).tolist() + raise NotImplementedError("asg_loss is currently not supported") + # trans = criterions[0].asg.trans.data + # args.asg_transitions = torch.flatten(trans).tolist() # Load dataset (possibly sharded) - itr = get_dataset_itr(args, task) - progress = progress_bar.progress_bar( - itr, - log_format=args.log_format, - log_interval=args.log_interval, - default_log_format=('tqdm' if not args.no_progress_bar else 'none'), - ) + itr = get_dataset_itr(args, task, models) # Initialize generator - gen_timer = meters.StopwatchMeter() - generator = task.build_generator(models, args) + gen_timer = StopwatchMeter() - num_sentences = 0 + def build_generator(args): + w2l_decoder = getattr(args, "w2l_decoder", None) + if w2l_decoder == "viterbi": + from examples.speech_recognition.w2l_decoder import W2lViterbiDecoder - if not os.path.exists(args.results_path): - os.makedirs(args.results_path) + return W2lViterbiDecoder(args, task.target_dictionary) + elif w2l_decoder == "kenlm": + from examples.speech_recognition.w2l_decoder import W2lKenLMDecoder - sp = spm.SentencePieceProcessor() - sp.Load(os.path.join(args.data, "spm.model")) - - res_files = prepare_result_files(args) - wps_meter = meters.TimeMeter() - for sample in progress: - sample = utils.move_to_cuda(sample) if use_cuda else sample - if "net_input" not in sample: - continue - - prefix_tokens = None - if args.prefix_size > 0: - prefix_tokens = sample["target"][:, : args.prefix_size] - - gen_timer.start() - hypos = task.inference_step(generator, models, sample, prefix_tokens) - num_generated_tokens = sum(len(h[0]["tokens"]) for h in hypos) - gen_timer.stop(num_generated_tokens) - - for i, sample_id in enumerate(sample["id"].tolist()): - speaker = task.dataset(args.gen_subset).speakers[int(sample_id)] - id = task.dataset(args.gen_subset).ids[int(sample_id)] - target_tokens = ( - utils.strip_pad(sample["target"][i, :], tgt_dict.pad()).int().cpu() - ) - # Process top predictions - process_predictions( - args, hypos[i], sp, tgt_dict, target_tokens, res_files, speaker, id + return W2lKenLMDecoder(args, task.target_dictionary) + elif w2l_decoder == "fairseqlm": + from examples.speech_recognition.w2l_decoder import W2lFairseqLMDecoder + + return W2lFairseqLMDecoder(args, task.target_dictionary) + else: + print( + "only flashlight decoders with (viterbi, kenlm, fairseqlm) options are supported at the moment" ) - wps_meter.update(num_generated_tokens) - progress.log({"wps": round(wps_meter.avg)}) - num_sentences += sample["nsentences"] + # please do not touch this unless you test both generate.py and infer.py with audio_pretraining task + generator = build_generator(args) - logger.info( - "| Processed {} sentences ({} tokens) in {:.1f}s ({:.2f}" - "sentences/s, {:.2f} tokens/s)".format( - num_sentences, - gen_timer.n, - gen_timer.sum, - num_sentences / gen_timer.sum, - 1.0 / gen_timer.avg, + if args.load_emissions: + generator = ExistingEmissionsDecoder( + generator, np.load(args.load_emissions, allow_pickle=True) ) + logger.info("loaded emissions from " + args.load_emissions) + + num_sentences = 0 + + if args.results_path is not None and not os.path.exists(args.results_path): + os.makedirs(args.results_path) + + max_source_pos = ( + utils.resolve_max_positions( + task.max_positions(), *[model.max_positions() for model in models] + ), ) - logger.info("| Generate {} with beam={}".format(args.gen_subset, args.beam)) + if max_source_pos is not None: + max_source_pos = max_source_pos[0] + if max_source_pos is not None: + max_source_pos = max_source_pos[0] - 1 + + if args.dump_emissions: + emissions = {} + if args.dump_features: + features = {} + models[0].bert.proj = None + else: + res_files = prepare_result_files(args) + errs_t = 0 + lengths_t = 0 + with progress_bar.build_progress_bar(args, itr) as t: + wps_meter = TimeMeter() + for sample in t: + sample = utils.move_to_cuda(sample) if use_cuda else sample + if "net_input" not in sample: + continue + + prefix_tokens = None + if args.prefix_size > 0: + prefix_tokens = sample["target"][:, : args.prefix_size] + + gen_timer.start() + if args.dump_emissions: + with torch.no_grad(): + encoder_out = models[0](**sample["net_input"]) + emm = models[0].get_normalized_probs(encoder_out, log_probs=True) + emm = emm.transpose(0, 1).cpu().numpy() + for i, id in enumerate(sample["id"]): + emissions[id.item()] = emm[i] + continue + elif args.dump_features: + with torch.no_grad(): + encoder_out = models[0](**sample["net_input"]) + feat = encoder_out["encoder_out"].transpose(0, 1).cpu().numpy() + for i, id in enumerate(sample["id"]): + padding = ( + encoder_out["encoder_padding_mask"][i].cpu().numpy() + if encoder_out["encoder_padding_mask"] is not None + else None + ) + features[id.item()] = (feat[i], padding) + continue + hypos = task.inference_step(generator, models, sample, prefix_tokens) + num_generated_tokens = sum(len(h[0]["tokens"]) for h in hypos) + gen_timer.stop(num_generated_tokens) + + for i, sample_id in enumerate(sample["id"].tolist()): + speaker = None + # id = task.dataset(args.gen_subset).ids[int(sample_id)] + id = sample_id + toks = ( + sample["target"][i, :] + if "target_label" not in sample + else sample["target_label"][i, :] + ) + target_tokens = utils.strip_pad(toks, tgt_dict.pad()).int().cpu() + # Process top predictions + errs, length = process_predictions( + args, + hypos[i], + None, + tgt_dict, + target_tokens, + res_files, + speaker, + id, + ) + errs_t += errs + lengths_t += length + + wps_meter.update(num_generated_tokens) + t.log({"wps": round(wps_meter.avg)}) + num_sentences += ( + sample["nsentences"] if "nsentences" in sample else sample["id"].numel() + ) -def cli_main(): + wer = None + if args.dump_emissions: + emm_arr = [] + for i in range(len(emissions)): + emm_arr.append(emissions[i]) + np.save(args.dump_emissions, emm_arr) + logger.info(f"saved {len(emissions)} emissions to {args.dump_emissions}") + elif args.dump_features: + feat_arr = [] + for i in range(len(features)): + feat_arr.append(features[i]) + np.save(args.dump_features, feat_arr) + logger.info(f"saved {len(features)} emissions to {args.dump_features}") + else: + if lengths_t > 0: + wer = errs_t * 100.0 / lengths_t + logger.info(f"WER: {wer}") + + logger.info( + "| Processed {} sentences ({} tokens) in {:.1f}s ({:.2f}" + "sentences/s, {:.2f} tokens/s)".format( + num_sentences, + gen_timer.n, + gen_timer.sum, + num_sentences / gen_timer.sum, + 1.0 / gen_timer.avg, + ) + ) + logger.info("| Generate {} with beam={}".format(args.gen_subset, args.beam)) + return task, wer + + +def make_parser(): parser = options.get_generation_parser() parser = add_asr_eval_argument(parser) + return parser + + +def cli_main(): + parser = make_parser() args = options.parse_args_and_arch(parser) main(args) diff --git a/examples/speech_recognition/kaldi/__init__.py b/examples/speech_recognition/kaldi/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/examples/speech_recognition/kaldi/add-self-loop-simple.cc b/examples/speech_recognition/kaldi/add-self-loop-simple.cc new file mode 100644 index 0000000000..e18fb62df5 --- /dev/null +++ b/examples/speech_recognition/kaldi/add-self-loop-simple.cc @@ -0,0 +1,94 @@ +/* + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ + +#include <iostream> +#include "fstext/fstext-lib.h" // @manual +#include "util/common-utils.h" // @manual + +/* + * This program is to modify a FST without self-loop by: + * for each incoming arc with non-eps input symbol, add a self-loop arc + * with that non-eps symbol as input and eps as output. + * + * This is to make sure the resultant FST can do deduplication for repeated + * symbols, which is very common in acoustic model + * + */ +namespace { +int32 AddSelfLoopsSimple(fst::StdVectorFst* fst) { + typedef fst::MutableArcIterator<fst::StdVectorFst> IterType; + + int32 num_states_before = fst->NumStates(); + fst::MakePrecedingInputSymbolsSame(false, fst); + int32 num_states_after = fst->NumStates(); + KALDI_LOG << "There are " << num_states_before + << " states in the original FST; " + << " after MakePrecedingInputSymbolsSame, there are " + << num_states_after << " states " << std::endl; + + auto weight_one = fst::StdArc::Weight::One(); + + int32 num_arc_added = 0; + + fst::StdArc self_loop_arc; + self_loop_arc.weight = weight_one; + + int32 num_states = fst->NumStates(); + std::vector<std::set<int32>> incoming_non_eps_label_per_state(num_states); + + for (int32 state = 0; state < num_states; state++) { + for (IterType aiter(fst, state); !aiter.Done(); aiter.Next()) { + fst::StdArc arc(aiter.Value()); + if (arc.ilabel != 0) { + incoming_non_eps_label_per_state[arc.nextstate].insert(arc.ilabel); + } + } + } + + for (int32 state = 0; state < num_states; state++) { + if (!incoming_non_eps_label_per_state[state].empty()) { + auto& ilabel_set = incoming_non_eps_label_per_state[state]; + for (auto it = ilabel_set.begin(); it != ilabel_set.end(); it++) { + self_loop_arc.ilabel = *it; + self_loop_arc.olabel = 0; + self_loop_arc.nextstate = state; + fst->AddArc(state, self_loop_arc); + num_arc_added++; + } + } + } + return num_arc_added; +} + +void print_usage() { + std::cout << "add-self-loop-simple usage:\n" + "\tadd-self-loop-simple <in-fst> <out-fst> \n"; +} +} // namespace + +int main(int argc, char** argv) { + if (argc != 3) { + print_usage(); + exit(1); + } + + auto input = argv[1]; + auto output = argv[2]; + + auto fst = fst::ReadFstKaldi(input); + auto num_states = fst->NumStates(); + KALDI_LOG << "Loading FST from " << input << " with " << num_states + << " states." << std::endl; + + int32 num_arc_added = AddSelfLoopsSimple(fst); + KALDI_LOG << "Adding " << num_arc_added << " self-loop arcs " << std::endl; + + fst::WriteFstKaldi(*fst, std::string(output)); + KALDI_LOG << "Writing FST to " << output << std::endl; + + delete fst; +} diff --git a/examples/speech_recognition/kaldi/config/kaldi_initializer.yaml b/examples/speech_recognition/kaldi/config/kaldi_initializer.yaml new file mode 100644 index 0000000000..be9ba98f55 --- /dev/null +++ b/examples/speech_recognition/kaldi/config/kaldi_initializer.yaml @@ -0,0 +1,8 @@ +# @package _group_ + +data_dir: ??? +fst_dir: ??? +in_labels: ??? +kaldi_root: ??? +lm_arpa: ??? +blank_symbol: <s> diff --git a/examples/speech_recognition/kaldi/kaldi_decoder.py b/examples/speech_recognition/kaldi/kaldi_decoder.py new file mode 100644 index 0000000000..5f62cc58ae --- /dev/null +++ b/examples/speech_recognition/kaldi/kaldi_decoder.py @@ -0,0 +1,244 @@ +#!/usr/bin/env python3 + +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from concurrent.futures import ThreadPoolExecutor +import logging +from omegaconf import MISSING +import os +import torch +from typing import Optional +import warnings + + +from dataclasses import dataclass +from fairseq.dataclass import FairseqDataclass +from .kaldi_initializer import KaldiInitializerConfig, initalize_kaldi + + +logger = logging.getLogger(__name__) + + +@dataclass +class KaldiDecoderConfig(FairseqDataclass): + hlg_graph_path: Optional[str] = None + output_dict: str = MISSING + + kaldi_initializer_config: Optional[KaldiInitializerConfig] = None + + acoustic_scale: float = 0.5 + max_active: int = 10000 + beam_delta: float = 0.5 + hash_ratio: float = 2.0 + + is_lattice: bool = False + lattice_beam: float = 10.0 + prune_interval: int = 25 + determinize_lattice: bool = True + prune_scale: float = 0.1 + max_mem: int = 0 + phone_determinize: bool = True + word_determinize: bool = True + minimize: bool = True + + num_threads: int = 1 + + +class KaldiDecoder(object): + def __init__( + self, + cfg: KaldiDecoderConfig, + beam: int, + nbest: int = 1, + ): + try: + from kaldi.asr import FasterRecognizer, LatticeFasterRecognizer + from kaldi.base import set_verbose_level + from kaldi.decoder import ( + FasterDecoder, + FasterDecoderOptions, + LatticeFasterDecoder, + LatticeFasterDecoderOptions, + ) + from kaldi.lat.functions import DeterminizeLatticePhonePrunedOptions + from kaldi.fstext import read_fst_kaldi, SymbolTable + except: + warnings.warn( + "pykaldi is required for this functionality. Please install from https://github.com/pykaldi/pykaldi" + ) + + # set_verbose_level(2) + + self.acoustic_scale = cfg.acoustic_scale + self.nbest = nbest + + if cfg.hlg_graph_path is None: + assert ( + cfg.kaldi_initializer_config is not None + ), "Must provide hlg graph path or kaldi initializer config" + cfg.hlg_graph_path = initalize_kaldi(cfg.kaldi_initializer_config) + + assert os.path.exists(cfg.hlg_graph_path), cfg.hlg_graph_path + + if cfg.is_lattice: + self.dec_cls = LatticeFasterDecoder + opt_cls = LatticeFasterDecoderOptions + self.rec_cls = LatticeFasterRecognizer + else: + assert self.nbest == 1, "nbest > 1 requires lattice decoder" + self.dec_cls = FasterDecoder + opt_cls = FasterDecoderOptions + self.rec_cls = FasterRecognizer + + self.decoder_options = opt_cls() + self.decoder_options.beam = beam + self.decoder_options.max_active = cfg.max_active + self.decoder_options.beam_delta = cfg.beam_delta + self.decoder_options.hash_ratio = cfg.hash_ratio + + if cfg.is_lattice: + self.decoder_options.lattice_beam = cfg.lattice_beam + self.decoder_options.prune_interval = cfg.prune_interval + self.decoder_options.determinize_lattice = cfg.determinize_lattice + self.decoder_options.prune_scale = cfg.prune_scale + det_opts = DeterminizeLatticePhonePrunedOptions() + det_opts.max_mem = cfg.max_mem + det_opts.phone_determinize = cfg.phone_determinize + det_opts.word_determinize = cfg.word_determinize + det_opts.minimize = cfg.minimize + self.decoder_options.det_opts = det_opts + + self.output_symbols = {} + with open(cfg.output_dict, "r") as f: + for line in f: + items = line.rstrip().split() + assert len(items) == 2 + self.output_symbols[int(items[1])] = items[0] + + logger.info(f"Loading FST from {cfg.hlg_graph_path}") + self.fst = read_fst_kaldi(cfg.hlg_graph_path) + self.symbol_table = SymbolTable.read_text(cfg.output_dict) + + self.executor = ThreadPoolExecutor(max_workers=cfg.num_threads) + + def generate(self, models, sample, **unused): + """Generate a batch of inferences.""" + # model.forward normally channels prev_output_tokens into the decoder + # separately, but SequenceGenerator directly calls model.encoder + encoder_input = { + k: v for k, v in sample["net_input"].items() if k != "prev_output_tokens" + } + emissions, padding = self.get_emissions(models, encoder_input) + return self.decode(emissions, padding) + + def get_emissions(self, models, encoder_input): + """Run encoder and normalize emissions""" + model = models[0] + + all_encoder_out = [m(**encoder_input) for m in models] + + if len(all_encoder_out) > 1: + + if "encoder_out" in all_encoder_out[0]: + encoder_out = { + "encoder_out": sum(e["encoder_out"] for e in all_encoder_out) + / len(all_encoder_out), + "encoder_padding_mask": all_encoder_out[0]["encoder_padding_mask"], + } + padding = encoder_out["encoder_padding_mask"] + else: + encoder_out = { + "logits": sum(e["logits"] for e in all_encoder_out) + / len(all_encoder_out), + "padding_mask": all_encoder_out[0]["padding_mask"], + } + padding = encoder_out["padding_mask"] + else: + encoder_out = all_encoder_out[0] + padding = ( + encoder_out["padding_mask"] + if "padding_mask" in encoder_out + else encoder_out["encoder_padding_mask"] + ) + + if hasattr(model, "get_logits"): + emissions = model.get_logits(encoder_out, normalize=True) + else: + emissions = model.get_normalized_probs(encoder_out, log_probs=True) + + return ( + emissions.cpu().float().transpose(0, 1), + padding.cpu() if padding is not None and padding.any() else None, + ) + + def decode_one(self, logits, padding): + from kaldi.matrix import Matrix + + decoder = self.dec_cls(self.fst, self.decoder_options) + asr = self.rec_cls( + decoder, self.symbol_table, acoustic_scale=self.acoustic_scale + ) + + if padding is not None: + logits = logits[~padding] + + mat = Matrix(logits.numpy()) + + out = asr.decode(mat) + + if self.nbest > 1: + from kaldi.fstext import shortestpath + from kaldi.fstext.utils import ( + convert_compact_lattice_to_lattice, + convert_lattice_to_std, + convert_nbest_to_list, + get_linear_symbol_sequence, + ) + + lat = out["lattice"] + + sp = shortestpath(lat, nshortest=self.nbest) + + sp = convert_compact_lattice_to_lattice(sp) + sp = convert_lattice_to_std(sp) + seq = convert_nbest_to_list(sp) + + results = [] + for s in seq: + _, o, w = get_linear_symbol_sequence(s) + words = list(self.output_symbols[z] for z in o) + results.append( + { + "tokens": words, + "words": words, + "score": w.value, + "emissions": logits, + } + ) + return results + else: + words = out["text"].split() + return [ + { + "tokens": words, + "words": words, + "score": out["likelihood"], + "emissions": logits, + } + ] + + def decode(self, emissions, padding): + if padding is None: + padding = [None] * len(emissions) + + ret = list( + map( + lambda e, p: self.executor.submit(self.decode_one, e, p), + emissions, + padding, + ) + ) + return ret diff --git a/examples/speech_recognition/kaldi/kaldi_initializer.py b/examples/speech_recognition/kaldi/kaldi_initializer.py new file mode 100644 index 0000000000..6d2a2a4b6b --- /dev/null +++ b/examples/speech_recognition/kaldi/kaldi_initializer.py @@ -0,0 +1,698 @@ +#!/usr/bin/env python3 + +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from dataclasses import dataclass +import hydra +from hydra.core.config_store import ConfigStore +import logging +from omegaconf import MISSING, OmegaConf +import os +import os.path as osp +from pathlib import Path +import subprocess +from typing import Optional + +from fairseq.data.dictionary import Dictionary +from fairseq.dataclass import FairseqDataclass + +script_dir = Path(__file__).resolve().parent +config_path = script_dir / "config" + + +logger = logging.getLogger(__name__) + + +@dataclass +class KaldiInitializerConfig(FairseqDataclass): + data_dir: str = MISSING + fst_dir: Optional[str] = None + in_labels: str = MISSING + out_labels: Optional[str] = None + wav2letter_lexicon: Optional[str] = None + lm_arpa: str = MISSING + kaldi_root: str = MISSING + blank_symbol: str = "<s>" + silence_symbol: Optional[str] = None + + +def create_units(fst_dir: Path, in_labels: str, vocab: Dictionary) -> Path: + in_units_file = fst_dir / f"kaldi_dict.{in_labels}.txt" + if not in_units_file.exists(): + + logger.info(f"Creating {in_units_file}") + + with open(in_units_file, "w") as f: + print("<eps> 0", file=f) + i = 1 + for symb in vocab.symbols[vocab.nspecial :]: + if not symb.startswith("madeupword"): + print(f"{symb} {i}", file=f) + i += 1 + return in_units_file + + +def create_lexicon( + cfg: KaldiInitializerConfig, + fst_dir: Path, + unique_label: str, + in_units_file: Path, + out_words_file: Path, +) -> (Path, Path): + + disambig_in_units_file = fst_dir / f"kaldi_dict.{cfg.in_labels}_disambig.txt" + lexicon_file = fst_dir / f"kaldi_lexicon.{unique_label}.txt" + disambig_lexicon_file = fst_dir / f"kaldi_lexicon.{unique_label}_disambig.txt" + if ( + not lexicon_file.exists() + or not disambig_lexicon_file.exists() + or not disambig_in_units_file.exists() + ): + logger.info(f"Creating {lexicon_file} (in units file: {in_units_file})") + + assert cfg.wav2letter_lexicon is not None or cfg.in_labels == cfg.out_labels + + if cfg.wav2letter_lexicon is not None: + lm_words = set() + with open(out_words_file, "r") as lm_dict_f: + for line in lm_dict_f: + lm_words.add(line.split()[0]) + + num_skipped = 0 + total = 0 + with open(cfg.wav2letter_lexicon, "r") as w2l_lex_f, open( + lexicon_file, "w" + ) as out_f: + for line in w2l_lex_f: + items = line.rstrip().split("\t") + assert len(items) == 2, items + if items[0] in lm_words: + print(items[0], items[1], file=out_f) + else: + num_skipped += 1 + logger.debug( + f"Skipping word {items[0]} as it was not found in LM" + ) + total += 1 + if num_skipped > 0: + logger.warning( + f"Skipped {num_skipped} out of {total} words as they were not found in LM" + ) + else: + with open(in_units_file, "r") as in_f, open(lexicon_file, "w") as out_f: + for line in in_f: + symb = line.split()[0] + if symb != "<eps>" and symb != "<ctc_blank>" and symb != "<SIL>": + print(symb, symb, file=out_f) + + lex_disambig_path = ( + Path(cfg.kaldi_root) / "egs/wsj/s5/utils/add_lex_disambig.pl" + ) + res = subprocess.run( + [lex_disambig_path, lexicon_file, disambig_lexicon_file], + check=True, + capture_output=True, + ) + ndisambig = int(res.stdout) + disamib_path = Path(cfg.kaldi_root) / "egs/wsj/s5/utils/add_disambig.pl" + res = subprocess.run( + [disamib_path, "--include-zero", in_units_file, str(ndisambig)], + check=True, + capture_output=True, + ) + with open(disambig_in_units_file, "wb") as f: + f.write(res.stdout) + + return disambig_lexicon_file, disambig_in_units_file + + +def create_G( + kaldi_root: Path, fst_dir: Path, lm_arpa: Path, arpa_base: str +) -> (Path, Path): + + out_words_file = fst_dir / f"kaldi_dict.{arpa_base}.txt" + grammar_graph = fst_dir / f"G_{arpa_base}.fst" + if not grammar_graph.exists() or not out_words_file.exists(): + logger.info(f"Creating {grammar_graph}") + arpa2fst = kaldi_root / "src/lmbin/arpa2fst" + subprocess.run( + [ + arpa2fst, + "--disambig-symbol=#0", + f"--write-symbol-table={out_words_file}", + lm_arpa, + grammar_graph, + ], + check=True, + ) + return grammar_graph, out_words_file + + +def create_L( + kaldi_root: Path, + fst_dir: Path, + unique_label: str, + lexicon_file: Path, + in_units_file: Path, + out_words_file: Path, +) -> Path: + lexicon_graph = fst_dir / f"L.{unique_label}.fst" + + if not lexicon_graph.exists(): + logger.info(f"Creating {lexicon_graph} (in units: {in_units_file})") + make_lex = kaldi_root / "egs/wsj/s5/utils/make_lexicon_fst.pl" + fstcompile = kaldi_root / "tools/openfst-1.6.7/bin/fstcompile" + fstaddselfloops = kaldi_root / "src/fstbin/fstaddselfloops" + fstarcsort = kaldi_root / "tools/openfst-1.6.7/bin/fstarcsort" + + def write_disambig_symbol(file): + with open(file, "r") as f: + for line in f: + items = line.rstrip().split() + if items[0] == "#0": + out_path = str(file) + "_disamig" + with open(out_path, "w") as out_f: + print(items[1], file=out_f) + return out_path + + return None + + in_disambig_sym = write_disambig_symbol(in_units_file) + assert in_disambig_sym is not None + out_disambig_sym = write_disambig_symbol(out_words_file) + assert out_disambig_sym is not None + + try: + with open(lexicon_graph, "wb") as out_f: + res = subprocess.run( + [make_lex, lexicon_file], capture_output=True, check=True + ) + assert len(res.stderr) == 0, res.stderr.decode("utf-8") + res = subprocess.run( + [ + fstcompile, + f"--isymbols={in_units_file}", + f"--osymbols={out_words_file}", + "--keep_isymbols=false", + "--keep_osymbols=false", + ], + input=res.stdout, + capture_output=True, + ) + assert len(res.stderr) == 0, res.stderr.decode("utf-8") + res = subprocess.run( + [fstaddselfloops, in_disambig_sym, out_disambig_sym], + input=res.stdout, + capture_output=True, + check=True, + ) + res = subprocess.run( + [fstarcsort, "--sort_type=olabel"], + input=res.stdout, + capture_output=True, + check=True, + ) + out_f.write(res.stdout) + except subprocess.CalledProcessError as e: + logger.error(f"cmd: {e.cmd}, err: {e.stderr.decode('utf-8')}") + os.remove(lexicon_graph) + raise + except AssertionError: + os.remove(lexicon_graph) + raise + + return lexicon_graph + + +def create_LG( + kaldi_root: Path, + fst_dir: Path, + unique_label: str, + lexicon_graph: Path, + grammar_graph: Path, +) -> Path: + lg_graph = fst_dir / f"LG.{unique_label}.fst" + + if not lg_graph.exists(): + logger.info(f"Creating {lg_graph}") + + fsttablecompose = kaldi_root / "src/fstbin/fsttablecompose" + fstdeterminizestar = kaldi_root / "src/fstbin/fstdeterminizestar" + fstminimizeencoded = kaldi_root / "src/fstbin/fstminimizeencoded" + fstpushspecial = kaldi_root / "src/fstbin/fstpushspecial" + fstarcsort = kaldi_root / "tools/openfst-1.6.7/bin/fstarcsort" + + try: + with open(lg_graph, "wb") as out_f: + res = subprocess.run( + [fsttablecompose, lexicon_graph, grammar_graph], + capture_output=True, + check=True, + ) + res = subprocess.run( + [ + fstdeterminizestar, + "--use-log=true", + ], + input=res.stdout, + capture_output=True, + ) + res = subprocess.run( + [fstminimizeencoded], + input=res.stdout, + capture_output=True, + check=True, + ) + res = subprocess.run( + [fstpushspecial], + input=res.stdout, + capture_output=True, + check=True, + ) + res = subprocess.run( + [fstarcsort, "--sort_type=ilabel"], + input=res.stdout, + capture_output=True, + check=True, + ) + out_f.write(res.stdout) + except subprocess.CalledProcessError as e: + logger.error(f"cmd: {e.cmd}, err: {e.stderr.decode('utf-8')}") + os.remove(lg_graph) + raise + + return lg_graph + + +def create_H( + kaldi_root: Path, + fst_dir: Path, + disambig_out_units_file: Path, + in_labels: str, + vocab: Dictionary, + blk_sym: str, + silence_symbol: Optional[str], +) -> (Path, Path, Path): + h_graph = ( + fst_dir / f"H.{in_labels}{'_' + silence_symbol if silence_symbol else ''}.fst" + ) + h_out_units_file = fst_dir / f"kaldi_dict.h_out.{in_labels}.txt" + disambig_in_units_file_int = Path(str(h_graph) + "isym_disambig.int") + disambig_out_units_file_int = Path(str(disambig_out_units_file) + ".int") + if ( + not h_graph.exists() + or not h_out_units_file.exists() + or not disambig_in_units_file_int.exists() + ): + logger.info(f"Creating {h_graph}") + eps_sym = "<eps>" + + num_disambig = 0 + osymbols = [] + + with open(disambig_out_units_file, "r") as f, open( + disambig_out_units_file_int, "w" + ) as out_f: + for line in f: + symb, id = line.rstrip().split() + if line.startswith("#"): + num_disambig += 1 + print(id, file=out_f) + else: + if len(osymbols) == 0: + assert symb == eps_sym, symb + osymbols.append((symb, id)) + + i_idx = 0 + isymbols = [(eps_sym, 0)] + + imap = {} + + for i, s in enumerate(vocab.symbols): + i_idx += 1 + isymbols.append((s, i_idx)) + imap[s] = i_idx + + fst_str = [] + + node_idx = 0 + root_node = node_idx + + special_symbols = [blk_sym] + if silence_symbol is not None: + special_symbols.append(silence_symbol) + + for ss in special_symbols: + fst_str.append("{} {} {} {}".format(root_node, root_node, ss, eps_sym)) + + for symbol, _ in osymbols: + if symbol == eps_sym or symbol.startswith("#"): + continue + + node_idx += 1 + # 1. from root to emitting state + fst_str.append("{} {} {} {}".format(root_node, node_idx, symbol, symbol)) + # 2. from emitting state back to root + fst_str.append("{} {} {} {}".format(node_idx, root_node, eps_sym, eps_sym)) + # 3. from emitting state to optional blank state + pre_node = node_idx + node_idx += 1 + for ss in special_symbols: + fst_str.append("{} {} {} {}".format(pre_node, node_idx, ss, eps_sym)) + # 4. from blank state back to root + fst_str.append("{} {} {} {}".format(node_idx, root_node, eps_sym, eps_sym)) + + fst_str.append("{}".format(root_node)) + + fst_str = "\n".join(fst_str) + h_str = str(h_graph) + isym_file = h_str + ".isym" + + with open(isym_file, "w") as f: + for sym, id in isymbols: + f.write("{} {}\n".format(sym, id)) + + with open(h_out_units_file, "w") as f: + for sym, id in osymbols: + f.write("{} {}\n".format(sym, id)) + + with open(disambig_in_units_file_int, "w") as f: + disam_sym_id = len(isymbols) + for _ in range(num_disambig): + f.write("{}\n".format(disam_sym_id)) + disam_sym_id += 1 + + fstcompile = kaldi_root / "tools/openfst-1.6.7/bin/fstcompile" + fstaddselfloops = kaldi_root / "src/fstbin/fstaddselfloops" + fstarcsort = kaldi_root / "tools/openfst-1.6.7/bin/fstarcsort" + + try: + with open(h_graph, "wb") as out_f: + res = subprocess.run( + [ + fstcompile, + f"--isymbols={isym_file}", + f"--osymbols={h_out_units_file}", + "--keep_isymbols=false", + "--keep_osymbols=false", + ], + input=str.encode(fst_str), + capture_output=True, + check=True, + ) + res = subprocess.run( + [ + fstaddselfloops, + disambig_in_units_file_int, + disambig_out_units_file_int, + ], + input=res.stdout, + capture_output=True, + check=True, + ) + res = subprocess.run( + [fstarcsort, "--sort_type=olabel"], + input=res.stdout, + capture_output=True, + check=True, + ) + out_f.write(res.stdout) + except subprocess.CalledProcessError as e: + logger.error(f"cmd: {e.cmd}, err: {e.stderr.decode('utf-8')}") + os.remove(h_graph) + raise + return h_graph, h_out_units_file, disambig_in_units_file_int + + +def create_HLGa( + kaldi_root: Path, + fst_dir: Path, + unique_label: str, + h_graph: Path, + lg_graph: Path, + disambig_in_words_file_int: Path, +) -> Path: + hlga_graph = fst_dir / f"HLGa.{unique_label}.fst" + + if not hlga_graph.exists(): + logger.info(f"Creating {hlga_graph}") + + fsttablecompose = kaldi_root / "src/fstbin/fsttablecompose" + fstdeterminizestar = kaldi_root / "src/fstbin/fstdeterminizestar" + fstrmsymbols = kaldi_root / "src/fstbin/fstrmsymbols" + fstrmepslocal = kaldi_root / "src/fstbin/fstrmepslocal" + fstminimizeencoded = kaldi_root / "src/fstbin/fstminimizeencoded" + + try: + with open(hlga_graph, "wb") as out_f: + res = subprocess.run( + [ + fsttablecompose, + h_graph, + lg_graph, + ], + capture_output=True, + check=True, + ) + res = subprocess.run( + [fstdeterminizestar, "--use-log=true"], + input=res.stdout, + capture_output=True, + check=True, + ) + res = subprocess.run( + [fstrmsymbols, disambig_in_words_file_int], + input=res.stdout, + capture_output=True, + check=True, + ) + res = subprocess.run( + [fstrmepslocal], + input=res.stdout, + capture_output=True, + check=True, + ) + res = subprocess.run( + [fstminimizeencoded], + input=res.stdout, + capture_output=True, + check=True, + ) + out_f.write(res.stdout) + except subprocess.CalledProcessError as e: + logger.error(f"cmd: {e.cmd}, err: {e.stderr.decode('utf-8')}") + os.remove(hlga_graph) + raise + + return hlga_graph + + +def create_HLa( + kaldi_root: Path, + fst_dir: Path, + unique_label: str, + h_graph: Path, + l_graph: Path, + disambig_in_words_file_int: Path, +) -> Path: + hla_graph = fst_dir / f"HLa.{unique_label}.fst" + + if not hla_graph.exists(): + logger.info(f"Creating {hla_graph}") + + fsttablecompose = kaldi_root / "src/fstbin/fsttablecompose" + fstdeterminizestar = kaldi_root / "src/fstbin/fstdeterminizestar" + fstrmsymbols = kaldi_root / "src/fstbin/fstrmsymbols" + fstrmepslocal = kaldi_root / "src/fstbin/fstrmepslocal" + fstminimizeencoded = kaldi_root / "src/fstbin/fstminimizeencoded" + + try: + with open(hla_graph, "wb") as out_f: + res = subprocess.run( + [ + fsttablecompose, + h_graph, + l_graph, + ], + capture_output=True, + check=True, + ) + res = subprocess.run( + [fstdeterminizestar, "--use-log=true"], + input=res.stdout, + capture_output=True, + check=True, + ) + res = subprocess.run( + [fstrmsymbols, disambig_in_words_file_int], + input=res.stdout, + capture_output=True, + check=True, + ) + res = subprocess.run( + [fstrmepslocal], + input=res.stdout, + capture_output=True, + check=True, + ) + res = subprocess.run( + [fstminimizeencoded], + input=res.stdout, + capture_output=True, + check=True, + ) + out_f.write(res.stdout) + except subprocess.CalledProcessError as e: + logger.error(f"cmd: {e.cmd}, err: {e.stderr.decode('utf-8')}") + os.remove(hla_graph) + raise + + return hla_graph + + +def create_HLG( + kaldi_root: Path, + fst_dir: Path, + unique_label: str, + hlga_graph: Path, + prefix: str = "HLG", +) -> Path: + hlg_graph = fst_dir / f"{prefix}.{unique_label}.fst" + + if not hlg_graph.exists(): + logger.info(f"Creating {hlg_graph}") + + add_self_loop = script_dir / "add-self-loop-simple" + kaldi_src = kaldi_root / "src" + kaldi_lib = kaldi_src / "lib" + + try: + if not add_self_loop.exists(): + fst_include = kaldi_root / "tools/openfst-1.6.7/include" + add_self_loop_src = script_dir / "add-self-loop-simple.cc" + + subprocess.run( + [ + "c++", + f"-I{kaldi_src}", + f"-I{fst_include}", + f"-L{kaldi_lib}", + add_self_loop_src, + "-lkaldi-base", + "-lkaldi-fstext", + "-o", + add_self_loop, + ], + check=True, + ) + + my_env = os.environ.copy() + my_env["LD_LIBRARY_PATH"] = f"{kaldi_lib}:{my_env['LD_LIBRARY_PATH']}" + + subprocess.run( + [ + add_self_loop, + hlga_graph, + hlg_graph, + ], + check=True, + capture_output=True, + env=my_env, + ) + except subprocess.CalledProcessError as e: + logger.error(f"cmd: {e.cmd}, err: {e.stderr.decode('utf-8')}") + raise + + return hlg_graph + + +def initalize_kaldi(cfg: KaldiInitializerConfig) -> Path: + if cfg.fst_dir is None: + cfg.fst_dir = osp.join(cfg.data_dir, "kaldi") + if cfg.out_labels is None: + cfg.out_labels = cfg.in_labels + + kaldi_root = Path(cfg.kaldi_root) + data_dir = Path(cfg.data_dir) + fst_dir = Path(cfg.fst_dir) + fst_dir.mkdir(parents=True, exist_ok=True) + + arpa_base = osp.splitext(osp.basename(cfg.lm_arpa))[0] + unique_label = f"{cfg.in_labels}.{arpa_base}" + + with open(data_dir / f"dict.{cfg.in_labels}.txt", "r") as f: + vocab = Dictionary.load(f) + + in_units_file = create_units(fst_dir, cfg.in_labels, vocab) + + grammar_graph, out_words_file = create_G( + kaldi_root, fst_dir, Path(cfg.lm_arpa), arpa_base + ) + + disambig_lexicon_file, disambig_L_in_units_file = create_lexicon( + cfg, fst_dir, unique_label, in_units_file, out_words_file + ) + + h_graph, h_out_units_file, disambig_in_units_file_int = create_H( + kaldi_root, + fst_dir, + disambig_L_in_units_file, + cfg.in_labels, + vocab, + cfg.blank_symbol, + cfg.silence_symbol, + ) + lexicon_graph = create_L( + kaldi_root, + fst_dir, + unique_label, + disambig_lexicon_file, + disambig_L_in_units_file, + out_words_file, + ) + lg_graph = create_LG( + kaldi_root, fst_dir, unique_label, lexicon_graph, grammar_graph + ) + hlga_graph = create_HLGa( + kaldi_root, fst_dir, unique_label, h_graph, lg_graph, disambig_in_units_file_int + ) + hlg_graph = create_HLG(kaldi_root, fst_dir, unique_label, hlga_graph) + + # for debugging + # hla_graph = create_HLa(kaldi_root, fst_dir, unique_label, h_graph, lexicon_graph, disambig_in_units_file_int) + # hl_graph = create_HLG(kaldi_root, fst_dir, unique_label, hla_graph, prefix="HL_looped") + # create_HLG(kaldi_root, fst_dir, "phnc", h_graph, prefix="H_looped") + + return hlg_graph + + +@hydra.main(config_path=config_path, config_name="kaldi_initializer") +def cli_main(cfg: KaldiInitializerConfig) -> None: + container = OmegaConf.to_container(cfg, resolve=True, enum_to_str=True) + cfg = OmegaConf.create(container) + OmegaConf.set_struct(cfg, True) + initalize_kaldi(cfg) + + +if __name__ == "__main__": + + logging.root.setLevel(logging.INFO) + logging.basicConfig(level=logging.INFO) + + try: + from hydra._internal.utils import ( + get_args, + ) # pylint: disable=import-outside-toplevel + + cfg_name = get_args().config_name or "kaldi_initializer" + except ImportError: + logger.warning("Failed to get config name from hydra args") + cfg_name = "kaldi_initializer" + + cs = ConfigStore.instance() + cs.store(name=cfg_name, node=KaldiInitializerConfig) + + cli_main() diff --git a/examples/speech_recognition/models/__init__.py b/examples/speech_recognition/models/__init__.py index 66ad2b0a1f..54b5a1c312 100644 --- a/examples/speech_recognition/models/__init__.py +++ b/examples/speech_recognition/models/__init__.py @@ -1,7 +1,8 @@ import importlib import os -for file in os.listdir(os.path.dirname(__file__)): - if file.endswith('.py') and not file.startswith('_'): - model_name = file[:file.find('.py')] - importlib.import_module('examples.speech_recognition.models.' + model_name) + +for file in sorted(os.listdir(os.path.dirname(__file__))): + if file.endswith(".py") and not file.startswith("_"): + model_name = file[: file.find(".py")] + importlib.import_module("examples.speech_recognition.models." + model_name) diff --git a/examples/speech_recognition/models/vggtransformer.py b/examples/speech_recognition/models/vggtransformer.py index 9191a3ffe2..bca0ae59a8 100644 --- a/examples/speech_recognition/models/vggtransformer.py +++ b/examples/speech_recognition/models/vggtransformer.py @@ -9,18 +9,22 @@ import torch import torch.nn as nn +from examples.speech_recognition.data.data_utils import lengths_to_encoder_padding_mask from fairseq import utils from fairseq.models import ( FairseqEncoder, + FairseqEncoderDecoderModel, FairseqEncoderModel, FairseqIncrementalDecoder, - FairseqEncoderDecoderModel, register_model, register_model_architecture, ) -from fairseq.modules import LinearizedConvolution -from examples.speech_recognition.data.data_utils import lengths_to_encoder_padding_mask -from fairseq.modules import TransformerDecoderLayer, TransformerEncoderLayer, VGGBlock +from fairseq.modules import ( + LinearizedConvolution, + TransformerDecoderLayer, + TransformerEncoderLayer, + VGGBlock, +) @register_model("asr_vggtransformer") @@ -29,6 +33,7 @@ class VGGTransformerModel(FairseqEncoderDecoderModel): Transformers with convolutional context for ASR https://arxiv.org/abs/1904.11660 """ + def __init__(self, encoder, decoder): super().__init__(encoder, decoder) @@ -198,6 +203,7 @@ def prepare_transformer_decoder_params( relu_dropout, ): args = argparse.Namespace() + args.encoder_embed_dim = None args.decoder_embed_dim = input_dim args.decoder_attention_heads = num_heads args.attention_dropout = attention_dropout @@ -251,6 +257,7 @@ def __init__( self.conv_layers = nn.ModuleList() self.in_channels = in_channels self.input_dim = input_feat_per_channel + self.pooling_kernel_sizes = [] if vggblock_config is not None: for _, config in enumerate(vggblock_config): @@ -272,6 +279,7 @@ def __init__( layer_norm=layer_norm, ) ) + self.pooling_kernel_sizes.append(pooling_kernel_size) in_channels = out_channels input_feat_per_channel = self.conv_layers[-1].output_dim @@ -336,9 +344,9 @@ def forward(self, src_tokens, src_lengths, **kwargs): x = x.transpose(1, 2).transpose(0, 1) x = x.contiguous().view(output_seq_len, bsz, -1) - subsampling_factor = int(max_seq_len * 1.0 / output_seq_len + 0.5) - # TODO: shouldn't subsampling_factor determined in advance ? - input_lengths = (src_lengths.float() / subsampling_factor).ceil().long() + input_lengths = src_lengths.clone() + for s in self.pooling_kernel_sizes: + input_lengths = (input_lengths.float() / s).ceil().long() encoder_padding_mask, _ = lengths_to_encoder_padding_mask( input_lengths, batch_first=True @@ -346,6 +354,7 @@ def forward(self, src_tokens, src_lengths, **kwargs): if not encoder_padding_mask.any(): encoder_padding_mask = None + subsampling_factor = int(max_seq_len * 1.0 / output_seq_len + 0.5) attn_mask = self.lengths_to_attn_mask(input_lengths, subsampling_factor) transformer_layer_idx = 0 @@ -394,9 +403,9 @@ def validate_transformer_config(self, transformer_config): input_dim, num_heads = config[:2] if input_dim % num_heads != 0: msg = ( - "ERROR in transformer config {}:".format(config) + "ERROR in transformer config {}: ".format(config) + "input dimension {} ".format(input_dim) - + "not dividable by number of heads".format(num_heads) + + "not dividable by number of heads {}".format(num_heads) ) raise ValueError(msg) @@ -459,7 +468,7 @@ def parse_transformer_sampling(self, transformer_sampling, num_layers): if len(transformer_sampling) != num_layers: raise ValueError( "transformer_sampling {} does not match with the number " - + "of layers {}".format(transformer_sampling, num_layers) + "of layers {}".format(transformer_sampling, num_layers) ) for layer, value in enumerate(transformer_sampling): @@ -599,18 +608,22 @@ def __init__( self.layers = nn.ModuleList() if conv_config[-1][0] != transformer_config[0][0]: self.layers.append(Linear(conv_config[-1][0], transformer_config[0][0])) - self.layers.append(TransformerDecoderLayer( - prepare_transformer_decoder_params(*transformer_config[0]) - )) + self.layers.append( + TransformerDecoderLayer( + prepare_transformer_decoder_params(*transformer_config[0]) + ) + ) for i in range(1, len(transformer_config)): if transformer_config[i - 1][0] != transformer_config[i][0]: self.layers.append( Linear(transformer_config[i - 1][0], transformer_config[i][0]) ) - self.layers.append(TransformerDecoderLayer( - prepare_transformer_decoder_params(*transformer_config[i]) - )) + self.layers.append( + TransformerDecoderLayer( + prepare_transformer_decoder_params(*transformer_config[i]) + ) + ) self.fc_out = Linear(transformer_config[-1][0], vocab_size) def forward(self, prev_output_tokens, encoder_out=None, incremental_state=None): @@ -710,6 +723,7 @@ def _transpose_if_inference(self, x, incremental_state): x = x.transpose(0, 1) return x + @register_model("asr_vggtransformer_encoder") class VGGTransformerEncoderModel(FairseqEncoderModel): def __init__(self, encoder): diff --git a/examples/speech_recognition/models/w2l_conv_glu_enc.py b/examples/speech_recognition/models/w2l_conv_glu_enc.py index 31cf3401b3..655a9b0d19 100644 --- a/examples/speech_recognition/models/w2l_conv_glu_enc.py +++ b/examples/speech_recognition/models/w2l_conv_glu_enc.py @@ -16,9 +16,10 @@ register_model, register_model_architecture, ) +from fairseq.modules.fairseq_dropout import FairseqDropout -default_conv_enc_config = """[ +default_conv_enc_config = """[ (400, 13, 170, 0.2), (440, 14, 0, 0.214), (484, 15, 0, 0.22898), @@ -106,7 +107,9 @@ def __init__( layer = nn.Conv1d(cur_channels, out_channels, kernel_size, padding=padding) layer.weight.data.mul_(math.sqrt(3)) # match wav2letter init self.conv_layers.append(nn.utils.weight_norm(layer)) - self.dropouts.append(dropout) + self.dropouts.append( + FairseqDropout(dropout, module_name=self.__class__.__name__) + ) if out_channels % 2 != 0: raise ValueError("odd # of out_channels is incompatible with GLU") cur_channels = out_channels // 2 # halved by GLU @@ -129,12 +132,12 @@ def forward(self, src_tokens, src_lengths, **kwargs): for layer_idx in range(len(self.conv_layers)): x = self.conv_layers[layer_idx](x) x = F.glu(x, dim=1) - x = F.dropout(x, p=self.dropouts[layer_idx], training=self.training) + x = self.dropouts[layer_idx](x) x = x.transpose(1, 2).contiguous() # (B, T, 908) x = self.linear_layers[0](x) x = F.glu(x, dim=2) - x = F.dropout(x, p=self.dropouts[-1]) + x = self.dropouts[-1](x) x = self.linear_layers[1](x) assert x.size(0) == B diff --git a/examples/speech_recognition/new/README.md b/examples/speech_recognition/new/README.md new file mode 100644 index 0000000000..5fa0e97245 --- /dev/null +++ b/examples/speech_recognition/new/README.md @@ -0,0 +1,43 @@ +# Flashlight Decoder + +This script runs decoding for pre-trained speech recognition models. + +## Usage + +Assuming a few variables: + +```bash +checkpoint=<path-to-checkpoint> +data=<path-to-data-directory> +lm_model=<path-to-language-model> +lexicon=<path-to-lexicon> +``` + +Example usage for decoding a fine-tuned Wav2Vec model: + +```bash +python $FAIRSEQ_ROOT/examples/speech_recognition/new/infer.py --multirun \ + task=audio_pretraining \ + task.data=$data \ + task.labels=ltr \ + common_eval.path=$checkpoint \ + decoding.type=kenlm \ + decoding.lexicon=$lexicon \ + decoding.lmpath=$lm_model \ + dataset.gen_subset=dev_clean,dev_other,test_clean,test_other +``` + +Example usage for using Ax to sweep WER parameters (requires `pip install hydra-ax-sweeper`): + +```bash +python $FAIRSEQ_ROOT/examples/speech_recognition/new/infer.py --multirun \ + hydra/sweeper=ax \ + task=audio_pretraining \ + task.data=$data \ + task.labels=ltr \ + common_eval.path=$checkpoint \ + decoding.type=kenlm \ + decoding.lexicon=$lexicon \ + decoding.lmpath=$lm_model \ + dataset.gen_subset=dev_other +``` diff --git a/examples/speech_recognition/new/__init__.py b/examples/speech_recognition/new/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/examples/speech_recognition/new/conf/hydra/sweeper/ax.yaml b/examples/speech_recognition/new/conf/hydra/sweeper/ax.yaml new file mode 100644 index 0000000000..fbeff17ca6 --- /dev/null +++ b/examples/speech_recognition/new/conf/hydra/sweeper/ax.yaml @@ -0,0 +1,26 @@ +# @package hydra.sweeper +_target_: hydra_plugins.hydra_ax_sweeper.ax_sweeper.AxSweeper +max_batch_size: null +ax_config: + max_trials: 128 + early_stop: + minimize: true + max_epochs_without_improvement: 32 + epsilon: 1.0e-05 + experiment: + name: ${dataset.gen_subset} + objective_name: wer + minimize: true + parameter_constraints: null + outcome_constraints: null + status_quo: null + client: + verbose_logging: false + random_seed: null + params: + decoding.lmweight: + type: range + bounds: [0.0, 5.0] + decoding.wordscore: + type: range + bounds: [-5.0, 5.0] diff --git a/examples/speech_recognition/new/conf/infer.yaml b/examples/speech_recognition/new/conf/infer.yaml new file mode 100644 index 0000000000..f176228082 --- /dev/null +++ b/examples/speech_recognition/new/conf/infer.yaml @@ -0,0 +1,25 @@ +# @package _group_ + +defaults: + - task: null + - model: null + +hydra: + run: + dir: ${common_eval.results_path}/${dataset.gen_subset} + sweep: + dir: ${common_eval.results_path} + subdir: ${dataset.gen_subset} +common_eval: + results_path: null + path: null + post_process: letter + quiet: true +dataset: + max_tokens: 1000000 + gen_subset: test +distributed_training: + distributed_world_size: 1 +decoding: + beam: 5 + type: viterbi diff --git a/examples/speech_recognition/new/decoders/__init__.py b/examples/speech_recognition/new/decoders/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/examples/speech_recognition/new/decoders/base_decoder.py b/examples/speech_recognition/new/decoders/base_decoder.py new file mode 100644 index 0000000000..a097969b3c --- /dev/null +++ b/examples/speech_recognition/new/decoders/base_decoder.py @@ -0,0 +1,62 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import itertools as it +from typing import Any, Dict, List + +import torch +from fairseq.data.dictionary import Dictionary +from fairseq.models.fairseq_model import FairseqModel + + +class BaseDecoder: + def __init__(self, tgt_dict: Dictionary) -> None: + self.tgt_dict = tgt_dict + self.vocab_size = len(tgt_dict) + + self.blank = ( + tgt_dict.index("<ctc_blank>") + if "<ctc_blank>" in tgt_dict.indices + else tgt_dict.bos() + ) + if "<sep>" in tgt_dict.indices: + self.silence = tgt_dict.index("<sep>") + elif "|" in tgt_dict.indices: + self.silence = tgt_dict.index("|") + else: + self.silence = tgt_dict.eos() + + def generate( + self, models: List[FairseqModel], sample: Dict[str, Any], **unused + ) -> List[List[Dict[str, torch.LongTensor]]]: + encoder_input = { + k: v for k, v in sample["net_input"].items() if k != "prev_output_tokens" + } + emissions = self.get_emissions(models, encoder_input) + return self.decode(emissions) + + def get_emissions( + self, + models: List[FairseqModel], + encoder_input: Dict[str, Any], + ) -> torch.FloatTensor: + model = models[0] + encoder_out = model(**encoder_input) + if hasattr(model, "get_logits"): + emissions = model.get_logits(encoder_out) + else: + emissions = model.get_normalized_probs(encoder_out, log_probs=True) + return emissions.transpose(0, 1).float().cpu().contiguous() + + def get_tokens(self, idxs: torch.IntTensor) -> torch.LongTensor: + idxs = (g[0] for g in it.groupby(idxs)) + idxs = filter(lambda x: x != self.blank, idxs) + return torch.LongTensor(list(idxs)) + + def decode( + self, + emissions: torch.FloatTensor, + ) -> List[List[Dict[str, torch.LongTensor]]]: + raise NotImplementedError diff --git a/examples/speech_recognition/new/decoders/decoder.py b/examples/speech_recognition/new/decoders/decoder.py new file mode 100644 index 0000000000..b5bec8cf70 --- /dev/null +++ b/examples/speech_recognition/new/decoders/decoder.py @@ -0,0 +1,32 @@ +#!/usr/bin/env python3 + +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from typing import Union + +from fairseq.data.dictionary import Dictionary + +from .decoder_config import DecoderConfig, FlashlightDecoderConfig +from .base_decoder import BaseDecoder + + +def Decoder( + cfg: Union[DecoderConfig, FlashlightDecoderConfig], tgt_dict: Dictionary +) -> BaseDecoder: + + if cfg.type == "viterbi": + from .viterbi_decoder import ViterbiDecoder + + return ViterbiDecoder(tgt_dict) + if cfg.type == "kenlm": + from .flashlight_decoder import KenLMDecoder + + return KenLMDecoder(cfg, tgt_dict) + if cfg.type == "fairseqlm": + from .flashlight_decoder import FairseqLMDecoder + + return FairseqLMDecoder(cfg, tgt_dict) + raise NotImplementedError(f"Invalid decoder name: {cfg.name}") diff --git a/examples/speech_recognition/new/decoders/decoder_config.py b/examples/speech_recognition/new/decoders/decoder_config.py new file mode 100644 index 0000000000..659eb94a9b --- /dev/null +++ b/examples/speech_recognition/new/decoders/decoder_config.py @@ -0,0 +1,70 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math +from dataclasses import dataclass, field +from typing import Optional + +from fairseq.dataclass.configs import FairseqDataclass +from fairseq.dataclass.constants import ChoiceEnum +from omegaconf import MISSING + + +DECODER_CHOICES = ChoiceEnum(["viterbi", "kenlm", "fairseqlm"]) + + +@dataclass +class DecoderConfig(FairseqDataclass): + type: DECODER_CHOICES = field( + default="viterbi", + metadata={"help": "The type of decoder to use"}, + ) + + +@dataclass +class FlashlightDecoderConfig(FairseqDataclass): + nbest: int = field( + default=1, + metadata={"help": "Number of decodings to return"}, + ) + unitlm: bool = field( + default=False, + metadata={"help": "If set, use unit language model"}, + ) + lmpath: str = field( + default=MISSING, + metadata={"help": "Language model for KenLM decoder"}, + ) + lexicon: Optional[str] = field( + default=None, + metadata={"help": "Lexicon for Flashlight decoder"}, + ) + beam: int = field( + default=50, + metadata={"help": "Number of beams to use for decoding"}, + ) + beamthreshold: float = field( + default=50.0, + metadata={"help": "Threshold for beam search decoding"}, + ) + beamsizetoken: Optional[int] = field( + default=None, metadata={"help": "Beam size to use"} + ) + wordscore: float = field( + default=-1, + metadata={"help": "Word score for KenLM decoder"}, + ) + unkweight: float = field( + default=-math.inf, + metadata={"help": "Unknown weight for KenLM decoder"}, + ) + silweight: float = field( + default=0, + metadata={"help": "Silence weight for KenLM decoder"}, + ) + lmweight: float = field( + default=2, + metadata={"help": "Weight for LM while interpolating score"}, + ) diff --git a/examples/speech_recognition/new/decoders/flashlight_decoder.py b/examples/speech_recognition/new/decoders/flashlight_decoder.py new file mode 100644 index 0000000000..38c7ac492f --- /dev/null +++ b/examples/speech_recognition/new/decoders/flashlight_decoder.py @@ -0,0 +1,431 @@ +#!/usr/bin/env python3 + +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import gc +import os.path as osp +import warnings +from collections import deque, namedtuple +from typing import Any, Dict, Tuple + +import numpy as np +import torch +from fairseq import tasks +from fairseq.data.dictionary import Dictionary +from fairseq.dataclass.utils import convert_namespace_to_omegaconf +from fairseq.models.fairseq_model import FairseqModel +from fairseq.utils import apply_to_sample +from omegaconf import open_dict, OmegaConf + +from typing import List + +from .decoder_config import FlashlightDecoderConfig +from .base_decoder import BaseDecoder + +try: + from flashlight.lib.text.decoder import ( + LM, + CriterionType, + DecodeResult, + KenLM, + LexiconDecoder, + LexiconDecoderOptions, + LexiconFreeDecoder, + LexiconFreeDecoderOptions, + LMState, + SmearingMode, + Trie, + ) + from flashlight.lib.text.dictionary import create_word_dict, load_words +except ImportError: + warnings.warn( + "flashlight python bindings are required to use this functionality. " + "Please install from " + "https://github.com/facebookresearch/flashlight/tree/master/bindings/python" + ) + LM = object + LMState = object + + +class KenLMDecoder(BaseDecoder): + def __init__(self, cfg: FlashlightDecoderConfig, tgt_dict: Dictionary) -> None: + super().__init__(tgt_dict) + + self.nbest = cfg.nbest + self.unitlm = cfg.unitlm + + if cfg.lexicon: + self.lexicon = load_words(cfg.lexicon) + self.word_dict = create_word_dict(self.lexicon) + self.unk_word = self.word_dict.get_index("<unk>") + + self.lm = KenLM(cfg.lmpath, self.word_dict) + self.trie = Trie(self.vocab_size, self.silence) + + start_state = self.lm.start(False) + for word, spellings in self.lexicon.items(): + word_idx = self.word_dict.get_index(word) + _, score = self.lm.score(start_state, word_idx) + for spelling in spellings: + spelling_idxs = [tgt_dict.index(token) for token in spelling] + assert ( + tgt_dict.unk() not in spelling_idxs + ), f"{word} {spelling} {spelling_idxs}" + self.trie.insert(spelling_idxs, word_idx, score) + self.trie.smear(SmearingMode.MAX) + + self.decoder_opts = LexiconDecoderOptions( + beam_size=cfg.beam, + beam_size_token=cfg.beamsizetoken or len(tgt_dict), + beam_threshold=cfg.beamthreshold, + lm_weight=cfg.lmweight, + word_score=cfg.wordscore, + unk_score=cfg.unkweight, + sil_score=cfg.silweight, + log_add=False, + criterion_type=CriterionType.CTC, + ) + + self.decoder = LexiconDecoder( + self.decoder_opts, + self.trie, + self.lm, + self.silence, + self.blank, + self.unk_word, + [], + self.unitlm, + ) + else: + assert self.unitlm, "Lexicon-free decoding requires unit LM" + + d = {w: [[w]] for w in tgt_dict.symbols} + self.word_dict = create_word_dict(d) + self.lm = KenLM(cfg.lmpath, self.word_dict) + self.decoder_opts = LexiconFreeDecoderOptions( + beam_size=cfg.beam, + beam_size_token=cfg.beamsizetoken or len(tgt_dict), + beam_threshold=cfg.beamthreshold, + lm_weight=cfg.lmweight, + sil_score=cfg.silweight, + log_add=False, + criterion_type=CriterionType.CTC, + ) + self.decoder = LexiconFreeDecoder( + self.decoder_opts, self.lm, self.silence, self.blank, [] + ) + + def get_timesteps(self, token_idxs: List[int]) -> List[int]: + """Returns frame numbers corresponding to every non-blank token. + + Parameters + ---------- + token_idxs : List[int] + IDs of decoded tokens. + + Returns + ------- + List[int] + Frame numbers corresponding to every non-blank token. + """ + timesteps = [] + for i, token_idx in enumerate(token_idxs): + if token_idx == self.blank: + continue + if i == 0 or token_idx != token_idxs[i-1]: + timesteps.append(i) + return timesteps + + def decode( + self, + emissions: torch.FloatTensor, + ) -> List[List[Dict[str, torch.LongTensor]]]: + B, T, N = emissions.size() + hypos = [] + for b in range(B): + emissions_ptr = emissions.data_ptr() + 4 * b * emissions.stride(0) + results = self.decoder.decode(emissions_ptr, T, N) + + nbest_results = results[: self.nbest] + hypos.append( + [ + { + "tokens": self.get_tokens(result.tokens), + "score": result.score, + "timesteps": self.get_timesteps(result.tokens), + "words": [ + self.word_dict.get_entry(x) for x in result.words if x >= 0 + ], + } + for result in nbest_results + ] + ) + return hypos + + +FairseqLMState = namedtuple( + "FairseqLMState", + [ + "prefix", + "incremental_state", + "probs", + ], +) + + +class FairseqLM(LM): + def __init__(self, dictionary: Dictionary, model: FairseqModel) -> None: + super().__init__() + + self.dictionary = dictionary + self.model = model + self.unk = self.dictionary.unk() + + self.save_incremental = False # this currently does not work properly + self.max_cache = 20_000 + + if torch.cuda.is_available(): + model.cuda() + model.eval() + model.make_generation_fast_() + + self.states = {} + self.stateq = deque() + + def start(self, start_with_nothing: bool) -> LMState: + state = LMState() + prefix = torch.LongTensor([[self.dictionary.eos()]]) + incremental_state = {} if self.save_incremental else None + with torch.no_grad(): + res = self.model(prefix.cuda(), incremental_state=incremental_state) + probs = self.model.get_normalized_probs(res, log_probs=True, sample=None) + + if incremental_state is not None: + incremental_state = apply_to_sample(lambda x: x.cpu(), incremental_state) + self.states[state] = FairseqLMState( + prefix.numpy(), incremental_state, probs[0, -1].cpu().numpy() + ) + self.stateq.append(state) + + return state + + def score( + self, + state: LMState, + token_index: int, + no_cache: bool = False, + ) -> Tuple[LMState, int]: + """ + Evaluate language model based on the current lm state and new word + Parameters: + ----------- + state: current lm state + token_index: index of the word + (can be lexicon index then you should store inside LM the + mapping between indices of lexicon and lm, or lm index of a word) + Returns: + -------- + (LMState, float): pair of (new state, score for the current word) + """ + curr_state = self.states[state] + + def trim_cache(targ_size: int) -> None: + while len(self.stateq) > targ_size: + rem_k = self.stateq.popleft() + rem_st = self.states[rem_k] + rem_st = FairseqLMState(rem_st.prefix, None, None) + self.states[rem_k] = rem_st + + if curr_state.probs is None: + new_incremental_state = ( + curr_state.incremental_state.copy() + if curr_state.incremental_state is not None + else None + ) + with torch.no_grad(): + if new_incremental_state is not None: + new_incremental_state = apply_to_sample( + lambda x: x.cuda(), new_incremental_state + ) + elif self.save_incremental: + new_incremental_state = {} + + res = self.model( + torch.from_numpy(curr_state.prefix).cuda(), + incremental_state=new_incremental_state, + ) + probs = self.model.get_normalized_probs( + res, log_probs=True, sample=None + ) + + if new_incremental_state is not None: + new_incremental_state = apply_to_sample( + lambda x: x.cpu(), new_incremental_state + ) + + curr_state = FairseqLMState( + curr_state.prefix, new_incremental_state, probs[0, -1].cpu().numpy() + ) + + if not no_cache: + self.states[state] = curr_state + self.stateq.append(state) + + score = curr_state.probs[token_index].item() + + trim_cache(self.max_cache) + + outstate = state.child(token_index) + if outstate not in self.states and not no_cache: + prefix = np.concatenate( + [curr_state.prefix, torch.LongTensor([[token_index]])], -1 + ) + incr_state = curr_state.incremental_state + + self.states[outstate] = FairseqLMState(prefix, incr_state, None) + + if token_index == self.unk: + score = float("-inf") + + return outstate, score + + def finish(self, state: LMState) -> Tuple[LMState, int]: + """ + Evaluate eos for language model based on the current lm state + Returns: + -------- + (LMState, float): pair of (new state, score for the current word) + """ + return self.score(state, self.dictionary.eos()) + + def empty_cache(self) -> None: + self.states = {} + self.stateq = deque() + gc.collect() + + +class FairseqLMDecoder(BaseDecoder): + def __init__(self, cfg: FlashlightDecoderConfig, tgt_dict: Dictionary) -> None: + super().__init__(tgt_dict) + + self.nbest = cfg.nbest + self.unitlm = cfg.unitlm + + self.lexicon = load_words(cfg.lexicon) if cfg.lexicon else None + self.idx_to_wrd = {} + + checkpoint = torch.load(cfg.lmpath, map_location="cpu") + + if "cfg" in checkpoint and checkpoint["cfg"] is not None: + lm_args = checkpoint["cfg"] + else: + lm_args = convert_namespace_to_omegaconf(checkpoint["args"]) + + if not OmegaConf.is_dict(lm_args): + lm_args = OmegaConf.create(lm_args) + + with open_dict(lm_args.task): + lm_args.task.data = osp.dirname(cfg.lmpath) + + task = tasks.setup_task(lm_args.task) + model = task.build_model(lm_args.model) + model.load_state_dict(checkpoint["model"], strict=False) + + self.trie = Trie(self.vocab_size, self.silence) + + self.word_dict = task.dictionary + self.unk_word = self.word_dict.unk() + self.lm = FairseqLM(self.word_dict, model) + + if self.lexicon: + start_state = self.lm.start(False) + for i, (word, spellings) in enumerate(self.lexicon.items()): + if self.unitlm: + word_idx = i + self.idx_to_wrd[i] = word + score = 0 + else: + word_idx = self.word_dict.index(word) + _, score = self.lm.score(start_state, word_idx, no_cache=True) + + for spelling in spellings: + spelling_idxs = [tgt_dict.index(token) for token in spelling] + assert ( + tgt_dict.unk() not in spelling_idxs + ), f"{spelling} {spelling_idxs}" + self.trie.insert(spelling_idxs, word_idx, score) + self.trie.smear(SmearingMode.MAX) + + self.decoder_opts = LexiconDecoderOptions( + beam_size=cfg.beam, + beam_size_token=cfg.beamsizetoken or len(tgt_dict), + beam_threshold=cfg.beamthreshold, + lm_weight=cfg.lmweight, + word_score=cfg.wordscore, + unk_score=cfg.unkweight, + sil_score=cfg.silweight, + log_add=False, + criterion_type=CriterionType.CTC, + ) + + self.decoder = LexiconDecoder( + self.decoder_opts, + self.trie, + self.lm, + self.silence, + self.blank, + self.unk_word, + [], + self.unitlm, + ) + else: + assert self.unitlm, "Lexicon-free decoding requires unit LM" + + d = {w: [[w]] for w in tgt_dict.symbols} + self.word_dict = create_word_dict(d) + self.lm = KenLM(cfg.lmpath, self.word_dict) + self.decoder_opts = LexiconFreeDecoderOptions( + beam_size=cfg.beam, + beam_size_token=cfg.beamsizetoken or len(tgt_dict), + beam_threshold=cfg.beamthreshold, + lm_weight=cfg.lmweight, + sil_score=cfg.silweight, + log_add=False, + criterion_type=CriterionType.CTC, + ) + self.decoder = LexiconFreeDecoder( + self.decoder_opts, self.lm, self.silence, self.blank, [] + ) + + def decode( + self, + emissions: torch.FloatTensor, + ) -> List[List[Dict[str, torch.LongTensor]]]: + B, T, N = emissions.size() + hypos = [] + + def make_hypo(result: DecodeResult) -> Dict[str, Any]: + hypo = { + "tokens": self.get_tokens(result.tokens), + "score": result.score, + } + if self.lexicon: + hypo["words"] = [ + self.idx_to_wrd[x] if self.unitlm else self.word_dict[x] + for x in result.words + if x >= 0 + ] + return hypo + + for b in range(B): + emissions_ptr = emissions.data_ptr() + 4 * b * emissions.stride(0) + results = self.decoder.decode(emissions_ptr, T, N) + + nbest_results = results[: self.nbest] + hypos.append([make_hypo(result) for result in nbest_results]) + self.lm.empty_cache() + + return hypos diff --git a/examples/speech_recognition/new/decoders/viterbi_decoder.py b/examples/speech_recognition/new/decoders/viterbi_decoder.py new file mode 100644 index 0000000000..b1c47868fa --- /dev/null +++ b/examples/speech_recognition/new/decoders/viterbi_decoder.py @@ -0,0 +1,24 @@ +#!/usr/bin/env python3 + +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch + +from typing import List, Dict + +from .base_decoder import BaseDecoder + + +class ViterbiDecoder(BaseDecoder): + def decode( + self, + emissions: torch.FloatTensor, + ) -> List[List[Dict[str, torch.LongTensor]]]: + def get_pred(e): + toks = e.argmax(dim=-1).unique_consecutive() + return toks[toks != self.blank] + + return [[{"tokens": get_pred(x), "score": 0}] for x in emissions] diff --git a/examples/speech_recognition/new/infer.py b/examples/speech_recognition/new/infer.py new file mode 100644 index 0000000000..3fb67151e0 --- /dev/null +++ b/examples/speech_recognition/new/infer.py @@ -0,0 +1,471 @@ +#!/usr/bin/env python -u +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import ast +import hashlib +import logging +import os +import shutil +import sys +from dataclasses import dataclass, field, is_dataclass +from pathlib import Path +from typing import Any, Dict, List, Optional, Tuple, Union + +import editdistance +import torch +import torch.distributed as dist +from examples.speech_recognition.new.decoders.decoder_config import ( + DecoderConfig, + FlashlightDecoderConfig, +) +from examples.speech_recognition.new.decoders.decoder import Decoder +from fairseq import checkpoint_utils, distributed_utils, progress_bar, tasks, utils +from fairseq.data.data_utils import post_process +from fairseq.dataclass.configs import ( + CheckpointConfig, + CommonConfig, + CommonEvalConfig, + DatasetConfig, + DistributedTrainingConfig, + FairseqDataclass, +) +from fairseq.logging.meters import StopwatchMeter, TimeMeter +from fairseq.logging.progress_bar import BaseProgressBar +from fairseq.models.fairseq_model import FairseqModel +from omegaconf import OmegaConf + +import hydra +from hydra.core.config_store import ConfigStore + +logging.root.setLevel(logging.INFO) +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + +config_path = Path(__file__).resolve().parent / "conf" + + +@dataclass +class DecodingConfig(DecoderConfig, FlashlightDecoderConfig): + unique_wer_file: bool = field( + default=False, + metadata={"help": "If set, use a unique file for storing WER"}, + ) + results_path: Optional[str] = field( + default=None, + metadata={ + "help": "If set, write hypothesis and reference sentences into this directory" + }, + ) + + +@dataclass +class InferConfig(FairseqDataclass): + task: Any = None + decoding: DecodingConfig = DecodingConfig() + common: CommonConfig = CommonConfig() + common_eval: CommonEvalConfig = CommonEvalConfig() + checkpoint: CheckpointConfig = CheckpointConfig() + distributed_training: DistributedTrainingConfig = DistributedTrainingConfig() + dataset: DatasetConfig = DatasetConfig() + is_ax: bool = field( + default=False, + metadata={ + "help": "if true, assumes we are using ax for tuning and returns a tuple for ax to consume" + }, + ) + + +def reset_logging(): + root = logging.getLogger() + for handler in root.handlers: + root.removeHandler(handler) + root.setLevel(os.environ.get("LOGLEVEL", "INFO").upper()) + handler = logging.StreamHandler(sys.stdout) + handler.setFormatter( + logging.Formatter( + fmt="%(asctime)s | %(levelname)s | %(name)s | %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", + ) + ) + root.addHandler(handler) + + +class InferenceProcessor: + cfg: InferConfig + + def __init__(self, cfg: InferConfig) -> None: + self.cfg = cfg + self.task = tasks.setup_task(cfg.task) + + models, saved_cfg = self.load_model_ensemble() + self.models = models + self.saved_cfg = saved_cfg + self.tgt_dict = self.task.target_dictionary + + self.task.load_dataset( + self.cfg.dataset.gen_subset, + task_cfg=saved_cfg.task, + ) + self.generator = Decoder(cfg.decoding, self.tgt_dict) + self.gen_timer = StopwatchMeter() + self.wps_meter = TimeMeter() + self.num_sentences = 0 + self.total_errors = 0 + self.total_length = 0 + + self.hypo_words_file = None + self.hypo_units_file = None + self.ref_words_file = None + self.ref_units_file = None + + self.progress_bar = self.build_progress_bar() + + def __enter__(self) -> "InferenceProcessor": + if self.cfg.decoding.results_path is not None: + self.hypo_words_file = self.get_res_file("hypo.word") + self.hypo_units_file = self.get_res_file("hypo.units") + self.ref_words_file = self.get_res_file("ref.word") + self.ref_units_file = self.get_res_file("ref.units") + return self + + def __exit__(self, *exc) -> bool: + if self.cfg.decoding.results_path is not None: + self.hypo_words_file.close() + self.hypo_units_file.close() + self.ref_words_file.close() + self.ref_units_file.close() + return False + + def __iter__(self) -> Any: + for sample in self.progress_bar: + if not self.cfg.common.cpu: + sample = utils.move_to_cuda(sample) + + # Happens on the last batch. + if "net_input" not in sample: + continue + yield sample + + def log(self, *args, **kwargs): + self.progress_bar.log(*args, **kwargs) + + def print(self, *args, **kwargs): + self.progress_bar.print(*args, **kwargs) + + def get_res_file(self, fname: str) -> None: + fname = os.path.join(self.cfg.decoding.results_path, fname) + if self.data_parallel_world_size > 1: + fname = f"{fname}.{self.data_parallel_rank}" + return open(fname, "w", buffering=1) + + def merge_shards(self) -> None: + """Merges all shard files into shard 0, then removes shard suffix.""" + + shard_id = self.data_parallel_rank + num_shards = self.data_parallel_world_size + + if self.data_parallel_world_size > 1: + + def merge_shards_with_root(fname: str) -> None: + fname = os.path.join(self.cfg.decoding.results_path, fname) + logger.info("Merging %s on shard %d", fname, shard_id) + base_fpath = Path(f"{fname}.0") + with open(base_fpath, "a") as out_file: + for s in range(1, num_shards): + shard_fpath = Path(f"{fname}.{s}") + with open(shard_fpath, "r") as in_file: + for line in in_file: + out_file.write(line) + shard_fpath.unlink() + shutil.move(f"{fname}.0", fname) + + dist.barrier() # ensure all shards finished writing + if shard_id == (0 % num_shards): + merge_shards_with_root("hypo.word") + if shard_id == (1 % num_shards): + merge_shards_with_root("hypo.units") + if shard_id == (2 % num_shards): + merge_shards_with_root("ref.word") + if shard_id == (3 % num_shards): + merge_shards_with_root("ref.units") + dist.barrier() + + def optimize_model(self, model: FairseqModel) -> None: + model.make_generation_fast_() + if self.cfg.common.fp16: + model.half() + if not self.cfg.common.cpu: + model.cuda() + + def load_model_ensemble(self) -> Tuple[List[FairseqModel], FairseqDataclass]: + arg_overrides = ast.literal_eval(self.cfg.common_eval.model_overrides) + models, saved_cfg = checkpoint_utils.load_model_ensemble( + utils.split_paths(self.cfg.common_eval.path, separator="\\"), + arg_overrides=arg_overrides, + task=self.task, + suffix=self.cfg.checkpoint.checkpoint_suffix, + strict=(self.cfg.checkpoint.checkpoint_shard_count == 1), + num_shards=self.cfg.checkpoint.checkpoint_shard_count, + ) + for model in models: + self.optimize_model(model) + return models, saved_cfg + + def get_dataset_itr(self, disable_iterator_cache: bool = False) -> None: + return self.task.get_batch_iterator( + dataset=self.task.dataset(self.cfg.dataset.gen_subset), + max_tokens=self.cfg.dataset.max_tokens, + max_sentences=self.cfg.dataset.batch_size, + max_positions=(sys.maxsize, sys.maxsize), + ignore_invalid_inputs=self.cfg.dataset.skip_invalid_size_inputs_valid_test, + required_batch_size_multiple=self.cfg.dataset.required_batch_size_multiple, + seed=self.cfg.common.seed, + num_shards=self.data_parallel_world_size, + shard_id=self.data_parallel_rank, + num_workers=self.cfg.dataset.num_workers, + data_buffer_size=self.cfg.dataset.data_buffer_size, + disable_iterator_cache=disable_iterator_cache, + ).next_epoch_itr(shuffle=False) + + def build_progress_bar( + self, + epoch: Optional[int] = None, + prefix: Optional[str] = None, + default_log_format: str = "tqdm", + ) -> BaseProgressBar: + return progress_bar.progress_bar( + iterator=self.get_dataset_itr(), + log_format=self.cfg.common.log_format, + log_interval=self.cfg.common.log_interval, + epoch=epoch, + prefix=prefix, + tensorboard_logdir=self.cfg.common.tensorboard_logdir, + default_log_format=default_log_format, + ) + + @property + def data_parallel_world_size(self): + if self.cfg.distributed_training.distributed_world_size == 1: + return 1 + return distributed_utils.get_data_parallel_world_size() + + @property + def data_parallel_rank(self): + if self.cfg.distributed_training.distributed_world_size == 1: + return 0 + return distributed_utils.get_data_parallel_rank() + + def process_sentence( + self, + sample: Dict[str, Any], + hypo: Dict[str, Any], + sid: int, + batch_id: int, + ) -> Tuple[int, int]: + speaker = None # Speaker can't be parsed from dataset. + + if "target_label" in sample: + toks = sample["target_label"] + else: + toks = sample["target"] + toks = toks[batch_id, :] + + # Processes hypothesis. + hyp_pieces = self.tgt_dict.string(hypo["tokens"].int().cpu()) + if "words" in hypo: + hyp_words = " ".join(hypo["words"]) + else: + hyp_words = post_process(hyp_pieces, self.cfg.common_eval.post_process) + + # Processes target. + target_tokens = utils.strip_pad(toks, self.tgt_dict.pad()) + tgt_pieces = self.tgt_dict.string(target_tokens.int().cpu()) + tgt_words = post_process(tgt_pieces, self.cfg.common_eval.post_process) + + if self.cfg.decoding.results_path is not None: + print(f"{hyp_pieces} ({speaker}-{sid})", file=self.hypo_units_file) + print(f"{hyp_words} ({speaker}-{sid})", file=self.hypo_words_file) + print(f"{tgt_pieces} ({speaker}-{sid})", file=self.ref_units_file) + print(f"{tgt_words} ({speaker}-{sid})", file=self.ref_words_file) + + if not self.cfg.common_eval.quiet: + logger.info(f"HYPO: {hyp_words}") + logger.info(f"REF: {tgt_words}") + logger.info("---------------------") + + hyp_words, tgt_words = hyp_words.split(), tgt_words.split() + + return editdistance.eval(hyp_words, tgt_words), len(tgt_words) + + def process_sample(self, sample: Dict[str, Any]) -> None: + self.gen_timer.start() + hypos = self.task.inference_step( + generator=self.generator, + models=self.models, + sample=sample, + ) + num_generated_tokens = sum(len(h[0]["tokens"]) for h in hypos) + self.gen_timer.stop(num_generated_tokens) + self.wps_meter.update(num_generated_tokens) + + for batch_id, sample_id in enumerate(sample["id"].tolist()): + errs, length = self.process_sentence( + sample=sample, + sid=sample_id, + batch_id=batch_id, + hypo=hypos[batch_id][0], + ) + self.total_errors += errs + self.total_length += length + + self.log({"wps": round(self.wps_meter.avg)}) + if "nsentences" in sample: + self.num_sentences += sample["nsentences"] + else: + self.num_sentences += sample["id"].numel() + + def log_generation_time(self) -> None: + logger.info( + "Processed %d sentences (%d tokens) in %.1fs %.2f " + "sentences per second, %.2f tokens per second)", + self.num_sentences, + self.gen_timer.n, + self.gen_timer.sum, + self.num_sentences / self.gen_timer.sum, + 1.0 / self.gen_timer.avg, + ) + + +def parse_wer(wer_file: Path) -> float: + with open(wer_file, "r") as f: + return float(f.readline().strip().split(" ")[1]) + + +def get_wer_file(cfg: InferConfig) -> Path: + """Hashes the decoding parameters to a unique file ID.""" + base_path = "wer" + if cfg.decoding.results_path is not None: + base_path = os.path.join(cfg.decoding.results_path, base_path) + + if cfg.decoding.unique_wer_file: + yaml_str = OmegaConf.to_yaml(cfg.decoding) + fid = int(hashlib.md5(yaml_str.encode("utf-8")).hexdigest(), 16) + return Path(f"{base_path}.{fid % 1000000}") + else: + return Path(base_path) + + +def main(cfg: InferConfig) -> float: + """Entry point for main processing logic. + + Args: + cfg: The inferance configuration to use. + wer: Optional shared memory pointer for returning the WER. If not None, + the final WER value will be written here instead of being returned. + + Returns: + The final WER if `wer` is None, otherwise None. + """ + + yaml_str, wer_file = OmegaConf.to_yaml(cfg.decoding), get_wer_file(cfg) + + # Validates the provided configuration. + if cfg.dataset.max_tokens is None and cfg.dataset.batch_size is None: + cfg.dataset.max_tokens = 4000000 + if not cfg.common.cpu and not torch.cuda.is_available(): + raise ValueError("CUDA not found; set `cpu=True` to run without CUDA") + + with InferenceProcessor(cfg) as processor: + for sample in processor: + processor.process_sample(sample) + + processor.log_generation_time() + + if cfg.decoding.results_path is not None: + processor.merge_shards() + + errs_t, leng_t = processor.total_errors, processor.total_length + + if cfg.common.cpu: + logger.warning("Merging WER requires CUDA.") + elif processor.data_parallel_world_size > 1: + stats = torch.LongTensor([errs_t, leng_t]).cuda() + dist.all_reduce(stats, op=dist.ReduceOp.SUM) + errs_t, leng_t = stats[0].item(), stats[1].item() + + wer = errs_t * 100.0 / leng_t + + if distributed_utils.is_master(cfg.distributed_training): + with open(wer_file, "w") as f: + f.write( + ( + f"WER: {wer}\n" + f"err / num_ref_words = {errs_t} / {leng_t}\n\n" + f"{yaml_str}" + ) + ) + + return wer + + +@hydra.main(config_path=config_path, config_name="infer") +def hydra_main(cfg: InferConfig) -> Union[float, Tuple[float, Optional[float]]]: + container = OmegaConf.to_container(cfg, resolve=True, enum_to_str=True) + cfg = OmegaConf.create(container) + OmegaConf.set_struct(cfg, True) + + if cfg.common.reset_logging: + reset_logging() + + # logger.info("Config:\n%s", OmegaConf.to_yaml(cfg)) + wer = float("inf") + + try: + if cfg.common.profile: + with torch.cuda.profiler.profile(): + with torch.autograd.profiler.emit_nvtx(): + distributed_utils.call_main(cfg, main) + else: + distributed_utils.call_main(cfg, main) + + wer = parse_wer(get_wer_file(cfg)) + except BaseException as e: # pylint: disable=broad-except + if not cfg.common.suppress_crashes: + raise + else: + logger.error("Crashed! %s", str(e)) + + logger.info("Word error rate: %.4f", wer) + if cfg.is_ax: + return wer, None + + return wer + + +def cli_main() -> None: + try: + from hydra._internal.utils import ( + get_args, + ) # pylint: disable=import-outside-toplevel + + cfg_name = get_args().config_name or "infer" + except ImportError: + logger.warning("Failed to get config name from hydra args") + cfg_name = "infer" + + cs = ConfigStore.instance() + cs.store(name=cfg_name, node=InferConfig) + + for k in InferConfig.__dataclass_fields__: + if is_dataclass(InferConfig.__dataclass_fields__[k].type): + v = InferConfig.__dataclass_fields__[k].default + cs.store(name=k, node=v) + + hydra_main() # pylint: disable=no-value-for-parameter + + +if __name__ == "__main__": + cli_main() diff --git a/examples/speech_recognition/tasks/__init__.py b/examples/speech_recognition/tasks/__init__.py index fb9e98372d..7ac3b8dc69 100644 --- a/examples/speech_recognition/tasks/__init__.py +++ b/examples/speech_recognition/tasks/__init__.py @@ -1,7 +1,8 @@ import importlib import os -for file in os.listdir(os.path.dirname(__file__)): - if file.endswith('.py') and not file.startswith('_'): - task_name = file[:file.find('.py')] - importlib.import_module('examples.speech_recognition.tasks.' + task_name) + +for file in sorted(os.listdir(os.path.dirname(__file__))): + if file.endswith(".py") and not file.startswith("_"): + task_name = file[: file.find(".py")] + importlib.import_module("examples.speech_recognition.tasks." + task_name) diff --git a/examples/speech_recognition/tasks/speech_recognition.py b/examples/speech_recognition/tasks/speech_recognition.py index b555cfeefa..d9f011d55f 100644 --- a/examples/speech_recognition/tasks/speech_recognition.py +++ b/examples/speech_recognition/tasks/speech_recognition.py @@ -6,12 +6,13 @@ import json import os import re +import sys import torch -from fairseq.data import Dictionary -from fairseq.tasks import FairseqTask, register_task from examples.speech_recognition.data import AsrDataset from examples.speech_recognition.data.replabels import replabel_symbol +from fairseq.data import Dictionary +from fairseq.tasks import LegacyFairseqTask, register_task def get_asr_dataset_from_json(data_json_path, tgt_dict): @@ -65,7 +66,7 @@ def get_asr_dataset_from_json(data_json_path, tgt_dict): @register_task("speech_recognition") -class SpeechRecognitionTask(FairseqTask): +class SpeechRecognitionTask(LegacyFairseqTask): """ Task for training speech recognition model. """ @@ -77,6 +78,20 @@ def add_args(parser): parser.add_argument( "--silence-token", default="\u2581", help="token for silence (used by w2l)" ) + parser.add_argument( + "--max-source-positions", + default=sys.maxsize, + type=int, + metavar="N", + help="max number of frames in the source sequence", + ) + parser.add_argument( + "--max-target-positions", + default=1024, + type=int, + metavar="N", + help="max number of tokens in the target sequence", + ) def __init__(self, args, tgt_dict): super().__init__(args) @@ -108,7 +123,7 @@ def load_dataset(self, split, combine=False, **kwargs): data_json_path = os.path.join(self.args.data, "{}.json".format(split)) self.datasets[split] = get_asr_dataset_from_json(data_json_path, self.tgt_dict) - def build_generator(self, models, args): + def build_generator(self, models, args, **unused): w2l_decoder = getattr(args, "w2l_decoder", None) if w2l_decoder == "viterbi": from examples.speech_recognition.w2l_decoder import W2lViterbiDecoder @@ -118,6 +133,10 @@ def build_generator(self, models, args): from examples.speech_recognition.w2l_decoder import W2lKenLMDecoder return W2lKenLMDecoder(args, self.target_dictionary) + elif w2l_decoder == "fairseqlm": + from examples.speech_recognition.w2l_decoder import W2lFairseqLMDecoder + + return W2lFairseqLMDecoder(args, self.target_dictionary) else: return super().build_generator(models, args) @@ -132,3 +151,7 @@ def source_dictionary(self): """Return the source :class:`~fairseq.data.Dictionary` (if applicable for this task).""" return None + + def max_positions(self): + """Return the max speech and sentence length allowed by the task.""" + return (self.args.max_source_positions, self.args.max_target_positions) diff --git a/examples/speech_recognition/w2l_decoder.py b/examples/speech_recognition/w2l_decoder.py index c56448ce3d..fbf2d3524e 100644 --- a/examples/speech_recognition/w2l_decoder.py +++ b/examples/speech_recognition/w2l_decoder.py @@ -6,29 +6,44 @@ # LICENSE file in the root directory of this source tree. """ -Wav2letter decoders. +Flashlight decoders. """ -import math + +import gc import itertools as it +import os.path as osp +from typing import List +import warnings +from collections import deque, namedtuple + +import numpy as np import torch -from fairseq import utils from examples.speech_recognition.data.replabels import unpack_replabels +from fairseq import tasks +from fairseq.utils import apply_to_sample +from omegaconf import open_dict +from fairseq.dataclass.utils import convert_namespace_to_omegaconf + try: - from wav2letter.common import create_word_dict, load_words - from wav2letter.criterion import CpuViterbiPath, get_data_ptr_as_bytes - from wav2letter.decoder import ( + from flashlight.lib.text.dictionary import create_word_dict, load_words + from flashlight.lib.sequence.criterion import CpuViterbiPath, get_data_ptr_as_bytes + from flashlight.lib.text.decoder import ( CriterionType, - DecoderOptions, + LexiconDecoderOptions, KenLM, + LM, + LMState, SmearingMode, Trie, - WordLMDecoder, + LexiconDecoder, ) -except ImportError: - # wav2letter is a required dependency for the speech_recognition - # example, but don't break on import - pass +except: + warnings.warn( + "flashlight python bindings are required to use this functionality. Please install from https://github.com/facebookresearch/flashlight/tree/master/bindings/python" + ) + LM = object + LMState = object class W2lDecoder(object): @@ -38,20 +53,21 @@ def __init__(self, args, tgt_dict): self.nbest = args.nbest # criterion-specific init - if args.criterion == "ctc_loss": - self.criterion_type = CriterionType.CTC - self.blank = tgt_dict.index("<ctc_blank>") - self.asg_transitions = None - elif args.criterion == "asg_loss": - self.criterion_type = CriterionType.ASG - self.blank = -1 - self.asg_transitions = args.asg_transitions - self.max_replabel = args.max_replabel - assert len(self.asg_transitions) == self.vocab_size ** 2 + self.criterion_type = CriterionType.CTC + self.blank = ( + tgt_dict.index("<ctc_blank>") + if "<ctc_blank>" in tgt_dict.indices + else tgt_dict.bos() + ) + if "<sep>" in tgt_dict.indices: + self.silence = tgt_dict.index("<sep>") + elif "|" in tgt_dict.indices: + self.silence = tgt_dict.index("|") else: - raise RuntimeError(f"unknown criterion: {args.criterion}") + self.silence = tgt_dict.eos() + self.asg_transitions = None - def generate(self, models, sample, prefix_tokens=None): + def generate(self, models, sample, **unused): """Generate a batch of inferences.""" # model.forward normally channels prev_output_tokens into the decoder # separately, but SequenceGenerator directly calls model.encoder @@ -63,21 +79,18 @@ def generate(self, models, sample, prefix_tokens=None): def get_emissions(self, models, encoder_input): """Run encoder and normalize emissions""" - encoder_out = models[0].encoder(**encoder_input) - if self.criterion_type == CriterionType.CTC: - emissions = models[0].get_normalized_probs(encoder_out, log_probs=True) - elif self.criterion_type == CriterionType.ASG: - emissions = encoder_out["encoder_out"] + model = models[0] + encoder_out = model(**encoder_input) + if hasattr(model, "get_logits"): + emissions = model.get_logits(encoder_out) # no need to normalize emissions + else: + emissions = model.get_normalized_probs(encoder_out, log_probs=True) return emissions.transpose(0, 1).float().cpu().contiguous() def get_tokens(self, idxs): """Normalize tokens by handling CTC blank, ASG replabels, etc.""" idxs = (g[0] for g in it.groupby(idxs)) - idxs = filter(lambda x: x >= 0, idxs) - if self.criterion_type == CriterionType.CTC: - idxs = filter(lambda x: x != self.blank, idxs) - elif self.criterion_type == CriterionType.ASG: - idxs = unpack_replabels(list(idxs), self.tgt_dict, self.max_replabel) + idxs = filter(lambda x: x != self.blank, idxs) return torch.LongTensor(list(idxs)) @@ -113,55 +126,361 @@ class W2lKenLMDecoder(W2lDecoder): def __init__(self, args, tgt_dict): super().__init__(args, tgt_dict) - self.silence = tgt_dict.index(args.silence_token) + self.unit_lm = getattr(args, "unit_lm", False) - self.lexicon = load_words(args.lexicon) - self.word_dict = create_word_dict(self.lexicon) - self.unk_word = self.word_dict.get_index("<unk>") + if args.lexicon: + self.lexicon = load_words(args.lexicon) + self.word_dict = create_word_dict(self.lexicon) + self.unk_word = self.word_dict.get_index("<unk>") - self.lm = KenLM(args.kenlm_model, self.word_dict) - self.trie = Trie(self.vocab_size, self.silence) + self.lm = KenLM(args.kenlm_model, self.word_dict) + self.trie = Trie(self.vocab_size, self.silence) - start_state = self.lm.start(False) - for word, spellings in self.lexicon.items(): - word_idx = self.word_dict.get_index(word) - _, score = self.lm.score(start_state, word_idx) - for spelling in spellings: - spelling_idxs = [tgt_dict.index(token) for token in spelling] - self.trie.insert(spelling_idxs, word_idx, score) - self.trie.smear(SmearingMode.MAX) - - self.decoder_opts = DecoderOptions( - args.beam, - args.beam_threshold, - args.lm_weight, - args.word_score, - args.unk_weight, - False, - args.sil_weight, - self.criterion_type, - ) + start_state = self.lm.start(False) + for i, (word, spellings) in enumerate(self.lexicon.items()): + word_idx = self.word_dict.get_index(word) + _, score = self.lm.score(start_state, word_idx) + for spelling in spellings: + spelling_idxs = [tgt_dict.index(token) for token in spelling] + assert ( + tgt_dict.unk() not in spelling_idxs + ), f"{spelling} {spelling_idxs}" + self.trie.insert(spelling_idxs, word_idx, score) + self.trie.smear(SmearingMode.MAX) - self.decoder = WordLMDecoder( - self.decoder_opts, - self.trie, - self.lm, - self.silence, - self.blank, - self.unk_word, - self.asg_transitions, - ) + self.decoder_opts = LexiconDecoderOptions( + beam_size=args.beam, + beam_size_token=int(getattr(args, "beam_size_token", len(tgt_dict))), + beam_threshold=args.beam_threshold, + lm_weight=args.lm_weight, + word_score=args.word_score, + unk_score=args.unk_weight, + sil_score=args.sil_weight, + log_add=False, + criterion_type=self.criterion_type, + ) + + if self.asg_transitions is None: + N = 768 + # self.asg_transitions = torch.FloatTensor(N, N).zero_() + self.asg_transitions = [] + + self.decoder = LexiconDecoder( + self.decoder_opts, + self.trie, + self.lm, + self.silence, + self.blank, + self.unk_word, + self.asg_transitions, + self.unit_lm, + ) + else: + assert args.unit_lm, "lexicon free decoding can only be done with a unit language model" + from flashlight.lib.text.decoder import LexiconFreeDecoder, LexiconFreeDecoderOptions + + d = {w: [[w]] for w in tgt_dict.symbols} + self.word_dict = create_word_dict(d) + self.lm = KenLM(args.kenlm_model, self.word_dict) + self.decoder_opts = LexiconFreeDecoderOptions( + beam_size=args.beam, + beam_size_token=int(getattr(args, "beam_size_token", len(tgt_dict))), + beam_threshold=args.beam_threshold, + lm_weight=args.lm_weight, + sil_score=args.sil_weight, + log_add=False, + criterion_type=self.criterion_type, + ) + self.decoder = LexiconFreeDecoder( + self.decoder_opts, self.lm, self.silence, self.blank, [] + ) + + def get_timesteps(self, token_idxs: List[int]) -> List[int]: + """Returns frame numbers corresponding to every non-blank token. + + Parameters + ---------- + token_idxs : List[int] + IDs of decoded tokens. + + Returns + ------- + List[int] + Frame numbers corresponding to every non-blank token. + """ + timesteps = [] + for i, token_idx in enumerate(token_idxs): + if token_idx == self.blank: + continue + if i == 0 or token_idx != token_idxs[i-1]: + timesteps.append(i) + return timesteps def decode(self, emissions): B, T, N = emissions.size() hypos = [] for b in range(B): emissions_ptr = emissions.data_ptr() + 4 * b * emissions.stride(0) - nbest_results = self.decoder.decode(emissions_ptr, T, N)[: self.nbest] + results = self.decoder.decode(emissions_ptr, T, N) + + nbest_results = results[: self.nbest] hypos.append( [ - {"tokens": self.get_tokens(result.tokens), "score": result.score} + { + "tokens": self.get_tokens(result.tokens), + "score": result.score, + "timesteps": self.get_timesteps(result.tokens), + "words": [ + self.word_dict.get_entry(x) for x in result.words if x >= 0 + ], + } for result in nbest_results ] ) return hypos + + +FairseqLMState = namedtuple("FairseqLMState", ["prefix", "incremental_state", "probs"]) + + +class FairseqLM(LM): + def __init__(self, dictionary, model): + LM.__init__(self) + self.dictionary = dictionary + self.model = model + self.unk = self.dictionary.unk() + + self.save_incremental = False # this currently does not work properly + self.max_cache = 20_000 + + model.cuda() + model.eval() + model.make_generation_fast_() + + self.states = {} + self.stateq = deque() + + def start(self, start_with_nothing): + state = LMState() + prefix = torch.LongTensor([[self.dictionary.eos()]]) + incremental_state = {} if self.save_incremental else None + with torch.no_grad(): + res = self.model(prefix.cuda(), incremental_state=incremental_state) + probs = self.model.get_normalized_probs(res, log_probs=True, sample=None) + + if incremental_state is not None: + incremental_state = apply_to_sample(lambda x: x.cpu(), incremental_state) + self.states[state] = FairseqLMState( + prefix.numpy(), incremental_state, probs[0, -1].cpu().numpy() + ) + self.stateq.append(state) + + return state + + def score(self, state: LMState, token_index: int, no_cache: bool = False): + """ + Evaluate language model based on the current lm state and new word + Parameters: + ----------- + state: current lm state + token_index: index of the word + (can be lexicon index then you should store inside LM the + mapping between indices of lexicon and lm, or lm index of a word) + + Returns: + -------- + (LMState, float): pair of (new state, score for the current word) + """ + curr_state = self.states[state] + + def trim_cache(targ_size): + while len(self.stateq) > targ_size: + rem_k = self.stateq.popleft() + rem_st = self.states[rem_k] + rem_st = FairseqLMState(rem_st.prefix, None, None) + self.states[rem_k] = rem_st + + if curr_state.probs is None: + new_incremental_state = ( + curr_state.incremental_state.copy() + if curr_state.incremental_state is not None + else None + ) + with torch.no_grad(): + if new_incremental_state is not None: + new_incremental_state = apply_to_sample( + lambda x: x.cuda(), new_incremental_state + ) + elif self.save_incremental: + new_incremental_state = {} + + res = self.model( + torch.from_numpy(curr_state.prefix).cuda(), + incremental_state=new_incremental_state, + ) + probs = self.model.get_normalized_probs( + res, log_probs=True, sample=None + ) + + if new_incremental_state is not None: + new_incremental_state = apply_to_sample( + lambda x: x.cpu(), new_incremental_state + ) + + curr_state = FairseqLMState( + curr_state.prefix, new_incremental_state, probs[0, -1].cpu().numpy() + ) + + if not no_cache: + self.states[state] = curr_state + self.stateq.append(state) + + score = curr_state.probs[token_index].item() + + trim_cache(self.max_cache) + + outstate = state.child(token_index) + if outstate not in self.states and not no_cache: + prefix = np.concatenate( + [curr_state.prefix, torch.LongTensor([[token_index]])], -1 + ) + incr_state = curr_state.incremental_state + + self.states[outstate] = FairseqLMState(prefix, incr_state, None) + + if token_index == self.unk: + score = float("-inf") + + return outstate, score + + def finish(self, state: LMState): + """ + Evaluate eos for language model based on the current lm state + + Returns: + -------- + (LMState, float): pair of (new state, score for the current word) + """ + return self.score(state, self.dictionary.eos()) + + def empty_cache(self): + self.states = {} + self.stateq = deque() + gc.collect() + + +class W2lFairseqLMDecoder(W2lDecoder): + def __init__(self, args, tgt_dict): + super().__init__(args, tgt_dict) + + self.unit_lm = getattr(args, "unit_lm", False) + + self.lexicon = load_words(args.lexicon) if args.lexicon else None + self.idx_to_wrd = {} + + checkpoint = torch.load(args.kenlm_model, map_location="cpu") + + if "cfg" in checkpoint and checkpoint["cfg"] is not None: + lm_args = checkpoint["cfg"] + else: + lm_args = convert_namespace_to_omegaconf(checkpoint["args"]) + + with open_dict(lm_args.task): + lm_args.task.data = osp.dirname(args.kenlm_model) + + task = tasks.setup_task(lm_args.task) + model = task.build_model(lm_args.model) + model.load_state_dict(checkpoint["model"], strict=False) + + self.trie = Trie(self.vocab_size, self.silence) + + self.word_dict = task.dictionary + self.unk_word = self.word_dict.unk() + self.lm = FairseqLM(self.word_dict, model) + + if self.lexicon: + start_state = self.lm.start(False) + for i, (word, spellings) in enumerate(self.lexicon.items()): + if self.unit_lm: + word_idx = i + self.idx_to_wrd[i] = word + score = 0 + else: + word_idx = self.word_dict.index(word) + _, score = self.lm.score(start_state, word_idx, no_cache=True) + + for spelling in spellings: + spelling_idxs = [tgt_dict.index(token) for token in spelling] + assert ( + tgt_dict.unk() not in spelling_idxs + ), f"{spelling} {spelling_idxs}" + self.trie.insert(spelling_idxs, word_idx, score) + self.trie.smear(SmearingMode.MAX) + + self.decoder_opts = LexiconDecoderOptions( + beam_size=args.beam, + beam_size_token=int(getattr(args, "beam_size_token", len(tgt_dict))), + beam_threshold=args.beam_threshold, + lm_weight=args.lm_weight, + word_score=args.word_score, + unk_score=args.unk_weight, + sil_score=args.sil_weight, + log_add=False, + criterion_type=self.criterion_type, + ) + + self.decoder = LexiconDecoder( + self.decoder_opts, + self.trie, + self.lm, + self.silence, + self.blank, + self.unk_word, + [], + self.unit_lm, + ) + else: + assert args.unit_lm, "lexicon free decoding can only be done with a unit language model" + from flashlight.lib.text.decoder import LexiconFreeDecoder, LexiconFreeDecoderOptions + + d = {w: [[w]] for w in tgt_dict.symbols} + self.word_dict = create_word_dict(d) + self.lm = KenLM(args.kenlm_model, self.word_dict) + self.decoder_opts = LexiconFreeDecoderOptions( + beam_size=args.beam, + beam_size_token=int(getattr(args, "beam_size_token", len(tgt_dict))), + beam_threshold=args.beam_threshold, + lm_weight=args.lm_weight, + sil_score=args.sil_weight, + log_add=False, + criterion_type=self.criterion_type, + ) + self.decoder = LexiconFreeDecoder( + self.decoder_opts, self.lm, self.silence, self.blank, [] + ) + + def decode(self, emissions): + B, T, N = emissions.size() + hypos = [] + + def idx_to_word(idx): + if self.unit_lm: + return self.idx_to_wrd[idx] + else: + return self.word_dict[idx] + + def make_hypo(result): + hypo = {"tokens": self.get_tokens(result.tokens), "score": result.score} + if self.lexicon: + hypo["words"] = [idx_to_word(x) for x in result.words if x >= 0] + return hypo + + for b in range(B): + emissions_ptr = emissions.data_ptr() + 4 * b * emissions.stride(0) + results = self.decoder.decode(emissions_ptr, T, N) + + nbest_results = results[: self.nbest] + hypos.append([make_hypo(result) for result in nbest_results]) + self.lm.empty_cache() + + return hypos diff --git a/examples/speech_synthesis/README.md b/examples/speech_synthesis/README.md new file mode 100644 index 0000000000..4a3ae54b85 --- /dev/null +++ b/examples/speech_synthesis/README.md @@ -0,0 +1,16 @@ +Speech Synthesis (S^2) +=== + +Speech synthesis with fairseq. + +- Autoregressive and non-autoregressive models +- Multi-speaker synthesis +- Audio preprocessing +- Automatic metrics +- Similar data configuration as [S2T](../speech_to_text/README.md) + + +## Examples +- [Single-speaker synthesis on LJSpeech](docs/ljspeech_example.md) +- [Multi-speaker synthesis on VCTK](docs/vctk_example.md) +- [Multi-speaker synthesis on Common Voice](docs/common_voice_example.md) diff --git a/examples/simultaneous_translation/eval/__init__.py b/examples/speech_synthesis/__init__.py similarity index 100% rename from examples/simultaneous_translation/eval/__init__.py rename to examples/speech_synthesis/__init__.py diff --git a/examples/speech_synthesis/data_utils.py b/examples/speech_synthesis/data_utils.py new file mode 100644 index 0000000000..f43a4a9004 --- /dev/null +++ b/examples/speech_synthesis/data_utils.py @@ -0,0 +1,320 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os +from pathlib import Path +from typing import Optional, List, Dict +import zipfile +import tempfile +from dataclasses import dataclass +from itertools import groupby + +import torch +import torch.nn.functional as F +import numpy as np +from tqdm import tqdm + +from examples.speech_to_text.data_utils import load_tsv_to_dicts +from fairseq.data.audio.audio_utils import TTSSpectrogram, TTSMelScale + + +def trim_or_pad_to_target_length( + data_1d_or_2d: np.ndarray, target_length: int +) -> np.ndarray: + assert len(data_1d_or_2d.shape) in {1, 2} + delta = data_1d_or_2d.shape[0] - target_length + if delta >= 0: # trim if being longer + data_1d_or_2d = data_1d_or_2d[: target_length] + else: # pad if being shorter + if len(data_1d_or_2d.shape) == 1: + data_1d_or_2d = np.concatenate( + [data_1d_or_2d, np.zeros(-delta)], axis=0 + ) + else: + data_1d_or_2d = np.concatenate( + [data_1d_or_2d, np.zeros((-delta, data_1d_or_2d.shape[1]))], + axis=0 + ) + return data_1d_or_2d + + +def extract_logmel_spectrogram( + waveform: torch.Tensor, sample_rate: int, + output_path: Optional[Path] = None, win_length: int = 1024, + hop_length: int = 256, n_fft: int = 1024, + win_fn: callable = torch.hann_window, n_mels: int = 80, + f_min: float = 0., f_max: float = 8000, eps: float = 1e-5, + overwrite: bool = False, target_length: Optional[int] = None +): + if output_path is not None and output_path.is_file() and not overwrite: + return + + spectrogram_transform = TTSSpectrogram( + n_fft=n_fft, win_length=win_length, hop_length=hop_length, + window_fn=win_fn + ) + mel_scale_transform = TTSMelScale( + n_mels=n_mels, sample_rate=sample_rate, f_min=f_min, f_max=f_max, + n_stft=n_fft // 2 + 1 + ) + spectrogram = spectrogram_transform(waveform) + mel_spec = mel_scale_transform(spectrogram) + logmel_spec = torch.clamp(mel_spec, min=eps).log() + assert len(logmel_spec.shape) == 3 and logmel_spec.shape[0] == 1 + logmel_spec = logmel_spec.squeeze().t() # D x T -> T x D + if target_length is not None: + trim_or_pad_to_target_length(logmel_spec, target_length) + + if output_path is not None: + np.save(output_path.as_posix(), logmel_spec) + else: + return logmel_spec + + +def extract_pitch( + waveform: torch.Tensor, sample_rate: int, + output_path: Optional[Path] = None, hop_length: int = 256, + log_scale: bool = True, phoneme_durations: Optional[List[int]] = None +): + if output_path is not None and output_path.is_file(): + return + + try: + import pyworld + except ImportError: + raise ImportError("Please install PyWORLD: pip install pyworld") + + _waveform = waveform.squeeze(0).double().numpy() + pitch, t = pyworld.dio( + _waveform, sample_rate, frame_period=hop_length / sample_rate * 1000 + ) + pitch = pyworld.stonemask(_waveform, pitch, t, sample_rate) + + if phoneme_durations is not None: + pitch = trim_or_pad_to_target_length(pitch, sum(phoneme_durations)) + try: + from scipy.interpolate import interp1d + except ImportError: + raise ImportError("Please install SciPy: pip install scipy") + nonzero_ids = np.where(pitch != 0)[0] + interp_fn = interp1d( + nonzero_ids, + pitch[nonzero_ids], + fill_value=(pitch[nonzero_ids[0]], pitch[nonzero_ids[-1]]), + bounds_error=False, + ) + pitch = interp_fn(np.arange(0, len(pitch))) + d_cumsum = np.cumsum(np.concatenate([np.array([0]), phoneme_durations])) + pitch = np.array( + [ + np.mean(pitch[d_cumsum[i-1]: d_cumsum[i]]) + for i in range(1, len(d_cumsum)) + ] + ) + assert len(pitch) == len(phoneme_durations) + + if log_scale: + pitch = np.log(pitch + 1) + + if output_path is not None: + np.save(output_path.as_posix(), pitch) + else: + return pitch + + +def extract_energy( + waveform: torch.Tensor, output_path: Optional[Path] = None, + hop_length: int = 256, n_fft: int = 1024, log_scale: bool = True, + phoneme_durations: Optional[List[int]] = None +): + if output_path is not None and output_path.is_file(): + return + + assert len(waveform.shape) == 2 and waveform.shape[0] == 1 + waveform = waveform.view(1, 1, waveform.shape[1]) + waveform = F.pad( + waveform.unsqueeze(1), [n_fft // 2, n_fft // 2, 0, 0], + mode="reflect" + ) + waveform = waveform.squeeze(1) + + fourier_basis = np.fft.fft(np.eye(n_fft)) + cutoff = int((n_fft / 2 + 1)) + fourier_basis = np.vstack( + [np.real(fourier_basis[:cutoff, :]), + np.imag(fourier_basis[:cutoff, :])] + ) + + forward_basis = torch.FloatTensor(fourier_basis[:, None, :]) + forward_transform = F.conv1d( + waveform, forward_basis, stride=hop_length, padding=0 + ) + + real_part = forward_transform[:, :cutoff, :] + imag_part = forward_transform[:, cutoff:, :] + magnitude = torch.sqrt(real_part ** 2 + imag_part ** 2) + energy = torch.norm(magnitude, dim=1).squeeze(0).numpy() + + if phoneme_durations is not None: + energy = trim_or_pad_to_target_length(energy, sum(phoneme_durations)) + d_cumsum = np.cumsum(np.concatenate([np.array([0]), phoneme_durations])) + energy = np.array( + [ + np.mean(energy[d_cumsum[i - 1]: d_cumsum[i]]) + for i in range(1, len(d_cumsum)) + ] + ) + assert len(energy) == len(phoneme_durations) + + if log_scale: + energy = np.log(energy + 1) + + if output_path is not None: + np.save(output_path.as_posix(), energy) + else: + return energy + + +def get_global_cmvn(feature_root: Path, output_path: Optional[Path] = None): + mean_x, mean_x2, n_frames = None, None, 0 + feature_paths = feature_root.glob("*.npy") + for p in tqdm(feature_paths): + with open(p, 'rb') as f: + frames = np.load(f).squeeze() + + n_frames += frames.shape[0] + + cur_mean_x = frames.sum(axis=0) + if mean_x is None: + mean_x = cur_mean_x + else: + mean_x += cur_mean_x + + cur_mean_x2 = (frames ** 2).sum(axis=0) + if mean_x2 is None: + mean_x2 = cur_mean_x2 + else: + mean_x2 += cur_mean_x2 + + mean_x /= n_frames + mean_x2 /= n_frames + var_x = mean_x2 - mean_x ** 2 + std_x = np.sqrt(np.maximum(var_x, 1e-10)) + + if output_path is not None: + with open(output_path, 'wb') as f: + np.savez(f, mean=mean_x, std=std_x) + else: + return {"mean": mean_x, "std": std_x} + + +def ipa_phonemize(text, lang="en-us", use_g2p=False): + if use_g2p: + assert lang == "en-us", "g2pE phonemizer only works for en-us" + try: + from g2p_en import G2p + g2p = G2p() + return " ".join("|" if p == " " else p for p in g2p(text)) + except ImportError: + raise ImportError( + "Please install phonemizer: pip install g2p_en" + ) + else: + try: + from phonemizer import phonemize + from phonemizer.separator import Separator + return phonemize( + text, backend='espeak', language=lang, + separator=Separator(word="| ", phone=" ") + ) + except ImportError: + raise ImportError( + "Please install phonemizer: pip install phonemizer" + ) + + +@dataclass +class ForceAlignmentInfo(object): + tokens: List[str] + frame_durations: List[int] + start_sec: Optional[float] + end_sec: Optional[float] + + +def get_mfa_alignment_by_sample_id( + textgrid_zip_path: str, sample_id: str, sample_rate: int, + hop_length: int, silence_phones: List[str] = ("sil", "sp", "spn") +) -> ForceAlignmentInfo: + try: + import tgt + except ImportError: + raise ImportError("Please install TextGridTools: pip install tgt") + + filename = f"{sample_id}.TextGrid" + out_root = Path(tempfile.gettempdir()) + tgt_path = out_root / filename + with zipfile.ZipFile(textgrid_zip_path) as f_zip: + f_zip.extract(filename, path=out_root) + textgrid = tgt.io.read_textgrid(tgt_path.as_posix()) + os.remove(tgt_path) + + phones, frame_durations = [], [] + start_sec, end_sec, end_idx = 0, 0, 0 + for t in textgrid.get_tier_by_name("phones")._objects: + s, e, p = t.start_time, t.end_time, t.text + # Trim leading silences + if len(phones) == 0: + if p in silence_phones: + continue + else: + start_sec = s + phones.append(p) + if p not in silence_phones: + end_sec = e + end_idx = len(phones) + r = sample_rate / hop_length + frame_durations.append(int(np.round(e * r) - np.round(s * r))) + # Trim tailing silences + phones = phones[:end_idx] + frame_durations = frame_durations[:end_idx] + + return ForceAlignmentInfo( + tokens=phones, frame_durations=frame_durations, start_sec=start_sec, + end_sec=end_sec + ) + + +def get_mfa_alignment( + textgrid_zip_path: str, sample_ids: List[str], sample_rate: int, + hop_length: int +) -> Dict[str, ForceAlignmentInfo]: + return { + i: get_mfa_alignment_by_sample_id( + textgrid_zip_path, i, sample_rate, hop_length + ) for i in tqdm(sample_ids) + } + + +def get_unit_alignment( + id_to_unit_tsv_path: str, sample_ids: List[str] +) -> Dict[str, ForceAlignmentInfo]: + id_to_units = { + e["id"]: e["units"] for e in load_tsv_to_dicts(id_to_unit_tsv_path) + } + id_to_units = {i: id_to_units[i].split() for i in sample_ids} + id_to_units_collapsed = { + i: [uu for uu, _ in groupby(u)] for i, u in id_to_units.items() + } + id_to_durations = { + i: [len(list(g)) for _, g in groupby(u)] for i, u in id_to_units.items() + } + + return { + i: ForceAlignmentInfo( + tokens=id_to_units_collapsed[i], frame_durations=id_to_durations[i], + start_sec=None, end_sec=None + ) + for i in sample_ids + } diff --git a/examples/speech_synthesis/docs/common_voice_example.md b/examples/speech_synthesis/docs/common_voice_example.md new file mode 100644 index 0000000000..40e841b284 --- /dev/null +++ b/examples/speech_synthesis/docs/common_voice_example.md @@ -0,0 +1,56 @@ +[[Back]](..) + +# Common Voice + +[Common Voice](https://commonvoice.mozilla.org/en/datasets) is a public domain speech corpus with 11.2K hours of read +speech in 76 languages (the latest version 7.0). We provide examples for building +[Transformer](https://arxiv.org/abs/1809.08895) models on this dataset. + + +## Data preparation +[Download](https://commonvoice.mozilla.org/en/datasets) and unpack Common Voice v4 to a path `${DATA_ROOT}/${LANG_ID}`. +Create splits and generate audio manifests with +```bash +python -m examples.speech_synthesis.preprocessing.get_common_voice_audio_manifest \ + --data-root ${DATA_ROOT} \ + --lang ${LANG_ID} \ + --output-manifest-root ${AUDIO_MANIFEST_ROOT} --convert-to-wav +``` + +Then, extract log-Mel spectrograms, generate feature manifest and create data configuration YAML with +```bash +python -m examples.speech_synthesis.preprocessing.get_feature_manifest \ + --audio-manifest-root ${AUDIO_MANIFEST_ROOT} \ + --output-root ${FEATURE_MANIFEST_ROOT} \ + --ipa-vocab --lang ${LANG_ID} +``` +where we use phoneme inputs (`--ipa-vocab`) as example. + +To denoise audio and trim leading/trailing silence using signal processing based VAD, run +```bash +for SPLIT in dev test train; do + python -m examples.speech_synthesis.preprocessing.denoise_and_vad_audio \ + --audio-manifest ${AUDIO_MANIFEST_ROOT}/${SPLIT}.audio.tsv \ + --output-dir ${PROCESSED_DATA_ROOT} \ + --denoise --vad --vad-agg-level 2 +done +``` + + +## Training +(Please refer to [the LJSpeech example](../docs/ljspeech_example.md#transformer).) + + +## Inference +(Please refer to [the LJSpeech example](../docs/ljspeech_example.md#inference).) + +## Automatic Evaluation +(Please refer to [the LJSpeech example](../docs/ljspeech_example.md#automatic-evaluation).) + +## Results + +| Language | Speakers | --arch | Params | Test MCD | Model | +|---|---|---|---|---|---| +| English | 200 | tts_transformer | 54M | 3.8 | [Download](https://dl.fbaipublicfiles.com/fairseq/s2/cv4_en200_transformer_phn.tar) | + +[[Back]](..) diff --git a/examples/speech_synthesis/docs/ljspeech_example.md b/examples/speech_synthesis/docs/ljspeech_example.md new file mode 100644 index 0000000000..90c524fac8 --- /dev/null +++ b/examples/speech_synthesis/docs/ljspeech_example.md @@ -0,0 +1,138 @@ +[[Back]](..) + +# LJSpeech + +[LJSpeech](https://keithito.com/LJ-Speech-Dataset) is a public domain TTS +corpus with around 24 hours of English speech sampled at 22.05kHz. We provide examples for building +[Transformer](https://arxiv.org/abs/1809.08895) and [FastSpeech 2](https://arxiv.org/abs/2006.04558) +models on this dataset. + + +## Data preparation + +Download data, create splits and generate audio manifests with +```bash +python -m examples.speech_synthesis.preprocessing.get_ljspeech_audio_manifest \ + --output-data-root ${AUDIO_DATA_ROOT} \ + --output-manifest-root ${AUDIO_MANIFEST_ROOT} +``` + +Then, extract log-Mel spectrograms, generate feature manifest and create data configuration YAML with +```bash +python -m examples.speech_synthesis.preprocessing.get_feature_manifest \ + --audio-manifest-root ${AUDIO_MANIFEST_ROOT} \ + --output-root ${FEATURE_MANIFEST_ROOT} \ + --ipa-vocab --use-g2p +``` +where we use phoneme inputs (`--ipa-vocab --use-g2p`) as example. + +FastSpeech 2 additionally requires frame durations, pitch and energy as auxiliary training targets. +Add `--add-fastspeech-targets` to include these fields in the feature manifests. We get frame durations either from +phoneme-level force-alignment or frame-level pseudo-text unit sequence. They should be pre-computed and specified via: +- `--textgrid-zip ${TEXT_GRID_ZIP_PATH}` for a ZIP file, inside which there is one + [TextGrid](https://www.fon.hum.uva.nl/praat/manual/TextGrid.html) file per sample to provide force-alignment info. +- `--id-to-units-tsv ${ID_TO_UNIT_TSV}` for a TSV file, where there are 2 columns for sample ID and + space-delimited pseudo-text unit sequence, respectively. + +For your convenience, we provide pre-computed +[force-alignment](https://dl.fbaipublicfiles.com/fairseq/s2/ljspeech_mfa.zip) from +[Montreal Forced Aligner](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner) and +[pseudo-text units](s3://dl.fbaipublicfiles.com/fairseq/s2/ljspeech_hubert.tsv) from +[HuBERT](https://github.com/pytorch/fairseq/tree/main/examples/hubert). You can also generate them by yourself using +a different software or model. + + +## Training +#### Transformer +```bash +fairseq-train ${FEATURE_MANIFEST_ROOT} --save-dir ${SAVE_DIR} \ + --config-yaml config.yaml --train-subset train --valid-subset dev \ + --num-workers 4 --max-tokens 30000 --max-update 200000 \ + --task text_to_speech --criterion tacotron2 --arch tts_transformer \ + --clip-norm 5.0 --n-frames-per-step 4 --bce-pos-weight 5.0 \ + --dropout 0.1 --attention-dropout 0.1 --activation-dropout 0.1 \ + --encoder-normalize-before --decoder-normalize-before \ + --optimizer adam --lr 2e-3 --lr-scheduler inverse_sqrt --warmup-updates 4000 \ + --seed 1 --update-freq 8 --eval-inference --best-checkpoint-metric mcd_loss +``` +where `SAVE_DIR` is the checkpoint root path. We set `--update-freq 8` to simulate 8 GPUs with 1 GPU. You may want to +update it accordingly when using more than 1 GPU. + +#### FastSpeech2 +```bash +fairseq-train ${FEATURE_MANIFEST_ROOT} --save-dir ${SAVE_DIR} \ + --config-yaml config.yaml --train-subset train --valid-subset dev \ + --num-workers 4 --max-sentences 6 --max-update 200000 \ + --task text_to_speech --criterion fastspeech2 --arch fastspeech2 \ + --clip-norm 5.0 --n-frames-per-step 1 \ + --dropout 0.1 --attention-dropout 0.1 --activation-dropout 0.1 \ + --encoder-normalize-before --decoder-normalize-before \ + --optimizer adam --lr 5e-4 --lr-scheduler inverse_sqrt --warmup-updates 4000 \ + --seed 1 --update-freq 8 --eval-inference --best-checkpoint-metric mcd_loss +``` + + +## Inference +Average the last 5 checkpoints, generate the test split spectrogram and waveform using the default Griffin-Lim vocoder: +```bash +SPLIT=test +CHECKPOINT_NAME=avg_last_5 +CHECKPOINT_PATH=${SAVE_DIR}/checkpoint_${CHECKPOINT_NAME}.pt +python scripts/average_checkpoints.py --inputs ${SAVE_DIR} \ + --num-epoch-checkpoints 5 \ + --output ${CHECKPOINT_PATH} + +python -m examples.speech_synthesis.generate_waveform ${FEATURE_MANIFEST_ROOT} \ + --config-yaml config.yaml --gen-subset ${SPLIT} --task text_to_speech \ + --path ${CHECKPOINT_PATH} --max-tokens 50000 --spec-bwd-max-iter 32 \ + --dump-waveforms +``` +which dumps files (waveform, feature, attention plot, etc.) to `${SAVE_DIR}/generate-${CHECKPOINT_NAME}-${SPLIT}`. To +re-synthesize target waveforms for automatic evaluation, add `--dump-target`. + +## Automatic Evaluation +To start with, generate the manifest for synthetic speech, which will be taken as inputs by evaluation scripts. +```bash +python -m examples.speech_synthesis.evaluation.get_eval_manifest \ + --generation-root ${SAVE_DIR}/generate-${CHECKPOINT_NAME}-${SPLIT} \ + --audio-manifest ${AUDIO_MANIFEST_ROOT}/${SPLIT}.audio.tsv \ + --output-path ${EVAL_OUTPUT_ROOT}/eval.tsv \ + --vocoder griffin_lim --sample-rate 22050 --audio-format flac \ + --use-resynthesized-target +``` +Speech recognition (ASR) models usually operate at lower sample rates (e.g. 16kHz). For the WER/CER metric, +you may need to resample the audios accordingly --- add `--output-sample-rate 16000` for `generate_waveform.py` and +use `--sample-rate 16000` for `get_eval_manifest.py`. + + +#### WER/CER metric +We use wav2vec 2.0 ASR model as example. [Download](https://github.com/pytorch/fairseq/tree/main/examples/wav2vec) +the model checkpoint and dictionary, then compute WER/CER with +```bash +python -m examples.speech_synthesis.evaluation.eval_asr \ + --audio-header syn --text-header text --err-unit char --split ${SPLIT} \ + --w2v-ckpt ${WAV2VEC2_CHECKPOINT_PATH} --w2v-dict-dir ${WAV2VEC2_DICT_DIR} \ + --raw-manifest ${EVAL_OUTPUT_ROOT}/eval_16khz.tsv --asr-dir ${EVAL_OUTPUT_ROOT}/asr +``` + +#### MCD/MSD metric +```bash +python -m examples.speech_synthesis.evaluation.eval_sp \ + ${EVAL_OUTPUT_ROOT}/eval.tsv --mcd --msd +``` + +#### F0 metrics +```bash +python -m examples.speech_synthesis.evaluation.eval_f0 \ + ${EVAL_OUTPUT_ROOT}/eval.tsv --gpe --vde --ffe +``` + + +## Results + +| --arch | Params | Test MCD | Model | +|---|---|---|---| +| tts_transformer | 54M | 3.8 | [Download](https://dl.fbaipublicfiles.com/fairseq/s2/ljspeech_transformer_phn.tar) | +| fastspeech2 | 41M | 3.8 | [Download](https://dl.fbaipublicfiles.com/fairseq/s2/ljspeech_fastspeech2_phn.tar) | + +[[Back]](..) diff --git a/examples/speech_synthesis/docs/vctk_example.md b/examples/speech_synthesis/docs/vctk_example.md new file mode 100644 index 0000000000..2ba78f3f73 --- /dev/null +++ b/examples/speech_synthesis/docs/vctk_example.md @@ -0,0 +1,51 @@ +[[Back]](..) + +# VCTK + +[VCTK](https://datashare.ed.ac.uk/handle/10283/3443) is an open English speech corpus. We provide examples +for building [Transformer](https://arxiv.org/abs/1809.08895) models on this dataset. + + +## Data preparation +Download data, create splits and generate audio manifests with +```bash +python -m examples.speech_synthesis.preprocessing.get_vctk_audio_manifest \ + --output-data-root ${AUDIO_DATA_ROOT} \ + --output-manifest-root ${AUDIO_MANIFEST_ROOT} +``` + +Then, extract log-Mel spectrograms, generate feature manifest and create data configuration YAML with +```bash +python -m examples.speech_synthesis.preprocessing.get_feature_manifest \ + --audio-manifest-root ${AUDIO_MANIFEST_ROOT} \ + --output-root ${FEATURE_MANIFEST_ROOT} \ + --ipa-vocab --use-g2p +``` +where we use phoneme inputs (`--ipa-vocab --use-g2p`) as example. + +To denoise audio and trim leading/trailing silence using signal processing based VAD, run +```bash +for SPLIT in dev test train; do + python -m examples.speech_synthesis.preprocessing.denoise_and_vad_audio \ + --audio-manifest ${AUDIO_MANIFEST_ROOT}/${SPLIT}.audio.tsv \ + --output-dir ${PROCESSED_DATA_ROOT} \ + --denoise --vad --vad-agg-level 3 +done +``` + +## Training +(Please refer to [the LJSpeech example](../docs/ljspeech_example.md#transformer).) + +## Inference +(Please refer to [the LJSpeech example](../docs/ljspeech_example.md#inference).) + +## Automatic Evaluation +(Please refer to [the LJSpeech example](../docs/ljspeech_example.md#automatic-evaluation).) + +## Results + +| --arch | Params | Test MCD | Model | +|---|---|---|---| +| tts_transformer | 54M | 3.4 | [Download](https://dl.fbaipublicfiles.com/fairseq/s2/vctk_transformer_phn.tar) | + +[[Back]](..) diff --git a/examples/speech_synthesis/evaluation/__init__.py b/examples/speech_synthesis/evaluation/__init__.py new file mode 100644 index 0000000000..6264236915 --- /dev/null +++ b/examples/speech_synthesis/evaluation/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. diff --git a/examples/speech_synthesis/evaluation/eval_asr.py b/examples/speech_synthesis/evaluation/eval_asr.py new file mode 100644 index 0000000000..005a11bfb3 --- /dev/null +++ b/examples/speech_synthesis/evaluation/eval_asr.py @@ -0,0 +1,128 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import editdistance +import re +import shutil +import soundfile as sf +import subprocess +from pathlib import Path + +from examples.speech_to_text.data_utils import load_tsv_to_dicts + + +def preprocess_text(text): + text = "|".join(re.sub(r"[^A-Z' ]", " ", text.upper()).split()) + text = " ".join(text) + return text + + +def prepare_w2v_data( + dict_dir, sample_rate, label, audio_paths, texts, split, data_dir +): + data_dir.mkdir(parents=True, exist_ok=True) + shutil.copyfile( + dict_dir / f"dict.{label}.txt", + data_dir / f"dict.{label}.txt" + ) + with open(data_dir / f"{split}.tsv", "w") as f: + f.write("/\n") + for audio_path in audio_paths: + wav, sr = sf.read(audio_path) + assert sr == sample_rate, f"{sr} != sample_rate" + nsample = len(wav) + f.write(f"{audio_path}\t{nsample}\n") + with open(data_dir / f"{split}.{label}", "w") as f: + for text in texts: + text = preprocess_text(text) + f.write(f"{text}\n") + + +def run_asr(asr_dir, split, w2v_ckpt, w2v_label, res_dir): + """ + results will be saved at + {res_dir}/{ref,hypo}.word-{w2v_ckpt.filename}-{split}.txt + """ + cmd = ["python", "-m", "examples.speech_recognition.infer"] + cmd += [str(asr_dir.resolve())] + cmd += ["--task", "audio_finetuning", "--nbest", "1", "--quiet"] + cmd += ["--w2l-decoder", "viterbi", "--criterion", "ctc"] + cmd += ["--post-process", "letter", "--max-tokens", "4000000"] + cmd += ["--path", str(w2v_ckpt.resolve()), "--labels", w2v_label] + cmd += ["--gen-subset", split, "--results-path", str(res_dir.resolve())] + + print(f"running cmd:\n{' '.join(cmd)}") + subprocess.run(cmd, check=True) + + +def compute_error_rate(hyp_wrd_path, ref_wrd_path, unit="word"): + """each line is "<text> (None-<index>)" """ + tokenize_line = { + "word": lambda x: re.sub(r" \(.*\)$", "", x.rstrip()).split(), + "char": lambda x: list(re.sub(r" \(.*\)$", "", x.rstrip())) + }.get(unit) + if tokenize_line is None: + raise ValueError(f"{unit} not supported") + + inds = [int(re.sub(r"\D*(\d*)\D*", r"\1", line)) + for line in open(hyp_wrd_path)] + hyps = [tokenize_line(line) for line in open(hyp_wrd_path)] + refs = [tokenize_line(line) for line in open(ref_wrd_path)] + assert(len(hyps) == len(refs)) + err_rates = [ + editdistance.eval(hyp, ref) / len(ref) for hyp, ref in zip(hyps, refs) + ] + ind_to_err_rates = {i: e for i, e in zip(inds, err_rates)} + return ind_to_err_rates + + +def main(args): + samples = load_tsv_to_dicts(args.raw_manifest) + ids = [ + sample[args.id_header] if args.id_header else "" for sample in samples + ] + audio_paths = [sample[args.audio_header] for sample in samples] + texts = [sample[args.text_header] for sample in samples] + + prepare_w2v_data( + args.w2v_dict_dir, + args.w2v_sample_rate, + args.w2v_label, + audio_paths, + texts, + args.split, + args.asr_dir + ) + run_asr(args.asr_dir, args.split, args.w2v_ckpt, args.w2v_label, args.asr_dir) + ind_to_err_rates = compute_error_rate( + args.asr_dir / f"hypo.word-{args.w2v_ckpt.name}-{args.split}.txt", + args.asr_dir / f"ref.word-{args.w2v_ckpt.name}-{args.split}.txt", + args.err_unit, + ) + + uer_path = args.asr_dir / f"uer_{args.err_unit}.{args.split}.tsv" + with open(uer_path, "w") as f: + f.write("id\taudio\tuer\n") + for ind, (id_, audio_path) in enumerate(zip(ids, audio_paths)): + f.write(f"{id_}\t{audio_path}\t{ind_to_err_rates[ind]:.4f}\n") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--raw-manifest", required=True, type=Path) + parser.add_argument("--asr-dir", required=True, type=Path) + parser.add_argument("--id-header", default="id", type=str) + parser.add_argument("--audio-header", default="audio", type=str) + parser.add_argument("--text-header", default="src_text", type=str) + parser.add_argument("--split", default="raw", type=str) + parser.add_argument("--w2v-ckpt", required=True, type=Path) + parser.add_argument("--w2v-dict-dir", required=True, type=Path) + parser.add_argument("--w2v-sample-rate", default=16000, type=int) + parser.add_argument("--w2v-label", default="ltr", type=str) + parser.add_argument("--err-unit", default="word", type=str) + args = parser.parse_args() + + main(args) diff --git a/examples/speech_synthesis/evaluation/eval_f0.py b/examples/speech_synthesis/evaluation/eval_f0.py new file mode 100644 index 0000000000..df721d6831 --- /dev/null +++ b/examples/speech_synthesis/evaluation/eval_f0.py @@ -0,0 +1,266 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +""" +Signal processing-based evaluation using waveforms +""" +import numpy as np +import os.path as op + +import torchaudio +import tqdm +from tabulate import tabulate + +from examples.speech_synthesis.utils import ( + gross_pitch_error, voicing_decision_error, f0_frame_error +) +from examples.speech_synthesis.evaluation.eval_sp import load_eval_spec + + +def difference_function(x, n, tau_max): + """ + Compute difference function of data x. This solution is implemented directly + with Numpy fft. + + + :param x: audio data + :param n: length of data + :param tau_max: integration window size + :return: difference function + :rtype: list + """ + + x = np.array(x, np.float64) + w = x.size + tau_max = min(tau_max, w) + x_cumsum = np.concatenate((np.array([0.]), (x * x).cumsum())) + size = w + tau_max + p2 = (size // 32).bit_length() + nice_numbers = (16, 18, 20, 24, 25, 27, 30, 32) + size_pad = min(x * 2 ** p2 for x in nice_numbers if x * 2 ** p2 >= size) + fc = np.fft.rfft(x, size_pad) + conv = np.fft.irfft(fc * fc.conjugate())[:tau_max] + return x_cumsum[w:w - tau_max:-1] + x_cumsum[w] - x_cumsum[:tau_max] - \ + 2 * conv + + +def cumulative_mean_normalized_difference_function(df, n): + """ + Compute cumulative mean normalized difference function (CMND). + + :param df: Difference function + :param n: length of data + :return: cumulative mean normalized difference function + :rtype: list + """ + + # scipy method + cmn_df = df[1:] * range(1, n) / np.cumsum(df[1:]).astype(float) + return np.insert(cmn_df, 0, 1) + + +def get_pitch(cmdf, tau_min, tau_max, harmo_th=0.1): + """ + Return fundamental period of a frame based on CMND function. + + :param cmdf: Cumulative Mean Normalized Difference function + :param tau_min: minimum period for speech + :param tau_max: maximum period for speech + :param harmo_th: harmonicity threshold to determine if it is necessary to + compute pitch frequency + :return: fundamental period if there is values under threshold, 0 otherwise + :rtype: float + """ + tau = tau_min + while tau < tau_max: + if cmdf[tau] < harmo_th: + while tau + 1 < tau_max and cmdf[tau + 1] < cmdf[tau]: + tau += 1 + return tau + tau += 1 + + return 0 # if unvoiced + + +def compute_yin(sig, sr, w_len=512, w_step=256, f0_min=100, f0_max=500, + harmo_thresh=0.1): + """ + + Compute the Yin Algorithm. Return fundamental frequency and harmonic rate. + + https://github.com/NVIDIA/mellotron adaption of + https://github.com/patriceguyot/Yin + + :param sig: Audio signal (list of float) + :param sr: sampling rate (int) + :param w_len: size of the analysis window (samples) + :param w_step: size of the lag between two consecutives windows (samples) + :param f0_min: Minimum fundamental frequency that can be detected (hertz) + :param f0_max: Maximum fundamental frequency that can be detected (hertz) + :param harmo_thresh: Threshold of detection. The yalgorithmù return the + first minimum of the CMND function below this threshold. + + :returns: + + * pitches: list of fundamental frequencies, + * harmonic_rates: list of harmonic rate values for each fundamental + frequency value (= confidence value) + * argmins: minimums of the Cumulative Mean Normalized DifferenceFunction + * times: list of time of each estimation + :rtype: tuple + """ + + tau_min = int(sr / f0_max) + tau_max = int(sr / f0_min) + + # time values for each analysis window + time_scale = range(0, len(sig) - w_len, w_step) + times = [t/float(sr) for t in time_scale] + frames = [sig[t:t + w_len] for t in time_scale] + + pitches = [0.0] * len(time_scale) + harmonic_rates = [0.0] * len(time_scale) + argmins = [0.0] * len(time_scale) + + for i, frame in enumerate(frames): + # Compute YIN + df = difference_function(frame, w_len, tau_max) + cm_df = cumulative_mean_normalized_difference_function(df, tau_max) + p = get_pitch(cm_df, tau_min, tau_max, harmo_thresh) + + # Get results + if np.argmin(cm_df) > tau_min: + argmins[i] = float(sr / np.argmin(cm_df)) + if p != 0: # A pitch was found + pitches[i] = float(sr / p) + harmonic_rates[i] = cm_df[p] + else: # No pitch, but we compute a value of the harmonic rate + harmonic_rates[i] = min(cm_df) + + return pitches, harmonic_rates, argmins, times + + +def extract_f0(samples): + f0_samples = [] + for sample in tqdm.tqdm(samples): + if not op.isfile(sample["ref"]) or not op.isfile(sample["syn"]): + f0_samples.append(None) + continue + + # assume single channel + yref, sr = torchaudio.load(sample["ref"]) + ysyn, _sr = torchaudio.load(sample["syn"]) + yref, ysyn = yref[0], ysyn[0] + assert sr == _sr, f"{sr} != {_sr}" + + yref_f0 = compute_yin(yref, sr) + ysyn_f0 = compute_yin(ysyn, sr) + + f0_samples += [ + { + "ref": yref_f0, + "syn": ysyn_f0 + } + ] + + return f0_samples + + +def eval_f0_error(samples, distortion_fn): + results = [] + for sample in tqdm.tqdm(samples): + if sample is None: + results.append(None) + continue + # assume single channel + yref_f, _, _, yref_t = sample["ref"] + ysyn_f, _, _, ysyn_t = sample["syn"] + + yref_f = np.array(yref_f) + yref_t = np.array(yref_t) + ysyn_f = np.array(ysyn_f) + ysyn_t = np.array(ysyn_t) + + distortion = distortion_fn(yref_t, yref_f, ysyn_t, ysyn_f) + results.append((distortion.item(), + len(yref_f), + len(ysyn_f) + )) + return results + + +def eval_gross_pitch_error(samples): + return eval_f0_error(samples, gross_pitch_error) + + +def eval_voicing_decision_error(samples): + return eval_f0_error(samples, voicing_decision_error) + + +def eval_f0_frame_error(samples): + return eval_f0_error(samples, f0_frame_error) + + +def print_results(results, show_bin): + results = np.array(list(filter(lambda x: x is not None, results))) + + np.set_printoptions(precision=3) + + def _print_result(results): + res = { + "nutt": len(results), + "error": results[:, 0].mean(), + "std": results[:, 0].std(), + "dur_ref": int(results[:, 1].sum()), + "dur_syn": int(results[:, 2].sum()), + } + print(tabulate([res.values()], res.keys(), floatfmt=".4f")) + + print(">>>> ALL") + _print_result(results) + + if show_bin: + edges = [0, 200, 400, 600, 800, 1000, 2000, 4000] + for i in range(1, len(edges)): + mask = np.logical_and(results[:, 1] >= edges[i-1], + results[:, 1] < edges[i]) + if not mask.any(): + continue + bin_results = results[mask] + print(f">>>> ({edges[i-1]}, {edges[i]})") + _print_result(bin_results) + + +def main(eval_f0, gpe, vde, ffe, show_bin): + samples = load_eval_spec(eval_f0) + if gpe or vde or ffe: + f0_samples = extract_f0(samples) + + if gpe: + print("===== Evaluate Gross Pitch Error =====") + results = eval_gross_pitch_error(f0_samples) + print_results(results, show_bin) + if vde: + print("===== Evaluate Voicing Decision Error =====") + results = eval_voicing_decision_error(f0_samples) + print_results(results, show_bin) + if ffe: + print("===== Evaluate F0 Frame Error =====") + results = eval_f0_frame_error(f0_samples) + print_results(results, show_bin) + + +if __name__ == "__main__": + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument("eval_f0") + parser.add_argument("--gpe", action="store_true") + parser.add_argument("--vde", action="store_true") + parser.add_argument("--ffe", action="store_true") + parser.add_argument("--show-bin", action="store_true") + args = parser.parse_args() + + main(args.eval_f0, args.gpe, args.vde, args.ffe, args.show_bin) diff --git a/examples/speech_synthesis/evaluation/eval_sp.py b/examples/speech_synthesis/evaluation/eval_sp.py new file mode 100644 index 0000000000..702c498038 --- /dev/null +++ b/examples/speech_synthesis/evaluation/eval_sp.py @@ -0,0 +1,131 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +""" +Signal processing-based evaluation using waveforms +""" + +import csv +import numpy as np +import os.path as op + +import torch +import tqdm +from tabulate import tabulate +import torchaudio + +from examples.speech_synthesis.utils import batch_mel_spectral_distortion +from fairseq.tasks.text_to_speech import batch_mel_cepstral_distortion + + +def load_eval_spec(path): + with open(path) as f: + reader = csv.DictReader(f, delimiter='\t') + samples = list(reader) + return samples + + +def eval_distortion(samples, distortion_fn, device="cuda"): + nmiss = 0 + results = [] + for sample in tqdm.tqdm(samples): + if not op.isfile(sample["ref"]) or not op.isfile(sample["syn"]): + nmiss += 1 + results.append(None) + continue + # assume single channel + yref, sr = torchaudio.load(sample["ref"]) + ysyn, _sr = torchaudio.load(sample["syn"]) + yref, ysyn = yref[0].to(device), ysyn[0].to(device) + assert sr == _sr, f"{sr} != {_sr}" + + distortion, extra = distortion_fn([yref], [ysyn], sr, None)[0] + _, _, _, _, _, pathmap = extra + nins = torch.sum(pathmap.sum(dim=1) - 1) # extra frames in syn + ndel = torch.sum(pathmap.sum(dim=0) - 1) # missing frames from syn + results.append( + (distortion.item(), # path distortion + pathmap.size(0), # yref num frames + pathmap.size(1), # ysyn num frames + pathmap.sum().item(), # path length + nins.item(), # insertion + ndel.item(), # deletion + ) + ) + return results + + +def eval_mel_cepstral_distortion(samples, device="cuda"): + return eval_distortion(samples, batch_mel_cepstral_distortion, device) + + +def eval_mel_spectral_distortion(samples, device="cuda"): + return eval_distortion(samples, batch_mel_spectral_distortion, device) + + +def print_results(results, show_bin): + results = np.array(list(filter(lambda x: x is not None, results))) + + np.set_printoptions(precision=3) + + def _print_result(results): + dist, dur_ref, dur_syn, dur_ali, nins, ndel = results.sum(axis=0) + res = { + "nutt": len(results), + "dist": dist, + "dur_ref": int(dur_ref), + "dur_syn": int(dur_syn), + "dur_ali": int(dur_ali), + "dist_per_ref_frm": dist/dur_ref, + "dist_per_syn_frm": dist/dur_syn, + "dist_per_ali_frm": dist/dur_ali, + "ins": nins/dur_ref, + "del": ndel/dur_ref, + } + print(tabulate( + [res.values()], + res.keys(), + floatfmt=".4f" + )) + + print(">>>> ALL") + _print_result(results) + + if show_bin: + edges = [0, 200, 400, 600, 800, 1000, 2000, 4000] + for i in range(1, len(edges)): + mask = np.logical_and(results[:, 1] >= edges[i-1], + results[:, 1] < edges[i]) + if not mask.any(): + continue + bin_results = results[mask] + print(f">>>> ({edges[i-1]}, {edges[i]})") + _print_result(bin_results) + + +def main(eval_spec, mcd, msd, show_bin): + samples = load_eval_spec(eval_spec) + device = "cpu" + if mcd: + print("===== Evaluate Mean Cepstral Distortion =====") + results = eval_mel_cepstral_distortion(samples, device) + print_results(results, show_bin) + if msd: + print("===== Evaluate Mean Spectral Distortion =====") + results = eval_mel_spectral_distortion(samples, device) + print_results(results, show_bin) + + +if __name__ == "__main__": + import argparse + parser = argparse.ArgumentParser() + parser.add_argument("eval_spec") + parser.add_argument("--mcd", action="store_true") + parser.add_argument("--msd", action="store_true") + parser.add_argument("--show-bin", action="store_true") + args = parser.parse_args() + + main(args.eval_spec, args.mcd, args.msd, args.show_bin) diff --git a/examples/speech_synthesis/evaluation/get_eval_manifest.py b/examples/speech_synthesis/evaluation/get_eval_manifest.py new file mode 100644 index 0000000000..a28cd607a0 --- /dev/null +++ b/examples/speech_synthesis/evaluation/get_eval_manifest.py @@ -0,0 +1,58 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +import csv +from pathlib import Path + + +def main(args): + """ + `uid syn ref text` + """ + in_root = Path(args.generation_root).resolve() + ext = args.audio_format + with open(args.audio_manifest) as f, open(args.output_path, "w") as f_out: + reader = csv.DictReader( + f, delimiter="\t", quotechar=None, doublequote=False, + lineterminator="\n", quoting=csv.QUOTE_NONE + ) + header = ["id", "syn", "ref", "text", "speaker"] + f_out.write("\t".join(header) + "\n") + for row in reader: + dir_name = f"{ext}_{args.sample_rate}hz_{args.vocoder}" + id_ = row["id"] + syn = (in_root / dir_name / f"{id_}.{ext}").as_posix() + ref = row["audio"] + if args.use_resynthesized_target: + ref = (in_root / f"{dir_name}_tgt" / f"{id_}.{ext}").as_posix() + sample = [id_, syn, ref, row["tgt_text"], row["speaker"]] + f_out.write("\t".join(sample) + "\n") + print(f"wrote evaluation file to {args.output_path}") + + +if __name__ == "__main__": + import argparse + parser = argparse.ArgumentParser() + parser.add_argument( + "--generation-root", help="output directory for generate_waveform.py" + ) + parser.add_argument( + "--audio-manifest", + help="used to determine the original utterance ID and text" + ) + parser.add_argument( + "--output-path", help="path to output evaluation spec file" + ) + parser.add_argument( + "--use-resynthesized-target", action="store_true", + help="use resynthesized reference instead of the original audio" + ) + parser.add_argument("--vocoder", type=str, default="griffin_lim") + parser.add_argument("--sample-rate", type=int, default=22_050) + parser.add_argument("--audio-format", type=str, default="wav") + args = parser.parse_args() + + main(args) diff --git a/examples/speech_synthesis/generate_waveform.py b/examples/speech_synthesis/generate_waveform.py new file mode 100644 index 0000000000..bfc2ef8eb3 --- /dev/null +++ b/examples/speech_synthesis/generate_waveform.py @@ -0,0 +1,191 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +import logging +import matplotlib.pyplot as plt +import numpy as np +from pathlib import Path +import soundfile as sf +import sys +import torch +import torchaudio + +from fairseq import checkpoint_utils, options, tasks, utils +from fairseq.logging import progress_bar +from fairseq.tasks.text_to_speech import plot_tts_output +from fairseq.data.audio.text_to_speech_dataset import TextToSpeechDataset + + +logging.basicConfig() +logging.root.setLevel(logging.INFO) +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + + +def make_parser(): + parser = options.get_speech_generation_parser() + parser.add_argument("--dump-features", action="store_true") + parser.add_argument("--dump-waveforms", action="store_true") + parser.add_argument("--dump-attentions", action="store_true") + parser.add_argument("--dump-eos-probs", action="store_true") + parser.add_argument("--dump-plots", action="store_true") + parser.add_argument("--dump-target", action="store_true") + parser.add_argument("--output-sample-rate", default=22050, type=int) + parser.add_argument("--teacher-forcing", action="store_true") + parser.add_argument( + "--audio-format", type=str, default="wav", choices=["wav", "flac"] + ) + return parser + + +def postprocess_results( + dataset: TextToSpeechDataset, sample, hypos, resample_fn, dump_target +): + def to_np(x): + return None if x is None else x.detach().cpu().numpy() + + sample_ids = [dataset.ids[i] for i in sample["id"].tolist()] + texts = sample["src_texts"] + attns = [to_np(hypo["attn"]) for hypo in hypos] + eos_probs = [to_np(hypo.get("eos_prob", None)) for hypo in hypos] + feat_preds = [to_np(hypo["feature"]) for hypo in hypos] + wave_preds = [to_np(resample_fn(h["waveform"])) for h in hypos] + if dump_target: + feat_targs = [to_np(hypo["targ_feature"]) for hypo in hypos] + wave_targs = [to_np(resample_fn(h["targ_waveform"])) for h in hypos] + else: + feat_targs = [None for _ in hypos] + wave_targs = [None for _ in hypos] + + return zip(sample_ids, texts, attns, eos_probs, feat_preds, wave_preds, + feat_targs, wave_targs) + + +def dump_result( + is_na_model, + args, + vocoder, + sample_id, + text, + attn, + eos_prob, + feat_pred, + wave_pred, + feat_targ, + wave_targ, +): + sample_rate = args.output_sample_rate + out_root = Path(args.results_path) + if args.dump_features: + feat_dir = out_root / "feat" + feat_dir.mkdir(exist_ok=True, parents=True) + np.save(feat_dir / f"{sample_id}.npy", feat_pred) + if args.dump_target: + feat_tgt_dir = out_root / "feat_tgt" + feat_tgt_dir.mkdir(exist_ok=True, parents=True) + np.save(feat_tgt_dir / f"{sample_id}.npy", feat_targ) + if args.dump_attentions: + attn_dir = out_root / "attn" + attn_dir.mkdir(exist_ok=True, parents=True) + np.save(attn_dir / f"{sample_id}.npy", attn.numpy()) + if args.dump_eos_probs and not is_na_model: + eos_dir = out_root / "eos" + eos_dir.mkdir(exist_ok=True, parents=True) + np.save(eos_dir / f"{sample_id}.npy", eos_prob) + + if args.dump_plots: + images = [feat_pred.T] if is_na_model else [feat_pred.T, attn] + names = ["output"] if is_na_model else ["output", "alignment"] + if feat_targ is not None: + images = [feat_targ.T] + images + names = [f"target (idx={sample_id})"] + names + if is_na_model: + plot_tts_output(images, names, attn, "alignment", suptitle=text) + else: + plot_tts_output(images, names, eos_prob, "eos prob", suptitle=text) + plot_dir = out_root / "plot" + plot_dir.mkdir(exist_ok=True, parents=True) + plt.savefig(plot_dir / f"{sample_id}.png") + plt.close() + + if args.dump_waveforms: + ext = args.audio_format + if wave_pred is not None: + wav_dir = out_root / f"{ext}_{sample_rate}hz_{vocoder}" + wav_dir.mkdir(exist_ok=True, parents=True) + sf.write(wav_dir / f"{sample_id}.{ext}", wave_pred, sample_rate) + if args.dump_target and wave_targ is not None: + wav_tgt_dir = out_root / f"{ext}_{sample_rate}hz_{vocoder}_tgt" + wav_tgt_dir.mkdir(exist_ok=True, parents=True) + sf.write(wav_tgt_dir / f"{sample_id}.{ext}", wave_targ, sample_rate) + + +def main(args): + assert(args.dump_features or args.dump_waveforms or args.dump_attentions + or args.dump_eos_probs or args.dump_plots) + if args.max_tokens is None and args.batch_size is None: + args.max_tokens = 8000 + logger.info(args) + + use_cuda = torch.cuda.is_available() and not args.cpu + task = tasks.setup_task(args) + models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task( + [args.path], + task=task, + ) + model = models[0].cuda() if use_cuda else models[0] + # use the original n_frames_per_step + task.args.n_frames_per_step = saved_cfg.task.n_frames_per_step + task.load_dataset(args.gen_subset, task_cfg=saved_cfg.task) + + data_cfg = task.data_cfg + sample_rate = data_cfg.config.get("features", {}).get("sample_rate", 22050) + resample_fn = { + False: lambda x: x, + True: lambda x: torchaudio.sox_effects.apply_effects_tensor( + x.detach().cpu().unsqueeze(0), sample_rate, + [['rate', str(args.output_sample_rate)]] + )[0].squeeze(0) + }.get(args.output_sample_rate != sample_rate) + if args.output_sample_rate != sample_rate: + logger.info(f"resampling to {args.output_sample_rate}Hz") + + generator = task.build_generator([model], args) + itr = task.get_batch_iterator( + dataset=task.dataset(args.gen_subset), + max_tokens=args.max_tokens, + max_sentences=args.batch_size, + max_positions=(sys.maxsize, sys.maxsize), + ignore_invalid_inputs=args.skip_invalid_size_inputs_valid_test, + required_batch_size_multiple=args.required_batch_size_multiple, + num_shards=args.num_shards, + shard_id=args.shard_id, + num_workers=args.num_workers, + data_buffer_size=args.data_buffer_size, + ).next_epoch_itr(shuffle=False) + + Path(args.results_path).mkdir(exist_ok=True, parents=True) + is_na_model = getattr(model, "NON_AUTOREGRESSIVE", False) + dataset = task.dataset(args.gen_subset) + vocoder = task.args.vocoder + with progress_bar.build_progress_bar(args, itr) as t: + for sample in t: + sample = utils.move_to_cuda(sample) if use_cuda else sample + hypos = generator.generate(model, sample, has_targ=args.dump_target) + for result in postprocess_results( + dataset, sample, hypos, resample_fn, args.dump_target + ): + dump_result(is_na_model, args, vocoder, *result) + + +def cli_main(): + parser = make_parser() + args = options.parse_args_and_arch(parser) + main(args) + + +if __name__ == "__main__": + cli_main() diff --git a/examples/speech_synthesis/preprocessing/__init__.py b/examples/speech_synthesis/preprocessing/__init__.py new file mode 100644 index 0000000000..6264236915 --- /dev/null +++ b/examples/speech_synthesis/preprocessing/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. diff --git a/examples/speech_synthesis/preprocessing/denoise_and_vad_audio.py b/examples/speech_synthesis/preprocessing/denoise_and_vad_audio.py new file mode 100644 index 0000000000..4e13b38a5d --- /dev/null +++ b/examples/speech_synthesis/preprocessing/denoise_and_vad_audio.py @@ -0,0 +1,204 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import logging +import os +import csv +import tempfile +from collections import defaultdict +from pathlib import Path + +import torchaudio +try: + import webrtcvad +except ImportError: + raise ImportError("Please install py-webrtcvad: pip install webrtcvad") +import pandas as pd +from tqdm import tqdm + +from examples.speech_synthesis.preprocessing.denoiser.pretrained import master64 +import examples.speech_synthesis.preprocessing.denoiser.utils as utils +from examples.speech_synthesis.preprocessing.vad import ( + frame_generator, vad_collector, read_wave, write_wave, FS_MS, THRESHOLD, + SCALE +) +from examples.speech_to_text.data_utils import save_df_to_tsv + + +log = logging.getLogger(__name__) + +PATHS = ["after_denoise", "after_vad"] +MIN_T = 0.05 + + +def generate_tmp_filename(extension="txt"): + return tempfile._get_default_tempdir() + "/" + \ + next(tempfile._get_candidate_names()) + "." + extension + + +def convert_sr(inpath, sr, output_path=None): + if not output_path: + output_path = generate_tmp_filename("wav") + cmd = f"sox {inpath} -r {sr} {output_path}" + os.system(cmd) + return output_path + + +def apply_vad(vad, inpath): + audio, sample_rate = read_wave(inpath) + frames = frame_generator(FS_MS, audio, sample_rate) + frames = list(frames) + segments = vad_collector(sample_rate, FS_MS, 300, vad, frames) + merge_segments = list() + timestamp_start = 0.0 + timestamp_end = 0.0 + # removing start, end, and long sequences of sils + for i, segment in enumerate(segments): + merge_segments.append(segment[0]) + if i and timestamp_start: + sil_duration = segment[1] - timestamp_end + if sil_duration > THRESHOLD: + merge_segments.append(int(THRESHOLD / SCALE) * (b'\x00')) + else: + merge_segments.append(int((sil_duration / SCALE)) * (b'\x00')) + timestamp_start = segment[1] + timestamp_end = segment[2] + segment = b''.join(merge_segments) + return segment, sample_rate + + +def write(wav, filename, sr=16_000): + # Normalize audio if it prevents clipping + wav = wav / max(wav.abs().max().item(), 1) + torchaudio.save(filename, wav.cpu(), sr, encoding="PCM_S", + bits_per_sample=16) + + +def process(args): + # making sure we are requested either denoise or vad + if not args.denoise and not args.vad: + log.error("No denoise or vad is requested.") + return + + log.info("Creating out directories...") + if args.denoise: + out_denoise = Path(args.output_dir).absolute().joinpath(PATHS[0]) + out_denoise.mkdir(parents=True, exist_ok=True) + if args.vad: + out_vad = Path(args.output_dir).absolute().joinpath(PATHS[1]) + out_vad.mkdir(parents=True, exist_ok=True) + + log.info("Loading pre-trained speech enhancement model...") + model = master64().to(args.device) + + log.info("Building the VAD model...") + vad = webrtcvad.Vad(int(args.vad_agg_level)) + + # preparing the output dict + output_dict = defaultdict(list) + + log.info(f"Parsing input manifest: {args.audio_manifest}") + with open(args.audio_manifest, "r") as f: + manifest_dict = csv.DictReader(f, delimiter="\t") + for row in tqdm(manifest_dict): + filename = str(row["audio"]) + + final_output = filename + keep_sample = True + n_frames = row["n_frames"] + snr = -1 + if args.denoise: + output_path_denoise = out_denoise.joinpath(Path(filename).name) + # convert to 16khz in case we use a differet sr + tmp_path = convert_sr(final_output, 16000) + + # loading audio file and generating the enhanced version + out, sr = torchaudio.load(tmp_path) + out = out.to(args.device) + estimate = model(out) + estimate = (1 - args.dry_wet) * estimate + args.dry_wet * out + write(estimate[0], str(output_path_denoise), sr) + + snr = utils.cal_snr(out, estimate) + snr = snr.cpu().detach().numpy()[0][0] + final_output = str(output_path_denoise) + + if args.vad: + output_path_vad = out_vad.joinpath(Path(filename).name) + sr = torchaudio.info(final_output).sample_rate + if sr in [16000, 32000, 48000]: + tmp_path = final_output + elif sr < 16000: + tmp_path = convert_sr(final_output, 16000) + elif sr < 32000: + tmp_path = convert_sr(final_output, 32000) + else: + tmp_path = convert_sr(final_output, 48000) + # apply VAD + segment, sample_rate = apply_vad(vad, tmp_path) + if len(segment) < sample_rate * MIN_T: + keep_sample = False + print(( + f"WARNING: skip {filename} because it is too short " + f"after VAD ({len(segment) / sample_rate} < {MIN_T})" + )) + else: + if sample_rate != sr: + tmp_path = generate_tmp_filename("wav") + write_wave(tmp_path, segment, sample_rate) + convert_sr(tmp_path, sr, + output_path=str(output_path_vad)) + else: + write_wave(str(output_path_vad), segment, sample_rate) + final_output = str(output_path_vad) + segment, _ = torchaudio.load(final_output) + n_frames = segment.size(1) + + if keep_sample: + output_dict["id"].append(row["id"]) + output_dict["audio"].append(final_output) + output_dict["n_frames"].append(n_frames) + output_dict["tgt_text"].append(row["tgt_text"]) + output_dict["speaker"].append(row["speaker"]) + output_dict["src_text"].append(row["src_text"]) + output_dict["snr"].append(snr) + + out_tsv_path = Path(args.output_dir) / Path(args.audio_manifest).name + log.info(f"Saving manifest to {out_tsv_path.as_posix()}") + save_df_to_tsv(pd.DataFrame.from_dict(output_dict), out_tsv_path) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--audio-manifest", "-i", required=True, + type=str, help="path to the input manifest.") + parser.add_argument( + "--output-dir", "-o", required=True, type=str, + help="path to the output dir. it will contain files after denoising and" + " vad" + ) + parser.add_argument("--vad-agg-level", "-a", type=int, default=2, + help="the aggresive level of the vad [0-3].") + parser.add_argument( + "--dry-wet", "-dw", type=float, default=0.01, + help="the level of linear interpolation between noisy and enhanced " + "files." + ) + parser.add_argument( + "--device", "-d", type=str, default="cpu", + help="the device to be used for the speech enhancement model: " + "cpu | cuda." + ) + parser.add_argument("--denoise", action="store_true", + help="apply a denoising") + parser.add_argument("--vad", action="store_true", help="apply a VAD") + args = parser.parse_args() + + process(args) + + +if __name__ == "__main__": + main() diff --git a/examples/speech_synthesis/preprocessing/denoiser/__init__.py b/examples/speech_synthesis/preprocessing/denoiser/__init__.py new file mode 100644 index 0000000000..6264236915 --- /dev/null +++ b/examples/speech_synthesis/preprocessing/denoiser/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. diff --git a/examples/speech_synthesis/preprocessing/denoiser/demucs.py b/examples/speech_synthesis/preprocessing/denoiser/demucs.py new file mode 100644 index 0000000000..3f70e73d6a --- /dev/null +++ b/examples/speech_synthesis/preprocessing/denoiser/demucs.py @@ -0,0 +1,473 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. +# author: adefossez + +import math +import time + +import torch as th +from torch import nn +from torch.nn import functional as F + +from .resample import downsample2, upsample2 +from .utils import capture_init + + +class BLSTM(nn.Module): + def __init__(self, dim, layers=2, bi=True): + super().__init__() + klass = nn.LSTM + self.lstm = klass( + bidirectional=bi, num_layers=layers, hidden_size=dim, input_size=dim + ) + self.linear = None + if bi: + self.linear = nn.Linear(2 * dim, dim) + + def forward(self, x, hidden=None): + x, hidden = self.lstm(x, hidden) + if self.linear: + x = self.linear(x) + return x, hidden + + +def rescale_conv(conv, reference): + std = conv.weight.std().detach() + scale = (std / reference)**0.5 + conv.weight.data /= scale + if conv.bias is not None: + conv.bias.data /= scale + + +def rescale_module(module, reference): + for sub in module.modules(): + if isinstance(sub, (nn.Conv1d, nn.ConvTranspose1d)): + rescale_conv(sub, reference) + + +class Demucs(nn.Module): + """ + Demucs speech enhancement model. + Args: + - chin (int): number of input channels. + - chout (int): number of output channels. + - hidden (int): number of initial hidden channels. + - depth (int): number of layers. + - kernel_size (int): kernel size for each layer. + - stride (int): stride for each layer. + - causal (bool): if false, uses BiLSTM instead of LSTM. + - resample (int): amount of resampling to apply to the input/output. + Can be one of 1, 2 or 4. + - growth (float): number of channels is multiplied by this for every layer. + - max_hidden (int): maximum number of channels. Can be useful to + control the size/speed of the model. + - normalize (bool): if true, normalize the input. + - glu (bool): if true uses GLU instead of ReLU in 1x1 convolutions. + - rescale (float): controls custom weight initialization. + See https://arxiv.org/abs/1911.13254. + - floor (float): stability flooring when normalizing. + + """ + @capture_init + def __init__(self, + chin=1, + chout=1, + hidden=48, + depth=5, + kernel_size=8, + stride=4, + causal=True, + resample=4, + growth=2, + max_hidden=10_000, + normalize=True, + glu=True, + rescale=0.1, + floor=1e-3): + + super().__init__() + if resample not in [1, 2, 4]: + raise ValueError("Resample should be 1, 2 or 4.") + + self.chin = chin + self.chout = chout + self.hidden = hidden + self.depth = depth + self.kernel_size = kernel_size + self.stride = stride + self.causal = causal + self.floor = floor + self.resample = resample + self.normalize = normalize + + self.encoder = nn.ModuleList() + self.decoder = nn.ModuleList() + activation = nn.GLU(1) if glu else nn.ReLU() + ch_scale = 2 if glu else 1 + + for index in range(depth): + encode = [] + encode += [ + nn.Conv1d(chin, hidden, kernel_size, stride), + nn.ReLU(), + nn.Conv1d(hidden, hidden * ch_scale, 1), activation, + ] + self.encoder.append(nn.Sequential(*encode)) + + decode = [] + decode += [ + nn.Conv1d(hidden, ch_scale * hidden, 1), activation, + nn.ConvTranspose1d(hidden, chout, kernel_size, stride), + ] + if index > 0: + decode.append(nn.ReLU()) + self.decoder.insert(0, nn.Sequential(*decode)) + chout = hidden + chin = hidden + hidden = min(int(growth * hidden), max_hidden) + + self.lstm = BLSTM(chin, bi=not causal) + if rescale: + rescale_module(self, reference=rescale) + + def valid_length(self, length): + """ + Return the nearest valid length to use with the model so that + there is no time steps left over in a convolutions, e.g. for all + layers, size of the input - kernel_size % stride = 0. + + If the mixture has a valid length, the estimated sources + will have exactly the same length. + """ + length = math.ceil(length * self.resample) + for _ in range(self.depth): + length = math.ceil((length - self.kernel_size) / self.stride) + 1 + length = max(length, 1) + for _ in range(self.depth): + length = (length - 1) * self.stride + self.kernel_size + length = int(math.ceil(length / self.resample)) + return int(length) + + @property + def total_stride(self): + return self.stride ** self.depth // self.resample + + def forward(self, mix): + if mix.dim() == 2: + mix = mix.unsqueeze(1) + + if self.normalize: + mono = mix.mean(dim=1, keepdim=True) + std = mono.std(dim=-1, keepdim=True) + mix = mix / (self.floor + std) + else: + std = 1 + length = mix.shape[-1] + x = mix + x = F.pad(x, (0, self.valid_length(length) - length)) + if self.resample == 2: + x = upsample2(x) + elif self.resample == 4: + x = upsample2(x) + x = upsample2(x) + skips = [] + for encode in self.encoder: + x = encode(x) + skips.append(x) + x = x.permute(2, 0, 1) + x, _ = self.lstm(x) + x = x.permute(1, 2, 0) + for decode in self.decoder: + skip = skips.pop(-1) + x = x + skip[..., :x.shape[-1]] + x = decode(x) + if self.resample == 2: + x = downsample2(x) + elif self.resample == 4: + x = downsample2(x) + x = downsample2(x) + + x = x[..., :length] + return std * x + + +def fast_conv(conv, x): + """ + Faster convolution evaluation if either kernel size is 1 + or length of sequence is 1. + """ + batch, chin, length = x.shape + chout, chin, kernel = conv.weight.shape + assert batch == 1 + if kernel == 1: + x = x.view(chin, length) + out = th.addmm(conv.bias.view(-1, 1), + conv.weight.view(chout, chin), x) + elif length == kernel: + x = x.view(chin * kernel, 1) + out = th.addmm(conv.bias.view(-1, 1), + conv.weight.view(chout, chin * kernel), x) + else: + out = conv(x) + return out.view(batch, chout, -1) + + +class DemucsStreamer: + """ + Streaming implementation for Demucs. It supports being fed with any amount + of audio at a time. You will get back as much audio as possible at that + point. + + Args: + - demucs (Demucs): Demucs model. + - dry (float): amount of dry (e.g. input) signal to keep. 0 is maximum + noise removal, 1 just returns the input signal. Small values > 0 + allows to limit distortions. + - num_frames (int): number of frames to process at once. Higher values + will increase overall latency but improve the real time factor. + - resample_lookahead (int): extra lookahead used for the resampling. + - resample_buffer (int): size of the buffer of previous inputs/outputs + kept for resampling. + """ + def __init__(self, demucs, + dry=0, + num_frames=1, + resample_lookahead=64, + resample_buffer=256): + device = next(iter(demucs.parameters())).device + self.demucs = demucs + self.lstm_state = None + self.conv_state = None + self.dry = dry + self.resample_lookahead = resample_lookahead + resample_buffer = min(demucs.total_stride, resample_buffer) + self.resample_buffer = resample_buffer + self.frame_length = demucs.valid_length(1) + \ + demucs.total_stride * (num_frames - 1) + self.total_length = self.frame_length + self.resample_lookahead + self.stride = demucs.total_stride * num_frames + self.resample_in = th.zeros(demucs.chin, resample_buffer, device=device) + self.resample_out = th.zeros( + demucs.chin, resample_buffer, device=device + ) + + self.frames = 0 + self.total_time = 0 + self.variance = 0 + self.pending = th.zeros(demucs.chin, 0, device=device) + + bias = demucs.decoder[0][2].bias + weight = demucs.decoder[0][2].weight + chin, chout, kernel = weight.shape + self._bias = bias.view(-1, 1).repeat(1, kernel).view(-1, 1) + self._weight = weight.permute(1, 2, 0).contiguous() + + def reset_time_per_frame(self): + self.total_time = 0 + self.frames = 0 + + @property + def time_per_frame(self): + return self.total_time / self.frames + + def flush(self): + """ + Flush remaining audio by padding it with zero. Call this + when you have no more input and want to get back the last chunk of audio. + """ + pending_length = self.pending.shape[1] + padding = th.zeros( + self.demucs.chin, self.total_length, device=self.pending.device + ) + out = self.feed(padding) + return out[:, :pending_length] + + def feed(self, wav): + """ + Apply the model to mix using true real time evaluation. + Normalization is done online as is the resampling. + """ + begin = time.time() + demucs = self.demucs + resample_buffer = self.resample_buffer + stride = self.stride + resample = demucs.resample + + if wav.dim() != 2: + raise ValueError("input wav should be two dimensional.") + chin, _ = wav.shape + if chin != demucs.chin: + raise ValueError(f"Expected {demucs.chin} channels, got {chin}") + + self.pending = th.cat([self.pending, wav], dim=1) + outs = [] + while self.pending.shape[1] >= self.total_length: + self.frames += 1 + frame = self.pending[:, :self.total_length] + dry_signal = frame[:, :stride] + if demucs.normalize: + mono = frame.mean(0) + variance = (mono**2).mean() + self.variance = variance / self.frames + \ + (1 - 1 / self.frames) * self.variance + frame = frame / (demucs.floor + math.sqrt(self.variance)) + frame = th.cat([self.resample_in, frame], dim=-1) + self.resample_in[:] = frame[:, stride - resample_buffer:stride] + + if resample == 4: + frame = upsample2(upsample2(frame)) + elif resample == 2: + frame = upsample2(frame) + # remove pre sampling buffer + frame = frame[:, resample * resample_buffer:] + # remove extra samples after window + frame = frame[:, :resample * self.frame_length] + + out, extra = self._separate_frame(frame) + padded_out = th.cat([self.resample_out, out, extra], 1) + self.resample_out[:] = out[:, -resample_buffer:] + if resample == 4: + out = downsample2(downsample2(padded_out)) + elif resample == 2: + out = downsample2(padded_out) + else: + out = padded_out + + out = out[:, resample_buffer // resample:] + out = out[:, :stride] + + if demucs.normalize: + out *= math.sqrt(self.variance) + out = self.dry * dry_signal + (1 - self.dry) * out + outs.append(out) + self.pending = self.pending[:, stride:] + + self.total_time += time.time() - begin + if outs: + out = th.cat(outs, 1) + else: + out = th.zeros(chin, 0, device=wav.device) + return out + + def _separate_frame(self, frame): + demucs = self.demucs + skips = [] + next_state = [] + first = self.conv_state is None + stride = self.stride * demucs.resample + x = frame[None] + for idx, encode in enumerate(demucs.encoder): + stride //= demucs.stride + length = x.shape[2] + if idx == demucs.depth - 1: + # This is sligthly faster for the last conv + x = fast_conv(encode[0], x) + x = encode[1](x) + x = fast_conv(encode[2], x) + x = encode[3](x) + else: + if not first: + prev = self.conv_state.pop(0) + prev = prev[..., stride:] + tgt = (length - demucs.kernel_size) // demucs.stride + 1 + missing = tgt - prev.shape[-1] + offset = length - demucs.kernel_size - \ + demucs.stride * (missing - 1) + x = x[..., offset:] + x = encode[1](encode[0](x)) + x = fast_conv(encode[2], x) + x = encode[3](x) + if not first: + x = th.cat([prev, x], -1) + next_state.append(x) + skips.append(x) + + x = x.permute(2, 0, 1) + x, self.lstm_state = demucs.lstm(x, self.lstm_state) + x = x.permute(1, 2, 0) + # In the following, x contains only correct samples, i.e. the one + # for which each time position is covered by two window of the upper + # layer. extra contains extra samples to the right, and is used only as + # a better padding for the online resampling. + extra = None + for idx, decode in enumerate(demucs.decoder): + skip = skips.pop(-1) + x += skip[..., :x.shape[-1]] + x = fast_conv(decode[0], x) + x = decode[1](x) + + if extra is not None: + skip = skip[..., x.shape[-1]:] + extra += skip[..., :extra.shape[-1]] + extra = decode[2](decode[1](decode[0](extra))) + x = decode[2](x) + next_state.append( + x[..., -demucs.stride:] - decode[2].bias.view(-1, 1) + ) + if extra is None: + extra = x[..., -demucs.stride:] + else: + extra[..., :demucs.stride] += next_state[-1] + x = x[..., :-demucs.stride] + + if not first: + prev = self.conv_state.pop(0) + x[..., :demucs.stride] += prev + if idx != demucs.depth - 1: + x = decode[3](x) + extra = decode[3](extra) + self.conv_state = next_state + return x[0], extra[0] + + +def test(): + import argparse + parser = argparse.ArgumentParser( + "denoiser.demucs", + description="Benchmark the streaming Demucs implementation, as well as " + "checking the delta with the offline implementation.") + parser.add_argument("--depth", default=5, type=int) + parser.add_argument("--resample", default=4, type=int) + parser.add_argument("--hidden", default=48, type=int) + parser.add_argument("--sample_rate", default=16000, type=float) + parser.add_argument("--device", default="cpu") + parser.add_argument("-t", "--num_threads", type=int) + parser.add_argument("-f", "--num_frames", type=int, default=1) + args = parser.parse_args() + if args.num_threads: + th.set_num_threads(args.num_threads) + sr = args.sample_rate + sr_ms = sr / 1000 + demucs = Demucs( + depth=args.depth, hidden=args.hidden, resample=args.resample + ).to(args.device) + x = th.randn(1, int(sr * 4)).to(args.device) + out = demucs(x[None])[0] + streamer = DemucsStreamer(demucs, num_frames=args.num_frames) + out_rt = [] + frame_size = streamer.total_length + with th.no_grad(): + while x.shape[1] > 0: + out_rt.append(streamer.feed(x[:, :frame_size])) + x = x[:, frame_size:] + frame_size = streamer.demucs.total_stride + out_rt.append(streamer.flush()) + out_rt = th.cat(out_rt, 1) + model_size = sum(p.numel() for p in demucs.parameters()) * 4 / 2**20 + initial_lag = streamer.total_length / sr_ms + tpf = 1000 * streamer.time_per_frame + print(f"model size: {model_size:.1f}MB, ", end='') + print(f"delta batch/streaming: {th.norm(out - out_rt) / th.norm(out):.2%}") + print(f"initial lag: {initial_lag:.1f}ms, ", end='') + print(f"stride: {streamer.stride * args.num_frames / sr_ms:.1f}ms") + print(f"time per frame: {tpf:.1f}ms, ", end='') + rtf = (1000 * streamer.time_per_frame) / (streamer.stride / sr_ms) + print(f"RTF: {rtf:.2f}") + print(f"Total lag with computation: {initial_lag + tpf:.1f}ms") + + +if __name__ == "__main__": + test() diff --git a/examples/speech_synthesis/preprocessing/denoiser/pretrained.py b/examples/speech_synthesis/preprocessing/denoiser/pretrained.py new file mode 100644 index 0000000000..2fa846075b --- /dev/null +++ b/examples/speech_synthesis/preprocessing/denoiser/pretrained.py @@ -0,0 +1,81 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. +# author: adefossez + +import logging + +import torch.hub + +from .demucs import Demucs +from .utils import deserialize_model + +logger = logging.getLogger(__name__) +ROOT = "https://dl.fbaipublicfiles.com/adiyoss/denoiser/" +DNS_48_URL = ROOT + "dns48-11decc9d8e3f0998.th" +DNS_64_URL = ROOT + "dns64-a7761ff99a7d5bb6.th" +MASTER_64_URL = ROOT + "master64-8a5dfb4bb92753dd.th" + + +def _demucs(pretrained, url, **kwargs): + model = Demucs(**kwargs) + if pretrained: + state_dict = torch.hub.load_state_dict_from_url(url, map_location='cpu') + model.load_state_dict(state_dict) + return model + + +def dns48(pretrained=True): + return _demucs(pretrained, DNS_48_URL, hidden=48) + + +def dns64(pretrained=True): + return _demucs(pretrained, DNS_64_URL, hidden=64) + + +def master64(pretrained=True): + return _demucs(pretrained, MASTER_64_URL, hidden=64) + + +def add_model_flags(parser): + group = parser.add_mutually_exclusive_group(required=False) + group.add_argument( + "-m", "--model_path", help="Path to local trained model." + ) + group.add_argument( + "--dns48", action="store_true", + help="Use pre-trained real time H=48 model trained on DNS." + ) + group.add_argument( + "--dns64", action="store_true", + help="Use pre-trained real time H=64 model trained on DNS." + ) + group.add_argument( + "--master64", action="store_true", + help="Use pre-trained real time H=64 model trained on DNS and Valentini." + ) + + +def get_model(args): + """ + Load local model package or torchhub pre-trained model. + """ + if args.model_path: + logger.info("Loading model from %s", args.model_path) + pkg = torch.load(args.model_path) + model = deserialize_model(pkg) + elif args.dns64: + logger.info("Loading pre-trained real time H=64 model trained on DNS.") + model = dns64() + elif args.master64: + logger.info( + "Loading pre-trained real time H=64 model trained on DNS and Valentini." + ) + model = master64() + else: + logger.info("Loading pre-trained real time H=48 model trained on DNS.") + model = dns48() + logger.debug(model) + return model diff --git a/examples/speech_synthesis/preprocessing/denoiser/resample.py b/examples/speech_synthesis/preprocessing/denoiser/resample.py new file mode 100644 index 0000000000..1222addc42 --- /dev/null +++ b/examples/speech_synthesis/preprocessing/denoiser/resample.py @@ -0,0 +1,79 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. +# author: adefossez + +import math + +import torch as th +from torch.nn import functional as F + + +def sinc(t): + """sinc. + + :param t: the input tensor + """ + return th.where(t == 0, th.tensor(1., device=t.device, dtype=t.dtype), + th.sin(t) / t) + + +def kernel_upsample2(zeros=56): + """kernel_upsample2. + + """ + win = th.hann_window(4 * zeros + 1, periodic=False) + winodd = win[1::2] + t = th.linspace(-zeros + 0.5, zeros - 0.5, 2 * zeros) + t *= math.pi + kernel = (sinc(t) * winodd).view(1, 1, -1) + return kernel + + +def upsample2(x, zeros=56): + """ + Upsampling the input by 2 using sinc interpolation. + Smith, Julius, and Phil Gossett. "A flexible sampling-rate conversion method." + ICASSP'84. IEEE International Conference on Acoustics, Speech, and Signal Processing. + Vol. 9. IEEE, 1984. + """ + *other, time = x.shape + kernel = kernel_upsample2(zeros).to(x) + out = F.conv1d(x.view(-1, 1, time), kernel, padding=zeros)[..., 1:].view( + *other, time + ) + y = th.stack([x, out], dim=-1) + return y.view(*other, -1) + + +def kernel_downsample2(zeros=56): + """kernel_downsample2. + + """ + win = th.hann_window(4 * zeros + 1, periodic=False) + winodd = win[1::2] + t = th.linspace(-zeros + 0.5, zeros - 0.5, 2 * zeros) + t.mul_(math.pi) + kernel = (sinc(t) * winodd).view(1, 1, -1) + return kernel + + +def downsample2(x, zeros=56): + """ + Downsampling the input by 2 using sinc interpolation. + Smith, Julius, and Phil Gossett. "A flexible sampling-rate conversion method." + ICASSP'84. IEEE International Conference on Acoustics, Speech, and Signal Processing. + Vol. 9. IEEE, 1984. + """ + if x.shape[-1] % 2 != 0: + x = F.pad(x, (0, 1)) + xeven = x[..., ::2] + xodd = x[..., 1::2] + *other, time = xodd.shape + kernel = kernel_downsample2(zeros).to(x) + out = xeven + F.conv1d( + xodd.view(-1, 1, time), kernel, padding=zeros + )[..., :-1].view(*other, time) + return out.view(*other, -1).mul(0.5) diff --git a/examples/speech_synthesis/preprocessing/denoiser/utils.py b/examples/speech_synthesis/preprocessing/denoiser/utils.py new file mode 100644 index 0000000000..734d047f1b --- /dev/null +++ b/examples/speech_synthesis/preprocessing/denoiser/utils.py @@ -0,0 +1,176 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. +# author: adefossez + +import functools +import logging +from contextlib import contextmanager +import inspect +import time + +logger = logging.getLogger(__name__) + +EPS = 1e-8 + + +def capture_init(init): + """capture_init. + + Decorate `__init__` with this, and you can then + recover the *args and **kwargs passed to it in `self._init_args_kwargs` + """ + @functools.wraps(init) + def __init__(self, *args, **kwargs): + self._init_args_kwargs = (args, kwargs) + init(self, *args, **kwargs) + + return __init__ + + +def deserialize_model(package, strict=False): + """deserialize_model. + + """ + klass = package['class'] + if strict: + model = klass(*package['args'], **package['kwargs']) + else: + sig = inspect.signature(klass) + kw = package['kwargs'] + for key in list(kw): + if key not in sig.parameters: + logger.warning("Dropping inexistant parameter %s", key) + del kw[key] + model = klass(*package['args'], **kw) + model.load_state_dict(package['state']) + return model + + +def copy_state(state): + return {k: v.cpu().clone() for k, v in state.items()} + + +def serialize_model(model): + args, kwargs = model._init_args_kwargs + state = copy_state(model.state_dict()) + return {"class": model.__class__, "args": args, "kwargs": kwargs, "state": state} + + +@contextmanager +def swap_state(model, state): + """ + Context manager that swaps the state of a model, e.g: + + # model is in old state + with swap_state(model, new_state): + # model in new state + # model back to old state + """ + old_state = copy_state(model.state_dict()) + model.load_state_dict(state) + try: + yield + finally: + model.load_state_dict(old_state) + + +def pull_metric(history, name): + out = [] + for metrics in history: + if name in metrics: + out.append(metrics[name]) + return out + + +class LogProgress: + """ + Sort of like tqdm but using log lines and not as real time. + Args: + - logger: logger obtained from `logging.getLogger`, + - iterable: iterable object to wrap + - updates (int): number of lines that will be printed, e.g. + if `updates=5`, log every 1/5th of the total length. + - total (int): length of the iterable, in case it does not support + `len`. + - name (str): prefix to use in the log. + - level: logging level (like `logging.INFO`). + """ + def __init__(self, + logger, + iterable, + updates=5, + total=None, + name="LogProgress", + level=logging.INFO): + self.iterable = iterable + self.total = total or len(iterable) + self.updates = updates + self.name = name + self.logger = logger + self.level = level + + def update(self, **infos): + self._infos = infos + + def __iter__(self): + self._iterator = iter(self.iterable) + self._index = -1 + self._infos = {} + self._begin = time.time() + return self + + def __next__(self): + self._index += 1 + try: + value = next(self._iterator) + except StopIteration: + raise + else: + return value + finally: + log_every = max(1, self.total // self.updates) + # logging is delayed by 1 it, in order to have the metrics from update + if self._index >= 1 and self._index % log_every == 0: + self._log() + + def _log(self): + self._speed = (1 + self._index) / (time.time() - self._begin) + infos = " | ".join(f"{k.capitalize()} {v}" for k, v in self._infos.items()) + if self._speed < 1e-4: + speed = "oo sec/it" + elif self._speed < 0.1: + speed = f"{1/self._speed:.1f} sec/it" + else: + speed = f"{self._speed:.1f} it/sec" + out = f"{self.name} | {self._index}/{self.total} | {speed}" + if infos: + out += " | " + infos + self.logger.log(self.level, out) + + +def colorize(text, color): + """ + Display text with some ANSI color in the terminal. + """ + code = f"\033[{color}m" + restore = "\033[0m" + return "".join([code, text, restore]) + + +def bold(text): + """ + Display text in bold in the terminal. + """ + return colorize(text, "1") + + +def cal_snr(lbl, est): + import torch + y = 10.0 * torch.log10( + torch.sum(lbl**2, dim=-1) / (torch.sum((est-lbl)**2, dim=-1) + EPS) + + EPS + ) + return y diff --git a/examples/speech_synthesis/preprocessing/get_common_voice_audio_manifest.py b/examples/speech_synthesis/preprocessing/get_common_voice_audio_manifest.py new file mode 100644 index 0000000000..a302546043 --- /dev/null +++ b/examples/speech_synthesis/preprocessing/get_common_voice_audio_manifest.py @@ -0,0 +1,140 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import logging +from pathlib import Path +from collections import defaultdict +from typing import List, Dict, Tuple + +import pandas as pd +import numpy as np +import torchaudio +from tqdm import tqdm + +from examples.speech_to_text.data_utils import load_df_from_tsv, save_df_to_tsv + + +log = logging.getLogger(__name__) + +SPLITS = ["train", "dev", "test"] + + +def get_top_n( + root: Path, n_speakers: int = 10, min_n_tokens: int = 5 +) -> pd.DataFrame: + df = load_df_from_tsv(root / "validated.tsv") + df["n_tokens"] = [len(s.split()) for s in df["sentence"]] + df = df[df["n_tokens"] >= min_n_tokens] + df["n_frames"] = [ + torchaudio.info((root / "clips" / p).as_posix()).num_frames + for p in tqdm(df["path"]) + ] + df["id"] = [Path(p).stem for p in df["path"]] + total_duration_ms = df.groupby("client_id")["n_frames"].agg(["sum"]) + total_duration_ms = total_duration_ms.sort_values("sum", ascending=False) + + top_n_total_duration_ms = total_duration_ms.head(n_speakers) + top_n_client_ids = set(top_n_total_duration_ms.index.tolist()) + df_top_n = df[df["client_id"].isin(top_n_client_ids)] + return df_top_n + + +def get_splits( + df, train_split_ratio=0.99, speaker_in_all_splits=False, rand_seed=0 +) -> Tuple[Dict[str, str], List[str]]: + np.random.seed(rand_seed) + dev_split_ratio = (1. - train_split_ratio) / 3 + grouped = list(df.groupby("client_id")) + id_to_split = {} + for _, cur_df in tqdm(grouped): + cur_n_examples = len(cur_df) + if speaker_in_all_splits and cur_n_examples < 3: + continue + cur_n_train = int(cur_n_examples * train_split_ratio) + cur_n_dev = int(cur_n_examples * dev_split_ratio) + cur_n_test = cur_n_examples - cur_n_dev - cur_n_train + if speaker_in_all_splits and cur_n_dev * cur_n_test == 0: + cur_n_dev, cur_n_test = 1, 1 + cur_n_train = cur_n_examples - cur_n_dev - cur_n_test + cur_indices = cur_df.index.tolist() + cur_shuffled_indices = np.random.permutation(cur_n_examples) + cur_shuffled_indices = [cur_indices[i] for i in cur_shuffled_indices] + cur_indices_by_split = { + "train": cur_shuffled_indices[:cur_n_train], + "dev": cur_shuffled_indices[cur_n_train: cur_n_train + cur_n_dev], + "test": cur_shuffled_indices[cur_n_train + cur_n_dev:] + } + for split in SPLITS: + for i in cur_indices_by_split[split]: + id_ = df["id"].loc[i] + id_to_split[id_] = split + return id_to_split, sorted(df["client_id"].unique()) + + +def convert_to_wav(root: Path, filenames: List[str], target_sr=16_000): + out_root = root / "wav" + out_root.mkdir(exist_ok=True, parents=True) + print("Converting to WAV...") + for n in tqdm(filenames): + in_path = (root / "clips" / n).as_posix() + waveform, sr = torchaudio.load(in_path) + converted, converted_sr = torchaudio.sox_effects.apply_effects_tensor( + waveform, sr, [["rate", str(target_sr)], ["channels", "1"]] + ) + out_path = (out_root / Path(n).with_suffix(".wav").name).as_posix() + torchaudio.save(out_path, converted, converted_sr, encoding="PCM_S", + bits_per_sample=16) + + +def process(args): + data_root = Path(args.data_root).absolute() / args.lang + + # Generate TSV manifest + print("Generating manifest...") + + df_top_n = get_top_n(data_root) + id_to_split, speakers = get_splits(df_top_n) + + if args.convert_to_wav: + convert_to_wav(data_root, df_top_n["path"].tolist()) + + manifest_by_split = {split: defaultdict(list) for split in SPLITS} + for sample in tqdm(df_top_n.to_dict(orient="index").values()): + sample_id = sample["id"] + split = id_to_split[sample_id] + manifest_by_split[split]["id"].append(sample_id) + if args.convert_to_wav: + audio_path = data_root / "wav" / f"{sample_id}.wav" + else: + audio_path = data_root / "clips" / f"{sample_id}.mp3" + manifest_by_split[split]["audio"].append(audio_path.as_posix()) + manifest_by_split[split]["n_frames"].append(sample["n_frames"]) + manifest_by_split[split]["tgt_text"].append(sample["sentence"]) + manifest_by_split[split]["speaker"].append(sample["client_id"]) + manifest_by_split[split]["src_text"].append(sample["sentence"]) + + output_root = Path(args.output_manifest_root).absolute() + output_root.mkdir(parents=True, exist_ok=True) + for split in SPLITS: + save_df_to_tsv( + pd.DataFrame.from_dict(manifest_by_split[split]), + output_root / f"{split}.audio.tsv" + ) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--data-root", "-d", required=True, type=str) + parser.add_argument("--output-manifest-root", "-m", required=True, type=str) + parser.add_argument("--lang", "-l", required=True, type=str) + parser.add_argument("--convert-to-wav", action="store_true") + args = parser.parse_args() + + process(args) + + +if __name__ == "__main__": + main() diff --git a/examples/speech_synthesis/preprocessing/get_feature_manifest.py b/examples/speech_synthesis/preprocessing/get_feature_manifest.py new file mode 100644 index 0000000000..516f2cc469 --- /dev/null +++ b/examples/speech_synthesis/preprocessing/get_feature_manifest.py @@ -0,0 +1,233 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import logging +from pathlib import Path +import shutil +from tempfile import NamedTemporaryFile +from collections import Counter, defaultdict + +import pandas as pd +import torchaudio +from tqdm import tqdm + +from fairseq.data.audio.audio_utils import convert_waveform +from examples.speech_to_text.data_utils import ( + create_zip, + gen_config_yaml, + gen_vocab, + get_zip_manifest, + load_tsv_to_dicts, + save_df_to_tsv +) +from examples.speech_synthesis.data_utils import ( + extract_logmel_spectrogram, extract_pitch, extract_energy, get_global_cmvn, + ipa_phonemize, get_mfa_alignment, get_unit_alignment +) + + +log = logging.getLogger(__name__) + + +def process(args): + assert "train" in args.splits + out_root = Path(args.output_root).absolute() + out_root.mkdir(exist_ok=True) + + print("Fetching data...") + audio_manifest_root = Path(args.audio_manifest_root).absolute() + samples = [] + for s in args.splits: + for e in load_tsv_to_dicts(audio_manifest_root / f"{s}.audio.tsv"): + e["split"] = s + samples.append(e) + sample_ids = [s["id"] for s in samples] + + # Get alignment info + id_to_alignment = None + if args.textgrid_zip is not None: + assert args.id_to_units_tsv is None + id_to_alignment = get_mfa_alignment( + args.textgrid_zip, sample_ids, args.sample_rate, args.hop_length + ) + elif args.id_to_units_tsv is not None: + # assume identical hop length on the unit sequence + id_to_alignment = get_unit_alignment(args.id_to_units_tsv, sample_ids) + + # Extract features and pack features into ZIP + feature_name = "logmelspec80" + zip_path = out_root / f"{feature_name}.zip" + pitch_zip_path = out_root / "pitch.zip" + energy_zip_path = out_root / "energy.zip" + gcmvn_npz_path = out_root / "gcmvn_stats.npz" + if zip_path.exists() and gcmvn_npz_path.exists(): + print(f"{zip_path} and {gcmvn_npz_path} exist.") + else: + feature_root = out_root / feature_name + feature_root.mkdir(exist_ok=True) + pitch_root = out_root / "pitch" + energy_root = out_root / "energy" + if args.add_fastspeech_targets: + pitch_root.mkdir(exist_ok=True) + energy_root.mkdir(exist_ok=True) + print("Extracting Mel spectrogram features...") + for sample in tqdm(samples): + waveform, sample_rate = torchaudio.load(sample["audio"]) + waveform, sample_rate = convert_waveform( + waveform, sample_rate, normalize_volume=args.normalize_volume, + to_sample_rate=args.sample_rate + ) + sample_id = sample["id"] + target_length = None + if id_to_alignment is not None: + a = id_to_alignment[sample_id] + target_length = sum(a.frame_durations) + if a.start_sec is not None and a.end_sec is not None: + start_frame = int(a.start_sec * sample_rate) + end_frame = int(a.end_sec * sample_rate) + waveform = waveform[:, start_frame: end_frame] + extract_logmel_spectrogram( + waveform, sample_rate, feature_root / f"{sample_id}.npy", + win_length=args.win_length, hop_length=args.hop_length, + n_fft=args.n_fft, n_mels=args.n_mels, f_min=args.f_min, + f_max=args.f_max, target_length=target_length + ) + if args.add_fastspeech_targets: + assert id_to_alignment is not None + extract_pitch( + waveform, sample_rate, pitch_root / f"{sample_id}.npy", + hop_length=args.hop_length, log_scale=True, + phoneme_durations=id_to_alignment[sample_id].frame_durations + ) + extract_energy( + waveform, energy_root / f"{sample_id}.npy", + hop_length=args.hop_length, n_fft=args.n_fft, + log_scale=True, + phoneme_durations=id_to_alignment[sample_id].frame_durations + ) + print("ZIPing features...") + create_zip(feature_root, zip_path) + get_global_cmvn(feature_root, gcmvn_npz_path) + shutil.rmtree(feature_root) + if args.add_fastspeech_targets: + create_zip(pitch_root, pitch_zip_path) + shutil.rmtree(pitch_root) + create_zip(energy_root, energy_zip_path) + shutil.rmtree(energy_root) + + print("Fetching ZIP manifest...") + audio_paths, audio_lengths = get_zip_manifest(zip_path) + pitch_paths, pitch_lengths, energy_paths, energy_lengths = [None] * 4 + if args.add_fastspeech_targets: + pitch_paths, pitch_lengths = get_zip_manifest(pitch_zip_path) + energy_paths, energy_lengths = get_zip_manifest(energy_zip_path) + # Generate TSV manifest + print("Generating manifest...") + manifest_by_split = {split: defaultdict(list) for split in args.splits} + for sample in tqdm(samples): + sample_id, split = sample["id"], sample["split"] + normalized_utt = sample["tgt_text"] + if id_to_alignment is not None: + normalized_utt = " ".join(id_to_alignment[sample_id].tokens) + elif args.ipa_vocab: + normalized_utt = ipa_phonemize( + normalized_utt, lang=args.lang, use_g2p=args.use_g2p + ) + manifest_by_split[split]["id"].append(sample_id) + manifest_by_split[split]["audio"].append(audio_paths[sample_id]) + manifest_by_split[split]["n_frames"].append(audio_lengths[sample_id]) + manifest_by_split[split]["tgt_text"].append(normalized_utt) + manifest_by_split[split]["speaker"].append(sample["speaker"]) + manifest_by_split[split]["src_text"].append(sample["src_text"]) + if args.add_fastspeech_targets: + assert id_to_alignment is not None + duration = " ".join( + str(d) for d in id_to_alignment[sample_id].frame_durations + ) + manifest_by_split[split]["duration"].append(duration) + manifest_by_split[split]["pitch"].append(pitch_paths[sample_id]) + manifest_by_split[split]["energy"].append(energy_paths[sample_id]) + for split in args.splits: + save_df_to_tsv( + pd.DataFrame.from_dict(manifest_by_split[split]), + out_root / f"{split}.tsv" + ) + # Generate vocab + vocab_name, spm_filename = None, None + if id_to_alignment is not None or args.ipa_vocab: + vocab = Counter() + for t in manifest_by_split["train"]["tgt_text"]: + vocab.update(t.split(" ")) + vocab_name = "vocab.txt" + with open(out_root / vocab_name, "w") as f: + for s, c in vocab.most_common(): + f.write(f"{s} {c}\n") + else: + spm_filename_prefix = "spm_char" + spm_filename = f"{spm_filename_prefix}.model" + with NamedTemporaryFile(mode="w") as f: + for t in manifest_by_split["train"]["tgt_text"]: + f.write(t + "\n") + f.flush() # needed to ensure gen_vocab sees dumped text + gen_vocab(Path(f.name), out_root / spm_filename_prefix, "char") + # Generate speaker list + speakers = sorted({sample["speaker"] for sample in samples}) + speakers_path = out_root / "speakers.txt" + with open(speakers_path, "w") as f: + for speaker in speakers: + f.write(f"{speaker}\n") + # Generate config YAML + win_len_t = args.win_length / args.sample_rate + hop_len_t = args.hop_length / args.sample_rate + extra = { + "sample_rate": args.sample_rate, + "features": { + "type": "spectrogram+melscale+log", + "eps": 1e-2, "n_mels": args.n_mels, "n_fft": args.n_fft, + "window_fn": "hann", "win_length": args.win_length, + "hop_length": args.hop_length, "sample_rate": args.sample_rate, + "win_len_t": win_len_t, "hop_len_t": hop_len_t, + "f_min": args.f_min, "f_max": args.f_max, + "n_stft": args.n_fft // 2 + 1 + } + } + if len(speakers) > 1: + extra["speaker_set_filename"] = "speakers.txt" + gen_config_yaml( + out_root, spm_filename=spm_filename, vocab_name=vocab_name, + audio_root=out_root.as_posix(), input_channels=None, + input_feat_per_channel=None, specaugment_policy=None, + cmvn_type="global", gcmvn_path=gcmvn_npz_path, extra=extra + ) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--audio-manifest-root", "-m", required=True, type=str) + parser.add_argument("--output-root", "-o", required=True, type=str) + parser.add_argument("--splits", "-s", type=str, nargs="+", + default=["train", "dev", "test"]) + parser.add_argument("--ipa-vocab", action="store_true") + parser.add_argument("--use-g2p", action="store_true") + parser.add_argument("--lang", type=str, default="en-us") + parser.add_argument("--win-length", type=int, default=1024) + parser.add_argument("--hop-length", type=int, default=256) + parser.add_argument("--n-fft", type=int, default=1024) + parser.add_argument("--n-mels", type=int, default=80) + parser.add_argument("--f-min", type=int, default=20) + parser.add_argument("--f-max", type=int, default=8000) + parser.add_argument("--sample-rate", type=int, default=22050) + parser.add_argument("--normalize-volume", "-n", action="store_true") + parser.add_argument("--textgrid-zip", type=str, default=None) + parser.add_argument("--id-to-units-tsv", type=str, default=None) + parser.add_argument("--add-fastspeech-targets", action="store_true") + args = parser.parse_args() + + process(args) + + +if __name__ == "__main__": + main() diff --git a/examples/speech_synthesis/preprocessing/get_ljspeech_audio_manifest.py b/examples/speech_synthesis/preprocessing/get_ljspeech_audio_manifest.py new file mode 100644 index 0000000000..7ec1fb7521 --- /dev/null +++ b/examples/speech_synthesis/preprocessing/get_ljspeech_audio_manifest.py @@ -0,0 +1,70 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import logging +from pathlib import Path +from collections import defaultdict + +import pandas as pd +from torchaudio.datasets import LJSPEECH +from tqdm import tqdm + +from examples.speech_to_text.data_utils import save_df_to_tsv + + +log = logging.getLogger(__name__) + +SPLITS = ["train", "dev", "test"] + + +def process(args): + out_root = Path(args.output_data_root).absolute() + out_root.mkdir(parents=True, exist_ok=True) + + # Generate TSV manifest + print("Generating manifest...") + # following FastSpeech's splits + dataset = LJSPEECH(out_root.as_posix(), download=True) + id_to_split = {} + for x in dataset._flist: + id_ = x[0] + speaker = id_.split("-")[0] + id_to_split[id_] = { + "LJ001": "test", "LJ002": "test", "LJ003": "dev" + }.get(speaker, "train") + manifest_by_split = {split: defaultdict(list) for split in SPLITS} + progress = tqdm(enumerate(dataset), total=len(dataset)) + for i, (waveform, _, utt, normalized_utt) in progress: + sample_id = dataset._flist[i][0] + split = id_to_split[sample_id] + manifest_by_split[split]["id"].append(sample_id) + audio_path = f"{dataset._path}/{sample_id}.wav" + manifest_by_split[split]["audio"].append(audio_path) + manifest_by_split[split]["n_frames"].append(len(waveform[0])) + manifest_by_split[split]["tgt_text"].append(normalized_utt) + manifest_by_split[split]["speaker"].append("ljspeech") + manifest_by_split[split]["src_text"].append(utt) + + manifest_root = Path(args.output_manifest_root).absolute() + manifest_root.mkdir(parents=True, exist_ok=True) + for split in SPLITS: + save_df_to_tsv( + pd.DataFrame.from_dict(manifest_by_split[split]), + manifest_root / f"{split}.audio.tsv" + ) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--output-data-root", "-d", required=True, type=str) + parser.add_argument("--output-manifest-root", "-m", required=True, type=str) + args = parser.parse_args() + + process(args) + + +if __name__ == "__main__": + main() diff --git a/examples/speech_synthesis/preprocessing/get_speaker_embedding.py b/examples/speech_synthesis/preprocessing/get_speaker_embedding.py new file mode 100644 index 0000000000..0e3e4c5cd7 --- /dev/null +++ b/examples/speech_synthesis/preprocessing/get_speaker_embedding.py @@ -0,0 +1,89 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +import argparse +from collections import defaultdict +from itertools import chain +from pathlib import Path + +import numpy as np +import torchaudio +import torchaudio.sox_effects as ta_sox +import yaml +from tqdm import tqdm + +from examples.speech_to_text.data_utils import load_tsv_to_dicts +from examples.speech_synthesis.preprocessing.speaker_embedder import SpkrEmbedder + + +def extract_embedding(audio_path, embedder): + wav, sr = torchaudio.load(audio_path) # 2D + if sr != embedder.RATE: + wav, sr = ta_sox.apply_effects_tensor( + wav, sr, [["rate", str(embedder.RATE)]] + ) + try: + emb = embedder([wav[0].cuda().float()]).cpu().numpy() + except RuntimeError: + emb = None + return emb + + +def process(args): + print("Fetching data...") + raw_manifest_root = Path(args.raw_manifest_root).absolute() + samples = [load_tsv_to_dicts(raw_manifest_root / (s + ".tsv")) + for s in args.splits] + samples = list(chain(*samples)) + with open(args.config, "r") as f: + config = yaml.load(f, Loader=yaml.FullLoader) + with open(f"{config['audio_root']}/{config['speaker_set_filename']}") as f: + speaker_to_id = {r.strip(): i for i, r in enumerate(f)} + + embedder = SpkrEmbedder(args.ckpt).cuda() + speaker_to_cnt = defaultdict(float) + speaker_to_emb = defaultdict(float) + for sample in tqdm(samples, desc="extract emb"): + emb = extract_embedding(sample["audio"], embedder) + if emb is not None: + speaker_to_cnt[sample["speaker"]] += 1 + speaker_to_emb[sample["speaker"]] += emb + if len(speaker_to_emb) != len(speaker_to_id): + missed = set(speaker_to_id) - set(speaker_to_emb.keys()) + print( + f"WARNING: missing embeddings for {len(missed)} speaker:\n{missed}" + ) + speaker_emb_mat = np.zeros((len(speaker_to_id), len(emb)), float) + for speaker in speaker_to_emb: + idx = speaker_to_id[speaker] + emb = speaker_to_emb[speaker] + cnt = speaker_to_cnt[speaker] + speaker_emb_mat[idx, :] = emb / cnt + speaker_emb_name = "speaker_emb.npy" + speaker_emb_path = f"{config['audio_root']}/{speaker_emb_name}" + np.save(speaker_emb_path, speaker_emb_mat) + config["speaker_emb_filename"] = speaker_emb_name + + with open(args.new_config, "w") as f: + yaml.dump(config, f) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--raw-manifest-root", "-m", required=True, type=str) + parser.add_argument("--splits", "-s", type=str, nargs="+", + default=["train"]) + parser.add_argument("--config", "-c", required=True, type=str) + parser.add_argument("--new-config", "-n", required=True, type=str) + parser.add_argument("--ckpt", required=True, type=str, + help="speaker embedder checkpoint") + args = parser.parse_args() + + process(args) + + +if __name__ == "__main__": + main() diff --git a/examples/speech_synthesis/preprocessing/get_vctk_audio_manifest.py b/examples/speech_synthesis/preprocessing/get_vctk_audio_manifest.py new file mode 100644 index 0000000000..7afa40fcd1 --- /dev/null +++ b/examples/speech_synthesis/preprocessing/get_vctk_audio_manifest.py @@ -0,0 +1,79 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import logging +import numpy as np +import re +from pathlib import Path +from collections import defaultdict + +import pandas as pd +from torchaudio.datasets import VCTK +from tqdm import tqdm + +from examples.speech_to_text.data_utils import save_df_to_tsv + + +log = logging.getLogger(__name__) + +SPLITS = ["train", "dev", "test"] + + +def normalize_text(text): + return re.sub(r"[^a-zA-Z.?!,'\- ]", '', text) + + +def process(args): + out_root = Path(args.output_data_root).absolute() + out_root.mkdir(parents=True, exist_ok=True) + + # Generate TSV manifest + print("Generating manifest...") + dataset = VCTK(out_root.as_posix(), download=False) + ids = list(dataset._walker) + np.random.seed(args.seed) + np.random.shuffle(ids) + n_train = len(ids) - args.n_dev - args.n_test + _split = ["train"] * n_train + ["dev"] * args.n_dev + ["test"] * args.n_test + id_to_split = dict(zip(ids, _split)) + manifest_by_split = {split: defaultdict(list) for split in SPLITS} + progress = tqdm(enumerate(dataset), total=len(dataset)) + for i, (waveform, _, text, speaker_id, _) in progress: + sample_id = dataset._walker[i] + _split = id_to_split[sample_id] + audio_dir = Path(dataset._path) / dataset._folder_audio / speaker_id + audio_path = audio_dir / f"{sample_id}.wav" + text = normalize_text(text) + manifest_by_split[_split]["id"].append(sample_id) + manifest_by_split[_split]["audio"].append(audio_path.as_posix()) + manifest_by_split[_split]["n_frames"].append(len(waveform[0])) + manifest_by_split[_split]["tgt_text"].append(text) + manifest_by_split[_split]["speaker"].append(speaker_id) + manifest_by_split[_split]["src_text"].append(text) + + manifest_root = Path(args.output_manifest_root).absolute() + manifest_root.mkdir(parents=True, exist_ok=True) + for _split in SPLITS: + save_df_to_tsv( + pd.DataFrame.from_dict(manifest_by_split[_split]), + manifest_root / f"{_split}.audio.tsv" + ) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--output-data-root", "-d", required=True, type=str) + parser.add_argument("--output-manifest-root", "-m", required=True, type=str) + parser.add_argument("--n-dev", default=50, type=int) + parser.add_argument("--n-test", default=100, type=int) + parser.add_argument("--seed", "-s", default=1234, type=int) + args = parser.parse_args() + + process(args) + + +if __name__ == "__main__": + main() diff --git a/examples/speech_synthesis/preprocessing/speaker_embedder/__init__.py b/examples/speech_synthesis/preprocessing/speaker_embedder/__init__.py new file mode 100644 index 0000000000..3b178676ba --- /dev/null +++ b/examples/speech_synthesis/preprocessing/speaker_embedder/__init__.py @@ -0,0 +1,135 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +import librosa +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.data +import torchaudio + + +EMBEDDER_PARAMS = { + 'num_mels': 40, + 'n_fft': 512, + 'emb_dim': 256, + 'lstm_hidden': 768, + 'lstm_layers': 3, + 'window': 80, + 'stride': 40, +} + + +def set_requires_grad(nets, requires_grad=False): + """Set requies_grad=Fasle for all the networks to avoid unnecessary + computations + Parameters: + nets (network list) -- a list of networks + requires_grad (bool) -- whether the networks require gradients or not + """ + if not isinstance(nets, list): + nets = [nets] + for net in nets: + if net is not None: + for param in net.parameters(): + param.requires_grad = requires_grad + + +class LinearNorm(nn.Module): + def __init__(self, hp): + super(LinearNorm, self).__init__() + self.linear_layer = nn.Linear(hp["lstm_hidden"], hp["emb_dim"]) + + def forward(self, x): + return self.linear_layer(x) + + +class SpeechEmbedder(nn.Module): + def __init__(self, hp): + super(SpeechEmbedder, self).__init__() + self.lstm = nn.LSTM(hp["num_mels"], + hp["lstm_hidden"], + num_layers=hp["lstm_layers"], + batch_first=True) + self.proj = LinearNorm(hp) + self.hp = hp + + def forward(self, mel): + # (num_mels, T) -> (num_mels, T', window) + mels = mel.unfold(1, self.hp["window"], self.hp["stride"]) + mels = mels.permute(1, 2, 0) # (T', window, num_mels) + x, _ = self.lstm(mels) # (T', window, lstm_hidden) + x = x[:, -1, :] # (T', lstm_hidden), use last frame only + x = self.proj(x) # (T', emb_dim) + x = x / torch.norm(x, p=2, dim=1, keepdim=True) # (T', emb_dim) + + x = x.mean(dim=0) + if x.norm(p=2) != 0: + x = x / x.norm(p=2) + return x + + +class SpkrEmbedder(nn.Module): + RATE = 16000 + + def __init__( + self, + embedder_path, + embedder_params=EMBEDDER_PARAMS, + rate=16000, + hop_length=160, + win_length=400, + pad=False, + ): + super(SpkrEmbedder, self).__init__() + embedder_pt = torch.load(embedder_path, map_location="cpu") + self.embedder = SpeechEmbedder(embedder_params) + self.embedder.load_state_dict(embedder_pt) + self.embedder.eval() + set_requires_grad(self.embedder, requires_grad=False) + self.embedder_params = embedder_params + + self.register_buffer('mel_basis', torch.from_numpy( + librosa.filters.mel( + sr=self.RATE, + n_fft=self.embedder_params["n_fft"], + n_mels=self.embedder_params["num_mels"]) + ) + ) + + self.resample = None + if rate != self.RATE: + self.resample = torchaudio.transforms.Resample(rate, self.RATE) + self.hop_length = hop_length + self.win_length = win_length + self.pad = pad + + def get_mel(self, y): + if self.pad and y.shape[-1] < 14000: + y = F.pad(y, (0, 14000 - y.shape[-1])) + + window = torch.hann_window(self.win_length).to(y) + y = torch.stft(y, n_fft=self.embedder_params["n_fft"], + hop_length=self.hop_length, + win_length=self.win_length, + window=window) + magnitudes = torch.norm(y, dim=-1, p=2) ** 2 + mel = torch.log10(self.mel_basis @ magnitudes + 1e-6) + return mel + + def forward(self, inputs): + dvecs = [] + for wav in inputs: + mel = self.get_mel(wav) + if mel.dim() == 3: + mel = mel.squeeze(0) + dvecs += [self.embedder(mel)] + dvecs = torch.stack(dvecs) + + dvec = torch.mean(dvecs, dim=0) + dvec = dvec / torch.norm(dvec) + + return dvec diff --git a/examples/speech_synthesis/preprocessing/vad/__init__.py b/examples/speech_synthesis/preprocessing/vad/__init__.py new file mode 100644 index 0000000000..9cf121081f --- /dev/null +++ b/examples/speech_synthesis/preprocessing/vad/__init__.py @@ -0,0 +1,192 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +import collections +import contextlib +import wave + +try: + import webrtcvad +except ImportError: + raise ImportError("Please install py-webrtcvad: pip install webrtcvad") +import argparse +import os +import logging +from tqdm import tqdm + +AUDIO_SUFFIX = '.wav' +FS_MS = 30 +SCALE = 6e-5 +THRESHOLD = 0.3 + + +def read_wave(path): + """Reads a .wav file. + Takes the path, and returns (PCM audio data, sample rate). + """ + with contextlib.closing(wave.open(path, 'rb')) as wf: + num_channels = wf.getnchannels() + assert num_channels == 1 + sample_width = wf.getsampwidth() + assert sample_width == 2 + sample_rate = wf.getframerate() + assert sample_rate in (8000, 16000, 32000, 48000) + pcm_data = wf.readframes(wf.getnframes()) + return pcm_data, sample_rate + + +def write_wave(path, audio, sample_rate): + """Writes a .wav file. + Takes path, PCM audio data, and sample rate. + """ + with contextlib.closing(wave.open(path, 'wb')) as wf: + wf.setnchannels(1) + wf.setsampwidth(2) + wf.setframerate(sample_rate) + wf.writeframes(audio) + + +class Frame(object): + """Represents a "frame" of audio data.""" + def __init__(self, bytes, timestamp, duration): + self.bytes = bytes + self.timestamp = timestamp + self.duration = duration + + +def frame_generator(frame_duration_ms, audio, sample_rate): + """Generates audio frames from PCM audio data. + Takes the desired frame duration in milliseconds, the PCM data, and + the sample rate. + Yields Frames of the requested duration. + """ + n = int(sample_rate * (frame_duration_ms / 1000.0) * 2) + offset = 0 + timestamp = 0.0 + duration = (float(n) / sample_rate) / 2.0 + while offset + n < len(audio): + yield Frame(audio[offset:offset + n], timestamp, duration) + timestamp += duration + offset += n + + +def vad_collector(sample_rate, frame_duration_ms, + padding_duration_ms, vad, frames): + """Filters out non-voiced audio frames. + Given a webrtcvad.Vad and a source of audio frames, yields only + the voiced audio. + Uses a padded, sliding window algorithm over the audio frames. + When more than 90% of the frames in the window are voiced (as + reported by the VAD), the collector triggers and begins yielding + audio frames. Then the collector waits until 90% of the frames in + the window are unvoiced to detrigger. + The window is padded at the front and back to provide a small + amount of silence or the beginnings/endings of speech around the + voiced frames. + Arguments: + sample_rate - The audio sample rate, in Hz. + frame_duration_ms - The frame duration in milliseconds. + padding_duration_ms - The amount to pad the window, in milliseconds. + vad - An instance of webrtcvad.Vad. + frames - a source of audio frames (sequence or generator). + Returns: A generator that yields PCM audio data. + """ + num_padding_frames = int(padding_duration_ms / frame_duration_ms) + # We use a deque for our sliding window/ring buffer. + ring_buffer = collections.deque(maxlen=num_padding_frames) + # We have two states: TRIGGERED and NOTTRIGGERED. We start in the + # NOTTRIGGERED state. + triggered = False + + voiced_frames = [] + for frame in frames: + is_speech = vad.is_speech(frame.bytes, sample_rate) + + # sys.stdout.write('1' if is_speech else '0') + if not triggered: + ring_buffer.append((frame, is_speech)) + num_voiced = len([f for f, speech in ring_buffer if speech]) + # If we're NOTTRIGGERED and more than 90% of the frames in + # the ring buffer are voiced frames, then enter the + # TRIGGERED state. + if num_voiced > 0.9 * ring_buffer.maxlen: + triggered = True + # We want to yield all the audio we see from now until + # we are NOTTRIGGERED, but we have to start with the + # audio that's already in the ring buffer. + for f, _ in ring_buffer: + voiced_frames.append(f) + ring_buffer.clear() + else: + # We're in the TRIGGERED state, so collect the audio data + # and add it to the ring buffer. + voiced_frames.append(frame) + ring_buffer.append((frame, is_speech)) + num_unvoiced = len([f for f, speech in ring_buffer if not speech]) + # If more than 90% of the frames in the ring buffer are + # unvoiced, then enter NOTTRIGGERED and yield whatever + # audio we've collected. + if num_unvoiced > 0.9 * ring_buffer.maxlen: + triggered = False + yield [b''.join([f.bytes for f in voiced_frames]), + voiced_frames[0].timestamp, voiced_frames[-1].timestamp] + ring_buffer.clear() + voiced_frames = [] + # If we have any leftover voiced audio when we run out of input, + # yield it. + if voiced_frames: + yield [b''.join([f.bytes for f in voiced_frames]), + voiced_frames[0].timestamp, voiced_frames[-1].timestamp] + + +def main(args): + # create output folder + try: + cmd = f"mkdir -p {args.out_path}" + os.system(cmd) + except Exception: + logging.error("Can not create output folder") + exit(-1) + + # build vad object + vad = webrtcvad.Vad(int(args.agg)) + # iterating over wavs in dir + for file in tqdm(os.listdir(args.in_path)): + if file.endswith(AUDIO_SUFFIX): + audio_inpath = os.path.join(args.in_path, file) + audio_outpath = os.path.join(args.out_path, file) + audio, sample_rate = read_wave(audio_inpath) + frames = frame_generator(FS_MS, audio, sample_rate) + frames = list(frames) + segments = vad_collector(sample_rate, FS_MS, 300, vad, frames) + merge_segments = list() + timestamp_start = 0.0 + timestamp_end = 0.0 + # removing start, end, and long sequences of sils + for i, segment in enumerate(segments): + merge_segments.append(segment[0]) + if i and timestamp_start: + sil_duration = segment[1] - timestamp_end + if sil_duration > THRESHOLD: + merge_segments.append(int(THRESHOLD / SCALE)*(b'\x00')) + else: + merge_segments.append(int((sil_duration / SCALE))*(b'\x00')) + timestamp_start = segment[1] + timestamp_end = segment[2] + segment = b''.join(merge_segments) + write_wave(audio_outpath, segment, sample_rate) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='Apply vad to a file of fils.') + parser.add_argument('in_path', type=str, help='Path to the input files') + parser.add_argument('out_path', type=str, + help='Path to save the processed files') + parser.add_argument('--agg', type=int, default=3, + help='The level of aggressiveness of the VAD: [0-3]') + args = parser.parse_args() + + main(args) diff --git a/examples/speech_synthesis/utils.py b/examples/speech_synthesis/utils.py new file mode 100644 index 0000000000..2c7b03733d --- /dev/null +++ b/examples/speech_synthesis/utils.py @@ -0,0 +1,101 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import numpy as np +import torch +from scipy.interpolate import interp1d +import torchaudio + +from fairseq.tasks.text_to_speech import ( + batch_compute_distortion, compute_rms_dist +) + + +def batch_mel_spectral_distortion( + y1, y2, sr, normalize_type="path", mel_fn=None +): + """ + https://arxiv.org/pdf/2011.03568.pdf + + Same as Mel Cepstral Distortion, but computed on log-mel spectrograms. + """ + if mel_fn is None or mel_fn.sample_rate != sr: + mel_fn = torchaudio.transforms.MelSpectrogram( + sr, n_fft=int(0.05 * sr), win_length=int(0.05 * sr), + hop_length=int(0.0125 * sr), f_min=20, n_mels=80, + window_fn=torch.hann_window + ).to(y1[0].device) + offset = 1e-6 + return batch_compute_distortion( + y1, y2, sr, lambda y: torch.log(mel_fn(y) + offset).transpose(-1, -2), + compute_rms_dist, normalize_type + ) + + +# This code is based on +# "https://github.com/bastibe/MAPS-Scripts/blob/master/helper.py" +def _same_t_in_true_and_est(func): + def new_func(true_t, true_f, est_t, est_f): + assert type(true_t) is np.ndarray + assert type(true_f) is np.ndarray + assert type(est_t) is np.ndarray + assert type(est_f) is np.ndarray + + interpolated_f = interp1d( + est_t, est_f, bounds_error=False, kind='nearest', fill_value=0 + )(true_t) + return func(true_t, true_f, true_t, interpolated_f) + + return new_func + + +@_same_t_in_true_and_est +def gross_pitch_error(true_t, true_f, est_t, est_f): + """The relative frequency in percent of pitch estimates that are + outside a threshold around the true pitch. Only frames that are + considered pitched by both the ground truth and the estimator (if + applicable) are considered. + """ + + correct_frames = _true_voiced_frames(true_t, true_f, est_t, est_f) + gross_pitch_error_frames = _gross_pitch_error_frames( + true_t, true_f, est_t, est_f + ) + return np.sum(gross_pitch_error_frames) / np.sum(correct_frames) + + +def _gross_pitch_error_frames(true_t, true_f, est_t, est_f, eps=1e-8): + voiced_frames = _true_voiced_frames(true_t, true_f, est_t, est_f) + true_f_p_eps = [x + eps for x in true_f] + pitch_error_frames = np.abs(est_f / true_f_p_eps - 1) > 0.2 + return voiced_frames & pitch_error_frames + + +def _true_voiced_frames(true_t, true_f, est_t, est_f): + return (est_f != 0) & (true_f != 0) + + +def _voicing_decision_error_frames(true_t, true_f, est_t, est_f): + return (est_f != 0) != (true_f != 0) + + +@_same_t_in_true_and_est +def f0_frame_error(true_t, true_f, est_t, est_f): + gross_pitch_error_frames = _gross_pitch_error_frames( + true_t, true_f, est_t, est_f + ) + voicing_decision_error_frames = _voicing_decision_error_frames( + true_t, true_f, est_t, est_f + ) + return (np.sum(gross_pitch_error_frames) + + np.sum(voicing_decision_error_frames)) / (len(true_t)) + + +@_same_t_in_true_and_est +def voicing_decision_error(true_t, true_f, est_t, est_f): + voicing_decision_error_frames = _voicing_decision_error_frames( + true_t, true_f, est_t, est_f + ) + return np.sum(voicing_decision_error_frames) / (len(true_t)) diff --git a/examples/speech_text_joint_to_text/README.md b/examples/speech_text_joint_to_text/README.md new file mode 100644 index 0000000000..e071d241e0 --- /dev/null +++ b/examples/speech_text_joint_to_text/README.md @@ -0,0 +1,46 @@ +# Joint Speech Text training in Fairseq +An extension of Fairseq s2t project with the speech to text task enhanced by the co-trained text to text mapping task. More details about Fairseq s2t can be found [here](../speech_to_text/README.md) + +## Examples +Examples of speech text joint training in fairseq +- [English-to-German MuST-C model](docs/ende-mustc.md) +- [IWSLT 2021 Multilingual Speech Translation](docs/iwslt2021.md) + +## Citation +Please cite as: +``` +@inproceedings{Tang2021AGM, + title={A General Multi-Task Learning Framework to Leverage Text Data for Speech to Text Tasks}, + author={Yun Tang and J. Pino and Changhan Wang and Xutai Ma and Dmitriy Genzel}, + booktitle={ICASSP}, + year={2021} +} + +@inproceedings{Tang2021IST, + title = {Improving Speech Translation by Understanding and Learning from the Auxiliary Text Translation Task}, + author = {Yun Tang and Juan Pino and Xian Li and Changhan Wang and Dmitriy Genzel}, + booktitle = {ACL}, + year = {2021}, +} + +@inproceedings{Tang2021FST, + title = {FST: the FAIR Speech Translation System for the IWSLT21 Multilingual Shared Task}, + author = {Yun Tang and Hongyu Gong and Xian Li and Changhan Wang and Juan Pino and Holger Schwenk and Naman Goyal}, + booktitle = {IWSLT}, + year = {2021}, +} + +@inproceedings{wang2020fairseqs2t, + title = {fairseq S2T: Fast Speech-to-Text Modeling with fairseq}, + author = {Changhan Wang and Yun Tang and Xutai Ma and Anne Wu and Dmytro Okhonko and Juan Pino}, + booktitle = {Proceedings of the 2020 Conference of the Asian Chapter of the Association for Computational Linguistics (AACL): System Demonstrations}, + year = {2020}, +} + +@inproceedings{ott2019fairseq, + title = {fairseq: A Fast, Extensible Toolkit for Sequence Modeling}, + author = {Myle Ott and Sergey Edunov and Alexei Baevski and Angela Fan and Sam Gross and Nathan Ng and David Grangier and Michael Auli}, + booktitle = {Proceedings of NAACL-HLT 2019: Demonstrations}, + year = {2019}, +} +``` diff --git a/examples/speech_text_joint_to_text/__init__.py b/examples/speech_text_joint_to_text/__init__.py new file mode 100644 index 0000000000..239d2e69f9 --- /dev/null +++ b/examples/speech_text_joint_to_text/__init__.py @@ -0,0 +1,6 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from . import tasks, criterions, models # noqa diff --git a/examples/speech_text_joint_to_text/configs/mustc_noise.list b/examples/speech_text_joint_to_text/configs/mustc_noise.list new file mode 100644 index 0000000000..02eeac4e00 --- /dev/null +++ b/examples/speech_text_joint_to_text/configs/mustc_noise.list @@ -0,0 +1,49 @@ +"(Applause) NOISE +"(Laughter) VOICE +"(Laughter)" VOICE +(Applause) NOISE +(Applause). NOISE +(Audience) VOICE +(Audio) NOISE +(Beat) NOISE +(Beatboxing) VOICE +(Beep) NOISE +(Beeps) NOISE +(Cheering) VOICE +(Cheers) VOICE +(Claps) NOISE +(Clicking) NOISE +(Clunk) NOISE +(Coughs) NOISE +(Drums) NOISE +(Explosion) NOISE +(Gasps) VOICE +(Guitar) NOISE +(Honk) NOISE +(Laugher) VOICE +(Laughing) VOICE +(Laughs) VOICE +(Laughter) VOICE +(Laughter). VOICE +(Laughter)... VOICE +(Mumbling) VOICE +(Music) NOISE +(Noise) NOISE +(Recording) VOICE +(Ringing) NOISE +(Shouts) VOICE +(Sigh) VOICE +(Sighs) VOICE +(Silence) NOISE +(Singing) VOICE +(Sings) VOICE +(Spanish) VOICE +(Static) NOISE +(Tones) NOISE +(Trumpet) NOISE +(Video) NOISE +(Video): NOISE +(Voice-over) NOISE +(Whistle) NOISE +(Whistling) NOISE +(video): NOISE diff --git a/examples/simultaneous_translation/criterions/__init__.py b/examples/speech_text_joint_to_text/criterions/__init__.py similarity index 83% rename from examples/simultaneous_translation/criterions/__init__.py rename to examples/speech_text_joint_to_text/criterions/__init__.py index 84dc80ad95..7faae73119 100644 --- a/examples/simultaneous_translation/criterions/__init__.py +++ b/examples/speech_text_joint_to_text/criterions/__init__.py @@ -6,9 +6,10 @@ import importlib import os + for file in os.listdir(os.path.dirname(__file__)): if file.endswith(".py") and not file.startswith("_"): criterion_name = file[: file.find(".py")] importlib.import_module( - "examples.simultaneous_translation.criterions." + criterion_name + "examples.speech_text_joint_to_text.criterions." + criterion_name ) diff --git a/examples/speech_text_joint_to_text/criterions/text_guide_cross_entropy_acc.py b/examples/speech_text_joint_to_text/criterions/text_guide_cross_entropy_acc.py new file mode 100644 index 0000000000..0d356e5a10 --- /dev/null +++ b/examples/speech_text_joint_to_text/criterions/text_guide_cross_entropy_acc.py @@ -0,0 +1,223 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +import math + +import torch +import torch.nn.functional as F +from fairseq.criterions import FairseqCriterion, register_criterion +from fairseq.criterions.label_smoothed_cross_entropy import label_smoothed_nll_loss +from fairseq import metrics, utils + + +@register_criterion("guided_label_smoothed_cross_entropy_with_accuracy") +class GuidedCrossEntAccCriterion(FairseqCriterion): + def __init__( + self, + task, + sentence_avg, + guide_alpha, + text_input_cost_ratio, + label_smoothing, + disable_text_guide_update_num=0, + attentive_cost_regularization=0, + ): + """ + guide_alpha: alpha to inteplate nll and kd loss + text_input_cost_ratio: loss ratio for text only input data + label_smoothing: label smoothing ratio + disable_text_guide_update_num: only use nll loss for the first N updates + attentive_cost_regularization: ratio fo attentive cost + """ + super().__init__(task) + self.alpha = guide_alpha + self.attn_beta = attentive_cost_regularization + self.sentence_avg = sentence_avg + self.eps = label_smoothing + self.text_input_cost_ratio = text_input_cost_ratio + self.disable_update_num = disable_text_guide_update_num + assert self.alpha >= 0 and self.alpha <= 1.0 + + @staticmethod + def add_args(parser): + """Add criterion-specific arguments to the parser.""" + # fmt: off + parser.add_argument('--label-smoothing', default=0., type=float, metavar='D', + help='epsilon for label smoothing, 0 means no label smoothing') + # fmt: off + parser.add_argument('--guide-alpha', default=0., type=float, metavar='D', + help='alpha to merge kd cost from text to speech input with ce loss') + # fmt: off + parser.add_argument('--disable-text-guide-update-num', default=0, type=int, metavar='D', + help='disable guided target from text for the first N updates.') + parser.add_argument("--attentive-cost-regularization", default=0.0, type=float, metavar='D', + help="use encoder attentive loss regularization with cost ratio D") + parser.add_argument("--attentive-cost-without-normalize", action='store_true', + help="Don't do normalization during attentive cost computation") + + def forward(self, model, sample, reduce=True): + reduction = 'sum' if reduce else 'none' + net_input = sample["net_input"] + net_output = model(**net_input) + attn_cost = None + lprobs = model.get_normalized_probs(net_output, log_probs=True) + is_dual_input = True if net_input['src_tokens'] is not None and net_input.get('src_txt_tokens') is not None else False + target = model.get_targets(sample, net_output) + src_token_num = 0 + if is_dual_input: + # lprobs_spch from speech encoder and lprobs_text from text encoder + lprobs_spch, lprobs_text = torch.chunk(lprobs, 2) + lprobs_spch.batch_first = lprobs.batch_first + lprobs_text.batch_first = lprobs.batch_first + + speech_loss, speech_nll_loss, speech_correct, speech_total = \ + self.guide_loss_and_acc(model, lprobs_spch, lprobs_text, target, reduce=(reduction == 'sum')) + text_loss, text_nll_loss, text_correct, text_total = self.compute_loss_and_acc(model, lprobs_text, target, reduction=reduction) + loss = (speech_loss + text_loss) + nll_loss = (speech_nll_loss + text_nll_loss) + correct = speech_correct + text_correct + total = speech_total + text_total + + attn_cost = net_output[1].get('attn_cost') + if attn_cost is not None: + # attn_cost is batch_first and padding tokens have been masked already + src_token_num = attn_cost.ne(0).sum() + attn_cost = attn_cost.sum() + loss = loss + attn_cost * self.attn_beta + else: + attn_cost = 0 + else: + loss, nll_loss, correct, total = self.compute_loss_and_acc(model, lprobs, target, reduction=reduction) + if sample["net_input"]['src_tokens'] is None: # text input only + loss = loss * self.text_input_cost_ratio + speech_loss = None + speech_nll_loss = None + + sample_size, logging_output = self.get_logging_output( + sample, loss, nll_loss, correct, total, src_token_num, speech_loss, speech_nll_loss, attn_cost, is_dual_input + ) + return loss, sample_size, logging_output + + def compute_loss_and_acc(self, model, lprobs, target, reduction='sum'): + if not lprobs.batch_first: + lprobs = lprobs.transpose(0, 1) + lprobs = lprobs.view(-1, lprobs.size(-1)) # -> (B x T) x C + target = target.view(-1) + loss, nll_loss = label_smoothed_nll_loss( + lprobs, target, self.eps, ignore_index=self.padding_idx, reduce=(reduction == 'sum'), + ) + + mask = target.ne(self.padding_idx) + correct = torch.sum(lprobs.argmax(1).masked_select(mask).eq(target.masked_select(mask))) + total = torch.sum(mask) + return loss, nll_loss, correct, total + + def guide_loss_and_acc(self, model, lprobs, lprobs_teacher, target, reduce=True): + """ lprobs_teacher is used as guide for lprobs """ + if self.alpha == 0.0 or model.num_updates < self.disable_update_num: + return self.compute_loss_and_acc(model, lprobs, target, reduction=('sum' if reduce else 'none')) + if not lprobs.batch_first: + lprobs = lprobs.transpose(0, 1) + lprobs_teacher = lprobs_teacher.transpose(0, 1) + + lprobs = lprobs.view(-1, lprobs.size(-1)).float() # -> (B x T) x C + lprobs_teacher = lprobs_teacher.view(-1, lprobs_teacher.size(-1)).float() # -> (B x T) x C + target = target.view(-1) + loss = F.nll_loss(lprobs, target, ignore_index=self.padding_idx, reduction='sum' if reduce else 'none') + nll_loss = loss + probs_teacher = lprobs_teacher.exp().masked_fill_(target.unsqueeze(-1).eq(self.padding_idx), 0) + probs_teacher = probs_teacher.detach() + guide_loss = -(probs_teacher*lprobs).sum() if reduce else -(probs_teacher*lprobs).sum(-1, keepdim=True) + loss = self.alpha*guide_loss + (1.0 - self.alpha)*loss + + mask = target.ne(self.padding_idx) + correct = torch.sum(lprobs.argmax(1).masked_select(mask).eq(target.masked_select(mask))) + total = torch.sum(mask) + return loss, nll_loss, correct, total + + def get_logging_output( + self, + sample, + loss, + nll_loss, + correct, + total, + src_token_num=0, + speech_loss=None, + speech_nll_loss=None, + attn_cost=None, + is_dual_input=False, + ): + + sample_size = ( + sample["target"].size(0) if self.sentence_avg else sample["ntokens"] + ) + mul_size = 2 if is_dual_input else 1 + + logging_output = { + "loss": utils.item(loss.data), # * sample['ntokens'], + "nll_loss": utils.item(nll_loss.data), # * sample['ntokens'], + "ntokens": sample["ntokens"]*mul_size, + "nsentences": sample["target"].size(0)*mul_size, + "sample_size": sample_size*mul_size, + "correct": utils.item(correct.data), + "total": utils.item(total.data), + "src_token_num": utils.item(src_token_num.data) if src_token_num > 0 else 0, + "nframes": torch.sum(sample["net_input"]["src_lengths"]).item(), + } + + if speech_loss is not None: + logging_output["speech_loss"] = utils.item(speech_loss.data) + logging_output["speech_nll_loss"] = utils.item(speech_nll_loss.data) + logging_output["sample_size_speech_cost"] = sample_size + logging_output["speech_attn_loss"] = attn_cost + + return sample_size*mul_size, logging_output + + @staticmethod + def aggregate_logging_outputs(logging_outputs): + """Aggregate logging outputs from data parallel training.""" + correct_sum = sum(log.get("correct", 0) for log in logging_outputs) + total_sum = sum(log.get("total", 0) for log in logging_outputs) + src_token_sum = sum(log.get("src_token_num", 0) for log in logging_outputs) + loss_sum = sum(log.get("loss", 0) for log in logging_outputs) + nll_loss_sum = sum(log.get("nll_loss", 0) for log in logging_outputs) + ntokens = sum(log.get("ntokens", 0) for log in logging_outputs) + nsentences = sum(log.get("nsentences", 0) for log in logging_outputs) + sample_size = sum(log.get("sample_size", 0) for log in logging_outputs) + nframes = sum(log.get("nframes", 0) for log in logging_outputs) + speech_loss_sum = sum(log.get("speech_loss", 0) for log in logging_outputs) + speech_nll_loss_sum = sum(log.get("speech_nll_loss", 0) for log in logging_outputs) + speech_attn_loss_sum = sum(log.get("speech_attn_loss", 0) for log in logging_outputs) + sample_size_speech = sum(log.get("sample_size_speech_cost", 0) for log in logging_outputs) + + agg_output = { + "loss": loss_sum / sample_size / math.log(2) if sample_size > 0 else 0.0, + "nll_loss": nll_loss_sum / sample_size / math.log(2) if sample_size > 0 else 0.0, + # if args.sentence_avg, then sample_size is nsentences, and loss + # is per-sentence loss; else sample_size is ntokens, and the loss + # becomes per-output token loss + "speech_loss": speech_loss_sum / sample_size_speech / math.log(2) if sample_size_speech > 0 else 0.0, + "speech_nll_loss": speech_nll_loss_sum / sample_size_speech / math.log(2) if sample_size_speech > 0 else 0.0, + "speech_attn_loss": speech_attn_loss_sum / src_token_sum / math.log(2) if src_token_sum > 0 else 0.0, + "ntokens": ntokens, + "nsentences": nsentences, + "nframes": nframes, + "sample_size": sample_size, + "acc": correct_sum * 100.0 / total_sum if total_sum > 0 else 0.0, + "correct": correct_sum, + "total": total_sum, + "src_token_num": src_token_sum, + # total is the number of validate tokens + } + return agg_output + + @classmethod + def reduce_metrics(cls, logging_outputs): + """Aggregate logging outputs from data parallel training.""" + agg_logging_outputs = cls.aggregate_logging_outputs(logging_outputs) + for k, v in agg_logging_outputs.items(): + if k in {'nsentences', 'ntokens', 'sample_size'}: + continue + metrics.log_scalar(k, v, round=3) diff --git a/examples/speech_text_joint_to_text/docs/ende-mustc.md b/examples/speech_text_joint_to_text/docs/ende-mustc.md new file mode 100644 index 0000000000..2897c4e27b --- /dev/null +++ b/examples/speech_text_joint_to_text/docs/ende-mustc.md @@ -0,0 +1,112 @@ +[[Back]](..) + +# Joint Speech Text Training for the MuST-C English to German Speech Translation task + +Joint Training Baseline: it is based on paper ["A general multi-task learning framework to leverage text data for speech to text tasks"](https://arxiv.org/pdf/2010.11338.pdf) + +Enhanced Joint Training: the joint training is enhanced with pre-trained models, cross attentive regularization and online knowledge distillation based on paper ["Improving Speech Translation by Understanding and Learning from the Auxiliary Text Translation Task"](https://research.fb.com/publications/improving-speech-translation-by-understanding-and-learning-from-the-auxiliary-text-translation-task) + +## Prepare Data +#### Download files +- Sentence piece model [spm.model](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/must_c/en_de/spm.model) +- Dictionary [dict.txt](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/must_c/en_de/dict.txt) +- config [config.yaml](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/must_c/en_de/config.yaml) +#### Prepare MuST-C data set +- [Please follow the data preparation in the S2T example](https://github.com/pytorch/fairseq/blob/main/examples/speech_to_text/docs/mustc_example.md) +- Append src_text in the tsv file with phoneme representation. +```bash + python examples/speech_text_joint_to_text/scripts/g2p_encode.py \ + --lower-case --do-filter --use-word-start --no-punc \ + --reserve-word examples/speech_text_joint_to_text/configs/mustc_noise.list \ + --data-path ${must_c_en_de_src_text} \ + --out-path ${must_c_en_de_src_text_pho} +``` +- Update tsv data with src_text generated above and save to $MANIFEST_ROOT +- Prepare phoneme dictionary and save to $MANIFEST_ROOT as [src_dict.txt](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/must_c/en_de/src_dict.txt) +#### Prepare WMT text data +- [Download wmt data](https://github.com/pytorch/fairseq/blob/main/examples/translation/prepare-wmt14en2de.sh) +- Convert source text (English) into phoneme representation as above +- Generate binary parallel file for training (as translation example) and save data in $parallel_text_data + +## Training +The model is trained with 8 v100 GPUs. + +#### Download pretrained models +- [pretrain_encoder](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_multilingual_asr_transformer_m.pt) +- [pretrain_nmt](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/must_c/en_de/checkpoint_mt.pt) + +#### Training scripts +- Jointly trained model from scratch +```bash +python train.py ${MANIFEST_ROOT} \ + --save-dir ${save_dir} \ + --num-workers 8 \ + --task speech_text_joint_to_text \ + --arch dualinputs2ttransformer_s \ + --user-dir examples/speech_text_joint_to_text \ + --max-epoch 100 --update-mix-data \ + --optimizer adam --lr-scheduler inverse_sqrt \ + --lr 0.001 --update-freq 4 --clip-norm 10.0 \ + --criterion guided_label_smoothed_cross_entropy_with_accuracy \ + --label-smoothing 0.1 --max-tokens 10000 --max-tokens-text 10000 \ + --max-positions-text 400 --seed 2 --speech-encoder-layers 12 \ + --text-encoder-layers 6 --encoder-shared-layers 6 --decoder-layers 6 \ + --dropout 0.1 --warmup-updates 20000 \ + --text-sample-ratio 0.25 --parallel-text-data ${parallel_text_data} \ + --text-input-cost-ratio 0.5 --enc-grad-mult 2.0 --add-speech-eos \ + --log-format json --langpairs en-de --noise-token '"'"'▁NOISE'"'"' \ + --mask-text-ratio 0.0 --max-tokens-valid 20000 --ddp-backend no_c10d \ + --log-interval 100 --data-buffer-size 50 --config-yaml config.yaml \ + --keep-last-epochs 10 +``` +- Jointly trained model with good initialization, cross attentive loss and online knowledge distillation +```bash +python train.py ${MANIFEST_ROOT} \ + --save-dir ${save_dir} \ + --num-workers 8 \ + --task speech_text_joint_to_text \ + --arch dualinputs2ttransformer_m \ + --user-dir examples/speech_text_joint_to_text \ + --max-epoch 100 --update-mix-data \ + --optimizer adam --lr-scheduler inverse_sqrt \ + --lr 0.002 --update-freq 4 --clip-norm 10.0 \ + --criterion guided_label_smoothed_cross_entropy_with_accuracy \ + --guide-alpha 0.8 --disable-text-guide-update-num 5000 \ + --label-smoothing 0.1 --max-tokens 10000 --max-tokens-text 10000 \ + --max-positions-text 400 --seed 2 --speech-encoder-layers 12 \ + --text-encoder-layers 6 --encoder-shared-layers 6 --decoder-layers 6 \ + --dropout 0.1 --warmup-updates 20000 --attentive-cost-regularization 0.02 \ + --text-sample-ratio 0.25 --parallel-text-data ${parallel_text_data} \ + --text-input-cost-ratio 0.5 --enc-grad-mult 2.0 --add-speech-eos \ + --log-format json --langpairs en-de --noise-token '"'"'▁NOISE'"'"' \ + --mask-text-ratio 0.0 --max-tokens-valid 20000 --ddp-backend no_c10d \ + --log-interval 100 --data-buffer-size 50 --config-yaml config.yaml \ + --load-pretrain-speech-encoder ${pretrain_encoder} \ + --load-pretrain-decoder ${pretrain_nmt} \ + --load-pretrain-text-encoder-last ${pretrain_nmt} \ + --keep-last-epochs 10 +``` + +## Evaluation +```bash +python ./fairseq_cli/generate.py \ + ${MANIFEST_ROOT} \ + --task speech_text_joint_to_text \ + --max-tokens 25000 \ + --nbest 1 \ + --results-path ${infer_results} \ + --batch-size 512 \ + --path ${model} \ + --gen-subset tst-COMMON \ + --config-yaml config_spm.yaml \ + --scoring sacrebleu \ + --beam 5 --lenpen 1.0 \ + --user-dir examples/speech_text_joint_to_text \ + --load-speech-only +``` + +## Results (Joint training with initialization + CAR + online KD) +|Direction|En-De | En-Es | En-Fr | +|---|---|---|---| +|BLEU|27.4| 31.2 | 37.6 | +|checkpoint | [link](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/must_c/en_de/checkpoint_ave_10.pt) |[link](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/must_c/en_es/checkpoint_ave_10.pt)|[link](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/must_c/en_fr/checkpoint_ave_10.pt)| diff --git a/examples/speech_text_joint_to_text/docs/iwslt2021.md b/examples/speech_text_joint_to_text/docs/iwslt2021.md new file mode 100644 index 0000000000..920ff271c2 --- /dev/null +++ b/examples/speech_text_joint_to_text/docs/iwslt2021.md @@ -0,0 +1,76 @@ +[[Back]](..) + +# Joint Speech Text Training for the 2021 IWSLT multilingual speech translation + +This directory contains the code from paper ["FST: the FAIR Speech Translation System for the IWSLT21 Multilingual Shared Task"](https://arxiv.org/pdf/2107.06959.pdf). + +## Prepare Data +#### Download files +- Sentence piece model [spm.model](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/iwslt/iwslt_data/spm.model) +- Dictionary [tgt_dict.txt](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/iwslt/iwslt_data/dict.txt) +- Config [config.yaml](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/iwslt/iwslt_data/config.yaml) + +#### Prepare +- [Please follow the data preparation in speech-to-text](https://github.com/pytorch/fairseq/blob/main/examples/speech_to_text/docs/mtedx_example.md) + + + +## Training + +#### Download pretrained models +- [Pretrained mbart model](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/iwslt/iwslt_data/mbart.pt) +- [Pretrained w2v model](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/iwslt/iwslt_data/xlsr_53_56k.pt) + + +#### Training scripts + +```bash +python train.py ${MANIFEST_ROOT} \ + --save-dir ${save_dir} \ + --user-dir examples/speech_text_joint_to_text \ + --train-subset train_es_en_tedx,train_es_es_tedx,train_fr_en_tedx,train_fr_es_tedx,train_fr_fr_tedx,train_it_it_tedx,train_pt_en_tedx,train_pt_pt_tedx \ + --valid-subset valid_es_en_tedx,valid_es_es_tedx,valid_es_fr_tedx,valid_es_it_tedx,valid_es_pt_tedx,valid_fr_en_tedx,valid_fr_es_tedx,valid_fr_fr_tedx,valid_fr_pt_tedx,valid_it_en_tedx,valid_it_es_tedx,valid_it_it_tedx,valid_pt_en_tedx,valid_pt_es_tedx,valid_pt_pt_tedx \ + --config-yaml config.yaml --ddp-backend no_c10d \ + --num-workers 2 --task speech_text_joint_to_text \ + --criterion guided_label_smoothed_cross_entropy_with_accuracy \ + --label-smoothing 0.3 --guide-alpha 0.8 \ + --disable-text-guide-update-num 5000 --arch dualinputxmtransformer_base \ + --max-tokens 500000 --max-sentences 3 --max-tokens-valid 800000 \ + --max-source-positions 800000 --enc-grad-mult 2.0 \ + --attentive-cost-regularization 0.02 --optimizer adam \ + --clip-norm 1.0 --log-format simple --log-interval 200 \ + --keep-last-epochs 5 --seed 1 \ + --w2v-path ${w2v_path} \ + --load-pretrained-mbart-from ${mbart_path} \ + --max-update 1000000 --update-freq 4 \ + --skip-invalid-size-inputs-valid-test \ + --skip-encoder-projection --save-interval 1 \ + --attention-dropout 0.3 --mbart-dropout 0.3 \ + --finetune-w2v-params all --finetune-mbart-decoder-params all \ + --finetune-mbart-encoder-params all --stack-w2v-mbart-encoder \ + --drop-w2v-layers 12 --normalize \ + --lr 5e-05 --lr-scheduler inverse_sqrt --warmup-updates 5000 +``` + +## Evaluation +```bash +python ./fairseq_cli/generate.py + ${MANIFEST_ROOT} \ + --task speech_text_joint_to_text \ + --user-dir ./examples/speech_text_joint_to_text \ + --load-speech-only --gen-subset test_es_en_tedx \ + --path ${model} \ + --max-source-positions 800000 \ + --skip-invalid-size-inputs-valid-test \ + --config-yaml config.yaml \ + --infer-target-lang en \ + --max-tokens 800000 \ + --beam 5 \ + --results-path ${RESULTS_DIR} \ + --scoring sacrebleu +``` +The trained model can be downloaded [here](https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/iwslt/iwslt_data/checkpoint17.pt) + +|direction|es_en|fr_en|pt_en|it_en|fr_es|pt_es|it_es|es_es|fr_fr|pt_pt|it_it| +|---|---|---|---|---|---|---|---|---|---|---|---| +|BLEU|31.62|36.93|35.07|27.12|38.87|35.57|34.13|74.59|74.64|70.84|69.76| diff --git a/examples/speech_text_joint_to_text/models/__init__.py b/examples/speech_text_joint_to_text/models/__init__.py new file mode 100644 index 0000000000..7a394c7e4f --- /dev/null +++ b/examples/speech_text_joint_to_text/models/__init__.py @@ -0,0 +1,14 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import importlib +import os + +for file in os.listdir(os.path.dirname(__file__)): + if file.endswith(".py") and not file.startswith("_"): + model_name = file[: file.find(".py")] + importlib.import_module( + "examples.speech_text_joint_to_text.models." + model_name + ) diff --git a/examples/speech_text_joint_to_text/models/s2t_dualinputtransformer.py b/examples/speech_text_joint_to_text/models/s2t_dualinputtransformer.py new file mode 100644 index 0000000000..7970a3c714 --- /dev/null +++ b/examples/speech_text_joint_to_text/models/s2t_dualinputtransformer.py @@ -0,0 +1,1090 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +from collections import namedtuple + +import torch +import torch.nn as nn +from fairseq import checkpoint_utils +from fairseq import utils +from fairseq.models import ( + FairseqEncoder, + FairseqDecoder, + FairseqEncoderDecoderModel, + register_model, + register_model_architecture, +) +from fairseq.models.fairseq_encoder import EncoderOut +from fairseq.models.speech_to_text import ( + TransformerDecoder, + S2TTransformerEncoder, +) +from fairseq.models.transformer import TransformerEncoder +from fairseq.modules import ( + TransformerEncoderLayer, + GradMultiply, + LayerNorm, +) + +logger = logging.getLogger(__name__) + + +class SpeechEoSEncoder(FairseqEncoder): + def __init__(self, encoder, eos_num, feat_dim, adapter_type="None", adapter_dim=0): + super().__init__(None) + self.encoder = encoder + self.eos_num = eos_num # downsampling rate for speech input feature + self.eos_emb = ( + nn.Parameter(torch.zeros(1, feat_dim), requires_grad=True) + if eos_num > 0 + else None + ) + self.adapter = self.add_adapter(adapter_type, adapter_dim) + + def add_adapter(self, adapter_type, adapter_dim): + def _make_identity(linear, eps=1e-5): + assert isinstance(linear, nn.Linear) + linear.weight.data.mul_(eps) + linear.weight.data.fill_diagonal_(1.0) + if linear.bias is not None: + linear.bias.data.mul_(eps) + + adapter = None + if adapter_type == "Linear": + assert adapter_dim > 0 + adapter = nn.Sequential( + nn.Linear(adapter_dim, adapter_dim), LayerNorm(adapter_dim) + ) + # initialize the adapter as identity matrix first + _make_identity(adapter[0]) + + elif adapter_type == "MLP": + assert adapter_dim > 0 + # assume the model is pre-norm model + adapter = nn.Sequential( + nn.Linear(adapter_dim, 2 * adapter_dim), + nn.ReLU(), + nn.Linear(2 * adapter_dim, adapter_dim), + LayerNorm(adapter_dim), + ) + _make_identity(adapter[0]) + _make_identity(adapter[2]) + return adapter + + def add_eos(self, src_tokens, src_lengths): + bsz, max_seq_len, fdim = src_tokens.size() + if self.eos_num > 0: + src_token_eos = torch.zeros( + [bsz, max_seq_len + self.eos_num, fdim], + dtype=src_tokens.dtype, + device=src_tokens.device, + ) + src_token_eos[:, :max_seq_len] = src_tokens + for bi in range(bsz): + src_token_eos[bi][ + src_lengths[bi] : src_lengths[bi] + self.eos_num + ] = self.eos_emb.expand(self.eos_num, fdim) + src_lengths = src_lengths + self.eos_num + src_tokens = src_token_eos + return src_tokens, src_lengths + + def apply_adapter(self, enc_out): + if self.adapter is None: + return enc_out + rst = self.adapter(enc_out.encoder_out) + if enc_out.encoder_padding_mask is not None: + rst.masked_fill_( + enc_out.encoder_padding_mask.transpose(0, 1).unsqueeze(-1), 0 + ) + return EncoderOut( + encoder_out=rst, + encoder_padding_mask=enc_out.encoder_padding_mask, + encoder_embedding=enc_out.encoder_embedding, + encoder_states=enc_out.encoder_states, + src_tokens=enc_out.src_tokens, + src_lengths=enc_out.src_lengths, + ) + + def forward(self, src_tokens, src_lengths=None, return_all_hiddens=False, **kwargs): + """ + src_tokens: padded tensor (B, T, C * feat) + src_lengths: tensor of original lengths of input utterances (B,) + """ + src_tokens, src_lengths = self.add_eos(src_tokens, src_lengths) + enc_out = self.encoder(src_tokens, src_lengths, return_all_hiddens) + enc_out = self.apply_adapter(enc_out) + return enc_out + + def reorder_encoder_out(self, encoder_out, new_order): + return self.encoder.reorder_encoder_out(encoder_out, new_order) + + +class DualInputEncoder(FairseqEncoder): + def __init__( + self, + args, + spch_encoder, + text_encoder, + dictionary, + cross_attentive_loss_before_last_layer=-1, + ): + super().__init__(dictionary) + + self.spch_encoder = spch_encoder + self.text_encoder = text_encoder + self.enc_grad_mult = args.enc_grad_mult + self.cross_attentive_loss_before_last_layer = ( + cross_attentive_loss_before_last_layer + ) + self.use_cross_attentive_loss = ( + False if cross_attentive_loss_before_last_layer <= -1 else True + ) + self.enc2_along_grad_mult = args.enc2_along_grad_mult + + @classmethod + def set_shared_layer(cls, share_level, src_layer, tgt_layer): + """ + share parameters from tgt_layer to src_layer + share_level: + 0: share everything + 1: share everything but different model + 2: share weight but not bias, layernorm + """ + if share_level == 0: + return tgt_layer + if isinstance(src_layer, nn.Linear): + return tgt_layer + if isinstance(src_layer, TransformerEncoderLayer): + assert src_layer.embed_dim == tgt_layer.embed_dim + assert src_layer.normalize_before == tgt_layer.normalize_before + if share_level == 1: + src_layer.fc1 = tgt_layer.fc1 + src_layer.fc2 = tgt_layer.fc2 + src_layer.self_attn = tgt_layer.self_attn + src_layer.final_layer_norm = tgt_layer.final_layer_norm + src_layer.self_attn_layer_norm = tgt_layer.self_attn_layer_norm + src_layer.layernorm_embedding = tgt_layer.layernorm_embedding + else: + src_layer.fc1.weight = tgt_layer.fc1.weight + src_layer.fc2.weight = tgt_layer.fc2.weight + src_layer.self_attn.k_proj.weight = tgt_layer.self_attn.k_proj.weight + src_layer.self_attn.v_proj.weight = tgt_layer.self_attn.v_proj.weight + src_layer.self_attn.q_proj.weight = tgt_layer.self_attn.q_proj.weight + src_layer.self_attn.out_proj.weight = ( + tgt_layer.self_attn.out_proj.weight + ) + else: + if share_level == 1: + return tgt_layer + return src_layer + + @classmethod + def build_spch_encoder(cls, args): + cfg = { + "input_feat_per_channel": args.input_feat_per_channel, + "input_channels": args.input_channels, + "conv_kernel_sizes": args.conv_kernel_sizes, + "conv_channels": args.conv_channels, + "encoder_embed_dim": args.encoder_embed_dim, + "encoder_ffn_embed_dim": args.encoder_ffn_embed_dim, + "encoder_layers": args.speech_encoder_layers, + "encoder_layerdrop": args.encoder_layerdrop, + "encoder_attention_heads": args.encoder_attention_heads, + "max_source_positions": args.max_source_positions, + "dropout": args.dropout, + "encoder_normalize_before": args.encoder_normalize_before, + "activation_dropout": args.activation_dropout, + "attention_dropout": args.attention_dropout, + "activation_fn": args.activation_fn, + "layernorm_embedding": args.layernorm_embedding, + "no_token_positional_embeddings": args.no_token_positional_embeddings, + "no_scale_embedding": args.no_scale_embedding, + "quant_noise_pq": args.quant_noise_pq, + "encoder_freezing_updates": 0, + } + model_args = namedtuple("args", cfg.keys())(*cfg.values()) + spch_encoder = S2TTransformerEncoder(model_args) + if args.add_speech_eos: + spch_encoder = SpeechEoSEncoder( + spch_encoder, + 2 * len(args.conv_kernel_sizes.split(",")), + args.input_feat_per_channel, + adapter_type=getattr(args, "speech_encoder_adapter_type", "None"), + adapter_dim=args.encoder_embed_dim, + ) + return spch_encoder + + @classmethod + def build_text_encoder(cls, args, src_dictionary, spch_encoder): + if args.encoder_shared_layers > 0: + mx_shared_layers = ( + args.speech_encoder_layers + if args.speech_encoder_layers < args.text_encoder_layers + else args.text_encoder_layers + ) + args.encoder_shared_layers = ( + args.encoder_shared_layers + if args.encoder_shared_layers <= mx_shared_layers + else mx_shared_layers + ) + cfg = { + "encoder_embed_dim": args.encoder_text_embed_dim, + "encoder_ffn_embed_dim": args.encoder_ffn_embed_dim, + "encoder_layers": args.text_encoder_layers, + "encoder_layerdrop": args.encoder_layerdrop, + "encoder_attention_heads": args.encoder_attention_heads, + "encoder_learned_pos": args.encoder_learned_pos, + "max_source_positions": args.max_source_positions, + "dropout": args.dropout, + "encoder_normalize_before": args.encoder_normalize_before, + "activation_dropout": args.activation_dropout, + "attention_dropout": args.attention_dropout, + "activation_fn": args.activation_fn, + "adaptive_input": args.adaptive_input, + "no_token_positional_embeddings": args.no_token_positional_embeddings, + "no_scale_embedding": args.no_scale_embedding, + "quant_noise_pq": args.quant_noise_pq, + } + model_args = namedtuple("args", cfg.keys())(*cfg.values()) + enc_emb = nn.Embedding( + len(src_dictionary), model_args.encoder_embed_dim, src_dictionary.pad() + ) + text_encoder = TransformerEncoder(model_args, src_dictionary, enc_emb) + if args.add_speech_eos: + spch_encoder = spch_encoder.encoder + if args.encoder_shared_layers > 0: + text_encoder.layer_norm = cls.set_shared_layer( + args.encoder_shared_layer_level, + text_encoder.layer_norm, + spch_encoder.layer_norm, + ) + for i, ly in enumerate( + spch_encoder.transformer_layers[-args.encoder_shared_layers :] + ): + ly_id = i + args.text_encoder_layers - args.encoder_shared_layers + assert isinstance(text_encoder.layers[ly_id], type(ly)) + text_encoder.layers[ly_id] = cls.set_shared_layer( + args.encoder_shared_layer_level, + text_encoder.layers[ly_id], + ly, + ) + return text_encoder + + def mult_rst_grad(self, rst, ratio): + assert isinstance(rst, dict) # instead of EncoderOut + assert len(rst["encoder_out"]) == 1 + rst["encoder_out"][0] = GradMultiply.apply(rst["encoder_out"][0], ratio) + return rst + + def process_attentive_loss_states(self, rst, interstates): + assert isinstance(rst, dict) # instead of EncoderOut + rst["encoder_states"] = interstates + return rst + + def forward( + self, + src_tokens, + src_lengths=None, + src_txt_tokens=None, + src_txt_lengths=None, + **kwargs + ): + """ + Args: + src_tokens: padded tensor (B, T, C * feat) + src_lengths: tensor of original lengths of input utterances (speech) (B,) + src_txt_tokens: padded tensor (B, T) + src_txt_lengths: tensor of original lengths of input utterances (text) (B,) + """ + # src_tokens only: inference + # src_tokens, src_lengths: speech only training + # src_txt_tokens, src_txt_lengths: text only training + # all valid: speech + text training + + if src_tokens is None and src_txt_tokens is None: + raise ValueError( + "src_tokens and src_txt_tokens cannot be None at the same time" + ) + ret1 = None + ret2 = None + return_all_hiddens = False + if src_tokens is not None: + if ( + self.use_cross_attentive_loss and src_txt_tokens is not None + ): # remove self.training so we can get attn score during validation step + return_all_hiddens = True + ret1 = self.spch_encoder( + src_tokens, src_lengths, return_all_hiddens=return_all_hiddens + ) + + if self.use_cross_attentive_loss and src_txt_tokens is not None: + assert self.cross_attentive_loss_before_last_layer < len( + ret1["encoder_states"] + ) + ret1 = self.process_attentive_loss_states( + ret1, + ret1["encoder_states"][ + -self.cross_attentive_loss_before_last_layer - 1 + ], + ) + + if src_txt_tokens is not None: + ret2 = self.text_encoder( + src_txt_tokens, src_txt_lengths, return_all_hiddens=return_all_hiddens + ) + if return_all_hiddens: + if self.cross_attentive_loss_before_last_layer == len( + self.text_encoder.layers + ): + text_embedding, _ = self.text_encoder.forward_embedding( + src_txt_tokens + ) + text_embedding = text_embedding.transpose(0, 1) + ret2 = self.process_attentive_loss_states(ret2, text_embedding) + else: + assert self.cross_attentive_loss_before_last_layer < len( + self.text_encoder.layers + ) + ret2 = self.process_attentive_loss_states( + ret2, + ret2["encoder_states"][ + -self.cross_attentive_loss_before_last_layer - 1 + ], + ) + + def merge_output(rst1, rst2): + if rst1 is None: + if not (self.enc2_along_grad_mult == 1.0 or self.training): + rst2 = self.mult_rst_grad(rst2, self.enc2_along_grad_mult) + return rst2 + if rst2 is None: + return rst1 + if self.enc_grad_mult != 1.0 and self.training: + rst1 = self.mult_rst_grad(rst1, self.enc_grad_mult) + rst2 = self.mult_rst_grad(rst2, self.enc_grad_mult) + rst = (rst1, rst2) + return rst + + return merge_output(ret1, ret2) + + def reorder_encoder_out(self, encoder_out, new_order): + assert self.training is False # used for inference only + return self.spch_encoder.reorder_encoder_out(encoder_out, new_order) + + +# TransformerMultiInputDecoder: take one or two encoder inputs +class TransformerMultiInputDecoder(FairseqDecoder): + def __init__( + self, + dictionary, + spch_decoder, + text_decoder, + compute_cross_attentive_loss=False, + cross_attentive_loss_with_norm=True, + cross_attentive_loss_reverse=False, + ): + + super().__init__(dictionary) + self.spch_decoder = spch_decoder + self.text_decoder = text_decoder + self.compute_cross_attentive_loss = compute_cross_attentive_loss + self.cross_attentive_loss_with_norm = cross_attentive_loss_with_norm + self.cross_attentive_loss_reverse = cross_attentive_loss_reverse + + @classmethod + def share_spchdecoder(cls, task_args, text_decoder, spch_decoder): + if task_args.decoder_shared_layer_level == 0: + return text_decoder + assert text_decoder.embed_tokens == spch_decoder.embed_tokens + spch_decoder.project_in_dim = text_decoder.project_in_dim + spch_decoder.embed_positions = text_decoder.embed_positions + spch_decoder.layernorm_embedding = text_decoder.layernorm_embedding + spch_decoder.project_out_dim = text_decoder.project_out_dim + spch_decoder.adaptive_softmax = text_decoder.adaptive_softmax + if task_args.decoder_shared_layer_level == 1: + spch_decoder.output_projection = text_decoder.output_projection + spch_decoder.layer_norm = text_decoder.layer_norm + else: # 2 + spch_decoder.output_projection.weight = ( + text_decoder.output_projection.weight + ) + for i, ly in enumerate(text_decoder.layers): + sly = spch_decoder.layers[i] + sly.self_attn = ly.self_attn + sly.self_attn_layer_norm = ly.self_attn_layer_norm + # sly.encoder_attn = ly.encoder_attn + if ( + task_args.decoder_shared_layer_level == 1 + ): # share everything, but under different models + sly.encoder_attn = ly.encoder_attn + sly.encoder_attn_layer_norm = ly.encoder_attn_layer_norm + sly.fc1 = ly.fc1 + sly.fc2 = ly.fc2 + sly.final_layer_norm = ly.final_layer_norm + else: # task_args.decoder_shared_layer_level == 2: #separated encoder_attn_layer_norm and bias + sly.encoder_attn.k_proj.weight = ly.encoder_attn.k_proj.weight + sly.encoder_attn.v_proj.weight = ly.encoder_attn.v_proj.weight + sly.encoder_attn.q_proj.weight = ly.encoder_attn.q_proj.weight + sly.encoder_attn.out_proj.weight = ly.encoder_attn.out_proj.weight + sly.fc1.weight = ly.fc1.weight + sly.fc2.weight = ly.fc2.weight + + return spch_decoder + + def cross_attentive_loss( + self, teacher_states, student_states, teacher_masking, student_masking, eps=1e-6 + ): + x = teacher_states.transpose(0, 1) # from T X B X D to B X T X D + y = student_states.transpose(0, 1) + if self.cross_attentive_loss_with_norm: + x = x / (x.norm(dim=2, keepdim=True) + eps) + y = y / (y.norm(dim=2, keepdim=True) + eps) + dim = x.size(-1) + # lengths: batch X seqLen + sim_scores_xy = torch.bmm(x, y.transpose(1, 2)) # batch X lenx X leny ] + if y.dtype == torch.float16: + sim_scores_xy = sim_scores_xy.float() + y = y.float() + x = x.float() + if teacher_masking != []: + assert len(teacher_masking) == 1 + sim_scores_xy = sim_scores_xy.masked_fill( + teacher_masking[0].unsqueeze(-1), float("-inf") + ) + if student_masking != []: + sim_scores_xy = sim_scores_xy.masked_fill( + student_masking[0].unsqueeze(1), float("-inf") + ) + # do masking + y_weights = utils.softmax(sim_scores_xy, dim=-1) + if teacher_masking != []: + y_weights = y_weights.masked_fill(teacher_masking[0].unsqueeze(-1), 0) + x_reconstruct_from_y = torch.bmm(y_weights, y) + + sim_scores_xx = torch.bmm(x, x.transpose(1, 2)) # batch X lenx X lenx ] + x_weights = utils.softmax(sim_scores_xx, dim=-1) + if teacher_masking != []: + x_weights = x_weights.masked_fill(teacher_masking[0].unsqueeze(-1), 0) + + # no gradient for teacher state + x_reconstruct_from_x = torch.bmm(x_weights, x).detach() + cost = (x_reconstruct_from_x - x_reconstruct_from_y).norm(dim=2) + if teacher_masking != []: + cost = cost.masked_fill(teacher_masking[0], 0) + + if not self.cross_attentive_loss_with_norm: + cost = cost / dim + return cost + + def forward( + self, + prev_output_tokens, + encoder_out, + incremental_state=None, + has_txt_input=False, + **kwargs + ): + """ + Args: + prev_output_tokens (LongTensor): previous decoder outputs of shape + `(batch, tgt_len)`, for input feeding/teacher forcing. If there are + two or more input during training, they will share the same prev_output_tokens + encoder_out (tuple[Tensor]): output from the encoder, used for + encoder-side attention. It will be tuple if there are more inputs, but a tensor + if only one input + incremental_state ([dict]): dictionary used for storing state during + :ref:`Incremental decoding`. It is only valid for inference, only from single + input + Returns: + tuple: + - the last decoder layer's output of shape `(batch, tgt_len, + vocab)`. If there are N inputs, batch will be N bigger than a single input + - the last decoder layer's attention weights of shape `(batch, + tgt_len, src_len)` + """ + assert not isinstance(encoder_out, EncoderOut) + if isinstance(encoder_out, tuple): # training with mulitple input + rst = [] + assert len(encoder_out) == 2 + for i, eo in enumerate(encoder_out): + assert incremental_state is None + if i == 0: + rst.append( + self.spch_decoder(prev_output_tokens, eo, incremental_state) + ) + else: + rst.append( + self.text_decoder(prev_output_tokens, eo, incremental_state) + ) + dec_out = torch.cat([r[0] for r in rst], dim=0) + attn_cost = None + if self.compute_cross_attentive_loss: + assert isinstance(encoder_out[0], dict) + if self.cross_attentive_loss_reverse: + attn_cost = self.cross_attentive_loss( + teacher_states=encoder_out[1]["encoder_states"], # text_states + student_states=encoder_out[0]["encoder_states"], # spch_states + teacher_masking=encoder_out[1]["encoder_padding_mask"], + student_masking=encoder_out[0]["encoder_padding_mask"], + ) + else: + attn_cost = self.cross_attentive_loss( + teacher_states=encoder_out[0]["encoder_states"], # spch_states + student_states=encoder_out[1]["encoder_states"], # text_states + teacher_masking=encoder_out[0]["encoder_padding_mask"], + student_masking=encoder_out[1]["encoder_padding_mask"], + ) + + return (dec_out, {"attn_cost": attn_cost}) + else: # inference or training with one input + if has_txt_input: + return self.text_decoder( + prev_output_tokens, encoder_out, incremental_state + ) + return self.spch_decoder(prev_output_tokens, encoder_out, incremental_state) + + +# Note: +# dual input transformer: +# encoder: S2TTransformerEncoder for speech + TransformerEncoder for text +# decoder: TransformerDecoder for text +@register_model("dual_input_s2t_transformer") +class DualInputS2TTransformerModel(FairseqEncoderDecoderModel): + def __init__(self, encoder, decoder): + super().__init__(encoder, decoder) + self.num_updates = 0 + + def max_positions(self): + return None # it is provided in task + + @staticmethod + def add_args(parser): + """Add model-specific arguments to the parser.""" + # encoder 1: S2TTransformerEncoder for speech + parser.add_argument( + "--conv-kernel-sizes", + type=str, + metavar="N", + help="kernel sizes of Conv1d subsampling layers", + ) + parser.add_argument( + "--conv-channels", + type=int, + metavar="N", + help="# of channels in Conv1d subsampling layers", + ) + parser.add_argument( + "--enc-output-dim", + type=int, + metavar="N", + help=""" + encoder output dimension, can be None. If specified, projecting the + transformer output to the specified dimension""", + ) + # standard Transformer + parser.add_argument( + "--activation-fn", + type=str, + default="relu", + choices=utils.get_available_activation_fns(), + help="activation function to use", + ) + parser.add_argument( + "--dropout", type=float, metavar="D", help="dropout probability" + ) + parser.add_argument( + "--attention-dropout", + type=float, + metavar="D", + help="dropout probability for attention weights", + ) + parser.add_argument( + "--activation-dropout", + "--relu-dropout", + type=float, + metavar="D", + help="dropout probability after activation in FFN.", + ) + parser.add_argument( + "--encoder-embed-dim", + type=int, + metavar="N", + help="encoder embedding dimension", + ) + parser.add_argument( + "--encoder-text-embed-dim", + type=int, + metavar="N", + help="encoder text embedding dimension", + ) + parser.add_argument( + "--encoder-ffn-embed-dim", + type=int, + metavar="N", + help="encoder embedding dimension for FFN", + ) + parser.add_argument( + "--encoder-attention-heads", + type=int, + metavar="N", + help="num encoder attention heads", + ) + parser.add_argument( + "--decoder-embed-dim", + type=int, + metavar="N", + help="decoder embedding dimension", + ) + parser.add_argument( + "--decoder-ffn-embed-dim", + type=int, + metavar="N", + help="decoder embedding dimension for FFN", + ) + parser.add_argument( + "--decoder-layers", type=int, metavar="N", help="num decoder layers" + ) + parser.add_argument( + "--decoder-attention-heads", + type=int, + metavar="N", + help="num decoder attention heads", + ) + parser.add_argument( + "--layernorm-embedding", + action="store_true", + help="add layernorm to embedding", + ) + parser.add_argument( + "--no-scale-embedding", + action="store_true", + help="if True, dont scale embeddings", + ) + # non-standard transformer parameters + parser.add_argument( + "--speech-encoder-layers", + type=int, + metavar="N", + help="num speech encoder layers", + ) + parser.add_argument( + "--text-encoder-layers", + type=int, + metavar="N", + help="num text encoder layers", + ) + parser.add_argument( + "--encoder-shared-layers", + type=int, + metavar="N", + help="num shared encoder layers", + ) + parser.add_argument( + "--encoder-shared-layer-level", + type=int, + metavar="N", + default=0, + choices=[0, 1, 2], + help="share layer level 0: all share 1: all share with separate model 2: share weight but not bias and layernorm", + ) + + parser.add_argument( + "--decoder-shared-layer-level", + default=0, + choices=[0, 1, 2], + type=int, + metavar="N", + help="0: share everything; 1: share everything with different model 2: no share layer_norm and bias", + ) + ### + parser.add_argument( + "--text-input-cost-ratio", + type=float, + default=1.0, + metavar="V", + help="text input cost ratio relative to speech input cost", + ) + parser.add_argument( + "--init-scale", + type=float, + default=1.0, + metavar="V", + help="scale the initial weight by given factor", + ) + parser.add_argument( + "--enc-grad-mult", + type=float, + metavar="V", + default=1.0, + help="multiply enc1 and enc2 gradient by V", + ) + parser.add_argument( + "--enc2-along-grad-mult", + type=float, + metavar="V", + default=1.0, + help="multiply enc2 gradient by V if only enc2 is used", + ) + parser.add_argument( + "--load-pretrain-encoder", + type=str, + default="", + metavar="EXPR", + help=""" path to the pretrained encoder """, + ) + parser.add_argument( + "--load-pretrain-speech-encoder", + type=str, + default="", + metavar="EXPR", + help=""" path to the pretrained speech encoder """, + ) + parser.add_argument( + "--load-pretrain-text-encoder", + type=str, + default="", + metavar="EXPR", + help=""" path to the pretrained text encoder """, + ) + parser.add_argument( + "--load-pretrain-text-encoder-last", + type=str, + default="", + metavar="EXPR", + help=""" path to the pretrained text encoder """, + ) + parser.add_argument( + "--load-pretrain-decoder", + type=str, + metavar="EXPR", + default="", + help=""" path to the pretrained encoder """, + ) + parser.add_argument( + "--add-speech-eos", + action="store_true", + help="add eos token at the end of input feature", + ) + parser.add_argument( + "--speech-encoder-adapter-type", + type=str, + metavar="EXPR", + default="None", + choices=["None", "Linear", "MLP"], + help="add speech encoder adapter", + ) + + @classmethod + def build_encoder(cls, args, task): + spch_encoder = DualInputEncoder.build_spch_encoder(args) + text_encoder = DualInputEncoder.build_text_encoder( + args, task.src_dict, spch_encoder + ) + cross_attentive_loss_before_last_layer = ( + 0 if getattr(args, "attentive_cost_regularization", 0.0) > 0.0 else -1 + ) + encoder = DualInputEncoder( + args, + spch_encoder, + text_encoder, + task.src_dict, + cross_attentive_loss_before_last_layer, + ) + if args.init_scale != 1.0: + with torch.no_grad(): + for param in encoder.parameters(): + param.data.mul_(args.init_scale) + if args.load_pretrain_text_encoder != "": + checkpoint_utils.load_pretrained_component_from_model( + text_encoder, args.load_pretrain_text_encoder + ) + if args.load_pretrain_speech_encoder != "": + if hasattr(spch_encoder, "encoder"): + checkpoint_utils.load_pretrained_component_from_model( + spch_encoder.encoder, args.load_pretrain_speech_encoder + ) + else: + checkpoint_utils.load_pretrained_component_from_model( + spch_encoder, args.load_pretrain_speech_encoder + ) + if ( + args.load_pretrain_text_encoder_last != "" + ): # if share encoder, speech encoder parameters will be used. + # It provides a chance to use pre-trained mt encoder instead + checkpoint_utils.load_pretrained_component_from_model( + text_encoder, args.load_pretrain_text_encoder_last + ) + + if args.load_pretrain_encoder != "": + checkpoint_utils.load_pretrained_component_from_model( + encoder, args.load_pretrain_encoder + ) + return encoder + + @classmethod + def build_decoder(cls, args, task): + dec_cfg = { + "decoder_layerdrop": args.decoder_layerdrop, + "share_decoder_input_output_embed": args.share_decoder_input_output_embed, + "decoder_embed_dim": args.decoder_embed_dim, + "max_target_positions": args.max_target_positions, + "dropout": args.dropout, + "encoder_learned_pos": args.encoder_learned_pos, + "decoder_learned_pos": args.decoder_learned_pos, + "layernorm_embedding": args.layernorm_embedding, + "decoder_normalize_before": args.decoder_normalize_before, + "activation_dropout": args.activation_dropout, + "attention_dropout": args.attention_dropout, + "decoder_ffn_embed_dim": args.decoder_ffn_embed_dim, + "decoder_layers": args.decoder_layers, + "decoder_attention_heads": args.decoder_attention_heads, + "decoder_output_dim": args.decoder_embed_dim, + "no_scale_embedding": args.no_scale_embedding, + "adaptive_input": args.adaptive_input, + "quant_noise_pq": args.quant_noise_pq, + "adaptive_softmax_cutoff": args.adaptive_softmax_cutoff, + "tie_adaptive_weights": args.tie_adaptive_weights, + "no_token_positional_embeddings": args.no_token_positional_embeddings, + } + dec_cfg = namedtuple("args", dec_cfg.keys())(*dec_cfg.values()) + dec_emb = nn.Embedding( + len(task.target_dictionary), + args.decoder_embed_dim, + task.target_dictionary.pad(), + ) + compute_cross_attentive_loss = ( + True if getattr(args, "attentive_cost_regularization", 0.0) > 0.0 else False + ) + cross_attentive_loss_without_norm = getattr( + args, "attentive_cost_without_normalize", False + ) + cross_attentive_loss_reverse = ( + False # getattr(args, "attentive_cost_reverse", False) + ) + + text_decoder = TransformerDecoder(dec_cfg, task.target_dictionary, dec_emb) + spch_decoder = TransformerDecoder(dec_cfg, task.target_dictionary, dec_emb) + spch_decoder = TransformerMultiInputDecoder.share_spchdecoder( + args, text_decoder, spch_decoder + ) + decoder = TransformerMultiInputDecoder( + dictionary=task.target_dictionary, + spch_decoder=spch_decoder, + text_decoder=text_decoder, + compute_cross_attentive_loss=compute_cross_attentive_loss, + cross_attentive_loss_with_norm=True + if not cross_attentive_loss_without_norm + else False, + cross_attentive_loss_reverse=cross_attentive_loss_reverse, + ) + if args.init_scale != 1.0: + with torch.no_grad(): + for param in decoder.parameters(): + param.data.mul_(args.init_scale) + if args.load_pretrain_decoder != "": + try: + checkpoint_utils.load_pretrained_component_from_model( + decoder, args.load_pretrain_decoder + ) + except RuntimeError: + checkpoint_utils.load_pretrained_component_from_model( + decoder.text_decoder, args.load_pretrain_decoder + ) + if args.decoder_shared_layer_level > 0: + checkpoint_utils.load_pretrained_component_from_model( + decoder.spch_decoder, args.load_pretrain_decoder + ) + + return decoder + + @classmethod + def build_model(cls, args, task): + """Build a new model instance.""" + # make sure that all args are properly defaulted + # (in case there are any new ones) + dualinputs2ttransformer_base(args) + + encoder = cls.build_encoder(args, task) + decoder = cls.build_decoder(args, task) + return cls(encoder, decoder) + + def get_normalized_probs(self, net_output, log_probs, sample=None): + # net_output['encoder_out'] is a (B, T, D) tensor + lprobs = super().get_normalized_probs(net_output, log_probs, sample) + lprobs.batch_first = True + return lprobs + + def set_num_updates(self, num_updates): + """Set the number of parameters updates.""" + super().set_num_updates(num_updates) + self.num_updates = num_updates + + def forward( + self, + src_tokens, + src_lengths, + prev_output_tokens, + use_encoder_outputs=False, + src_txt_tokens=None, + src_txt_lengths=None, + mode="sup_speech", + **kwargs + ): + """ + Run the forward pass for an encoder-decoder model. + + First feed a batch of source tokens through the encoder. Then, feed the + encoder output and previous decoder outputs (i.e., teacher forcing) to + the decoder to produce the next outputs:: + + encoder_out = self.encoder(src_tokens, src_lengths) + return self.decoder(prev_output_tokens, encoder_out) + + Args: + src_tokens (LongTensor): tokens in the source language of shape + `(batch, src_len)` + src_lengths (LongTensor): source sentence lengths of shape `(batch)` + prev_output_tokens (LongTensor): previous decoder outputs of shape + `(batch, tgt_len)`, for teacher forcing + mode = 'sup_speech' or 'text' + + Returns: + tuple: + - the decoder's output of shape `(batch, tgt_len, vocab)` + - a dictionary with any model-specific outputs + """ + if mode == "text": + assert src_txt_tokens is None + src_txt_tokens = src_tokens + src_txt_lengths = src_lengths + src_tokens = None + src_lengths = None + encoder_out = self.encoder( + src_tokens, + src_lengths=src_lengths, + src_txt_tokens=src_txt_tokens, + src_txt_lengths=src_txt_lengths, + **kwargs + ) + has_txt_input = True if src_txt_tokens is not None else False + decoder_out = self.decoder( + prev_output_tokens, + encoder_out=encoder_out, + has_txt_input=has_txt_input, + **kwargs + ) + if use_encoder_outputs: + return decoder_out, encoder_out + return decoder_out + + +@register_model_architecture( + "dual_input_s2t_transformer", "dualinputs2ttransformer_base" +) +def dualinputs2ttransformer_base(args): + args.encoder_freezing_updates = getattr(args, "encoder_freezing_updates", 0) + # Convolutional subsampler + args.input_feat_per_channel = getattr(args, "input_feat_per_channel", 80) + args.conv_kernel_sizes = getattr(args, "conv_kernel_sizes", "5,5") + args.conv_channels = getattr(args, "conv_channels", 1024) + # Transformer + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) + args.encoder_text_embed_dim = getattr( + args, "encoder_text_embed_dim", args.encoder_embed_dim + ) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8) + args.encoder_normalize_before = getattr(args, "encoder_normalize_before", True) + args.encoder_layerdrop = getattr(args, "encoder_layerdrop", 0) + args.encoder_learned_pos = getattr(args, "encoder_learned_pos", False) + + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim) + args.decoder_ffn_embed_dim = getattr( + args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim + ) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8) + args.decoder_normalize_before = getattr(args, "decoder_normalize_before", True) + args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False) + args.dropout = getattr(args, "dropout", 0.1) + args.attention_dropout = getattr(args, "attention_dropout", args.dropout) + args.activation_dropout = getattr(args, "activation_dropout", args.dropout) + args.activation_fn = getattr(args, "activation_fn", "relu") + args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None) + args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0) + args.tie_adaptive_weights = getattr(args, "tie_adaptive_weights", False) + args.share_decoder_input_output_embed = getattr( + args, "share_decoder_input_output_embed", False + ) + args.no_token_positional_embeddings = getattr( + args, "no_token_positional_embeddings", False + ) + args.adaptive_input = getattr(args, "adaptive_input", False) + args.decoder_layerdrop = getattr(args, "decoder_layerdrop", 0.0) + args.decoder_output_dim = getattr( + args, "decoder_output_dim", args.decoder_embed_dim + ) + args.layernorm_embedding = getattr(args, "layernorm_embedding", False) + args.no_scale_embedding = getattr(args, "no_scale_embedding", False) + args.quant_noise_pq = getattr(args, "quant_noise_pq", 0) + + args.speech_encoder_layers = getattr(args, "speech_encoder_layers", 10) + args.text_encoder_layers = getattr(args, "text_encoder_layers", 6) + args.encoder_shared_layers = getattr(args, "encoder_shared_layers", 0) + args.decoder_layers = getattr(args, "decoder_layers", 6) + + args.add_speech_eos = getattr(args, "add_speech_eos", False) + + +@register_model_architecture("dual_input_s2t_transformer", "dualinputs2ttransformer_s") +def dualinputs2ttransformer_s(args): + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 256) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 256 * 4) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 4) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 4) + args.dropout = getattr(args, "dropout", 0.1) + args.speech_encoder_layers = getattr(args, "speech_encoder_layers", 7) + args.text_encoder_layers = getattr(args, "text_encoder_layers", 7) + args.decoder_layers = getattr(args, "decoder_layers", 7) + dualinputs2ttransformer_base(args) + + +@register_model_architecture("dual_input_s2t_transformer", "dualinputs2ttransformer_m") +def dualinputs2ttransformer_m(args): + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 512 * 4) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8) + args.dropout = getattr(args, "dropout", 0.15) + args.speech_encoder_layers = getattr(args, "speech_encoder_layers", 10) + args.text_encoder_layers = getattr(args, "text_encoder_layers", 6) + args.decoder_layers = getattr(args, "decoder_layers", 6) + dualinputs2ttransformer_base(args) + + +@register_model_architecture("dual_input_s2t_transformer", "dualinputs2ttransformer_b") +def dualinputs2ttransformer_b(args): + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 768) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 768 * 4) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 12) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 12) + args.dropout = getattr(args, "dropout", 0.15) + args.speech_encoder_layers = getattr(args, "speech_encoder_layers", 12) + args.text_encoder_layers = getattr(args, "text_encoder_layers", 6) + args.decoder_layers = getattr(args, "decoder_layers", 6) + dualinputs2ttransformer_base(args) + + +@register_model_architecture("dual_input_s2t_transformer", "dualinputs2ttransformer_l") +def dualinputs2ttransformer_l(args): + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 1024 * 4) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 16) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 16) + args.dropout = getattr(args, "dropout", 0.2) + args.speech_encoder_layers = getattr(args, "speech_encoder_layers", 12) + args.text_encoder_layers = getattr(args, "text_encoder_layers", 6) + args.decoder_layers = getattr(args, "decoder_layers", 6) + dualinputs2ttransformer_base(args) diff --git a/examples/speech_text_joint_to_text/models/s2t_dualinputxmtransformer.py b/examples/speech_text_joint_to_text/models/s2t_dualinputxmtransformer.py new file mode 100644 index 0000000000..6c853b96ed --- /dev/null +++ b/examples/speech_text_joint_to_text/models/s2t_dualinputxmtransformer.py @@ -0,0 +1,584 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import copy + +import torch.nn as nn +from fairseq import checkpoint_utils +from fairseq import utils +from fairseq.data.data_utils import lengths_to_padding_mask +from fairseq.models import ( + register_model, + register_model_architecture, + FairseqEncoder, +) +from fairseq.models.speech_to_text import XMTransformerModel, Wav2VecEncoderWithAdaptor +from fairseq.models.speech_to_text.xm_transformer import ( + set_default_adaptor_args, + set_default_w2v_encoder_args, +) +from fairseq.models.transformer import TransformerEncoder, TransformerDecoder +from fairseq.models.wav2vec import TransformerSentenceEncoderLayer + +from .s2t_dualinputtransformer import ( + DualInputS2TTransformerModel, + TransformerMultiInputDecoder, + DualInputEncoder, +) + + +class TransformerSentenceEncoderLayerStd(TransformerSentenceEncoderLayer): + def __init__(self, sent_enc_layer): + super(TransformerSentenceEncoderLayer, self).__init__() + self.embedding_dim = sent_enc_layer.embedding_dim + self.dropout = sent_enc_layer.dropout + self.activation_dropout = sent_enc_layer.activation_dropout + + # Initialize blocks + self.activation_fn = sent_enc_layer.activation_fn + self.self_attn = sent_enc_layer.self_attn + + self.dropout1 = sent_enc_layer.dropout1 + self.dropout2 = sent_enc_layer.dropout2 + self.dropout3 = sent_enc_layer.dropout3 + + self.layer_norm_first = sent_enc_layer.layer_norm_first + + # layer norm associated with the self attention layer + self.self_attn_layer_norm = sent_enc_layer.self_attn_layer_norm + self.fc1 = sent_enc_layer.fc1 + self.fc2 = sent_enc_layer.fc2 + + # layer norm associated with the position wise feed-forward NN + self.final_layer_norm = sent_enc_layer.final_layer_norm + + def forward( + self, + x, + self_attn_mask=None, + self_attn_padding_mask=None, + need_weights=None, + att_args=None, + ): + x, attn = super().forward( + x, self_attn_mask, self_attn_padding_mask, need_weights, att_args + ) + return x + + +# TODO retire SharedEncoder +class SharedEncoder(FairseqEncoder): + def __init__(self, wav2vec_enc, mbart_enc, adaptor, shared_layers): + super().__init__(None) + self.w2v_encoder = wav2vec_enc + self.shared_layers = self.w2v_encoder.w2v_model.encoder.layers[-shared_layers:] + self.w2v_encoder.w2v_model.encoder.layers = ( + self.w2v_encoder.w2v_model.encoder.layers[:-shared_layers] + ) + self.adaptor = adaptor + if self.shared_layers[-1].layer_norm_first: + self.final_layer_norm = mbart_enc.layer_norm + else: + mbart_enc.layer_norm = None + self.final_layer_norm = None + shared_layer_from = len(mbart_enc.layers) - shared_layers + if shared_layer_from < 0: + shared_layer_from = 0 + for layer_id, layer in enumerate(self.shared_layers): + mbart_enc.layers[ + shared_layer_from + layer_id + ] = TransformerSentenceEncoderLayerStd(layer) + + def forward(self, src_tokens, src_lengths=None, **kwargs): + padding_mask = lengths_to_padding_mask(src_lengths) + if not padding_mask.any(): + padding_mask = None + + out = self.w2v_encoder.forward(src_tokens, padding_mask, tbc=True) + x = out["encoder_out"] + enc_padding_mask = None + if out["encoder_padding_mask"] is not None: + enc_padding_mask = out["encoder_padding_mask"].transpose( + 0, 1 + ) # T X B --> B X T + + x, enc_padding_mask = self.adaptor(x, enc_padding_mask) + for layer in self.shared_layers: + x, _ = layer(x, enc_padding_mask) + if self.final_layer_norm is not None: + x = self.final_layer_norm(x) + + return { + "encoder_out": [x], # T x B x C + "encoder_padding_mask": [enc_padding_mask] + if enc_padding_mask is not None + else [], # B x T + "encoder_embedding": [], # B x T x C + "encoder_states": [], # List[T x B x C] + "src_tokens": [], + "src_lengths": [], + } + + +class StackedWav2VecEncoderWithAdaptor(FairseqEncoder): + def __init__( + self, + wav2vec_enc, + mbart_enc_layers, + mbart_layer_norm, + adaptor, + drop_w2v_layers=0, + ): + super().__init__(None) + self.w2v_encoder = wav2vec_enc + self.adaptor = adaptor + self.mbart_encoder_layers = mbart_enc_layers + self.final_layer_norm = mbart_layer_norm + if drop_w2v_layers > 0: + self.w2v_encoder.w2v_model.encoder.layers = ( + self.w2v_encoder.w2v_model.encoder.layers[:-drop_w2v_layers] + ) + + def forward(self, src_tokens, src_lengths=None, return_all_hiddens=False, **kwargs): + padding_mask = lengths_to_padding_mask(src_lengths) + if not padding_mask.any(): + padding_mask = None + + out = self.w2v_encoder.forward(src_tokens, padding_mask, tbc=True) + x = out["encoder_out"] + enc_padding_mask = None + if out["encoder_padding_mask"] is not None: + enc_padding_mask = out["encoder_padding_mask"].transpose( + 0, 1 + ) # T X B --> B X T + + x, enc_padding_mask = self.adaptor(x, enc_padding_mask) + encoder_states = [] + for layer in self.mbart_encoder_layers: + x = layer(x, enc_padding_mask) + if return_all_hiddens: + encoder_states.append(x) + if self.final_layer_norm is not None: + x = self.final_layer_norm(x) + + return { + "encoder_out": [x], # T x B x C + "encoder_padding_mask": [enc_padding_mask] + if enc_padding_mask is not None + else [], # B x T + "encoder_embedding": [], # B x T x C + "encoder_states": encoder_states, # List[T x B x C] + "src_tokens": [], + "src_lengths": [], + } + + def reorder_encoder_out(self, encoder_out, new_order): + new_encoder_out = ( + [] + if len(encoder_out["encoder_out"]) == 0 + else [x.index_select(1, new_order) for x in encoder_out["encoder_out"]] + ) + + new_encoder_padding_mask = ( + [] + if len(encoder_out["encoder_padding_mask"]) == 0 + else [ + x.index_select(0, new_order) + for x in encoder_out["encoder_padding_mask"] + ] + ) + + new_encoder_embedding = ( + [] + if len(encoder_out["encoder_embedding"]) == 0 + else [ + x.index_select(0, new_order) for x in encoder_out["encoder_embedding"] + ] + ) + + encoder_states = encoder_out["encoder_states"] + if len(encoder_states) > 0: + for idx, state in enumerate(encoder_states): + encoder_states[idx] = state.index_select(1, new_order) + + return { + "encoder_out": new_encoder_out, # T x B x C + "encoder_padding_mask": new_encoder_padding_mask, # B x T + "encoder_embedding": new_encoder_embedding, # B x T x C + "encoder_states": encoder_states, # List[T x B x C] + "src_tokens": [], # B x T + "src_lengths": [], # B x 1 + } + + +# Note: +# dual input transformer: +# encoder: wav2vec for speech + mbart encoder for text +# decoder: mbart decoder for text +@register_model("dual_input_xm_transformer") +class DualInputXMTransformerModel(DualInputS2TTransformerModel): + def __init__(self, encoder, decoder): + super().__init__(encoder, decoder) + + @staticmethod + def add_args(parser): + """Add model-specific arguments to the parser.""" + # wav2vec encoder + Wav2VecEncoderWithAdaptor.add_args(parser) + # add_decoder_args(parser) + # mbart Transformer + parser.add_argument( + "--activation-fn", + type=str, + default="relu", + choices=utils.get_available_activation_fns(), + help="activation function to use", + ) + + parser.add_argument( + "--mbart-dropout", type=float, metavar="D", help="dropout probability" + ) + parser.add_argument( + "--mbart-attention-dropout", + type=float, + metavar="D", + help="dropout probability for attention weights", + ) + parser.add_argument( + "--mbart-activation-dropout", + type=float, + metavar="D", + help="dropout probability after activation in FFN.", + ) + + parser.add_argument( + "--encoder-embed-dim", + type=int, + metavar="N", + help="encoder embedding dimension", + ) + parser.add_argument( + "--encoder-ffn-embed-dim", + type=int, + metavar="N", + help="encoder embedding dimension for FFN", + ) + parser.add_argument( + "--encoder-layers", type=int, metavar="N", help="num encoder layers" + ) + parser.add_argument( + "--encoder-attention-heads", + type=int, + metavar="N", + help="num encoder attention heads", + ) + parser.add_argument( + "--encoder-normalize-before", + action="store_true", + help="apply layernorm before each encoder block", + ) + + parser.add_argument( + "--decoder-embed-dim", + type=int, + metavar="N", + help="decoder embedding dimension", + ) + parser.add_argument( + "--decoder-ffn-embed-dim", + type=int, + metavar="N", + help="decoder embedding dimension for FFN", + ) + parser.add_argument( + "--decoder-layers", type=int, metavar="N", help="num decoder layers" + ) + parser.add_argument( + "--decoder-attention-heads", + type=int, + metavar="N", + help="num decoder attention heads", + ) + parser.add_argument( + "--decoder-normalize-before", + action="store_true", + help="apply layernorm before each decoder block", + ) + parser.add_argument( + "--layernorm-embedding", + action="store_true", + help="add layernorm to embedding", + ) + parser.add_argument( + "--no-scale-embedding", + action="store_true", + help="if True, dont scale embeddings", + ) + parser.add_argument( + "--load-pretrained-mbart-from", + type=str, + metavar="STR", + help="model to take text encoder decoder weights from (for initialization)", + ) + # parser.add_argument("--finetune-w2v-params", type=str, metavar="STR", + # help="comma-separated param strings to finetune.") + parser.add_argument( + "--finetune-mbart-decoder-params", + type=str, + metavar="STR", + help="comma-separated param strings to finetune.", + ) + parser.add_argument( + "--finetune-mbart-encoder-params", + type=str, + metavar="STR", + help="comma-separated param strings to finetune.", + ) + parser.add_argument( + "--skip-encoder-projection", + action="store_true", + help="skip the projection layer in encoder", + ) + + parser.add_argument( + "--enc-grad-mult", + type=float, + metavar="V", + default=1.0, + help="multiply enc1 and enc2 gradient by V", + ) + parser.add_argument( + "--enc2-along-grad-mult", + type=float, + metavar="V", + default=1.0, + help="multiply enc2 gradient by V if only enc2 is used", + ) + parser.add_argument( + "--text-input-cost-ratio", + type=float, + default=1.0, + metavar="V", + help="text input cost ratio relative to speech input cost", + ) + parser.add_argument( + "--stack-w2v-mbart-encoder", + action="store_true", + help="stack w2v and mbart encoder", + ) + parser.add_argument( + "--stack-w2v-mbart-nonorm-encoder", + action="store_true", + help="stack w2v and mbart encoder", + ) + parser.add_argument( + "--no-final-norm-decoder", action="store_true", help="no layer norm" + ) + parser.add_argument( + "--drop-w2v-layers", + type=int, + default=0, + metavar="N", + help="drop w2v encoder layers", + ) + + parser.add_argument( + "--share-w2v-text-encoder", + action="store_true", + help="share w2v encoder layers with text encoder", + ) + parser.add_argument( + "--shared-w2v-layers", + type=int, + default=0, + metavar="N", + help="shared encoder layers from w2v encoder", + ) + + @classmethod + def build_encoder(cls, args, task): + _args = copy.deepcopy(args) + _args.dropout = args.mbart_dropout + _args.attention_dropout = args.mbart_attention_dropout + _args.activation_dropout = args.mbart_activation_dropout + _args.max_source_positions = 1024 + enc_emb = nn.Embedding( + len(task.src_dict), _args.encoder_embed_dim, task.src_dict.pad() + ) + text_encoder = TransformerEncoder(_args, task.src_dict, enc_emb) + spch_encoder = Wav2VecEncoderWithAdaptor(args) + if getattr(args, "load_pretrained_mbart_from", None): + text_encoder = checkpoint_utils.load_pretrained_component_from_model( + component=text_encoder, checkpoint=args.load_pretrained_mbart_from + ) + if getattr(args, "stack_w2v_mbart_encoder", False): + assert getattr(args, "share_w2v_text_encoder", False) is False + spch_encoder = StackedWav2VecEncoderWithAdaptor( + spch_encoder.w2v_encoder, + text_encoder.layers, + text_encoder.layer_norm, + spch_encoder.adaptor, + args.drop_w2v_layers, + ) + elif getattr(args, "stack_w2v_mbart_nonorm_encoder", False): + text_encoder.layer_norm = None + spch_encoder = StackedWav2VecEncoderWithAdaptor( + spch_encoder.w2v_encoder, + text_encoder.layers, + text_encoder.layer_norm, + spch_encoder.adaptor, + args.drop_w2v_layers, + ) + elif getattr(args, "share_w2v_text_encoder", False): + spch_encoder = SharedEncoder( + spch_encoder.w2v_encoder, + text_encoder, + spch_encoder.adaptor, + args.shared_w2v_layers, + ) + + for k, p in spch_encoder.named_parameters(): + # Freeze pretrained models by default + if hasattr( + args, "finetune_w2v_params" + ) and XMTransformerModel.finetune_params(args.finetune_w2v_params, k): + p.requires_grad = True + else: + p.requires_grad = False + for k, p in text_encoder.named_parameters(): + # Freeze pretrained models by default + if hasattr( + args, "finetune_mbart_encoder_params" + ) and XMTransformerModel.finetune_params( + args.finetune_mbart_encoder_params, k + ): + p.requires_grad = True + else: + p.requires_grad = False + cross_attentive_loss_before_last_layer = ( + 0 if getattr(args, "attentive_cost_regularization", 0.0) > 0.0 else -1 + ) + encoder = DualInputEncoder( + args, + spch_encoder, + text_encoder, + task.src_dict, + cross_attentive_loss_before_last_layer, + ) + return encoder + + @classmethod + def build_decoder(cls, args, task): + _args = copy.deepcopy(args) + _args.dropout = args.mbart_dropout + _args.attention_dropout = args.mbart_attention_dropout + _args.activation_dropout = args.mbart_activation_dropout + _args.max_target_positions = 1024 + dec_emb = nn.Embedding( + len(task.tgt_dict), _args.encoder_embed_dim, task.tgt_dict.pad() + ) + decoder = TransformerDecoder(_args, task.tgt_dict, dec_emb) + if getattr(args, "load_pretrained_mbart_from", None): + decoder = checkpoint_utils.load_pretrained_component_from_model( + component=decoder, checkpoint=args.load_pretrained_mbart_from + ) + if getattr(args, "no_final_norm_decoder", False): + decoder.layer_norm = None + for k, p in decoder.named_parameters(): + # Freeze pretrained models by default + if hasattr( + args, "finetune_mbart_decoder_params" + ) and XMTransformerModel.finetune_params( + args.finetune_mbart_decoder_params, k + ): + p.requires_grad = True + else: + p.requires_grad = False + + compute_cross_attentive_loss = ( + True if getattr(args, "attentive_cost_regularization", 0.0) > 0.0 else False + ) + cross_attentive_loss_without_norm = getattr( + args, "attentive_cost_without_normalize", False + ) + cross_attentive_loss_reverse = ( + False # getattr(args, "attentive_cost_reverse", False) + ) + decoder = TransformerMultiInputDecoder( + dictionary=task.target_dictionary, + spch_decoder=decoder, + text_decoder=decoder, + compute_cross_attentive_loss=compute_cross_attentive_loss, + cross_attentive_loss_with_norm=True + if not cross_attentive_loss_without_norm + else False, + cross_attentive_loss_reverse=cross_attentive_loss_reverse, + ) + return decoder + + @classmethod + def build_model(cls, args, task): + """Build a new model instance.""" + # make sure that all args are properly defaulted + # (in case there are any new ones) + dualinputxmtransformer_base(args) + + encoder = cls.build_encoder(args, task) + decoder = cls.build_decoder(args, task) + return cls(encoder, decoder) + + +@register_model_architecture("dual_input_xm_transformer", "dualinputxmtransformer_base") +def dualinputxmtransformer_base(args): + # wav2vec encoder + set_default_w2v_encoder_args(args) + set_default_adaptor_args(args) + + # mbart model + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024) + args.encoder_ffn_embed_dim = getattr( + args, "encoder_ffn_embed_dim", 4 * args.encoder_embed_dim + ) + args.encoder_layers = getattr(args, "encoder_layers", 12) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 16) + args.encoder_normalize_before = getattr(args, "encoder_normalize_before", True) + args.encoder_layerdrop = getattr(args, "encoder_layerdrop", 0) + args.encoder_learned_pos = getattr(args, "encoder_learned_pos", True) + + args.decoder_embed_path = getattr(args, "decoder_embed_path", None) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 1024) + args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 4 * 1024) + args.decoder_layers = getattr(args, "decoder_layers", 12) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 16) + args.decoder_normalize_before = getattr(args, "decoder_normalize_before", True) + args.decoder_learned_pos = getattr(args, "decoder_learned_pos", True) + args.decoder_layerdrop = getattr(args, "decoder_layerdrop", 0.0) + + args.adaptive_input = getattr(args, "adaptive_input", False) + + args.mbart_attention_dropout = getattr(args, "mbart_attention_dropout", 0.0) + args.mbart_activation_dropout = getattr(args, "mbart_activation_dropout", 0.0) + args.mbart_dropout = getattr(args, "mbart_dropout", 0.1) + args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None) + args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0) + args.share_decoder_input_output_embed = getattr( + args, "share_decoder_input_output_embed", True + ) + args.no_token_positional_embeddings = getattr( + args, "no_token_positional_embeddings", False + ) + + args.decoder_output_dim = getattr( + args, "decoder_output_dim", args.decoder_embed_dim + ) + args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim) + + args.no_scale_embedding = getattr(args, "no_scale_embedding", False) + args.quant_noise_pq = getattr(args, "quant_noise_pq", 0) + args.layernorm_embedding = getattr(args, "layernorm_embedding", True) + + args.activation_fn = getattr(args, "activation_fn", "gelu") + args.pooler_activation_fn = getattr(args, "pooler_activation_fn", "tanh") + args.pooler_dropout = getattr(args, "pooler_dropout", 0.0) diff --git a/examples/speech_text_joint_to_text/scripts/g2p_encode.py b/examples/speech_text_joint_to_text/scripts/g2p_encode.py new file mode 100644 index 0000000000..9db779396f --- /dev/null +++ b/examples/speech_text_joint_to_text/scripts/g2p_encode.py @@ -0,0 +1,191 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import itertools +import logging +import re +import time + +from g2p_en import G2p + +logger = logging.getLogger(__name__) + +FAIL_SENT = "FAILED_SENTENCE" + + +def parse(): + parser = argparse.ArgumentParser() + parser.add_argument("--data-path", type=str, required=True) + parser.add_argument("--out-path", type=str, required=True) + parser.add_argument("--lower-case", action="store_true") + parser.add_argument("--do-filter", action="store_true") + parser.add_argument("--use-word-start", action="store_true") + parser.add_argument("--dup-vowel", default=1, type=int) + parser.add_argument("--dup-consonant", default=1, type=int) + parser.add_argument("--no-punc", action="store_true") + parser.add_argument("--reserve-word", type=str, default="") + parser.add_argument( + "--reserve-first-column", + action="store_true", + help="first column is sentence id", + ) + ### + parser.add_argument("--parallel-process-num", default=1, type=int) + parser.add_argument("--logdir", default="") + args = parser.parse_args() + return args + + +def process_sent(sent, g2p, res_wrds, args): + sents = pre_process_sent(sent, args.do_filter, args.lower_case, res_wrds) + pho_seqs = [do_g2p(g2p, s, res_wrds, i == 0) for i, s in enumerate(sents)] + pho_seq = ( + [FAIL_SENT] + if [FAIL_SENT] in pho_seqs + else list(itertools.chain.from_iterable(pho_seqs)) + ) + if args.no_punc: + pho_seq = remove_punc(pho_seq) + if args.dup_vowel > 1 or args.dup_consonant > 1: + pho_seq = dup_pho(pho_seq, args.dup_vowel, args.dup_consonant) + if args.use_word_start: + pho_seq = add_word_start(pho_seq) + return " ".join(pho_seq) + + +def remove_punc(sent): + ns = [] + regex = re.compile("[^a-zA-Z0-9 ]") + for p in sent: + if (not regex.search(p)) or p == FAIL_SENT: + if p == " " and (len(ns) == 0 or ns[-1] == " "): + continue + ns.append(p) + return ns + + +def do_g2p(g2p, sent, res_wrds, is_first_sent): + if sent in res_wrds: + pho_seq = [res_wrds[sent]] + else: + pho_seq = g2p(sent) + if not is_first_sent: + pho_seq = [" "] + pho_seq # add space to separate + return pho_seq + + +def pre_process_sent(sent, do_filter, lower_case, res_wrds): + if do_filter: + sent = re.sub("-", " ", sent) + sent = re.sub("—", " ", sent) + if len(res_wrds) > 0: + wrds = sent.split() + wrds = ["SPLIT_ME " + w + " SPLIT_ME" if w in res_wrds else w for w in wrds] + sents = [x.strip() for x in " ".join(wrds).split("SPLIT_ME") if x.strip() != ""] + else: + sents = [sent] + if lower_case: + sents = [s.lower() if s not in res_wrds else s for s in sents] + return sents + + +def dup_pho(sent, dup_v_num, dup_c_num): + """ + duplicate phoneme defined as cmudict + http://www.speech.cs.cmu.edu/cgi-bin/cmudict + """ + if dup_v_num == 1 and dup_c_num == 1: + return sent + ns = [] + for p in sent: + ns.append(p) + if re.search(r"\d$", p): + for i in range(1, dup_v_num): + ns.append(f"{p}-{i}P") + elif re.search(r"\w", p): + for i in range(1, dup_c_num): + ns.append(f"{p}-{i}P") + return ns + + +def add_word_start(sent): + ns = [] + do_add = True + ws = "▁" + for p in sent: + if do_add: + p = ws + p + do_add = False + if p == " ": + do_add = True + else: + ns.append(p) + return ns + + +def load_reserve_word(reserve_word): + if reserve_word == "": + return [] + with open(reserve_word, "r") as fp: + res_wrds = [x.strip().split() for x in fp.readlines() if x.strip() != ""] + assert sum([0 if len(x) == 2 else 1 for x in res_wrds]) == 0 + res_wrds = dict(res_wrds) + return res_wrds + + +def process_sents(sents, args): + g2p = G2p() + out_sents = [] + res_wrds = load_reserve_word(args.reserve_word) + for sent in sents: + col1 = "" + if args.reserve_first_column: + col1, sent = sent.split(None, 1) + sent = process_sent(sent, g2p, res_wrds, args) + if args.reserve_first_column and col1 != "": + sent = f"{col1} {sent}" + out_sents.append(sent) + return out_sents + + +def main(): + args = parse() + out_sents = [] + with open(args.data_path, "r") as fp: + sent_list = [x.strip() for x in fp.readlines()] + if args.parallel_process_num > 1: + try: + import submitit + except ImportError: + logger.warn( + "submitit is not found and only one job is used to process the data" + ) + submitit = None + + if args.parallel_process_num == 1 or submitit is None: + out_sents = process_sents(sent_list, args) + else: + # process sentences with parallel computation + lsize = len(sent_list) // args.parallel_process_num + 1 + executor = submitit.AutoExecutor(folder=args.logdir) + executor.update_parameters(timeout_min=1000, cpus_per_task=4) + jobs = [] + for i in range(args.parallel_process_num): + job = executor.submit( + process_sents, sent_list[lsize * i : lsize * (i + 1)], args + ) + jobs.append(job) + is_running = True + while is_running: + time.sleep(5) + is_running = sum([job.done() for job in jobs]) < len(jobs) + out_sents = list(itertools.chain.from_iterable([job.result() for job in jobs])) + with open(args.out_path, "w") as fp: + fp.write("\n".join(out_sents) + "\n") + + +if __name__ == "__main__": + main() diff --git a/examples/speech_text_joint_to_text/tasks/__init__.py b/examples/speech_text_joint_to_text/tasks/__init__.py new file mode 100644 index 0000000000..d878278475 --- /dev/null +++ b/examples/speech_text_joint_to_text/tasks/__init__.py @@ -0,0 +1,12 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import importlib +import os + +for file in os.listdir(os.path.dirname(__file__)): + if file.endswith(".py") and not file.startswith("_"): + task_name = file[: file.find(".py")] + importlib.import_module("examples.speech_text_joint_to_text.tasks." + task_name) diff --git a/examples/speech_text_joint_to_text/tasks/speech_text_joint.py b/examples/speech_text_joint_to_text/tasks/speech_text_joint.py new file mode 100644 index 0000000000..f2b3966d2d --- /dev/null +++ b/examples/speech_text_joint_to_text/tasks/speech_text_joint.py @@ -0,0 +1,372 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +import logging +import os +from argparse import Namespace +from pathlib import Path + +import torch +from fairseq.data import ( + encoders, + Dictionary, + ResamplingDataset, + TransformEosLangPairDataset, + ConcatDataset, +) +from fairseq.data.iterators import GroupedEpochBatchIterator +from fairseq.data.audio.multi_modality_dataset import ( + MultiModalityDataset, + LangPairMaskDataset, + ModalityDatasetItem, +) +from fairseq.data.audio.speech_to_text_dataset import SpeechToTextDataset, SpeechToTextDatasetCreator +from fairseq.data.audio.speech_to_text_joint_dataset import ( + S2TJointDataConfig, + SpeechToTextJointDatasetCreator, +) +from fairseq.tasks import register_task +from fairseq.tasks.speech_to_text import SpeechToTextTask +from fairseq.tasks.translation import load_langpair_dataset + +logger = logging.getLogger(__name__) +LANG_TAG_TEMPLATE = "<lang:{}>" + + +@register_task("speech_text_joint_to_text") +class SpeechTextJointToTextTask(SpeechToTextTask): + """ + Task for joint training speech and text to text. + """ + + @classmethod + def add_args(cls, parser): + """Add task-specific arguments to the parser.""" + super(SpeechTextJointToTextTask, cls).add_args(parser) + ### + parser.add_argument( + "--parallel-text-data", + default="", + help="path to parallel text data directory", + ) + parser.add_argument( + "--max-tokens-text", + type=int, + metavar="N", + help="maximum tokens for encoder text input ", + ) + parser.add_argument( + "--max-positions-text", + type=int, + metavar="N", + default=400, + help="maximum tokens for per encoder text input ", + ) + parser.add_argument( + "--langpairs", + default=None, + metavar="S", + help='language pairs for text training, separated with ","', + ) + parser.add_argument( + "--speech-sample-ratio", + default=1, + type=float, + metavar="N", + help="Multiple Ratio for speech dataset with transcripts ", + ) + parser.add_argument( + "--text-sample-ratio", + default=1, + type=float, + metavar="N", + help="Multiple Ratio for text set ", + ) + parser.add_argument( + "--update-mix-data", + action="store_true", + help="use mixed data in one update when update-freq > 1", + ) + parser.add_argument( + "--load-speech-only", + action="store_true", + help="load speech data only", + ) + parser.add_argument( + "--mask-text-ratio", + type=float, + metavar="V", + default=0.0, + help="mask V source tokens for text only mode", + ) + parser.add_argument( + "--mask-text-type", + default="random", + choices=["random", "tail"], + help="mask text typed", + ) + parser.add_argument( + "--noise-token", + default="", + help="noise token for masking src text tokens if mask-text-ratio > 0", + ) + parser.add_argument( + "--infer-target-lang", + default="", + metavar="S", + help="target language for inference", + ) + + def __init__(self, args, src_dict, tgt_dict, infer_tgt_lang_id=None): + super().__init__(args, tgt_dict) + self.src_dict = src_dict + self.data_cfg = S2TJointDataConfig(Path(args.data) / args.config_yaml) + assert self.tgt_dict.pad() == self.src_dict.pad() + assert self.tgt_dict.eos() == self.src_dict.eos() + self.speech_only = args.load_speech_only + self._infer_tgt_lang_id = infer_tgt_lang_id + + @classmethod + def setup_task(cls, args, **kwargs): + """Setup the task (e.g., load dictionaries).""" + data_cfg = S2TJointDataConfig(Path(args.data) / args.config_yaml) + tgt_dict_path = Path(args.data) / data_cfg.vocab_filename + src_dict_path = Path(args.data) / data_cfg.src_vocab_filename + if (not os.path.isfile(src_dict_path)) or (not os.path.isfile(tgt_dict_path)): + raise FileNotFoundError("Dict not found: {}".format(args.data)) + src_dict = Dictionary.load(src_dict_path.as_posix()) + tgt_dict = Dictionary.load(tgt_dict_path.as_posix()) + + print("| src dictionary: {} types".format(len(src_dict))) + print("| tgt dictionary: {} types".format(len(tgt_dict))) + + if args.parallel_text_data != "": + if not os.path.isabs(args.parallel_text_data): + args.parallel_text_data = os.path.join( + args.data, args.parallel_text_data + ) + + if args.langpairs is None: + raise Exception( + "Could not infer language pair, please provide it explicitly" + ) + infer_tgt_lang_id = None + if args.infer_target_lang != "" and data_cfg.prepend_tgt_lang_tag_no_change: + tgt_lang_tag = SpeechToTextDataset.LANG_TAG_TEMPLATE.format( + args.infer_target_lang + ) + infer_tgt_lang_id = tgt_dict.index(tgt_lang_tag) + assert infer_tgt_lang_id != tgt_dict.unk() + return cls(args, src_dict, tgt_dict, infer_tgt_lang_id=infer_tgt_lang_id) + + def load_langpair_dataset(self, prepend_tgt_lang_tag=False, sampling_alpha=1.0, epoch=0): + lang_pairs = [] + text_dataset = None + split = "train" + for lp in self.args.langpairs.split(","): + src, tgt = lp.split("-") + text_dataset = load_langpair_dataset( + self.args.parallel_text_data, + split, + src, + self.src_dict, + tgt, + self.tgt_dict, + combine=True, + dataset_impl=None, + upsample_primary=1, + left_pad_source=False, + left_pad_target=False, + max_source_positions=self.args.max_positions_text, + max_target_positions=self.args.max_target_positions, + load_alignments=False, + truncate_source=False, + ) + if prepend_tgt_lang_tag: + # TODO + text_dataset = TransformEosLangPairDataset( + text_dataset, + src_eos=self.src_dict.eos(), + tgt_bos=self.tgt_dict.eos(), # 'prev_output_tokens' starts with eos + new_tgt_bos=self.tgt_dict.index(LANG_TAG_TEMPLATE.format(tgt)), + ) + lang_pairs.append(text_dataset) + if len(lang_pairs) > 1: + if sampling_alpha != 1.0: + size_ratios = SpeechToTextDatasetCreator.get_size_ratios( + self.args.langpairs.split(","), + [len(s) for s in lang_pairs], + alpha=sampling_alpha, + ) + lang_pairs = [ + ResamplingDataset( + d, size_ratio=r, epoch=epoch, replace=(r >= 1.0) + ) + for d, r in zip(lang_pairs, size_ratios) + ] + return ConcatDataset(lang_pairs) + return text_dataset + + def inference_step( + self, generator, models, sample, prefix_tokens=None, constraints=None + ): + with torch.no_grad(): + return generator.generate( + models, + sample, + prefix_tokens=prefix_tokens, + constraints=constraints, + bos_token=self._infer_tgt_lang_id, + ) + + def build_src_tokenizer(self, args): + logger.info(f"src-pre-tokenizer: {self.data_cfg.src_pre_tokenizer}") + return encoders.build_tokenizer(Namespace(**self.data_cfg.src_pre_tokenizer)) + + def build_src_bpe(self, args): + logger.info(f"tokenizer: {self.data_cfg.src_bpe_tokenizer}") + return encoders.build_bpe(Namespace(**self.data_cfg.src_bpe_tokenizer)) + + def load_dataset(self, split, epoch=1, combine=False, **kwargs): + """Load a given dataset split. + + Args: + split (str): name of the split (e.g., train, valid, test) + """ + is_train_split = split.startswith("train") + pre_tokenizer = self.build_tokenizer(self.args) + bpe_tokenizer = self.build_bpe(self.args) + src_pre_tokenizer = self.build_src_tokenizer(self.args) + src_bpe_tokenizer = self.build_src_bpe(self.args) + ast_dataset = SpeechToTextJointDatasetCreator.from_tsv( + self.args.data, + self.data_cfg, + split, + self.tgt_dict, + src_dict=None if self.speech_only else self.src_dict, + pre_tokenizer=pre_tokenizer, + bpe_tokenizer=bpe_tokenizer, + src_pre_tokenizer=src_pre_tokenizer, + src_bpe_tokenizer=src_bpe_tokenizer, + is_train_split=is_train_split, + epoch=epoch, + seed=self.args.seed, + ) + noise_token_id = -1 + text_dataset = None + if self.args.parallel_text_data != "" and is_train_split: + text_dataset = self.load_langpair_dataset( + self.data_cfg.prepend_tgt_lang_tag_no_change, + 1.0, + epoch=epoch, + ) + if self.args.mask_text_ratio > 0: + # add mask + noise_token_id = ( + self.src_dict.unk() + if self.args.noise_token == "" + else self.src_dict.index(self.args.noise_token) + ) + text_dataset = LangPairMaskDataset( + text_dataset, + src_bos=self.src_dict.bos(), + src_eos=self.src_dict.eos(), + noise_id=noise_token_id, + mask_ratio=self.args.mask_text_ratio, + mask_type=self.args.mask_text_type, + ) + + if text_dataset is not None: + mdsets = [ + ModalityDatasetItem( + "sup_speech", + ast_dataset, + (self.args.max_source_positions, self.args.max_target_positions), + self.args.max_tokens, + self.args.batch_size, + ), + ModalityDatasetItem( + "text", + text_dataset, + (self.args.max_positions_text, self.args.max_target_positions), + self.args.max_tokens_text + if self.args.max_tokens_text is not None + else self.args.max_tokens, + self.args.batch_size, + ), + ] + ast_dataset = MultiModalityDataset(mdsets) + self.datasets[split] = ast_dataset + + @property + def target_dictionary(self): + """Return the :class:`~fairseq.data.Dictionary` for the language + model.""" + return self.tgt_dict + + @property + def source_dictionary(self): + """Return the source :class:`~fairseq.data.Dictionary` (if applicable + for this task).""" + return None if self.speech_only else self.src_dict + + def get_batch_iterator( + self, + dataset, + max_tokens=None, + max_sentences=None, + max_positions=None, + ignore_invalid_inputs=False, + required_batch_size_multiple=1, + seed=1, + num_shards=1, + shard_id=0, + num_workers=0, + epoch=0, + data_buffer_size=0, + disable_iterator_cache=False, + ): + + if not isinstance(dataset, MultiModalityDataset): + return super(SpeechTextJointToTextTask, self).get_batch_iterator( + dataset, + max_tokens, + max_sentences, + max_positions, + ignore_invalid_inputs, + required_batch_size_multiple, + seed, + num_shards, + shard_id, + num_workers, + epoch, + data_buffer_size, + disable_iterator_cache, + ) + + mult_ratio = [self.args.speech_sample_ratio, self.args.text_sample_ratio] + assert len(dataset.datasets) == 2 + + # initialize the dataset with the correct starting epoch + dataset.set_epoch(epoch) + + batch_samplers = dataset.get_batch_samplers( + mult_ratio, required_batch_size_multiple, seed + ) + + # return a reusable, sharded iterator + epoch_iter = GroupedEpochBatchIterator( + dataset=dataset, + collate_fn=dataset.collater, + batch_samplers=batch_samplers, + seed=seed, + num_shards=num_shards, + shard_id=shard_id, + num_workers=num_workers, + epoch=epoch, + mult_rate=1 if self.args.update_mix_data else max(self.args.update_freq), + buffer_size=data_buffer_size, + ) + self.dataset_to_epoch_iter[dataset] = {} # refresh it every epoch + return epoch_iter diff --git a/examples/speech_to_text/README.md b/examples/speech_to_text/README.md new file mode 100644 index 0000000000..f639d300d3 --- /dev/null +++ b/examples/speech_to_text/README.md @@ -0,0 +1,77 @@ +# Speech-to-Text (S2T) Modeling + +[https://www.aclweb.org/anthology/2020.aacl-demo.6](https://www.aclweb.org/anthology/2020.aacl-demo.6.pdf) + +Speech recognition (ASR) and speech-to-text translation (ST) with fairseq. + +## Data Preparation +S2T modeling data consists of source speech features, target text and other optional information +(source text, speaker id, etc.). Fairseq S2T uses per-dataset-split TSV manifest files +to store these information. Each data field is represented by a column in the TSV file. + +Unlike text token embeddings, speech features (e.g. log mel-scale filter banks) are usually fixed +during model training and can be pre-computed. The manifest file contains the path to +either the feature file in NumPy format or the WAV/FLAC audio file. For the latter, +features will be extracted on-the-fly by fairseq S2T. Optionally, feature/audio files can be packed +into uncompressed ZIP files (then accessed via byte offset and length) to improve I/O performance. + +Fairseq S2T also employs a YAML file for data related configurations: tokenizer type and dictionary path +for the target text, feature transforms such as CMVN (cepstral mean and variance normalization) and SpecAugment, +temperature-based resampling, etc. + +## Model Training +Fairseq S2T uses the unified `fairseq-train` interface for model training. It requires arguments `--task speech_to_text`, + `--arch <model architecture in fairseq.models.speech_to_text.*>` and `--config-yaml <config YAML filename>`. + +## Inference & Evaluation +Fairseq S2T uses the unified `fairseq-generate`/`fairseq-interactive` interface for inference and evaluation. It +requires arguments `--task speech_to_text` and `--config-yaml <config YAML filename>`. The interactive console takes +audio paths (one per line) as inputs. + + +## Examples +- [Speech Recognition (ASR) on LibriSpeech](docs/librispeech_example.md) + +- [Speech-to-Text Translation (ST) on MuST-C](docs/mustc_example.md) + +- [Speech-to-Text Translation (ST) on CoVoST 2](docs/covost_example.md) + +- [Speech-to-Text Translation (ST) on Multilingual TEDx](docs/mtedx_example.md) +- [Simultaneous Speech-to-Text Translation (SimulST) on MuST-C](docs/simulst_mustc_example.md) + +## Updates +- 02/04/2021: Added interactive decoding (`fairseq-interactive`) support. Examples: + [ASR (LibriSpeech)](docs/librispeech_example.md#interactive-decoding) + and [ST (CoVoST 2)](docs/covost_example.md#interactive-decoding). +- 01/08/2021: Several fixes for S2T Transformer model, inference-time de-tokenization, scorer configuration and data + preparation scripts. We also add pre-trained models to the examples and revise the instructions. + Breaking changes: the data preparation scripts now extract filterbank features without CMVN. CMVN is instead applied + on-the-fly (defined in the config YAML). + +## What's Next +- We are migrating the old fairseq [ASR example](../speech_recognition) into this S2T framework and + merging the features from both sides. +- The following papers also base their experiments on fairseq S2T. We are adding more examples for replication. + - [Improving Cross-Lingual Transfer Learning for End-to-End Speech Recognition with Speech Translation (Wang et al., 2020)](https://arxiv.org/abs/2006.05474) + - [Self-Supervised Representations Improve End-to-End Speech Translation (Wu et al., 2020)](https://arxiv.org/abs/2006.12124) + - [Self-Training for End-to-End Speech Translation (Pino et al., 2020)](https://arxiv.org/abs/2006.02490) + - [CoVoST: A Diverse Multilingual Speech-To-Text Translation Corpus (Wang et al., 2020)](https://arxiv.org/abs/2002.01320) + - [Harnessing Indirect Training Data for End-to-End Automatic Speech Translation: Tricks of the Trade (Pino et al., 2019)](https://arxiv.org/abs/1909.06515) + +## Citation +Please cite as: +``` +@inproceedings{wang2020fairseqs2t, + title = {fairseq S2T: Fast Speech-to-Text Modeling with fairseq}, + author = {Changhan Wang and Yun Tang and Xutai Ma and Anne Wu and Dmytro Okhonko and Juan Pino}, + booktitle = {Proceedings of the 2020 Conference of the Asian Chapter of the Association for Computational Linguistics (AACL): System Demonstrations}, + year = {2020}, +} + +@inproceedings{ott2019fairseq, + title = {fairseq: A Fast, Extensible Toolkit for Sequence Modeling}, + author = {Myle Ott and Sergey Edunov and Alexei Baevski and Angela Fan and Sam Gross and Nathan Ng and David Grangier and Michael Auli}, + booktitle = {Proceedings of NAACL-HLT 2019: Demonstrations}, + year = {2019}, +} +``` diff --git a/examples/speech_to_text/data_utils.py b/examples/speech_to_text/data_utils.py new file mode 100644 index 0000000000..41afac0bf8 --- /dev/null +++ b/examples/speech_to_text/data_utils.py @@ -0,0 +1,382 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import csv +from pathlib import Path +import zipfile +from functools import reduce +from multiprocessing import cpu_count +from typing import Any, Dict, List, Optional, Union +import io + +import numpy as np +import pandas as pd +import sentencepiece as sp +from fairseq.data.audio.audio_utils import ( + convert_waveform, _get_kaldi_fbank, _get_torchaudio_fbank, is_npy_data, + is_sf_audio_data +) +import torch +import soundfile as sf +from tqdm import tqdm + + +UNK_TOKEN, UNK_TOKEN_ID = "<unk>", 3 +BOS_TOKEN, BOS_TOKEN_ID = "<s>", 0 +EOS_TOKEN, EOS_TOKEN_ID = "</s>", 2 +PAD_TOKEN, PAD_TOKEN_ID = "<pad>", 1 + + +def gen_vocab( + input_path: Path, output_path_prefix: Path, model_type="bpe", + vocab_size=1000, special_symbols: Optional[List[str]] = None +): + # Train SentencePiece Model + arguments = [ + f"--input={input_path.as_posix()}", + f"--model_prefix={output_path_prefix.as_posix()}", + f"--model_type={model_type}", + f"--vocab_size={vocab_size}", + "--character_coverage=1.0", + f"--num_threads={cpu_count()}", + f"--unk_id={UNK_TOKEN_ID}", + f"--bos_id={BOS_TOKEN_ID}", + f"--eos_id={EOS_TOKEN_ID}", + f"--pad_id={PAD_TOKEN_ID}", + ] + if special_symbols is not None: + _special_symbols = ",".join(special_symbols) + arguments.append(f"--user_defined_symbols={_special_symbols}") + sp.SentencePieceTrainer.Train(" ".join(arguments)) + # Export fairseq dictionary + spm = sp.SentencePieceProcessor() + spm.Load(output_path_prefix.as_posix() + ".model") + vocab = {i: spm.IdToPiece(i) for i in range(spm.GetPieceSize())} + assert ( + vocab.get(UNK_TOKEN_ID) == UNK_TOKEN + and vocab.get(PAD_TOKEN_ID) == PAD_TOKEN + and vocab.get(BOS_TOKEN_ID) == BOS_TOKEN + and vocab.get(EOS_TOKEN_ID) == EOS_TOKEN + ) + vocab = { + i: s + for i, s in vocab.items() + if s not in {UNK_TOKEN, BOS_TOKEN, EOS_TOKEN, PAD_TOKEN} + } + with open(output_path_prefix.as_posix() + ".txt", "w") as f_out: + for _, s in sorted(vocab.items(), key=lambda x: x[0]): + f_out.write(f"{s} 1\n") + + +def extract_fbank_features( + waveform: torch.FloatTensor, + sample_rate: int, + output_path: Optional[Path] = None, + n_mel_bins: int = 80, + overwrite: bool = False, +): + if output_path is not None and output_path.is_file() and not overwrite: + return + + _waveform = convert_waveform(waveform, sample_rate, to_mono=True) + # Kaldi compliance: 16-bit signed integers + _waveform = _waveform * (2 ** 15) + _waveform = _waveform.numpy() + + features = _get_kaldi_fbank(_waveform, sample_rate, n_mel_bins) + if features is None: + features = _get_torchaudio_fbank(_waveform, sample_rate, n_mel_bins) + if features is None: + raise ImportError( + "Please install pyKaldi or torchaudio to enable fbank feature extraction" + ) + + if output_path is not None: + np.save(output_path.as_posix(), features) + return features + + +def create_zip(data_root: Path, zip_path: Path): + paths = list(data_root.glob("*.npy")) + with zipfile.ZipFile(zip_path, "w", zipfile.ZIP_STORED) as f: + for path in tqdm(paths): + f.write(path, arcname=path.name) + + +def get_zip_manifest( + zip_path: Path, zip_root: Optional[Path] = None, is_audio=False +): + _zip_path = Path.joinpath(zip_root or Path(""), zip_path) + with zipfile.ZipFile(_zip_path, mode="r") as f: + info = f.infolist() + paths, lengths = {}, {} + for i in tqdm(info): + utt_id = Path(i.filename).stem + offset, file_size = i.header_offset + 30 + len(i.filename), i.file_size + paths[utt_id] = f"{zip_path.as_posix()}:{offset}:{file_size}" + with open(_zip_path, "rb") as f: + f.seek(offset) + byte_data = f.read(file_size) + assert len(byte_data) > 1 + if is_audio: + assert is_sf_audio_data(byte_data), i + else: + assert is_npy_data(byte_data), i + byte_data_fp = io.BytesIO(byte_data) + if is_audio: + lengths[utt_id] = sf.info(byte_data_fp).frames + else: + lengths[utt_id] = np.load(byte_data_fp).shape[0] + return paths, lengths + + +def gen_config_yaml( + manifest_root: Path, + spm_filename: Optional[str] = None, + vocab_name: Optional[str] = None, + yaml_filename: str = "config.yaml", + specaugment_policy: Optional[str] = "lb", + prepend_tgt_lang_tag: bool = False, + sampling_alpha: Optional[float] = None, + input_channels: Optional[int] = 1, + input_feat_per_channel: Optional[int] = 80, + audio_root: str = "", + cmvn_type: str = "utterance", + gcmvn_path: Optional[Path] = None, + extra=None +): + manifest_root = manifest_root.absolute() + writer = S2TDataConfigWriter(manifest_root / yaml_filename) + assert spm_filename is not None or vocab_name is not None + vocab_name = spm_filename.replace(".model", ".txt") if vocab_name is None \ + else vocab_name + writer.set_vocab_filename(vocab_name) + if input_channels is not None: + writer.set_input_channels(input_channels) + if input_feat_per_channel is not None: + writer.set_input_feat_per_channel(input_feat_per_channel) + specaugment_setters = { + "lb": writer.set_specaugment_lb_policy, + "ld": writer.set_specaugment_ld_policy, + "sm": writer.set_specaugment_sm_policy, + "ss": writer.set_specaugment_ss_policy, + } + specaugment_setter = specaugment_setters.get(specaugment_policy, None) + if specaugment_setter is not None: + specaugment_setter() + if spm_filename is not None: + writer.set_bpe_tokenizer( + { + "bpe": "sentencepiece", + "sentencepiece_model": (manifest_root / spm_filename).as_posix(), + } + ) + if prepend_tgt_lang_tag: + writer.set_prepend_tgt_lang_tag(True) + if sampling_alpha is not None: + writer.set_sampling_alpha(sampling_alpha) + + if cmvn_type not in ["global", "utterance"]: + raise NotImplementedError + + if specaugment_policy is not None: + writer.set_feature_transforms( + "_train", [f"{cmvn_type}_cmvn", "specaugment"] + ) + writer.set_feature_transforms("*", [f"{cmvn_type}_cmvn"]) + + if cmvn_type == "global": + if gcmvn_path is None: + raise ValueError("Please provide path of global cmvn file.") + else: + writer.set_global_cmvn(gcmvn_path.as_posix()) + + if len(audio_root) > 0: + writer.set_audio_root(audio_root) + + if extra is not None: + writer.set_extra(extra) + writer.flush() + + +def load_df_from_tsv(path: Union[str, Path]) -> pd.DataFrame: + _path = path if isinstance(path, str) else path.as_posix() + return pd.read_csv( + _path, + sep="\t", + header=0, + encoding="utf-8", + escapechar="\\", + quoting=csv.QUOTE_NONE, + na_filter=False, + ) + + +def save_df_to_tsv(dataframe, path: Union[str, Path]): + _path = path if isinstance(path, str) else path.as_posix() + dataframe.to_csv( + _path, + sep="\t", + header=True, + index=False, + encoding="utf-8", + escapechar="\\", + quoting=csv.QUOTE_NONE, + ) + + +def load_tsv_to_dicts(path: Union[str, Path]) -> List[dict]: + with open(path, "r") as f: + reader = csv.DictReader( + f, + delimiter="\t", + quotechar=None, + doublequote=False, + lineterminator="\n", + quoting=csv.QUOTE_NONE, + ) + rows = [dict(e) for e in reader] + return rows + + +def filter_manifest_df( + df, is_train_split=False, extra_filters=None, min_n_frames=5, max_n_frames=3000 +): + filters = { + "no speech": df["audio"] == "", + f"short speech (<{min_n_frames} frames)": df["n_frames"] < min_n_frames, + "empty sentence": df["tgt_text"] == "", + } + if is_train_split: + filters[f"long speech (>{max_n_frames} frames)"] = df["n_frames"] > max_n_frames + if extra_filters is not None: + filters.update(extra_filters) + invalid = reduce(lambda x, y: x | y, filters.values()) + valid = ~invalid + print( + "| " + + ", ".join(f"{n}: {f.sum()}" for n, f in filters.items()) + + f", total {invalid.sum()} filtered, {valid.sum()} remained." + ) + return df[valid] + + +def cal_gcmvn_stats(features_list): + features = np.concatenate(features_list) + square_sums = (features ** 2).sum(axis=0) + mean = features.mean(axis=0) + features = np.subtract(features, mean) + var = square_sums / features.shape[0] - mean ** 2 + std = np.sqrt(np.maximum(var, 1e-8)) + return {"mean": mean.astype("float32"), "std": std.astype("float32")} + + +class S2TDataConfigWriter(object): + DEFAULT_VOCAB_FILENAME = "dict.txt" + DEFAULT_INPUT_FEAT_PER_CHANNEL = 80 + DEFAULT_INPUT_CHANNELS = 1 + + def __init__(self, yaml_path: Path): + try: + import yaml + except ImportError: + print("Please install PyYAML for S2T data config YAML files") + self.yaml = yaml + self.yaml_path = yaml_path + self.config = {} + + def flush(self): + with open(self.yaml_path, "w") as f: + self.yaml.dump(self.config, f) + + def set_audio_root(self, audio_root=""): + self.config["audio_root"] = audio_root + + def set_vocab_filename(self, vocab_filename: str = "dict.txt"): + self.config["vocab_filename"] = vocab_filename + + def set_specaugment( + self, + time_wrap_w: int, + freq_mask_n: int, + freq_mask_f: int, + time_mask_n: int, + time_mask_t: int, + time_mask_p: float, + ): + self.config["specaugment"] = { + "time_wrap_W": time_wrap_w, + "freq_mask_N": freq_mask_n, + "freq_mask_F": freq_mask_f, + "time_mask_N": time_mask_n, + "time_mask_T": time_mask_t, + "time_mask_p": time_mask_p, + } + + def set_specaugment_lb_policy(self): + self.set_specaugment( + time_wrap_w=0, + freq_mask_n=1, + freq_mask_f=27, + time_mask_n=1, + time_mask_t=100, + time_mask_p=1.0, + ) + + def set_specaugment_ld_policy(self): + self.set_specaugment( + time_wrap_w=0, + freq_mask_n=2, + freq_mask_f=27, + time_mask_n=2, + time_mask_t=100, + time_mask_p=1.0, + ) + + def set_specaugment_sm_policy(self): + self.set_specaugment( + time_wrap_w=0, + freq_mask_n=2, + freq_mask_f=15, + time_mask_n=2, + time_mask_t=70, + time_mask_p=0.2, + ) + + def set_specaugment_ss_policy(self): + self.set_specaugment( + time_wrap_w=0, + freq_mask_n=2, + freq_mask_f=27, + time_mask_n=2, + time_mask_t=70, + time_mask_p=0.2, + ) + + def set_input_channels(self, input_channels: int = 1): + self.config["input_channels"] = input_channels + + def set_input_feat_per_channel(self, input_feat_per_channel: int = 80): + self.config["input_feat_per_channel"] = input_feat_per_channel + + def set_bpe_tokenizer(self, bpe_tokenizer: Dict[str, Any]): + self.config["bpe_tokenizer"] = bpe_tokenizer + + def set_global_cmvn(self, stats_npz_path: str): + self.config["global_cmvn"] = {"stats_npz_path": stats_npz_path} + + def set_feature_transforms(self, split: str, transforms: List[str]): + if "transforms" not in self.config: + self.config["transforms"] = {} + self.config["transforms"][split] = transforms + + def set_prepend_tgt_lang_tag(self, flag: bool = True): + self.config["prepend_tgt_lang_tag"] = flag + + def set_sampling_alpha(self, sampling_alpha: float = 1.0): + self.config["sampling_alpha"] = sampling_alpha + + def set_extra(self, data): + self.config.update(data) diff --git a/examples/speech_to_text/docs/covost_example.md b/examples/speech_to_text/docs/covost_example.md new file mode 100644 index 0000000000..16447f041e --- /dev/null +++ b/examples/speech_to_text/docs/covost_example.md @@ -0,0 +1,102 @@ +[[Back]](..) + +# S2T Example: ST on CoVoST +We replicate the experiments in +[CoVoST 2 and Massively Multilingual Speech-to-Text Translation (Wang et al., 2020)](https://arxiv.org/abs/2007.10310). + +## Data Preparation +[Download](https://commonvoice.mozilla.org/en/datasets) and unpack Common Voice v4 to a path +`${COVOST_ROOT}/${SOURCE_LANG_ID}`, then preprocess it with +```bash +# additional Python packages for S2T data processing/model training +pip install pandas torchaudio sentencepiece + +# En ASR +python examples/speech_to_text/prep_covost_data.py \ + --data-root ${COVOST_ROOT} --vocab-type char --src-lang en +# ST +python examples/speech_to_text/prep_covost_data.py \ + --data-root ${COVOST_ROOT} --vocab-type char \ + --src-lang fr --tgt-lang en +``` +The generated files (manifest, features, vocabulary and data configuration) will be added to +`${COVOST_ROOT}/${SOURCE_LANG_ID}`. + +Download our vocabulary files if you want to use our pre-trained models: +- ASR: [En](https://dl.fbaipublicfiles.com/fairseq/s2t/covost2_en_asr_vocab_char.zip) +- ST: [Fr-En](https://dl.fbaipublicfiles.com/fairseq/s2t/covost2_fr_en_st_vocab_char.zip), [De-En](https://dl.fbaipublicfiles.com/fairseq/s2t/covost2_de_en_st_vocab_char.zip), [Es-En](https://dl.fbaipublicfiles.com/fairseq/s2t/covost2_es_en_st_vocab_char.zip), [Ca-En](https://dl.fbaipublicfiles.com/fairseq/s2t/covost2_ca_en_st_vocab_char.zip), [En-De](https://dl.fbaipublicfiles.com/fairseq/s2t/covost2_en_de_st_vocab_char.zip), [En-Ca](https://dl.fbaipublicfiles.com/fairseq/s2t/covost2_en_ca_st_vocab_char.zip), [En-Fa](https://dl.fbaipublicfiles.com/fairseq/s2t/covost2_en_fa_st_vocab_char.zip), [En-Et](https://dl.fbaipublicfiles.com/fairseq/s2t/covost2_en_et_st_vocab_char.zip) + +## ASR +#### Training +We train an En ASR model for encoder pre-training of all ST models: +```bash +fairseq-train ${COVOST_ROOT}/en \ + --config-yaml config_asr_en.yaml --train-subset train_asr_en --valid-subset dev_asr_en \ + --save-dir ${ASR_SAVE_DIR} --num-workers 4 --max-tokens 50000 --max-update 60000 \ + --task speech_to_text --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \ + --report-accuracy --arch s2t_transformer_s --dropout 0.15 --optimizer adam --lr 2e-3 \ + --lr-scheduler inverse_sqrt --warmup-updates 10000 --clip-norm 10.0 --seed 1 --update-freq 8 +``` +where `ASR_SAVE_DIR` is the checkpoint root path. We set `--update-freq 8` to simulate 8 GPUs with 1 GPU. +You may want to update it accordingly when using more than 1 GPU. + +#### Inference & Evaluation +```bash +CHECKPOINT_FILENAME=avg_last_10_checkpoint.pt +python scripts/average_checkpoints.py \ + --inputs ${ASR_SAVE_DIR} --num-epoch-checkpoints 10 \ + --output "${ASR_SAVE_DIR}/${CHECKPOINT_FILENAME}" +fairseq-generate ${COVOST_ROOT}/en \ + --config-yaml config_asr_en.yaml --gen-subset test_asr_en --task speech_to_text \ + --path ${ASR_SAVE_DIR}/${CHECKPOINT_FILENAME} --max-tokens 50000 --beam 5 \ + --scoring wer --wer-tokenizer 13a --wer-lowercase --wer-remove-punct +``` +#### Results +| --arch | Params | En | Model | +|---|---|---|---| +| s2t_transformer_s | 31M | 25.6 | [Download](https://dl.fbaipublicfiles.com/fairseq/s2t/covost2_en_asr_transformer_s.pt) | + +## ST +#### Training +Fr-En as example: +```bash +fairseq-train ${COVOST_ROOT}/fr \ + --config-yaml config_st_fr_en.yaml --train-subset train_st_fr_en --valid-subset dev_st_fr_en \ + --save-dir ${ST_SAVE_DIR} --num-workers 4 --max-update 30000 --max-tokens 40000 \ # --max-tokens 50000 for en-* + --task speech_to_text --criterion label_smoothed_cross_entropy --label-smoothing 0.1 --report-accuracy \ + --arch s2t_transformer_s --encoder-freezing-updates 1000 --optimizer adam --lr 2e-3 \ + --lr-scheduler inverse_sqrt --warmup-updates 10000 --clip-norm 10.0 --seed 1 --update-freq 8 \ + --load-pretrained-encoder-from ${ASR_SAVE_DIR}/${CHECKPOINT_FILENAME} +``` +where `ST_SAVE_DIR` is the checkpoint root path. The ST encoder is pre-trained by En ASR for faster training and better +performance: `--load-pretrained-encoder-from <ASR checkpoint path>`. We set `--update-freq 8` to simulate 8 GPUs with 1 GPU. +You may want to update it accordingly when using more than 1 GPU. + +#### Inference & Evaluation +Average the last 10 checkpoints and evaluate on test split: +```bash +CHECKPOINT_FILENAME=avg_last_10_checkpoint.pt +python scripts/average_checkpoints.py \ + --inputs ${ST_SAVE_DIR} --num-epoch-checkpoints 10 \ + --output "${ST_SAVE_DIR}/${CHECKPOINT_FILENAME}" +fairseq-generate ${COVOST_ROOT}/fr \ + --config-yaml config_st_fr_en.yaml --gen-subset test_st_fr_en --task speech_to_text \ + --path ${ST_SAVE_DIR}/${CHECKPOINT_FILENAME} \ + --max-tokens 50000 --beam 5 --scoring sacrebleu +``` + +## Interactive Decoding +Launch the interactive console via +```bash +fairseq-interactive ${COVOST_ROOT}/fr --config-yaml config_st_fr_en.yaml \ + --task speech_to_text --path ${SAVE_DIR}/${CHECKPOINT_FILENAME} \ + --max-tokens 50000 --beam 5 +``` +Type in WAV/FLAC/OGG audio paths (one per line) after the prompt. + +#### Results +| --arch | Params | Fr-En | De-En | Es-En | Ca-En | En-De | En-Ca | En-Fa | En-Et | Model | +|---|---|---|---|---|---|---|---|---|---|---| +| s2t_transformer_s | 31M | [27.2](https://dl.fbaipublicfiles.com/fairseq/s2t/covost2_fr_en_st_transformer_s.pt) | [17.7](https://dl.fbaipublicfiles.com/fairseq/s2t/covost2_de_en_st_transformer_s.pt) | [23.1](https://dl.fbaipublicfiles.com/fairseq/s2t/covost2_es_en_st_transformer_s.pt) | [19.3](https://dl.fbaipublicfiles.com/fairseq/s2t/covost2_ca_en_st_transformer_s.pt) | [16.1](https://dl.fbaipublicfiles.com/fairseq/s2t/covost2_en_de_st_transformer_s.pt) | [21.6](https://dl.fbaipublicfiles.com/fairseq/s2t/covost2_en_ca_st_transformer_s.pt) | [12.9](https://dl.fbaipublicfiles.com/fairseq/s2t/covost2_en_fa_st_transformer_s.pt) | [12.8](https://dl.fbaipublicfiles.com/fairseq/s2t/covost2_en_et_st_transformer_s.pt) | (<-Download) | + +[[Back]](..) diff --git a/examples/speech_to_text/docs/librispeech_example.md b/examples/speech_to_text/docs/librispeech_example.md new file mode 100644 index 0000000000..4040fda942 --- /dev/null +++ b/examples/speech_to_text/docs/librispeech_example.md @@ -0,0 +1,69 @@ +[[Back]](..) + +# S2T Example: Speech Recognition (ASR) on LibriSpeech +[LibriSpeech](https://www.danielpovey.com/files/2015_icassp_librispeech.pdf) is a de-facto standard English ASR +benchmark. We provide competitive +vanilla [Transformer](https://papers.nips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf) baselines. + +## Data preparation +Download and preprocess LibriSpeech data with +```bash +# additional Python packages for S2T data processing/model training +pip install pandas torchaudio sentencepiece + +python examples/speech_to_text/prep_librispeech_data.py \ + --output-root ${LS_ROOT} --vocab-type unigram --vocab-size 10000 +``` +where `LS_ROOT` is the root path for downloaded data as well as generated files (manifest, features, vocabulary and +data configuration). + +[Download](https://dl.fbaipublicfiles.com/fairseq/s2t/librispeech_vocab_unigram10000.zip) our vocabulary files +if you want to use our pre-trained models. + +## Training +```bash +fairseq-train ${LS_ROOT} --save-dir ${SAVE_DIR} \ + --config-yaml config.yaml --train-subset train-clean-100,train-clean-360,train-other-500 --valid-subset dev-clean,dev-other \ + --num-workers 4 --max-tokens 40000 --max-update 300000 \ + --task speech_to_text --criterion label_smoothed_cross_entropy --label-smoothing 0.1 --report-accuracy \ + --arch s2t_transformer_s --share-decoder-input-output-embed \ + --optimizer adam --lr 2e-3 --lr-scheduler inverse_sqrt --warmup-updates 10000 \ + --clip-norm 10.0 --seed 1 --update-freq 8 +``` +where `SAVE_DIR` is the checkpoint root path. Here we use `--arch s2t_transformer_s` (31M parameters) as example. +For better performance, you may switch to `s2t_transformer_m` (71M, with `--lr 1e-3`) or `s2t_transformer_l` +(268M, with `--lr 5e-4`). We set `--update-freq 8` to simulate 8 GPUs with 1 GPU. You may want to update it accordingly +when using more than 1 GPU. + +## Inference & Evaluation +Average the last 10 checkpoints and evaluate on the 4 splits +(`dev-clean`, `dev-other`, `test-clean` and `test-other`): +```bash +CHECKPOINT_FILENAME=avg_last_10_checkpoint.pt +python scripts/average_checkpoints.py --inputs ${SAVE_DIR} \ + --num-epoch-checkpoints 10 \ + --output "${SAVE_DIR}/${CHECKPOINT_FILENAME}" +for SUBSET in dev-clean dev-other test-clean test-other; do + fairseq-generate ${LS_ROOT} --config-yaml config.yaml --gen-subset ${SUBSET} \ + --task speech_to_text --path ${SAVE_DIR}/${CHECKPOINT_FILENAME} \ + --max-tokens 50000 --beam 5 --scoring wer +done +``` + +## Interactive Decoding +Launch the interactive console via +```bash +fairseq-interactive ${LS_ROOT} --config-yaml config.yaml --task speech_to_text \ + --path ${SAVE_DIR}/${CHECKPOINT_FILENAME} --max-tokens 50000 --beam 5 +``` +Type in WAV/FLAC/OGG audio paths (one per line) after the prompt. + +## Results + +| --arch | Params | dev-clean | dev-other | test-clean | test-other | Model | +|---|---|---|---|---|---|---| +| s2t_transformer_s | 30M | 3.8 | 8.9 | 4.4 | 9.0 | [Download](https://dl.fbaipublicfiles.com/fairseq/s2t/librispeech_transformer_s.pt) | +| s2t_transformer_m | 71M | 3.2 | 8.0 | 3.4 | 7.9 | [Download](https://dl.fbaipublicfiles.com/fairseq/s2t/librispeech_transformer_m.pt) | +| s2t_transformer_l | 268M | 3.0 | 7.5 | 3.2 | 7.5 | [Download](https://dl.fbaipublicfiles.com/fairseq/s2t/librispeech_transformer_l.pt) | + +[[Back]](..) diff --git a/examples/speech_to_text/docs/mtedx_example.md b/examples/speech_to_text/docs/mtedx_example.md new file mode 100644 index 0000000000..25b4556aff --- /dev/null +++ b/examples/speech_to_text/docs/mtedx_example.md @@ -0,0 +1,200 @@ +[[Back]](..) + +# S2T Example: Speech Translation (ST) on Multilingual TEDx + +[Multilingual TEDx](https://arxiv.org/abs/2102.01757) is multilingual corpus for speech recognition and +speech translation. The data is derived from TEDx talks in 8 source languages +with translations to a subset of 5 target languages. + +## Data Preparation +[Download](http://openslr.org/100/) and unpack Multilingual TEDx data to a path +`${MTEDX_ROOT}/${LANG_PAIR}`, then preprocess it with +```bash +# additional Python packages for S2T data processing/model training +pip install pandas torchaudio soundfile sentencepiece + +# Generate TSV manifests, features, vocabulary +# and configuration for each language +python examples/speech_to_text/prep_mtedx_data.py \ + --data-root ${MTEDX_ROOT} --task asr \ + --vocab-type unigram --vocab-size 1000 +python examples/speech_to_text/prep_mtedx_data.py \ + --data-root ${MTEDX_ROOT} --task st \ + --vocab-type unigram --vocab-size 1000 + +# Add vocabulary and configuration for joint data +# (based on the manifests and features generated above) +python examples/speech_to_text/prep_mtedx_data.py \ + --data-root ${MTEDX_ROOT} --task asr --joint \ + --vocab-type unigram --vocab-size 8000 +python examples/speech_to_text/prep_mtedx_data.py \ + --data-root ${MTEDX_ROOT} --task st --joint \ + --vocab-type unigram --vocab-size 8000 +``` +The generated files (manifest, features, vocabulary and data configuration) will be added to +`${MTEDX_ROOT}/${LANG_PAIR}` (per-language data) and `MTEDX_ROOT` (joint data). + + +## ASR +#### Training +Spanish as example: +```bash +fairseq-train ${MTEDX_ROOT}/es-es \ + --config-yaml config_asr.yaml --train-subset train_asr --valid-subset valid_asr \ + --save-dir ${ASR_SAVE_DIR} --num-workers 4 --max-tokens 40000 --max-epoch 200 \ + --task speech_to_text --criterion label_smoothed_cross_entropy --report-accuracy \ + --arch s2t_transformer_xs --optimizer adam --lr 2e-3 --lr-scheduler inverse_sqrt \ + --warmup-updates 10000 --clip-norm 10.0 --seed 1 --dropout 0.3 --label-smoothing 0.1 \ + --load-pretrained-encoder-from ${PRETRAINED_ENCODER} \ + --skip-invalid-size-inputs-valid-test \ + --keep-last-epochs 10 --update-freq 8 --patience 10 +``` +For joint model (using ASR data from all 8 languages): +```bash +fairseq-train ${MTEDX_ROOT} \ + --config-yaml config_asr.yaml \ + --train-subset train_es-es_asr,train_fr-fr_asr,train_pt-pt_asr,train_it-it_asr,train_ru-ru_asr,train_el-el_asr,train_ar-ar_asr,train_de-de_asr \ + --valid-subset valid_es-es_asr,valid_fr-fr_asr,valid_pt-pt_asr,valid_it-it_asr,valid_ru-ru_asr,valid_el-el_asr,valid_ar-ar_asr,valid_de-de_asr \ + --save-dir ${MULTILINGUAL_ASR_SAVE_DIR} --num-workers 4 --max-tokens 40000 --max-epoch 200 \ + --task speech_to_text --criterion label_smoothed_cross_entropy --report-accuracy \ + --arch s2t_transformer_s --optimizer adam --lr 2e-3 --lr-scheduler inverse_sqrt \ + --warmup-updates 10000 --clip-norm 10.0 --seed 1 --dropout 0.3 --label-smoothing 0.1 \ + --skip-invalid-size-inputs-valid-test \ + --keep-last-epochs 10 --update-freq 8 --patience 10 \ + --ignore-prefix-size 1 +``` +where `MULTILINGUAL_ASR_SAVE_DIR` is the checkpoint root path. We set `--update-freq 8` to simulate 8 GPUs +with 1 GPU. You may want to update it accordingly when using more than 1 GPU. +For multilingual models, we prepend target language ID token as target BOS, which should be excluded from +the training loss via `--ignore-prefix-size 1`. + +#### Inference & Evaluation +```bash +CHECKPOINT_FILENAME=avg_last_10_checkpoint.pt +python scripts/average_checkpoints.py \ + --inputs ${ASR_SAVE_DIR} --num-epoch-checkpoints 10 \ + --output "${ASR_SAVE_DIR}/${CHECKPOINT_FILENAME}" + +fairseq-generate ${MTEDX_ROOT}/es-es \ + --config-yaml config_asr.yaml --gen-subset test --task speech_to_text \ + --path ${ASR_SAVE_DIR}/${CHECKPOINT_FILENAME} --max-tokens 50000 --beam 5 \ + --skip-invalid-size-inputs-valid-test \ + --scoring wer --wer-tokenizer 13a --wer-lowercase --wer-remove-punct --remove-bpe + +# For models trained on joint data +CHECKPOINT_FILENAME=avg_last_10_checkpoint.pt +python scripts/average_checkpoints.py \ + --inputs ${MULTILINGUAL_ASR_SAVE_DIR} --num-epoch-checkpoints 10 \ + --output "${MULTILINGUAL_ASR_SAVE_DIR}/${CHECKPOINT_FILENAME}" + +for LANG in es fr pt it ru el ar de; do + fairseq-generate ${MTEDX_ROOT} \ + --config-yaml config_asr.yaml --gen-subset test_${LANG}-${LANG}_asr --task speech_to_text \ + --prefix-size 1 --path ${MULTILINGUAL_ASR_SAVE_DIR}/${CHECKPOINT_FILENAME} \ + --max-tokens 40000 --beam 5 \ + --skip-invalid-size-inputs-valid-test \ + --scoring wer --wer-tokenizer 13a --wer-lowercase --wer-remove-punct --remove-bpe +done +``` +#### Results +| Data | --arch | Params | Es | Fr | Pt | It | Ru | El | Ar | De | +|--------------|--------------------|--------|------|------|------|------|------|-------|-------|-------| +| Monolingual | s2t_transformer_xs | 10M | 46.4 | 45.6 | 54.8 | 48.0 | 74.7 | 109.5 | 104.4 | 111.1 | + + +## ST +#### Training +Es-En as example: +```bash +fairseq-train ${MTEDX_ROOT}/es-en \ + --config-yaml config_st.yaml --train-subset train_st --valid-subset valid_st \ + --save-dir ${ST_SAVE_DIR} --num-workers 4 --max-tokens 40000 --max-epoch 200 \ + --task speech_to_text --criterion label_smoothed_cross_entropy --report-accuracy \ + --arch s2t_transformer_xs --optimizer adam --lr 2e-3 --lr-scheduler inverse_sqrt \ + --warmup-updates 10000 --clip-norm 10.0 --seed 1 --dropout 0.3 --label-smoothing 0.1 \ + --load-pretrained-encoder-from ${PRETRAINED_ENCODER} \ + --skip-invalid-size-inputs-valid-test \ + --keep-last-epochs 10 --update-freq 8 --patience 10 +``` +For multilingual model (all 12 directions): +```bash +fairseq-train ${MTEDX_ROOT} \ + --config-yaml config_st.yaml \ + --train-subset train_el-en_st,train_es-en_st,train_es-fr_st,train_es-it_st,train_es-pt_st,train_fr-en_st,train_fr-es_st,train_fr-pt_st,train_it-en_st,train_it-es_st,train_pt-en_st,train_pt-es_st,train_ru-en_st \ + --valid-subset valid_el-en_st,valid_es-en_st,valid_es-fr_st,valid_es-it_st,valid_es-pt_st,valid_fr-en_st,valid_fr-es_st,valid_fr-pt_st,valid_it-en_st,valid_it-es_st,valid_pt-en_st,valid_pt-es_st,valid_ru-en_st \ + --save-dir ${MULTILINGUAL_ST_SAVE_DIR} --num-workers 4 --max-tokens 40000 --max-epoch 200 \ + --task speech_to_text --criterion label_smoothed_cross_entropy --report-accuracy \ + --arch s2t_transformer_s --optimizer adam --lr 2e-3 --lr-scheduler inverse_sqrt \ + --warmup-updates 10000 --clip-norm 10.0 --seed 1 --dropout 0.3 --label-smoothing 0.1 \ + --skip-invalid-size-inputs-valid-test \ + --keep-last-epochs 10 --update-freq 8 --patience 10 \ + --ignore-prefix-size 1 \ + --load-pretrained-encoder-from ${PRETRAINED_ENCODER} +``` +where `ST_SAVE_DIR` (`MULTILINGUAL_ST_SAVE_DIR`) is the checkpoint root path. The ST encoder is pre-trained by ASR +for faster training and better performance: `--load-pretrained-encoder-from <(JOINT_)ASR checkpoint path>`. We set +`--update-freq 8` to simulate 8 GPUs with 1 GPU. You may want to update it accordingly when using more than 1 GPU. +For multilingual models, we prepend target language ID token as target BOS, which should be excluded from +the training loss via `--ignore-prefix-size 1`. + +#### Inference & Evaluation +Average the last 10 checkpoints and evaluate on the `test` split: +```bash +CHECKPOINT_FILENAME=avg_last_10_checkpoint.pt +python scripts/average_checkpoints.py \ + --inputs ${ST_SAVE_DIR} --num-epoch-checkpoints 10 \ + --output "${ST_SAVE_DIR}/${CHECKPOINT_FILENAME}" + +fairseq-generate ${MTEDX_ROOT}/es-en \ + --config-yaml config_st.yaml --gen-subset test --task speech_to_text \ + --path ${ST_SAVE_DIR}/${CHECKPOINT_FILENAME} \ + --max-tokens 50000 --beam 5 --scoring sacrebleu --remove-bpe + +# For multilingual models +python scripts/average_checkpoints.py \ + --inputs ${MULTILINGUAL_ST_SAVE_DIR} --num-epoch-checkpoints 10 \ + --output "${MULTILINGUAL_ST_SAVE_DIR}/${CHECKPOINT_FILENAME}" + +for LANGPAIR in es-en es-fr es-pt fr-en fr-es fr-pt pt-en pt-es it-en it-es ru-en el-en; do + fairseq-generate ${MTEDX_ROOT} \ + --config-yaml config_st.yaml --gen-subset test_${LANGPAIR}_st --task speech_to_text \ + --prefix-size 1 --path ${MULTILINGUAL_ST_SAVE_DIR}/${CHECKPOINT_FILENAME} \ + --max-tokens 40000 --beam 5 \ + --skip-invalid-size-inputs-valid-test \ + --scoring sacrebleu --remove-bpe +done +``` +For multilingual models, we force decoding from the target language ID token (as BOS) via `--prefix-size 1`. + +#### Results +| Data | --arch | Params | Es-En | Es-Pt | Es-Fr | Fr-En | Fr-Es | Fr-Pt | Pt-En | Pt-Es | It-En | It-Es | Ru-En | El-En | +|--------------|--------------------|-----|-------|-------|-------|-------|-------|-------|-------|-------|-------|-------|-------|-------| +| Bilingual | s2t_transformer_xs | 10M | 7.0 | 12.2 | 1.7 | 8.9 | 10.6 | 7.9 | 8.1 | 8.7 | 6.4 | 1.0 | 0.7 | 0.6 | +| Multilingual | s2t_transformer_s | 31M | 12.3 | 17.4 | 6.1 | 12.0 | 13.6 | 13.2 | 12.0 | 13.7 | 10.7 | 13.1 | 0.6 | 0.8 | + + +## Citation +Please cite as: +``` +@misc{salesky2021mtedx, + title={Multilingual TEDx Corpus for Speech Recognition and Translation}, + author={Elizabeth Salesky and Matthew Wiesner and Jacob Bremerman and Roldano Cattoni and Matteo Negri and Marco Turchi and Douglas W. Oard and Matt Post}, + year={2021}, +} + +@inproceedings{wang2020fairseqs2t, + title = {fairseq S2T: Fast Speech-to-Text Modeling with fairseq}, + author = {Changhan Wang and Yun Tang and Xutai Ma and Anne Wu and Dmytro Okhonko and Juan Pino}, + booktitle = {Proceedings of the 2020 Conference of the Asian Chapter of the Association for Computational Linguistics (AACL): System Demonstrations}, + year = {2020}, +} + +@inproceedings{ott2019fairseq, + title = {fairseq: A Fast, Extensible Toolkit for Sequence Modeling}, + author = {Myle Ott and Sergey Edunov and Alexei Baevski and Angela Fan and Sam Gross and Nathan Ng and David Grangier and Michael Auli}, + booktitle = {Proceedings of NAACL-HLT 2019: Demonstrations}, + year = {2019}, +} +``` + +[[Back]](..) diff --git a/examples/speech_to_text/docs/mustc_example.md b/examples/speech_to_text/docs/mustc_example.md new file mode 100644 index 0000000000..c95ef3e156 --- /dev/null +++ b/examples/speech_to_text/docs/mustc_example.md @@ -0,0 +1,155 @@ +[[Back]](..) + +# S2T Example: Speech Translation (ST) on MuST-C + +[MuST-C](https://www.aclweb.org/anthology/N19-1202) is multilingual speech-to-text translation corpus with +8-language translations on English TED talks. We match the state-of-the-art performance in +[ESPNet-ST](https://arxiv.org/pdf/2004.10234.pdf) with a simpler model training pipeline. + +## Data Preparation +[Download](https://ict.fbk.eu/must-c) and unpack MuST-C data to a path +`${MUSTC_ROOT}/en-${TARGET_LANG_ID}`, then preprocess it with +```bash +# additional Python packages for S2T data processing/model training +pip install pandas torchaudio soundfile sentencepiece + +# Generate TSV manifests, features, vocabulary +# and configuration for each language +python examples/speech_to_text/prep_mustc_data.py \ + --data-root ${MUSTC_ROOT} --task asr \ + --vocab-type unigram --vocab-size 5000 +python examples/speech_to_text/prep_mustc_data.py \ + --data-root ${MUSTC_ROOT} --task st \ + --vocab-type unigram --vocab-size 8000 + +# Add vocabulary and configuration for joint data +# (based on the manifests and features generated above) +python examples/speech_to_text/prep_mustc_data.py \ + --data-root ${MUSTC_ROOT} --task asr --joint \ + --vocab-type unigram --vocab-size 10000 +python examples/speech_to_text/prep_mustc_data.py \ + --data-root ${MUSTC_ROOT} --task st --joint \ + --vocab-type unigram --vocab-size 10000 +``` +The generated files (manifest, features, vocabulary and data configuration) will be added to +`${MUSTC_ROOT}/en-${TARGET_LANG_ID}` (per-language data) and `MUSTC_ROOT` (joint data). + +Download our vocabulary files if you want to use our pre-trained models: +- ASR: [En-De](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_de_asr_vocab_unigram5000.zip), [En-Nl](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_nl_asr_vocab_unigram5000.zip), [En-Es](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_es_asr_vocab_unigram5000.zip), [En-Fr](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_fr_asr_vocab_unigram5000.zip), [En-It](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_it_asr_vocab_unigram5000.zip), [En-Pt](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_pt_asr_vocab_unigram5000.zip), [En-Ro](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_ro_asr_vocab_unigram5000.zip), [En-Ru](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_ru_asr_vocab_unigram5000.zip), [Joint](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_joint_asr_vocab_unigram10000.zip) +- ST: [En-De](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_de_st_vocab_unigram8000.zip), [En-Nl](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_nl_st_vocab_unigram8000.zip), [En-Es](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_es_st_vocab_unigram8000.zip), [En-Fr](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_fr_st_vocab_unigram8000.zip), [En-It](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_it_st_vocab_unigram8000.zip), [En-Pt](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_pt_st_vocab_unigram8000.zip), [En-Ro](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_ro_st_vocab_unigram8000.zip), [En-Ru](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_ru_st_vocab_unigram8000.zip), [Multilingual](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_multilingual_st_vocab_unigram10000.zip) + +## ASR +#### Training +En-De as example: +```bash +fairseq-train ${MUSTC_ROOT}/en-de \ + --config-yaml config_asr.yaml --train-subset train_asr --valid-subset dev_asr \ + --save-dir ${ASR_SAVE_DIR} --num-workers 4 --max-tokens 40000 --max-update 100000 \ + --task speech_to_text --criterion label_smoothed_cross_entropy --label-smoothing 0.1 --report-accuracy \ + --arch s2t_transformer_s --optimizer adam --lr 1e-3 --lr-scheduler inverse_sqrt \ + --warmup-updates 10000 --clip-norm 10.0 --seed 1 --update-freq 8 +``` +For joint model (using ASR data from all 8 directions): +```bash +fairseq-train ${MUSTC_ROOT} \ + --config-yaml config_asr.yaml \ + --train-subset train_de_asr,train_nl_asr,train_es_asr,train_fr_asr,train_it_asr,train_pt_asr,train_ro_asr,train_ru_asr \ + --valid-subset dev_de_asr,dev_nl_asr,dev_es_asr,dev_fr_asr,dev_it_asr,dev_pt_asr,dev_ro_asr,dev_ru_asr \ + --save-dir ${JOINT_ASR_SAVE_DIR} --num-workers 4 --max-tokens 40000 --max-update 100000 \ + --task speech_to_text --criterion label_smoothed_cross_entropy --label-smoothing 0.1 --report-accuracy \ + --arch s2t_transformer_s --optimizer adam --lr 1e-3 --lr-scheduler inverse_sqrt \ + --warmup-updates 10000 --clip-norm 10.0 --seed 1 --update-freq 8 +``` +where `ASR_SAVE_DIR` (`JOINT_ASR_SAVE_DIR`) is the checkpoint root path. We set `--update-freq 8` to simulate 8 GPUs +with 1 GPU. You may want to update it accordingly when using more than 1 GPU. + +#### Inference & Evaluation +```bash +CHECKPOINT_FILENAME=avg_last_10_checkpoint.pt +python scripts/average_checkpoints.py \ + --inputs ${ASR_SAVE_DIR} --num-epoch-checkpoints 10 \ + --output "${ASR_SAVE_DIR}/${CHECKPOINT_FILENAME}" +fairseq-generate ${MUSTC_ROOT}/en-de \ + --config-yaml config_asr.yaml --gen-subset tst-COMMON_asr --task speech_to_text \ + --path ${ASR_SAVE_DIR}/${CHECKPOINT_FILENAME} --max-tokens 50000 --beam 5 \ + --scoring wer --wer-tokenizer 13a --wer-lowercase --wer-remove-punct + +# For models trained on joint data +python scripts/average_checkpoints.py \ + --inputs ${JOINT_ASR_SAVE_DIR} --num-epoch-checkpoints 10 \ + --output "${JOINT_ASR_SAVE_DIR}/${CHECKPOINT_FILENAME}" +for LANG in de nl es fr it pt ro ru; do + fairseq-generate ${MUSTC_ROOT} \ + --config-yaml config_asr.yaml --gen-subset tst-COMMON_${LANG}_asr --task speech_to_text \ + --path ${JOINT_ASR_SAVE_DIR}/${CHECKPOINT_FILENAME} --max-tokens 50000 --beam 5 \ + --scoring wer --wer-tokenizer 13a --wer-lowercase --wer-remove-punct +done +``` +#### Results +| Data | --arch | Params | En-De | En-Nl | En-Es | En-Fr | En-It | En-Pt | En-Ro | En-Ru | Model | +|---|---|---|---|---|---|---|---|---|---|---|---| +| Single | s2t_transformer_s | 31M | [18.2](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_de_asr_transformer_s.pt) | [17.6](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_nl_asr_transformer_s.pt) | [17.7](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_es_asr_transformer_s.pt) | [17.2](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_fr_asr_transformer_s.pt) | [17.9](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_it_asr_transformer_s.pt) | [19.1](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_pt_asr_transformer_s.pt) | [18.1](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_ro_asr_transformer_s.pt) | [17.7](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_ru_asr_transformer_s.pt) | (<-Download) | +| Joint | s2t_transformer_m | 76M | 16.8 | 16.7 | 16.9 | 16.9 | 17.0 | 17.4 | 17.0 | 16.9 | [Download](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_joint_asr_transformer_m.pt) | + +## ST +#### Training +En-De as example: +```bash +fairseq-train ${MUSTC_ROOT}/en-de \ + --config-yaml config_st.yaml --train-subset train_st --valid-subset dev_st \ + --save-dir ${ST_SAVE_DIR} --num-workers 4 --max-tokens 40000 --max-update 100000 \ + --task speech_to_text --criterion label_smoothed_cross_entropy --label-smoothing 0.1 --report-accuracy \ + --arch s2t_transformer_s --optimizer adam --lr 2e-3 --lr-scheduler inverse_sqrt \ + --warmup-updates 10000 --clip-norm 10.0 --seed 1 --update-freq 8 \ + --load-pretrained-encoder-from ${ASR_SAVE_DIR}/${CHECKPOINT_FILENAME} +``` +For multilingual model (all 8 directions): +```bash +fairseq-train ${MUSTC_ROOT} \ + --config-yaml config_st.yaml \ + --train-subset train_de_st,train_nl_st,train_es_st,train_fr_st,train_it_st,train_pt_st,train_ro_st,train_ru_st \ + --valid-subset dev_de_st,dev_nl_st,dev_es_st,dev_fr_st,dev_it_st,dev_pt_st,dev_ro_st,dev_ru_st \ + --save-dir ${MULTILINGUAL_ST_SAVE_DIR} --num-workers 4 --max-tokens 40000 --max-update 100000 \ + --task speech_to_text --criterion label_smoothed_cross_entropy --label-smoothing 0.1 --report-accuracy \ + --arch s2t_transformer_s --ignore-prefix-size 1 --optimizer adam --lr 2e-3 --lr-scheduler inverse_sqrt \ + --warmup-updates 10000 --clip-norm 10.0 --seed 1 --update-freq 8 \ + --load-pretrained-encoder-from ${JOINT_ASR_SAVE_DIR}/${CHECKPOINT_FILENAME} +``` +where `ST_SAVE_DIR` (`MULTILINGUAL_ST_SAVE_DIR`) is the checkpoint root path. The ST encoder is pre-trained by ASR +for faster training and better performance: `--load-pretrained-encoder-from <(JOINT_)ASR checkpoint path>`. We set +`--update-freq 8` to simulate 8 GPUs with 1 GPU. You may want to update it accordingly when using more than 1 GPU. +For multilingual models, we prepend target language ID token as target BOS, which should be excluded from +the training loss via `--ignore-prefix-size 1`. + +#### Inference & Evaluation +Average the last 10 checkpoints and evaluate on the `tst-COMMON` split: +```bash +CHECKPOINT_FILENAME=avg_last_10_checkpoint.pt +python scripts/average_checkpoints.py \ + --inputs ${ST_SAVE_DIR} --num-epoch-checkpoints 10 \ + --output "${ST_SAVE_DIR}/${CHECKPOINT_FILENAME}" +fairseq-generate ${MUSTC_ROOT}/en-de \ + --config-yaml config_st.yaml --gen-subset tst-COMMON_st --task speech_to_text \ + --path ${ST_SAVE_DIR}/${CHECKPOINT_FILENAME} \ + --max-tokens 50000 --beam 5 --scoring sacrebleu + +# For multilingual models +python scripts/average_checkpoints.py \ + --inputs ${MULTILINGUAL_ST_SAVE_DIR} --num-epoch-checkpoints 10 \ + --output "${MULTILINGUAL_ST_SAVE_DIR}/${CHECKPOINT_FILENAME}" +for LANG in de nl es fr it pt ro ru; do + fairseq-generate ${MUSTC_ROOT} \ + --config-yaml config_st.yaml --gen-subset tst-COMMON_${LANG}_st --task speech_to_text \ + --prefix-size 1 --path ${MULTILINGUAL_ST_SAVE_DIR}/${CHECKPOINT_FILENAME} \ + --max-tokens 50000 --beam 5 --scoring sacrebleu +done +``` +For multilingual models, we force decoding from the target language ID token (as BOS) via `--prefix-size 1`. + +#### Results +| Data | --arch | Params | En-De | En-Nl | En-Es | En-Fr | En-It | En-Pt | En-Ro | En-Ru | Model | +|---|---|---|---|---|---|---|---|---|---|---|---| +| Bilingual | s2t_transformer_s | 31M | [22.7](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_de_st_transformer_s.pt) | [27.3](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_nl_st_transformer_s.pt) | [27.2](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_es_st_transformer_s.pt) | [32.9](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_fr_st_transformer_s.pt) | [22.7](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_it_st_transformer_s.pt) | [28.1](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_pt_st_transformer_s.pt) | [21.9](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_ro_st_transformer_s.pt) | [15.3](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_ru_st_transformer_s.pt) | (<-Download) | +| Multilingual | s2t_transformer_m | 76M | 24.5 | 28.6 | 28.2 | 34.9 | 24.6 | 31.1 | 23.8 | 16.0 | [Download](https://dl.fbaipublicfiles.com/fairseq/s2t/mustc_multilingual_st_transformer_m.pt) | + +[[Back]](..) diff --git a/examples/speech_to_text/docs/simulst_mustc_example.md b/examples/speech_to_text/docs/simulst_mustc_example.md new file mode 100644 index 0000000000..f3b5a413a2 --- /dev/null +++ b/examples/speech_to_text/docs/simulst_mustc_example.md @@ -0,0 +1,190 @@ +# Simultaneous Speech Translation (SimulST) on MuST-C + +This is a tutorial of training and evaluating a transformer *wait-k* simultaneous model on MUST-C English-Germen Dataset, from [SimulMT to SimulST: Adapting Simultaneous Text Translation to End-to-End Simultaneous Speech Translation](https://www.aclweb.org/anthology/2020.aacl-main.58.pdf). + +[MuST-C](https://www.aclweb.org/anthology/N19-1202) is multilingual speech-to-text translation corpus with 8-language translations on English TED talks. + +## Data Preparation +This section introduces the data preparation for training and evaluation. +If you only want to evaluate the model, please jump to [Inference & Evaluation](#inference--evaluation) + +[Download](https://ict.fbk.eu/must-c) and unpack MuST-C data to a path +`${MUSTC_ROOT}/en-${TARGET_LANG_ID}`, then preprocess it with +```bash +# Additional Python packages for S2T data processing/model training +pip install pandas torchaudio sentencepiece + +# Generate TSV manifests, features, vocabulary, +# global cepstral and mean estimation, +# and configuration for each language +cd fairseq + +python examples/speech_to_text/prep_mustc_data.py \ + --data-root ${MUSTC_ROOT} --task asr \ + --vocab-type unigram --vocab-size 10000 \ + --cmvn-type global + +python examples/speech_to_text/prep_mustc_data.py \ + --data-root ${MUSTC_ROOT} --task st \ + --vocab-type unigram --vocab-size 10000 \ + --cmvn-type global +``` + +## ASR Pretraining +We need a pretrained offline ASR model. Assuming the save directory of the ASR model is `${ASR_SAVE_DIR}`. +The following command (and the subsequent training commands in this tutorial) assume training on 1 GPU (you can also train on 8 GPUs and remove the `--update-freq 8` option). +``` +fairseq-train ${MUSTC_ROOT}/en-de \ + --config-yaml config_asr.yaml --train-subset train_asr --valid-subset dev_asr \ + --save-dir ${ASR_SAVE_DIR} --num-workers 4 --max-tokens 40000 --max-update 100000 \ + --task speech_to_text --criterion label_smoothed_cross_entropy --report-accuracy \ + --arch convtransformer_espnet --optimizer adam --lr 0.0005 --lr-scheduler inverse_sqrt \ + --warmup-updates 10000 --clip-norm 10.0 --seed 1 --update-freq 8 +``` +A pretrained ASR checkpoint can be downloaded [here](https://dl.fbaipublicfiles.com/simultaneous_translation/must_c_v1_en_de_pretrained_asr) + +## Simultaneous Speech Translation Training + +### Wait-K with fixed pre-decision module +Fixed pre-decision indicates that the model operate simultaneous policy on the boundaries of fixed chunks. +Here is a example of fixed pre-decision ratio 7 (the simultaneous decision is made every 7 encoder states) and +a wait-3 policy model. Assuming the save directory is `${ST_SAVE_DIR}` +```bash + fairseq-train ${MUSTC_ROOT}/en-de \ + --config-yaml config_st.yaml --train-subset train_st --valid-subset dev_st \ + --save-dir ${ST_SAVE_DIR} --num-workers 8 \ + --optimizer adam --lr 0.0001 --lr-scheduler inverse_sqrt --clip-norm 10.0 \ + --criterion label_smoothed_cross_entropy \ + --warmup-updates 4000 --max-update 100000 --max-tokens 40000 --seed 2 \ + --load-pretrained-encoder-from ${ASR_SAVE_DIR}/checkpoint_best.pt \ + --task speech_to_text \ + --arch convtransformer_simul_trans_espnet \ + --simul-type waitk_fixed_pre_decision \ + --waitk-lagging 3 \ + --fixed-pre-decision-ratio 7 \ + --update-freq 8 + +``` +### Monotonic multihead attention with fixed pre-decision module +``` + fairseq-train ${MUSTC_ROOT}/en-de \ + --config-yaml config_st.yaml --train-subset train_st --valid-subset dev_st \ + --save-dir ${ST_SAVE_DIR} --num-workers 8 \ + --optimizer adam --lr 0.0001 --lr-scheduler inverse_sqrt --clip-norm 10.0 \ + --warmup-updates 4000 --max-update 100000 --max-tokens 40000 --seed 2 \ + --load-pretrained-encoder-from ${ASR_SAVE_DIR}/${CHECKPOINT_FILENAME} \ + --task speech_to_text \ + --criterion latency_augmented_label_smoothed_cross_entropy \ + --latency-weight-avg 0.1 \ + --arch convtransformer_simul_trans_espnet \ + --simul-type infinite_lookback_fixed_pre_decision \ + --fixed-pre-decision-ratio 7 \ + --update-freq 8 +``` +## Inference & Evaluation +[SimulEval](https://github.com/facebookresearch/SimulEval) is used for evaluation. +The following command is for evaluation. + +``` +git clone https://github.com/facebookresearch/SimulEval.git +cd SimulEval +pip install -e . + +simuleval \ + --agent ${FAIRSEQ}/examples/speech_to_text/simultaneous_translation/agents/fairseq_simul_st_agent.py + --source ${SRC_LIST_OF_AUDIO} + --target ${TGT_FILE} + --data-bin ${MUSTC_ROOT}/en-de \ + --config config_st.yaml \ + --model-path ${ST_SAVE_DIR}/${CHECKPOINT_FILENAME} \ + --output ${OUTPUT} \ + --scores +``` + +The source file `${SRC_LIST_OF_AUDIO}` is a list of paths of audio files. Assuming your audio files stored at `/home/user/data`, +it should look like this + +```bash +/home/user/data/audio-1.wav +/home/user/data/audio-2.wav +``` + +Each line of target file `${TGT_FILE}` is the translation for each audio file input. +```bash +Translation_1 +Translation_2 +``` +The evaluation runs on the original MUSTC segmentation. +The following command will generate the wav list and text file for a evaluation set `${SPLIT}` (chose from `dev`, `tst-COMMON` and `tst-HE`) in MUSTC to `${EVAL_DATA}`. +```bash +python ${FAIRSEQ}/examples/speech_to_text/seg_mustc_data.py \ + --data-root ${MUSTC_ROOT} --lang de \ + --split ${SPLIT} --task st \ + --output ${EVAL_DATA} +``` + +The `--data-bin` and `--config` should be the same in previous section if you prepare the data from the scratch. +If only for evaluation, a prepared data directory can be found [here](https://dl.fbaipublicfiles.com/simultaneous_translation/must_c_v1.0_en_de_databin.tgz). It contains +- `spm_unigram10000_st.model`: a sentencepiece model binary. +- `spm_unigram10000_st.txt`: the dictionary file generated by the sentencepiece model. +- `gcmvn.npz`: the binary for global cepstral mean and variance. +- `config_st.yaml`: the config yaml file. It looks like this. +You will need to set the absolute paths for `sentencepiece_model` and `stats_npz_path` if the data directory is downloaded. +```yaml +bpe_tokenizer: + bpe: sentencepiece + sentencepiece_model: ABS_PATH_TO_SENTENCEPIECE_MODEL +global_cmvn: + stats_npz_path: ABS_PATH_TO_GCMVN_FILE +input_channels: 1 +input_feat_per_channel: 80 +sampling_alpha: 1.0 +specaugment: + freq_mask_F: 27 + freq_mask_N: 1 + time_mask_N: 1 + time_mask_T: 100 + time_mask_p: 1.0 + time_wrap_W: 0 +transforms: + '*': + - global_cmvn + _train: + - global_cmvn + - specaugment +vocab_filename: spm_unigram10000_st.txt +``` + +Notice that once a `--data-bin` is set, the `--config` is the base name of the config yaml, not the full path. + +Set `--model-path` to the model checkpoint. +A pretrained checkpoint can be downloaded from [here](https://dl.fbaipublicfiles.com/simultaneous_translation/convtransformer_wait5_pre7), which is a wait-5 model with a pre-decision of 280 ms. + +The result of this model on `tst-COMMON` is: +```bash +{ + "Quality": { + "BLEU": 13.94974229366959 + }, + "Latency": { + "AL": 1751.8031870037803, + "AL_CA": 2338.5911762796536, + "AP": 0.7931395378788959, + "AP_CA": 0.9405103863210942, + "DAL": 1987.7811616943081, + "DAL_CA": 2425.2751560926167 + } +} +``` + +If `--output ${OUTPUT}` option is used, the detailed log and scores will be stored under the `${OUTPUT}` directory. + + +The quality is measured by detokenized BLEU. So make sure that the predicted words sent to the server are detokenized. + +The latency metrics are +* Average Proportion +* Average Lagging +* Differentiable Average Lagging + +Again they will also be evaluated on detokenized text. diff --git a/examples/speech_to_text/prep_covost_data.py b/examples/speech_to_text/prep_covost_data.py new file mode 100644 index 0000000000..411e9b5515 --- /dev/null +++ b/examples/speech_to_text/prep_covost_data.py @@ -0,0 +1,279 @@ +#!/usr/bin/env python3 +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import logging +from pathlib import Path +import shutil +from tempfile import NamedTemporaryFile +from typing import Optional, Tuple + +import pandas as pd +import torchaudio +from examples.speech_to_text.data_utils import ( + create_zip, + extract_fbank_features, + filter_manifest_df, + gen_config_yaml, + gen_vocab, + get_zip_manifest, + load_df_from_tsv, + save_df_to_tsv, +) +from torch import Tensor +from torch.utils.data import Dataset +from torchaudio.datasets.utils import download_url, extract_archive +from tqdm import tqdm + + +log = logging.getLogger(__name__) + + +MANIFEST_COLUMNS = ["id", "audio", "n_frames", "tgt_text", "speaker"] + + +class CoVoST(Dataset): + """Create a Dataset for CoVoST (https://github.com/facebookresearch/covost). + + Args: + root (str): root path to the dataset and generated manifests/features + source_language (str): source (audio) language + target_language (str, optional): target (text) language, + None for no translation (default: None) + version (int, optional): CoVoST version. (default: 2) + download (bool, optional): Whether to download the dataset if it is not + found at root path. (default: ``False``). + """ + + COVOST_URL_TEMPLATE = ( + "https://dl.fbaipublicfiles.com/covost/" + "covost_v2.{src_lang}_{tgt_lang}.tsv.tar.gz" + ) + + VERSIONS = {2} + SPLITS = ["train", "dev", "test"] + + XX_EN_LANGUAGES = { + 1: ["fr", "de", "nl", "ru", "es", "it", "tr", "fa", "sv-SE", "mn", "zh-CN"], + 2: [ + "fr", + "de", + "es", + "ca", + "it", + "ru", + "zh-CN", + "pt", + "fa", + "et", + "mn", + "nl", + "tr", + "ar", + "sv-SE", + "lv", + "sl", + "ta", + "ja", + "id", + "cy", + ], + } + EN_XX_LANGUAGES = { + 1: [], + 2: [ + "de", + "tr", + "fa", + "sv-SE", + "mn", + "zh-CN", + "cy", + "ca", + "sl", + "et", + "id", + "ar", + "ta", + "lv", + "ja", + ], + } + + def __init__( + self, + root: str, + split: str, + source_language: str, + target_language: Optional[str] = None, + version: int = 2, + ) -> None: + assert version in self.VERSIONS and split in self.SPLITS + assert source_language is not None + self.no_translation = target_language is None + if not self.no_translation: + assert "en" in {source_language, target_language} + if source_language == "en": + assert target_language in self.EN_XX_LANGUAGES[version] + else: + assert source_language in self.XX_EN_LANGUAGES[version] + else: + # Hack here so that we can get "split" column from CoVoST TSV. + # Note that we use CoVoST train split for ASR which is an extension + # to Common Voice train split. + target_language = "de" if source_language == "en" else "en" + + self.root: Path = Path(root) + + cv_tsv_path = self.root / "validated.tsv" + assert cv_tsv_path.is_file() + + covost_url = self.COVOST_URL_TEMPLATE.format( + src_lang=source_language, tgt_lang=target_language + ) + covost_archive = self.root / Path(covost_url).name + if not covost_archive.is_file(): + download_url(covost_url, self.root.as_posix(), hash_value=None) + extract_archive(covost_archive.as_posix()) + + cv_tsv = load_df_from_tsv(cv_tsv_path) + covost_tsv = load_df_from_tsv( + self.root / Path(covost_url).name.replace(".tar.gz", "") + ) + df = pd.merge( + left=cv_tsv[["path", "sentence", "client_id"]], + right=covost_tsv[["path", "translation", "split"]], + how="inner", + on="path", + ) + if split == "train": + df = df[(df["split"] == split) | (df["split"] == f"{split}_covost")] + else: + df = df[df["split"] == split] + data = df.to_dict(orient="index").items() + data = [v for k, v in sorted(data, key=lambda x: x[0])] + self.data = [] + for e in data: + try: + path = self.root / "clips" / e["path"] + _ = torchaudio.info(path.as_posix()) + self.data.append(e) + except RuntimeError: + pass + + def __getitem__( + self, n: int + ) -> Tuple[Tensor, int, str, str, Optional[str], str, str]: + """Load the n-th sample from the dataset. + + Args: + n (int): The index of the sample to be loaded + + Returns: + tuple: ``(waveform, sample_rate, sentence, translation, speaker_id, + sample_id)`` + """ + data = self.data[n] + path = self.root / "clips" / data["path"] + waveform, sample_rate = torchaudio.load(path) + sentence = data["sentence"] + translation = None if self.no_translation else data["translation"] + speaker_id = data["client_id"] + _id = data["path"].replace(".mp3", "") + return waveform, sample_rate, sentence, translation, speaker_id, _id + + def __len__(self) -> int: + return len(self.data) + + +def process(args): + root = Path(args.data_root).absolute() / args.src_lang + if not root.is_dir(): + raise NotADirectoryError(f"{root} does not exist") + # Extract features + feature_root = root / "fbank80" + feature_root.mkdir(exist_ok=True) + for split in CoVoST.SPLITS: + print(f"Fetching split {split}...") + dataset = CoVoST(root, split, args.src_lang, args.tgt_lang) + print("Extracting log mel filter bank features...") + for waveform, sample_rate, _, _, _, utt_id in tqdm(dataset): + extract_fbank_features( + waveform, sample_rate, feature_root / f"{utt_id}.npy" + ) + # Pack features into ZIP + zip_path = root / "fbank80.zip" + print("ZIPing features...") + create_zip(feature_root, zip_path) + print("Fetching ZIP manifest...") + audio_paths, audio_lengths = get_zip_manifest(zip_path) + # Generate TSV manifest + print("Generating manifest...") + train_text = [] + task = f"asr_{args.src_lang}" + if args.tgt_lang is not None: + task = f"st_{args.src_lang}_{args.tgt_lang}" + for split in CoVoST.SPLITS: + manifest = {c: [] for c in MANIFEST_COLUMNS} + dataset = CoVoST(root, split, args.src_lang, args.tgt_lang) + for _, _, src_utt, tgt_utt, speaker_id, utt_id in tqdm(dataset): + manifest["id"].append(utt_id) + manifest["audio"].append(audio_paths[utt_id]) + manifest["n_frames"].append(audio_lengths[utt_id]) + manifest["tgt_text"].append(src_utt if args.tgt_lang is None else tgt_utt) + manifest["speaker"].append(speaker_id) + is_train_split = split.startswith("train") + if is_train_split: + train_text.extend(manifest["tgt_text"]) + df = pd.DataFrame.from_dict(manifest) + df = filter_manifest_df(df, is_train_split=is_train_split) + save_df_to_tsv(df, root / f"{split}_{task}.tsv") + # Generate vocab + vocab_size_str = "" if args.vocab_type == "char" else str(args.vocab_size) + spm_filename_prefix = f"spm_{args.vocab_type}{vocab_size_str}_{task}" + with NamedTemporaryFile(mode="w") as f: + for t in train_text: + f.write(t + "\n") + gen_vocab( + Path(f.name), + root / spm_filename_prefix, + args.vocab_type, + args.vocab_size + ) + # Generate config YAML + gen_config_yaml( + root, + spm_filename=spm_filename_prefix + ".model", + yaml_filename=f"config_{task}.yaml", + specaugment_policy="lb", + ) + # Clean up + shutil.rmtree(feature_root) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--data-root", "-d", required=True, type=str, + help="data root with sub-folders for each language <root>/<src_lang>" + ) + parser.add_argument( + "--vocab-type", + default="unigram", + required=True, + type=str, + choices=["bpe", "unigram", "char"], + ), + parser.add_argument("--vocab-size", default=1000, type=int) + parser.add_argument("--src-lang", "-s", required=True, type=str) + parser.add_argument("--tgt-lang", "-t", type=str) + args = parser.parse_args() + + process(args) + + +if __name__ == "__main__": + main() diff --git a/examples/speech_to_text/prep_librispeech_data.py b/examples/speech_to_text/prep_librispeech_data.py new file mode 100644 index 0000000000..f379fa7bf1 --- /dev/null +++ b/examples/speech_to_text/prep_librispeech_data.py @@ -0,0 +1,119 @@ +#!/usr/bin/env python3 +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import logging +from pathlib import Path +import shutil +from tempfile import NamedTemporaryFile + +import pandas as pd +from examples.speech_to_text.data_utils import ( + create_zip, + extract_fbank_features, + gen_config_yaml, + gen_vocab, + get_zip_manifest, + save_df_to_tsv, +) +from torchaudio.datasets import LIBRISPEECH +from tqdm import tqdm + + +log = logging.getLogger(__name__) + +SPLITS = [ + "train-clean-100", + "train-clean-360", + "train-other-500", + "dev-clean", + "dev-other", + "test-clean", + "test-other", +] + +MANIFEST_COLUMNS = ["id", "audio", "n_frames", "tgt_text", "speaker"] + + +def process(args): + out_root = Path(args.output_root).absolute() + out_root.mkdir(exist_ok=True) + # Extract features + feature_root = out_root / "fbank80" + feature_root.mkdir(exist_ok=True) + for split in SPLITS: + print(f"Fetching split {split}...") + dataset = LIBRISPEECH(out_root.as_posix(), url=split, download=True) + print("Extracting log mel filter bank features...") + for wav, sample_rate, _, spk_id, chapter_no, utt_no in tqdm(dataset): + sample_id = f"{spk_id}-{chapter_no}-{utt_no}" + extract_fbank_features( + wav, sample_rate, feature_root / f"{sample_id}.npy" + ) + # Pack features into ZIP + zip_path = out_root / "fbank80.zip" + print("ZIPing features...") + create_zip(feature_root, zip_path) + print("Fetching ZIP manifest...") + audio_paths, audio_lengths = get_zip_manifest(zip_path) + # Generate TSV manifest + print("Generating manifest...") + train_text = [] + for split in SPLITS: + manifest = {c: [] for c in MANIFEST_COLUMNS} + dataset = LIBRISPEECH(out_root.as_posix(), url=split) + for _, _, utt, spk_id, chapter_no, utt_no in tqdm(dataset): + sample_id = f"{spk_id}-{chapter_no}-{utt_no}" + manifest["id"].append(sample_id) + manifest["audio"].append(audio_paths[sample_id]) + manifest["n_frames"].append(audio_lengths[sample_id]) + manifest["tgt_text"].append(utt.lower()) + manifest["speaker"].append(spk_id) + save_df_to_tsv( + pd.DataFrame.from_dict(manifest), out_root / f"{split}.tsv" + ) + if split.startswith("train"): + train_text.extend(manifest["tgt_text"]) + # Generate vocab + vocab_size = "" if args.vocab_type == "char" else str(args.vocab_size) + spm_filename_prefix = f"spm_{args.vocab_type}{vocab_size}" + with NamedTemporaryFile(mode="w") as f: + for t in train_text: + f.write(t + "\n") + gen_vocab( + Path(f.name), + out_root / spm_filename_prefix, + args.vocab_type, + args.vocab_size, + ) + # Generate config YAML + gen_config_yaml( + out_root, + spm_filename=spm_filename_prefix + ".model", + specaugment_policy="ld" + ) + # Clean up + shutil.rmtree(feature_root) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--output-root", "-o", required=True, type=str) + parser.add_argument( + "--vocab-type", + default="unigram", + required=True, + type=str, + choices=["bpe", "unigram", "char"], + ), + parser.add_argument("--vocab-size", default=10000, type=int) + args = parser.parse_args() + + process(args) + + +if __name__ == "__main__": + main() diff --git a/examples/speech_to_text/prep_mtedx_data.py b/examples/speech_to_text/prep_mtedx_data.py new file mode 100644 index 0000000000..2dfd631763 --- /dev/null +++ b/examples/speech_to_text/prep_mtedx_data.py @@ -0,0 +1,271 @@ +#!/usr/bin/env python3 +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import logging +import os +from pathlib import Path +import shutil +from itertools import groupby +from tempfile import NamedTemporaryFile +from typing import Tuple + +import pandas as pd +import soundfile as sf +from examples.speech_to_text.data_utils import ( + create_zip, + extract_fbank_features, + filter_manifest_df, + gen_config_yaml, + gen_vocab, + get_zip_manifest, + load_df_from_tsv, + save_df_to_tsv, +) +import torch +from torch.utils.data import Dataset +from tqdm import tqdm + +from fairseq.data.audio.audio_utils import get_waveform, convert_waveform + + +log = logging.getLogger(__name__) + + +MANIFEST_COLUMNS = [ + "id", "audio", "n_frames", "tgt_text", "speaker", "tgt_lang" +] + + +class mTEDx(Dataset): + """ + Create a Dataset for Multilingual TEDx. + Each item is a tuple of the form: waveform, sample_rate, source utterance, + target utterance, speaker_id, utterance_id + """ + + SPLITS = ["train", "valid", "test"] + LANGPAIRS = ["es-es", "fr-fr", "pt-pt", "it-it", "ru-ru", "el-el", "ar-ar", + "de-de", "es-en", "es-fr", "es-pt", "es-it", "fr-en", "fr-es", + "fr-pt", "pt-en", "pt-es", "it-en", "it-es", "ru-en", "el-en"] + + def __init__(self, root: str, lang: str, split: str) -> None: + assert split in self.SPLITS and lang in self.LANGPAIRS + _root = Path(root) / f"{lang}" / "data" / split + wav_root, txt_root = _root / "wav", _root / "txt" + assert _root.is_dir() and wav_root.is_dir() and txt_root.is_dir() + # Load audio segments + try: + import yaml + except ImportError: + print( + "Please install PyYAML to load the Multilingual TEDx YAML files" + ) + with open(txt_root / f"{split}.yaml") as f: + segments = yaml.load(f, Loader=yaml.BaseLoader) + # Load source and target utterances + src, tgt = lang.split("-") + for _lang in [src, tgt]: + with open(txt_root / f"{split}.{_lang}") as f: + utterances = [r.strip() for r in f] + assert len(segments) == len(utterances) + for i, u in enumerate(utterances): + segments[i][_lang] = u + # Gather info + self.data = [] + for wav_filename, _seg_group in groupby(segments, lambda x: x["wav"]): + wav_filename = wav_filename.replace(".wav", ".flac") + wav_path = wav_root / wav_filename + sample_rate = sf.info(wav_path.as_posix()).samplerate + seg_group = sorted(_seg_group, key=lambda x: float(x["offset"])) + for i, segment in enumerate(seg_group): + offset = int(float(segment["offset"]) * sample_rate) + n_frames = int(float(segment["duration"]) * sample_rate) + _id = f"{wav_path.stem}_{i}" + self.data.append( + ( + wav_path.as_posix(), + offset, + n_frames, + sample_rate, + segment[src], + segment[tgt], + segment["speaker_id"], + tgt, + _id, + ) + ) + + def __getitem__( + self, n: int + ) -> Tuple[torch.Tensor, int, str, str, str, str, str]: + wav_path, offset, n_frames, sr, src_utt, tgt_utt, spk_id, tgt_lang, \ + utt_id = self.data[n] + waveform, _ = get_waveform(wav_path, frames=n_frames, start=offset) + waveform = torch.from_numpy(waveform) + return waveform, sr, src_utt, tgt_utt, spk_id, tgt_lang, utt_id + + def __len__(self) -> int: + return len(self.data) + + +def process(args): + root = Path(args.data_root).absolute() + for lang in mTEDx.LANGPAIRS: + cur_root = root / f"{lang}" + if not cur_root.is_dir(): + print(f"{cur_root.as_posix()} does not exist. Skipped.") + continue + # Extract features + audio_root = cur_root / ("flac" if args.use_audio_input else "fbank80") + audio_root.mkdir(exist_ok=True) + for split in mTEDx.SPLITS: + print(f"Fetching split {split}...") + dataset = mTEDx(root.as_posix(), lang, split) + if args.use_audio_input: + print("Converting audios...") + for waveform, sample_rate, _, _, _, utt_id in tqdm(dataset): + tgt_sample_rate = 16_000 + _wavform, _ = convert_waveform( + waveform, sample_rate, to_mono=True, + to_sample_rate=tgt_sample_rate + ) + sf.write( + (audio_root / f"{utt_id}.flac").as_posix(), + _wavform.numpy(), tgt_sample_rate + ) + else: + print("Extracting log mel filter bank features...") + for waveform, sample_rate, _, _, _, _, utt_id in tqdm(dataset): + extract_fbank_features( + waveform, sample_rate, audio_root / f"{utt_id}.npy" + ) + # Pack features into ZIP + zip_path = cur_root / f"{audio_root.name}.zip" + print("ZIPing audios/features...") + create_zip(audio_root, zip_path) + print("Fetching ZIP manifest...") + audio_paths, audio_lengths = get_zip_manifest(zip_path) + # Generate TSV manifest + print("Generating manifest...") + train_text = [] + for split in mTEDx.SPLITS: + is_train_split = split.startswith("train") + manifest = {c: [] for c in MANIFEST_COLUMNS} + ds = mTEDx(args.data_root, lang, split) + for _, _, src_utt, tgt_utt, spk_id, tgt_lang, utt_id in tqdm(ds): + manifest["id"].append(utt_id) + manifest["audio"].append(audio_paths[utt_id]) + manifest["n_frames"].append(audio_lengths[utt_id]) + manifest["tgt_text"].append( + src_utt if args.task == "asr" else tgt_utt + ) + manifest["speaker"].append(spk_id) + manifest["tgt_lang"].append(tgt_lang) + if is_train_split: + train_text.extend(manifest["tgt_text"]) + df = pd.DataFrame.from_dict(manifest) + df = filter_manifest_df(df, is_train_split=is_train_split) + save_df_to_tsv(df, cur_root / f"{split}_{args.task}.tsv") + # Generate vocab + v_size_str = "" if args.vocab_type == "char" else str(args.vocab_size) + spm_filename_prefix = f"spm_{args.vocab_type}{v_size_str}_{args.task}" + with NamedTemporaryFile(mode="w") as f: + for t in train_text: + f.write(t + "\n") + gen_vocab( + Path(f.name), + cur_root / spm_filename_prefix, + args.vocab_type, + args.vocab_size, + ) + # Generate config YAML + if args.use_audio_input: + gen_config_yaml( + cur_root, + spm_filename=spm_filename_prefix + ".model", + yaml_filename=f"config_{args.task}.yaml", + specaugment_policy=None, + extra={"use_audio_input": True} + ) + else: + gen_config_yaml( + cur_root, + spm_filename=spm_filename_prefix + ".model", + yaml_filename=f"config_{args.task}.yaml", + specaugment_policy="lb", + ) + # Clean up + shutil.rmtree(audio_root) + + +def process_joint(args): + cur_root = Path(args.data_root) + assert all((cur_root / f"{lang}").is_dir() for lang in mTEDx.LANGPAIRS), \ + "do not have downloaded data available for all languages" + # Generate vocab + vocab_size_str = "" if args.vocab_type == "char" else str(args.vocab_size) + spm_filename_prefix = f"spm_{args.vocab_type}{vocab_size_str}_{args.task}" + with NamedTemporaryFile(mode="w") as f: + for lang in mTEDx.LANGPAIRS: + tsv_path = cur_root / f"{lang}" / f"train_{args.task}.tsv" + df = load_df_from_tsv(tsv_path) + for t in df["tgt_text"]: + f.write(t + "\n") + special_symbols = None + if args.joint: + # Add tgt_lang tags to dict + special_symbols = list( + {f'<lang:{lang.split("-")[1]}>' for lang in mTEDx.LANGPAIRS} + ) + gen_vocab( + Path(f.name), + cur_root / spm_filename_prefix, + args.vocab_type, + args.vocab_size, + special_symbols=special_symbols + ) + # Generate config YAML + gen_config_yaml( + cur_root, + spm_filename=spm_filename_prefix + ".model", + yaml_filename=f"config_{args.task}.yaml", + specaugment_policy="ld", + prepend_tgt_lang_tag=(args.joint), + ) + # Make symbolic links to manifests + for lang in mTEDx.LANGPAIRS: + for split in mTEDx.SPLITS: + src_path = cur_root / f"{lang}" / f"{split}_{args.task}.tsv" + desc_path = cur_root / f"{split}_{lang}_{args.task}.tsv" + if not desc_path.is_symlink(): + os.symlink(src_path, desc_path) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--data-root", "-d", required=True, type=str) + parser.add_argument( + "--vocab-type", + default="unigram", + required=True, + type=str, + choices=["bpe", "unigram", "char"], + ), + parser.add_argument("--vocab-size", default=8000, type=int) + parser.add_argument("--task", type=str, choices=["asr", "st"]) + parser.add_argument("--joint", action="store_true", help="") + parser.add_argument("--use-audio-input", action="store_true") + args = parser.parse_args() + + if args.joint: + process_joint(args) + else: + process(args) + + +if __name__ == "__main__": + main() diff --git a/examples/speech_to_text/prep_mustc_data.py b/examples/speech_to_text/prep_mustc_data.py new file mode 100644 index 0000000000..3f0d3fcbd9 --- /dev/null +++ b/examples/speech_to_text/prep_mustc_data.py @@ -0,0 +1,291 @@ +#!/usr/bin/env python3 +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import logging +import os +from pathlib import Path +import shutil +from itertools import groupby +from tempfile import NamedTemporaryFile +from typing import Tuple + +import numpy as np +import pandas as pd +import soundfile as sf +from examples.speech_to_text.data_utils import ( + create_zip, + extract_fbank_features, + filter_manifest_df, + gen_config_yaml, + gen_vocab, + get_zip_manifest, + load_df_from_tsv, + save_df_to_tsv, + cal_gcmvn_stats, +) +import torch +from torch.utils.data import Dataset +from tqdm import tqdm + +from fairseq.data.audio.audio_utils import get_waveform, convert_waveform + + +log = logging.getLogger(__name__) + + +MANIFEST_COLUMNS = ["id", "audio", "n_frames", "tgt_text", "speaker"] + + +class MUSTC(Dataset): + """ + Create a Dataset for MuST-C. Each item is a tuple of the form: + waveform, sample_rate, source utterance, target utterance, speaker_id, + utterance_id + """ + + SPLITS = ["train", "dev", "tst-COMMON", "tst-HE"] + LANGUAGES = ["de", "es", "fr", "it", "nl", "pt", "ro", "ru"] + + def __init__(self, root: str, lang: str, split: str) -> None: + assert split in self.SPLITS and lang in self.LANGUAGES + _root = Path(root) / f"en-{lang}" / "data" / split + wav_root, txt_root = _root / "wav", _root / "txt" + assert _root.is_dir() and wav_root.is_dir() and txt_root.is_dir() + # Load audio segments + try: + import yaml + except ImportError: + print("Please install PyYAML to load the MuST-C YAML files") + with open(txt_root / f"{split}.yaml") as f: + segments = yaml.load(f, Loader=yaml.BaseLoader) + # Load source and target utterances + for _lang in ["en", lang]: + with open(txt_root / f"{split}.{_lang}") as f: + utterances = [r.strip() for r in f] + assert len(segments) == len(utterances) + for i, u in enumerate(utterances): + segments[i][_lang] = u + # Gather info + self.data = [] + for wav_filename, _seg_group in groupby(segments, lambda x: x["wav"]): + wav_path = wav_root / wav_filename + sample_rate = sf.info(wav_path.as_posix()).samplerate + seg_group = sorted(_seg_group, key=lambda x: x["offset"]) + for i, segment in enumerate(seg_group): + offset = int(float(segment["offset"]) * sample_rate) + n_frames = int(float(segment["duration"]) * sample_rate) + _id = f"{wav_path.stem}_{i}" + self.data.append( + ( + wav_path.as_posix(), + offset, + n_frames, + sample_rate, + segment["en"], + segment[lang], + segment["speaker_id"], + _id, + ) + ) + + def __getitem__( + self, n: int + ) -> Tuple[torch.Tensor, int, str, str, str, str]: + wav_path, offset, n_frames, sr, src_utt, tgt_utt, spk_id, \ + utt_id = self.data[n] + waveform, _ = get_waveform(wav_path, frames=n_frames, start=offset) + waveform = torch.from_numpy(waveform) + return waveform, sr, src_utt, tgt_utt, spk_id, utt_id + + def __len__(self) -> int: + return len(self.data) + + +def process(args): + root = Path(args.data_root).absolute() + for lang in MUSTC.LANGUAGES: + cur_root = root / f"en-{lang}" + if not cur_root.is_dir(): + print(f"{cur_root.as_posix()} does not exist. Skipped.") + continue + # Extract features + audio_root = cur_root / ("flac" if args.use_audio_input else "fbank80") + audio_root.mkdir(exist_ok=True) + + for split in MUSTC.SPLITS: + print(f"Fetching split {split}...") + dataset = MUSTC(root.as_posix(), lang, split) + if args.use_audio_input: + print("Converting audios...") + for waveform, sample_rate, _, _, _, utt_id in tqdm(dataset): + tgt_sample_rate = 16_000 + _wavform, _ = convert_waveform( + waveform, sample_rate, to_mono=True, + to_sample_rate=tgt_sample_rate + ) + sf.write( + (audio_root / f"{utt_id}.flac").as_posix(), + _wavform.numpy(), tgt_sample_rate + ) + else: + print("Extracting log mel filter bank features...") + gcmvn_feature_list = [] + if split == 'train' and args.cmvn_type == "global": + print("And estimating cepstral mean and variance stats...") + + for waveform, sample_rate, _, _, _, utt_id in tqdm(dataset): + features = extract_fbank_features( + waveform, sample_rate, audio_root / f"{utt_id}.npy" + ) + if split == 'train' and args.cmvn_type == "global": + if len(gcmvn_feature_list) < args.gcmvn_max_num: + gcmvn_feature_list.append(features) + + if split == 'train' and args.cmvn_type == "global": + # Estimate and save cmv + stats = cal_gcmvn_stats(gcmvn_feature_list) + with open(cur_root / "gcmvn.npz", "wb") as f: + np.savez(f, mean=stats["mean"], std=stats["std"]) + + # Pack features into ZIP + zip_path = cur_root / f"{audio_root.name}.zip" + print("ZIPing audios/features...") + create_zip(audio_root, zip_path) + print("Fetching ZIP manifest...") + audio_paths, audio_lengths = get_zip_manifest(zip_path) + # Generate TSV manifest + print("Generating manifest...") + train_text = [] + for split in MUSTC.SPLITS: + is_train_split = split.startswith("train") + manifest = {c: [] for c in MANIFEST_COLUMNS} + dataset = MUSTC(args.data_root, lang, split) + for _, _, src_utt, tgt_utt, speaker_id, utt_id in tqdm(dataset): + manifest["id"].append(utt_id) + manifest["audio"].append(audio_paths[utt_id]) + manifest["n_frames"].append(audio_lengths[utt_id]) + manifest["tgt_text"].append( + src_utt if args.task == "asr" else tgt_utt + ) + manifest["speaker"].append(speaker_id) + if is_train_split: + train_text.extend(manifest["tgt_text"]) + df = pd.DataFrame.from_dict(manifest) + df = filter_manifest_df(df, is_train_split=is_train_split) + save_df_to_tsv(df, cur_root / f"{split}_{args.task}.tsv") + # Generate vocab + v_size_str = "" if args.vocab_type == "char" else str(args.vocab_size) + spm_filename_prefix = f"spm_{args.vocab_type}{v_size_str}_{args.task}" + with NamedTemporaryFile(mode="w") as f: + for t in train_text: + f.write(t + "\n") + gen_vocab( + Path(f.name), + cur_root / spm_filename_prefix, + args.vocab_type, + args.vocab_size, + ) + # Generate config YAML + if args.use_audio_input: + gen_config_yaml( + cur_root, + spm_filename=spm_filename_prefix + ".model", + yaml_filename=f"config_{args.task}.yaml", + specaugment_policy=None, + extra={"use_audio_input": True} + ) + else: + gen_config_yaml( + cur_root, + spm_filename=spm_filename_prefix + ".model", + yaml_filename=f"config_{args.task}.yaml", + specaugment_policy="lb", + cmvn_type=args.cmvn_type, + gcmvn_path=( + cur_root / "gcmvn.npz" if args.cmvn_type == "global" + else None + ), + ) + # Clean up + shutil.rmtree(audio_root) + + +def process_joint(args): + cur_root = Path(args.data_root) + assert all( + (cur_root / f"en-{lang}").is_dir() for lang in MUSTC.LANGUAGES + ), "do not have downloaded data available for all 8 languages" + # Generate vocab + vocab_size_str = "" if args.vocab_type == "char" else str(args.vocab_size) + spm_filename_prefix = f"spm_{args.vocab_type}{vocab_size_str}_{args.task}" + with NamedTemporaryFile(mode="w") as f: + for lang in MUSTC.LANGUAGES: + tsv_path = cur_root / f"en-{lang}" / f"train_{args.task}.tsv" + df = load_df_from_tsv(tsv_path) + for t in df["tgt_text"]: + f.write(t + "\n") + special_symbols = None + if args.task == 'st': + special_symbols = [f'<lang:{lang}>' for lang in MUSTC.LANGUAGES] + gen_vocab( + Path(f.name), + cur_root / spm_filename_prefix, + args.vocab_type, + args.vocab_size, + special_symbols=special_symbols + ) + # Generate config YAML + gen_config_yaml( + cur_root, + spm_filename=spm_filename_prefix + ".model", + yaml_filename=f"config_{args.task}.yaml", + specaugment_policy="ld", + prepend_tgt_lang_tag=(args.task == "st"), + ) + # Make symbolic links to manifests + for lang in MUSTC.LANGUAGES: + for split in MUSTC.SPLITS: + src_path = cur_root / f"en-{lang}" / f"{split}_{args.task}.tsv" + desc_path = cur_root / f"{split}_{lang}_{args.task}.tsv" + if not desc_path.is_symlink(): + os.symlink(src_path, desc_path) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--data-root", "-d", required=True, type=str) + parser.add_argument( + "--vocab-type", + default="unigram", + required=True, + type=str, + choices=["bpe", "unigram", "char"], + ), + parser.add_argument("--vocab-size", default=8000, type=int) + parser.add_argument("--task", type=str, choices=["asr", "st"]) + parser.add_argument("--joint", action="store_true", help="") + parser.add_argument( + "--cmvn-type", default="utterance", + choices=["global", "utterance"], + help="The type of cepstral mean and variance normalization" + ) + parser.add_argument( + "--gcmvn-max-num", default=150000, type=int, + help="Maximum number of sentences to use to estimate global mean and " + "variance" + ) + parser.add_argument("--use-audio-input", action="store_true") + args = parser.parse_args() + + if args.joint: + process_joint(args) + else: + process(args) + + +if __name__ == "__main__": + main() diff --git a/examples/speech_to_text/seg_mustc_data.py b/examples/speech_to_text/seg_mustc_data.py new file mode 100644 index 0000000000..1ee665d639 --- /dev/null +++ b/examples/speech_to_text/seg_mustc_data.py @@ -0,0 +1,54 @@ +#!/usr/bin/env python3 +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import logging +from pathlib import Path +import soundfile as sf +from examples.speech_to_text.prep_mustc_data import ( + MUSTC +) + +from tqdm import tqdm + +log = logging.getLogger(__name__) + + +def main(args): + root = Path(args.data_root).absolute() + lang = args.lang + split = args.split + + cur_root = root / f"en-{lang}" + assert cur_root.is_dir(), ( + f"{cur_root.as_posix()} does not exist. Skipped." + ) + + dataset = MUSTC(root.as_posix(), lang, split) + output = Path(args.output).absolute() + output.mkdir(exist_ok=True) + f_text = open(output / f"{split}.{lang}", "w") + f_wav_list = open(output / f"{split}.wav_list", "w") + for waveform, sample_rate, _, text, _, utt_id in tqdm(dataset): + sf.write( + output / f"{utt_id}.wav", + waveform.squeeze(0).numpy(), + samplerate=int(sample_rate) + ) + f_text.write(text + "\n") + f_wav_list.write(str(output / f"{utt_id}.wav") + "\n") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--data-root", "-d", required=True, type=str) + parser.add_argument("--task", required=True, type=str, choices=["asr", "st"]) + parser.add_argument("--lang", required=True, type=str) + parser.add_argument("--output", required=True, type=str) + parser.add_argument("--split", required=True, choices=MUSTC.SPLITS) + args = parser.parse_args() + + main(args) diff --git a/examples/speech_to_text/simultaneous_translation/agents/fairseq_simul_st_agent.py b/examples/speech_to_text/simultaneous_translation/agents/fairseq_simul_st_agent.py new file mode 100644 index 0000000000..61617a1739 --- /dev/null +++ b/examples/speech_to_text/simultaneous_translation/agents/fairseq_simul_st_agent.py @@ -0,0 +1,363 @@ +import math +import os +import json +import numpy as np +import torch +import torchaudio.compliance.kaldi as kaldi +import yaml +from fairseq import checkpoint_utils, tasks +from fairseq.file_io import PathManager + +try: + from simuleval import READ_ACTION, WRITE_ACTION, DEFAULT_EOS + from simuleval.agents import SpeechAgent + from simuleval.states import ListEntry, SpeechStates +except ImportError: + print("Please install simuleval 'pip install simuleval'") + +SHIFT_SIZE = 10 +WINDOW_SIZE = 25 +SAMPLE_RATE = 16000 +FEATURE_DIM = 80 +BOW_PREFIX = "\u2581" + + +class OnlineFeatureExtractor: + """ + Extract speech feature on the fly. + """ + + def __init__(self, args): + self.shift_size = args.shift_size + self.window_size = args.window_size + assert self.window_size >= self.shift_size + + self.sample_rate = args.sample_rate + self.feature_dim = args.feature_dim + self.num_samples_per_shift = int(self.shift_size * self.sample_rate / 1000) + self.num_samples_per_window = int(self.window_size * self.sample_rate / 1000) + self.len_ms_to_samples = lambda x: x * self.sample_rate / 1000 + self.previous_residual_samples = [] + self.global_cmvn = args.global_cmvn + + def clear_cache(self): + self.previous_residual_samples = [] + + def __call__(self, new_samples): + samples = self.previous_residual_samples + new_samples + if len(samples) < self.num_samples_per_window: + self.previous_residual_samples = samples + return + + # num_frames is the number of frames from the new segment + num_frames = math.floor( + (len(samples) - self.len_ms_to_samples(self.window_size - self.shift_size)) + / self.num_samples_per_shift + ) + + # the number of frames used for feature extraction + # including some part of thte previous segment + effective_num_samples = int( + num_frames * self.len_ms_to_samples(self.shift_size) + + self.len_ms_to_samples(self.window_size - self.shift_size) + ) + + input_samples = samples[:effective_num_samples] + self.previous_residual_samples = samples[ + num_frames * self.num_samples_per_shift: + ] + + torch.manual_seed(1) + output = kaldi.fbank( + torch.FloatTensor(input_samples).unsqueeze(0), + num_mel_bins=self.feature_dim, + frame_length=self.window_size, + frame_shift=self.shift_size, + ).numpy() + + output = self.transform(output) + + return torch.from_numpy(output) + + def transform(self, input): + if self.global_cmvn is None: + return input + + mean = self.global_cmvn["mean"] + std = self.global_cmvn["std"] + + x = np.subtract(input, mean) + x = np.divide(x, std) + return x + + +class TensorListEntry(ListEntry): + """ + Data structure to store a list of tensor. + """ + + def append(self, value): + + if len(self.value) == 0: + self.value = value + return + + self.value = torch.cat([self.value] + [value], dim=0) + + def info(self): + return { + "type": str(self.new_value_type), + "length": self.__len__(), + "value": "" if type(self.value) is list else self.value.size(), + } + + +class FairseqSimulSTAgent(SpeechAgent): + + speech_segment_size = 40 # in ms, 4 pooling ratio * 10 ms step size + + def __init__(self, args): + super().__init__(args) + + self.eos = DEFAULT_EOS + + self.gpu = getattr(args, "gpu", False) + + self.args = args + + self.load_model_vocab(args) + + if getattr( + self.model.decoder.layers[0].encoder_attn, + 'pre_decision_ratio', + None + ) is not None: + self.speech_segment_size *= ( + self.model.decoder.layers[0].encoder_attn.pre_decision_ratio + ) + + args.global_cmvn = None + if args.config: + with open(os.path.join(args.data_bin, args.config), "r") as f: + config = yaml.load(f, Loader=yaml.BaseLoader) + + if "global_cmvn" in config: + args.global_cmvn = np.load(config["global_cmvn"]["stats_npz_path"]) + + if args.global_stats: + with PathManager.open(args.global_stats, "r") as f: + global_cmvn = json.loads(f.read()) + self.global_cmvn = {"mean": global_cmvn["mean"], "std": global_cmvn["stddev"]} + + self.feature_extractor = OnlineFeatureExtractor(args) + + self.max_len = args.max_len + + self.force_finish = args.force_finish + + torch.set_grad_enabled(False) + + def build_states(self, args, client, sentence_id): + # Initialize states here, for example add customized entry to states + # This function will be called at beginning of every new sentence + states = SpeechStates(args, client, sentence_id, self) + self.initialize_states(states) + return states + + def to_device(self, tensor): + if self.gpu: + return tensor.cuda() + else: + return tensor.cpu() + + @staticmethod + def add_args(parser): + # fmt: off + parser.add_argument('--model-path', type=str, required=True, + help='path to your pretrained model.') + parser.add_argument("--data-bin", type=str, required=True, + help="Path of data binary") + parser.add_argument("--config", type=str, default=None, + help="Path to config yaml file") + parser.add_argument("--global-stats", type=str, default=None, + help="Path to json file containing cmvn stats") + parser.add_argument("--tgt-splitter-type", type=str, default="SentencePiece", + help="Subword splitter type for target text") + parser.add_argument("--tgt-splitter-path", type=str, default=None, + help="Subword splitter model path for target text") + parser.add_argument("--user-dir", type=str, default="examples/simultaneous_translation", + help="User directory for simultaneous translation") + parser.add_argument("--max-len", type=int, default=200, + help="Max length of translation") + parser.add_argument("--force-finish", default=False, action="store_true", + help="Force the model to finish the hypothsis if the source is not finished") + parser.add_argument("--shift-size", type=int, default=SHIFT_SIZE, + help="Shift size of feature extraction window.") + parser.add_argument("--window-size", type=int, default=WINDOW_SIZE, + help="Window size of feature extraction window.") + parser.add_argument("--sample-rate", type=int, default=SAMPLE_RATE, + help="Sample rate") + parser.add_argument("--feature-dim", type=int, default=FEATURE_DIM, + help="Acoustic feature dimension.") + + # fmt: on + return parser + + def load_model_vocab(self, args): + + filename = args.model_path + if not os.path.exists(filename): + raise IOError("Model file not found: {}".format(filename)) + + state = checkpoint_utils.load_checkpoint_to_cpu(filename) + + task_args = state["cfg"]["task"] + task_args.data = args.data_bin + + if args.config is not None: + task_args.config_yaml = args.config + + task = tasks.setup_task(task_args) + + # build model for ensemble + state["cfg"]["model"].load_pretrained_encoder_from = None + state["cfg"]["model"].load_pretrained_decoder_from = None + self.model = task.build_model(state["cfg"]["model"]) + self.model.load_state_dict(state["model"], strict=True) + self.model.eval() + self.model.share_memory() + + if self.gpu: + self.model.cuda() + + # Set dictionary + self.dict = {} + self.dict["tgt"] = task.target_dictionary + + def initialize_states(self, states): + self.feature_extractor.clear_cache() + states.units.source = TensorListEntry() + states.units.target = ListEntry() + states.incremental_states = dict() + + def segment_to_units(self, segment, states): + # Convert speech samples to features + features = self.feature_extractor(segment) + if features is not None: + return [features] + else: + return [] + + def units_to_segment(self, units, states): + # Merge sub word to full word. + if self.model.decoder.dictionary.eos() == units[0]: + return DEFAULT_EOS + + segment = [] + if None in units.value: + units.value.remove(None) + + for index in units: + if index is None: + units.pop() + token = self.model.decoder.dictionary.string([index]) + if token.startswith(BOW_PREFIX): + if len(segment) == 0: + segment += [token.replace(BOW_PREFIX, "")] + else: + for j in range(len(segment)): + units.pop() + + string_to_return = ["".join(segment)] + + if self.model.decoder.dictionary.eos() == units[0]: + string_to_return += [DEFAULT_EOS] + + return string_to_return + else: + segment += [token.replace(BOW_PREFIX, "")] + + if ( + len(units) > 0 + and self.model.decoder.dictionary.eos() == units[-1] + or len(states.units.target) > self.max_len + ): + tokens = [self.model.decoder.dictionary.string([unit]) for unit in units] + return ["".join(tokens).replace(BOW_PREFIX, "")] + [DEFAULT_EOS] + + return None + + def update_model_encoder(self, states): + if len(states.units.source) == 0: + return + src_indices = self.to_device( + states.units.source.value.unsqueeze(0) + ) + src_lengths = self.to_device( + torch.LongTensor([states.units.source.value.size(0)]) + ) + + states.encoder_states = self.model.encoder(src_indices, src_lengths) + torch.cuda.empty_cache() + + def update_states_read(self, states): + # Happens after a read action. + self.update_model_encoder(states) + + def policy(self, states): + if not getattr(states, "encoder_states", None): + return READ_ACTION + + tgt_indices = self.to_device( + torch.LongTensor( + [self.model.decoder.dictionary.eos()] + + [x for x in states.units.target.value if x is not None] + ).unsqueeze(0) + ) + + states.incremental_states["steps"] = { + "src": states.encoder_states["encoder_out"][0].size(0), + "tgt": 1 + len(states.units.target), + } + + states.incremental_states["online"] = {"only": torch.tensor(not states.finish_read())} + + x, outputs = self.model.decoder.forward( + prev_output_tokens=tgt_indices, + encoder_out=states.encoder_states, + incremental_state=states.incremental_states, + ) + + states.decoder_out = x + + states.decoder_out_extra = outputs + + torch.cuda.empty_cache() + + if outputs.action == 0: + return READ_ACTION + else: + return WRITE_ACTION + + def predict(self, states): + decoder_states = states.decoder_out + + lprobs = self.model.get_normalized_probs( + [decoder_states[:, -1:]], log_probs=True + ) + + index = lprobs.argmax(dim=-1) + + index = index[0, 0].item() + + if ( + self.force_finish + and index == self.model.decoder.dictionary.eos() + and not states.finish_read() + ): + # If we want to force finish the translation + # (don't stop before finish reading), return a None + # self.model.decoder.clear_cache(states.incremental_states) + index = None + + return index diff --git a/examples/stories/README.md b/examples/stories/README.md index 28579de355..588941eddc 100644 --- a/examples/stories/README.md +++ b/examples/stories/README.md @@ -44,7 +44,7 @@ fairseq-preprocess --source-lang wp_source --target-lang wp_target \ --destdir data-bin/writingPrompts --padding-factor 1 --thresholdtgt 10 --thresholdsrc 10 # Train the model: -fairseq-train data-bin/writingPrompts -a fconv_self_att_wp --lr 0.25 --clip-norm 0.1 --max-tokens 1500 --lr-scheduler reduce_lr_on_plateau --decoder-attention True --encoder-attention False --criterion label_smoothed_cross_entropy --weight-decay .0000001 --label-smoothing 0 --source-lang wp_source --target-lang wp_target --gated-attention True --self-attention True --project-input True --pretrained False +fairseq-train data-bin/writingPrompts -a fconv_self_att_wp --lr 0.25 --optimizer nag --clip-norm 0.1 --max-tokens 1500 --lr-scheduler reduce_lr_on_plateau --decoder-attention True --encoder-attention False --criterion label_smoothed_cross_entropy --weight-decay .0000001 --label-smoothing 0 --source-lang wp_source --target-lang wp_target --gated-attention True --self-attention True --project-input True --pretrained False # Train a fusion model: # add the arguments: --pretrained True --pretrained-checkpoint path/to/checkpoint diff --git a/examples/textless_nlp/gslm/README.md b/examples/textless_nlp/gslm/README.md new file mode 100644 index 0000000000..7a76ffd57c --- /dev/null +++ b/examples/textless_nlp/gslm/README.md @@ -0,0 +1,21 @@ +# Generative Spoken Language Modeling + +* [Paper](https://arxiv.org/abs/2102.01192) +* [Demo](https://speechbot.github.io/gslm/index.html) + +We build and evaluate generative speech2speech systems using [Log Mel Filtebank](https://pytorch.org/audio/stable/compliance.kaldi.html#fbank), [Modified CPC](https://github.com/facebookresearch/CPC_audio), [HuBERT Base](https://github.com/pytorch/fairseq/tree/main/examples/hubert) and [Wav2Vec 2.0 Large](https://github.com/pytorch/fairseq/tree/main/examples/wav2vec). Our system is composed of three components, namely, *speech2unit*, *ulm* and *unit2speech*. We explain about models and usage of these components in their respective sub-directories. See the links below. + +## Speech to Unit Model (speech2unit) +Speech to unit model is used for quantizing raw speech into learned discrete speech units. [More details](speech2unit) + +## Unit Language Model (ulm) +Unit Language Model is a generative language model trained on discrete speech units. [More details](ulm) + +## Unit to Speech Model (unit2speech) +Unit to speech model is used for synthesizing speech from discrete speech units. [More details](unit2speech) + +## Metrics +We show how to compute ASR based metrics as well as zero-shot metrics proposed in our paper [here](metrics). + +## Tools +We share two tools to resynthesize a given spoken utterance, and generate novel spoken language given a spoken prompt. [More detail](tools) diff --git a/examples/textless_nlp/gslm/metrics/README.md b/examples/textless_nlp/gslm/metrics/README.md new file mode 100644 index 0000000000..0a63e2f0d8 --- /dev/null +++ b/examples/textless_nlp/gslm/metrics/README.md @@ -0,0 +1,10 @@ +# GSLM Metrics + +## ASR Metrics +The suite of metrics here uses an ASR model to transcribe the synthesized speech into text, and then uses text-based metrics. We also use word error rate from ASR transcription itself as one of the metrics. [More details](asr_metrics) + +## ABX Metrics +We use [ABX](https://www.semanticscholar.org/paper/ABX-Discriminability-Measures-and-Applications-Schatz/13d3537228f728c1063cc83743cb118bba3367a0) to evaluate how well-separated phonetic categories are with quantized representations. [More details](abx_metrics) + +## sWUGGY and sBLIMP +We refer to [ZeroSpeech challenge](https://www.zerospeech.com/2021/track_s.html#scoring-based-metrics) for details on the sWUGGY and sBLIMP metrics. diff --git a/examples/textless_nlp/gslm/metrics/abx_metrics/README.md b/examples/textless_nlp/gslm/metrics/abx_metrics/README.md new file mode 100644 index 0000000000..aa2560f045 --- /dev/null +++ b/examples/textless_nlp/gslm/metrics/abx_metrics/README.md @@ -0,0 +1,77 @@ +# ABX-based evaluation + +ABX is used to evaluate the quality of the obtained discrete units. + +The life cycle of the ABX-based evaluation for the Speech-to-Unit contains the following steps: +1. Training an acoustic model (or use an existing acoustic model) ([description](./../..)) +2. Perform quantization of speech by learning a K-means clustering model ([description](./../..)) +3. Compute discrete features for ABX computation using the learned clusters +4. Compute the ABX score over the discrete features taking advantage of [libri-light's ABX evaluation script][ll-abx] + +Here we assume that you already went throught the first two steps and focus solely on extracting features and computing ABX scores. + +## Libri-light setup + +Follow [libri-light's instructions][ll-instructions] for installation and [ABX evaluation setup][ll-abx] (including the download of the data items required for ABX computation). + +## Computing ABX + +### Dumping quantized features + +The first step for the ABX computation is to dump the quantized representations corresponding to the test files. + +```shell +TYPE="hubert" +LAYER=6 +CKPT_PATH="<PATH_TO_HUBERT_MODEL_CHECKPOINT_FILE>" +KM_MODEL_PATH="<PATH_TO_PRETRAINED_KM_MODEL_FILE>" + +SUBSET="dev-clean" +MANIFEST="<PATH_TO_MANIFEST_FOR_LS_DEV-CLEAN>" +DATA_DIR="<PATH_TO_DIR_TO_STORE_FEATURES>/$SUBSET" + +PYTHONPATH=. python examples/textless_nlp/gslm/metrics/abx_metrics/dump_abx_feats.py \ + --feature_type $TYPE \ + --kmeans_model_path $KM_MODEL_PATH \ + --checkpoint_path $CKPT_PATH \ + --layer $LAYER \ + --manifest_path $MANIFEST \ + --out_dir_path $DATA_DIR \ + --extension ".flac" +``` + +Again the manifest file follows the same structure than elsewhere in the codebase. + +### Compute ABX with Libri-light + +Use libri-light's `eval_ABX.py` script (within the appropriate environment set up) as followed: + +```shell +LIBRILIGHT_ROOT="<PATH_TO_LIBRILIGHT>" + +SUBSET="dev-clean" +DATA_DIR="<PATH_TO_DIR_TO_STORE_FEATURES>/$SUBSET" +ITEM_FILE_PATH="$LIBRILIGHT_ROOT/eval/ABX_data/$SUBSET.item" +OUT_DIR="<PATH_TO_DIR_TO_STORE_ABX_SCORES>/$SUBSET" + +FILE_EXTENSION=".npy" +FEATURE_SIZE=0.02 # depends on the model used + +PYTHONPATH=$LIBRILIGHT_ROOT \ + python $LIBRILIGHT_ROOT/eval/eval_ABX.py \ + $DATA_DIR \ + $ITEM_FILE_PATH \ + --file_extension $FILE_EXTENSION \ + --feature_size $FEATURE_SIZE \ + --out $OUT_DIR \ + --mode "all" +``` + +Note that `FEATURE_SIZE` will depend on the model type you are using to extract the acoustic features: +* For HuBERT and Wav2Vec2.0, use `FEATURE_SIZE=0.02` +* For CPC and Log Mel, use `FEATURE_SIZE=0.01` + +If you have a gpu available, make sure you add the `--cuda` flag for faster computation. + +[ll-instructions]: https://github.com/facebookresearch/libri-light +[ll-abx]: https://github.com/facebookresearch/libri-light/tree/master/eval#abx diff --git a/examples/textless_nlp/gslm/metrics/abx_metrics/dump_abx_feats.py b/examples/textless_nlp/gslm/metrics/abx_metrics/dump_abx_feats.py new file mode 100644 index 0000000000..41cf558970 --- /dev/null +++ b/examples/textless_nlp/gslm/metrics/abx_metrics/dump_abx_feats.py @@ -0,0 +1,107 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import logging +import os + +import joblib +import numpy as np + +from examples.textless_nlp.gslm.speech2unit.clustering.utils import get_audio_files +from examples.textless_nlp.gslm.speech2unit.pretrained.utils import get_features + +def get_logger(): + log_format = "[%(asctime)s] [%(levelname)s]: %(message)s" + logging.basicConfig(format=log_format, level=logging.INFO) + logger = logging.getLogger(__name__) + return logger + +def get_parser(): + parser = argparse.ArgumentParser( + description="Quantize using K-means clustering over acoustic features." + ) + parser.add_argument( + "--feature_type", + type=str, + choices=["logmel", "hubert", "w2v2", "cpc"], + default=None, + required=True, + help="Acoustic feature type", + ) + parser.add_argument( + "--kmeans_model_path", + type=str, + required=True, + help="K-means model file path to use for inference", + ) + parser.add_argument( + "--manifest_path", + type=str, + default=None, + help="Manifest file containing the root dir and file names", + ) + parser.add_argument( + "--checkpoint_path", + type=str, + help="Pretrained model checkpoint", + ) + parser.add_argument( + "--layer", + type=int, + help="The layer of the pretrained model to extract features from", + default=-1, + ) + parser.add_argument( + "--out_dir_path", + required=True, + type=str, + help="File path of quantized output.", + ) + parser.add_argument( + "--extension", type=str, default=".flac", help="Features file path" + ) + return parser + + +def one_hot(feat, n_clusters): + return np.eye(n_clusters)[feat] + +def main(args, logger): + # Feature extraction + logger.info(f"Extracting {args.feature_type} acoustic features...") + features_batch = get_features( + feature_type=args.feature_type, + checkpoint_path=args.checkpoint_path, + layer=args.layer, + manifest_path=args.manifest_path, + sample_pct=1.0, + flatten=False, + ) + logger.info(f"Features extracted for {len(features_batch)} utterances.\n") + logger.info(f"Dimensionality of representation = {features_batch[0].shape[1]}") + + logger.info(f"Loading K-means model from {args.kmeans_model_path} ...") + kmeans_model = joblib.load(open(args.kmeans_model_path, "rb")) + kmeans_model.verbose = False + + _, fnames, _ = get_audio_files(args.manifest_path) + + os.makedirs(args.out_dir_path, exist_ok=True) + logger.info(f"Writing quantized features to {args.out_dir_path}") + for i, feats in enumerate(features_batch): + pred = kmeans_model.predict(feats) + emb = one_hot(pred, kmeans_model.n_clusters) + base_fname = os.path.basename(fnames[i]).rstrip(args.extension) + output_path = os.path.join(args.out_dir_path, f"{base_fname}.npy") + with open(output_path, "wb") as f: + np.save(f, emb) + +if __name__ == "__main__": + parser = get_parser() + args = parser.parse_args() + logger = get_logger() + logger.info(args) + main(args, logger) diff --git a/examples/textless_nlp/gslm/metrics/asr_metrics/README.md b/examples/textless_nlp/gslm/metrics/asr_metrics/README.md new file mode 100644 index 0000000000..90741f42b0 --- /dev/null +++ b/examples/textless_nlp/gslm/metrics/asr_metrics/README.md @@ -0,0 +1,87 @@ +# ASR-based evaluation + +Overall, the life cycle of the ASR-based evaluation for an ULM contains the following steps: + 1. Training an ULM and sampling from it [[description]](./../../ulm) + 2. Running UTS on the sampled unit sequences [[description]](./../../unit2speech) + 3. Pre-processing for the ASR (down-sampling to 16 KHz, aligning length of the generated audio with ground-truth utterances) + 4. Running ASR + 5. Calculation of the post-ASR evaluation metrics + +Here we assume that you have already went throught the first two steps and focus on the rest. + +## Preprocessing +### Down-sampling to 16KHz +The bulk conversion can be done by running +```bash + python $FAIRSEQ_ROOT/examples/textless_nlp/gslm/unit2speech/convert_to_16k.py $UTS_OUTPUT $UTS_OUTPUT_DOWNSAMPLE + ``` + where `$UTS_OUTPUT` specifies the directory with the generated audio and `$UTS_OUTPUT_DOWNSAMPLE` is the directory where downsampled audio would be saved. + + ### Matching by length +This step is somewhat optional. However, if you want to compare the fluency and diversity of a generated speech utterance to that of the ground-truth speech with the same prefix, it is a good idea to force them to be of the same length. +```bash +python $FAIRSEQ_ROOT/examples/textless_nlp/asr_metrics/cut_as.py \ + --samples_dir=$UTS_OUTPUT_DOWNSAMPLE --out_dir=$UTS_OUTPUT_DOWNSAMPLE_CUT \ + --prompts_description=data/ground_truth_continuation_dev.json +``` + +Here `ground_truth_continuation_dev.json` is a json file with ground-truth text from LibriSpeech dev-clean, associated with some meta-data (assuming the evaluation is done on dev-clean). This file can be downloaded [[here]](https://dl.fbaipublicfiles.com/textless_nlp/gslm/eval_data/ground_truth_continuation_dev.json). A similar file for the test-clean is [[here]](https://dl.fbaipublicfiles.com/textless_nlp/gslm/eval_data/ground_truth_continuation_test.json). These files are used for the evaluation and contain texts for audio sequences that are at least 6s long. + +## Running ASR +We use a pre-trained wav2vec model to run the ASR step. We firstly need to prepare manifest files which, roughly, tell the ASR system which files we want to transcribe. You can find more details and download the `960h_scratch.pt` checkpoint +[[here]](https://github.com/pytorch/fairseq/blob/main/examples/wav2vec/README.md)). To run ASR, you would also need to +install KenLM, Flashlight decoder, and download the KenLM 4-gram English language model. + +```bash + python $FAIRSEQ_ROOT/examples/wav2vec/wav2vec_manifest.py \ + $UTS_OUTPUT_DOWNSAMPLE_CUT --valid-percent 0.0 --dest $MANIFEST_DIR --ext wav +``` +where `$UTS_OUTPUT_DOWNSAMPLE_CUT` speficies the directory with the preprocessed UTS outputs and `$MANIFEST_DIR` is the output directory. + +We will be running an out-of-the-box evaluation script which requires ground-truth transcripts to measure quality metrics. We are only +interested in the transcripts (and we don't have ground-truth outputs for when our ULM generated!), hence we will just generate +some dummy transcripts instead: +```bash +cp $FAIRSEQ_ROOT/examples/textless_nlp/gslm/asr_metrics/misc/dict.ltr.txt $MANIFEST_DIR +python $FAIRSEQ_ROOT/examples/textless_nlp/gslm/asr_metrics/misc/dummy_asr_data.py --tsv=$MANIFEST_DIR/train.tsv \ + --output-dir=$MANIFEST_DIR +``` + +Now we are ready for running ASR: +``` +mkdir -p asr +python $FAIRSEQ_ROOT/examples/speech_recognition/infer.py \ + $MANIFEST_DIR \ + --task audio_pretraining --nbest 1 --path 960h_scratch.pt \ + --gen-subset=train --results-path $PATH_TO_ASR_OUTPUT \ + --w2l-decoder kenlm --lm-model 4-gram.bin \ + --lexicon librispeech/lexicon_ltr.lst --word-score -1 \ + --sil-weight 0 --lm-weight 2 --criterion ctc --labels ltr --max-tokens 300000 --remove-bpe letter +``` +where `lexicon_ltr.lst` is the LibriSpeech lexicon and `$PATH_TO_ASR_OUTPUT` is the output directory (can be downloaded [[here]](https://dl.fbaipublicfiles.com/textless_nlp/gslm/eval_data/lexicon_ltr.lst)). + +## Evaluation metrics +We run evaluation on the 1_000 shortest sequences that are at least 6s long. To filter those from the ASR transcript, we additionally provide each metric script with the paths to the manifest and `ground_truth_continuation_*` files. + +### Perplexity (PPX) +To get a PPX metric estimate on an ASR transcript, you need to run the following command: +```bash +python ppx.py $PATH_TO_ASR_OUTPUT/hypo.word-960h_scratch.pt-train.txt --cut-tail\ + --manifest=$MANIFEST_DIR/train.tsv --prompts-description=data/ground_truth_continuation_dev.json +``` +where `--cut-tail` tells the script to ignore the last token on each line (ASR puts the sequence ID there). + +### Self- and Auto-BLEU +```bash +python self_bleu.py $PATH_TO_ASR_OUTPUT/hypo.word-960h_scratch.pt-train.txt --cut-tail \ + --manifest=$MANIFEST_DIR/train.tsv --prompts-description=data/ground_truth_continuation_dev.json +``` + +### Continuation-BLEU +```bash +python continuation_eval.py --asr-transcript $PATH_TO_ASR_OUTPUT/hypo.word-960h_scratch.pt-train.txt \ + --manifest=$MANIFEST_DIR/train.tsv --prompts-description=data/ground_truth_continuation_dev.json +``` + +### AUC +Based on the metrics calculated above, we can estimate the AUC of the perplexity/diversity trade-off. We provide an illustration in a [Colab notebook](https://colab.research.google.com/drive/1pVPfOVax_PU3MkYdHRSsa-SI8GBUldNt?usp=sharing). diff --git a/examples/textless_nlp/gslm/metrics/asr_metrics/continuation_eval.py b/examples/textless_nlp/gslm/metrics/asr_metrics/continuation_eval.py new file mode 100644 index 0000000000..72b92a341d --- /dev/null +++ b/examples/textless_nlp/gslm/metrics/asr_metrics/continuation_eval.py @@ -0,0 +1,99 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +from collections import defaultdict +import numpy as np +from misc.bleu_utils import sentence_bleu +import json +import warnings + + +def get_args(): + import argparse + + parser = argparse.ArgumentParser("Tool to calculate Continuation-BLEU2") + parser.add_argument('--asr-transcript', type=str, + help='Path to the transcript file.') + parser.add_argument('--prompts-description', type=str, + help='Path to the ground-truth continuation') + parser.add_argument('--manifest', type=str, required=True) + parser.add_argument('--take-shortest', type=int, default=1000) + + args = parser.parse_args() + + return args + + +def main(): + # NLTK produces warnings + warnings.filterwarnings("ignore") + + args = get_args() + + with open(args.prompts_description, 'r') as fin: + original_continuations = json.loads(fin.read()) + + sequence2length = [(k, v[0]) for k, v in original_continuations.items()] + assert all(float(v) >= 6.0 for (_, v) in sequence2length) # 6 seconds + + sequence2length.sort(key=lambda x: x[1]) + to_take = set(v[0] for v in sequence2length[:args.take_shortest]) + + with open(args.manifest, 'r') as fin: + fin.readline() + + linenum2file = dict([ + (i, l.split("__")[0]) for (i, l) in enumerate(fin) + ]) + + max_files = max(linenum2file.keys()) + continuations = defaultdict(list) + + mean_length_after = 0 + n_examples = 0 + + with open(args.asr_transcript, 'r') as fin: + for line in fin: + n_examples += 1 + line = line.split() + sequence_id = int(line[-1].split('-')[1][:-1]) + + assert sequence_id <= max_files + + sequence_name = linenum2file[sequence_id] + + continuations[sequence_name].append(line[:-1]) + mean_length_after += len(line) + + mean_length_after /= n_examples + print(f'Mean length of continuations, in words: {mean_length_after}') + metric_values = [] + + mean_ground_truth_words = 0 + n_examples = 0 + n_candidates = 0 + + for k, candidates in continuations.items(): + if k not in to_take: + continue + + n_examples += 1 + + ground_truth = original_continuations[k][1].split() + n_candidates += len(candidates) + bleu = sentence_bleu(candidates, ground_truth, weights=( + 0.5, 0.5), no_length_penalty=True, averaging_mode="geometric") + mean_ground_truth_words += len(ground_truth) + + metric_values.append(bleu) + + n = len(metric_values) + print( + f'Median BLEU over {n} examples: {np.median(metric_values)} +- {np.std(metric_values) / np.sqrt(n)}') + + +if __name__ == '__main__': + main() diff --git a/examples/textless_nlp/gslm/metrics/asr_metrics/misc/bleu_utils.py b/examples/textless_nlp/gslm/metrics/asr_metrics/misc/bleu_utils.py new file mode 100644 index 0000000000..75cc5272d3 --- /dev/null +++ b/examples/textless_nlp/gslm/metrics/asr_metrics/misc/bleu_utils.py @@ -0,0 +1,166 @@ +""" + +TODO: the code is take from Apache-2 Licensed NLTK: make sure we do this properly! + + +Copied over from nltk.tranlate.bleu_score. This code has two major changes: + - allows to turn off length/brevity penalty --- it has no sense for self-bleu, + - allows to use arithmetic instead of geometric mean +""" + +import math +import sys +from fractions import Fraction +import warnings +from collections import Counter +from nltk.translate.bleu_score import modified_precision, closest_ref_length, brevity_penalty, SmoothingFunction + + +def corpus_bleu( + list_of_references, + hypotheses, + weights=(0.25, 0.25, 0.25, 0.25), + smoothing_function=None, + auto_reweigh=False, + averaging_mode="geometric", + no_length_penalty=False +): + """ + Calculate a single corpus-level BLEU score (aka. system-level BLEU) for all + the hypotheses and their respective references. + + Instead of averaging the sentence level BLEU scores (i.e. marco-average + precision), the original BLEU metric (Papineni et al. 2002) accounts for + the micro-average precision (i.e. summing the numerators and denominators + for each hypothesis-reference(s) pairs before the division). + + >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which', + ... 'ensures', 'that', 'the', 'military', 'always', + ... 'obeys', 'the', 'commands', 'of', 'the', 'party'] + >>> ref1a = ['It', 'is', 'a', 'guide', 'to', 'action', 'that', + ... 'ensures', 'that', 'the', 'military', 'will', 'forever', + ... 'heed', 'Party', 'commands'] + >>> ref1b = ['It', 'is', 'the', 'guiding', 'principle', 'which', + ... 'guarantees', 'the', 'military', 'forces', 'always', + ... 'being', 'under', 'the', 'command', 'of', 'the', 'Party'] + >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the', + ... 'army', 'always', 'to', 'heed', 'the', 'directions', + ... 'of', 'the', 'party'] + + >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was', + ... 'interested', 'in', 'world', 'history'] + >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history', + ... 'because', 'he', 'read', 'the', 'book'] + + >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]] + >>> hypotheses = [hyp1, hyp2] + >>> corpus_bleu(list_of_references, hypotheses) # doctest: +ELLIPSIS + 0.5920... + + The example below show that corpus_bleu() is different from averaging + sentence_bleu() for hypotheses + + >>> score1 = sentence_bleu([ref1a, ref1b, ref1c], hyp1) + >>> score2 = sentence_bleu([ref2a], hyp2) + >>> (score1 + score2) / 2 # doctest: +ELLIPSIS + 0.6223... + + :param list_of_references: a corpus of lists of reference sentences, w.r.t. hypotheses + :type list_of_references: list(list(list(str))) + :param hypotheses: a list of hypothesis sentences + :type hypotheses: list(list(str)) + :param weights: weights for unigrams, bigrams, trigrams and so on + :type weights: list(float) + :param smoothing_function: + :type smoothing_function: SmoothingFunction + :param auto_reweigh: Option to re-normalize the weights uniformly. + :type auto_reweigh: bool + :return: The corpus-level BLEU score. + :rtype: float + """ + # Before proceeding to compute BLEU, perform sanity checks. + + p_numerators = Counter() # Key = ngram order, and value = no. of ngram matches. + p_denominators = Counter() # Key = ngram order, and value = no. of ngram in ref. + hyp_lengths, ref_lengths = 0, 0 + + assert len(list_of_references) == len(hypotheses), ( + "The number of hypotheses and their reference(s) should be the " "same " + ) + + # Iterate through each hypothesis and their corresponding references. + for references, hypothesis in zip(list_of_references, hypotheses): + # For each order of ngram, calculate the numerator and + # denominator for the corpus-level modified precision. + for i, _ in enumerate(weights, start=1): + p_i = modified_precision(references, hypothesis, i) + p_numerators[i] += p_i.numerator + p_denominators[i] += p_i.denominator + + # Calculate the hypothesis length and the closest reference length. + # Adds them to the corpus-level hypothesis and reference counts. + hyp_len = len(hypothesis) + hyp_lengths += hyp_len + ref_lengths += closest_ref_length(references, hyp_len) + + # Calculate corpus-level brevity penalty. + if no_length_penalty and averaging_mode == 'geometric': + bp = 1.0 + elif no_length_penalty and averaging_mode == 'arithmetic': + bp = 0.0 + else: + assert not no_length_penalty + assert averaging_mode != 'arithmetic', 'Not sure how to apply length penalty when aurithmetic mode' + bp = brevity_penalty(ref_lengths, hyp_lengths) + + # Uniformly re-weighting based on maximum hypothesis lengths if largest + # order of n-grams < 4 and weights is set at default. + if auto_reweigh: + if hyp_lengths < 4 and weights == (0.25, 0.25, 0.25, 0.25): + weights = (1 / hyp_lengths,) * hyp_lengths + + # Collects the various precision values for the different ngram orders. + p_n = [ + Fraction(p_numerators[i], p_denominators[i], _normalize=False) + for i, _ in enumerate(weights, start=1) + ] + + # Returns 0 if there's no matching n-grams + # We only need to check for p_numerators[1] == 0, since if there's + # no unigrams, there won't be any higher order ngrams. + if p_numerators[1] == 0: + return 0 + + # If there's no smoothing, set use method0 from SmoothinFunction class. + if not smoothing_function: + smoothing_function = SmoothingFunction().method0 + # Smoothen the modified precision. + # Note: smoothing_function() may convert values into floats; + # it tries to retain the Fraction object as much as the + # smoothing method allows. + p_n = smoothing_function( + p_n, references=references, hypothesis=hypothesis, hyp_len=hyp_lengths + ) + + if averaging_mode == "geometric": + s = (w_i * math.log(p_i) for w_i, p_i in zip(weights, p_n)) + s = bp * math.exp(math.fsum(s)) + elif averaging_mode == "arithmetic": + s = (w_i * p_i for w_i, p_i in zip(weights, p_n)) + s = math.fsum(s) + + return s + + +def sentence_bleu( + references, + hypothesis, + weights=(0.25, 0.25, 0.25, 0.25), + smoothing_function=None, + auto_reweigh=False, + averaging_mode="geometric", + no_length_penalty=False +): + return corpus_bleu( + [references], [hypothesis], weights, smoothing_function, auto_reweigh, averaging_mode, no_length_penalty + ) \ No newline at end of file diff --git a/examples/textless_nlp/gslm/metrics/asr_metrics/misc/cut_as.py b/examples/textless_nlp/gslm/metrics/asr_metrics/misc/cut_as.py new file mode 100644 index 0000000000..5b7e1e9685 --- /dev/null +++ b/examples/textless_nlp/gslm/metrics/asr_metrics/misc/cut_as.py @@ -0,0 +1,69 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +import torchaudio +import argparse +import json +import pathlib + + +def get_args(): + parser = argparse.ArgumentParser( + "Assuring generated audio have the same length as ground-truth audio") + parser.add_argument('--samples_dir', required=True, type=str) + parser.add_argument('--out_dir', required=True, type=str) + parser.add_argument('--prompts_description', required=True, type=str) + return parser.parse_args() + + +def cut(src, tgt, l): + x, sr = torchaudio.load(str(src)) + assert sr == 16_000 + + x = x.squeeze() + target_frames = int(l * sr) + + flag = 0 + if target_frames <= x.size(0): + x = x[:target_frames] + flag = 1 + else: + flag = 0 + torchaudio.save(str(tgt), x.unsqueeze(0), sr) + return flag + + +def main(): + args = get_args() + tgt_dir = pathlib.Path(args.out_dir) + tgt_dir.mkdir(exist_ok=True, parents=True) + + total_files, sufficiently_long = 0, 0 + + with open(args.prompts_description, 'r') as f: + description = json.loads(f.read()) + + for src_f in pathlib.Path(args.samples_dir).glob('*.wav'): + name_prompt = src_f.with_suffix('').name.split('__')[0] + + assert name_prompt in description, f'Cannot find {name_prompt}!' + + target_length = description[name_prompt][0] + tgt_f = tgt_dir / (src_f.name) + + is_long_enough = cut(src_f, tgt_f, target_length) + sufficiently_long += is_long_enough + if not is_long_enough: + print(f'{src_f} is not long enough') + + total_files += 1 + + print( + f'Total files: {total_files}; sufficiently long: {sufficiently_long}') + + +if __name__ == '__main__': + main() diff --git a/examples/textless_nlp/gslm/metrics/asr_metrics/misc/dict.ltr.txt b/examples/textless_nlp/gslm/metrics/asr_metrics/misc/dict.ltr.txt new file mode 100644 index 0000000000..69929e1666 --- /dev/null +++ b/examples/textless_nlp/gslm/metrics/asr_metrics/misc/dict.ltr.txt @@ -0,0 +1,28 @@ +| 94802 +E 51860 +T 38431 +A 33152 +O 31495 +N 28855 +I 28794 +H 27187 +S 26071 +R 23546 +D 18289 +L 16308 +U 12400 +M 10685 +W 10317 +C 9844 +F 9062 +G 8924 +Y 8226 +P 6890 +B 6339 +V 3936 +K 3456 +' 1023 +X 636 +J 598 +Q 437 +Z 213 diff --git a/examples/textless_nlp/gslm/metrics/asr_metrics/ppx.py b/examples/textless_nlp/gslm/metrics/asr_metrics/ppx.py new file mode 100644 index 0000000000..d6a40e4d35 --- /dev/null +++ b/examples/textless_nlp/gslm/metrics/asr_metrics/ppx.py @@ -0,0 +1,122 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +import torch +import numpy as np +import warnings + + +def get_target_sequences(manifest, ground_truth, to_take=1000): + import json + import pathlib + + with open(ground_truth, 'r') as fin: + original_continuations = json.loads(fin.read()) + + sequence2length = [(k, v[0]) for k, v in original_continuations.items()] + assert all(float(v) >= 6.0 for (_, v) in sequence2length) # 6 seconds + + sequence2length.sort(key=lambda x: x[1]) + to_take_sequences = set(v[0] for v in sequence2length[:to_take]) + to_take_ids = [] + + with open(manifest, 'r') as f: + f.readline() + + for i, line in enumerate(f.readlines()): + seq_id = line.split()[0] + seq_id = pathlib.Path(seq_id).name.split('__')[0] + + if seq_id in to_take_sequences: + to_take_ids.append(i) + + print(f'Took {len(to_take_ids)} ids') + return set(to_take_ids) + + +def get_args(): + import argparse + + parser = argparse.ArgumentParser("Evaluate PPX metric of a transcript.") + parser.add_argument('--asr-transcript', type=str, + help='Path to the transcript file.') + parser.add_argument('--cut-id', action='store_true', + help='Whether cut the first token (typically a seq id)') + parser.add_argument('--cut-tail', action='store_true', + help='Whether cut the last token (typically a speaker id)') + + parser.add_argument('--manifest', type=str, default=None) + parser.add_argument('--prompts-description', type=str, default=None) + + args = parser.parse_args() + + return args + + +def main(): + args = get_args() + + lm = torch.hub.load( + 'pytorch/fairseq', 'transformer_lm.wmt19.en', tokenizer='moses', bpe='fastbpe') + + lm.eval().cuda() # disable dropout + + if args.manifest is None and args.prompts_description is None: + target_ids = None + else: + target_ids = get_target_sequences( + args.manifest, args.prompts_description) + + with open(args.asr_transcript, 'r') as fin: + lines = fin.readlines() + + if target_ids is not None: + filtered = [] + for line in lines: + line_id = line.split()[-1] + line_id = int(line_id.split('-')[1][:-1]) + if line_id in target_ids: + filtered.append(line) + lines = filtered + else: + pass + + if args.cut_id: + lines = [' '.join(x.split()[1:]) for x in lines] + if args.cut_tail: + lines = [' '.join(x.split()[:-1]) for x in lines] + lines = [x.strip().lower() for x in lines] + + def get_logprob(sent): return \ + lm.score(sent)['positional_scores'].mean().neg().item() + + logprobs = [get_logprob(l) for l in lines] + + filtered = [x for x in logprobs if not np.isnan(x)] + if len(filtered) != len(logprobs): + warnings.warn("NaNs detected!") + logprobs = filtered + + perplexities = [np.exp(l) for l in logprobs] + + for name, stats in [('logprob', logprobs), ('perplexity', perplexities)]: + mean = np.mean(stats) + sem = np.std(stats) / np.sqrt(len(stats)) + + median = np.median(stats) + interval = list(np.percentile(stats, [10, 90])) + + mean, sem, median, percentile10, percentile90 = [ + round(x, 2) for x in [mean, sem, median] + interval] + + print(name) + print(f"\tMean {mean} +- {sem}") + print( + f"\tMedian {median}, 90% confidence interval {percentile10}...{percentile90}") + + +if __name__ == '__main__': + main() diff --git a/examples/textless_nlp/gslm/metrics/asr_metrics/self_auto_bleu.py b/examples/textless_nlp/gslm/metrics/asr_metrics/self_auto_bleu.py new file mode 100644 index 0000000000..062bb82f66 --- /dev/null +++ b/examples/textless_nlp/gslm/metrics/asr_metrics/self_auto_bleu.py @@ -0,0 +1,201 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import numpy as np +import nltk +from misc.bleu_utils import sentence_bleu +import warnings + + +def get_target_sequences(manifest, ground_truth, to_take=1000): + import json + import pathlib + + with open(ground_truth, 'r') as fin: + original_continuations = json.loads(fin.read()) + + sequence2length = [(k, v[0]) for k, v in original_continuations.items()] + assert all(float(v) >= 6.0 for (_, v) in sequence2length) # 6 seconds + + sequence2length.sort(key=lambda x: x[1]) + to_take_sequences = set(v[0] for v in sequence2length[:to_take]) + to_take_ids = [] + + with open(manifest, 'r') as f: + f.readline() + + for i, line in enumerate(f.readlines()): + seq_id = line.split()[0] + seq_id = pathlib.Path(seq_id).name.split('__')[0] + + if seq_id in to_take_sequences: + to_take_ids.append(i) + + print(f'Took {len(to_take_ids)} ids') + return set(to_take_ids) + + +def get_args(): + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument('--asr-transcript', type=str, + help='Path to the transcript file.') + + parser.add_argument('--manifest', required=True) + parser.add_argument('--prompts-description', required=True) + + parser.add_argument('--cut-id', action='store_true', + help='Whether cut the first token (typically a seq id)') + parser.add_argument('--cut-tail', action='store_true', + help='Whether cut the last token (typically a speaker id)') + parser.add_argument('--debug', action='store_true') + + args = parser.parse_args() + + return args + + +def get_self_bleu(utterances, averaging_mode, weights): + self_bleu = [] + + for i in range(len(utterances)): + hypo = utterances[i] + rest = utterances[:i] + utterances[i+1:] + + self_bleu.append(sentence_bleu(rest, hypo, weights, + no_length_penalty=True, averaging_mode=averaging_mode)) + + return self_bleu + + +def get_self_bleu2_arithmetic(utterances): + weights = (0.5, 0.5) # equal weight for unigrams and bigrams + return get_self_bleu(utterances, averaging_mode='arithmetic', weights=weights) + + +def get_self_bleu2_geometric(utterances): + weights = (0.5, 0.5) + return get_self_bleu(utterances, averaging_mode='geometric', weights=weights) + + +def get_auto_bleu2_arithmetic(utterances): + weights = (0.5, 0.5) + return [auto_bleu(u, mean_mode='arithmetic', weights=weights) for u in utterances] + + +def get_auto_bleu2_geometric(utterances): + weights = (0.5, 0.5) + return [auto_bleu(u, mean_mode='geometric', weights=weights) for u in utterances] + + +def get_auto_bleu3_geometric(utterances): + weights = (1./3, 1./3, 1./3) + return [auto_bleu(u, mean_mode='geometric', weights=weights) for u in utterances] + + +def get_auto_bleu3_arithmetic(utterances): + weights = (1./3, 1./3, 1./3) + return [auto_bleu(u, mean_mode='arithmetic', weights=weights) for u in utterances] + + +def get_self_bleu3_arithmetic(utterances): + weights = (1./3, 1./3, 1./3) + return get_self_bleu(utterances, averaging_mode='arithmetic', weights=weights) + + +def get_self_bleu3_geometric(utterances): + weights = (1./3, 1./3, 1./3) + return get_self_bleu(utterances, averaging_mode='geometric', weights=weights) + + +def auto_bleu(sentence, weights, mean_mode='arithmetic'): + if len(sentence) <= 1: + return 0 + + N = len(weights) + + bleu_n = np.zeros([N]) + for n in range(N): + targ_ngrams = list(nltk.ngrams(sentence, n+1)) + for p in range(len(targ_ngrams)): + left = sentence[:p] + right = sentence[(p+n+1):] + rest_ngrams = list(nltk.ngrams(left, n+1)) + \ + list(nltk.ngrams(right, n+1)) + # compute the nb of matching ngrams + bleu_n[n] += targ_ngrams[p] in rest_ngrams + bleu_n[n] /= len(targ_ngrams) # average them to get a proportion + + weights = np.array(weights) + if mean_mode == 'arithmetic': + return (bleu_n * weights).sum() + elif mean_mode == 'geometric': + return (bleu_n ** weights).prod() + else: + raise ValueError(f'Unknown agggregation mode {mean_mode}') + + +def main(): + from multiprocessing import Pool + + args = get_args() + target_ids = get_target_sequences(args.manifest, args.prompts_description) + + with open(args.asr_transcript, 'r') as fin: + lines = fin.readlines() + + terms = [x.strip().split() for x in lines] + filtered = [] + for term in terms: + line_id = int(term[-1].split('-')[1][:-1]) + if line_id in target_ids: + filtered.append(term) + terms = filtered + + if args.cut_id: + terms = [x[1:] for x in terms] + if args.cut_tail: + terms = [x[:-1] for x in terms] + + if args.debug: + terms = terms[:10] + + tasks = [ + ('Self-BLEU2-arithmetic', get_self_bleu2_arithmetic), + ('Self-BLEU2-geometric', get_self_bleu2_geometric), + ('Auto-BLEU2-arithmetic', get_auto_bleu2_arithmetic), + ('Auto-BLEU2-geometric', get_auto_bleu2_geometric), + + ('Self-BLEU3-arithmetic', get_self_bleu3_arithmetic), + ('Self-BLEU3-geometric', get_self_bleu3_geometric), + ('Auto-BLEU3-arithmetic', get_auto_bleu3_arithmetic), + ('Auto-BLEU3-geometric', get_auto_bleu3_geometric), + ] + + n_processes = min(16, len(tasks)) + with Pool(n_processes) as pool: + metrics = pool.map(run_f, [(t[1], terms) for t in tasks]) + + for (metric_name, _), metric in zip(tasks, metrics): + metric, sem = np.mean(metric), np.std(metric) / np.sqrt(len(metric)) + + metric, sem = [ + round(100 * x, 2) for x in [metric, sem] + ] + + print(f'{metric_name} {metric} +- {sem}') + + +def run_f(task_params): + f, terms = task_params + return f(terms) + + +if __name__ == '__main__': + # NLTK produces warnings + warnings.filterwarnings("ignore") + + main() diff --git a/examples/textless_nlp/gslm/speech2unit/README.md b/examples/textless_nlp/gslm/speech2unit/README.md new file mode 100644 index 0000000000..1a3d131ec1 --- /dev/null +++ b/examples/textless_nlp/gslm/speech2unit/README.md @@ -0,0 +1,71 @@ +# Speech to Unit Model (speech2unit) + +## Acoustic Model +For quantizing speech we learn a K-means clustering over acoustic representations for which we either use Log-Mel Filterbank or pretrained acoustic representation models. For using pretrained models, please download from their respective locations linked below. +* [Modified CPC](https://dl.fbaipublicfiles.com/textless_nlp/gslm/cpc/cpc_big_ll6kh_top_ctc.pt) +* [HuBERT-Base](https://dl.fbaipublicfiles.com/hubert/hubert_base_ls960.pt) +* [Wav2Vec 2.0-Base](https://dl.fbaipublicfiles.com/fairseq/wav2vec/wav2vec_vox_new.pt) + +## Quantization Model +You can download pretrained quantized model from the list below. + +K-Means Model | Download Link +|-|- +Log Mel Filterbank + KM50 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/logmel/km50/km.bin) +Log Mel Filterbank + KM100 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/logmel/km100/km.bin) +Log Mel Filterbank + KM200 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/logmel/km200/km.bin) +Log Mel Filterbank + KM500 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/logmel/km500/km.bin) +Modified CPC + KM50 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/cpc/km50/km.bin) +Modified CPC + KM100 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/cpc/km100/km.bin) +Modified CPC + KM200 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/cpc/km200/km.bin) +Modified CPC + KM500 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/cpc/km500/km.bin) +HuBERT Base + KM50 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/hubert/km50/km.bin) +HuBERT Base + KM100 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/hubert/km100/km.bin) +HuBERT Base + KM200 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/hubert/km200/km.bin) +HuBERT Base + KM500 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/hubert/km500/km.bin) +wav2vec 2.0 Large + KM50 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/w2v2/km50/km.bin) +wav2vec 2.0 Large + KM100 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/w2v2/km100/km.bin) +wav2vec 2.0 Large + KM200 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/w2v2/km200/km.bin) +wav2vec 2.0 Large + KM500 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/w2v2/km500/km.bin) + +### Quantization +For quantizing speech with a given acoustic representation, please follow the steps below. +1. Learn K-means clustering model +``` +N_CLUSTERS=<number_of_clusters_used_for_kmeans> +TYPE=<one_of_logmel/cpc/hubert/w2v2> +CKPT_PATH=<path_of_pretrained_acoustic_model> +LAYER=<layer_of_acoustic_model_to_extract_features_from> +MANIFEST=<tab_separated_manifest_of_audio_files_for_training_kmeans> +KM_MODEL_PATH=<output_path_of_the_kmeans_model> + +PYTHONPATH=. python examples/textless_nlp/gslm/speech2unit/clustering/cluster_kmeans.py \ + --num_clusters $N_CLUSTERS \ + --feature_type $TYPE \ + --checkpoint_path $CKPT_PATH \ + --layer $LAYER \ + --manifest_path $MANIFEST \ + --out_kmeans_model_path $KM_MODEL_PATH +``` +2. Quantize using the learned clusters +``` +MANIFEST=<tab_separated_manifest_of_audio_files_to_quantize> +OUT_QUANTIZED_FILE=<output_quantized_audio_file_path> + +python examples/textless_nlp/gslm/speech2unit/clustering/del/quantize_with_kmeans.py \ + --feature_type $TYPE \ + --kmeans_model_path $KM_MODEL_PATH \ + --checkpoint_path $CKPT_PATH \ + --layer $LAYER \ + --manifest_path $MANIFEST \ + --out_quantized_file_path $OUT_QUANTIZED_FILE \ + --extension ".flac" +``` + +Note about the manifest file is a file with paths and length of input audio files. The format of the file is as follows: +``` +<path_of_root_directory_containing_audio_files> +<relative_path_of_audio_file_1>\t<number_of_frames_1> +<relative_path_of_audio_file_2>\t<number_of_frames_1> +... +``` \ No newline at end of file diff --git a/examples/textless_nlp/gslm/speech2unit/__init__.py b/examples/textless_nlp/gslm/speech2unit/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/examples/textless_nlp/gslm/speech2unit/clustering/__init__.py b/examples/textless_nlp/gslm/speech2unit/clustering/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/examples/textless_nlp/gslm/speech2unit/clustering/cluster_kmeans.py b/examples/textless_nlp/gslm/speech2unit/clustering/cluster_kmeans.py new file mode 100644 index 0000000000..7cf844a95a --- /dev/null +++ b/examples/textless_nlp/gslm/speech2unit/clustering/cluster_kmeans.py @@ -0,0 +1,212 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import logging +import os +import time + +import numpy as np +from sklearn.cluster import MiniBatchKMeans + +import joblib +from examples.textless_nlp.gslm.speech2unit.pretrained.utils import ( + get_and_dump_features, + get_features, +) + + +def get_logger(): + log_format = "[%(asctime)s] [%(levelname)s]: %(message)s" + logging.basicConfig(format=log_format, level=logging.INFO) + logger = logging.getLogger(__name__) + return logger + + +def get_parser(): + parser = argparse.ArgumentParser( + description="Learn K-means clustering over acoustic features." + ) + + # Features arguments + parser.add_argument( + "--in_features_path", type=str, default=None, help="Features file path" + ) + parser.add_argument( + "--feature_type", + type=str, + choices=["logmel", "hubert", "w2v2", "cpc"], + default=None, + help="Acoustic feature type", + ) + parser.add_argument( + "--manifest_path", + type=str, + default=None, + help="Manifest file containing the root dir and file names", + ) + parser.add_argument( + "--out_features_path", + type=str, + default=None, + help="Features file path to write to", + ) + parser.add_argument( + "--checkpoint_path", + type=str, + help="Pretrained acoustic model checkpoint", + ) + parser.add_argument( + "--layer", + type=int, + help="The layer of the pretrained model to extract features from", + default=-1, + ) + parser.add_argument( + "--sample_pct", + type=float, + help="Percent data to use for K-means training", + default=0.1, + ) + + # K-means arguments + parser.add_argument( + "--num_clusters", type=int, help="Nubmer of clusters", default=50 + ) + parser.add_argument("--init", default="k-means++") + parser.add_argument( + "--max_iter", + type=int, + help="Maximum number of iterations for K-means training", + default=150, + ) + parser.add_argument( + "--batch_size", + type=int, + help="Batch size for K-means training", + default=10000, + ) + parser.add_argument("--tol", default=0.0, type=float) + parser.add_argument("--max_no_improvement", default=100, type=int) + parser.add_argument("--n_init", default=20, type=int) + parser.add_argument("--reassignment_ratio", default=0.5, type=float) + parser.add_argument( + "--out_kmeans_model_path", + type=str, + required=True, + help="Path to save K-means model", + ) + + # Leftovers + parser.add_argument( + "--seed", + type=int, + help="Random seed to use for K-means training", + default=1369, + ) + + return parser + + +def get_kmeans_model( + n_clusters, + init, + max_iter, + batch_size, + tol, + max_no_improvement, + n_init, + reassignment_ratio, + random_state, +): + return MiniBatchKMeans( + n_clusters=n_clusters, + init=init, + max_iter=max_iter, + batch_size=batch_size, + tol=tol, + max_no_improvement=max_no_improvement, + n_init=n_init, + reassignment_ratio=reassignment_ratio, + random_state=random_state, + verbose=1, + compute_labels=True, + init_size=None, + ) + + +def train_kmeans(kmeans_model, features_batch): + start_time = time.time() + kmeans_model.fit(features_batch) + time_taken = round((time.time() - start_time) // 60, 2) + return kmeans_model, time_taken + + +def main(args, logger): + # Features loading/extraction for K-means + if args.in_features_path: + # Feature loading + logger.info(f"Loading features from {args.in_features_path}...") + features_batch = np.load(args.in_features_path, allow_pickle=True) + else: + # Feature extraction + logger.info(f"Extracting {args.feature_type} acoustic features...") + features_batch = ( + get_features( + feature_type=args.feature_type, + checkpoint_path=args.checkpoint_path, + layer=args.layer, + manifest_path=args.manifest_path, + sample_pct=args.sample_pct, + flatten=True, + ) + if not args.out_features_path + else get_and_dump_features( + feature_type=args.feature_type, + checkpoint_path=args.checkpoint_path, + layer=args.layer, + manifest_path=args.manifest_path, + sample_pct=args.sample_pct, + flatten=True, + out_features_path=args.out_features_path, + ) + ) + if args.out_features_path: + logger.info( + f"Saved extracted features at {args.out_features_path}" + ) + logger.info(f"Features shape = {features_batch.shape}\n") + + # Learn and save K-means model + kmeans_model = get_kmeans_model( + n_clusters=args.num_clusters, + init=args.init, + max_iter=args.max_iter, + batch_size=args.batch_size, + tol=args.tol, + max_no_improvement=args.max_no_improvement, + n_init=args.n_init, + reassignment_ratio=args.reassignment_ratio, + random_state=args.seed, + ) + logger.info("Starting k-means training...") + kmeans_model, time_taken = train_kmeans( + kmeans_model=kmeans_model, features_batch=features_batch + ) + logger.info(f"...done k-means training in {time_taken} minutes") + inertia = -kmeans_model.score(features_batch) / len(features_batch) + logger.info(f"Total intertia: {round(inertia, 2)}\n") + + logger.info(f"Saving k-means model to {args.out_kmeans_model_path}") + os.makedirs(os.path.dirname(args.out_kmeans_model_path), exist_ok=True) + joblib.dump(kmeans_model, open(args.out_kmeans_model_path, "wb")) + + +if __name__ == "__main__": + parser = get_parser() + args = parser.parse_args() + logger = get_logger() + logger.info(args) + main(args, logger) diff --git a/examples/textless_nlp/gslm/speech2unit/clustering/dump_feats.py b/examples/textless_nlp/gslm/speech2unit/clustering/dump_feats.py new file mode 100644 index 0000000000..031567c6d8 --- /dev/null +++ b/examples/textless_nlp/gslm/speech2unit/clustering/dump_feats.py @@ -0,0 +1,91 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import logging + +from examples.textless_nlp.gslm.speech2unit.pretrained.utils import ( + get_and_dump_features, +) + + +def get_parser(): + parser = argparse.ArgumentParser( + description="Compute and dump log mel fbank features." + ) + parser.add_argument( + "--feature_type", + type=str, + choices=["logmel", "hubert", "w2v2", "cpc"], + default=None, + help="Acoustic feature type", + ) + parser.add_argument( + "--manifest_path", + type=str, + default=None, + help="Manifest file containing the root dir and file names", + ) + parser.add_argument( + "--out_features_path", + type=str, + default=None, + help="Features file path to write to", + ) + parser.add_argument( + "--checkpoint_path", + type=str, + help="Pretrained acoustic model checkpoint", + ) + parser.add_argument( + "--layer", + type=int, + help="The layer of the pretrained model to extract features from", + default=-1, + ) + parser.add_argument( + "--sample_pct", + type=float, + help="Percent data to use for K-means training", + default=0.1, + ) + parser.add_argument( + "--out_features_path", + type=str, + help="Path to save log mel fbank features", + ) + return parser + + +def get_logger(): + log_format = "[%(asctime)s] [%(levelname)s]: %(message)s" + logging.basicConfig(format=log_format, level=logging.INFO) + logger = logging.getLogger(__name__) + return logger + + +if __name__ == "__main__": + """ + Example command: + python ~/speechbot/clustering/dump_logmelfank_feats.py \ + --manifest_path /checkpoint/kushall/data/LJSpeech-1.1/asr_input_wavs_16k/train.tsv + --out_features_path /checkpoint/kushall/experiments/speechbot/logmelfbank/features/ljspeech/train.npy + """ + parser = get_parser() + args = parser.parse_args() + logger = get_logger() + logger.info(args) + + logger.info(f"Extracting {args.feature_type} acoustic features...") + get_and_dump_features( + feature_type=args.feature_type, + checkpoint_path=args.checkpoint_path, + layer=args.layer, + manifest_path=args.manifest_path, + sample_pct=args.sample_pct, + flatten=True, + out_features_path=args.out_features_path, + ) + logger.info(f"Saved extracted features at {args.out_features_path}") diff --git a/examples/textless_nlp/gslm/speech2unit/clustering/quantize_with_kmeans.py b/examples/textless_nlp/gslm/speech2unit/clustering/quantize_with_kmeans.py new file mode 100644 index 0000000000..2c87445d81 --- /dev/null +++ b/examples/textless_nlp/gslm/speech2unit/clustering/quantize_with_kmeans.py @@ -0,0 +1,125 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import logging +import os + +import numpy as np + +import joblib +from examples.textless_nlp.gslm.speech2unit.clustering.utils import ( + get_audio_files, +) +from examples.textless_nlp.gslm.speech2unit.pretrained.utils import ( + get_features, +) + + +def get_logger(): + log_format = "[%(asctime)s] [%(levelname)s]: %(message)s" + logging.basicConfig(format=log_format, level=logging.INFO) + logger = logging.getLogger(__name__) + return logger + + +def get_parser(): + parser = argparse.ArgumentParser( + description="Quantize using K-means clustering over acoustic features." + ) + parser.add_argument( + "--feature_type", + type=str, + choices=["logmel", "hubert", "w2v2", "cpc"], + default=None, + required=True, + help="Acoustic feature type", + ) + parser.add_argument( + "--acoustic_model_path", + type=str, + help="Pretrained acoustic model checkpoint" + ) + parser.add_argument( + "--layer", + type=int, + help="The layer of the pretrained model to extract features from", + default=-1, + ) + parser.add_argument( + "--kmeans_model_path", + type=str, + required=True, + help="K-means model file path to use for inference", + ) + parser.add_argument( + "--features_path", + type=str, + default=None, + help="Features file path. You don't need to enter acoustic model details if you have dumped features", + ) + parser.add_argument( + "--manifest_path", + type=str, + default=None, + help="Manifest file containing the root dir and file names", + ) + parser.add_argument( + "--out_quantized_file_path", + required=True, + type=str, + help="File path of quantized output.", + ) + parser.add_argument( + "--extension", type=str, default=".flac", help="Features file path" + ) + return parser + + +def main(args, logger): + # Feature extraction + if args.features_path is not None: + logger.info(f"Loading acoustic features from {args.features_path}...") + features_batch = np.load(args.features_path) + else: + logger.info(f"Extracting {args.feature_type} acoustic features...") + features_batch = get_features( + feature_type=args.feature_type, + checkpoint_path=args.acoustic_model_path, + layer=args.layer, + manifest_path=args.manifest_path, + sample_pct=1.0, + flatten=False, + ) + logger.info( + f"Features extracted for {len(features_batch)} utterances.\n" + ) + logger.info( + f"Dimensionality of representation = {features_batch[0].shape[1]}" + ) + + # K-means model + logger.info(f"Loading K-means model from {args.kmeans_model_path} ...") + kmeans_model = joblib.load(open(args.kmeans_model_path, "rb")) + kmeans_model.verbose = False + + _, fnames, _ = get_audio_files(args.manifest_path) + + os.makedirs(os.path.dirname(args.out_quantized_file_path), exist_ok=True) + print(f"Writing quantized predictions to {args.out_quantized_file_path}") + with open(args.out_quantized_file_path, "w") as fout: + for i, feats in enumerate(features_batch): + pred = kmeans_model.predict(feats) + pred_str = " ".join(str(p) for p in pred) + base_fname = os.path.basename(fnames[i]).rstrip(args.extension) + fout.write(f"{base_fname}|{pred_str}\n") + + +if __name__ == "__main__": + parser = get_parser() + args = parser.parse_args() + logger = get_logger() + logger.info(args) + main(args, logger) diff --git a/examples/textless_nlp/gslm/speech2unit/clustering/utils.py b/examples/textless_nlp/gslm/speech2unit/clustering/utils.py new file mode 100644 index 0000000000..cf08d1fe4b --- /dev/null +++ b/examples/textless_nlp/gslm/speech2unit/clustering/utils.py @@ -0,0 +1,20 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from typing import List, Tuple + + +def get_audio_files(manifest_path: str) -> Tuple[str, List[str], List[int]]: + fnames, sizes = [], [] + with open(manifest_path, "r") as f: + root_dir = f.readline().strip() + for line in f: + items = line.strip().split("\t") + assert ( + len(items) == 2 + ), f"File must have two columns separated by tab. Got {line}" + fnames.append(items[0]) + sizes.append(int(items[1])) + return root_dir, fnames, sizes diff --git a/examples/textless_nlp/gslm/speech2unit/pretrained/cpc_feature_reader.py b/examples/textless_nlp/gslm/speech2unit/pretrained/cpc_feature_reader.py new file mode 100644 index 0000000000..c613f52d3c --- /dev/null +++ b/examples/textless_nlp/gslm/speech2unit/pretrained/cpc_feature_reader.py @@ -0,0 +1,192 @@ +import soundfile as sf +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class CpcFeatureReader: + """ + Wrapper class to run inference on CPC model. + Helps extract features for a given audio file. + """ + + def __init__( + self, + checkpoint_path, + layer, + use_encoder_layer=False, + norm_features=False, + sample_rate=16000, + max_chunk=64000, + ): + self.model = load_cpc_model(checkpoint_path, layer).eval().cuda() + self.sample_rate = sample_rate + self.max_chunk = max_chunk + self.norm_features = norm_features + self.use_encoder_layer = use_encoder_layer + + def read_audio(self, path, ref_len=None): + wav, sr = sf.read(path) + if wav.ndim == 2: + wav = wav.mean(-1) + assert wav.ndim == 1, wav.ndim + assert sr == self.sample_rate, sr + if ref_len is not None and abs(ref_len - len(wav)) > 160: + print(f"ref {ref_len} != read {len(wav)} ({path})") + return wav + + def get_feats(self, file_path, ref_len=None): + x = self.read_audio(file_path, ref_len) + # Inspired from CPC_audio feature_loader.py + with torch.no_grad(): + x = torch.from_numpy(x).float().cuda() + x = x.view(1, 1, -1) + size = x.size(2) + feat = [] + start = 0 + while start < size: + if start + self.max_chunk > size: + break + x_chunk = x[..., start : start + self.max_chunk] + feat_chunk = self.model.extract_features( + source=x_chunk, + get_encoded=self.use_encoder_layer, + norm_output=self.norm_features, + ) + feat.append(feat_chunk) + start += self.max_chunk + + if start < size: + x_chunk = x[:, -self.max_chunk :] + feat_chunk = self.model.extract_features( + source=x_chunk, + get_encoded=self.use_encoder_layer, + norm_output=self.norm_features, + ) + df = x_chunk.size(2) // feat_chunk.size(1) + delta = (size - start) // df + feat.append(feat_chunk[:, -delta:]) + return torch.cat(feat, 1).squeeze(0) + + +def load_cpc_model(checkpoint_path, layer=None): + state_dict = torch.load(checkpoint_path) + weights = state_dict["weights"] + config = state_dict["config"] + if layer is not None: + config["nLevelsGRU"] = layer + + encoder = CPCEncoder(config["hiddenEncoder"]) + ar_net = CPCAR( + config["hiddenEncoder"], config["hiddenGar"], False, config["nLevelsGRU"] + ) + + model = CPCModel(encoder, ar_net) + model.load_state_dict(weights, strict=False) + model.config = config + + return model + + +class ChannelNorm(nn.Module): + def __init__(self, num_features, epsilon=1e-05, affine=True): + super(ChannelNorm, self).__init__() + if affine: + self.weight = nn.parameter.Parameter(torch.Tensor(1, num_features, 1)) + self.bias = nn.parameter.Parameter(torch.Tensor(1, num_features, 1)) + else: + self.weight = None + self.bias = None + self.epsilon = epsilon + self.p = 0 + self.affine = affine + self.reset_parameters() + + def reset_parameters(self): + if self.affine: + torch.nn.init.ones_(self.weight) + torch.nn.init.zeros_(self.bias) + + def forward(self, x): + cum_mean = x.mean(dim=1, keepdim=True) + cum_var = x.var(dim=1, keepdim=True) + x = (x - cum_mean) * torch.rsqrt(cum_var + self.epsilon) + if self.weight is not None: + x = x * self.weight + self.bias + return x + + +class CPCEncoder(nn.Module): + def __init__(self, hidden_dim=512): + super(CPCEncoder, self).__init__() + self.conv0 = nn.Conv1d(1, hidden_dim, 10, stride=5, padding=3) + self.batchNorm0 = ChannelNorm(hidden_dim) + self.conv1 = nn.Conv1d(hidden_dim, hidden_dim, 8, stride=4, padding=2) + self.batchNorm1 = ChannelNorm(hidden_dim) + self.conv2 = nn.Conv1d(hidden_dim, hidden_dim, 4, stride=2, padding=1) + self.batchNorm2 = ChannelNorm(hidden_dim) + self.conv3 = nn.Conv1d(hidden_dim, hidden_dim, 4, stride=2, padding=1) + self.batchNorm3 = ChannelNorm(hidden_dim) + self.conv4 = nn.Conv1d(hidden_dim, hidden_dim, 4, stride=2, padding=1) + self.batchNorm4 = ChannelNorm(hidden_dim) + self.DOWNSAMPLING = 160 + + def get_output_dim(self): + return self.conv4.out_channels + + def forward(self, x): + x = F.relu(self.batchNorm0(self.conv0(x))) + x = F.relu(self.batchNorm1(self.conv1(x))) + x = F.relu(self.batchNorm2(self.conv2(x))) + x = F.relu(self.batchNorm3(self.conv3(x))) + x = F.relu(self.batchNorm4(self.conv4(x))) + return x + + +class CPCAR(nn.Module): + def __init__(self, dim_encoded, dim_output, keep_hidden, num_layers): + super(CPCAR, self).__init__() + self.baseNet = nn.LSTM( + dim_encoded, dim_output, num_layers=num_layers, batch_first=True + ) + self.hidden = None + self.keep_hidden = keep_hidden + + def get_output_dim(self): + return self.baseNet.hidden_size + + def forward(self, x): + try: + self.baseNet.flatten_parameters() + except RuntimeError: + pass + x, h = self.baseNet(x, self.hidden) + if self.keep_hidden: + if isinstance(h, tuple): + self.hidden = tuple(x.detach() for x in h) + else: + self.hidden = h.detach() + return x + + +class CPCModel(nn.Module): + def __init__(self, encoder, ar_net): + super(CPCModel, self).__init__() + self.gEncoder = encoder + self.gAR = ar_net + self.config = None + + def forward(self, x, label): + encoded = self.gEncoder(x).permute(0, 2, 1) + cpc_feature = self.gAR(encoded) + return cpc_feature, encoded, label + + def extract_features(self, source, get_encoded=False, norm_output=False): + cpc_feature, encoded, _ = self.forward(source, None) + if get_encoded: + cpc_feature = encoded + if norm_output: + mean = cpc_feature.mean(dim=1, keepdim=True) + var = cpc_feature.var(dim=1, keepdim=True) + cpc_feature = (cpc_feature - mean) / torch.sqrt(var + 1e-08) + return cpc_feature diff --git a/examples/textless_nlp/gslm/speech2unit/pretrained/hubert_feature_reader.py b/examples/textless_nlp/gslm/speech2unit/pretrained/hubert_feature_reader.py new file mode 100644 index 0000000000..09442206e1 --- /dev/null +++ b/examples/textless_nlp/gslm/speech2unit/pretrained/hubert_feature_reader.py @@ -0,0 +1,59 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import fairseq +import soundfile as sf +import torch.nn.functional as F + + +class HubertFeatureReader: + """ + Wrapper class to run inference on HuBERT model. + Helps extract features for a given audio file. + """ + + def __init__(self, checkpoint_path, layer, max_chunk=1600000): + ( + model, + cfg, + task, + ) = fairseq.checkpoint_utils.load_model_ensemble_and_task( + [checkpoint_path] + ) + self.model = model[0].eval().cuda() + self.task = task + self.layer = layer + self.max_chunk = max_chunk + + def read_audio(self, path, ref_len=None): + wav, sr = sf.read(path) + if wav.ndim == 2: + wav = wav.mean(-1) + assert wav.ndim == 1, wav.ndim + assert sr == self.task.cfg.sample_rate, sr + if ref_len is not None and abs(ref_len - len(wav)) > 160: + print(f"ref {ref_len} != read {len(wav)} ({path})") + return wav + + def get_feats(self, file_path, ref_len=None): + x = self.read_audio(file_path, ref_len) + with torch.no_grad(): + x = torch.from_numpy(x).float().cuda() + if self.task.cfg.normalize: + x = F.layer_norm(x, x.shape) + x = x.view(1, -1) + + feat = [] + for start in range(0, x.size(1), self.max_chunk): + x_chunk = x[:, start: start + self.max_chunk] + feat_chunk, _ = self.model.extract_features( + source=x_chunk, + padding_mask=None, + mask=False, + output_layer=self.layer, + ) + feat.append(feat_chunk) + return torch.cat(feat, 1).squeeze(0) diff --git a/examples/textless_nlp/gslm/speech2unit/pretrained/logmel_feature_reader.py b/examples/textless_nlp/gslm/speech2unit/pretrained/logmel_feature_reader.py new file mode 100644 index 0000000000..106f502476 --- /dev/null +++ b/examples/textless_nlp/gslm/speech2unit/pretrained/logmel_feature_reader.py @@ -0,0 +1,30 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import soundfile as sf +import torch +import torchaudio.compliance.kaldi as kaldi + + +class LogMelFeatureReader: + """ + Wrapper class to run inference on HuBERT model. + Helps extract features for a given audio file. + """ + + def __init__(self, *args, **kwargs): + self.num_mel_bins = kwargs.get("num_mel_bins", 80) + self.frame_length = kwargs.get("frame_length", 25.0) + + def get_feats(self, file_path): + wav, sr = sf.read(file_path) + feats = torch.from_numpy(wav).float() + feats = kaldi.fbank( + feats.unsqueeze(0), + num_mel_bins=self.num_mel_bins, + frame_length=self.frame_length, + sample_frequency=sr, + ) + return feats diff --git a/examples/textless_nlp/gslm/speech2unit/pretrained/utils.py b/examples/textless_nlp/gslm/speech2unit/pretrained/utils.py new file mode 100644 index 0000000000..5aaddf6421 --- /dev/null +++ b/examples/textless_nlp/gslm/speech2unit/pretrained/utils.py @@ -0,0 +1,126 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import gc +import os +import random +import shutil +import numpy as np + +import torch +import tqdm +from examples.textless_nlp.gslm.speech2unit.pretrained.cpc_feature_reader import ( + CpcFeatureReader, +) +from examples.textless_nlp.gslm.speech2unit.pretrained.hubert_feature_reader import ( + HubertFeatureReader, +) +from examples.textless_nlp.gslm.speech2unit.pretrained.logmel_feature_reader import ( + LogMelFeatureReader, +) +from examples.textless_nlp.gslm.speech2unit.pretrained.w2v2_feature_reader import ( + Wav2VecFeatureReader, +) + + +def get_feature_reader(feature_type): + if feature_type == "logmel": + return LogMelFeatureReader + elif feature_type == "hubert": + return HubertFeatureReader + elif feature_type == "w2v2": + return Wav2VecFeatureReader + elif feature_type == "cpc": + return CpcFeatureReader + else: + raise NotImplementedError(f"{feature_type} is not supported.") + + +def get_feature_iterator( + feature_type, checkpoint_path, layer, manifest_path, sample_pct +): + feature_reader_cls = get_feature_reader(feature_type) + with open(manifest_path, "r") as fp: + lines = fp.read().split("\n") + root = lines.pop(0).strip() + file_path_list = [ + os.path.join(root, line.split("\t")[0]) + for line in lines + if len(line) > 0 + ] + if sample_pct < 1.0: + file_path_list = random.sample( + file_path_list, int(sample_pct * len(file_path_list)) + ) + num_files = len(file_path_list) + reader = feature_reader_cls( + checkpoint_path=checkpoint_path, layer=layer + ) + + def iterate(): + for file_path in file_path_list: + feats = reader.get_feats(file_path) + yield feats.cpu().numpy() + + return iterate, num_files + + +def get_features( + feature_type, checkpoint_path, layer, manifest_path, sample_pct, flatten +): + generator, num_files = get_feature_iterator( + feature_type=feature_type, + checkpoint_path=checkpoint_path, + layer=layer, + manifest_path=manifest_path, + sample_pct=sample_pct, + ) + iterator = generator() + + features_list = [] + for features in tqdm.tqdm(iterator, total=num_files): + features_list.append(features) + + # Explicit clean up + del iterator + del generator + gc.collect() + torch.cuda.empty_cache() + + if flatten: + return np.concatenate(features_list) + + return features_list + + +def get_and_dump_features( + feature_type, + checkpoint_path, + layer, + manifest_path, + sample_pct, + flatten, + out_features_path, +): + # Feature extraction + features_batch = get_features( + feature_type=feature_type, + checkpoint_path=checkpoint_path, + layer=layer, + manifest_path=manifest_path, + sample_pct=sample_pct, + flatten=flatten, + ) + + # Save features + out_dir_path = os.path.dirname(out_features_path) + os.makedirs(out_dir_path, exist_ok=True) + shutil.copyfile( + manifest_path, + os.path.join(out_dir_path, os.path.basename(manifest_path)), + ) + np.save(out_features_path, features_batch) + + return features_batch diff --git a/examples/textless_nlp/gslm/speech2unit/pretrained/w2v2_feature_reader.py b/examples/textless_nlp/gslm/speech2unit/pretrained/w2v2_feature_reader.py new file mode 100644 index 0000000000..b878321e44 --- /dev/null +++ b/examples/textless_nlp/gslm/speech2unit/pretrained/w2v2_feature_reader.py @@ -0,0 +1,46 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import fairseq +import soundfile as sf + + +class Wav2VecFeatureReader: + """ + Wrapper class to run inference on Wav2Vec 2.0 model. + Helps extract features for a given audio file. + """ + + def __init__(self, checkpoint_path, layer): + state = fairseq.checkpoint_utils.load_checkpoint_to_cpu( + checkpoint_path + ) + + w2v_args = state["args"] + self.task = fairseq.tasks.setup_task(w2v_args) + model = self.task.build_model(w2v_args) + model.load_state_dict(state["model"], strict=True) + model.eval() + model.cuda() + self.model = model + self.layer = layer + + def read_audio(self, fname): + wav, sr = sf.read(fname) + if wav.ndim == 2: + wav = wav.mean(-1) + assert wav.ndim == 1, wav.ndim + assert sr == self.task.cfg.sample_rate, sr + return wav + + def get_feats(self, file_path): + x = self.read_audio(file_path) + with torch.no_grad(): + source = torch.from_numpy(x).view(1, -1).float().cuda() + res = self.model( + source=source, mask=False, features_only=True, layer=self.layer + ) + return res["layer_results"][self.layer][0].squeeze(1) diff --git a/examples/textless_nlp/gslm/tools/README.md b/examples/textless_nlp/gslm/tools/README.md new file mode 100644 index 0000000000..61fcbbded8 --- /dev/null +++ b/examples/textless_nlp/gslm/tools/README.md @@ -0,0 +1,22 @@ +# GSLM Tools + +## Resynthesis +You can use the command line tool below to input an audio file and get the resynthesized audio. This tool implements the unsupervised method for resynthesis described in the paper. The way to invoke the command line tool is shown below. +``` +FAIRSEQ_ROOT=<path_to_your_fairseq_repo_root> +TYPE=<one_of_logmel/cpc/hubert/w2v2> +ACOUSTIC_MODEL_PATH=<path_of_pretrained_acoustic_model> +LAYER=<layer_of_acoustic_model_to_extract_features_from> +KM_MODEL_PATH=<output_path_of_the_kmeans_model> +TTS_MODEL_PATH=<unit2speech_model_file_path> +WAVEGLOW_PATH=<path_where_you_have_downloaded_waveglow_checkpoint> + +PYTHONPATH=${FAIRSEQ_ROOT}:${FAIRSEQ_ROOT}/examples/textless_nlp/gslm/unit2speech python ${FAIRSEQ_ROOT}/examples/textless_nlp/gslm/tools/gen_speech.py \ + --feature_type $TYPE \ + --acoustic_model_path $ACOUSTIC_MODEL_PATH \ + --layer $LAYER \ + --kmeans_model_path $KM_MODEL_PATH \ + --tts_model_path $TTS_MODEL_PATH \ + --waveglow_path $WAVEGLOW_PATH \ + --max_decoder_steps 2000 +``` \ No newline at end of file diff --git a/examples/textless_nlp/gslm/tools/resynthesize_speech.py b/examples/textless_nlp/gslm/tools/resynthesize_speech.py new file mode 100644 index 0000000000..2b6215d372 --- /dev/null +++ b/examples/textless_nlp/gslm/tools/resynthesize_speech.py @@ -0,0 +1,138 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import gc +import logging + +import joblib +import soundfile as sf +import torch +from examples.textless_nlp.gslm.speech2unit.pretrained.utils import ( + get_feature_reader, +) +from examples.textless_nlp.gslm.unit2speech.tts_data import ( + TacotronInputDataset, +) +from examples.textless_nlp.gslm.unit2speech.utils import ( + load_tacotron, + load_waveglow, + synthesize_audio, +) + + +def get_logger(): + log_format = "[%(asctime)s] [%(levelname)s]: %(message)s" + logging.basicConfig(format=log_format, level=logging.INFO) + logger = logging.getLogger(__name__) + return logger + + +def get_parser(): + parser = argparse.ArgumentParser( + description="GSLM speech resynthesis tool." + ) + parser.add_argument( + "--feature_type", + type=str, + choices=["logmel", "hubert", "w2v2", "cpc"], + default=None, + required=True, + help="Acoustic feature type", + ) + parser.add_argument( + "--acoustic_model_path", + type=str, + help="Pretrained acoustic model checkpoint", + ) + parser.add_argument( + "--layer", type=int, help="Layer of acoustic model" + ) + parser.add_argument( + "--kmeans_model_path", + type=str, + required=True, + help="K-means model file path to use for inference", + ) + parser.add_argument( + "--tts_model_path", + type=str, + help="TTS model file path to use for inference", + ) + parser.add_argument( + "--waveglow_path", + type=str, + help="Waveglow (vocoder) model file path to use for inference", + ) + parser.add_argument("--max_decoder_steps", type=int, default=2000) + parser.add_argument("--denoiser_strength", type=float, default=0.1) + return parser + + +################################################ +def main(args, logger): + # Acoustic Model + logger.info(f"Loading acoustic model from {args.tts_model_path}...") + feature_reader_cls = get_feature_reader(args.feature_type) + reader = feature_reader_cls( + checkpoint_path=args.acoustic_model_path, layer=args.layer + ) + + # K-means Model + logger.info(f"Loading K-means model from {args.kmeans_model_path} ...") + kmeans_model = joblib.load(open(args.kmeans_model_path, "rb")) + kmeans_model.verbose = False + + # TTS Model + logger.info(f"Loading TTS model from {args.tts_model_path}...") + tacotron_model, sample_rate, hparams = load_tacotron( + tacotron_model_path=args.tts_model_path, + max_decoder_steps=args.max_decoder_steps, + ) + + # Waveglow Model + logger.info(f"Loading Waveglow model from {args.waveglow_path}...") + waveglow, denoiser = load_waveglow(waveglow_path=args.waveglow_path) + + # Dataset + tts_dataset = TacotronInputDataset(hparams) + + iters = 0 + while True: + in_file_path = input( + "Input: Enter the full file path of audio file...\n" + ) + out_file_path = input( + "Output: Enter the full file path of audio file...\n" + ) + feats = reader.get_feats(in_file_path).cpu().numpy() + iters += 1 + if iters == 1000: + gc.collect() + torch.cuda.empty_cache() + + quantized_units = kmeans_model.predict(feats) + quantized_units_str = " ".join(map(str, quantized_units)) + + tts_input = tts_dataset.get_tensor(quantized_units_str) + mel, aud, aud_dn, has_eos = synthesize_audio( + tacotron_model, + waveglow, + denoiser, + tts_input.unsqueeze(0), + strength=args.denoiser_strength, + ) + sf.write( + f"{out_file_path}", aud_dn[0].cpu().float().numpy(), sample_rate + ) + logger.info("Resynthesis done!\n") + + +if __name__ == "__main__": + parser = get_parser() + args = parser.parse_args() + logger = get_logger() + logger.info(args) + main(args, logger) diff --git a/examples/textless_nlp/gslm/ulm/README.md b/examples/textless_nlp/gslm/ulm/README.md new file mode 100644 index 0000000000..01459121ce --- /dev/null +++ b/examples/textless_nlp/gslm/ulm/README.md @@ -0,0 +1,72 @@ +# Unit Language Model (ULM) + +Here you can find links to the pre-trained ULMs and instructions on training new models using fairseq. At the end of the page, we also share how to run sampling for those models and provide pointers to the transcribed prompts we used. + +## Pre-trained models + +Using the links below, you can download pre-trained models for various unit types and vocabulary sizes: + +| | 50 | 100 | 200 +|-|-|-|- +| LogMel Filterbank | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/logmel/lm_km50/logmel50_lm.tgz) | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/logmel/lm_km100/logmel100_lm.tgz) | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/logmel/lm_km200/logmel200_lm.tgz) +| Modified CPC | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/cpc/lm_km50/cpc50_lm.tgz) | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/cpc/lm_km100/cpc100_lm.tgz) | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/cpc/lm_km200/cpc200_lm.tgz) +| HuBERT | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/hubert/lm_km50/hubert50_lm.tgz) | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/hubert/lm_km100/hubert100_lm.tgz) | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/hubert/lm_km200/hubert200_lm.tgz) +| Wav2Vec 2.0 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/w2v2/lm_km50/w2v2_50_lm.tgz) | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/w2v2/lm_km100/w2v2_100_lm.tgz) | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/w2v2/lm_km200/w2v2_200_lm.tgz) + + +## Preprocessing data +Assuming that unit-transcribed train, valid, and test sets are located in `data/train.txt`, `data/valid.txt`, and `data/test.txt`, respectively, +we run the following command to get a preprocessed version of the datast in `data-bin`: + +```bash +fairseq-preprocess --only-source \ + --trainpref data/train.txt --validpref data/valid.txt --testpref data/test.txt \ + --destdir data-bin/ --workers 40 +``` +As a result, the `data-bin` directory should appear. + +## Fitting a Unit Language Model (ULM) +As an ULM, we train a standard fairseq Transformer LM. Assuming 8 GPUs used for training, a good starting point for an ULM training would be: +```bash + fairseq-train data-bin/ \ + --task=language_modeling \ + --arch=transformer_lm_big \ + --share-decoder-input-output-embed \ + --dropout=0.1 \ + --attention-dropout=0.1 \ + --optimizer=adam \ + --adam-betas='(0.9, 0.98)' \ + --clip-norm=1.0 \ + --lr=0.0005 \ + --lr-scheduler=inverse_sqrt \ + --warmup-updates=4000 \ + --warmup-init-lr=1e-07 \ + --tokens-per-sample=3072 \ + --update-freq=16 \ + --max-tokens=4096 \ + --num-workers=4 \ + --skip-invalid-size-inputs-valid-test \ + --max-update=500000 \ + --log-interval=10 \ + --seed=100501 \ + --fp16 \ + --sample-break-mode=eos +``` +This command will train a Transformer-large model (12 layers). You can train other standard LM models provided by fairseq, e.g. specify `--arch=transformer_lm` to train a smaller (6-layer) Transformer model. When training with a different number of GPUs, it might be a good idea to adjust the `update-freq` parameter. To save the GPU memory at an expense of additional computation, it can be useful to enable activation checkpointing with `--checkpoint-activations`. + +## Sampling from an ULM +Once an ULM was trained, we can use it for generating new utterances. Suppose, that the prompts are given in a file named `prompts.txt`. Then we can sample continuations by running the following command: + +```bash + python sample.py data-bin/ \ + --path=checkpoints/checkpoint_best.pt --task=language_modeling --sampling --temperature=0.7 \ + --seed=1 --prompts=prompts.txt --output=samples.txt --max-len-a=0 --max-len-b=500 \ + --prefix-size=-1 --batch-size=16 --fp16 --samples-per-prompt=10 +``` +Here, `--prefix-size` controls the number of tokens that are used to prime the ULM. When set to a positive value, the sampling script will take first `prefix-size` tokens to prompt the ULM; with `0` it runs unconditional sampling and with `-1` the entire prompt is used. +`--samples-per-prompt` specifies how many utterances are generated with every prompt which can be useful when generating multiple prompt continuations. In this command, `--max-len-a` and `--max-len-b` control the number of generated tokens. + +When using a pretrained model from above, `data-bin` should point to the unpacked directory (with `dict.txt` file). + +Evaluation-time, to generate prompts, we used utterances from LibriSpeech dev-clean and test-clean that are longer than 6s. We took first 3s from an utterance as a prompt. Unit transcripts of those prompts can be downloaded here: [[dev]](https://dl.fbaipublicfiles.com/textless_nlp/gslm/eval_data/dev_prompts.tgz) [[test]](https://dl.fbaipublicfiles.com/textless_nlp/gslm/eval_data/test_prompts.tgz) + diff --git a/examples/textless_nlp/gslm/ulm/sample.py b/examples/textless_nlp/gslm/ulm/sample.py new file mode 100644 index 0000000000..77302a6894 --- /dev/null +++ b/examples/textless_nlp/gslm/ulm/sample.py @@ -0,0 +1,174 @@ +#!/usr/bin/env python3 -u +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +""" +Sample from a trained LM; hacked fairseq-interactive +""" +from collections import namedtuple +import os +import ast +import numpy as np + +from fairseq import checkpoint_utils, options, tasks, utils + +import tqdm + +Batch = namedtuple('Batch', 'ids src_tokens src_lengths') +Translation = namedtuple('Translation', 'src_str hypos pos_scores alignments') + + +def make_batches(lines, args, task, max_positions): + tokens = [ + task.source_dictionary.encode_line( + src_str, add_if_not_exist=False + ).long() + for src_str in lines + ] + lengths = [t.numel() for t in tokens] + itr = task.get_batch_iterator( + dataset=task.build_dataset_for_inference(tokens, lengths), + max_tokens=args.dataset.max_tokens, + max_sentences=args.dataset.batch_size, + max_positions=max_positions, + ignore_invalid_inputs=args.dataset.skip_invalid_size_inputs_valid_test + ).next_epoch_itr(shuffle=False) + for batch in itr: + yield Batch( + ids=batch['id'], + src_tokens=batch['net_input']['src_tokens'], src_lengths=batch['net_input']['src_lengths'], + ) + + +def main(args): + arg_prompts = args.prompts + arg_output = args.output + arg_debug = args.debug + arg_sample_size = args.samples_per_prompt + + try: + from fairseq.dataclass.utils import convert_namespace_to_omegaconf + args = convert_namespace_to_omegaconf(args) + except: + pass + + # if args.max_tokens is None and args.max_sentences is None: + if args.common.seed is not None: + np.random.seed(args.common.seed) + utils.set_torch_seed(args.common.seed) + + if args.generation.sampling: + args.generation.nbest = args.generation.beam = arg_sample_size + + task = tasks.setup_task(args.task) + + overrides = ast.literal_eval(args.common_eval.model_overrides) + + models, _model_args = checkpoint_utils.load_model_ensemble( + args.common_eval.path.split(os.pathsep), + arg_overrides=overrides, + task=task, + suffix=getattr(args, "checkpoint_suffix", ""), + ) + + # Set dictionaries + src_dict = task.source_dictionary + tgt_dict = task.target_dictionary + + # Optimize ensemble for generation + for model in models: + model.prepare_for_inference_(args) + model.cuda() + + # Load alignment dictionary for unknown word replacement + # (None if no unknown word replacement, empty if no path to align dictionary) + align_dict = utils.load_align_dict(args.generation.replace_unk) + + max_positions = utils.resolve_max_positions( + task.max_positions(), + *[model.max_positions() for model in models] + ) + + output_file = open(arg_output, 'w') + + with open(arg_prompts, 'r') as fin: + lines = fin.readlines() + + split = [x.split('|', 1) for x in lines] + seq_id = [x[0] for x in split] + prompts = [x[1] for x in split] + + if args.generation.prefix_size >= 0: + prompts = [' '.join(l.split()[:args.generation.prefix_size]) + for l in prompts] + + if arg_debug: + prompts = prompts[:10] + + generator = task.build_generator(models, args.generation) + + start_id = 0 + pbar = tqdm.tqdm(total=len(prompts)) + for batch in make_batches(prompts, args, task, max_positions): + src_tokens = batch.src_tokens + src_lengths = batch.src_lengths + src_tokens = src_tokens.cuda() + src_lengths = src_lengths.cuda() + + sample = { + 'net_input': { + 'src_tokens': src_tokens, + 'src_lengths': src_lengths, + }, + } + + results = [] + translations = task.inference_step(generator, models, sample) + for i, (id, hypos) in enumerate(zip(batch.ids.tolist(), translations)): + src_tokens_i = utils.strip_pad(src_tokens[i], tgt_dict.pad()) + results.append((i + start_id, src_tokens_i, hypos)) + + # sort output to match input order + for id, src_tokens, hypos in sorted(results, key=lambda x: x[0]): + if src_dict is not None: + src_str = src_dict.string( + src_tokens, args.common_eval.post_process) + + # Process top predictions + for hypo_id, hypo in enumerate(hypos): + _hypo_tokens, hypo_str, _alignment = utils.post_process_prediction( + hypo_tokens=hypo['tokens'].int().cpu(), + src_str=src_str, + alignment=hypo['alignment'], + align_dict=align_dict, + tgt_dict=tgt_dict, + remove_bpe=args.common_eval.post_process, + ) + + detok_hypo_str = hypo_str + utterance = detok_hypo_str + print(f'{seq_id[id]}__{hypo_id}|{utterance}', file=output_file) + pbar.update(1) + start_id += len(results) + + # output_file.close() + + +def cli_main(): + parser = options.get_interactive_generation_parser() + parser.add_argument('--prompts', type=str, default=None, required=True) + parser.add_argument('--output', type=str, default=None, required=True) + parser.add_argument('--debug', action='store_true') + parser.add_argument('--samples-per-prompt', type=int, default=1) + + args = options.parse_args_and_arch(parser) + + np.random.seed(args.seed) + utils.set_torch_seed(args.seed) + + main(args) + + +if __name__ == '__main__': + cli_main() diff --git a/examples/textless_nlp/gslm/unit2speech/README.md b/examples/textless_nlp/gslm/unit2speech/README.md new file mode 100644 index 0000000000..5710423065 --- /dev/null +++ b/examples/textless_nlp/gslm/unit2speech/README.md @@ -0,0 +1,42 @@ +# Unit to Speech Model (unit2speech) + +Unit to speech model is modified Tacotron2 model that learns to synthesize speech from discrete speech units. All models are trained on quantized [LJSpeech](https://keithito.com/LJ-Speech-Dataset/). + +Upstream Units | Download Link +|-|- +Log Mel Filterbank + KM50 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/logmel/tts_km50/tts_checkpoint_best.pt) +Log Mel Filterbank + KM100 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/logmel/tts_km100/tts_checkpoint_best.pt) +Log Mel Filterbank + KM200 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/logmel/tts_km200/tts_checkpoint_best.pt) +Log Mel Filterbank + KM500 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/logmel/tts_km500/tts_checkpoint_best.pt) +Modified CPC + KM50 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/cpc/tts_km50/tts_checkpoint_best.pt) +Modified CPC + KM100 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/cpc/tts_km100/tts_checkpoint_best.pt) +Modified CPC + KM200 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/cpc/tts_km200/tts_checkpoint_best.pt) +Modified CPC + KM500 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/cpc/tts_km500/tts_checkpoint_best.pt) +HuBERT Base + KM50 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/hubert/tts_km50/tts_checkpoint_best.pt) +HuBERT Base + KM100 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/hubert/tts_km100/tts_checkpoint_best.pt) +HuBERT Base + KM200 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/hubert/tts_km200/tts_checkpoint_best.pt) +HuBERT Base + KM500 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/hubert/tts_km500/tts_checkpoint_best.pt) +wav2vec 2.0 Large + KM50 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/w2v2/tts_km50/tts_checkpoint_best.pt) +wav2vec 2.0 Large + KM100 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/w2v2/tts_km100/tts_checkpoint_best.pt) +wav2vec 2.0 Large + KM200 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/w2v2/tts_km200/tts_checkpoint_best.pt) +wav2vec 2.0 Large + KM500 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/w2v2/tts_km500/tts_checkpoint_best.pt) + +## Run inference using a unit2speech model +* Install librosa, unidecode and inflect using `pip install librosa, unidecode, inflect` +* Download [Waveglow checkpoint](https://dl.fbaipublicfiles.com/textless_nlp/gslm/waveglow_256channels_new.pt). This is the vocoder. + +Sample commnd to run inference using trained unit2speech models. Please note that the quantized audio to synthesized should be using the same units as the unit2speech model was trained with. +``` +FAIRSEQ_ROOT=<path_to_your_fairseq_repo_root> +TTS_MODEL_PATH=<unit2speech_model_file_path> +QUANTIZED_UNIT_PATH=<quantized_audio_file_path> +OUT_DIR=<dir_to_dump_synthesized_audio_files> +WAVEGLOW_PATH=<path_where_you_have_downloaded_waveglow_checkpoint> + +PYTHONPATH=${FAIRSEQ_ROOT}:${FAIRSEQ_ROOT}/examples/textless_nlp/gslm/unit2speech python ${FAIRSEQ_ROOT}/examples/textless_nlp/gslm/unit2speech/synthesize_audio_from_units.py \ + --tts_model_path $TTS_MODEL_PATH \ + --quantized_unit_path $QUANTIZED_UNIT_PATH \ + --out_audio_dir $OUT_DIR \ + --waveglow_path $WAVEGLOW_PATH \ + --max_decoder_steps 2000 +``` \ No newline at end of file diff --git a/examples/textless_nlp/gslm/unit2speech/convert_to_16k.py b/examples/textless_nlp/gslm/unit2speech/convert_to_16k.py new file mode 100644 index 0000000000..2be848fcea --- /dev/null +++ b/examples/textless_nlp/gslm/unit2speech/convert_to_16k.py @@ -0,0 +1,56 @@ +import os +import shlex +import subprocess +import progressbar +from time import time +from pathlib import Path + +def find_all_files(path_dir, extension): + out = [] + for root, dirs, filenames in os.walk(path_dir): + for f in filenames: + if f.endswith(extension): + out.append(((str(Path(f).stem)), os.path.join(root, f))) + return out + +def convert16k(inputfile, outputfile16k): + command = ('sox -c 1 -b 16 {} -t wav {} rate 16k'.format(inputfile, outputfile16k)) + subprocess.call(shlex.split(command)) + +if __name__ == "__main__": + import argparse + + parser = argparse.ArgumentParser(description='Convert to wav 16k audio using sox.') + parser.add_argument('input_dir', type=str, + help='Path to the input dir.') + parser.add_argument('output_dir', type=str, + help='Path to the output dir.') + parser.add_argument('--extension', type=str, default='wav', + help='Audio file extension in the input. Default: mp3') + args = parser.parse_args() + + # Find all sequences + print(f"Finding all audio files with extension '{args.extension}' from {args.input_dir}...") + audio_files = find_all_files(args.input_dir, args.extension) + print(f"Done! Found {len(audio_files)} files.") + + # Convert to relative path + audio_files = [os.path.relpath(file[-1], start=args.input_dir) for file in audio_files] + + # Create all the directories needed + rel_dirs_set = set([os.path.dirname(file) for file in audio_files]) + for rel_dir in rel_dirs_set: + Path(os.path.join(args.output_dir, rel_dir)).mkdir(parents=True, exist_ok=True) + + # Converting wavs files + print("Converting the audio to wav files...") + bar = progressbar.ProgressBar(maxval=len(audio_files)) + bar.start() + start_time = time() + for index, file in enumerate(audio_files): + bar.update(index) + input_file = os.path.join(args.input_dir, file) + output_file = os.path.join(args.output_dir, os.path.splitext(file)[0]+".wav") + convert16k(input_file, output_file) + bar.finish() + print(f"...done {len(audio_files)} files in {time()-start_time} seconds.") \ No newline at end of file diff --git a/examples/textless_nlp/gslm/unit2speech/glow.py b/examples/textless_nlp/gslm/unit2speech/glow.py new file mode 100644 index 0000000000..7a7696403d --- /dev/null +++ b/examples/textless_nlp/gslm/unit2speech/glow.py @@ -0,0 +1,311 @@ +# ***************************************************************************** +# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: +# * Redistributions of source code must retain the above copyright +# notice, this list of conditions and the following disclaimer. +# * Redistributions in binary form must reproduce the above copyright +# notice, this list of conditions and the following disclaimer in the +# documentation and/or other materials provided with the distribution. +# * Neither the name of the NVIDIA CORPORATION nor the +# names of its contributors may be used to endorse or promote products +# derived from this software without specific prior written permission. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +# DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY +# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES +# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; +# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND +# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS +# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +# +# ***************************************************************************** +import copy +import torch +from torch.autograd import Variable +import torch.nn.functional as F + + +@torch.jit.script +def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels): + n_channels_int = n_channels[0] + in_act = input_a+input_b + t_act = torch.tanh(in_act[:, :n_channels_int, :]) + s_act = torch.sigmoid(in_act[:, n_channels_int:, :]) + acts = t_act * s_act + return acts + + +class WaveGlowLoss(torch.nn.Module): + def __init__(self, sigma=1.0): + super(WaveGlowLoss, self).__init__() + self.sigma = sigma + + def forward(self, model_output): + z, log_s_list, log_det_W_list = model_output + for i, log_s in enumerate(log_s_list): + if i == 0: + log_s_total = torch.sum(log_s) + log_det_W_total = log_det_W_list[i] + else: + log_s_total = log_s_total + torch.sum(log_s) + log_det_W_total += log_det_W_list[i] + + loss = torch.sum(z*z)/(2*self.sigma*self.sigma) - log_s_total - log_det_W_total + return loss/(z.size(0)*z.size(1)*z.size(2)) + + +class Invertible1x1Conv(torch.nn.Module): + """ + The layer outputs both the convolution, and the log determinant + of its weight matrix. If reverse=True it does convolution with + inverse + """ + def __init__(self, c): + super(Invertible1x1Conv, self).__init__() + self.conv = torch.nn.Conv1d(c, c, kernel_size=1, stride=1, padding=0, + bias=False) + + # Sample a random orthonormal matrix to initialize weights + W = torch.qr(torch.FloatTensor(c, c).normal_())[0] + + # Ensure determinant is 1.0 not -1.0 + if torch.det(W) < 0: + W[:,0] = -1*W[:,0] + W = W.view(c, c, 1) + self.conv.weight.data = W + + def forward(self, z, reverse=False): + # shape + batch_size, group_size, n_of_groups = z.size() + + W = self.conv.weight.squeeze() + + if reverse: + if not hasattr(self, 'W_inverse'): + # Reverse computation + W_inverse = W.float().inverse() + W_inverse = Variable(W_inverse[..., None]) + if z.type() == 'torch.cuda.HalfTensor': + W_inverse = W_inverse.half() + self.W_inverse = W_inverse + z = F.conv1d(z, self.W_inverse, bias=None, stride=1, padding=0) + return z + else: + # Forward computation + log_det_W = batch_size * n_of_groups * torch.logdet(W) + z = self.conv(z) + return z, log_det_W + + +class WN(torch.nn.Module): + """ + This is the WaveNet like layer for the affine coupling. The primary difference + from WaveNet is the convolutions need not be causal. There is also no dilation + size reset. The dilation only doubles on each layer + """ + def __init__(self, n_in_channels, n_mel_channels, n_layers, n_channels, + kernel_size): + super(WN, self).__init__() + assert(kernel_size % 2 == 1) + assert(n_channels % 2 == 0) + self.n_layers = n_layers + self.n_channels = n_channels + self.in_layers = torch.nn.ModuleList() + self.res_skip_layers = torch.nn.ModuleList() + + start = torch.nn.Conv1d(n_in_channels, n_channels, 1) + start = torch.nn.utils.weight_norm(start, name='weight') + self.start = start + + # Initializing last layer to 0 makes the affine coupling layers + # do nothing at first. This helps with training stability + end = torch.nn.Conv1d(n_channels, 2*n_in_channels, 1) + end.weight.data.zero_() + end.bias.data.zero_() + self.end = end + + cond_layer = torch.nn.Conv1d(n_mel_channels, 2*n_channels*n_layers, 1) + self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight') + + for i in range(n_layers): + dilation = 2 ** i + padding = int((kernel_size*dilation - dilation)/2) + in_layer = torch.nn.Conv1d(n_channels, 2*n_channels, kernel_size, + dilation=dilation, padding=padding) + in_layer = torch.nn.utils.weight_norm(in_layer, name='weight') + self.in_layers.append(in_layer) + + + # last one is not necessary + if i < n_layers - 1: + res_skip_channels = 2*n_channels + else: + res_skip_channels = n_channels + res_skip_layer = torch.nn.Conv1d(n_channels, res_skip_channels, 1) + res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight') + self.res_skip_layers.append(res_skip_layer) + + def forward(self, forward_input): + audio, spect = forward_input + audio = self.start(audio) + output = torch.zeros_like(audio) + n_channels_tensor = torch.IntTensor([self.n_channels]) + + spect = self.cond_layer(spect) + + for i in range(self.n_layers): + spect_offset = i*2*self.n_channels + acts = fused_add_tanh_sigmoid_multiply( + self.in_layers[i](audio), + spect[:,spect_offset:spect_offset+2*self.n_channels,:], + n_channels_tensor) + + res_skip_acts = self.res_skip_layers[i](acts) + if i < self.n_layers - 1: + audio = audio + res_skip_acts[:,:self.n_channels,:] + output = output + res_skip_acts[:,self.n_channels:,:] + else: + output = output + res_skip_acts + + return self.end(output) + + +class WaveGlow(torch.nn.Module): + def __init__(self, n_mel_channels, n_flows, n_group, n_early_every, + n_early_size, WN_config): + super(WaveGlow, self).__init__() + + self.upsample = torch.nn.ConvTranspose1d(n_mel_channels, + n_mel_channels, + 1024, stride=256) + assert(n_group % 2 == 0) + self.n_flows = n_flows + self.n_group = n_group + self.n_early_every = n_early_every + self.n_early_size = n_early_size + self.WN = torch.nn.ModuleList() + self.convinv = torch.nn.ModuleList() + + n_half = int(n_group/2) + + # Set up layers with the right sizes based on how many dimensions + # have been output already + n_remaining_channels = n_group + for k in range(n_flows): + if k % self.n_early_every == 0 and k > 0: + n_half = n_half - int(self.n_early_size/2) + n_remaining_channels = n_remaining_channels - self.n_early_size + self.convinv.append(Invertible1x1Conv(n_remaining_channels)) + self.WN.append(WN(n_half, n_mel_channels*n_group, **WN_config)) + self.n_remaining_channels = n_remaining_channels # Useful during inference + + def forward(self, forward_input): + """ + forward_input[0] = mel_spectrogram: batch x n_mel_channels x frames + forward_input[1] = audio: batch x time + """ + spect, audio = forward_input + + # Upsample spectrogram to size of audio + spect = self.upsample(spect) + assert(spect.size(2) >= audio.size(1)) + if spect.size(2) > audio.size(1): + spect = spect[:, :, :audio.size(1)] + + spect = spect.unfold(2, self.n_group, self.n_group).permute(0, 2, 1, 3) + spect = spect.contiguous().view(spect.size(0), spect.size(1), -1).permute(0, 2, 1) + + audio = audio.unfold(1, self.n_group, self.n_group).permute(0, 2, 1) + output_audio = [] + log_s_list = [] + log_det_W_list = [] + + for k in range(self.n_flows): + if k % self.n_early_every == 0 and k > 0: + output_audio.append(audio[:,:self.n_early_size,:]) + audio = audio[:,self.n_early_size:,:] + + audio, log_det_W = self.convinv[k](audio) + log_det_W_list.append(log_det_W) + + n_half = int(audio.size(1)/2) + audio_0 = audio[:,:n_half,:] + audio_1 = audio[:,n_half:,:] + + output = self.WN[k]((audio_0, spect)) + log_s = output[:, n_half:, :] + b = output[:, :n_half, :] + audio_1 = torch.exp(log_s)*audio_1 + b + log_s_list.append(log_s) + + audio = torch.cat([audio_0, audio_1],1) + + output_audio.append(audio) + return torch.cat(output_audio,1), log_s_list, log_det_W_list + + def infer(self, spect, sigma=1.0): + spect = self.upsample(spect) + # trim conv artifacts. maybe pad spec to kernel multiple + time_cutoff = self.upsample.kernel_size[0] - self.upsample.stride[0] + spect = spect[:, :, :-time_cutoff] + + spect = spect.unfold(2, self.n_group, self.n_group).permute(0, 2, 1, 3) + spect = spect.contiguous().view(spect.size(0), spect.size(1), -1).permute(0, 2, 1) + + if spect.type() == 'torch.cuda.HalfTensor': + audio = torch.cuda.HalfTensor(spect.size(0), + self.n_remaining_channels, + spect.size(2)).normal_() + else: + audio = torch.cuda.FloatTensor(spect.size(0), + self.n_remaining_channels, + spect.size(2)).normal_() + + audio = torch.autograd.Variable(sigma*audio) + + for k in reversed(range(self.n_flows)): + n_half = int(audio.size(1)/2) + audio_0 = audio[:,:n_half,:] + audio_1 = audio[:,n_half:,:] + + output = self.WN[k]((audio_0, spect)) + + s = output[:, n_half:, :] + b = output[:, :n_half, :] + audio_1 = (audio_1 - b)/torch.exp(s) + audio = torch.cat([audio_0, audio_1],1) + + audio = self.convinv[k](audio, reverse=True) + + if k % self.n_early_every == 0 and k > 0: + if spect.type() == 'torch.cuda.HalfTensor': + z = torch.cuda.HalfTensor(spect.size(0), self.n_early_size, spect.size(2)).normal_() + else: + z = torch.cuda.FloatTensor(spect.size(0), self.n_early_size, spect.size(2)).normal_() + audio = torch.cat((sigma*z, audio),1) + + audio = audio.permute(0,2,1).contiguous().view(audio.size(0), -1).data + return audio + + @staticmethod + def remove_weightnorm(model): + waveglow = model + for WN in waveglow.WN: + WN.start = torch.nn.utils.remove_weight_norm(WN.start) + WN.in_layers = remove(WN.in_layers) + WN.cond_layer = torch.nn.utils.remove_weight_norm(WN.cond_layer) + WN.res_skip_layers = remove(WN.res_skip_layers) + return waveglow + + +def remove(conv_list): + new_conv_list = torch.nn.ModuleList() + for old_conv in conv_list: + old_conv = torch.nn.utils.remove_weight_norm(old_conv) + new_conv_list.append(old_conv) + return new_conv_list diff --git a/examples/textless_nlp/gslm/unit2speech/multiproc.py b/examples/textless_nlp/gslm/unit2speech/multiproc.py new file mode 100644 index 0000000000..2a287a4e97 --- /dev/null +++ b/examples/textless_nlp/gslm/unit2speech/multiproc.py @@ -0,0 +1,27 @@ +import os +import time +import torch +import sys +import subprocess + +argslist = list(sys.argv)[1:] +log_dir = argslist[-1] +num_gpus = torch.cuda.device_count() +argslist.append('--n_gpus={}'.format(num_gpus)) +workers = [] +job_id = time.strftime("%Y_%m_%d-%H%M%S") +argslist.append("--group_name=group_{}".format(job_id)) + +print("GPU log directory is {}".format(log_dir)) +os.makedirs(log_dir, exist_ok=True) +for i in range(num_gpus): + argslist.append('--rank={}'.format(i)) + stdout = None if i == 0 else open("{}/{}_GPU_{}.log".format(log_dir, job_id, i), + "w") + print(argslist) + p = subprocess.Popen([str(sys.executable)]+argslist, stdout=stdout) + workers.append(p) + argslist = argslist[:-1] + +for p in workers: + p.wait() diff --git a/examples/textless_nlp/gslm/unit2speech/synthesize_audio_from_units.py b/examples/textless_nlp/gslm/unit2speech/synthesize_audio_from_units.py new file mode 100644 index 0000000000..f226d5f505 --- /dev/null +++ b/examples/textless_nlp/gslm/unit2speech/synthesize_audio_from_units.py @@ -0,0 +1,97 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import logging +import os + +import soundfile as sf +from examples.textless_nlp.gslm.unit2speech.tts_data import ( + TacotronInputDataset, +) +from examples.textless_nlp.gslm.unit2speech.utils import ( + load_quantized_audio_from_file, + load_tacotron, + load_waveglow, + synthesize_audio, +) + + +def get_logger(): + log_format = "[%(asctime)s] [%(levelname)s]: %(message)s" + logging.basicConfig(format=log_format, level=logging.INFO) + logger = logging.getLogger(__name__) + return logger + + +def get_parser(): + parser = argparse.ArgumentParser( + description="Wav2Vec 2.0 speech generator." + ) + parser.add_argument( + "--quantized_unit_path", + type=str, + help="K-means model file path to use for inference", + ) + parser.add_argument( + "--tts_model_path", + type=str, + help="TTS model file path to use for inference", + ) + parser.add_argument( + "--waveglow_path", + type=str, + help="Path to the waveglow checkpoint (vocoder).", + ) + parser.add_argument("--max_decoder_steps", type=int, default=2000) + parser.add_argument("--denoiser_strength", type=float, default=0.1) + parser.add_argument( + "--out_audio_dir", + type=str, + help="Output directory to dump audio files", + ) + + return parser + + +def main(args, logger): + # Load quantized audio + logger.info(f"Loading quantized audio from {args.quantized_unit_path}...") + names_batch, quantized_units_batch = load_quantized_audio_from_file( + file_path=args.quantized_unit_path + ) + + logger.info(f"Loading TTS model from {args.tts_model_path}...") + tacotron_model, sample_rate, hparams = load_tacotron( + tacotron_model_path=args.tts_model_path, + max_decoder_steps=args.max_decoder_steps, + ) + + logger.info(f"Loading Waveglow model from {args.waveglow_path}...") + waveglow, denoiser = load_waveglow(waveglow_path=args.waveglow_path) + + tts_dataset = TacotronInputDataset(hparams) + for name, quantized_units in zip(names_batch, quantized_units_batch): + quantized_units_str = " ".join(map(str, quantized_units)) + tts_input = tts_dataset.get_tensor(quantized_units_str) + mel, aud, aud_dn, has_eos = synthesize_audio( + tacotron_model, + waveglow, + denoiser, + tts_input.unsqueeze(0), + strength=args.denoiser_strength, + ) + out_file_path = os.path.join(args.out_audio_dir, f"{name}.wav") + sf.write( + f"{out_file_path}", aud_dn[0].cpu().float().numpy(), sample_rate + ) + + +if __name__ == "__main__": + parser = get_parser() + args = parser.parse_args() + logger = get_logger() + logger.info(args) + main(args, logger) diff --git a/examples/textless_nlp/gslm/unit2speech/tacotron2/__init__.py b/examples/textless_nlp/gslm/unit2speech/tacotron2/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/examples/textless_nlp/gslm/unit2speech/tacotron2/audio_processing.py b/examples/textless_nlp/gslm/unit2speech/tacotron2/audio_processing.py new file mode 100644 index 0000000000..b5af7f723e --- /dev/null +++ b/examples/textless_nlp/gslm/unit2speech/tacotron2/audio_processing.py @@ -0,0 +1,93 @@ +import torch +import numpy as np +from scipy.signal import get_window +import librosa.util as librosa_util + + +def window_sumsquare(window, n_frames, hop_length=200, win_length=800, + n_fft=800, dtype=np.float32, norm=None): + """ + # from librosa 0.6 + Compute the sum-square envelope of a window function at a given hop length. + + This is used to estimate modulation effects induced by windowing + observations in short-time fourier transforms. + + Parameters + ---------- + window : string, tuple, number, callable, or list-like + Window specification, as in `get_window` + + n_frames : int > 0 + The number of analysis frames + + hop_length : int > 0 + The number of samples to advance between frames + + win_length : [optional] + The length of the window function. By default, this matches `n_fft`. + + n_fft : int > 0 + The length of each analysis frame. + + dtype : np.dtype + The data type of the output + + Returns + ------- + wss : np.ndarray, shape=`(n_fft + hop_length * (n_frames - 1))` + The sum-squared envelope of the window function + """ + if win_length is None: + win_length = n_fft + + n = n_fft + hop_length * (n_frames - 1) + x = np.zeros(n, dtype=dtype) + + # Compute the squared window at the desired length + win_sq = get_window(window, win_length, fftbins=True) + win_sq = librosa_util.normalize(win_sq, norm=norm)**2 + win_sq = librosa_util.pad_center(win_sq, n_fft) + + # Fill the envelope + for i in range(n_frames): + sample = i * hop_length + x[sample:min(n, sample + n_fft)] += win_sq[:max(0, min(n_fft, n - sample))] + return x + + +def griffin_lim(magnitudes, stft_fn, n_iters=30): + """ + PARAMS + ------ + magnitudes: spectrogram magnitudes + stft_fn: STFT class with transform (STFT) and inverse (ISTFT) methods + """ + + angles = np.angle(np.exp(2j * np.pi * np.random.rand(*magnitudes.size()))) + angles = angles.astype(np.float32) + angles = torch.autograd.Variable(torch.from_numpy(angles)) + signal = stft_fn.inverse(magnitudes, angles).squeeze(1) + + for i in range(n_iters): + _, angles = stft_fn.transform(signal) + signal = stft_fn.inverse(magnitudes, angles).squeeze(1) + return signal + + +def dynamic_range_compression(x, C=1, clip_val=1e-5): + """ + PARAMS + ------ + C: compression factor + """ + return torch.log(torch.clamp(x, min=clip_val) * C) + + +def dynamic_range_decompression(x, C=1): + """ + PARAMS + ------ + C: compression factor used to compress + """ + return torch.exp(x) / C diff --git a/examples/textless_nlp/gslm/unit2speech/tacotron2/cleaners.py b/examples/textless_nlp/gslm/unit2speech/tacotron2/cleaners.py new file mode 100644 index 0000000000..e2e35c1a8c --- /dev/null +++ b/examples/textless_nlp/gslm/unit2speech/tacotron2/cleaners.py @@ -0,0 +1,90 @@ +""" from https://github.com/keithito/tacotron """ + +''' +Cleaners are transformations that run over the input text at both training and eval time. + +Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners" +hyperparameter. Some cleaners are English-specific. You'll typically want to use: + 1. "english_cleaners" for English text + 2. "transliteration_cleaners" for non-English text that can be transliterated to ASCII using + the Unidecode library (https://pypi.python.org/pypi/Unidecode) + 3. "basic_cleaners" if you do not want to transliterate (in this case, you should also update + the symbols in symbols.py to match your data). +''' + +import re +from unidecode import unidecode +from .numbers import normalize_numbers + + +# Regular expression matching whitespace: +_whitespace_re = re.compile(r'\s+') + +# List of (regular expression, replacement) pairs for abbreviations: +_abbreviations = [(re.compile('\\b%s\\.' % x[0], re.IGNORECASE), x[1]) for x in [ + ('mrs', 'misess'), + ('mr', 'mister'), + ('dr', 'doctor'), + ('st', 'saint'), + ('co', 'company'), + ('jr', 'junior'), + ('maj', 'major'), + ('gen', 'general'), + ('drs', 'doctors'), + ('rev', 'reverend'), + ('lt', 'lieutenant'), + ('hon', 'honorable'), + ('sgt', 'sergeant'), + ('capt', 'captain'), + ('esq', 'esquire'), + ('ltd', 'limited'), + ('col', 'colonel'), + ('ft', 'fort'), +]] + + +def expand_abbreviations(text): + for regex, replacement in _abbreviations: + text = re.sub(regex, replacement, text) + return text + + +def expand_numbers(text): + return normalize_numbers(text) + + +def lowercase(text): + return text.lower() + + +def collapse_whitespace(text): + return re.sub(_whitespace_re, ' ', text) + + +def convert_to_ascii(text): + return unidecode(text) + + +def basic_cleaners(text): + '''Basic pipeline that lowercases and collapses whitespace without transliteration.''' + text = lowercase(text) + text = collapse_whitespace(text) + return text + + +def transliteration_cleaners(text): + '''Pipeline for non-English text that transliterates to ASCII.''' + text = convert_to_ascii(text) + text = lowercase(text) + text = collapse_whitespace(text) + return text + + +def english_cleaners(text): + '''Pipeline for English text, including number and abbreviation expansion.''' + text = convert_to_ascii(text) + text = lowercase(text) + text = expand_numbers(text) + text = expand_abbreviations(text) + text = collapse_whitespace(text) + return text diff --git a/examples/textless_nlp/gslm/unit2speech/tacotron2/cmudict.py b/examples/textless_nlp/gslm/unit2speech/tacotron2/cmudict.py new file mode 100644 index 0000000000..62bfef745c --- /dev/null +++ b/examples/textless_nlp/gslm/unit2speech/tacotron2/cmudict.py @@ -0,0 +1,65 @@ +""" from https://github.com/keithito/tacotron """ + +import re + + +valid_symbols = [ + 'AA', 'AA0', 'AA1', 'AA2', 'AE', 'AE0', 'AE1', 'AE2', 'AH', 'AH0', 'AH1', 'AH2', + 'AO', 'AO0', 'AO1', 'AO2', 'AW', 'AW0', 'AW1', 'AW2', 'AY', 'AY0', 'AY1', 'AY2', + 'B', 'CH', 'D', 'DH', 'EH', 'EH0', 'EH1', 'EH2', 'ER', 'ER0', 'ER1', 'ER2', 'EY', + 'EY0', 'EY1', 'EY2', 'F', 'G', 'HH', 'IH', 'IH0', 'IH1', 'IH2', 'IY', 'IY0', 'IY1', + 'IY2', 'JH', 'K', 'L', 'M', 'N', 'NG', 'OW', 'OW0', 'OW1', 'OW2', 'OY', 'OY0', + 'OY1', 'OY2', 'P', 'R', 'S', 'SH', 'T', 'TH', 'UH', 'UH0', 'UH1', 'UH2', 'UW', + 'UW0', 'UW1', 'UW2', 'V', 'W', 'Y', 'Z', 'ZH' +] + +_valid_symbol_set = set(valid_symbols) + + +class CMUDict: + '''Thin wrapper around CMUDict data. http://www.speech.cs.cmu.edu/cgi-bin/cmudict''' + def __init__(self, file_or_path, keep_ambiguous=True): + if isinstance(file_or_path, str): + with open(file_or_path, encoding='latin-1') as f: + entries = _parse_cmudict(f) + else: + entries = _parse_cmudict(file_or_path) + if not keep_ambiguous: + entries = {word: pron for word, pron in entries.items() if len(pron) == 1} + self._entries = entries + + + def __len__(self): + return len(self._entries) + + + def lookup(self, word): + '''Returns list of ARPAbet pronunciations of the given word.''' + return self._entries.get(word.upper()) + + + +_alt_re = re.compile(r'\([0-9]+\)') + + +def _parse_cmudict(file): + cmudict = {} + for line in file: + if len(line) and (line[0] >= 'A' and line[0] <= 'Z' or line[0] == "'"): + parts = line.split(' ') + word = re.sub(_alt_re, '', parts[0]) + pronunciation = _get_pronunciation(parts[1]) + if pronunciation: + if word in cmudict: + cmudict[word].append(pronunciation) + else: + cmudict[word] = [pronunciation] + return cmudict + + +def _get_pronunciation(s): + parts = s.strip().split(' ') + for part in parts: + if part not in _valid_symbol_set: + return None + return ' '.join(parts) diff --git a/examples/textless_nlp/gslm/unit2speech/tacotron2/layers.py b/examples/textless_nlp/gslm/unit2speech/tacotron2/layers.py new file mode 100644 index 0000000000..f10d557ff5 --- /dev/null +++ b/examples/textless_nlp/gslm/unit2speech/tacotron2/layers.py @@ -0,0 +1,103 @@ +import torch +from librosa.filters import mel as librosa_mel_fn +from .audio_processing import dynamic_range_compression +from .audio_processing import dynamic_range_decompression +from .stft import STFT +from .utils import get_mask_from_lengths + + +class LinearNorm(torch.nn.Module): + def __init__(self, in_dim, out_dim, bias=True, w_init_gain='linear'): + super(LinearNorm, self).__init__() + self.linear_layer = torch.nn.Linear(in_dim, out_dim, bias=bias) + + torch.nn.init.xavier_uniform_( + self.linear_layer.weight, + gain=torch.nn.init.calculate_gain(w_init_gain)) + + def forward(self, x): + return self.linear_layer(x) + + +class ConvNorm(torch.nn.Module): + def __init__(self, in_channels, out_channels, kernel_size=1, stride=1, + padding=None, dilation=1, bias=True, w_init_gain='linear'): + super(ConvNorm, self).__init__() + if padding is None: + assert(kernel_size % 2 == 1) + padding = int(dilation * (kernel_size - 1) / 2) + + self.conv = torch.nn.Conv1d(in_channels, out_channels, + kernel_size=kernel_size, stride=stride, + padding=padding, dilation=dilation, + bias=bias) + + torch.nn.init.xavier_uniform_( + self.conv.weight, gain=torch.nn.init.calculate_gain(w_init_gain)) + + def forward(self, signal): + conv_signal = self.conv(signal) + return conv_signal + + +class GlobalAvgPool(torch.nn.Module): + def __init__(self): + super(GlobalAvgPool, self).__init__() + + def forward(self, x, lengths=None): + """Average pooling across time steps (dim=1) with optionally lengths. + Args: + x: torch.Tensor of shape (N, T, ...) + lengths: None or torch.Tensor of shape (N,) + dim: dimension to pool + """ + if lengths is None: + return x.mean(dim=1, keepdim=False) + else: + mask = get_mask_from_lengths(lengths).type(x.type()).to(x.device) + mask_shape = list(mask.size()) + [1 for _ in range(x.ndimension()-2)] + mask = mask.reshape(*mask_shape) + numer = (x * mask).sum(dim=1, keepdim=False) + denom = mask.sum(dim=1, keepdim=False) + return numer / denom + + +class TacotronSTFT(torch.nn.Module): + def __init__(self, filter_length=1024, hop_length=256, win_length=1024, + n_mel_channels=80, sampling_rate=22050, mel_fmin=0.0, + mel_fmax=8000.0): + super(TacotronSTFT, self).__init__() + self.n_mel_channels = n_mel_channels + self.sampling_rate = sampling_rate + self.stft_fn = STFT(filter_length, hop_length, win_length) + mel_basis = librosa_mel_fn( + sampling_rate, filter_length, n_mel_channels, mel_fmin, mel_fmax) + mel_basis = torch.from_numpy(mel_basis).float() + self.register_buffer('mel_basis', mel_basis) + + def spectral_normalize(self, magnitudes): + output = dynamic_range_compression(magnitudes) + return output + + def spectral_de_normalize(self, magnitudes): + output = dynamic_range_decompression(magnitudes) + return output + + def mel_spectrogram(self, y): + """Computes mel-spectrograms from a batch of waves + PARAMS + ------ + y: Variable(torch.FloatTensor) with shape (B, T) in range [-1, 1] + + RETURNS + ------- + mel_output: torch.FloatTensor of shape (B, n_mel_channels, T) + """ + assert(torch.min(y.data) >= -1) + assert(torch.max(y.data) <= 1) + + magnitudes, phases = self.stft_fn.transform(y) + magnitudes = magnitudes.data + mel_output = torch.matmul(self.mel_basis, magnitudes) + mel_output = self.spectral_normalize(mel_output) + return mel_output diff --git a/examples/textless_nlp/gslm/unit2speech/tacotron2/model.py b/examples/textless_nlp/gslm/unit2speech/tacotron2/model.py new file mode 100644 index 0000000000..ccf132b150 --- /dev/null +++ b/examples/textless_nlp/gslm/unit2speech/tacotron2/model.py @@ -0,0 +1,669 @@ +from math import sqrt +import torch +import torch.distributions as distr +from torch.autograd import Variable +from torch import nn +from torch.nn import functional as F +from .layers import ConvNorm, LinearNorm, GlobalAvgPool +from .utils import to_gpu, get_mask_from_lengths + + +class LocationLayer(nn.Module): + def __init__(self, attention_n_filters, attention_kernel_size, + attention_dim): + super(LocationLayer, self).__init__() + padding = int((attention_kernel_size - 1) / 2) + self.location_conv = ConvNorm(2, attention_n_filters, + kernel_size=attention_kernel_size, + padding=padding, bias=False, stride=1, + dilation=1) + self.location_dense = LinearNorm(attention_n_filters, attention_dim, + bias=False, w_init_gain='tanh') + + def forward(self, attention_weights_cat): + processed_attention = self.location_conv(attention_weights_cat) + processed_attention = processed_attention.transpose(1, 2) + processed_attention = self.location_dense(processed_attention) + return processed_attention + + +class Attention(nn.Module): + def __init__(self, attention_rnn_dim, embedding_dim, attention_dim, + attention_location_n_filters, attention_location_kernel_size): + super(Attention, self).__init__() + self.query_layer = LinearNorm(attention_rnn_dim, attention_dim, + bias=False, w_init_gain='tanh') + self.memory_layer = LinearNorm(embedding_dim, attention_dim, bias=False, + w_init_gain='tanh') + self.v = LinearNorm(attention_dim, 1, bias=False) + self.location_layer = LocationLayer(attention_location_n_filters, + attention_location_kernel_size, + attention_dim) + self.score_mask_value = -float("inf") + + def get_alignment_energies(self, query, processed_memory, + attention_weights_cat): + """ + PARAMS + ------ + query: decoder output (batch, n_mel_channels * n_frames_per_step) + processed_memory: processed encoder outputs (B, T_in, attention_dim) + attention_weights_cat: cumulative and prev. att weights (B, 2, max_time) + + RETURNS + ------- + alignment (batch, max_time) + """ + + processed_query = self.query_layer(query.unsqueeze(1)) + processed_attention_weights = self.location_layer(attention_weights_cat) + energies = self.v(torch.tanh( + processed_query + processed_attention_weights + processed_memory)) + + energies = energies.squeeze(-1) + return energies + + def forward(self, attention_hidden_state, memory, processed_memory, + attention_weights_cat, mask): + """ + PARAMS + ------ + attention_hidden_state: attention rnn last output + memory: encoder outputs + processed_memory: processed encoder outputs + attention_weights_cat: previous and cummulative attention weights + mask: binary mask for padded data + """ + alignment = self.get_alignment_energies( + attention_hidden_state, processed_memory, attention_weights_cat) + + if mask is not None: + alignment.data.masked_fill_(mask, self.score_mask_value) + + attention_weights = F.softmax(alignment, dim=1) + attention_context = torch.bmm(attention_weights.unsqueeze(1), memory) + attention_context = attention_context.squeeze(1) + + return attention_context, attention_weights + + +class Prenet(nn.Module): + def __init__(self, in_dim, sizes): + super(Prenet, self).__init__() + in_sizes = [in_dim] + sizes[:-1] + self.layers = nn.ModuleList( + [LinearNorm(in_size, out_size, bias=False) + for (in_size, out_size) in zip(in_sizes, sizes)]) + + def forward(self, x): + for linear in self.layers: + x = F.dropout(F.relu(linear(x)), p=0.5, training=True) + return x + + +class Postnet(nn.Module): + """Postnet + - Five 1-d convolution with 512 channels and kernel size 5 + """ + + def __init__(self, hparams): + super(Postnet, self).__init__() + self.convolutions = nn.ModuleList() + + self.convolutions.append( + nn.Sequential( + ConvNorm(hparams.n_mel_channels, hparams.postnet_embedding_dim, + kernel_size=hparams.postnet_kernel_size, stride=1, + padding=int((hparams.postnet_kernel_size - 1) / 2), + dilation=1, w_init_gain='tanh'), + nn.BatchNorm1d(hparams.postnet_embedding_dim)) + ) + + for i in range(1, hparams.postnet_n_convolutions - 1): + self.convolutions.append( + nn.Sequential( + ConvNorm(hparams.postnet_embedding_dim, + hparams.postnet_embedding_dim, + kernel_size=hparams.postnet_kernel_size, stride=1, + padding=int((hparams.postnet_kernel_size - 1) / 2), + dilation=1, w_init_gain='tanh'), + nn.BatchNorm1d(hparams.postnet_embedding_dim)) + ) + + self.convolutions.append( + nn.Sequential( + ConvNorm(hparams.postnet_embedding_dim, hparams.n_mel_channels, + kernel_size=hparams.postnet_kernel_size, stride=1, + padding=int((hparams.postnet_kernel_size - 1) / 2), + dilation=1, w_init_gain='linear'), + nn.BatchNorm1d(hparams.n_mel_channels)) + ) + + def forward(self, x): + for i in range(len(self.convolutions) - 1): + x = F.dropout(torch.tanh(self.convolutions[i](x)), 0.5, self.training) + x = F.dropout(self.convolutions[-1](x), 0.5, self.training) + + return x + + +class Encoder(nn.Module): + """Encoder module: + - Three 1-d convolution banks + - Bidirectional LSTM + """ + def __init__(self, hparams): + super(Encoder, self).__init__() + + convolutions = [] + for _ in range(hparams.encoder_n_convolutions): + conv_layer = nn.Sequential( + ConvNorm(hparams.encoder_embedding_dim, + hparams.encoder_embedding_dim, + kernel_size=hparams.encoder_kernel_size, stride=1, + padding=int((hparams.encoder_kernel_size - 1) / 2), + dilation=1, w_init_gain='relu'), + nn.BatchNorm1d(hparams.encoder_embedding_dim)) + convolutions.append(conv_layer) + self.convolutions = nn.ModuleList(convolutions) + + self.lstm = nn.LSTM(hparams.encoder_embedding_dim, + int(hparams.encoder_embedding_dim / 2), 1, + batch_first=True, bidirectional=True) + + def forward(self, x, input_lengths): + for conv in self.convolutions: + x = F.dropout(F.relu(conv(x)), 0.5, self.training) + + x = x.transpose(1, 2) + + # pytorch tensor are not reversible, hence the conversion + input_lengths = input_lengths.cpu().numpy() + x = nn.utils.rnn.pack_padded_sequence( + x, input_lengths, batch_first=True) + + self.lstm.flatten_parameters() + outputs, _ = self.lstm(x) + + outputs, _ = nn.utils.rnn.pad_packed_sequence( + outputs, batch_first=True) + + return outputs + + def inference(self, x): + for conv in self.convolutions: + x = F.dropout(F.relu(conv(x)), 0.5, self.training) + + x = x.transpose(1, 2) + + self.lstm.flatten_parameters() + outputs, _ = self.lstm(x) + + return outputs + + +class AudioEncoder(nn.Module): + def __init__(self, hparams): + super(AudioEncoder, self).__init__() + + assert hparams.lat_dim > 0 + + convolutions = [] + inp_dim = hparams.n_mel_channels + for _ in range(hparams.lat_n_convolutions): + conv_layer = nn.Sequential( + ConvNorm(inp_dim, hparams.lat_n_filters, + kernel_size=hparams.lat_kernel_size, stride=1, + padding=int((hparams.lat_kernel_size - 1) / 2), + dilation=1, w_init_gain='tanh'), + nn.BatchNorm1d(hparams.lat_n_filters)) + inp_dim = hparams.lat_n_filters + convolutions.append(conv_layer) + self.convolutions = nn.ModuleList(convolutions) + + self.lstm = nn.LSTM(hparams.lat_n_filters, + int(hparams.lat_n_filters / 2), + hparams.lat_n_blstms, batch_first=True, + bidirectional=True) + self.pool = GlobalAvgPool() + + self.mu_proj = LinearNorm(hparams.lat_n_filters, hparams.lat_dim) + self.logvar_proj = LinearNorm(hparams.lat_n_filters, hparams.lat_dim) + self.lat_dim = hparams.lat_dim + + def forward(self, x, lengths): + """ + Args: + x (torch.Tensor): (B, F, T) + """ + + for conv in self.convolutions: + x = F.dropout(F.tanh(conv(x)), 0.5, self.training) + + x = x.transpose(1, 2) # (B, T, D) + + # x may not be sorted by length. Sort->process->unsort + max_len = x.size(1) + assert max_len == torch.max(lengths).item() + + lengths, perm_idx = lengths.sort(0, descending=True) + x = x[perm_idx] + x = nn.utils.rnn.pack_padded_sequence(x, lengths, batch_first=True) + + self.lstm.flatten_parameters() + outputs, _ = self.lstm(x) + outputs, _ = nn.utils.rnn.pad_packed_sequence(outputs, batch_first=True) + + _, unperm_idx = perm_idx.sort(0) + outputs = outputs[unperm_idx] # (B, T, D) + lengths = lengths[unperm_idx] # (B, T, D) + + outputs = self.pool(outputs, lengths) # (B, D) + + mu = self.mu_proj(outputs) + logvar = self.logvar_proj(outputs) + z = distr.Normal(mu, logvar).rsample() + return z, mu, logvar + + +class Decoder(nn.Module): + def __init__(self, hparams): + super(Decoder, self).__init__() + self.n_mel_channels = hparams.n_mel_channels + self.n_frames_per_step = hparams.n_frames_per_step + self.encoder_embedding_dim = hparams.encoder_embedding_dim + self.obs_dim = hparams.obs_dim + self.lat_dim = hparams.lat_dim + self.attention_rnn_dim = hparams.attention_rnn_dim + self.decoder_rnn_dim = hparams.decoder_rnn_dim + self.prenet_dim = hparams.prenet_dim + self.max_decoder_steps = hparams.max_decoder_steps + self.gate_threshold = hparams.gate_threshold + self.p_attention_dropout = hparams.p_attention_dropout + self.p_decoder_dropout = hparams.p_decoder_dropout + + self.prenet = Prenet( + hparams.n_mel_channels * hparams.n_frames_per_step, + [hparams.prenet_dim, hparams.prenet_dim]) + + self.attention_rnn = nn.LSTMCell( + hparams.prenet_dim + hparams.encoder_embedding_dim, + hparams.attention_rnn_dim) + + self.attention_layer = Attention( + hparams.attention_rnn_dim, hparams.encoder_embedding_dim, + hparams.attention_dim, hparams.attention_location_n_filters, + hparams.attention_location_kernel_size) + + encoder_tot_dim = (hparams.encoder_embedding_dim + \ + hparams.lat_dim + hparams.obs_dim) + self.decoder_rnn = nn.LSTMCell( + hparams.attention_rnn_dim + encoder_tot_dim, + hparams.decoder_rnn_dim, 1) + + self.linear_projection = LinearNorm( + hparams.decoder_rnn_dim + encoder_tot_dim, + hparams.n_mel_channels * hparams.n_frames_per_step) + + self.gate_layer = LinearNorm( + hparams.decoder_rnn_dim + encoder_tot_dim, 1, + bias=True, w_init_gain='sigmoid') + + def get_go_frame(self, memory): + """ Gets all zeros frames to use as first decoder input + PARAMS + ------ + memory: decoder outputs + + RETURNS + ------- + decoder_input: all zeros frames + """ + B = memory.size(0) + decoder_input = Variable(memory.data.new( + B, self.n_mel_channels * self.n_frames_per_step).zero_()) + return decoder_input + + def initialize_decoder_states(self, memory, obs_and_lat, mask): + """ Initializes attention rnn states, decoder rnn states, attention + weights, attention cumulative weights, attention context, stores memory + and stores processed memory + PARAMS + ------ + memory: Encoder outputs + obs_and_lat: Observed and latent attribute embeddings + mask: Mask for padded data if training, expects None for inference + """ + B = memory.size(0) + MAX_TIME = memory.size(1) + + self.attention_hidden = Variable(memory.data.new( + B, self.attention_rnn_dim).zero_()) + self.attention_cell = Variable(memory.data.new( + B, self.attention_rnn_dim).zero_()) + + self.decoder_hidden = Variable(memory.data.new( + B, self.decoder_rnn_dim).zero_()) + self.decoder_cell = Variable(memory.data.new( + B, self.decoder_rnn_dim).zero_()) + + self.attention_weights = Variable(memory.data.new( + B, MAX_TIME).zero_()) + self.attention_weights_cum = Variable(memory.data.new( + B, MAX_TIME).zero_()) + self.attention_context = Variable(memory.data.new( + B, self.encoder_embedding_dim).zero_()) + + self.memory = memory + self.processed_memory = self.attention_layer.memory_layer(memory) + self.obs_and_lat = obs_and_lat + self.mask = mask + + def parse_decoder_inputs(self, decoder_inputs): + """ Prepares decoder inputs, i.e. mel outputs + PARAMS + ------ + decoder_inputs: inputs used for teacher-forced training, i.e. mel-specs + + RETURNS + ------- + inputs: processed decoder inputs + + """ + # (B, n_mel_channels, T_out) -> (B, T_out, n_mel_channels) + decoder_inputs = decoder_inputs.transpose(1, 2) + decoder_inputs = decoder_inputs.view( + decoder_inputs.size(0), + int(decoder_inputs.size(1)/self.n_frames_per_step), -1) + # (B, T_out, n_mel_channels) -> (T_out, B, n_mel_channels) + decoder_inputs = decoder_inputs.transpose(0, 1) + return decoder_inputs + + def parse_decoder_outputs(self, mel_outputs, gate_outputs, alignments): + """ Prepares decoder outputs for output + PARAMS + ------ + mel_outputs: + gate_outputs: gate output energies + alignments: + + RETURNS + ------- + mel_outputs: + gate_outpust: gate output energies + alignments: + """ + # (T_out, B) -> (B, T_out) + alignments = torch.stack(alignments).transpose(0, 1) + # (T_out, B) -> (B, T_out) + gate_outputs = torch.stack(gate_outputs).transpose(0, 1) + gate_outputs = gate_outputs.contiguous() + # (T_out, B, n_mel_channels) -> (B, T_out, n_mel_channels) + mel_outputs = torch.stack(mel_outputs).transpose(0, 1).contiguous() + # decouple frames per step + mel_outputs = mel_outputs.view( + mel_outputs.size(0), -1, self.n_mel_channels) + # (B, T_out, n_mel_channels) -> (B, n_mel_channels, T_out) + mel_outputs = mel_outputs.transpose(1, 2) + + return mel_outputs, gate_outputs, alignments + + def decode(self, decoder_input): + """ Decoder step using stored states, attention and memory + PARAMS + ------ + decoder_input: previous mel output + + RETURNS + ------- + mel_output: + gate_output: gate output energies + attention_weights: + """ + cell_input = torch.cat((decoder_input, self.attention_context), -1) + self.attention_hidden, self.attention_cell = self.attention_rnn( + cell_input, (self.attention_hidden, self.attention_cell)) + self.attention_hidden = F.dropout( + self.attention_hidden, self.p_attention_dropout, self.training) + + attention_weights_cat = torch.cat( + (self.attention_weights.unsqueeze(1), + self.attention_weights_cum.unsqueeze(1)), dim=1) + self.attention_context, self.attention_weights = self.attention_layer( + self.attention_hidden, self.memory, self.processed_memory, + attention_weights_cat, self.mask) + + self.attention_weights_cum += self.attention_weights + decoder_input = torch.cat( + (self.attention_hidden, self.attention_context), -1) + if self.obs_and_lat is not None: + decoder_input = torch.cat((decoder_input, self.obs_and_lat), -1) + self.decoder_hidden, self.decoder_cell = self.decoder_rnn( + decoder_input, (self.decoder_hidden, self.decoder_cell)) + self.decoder_hidden = F.dropout( + self.decoder_hidden, self.p_decoder_dropout, self.training) + + decoder_hidden_attention_context = torch.cat( + (self.decoder_hidden, self.attention_context), dim=1) + if self.obs_and_lat is not None: + decoder_hidden_attention_context = torch.cat( + (decoder_hidden_attention_context, self.obs_and_lat), dim=1) + decoder_output = self.linear_projection( + decoder_hidden_attention_context) + + gate_prediction = self.gate_layer(decoder_hidden_attention_context) + return decoder_output, gate_prediction, self.attention_weights + + def forward(self, memory, obs_and_lat, decoder_inputs, memory_lengths): + """ Decoder forward pass for training + PARAMS + ------ + memory: Encoder outputs + obs_and_lat: Observed and latent attribute embeddings + decoder_inputs: Decoder inputs for teacher forcing. i.e. mel-specs + memory_lengths: Encoder output lengths for attention masking. + + RETURNS + ------- + mel_outputs: mel outputs from the decoder + gate_outputs: gate outputs from the decoder + alignments: sequence of attention weights from the decoder + """ + + decoder_input = self.get_go_frame(memory).unsqueeze(0) + decoder_inputs = self.parse_decoder_inputs(decoder_inputs) + decoder_inputs = torch.cat((decoder_input, decoder_inputs), dim=0) + decoder_inputs = self.prenet(decoder_inputs) + + self.initialize_decoder_states( + memory, obs_and_lat, mask=~get_mask_from_lengths(memory_lengths)) + + mel_outputs, gate_outputs, alignments = [], [], [] + while len(mel_outputs) < decoder_inputs.size(0) - 1: + decoder_input = decoder_inputs[len(mel_outputs)] + mel_output, gate_output, attention_weights = self.decode( + decoder_input) + mel_outputs += [mel_output.squeeze(1)] + gate_outputs += [gate_output.squeeze()] + alignments += [attention_weights] + + mel_outputs, gate_outputs, alignments = self.parse_decoder_outputs( + mel_outputs, gate_outputs, alignments) + + return mel_outputs, gate_outputs, alignments + + def inference(self, memory, obs_and_lat, ret_has_eos=False): + """ Decoder inference + PARAMS + ------ + memory: Encoder outputs + obs_and_lat: Observed and latent attribute embeddings + + RETURNS + ------- + mel_outputs: mel outputs from the decoder + gate_outputs: gate outputs from the decoder + alignments: sequence of attention weights from the decoder + """ + decoder_input = self.get_go_frame(memory) + + self.initialize_decoder_states(memory, obs_and_lat, mask=None) + + mel_outputs, gate_outputs, alignments = [], [], [] + has_eos = False + while True: + decoder_input = self.prenet(decoder_input) + mel_output, gate_output, alignment = self.decode(decoder_input) + + mel_outputs += [mel_output.squeeze(1)] + gate_outputs += [gate_output] + alignments += [alignment] + + if torch.sigmoid(gate_output.data) > self.gate_threshold: + has_eos = True + break + elif len(mel_outputs) == self.max_decoder_steps: + # print("Warning! Reached max decoder steps") + break + + decoder_input = mel_output + + mel_outputs, gate_outputs, alignments = self.parse_decoder_outputs( + mel_outputs, gate_outputs, alignments) + + if ret_has_eos: + return mel_outputs, gate_outputs, alignments, has_eos + else: + return mel_outputs, gate_outputs, alignments + + +class Tacotron2(nn.Module): + def __init__(self, hparams): + super(Tacotron2, self).__init__() + self.mask_padding = hparams.mask_padding + self.fp16_run = hparams.fp16_run + self.n_mel_channels = hparams.n_mel_channels + self.n_frames_per_step = hparams.n_frames_per_step + + # initialize text encoder embedding + self.embedding = nn.Embedding( + hparams.n_symbols, hparams.symbols_embedding_dim) + std = sqrt(2.0 / (hparams.n_symbols + hparams.symbols_embedding_dim)) + val = sqrt(3.0) * std # uniform bounds for std + self.embedding.weight.data.uniform_(-val, val) + + # initialize observed attribute embedding + self.obs_embedding = None + if hparams.obs_dim > 0: + self.obs_embedding = nn.Embedding( + hparams.obs_n_class, hparams.obs_dim) + std = sqrt(2.0 / (hparams.obs_n_class + hparams.obs_dim)) + val = sqrt(3.0) * std # uniform bounds for std + self.obs_embedding.weight.data.uniform_(-val, val) + + self.encoder = Encoder(hparams) + self.decoder = Decoder(hparams) + self.postnet = Postnet(hparams) + + self.lat_encoder = None + if hparams.lat_dim > 0: + self.lat_encoder = AudioEncoder(hparams) + + def parse_batch(self, batch): + (text_padded, input_lengths, obs_labels, + mel_padded, gate_padded, output_lengths) = batch + text_padded = to_gpu(text_padded).long() + input_lengths = to_gpu(input_lengths).long() + obs_labels = to_gpu(obs_labels).long() + max_len = torch.max(input_lengths.data).item() + mel_padded = to_gpu(mel_padded).float() + gate_padded = to_gpu(gate_padded).float() + output_lengths = to_gpu(output_lengths).long() + + return ( + (text_padded, input_lengths, obs_labels, + mel_padded, max_len, output_lengths), + (mel_padded, gate_padded)) + + def parse_output(self, outputs, output_lengths=None): + if self.mask_padding and output_lengths is not None: + mask = ~get_mask_from_lengths(output_lengths) + mask = mask.expand(self.n_mel_channels, mask.size(0), mask.size(1)) + mask = mask.permute(1, 0, 2) + + outputs[0].data.masked_fill_(mask, 0.0) + outputs[1].data.masked_fill_(mask, 0.0) + outputs[2].data.masked_fill_(mask[:, 0, :], 1e3) # gate energies + + return outputs + + def forward(self, inputs): + (text_inputs, text_lengths, obs_labels, + mels, max_len, output_lengths) = inputs + text_lengths, output_lengths = text_lengths.data, output_lengths.data + + embedded_inputs = self.embedding(text_inputs).transpose(1, 2) + + encoder_outputs = self.encoder(embedded_inputs, text_lengths) + + obs = None + if self.obs_embedding is not None: + obs = self.obs_embedding(obs_labels) + + lat, lat_mu, lat_logvar = None, None, None + if self.lat_encoder is not None: + (lat, lat_mu, lat_logvar) = self.lat_encoder(mels, output_lengths) + + obs_and_lat = [x for x in [obs, lat] if x is not None] + if bool(obs_and_lat): + obs_and_lat = torch.cat(obs_and_lat, dim=-1) + else: + obs_and_lat = None + + mel_outputs, gate_outputs, alignments = self.decoder( + encoder_outputs, obs_and_lat, mels, memory_lengths=text_lengths) + + mel_outputs_postnet = self.postnet(mel_outputs) + mel_outputs_postnet = mel_outputs + mel_outputs_postnet + + return self.parse_output( + [mel_outputs, mel_outputs_postnet, gate_outputs, alignments, + lat_mu, lat_logvar], + output_lengths) + + def inference(self, inputs, obs_labels=None, lat=None, ret_has_eos=False): + embedded_inputs = self.embedding(inputs).transpose(1, 2) + encoder_outputs = self.encoder.inference(embedded_inputs) + + if obs_labels is None: + obs_labels = torch.LongTensor(len(inputs)) + obs_labels = obs_labels.to(inputs.device).zero_() + + obs = None + if self.obs_embedding is not None: + obs = self.obs_embedding(obs_labels) + + if self.lat_encoder is not None: + if lat is None: + lat = torch.FloatTensor(len(inputs), self.lat_encoder.lat_dim) + lat = lat.to(inputs.device).zero_().type(encoder_outputs.type()) + + obs_and_lat = [x for x in [obs, lat] if x is not None] + if bool(obs_and_lat): + obs_and_lat = torch.cat(obs_and_lat, dim=-1) + else: + obs_and_lat = None + + mel_outputs, gate_outputs, alignments, has_eos = self.decoder.inference( + encoder_outputs, obs_and_lat, ret_has_eos=True) + + mel_outputs_postnet = self.postnet(mel_outputs) + mel_outputs_postnet = mel_outputs + mel_outputs_postnet + + outputs = self.parse_output( + [mel_outputs, mel_outputs_postnet, gate_outputs, alignments]) + + if ret_has_eos: + return outputs + [has_eos] + else: + return outputs diff --git a/examples/textless_nlp/gslm/unit2speech/tacotron2/numbers.py b/examples/textless_nlp/gslm/unit2speech/tacotron2/numbers.py new file mode 100644 index 0000000000..0d5f7fa818 --- /dev/null +++ b/examples/textless_nlp/gslm/unit2speech/tacotron2/numbers.py @@ -0,0 +1,71 @@ +""" from https://github.com/keithito/tacotron """ + +import inflect +import re + + +_inflect = inflect.engine() +_comma_number_re = re.compile(r'([0-9][0-9\,]+[0-9])') +_decimal_number_re = re.compile(r'([0-9]+\.[0-9]+)') +_pounds_re = re.compile(r'£([0-9\,]*[0-9]+)') +_dollars_re = re.compile(r'\$([0-9\.\,]*[0-9]+)') +_ordinal_re = re.compile(r'[0-9]+(st|nd|rd|th)') +_number_re = re.compile(r'[0-9]+') + + +def _remove_commas(m): + return m.group(1).replace(',', '') + + +def _expand_decimal_point(m): + return m.group(1).replace('.', ' point ') + + +def _expand_dollars(m): + match = m.group(1) + parts = match.split('.') + if len(parts) > 2: + return match + ' dollars' # Unexpected format + dollars = int(parts[0]) if parts[0] else 0 + cents = int(parts[1]) if len(parts) > 1 and parts[1] else 0 + if dollars and cents: + dollar_unit = 'dollar' if dollars == 1 else 'dollars' + cent_unit = 'cent' if cents == 1 else 'cents' + return '%s %s, %s %s' % (dollars, dollar_unit, cents, cent_unit) + elif dollars: + dollar_unit = 'dollar' if dollars == 1 else 'dollars' + return '%s %s' % (dollars, dollar_unit) + elif cents: + cent_unit = 'cent' if cents == 1 else 'cents' + return '%s %s' % (cents, cent_unit) + else: + return 'zero dollars' + + +def _expand_ordinal(m): + return _inflect.number_to_words(m.group(0)) + + +def _expand_number(m): + num = int(m.group(0)) + if num > 1000 and num < 3000: + if num == 2000: + return 'two thousand' + elif num > 2000 and num < 2010: + return 'two thousand ' + _inflect.number_to_words(num % 100) + elif num % 100 == 0: + return _inflect.number_to_words(num // 100) + ' hundred' + else: + return _inflect.number_to_words(num, andword='', zero='oh', group=2).replace(', ', ' ') + else: + return _inflect.number_to_words(num, andword='') + + +def normalize_numbers(text): + text = re.sub(_comma_number_re, _remove_commas, text) + text = re.sub(_pounds_re, r'\1 pounds', text) + text = re.sub(_dollars_re, _expand_dollars, text) + text = re.sub(_decimal_number_re, _expand_decimal_point, text) + text = re.sub(_ordinal_re, _expand_ordinal, text) + text = re.sub(_number_re, _expand_number, text) + return text diff --git a/examples/textless_nlp/gslm/unit2speech/tacotron2/stft.py b/examples/textless_nlp/gslm/unit2speech/tacotron2/stft.py new file mode 100644 index 0000000000..63fcd431e2 --- /dev/null +++ b/examples/textless_nlp/gslm/unit2speech/tacotron2/stft.py @@ -0,0 +1,141 @@ +""" +BSD 3-Clause License + +Copyright (c) 2017, Prem Seetharaman +All rights reserved. + +* Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are met: + +* Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + +* Redistributions in binary form must reproduce the above copyright notice, this + list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + +* Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived from this + software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR +ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES +(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; +LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON +ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS +SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +""" + +import torch +import numpy as np +import torch.nn.functional as F +from torch.autograd import Variable +from scipy.signal import get_window +from librosa.util import pad_center, tiny +from .audio_processing import window_sumsquare + + +class STFT(torch.nn.Module): + """adapted from Prem Seetharaman's https://github.com/pseeth/pytorch-stft""" + def __init__(self, filter_length=800, hop_length=200, win_length=800, + window='hann'): + super(STFT, self).__init__() + self.filter_length = filter_length + self.hop_length = hop_length + self.win_length = win_length + self.window = window + self.forward_transform = None + scale = self.filter_length / self.hop_length + fourier_basis = np.fft.fft(np.eye(self.filter_length)) + + cutoff = int((self.filter_length / 2 + 1)) + fourier_basis = np.vstack([np.real(fourier_basis[:cutoff, :]), + np.imag(fourier_basis[:cutoff, :])]) + + forward_basis = torch.FloatTensor(fourier_basis[:, None, :]) + inverse_basis = torch.FloatTensor( + np.linalg.pinv(scale * fourier_basis).T[:, None, :]) + + if window is not None: + assert(filter_length >= win_length) + # get window and zero center pad it to filter_length + fft_window = get_window(window, win_length, fftbins=True) + fft_window = pad_center(fft_window, filter_length) + fft_window = torch.from_numpy(fft_window).float() + + # window the bases + forward_basis *= fft_window + inverse_basis *= fft_window + + self.register_buffer('forward_basis', forward_basis.float()) + self.register_buffer('inverse_basis', inverse_basis.float()) + + def transform(self, input_data): + num_batches = input_data.size(0) + num_samples = input_data.size(1) + + self.num_samples = num_samples + + # similar to librosa, reflect-pad the input + input_data = input_data.view(num_batches, 1, num_samples) + input_data = F.pad( + input_data.unsqueeze(1), + (int(self.filter_length / 2), int(self.filter_length / 2), 0, 0), + mode='reflect') + input_data = input_data.squeeze(1) + + forward_transform = F.conv1d( + input_data, + Variable(self.forward_basis, requires_grad=False), + stride=self.hop_length, + padding=0) + + cutoff = int((self.filter_length / 2) + 1) + real_part = forward_transform[:, :cutoff, :] + imag_part = forward_transform[:, cutoff:, :] + + magnitude = torch.sqrt(real_part**2 + imag_part**2) + phase = torch.autograd.Variable( + torch.atan2(imag_part.data, real_part.data)) + + return magnitude, phase + + def inverse(self, magnitude, phase): + recombine_magnitude_phase = torch.cat( + [magnitude*torch.cos(phase), magnitude*torch.sin(phase)], dim=1) + + inverse_transform = F.conv_transpose1d( + recombine_magnitude_phase, + Variable(self.inverse_basis, requires_grad=False), + stride=self.hop_length, + padding=0) + + if self.window is not None: + window_sum = window_sumsquare( + self.window, magnitude.size(-1), hop_length=self.hop_length, + win_length=self.win_length, n_fft=self.filter_length, + dtype=np.float32) + # remove modulation effects + approx_nonzero_indices = torch.from_numpy( + np.where(window_sum > tiny(window_sum))[0]) + window_sum = torch.autograd.Variable( + torch.from_numpy(window_sum), requires_grad=False) + window_sum = window_sum.cuda() if magnitude.is_cuda else window_sum + inverse_transform[:, :, approx_nonzero_indices] /= window_sum[approx_nonzero_indices] + + # scale by hop ratio + inverse_transform *= float(self.filter_length) / self.hop_length + + inverse_transform = inverse_transform[:, :, int(self.filter_length/2):] + inverse_transform = inverse_transform[:, :, :-int(self.filter_length/2):] + + return inverse_transform + + def forward(self, input_data): + self.magnitude, self.phase = self.transform(input_data) + reconstruction = self.inverse(self.magnitude, self.phase) + return reconstruction diff --git a/examples/textless_nlp/gslm/unit2speech/tacotron2/symbols.py b/examples/textless_nlp/gslm/unit2speech/tacotron2/symbols.py new file mode 100644 index 0000000000..5f0d70fdad --- /dev/null +++ b/examples/textless_nlp/gslm/unit2speech/tacotron2/symbols.py @@ -0,0 +1,18 @@ +""" from https://github.com/keithito/tacotron """ + +''' +Defines the set of symbols used in text input to the model. + +The default is a set of ASCII characters that works well for English or text that has been run through Unidecode. For other data, you can modify _characters. See TRAINING_DATA.md for details. ''' +from . import cmudict + +_pad = '_' +_punctuation = '!\'(),.:;? ' +_special = '-' +_letters = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz' + +# Prepend "@" to ARPAbet symbols to ensure uniqueness (some are the same as uppercase letters): +_arpabet = ['@' + s for s in cmudict.valid_symbols] + +# Export all symbols: +symbols = [_pad] + list(_special) + list(_punctuation) + list(_letters) + _arpabet diff --git a/examples/textless_nlp/gslm/unit2speech/tacotron2/text.py b/examples/textless_nlp/gslm/unit2speech/tacotron2/text.py new file mode 100644 index 0000000000..49e2ca498b --- /dev/null +++ b/examples/textless_nlp/gslm/unit2speech/tacotron2/text.py @@ -0,0 +1,107 @@ +""" from https://github.com/keithito/tacotron """ +import numpy as np +import re +from . import cleaners +from .symbols import symbols + + +# Mappings from symbol to numeric ID and vice versa: +_symbol_to_id = {s: i for i, s in enumerate(symbols)} +_id_to_symbol = {i: s for i, s in enumerate(symbols)} + +# Regular expression matching text enclosed in curly braces: +_curly_re = re.compile(r'(.*?)\{(.+?)\}(.*)') + +# Special symbols +SOS_TOK = '<s>' +EOS_TOK = '</s>' + +def text_to_sequence(text, cleaner_names): + '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text. + + The text can optionally have ARPAbet sequences enclosed in curly braces embedded + in it. For example, "Turn left on {HH AW1 S S T AH0 N} Street." + + Args: + text: string to convert to a sequence + cleaner_names: names of the cleaner functions to run the text through + + Returns: + List of integers corresponding to the symbols in the text + ''' + sequence = [] + + # Check for curly braces and treat their contents as ARPAbet: + while len(text): + m = _curly_re.match(text) + if not m: + sequence += _symbols_to_sequence(_clean_text(text, cleaner_names)) + break + sequence += _symbols_to_sequence(_clean_text(m.group(1), cleaner_names)) + sequence += _arpabet_to_sequence(m.group(2)) + text = m.group(3) + + return sequence + + +def sample_code_chunk(code, size): + assert(size > 0 and size <= len(code)) + start = np.random.randint(len(code) - size + 1) + end = start + size + return code[start:end], start, end + + +def code_to_sequence(code, code_dict, collapse_code): + if collapse_code: + prev_c = None + sequence = [] + for c in code: + if c in code_dict and c != prev_c: + sequence.append(code_dict[c]) + prev_c = c + else: + sequence = [code_dict[c] for c in code if c in code_dict] + if len(sequence) < 0.95 * len(code): + print('WARNING : over 5%% codes are OOV') + + return sequence + + +def sequence_to_text(sequence): + '''Converts a sequence of IDs back to a string''' + result = '' + for symbol_id in sequence: + if symbol_id in _id_to_symbol: + s = _id_to_symbol[symbol_id] + # Enclose ARPAbet back in curly braces: + if len(s) > 1 and s[0] == '@': + s = '{%s}' % s[1:] + result += s + return result.replace('}{', ' ') + + +def sequence_to_code(sequence, code_dict): + '''Analogous to sequence_to_text''' + id_to_code = {i: c for c, i in code_dict.items()} + return ' '.join([id_to_code[i] for i in sequence]) + + +def _clean_text(text, cleaner_names): + for name in cleaner_names: + cleaner = getattr(cleaners, name) + if not cleaner: + raise Exception('Unknown cleaner: %s' % name) + text = cleaner(text) + return text + + +def _symbols_to_sequence(symbols): + return [_symbol_to_id[s] for s in symbols if _should_keep_symbol(s)] + + +def _arpabet_to_sequence(text): + return _symbols_to_sequence(['@' + s for s in text.split()]) + + +def _should_keep_symbol(s): + return s in _symbol_to_id and s != '_' and s != '~' diff --git a/examples/textless_nlp/gslm/unit2speech/tacotron2/utils.py b/examples/textless_nlp/gslm/unit2speech/tacotron2/utils.py new file mode 100644 index 0000000000..66a426d222 --- /dev/null +++ b/examples/textless_nlp/gslm/unit2speech/tacotron2/utils.py @@ -0,0 +1,167 @@ +import collections +import io +import json +import librosa +import numpy as np +import soundfile as sf +import time +import torch +from scipy.io.wavfile import read +from .text import SOS_TOK, EOS_TOK + + +def get_mask_from_lengths(lengths): + max_len = torch.max(lengths).item() + ids = torch.arange(0, max_len, out=torch.cuda.LongTensor(max_len)) + mask = (ids < lengths.unsqueeze(1)) + return mask + + +def load_wav_to_torch(full_path, sr=None): + data, sr = librosa.load(full_path, sr=sr) + data = np.clip(data, -1, 1) # potentially out of [-1, 1] due to resampling + data = data * 32768.0 # match values loaded by scipy + return torch.FloatTensor(data.astype(np.float32)), sr + + +def read_binary_audio(bin_data, tar_sr=None): + """ + read binary audio (`bytes` or `uint8` `numpy.ndarray`) to `float32` + `numpy.ndarray` + + RETURNS: + data (np.ndarray) : audio of shape (n,) or (2, n) + tar_sr (int) : sample rate + """ + data, ori_sr = sf.read(io.BytesIO(bin_data), dtype='float32') + data = data.T + if (tar_sr is not None) and (ori_sr != tar_sr): + data = librosa.resample(data, ori_sr, tar_sr) + else: + tar_sr = ori_sr + data = np.clip(data, -1, 1) + data = data * 32768.0 + return torch.FloatTensor(data.astype(np.float32)), tar_sr + + +def load_filepaths_and_text(filename): + with open(filename, encoding='utf-8') as f: + data = [json.loads(line.rstrip()) for line in f] + return data + + +def to_gpu(x): + x = x.contiguous() + + if torch.cuda.is_available(): + x = x.cuda(non_blocking=True) + return torch.autograd.Variable(x) + + +def load_code_dict(path, add_sos=False, add_eos=False): + if not path: + return {} + + with open(path, 'r') as f: + codes = ['_'] + [line.rstrip() for line in f] # '_' for pad + code_dict = {c: i for i, c in enumerate(codes)} + + if add_sos: + code_dict[SOS_TOK] = len(code_dict) + if add_eos: + code_dict[EOS_TOK] = len(code_dict) + assert(set(code_dict.values()) == set(range(len(code_dict)))) + + return code_dict + + +def load_obs_label_dict(path): + if not path: + return {} + with open(path, 'r') as f: + obs_labels = [line.rstrip() for line in f] + return {c: i for i, c in enumerate(obs_labels)} + + +# A simple timer class inspired from `tnt.TimeMeter` +class CudaTimer: + def __init__(self, keys): + self.keys = keys + self.reset() + + def start(self, key): + s = torch.cuda.Event(enable_timing=True) + s.record() + self.start_events[key].append(s) + return self + + def stop(self, key): + e = torch.cuda.Event(enable_timing=True) + e.record() + self.end_events[key].append(e) + return self + + def reset(self): + self.start_events = collections.defaultdict(list) + self.end_events = collections.defaultdict(list) + self.running_times = collections.defaultdict(float) + self.n = collections.defaultdict(int) + return self + + def value(self): + self._synchronize() + return {k: self.running_times[k] / self.n[k] for k in self.keys} + + def _synchronize(self): + torch.cuda.synchronize() + for k in self.keys: + starts = self.start_events[k] + ends = self.end_events[k] + if len(starts) == 0: + raise ValueError("Trying to divide by zero in TimeMeter") + if len(ends) != len(starts): + raise ValueError("Call stop before checking value!") + time = 0 + for start, end in zip(starts, ends): + time += start.elapsed_time(end) + self.running_times[k] += time * 1e-3 + self.n[k] += len(starts) + self.start_events = collections.defaultdict(list) + self.end_events = collections.defaultdict(list) + + +# Used to measure the time taken for multiple events +class Timer: + def __init__(self, keys): + self.keys = keys + self.n = {} + self.running_time = {} + self.total_time = {} + self.reset() + + def start(self, key): + self.running_time[key] = time.time() + return self + + def stop(self, key): + self.total_time[key] = time.time() - self.running_time[key] + self.n[key] += 1 + self.running_time[key] = None + return self + + def reset(self): + for k in self.keys: + self.total_time[k] = 0 + self.running_time[k] = None + self.n[k] = 0 + return self + + def value(self): + vals = {} + for k in self.keys: + if self.n[k] == 0: + raise ValueError("Trying to divide by zero in TimeMeter") + else: + vals[k] = self.total_time[k] / self.n[k] + return vals + diff --git a/examples/textless_nlp/gslm/unit2speech/tacotron2/waveglow_denoiser.py b/examples/textless_nlp/gslm/unit2speech/tacotron2/waveglow_denoiser.py new file mode 100644 index 0000000000..6a6585e8b6 --- /dev/null +++ b/examples/textless_nlp/gslm/unit2speech/tacotron2/waveglow_denoiser.py @@ -0,0 +1,40 @@ +# import sys +# sys.path.append('tacotron2') +import torch +from .layers import STFT + + +class Denoiser(torch.nn.Module): + """ Removes model bias from audio produced with waveglow """ + + def __init__(self, waveglow, filter_length=1024, n_overlap=4, + win_length=1024, mode='zeros'): + super(Denoiser, self).__init__() + self.stft = STFT(filter_length=filter_length, + hop_length=int(filter_length/n_overlap), + win_length=win_length).cuda() + if mode == 'zeros': + mel_input = torch.zeros( + (1, 80, 88), + dtype=waveglow.upsample.weight.dtype, + device=waveglow.upsample.weight.device) + elif mode == 'normal': + mel_input = torch.randn( + (1, 80, 88), + dtype=waveglow.upsample.weight.dtype, + device=waveglow.upsample.weight.device) + else: + raise Exception("Mode {} if not supported".format(mode)) + + with torch.no_grad(): + bias_audio = waveglow.infer(mel_input, sigma=0.0).float() + bias_spec, _ = self.stft.transform(bias_audio) + + self.register_buffer('bias_spec', bias_spec[:, :, 0][:, :, None]) + + def forward(self, audio, strength=0.1): + audio_spec, audio_angles = self.stft.transform(audio.cuda().float()) + audio_spec_denoised = audio_spec - self.bias_spec * strength + audio_spec_denoised = torch.clamp(audio_spec_denoised, 0.0) + audio_denoised = self.stft.inverse(audio_spec_denoised, audio_angles) + return audio_denoised diff --git a/examples/textless_nlp/gslm/unit2speech/tts_data.py b/examples/textless_nlp/gslm/unit2speech/tts_data.py new file mode 100644 index 0000000000..eb0f7c360d --- /dev/null +++ b/examples/textless_nlp/gslm/unit2speech/tts_data.py @@ -0,0 +1,52 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +import torch +import numpy as np +from examples.textless_nlp.gslm.unit2speech.tacotron2.text import ( + EOS_TOK, + SOS_TOK, + code_to_sequence, + text_to_sequence, +) +from examples.textless_nlp.gslm.unit2speech.tacotron2.utils import ( + load_code_dict, +) + + +class TacotronInputDataset: + def __init__(self, hparams, append_str=""): + self.is_text = getattr(hparams, "text_or_code", "text") == "text" + if not self.is_text: + self.code_dict = load_code_dict(hparams.code_dict) + self.code_key = hparams.code_key + self.add_sos = hparams.add_sos + self.add_eos = hparams.add_eos + self.collapse_code = hparams.collapse_code + self.append_str = append_str + + def process_code(self, inp_str): + inp_toks = inp_str.split() + if self.add_sos: + inp_toks = [SOS_TOK] + inp_toks + if self.add_eos: + inp_toks = inp_toks + [EOS_TOK] + return code_to_sequence(inp_toks, self.code_dict, self.collapse_code) + + def process_text(self, inp_str): + return text_to_sequence(inp_str, ["english_cleaners"]) + + def get_tensor(self, inp_str): + # uid, txt, inp_str = self._get_data(idx) + inp_str = inp_str + self.append_str + if self.is_text: + inp_toks = self.process_text(inp_str) + else: + inp_toks = self.process_code(inp_str) + return torch.from_numpy(np.array(inp_toks)).long() + + def __len__(self): + return len(self.data) diff --git a/examples/textless_nlp/gslm/unit2speech/utils.py b/examples/textless_nlp/gslm/unit2speech/utils.py new file mode 100644 index 0000000000..7aced08d38 --- /dev/null +++ b/examples/textless_nlp/gslm/unit2speech/utils.py @@ -0,0 +1,55 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +import torch +from examples.textless_nlp.gslm.unit2speech.tacotron2.model import Tacotron2 +from examples.textless_nlp.gslm.unit2speech.tacotron2.waveglow_denoiser import ( + Denoiser, +) + + +def load_quantized_audio_from_file(file_path): + base_fname_batch, quantized_units_batch = [], [] + with open(file_path) as f: + for line in f: + base_fname, quantized_units_str = line.rstrip().split("|") + quantized_units = [int(q) for q in quantized_units_str.split(" ")] + base_fname_batch.append(base_fname) + quantized_units_batch.append(quantized_units) + return base_fname_batch, quantized_units_batch + + +def synthesize_audio(model, waveglow, denoiser, inp, lab=None, strength=0.0): + assert inp.size(0) == 1 + inp = inp.cuda() + if lab is not None: + lab = torch.LongTensor(1).cuda().fill_(lab) + + with torch.no_grad(): + _, mel, _, ali, has_eos = model.inference(inp, lab, ret_has_eos=True) + aud = waveglow.infer(mel, sigma=0.666) + aud_dn = denoiser(aud, strength=strength).squeeze(1) + return mel, aud, aud_dn, has_eos + + +def load_tacotron(tacotron_model_path, max_decoder_steps): + ckpt_dict = torch.load(tacotron_model_path) + hparams = ckpt_dict["hparams"] + hparams.max_decoder_steps = max_decoder_steps + sr = hparams.sampling_rate + model = Tacotron2(hparams) + model.load_state_dict(ckpt_dict["model_dict"]) + model = model.cuda().eval().half() + return model, sr, hparams + + +def load_waveglow(waveglow_path): + waveglow = torch.load(waveglow_path)["model"] + waveglow = waveglow.cuda().eval().half() + for k in waveglow.convinv: + k.float() + denoiser = Denoiser(waveglow) + return waveglow, denoiser diff --git a/examples/translation/README.md b/examples/translation/README.md index 67e99f6efd..2941f5eb84 100644 --- a/examples/translation/README.md +++ b/examples/translation/README.md @@ -175,9 +175,11 @@ mkdir -p checkpoints/fconv_wmt_en_de fairseq-train \ data-bin/wmt17_en_de \ --arch fconv_wmt_en_de \ - --lr 0.5 --clip-norm 0.1 --dropout 0.2 --max-tokens 4000 \ + --dropout 0.2 \ --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \ - --lr-scheduler fixed --force-anneal 50 \ + --optimizer nag --clip-norm 0.1 \ + --lr 0.5 --lr-scheduler fixed --force-anneal 50 \ + --max-tokens 4000 \ --save-dir checkpoints/fconv_wmt_en_de # Evaluate @@ -205,10 +207,12 @@ fairseq-preprocess \ mkdir -p checkpoints/fconv_wmt_en_fr fairseq-train \ data-bin/wmt14_en_fr \ - --lr 0.5 --clip-norm 0.1 --dropout 0.1 --max-tokens 3000 \ - --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \ - --lr-scheduler fixed --force-anneal 50 \ --arch fconv_wmt_en_fr \ + --dropout 0.1 \ + --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \ + --optimizer nag --clip-norm 0.1 \ + --lr 0.5 --lr-scheduler fixed --force-anneal 50 \ + --max-tokens 3000 \ --save-dir checkpoints/fconv_wmt_en_fr # Evaluate @@ -225,7 +229,7 @@ train a multilingual `{de,fr}-en` translation model using the IWSLT'17 datasets. Note that we use slightly different preprocessing here than for the IWSLT'14 En-De data above. In particular we learn a joint BPE code for all three -languages and use interactive.py and sacrebleu for scoring the test set. +languages and use fairseq-interactive and sacrebleu for scoring the test set. ```bash # First install sacrebleu and sentencepiece @@ -259,12 +263,12 @@ fairseq-preprocess --source-lang fr --target-lang en \ mkdir -p checkpoints/multilingual_transformer CUDA_VISIBLE_DEVICES=0 fairseq-train data-bin/iwslt17.de_fr.en.bpe16k/ \ --max-epoch 50 \ - --ddp-backend=no_c10d \ + --ddp-backend=legacy_ddp \ --task multilingual_translation --lang-pairs de-en,fr-en \ --arch multilingual_transformer_iwslt_de_en \ --share-decoders --share-decoder-input-output-embed \ --optimizer adam --adam-betas '(0.9, 0.98)' \ - --lr 0.0005 --lr-scheduler inverse_sqrt --min-lr '1e-09' \ + --lr 0.0005 --lr-scheduler inverse_sqrt \ --warmup-updates 4000 --warmup-init-lr '1e-07' \ --label-smoothing 0.1 --criterion label_smoothed_cross_entropy \ --dropout 0.3 --weight-decay 0.0001 \ diff --git a/examples/translation/prepare-iwslt14.sh b/examples/translation/prepare-iwslt14.sh index 0bf0dc2a2e..2fb6643fbc 100644 --- a/examples/translation/prepare-iwslt14.sh +++ b/examples/translation/prepare-iwslt14.sh @@ -15,7 +15,7 @@ CLEAN=$SCRIPTS/training/clean-corpus-n.perl BPEROOT=subword-nmt/subword_nmt BPE_TOKENS=10000 -URL="https://wit3.fbk.eu/archive/2014-01/texts/de/en/de-en.tgz" +URL="http://dl.fbaipublicfiles.com/fairseq/data/iwslt14/de-en.tgz" GZ=de-en.tgz if [ ! -d "$SCRIPTS" ]; then diff --git a/examples/translation_moe/README.md b/examples/translation_moe/README.md index 33f1bee5cb..2e5c8af617 100644 --- a/examples/translation_moe/README.md +++ b/examples/translation_moe/README.md @@ -15,16 +15,16 @@ The model is trained with online responsibility assignment and shared parameteri The following command will train a `hMoElp` model with `3` experts: ```bash -fairseq-train --ddp-backend='no_c10d' \ +fairseq-train --ddp-backend='legacy_ddp' \ data-bin/wmt17_en_de \ --max-update 100000 \ - --task translation_moe --user-dir examples/translation_moe/src \ + --task translation_moe --user-dir examples/translation_moe/translation_moe_src \ --method hMoElp --mean-pool-gating-network \ --num-experts 3 \ --arch transformer_wmt_en_de --share-all-embeddings \ --optimizer adam --adam-betas '(0.9, 0.98)' --clip-norm 0.0 \ --lr-scheduler inverse_sqrt --warmup-init-lr 1e-07 --warmup-updates 4000 \ - --lr 0.0007 --min-lr 1e-09 \ + --lr 0.0007 \ --dropout 0.1 --weight-decay 0.0 --criterion cross_entropy \ --max-tokens 3584 ``` @@ -37,7 +37,7 @@ For example, to generate from expert 0: fairseq-generate data-bin/wmt17_en_de \ --path checkpoints/checkpoint_best.pt \ --beam 1 --remove-bpe \ - --task translation_moe --user-dir examples/translation_moe/src \ + --task translation_moe --user-dir examples/translation_moe/translation_moe_src \ --method hMoElp --mean-pool-gating-network \ --num-experts 3 \ --gen-expert 0 @@ -61,7 +61,7 @@ for EXPERT in $(seq 0 2); do \ --beam 1 \ --bpe subword_nmt --bpe-codes $BPE_CODE \ --buffer-size 500 --max-tokens 6000 \ - --task translation_moe --user-dir examples/translation_moe/src \ + --task translation_moe --user-dir examples/translation_moe/translation_moe_src \ --method hMoElp --mean-pool-gating-network \ --num-experts 3 \ --gen-expert $EXPERT ; \ diff --git a/examples/translation_moe/score.py b/examples/translation_moe/score.py index 8e207093db..9a529a9850 100644 --- a/examples/translation_moe/score.py +++ b/examples/translation_moe/score.py @@ -12,9 +12,9 @@ """ import argparse -from itertools import chain -import sys import random +import sys +from itertools import chain import numpy as np from sacrebleu import compute_bleu, corpus_bleu as _corpus_bleu @@ -22,17 +22,21 @@ def main(): parser = argparse.ArgumentParser(sys.argv[0]) - parser.add_argument('--sys', nargs='*', default='', metavar='FILE', - help='path to system output') - parser.add_argument('--ref', default='', metavar='FILE', - help='path to references') - parser.add_argument('--output', default='', metavar='FILE', - help='print outputs into a pretty format') + parser.add_argument( + "--sys", nargs="*", default="", metavar="FILE", help="path to system output" + ) + parser.add_argument("--ref", default="", metavar="FILE", help="path to references") + parser.add_argument( + "--output", + default="", + metavar="FILE", + help="print outputs into a pretty format", + ) args = parser.parse_args() if args.sys: src, tgt, hypos, log_probs = load_sys(args.sys) - print('pairwise BLEU: %.2f' % pairwise(hypos)) + print("pairwise BLEU: %.2f" % pairwise(hypos)) if args.output: merge(src, tgt, hypos, log_probs, args.output) @@ -55,18 +59,21 @@ def load_sys(paths): with open(path) as f: for line in f: line = line.rstrip() - if line.startswith(('S-', 'T-', 'H-')): - i = int(line[line.find('-')+1:line.find('\t')]) - if line.startswith('S-'): - src[i] = line.split('\t')[1] - if line.startswith('T-'): - tgt[i] = line.split('\t')[1] - if line.startswith('H-'): + # S: source + # T: target + # D: detokenized system output + if line.startswith(("S-", "T-", "D-")): + i = int(line[line.find("-") + 1 : line.find("\t")]) + if line.startswith("S-"): + src[i] = line.split("\t")[1] + if line.startswith("T-"): + tgt[i] = line.split("\t")[1] + if line.startswith("D-"): if i not in hypos: hypos[i] = [] log_probs[i] = [] - hypos[i].append(line.split('\t')[2]) - log_probs[i].append(float(line.split('\t')[1])) + hypos[i].append(line.split("\t")[2]) + log_probs[i].append(float(line.split("\t")[1])) return dictolist(src), dictolist(tgt), dictolist(hypos), dictolist(log_probs) @@ -76,34 +83,34 @@ def load_ref(path): src, tgt, refs = [], [], [] i = 0 while i < len(lines): - if lines[i].startswith('S-'): - src.append(lines[i].split('\t')[1].rstrip()) + if lines[i].startswith("S-"): + src.append(lines[i].split("\t")[1].rstrip()) i += 1 - elif lines[i].startswith('T-'): - tgt.append(lines[i].split('\t')[1].rstrip()) + elif lines[i].startswith("T-"): + tgt.append(lines[i].split("\t")[1].rstrip()) i += 1 else: a = [] - while i < len(lines) and lines[i].startswith('R'): - a.append(lines[i].split('\t')[1].rstrip()) + while i < len(lines) and lines[i].startswith("R"): + a.append(lines[i].split("\t")[1].rstrip()) i += 1 refs.append(a) return src, tgt, refs def merge(src, tgt, hypos, log_probs, path): - with open(path, 'w') as f: + with open(path, "w") as f: for s, t, hs, lps in zip(src, tgt, hypos, log_probs): - f.write(s + '\n') - f.write(t + '\n') - f.write('\n') + f.write(s + "\n") + f.write(t + "\n") + f.write("\n") for h, lp in zip(hs, lps): - f.write('\t%f\t%s\n' % (lp, h.strip())) - f.write('------------------------------------------------------\n') + f.write("\t%f\t%s\n" % (lp, h.strip())) + f.write("------------------------------------------------------\n") def corpus_bleu(sys_stream, ref_streams): - bleu = _corpus_bleu(sys_stream, ref_streams, tokenize='none') + bleu = _corpus_bleu(sys_stream, ref_streams, tokenize="none") return bleu.score @@ -113,9 +120,11 @@ def sentence_bleu(hypothesis, reference): bleu.counts[i] += 1 bleu.totals[i] += 1 bleu = compute_bleu( - bleu.counts, bleu.totals, - bleu.sys_len, bleu.ref_len, - smooth='exp', smooth_floor=0.0, + bleu.counts, + bleu.totals, + bleu.sys_len, + bleu.ref_len, + smooth_method="exp", ) return bleu.score @@ -147,7 +156,7 @@ def multi_ref(refs, hypos): best = [k for k in range(len(rs)) if s[k] == s[j]] a.add(random.choice(best)) ref_cnt += len(a) - print('#refs covered: %.2f' % (ref_cnt / len(refs))) + print("#refs covered: %.2f" % (ref_cnt / len(refs))) # transpose refs and hypos refs = list(zip(*refs)) @@ -157,33 +166,32 @@ def multi_ref(refs, hypos): k = len(hypos) m = len(refs) flat_hypos = [hypos[j][i] for i in range(len(hypos[0])) for j in range(k)] - duplicated_refs = [ - [ref for ref in refs_i for _ in range(k)] - for refs_i in refs - ] + duplicated_refs = [[ref for ref in refs_i for _ in range(k)] for refs_i in refs] loo_bleus = [] for held_out_ref in range(m): - remaining_refs = duplicated_refs[:held_out_ref] + duplicated_refs[held_out_ref+1:] + remaining_refs = ( + duplicated_refs[:held_out_ref] + duplicated_refs[held_out_ref + 1 :] + ) assert len(remaining_refs) == m - 1 loo_bleus.append(corpus_bleu(flat_hypos, remaining_refs)) - print('average multi-reference BLEU (leave-one-out): %.2f' % np.mean(loo_bleus)) + print("average multi-reference BLEU (leave-one-out): %.2f" % np.mean(loo_bleus)) def intra_ref(refs): - print('ref pairwise BLEU: %.2f' % pairwise(refs)) + print("ref pairwise BLEU: %.2f" % pairwise(refs)) refs = list(zip(*refs)) m = len(refs) concat_h = [] concat_rest = [[] for j in range(m - 1)] for i, h in enumerate(refs): - rest = refs[:i] + refs[i+1:] + rest = refs[:i] + refs[i + 1 :] concat_h.append(h) for j in range(m - 1): concat_rest[j].extend(rest[j]) concat_h = list(chain.from_iterable(concat_h)) bleu = corpus_bleu(concat_h, concat_rest) - print('multi-reference BLEU (leave-one-out): %.2f' % bleu) + print("multi-reference BLEU (leave-one-out): %.2f" % bleu) -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/examples/translation_moe/src/__init__.py b/examples/translation_moe/translation_moe_src/__init__.py similarity index 100% rename from examples/translation_moe/src/__init__.py rename to examples/translation_moe/translation_moe_src/__init__.py diff --git a/examples/translation_moe/src/logsumexp_moe.py b/examples/translation_moe/translation_moe_src/logsumexp_moe.py similarity index 95% rename from examples/translation_moe/src/logsumexp_moe.py rename to examples/translation_moe/translation_moe_src/logsumexp_moe.py index 0379f226b0..fb299daecb 100644 --- a/examples/translation_moe/src/logsumexp_moe.py +++ b/examples/translation_moe/translation_moe_src/logsumexp_moe.py @@ -21,6 +21,6 @@ def forward(ctx, logp, posterior, dim=-1): @staticmethod def backward(ctx, grad_output): - posterior, = ctx.saved_tensors + (posterior,) = ctx.saved_tensors grad_logp = grad_output.unsqueeze(ctx.dim) * posterior return grad_logp, None, None diff --git a/examples/translation_moe/src/mean_pool_gating_network.py b/examples/translation_moe/translation_moe_src/mean_pool_gating_network.py similarity index 80% rename from examples/translation_moe/src/mean_pool_gating_network.py rename to examples/translation_moe/translation_moe_src/mean_pool_gating_network.py index 25743b4e98..efc7ae40bf 100644 --- a/examples/translation_moe/src/mean_pool_gating_network.py +++ b/examples/translation_moe/translation_moe_src/mean_pool_gating_network.py @@ -26,15 +26,15 @@ def __init__(self, embed_dim, num_experts, dropout=None): def forward(self, encoder_out): if not ( - hasattr(encoder_out, 'encoder_out') - and hasattr(encoder_out, 'encoder_padding_mask') - and encoder_out.encoder_out.size(2) == self.embed_dim + "encoder_out" in encoder_out + and "encoder_padding_mask" in encoder_out + and encoder_out["encoder_out"][0].size(2) == self.embed_dim ): - raise ValueError('Unexpected format for encoder_out') + raise ValueError("Unexpected format for encoder_out") # mean pooling over time - encoder_padding_mask = encoder_out.encoder_padding_mask # B x T - encoder_out = encoder_out.encoder_out.transpose(0, 1) # B x T x C + encoder_padding_mask = encoder_out["encoder_padding_mask"][0] # B x T + encoder_out = encoder_out["encoder_out"][0].transpose(0, 1) # B x T x C if encoder_padding_mask is not None: encoder_out = encoder_out.clone() # required because of transpose above encoder_out[encoder_padding_mask] = 0 diff --git a/examples/translation_moe/src/translation_moe.py b/examples/translation_moe/translation_moe_src/translation_moe.py similarity index 53% rename from examples/translation_moe/src/translation_moe.py rename to examples/translation_moe/translation_moe_src/translation_moe.py index 61e4bed809..7f28c32dd6 100644 --- a/examples/translation_moe/src/translation_moe.py +++ b/examples/translation_moe/translation_moe_src/translation_moe.py @@ -3,17 +3,52 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. +from dataclasses import dataclass, field import torch +from omegaconf import II from fairseq import metrics, utils +from fairseq.dataclass import ChoiceEnum from fairseq.tasks import register_task -from fairseq.tasks.translation import TranslationTask +from fairseq.tasks.translation import TranslationConfig, TranslationTask from .logsumexp_moe import LogSumExpMoE from .mean_pool_gating_network import MeanPoolGatingNetwork -@register_task('translation_moe') +METHOD_CHOICES = ChoiceEnum(["sMoElp", "sMoEup", "hMoElp", "hMoEup"]) + + +@dataclass +class TranslationMoEConfig(TranslationConfig): + method: METHOD_CHOICES = field( + default="hMoEup", + metadata={"help": "MoE method"}, + ) + num_experts: int = field( + default=3, + metadata={"help": "number of experts"}, + ) + mean_pool_gating_network: bool = field( + default=False, + metadata={"help": "use a simple mean-pooling gating network"}, + ) + mean_pool_gating_network_dropout: float = field( + default=0, + metadata={"help": "dropout for mean-pooling gating network"}, + ) + mean_pool_gating_network_encoder_dim: int = field( + default=0, + metadata={"help": "encoder output dim for mean-pooling gating network"}, + ) + gen_expert: int = field( + default=0, + metadata={"help": "which expert to use for generation"}, + ) + sentence_avg: bool = II("optimization.sentence_avg") + + +@register_task("translation_moe", dataclass=TranslationMoEConfig) class TranslationMoETask(TranslationTask): """ Translation task for Mixture of Experts (MoE) models. @@ -38,90 +73,79 @@ class TranslationMoETask(TranslationTask): :prog: """ - @staticmethod - def add_args(parser): - """Add task-specific arguments to the parser.""" - # fmt: off - TranslationTask.add_args(parser) - parser.add_argument('--method', default='hMoEup', - choices=['sMoElp', 'sMoEup', 'hMoElp', 'hMoEup']) - parser.add_argument('--num-experts', default=3, type=int, metavar='N', - help='number of experts') - parser.add_argument('--mean-pool-gating-network', action='store_true', - help='use a simple mean-pooling gating network') - parser.add_argument('--mean-pool-gating-network-dropout', type=float, - help='dropout for mean-pooling gating network') - parser.add_argument('--mean-pool-gating-network-encoder-dim', type=float, - help='encoder output dim for mean-pooling gating network') - parser.add_argument('--gen-expert', type=int, default=0, - help='which expert to use for generation') - # fmt: on - - def __init__(self, args, src_dict, tgt_dict): - if args.method == 'sMoElp': + cfg: TranslationMoEConfig + + def __init__(self, cfg: TranslationMoEConfig, src_dict, tgt_dict): + if cfg.method == "sMoElp": # soft MoE with learned prior self.uniform_prior = False self.hard_selection = False - elif args.method == 'sMoEup': + elif cfg.method == "sMoEup": # soft MoE with uniform prior self.uniform_prior = True self.hard_selection = False - elif args.method == 'hMoElp': + elif cfg.method == "hMoElp": # hard MoE with learned prior self.uniform_prior = False self.hard_selection = True - elif args.method == 'hMoEup': + elif cfg.method == "hMoEup": # hard MoE with uniform prior self.uniform_prior = True self.hard_selection = True # add indicator tokens for each expert - for i in range(args.num_experts): + for i in range(cfg.num_experts): # add to both dictionaries in case we're sharing embeddings - src_dict.add_symbol('<expert_{}>'.format(i)) - tgt_dict.add_symbol('<expert_{}>'.format(i)) + src_dict.add_symbol("<expert_{}>".format(i)) + tgt_dict.add_symbol("<expert_{}>".format(i)) - super().__init__(args, src_dict, tgt_dict) + super().__init__(cfg, src_dict, tgt_dict) - def build_model(self, args): + def build_model(self, cfg): from fairseq import models - model = models.build_model(args, self) - if not self.uniform_prior and not hasattr(model, 'gating_network'): - if self.args.mean_pool_gating_network: - if getattr(args, 'mean_pool_gating_network_encoder_dim', None): - encoder_dim = args.mean_pool_gating_network_encoder_dim - elif getattr(args, 'encoder_embed_dim', None): + + model = models.build_model(cfg, self) + if not self.uniform_prior and not hasattr(model, "gating_network"): + if self.cfg.mean_pool_gating_network: + if self.cfg.mean_pool_gating_network_encoder_dim > 0: + encoder_dim = self.cfg.mean_pool_gating_network_encoder_dim + elif getattr(cfg, "encoder_embed_dim", None): # assume that encoder_embed_dim is the encoder's output dimension - encoder_dim = args.encoder_embed_dim + encoder_dim = cfg.encoder_embed_dim else: - raise ValueError('Must specify --mean-pool-gating-network-encoder-dim') - - if getattr(args, 'mean_pool_gating_network_dropout', None): - dropout = args.mean_pool_gating_network_dropout - elif getattr(args, 'dropout', None): - dropout = args.dropout + raise ValueError( + "Must specify --mean-pool-gating-network-encoder-dim" + ) + + if self.cfg.mean_pool_gating_network_dropout > 0: + dropout = self.cfg.mean_pool_gating_network_dropout + elif getattr(cfg, "dropout", None): + dropout = cfg.dropout else: - raise ValueError('Must specify --mean-pool-gating-network-dropout') + raise ValueError("Must specify task.mean_pool_gating_network_dropout") model.gating_network = MeanPoolGatingNetwork( - encoder_dim, args.num_experts, dropout, + encoder_dim, + self.cfg.num_experts, + dropout, ) else: raise ValueError( - 'translation_moe task with learned prior requires the model to ' - 'have a gating network; try using --mean-pool-gating-network' + "translation_moe task with learned prior requires the model to " + "have a gating network; try using --mean-pool-gating-network" ) return model def expert_index(self, i): - return i + self.tgt_dict.index('<expert_0>') + return i + self.tgt_dict.index("<expert_0>") def _get_loss(self, sample, model, criterion): - assert hasattr(criterion, 'compute_loss'), \ - 'translation_moe task requires the criterion to implement the compute_loss() method' + assert hasattr( + criterion, "compute_loss" + ), "translation_moe task requires the criterion to implement the compute_loss() method" - k = self.args.num_experts - bsz = sample['target'].size(0) + k = self.cfg.num_experts + bsz = sample["target"].size(0) def get_lprob_y(encoder_out, prev_output_tokens_k): net_output = model.decoder( @@ -134,20 +158,22 @@ def get_lprob_y(encoder_out, prev_output_tokens_k): def get_lprob_yz(winners=None): encoder_out = model.encoder( - src_tokens=sample['net_input']['src_tokens'], - src_lengths=sample['net_input']['src_lengths'], + src_tokens=sample["net_input"]["src_tokens"], + src_lengths=sample["net_input"]["src_lengths"], ) if winners is None: lprob_y = [] for i in range(k): - prev_output_tokens_k = sample['net_input']['prev_output_tokens'].clone() + prev_output_tokens_k = sample["net_input"][ + "prev_output_tokens" + ].clone() assert not prev_output_tokens_k.requires_grad prev_output_tokens_k[:, 0] = self.expert_index(i) lprob_y.append(get_lprob_y(encoder_out, prev_output_tokens_k)) lprob_y = torch.cat(lprob_y, dim=1) # -> B x K else: - prev_output_tokens_k = sample['net_input']['prev_output_tokens'].clone() + prev_output_tokens_k = sample["net_input"]["prev_output_tokens"].clone() prev_output_tokens_k[:, 0] = self.expert_index(winners) lprob_y = get_lprob_y(encoder_out, prev_output_tokens_k) # -> B @@ -162,7 +188,7 @@ def get_lprob_yz(winners=None): return lprob_yz # compute responsibilities without dropout - with utils.eval(model): # disable dropout + with utils.model_eval(model): # disable dropout with torch.no_grad(): # disable autograd lprob_yz = get_lprob_yz() # B x K prob_z_xy = torch.nn.functional.softmax(lprob_yz, dim=1) @@ -177,17 +203,21 @@ def get_lprob_yz(winners=None): loss = -LogSumExpMoE.apply(lprob_yz, prob_z_xy, 1) loss = loss.sum() - sample_size = sample['target'].size(0) if self.args.sentence_avg else sample['ntokens'] + sample_size = ( + sample["target"].size(0) if self.cfg.sentence_avg else sample["ntokens"] + ) logging_output = { - 'loss': utils.item(loss.data), - 'ntokens': sample['ntokens'], - 'nsentences': bsz, - 'sample_size': sample_size, - 'posterior': prob_z_xy.float().sum(dim=0).cpu(), + "loss": utils.item(loss.data), + "ntokens": sample["ntokens"], + "nsentences": bsz, + "sample_size": sample_size, + "posterior": prob_z_xy.float().sum(dim=0).cpu(), } return loss, sample_size, logging_output - def train_step(self, sample, model, criterion, optimizer, update_num, ignore_grad=False): + def train_step( + self, sample, model, criterion, optimizer, update_num, ignore_grad=False + ): model.train() loss, sample_size, logging_output = self._get_loss(sample, model, criterion) if ignore_grad: @@ -201,19 +231,28 @@ def valid_step(self, sample, model, criterion): loss, sample_size, logging_output = self._get_loss(sample, model, criterion) return loss, sample_size, logging_output - def inference_step(self, generator, models, sample, prefix_tokens=None, expert=None): - expert = expert or self.args.gen_expert + def inference_step( + self, + generator, + models, + sample, + prefix_tokens=None, + expert=None, + constraints=None, + ): + expert = expert or self.cfg.gen_expert with torch.no_grad(): return generator.generate( models, sample, prefix_tokens=prefix_tokens, + constraints=constraints, bos_token=self.expert_index(expert), ) def reduce_metrics(self, logging_outputs, criterion): super().reduce_metrics(logging_outputs, criterion) metrics.log_scalar( - 'posterior', - sum(log['posterior'] for log in logging_outputs if 'posterior' in log) + "posterior", + sum(log["posterior"] for log in logging_outputs if "posterior" in log), ) diff --git a/examples/truncated_bptt/README.md b/examples/truncated_bptt/README.md new file mode 100644 index 0000000000..86518c9d5e --- /dev/null +++ b/examples/truncated_bptt/README.md @@ -0,0 +1,70 @@ +# Truncated Backpropagation Through Time (BPTT) + +Truncated BPTT is a useful technique for training language models on very long +sequences. Typically a long sequences is split into chunks and a language model +is trained over the chunks sequentially. The LM may condition on previous +chunks, but gradients only flow through the current chunk. This technique was +the basis for the paper: [Transformer-XL: Attentive Language Models Beyond a +Fixed-Length Context](https://arxiv.org/abs/1901.02860), which achieved +state-of-the-art language modeling results at the time of publication. + +It is slightly tricky to implement Truncated BPTT efficiently in fairseq, since +we need to iterate over the data sequentially and disable any batch shuffling +logic. The code provided in this example illustrates how to implement Truncated +BPTT in fairseq by overriding ``FairseqTask::get_batch_iterator`` to iterate +over the data sequentially. Crucially, this example supports batching and +multi-GPU (data parallel) training. + +##### 0. Setup + +First, see the general [language modeling README](README.md) for instructions on +preprocessing the WikiText-103 data. + +##### 1. Train a Transformer-XL model on WikiText-103 + +We will train a 16-layer Transformer-XL model following the [hyperparameters +used in the original +paper](https://github.com/kimiyoung/transformer-xl/blob/master/pytorch/run_wt103_base.sh). + +The following command assumes 4 GPUs, so that the total batch size is 60 +sequences (15 x 4). Training should take ~24 hours on 4 V100 GPUs: +```bash +CUDA_VISIBLE_DEVICES=0,1,2,3 fairseq-train \ + --user-dir examples/truncated_bptt \ + data-bin/wikitext-103/ \ + --task truncated_bptt_lm --tokens-per-sample 150 \ + --batch-size 15 --max-update 200000 \ + --arch transformer_xl --n-layer 16 --d-model 410 --n-head 10 \ + --d-head 41 --d-inner 2100 --dropout 0.1 --dropatt 0.0 --mem-len 150 \ + --optimizer adam --clip-norm 0.25 \ + --lr-scheduler cosine --warmup-updates 0 --min-lr 0.0 --lr 0.00025 \ + --log-format json --log-interval 25 \ + --fp16 +``` + +If training on a single GPU, set `--update-freq=4` to accumulate 4x gradients +and simulate training on 4 GPUs. + +##### 2. Evaluate + +```bash +fairseq-eval-lm data-bin/wikitext-103/ \ + --path checkpoints/checkpoint_best.pt \ + --user-dir examples/truncated_bptt/ \ + --task truncated_bptt_lm \ + --batch-size 1 --required-batch-size-multiple 1 \ + --model-overrides '{"mem_len":640,"clamp_len":400,"same_length":True}' \ + --tokens-per-sample 64 +# ... | INFO | fairseq_cli.eval_lm | num. model params: 151123537 +# ... | INFO | fairseq_cli.eval_lm | Evaluated 245569 tokens in 83.1s (2956.82 tokens/s) +# ... | INFO | fairseq_cli.eval_lm | Loss (base 2): 4.5668, Perplexity: 23.70 +# Compare to 24.0 test perplexity from the paper +``` + +*Note:* During training the model saw 150 tokens of context +(``--tokens-per-sample=150``) and 150 extra memory tokens (``--mem-len=150``). +During evaluation we measure perplexity on sequences of 64 tokens +(``--tokens-per-sample=64``) and increase the memory length +(``--model-overrides='{"mem_len":640}'``). These settings match the evaluation +settings from [the original +paper](https://github.com/kimiyoung/transformer-xl/blob/master/pytorch/run_wt103_base.sh). diff --git a/examples/truncated_bptt/__init__.py b/examples/truncated_bptt/__init__.py new file mode 100644 index 0000000000..eee484d427 --- /dev/null +++ b/examples/truncated_bptt/__init__.py @@ -0,0 +1,6 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from . import transformer_xl_model, truncated_bptt_lm_task # noqa diff --git a/examples/truncated_bptt/transformer_xl_model.py b/examples/truncated_bptt/transformer_xl_model.py new file mode 100644 index 0000000000..a6c8b25a07 --- /dev/null +++ b/examples/truncated_bptt/transformer_xl_model.py @@ -0,0 +1,155 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +from dataclasses import dataclass, field +from typing import Dict, List, Optional + +import torch +from fairseq.dataclass import FairseqDataclass +from fairseq.models import ( + FairseqIncrementalDecoder, + FairseqLanguageModel, + register_model, +) +from fairseq.modules.checkpoint_activations import checkpoint_wrapper +from omegaconf import II + + +logger = logging.getLogger(__name__) + + +@dataclass +class TransformerXLConfig(FairseqDataclass): + # defaults come from the original Transformer-XL code + cutoffs: List[int] = field(default_factory=lambda: [20000, 40000, 200000]) + d_model: int = 500 + n_head: int = 10 + d_head: int = 50 + d_inner: int = 1000 + div_val: int = 1 + n_layer: int = 12 + mem_len: int = 0 + clamp_len: int = -1 + same_length: bool = False + dropout: float = 0.0 + dropatt: float = 0.0 + checkpoint_activations: bool = False + offload_activations: bool = False + max_target_positions: int = II("task.max_target_positions") + + +@register_model("transformer_xl", dataclass=TransformerXLConfig) +class TransformerXLLanguageModel(FairseqLanguageModel): + @classmethod + def build_model(cls, cfg: TransformerXLConfig, task): + return cls(TransformerXLDecoder(cfg, task)) + + +class TransformerXLDecoder(FairseqIncrementalDecoder): + def __init__(self, cfg, task): + try: + from transformers.models.transfo_xl import ( + TransfoXLConfig, + TransfoXLLMHeadModel, + ) + except ImportError: + from transformers.configuration_transfo_xl import TransfoXLConfig + from transformers.modeling_transfo_xl import TransfoXLLMHeadModel + + super().__init__(task.target_dictionary) + self.cfg = cfg + + # remove any cutoffs larger than the vocab size + cutoffs = [ + cutoff for cutoff in cfg.cutoffs if cutoff < len(task.target_dictionary) + ] + + config = TransfoXLConfig( + vocab_size=len(task.target_dictionary), + cutoffs=cutoffs, + d_model=cfg.d_model, + d_embed=cfg.d_model, + n_head=cfg.n_head, + d_head=cfg.d_head, + d_inner=cfg.d_inner, + div_val=cfg.div_val, + n_layer=cfg.n_layer, + mem_len=cfg.mem_len, + clamp_len=cfg.clamp_len, + same_length=cfg.same_length, + dropout=cfg.dropout, + dropatt=cfg.dropatt, + ) + logger.info(config) + self.model = TransfoXLLMHeadModel(config) + + # Workaround a bug in huggingface's ``ProjectedAdaptiveLogSoftmax`` + # which adds ``None`` values to an ``nn.ParameterList``, which is not + # supported in PyTorch. Instead we can replace this with an + # ``nn.ModuleList``, which does support ``None`` values. + try: + if all(p is None for p in self.model.crit.out_projs._parameters.values()): + self.model.crit.out_projs = torch.nn.ModuleList( + [None] * len(self.model.crit.out_projs._parameters) + ) + except Exception: + pass + + if cfg.checkpoint_activations or cfg.offload_activations: + for i in range(len(self.model.transformer.layers)): + self.model.transformer.layers[i] = checkpoint_wrapper( + self.model.transformer.layers[i], + offload_to_cpu=cfg.offload_activations, + ) + # TODO: may save mem to wrap(layer.pos_ff.CoreNet[3]) + + self._mems = None + + def forward( + self, + src_tokens, + src_lengths=None, # unused + incremental_state: Optional[Dict[str, List[torch.Tensor]]] = None, + encoder_out=None, + ): + if incremental_state is not None: # used during inference + mems = self.get_incremental_state(incremental_state, "mems") + src_tokens = src_tokens[:, -1:] # only keep the most recent token + else: + mems = self._mems + + output = self.model( + input_ids=src_tokens, + mems=mems, + return_dict=False, + ) + + if len(output) >= 2: + if incremental_state is not None: + self.set_incremental_state(incremental_state, "mems", output[1]) + else: + self._mems = output[1] + + return (output[0],) + + def max_positions(self): + return self.cfg.max_target_positions + + def reorder_incremental_state( + self, + incremental_state: Dict[str, Dict[str, Optional[torch.Tensor]]], + new_order: torch.Tensor, + ): + """Reorder incremental state. + + This will be called when the order of the input has changed from the + previous time step. A typical use case is beam search, where the input + order changes between time steps based on the selection of beams. + """ + mems = self.get_incremental_state(incremental_state, "mems") + if mems is not None: + new_mems = [mems_i.index_select(1, new_order) for mems_i in mems] + self.set_incremental_state(incremental_state, "mems", new_mems) diff --git a/examples/truncated_bptt/truncated_bptt_lm_task.py b/examples/truncated_bptt/truncated_bptt_lm_task.py new file mode 100644 index 0000000000..02be0e7fb4 --- /dev/null +++ b/examples/truncated_bptt/truncated_bptt_lm_task.py @@ -0,0 +1,281 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +import os +from dataclasses import dataclass, field +from typing import List, Optional, Tuple + +import torch +from fairseq import utils +from fairseq.data import ( + Dictionary, + TokenBlockDataset, + data_utils, + iterators, +) +from fairseq.dataclass import FairseqDataclass +from fairseq.distributed import utils as dist_utils +from fairseq.tasks import FairseqTask, register_task +from omegaconf import II + + +logger = logging.getLogger(__name__) + + +@dataclass +class TruncatedBPTTLMConfig(FairseqDataclass): + data: str = field(default="???", metadata={"help": "path to data directory"}) + tokens_per_sample: int = field( + default=1024, + metadata={"help": "max number of tokens per sequence"}, + ) + batch_size: int = II("dataset.batch_size") + # Some models use *max_target_positions* to know how many positional + # embeddings to learn. We use II(...) to make it default to + # *tokens_per_sample*, but in principle there could be more positional + # embeddings than tokens in a single batch. This may also be irrelevant for + # custom model implementations. + max_target_positions: int = II("task.tokens_per_sample") + # these will be populated automatically if not provided + data_parallel_rank: Optional[int] = None + data_parallel_size: Optional[int] = None + + +@register_task("truncated_bptt_lm", dataclass=TruncatedBPTTLMConfig) +class TruncatedBPTTLMTask(FairseqTask): + def __init__(self, cfg: TruncatedBPTTLMConfig): + super().__init__(cfg) + + if cfg.data_parallel_rank is None or cfg.data_parallel_size is None: + if torch.distributed.is_initialized(): + cfg.data_parallel_rank = dist_utils.get_data_parallel_rank() + cfg.data_parallel_size = dist_utils.get_data_parallel_world_size() + else: + cfg.data_parallel_rank = 0 + cfg.data_parallel_size = 1 + + # load the dictionary + paths = utils.split_paths(cfg.data) + assert len(paths) > 0 + self.dictionary = Dictionary.load(os.path.join(paths[0], "dict.txt")) + logger.info("dictionary: {} types".format(len(self.dictionary))) + + def load_dataset(self, split, epoch=1, combine=False, **kwargs): + """Load a given dataset split (e.g., train, valid, test)""" + + # support sharded datasets + paths = utils.split_paths(self.cfg.data) + assert len(paths) > 0 + data_path = paths[(epoch - 1) % len(paths)] + split_path = os.path.join(data_path, split) + + # each element of *data* will be a tensorized line from the original + # text dataset, similar to ``open(split_path).readlines()`` + data = data_utils.load_indexed_dataset( + split_path, self.dictionary, combine=combine + ) + if data is None: + raise FileNotFoundError( + "Dataset not found: {} ({})".format(split, split_path) + ) + + # this is similar to ``data.view(-1).split(tokens_per_sample)`` + data = TokenBlockDataset( + data, + data.sizes, + block_size=self.cfg.tokens_per_sample, + pad=None, # unused + eos=None, # unused + break_mode="none", + ) + + self.datasets[split] = TruncatedBPTTDataset( + data=data, + bsz_per_shard=self.cfg.batch_size, + shard_id=self.cfg.data_parallel_rank, + num_shards=self.cfg.data_parallel_size, + ) + + def dataset(self, split): + return self.datasets[split] + + def get_batch_iterator( + self, dataset, num_workers=0, epoch=1, data_buffer_size=0, **kwargs + ): + return iterators.EpochBatchIterator( + dataset=dataset, + collate_fn=self._collate_fn, + num_workers=num_workers, + epoch=epoch, + buffer_size=data_buffer_size, + # we don't use the batching functionality from EpochBatchIterator; + # instead every item in *dataset* is a whole batch + batch_sampler=[[i] for i in range(len(dataset))], + disable_shuffling=True, + ) + + def _collate_fn(self, items: List[List[torch.Tensor]]): + # we don't use fairseq's batching functionality, so we expect a single + # Tensor of type List[torch.Tensor] + assert len(items) == 1 + + # item will have shape B x T (the last batch may have length < T) + id, item = items[0] + item = data_utils.collate_tokens(item, pad_idx=self.source_dictionary.pad()) + B, T = item.size() + + # shift item one position over and append a padding token for the target + target = torch.nn.functional.pad( + item[:, 1:], (0, 1, 0, 0), value=self.target_dictionary.pad() + ) + + # fairseq expects batches to have the following structure + return { + "id": torch.tensor([id]*item.size(0)), + "net_input": { + "src_tokens": item, + }, + "target": target, + "nsentences": item.size(0), + "ntokens": item.numel(), + } + + def build_dataset_for_inference( + self, src_tokens: List[torch.Tensor], src_lengths: List[int], **kwargs + ) -> torch.utils.data.Dataset: + eos = self.source_dictionary.eos() + dataset = TokenBlockDataset( + src_tokens, + src_lengths, + block_size=None, # ignored for "eos" break mode + pad=self.source_dictionary.pad(), + eos=eos, + break_mode="eos", + ) + + class Dataset(torch.utils.data.Dataset): + def __getitem__(self, i): + item = dataset[i] + if item[-1] == eos: + # remove eos to support generating with a prefix + item = item[:-1] + return (i, [item]) + + def __len__(self): + return len(dataset) + + return Dataset() + + def inference_step( + self, generator, models, sample, prefix_tokens=None, constraints=None + ): + with torch.no_grad(): + if constraints is not None: + raise NotImplementedError + + # SequenceGenerator doesn't use *src_tokens* directly, we need to + # pass the *prefix_tokens* argument instead. + if prefix_tokens is None and sample["net_input"]["src_tokens"].nelement(): + prefix_tokens = sample["net_input"]["src_tokens"] + + # begin generation with the end-of-sentence token + bos_token = self.source_dictionary.eos() + + return generator.generate( + models, sample, prefix_tokens=prefix_tokens, bos_token=bos_token + ) + + def eval_lm_dataloader( + self, + dataset, + max_tokens: Optional[int] = 36000, + batch_size: Optional[int] = None, + max_positions: Optional[int] = None, + num_shards: int = 1, + shard_id: int = 0, + num_workers: int = 1, + data_buffer_size: int = 10, + context_window: int = 0, + ): + if context_window > 0: + raise NotImplementedError( + "Transformer-XL doesn't need --context-window, try " + "--model-overrides '{\"mem_len\":42}' instead " + ) + return self.get_batch_iterator( + dataset=dataset, + max_tokens=max_tokens, + max_sentences=batch_size, + max_positions=max_positions, + ignore_invalid_inputs=True, + num_shards=num_shards, + shard_id=shard_id, + num_workers=num_workers, + data_buffer_size=data_buffer_size, + ).next_epoch_itr(shuffle=False) + + @property + def source_dictionary(self): + return self.dictionary + + @property + def target_dictionary(self): + return self.dictionary + + +class TruncatedBPTTDataset(torch.utils.data.Dataset): + def __init__( + self, + data: List[torch.Tensor], # ordered list of items + bsz_per_shard, # number of items processed per GPUs per forward + shard_id, # current GPU ID + num_shards, # number of GPUs + ): + super().__init__() + self.data = data + + def batchify(data, bsz): + # Work out how cleanly we can divide the dataset into bsz parts. + nbatch = data.size(0) // bsz + # Trim off any extra elements that wouldn't cleanly fit (remainders). + data = data.narrow(0, 0, nbatch * bsz) + # Evenly divide the data across the bsz batches. + data = data.view(bsz, -1).contiguous() + return data + + # total number of sequences processed by all GPUs in each forward pass + global_batch_size = bsz_per_shard * num_shards + + """ + With a 16 item dataset, bsz_per_shard=2 and num_shards=3, + *indices* might look like: + + indices = [[0, 1], + [2, 3], + [4, 5], + [6, 7], + [8, 9], + [10, 11]] + + The size of the TruncatedBPTTDataset instance will be 2, + and shard 1 will see items: + + [(0, [data[4], data[6]]), + (1, [data[5], data[7]])] + """ + indices = batchify(torch.arange(len(data)), global_batch_size) + assert indices.size(0) == global_batch_size + + self.my_indices = indices[ + shard_id * bsz_per_shard : (shard_id + 1) * bsz_per_shard + ] + assert self.my_indices.size(0) == bsz_per_shard + + def __len__(self): + return self.my_indices.size(1) + + def __getitem__(self, i) -> Tuple[int, List[torch.Tensor]]: + return (i, [self.data[idx] for idx in self.my_indices[:, i]]) diff --git a/examples/unsupervised_quality_estimation/README.md b/examples/unsupervised_quality_estimation/README.md new file mode 100644 index 0000000000..e86a0d13b8 --- /dev/null +++ b/examples/unsupervised_quality_estimation/README.md @@ -0,0 +1,126 @@ +# Unsupervised Quality Estimation for Neural Machine Translation (Fomicheva et al., 2020) + +This page includes instructions for reproducing results from the paper [Unsupervised Quality Estimation for Neural +Machine Translation (Fomicheva et al., 2020)](https://arxiv.org/abs/2005.10608) + +## Requirements: + +* mosesdecoder: https://github.com/moses-smt/mosesdecoder +* subword-nmt: https://github.com/rsennrich/subword-nmt +* flores: https://github.com/facebookresearch/flores + +## Download Models and Test Data + +Download translation models and test data from [MLQE dataset repository](https://github.com/facebookresearch/mlqe). + +## Set up: + +Given a testset consisting of source sentences and reference translations: + +* `SRC_LANG`: source language +* `TGT_LANG`: target language +* `INPUT`: input prefix, such that the file `$INPUT.$SRC_LANG` contains source sentences and `$INPUT.$TGT_LANG` +contains the reference sentences +* `OUTPUT_DIR`: output path to store results +* `MOSES_DECODER`: path to mosesdecoder installation +* `BPE_ROOT`: path to subword-nmt installation +* `BPE`: path to BPE model +* `MODEL_DIR`: directory containing the NMT model `.pt` file as well as the source and target vocabularies. +* `TMP`: directory for intermediate temporary files +* `GPU`: if translating with GPU, id of the GPU to use for inference +* `DROPOUT_N`: number of stochastic forward passes + +`$DROPOUT_N` is set to 30 in the experiments reported in the paper. However, we observed that increasing it beyond 10 +does not bring substantial improvements. + +## Translate the data using standard decoding + +Preprocess the input data: +``` +for LANG in $SRC_LANG $TGT_LANG; do + perl $MOSES_DECODER/scripts/tokenizer/tokenizer.perl -threads 80 -a -l $LANG < $INPUT.$LANG > $TMP/preprocessed.tok.$LANG + python $BPE_ROOT/apply_bpe.py -c ${BPE} < $TMP/preprocessed.tok.$LANG > $TMP/preprocessed.tok.bpe.$LANG +done +``` + +Binarize the data for faster translation: + +``` +fairseq-preprocess --srcdict $MODEL_DIR/dict.$SRC_LANG.txt --tgtdict $MODEL_DIR/dict.$TGT_LANG.txt +--source-lang ${SRC_LANG} --target-lang ${TGT_LANG} --testpref $TMP/preprocessed.tok.bpe --destdir $TMP/bin --workers 4 +``` + +Translate + +``` +CUDA_VISIBLE_DEVICES=$GPU fairseq-generate $TMP/bin --path ${MODEL_DIR}/${SRC_LANG}-${TGT_LANG}.pt --beam 5 +--source-lang $SRC_LANG --target-lang $TGT_LANG --no-progress-bar --unkpen 5 > $TMP/fairseq.out +grep ^H $TMP/fairseq.out | cut -d- -f2- | sort -n | cut -f3- > $TMP/mt.out +``` + +Post-process + +``` +sed -r 's/(@@ )| (@@ ?$)//g' < $TMP/mt.out | perl $MOSES_DECODER/scripts/tokenizer/detokenizer.perl +-l $TGT_LANG > $OUTPUT_DIR/mt.out +``` + +## Produce uncertainty estimates + +### Scoring + +Make temporary files to store the translations repeated N times. + +``` +python ${SCRIPTS}/scripts/uncertainty/repeat_lines.py -i $TMP/preprocessed.tok.bpe.$SRC_LANG -n $DROPOUT_N +-o $TMP/repeated.$SRC_LANG +python ${SCRIPTS}/scripts/uncertainty/repeat_lines.py -i $TMP/mt.out -n $DROPOUT_N -o $TMP/repeated.$TGT_LANG + +fairseq-preprocess --srcdict ${MODEL_DIR}/dict.${SRC_LANG}.txt $TGT_DIC --source-lang ${SRC_LANG} +--target-lang ${TGT_LANG} --testpref ${TMP}/repeated --destdir ${TMP}/bin-repeated +``` + +Produce model scores for the generated translations using `--retain-dropout` option to apply dropout at inference time: + +``` +CUDA_VISIBLE_DEVICES=${GPU} fairseq-generate ${TMP}/bin-repeated --path ${MODEL_DIR}/${LP}.pt --beam 5 + --source-lang $SRC_LANG --target-lang $TGT_LANG --no-progress-bar --unkpen 5 --score-reference --retain-dropout + --retain-dropout-modules '["TransformerModel","TransformerEncoder","TransformerDecoder","TransformerEncoderLayer"]' + TransformerDecoderLayer --seed 46 > $TMP/dropout.scoring.out + +grep ^H $TMP/dropout.scoring.out | cut -d- -f2- | sort -n | cut -f2 > $TMP/dropout.scores + +``` + +Use `--retain-dropout-modules` to specify the modules. By default, dropout is applied in the same places +as for training. + +Compute the mean of the resulting output distribution: + +``` +python $SCRIPTS/scripts/uncertainty/aggregate_scores.py -i $TMP/dropout.scores -o $OUTPUT_DIR/dropout.scores.mean +-n $DROPOUT_N +``` + +### Generation + +Produce multiple translation hypotheses for the same source using `--retain-dropout` option: + +``` +CUDA_VISIBLE_DEVICES=${GPU} fairseq-generate ${TMP}/bin-repeated --path ${MODEL_DIR}/${LP}.pt + --beam 5 --source-lang $SRC_LANG --target-lang $TGT_LANG --no-progress-bar --retain-dropout + --unkpen 5 --retain-dropout-modules TransformerModel TransformerEncoder TransformerDecoder +TransformerEncoderLayer TransformerDecoderLayer --seed 46 > $TMP/dropout.generation.out + +grep ^H $TMP/dropout.generation.out | cut -d- -f2- | sort -n | cut -f3- > $TMP/dropout.hypotheses_ + +sed -r 's/(@@ )| (@@ ?$)//g' < $TMP/dropout.hypotheses_ | perl $MOSES_DECODER/scripts/tokenizer/detokenizer.perl +-l $TGT_LANG > $TMP/dropout.hypotheses +``` + +Compute similarity between multiple hypotheses corresponding to the same source sentence using Meteor +evaluation metric: +``` +python meteor.py -i $TMP/dropout.hypotheses -m <path_to_meteor_installation> -n $DROPOUT_N -o +$OUTPUT_DIR/dropout.gen.sim.meteor +``` diff --git a/examples/unsupervised_quality_estimation/aggregate_scores.py b/examples/unsupervised_quality_estimation/aggregate_scores.py new file mode 100644 index 0000000000..66d50d07ff --- /dev/null +++ b/examples/unsupervised_quality_estimation/aggregate_scores.py @@ -0,0 +1,41 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import sys + +import numpy as np + + +aggregate_funcs = { + "std": np.std, + "var": np.var, + "median": np.median, + "mean": np.mean, + "min": np.min, + "max": np.max, +} + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("-i", "--input_file", required=True, type=str) + parser.add_argument("-n", "--repeat_times", required=True, type=int) + parser.add_argument("-o", "--output_file", required=False) + parser.add_argument("-f", "--func", required=False, default="mean") + args = parser.parse_args() + + stream = open(args.output_file, "w") if args.output_file else sys.stdout + + segment_scores = [] + for line in open(args.input_file): + segment_scores.append(float(line.strip())) + if len(segment_scores) == args.repeat_times: + stream.write("{}\n".format(aggregate_funcs[args.func](segment_scores))) + segment_scores = [] + + +if __name__ == "__main__": + main() diff --git a/examples/unsupervised_quality_estimation/meteor.py b/examples/unsupervised_quality_estimation/meteor.py new file mode 100644 index 0000000000..2ee0448cf1 --- /dev/null +++ b/examples/unsupervised_quality_estimation/meteor.py @@ -0,0 +1,109 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import math +import os +import subprocess +import sys +import tempfile +from collections import defaultdict +from itertools import combinations + + +def read_translations(path, n_repeats): + segment_counter = 0 + segment_translations = [] + translations = defaultdict(list) + for line in open(path): + segment_translations.append(" ".join(line.split())) + if len(segment_translations) == n_repeats: + translations[segment_counter] = segment_translations + segment_translations = [] + segment_counter += 1 + return translations + + +def generate_input(translations, n_repeats): + _, ref_path = tempfile.mkstemp() + _, mt_path = tempfile.mkstemp() + ref_fh = open(ref_path, "w") + mt_fh = open(mt_path, "w") + for segid in sorted(translations.keys()): + assert len(translations[segid]) == n_repeats + indexes = combinations(range(n_repeats), 2) + for idx1, idx2 in indexes: + mt_fh.write(translations[segid][idx1].strip() + "\n") + ref_fh.write(translations[segid][idx2].strip() + "\n") + sys.stderr.write("\nSaved translations to %s and %s" % (ref_path, mt_path)) + return ref_path, mt_path + + +def run_meteor(ref_path, mt_path, metric_path, lang="en"): + _, out_path = tempfile.mkstemp() + subprocess.call( + [ + "java", + "-Xmx2G", + "-jar", + metric_path, + mt_path, + ref_path, + "-p", + "0.5 0.2 0.6 0.75", # default parameters, only changed alpha to give equal weight to P and R + "-norm", + "-l", + lang, + ], + stdout=open(out_path, "w"), + ) + os.remove(ref_path) + os.remove(mt_path) + sys.stderr.write("\nSaved Meteor output to %s" % out_path) + return out_path + + +def read_output(meteor_output_path, n_repeats): + n_combinations = math.factorial(n_repeats) / ( + math.factorial(2) * math.factorial(n_repeats - 2) + ) + raw_scores = [] + average_scores = [] + for line in open(meteor_output_path): + if not line.startswith("Segment "): + continue + score = float(line.strip().split("\t")[1]) + raw_scores.append(score) + if len(raw_scores) == n_combinations: + average_scores.append(sum(raw_scores) / n_combinations) + raw_scores = [] + os.remove(meteor_output_path) + return average_scores + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("-i", "--infile") + parser.add_argument("-n", "--repeat_times", type=int) + parser.add_argument("-m", "--meteor") + parser.add_argument("-o", "--output") + args = parser.parse_args() + + translations = read_translations(args.infile, args.repeat_times) + sys.stderr.write("\nGenerating input for Meteor...") + ref_path, mt_path = generate_input(translations, args.repeat_times) + sys.stderr.write("\nRunning Meteor...") + out_path = run_meteor(ref_path, mt_path, args.meteor) + sys.stderr.write("\nReading output...") + scores = read_output(out_path, args.repeat_times) + sys.stderr.write("\nWriting results...") + with open(args.output, "w") as o: + for scr in scores: + o.write("{}\n".format(scr)) + o.close() + + +if __name__ == "__main__": + main() diff --git a/examples/unsupervised_quality_estimation/repeat_lines.py b/examples/unsupervised_quality_estimation/repeat_lines.py new file mode 100644 index 0000000000..5a04851a74 --- /dev/null +++ b/examples/unsupervised_quality_estimation/repeat_lines.py @@ -0,0 +1,28 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import sys + + +def _normalize_spaces(line): + return " ".join(line.split()) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("-i", "--input_file", required=True, type=str) + parser.add_argument("-n", "--repeat_times", required=True, type=int) + parser.add_argument("-o", "--output_file", required=False, type=str) + args = parser.parse_args() + stream = open(args.output_file, "w") if args.output_file else sys.stdout + + for line in open(args.input_file): + for _ in range(args.repeat_times): + stream.write(_normalize_spaces(line) + "\n") + + +if __name__ == "__main__": + main() diff --git a/examples/wav2vec/README.md b/examples/wav2vec/README.md index 963c133f83..253c8af251 100644 --- a/examples/wav2vec/README.md +++ b/examples/wav2vec/README.md @@ -1,21 +1,228 @@ +# wav2vec 2.0 + +wav2vec 2.0 learns speech representations on unlabeled data as described in [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations (Baevski et al., 2020)](https://arxiv.org/abs/2006.11477). + +We learned speech representations in multiple languages as well in [Unsupervised Cross-lingual Representation Learning for Speech Recognition (Conneau et al., 2020)](https://arxiv.org/abs/2006.13979). + +We also combined wav2vec 2.0 with self-training in [Self-training and Pre-training are Complementary for Speech Recognition (Xu et al., 2020)](https://arxiv.org/abs/2010.11430). + +We combined speech data from multiple domains in [Robust wav2vec 2.0: Analyzing Domain Shift in Self-Supervised Pre-Training (Hsu, et al., 2021)](https://arxiv.org/abs/2104.01027) + +## Pre-trained models + +Model | Finetuning split | Dataset | Model +|---|---|---|--- +Wav2Vec 2.0 Base | No finetuning | [Librispeech](http://www.openslr.org/12) | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/wav2vec_small.pt) +Wav2Vec 2.0 Base | 10 minutes | [Librispeech](http://www.openslr.org/12) | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/wav2vec_small_10m.pt) +Wav2Vec 2.0 Base | 100 hours | [Librispeech](http://www.openslr.org/12) | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/wav2vec_small_100h.pt) +Wav2Vec 2.0 Base | 960 hours | [Librispeech](http://www.openslr.org/12) | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/wav2vec_small_960h.pt) +Wav2Vec 2.0 Large | No finetuning | [Librispeech](http://www.openslr.org/12) | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/libri960_big.pt) +Wav2Vec 2.0 Large | 10 minutes | [Librispeech](http://www.openslr.org/12) | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/wav2vec_big_10m.pt) +Wav2Vec 2.0 Large | 100 hours | [Librispeech](http://www.openslr.org/12) | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/wav2vec_big_100h.pt) +Wav2Vec 2.0 Large | 960 hours | [Librispeech](http://www.openslr.org/12) | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/wav2vec_big_960h.pt) +Wav2Vec 2.0 Large (LV-60)* | No finetuning | [Libri-Light](https://github.com/facebookresearch/libri-light) | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/wav2vec_vox_new.pt) +Wav2Vec 2.0 Large (LV-60)* | 10 minutes | [Libri-Light](https://github.com/facebookresearch/libri-light) + [Librispeech](http://www.openslr.org/12) | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/wav2vec_vox_10m_new.pt) +Wav2Vec 2.0 Large (LV-60)* | 100 hours | [Libri-Light](https://github.com/facebookresearch/libri-light) + [Librispeech](http://www.openslr.org/12) | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/wav2vec_vox_100h_new.pt) +Wav2Vec 2.0 Large (LV-60)* | 960 hours | [Libri-Light](https://github.com/facebookresearch/libri-light) + [Librispeech](http://www.openslr.org/12) | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/wav2vec2_vox_960h_new.pt) +Wav2Vec 2.0 Large (LV-60) + Self Training * | 10 minutes | [Libri-Light](https://github.com/facebookresearch/libri-light) + [Librispeech](http://www.openslr.org/12) | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/wav2vec_vox_10m_pl.pt) +Wav2Vec 2.0 Large (LV-60) + Self Training * | 100 hours | [Libri-Light](https://github.com/facebookresearch/libri-light) + [Librispeech](http://www.openslr.org/12) | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/wav2vec_vox_100h_pl.pt) +Wav2Vec 2.0 Large (LV-60) + Self Training * | 960 hours | [Libri-Light](https://github.com/facebookresearch/libri-light) + [Librispeech](http://www.openslr.org/12) | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/wav2vec_vox_960h_pl.pt) +Wav2Vec 2.0 Large (LV-60 + CV + SWBD + FSH) ** | No finetuning | [Libri-Light](https://github.com/facebookresearch/libri-light) + [CommonVoice](https://commonvoice.mozilla.org/en/languages) + [Switchboard](https://catalog.ldc.upenn.edu/LDC97S62) + [Fisher](https://catalog.ldc.upenn.edu/LDC2004T19) | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/w2v_large_lv_fsh_swbd_cv.pt) +Wav2Vec 2.0 Large (LV-60 + CV + SWBD + FSH) ** | 960 hours Librispeech | [Libri-Light](https://github.com/facebookresearch/libri-light) + [CommonVoice](https://commonvoice.mozilla.org/en/languages) + [Switchboard](https://catalog.ldc.upenn.edu/LDC97S62) + [Fisher](https://catalog.ldc.upenn.edu/LDC2004T19) | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/w2v_large_lv_fsh_swbd_cv_ftls960.pt) +Wav2Vec 2.0 Large (LV-60 + CV + SWBD + FSH) ** | 300 hours Switchboard | [Libri-Light](https://github.com/facebookresearch/libri-light) + [CommonVoice](https://commonvoice.mozilla.org/en/languages) + [Switchboard](https://catalog.ldc.upenn.edu/LDC97S62) + [Fisher](https://catalog.ldc.upenn.edu/LDC2004T19) | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/w2v_large_lv_fsh_swbd_cv_ftsb300.pt) + +\* updated (Oct. 24, 2020)\ +** updated (Jul. 8, 2021) + +We also release multilingual pre-trained wav2vec 2.0 (XLSR) models: + +Model | Architecture | Hours | Languages | Datasets | Model +|---|---|---|---|---|--- +XLSR-53 | Large | 56k | 53 | MLS, CommonVoice, BABEL | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/xlsr_53_56k.pt) + +The XLSR model uses the following datasets for multilingual pretraining: + +* **[MLS: Multilingual LibriSpeech](https://indico2.conference4me.psnc.pl/event/35/contributions/3585/attachments/1060/1101/Wed-2-6-10.pdf)** (8 languages, 50.7k hours): *Dutch, English, French, German, Italian, Polish, Portuguese, Spanish* + +* **[CommonVoice](https://commonvoice.mozilla.org/en/languages)** (36 languages, 3.6k hours): *Arabic, Basque, Breton, Chinese (CN), Chinese (HK), Chinese (TW), Chuvash, Dhivehi, Dutch, English, Esperanto, Estonian, French, German, Hakh-Chin, Indonesian, Interlingua, Irish, Italian, Japanese, Kabyle, Kinyarwanda, Kyrgyz, Latvian, Mongolian, Persian, Portuguese, Russian, Sakha, Slovenian, Spanish, Swedish, Tamil, Tatar, Turkish, Welsh* (see also [finetuning splits]([https://dl.fbaipublicfiles.com/cpc_audio/common_voices_splits.tar.gz]) from [this paper](https://arxiv.org/abs/2002.02848)). + +* **[Babel](https://catalog.ldc.upenn.edu/byyear)** (17 languages, 1.7k hours): *Assamese, Bengali, Cantonese, Cebuano, Georgian, Haitian, Kazakh, Kurmanji, Lao, Pashto, Swahili, Tagalog, Tamil, Tok, Turkish, Vietnamese, Zulu* + + +## Training a new model with the CLI tools + +Given a directory containing wav files to be used for pretraining (we recommend splitting each file into separate file 10 to 30 seconds in length) + +### Prepare training data manifest: + +First, install the `soundfile` library: +```shell script +pip install soundfile +``` + +Next, run: + +```shell script +$ python examples/wav2vec/wav2vec_manifest.py /path/to/waves --dest /manifest/path --ext $ext --valid-percent $valid +``` + +$ext should be set to flac, wav, or whatever format your dataset happens to use that soundfile can read. + +$valid should be set to some reasonable percentage (like 0.01) of training data to use for validation. +To use a pre-defined validation set (like dev-other from librispeech), set to it 0 and then overwrite valid.tsv with a +separately pre-processed manifest file. + +### Train a wav2vec 2.0 base model: + +This configuration was used for the base model trained on the Librispeech dataset in the wav2vec 2.0 paper + +Note that the input is expected to be single channel, sampled at 16 kHz + +```shell script +$ fairseq-hydra-train \ + task.data=/path/to/data \ + --config-dir /path/to/fairseq-py/examples/wav2vec/config/pretraining \ + --config-name wav2vec2_base_librispeech +``` + +Note: you can simulate 64 GPUs by using k GPUs and adding command line parameters (before `--config-dir`) +`distributed_training.distributed_world_size=k` `+optimization.update_freq='[x]'` where x = 64/k + +### Train a wav2vec 2.0 large model: + +This configuration was used for the large model trained on the Libri-light dataset in the wav2vec 2.0 paper + +```shell script +$ fairseq-hydra-train \ + task.data=/path/to/data \ + --config-dir /path/to/fairseq-py/examples/wav2vec/config/pretraining \ + --config-name wav2vec2_large_librivox +``` + +Note: you can simulate 128 GPUs by using k GPUs and adding command line parameters (before `--config-dir`) +`distributed_training.distributed_world_size=k` `+optimization.update_freq='[x]'` where x = 128/k + +### Fine-tune a pre-trained model with CTC: + +Fine-tuning a model requires parallel audio and labels file, as well as a vocabulary file in fairseq format. +A letter vocabulary can be downloaded [here](https://dl.fbaipublicfiles.com/fairseq/wav2vec/dict.ltr.txt). +An example [script](libri_labels.py) that generates labels for the Librispeech dataset from the tsv file produced by wav2vec_manifest.py can be used as follows: + +```shell script +split=train +$ python libri_labels.py /path/to/tsv --output-dir /output/dir --output-name $split +``` + +Fine-tuning on 100h of Librispeech with letter targets: +```shell script +$ fairseq-hydra-train \ + distributed_training.distributed_port=$PORT \ + task.data=/path/to/data \ + model.w2v_path=/path/to/model.pt \ + --config-dir /path/to/fairseq-py/examples/wav2vec/config/finetuning \ + --config-name base_100h +``` + +There are other config files in the config/finetuning directory that can be used to fine-tune on other splits. +You can specify the right config via the `--config-name` parameter. + +Note: you can simulate 24 GPUs by using k GPUs and adding command line parameters (before `--config-dir`) +`distributed_training.distributed_world_size=k` `+optimization.update_freq='[x]'` where x = 24/k + +Decoding with a language model during training requires flashlight [python bindings](https://github.com/facebookresearch/flashlight/tree/master/bindings/python) (previously called [wav2letter](https://github.com/facebookresearch/wav2letter). +If you want to use a language model, add `+criterion.wer_args='[/path/to/kenlm, /path/to/lexicon, 2, -1]'` to the command line. + +### Evaluating a CTC model: + +Evaluating a CTC model with a language model requires [flashlight python bindings](https://github.com/facebookresearch/flashlight/tree/master/bindings/python) (previously called [wav2letter](https://github.com/facebookresearch/wav2letter) to be installed. + +Fairseq transformer language model used in the wav2vec 2.0 paper can be obtained from the [wav2letter model repository](https://github.com/facebookresearch/wav2letter/tree/master/recipes/sota/2019). +Be sure to upper-case the language model vocab after downloading it. + +Letter dictionary for pre-trained models can be found [here](https://dl.fbaipublicfiles.com/fairseq/wav2vec/dict.ltr.txt). + +Next, run the evaluation command: + +```shell script +$subset=dev_other +python examples/speech_recognition/infer.py /checkpoint/abaevski/data/speech/libri/10h/wav2vec/raw --task audio_finetuning \ +--nbest 1 --path /path/to/model --gen-subset $subset --results-path /path/to/save/results/for/sclite --w2l-decoder kenlm \ +--lm-model /path/to/kenlm.bin --lm-weight 2 --word-score -1 --sil-weight 0 --criterion ctc --labels ltr --max-tokens 4000000 \ +--post-process letter +``` + +To get raw numbers, use --w2l-decoder viterbi and omit the lexicon. To use the transformer language model, use --w2l-decoder fairseqlm. + +## Use wav2vec 2.0 with 🤗Transformers: + +Wav2Vec2 is also available in the [🤗Transformers library](https://github.com/huggingface/transformers) since version 4.4. + +Pretrained Models can be found on the [hub](https://huggingface.co/models?filter=wav2vec2) +and documentation can be found [here](https://huggingface.co/transformers/master/model_doc/wav2vec2.html). + +Usage example: + +```python +# !pip install transformers +# !pip install datasets +import soundfile as sf +import torch +from datasets import load_dataset +from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor + +# load pretrained model +processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h") +model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h") + + +librispeech_samples_ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation") + +# load audio +audio_input, sample_rate = sf.read(librispeech_samples_ds[0]["file"]) + +# pad input values and return pt tensor +input_values = processor(audio_input, sampling_rate=sample_rate, return_tensors="pt").input_values + +# INFERENCE + +# retrieve logits & take argmax +logits = model(input_values).logits +predicted_ids = torch.argmax(logits, dim=-1) + +# transcribe +transcription = processor.decode(predicted_ids[0]) + +# FINE-TUNE + +target_transcription = "A MAN SAID TO THE UNIVERSE I EXIST" + +# encode labels +with processor.as_target_processor(): + labels = processor(target_transcription, return_tensors="pt").input_ids + +# compute loss by passing labels +loss = model(input_values, labels=labels).loss +loss.backward() +``` + # wav2vec Example to train a wav2vec model as described in [wav2vec: Unsupervised Pre-training for Speech Recognition (Schneider et al., 2019)](https://arxiv.org/abs/1904.05862). ## Pre-trained models -Description | Parameters | Dataset | Model ----|---:|---|--- -Wav2Vec large <br> ([(Schneider et al., 2019)](https://arxiv.org/abs/1904.05862)) | 32.5M | [Librispeech](http://www.openslr.org/12) | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/wav2vec_large.pt) +Description | Dataset | Model +---|---|--- +Wav2Vec large | [Librispeech](http://www.openslr.org/12) | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/wav2vec_large.pt) #### Example usage: ```python import torch -from fairseq.models.wav2vec import Wav2VecModel +import fairseq -cp = torch.load('/path/to/wav2vec.pt') -model = Wav2VecModel.build_model(cp['args'], task=None) -model.load_state_dict(cp['model']) +cp_path = '/path/to/wav2vec.pt' +model, cfg, task = fairseq.checkpoint_utils.load_model_ensemble_and_task([cp_path]) +model = model[0] model.eval() wav_input_16khz = torch.randn(1,10000) @@ -25,23 +232,76 @@ c = model.feature_aggregator(z) ## Training a new model with the CLI tools -Given a directory containing wav files to be used for pretraining (we recommend splitting each file into separate file 10 to 30 seconds in length) +Given a directory containing wav files to be used for pretraining (we recommend splitting each file into separate files 10 to 30 seconds in length) ### Prepare training data manifest: ``` -$ python scripts/wav2vec_manifest.py /path/to/waves --dest /manifest/path --ext wav +$ python examples/wav2vec/wav2vec_manifest.py /path/to/waves --dest /manifest/path --ext wav ``` ### Train a wav2vec model: ``` $ python train.py /manifest/path --save-dir /model/path --num-workers 6 --fp16 --max-update 400000 --save-interval 1 --no-epoch-checkpoints \ ---arch wav2vec --task audio_pretraining --lr 1e-06 --min-lr 1e-09 --optimizer adam --max-lr 0.005 --lr-scheduler cosine \ +--arch wav2vec --task audio_pretraining --min-lr 1e-06 --stop-min-lr 1e-09 --optimizer adam --lr 0.005 --lr-scheduler cosine \ --conv-feature-layers [(512, 10, 5), (512, 8, 4), (512, 4, 2), (512, 4, 2), (512, 4, 2), (512, 1, 1), (512, 1, 1)] \ --conv-aggregator-layers [(512, 2, 1), (512, 3, 1), (512, 4, 1), (512, 5, 1), (512, 6, 1), (512, 7, 1), (512, 8, 1), (512, 9, 1), (512, 10, 1), (512, 11, 1), (512, 12, 1), (512, 13, 1)] \ ---skip-connections-agg --residual-scale 0.5 --log-compression --warmup-updates 500 --warmup-init-lr 1e-07 --criterion binary_cross_entropy --num-negatives 10 \ ---max-sample-size 150000 --max-tokens 1500000 ---skip-invalid-size-inputs-valid-test +--skip-connections-agg --residual-scale 0.5 --log-compression --warmup-updates 500 --warmup-init-lr 1e-07 --criterion wav2vec --num-negatives 10 \ +--max-sample-size 150000 --max-tokens 1500000 --skip-invalid-size-inputs-valid-test +``` + +### Run wav2vec2 pre-training on Google Cloud TPUs: + +Wav2Vec2 is now supported on TPUs! It's currently pre-training only. + +#### Using hydra on a v3-8: + +``` +$ OMP_NUM_THREADS=1 fairseq-hydra-train \ + task.data=/manifest/path \ + --config-dir /PATH/TO/FAIRSEQ/examples/wav2vec/config/pretraining \ + --config-name wav2vec2_large_librivox_tpu.yaml +``` + +#### Using command line arguments on a v3-8: +Note: Commandline arguments way of execution has a [known-problem](https://github.com/pytorch/fairseq/issues/3741) currently. + +``` +$ OMP_NUM_THREADS=1 python train.py /manifest/path --save-dir /model/path --num-workers 6 --fp16 --max-update 400000 --save-interval 1 --no-epoch-checkpoints \ +--arch wav2vec2 --task audio_pretraining --min-lr 1e-06 --stop-min-lr 1e-09 --optimizer adam --lr 0.005 --lr-scheduler cosine \ +--conv-feature-layers [(512, 10, 5), (512, 8, 4), (512, 4, 2), (512, 4, 2), (512, 4, 2), (512, 1, 1), (512, 1, 1)] \ +--conv-aggregator-layers [(512, 2, 1), (512, 3, 1), (512, 4, 1), (512, 5, 1), (512, 6, 1), (512, 7, 1), (512, 8, 1), (512, 9, 1), (512, 10, 1), (512, 11, 1), (512, 12, 1), (512, 13, 1)] \ +--skip-connections-agg --residual-scale 0.5 --log-compression --warmup-updates 500 --warmup-init-lr 1e-07 --criterion wav2vec --num-negatives 10 \ +--max-sample-size 150000 --max-tokens 1500000 --skip-invalid-size-inputs-valid-test \ +--tpu --distributed-world-size 8 --num-batch-buckets 3 --enable-padding \ +--encoder-layerdrop 0 --mask-channel-prob 0.1 +``` + +#### Using hydra on a pod slice (v3-N with N > 8): + +``` +$ OMP_NUM_THREADS=1 fairseq-hydra-train \ + task.data=/manifest/path \ + --config-dir /PATH/TO/FAIRSEQ/examples/wav2vec/config/pretraining \ + --config-name wav2vec2_large_librivox_tpu-pod.yaml # edit distributed-world-size accordingly +``` + +#### Using command line arguments on a pod slice (v3-N with N > 8): +Note: Commandline arguments way of execution has a [known-problem](https://github.com/pytorch/fairseq/issues/3741) currently. + +``` +$ python -m torch_xla.distributed.xla_dist \ + --tpu ${TPUNAME} --conda-env=torch-xla-${TORCH_XLA_VERSION} --env OMP_NUM_THREADS=1 \ + -- \ +python train.py /manifest/path --save-dir /model/path --num-workers 6 --fp16 --max-update 400000 --save-interval 1 --no-epoch-checkpoints \ +--arch wav2vec2 --task audio_pretraining --min-lr 1e-06 --stop-min-lr 1e-09 --optimizer adam --lr 0.005 --lr-scheduler cosine \ +--conv-feature-layers [(512, 10, 5), (512, 8, 4), (512, 4, 2), (512, 4, 2), (512, 4, 2), (512, 1, 1), (512, 1, 1)] \ +--conv-aggregator-layers [(512, 2, 1), (512, 3, 1), (512, 4, 1), (512, 5, 1), (512, 6, 1), (512, 7, 1), (512, 8, 1), (512, 9, 1), (512, 10, 1), (512, 11, 1), (512, 12, 1), (512, 13, 1)] \ +--skip-connections-agg --residual-scale 0.5 --log-compression --warmup-updates 500 --warmup-init-lr 1e-07 --criterion wav2vec --num-negatives 10 \ +--max-sample-size 150000 --max-tokens 1500000 --skip-invalid-size-inputs-valid-test \ +--tpu --distributed-world-size ${WORLD_SIZE} --num-batch-buckets 3 --enable-padding \ +--encoder-layerdrop 0 --mask-channel-prob 0.1 ``` ### Extract embeddings from the downstream task data: @@ -55,22 +315,24 @@ $ PYTHONPATH=/path/to/fairseq python examples/wav2vec/wav2vec_featurize.py --inp Example to train a vq-wav2vec model as described in [vq-wav2vec: Self-Supervised Learning of Discrete Speech Representations (Baevski et al., 2019)](https://arxiv.org/abs/1910.05453). +These models are also used in [Effectiveness of self-supervised pre-training for speech recognition (Baevski et al., 2019)](https://arxiv.org/abs/1911.03912). + ## Pre-trained models -Description | Parameters | Dataset | Model ----|---:|---|--- -vq-wav2vec Gumbel <br> ([(Baevski et al., 2019)](https://arxiv.org/abs/1910.05453)) | 34.1M | [Librispeech](http://www.openslr.org/12) | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/vq-wav2vec.pt) -vq-wav2vec K-means <br> ([(Baevski et al., 2019)](https://arxiv.org/abs/1910.05453)) | 33.0M | [Librispeech](http://www.openslr.org/12) | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/vq-wav2vec_kmeans.pt) -Roberta on K-means codes <br> ([(Baevski et al., 2019)](https://arxiv.org/abs/1910.05453)) | 123.6M | [Librispeech](http://www.openslr.org/12) | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/bert_kmeans.tar) +Description | Dataset | Model +---|---|--- +vq-wav2vec Gumbel | [Librispeech](http://www.openslr.org/12) | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/vq-wav2vec.pt) +vq-wav2vec K-means | [Librispeech](http://www.openslr.org/12) | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/vq-wav2vec_kmeans.pt) +Roberta on K-means codes | [Librispeech](http://www.openslr.org/12) | [download](https://dl.fbaipublicfiles.com/fairseq/wav2vec/bert_kmeans.tar) #### Example usage: ```python import torch -from fairseq.models.wav2vec import Wav2VecModel +import fairseq cp = torch.load('/path/to/vq-wav2vec.pt') -model = Wav2VecModel.build_model(cp['args'], task=None) -model.load_state_dict(cp['model']) +model, cfg, task = fairseq.checkpoint_utils.load_model_ensemble_and_task([cp]) +model = model[0] model.eval() wav_input_16khz = torch.randn(1,10000) @@ -93,14 +355,14 @@ $ python examples/wav2vec/wav2vec_manifest.py /path/to/waves --dest /manifest/pa ``` $ python train.py /manifest/path --save-dir /model/path --num-workers 6 --fp16 --max-update 400000 \ ---save-interval 1 --no-epoch-checkpoints --arch wav2vec --task audio_pretraining --lr 1e-06 --min-lr 1e-09 \ ---optimizer adam --max-lr 1e-05 --lr-scheduler cosine \ +--save-interval 1 --no-epoch-checkpoints --arch wav2vec --task audio_pretraining --min-lr 1e-06 --stop-min-lr 1e-09 \ +--optimizer adam --lr 1e-05 --lr-scheduler cosine \ --conv-feature-layers [(512, 10, 5), (512, 8, 4), (512, 4, 2), (512, 4, 2), (512, 4, 2), (512, 1, 1), (512, 1, 1), (512, 1, 1)] \ --conv-aggregator-layers [(512, 2, 1), (512, 3, 1), (512, 4, 1), (512, 5, 1), (512, 6, 1), (512, 7, 1), (512, 8, 1), (512, 9, 1), (512, 10, 1), (512, 11, 1), (512, 12, 1), (512, 13, 1)] \ --activation gelu --offset auto --skip-connections-agg --residual-scale 0.5 \ --log-keys ["prob_perplexity","code_perplexity","temp"] --vq-type gumbel --vq-groups 2 --vq-depth 2 \ --combine-groups --vq-vars 320 --vq-temp (2,0.5,0.999995) --prediction-steps 12 --warmup-updates 1000 \ ---warmup-init-lr 1e-07 --criterion binary_cross_entropy --num-negatives 10 --max-sample-size 150000 \ +--warmup-init-lr 1e-07 --criterion wav2vec --num-negatives 10 --max-sample-size 150000 \ --max-tokens 300000 --cross-sample-negatives 0 --update-freq 1 --seed 2 --skip-invalid-size-inputs-valid-test ``` diff --git a/examples/wav2vec/__init__.py b/examples/wav2vec/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/examples/wav2vec/config/finetuning/base_100h.yaml b/examples/wav2vec/config/finetuning/base_100h.yaml new file mode 100644 index 0000000000..153b5df170 --- /dev/null +++ b/examples/wav2vec/config/finetuning/base_100h.yaml @@ -0,0 +1,58 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + +checkpoint: + no_epoch_checkpoints: true + best_checkpoint_metric: wer + +task: + _name: audio_finetuning + data: ??? + normalize: false + labels: ltr + +dataset: + num_workers: 6 + max_tokens: 3200000 + skip_invalid_size_inputs_valid_test: true + valid_subset: dev_other + +distributed_training: + ddp_backend: legacy_ddp + distributed_world_size: 2 + +criterion: + _name: ctc + zero_infinity: true + +optimization: + max_update: 80000 + lr: [0.00003] + sentence_avg: true + update_freq: [4] + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-08 + +lr_scheduler: + _name: tri_stage + phase_ratio: [0.1, 0.4, 0.5] + final_lr_scale: 0.05 + +model: + _name: wav2vec_ctc + w2v_path: ??? + apply_mask: true + mask_prob: 0.65 + mask_channel_prob: 0.5 + mask_channel_length: 64 + layerdrop: 0.1 + activation_dropout: 0.1 + feature_grad_mult: 0.0 + freeze_finetune_updates: 0 diff --git a/examples/wav2vec/config/finetuning/base_10h.yaml b/examples/wav2vec/config/finetuning/base_10h.yaml new file mode 100644 index 0000000000..5044518025 --- /dev/null +++ b/examples/wav2vec/config/finetuning/base_10h.yaml @@ -0,0 +1,63 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + +checkpoint: + save_interval: 50 + save_interval_updates: 10000 + keep_interval_updates: 1 + no_epoch_checkpoints: true + best_checkpoint_metric: wer + +task: + _name: audio_finetuning + data: ??? + normalize: false + labels: ltr + +dataset: + num_workers: 6 + max_tokens: 3200000 + skip_invalid_size_inputs_valid_test: true + validate_after_updates: 10000 + validate_interval: 50 + valid_subset: dev_other + +distributed_training: + ddp_backend: legacy_ddp + distributed_world_size: 2 + +criterion: + _name: ctc + zero_infinity: true + +optimization: + max_update: 20000 + lr: [0.00005] + sentence_avg: true + update_freq: [4] + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-08 + +lr_scheduler: + _name: tri_stage + phase_ratio: [0.1, 0.4, 0.5] + final_lr_scale: 0.05 + +model: + _name: wav2vec_ctc + w2v_path: ??? + apply_mask: true + mask_prob: 0.65 + mask_channel_prob: 0.5 + mask_channel_length: 64 + layerdrop: 0.05 + activation_dropout: 0.1 + feature_grad_mult: 0.0 + freeze_finetune_updates: 10000 diff --git a/examples/wav2vec/config/finetuning/base_10m.yaml b/examples/wav2vec/config/finetuning/base_10m.yaml new file mode 100644 index 0000000000..14abc013bd --- /dev/null +++ b/examples/wav2vec/config/finetuning/base_10m.yaml @@ -0,0 +1,63 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + +checkpoint: + save_interval: 1000 + save_interval_updates: 50 + keep_interval_updates: 1 + no_epoch_checkpoints: true + best_checkpoint_metric: wer + +task: + _name: audio_finetuning + data: ??? + normalize: false + labels: ltr + +dataset: + num_workers: 6 + max_tokens: 3200000 + skip_invalid_size_inputs_valid_test: true + validate_after_updates: 10000 + validate_interval: 1000 + valid_subset: dev_other + +distributed_training: + ddp_backend: legacy_ddp + distributed_world_size: 2 + +criterion: + _name: ctc + zero_infinity: true + +optimization: + max_update: 13000 + lr: [0.00005] + sentence_avg: true + update_freq: [4] + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-08 + +lr_scheduler: + _name: tri_stage + phase_ratio: [0.1, 0.4, 0.5] + final_lr_scale: 0.05 + +model: + _name: wav2vec_ctc + w2v_path: ??? + apply_mask: true + mask_prob: 0.65 + mask_channel_prob: 0.25 + mask_channel_length: 64 + layerdrop: 0.1 + activation_dropout: 0.1 + feature_grad_mult: 0.0 + freeze_finetune_updates: 10000 diff --git a/examples/wav2vec/config/finetuning/base_1h.yaml b/examples/wav2vec/config/finetuning/base_1h.yaml new file mode 100644 index 0000000000..14abc013bd --- /dev/null +++ b/examples/wav2vec/config/finetuning/base_1h.yaml @@ -0,0 +1,63 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + +checkpoint: + save_interval: 1000 + save_interval_updates: 50 + keep_interval_updates: 1 + no_epoch_checkpoints: true + best_checkpoint_metric: wer + +task: + _name: audio_finetuning + data: ??? + normalize: false + labels: ltr + +dataset: + num_workers: 6 + max_tokens: 3200000 + skip_invalid_size_inputs_valid_test: true + validate_after_updates: 10000 + validate_interval: 1000 + valid_subset: dev_other + +distributed_training: + ddp_backend: legacy_ddp + distributed_world_size: 2 + +criterion: + _name: ctc + zero_infinity: true + +optimization: + max_update: 13000 + lr: [0.00005] + sentence_avg: true + update_freq: [4] + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-08 + +lr_scheduler: + _name: tri_stage + phase_ratio: [0.1, 0.4, 0.5] + final_lr_scale: 0.05 + +model: + _name: wav2vec_ctc + w2v_path: ??? + apply_mask: true + mask_prob: 0.65 + mask_channel_prob: 0.25 + mask_channel_length: 64 + layerdrop: 0.1 + activation_dropout: 0.1 + feature_grad_mult: 0.0 + freeze_finetune_updates: 10000 diff --git a/examples/wav2vec/config/finetuning/base_960h.yaml b/examples/wav2vec/config/finetuning/base_960h.yaml new file mode 100644 index 0000000000..3eadc36b37 --- /dev/null +++ b/examples/wav2vec/config/finetuning/base_960h.yaml @@ -0,0 +1,57 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + +checkpoint: + no_epoch_checkpoints: true + best_checkpoint_metric: wer + +task: + _name: audio_finetuning + data: ??? + normalize: false + labels: ltr + +dataset: + num_workers: 6 + max_tokens: 3200000 + skip_invalid_size_inputs_valid_test: true + valid_subset: dev_other + +distributed_training: + ddp_backend: legacy_ddp + distributed_world_size: 8 + +criterion: + _name: ctc + zero_infinity: true + +optimization: + max_update: 320000 + lr: [0.0001] + sentence_avg: true + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-08 + +lr_scheduler: + _name: tri_stage + phase_ratio: [0.1, 0.4, 0.5] + final_lr_scale: 0.05 + +model: + _name: wav2vec_ctc + w2v_path: ??? + apply_mask: true + mask_prob: 0.5 + mask_channel_prob: 0.1 + mask_channel_length: 64 + layerdrop: 0.1 + activation_dropout: 0.1 + feature_grad_mult: 0.0 + freeze_finetune_updates: 0 diff --git a/examples/wav2vec/config/finetuning/vox_100h.yaml b/examples/wav2vec/config/finetuning/vox_100h.yaml new file mode 100644 index 0000000000..b8f81e5e18 --- /dev/null +++ b/examples/wav2vec/config/finetuning/vox_100h.yaml @@ -0,0 +1,58 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + +checkpoint: + no_epoch_checkpoints: true + best_checkpoint_metric: wer + +task: + _name: audio_finetuning + data: ??? + normalize: true + labels: ltr + +dataset: + num_workers: 6 + max_tokens: 1280000 + skip_invalid_size_inputs_valid_test: true + valid_subset: dev_other + +distributed_training: + ddp_backend: legacy_ddp + distributed_world_size: 4 + +criterion: + _name: ctc + zero_infinity: true + +optimization: + max_update: 80000 + lr: [0.00003] + sentence_avg: true + update_freq: [5] + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-08 + +lr_scheduler: + _name: tri_stage + phase_ratio: [0.1, 0.4, 0.5] + final_lr_scale: 0.05 + +model: + _name: wav2vec_ctc + w2v_path: ??? + apply_mask: true + mask_prob: 0.5 + mask_channel_prob: 0.5 + mask_channel_length: 64 + layerdrop: 0.1 + activation_dropout: 0.1 + feature_grad_mult: 0.0 + freeze_finetune_updates: 10000 diff --git a/examples/wav2vec/config/finetuning/vox_10h.yaml b/examples/wav2vec/config/finetuning/vox_10h.yaml new file mode 100644 index 0000000000..8f1ca71ee2 --- /dev/null +++ b/examples/wav2vec/config/finetuning/vox_10h.yaml @@ -0,0 +1,63 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + +checkpoint: + save_interval: 50 + save_interval_updates: 10000 + keep_interval_updates: 1 + no_epoch_checkpoints: true + best_checkpoint_metric: wer + +task: + _name: audio_finetuning + data: ??? + normalize: true + labels: ltr + +dataset: + num_workers: 6 + max_tokens: 1280000 + skip_invalid_size_inputs_valid_test: true + validate_after_updates: 10000 + validate_interval: 50 + valid_subset: dev_other + +distributed_training: + ddp_backend: legacy_ddp + distributed_world_size: 4 + +criterion: + _name: ctc + zero_infinity: true + +optimization: + max_update: 20000 + lr: [0.0001] + sentence_avg: true + update_freq: [5] + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-08 + +lr_scheduler: + _name: tri_stage + phase_ratio: [0.1, 0.4, 0.5] + final_lr_scale: 0.05 + +model: + _name: wav2vec_ctc + w2v_path: ??? + apply_mask: true + mask_prob: 0.75 + mask_channel_prob: 0.25 + mask_channel_length: 64 + layerdrop: 0.1 + activation_dropout: 0.1 + feature_grad_mult: 0.0 + freeze_finetune_updates: 10000 diff --git a/examples/wav2vec/config/finetuning/vox_10m.yaml b/examples/wav2vec/config/finetuning/vox_10m.yaml new file mode 100644 index 0000000000..07e327fe74 --- /dev/null +++ b/examples/wav2vec/config/finetuning/vox_10m.yaml @@ -0,0 +1,63 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + +checkpoint: + save_interval: 1000 + save_interval_updates: 50 + keep_interval_updates: 1 + no_epoch_checkpoints: true + best_checkpoint_metric: wer + +task: + _name: audio_finetuning + data: ??? + normalize: true + labels: ltr + +dataset: + num_workers: 6 + max_tokens: 1280000 + skip_invalid_size_inputs_valid_test: true + validate_after_updates: 10000 + validate_interval: 1000 + valid_subset: dev_other + +distributed_training: + ddp_backend: legacy_ddp + distributed_world_size: 4 + +criterion: + _name: ctc + zero_infinity: true + +optimization: + max_update: 13000 + lr: [0.0001] + sentence_avg: true + update_freq: [5] + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-08 + +lr_scheduler: + _name: tri_stage + phase_ratio: [0.1, 0.4, 0.5] + final_lr_scale: 0.05 + +model: + _name: wav2vec_ctc + w2v_path: ??? + apply_mask: true + mask_prob: 0.65 + mask_channel_prob: 0.25 + mask_channel_length: 64 + layerdrop: 0.1 + activation_dropout: 0.1 + feature_grad_mult: 0.0 + freeze_finetune_updates: 10000 diff --git a/examples/wav2vec/config/finetuning/vox_1h.yaml b/examples/wav2vec/config/finetuning/vox_1h.yaml new file mode 100644 index 0000000000..fac1bbb32f --- /dev/null +++ b/examples/wav2vec/config/finetuning/vox_1h.yaml @@ -0,0 +1,63 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + +checkpoint: + save_interval: 1000 + save_interval_updates: 50 + keep_interval_updates: 1 + no_epoch_checkpoints: true + best_checkpoint_metric: wer + +task: + _name: audio_finetuning + data: ??? + normalize: true + labels: ltr + +dataset: + num_workers: 6 + max_tokens: 1280000 + skip_invalid_size_inputs_valid_test: true + validate_after_updates: 10000 + validate_interval: 1000 + valid_subset: dev_other + +distributed_training: + ddp_backend: legacy_ddp + distributed_world_size: 4 + +criterion: + _name: ctc + zero_infinity: true + +optimization: + max_update: 13000 + lr: [0.0003] + sentence_avg: true + update_freq: [5] + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-08 + +lr_scheduler: + _name: tri_stage + phase_ratio: [0.1, 0.4, 0.5] + final_lr_scale: 0.05 + +model: + _name: wav2vec_ctc + w2v_path: ??? + apply_mask: true + mask_prob: 0.75 + mask_channel_prob: 0.25 + mask_channel_length: 64 + layerdrop: 0.1 + activation_dropout: 0.1 + feature_grad_mult: 0.0 + freeze_finetune_updates: 10000 diff --git a/examples/wav2vec/config/finetuning/vox_960h.yaml b/examples/wav2vec/config/finetuning/vox_960h.yaml new file mode 100644 index 0000000000..9d72404fa3 --- /dev/null +++ b/examples/wav2vec/config/finetuning/vox_960h.yaml @@ -0,0 +1,57 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + +checkpoint: + no_epoch_checkpoints: true + best_checkpoint_metric: wer + +task: + _name: audio_finetuning + data: ??? + normalize: true + labels: ltr + +dataset: + num_workers: 6 + max_tokens: 1280000 + skip_invalid_size_inputs_valid_test: true + valid_subset: dev_other + +distributed_training: + ddp_backend: legacy_ddp + distributed_world_size: 24 + +criterion: + _name: ctc + zero_infinity: true + +optimization: + max_update: 320000 + lr: [0.00003] + sentence_avg: true + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-08 + +lr_scheduler: + _name: tri_stage + phase_ratio: [0.1, 0.4, 0.5] + final_lr_scale: 0.05 + +model: + _name: wav2vec_ctc + w2v_path: ??? + apply_mask: true + mask_prob: 0.5 + mask_channel_prob: 0.25 + mask_channel_length: 64 + layerdrop: 0.1 + activation_dropout: 0.1 + feature_grad_mult: 0.0 + freeze_finetune_updates: 10000 diff --git a/examples/wav2vec/config/pretraining/wav2vec2_base_librispeech.yaml b/examples/wav2vec/config/pretraining/wav2vec2_base_librispeech.yaml new file mode 100644 index 0000000000..b686e21ab1 --- /dev/null +++ b/examples/wav2vec/config/pretraining/wav2vec2_base_librispeech.yaml @@ -0,0 +1,57 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + +checkpoint: + save_interval_updates: 25000 + keep_interval_updates: 1 + no_epoch_checkpoints: true + +task: + _name: audio_pretraining + data: ??? + max_sample_size: 250000 + min_sample_size: 32000 + normalize: false + +dataset: + num_workers: 6 + max_tokens: 1400000 + skip_invalid_size_inputs_valid_test: true + +distributed_training: + distributed_world_size: 64 + ddp_backend: legacy_ddp + +criterion: + _name: wav2vec + infonce: true + log_keys: ["prob_perplexity","code_perplexity","temp"] + loss_weights: [0.1, 10] + +optimization: + max_update: 400000 + lr: [0.0005] + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-06 + weight_decay: 0.01 + +lr_scheduler: + _name: polynomial_decay + warmup_updates: 32000 + +model: + _name: wav2vec2 + quantize_targets: true + final_dim: 256 + encoder_layerdrop: 0.05 + dropout_input: 0.1 + dropout_features: 0.1 + feature_grad_mult: 0.1 + encoder_embed_dim: 768 diff --git a/examples/wav2vec/config/pretraining/wav2vec2_large_librivox.yaml b/examples/wav2vec/config/pretraining/wav2vec2_large_librivox.yaml new file mode 100644 index 0000000000..3192ce4cba --- /dev/null +++ b/examples/wav2vec/config/pretraining/wav2vec2_large_librivox.yaml @@ -0,0 +1,70 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + +checkpoint: + save_interval_updates: 25000 + keep_interval_updates: 1 + no_epoch_checkpoints: true + +task: + _name: audio_pretraining + data: ??? + max_sample_size: 320000 + min_sample_size: 32000 + normalize: true + +dataset: + batch_size: 4 + num_workers: 6 + max_tokens: 1200000 + skip_invalid_size_inputs_valid_test: true + +distributed_training: + distributed_world_size: 128 + ddp_backend: legacy_ddp + +criterion: + _name: wav2vec + infonce: true + log_keys: ["prob_perplexity","code_perplexity","temp"] + loss_weights: [0.1, 0] + +optimization: + max_update: 1000000 + lr: [0.005] + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-06 + weight_decay: 0.01 + +lr_scheduler: + _name: polynomial_decay + warmup_updates: 32000 + +model: + _name: wav2vec2 + quantize_targets: true + extractor_mode: layer_norm + layer_norm_first: true + final_dim: 768 + latent_temp: [2.0,0.1,0.999995] + encoder_layerdrop: 0.00 + dropout_input: 0.0 + dropout_features: 0.0 + dropout: 0.0 + attention_dropout: 0.0 + conv_bias: true + + encoder_layers: 24 + encoder_embed_dim: 1024 + encoder_ffn_embed_dim: 4096 + encoder_attention_heads: 16 + + feature_grad_mult: 1.0 + diff --git a/examples/wav2vec/config/pretraining/wav2vec2_large_librivox_tpu-pod.yaml b/examples/wav2vec/config/pretraining/wav2vec2_large_librivox_tpu-pod.yaml new file mode 100644 index 0000000000..ff35a95b65 --- /dev/null +++ b/examples/wav2vec/config/pretraining/wav2vec2_large_librivox_tpu-pod.yaml @@ -0,0 +1,72 @@ +# @package _group_ + +common: + tpu: true + fp16: false + log_format: json + log_interval: 10 + +checkpoint: + save_interval_updates: 25000 + keep_interval_updates: 1 + no_epoch_checkpoints: true + +task: + _name: audio_pretraining + data: ??? + max_sample_size: 250000 + min_sample_size: 32000 + normalize: true + num_batch_buckets: 3 + precompute_mask_indices: true + enable_padding: true + +dataset: + num_workers: 6 + max_tokens: 1200000 + skip_invalid_size_inputs_valid_test: true + +distributed_training: + distributed_world_size: 128 + ddp_backend: legacy_ddp + +criterion: + _name: wav2vec + infonce: true + log_keys: ["prob_perplexity","code_perplexity","temp"] + loss_weights: [0.1, 0] + +optimization: + max_update: 1000000 + lr: [0.005] + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-06 + weight_decay: 0.01 + +lr_scheduler: + _name: polynomial_decay + warmup_updates: 32000 + +model: + _name: wav2vec2 + quantize_targets: true + extractor_mode: layer_norm + layer_norm_first: true + final_dim: 768 + latent_temp: [2.0,0.1,0.999995] + encoder_layerdrop: 0.00 + dropout_input: 0.0 + dropout_features: 0.0 + dropout: 0.0 + attention_dropout: 0.0 + conv_bias: true + + encoder_layers: 24 + encoder_embed_dim: 1024 + encoder_ffn_embed_dim: 4096 + encoder_attention_heads: 16 + + feature_grad_mult: 1.0 diff --git a/examples/wav2vec/config/pretraining/wav2vec2_large_librivox_tpu.yaml b/examples/wav2vec/config/pretraining/wav2vec2_large_librivox_tpu.yaml new file mode 100644 index 0000000000..ee55bdab72 --- /dev/null +++ b/examples/wav2vec/config/pretraining/wav2vec2_large_librivox_tpu.yaml @@ -0,0 +1,77 @@ +# @package _group_ + +common: + tpu: true + fp16: false + log_format: json + log_interval: 10 + +checkpoint: + save_interval_updates: 25000 + keep_interval_updates: 1 + no_epoch_checkpoints: true + +task: + _name: audio_pretraining + data: ??? + max_sample_size: 250000 + min_sample_size: 32000 + normalize: true + num_batch_buckets: 3 + precompute_mask_indices: true + enable_padding: true + inferred_w2v_config: + mask_prob: 0.65 + mask_selection: 'static' + mask_other: 0 + mask_channel_prob: 0.1 + +dataset: + num_workers: 6 + max_tokens: 1200000 + skip_invalid_size_inputs_valid_test: true + +distributed_training: + distributed_world_size: 8 + ddp_backend: legacy_ddp + +criterion: + _name: wav2vec + infonce: true + log_keys: ["prob_perplexity","code_perplexity","temp"] + loss_weights: [0.1, 0] + +optimization: + max_update: 1000000 + lr: [0.005] + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-06 + weight_decay: 0.01 + +lr_scheduler: + _name: polynomial_decay + warmup_updates: 32000 + +model: + _name: wav2vec2 + quantize_targets: true + extractor_mode: layer_norm + layer_norm_first: true + final_dim: 768 + latent_temp: [2.0,0.1,0.999995] + encoder_layerdrop: 0.00 + dropout_input: 0.0 + dropout_features: 0.0 + dropout: 0.0 + attention_dropout: 0.0 + conv_bias: true + + encoder_layers: 24 + encoder_embed_dim: 1024 + encoder_ffn_embed_dim: 4096 + encoder_attention_heads: 16 + + feature_grad_mult: 1.0 diff --git a/examples/wav2vec/libri_labels.py b/examples/wav2vec/libri_labels.py new file mode 100644 index 0000000000..694a202604 --- /dev/null +++ b/examples/wav2vec/libri_labels.py @@ -0,0 +1,56 @@ +#!/usr/bin/env python3 +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +""" +Helper script to pre-compute embeddings for a flashlight (previously called wav2letter++) dataset +""" + +import argparse +import os + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("tsv") + parser.add_argument("--output-dir", required=True) + parser.add_argument("--output-name", required=True) + args = parser.parse_args() + + os.makedirs(args.output_dir, exist_ok=True) + + transcriptions = {} + + with open(args.tsv, "r") as tsv, open( + os.path.join(args.output_dir, args.output_name + ".ltr"), "w" + ) as ltr_out, open( + os.path.join(args.output_dir, args.output_name + ".wrd"), "w" + ) as wrd_out: + root = next(tsv).strip() + for line in tsv: + line = line.strip() + dir = os.path.dirname(line) + if dir not in transcriptions: + parts = dir.split(os.path.sep) + trans_path = f"{parts[-2]}-{parts[-1]}.trans.txt" + path = os.path.join(root, dir, trans_path) + assert os.path.exists(path) + texts = {} + with open(path, "r") as trans_f: + for tline in trans_f: + items = tline.strip().split() + texts[items[0]] = " ".join(items[1:]) + transcriptions[dir] = texts + part = os.path.basename(line).split(".")[0] + assert part in transcriptions[dir] + print(transcriptions[dir][part], file=wrd_out) + print( + " ".join(list(transcriptions[dir][part].replace(" ", "|"))) + " |", + file=ltr_out, + ) + + +if __name__ == "__main__": + main() diff --git a/examples/wav2vec/scripts/binarize_manifest.sh b/examples/wav2vec/scripts/binarize_manifest.sh new file mode 100644 index 0000000000..6f201bdb52 --- /dev/null +++ b/examples/wav2vec/scripts/binarize_manifest.sh @@ -0,0 +1,33 @@ +#!/usr/bin/env bash + +# usage: bash binarize_manifest <dest_dir> <train_split> <valid_split> + +DEST_DIR=$1 +TRAIN_SPLIT=$2 +VALID_SPLIT=$3 +FAIRSEQ_ROOT=$4 + +mkdir -p $DEST_DIR + +# split file path and lengths into separate files +cut -f1 $TRAIN_SPLIT.tsv > $DEST_DIR/train_fnames.txt +cut -f1 $VALID_SPLIT.tsv > $DEST_DIR/valid_fnames.txt +cut -f2 $TRAIN_SPLIT.tsv > $DEST_DIR/train.lengths +cut -f2 $VALID_SPLIT.tsv > $DEST_DIR/valid.lengths + +# copy root directory +head -1 $TRAIN_SPLIT.tsv > $DEST_DIR/train.root +head -1 $VALID_SPLIT.tsv > $DEST_DIR/valid.root + +# remove root directory +sed -i '1d' $DEST_DIR/train_fnames.txt +sed -i '1d' $DEST_DIR/valid_fnames.txt +sed -i '1d' $DEST_DIR/train.lengths +sed -i '1d' $DEST_DIR/valid.lengths + +# insert spaces between characters +sed -i -e 's/\(.\)/\1 /g' $DEST_DIR/train_fnames.txt +sed -i -e 's/\(.\)/\1 /g' $DEST_DIR/valid_fnames.txt + +# run preprocessor +PYTHONPATH=$FAIRSEQ_ROOT python $FAIRSEQ_ROOT/fairseq_cli/preprocess.py --dataset-impl mmap --trainpref $DEST_DIR/train_fnames.txt --validpref $DEST_DIR/valid_fnames.txt --workers 60 --only-source --destdir $DEST_DIR diff --git a/examples/wav2vec/unsupervised/README.md b/examples/wav2vec/unsupervised/README.md new file mode 100644 index 0000000000..0b213fd202 --- /dev/null +++ b/examples/wav2vec/unsupervised/README.md @@ -0,0 +1,103 @@ +# wav2vec Unsupervised (wav2vec-U) + +Wav2vec Unsupervised (wav2vec-U) is a framework for building speech recognition systems without any labeled training data as described in [Unsupervised Speech Recognition (Baevski et al., 2021)](https://ai.facebook.com/research/publications/unsupervised-speech-recognition). The model takes as input wav2vec 2.0 or XLSR representations (see [pretrained models](https://github.com/pytorch/fairseq/blob/main/examples/wav2vec)) as well as unlabeled speech and text data. + + The wav2vec-U training procedure consists of three consecutive main steps: +* Preparation of speech representations and text data +* Generative adversarial training (GAN) +* Iterative self-training + Kaldi LM-decoding + +## Preparation of speech and text data +Similar to [wav2vec 2.0](https://github.com/pytorch/fairseq/blob/main/examples/wav2vec/README.md), data folders contain {train,valid,test}.{tsv,wrd,phn} files, where audio paths are stored in tsv files, and word, letter or phoneme transcriptions are stored in .{wrd,ltr,phn}. + +In **/path/to/data/with_silence** you need a *train.tsv* file as well as (optionally) *{valid,test}.{tsv,wrd,phn}*. It is nice to have *10h.{tsv,phn}* files there too for reproducing the ablation study on layer selection. In **/path/to/data/without_silence** you have the same files, except *.tsv* files contain audios with silences removed using rVAD. + +Pre-requisites: +* set FAIRSEQ_ROOT environmental variable to your fairseq installation +* set RVAD_ROOT environmental variable to a checkout of [rVADfast](https://github.com/zhenghuatan/rVADfast) +* set KENLM_ROOT environmental variable to the location of [KenLM](https://github.com/kpu/kenlm) binaries +* install [PyKaldi](https://github.com/pykaldi/pykaldi) and set KALDI_ROOT environmental variable to the location of your kaldi installation. To use the version bundled with PyKaldi, you can use /path/to/pykaldi/tools/kaldi + +Create new audio files without silences: +```shell +# create a manifest file for the set original of audio files +python $FAIRSEQ_ROOT/examples/wav2vec/wav2vec_manifest.py /dir/to/save/audio/files --ext wav --dest /path/to/new/train.tsv --valid-percent 0 + +python scripts/vads.py -r $RVAD_ROOT < /path/to/train.tsv > train.vads + +python scripts/remove_silence.py --tsv /path/to/train.tsv --vads train.vads --out /dir/to/save/audio/files + +python $FAIRSEQ_ROOT/examples/wav2vec/wav2vec_manifest.py /dir/to/save/audio/files --ext wav --dest /path/to/new/train.tsv --valid-percent 0.01 +``` + +Next, we need to preprocess the audio data to better match phonemized text data: + +```shell +zsh scripts/prepare_audio.sh /dir/with/{train,test,valid}.tsv /output/dir /path/to/wav2vec2/model.pt 512 14 +``` +Note that if you have splits different than train/valid/test, you will need to modify this script. The last two arguments are the PCA dimensionality and the 0-based index of the layer from which to extract representations. + +Now we need to prepare text data: +```shell +zsh scripts/prepare_text.sh language /path/to/text/file /output/dir 1000 espeak /path/to/fasttext/lid/model +``` + +The fourth argument is minimum number observations of phones to keep. If your text corpus is small, you might want to reduce this number. + +The fifth argument is which phonemizer to use. Supported values are [espeak](http://espeak.sourceforge.net/), [espeak-ng](https://github.com/espeak-ng/espeak-ng), and [G2P](https://github.com/Kyubyong/g2p) (english only). + +Pre-trained fasttext LID models can be downloaded [here](https://fasttext.cc/docs/en/language-identification.html). + +### Prepare TIMIT data +TIMIT transcripts include silence. Therefore VAD is not used for audio preprocessing, and we do not wrap transcripts with silences or insert random silence in between words. + +To prepare TIMIT data for both the matched an unmatched setup: +```shell +bash scripts/prepare_timit.sh /dir/to/timit/raw/data /output/dir /path/to/wav2vec2/model.pt +``` + +Note that we assume the TIMIT distribution with capitalized directories and filenames are used (e.g., `TRAIN/DR1/FCJF0/SA1.PHN`). + +## Generative adversarial training (GAN) + +We then use a GAN model to build a first unsupervised ASR model. The data preparation above of both speech features and text data is a necessary procedure that enables the generator to match speech to text in an unsupervised way. + +Launching GAN training on top of preprocessed features, with default hyperparameters can be done with: + +``` +PREFIX=w2v_unsup_gan_xp +TASK_DATA=/path/to/features/precompute_unfiltered_pca512_cls128_mean_pooled +TEXT_DATA=/path/to/data/phones # path to fairseq-preprocessed GAN data (phones dir) +KENLM_PATH=/path/to/data/phones/kenlm.phn.o4.bin # KenLM 4-gram phoneme language model (LM data = GAN data here) + +PYTHONPATH=$FAIRSEQ_ROOT PREFIX=$PREFIX fairseq-hydra-train \ + -m --config-dir config/gan \ + --config-name w2vu \ + task.data=${TASK_DATA} \ + task.text_data=${TEXT_DATA} \ + task.kenlm_path=${KENLM_PATH} \ + common.user_dir=${FAIRSEQ_ROOT}/examples/wav2vec/unsupervised \ + model.code_penalty=2,4 model.gradient_penalty=1.5,2.0 \ + model.smoothness_weight=0.5,0.75,1.0 'common.seed=range(0,5)' +``` + + +Once we find the best checkpoint (chosen using unsupervised metric that combined language model perplexity and vocabulary usage), we can use it to generate phone labels (or word labels with an appropriate kaldi WFST): + +```shell +python w2vu_generate.py --config-dir config/generate --config-name viterbi \ +fairseq.common.user_dir=${FAIRSEQ_ROOT}/examples/wav2vec/unsupervised \ +fairseq.task.data=/path/to/dir/with/features \ +fairseq.common_eval.path=/path/to/gan/checkpoint \ +fairseq.dataset.gen_subset=valid results_path=/where/to/save/transcriptions +``` + +The decoding without LM works best on the same adjacent-mean-pooled features that the gan was trained on, while decoding with LM works better on features before the adjacent timestep mean-pooling step (without the "_pooled" suffix). + +## Iterative self-training + Kaldi LM-decoding +After the GAN training provides a first unsupervised model, we can then progressively refine the quality of transcriptions using several iterations of semi-supervised learning. We perform two iterations: first, pseudo-label the training data with the unsupervised GAN model and train an HMM on the pseudo-labels. Second, we relabel the training data with the HMM and then fine-tune the original wav2vec 2.0 model using the HMM pseudo-labels with a CTC loss. Note that HMM models use phonemes as output, while wav2vec 2.0 use letter. Both are decoded using WFST decoders into words. + + +Please see [this README](kaldi_self_train/README.md) for more instructions on how to do iterative self-training + Kaldi LM-decoding. + +*** Note: these instructions are a work in progress and will be updated over the next few days diff --git a/examples/wav2vec/unsupervised/__init__.py b/examples/wav2vec/unsupervised/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/examples/wav2vec/unsupervised/config/finetuning/w2v_finetune.yaml b/examples/wav2vec/unsupervised/config/finetuning/w2v_finetune.yaml new file mode 100644 index 0000000000..19a3ef3484 --- /dev/null +++ b/examples/wav2vec/unsupervised/config/finetuning/w2v_finetune.yaml @@ -0,0 +1,62 @@ +# @package _group_ + +common: + fp16: true + log_format: json + log_interval: 200 + tensorboard_logdir: tb + +checkpoint: + no_epoch_checkpoints: true + save_interval_updates: 20000 + +task: + _name: audio_finetuning + data: ??? + normalize: true + labels: ltr + +dataset: + num_workers: 6 + max_tokens: 800000 + skip_invalid_size_inputs_valid_test: true + train_subset: train + valid_subset: valid + +distributed_training: + ddp_backend: legacy_ddp + distributed_world_size: 8 + find_unused_parameters: True + +criterion: + _name: ctc + zero_infinity: true + post_process: letter + +optimization: + max_update: 80000 + lr: [0.00003] + sentence_avg: true + update_freq: [1] + +optimizer: + _name: adam + adam_betas: (0.9,0.98) + adam_eps: 1e-08 + +lr_scheduler: + _name: tri_stage + phase_ratio: [0.1, 0.4, 0.5] + final_lr_scale: 0.05 + +model: + _name: wav2vec_ctc + w2v_path: ??? + apply_mask: true + mask_prob: 0.25 + mask_channel_prob: 0.1 + mask_channel_length: 64 + layerdrop: 0.1 + activation_dropout: 0.1 + feature_grad_mult: 0.0 + freeze_finetune_updates: 0 diff --git a/examples/wav2vec/unsupervised/config/gan/w2vu.yaml b/examples/wav2vec/unsupervised/config/gan/w2vu.yaml new file mode 100644 index 0000000000..74f1829d14 --- /dev/null +++ b/examples/wav2vec/unsupervised/config/gan/w2vu.yaml @@ -0,0 +1,115 @@ +# @package _group_ + +common: + fp16: false + fp16_no_flatten_grads: true + log_format: json + log_interval: 100 + tensorboard_logdir: tb + reset_logging: false + suppress_crashes: false + +checkpoint: + save_interval: 1000 + save_interval_updates: 1000 + no_epoch_checkpoints: true + best_checkpoint_metric: weighted_lm_ppl + save_dir: . + +distributed_training: + distributed_world_size: 1 + +task: + _name: unpaired_audio_text + data: ??? + text_data: ??? + labels: phn + sort_by_length: false + unfiltered: false + max_length: null + append_eos: false + kenlm_path: ??? + +dataset: + num_workers: 6 + batch_size: 160 + skip_invalid_size_inputs_valid_test: true + valid_subset: valid + validate_interval: 1000 + validate_interval_updates: 1000 + +criterion: + _name: model + log_keys: + - accuracy_dense + - accuracy_token + - temp + - code_ppl + +optimization: + max_update: 150000 + clip_norm: 5.0 + lr: [0] + +optimizer: + _name: composite + groups: + generator: + lr: [0.0004] + lr_float: null + optimizer: + _name: adam + adam_betas: [0.5,0.98] + adam_eps: 1e-06 + weight_decay: 0 + amsgrad: false + lr_scheduler: + _name: fixed + warmup_updates: 0 + discriminator: + lr: [ 0.0005 ] + lr_float: null + optimizer: + _name: adam + adam_betas: [0.5,0.98] + adam_eps: 1e-06 + weight_decay: 0.0001 + amsgrad: false + lr_scheduler: + _name: fixed + warmup_updates: 0 + +lr_scheduler: pass_through + +model: + _name: wav2vec_u + + discriminator_dim: 384 + discriminator_depth: 2 + discriminator_kernel: 6 + discriminator_linear_emb: false + discriminator_causal: true + discriminator_max_pool: false + discriminator_act_after_linear: false + discriminator_dropout: 0.0 + discriminator_weight_norm: false + + generator_stride: 1 + generator_kernel: 4 + generator_bias: false + generator_dropout: 0.1 + + smoothness_weight: 0.5 + smoothing: 0 + smoothing_one_sided: false + gumbel: false + hard_gumbel: false + gradient_penalty: 1.5 + code_penalty: 4.0 + temp: [ 2,0.1,0.99995 ] + input_dim: 512 + + segmentation: + type: JOIN + mean_pool_join: false + remove_zeros: false diff --git a/examples/wav2vec/unsupervised/config/generate/viterbi.yaml b/examples/wav2vec/unsupervised/config/generate/viterbi.yaml new file mode 100644 index 0000000000..9c88beebcb --- /dev/null +++ b/examples/wav2vec/unsupervised/config/generate/viterbi.yaml @@ -0,0 +1,21 @@ +# @package _group_ + +fairseq: + task: + _name: unpaired_audio_text + labels: phn + data: ??? + sort_by_length: false + shuffle: false + text_data: '' + + common_eval: + path: ??? + quiet: true + + dataset: + gen_subset: valid + batch_size: 1 + +w2l_decoder: VITERBI +post_process: silence diff --git a/examples/wav2vec/unsupervised/config/timit_matched/test.uid b/examples/wav2vec/unsupervised/config/timit_matched/test.uid new file mode 100644 index 0000000000..401008246a --- /dev/null +++ b/examples/wav2vec/unsupervised/config/timit_matched/test.uid @@ -0,0 +1,192 @@ 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root directory of this source tree. + +from .extracted_features_dataset import ExtractedFeaturesDataset +from .random_input_dataset import RandomInputDataset + + +__all__ = [ + "ExtractedFeaturesDataset", + "RandomInputDataset", +] diff --git a/examples/wav2vec/unsupervised/data/extracted_features_dataset.py b/examples/wav2vec/unsupervised/data/extracted_features_dataset.py new file mode 100644 index 0000000000..d6ee9c4a36 --- /dev/null +++ b/examples/wav2vec/unsupervised/data/extracted_features_dataset.py @@ -0,0 +1,144 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +import logging +import os +import contextlib + +import numpy as np +import torch + +from fairseq.data import FairseqDataset, data_utils + + +logger = logging.getLogger(__name__) + + +class ExtractedFeaturesDataset(FairseqDataset): + def __init__( + self, + path, + split, + min_length=3, + max_length=None, + labels=None, + label_dict=None, + shuffle=True, + sort_by_length=True, + ): + super().__init__() + + self.min_length = min_length + self.max_length = max_length + self.shuffle = shuffle + self.sort_by_length = sort_by_length + self.label_dict = label_dict + + if labels is not None: + assert label_dict is not None + + self.sizes = [] + self.offsets = [] + self.labels = [] + + path = os.path.join(path, split) + data_path = path + self.data = np.load(data_path + ".npy", mmap_mode="r") + + offset = 0 + skipped = 0 + + if not os.path.exists(path + f".{labels}"): + labels = None + + with open(data_path + ".lengths", "r") as len_f, open( + path + f".{labels}", "r" + ) if labels is not None else contextlib.ExitStack() as lbl_f: + for line in len_f: + length = int(line.rstrip()) + lbl = None if labels is None else next(lbl_f).rstrip().split() + if length >= min_length and ( + max_length is None or length <= max_length + ): + self.sizes.append(length) + self.offsets.append(offset) + if lbl is not None: + self.labels.append(lbl) + offset += length + + self.sizes = np.asarray(self.sizes) + self.offsets = np.asarray(self.offsets) + + logger.info(f"loaded {len(self.offsets)}, skipped {skipped} samples") + + def __getitem__(self, index): + offset = self.offsets[index] + end = self.sizes[index] + offset + feats = torch.from_numpy(self.data[offset:end].copy()).float() + + res = {"id": index, "features": feats} + if len(self.labels) > 0: + res["target"] = self.label_dict.encode_line( + self.labels[index], + line_tokenizer=lambda x: x, + append_eos=False, + ) + + return res + + def __len__(self): + return len(self.sizes) + + def collater(self, samples): + if len(samples) == 0: + return {} + + features = [s["features"] for s in samples] + sizes = [len(s) for s in features] + + target_size = max(sizes) + + collated_features = features[0].new_zeros( + len(features), target_size, features[0].size(-1) + ) + padding_mask = torch.BoolTensor(collated_features.shape[:-1]).fill_(False) + for i, (f, size) in enumerate(zip(features, sizes)): + collated_features[i, :size] = f + padding_mask[i, size:] = True + + res = { + "id": torch.LongTensor([s["id"] for s in samples]), + "net_input": {"features": collated_features, "padding_mask": padding_mask}, + } + + if len(self.labels) > 0: + target = data_utils.collate_tokens( + [s["target"] for s in samples], + pad_idx=self.label_dict.pad(), + left_pad=False, + ) + res["target"] = target + return res + + def num_tokens(self, index): + return self.size(index) + + def size(self, index): + return self.sizes[index] + + def ordered_indices(self): + """Return an ordered list of indices. Batches will be constructed based + on this order.""" + if self.shuffle: + order = [np.random.permutation(len(self))] + else: + order = [np.arange(len(self))] + + if self.sort_by_length: + order.append(self.sizes) + return np.lexsort(order)[::-1] + else: + return order[0] diff --git a/examples/wav2vec/unsupervised/data/random_input_dataset.py b/examples/wav2vec/unsupervised/data/random_input_dataset.py new file mode 100644 index 0000000000..886505616c --- /dev/null +++ b/examples/wav2vec/unsupervised/data/random_input_dataset.py @@ -0,0 +1,62 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import random +from typing import List + +from fairseq.data import BaseWrapperDataset, data_utils + + +class RandomInputDataset(BaseWrapperDataset): + def __init__( + self, + dataset, + random_input_dataset, + input_key_path: List[str], + add_to_input, + pad_idx, + ): + super().__init__(dataset) + self.random_input_dataset = random_input_dataset + if isinstance(input_key_path, str): + input_key_path = [input_key_path] + assert len(input_key_path) > 0 + self.input_key_path = input_key_path + self.add_to_input = add_to_input + self.pad_idx = pad_idx + + def get_target(self, item): + target_loc = item + for p in self.input_key_path[:-1]: + target_loc = target_loc[p] + return self.input_key_path[-1], target_loc + + def get_target_value(self, item): + k, target_loc = self.get_target(item) + return target_loc[k] + + def __getitem__(self, index): + item = self.dataset[index] + k, target_loc = self.get_target(item) + target_loc[k] = random.choice(self.random_input_dataset) + return item + + def collater(self, samples): + collated = self.dataset.collater(samples) + if len(collated) == 0: + return collated + indices = set(collated["id"].tolist()) + + random_inputs = data_utils.collate_tokens( + [self.get_target_value(s) for s in samples if s["id"] in indices], + pad_idx=self.pad_idx, + left_pad=False, + ) + k, target_loc = self.get_target( + collated if not self.add_to_input else collated["net_input"] + ) + target_loc[k] = random_inputs + + return collated diff --git a/examples/wav2vec/unsupervised/kaldi_self_train/README.md b/examples/wav2vec/unsupervised/kaldi_self_train/README.md new file mode 100644 index 0000000000..314984fcbb --- /dev/null +++ b/examples/wav2vec/unsupervised/kaldi_self_train/README.md @@ -0,0 +1,56 @@ +# Self-Training with Kaldi HMM Models +This folder contains recipes for self-training on pseudo phone transcripts and +decoding into phones or words with [kaldi](https://github.com/kaldi-asr/kaldi). + +To start, download and install kaldi follow its instruction, and place this +folder in `path/to/kaldi/egs`. + +## Training +Assuming the following has been prepared: +- `w2v_dir`: contains features `{train,valid}.{npy,lengths}`, real transcripts `{train,valid}.${label}`, and dict `dict.${label}.txt` +- `lab_dir`: contains pseudo labels `{train,valid}.txt` +- `arpa_lm`: Arpa-format n-gram phone LM for decoding +- `arpa_lm_bin`: Arpa-format n-gram phone LM for unsupervised model selection to be used with KenLM + +Set these variables in `train.sh`, as well as `out_dir`, the output directory, +and then run it. + +The output will be: +``` +==== WER w.r.t. real transcript (select based on unsupervised metric) +INFO:root:./out/exp/mono/decode_valid/scoring/14.0.0.tra.txt: score 0.9178 wer 28.71% lm_ppl 24.4500 gt_wer 25.57% +INFO:root:./out/exp/tri1/decode_valid/scoring/17.1.0.tra.txt: score 0.9257 wer 26.99% lm_ppl 30.8494 gt_wer 21.90% +INFO:root:./out/exp/tri2b/decode_valid/scoring/8.0.0.tra.txt: score 0.7506 wer 23.15% lm_ppl 25.5944 gt_wer 15.78% +``` +where `wer` is the word eror rate with respect to the pseudo label, `gt_wer` to +the ground truth label, `lm_ppl` the language model perplexity of HMM prediced +transcripts, and `score` is the unsupervised metric for model selection. We +choose the model and the LM parameter of the one with the lowest score. In the +example above, it is `tri2b`, `8.0.0`. + + +## Decoding into Phones +In `decode_phone.sh`, set `out_dir` the same as used in `train.sh`, set +`dec_exp` and `dec_lmparam` to the selected model and LM parameter (e.g. +`tri2b` and `8.0.0` in the above example). `dec_script` needs to be set +according to `dec_exp`: for mono/tri1/tri2b, use `decode.sh`; for tri3b, use +`decode_fmllr.sh`. + +The output will be saved at `out_dir/dec_data` + + +## Decoding into Words +`decode_word_step1.sh` prepares WFSTs for word decoding. Besides the variables +mentioned above, set +- `wrd_arpa_lm`: Arpa-format n-gram word LM for decoding +- `wrd_arpa_lm_bin`: Arpa-format n-gram word LM for unsupervised model selection + +`decode_word_step1.sh` decodes the `train` and `valid` split into word and runs +unsupervised model selection using the `valid` split. The output is like: +``` +INFO:root:./out/exp/tri2b/decodeword_valid/scoring/17.0.0.tra.txt: score 1.8693 wer 24.97% lm_ppl 1785.5333 gt_wer 31.45% +``` + +After determining the LM parameter (`17.0.0` in the example above), set it in +`decode_word_step2.sh` and run it. The output will be saved at +`out_dir/dec_data_word`. diff --git a/examples/wav2vec/unsupervised/kaldi_self_train/st/cmd.sh b/examples/wav2vec/unsupervised/kaldi_self_train/st/cmd.sh new file mode 100644 index 0000000000..e74953194d --- /dev/null +++ b/examples/wav2vec/unsupervised/kaldi_self_train/st/cmd.sh @@ -0,0 +1,15 @@ +# you can change cmd.sh depending on what type of queue you are using. +# If you have no queueing system and want to run on a local machine, you +# can change all instances 'queue.pl' to run.pl (but be careful and run +# commands one by one: most recipes will exhaust the memory on your +# machine). queue.pl works with GridEngine (qsub). slurm.pl works +# with slurm. Different queues are configured differently, with different +# queue names and different ways of specifying things like memory; +# to account for these differences you can create and edit the file +# conf/queue.conf to match your queue's configuration. Search for +# conf/queue.conf in http://kaldi-asr.org/doc/queue.html for more information, +# or search for the string 'default_config' in utils/queue.pl or utils/slurm.pl. + +export train_cmd="run.pl --mem 2G" +export decode_cmd="run.pl --mem 4G" +export mkgraph_cmd="run.pl --mem 8G" diff --git a/examples/wav2vec/unsupervised/kaldi_self_train/st/decode_phone.sh b/examples/wav2vec/unsupervised/kaldi_self_train/st/decode_phone.sh new file mode 100644 index 0000000000..947342a0b7 --- /dev/null +++ b/examples/wav2vec/unsupervised/kaldi_self_train/st/decode_phone.sh @@ -0,0 +1,33 @@ +#!/bin/bash + +# decode into phones (and prepare a new data directory for HMM outputs) + +. ./path.sh + +set -eu + +out_dir= # same as in train.sh +dec_lmparam= # LM hyperparameters (e.g., 7.0.0) +dec_exp= +dec_script= +dec_splits="train valid" +dec_data_dir=$out_dir/dec_data # where to write HMM output + +data_dir=${out_dir}/data + +local/decode.sh --nj 40 --graph_name graph \ + --val_sets "$dec_splits" --decode_script $dec_script \ + $out_dir/exp/$dec_exp $data_dir $data_dir/lang_test + +if [ ! -z $dec_lmparam ]; then + for x in $dec_splits; do + mkdir -p $dec_data_dir/$x + cp $data_dir/$x/{feats.scp,cmvn.scp,utt2spk,spk2utt} $dec_data_dir/$x/ + + tra=$out_dir/exp/$dec_exp/decode_${x}/scoring/${dec_lmparam}.tra + cat $tra | utils/int2sym.pl -f 2- $data_dir/lang/words.txt | \ + sed 's:<UNK>::g' | sed 's:<SIL>::g' > $dec_data_dir/${x}/text + utils/fix_data_dir.sh $dec_data_dir/${x} + echo "WER on ${x} is" $(compute-wer ark:$data_dir/${x}_gt/text ark:$dec_data_dir/$x/text | cut -d" " -f2-) + done +fi diff --git a/examples/wav2vec/unsupervised/kaldi_self_train/st/decode_word_step1.sh b/examples/wav2vec/unsupervised/kaldi_self_train/st/decode_word_step1.sh new file mode 100644 index 0000000000..c1276bbe4d --- /dev/null +++ b/examples/wav2vec/unsupervised/kaldi_self_train/st/decode_word_step1.sh @@ -0,0 +1,46 @@ +#!/bin/bash + +# prepare word WFSTs, reference data, and decode + +set -eu + +w2v_dir= # same as in train.sh +out_dir= # same as in train.sh +lexicon= # word to phone mapping +wrd_arpa_lm= # word LM +wrd_arpa_lm_bin= # word LM for KenLM, used in unsupervised selection + +dec_exp= # what HMM stage to decode (e.g., tri3b) +dec_script= # what decoding script to use (e.g., steps/decode_fmllr.sh) +phn_label=phnc +wrd_label=wrd +dec_suffix=word +dec_splits="train valid" +valid_split="valid" + +data_dir=$out_dir/data +wrd_data_dir=$out_dir/data_word + +lexicon_clean=$(mktemp) +cat $lexicon | sort | uniq > $lexicon_clean +local/prepare_lang_word.sh $w2v_dir/dict.${phn_label}.txt $data_dir $lexicon_clean && rm $lexicon_clean +local/prepare_lm.sh --langdir $data_dir/lang_word --lmdir $data_dir/lang_test_word $wrd_arpa_lm $data_dir + +for x in $dec_splits; do + x_gt=${x}_gt + mkdir -p $wrd_data_dir/$x_gt + cp $data_dir/$x_gt/{feats.scp,cmvn.scp,utt2spk,spk2utt} $wrd_data_dir/$x_gt/ + python local/copy_aligned_text.py < $w2v_dir/$x.$wrd_label > $wrd_data_dir/$x_gt/text +done + +local/decode.sh --nj 40 --graph_name graph${dec_suffix} --decode_suffix $dec_suffix \ + --val_sets "$dec_splits" --decode_script $dec_script \ + $out_dir/exp/$dec_exp $data_dir $data_dir/lang_test_word + +local/unsup_select_decode_word.sh \ + --split $valid_split --kenlm_path $wrd_arpa_lm_bin \ + --ref_txt $wrd_data_dir/${valid_split}_gt/text \ + --psd_txt $data_dir/${valid_split}/text \ + --dec_name decode${dec_suffix} --graph_name graph${dec_suffix} \ + --phonemize_lexicon $data_dir/local/dict_word/lexicon.txt \ + $out_dir/exp diff --git a/examples/wav2vec/unsupervised/kaldi_self_train/st/decode_word_step2.sh b/examples/wav2vec/unsupervised/kaldi_self_train/st/decode_word_step2.sh new file mode 100644 index 0000000000..59a6cbb125 --- /dev/null +++ b/examples/wav2vec/unsupervised/kaldi_self_train/st/decode_word_step2.sh @@ -0,0 +1,30 @@ +#!/bin/bash + +# prepare a new data directory of HMM word output + +. ./path.sh + +set -eu + +out_dir= # same as in train.sh +dec_lmparam= # LM hyperparameters (e.g., 7.0.0) + +dec_exp=tri3b # what HMM stage to decode (e.g., tri3b) +dec_suffix=word +dec_splits="train valid" +dec_data_dir=$out_dir/dec_data_word # where to write HMM output + +data_dir=$out_dir/data +wrd_data_dir=$out_dir/data_word + +for x in $dec_splits; do + mkdir -p $dec_data_dir/$x + cp $data_dir/$x/{feats.scp,cmvn.scp,utt2spk,spk2utt} $dec_data_dir/$x/ + + tra=$out_dir/exp/$dec_exp/decode${dec_suffix}_${x}/scoring/${dec_lmparam}.tra + cat $tra | utils/int2sym.pl -f 2- $data_dir/lang_word/words.txt | \ + sed 's:<UNK>::g' | sed 's:<SIL>::g' > $dec_data_dir/$x/text + utils/fix_data_dir.sh $dec_data_dir/$x + echo "WER on $x is" $(compute-wer ark:$wrd_data_dir/${x}_gt/text ark:$dec_data_dir/$x/text | cut -d" " -f2-) +done + diff --git a/examples/wav2vec/unsupervised/kaldi_self_train/st/local/copy_aligned_text.py b/examples/wav2vec/unsupervised/kaldi_self_train/st/local/copy_aligned_text.py new file mode 100644 index 0000000000..5f4faa9921 --- /dev/null +++ b/examples/wav2vec/unsupervised/kaldi_self_train/st/local/copy_aligned_text.py @@ -0,0 +1,4 @@ +import sys + +for idx, line in enumerate(sys.stdin): + print(f"utt{idx:010d} {line}", end='') \ No newline at end of file diff --git a/examples/wav2vec/unsupervised/kaldi_self_train/st/local/decode.sh b/examples/wav2vec/unsupervised/kaldi_self_train/st/local/decode.sh new file mode 100755 index 0000000000..811cb63c88 --- /dev/null +++ b/examples/wav2vec/unsupervised/kaldi_self_train/st/local/decode.sh @@ -0,0 +1,38 @@ +#!/bin/bash + +set -u + +val_sets="dev_other" +graph_name=graph +decode_suffix="" +decode_script="steps/decode_fmllr.sh" +decode_args="" +nj=60 + +. ./cmd.sh +. ./path.sh +. parse_options.sh + +set -x +exp_dir=$1 +data_root=$2 +lang_test=$3 + +graph=$exp_dir/$graph_name + +if [ ! -d $graph ]; then + utils/mkgraph.sh $lang_test $exp_dir $graph +fi + +for part in $val_sets; do + dec_dir=$exp_dir/decode${decode_suffix}_${part} + if [ ! -d $dec_dir ]; then + echo "decoding $part for $exp_dir" + $decode_script --nj $nj --cmd "$decode_cmd" $decode_args \ + $graph $data_root/$part $dec_dir & + else + echo "$dec_dir exists. skip" + fi +done + +wait diff --git a/examples/wav2vec/unsupervised/kaldi_self_train/st/local/prepare_data_from_w2v.py b/examples/wav2vec/unsupervised/kaldi_self_train/st/local/prepare_data_from_w2v.py new file mode 100644 index 0000000000..66954ea5c9 --- /dev/null +++ b/examples/wav2vec/unsupervised/kaldi_self_train/st/local/prepare_data_from_w2v.py @@ -0,0 +1,56 @@ +import kaldi_io +import numpy as np +import os + + +def get_parser(): + import argparse + parser = argparse.ArgumentParser() + parser.add_argument("w2v_dir", help="wav2vec feature and text directory") + parser.add_argument("tar_root", help="output data directory in kaldi's format") + parser.add_argument("split", help="name of the subset") + parser.add_argument("--label", default="", help="if specified, copy labels too") + return parser + +def main(): + parser = get_parser() + args = parser.parse_args() + + tar_dir = os.path.join(args.tar_root, args.split) + os.makedirs(tar_dir, exist_ok=True) + + lengths_path = os.path.join(args.w2v_dir, f"{args.split}.lengths") + with open(lengths_path) as f: + lengths = [int(line.rstrip()) for line in f] + offsets = [0] + np.cumsum(lengths[:-1]).tolist() + feats = np.load( + os.path.join(args.w2v_dir, f"{args.split}.npy"), + mmap_mode="r" + ) + assert feats.shape[0] == sum(lengths), \ + f"lengths mismatch {feats.shape[0]} != {sum(lengths)}" + + ark_path = os.path.join(tar_dir, "feats.ark") + scp_path = os.path.join(tar_dir, "feats.scp") + wspec = f"ark:| copy-feats --compress=true ark:- ark,scp:{ark_path},{scp_path}" + with kaldi_io.open_or_fd(wspec, "wb") as f: + for idx, (offset, length) in enumerate(zip(offsets, lengths)): + feat = feats[offset:offset+length] + kaldi_io.write_mat(f, feat, key=f"utt{idx:010d}") + + u2s_path = os.path.join(tar_dir, "utt2spk") + s2u_path = os.path.join(tar_dir, "spk2utt") + with open(u2s_path, "w") as f_u2s, open(s2u_path, "w") as f_s2u: + for idx in range(len(lengths)): + f_u2s.write(f"utt{idx:010d} utt{idx:010d}\n") + f_s2u.write(f"utt{idx:010d} utt{idx:010d}\n") + + if bool(args.label): + lab_path = os.path.join(args.w2v_dir, f"{args.split}.{args.label}") + txt_path = os.path.join(tar_dir, "text") + with open(lab_path) as f_lab, open(txt_path, "w") as f_txt: + for idx, line in enumerate(f_lab): + f_txt.write(f"utt{idx:010d} {line}") + +if __name__ == "__main__": + main() diff --git a/examples/wav2vec/unsupervised/kaldi_self_train/st/local/prepare_lang.sh b/examples/wav2vec/unsupervised/kaldi_self_train/st/local/prepare_lang.sh new file mode 100755 index 0000000000..e9a80001eb --- /dev/null +++ b/examples/wav2vec/unsupervised/kaldi_self_train/st/local/prepare_lang.sh @@ -0,0 +1,37 @@ +#!/bin/bash + +sil_prob=0.5 +num_sil_states=3 +num_nonsil_states=1 + +. ./cmd.sh +. ./path.sh +. parse_options.sh + +set -eux + +dict=$1 +data_dir=$2 + +dict_dir=$data_dir/local/dict +tmplm_dir=$data_dir/local/lang_tmp +lm_dir=$data_dir/lang + +mkdir -p $dict_dir $tmplm_dir $lm_dir + +# prepare dict +echo "SIL" > $dict_dir/silence_phones.txt +echo "SIL" > $dict_dir/optional_silence.txt +awk '{print $1}' $dict > $dict_dir/nonsilence_phones.txt + +echo "SIL SIL" > $dict_dir/lexicon.txt +echo "<UNK> SIL" >> $dict_dir/lexicon.txt +awk '{print $1" "$1}' $dict >> $dict_dir/lexicon.txt + +echo "SIL" > $dict_dir/extra_questions.txt +awk '{printf $1" "} END {printf "\n"}' $dict >> $dict_dir/extra_questions.txt + +# prepare lang +utils/prepare_lang.sh --sil-prob $sil_prob --position-dependent-phones false \ + --num_sil_states $num_sil_states --num_nonsil_states $num_nonsil_states \ + $dict_dir "<UNK>" $tmplm_dir $lm_dir diff --git a/examples/wav2vec/unsupervised/kaldi_self_train/st/local/prepare_lang_word.sh b/examples/wav2vec/unsupervised/kaldi_self_train/st/local/prepare_lang_word.sh new file mode 100755 index 0000000000..a7ea3877be --- /dev/null +++ b/examples/wav2vec/unsupervised/kaldi_self_train/st/local/prepare_lang_word.sh @@ -0,0 +1,35 @@ +#!/bin/bash + +num_sil_states=3 +num_nonsil_states=1 + +. ./cmd.sh +. ./path.sh +. parse_options.sh + +set -eux + +dict=$1 +data_dir=$2 +lexicon=$3 + +dict_dir=$data_dir/local/dict_word +tmplm_dir=$data_dir/local/lang_tmp_word +lm_dir=$data_dir/lang_word + +mkdir -p $dict_dir $tmplm_dir $lm_dir + +# prepare dict +echo "SIL" > $dict_dir/silence_phones.txt +echo "SIL" > $dict_dir/optional_silence.txt +awk '{print $1}' $dict > $dict_dir/nonsilence_phones.txt + +(echo "!SIL SIL"; echo "<UNK> SIL";) | cat - $lexicon > $dict_dir/lexicon.txt + +echo "SIL" > $dict_dir/extra_questions.txt +awk '{printf $1" "} END {printf "\n"}' $dict >> $dict_dir/extra_questions.txt + +# prepare lang +utils/prepare_lang.sh --position-dependent-phones false \ + --num_sil_states $num_sil_states --num_nonsil_states $num_nonsil_states \ + $dict_dir "<UNK>" $tmplm_dir $lm_dir diff --git a/examples/wav2vec/unsupervised/kaldi_self_train/st/local/prepare_lm.sh b/examples/wav2vec/unsupervised/kaldi_self_train/st/local/prepare_lm.sh new file mode 100755 index 0000000000..c2edcefede --- /dev/null +++ b/examples/wav2vec/unsupervised/kaldi_self_train/st/local/prepare_lm.sh @@ -0,0 +1,35 @@ +#!/usr/bin/env bash + +langdir="" +lmdir="" + +. ./cmd.sh +. ./path.sh +. parse_options.sh + +arpa_lm=$1 +data=$2 + +if [ -z $langdir ]; then + langdir=$data/lang +fi +if [ -z $lmdir ]; then + lmdir=$data/lang_test +fi + +if [ ! -d $langdir ]; then + echo "$langdir not found. run local/prepare_lang.sh first" && exit 1 +fi + +mkdir -p $lmdir +cp -r $langdir/* $lmdir + +if [[ "$arpa_lm" == *.gz ]]; then + gunzip -c $arpa_lm | arpa2fst --disambig-symbol=#0 --read-symbol-table=$lmdir/words.txt - $lmdir/G.fst +else + arpa2fst --disambig-symbol=#0 --read-symbol-table=$lmdir/words.txt $arpa_lm $lmdir/G.fst +fi +fstisstochastic $lmdir/G.fst +utils/validate_lang.pl $lmdir || exit 1 + +echo "done preparing lm ($lmdir)" diff --git a/examples/wav2vec/unsupervised/kaldi_self_train/st/local/score.sh b/examples/wav2vec/unsupervised/kaldi_self_train/st/local/score.sh new file mode 100755 index 0000000000..cb5bbb7277 --- /dev/null +++ b/examples/wav2vec/unsupervised/kaldi_self_train/st/local/score.sh @@ -0,0 +1,63 @@ +#!/usr/bin/env bash +# Copyright 2012 Johns Hopkins University (Author: Daniel Povey) +# 2014 Guoguo Chen +# Apache 2.0 + +[ -f ./path.sh ] && . ./path.sh + +# begin configuration section. +cmd=run.pl +stage=0 +decode_mbr=true +word_ins_penalty=0.0,0.5,1.0 +min_lmwt=7 +max_lmwt=17 +iter=final +#end configuration section. + +[ -f ./path.sh ] && . ./path.sh +. parse_options.sh || exit 1; + +if [ $# -ne 3 ]; then + echo "Usage: local/score.sh [--cmd (run.pl|queue.pl...)] <data-dir> <lang-dir|graph-dir> <decode-dir>" + echo " Options:" + echo " --cmd (run.pl|queue.pl...) # specify how to run the sub-processes." + echo " --stage (0|1|2) # start scoring script from part-way through." + echo " --decode_mbr (true/false) # maximum bayes risk decoding (confusion network)." + echo " --min_lmwt <int> # minumum LM-weight for lattice rescoring " + echo " --max_lmwt <int> # maximum LM-weight for lattice rescoring " + exit 1; +fi + +data=$1 +lang_or_graph=$2 +dir=$3 + +symtab=$lang_or_graph/words.txt + +for f in $symtab $dir/lat.1.gz $data/text; do + [ ! -f $f ] && echo "score.sh: no such file $f" && exit 1; +done + +mkdir -p $dir/scoring/log + +cat $data/text | sed 's:<NOISE>::g' | sed 's:<SPOKEN_NOISE>::g' > $dir/scoring/test_filt.txt + +for wip in $(echo $word_ins_penalty | sed 's/,/ /g'); do + $cmd LMWT=$min_lmwt:$max_lmwt $dir/scoring/log/best_path.LMWT.$wip.log \ + lattice-scale --inv-acoustic-scale=LMWT "ark:gunzip -c $dir/lat.*.gz|" ark:- \| \ + lattice-add-penalty --word-ins-penalty=$wip ark:- ark:- \| \ + lattice-best-path --word-symbol-table=$symtab \ + ark:- ark,t:$dir/scoring/LMWT.$wip.tra || exit 1; +done + +# Note: the double level of quoting for the sed command +for wip in $(echo $word_ins_penalty | sed 's/,/ /g'); do + $cmd LMWT=$min_lmwt:$max_lmwt $dir/scoring/log/score.LMWT.$wip.log \ + cat $dir/scoring/LMWT.$wip.tra \| \ + utils/int2sym.pl -f 2- $symtab \| sed 's:\<UNK\>::g' \| \ + compute-wer --text --mode=present \ + ark:$dir/scoring/test_filt.txt ark,p:- ">&" $dir/wer_LMWT_$wip || exit 1; +done + +exit 0; diff --git a/examples/wav2vec/unsupervised/kaldi_self_train/st/local/show_wer.sh b/examples/wav2vec/unsupervised/kaldi_self_train/st/local/show_wer.sh new file mode 100755 index 0000000000..9ecf1690c6 --- /dev/null +++ b/examples/wav2vec/unsupervised/kaldi_self_train/st/local/show_wer.sh @@ -0,0 +1,52 @@ +#!/bin/bash + +split="dev_other" +ref_data="" +get_best_wer=true +dec_name="decode" +graph_name="graph" + +. ./cmd.sh +. ./path.sh +. parse_options.sh + +exp_root=$1 + +set -eu + +echo "==== WER w.r.t. pseudo transcript" +for x in $exp_root/*/${dec_name}_${split}*; do grep WER $x/wer_* 2>/dev/null | utils/best_wer.sh; done + + +if [ ! -z $ref_data ]; then + echo "==== WER w.r.t. real transcript (select based on pseudo WER)" + ref_txt=$ref_data/$split/text + for x in $exp_root/*/${dec_name}_${split}*; do + lang=$(dirname $x)/$graph_name + + lmwt=$( + grep WER $x/wer_* 2>/dev/null | utils/best_wer.sh | + sed 's/.*wer_\(.*\)$/\1/g' | sed 's/_/./g' + ) + tra=$x/scoring/$lmwt.tra + cat $tra | utils/int2sym.pl -f 2- $lang/words.txt | sed 's:<UNK>::g' | sed 's:<SIL>::g' | \ + compute-wer --text --mode=present \ + ark:$ref_txt ark,p:- 2> /dev/null | grep WER | xargs -I{} echo {} $tra + done +fi + +if [ ! -z $ref_data ] && $get_best_wer; then + echo "==== WER w.r.t. real transcript (select based on true WER)" + ref_txt=$ref_data/$split/text + for x in $exp_root/*/${dec_name}_${split}*; do + lang=$(dirname $x)/$graph_name + + for tra in $x/scoring/*.tra; do + cat $tra | utils/int2sym.pl -f 2- $lang/words.txt | sed 's:<UNK>::g' | sed 's:<SIL>::g' | \ + compute-wer --text --mode=present \ + ark:$ref_txt ark,p:- 2> /dev/null | grep WER | xargs -I{} echo {} $tra + done | sort -k2n | head -n1 + done +fi + +exit 0; diff --git a/examples/wav2vec/unsupervised/kaldi_self_train/st/local/train_subset_lgbeam.sh b/examples/wav2vec/unsupervised/kaldi_self_train/st/local/train_subset_lgbeam.sh new file mode 100755 index 0000000000..913c1d8e43 --- /dev/null +++ b/examples/wav2vec/unsupervised/kaldi_self_train/st/local/train_subset_lgbeam.sh @@ -0,0 +1,129 @@ +#!/usr/bin/env bash + +out_root=/tmp +out_name=train_${RANDOM} +num_nonsil_states=1 + +valid="dev_other" +train="train" +mono_size="-1" # 2000 +tri1_size="-1" # 5000 +tri2b_size="-1" # 10000 +tri3b_size="-1" # 10000 + +# Acoustic model parameters +numLeavesTri1=2000 +numGaussTri1=10000 +numLeavesMLLT=2500 +numGaussMLLT=15000 +numLeavesSAT=2500 +numGaussSAT=15000 + +stage=1 +max_stage=1 + +. ./cmd.sh +. ./path.sh +. parse_options.sh + +data=$1 +lang=$2 +lang_test=$3 + +exp_root=$out_root/$out_name + +# you might not want to do this for interactive shells. +set -e + + +if [ $stage -le 1 ] && [ $max_stage -ge 1 ]; then + # train a monophone system + if [ ! $mono_size -eq -1 ]; then + utils/subset_data_dir.sh $data/$train $mono_size $data/${train}_${mono_size} + mono_train=${train}_${mono_size} + else + mono_train=${train} + fi + + steps/train_mono.sh --boost-silence 1.25 --nj 20 --cmd "$train_cmd" \ + --initial-beam 40 --regular-beam 60 --retry-beam 120 \ + $data/$mono_train $lang $exp_root/mono + + utils/mkgraph.sh $lang_test $exp_root/mono $exp_root/mono/graph + steps/decode.sh --nj 20 --cmd "$decode_cmd" \ + $exp_root/mono/graph $data/$valid $exp_root/mono/decode_$valid & +fi + + +if [ $stage -le 2 ] && [ $max_stage -ge 2 ]; then + # train a first delta + delta-delta triphone system on a subset of 5000 utterances + if [ ! $tri1_size -eq -1 ]; then + utils/subset_data_dir.sh $data/$train $tri1_size $data/${train}_${tri1_size} + tri1_train=${train}_${tri1_size} + else + tri1_train=${train} + fi + + steps/align_si.sh --boost-silence 1.25 --nj 10 --cmd "$train_cmd" \ + $data/$tri1_train $lang \ + $exp_root/mono $exp_root/mono_ali_${tri1_train} + + steps_gan/train_deltas.sh --boost-silence 1.25 --cmd "$train_cmd" \ + --num_nonsil_states $num_nonsil_states $numLeavesTri1 $numGaussTri1 \ + $data/$tri1_train $lang \ + $exp_root/mono_ali_${tri1_train} $exp_root/tri1 + + utils/mkgraph.sh $lang_test $exp_root/tri1 $exp_root/tri1/graph + steps/decode.sh --nj 20 --cmd "$decode_cmd" \ + $exp_root/tri1/graph $data/$valid $exp_root/tri1/decode_$valid & +fi + +if [ $stage -le 3 ] && [ $max_stage -ge 3 ]; then + # train an LDA+MLLT system. + if [ ! $tri2b_size -eq -1 ]; then + utils/subset_data_dir.sh $data/$train $tri2b_size $data/${train}_${tri2b_size} + tri2b_train=${train}_${tri2b_size} + else + tri2b_train=${train} + fi + + steps/align_si.sh --nj 10 --cmd "$train_cmd" \ + $data/$tri2b_train $lang \ + $exp_root/tri1 $exp_root/tri1_ali_${tri2b_train} + + steps_gan/train_lda_mllt.sh --cmd "$train_cmd" \ + --num_nonsil_states $num_nonsil_states \ + --splice-opts "--left-context=3 --right-context=3" $numLeavesMLLT $numGaussMLLT \ + $data/$tri2b_train $lang \ + $exp_root/tri1_ali_${tri2b_train} $exp_root/tri2b + + utils/mkgraph.sh $lang_test $exp_root/tri2b $exp_root/tri2b/graph + steps/decode.sh --nj 20 --cmd "$decode_cmd" \ + $exp_root/tri2b/graph $data/$valid $exp_root/tri2b/decode_$valid & +fi + + +if [ $stage -le 4 ] && [ $max_stage -ge 4 ]; then + # Train tri3b, which is LDA+MLLT+SAT on 10k utts + if [ ! $tri3b_size -eq -1 ]; then + utils/subset_data_dir.sh $data/$train $tri3b_size $data/${train}_${tri3b_size} + tri3b_train=${train}_${tri3b_size} + else + tri3b_train=${train} + fi + + steps/align_si.sh --nj 10 --cmd "$train_cmd" --use-graphs true \ + $data/$tri3b_train $lang \ + $exp_root/tri2b $exp_root/tri2b_ali_${tri2b_train} + + steps_gan/train_sat.sh --cmd "$train_cmd" \ + --num_nonsil_states $num_nonsil_states $numLeavesSAT $numGaussSAT \ + $data/$tri3b_train $lang \ + $exp_root/tri2b_ali_${tri2b_train} $exp_root/tri3b + + utils/mkgraph.sh $lang_test $exp_root/tri3b $exp_root/tri3b/graph + steps/decode_fmllr.sh --nj 20 --cmd "$decode_cmd" \ + $exp_root/tri3b/graph $data/$valid $exp_root/tri3b/decode_$valid & +fi + +wait diff --git a/examples/wav2vec/unsupervised/kaldi_self_train/st/local/unsup_select.py b/examples/wav2vec/unsupervised/kaldi_self_train/st/local/unsup_select.py new file mode 100644 index 0000000000..1122c88c19 --- /dev/null +++ b/examples/wav2vec/unsupervised/kaldi_self_train/st/local/unsup_select.py @@ -0,0 +1,135 @@ +""" +Implement unsupervised metric for decoding hyperparameter selection: + $$ alpha * LM_PPL + ViterbitUER(%) * 100 $$ +""" +import argparse +import logging +import math +import sys + +import kenlm +import editdistance +from g2p_en import G2p + +logging.root.setLevel(logging.INFO) +logging.basicConfig(stream=sys.stdout, level=logging.INFO) +logger = logging.getLogger(__name__) + + +def get_parser(): + parser = argparse.ArgumentParser() + parser.add_argument("ref_tra", help="reference pseudo labels") + parser.add_argument("hyp_tra", help="decoded pseudo labels to be assess") + parser.add_argument("--kenlm_path", default="/checkpoint/abaevski/data/speech/libri/librispeech_lm_novox.phnc_o5.bin", help="") + parser.add_argument("--uppercase", action="store_true", help="") + parser.add_argument("--skipwords", default="", help="") + parser.add_argument("--gt_tra", default="", help="ground truth pseudo labels for computing oracle WER") + parser.add_argument("--min_vt_uer", default=0.0, type=float) + parser.add_argument("--phonemize", action="store_true", help="phonemize word hypotheses, used when reference is phone transcript") + parser.add_argument("--phonemize_lexicon", default="", type=str, help="use a lexicon for phonemizing") + return parser + +def load_tra(tra_path): + with open(tra_path, "r") as f: + uid_to_tra = {} + for line in f: + toks = line.rstrip().split() + uid, tra = toks[0], " ".join(toks[1:]) + uid_to_tra[uid] = tra + logger.debug(f"loaded {len(uid_to_tra)} utterances from {tra_path}") + return uid_to_tra + +def load_lex(lex_path): + with open(lex_path, "r") as f: + w2p = {} + for line in f: + w, p = line.rstrip().split(None, 1) + w2p[w] = p.split() + return w2p + +def compute_wer(ref_uid_to_tra, hyp_uid_to_tra, g2p, g2p_dict): + d_cnt = 0 + w_cnt = 0 + w_cnt_h = 0 + for uid in hyp_uid_to_tra: + ref = ref_uid_to_tra[uid].split() + if g2p_dict is not None: + hyp = [] + for word in hyp_uid_to_tra[uid].split(): + if word in g2p_dict: + hyp = hyp + g2p_dict[word] + else: + logger.warning(f"{word} not in g2p_dict") + elif g2p is not None: + hyp = g2p(hyp_uid_to_tra[uid]) + hyp = [p for p in hyp if p != "'" and p != " "] + hyp = [p[:-1] if p[-1].isnumeric() else p for p in hyp] + else: + hyp = hyp_uid_to_tra[uid].split() + logger.debug(( + f"======================\n" + f"HYP: {' '.join(hyp)}\n" + f"REF: {' '.join(ref)}" + )) + d_cnt += editdistance.eval(ref, hyp) + w_cnt += len(ref) + w_cnt_h += len(hyp) + wer = float(d_cnt) / w_cnt + logger.debug(( + f"wer = {wer*100:.2f}%; num. of ref words = {w_cnt}; " + f"num. of hyp words = {w_cnt_h}; num. of sentences = {len(ref_uid_to_tra)}" + )) + return wer + +def compute_lm_ppl(hyp_uid_to_tra, score_fn): + lm_score = 0. + w_cnt = 0 + for hyp in hyp_uid_to_tra.values(): + cur_score = score_fn(hyp) + cur_cnt = len(hyp.split()) + 1 # plus one for </s> + lm_score += cur_score + w_cnt += cur_cnt + logger.debug(( + f"======================\n" + f"score sum/avg = {cur_score:.2f}/{cur_score/cur_cnt:.2f}\n" + f"hyp = {hyp}" + )) + lm_ppl = math.pow(10, -lm_score / w_cnt) + logger.debug(f"lm ppl = {lm_ppl:.2f}; num. of words = {w_cnt}") + return lm_ppl + +def main(): + args = get_parser().parse_args() + logger.debug(f"Args: {args}") + + ref_uid_to_tra = load_tra(args.ref_tra) + hyp_uid_to_tra = load_tra(args.hyp_tra) + assert not bool(set(hyp_uid_to_tra.keys()) - set(ref_uid_to_tra.keys())) + + lm = kenlm.Model(args.kenlm_path) + skipwords = set(args.skipwords.split(",")) + def compute_lm_score(s): + s = " ".join(w for w in s.split() if w not in skipwords) + s = s.upper() if args.uppercase else s + return lm.score(s) + + g2p, g2p_dict = None, None + if args.phonemize: + if args.phonemize_lexicon: + g2p_dict = load_lex(args.phonemize_lexicon) + else: + g2p = G2p() + + wer = compute_wer(ref_uid_to_tra, hyp_uid_to_tra, g2p, g2p_dict) + lm_ppl = compute_lm_ppl(hyp_uid_to_tra, compute_lm_score) + + gt_wer = -math.inf + if args.gt_tra: + gt_uid_to_tra = load_tra(args.gt_tra) + gt_wer = compute_wer(gt_uid_to_tra, hyp_uid_to_tra, None, None) + + score = math.log(lm_ppl) * max(wer, args.min_vt_uer) + logging.info(f"{args.hyp_tra}: score={score:.4f}; wer={wer*100:.2f}%; lm_ppl={lm_ppl:.4f}; gt_wer={gt_wer*100:.2f}%") + +if __name__ == "__main__": + main() diff --git a/examples/wav2vec/unsupervised/kaldi_self_train/st/local/unsup_select_decode.sh b/examples/wav2vec/unsupervised/kaldi_self_train/st/local/unsup_select_decode.sh new file mode 100755 index 0000000000..b34c5b6e06 --- /dev/null +++ b/examples/wav2vec/unsupervised/kaldi_self_train/st/local/unsup_select_decode.sh @@ -0,0 +1,37 @@ +#!/bin/bash + +split="dev_other" +ref_txt="" # ground truth transcript path +psd_txt="" # pseudo transcript path +get_best_wer=true +dec_name="decode" +graph_name="graph" +kenlm_path=/checkpoint/abaevski/data/speech/libri/librispeech_lm_novox.phnc_o6.bin + +. ./cmd.sh +. ./path.sh +. parse_options.sh + +exp_root=$1 +unsup_args="" +if [ $# -ge 2 ]; then + unsup_args=$2 +fi + +set -eu + +if [ ! -z $ref_txt ] && $get_best_wer; then + echo "==== WER w.r.t. real transcript (select based on unsupervised metric)" + for x in $exp_root/*/${dec_name}_${split}*; do + lang=$(dirname $x)/$graph_name + + ( + for tra in $x/scoring/*.tra; do + cat $tra | utils/int2sym.pl -f 2- $lang/words.txt | sed 's:<UNK>::g' | sed 's:<SIL>::g' > $tra.txt + python local/unsup_select.py $psd_txt $tra.txt --kenlm_path $kenlm_path --gt_tra $ref_txt $unsup_args + done 2>/dev/null | grep "score=" | sed 's/=/ /g' | sed 's/;//g' | sort -k3n | head -n1 + ) & + done +fi +wait + diff --git a/examples/wav2vec/unsupervised/kaldi_self_train/st/local/unsup_select_decode_word.sh b/examples/wav2vec/unsupervised/kaldi_self_train/st/local/unsup_select_decode_word.sh new file mode 100755 index 0000000000..c10a6b8809 --- /dev/null +++ b/examples/wav2vec/unsupervised/kaldi_self_train/st/local/unsup_select_decode_word.sh @@ -0,0 +1,35 @@ +#!/bin/bash + +split="dev_other" +ref_txt="" # ground truth transcript path +psd_txt="" # pseudo transcript path +get_best_wer=true +dec_name="decode" +graph_name="graph" +kenlm_path=/checkpoint/abaevski/data/speech/libri/librispeech_lm_novox.phnc_o6.bin +phonemize_lexicon="" + +. ./cmd.sh +. ./path.sh +. parse_options.sh +. /private/home/wnhsu/unsup_asr/fairseq-py-unsup/env.sh + +exp_root=$1 + +set -eu + +if [ ! -z $ref_txt ] && $get_best_wer; then + echo "==== WER w.r.t. real transcript (select based on unsupervised metric)" + for x in $exp_root/*/${dec_name}_${split}*; do + lang=$(dirname $x)/$graph_name + + for tra in $x/scoring/*.tra; do + cat $tra | utils/int2sym.pl -f 2- $lang/words.txt | sed 's:\<UNK\>::g' > $tra.txt + python local/unsup_select.py $psd_txt $tra.txt \ + --kenlm_path $kenlm_path --gt_tra $ref_txt --phonemize \ + --phonemize_lexicon "$phonemize_lexicon" + done | grep "score=" | sed 's/=/ /g' | sed 's/;//g' | sort -k3n | head -n1 + done +fi + + diff --git a/examples/wav2vec/unsupervised/kaldi_self_train/st/path.sh b/examples/wav2vec/unsupervised/kaldi_self_train/st/path.sh new file mode 100755 index 0000000000..1a6fb5f891 --- /dev/null +++ b/examples/wav2vec/unsupervised/kaldi_self_train/st/path.sh @@ -0,0 +1,5 @@ +export KALDI_ROOT=`pwd`/../../.. +export PATH=$PWD/utils/:$KALDI_ROOT/tools/openfst/bin:$PWD:$PATH +[ ! -f $KALDI_ROOT/tools/config/common_path.sh ] && echo >&2 "The standard file $KALDI_ROOT/tools/config/common_path.sh is not present -> Exit!" && exit 1 +. $KALDI_ROOT/tools/config/common_path.sh +export LC_ALL=C diff --git a/examples/wav2vec/unsupervised/kaldi_self_train/st/steps b/examples/wav2vec/unsupervised/kaldi_self_train/st/steps new file mode 120000 index 0000000000..6e99bf5b5a --- /dev/null +++ b/examples/wav2vec/unsupervised/kaldi_self_train/st/steps @@ -0,0 +1 @@ +../../wsj/s5/steps \ No newline at end of file diff --git a/examples/wav2vec/unsupervised/kaldi_self_train/st/steps_gan/train_deltas.sh b/examples/wav2vec/unsupervised/kaldi_self_train/st/steps_gan/train_deltas.sh new file mode 100755 index 0000000000..af68715ab0 --- /dev/null +++ b/examples/wav2vec/unsupervised/kaldi_self_train/st/steps_gan/train_deltas.sh @@ -0,0 +1,175 @@ +#!/usr/bin/env bash + +# Copyright 2012 Johns Hopkins University (Author: Daniel Povey) +# Apache 2.0 + +# Begin configuration. +stage=-4 # This allows restarting after partway, when something when wrong. +config= +cmd=run.pl +scale_opts="--transition-scale=1.0 --acoustic-scale=0.1 --self-loop-scale=0.1" +realign_iters="10 20 30"; +num_iters=35 # Number of iterations of training +max_iter_inc=25 # Last iter to increase #Gauss on. +beam=10 +careful=false +retry_beam=40 +boost_silence=1.0 # Factor by which to boost silence likelihoods in alignment +power=0.25 # Exponent for number of gaussians according to occurrence counts +cluster_thresh=-1 # for build-tree control final bottom-up clustering of leaves +norm_vars=false # deprecated. Prefer --cmvn-opts "--norm-vars=true" + # use the option --cmvn-opts "--norm-means=false" +cmvn_opts= +delta_opts= +context_opts= # use"--context-width=5 --central-position=2" for quinphone +num_nonsil_states=3 +# End configuration. + +echo "$0 $@" # Print the command line for logging + +[ -f path.sh ] && . ./path.sh; +. parse_options.sh || exit 1; + +if [ $# != 6 ]; then + echo "Usage: steps/train_deltas.sh <num-leaves> <tot-gauss> <data-dir> <lang-dir> <alignment-dir> <exp-dir>" + echo "e.g.: steps/train_deltas.sh 2000 10000 data/train_si84_half data/lang exp/mono_ali exp/tri1" + echo "main options (for others, see top of script file)" + echo " --cmd (utils/run.pl|utils/queue.pl <queue opts>) # how to run jobs." + echo " --config <config-file> # config containing options" + echo " --stage <stage> # stage to do partial re-run from." + exit 1; +fi + +numleaves=$1 +totgauss=$2 +data=$3 +lang=$4 +alidir=$5 +dir=$6 + +for f in $alidir/final.mdl $alidir/ali.1.gz $data/feats.scp $lang/phones.txt; do + [ ! -f $f ] && echo "train_deltas.sh: no such file $f" && exit 1; +done + +numgauss=$numleaves +incgauss=$[($totgauss-$numgauss)/$max_iter_inc] # per-iter increment for #Gauss +oov=`cat $lang/oov.int` || exit 1; +ciphonelist=`cat $lang/phones/context_indep.csl` || exit 1; +nj=`cat $alidir/num_jobs` || exit 1; +mkdir -p $dir/log +echo $nj > $dir/num_jobs + +utils/lang/check_phones_compatible.sh $lang/phones.txt $alidir/phones.txt || exit 1; +cp $lang/phones.txt $dir || exit 1; + +sdata=$data/split$nj; +split_data.sh $data $nj || exit 1; + + +[ $(cat $alidir/cmvn_opts 2>/dev/null | wc -c) -gt 1 ] && [ -z "$cmvn_opts" ] && \ + echo "$0: warning: ignoring CMVN options from source directory $alidir" +$norm_vars && cmvn_opts="--norm-vars=true $cmvn_opts" +echo $cmvn_opts > $dir/cmvn_opts # keep track of options to CMVN. +[ ! -z $delta_opts ] && echo $delta_opts > $dir/delta_opts + +feats="ark,s,cs:apply-cmvn $cmvn_opts --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | add-deltas $delta_opts ark:- ark:- |" + +rm $dir/.error 2>/dev/null + +if [ $stage -le -3 ]; then + echo "$0: accumulating tree stats" + $cmd JOB=1:$nj $dir/log/acc_tree.JOB.log \ + acc-tree-stats $context_opts \ + --ci-phones=$ciphonelist $alidir/final.mdl "$feats" \ + "ark:gunzip -c $alidir/ali.JOB.gz|" $dir/JOB.treeacc || exit 1; + sum-tree-stats $dir/treeacc $dir/*.treeacc 2>$dir/log/sum_tree_acc.log || exit 1; + rm $dir/*.treeacc +fi + +if [ $stage -le -2 ]; then + echo "$0: getting questions for tree-building, via clustering" + # preparing questions, roots file... + cluster-phones --pdf-class-list=$(($num_nonsil_states / 2)) $context_opts \ + $dir/treeacc $lang/phones/sets.int \ + $dir/questions.int 2> $dir/log/questions.log || exit 1; + cat $lang/phones/extra_questions.int >> $dir/questions.int + compile-questions $context_opts $lang/topo $dir/questions.int \ + $dir/questions.qst 2>$dir/log/compile_questions.log || exit 1; + + echo "$0: building the tree" + $cmd $dir/log/build_tree.log \ + build-tree $context_opts --verbose=1 --max-leaves=$numleaves \ + --cluster-thresh=$cluster_thresh $dir/treeacc $lang/phones/roots.int \ + $dir/questions.qst $lang/topo $dir/tree || exit 1; + + $cmd $dir/log/init_model.log \ + gmm-init-model --write-occs=$dir/1.occs \ + $dir/tree $dir/treeacc $lang/topo $dir/1.mdl || exit 1; + if grep 'no stats' $dir/log/init_model.log; then + echo "** The warnings above about 'no stats' generally mean you have phones **" + echo "** (or groups of phones) in your phone set that had no corresponding data. **" + echo "** You should probably figure out whether something went wrong, **" + echo "** or whether your data just doesn't happen to have examples of those **" + echo "** phones. **" + fi + + gmm-mixup --mix-up=$numgauss $dir/1.mdl $dir/1.occs $dir/1.mdl 2>$dir/log/mixup.log || exit 1; + rm $dir/treeacc +fi + +if [ $stage -le -1 ]; then + # Convert the alignments. + echo "$0: converting alignments from $alidir to use current tree" + $cmd JOB=1:$nj $dir/log/convert.JOB.log \ + convert-ali $alidir/final.mdl $dir/1.mdl $dir/tree \ + "ark:gunzip -c $alidir/ali.JOB.gz|" "ark:|gzip -c >$dir/ali.JOB.gz" || exit 1; +fi + +if [ $stage -le 0 ]; then + echo "$0: compiling graphs of transcripts" + $cmd JOB=1:$nj $dir/log/compile_graphs.JOB.log \ + compile-train-graphs --read-disambig-syms=$lang/phones/disambig.int $dir/tree $dir/1.mdl $lang/L.fst \ + "ark:utils/sym2int.pl --map-oov $oov -f 2- $lang/words.txt < $sdata/JOB/text |" \ + "ark:|gzip -c >$dir/fsts.JOB.gz" || exit 1; +fi + +x=1 +while [ $x -lt $num_iters ]; do + echo "$0: training pass $x" + if [ $stage -le $x ]; then + if echo $realign_iters | grep -w $x >/dev/null; then + echo "$0: aligning data" + mdl="gmm-boost-silence --boost=$boost_silence `cat $lang/phones/optional_silence.csl` $dir/$x.mdl - |" + $cmd JOB=1:$nj $dir/log/align.$x.JOB.log \ + gmm-align-compiled $scale_opts --beam=$beam --retry-beam=$retry_beam --careful=$careful "$mdl" \ + "ark:gunzip -c $dir/fsts.JOB.gz|" "$feats" \ + "ark:|gzip -c >$dir/ali.JOB.gz" || exit 1; + fi + $cmd JOB=1:$nj $dir/log/acc.$x.JOB.log \ + gmm-acc-stats-ali $dir/$x.mdl "$feats" \ + "ark,s,cs:gunzip -c $dir/ali.JOB.gz|" $dir/$x.JOB.acc || exit 1; + $cmd $dir/log/update.$x.log \ + gmm-est --mix-up=$numgauss --power=$power \ + --write-occs=$dir/$[$x+1].occs $dir/$x.mdl \ + "gmm-sum-accs - $dir/$x.*.acc |" $dir/$[$x+1].mdl || exit 1; + rm $dir/$x.mdl $dir/$x.*.acc + rm $dir/$x.occs + fi + [ $x -le $max_iter_inc ] && numgauss=$[$numgauss+$incgauss]; + x=$[$x+1]; +done + +rm $dir/final.mdl $dir/final.occs 2>/dev/null +ln -s $x.mdl $dir/final.mdl +ln -s $x.occs $dir/final.occs + +steps/diagnostic/analyze_alignments.sh --cmd "$cmd" $lang $dir + +# Summarize warning messages... +utils/summarize_warnings.pl $dir/log + +steps/info/gmm_dir_info.pl $dir + +echo "$0: Done training system with delta+delta-delta features in $dir" + +exit 0 diff --git a/examples/wav2vec/unsupervised/kaldi_self_train/st/steps_gan/train_lda_mllt.sh b/examples/wav2vec/unsupervised/kaldi_self_train/st/steps_gan/train_lda_mllt.sh new file mode 100755 index 0000000000..9d8c319ce8 --- /dev/null +++ b/examples/wav2vec/unsupervised/kaldi_self_train/st/steps_gan/train_lda_mllt.sh @@ -0,0 +1,239 @@ +#!/usr/bin/env bash + +# Copyright 2012 Johns Hopkins University (Author: Daniel Povey) +# +# LDA+MLLT refers to the way we transform the features after computing +# the MFCCs: we splice across several frames, reduce the dimension (to 40 +# by default) using Linear Discriminant Analysis), and then later estimate, +# over multiple iterations, a diagonalizing transform known as MLLT or STC. +# See http://kaldi-asr.org/doc/transform.html for more explanation. +# +# Apache 2.0. + +# Begin configuration. +cmd=run.pl +config= +stage=-5 +scale_opts="--transition-scale=1.0 --acoustic-scale=0.1 --self-loop-scale=0.1" +realign_iters="10 20 30"; +mllt_iters="2 4 6 12"; +num_iters=35 # Number of iterations of training +max_iter_inc=25 # Last iter to increase #Gauss on. +dim=40 +beam=10 +retry_beam=40 +careful=false +boost_silence=1.0 # Factor by which to boost silence likelihoods in alignment +power=0.25 # Exponent for number of gaussians according to occurrence counts +randprune=4.0 # This is approximately the ratio by which we will speed up the + # LDA and MLLT calculations via randomized pruning. +splice_opts= +cluster_thresh=-1 # for build-tree control final bottom-up clustering of leaves +norm_vars=false # deprecated. Prefer --cmvn-opts "--norm-vars=false" +cmvn_opts= +context_opts= # use "--context-width=5 --central-position=2" for quinphone. +# End configuration. +train_tree=true # if false, don't actually train the tree. +use_lda_mat= # If supplied, use this LDA[+MLLT] matrix. +num_nonsil_states=3 + +echo "$0 $@" # Print the command line for logging + +[ -f path.sh ] && . ./path.sh +. parse_options.sh || exit 1; + +if [ $# != 6 ]; then + echo "Usage: steps/train_lda_mllt.sh [options] <#leaves> <#gauss> <data> <lang> <alignments> <dir>" + echo " e.g.: steps/train_lda_mllt.sh 2500 15000 data/train_si84 data/lang exp/tri1_ali_si84 exp/tri2b" + echo "Main options (for others, see top of script file)" + echo " --cmd (utils/run.pl|utils/queue.pl <queue opts>) # how to run jobs." + echo " --config <config-file> # config containing options" + echo " --stage <stage> # stage to do partial re-run from." + exit 1; +fi + +numleaves=$1 +totgauss=$2 +data=$3 +lang=$4 +alidir=$5 +dir=$6 + +for f in $alidir/final.mdl $alidir/ali.1.gz $data/feats.scp $lang/phones.txt; do + [ ! -f $f ] && echo "train_lda_mllt.sh: no such file $f" && exit 1; +done + +numgauss=$numleaves +incgauss=$[($totgauss-$numgauss)/$max_iter_inc] # per-iter #gauss increment +oov=`cat $lang/oov.int` || exit 1; +nj=`cat $alidir/num_jobs` || exit 1; +silphonelist=`cat $lang/phones/silence.csl` || exit 1; +ciphonelist=`cat $lang/phones/context_indep.csl` || exit 1; + +mkdir -p $dir/log + +utils/lang/check_phones_compatible.sh $lang/phones.txt $alidir/phones.txt || exit 1; +cp $lang/phones.txt $dir || exit 1; + +echo $nj >$dir/num_jobs +echo "$splice_opts" >$dir/splice_opts # keep track of frame-splicing options + # so that later stages of system building can know what they were. + + +[ $(cat $alidir/cmvn_opts 2>/dev/null | wc -c) -gt 1 ] && [ -z "$cmvn_opts" ] && \ + echo "$0: warning: ignoring CMVN options from source directory $alidir" +$norm_vars && cmvn_opts="--norm-vars=true $cmvn_opts" +echo $cmvn_opts > $dir/cmvn_opts # keep track of options to CMVN. + +sdata=$data/split$nj; +split_data.sh $data $nj || exit 1; + +splicedfeats="ark,s,cs:apply-cmvn $cmvn_opts --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | splice-feats $splice_opts ark:- ark:- |" +# Note: $feats gets overwritten later in the script. +feats="$splicedfeats transform-feats $dir/0.mat ark:- ark:- |" + + + +if [ $stage -le -5 ]; then + if [ -z "$use_lda_mat" ]; then + echo "$0: Accumulating LDA statistics." + rm $dir/lda.*.acc 2>/dev/null + $cmd JOB=1:$nj $dir/log/lda_acc.JOB.log \ + ali-to-post "ark:gunzip -c $alidir/ali.JOB.gz|" ark:- \| \ + weight-silence-post 0.0 $silphonelist $alidir/final.mdl ark:- ark:- \| \ + acc-lda --rand-prune=$randprune $alidir/final.mdl "$splicedfeats" ark,s,cs:- \ + $dir/lda.JOB.acc || exit 1; + est-lda --write-full-matrix=$dir/full.mat --dim=$dim $dir/0.mat $dir/lda.*.acc \ + 2>$dir/log/lda_est.log || exit 1; + rm $dir/lda.*.acc + else + echo "$0: Using supplied LDA matrix $use_lda_mat" + cp $use_lda_mat $dir/0.mat || exit 1; + [ ! -z "$mllt_iters" ] && \ + echo "$0: Warning: using supplied LDA matrix $use_lda_mat but we will do MLLT," && \ + echo " which you might not want; to disable MLLT, specify --mllt-iters ''" && \ + sleep 5 + fi +fi + +cur_lda_iter=0 + +if [ $stage -le -4 ] && $train_tree; then + echo "$0: Accumulating tree stats" + $cmd JOB=1:$nj $dir/log/acc_tree.JOB.log \ + acc-tree-stats $context_opts \ + --ci-phones=$ciphonelist $alidir/final.mdl "$feats" \ + "ark:gunzip -c $alidir/ali.JOB.gz|" $dir/JOB.treeacc || exit 1; + [ `ls $dir/*.treeacc | wc -w` -ne "$nj" ] && echo "$0: Wrong #tree-accs" && exit 1; + $cmd $dir/log/sum_tree_acc.log \ + sum-tree-stats $dir/treeacc $dir/*.treeacc || exit 1; + rm $dir/*.treeacc +fi + + +if [ $stage -le -3 ] && $train_tree; then + echo "$0: Getting questions for tree clustering." + # preparing questions, roots file... + cluster-phones --pdf-class-list=$(($num_nonsil_states / 2)) $context_opts $dir/treeacc $lang/phones/sets.int \ + $dir/questions.int 2> $dir/log/questions.log || exit 1; + cat $lang/phones/extra_questions.int >> $dir/questions.int + compile-questions $context_opts $lang/topo $dir/questions.int \ + $dir/questions.qst 2>$dir/log/compile_questions.log || exit 1; + + echo "$0: Building the tree" + $cmd $dir/log/build_tree.log \ + build-tree $context_opts --verbose=1 --max-leaves=$numleaves \ + --cluster-thresh=$cluster_thresh $dir/treeacc $lang/phones/roots.int \ + $dir/questions.qst $lang/topo $dir/tree || exit 1; +fi + +if [ $stage -le -2 ]; then + echo "$0: Initializing the model" + if $train_tree; then + gmm-init-model --write-occs=$dir/1.occs \ + $dir/tree $dir/treeacc $lang/topo $dir/1.mdl 2> $dir/log/init_model.log || exit 1; + grep 'no stats' $dir/log/init_model.log && echo "This is a bad warning."; + rm $dir/treeacc + else + cp $alidir/tree $dir/ || exit 1; + $cmd JOB=1 $dir/log/init_model.log \ + gmm-init-model-flat $dir/tree $lang/topo $dir/1.mdl \ + "$feats subset-feats ark:- ark:-|" || exit 1; + fi +fi + + +if [ $stage -le -1 ]; then + # Convert the alignments. + echo "$0: Converting alignments from $alidir to use current tree" + $cmd JOB=1:$nj $dir/log/convert.JOB.log \ + convert-ali $alidir/final.mdl $dir/1.mdl $dir/tree \ + "ark:gunzip -c $alidir/ali.JOB.gz|" "ark:|gzip -c >$dir/ali.JOB.gz" || exit 1; +fi + +if [ $stage -le 0 ] && [ "$realign_iters" != "" ]; then + echo "$0: Compiling graphs of transcripts" + $cmd JOB=1:$nj $dir/log/compile_graphs.JOB.log \ + compile-train-graphs --read-disambig-syms=$lang/phones/disambig.int $dir/tree $dir/1.mdl $lang/L.fst \ + "ark:utils/sym2int.pl --map-oov $oov -f 2- $lang/words.txt < $data/split$nj/JOB/text |" \ + "ark:|gzip -c >$dir/fsts.JOB.gz" || exit 1; +fi + + +x=1 +while [ $x -lt $num_iters ]; do + echo Training pass $x + if echo $realign_iters | grep -w $x >/dev/null && [ $stage -le $x ]; then + echo Aligning data + mdl="gmm-boost-silence --boost=$boost_silence `cat $lang/phones/optional_silence.csl` $dir/$x.mdl - |" + $cmd JOB=1:$nj $dir/log/align.$x.JOB.log \ + gmm-align-compiled $scale_opts --beam=$beam --retry-beam=$retry_beam --careful=$careful "$mdl" \ + "ark:gunzip -c $dir/fsts.JOB.gz|" "$feats" \ + "ark:|gzip -c >$dir/ali.JOB.gz" || exit 1; + fi + if echo $mllt_iters | grep -w $x >/dev/null; then + if [ $stage -le $x ]; then + echo "$0: Estimating MLLT" + $cmd JOB=1:$nj $dir/log/macc.$x.JOB.log \ + ali-to-post "ark:gunzip -c $dir/ali.JOB.gz|" ark:- \| \ + weight-silence-post 0.0 $silphonelist $dir/$x.mdl ark:- ark:- \| \ + gmm-acc-mllt --rand-prune=$randprune $dir/$x.mdl "$feats" ark:- $dir/$x.JOB.macc \ + || exit 1; + est-mllt $dir/$x.mat.new $dir/$x.*.macc 2> $dir/log/mupdate.$x.log || exit 1; + gmm-transform-means $dir/$x.mat.new $dir/$x.mdl $dir/$x.mdl \ + 2> $dir/log/transform_means.$x.log || exit 1; + compose-transforms --print-args=false $dir/$x.mat.new $dir/$cur_lda_iter.mat $dir/$x.mat || exit 1; + rm $dir/$x.*.macc + fi + feats="$splicedfeats transform-feats $dir/$x.mat ark:- ark:- |" + cur_lda_iter=$x + fi + + if [ $stage -le $x ]; then + $cmd JOB=1:$nj $dir/log/acc.$x.JOB.log \ + gmm-acc-stats-ali $dir/$x.mdl "$feats" \ + "ark,s,cs:gunzip -c $dir/ali.JOB.gz|" $dir/$x.JOB.acc || exit 1; + $cmd $dir/log/update.$x.log \ + gmm-est --write-occs=$dir/$[$x+1].occs --mix-up=$numgauss --power=$power \ + $dir/$x.mdl "gmm-sum-accs - $dir/$x.*.acc |" $dir/$[$x+1].mdl || exit 1; + rm $dir/$x.mdl $dir/$x.*.acc $dir/$x.occs + fi + [ $x -le $max_iter_inc ] && numgauss=$[$numgauss+$incgauss]; + x=$[$x+1]; +done + +rm $dir/final.{mdl,mat,occs} 2>/dev/null +ln -s $x.mdl $dir/final.mdl +ln -s $x.occs $dir/final.occs +ln -s $cur_lda_iter.mat $dir/final.mat + +steps/diagnostic/analyze_alignments.sh --cmd "$cmd" $lang $dir + +# Summarize warning messages... +utils/summarize_warnings.pl $dir/log + +steps/info/gmm_dir_info.pl $dir + +echo "$0: Done training system with LDA+MLLT features in $dir" + +exit 0 diff --git a/examples/wav2vec/unsupervised/kaldi_self_train/st/steps_gan/train_sat.sh b/examples/wav2vec/unsupervised/kaldi_self_train/st/steps_gan/train_sat.sh new file mode 100755 index 0000000000..f75afafb1c --- /dev/null +++ b/examples/wav2vec/unsupervised/kaldi_self_train/st/steps_gan/train_sat.sh @@ -0,0 +1,281 @@ +#!/usr/bin/env bash +# Copyright 2012 Johns Hopkins University (Author: Daniel Povey). Apache 2.0. + + +# This does Speaker Adapted Training (SAT), i.e. train on +# fMLLR-adapted features. It can be done on top of either LDA+MLLT, or +# delta and delta-delta features. If there are no transforms supplied +# in the alignment directory, it will estimate transforms itself before +# building the tree (and in any case, it estimates transforms a number +# of times during training). + + +# Begin configuration section. +stage=-5 +exit_stage=-100 # you can use this to require it to exit at the + # beginning of a specific stage. Not all values are + # supported. +fmllr_update_type=full +cmd=run.pl +scale_opts="--transition-scale=1.0 --acoustic-scale=0.1 --self-loop-scale=0.1" +beam=10 +retry_beam=40 +careful=false +boost_silence=1.0 # Factor by which to boost silence likelihoods in alignment +context_opts= # e.g. set this to "--context-width 5 --central-position 2" for quinphone. +realign_iters="10 20 30"; +fmllr_iters="2 4 6 12"; +silence_weight=0.0 # Weight on silence in fMLLR estimation. +num_iters=35 # Number of iterations of training +max_iter_inc=25 # Last iter to increase #Gauss on. +power=0.2 # Exponent for number of gaussians according to occurrence counts +cluster_thresh=-1 # for build-tree control final bottom-up clustering of leaves +phone_map= +train_tree=true +tree_stats_opts= +cluster_phones_opts= +compile_questions_opts= +# End configuration section. +num_nonsil_states=3 + +echo "$0 $@" # Print the command line for logging + +[ -f path.sh ] && . ./path.sh +. parse_options.sh || exit 1; + +if [ $# != 6 ]; then + echo "Usage: steps/train_sat.sh <#leaves> <#gauss> <data> <lang> <ali-dir> <exp-dir>" + echo " e.g.: steps/train_sat.sh 2500 15000 data/train_si84 data/lang exp/tri2b_ali_si84 exp/tri3b" + echo "Main options (for others, see top of script file)" + echo " --cmd (utils/run.pl|utils/queue.pl <queue opts>) # how to run jobs." + echo " --config <config-file> # config containing options" + echo " --stage <stage> # stage to do partial re-run from." + exit 1; +fi + +numleaves=$1 +totgauss=$2 +data=$3 +lang=$4 +alidir=$5 +dir=$6 + +for f in $data/feats.scp $lang/phones.txt $alidir/final.mdl $alidir/ali.1.gz; do + [ ! -f $f ] && echo "train_sat.sh: no such file $f" && exit 1; +done + +numgauss=$numleaves +incgauss=$[($totgauss-$numgauss)/$max_iter_inc] # per-iter #gauss increment +oov=`cat $lang/oov.int` +nj=`cat $alidir/num_jobs` || exit 1; +silphonelist=`cat $lang/phones/silence.csl` +ciphonelist=`cat $lang/phones/context_indep.csl` || exit 1; +sdata=$data/split$nj; +splice_opts=`cat $alidir/splice_opts 2>/dev/null` # frame-splicing options. +cmvn_opts=`cat $alidir/cmvn_opts 2>/dev/null` +delta_opts=`cat $alidir/delta_opts 2>/dev/null` +phone_map_opt= +[ ! -z "$phone_map" ] && phone_map_opt="--phone-map='$phone_map'" + +mkdir -p $dir/log +cp $alidir/splice_opts $dir 2>/dev/null # frame-splicing options. +cp $alidir/cmvn_opts $dir 2>/dev/null # cmn/cmvn option. +cp $alidir/delta_opts $dir 2>/dev/null # delta option. + +utils/lang/check_phones_compatible.sh $lang/phones.txt $alidir/phones.txt || exit 1; +cp $lang/phones.txt $dir || exit 1; + +echo $nj >$dir/num_jobs +[[ -d $sdata && $data/feats.scp -ot $sdata ]] || split_data.sh $data $nj || exit 1; + +# Set up features. + +if [ -f $alidir/final.mat ]; then feat_type=lda; else feat_type=delta; fi +echo "$0: feature type is $feat_type" + +## Set up speaker-independent features. +case $feat_type in + delta) sifeats="ark,s,cs:apply-cmvn $cmvn_opts --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | add-deltas $delta_opts ark:- ark:- |";; + lda) sifeats="ark,s,cs:apply-cmvn $cmvn_opts --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | splice-feats $splice_opts ark:- ark:- | transform-feats $alidir/final.mat ark:- ark:- |" + cp $alidir/final.mat $dir + cp $alidir/full.mat $dir 2>/dev/null + ;; + *) echo "$0: invalid feature type $feat_type" && exit 1; +esac + +## Get initial fMLLR transforms (possibly from alignment dir) +if [ -f $alidir/trans.1 ]; then + echo "$0: Using transforms from $alidir" + feats="$sifeats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark,s,cs:$alidir/trans.JOB ark:- ark:- |" + cur_trans_dir=$alidir +else + if [ $stage -le -5 ]; then + echo "$0: obtaining initial fMLLR transforms since not present in $alidir" + # The next line is necessary because of $silphonelist otherwise being incorrect; would require + # old $lang dir which would require another option. Not needed anyway. + [ ! -z "$phone_map" ] && \ + echo "$0: error: you must provide transforms if you use the --phone-map option." && exit 1; + $cmd JOB=1:$nj $dir/log/fmllr.0.JOB.log \ + ali-to-post "ark:gunzip -c $alidir/ali.JOB.gz|" ark:- \| \ + weight-silence-post $silence_weight $silphonelist $alidir/final.mdl ark:- ark:- \| \ + gmm-est-fmllr --fmllr-update-type=$fmllr_update_type \ + --spk2utt=ark:$sdata/JOB/spk2utt $alidir/final.mdl "$sifeats" \ + ark:- ark:$dir/trans.JOB || exit 1; + fi + feats="$sifeats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark,s,cs:$dir/trans.JOB ark:- ark:- |" + cur_trans_dir=$dir +fi + +if [ $stage -le -4 ] && $train_tree; then + # Get tree stats. + echo "$0: Accumulating tree stats" + $cmd JOB=1:$nj $dir/log/acc_tree.JOB.log \ + acc-tree-stats $context_opts $tree_stats_opts $phone_map_opt --ci-phones=$ciphonelist $alidir/final.mdl "$feats" \ + "ark:gunzip -c $alidir/ali.JOB.gz|" $dir/JOB.treeacc || exit 1; + [ "`ls $dir/*.treeacc | wc -w`" -ne "$nj" ] && echo "$0: Wrong #tree-accs" && exit 1; + $cmd $dir/log/sum_tree_acc.log \ + sum-tree-stats $dir/treeacc $dir/*.treeacc || exit 1; + rm $dir/*.treeacc +fi + +if [ $stage -le -3 ] && $train_tree; then + echo "$0: Getting questions for tree clustering." + # preparing questions, roots file... + cluster-phones --pdf-class-list=$(($num_nonsil_states / 2)) \ + $cluster_phones_opts $context_opts \ + $dir/treeacc $lang/phones/sets.int $dir/questions.int 2>$dir/log/questions.log || exit 1; + cat $lang/phones/extra_questions.int >> $dir/questions.int + compile-questions $context_opts $compile_questions_opts $lang/topo $dir/questions.int $dir/questions.qst 2>$dir/log/compile_questions.log || exit 1; + + echo "$0: Building the tree" + $cmd $dir/log/build_tree.log \ + build-tree $context_opts --verbose=1 --max-leaves=$numleaves \ + --cluster-thresh=$cluster_thresh $dir/treeacc $lang/phones/roots.int \ + $dir/questions.qst $lang/topo $dir/tree || exit 1; +fi + +if [ $stage -le -2 ]; then + echo "$0: Initializing the model" + if $train_tree; then + gmm-init-model --write-occs=$dir/1.occs \ + $dir/tree $dir/treeacc $lang/topo $dir/1.mdl 2> $dir/log/init_model.log || exit 1; + grep 'no stats' $dir/log/init_model.log && echo "This is a bad warning."; + rm $dir/treeacc + else + cp $alidir/tree $dir/ || exit 1; + $cmd JOB=1 $dir/log/init_model.log \ + gmm-init-model-flat $dir/tree $lang/topo $dir/1.mdl \ + "$feats subset-feats ark:- ark:-|" || exit 1; + fi +fi + +if [ $stage -le -1 ]; then + # Convert the alignments. + echo "$0: Converting alignments from $alidir to use current tree" + $cmd JOB=1:$nj $dir/log/convert.JOB.log \ + convert-ali $phone_map_opt $alidir/final.mdl $dir/1.mdl $dir/tree \ + "ark:gunzip -c $alidir/ali.JOB.gz|" "ark:|gzip -c >$dir/ali.JOB.gz" || exit 1; +fi + +[ "$exit_stage" -eq 0 ] && echo "$0: Exiting early: --exit-stage $exit_stage" && exit 0; + +if [ $stage -le 0 ] && [ "$realign_iters" != "" ]; then + echo "$0: Compiling graphs of transcripts" + $cmd JOB=1:$nj $dir/log/compile_graphs.JOB.log \ + compile-train-graphs --read-disambig-syms=$lang/phones/disambig.int $dir/tree $dir/1.mdl $lang/L.fst \ + "ark:utils/sym2int.pl --map-oov $oov -f 2- $lang/words.txt < $sdata/JOB/text |" \ + "ark:|gzip -c >$dir/fsts.JOB.gz" || exit 1; +fi + +x=1 +while [ $x -lt $num_iters ]; do + echo Pass $x + if echo $realign_iters | grep -w $x >/dev/null && [ $stage -le $x ]; then + echo Aligning data + mdl="gmm-boost-silence --boost=$boost_silence `cat $lang/phones/optional_silence.csl` $dir/$x.mdl - |" + $cmd JOB=1:$nj $dir/log/align.$x.JOB.log \ + gmm-align-compiled $scale_opts --beam=$beam --retry-beam=$retry_beam --careful=$careful "$mdl" \ + "ark:gunzip -c $dir/fsts.JOB.gz|" "$feats" \ + "ark:|gzip -c >$dir/ali.JOB.gz" || exit 1; + fi + + if echo $fmllr_iters | grep -w $x >/dev/null; then + if [ $stage -le $x ]; then + echo Estimating fMLLR transforms + # We estimate a transform that's additional to the previous transform; + # we'll compose them. + $cmd JOB=1:$nj $dir/log/fmllr.$x.JOB.log \ + ali-to-post "ark:gunzip -c $dir/ali.JOB.gz|" ark:- \| \ + weight-silence-post $silence_weight $silphonelist $dir/$x.mdl ark:- ark:- \| \ + gmm-est-fmllr --fmllr-update-type=$fmllr_update_type \ + --spk2utt=ark:$sdata/JOB/spk2utt $dir/$x.mdl \ + "$feats" ark:- ark:$dir/tmp_trans.JOB || exit 1; + for n in `seq $nj`; do + ! ( compose-transforms --b-is-affine=true \ + ark:$dir/tmp_trans.$n ark:$cur_trans_dir/trans.$n ark:$dir/composed_trans.$n \ + && mv $dir/composed_trans.$n $dir/trans.$n && \ + rm $dir/tmp_trans.$n ) 2>$dir/log/compose_transforms.$x.log \ + && echo "$0: Error composing transforms" && exit 1; + done + fi + feats="$sifeats transform-feats --utt2spk=ark:$sdata/JOB/utt2spk ark:$dir/trans.JOB ark:- ark:- |" + cur_trans_dir=$dir + fi + + if [ $stage -le $x ]; then + $cmd JOB=1:$nj $dir/log/acc.$x.JOB.log \ + gmm-acc-stats-ali $dir/$x.mdl "$feats" \ + "ark,s,cs:gunzip -c $dir/ali.JOB.gz|" $dir/$x.JOB.acc || exit 1; + [ `ls $dir/$x.*.acc | wc -w` -ne "$nj" ] && echo "$0: Wrong #accs" && exit 1; + $cmd $dir/log/update.$x.log \ + gmm-est --power=$power --write-occs=$dir/$[$x+1].occs --mix-up=$numgauss $dir/$x.mdl \ + "gmm-sum-accs - $dir/$x.*.acc |" $dir/$[$x+1].mdl || exit 1; + rm $dir/$x.mdl $dir/$x.*.acc + rm $dir/$x.occs + fi + [ $x -le $max_iter_inc ] && numgauss=$[$numgauss+$incgauss]; + x=$[$x+1]; +done + + +if [ $stage -le $x ]; then + # Accumulate stats for "alignment model"-- this model is + # computed with the speaker-independent features, but matches Gaussian-for-Gaussian + # with the final speaker-adapted model. + $cmd JOB=1:$nj $dir/log/acc_alimdl.JOB.log \ + ali-to-post "ark:gunzip -c $dir/ali.JOB.gz|" ark:- \| \ + gmm-acc-stats-twofeats $dir/$x.mdl "$feats" "$sifeats" \ + ark,s,cs:- $dir/$x.JOB.acc || exit 1; + [ `ls $dir/$x.*.acc | wc -w` -ne "$nj" ] && echo "$0: Wrong #accs" && exit 1; + # Update model. + $cmd $dir/log/est_alimdl.log \ + gmm-est --power=$power --remove-low-count-gaussians=false $dir/$x.mdl \ + "gmm-sum-accs - $dir/$x.*.acc|" $dir/$x.alimdl || exit 1; + rm $dir/$x.*.acc +fi + +rm $dir/final.{mdl,alimdl,occs} 2>/dev/null +ln -s $x.mdl $dir/final.mdl +ln -s $x.occs $dir/final.occs +ln -s $x.alimdl $dir/final.alimdl + + +steps/diagnostic/analyze_alignments.sh --cmd "$cmd" $lang $dir + +utils/summarize_warnings.pl $dir/log +( + echo "$0: Likelihood evolution:" + for x in `seq $[$num_iters-1]`; do + tail -n 30 $dir/log/acc.$x.*.log | awk '/Overall avg like/{l += $(NF-3)*$(NF-1); t += $(NF-1); } + /Overall average logdet/{d += $(NF-3)*$(NF-1); t2 += $(NF-1);} + END{ d /= t2; l /= t; printf("%s ", d+l); } ' + done + echo +) | tee $dir/log/summary.log + + +steps/info/gmm_dir_info.pl $dir + +echo "$0: done training SAT system in $dir" + +exit 0 diff --git a/examples/wav2vec/unsupervised/kaldi_self_train/st/train.sh b/examples/wav2vec/unsupervised/kaldi_self_train/st/train.sh new file mode 100644 index 0000000000..f3a3d3fc7c --- /dev/null +++ b/examples/wav2vec/unsupervised/kaldi_self_train/st/train.sh @@ -0,0 +1,43 @@ +#!/bin/bash + +set -eu + +w2v_dir= # contains features `{train,valid}.{npy,lengths}`, real transcripts `{train,valid}.${label}`, and dict `dict.${label}.txt` +lab_dir= # contains pseudo labels `{train,valid}.txt` +out_dir= # output root +arpa_lm= # phone LM +arpa_lm_bin= # (binary) phone LM for KenLM, used in unsupervised selection + +label=phnc +train_name="train" +valid_name="valid" +data_dir=${out_dir}/data + +mkdir -p ${out_dir}/exp +local/prepare_lang.sh $w2v_dir/dict.${label}.txt $data_dir +local/prepare_lm.sh $arpa_lm $data_dir + +for x in $train_name $valid_name; do + x_gt=${x}_gt + + # prepare pseudo data + python local/prepare_data_from_w2v.py $w2v_dir $data_dir $x + steps/compute_cmvn_stats.sh $data_dir/$x $out_dir/exp/make_feat/$x $out_dir/feats/$x + python local/copy_aligned_text.py < $lab_dir/$x.txt > $data_dir/$x/text + + # prepare ground truth data + mkdir $data_dir/$x_gt + cp $data_dir/$x/{feats.scp,cmvn.scp,utt2spk,spk2utt} $data_dir/$x_gt/ + python local/copy_aligned_text.py < $w2v_dir/$x.$label > $data_dir/$x_gt/text +done + +local/train_subset_lgbeam.sh \ + --out_root ${out_dir} --out_name exp --train $train_name --valid $valid_name \ + --mono_size 2000 --tri1_size 5000 --tri2b_size -1 --tri3b_size -1 \ + --stage 1 --max_stage 3 $data_dir $data_dir/lang $data_dir/lang_test + +local/unsup_select_decode.sh \ + --split $valid_name --kenlm_path $arpa_lm_bin \ + --ref_txt $data_dir/${valid_name}_gt/text \ + --psd_txt $data_dir/${valid_name}/text \ + $out_dir/exp diff --git a/examples/wav2vec/unsupervised/kaldi_self_train/st/utils b/examples/wav2vec/unsupervised/kaldi_self_train/st/utils new file mode 120000 index 0000000000..b240885218 --- /dev/null +++ b/examples/wav2vec/unsupervised/kaldi_self_train/st/utils @@ -0,0 +1 @@ +../../wsj/s5/utils \ No newline at end of file diff --git a/score.py b/examples/wav2vec/unsupervised/models/__init__.py similarity index 62% rename from score.py rename to examples/wav2vec/unsupervised/models/__init__.py index 153d037294..3e3039b708 100644 --- a/score.py +++ b/examples/wav2vec/unsupervised/models/__init__.py @@ -1,11 +1,11 @@ -#!/usr/bin/env python3 -u # Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. -from fairseq_cli.score import cli_main +from .wav2vec_u import Wav2vec_U -if __name__ == '__main__': - cli_main() +__all__ = [ + "Wav2vec_U", +] diff --git a/examples/wav2vec/unsupervised/models/wav2vec_u.py b/examples/wav2vec/unsupervised/models/wav2vec_u.py new file mode 100644 index 0000000000..27792ebda8 --- /dev/null +++ b/examples/wav2vec/unsupervised/models/wav2vec_u.py @@ -0,0 +1,637 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from dataclasses import dataclass +from enum import Enum, auto +import math +import numpy as np +from typing import Tuple, List, Optional, Dict + +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch import autograd + +from fairseq import checkpoint_utils, utils +from fairseq.dataclass import FairseqDataclass +from fairseq.models import BaseFairseqModel, register_model +from fairseq.modules import ( + SamePad, + TransposeLast, +) + + +class SegmentationType(Enum): + NONE = auto() + RANDOM = auto() + UNIFORM_RANDOM = auto() + UNIFORM_RANDOM_JOIN = auto() + JOIN = auto() + + +@dataclass +class SegmentationConfig(FairseqDataclass): + type: SegmentationType = SegmentationType.NONE + subsample_rate: float = 0.25 + mean_pool: bool = True + mean_pool_join: bool = False + remove_zeros: bool = False + + +@dataclass +class Wav2vec_UConfig(FairseqDataclass): + + discriminator_kernel: int = 3 + discriminator_dilation: int = 1 + discriminator_dim: int = 256 + discriminator_causal: bool = True + discriminator_linear_emb: bool = False + discriminator_depth: int = 1 + discriminator_max_pool: bool = False + discriminator_act_after_linear: bool = False + discriminator_dropout: float = 0.0 + discriminator_spectral_norm: bool = False + discriminator_weight_norm: bool = False + + generator_kernel: int = 4 + generator_dilation: int = 1 + generator_stride: int = 1 + generator_bias: bool = False + generator_dropout: float = 0.0 + + blank_weight: float = 0 + blank_mode: str = "add" + blank_is_sil: bool = False + no_softmax: bool = False + + smoothness_weight: float = 0.0 + smoothing: float = 0.0 + smoothing_one_sided: bool = False + gradient_penalty: float = 0.0 + probabilistic_grad_penalty_slicing: bool = False + code_penalty: float = 0.0 + gumbel: bool = False + hard_gumbel: bool = True + temp: Tuple[float, float, float] = (2, 0.1, 0.99995) + input_dim: int = 128 + + segmentation: SegmentationConfig = SegmentationConfig() + + +class Segmenter(nn.Module): + cfg: SegmentationConfig + + def __init__(self, cfg: SegmentationConfig): + super().__init__() + self.cfg = cfg + self.subsample_rate = cfg.subsample_rate + + def pre_segment(self, dense_x, dense_padding_mask): + return dense_x, dense_padding_mask + + def logit_segment(self, logits, padding_mask): + return logits, padding_mask + + +class RandomSegmenter(Segmenter): + def pre_segment(self, dense_x, dense_padding_mask): + target_num = math.ceil(dense_x.size(1) * self.subsample_rate) + ones = torch.ones(dense_x.shape[:-1], device=dense_x.device) + indices, _ = ones.multinomial(target_num).sort(dim=-1) + indices_ld = indices.unsqueeze(-1).expand(-1, -1, dense_x.size(-1)) + dense_x = dense_x.gather(1, indices_ld) + dense_padding_mask = dense_padding_mask.gather(1, index=indices) + return dense_x, dense_padding_mask + + +class UniformRandomSegmenter(Segmenter): + def pre_segment(self, dense_x, dense_padding_mask): + bsz, tsz, fsz = dense_x.shape + + target_num = math.ceil(tsz * self.subsample_rate) + + rem = tsz % target_num + + if rem > 0: + dense_x = F.pad(dense_x, [0, 0, 0, target_num - rem]) + dense_padding_mask = F.pad( + dense_padding_mask, [0, target_num - rem], value=True + ) + + dense_x = dense_x.view(bsz, target_num, -1, fsz) + dense_padding_mask = dense_padding_mask.view(bsz, target_num, -1) + + if self.cfg.mean_pool: + dense_x = dense_x.mean(dim=-2) + dense_padding_mask = dense_padding_mask.all(dim=-1) + else: + ones = torch.ones((bsz, dense_x.size(2)), device=dense_x.device) + indices = ones.multinomial(1) + indices = indices.unsqueeze(-1).expand(-1, target_num, -1) + indices_ld = indices.unsqueeze(-1).expand(-1, -1, -1, fsz) + dense_x = dense_x.gather(2, indices_ld).reshape(bsz, -1, fsz) + dense_padding_mask = dense_padding_mask.gather(2, index=indices).reshape( + bsz, -1 + ) + return dense_x, dense_padding_mask + + +class JoinSegmenter(Segmenter): + def logit_segment(self, logits, padding_mask): + preds = logits.argmax(dim=-1) + + if padding_mask.any(): + preds[padding_mask] = -1 # mark pad + uniques = [] + + bsz, tsz, csz = logits.shape + + for p in preds: + uniques.append( + p.cpu().unique_consecutive(return_inverse=True, return_counts=True) + ) + + new_tsz = max(u[0].numel() for u in uniques) + new_logits = logits.new_zeros(bsz, new_tsz, csz) + new_pad = padding_mask.new_zeros(bsz, new_tsz) + + for b in range(bsz): + u, idx, c = uniques[b] + keep = u != -1 + + if self.cfg.remove_zeros: + keep.logical_and_(u != 0) + + if self.training and not self.cfg.mean_pool_join: + u[0] = 0 + u[1:] = c.cumsum(0)[:-1] + m = c > 1 + r = torch.rand(m.sum()) + o = (c[m] * r).long() + u[m] += o + new_logits[b, : u.numel()] = logits[b, u] + else: + new_logits[b].index_add_( + dim=0, index=idx.to(new_logits.device), source=logits[b] + ) + new_logits[b, : c.numel()] /= c.unsqueeze(-1).to(new_logits.device) + + new_sz = keep.sum() + if not keep.all(): + kept_logits = new_logits[b, : c.numel()][keep] + new_logits[b, :new_sz] = kept_logits + + if new_sz < new_tsz: + pad = new_tsz - new_sz + new_logits[b, -pad:] = 0 + new_pad[b, -pad:] = True + + return new_logits, new_pad + + +class UniformRandomJoinSegmenter(UniformRandomSegmenter, JoinSegmenter): + pass + + +SEGMENT_FACTORY = { + SegmentationType.NONE: Segmenter, + SegmentationType.RANDOM: RandomSegmenter, + SegmentationType.UNIFORM_RANDOM: UniformRandomSegmenter, + SegmentationType.UNIFORM_RANDOM_JOIN: UniformRandomJoinSegmenter, + SegmentationType.JOIN: JoinSegmenter, +} + + +class Discriminator(nn.Module): + def __init__(self, dim, cfg: Wav2vec_UConfig): + super().__init__() + + inner_dim = cfg.discriminator_dim + kernel = cfg.discriminator_kernel + dilation = cfg.discriminator_dilation + self.max_pool = cfg.discriminator_max_pool + + if cfg.discriminator_causal: + padding = kernel - 1 + else: + padding = kernel // 2 + + def make_conv(in_d, out_d, k, p=0, has_dilation=True): + conv = nn.Conv1d( + in_d, + out_d, + kernel_size=k, + padding=p, + dilation=dilation if has_dilation else 1, + ) + if cfg.discriminator_spectral_norm: + conv = nn.utils.spectral_norm(conv) + elif cfg.discriminator_weight_norm: + conv = nn.utils.weight_norm(conv) + return conv + + inner_net = [ + nn.Sequential( + make_conv(inner_dim, inner_dim, kernel, padding), + SamePad(kernel_size=kernel, causal=cfg.discriminator_causal), + nn.Dropout(cfg.discriminator_dropout), + nn.GELU(), + ) + for _ in range(cfg.discriminator_depth - 1) + ] + [ + make_conv(inner_dim, 1, kernel, padding, has_dilation=False), + SamePad(kernel_size=kernel, causal=cfg.discriminator_causal), + ] + + if cfg.discriminator_linear_emb: + emb_net = [make_conv(dim, inner_dim, 1)] + else: + emb_net = [ + make_conv(dim, inner_dim, kernel, padding), + SamePad(kernel_size=kernel, causal=cfg.discriminator_causal), + ] + + if cfg.discriminator_act_after_linear: + emb_net.append(nn.GELU()) + + self.net = nn.Sequential( + *emb_net, + nn.Dropout(cfg.discriminator_dropout), + *inner_net, + ) + + def forward(self, x, padding_mask): + x = x.transpose(1, 2) # BTC -> BCT + x = self.net(x) + x = x.transpose(1, 2) + x_sz = x.size(1) + if padding_mask is not None and padding_mask.any() and padding_mask.dim() > 1: + padding_mask = padding_mask[:, : x.size(1)] + x[padding_mask] = float("-inf") if self.max_pool else 0 + x_sz = x_sz - padding_mask.sum(dim=-1) + x = x.squeeze(-1) + if self.max_pool: + x, _ = x.max(dim=-1) + else: + x = x.sum(dim=-1) + x = x / x_sz + return x + + +class Generator(nn.Module): + def __init__(self, input_dim, output_dim, cfg: Wav2vec_UConfig): + super().__init__() + + self.cfg = cfg + self.output_dim = output_dim + self.stride = cfg.generator_stride + self.dropout = nn.Dropout(cfg.generator_dropout) + + padding = cfg.generator_kernel // 2 + self.proj = nn.Sequential( + TransposeLast(), + nn.Conv1d( + input_dim, + output_dim, + kernel_size=cfg.generator_kernel, + stride=cfg.generator_stride, + dilation=cfg.generator_dilation, + padding=padding, + bias=cfg.generator_bias, + ), + TransposeLast(), + ) + + def forward(self, dense_x, tokens, dense_padding_mask): + dense_x = self.dropout(dense_x) + + dense_x = self.proj(dense_x) + if self.stride > 1: + dense_padding_mask = dense_padding_mask[:, :: self.stride] + + if dense_padding_mask.size(1) != dense_x.size(1): + new_padding = dense_padding_mask.new_zeros(dense_x.shape[:-1]) + diff = new_padding.size(1) - dense_padding_mask.size(1) + assert ( + diff > 0 + ), f"{new_padding.shape}, {dense_padding_mask.shape}, {dense_x.shape}, {diff}" + if diff > 0: + new_padding[:, diff:] = dense_padding_mask + else: + assert diff < 0 + new_padding = dense_padding_mask[:, :diff] + + dense_padding_mask = new_padding + + result = {} + + token_x = None + if tokens is not None: + token_x = dense_x.new_zeros(tokens.numel(), self.output_dim) + token_x.scatter_(1, tokens.view(-1, 1).long(), 1) + token_x = token_x.view(tokens.shape + (self.output_dim,)) + + result["dense_x"] = dense_x + result["token_x"] = token_x + result["dense_padding_mask"] = dense_padding_mask + + return result + + +@register_model("wav2vec_u", dataclass=Wav2vec_UConfig) +class Wav2vec_U(BaseFairseqModel): + def calc_gradient_penalty(self, real_data, fake_data): + + b_size = min(real_data.size(0), fake_data.size(0)) + t_size = min(real_data.size(1), fake_data.size(1)) + + if self.cfg.probabilistic_grad_penalty_slicing: + + def get_slice(data, dim, target_size): + + size = data.size(dim) + diff = size - target_size + if diff <= 0: + return data + + start = np.random.randint(0, diff + 1) + return data.narrow(dim=dim, start=start, length=target_size) + + real_data = get_slice(real_data, 0, b_size) + real_data = get_slice(real_data, 1, t_size) + fake_data = get_slice(fake_data, 0, b_size) + fake_data = get_slice(fake_data, 1, t_size) + + else: + real_data = real_data[:b_size, :t_size] + fake_data = fake_data[:b_size, :t_size] + + alpha = torch.rand(real_data.size(0), 1, 1) + alpha = alpha.expand(real_data.size()) + alpha = alpha.to(real_data.device) + + interpolates = alpha * real_data + ((1 - alpha) * fake_data) + + disc_interpolates = self.discriminator(interpolates, None) + + gradients = autograd.grad( + outputs=disc_interpolates, + inputs=interpolates, + grad_outputs=torch.ones(disc_interpolates.size(), device=real_data.device), + create_graph=True, + retain_graph=True, + only_inputs=True, + )[0] + + gradient_penalty = (gradients.norm(2, dim=1) - 1) ** 2 + return gradient_penalty + + def set_num_updates(self, num_updates): + super().set_num_updates(num_updates) + self.update_num = num_updates + self.curr_temp = max( + self.max_temp * self.temp_decay ** num_updates, self.min_temp + ) + + def discrim_step(self, num_updates): + return num_updates % 2 == 1 + + def get_groups_for_update(self, num_updates): + return "discriminator" if self.discrim_step(num_updates) else "generator" + + def __init__(self, cfg: Wav2vec_UConfig, target_dict): + super().__init__() + + self.cfg = cfg + self.zero_index = target_dict.index("<SIL>") if "<SIL>" in target_dict else 0 + self.smoothness_weight = cfg.smoothness_weight + + output_size = len(target_dict) + self.pad = target_dict.pad() + self.eos = target_dict.eos() + self.smoothing = cfg.smoothing + self.smoothing_one_sided = cfg.smoothing_one_sided + self.no_softmax = cfg.no_softmax + self.gumbel = cfg.gumbel + self.hard_gumbel = cfg.hard_gumbel + self.last_acc = None + + self.gradient_penalty = cfg.gradient_penalty + self.code_penalty = cfg.code_penalty + self.blank_weight = cfg.blank_weight + self.blank_mode = cfg.blank_mode + self.blank_index = target_dict.index("<SIL>") if cfg.blank_is_sil else 0 + assert self.blank_index != target_dict.unk() + + self.discriminator = Discriminator(output_size, cfg) + for p in self.discriminator.parameters(): + p.param_group = "discriminator" + + self.pca_A = self.pca_b = None + d = cfg.input_dim + + self.segmenter = SEGMENT_FACTORY[cfg.segmentation.type](cfg.segmentation) + + self.generator = Generator(d, output_size, cfg) + + for p in self.generator.parameters(): + p.param_group = "generator" + + for p in self.segmenter.parameters(): + p.param_group = "generator" + + self.max_temp, self.min_temp, self.temp_decay = cfg.temp + self.curr_temp = self.max_temp + self.update_num = 0 + + @classmethod + def build_model(cls, cfg, task): + return cls(cfg, task.target_dictionary) + + def get_logits( + self, + net_output: Optional[Dict[str, List[Optional[torch.Tensor]]]], + normalize: bool = False, + ): + logits = net_output["logits"] + + if self.blank_weight != 0: + if self.blank_mode == "add": + logits[..., self.blank_index] += self.blank_weight + elif self.blank_mode == "set": + logits[..., self.blank_index] = self.blank_weight + else: + raise Exception(f"invalid blank mode {self.blank_mode}") + + padding = net_output["padding_mask"] + if padding.any(): + logits[padding] = float("-inf") + logits[padding][..., self.blank_index] = float("inf") + + if normalize: + logits = utils.log_softmax(logits.float(), dim=-1) + + return logits.transpose(0, 1) + + def get_normalized_probs( + self, + net_output: Tuple[ + torch.Tensor, Optional[Dict[str, List[Optional[torch.Tensor]]]] + ], + log_probs: bool, + sample: Optional[Dict[str, torch.Tensor]] = None, + ): + logits = self.get_logits(net_output) + + probs = super().get_normalized_probs(logits, log_probs, sample) + # BTC -> TBC for ctc + probs = probs.transpose(0, 1) + return probs + + def normalize(self, dense_x): + + bsz, tsz, csz = dense_x.shape + + if dense_x.numel() == 0: + raise Exception(dense_x.shape) + _, k = dense_x.max(-1) + hard_x = ( + dense_x.new_zeros(bsz * tsz, csz) + .scatter_(-1, k.view(-1, 1), 1.0) + .view(-1, csz) + ) + hard_probs = torch.mean(hard_x.float(), dim=0) + code_perplexity = torch.exp( + -torch.sum(hard_probs * torch.log(hard_probs + 1e-7), dim=-1) + ) + + avg_probs = torch.softmax(dense_x.reshape(-1, csz).float(), dim=-1).mean(dim=0) + prob_perplexity = torch.exp( + -torch.sum(avg_probs * torch.log(avg_probs + 1e-7), dim=-1) + ) + + if not self.no_softmax: + if self.training and self.gumbel: + dense_x = F.gumbel_softmax( + dense_x.float(), tau=self.curr_temp, hard=self.hard_gumbel + ).type_as(dense_x) + else: + dense_x = dense_x.softmax(-1) + + return dense_x, code_perplexity, prob_perplexity + + def forward( + self, + features, + padding_mask, + random_label=None, + dense_x_only=False, + segment=True, + ): + if segment: + features, padding_mask = self.segmenter.pre_segment(features, padding_mask) + + orig_size = features.size(0) * features.size(1) - padding_mask.sum() + + gen_result = self.generator(features, random_label, padding_mask) + + orig_dense_x, token_x = gen_result["dense_x"], gen_result["token_x"] + orig_dense_padding_mask = gen_result["dense_padding_mask"] + + if segment: + dense_x, dense_padding_mask = self.segmenter.logit_segment( + orig_dense_x, orig_dense_padding_mask + ) + else: + dense_x = orig_dense_x + dense_padding_mask = orig_dense_padding_mask + + dense_logits = dense_x + prob_perplexity = None + code_perplexity = None + + if not (self.no_softmax and dense_x_only): + dense_x, code_perplexity, prob_perplexity = self.normalize(dense_logits) + + if dense_x_only or self.discriminator is None: + return { + "logits": dense_x, + "padding_mask": dense_padding_mask, + } + + token_padding_mask = random_label == self.pad + + dense_y = self.discriminator(dense_x, dense_padding_mask) + token_y = self.discriminator(token_x, token_padding_mask) + + sample_size = features.size(0) + + d_step = self.discrim_step(self.update_num) + + fake_smooth = self.smoothing + real_smooth = self.smoothing + if self.smoothing_one_sided: + fake_smooth = 0 + + zero_loss = None + smoothness_loss = None + code_pen = None + + if d_step: + loss_dense = F.binary_cross_entropy_with_logits( + dense_y, + dense_y.new_ones(dense_y.shape) - fake_smooth, + reduction="sum", + ) + loss_token = F.binary_cross_entropy_with_logits( + token_y, + token_y.new_zeros(token_y.shape) + real_smooth, + reduction="sum", + ) + if self.training and self.gradient_penalty > 0: + grad_pen = self.calc_gradient_penalty(token_x, dense_x) + grad_pen = grad_pen.sum() * self.gradient_penalty + else: + grad_pen = None + else: + grad_pen = None + loss_token = None + loss_dense = F.binary_cross_entropy_with_logits( + dense_y, + dense_y.new_zeros(dense_y.shape) + fake_smooth, + reduction="sum", + ) + num_vars = dense_x.size(-1) + if prob_perplexity is not None: + code_pen = (num_vars - prob_perplexity) / num_vars + code_pen = code_pen * sample_size * self.code_penalty + + if self.smoothness_weight > 0: + smoothness_loss = F.mse_loss( + dense_logits[:, :-1], dense_logits[:, 1:], reduction="none" + ) + smoothness_loss[dense_padding_mask[:, 1:]] = 0 + smoothness_loss = ( + smoothness_loss.mean() * sample_size * self.smoothness_weight + ) + + result = { + "losses": { + "grad_pen": grad_pen, + "code_pen": code_pen, + "smoothness": smoothness_loss, + }, + "temp": self.curr_temp, + "code_ppl": code_perplexity, + "prob_ppl": prob_perplexity, + "d_steps": int(d_step), + "sample_size": sample_size, + } + + suff = "_d" if d_step else "_g" + result["losses"]["dense" + suff] = loss_dense + result["losses"]["token" + suff] = loss_token + + return result diff --git a/examples/wav2vec/unsupervised/scripts/apply_pca.py b/examples/wav2vec/unsupervised/scripts/apply_pca.py new file mode 100644 index 0000000000..10ad6ce47c --- /dev/null +++ b/examples/wav2vec/unsupervised/scripts/apply_pca.py @@ -0,0 +1,76 @@ +#!/usr/bin/env python3 -u +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import os +import os.path as osp +import math +import numpy as np +import tqdm +import torch +from shutil import copyfile + +from npy_append_array import NpyAppendArray + + +def get_parser(): + parser = argparse.ArgumentParser( + description="transforms features via a given pca and stored them in target dir" + ) + # fmt: off + parser.add_argument('source', help='directory with features') + parser.add_argument('--split', help='which split to read', required=True) + parser.add_argument('--save-dir', help='where to save the output', required=True) + parser.add_argument('--pca-path', type=str, help='pca location. will append _A.npy and _b.npy', required=True) + parser.add_argument('--batch-size', type=int, default=2048000, help='batch size') + parser.add_argument('--unfiltered', action='store_true', help='process the unfiltered version') + # fmt: on + + return parser + + +def main(): + parser = get_parser() + args = parser.parse_args() + + source_path = osp.join(args.source, args.split) + data_poth = source_path + "_unfiltered" if args.unfiltered else source_path + + print(f"data path: {data_poth}") + + features = np.load(data_poth + ".npy", mmap_mode="r") + pca_A = torch.from_numpy(np.load(args.pca_path + "_A.npy")).cuda() + pca_b = torch.from_numpy(np.load(args.pca_path + "_b.npy")).cuda() + + os.makedirs(args.save_dir, exist_ok=True) + save_path = osp.join(args.save_dir, args.split) + + copyfile(source_path + ".tsv", save_path + ".tsv") + copyfile(data_poth + ".lengths", save_path + ".lengths") + + if osp.exists(source_path + ".phn"): + copyfile(source_path + ".phn", save_path + ".phn") + + if osp.exists(source_path + ".wrd"): + copyfile(source_path + ".wrd", save_path + ".wrd") + + if osp.exists(save_path + ".npy"): + os.remove(save_path + ".npy") + npaa = NpyAppendArray(save_path + ".npy") + + batches = math.ceil(features.shape[0] / args.batch_size) + + with torch.no_grad(): + for b in tqdm.trange(batches): + start = b * args.batch_size + end = start + args.batch_size + x = torch.from_numpy(features[start:end]).cuda() + x = torch.matmul(x, pca_A) + pca_b + npaa.append(x.cpu().numpy()) + + +if __name__ == "__main__": + main() diff --git a/preprocess.py b/examples/wav2vec/unsupervised/scripts/copy_labels.py similarity index 68% rename from preprocess.py rename to examples/wav2vec/unsupervised/scripts/copy_labels.py index 6a64ce71e7..989868388e 100644 --- a/preprocess.py +++ b/examples/wav2vec/unsupervised/scripts/copy_labels.py @@ -4,8 +4,7 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. -from fairseq_cli.preprocess import cli_main +import sys - -if __name__ == '__main__': - cli_main() +for idx, line in enumerate(sys.stdin): + print(f"utt{idx:010d} {line}", end="") diff --git a/examples/wav2vec/unsupervised/scripts/filter_lexicon.py b/examples/wav2vec/unsupervised/scripts/filter_lexicon.py new file mode 100644 index 0000000000..5bf3e51e7a --- /dev/null +++ b/examples/wav2vec/unsupervised/scripts/filter_lexicon.py @@ -0,0 +1,40 @@ +#!/usr/bin/env python3 -u +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import sys + +from fairseq.data import Dictionary + + +def get_parser(): + parser = argparse.ArgumentParser( + description="filters a lexicon given a unit dictionary" + ) + parser.add_argument("-d", "--unit-dict", help="unit dictionary", required=True) + return parser + + +def main(): + parser = get_parser() + args = parser.parse_args() + + d = Dictionary.load(args.unit_dict) + symbols = set(d.symbols) + + for line in sys.stdin: + items = line.rstrip().split() + skip = len(items) < 2 + for x in items[1:]: + if x not in symbols: + skip = True + break + if not skip: + print(line, end="") + + +if __name__ == "__main__": + main() diff --git a/examples/wav2vec/unsupervised/scripts/filter_tsv.py b/examples/wav2vec/unsupervised/scripts/filter_tsv.py new file mode 100644 index 0000000000..a09d79acf3 --- /dev/null +++ b/examples/wav2vec/unsupervised/scripts/filter_tsv.py @@ -0,0 +1,37 @@ +#!/usr/bin/env python3 -u +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os +import argparse +import sys + + +parser = argparse.ArgumentParser() +parser.add_argument("--tsv", required=True, type=str) +parser.add_argument("--no-skip", action="store_true") +parser.add_argument("--keep", action="store_true") +params = parser.parse_args() + + +def get_fname(line): + p = os.path.basename(line.split("\t")[0]) + p = os.path.splitext(p)[0] + return p + + +# filenames to exclude +seen = set() +with open(params.tsv) as f: + if not params.no_skip: + root = next(f).rstrip() + for line in f: + seen.add(get_fname(line)) + +for i, line in enumerate(sys.stdin): + exists = get_fname(line) in seen + keep = (exists and params.keep) or (not exists and not params.keep) + if i == 0 or keep: + print(line, end="") diff --git a/examples/wav2vec/unsupervised/scripts/g2p_wrd_to_phn.py b/examples/wav2vec/unsupervised/scripts/g2p_wrd_to_phn.py new file mode 100644 index 0000000000..2e31c307bd --- /dev/null +++ b/examples/wav2vec/unsupervised/scripts/g2p_wrd_to_phn.py @@ -0,0 +1,45 @@ +#!/usr/bin/env python3 -u +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import sys + +from g2p_en import G2p + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--compact", + action="store_true", + help="if set, compacts phones", + ) + args = parser.parse_args() + + compact = args.compact + + wrd_to_phn = {} + g2p = G2p() + for line in sys.stdin: + words = line.strip().split() + phones = [] + for w in words: + if w not in wrd_to_phn: + wrd_to_phn[w] = g2p(w) + if compact: + wrd_to_phn[w] = [ + p[:-1] if p[-1].isnumeric() else p for p in wrd_to_phn[w] + ] + phones.extend(wrd_to_phn[w]) + try: + print(" ".join(phones)) + except: + print(wrd_to_phn, words, phones, file=sys.stderr) + raise + + +if __name__ == "__main__": + main() diff --git a/examples/wav2vec/unsupervised/scripts/ltr_to_wrd.py b/examples/wav2vec/unsupervised/scripts/ltr_to_wrd.py new file mode 100644 index 0000000000..36c85d1e2f --- /dev/null +++ b/examples/wav2vec/unsupervised/scripts/ltr_to_wrd.py @@ -0,0 +1,16 @@ +#!/usr/bin/env python3 -u +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import sys + + +def main(): + for line in sys.stdin: + print(line.replace(" ", "").replace("|", " ").strip()) + + +if __name__ == "__main__": + main() diff --git a/examples/wav2vec/unsupervised/scripts/mean_pool.py b/examples/wav2vec/unsupervised/scripts/mean_pool.py new file mode 100644 index 0000000000..4eea048ef3 --- /dev/null +++ b/examples/wav2vec/unsupervised/scripts/mean_pool.py @@ -0,0 +1,99 @@ +#!/usr/bin/env python3 -u +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import os +import os.path as osp +import math +import numpy as np +import tqdm +import torch +import torch.nn.functional as F +from shutil import copyfile + +from npy_append_array import NpyAppendArray + + +def get_parser(): + parser = argparse.ArgumentParser( + description="mean pools representations by compressing uniform splits of the data" + ) + # fmt: off + parser.add_argument('source', help='directory with features') + parser.add_argument('--split', help='which split to read', required=True) + parser.add_argument('--save-dir', help='where to save the output', required=True) + parser.add_argument('--subsample-rate', type=float, default=0.5, help='size to subsample data to') + + parser.add_argument('--remove-extra', action='store_true', help='if true, removes extra states that cant be pooled, otherwise pads with 0s') + # fmt: on + + return parser + + +def main(): + parser = get_parser() + args = parser.parse_args() + + source_path = osp.join(args.source, args.split) + + print(f"data path: {source_path}") + + features = np.load(source_path + ".npy", mmap_mode="r") + + os.makedirs(args.save_dir, exist_ok=True) + save_path = osp.join(args.save_dir, args.split) + + copyfile(source_path + ".tsv", save_path + ".tsv") + + if os.path.exists(source_path + ".phn"): + copyfile(source_path + ".phn", save_path + ".phn") + if os.path.exists(source_path + ".wrd"): + copyfile(source_path + ".wrd", save_path + ".wrd") + + if os.path.exists(osp.join(args.source, "dict.phn.txt")): + copyfile( + osp.join(args.source, "dict.phn.txt"), + osp.join(args.save_dir, "dict.phn.txt"), + ) + + if osp.exists(save_path + ".npy"): + os.remove(save_path + ".npy") + npaa = NpyAppendArray(save_path + ".npy") + + with open(source_path + ".lengths", "r") as lf: + lengths = lf.readlines() + + fsz = features.shape[-1] + start = 0 + with torch.no_grad(): + with open(save_path + ".lengths", "w") as lengths_out: + for length in tqdm.tqdm(lengths): + length = int(length) + end = start + length + feats = features[start:end] + start += length + x = torch.from_numpy(feats).cuda() + target_num = math.ceil(length * args.subsample_rate) + rem = length % target_num + + if rem > 0: + if args.remove_extra: + to_rem = target_num - rem + target_num -= 1 + x = x[:-to_rem] + else: + to_add = target_num - rem + x = F.pad(x, [0, 0, 0, to_add]) + x[-to_add:] = x[-to_add - 1] + + x = x.view(target_num, -1, fsz) + x = x.mean(dim=-2) + print(target_num, file=lengths_out) + npaa.append(x.cpu().numpy()) + + +if __name__ == "__main__": + main() diff --git a/examples/wav2vec/unsupervised/scripts/merge_clusters.py b/examples/wav2vec/unsupervised/scripts/merge_clusters.py new file mode 100644 index 0000000000..2780f9d971 --- /dev/null +++ b/examples/wav2vec/unsupervised/scripts/merge_clusters.py @@ -0,0 +1,114 @@ +#!/usr/bin/env python3 -u +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import os +import os.path as osp +import numpy as np +import tqdm +import torch +import random +from shutil import copyfile + +from npy_append_array import NpyAppendArray + + +def get_parser(): + parser = argparse.ArgumentParser( + description="transforms features via a given pca and stored them in target dir" + ) + # fmt: off + parser.add_argument('source', help='directory with features') + parser.add_argument('--split', help='which split to read', required=True) + parser.add_argument('--save-dir', help='where to save the output', required=True) + parser.add_argument('--cluster-dir', help='where the clusters are') + parser.add_argument('--pooling', type=str, default='mean', choices=['mean', 'sample'], help='how to pool') + # fmt: on + + return parser + + +def main(): + parser = get_parser() + args = parser.parse_args() + + source_path = osp.join(args.source, args.split) + cluster_path = osp.join(args.cluster_dir, args.split + ".src") + print(f"data path: {source_path}") + + features = np.load(source_path + ".npy", mmap_mode="r") + sizes = [] + offsets = [] + offset = 0 + with open(source_path + ".lengths", "r") as len_f: + for line in len_f: + length = int(line.rstrip()) + sizes.append(length) + offsets.append(offset) + offset += length + + clusters = [] + with open(cluster_path, "r") as cf: + for line in cf: + line = line.rstrip() + items = line.split() + items = list(map(int, items)) + clusters.append(items) + + os.makedirs(args.save_dir, exist_ok=True) + save_path = osp.join(args.save_dir, args.split) + + copyfile(source_path + ".tsv", save_path + ".tsv") + + if os.path.exists(source_path + ".phn"): + copyfile(source_path + ".phn", save_path + ".phn") + if os.path.exists(osp.join(args.source, "dict.phn.txt")): + copyfile( + osp.join(args.source, "dict.phn.txt"), + osp.join(args.save_dir, "dict.phn.txt"), + ) + if os.path.exists(source_path + ".wrd"): + copyfile(source_path + ".wrd", save_path + ".wrd") + + if osp.exists(save_path + ".npy"): + os.remove(save_path + ".npy") + npaa = NpyAppendArray(save_path + ".npy") + + def merge(feats, clust): + feats = torch.from_numpy(feats.copy()) + clust = torch.LongTensor(clust) + _, counts = clust.unique_consecutive(return_counts=True) + curr = 0 + + merged = [] + for c in counts: + c = c.item() + start = curr + end = curr + c + curr += c + if args.pooling == "mean": + new_x = feats[start:end].mean(dim=0) + elif args.pooling == "sample": + new_x = feats[start + int(random.random() * c)] + else: + raise NotImplementedError() + merged.append(new_x) + + return torch.stack(merged, dim=0).numpy() + + with open(save_path + ".lengths", "w") as l_f: + for size, offset, clust in tqdm.tqdm( + zip(sizes, offsets, clusters), total=len(sizes) + ): + end = size + offset + feats = features[offset:end] + feats = merge(feats, clust) + print(len(feats), file=l_f) + npaa.append(feats) + + +if __name__ == "__main__": + main() diff --git a/examples/wav2vec/unsupervised/scripts/normalize_and_filter_text.py b/examples/wav2vec/unsupervised/scripts/normalize_and_filter_text.py new file mode 100644 index 0000000000..c2bd16efb5 --- /dev/null +++ b/examples/wav2vec/unsupervised/scripts/normalize_and_filter_text.py @@ -0,0 +1,72 @@ +#!/usr/bin/env python3 +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import fasttext as ft +import os +import regex +import sys + + +def get_parser(): + parser = argparse.ArgumentParser( + description="reads text from stdin and outputs normalized, lid-filtered version to stdout" + ) + parser.add_argument( + "--fasttext-model", + help="path to fasttext model", + default="lid.187.bin", + ) + parser.add_argument("--lang", help="language id", required=True) + parser.add_argument( + "--lid-threshold", + type=float, + help="threshold for this lang id probability", + default=0.4, + ) + + return parser + + +def main(): + parser = get_parser() + args = parser.parse_args() + filter_r = regex.compile(r"[^\p{L}\p{N}\p{M}\' \-]") + + lg = args.lang.lower() + lg_label = f"__label__{lg}" + thresh = args.lid_threshold + + if os.path.exists(args.fasttext_model): + model = ft.load_model(args.fasttext_model) + else: + print( + f"fasttext language id model {args.fasttext_model} not found. Proceeding without language filtering. " + f"To enable language filtering, please download the latest language id model " + f"from https://fasttext.cc/docs/en/language-identification.html", + file=sys.stderr, + ) + model = None + + for line in sys.stdin: + line = line.strip() + line = filter_r.sub(" ", line) + line = " ".join(line.split()) + + if model is not None: + lid, prob = model.predict(line, k=100) + try: + target_idx = lid.index(lg_label) + except ValueError: + continue + if target_idx == 0 or prob[target_idx] >= thresh: + print(line) + else: + print(line) + + +if __name__ == "__main__": + main() diff --git a/examples/wav2vec/unsupervised/scripts/normalize_text.py b/examples/wav2vec/unsupervised/scripts/normalize_text.py new file mode 100644 index 0000000000..9d0ffeb27d --- /dev/null +++ b/examples/wav2vec/unsupervised/scripts/normalize_text.py @@ -0,0 +1,22 @@ +#!/usr/bin/env python3 +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import regex +import sys + + +def main(): + filter_r = regex.compile(r"[^\p{L}\p{N}\p{M}\' \-]") + + for line in sys.stdin: + line = line.strip() + line = filter_r.sub(" ", line) + line = " ".join(line.split()) + print(line) + + +if __name__ == "__main__": + main() diff --git a/examples/wav2vec/unsupervised/scripts/pca.py b/examples/wav2vec/unsupervised/scripts/pca.py new file mode 100644 index 0000000000..948cf5319f --- /dev/null +++ b/examples/wav2vec/unsupervised/scripts/pca.py @@ -0,0 +1,53 @@ +#!/usr/bin/env python3 -u +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import os +import os.path as osp +import numpy as np + +import faiss + + + +def get_parser(): + parser = argparse.ArgumentParser( + description="compute a pca matrix given an array of numpy features" + ) + # fmt: off + parser.add_argument('data', help='numpy file containing features') + parser.add_argument('--output', help='where to save the pca matrix', required=True) + parser.add_argument('--dim', type=int, help='dim for pca reduction', required=True) + parser.add_argument('--eigen-power', type=float, default=0, help='eigen power, -0.5 for whitening') + + return parser + + +def main(): + parser = get_parser() + args = parser.parse_args() + + print("Reading features") + x = np.load(args.data, mmap_mode="r") + + print("Computing PCA") + pca = faiss.PCAMatrix(x.shape[-1], args.dim, args.eigen_power) + pca.train(x) + b = faiss.vector_to_array(pca.b) + A = faiss.vector_to_array(pca.A).reshape(pca.d_out, pca.d_in) + + os.makedirs(args.output, exist_ok=True) + + prefix = str(args.dim) + if args.eigen_power != 0: + prefix += f"_{args.eigen_power}" + + np.save(osp.join(args.output, f"{prefix}_pca_A"), A.T) + np.save(osp.join(args.output, f"{prefix}_pca_b"), b) + + +if __name__ == "__main__": + main() diff --git a/examples/wav2vec/unsupervised/scripts/phonemize_with_sil.py b/examples/wav2vec/unsupervised/scripts/phonemize_with_sil.py new file mode 100644 index 0000000000..c6512d7322 --- /dev/null +++ b/examples/wav2vec/unsupervised/scripts/phonemize_with_sil.py @@ -0,0 +1,83 @@ +#!/usr/bin/env python3 -u +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import numpy as np +import sys + + +def get_parser(): + parser = argparse.ArgumentParser( + description="converts words to phones adding optional silences around in between words" + ) + parser.add_argument( + "--sil-prob", + "-s", + type=float, + default=0, + help="probability of inserting silence between each word", + ) + parser.add_argument( + "--surround", + action="store_true", + help="if set, surrounds each example with silence", + ) + parser.add_argument( + "--lexicon", + help="lexicon to convert to phones", + required=True, + ) + + return parser + + +def main(): + parser = get_parser() + args = parser.parse_args() + + sil_prob = args.sil_prob + surround = args.surround + sil = "<SIL>" + + wrd_to_phn = {} + + with open(args.lexicon, "r") as lf: + for line in lf: + items = line.rstrip().split() + assert len(items) > 1, line + assert items[0] not in wrd_to_phn, items + wrd_to_phn[items[0]] = items[1:] + + for line in sys.stdin: + words = line.strip().split() + + if not all(w in wrd_to_phn for w in words): + continue + + phones = [] + if surround: + phones.append(sil) + + sample_sil_probs = None + if sil_prob > 0 and len(words) > 1: + sample_sil_probs = np.random.random(len(words) - 1) + + for i, w in enumerate(words): + phones.extend(wrd_to_phn[w]) + if ( + sample_sil_probs is not None + and i < len(sample_sil_probs) + and sample_sil_probs[i] < sil_prob + ): + phones.append(sil) + + if surround: + phones.append(sil) + print(" ".join(phones)) + + +if __name__ == "__main__": + main() diff --git a/examples/wav2vec/unsupervised/scripts/prepare_audio.sh b/examples/wav2vec/unsupervised/scripts/prepare_audio.sh new file mode 100644 index 0000000000..013f7a9b05 --- /dev/null +++ b/examples/wav2vec/unsupervised/scripts/prepare_audio.sh @@ -0,0 +1,78 @@ +#!/usr/bin/env zsh +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +source_dir=$1 +tgt_dir=$2 +model=$3 + +if [ -z "$4" ] + then + dim=512 + else + dim=$4 +fi + +echo "using $dim dim for PCA" + +if [ -z "$5" ] + then + layer=14 + else + layer=$5 +fi + +echo "extracting from layer $layer" + +train_split=train +valid_split=valid +test_split=test + +all_splits=($train_split) + +if [[ -f "$source_dir/valid.tsv" ]]; then + all_splits+=('valid') +fi + +if [[ -f "$source_dir/test.tsv" ]]; then + all_splits+=('test') +fi + +echo "processing splits: $all_splits" + +mkdir -p $tgt_dir + +cp $source_dir/*.tsv $tgt_dir +cp $source_dir/*.wrd $tgt_dir +cp $source_dir/*.ltr $tgt_dir +cp $source_dir/*.phn $tgt_dir +cp $source_dir/dict* $tgt_dir + +setopt shwordsplit + +for split in $all_splits; do + python $FAIRSEQ_ROOT/examples/wav2vec/unsupervised/scripts/wav2vec_extract_features.py $source_dir --split $split \ + --save-dir $tgt_dir --checkpoint $model --layer $layer +done + +python $FAIRSEQ_ROOT/examples/wav2vec/unsupervised/scripts/wav2vec_cluster_faiss.py $tgt_dir/${train_split}.tsv \ +--checkpoint $model --save-dir $tgt_dir -f "CLUS128" --sample-pct 1.0 + +for split in $all_splits; do + python $FAIRSEQ_ROOT/examples/wav2vec/unsupervised/scripts/wav2vec_apply_cluster_faiss.py $tgt_dir \ + --checkpoint $model --path $tgt_dir/CLUS128 --split $split +done + +python $FAIRSEQ_ROOT/examples/wav2vec/unsupervised/scripts/pca.py $tgt_dir/${train_split}.npy --output $tgt_dir/pca --dim $dim + +for split in $all_splits; do + python $FAIRSEQ_ROOT/examples/wav2vec/unsupervised/scripts/apply_pca.py $tgt_dir --split $split --save-dir $tgt_dir/precompute_pca$dim --pca-path $tgt_dir/pca/${dim}_pca --batch-size 1048000 + + python $FAIRSEQ_ROOT/examples/wav2vec/unsupervised/scripts/merge_clusters.py $tgt_dir/precompute_pca$dim --cluster-dir $tgt_dir/CLUS128 \ + --split $split --save-dir $tgt_dir/precompute_pca${dim}_cls128_mean --pooling mean + + python $FAIRSEQ_ROOT/examples/wav2vec/unsupervised/scripts/mean_pool.py $tgt_dir/precompute_pca${dim}_cls128_mean \ + --save-dir $tgt_dir/precompute_pca${dim}_cls128_mean_pooled --split $split +done diff --git a/examples/wav2vec/unsupervised/scripts/prepare_text.sh b/examples/wav2vec/unsupervised/scripts/prepare_text.sh new file mode 100644 index 0000000000..1caf13cb6a --- /dev/null +++ b/examples/wav2vec/unsupervised/scripts/prepare_text.sh @@ -0,0 +1,82 @@ +#!/usr/bin/env zsh +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +lg=$1 +text_path=$2 +target_dir=$3 +min_phones=$4 +phonemizer=$5 +lid_path=$6 + +if [ -z "$lid_path" ]; then + lid_path="lid.187.bin" +fi + +ph_lg=${lg:l} +if test "$lg" = 'fr'; then + ph_lg='fr-fr' +elif test "$lg" = 'en'; then + ph_lg='en-us' +elif test "$lg" = 'pt'; then + ph_lg='pt-br' +fi + +ESPEAK_PATH='' +if test "$phonemizer" = 'espeak'; then + ESPEAK_PATH=$(which espeak) +elif test "$phonemizer" = 'espeak-ng'; then + ESPEAK_PATH=$(which espeak-ng) +elif test "$phonemizer" = 'G2P'; then + ESPEAK_PATH='' +else + echo "Unknown phonemizer $phonemizer. Valid options are espeak, espean-ng and G2P" + exit 1 +fi + +echo $lg +echo $ph_lg +echo $text_path +echo $target_dir +echo "min phone seen threshold is $min_phones" + +mkdir -p $target_dir +python $FAIRSEQ_ROOT/examples/wav2vec/unsupervised/scripts/normalize_and_filter_text.py --lang $lg --fasttext-model $lid_path < $text_path | grep -v '\-\-\-' >! $target_dir/lm.upper.lid.txt +python $FAIRSEQ_ROOT/fairseq_cli/preprocess.py --dataset-impl mmap --trainpref $target_dir/lm.upper.lid.txt --only-source --destdir $target_dir --thresholdsrc 2 --padding-factor 1 --dict-only +cut -f1 -d' ' $target_dir/dict.txt | grep -v -x '[[:punct:]]*' | grep -Pv '\d\d\d\d\d+' >! $target_dir/words.txt + + +if [ -z "$ESPEAK_PATH" ]; then + python $FAIRSEQ_ROOT/examples/wav2vec/unsupervised/scripts/g2p_wrd_to_phn.py --compact < $target_dir/words.txt > $target_dir/phones.txt +else + # echoing 1 into corpus will prevent the mismatch lines between lexicon and phones in case the phonemizer fails + one=$(echo "1" | PHONEMIZER_ESPEAK_PATH=$ESPEAK_PATH phonemize -p ' ' -w '' -l $ph_lg --language-switch remove-flags) + sed 's/$/ 1/' $target_dir/words.txt | PHONEMIZER_ESPEAK_PATH=$ESPEAK_PATH phonemize -o $target_dir/phones.txt -p ' ' -w '' -l $ph_lg -j 70 --language-switch remove-flags + echo "one is ${one}" + sed -i "s/${one}$//" $target_dir/phones.txt +fi + +paste $target_dir/words.txt $target_dir/phones.txt >! $target_dir/lexicon.lst + +python $FAIRSEQ_ROOT/fairseq_cli/preprocess.py --dataset-impl mmap --trainpref $target_dir/phones.txt --only-source --destdir $target_dir/phones --thresholdsrc $min_phones --padding-factor 1 --dict-only + +python $FAIRSEQ_ROOT/examples/wav2vec/unsupervised/scripts/filter_lexicon.py -d $target_dir/phones/dict.txt < $target_dir/lexicon.lst >! $target_dir/lexicon_filtered.lst +python $FAIRSEQ_ROOT/examples/wav2vec/unsupervised/scripts/phonemize_with_sil.py -s 0.25 --surround --lexicon $target_dir/lexicon_filtered.lst < $target_dir/lm.upper.lid.txt >! $target_dir/phones/lm.phones.filtered.txt +cp $target_dir/phones/dict.txt $target_dir/phones/dict.phn.txt +echo "<SIL> 0" >> $target_dir/phones/dict.phn.txt +python $FAIRSEQ_ROOT/fairseq_cli/preprocess.py --dataset-impl mmap --trainpref $target_dir/phones/lm.phones.filtered.txt --workers 70 --only-source --destdir $target_dir/phones --srcdict $target_dir/phones/dict.phn.txt + +$KENLM_ROOT/lmplz -o 4 < $target_dir/lm.upper.lid.txt --discount_fallback --prune 0 0 0 3 >! $target_dir/kenlm.wrd.o40003.arpa +$KENLM_ROOT/build_binary $target_dir/kenlm.wrd.o40003.arpa $target_dir/kenlm.wrd.o40003.bin + +lg=$lg python $FAIRSEQ_ROOT/examples/speech_recognition/kaldi/kaldi_initializer.py kaldi_root=$KALDI_ROOT fst_dir=$target_dir/fst/phn_to_words_sil lm_arpa=$target_dir/kenlm.wrd.o40003.arpa wav2letter_lexicon=$target_dir/lexicon_filtered.lst data_dir=$target_dir/phones in_labels=phn "blank_symbol='<SIL>'" +lg=$lg python $FAIRSEQ_ROOT/examples/speech_recognition/kaldi/kaldi_initializer.py kaldi_root=$KALDI_ROOT fst_dir=$target_dir/fst/phn_to_words lm_arpa=$target_dir/kenlm.wrd.o40003.arpa wav2letter_lexicon=$target_dir/lexicon_filtered.lst data_dir=$target_dir/phones in_labels=phn + +$KENLM_ROOT/lmplz -o 4 < $target_dir/phones/lm.phones.filtered.txt --discount_fallback >! $target_dir/phones/lm.phones.filtered.04.arpa +$KENLM_ROOT/build_binary $target_dir/phones/lm.phones.filtered.04.arpa $target_dir/phones/lm.phones.filtered.04.bin +$KENLM_ROOT/lmplz -o 6 < $target_dir/phones/lm.phones.filtered.txt --discount_fallback >! $target_dir/phones/lm.phones.filtered.06.arpa +$KENLM_ROOT/build_binary $target_dir/phones/lm.phones.filtered.06.arpa $target_dir/phones/lm.phones.filtered.06.bin + +lg=$lg python $FAIRSEQ_ROOT/examples/speech_recognition/kaldi/kaldi_initializer.py kaldi_root=$KALDI_ROOT fst_dir=$target_dir/fst/phn_to_phn_sil lm_arpa=$target_dir/phones/lm.phones.filtered.06.arpa data_dir=$target_dir/phones in_labels=phn "blank_symbol='<SIL>'" diff --git a/examples/wav2vec/unsupervised/scripts/prepare_timit.sh b/examples/wav2vec/unsupervised/scripts/prepare_timit.sh new file mode 100644 index 0000000000..d8f5d596b4 --- /dev/null +++ b/examples/wav2vec/unsupervised/scripts/prepare_timit.sh @@ -0,0 +1,79 @@ +#!/bin/bash +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +timit_root=$1 # assume it is the upper-cased version +tgt_dir=$2 +model=$3 + +set -eu + +setups="matched unmatched" +splits="test valid train train_text" + +tgt_dir=$(realpath $tgt_dir) +sph2wav=$KALDI_ROOT/tools/sph2pipe_v2.5/sph2pipe +wav_dir=$tgt_dir/wav + + +mkdir -p $tgt_dir $wav_dir +find $timit_root/{TRAIN,TEST} -iname "*.WAV" > $tgt_dir/all_sph.flist +cat $tgt_dir/all_sph.flist | sed -e 's#//*#/#g' -e 's#.*/\([^/]*\)/\([^/]*\).WAV#\1_\2#g' > $tgt_dir/all.uid +paste -d' ' $tgt_dir/{all_sph.flist,all.uid} | \ + awk -v sph2wav=$sph2wav -v wav_dir=$wav_dir '{print sph2wav " -f wav " $1 " > " wav_dir "/" $2 ".wav"}' \ + > $tgt_dir/sph2wav.sh +bash $tgt_dir/sph2wav.sh +cat $tgt_dir/all.uid | awk -v wav_dir=$(pwd)/$wav_dir '{print $1" "wav_dir"/"$1".wav"}' | sort > $tgt_dir/all_wav.scp +cut -d' ' -f2 $tgt_dir/all_wav.scp | xargs -I{} soxi -s {} > $tgt_dir/all.dur +paste -d' ' $tgt_dir/{all_wav.scp,all.dur} > $tgt_dir/all_wav_dur.scp +rm $tgt_dir/{all.uid,all_sph.flist,sph2wav.sh} + +find $timit_root/{TRAIN,TEST} -iname "*.PHN" > $tgt_dir/all_phn60.flist +while read line; do + if [ ! -f $line ]; then + >&2 echo "Cannot find transcription file '$line'" && exit 1; + fi + cut -f3 -d' ' "$line" | tr '\n' ' ' | perl -ape 's: *$:\n:;' +done < $tgt_dir/all_phn60.flist > $tgt_dir/all.phn60 +cat $tgt_dir/all_phn60.flist | sed -e 's#//*#/#g' -e 's#.*/\([^/]*\)/\([^/]*\).PHN#\1_\2#g' | \ + paste -d' ' - $tgt_dir/all.phn60 | \ + $KALDI_ROOT/egs/timit/s5/local/timit_norm_trans.pl -i - -m $KALDI_ROOT/egs/timit/s5/conf/phones.60-48-39.map -to 39 | \ + sort > $tgt_dir/all.phn +echo "done preparing wav and 39-phone transcripts" + + +for s in $setups; do + mkdir -p $tgt_dir/$s + for x in $splits; do + uid_path=config/timit_${s}/${x}.uid + grep -w -f $uid_path $tgt_dir/all.phn | cut -d' ' -f2- > $tgt_dir/$s/$x.phn + ln -sf $(realpath $tgt_dir/$s/$x.phn) $tgt_dir/$s/$x.wrd + + echo "/" > $tgt_dir/$s/$x.tsv && grep -w -f $uid_path $tgt_dir/all_wav_dur.scp | cut -d' ' -f2- | sed 's# #\t#' >> $tgt_dir/$s/$x.tsv + done + + for x in $splits; do + cat $tgt_dir/$s/$x.phn + done | tr ' ' '\n' | sort -u | awk '{print $1" "1}' > $tgt_dir/$s/dict.phn.txt + ln -sf $(realpath $tgt_dir/$s/dict.phn.txt) $tgt_dir/$s/dict.wrd.txt +done +echo "done preparing unmatched and matched setups for TIMIT" + + +for s in $setups; do + zsh scripts/prepare_audio.sh $tgt_dir/$s $tgt_dir/$s/feat $model + + lm_dir=$tgt_dir/$s/phones + fst_dir=$tgt_dir/$s/fst/phn_to_phn + + python $FAIRSEQ_ROOT/fairseq_cli/preprocess.py --dataset-impl mmap --trainpref $tgt_dir/$s/train_text.phn --workers 10 --only-source --destdir $lm_dir --srcdict $tgt_dir/$s/dict.phn.txt + $KENLM_ROOT/lmplz -o 3 < $tgt_dir/$s/train_text.phn --discount_fallback >$lm_dir/train_text_phn.03.arpa + $KENLM_ROOT/build_binary $lm_dir/train_text_phn.03.arpa $lm_dir/train_text_phn.03.bin + $KENLM_ROOT/lmplz -o 4 < $tgt_dir/$s/train_text.phn --discount_fallback >$lm_dir/train_text_phn.04.arpa + $KENLM_ROOT/build_binary $lm_dir/train_text_phn.04.arpa $lm_dir/train_text_phn.04.bin + + python $FAIRSEQ_ROOT/examples/speech_recognition/kaldi/kaldi_initializer.py kaldi_root=$KALDI_ROOT fst_dir=$fst_dir lm_arpa=$lm_dir/train_text_phn.03.arpa data_dir=$tgt_dir/$s in_labels=phn +done +echo "done preprocessing audio and text for wav2vec-U" diff --git a/examples/wav2vec/unsupervised/scripts/remove_silence.py b/examples/wav2vec/unsupervised/scripts/remove_silence.py new file mode 100644 index 0000000000..fac88b9897 --- /dev/null +++ b/examples/wav2vec/unsupervised/scripts/remove_silence.py @@ -0,0 +1,63 @@ +#!/usr/bin/env python3 -u +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +""" +get intervals from .vads file, specify output data, and this script removes silences and saves the audio data in out path folder +paths=shards/train.tsv +vads=shards/train.vads +python remove_silence.py --paths $paths --vads $vads +""" + +import os +import argparse +import torch +import torchaudio +import tqdm + + +parser = argparse.ArgumentParser() +parser.add_argument("--tsv", default="", type=str) +parser.add_argument("--vads", default="", type=str) +parser.add_argument("--out", type=str) +params = parser.parse_args() + +# load paths +paths = [] +with open(params.tsv) as f: + root = next(f).rstrip() + for line in f: + paths.append(os.path.join(root, line.rstrip().split("\t")[0])) + +# load vads +list_intervals = [] +with open(params.vads) as f: + for line in f: + interval = [ + [int(w.split(":")[0]), int(w.split(":")[1])] for w in line.rstrip().split() + ] + list_intervals.append(interval) + + +# load audio and keep only intervals (i.e. remove silences) +for i in tqdm.trange(len(paths)): + data, _ = torchaudio.load(paths[i]) + if len(list_intervals[i]) > 0: + data_filtered = torch.cat( + [data[0][int(it[0]) : int(it[1])] for it in list_intervals[i]] + ).unsqueeze(0) + else: + data_filtered = data + + # YOU MAY NEED TO MODIFY THIS TO GET THE RIGHT SUBPATH + # outpath = params.out + '/'.join(paths[i].split('/')[-1]) + outpath = params.out + "/" + "/".join(paths[i].split("/")[-2:]) + + if not os.path.isdir("/".join(outpath.split("/")[:-1])): + os.makedirs("/".join(outpath.split("/")[:-1])) + if not os.path.exists(outpath): + torchaudio.save(outpath, data_filtered, sample_rate=16000) + else: + print(outpath, "exists!") diff --git a/examples/wav2vec/unsupervised/scripts/vads.py b/examples/wav2vec/unsupervised/scripts/vads.py new file mode 100644 index 0000000000..2398da97d8 --- /dev/null +++ b/examples/wav2vec/unsupervised/scripts/vads.py @@ -0,0 +1,98 @@ +#!/usr/bin/env python3 -u +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import sys + +from copy import deepcopy +from scipy.signal import lfilter + +import numpy as np +from tqdm import tqdm +import soundfile as sf +import os.path as osp + + +def get_parser(): + parser = argparse.ArgumentParser(description="compute vad segments") + parser.add_argument( + "--rvad-home", + "-r", + help="path to rvad home (see https://github.com/zhenghuatan/rVADfast)", + required=True, + ) + + return parser + + +def rvad(speechproc, path): + winlen, ovrlen, pre_coef, nfilter, nftt = 0.025, 0.01, 0.97, 20, 512 + ftThres = 0.5 + vadThres = 0.4 + opts = 1 + + data, fs = sf.read(path) + assert fs == 16_000, "sample rate must be 16khz" + ft, flen, fsh10, nfr10 = speechproc.sflux(data, fs, winlen, ovrlen, nftt) + + # --spectral flatness -- + pv01 = np.zeros(ft.shape[0]) + pv01[np.less_equal(ft, ftThres)] = 1 + pitch = deepcopy(ft) + + pvblk = speechproc.pitchblockdetect(pv01, pitch, nfr10, opts) + + # --filtering-- + ENERGYFLOOR = np.exp(-50) + b = np.array([0.9770, -0.9770]) + a = np.array([1.0000, -0.9540]) + fdata = lfilter(b, a, data, axis=0) + + # --pass 1-- + noise_samp, noise_seg, n_noise_samp = speechproc.snre_highenergy( + fdata, nfr10, flen, fsh10, ENERGYFLOOR, pv01, pvblk + ) + + # sets noisy segments to zero + for j in range(n_noise_samp): + fdata[range(int(noise_samp[j, 0]), int(noise_samp[j, 1]) + 1)] = 0 + + vad_seg = speechproc.snre_vad( + fdata, nfr10, flen, fsh10, ENERGYFLOOR, pv01, pvblk, vadThres + ) + return vad_seg, data + + +def main(): + parser = get_parser() + args = parser.parse_args() + + sys.path.append(args.rvad_home) + import speechproc + + stride = 160 + lines = sys.stdin.readlines() + root = lines[0].rstrip() + for fpath in tqdm(lines[1:]): + path = osp.join(root, fpath.split()[0]) + vads, wav = rvad(speechproc, path) + + start = None + vad_segs = [] + for i, v in enumerate(vads): + if start is None and v == 1: + start = i * stride + elif start is not None and v == 0: + vad_segs.append((start, i * stride)) + start = None + if start is not None: + vad_segs.append((start, len(wav))) + + print(" ".join(f"{v[0]}:{v[1]}" for v in vad_segs)) + + +if __name__ == "__main__": + main() diff --git a/examples/wav2vec/unsupervised/scripts/wav2vec_apply_cluster_faiss.py b/examples/wav2vec/unsupervised/scripts/wav2vec_apply_cluster_faiss.py new file mode 100644 index 0000000000..a5dd7ae6c1 --- /dev/null +++ b/examples/wav2vec/unsupervised/scripts/wav2vec_apply_cluster_faiss.py @@ -0,0 +1,128 @@ +#!/usr/bin/env python3 -u +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import os +import os.path as osp +import numpy as np +import tqdm +import torch +import sys + +import faiss +import torch.nn.functional as F + +from wav2vec_cluster_faiss import parse_faiss_specs, Wav2VecFeatureReader + + +def get_parser(): + parser = argparse.ArgumentParser(description="apply clusters") + # fmt: off + parser.add_argument('data', help='location of tsv files') + parser.add_argument('--split', help='split to process', required=True) + parser.add_argument('--labels', help='split to process', default="phn") + parser.add_argument('--path', help='path to pca and centroids', required=True) + parser.add_argument('--checkpoint', type=str, help='checkpoint for wav2vec model (if using wav2vec features)', required=True) + parser.add_argument('--layer', '-l', type=int, help='which layer to read', default=14) + parser.add_argument('--max-tsz', type=int, help='batch kmeans up to this much', default=14) + # fmt: on + + return parser + + +def get_iterator(args): + label_path = osp.join(args.data, f"{args.split}.{args.labels}") + if osp.exists(label_path): + lp = open(label_path, "r") + else: + lp = None + + with open(osp.join(args.data, f"{args.split}.tsv"), "r") as fp: + lines = fp.read().split("\n") + root = lines.pop(0).strip() + files = [line.rstrip() for line in lines if len(line) > 0] + + if lp is not None: + lbls = [line.rstrip() for line in lp] + else: + lbls = [None] * len(files) + + num = len(files) + reader = Wav2VecFeatureReader(args.checkpoint, args.layer) + + def iterate(): + for fname, lbl in zip(files, lbls): + file = osp.join(root, fname.split("\t")[0]) + feats = reader.get_feats(file) + yield feats.data, fname, lbl + + return iterate, num, root + + +def main(): + parser = get_parser() + args = parser.parse_args() + + spec = osp.basename(args.path) + + try: + faiss_spec = parse_faiss_specs(spec.rstrip("/"))[0] + except: + print(spec) + raise + + print("Faiss Spec:", faiss_spec, file=sys.stderr) + + if faiss_spec.pca: + A = torch.from_numpy(np.load(osp.join(args.path, "pca_A.npy"))).cuda() + b = torch.from_numpy(np.load(osp.join(args.path, "pca_b.npy"))).cuda() + print("Loaded PCA", file=sys.stderr) + + centroids = np.load(osp.join(args.path, "centroids.npy")) + print("Loaded centroids", centroids.shape, file=sys.stderr) + + res = faiss.StandardGpuResources() + index_flat = ( + faiss.IndexFlatL2(centroids.shape[1]) + if not faiss_spec.sphere + else faiss.IndexFlatIP(centroids.shape[1]) + ) + faiss_index = faiss.index_cpu_to_gpu(res, 0, index_flat) + faiss_index.add(centroids) + + generator, num, root = get_iterator(args) + iterator = generator() + + had_labels = False + label_path = osp.join(args.path, f"{args.split}.{args.labels}") + + with torch.no_grad(): + with open(osp.join(args.path, f"{args.split}.src"), "w") as fp, open( + osp.join(args.path, f"{args.split}.tsv"), "w" + ) as pp, open(label_path, "w") as lp: + print(root, file=pp) + for f, fname, lbl in tqdm.tqdm(iterator, total=num): + if faiss_spec.pca: + f = torch.mm(f, A) + b + if faiss_spec.norm: + f = F.normalize(f, p=2, dim=-1) + + f = f.cpu().numpy() + + _, z = faiss_index.search(f, 1) + + print(" ".join(str(x.item()) for x in z), file=fp) + print(fname, file=pp) + + if lbl is not None: + print(lbl, file=lp) + had_labels = True + if not had_labels: + os.remove(label_path) + + +if __name__ == "__main__": + main() diff --git a/examples/wav2vec/unsupervised/scripts/wav2vec_cluster_faiss.py b/examples/wav2vec/unsupervised/scripts/wav2vec_cluster_faiss.py new file mode 100644 index 0000000000..632a69e9f4 --- /dev/null +++ b/examples/wav2vec/unsupervised/scripts/wav2vec_cluster_faiss.py @@ -0,0 +1,210 @@ +#!/usr/bin/env python3 -u +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import gc +import os +import os.path as osp +import random +import numpy as np +import tqdm +import torch + +from collections import namedtuple + +import faiss + +import fairseq +import soundfile as sf + + +def get_parser(): + parser = argparse.ArgumentParser( + description="compute kmeans codebook from kaldi-computed feats" + ) + # fmt: off + parser.add_argument('data', help='location of tsv files') + parser.add_argument('--save-dir', help='where to save the output', required=True) + parser.add_argument('--checkpoint', type=str, help='checkpoint for wav2vec model (if using wav2vec features)', required=True) + parser.add_argument('--sample-pct', '-r', type=float, help='percentage of timesteps to sample', default=0) + parser.add_argument('--layer', '-l', type=int, help='which layer to read', default=14) + parser.add_argument('--faiss-specs', '-f', type=str, + help='faiss index specs; separated by space ' + 'format is: PCAx_NORM_CLUSx_SPHERICAL -> ' + 'PCAx if exists first apply PCA ' + 'NORM if exists, normalize the vector by L2 norm ' + 'CLUSx must exist, cluster to x clusters ' + 'SPEHRICAL if exists, apply spherical kmeans', + default='l2') + # fmt: on + + return parser + + +faiss_spec = namedtuple("faiss_spec", ["pca", "norm", "n_clus", "sphere", "spec_str"]) + + +def parse_faiss_specs(specs_str): + specs = [] + for ss in specs_str.split(): + comps = ss.split("_") + pca = 0 + norm = False + n_clus = 0 + sphere = False + for c in comps: + if c.startswith("PCA"): + pca = int(c[3:]) + elif c == "NORM": + norm = True + elif c.startswith("CLUS"): + n_clus = int(c[4:]) + elif c == "SPHERICAL": + sphere = True + assert n_clus > 0 + specs.append( + faiss_spec(pca=pca, norm=norm, n_clus=n_clus, sphere=sphere, spec_str=ss) + ) + return specs + + +class Wav2VecFeatureReader(object): + def __init__(self, cp_file, layer): + state = fairseq.checkpoint_utils.load_checkpoint_to_cpu(cp_file) + + self.layer = layer + + if "cfg" in state: + w2v_args = state["cfg"] + task = fairseq.tasks.setup_task(w2v_args.task) + model = task.build_model(w2v_args.model) + else: + w2v_args = state["args"] + task = fairseq.tasks.setup_task(w2v_args) + model = task.build_model(w2v_args) + model.load_state_dict(state["model"], strict=True) + model.eval() + model.cuda() + self.model = model + + def read_audio(self, fname): + """Load an audio file and return PCM along with the sample rate""" + wav, sr = sf.read(fname) + assert sr == 16e3 + + return wav + + def get_feats(self, loc): + x = self.read_audio(loc) + with torch.no_grad(): + source = torch.from_numpy(x).view(1, -1).float().cuda() + res = self.model( + source=source, mask=False, features_only=True, layer=self.layer + ) + return res["layer_results"][self.layer][0].squeeze(1) + + +def get_iterator(args): + with open(args.data, "r") as fp: + lines = fp.read().split("\n") + root = lines.pop(0).strip() + files = [osp.join(root, line.split("\t")[0]) for line in lines if len(line) > 0] + + if getattr(args, "sample_pct", 0) > 0: + files = random.sample(files, int(args.sample_pct * len(files))) + num = len(files) + reader = Wav2VecFeatureReader(args.checkpoint, args.layer) + + def iterate(): + for fname in files: + feats = reader.get_feats(fname) + yield feats.cpu().numpy() + + return iterate, num + + +def main(): + parser = get_parser() + args = parser.parse_args() + + faiss_specs = parse_faiss_specs(args.faiss_specs) + print("Faiss Specs:", faiss_specs) + + feat_path = osp.join(args.save_dir, "features") + if osp.exists(feat_path + ".npy"): + feats = np.load(feat_path + ".npy") + else: + generator, num = get_iterator(args) + iterator = generator() + + feats = [] + for f in tqdm.tqdm(iterator, total=num): + feats.append(f) + + del iterator + del generator + + feats = np.concatenate(feats) + + print(feats.shape) + + os.makedirs(args.save_dir, exist_ok=True) + # np.save(feat_path, feats) + + gc.collect() + torch.cuda.empty_cache() + + reload = False + for spec in faiss_specs: + print("Processing spec", spec) + + if reload: + print("Reloading...") + del feats + gc.collect() + feats = np.load(feat_path + ".npy") + + save_path = osp.join(args.save_dir, spec.spec_str) + os.makedirs(save_path, exist_ok=True) + d = feats.shape[-1] + x = feats + if spec.pca > 0: + print("Computing PCA") + pca = faiss.PCAMatrix(d, spec.pca) + pca.train(x) + d = spec.pca + b = faiss.vector_to_array(pca.b) + A = faiss.vector_to_array(pca.A).reshape(pca.d_out, pca.d_in) + np.save(osp.join(save_path, "pca_A"), A.T) + np.save(osp.join(save_path, "pca_b"), b) + print("Applying PCA") + x = pca.apply_py(x) + + if spec.norm: + reload = spec.pca <= 0 + print("Normalizing") + faiss.normalize_L2(x) + + print("Computing kmeans") + kmeans = faiss.Kmeans( + d, + spec.n_clus, + niter=50, + verbose=True, + spherical=spec.sphere, + max_points_per_centroid=feats.shape[0], + gpu=True, + nredo=3, + ) + kmeans.train(x) + np.save(osp.join(save_path, "centroids"), kmeans.centroids) + del kmeans + del x + gc.collect() + + +if __name__ == "__main__": + main() diff --git a/examples/wav2vec/unsupervised/scripts/wav2vec_extract_features.py b/examples/wav2vec/unsupervised/scripts/wav2vec_extract_features.py new file mode 100644 index 0000000000..b07e274d20 --- /dev/null +++ b/examples/wav2vec/unsupervised/scripts/wav2vec_extract_features.py @@ -0,0 +1,119 @@ +#!/usr/bin/env python3 -u +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import os +import os.path as osp +import tqdm +import torch +import torch.nn.functional as F +from shutil import copyfile + +from npy_append_array import NpyAppendArray + +import fairseq +import soundfile as sf + + +def get_parser(): + parser = argparse.ArgumentParser( + description="compute kmeans codebook from kaldi-computed feats" + ) + # fmt: off + parser.add_argument('data', help='location of tsv files') + parser.add_argument('--split', help='which split to read', required=True) + parser.add_argument('--save-dir', help='where to save the output', required=True) + parser.add_argument('--checkpoint', type=str, help='checkpoint for wav2vec ctc model', required=True) + parser.add_argument('--layer', type=int, default=14, help='which layer to use') + # fmt: on + + return parser + + +class Wav2VecFeatureReader(object): + def __init__(self, cp_file, layer): + model, cfg, task = fairseq.checkpoint_utils.load_model_ensemble_and_task( + [cp_file] + ) + model = model[0] + model.eval() + model.cuda() + self.model = model + self.task = task + self.layer = layer + + def read_audio(self, fname): + """Load an audio file and return PCM along with the sample rate""" + wav, sr = sf.read(fname) + assert sr == 16e3 + + return wav + + def get_feats(self, loc): + x = self.read_audio(loc) + with torch.no_grad(): + source = torch.from_numpy(x).float().cuda() + if self.task.cfg.normalize: + assert source.dim() == 1, source.dim() + with torch.no_grad(): + source = F.layer_norm(source, source.shape) + source = source.view(1, -1) + + m_res = self.model(source=source, mask=False, features_only=True, layer=self.layer) + return m_res["x"].squeeze(0).cpu() + + +def get_iterator(args): + with open(osp.join(args.data, args.split) + ".tsv", "r") as fp: + lines = fp.read().split("\n") + root = lines.pop(0).strip() + files = [osp.join(root, line.split("\t")[0]) for line in lines if len(line) > 0] + + num = len(files) + reader = Wav2VecFeatureReader(args.checkpoint, args.layer) + + def iterate(): + for fname in files: + w2v_feats = reader.get_feats(fname) + yield w2v_feats + + return iterate, num + + +def main(): + parser = get_parser() + args = parser.parse_args() + + os.makedirs(args.save_dir, exist_ok=True) + + def create_files(dest): + copyfile(osp.join(args.data, args.split) + ".tsv", dest + ".tsv") + if osp.exists(osp.join(args.data, args.split) + ".wrd"): + copyfile(osp.join(args.data, args.split) + ".wrd", dest + ".wrd") + if osp.exists(osp.join(args.data, args.split) + ".phn"): + copyfile(osp.join(args.data, args.split) + ".phn", dest + ".phn") + + if osp.exists(dest + ".npy"): + os.remove(dest + ".npy") + npaa = NpyAppendArray(dest + ".npy") + return npaa + + save_path = osp.join(args.save_dir, args.split) + npaa = create_files(save_path) + + generator, num = get_iterator(args) + iterator = generator() + + with open(save_path + ".lengths", "w") as l_f: + for w2v_feats in tqdm.tqdm(iterator, total=num): + print(len(w2v_feats), file=l_f) + + if len(w2v_feats) > 0: + npaa.append(w2v_feats.numpy()) + + +if __name__ == "__main__": + main() diff --git a/examples/wav2vec/unsupervised/scripts/wer.py b/examples/wav2vec/unsupervised/scripts/wer.py new file mode 100644 index 0000000000..613ab50d39 --- /dev/null +++ b/examples/wav2vec/unsupervised/scripts/wer.py @@ -0,0 +1,82 @@ +#!/usr/bin/env python3 -u +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +""" +Implement unsupervised metric for decoding hyperparameter selection: + $$ alpha * LM_PPL + ViterbitUER(%) * 100 $$ +""" +import argparse +import logging +import sys + +import editdistance + +logging.root.setLevel(logging.INFO) +logging.basicConfig(stream=sys.stdout, level=logging.INFO) +logger = logging.getLogger(__name__) + + +def get_parser(): + parser = argparse.ArgumentParser() + parser.add_argument("-s", "--hypo", help="hypo transcription", required=True) + parser.add_argument( + "-r", "--reference", help="reference transcription", required=True + ) + return parser + + +def compute_wer(ref_uid_to_tra, hyp_uid_to_tra, g2p): + d_cnt = 0 + w_cnt = 0 + w_cnt_h = 0 + for uid in hyp_uid_to_tra: + ref = ref_uid_to_tra[uid].split() + if g2p is not None: + hyp = g2p(hyp_uid_to_tra[uid]) + hyp = [p for p in hyp if p != "'" and p != " "] + hyp = [p[:-1] if p[-1].isnumeric() else p for p in hyp] + else: + hyp = hyp_uid_to_tra[uid].split() + d_cnt += editdistance.eval(ref, hyp) + w_cnt += len(ref) + w_cnt_h += len(hyp) + wer = float(d_cnt) / w_cnt + logger.debug( + ( + f"wer = {wer * 100:.2f}%; num. of ref words = {w_cnt}; " + f"num. of hyp words = {w_cnt_h}; num. of sentences = {len(ref_uid_to_tra)}" + ) + ) + return wer + + +def main(): + args = get_parser().parse_args() + + errs = 0 + count = 0 + with open(args.hypo, "r") as hf, open(args.reference, "r") as rf: + for h, r in zip(hf, rf): + h = h.rstrip().split() + r = r.rstrip().split() + errs += editdistance.eval(r, h) + count += len(r) + + logger.info(f"UER: {errs / count * 100:.2f}%") + + +if __name__ == "__main__": + main() + + +def load_tra(tra_path): + with open(tra_path, "r") as f: + uid_to_tra = {} + for line in f: + uid, tra = line.split(None, 1) + uid_to_tra[uid] = tra + logger.debug(f"loaded {len(uid_to_tra)} utterances from {tra_path}") + return uid_to_tra diff --git a/examples/wav2vec/unsupervised/scripts/wrd_to_ltr.py b/examples/wav2vec/unsupervised/scripts/wrd_to_ltr.py new file mode 100644 index 0000000000..f83471409a --- /dev/null +++ b/examples/wav2vec/unsupervised/scripts/wrd_to_ltr.py @@ -0,0 +1,16 @@ +#!/usr/bin/env python3 -u +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import sys + + +def main(): + for line in sys.stdin: + print(" ".join(list(line.strip().replace(" ", "|"))) + " |") + + +if __name__ == "__main__": + main() diff --git a/eval_lm.py b/examples/wav2vec/unsupervised/tasks/__init__.py similarity index 62% rename from eval_lm.py rename to examples/wav2vec/unsupervised/tasks/__init__.py index b5e965a19f..6d7dd625e0 100644 --- a/eval_lm.py +++ b/examples/wav2vec/unsupervised/tasks/__init__.py @@ -1,11 +1,11 @@ -#!/usr/bin/env python3 -u # Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. -from fairseq_cli.eval_lm import cli_main +from .unpaired_audio_text import UnpairedAudioText -if __name__ == '__main__': - cli_main() +__all__ = [ + "UnpairedAudioText", +] diff --git a/examples/wav2vec/unsupervised/tasks/unpaired_audio_text.py b/examples/wav2vec/unsupervised/tasks/unpaired_audio_text.py new file mode 100644 index 0000000000..5f292528f8 --- /dev/null +++ b/examples/wav2vec/unsupervised/tasks/unpaired_audio_text.py @@ -0,0 +1,447 @@ +# Copyright (c) 2017-present, Facebook, Inc. +# All rights reserved. +# +# This source code is licensed under the license found in the LICENSE file in +# the root directory of this source tree. An additional grant of patent rights +# can be found in the PATENTS file in the same directory. + +from dataclasses import dataclass, field +import logging +import math +import os +from typing import Optional +import torch + +from fairseq.logging import metrics +from fairseq.tasks import FairseqTask, register_task +from ..data import ExtractedFeaturesDataset, RandomInputDataset + +from fairseq.data import ( + Dictionary, + data_utils, + StripTokenDataset, +) +from fairseq.dataclass import FairseqDataclass +from fairseq.distributed.utils import get_data_parallel_world_size +from omegaconf import MISSING + +from examples.speech_recognition.kaldi.kaldi_decoder import ( + KaldiDecoder, + KaldiDecoderConfig, +) + + +logger = logging.getLogger(__name__) + + +@dataclass +class DecodingConfig(FairseqDataclass): + kenlm_path: Optional[str] = None + lm_weight: float = 0 + blank_weight: float = 0 + + +@dataclass +class UnpairedAudioTextConfig(FairseqDataclass): + data: str = field( + default=MISSING, metadata={"help": "path to data directory containing audio"} + ) + text_data: str = field( + default=MISSING, metadata={"help": "path to data directory containing text"} + ) + max_length: Optional[int] = None + labels: Optional[str] = field( + default=None, + metadata={"help": "extension of the label file to load, used for fine-tuning"}, + ) + unfiltered: bool = field( + default=False, metadata={"help": "load data with _unfiltered suffix"} + ) + ctc_eval: bool = field( + default=False, metadata={"help": "eval UER as if computed by CTC"} + ) + sort_by_length: bool = field( + default=True, metadata={"help": "sort examples by length of audio timesteps"} + ) + shuffle: bool = field(default=True, metadata={"help": "shuffle examples"}) + append_eos: bool = field(default=False, metadata={"help": "append eos"}) + uppercase: Optional[bool] = field( + default=False, metadata={"help": "uppercase for LM score computation"} + ) + skipwords: Optional[str] = field( + default="", + metadata={ + "help": "comma-separated words to be removed for LM score computation" + }, + ) + kenlm_path: Optional[str] = None + vocab_usage_power: float = 2 + + word_decoder_config: Optional[KaldiDecoderConfig] = None + word_kenlm_path: Optional[str] = None + + decoding_config: DecodingConfig = DecodingConfig() + + +@register_task("unpaired_audio_text", dataclass=UnpairedAudioTextConfig) +class UnpairedAudioText(FairseqTask): + """ """ + + cfg: UnpairedAudioTextConfig + + def __init__( + self, + cfg: UnpairedAudioTextConfig, + source_dictionary=None, + target_dictionary=None, + ): + super().__init__(cfg) + + self._target_dictionary = target_dictionary + self._source_dictionary = source_dictionary + self.num_symbols = ( + len([s for s in target_dictionary.symbols if not s.startswith("madeup")]) + - target_dictionary.nspecial + ) + self.sil_id = ( + target_dictionary.index("<SIL>") if "<SIL>" in target_dictionary else -1 + ) + self.kenlm = None + if cfg.kenlm_path is not None: + import kenlm + + self.kenlm = kenlm.Model(cfg.kenlm_path) + + self.word_kenlm = None + if cfg.word_kenlm_path is not None: + import kenlm + + self.word_kenlm = kenlm.Model(cfg.word_kenlm_path) + + self.uppercase = cfg.uppercase + self.skipwords = set(cfg.skipwords.split(",")) + + def str_postprocess(s): + s = " ".join(w for w in s.split() if w not in self.skipwords) + s = s.upper() if self.uppercase else s + return s + + self.str_postprocess = str_postprocess + self.compute_lm_score = lambda s: self.kenlm.score(self.str_postprocess(s)) + + self.compute_word_score = None + if cfg.word_decoder_config is not None: + self.kaldi_decoder = KaldiDecoder(cfg.word_decoder_config, beam=10) + + def compute_word_score(logits, padding): + res = self.kaldi_decoder.decode(logits, padding) + for r in res: + r = r.result() + assert len(r) == 1 + r = r[0] + yield r["score"], r["words"] + + self.compute_word_score = compute_word_score + + @classmethod + def setup_task(cls, cfg: UnpairedAudioTextConfig, **kwargs): + """Setup the task (e.g., load dictionaries). + + Args: + cfg (AudioPretrainingConfig): configuration of this task + """ + + dict_path = os.path.join(cfg.text_data, "dict.txt") + if os.path.exists(dict_path): + target_dictionary = Dictionary.load(dict_path) + else: + dict_path = os.path.join(cfg.data, f"dict.{cfg.labels}.txt") + target_dictionary = Dictionary.load(dict_path) + + return cls(cfg, target_dictionary=target_dictionary) + + def optimizer_step(self, optimizer, model, update_num): + if hasattr(model, "get_groups_for_update"): + groups = model.get_groups_for_update(update_num) + optimizer.step(groups={groups}) + else: + optimizer.step() + + def valid_step(self, sample, model, criterion): + res = model( + **sample["net_input"], + dense_x_only=True, + ) + + dense_x = res["logits"] + padding_mask = res["padding_mask"] + + word_scores = None + if self.compute_word_score is not None: + word_scores = self.compute_word_score(dense_x.cpu(), padding_mask.cpu()) + + z = dense_x.argmax(-1) + z[padding_mask] = self.target_dictionary.pad() + + vocab_seen = torch.zeros(self.num_symbols, dtype=torch.bool) + + import editdistance + + c_err = 0 + c_len = 0 + pred_c_len = 0 + lm_score_sum = 0 + for i, (x, t, id) in enumerate( + zip( + z, + sample["target"] if "target" in sample else [None] * len(z), + sample["id"], + ) + ): + + if t is not None: + t = t[(t >= self.target_dictionary.nspecial)] + x = x[ + (x >= self.target_dictionary.nspecial) + & (x < (self.num_symbols + self.target_dictionary.nspecial)) + ] + if self.sil_id >= 0: + x = x[x != self.sil_id] + + vocab_seen[x - self.target_dictionary.nspecial] = True + + pred_units_arr = x + if self.cfg.ctc_eval: + pred_units_arr = pred_units_arr.unique_consecutive() + pred_units_arr = pred_units_arr[pred_units_arr != 0] + + if id == 0: + if t is not None: + logger.info(f"REF: {self.target_dictionary.string(t)}") + logger.info(f"HYP: {self.target_dictionary.string(pred_units_arr)}") + + if self.kenlm is not None: + if t is not None: + ref_lm_s = self.compute_lm_score( + self.target_dictionary.string(t) + ) + logger.info( + f"LM [REF]: {ref_lm_s}, {math.pow(10, -ref_lm_s / (len(t) + 1))}" + ) + + hyp_lm_s = self.compute_lm_score( + self.target_dictionary.string(pred_units_arr) + ) + logger.info( + f"LM [HYP]: {hyp_lm_s}, {math.pow(10, -hyp_lm_s / (len(pred_units_arr) + 1))}" + ) + + pred_units_arr = pred_units_arr.tolist() + + pred_c_len += len(pred_units_arr) + + if t is not None: + t = t.tolist() + c_err += editdistance.eval(pred_units_arr, t) + c_len += len(t) + else: + c_len = pred_c_len + + if self.kenlm is not None: + pred_str = self.target_dictionary.string(pred_units_arr) + lm_score = self.compute_lm_score(pred_str) + lm_score_sum += lm_score + + kaldi_score_sum = 0 + word_lm_sum = 0 + num_words = 0 + if word_scores is not None: + for score, words in word_scores: + kaldi_score_sum += score + num_words += len(words) + if self.word_kenlm is not None: + word_lm_sum += self.kenlm.score(" ".join(words)) + + try: + world_size = get_data_parallel_world_size() + except: + world_size = 1 + + logging_output = { + "loss": c_err, + "_num_char_errors": c_err, + "_num_chars": c_len, + "_num_pred_chars": pred_c_len, + "ntokens": c_len, + "nsentences": z.size(0), + "sample_size": c_len, + "_world_size": world_size, + "_lm_score_sum": lm_score_sum, + "_kaldi_score_sum": kaldi_score_sum, + "_word_lm_sum": word_lm_sum, + "_num_words": num_words, + "_vocab_seen": vocab_seen, + } + + return c_err, c_len, logging_output + + def load_dataset(self, split: str, task_cfg: FairseqDataclass = None, **kwargs): + data_path = self.cfg.data + task_cfg = task_cfg or self.cfg + + has_unpaired_text = os.path.exists( + os.path.join(self.cfg.text_data, f"{split}.idx") + ) + + self.datasets[split] = ExtractedFeaturesDataset( + path=data_path, + split=split, + min_length=3, + max_length=task_cfg.max_length, + labels=None if has_unpaired_text else task_cfg.labels, + label_dict=self.target_dictionary, + shuffle=getattr(task_cfg, "shuffle", True), + sort_by_length=task_cfg.sort_by_length, + ) + + logger.info(f"split {split} has unpaired text? {has_unpaired_text}") + if has_unpaired_text: + text_dataset = data_utils.load_indexed_dataset( + os.path.join(self.cfg.text_data, split), self.target_dictionary + ) + text_dataset = StripTokenDataset(text_dataset, self.target_dictionary.eos()) + self.datasets[split] = RandomInputDataset( + self.datasets[split], + text_dataset, + ["random_label"], + add_to_input=True, + pad_idx=self.target_dictionary.pad(), + ) + + @property + def source_dictionary(self): + return self._source_dictionary + + @property + def target_dictionary(self): + """Return the :class:`~fairseq.data.Dictionary` for the language + model.""" + return self._target_dictionary + + def max_positions(self): + """Maximum input length supported by the encoder.""" + return None + + def reduce_metrics(self, logging_outputs, criterion): + super().reduce_metrics(logging_outputs, criterion) + + zero = torch.scalar_tensor(0.0) + num_char_errors = sum( + log.get("_num_char_errors", zero) for log in logging_outputs + ) + num_chars = sum(log.get("_num_chars", zero) for log in logging_outputs) + num_word_errors = sum( + log.get("_num_word_errors", zero) for log in logging_outputs + ) + num_words = sum(log.get("_num_words", zero) for log in logging_outputs) + num_pred_chars = sum( + log.get("_num_pred_chars", zero) for log in logging_outputs + ) + + lm_score_sum = sum(log.get("_lm_score_sum", zero) for log in logging_outputs) + vocab_seen = ( + sum(log.get("_vocab_seen", zero) for log in logging_outputs) + .bool() + .sum() + .item() + ) + kaldi_score_sum = sum( + log.get("_kaldi_score_sum", zero) for log in logging_outputs + ) + word_lm_sum = sum(log.get("_word_lm_sum", zero) for log in logging_outputs) + + metrics.log_scalar_sum("_num_char_errors", num_char_errors) + metrics.log_scalar_sum("_num_chars", num_chars) + metrics.log_scalar_sum("_num_word_errors", num_word_errors) + metrics.log_scalar_sum("_num_words", num_words) + + metrics.log_scalar_sum("lm_score_sum", lm_score_sum) + metrics.log_scalar_sum("num_pred_chars", num_pred_chars) + + if self.cfg.word_kenlm_path is not None: + metrics.log_scalar_sum("kaldi_score_sum", kaldi_score_sum) + metrics.log_scalar_sum("word_lm_sum", word_lm_sum) + + if num_chars > 0: + metrics.log_derived( + "uer", + lambda meters: meters["_num_char_errors"].sum + * 100.0 + / meters["_num_chars"].sum + if meters["_num_chars"].sum > 0 + else float("nan"), + ) + + if lm_score_sum < 0 and vocab_seen > 0: + metrics.log_scalar("vocab_seen_pct", vocab_seen / self.num_symbols) + + metrics.log_derived( + "weighted_lm_ppl", + lambda meters: math.pow( + 10, + -meters["lm_score_sum"].sum + / ( + meters["num_pred_chars"].sum + meters["nsentences"].sum + ), # account for </s> + ) + / meters["vocab_seen_pct"].avg ** self.cfg.vocab_usage_power, + ) + + metrics.log_derived( + "lm_ppl", + lambda meters: math.pow( + 10, + -meters["lm_score_sum"].sum + / ( + meters["num_pred_chars"].sum + meters["nsentences"].sum + ), # account for </s> + ), + ) + else: + metrics.log_derived("weighted_lm_ppl", lambda meters: float("inf")) + + if num_words > 0: + if word_lm_sum != 0: + metrics.log_derived( + "word_lm_ppl", + lambda meters: math.pow( + 10, + -meters["word_lm_sum"].sum + / ( + meters["_num_words"].sum + meters["nsentences"].sum + ), # account for </s> + ), + ) + metrics.log_derived( + "weighted_word_lm_ppl", + lambda meters: math.pow( + 10, + -meters["word_lm_sum"].sum + / ( + meters["_num_words"].sum + meters["nsentences"].sum + ), # account for </s> + ) + / meters["vocab_seen_pct"].avg ** self.cfg.vocab_usage_power, + ) + + if self.cfg.word_kenlm_path is not None: + metrics.log_derived( + "kaldi_score", + lambda meters: meters["kaldi_score_sum"].sum + / meters["nsentences"].sum, + ) + + def build_model(self, cfg: FairseqDataclass): + model = super().build_model(cfg) + + return model diff --git a/examples/wav2vec/unsupervised/w2vu_generate.py b/examples/wav2vec/unsupervised/w2vu_generate.py new file mode 100644 index 0000000000..6177239dc7 --- /dev/null +++ b/examples/wav2vec/unsupervised/w2vu_generate.py @@ -0,0 +1,707 @@ +#!/usr/bin/env python3 -u +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +""" +Run inference for pre-processed data with a trained model. +""" + +import ast +from collections import namedtuple +from dataclasses import dataclass, field +from enum import Enum, auto +import hydra +from hydra.core.config_store import ConfigStore +import logging +import math +import os +from omegaconf import OmegaConf +from typing import Optional +import sys + +import editdistance +import torch + +from hydra.core.hydra_config import HydraConfig + +from fairseq import checkpoint_utils, progress_bar, tasks, utils +from fairseq.data.data_utils import post_process +from fairseq.dataclass.configs import FairseqDataclass, FairseqConfig +from fairseq.logging.meters import StopwatchMeter +from omegaconf import open_dict + +from examples.speech_recognition.kaldi.kaldi_decoder import KaldiDecoderConfig + +logging.root.setLevel(logging.INFO) +logging.basicConfig(stream=sys.stdout, level=logging.INFO) +logger = logging.getLogger(__name__) + + +class DecoderType(Enum): + VITERBI = auto() + KENLM = auto() + FAIRSEQ = auto() + KALDI = auto() + + +@dataclass +class UnsupGenerateConfig(FairseqDataclass): + fairseq: FairseqConfig = FairseqConfig() + lm_weight: float = field( + default=2.0, + metadata={"help": "language model weight"}, + ) + w2l_decoder: DecoderType = field( + default=DecoderType.VITERBI, + metadata={"help": "type of decoder to use"}, + ) + kaldi_decoder_config: Optional[KaldiDecoderConfig] = None + lexicon: Optional[str] = field( + default=None, + metadata={ + "help": "path to lexicon. This is also used to 'phonemize' for unsupvised param tuning" + }, + ) + lm_model: Optional[str] = field( + default=None, + metadata={"help": "path to language model (kenlm or fairseq)"}, + ) + unit_lm: bool = field( + default=False, + metadata={"help": "whether to use unit lm"}, + ) + beam_threshold: float = field( + default=50.0, + metadata={"help": "beam score threshold"}, + ) + beam_size_token: float = field( + default=100.0, + metadata={"help": "max tokens per beam"}, + ) + beam: int = field( + default=5, + metadata={"help": "decoder beam size"}, + ) + nbest: int = field( + default=1, + metadata={"help": "number of results to return"}, + ) + word_score: float = field( + default=1.0, + metadata={"help": "word score to add at end of word"}, + ) + unk_weight: float = field( + default=-math.inf, + metadata={"help": "unknown token weight"}, + ) + sil_weight: float = field( + default=0.0, + metadata={"help": "silence token weight"}, + ) + targets: Optional[str] = field( + default=None, + metadata={"help": "extension of ground truth labels to compute UER"}, + ) + results_path: Optional[str] = field( + default=None, + metadata={"help": "where to store results"}, + ) + post_process: Optional[str] = field( + default=None, + metadata={"help": "how to post process results"}, + ) + vocab_usage_power: float = field( + default=2, + metadata={"help": "for unsupervised param tuning"}, + ) + + viterbi_transcript: Optional[str] = field( + default=None, + metadata={"help": "for unsupervised param tuning"}, + ) + min_lm_ppl: float = field( + default=0, + metadata={"help": "for unsupervised param tuning"}, + ) + min_vt_uer: float = field( + default=0, + metadata={"help": "for unsupervised param tuning"}, + ) + + blank_weight: float = field( + default=0, + metadata={"help": "value to add or set for blank emission"}, + ) + blank_mode: str = field( + default="set", + metadata={ + "help": "can be add or set, how to modify blank emission with blank weight" + }, + ) + sil_is_blank: bool = field( + default=False, + metadata={"help": "if true, <SIL> token is same as blank token"}, + ) + + unsupervised_tuning: bool = field( + default=False, + metadata={ + "help": "if true, returns a score based on unsupervised param selection metric instead of UER" + }, + ) + is_ax: bool = field( + default=False, + metadata={ + "help": "if true, assumes we are using ax for tuning and returns a tuple for ax to consume" + }, + ) + + +def get_dataset_itr(cfg, task): + return task.get_batch_iterator( + dataset=task.dataset(cfg.fairseq.dataset.gen_subset), + max_tokens=cfg.fairseq.dataset.max_tokens, + max_sentences=cfg.fairseq.dataset.batch_size, + max_positions=(sys.maxsize, sys.maxsize), + ignore_invalid_inputs=cfg.fairseq.dataset.skip_invalid_size_inputs_valid_test, + required_batch_size_multiple=cfg.fairseq.dataset.required_batch_size_multiple, + num_shards=cfg.fairseq.dataset.num_shards, + shard_id=cfg.fairseq.dataset.shard_id, + num_workers=cfg.fairseq.dataset.num_workers, + data_buffer_size=cfg.fairseq.dataset.data_buffer_size, + ).next_epoch_itr(shuffle=False) + + +def process_predictions( + cfg: UnsupGenerateConfig, + hypos, + tgt_dict, + target_tokens, + res_files, +): + retval = [] + word_preds = [] + transcriptions = [] + dec_scores = [] + + for i, hypo in enumerate(hypos[: min(len(hypos), cfg.nbest)]): + if torch.is_tensor(hypo["tokens"]): + tokens = hypo["tokens"].int().cpu() + tokens = tokens[tokens >= tgt_dict.nspecial] + hyp_pieces = tgt_dict.string(tokens) + else: + hyp_pieces = " ".join(hypo["tokens"]) + + if "words" in hypo and len(hypo["words"]) > 0: + hyp_words = " ".join(hypo["words"]) + else: + hyp_words = post_process(hyp_pieces, cfg.post_process) + + to_write = {} + if res_files is not None: + to_write[res_files["hypo.units"]] = hyp_pieces + to_write[res_files["hypo.words"]] = hyp_words + + tgt_words = "" + if target_tokens is not None: + if isinstance(target_tokens, str): + tgt_pieces = tgt_words = target_tokens + else: + tgt_pieces = tgt_dict.string(target_tokens) + tgt_words = post_process(tgt_pieces, cfg.post_process) + + if res_files is not None: + to_write[res_files["ref.units"]] = tgt_pieces + to_write[res_files["ref.words"]] = tgt_words + + if not cfg.fairseq.common_eval.quiet: + logger.info(f"HYPO {i}:" + hyp_words) + if tgt_words: + logger.info("TARGET:" + tgt_words) + + if "am_score" in hypo and "lm_score" in hypo: + logger.info( + f"DECODER AM SCORE: {hypo['am_score']}, DECODER LM SCORE: {hypo['lm_score']}, DECODER SCORE: {hypo['score']}" + ) + elif "score" in hypo: + logger.info(f"DECODER SCORE: {hypo['score']}") + + logger.info("___________________") + + hyp_words_arr = hyp_words.split() + tgt_words_arr = tgt_words.split() + + retval.append( + ( + editdistance.eval(hyp_words_arr, tgt_words_arr), + len(hyp_words_arr), + len(tgt_words_arr), + hyp_pieces, + hyp_words, + ) + ) + word_preds.append(hyp_words_arr) + transcriptions.append(to_write) + dec_scores.append(-hypo.get("score", 0)) # negate cuz kaldi returns NLL + + if len(retval) > 1: + best = None + for r, t in zip(retval, transcriptions): + if best is None or r[0] < best[0][0]: + best = r, t + for dest, tran in best[1].items(): + print(tran, file=dest) + dest.flush() + return best[0] + + assert len(transcriptions) == 1 + for dest, tran in transcriptions[0].items(): + print(tran, file=dest) + + return retval[0] + + +def prepare_result_files(cfg: UnsupGenerateConfig): + def get_res_file(file_prefix): + if cfg.fairseq.dataset.num_shards > 1: + file_prefix = f"{cfg.fairseq.dataset.shard_id}_{file_prefix}" + path = os.path.join( + cfg.results_path, + "{}{}.txt".format( + cfg.fairseq.dataset.gen_subset, + file_prefix, + ), + ) + return open(path, "w", buffering=1) + + if not cfg.results_path: + return None + + return { + "hypo.words": get_res_file(""), + "hypo.units": get_res_file("_units"), + "ref.words": get_res_file("_ref"), + "ref.units": get_res_file("_ref_units"), + "hypo.nbest.words": get_res_file("_nbest_words"), + } + + +def optimize_models(cfg: UnsupGenerateConfig, use_cuda, models): + """Optimize ensemble for generation""" + for model in models: + model.eval() + if cfg.fairseq.common.fp16: + model.half() + if use_cuda: + model.cuda() + + +GenResult = namedtuple( + "GenResult", + [ + "count", + "errs_t", + "gen_timer", + "lengths_hyp_unit_t", + "lengths_hyp_t", + "lengths_t", + "lm_score_t", + "num_feats", + "num_sentences", + "num_symbols", + "vt_err_t", + "vt_length_t", + ], +) + + +def generate(cfg: UnsupGenerateConfig, models, saved_cfg, use_cuda): + task = tasks.setup_task(cfg.fairseq.task) + saved_cfg.task.labels = cfg.fairseq.task.labels + task.load_dataset(cfg.fairseq.dataset.gen_subset, task_cfg=saved_cfg.task) + # Set dictionary + tgt_dict = task.target_dictionary + logger.info( + "| {} {} {} examples".format( + cfg.fairseq.task.data, + cfg.fairseq.dataset.gen_subset, + len(task.dataset(cfg.fairseq.dataset.gen_subset)), + ) + ) + # Load dataset (possibly sharded) + itr = get_dataset_itr(cfg, task) + # Initialize generator + gen_timer = StopwatchMeter() + + def build_generator(cfg: UnsupGenerateConfig): + w2l_decoder = cfg.w2l_decoder + if w2l_decoder == DecoderType.VITERBI: + from examples.speech_recognition.w2l_decoder import W2lViterbiDecoder + + return W2lViterbiDecoder(cfg, task.target_dictionary) + elif w2l_decoder == DecoderType.KENLM: + from examples.speech_recognition.w2l_decoder import W2lKenLMDecoder + + return W2lKenLMDecoder(cfg, task.target_dictionary) + elif w2l_decoder == DecoderType.FAIRSEQ: + from examples.speech_recognition.w2l_decoder import W2lFairseqLMDecoder + + return W2lFairseqLMDecoder(cfg, task.target_dictionary) + elif w2l_decoder == DecoderType.KALDI: + from examples.speech_recognition.kaldi.kaldi_decoder import KaldiDecoder + + assert cfg.kaldi_decoder_config is not None + + return KaldiDecoder( + cfg.kaldi_decoder_config, + cfg.beam, + ) + else: + raise NotImplementedError( + "only wav2letter decoders with (viterbi, kenlm, fairseqlm) options are supported at the moment but found " + + str(w2l_decoder) + ) + + generator = build_generator(cfg) + + kenlm = None + fairseq_lm = None + if cfg.lm_model is not None: + import kenlm + + kenlm = kenlm.Model(cfg.lm_model) + + num_sentences = 0 + if cfg.results_path is not None and not os.path.exists(cfg.results_path): + os.makedirs(cfg.results_path) + + res_files = prepare_result_files(cfg) + errs_t = 0 + lengths_hyp_t = 0 + lengths_hyp_unit_t = 0 + lengths_t = 0 + count = 0 + num_feats = 0 + all_hyp_pieces = [] + all_hyp_words = [] + + num_symbols = ( + len([s for s in tgt_dict.symbols if not s.startswith("madeup")]) + - tgt_dict.nspecial + ) + targets = None + if cfg.targets is not None: + tgt_path = os.path.join( + cfg.fairseq.task.data, cfg.fairseq.dataset.gen_subset + "." + cfg.targets + ) + if os.path.exists(tgt_path): + with open(tgt_path, "r") as f: + targets = f.read().splitlines() + viterbi_transcript = None + if cfg.viterbi_transcript is not None and len(cfg.viterbi_transcript) > 0: + logger.info(f"loading viterbi transcript from {cfg.viterbi_transcript}") + with open(cfg.viterbi_transcript, "r") as vf: + viterbi_transcript = vf.readlines() + viterbi_transcript = [v.rstrip().split() for v in viterbi_transcript] + + gen_timer.start() + + start = 0 + end = len(itr) + + hypo_futures = None + if cfg.w2l_decoder == DecoderType.KALDI: + logger.info("Extracting features") + hypo_futures = [] + samples = [] + with progress_bar.build_progress_bar(cfg.fairseq.common, itr) as t: + for i, sample in enumerate(t): + if "net_input" not in sample or i < start or i >= end: + continue + if "padding_mask" not in sample["net_input"]: + sample["net_input"]["padding_mask"] = None + + hypos, num_feats = gen_hypos( + generator, models, num_feats, sample, task, use_cuda + ) + hypo_futures.append(hypos) + samples.append(sample) + itr = list(zip(hypo_futures, samples)) + start = 0 + end = len(itr) + logger.info("Finished extracting features") + + with progress_bar.build_progress_bar(cfg.fairseq.common, itr) as t: + for i, sample in enumerate(t): + if i < start or i >= end: + continue + + if hypo_futures is not None: + hypos, sample = sample + hypos = [h.result() for h in hypos] + else: + if "net_input" not in sample: + continue + + hypos, num_feats = gen_hypos( + generator, models, num_feats, sample, task, use_cuda + ) + + for i, sample_id in enumerate(sample["id"].tolist()): + if targets is not None: + target_tokens = targets[sample_id] + elif "target" in sample or "target_label" in sample: + toks = ( + sample["target"][i, :] + if "target_label" not in sample + else sample["target_label"][i, :] + ) + + target_tokens = utils.strip_pad(toks, tgt_dict.pad()).int().cpu() + else: + target_tokens = None + + # Process top predictions + ( + errs, + length_hyp, + length, + hyp_pieces, + hyp_words, + ) = process_predictions( + cfg, + hypos[i], + tgt_dict, + target_tokens, + res_files, + ) + errs_t += errs + lengths_hyp_t += length_hyp + lengths_hyp_unit_t += ( + len(hyp_pieces) if len(hyp_pieces) > 0 else len(hyp_words) + ) + lengths_t += length + count += 1 + all_hyp_pieces.append(hyp_pieces) + all_hyp_words.append(hyp_words) + + num_sentences += ( + sample["nsentences"] if "nsentences" in sample else sample["id"].numel() + ) + + lm_score_sum = 0 + if kenlm is not None: + + if cfg.unit_lm: + lm_score_sum = sum(kenlm.score(w) for w in all_hyp_pieces) + else: + lm_score_sum = sum(kenlm.score(w) for w in all_hyp_words) + elif fairseq_lm is not None: + lm_score_sum = sum(fairseq_lm.score([h.split() for h in all_hyp_words])[0]) + + vt_err_t = 0 + vt_length_t = 0 + if viterbi_transcript is not None: + unit_hyps = [] + if cfg.targets is not None and cfg.lexicon is not None: + lex = {} + with open(cfg.lexicon, "r") as lf: + for line in lf: + items = line.rstrip().split() + lex[items[0]] = items[1:] + for h in all_hyp_pieces: + hyp_ws = [] + for w in h.split(): + assert w in lex, w + hyp_ws.extend(lex[w]) + unit_hyps.append(hyp_ws) + + else: + unit_hyps.extend([h.split() for h in all_hyp_words]) + + vt_err_t = sum( + editdistance.eval(vt, h) for vt, h in zip(viterbi_transcript, unit_hyps) + ) + + vt_length_t = sum(len(h) for h in viterbi_transcript) + + if res_files is not None: + for r in res_files.values(): + r.close() + + gen_timer.stop(lengths_hyp_t) + + return GenResult( + count, + errs_t, + gen_timer, + lengths_hyp_unit_t, + lengths_hyp_t, + lengths_t, + lm_score_sum, + num_feats, + num_sentences, + num_symbols, + vt_err_t, + vt_length_t, + ) + + +def gen_hypos(generator, models, num_feats, sample, task, use_cuda): + sample = utils.move_to_cuda(sample) if use_cuda else sample + + if "features" in sample["net_input"]: + sample["net_input"]["dense_x_only"] = True + num_feats += ( + sample["net_input"]["features"].shape[0] + * sample["net_input"]["features"].shape[1] + ) + hypos = task.inference_step(generator, models, sample, None) + return hypos, num_feats + + +def main(cfg: UnsupGenerateConfig, model=None): + if ( + cfg.fairseq.dataset.max_tokens is None + and cfg.fairseq.dataset.batch_size is None + ): + cfg.fairseq.dataset.max_tokens = 1024000 + + use_cuda = torch.cuda.is_available() and not cfg.fairseq.common.cpu + + task = tasks.setup_task(cfg.fairseq.task) + + overrides = ast.literal_eval(cfg.fairseq.common_eval.model_overrides) + + if cfg.fairseq.task._name == "unpaired_audio_text": + overrides["model"] = { + "blank_weight": cfg.blank_weight, + "blank_mode": cfg.blank_mode, + "blank_is_sil": cfg.sil_is_blank, + "no_softmax": True, + "segmentation": { + "type": "NONE", + }, + } + else: + overrides["model"] = { + "blank_weight": cfg.blank_weight, + "blank_mode": cfg.blank_mode, + } + + if model is None: + # Load ensemble + logger.info("| loading model(s) from {}".format(cfg.fairseq.common_eval.path)) + models, saved_cfg = checkpoint_utils.load_model_ensemble( + cfg.fairseq.common_eval.path.split("\\"), + arg_overrides=overrides, + task=task, + suffix=cfg.fairseq.checkpoint.checkpoint_suffix, + strict=(cfg.fairseq.checkpoint.checkpoint_shard_count == 1), + num_shards=cfg.fairseq.checkpoint.checkpoint_shard_count, + ) + optimize_models(cfg, use_cuda, models) + else: + models = [model] + saved_cfg = cfg.fairseq + + with open_dict(saved_cfg.task): + saved_cfg.task.shuffle = False + saved_cfg.task.sort_by_length = False + + gen_result = generate(cfg, models, saved_cfg, use_cuda) + + wer = None + if gen_result.lengths_t > 0: + wer = gen_result.errs_t * 100.0 / gen_result.lengths_t + logger.info(f"WER: {wer}") + + lm_ppl = float("inf") + + if gen_result.lm_score_t != 0 and gen_result.lengths_hyp_t > 0: + hyp_len = gen_result.lengths_hyp_t + lm_ppl = math.pow( + 10, -gen_result.lm_score_t / (hyp_len + gen_result.num_sentences) + ) + logger.info(f"LM PPL: {lm_ppl}") + + logger.info( + "| Processed {} sentences ({} tokens) in {:.1f}s ({:.2f}" + " sentences/s, {:.2f} tokens/s)".format( + gen_result.num_sentences, + gen_result.gen_timer.n, + gen_result.gen_timer.sum, + gen_result.num_sentences / gen_result.gen_timer.sum, + 1.0 / gen_result.gen_timer.avg, + ) + ) + + vt_diff = None + if gen_result.vt_length_t > 0: + vt_diff = gen_result.vt_err_t / gen_result.vt_length_t + vt_diff = max(cfg.min_vt_uer, vt_diff) + + lm_ppl = max(cfg.min_lm_ppl, lm_ppl) + + if not cfg.unsupervised_tuning == 0: + weighted_score = wer + else: + weighted_score = math.log(lm_ppl) * (vt_diff or 1.0) + + res = ( + f"| Generate {cfg.fairseq.dataset.gen_subset} with beam={cfg.beam}, " + f"lm_weight={cfg.kaldi_decoder_config.acoustic_scale if cfg.kaldi_decoder_config else cfg.lm_weight}, " + f"word_score={cfg.word_score}, sil_weight={cfg.sil_weight}, blank_weight={cfg.blank_weight}, " + f"WER: {wer}, LM_PPL: {lm_ppl}, num feats: {gen_result.num_feats}, " + f"length: {gen_result.lengths_hyp_t}, UER to viterbi: {(vt_diff or 0) * 100}, score: {weighted_score}" + ) + + logger.info(res) + # print(res) + + return task, weighted_score + + +@hydra.main( + config_path=os.path.join("../../..", "fairseq", "config"), config_name="config" +) +def hydra_main(cfg): + with open_dict(cfg): + # make hydra logging work with ddp (see # see https://github.com/facebookresearch/hydra/issues/1126) + cfg.job_logging_cfg = OmegaConf.to_container( + HydraConfig.get().job_logging, resolve=True + ) + + cfg = OmegaConf.create( + OmegaConf.to_container(cfg, resolve=False, enum_to_str=False) + ) + OmegaConf.set_struct(cfg, True) + logger.info(cfg) + + utils.import_user_module(cfg.fairseq.common) + + _, score = main(cfg) + + if cfg.is_ax: + return score, None + return score + + +def cli_main(): + try: + from hydra._internal.utils import get_args + + cfg_name = get_args().config_name or "config" + except: + logger.warning("Failed to get config name from hydra args") + cfg_name = "config" + + cs = ConfigStore.instance() + cs.store(name=cfg_name, node=UnsupGenerateConfig) + hydra_main() + + +if __name__ == "__main__": + cli_main() diff --git a/examples/wav2vec/vq-wav2vec_featurize.py b/examples/wav2vec/vq-wav2vec_featurize.py index ce9afd2431..627072ee17 100644 --- a/examples/wav2vec/vq-wav2vec_featurize.py +++ b/examples/wav2vec/vq-wav2vec_featurize.py @@ -5,33 +5,33 @@ # LICENSE file in the root directory of this source tree. """ -Helper script to pre-compute embeddings for a wav2letter++ dataset +Helper script to pre-compute embeddings for a flashlight (previously called wav2letter++) dataset """ +import argparse +import glob +import os +import os.path as osp import pprint -import glob, os, argparse +import soundfile as sf import torch +import fairseq from torch import nn +from torch.utils.data import DataLoader + try: import tqdm except: print("Install tqdm to use --log-format=tqdm") -from fairseq.models.wav2vec import Wav2VecModel - -import tqdm -import soundfile as sf -from torch.utils.data import DataLoader -import os.path as osp - class FilesDataset: def __init__(self, files, labels): self.files = files if labels and osp.exists(labels): - with open(labels, 'r') as lbl_f: + with open(labels, "r") as lbl_f: self.labels = [line.rstrip() for line in lbl_f] else: self.labels = labels @@ -50,7 +50,7 @@ def __getitem__(self, index): if self.labels: if isinstance(self.labels, str): lbl_file = osp.splitext(fname)[0] + "." + self.labels - with open(lbl_file, 'r') as lblf: + with open(lbl_file, "r") as lblf: lbls = lblf.readline() assert lbls is not None else: @@ -116,24 +116,24 @@ def process_splits(self): assert len(files) > 0 if self.args.shard is not None: - files = files[self.args.shard::self.args.num_shards] + files = files[self.args.shard :: self.args.num_shards] lbls = [] - with open(self.data_file(split), 'w') as srcf: + with open(self.data_file(split), "w") as srcf: for line, lbl in self.iterate(files): print(line, file=srcf) if self.args.labels: - lbls.append(lbl + '\n') + lbls.append(lbl + "\n") if self.args.labels: assert all(a is not None for a in lbls) - with open(self.lbl_file(split), 'w') as lblf: + with open(self.lbl_file(split), "w") as lblf: lblf.writelines(lbls) def iterate(self, files): data = self.load_data(files) - for samples in tqdm.tqdm(data, total=len(files)//32): + for samples in tqdm.tqdm(data, total=len(files) // 32): for wav, lbl in samples: x = wav.unsqueeze(0).float().cuda() @@ -162,7 +162,6 @@ def iterate(self, files): idx = torch.cat(result, dim=0) yield " ".join("-".join(map(str, a.tolist())) for a in idx), lbl - def lbl_file(self, name): shard_part = "" if self.args.shard is None else f".{self.args.shard}" return osp.join(self.output_dir, f"{name}.lbl{shard_part}") @@ -211,13 +210,11 @@ def load_data(self, fnames): return loader def load_model(self): - cp = torch.load(self.checkpoint, map_location=lambda x, _: x) - - model = Wav2VecModel.build_model(cp["args"], None) + model, cfg, task = fairseq.checkpoint_utils.load_model_ensemble_and_task([self.checkpoint]) + model = model[0] - self.quantize_location = getattr(cp["args"], "vq", "encoder") + self.quantize_location = getattr(cfg.model, "vq", "encoder") - model.load_state_dict(cp["model"]) model.eval().float() model.cuda() @@ -230,7 +227,9 @@ def __call__(self): self.process_splits() - if hasattr(self.model.feature_extractor, "vars") and (self.args.shard is None or self.args.shard == 0): + if hasattr(self.model.feature_extractor, "vars") and ( + self.args.shard is None or self.args.shard == 0 + ): vars = ( self.model.feature_extractor.vars.view( self.model.feature_extractor.banks, @@ -248,4 +247,4 @@ def __call__(self): write_data = DatasetWriter() write_data() - print("Done.") \ No newline at end of file + print("Done.") diff --git a/examples/wav2vec/wav2vec_featurize.py b/examples/wav2vec/wav2vec_featurize.py index 31e12433f9..588268b708 100644 --- a/examples/wav2vec/wav2vec_featurize.py +++ b/examples/wav2vec/wav2vec_featurize.py @@ -5,7 +5,7 @@ # LICENSE file in the root directory of this source tree. """ -Helper script to pre-compute embeddings for a wav2letter++ dataset +Helper script to pre-compute embeddings for a flashlight (previously called wav2letter++) dataset """ import argparse @@ -14,13 +14,12 @@ from shutil import copy import h5py -import soundfile as sf import numpy as np +import soundfile as sf import torch -from torch import nn import tqdm - -from fairseq.models.wav2vec import Wav2VecModel +import fairseq +from torch import nn def read_audio(fname): @@ -33,14 +32,11 @@ def read_audio(fname): class PretrainedWav2VecModel(nn.Module): - def __init__(self, fname): super().__init__() - checkpoint = torch.load(fname) - self.args = checkpoint["args"] - model = Wav2VecModel.build_model(self.args, None) - model.load_state_dict(checkpoint["model"]) + model, cfg, task = fairseq.checkpoint_utils.load_model_ensemble_and_task([fname]) + model = model[0] model.eval() self.model = model @@ -55,32 +51,33 @@ def forward(self, x): class EmbeddingWriterConfig(argparse.ArgumentParser): - def __init__(self): - super().__init__("Pre-compute embeddings for wav2letter++ datasets") + super().__init__("Pre-compute embeddings for flashlight datasets") kwargs = {"action": "store", "type": str, "required": True} - self.add_argument("--input", "-i", - help="Input Directory", **kwargs) - self.add_argument("--output", "-o", - help="Output Directory", **kwargs) - self.add_argument("--model", - help="Path to model checkpoint", **kwargs) - self.add_argument("--split", - help="Dataset Splits", nargs='+', **kwargs) - self.add_argument("--ext", default="wav", required=False, - help="Audio file extension") - - self.add_argument("--no-copy-labels", action="store_true", - help="Do not copy label files. Useful for large datasets, use --targetdir in wav2letter then.") - self.add_argument("--use-feat", action="store_true", - help="Use the feature vector ('z') instead of context vector ('c') for features") - self.add_argument("--gpu", - help="GPU to use", default=0, type=int) - - -class Prediction(): + self.add_argument("--input", "-i", help="Input Directory", **kwargs) + self.add_argument("--output", "-o", help="Output Directory", **kwargs) + self.add_argument("--model", help="Path to model checkpoint", **kwargs) + self.add_argument("--split", help="Dataset Splits", nargs="+", **kwargs) + self.add_argument( + "--ext", default="wav", required=False, help="Audio file extension" + ) + + self.add_argument( + "--no-copy-labels", + action="store_true", + help="Do not copy label files. Useful for large datasets, use --targetdir in flashlight then.", + ) + self.add_argument( + "--use-feat", + action="store_true", + help="Use the feature vector ('z') instead of context vector ('c') for features", + ) + self.add_argument("--gpu", help="GPU to use", default=0, type=int) + + +class Prediction: """ Lightweight wrapper around a fairspeech embedding model """ def __init__(self, fname, gpu=0): @@ -95,8 +92,8 @@ def __call__(self, x): return z.squeeze(0).cpu().numpy(), c.squeeze(0).cpu().numpy() -class H5Writer(): - """ Write features as hdf5 file in wav2letter++ compatible format """ +class H5Writer: + """ Write features as hdf5 file in flashlight compatible format """ def __init__(self, fname): self.fname = fname @@ -112,24 +109,28 @@ def write(self, data): class EmbeddingDatasetWriter(object): - """ Given a model and a wav2letter++ dataset, pre-compute and store embeddings + """Given a model and a flashlight dataset, pre-compute and store embeddings Args: input_root, str : - Path to the wav2letter++ dataset + Path to the flashlight dataset output_root, str : Desired output directory. Will be created if non-existent split, str : Dataset split """ - def __init__(self, input_root, output_root, split, - model_fname, - extension="wav", - gpu=0, - verbose=False, - use_feat=False, - ): + def __init__( + self, + input_root, + output_root, + split, + model_fname, + extension="wav", + gpu=0, + verbose=False, + use_feat=False, + ): assert os.path.exists(model_fname) @@ -143,8 +144,9 @@ def __init__(self, input_root, output_root, split, self.extension = extension self.use_feat = use_feat - assert os.path.exists(self.input_path), \ - "Input path '{}' does not exist".format(self.input_path) + assert os.path.exists(self.input_path), "Input path '{}' does not exist".format( + self.input_path + ) def _progress(self, iterable, **kwargs): if self.verbose: @@ -176,7 +178,11 @@ def get_output_path(self, fname=None): def copy_labels(self): self.require_output_path() - labels = list(filter(lambda x: self.extension not in x, glob.glob(self.get_input_path("*")))) + labels = list( + filter( + lambda x: self.extension not in x, glob.glob(self.get_input_path("*")) + ) + ) for fname in tqdm.tqdm(labels): copy(fname, self.output_path) @@ -191,10 +197,16 @@ def write_features(self): paths = self.input_fnames - fnames_context = map(lambda x: os.path.join(self.output_path, x.replace("." + self.extension, ".h5context")), \ - map(os.path.basename, paths)) + fnames_context = map( + lambda x: os.path.join( + self.output_path, x.replace("." + self.extension, ".h5context") + ), + map(os.path.basename, paths), + ) - for name, target_fname in self._progress(zip(paths, fnames_context), total=len(self)): + for name, target_fname in self._progress( + zip(paths, fnames_context), total=len(self) + ): wav, sr = read_audio(name) z, c = self.model(wav) feat = z if self.use_feat else c @@ -204,7 +216,8 @@ def write_features(self): def __repr__(self): return "EmbeddingDatasetWriter ({n_files} files)\n\tinput:\t{input_root}\n\toutput:\t{output_root}\n\tsplit:\t{split})".format( - n_files=len(self), **self.__dict__) + n_files=len(self), **self.__dict__ + ) if __name__ == "__main__": diff --git a/examples/wav2vec/wav2vec_manifest.py b/examples/wav2vec/wav2vec_manifest.py index c80f9883df..9b8aa180e8 100644 --- a/examples/wav2vec/wav2vec_manifest.py +++ b/examples/wav2vec/wav2vec_manifest.py @@ -10,34 +10,61 @@ import argparse import glob import os -import soundfile import random +import soundfile + def get_parser(): parser = argparse.ArgumentParser() - parser.add_argument('root', metavar='DIR', help='root directory containing flac files to index') - parser.add_argument('--valid-percent', default=0.01, type=float, metavar='D', - help='percentage of data to use as validation set (between 0 and 1)') - parser.add_argument('--dest', default='.', type=str, metavar='DIR', help='output directory') - parser.add_argument('--ext', default='flac', type=str, metavar='EXT', help='extension to look for') - parser.add_argument('--seed', default=42, type=int, metavar='N', help='random seed') - parser.add_argument('--path-must-contain', default=None, type=str, metavar='FRAG', - help='if set, path must contain this substring for a file to be included in the manifest') + parser.add_argument( + "root", metavar="DIR", help="root directory containing flac files to index" + ) + parser.add_argument( + "--valid-percent", + default=0.01, + type=float, + metavar="D", + help="percentage of data to use as validation set (between 0 and 1)", + ) + parser.add_argument( + "--dest", default=".", type=str, metavar="DIR", help="output directory" + ) + parser.add_argument( + "--ext", default="flac", type=str, metavar="EXT", help="extension to look for" + ) + parser.add_argument("--seed", default=42, type=int, metavar="N", help="random seed") + parser.add_argument( + "--path-must-contain", + default=None, + type=str, + metavar="FRAG", + help="if set, path must contain this substring for a file to be included in the manifest", + ) return parser def main(args): - assert args.valid_percent >= 0 and args.valid_percent <= 1. + assert args.valid_percent >= 0 and args.valid_percent <= 1.0 + + if not os.path.exists(args.dest): + os.makedirs(args.dest) dir_path = os.path.realpath(args.root) - search_path = os.path.join(dir_path, '**/*.' + args.ext) + search_path = os.path.join(dir_path, "**/*." + args.ext) rand = random.Random(args.seed) - with open(os.path.join(args.dest, 'train.tsv'), 'w') as train_f, open( - os.path.join(args.dest, 'valid.tsv'), 'w') as valid_f: + valid_f = ( + open(os.path.join(args.dest, "valid.tsv"), "w") + if args.valid_percent > 0 + else None + ) + + with open(os.path.join(args.dest, "train.tsv"), "w") as train_f: print(dir_path, file=train_f) - print(dir_path, file=valid_f) + + if valid_f is not None: + print(dir_path, file=valid_f) for fname in glob.iglob(search_path, recursive=True): file_path = os.path.realpath(fname) @@ -47,10 +74,14 @@ def main(args): frames = soundfile.info(fname).frames dest = train_f if rand.random() > args.valid_percent else valid_f - print('{}\t{}'.format(os.path.relpath(file_path, dir_path), frames), file=dest) + print( + "{}\t{}".format(os.path.relpath(file_path, dir_path), frames), file=dest + ) + if valid_f is not None: + valid_f.close() -if __name__ == '__main__': +if __name__ == "__main__": parser = get_parser() args = parser.parse_args() main(args) diff --git a/examples/wmt19/README.md b/examples/wmt19/README.md index 3c59851264..5c90d0e6c4 100644 --- a/examples/wmt19/README.md +++ b/examples/wmt19/README.md @@ -14,6 +14,15 @@ Model | Description | Download `transformer_lm.wmt19.de` | De Language Model | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt19.de.tar.gz) `transformer_lm.wmt19.ru` | Ru Language Model | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt19.ru.tar.gz) +## Pre-trained single models before finetuning + +Model | Description | Download +---|---|--- +`transformer.wmt19.en-de` | En->De Single, no finetuning | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt19.en-de.ffn8192.tar.gz) +`transformer.wmt19.de-en` | De->En Single, no finetuning | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt19.de-en.ffn8192.tar.gz) +`transformer.wmt19.en-ru` | En->Ru Single, no finetuning | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt19.en-ru.ffn8192.tar.gz) +`transformer.wmt19.ru-en` | Ru->En Single, no finetuning | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt19.ru-en.ffn8192.tar.gz) + ## Example usage (torch.hub) #### Requirements diff --git a/examples/wmt20/README.md b/examples/wmt20/README.md new file mode 100644 index 0000000000..b4f2874652 --- /dev/null +++ b/examples/wmt20/README.md @@ -0,0 +1,72 @@ +# WMT 20 + +This page provides pointers to the models of Facebook-FAIR's WMT'20 news translation task submission [(Chen et al., 2020)](https://arxiv.org/abs/2011.08298). + +## Single best MT models (after finetuning on part of WMT20 news dev set) + +Model | Description | Download +---|---|--- +`transformer.wmt20.ta-en` | Ta->En | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt20.ta-en.single.tar.gz) +`transformer.wmt20.en-ta` | En->Ta | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt20.en-ta.single.tar.gz) +`transformer.wmt20.iu-en.news` | Iu->En (News domain) | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt20.iu-en.news.single.tar.gz) +`transformer.wmt20.en-iu.news` | En->Iu (News domain) | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt20.en-iu.news.single.tar.gz) +`transformer.wmt20.iu-en.nh` | Iu->En (Nunavut Hansard domain) | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt20.iu-en.nh.single.tar.gz) +`transformer.wmt20.en-iu.nh` | En->Iu (Nunavut Hansard domain) | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt20.en-iu.nh.single.tar.gz) + +## Language models +Model | Description | Download +---|---|--- +`transformer_lm.wmt20.en` | En Language Model | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt20.en.tar.gz) +`transformer_lm.wmt20.ta` | Ta Language Model | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt20.ta.tar.gz) +`transformer_lm.wmt20.iu.news` | Iu Language Model (News domain) | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt20.iu.news.tar.gz) +`transformer_lm.wmt20.iu.nh` | Iu Language Model (Nunavut Hansard domain) | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt20.iu.nh.tar.gz) + +## Example usage (torch.hub) + +#### Translation + +```python +import torch + +# English to Tamil translation +en2ta = torch.hub.load('pytorch/fairseq', 'transformer.wmt20.en-ta') +en2ta.translate("Machine learning is great!") # 'இயந்திரக் கற்றல் அருமை!' + +# Tamil to English translation +ta2en = torch.hub.load('pytorch/fairseq', 'transformer.wmt20.ta-en') +ta2en.translate("இயந்திரக் கற்றல் அருமை!") # 'Machine learning is great!' + +# English to Inuktitut translation +en2iu = torch.hub.load('pytorch/fairseq', 'transformer.wmt20.en-iu.news') +en2iu.translate("machine learning is great!") # 'ᖃᒧᑕᐅᔭᓄᑦ ᐃᓕᓐᓂᐊᕐᓂᖅ ᐱᐅᔪᒻᒪᕆᒃ!' + +# Inuktitut to English translation +iu2en = torch.hub.load('pytorch/fairseq', 'transformer.wmt20.iu-en.news') +iu2en.translate("ᖃᒧᑕᐅᔭᓄᑦ ᐃᓕᓐᓂᐊᕐᓂᖅ ᐱᐅᔪᒻᒪᕆᒃ!") # 'Machine learning excellence!' +``` + +#### Language Modeling + +```python +# Sample from the English LM +en_lm = torch.hub.load('pytorch/fairseq', 'transformer_lm.wmt20.en') +en_lm.sample("Machine learning is") # 'Machine learning is a type of artificial intelligence that uses machine learning to learn from data and make predictions.' + +# Sample from the Tamil LM +ta_lm = torch.hub.load('pytorch/fairseq', 'transformer_lm.wmt20.ta') +ta_lm.sample("இயந்திரக் கற்றல் என்பது செயற்கை நுண்ணறிவின்") # 'இயந்திரக் கற்றல் என்பது செயற்கை நுண்ணறிவின் ஒரு பகுதியாகும்.' + +# Sample from the Inuktitut LM +iu_lm = torch.hub.load('pytorch/fairseq', 'transformer_lm.wmt20.iu.news') +iu_lm.sample("ᖃᒧᑕᐅᔭᓄᑦ ᐃᓕᓐᓂᐊᕐᓂᖅ") # 'ᖃᒧᑕᐅᔭᓄᑦ ᐃᓕᓐᓂᐊᕐᓂᖅ, ᐊᒻᒪᓗ ᓯᓚᐅᑉ ᐊᓯᙳᖅᐸᓪᓕᐊᓂᖓᓄᑦ ᖃᓄᐃᓕᐅᕈᑎᒃᓴᑦ, ᐃᓚᖃᖅᖢᑎᒃ ᐅᑯᓂᖓ:' +``` + +## Citation +```bibtex +@inproceedings{chen2020facebook + title={Facebook AI's WMT20 News Translation Task Submission}, + author={Peng-Jen Chen and Ann Lee and Changhan Wang and Naman Goyal and Angela Fan and Mary Williamson and Jiatao Gu}, + booktitle={Proc. of WMT}, + year={2020}, +} +``` diff --git a/examples/xlmr/README.md b/examples/xlmr/README.md index 9e0dfc1254..b95bfe15d3 100644 --- a/examples/xlmr/README.md +++ b/examples/xlmr/README.md @@ -1,9 +1,16 @@ # Unsupervised Cross-lingual Representation Learning at Scale (XLM-RoBERTa) https://arxiv.org/pdf/1911.02116.pdf +# Larger-Scale Transformers for Multilingual Masked Language Modeling +https://arxiv.org/pdf/2105.00572.pdf + + +## What's New: +- June 2021: `XLMR-XL` AND `XLMR-XXL` models released. + ## Introduction -XLM-R (XLM-RoBERTa) is a generic cross lingual sentence encoder that obtains state-of-the-art results on many cross-lingual understanding (XLU) benchmarks. It is trained on 2.5T of filtered CommonCrawl data in 100 languages (list below). +`XLM-R` (`XLM-RoBERTa`) is a generic cross lingual sentence encoder that obtains state-of-the-art results on many cross-lingual understanding (XLU) benchmarks. It is trained on `2.5T` of filtered CommonCrawl data in 100 languages (list below). Language | Language|Language |Language | Language ---|---|---|---|--- @@ -21,7 +28,7 @@ Kyrgyz | Lao | Latin | Latvian | Lithuanian Macedonian | Malagasy | Malay | Malayalam | Marathi Mongolian | Nepali | Norwegian | Oriya | Oromo Pashto | Persian | Polish | Portuguese | Punjabi -Romanian | Russian | Sanskri | Scottish Gaelic | Serbian +Romanian | Russian | Sanskrit | Scottish Gaelic | Serbian Sindhi | Sinhala | Slovak | Slovenian | Somali Spanish | Sundanese | Swahili | Swedish | Tamil Tamil Romanize | Telugu | Telugu Romanize | Thai | Turkish @@ -34,8 +41,8 @@ Model | Description | #params | vocab size | Download ---|---|---|---|--- `xlmr.base` | XLM-R using the BERT-base architecture | 250M | 250k | [xlm.base.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/xlmr.base.tar.gz) `xlmr.large` | XLM-R using the BERT-large architecture | 560M | 250k | [xlm.large.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/xlmr.large.tar.gz) - -(Note: Above are final model checkpoints. If you were using previously released `v0` version, we recommend using above. They have same architecture and dictionary.) +`xlmr.xl` | XLM-R (`layers=36, model_dim=2560`) | 3.5B | 250k | [xlm.xl.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/xlmr/xlmr.xl.tar.gz) +`xlmr.xxl` | XLM-R (`layers=48, model_dim=4096`) | 10.7B | 250k | [xlm.xxl.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/xlmr/xlmr.xxl.tar.gz) ## Results @@ -44,7 +51,9 @@ Model | Description | #params | vocab size | Download Model | average | en | fr | es | de | el | bg | ru | tr | ar | vi | th | zh | hi | sw | ur ---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|--- `roberta.large.mnli` _(TRANSLATE-TEST)_ | 77.8 | 91.3 | 82.9 | 84.3 | 81.2 | 81.7 | 83.1 | 78.3 | 76.8 | 76.6 | 74.2 | 74.1 | 77.5 | 70.9 | 66.7 | 66.8 -`xlmr.large` _(TRANSLATE-TRAIN-ALL)_ | **83.6** | 89.1 | 85.1 | 86.6 | 85.7 | 85.3 | 85.9 | 83.5 | 83.2 | 83.1 | 83.7 | 81.5 | 83.7 | 81.6 | 78.0 | 78.1 +`xlmr.large` _(TRANSLATE-TRAIN-ALL)_ | 83.6 | 89.1 | 85.1 | 86.6 | 85.7 | 85.3 | 85.9 | 83.5 | 83.2 | 83.1 | 83.7 | 81.5 | 83.7 | 81.6 | 78.0 | 78.1 +`xlmr.xl` _(TRANSLATE-TRAIN-ALL)_ | 85.4 | 91.1 | 87.2 | 88.1 | 87.0 | 87.4 | 87.8 | 85.3 | 85.2 | 85.3 | 86.2 | 83.8 | 85.3 | 83.1 | 79.8 | 78.2 | 85.4 +`xlmr.xxl` _(TRANSLATE-TRAIN-ALL)_ | 86.0 | 91.5 | 87.6 | 88.7 | 87.8 | 87.4 | 88.2 | 85.6 | 85.1 | 85.8 | 86.3 | 83.9 | 85.6 | 84.6 | 81.7 | 80.6 **[MLQA (Lewis et al., 2018)](https://arxiv.org/abs/1910.07475)** @@ -52,7 +61,9 @@ Model | average | en | es | de | ar | hi | vi | zh ---|---|---|---|---|---|---|---|--- `BERT-large` | - | 80.2/67.4 | - | - | - | - | - | - `mBERT` | 57.7 / 41.6 | 77.7 / 65.2 | 64.3 / 46.6 | 57.9 / 44.3 | 45.7 / 29.8| 43.8 / 29.7 | 57.1 / 38.6 | 57.5 / 37.3 -`xlmr.large` | **70.7 / 52.7** | 80.6 / 67.8 | 74.1 / 56.0 | 68.5 / 53.6 | 63.1 / 43.5 | 69.2 / 51.6 | 71.3 / 50.9 | 68.0 / 45.4 +`xlmr.large` | 70.7 / 52.7 | 80.6 / 67.8 | 74.1 / 56.0 | 68.5 / 53.6 | 63.1 / 43.5 | 69.2 / 51.6 | 71.3 / 50.9 | 68.0 / 45.4 +`xlmr.xl` | 73.4 / 55.3 | 85.1 / 72.6 | 66.7 / 46.2 | 70.5 / 55.5 | 74.3 / 56.9 | 72.2 / 54.7 | 74.4 / 52.9 | 70.9 / 48.5 +`xlmr.xxl` | 74.8 / 56.6 | 85.5 / 72.4 | 68.6 / 48.4 | 72.7 / 57.8 | 75.4 / 57.6 | 73.7 / 55.8 | 76.0 / 55.0 | 71.7 / 48.9 ## Example usage @@ -121,3 +132,13 @@ assert torch.all(all_layers[-1] == last_layer_features) year={2019} } ``` + + +```bibtex +@article{goyal2021larger, + title={Larger-Scale Transformers for Multilingual Masked Language Modeling}, + author={Goyal, Naman and Du, Jingfei and Ott, Myle and Anantharaman, Giri and Conneau, Alexis}, + journal={arXiv preprint arXiv:2105.00572}, + year={2021} +} +``` diff --git a/fairseq/__init__.py b/fairseq/__init__.py index 3dd29637af..dc9fd1886d 100644 --- a/fairseq/__init__.py +++ b/fairseq/__init__.py @@ -2,25 +2,43 @@ # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. +"""isort:skip_file""" -__all__ = ['pdb'] -__version__ = '0.9.0' - +import os import sys -# backwards compatibility to support `from fairseq.meters import AverageMeter` +try: + from .version import __version__ # noqa +except ImportError: + version_txt = os.path.join(os.path.dirname(__file__), "version.txt") + with open(version_txt) as f: + __version__ = f.read().strip() + +__all__ = ["pdb"] + +# backwards compatibility to support `from fairseq.X import Y` +from fairseq.distributed import utils as distributed_utils from fairseq.logging import meters, metrics, progress_bar # noqa -sys.modules['fairseq.meters'] = meters -sys.modules['fairseq.metrics'] = metrics -sys.modules['fairseq.progress_bar'] = progress_bar + +sys.modules["fairseq.distributed_utils"] = distributed_utils +sys.modules["fairseq.meters"] = meters +sys.modules["fairseq.metrics"] = metrics +sys.modules["fairseq.progress_bar"] = progress_bar + +# initialize hydra +from fairseq.dataclass.initialize import hydra_init +hydra_init() import fairseq.criterions # noqa +import fairseq.distributed # noqa import fairseq.models # noqa import fairseq.modules # noqa import fairseq.optim # noqa import fairseq.optim.lr_scheduler # noqa import fairseq.pdb # noqa +import fairseq.scoring # noqa import fairseq.tasks # noqa +import fairseq.token_generation_constraints # noqa import fairseq.benchmark # noqa import fairseq.model_parallel # noqa diff --git a/fairseq/benchmark/__init__.py b/fairseq/benchmark/__init__.py index ea67844ef7..0317d5c623 100644 --- a/fairseq/benchmark/__init__.py +++ b/fairseq/benchmark/__init__.py @@ -4,8 +4,4 @@ # LICENSE file in the root directory of this source tree. # import models/tasks to register them -from . import ( # noqa - dummy_lm, - dummy_masked_lm, - dummy_model, -) +from . import dummy_dataset, dummy_lm, dummy_masked_lm, dummy_model, dummy_mt # noqa diff --git a/fairseq/benchmark/dummy_dataset.py b/fairseq/benchmark/dummy_dataset.py new file mode 100644 index 0000000000..2f051754af --- /dev/null +++ b/fairseq/benchmark/dummy_dataset.py @@ -0,0 +1,36 @@ +import numpy as np +from fairseq.data import FairseqDataset + + +class DummyDataset(FairseqDataset): + def __init__(self, batch, num_items, item_size): + super().__init__() + self.batch = batch + self.num_items = num_items + self.item_size = item_size + + def __getitem__(self, index): + return index + + def __len__(self): + return self.num_items + + def collater(self, samples): + return self.batch + + @property + def sizes(self): + return np.array([self.item_size] * self.num_items) + + def num_tokens(self, index): + return self.item_size + + def size(self, index): + return self.item_size + + def ordered_indices(self): + return np.arange(self.num_items) + + @property + def supports_prefetch(self): + return False diff --git a/fairseq/benchmark/dummy_lm.py b/fairseq/benchmark/dummy_lm.py index 710eb67437..c6246a0c0e 100644 --- a/fairseq/benchmark/dummy_lm.py +++ b/fairseq/benchmark/dummy_lm.py @@ -4,69 +4,74 @@ # LICENSE file in the root directory of this source tree. import logging +from dataclasses import dataclass, field +from typing import Optional -import numpy as np import torch - -from fairseq.data import Dictionary, FairseqDataset +from .dummy_dataset import DummyDataset +from fairseq.data import Dictionary +from fairseq.dataclass import FairseqDataclass from fairseq.tasks import FairseqTask, register_task +from omegaconf import II logger = logging.getLogger(__name__) -@register_task('dummy_lm') -class DummyLMTask(FairseqTask): +@dataclass +class DummyLMConfig(FairseqDataclass): + dict_size: int = 49996 + dataset_size: int = 100000 + tokens_per_sample: int = field( + default=512, metadata={"help": "max sequence length"} + ) + add_bos_token: bool = False + batch_size: Optional[int] = II("dataset.batch_size") + max_tokens: Optional[int] = II("dataset.max_tokens") + max_target_positions: int = II("task.tokens_per_sample") - @staticmethod - def add_args(parser): - """Add task-specific arguments to the parser.""" - parser.add_argument('--dict-size', default=50000, type=int) - parser.add_argument('--dataset-size', default=100000, type=int) - parser.add_argument('--tokens-per-sample', default=512, type=int, - help='max number of total tokens over all segments ' - 'per sample for BERT dataset') - def __init__(self, args, dictionary): - super().__init__(args) - self.dictionary = dictionary - self.seed = args.seed +@register_task("dummy_lm", dataclass=DummyLMConfig) +class DummyLMTask(FairseqTask): + def __init__(self, cfg: DummyLMConfig): + super().__init__(cfg) - seq = torch.arange(args.tokens_per_sample + 1) + dictionary.pad() + 1 + # load dictionary + self.dictionary = Dictionary() + for i in range(cfg.dict_size): + self.dictionary.add_symbol("word{}".format(i)) + self.dictionary.pad_to_multiple_(8) # often faster if divisible by 8 + logger.info("dictionary: {} types".format(len(self.dictionary))) + + seq = torch.arange(cfg.tokens_per_sample + 1) + self.dictionary.pad() + 1 self.dummy_src = seq[:-1] self.dummy_tgt = seq[1:] - @classmethod - def setup_task(cls, args, **kwargs): - """Setup the task. """ - dictionary = Dictionary() - for i in range(args.dict_size): - dictionary.add_symbol('word{}'.format(i)) - logger.info('dictionary: {} types'.format(len(dictionary))) - return cls(args, dictionary) - def load_dataset(self, split, epoch=1, combine=False, **kwargs): """Load a given dataset split. Args: split (str): name of the split (e.g., train, valid, test) """ - bsz = self.args.max_sentences + if self.cfg.batch_size is not None: + bsz = self.cfg.batch_size + else: + bsz = max(1, self.cfg.max_tokens // self.cfg.tokens_per_sample) self.datasets[split] = DummyDataset( { - 'id': 1, - 'net_input': { - 'src_tokens': torch.stack([self.dummy_src for _ in range(bsz)]), - 'src_lengths': torch.full( - (bsz, ), self.args.tokens_per_sample, dtype=torch.long + "id": 1, + "net_input": { + "src_tokens": torch.stack([self.dummy_src for _ in range(bsz)]), + "src_lengths": torch.full( + (bsz,), self.cfg.tokens_per_sample, dtype=torch.long ), }, - 'target': torch.stack([self.dummy_tgt for _ in range(bsz)]), - 'nsentences': bsz, - 'ntokens': bsz * self.args.tokens_per_sample, + "target": torch.stack([self.dummy_tgt for _ in range(bsz)]), + "nsentences": bsz, + "ntokens": bsz * self.cfg.tokens_per_sample, }, - num_items=self.args.dataset_size, - item_size=self.args.tokens_per_sample, + num_items=self.cfg.dataset_size, + item_size=self.cfg.tokens_per_sample, ) @property @@ -76,38 +81,3 @@ def source_dictionary(self): @property def target_dictionary(self): return self.dictionary - - -class DummyDataset(FairseqDataset): - - def __init__(self, batch, num_items, item_size): - super().__init__() - self.batch = batch - self.num_items = num_items - self.item_size = item_size - - def __getitem__(self, index): - return index - - def __len__(self): - return self.num_items - - def collater(self, samples): - return self.batch - - @property - def sizes(self): - return np.array([self.item_size] * self.num_items) - - def num_tokens(self, index): - return self.item_size - - def size(self, index): - return self.item_size - - def ordered_indices(self): - return np.arange(self.num_items) - - @property - def supports_prefetch(self): - return False diff --git a/fairseq/benchmark/dummy_masked_lm.py b/fairseq/benchmark/dummy_masked_lm.py index bacd0e8acc..12b9c5d0f5 100644 --- a/fairseq/benchmark/dummy_masked_lm.py +++ b/fairseq/benchmark/dummy_masked_lm.py @@ -4,42 +4,53 @@ # LICENSE file in the root directory of this source tree. import logging +from dataclasses import dataclass, field +from typing import Optional -import numpy as np import torch +from omegaconf import II -from fairseq.data import Dictionary, FairseqDataset +from .dummy_dataset import DummyDataset +from fairseq.data import Dictionary +from fairseq.dataclass import FairseqDataclass from fairseq.tasks import FairseqTask, register_task - logger = logging.getLogger(__name__) -@register_task('dummy_masked_lm') -class DummyMaskedLMTask(FairseqTask): +@dataclass +class DummyMaskedLMConfig(FairseqDataclass): + dict_size: int = 49996 + dataset_size: int = 100000 + tokens_per_sample: int = field( + default=512, + metadata={ + "help": "max number of total tokens over all" + " segments per sample for BERT dataset" + }, + ) + batch_size: Optional[int] = II("dataset.batch_size") + max_tokens: Optional[int] = II("dataset.max_tokens") + max_target_positions: int = II("task.tokens_per_sample") - @staticmethod - def add_args(parser): - """Add task-specific arguments to the parser.""" - parser.add_argument('--dict-size', default=50000, type=int) - parser.add_argument('--dataset-size', default=100000, type=int) - parser.add_argument('--tokens-per-sample', default=512, type=int, - help='max number of total tokens over all segments ' - 'per sample for BERT dataset') - def __init__(self, args, dictionary): - super().__init__(args) - self.dictionary = dictionary - self.seed = args.seed +@register_task("dummy_masked_lm", dataclass=DummyMaskedLMConfig) +class DummyMaskedLMTask(FairseqTask): + def __init__(self, cfg: DummyMaskedLMConfig): + super().__init__(cfg) + self.dictionary = Dictionary() + for i in range(cfg.dict_size): + self.dictionary.add_symbol("word{}".format(i)) + logger.info("dictionary: {} types".format(len(self.dictionary))) # add mask token - self.mask_idx = dictionary.add_symbol('<mask>') - assert len(dictionary) % 8 == 0 + self.mask_idx = self.dictionary.add_symbol("<mask>") + self.dictionary.pad_to_multiple_(8) # often faster if divisible by 8 mask_idx = 0 pad_idx = 1 - seq = torch.arange(args.tokens_per_sample) + pad_idx + 1 - mask = torch.arange(2, args.tokens_per_sample, 7) # ~15% + seq = torch.arange(cfg.tokens_per_sample) + pad_idx + 1 + mask = torch.arange(2, cfg.tokens_per_sample, 7) # ~15% src = seq.clone() src[mask] = mask_idx tgt = torch.full_like(seq, pad_idx) @@ -48,36 +59,30 @@ def __init__(self, args, dictionary): self.dummy_src = src self.dummy_tgt = tgt - @classmethod - def setup_task(cls, args, **kwargs): - """Setup the task. """ - dictionary = Dictionary() - for i in range(args.dict_size): - dictionary.add_symbol('word{}'.format(i)) - logger.info('dictionary: {} types'.format(len(dictionary))) - return cls(args, dictionary) - def load_dataset(self, split, epoch=1, combine=False, **kwargs): """Load a given dataset split. Args: split (str): name of the split (e.g., train, valid, test) """ - bsz = self.args.max_sentences + if self.cfg.batch_size is not None: + bsz = self.cfg.batch_size + else: + bsz = max(1, self.cfg.max_tokens // self.cfg.tokens_per_sample) self.datasets[split] = DummyDataset( { - 'id': 1, - 'net_input': { - 'src_tokens': torch.stack([self.dummy_src for _ in range(bsz)]), - 'src_lengths': torch.full( - (bsz, ), self.args.tokens_per_sample, dtype=torch.long + "id": 1, + "net_input": { + "src_tokens": torch.stack([self.dummy_src for _ in range(bsz)]), + "src_lengths": torch.full( + (bsz,), self.cfg.tokens_per_sample, dtype=torch.long ), }, - 'target': torch.stack([self.dummy_tgt for _ in range(bsz)]), - 'nsentences': bsz, - 'ntokens': bsz * self.args.tokens_per_sample, + "target": torch.stack([self.dummy_tgt for _ in range(bsz)]), + "nsentences": bsz, + "ntokens": bsz * self.cfg.tokens_per_sample, }, - num_items=self.args.dataset_size, - item_size=self.args.tokens_per_sample, + num_items=self.cfg.dataset_size, + item_size=self.cfg.tokens_per_sample, ) @property @@ -87,38 +92,3 @@ def source_dictionary(self): @property def target_dictionary(self): return self.dictionary - - -class DummyDataset(FairseqDataset): - - def __init__(self, batch, num_items, item_size): - super().__init__() - self.batch = batch - self.num_items = num_items - self.item_size = item_size - - def __getitem__(self, index): - return index - - def __len__(self): - return self.num_items - - def collater(self, samples): - return self.batch - - @property - def sizes(self): - return np.array([self.item_size] * self.num_items) - - def num_tokens(self, index): - return self.item_size - - def size(self, index): - return self.item_size - - def ordered_indices(self): - return np.arange(self.num_items) - - @property - def supports_prefetch(self): - return False diff --git a/fairseq/benchmark/dummy_model.py b/fairseq/benchmark/dummy_model.py index 817cdb34bb..ff26e4fe65 100644 --- a/fairseq/benchmark/dummy_model.py +++ b/fairseq/benchmark/dummy_model.py @@ -5,7 +5,6 @@ import torch.nn as nn import torch.nn.functional as F - from fairseq.data import Dictionary from fairseq.models import ( FairseqDecoder, @@ -15,17 +14,16 @@ ) -@register_model('dummy_model') +@register_model("dummy_model") class DummyModel(FairseqLanguageModel): - def __init__(self, args, encoder): super().__init__(encoder) self.args = args @staticmethod def add_args(parser): - parser.add_argument('--num-layers', type=int, default=24) - parser.add_argument('--embed-dim', type=int, default=1024) + parser.add_argument("--num-layers", type=int, default=24) + parser.add_argument("--embed-dim", type=int, default=1024) @classmethod def build_model(cls, args, task): @@ -41,32 +39,35 @@ def forward(self, src_tokens, masked_tokens=None, **kwargs): class DummyEncoder(FairseqDecoder): - def __init__(self, num_embed=50000, embed_dim=1024, num_layers=24): super().__init__(Dictionary()) self.embed = nn.Embedding( num_embeddings=num_embed, embedding_dim=embed_dim, padding_idx=0 ) - self.layers_a = nn.ModuleList([ - nn.Sequential( - nn.LayerNorm(embed_dim), - nn.Linear(embed_dim, 3*embed_dim), # q, k, v input projection - nn.Linear(3*embed_dim, embed_dim), # skip self-attention - nn.Linear(embed_dim, embed_dim), # output projection - nn.Dropout(), - ) - for i in range(num_layers) - ]) - self.layers_b = nn.ModuleList([ - nn.Sequential( - nn.LayerNorm(embed_dim), - nn.Linear(embed_dim, 4*embed_dim), # FFN - nn.ReLU(), - nn.Linear(4*embed_dim, embed_dim), # FFN - nn.Dropout(0.1), - ) - for i in range(num_layers) - ]) + self.layers_a = nn.ModuleList( + [ + nn.Sequential( + nn.LayerNorm(embed_dim), + nn.Linear(embed_dim, 3 * embed_dim), # q, k, v input projection + nn.Linear(3 * embed_dim, embed_dim), # skip self-attention + nn.Linear(embed_dim, embed_dim), # output projection + nn.Dropout(), + ) + for i in range(num_layers) + ] + ) + self.layers_b = nn.ModuleList( + [ + nn.Sequential( + nn.LayerNorm(embed_dim), + nn.Linear(embed_dim, 4 * embed_dim), # FFN + nn.ReLU(), + nn.Linear(4 * embed_dim, embed_dim), # FFN + nn.Dropout(0.1), + ) + for i in range(num_layers) + ] + ) self.out_proj = nn.Linear(embed_dim, num_embed) def forward(self, tokens, masked_tokens=None): @@ -90,6 +91,6 @@ def get_normalized_probs(self, net_output, log_probs, sample=None): return F.softmax(logits, dim=-1) -@register_model_architecture('dummy_model', 'dummy_model') +@register_model_architecture("dummy_model", "dummy_model") def base_architecture(args): pass diff --git a/fairseq/benchmark/dummy_mt.py b/fairseq/benchmark/dummy_mt.py new file mode 100644 index 0000000000..4ca7be93a3 --- /dev/null +++ b/fairseq/benchmark/dummy_mt.py @@ -0,0 +1,119 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging + +import numpy as np +import torch +from fairseq.data import Dictionary, FairseqDataset +from fairseq.tasks import LegacyFairseqTask, register_task + + +logger = logging.getLogger(__name__) + + +@register_task("dummy_mt") +class DummyMTTask(LegacyFairseqTask): + @staticmethod + def add_args(parser): + """Add task-specific arguments to the parser.""" + parser.add_argument("--dict-size", default=49996, type=int) + parser.add_argument("--dataset-size", default=100000, type=int) + parser.add_argument("--src-len", default=30, type=int) + parser.add_argument("--tgt-len", default=30, type=int) + + def __init__(self, args, dictionary): + super().__init__(args) + self.dictionary = dictionary + self.seed = args.seed + + dictionary.pad_to_multiple_(8) # often faster if divisible by 8 + + self.dummy_src = torch.arange(args.src_len + 1) + dictionary.pad() + 1 + self.dummy_tgt = torch.arange(args.tgt_len + 1) + dictionary.pad() + 1 + + @classmethod + def setup_task(cls, args, **kwargs): + """Setup the task. """ + dictionary = Dictionary() + for i in range(args.dict_size): + dictionary.add_symbol("word{}".format(i)) + logger.info("dictionary: {} types".format(len(dictionary))) + + args.max_source_positions = args.src_len + dictionary.pad() + 2 + args.max_target_positions = args.tgt_len + dictionary.pad() + 2 + + return cls(args, dictionary) + + def load_dataset(self, split, epoch=1, combine=False, **kwargs): + """Load a given dataset split. + Args: + split (str): name of the split (e.g., train, valid, test) + """ + item_size = max(self.args.src_len, self.args.tgt_len) + if self.args.batch_size is not None: + bsz = self.args.batch_size + else: + bsz = max(1, self.args.max_tokens // item_size) + tgt = torch.stack([self.dummy_tgt for _ in range(bsz)]) + self.datasets[split] = DummyDataset( + { + "id": 1, + "net_input": { + "src_tokens": torch.stack([self.dummy_src for _ in range(bsz)]), + "src_lengths": torch.full( + (bsz,), self.args.src_len, dtype=torch.long + ), + "prev_output_tokens": tgt.clone(), + }, + "target": tgt, + "nsentences": bsz, + "ntokens": bsz * self.args.tgt_len, + }, + num_items=self.args.dataset_size, + item_size=item_size, + ) + + @property + def source_dictionary(self): + return self.dictionary + + @property + def target_dictionary(self): + return self.dictionary + + +class DummyDataset(FairseqDataset): + def __init__(self, batch, num_items, item_size): + super().__init__() + self.batch = batch + self.num_items = num_items + self.item_size = item_size + + def __getitem__(self, index): + return index + + def __len__(self): + return self.num_items + + def collater(self, samples): + return self.batch + + @property + def sizes(self): + return np.array([self.item_size] * self.num_items) + + def num_tokens(self, index): + return self.item_size + + def size(self, index): + return self.item_size + + def ordered_indices(self): + return np.arange(self.num_items) + + @property + def supports_prefetch(self): + return False diff --git a/fairseq/binarizer.py b/fairseq/binarizer.py index 3a7ee0854f..ae4d02a6db 100644 --- a/fairseq/binarizer.py +++ b/fairseq/binarizer.py @@ -3,21 +3,14 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. -import os from collections import Counter +from typing import Dict -from fairseq.tokenizer import tokenize_line import torch - -def safe_readline(f): - pos = f.tell() - while True: - try: - return f.readline() - except UnicodeDecodeError: - pos -= 1 - f.seek(pos) # search where this character begins +from fairseq.file_chunker_utils import Chunker +from fairseq.file_io import PathManager +from fairseq.tokenizer import tokenize_line class Binarizer: @@ -32,7 +25,7 @@ def binarize( offset=0, end=-1, already_numberized=False, - ): + ) -> Dict[str, int]: nseq, ntok = 0, 0 replaced = Counter() @@ -40,13 +33,10 @@ def replaced_consumer(word, idx): if idx == dict.unk_index and word != dict.unk_word: replaced.update([word]) - with open(filename, "r", encoding="utf-8") as f: - f.seek(offset) - # next(f) breaks f.tell(), hence readline() must be used - line = safe_readline(f) - while line: - if end > 0 and f.tell() > end: - break + with Chunker( + PathManager.get_local_path(filename), offset, end + ) as line_iterator: + for line in line_iterator: if already_numberized: id_strings = line.strip().split() id_list = [int(id_string) for id_string in id_strings] @@ -67,7 +57,6 @@ def replaced_consumer(word, idx): nseq += 1 ntok += len(ids) consumer(ids) - line = f.readline() return { "nseq": nseq, "nunk": sum(replaced.values()), @@ -76,29 +65,16 @@ def replaced_consumer(word, idx): } @staticmethod - def binarize_alignments(filename, alignment_parser, consumer, offset=0, end=-1): + def binarize_alignments( + filename, alignment_parser, consumer, offset=0, end=-1 + ) -> Dict[str, int]: nseq = 0 - with open(filename, "r") as f: - f.seek(offset) - line = safe_readline(f) - while line: - if end > 0 and f.tell() > end: - break + with Chunker( + PathManager.get_local_path(filename), offset, end + ) as line_iterator: + for line in line_iterator: ids = alignment_parser(line) nseq += 1 consumer(ids) - line = f.readline() return {"nseq": nseq} - - @staticmethod - def find_offsets(filename, num_chunks): - with open(filename, "r", encoding="utf-8") as f: - size = os.fstat(f.fileno()).st_size - chunk_size = size // num_chunks - offsets = [0 for _ in range(num_chunks + 1)] - for i in range(1, num_chunks): - f.seek(chunk_size * i) - safe_readline(f) - offsets[i] = f.tell() - return offsets diff --git a/fairseq/bleu.py b/fairseq/bleu.py deleted file mode 100644 index 36b15286fc..0000000000 --- a/fairseq/bleu.py +++ /dev/null @@ -1,129 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import ctypes -import math -import torch - -try: - from fairseq import libbleu -except ImportError as e: - import sys - sys.stderr.write('ERROR: missing libbleu.so. run `pip install --editable .`\n') - raise e - - -C = ctypes.cdll.LoadLibrary(libbleu.__file__) - - -class BleuStat(ctypes.Structure): - _fields_ = [ - ('reflen', ctypes.c_size_t), - ('predlen', ctypes.c_size_t), - ('match1', ctypes.c_size_t), - ('count1', ctypes.c_size_t), - ('match2', ctypes.c_size_t), - ('count2', ctypes.c_size_t), - ('match3', ctypes.c_size_t), - ('count3', ctypes.c_size_t), - ('match4', ctypes.c_size_t), - ('count4', ctypes.c_size_t), - ] - - -class SacrebleuScorer(object): - def __init__(self): - import sacrebleu - self.sacrebleu = sacrebleu - self.reset() - - def reset(self, one_init=False): - if one_init: - raise NotImplementedError - self.ref = [] - self.sys = [] - - def add_string(self, ref, pred): - self.ref.append(ref) - self.sys.append(pred) - - def score(self, order=4): - return self.result_string(order).score - - def result_string(self, order=4): - if order != 4: - raise NotImplementedError - return self.sacrebleu.corpus_bleu(self.sys, [self.ref]) - - -class Scorer(object): - def __init__(self, pad, eos, unk): - self.stat = BleuStat() - self.pad = pad - self.eos = eos - self.unk = unk - self.reset() - - def reset(self, one_init=False): - if one_init: - C.bleu_one_init(ctypes.byref(self.stat)) - else: - C.bleu_zero_init(ctypes.byref(self.stat)) - - def add(self, ref, pred): - if not isinstance(ref, torch.IntTensor): - raise TypeError('ref must be a torch.IntTensor (got {})' - .format(type(ref))) - if not isinstance(pred, torch.IntTensor): - raise TypeError('pred must be a torch.IntTensor(got {})' - .format(type(pred))) - - # don't match unknown words - rref = ref.clone() - assert not rref.lt(0).any() - rref[rref.eq(self.unk)] = -999 - - rref = rref.contiguous().view(-1) - pred = pred.contiguous().view(-1) - - C.bleu_add( - ctypes.byref(self.stat), - ctypes.c_size_t(rref.size(0)), - ctypes.c_void_p(rref.data_ptr()), - ctypes.c_size_t(pred.size(0)), - ctypes.c_void_p(pred.data_ptr()), - ctypes.c_int(self.pad), - ctypes.c_int(self.eos)) - - def score(self, order=4): - psum = sum(math.log(p) if p > 0 else float('-Inf') - for p in self.precision()[:order]) - return self.brevity() * math.exp(psum / order) * 100 - - def precision(self): - def ratio(a, b): - return a / b if b > 0 else 0 - - return [ - ratio(self.stat.match1, self.stat.count1), - ratio(self.stat.match2, self.stat.count2), - ratio(self.stat.match3, self.stat.count3), - ratio(self.stat.match4, self.stat.count4), - ] - - def brevity(self): - r = self.stat.reflen / self.stat.predlen - return min(1, math.exp(1 - r)) - - def result_string(self, order=4): - assert order <= 4, "BLEU scores for order > 4 aren't supported" - fmt = 'BLEU{} = {:2.2f}, {:2.1f}' - for _ in range(1, order): - fmt += '/{:2.1f}' - fmt += ' (BP={:.3f}, ratio={:.3f}, syslen={}, reflen={})' - bleup = [p * 100 for p in self.precision()[:order]] - return fmt.format(order, self.score(order=order), *bleup, - self.brevity(), self.stat.predlen/self.stat.reflen, - self.stat.predlen, self.stat.reflen) diff --git a/fairseq/checkpoint_utils.py b/fairseq/checkpoint_utils.py index ecda632264..ef5d4c9022 100644 --- a/fairseq/checkpoint_utils.py +++ b/fairseq/checkpoint_utils.py @@ -3,40 +3,55 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. +import ast import collections +import contextlib import logging +import numpy as np import os import re +import time import traceback from collections import OrderedDict -from typing import Union +from typing import Any, Dict, Optional, Union import torch +from fairseq.data import data_utils +from fairseq.dataclass.configs import CheckpointConfig +from fairseq.dataclass.utils import ( + convert_namespace_to_omegaconf, + overwrite_args_by_name, +) +from fairseq.distributed.fully_sharded_data_parallel import FSDP, has_FSDP from fairseq.file_io import PathManager from fairseq.models import FairseqDecoder, FairseqEncoder -from torch.serialization import default_restore_location +from omegaconf import DictConfig, open_dict, OmegaConf logger = logging.getLogger(__name__) -def save_checkpoint(args, trainer, epoch_itr, val_loss): - from fairseq import distributed_utils, meters +def save_checkpoint(cfg: CheckpointConfig, trainer, epoch_itr, val_loss): + from fairseq import meters # only one worker should attempt to create the required dir - if args.distributed_rank == 0: - os.makedirs(args.save_dir, exist_ok=True) + if trainer.data_parallel_rank == 0: + os.makedirs(cfg.save_dir, exist_ok=True) prev_best = getattr(save_checkpoint, "best", val_loss) if val_loss is not None: - best_function = max if args.maximize_best_checkpoint_metric else min + best_function = max if cfg.maximize_best_checkpoint_metric else min save_checkpoint.best = best_function(val_loss, prev_best) - if args.no_save or not trainer.is_data_parallel_master: + if cfg.no_save: return - def is_better(a, b): - return a >= b if args.maximize_best_checkpoint_metric else a <= b + trainer.consolidate_optimizer() # TODO(SS): do we need this if no_save_optimizer_state + + if not trainer.should_save_checkpoint_on_current_rank: + if trainer.always_call_state_dict_during_save_checkpoint: + trainer.state_dict() + return write_timer = meters.StopwatchMeter() write_timer.start() @@ -45,77 +60,134 @@ def is_better(a, b): end_of_epoch = epoch_itr.end_of_epoch() updates = trainer.get_num_updates() - suffix = getattr(args, "checkpoint_suffix", "") + logger.info(f"Preparing to save checkpoint for epoch {epoch} @ {updates} updates") + + def is_better(a, b): + return a >= b if cfg.maximize_best_checkpoint_metric else a <= b + + suffix = trainer.checkpoint_suffix checkpoint_conds = collections.OrderedDict() checkpoint_conds["checkpoint{}{}.pt".format(epoch, suffix)] = ( - end_of_epoch - and not args.no_epoch_checkpoints - and epoch % args.save_interval == 0 + end_of_epoch and not cfg.no_epoch_checkpoints and epoch % cfg.save_interval == 0 ) checkpoint_conds["checkpoint_{}_{}{}.pt".format(epoch, updates, suffix)] = ( not end_of_epoch - and args.save_interval_updates > 0 - and updates % args.save_interval_updates == 0 + and cfg.save_interval_updates > 0 + and updates % cfg.save_interval_updates == 0 ) checkpoint_conds["checkpoint_best{}.pt".format(suffix)] = val_loss is not None and ( not hasattr(save_checkpoint, "best") or is_better(val_loss, save_checkpoint.best) ) - if val_loss is not None and args.keep_best_checkpoints > 0: - checkpoint_conds["checkpoint.best_{}_{:.2f}.pt".format( - args.best_checkpoint_metric, val_loss)] = ( - not hasattr(save_checkpoint, "best") - or is_better(val_loss, save_checkpoint.best) + if val_loss is not None and cfg.keep_best_checkpoints > 0: + worst_best = getattr(save_checkpoint, "best", None) + chkpts = checkpoint_paths( + cfg.save_dir, + pattern=r"checkpoint\.best_{}_(\d+\.?\d*){}\.pt".format( + cfg.best_checkpoint_metric, suffix + ), ) - checkpoint_conds["checkpoint_last{}.pt".format(suffix)] = not args.no_last_checkpoints + if len(chkpts) > 0: + p = chkpts[-1] if cfg.maximize_best_checkpoint_metric else chkpts[0] + worst_best = float(p.rsplit("_")[-1].replace("{}.pt".format(suffix), "")) + # add random digits to resolve ties + with data_utils.numpy_seed(epoch, updates, val_loss): + rand_sfx = np.random.randint(0, cfg.keep_best_checkpoints) + + checkpoint_conds[ + "checkpoint.best_{}_{:.3f}{}{}.pt".format( + cfg.best_checkpoint_metric, + val_loss, + rand_sfx, + suffix + ) + ] = worst_best is None or is_better(val_loss, worst_best) + checkpoint_conds[ + "checkpoint_last{}.pt".format(suffix) + ] = not cfg.no_last_checkpoints extra_state = {"train_iterator": epoch_itr.state_dict(), "val_loss": val_loss} if hasattr(save_checkpoint, "best"): extra_state.update({"best": save_checkpoint.best}) checkpoints = [ - os.path.join(args.save_dir, fn) for fn, cond in checkpoint_conds.items() if cond + os.path.join(cfg.save_dir, fn) for fn, cond in checkpoint_conds.items() if cond ] if len(checkpoints) > 0: trainer.save_checkpoint(checkpoints[0], extra_state) for cp in checkpoints[1:]: - PathManager.copy(checkpoints[0], cp, overwrite=True) + if cfg.write_checkpoints_asynchronously: + # TODO[ioPath]: Need to implement a delayed asynchronous + # file copying/moving feature. + logger.warning( + f"ioPath is not copying {checkpoints[0]} to {cp} " + "since async write mode is on." + ) + else: + assert PathManager.copy( + checkpoints[0], cp, overwrite=True + ), f"Failed to copy {checkpoints[0]} to {cp}" write_timer.stop() logger.info( - "saved checkpoint {} (epoch {} @ {} updates, score {}) (writing took {} seconds)".format( + "Saved checkpoint {} (epoch {} @ {} updates, score {}) (writing took {} seconds)".format( checkpoints[0], epoch, updates, val_loss, write_timer.sum ) ) - if not end_of_epoch and args.keep_interval_updates > 0: + if not end_of_epoch and cfg.keep_interval_updates > 0: # remove old checkpoints; checkpoints are sorted in descending order - checkpoints = checkpoint_paths( - args.save_dir, pattern=r"checkpoint_\d+_(\d+)\.pt" - ) - for old_chk in checkpoints[args.keep_interval_updates :]: + if cfg.keep_interval_updates_pattern == -1: + checkpoints = checkpoint_paths( + cfg.save_dir, pattern=r"checkpoint_\d+_(\d+){}\.pt".format(suffix) + ) + else: + checkpoints = checkpoint_paths( + cfg.save_dir, + pattern=r"checkpoint_\d+_(\d+){}\.pt".format(suffix), + keep_match=True, + ) + checkpoints = [ + x[0] + for x in checkpoints + if x[1] % cfg.keep_interval_updates_pattern != 0 + ] + + for old_chk in checkpoints[cfg.keep_interval_updates :]: if os.path.lexists(old_chk): os.remove(old_chk) + elif PathManager.exists(old_chk): + PathManager.rm(old_chk) - if args.keep_last_epochs > 0: + if cfg.keep_last_epochs > 0: # remove old epoch checkpoints; checkpoints are sorted in descending order - checkpoints = checkpoint_paths(args.save_dir, pattern=r"checkpoint(\d+)\.pt") - for old_chk in checkpoints[args.keep_last_epochs :]: + checkpoints = checkpoint_paths( + cfg.save_dir, pattern=r"checkpoint(\d+){}\.pt".format(suffix) + ) + for old_chk in checkpoints[cfg.keep_last_epochs :]: if os.path.lexists(old_chk): os.remove(old_chk) + elif PathManager.exists(old_chk): + PathManager.rm(old_chk) - if args.keep_best_checkpoints > 0: + if cfg.keep_best_checkpoints > 0: # only keep the best N checkpoints according to validation metric checkpoints = checkpoint_paths( - args.save_dir, pattern=r"checkpoint\.best_{}_(\d+\.?\d*)\.pt".format(args.best_checkpoint_metric)) - if not args.maximize_best_checkpoint_metric: + cfg.save_dir, + pattern=r"checkpoint\.best_{}_(\d+\.?\d*){}\.pt".format( + cfg.best_checkpoint_metric, suffix + ), + ) + if not cfg.maximize_best_checkpoint_metric: checkpoints = checkpoints[::-1] - for old_chk in checkpoints[args.keep_best_checkpoints:]: + for old_chk in checkpoints[cfg.keep_best_checkpoints :]: if os.path.lexists(old_chk): os.remove(old_chk) + elif PathManager.exists(old_chk): + PathManager.rm(old_chk) -def load_checkpoint(args, trainer, **passthrough_args): +def load_checkpoint(cfg: CheckpointConfig, trainer, **passthrough_args): """ Load a checkpoint and restore the training iterator. @@ -123,29 +195,73 @@ def load_checkpoint(args, trainer, **passthrough_args): ``trainer.get_train_iterator``. """ - suffix = getattr(args, "checkpoint_suffix", "") - if args.restore_file == "checkpoint_last.pt": - checkpoint_path = os.path.join(args.save_dir, "checkpoint_last{}.pt".format(suffix)) + reset_optimizer = cfg.reset_optimizer + reset_lr_scheduler = cfg.reset_lr_scheduler + optimizer_overrides = ast.literal_eval(cfg.optimizer_overrides) + reset_meters = cfg.reset_meters + reset_dataloader = cfg.reset_dataloader + + if cfg.finetune_from_model is not None and ( + reset_optimizer or reset_lr_scheduler or reset_meters or reset_dataloader + ): + raise ValueError( + "--finetune-from-model can not be set together with either --reset-optimizer" + " or reset_lr_scheduler or reset_meters or reset_dataloader" + ) + + suffix = trainer.checkpoint_suffix + if ( + cfg.restore_file == "checkpoint_last.pt" + ): # default value of restore_file is 'checkpoint_last.pt' + checkpoint_path = os.path.join( + cfg.save_dir, "checkpoint_last{}.pt".format(suffix) + ) + first_launch = not PathManager.exists(checkpoint_path) + if cfg.finetune_from_model is not None and first_launch: + # if there is no last checkpoint to restore, start the finetune from pretrained model + # else just use usual logic to load checkpoint, e.g. restart from last checkpoint and etc. + if PathManager.exists(cfg.finetune_from_model): + checkpoint_path = cfg.finetune_from_model + reset_optimizer = True + reset_lr_scheduler = True + reset_meters = True + reset_dataloader = True + logger.info( + f"loading pretrained model from {checkpoint_path}: " + "optimizer, lr scheduler, meters, dataloader will be reset" + ) + else: + raise ValueError( + f"--funetune-from-model {cfg.finetune_from_model} does not exist" + ) + elif suffix is not None: + checkpoint_path = cfg.restore_file.replace(".pt", suffix + ".pt") else: - checkpoint_path = args.restore_file + checkpoint_path = cfg.restore_file + + if cfg.restore_file != "checkpoint_last.pt" and cfg.finetune_from_model: + raise ValueError( + "--finetune-from-model and --restore-file (non-default value) " + "can not be specified together: " + str(cfg) + ) extra_state = trainer.load_checkpoint( checkpoint_path, - args.reset_optimizer, - args.reset_lr_scheduler, - eval(args.optimizer_overrides), - reset_meters=args.reset_meters, + reset_optimizer, + reset_lr_scheduler, + optimizer_overrides, + reset_meters=reset_meters, ) if ( extra_state is not None and "best" in extra_state - and not args.reset_optimizer - and not args.reset_meters + and not reset_optimizer + and not reset_meters ): save_checkpoint.best = extra_state["best"] - if extra_state is not None and not args.reset_dataloader: + if extra_state is not None and not reset_dataloader: # restore iterator from checkpoint itr_state = extra_state["train_iterator"] epoch_itr = trainer.get_train_iterator( @@ -162,22 +278,77 @@ def load_checkpoint(args, trainer, **passthrough_args): return extra_state, epoch_itr -def load_checkpoint_to_cpu(path, arg_overrides=None): - """Loads a checkpoint to CPU (with upgrading for backward compatibility).""" - with PathManager.open(path, "rb") as f: - state = torch.load( - f, map_location=lambda s, l: default_restore_location(s, "cpu") - ) +def load_checkpoint_to_cpu(path, arg_overrides=None, load_on_all_ranks=False): + """Loads a checkpoint to CPU (with upgrading for backward compatibility). + + If doing single-GPU training or if the checkpoint is only being loaded by at + most one process on each node (current default behavior is for only rank 0 + to read the checkpoint from disk), load_on_all_ranks should be False to + avoid errors from torch.distributed not having been initialized or + torch.distributed.barrier() hanging. - args = state["args"] - if arg_overrides is not None: + If all processes on each node may be loading the checkpoint + simultaneously, load_on_all_ranks should be set to True to avoid I/O + conflicts. + + There's currently no support for > 1 but < all processes loading the + checkpoint on each node. + """ + local_path = PathManager.get_local_path(path) + # The locally cached file returned by get_local_path() may be stale for + # remote files that are periodically updated/overwritten (ex: + # checkpoint_last.pt) - so we remove the local copy, sync across processes + # (if needed), and then download a fresh copy. + if local_path != path and PathManager.path_requires_pathmanager(path): + try: + os.remove(local_path) + except FileNotFoundError: + # With potentially multiple processes removing the same file, the + # file being missing is benign (missing_ok isn't available until + # Python 3.8). + pass + if load_on_all_ranks: + torch.distributed.barrier() + local_path = PathManager.get_local_path(path) + + with open(local_path, "rb") as f: + state = torch.load(f, map_location=torch.device("cpu")) + + if "args" in state and state["args"] is not None and arg_overrides is not None: + args = state["args"] for arg_name, arg_val in arg_overrides.items(): setattr(args, arg_name, arg_val) + + if "cfg" in state and state["cfg"] is not None: + + # hack to be able to set Namespace in dict config. this should be removed when we update to newer + # omegaconf version that supports object flags, or when we migrate all existing models + from omegaconf import _utils + + old_primitive = _utils.is_primitive_type + _utils.is_primitive_type = lambda _: True + + state["cfg"] = OmegaConf.create(state["cfg"]) + + _utils.is_primitive_type = old_primitive + OmegaConf.set_struct(state["cfg"], True) + + if arg_overrides is not None: + overwrite_args_by_name(state["cfg"], arg_overrides) + state = _upgrade_state_dict(state) return state -def load_model_ensemble(filenames, arg_overrides=None, task=None, strict=True, suffix=''): +def load_model_ensemble( + filenames, + arg_overrides: Optional[Dict[str, Any]] = None, + task=None, + strict=True, + suffix="", + num_shards=1, + state=None, +): """Loads an ensemble of models. Args: @@ -186,34 +357,122 @@ def load_model_ensemble(filenames, arg_overrides=None, task=None, strict=True, s were used during model training task (fairseq.tasks.FairseqTask, optional): task to use for loading """ + assert not ( + strict and num_shards > 1 + ), "Cannot load state dict with strict=True and checkpoint shards > 1" ensemble, args, _task = load_model_ensemble_and_task( - filenames, arg_overrides, task, strict, suffix, + filenames, + arg_overrides, + task, + strict, + suffix, + num_shards, + state, ) return ensemble, args -def load_model_ensemble_and_task(filenames, arg_overrides=None, task=None, strict=True, suffix=''): +def get_maybe_sharded_checkpoint_filename( + filename: str, suffix: str, shard_idx: int, num_shards: int +) -> str: + orig_filename = filename + filename = filename.replace(".pt", suffix + ".pt") + fsdp_filename = filename[:-3] + f"-shard{shard_idx}.pt" + model_parallel_filename = orig_filename[:-3] + f"_part{shard_idx}.pt" + if PathManager.exists(fsdp_filename): + return fsdp_filename + elif num_shards > 1: + return model_parallel_filename + else: + return filename + + +def load_model_ensemble_and_task( + filenames, + arg_overrides: Optional[Dict[str, Any]] = None, + task=None, + strict=True, + suffix="", + num_shards=1, + state=None, +): + assert state is None or len(filenames) == 1 + from fairseq import tasks + assert not ( + strict and num_shards > 1 + ), "Cannot load state dict with strict=True and checkpoint shards > 1" ensemble = [] + cfg = None for filename in filenames: - filename = filename.replace(".pt", suffix + ".pt") - if not PathManager.exists(filename): - raise IOError("Model file not found: {}".format(filename)) - state = load_checkpoint_to_cpu(filename, arg_overrides) + orig_filename = filename + model_shard_state = {"shard_weights": [], "shard_metadata": []} + assert num_shards > 0 + st = time.time() + for shard_idx in range(num_shards): + filename = get_maybe_sharded_checkpoint_filename( + orig_filename, suffix, shard_idx, num_shards + ) - args = state["args"] - if task is None: - task = tasks.setup_task(args) + if not PathManager.exists(filename): + raise IOError("Model file not found: {}".format(filename)) + if state is None: + state = load_checkpoint_to_cpu(filename, arg_overrides) + if "args" in state and state["args"] is not None: + cfg = convert_namespace_to_omegaconf(state["args"]) + elif "cfg" in state and state["cfg"] is not None: + cfg = state["cfg"] + else: + raise RuntimeError( + f"Neither args nor cfg exist in state keys = {state.keys()}" + ) + + if task is None: + task = tasks.setup_task(cfg.task) + + if "task_state" in state: + task.load_state_dict(state["task_state"]) + + if "fsdp_metadata" in state and num_shards > 1: + model_shard_state["shard_weights"].append(state["model"]) + model_shard_state["shard_metadata"].append(state["fsdp_metadata"]) + # check FSDP import before the code goes too far + if not has_FSDP: + raise ImportError( + "Cannot find FullyShardedDataParallel. " + "Please install fairscale with: pip install fairscale" + ) + if shard_idx == num_shards - 1: + consolidated_model_state = FSDP.consolidate_shard_weights( + shard_weights=model_shard_state["shard_weights"], + shard_metadata=model_shard_state["shard_metadata"], + ) + model = task.build_model(cfg.model) + model.load_state_dict( + consolidated_model_state, strict=strict, model_cfg=cfg.model + ) + else: + # model parallel checkpoint or unsharded checkpoint + model = task.build_model(cfg.model) + model.load_state_dict( + state["model"], strict=strict, model_cfg=cfg.model + ) + + # reset state so it gets loaded for the next model in ensemble + state = None + if shard_idx % 10 == 0 and shard_idx > 0: + elapsed = time.time() - st + logger.info( + f"Loaded {shard_idx} shards in {elapsed:.2f}s, {elapsed / (shard_idx+1):.2f}s/shard" + ) # build model for ensemble - model = task.build_model(args) - model.load_state_dict(state["model"], strict=strict, args=args) ensemble.append(model) - return ensemble, args, task + return ensemble, cfg, task -def checkpoint_paths(path, pattern=r"checkpoint(\d+)\.pt"): +def checkpoint_paths(path, pattern=r"checkpoint(\d+)\.pt", keep_match=False): """Retrieves all checkpoints found in `path` directory. Checkpoints are identified by matching filename to the specified pattern. If @@ -221,7 +480,7 @@ def checkpoint_paths(path, pattern=r"checkpoint(\d+)\.pt"): descending order. """ pt_regexp = re.compile(pattern) - files = os.listdir(path) + files = PathManager.ls(path) entries = [] for i, f in enumerate(files): @@ -229,64 +488,44 @@ def checkpoint_paths(path, pattern=r"checkpoint(\d+)\.pt"): if m is not None: idx = float(m.group(1)) if len(m.groups()) > 0 else i entries.append((idx, m.group(0))) - return [os.path.join(path, x[1]) for x in sorted(entries, reverse=True)] + if keep_match: + return [(os.path.join(path, x[1]), x[0]) for x in sorted(entries, reverse=True)] + else: + return [os.path.join(path, x[1]) for x in sorted(entries, reverse=True)] -def torch_persistent_save(*args, **kwargs): +def torch_persistent_save(obj, filename, async_write: bool = False): + if async_write: + with PathManager.opena(filename, "wb") as f: + _torch_persistent_save(obj, f) + else: + if PathManager.supports_rename(filename): + # do atomic save + with PathManager.open(filename + ".tmp", "wb") as f: + _torch_persistent_save(obj, f) + PathManager.rename(filename + ".tmp", filename) + else: + # fallback to non-atomic save + with PathManager.open(filename, "wb") as f: + _torch_persistent_save(obj, f) + + +def _torch_persistent_save(obj, f): + if isinstance(f, str): + with PathManager.open(f, "wb") as h: + torch_persistent_save(obj, h) + return for i in range(3): try: - return torch.save(*args, **kwargs) + return torch.save(obj, f) except Exception: if i == 2: logger.error(traceback.format_exc()) - - -def save_state( - filename, - args, - model_state_dict, - criterion, - optimizer, - lr_scheduler, - num_updates, - optim_history=None, - extra_state=None, -): - from fairseq import utils - - if optim_history is None: - optim_history = [] - if extra_state is None: - extra_state = {} - state_dict = { - "args": args, - "model": model_state_dict or {}, - "optimizer_history": optim_history - + [ - { - "criterion_name": criterion.__class__.__name__, - "optimizer_name": optimizer.__class__.__name__, - "lr_scheduler_state": lr_scheduler.state_dict(), - "num_updates": num_updates, - } - ], - "extra_state": extra_state, - } - if utils.has_parameters(criterion): - state_dict["criterion"] = criterion.state_dict() - if not args.no_save_optimizer_state: - state_dict["last_optimizer_state"] = optimizer.state_dict() - - # convert all state to CPU - state_dict = utils.move_to_cpu(state_dict) - - with PathManager.open(filename, "wb") as f: - torch_persistent_save(state_dict, f) + raise def _upgrade_state_dict(state): """Helper for upgrading old model checkpoints.""" - from fairseq import models, registry, tasks # add optimizer_history if "optimizer_history" not in state: @@ -324,8 +563,10 @@ def _upgrade_state_dict(state): if "num_updates" not in state["optimizer_history"][-1]: state["optimizer_history"][-1]["num_updates"] = 0 # old model checkpoints may not have separate source/target positions - if hasattr(state["args"], "max_positions") and not hasattr( - state["args"], "max_source_positions" + if ( + "args" in state + and hasattr(state["args"], "max_positions") + and not hasattr(state["args"], "max_source_positions") ): state["args"].max_source_positions = state["args"].max_positions state["args"].max_target_positions = state["args"].max_positions @@ -335,34 +576,103 @@ def _upgrade_state_dict(state): "epoch": state["extra_state"]["epoch"], "iterations_in_epoch": state["extra_state"].get("batch_offset", 0), } - # default to translation task - if not hasattr(state["args"], "task"): - state["args"].task = "translation" - # --raw-text and --lazy-load are deprecated - if getattr(state["args"], "raw_text", False): - state["args"].dataset_impl = "raw" - elif getattr(state["args"], "lazy_load", False): - state["args"].dataset_impl = "lazy" - # epochs start at 1 - if state["extra_state"]["train_iterator"] is not None: - state["extra_state"]["train_iterator"]["epoch"] = max( - state["extra_state"]["train_iterator"].get("epoch", 1), - 1, - ) - # set any missing default values in the task, model or other registries - registry.set_defaults(state["args"], tasks.TASK_REGISTRY[state["args"].task]) - registry.set_defaults(state["args"], models.ARCH_MODEL_REGISTRY[state["args"].arch]) - for registry_name, REGISTRY in registry.REGISTRIES.items(): - choice = getattr(state["args"], registry_name, None) - if choice is not None: - cls = REGISTRY["registry"][choice] - registry.set_defaults(state["args"], cls) + # backward compatibility, cfg updates + if "args" in state and state["args"] is not None: + # default to translation task + if not hasattr(state["args"], "task"): + state["args"].task = "translation" + # --raw-text and --lazy-load are deprecated + if getattr(state["args"], "raw_text", False): + state["args"].dataset_impl = "raw" + elif getattr(state["args"], "lazy_load", False): + state["args"].dataset_impl = "lazy" + # epochs start at 1 + if state["extra_state"]["train_iterator"] is not None: + state["extra_state"]["train_iterator"]["epoch"] = max( + state["extra_state"]["train_iterator"].get("epoch", 1), 1 + ) + # --remove-bpe ==> --postprocess + if hasattr(state["args"], "remove_bpe"): + state["args"].post_process = state["args"].remove_bpe + # --min-lr ==> --stop-min-lr + if hasattr(state["args"], "min_lr"): + state["args"].stop_min_lr = state["args"].min_lr + del state["args"].min_lr + # binary_cross_entropy / kd_binary_cross_entropy => wav2vec criterion + if ( + hasattr(state["args"], "criterion") + and state["args"].criterion in [ + "binary_cross_entropy", + "kd_binary_cross_entropy", + ] + ): + state["args"].criterion = "wav2vec" + # remove log_keys if it's None (criteria will supply a default value of []) + if hasattr(state["args"], "log_keys") and state["args"].log_keys is None: + delattr(state["args"], "log_keys") + # speech_pretraining => audio pretraining + if ( + hasattr(state["args"], "task") + and state["args"].task == "speech_pretraining" + ): + state["args"].task = "audio_pretraining" + # audio_cpc => wav2vec + if hasattr(state["args"], "arch") and state["args"].arch == "audio_cpc": + state["args"].arch = "wav2vec" + # convert legacy float learning rate to List[float] + if hasattr(state["args"], "lr") and isinstance(state["args"].lr, float): + state["args"].lr = [state["args"].lr] + # convert task data arg to a string instead of List[string] + if ( + hasattr(state["args"], "data") + and isinstance(state["args"].data, list) + and len(state["args"].data) > 0 + ): + state["args"].data = state["args"].data[0] + # remove keys in state["args"] related to teacher-student learning + for key in [ + "static_teachers", + "static_teacher_weights", + "dynamic_teachers", + "dynamic_teacher_weights", + ]: + if key in state["args"]: + delattr(state["args"], key) + + state["cfg"] = convert_namespace_to_omegaconf(state["args"]) + + if "cfg" in state and state["cfg"] is not None: + cfg = state["cfg"] + with open_dict(cfg): + # any upgrades for Hydra-based configs + if ( + "task" in cfg + and "eval_wer_config" in cfg.task + and isinstance(cfg.task.eval_wer_config.print_alignment, bool) + ): + cfg.task.eval_wer_config.print_alignment = "hard" + if "generation" in cfg and isinstance(cfg.generation.print_alignment, bool): + cfg.generation.print_alignment = "hard" if cfg.generation.print_alignment else None + if ( + "model" in cfg + and "w2v_args" in cfg.model + and cfg.model.w2v_args is not None + and ( + hasattr(cfg.model.w2v_args, "task") or "task" in cfg.model.w2v_args + ) + and hasattr(cfg.model.w2v_args.task, "eval_wer_config") + and cfg.model.w2v_args.task.eval_wer_config is not None + and isinstance( + cfg.model.w2v_args.task.eval_wer_config.print_alignment, bool + ) + ): + cfg.model.w2v_args.task.eval_wer_config.print_alignment = "hard" return state -def prune_state_dict(state_dict, args): +def prune_state_dict(state_dict, model_cfg: Optional[DictConfig]): """Prune the given state_dict if desired for LayerDrop (https://arxiv.org/abs/1909.11556). @@ -373,16 +683,20 @@ def prune_state_dict(state_dict, args): It's called by functions that load models from checkpoints and does not need to be called directly. """ - if not args or args.arch == "ptt_transformer": + arch = None + if model_cfg is not None: + arch = ( + model_cfg._name + if isinstance(model_cfg, DictConfig) + else getattr(model_cfg, "arch", None) + ) + + if not model_cfg or arch is None or arch == "ptt_transformer": # args should not be none, but don't crash if it is. return state_dict - encoder_layers_to_keep = ( - args.encoder_layers_to_keep if "encoder_layers_to_keep" in vars(args) else None - ) - decoder_layers_to_keep = ( - args.decoder_layers_to_keep if "decoder_layers_to_keep" in vars(args) else None - ) + encoder_layers_to_keep = getattr(model_cfg, "encoder_layers_to_keep", None) + decoder_layers_to_keep = getattr(model_cfg, "decoder_layers_to_keep", None) if not encoder_layers_to_keep and not decoder_layers_to_keep: return state_dict @@ -394,13 +708,13 @@ def prune_state_dict(state_dict, args): def create_pruning_pass(layers_to_keep, layer_name): keep_layers = sorted( - [int(layer_string) for layer_string in layers_to_keep.split(",")] + int(layer_string) for layer_string in layers_to_keep.split(",") ) mapping_dict = {} for i in range(len(keep_layers)): mapping_dict[str(keep_layers[i])] = str(i) - regex = re.compile("^{layer}.*\.layers\.(\d+)".format(layer=layer_name)) + regex = re.compile(r"^{layer}.*\.layers\.(\d+)".format(layer=layer_name)) return {"substitution_regex": regex, "mapping_dict": mapping_dict} pruning_passes = [] @@ -411,7 +725,7 @@ def create_pruning_pass(layers_to_keep, layer_name): new_state_dict = {} for layer_name in state_dict.keys(): - match = re.search("\.layers\.(\d+)\.", layer_name) + match = re.search(r"\.layers\.(\d+)\.", layer_name) # if layer has no number in it, it is a supporting layer, such as an # embedding if not match: @@ -438,10 +752,15 @@ def create_pruning_pass(layers_to_keep, layer_name): # Since layers are now pruned, *_layers_to_keep are no longer needed. # This is more of "It would make it work fix" rather than a proper fix. - if "encoder_layers_to_keep" in vars(args): - args.encoder_layers_to_keep = None - if "decoder_layers_to_keep" in vars(args): - args.decoder_layers_to_keep = None + if isinstance(model_cfg, DictConfig): + context = open_dict(model_cfg) + else: + context = contextlib.ExitStack() + with context: + if hasattr(model_cfg, "encoder_layers_to_keep"): + model_cfg.encoder_layers_to_keep = None + if hasattr(model_cfg, "decoder_layers_to_keep"): + model_cfg.decoder_layers_to_keep = None return new_state_dict @@ -485,7 +804,55 @@ def verify_checkpoint_directory(save_dir: str) -> None: with open(temp_file_path, "w"): pass except OSError as e: - logger.warning("Unable to access checkpoint save directory: {}".format(save_dir)) + logger.warning( + "Unable to access checkpoint save directory: {}".format(save_dir) + ) raise e else: os.remove(temp_file_path) + + +def load_ema_from_checkpoint(fpath): + """Loads exponential moving averaged (EMA) checkpoint from input and + returns a model with ema weights. + + Args: + fpath: A string path of checkpoint to load from. + + Returns: + A dict of string keys mapping to various values. The 'model' key + from the returned dict should correspond to an OrderedDict mapping + string parameter names to torch Tensors. + """ + params_dict = collections.OrderedDict() + new_state = None + + with PathManager.open(fpath, 'rb') as f: + new_state = torch.load( + f, + map_location=( + lambda s, _: torch.serialization.default_restore_location(s, 'cpu') + ), + ) + + # EMA model is stored in a separate "extra state" + model_params = new_state['extra_state']['ema'] + + for key in list(model_params.keys()): + p = model_params[key] + if isinstance(p, torch.HalfTensor): + p = p.float() + if key not in params_dict: + params_dict[key] = p.clone() + # NOTE: clone() is needed in case of p is a shared parameter + else: + raise ValueError("Key {} is repeated in EMA model params.".format(key)) + + if len(params_dict) == 0: + raise ValueError( + f"Input checkpoint path '{fpath}' does not contain " + "ema model weights, is this model trained with EMA?" + ) + + new_state['model'] = params_dict + return new_state diff --git a/fairseq/clib/cuda/ngram_repeat_block_cuda.cpp b/fairseq/clib/cuda/ngram_repeat_block_cuda.cpp new file mode 100644 index 0000000000..707219105a --- /dev/null +++ b/fairseq/clib/cuda/ngram_repeat_block_cuda.cpp @@ -0,0 +1,55 @@ +/* +Copyright (c) Microsoft Corporation. +Licensed under the MIT License. +*/ + +#include <torch/extension.h> +#include <vector> + +/* +CPP Binding for CUDA OP +*/ + +// CUDA forward declarations +torch::Tensor ngram_repeat_block_cuda_forward( + torch::Tensor tokens, + torch::Tensor lprobs, + int bsz, + int step, + int beam_size, + int no_repeat_ngram_size); + +#define CHECK_CUDA(x) \ + TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor") +#define CHECK_CONTIGUOUS(x) \ + TORCH_CHECK(x.is_contiguous(), #x " must be contiguous") +#define CHECK_INPUT(x) \ + CHECK_CUDA(x); \ + CHECK_CONTIGUOUS(x) + +// Input check and call to CUDA OP +// Backward method not required +torch::Tensor ngram_repeat_block_forward( + torch::Tensor tokens, + torch::Tensor lprobs, + int bsz, + int step, + int beam_size, + int no_repeat_ngram_size) { + CHECK_INPUT(tokens); + CHECK_INPUT(lprobs); + assert(bsz > 0); + assert(step >= 0); + assert(beam_size > 0); + assert(no_repeat_ngram_size > 0); + + return ngram_repeat_block_cuda_forward( + tokens, lprobs, bsz, step, beam_size, no_repeat_ngram_size); +} + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { + m.def( + "forward", + &ngram_repeat_block_forward, + "No Repeat Ngram Block forward (CUDA)"); +} diff --git a/fairseq/clib/cuda/ngram_repeat_block_cuda_kernel.cu b/fairseq/clib/cuda/ngram_repeat_block_cuda_kernel.cu new file mode 100644 index 0000000000..bd6106cba0 --- /dev/null +++ b/fairseq/clib/cuda/ngram_repeat_block_cuda_kernel.cu @@ -0,0 +1,82 @@ +/* +Copyright (c) Microsoft Corporation. +Licensed under the MIT License. +*/ + +/* +Kernel implementation for blocking repeated n-grams. +*/ + +#include <cuda.h> +#include <cuda_runtime.h> +#include <math.h> +#include <torch/extension.h> +#include <vector> + +// Ban repeated ngrams of length = 'no_repeat_ngram_size' +__global__ void banRepeatedTokens( + long* __restrict__ tokens, + float* __restrict__ lprobs, + int max_predict_len, + int vocab_size, + int no_repeat_ngram_size) { + auto row = blockIdx.x; + auto col = threadIdx.x; + auto start = row * (max_predict_len) + col; + // Each thread compares ngram starting from + // thread index with final ngram starting from + // step - no_repeat_ngram_size +2 + auto check_start_pos = blockDim.x; + auto lprob_start = row * vocab_size; + bool is_banned = true; + extern __shared__ long tokens_shm[]; + tokens_shm[col] = tokens[start]; + if (col == blockDim.x - 1) { + for (int i = 1; i < no_repeat_ngram_size; i++) { + if (col + i < max_predict_len) { + tokens_shm[col + i] = tokens[start + i]; + } + } + } + __syncthreads(); + + for (int k = 0; k < no_repeat_ngram_size - 1; k++) { + if (tokens_shm[col + k] != tokens_shm[check_start_pos + k]) { + is_banned = false; + } + } + if (is_banned == true) { + auto token_to_be_banned = tokens_shm[col + no_repeat_ngram_size - 1]; + lprobs[lprob_start + token_to_be_banned] = -INFINITY; + } +} + +// Allocate blocks and threads based on +// batch size and sequence length and launch +// kernel +torch::Tensor ngram_repeat_block_cuda_forward( + const torch::Tensor tokens, + torch::Tensor lprobs, + int bsz, + int step, + int beam_size, + int no_repeat_ngram_size) { + int threads = step - no_repeat_ngram_size + 2; + if (threads <= 0) + return lprobs; + int max_predict_len = tokens.size(1); + int vocab_size = lprobs.size(1); + auto token_ptr = tokens.data_ptr<long>(); + auto lprob_ptr = lprobs.data_ptr<float>(); + int blocks = bsz * beam_size; + int shared_mem_size = (step + 1) * sizeof(long); + + // Launching N blocks where N is number of samples in a batch (beams*bsz) + // Launching T threads where T is number of previous ngrams in a sample + // Allocating shared mem per block for fastser access of input tokens since + // each token will be accessed N times to compare with current Ngram where + // N is Ngram size. + banRepeatedTokens<<<blocks, threads, shared_mem_size>>>( + token_ptr, lprob_ptr, max_predict_len, vocab_size, no_repeat_ngram_size); + return lprobs; +} diff --git a/fairseq/clib/libbase/balanced_assignment.cpp b/fairseq/clib/libbase/balanced_assignment.cpp new file mode 100644 index 0000000000..1a5a1061f3 --- /dev/null +++ b/fairseq/clib/libbase/balanced_assignment.cpp @@ -0,0 +1,109 @@ +/** + * Copyright 2017-present, Facebook, Inc. + * All rights reserved. + * + * This source code is licensed under the license found in the + * LICENSE file in the root directory of this source tree. + */ + +/* +C++ code for solving the linear assignment problem. +Based on the Auction Algorithm from +https://dspace.mit.edu/bitstream/handle/1721.1/3265/P-2108-26912652.pdf and the +implementation from: https://github.com/bkj/auction-lap Adapted to be more +efficient when each worker is looking for k jobs instead of 1. +*/ +#include <torch/extension.h> +#include <iostream> +using namespace torch::indexing; +torch::Tensor balanced_assignment(torch::Tensor job_and_worker_to_score) { + int max_iterations = 100; + torch::Tensor epsilon = + (job_and_worker_to_score.max() - job_and_worker_to_score.min()) / 50; + epsilon.clamp_min_(1e-04); + torch::Tensor worker_and_job_to_score = + job_and_worker_to_score.detach().transpose(0, 1).contiguous(); + int num_workers = worker_and_job_to_score.size(0); + int num_jobs = worker_and_job_to_score.size(1); + auto device = worker_and_job_to_score.device(); + int jobs_per_worker = num_jobs / num_workers; + torch::Tensor value = worker_and_job_to_score.clone(); + int counter = 0; + torch::Tensor max_value = worker_and_job_to_score.max(); + + torch::Tensor bid_indices; + torch::Tensor cost = worker_and_job_to_score.new_zeros({1, num_jobs}); + torch::Tensor bids = + worker_and_job_to_score.new_empty({num_workers, num_jobs}); + torch::Tensor bid_increments = + worker_and_job_to_score.new_empty({num_workers, jobs_per_worker}); + torch::Tensor top_values = + worker_and_job_to_score.new_empty({num_workers, jobs_per_worker + 1}); + torch::Tensor high_bids = worker_and_job_to_score.new_empty({num_jobs}); + + torch::Tensor top_index = top_values.to(torch::kLong); + torch::Tensor high_bidders = top_index.new_empty({num_jobs}); + torch::Tensor have_bids = high_bidders.to(torch::kBool); + torch::Tensor jobs_indices = + torch::arange({num_jobs}, torch::dtype(torch::kLong).device(device)); + torch::Tensor true_tensor = + torch::ones({1}, torch::dtype(torch::kBool).device(device)); + + while (true) { + bids.zero_(); + torch::topk_out(top_values, top_index, value, jobs_per_worker + 1, 1); + + // Each worker bids the difference in value between that job and the k+1th + // job + torch::sub_out( + bid_increments, + top_values.index({Slice(None, None), Slice(0, jobs_per_worker)}), + top_values.index({Slice(None, None), jobs_per_worker}).unsqueeze(1)); + + bid_increments.add_(epsilon); + bids.scatter_( + 1, + top_index.index({Slice(None, None), Slice(0, jobs_per_worker)}), + bid_increments); + + if (counter < max_iterations && counter > 0) { + // Put in a minimal bid to retain items from the last round if no-one else + // bids for them this round + bids.view(-1).index_put_({bid_indices}, epsilon); + } + + // Find the highest bidding worker per job + torch::max_out(high_bids, high_bidders, bids, 0); + torch::gt_out(have_bids, high_bids, 0); + + if (have_bids.all().item<bool>()) { + // All jobs were bid for + break; + } + + // Make popular items more expensive + cost.add_(high_bids); + torch::sub_out(value, worker_and_job_to_score, cost); + + bid_indices = ((high_bidders * num_jobs) + jobs_indices).index({have_bids}); + + if (counter < max_iterations) { + // Make sure that this item will be in the winning worker's top-k next + // time. + value.view(-1).index_put_({bid_indices}, max_value); + } else { + // Suboptimal approximation that converges quickly from current solution + value.view(-1).index_put_( + {bid_indices}, worker_and_job_to_score.view(-1).index({bid_indices})); + } + + counter += 1; + } + + return top_index.index({Slice(None, None), Slice(0, jobs_per_worker)}) + .reshape(-1); +} + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { + m.def("balanced_assignment", &balanced_assignment, "Balanced Assignment"); +} diff --git a/fairseq/clib/libbleu/libbleu.cpp b/fairseq/clib/libbleu/libbleu.cpp index 3cf2d65b6d..939d9e1174 100644 --- a/fairseq/clib/libbleu/libbleu.cpp +++ b/fairseq/clib/libbleu/libbleu.cpp @@ -6,30 +6,32 @@ * LICENSE file in the root directory of this source tree. */ -#include <map> #include <array> -#include <cstring> #include <cstdio> +#include <cstring> +#include <map> -typedef struct -{ - size_t reflen; - size_t predlen; - size_t match1; - size_t count1; - size_t match2; - size_t count2; - size_t match3; - size_t count3; - size_t match4; - size_t count4; +// NOLINTNEXTLINE +typedef struct { + size_t reflen; + size_t predlen; + size_t match1; + size_t count1; + size_t match2; + size_t count2; + size_t match3; + size_t count3; + size_t match4; + size_t count4; } bleu_stat; // left trim (remove pad) void bleu_ltrim(size_t* len, int** sent, int pad) { size_t start = 0; - while(start < *len) { - if (*(*sent + start) != pad) { break; } + while (start < *len) { + if (*(*sent + start) != pad) { + break; + } start++; } *sent += start; @@ -40,7 +42,9 @@ void bleu_ltrim(size_t* len, int** sent, int pad) { void bleu_rtrim(size_t* len, int** sent, int pad, int eos) { size_t end = *len - 1; while (end > 0) { - if (*(*sent + end) != eos && *(*sent + end) != pad) { break; } + if (*(*sent + end) != eos && *(*sent + end) != pad) { + break; + } end--; } *len = end + 1; @@ -53,10 +57,10 @@ void bleu_trim(size_t* len, int** sent, int pad, int eos) { } size_t bleu_hash(int len, int* data) { - size_t h = 14695981039346656037ul; + size_t h = 14695981039346656037ul; size_t prime = 0x100000001b3; - char* b = (char*) data; - size_t blen = sizeof(int) * len; + char* b = (char*)data; + size_t blen = sizeof(int) * len; while (blen-- > 0) { h ^= *b++; @@ -67,15 +71,23 @@ size_t bleu_hash(int len, int* data) { } void bleu_addngram( - size_t *ntotal, size_t *nmatch, size_t n, - size_t reflen, int* ref, size_t predlen, int* pred) { - - if (predlen < n) { return; } + size_t* ntotal, + size_t* nmatch, + size_t n, + size_t reflen, + int* ref, + size_t predlen, + int* pred) { + if (predlen < n) { + return; + } predlen = predlen - n + 1; (*ntotal) += predlen; - if (reflen < n) { return; } + if (reflen < n) { + return; + } reflen = reflen - n + 1; @@ -90,7 +102,7 @@ void bleu_addngram( size_t w = bleu_hash(n, ref++); if (count[w] > 0) { (*nmatch)++; - count[w] -=1; + count[w] -= 1; } reflen--; } @@ -99,16 +111,16 @@ void bleu_addngram( extern "C" { #ifdef _WIN64 -__declspec(dllexport) +__declspec(dllexport) #endif -void bleu_zero_init(bleu_stat* stat) { + void bleu_zero_init(bleu_stat* stat) { std::memset(stat, 0, sizeof(bleu_stat)); } #ifdef _WIN64 -__declspec(dllexport) +__declspec(dllexport) #endif -void bleu_one_init(bleu_stat* stat) { + void bleu_one_init(bleu_stat* stat) { bleu_zero_init(stat); stat->count1 = 0; stat->count2 = 1; @@ -121,11 +133,16 @@ void bleu_one_init(bleu_stat* stat) { } #ifdef _WIN64 -__declspec(dllexport) +__declspec(dllexport) #endif -void bleu_add( - bleu_stat* stat, - size_t reflen, int* ref, size_t predlen, int* pred, int pad, int eos) { + void bleu_add( + bleu_stat* stat, + size_t reflen, + int* ref, + size_t predlen, + int* pred, + int pad, + int eos) { bleu_trim(&reflen, &ref, pad, eos); bleu_trim(&predlen, &pred, pad, eos); @@ -137,5 +154,4 @@ void bleu_add( bleu_addngram(&stat->count3, &stat->match3, 3, reflen, ref, predlen, pred); bleu_addngram(&stat->count4, &stat->match4, 4, reflen, ref, predlen, pred); } - } diff --git a/fairseq/clib/libbleu/module.cpp b/fairseq/clib/libbleu/module.cpp index 8ed9a84b1c..35288b3177 100644 --- a/fairseq/clib/libbleu/module.cpp +++ b/fairseq/clib/libbleu/module.cpp @@ -8,20 +8,16 @@ #include <Python.h> - -static PyMethodDef method_def[] = { - {NULL, NULL, 0, NULL} -}; +static PyMethodDef method_def[] = {{NULL, NULL, 0, NULL}}; // NOLINT static struct PyModuleDef module_def = { - PyModuleDef_HEAD_INIT, - "libbleu", /* name of module */ - NULL, /* module documentation, may be NULL */ - -1, /* size of per-interpreter state of the module, - or -1 if the module keeps state in global variables. */ - method_def -}; - + PyModuleDef_HEAD_INIT, + "libbleu", /* name of module */ + // NOLINTNEXTLINE + NULL, /* module documentation, may be NULL */ + -1, /* size of per-interpreter state of the module, + or -1 if the module keeps state in global variables. */ + method_def}; // NOLINT #if PY_MAJOR_VERSION == 2 PyMODINIT_FUNC init_libbleu() @@ -29,7 +25,7 @@ PyMODINIT_FUNC init_libbleu() PyMODINIT_FUNC PyInit_libbleu() #endif { - PyObject *m = PyModule_Create(&module_def); + PyObject* m = PyModule_Create(&module_def); if (!m) { return NULL; } diff --git a/fairseq/clib/libnat/edit_dist.cpp b/fairseq/clib/libnat/edit_dist.cpp index 6bc6a937d6..9ffb60569d 100644 --- a/fairseq/clib/libnat/edit_dist.cpp +++ b/fairseq/clib/libnat/edit_dist.cpp @@ -6,10 +6,9 @@ * LICENSE file in the root directory of this source tree. */ -#include <torch/torch.h> // @manual=//caffe2:torch_extension #include <pybind11/detail/common.h> #include <pybind11/pybind11.h> -#include <vector> +#include <torch/torch.h> // @manual=//caffe2:torch_extension #include <algorithm> #include <cstdint> #include <iosfwd> @@ -17,6 +16,7 @@ #include <new> #include <string> #include <utility> +#include <vector> using namespace ::std; diff --git a/fairseq/clib/libnat_cuda/binding.cpp b/fairseq/clib/libnat_cuda/binding.cpp index aaa6244d5c..ced91c0d0a 100644 --- a/fairseq/clib/libnat_cuda/binding.cpp +++ b/fairseq/clib/libnat_cuda/binding.cpp @@ -7,54 +7,61 @@ */ /* - This code is partially adpoted from https://github.com/1ytic/pytorch-edit-distance + This code is partially adpoted from + https://github.com/1ytic/pytorch-edit-distance */ -#include "edit_dist.h" #include <torch/types.h> +#include "edit_dist.h" #ifndef TORCH_CHECK #define TORCH_CHECK AT_CHECK #endif -#define CHECK_CUDA(x) TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor") -#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous") -#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x) - +#define CHECK_CUDA(x) \ + TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor") +#define CHECK_CONTIGUOUS(x) \ + TORCH_CHECK(x.is_contiguous(), #x " must be contiguous") +#define CHECK_INPUT(x) \ + CHECK_CUDA(x); \ + CHECK_CONTIGUOUS(x) torch::Tensor LevenshteinDistance( - torch::Tensor source, - torch::Tensor target, - torch::Tensor source_length, - torch::Tensor target_length) { - - CHECK_INPUT(source); - CHECK_INPUT(target); - CHECK_INPUT(source_length); - CHECK_INPUT(target_length); - return LevenshteinDistanceCuda(source, target, source_length, target_length); + torch::Tensor source, + torch::Tensor target, + torch::Tensor source_length, + torch::Tensor target_length) { + CHECK_INPUT(source); + CHECK_INPUT(target); + CHECK_INPUT(source_length); + CHECK_INPUT(target_length); + return LevenshteinDistanceCuda(source, target, source_length, target_length); } torch::Tensor GenerateDeletionLabel( - torch::Tensor source, - torch::Tensor operations) { - - CHECK_INPUT(source); - CHECK_INPUT(operations); - return GenerateDeletionLabelCuda(source, operations); + torch::Tensor source, + torch::Tensor operations) { + CHECK_INPUT(source); + CHECK_INPUT(operations); + return GenerateDeletionLabelCuda(source, operations); } std::pair<torch::Tensor, torch::Tensor> GenerateInsertionLabel( - torch::Tensor target, - torch::Tensor operations) { - - CHECK_INPUT(target); - CHECK_INPUT(operations); - return GenerateInsertionLabelCuda(target, operations); + torch::Tensor target, + torch::Tensor operations) { + CHECK_INPUT(target); + CHECK_INPUT(operations); + return GenerateInsertionLabelCuda(target, operations); } PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { - m.def("levenshtein_distance", &LevenshteinDistance, "Levenshtein distance"); - m.def("generate_deletion_labels", &GenerateDeletionLabel, "Generate Deletion Label"); - m.def("generate_insertion_labels", &GenerateInsertionLabel, "Generate Insertion Label"); + m.def("levenshtein_distance", &LevenshteinDistance, "Levenshtein distance"); + m.def( + "generate_deletion_labels", + &GenerateDeletionLabel, + "Generate Deletion Label"); + m.def( + "generate_insertion_labels", + &GenerateInsertionLabel, + "Generate Insertion Label"); } diff --git a/fairseq/clib/libnat_cuda/edit_dist.cu b/fairseq/clib/libnat_cuda/edit_dist.cu index b6486a8c22..96569d46c8 100644 --- a/fairseq/clib/libnat_cuda/edit_dist.cu +++ b/fairseq/clib/libnat_cuda/edit_dist.cu @@ -1,332 +1,344 @@ /** -* Copyright 2017-present, Facebook, Inc. -* All rights reserved. -* -* This source code is licensed under the license found in the -* LICENSE file in the root directory of this source tree. -*/ + * Copyright 2017-present, Facebook, Inc. + * All rights reserved. + * + * This source code is licensed under the license found in the + * LICENSE file in the root directory of this source tree. + */ #include "edit_dist.h" + #include <THC/THC.h> #include <cuda.h> #include <cuda_runtime.h> #include <device_launch_parameters.h> -#include <utility> // std::pair +#include <utility> // std::pair template <typename scalar_t> __global__ void generate_deletion_label_kernel( - const scalar_t* __restrict__ source, - const size_t source_size, - const size_t operation_size, - int* __restrict__ operations, - int* __restrict__ labels) { - - const int index = blockIdx.x; - const int offset = index * operation_size; - const int offset_label = index * source_size; - - for (int i = 0; i < source_size; i++) { - labels[offset_label + i] = 0; - } - - int k = 0; - for (int i = 0; i < operation_size; i++){ - if (operations[offset + i] == 0){ - break; - } else if (operations[offset + i] == 1){ - continue; - } else { - labels[offset_label + k] = 3 - operations[offset + i]; - k++; - } + const scalar_t* __restrict__ source, + const size_t source_size, + const size_t operation_size, + int* __restrict__ operations, + int* __restrict__ labels) { + const int index = blockIdx.x; + const int offset = index * operation_size; + const int offset_label = index * source_size; + + for (int i = 0; i < source_size; i++) { + labels[offset_label + i] = 0; + } + + int k = 0; + for (int i = 0; i < operation_size; i++) { + if (operations[offset + i] == 0) { + break; + } else if (operations[offset + i] == 1) { + continue; + } else { + labels[offset_label + k] = 3 - operations[offset + i]; + k++; } + } } template <typename scalar_t> __global__ void generate_insertion_label_kernel( - const scalar_t* __restrict__ target, - const size_t target_size, - const size_t operation_size, - int* __restrict__ operations, - int* __restrict__ labels, - int* __restrict__ masks) { - - const int index = blockIdx.x; - const int offset = index * operation_size; - const int offset_label = index * target_size; - - int k = 0; - int u = 0; - int m = 0; - - for (int i = 0; i < target_size; i++) { - labels[offset_label + i] = 0; - masks[offset_label + i] = 0; - } - - for (int i = 0; i < operation_size-1; i++){ - if (operations[offset + i] == 0){ - break; - } else if (operations[offset + i] == 2){ - continue; - } else if (operations[offset + i] == 1){ - masks[offset_label + m] = 1; - u++; m++; - } else { - labels[offset_label + k] = u; - masks[offset_label + m] = 0; - k++; m++; - u = 0; - } + const scalar_t* __restrict__ target, + const size_t target_size, + const size_t operation_size, + int* __restrict__ operations, + int* __restrict__ labels, + int* __restrict__ masks) { + const int index = blockIdx.x; + const int offset = index * operation_size; + const int offset_label = index * target_size; + + int k = 0; + int u = 0; + int m = 0; + + for (int i = 0; i < target_size; i++) { + labels[offset_label + i] = 0; + masks[offset_label + i] = 0; + } + + for (int i = 0; i < operation_size - 1; i++) { + if (operations[offset + i] == 0) { + break; + } else if (operations[offset + i] == 2) { + continue; + } else if (operations[offset + i] == 1) { + masks[offset_label + m] = 1; + u++; + m++; + } else { + labels[offset_label + k] = u; + masks[offset_label + m] = 0; + k++; + m++; + u = 0; } + } } template <typename scalar_t> __global__ void levenshtein_distance_kernel( - const scalar_t* __restrict__ source, - const scalar_t* __restrict__ target, - const int* __restrict__ source_length, - const int* __restrict__ target_length, - const size_t source_size, - const size_t target_size, - int* __restrict__ operations, - int* __restrict__ errors_curr) { - - const int index = blockIdx.x; - const int offset = index * (source_size + target_size); - const int d = index * (source_size + 1) * (target_size + 1); - const int t = target_size + 1; - - auto err_idx = [d, t](int i, int j) { return d + i * t + j; }; - auto opt_idx = [offset](int k) { return offset + k; }; - - const int hyp_len = source_length[index]; - const int ref_len = target_length[index]; - const scalar_t* hyp_begin = source + index * source_size; - const scalar_t* ref_begin = target + index * target_size; - - // dynamic programming - for (int i = 0; i <= hyp_len; i++){ - errors_curr[err_idx(i, 0)] = i; - } - for (int j = 0; j <= ref_len; j++){ - errors_curr[err_idx(0, j)] = j; - } - for (int i = 1; i <= hyp_len; i++){ - for (int j = 1; j <= ref_len; j++){ - errors_curr[err_idx(i, j)] = min( - min( - errors_curr[err_idx(i-1, j)], - errors_curr[err_idx(i, j-1)] - ) + 1, - errors_curr[err_idx(i-1, j-1)] + 2 * ( - *(hyp_begin+i-1) == *(ref_begin+j-1) ? 0 : 1 - ) - ); - } + const scalar_t* __restrict__ source, + const scalar_t* __restrict__ target, + const int* __restrict__ source_length, + const int* __restrict__ target_length, + const size_t source_size, + const size_t target_size, + int* __restrict__ operations, + int* __restrict__ errors_curr) { + const int index = blockIdx.x; + const int offset = index * (source_size + target_size); + const int d = index * (source_size + 1) * (target_size + 1); + const int t = target_size + 1; + + auto err_idx = [d, t](int i, int j) { return d + i * t + j; }; + auto opt_idx = [offset](int k) { return offset + k; }; + + const int hyp_len = source_length[index]; + const int ref_len = target_length[index]; + const scalar_t* hyp_begin = source + index * source_size; + const scalar_t* ref_begin = target + index * target_size; + + // dynamic programming + for (int i = 0; i <= hyp_len; i++) { + errors_curr[err_idx(i, 0)] = i; + } + for (int j = 0; j <= ref_len; j++) { + errors_curr[err_idx(0, j)] = j; + } + for (int i = 1; i <= hyp_len; i++) { + for (int j = 1; j <= ref_len; j++) { + errors_curr[err_idx(i, j)] = min( + min(errors_curr[err_idx(i - 1, j)], errors_curr[err_idx(i, j - 1)]) + + 1, + errors_curr[err_idx(i - 1, j - 1)] + + 2 * (*(hyp_begin + i - 1) == *(ref_begin + j - 1) ? 0 : 1)); } + } - // back-tracing - int i = hyp_len; - int j = ref_len; - int o = hyp_len + ref_len; + // back-tracing + int i = hyp_len; + int j = ref_len; + int o = hyp_len + ref_len; - for (int k = 0; k < source_size + target_size; k++) { - operations[opt_idx(k)] = 0; - } + for (int k = 0; k < source_size + target_size; k++) { + operations[opt_idx(k)] = 0; + } - while ((i >= 0) && (j >= 0)) { - if ((i == 0) && (j == 0)) { - break; - } - - if ((j > 0) && (errors_curr[err_idx(i, j-1)] < errors_curr[err_idx(i, j)])) { - o--; operations[opt_idx(o)] = 1; j--; // insertion - } else if ((i > 0) && (errors_curr[err_idx(i-1, j)] < errors_curr[err_idx(i, j)])) { - o--; operations[opt_idx(o)] = 2; i--; // deletion - } else { - o--; operations[opt_idx(o)] = 3; i--; j--; // do nothing - } + while ((i >= 0) && (j >= 0)) { + if ((i == 0) && (j == 0)) { + break; } - // moving to the left - for (int k = 0; k < hyp_len + ref_len; k++) { - if (k + o < hyp_len + ref_len){ - operations[opt_idx(k)] = operations[opt_idx(k+o)]; - } else{ - operations[opt_idx(k)] = 0; // padding - } + if ((j > 0) && + (errors_curr[err_idx(i, j - 1)] < errors_curr[err_idx(i, j)])) { + o--; + operations[opt_idx(o)] = 1; + j--; // insertion + } else if ( + (i > 0) && + (errors_curr[err_idx(i - 1, j)] < errors_curr[err_idx(i, j)])) { + o--; + operations[opt_idx(o)] = 2; + i--; // deletion + } else { + o--; + operations[opt_idx(o)] = 3; + i--; + j--; // do nothing } + } + // moving to the left + for (int k = 0; k < hyp_len + ref_len; k++) { + if (k + o < hyp_len + ref_len) { + operations[opt_idx(k)] = operations[opt_idx(k + o)]; + } else { + operations[opt_idx(k)] = 0; // padding + } + } } template <typename scalar_t> __global__ void faster_levenshtein_distance_kernel( - const scalar_t* __restrict__ source, - const scalar_t* __restrict__ target, - const int* __restrict__ source_length, - const int* __restrict__ target_length, - const size_t source_size, - const size_t target_size, - int* __restrict__ operations) { - - extern __shared__ short errors[]; - auto errors_curr = errors; - - const int index = blockIdx.x; - const int offset = index * (source_size + target_size); - const int t = target_size + 1; - - auto err_idx = [t](int i, int j) { return i * t + j; }; - auto opt_idx = [offset](int k) { return offset + k; }; - - const int hyp_len = source_length[index]; - const int ref_len = target_length[index]; - const scalar_t* hyp_begin = source + index * source_size; - const scalar_t* ref_begin = target + index * target_size; - - // dynamic programming - for (int i = 0; i <= hyp_len; i++){ - errors_curr[err_idx(i, 0)] = i; - } - for (int j = 0; j <= ref_len; j++){ - errors_curr[err_idx(0, j)] = j; - } - for (int i = 1; i <= hyp_len; i++){ - for (int j = 1; j <= ref_len; j++){ - errors_curr[err_idx(i, j)] = min( - min( - errors_curr[err_idx(i-1, j)], - errors_curr[err_idx(i, j-1)] - ) + 1, - errors_curr[err_idx(i-1, j-1)] + 2 * ( - *(hyp_begin+i-1) == *(ref_begin+j-1) ? 0 : 1 - ) - ); - } + const scalar_t* __restrict__ source, + const scalar_t* __restrict__ target, + const int* __restrict__ source_length, + const int* __restrict__ target_length, + const size_t source_size, + const size_t target_size, + int* __restrict__ operations) { + extern __shared__ short errors[]; + auto errors_curr = errors; + + const int index = blockIdx.x; + const int offset = index * (source_size + target_size); + const int t = target_size + 1; + + auto err_idx = [t](int i, int j) { return i * t + j; }; + auto opt_idx = [offset](int k) { return offset + k; }; + + const int hyp_len = source_length[index]; + const int ref_len = target_length[index]; + const scalar_t* hyp_begin = source + index * source_size; + const scalar_t* ref_begin = target + index * target_size; + + // dynamic programming + for (int i = 0; i <= hyp_len; i++) { + errors_curr[err_idx(i, 0)] = i; + } + for (int j = 0; j <= ref_len; j++) { + errors_curr[err_idx(0, j)] = j; + } + for (int i = 1; i <= hyp_len; i++) { + for (int j = 1; j <= ref_len; j++) { + errors_curr[err_idx(i, j)] = min( + min(errors_curr[err_idx(i - 1, j)], errors_curr[err_idx(i, j - 1)]) + + 1, + errors_curr[err_idx(i - 1, j - 1)] + + 2 * (*(hyp_begin + i - 1) == *(ref_begin + j - 1) ? 0 : 1)); } + } - // back-tracing - int i = hyp_len; - int j = ref_len; - int o = hyp_len + ref_len; + // back-tracing + int i = hyp_len; + int j = ref_len; + int o = hyp_len + ref_len; - for (int k = 0; k < source_size + target_size; k++) { - operations[opt_idx(k)] = 0; - } + for (int k = 0; k < source_size + target_size; k++) { + operations[opt_idx(k)] = 0; + } - while ((i >= 0) && (j >= 0)) { - if ((i == 0) && (j == 0)) { - break; - } - - if ((j > 0) && (errors_curr[err_idx(i, j-1)] < errors_curr[err_idx(i, j)])) { - o--; operations[opt_idx(o)] = 1; j--; // insertion - } else if ((i > 0) && (errors_curr[err_idx(i-1, j)] < errors_curr[err_idx(i, j)])) { - o--; operations[opt_idx(o)] = 2; i--; // deletion - } else { - o--; operations[opt_idx(o)] = 3; i--; j--; // do nothing - } + while ((i >= 0) && (j >= 0)) { + if ((i == 0) && (j == 0)) { + break; } - // moving to the left - for (int k = 0; k < hyp_len + ref_len; k++) { - if (k + o < hyp_len + ref_len){ - operations[opt_idx(k)] = operations[opt_idx(k+o)]; - } else{ - operations[opt_idx(k)] = 0; // padding - } + if ((j > 0) && + (errors_curr[err_idx(i, j - 1)] < errors_curr[err_idx(i, j)])) { + o--; + operations[opt_idx(o)] = 1; + j--; // insertion + } else if ( + (i > 0) && + (errors_curr[err_idx(i - 1, j)] < errors_curr[err_idx(i, j)])) { + o--; + operations[opt_idx(o)] = 2; + i--; // deletion + } else { + o--; + operations[opt_idx(o)] = 3; + i--; + j--; // do nothing } + } + // moving to the left + for (int k = 0; k < hyp_len + ref_len; k++) { + if (k + o < hyp_len + ref_len) { + operations[opt_idx(k)] = operations[opt_idx(k + o)]; + } else { + operations[opt_idx(k)] = 0; // padding + } + } } - torch::Tensor GenerateDeletionLabelCuda( - torch::Tensor source, - torch::Tensor operations) { - - const auto batch_size = source.size(0); - at::TensorOptions options(source.device()); - options = options.dtype(at::ScalarType::Int); - auto labels = torch::empty({batch_size, source.size(1)}, options); - auto stream = at::cuda::getCurrentCUDAStream(source.device().index()); - - AT_DISPATCH_ALL_TYPES(source.scalar_type(), "generate_deletion_labels", ([&] { - generate_deletion_label_kernel<scalar_t><<<batch_size, 1, 0, stream>>>( - source.data<scalar_t>(), - source.size(1), - operations.size(1), - operations.data<int>(), - labels.data<int>()); - })); - - return labels; + torch::Tensor source, + torch::Tensor operations) { + const auto batch_size = source.size(0); + at::TensorOptions options(source.device()); + options = options.dtype(at::ScalarType::Int); + auto labels = torch::empty({batch_size, source.size(1)}, options); + auto stream = at::cuda::getCurrentCUDAStream(source.device().index()); + + AT_DISPATCH_ALL_TYPES(source.scalar_type(), "generate_deletion_labels", ([&] { + generate_deletion_label_kernel<scalar_t> + <<<batch_size, 1, 0, stream>>>( + source.data_ptr<scalar_t>(), + source.size(1), + operations.size(1), + operations.data_ptr<int>(), + labels.data_ptr<int>()); + })); + + return labels; } std::pair<torch::Tensor, torch::Tensor> GenerateInsertionLabelCuda( torch::Tensor target, torch::Tensor operations) { + const auto batch_size = target.size(0); + at::TensorOptions options(target.device()); + options = options.dtype(at::ScalarType::Int); + auto labels = torch::empty({batch_size, target.size(1)}, options); + auto masks = torch::empty({batch_size, target.size(1)}, options); + auto stream = at::cuda::getCurrentCUDAStream(target.device().index()); + + AT_DISPATCH_ALL_TYPES( + target.scalar_type(), "generate_insertion_labels", ([&] { + generate_insertion_label_kernel<scalar_t><<<batch_size, 1, 0, stream>>>( + target.data_ptr<scalar_t>(), + target.size(1), + operations.size(1), + operations.data_ptr<int>(), + labels.data_ptr<int>(), + masks.data_ptr<int>()); + })); -const auto batch_size = target.size(0); -at::TensorOptions options(target.device()); -options = options.dtype(at::ScalarType::Int); -auto labels = torch::empty({batch_size, target.size(1)}, options); -auto masks = torch::empty({batch_size, target.size(1)}, options); -auto stream = at::cuda::getCurrentCUDAStream(target.device().index()); - -AT_DISPATCH_ALL_TYPES(target.scalar_type(), "generate_insertion_labels", ([&] { - generate_insertion_label_kernel<scalar_t><<<batch_size, 1, 0, stream>>>( - target.data<scalar_t>(), - target.size(1), - operations.size(1), - operations.data<int>(), - labels.data<int>(), - masks.data<int>()); -})); - -return std::make_pair(labels, masks); + return std::make_pair(labels, masks); } - torch::Tensor LevenshteinDistanceCuda( - torch::Tensor source, - torch::Tensor target, - torch::Tensor source_length, - torch::Tensor target_length) { - - const auto batch_size = source.size(0); - const auto shared_size = (source.size(1) + 1) * (target.size(1) + 1) * sizeof(short); - - at::TensorOptions options(source.device()); - options = options.dtype(at::ScalarType::Int); - auto operations = torch::empty({batch_size, source.size(1) + target.size(1)}, options); - auto stream = at::cuda::getCurrentCUDAStream(source.device().index()); - - if (shared_size > 40000) { - auto distances = torch::empty({batch_size, (source.size(1) + 1) * (target.size(1) + 1)}, options); - AT_DISPATCH_ALL_TYPES(source.scalar_type(), "levenshtein_distance", ([&] { - levenshtein_distance_kernel<scalar_t><<<batch_size, 1, 0, stream>>>( - source.data<scalar_t>(), - target.data<scalar_t>(), - source_length.data<int>(), - target_length.data<int>(), - source.size(1), - target.size(1), - operations.data<int>(), - distances.data<int>()); - })); - } else { - AT_DISPATCH_ALL_TYPES(source.scalar_type(), "faster_levenshtein_distance", ([&] { - faster_levenshtein_distance_kernel<scalar_t><<<batch_size, 1, shared_size, stream>>>( - source.data<scalar_t>(), - target.data<scalar_t>(), - source_length.data<int>(), - target_length.data<int>(), - source.size(1), - target.size(1), - operations.data<int>()); + torch::Tensor source, + torch::Tensor target, + torch::Tensor source_length, + torch::Tensor target_length) { + const auto batch_size = source.size(0); + const auto shared_size = + (source.size(1) + 1) * (target.size(1) + 1) * sizeof(short); + + at::TensorOptions options(source.device()); + options = options.dtype(at::ScalarType::Int); + auto operations = + torch::empty({batch_size, source.size(1) + target.size(1)}, options); + auto stream = at::cuda::getCurrentCUDAStream(source.device().index()); + + if (shared_size > 40000) { + auto distances = torch::empty( + {batch_size, (source.size(1) + 1) * (target.size(1) + 1)}, options); + AT_DISPATCH_ALL_TYPES(source.scalar_type(), "levenshtein_distance", ([&] { + levenshtein_distance_kernel<scalar_t> + <<<batch_size, 1, 0, stream>>>( + source.data_ptr<scalar_t>(), + target.data_ptr<scalar_t>(), + source_length.data_ptr<int>(), + target_length.data_ptr<int>(), + source.size(1), + target.size(1), + operations.data_ptr<int>(), + distances.data_ptr<int>()); + })); + } else { + AT_DISPATCH_ALL_TYPES( + source.scalar_type(), "faster_levenshtein_distance", ([&] { + faster_levenshtein_distance_kernel<scalar_t> + <<<batch_size, 1, shared_size, stream>>>( + source.data_ptr<scalar_t>(), + target.data_ptr<scalar_t>(), + source_length.data_ptr<int>(), + target_length.data_ptr<int>(), + source.size(1), + target.size(1), + operations.data_ptr<int>()); })); - } + } - return operations; + return operations; } diff --git a/fairseq/clib/libnat_cuda/edit_dist.h b/fairseq/clib/libnat_cuda/edit_dist.h index e3506cd34d..5220c52fd8 100644 --- a/fairseq/clib/libnat_cuda/edit_dist.h +++ b/fairseq/clib/libnat_cuda/edit_dist.h @@ -11,15 +11,15 @@ #include <torch/extension.h> torch::Tensor LevenshteinDistanceCuda( - torch::Tensor source, - torch::Tensor target, - torch::Tensor source_length, - torch::Tensor target_length); + torch::Tensor source, + torch::Tensor target, + torch::Tensor source_length, + torch::Tensor target_length); torch::Tensor GenerateDeletionLabelCuda( - torch::Tensor source, - torch::Tensor operations); + torch::Tensor source, + torch::Tensor operations); std::pair<torch::Tensor, torch::Tensor> GenerateInsertionLabelCuda( - torch::Tensor source, - torch::Tensor operations); + torch::Tensor source, + torch::Tensor operations); diff --git a/fairseq/config/__init__.py b/fairseq/config/__init__.py new file mode 100644 index 0000000000..6264236915 --- /dev/null +++ b/fairseq/config/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. diff --git a/fairseq/config/config.yaml b/fairseq/config/config.yaml new file mode 100644 index 0000000000..2ed7168cb7 --- /dev/null +++ b/fairseq/config/config.yaml @@ -0,0 +1,19 @@ +# @package _group_ + +hydra: + run: + dir: . + +defaults: + - _self_ + - task: null + - model: null + - criterion: cross_entropy + - optimizer: null + - lr_scheduler: fixed + - bpe: null + - tokenizer: null + - scoring: null + - generation: null + - common_eval: null + - eval_lm: null diff --git a/fairseq/config/model/transformer_lm/transformer_lm_baevski_gbw.yaml b/fairseq/config/model/transformer_lm/transformer_lm_baevski_gbw.yaml new file mode 100644 index 0000000000..30b1a4f1e0 --- /dev/null +++ b/fairseq/config/model/transformer_lm/transformer_lm_baevski_gbw.yaml @@ -0,0 +1,36 @@ +# @package _group_ +activation_fn: "relu" +dropout: 0.1 +attention_dropout: 0.1 +activation_dropout: 0.0 +relu_dropout: 0.0 +decoder_embed_dim: 512 +decoder_output_dim: 512 +decoder_input_dim: 512 +decoder_ffn_embed_dim: 4096 +decoder_layers: 12 +decoder_attention_heads: 16 +decoder_normalize_before: true +no_decoder_final_norm: true +adaptive_softmax_cutoff: null +adaptive_softmax_dropout: 0 +adaptive_softmax_factor: 4 +no_token_positional_embeddings: false +share_decoder_input_output_embed: false +character_embeddings: false +character_filters: "[(1, 64), (2, 128), (3, 192), (4, 256), (5, 256), (6, 256), (7, 256)]" +character_embedding_dim: 4 +char_embedder_highway_layers: 2 +adaptive_input: false +adaptive_input_factor: 4 +adaptive_input_cutoff: null +tie_adaptive_weights: false +tie_adaptive_proj: false +decoder_learned_pos: false +decoder_layerdrop: 0 +decoder_layers_to_keep: null +layernorm_embedding: false +no_scale_embedding: false +quant_noise_pq: 0 +quant_noise_pq_block_size: 8 +quant_noise_scalar: 0 diff --git a/fairseq/config/model/transformer_lm/transformer_lm_baevski_wiki103.yaml b/fairseq/config/model/transformer_lm/transformer_lm_baevski_wiki103.yaml new file mode 100644 index 0000000000..1154cfa660 --- /dev/null +++ b/fairseq/config/model/transformer_lm/transformer_lm_baevski_wiki103.yaml @@ -0,0 +1,36 @@ +# @package _group_ +activation_fn: "relu" +dropout: 0.3 +attention_dropout: 0.1 +activation_dropout: 0.1 +relu_dropout: 0.1 +decoder_embed_dim: 1024 +decoder_output_dim: 1024 +decoder_input_dim: 1024 +decoder_ffn_embed_dim: 4096 +decoder_layers: 16 +decoder_attention_heads: 8 +decoder_normalize_before: true +no_decoder_final_norm: true +adaptive_softmax_cutoff: "20000,60000" +adaptive_softmax_dropout: 0.2 +adaptive_softmax_factor: 4 +no_token_positional_embeddings: false +share_decoder_input_output_embed: false +character_embeddings: false +character_filters: "[(1, 64), (2, 128), (3, 192), (4, 256), (5, 256), (6, 256), (7, 256)]" +character_embedding_dim: 4 +char_embedder_highway_layers: 2 +adaptive_input: true +adaptive_input_factor: 4 +adaptive_input_cutoff: "20000,60000" +tie_adaptive_weights: true +tie_adaptive_proj: true +decoder_learned_pos: false +decoder_layerdrop: 0 +decoder_layers_to_keep: null +layernorm_embedding: false +no_scale_embedding: false +quant_noise_pq: 0 +quant_noise_pq_block_size: 8 +quant_noise_scalar: 0 diff --git a/fairseq/config/model/transformer_lm/transformer_lm_big.yaml b/fairseq/config/model/transformer_lm/transformer_lm_big.yaml new file mode 100644 index 0000000000..309575310b --- /dev/null +++ b/fairseq/config/model/transformer_lm/transformer_lm_big.yaml @@ -0,0 +1,36 @@ +# @package _group_ +activation_fn: "relu" +dropout: 0.1 +attention_dropout: 0.0 +activation_dropout: 0.0 +relu_dropout: 0.0 +decoder_embed_dim: 1024 +decoder_output_dim: 1024 +decoder_input_dim: 1024 +decoder_ffn_embed_dim: 4096 +decoder_layers: 12 +decoder_attention_heads: 16 +decoder_normalize_before: true +no_decoder_final_norm: false +adaptive_softmax_cutoff: null +adaptive_softmax_dropout: 0 +adaptive_softmax_factor: 4 +no_token_positional_embeddings: false +share_decoder_input_output_embed: false +character_embeddings: false +character_filters: "[(1, 64), (2, 128), (3, 192), (4, 256), (5, 256), (6, 256), (7, 256)]" +character_embedding_dim: 4 +char_embedder_highway_layers: 2 +adaptive_input: false +adaptive_input_factor: 4 +adaptive_input_cutoff: null +tie_adaptive_weights: false +tie_adaptive_proj: false +decoder_learned_pos: false +decoder_layerdrop: 0 +decoder_layers_to_keep: null +layernorm_embedding: false +no_scale_embedding: false +quant_noise_pq: 0 +quant_noise_pq_block_size: 8 +quant_noise_scalar: 0 diff --git a/fairseq/config/model/transformer_lm/transformer_lm_gbw.yaml b/fairseq/config/model/transformer_lm/transformer_lm_gbw.yaml new file mode 100644 index 0000000000..30b1a4f1e0 --- /dev/null +++ b/fairseq/config/model/transformer_lm/transformer_lm_gbw.yaml @@ -0,0 +1,36 @@ +# @package _group_ +activation_fn: "relu" +dropout: 0.1 +attention_dropout: 0.1 +activation_dropout: 0.0 +relu_dropout: 0.0 +decoder_embed_dim: 512 +decoder_output_dim: 512 +decoder_input_dim: 512 +decoder_ffn_embed_dim: 4096 +decoder_layers: 12 +decoder_attention_heads: 16 +decoder_normalize_before: true +no_decoder_final_norm: true +adaptive_softmax_cutoff: null +adaptive_softmax_dropout: 0 +adaptive_softmax_factor: 4 +no_token_positional_embeddings: false +share_decoder_input_output_embed: false +character_embeddings: false +character_filters: "[(1, 64), (2, 128), (3, 192), (4, 256), (5, 256), (6, 256), (7, 256)]" +character_embedding_dim: 4 +char_embedder_highway_layers: 2 +adaptive_input: false +adaptive_input_factor: 4 +adaptive_input_cutoff: null +tie_adaptive_weights: false +tie_adaptive_proj: false +decoder_learned_pos: false +decoder_layerdrop: 0 +decoder_layers_to_keep: null +layernorm_embedding: false +no_scale_embedding: false +quant_noise_pq: 0 +quant_noise_pq_block_size: 8 +quant_noise_scalar: 0 diff --git a/fairseq/config/model/transformer_lm/transformer_lm_gpt.yaml b/fairseq/config/model/transformer_lm/transformer_lm_gpt.yaml new file mode 100644 index 0000000000..2c6cb7be38 --- /dev/null +++ b/fairseq/config/model/transformer_lm/transformer_lm_gpt.yaml @@ -0,0 +1,36 @@ +# @package _group_ +activation_fn: "gelu" +dropout: 0.1 +attention_dropout: 0.1 +activation_dropout: 0.0 +relu_dropout: 0.0 +decoder_embed_dim: 768 +decoder_output_dim: 768 +decoder_input_dim: 768 +decoder_ffn_embed_dim: 3072 +decoder_layers: 12 +decoder_attention_heads: 12 +decoder_normalize_before: true +no_decoder_final_norm: false +adaptive_softmax_cutoff: null +adaptive_softmax_dropout: 0 +adaptive_softmax_factor: 4 +no_token_positional_embeddings: false +share_decoder_input_output_embed: false +character_embeddings: false +character_filters: "[(1, 64), (2, 128), (3, 192), (4, 256), (5, 256), (6, 256), (7, 256)]" +character_embedding_dim: 4 +char_embedder_highway_layers: 2 +adaptive_input: false +adaptive_input_factor: 4 +adaptive_input_cutoff: null +tie_adaptive_weights: false +tie_adaptive_proj: false +decoder_learned_pos: false +decoder_layerdrop: 0 +decoder_layers_to_keep: null +layernorm_embedding: false +no_scale_embedding: false +quant_noise_pq: 0 +quant_noise_pq_block_size: 8 +quant_noise_scalar: 0 diff --git a/fairseq/config/model/transformer_lm/transformer_lm_gpt2_big.yaml b/fairseq/config/model/transformer_lm/transformer_lm_gpt2_big.yaml new file mode 100644 index 0000000000..a08769a178 --- /dev/null +++ b/fairseq/config/model/transformer_lm/transformer_lm_gpt2_big.yaml @@ -0,0 +1,36 @@ +# @package _group_ +activation_fn: "gelu" +dropout: 0.1 +attention_dropout: 0.1 +activation_dropout: 0.0 +relu_dropout: 0.0 +decoder_embed_dim: 1600 +decoder_output_dim: 1600 +decoder_input_dim: 1600 +decoder_ffn_embed_dim: 6400 +decoder_layers: 48 +decoder_attention_heads: 25 +decoder_normalize_before: true +no_decoder_final_norm: false +adaptive_softmax_cutoff: null +adaptive_softmax_dropout: 0 +adaptive_softmax_factor: 4 +no_token_positional_embeddings: false +share_decoder_input_output_embed: false +character_embeddings: false +character_filters: "[(1, 64), (2, 128), (3, 192), (4, 256), (5, 256), (6, 256), (7, 256)]" +character_embedding_dim: 4 +char_embedder_highway_layers: 2 +adaptive_input: false +adaptive_input_factor: 4 +adaptive_input_cutoff: null +tie_adaptive_weights: false +tie_adaptive_proj: false +decoder_learned_pos: false +decoder_layerdrop: 0 +decoder_layers_to_keep: null +layernorm_embedding: false +no_scale_embedding: false +quant_noise_pq: 0 +quant_noise_pq_block_size: 8 +quant_noise_scalar: 0 diff --git a/fairseq/config/model/transformer_lm/transformer_lm_gpt2_medium.yaml b/fairseq/config/model/transformer_lm/transformer_lm_gpt2_medium.yaml new file mode 100644 index 0000000000..64261d793c --- /dev/null +++ b/fairseq/config/model/transformer_lm/transformer_lm_gpt2_medium.yaml @@ -0,0 +1,36 @@ +# @package _group_ +activation_fn: "gelu" +dropout: 0.1 +attention_dropout: 0.1 +activation_dropout: 0.0 +relu_dropout: 0.0 +decoder_embed_dim: 1280 +decoder_output_dim: 1280 +decoder_input_dim: 1280 +decoder_ffn_embed_dim: 5120 +decoder_layers: 36 +decoder_attention_heads: 20 +decoder_normalize_before: true +no_decoder_final_norm: false +adaptive_softmax_cutoff: null +adaptive_softmax_dropout: 0 +adaptive_softmax_factor: 4 +no_token_positional_embeddings: false +share_decoder_input_output_embed: false +character_embeddings: false +character_filters: "[(1, 64), (2, 128), (3, 192), (4, 256), (5, 256), (6, 256), (7, 256)]" +character_embedding_dim: 4 +char_embedder_highway_layers: 2 +adaptive_input: false +adaptive_input_factor: 4 +adaptive_input_cutoff: null +tie_adaptive_weights: false +tie_adaptive_proj: false +decoder_learned_pos: false +decoder_layerdrop: 0 +decoder_layers_to_keep: null +layernorm_embedding: false +no_scale_embedding: false +quant_noise_pq: 0 +quant_noise_pq_block_size: 8 +quant_noise_scalar: 0 diff --git a/fairseq/config/model/transformer_lm/transformer_lm_gpt2_small.yaml b/fairseq/config/model/transformer_lm/transformer_lm_gpt2_small.yaml new file mode 100644 index 0000000000..702e81f466 --- /dev/null +++ b/fairseq/config/model/transformer_lm/transformer_lm_gpt2_small.yaml @@ -0,0 +1,36 @@ +# @package _group_ +activation_fn: "gelu" +dropout: 0.1 +attention_dropout: 0.1 +activation_dropout: 0.0 +relu_dropout: 0.0 +decoder_embed_dim: 1024 +decoder_output_dim: 1024 +decoder_input_dim: 1024 +decoder_ffn_embed_dim: 4096 +decoder_layers: 24 +decoder_attention_heads: 16 +decoder_normalize_before: true +no_decoder_final_norm: false +adaptive_softmax_cutoff: null +adaptive_softmax_dropout: 0 +adaptive_softmax_factor: 4 +no_token_positional_embeddings: false +share_decoder_input_output_embed: false +character_embeddings: false +character_filters: "[(1, 64), (2, 128), (3, 192), (4, 256), (5, 256), (6, 256), (7, 256)]" +character_embedding_dim: 4 +char_embedder_highway_layers: 2 +adaptive_input: false +adaptive_input_factor: 4 +adaptive_input_cutoff: null +tie_adaptive_weights: false +tie_adaptive_proj: false +decoder_learned_pos: false +decoder_layerdrop: 0 +decoder_layers_to_keep: null +layernorm_embedding: false +no_scale_embedding: false +quant_noise_pq: 0 +quant_noise_pq_block_size: 8 +quant_noise_scalar: 0 diff --git a/fairseq/config/model/transformer_lm/transformer_lm_wiki103.yaml b/fairseq/config/model/transformer_lm/transformer_lm_wiki103.yaml new file mode 100644 index 0000000000..1154cfa660 --- /dev/null +++ b/fairseq/config/model/transformer_lm/transformer_lm_wiki103.yaml @@ -0,0 +1,36 @@ +# @package _group_ +activation_fn: "relu" +dropout: 0.3 +attention_dropout: 0.1 +activation_dropout: 0.1 +relu_dropout: 0.1 +decoder_embed_dim: 1024 +decoder_output_dim: 1024 +decoder_input_dim: 1024 +decoder_ffn_embed_dim: 4096 +decoder_layers: 16 +decoder_attention_heads: 8 +decoder_normalize_before: true +no_decoder_final_norm: true +adaptive_softmax_cutoff: "20000,60000" +adaptive_softmax_dropout: 0.2 +adaptive_softmax_factor: 4 +no_token_positional_embeddings: false +share_decoder_input_output_embed: false +character_embeddings: false +character_filters: "[(1, 64), (2, 128), (3, 192), (4, 256), (5, 256), (6, 256), (7, 256)]" +character_embedding_dim: 4 +char_embedder_highway_layers: 2 +adaptive_input: true +adaptive_input_factor: 4 +adaptive_input_cutoff: "20000,60000" +tie_adaptive_weights: true +tie_adaptive_proj: true +decoder_learned_pos: false +decoder_layerdrop: 0 +decoder_layers_to_keep: null +layernorm_embedding: false +no_scale_embedding: false +quant_noise_pq: 0 +quant_noise_pq_block_size: 8 +quant_noise_scalar: 0 diff --git a/fairseq/config/model/wav2vec/vq_wav2vec_gumbel.yaml b/fairseq/config/model/wav2vec/vq_wav2vec_gumbel.yaml new file mode 100644 index 0000000000..ee1329bf46 --- /dev/null +++ b/fairseq/config/model/wav2vec/vq_wav2vec_gumbel.yaml @@ -0,0 +1,5 @@ +# @package _group_ +activation: gelu +vq_type: gumbel +vq_depth: 2 +combine_groups: true diff --git a/fairseq/config/model/wav2vec2/wav2vec2_base.yaml b/fairseq/config/model/wav2vec2/wav2vec2_base.yaml new file mode 100644 index 0000000000..ce65499b80 --- /dev/null +++ b/fairseq/config/model/wav2vec2/wav2vec2_base.yaml @@ -0,0 +1,8 @@ +# @package _group_ + +quantize_targets: true +final_dim: 256 +encoder_layerdrop: 0.05 +dropout_input: 0.1 +dropout_features: 0.1 +feature_grad_mult: 0.1 diff --git a/fairseq/config/model/wav2vec2/wav2vec2_large.yaml b/fairseq/config/model/wav2vec2/wav2vec2_large.yaml new file mode 100644 index 0000000000..5846f75243 --- /dev/null +++ b/fairseq/config/model/wav2vec2/wav2vec2_large.yaml @@ -0,0 +1,20 @@ +# @package _group_ + +quantize_targets: true +extractor_mode: layer_norm +layer_norm_first: true +final_dim: 768 +latent_temp: [2.0,0.1,0.999995] +encoder_layerdrop: 0.0 +dropout_input: 0.0 +dropout_features: 0.0 +dropout: 0.0 +attention_dropout: 0.0 +conv_bias: true + +encoder_layers: 24 +encoder_embed_dim: 1024 +encoder_ffn_embed_dim: 4096 +encoder_attention_heads: 16 + +feature_grad_mult: 1.0 diff --git a/fairseq/criterions/__init__.py b/fairseq/criterions/__init__.py index 1c28780111..4dbf46a1cb 100644 --- a/fairseq/criterions/__init__.py +++ b/fairseq/criterions/__init__.py @@ -2,23 +2,35 @@ # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. +"""isort:skip_file""" import importlib import os from fairseq import registry -from fairseq.criterions.fairseq_criterion import FairseqCriterion, LegacyFairseqCriterion +from fairseq.criterions.fairseq_criterion import ( # noqa + FairseqCriterion, + LegacyFairseqCriterion, +) +from omegaconf import DictConfig -build_criterion, register_criterion, CRITERION_REGISTRY = registry.setup_registry( - '--criterion', - base_class=FairseqCriterion, - default='cross_entropy', +( + build_criterion_, + register_criterion, + CRITERION_REGISTRY, + CRITERION_DATACLASS_REGISTRY, +) = registry.setup_registry( + "--criterion", base_class=FairseqCriterion, default="cross_entropy" ) +def build_criterion(cfg: DictConfig, task): + return build_criterion_(cfg, task) + + # automatically import any Python files in the criterions/ directory -for file in os.listdir(os.path.dirname(__file__)): - if file.endswith('.py') and not file.startswith('_'): - module = file[:file.find('.py')] - importlib.import_module('fairseq.criterions.' + module) +for file in sorted(os.listdir(os.path.dirname(__file__))): + if file.endswith(".py") and not file.startswith("_"): + file_name = file[: file.find(".py")] + importlib.import_module("fairseq.criterions." + file_name) diff --git a/fairseq/criterions/adaptive_loss.py b/fairseq/criterions/adaptive_loss.py index 33e9317e84..6209ceaedb 100644 --- a/fairseq/criterions/adaptive_loss.py +++ b/fairseq/criterions/adaptive_loss.py @@ -4,14 +4,23 @@ # LICENSE file in the root directory of this source tree. import math +from dataclasses import dataclass import torch.nn.functional as F - from fairseq import metrics, utils from fairseq.criterions import FairseqCriterion, register_criterion +from fairseq.dataclass import FairseqDataclass +from fairseq.dataclass.constants import DDP_BACKEND_CHOICES +from omegaconf import II + +@dataclass +class AdaptiveLossConfig(FairseqDataclass): + sentence_avg: bool = II("optimization.sentence_avg") + ddp_backend: DDP_BACKEND_CHOICES = II("distributed_training.ddp_backend") -@register_criterion('adaptive_loss') + +@register_criterion("adaptive_loss", dataclass=AdaptiveLossConfig) class AdaptiveLoss(FairseqCriterion): """This is an implementation of the loss function accompanying the adaptive softmax approximation for graphical processing units (GPU), described in the paper "Efficient softmax approximation for GPUs" @@ -22,14 +31,14 @@ def __init__(self, task, sentence_avg): self.sentence_avg = sentence_avg @classmethod - def build_criterion(cls, args, task): - if args.ddp_backend == 'c10d': + def build_criterion(cls, cfg: AdaptiveLossConfig, task): + if cfg.ddp_backend in {"c10d", "pytorch_ddp"}: raise Exception( - 'AdaptiveLoss is not compatible with the c10d ' - 'version of DistributedDataParallel. Please use ' - '`--ddp-backend=no_c10d` instead.' + "AdaptiveLoss is not compatible with the PyTorch " + "version of DistributedDataParallel. Please use " + "`--ddp-backend=legacy_ddp` instead." ) - return cls(task, args.sentence_avg) + return cls(task, cfg.sentence_avg) def forward(self, model, sample, reduce=True): """Compute the loss for the given sample. @@ -40,10 +49,13 @@ def forward(self, model, sample, reduce=True): 3) logging outputs to display while training """ - assert hasattr(model.decoder, 'adaptive_softmax') and model.decoder.adaptive_softmax is not None + assert ( + hasattr(model.decoder, "adaptive_softmax") + and model.decoder.adaptive_softmax is not None + ) adaptive_softmax = model.decoder.adaptive_softmax - net_output = model(**sample['net_input']) + net_output = model(**sample["net_input"]) orig_target = model.get_targets(sample, net_output) nsentences = orig_target.size(0) @@ -58,38 +70,48 @@ def forward(self, model, sample, reduce=True): for i in range(len(target)): if target[i] is not None: - assert (target[i].min() >= 0 and target[i].max() <= logits[i].size(1)) + assert target[i].min() >= 0 and target[i].max() <= logits[i].size(1) loss += F.cross_entropy( logits[i], target[i], ignore_index=self.padding_idx, - reduction='sum' if reduce else 'none', + reduction="sum" if reduce else "none", ) orig = utils.strip_pad(orig_target, self.padding_idx) ntokens = orig.numel() - sample_size = sample['target'].size(0) if self.sentence_avg else ntokens + sample_size = sample["target"].size(0) if self.sentence_avg else ntokens logging_output = { - 'loss': loss.data, - 'ntokens': ntokens, - 'nsentences': nsentences, - 'sample_size': sample_size, + "loss": loss.data, + "ntokens": ntokens, + "nsentences": nsentences, + "sample_size": sample_size, } return loss, sample_size, logging_output @staticmethod def reduce_metrics(logging_outputs) -> None: """Aggregate logging outputs from data parallel training.""" - loss_sum = utils.item(sum(log.get('loss', 0) for log in logging_outputs)) - ntokens = utils.item(sum(log.get('ntokens', 0) for log in logging_outputs)) - sample_size = utils.item(sum(log.get('sample_size', 0) for log in logging_outputs)) - - metrics.log_scalar('loss', loss_sum / sample_size / math.log(2), sample_size, round=3) + loss_sum = utils.item(sum(log.get("loss", 0) for log in logging_outputs)) + ntokens = utils.item(sum(log.get("ntokens", 0) for log in logging_outputs)) + sample_size = utils.item( + sum(log.get("sample_size", 0) for log in logging_outputs) + ) + + metrics.log_scalar( + "loss", loss_sum / sample_size / math.log(2), sample_size, round=3 + ) if sample_size != ntokens: - metrics.log_scalar('nll_loss', loss_sum / ntokens / math.log(2), ntokens, round=3) - metrics.log_derived('ppl', lambda meters: utils.get_perplexity(meters['nll_loss'].avg)) + metrics.log_scalar( + "nll_loss", loss_sum / ntokens / math.log(2), ntokens, round=3 + ) + metrics.log_derived( + "ppl", lambda meters: utils.get_perplexity(meters["nll_loss"].avg) + ) else: - metrics.log_derived('ppl', lambda meters: utils.get_perplexity(meters['loss'].avg)) + metrics.log_derived( + "ppl", lambda meters: utils.get_perplexity(meters["loss"].avg) + ) @staticmethod def logging_outputs_can_be_summed() -> bool: diff --git a/fairseq/criterions/binary_cross_entropy.py b/fairseq/criterions/binary_cross_entropy.py deleted file mode 100644 index 557f50bd90..0000000000 --- a/fairseq/criterions/binary_cross_entropy.py +++ /dev/null @@ -1,142 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import math - -import torch -import torch.nn.functional as F - -from fairseq import utils -from fairseq.criterions import FairseqCriterion, register_criterion - - -@register_criterion('binary_cross_entropy') -class BinaryCrossEntropyCriterion(FairseqCriterion): - - def __init__(self, task, infonce=False, loss_weights=None, log_keys=None): - super().__init__(task) - self.infonce = infonce - self.loss_weights = None if loss_weights is None else eval(loss_weights) - self.log_keys = [] if log_keys is None else eval(log_keys) - - @staticmethod - def add_args(parser): - """Add criterion-specific arguments to the parser.""" - # fmt: off - parser.add_argument('--infonce', action='store_true', - help='if set, uses cross entropy instead of binary cross entropy (i.e. InfoNCE loss)') - parser.add_argument('--loss-weights', type=str, default=None, - help='weights for additional loss terms (not first one)') - parser.add_argument('--log-keys', type=str, default=None, - help='output keys to log') - - def forward(self, model, sample, reduce=True, log_pred=False): - """Compute the loss for the given sample. - - Returns a tuple with three elements: - 1) the loss - 2) the sample size, which is used as the denominator for the gradient - 3) logging outputs to display while training - """ - net_output = model(**sample['net_input']) - logits = model.get_logits(net_output).float() - target = model.get_targets(sample, net_output) - - weights = None - if hasattr(model, 'get_target_weights') and not self.infonce: - weights = model.get_target_weights(target, net_output) - if torch.is_tensor(weights): - weights = weights.float() - - losses = [] - - if self.infonce: - loss = F.cross_entropy(logits, target, reduction="sum" if reduce else "none",) - else: - loss = F.binary_cross_entropy_with_logits(logits, target.float(), weights, reduction="sum" if reduce else "none",) - - sample_size = target.numel() if self.infonce else target.long().sum().item() - losses.append(loss) - - if self.loss_weights is not None and hasattr(model, "get_extra_losses"): - extra_losses = model.get_extra_losses(net_output) - if torch.is_tensor(extra_losses): - extra_losses = [extra_losses] - if len(self.loss_weights) == 1 and len(extra_losses) != 1: - self.loss_weights = [self.loss_weights[0]] * len(extra_losses) - assert len(extra_losses) == len(self.loss_weights), f'{len(extra_losses)}, {len(self.loss_weights)}' - for p, coef in zip(extra_losses, self.loss_weights): - if coef != 0 and p is not None: - p = coef * p.float() * sample_size - loss += p - losses.append(p) - - logging_output = { - 'loss': loss.item() if reduce else loss, - 'ntokens': sample_size, - 'nsentences': logits.size(0), - 'sample_size': sample_size, - } - - for lk in self.log_keys: - if lk in net_output: - logging_output[lk] = float((net_output[lk])) - - if len(losses) > 1: - for i, l in enumerate(losses): - logging_output[f'loss_{i}'] = l.item() - - if self.infonce: - with torch.no_grad(): - if logits.numel() == 0: - corr = 0 - count = 0 - else: - assert logits.dim() > 1, logits.shape - max = logits.argmax(-1) == 0 - min = logits.argmin(-1) == 0 - both = max & min - corr = max.long().sum().item() - both.long().sum().item() - count = max.numel() - - logging_output["correct"] = corr - logging_output["count"] = count - - if log_pred: - logging_output['logits'] = logits.cpu().numpy() - logging_output['target'] = target.cpu().numpy() - return loss, sample_size, logging_output - - @staticmethod - def aggregate_logging_outputs(logging_outputs): - """Aggregate logging outputs from data parallel training.""" - loss_sum = utils.item(sum(log.get('loss', 0) for log in logging_outputs)) - ntokens = utils.item(sum(log.get('ntokens', 0) for log in logging_outputs)) - nsentences = utils.item(sum(log.get('nsentences', 0) for log in logging_outputs)) - sample_size = utils.item(sum(log.get('sample_size', 0) for log in logging_outputs)) - agg_output = { - 'loss': loss_sum / sample_size / math.log(2), - 'ntokens': ntokens, - 'nsentences': nsentences, - 'sample_size': sample_size, - } - if sample_size != ntokens: - agg_output['nll_loss'] = loss_sum / ntokens / math.log(2) - - correct = sum(log.get("correct", 0) for log in logging_outputs) - total = sum(log.get("count", 0) for log in logging_outputs) - if total > 0: - agg_output['accuracy'] = correct / total - - builtin_keys = {'loss', 'ntokens', 'nsentences', 'sample_size', 'correct', 'count'} - - for k in logging_outputs[0]: - if k not in builtin_keys: - val = sum(log.get(k, 0) for log in logging_outputs) / len(logging_outputs) - if k.startswith('loss'): - val = val / ntokens if ntokens > 0 else float('nan') - agg_output[k] = val - - return agg_output diff --git a/fairseq/criterions/composite_loss.py b/fairseq/criterions/composite_loss.py index 6671c696e9..98e835fa6e 100644 --- a/fairseq/criterions/composite_loss.py +++ b/fairseq/criterions/composite_loss.py @@ -3,20 +3,19 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. -from torch import nn - from fairseq import utils -from fairseq.criterions import FairseqCriterion, register_criterion +from fairseq.criterions import LegacyFairseqCriterion, register_criterion +from torch import nn -@register_criterion('composite_loss') -class CompositeLoss(FairseqCriterion): +@register_criterion("composite_loss") +class CompositeLoss(LegacyFairseqCriterion): """This is a composite loss that, given a list of model outputs and a list of targets, computes an average of losses for each output-target pair""" - def __init__(self, task, underlying_criterion): - super().__init__(task) - self.underlying_criterion = underlying_criterion + def __init__(self, args, task): + super().__init__(args, task) + self.underlying_criterion = args.underlying_criterion @staticmethod def add_args(parser): @@ -40,7 +39,6 @@ def build_criterion(cls, args, task): underlying_criterion = CompositeLoss.build_underlying_criterion(args, task) class FakeModel(nn.Module): - def __init__(self, model, net_out, target): super().__init__() self.model = model @@ -51,7 +49,9 @@ def forward(self, **unused): return self.net_out def get_normalized_probs(self, net_output, log_probs, sample=None): - return self.model.get_normalized_probs(net_output, log_probs, sample=sample) + return self.model.get_normalized_probs( + net_output, log_probs, sample=sample + ) def get_targets(self, *unused): return self.target @@ -60,15 +60,14 @@ def get_targets(self, *unused): def decoder(self): return self.model.decoder - class _CompositeLoss(FairseqCriterion): - - def __init__(self, task, underlying_criterion): - super().__init__(task) + class _CompositeLoss(LegacyFairseqCriterion): + def __init__(self, args, task, underlying_criterion): + super().__init__(args, task) self.underlying_criterion = underlying_criterion def forward(self, model, sample, reduce=True): - net_outputs = model(**sample['net_input']) - targets = sample['target'] + net_outputs = model(**sample["net_input"]) + targets = sample["target"] bsz = targets[0].size(0) loss = net_outputs[0][0].new(1 if reduce else bsz).float().zero_() @@ -77,7 +76,7 @@ def forward(self, model, sample, reduce=True): logging_output = {} for o, t in zip(net_outputs[0], targets): m = FakeModel(model, (o, net_outputs[1]), t) - sample['target'] = t + sample["target"] = t l, ss, logging_output = self.underlying_criterion(m, sample, reduce) loss += l sample_size += ss @@ -85,15 +84,17 @@ def forward(self, model, sample, reduce=True): loss.div_(len(targets)) sample_size /= len(targets) - logging_output['loss'] = utils.item(loss.data) if reduce else loss.data + logging_output["loss"] = utils.item(loss.data) if reduce else loss.data return loss, sample_size, logging_output @staticmethod def aggregate_logging_outputs(logging_outputs): - return underlying_criterion.__class__.aggregate_logging_outputs(logging_outputs) + return underlying_criterion.__class__.aggregate_logging_outputs( + logging_outputs + ) @staticmethod def reduce_metrics(logging_outputs) -> None: underlying_criterion.__class__.reduce_metrics(logging_outputs) - return _CompositeLoss(task, underlying_criterion) + return _CompositeLoss(args, task, underlying_criterion) diff --git a/fairseq/criterions/cross_entropy.py b/fairseq/criterions/cross_entropy.py index 4e750f62e3..fe46106471 100644 --- a/fairseq/criterions/cross_entropy.py +++ b/fairseq/criterions/cross_entropy.py @@ -4,16 +4,22 @@ # LICENSE file in the root directory of this source tree. import math +from dataclasses import dataclass import torch.nn.functional as F - from fairseq import metrics, utils from fairseq.criterions import FairseqCriterion, register_criterion +from fairseq.dataclass import FairseqDataclass +from omegaconf import II -@register_criterion('cross_entropy') -class CrossEntropyCriterion(FairseqCriterion): +@dataclass +class CrossEntropyCriterionConfig(FairseqDataclass): + sentence_avg: bool = II("optimization.sentence_avg") + +@register_criterion("cross_entropy", dataclass=CrossEntropyCriterionConfig) +class CrossEntropyCriterion(FairseqCriterion): def __init__(self, task, sentence_avg): super().__init__(task) self.sentence_avg = sentence_avg @@ -26,14 +32,16 @@ def forward(self, model, sample, reduce=True): 2) the sample size, which is used as the denominator for the gradient 3) logging outputs to display while training """ - net_output = model(**sample['net_input']) + net_output = model(**sample["net_input"]) loss, _ = self.compute_loss(model, net_output, sample, reduce=reduce) - sample_size = sample['target'].size(0) if self.sentence_avg else sample['ntokens'] + sample_size = ( + sample["target"].size(0) if self.sentence_avg else sample["ntokens"] + ) logging_output = { - 'loss': loss.data, - 'ntokens': sample['ntokens'], - 'nsentences': sample['target'].size(0), - 'sample_size': sample_size, + "loss": loss.data, + "ntokens": sample["ntokens"], + "nsentences": sample["target"].size(0), + "sample_size": sample_size, } return loss, sample_size, logging_output @@ -45,23 +53,32 @@ def compute_loss(self, model, net_output, sample, reduce=True): lprobs, target, ignore_index=self.padding_idx, - reduction='sum' if reduce else 'none', + reduction="sum" if reduce else "none", ) return loss, loss @staticmethod def reduce_metrics(logging_outputs) -> None: """Aggregate logging outputs from data parallel training.""" - loss_sum = sum(log.get('loss', 0) for log in logging_outputs) - ntokens = sum(log.get('ntokens', 0) for log in logging_outputs) - sample_size = sum(log.get('sample_size', 0) for log in logging_outputs) + loss_sum = sum(log.get("loss", 0) for log in logging_outputs) + ntokens = sum(log.get("ntokens", 0) for log in logging_outputs) + sample_size = sum(log.get("sample_size", 0) for log in logging_outputs) - metrics.log_scalar('loss', loss_sum / sample_size / math.log(2), sample_size, round=3) + # we divide by log(2) to convert the loss from base e to base 2 + metrics.log_scalar( + "loss", loss_sum / sample_size / math.log(2), sample_size, round=3 + ) if sample_size != ntokens: - metrics.log_scalar('nll_loss', loss_sum / ntokens / math.log(2), ntokens, round=3) - metrics.log_derived('ppl', lambda meters: utils.get_perplexity(meters['nll_loss'].avg)) + metrics.log_scalar( + "nll_loss", loss_sum / ntokens / math.log(2), ntokens, round=3 + ) + metrics.log_derived( + "ppl", lambda meters: utils.get_perplexity(meters["nll_loss"].avg) + ) else: - metrics.log_derived('ppl', lambda meters: utils.get_perplexity(meters['loss'].avg)) + metrics.log_derived( + "ppl", lambda meters: utils.get_perplexity(meters["loss"].avg) + ) @staticmethod def logging_outputs_can_be_summed() -> bool: diff --git a/fairseq/criterions/ctc.py b/fairseq/criterions/ctc.py new file mode 100644 index 0000000000..10e3618382 --- /dev/null +++ b/fairseq/criterions/ctc.py @@ -0,0 +1,295 @@ +# All rights reserved. +# +# This source code is licensed under the license found in the LICENSE file in +# the root directory of this source tree. An additional grant of patent rights +# can be found in the PATENTS file in the same directory. + +import math +from argparse import Namespace +from dataclasses import dataclass, field +from omegaconf import II +from typing import Optional + +import torch +import torch.nn.functional as F +from fairseq import metrics, utils +from fairseq.criterions import FairseqCriterion, register_criterion +from fairseq.dataclass import FairseqDataclass +from fairseq.data.data_utils import post_process +from fairseq.tasks import FairseqTask +from fairseq.logging.meters import safe_round + + +@dataclass +class CtcCriterionConfig(FairseqDataclass): + zero_infinity: bool = field( + default=False, + metadata={"help": "zero inf loss when source length <= target length"}, + ) + sentence_avg: bool = II("optimization.sentence_avg") + post_process: str = field( + default="letter", + metadata={ + "help": "how to post process predictions into words. can be letter, " + "wordpiece, BPE symbols, etc. " + "See fairseq.data.data_utils.post_process() for full list of options" + }, + ) + wer_kenlm_model: Optional[str] = field( + default=None, + metadata={ + "help": "if this is provided, use kenlm to compute wer (along with other wer_* args)" + }, + ) + wer_lexicon: Optional[str] = field( + default=None, + metadata={"help": "lexicon to use with wer_kenlm_model"}, + ) + wer_lm_weight: float = field( + default=2.0, + metadata={"help": "lm weight to use with wer_kenlm_model"}, + ) + wer_word_score: float = field( + default=-1.0, + metadata={"help": "lm word score to use with wer_kenlm_model"}, + ) + + wer_args: Optional[str] = field( + default=None, + metadata={ + "help": "DEPRECATED: tuple of (wer_kenlm_model, wer_lexicon, wer_lm_weight, wer_word_score)" + }, + ) + + +@register_criterion("ctc", dataclass=CtcCriterionConfig) +class CtcCriterion(FairseqCriterion): + def __init__(self, cfg: CtcCriterionConfig, task: FairseqTask): + super().__init__(task) + self.blank_idx = ( + task.target_dictionary.index(task.blank_symbol) + if hasattr(task, "blank_symbol") + else 0 + ) + self.pad_idx = task.target_dictionary.pad() + self.eos_idx = task.target_dictionary.eos() + self.post_process = cfg.post_process + + if cfg.wer_args is not None: + ( + cfg.wer_kenlm_model, + cfg.wer_lexicon, + cfg.wer_lm_weight, + cfg.wer_word_score, + ) = eval(cfg.wer_args) + + if cfg.wer_kenlm_model is not None: + from examples.speech_recognition.w2l_decoder import W2lKenLMDecoder + + dec_args = Namespace() + dec_args.nbest = 1 + dec_args.criterion = "ctc" + dec_args.kenlm_model = cfg.wer_kenlm_model + dec_args.lexicon = cfg.wer_lexicon + dec_args.beam = 50 + dec_args.beam_size_token = min(50, len(task.target_dictionary)) + dec_args.beam_threshold = min(50, len(task.target_dictionary)) + dec_args.lm_weight = cfg.wer_lm_weight + dec_args.word_score = cfg.wer_word_score + dec_args.unk_weight = -math.inf + dec_args.sil_weight = 0 + + self.w2l_decoder = W2lKenLMDecoder(dec_args, task.target_dictionary) + else: + self.w2l_decoder = None + + self.zero_infinity = cfg.zero_infinity + self.sentence_avg = cfg.sentence_avg + + def forward(self, model, sample, reduce=True): + net_output = model(**sample["net_input"]) + lprobs = model.get_normalized_probs( + net_output, log_probs=True + ).contiguous() # (T, B, C) from the encoder + + if "src_lengths" in sample["net_input"]: + input_lengths = sample["net_input"]["src_lengths"] + else: + if net_output["padding_mask"] is not None: + non_padding_mask = ~net_output["padding_mask"] + input_lengths = non_padding_mask.long().sum(-1) + else: + input_lengths = lprobs.new_full( + (lprobs.size(1),), lprobs.size(0), dtype=torch.long + ) + + pad_mask = (sample["target"] != self.pad_idx) & ( + sample["target"] != self.eos_idx + ) + targets_flat = sample["target"].masked_select(pad_mask) + if "target_lengths" in sample: + target_lengths = sample["target_lengths"] + else: + target_lengths = pad_mask.sum(-1) + + with torch.backends.cudnn.flags(enabled=False): + loss = F.ctc_loss( + lprobs, + targets_flat, + input_lengths, + target_lengths, + blank=self.blank_idx, + reduction="sum", + zero_infinity=self.zero_infinity, + ) + + ntokens = ( + sample["ntokens"] if "ntokens" in sample else target_lengths.sum().item() + ) + + sample_size = sample["target"].size(0) if self.sentence_avg else ntokens + logging_output = { + "loss": utils.item(loss.data), # * sample['ntokens'], + "ntokens": ntokens, + "nsentences": sample["id"].numel(), + "sample_size": sample_size, + } + + if not model.training: + import editdistance + + with torch.no_grad(): + lprobs_t = lprobs.transpose(0, 1).float().contiguous().cpu() + + c_err = 0 + c_len = 0 + w_errs = 0 + w_len = 0 + wv_errs = 0 + for lp, t, inp_l in zip( + lprobs_t, + sample["target_label"] + if "target_label" in sample + else sample["target"], + input_lengths, + ): + lp = lp[:inp_l].unsqueeze(0) + + decoded = None + if self.w2l_decoder is not None: + decoded = self.w2l_decoder.decode(lp) + if len(decoded) < 1: + decoded = None + else: + decoded = decoded[0] + if len(decoded) < 1: + decoded = None + else: + decoded = decoded[0] + + p = (t != self.task.target_dictionary.pad()) & ( + t != self.task.target_dictionary.eos() + ) + targ = t[p] + targ_units = self.task.target_dictionary.string(targ) + targ_units_arr = targ.tolist() + + toks = lp.argmax(dim=-1).unique_consecutive() + pred_units_arr = toks[toks != self.blank_idx].tolist() + + c_err += editdistance.eval(pred_units_arr, targ_units_arr) + c_len += len(targ_units_arr) + + targ_words = post_process(targ_units, self.post_process).split() + + pred_units = self.task.target_dictionary.string(pred_units_arr) + pred_words_raw = post_process(pred_units, self.post_process).split() + + if decoded is not None and "words" in decoded: + pred_words = decoded["words"] + w_errs += editdistance.eval(pred_words, targ_words) + wv_errs += editdistance.eval(pred_words_raw, targ_words) + else: + dist = editdistance.eval(pred_words_raw, targ_words) + w_errs += dist + wv_errs += dist + + w_len += len(targ_words) + + logging_output["wv_errors"] = wv_errs + logging_output["w_errors"] = w_errs + logging_output["w_total"] = w_len + logging_output["c_errors"] = c_err + logging_output["c_total"] = c_len + + return loss, sample_size, logging_output + + @staticmethod + def reduce_metrics(logging_outputs) -> None: + """Aggregate logging outputs from data parallel training.""" + + loss_sum = utils.item(sum(log.get("loss", 0) for log in logging_outputs)) + ntokens = utils.item(sum(log.get("ntokens", 0) for log in logging_outputs)) + nsentences = utils.item( + sum(log.get("nsentences", 0) for log in logging_outputs) + ) + sample_size = utils.item( + sum(log.get("sample_size", 0) for log in logging_outputs) + ) + + metrics.log_scalar( + "loss", loss_sum / sample_size / math.log(2), sample_size, round=3 + ) + metrics.log_scalar("ntokens", ntokens) + metrics.log_scalar("nsentences", nsentences) + if sample_size != ntokens: + metrics.log_scalar( + "nll_loss", loss_sum / ntokens / math.log(2), ntokens, round=3 + ) + + c_errors = sum(log.get("c_errors", 0) for log in logging_outputs) + metrics.log_scalar("_c_errors", c_errors) + c_total = sum(log.get("c_total", 0) for log in logging_outputs) + metrics.log_scalar("_c_total", c_total) + w_errors = sum(log.get("w_errors", 0) for log in logging_outputs) + metrics.log_scalar("_w_errors", w_errors) + wv_errors = sum(log.get("wv_errors", 0) for log in logging_outputs) + metrics.log_scalar("_wv_errors", wv_errors) + w_total = sum(log.get("w_total", 0) for log in logging_outputs) + metrics.log_scalar("_w_total", w_total) + + if c_total > 0: + metrics.log_derived( + "uer", + lambda meters: safe_round( + meters["_c_errors"].sum * 100.0 / meters["_c_total"].sum, 3 + ) + if meters["_c_total"].sum > 0 + else float("nan"), + ) + if w_total > 0: + metrics.log_derived( + "wer", + lambda meters: safe_round( + meters["_w_errors"].sum * 100.0 / meters["_w_total"].sum, 3 + ) + if meters["_w_total"].sum > 0 + else float("nan"), + ) + metrics.log_derived( + "raw_wer", + lambda meters: safe_round( + meters["_wv_errors"].sum * 100.0 / meters["_w_total"].sum, 3 + ) + if meters["_w_total"].sum > 0 + else float("nan"), + ) + + @staticmethod + def logging_outputs_can_be_summed() -> bool: + """ + Whether the logging outputs returned by `forward` can be summed + across workers prior to calling `reduce_metrics`. Setting this + to True will improves distributed training speed. + """ + return True diff --git a/fairseq/criterions/fairseq_criterion.py b/fairseq/criterions/fairseq_criterion.py index 9873574d47..ff4beb0250 100644 --- a/fairseq/criterions/fairseq_criterion.py +++ b/fairseq/criterions/fairseq_criterion.py @@ -6,30 +6,30 @@ import inspect from typing import Any, Dict, List -from torch.nn.modules.loss import _Loss - from fairseq import metrics, utils +from fairseq.dataclass import FairseqDataclass +from fairseq.dataclass.utils import gen_parser_from_dataclass +from torch.nn.modules.loss import _Loss class FairseqCriterion(_Loss): - def __init__(self, task): super().__init__() self.task = task - if hasattr(task, 'target_dictionary'): + if hasattr(task, "target_dictionary"): tgt_dict = task.target_dictionary self.padding_idx = tgt_dict.pad() if tgt_dict is not None else -100 - @staticmethod - def add_args(parser): + @classmethod + def add_args(cls, parser): """Add criterion-specific arguments to the parser.""" - pass + dc = getattr(cls, "__dataclass", None) + if dc is not None: + gen_parser_from_dataclass(parser, dc()) @classmethod - def build_criterion(cls, args, task): + def build_criterion(cls, cfg: FairseqDataclass, task): """Construct a criterion from command-line args.""" - # Criterions can override this, but for convenience we also try - # to automatically map argparse.Namespace keys to corresponding # arguments in the __init__. init_args = {} for p in inspect.signature(cls).parameters.values(): @@ -40,20 +40,22 @@ def build_criterion(cls, args, task): ): # we haven't implemented inference for these argument types, # but PRs welcome :) - raise NotImplementedError('{} not supported'.format(p.kind)) + raise NotImplementedError("{} not supported".format(p.kind)) assert p.kind in {p.POSITIONAL_OR_KEYWORD, p.KEYWORD_ONLY} - if p.name == 'task': - init_args['task'] = task - elif hasattr(args, p.name): - init_args[p.name] = getattr(args, p.name) + if p.name == "task": + init_args["task"] = task + elif p.name == "cfg": + init_args["cfg"] = cfg + elif hasattr(cfg, p.name): + init_args[p.name] = getattr(cfg, p.name) elif p.default != p.empty: pass # we'll use the default value else: raise NotImplementedError( - 'Unable to infer Criterion arguments, please implement ' - '{}.build_criterion'.format(cls.__name__) + "Unable to infer Criterion arguments, please implement " + "{}.build_criterion".format(cls.__name__) ) return cls(**init_args) @@ -69,12 +71,12 @@ def forward(self, model, sample, reduce=True): @staticmethod def aggregate_logging_outputs( - logging_outputs: List[Dict[str, Any]], + logging_outputs: List[Dict[str, Any]] ) -> Dict[str, Any]: """Aggregate logging outputs from data parallel training.""" utils.deprecation_warning( - 'The aggregate_logging_outputs API is deprecated. ' - 'Please use the reduce_metrics API instead.' + "The aggregate_logging_outputs API is deprecated. " + "Please use the reduce_metrics API instead." ) raise NotImplementedError @@ -82,12 +84,12 @@ def aggregate_logging_outputs( def reduce_metrics(cls, logging_outputs: List[Dict[str, Any]]) -> None: """Aggregate logging outputs from data parallel training.""" utils.deprecation_warning( - 'Criterions should implement the reduce_metrics API. ' - 'Falling back to deprecated aggregate_logging_outputs API.' + "Criterions should implement the reduce_metrics API. " + "Falling back to deprecated aggregate_logging_outputs API." ) agg_logging_outputs = cls.aggregate_logging_outputs(logging_outputs) for k, v in agg_logging_outputs.items(): - if k in {'nsentences', 'ntokens', 'sample_size'}: + if k in {"nsentences", "ntokens", "sample_size"}: continue metrics.log_scalar(k, v) @@ -102,15 +104,14 @@ def logging_outputs_can_be_summed() -> bool: class LegacyFairseqCriterion(FairseqCriterion): - def __init__(self, args, task): super().__init__(task=task) self.args = args utils.deprecation_warning( - 'Criterions should take explicit arguments instead of an ' - 'argparse.Namespace object, please update your criterion by ' - 'extending FairseqCriterion instead of LegacyFairseqCriterion.' + "Criterions should take explicit arguments instead of an " + "argparse.Namespace object, please update your criterion by " + "extending FairseqCriterion instead of LegacyFairseqCriterion." ) @classmethod diff --git a/fairseq/criterions/fastspeech2_loss.py b/fairseq/criterions/fastspeech2_loss.py new file mode 100644 index 0000000000..085d5628d4 --- /dev/null +++ b/fairseq/criterions/fastspeech2_loss.py @@ -0,0 +1,125 @@ +# Copyright (c) 2017-present, Facebook, Inc. +# All rights reserved. +# +# This source code is licensed under the license found in the LICENSE file in +# the root directory of this source tree. An additional grant of patent rights +# can be found in the PATENTS file in the same directory. + +from typing import List, Dict, Any +from dataclasses import dataclass, field + +import torch +import torch.nn.functional as F + +from fairseq import metrics, utils +from fairseq.criterions import FairseqCriterion, register_criterion +from fairseq.dataclass import FairseqDataclass +from fairseq.data.data_utils import lengths_to_mask +from fairseq.models.fairseq_model import FairseqEncoderModel + + +@dataclass +class FastSpeech2CriterionConfig(FairseqDataclass): + ctc_weight: float = field( + default=0.0, metadata={"help": "weight for CTC loss"} + ) + + +@register_criterion("fastspeech2", dataclass=FastSpeech2CriterionConfig) +class FastSpeech2Loss(FairseqCriterion): + def __init__(self, task, ctc_weight): + super().__init__(task) + self.ctc_weight = ctc_weight + + def forward(self, model: FairseqEncoderModel, sample, reduction="mean"): + src_tokens = sample["net_input"]["src_tokens"] + src_lens = sample["net_input"]["src_lengths"] + tgt_lens = sample["target_lengths"] + _feat_out, _, log_dur_out, pitch_out, energy_out = model( + src_tokens=src_tokens, + src_lengths=src_lens, + prev_output_tokens=sample["net_input"]["prev_output_tokens"], + incremental_state=None, + target_lengths=tgt_lens, + speaker=sample["speaker"], + durations=sample["durations"], + pitches=sample["pitches"], + energies=sample["energies"] + ) + + src_mask = lengths_to_mask(sample["net_input"]["src_lengths"]) + tgt_mask = lengths_to_mask(sample["target_lengths"]) + + pitches, energies = sample["pitches"], sample["energies"] + pitch_out, pitches = pitch_out[src_mask], pitches[src_mask] + energy_out, energies = energy_out[src_mask], energies[src_mask] + + feat_out, feat = _feat_out[tgt_mask], sample["target"][tgt_mask] + l1_loss = F.l1_loss(feat_out, feat, reduction=reduction) + + pitch_loss = F.mse_loss(pitch_out, pitches, reduction=reduction) + energy_loss = F.mse_loss(energy_out, energies, reduction=reduction) + + log_dur_out = log_dur_out[src_mask] + dur = sample["durations"].float() + dur = dur.half() if log_dur_out.type().endswith(".HalfTensor") else dur + log_dur = torch.log(dur + 1)[src_mask] + dur_loss = F.mse_loss(log_dur_out, log_dur, reduction=reduction) + + ctc_loss = torch.tensor(0.).type_as(l1_loss) + if self.ctc_weight > 0.: + lprobs = model.get_normalized_probs((_feat_out,), log_probs=True) + lprobs = lprobs.transpose(0, 1) # T x B x C + src_mask = lengths_to_mask(src_lens) + src_tokens_flat = src_tokens.masked_select(src_mask) + ctc_loss = F.ctc_loss( + lprobs, src_tokens_flat, tgt_lens, src_lens, + reduction=reduction, zero_infinity=True + ) * self.ctc_weight + + loss = l1_loss + dur_loss + pitch_loss + energy_loss + ctc_loss + + sample_size = sample["nsentences"] + logging_output = { + "loss": utils.item(loss.data), + "ntokens": sample["ntokens"], + "nsentences": sample["nsentences"], + "sample_size": sample_size, + "l1_loss": utils.item(l1_loss.data), + "dur_loss": utils.item(dur_loss.data), + "pitch_loss": utils.item(pitch_loss.data), + "energy_loss": utils.item(energy_loss.data), + "ctc_loss": utils.item(ctc_loss.data), + } + return loss, sample_size, logging_output + + @classmethod + def reduce_metrics(cls, logging_outputs: List[Dict[str, Any]]) -> None: + ns = [log.get("sample_size", 0) for log in logging_outputs] + ntot = sum(ns) + ws = [n / (ntot + 1e-8) for n in ns] + for key in [ + "loss", "l1_loss", "dur_loss", "pitch_loss", "energy_loss", + "ctc_loss" + ]: + vals = [log.get(key, 0) for log in logging_outputs] + val = sum(val * w for val, w in zip(vals, ws)) + metrics.log_scalar(key, val, ntot, round=3) + metrics.log_scalar("sample_size", ntot, len(logging_outputs)) + + # inference metrics + if "targ_frames" not in logging_outputs[0]: + return + n = sum(log.get("targ_frames", 0) for log in logging_outputs) + for key, new_key in [ + ("mcd_loss", "mcd_loss"), + ("pred_frames", "pred_ratio"), + ("nins", "ins_rate"), + ("ndel", "del_rate"), + ]: + val = sum(log.get(key, 0) for log in logging_outputs) + metrics.log_scalar(new_key, val / n, n, round=3) + + @staticmethod + def logging_outputs_can_be_summed() -> bool: + return False diff --git a/fairseq/criterions/hubert_criterion.py b/fairseq/criterions/hubert_criterion.py new file mode 100644 index 0000000000..68cb24e6f1 --- /dev/null +++ b/fairseq/criterions/hubert_criterion.py @@ -0,0 +1,177 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math +import re +from dataclasses import dataclass, field +from typing import List, Optional + +import torch +import torch.nn.functional as F +from fairseq import metrics, utils +from fairseq.criterions import FairseqCriterion, register_criterion +from fairseq.dataclass import FairseqDataclass + + +@dataclass +class HubertCriterionConfig(FairseqDataclass): + pred_masked_weight: float = field( + default=1.0, + metadata={"help": "weight for predictive loss for masked frames"}, + ) + pred_nomask_weight: float = field( + default=0.0, + metadata={"help": "weight for predictive loss for unmasked frames"}, + ) + loss_weights: Optional[List[float]] = field( + default=None, + metadata={"help": "weights for additional loss terms (not first one)"}, + ) + log_keys: List[str] = field( + default_factory=lambda: [], + metadata={"help": "output keys to log"}, + ) + + +@register_criterion("hubert", dataclass=HubertCriterionConfig) +class HubertCriterion(FairseqCriterion): + def __init__(self, task, pred_masked_weight, pred_nomask_weight, loss_weights=None, log_keys=None): + super().__init__(task) + self.pred_masked_weight = pred_masked_weight + self.pred_nomask_weight = pred_nomask_weight + self.loss_weights = loss_weights + self.log_keys = [] if log_keys is None else log_keys + + def forward(self, model, sample, reduce=True, log_pred=False): + """Compute the loss for the given sample. + Returns a tuple with three elements: + 1) the loss + 2) the sample size, which is used as the denominator for the gradient + 3) logging outputs to display while training + """ + net_output = model(target_list=sample["target_list"], **sample["net_input"]) + loss = 0. + sample_size = 0 + logging_output = {} + reduction = "sum" if reduce else "none" + + loss_m_list = [] + logp_m_list = model.get_logits(net_output, True) + targ_m_list = model.get_targets(net_output, True) + assert self.pred_masked_weight == 0 or len(logp_m_list) > 0 + for i, (logp_m, targ_m) in enumerate(zip(logp_m_list, targ_m_list)): + loss_m = F.cross_entropy(logp_m, targ_m, reduction=reduction) + loss_m_list.append(loss_m) + logging_output[f"loss_m_{i}"] = loss_m.detach().item() + if self.pred_masked_weight > 0: + loss += self.pred_masked_weight * sum(loss_m_list) + sample_size += targ_m_list[0].numel() + + loss_u_list = [] + logp_u_list = model.get_logits(net_output, False) + targ_u_list = model.get_targets(net_output, False) + assert self.pred_nomask_weight == 0 or len(logp_u_list) > 0 + for i, (logp_u, targ_u) in enumerate(zip(logp_u_list, targ_u_list)): + loss_u = F.cross_entropy(logp_u, targ_u, reduction=reduction) + loss_u_list.append(loss_u) + logging_output[f"loss_u_{i}"] = loss_u.detach().item() + if self.pred_nomask_weight > 0: + loss += self.pred_nomask_weight * sum(loss_u_list) + sample_size += targ_u_list[0].numel() + + if self.loss_weights is not None: + assert hasattr(model, "get_extra_losses") + extra_losses, names = model.get_extra_losses(net_output) + if torch.is_tensor(extra_losses): + extra_losses = [extra_losses] + names = [names] + if len(self.loss_weights) == 1 and len(extra_losses) != 1: + self.loss_weights = [self.loss_weights[0]] * len(extra_losses) + assert len(extra_losses) == len(self.loss_weights), f"{len(extra_losses)}, {len(self.loss_weights)}" + for p, n, coef in zip(extra_losses, names, self.loss_weights): + if coef != 0 and p is not None: + p = coef * p.float() * sample_size + loss += p + logging_output[f"loss_{n}"] = p.item() + + logging_output = { + "loss": loss.item() if reduce else loss, + "ntokens": sample_size, + "nsentences": sample["id"].numel(), + "sample_size": sample_size, + **logging_output, + } + + for lk in self.log_keys: + if lk in net_output: + logging_output[lk] = float((net_output[lk])) + + def compute_correct(logits): + if logits.numel() == 0: + return 0, 0 + else: + assert logits.dim() > 1, logits.shape + max = logits.argmax(-1) == 0 + min = logits.argmin(-1) == 0 + both = max & min + corr = max.long().sum().item() - both.long().sum().item() + count = max.numel() + return corr, count + + with torch.no_grad(): + for i, logp_m in enumerate(logp_m_list): + corr_m, count_m = compute_correct(logp_m) + logging_output[f"correct_m_{i}"] = corr_m + logging_output[f"count_m_{i}"] = count_m + + for i, logp_u in enumerate(logp_u_list): + corr_u, count_u = compute_correct(logp_u) + logging_output[f"correct_u_{i}"] = corr_u + logging_output[f"count_u_{i}"] = count_u + + return loss, sample_size, logging_output + + @staticmethod + def reduce_metrics(logging_outputs) -> None: + """Aggregate logging outputs from data parallel training (copied from normal cross entropy).""" + loss_sum = sum(log.get("loss", 0) for log in logging_outputs) + ntokens = sum(log.get("ntokens", 0) for log in logging_outputs) + sample_size = sum(log.get("sample_size", 0) for log in logging_outputs) + + metrics.log_scalar("loss", loss_sum / sample_size / math.log(2), sample_size, round=3) + if sample_size != ntokens: + metrics.log_scalar("nll_loss", loss_sum / ntokens / math.log(2), ntokens, round=3) + metrics.log_derived("ppl", lambda meters: utils.get_perplexity(meters["nll_loss"].avg)) + else: + metrics.log_derived("ppl", lambda meters: utils.get_perplexity(meters["loss"].avg)) + + counts = {} + for lk in logging_outputs[0].keys(): + if lk.startswith("count_"): + val = sum(log[lk] for log in logging_outputs) + metrics.log_scalar(lk, val) + counts[lk] = val + + for lk in logging_outputs[0].keys(): + if lk.startswith("loss_"): + val = sum(log[lk] for log in logging_outputs) + metrics.log_scalar(lk, val / sample_size / math.log(2), round=3) + elif lk.startswith("correct_"): + val = sum(log[lk] for log in logging_outputs) + metrics.log_scalar(lk, val / counts[re.sub("correct", "count", lk)]) + + @staticmethod + def aggregate_logging_outputs(logging_outputs): + """Aggregate logging outputs from data parallel training.""" + raise NotImplementedError() + + @staticmethod + def logging_outputs_can_be_summed() -> bool: + """ + Whether the logging outputs returned by `forward` can be summed + across workers prior to calling `reduce_metrics`. Setting this + to True will improves distributed training speed. + """ + return False diff --git a/fairseq/criterions/label_smoothed_cross_entropy.py b/fairseq/criterions/label_smoothed_cross_entropy.py index d010c3d03d..56d63e3e1b 100644 --- a/fairseq/criterions/label_smoothed_cross_entropy.py +++ b/fairseq/criterions/label_smoothed_cross_entropy.py @@ -4,9 +4,30 @@ # LICENSE file in the root directory of this source tree. import math +from dataclasses import dataclass, field +import torch from fairseq import metrics, utils from fairseq.criterions import FairseqCriterion, register_criterion +from fairseq.dataclass import FairseqDataclass +from omegaconf import II + + +@dataclass +class LabelSmoothedCrossEntropyCriterionConfig(FairseqDataclass): + label_smoothing: float = field( + default=0.0, + metadata={"help": "epsilon for label smoothing, 0 means no label smoothing"}, + ) + report_accuracy: bool = field( + default=False, + metadata={"help": "report accuracy metric"}, + ) + ignore_prefix_size: int = field( + default=0, + metadata={"help": "Ignore first N tokens"}, + ) + sentence_avg: bool = II("optimization.sentence_avg") def label_smoothed_nll_loss(lprobs, target, epsilon, ignore_index=None, reduce=True): @@ -16,34 +37,36 @@ def label_smoothed_nll_loss(lprobs, target, epsilon, ignore_index=None, reduce=T smooth_loss = -lprobs.sum(dim=-1, keepdim=True) if ignore_index is not None: pad_mask = target.eq(ignore_index) - nll_loss.masked_fill_(pad_mask, 0.) - smooth_loss.masked_fill_(pad_mask, 0.) + nll_loss.masked_fill_(pad_mask, 0.0) + smooth_loss.masked_fill_(pad_mask, 0.0) else: nll_loss = nll_loss.squeeze(-1) smooth_loss = smooth_loss.squeeze(-1) if reduce: nll_loss = nll_loss.sum() smooth_loss = smooth_loss.sum() - eps_i = epsilon / lprobs.size(-1) - loss = (1. - epsilon) * nll_loss + eps_i * smooth_loss + eps_i = epsilon / (lprobs.size(-1) - 1) + loss = (1.0 - epsilon - eps_i) * nll_loss + eps_i * smooth_loss return loss, nll_loss -@register_criterion('label_smoothed_cross_entropy') +@register_criterion( + "label_smoothed_cross_entropy", dataclass=LabelSmoothedCrossEntropyCriterionConfig +) class LabelSmoothedCrossEntropyCriterion(FairseqCriterion): - - def __init__(self, task, sentence_avg, label_smoothing): + def __init__( + self, + task, + sentence_avg, + label_smoothing, + ignore_prefix_size=0, + report_accuracy=False, + ): super().__init__(task) self.sentence_avg = sentence_avg self.eps = label_smoothing - - @staticmethod - def add_args(parser): - """Add criterion-specific arguments to the parser.""" - # fmt: off - parser.add_argument('--label-smoothing', default=0., type=float, metavar='D', - help='epsilon for label smoothing, 0 means no label smoothing') - # fmt: on + self.ignore_prefix_size = ignore_prefix_size + self.report_accuracy = report_accuracy def forward(self, model, sample, reduce=True): """Compute the loss for the given sample. @@ -53,38 +76,89 @@ def forward(self, model, sample, reduce=True): 2) the sample size, which is used as the denominator for the gradient 3) logging outputs to display while training """ - net_output = model(**sample['net_input']) + net_output = model(**sample["net_input"]) loss, nll_loss = self.compute_loss(model, net_output, sample, reduce=reduce) - sample_size = sample['target'].size(0) if self.sentence_avg else sample['ntokens'] + sample_size = ( + sample["target"].size(0) if self.sentence_avg else sample["ntokens"] + ) logging_output = { - 'loss': loss.data, - 'nll_loss': nll_loss.data, - 'ntokens': sample['ntokens'], - 'nsentences': sample['target'].size(0), - 'sample_size': sample_size, + "loss": loss.data, + "nll_loss": nll_loss.data, + "ntokens": sample["ntokens"], + "nsentences": sample["target"].size(0), + "sample_size": sample_size, } + if self.report_accuracy: + n_correct, total = self.compute_accuracy(model, net_output, sample) + logging_output["n_correct"] = utils.item(n_correct.data) + logging_output["total"] = utils.item(total.data) return loss, sample_size, logging_output - def compute_loss(self, model, net_output, sample, reduce=True): + def get_lprobs_and_target(self, model, net_output, sample): lprobs = model.get_normalized_probs(net_output, log_probs=True) - lprobs = lprobs.view(-1, lprobs.size(-1)) - target = model.get_targets(sample, net_output).view(-1, 1) + target = model.get_targets(sample, net_output) + if self.ignore_prefix_size > 0: + if getattr(lprobs, "batch_first", False): + lprobs = lprobs[:, self.ignore_prefix_size :, :].contiguous() + target = target[:, self.ignore_prefix_size :].contiguous() + else: + lprobs = lprobs[self.ignore_prefix_size :, :, :].contiguous() + target = target[self.ignore_prefix_size :, :].contiguous() + return lprobs.view(-1, lprobs.size(-1)), target.view(-1) + + def compute_loss(self, model, net_output, sample, reduce=True): + lprobs, target = self.get_lprobs_and_target(model, net_output, sample) loss, nll_loss = label_smoothed_nll_loss( - lprobs, target, self.eps, ignore_index=self.padding_idx, reduce=reduce, + lprobs, + target, + self.eps, + ignore_index=self.padding_idx, + reduce=reduce, ) return loss, nll_loss - @staticmethod - def reduce_metrics(logging_outputs) -> None: + def compute_accuracy(self, model, net_output, sample): + lprobs, target = self.get_lprobs_and_target(model, net_output, sample) + mask = target.ne(self.padding_idx) + n_correct = torch.sum( + lprobs.argmax(1).masked_select(mask).eq(target.masked_select(mask)) + ) + total = torch.sum(mask) + return n_correct, total + + @classmethod + def reduce_metrics(cls, logging_outputs) -> None: """Aggregate logging outputs from data parallel training.""" - loss_sum = sum(log.get('loss', 0) for log in logging_outputs) - nll_loss_sum = sum(log.get('nll_loss', 0) for log in logging_outputs) - ntokens = sum(log.get('ntokens', 0) for log in logging_outputs) - sample_size = sum(log.get('sample_size', 0) for log in logging_outputs) - - metrics.log_scalar('loss', loss_sum / sample_size / math.log(2), sample_size, round=3) - metrics.log_scalar('nll_loss', nll_loss_sum / ntokens / math.log(2), ntokens, round=3) - metrics.log_derived('ppl', lambda meters: utils.get_perplexity(meters['nll_loss'].avg)) + loss_sum = sum(log.get("loss", 0) for log in logging_outputs) + nll_loss_sum = sum(log.get("nll_loss", 0) for log in logging_outputs) + ntokens = sum(log.get("ntokens", 0) for log in logging_outputs) + sample_size = sum(log.get("sample_size", 0) for log in logging_outputs) + + metrics.log_scalar( + "loss", loss_sum / sample_size / math.log(2), sample_size, round=3 + ) + metrics.log_scalar( + "nll_loss", nll_loss_sum / ntokens / math.log(2), ntokens, round=3 + ) + metrics.log_derived( + "ppl", lambda meters: utils.get_perplexity(meters["nll_loss"].avg) + ) + + total = utils.item(sum(log.get("total", 0) for log in logging_outputs)) + if total > 0: + metrics.log_scalar("total", total) + n_correct = utils.item( + sum(log.get("n_correct", 0) for log in logging_outputs) + ) + metrics.log_scalar("n_correct", n_correct) + metrics.log_derived( + "accuracy", + lambda meters: round( + meters["n_correct"].sum * 100.0 / meters["total"].sum, 3 + ) + if meters["total"].sum > 0 + else float("nan"), + ) @staticmethod def logging_outputs_can_be_summed() -> bool: diff --git a/fairseq/criterions/label_smoothed_cross_entropy_latency_augmented.py b/fairseq/criterions/label_smoothed_cross_entropy_latency_augmented.py new file mode 100644 index 0000000000..223a16f740 --- /dev/null +++ b/fairseq/criterions/label_smoothed_cross_entropy_latency_augmented.py @@ -0,0 +1,233 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from dataclasses import dataclass, field +import torch +from fairseq import metrics, utils +from fairseq.criterions import register_criterion +from fairseq.criterions.label_smoothed_cross_entropy import ( + LabelSmoothedCrossEntropyCriterion, + LabelSmoothedCrossEntropyCriterionConfig +) + +try: + from simuleval.metrics.latency import ( + AverageLagging, + AverageProportion, + DifferentiableAverageLagging + ) + LATENCY_METRICS = { + "average_lagging": AverageLagging, + "average_proportion": AverageProportion, + "differentiable_average_lagging": DifferentiableAverageLagging, + } +except ImportError: + LATENCY_METRICS = None + + +@dataclass +class LabelSmoothedCrossEntropyCriterionLatencyAugmentConfig( + LabelSmoothedCrossEntropyCriterionConfig +): + latency_avg_weight: float = field( + default=0.0, + metadata={"help": "weight fot average latency loss."}, + ) + latency_var_weight: float = field( + default=0.0, + metadata={"help": "weight fot variance latency loss."}, + ) + latency_avg_type: str = field( + default="differentiable_average_lagging", + metadata={"help": "latency type for average loss"}, + ) + latency_var_type: str = field( + default="variance_delay", + metadata={"help": "latency typ for variance loss"}, + ) + latency_gather_method: str = field( + default="weighted_average", + metadata={"help": "method to gather latency loss for all heads"}, + ) + latency_update_after: int = field( + default=0, + metadata={"help": "Add latency loss after certain steps"}, + ) + +@register_criterion( + "latency_augmented_label_smoothed_cross_entropy", + dataclass=LabelSmoothedCrossEntropyCriterionLatencyAugmentConfig +) +class LatencyAugmentedLabelSmoothedCrossEntropyCriterion( + LabelSmoothedCrossEntropyCriterion +): + def __init__( + self, + task, + sentence_avg, + label_smoothing, + ignore_prefix_size, + report_accuracy, + latency_avg_weight, + latency_var_weight, + latency_avg_type, + latency_var_type, + latency_gather_method, + latency_update_after, + ): + super().__init__( + task, sentence_avg, label_smoothing, ignore_prefix_size, report_accuracy + ) + assert LATENCY_METRICS is not None, "Please make sure SimulEval is installed." + + self.latency_avg_weight = latency_avg_weight + self.latency_var_weight = latency_var_weight + self.latency_avg_type = latency_avg_type + self.latency_var_type = latency_var_type + self.latency_gather_method = latency_gather_method + self.latency_update_after = latency_update_after + + def forward(self, model, sample, reduce=True): + net_output = model(**sample["net_input"]) + # 1. Compute cross entropy loss + loss, nll_loss = self.compute_loss(model, net_output, sample, reduce=reduce) + + # 2. Compute cross latency loss + latency_loss, expected_latency, expected_delays_var = self.compute_latency_loss( + model, sample, net_output + ) + + if self.latency_update_after > 0: + num_updates = getattr(model.decoder, "num_updates", None) + assert num_updates is not None, ( + "model.decoder doesn't have attribute 'num_updates'" + ) + if num_updates <= self.latency_update_after: + latency_loss = 0 + + loss += latency_loss + + sample_size = ( + sample["target"].size(0) if self.sentence_avg else sample["ntokens"] + ) + + logging_output = { + "loss": loss.data, + "nll_loss": nll_loss.data, + "ntokens": sample["ntokens"], + "nsentences": sample["target"].size(0), + "sample_size": sample_size, + "latency": expected_latency, + "delays_var": expected_delays_var, + "latency_loss": latency_loss, + } + + if self.report_accuracy: + n_correct, total = self.compute_accuracy(model, net_output, sample) + logging_output["n_correct"] = utils.item(n_correct.data) + logging_output["total"] = utils.item(total.data) + return loss, sample_size, logging_output + + def compute_latency_loss(self, model, sample, net_output): + assert ( + net_output[-1].encoder_padding_mask is None + or not net_output[-1].encoder_padding_mask[:, 0].any() + ), ( + "Only right padding on source is supported." + ) + # 1. Obtain the expected alignment + alpha_list = [item["alpha"] for item in net_output[1].attn_list] + num_layers = len(alpha_list) + bsz, num_heads, tgt_len, src_len = alpha_list[0].size() + + # bsz * num_layers * num_heads, tgt_len, src_len + alpha_all = torch.cat(alpha_list, dim=1).view(-1, tgt_len, src_len) + + # 2 compute expected delays + # bsz * num_heads * num_layers, tgt_len, src_len for MMA + steps = ( + torch.arange(1, 1 + src_len) + .unsqueeze(0) + .unsqueeze(1) + .expand_as(alpha_all) + .type_as(alpha_all) + ) + + expected_delays = torch.sum(steps * alpha_all, dim=-1) + + target_padding_mask = ( + model.get_targets(sample, net_output) + .eq(self.padding_idx) + .unsqueeze(1) + .expand(bsz, num_layers * num_heads, tgt_len) + .contiguous() + .view(-1, tgt_len) + ) + + src_lengths = ( + sample["net_input"]["src_lengths"] + .unsqueeze(1) + .expand(bsz, num_layers * num_heads) + .contiguous() + .view(-1) + ) + expected_latency = LATENCY_METRICS[self.latency_avg_type]( + expected_delays, src_lengths, None, + target_padding_mask=target_padding_mask + ) + + # 2.1 average expected latency of heads + # bsz, num_layers * num_heads + expected_latency = expected_latency.view(bsz, -1) + if self.latency_gather_method == "average": + # bsz * tgt_len + expected_latency = expected_delays.mean(dim=1) + elif self.latency_gather_method == "weighted_average": + weights = torch.nn.functional.softmax(expected_latency, dim=1) + expected_latency = torch.sum(expected_latency * weights, dim=1) + elif self.latency_gather_method == "max": + expected_latency = expected_latency.max(dim=1)[0] + else: + raise NotImplementedError + + expected_latency = expected_latency.sum() + avg_loss = self.latency_avg_weight * expected_latency + + # 2.2 variance of expected delays + expected_delays_var = ( + expected_delays.view(bsz, -1, tgt_len).var(dim=1).mean(dim=1) + ) + expected_delays_var = expected_delays_var.sum() + var_loss = self.latency_avg_weight * expected_delays_var + + # 3. Final loss + latency_loss = avg_loss + var_loss + + return latency_loss, expected_latency, expected_delays_var + + @classmethod + def reduce_metrics(cls, logging_outputs) -> None: + super().reduce_metrics(logging_outputs) + latency = sum( + log.get("latency", 0) for log in logging_outputs + ) + delays_var = sum( + log.get("delays_var", 0) for log in logging_outputs + ) + latency_loss = sum( + log.get("latency_loss", 0) for log in logging_outputs + ) + nsentences = sum(log.get("nsentences", 0) for log in logging_outputs) + metrics.log_scalar( + "latency", latency.float() / nsentences, nsentences, round=3 + ) + metrics.log_scalar( + "delays_var", delays_var / nsentences, + nsentences, round=3 + ) + metrics.log_scalar( + "latency_loss", latency_loss / nsentences, + nsentences, round=3 + ) diff --git a/fairseq/criterions/label_smoothed_cross_entropy_with_alignment.py b/fairseq/criterions/label_smoothed_cross_entropy_with_alignment.py index cfc7e008cd..2ea37c16b4 100644 --- a/fairseq/criterions/label_smoothed_cross_entropy_with_alignment.py +++ b/fairseq/criterions/label_smoothed_cross_entropy_with_alignment.py @@ -8,23 +8,34 @@ from fairseq import metrics, utils from fairseq.criterions import register_criterion -from .label_smoothed_cross_entropy import LabelSmoothedCrossEntropyCriterion - - -@register_criterion('label_smoothed_cross_entropy_with_alignment') -class LabelSmoothedCrossEntropyCriterionWithAlignment(LabelSmoothedCrossEntropyCriterion): - +from .label_smoothed_cross_entropy import ( + LabelSmoothedCrossEntropyCriterion, + LabelSmoothedCrossEntropyCriterionConfig, +) + +from dataclasses import dataclass, field + + +@dataclass +class LabelSmoothedCrossEntropyCriterionWithAlignmentConfig( + LabelSmoothedCrossEntropyCriterionConfig +): + alignment_lambda: float = field( + default=0.05, metadata={"help": "weight for the alignment loss"} + ) + + +@register_criterion( + "label_smoothed_cross_entropy_with_alignment", + dataclass=LabelSmoothedCrossEntropyCriterionWithAlignmentConfig, +) +class LabelSmoothedCrossEntropyCriterionWithAlignment( + LabelSmoothedCrossEntropyCriterion +): def __init__(self, task, sentence_avg, label_smoothing, alignment_lambda): super().__init__(task, sentence_avg, label_smoothing) self.alignment_lambda = alignment_lambda - @staticmethod - def add_args(parser): - """Add criterion-specific arguments to the parser.""" - LabelSmoothedCrossEntropyCriterion.add_args(parser) - parser.add_argument('--alignment-lambda', default=0.05, type=float, metavar='D', - help='weight for the alignment loss') - def forward(self, model, sample, reduce=True): """Compute the loss for the given sample. @@ -33,41 +44,46 @@ def forward(self, model, sample, reduce=True): 2) the sample size, which is used as the denominator for the gradient 3) logging outputs to display while training """ - net_output = model(**sample['net_input']) + net_output = model(**sample["net_input"]) loss, nll_loss = self.compute_loss(model, net_output, sample, reduce=reduce) - sample_size = sample['target'].size(0) if self.sentence_avg else sample['ntokens'] + sample_size = ( + sample["target"].size(0) if self.sentence_avg else sample["ntokens"] + ) logging_output = { - 'loss': utils.item(loss.data) if reduce else loss.data, - 'nll_loss': utils.item(nll_loss.data) if reduce else nll_loss.data, - 'ntokens': sample['ntokens'], - 'nsentences': sample['target'].size(0), - 'sample_size': sample_size, + "loss": utils.item(loss.data) if reduce else loss.data, + "nll_loss": utils.item(nll_loss.data) if reduce else nll_loss.data, + "ntokens": sample["ntokens"], + "nsentences": sample["target"].size(0), + "sample_size": sample_size, } alignment_loss = None # Compute alignment loss only for training set and non dummy batches. - if 'alignments' in sample and sample['alignments'] is not None: + if "alignments" in sample and sample["alignments"] is not None: alignment_loss = self.compute_alignment_loss(sample, net_output) if alignment_loss is not None: - logging_output['alignment_loss'] = utils.item(alignment_loss.data) + logging_output["alignment_loss"] = utils.item(alignment_loss.data) loss += self.alignment_lambda * alignment_loss return loss, sample_size, logging_output def compute_alignment_loss(self, sample, net_output): - attn_prob = net_output[1]['attn'][0] + attn_prob = net_output[1]["attn"][0] bsz, tgt_sz, src_sz = attn_prob.shape attn = attn_prob.view(bsz * tgt_sz, src_sz) - align = sample['alignments'] - align_weights = sample['align_weights'].float() + align = sample["alignments"] + align_weights = sample["align_weights"].float() if len(align) > 0: # Alignment loss computation. align (shape [:, 2]) contains the src-tgt index pairs corresponding to # the alignments. align_weights (shape [:]) contains the 1 / frequency of a tgt index for normalizing. - loss = -((attn[align[:, 1][:, None], align[:, 0][:, None]]).log() * align_weights[:, None]).sum() + loss = -( + (attn[align[:, 1][:, None], align[:, 0][:, None]]).log() + * align_weights[:, None] + ).sum() else: return None @@ -76,16 +92,33 @@ def compute_alignment_loss(self, sample, net_output): @staticmethod def reduce_metrics(logging_outputs) -> None: """Aggregate logging outputs from data parallel training.""" - loss_sum = utils.item(sum(log.get('loss', 0) for log in logging_outputs)) - nll_loss_sum = utils.item(sum(log.get('nll_loss', 0) for log in logging_outputs)) - alignment_loss_sum = utils.item(sum(log.get('alignment_loss', 0) for log in logging_outputs)) - ntokens = utils.item(sum(log.get('ntokens', 0) for log in logging_outputs)) - sample_size = utils.item(sum(log.get('sample_size', 0) for log in logging_outputs)) - - metrics.log_scalar('loss', loss_sum / sample_size / math.log(2), sample_size, round=3) - metrics.log_scalar('nll_loss', nll_loss_sum / ntokens / math.log(2), ntokens, round=3) - metrics.log_scalar('alignment_loss', alignment_loss_sum / sample_size / math.log(2), sample_size, round=3) - metrics.log_derived('ppl', lambda meters: utils.get_perplexity(meters['nll_loss'].avg)) + loss_sum = utils.item(sum(log.get("loss", 0) for log in logging_outputs)) + nll_loss_sum = utils.item( + sum(log.get("nll_loss", 0) for log in logging_outputs) + ) + alignment_loss_sum = utils.item( + sum(log.get("alignment_loss", 0) for log in logging_outputs) + ) + ntokens = utils.item(sum(log.get("ntokens", 0) for log in logging_outputs)) + sample_size = utils.item( + sum(log.get("sample_size", 0) for log in logging_outputs) + ) + + metrics.log_scalar( + "loss", loss_sum / sample_size / math.log(2), sample_size, round=3 + ) + metrics.log_scalar( + "nll_loss", nll_loss_sum / ntokens / math.log(2), ntokens, round=3 + ) + metrics.log_scalar( + "alignment_loss", + alignment_loss_sum / sample_size / math.log(2), + sample_size, + round=3, + ) + metrics.log_derived( + "ppl", lambda meters: utils.get_perplexity(meters["nll_loss"].avg) + ) @staticmethod def logging_outputs_can_be_summed() -> bool: diff --git a/fairseq/criterions/legacy_masked_lm.py b/fairseq/criterions/legacy_masked_lm.py index 10dea76e4b..c70608c5a1 100644 --- a/fairseq/criterions/legacy_masked_lm.py +++ b/fairseq/criterions/legacy_masked_lm.py @@ -7,8 +7,7 @@ import torch import torch.nn.functional as F - -from fairseq import utils +from fairseq import metrics, utils from fairseq.criterions import FairseqCriterion, register_criterion @@ -18,8 +17,9 @@ def compute_cross_entropy_loss(logits, targets, ignore_index=-100): ignore_index is the same as the default value for F.cross_entropy in pytorch. """ - assert logits.size(0) == targets.size(-1), \ - "Logits and Targets tensor shapes don't match up" + assert logits.size(0) == targets.size( + -1 + ), "Logits and Targets tensor shapes don't match up" loss = F.nll_loss( F.log_softmax(logits, -1, dtype=torch.float32), @@ -30,7 +30,7 @@ def compute_cross_entropy_loss(logits, targets, ignore_index=-100): return loss -@register_criterion('legacy_masked_lm_loss') +@register_criterion("legacy_masked_lm_loss") class LegacyMaskedLmLoss(FairseqCriterion): """ Implementation for the loss used in masked language model (MLM) training. @@ -57,11 +57,18 @@ def __init__(self, task, masked_lm_only, nsp_loss_weight): def add_args(parser): """Args for MaskedLM Loss""" # Default for masked_lm_only is False so as to not break BERT training - parser.add_argument('--masked-lm-only', default=False, - action='store_true', help='compute MLM loss only') - parser.add_argument('--nsp-loss-weight', default=1.0, type=float, - help='weight for next sentence prediction' - ' loss (default 1)') + parser.add_argument( + "--masked-lm-only", + default=False, + action="store_true", + help="compute MLM loss only", + ) + parser.add_argument( + "--nsp-loss-weight", + default=1.0, + type=float, + help="weight for next sentence prediction" " loss (default 1)", + ) def forward(self, model, sample, reduce=True): """Compute the loss for the given sample. @@ -74,22 +81,21 @@ def forward(self, model, sample, reduce=True): # reshape lm_logits from (N,T,C) to (N*T,C) lm_logits = lm_logits.view(-1, lm_logits.size(-1)) - lm_targets = sample['lm_target'].view(-1) - lm_loss = compute_cross_entropy_loss( - lm_logits, lm_targets, self.padding_idx) + lm_targets = sample["lm_target"].view(-1) + lm_loss = compute_cross_entropy_loss(lm_logits, lm_targets, self.padding_idx) # compute the number of tokens for which loss is computed. This is used # to normalize the loss ntokens = utils.strip_pad(lm_targets, self.padding_idx).numel() loss = lm_loss / ntokens - nsentences = sample['nsentences'] + nsentences = sample["nsentences"] # nsentences = 0 # Compute sentence loss if masked_lm_only is False sentence_loss = None if not self.masked_lm_only: - sentence_logits = output_metadata['sentence_logits'] - sentence_targets = sample['sentence_target'].view(-1) + sentence_logits = output_metadata["sentence_logits"] + sentence_targets = sample["sentence_target"].view(-1) # This needs to be recomputed due to some differences between # TokenBlock and BlockPair dataset. This can be resolved with a # refactor of BERTModel which we will do in the future. @@ -102,7 +108,8 @@ def forward(self, model, sample, reduce=True): # refactor in the BERT model. if sentence_logits is not None: sentence_loss = compute_cross_entropy_loss( - sentence_logits, sentence_targets) + sentence_logits, sentence_targets + ) loss += self.nsp_loss_weight * (sentence_loss / nsentences) @@ -111,42 +118,54 @@ def forward(self, model, sample, reduce=True): # here sample_size is just used for logging sample_size = 1 logging_output = { - 'loss': utils.item(loss.data) if reduce else loss.data, - 'lm_loss': utils.item(lm_loss.data) if reduce else lm_loss.data, + "loss": utils.item(loss.data) if reduce else loss.data, + "lm_loss": utils.item(lm_loss.data) if reduce else lm_loss.data, # sentence loss is not always computed - 'sentence_loss': ( - ( - utils.item(sentence_loss.data) if reduce - else sentence_loss.data - ) if sentence_loss is not None else 0.0 + "sentence_loss": ( + (utils.item(sentence_loss.data) if reduce else sentence_loss.data) + if sentence_loss is not None + else 0.0 ), - 'ntokens': ntokens, - 'nsentences': nsentences, - 'sample_size': sample_size, + "ntokens": ntokens, + "nsentences": nsentences, + "sample_size": sample_size, } return loss, sample_size, logging_output @staticmethod - def aggregate_logging_outputs(logging_outputs): + def reduce_metrics(logging_outputs) -> None: """Aggregate logging outputs from data parallel training.""" - lm_loss_sum = sum(log.get('lm_loss', 0) for log in logging_outputs) - sentence_loss_sum = sum( - log.get('sentence_loss', 0) for log in logging_outputs) - ntokens = sum(log.get('ntokens', 0) for log in logging_outputs) - nsentences = sum(log.get('nsentences', 0) for log in logging_outputs) - sample_size = sum(log.get('sample_size', 0) for log in logging_outputs) - agg_loss = sum(log.get('loss', 0) for log in logging_outputs) - - agg_output = { - 'loss': agg_loss / sample_size / math.log(2) if sample_size > 0 else 0., - 'lm_loss': lm_loss_sum / ntokens / math.log(2) if ntokens > 0 else 0., - 'sentence_loss': sentence_loss_sum / nsentences / math.log(2) if nsentences > 0 else 0., - 'nll_loss': lm_loss_sum / ntokens / math.log(2) if ntokens > 0 else 0., - 'ntokens': ntokens, - 'nsentences': nsentences, - 'sample_size': sample_size, - } - return agg_output + lm_loss_sum = sum(log.get("lm_loss", 0) for log in logging_outputs) + sentence_loss_sum = sum(log.get("sentence_loss", 0) for log in logging_outputs) + ntokens = sum(log.get("ntokens", 0) for log in logging_outputs) + nsentences = sum(log.get("nsentences", 0) for log in logging_outputs) + sample_size = sum(log.get("sample_size", 0) for log in logging_outputs) + agg_loss = sum(log.get("loss", 0) for log in logging_outputs) + + metrics.log_scalar( + "loss", + agg_loss / sample_size / math.log(2) if sample_size > 0 else 0.0, + sample_size, + round=3, + ) + metrics.log_scalar( + "lm_loss", + lm_loss_sum / ntokens / math.log(2) if ntokens > 0 else 0.0, + ntokens, + round=3, + ) + metrics.log_scalar( + "sentence_loss", + sentence_loss_sum / nsentences / math.log(2) if nsentences > 0 else 0.0, + nsentences, + round=3, + ) + metrics.log_scalar( + "nll_loss", + lm_loss_sum / ntokens / math.log(2) if ntokens > 0 else 0.0, + ntokens, + round=3, + ) @staticmethod def logging_outputs_can_be_summed() -> bool: diff --git a/fairseq/criterions/masked_lm.py b/fairseq/criterions/masked_lm.py index bb5b35b41f..279458f317 100644 --- a/fairseq/criterions/masked_lm.py +++ b/fairseq/criterions/masked_lm.py @@ -3,24 +3,30 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. +from dataclasses import dataclass import math +from omegaconf import II import torch -import torch.nn.functional as F - from fairseq import metrics, modules, utils from fairseq.criterions import FairseqCriterion, register_criterion +from fairseq.dataclass import FairseqDataclass + + +@dataclass +class MaskedLmConfig(FairseqDataclass): + tpu: bool = II("common.tpu") -@register_criterion('masked_lm') +@register_criterion("masked_lm", dataclass=MaskedLmConfig) class MaskedLmLoss(FairseqCriterion): """ Implementation for the loss used in masked language model (MLM) training. """ - def __init__(self, task, tpu): + def __init__(self, cfg: MaskedLmConfig, task): super().__init__(task) - self.tpu = tpu + self.tpu = cfg.tpu def forward(self, model, sample, reduce=True): """Compute the loss for the given sample. @@ -30,7 +36,7 @@ def forward(self, model, sample, reduce=True): 2) the sample size, which is used as the denominator for the gradient 3) logging outputs to display while training """ - masked_tokens = sample['target'].ne(self.padding_idx) + masked_tokens = sample["target"].ne(self.padding_idx) sample_size = masked_tokens.int().sum() # Rare: when all tokens are masked, project all tokens. @@ -39,7 +45,7 @@ def forward(self, model, sample, reduce=True): # (see github.com/pytorch/pytorch/issues/26247). if self.tpu: masked_tokens = None # always project all tokens on TPU - elif masked_tokens.device == torch.device('cpu'): + elif masked_tokens.device == torch.device("cpu"): if not masked_tokens.any(): masked_tokens = None else: @@ -49,7 +55,7 @@ def forward(self, model, sample, reduce=True): masked_tokens.new([True]), ) - logits = model(**sample['net_input'], masked_tokens=masked_tokens)[0] + logits = model(**sample["net_input"], masked_tokens=masked_tokens)[0] targets = model.get_targets(sample, [logits]) if masked_tokens is not None: targets = targets[masked_tokens] @@ -57,26 +63,30 @@ def forward(self, model, sample, reduce=True): loss = modules.cross_entropy( logits.view(-1, logits.size(-1)), targets.view(-1), - reduction='sum', + reduction="sum", ignore_index=self.padding_idx, ) logging_output = { - 'loss': loss, - 'ntokens': sample['ntokens'], - 'nsentences': sample['nsentences'], - 'sample_size': sample_size, + "loss": loss if self.tpu else loss.data, + "ntokens": sample["ntokens"], + "nsentences": sample["nsentences"], + "sample_size": sample_size, } return loss, sample_size, logging_output @staticmethod def reduce_metrics(logging_outputs) -> None: """Aggregate logging outputs from data parallel training.""" - loss_sum = sum(log.get('loss', 0) for log in logging_outputs) - sample_size = sum(log.get('sample_size', 0) for log in logging_outputs) + loss_sum = sum(log.get("loss", 0) for log in logging_outputs) + sample_size = sum(log.get("sample_size", 0) for log in logging_outputs) - metrics.log_scalar('loss', loss_sum / sample_size / math.log(2), sample_size, round=3) - metrics.log_derived('ppl', lambda meters: utils.get_perplexity(meters['loss'].avg)) + metrics.log_scalar( + "loss", loss_sum / sample_size / math.log(2), sample_size, round=3 + ) + metrics.log_derived( + "ppl", lambda meters: utils.get_perplexity(meters["loss"].avg) + ) @staticmethod def logging_outputs_can_be_summed() -> bool: diff --git a/fairseq/criterions/model_criterion.py b/fairseq/criterions/model_criterion.py new file mode 100644 index 0000000000..30350f13b1 --- /dev/null +++ b/fairseq/criterions/model_criterion.py @@ -0,0 +1,138 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +from dataclasses import dataclass, field +from typing import Dict, List + +from fairseq import metrics, utils +from fairseq.criterions import FairseqCriterion, register_criterion +from fairseq.dataclass import FairseqDataclass + + +logger = logging.getLogger(__name__) + + +@dataclass +class ModelCriterionConfig(FairseqDataclass): + loss_weights: Dict[str, float] = field( + default_factory=dict, + metadata={"help": "weights for the loss terms"}, + ) + log_keys: List[str] = field( + default_factory=list, + metadata={"help": "additional output keys to log"}, + ) + + +@register_criterion("model", dataclass=ModelCriterionConfig) +class ModelCriterion(FairseqCriterion): + """ + This criterion relies on the model to supply losses. + The losses should be a dictionary of name -> scalar returned by + the model either by including it in the net_output dict or by + implementing a get_losses(net_output, sample) method. The final loss is + a scaled sum of all losses according to weights in loss_weights. + If no weights are provided, then all losses are scaled by 1.0. + + The losses will be automatically logged. Additional keys from + net_output dict can be logged via the log_keys parameter. + """ + + def __init__(self, task, loss_weights=None, log_keys=None): + super().__init__(task) + self.loss_weights = loss_weights + self.log_keys = log_keys + + def forward(self, model, sample, reduce=True): + net_output = model(**sample["net_input"]) + + sample_size = net_output["sample_size"] + scaled_losses = {} + + if hasattr(model, "get_losses"): + losses = model.get_losses(net_output, sample) + elif isinstance(net_output, dict) and "losses" in net_output: + losses = net_output["losses"] + else: + raise Exception("Could not retrieve losses") + + for lk, p in losses.items(): + try: + coef = 1.0 if len(self.loss_weights) == 0 else self.loss_weights[lk] + except KeyError: + logger.error( + f"weight for loss {lk} is not in loss_weights ({self.loss_weights})" + ) + raise + if coef != 0 and p is not None: + scaled_losses[lk] = coef * p.float() + + loss = sum(scaled_losses.values()) + if reduce and loss.numel() > 1: + loss = loss.sum() + + logging_output = { + "loss": loss.data, + "ntokens": sample_size, + "nsentences": sample["id"].numel(), + "sample_size": sample_size, + "_world_size": 1, + } + + for lk in self.log_keys: + if lk in net_output and net_output[lk] is not None: + logging_output[lk] = float(net_output[lk]) + + if len(scaled_losses) > 1: + for lk, l in scaled_losses.items(): + logging_output[f"loss_{lk}"] = l.item() + + return loss, sample_size, logging_output + + @staticmethod + def reduce_metrics(logging_outputs) -> None: + """Aggregate logging outputs from data parallel training.""" + loss_sum = utils.item(sum(log.get("loss", 0) for log in logging_outputs)) + ntokens = utils.item(sum(log.get("ntokens", 0) for log in logging_outputs)) + nsentences = utils.item( + sum(log.get("nsentences", 0) for log in logging_outputs) + ) + sample_size = utils.item( + sum(log.get("sample_size", 0) for log in logging_outputs) + ) + + metrics.log_scalar("loss", loss_sum / sample_size, sample_size, round=3) + metrics.log_scalar("ntokens", ntokens) + metrics.log_scalar("nsentences", nsentences) + + builtin_keys = { + "loss", + "ntokens", + "nsentences", + "sample_size", + "_world_size", + } + + world_size = utils.item( + sum(log.get("_world_size", 0) for log in logging_outputs) + ) + + for k in logging_outputs[0]: + if k not in builtin_keys: + val = sum(log.get(k, 0) for log in logging_outputs) + if k.startswith("loss_"): + metrics.log_scalar(k, val / sample_size, sample_size, round=3) + else: + metrics.log_scalar(k, val / world_size, round=3) + + @staticmethod + def logging_outputs_can_be_summed() -> bool: + """ + Whether the logging outputs returned by `forward` can be summed + across workers prior to calling `reduce_metrics`. Setting this + to True will improves distributed training speed. + """ + return True diff --git a/fairseq/criterions/nat_loss.py b/fairseq/criterions/nat_loss.py index 3326734d55..7dac32fbaf 100644 --- a/fairseq/criterions/nat_loss.py +++ b/fairseq/criterions/nat_loss.py @@ -5,42 +5,40 @@ import math -import torch.nn.functional as F import torch -from torch import Tensor - +import torch.nn.functional as F from fairseq import metrics, utils from fairseq.criterions import FairseqCriterion, register_criterion +from fairseq.dataclass import FairseqDataclass +from torch import Tensor +from dataclasses import dataclass, field -@register_criterion("nat_loss") -class LabelSmoothedDualImitationCriterion(FairseqCriterion): +@dataclass +class LabelSmoothedDualImitationCriterionConfig(FairseqDataclass): + label_smoothing: float = field( + default=0.0, + metadata={"help": "epsilon for label smoothing, 0 means no label smoothing"}, + ) + + +@register_criterion("nat_loss", dataclass=LabelSmoothedDualImitationCriterionConfig) +class LabelSmoothedDualImitationCriterion(FairseqCriterion): def __init__(self, task, label_smoothing): super().__init__(task) self.label_smoothing = label_smoothing - @staticmethod - def add_args(parser): - """Add criterion-specific arguments to the parser.""" - parser.add_argument( - '--label-smoothing', - default=0., - type=float, - metavar='D', - help='epsilon for label smoothing, 0 means no label smoothing', - ) - def _compute_loss( self, outputs, targets, masks=None, label_smoothing=0.0, name="loss", factor=1.0 ): """ - outputs: batch x len x d_model - targets: batch x len - masks: batch x len + outputs: batch x len x d_model + targets: batch x len + masks: batch x len - policy_logprob: if there is some policy - depends on the likelihood score as rewards. + policy_logprob: if there is some policy + depends on the likelihood score as rewards. """ def mean_ds(x: Tensor, dim=None) -> Tensor: @@ -49,6 +47,7 @@ def mean_ds(x: Tensor, dim=None) -> Tensor: if dim is None else x.float().mean(dim).type_as(x) ) + if masks is not None: outputs, targets = outputs[masks], targets[masks] @@ -58,16 +57,17 @@ def mean_ds(x: Tensor, dim=None) -> Tensor: else: logits = F.log_softmax(outputs, dim=-1) if targets.dim() == 1: - losses = F.nll_loss(logits, targets.to(logits.device), reduction='none') + losses = F.nll_loss(logits, targets.to(logits.device), reduction="none") else: # soft-labels - losses = F.kl_div(logits, targets.to(logits.device), reduction='none') + losses = F.kl_div(logits, targets.to(logits.device), reduction="none") losses = losses.sum(-1) nll_loss = mean_ds(losses) if label_smoothing > 0: - loss = nll_loss * ( - 1 - label_smoothing) - mean_ds(logits) * label_smoothing + loss = ( + nll_loss * (1 - label_smoothing) - mean_ds(logits) * label_smoothing + ) else: loss = nll_loss @@ -103,14 +103,14 @@ def forward(self, model, sample, reduce=True): outputs[obj].get("tgt"), outputs[obj].get("mask", None), outputs[obj].get("ls", 0.0), - name=obj + '-loss', - factor=outputs[obj].get("factor", 1.0) + name=obj + "-loss", + factor=outputs[obj].get("factor", 1.0), ) else: _losses = self._custom_loss( outputs[obj].get("loss"), - name=obj + '-loss', - factor=outputs[obj].get("factor", 1.0) + name=obj + "-loss", + factor=outputs[obj].get("factor", 1.0), ) losses += [_losses] @@ -118,8 +118,7 @@ def forward(self, model, sample, reduce=True): nll_loss += [_losses.get("nll_loss", 0.0)] loss = sum(l["loss"] for l in losses) - nll_loss = sum(l for l in nll_loss) if len(nll_loss) > 0 \ - else loss.new_tensor(0) + nll_loss = sum(l for l in nll_loss) if len(nll_loss) > 0 else loss.new_tensor(0) # NOTE: # we don't need to use sample_size as denominator for the gradient @@ -145,13 +144,21 @@ def forward(self, model, sample, reduce=True): @staticmethod def reduce_metrics(logging_outputs) -> None: """Aggregate logging outputs from data parallel training.""" - sample_size = utils.item(sum(log.get("sample_size", 0) for log in logging_outputs)) + sample_size = utils.item( + sum(log.get("sample_size", 0) for log in logging_outputs) + ) loss = utils.item(sum(log.get("loss", 0) for log in logging_outputs)) nll_loss = utils.item(sum(log.get("nll_loss", 0) for log in logging_outputs)) - metrics.log_scalar('loss', loss / sample_size / math.log(2), sample_size, round=3) - metrics.log_scalar('nll_loss', nll_loss / sample_size / math.log(2), sample_size, round=3) - metrics.log_derived('ppl', lambda meters: utils.get_perplexity(meters['loss'].avg)) + metrics.log_scalar( + "loss", loss / sample_size / math.log(2), sample_size, round=3 + ) + metrics.log_scalar( + "nll_loss", nll_loss / sample_size / math.log(2), sample_size, round=3 + ) + metrics.log_derived( + "ppl", lambda meters: utils.get_perplexity(meters["loss"].avg) + ) for key in logging_outputs[0]: if key[-5:] == "-loss": diff --git a/fairseq/criterions/sentence_prediction.py b/fairseq/criterions/sentence_prediction.py index 4ba1317856..482b97985a 100644 --- a/fairseq/criterions/sentence_prediction.py +++ b/fairseq/criterions/sentence_prediction.py @@ -4,29 +4,32 @@ # LICENSE file in the root directory of this source tree. import math +from dataclasses import dataclass, field import torch import torch.nn.functional as F - from fairseq import metrics, utils from fairseq.criterions import FairseqCriterion, register_criterion +from fairseq.dataclass import FairseqDataclass -@register_criterion('sentence_prediction') -class SentencePredictionCriterion(FairseqCriterion): +@dataclass +class SentencePredictionConfig(FairseqDataclass): + classification_head_name: str = field( + default="sentence_classification_head", + metadata={"help": "name of the classification head to use"}, + ) + regression_target: bool = field( + default=False, + ) - def __init__(self, task, classification_head_name, regression_target): - super().__init__(task) - self.classification_head_name = classification_head_name - self.regression_target = regression_target - @staticmethod - def add_args(parser): - # fmt: off - parser.add_argument('--classification-head-name', - default='sentence_classification_head', - help='name of the classification head to use') - # fmt: on +@register_criterion("sentence_prediction", dataclass=SentencePredictionConfig) +class SentencePredictionCriterion(FairseqCriterion): + def __init__(self, cfg: SentencePredictionConfig, task): + super().__init__(task) + self.classification_head_name = cfg.classification_head_name + self.regression_target = cfg.regression_target def forward(self, model, sample, reduce=True): """Compute the loss for the given sample. @@ -37,12 +40,12 @@ def forward(self, model, sample, reduce=True): 3) logging outputs to display while training """ assert ( - hasattr(model, 'classification_heads') + hasattr(model, "classification_heads") and self.classification_head_name in model.classification_heads - ), 'model must provide sentence classification head for --criterion=sentence_prediction' + ), "model must provide sentence classification head for --criterion=sentence_prediction" logits, _ = model( - **sample['net_input'], + **sample["net_input"], features_only=True, classification_head_name=self.classification_head_name, ) @@ -51,39 +54,45 @@ def forward(self, model, sample, reduce=True): if not self.regression_target: lprobs = F.log_softmax(logits, dim=-1, dtype=torch.float32) - loss = F.nll_loss(lprobs, targets, reduction='sum') + loss = F.nll_loss(lprobs, targets, reduction="sum") else: logits = logits.view(-1).float() targets = targets.float() - loss = F.mse_loss(logits, targets, reduction='sum') + loss = F.mse_loss(logits, targets, reduction="sum") logging_output = { - 'loss': loss.data, - 'ntokens': sample['ntokens'], - 'nsentences': sample_size, - 'sample_size': sample_size, + "loss": loss.data, + "ntokens": sample["ntokens"], + "nsentences": sample_size, + "sample_size": sample_size, } if not self.regression_target: preds = logits.argmax(dim=1) - logging_output['ncorrect'] = (preds == targets).sum() + logging_output["ncorrect"] = (preds == targets).sum() return loss, sample_size, logging_output @staticmethod def reduce_metrics(logging_outputs) -> None: """Aggregate logging outputs from data parallel training.""" - loss_sum = sum(log.get('loss', 0) for log in logging_outputs) - ntokens = sum(log.get('ntokens', 0) for log in logging_outputs) - nsentences = sum(log.get('nsentences', 0) for log in logging_outputs) - sample_size = sum(log.get('sample_size', 0) for log in logging_outputs) + loss_sum = sum(log.get("loss", 0) for log in logging_outputs) + ntokens = sum(log.get("ntokens", 0) for log in logging_outputs) + nsentences = sum(log.get("nsentences", 0) for log in logging_outputs) + sample_size = sum(log.get("sample_size", 0) for log in logging_outputs) - metrics.log_scalar('loss', loss_sum / sample_size / math.log(2), sample_size, round=3) + metrics.log_scalar( + "loss", loss_sum / sample_size / math.log(2), sample_size, round=3 + ) if sample_size != ntokens: - metrics.log_scalar('nll_loss', loss_sum / ntokens / math.log(2), ntokens, round=3) - - if len(logging_outputs) > 0 and 'ncorrect' in logging_outputs[0]: - ncorrect = sum(log.get('ncorrect', 0) for log in logging_outputs) - metrics.log_scalar('accuracy', 100.0 * ncorrect / nsentences, nsentences, round=1) + metrics.log_scalar( + "nll_loss", loss_sum / ntokens / math.log(2), ntokens, round=3 + ) + + if len(logging_outputs) > 0 and "ncorrect" in logging_outputs[0]: + ncorrect = sum(log.get("ncorrect", 0) for log in logging_outputs) + metrics.log_scalar( + "accuracy", 100.0 * ncorrect / nsentences, nsentences, round=1 + ) @staticmethod def logging_outputs_can_be_summed() -> bool: diff --git a/fairseq/criterions/sentence_ranking.py b/fairseq/criterions/sentence_ranking.py index 52a0a177d8..d4c76341d4 100644 --- a/fairseq/criterions/sentence_ranking.py +++ b/fairseq/criterions/sentence_ranking.py @@ -7,19 +7,17 @@ import torch import torch.nn.functional as F - from fairseq import metrics, utils from fairseq.criterions import FairseqCriterion, register_criterion -@register_criterion('sentence_ranking') +@register_criterion("sentence_ranking") class SentenceRankingCriterion(FairseqCriterion): - def __init__(self, task, ranking_head_name, save_predictions, num_classes): super().__init__(task) self.ranking_head_name = ranking_head_name if save_predictions is not None: - self.prediction_h = open(save_predictions, 'w') + self.prediction_h = open(save_predictions, "w") else: self.prediction_h = None self.num_classes = num_classes @@ -47,14 +45,14 @@ def forward(self, model, sample, reduce=True): 3) logging outputs to display while training """ assert ( - hasattr(model, 'classification_heads') + hasattr(model, "classification_heads") and self.ranking_head_name in model.classification_heads - ), 'model must provide sentence ranking head for --criterion=sentence_ranking' + ), "model must provide sentence ranking head for --criterion=sentence_ranking" scores = [] for idx in range(self.num_classes): score, _ = model( - **sample['net_input{idx}'.format(idx=idx+1)], + **sample["net_input{idx}".format(idx=idx + 1)], classification_head_name=self.ranking_head_name, ) scores.append(score) @@ -62,49 +60,55 @@ def forward(self, model, sample, reduce=True): logits = torch.cat(scores, dim=1) sample_size = logits.size(0) - if 'target' in sample: + if "target" in sample: targets = model.get_targets(sample, [logits]).view(-1) lprobs = F.log_softmax(logits, dim=-1, dtype=torch.float32) - loss = F.nll_loss(lprobs, targets, reduction='sum') + loss = F.nll_loss(lprobs, targets, reduction="sum") else: targets = None loss = torch.tensor(0.0, requires_grad=True) if self.prediction_h is not None: preds = logits.argmax(dim=1) - for i, (id, pred) in enumerate(zip(sample['id'].tolist(), preds.tolist())): + for i, (id, pred) in enumerate(zip(sample["id"].tolist(), preds.tolist())): if targets is not None: label = targets[i].item() - print('{}\t{}\t{}'.format(id, pred, label), file=self.prediction_h) + print("{}\t{}\t{}".format(id, pred, label), file=self.prediction_h) else: - print('{}\t{}'.format(id, pred), file=self.prediction_h) + print("{}\t{}".format(id, pred), file=self.prediction_h) logging_output = { - 'loss': loss.data, - 'ntokens': sample['ntokens'], - 'nsentences': sample_size, - 'sample_size': sample_size, + "loss": loss.data, + "ntokens": sample["ntokens"], + "nsentences": sample_size, + "sample_size": sample_size, } if targets is not None: - logging_output['ncorrect'] = (logits.argmax(dim=1) == targets).sum() + logging_output["ncorrect"] = (logits.argmax(dim=1) == targets).sum() return loss, sample_size, logging_output @staticmethod def reduce_metrics(logging_outputs) -> None: """Aggregate logging outputs from data parallel training.""" - loss_sum = sum(log.get('loss', 0) for log in logging_outputs) - ntokens = sum(log.get('ntokens', 0) for log in logging_outputs) - nsentences = sum(log.get('nsentences', 0) for log in logging_outputs) - sample_size = sum(log.get('sample_size', 0) for log in logging_outputs) - - metrics.log_scalar('loss', loss_sum / sample_size / math.log(2), sample_size, round=3) + loss_sum = sum(log.get("loss", 0) for log in logging_outputs) + ntokens = sum(log.get("ntokens", 0) for log in logging_outputs) + nsentences = sum(log.get("nsentences", 0) for log in logging_outputs) + sample_size = sum(log.get("sample_size", 0) for log in logging_outputs) + + metrics.log_scalar( + "loss", loss_sum / sample_size / math.log(2), sample_size, round=3 + ) if sample_size != ntokens: - metrics.log_scalar('nll_loss', loss_sum / ntokens / math.log(2), ntokens, round=3) + metrics.log_scalar( + "nll_loss", loss_sum / ntokens / math.log(2), ntokens, round=3 + ) - if len(logging_outputs) > 0 and 'ncorrect' in logging_outputs[0]: - ncorrect = sum(log.get('ncorrect', 0) for log in logging_outputs) - metrics.log_scalar('accuracy', 100.0 * ncorrect / nsentences, nsentences, round=1) + if len(logging_outputs) > 0 and "ncorrect" in logging_outputs[0]: + ncorrect = sum(log.get("ncorrect", 0) for log in logging_outputs) + metrics.log_scalar( + "accuracy", 100.0 * ncorrect / nsentences, nsentences, round=1 + ) @staticmethod def logging_outputs_can_be_summed() -> bool: diff --git a/fairseq/criterions/tacotron2_loss.py b/fairseq/criterions/tacotron2_loss.py new file mode 100644 index 0000000000..8c7b655c8c --- /dev/null +++ b/fairseq/criterions/tacotron2_loss.py @@ -0,0 +1,210 @@ +# Copyright (c) 2017-present, Facebook, Inc. +# All rights reserved. +# +# This source code is licensed under the license found in the LICENSE file in +# the root directory of this source tree. An additional grant of patent rights +# can be found in the PATENTS file in the same directory. + +import logging +from typing import Any, Dict, List +from functools import lru_cache +from dataclasses import dataclass, field + +import torch +from omegaconf import II + +from fairseq import metrics, utils +from fairseq.criterions import FairseqCriterion, register_criterion +from fairseq.dataclass import FairseqDataclass +from fairseq.data.data_utils import lengths_to_mask +import torch.nn.functional as F + + +logger = logging.getLogger(__name__) + + +@dataclass +class Tacotron2CriterionConfig(FairseqDataclass): + bce_pos_weight: float = field( + default=1.0, + metadata={"help": "weight of positive examples for BCE loss"}, + ) + n_frames_per_step: int = field( + default=0, + metadata={"help": "Number of frames per decoding step"}, + ) + use_guided_attention_loss: bool = field( + default=False, + metadata={"help": "use guided attention loss"}, + ) + guided_attention_loss_sigma: float = field( + default=0.4, + metadata={"help": "weight of positive examples for BCE loss"}, + ) + ctc_weight: float = field( + default=0.0, metadata={"help": "weight for CTC loss"} + ) + sentence_avg: bool = II("optimization.sentence_avg") + + +class GuidedAttentionLoss(torch.nn.Module): + """ + Efficiently Trainable Text-to-Speech System Based on Deep Convolutional + Networks with Guided Attention (https://arxiv.org/abs/1710.08969) + """ + + def __init__(self, sigma): + super().__init__() + self.sigma = sigma + + @staticmethod + @lru_cache(maxsize=8) + def _get_weight(s_len, t_len, sigma): + grid_x, grid_y = torch.meshgrid(torch.arange(t_len), torch.arange(s_len)) + grid_x = grid_x.to(s_len.device) + grid_y = grid_y.to(s_len.device) + w = (grid_y.float() / s_len - grid_x.float() / t_len) ** 2 + return 1.0 - torch.exp(-w / (2 * (sigma ** 2))) + + def _get_weights(self, src_lens, tgt_lens): + bsz, max_s_len, max_t_len = len(src_lens), max(src_lens), max(tgt_lens) + weights = torch.zeros((bsz, max_t_len, max_s_len)) + for i, (s_len, t_len) in enumerate(zip(src_lens, tgt_lens)): + weights[i, :t_len, :s_len] = self._get_weight(s_len, t_len, + self.sigma) + return weights + + @staticmethod + def _get_masks(src_lens, tgt_lens): + in_masks = lengths_to_mask(src_lens) + out_masks = lengths_to_mask(tgt_lens) + return out_masks.unsqueeze(2) & in_masks.unsqueeze(1) + + def forward(self, attn, src_lens, tgt_lens, reduction="mean"): + weights = self._get_weights(src_lens, tgt_lens).to(attn.device) + masks = self._get_masks(src_lens, tgt_lens).to(attn.device) + loss = (weights * attn.transpose(1, 2)).masked_select(masks) + loss = torch.sum(loss) if reduction == "sum" else torch.mean(loss) + return loss + + +@register_criterion("tacotron2", dataclass=Tacotron2CriterionConfig) +class Tacotron2Criterion(FairseqCriterion): + def __init__(self, task, sentence_avg, n_frames_per_step, + use_guided_attention_loss, guided_attention_loss_sigma, + bce_pos_weight, ctc_weight): + super().__init__(task) + self.sentence_avg = sentence_avg + self.n_frames_per_step = n_frames_per_step + self.bce_pos_weight = bce_pos_weight + + self.guided_attn = None + if use_guided_attention_loss: + self.guided_attn = GuidedAttentionLoss(guided_attention_loss_sigma) + self.ctc_weight = ctc_weight + + def forward(self, model, sample, reduction="mean"): + bsz, max_len, _ = sample["target"].size() + feat_tgt = sample["target"] + feat_len = sample["target_lengths"].view(bsz, 1).expand(-1, max_len) + eos_tgt = torch.arange(max_len).to(sample["target"].device) + eos_tgt = eos_tgt.view(1, max_len).expand(bsz, -1) + eos_tgt = (eos_tgt == (feat_len - 1)).float() + src_tokens = sample["net_input"]["src_tokens"] + src_lens = sample["net_input"]["src_lengths"] + tgt_lens = sample["target_lengths"] + + feat_out, eos_out, extra = model( + src_tokens=src_tokens, + src_lengths=src_lens, + prev_output_tokens=sample["net_input"]["prev_output_tokens"], + incremental_state=None, + target_lengths=tgt_lens, + speaker=sample["speaker"] + ) + + l1_loss, mse_loss, eos_loss = self.compute_loss( + extra["feature_out"], feat_out, eos_out, feat_tgt, eos_tgt, + tgt_lens, reduction, + ) + attn_loss = torch.tensor(0.).type_as(l1_loss) + if self.guided_attn is not None: + attn_loss = self.guided_attn(extra['attn'], src_lens, tgt_lens, reduction) + ctc_loss = torch.tensor(0.).type_as(l1_loss) + if self.ctc_weight > 0.: + net_output = (feat_out, eos_out, extra) + lprobs = model.get_normalized_probs(net_output, log_probs=True) + lprobs = lprobs.transpose(0, 1) # T x B x C + src_mask = lengths_to_mask(src_lens) + src_tokens_flat = src_tokens.masked_select(src_mask) + ctc_loss = F.ctc_loss( + lprobs, src_tokens_flat, tgt_lens, src_lens, + reduction=reduction, zero_infinity=True + ) * self.ctc_weight + loss = l1_loss + mse_loss + eos_loss + attn_loss + ctc_loss + + sample_size = sample["nsentences"] if self.sentence_avg \ + else sample["ntokens"] + logging_output = { + "loss": utils.item(loss.data), + "ntokens": sample["ntokens"], + "nsentences": sample["nsentences"], + "sample_size": sample_size, + "l1_loss": utils.item(l1_loss.data), + "mse_loss": utils.item(mse_loss.data), + "eos_loss": utils.item(eos_loss.data), + "attn_loss": utils.item(attn_loss.data), + "ctc_loss": utils.item(ctc_loss.data), + } + return loss, sample_size, logging_output + + def compute_loss(self, feat_out, feat_out_post, eos_out, feat_tgt, + eos_tgt, tgt_lens, reduction="mean"): + mask = lengths_to_mask(tgt_lens) + _eos_out = eos_out[mask].squeeze() + _eos_tgt = eos_tgt[mask] + _feat_tgt = feat_tgt[mask] + _feat_out = feat_out[mask] + _feat_out_post = feat_out_post[mask] + + l1_loss = ( + F.l1_loss(_feat_out, _feat_tgt, reduction=reduction) + + F.l1_loss(_feat_out_post, _feat_tgt, reduction=reduction) + ) + mse_loss = ( + F.mse_loss(_feat_out, _feat_tgt, reduction=reduction) + + F.mse_loss(_feat_out_post, _feat_tgt, reduction=reduction) + ) + eos_loss = F.binary_cross_entropy_with_logits( + _eos_out, _eos_tgt, pos_weight=torch.tensor(self.bce_pos_weight), + reduction=reduction + ) + return l1_loss, mse_loss, eos_loss + + @classmethod + def reduce_metrics(cls, logging_outputs: List[Dict[str, Any]]) -> None: + ns = [log.get("sample_size", 0) for log in logging_outputs] + ntot = sum(ns) + ws = [n / (ntot + 1e-8) for n in ns] + for key in ["loss", "l1_loss", "mse_loss", "eos_loss", "attn_loss", "ctc_loss"]: + vals = [log.get(key, 0) for log in logging_outputs] + val = sum(val * w for val, w in zip(vals, ws)) + metrics.log_scalar(key, val, ntot, round=3) + metrics.log_scalar("sample_size", ntot, len(logging_outputs)) + + # inference metrics + if "targ_frames" not in logging_outputs[0]: + return + n = sum(log.get("targ_frames", 0) for log in logging_outputs) + for key, new_key in [ + ("mcd_loss", "mcd_loss"), + ("pred_frames", "pred_ratio"), + ("nins", "ins_rate"), + ("ndel", "del_rate"), + ]: + val = sum(log.get(key, 0) for log in logging_outputs) + metrics.log_scalar(new_key, val / n, n, round=3) + + @staticmethod + def logging_outputs_can_be_summed() -> bool: + return False diff --git a/fairseq/criterions/wav2vec_criterion.py b/fairseq/criterions/wav2vec_criterion.py new file mode 100644 index 0000000000..e04786cc3b --- /dev/null +++ b/fairseq/criterions/wav2vec_criterion.py @@ -0,0 +1,229 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math +from dataclasses import dataclass, field +from typing import List, Optional + +import torch +import torch.nn.functional as F +from fairseq import metrics, utils +from fairseq.criterions import FairseqCriterion, register_criterion +from fairseq.dataclass import FairseqDataclass +from fairseq.logging.meters import safe_round +from fairseq.utils import is_xla_tensor + + +@dataclass +class Wav2VecCriterionConfig(FairseqDataclass): + infonce: bool = field( + default=False, + metadata={ + "help": "if set, uses cross entropy instead of binary cross entropy (i.e. InfoNCE loss)" + }, + ) + loss_weights: Optional[List[float]] = field( + default=None, + metadata={"help": "weights for additional loss terms (not first one)"}, + ) + log_keys: List[str] = field( + default_factory=lambda: [], + metadata={"help": "output keys to log"}, + ) + +@register_criterion("wav2vec", dataclass=Wav2VecCriterionConfig) +class Wav2vecCriterion(FairseqCriterion): + def __init__(self, task, infonce=False, loss_weights=None, log_keys=None): + super().__init__(task) + self.infonce = infonce + self.loss_weights = loss_weights + self.log_keys = [] if log_keys is None else log_keys + + def forward(self, model, sample, reduce=True): + """Compute the loss for the given sample. + + Returns a tuple with three elements: + 1) the loss + 2) the sample size, which is used as the denominator for the gradient + 3) logging outputs to display while training + """ + net_output = model(**sample["net_input"]) + logits = model.get_logits(net_output).float() + target = model.get_targets(sample, net_output) + self.xla = is_xla_tensor(logits) + + # XXX: handle weights on xla. + weights = None + if hasattr(model, "get_target_weights") and not self.infonce: + weights = model.get_target_weights(target, net_output) + if torch.is_tensor(weights): + weights = weights.float() + + losses = [] + + reduction = "none" if ((not reduce) or self.xla) else "sum" + if self.infonce: + loss = F.cross_entropy(logits, target, reduction=reduction) + else: + loss = F.binary_cross_entropy_with_logits( + logits, target.float(), weights, reduction=reduction + ) + + if self.xla: + # tpu-comment: since dynamic shapes lead to recompilations on xla, + # we don't shrink tensors using mask_indices. + # Instead, we use mask indices to adjust loss. + mi = ( + sample['net_input']['mask_indices'] + .transpose(0, 1) # logits are transposed in `model.get_logits` + .reshape(logits.size(0)) + ) + loss = (loss * mi).sum() if reduce else (loss * mi) + + if 'sample_size' in sample: + sample_size = sample['sample_size'] + elif 'mask_indices' in sample['net_input']: + sample_size = sample['net_input']['mask_indices'].sum() + else: + sample_size = target.numel() if self.infonce else target.long().sum().item() + losses.append(loss.detach().clone()) + + if self.loss_weights is not None: + assert hasattr(model, "get_extra_losses") + extra_losses = model.get_extra_losses(net_output) + if torch.is_tensor(extra_losses): + extra_losses = [extra_losses] + if len(self.loss_weights) == 1 and len(extra_losses) != 1: + self.loss_weights = [self.loss_weights[0]] * len(extra_losses) + assert len(extra_losses) == len( + self.loss_weights + ), f"{len(extra_losses)}, {len(self.loss_weights)}" + for p, coef in zip(extra_losses, self.loss_weights): + if coef != 0 and p is not None: + p = coef * p.float() * sample_size + loss += p + losses.append(p) + + logging_output = { + "loss": loss.item() if (reduce and not self.xla) else loss.detach(), + "ntokens": sample_size, + "nsentences": sample["id"].numel(), + "sample_size": sample_size, + } + + for lk in self.log_keys: + # Only store "logits" and "target" for computing MAP and MAUC + # during validation + if lk == "logits": + if not self.training: + logging_output["logits"] = logits.cpu().numpy() + elif lk == "target": + if not self.training: + # If the targets have been mixed with the predictions of + # teacher models, find the original targets + if hasattr(model, "get_original_targets"): + original_target = model.get_original_targets(sample, net_output) + else: + original_target = target + logging_output["target"] = original_target.cpu().numpy() + elif lk in net_output: + value = net_output[lk] + if not is_xla_tensor(value): + value = float(value) + logging_output[lk] = value + + if len(losses) > 1: + for i, l in enumerate(losses): + logging_output[f"loss_{i}"] = l.item() if not self.xla else l.detach() + + if self.infonce: + with torch.no_grad(): + if logits.numel() == 0: + corr = 0 + count = 0 + else: + assert logits.dim() > 1, logits.shape + max = logits.argmax(-1) == 0 + min = logits.argmin(-1) == 0 + if is_xla_tensor(logits): + max, min = max * mi, min * mi + both = max & min + corr = max.long().sum() - both.long().sum() + count = mi.sum() + else: + both = max & min + corr = max.long().sum().item() - both.long().sum().item() + count = float(max.numel()) + + logging_output["correct"] = corr + logging_output["count"] = count + + return loss, sample_size, logging_output + + @staticmethod + def reduce_metrics(logging_outputs) -> None: + """Aggregate logging outputs from data parallel training.""" + loss_sum = utils.item(sum(log.get("loss", 0) for log in logging_outputs)) + ntokens = utils.item(sum(log.get("ntokens", 0) for log in logging_outputs)) + nsentences = utils.item( + sum(log.get("nsentences", 0) for log in logging_outputs) + ) + sample_size = utils.item( + sum(log.get("sample_size", 0) for log in logging_outputs) + ) + + metrics.log_scalar( + "loss", loss_sum / (sample_size or 1) / math.log(2), sample_size, round=3 + ) + metrics.log_scalar("ntokens", ntokens) + metrics.log_scalar("nsentences", nsentences) + + correct = sum(log.get("correct", 0) for log in logging_outputs) + metrics.log_scalar("_correct", correct) + + total = sum(log.get("count", 0) for log in logging_outputs) + metrics.log_scalar("_total", total) + + if total > 0: + metrics.log_derived( + "accuracy", + lambda meters: safe_round( + meters["_correct"].sum / meters["_total"].sum, 5 + ) + if meters["_total"].sum > 0 + else float("nan"), + ) + + builtin_keys = { + "loss", + "ntokens", + "nsentences", + "sample_size", + "correct", + "count", + } + + for k in logging_outputs[0]: + if k not in builtin_keys: + val = sum(log.get(k, 0) for log in logging_outputs) + if k.startswith("loss"): + metrics.log_scalar( + k, val / (sample_size or 1) / math.log(2), sample_size, round=3 + ) + else: + metrics.log_scalar(k, val / len(logging_outputs), round=3) + + # FIXME: revert when gather based xla reduction is implemented + #@staticmethod + #def logging_outputs_can_be_summed() -> bool: + def logging_outputs_can_be_summed(self) -> bool: + """ + Whether the logging outputs returned by `forward` can be summed + across workers prior to calling `reduce_metrics`. Setting this + to True will improves distributed training speed. + """ + # XXX: Gather based reduction not implemented for xla yet. + # So we fall to sum based reduction for xla. + return self.xla diff --git a/fairseq/data/__init__.py b/fairseq/data/__init__.py index f844564dbc..8b7eb2ec4f 100644 --- a/fairseq/data/__init__.py +++ b/fairseq/data/__init__.py @@ -2,6 +2,7 @@ # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. +"""isort:skip_file""" from .dictionary import Dictionary, TruncatedDictionary @@ -9,15 +10,23 @@ from .base_wrapper_dataset import BaseWrapperDataset +from .add_target_dataset import AddTargetDataset from .append_token_dataset import AppendTokenDataset -from .audio.raw_audio_dataset import FileAudioDataset +from .audio.raw_audio_dataset import BinarizedAudioDataset, FileAudioDataset +from .audio.hubert_dataset import HubertDataset from .backtranslation_dataset import BacktranslationDataset +from .bucket_pad_length_dataset import BucketPadLengthDataset from .colorize_dataset import ColorizeDataset from .concat_dataset import ConcatDataset from .concat_sentences_dataset import ConcatSentencesDataset from .denoising_dataset import DenoisingDataset from .id_dataset import IdDataset -from .indexed_dataset import IndexedCachedDataset, IndexedDataset, IndexedRawTextDataset, MMapIndexedDataset +from .indexed_dataset import ( + IndexedCachedDataset, + IndexedDataset, + IndexedRawTextDataset, + MMapIndexedDataset, +) from .language_pair_dataset import LanguagePairDataset from .list_dataset import ListDataset from .lm_context_window_dataset import LMContextWindowDataset @@ -44,7 +53,10 @@ from .token_block_dataset import TokenBlockDataset from .transform_eos_dataset import TransformEosDataset from .transform_eos_lang_pair_dataset import TransformEosLangPairDataset -from .truncate_dataset import TruncateDataset +from .shorten_dataset import TruncateDataset, RandomCropDataset +from .multilingual.sampled_multi_dataset import SampledMultiDataset +from .multilingual.sampled_multi_epoch_dataset import SampledMultiEpochDataset +from .fasta_dataset import FastaDataset, EncodedFastaDataset from .iterators import ( CountingIterator, @@ -54,54 +66,63 @@ ) __all__ = [ - 'AppendTokenDataset', - 'BacktranslationDataset', - 'BaseWrapperDataset', - 'ColorizeDataset', - 'ConcatDataset', - 'ConcatSentencesDataset', - 'CountingIterator', - 'DenoisingDataset', - 'Dictionary', - 'EpochBatchIterator', - 'FairseqDataset', - 'FairseqIterableDataset', - 'GroupedIterator', - 'IdDataset', - 'IndexedCachedDataset', - 'IndexedDataset', - 'IndexedRawTextDataset', - 'LanguagePairDataset', - 'LeftPadDataset', - 'ListDataset', - 'LMContextWindowDataset', - 'LRUCacheDataset', - 'MaskTokensDataset', - 'MMapIndexedDataset', - 'MonolingualDataset', - 'MultiCorpusSampledDataset', - 'NestedDictionaryDataset', - 'NoisingDataset', - 'NumelDataset', - 'NumSamplesDataset', - 'OffsetTokensDataset', - 'PadDataset', - 'PrependDataset', - 'PrependTokenDataset', - 'ReplaceDataset', - 'RollDataset', - 'FileAudioDataset', - 'RawLabelDataset', - 'ResamplingDataset', - 'RightPadDataset', - 'RoundRobinZipDatasets', - 'ShardedIterator', - 'SortDataset', - 'StripTokenDataset', - 'SubsampleDataset', - 'TokenBlockDataset', - 'TransformEosDataset', - 'TransformEosLangPairDataset', - 'TruncateDataset', - 'TruncatedDictionary', + "AddTargetDataset", + "AppendTokenDataset", + "BacktranslationDataset", + "BaseWrapperDataset", + "BinarizedAudioDataset", + "BucketPadLengthDataset", + "ColorizeDataset", + "ConcatDataset", + "ConcatSentencesDataset", + "CountingIterator", + "DenoisingDataset", + "Dictionary", + "EncodedFastaDataset", + "EpochBatchIterator", + "FairseqDataset", + "FairseqIterableDataset", + "FastaDataset", + "FileAudioDataset", + "GroupedIterator", + "HubertDataset", + "IdDataset", + "IndexedCachedDataset", + "IndexedDataset", + "IndexedRawTextDataset", + "LanguagePairDataset", + "LeftPadDataset", + "ListDataset", + "LMContextWindowDataset", + "LRUCacheDataset", + "MaskTokensDataset", + "MMapIndexedDataset", + "MonolingualDataset", + "MultiCorpusSampledDataset", + "NestedDictionaryDataset", + "NoisingDataset", + "NumelDataset", + "NumSamplesDataset", + "OffsetTokensDataset", + "PadDataset", + "PrependDataset", + "PrependTokenDataset", + "RandomCropDataset", + "RawLabelDataset", + "ResamplingDataset", + "ReplaceDataset", + "RightPadDataset", + "RollDataset", + "RoundRobinZipDatasets", + "SampledMultiDataset", + "SampledMultiEpochDataset", + "ShardedIterator", + "SortDataset", + "StripTokenDataset", + "SubsampleDataset", + "TokenBlockDataset", + "TransformEosDataset", + "TransformEosLangPairDataset", + "TruncateDataset", + "TruncatedDictionary", ] diff --git a/fairseq/data/add_target_dataset.py b/fairseq/data/add_target_dataset.py new file mode 100644 index 0000000000..d8a08e746d --- /dev/null +++ b/fairseq/data/add_target_dataset.py @@ -0,0 +1,79 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch + +from . import BaseWrapperDataset, data_utils +from fairseq.data.text_compressor import TextCompressor, TextCompressionLevel + + +class AddTargetDataset(BaseWrapperDataset): + def __init__( + self, + dataset, + labels, + pad, + eos, + batch_targets, + process_label=None, + label_len_fn=None, + add_to_input=False, + text_compression_level=TextCompressionLevel.none + ): + super().__init__(dataset) + self.labels = labels + self.batch_targets = batch_targets + self.pad = pad + self.eos = eos + self.process_label = process_label + self.label_len_fn = label_len_fn + self.add_to_input = add_to_input + self.text_compressor = TextCompressor(level=text_compression_level) + + def get_label(self, index, process_fn=None): + lbl = self.labels[index] + lbl = self.text_compressor.decompress(lbl) + return lbl if process_fn is None else process_fn(lbl) + + def __getitem__(self, index): + item = self.dataset[index] + item["label"] = self.get_label(index, process_fn=self.process_label) + return item + + def size(self, index): + sz = self.dataset.size(index) + own_sz = self.label_len_fn(self.get_label(index)) + return sz, own_sz + + def collater(self, samples): + collated = self.dataset.collater(samples) + if len(collated) == 0: + return collated + indices = set(collated["id"].tolist()) + target = [s["label"] for s in samples if s["id"] in indices] + + if self.batch_targets: + collated["target_lengths"] = torch.LongTensor([len(t) for t in target]) + target = data_utils.collate_tokens(target, pad_idx=self.pad, left_pad=False) + collated["ntokens"] = collated["target_lengths"].sum().item() + else: + collated["ntokens"] = sum([len(t) for t in target]) + + collated["target"] = target + + if self.add_to_input: + eos = target.new_full((target.size(0), 1), self.eos) + collated["target"] = torch.cat([target, eos], dim=-1).long() + collated["net_input"]["prev_output_tokens"] = torch.cat( + [eos, target], dim=-1 + ).long() + collated["ntokens"] += target.size(0) + return collated + + def filter_indices_by_size(self, indices, max_sizes): + indices, ignored = data_utils._filter_by_size_dynamic( + indices, self.size, max_sizes + ) + return indices, ignored diff --git a/fairseq/data/append_token_dataset.py b/fairseq/data/append_token_dataset.py index 7298129f62..87695bd0f5 100644 --- a/fairseq/data/append_token_dataset.py +++ b/fairseq/data/append_token_dataset.py @@ -10,7 +10,6 @@ class AppendTokenDataset(BaseWrapperDataset): - def __init__(self, dataset, token=None): super().__init__(dataset) self.token = token diff --git a/fairseq/data/audio/audio_utils.py b/fairseq/data/audio/audio_utils.py new file mode 100644 index 0000000000..b9444cb8d0 --- /dev/null +++ b/fairseq/data/audio/audio_utils.py @@ -0,0 +1,280 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +from pathlib import Path +from typing import BinaryIO, Optional, Tuple, Union, List + +import numpy as np +import torch +import torch.nn.functional as F + + +SF_AUDIO_FILE_EXTENSIONS = {".wav", ".flac", ".ogg"} +FEATURE_OR_SF_AUDIO_FILE_EXTENSIONS = {".npy", ".wav", ".flac", ".ogg"} + + +def convert_waveform( + waveform: Union[np.ndarray, torch.Tensor], sample_rate: int, + normalize_volume: bool = False, to_mono: bool = False, + to_sample_rate: Optional[int] = None +) -> Tuple[Union[np.ndarray, torch.Tensor], int]: + """convert a waveform: + - to a target sample rate + - from multi-channel to mono channel + - volume normalization + + Args: + waveform (numpy.ndarray or torch.Tensor): 2D original waveform + (channels x length) + sample_rate (int): original sample rate + normalize_volume (bool): perform volume normalization + to_mono (bool): convert to mono channel if having multiple channels + to_sample_rate (Optional[int]): target sample rate + Returns: + waveform (numpy.ndarray): converted 2D waveform (channels x length) + sample_rate (float): target sample rate + """ + try: + import torchaudio.sox_effects as ta_sox + except ImportError: + raise ImportError("Please install torchaudio: pip install torchaudio") + + effects = [] + if normalize_volume: + effects.append(["gain", "-n"]) + if to_sample_rate is not None and to_sample_rate != sample_rate: + effects.append(["rate", f"{to_sample_rate}"]) + if to_mono and waveform.shape[0] > 1: + effects.append(["channels", "1"]) + if len(effects) > 0: + is_np_input = isinstance(waveform, np.ndarray) + _waveform = torch.from_numpy(waveform) if is_np_input else waveform + converted, converted_sample_rate = ta_sox.apply_effects_tensor( + _waveform, sample_rate, effects + ) + if is_np_input: + converted = converted.numpy() + return converted, converted_sample_rate + return waveform, sample_rate + + +def get_waveform( + path_or_fp: Union[str, BinaryIO], normalization: bool = True, + mono: bool = True, frames: int = -1, start: int = 0, + always_2d: bool = True, output_sample_rate: Optional[int] = None, + normalize_volume: bool = False +) -> Tuple[np.ndarray, int]: + """Get the waveform and sample rate of a 16-bit WAV/FLAC/OGG Vorbis audio. + + Args: + path_or_fp (str or BinaryIO): the path or file-like object + normalization (bool): normalize values to [-1, 1] (Default: True) + mono (bool): convert multi-channel audio to mono-channel one + frames (int): the number of frames to read. (-1 for reading all) + start (int): Where to start reading. A negative value counts from the end. + always_2d (bool): always return 2D array even for mono-channel audios + output_sample_rate (Optional[int]): output sample rate + normalize_volume (bool): normalize volume + Returns: + waveform (numpy.ndarray): 1D or 2D waveform (channels x length) + sample_rate (float): sample rate + """ + if isinstance(path_or_fp, str): + ext = Path(path_or_fp).suffix + if ext not in SF_AUDIO_FILE_EXTENSIONS: + raise ValueError(f"Unsupported audio format: {ext}") + + try: + import soundfile as sf + except ImportError: + raise ImportError("Please install soundfile: pip install soundfile") + + waveform, sample_rate = sf.read( + path_or_fp, dtype="float32", always_2d=True, frames=frames, start=start + ) + waveform = waveform.T # T x C -> C x T + waveform, sample_rate = convert_waveform( + waveform, sample_rate, normalize_volume=normalize_volume, to_mono=mono, + to_sample_rate=output_sample_rate + ) + + if not normalization: + waveform *= 2 ** 15 # denormalized to 16-bit signed integers + if not always_2d: + waveform = waveform.squeeze(axis=0) + return waveform, sample_rate + + +def _get_kaldi_fbank( + waveform: np.ndarray, sample_rate: int, n_bins=80 +) -> Optional[np.ndarray]: + """Get mel-filter bank features via PyKaldi.""" + try: + from kaldi.feat.fbank import FbankOptions, Fbank + from kaldi.feat.mel import MelBanksOptions + from kaldi.feat.window import FrameExtractionOptions + from kaldi.matrix import Vector + + mel_opts = MelBanksOptions() + mel_opts.num_bins = n_bins + frame_opts = FrameExtractionOptions() + frame_opts.samp_freq = sample_rate + opts = FbankOptions() + opts.mel_opts = mel_opts + opts.frame_opts = frame_opts + fbank = Fbank(opts=opts) + features = fbank.compute(Vector(waveform.squeeze()), 1.0).numpy() + return features + except ImportError: + return None + + +def _get_torchaudio_fbank( + waveform: np.ndarray, sample_rate, n_bins=80 +) -> Optional[np.ndarray]: + """Get mel-filter bank features via TorchAudio.""" + try: + import torchaudio.compliance.kaldi as ta_kaldi + + waveform = torch.from_numpy(waveform) + features = ta_kaldi.fbank( + waveform, num_mel_bins=n_bins, sample_frequency=sample_rate + ) + return features.numpy() + except ImportError: + return None + + +def get_fbank(path_or_fp: Union[str, BinaryIO], n_bins=80) -> np.ndarray: + """Get mel-filter bank features via PyKaldi or TorchAudio. Prefer PyKaldi + (faster CPP implementation) to TorchAudio (Python implementation). Note that + Kaldi/TorchAudio requires 16-bit signed integers as inputs and hence the + waveform should not be normalized.""" + waveform, sample_rate = get_waveform(path_or_fp, normalization=False) + + features = _get_kaldi_fbank(waveform, sample_rate, n_bins) + if features is None: + features = _get_torchaudio_fbank(waveform, sample_rate, n_bins) + if features is None: + raise ImportError( + "Please install pyKaldi or torchaudio to enable " + "online filterbank feature extraction" + ) + + return features + + +def is_npy_data(data: bytes) -> bool: + return data[0] == 147 and data[1] == 78 + + +def is_sf_audio_data(data: bytes) -> bool: + is_wav = data[0] == 82 and data[1] == 73 and data[2] == 70 + is_flac = data[0] == 102 and data[1] == 76 and data[2] == 97 + is_ogg = data[0] == 79 and data[1] == 103 and data[2] == 103 + return is_wav or is_flac or is_ogg + + +def read_from_stored_zip(zip_path: str, offset: int, file_size: int) -> bytes: + with open(zip_path, "rb") as f: + f.seek(offset) + data = f.read(file_size) + return data + + +def parse_path(path: str) -> Tuple[str, List[int]]: + """Parse data path which is either a path to + 1. a .npy/.wav/.flac/.ogg file + 2. a stored ZIP file with slicing info: "[zip_path]:[offset]:[length]" + + Args: + path (str): the data path to parse + + Returns: + file_path (str): the file path + slice_ptr (list of int): empty in case 1; + byte offset and length for the slice in case 2 + """ + + if Path(path).suffix in FEATURE_OR_SF_AUDIO_FILE_EXTENSIONS: + _path, slice_ptr = path, [] + else: + _path, *slice_ptr = path.split(":") + if not Path(_path).is_file(): + raise FileNotFoundError(f"File not found: {_path}") + assert len(slice_ptr) in {0, 2}, f"Invalid path: {path}" + slice_ptr = [int(i) for i in slice_ptr] + return _path, slice_ptr + + +def get_window( + window_fn: callable, n_fft: int, win_length: int +) -> torch.Tensor: + padding = n_fft - win_length + assert padding >= 0 + return F.pad(window_fn(win_length), (padding // 2, padding - padding // 2)) + + +def get_fourier_basis(n_fft: int) -> torch.Tensor: + basis = np.fft.fft(np.eye(n_fft)) + basis = np.vstack( + [np.real(basis[:n_fft // 2 + 1, :]), np.imag(basis[:n_fft // 2 + 1, :])] + ) + return torch.from_numpy(basis).float() + + +def get_mel_filters( + sample_rate: int, n_fft: int, n_mels: int, f_min: float, f_max: float +) -> torch.Tensor: + try: + import librosa + except ImportError: + raise ImportError("Please install librosa: pip install librosa") + basis = librosa.filters.mel(sample_rate, n_fft, n_mels, f_min, f_max) + return torch.from_numpy(basis).float() + + +class TTSSpectrogram(torch.nn.Module): + def __init__( + self, n_fft: int, win_length: int, hop_length: int, + window_fn: callable = torch.hann_window, return_phase: bool = False + ) -> None: + super(TTSSpectrogram, self).__init__() + self.n_fft = n_fft + self.hop_length = hop_length + self.return_phase = return_phase + + basis = get_fourier_basis(n_fft).unsqueeze(1) + basis *= get_window(window_fn, n_fft, win_length) + self.register_buffer('basis', basis) + + def forward( + self, waveform: torch.Tensor + ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]: + padding = (self.n_fft // 2, self.n_fft // 2) + x = F.pad(waveform.unsqueeze(1), padding, mode='reflect') + x = F.conv1d(x, self.basis, stride=self.hop_length) + real_part = x[:, :self.n_fft // 2 + 1, :] + imag_part = x[:, self.n_fft // 2 + 1:, :] + magnitude = torch.sqrt(real_part ** 2 + imag_part ** 2) + if self.return_phase: + phase = torch.atan2(imag_part, real_part) + return magnitude, phase + return magnitude + + +class TTSMelScale(torch.nn.Module): + def __init__( + self, n_mels: int, sample_rate: int, f_min: float, f_max: float, + n_stft: int + ) -> None: + super(TTSMelScale, self).__init__() + basis = get_mel_filters(sample_rate, (n_stft - 1) * 2, n_mels, f_min, + f_max) + self.register_buffer('basis', basis) + + def forward(self, specgram: torch.Tensor) -> torch.Tensor: + return torch.matmul(self.basis, specgram) diff --git a/fairseq/data/audio/data_cfg.py b/fairseq/data/audio/data_cfg.py new file mode 100644 index 0000000000..95b403ad9c --- /dev/null +++ b/fairseq/data/audio/data_cfg.py @@ -0,0 +1,139 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from pathlib import Path +from typing import Dict, Optional + + +class S2TDataConfig(object): + """Wrapper class for data config YAML""" + + def __init__(self, yaml_path: Path): + try: + import yaml + except ImportError: + print("Please install PyYAML: pip install PyYAML") + self.config = {} + if yaml_path.is_file(): + try: + with open(yaml_path) as f: + self.config = yaml.load(f, Loader=yaml.FullLoader) + except Exception as e: + raise Exception( + f"Failed to load config from {yaml_path.as_posix()}: {e}" + ) + else: + raise FileNotFoundError(f"{yaml_path.as_posix()} not found") + self.root = yaml_path.parent + + def _auto_convert_to_abs_path(self, x): + if isinstance(x, str): + if not Path(x).exists() and (self.root / x).exists(): + return (self.root / x).as_posix() + elif isinstance(x, dict): + return {k: self._auto_convert_to_abs_path(v) for k, v in x.items()} + return x + + @property + def vocab_filename(self): + """fairseq vocabulary file under data root""" + return self.config.get("vocab_filename", "dict.txt") + + @property + def speaker_set_filename(self): + """fairseq vocabulary file under data root""" + return self.config.get("speaker_set_filename", None) + + @property + def shuffle(self) -> bool: + """Shuffle dataset samples before batching""" + return self.config.get("shuffle", False) + + @property + def pre_tokenizer(self) -> Dict: + """Pre-tokenizer to apply before subword tokenization. Returning + a dictionary with `tokenizer` providing the tokenizer name and + the other items providing the tokenizer-specific arguments. + Tokenizers are defined in `fairseq.data.encoders.*`""" + tokenizer = self.config.get("pre_tokenizer", {"tokenizer": None}) + return self._auto_convert_to_abs_path(tokenizer) + + @property + def bpe_tokenizer(self) -> Dict: + """Subword tokenizer to apply after pre-tokenization. Returning + a dictionary with `bpe` providing the tokenizer name and + the other items providing the tokenizer-specific arguments. + Tokenizers are defined in `fairseq.data.encoders.*`""" + tokenizer = self.config.get("bpe_tokenizer", {"bpe": None}) + return self._auto_convert_to_abs_path(tokenizer) + + @property + def prepend_tgt_lang_tag(self) -> bool: + """Prepend target lang ID token as the target BOS (e.g. for to-many + multilingual setting). During inference, this requires `--prefix-size 1` + to force BOS to be lang ID token.""" + return self.config.get("prepend_tgt_lang_tag", False) + + @property + def input_feat_per_channel(self): + """The dimension of input features (per audio channel)""" + return self.config.get("input_feat_per_channel", 80) + + @property + def input_channels(self): + """The number of channels in the input audio""" + return self.config.get("input_channels", 1) + + @property + def sample_rate(self): + return self.config.get("sample_rate", 16_000) + + @property + def sampling_alpha(self): + """Hyper-parameter alpha = 1/T for temperature-based resampling. + (alpha = 1 for no resampling)""" + return self.config.get("sampling_alpha", 1.0) + + @property + def use_audio_input(self): + """Needed by the dataset loader to see if the model requires + raw audio as inputs.""" + return self.config.get("use_audio_input", False) + + @property + def use_sample_rate(self): + """Needed by the dataset loader to see if the model requires + raw audio with specific sample rate as inputs.""" + return self.config.get("use_sample_rate", 16000) + + @property + def audio_root(self): + """Audio paths in the manifest TSV can be relative and this provides + the root path. Set this to empty string when using absolute paths.""" + return self.config.get("audio_root", "") + + def get_feature_transforms(self, split, is_train): + """Split-specific feature transforms. Allowing train set + wildcard `_train`, evaluation set wildcard `_eval` and general + wildcard `*` for matching.""" + from copy import deepcopy + + cfg = deepcopy(self.config) + _cur = cfg.get("transforms", {}) + cur = _cur.get(split) + cur = _cur.get("_train") if cur is None and is_train else cur + cur = _cur.get("_eval") if cur is None and not is_train else cur + cur = _cur.get("*") if cur is None else cur + cfg["transforms"] = cur + return cfg + + @property + def global_cmvn_stats_npz(self) -> Optional[str]: + path = self.config.get("global_cmvn", {}).get("stats_npz_path", None) + return self._auto_convert_to_abs_path(path) + + @property + def vocoder(self) -> Optional[Dict[str, str]]: + return self.config.get("vocoder", None) diff --git a/fairseq/data/audio/feature_transforms/__init__.py b/fairseq/data/audio/feature_transforms/__init__.py new file mode 100644 index 0000000000..359fa06971 --- /dev/null +++ b/fairseq/data/audio/feature_transforms/__init__.py @@ -0,0 +1,82 @@ +import importlib +import os +from abc import ABC, abstractmethod +from typing import Dict, Optional + + +class AudioFeatureTransform(ABC): + @classmethod + @abstractmethod + def from_config_dict(cls, config: Optional[Dict] = None): + pass + + +AUDIO_FEATURE_TRANSFORM_REGISTRY = {} +AUDIO_FEATURE_TRANSFORM_CLASS_NAMES = set() + + +def register_audio_feature_transform(name): + def register_audio_feature_transform_cls(cls): + if name in AUDIO_FEATURE_TRANSFORM_REGISTRY: + raise ValueError(f"Cannot register duplicate transform ({name})") + if not issubclass(cls, AudioFeatureTransform): + raise ValueError( + f"Transform ({name}: {cls.__name__}) must extend " + "AudioFeatureTransform" + ) + if cls.__name__ in AUDIO_FEATURE_TRANSFORM_CLASS_NAMES: + raise ValueError( + f"Cannot register audio feature transform with duplicate " + f"class name ({cls.__name__})" + ) + AUDIO_FEATURE_TRANSFORM_REGISTRY[name] = cls + AUDIO_FEATURE_TRANSFORM_CLASS_NAMES.add(cls.__name__) + return cls + + return register_audio_feature_transform_cls + + +def get_audio_feature_transform(name): + return AUDIO_FEATURE_TRANSFORM_REGISTRY[name] + + +transforms_dir = os.path.dirname(__file__) +for file in os.listdir(transforms_dir): + path = os.path.join(transforms_dir, file) + if ( + not file.startswith("_") + and not file.startswith(".") + and (file.endswith(".py") or os.path.isdir(path)) + ): + name = file[: file.find(".py")] if file.endswith(".py") else file + importlib.import_module("fairseq.data.audio.feature_transforms." + name) + + +class CompositeAudioFeatureTransform(AudioFeatureTransform): + @classmethod + def from_config_dict(cls, config=None): + _config = {} if config is None else config + _transforms = _config.get("transforms") + if _transforms is None: + return None + transforms = [ + get_audio_feature_transform(_t).from_config_dict(_config.get(_t)) + for _t in _transforms + ] + return CompositeAudioFeatureTransform(transforms) + + def __init__(self, transforms): + self.transforms = [t for t in transforms if t is not None] + + def __call__(self, x): + for t in self.transforms: + x = t(x) + return x + + def __repr__(self): + format_string = ( + [self.__class__.__name__ + "("] + + [f" {t.__repr__()}" for t in self.transforms] + + [")"] + ) + return "\n".join(format_string) diff --git a/fairseq/data/audio/feature_transforms/global_cmvn.py b/fairseq/data/audio/feature_transforms/global_cmvn.py new file mode 100644 index 0000000000..e457ff176f --- /dev/null +++ b/fairseq/data/audio/feature_transforms/global_cmvn.py @@ -0,0 +1,29 @@ +import numpy as np +from fairseq.data.audio.feature_transforms import ( + AudioFeatureTransform, + register_audio_feature_transform, +) + + +@register_audio_feature_transform("global_cmvn") +class GlobalCMVN(AudioFeatureTransform): + """Global CMVN (cepstral mean and variance normalization). The global mean + and variance need to be pre-computed and stored in NumPy format (.npz).""" + + @classmethod + def from_config_dict(cls, config=None): + _config = {} if config is None else config + return GlobalCMVN(_config.get("stats_npz_path")) + + def __init__(self, stats_npz_path): + self.stats_npz_path = stats_npz_path + stats = np.load(stats_npz_path) + self.mean, self.std = stats["mean"], stats["std"] + + def __repr__(self): + return self.__class__.__name__ + f'(stats_npz_path="{self.stats_npz_path}")' + + def __call__(self, x): + x = np.subtract(x, self.mean) + x = np.divide(x, self.std) + return x diff --git a/fairseq/data/audio/feature_transforms/specaugment.py b/fairseq/data/audio/feature_transforms/specaugment.py new file mode 100644 index 0000000000..ce5802b41a --- /dev/null +++ b/fairseq/data/audio/feature_transforms/specaugment.py @@ -0,0 +1,131 @@ +import math +import numbers +from typing import Optional + +import numpy as np +from fairseq.data.audio.feature_transforms import ( + AudioFeatureTransform, + register_audio_feature_transform, +) + + +@register_audio_feature_transform("specaugment") +class SpecAugmentTransform(AudioFeatureTransform): + """SpecAugment (https://arxiv.org/abs/1904.08779)""" + + @classmethod + def from_config_dict(cls, config=None): + _config = {} if config is None else config + return SpecAugmentTransform( + _config.get("time_warp_W", 0), + _config.get("freq_mask_N", 0), + _config.get("freq_mask_F", 0), + _config.get("time_mask_N", 0), + _config.get("time_mask_T", 0), + _config.get("time_mask_p", 0.0), + _config.get("mask_value", None), + ) + + def __init__( + self, + time_warp_w: int = 0, + freq_mask_n: int = 0, + freq_mask_f: int = 0, + time_mask_n: int = 0, + time_mask_t: int = 0, + time_mask_p: float = 0.0, + mask_value: Optional[float] = 0.0, + ): + # Sanity checks + assert mask_value is None or isinstance( + mask_value, numbers.Number + ), f"mask_value (type: {type(mask_value)}) must be None or a number" + if freq_mask_n > 0: + assert freq_mask_f > 0, ( + f"freq_mask_F ({freq_mask_f}) " + f"must be larger than 0 when doing freq masking." + ) + if time_mask_n > 0: + assert time_mask_t > 0, ( + f"time_mask_T ({time_mask_t}) must be larger than 0 when " + f"doing time masking." + ) + + self.time_warp_w = time_warp_w + self.freq_mask_n = freq_mask_n + self.freq_mask_f = freq_mask_f + self.time_mask_n = time_mask_n + self.time_mask_t = time_mask_t + self.time_mask_p = time_mask_p + self.mask_value = mask_value + + def __repr__(self): + return ( + self.__class__.__name__ + + "(" + + ", ".join( + [ + f"time_warp_w={self.time_warp_w}", + f"freq_mask_n={self.freq_mask_n}", + f"freq_mask_f={self.freq_mask_f}", + f"time_mask_n={self.time_mask_n}", + f"time_mask_t={self.time_mask_t}", + f"time_mask_p={self.time_mask_p}", + ] + ) + + ")" + ) + + def __call__(self, spectrogram): + assert len(spectrogram.shape) == 2, "spectrogram must be a 2-D tensor." + + distorted = spectrogram.copy() # make a copy of input spectrogram. + num_frames = spectrogram.shape[0] # or 'tau' in the paper. + num_freqs = spectrogram.shape[1] # or 'miu' in the paper. + mask_value = self.mask_value + + if mask_value is None: # if no value was specified, use local mean. + mask_value = spectrogram.mean() + + if num_frames == 0: + return spectrogram + + if num_freqs < self.freq_mask_f: + return spectrogram + + if self.time_warp_w > 0: + if 2 * self.time_warp_w < num_frames: + import cv2 + + w0 = np.random.randint(self.time_warp_w, num_frames - self.time_warp_w) + w = np.random.randint(-self.time_warp_w + 1, self.time_warp_w) + upper, lower = distorted[:w0, :], distorted[w0:, :] + upper = cv2.resize( + upper, dsize=(num_freqs, w0 + w), interpolation=cv2.INTER_LINEAR + ) + lower = cv2.resize( + lower, + dsize=(num_freqs, num_frames - w0 - w), + interpolation=cv2.INTER_LINEAR, + ) + distorted = np.concatenate((upper, lower), axis=0) + + for _i in range(self.freq_mask_n): + f = np.random.randint(0, self.freq_mask_f) + f0 = np.random.randint(0, num_freqs - f) + if f != 0: + distorted[:, f0 : f0 + f] = mask_value + + max_time_mask_t = min( + self.time_mask_t, math.floor(num_frames * self.time_mask_p) + ) + if max_time_mask_t < 1: + return distorted + + for _i in range(self.time_mask_n): + t = np.random.randint(0, max_time_mask_t) + t0 = np.random.randint(0, num_frames - t) + if t != 0: + distorted[t0 : t0 + t, :] = mask_value + + return distorted diff --git a/fairseq/data/audio/feature_transforms/utterance_cmvn.py b/fairseq/data/audio/feature_transforms/utterance_cmvn.py new file mode 100644 index 0000000000..6bbd0ae821 --- /dev/null +++ b/fairseq/data/audio/feature_transforms/utterance_cmvn.py @@ -0,0 +1,40 @@ +import numpy as np +from fairseq.data.audio.feature_transforms import ( + AudioFeatureTransform, + register_audio_feature_transform, +) + + +@register_audio_feature_transform("utterance_cmvn") +class UtteranceCMVN(AudioFeatureTransform): + """Utterance-level CMVN (cepstral mean and variance normalization)""" + + @classmethod + def from_config_dict(cls, config=None): + _config = {} if config is None else config + return UtteranceCMVN( + _config.get("norm_means", True), + _config.get("norm_vars", True), + ) + + def __init__(self, norm_means=True, norm_vars=True): + self.norm_means, self.norm_vars = norm_means, norm_vars + + def __repr__(self): + return ( + self.__class__.__name__ + + f"(norm_means={self.norm_means}, norm_vars={self.norm_vars})" + ) + + def __call__(self, x): + mean = x.mean(axis=0) + square_sums = (x ** 2).sum(axis=0) + + if self.norm_means: + x = np.subtract(x, mean) + if self.norm_vars: + var = square_sums / x.shape[0] - mean ** 2 + std = np.sqrt(np.maximum(var, 1e-10)) + x = np.divide(x, std) + + return x diff --git a/fairseq/data/audio/frm_text_to_speech_dataset.py b/fairseq/data/audio/frm_text_to_speech_dataset.py new file mode 100644 index 0000000000..125b1fc0c0 --- /dev/null +++ b/fairseq/data/audio/frm_text_to_speech_dataset.py @@ -0,0 +1,207 @@ +# Copyright (c) 2017-present, Facebook, Inc. +# All rights reserved. +# +# This source code is licensed under the license found in the LICENSE file in +# the root directory of this source tree. An additional grant of patent rights +# can be found in the PATENTS file in the same directory.abs + +import csv +import logging +import os.path as op +from typing import List, Optional + +import numpy as np +import torch +from fairseq.data import Dictionary +from fairseq.data.audio.speech_to_text_dataset import ( + S2TDataConfig +) +from fairseq.data.audio.text_to_speech_dataset import ( + TextToSpeechDataset, TextToSpeechDatasetCreator +) + +logger = logging.getLogger(__name__) + + +class FrmTextToSpeechDataset(TextToSpeechDataset): + def __init__( + self, + split: str, + is_train_split: bool, + data_cfg: S2TDataConfig, + audio_paths: List[str], + n_frames: List[int], + src_texts: Optional[List[str]] = None, + tgt_texts: Optional[List[str]] = None, + speakers: Optional[List[str]] = None, + src_langs: Optional[List[str]] = None, + tgt_langs: Optional[List[str]] = None, + ids: Optional[List[str]] = None, + tgt_dict: Optional[Dictionary] = None, + pre_tokenizer=None, + bpe_tokenizer=None, + n_frames_per_step=1, + speaker_to_id=None, + do_chunk=False, + chunk_bound=-1, + chunk_init=50, + chunk_incr=5, + add_eos=True, + dedup=True, + ref_fpu=-1 + ): + # It assumes texts are encoded at a fixed frame-rate + super().__init__( + split=split, + is_train_split=is_train_split, + data_cfg=data_cfg, + audio_paths=audio_paths, + n_frames=n_frames, + src_texts=src_texts, + tgt_texts=tgt_texts, + speakers=speakers, + src_langs=src_langs, + tgt_langs=tgt_langs, + ids=ids, + tgt_dict=tgt_dict, + pre_tokenizer=pre_tokenizer, + bpe_tokenizer=bpe_tokenizer, + n_frames_per_step=n_frames_per_step, + speaker_to_id=speaker_to_id + ) + + self.do_chunk = do_chunk + self.chunk_bound = chunk_bound + self.chunk_init = chunk_init + self.chunk_incr = chunk_incr + self.add_eos = add_eos + self.dedup = dedup + self.ref_fpu = ref_fpu + + self.chunk_size = -1 + + if do_chunk: + assert self.chunk_incr >= 0 + assert self.pre_tokenizer is None + + def __getitem__(self, index): + index, source, target, speaker_id, _, _, _ = super().__getitem__(index) + if target[-1].item() == self.tgt_dict.eos_index: + target = target[:-1] + + fpu = source.size(0) / target.size(0) # frame-per-unit + fps = self.n_frames_per_step + assert ( + self.ref_fpu == -1 or + abs((fpu * fps - self.ref_fpu) / self.ref_fpu) < 0.1 + ), f"{fpu*fps} != {self.ref_fpu}" + + # only chunk training split + if self.is_train_split and self.do_chunk and self.chunk_size > 0: + lang = target[:int(self.data_cfg.prepend_tgt_lang_tag)] + text = target[int(self.data_cfg.prepend_tgt_lang_tag):] + size = len(text) + chunk_size = min(self.chunk_size, size) + chunk_start = np.random.randint(size - chunk_size + 1) + text = text[chunk_start:chunk_start+chunk_size] + target = torch.cat((lang, text), 0) + + f_size = int(np.floor(chunk_size * fpu)) + f_start = int(np.floor(chunk_start * fpu)) + assert(f_size > 0) + source = source[f_start:f_start+f_size, :] + + if self.dedup: + target = torch.unique_consecutive(target) + + if self.add_eos: + eos_idx = self.tgt_dict.eos_index + target = torch.cat((target, torch.LongTensor([eos_idx])), 0) + + return index, source, target, speaker_id + + def set_epoch(self, epoch): + if self.is_train_split and self.do_chunk: + old = self.chunk_size + self.chunk_size = self.chunk_init + epoch * self.chunk_incr + if self.chunk_bound > 0: + self.chunk_size = min(self.chunk_size, self.chunk_bound) + logger.info(( + f"{self.split}: setting chunk size " + f"from {old} to {self.chunk_size}" + )) + + +class FrmTextToSpeechDatasetCreator(TextToSpeechDatasetCreator): + # inherit for key names + @classmethod + def from_tsv( + cls, + root: str, + data_cfg: S2TDataConfig, + split: str, + tgt_dict, + pre_tokenizer, + bpe_tokenizer, + is_train_split: bool, + n_frames_per_step: int, + speaker_to_id, + do_chunk: bool = False, + chunk_bound: int = -1, + chunk_init: int = 50, + chunk_incr: int = 5, + add_eos: bool = True, + dedup: bool = True, + ref_fpu: float = -1 + ) -> FrmTextToSpeechDataset: + tsv_path = op.join(root, f"{split}.tsv") + if not op.isfile(tsv_path): + raise FileNotFoundError(f"Dataset not found: {tsv_path}") + with open(tsv_path) as f: + reader = csv.DictReader( + f, + delimiter="\t", + quotechar=None, + doublequote=False, + lineterminator="\n", + quoting=csv.QUOTE_NONE, + ) + s = [dict(e) for e in reader] + assert len(s) > 0 + + ids = [ss[cls.KEY_ID] for ss in s] + audio_paths = [ + op.join(data_cfg.audio_root, ss[cls.KEY_AUDIO]) for ss in s + ] + n_frames = [int(ss[cls.KEY_N_FRAMES]) for ss in s] + tgt_texts = [ss[cls.KEY_TGT_TEXT] for ss in s] + src_texts = [ss.get(cls.KEY_SRC_TEXT, cls.DEFAULT_SRC_TEXT) for ss in s] + speakers = [ss.get(cls.KEY_SPEAKER, cls.DEFAULT_SPEAKER) for ss in s] + src_langs = [ss.get(cls.KEY_SRC_LANG, cls.DEFAULT_LANG) for ss in s] + tgt_langs = [ss.get(cls.KEY_TGT_LANG, cls.DEFAULT_LANG) for ss in s] + + return FrmTextToSpeechDataset( + split=split, + is_train_split=is_train_split, + data_cfg=data_cfg, + audio_paths=audio_paths, + n_frames=n_frames, + src_texts=src_texts, + tgt_texts=tgt_texts, + speakers=speakers, + src_langs=src_langs, + tgt_langs=tgt_langs, + ids=ids, + tgt_dict=tgt_dict, + pre_tokenizer=pre_tokenizer, + bpe_tokenizer=bpe_tokenizer, + n_frames_per_step=n_frames_per_step, + speaker_to_id=speaker_to_id, + do_chunk=do_chunk, + chunk_bound=chunk_bound, + chunk_init=chunk_init, + chunk_incr=chunk_incr, + add_eos=add_eos, + dedup=dedup, + ref_fpu=ref_fpu + ) diff --git a/fairseq/data/audio/hubert_dataset.py b/fairseq/data/audio/hubert_dataset.py new file mode 100644 index 0000000000..f00fe301a6 --- /dev/null +++ b/fairseq/data/audio/hubert_dataset.py @@ -0,0 +1,358 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import itertools +import logging +import os +import sys +from typing import Any, List, Optional, Union + +import numpy as np + +import torch +import torch.nn.functional as F +from fairseq.data import data_utils +from fairseq.data.fairseq_dataset import FairseqDataset + +logger = logging.getLogger(__name__) + + +def load_audio(manifest_path, max_keep, min_keep): + n_long, n_short = 0, 0 + names, inds, sizes = [], [], [] + with open(manifest_path) as f: + root = f.readline().strip() + for ind, line in enumerate(f): + items = line.strip().split("\t") + assert len(items) == 2, line + sz = int(items[1]) + if min_keep is not None and sz < min_keep: + n_short += 1 + elif max_keep is not None and sz > max_keep: + n_long += 1 + else: + names.append(items[0]) + inds.append(ind) + sizes.append(sz) + tot = ind + 1 + logger.info( + ( + f"max_keep={max_keep}, min_keep={min_keep}, " + f"loaded {len(names)}, skipped {n_short} short and {n_long} long, " + f"longest-loaded={max(sizes)}, shortest-loaded={min(sizes)}" + ) + ) + return root, names, inds, tot, sizes + + +def load_label(label_path, inds, tot): + with open(label_path) as f: + labels = [line.rstrip() for line in f] + assert ( + len(labels) == tot + ), f"number of labels does not match ({len(labels)} != {tot})" + labels = [labels[i] for i in inds] + return labels + + +def load_label_offset(label_path, inds, tot): + with open(label_path) as f: + code_lengths = [len(line.encode("utf-8")) for line in f] + assert ( + len(code_lengths) == tot + ), f"number of labels does not match ({len(code_lengths)} != {tot})" + offsets = list(itertools.accumulate([0] + code_lengths)) + offsets = [(offsets[i], offsets[i + 1]) for i in inds] + return offsets + + +def verify_label_lengths( + audio_sizes, + audio_rate, + label_path, + label_rate, + inds, + tot, + tol=0.1, # tolerance in seconds +): + if label_rate < 0: + logger.info(f"{label_path} is sequence label. skipped") + return + + with open(label_path) as f: + lengths = [len(line.rstrip().split()) for line in f] + assert len(lengths) == tot + lengths = [lengths[i] for i in inds] + num_invalid = 0 + for i, ind in enumerate(inds): + dur_from_audio = audio_sizes[i] / audio_rate + dur_from_label = lengths[i] / label_rate + if abs(dur_from_audio - dur_from_label) > tol: + logger.warning( + ( + f"audio and label duration differ too much " + f"(|{dur_from_audio} - {dur_from_label}| > {tol}) " + f"in line {ind+1} of {label_path}. Check if `label_rate` " + f"is correctly set (currently {label_rate}). " + f"num. of samples = {audio_sizes[i]}; " + f"label length = {lengths[i]}" + ) + ) + num_invalid += 1 + if num_invalid > 0: + logger.warning( + f"total {num_invalid} (audio, label) pairs with mismatched lengths" + ) + + +class HubertDataset(FairseqDataset): + def __init__( + self, + manifest_path: str, + sample_rate: float, + label_paths: List[str], + label_rates: Union[List[float], float], # -1 for sequence labels + pad_list: List[str], + eos_list: List[str], + label_processors: Optional[List[Any]] = None, + max_keep_sample_size: Optional[int] = None, + min_keep_sample_size: Optional[int] = None, + max_sample_size: Optional[int] = None, + shuffle: bool = True, + pad_audio: bool = False, + normalize: bool = False, + store_labels: bool = True, + random_crop: bool = False, + single_target: bool = False, + ): + self.audio_root, self.audio_names, inds, tot, self.sizes = load_audio( + manifest_path, max_keep_sample_size, min_keep_sample_size + ) + self.sample_rate = sample_rate + self.shuffle = shuffle + self.random_crop = random_crop + + self.num_labels = len(label_paths) + self.pad_list = pad_list + self.eos_list = eos_list + self.label_processors = label_processors + self.single_target = single_target + self.label_rates = ( + [label_rates for _ in range(len(label_paths))] + if isinstance(label_rates, int) + else label_rates + ) + self.store_labels = store_labels + if store_labels: + self.label_list = [load_label(p, inds, tot) for p in label_paths] + else: + self.label_paths = label_paths + self.label_offsets_list = [ + load_label_offset(p, inds, tot) for p in label_paths + ] + assert ( + label_processors is None + or len(label_processors) == self.num_labels + ) + for label_path, label_rate in zip(label_paths, self.label_rates): + verify_label_lengths( + self.sizes, sample_rate, label_path, label_rate, inds, tot + ) + + self.max_sample_size = ( + max_sample_size if max_sample_size is not None else sys.maxsize + ) + self.pad_audio = pad_audio + self.normalize = normalize + logger.info( + f"pad_audio={pad_audio}, random_crop={random_crop}, " + f"normalize={normalize}, max_sample_size={self.max_sample_size}" + ) + + def get_audio(self, index): + import soundfile as sf + + wav_path = os.path.join(self.audio_root, self.audio_names[index]) + wav, cur_sample_rate = sf.read(wav_path) + wav = torch.from_numpy(wav).float() + wav = self.postprocess(wav, cur_sample_rate) + return wav + + def get_label(self, index, label_idx): + if self.store_labels: + label = self.label_list[label_idx][index] + else: + with open(self.label_paths[label_idx]) as f: + offset_s, offset_e = self.label_offsets_list[label_idx][index] + f.seek(offset_s) + label = f.read(offset_e - offset_s) + + if self.label_processors is not None: + label = self.label_processors[label_idx](label) + return label + + def get_labels(self, index): + return [self.get_label(index, i) for i in range(self.num_labels)] + + def __getitem__(self, index): + wav = self.get_audio(index) + labels = self.get_labels(index) + return {"id": index, "source": wav, "label_list": labels} + + def __len__(self): + return len(self.sizes) + + def crop_to_max_size(self, wav, target_size): + size = len(wav) + diff = size - target_size + if diff <= 0: + return wav, 0 + + start, end = 0, target_size + if self.random_crop: + start = np.random.randint(0, diff + 1) + end = size - diff + start + return wav[start:end], start + + def collater(self, samples): + # target = max(sizes) -> random_crop not used + # target = max_sample_size -> random_crop used for long + samples = [s for s in samples if s["source"] is not None] + if len(samples) == 0: + return {} + + audios = [s["source"] for s in samples] + audio_sizes = [len(s) for s in audios] + if self.pad_audio: + audio_size = min(max(audio_sizes), self.max_sample_size) + else: + audio_size = min(min(audio_sizes), self.max_sample_size) + collated_audios, padding_mask, audio_starts = self.collater_audio( + audios, audio_size + ) + + targets_by_label = [ + [s["label_list"][i] for s in samples] + for i in range(self.num_labels) + ] + targets_list, lengths_list, ntokens_list = self.collater_label( + targets_by_label, audio_size, audio_starts + ) + + net_input = {"source": collated_audios, "padding_mask": padding_mask} + batch = { + "id": torch.LongTensor([s["id"] for s in samples]), + "net_input": net_input, + } + + if self.single_target: + batch["target_lengths"] = lengths_list[0] + batch["ntokens"] = ntokens_list[0] + batch["target"] = targets_list[0] + else: + batch["target_lengths_list"] = lengths_list + batch["ntokens_list"] = ntokens_list + batch["target_list"] = targets_list + return batch + + def collater_audio(self, audios, audio_size): + collated_audios = audios[0].new_zeros(len(audios), audio_size) + padding_mask = ( + torch.BoolTensor(collated_audios.shape).fill_(False) + # if self.pad_audio else None + ) + audio_starts = [0 for _ in audios] + for i, audio in enumerate(audios): + diff = len(audio) - audio_size + if diff == 0: + collated_audios[i] = audio + elif diff < 0: + assert self.pad_audio + collated_audios[i] = torch.cat( + [audio, audio.new_full((-diff,), 0.0)] + ) + padding_mask[i, diff:] = True + else: + collated_audios[i], audio_starts[i] = self.crop_to_max_size( + audio, audio_size + ) + return collated_audios, padding_mask, audio_starts + + def collater_frm_label( + self, targets, audio_size, audio_starts, label_rate, pad + ): + assert label_rate > 0 + s2f = label_rate / self.sample_rate + frm_starts = [int(round(s * s2f)) for s in audio_starts] + frm_size = int(round(audio_size * s2f)) + if not self.pad_audio: + rem_size = [len(t) - s for t, s in zip(targets, frm_starts)] + frm_size = min(frm_size, *rem_size) + targets = [t[s: s + frm_size] for t, s in zip(targets, frm_starts)] + logger.debug(f"audio_starts={audio_starts}") + logger.debug(f"frame_starts={frm_starts}") + logger.debug(f"frame_size={frm_size}") + + lengths = torch.LongTensor([len(t) for t in targets]) + ntokens = lengths.sum().item() + targets = data_utils.collate_tokens( + targets, pad_idx=pad, left_pad=False + ) + return targets, lengths, ntokens + + def collater_seq_label(self, targets, pad): + lengths = torch.LongTensor([len(t) for t in targets]) + ntokens = lengths.sum().item() + targets = data_utils.collate_tokens( + targets, pad_idx=pad, left_pad=False + ) + return targets, lengths, ntokens + + def collater_label(self, targets_by_label, audio_size, audio_starts): + targets_list, lengths_list, ntokens_list = [], [], [] + itr = zip(targets_by_label, self.label_rates, self.pad_list) + for targets, label_rate, pad in itr: + if label_rate == -1: + targets, lengths, ntokens = self.collater_seq_label( + targets, pad + ) + else: + targets, lengths, ntokens = self.collater_frm_label( + targets, audio_size, audio_starts, label_rate, pad + ) + targets_list.append(targets) + lengths_list.append(lengths) + ntokens_list.append(ntokens) + return targets_list, lengths_list, ntokens_list + + def num_tokens(self, index): + return self.size(index) + + def size(self, index): + if self.pad_audio: + return self.sizes[index] + return min(self.sizes[index], self.max_sample_size) + + def ordered_indices(self): + if self.shuffle: + order = [np.random.permutation(len(self))] + else: + order = [np.arange(len(self))] + + order.append(self.sizes) + return np.lexsort(order)[::-1] + + def postprocess(self, wav, cur_sample_rate): + if wav.dim() == 2: + wav = wav.mean(-1) + assert wav.dim() == 1, wav.dim() + + if cur_sample_rate != self.sample_rate: + raise Exception(f"sr {cur_sample_rate} != {self.sample_rate}") + + if self.normalize: + with torch.no_grad(): + wav = F.layer_norm(wav, wav.shape) + return wav diff --git a/fairseq/data/audio/multi_modality_dataset.py b/fairseq/data/audio/multi_modality_dataset.py new file mode 100644 index 0000000000..69d23d31c1 --- /dev/null +++ b/fairseq/data/audio/multi_modality_dataset.py @@ -0,0 +1,263 @@ +# Copyright (c) 2021-present, Facebook, Inc. +# All rights reserved. +# +# This source code is licensed under the license found in the LICENSE file in +# the root directory of this source tree. An additional grant of patent rights +# can be found in the PATENTS file in the same directory. + +import logging +import math +from typing import List, Optional, NamedTuple + +import numpy as np +import torch +from fairseq.data import ( + ConcatDataset, + LanguagePairDataset, + FileAudioDataset, + data_utils, +) +from fairseq.data import FairseqDataset + +logger = logging.getLogger(__name__) + + +class ModalityDatasetItem(NamedTuple): + datasetname: str + dataset: any + max_positions: List[int] + max_tokens: Optional[int] = None + max_sentences: Optional[int] = None + +# MultiModalityDataset: it concate multiple datasets with different modalities. +# Compared with ConcatDataset it can 1) sample data given the ratios for different datasets +# 2) it adds mode to indicate what type of the data samples come from. +# It will be used with GroupedEpochBatchIterator together to generate mini-batch with samples +# from the same type of dataset +# If only one dataset is used, it will perform like the original dataset with mode added +class MultiModalityDataset(ConcatDataset): + def __init__(self, datasets: List[ModalityDatasetItem]): + id_to_mode = [] + dsets = [] + max_tokens = [] + max_sentences = [] + max_positions = [] + for dset in datasets: + id_to_mode.append(dset.datasetname) + dsets.append(dset.dataset) + max_tokens.append(dset.max_tokens) + max_positions.append(dset.max_positions) + max_sentences.append(dset.max_sentences) + weights = [1.0 for s in dsets] + super().__init__(dsets, weights) + self.max_tokens = max_tokens + self.max_positions = max_positions + self.max_sentences = max_sentences + self.id_to_mode = id_to_mode + self.raw_sub_batch_samplers = [] + self._cur_epoch = 0 + + def set_epoch(self, epoch): + super().set_epoch(epoch) + self._cur_epoch = epoch + + def __getitem__(self, idx): + dataset_idx, sample_idx = self._get_dataset_and_sample_index(idx) + sample = self.datasets[dataset_idx][sample_idx] + return (dataset_idx, sample) + + def collater(self, samples): + if len(samples) == 0: + return {} + dataset_idx = samples[0][0] + # make sure all samples in samples are from same dataset + assert sum([0 if dataset_idx == s[0] else 1 for s in samples]) == 0 + samples = self.datasets[dataset_idx].collater([x[1] for x in samples]) + # add mode + samples["net_input"]["mode"] = self.id_to_mode[dataset_idx] + + return samples + + def size(self, index: int): + if len(self.datasets) == 1: + return self.datasets[0].size(index) + return super().size(index) + + @property + def sizes(self): + if len(self.datasets) == 1: + return self.datasets[0].sizes + super().sizes + + def ordered_indices(self): + """ + Returns indices sorted by length. So less padding is needed. + """ + if len(self.datasets) == 1: + return self.datasets[0].ordered_indices() + indices_group = [] + for d_idx, ds in enumerate(self.datasets): + sample_num = self.cumulative_sizes[d_idx] + if d_idx > 0: + sample_num = sample_num - self.cumulative_sizes[d_idx - 1] + assert sample_num == len(ds) + indices_group.append(ds.ordered_indices()) + return indices_group + + def get_raw_batch_samplers(self, required_batch_size_multiple, seed): + if len(self.raw_sub_batch_samplers) > 0: + logger.info(" raw_sub_batch_samplers exists. No action is taken") + return + with data_utils.numpy_seed(seed): + indices = self.ordered_indices() + for i, ds in enumerate(self.datasets): + indices[i] = ds.filter_indices_by_size( + indices[i], + self.max_positions[i], + )[0] + sub_batch_sampler = ds.batch_by_size( + indices[i], + max_tokens=self.max_tokens[i], + max_sentences=self.max_sentences[i], + required_batch_size_multiple=required_batch_size_multiple, + ) + self.raw_sub_batch_samplers.append(sub_batch_sampler) + + def get_batch_samplers(self, mult_ratios, required_batch_size_multiple, seed): + self.get_raw_batch_samplers(required_batch_size_multiple, seed) + batch_samplers = [] + for i, _ in enumerate(self.datasets): + if i > 0: + sub_batch_sampler = [ + [y + self.cumulative_sizes[i - 1] for y in x] + for x in self.raw_sub_batch_samplers[i] + ] + else: + sub_batch_sampler = list(self.raw_sub_batch_samplers[i]) + smp_r = mult_ratios[i] + if smp_r != 1: + is_increase = "increased" if smp_r > 1 else "decreased" + logger.info( + "number of batch for the dataset {} is {} from {} to {}".format( + self.id_to_mode[i], + is_increase, + len(sub_batch_sampler), + int(len(sub_batch_sampler) * smp_r), + ) + ) + mul_samplers = [] + for _ in range(math.floor(smp_r)): + mul_samplers = mul_samplers + sub_batch_sampler + if math.floor(smp_r) != smp_r: + with data_utils.numpy_seed(seed + self._cur_epoch): + np.random.shuffle(sub_batch_sampler) + smp_num = int( + (smp_r - math.floor(smp_r)) * len(sub_batch_sampler) + ) + mul_samplers = mul_samplers + sub_batch_sampler[:smp_num] + sub_batch_sampler = mul_samplers + else: + logger.info( + "dataset {} batch number is {} ".format( + self.id_to_mode[i], len(sub_batch_sampler) + ) + ) + batch_samplers.append(sub_batch_sampler) + + return batch_samplers + + +class LangPairMaskDataset(FairseqDataset): + def __init__( + self, + dataset: LanguagePairDataset, + src_eos: int, + src_bos: Optional[int] = None, + noise_id: Optional[int] = -1, + mask_ratio: Optional[float] = 0, + mask_type: Optional[str] = "random", + ): + self.dataset = dataset + self.src_eos = src_eos + self.src_bos = src_bos + self.noise_id = noise_id + self.mask_ratio = mask_ratio + self.mask_type = mask_type + assert mask_type in ("random", "tail") + + @property + def src_sizes(self): + return self.dataset.src_sizes + + @property + def tgt_sizes(self): + return self.dataset.tgt_sizes + + @property + def sizes(self): + # dataset.sizes can be a dynamically computed sizes: + return self.dataset.sizes + + def get_batch_shapes(self): + return self.dataset.buckets + + def num_tokens_vec(self, indices): + return self.dataset.num_tokens_vec(indices) + + def __len__(self): + return len(self.dataset) + + def num_tokens(self, index): + return self.dataset.num_tokens(index) + + def size(self, index): + return self.dataset.size(index) + + def ordered_indices(self): + return self.dataset.ordered_indices() + + @property + def supports_prefetch(self): + return getattr(self.dataset, "supports_prefetch", False) + + def prefetch(self, indices): + return self.dataset.prefetch(indices) + + def mask_src_tokens(self, sample): + src_item = sample["source"] + mask = None + if self.mask_type == "random": + mask = torch.rand(len(src_item)).le(self.mask_ratio) + else: + mask = torch.ones(len(src_item)) + mask[: int(len(src_item) * (1 - self.mask_ratio))] = 0 + mask = mask.eq(1) + if src_item[0] == self.src_bos: + mask[0] = False + if src_item[-1] == self.src_eos: + mask[-1] = False + mask_src_item = src_item.masked_fill(mask, self.noise_id) + smp = {"id": sample["id"], "source": mask_src_item, "target": sample["target"]} + return smp + + def __getitem__(self, index): + sample = self.dataset[index] + if self.mask_ratio > 0: + sample = self.mask_src_tokens(sample) + return sample + + def collater(self, samples, pad_to_length=None): + return self.dataset.collater(samples, pad_to_length) + + +class FileAudioDatasetWrapper(FileAudioDataset): + def collater(self, samples): + samples = super().collater(samples) + if len(samples) == 0: + return {} + samples["net_input"]["src_tokens"] = samples["net_input"]["source"] + samples["net_input"]["prev_output_tokens"] = None + del samples["net_input"]["source"] + samples["net_input"]["src_lengths"] = None + samples["net_input"]["alignment"] = None + return samples diff --git a/fairseq/data/audio/raw_audio_dataset.py b/fairseq/data/audio/raw_audio_dataset.py index 40cbc20680..f4e965493c 100644 --- a/fairseq/data/audio/raw_audio_dataset.py +++ b/fairseq/data/audio/raw_audio_dataset.py @@ -4,14 +4,26 @@ # LICENSE file in the root directory of this source tree. +import logging import os -import numpy as np import sys +import io +import numpy as np import torch import torch.nn.functional as F from .. import FairseqDataset +from ..data_utils import compute_mask_indices, get_buckets, get_bucketed_sizes +from fairseq.data.audio.audio_utils import ( + parse_path, + read_from_stored_zip, + is_sf_audio_data, +) +from fairseq.data.text_compressor import TextCompressor, TextCompressionLevel + + +logger = logging.getLogger(__name__) class RawAudioDataset(FairseqDataset): @@ -19,9 +31,12 @@ def __init__( self, sample_rate, max_sample_size=None, - min_sample_size=None, + min_sample_size=0, shuffle=True, - min_length=0, + pad=False, + normalize=False, + compute_mask_indices=False, + **mask_compute_kwargs, ): super().__init__() @@ -30,11 +45,16 @@ def __init__( self.max_sample_size = ( max_sample_size if max_sample_size is not None else sys.maxsize ) - self.min_sample_size = ( - min_sample_size if min_sample_size is not None else self.max_sample_size - ) - self.min_length = min_length + self.min_sample_size = min_sample_size + self.pad = pad self.shuffle = shuffle + self.normalize = normalize + self.compute_mask_indices = compute_mask_indices + if self.compute_mask_indices: + self.mask_compute_kwargs = mask_compute_kwargs + self._features_size_map = {} + self._C = mask_compute_kwargs["encoder_embed_dim"] + self._conv_feature_layers = eval(mask_compute_kwargs["conv_feature_layers"]) def __getitem__(self, index): raise NotImplementedError() @@ -43,17 +63,17 @@ def __len__(self): return len(self.sizes) def postprocess(self, feats, curr_sample_rate): - def resample(x, factor): - return F.interpolate(x.view(1, 1, -1), scale_factor=factor).squeeze() - if feats.dim() == 2: feats = feats.mean(-1) if curr_sample_rate != self.sample_rate: - factor = self.sample_rate / curr_sample_rate - feats = resample(feats, factor) + raise Exception(f"sample rate: {curr_sample_rate}, need {self.sample_rate}") assert feats.dim() == 1, feats.dim() + + if self.normalize: + with torch.no_grad(): + feats = F.layer_norm(feats, feats.shape) return feats def crop_to_max_size(self, wav, target_size): @@ -66,36 +86,118 @@ def crop_to_max_size(self, wav, target_size): end = size - diff + start return wav[start:end] + def _compute_mask_indices(self, dims, padding_mask): + B, T, C = dims + mask_indices, mask_channel_indices = None, None + if self.mask_compute_kwargs["mask_prob"] > 0: + mask_indices = compute_mask_indices( + (B, T), + padding_mask, + self.mask_compute_kwargs["mask_prob"], + self.mask_compute_kwargs["mask_length"], + self.mask_compute_kwargs["mask_selection"], + self.mask_compute_kwargs["mask_other"], + min_masks=2, + no_overlap=self.mask_compute_kwargs["no_mask_overlap"], + min_space=self.mask_compute_kwargs["mask_min_space"], + ) + mask_indices = torch.from_numpy(mask_indices) + if self.mask_compute_kwargs["mask_channel_prob"] > 0: + mask_channel_indices = compute_mask_indices( + (B, C), + None, + self.mask_compute_kwargs["mask_channel_prob"], + self.mask_compute_kwargs["mask_channel_length"], + self.mask_compute_kwargs["mask_channel_selection"], + self.mask_compute_kwargs["mask_channel_other"], + no_overlap=self.mask_compute_kwargs["no_mask_channel_overlap"], + min_space=self.mask_compute_kwargs["mask_channel_min_space"], + ) + mask_channel_indices = ( + torch.from_numpy(mask_channel_indices).unsqueeze(1).expand(-1, T, -1) + ) + + return mask_indices, mask_channel_indices + + @staticmethod + def _bucket_tensor(tensor, num_pad, value): + return F.pad(tensor, (0, num_pad), value=value) + def collater(self, samples): - samples = [ - s for s in samples if s["source"] is not None and len(s["source"]) > 0 - ] + samples = [s for s in samples if s["source"] is not None] if len(samples) == 0: return {} sources = [s["source"] for s in samples] sizes = [len(s) for s in sources] - target_size = min(min(sizes), self.max_sample_size) - - if target_size < self.min_length: - return {} - if self.min_sample_size < target_size: - target_size = np.random.randint(self.min_sample_size, target_size + 1) + if self.pad: + target_size = min(max(sizes), self.max_sample_size) + else: + target_size = min(min(sizes), self.max_sample_size) - collated_sources = sources[0].new(len(sources), target_size) + collated_sources = sources[0].new_zeros(len(sources), target_size) + padding_mask = ( + torch.BoolTensor(collated_sources.shape).fill_(False) if self.pad else None + ) for i, (source, size) in enumerate(zip(sources, sizes)): diff = size - target_size - assert diff >= 0 if diff == 0: collated_sources[i] = source + elif diff < 0: + assert self.pad + collated_sources[i] = torch.cat( + [source, source.new_full((-diff,), 0.0)] + ) + padding_mask[i, diff:] = True else: collated_sources[i] = self.crop_to_max_size(source, target_size) - return { - "id": torch.LongTensor([s["id"] for s in samples]), - "net_input": {"source": collated_sources}, - } + input = {"source": collated_sources} + out = {"id": torch.LongTensor([s["id"] for s in samples])} + if self.pad: + input["padding_mask"] = padding_mask + + if hasattr(self, "num_buckets") and self.num_buckets > 0: + assert self.pad, "Cannot bucket without padding first." + bucket = max(self._bucketed_sizes[s["id"]] for s in samples) + num_pad = bucket - collated_sources.size(-1) + if num_pad: + input["source"] = self._bucket_tensor(collated_sources, num_pad, 0) + input["padding_mask"] = self._bucket_tensor(padding_mask, num_pad, True) + + if self.compute_mask_indices: + B = input["source"].size(0) + T = self._get_mask_indices_dims(input["source"].size(-1)) + padding_mask_reshaped = input["padding_mask"].clone() + extra = padding_mask_reshaped.size(1) % T + if extra > 0: + padding_mask_reshaped = padding_mask_reshaped[:, :-extra] + padding_mask_reshaped = padding_mask_reshaped.view( + padding_mask_reshaped.size(0), T, -1 + ) + padding_mask_reshaped = padding_mask_reshaped.all(-1) + input["padding_count"] = padding_mask_reshaped.sum(-1).max().item() + mask_indices, mask_channel_indices = self._compute_mask_indices( + (B, T, self._C), + padding_mask_reshaped, + ) + input["mask_indices"] = mask_indices + input["mask_channel_indices"] = mask_channel_indices + out["sample_size"] = mask_indices.sum().item() + + out["net_input"] = input + return out + + def _get_mask_indices_dims(self, size, padding=0, dilation=1): + if size not in self._features_size_map: + L_in = size + for (_, kernel_size, stride) in self._conv_feature_layers: + L_out = L_in + 2 * padding - dilation * (kernel_size - 1) - 1 + L_out = 1 + L_out // stride + L_in = L_out + self._features_size_map[size] = L_out + return self._features_size_map[size] def num_tokens(self, index): return self.size(index) @@ -103,6 +205,8 @@ def num_tokens(self, index): def size(self, index): """Return an example's size as a float or tuple. This value is used when filtering a dataset with ``--max-positions``.""" + if self.pad: + return self.sizes[index] return min(self.sizes[index], self.max_sample_size) def ordered_indices(self): @@ -111,11 +215,34 @@ def ordered_indices(self): if self.shuffle: order = [np.random.permutation(len(self))] + order.append( + np.minimum( + np.array(self.sizes), + self.max_sample_size, + ) + ) + return np.lexsort(order)[::-1] else: - order = [np.arange(len(self))] - - order.append(self.sizes) - return np.lexsort(order) + return np.arange(len(self)) + + def set_bucket_info(self, num_buckets): + self.num_buckets = num_buckets + if self.num_buckets > 0: + self._collated_sizes = np.minimum( + np.array(self.sizes), + self.max_sample_size, + ) + self.buckets = get_buckets( + self._collated_sizes, + self.num_buckets, + ) + self._bucketed_sizes = get_bucketed_sizes( + self._collated_sizes, self.buckets + ) + logger.info( + f"{len(self.buckets)} bucket(s) for the audio dataset: " + f"{self.buckets}" + ) class FileAudioDataset(RawAudioDataset): @@ -124,32 +251,141 @@ def __init__( manifest_path, sample_rate, max_sample_size=None, - min_sample_size=None, + min_sample_size=0, shuffle=True, - min_length=0, + pad=False, + normalize=False, + num_buckets=0, + compute_mask_indices=False, + text_compression_level=TextCompressionLevel.none, + **mask_compute_kwargs, ): super().__init__( sample_rate=sample_rate, max_sample_size=max_sample_size, min_sample_size=min_sample_size, shuffle=shuffle, - min_length=min_length, + pad=pad, + normalize=normalize, + compute_mask_indices=compute_mask_indices, + **mask_compute_kwargs, ) + self.text_compressor = TextCompressor(level=text_compression_level) + + skipped = 0 self.fnames = [] + sizes = [] + self.skipped_indices = set() with open(manifest_path, "r") as f: self.root_dir = f.readline().strip() - for line in f: + for i, line in enumerate(f): items = line.strip().split("\t") assert len(items) == 2, line - self.fnames.append(items[0]) - self.sizes.append(int(items[1])) + sz = int(items[1]) + if min_sample_size is not None and sz < min_sample_size: + skipped += 1 + self.skipped_indices.add(i) + continue + self.fnames.append(self.text_compressor.compress(items[0])) + sizes.append(sz) + logger.info(f"loaded {len(self.fnames)}, skipped {skipped} samples") + + self.sizes = np.array(sizes, dtype=np.int64) + + try: + import pyarrow + + self.fnames = pyarrow.array(self.fnames) + except: + logger.debug( + "Could not create a pyarrow array. Please install pyarrow for better performance" + ) + pass + + self.set_bucket_info(num_buckets) + + def __getitem__(self, index): + import soundfile as sf + fn = self.fnames[index] + fn = fn if isinstance(self.fnames, list) else fn.as_py() + fn = self.text_compressor.decompress(fn) + path_or_fp = os.path.join(self.root_dir, fn) + _path, slice_ptr = parse_path(path_or_fp) + if len(slice_ptr) == 2: + byte_data = read_from_stored_zip(_path, slice_ptr[0], slice_ptr[1]) + assert is_sf_audio_data(byte_data) + path_or_fp = io.BytesIO(byte_data) + + wav, curr_sample_rate = sf.read(path_or_fp, dtype="float32") + + feats = torch.from_numpy(wav).float() + feats = self.postprocess(feats, curr_sample_rate) + return {"id": index, "source": feats} + + +class BinarizedAudioDataset(RawAudioDataset): + def __init__( + self, + data_dir, + split, + sample_rate, + max_sample_size=None, + min_sample_size=0, + shuffle=True, + pad=False, + normalize=False, + num_buckets=0, + compute_mask_indices=False, + **mask_compute_kwargs, + ): + super().__init__( + sample_rate=sample_rate, + max_sample_size=max_sample_size, + min_sample_size=min_sample_size, + shuffle=shuffle, + pad=pad, + normalize=normalize, + compute_mask_indices=compute_mask_indices, + **mask_compute_kwargs, + ) + + from fairseq.data import data_utils, Dictionary + + self.fnames_dict = Dictionary.load(os.path.join(data_dir, "dict.txt")) + + root_path = os.path.join(data_dir, f"{split}.root") + if os.path.exists(root_path): + with open(root_path, "r") as f: + self.root_dir = next(f).strip() + else: + self.root_dir = None + + fnames_path = os.path.join(data_dir, split) + self.fnames = data_utils.load_indexed_dataset(fnames_path, self.fnames_dict) + lengths_path = os.path.join(data_dir, f"{split}.lengths") + + with open(lengths_path, "r") as f: + for line in f: + sz = int(line.rstrip()) + assert ( + sz >= min_sample_size + ), f"Min sample size is not supported for binarized dataset, but found a sample with size {sz}" + self.sizes.append(sz) + + self.sizes = np.array(self.sizes, dtype=np.int64) + + self.set_bucket_info(num_buckets) + logger.info(f"loaded {len(self.fnames)} samples") def __getitem__(self, index): import soundfile as sf - fname = os.path.join(self.root_dir, self.fnames[index]) + fname = self.fnames_dict.string(self.fnames[index], separator="") + if self.root_dir: + fname = os.path.join(self.root_dir, fname) + wav, curr_sample_rate = sf.read(fname) feats = torch.from_numpy(wav).float() feats = self.postprocess(feats, curr_sample_rate) diff --git a/fairseq/data/audio/speech_to_text_dataset.py b/fairseq/data/audio/speech_to_text_dataset.py new file mode 100644 index 0000000000..164bf413e4 --- /dev/null +++ b/fairseq/data/audio/speech_to_text_dataset.py @@ -0,0 +1,525 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import csv +import io +import logging +import re +from collections import defaultdict +from pathlib import Path +from typing import Dict, List, Optional +from dataclasses import dataclass + +import numpy as np +import torch +from fairseq.data import ( + ConcatDataset, + Dictionary, + FairseqDataset, + ResamplingDataset, + data_utils as fairseq_data_utils, +) +from fairseq.data.audio.audio_utils import ( + get_fbank, + get_waveform, + read_from_stored_zip, + is_npy_data, + is_sf_audio_data, + parse_path, + FEATURE_OR_SF_AUDIO_FILE_EXTENSIONS, +) +from fairseq.data.audio.feature_transforms import CompositeAudioFeatureTransform +from fairseq.data.audio.data_cfg import S2TDataConfig + + +logger = logging.getLogger(__name__) + + +def get_features_from_npy_or_audio(path): + ext = Path(path).suffix + if ext not in FEATURE_OR_SF_AUDIO_FILE_EXTENSIONS: + raise ValueError(f'Unsupported file format for "{path}"') + return np.load(path) if ext == ".npy" else get_fbank(path) + + +def get_features_or_waveform_from_stored_zip( + path, byte_offset, byte_size, need_waveform=False, use_sample_rate=None, +): + assert path.endswith(".zip") + data = read_from_stored_zip(path, byte_offset, byte_size) + f = io.BytesIO(data) + if is_npy_data(data): + features_or_waveform = np.load(f) + elif is_sf_audio_data(data): + features_or_waveform = \ + get_waveform( + f, always_2d=False, output_sample_rate=use_sample_rate + )[0] if need_waveform else get_fbank(f) + else: + raise ValueError(f'Unknown file format for "{path}"') + return features_or_waveform + + +def get_features_or_waveform( + path: str, need_waveform=False, use_sample_rate=None +): + """Get speech features from .npy file or waveform from .wav/.flac file. + The file may be inside an uncompressed ZIP file and is accessed via byte + offset and length. + + Args: + path (str): File path in the format of "<.npy/.wav/.flac path>" or + "<zip path>:<byte offset>:<byte length>". + need_waveform (bool): return waveform instead of features. + use_sample_rate (int): change sample rate for the input wave file + + Returns: + features_or_waveform (numpy.ndarray): speech features or waveform. + """ + _path, slice_ptr = parse_path(path) + if len(slice_ptr) == 0: + if need_waveform: + return get_waveform( + _path, always_2d=False, output_sample_rate=use_sample_rate + )[0] + return get_features_from_npy_or_audio(_path) + elif len(slice_ptr) == 2: + features_or_waveform = get_features_or_waveform_from_stored_zip( + _path, slice_ptr[0], slice_ptr[1], need_waveform=need_waveform, + use_sample_rate=use_sample_rate + ) + else: + raise ValueError(f"Invalid path: {path}") + + return features_or_waveform + + +def _collate_frames( + frames: List[torch.Tensor], is_audio_input: bool = False +) -> torch.Tensor: + """ + Convert a list of 2D frames into a padded 3D tensor + Args: + frames (list): list of 2D frames of size L[i]*f_dim. Where L[i] is + length of i-th frame and f_dim is static dimension of features + Returns: + 3D tensor of size len(frames)*len_max*f_dim where len_max is max of L[i] + """ + max_len = max(frame.size(0) for frame in frames) + if is_audio_input: + out = frames[0].new_zeros((len(frames), max_len)) + else: + out = frames[0].new_zeros((len(frames), max_len, frames[0].size(1))) + for i, v in enumerate(frames): + out[i, : v.size(0)] = v + return out + + +@dataclass +class SpeechToTextDatasetItem(object): + index: int + source: torch.Tensor + target: Optional[torch.Tensor] = None + speaker_id: Optional[int] = None + + +class SpeechToTextDataset(FairseqDataset): + LANG_TAG_TEMPLATE = "<lang:{}>" + + def __init__( + self, + split: str, + is_train_split: bool, + cfg: S2TDataConfig, + audio_paths: List[str], + n_frames: List[int], + src_texts: Optional[List[str]] = None, + tgt_texts: Optional[List[str]] = None, + speakers: Optional[List[str]] = None, + src_langs: Optional[List[str]] = None, + tgt_langs: Optional[List[str]] = None, + ids: Optional[List[str]] = None, + tgt_dict: Optional[Dictionary] = None, + pre_tokenizer=None, + bpe_tokenizer=None, + n_frames_per_step=1, + speaker_to_id=None + ): + self.split, self.is_train_split = split, is_train_split + self.cfg = cfg + self.audio_paths, self.n_frames = audio_paths, n_frames + self.n_samples = len(audio_paths) + assert len(n_frames) == self.n_samples > 0 + assert src_texts is None or len(src_texts) == self.n_samples + assert tgt_texts is None or len(tgt_texts) == self.n_samples + assert speakers is None or len(speakers) == self.n_samples + assert src_langs is None or len(src_langs) == self.n_samples + assert tgt_langs is None or len(tgt_langs) == self.n_samples + assert ids is None or len(ids) == self.n_samples + assert (tgt_dict is None and tgt_texts is None) or ( + tgt_dict is not None and tgt_texts is not None + ) + self.src_texts, self.tgt_texts = src_texts, tgt_texts + self.src_langs, self.tgt_langs = src_langs, tgt_langs + self.speakers = speakers + self.tgt_dict = tgt_dict + self.check_tgt_lang_tag() + self.ids = ids + self.shuffle = cfg.shuffle if is_train_split else False + + self.feature_transforms = CompositeAudioFeatureTransform.from_config_dict( + self.cfg.get_feature_transforms(split, is_train_split) + ) + + self.pre_tokenizer = pre_tokenizer + self.bpe_tokenizer = bpe_tokenizer + self.n_frames_per_step = n_frames_per_step + self.speaker_to_id = speaker_to_id + + self.tgt_lens = self.get_tgt_lens_and_check_oov() + + logger.info(self.__repr__()) + + def get_tgt_lens_and_check_oov(self): + if self.tgt_texts is None: + return [0 for _ in range(self.n_samples)] + tgt_lens = [] + n_tokens, n_oov_tokens = 0, 0 + for i in range(self.n_samples): + tokenized = self.get_tokenized_tgt_text(i).split(" ") + oov_tokens = [ + t + for t in tokenized + if self.tgt_dict.index(t) == self.tgt_dict.unk_index + ] + n_tokens += len(tokenized) + n_oov_tokens += len(oov_tokens) + tgt_lens.append(len(tokenized)) + logger.info(f"'{self.split}' has {n_oov_tokens / n_tokens * 100:.2f}% OOV") + return tgt_lens + + def __repr__(self): + return ( + self.__class__.__name__ + + f'(split="{self.split}", n_samples={self.n_samples:_}, ' + f"prepend_tgt_lang_tag={self.cfg.prepend_tgt_lang_tag}, " + f"shuffle={self.shuffle}, transforms={self.feature_transforms}, " + f"n_frames_per_step={self.n_frames_per_step}" + ) + + @classmethod + def is_lang_tag(cls, token): + pattern = cls.LANG_TAG_TEMPLATE.replace("{}", "(.*)") + return re.match(pattern, token) + + def check_tgt_lang_tag(self): + if self.cfg.prepend_tgt_lang_tag: + assert self.tgt_langs is not None and self.tgt_dict is not None + tgt_lang_tags = [ + self.LANG_TAG_TEMPLATE.format(t) for t in set(self.tgt_langs) + ] + assert all(t in self.tgt_dict for t in tgt_lang_tags) + + @classmethod + def tokenize(cls, tokenizer, text: str): + return text if tokenizer is None else tokenizer.encode(text) + + def get_tokenized_tgt_text(self, index: int): + text = self.tokenize(self.pre_tokenizer, self.tgt_texts[index]) + text = self.tokenize(self.bpe_tokenizer, text) + return text + + def pack_frames(self, feature: torch.Tensor): + if self.n_frames_per_step == 1: + return feature + n_packed_frames = feature.shape[0] // self.n_frames_per_step + feature = feature[:self.n_frames_per_step * n_packed_frames] + return feature.reshape(n_packed_frames, -1) + + @classmethod + def get_lang_tag_idx(cls, lang: str, dictionary: Dictionary): + lang_tag_idx = dictionary.index(cls.LANG_TAG_TEMPLATE.format(lang)) + assert lang_tag_idx != dictionary.unk() + return lang_tag_idx + + def __getitem__(self, index: int) -> SpeechToTextDatasetItem: + source = get_features_or_waveform( + self.audio_paths[index], + need_waveform=self.cfg.use_audio_input, + use_sample_rate=self.cfg.use_sample_rate, + ) + if self.feature_transforms is not None: + assert not self.cfg.use_audio_input + source = self.feature_transforms(source) + source = torch.from_numpy(source).float() + source = self.pack_frames(source) + + target = None + if self.tgt_texts is not None: + tokenized = self.get_tokenized_tgt_text(index) + target = self.tgt_dict.encode_line( + tokenized, add_if_not_exist=False, append_eos=True + ).long() + if self.cfg.prepend_tgt_lang_tag: + lang_tag_idx = self.get_lang_tag_idx( + self.tgt_langs[index], self.tgt_dict + ) + target = torch.cat((torch.LongTensor([lang_tag_idx]), target), 0) + + speaker_id = None + if self.speaker_to_id is not None: + speaker_id = self.speaker_to_id[self.speakers[index]] + return SpeechToTextDatasetItem( + index=index, source=source, target=target, speaker_id=speaker_id + ) + + def __len__(self): + return self.n_samples + + def collater( + self, samples: List[SpeechToTextDatasetItem], return_order: bool = False + ) -> Dict: + if len(samples) == 0: + return {} + indices = torch.tensor([x.index for x in samples], dtype=torch.long) + frames = _collate_frames([x.source for x in samples], self.cfg.use_audio_input) + # sort samples by descending number of frames + n_frames = torch.tensor([x.source.size(0) for x in samples], dtype=torch.long) + n_frames, order = n_frames.sort(descending=True) + indices = indices.index_select(0, order) + frames = frames.index_select(0, order) + + target, target_lengths = None, None + prev_output_tokens = None + ntokens = None + if self.tgt_texts is not None: + target = fairseq_data_utils.collate_tokens( + [x.target for x in samples], + self.tgt_dict.pad(), + self.tgt_dict.eos(), + left_pad=False, + move_eos_to_beginning=False, + ) + target = target.index_select(0, order) + target_lengths = torch.tensor( + [x.target.size(0) for x in samples], dtype=torch.long + ).index_select(0, order) + prev_output_tokens = fairseq_data_utils.collate_tokens( + [x.target for x in samples], + self.tgt_dict.pad(), + self.tgt_dict.eos(), + left_pad=False, + move_eos_to_beginning=True, + ) + prev_output_tokens = prev_output_tokens.index_select(0, order) + ntokens = sum(x.target.size(0) for x in samples) + + speaker = None + if self.speaker_to_id is not None: + speaker = torch.tensor( + [s.speaker_id for s in samples], dtype=torch.long + ).index_select(0, order).view(-1, 1) + + net_input = { + "src_tokens": frames, + "src_lengths": n_frames, + "prev_output_tokens": prev_output_tokens, + } + out = { + "id": indices, + "net_input": net_input, + "speaker": speaker, + "target": target, + "target_lengths": target_lengths, + "ntokens": ntokens, + "nsentences": len(samples), + } + if return_order: + out["order"] = order + return out + + def num_tokens(self, index): + return self.n_frames[index] + + def size(self, index): + return self.n_frames[index], self.tgt_lens[index] + + @property + def sizes(self): + return np.array(self.n_frames) + + @property + def can_reuse_epoch_itr_across_epochs(self): + return True + + def ordered_indices(self): + if self.shuffle: + order = [np.random.permutation(len(self))] + else: + order = [np.arange(len(self))] + # first by descending order of # of frames then by original/random order + order.append([-n for n in self.n_frames]) + return np.lexsort(order) + + def prefetch(self, indices): + raise False + + +class SpeechToTextDatasetCreator(object): + # mandatory columns + KEY_ID, KEY_AUDIO, KEY_N_FRAMES = "id", "audio", "n_frames" + KEY_TGT_TEXT = "tgt_text" + # optional columns + KEY_SPEAKER, KEY_SRC_TEXT = "speaker", "src_text" + KEY_SRC_LANG, KEY_TGT_LANG = "src_lang", "tgt_lang" + # default values + DEFAULT_SPEAKER = DEFAULT_SRC_TEXT = DEFAULT_LANG = "" + + @classmethod + def _from_list( + cls, + split_name: str, + is_train_split, + samples: List[Dict], + cfg: S2TDataConfig, + tgt_dict, + pre_tokenizer, + bpe_tokenizer, + n_frames_per_step, + speaker_to_id + ) -> SpeechToTextDataset: + audio_root = Path(cfg.audio_root) + ids = [s[cls.KEY_ID] for s in samples] + audio_paths = [(audio_root / s[cls.KEY_AUDIO]).as_posix() for s in samples] + n_frames = [int(s[cls.KEY_N_FRAMES]) for s in samples] + tgt_texts = [s[cls.KEY_TGT_TEXT] for s in samples] + src_texts = [s.get(cls.KEY_SRC_TEXT, cls.DEFAULT_SRC_TEXT) for s in samples] + speakers = [s.get(cls.KEY_SPEAKER, cls.DEFAULT_SPEAKER) for s in samples] + src_langs = [s.get(cls.KEY_SRC_LANG, cls.DEFAULT_LANG) for s in samples] + tgt_langs = [s.get(cls.KEY_TGT_LANG, cls.DEFAULT_LANG) for s in samples] + return SpeechToTextDataset( + split_name, + is_train_split, + cfg, + audio_paths, + n_frames, + src_texts=src_texts, + tgt_texts=tgt_texts, + speakers=speakers, + src_langs=src_langs, + tgt_langs=tgt_langs, + ids=ids, + tgt_dict=tgt_dict, + pre_tokenizer=pre_tokenizer, + bpe_tokenizer=bpe_tokenizer, + n_frames_per_step=n_frames_per_step, + speaker_to_id=speaker_to_id + ) + + @classmethod + def get_size_ratios( + cls, datasets: List[SpeechToTextDataset], alpha: float = 1.0 + ) -> List[float]: + """Size ratios for temperature-based sampling + (https://arxiv.org/abs/1907.05019)""" + + id_to_lp, lp_to_sz = {}, defaultdict(int) + for ds in datasets: + lang_pairs = {f"{s}->{t}" for s, t in zip(ds.src_langs, ds.tgt_langs)} + assert len(lang_pairs) == 1 + lang_pair = list(lang_pairs)[0] + id_to_lp[ds.split] = lang_pair + lp_to_sz[lang_pair] += sum(ds.n_frames) + + sz_sum = sum(v for v in lp_to_sz.values()) + lp_to_prob = {k: v / sz_sum for k, v in lp_to_sz.items()} + lp_to_tgt_prob = {k: v ** alpha for k, v in lp_to_prob.items()} + prob_sum = sum(v for v in lp_to_tgt_prob.values()) + lp_to_tgt_prob = {k: v / prob_sum for k, v in lp_to_tgt_prob.items()} + lp_to_sz_ratio = { + k: (lp_to_tgt_prob[k] * sz_sum) / v for k, v in lp_to_sz.items() + } + size_ratio = [lp_to_sz_ratio[id_to_lp[ds.split]] for ds in datasets] + + p_formatted = { + k: f"{lp_to_prob[k]:.3f}->{lp_to_tgt_prob[k]:.3f}" for k in lp_to_sz + } + logger.info(f"sampling probability balancing: {p_formatted}") + sr_formatted = {ds.split: f"{r:.3f}" for ds, r in zip(datasets, size_ratio)} + logger.info(f"balanced sampling size ratio: {sr_formatted}") + return size_ratio + + @classmethod + def _load_samples_from_tsv(cls, root: str, split: str): + tsv_path = Path(root) / f"{split}.tsv" + if not tsv_path.is_file(): + raise FileNotFoundError(f"Dataset not found: {tsv_path}") + with open(tsv_path) as f: + reader = csv.DictReader( + f, + delimiter="\t", + quotechar=None, + doublequote=False, + lineterminator="\n", + quoting=csv.QUOTE_NONE, + ) + samples = [dict(e) for e in reader] + if len(samples) == 0: + raise ValueError(f"Empty manifest: {tsv_path}") + return samples + + @classmethod + def _from_tsv( + cls, + root: str, + cfg: S2TDataConfig, + split: str, + tgt_dict, + is_train_split: bool, + pre_tokenizer, + bpe_tokenizer, + n_frames_per_step, + speaker_to_id + ) -> SpeechToTextDataset: + samples = cls._load_samples_from_tsv(root, split) + return cls._from_list( + split, is_train_split, samples, cfg, tgt_dict, pre_tokenizer, + bpe_tokenizer, n_frames_per_step, speaker_to_id + ) + + @classmethod + def from_tsv( + cls, + root: str, + cfg: S2TDataConfig, + splits: str, + tgt_dict, + pre_tokenizer, + bpe_tokenizer, + is_train_split: bool, + epoch: int, + seed: int, + n_frames_per_step: int = 1, + speaker_to_id=None + ) -> SpeechToTextDataset: + datasets = [ + cls._from_tsv( + root, cfg, split, tgt_dict, is_train_split, pre_tokenizer, + bpe_tokenizer, n_frames_per_step, speaker_to_id + ) + for split in splits.split(",") + ] + + if is_train_split and len(datasets) > 1 and cfg.sampling_alpha != 1.0: + # temperature-based sampling + size_ratios = cls.get_size_ratios(datasets, alpha=cfg.sampling_alpha) + datasets = [ + ResamplingDataset( + d, size_ratio=r, seed=seed, epoch=epoch, replace=(r >= 1.0) + ) + for r, d in zip(size_ratios, datasets) + ] + + return ConcatDataset(datasets) if len(datasets) > 1 else datasets[0] diff --git a/fairseq/data/audio/speech_to_text_joint_dataset.py b/fairseq/data/audio/speech_to_text_joint_dataset.py new file mode 100644 index 0000000000..885ee7e0a3 --- /dev/null +++ b/fairseq/data/audio/speech_to_text_joint_dataset.py @@ -0,0 +1,288 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +from pathlib import Path +from typing import Dict, List, Optional, NamedTuple + +import torch +from fairseq.data import ( + ConcatDataset, + Dictionary, + ResamplingDataset, + data_utils as fairseq_data_utils, +) +from fairseq.data.audio.speech_to_text_dataset import ( + SpeechToTextDataset, + S2TDataConfig, + SpeechToTextDatasetCreator, +) + + +logger = logging.getLogger(__name__) + + +class S2TJointDataConfig(S2TDataConfig): + """Wrapper class for data config YAML""" + + @property + def src_vocab_filename(self): + """fairseq vocabulary file under data root""" + return self.config.get("src_vocab_filename", "src_dict.txt") + + @property + def src_pre_tokenizer(self) -> Dict: + """Pre-tokenizer to apply before subword tokenization. Returning + a dictionary with `tokenizer` providing the tokenizer name and + the other items providing the tokenizer-specific arguments. + Tokenizers are defined in `fairseq.data.encoders.*`""" + return self.config.get("src_pre_tokenizer", {"tokenizer": None}) + + @property + def src_bpe_tokenizer(self) -> Dict: + """Subword tokenizer to apply on source text after pre-tokenization. + Returning a dictionary with `bpe` providing the tokenizer name and + the other items providing the tokenizer-specific arguments. + Tokenizers are defined in `fairseq.data.encoders.*`""" + return self.config.get("src_bpe_tokenizer", {"bpe": None}) + + @property + def prepend_tgt_lang_tag_no_change(self) -> bool: + """Prepend target lang ID token as the prev_output_tokens BOS (e.g. for + to-many multilingual setting). No change needed during inference. + """ + return self.config.get("prepend_tgt_lang_tag_no_change", False) + + +class SpeechToTextJointDatasetItem(NamedTuple): + index: int + source: torch.Tensor + target: Optional[torch.Tensor] = None + src_txt_tokens: Optional[torch.Tensor] = None + tgt_lang_tag: Optional[int] = None + + +class SpeechToTextJointDataset(SpeechToTextDataset): + def __init__( + self, + split: str, + is_train_split: bool, + cfg: S2TJointDataConfig, + audio_paths: List[str], + n_frames: List[int], + src_texts: Optional[List[str]] = None, + tgt_texts: Optional[List[str]] = None, + speakers: Optional[List[str]] = None, + src_langs: Optional[List[str]] = None, + tgt_langs: Optional[List[str]] = None, + ids: Optional[List[str]] = None, + tgt_dict: Optional[Dictionary] = None, + src_dict: Optional[Dictionary] = None, + pre_tokenizer=None, + bpe_tokenizer=None, + src_pre_tokenizer=None, + src_bpe_tokenizer=None, + ): + super().__init__( + split, + is_train_split, + cfg, + audio_paths, + n_frames, + src_texts=src_texts, + tgt_texts=tgt_texts, + speakers=speakers, + src_langs=src_langs, + tgt_langs=tgt_langs, + ids=ids, + tgt_dict=tgt_dict, + pre_tokenizer=pre_tokenizer, + bpe_tokenizer=bpe_tokenizer, + ) + + self.src_dict = src_dict + self.src_pre_tokenizer = src_pre_tokenizer + self.src_bpe_tokenizer = src_bpe_tokenizer + + def get_tokenized_src_text(self, index: int): + text = self.tokenize(self.src_pre_tokenizer, self.src_texts[index]) + text = self.tokenize(self.src_bpe_tokenizer, text) + return text + + def __getitem__(self, index: int) -> SpeechToTextJointDatasetItem: + s2t_dataset_item = super().__getitem__(index) + src_tokens = None + if self.src_texts is not None and self.src_dict is not None: + src_tokens = self.get_tokenized_src_text(index) + src_tokens = self.src_dict.encode_line( + src_tokens, add_if_not_exist=False, append_eos=True + ).long() + tgt_lang_tag = None + if self.cfg.prepend_tgt_lang_tag_no_change: + # prepend_tgt_lang_tag_no_change: modify prev_output_tokens instead + tgt_lang_tag = self.get_lang_tag_idx(self.tgt_langs[index], self.tgt_dict) + + return SpeechToTextJointDatasetItem( + index=index, + source=s2t_dataset_item.source, + target=s2t_dataset_item.target, + src_txt_tokens=src_tokens, + tgt_lang_tag=tgt_lang_tag, + ) + + def __len__(self): + return self.n_samples + + def collater(self, samples: List[SpeechToTextJointDatasetItem]) -> Dict: + s2t_out = super().collater(samples, return_order=True) + if s2t_out == {}: + return s2t_out + net_input, order = s2t_out["net_input"], s2t_out["order"] + + if self.src_texts is not None and self.src_dict is not None: + src_txt_tokens = fairseq_data_utils.collate_tokens( + [x.src_txt_tokens for x in samples], + self.src_dict.pad(), + self.src_dict.eos(), + left_pad=False, + move_eos_to_beginning=False, + ) + src_txt_tokens = src_txt_tokens.index_select(0, order) + src_txt_lengths = torch.tensor( + [x.src_txt_tokens.size()[0] for x in samples], dtype=torch.long + ).index_select(0, order) + net_input["src_txt_tokens"] = src_txt_tokens + net_input["src_txt_lengths"] = src_txt_lengths + + if self.tgt_texts is not None and samples[0].tgt_lang_tag is not None: + for i in range(len(samples)): + net_input["prev_output_tokens"][i][0] = samples[order[i]].tgt_lang_tag + + out = { + "id": s2t_out["id"], + "net_input": net_input, + "target": s2t_out["target"], + "target_lengths": s2t_out["target_lengths"], + "ntokens": s2t_out["ntokens"], + "nsentences": len(samples), + } + return out + + +class SpeechToTextJointDatasetCreator(SpeechToTextDatasetCreator): + @classmethod + def _from_list( + cls, + split_name: str, + is_train_split, + samples: List[Dict], + cfg: S2TJointDataConfig, + tgt_dict, + src_dict, + pre_tokenizer, + bpe_tokenizer, + src_pre_tokenizer, + src_bpe_tokenizer, + ) -> SpeechToTextJointDataset: + audio_root = Path(cfg.audio_root) + ids = [s[cls.KEY_ID] for s in samples] + audio_paths = [(audio_root / s[cls.KEY_AUDIO]).as_posix() for s in samples] + n_frames = [int(s[cls.KEY_N_FRAMES]) for s in samples] + tgt_texts = [s[cls.KEY_TGT_TEXT] for s in samples] + src_texts = [s.get(cls.KEY_SRC_TEXT, cls.DEFAULT_SRC_TEXT) for s in samples] + speakers = [s.get(cls.KEY_SPEAKER, cls.DEFAULT_SPEAKER) for s in samples] + src_langs = [s.get(cls.KEY_SRC_LANG, cls.DEFAULT_LANG) for s in samples] + tgt_langs = [s.get(cls.KEY_TGT_LANG, cls.DEFAULT_LANG) for s in samples] + return SpeechToTextJointDataset( + split_name, + is_train_split, + cfg, + audio_paths, + n_frames, + src_texts=src_texts, + tgt_texts=tgt_texts, + speakers=speakers, + src_langs=src_langs, + tgt_langs=tgt_langs, + ids=ids, + tgt_dict=tgt_dict, + src_dict=src_dict, + pre_tokenizer=pre_tokenizer, + bpe_tokenizer=bpe_tokenizer, + src_pre_tokenizer=src_pre_tokenizer, + src_bpe_tokenizer=src_bpe_tokenizer, + ) + + @classmethod + def _from_tsv( + cls, + root: str, + cfg: S2TJointDataConfig, + split: str, + tgt_dict, + src_dict, + is_train_split: bool, + pre_tokenizer, + bpe_tokenizer, + src_pre_tokenizer, + src_bpe_tokenizer, + ) -> SpeechToTextJointDataset: + samples = cls._load_samples_from_tsv(root, split) + return cls._from_list( + split, + is_train_split, + samples, + cfg, + tgt_dict, + src_dict, + pre_tokenizer, + bpe_tokenizer, + src_pre_tokenizer, + src_bpe_tokenizer, + ) + + @classmethod + def from_tsv( + cls, + root: str, + cfg: S2TJointDataConfig, + splits: str, + tgt_dict, + src_dict, + pre_tokenizer, + bpe_tokenizer, + src_pre_tokenizer, + src_bpe_tokenizer, + is_train_split: bool, + epoch: int, + seed: int, + ) -> SpeechToTextJointDataset: + datasets = [ + cls._from_tsv( + root, + cfg, + split, + tgt_dict, + src_dict, + is_train_split, + pre_tokenizer, + bpe_tokenizer, + src_pre_tokenizer, + src_bpe_tokenizer, + ) + for split in splits.split(",") + ] + + if is_train_split and len(datasets) > 1 and cfg.sampling_alpha != 1.0: + # temperature-based sampling + size_ratios = cls.get_size_ratios(datasets, alpha=cfg.sampling_alpha) + datasets = [ + ResamplingDataset( + d, size_ratio=r, seed=seed, epoch=epoch, replace=(r >= 1.0) + ) + for r, d in zip(size_ratios, datasets) + ] + + return ConcatDataset(datasets) if len(datasets) > 1 else datasets[0] diff --git a/fairseq/data/audio/text_to_speech_dataset.py b/fairseq/data/audio/text_to_speech_dataset.py new file mode 100644 index 0000000000..abfcb2be40 --- /dev/null +++ b/fairseq/data/audio/text_to_speech_dataset.py @@ -0,0 +1,215 @@ +# Copyright (c) 2017-present, Facebook, Inc. +# All rights reserved. +# +# This source code is licensed under the license found in the LICENSE file in +# the root directory of this source tree. An additional grant of patent rights +# can be found in the PATENTS file in the same directory.abs + +from pathlib import Path +from typing import List, Dict, Optional, Any +from dataclasses import dataclass + +import numpy as np +import torch + +from fairseq.data.audio.speech_to_text_dataset import ( + SpeechToTextDataset, SpeechToTextDatasetCreator, S2TDataConfig, + _collate_frames, get_features_or_waveform +) +from fairseq.data import Dictionary, data_utils as fairseq_data_utils + + +@dataclass +class TextToSpeechDatasetItem(object): + index: int + source: torch.Tensor + target: Optional[torch.Tensor] = None + speaker_id: Optional[int] = None + duration: Optional[torch.Tensor] = None + pitch: Optional[torch.Tensor] = None + energy: Optional[torch.Tensor] = None + + +class TextToSpeechDataset(SpeechToTextDataset): + def __init__( + self, + split: str, + is_train_split: bool, + cfg: S2TDataConfig, + audio_paths: List[str], + n_frames: List[int], + src_texts: Optional[List[str]] = None, + tgt_texts: Optional[List[str]] = None, + speakers: Optional[List[str]] = None, + src_langs: Optional[List[str]] = None, + tgt_langs: Optional[List[str]] = None, + ids: Optional[List[str]] = None, + tgt_dict: Optional[Dictionary] = None, + pre_tokenizer=None, + bpe_tokenizer=None, + n_frames_per_step=1, + speaker_to_id=None, + durations: Optional[List[List[int]]] = None, + pitches: Optional[List[str]] = None, + energies: Optional[List[str]] = None + ): + super(TextToSpeechDataset, self).__init__( + split, is_train_split, cfg, audio_paths, n_frames, + src_texts=src_texts, tgt_texts=tgt_texts, speakers=speakers, + src_langs=src_langs, tgt_langs=tgt_langs, ids=ids, + tgt_dict=tgt_dict, pre_tokenizer=pre_tokenizer, + bpe_tokenizer=bpe_tokenizer, n_frames_per_step=n_frames_per_step, + speaker_to_id=speaker_to_id + ) + self.durations = durations + self.pitches = pitches + self.energies = energies + + def __getitem__(self, index: int) -> TextToSpeechDatasetItem: + s2t_item = super().__getitem__(index) + + duration, pitch, energy = None, None, None + if self.durations is not None: + duration = torch.tensor( + self.durations[index] + [0], dtype=torch.long # pad 0 for EOS + ) + if self.pitches is not None: + pitch = get_features_or_waveform(self.pitches[index]) + pitch = torch.from_numpy( + np.concatenate((pitch, [0])) # pad 0 for EOS + ).float() + if self.energies is not None: + energy = get_features_or_waveform(self.energies[index]) + energy = torch.from_numpy( + np.concatenate((energy, [0])) # pad 0 for EOS + ).float() + return TextToSpeechDatasetItem( + index=index, source=s2t_item.source, target=s2t_item.target, + speaker_id=s2t_item.speaker_id, duration=duration, pitch=pitch, + energy=energy + ) + + def collater(self, samples: List[TextToSpeechDatasetItem]) -> Dict[str, Any]: + if len(samples) == 0: + return {} + + src_lengths, order = torch.tensor( + [s.target.shape[0] for s in samples], dtype=torch.long + ).sort(descending=True) + id_ = torch.tensor([s.index for s in samples], + dtype=torch.long).index_select(0, order) + feat = _collate_frames( + [s.source for s in samples], self.cfg.use_audio_input + ).index_select(0, order) + target_lengths = torch.tensor( + [s.source.shape[0] for s in samples], dtype=torch.long + ).index_select(0, order) + + src_tokens = fairseq_data_utils.collate_tokens( + [s.target for s in samples], + self.tgt_dict.pad(), + self.tgt_dict.eos(), + left_pad=False, + move_eos_to_beginning=False, + ).index_select(0, order) + + speaker = None + if self.speaker_to_id is not None: + speaker = torch.tensor( + [s.speaker_id for s in samples], dtype=torch.long + ).index_select(0, order).view(-1, 1) + + bsz, _, d = feat.size() + prev_output_tokens = torch.cat( + (feat.new_zeros((bsz, 1, d)), feat[:, :-1, :]), dim=1 + ) + + durations, pitches, energies = None, None, None + if self.durations is not None: + durations = fairseq_data_utils.collate_tokens( + [s.duration for s in samples], 0 + ).index_select(0, order) + assert src_tokens.shape[1] == durations.shape[1] + if self.pitches is not None: + pitches = _collate_frames([s.pitch for s in samples], True) + pitches = pitches.index_select(0, order) + assert src_tokens.shape[1] == pitches.shape[1] + if self.energies is not None: + energies = _collate_frames([s.energy for s in samples], True) + energies = energies.index_select(0, order) + assert src_tokens.shape[1] == energies.shape[1] + src_texts = [self.tgt_dict.string(samples[i].target) for i in order] + + return { + "id": id_, + "net_input": { + "src_tokens": src_tokens, + "src_lengths": src_lengths, + "prev_output_tokens": prev_output_tokens, + }, + "speaker": speaker, + "target": feat, + "durations": durations, + "pitches": pitches, + "energies": energies, + "target_lengths": target_lengths, + "ntokens": sum(target_lengths).item(), + "nsentences": len(samples), + "src_texts": src_texts, + } + + +class TextToSpeechDatasetCreator(SpeechToTextDatasetCreator): + KEY_DURATION = "duration" + KEY_PITCH = "pitch" + KEY_ENERGY = "energy" + + @classmethod + def _from_list( + cls, + split_name: str, + is_train_split, + samples: List[Dict], + cfg: S2TDataConfig, + tgt_dict, + pre_tokenizer, + bpe_tokenizer, + n_frames_per_step, + speaker_to_id + ) -> TextToSpeechDataset: + audio_root = Path(cfg.audio_root) + ids = [s[cls.KEY_ID] for s in samples] + audio_paths = [(audio_root / s[cls.KEY_AUDIO]).as_posix() for s in samples] + n_frames = [int(s[cls.KEY_N_FRAMES]) for s in samples] + tgt_texts = [s[cls.KEY_TGT_TEXT] for s in samples] + src_texts = [s.get(cls.KEY_SRC_TEXT, cls.DEFAULT_SRC_TEXT) for s in samples] + speakers = [s.get(cls.KEY_SPEAKER, cls.DEFAULT_SPEAKER) for s in samples] + src_langs = [s.get(cls.KEY_SRC_LANG, cls.DEFAULT_LANG) for s in samples] + tgt_langs = [s.get(cls.KEY_TGT_LANG, cls.DEFAULT_LANG) for s in samples] + + durations = [s.get(cls.KEY_DURATION, None) for s in samples] + durations = [ + None if dd is None else [int(d) for d in dd.split(" ")] + for dd in durations + ] + durations = None if any(dd is None for dd in durations) else durations + + pitches = [s.get(cls.KEY_PITCH, None) for s in samples] + pitches = [ + None if pp is None else (audio_root / pp).as_posix() + for pp in pitches + ] + pitches = None if any(pp is None for pp in pitches) else pitches + + energies = [s.get(cls.KEY_ENERGY, None) for s in samples] + energies = [ + None if ee is None else (audio_root / ee).as_posix() + for ee in energies] + energies = None if any(ee is None for ee in energies) else energies + + return TextToSpeechDataset( + split_name, is_train_split, cfg, audio_paths, n_frames, + src_texts, tgt_texts, speakers, src_langs, tgt_langs, ids, tgt_dict, + pre_tokenizer, bpe_tokenizer, n_frames_per_step, speaker_to_id, + durations, pitches, energies + ) diff --git a/fairseq/data/backtranslation_dataset.py b/fairseq/data/backtranslation_dataset.py index 0007a01506..8f70c90df3 100644 --- a/fairseq/data/backtranslation_dataset.py +++ b/fairseq/data/backtranslation_dataset.py @@ -4,7 +4,6 @@ # LICENSE file in the root directory of this source tree. import torch - from fairseq import utils from . import FairseqDataset @@ -36,16 +35,18 @@ def backtranslate_samples(samples, collate_fn, generate_fn, cuda=True): s = utils.move_to_cuda(collated_samples) if cuda else collated_samples generated_sources = generate_fn(s) - id_to_src = { - sample['id']: sample['source'] for sample in samples - } + id_to_src = {sample["id"]: sample["source"] for sample in samples} # Go through each tgt sentence in batch and its corresponding best # generated hypothesis and create a backtranslation data pair # {id: id, source: generated backtranslation, target: original tgt} return [ - {'id': id.item(), 'target': id_to_src[id.item()], 'source': hypos[0]['tokens'].cpu()} - for id, hypos in zip(collated_samples['id'], generated_sources) + { + "id": id.item(), + "target": id_to_src[id.item()], + "source": hypos[0]["tokens"].cpu(), + } + for id, hypos in zip(collated_samples["id"], generated_sources) ] @@ -87,8 +88,9 @@ def __init__( ): self.tgt_dataset = tgt_dataset self.backtranslation_fn = backtranslation_fn - self.output_collater = output_collater if output_collater is not None \ - else tgt_dataset.collater + self.output_collater = ( + output_collater if output_collater is not None else tgt_dataset.collater + ) self.cuda = cuda if torch.cuda.is_available() else False self.src_dict = src_dict self.tgt_dict = tgt_dict @@ -126,14 +128,12 @@ def collater(self, samples): Returns: dict: a mini-batch with keys coming from *output_collater* """ - if samples[0].get('is_dummy', False): + if samples[0].get("is_dummy", False): return samples samples = backtranslate_samples( samples=samples, collate_fn=self.tgt_dataset.collater, - generate_fn=( - lambda net_input: self.backtranslation_fn(net_input) - ), + generate_fn=(lambda net_input: self.backtranslation_fn(net_input)), cuda=self.cuda, ) return self.output_collater(samples) @@ -159,7 +159,7 @@ def size(self, index): @property def supports_prefetch(self): - return getattr(self.tgt_dataset, 'supports_prefetch', False) + return getattr(self.tgt_dataset, "supports_prefetch", False) def prefetch(self, indices): return self.tgt_dataset.prefetch(indices) diff --git a/fairseq/data/base_wrapper_dataset.py b/fairseq/data/base_wrapper_dataset.py index d14c3c76f3..134d398b47 100644 --- a/fairseq/data/base_wrapper_dataset.py +++ b/fairseq/data/base_wrapper_dataset.py @@ -9,7 +9,6 @@ class BaseWrapperDataset(FairseqDataset): - def __init__(self, dataset): super().__init__() self.dataset = dataset @@ -21,7 +20,7 @@ def __len__(self): return len(self.dataset) def collater(self, samples): - if hasattr(self.dataset, 'collater'): + if hasattr(self.dataset, "collater"): return self.dataset.collater(samples) else: return default_collate(samples) @@ -41,12 +40,39 @@ def ordered_indices(self): @property def supports_prefetch(self): - return getattr(self.dataset, 'supports_prefetch', False) + return getattr(self.dataset, "supports_prefetch", False) + + def attr(self, attr: str, index: int): + return self.dataset.attr(attr, index) def prefetch(self, indices): self.dataset.prefetch(indices) + def get_batch_shapes(self): + return self.dataset.get_batch_shapes() + + def batch_by_size( + self, + indices, + max_tokens=None, + max_sentences=None, + required_batch_size_multiple=1, + ): + return self.dataset.batch_by_size( + indices, + max_tokens=max_tokens, + max_sentences=max_sentences, + required_batch_size_multiple=required_batch_size_multiple, + ) + + def filter_indices_by_size(self, indices, max_sizes): + return self.dataset.filter_indices_by_size(indices, max_sizes) + + @property + def can_reuse_epoch_itr_across_epochs(self): + return self.dataset.can_reuse_epoch_itr_across_epochs + def set_epoch(self, epoch): super().set_epoch(epoch) - if hasattr(self.dataset, 'set_epoch'): + if hasattr(self.dataset, "set_epoch"): self.dataset.set_epoch(epoch) diff --git a/fairseq/data/bucket_pad_length_dataset.py b/fairseq/data/bucket_pad_length_dataset.py new file mode 100644 index 0000000000..0f94100148 --- /dev/null +++ b/fairseq/data/bucket_pad_length_dataset.py @@ -0,0 +1,78 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import numpy as np +import torch.nn.functional as F +from fairseq.data import BaseWrapperDataset +from fairseq.data.data_utils import get_buckets, get_bucketed_sizes + + +class BucketPadLengthDataset(BaseWrapperDataset): + """ + Bucket and pad item lengths to the nearest bucket size. This can be used to + reduce the number of unique batch shapes, which is important on TPUs since + each new batch shape requires a recompilation. + + Args: + dataset (FairseqDatset): dataset to bucket + sizes (List[int]): all item sizes + num_buckets (int): number of buckets to create + pad_idx (int): padding symbol + left_pad (bool): if True, pad on the left; otherwise right pad + """ + + def __init__( + self, + dataset, + sizes, + num_buckets, + pad_idx, + left_pad, + tensor_key=None, + ): + super().__init__(dataset) + self.pad_idx = pad_idx + self.left_pad = left_pad + + assert num_buckets > 0 + self.buckets = get_buckets(sizes, num_buckets) + self._bucketed_sizes = get_bucketed_sizes(sizes, self.buckets) + self._tensor_key = tensor_key + + def _set_tensor(self, item, val): + if self._tensor_key is None: + return val + item[self._tensor_key] = val + return item + + def _get_tensor(self, item): + if self._tensor_key is None: + return item + return item[self._tensor_key] + + def _pad(self, tensor, bucket_size, dim=-1): + num_pad = bucket_size - tensor.size(dim) + return F.pad( + tensor, + (num_pad if self.left_pad else 0, 0 if self.left_pad else num_pad), + value=self.pad_idx, + ) + + def __getitem__(self, index): + item = self.dataset[index] + bucket_size = self._bucketed_sizes[index] + tensor = self._get_tensor(item) + padded = self._pad(tensor, bucket_size) + return self._set_tensor(item, padded) + + @property + def sizes(self): + return self._bucketed_sizes + + def num_tokens(self, index): + return self._bucketed_sizes[index] + + def size(self, index): + return self._bucketed_sizes[index] diff --git a/fairseq/data/colorize_dataset.py b/fairseq/data/colorize_dataset.py index 89e0e04142..6ef097bff1 100644 --- a/fairseq/data/colorize_dataset.py +++ b/fairseq/data/colorize_dataset.py @@ -10,6 +10,7 @@ class ColorizeDataset(BaseWrapperDataset): """ Adds 'colors' property to net input that is obtained from the provided color getter for use by models """ + def __init__(self, dataset, color_getter): super().__init__(dataset) self.color_getter = color_getter diff --git a/fairseq/data/concat_dataset.py b/fairseq/data/concat_dataset.py index 0376b3ae1d..01a4078bb1 100644 --- a/fairseq/data/concat_dataset.py +++ b/fairseq/data/concat_dataset.py @@ -47,12 +47,12 @@ def _get_dataset_and_sample_index(self, idx: int): sample_idx = sample_idx % self.real_sizes[dataset_idx] return dataset_idx, sample_idx - def collater(self, samples): + def collater(self, samples, **extra_args): # For now only supports datasets with same underlying collater implementations - if hasattr(self.datasets[0], 'collater'): - return self.datasets[0].collater(samples) + if hasattr(self.datasets[0], "collater"): + return self.datasets[0].collater(samples, **extra_args) else: - return default_collate(samples) + return default_collate(samples, **extra_args) def size(self, idx: int): """ @@ -88,18 +88,37 @@ def ordered_indices(self): """ Returns indices sorted by length. So less padding is needed. """ - return np.argsort(self.sizes) + if isinstance(self.sizes, np.ndarray) and len(self.sizes.shape) > 1: + # special handling for concatenating lang_pair_datasets + indices = np.arange(len(self)) + sizes = self.sizes + tgt_sizes = ( + sizes[:, 1] if len(sizes.shape) > 0 and sizes.shape[1] > 1 else None + ) + src_sizes = ( + sizes[:, 0] if len(sizes.shape) > 0 and sizes.shape[1] > 1 else sizes + ) + # sort by target length, then source length + if tgt_sizes is not None: + indices = indices[np.argsort(tgt_sizes[indices], kind="mergesort")] + return indices[np.argsort(src_sizes[indices], kind="mergesort")] + else: + return np.argsort(self.sizes) def prefetch(self, indices): frm = 0 for to, ds in zip(self.cumulative_sizes, self.datasets): real_size = len(ds) - if getattr(ds, 'supports_prefetch', False): + if getattr(ds, "supports_prefetch", False): ds.prefetch([(i - frm) % real_size for i in indices if frm <= i < to]) frm = to + @property + def can_reuse_epoch_itr_across_epochs(self): + return all(d.can_reuse_epoch_itr_across_epochs for d in self.datasets) + def set_epoch(self, epoch): super().set_epoch(epoch) for ds in self.datasets: - if hasattr(ds, 'set_epoch'): + if hasattr(ds, "set_epoch"): ds.set_epoch(epoch) diff --git a/fairseq/data/concat_sentences_dataset.py b/fairseq/data/concat_sentences_dataset.py index 55445ee1c7..625a29370e 100644 --- a/fairseq/data/concat_sentences_dataset.py +++ b/fairseq/data/concat_sentences_dataset.py @@ -9,12 +9,12 @@ class ConcatSentencesDataset(FairseqDataset): - def __init__(self, *datasets): super().__init__() self.datasets = datasets - assert all(len(ds) == len(datasets[0]) for ds in datasets), \ - 'datasets must have the same length' + assert all( + len(ds) == len(datasets[0]) for ds in datasets + ), "datasets must have the same length" def __getitem__(self, index): return torch.cat([ds[index] for ds in self.datasets]) @@ -40,17 +40,15 @@ def ordered_indices(self): @property def supports_prefetch(self): - return any( - getattr(ds, 'supports_prefetch', False) for ds in self.datasets - ) + return any(getattr(ds, "supports_prefetch", False) for ds in self.datasets) def prefetch(self, indices): for ds in self.datasets: - if getattr(ds, 'supports_prefetch', False): + if getattr(ds, "supports_prefetch", False): ds.prefetch(indices) def set_epoch(self, epoch): super().set_epoch(epoch) for ds in self.datasets: - if hasattr(ds, 'set_epoch'): + if hasattr(ds, "set_epoch"): ds.set_epoch(epoch) diff --git a/fairseq/data/data_utils.py b/fairseq/data/data_utils.py index ab82ea4594..b3de57681e 100644 --- a/fairseq/data/data_utils.py +++ b/fairseq/data/data_utils.py @@ -10,12 +10,16 @@ import contextlib import itertools import logging -import os -import sys -import types +import re +import warnings +from typing import Optional, Tuple import numpy as np +import torch +from fairseq.file_io import PathManager +from fairseq import utils +import os logger = logging.getLogger(__name__) @@ -23,32 +27,51 @@ def infer_language_pair(path): """Infer language pair from filename: <split>.<lang1>-<lang2>.(...).idx""" src, dst = None, None - for filename in os.listdir(path): - parts = filename.split('.') - if len(parts) >= 3 and len(parts[1].split('-')) == 2: - return parts[1].split('-') + for filename in PathManager.ls(path): + parts = filename.split(".") + if len(parts) >= 3 and len(parts[1].split("-")) == 2: + return parts[1].split("-") return src, dst -def collate_tokens(values, pad_idx, eos_idx=None, left_pad=False, move_eos_to_beginning=False): +def collate_tokens( + values, + pad_idx, + eos_idx=None, + left_pad=False, + move_eos_to_beginning=False, + pad_to_length=None, + pad_to_multiple=1, + pad_to_bsz=None, +): """Convert a list of 1d tensors into a padded 2d tensor.""" size = max(v.size(0) for v in values) - res = values[0].new(len(values), size).fill_(pad_idx) + size = size if pad_to_length is None else max(size, pad_to_length) + if pad_to_multiple != 1 and size % pad_to_multiple != 0: + size = int(((size - 0.1) // pad_to_multiple + 1) * pad_to_multiple) + + batch_size = len(values) if pad_to_bsz is None else max(len(values), pad_to_bsz) + res = values[0].new(batch_size, size).fill_(pad_idx) def copy_tensor(src, dst): assert dst.numel() == src.numel() if move_eos_to_beginning: - dst[0] = eos_idx + if eos_idx is None: + # if no eos_idx is specified, then use the last token in src + dst[0] = src[-1] + else: + dst[0] = eos_idx dst[1:] = src[:-1] else: dst.copy_(src) for i, v in enumerate(values): - copy_tensor(v, res[i][size - len(v):] if left_pad else res[i][:len(v)]) + copy_tensor(v, res[i][size - len(v) :] if left_pad else res[i][: len(v)]) return res - -def load_indexed_dataset(path, dictionary, dataset_impl=None, combine=False, default='cached'): +def load_indexed_dataset( + path, dictionary=None, dataset_impl=None, combine=False, default="cached" +): """A helper function for loading indexed datasets. Args: @@ -62,17 +85,23 @@ def load_indexed_dataset(path, dictionary, dataset_impl=None, combine=False, def combine 'data-bin/train', 'data-bin/train1', ... and return a single ConcatDataset instance. """ - from fairseq.data.concat_dataset import ConcatDataset import fairseq.data.indexed_dataset as indexed_dataset + from fairseq.data.concat_dataset import ConcatDataset datasets = [] for k in itertools.count(): - path_k = path + (str(k) if k > 0 else '') + path_k = path + (str(k) if k > 0 else "") + try: + path_k = indexed_dataset.get_indexed_dataset_to_local(path_k) + except Exception as e: + if "StorageException: [404] Path not found" in str(e): + logger.warning(f"path_k: {e} not found") + else: + raise e dataset_impl_k = dataset_impl if dataset_impl_k is None: dataset_impl_k = indexed_dataset.infer_dataset_impl(path_k) - dataset = indexed_dataset.make_dataset( path_k, impl=dataset_impl_k or default, @@ -81,7 +110,7 @@ def load_indexed_dataset(path, dictionary, dataset_impl=None, combine=False, def ) if dataset is None: break - logger.info('loaded {} examples from: {}'.format(len(dataset), path_k)) + logger.info("loaded {:,} examples from: {}".format(len(dataset), path_k)) datasets.append(dataset) if not combine: break @@ -128,6 +157,9 @@ def collect_filtered(function, iterable, filtered): def _filter_by_size_dynamic(indices, size_fn, max_positions, raise_exception=False): + def compare_leq(a, b): + return a <= b if not isinstance(a, tuple) else max(a) <= b + def check_size(idx): if isinstance(max_positions, float) or isinstance(max_positions, int): return size_fn(idx) <= max_positions @@ -136,17 +168,13 @@ def check_size(idx): assert isinstance(idx_size, dict) intersect_keys = set(max_positions.keys()) & set(idx_size.keys()) return all( - all(a is None or b is None or a <= b - for a, b in zip(idx_size[key], max_positions[key])) + all( + a is None or b is None or a <= b + for a, b in zip(idx_size[key], max_positions[key]) + ) for key in intersect_keys ) else: - # Hacky as heck, for the specific case of multilingual training with RoundRobin. - if isinstance(size_fn(idx), dict) and isinstance(max_positions, tuple): - return all( - a is None or b is None or a <= b - for a, b in zip(size_fn(idx).values(), max_positions) - ) # For MultiCorpusSampledDataset, will generalize it later if not isinstance(size_fn(idx), Iterable): return all(size_fn(idx) <= b for b in max_positions) @@ -154,6 +182,7 @@ def check_size(idx): a is None or b is None or a <= b for a, b in zip(size_fn(idx), max_positions) ) + ignored = [] itr = collect_filtered(check_size, indices, ignored) indices = np.fromiter(itr, dtype=np.int64, count=-1) @@ -162,7 +191,8 @@ def check_size(idx): def filter_by_size(indices, dataset, max_positions, raise_exception=False): """ - Filter indices based on their size. + [deprecated] Filter indices based on their size. + Use `FairseqDataset::filter_indices_by_size` instead. Args: indices (List[int]): ordered list of dataset indices @@ -172,34 +202,90 @@ def filter_by_size(indices, dataset, max_positions, raise_exception=False): raise_exception (bool, optional): if ``True``, raise an exception if any elements are filtered (default: False). """ + warnings.warn( + "data_utils.filter_by_size is deprecated. " + "Use `FairseqDataset::filter_indices_by_size` instead.", + stacklevel=2, + ) if isinstance(max_positions, float) or isinstance(max_positions, int): - if hasattr(dataset, 'sizes') and isinstance(dataset.sizes, np.ndarray): + if hasattr(dataset, "sizes") and isinstance(dataset.sizes, np.ndarray): ignored = indices[dataset.sizes[indices] > max_positions].tolist() indices = indices[dataset.sizes[indices] <= max_positions] - elif hasattr(dataset, 'sizes') and isinstance(dataset.sizes, list) and len(dataset.sizes) == 1: + elif ( + hasattr(dataset, "sizes") + and isinstance(dataset.sizes, list) + and len(dataset.sizes) == 1 + ): ignored = indices[dataset.sizes[0][indices] > max_positions].tolist() indices = indices[dataset.sizes[0][indices] <= max_positions] else: - indices, ignored = _filter_by_size_dynamic(indices, dataset.size, max_positions) + indices, ignored = _filter_by_size_dynamic( + indices, dataset.size, max_positions + ) else: indices, ignored = _filter_by_size_dynamic(indices, dataset.size, max_positions) if len(ignored) > 0 and raise_exception: - raise Exception(( - 'Size of sample #{} is invalid (={}) since max_positions={}, ' - 'skip this example with --skip-invalid-size-inputs-valid-test' - ).format(ignored[0], dataset.size(ignored[0]), max_positions)) + raise Exception( + ( + "Size of sample #{} is invalid (={}) since max_positions={}, " + "skip this example with --skip-invalid-size-inputs-valid-test" + ).format(ignored[0], dataset.size(ignored[0]), max_positions) + ) if len(ignored) > 0: - logger.warning(( - '{} samples have invalid sizes and will be skipped, ' - 'max_positions={}, first few sample ids={}' - ).format(len(ignored), max_positions, ignored[:10])) + logger.warning( + ( + "{} samples have invalid sizes and will be skipped, " + "max_positions={}, first few sample ids={}" + ).format(len(ignored), max_positions, ignored[:10]) + ) return indices +def filter_paired_dataset_indices_by_size(src_sizes, tgt_sizes, indices, max_sizes): + """Filter a list of sample indices. Remove those that are longer + than specified in max_sizes. + + Args: + indices (np.array): original array of sample indices + max_sizes (int or list[int] or tuple[int]): max sample size, + can be defined separately for src and tgt (then list or tuple) + + Returns: + np.array: filtered sample array + list: list of removed indices + """ + if max_sizes is None: + return indices, [] + if type(max_sizes) in (int, float): + max_src_size, max_tgt_size = max_sizes, max_sizes + else: + max_src_size, max_tgt_size = max_sizes + if tgt_sizes is None: + ignored = indices[src_sizes[indices] > max_src_size] + else: + ignored = indices[ + (src_sizes[indices] > max_src_size) | (tgt_sizes[indices] > max_tgt_size) + ] + if len(ignored) > 0: + if tgt_sizes is None: + indices = indices[src_sizes[indices] <= max_src_size] + else: + indices = indices[ + (src_sizes[indices] <= max_src_size) + & (tgt_sizes[indices] <= max_tgt_size) + ] + return indices, ignored.tolist() + + def batch_by_size( - indices, num_tokens_fn, max_tokens=None, max_sentences=None, + indices, + num_tokens_fn, + num_tokens_vec=None, + max_tokens=None, + max_sentences=None, required_batch_size_multiple=1, + fixed_shapes=None, ): """ Yield mini-batches of indices bucketed by size. Batches may contain @@ -209,36 +295,301 @@ def batch_by_size( indices (List[int]): ordered list of dataset indices num_tokens_fn (callable): function that returns the number of tokens at a given index + num_tokens_vec (List[int], optional): precomputed vector of the number + of tokens for each index in indices (to enable faster batch generation) max_tokens (int, optional): max number of tokens in each batch (default: None). max_sentences (int, optional): max number of sentences in each batch (default: None). required_batch_size_multiple (int, optional): require batch size to - be a multiple of N (default: 1). + be less than N or a multiple of N (default: 1). + fixed_shapes (List[Tuple[int, int]], optional): if given, batches will + only be created with the given shapes. *max_sentences* and + *required_batch_size_multiple* will be ignored (default: None). """ try: - from fairseq.data.data_utils_fast import batch_by_size_fast + from fairseq.data.data_utils_fast import ( + batch_by_size_fn, + batch_by_size_vec, + batch_fixed_shapes_fast, + ) except ImportError: raise ImportError( - 'Please build Cython components with: `pip install --editable .` ' - 'or `python setup.py build_ext --inplace`' + "Please build Cython components with: " + "`python setup.py build_ext --inplace`" + ) + except ValueError: + raise ValueError( + "Please build (or rebuild) Cython components with `python setup.py build_ext --inplace`." ) - max_tokens = max_tokens if max_tokens is not None else -1 + # added int() to avoid TypeError: an integer is required + max_tokens = ( + int(max_tokens) if max_tokens is not None else -1 + ) max_sentences = max_sentences if max_sentences is not None else -1 bsz_mult = required_batch_size_multiple - if isinstance(indices, types.GeneratorType): + if not isinstance(indices, np.ndarray): indices = np.fromiter(indices, dtype=np.int64, count=-1) - return batch_by_size_fast(indices, num_tokens_fn, max_tokens, max_sentences, bsz_mult) - + if num_tokens_vec is not None and not isinstance(num_tokens_vec, np.ndarray): + num_tokens_vec = np.fromiter(num_tokens_vec, dtype=np.int64, count=-1) + + if fixed_shapes is None: + if num_tokens_vec is None: + return batch_by_size_fn( + indices, + num_tokens_fn, + max_tokens, + max_sentences, + bsz_mult, + ) + else: + return batch_by_size_vec( + indices, + num_tokens_vec, + max_tokens, + max_sentences, + bsz_mult, + ) -def process_bpe_symbol(sentence: str, bpe_symbol: str): - if bpe_symbol == 'sentencepiece': - sentence = sentence.replace(' ', '').replace('\u2581', ' ').strip() - elif bpe_symbol == '_EOW': - sentence = sentence.replace(' ', '').replace('_EOW', ' ').strip() - elif bpe_symbol is not None: - sentence = (sentence + ' ').replace(bpe_symbol, '').rstrip() + else: + fixed_shapes = np.array(fixed_shapes, dtype=np.int64) + sort_order = np.lexsort( + [ + fixed_shapes[:, 1].argsort(), # length + fixed_shapes[:, 0].argsort(), # bsz + ] + ) + fixed_shapes_sorted = fixed_shapes[sort_order] + return batch_fixed_shapes_fast(indices, num_tokens_fn, fixed_shapes_sorted) + + +def post_process(sentence: str, symbol: str): + if symbol == "sentencepiece": + sentence = sentence.replace(" ", "").replace("\u2581", " ").strip() + elif symbol == "wordpiece": + sentence = sentence.replace(" ", "").replace("_", " ").strip() + elif symbol == "letter": + sentence = sentence.replace(" ", "").replace("|", " ").strip() + elif symbol == "silence": + import re + sentence = sentence.replace("<SIL>", "") + sentence = re.sub(' +', ' ', sentence).strip() + elif symbol == "_EOW": + sentence = sentence.replace(" ", "").replace("_EOW", " ").strip() + elif symbol in {"subword_nmt", "@@ ", "@@"}: + if symbol == "subword_nmt": + symbol = "@@ " + sentence = (sentence + " ").replace(symbol, "").rstrip() + elif symbol == "none": + pass + elif symbol is not None: + raise NotImplementedError(f"Unknown post_process option: {symbol}") return sentence + + +def compute_mask_indices( + shape: Tuple[int, int], + padding_mask: Optional[torch.Tensor], + mask_prob: float, + mask_length: int, + mask_type: str = "static", + mask_other: float = 0.0, + min_masks: int = 0, + no_overlap: bool = False, + min_space: int = 0, +) -> np.ndarray: + """ + Computes random mask spans for a given shape + + Args: + shape: the the shape for which to compute masks. + should be of size 2 where first element is batch size and 2nd is timesteps + padding_mask: optional padding mask of the same size as shape, which will prevent masking padded elements + mask_prob: probability for each token to be chosen as start of the span to be masked. this will be multiplied by + number of timesteps divided by length of mask span to mask approximately this percentage of all elements. + however due to overlaps, the actual number will be smaller (unless no_overlap is True) + mask_type: how to compute mask lengths + static = fixed size + uniform = sample from uniform distribution [mask_other, mask_length*2] + normal = sample from normal distribution with mean mask_length and stdev mask_other. mask is min 1 element + poisson = sample from possion distribution with lambda = mask length + min_masks: minimum number of masked spans + no_overlap: if false, will switch to an alternative recursive algorithm that prevents spans from overlapping + min_space: only used if no_overlap is True, this is how many elements to keep unmasked between spans + """ + + bsz, all_sz = shape + mask = np.full((bsz, all_sz), False) + + all_num_mask = int( + # add a random number for probabilistic rounding + mask_prob * all_sz / float(mask_length) + + np.random.rand() + ) + + all_num_mask = max(min_masks, all_num_mask) + + mask_idcs = [] + for i in range(bsz): + if padding_mask is not None: + sz = all_sz - padding_mask[i].long().sum().item() + num_mask = int( + # add a random number for probabilistic rounding + mask_prob * sz / float(mask_length) + + np.random.rand() + ) + num_mask = max(min_masks, num_mask) + else: + sz = all_sz + num_mask = all_num_mask + + if mask_type == "static": + lengths = np.full(num_mask, mask_length) + elif mask_type == "uniform": + lengths = np.random.randint(mask_other, mask_length * 2 + 1, size=num_mask) + elif mask_type == "normal": + lengths = np.random.normal(mask_length, mask_other, size=num_mask) + lengths = [max(1, int(round(x))) for x in lengths] + elif mask_type == "poisson": + lengths = np.random.poisson(mask_length, size=num_mask) + lengths = [int(round(x)) for x in lengths] + else: + raise Exception("unknown mask selection " + mask_type) + + if sum(lengths) == 0: + lengths[0] = min(mask_length, sz - 1) + + if no_overlap: + mask_idc = [] + + def arrange(s, e, length, keep_length): + span_start = np.random.randint(s, e - length) + mask_idc.extend(span_start + i for i in range(length)) + + new_parts = [] + if span_start - s - min_space >= keep_length: + new_parts.append((s, span_start - min_space + 1)) + if e - span_start - keep_length - min_space > keep_length: + new_parts.append((span_start + length + min_space, e)) + return new_parts + + parts = [(0, sz)] + min_length = min(lengths) + for length in sorted(lengths, reverse=True): + lens = np.fromiter( + (e - s if e - s >= length + min_space else 0 for s, e in parts), + np.int, + ) + l_sum = np.sum(lens) + if l_sum == 0: + break + probs = lens / np.sum(lens) + c = np.random.choice(len(parts), p=probs) + s, e = parts.pop(c) + parts.extend(arrange(s, e, length, min_length)) + mask_idc = np.asarray(mask_idc) + else: + min_len = min(lengths) + if sz - min_len <= num_mask: + min_len = sz - num_mask - 1 + + mask_idc = np.random.choice(sz - min_len, num_mask, replace=False) + + mask_idc = np.asarray( + [ + mask_idc[j] + offset + for j in range(len(mask_idc)) + for offset in range(lengths[j]) + ] + ) + + mask_idcs.append(np.unique(mask_idc[mask_idc < sz])) + + min_len = min([len(m) for m in mask_idcs]) + for i, mask_idc in enumerate(mask_idcs): + if len(mask_idc) > min_len: + mask_idc = np.random.choice(mask_idc, min_len, replace=False) + mask[i, mask_idc] = True + + return mask + + +def get_mem_usage(): + try: + import psutil + + mb = 1024 * 1024 + return f"used={psutil.virtual_memory().used / mb}Mb; avail={psutil.virtual_memory().available / mb}Mb" + except ImportError: + return "N/A" + + +# lens: torch.LongTensor +# returns: torch.BoolTensor +def lengths_to_padding_mask(lens): + bsz, max_lens = lens.size(0), torch.max(lens).item() + mask = torch.arange(max_lens).to(lens.device).view(1, max_lens) + mask = mask.expand(bsz, -1) >= lens.view(bsz, 1).expand(-1, max_lens) + return mask + + +# lens: torch.LongTensor +# returns: torch.BoolTensor +def lengths_to_mask(lens): + return ~lengths_to_padding_mask(lens) + + +def get_buckets(sizes, num_buckets): + buckets = np.unique( + np.percentile( + sizes, + np.linspace(0, 100, num_buckets + 1), + interpolation='lower', + )[1:] + ) + return buckets + + +def get_bucketed_sizes(orig_sizes, buckets): + sizes = np.copy(orig_sizes) + assert np.min(sizes) >= 0 + start_val = -1 + for end_val in buckets: + mask = (sizes > start_val) & (sizes <= end_val) + sizes[mask] = end_val + start_val = end_val + return sizes + + + +def _find_extra_valid_paths(dataset_path: str) -> set: + paths = utils.split_paths(dataset_path) + all_valid_paths = set() + for sub_dir in paths: + contents = PathManager.ls(sub_dir) + valid_paths = [c for c in contents if re.match("valid*[0-9].*", c) is not None] + all_valid_paths |= {os.path.basename(p) for p in valid_paths} + # Remove .bin, .idx etc + roots = {os.path.splitext(p)[0] for p in all_valid_paths} + return roots + + +def raise_if_valid_subsets_unintentionally_ignored(train_cfg) -> None: + """Raises if there are paths matching 'valid*[0-9].*' which are not combined or ignored.""" + if ( + train_cfg.dataset.ignore_unused_valid_subsets + or train_cfg.dataset.combine_valid_subsets + or train_cfg.dataset.disable_validation + or not hasattr(train_cfg.task, "data") + ): + return + other_paths = _find_extra_valid_paths(train_cfg.task.data) + specified_subsets = train_cfg.dataset.valid_subset.split(",") + ignored_paths = [p for p in other_paths if p not in specified_subsets] + if ignored_paths: + advice = "Set --combine-val to combine them or --ignore-unused-valid-subsets to ignore them." + msg = f"Valid paths {ignored_paths} will be ignored. {advice}" + raise ValueError(msg) diff --git a/fairseq/data/data_utils_fast.pyx b/fairseq/data/data_utils_fast.pyx index 6fa8acc09f..c61f31d6b2 100644 --- a/fairseq/data/data_utils_fast.pyx +++ b/fairseq/data/data_utils_fast.pyx @@ -9,37 +9,149 @@ import numpy as np cimport cython cimport numpy as np -DTYPE = np.int64 -ctypedef np.int64_t DTYPE_t +from libc.stdint cimport int32_t, int64_t +from libcpp cimport bool as bool_t +ctypedef int64_t DTYPE_t -cdef _is_batch_full(list batch, long num_tokens, long max_tokens, long max_sentences): - if len(batch) == 0: - return 0 - if max_sentences > 0 and len(batch) == max_sentences: - return 1 - if max_tokens > 0 and num_tokens > max_tokens: - return 1 - return 0 +@cython.cdivision(True) +@cython.boundscheck(False) +@cython.wraparound(False) +cpdef list batch_by_size_vec( + np.ndarray[int64_t, ndim=1] indices, + np.ndarray[int64_t, ndim=1] num_tokens_vec, + int64_t max_tokens, + int64_t max_sentences, + int32_t bsz_mult, +): + if indices.shape[0] == 0: + return [] + + assert max_tokens <= 0 or np.max(num_tokens_vec) <= max_tokens, ( + f"Sentences lengths should not exceed max_tokens={max_tokens}" + ) + + cdef int32_t indices_len = indices.shape[0] + cdef np.ndarray[int32_t, ndim=1] batches_ends = \ + np.zeros(indices_len, dtype=np.int32) + cdef int32_t[:] batches_ends_view = batches_ends + cdef int64_t[:] num_tokens_view = num_tokens_vec + + cdef int32_t pos = 0 + cdef int32_t new_batch_end = 0 + + cdef int64_t new_batch_max_tokens = 0 + cdef int32_t new_batch_sentences = 0 + cdef int64_t new_batch_num_tokens = 0 + + cdef bool_t overflow = False + cdef bool_t size_matches_with_bsz_mult = False + + cdef int32_t batches_count = 0 + cdef int32_t batch_start = 0 + cdef int64_t tail_max_tokens = 0 + cdef int64_t batch_max_tokens = 0 + + for pos in range(indices_len): + # At every pos we keep stats about the last complete batch [batch_start:batch_end), + # and tail [batch_end:pos]. + # 1) Every time when (batch + tail) forms a valid batch + # (according to max_tokens, max_sentences and bsz_mult) we append tail to batch. + # 2) When (batch+tail) violates max_tokens or max_sentences constraints + # we finalize running batch, and tail becomes a new batch. + # 3) There is a corner case when tail also violates constraints. + # In that situation [batch_end:pos-1] (tail without the current pos) + # gets added to the finalized batches, while [pos:pos] becomes a new tail. + # + # Important: For the sake of performance try to avoid using function calls within this loop. + + tail_max_tokens = tail_max_tokens \ + if tail_max_tokens > num_tokens_view[pos] \ + else num_tokens_view[pos] + new_batch_end = pos + 1 + new_batch_max_tokens = batch_max_tokens \ + if batch_max_tokens > tail_max_tokens \ + else tail_max_tokens + new_batch_sentences = new_batch_end - batch_start + new_batch_num_tokens = new_batch_sentences * new_batch_max_tokens + + overflow = (new_batch_sentences > max_sentences > 0 or + new_batch_num_tokens > max_tokens > 0) + size_matches_with_bsz_mult = (new_batch_sentences < bsz_mult or + new_batch_sentences % bsz_mult == 0) + + if overflow: + tail_num_tokens = tail_max_tokens * \ + (new_batch_end - batches_ends_view[batches_count]) + tail_overflow = tail_num_tokens > max_tokens > 0 + # In case of a tail overflow finalize two batches + if tail_overflow: + batches_count += 1 + batches_ends_view[batches_count] = pos + tail_max_tokens = num_tokens_view[pos] + batch_start = batches_ends_view[batches_count] + batches_count += 1 + new_batch_max_tokens = tail_max_tokens + + if overflow or size_matches_with_bsz_mult: + batches_ends_view[batches_count] = new_batch_end + batch_max_tokens = new_batch_max_tokens + tail_max_tokens = 0 + if batches_ends_view[batches_count] != indices_len: + batches_count += 1 + # Memory and time-efficient split + return np.split(indices, batches_ends[:batches_count]) + + +@cython.boundscheck(False) +@cython.wraparound(False) +cpdef list batch_by_size_fn( + np.ndarray[DTYPE_t, ndim=1] indices, + num_tokens_fn, + int64_t max_tokens, + int64_t max_sentences, + int32_t bsz_mult, +): + cdef int32_t indices_len = indices.shape[0] + cdef np.ndarray[int64_t, ndim=1] num_tokens_vec = np.zeros(indices_len, + dtype=np.int64) + cdef DTYPE_t[:] indices_view = indices + cdef DTYPE_t[:] num_tokens_vec_view = num_tokens_vec + cdef int64_t pos + for pos in range(indices_len): + num_tokens_vec[pos] = num_tokens_fn(indices_view[pos]) + return batch_by_size_vec(indices, num_tokens_vec, max_tokens, + max_sentences, bsz_mult,) + + +cdef _find_valid_shape( + DTYPE_t[:, :] shapes_view, + int64_t num_sentences, + int64_t num_tokens, +): + """Return index of first valid shape of -1 if none is found.""" + for i in range(shapes_view.shape[0]): + if num_sentences <= shapes_view[i][0] and num_tokens <= shapes_view[i][1]: + return i + return -1 @cython.cdivision(True) -cpdef list batch_by_size_fast( +cpdef list batch_fixed_shapes_fast( np.ndarray[DTYPE_t, ndim=1] indices, num_tokens_fn, - long max_tokens, - long max_sentences, - int bsz_mult, + np.ndarray[DTYPE_t, ndim=2] fixed_shapes_sorted, ): - cdef long sample_len = 0 + cdef int64_t sample_len = 0 cdef list sample_lens = [] cdef list batch = [] cdef list batches = [] - cdef long mod_len - cdef long i - cdef long idx - cdef long num_tokens + cdef int64_t mod_len + cdef int64_t i + cdef int64_t idx + cdef int64_t num_tokens cdef DTYPE_t[:] indices_view = indices + cdef DTYPE_t[:, :] shapes_view = fixed_shapes_sorted for i in range(len(indices_view)): idx = indices_view[i] @@ -47,22 +159,20 @@ cpdef list batch_by_size_fast( sample_lens.append(num_tokens) sample_len = max(sample_len, num_tokens) - assert max_tokens <= 0 or sample_len <= max_tokens, ( - "sentence at index {} of size {} exceeds max_tokens " - "limit of {}!".format(idx, sample_len, max_tokens) - ) - num_tokens = (len(batch) + 1) * sample_len - - if _is_batch_full(batch, num_tokens, max_tokens, max_sentences): - mod_len = max( - bsz_mult * (len(batch) // bsz_mult), - len(batch) % bsz_mult, - ) - batches.append(batch[:mod_len]) - batch = batch[mod_len:] - sample_lens = sample_lens[mod_len:] - sample_len = max(sample_lens) if len(sample_lens) > 0 else 0 + shape_idx = _find_valid_shape(shapes_view, len(batch) + 1, sample_len) + if shape_idx == -1: + batches.append(batch) + batch = [] + sample_lens = [] + sample_len = 0 + shapes_view = fixed_shapes_sorted + elif shape_idx > 0: + # small optimization for the next call to _find_valid_shape + shapes_view = shapes_view[shape_idx:] + batch.append(idx) + if len(batch) > 0: batches.append(batch) + return batches diff --git a/fairseq/data/denoising_dataset.py b/fairseq/data/denoising_dataset.py index 8f96600c6d..bdb62c8d5d 100644 --- a/fairseq/data/denoising_dataset.py +++ b/fairseq/data/denoising_dataset.py @@ -3,11 +3,12 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. +import math + import numpy as np import torch -import math -from . import data_utils, FairseqDataset +from . import FairseqDataset, data_utils def collate( @@ -18,56 +19,75 @@ def collate( left_pad_source=False, left_pad_target=False, input_feeding=True, + pad_to_length=None, ): assert input_feeding if len(samples) == 0: return {} - def merge(key, left_pad, move_eos_to_beginning=False): + def merge(key, left_pad, move_eos_to_beginning=False, pad_to_length=None): return data_utils.collate_tokens( [s[key] for s in samples], - pad_idx, eos_idx, left_pad, move_eos_to_beginning, + pad_idx, + eos_idx=None, # use eos_idx of each sample instead of vocab.eos() + left_pad=left_pad, + move_eos_to_beginning=move_eos_to_beginning, + pad_to_length=pad_to_length, ) - id = torch.LongTensor([s['id'] for s in samples]) - src_tokens = merge('source', left_pad=left_pad_source) + id = torch.LongTensor([s["id"] for s in samples]) + src_tokens = merge( + "source", + left_pad=left_pad_source, + pad_to_length=pad_to_length["source"] if pad_to_length is not None else None, + ) # sort by descending source length - src_lengths = torch.LongTensor([s['source'].numel() for s in samples]) + src_lengths = torch.LongTensor([s["source"].numel() for s in samples]) src_lengths, sort_order = src_lengths.sort(descending=True) id = id.index_select(0, sort_order) src_tokens = src_tokens.index_select(0, sort_order) prev_output_tokens = None target = None - if samples[0].get('target', None) is not None: - target = merge('target', left_pad=left_pad_target) + if samples[0].get("target", None) is not None: + target = merge( + "target", + left_pad=left_pad_target, + pad_to_length=pad_to_length["target"] + if pad_to_length is not None + else None, + ) target = target.index_select(0, sort_order) - ntokens = sum(len(s['target']) for s in samples) + ntokens = sum(len(s["target"]) for s in samples) if input_feeding: # we create a shifted version of targets for feeding the # previous output token(s) into the next decoder step prev_output_tokens = merge( - 'target', + "target", left_pad=left_pad_target, move_eos_to_beginning=True, + pad_to_length=pad_to_length["target"] + if pad_to_length is not None + else None, ) prev_output_tokens = prev_output_tokens.index_select(0, sort_order) else: - ntokens = sum(len(s['source']) for s in samples) + ntokens = sum(len(s["source"]) for s in samples) batch = { - 'id': id, - 'ntokens': ntokens, - 'net_input': { - 'src_tokens': src_tokens, - 'src_lengths': src_lengths, + "id": id, + "ntokens": ntokens, + "net_input": { + "src_tokens": src_tokens, + "src_lengths": src_lengths, }, - 'target': target, - 'nsentences': samples[0]['source'].size(0), + "target": target, + "nsentences": samples[0]["source"].size(0), + "sort_order": sort_order, } if prev_output_tokens is not None: - batch['net_input']['prev_output_tokens'] = prev_output_tokens + batch["net_input"]["prev_output_tokens"] = prev_output_tokens return batch @@ -100,7 +120,8 @@ def __init__( shuffle, seed, args, - eos=None + eos=None, + item_transform_func=None, ): self.dataset = dataset @@ -116,24 +137,25 @@ def __init__( self.insert_ratio = args.insert self.rotate_ratio = args.rotate self.permute_sentence_ratio = args.permute_sentences - self.eos = (eos if eos is not None else vocab.eos()) + self.eos = eos if eos is not None else vocab.eos() + self.item_transform_func = item_transform_func - if args.bpe != 'gpt2': + if args.bpe != "gpt2": self.full_stop_index = self.vocab.eos() else: - assert args.bpe == 'gpt2' - self.full_stop_index = self.vocab.index('13') + assert args.bpe == "gpt2" + self.full_stop_index = self.vocab.index("13") self.replace_length = args.replace_length - if not self.replace_length in [-1, 0, 1]: - raise (f'invalid arg: replace_length={self.replace_length}') - if not args.mask_length in ['subword', 'word', 'span-poisson']: - raise (f'invalid arg: mask-length={args.mask_length}') - if args.mask_length == 'subword' and not args.replace_length in [0, 1]: - raise (f'if using subwords, use replace-length=1 or 0') + if self.replace_length not in [-1, 0, 1]: + raise ValueError(f"invalid arg: replace_length={self.replace_length}") + if args.mask_length not in ["subword", "word", "span-poisson"]: + raise ValueError(f"invalid arg: mask-length={args.mask_length}") + if args.mask_length == "subword" and args.replace_length not in [0, 1]: + raise ValueError(f"if using subwords, use replace-length=1 or 0") self.mask_span_distribution = None - if args.mask_length == 'span-poisson': + if args.mask_length == "span-poisson": _lambda = args.poisson_lambda lambda_to_the_k = 1 @@ -143,7 +165,7 @@ def __init__( for k in range(0, 128): ps.append(e_to_the_minus_lambda * lambda_to_the_k / k_factorial) lambda_to_the_k *= _lambda - k_factorial *= (k + 1) + k_factorial *= k + 1 if ps[-1] < 0.0000001: break ps = torch.FloatTensor(ps) @@ -151,6 +173,10 @@ def __init__( self.epoch = 0 + @property + def can_reuse_epoch_itr_across_epochs(self): + return True # only the noise changes, not item sizes + def set_epoch(self, epoch, **unused): self.epoch = epoch @@ -171,6 +197,9 @@ def __getitem__(self, index): if self.rotate_ratio > 0.0 and np.random.random() < self.rotate_ratio: source = self.add_rolling_noise(source) + # there can additional changes to make: + if self.item_transform_func is not None: + source, target = self.item_transform_func(source, target) assert (source >= 0).all() assert (source[1:-1] >= 1).all() @@ -178,21 +207,21 @@ def __getitem__(self, index): assert source[0] == self.vocab.bos() assert source[-1] == self.eos return { - 'id': index, - 'source': source, - 'target': target, + "id": index, + "source": source, + "target": target, } def __len__(self): return len(self.dataset) def permute_sentences(self, source, p=1.0): - full_stops = (source == self.full_stop_index) + full_stops = source == self.full_stop_index # Pretend it ends with a full stop so last span is a sentence full_stops[-2] = 1 # Tokens that are full stops, where the previous token is not - sentence_ends = (full_stops[1:] * ~full_stops[:-1]).nonzero() + 2 + sentence_ends = (full_stops[1:] * ~full_stops[:-1]).nonzero(as_tuple=False) + 2 result = source.clone() num_sentences = sentence_ends.size(0) @@ -204,8 +233,8 @@ def permute_sentences(self, source, p=1.0): # Ignore <bos> at start index = 1 for i in ordering: - sentence = source[(sentence_ends[i - 1] if i > 0 else 1):sentence_ends[i]] - result[index:index + sentence.size(0)] = sentence + sentence = source[(sentence_ends[i - 1] if i > 0 else 1) : sentence_ends[i]] + result[index : index + sentence.size(0)] = sentence index += sentence.size(0) return result @@ -231,7 +260,13 @@ def add_whole_word_mask(self, source, p): # Make sure we have enough to mask cum_length = torch.cumsum(lengths, 0) while cum_length[-1] < num_to_mask: - lengths = torch.cat([lengths, self.mask_span_distribution.sample(sample_shape=(num_to_mask,))], dim=0) + lengths = torch.cat( + [ + lengths, + self.mask_span_distribution.sample(sample_shape=(num_to_mask,)), + ], + dim=0, + ) cum_length = torch.cumsum(lengths, 0) # Trim to masking budget @@ -253,20 +288,26 @@ def add_whole_word_mask(self, source, p): else: lengths = torch.ones((num_to_mask,)).long() assert is_word_start[-1] == 0 - word_starts = is_word_start.nonzero() - indices = word_starts[torch.randperm(word_starts.size(0))[:num_to_mask]].squeeze(1) + word_starts = is_word_start.nonzero(as_tuple=False) + indices = word_starts[ + torch.randperm(word_starts.size(0))[:num_to_mask] + ].squeeze(1) mask_random = torch.FloatTensor(num_to_mask).uniform_() < self.random_ratio source_length = source.size(0) assert source_length - 1 not in indices to_keep = torch.ones(source_length, dtype=torch.bool) - is_word_start[-1] = 255 # acts as a long length, so spans don't go over the end of doc + is_word_start[ + -1 + ] = 255 # acts as a long length, so spans don't go over the end of doc if self.replace_length == 0: to_keep[indices] = 0 else: # keep index, but replace it with [MASK] source[indices] = self.mask_idx - source[indices[mask_random]] = torch.randint(1, len(self.vocab), size=(mask_random.sum(),)) + source[indices[mask_random]] = torch.randint( + 1, len(self.vocab), size=(mask_random.sum(),) + ) if self.mask_span_distribution is not None: assert len(lengths.size()) == 1 @@ -285,7 +326,9 @@ def add_whole_word_mask(self, source, p): else: # keep index, but replace it with [MASK] source[indices] = self.mask_idx - source[indices[mask_random]] = torch.randint(1, len(self.vocab), size=(mask_random.sum(),)) + source[indices[mask_random]] = torch.randint( + 1, len(self.vocab), size=(mask_random.sum(),) + ) else: # A bit faster when all lengths are 1 while indices.size(0) > 0: @@ -298,7 +341,9 @@ def add_whole_word_mask(self, source, p): else: # keep index, but replace it with [MASK] source[indices] = self.mask_idx - source[indices[mask_random]] = torch.randint(1, len(self.vocab), size=(mask_random.sum(),)) + source[indices[mask_random]] = torch.randint( + 1, len(self.vocab), size=(mask_random.sum(),) + ) assert source_length - 1 not in indices @@ -338,21 +383,25 @@ def add_insertion_noise(self, tokens, p): num_random = int(math.ceil(n * self.random_ratio)) result[noise_indices[num_random:]] = self.mask_idx - result[noise_indices[:num_random]] = torch.randint(low=1, high=len(self.vocab), size=(num_random,)) + result[noise_indices[:num_random]] = torch.randint( + low=1, high=len(self.vocab), size=(num_random,) + ) result[~noise_mask] = tokens assert (result >= 0).all() return result - def collater(self, samples): + def collater(self, samples, pad_to_length=None): """Merge a list of samples to form a mini-batch. Args: samples (List[dict]): samples to collate Returns: dict: a mini-batch of data """ - return collate(samples, self.vocab.pad(), self.vocab.eos(), self.vocab) + return collate( + samples, self.vocab.pad(), self.eos, self.vocab, pad_to_length=pad_to_length + ) def num_tokens(self, index): """Return the number of tokens in a sample. This value is used to @@ -371,7 +420,7 @@ def ordered_indices(self): indices = np.random.permutation(len(self)) else: indices = np.arange(len(self)) - return indices[np.argsort(self.sizes[indices], kind='mergesort')] + return indices[np.argsort(self.sizes[indices], kind="mergesort")] def prefetch(self, indices): self.src.prefetch(indices) @@ -380,8 +429,8 @@ def prefetch(self, indices): @property def supports_prefetch(self): return ( - hasattr(self.src, 'supports_prefetch') + hasattr(self.src, "supports_prefetch") and self.src.supports_prefetch - and hasattr(self.tgt, 'supports_prefetch') + and hasattr(self.tgt, "supports_prefetch") and self.tgt.supports_prefetch ) diff --git a/fairseq/data/dictionary.py b/fairseq/data/dictionary.py index 5ff010d5e2..d6495389f0 100644 --- a/fairseq/data/dictionary.py +++ b/fairseq/data/dictionary.py @@ -9,25 +9,25 @@ import torch from fairseq import utils -from fairseq.binarizer import safe_readline from fairseq.data import data_utils +from fairseq.file_chunker_utils import Chunker, find_offsets from fairseq.file_io import PathManager from fairseq.tokenizer import tokenize_line -class Dictionary(object): +class Dictionary: """A mapping from symbols to consecutive integers""" def __init__( self, *, # begin keyword-only arguments + bos="<s>", pad="<pad>", eos="</s>", unk="<unk>", - bos="<s>", extra_special_symbols=None, ): - self.unk_word, self.pad_word, self.eos_word = unk, pad, eos + self.bos_word, self.unk_word, self.pad_word, self.eos_word = bos, unk, pad, eos self.symbols = [] self.count = [] self.indices = {} @@ -48,6 +48,9 @@ def __getitem__(self, idx): return self.symbols[idx] return self.unk_word + def get_count(self, idx): + return self.count[idx] + def __len__(self): """Returns the number of symbols in the dictionary""" return len(self.symbols) @@ -69,6 +72,8 @@ def string( escape_unk=False, extra_symbols_to_ignore=None, unk_string=None, + include_eos=False, + separator=" ", ): """Helper for converting a tensor of token indices to a string. @@ -76,12 +81,19 @@ def string( """ if torch.is_tensor(tensor) and tensor.dim() == 2: return "\n".join( - self.string(t, bpe_symbol, escape_unk, extra_symbols_to_ignore) + self.string( + t, + bpe_symbol, + escape_unk, + extra_symbols_to_ignore, + include_eos=include_eos, + ) for t in tensor ) extra_symbols_to_ignore = set(extra_symbols_to_ignore or []) - extra_symbols_to_ignore.add(self.eos()) + if not include_eos: + extra_symbols_to_ignore.add(self.eos()) def token_string(i): if i == self.unk(): @@ -95,13 +107,13 @@ def token_string(i): if hasattr(self, "bos_index"): extra_symbols_to_ignore.add(self.bos()) - sent = " ".join( + sent = separator.join( token_string(i) for i in tensor if utils.item(i) not in extra_symbols_to_ignore ) - return data_utils.process_bpe_symbol(sent, bpe_symbol) + return data_utils.post_process(sent, bpe_symbol) def unk_string(self, escape=False): """Return unknown string, optionally escaped as: <<unk>>""" @@ -221,7 +233,7 @@ def add_from_file(self, f): """ if isinstance(f, str): try: - with PathManager.open(f, "r", encoding="utf-8") as fd: + with open(PathManager.get_local_path(f), "r", encoding="utf-8") as fd: self.add_from_file(fd) except FileNotFoundError as fnfe: raise fnfe @@ -251,13 +263,12 @@ def add_from_file(self, f): "Duplicate words can overwrite earlier ones by adding the " "#fairseq:overwrite flag at the end of the corresponding row " "in the dictionary file. If using the Camembert model, please " - "download an updated copy of the model file." - .format(word) + "download an updated copy of the model file.".format(word) ) self.add_symbol(word, n=count, overwrite=overwrite) except ValueError: raise ValueError( - "Incorrect dictionary format, expected '<token> <cnt> [flags]'" + f"Incorrect dictionary format, expected '<token> <cnt> [flags]': \"{line}\"" ) def _save(self, f, kv_iterator): @@ -298,7 +309,7 @@ def encode_line( consumer=None, append_eos=True, reverse_order=False, - ): + ) -> torch.IntTensor: words = line_tokenizer(line) if reverse_order: words = list(reversed(words)) @@ -319,25 +330,18 @@ def encode_line( @staticmethod def _add_file_to_dictionary_single_worker( - filename, tokenize, eos_word, worker_id=0, num_workers=1 + filename, + tokenize, + eos_word, + start_offset, + end_offset, ): counter = Counter() - with open(PathManager.get_local_path(filename), "r", encoding="utf-8") as f: - size = os.fstat(f.fileno()).st_size - chunk_size = size // num_workers - offset = worker_id * chunk_size - end = offset + chunk_size - f.seek(offset) - if offset > 0: - safe_readline(f) # drop first incomplete line - line = f.readline() - while line: + with Chunker(filename, start_offset, end_offset) as line_iterator: + for line in line_iterator: for word in tokenize(line): counter.update([word]) counter.update([eos_word]) - if f.tell() > end: - break - line = f.readline() return counter @staticmethod @@ -346,14 +350,23 @@ def merge_result(counter): for w, c in sorted(counter.items()): dict.add_symbol(w, c) + local_file = PathManager.get_local_path(filename) + offsets = find_offsets(local_file, num_workers) if num_workers > 1: + chunks = zip(offsets, offsets[1:]) pool = Pool(processes=num_workers) results = [] - for worker_id in range(num_workers): + for (start_offset, end_offset) in chunks: results.append( pool.apply_async( Dictionary._add_file_to_dictionary_single_worker, - (filename, tokenize, dict.eos_word, worker_id, num_workers), + ( + local_file, + tokenize, + dict.eos_word, + start_offset, + end_offset, + ), ) ) pool.close() @@ -363,7 +376,7 @@ def merge_result(counter): else: merge_result( Dictionary._add_file_to_dictionary_single_worker( - filename, tokenize, dict.eos_word + local_file, tokenize, dict.eos_word, offsets[0], offsets[1] ) ) diff --git a/fairseq/data/encoders/__init__.py b/fairseq/data/encoders/__init__.py index c0909b6697..7cbe00a105 100644 --- a/fairseq/data/encoders/__init__.py +++ b/fairseq/data/encoders/__init__.py @@ -10,20 +10,20 @@ from fairseq import registry -build_tokenizer, register_tokenizer, TOKENIZER_REGISTRY = registry.setup_registry( - '--tokenizer', +build_tokenizer, register_tokenizer, TOKENIZER_REGISTRY, _ = registry.setup_registry( + "--tokenizer", default=None, ) -build_bpe, register_bpe, BPE_REGISTRY = registry.setup_registry( - '--bpe', +build_bpe, register_bpe, BPE_REGISTRY, _ = registry.setup_registry( + "--bpe", default=None, ) # automatically import any Python files in the encoders/ directory -for file in os.listdir(os.path.dirname(__file__)): - if file.endswith('.py') and not file.startswith('_'): - module = file[:file.find('.py')] - importlib.import_module('fairseq.data.encoders.' + module) +for file in sorted(os.listdir(os.path.dirname(__file__))): + if file.endswith(".py") and not file.startswith("_"): + module = file[: file.find(".py")] + importlib.import_module("fairseq.data.encoders." + module) diff --git a/fairseq/data/encoders/byte_bpe.py b/fairseq/data/encoders/byte_bpe.py index 1d78ff9150..31e3a06278 100644 --- a/fairseq/data/encoders/byte_bpe.py +++ b/fairseq/data/encoders/byte_bpe.py @@ -4,29 +4,39 @@ # LICENSE file in the root directory of this source tree. +from dataclasses import dataclass, field + from fairseq import file_utils from fairseq.data.encoders import register_bpe -from fairseq.data.encoders.byte_utils import (byte_encode, smart_byte_decode, - SPACE, SPACE_ESCAPE) +from fairseq.data.encoders.byte_utils import ( + SPACE, + SPACE_ESCAPE, + byte_encode, + smart_byte_decode, +) +from fairseq.dataclass import FairseqDataclass + + +@dataclass +class ByteBpeConfig(FairseqDataclass): + sentencepiece_model_path: str = field( + default="???", metadata={"help": "path to sentencepiece model"} + ) -@register_bpe('byte_bpe') +@register_bpe("byte_bpe", dataclass=ByteBpeConfig) class ByteBPE(object): - @staticmethod - def add_args(parser): - # fmt: off - parser.add_argument('--sentencepiece-model-path', type=str, - help='path to sentencepiece model') - # fmt: on - - def __init__(self, args): - vocab = file_utils.cached_path(args.sentencepiece_model_path) + def __init__(self, cfg): + vocab = file_utils.cached_path(cfg.sentencepiece_model_path) try: import sentencepiece as spm + self.sp = spm.SentencePieceProcessor() self.sp.Load(vocab) except ImportError: - raise ImportError('Please install sentencepiece with: pip install sentencepiece') + raise ImportError( + "Please install sentencepiece with: pip install sentencepiece" + ) def encode(self, x: str) -> str: byte_encoded = byte_encode(x) @@ -34,5 +44,5 @@ def encode(self, x: str) -> str: @staticmethod def decode(x: str) -> str: - unescaped = x.replace(SPACE, '').replace(SPACE_ESCAPE, SPACE) + unescaped = x.replace(SPACE, "").replace(SPACE_ESCAPE, SPACE) return smart_byte_decode(unescaped) diff --git a/fairseq/data/encoders/byte_utils.py b/fairseq/data/encoders/byte_utils.py index 7c4bb74713..a305c08092 100644 --- a/fairseq/data/encoders/byte_utils.py +++ b/fairseq/data/encoders/byte_utils.py @@ -5,13 +5,13 @@ import re -WHITESPACE_NORMALIZER = re.compile(r'\s+') + +WHITESPACE_NORMALIZER = re.compile(r"\s+") SPACE = chr(32) SPACE_ESCAPE = chr(9601) # excluding non-breaking space (160) here PRINTABLE_LATIN = set( - list(range(32, 126 + 1)) + list(range(161, 172 + 1)) + - list(range(174, 255 + 1)) + list(range(32, 126 + 1)) + list(range(161, 172 + 1)) + list(range(174, 255 + 1)) ) BYTE_TO_BCHAR = { b: chr(b) if b in PRINTABLE_LATIN else chr(256 + b) for b in range(256) @@ -21,19 +21,19 @@ def byte_encode(x: str) -> str: normalized = WHITESPACE_NORMALIZER.sub(SPACE, x) - return ''.join([BYTE_TO_BCHAR[b] for b in normalized.encode('utf-8')]) + return "".join([BYTE_TO_BCHAR[b] for b in normalized.encode("utf-8")]) def byte_decode(x: str) -> str: try: - return bytes([BCHAR_TO_BYTE[bc] for bc in x]).decode('utf-8') + return bytes([BCHAR_TO_BYTE[bc] for bc in x]).decode("utf-8") except ValueError: - return '' + return "" def smart_byte_decode(x: str) -> str: output = byte_decode(x) - if output == '': + if output == "": # DP the best recovery (max valid chars) if it's broken n_bytes = len(x) f = [0 for _ in range(n_bytes + 1)] @@ -41,11 +41,11 @@ def smart_byte_decode(x: str) -> str: for i in range(1, n_bytes + 1): f[i], pt[i] = f[i - 1], i - 1 for j in range(1, min(4, i) + 1): - if f[i - j] + 1 > f[i] and len(byte_decode(x[i - j: i])) > 0: + if f[i - j] + 1 > f[i] and len(byte_decode(x[i - j : i])) > 0: f[i], pt[i] = f[i - j] + 1, i - j cur_pt = n_bytes while cur_pt > 0: if f[cur_pt] == f[pt[cur_pt]] + 1: - output = byte_decode(x[pt[cur_pt]: cur_pt]) + output + output = byte_decode(x[pt[cur_pt] : cur_pt]) + output cur_pt = pt[cur_pt] return output diff --git a/fairseq/data/encoders/bytes.py b/fairseq/data/encoders/bytes.py index 8bace19c53..f88f8f6929 100644 --- a/fairseq/data/encoders/bytes.py +++ b/fairseq/data/encoders/bytes.py @@ -5,13 +5,17 @@ from fairseq.data.encoders import register_bpe -from fairseq.data.encoders.byte_utils import (byte_encode, smart_byte_decode, - SPACE, SPACE_ESCAPE) +from fairseq.data.encoders.byte_utils import ( + SPACE, + SPACE_ESCAPE, + byte_encode, + smart_byte_decode, +) -@register_bpe('bytes') +@register_bpe("bytes") class Bytes(object): - def __init__(self, args): + def __init__(self, *unused): pass @staticmethod @@ -26,5 +30,5 @@ def encode(x: str) -> str: @staticmethod def decode(x: str) -> str: - unescaped = x.replace(SPACE, '').replace(SPACE_ESCAPE, SPACE) + unescaped = x.replace(SPACE, "").replace(SPACE_ESCAPE, SPACE) return smart_byte_decode(unescaped) diff --git a/fairseq/data/encoders/characters.py b/fairseq/data/encoders/characters.py index db6a58a650..494ea21939 100644 --- a/fairseq/data/encoders/characters.py +++ b/fairseq/data/encoders/characters.py @@ -6,13 +6,14 @@ from fairseq.data.encoders import register_bpe + SPACE = chr(32) SPACE_ESCAPE = chr(9601) -@register_bpe('characters') +@register_bpe("characters") class Characters(object): - def __init__(self, args): + def __init__(self, *unused): pass @staticmethod @@ -26,4 +27,4 @@ def encode(x: str) -> str: @staticmethod def decode(x: str) -> str: - return x.replace(SPACE, '').replace(SPACE_ESCAPE, SPACE) + return x.replace(SPACE, "").replace(SPACE_ESCAPE, SPACE) diff --git a/fairseq/data/encoders/fastbpe.py b/fairseq/data/encoders/fastbpe.py index 376e22cd85..f7c2103954 100644 --- a/fairseq/data/encoders/fastbpe.py +++ b/fairseq/data/encoders/fastbpe.py @@ -3,33 +3,34 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. +from dataclasses import dataclass, field + from fairseq import file_utils from fairseq.data.encoders import register_bpe +from fairseq.dataclass import FairseqDataclass -@register_bpe('fastbpe') -class fastBPE(object): +@dataclass +class fastBPEConfig(FairseqDataclass): + bpe_codes: str = field(default="???", metadata={"help": "path to fastBPE BPE"}) + - @staticmethod - def add_args(parser): - # fmt: off - parser.add_argument('--bpe-codes', type=str, - help='path to fastBPE BPE') - # fmt: on - - def __init__(self, args): - if args.bpe_codes is None: - raise ValueError('--bpe-codes is required for --bpe=subword_nmt') - codes = file_utils.cached_path(args.bpe_codes) +@register_bpe("fastbpe", dataclass=fastBPEConfig) +class fastBPE(object): + def __init__(self, cfg): + if cfg.bpe_codes is None: + raise ValueError("--bpe-codes is required for --bpe=fastbpe") + codes = file_utils.cached_path(cfg.bpe_codes) try: import fastBPE + self.bpe = fastBPE.fastBPE(codes) self.bpe_symbol = "@@ " except ImportError: - raise ImportError('Please install fastBPE with: pip install fastBPE') + raise ImportError("Please install fastBPE with: pip install fastBPE") def encode(self, x: str) -> str: return self.bpe.apply([x])[0] def decode(self, x: str) -> str: - return (x + ' ').replace(self.bpe_symbol, '').rstrip() + return (x + " ").replace(self.bpe_symbol, "").rstrip() diff --git a/fairseq/data/encoders/gpt2_bpe.py b/fairseq/data/encoders/gpt2_bpe.py index 54e0593d00..e661426a73 100644 --- a/fairseq/data/encoders/gpt2_bpe.py +++ b/fairseq/data/encoders/gpt2_bpe.py @@ -3,47 +3,43 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. +from dataclasses import dataclass, field + from fairseq import file_utils from fairseq.data.encoders import register_bpe +from fairseq.dataclass import FairseqDataclass from .gpt2_bpe_utils import get_encoder -DEFAULT_ENCODER_JSON = 'https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/encoder.json' -DEFAULT_VOCAB_BPE = 'https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/vocab.bpe' +DEFAULT_ENCODER_JSON = "https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/encoder.json" +DEFAULT_VOCAB_BPE = "https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/vocab.bpe" -@register_bpe('gpt2') -class GPT2BPE(object): +@dataclass +class GPT2BPEConfig(FairseqDataclass): + gpt2_encoder_json: str = field( + default=DEFAULT_ENCODER_JSON, metadata={"help": "path to encoder.json"} + ) + gpt2_vocab_bpe: str = field( + default=DEFAULT_VOCAB_BPE, metadata={"help": "path to vocab.bpe"} + ) - @staticmethod - def add_args(parser): - # fmt: off - parser.add_argument('--gpt2-encoder-json', type=str, - default=DEFAULT_ENCODER_JSON, - help='path to encoder.json') - parser.add_argument('--gpt2-vocab-bpe', type=str, - default=DEFAULT_VOCAB_BPE, - help='path to vocab.bpe') - # fmt: on - - def __init__(self, args): - encoder_json = file_utils.cached_path( - getattr(args, 'gpt2_encoder_json', DEFAULT_ENCODER_JSON) - ) - vocab_bpe = file_utils.cached_path( - getattr(args, 'gpt2_vocab_bpe', DEFAULT_VOCAB_BPE) - ) + +@register_bpe("gpt2", dataclass=GPT2BPEConfig) +class GPT2BPE(object): + def __init__(self, cfg): + encoder_json = file_utils.cached_path(cfg.gpt2_encoder_json) + vocab_bpe = file_utils.cached_path(cfg.gpt2_vocab_bpe) self.bpe = get_encoder(encoder_json, vocab_bpe) def encode(self, x: str) -> str: - return ' '.join(map(str, self.bpe.encode(x))) + return " ".join(map(str, self.bpe.encode(x))) def decode(self, x: str) -> str: - return self.bpe.decode([ - int(tok) if tok not in {'<unk>', '<mask>'} else tok - for tok in x.split() - ]) + return self.bpe.decode( + [int(tok) if tok not in {"<unk>", "<mask>"} else tok for tok in x.split()] + ) def is_beginning_of_word(self, x: str) -> bool: - return self.decode(x).startswith(' ') + return self.decode(x).startswith(" ") diff --git a/fairseq/data/encoders/gpt2_bpe_utils.py b/fairseq/data/encoders/gpt2_bpe_utils.py index 1917f82314..688d4e36e3 100644 --- a/fairseq/data/encoders/gpt2_bpe_utils.py +++ b/fairseq/data/encoders/gpt2_bpe_utils.py @@ -5,8 +5,8 @@ Original license: MIT """ -from functools import lru_cache import json +from functools import lru_cache @lru_cache() @@ -20,17 +20,22 @@ def bytes_to_unicode(): To avoid that, we want lookup tables between utf-8 bytes and unicode strings. And avoids mapping to whitespace/control characters the bpe code barfs on. """ - bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1)) + bs = ( + list(range(ord("!"), ord("~") + 1)) + + list(range(ord("¡"), ord("¬") + 1)) + + list(range(ord("®"), ord("ÿ") + 1)) + ) cs = bs[:] n = 0 - for b in range(2**8): + for b in range(2 ** 8): if b not in bs: bs.append(b) - cs.append(2**8+n) + cs.append(2 ** 8 + n) n += 1 cs = [chr(n) for n in cs] return dict(zip(bs, cs)) + def get_pairs(word): """Return set of symbol pairs in a word. Word is represented as tuple of symbols (symbols being variable-length strings). @@ -42,25 +47,28 @@ def get_pairs(word): prev_char = char return pairs -class Encoder: - def __init__(self, encoder, bpe_merges, errors='replace'): +class Encoder: + def __init__(self, encoder, bpe_merges, errors="replace"): self.encoder = encoder - self.decoder = {v:k for k,v in self.encoder.items()} - self.errors = errors # how to handle errors in decoding + self.decoder = {v: k for k, v in self.encoder.items()} + self.errors = errors # how to handle errors in decoding self.byte_encoder = bytes_to_unicode() - self.byte_decoder = {v:k for k, v in self.byte_encoder.items()} + self.byte_decoder = {v: k for k, v in self.byte_encoder.items()} self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges)))) self.cache = {} try: import regex as re + self.re = re except ImportError: - raise ImportError('Please install regex with: pip install regex') + raise ImportError("Please install regex with: pip install regex") # Should haved added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions - self.pat = self.re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""") + self.pat = self.re.compile( + r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""" + ) def bpe(self, token): if token in self.cache: @@ -72,7 +80,7 @@ def bpe(self, token): return token while True: - bigram = min(pairs, key = lambda pair: self.bpe_ranks.get(pair, float('inf'))) + bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf"))) if bigram not in self.bpe_ranks: break first, second = bigram @@ -87,8 +95,8 @@ def bpe(self, token): new_word.extend(word[i:]) break - if word[i] == first and i < len(word)-1 and word[i+1] == second: - new_word.append(first+second) + if word[i] == first and i < len(word) - 1 and word[i + 1] == second: + new_word.append(first + second) i += 2 else: new_word.append(word[i]) @@ -99,28 +107,33 @@ def bpe(self, token): break else: pairs = get_pairs(word) - word = ' '.join(word) + word = " ".join(word) self.cache[token] = word return word def encode(self, text): bpe_tokens = [] for token in self.re.findall(self.pat, text): - token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8')) - bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(' ')) + token = "".join(self.byte_encoder[b] for b in token.encode("utf-8")) + bpe_tokens.extend( + self.encoder[bpe_token] for bpe_token in self.bpe(token).split(" ") + ) return bpe_tokens def decode(self, tokens): - text = ''.join([self.decoder.get(token, token) for token in tokens]) - text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors=self.errors) + text = "".join([self.decoder.get(token, token) for token in tokens]) + text = bytearray([self.byte_decoder[c] for c in text]).decode( + "utf-8", errors=self.errors + ) return text + def get_encoder(encoder_json_path, vocab_bpe_path): - with open(encoder_json_path, 'r') as f: + with open(encoder_json_path, "r") as f: encoder = json.load(f) - with open(vocab_bpe_path, 'r', encoding="utf-8") as f: + with open(vocab_bpe_path, "r", encoding="utf-8") as f: bpe_data = f.read() - bpe_merges = [tuple(merge_str.split()) for merge_str in bpe_data.split('\n')[1:-1]] + bpe_merges = [tuple(merge_str.split()) for merge_str in bpe_data.split("\n")[1:-1]] return Encoder( encoder=encoder, bpe_merges=bpe_merges, diff --git a/fairseq/data/encoders/hf_bert_bpe.py b/fairseq/data/encoders/hf_bert_bpe.py index 40c69d53c5..a41c059343 100644 --- a/fairseq/data/encoders/hf_bert_bpe.py +++ b/fairseq/data/encoders/hf_bert_bpe.py @@ -3,49 +3,48 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. +from dataclasses import dataclass, field +from typing import Optional + from fairseq.data.encoders import register_bpe +from fairseq.dataclass import FairseqDataclass -@register_bpe('bert') -class BertBPE(object): +@dataclass +class BertBPEConfig(FairseqDataclass): + bpe_cased: bool = field(default=False, metadata={"help": "set for cased BPE"}) + bpe_vocab_file: Optional[str] = field( + default=None, metadata={"help": "bpe vocab file"} + ) + - @staticmethod - def add_args(parser): - # fmt: off - parser.add_argument('--bpe-cased', action='store_true', - help='set for cased BPE', - default=False) - parser.add_argument('--bpe-vocab-file', type=str, - help='bpe vocab file.') - # fmt: on - - def __init__(self, args): +@register_bpe("bert", dataclass=BertBPEConfig) +class BertBPE(object): + def __init__(self, cfg): try: - from pytorch_transformers import BertTokenizer - from pytorch_transformers.tokenization_utils import clean_up_tokenization + from transformers import BertTokenizer except ImportError: raise ImportError( - 'Please install 1.0.0 version of pytorch_transformers' - 'with: pip install pytorch-transformers' + "Please install transformers with: pip install transformers" ) - if 'bpe_vocab_file' in args: + if cfg.bpe_vocab_file: self.bert_tokenizer = BertTokenizer( - args.bpe_vocab_file, - do_lower_case=not args.bpe_cased + cfg.bpe_vocab_file, do_lower_case=not cfg.bpe_cased ) else: - vocab_file_name = 'bert-base-cased' if args.bpe_cased else 'bert-base-uncased' + vocab_file_name = ( + "bert-base-cased" if cfg.bpe_cased else "bert-base-uncased" + ) self.bert_tokenizer = BertTokenizer.from_pretrained(vocab_file_name) - self.clean_up_tokenization = clean_up_tokenization def encode(self, x: str) -> str: - return ' '.join(self.bert_tokenizer.tokenize(x)) + return " ".join(self.bert_tokenizer.tokenize(x)) def decode(self, x: str) -> str: - return self.clean_up_tokenization( - self.bert_tokenizer.convert_tokens_to_string(x.split(' ')) + return self.bert_tokenizer.clean_up_tokenization( + self.bert_tokenizer.convert_tokens_to_string(x.split(" ")) ) def is_beginning_of_word(self, x: str) -> bool: - return not x.startswith('##') + return not x.startswith("##") diff --git a/fairseq/data/encoders/hf_byte_bpe.py b/fairseq/data/encoders/hf_byte_bpe.py index 2767df044e..c508578d41 100644 --- a/fairseq/data/encoders/hf_byte_bpe.py +++ b/fairseq/data/encoders/hf_byte_bpe.py @@ -3,44 +3,48 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. +from dataclasses import dataclass, field + from fairseq.data.encoders import register_bpe +from fairseq.dataclass import FairseqDataclass +from fairseq import file_utils -@register_bpe('hf_byte_bpe') -class HuggingFaceByteLevelBPE(object): +@dataclass +class HuggingFaceByteLevelBPEConfig(FairseqDataclass): + bpe_merges: str = field(default="???", metadata={"help": "path to merges.txt"}) + bpe_vocab: str = field(default="???", metadata={"help": "path to vocab.json"}) + bpe_add_prefix_space: bool = field( + default=False, metadata={"help": "add prefix space before encoding"} + ) - @staticmethod - def add_args(parser): - # fmt: off - parser.add_argument('--bpe-merges', help='path to merges.txt') - parser.add_argument('--bpe-vocab', help='path to vocab.json') - parser.add_argument('--bpe-add-prefix-space', action='store_true', - help='add prefix space before encoding') - # fmt: on - def __init__(self, args): +@register_bpe("hf_byte_bpe", dataclass=HuggingFaceByteLevelBPEConfig) +class HuggingFaceByteLevelBPE(object): + def __init__(self, cfg): try: from tokenizers import ByteLevelBPETokenizer except ImportError: raise ImportError( - 'Please install huggingface/tokenizers with: ' - 'pip install tokenizers' + "Please install huggingface/tokenizers with: " "pip install tokenizers" ) + bpe_vocab = file_utils.cached_path(cfg.bpe_vocab) + bpe_merges = file_utils.cached_path(cfg.bpe_merges) + self.bpe = ByteLevelBPETokenizer( - args.bpe_vocab, - args.bpe_merges, - add_prefix_space=getattr(args, 'bpe_add_prefix_space', False), + bpe_vocab, + bpe_merges, + add_prefix_space=cfg.bpe_add_prefix_space, ) def encode(self, x: str) -> str: - return ' '.join(map(str, self.bpe.encode(x).ids)) + return " ".join(map(str, self.bpe.encode(x).ids)) def decode(self, x: str) -> str: - return self.bpe.decode([ - int(tok) if tok not in {'<unk>', '<mask>'} else tok - for tok in x.split() - ]) + return self.bpe.decode( + [int(tok) if tok not in {"<unk>", "<mask>"} else tok for tok in x.split()] + ) def is_beginning_of_word(self, x: str) -> bool: - return self.decode(x).startswith(' ') + return self.decode(x).startswith(" ") diff --git a/fairseq/data/encoders/moses_tokenizer.py b/fairseq/data/encoders/moses_tokenizer.py index b1e7478b9d..e236dad167 100644 --- a/fairseq/data/encoders/moses_tokenizer.py +++ b/fairseq/data/encoders/moses_tokenizer.py @@ -3,46 +3,46 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. +from dataclasses import dataclass, field + from fairseq.data.encoders import register_tokenizer +from fairseq.dataclass import FairseqDataclass -@register_tokenizer('moses') -class MosesTokenizer(object): +@dataclass +class MosesTokenizerConfig(FairseqDataclass): + source_lang: str = field(default="en", metadata={"help": "source language"}) + target_lang: str = field(default="en", metadata={"help": "target language"}) + moses_no_dash_splits: bool = field( + default=False, metadata={"help": "don't apply dash split rules"} + ) + moses_no_escape: bool = field( + default=False, + metadata={"help": "don't perform HTML escaping on apostrophe, quotes, etc."}, + ) - @staticmethod - def add_args(parser): - # fmt: off - parser.add_argument('--moses-source-lang', metavar='SRC', - help='source language') - parser.add_argument('--moses-target-lang', metavar='TARGET', - help='target language') - parser.add_argument('--moses-no-dash-splits', action='store_true', default=False, - help='don\'t apply dash split rules') - parser.add_argument('--moses-no-escape', action='store_true', default=False, - help='don\'t perform HTML escaping on apostrophy, quotes, etc.') - # fmt: on - - def __init__(self, args): - self.args = args - - if getattr(args, 'moses_source_lang', None) is None: - args.moses_source_lang = getattr(args, 'source_lang', 'en') - if getattr(args, 'moses_target_lang', None) is None: - args.moses_target_lang = getattr(args, 'target_lang', 'en') + +@register_tokenizer("moses", dataclass=MosesTokenizerConfig) +class MosesTokenizer(object): + def __init__(self, cfg: MosesTokenizerConfig): + self.cfg = cfg try: from sacremoses import MosesTokenizer, MosesDetokenizer - self.tok = MosesTokenizer(args.moses_source_lang) - self.detok = MosesDetokenizer(args.moses_target_lang) + + self.tok = MosesTokenizer(cfg.source_lang) + self.detok = MosesDetokenizer(cfg.target_lang) except ImportError: - raise ImportError('Please install Moses tokenizer with: pip install sacremoses') + raise ImportError( + "Please install Moses tokenizer with: pip install sacremoses" + ) def encode(self, x: str) -> str: return self.tok.tokenize( x, - aggressive_dash_splits=(not self.args.moses_no_dash_splits), + aggressive_dash_splits=(not self.cfg.moses_no_dash_splits), return_str=True, - escape=(not self.args.moses_no_escape), + escape=(not self.cfg.moses_no_escape), ) def decode(self, x: str) -> str: diff --git a/fairseq/data/encoders/nltk_tokenizer.py b/fairseq/data/encoders/nltk_tokenizer.py index 3db8ee5652..0ab92377b3 100644 --- a/fairseq/data/encoders/nltk_tokenizer.py +++ b/fairseq/data/encoders/nltk_tokenizer.py @@ -4,20 +4,21 @@ # LICENSE file in the root directory of this source tree. from fairseq.data.encoders import register_tokenizer +from fairseq.dataclass import FairseqDataclass -@register_tokenizer('nltk') +@register_tokenizer("nltk", dataclass=FairseqDataclass) class NLTKTokenizer(object): - - def __init__(self, source_lang=None, target_lang=None): + def __init__(self, *unused): try: from nltk.tokenize import word_tokenize + self.word_tokenize = word_tokenize except ImportError: - raise ImportError('Please install nltk with: pip install nltk') + raise ImportError("Please install nltk with: pip install nltk") def encode(self, x: str) -> str: - return ' '.join(self.word_tokenize(x)) + return " ".join(self.word_tokenize(x)) def decode(self, x: str) -> str: return x diff --git a/fairseq/data/encoders/sentencepiece_bpe.py b/fairseq/data/encoders/sentencepiece_bpe.py index 7ae4fc57d1..a76d46a201 100644 --- a/fairseq/data/encoders/sentencepiece_bpe.py +++ b/fairseq/data/encoders/sentencepiece_bpe.py @@ -3,41 +3,46 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. +from dataclasses import dataclass, field + from fairseq import file_utils from fairseq.data.encoders import register_bpe +from fairseq.dataclass import FairseqDataclass -@register_bpe('sentencepiece') -class SentencepieceBPE(object): +@dataclass +class SentencepieceConfig(FairseqDataclass): + sentencepiece_model: str = field( + default="???", metadata={"help": "path to sentencepiece model"} + ) - @staticmethod - def add_args(parser): - # fmt: off - parser.add_argument('--sentencepiece-vocab', type=str, - help='path to sentencepiece vocab') - # fmt: on - def __init__(self, args): - vocab = file_utils.cached_path(args.sentencepiece_vocab) +@register_bpe("sentencepiece", dataclass=SentencepieceConfig) +class SentencepieceBPE(object): + def __init__(self, cfg): + sentencepiece_model = file_utils.cached_path(cfg.sentencepiece_model) try: import sentencepiece as spm + self.sp = spm.SentencePieceProcessor() - self.sp.Load(vocab) + self.sp.Load(sentencepiece_model) except ImportError: - raise ImportError('Please install sentencepiece with: pip install sentencepiece') + raise ImportError( + "Please install sentencepiece with: pip install sentencepiece" + ) def encode(self, x: str) -> str: - return ' '.join(self.sp.EncodeAsPieces(x)) + return " ".join(self.sp.EncodeAsPieces(x)) def decode(self, x: str) -> str: - return x.replace(' ', '').replace('\u2581', ' ').strip() + return x.replace(" ", "").replace("\u2581", " ").strip() def is_beginning_of_word(self, x: str) -> bool: - if x in ['<unk>', '<s>', '</s>', '<pad>']: + if x in ["<unk>", "<s>", "</s>", "<pad>"]: # special elements are always considered beginnings # HACK: this logic is already present in fairseq/tasks/masked_lm.py # but these special tokens are also contained in the sentencepiece # vocabulary which causes duplicate special tokens. This hack makes # sure that they are all taken into account. return True - return x.startswith('\u2581') + return x.startswith("\u2581") diff --git a/fairseq/data/encoders/space_tokenizer.py b/fairseq/data/encoders/space_tokenizer.py index 670001a8e8..925ad41b7c 100644 --- a/fairseq/data/encoders/space_tokenizer.py +++ b/fairseq/data/encoders/space_tokenizer.py @@ -6,16 +6,16 @@ import re from fairseq.data.encoders import register_tokenizer +from fairseq.dataclass import FairseqDataclass -@register_tokenizer('space') +@register_tokenizer("space", dataclass=FairseqDataclass) class SpaceTokenizer(object): - - def __init__(self, source_lang=None, target_lang=None): + def __init__(self, *unused): self.space_tok = re.compile(r"\s+") def encode(self, x: str) -> str: - return self.space_tok.sub(' ', x) + return self.space_tok.sub(" ", x) def decode(self, x: str) -> str: return x diff --git a/fairseq/data/encoders/subword_nmt_bpe.py b/fairseq/data/encoders/subword_nmt_bpe.py index 78f19b43ea..5d724d2730 100644 --- a/fairseq/data/encoders/subword_nmt_bpe.py +++ b/fairseq/data/encoders/subword_nmt_bpe.py @@ -3,33 +3,37 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. +from dataclasses import dataclass, field + from fairseq import file_utils from fairseq.data.encoders import register_bpe +from fairseq.dataclass import FairseqDataclass -@register_bpe('subword_nmt') -class SubwordNMTBPE(object): +@dataclass +class SubwordNMTBPEConfig(FairseqDataclass): + bpe_codes: str = field(default="???", metadata={"help": "path to subword NMT BPE"}) + bpe_separator: str = field(default="@@", metadata={"help": "BPE separator"}) - @staticmethod - def add_args(parser): - # fmt: off - parser.add_argument('--bpe-codes', type=str, - help='path to subword NMT BPE') - parser.add_argument('--bpe-separator', default='@@', - help='BPE separator') - # fmt: on - - def __init__(self, args): - if args.bpe_codes is None: - raise ValueError('--bpe-codes is required for --bpe=subword_nmt') - codes = file_utils.cached_path(args.bpe_codes) + +@register_bpe("subword_nmt", dataclass=SubwordNMTBPEConfig) +class SubwordNMTBPE(object): + def __init__(self, cfg): + if cfg.bpe_codes is None: + raise ValueError("--bpe-codes is required for --bpe=subword_nmt") + codes = file_utils.cached_path(cfg.bpe_codes) try: from subword_nmt import apply_bpe + bpe_parser = apply_bpe.create_parser() - bpe_args = bpe_parser.parse_args([ - '--codes', codes, - '--separator', args.bpe_separator, - ]) + bpe_args = bpe_parser.parse_args( + [ + "--codes", + codes, + "--separator", + cfg.bpe_separator, + ] + ) self.bpe = apply_bpe.BPE( bpe_args.codes, bpe_args.merges, @@ -37,12 +41,14 @@ def __init__(self, args): None, bpe_args.glossaries, ) - self.bpe_symbol = bpe_args.separator + ' ' + self.bpe_symbol = bpe_args.separator + " " except ImportError: - raise ImportError('Please install subword_nmt with: pip install subword-nmt') + raise ImportError( + "Please install subword_nmt with: pip install subword-nmt" + ) def encode(self, x: str) -> str: return self.bpe.process_line(x) def decode(self, x: str) -> str: - return (x + ' ').replace(self.bpe_symbol, '').rstrip() + return (x + " ").replace(self.bpe_symbol, "").rstrip() diff --git a/fairseq/data/encoders/utils.py b/fairseq/data/encoders/utils.py index a0e491c143..d93eb532ef 100644 --- a/fairseq/data/encoders/utils.py +++ b/fairseq/data/encoders/utils.py @@ -10,19 +10,21 @@ def get_whole_word_mask(args, dictionary): bpe = encoders.build_bpe(args) if bpe is not None: + def is_beginning_of_word(i): if i < dictionary.nspecial: # special elements are always considered beginnings return True tok = dictionary[i] - if tok.startswith('madeupword'): + if tok.startswith("madeupword"): return True try: return bpe.is_beginning_of_word(tok) except ValueError: return True - mask_whole_words = torch.ByteTensor(list( - map(is_beginning_of_word, range(len(dictionary))) - )) + + mask_whole_words = torch.ByteTensor( + list(map(is_beginning_of_word, range(len(dictionary)))) + ) return mask_whole_words return None diff --git a/fairseq/data/fairseq_dataset.py b/fairseq/data/fairseq_dataset.py index fe5681be5a..23e6992dba 100644 --- a/fairseq/data/fairseq_dataset.py +++ b/fairseq/data/fairseq_dataset.py @@ -3,15 +3,32 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. +import logging import numpy as np import torch.utils.data +from fairseq.data import data_utils + +logger = logging.getLogger(__name__) class EpochListening: """Mixin for receiving updates whenever the epoch increments.""" - def set_epoch(self, epoch): - """Will receive the updated epoch number at the beginning of the epoch. + + @property + def can_reuse_epoch_itr_across_epochs(self): """ + Whether we can reuse the :class:`fairseq.data.EpochBatchIterator` for + this dataset across epochs. + + This needs to return ``False`` if the sample sizes can change across + epochs, in which case we may need to regenerate batches at each epoch. + If your dataset relies in ``set_epoch`` then you should consider setting + this to ``False``. + """ + return True + + def set_epoch(self, epoch): + """Will receive the updated epoch number at the beginning of the epoch.""" pass @@ -40,6 +57,11 @@ def num_tokens(self, index): enforce ``--max-tokens`` during batching.""" raise NotImplementedError + def num_tokens_vec(self, indices): + """Return the number of tokens for a set of positions defined by indices. + This value is used to enforce ``--max-tokens`` during batching.""" + raise NotImplementedError + def size(self, index): """Return an example's size as a float or tuple. This value is used when filtering a dataset with ``--max-positions``.""" @@ -48,7 +70,7 @@ def size(self, index): def ordered_indices(self): """Return an ordered list of indices. Batches will be constructed based on this order.""" - return np.arange(len(self)) + return np.arange(len(self), dtype=np.int64) @property def supports_prefetch(self): @@ -62,10 +84,121 @@ def prefetch(self, indices): """Prefetch the data required for this epoch.""" raise NotImplementedError + def get_batch_shapes(self): + """ + Return a list of valid batch shapes, for example:: + + [(8, 512), (16, 256), (32, 128)] + + The first dimension of each tuple is the batch size and can be ``None`` + to automatically infer the max batch size based on ``--max-tokens``. + The second dimension of each tuple is the max supported length as given + by :func:`fairseq.data.FairseqDataset.num_tokens`. + + This will be used by :func:`fairseq.data.FairseqDataset.batch_by_size` + to restrict batch shapes. This is useful on TPUs to avoid too many + dynamic shapes (and recompilations). + """ + return None + + def batch_by_size( + self, + indices, + max_tokens=None, + max_sentences=None, + required_batch_size_multiple=1, + ): + """ + Given an ordered set of indices, return batches according to + *max_tokens*, *max_sentences* and *required_batch_size_multiple*. + """ + from fairseq.data import data_utils + + fixed_shapes = self.get_batch_shapes() + if fixed_shapes is not None: + + def adjust_bsz(bsz, num_tokens): + if bsz is None: + assert max_tokens is not None, "Must specify --max-tokens" + bsz = max_tokens // num_tokens + if max_sentences is not None: + bsz = min(bsz, max_sentences) + elif ( + bsz >= required_batch_size_multiple + and bsz % required_batch_size_multiple != 0 + ): + bsz -= bsz % required_batch_size_multiple + return bsz + + fixed_shapes = np.array( + [ + [adjust_bsz(bsz, num_tokens), num_tokens] + for (bsz, num_tokens) in fixed_shapes + ] + ) + + try: + num_tokens_vec = self.num_tokens_vec(indices).astype('int64') + except NotImplementedError: + num_tokens_vec = None + + return data_utils.batch_by_size( + indices, + num_tokens_fn=self.num_tokens, + num_tokens_vec=num_tokens_vec, + max_tokens=max_tokens, + max_sentences=max_sentences, + required_batch_size_multiple=required_batch_size_multiple, + fixed_shapes=fixed_shapes, + ) + + def filter_indices_by_size(self, indices, max_sizes): + """ + Filter a list of sample indices. Remove those that are longer than + specified in *max_sizes*. + + WARNING: don't update, override method in child classes + + Args: + indices (np.array): original array of sample indices + max_sizes (int or list[int] or tuple[int]): max sample size, + can be defined separately for src and tgt (then list or tuple) + + Returns: + np.array: filtered sample array + list: list of removed indices + """ + if isinstance(max_sizes, float) or isinstance(max_sizes, int): + if hasattr(self, "sizes") and isinstance(self.sizes, np.ndarray): + ignored = indices[self.sizes[indices] > max_sizes].tolist() + indices = indices[self.sizes[indices] <= max_sizes] + elif ( + hasattr(self, "sizes") + and isinstance(self.sizes, list) + and len(self.sizes) == 1 + ): + ignored = indices[self.sizes[0][indices] > max_sizes].tolist() + indices = indices[self.sizes[0][indices] <= max_sizes] + else: + indices, ignored = data_utils._filter_by_size_dynamic( + indices, self.size, max_sizes + ) + else: + indices, ignored = data_utils._filter_by_size_dynamic( + indices, self.size, max_sizes + ) + return indices, ignored + + @property + def supports_fetch_outside_dataloader(self): + """Whether this dataset supports fetching outside the workers of the dataloader.""" + return True + class FairseqIterableDataset(torch.utils.data.IterableDataset, EpochListening): - """For datasets that need to be read sequentially, usually because the data - is being streamed or otherwise can't be manipulated on a single machine. + """ + For datasets that need to be read sequentially, usually because the data is + being streamed or otherwise can't be manipulated on a single machine. """ def __iter__(self): diff --git a/fairseq/data/fasta_dataset.py b/fairseq/data/fasta_dataset.py new file mode 100644 index 0000000000..007011974a --- /dev/null +++ b/fairseq/data/fasta_dataset.py @@ -0,0 +1,107 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os +import subprocess +import threading +from pathlib import Path + +import numpy as np +import torch + + +def fasta_file_path(prefix_path): + return prefix_path + ".fasta" + + +class FastaDataset(torch.utils.data.Dataset): + """ + For loading protein sequence datasets in the common FASTA data format + """ + + def __init__(self, path: str, cache_indices=False): + self.fn = fasta_file_path(path) + self.threadlocal = threading.local() + self.cache = Path(f"{path}.fasta.idx.npy") + if cache_indices: + if self.cache.exists(): + self.offsets, self.sizes = np.load(self.cache) + else: + self.offsets, self.sizes = self._build_index(path) + np.save(self.cache, np.stack([self.offsets, self.sizes])) + else: + self.offsets, self.sizes = self._build_index(path) + + def _get_file(self): + if not hasattr(self.threadlocal, "f"): + self.threadlocal.f = open(self.fn, "r") + return self.threadlocal.f + + def __getitem__(self, idx): + f = self._get_file() + f.seek(self.offsets[idx]) + desc = f.readline().strip() + line = f.readline() + seq = "" + while line != "" and line[0] != ">": + seq += line.strip() + line = f.readline() + return desc, seq + + def __len__(self): + return self.offsets.size + + def _build_index(self, path: str): + # Use grep and awk to get 100M/s on local SSD. + # Should process your enormous 100G fasta in ~10 min single core... + path = fasta_file_path(path) + bytes_offsets = subprocess.check_output( + f"cat {path} | tqdm --bytes --total $(wc -c < {path})" + "| grep --byte-offset '^>' -o | cut -d: -f1", + shell=True, + ) + fasta_lengths = subprocess.check_output( + f"cat {path} | tqdm --bytes --total $(wc -c < {path})" + "| awk '/^>/ {print \"\";next;} { printf(\"%s\",$0);}' | tail -n+2 | awk '{print length($1)}'", + shell=True, + ) + bytes_np = np.fromstring(bytes_offsets, dtype=np.int64, sep=" ") + sizes_np = np.fromstring(fasta_lengths, dtype=np.int64, sep=" ") + return bytes_np, sizes_np + + def __setstate__(self, state): + self.__dict__ = state + self.threadlocal = threading.local() + + def __getstate__(self): + d = {} + for i, v in self.__dict__.items(): + if i != "threadlocal": + d[i] = v + return d + + def __del__(self): + if hasattr(self.threadlocal, "f"): + self.threadlocal.f.close() + del self.threadlocal.f + + @staticmethod + def exists(path): + return os.path.exists(fasta_file_path(path)) + + +class EncodedFastaDataset(FastaDataset): + """ + The FastaDataset returns raw sequences - this allows us to return + indices with a dictionary instead. + """ + + def __init__(self, path, dictionary): + super().__init__(path, cache_indices=True) + self.dictionary = dictionary + + def __getitem__(self, idx): + desc, seq = super().__getitem__(idx) + return self.dictionary.encode_line(seq, line_tokenizer=list).long() diff --git a/fairseq/data/huffman/__init__.py b/fairseq/data/huffman/__init__.py new file mode 100644 index 0000000000..9b61fafadb --- /dev/null +++ b/fairseq/data/huffman/__init__.py @@ -0,0 +1,21 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from .huffman_coder import HuffmanCodeBuilder, HuffmanCoder +from .huffman_mmap_indexed_dataset import ( + HuffmanMMapIndex, + HuffmanMMapIndexedDataset, + HuffmanMMapIndexedDatasetBuilder, + vocab_file_path, +) + +__all__ = [ + "HuffmanCoder", + "HuffmanCodeBuilder", + "HuffmanMMapIndexedDatasetBuilder", + "HuffmanMMapIndexedDataset", + "HuffmanMMapIndex", + "vocab_file_path", +] diff --git a/fairseq/data/huffman/huffman_coder.py b/fairseq/data/huffman/huffman_coder.py new file mode 100644 index 0000000000..6531f1547c --- /dev/null +++ b/fairseq/data/huffman/huffman_coder.py @@ -0,0 +1,265 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import re +import typing as tp +from collections import Counter, deque +from dataclasses import dataclass + +from bitarray import bitarray, util +from fairseq.data import Dictionary + +# basically we have to write to addressable bytes for the memory mapped +# dataset loader. Sentences that get encoded to a length that is not a +# multiple of BLOCKSIZE (a byte) will be padded to fit. (see _pad in the coder) +BLOCKSIZE = 8 + + +class HuffmanCoder: + def __init__( + self, root: "HuffmanNode", bos="<s>", pad="<pad>", eos="</s>", unk="<unk>" + ): + self.root = root + self.table = root.code_table() + self.bos_word, self.unk_word, self.pad_word, self.eos_word = bos, unk, pad, eos + + def _pad(self, a: bitarray) -> bitarray: + """ + bitpadding, 1 then 0. + + If the array is already a multiple of blocksize, we add a full block. + """ + pad_len = BLOCKSIZE - (len(a) % BLOCKSIZE) - 1 + padding = bitarray("1" + "0" * pad_len) + return a + padding + + def _unpad(self, a: bitarray) -> bitarray: + """ + remove the bitpadding. + + There will be a set of 0s preceded by a 1 at the end of the bitarray, we remove that + """ + # count the 0 padding at the end until we find the first 1 + # we want to remove the one too + remove_cnt = util.rindex(a, 1) + return a[:remove_cnt] + + def encode(self, iter: tp.List[str]) -> bytes: + """ + encode a list of tokens a return bytes. We use bitpadding to make sure the encoded bits fit in bytes. + """ + a = bitarray() + for token in iter: + code = self.get_code(token) + if code is None: + if self.unk_word is None: + raise Exception(f"unknown token {token} cannot be encoded.") + else: + token = self.unk_word + a = a + self.get_code(token) + return self._pad(a).tobytes() + + def decode(self, bits: bytes) -> tp.Iterator["HuffmanNode"]: + """ + take bitpadded bytes and decode it to a set of leaves. You can then use each node to find the symbol/id + """ + a = bitarray() + a.frombytes(bits) + return self.root.decode(self._unpad(a)) + + def get_code(self, symbol: str) -> tp.Optional[bitarray]: + node = self.get_node(symbol) + return None if node is None else node.code + + def get_node(self, symbol: str) -> "HuffmanNode": + return self.table.get(symbol) + + @classmethod + def from_file( + cls, + filename: str, + bos="<s>", + pad="<pad>", + eos="</s>", + unk="<unk>", + ) -> "HuffmanCoder": + builder = HuffmanCodeBuilder.from_file(filename) + return builder.build_code(bos=bos, pad=pad, eos=eos, unk=unk) + + def to_file(self, filename, sep="\t"): + nodes = list(self.table.values()) + nodes.sort(key=lambda n: n.id) + with open(filename, "w", encoding="utf-8") as output: + for n in nodes: + output.write(f"{n.symbol}{sep}{n.count}\n") + + def __iter__(self): + for n in self.table.values(): + yield n + + def merge(self, other_coder: "HuffmanCoder") -> "HuffmanCoder": + builder = HuffmanCodeBuilder() + for n in self: + builder.increment(n.symbol, n.count) + for n in other_coder: + builder.increment(n.symbol, n.count) + return builder.build_code() + + def __eq__(self, other: "HuffmanCoder") -> bool: + return self.table == other.table + + def __len__(self) -> int: + return len(self.table) + + def __contains__(self, sym: str) -> bool: + return sym in self.table + + def to_dictionary(self) -> Dictionary: + dictionary = Dictionary(bos=self.bos, unk=self.unk, pad=self.pad, eos=self.eos) + for n in self: + dictionary.add_symbol(n.symbol, n=n.count) + dictionary.finalize() + return dictionary + + +@dataclass +class HuffmanNode: + """ + a node in a Huffman tree + """ + + id: int + count: int + symbol: tp.Optional[str] = None + left: tp.Optional["HuffmanNode"] = None + right: tp.Optional["HuffmanNode"] = None + code: tp.Optional[bitarray] = None + + def is_leaf(self) -> bool: + return self.left is None and self.right is None + + def code_table(self, prefix: tp.Optional[bitarray] = None) -> tp.Dict[str, "HuffmanNode"]: + defaulted_prefix = prefix if prefix is not None else bitarray() + if self.is_leaf(): + self.code = ( + defaulted_prefix if len(defaulted_prefix) > 0 else bitarray("0") + ) # leaf could be the root if there is only one symbol + return {self.symbol: self} + + codes_right = self.right.code_table(defaulted_prefix + bitarray([0])) + codes_left = self.left.code_table(defaulted_prefix + bitarray([1])) + return {**codes_left, **codes_right} + + def decode(self, bits: bitarray) -> tp.Iterator["HuffmanNode"]: + current_node = self + for bit in bits: + if bit == 0: # go right + current_node = current_node.right + else: # go left + current_node = current_node.left + if current_node is None: + # we shouldn't be on a leaf here + raise Exception("fell off a leaf") + if current_node.is_leaf(): + yield current_node + current_node = self + if current_node != self: + raise Exception("couldn't decode all the bits") + + +class HuffmanCodeBuilder: + """ + build a dictionary with occurence count and then build the Huffman code for it. + """ + + def __init__(self): + self.symbols = Counter() + + def add_symbols(self, *syms) -> None: + self.symbols.update(syms) + + def increment(self, symbol: str, cnt: int) -> None: + self.symbols[symbol] += cnt + + @classmethod + def from_file(cls, filename): + c = cls() + with open(filename, "r", encoding="utf-8") as input: + for line in input: + split = re.split(r"[\s]+", line) + c.increment(split[0], int(split[1])) + return c + + def to_file(self, filename, sep="\t"): + with open(filename, "w", encoding="utf-8") as output: + for (tok, cnt) in self.symbols.most_common(): + output.write(f"{tok}{sep}{cnt}\n") + + def _smallest(self, q1: deque, q2: deque) -> HuffmanNode: + if len(q1) == 0: + return q2.pop() + + if len(q2) == 0: + return q1.pop() + + if q1[-1].count < q2[-1].count: + return q1.pop() + + return q2.pop() + + def __add__(self, c: "HuffmanCodeBuilder") -> "HuffmanCodeBuilder": + new_c = self.symbols + c.symbols + new_b = HuffmanCodeBuilder() + new_b.symbols = new_c + return new_b + + def build_code( + self, + bos="<s>", + pad="<pad>", + eos="</s>", + unk="<unk>", + ) -> HuffmanCoder: + assert len(self.symbols) > 0, "cannot build code from empty list of symbols" + + if self.symbols[bos] == 0: + self.add_symbols(bos) + if self.symbols[pad] == 0: + self.add_symbols(pad) + if self.symbols[eos] == 0: + self.add_symbols(eos) + if self.symbols[unk] == 0: + self.add_symbols(unk) + + node_id = 0 + leaves_queue = deque( + [ + HuffmanNode(symbol=symbol, count=count, id=idx) + for idx, (symbol, count) in enumerate(self.symbols.most_common()) + ] + ) # left are the most common, right are the least common + + if len(leaves_queue) == 1: + root = leaves_queue.pop() + root.id = 0 + return HuffmanCoder(root) + + nodes_queue = deque() + + while len(leaves_queue) > 0 or len(nodes_queue) != 1: + # get the lowest two nodes at the head of each queue + node1 = self._smallest(leaves_queue, nodes_queue) + node2 = self._smallest(leaves_queue, nodes_queue) + + # add new node + nodes_queue.appendleft( + HuffmanNode( + count=node1.count + node2.count, left=node1, right=node2, id=node_id + ) + ) + node_id += 1 + + # we are left with the root + return HuffmanCoder(nodes_queue.pop(), bos=bos, pad=pad, eos=eos, unk=unk) diff --git a/fairseq/data/huffman/huffman_mmap_indexed_dataset.py b/fairseq/data/huffman/huffman_mmap_indexed_dataset.py new file mode 100644 index 0000000000..3279dae89a --- /dev/null +++ b/fairseq/data/huffman/huffman_mmap_indexed_dataset.py @@ -0,0 +1,287 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import mmap +import os +import shutil +import struct +import typing as tp +from functools import lru_cache + +import numpy as np +import torch +from fairseq.data import indexed_dataset +from fairseq.data.huffman import HuffmanCoder +from fairseq.file_io import PathManager + + +class HuffmanMMapIndex: + """ + keep an index of the offsets in the huffman binary file. + First a header, then the list of sizes (num tokens) for each instance and finally + the addresses of each instance. + """ + + _HDR_MAGIC = b"HUFFIDX\x00\x00" + _VERSION = 1 + + @classmethod + def writer(cls, path: str, data_len: int): + class _Writer: + def __enter__(self): + self._file = open(path, "wb") + + # write header (magic + version) + self._file.write(cls._HDR_MAGIC) + self._file.write(struct.pack("<Q", cls._VERSION)) + self._file.write(struct.pack("<Q", data_len)) + + return self + + def write(self, sizes, pointers): + # add number of items in the index to the header + self._file.write(struct.pack("<Q", len(sizes))) + + # write sizes + sizes = np.array(sizes, dtype=np.int32) + self._file.write(sizes.tobytes(order="C")) + del sizes + + # write address pointers + pointers = np.array(pointers, dtype=np.int64) + self._file.write(pointers.tobytes(order="C")) + del pointers + + def __exit__(self, exc_type, exc_val, exc_tb): + self._file.close() + + return _Writer() + + def __init__(self, path): + with open(path, "rb") as stream: + # read headers + magic_test = stream.read(9) + assert self._HDR_MAGIC == magic_test, ( + "Index file doesn't match expected format. " + "Make sure that --dataset-impl is configured properly." + ) + (version,) = struct.unpack("<Q", stream.read(8)) + assert ( + self._VERSION == version + ), "Unexpected file version f{version} != code version f{self._VERSION}" + + # read length of data file + (self._data_len,) = struct.unpack("<Q", stream.read(8)) + # read number of items in data file/index + (self._len,) = struct.unpack("<Q", stream.read(8)) + offset = stream.tell() + + indexed_dataset._warmup_mmap_file(path) + + self._bin_buffer_mmap = np.memmap(path, mode="r", order="C") + self._bin_buffer = memoryview(self._bin_buffer_mmap) + self._sizes = np.frombuffer( + self._bin_buffer, dtype=np.int32, count=self._len, offset=offset + ) + self._pointers = np.frombuffer( + self._bin_buffer, + dtype=np.int64, + count=self._len, + offset=offset + self._sizes.nbytes, + ) + + def __del__(self): + self._bin_buffer_mmap._mmap.close() + del self._bin_buffer_mmap + + def __iter__(self): + for i in range(self._len): + yield self[i] + + @property + def data_len(self): + return self._data_len + + @property + def sizes(self): + return self._sizes + + @lru_cache(maxsize=8) + def __getitem__(self, i): + return self._pointers[i], self._sizes[i] + + def __len__(self): + return self._len + + +def vocab_file_path(prefix_path): + return prefix_path + ".vocab" + + +class HuffmanMMapIndexedDataset(torch.utils.data.Dataset): + """ + an indexed dataset that use mmap and memoryview to access data from disk + that was compressed with a HuffmanCoder. + """ + + def __init__(self, prefix_path): + super().__init__() + + self._prefix_path = None + self._index = None + self._bin_buffer = None + self._coder = None + self._file = None + + self._bin_buffer_mmap = None + + self._do_init(prefix_path) + + def __getstate__(self): + return self._prefix_path + + def __setstate__(self, state): + self._do_init(state) + + def _do_init(self, prefix_path): + self._prefix_path = prefix_path + self._index = HuffmanMMapIndex( + indexed_dataset.index_file_path(self._prefix_path) + ) + self._coder = HuffmanCoder.from_file(vocab_file_path(self._prefix_path)) + + indexed_dataset._warmup_mmap_file( + indexed_dataset.data_file_path(self._prefix_path) + ) + self._file = os.open( + indexed_dataset.data_file_path(self._prefix_path), os.O_RDONLY + ) + self._bin_buffer_mmap = mmap.mmap( + self._file, + self._index.data_len, + access=mmap.ACCESS_READ, + ) + self._bin_buffer = memoryview(self._bin_buffer_mmap) + + def __del__(self): + del self._bin_buffer + if self._file: + os.close(self._file) + del self._index + + def __len__(self): + return len(self._index) + + def _decode(self, i): + ptr, _ = self._index[i] + if i == 0: + raw_bytes = self._bin_buffer[:ptr] + else: + (prev_ptr, _) = self._index[i - 1] + raw_bytes = self._bin_buffer[prev_ptr:ptr] + + return self._coder.decode(raw_bytes.tobytes()) + + @lru_cache(maxsize=8) + def __getitem__(self, i): + nodes = self._decode(i) + return torch.tensor([n.id for n in nodes], dtype=torch.int64) + + def __iter__(self): + for idx in range(len(self)): + yield self[idx] + + def get_symbols(self, i): + nodes = self._decode(i) + for n in nodes: + yield n.symbol + + @property + def sizes(self): + return self._index.sizes + + @property + def supports_prefetch(self): + return False + + @property + def coder(self): + return self._coder + + @staticmethod + def exists(prefix_path): + return ( + PathManager.exists(indexed_dataset.index_file_path(prefix_path)) + and PathManager.exists(indexed_dataset.data_file_path(prefix_path)) + and PathManager.exists(vocab_file_path(prefix_path)) + ) + + +class HuffmanMMapIndexedDatasetBuilder: + """ + Helper to build a memory mapped datasets with a huffman encoder. + You can either open/close this manually or use it as a ContextManager. + Provide your own coder, it will then be stored alongside the dataset. + The builder will first write the vocab file, then open the binary file so you can stream + into it, finally the index will be written when the builder is closed (your index should fit in memory). + """ + + def __init__(self, path_prefix: str, coder: HuffmanCoder) -> None: + self._path_prefix = path_prefix + self._coder = coder + self._sizes = [] + self._ptrs = [] + self._data_len = 0 + + def open(self): + self._coder.to_file(vocab_file_path(self._path_prefix)) + self._data_file = open(indexed_dataset.data_file_path(self._path_prefix), "wb") + + def __enter__(self) -> "HuffmanMMapIndexedDatasetBuilder": + self.open() + return self + + def add_item(self, tokens: tp.List[str]) -> None: + """ + add a list of tokens to the dataset, they will compressed with the + provided coder before being written to file. + """ + encoded = self._coder.encode(tokens) + code_len = len(encoded) + last_ptr = 0 + if len(self._ptrs) > 0: + last_ptr = self._ptrs[-1] + self._sizes.append(len(tokens)) + self._ptrs.append(last_ptr + code_len) + self._data_len += code_len + self._data_file.write(encoded) + + def append(self, other_dataset_path_prefix: str) -> None: + """ + append an existing dataset. + Beware, if it wasn't built with the same coder, you are in trouble. + """ + other_index = HuffmanMMapIndex( + indexed_dataset.index_file_path(other_dataset_path_prefix) + ) + for (ptr, size) in other_index: + self._ptrs.append(ptr + self._data_len) + self._sizes.append(size) + + # Concatenate data + with open(indexed_dataset.data_file_path(other_dataset_path_prefix), "rb") as f: + shutil.copyfileobj(f, self._data_file) + + self._data_len += other_index.data_len + + def close(self): + self._data_file.close() + with HuffmanMMapIndex.writer( + indexed_dataset.index_file_path(self._path_prefix), self._data_len + ) as index: + index.write(self._sizes, self._ptrs) + + def __exit__(self, exc_type, exc_val, exc_tb) -> None: + self.close() diff --git a/fairseq/data/id_dataset.py b/fairseq/data/id_dataset.py index 6a73ba1ff7..3e4d7969cf 100644 --- a/fairseq/data/id_dataset.py +++ b/fairseq/data/id_dataset.py @@ -9,7 +9,6 @@ class IdDataset(FairseqDataset): - def __getitem__(self, index): return index diff --git a/fairseq/data/indexed_dataset.py b/fairseq/data/indexed_dataset.py index 12497989bb..23afb43356 100644 --- a/fairseq/data/indexed_dataset.py +++ b/fairseq/data/indexed_dataset.py @@ -3,69 +3,101 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. -from functools import lru_cache -import os import shutil import struct +from functools import lru_cache import numpy as np import torch +from fairseq.dataclass.constants import DATASET_IMPL_CHOICES +from fairseq.data.fasta_dataset import FastaDataset +from fairseq.file_io import PathManager +from fairseq.data.huffman import HuffmanMMapIndexedDataset, HuffmanMMapIndex from . import FairseqDataset +from typing import Union + -def __best_fitting_dtype(vocab_size=None): - if vocab_size is not None and vocab_size < 65500: +def best_fitting_int_dtype( + max_int_to_represent, +) -> Union[np.uint16, np.uint32, np.int64]: + + if max_int_to_represent is None: + return np.uint32 # Safe guess + elif max_int_to_represent < 65500: return np.uint16 + elif max_int_to_represent < 4294967295: + return np.uint32 else: - return np.int32 + return np.int64 + # we avoid np.uint64 because it doesn't save space and its type promotion behaves unexpectedly + # https://github.com/numpy/numpy/issues/5745 def get_available_dataset_impl(): - return ['raw', 'lazy', 'cached', 'mmap'] + return list(map(str, DATASET_IMPL_CHOICES)) def infer_dataset_impl(path): if IndexedRawTextDataset.exists(path): - return 'raw' + return "raw" elif IndexedDataset.exists(path): - with open(index_file_path(path), 'rb') as f: + with open(index_file_path(path), "rb") as f: magic = f.read(8) if magic == IndexedDataset._HDR_MAGIC: - return 'cached' + return "cached" elif magic == MMapIndexedDataset.Index._HDR_MAGIC[:8]: - return 'mmap' + return "mmap" + elif magic == HuffmanMMapIndex._HDR_MAGIC[:8]: + return "huffman" else: return None + elif FastaDataset.exists(path): + return "fasta" else: return None def make_builder(out_file, impl, vocab_size=None): - if impl == 'mmap': - return MMapIndexedDatasetBuilder(out_file, dtype=__best_fitting_dtype(vocab_size)) + if impl == "mmap": + return MMapIndexedDatasetBuilder( + out_file, dtype=best_fitting_int_dtype(vocab_size) + ) + elif impl == "fasta": + raise NotImplementedError + elif impl == "huffman": + raise ValueError("Use HuffmanCodeBuilder directly as it has a different interface.") else: return IndexedDatasetBuilder(out_file) def make_dataset(path, impl, fix_lua_indexing=False, dictionary=None): - if impl == 'raw' and IndexedRawTextDataset.exists(path): + if impl == "raw" and IndexedRawTextDataset.exists(path): assert dictionary is not None return IndexedRawTextDataset(path, dictionary) - elif impl == 'lazy' and IndexedDataset.exists(path): + elif impl == "lazy" and IndexedDataset.exists(path): return IndexedDataset(path, fix_lua_indexing=fix_lua_indexing) - elif impl == 'cached' and IndexedDataset.exists(path): + elif impl == "cached" and IndexedDataset.exists(path): return IndexedCachedDataset(path, fix_lua_indexing=fix_lua_indexing) - elif impl == 'mmap' and MMapIndexedDataset.exists(path): + elif impl == "mmap" and MMapIndexedDataset.exists(path): return MMapIndexedDataset(path) + elif impl == "fasta" and FastaDataset.exists(path): + from fairseq.data.fasta_dataset import EncodedFastaDataset + + return EncodedFastaDataset(path, dictionary) + elif impl == "huffman" and HuffmanMMapIndexedDataset.exists(path): + return HuffmanMMapIndexedDataset(path) return None def dataset_exists(path, impl): - if impl == 'raw': + if impl == "raw": return IndexedRawTextDataset.exists(path) - elif impl == 'mmap': + elif impl == "mmap": return MMapIndexedDataset.exists(path) + elif impl == "huffman": + return HuffmanMMapIndexedDataset.exists(path) else: return IndexedDataset.exists(path) @@ -80,7 +112,7 @@ def write_longs(f, a): f.write(np.array(a, dtype=np.int64)) -dtypes = { +_code_to_dtype = { 1: np.uint8, 2: np.int8, 3: np.int16, @@ -88,28 +120,31 @@ def write_longs(f, a): 5: np.int64, 6: np.float, 7: np.double, - 8: np.uint16 + 8: np.uint16, + 9: np.uint32, + 10: np.uint64, } -def code(dtype): - for k in dtypes.keys(): - if dtypes[k] == dtype: +def _dtype_header_code(dtype) -> int: + for k in _code_to_dtype.keys(): + if _code_to_dtype[k] == dtype: return k raise ValueError(dtype) def index_file_path(prefix_path): - return prefix_path + '.idx' + return prefix_path + ".idx" def data_file_path(prefix_path): - return prefix_path + '.bin' + return prefix_path + ".bin" class IndexedDataset(FairseqDataset): """Loader for TorchNet IndexedDataset""" - _HDR_MAGIC = b'TNTIDX\x00\x00' + + _HDR_MAGIC = b"TNTIDX\x00\x00" def __init__(self, path, fix_lua_indexing=False): super().__init__() @@ -119,38 +154,38 @@ def __init__(self, path, fix_lua_indexing=False): self.read_index(path) def read_index(self, path): - with open(index_file_path(path), 'rb') as f: + with open(index_file_path(path), "rb") as f: magic = f.read(8) assert magic == self._HDR_MAGIC, ( - 'Index file doesn\'t match expected format. ' - 'Make sure that --dataset-impl is configured properly.' + "Index file doesn't match expected format. " + "Make sure that --dataset-impl is configured properly." ) version = f.read(8) - assert struct.unpack('<Q', version) == (1,) - code, self.element_size = struct.unpack('<QQ', f.read(16)) - self.dtype = dtypes[code] - self._len, self.s = struct.unpack('<QQ', f.read(16)) + assert struct.unpack("<Q", version) == (1,) + code, self.element_size = struct.unpack("<QQ", f.read(16)) + self.dtype = _code_to_dtype[code] + self._len, self.s = struct.unpack("<QQ", f.read(16)) self.dim_offsets = read_longs(f, self._len + 1) self.data_offsets = read_longs(f, self._len + 1) self.sizes = read_longs(f, self.s) def read_data(self, path): - self.data_file = open(data_file_path(path), 'rb', buffering=0) + self.data_file = open(data_file_path(path), "rb", buffering=0) def check_index(self, i): if i < 0 or i >= self._len: - raise IndexError('index out of range') + raise IndexError("index out of range") def __del__(self): if self.data_file: self.data_file.close() @lru_cache(maxsize=8) - def __getitem__(self, i): + def __getitem__(self, i) -> torch.Tensor: if not self.data_file: self.read_data(self.path) self.check_index(i) - tensor_size = self.sizes[self.dim_offsets[i]:self.dim_offsets[i + 1]] + tensor_size = self.sizes[self.dim_offsets[i] : self.dim_offsets[i + 1]] a = np.empty(tensor_size, dtype=self.dtype) self.data_file.seek(self.data_offsets[i] * self.element_size) self.data_file.readinto(a) @@ -170,8 +205,8 @@ def size(self, index): @staticmethod def exists(path): - return ( - os.path.exists(index_file_path(path)) and os.path.exists(data_file_path(path)) + return PathManager.exists(index_file_path(path)) and PathManager.exists( + data_file_path(path) ) @property @@ -180,7 +215,6 @@ def supports_prefetch(self): class IndexedCachedDataset(IndexedDataset): - def __init__(self, path, fix_lua_indexing=False): super().__init__(path, fix_lua_indexing=fix_lua_indexing) self.cache = None @@ -205,7 +239,7 @@ def prefetch(self, indices): for i in indices: self.cache_index[i] = ptx size = self.data_offsets[i + 1] - self.data_offsets[i] - a = self.cache[ptx: ptx + size] + a = self.cache[ptx : ptx + size] self.data_file.seek(self.data_offsets[i] * self.element_size) self.data_file.readinto(a) ptx += size @@ -217,10 +251,10 @@ def prefetch(self, indices): @lru_cache(maxsize=8) def __getitem__(self, i): self.check_index(i) - tensor_size = self.sizes[self.dim_offsets[i]:self.dim_offsets[i + 1]] + tensor_size = self.sizes[self.dim_offsets[i] : self.dim_offsets[i + 1]] a = np.empty(tensor_size, dtype=self.dtype) ptx = self.cache_index[i] - np.copyto(a, self.cache[ptx: ptx + a.size]) + np.copyto(a, self.cache[ptx : ptx + a.size]) item = torch.from_numpy(a).long() if self.fix_lua_indexing: item -= 1 # subtract 1 for 0-based indexing @@ -241,12 +275,14 @@ def __init__(self, path, dictionary, append_eos=True, reverse_order=False): self.size = len(self.tokens_list) def read_data(self, path, dictionary): - with open(path, 'r', encoding='utf-8') as f: + with open(path, "r", encoding="utf-8") as f: for line in f: - self.lines.append(line.strip('\n')) + self.lines.append(line.strip("\n")) tokens = dictionary.encode_line( - line, add_if_not_exist=False, - append_eos=self.append_eos, reverse_order=self.reverse_order, + line, + add_if_not_exist=False, + append_eos=self.append_eos, + reverse_order=self.reverse_order, ).long() self.tokens_list.append(tokens) self.sizes.append(len(tokens)) @@ -254,7 +290,7 @@ def read_data(self, path, dictionary): def check_index(self, i): if i < 0 or i >= self.size: - raise IndexError('index out of range') + raise IndexError("index out of range") @lru_cache(maxsize=8) def __getitem__(self, i): @@ -279,10 +315,10 @@ def size(self, index): @staticmethod def exists(path): - return os.path.exists(path) + return PathManager.exists(path) -class IndexedDatasetBuilder(object): +class IndexedDatasetBuilder: element_sizes = { np.uint8: 1, np.int8: 1, @@ -290,11 +326,11 @@ class IndexedDatasetBuilder(object): np.int32: 4, np.int64: 8, np.float: 4, - np.double: 8 + np.double: 8, } def __init__(self, out_file, dtype=np.int32): - self.out_file = open(out_file, 'wb') + self.out_file = open(out_file, "wb") self.dtype = dtype self.data_offsets = [0] self.dim_offsets = [0] @@ -321,7 +357,7 @@ def merge_file_(self, another_file): for dim_offset in index.dim_offsets[1:]: self.dim_offsets.append(begin + dim_offset) - with open(data_file_path(another_file), 'rb') as f: + with open(data_file_path(another_file), "rb") as f: while True: data = f.read(1024) if data: @@ -331,11 +367,13 @@ def merge_file_(self, another_file): def finalize(self, index_file): self.out_file.close() - index = open(index_file, 'wb') - index.write(b'TNTIDX\x00\x00') - index.write(struct.pack('<Q', 1)) - index.write(struct.pack('<QQ', code(self.dtype), self.element_size)) - index.write(struct.pack('<QQ', len(self.data_offsets) - 1, len(self.sizes))) + index = open(index_file, "wb") + index.write(b"TNTIDX\x00\x00") + index.write(struct.pack("<Q", 1)) + index.write( + struct.pack("<QQ", _dtype_header_code(self.dtype), self.element_size) + ) + index.write(struct.pack("<QQ", len(self.data_offsets) - 1, len(self.sizes))) write_longs(index, self.dim_offsets) write_longs(index, self.data_offsets) write_longs(index, self.sizes) @@ -343,24 +381,24 @@ def finalize(self, index_file): def _warmup_mmap_file(path): - with open(path, 'rb') as stream: + with open(path, "rb") as stream: while stream.read(100 * 1024 * 1024): pass class MMapIndexedDataset(torch.utils.data.Dataset): - class Index(object): - _HDR_MAGIC = b'MMIDIDX\x00\x00' + class Index: + _HDR_MAGIC = b"MMIDIDX\x00\x00" @classmethod def writer(cls, path, dtype): - class _Writer(object): + class _Writer: def __enter__(self): - self._file = open(path, 'wb') + self._file = open(path, "wb") self._file.write(cls._HDR_MAGIC) - self._file.write(struct.pack('<Q', 1)) - self._file.write(struct.pack('<B', code(dtype))) + self._file.write(struct.pack("<Q", 1)) + self._file.write(struct.pack("<B", _dtype_header_code(dtype))) return self @@ -379,14 +417,14 @@ def _get_pointers(sizes): def write(self, sizes): pointers = self._get_pointers(sizes) - self._file.write(struct.pack('<Q', len(sizes))) + self._file.write(struct.pack("<Q", len(sizes))) sizes = np.array(sizes, dtype=np.int32) - self._file.write(sizes.tobytes(order='C')) + self._file.write(sizes.tobytes(order="C")) del sizes pointers = np.array(pointers, dtype=np.int64) - self._file.write(pointers.tobytes(order='C')) + self._file.write(pointers.tobytes(order="C")) del pointers def __exit__(self, exc_type, exc_val, exc_tb): @@ -395,29 +433,35 @@ def __exit__(self, exc_type, exc_val, exc_tb): return _Writer() def __init__(self, path): - with open(path, 'rb') as stream: + with open(path, "rb") as stream: magic_test = stream.read(9) assert self._HDR_MAGIC == magic_test, ( - 'Index file doesn\'t match expected format. ' - 'Make sure that --dataset-impl is configured properly.' + "Index file doesn't match expected format. " + "Make sure that --dataset-impl is configured properly." ) - version = struct.unpack('<Q', stream.read(8)) + version = struct.unpack("<Q", stream.read(8)) assert (1,) == version - dtype_code, = struct.unpack('<B', stream.read(1)) - self._dtype = dtypes[dtype_code] + (dtype_code,) = struct.unpack("<B", stream.read(1)) + self._dtype = _code_to_dtype[dtype_code] self._dtype_size = self._dtype().itemsize - self._len = struct.unpack('<Q', stream.read(8))[0] + self._len = struct.unpack("<Q", stream.read(8))[0] offset = stream.tell() _warmup_mmap_file(path) - self._bin_buffer_mmap = np.memmap(path, mode='r', order='C') + self._bin_buffer_mmap = np.memmap(path, mode="r", order="C") self._bin_buffer = memoryview(self._bin_buffer_mmap) - self._sizes = np.frombuffer(self._bin_buffer, dtype=np.int32, count=self._len, offset=offset) - self._pointers = np.frombuffer(self._bin_buffer, dtype=np.int64, count=self._len, - offset=offset + self._sizes.nbytes) + self._sizes = np.frombuffer( + self._bin_buffer, dtype=np.int32, count=self._len, offset=offset + ) + self._pointers = np.frombuffer( + self._bin_buffer, + dtype=np.int64, + count=self._len, + offset=offset + self._sizes.nbytes, + ) def __del__(self): self._bin_buffer_mmap._mmap.close() @@ -458,7 +502,9 @@ def _do_init(self, path): self._index = self.Index(index_file_path(self._path)) _warmup_mmap_file(data_file_path(self._path)) - self._bin_buffer_mmap = np.memmap(data_file_path(self._path), mode='r', order='C') + self._bin_buffer_mmap = np.memmap( + data_file_path(self._path), mode="r", order="C" + ) self._bin_buffer = memoryview(self._bin_buffer_mmap) def __del__(self): @@ -472,7 +518,9 @@ def __len__(self): @lru_cache(maxsize=8) def __getitem__(self, i): ptr, size = self._index[i] - np_array = np.frombuffer(self._bin_buffer, dtype=self._index.dtype, count=size, offset=ptr) + np_array = np.frombuffer( + self._bin_buffer, dtype=self._index.dtype, count=size, offset=ptr + ) if self._index.dtype != np.int64: np_array = np_array.astype(np.int64) @@ -488,20 +536,34 @@ def supports_prefetch(self): @staticmethod def exists(path): - return ( - os.path.exists(index_file_path(path)) and os.path.exists(data_file_path(path)) + return PathManager.exists(index_file_path(path)) and PathManager.exists( + data_file_path(path) ) -class MMapIndexedDatasetBuilder(object): +def get_indexed_dataset_to_local(path) -> str: + local_index_path = PathManager.get_local_path(index_file_path(path)) + local_data_path = PathManager.get_local_path(data_file_path(path)) + + assert local_index_path.endswith(".idx") and local_data_path.endswith(".bin"), ( + "PathManager.get_local_path does not return files with expected patterns: " + f"{local_index_path} and {local_data_path}" + ) + + local_path = local_data_path[:-4] # stripping surfix ".bin" + assert local_path == local_index_path[:-4] # stripping surfix ".idx" + return local_path + + +class MMapIndexedDatasetBuilder: def __init__(self, out_file, dtype=np.int64): - self._data_file = open(out_file, 'wb') + self._data_file = open(out_file, "wb") self._dtype = dtype self._sizes = [] def add_item(self, tensor): np_array = np.array(tensor.numpy(), dtype=self._dtype) - self._data_file.write(np_array.tobytes(order='C')) + self._data_file.write(np_array.tobytes(order="C")) self._sizes.append(np_array.size) def merge_file_(self, another_file): @@ -513,7 +575,7 @@ def merge_file_(self, another_file): self._sizes.append(size) # Concatenate data - with open(data_file_path(another_file), 'rb') as f: + with open(data_file_path(another_file), "rb") as f: shutil.copyfileobj(f, self._data_file) def finalize(self, index_file): diff --git a/fairseq/data/iterators.py b/fairseq/data/iterators.py index 169a4e72f2..1ce26e57e5 100644 --- a/fairseq/data/iterators.py +++ b/fairseq/data/iterators.py @@ -4,19 +4,20 @@ # LICENSE file in the root directory of this source tree. import itertools +import logging import math import operator import os +import queue import time +from threading import Thread + import numpy as np import torch -import queue -import logging -from threading import Thread -from . import data_utils +from fairseq.data import data_utils + logger = logging.getLogger(__name__) -logger.setLevel(logging.DEBUG) # Object used by _background_consumer to signal the source is exhausted # to the main thread. @@ -30,54 +31,52 @@ class CountingIterator(object): iterable (iterable): iterable to wrap start (int): starting iteration count. Note that this doesn't actually advance the iterator. - total (int): override the iterator length returned by - ``__len__``. This can be used to truncate *iterator*. + total (int): override the iterator length returned by ``__len``. + This can be used to truncate *iterator*. Attributes: n (int): number of elements consumed from this iterator """ def __init__(self, iterable, start=None, total=None): - self.iterable = iterable - self.itr = iter(self) - - if start is None: - self.n = getattr(iterable, 'n', 0) - else: - self.n = start - - if total is None: - self.total = self.n + len(iterable) - else: - self.total = total + self._itr = iter(iterable) + self.n = start or getattr(iterable, "n", 0) + self.total = total or self.n + len(iterable) def __len__(self): return self.total def __iter__(self): - for x in self.iterable: - if self.n >= self.total: - return - self.n += 1 - yield x + return self def __next__(self): - return next(self.itr) + if not self.has_next(): + raise StopIteration + try: + x = next(self._itr) + except StopIteration: + raise IndexError(f"Iterator expected to have length {self.total}, " + "but exhausted at position {self.n}.") + self.n += 1 + return x def has_next(self): """Whether the iterator has been exhausted.""" - return self.n < len(self) + return self.n < self.total - def skip(self, num_to_skip): - """Fast-forward the iterator by skipping *num_to_skip* elements.""" - next(itertools.islice(self.itr, num_to_skip, num_to_skip), None) + def skip(self, n): + """Fast-forward the iterator by skipping n elements.""" + for _ in range(n): + next(self) return self def take(self, n): - """ - Truncates the iterator to n elements at most. - """ + """Truncate the iterator to n elements at most.""" self.total = min(self.total, n) + # Propagate this change to the underlying iterator + if hasattr(self._itr, "take"): + self._itr.take(max(n - self.n, 0)) + return self class EpochBatchIterating(object): @@ -88,15 +87,19 @@ def __len__(self) -> int: def next_epoch_idx(self): raise NotImplementedError - def next_epoch_itr(self, shuffle=True, fix_batches_to_gpus=False): + def next_epoch_itr( + self, shuffle=True, fix_batches_to_gpus=False, set_dataset_epoch=True + ): """Return a new iterator over the dataset. Args: shuffle (bool, optional): shuffle batches before returning the iterator (default: True). - fix_batches_to_gpus: ensure that batches are always + fix_batches_to_gpus (bool, optional): ensure that batches are always allocated to the same shards across epochs. Requires that :attr:`dataset` supports prefetching (default: False). + set_dataset_epoch (bool, optional): update the wrapped Dataset with + the new epoch number (default: True). """ raise NotImplementedError @@ -117,17 +120,52 @@ def load_state_dict(self, state_dict): """Copies the state of the iterator from the given *state_dict*.""" raise NotImplementedError + @property + def first_batch(self): + return "DUMMY" + class StreamingEpochBatchIterator(EpochBatchIterating): + """A steaming-style iterator over a :class:`torch.utils.data.IterableDataset`. + + Args: + dataset (~torch.utils.data.Dataset): dataset from which to load the data + max_sentences: batch size + collate_fn (callable): merges a list of samples to form a mini-batch + num_workers (int, optional): how many subprocesses to use for data + loading. 0 means the data will be loaded in the main process + (default: 0). + epoch (int, optional): the epoch to start the iterator from + (default: 1). + buffer_size (int, optional): the number of batches to keep ready in the + queue. Helps speeding up dataloading. When buffer_size is zero, the + default torch.utils.data.DataLoader preloading is used. + timeout (int, optional): if positive, the timeout value for collecting a batch + from workers. Should always be non-negative (default: ``0``). + """ + def __init__( - self, dataset, epoch=1, num_shards=1, shard_id=0, + self, + dataset, + max_sentences=1, + collate_fn=None, + epoch=1, + num_workers=0, + buffer_size=0, + timeout=0, ): assert isinstance(dataset, torch.utils.data.IterableDataset) self.dataset = dataset + self.max_sentences = max_sentences + self.collate_fn = collate_fn self.epoch = max(epoch, 1) # we use 1-based indexing for epochs + self.num_workers = num_workers + # This upper limit here is to prevent people from abusing this feature + # in a shared computing environment. + self.buffer_size = min(buffer_size, 20) + self.timeout = timeout + self._current_epoch_iterator = None - self.num_shards = num_shards - self.shard_id = shard_id @property def next_epoch_idx(self): @@ -137,16 +175,13 @@ def next_epoch_idx(self): else: return self.epoch - def next_epoch_itr(self, shuffle=True, fix_batches_to_gpus=False): + def next_epoch_itr( + self, shuffle=True, fix_batches_to_gpus=False, set_dataset_epoch=True + ): self.epoch = self.next_epoch_idx - self.dataset.set_epoch(self.epoch) - self._current_epoch_iterator = CountingIterator( - iterable=ShardedIterator( - iterable=self.dataset, - num_shards=self.num_shards, - shard_id=self.shard_id, - ), - ) + if set_dataset_epoch and hasattr(self.dataset, "set_epoch"): + self.dataset.set_epoch(self.epoch) + self._current_epoch_iterator = self._get_iterator_for_epoch(self.epoch, shuffle) return self._current_epoch_iterator def end_of_epoch(self) -> bool: @@ -160,11 +195,36 @@ def iterations_in_epoch(self) -> int: def state_dict(self): return { - 'epoch': self.epoch, + "epoch": self.epoch, } def load_state_dict(self, state_dict): - self.epoch = state_dict['epoch'] + self.epoch = state_dict["epoch"] + + def _get_iterator_for_epoch(self, epoch, shuffle, offset=0): + if self.num_workers > 0: + os.environ["PYTHONWARNINGS"] = "ignore:semaphore_tracker:UserWarning" + + # Create data loader + worker_init_fn = getattr(self.dataset, "worker_init_fn", None) + itr = torch.utils.data.DataLoader( + self.dataset, + batch_size=self.max_sentences, + collate_fn=self.collate_fn, + num_workers=self.num_workers, + timeout=self.timeout, + worker_init_fn=worker_init_fn, + pin_memory=True, + ) + + # Wrap with a BufferedIterator if needed + if self.buffer_size > 0: + itr = BufferedIterator(self.buffer_size, itr) + + # Wrap with CountingIterator + itr = CountingIterator(itr, start=offset) + + return itr class EpochBatchIterator(EpochBatchIterating): @@ -181,8 +241,10 @@ class EpochBatchIterator(EpochBatchIterating): Args: dataset (~torch.utils.data.Dataset): dataset from which to load the data collate_fn (callable): merges a list of samples to form a mini-batch - batch_sampler (~torch.utils.data.Sampler): an iterator over batches of - indices + batch_sampler (~torch.utils.data.Sampler or a callable): an iterator over batches of + indices, or a callable to create such an iterator (~torch.utils.data.Sampler). + A callable batch_sampler will be called for each epoch to enable per epoch dynamic + batch iterators defined by this callable batch_sampler. seed (int, optional): seed for random number generator for reproducibility (default: 1). num_shards (int, optional): shard the data iterator into N @@ -197,27 +259,69 @@ class EpochBatchIterator(EpochBatchIterating): buffer_size (int, optional): the number of batches to keep ready in the queue. Helps speeding up dataloading. When buffer_size is zero, the default torch.utils.data.DataLoader preloading is used. + timeout (int, optional): if positive, the timeout value for collecting a batch + from workers. Should always be non-negative (default: ``0``). + disable_shuffling (bool, optional): force disable shuffling + (default: ``False``). """ def __init__( - self, dataset, collate_fn, batch_sampler, seed=1, num_shards=1, shard_id=0, - num_workers=0, epoch=1, buffer_size=0 + self, + dataset, + collate_fn, + batch_sampler, + seed=1, + num_shards=1, + shard_id=0, + num_workers=0, + epoch=1, + buffer_size=0, + timeout=0, + disable_shuffling=False, ): assert isinstance(dataset, torch.utils.data.Dataset) self.dataset = dataset self.collate_fn = collate_fn - self.frozen_batches = tuple(batch_sampler) + self.batch_sampler = batch_sampler + self._frozen_batches = ( + tuple(batch_sampler) if not callable(batch_sampler) else None + ) self.seed = seed self.num_shards = num_shards self.shard_id = shard_id self.num_workers = num_workers - self.buffer_size = buffer_size + # This upper limit here is to prevent people from abusing this feature + # in a shared computing environment. + self.buffer_size = min(buffer_size, 20) + self.timeout = timeout + self.disable_shuffling = disable_shuffling self.epoch = max(epoch, 1) # we use 1-based indexing for epochs - self.shuffle = True + self.shuffle = not disable_shuffling self._cur_epoch_itr = None self._next_epoch_itr = None - self._supports_prefetch = getattr(dataset, 'supports_prefetch', False) + self._supports_prefetch = getattr(dataset, "supports_prefetch", False) + + @property + def frozen_batches(self): + if self._frozen_batches is None: + self._frozen_batches = tuple(self.batch_sampler(self.dataset, self.epoch)) + return self._frozen_batches + + @property + def first_batch(self): + if len(self.frozen_batches) == 0: + raise Exception( + "The dataset is empty. This could indicate " + "that all elements in the dataset have been skipped. " + "Try increasing the max number of allowed tokens or using " + "a larger dataset." + ) + + if getattr(self.dataset, "supports_fetch_outside_dataloader", True): + return self.collate_fn([self.dataset[i] for i in self.frozen_batches[0]]) + else: + return "DUMMY" def __len__(self): return int(math.ceil(len(self.frozen_batches) / float(self.num_shards))) @@ -236,25 +340,38 @@ def next_epoch_idx(self): else: return self.epoch - def next_epoch_itr(self, shuffle=True, fix_batches_to_gpus=False): + def next_epoch_itr( + self, shuffle=True, fix_batches_to_gpus=False, set_dataset_epoch=True + ): """Return a new iterator over the dataset. Args: shuffle (bool, optional): shuffle batches before returning the iterator (default: True). - fix_batches_to_gpus: ensure that batches are always + fix_batches_to_gpus (bool, optional): ensure that batches are always allocated to the same shards across epochs. Requires that :attr:`dataset` supports prefetching (default: False). + set_dataset_epoch (bool, optional): update the wrapped Dataset with + the new epoch number (default: True). """ + if self.disable_shuffling: + shuffle = False + prev_epoch = self.epoch self.epoch = self.next_epoch_idx + if set_dataset_epoch and hasattr(self.dataset, "set_epoch"): + self.dataset.set_epoch(self.epoch) if self._next_epoch_itr is not None: self._cur_epoch_itr = self._next_epoch_itr self._next_epoch_itr = None else: + if callable(self.batch_sampler) and prev_epoch != self.epoch: + # reset _frozen_batches to refresh the next epoch + self._frozen_batches = None self._cur_epoch_itr = self._get_iterator_for_epoch( - self.epoch, shuffle, fix_batches_to_gpus=fix_batches_to_gpus, + self.epoch, + shuffle, + fix_batches_to_gpus=fix_batches_to_gpus, ) - self.dataset.set_epoch(self.epoch) self.shuffle = shuffle return self._cur_epoch_itr @@ -273,31 +390,47 @@ def iterations_in_epoch(self): def state_dict(self): """Returns a dictionary containing a whole state of the iterator.""" + if self.end_of_epoch(): + epoch = self.epoch + 1 + iter_in_epoch = 0 + else: + epoch = self.epoch + iter_in_epoch = self.iterations_in_epoch return { - 'epoch': self.epoch, - 'iterations_in_epoch': self.iterations_in_epoch, - 'shuffle': self.shuffle, + "version": 2, + "epoch": epoch, + "iterations_in_epoch": iter_in_epoch, + "shuffle": self.shuffle, } def load_state_dict(self, state_dict): """Copies the state of the iterator from the given *state_dict*.""" - self.epoch = state_dict['epoch'] - itr_pos = state_dict.get('iterations_in_epoch', 0) + self.epoch = state_dict["epoch"] + itr_pos = state_dict.get("iterations_in_epoch", 0) + version = state_dict.get("version", 1) if itr_pos > 0: # fast-forward epoch iterator self._next_epoch_itr = self._get_iterator_for_epoch( self.epoch, - shuffle=state_dict.get('shuffle', True), + shuffle=state_dict.get("shuffle", True), offset=itr_pos, ) if self._next_epoch_itr is None: - # we finished the epoch, increment epoch counter - self.epoch += 1 + if version == 1: + # legacy behavior: we finished the epoch, increment epoch counter + self.epoch += 1 + else: + raise RuntimeError( + "Cannot resume training due to dataloader mismatch, please " + "report this to the fairseq developers. You can relaunch " + "training with `--reset-dataloader` and it should work." + ) else: self._next_epoch_itr = None - def _get_iterator_for_epoch(self, epoch, shuffle, fix_batches_to_gpus=False, offset=0): - + def _get_iterator_for_epoch( + self, epoch, shuffle, fix_batches_to_gpus=False, offset=0 + ): def shuffle_batches(batches, seed): with data_utils.numpy_seed(seed): np.random.shuffle(batches) @@ -309,9 +442,9 @@ def shuffle_batches(batches, seed): if shuffle and not fix_batches_to_gpus: batches = shuffle_batches(list(batches), self.seed + epoch) - batches = list(ShardedIterator( - batches, self.num_shards, self.shard_id, fill_value=[] - )) + batches = list( + ShardedIterator(batches, self.num_shards, self.shard_id, fill_value=[]) + ) self.dataset.prefetch([i for s in batches for i in s]) if shuffle and fix_batches_to_gpus: @@ -321,15 +454,15 @@ def shuffle_batches(batches, seed): batches = shuffle_batches(list(self.frozen_batches), self.seed + epoch) else: batches = self.frozen_batches - batches = list(ShardedIterator( - batches, self.num_shards, self.shard_id, fill_value=[] - )) + batches = list( + ShardedIterator(batches, self.num_shards, self.shard_id, fill_value=[]) + ) if offset > 0 and offset >= len(batches): return None if self.num_workers > 0: - os.environ['PYTHONWARNINGS'] = 'ignore:semaphore_tracker:UserWarning' + os.environ["PYTHONWARNINGS"] = "ignore:semaphore_tracker:UserWarning" # Create data loader itr = torch.utils.data.DataLoader( @@ -337,13 +470,15 @@ def shuffle_batches(batches, seed): collate_fn=self.collate_fn, batch_sampler=batches[offset:], num_workers=self.num_workers, + timeout=self.timeout, + pin_memory=True, ) # Wrap with a BufferedIterator if needed if self.buffer_size > 0: itr = BufferedIterator(self.buffer_size, itr) - # Wrap with CoutingIterator + # Wrap with CountingIterator itr = CountingIterator(itr, start=offset) return itr @@ -363,7 +498,7 @@ def __init__(self, iterable, chunk_size): itr = _chunk_iterator(iterable, chunk_size) super().__init__( itr, - start=int(math.ceil(getattr(iterable, 'n', 0) / float(chunk_size))), + start=int(math.ceil(getattr(iterable, "n", 0) / float(chunk_size))), total=int(math.ceil(len(iterable) / float(chunk_size))), ) self.chunk_size = chunk_size @@ -396,7 +531,7 @@ class ShardedIterator(CountingIterator): def __init__(self, iterable, num_shards, shard_id, fill_value=None): if shard_id < 0 or shard_id >= num_shards: - raise ValueError('shard_id must be between 0 and num_shards') + raise ValueError("shard_id must be between 0 and num_shards") sharded_len = int(math.ceil(len(iterable) / float(num_shards))) itr = map( operator.itemgetter(1), @@ -408,58 +543,223 @@ def __init__(self, iterable, num_shards, shard_id, fill_value=None): ) super().__init__( itr, - start=int(math.ceil(getattr(iterable, 'n', 0) / float(num_shards))), + start=int(math.ceil(getattr(iterable, "n", 0) / float(num_shards))), total=sharded_len, ) class BackgroundConsumer(Thread): - def __init__(self, queue, source): + def __init__(self, queue, source, max_len, cuda_device): Thread.__init__(self) self._queue = queue self._source = source + self._max_len = max_len + self.count = 0 + self.cuda_device = cuda_device def run(self): - for item in self._source: - self._queue.put(item) + # set_device to avoid creation of GPU0 context when using pin_memory + if self.cuda_device is not None: + torch.cuda.set_device(self.cuda_device) - # Signal the consumer we are done. - self._queue.put(_sentinel) + try: + for item in self._source: + self._queue.put(item) + + # Stop if we reached the maximum length + self.count += 1 + if self._max_len is not None and self.count >= self._max_len: + break + + # Signal the consumer we are done. + self._queue.put(_sentinel) + except Exception as e: + self._queue.put(e) class BufferedIterator(object): def __init__(self, size, iterable): self._queue = queue.Queue(size) self._iterable = iterable - - self._consumer = BackgroundConsumer(self._queue, iterable) - self._consumer.daemon = True - self._consumer.start() + self._consumer = None self.start_time = time.time() self.warning_time = None + self.total = len(iterable) + + def _create_consumer(self): + self._consumer = BackgroundConsumer( + self._queue, + self._iterable, + self.total, + torch.cuda.current_device() if torch.cuda.is_available() else None + ) + self._consumer.daemon = True + self._consumer.start() + def __iter__(self): return self def __len__(self): - return len(self._iterable) + return self.total + + def take(self, n): + self.total = min(self.total, n) + # Propagate this change to the underlying iterator + if hasattr(self._iterable, "take"): + self._iterable.take(n) + return self def __next__(self): + # Create consumer if not created yet + if self._consumer is None: + self._create_consumer() + # Notify the user if there is a data loading bottleneck - if self._queue.qsize() < 2: + if self._queue.qsize() < min(2, max(1, self._queue.maxsize // 2)): if time.time() - self.start_time > 5 * 60: - if self.warning_time is None or time.time() - self.warning_time > 15 * 60: - logger.info( + if ( + self.warning_time is None + or time.time() - self.warning_time > 15 * 60 + ): + logger.debug( "Data loading buffer is empty or nearly empty. This may " "indicate a data loading bottleneck, and increasing the " - "number of workers may help." + "number of workers (--num-workers) may help." ) self.warning_time = time.time() # Get next example item = self._queue.get(True) + if isinstance(item, Exception): + raise item if item is _sentinel: raise StopIteration() return item + +class GroupedEpochBatchIterator(EpochBatchIterator): + """Grouped version of EpochBatchIterator + It takes several samplers from different datasets. + Each epoch shuffle the dataset wise sampler individually with different + random seed. The those sub samplers are combined with into + one big samplers with deterministic permutation to mix batches from + different datasets. It will act like EpochBatchIterator but make sure + 1) data from one data set each time + 2) for different workers, they use the same order to fetch the data + so they will use data from the same dataset everytime + mult_rate is used for update_freq > 1 case where we want to make sure update_freq + mini-batches come from same source + """ + + def __init__( + self, + dataset, + collate_fn, + batch_samplers, + seed=1, + num_shards=1, + shard_id=0, + num_workers=0, + epoch=0, + mult_rate=1, + buffer_size=0, + ): + super().__init__( + dataset, + collate_fn, + batch_samplers, + seed, + num_shards, + shard_id, + num_workers, + epoch, + buffer_size, + ) + # level 0: sub-samplers 1: batch_idx 2: batches + self._frozen_batches = tuple([tuple(sub_batch) for sub_batch in batch_samplers]) + self.step_size = mult_rate * num_shards + + self.lengths = [ + (len(x) // self.step_size) * self.step_size for x in self.frozen_batches + ] + + def __len__(self): + return sum(self.lengths) + + @property + def first_batch(self): + if len(self.frozen_batches) == 0: + raise Exception( + "The dataset is empty. This could indicate " + "that all elements in the dataset have been skipped. " + "Try increasing the max number of allowed tokens or using " + "a larger dataset." + ) + + if self.dataset.supports_fetch_outside_dataloader: + return self.collate_fn([self.dataset[i] for i in self.frozen_batches[0][0]]) + else: + return "DUMMY" + + def _get_iterator_for_epoch( + self, epoch, shuffle, fix_batches_to_gpus=False, offset=0 + ): + def shuffle_batches(batches, seed): + with data_utils.numpy_seed(seed): + np.random.shuffle(batches) + return batches + + def return_full_batches(batch_sets, seed, shuffle): + if shuffle: + batch_sets = [shuffle_batches(list(x), seed) for x in batch_sets] + + batch_sets = [ + batch_sets[i][: self.lengths[i]] for i in range(len(batch_sets)) + ] + batches = list(itertools.chain.from_iterable(batch_sets)) + + if shuffle: + with data_utils.numpy_seed(seed): + idx = np.random.permutation(len(batches) // self.step_size) + if len(idx) * self.step_size != len(batches): + raise ValueError( + "ERROR: %d %d %d %d" + % (len(idx), self.step_size, len(batches), self.shard_id), + ":".join(["%d" % x for x in self.lengths]), + ) + mini_shards = [ + batches[i * self.step_size : (i + 1) * self.step_size] + for i in idx + ] + batches = list(itertools.chain.from_iterable(mini_shards)) + + return batches + + if self._supports_prefetch: + raise NotImplementedError("To be implemented") + else: + batches = return_full_batches( + self.frozen_batches, self.seed + epoch, shuffle + ) + batches = list( + ShardedIterator(batches, self.num_shards, self.shard_id, fill_value=[]) + ) + + if offset > 0 and offset >= len(batches): + return None + + if self.num_workers > 0: + os.environ["PYTHONWARNINGS"] = "ignore:semaphore_tracker:UserWarning" + + itr = torch.utils.data.DataLoader( + self.dataset, + collate_fn=self.collate_fn, + batch_sampler=batches[offset:], + num_workers=self.num_workers, + ) + if self.buffer_size > 0: + itr = BufferedIterator(self.buffer_size, itr) + + return CountingIterator(itr, start=offset) diff --git a/fairseq/data/language_pair_dataset.py b/fairseq/data/language_pair_dataset.py index d18a92d786..ff3e14bf14 100644 --- a/fairseq/data/language_pair_dataset.py +++ b/fairseq/data/language_pair_dataset.py @@ -7,30 +7,43 @@ import numpy as np import torch - -from . import data_utils, FairseqDataset +from fairseq.data import FairseqDataset, data_utils logger = logging.getLogger(__name__) def collate( - samples, pad_idx, eos_idx, left_pad_source=True, left_pad_target=False, + samples, + pad_idx, + eos_idx, + left_pad_source=True, + left_pad_target=False, input_feeding=True, + pad_to_length=None, + pad_to_multiple=1, ): if len(samples) == 0: return {} - def merge(key, left_pad, move_eos_to_beginning=False): + def merge(key, left_pad, move_eos_to_beginning=False, pad_to_length=None): return data_utils.collate_tokens( [s[key] for s in samples], - pad_idx, eos_idx, left_pad, move_eos_to_beginning, + pad_idx, + eos_idx, + left_pad, + move_eos_to_beginning, + pad_to_length=pad_to_length, + pad_to_multiple=pad_to_multiple, ) def check_alignment(alignment, src_len, tgt_len): if alignment is None or len(alignment) == 0: return False - if alignment[:, 0].max().item() >= src_len - 1 or alignment[:, 1].max().item() >= tgt_len - 1: + if ( + alignment[:, 0].max().item() >= src_len - 1 + or alignment[:, 1].max().item() >= tgt_len - 1 + ): logger.warning("alignment size mismatch found, skipping alignment!") return False return True @@ -45,66 +58,87 @@ def compute_alignment_weights(alignments): index 3 is repeated twice) """ align_tgt = alignments[:, 1] - _, align_tgt_i, align_tgt_c = torch.unique(align_tgt, return_inverse=True, return_counts=True) + _, align_tgt_i, align_tgt_c = torch.unique( + align_tgt, return_inverse=True, return_counts=True + ) align_weights = align_tgt_c[align_tgt_i[np.arange(len(align_tgt))]] - return 1. / align_weights.float() - - id = torch.LongTensor([s['id'] for s in samples]) - src_tokens = merge('source', left_pad=left_pad_source) + return 1.0 / align_weights.float() + + id = torch.LongTensor([s["id"] for s in samples]) + src_tokens = merge( + "source", + left_pad=left_pad_source, + pad_to_length=pad_to_length["source"] if pad_to_length is not None else None, + ) # sort by descending source length - src_lengths = torch.LongTensor([s['source'].numel() for s in samples]) + src_lengths = torch.LongTensor( + [s["source"].ne(pad_idx).long().sum() for s in samples] + ) src_lengths, sort_order = src_lengths.sort(descending=True) id = id.index_select(0, sort_order) src_tokens = src_tokens.index_select(0, sort_order) prev_output_tokens = None target = None - if samples[0].get('target', None) is not None: - target = merge('target', left_pad=left_pad_target) + if samples[0].get("target", None) is not None: + target = merge( + "target", + left_pad=left_pad_target, + pad_to_length=pad_to_length["target"] + if pad_to_length is not None + else None, + ) target = target.index_select(0, sort_order) - tgt_lengths = torch.LongTensor([s['target'].numel() for s in samples]).index_select(0, sort_order) - ntokens = sum(len(s['target']) for s in samples) - - if input_feeding: + tgt_lengths = torch.LongTensor( + [s["target"].ne(pad_idx).long().sum() for s in samples] + ).index_select(0, sort_order) + ntokens = tgt_lengths.sum().item() + + if samples[0].get("prev_output_tokens", None) is not None: + prev_output_tokens = merge("prev_output_tokens", left_pad=left_pad_target) + elif input_feeding: # we create a shifted version of targets for feeding the # previous output token(s) into the next decoder step prev_output_tokens = merge( - 'target', + "target", left_pad=left_pad_target, move_eos_to_beginning=True, + pad_to_length=pad_to_length["target"] + if pad_to_length is not None + else None, ) - prev_output_tokens = prev_output_tokens.index_select(0, sort_order) else: - ntokens = sum(len(s['source']) for s in samples) + ntokens = src_lengths.sum().item() batch = { - 'id': id, - 'nsentences': len(samples), - 'ntokens': ntokens, - 'net_input': { - 'src_tokens': src_tokens, - 'src_lengths': src_lengths, - }, - 'target': target, + "id": id, + "nsentences": len(samples), + "ntokens": ntokens, + "net_input": {"src_tokens": src_tokens, "src_lengths": src_lengths,}, + "target": target, } if prev_output_tokens is not None: - batch['net_input']['prev_output_tokens'] = prev_output_tokens + batch["net_input"]["prev_output_tokens"] = prev_output_tokens.index_select( + 0, sort_order + ) - if samples[0].get('alignment', None) is not None: - bsz, tgt_sz = batch['target'].shape - src_sz = batch['net_input']['src_tokens'].shape[1] + if samples[0].get("alignment", None) is not None: + bsz, tgt_sz = batch["target"].shape + src_sz = batch["net_input"]["src_tokens"].shape[1] offsets = torch.zeros((len(sort_order), 2), dtype=torch.long) - offsets[:, 1] += (torch.arange(len(sort_order), dtype=torch.long) * tgt_sz) + offsets[:, 1] += torch.arange(len(sort_order), dtype=torch.long) * tgt_sz if left_pad_source: - offsets[:, 0] += (src_sz - src_lengths) + offsets[:, 0] += src_sz - src_lengths if left_pad_target: - offsets[:, 1] += (tgt_sz - tgt_lengths) + offsets[:, 1] += tgt_sz - tgt_lengths alignments = [ alignment + offset - for align_idx, offset, src_len, tgt_len in zip(sort_order, offsets, src_lengths, tgt_lengths) - for alignment in [samples[align_idx]['alignment'].view(-1, 2)] + for align_idx, offset, src_len, tgt_len in zip( + sort_order, offsets, src_lengths, tgt_lengths + ) + for alignment in [samples[align_idx]["alignment"].view(-1, 2)] if check_alignment(alignment, src_len, tgt_len) ] @@ -112,8 +146,18 @@ def compute_alignment_weights(alignments): alignments = torch.cat(alignments, dim=0) align_weights = compute_alignment_weights(alignments) - batch['alignments'] = alignments - batch['align_weights'] = align_weights + batch["alignments"] = alignments + batch["align_weights"] = align_weights + + if samples[0].get("constraints", None) is not None: + # Collate the packed constraints across the samples, padding to + # the length of the longest sample. + lens = [sample.get("constraints").size(0) for sample in samples] + max_len = max(lens) + constraints = torch.zeros((len(samples), max(lens))).long() + for i, sample in enumerate(samples): + constraints[i, 0 : lens[i]] = samples[i].get("constraints") + batch["constraints"] = constraints.index_select(0, sort_order) return batch @@ -133,10 +177,6 @@ class LanguagePairDataset(FairseqDataset): (default: True). left_pad_target (bool, optional): pad target tensors on the left side (default: False). - max_source_positions (int, optional): max number of tokens in the - source sentence (default: 1024). - max_target_positions (int, optional): max number of tokens in the - target sentence (default: 1024). shuffle (bool, optional): shuffle dataset elements before batching (default: True). input_feeding (bool, optional): create a shifted version of the targets @@ -147,45 +187,116 @@ class LanguagePairDataset(FairseqDataset): target if it's absent (default: False). align_dataset (torch.utils.data.Dataset, optional): dataset containing alignments. + constraints (Tensor, optional): 2d tensor with a concatenated, zero- + delimited list of constraints for each sentence. append_bos (bool, optional): if set, appends bos to the beginning of source/target sentence. + num_buckets (int, optional): if set to a value greater than 0, then + batches will be bucketed into the given number of batch shapes. + src_lang_id (int, optional): source language ID, if set, the collated batch + will contain a field 'src_lang_id' in 'net_input' which indicates the + source language of the samples. + tgt_lang_id (int, optional): target language ID, if set, the collated batch + will contain a field 'tgt_lang_id' which indicates the target language + of the samples. """ def __init__( - self, src, src_sizes, src_dict, - tgt=None, tgt_sizes=None, tgt_dict=None, - left_pad_source=True, left_pad_target=False, - max_source_positions=1024, max_target_positions=1024, - shuffle=True, input_feeding=True, - remove_eos_from_source=False, append_eos_to_target=False, + self, + src, + src_sizes, + src_dict, + tgt=None, + tgt_sizes=None, + tgt_dict=None, + left_pad_source=True, + left_pad_target=False, + shuffle=True, + input_feeding=True, + remove_eos_from_source=False, + append_eos_to_target=False, align_dataset=None, - append_bos=False, eos=None + constraints=None, + append_bos=False, + eos=None, + num_buckets=0, + src_lang_id=None, + tgt_lang_id=None, + pad_to_multiple=1, ): if tgt_dict is not None: assert src_dict.pad() == tgt_dict.pad() assert src_dict.eos() == tgt_dict.eos() assert src_dict.unk() == tgt_dict.unk() if tgt is not None: - assert len(src) == len(tgt), "Source and target must contain the same number of examples" + assert len(src) == len( + tgt + ), "Source and target must contain the same number of examples" self.src = src self.tgt = tgt self.src_sizes = np.array(src_sizes) self.tgt_sizes = np.array(tgt_sizes) if tgt_sizes is not None else None + self.sizes = ( + np.vstack((self.src_sizes, self.tgt_sizes)).T + if self.tgt_sizes is not None + else self.src_sizes + ) self.src_dict = src_dict self.tgt_dict = tgt_dict self.left_pad_source = left_pad_source self.left_pad_target = left_pad_target - self.max_source_positions = max_source_positions - self.max_target_positions = max_target_positions self.shuffle = shuffle self.input_feeding = input_feeding self.remove_eos_from_source = remove_eos_from_source self.append_eos_to_target = append_eos_to_target self.align_dataset = align_dataset if self.align_dataset is not None: - assert self.tgt_sizes is not None, "Both source and target needed when alignments are provided" + assert ( + self.tgt_sizes is not None + ), "Both source and target needed when alignments are provided" + self.constraints = constraints self.append_bos = append_bos - self.eos = (eos if eos is not None else src_dict.eos()) + self.eos = eos if eos is not None else src_dict.eos() + self.src_lang_id = src_lang_id + self.tgt_lang_id = tgt_lang_id + if num_buckets > 0: + from fairseq.data import BucketPadLengthDataset + + self.src = BucketPadLengthDataset( + self.src, + sizes=self.src_sizes, + num_buckets=num_buckets, + pad_idx=self.src_dict.pad(), + left_pad=self.left_pad_source, + ) + self.src_sizes = self.src.sizes + logger.info("bucketing source lengths: {}".format(list(self.src.buckets))) + if self.tgt is not None: + self.tgt = BucketPadLengthDataset( + self.tgt, + sizes=self.tgt_sizes, + num_buckets=num_buckets, + pad_idx=self.tgt_dict.pad(), + left_pad=self.left_pad_target, + ) + self.tgt_sizes = self.tgt.sizes + logger.info( + "bucketing target lengths: {}".format(list(self.tgt.buckets)) + ) + + # determine bucket sizes using self.num_tokens, which will return + # the padded lengths (thanks to BucketPadLengthDataset) + num_tokens = np.vectorize(self.num_tokens, otypes=[np.compat.long]) + self.bucketed_num_tokens = num_tokens(np.arange(len(self.src))) + self.buckets = [ + (None, num_tokens) for num_tokens in np.unique(self.bucketed_num_tokens) + ] + else: + self.buckets = None + self.pad_to_multiple = pad_to_multiple + + def get_batch_shapes(self): + return self.buckets def __getitem__(self, index): tgt_item = self.tgt[index] if self.tgt is not None else None @@ -205,7 +316,7 @@ def __getitem__(self, index): tgt_item = torch.cat([torch.LongTensor([bos]), self.tgt[index]]) bos = self.src_dict.bos() - if self.src[index][-1] != bos: + if self.src[index][0] != bos: src_item = torch.cat([torch.LongTensor([bos]), self.src[index]]) if self.remove_eos_from_source: @@ -214,22 +325,27 @@ def __getitem__(self, index): src_item = self.src[index][:-1] example = { - 'id': index, - 'source': src_item, - 'target': tgt_item, + "id": index, + "source": src_item, + "target": tgt_item, } if self.align_dataset is not None: - example['alignment'] = self.align_dataset[index] + example["alignment"] = self.align_dataset[index] + if self.constraints is not None: + example["constraints"] = self.constraints[index] return example def __len__(self): return len(self.src) - def collater(self, samples): + def collater(self, samples, pad_to_length=None): """Merge a list of samples to form a mini-batch. Args: samples (List[dict]): samples to collate + pad_to_length (dict, optional): a dictionary of + {'source': source_pad_to_length, 'target': target_pad_to_length} + to indicate the max length to pad to in source and target respectively. Returns: dict: a mini-batch with the following keys: @@ -249,43 +365,85 @@ def collater(self, samples): This key will not be present if *input_feeding* is ``False``. Padding will appear on the left if *left_pad_target* is ``True``. + - `src_lang_id` (LongTensor): a long Tensor which contains source + language IDs of each sample in the batch - `target` (LongTensor): a padded 2D Tensor of tokens in the target sentence of shape `(bsz, tgt_len)`. Padding will appear on the left if *left_pad_target* is ``True``. + - `tgt_lang_id` (LongTensor): a long Tensor which contains target language + IDs of each sample in the batch """ - return collate( - samples, pad_idx=self.src_dict.pad(), eos_idx=self.eos, - left_pad_source=self.left_pad_source, left_pad_target=self.left_pad_target, + res = collate( + samples, + pad_idx=self.src_dict.pad(), + eos_idx=self.eos, + left_pad_source=self.left_pad_source, + left_pad_target=self.left_pad_target, input_feeding=self.input_feeding, + pad_to_length=pad_to_length, + pad_to_multiple=self.pad_to_multiple, ) + if self.src_lang_id is not None or self.tgt_lang_id is not None: + src_tokens = res["net_input"]["src_tokens"] + bsz = src_tokens.size(0) + if self.src_lang_id is not None: + res["net_input"]["src_lang_id"] = ( + torch.LongTensor([[self.src_lang_id]]).expand(bsz, 1).to(src_tokens) + ) + if self.tgt_lang_id is not None: + res["tgt_lang_id"] = ( + torch.LongTensor([[self.tgt_lang_id]]).expand(bsz, 1).to(src_tokens) + ) + return res def num_tokens(self, index): """Return the number of tokens in a sample. This value is used to enforce ``--max-tokens`` during batching.""" - return max(self.src_sizes[index], self.tgt_sizes[index] if self.tgt_sizes is not None else 0) + return max( + self.src_sizes[index], + self.tgt_sizes[index] if self.tgt_sizes is not None else 0, + ) + + def num_tokens_vec(self, indices): + """Return the number of tokens for a set of positions defined by indices. + This value is used to enforce ``--max-tokens`` during batching.""" + sizes = self.src_sizes[indices] + if self.tgt_sizes is not None: + sizes = np.maximum(sizes, self.tgt_sizes[indices]) + return sizes def size(self, index): """Return an example's size as a float or tuple. This value is used when filtering a dataset with ``--max-positions``.""" - return (self.src_sizes[index], self.tgt_sizes[index] if self.tgt_sizes is not None else 0) + return ( + self.src_sizes[index], + self.tgt_sizes[index] if self.tgt_sizes is not None else 0, + ) def ordered_indices(self): """Return an ordered list of indices. Batches will be constructed based on this order.""" if self.shuffle: - indices = np.random.permutation(len(self)) + indices = np.random.permutation(len(self)).astype(np.int64) else: - indices = np.arange(len(self)) - if self.tgt_sizes is not None: - indices = indices[np.argsort(self.tgt_sizes[indices], kind='mergesort')] - return indices[np.argsort(self.src_sizes[indices], kind='mergesort')] + indices = np.arange(len(self), dtype=np.int64) + if self.buckets is None: + # sort by target length, then source length + if self.tgt_sizes is not None: + indices = indices[np.argsort(self.tgt_sizes[indices], kind="mergesort")] + return indices[np.argsort(self.src_sizes[indices], kind="mergesort")] + else: + # sort by bucketed_num_tokens, which is: + # max(padded_src_len, padded_tgt_len) + return indices[ + np.argsort(self.bucketed_num_tokens[indices], kind="mergesort") + ] @property def supports_prefetch(self): - return ( - getattr(self.src, 'supports_prefetch', False) - and (getattr(self.tgt, 'supports_prefetch', False) or self.tgt is None) + return getattr(self.src, "supports_prefetch", False) and ( + getattr(self.tgt, "supports_prefetch", False) or self.tgt is None ) def prefetch(self, indices): @@ -294,3 +452,20 @@ def prefetch(self, indices): self.tgt.prefetch(indices) if self.align_dataset is not None: self.align_dataset.prefetch(indices) + + def filter_indices_by_size(self, indices, max_sizes): + """Filter a list of sample indices. Remove those that are longer + than specified in max_sizes. + + Args: + indices (np.array): original array of sample indices + max_sizes (int or list[int] or tuple[int]): max sample size, + can be defined separately for src and tgt (then list or tuple) + + Returns: + np.array: filtered sample array + list: list of removed indices + """ + return data_utils.filter_paired_dataset_indices_by_size( + self.src_sizes, self.tgt_sizes, indices, max_sizes, + ) diff --git a/fairseq/data/legacy/__init__.py b/fairseq/data/legacy/__init__.py index 1acaafeb09..9bd5c72b5e 100644 --- a/fairseq/data/legacy/__init__.py +++ b/fairseq/data/legacy/__init__.py @@ -3,13 +3,14 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. -from .masked_lm_dictionary import BertDictionary, MaskedLMDictionary from .block_pair_dataset import BlockPairDataset from .masked_lm_dataset import MaskedLMDataset +from .masked_lm_dictionary import BertDictionary, MaskedLMDictionary + __all__ = [ - 'BertDictionary', - 'BlockPairDataset', - 'MaskedLMDataset', - 'MaskedLMDictionary', + "BertDictionary", + "BlockPairDataset", + "MaskedLMDataset", + "MaskedLMDictionary", ] diff --git a/fairseq/data/legacy/block_pair_dataset.py b/fairseq/data/legacy/block_pair_dataset.py index b9fc814147..ba069b4605 100644 --- a/fairseq/data/legacy/block_pair_dataset.py +++ b/fairseq/data/legacy/block_pair_dataset.py @@ -7,7 +7,6 @@ import numpy as np import torch - from fairseq.data import FairseqDataset diff --git a/fairseq/data/legacy/masked_lm_dataset.py b/fairseq/data/legacy/masked_lm_dataset.py index 953aa85dd4..dd8ea2c60a 100644 --- a/fairseq/data/legacy/masked_lm_dataset.py +++ b/fairseq/data/legacy/masked_lm_dataset.py @@ -4,18 +4,14 @@ # LICENSE file in the root directory of this source tree. import math +from typing import Dict, List, Tuple import numpy as np import torch - -from typing import Dict, List, Tuple - -from fairseq.data import FairseqDataset, data_utils - -from fairseq.data import Dictionary +from fairseq.data import Dictionary, FairseqDataset, data_utils +from fairseq.data.concat_dataset import ConcatDataset from fairseq.data.legacy.block_pair_dataset import BlockPairDataset from fairseq.data.token_block_dataset import TokenBlockDataset -from fairseq.data.concat_dataset import ConcatDataset class MaskedLMDataset(FairseqDataset): @@ -55,29 +51,31 @@ class MaskedLMDataset(FairseqDataset): """ def __init__( - self, - dataset: FairseqDataset, - sizes: np.ndarray, - vocab: Dictionary, - pad_idx: int, - mask_idx: int, - classif_token_idx: int, - sep_token_idx: int, - seed: int = 1, - shuffle: bool = True, - has_pairs: bool = True, - segment_id: int = 0, - masking_ratio: float = 0.15, - masking_prob: float = 0.8, - random_token_prob: float = 0.1 + self, + dataset: FairseqDataset, + sizes: np.ndarray, + vocab: Dictionary, + pad_idx: int, + mask_idx: int, + classif_token_idx: int, + sep_token_idx: int, + seed: int = 1, + shuffle: bool = True, + has_pairs: bool = True, + segment_id: int = 0, + masking_ratio: float = 0.15, + masking_prob: float = 0.8, + random_token_prob: float = 0.1, ): # Make sure the input datasets are the ones supported assert ( - isinstance(dataset, TokenBlockDataset) or - isinstance(dataset, BlockPairDataset) or - isinstance(dataset, ConcatDataset) - ), "MaskedLMDataset only wraps TokenBlockDataset or BlockPairDataset or " \ - "ConcatDataset" + isinstance(dataset, TokenBlockDataset) + or isinstance(dataset, BlockPairDataset) + or isinstance(dataset, ConcatDataset) + ), ( + "MaskedLMDataset only wraps TokenBlockDataset or BlockPairDataset or " + "ConcatDataset" + ) self.dataset = dataset self.sizes = np.array(sizes) @@ -99,10 +97,7 @@ def __init__( if not has_pairs: self.sizes = self.sizes + 1 - def __getitem__( - self, - index: int - ): + def __getitem__(self, index: int): # if has_pairs, then expect 2 blocks and a sentence target if self.has_pairs: (block_one, block_two, sentence_target) = self.dataset[index] @@ -120,11 +115,11 @@ def __len__(self): return len(self.dataset) def _mask_block( - self, - sentence: np.ndarray, - mask_idx: int, - pad_idx: int, - dictionary_token_range: Tuple, + self, + sentence: np.ndarray, + mask_idx: int, + pad_idx: int, + dictionary_token_range: Tuple, ): """ Mask tokens for Masked Language Model training @@ -166,22 +161,15 @@ def _mask_block( # masking_prob + random_token_prob (Eg: 0.9) elif rand < (self.masking_prob + self.random_token_prob): # sample random token from dictionary - masked_sent[i] = ( - np.random.randint( - dictionary_token_range[0], dictionary_token_range[1] - ) + masked_sent[i] = np.random.randint( + dictionary_token_range[0], dictionary_token_range[1] ) else: target[i] = pad_idx return masked_sent, target - def _collate( - self, - samples: List[Dict], - pad_idx: int, - eos_idx: int - ): + def _collate(self, samples: List[Dict], pad_idx: int, eos_idx: int): """ Does the heavy lifting for creating a batch from the input list of examples. The logic is as follows: @@ -215,12 +203,13 @@ def _collate( # mask according to specified probabilities. masked_blk_one, masked_tgt_one = self._mask_block( - s["block_one"], self.mask_idx, self.pad_idx, token_range, + s["block_one"], + self.mask_idx, + self.pad_idx, + token_range, ) - tokens = np.concatenate([ - [self.classif_token_idx], masked_blk_one - ]) + tokens = np.concatenate([[self.classif_token_idx], masked_blk_one]) targets = np.concatenate([[self.pad_idx], masked_tgt_one]) segments = np.ones(len(tokens)) * self.segment_id @@ -232,9 +221,9 @@ def _collate( targets_one = np.concatenate([targets, [self.pad_idx]]) masked_blk_two, masked_tgt_two = self._mask_block( - s["block_two"], self.mask_idx, self.pad_idx, token_range) - tokens_two = np.concatenate( - [masked_blk_two, [self.sep_token_idx]]) + s["block_two"], self.mask_idx, self.pad_idx, token_range + ) + tokens_two = np.concatenate([masked_blk_two, [self.sep_token_idx]]) targets_two = np.concatenate([masked_tgt_two, [self.pad_idx]]) # block + 1 sep + 1 special (CLS) @@ -254,6 +243,7 @@ def merge(key): return data_utils.collate_tokens( [s[key] for s in samples], pad_idx, eos_idx, left_pad=False ) + return { "id": torch.LongTensor([s["id"] for s in samples]), "ntokens": sum(len(s["source"]) for s in samples), @@ -262,16 +252,13 @@ def merge(key): "segment_labels": merge("segment_labels"), }, "lm_target": merge("lm_target"), - "sentence_target": torch.LongTensor( - [s["sentence_target"] for s in samples] - ) if self.has_pairs else None, + "sentence_target": torch.LongTensor([s["sentence_target"] for s in samples]) + if self.has_pairs + else None, "nsentences": len(samples), } - def collater( - self, - samples: List[Dict] - ): + def collater(self, samples: List[Dict]): """Merge a list of samples to form a mini-batch. Args: @@ -282,20 +269,14 @@ def collater( """ return self._collate(samples, self.vocab.pad(), self.vocab.eos()) - def num_tokens( - self, - index: int - ): + def num_tokens(self, index: int): """ Return the number of tokens in a sample. This value is used to enforce max-tokens during batching. """ return self.sizes[index] - def size( - self, - index: int - ): + def size(self, index: int): """ Return an example's size as a float or tuple. This value is used when filtering a dataset with max-positions. diff --git a/fairseq/data/legacy/masked_lm_dictionary.py b/fairseq/data/legacy/masked_lm_dictionary.py index 68f9b83fbd..dee88f7a3e 100644 --- a/fairseq/data/legacy/masked_lm_dictionary.py +++ b/fairseq/data/legacy/masked_lm_dictionary.py @@ -11,12 +11,13 @@ class MaskedLMDictionary(Dictionary): Dictionary for Masked Language Modelling tasks. This extends Dictionary by adding the mask symbol. """ + def __init__( self, - pad='<pad>', - eos='</s>', - unk='<unk>', - mask='<mask>', + pad="<pad>", + eos="</s>", + unk="<unk>", + mask="<mask>", ): super().__init__(pad=pad, eos=eos, unk=unk) self.mask_word = mask @@ -33,16 +34,17 @@ class BertDictionary(MaskedLMDictionary): Dictionary for BERT task. This extends MaskedLMDictionary by adding support for cls and sep symbols. """ + def __init__( self, - pad='<pad>', - eos='</s>', - unk='<unk>', - mask='<mask>', - cls='<cls>', - sep='<sep>' + pad="<pad>", + eos="</s>", + unk="<unk>", + mask="<mask>", + cls="<cls>", + sep="<sep>", ): - super().__init__(pad=pad, eos=eos, unk=unk) + super().__init__(pad=pad, eos=eos, unk=unk, mask=mask) self.cls_word = cls self.sep_word = sep self.cls_index = self.add_symbol(cls) diff --git a/fairseq/data/list_dataset.py b/fairseq/data/list_dataset.py index b96bba3437..12f00aa436 100644 --- a/fairseq/data/list_dataset.py +++ b/fairseq/data/list_dataset.py @@ -7,7 +7,6 @@ class ListDataset(BaseWrapperDataset): - def __init__(self, dataset, sizes=None): super().__init__(dataset) self._sizes = sizes diff --git a/fairseq/data/lm_context_window_dataset.py b/fairseq/data/lm_context_window_dataset.py index 17ba08bc7f..1a945927cf 100644 --- a/fairseq/data/lm_context_window_dataset.py +++ b/fairseq/data/lm_context_window_dataset.py @@ -5,6 +5,7 @@ import numpy as np import torch +from typing import Dict from fairseq.data.monolingual_dataset import MonolingualDataset @@ -12,10 +13,27 @@ class LMContextWindowDataset(FairseqDataset): - """Wraps a MonolingualDataset and provides more context for evaluation.""" - - def __init__(self, dataset, tokens_per_sample, context_window, pad_idx): - assert isinstance(dataset, MonolingualDataset) + """ + Wraps a MonolingualDataset and provides more context for evaluation. + + Each item in the new dataset will have a maximum size of + ``tokens_per_sample + context_window``. + + Args: + dataset: dataset to wrap + tokens_per_sample (int): the max number of tokens in each dataset item + context_window (int): the number of accumulated tokens to add to each + dataset item + pad_idx (int): padding symbol + """ + + def __init__( + self, + dataset: MonolingualDataset, + tokens_per_sample: int, + context_window: int, + pad_idx: int, + ): assert context_window > 0 self.dataset = dataset self.tokens_per_sample = tokens_per_sample @@ -29,17 +47,17 @@ def __getitem__(self, index): def __len__(self): return len(self.dataset) - def collater(self, samples): + def collater(self, samples) -> Dict: sample = self.dataset.collater(samples) pad = self.pad_idx max_sample_len = self.tokens_per_sample + self.context_window - bsz, tsz = sample['net_input']['src_tokens'].shape + bsz, tsz = sample["net_input"]["src_tokens"].shape start_idxs = [0] * bsz - toks = sample['net_input']['src_tokens'] - lengths = sample['net_input']['src_lengths'] - tgt = sample['target'] + toks = sample["net_input"]["src_tokens"] + lengths = sample["net_input"]["src_lengths"] + tgt = sample["target"] new_toks = np.empty([bsz, tsz + self.context_window], dtype=np.int64) new_tgt = np.full([bsz, tsz + self.context_window], pad, dtype=np.int64) sample_lens = toks.ne(pad).long().sum(dim=1).cpu() @@ -50,14 +68,15 @@ def collater(self, samples): self.prev_tokens = self.prev_tokens[extra:] pads = np.full(self.context_window - len(self.prev_tokens), pad) new_toks[i] = np.concatenate([self.prev_tokens, toks[i].numpy(), pads]) - new_tgt[i, len(self.prev_tokens):len(self.prev_tokens) + len(tgt[i])] = tgt[i] + new_tgt[ + i, len(self.prev_tokens) : len(self.prev_tokens) + len(tgt[i]) + ] = tgt[i] start_idxs[i] = len(self.prev_tokens) lengths[i] += len(self.prev_tokens) - self.prev_tokens = new_toks[i][new_toks[i] != pad][-self.context_window:] - sample['net_input']['src_tokens'] = torch.from_numpy(new_toks) - sample['target'] = torch.from_numpy(new_tgt) - sample['start_indices'] = start_idxs - + self.prev_tokens = new_toks[i][new_toks[i] != pad][-self.context_window :] + sample["net_input"]["src_tokens"] = torch.from_numpy(new_toks) + sample["target"] = torch.from_numpy(new_tgt) + sample["start_indices"] = start_idxs return sample def num_tokens(self, index): @@ -72,7 +91,7 @@ def ordered_indices(self): @property def supports_prefetch(self): - return getattr(self.dataset, 'supports_prefetch', False) + return getattr(self.dataset, "supports_prefetch", False) def prefetch(self, indices): return self.dataset.prefetch(indices) diff --git a/fairseq/data/lru_cache_dataset.py b/fairseq/data/lru_cache_dataset.py index 833a2c75cb..a7854ac170 100644 --- a/fairseq/data/lru_cache_dataset.py +++ b/fairseq/data/lru_cache_dataset.py @@ -9,7 +9,6 @@ class LRUCacheDataset(BaseWrapperDataset): - def __init__(self, dataset, token=None): super().__init__(dataset) diff --git a/fairseq/data/mask_tokens_dataset.py b/fairseq/data/mask_tokens_dataset.py index 84d313a096..9123235594 100644 --- a/fairseq/data/mask_tokens_dataset.py +++ b/fairseq/data/mask_tokens_dataset.py @@ -7,8 +7,7 @@ import numpy as np import torch - -from fairseq.data import data_utils, Dictionary +from fairseq.data import Dictionary, data_utils from . import BaseWrapperDataset, LRUCacheDataset @@ -40,6 +39,10 @@ class MaskTokensDataset(BaseWrapperDataset): over vocab indices, indicating whether it is the beginning of a word. We will extend any mask to encompass the whole word. bpe: BPE to use for whole-word masking. + mask_multiple_length : repeat each mask index multiple times. Default + value is 1. + mask_stdev : standard deviation of masks distribution in case of + multiple masking. Default value is 0. """ @classmethod @@ -64,11 +67,15 @@ def __init__( random_token_prob: float = 0.1, freq_weighted_replacement: bool = False, mask_whole_words: torch.Tensor = None, + mask_multiple_length: int = 1, + mask_stdev: float = 0.0, ): assert 0.0 < mask_prob < 1.0 assert 0.0 <= random_token_prob <= 1.0 assert 0.0 <= leave_unmasked_prob <= 1.0 assert random_token_prob + leave_unmasked_prob <= 1.0 + assert mask_multiple_length >= 1 + assert mask_stdev >= 0.0 self.dataset = dataset self.vocab = vocab @@ -80,30 +87,41 @@ def __init__( self.leave_unmasked_prob = leave_unmasked_prob self.random_token_prob = random_token_prob self.mask_whole_words = mask_whole_words + self.mask_multiple_length = mask_multiple_length + self.mask_stdev = mask_stdev if random_token_prob > 0.0: if freq_weighted_replacement: weights = np.array(self.vocab.count) else: weights = np.ones(len(self.vocab)) - weights[:self.vocab.nspecial] = 0 + weights[: self.vocab.nspecial] = 0 self.weights = weights / weights.sum() self.epoch = 0 + @property + def can_reuse_epoch_itr_across_epochs(self): + return True # only the noise changes, not item sizes + def set_epoch(self, epoch, **unused): + super().set_epoch(epoch) self.epoch = epoch - @lru_cache(maxsize=8) def __getitem__(self, index: int): + return self.__getitem_cached__(self.seed, self.epoch, index) + + @lru_cache(maxsize=8) + def __getitem_cached__(self, seed: int, epoch: int, index: int): with data_utils.numpy_seed(self.seed, self.epoch, index): item = self.dataset[index] sz = len(item) - assert self.mask_idx not in item, \ - 'Dataset contains mask_idx (={}), this is not expected!'.format( - self.mask_idx, - ) + assert ( + self.mask_idx not in item + ), "Dataset contains mask_idx (={}), this is not expected!".format( + self.mask_idx, + ) if self.mask_whole_words is not None: word_begins_mask = self.mask_whole_words.gather(0, item) @@ -117,9 +135,39 @@ def __getitem__(self, index: int): mask = np.full(sz, False) num_mask = int( # add a random number for probabilistic rounding - self.mask_prob * sz + np.random.rand() + self.mask_prob * sz / float(self.mask_multiple_length) + + np.random.rand() ) - mask[np.random.choice(sz, num_mask, replace=False)] = True + + # multiple masking as described in the vq-wav2vec paper (https://arxiv.org/abs/1910.05453) + mask_idc = np.random.choice(sz, num_mask, replace=False) + if self.mask_stdev > 0.0: + lengths = np.random.normal( + self.mask_multiple_length, self.mask_stdev, size=num_mask + ) + lengths = [max(0, int(round(x))) for x in lengths] + mask_idc = np.asarray( + [ + mask_idc[j] + offset + for j in range(len(mask_idc)) + for offset in range(lengths[j]) + ], + dtype=np.int64, + ) + else: + mask_idc = np.concatenate( + [mask_idc + i for i in range(self.mask_multiple_length)] + ) + mask_idc = mask_idc[mask_idc < len(mask)] + try: + mask[mask_idc] = True + except: # something wrong + print( + "Assigning mask indexes {} to mask {} failed!".format( + mask_idc, mask + ) + ) + raise if self.return_masked_tokens: # exit early if we're just returning the masked tokens diff --git a/fairseq/data/monolingual_dataset.py b/fairseq/data/monolingual_dataset.py index 76c3772374..54fd583b64 100644 --- a/fairseq/data/monolingual_dataset.py +++ b/fairseq/data/monolingual_dataset.py @@ -6,10 +6,10 @@ import numpy as np import torch -from . import data_utils, FairseqDataset +from . import FairseqDataset, data_utils -def collate(samples, pad_idx, eos_idx): +def collate(samples, pad_idx, eos_idx, fixed_pad_length=None, pad_to_bsz=None): if len(samples) == 0: return {} @@ -17,33 +17,43 @@ def merge(key, is_list=False): if is_list: res = [] for i in range(len(samples[0][key])): - res.append(data_utils.collate_tokens( - [s[key][i] for s in samples], pad_idx, eos_idx, left_pad=False, - )) + res.append( + data_utils.collate_tokens( + [s[key][i] for s in samples], + pad_idx, + eos_idx, + left_pad=False, + pad_to_length=fixed_pad_length, + pad_to_bsz=pad_to_bsz, + ) + ) return res else: return data_utils.collate_tokens( - [s[key] for s in samples], pad_idx, eos_idx, left_pad=False, + [s[key] for s in samples], + pad_idx, + eos_idx, + left_pad=False, + pad_to_length=fixed_pad_length, + pad_to_bsz=pad_to_bsz, ) - src_tokens = merge('source') - if samples[0]['target'] is not None: - is_target_list = isinstance(samples[0]['target'], list) - target = merge('target', is_target_list) + src_tokens = merge("source") + if samples[0]["target"] is not None: + is_target_list = isinstance(samples[0]["target"], list) + target = merge("target", is_target_list) else: target = src_tokens return { - 'id': torch.LongTensor([s['id'] for s in samples]), - 'nsentences': len(samples), - 'ntokens': sum(len(s['source']) for s in samples), - 'net_input': { - 'src_tokens': src_tokens, - 'src_lengths': torch.LongTensor([ - s['source'].numel() for s in samples - ]), + "id": torch.LongTensor([s["id"] for s in samples]), + "nsentences": len(samples), + "ntokens": sum(len(s["source"]) for s in samples), + "net_input": { + "src_tokens": src_tokens, + "src_lengths": torch.LongTensor([s["source"].numel() for s in samples]), }, - 'target': target, + "target": target, } @@ -59,18 +69,36 @@ class MonolingualDataset(FairseqDataset): (default: True). """ - def __init__(self, dataset, sizes, src_vocab, tgt_vocab, add_eos_for_other_targets, shuffle, - targets=None, add_bos_token=False): + def __init__( + self, + dataset, + sizes, + src_vocab, + tgt_vocab=None, + add_eos_for_other_targets=False, + shuffle=False, + targets=None, + add_bos_token=False, + fixed_pad_length=None, + pad_to_bsz=None, + src_lang_idx=None, + tgt_lang_idx=None, + ): self.dataset = dataset self.sizes = np.array(sizes) self.vocab = src_vocab - self.tgt_vocab = tgt_vocab + self.tgt_vocab = tgt_vocab or src_vocab self.add_eos_for_other_targets = add_eos_for_other_targets self.shuffle = shuffle self.add_bos_token = add_bos_token - - assert targets is None or all(t in {'self', 'future', 'past'} for t in targets), \ - "targets must be none or one of 'self', 'future', 'past'" + self.fixed_pad_length = fixed_pad_length + self.pad_to_bsz = pad_to_bsz + self.src_lang_idx = src_lang_idx + self.tgt_lang_idx = tgt_lang_idx + + assert targets is None or all( + t in {"self", "future", "past"} for t in targets + ), "targets must be none or one of 'self', 'future', 'past'" if targets is not None and len(targets) == 0: targets = None self.targets = targets @@ -86,12 +114,14 @@ def __getitem__(self, index): # Right-to-left language models should condition on *source* and # predict *past_target*. source, future_target, past_target = self.dataset[index] - source, target = self._make_source_target(source, future_target, past_target) + source, target = self._make_source_target( + source, future_target, past_target + ) else: source = self.dataset[index] target = None source, target = self._maybe_add_bos(source, target) - return {'id': index, 'source': source, 'target': target} + return {"id": index, "source": source, "target": target} def __len__(self): return len(self.dataset) @@ -100,27 +130,38 @@ def _make_source_target(self, source, future_target, past_target): if self.targets is not None: target = [] - if self.add_eos_for_other_targets and (('self' in self.targets) or ('past' in self.targets)) \ - and source[-1] != self.vocab.eos(): + if ( + self.add_eos_for_other_targets + and (("self" in self.targets) or ("past" in self.targets)) + and source[-1] != self.vocab.eos() + ): # append eos at the end of source source = torch.cat([source, source.new([self.vocab.eos()])]) - if 'future' in self.targets: - future_target = torch.cat([future_target, future_target.new([self.vocab.pad()])]) - if 'past' in self.targets: + if "future" in self.targets: + future_target = torch.cat( + [future_target, future_target.new([self.vocab.pad()])] + ) + if "past" in self.targets: # first token is before the start of sentence which is only used in "none" break mode when # add_eos_for_other_targets is False - past_target = torch.cat([past_target.new([self.vocab.pad()]), past_target[1:], source[-2, None]]) + past_target = torch.cat( + [ + past_target.new([self.vocab.pad()]), + past_target[1:], + source[-2, None], + ] + ) for t in self.targets: - if t == 'self': + if t == "self": target.append(source) - elif t == 'future': + elif t == "future": target.append(future_target) - elif t == 'past': + elif t == "past": target.append(past_target) else: - raise Exception('invalid target ' + t) + raise Exception("invalid target " + t) if len(target) == 1: target = target[0] @@ -136,8 +177,14 @@ def _maybe_add_bos(self, source, target): target = torch.cat([target.new([self.tgt_vocab.bos()]), target]) return source, target + def num_tokens_vec(self, indices): + """Return the number of tokens for a set of positions defined by indices. + This value is used to enforce ``--max-tokens`` during batching.""" + return self.sizes[indices] + def _filter_vocab(self, target): if len(self.tgt_vocab) != len(self.vocab): + def _filter(target): mask = target.ge(len(self.tgt_vocab)) if mask.any(): @@ -170,7 +217,13 @@ def collater(self, samples): target sentence of shape `(bsz, tgt_len)`. Padding will appear on the right. """ - return collate(samples, self.vocab.pad(), self.vocab.eos()) + return collate( + samples, + self.vocab.pad(), + self.vocab.eos(), + self.fixed_pad_length, + self.pad_to_bsz, + ) def num_tokens(self, index): """Return the number of tokens in a sample. This value is used to @@ -194,7 +247,7 @@ def ordered_indices(self): @property def supports_prefetch(self): - return getattr(self.dataset, 'supports_prefetch', False) + return getattr(self.dataset, "supports_prefetch", False) def prefetch(self, indices): self.dataset.prefetch(indices) diff --git a/fairseq/data/multi_corpus_dataset.py b/fairseq/data/multi_corpus_dataset.py new file mode 100644 index 0000000000..746155e515 --- /dev/null +++ b/fairseq/data/multi_corpus_dataset.py @@ -0,0 +1,245 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +import time +from collections import OrderedDict +from typing import Dict, List + +import numpy as np +from fairseq.data import data_utils + +from . import FairseqDataset + +logger = logging.getLogger(__name__) + + +class MultiCorpusDataset(FairseqDataset): + """ + Stores multiple instances of FairseqDataset together. Requires each instance + to be the same dataset, as the collate method needs to work on batches with + samples from each dataset. + + Allows specifying a distribution over the datasets to use. Note that unlike + MultiCorpusSampledDataset, this distribution allows sampling for each item, + rather than on a batch level. + + Each time ordered_indices() is called, a new sample is generated with + the specified distribution. + + Args: + datasets: a OrderedDict of FairseqDataset instances. + distribution: a List containing the probability of getting an utterance from + corresponding dataset + seed: random seed for sampling the datsets + sort_indices: if true, will sort the ordered indices by size + batch_sample: if true, will ensure each batch is from a single dataset + """ + + def __init__( + self, + datasets: Dict[str, FairseqDataset], + distribution: List[float], + seed: int, + sort_indices: bool = False, + batch_sample: bool = False, + distributed_rank=None, + ): + super().__init__() + assert isinstance(datasets, OrderedDict) + assert len(datasets) == len(distribution) + assert sum(distribution) == 1 + self.datasets = datasets + self.distribution = distribution + self.seed = seed + self.sort_indices = sort_indices + self.batch_sample = batch_sample + self.distributed_rank = distributed_rank + + # Avoid repeated conversions to list later + self.dataset_list = list(datasets.values()) + self.total_num_instances = 0 + + first_dataset = list(self.datasets.values())[0] + + self.dataset_offsets = [] + for dataset in datasets.values(): + assert isinstance(dataset, FairseqDataset) + assert type(dataset) is type(first_dataset) + self.dataset_offsets.append(self.total_num_instances) + self.total_num_instances += len(dataset) + + def ordered_indices(self): + start = time.time() + with data_utils.numpy_seed(self.seed, self.epoch): + logger.info(f"sampling new dataset with seed {self.seed} epoch {self.epoch}") + sampled_indices = [] + num_selected_instances = 0 + + # For each dataset i, sample self.distribution[i] * self.total_num_instances + for i, key in enumerate(self.datasets): + + if i < len(self.datasets) - 1: + num_instances = int(self.distribution[i] * self.total_num_instances) + high = self.dataset_offsets[i + 1] + else: + num_instances = self.total_num_instances - num_selected_instances + high = self.total_num_instances + + logger.info(f"sampling {num_instances} from {key} dataset") + num_selected_instances += num_instances + + # First, add k copies of the dataset where k = num_instances // len(dataset). + # This ensures an equal distribution of the data points as much as possible. + # For the remaining entries randomly sample them + dataset_size = len(self.datasets[key]) + num_copies = num_instances // dataset_size + dataset_indices = ( + np.random.permutation(high - self.dataset_offsets[i]) + + self.dataset_offsets[i] + )[: num_instances - num_copies * dataset_size] + if num_copies > 0: + sampled_indices += list( + np.concatenate( + ( + np.repeat( + np.arange(self.dataset_offsets[i], high), num_copies + ), + dataset_indices, + ) + ) + ) + else: + sampled_indices += list(dataset_indices) + + assert ( + len(sampled_indices) == self.total_num_instances + ), f"{len(sampled_indices)} vs {self.total_num_instances}" + + np.random.shuffle(sampled_indices) + if self.sort_indices: + sampled_indices.sort(key=lambda i: self.num_tokens(i)) + + logger.info( + "multi_corpus_dataset ordered_indices took {}s".format( + time.time() - start + ) + ) + return np.array(sampled_indices, dtype=np.int64) + + def _map_index(self, index: int): + """ + If dataset A has length N and dataset B has length M + then index 1 maps to index 1 of dataset A, and index N + 1 + maps to index 1 of B. + """ + counter = 0 + for key, dataset in self.datasets.items(): + if index < counter + len(dataset): + return index - counter, key + counter += len(dataset) + raise ValueError( + "Invalid index: {}, max: {}".format(index, self.total_num_instances) + ) + + def __len__(self): + """ + Length of this dataset is the sum of individual datasets + """ + return self.total_num_instances + + def __getitem__(self, index): + new_index, key = self._map_index(index) + try: + item = self.datasets[key][new_index] + item["full_id"] = index + return item + except Exception as e: + e.args = (f"Error from {key} dataset", *e.args) + raise + + def collater(self, samples): + """ + If we are doing batch sampling, then pick the right collater to use. + + Otherwise we assume all collaters are the same. + """ + if len(samples) == 0: + return None + if "full_id" in samples[0]: + _, key = self._map_index(samples[0]["full_id"]) + try: + batch = self.datasets[key].collater(samples) + except Exception: + print(f"Collating failed for key {key}", flush=True) + raise + return batch + else: + # Subclasses may override __getitem__ to not specify full_id + return list(self.datasets.values())[0].collater(samples) + + def num_tokens(self, index: int): + index, key = self._map_index(index) + return self.datasets[key].num_tokens(index) + + def size(self, index: int): + index, key = self._map_index(index) + return self.datasets[key].size(index) + + @property + def can_reuse_epoch_itr_across_epochs(self): + return False + + def set_epoch(self, epoch, **unused): + super().set_epoch(epoch) + logger.info(f"setting epoch of multi_corpus_dataset to {epoch}") + self.epoch = epoch + + @property + def supports_prefetch(self): + return False + + @property + def supports_fetch_outside_dataloader(self): + return all( + self.datasets[key].supports_fetch_outside_dataloader + for key in self.datasets + ) + + def batch_by_size( + self, + indices, + max_tokens=None, + max_sentences=None, + required_batch_size_multiple=1, + ): + if not self.batch_sample: + return super().batch_by_size( + indices, max_tokens, max_sentences, required_batch_size_multiple + ) + + dataset_indices = {key: [] for key in self.datasets} + for i in indices: + _, key = self._map_index(i) + dataset_indices[key].append(i) + + batches = [] + for key in dataset_indices: + cur_batches = super().batch_by_size( + np.array(dataset_indices[key], dtype=np.int64), + max_tokens, + max_sentences, + required_batch_size_multiple, + ) + logger.info(f"Created {len(cur_batches)} batches for dataset {key}") + batches += cur_batches + + # If this dataset is used in a distributed training setup, + # then shuffle such that the order is seeded by the distributed rank + # as well + if self.distributed_rank is not None: + with data_utils.numpy_seed(self.seed, self.epoch, self.distributed_rank): + np.random.shuffle(batches) + return batches diff --git a/fairseq/data/multi_corpus_sampled_dataset.py b/fairseq/data/multi_corpus_sampled_dataset.py index ad8e951cc9..e2e9fdf004 100644 --- a/fairseq/data/multi_corpus_sampled_dataset.py +++ b/fairseq/data/multi_corpus_sampled_dataset.py @@ -143,3 +143,10 @@ def prefetch(self, indices): dataset.prefetch( [self._map_index_to_dataset(key, index) for index in indices] ) + + @property + def supports_fetch_outside_dataloader(self): + return all( + self.datasets[key].supports_fetch_outside_dataloader + for key in self.datasets + ) diff --git a/fairseq/data/multilingual/__init__.py b/fairseq/data/multilingual/__init__.py new file mode 100644 index 0000000000..6264236915 --- /dev/null +++ b/fairseq/data/multilingual/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. diff --git a/fairseq/data/multilingual/multilingual_data_manager.py b/fairseq/data/multilingual/multilingual_data_manager.py new file mode 100644 index 0000000000..137481b449 --- /dev/null +++ b/fairseq/data/multilingual/multilingual_data_manager.py @@ -0,0 +1,1136 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import itertools +import json +import logging +import math +import os +from collections import OrderedDict, defaultdict +from argparse import ArgumentError + +from fairseq import utils +from fairseq.data import ( + AppendTokenDataset, + ConcatDataset, + Dictionary, + LanguagePairDataset, + PrependTokenDataset, + SampledMultiDataset, + SampledMultiEpochDataset, + StripTokenDataset, + TransformEosLangPairDataset, + TruncateDataset, + data_utils, + indexed_dataset, +) +from fairseq.data.multilingual.multilingual_utils import ( + EncoderLangtok, + LangTokSpec, + LangTokStyle, + augment_dictionary, + get_lang_tok, +) +from fairseq.data.multilingual.sampled_multi_dataset import CollateFormat +from fairseq.file_io import PathManager +from fairseq.utils import FileContentsAction, csv_str_list, eval_str_dict + + +logger = logging.getLogger(__name__) + +SRC_DICT_NAME = 'src' +TGT_DICT_NAME = 'tgt' + + +def _lang_id(dic: Dictionary, lang: str): + """Return language ID index.""" + idx = dic.index(lang) + assert idx != dic.unk_index, "cannot find language ID for lang {}".format(lang) + return idx + + +def load_sampling_weights(from_file): + with open(from_file) as f: + weights = json.load(f) + return weights + + +class MultilingualDatasetManager(object): + def __init__(self, args, lang_pairs, langs, dicts, sampling_method): + super().__init__() + self.args = args + self.seed = args.seed + self.lang_pairs = lang_pairs + self.extra_lang_pairs = ( + list( + {p for _, v in args.extra_lang_pairs.items() for p in v.split(",")} + ) + if args.extra_lang_pairs + else [] + ) + self.src_langs = {p.split("-")[0] for p in args.lang_pairs + self.extra_lang_pairs} + self.tgt_langs = {p.split("-")[1] for p in args.lang_pairs + self.extra_lang_pairs} + self.langs = langs + self.dicts = dicts + self.lang_dict = self.create_lang_dictionary(self.langs) + self.sampling_method = sampling_method + self.sampling_scheduler = None + self._has_sharded_data = False + self._num_shards_dict = {} + self._training_data_sizes = defaultdict(lambda: {}) + + @classmethod + def setup_data_manager(cls, args, lang_pairs, langs, dicts, sampling_method): + return MultilingualDatasetManager( + args, lang_pairs, langs, dicts, sampling_method + ) + + @staticmethod + def add_args(parser): + parser.add_argument( + "data", + help="colon separated path to data directories list, \ + will be iterated upon during epochs in round-robin manner", + action=FileContentsAction, + ) + parser.add_argument( + "--langs", + default=None, + type=csv_str_list, + help="a list of languages comma sperated languages which can appear in lang-pairs; " + "note that the ordering determines language token IDs", + ) + parser.add_argument( + "--lang-dict", + default=None, + type=str, + help="an external file which contains a list of " + "languages which can appear in lang-pairs; " + "note that the ordering determines language token IDs; " + "--langs and --lang-dict are two exclusive options", + ) + parser.add_argument('--source-dict', default=None, type=str, + help='path to source dictionary; if specified it will override per language dictionary loading') + parser.add_argument('--target-dict', default=None, type=str, + help='path to target dictionary; if specified it will override per language dictionary loading') + parser.add_argument( + "--lang-tok-style", + default=LangTokStyle.multilingual.value, + type=str, + choices=[LangTokStyle.multilingual.value, LangTokStyle.mbart.value], + help="language token styles", + ) + + parser.add_argument( + "--load-alignments", + action="store_true", + help="load the binarized alignments", + ) + parser.add_argument( + "--left-pad-source", + default="True", + type=str, + metavar="BOOL", + help="pad the source on the left", + ) + parser.add_argument( + "--left-pad-target", + default="False", + type=str, + metavar="BOOL", + help="pad the target on the left", + ) + try: + parser.add_argument( + "--max-source-positions", + default=1024, + type=int, + metavar="N", + help="max number of tokens in the source sequence", + ) + parser.add_argument( + "--max-target-positions", + default=1024, + type=int, + metavar="N", + help="max number of tokens in the target sequence", + ) + except ArgumentError: + # this might have already been defined. Once we transition this to hydra it should be fine to add it here. + pass + parser.add_argument( + "--upsample-primary", + default=1, + type=int, + help="amount to upsample primary dataset", + ) + parser.add_argument( + "--truncate-source", + action="store_true", + default=False, + help="truncate source to max-source-positions", + ) + parser.add_argument( + "--encoder-langtok", + default=None, + type=str, + choices=[EncoderLangtok.src.value, EncoderLangtok.tgt.value], + metavar="SRCTGT", + help="prepend to the beginning of source sentence the source or target " + "language token. (src/tgt)", + ) + parser.add_argument( + "--decoder-langtok", + action="store_true", + help="prepend to the beginning of target sentence the target language token", + ) + parser.add_argument( + "--lang-tok-replacing-bos-eos", action="store_true", default=False + ) + parser.add_argument( + "--enable-lang-ids", + default=False, + action="store_true", + help="whether to include language IDs in samples", + ) + parser.add_argument( + "--enable-reservsed-directions-shared-datasets", + default=False, + action="store_true", + help="whether to allow datasets be used in reversed directions", + ) + + parser.add_argument( + "--extra-data", + help='a dictionary of data name to this path, \ + e.g. {"mined", path_to_mined_data, "denoised": path_to_denoised_data}', + type=lambda uf: eval_str_dict(uf, type=str), + default=None, + ) + parser.add_argument( + "--extra-lang-pairs", + help='a dictionary of data name to the language pairs they serve, \ + e.g. {"mined": comma-separated-lang-pairs, "denoised": comma-separated-lang-pairs}', + type=lambda uf: eval_str_dict(uf, type=str), + default=None, + ) + parser.add_argument( + "--fixed-dictionary", + help="Fixed dictionary to use with model path", + default=None, + type=str, + ) + parser.add_argument( + "--langtoks-specs", + help='a list of comma separated data types that a set of language tokens to be specialized for, \ + e.g. "main,dae,mined". There will be a set of language tokens added to the vocab to \ + distinguish languages in different training data types. If not specified, default language \ + tokens per languages will be added', + default=LangTokSpec.main.value, + type=csv_str_list, + ) + parser.add_argument( + "--langtoks", + help='a dictionary of how to add language tokens, \ + e.g. {"mined": (None, "tgt"), "mono_dae": ("src.dae", "tgt"), "main": \ + ("src", "tgt")}, or {"mined": ("src.mined", "tgt")}', + default=None, + type=lambda uf: eval_str_dict(uf, type=str), + ) + parser.add_argument( + "--sampling-weights-from-file", + help='a file contain a python dictionary of how to sample data sets, \ + e.g. { "main:en_XX-es_XX": 0.2, "mined:en_XX-pt_XX": 0.5, \ + "mono_dae:es_XX-es_XX: 0.3, "main:en_xx-fr_XX": 0.8 }', + default=None, + type=str, + ) + parser.add_argument( + "--sampling-weights", + help='a dictionary of how to sample data sets, \ + e.g. { "main:en_XX-es_XX": 0.2, "mined:en_XX-pt_XX": 0.5, \ + "mono_dae:es_XX-es_XX: 0.3, "main:en_xx-fr_XX": 0.8 }', + default=None, + type=lambda uf: eval_str_dict(uf, type=str), + ) + parser.add_argument( + "--virtual-epoch-size", + default=None, + type=int, + help="virtual epoch size to speed up data loading", + ) + parser.add_argument( + "--virtual-data-size", + default=None, + type=int, + help="virtual data size of the whole joint dataset to speed" + "up data loading and have specific dynamic sampling strategy interval", + ) + + @classmethod + def load_langs(cls, args, **kwargs): + if args.lang_dict and args.langs: + raise ValueError("--langs and --lang-dict can not both be specified") + if args.lang_dict is None and args.langs is None: + logger.warning( + "External language dictionary is not provided; " + "use lang-pairs to infer the set of supported languages. " + "The language ordering is not stable which might cause " + "misalignment in pretraining and finetuning." + ) + # infer from lang_pairs as it is + langs = list( + {x for lang_pair in args.lang_pairs for x in lang_pair.split("-")} + ) + langs = sorted(langs) + logger.info(f"inferred language list: {langs}") + elif args.lang_dict: + with open( + PathManager.get_local_path(args.lang_dict), "r", encoding="utf-8" + ) as f: + langs = [lang.strip() for lang in f.readlines() if lang.strip()] + logger.info( + f"loaded language list from {args.lang_dict} as they are ordered in file" + ) + elif args.langs: + langs = args.langs + logger.info( + f"parsed the language list as they are ordered in the option: {langs}" + ) + return langs + + def has_sharded_data(self, split): + return self._has_sharded_data and split == getattr( + self.args, "train_subset", None + ) + + def _shared_collater(self): + return not (self.args.extra_data and "mono_dae" in self.args.extra_data) and ( + not self.args.lang_tok_replacing_bos_eos + ) + + def estimate_global_pass_epoch(self, epoch): + if self.args.virtual_epoch_size is None or self.args.virtual_data_size is None: + return None + # one epoch more for remaining data in each shard + virtual_epochs_per_shard = math.ceil( + self.args.virtual_data_size / self.args.virtual_epoch_size + ) + # note that fairseq epoch / shard_epoch starts from 1 + shard_epoch = (epoch - 1) // virtual_epochs_per_shard + 1 + return shard_epoch + + @classmethod + def prepare(cls, load_dictionary, args, **kargs): + args.left_pad_source = utils.eval_bool(args.left_pad_source) + args.left_pad_target = utils.eval_bool(args.left_pad_target) + + if not hasattr(args, "shuffle_instance"): + args.shuffle_instance = False + if args.langtoks is None: + args.langtoks = {} + if "main" not in args.langtoks: + src_langtok_spec = args.encoder_langtok if args.encoder_langtok else None + tgt_langtok_spec = "tgt" if args.decoder_langtok else None + args.langtoks["main"] = (src_langtok_spec, tgt_langtok_spec) + + def check_langs(langs, pairs): + messages = [] + for src, tgt in pairs: + if src not in langs or tgt not in langs: + messages.append( + f"language pair {src}-{tgt} contains languages " + "that are not in the language dictionary" + ) + if len(messages) > 0: + raise ValueError(" ".join(messages) + f"; langs: {langs}") + + if args.lang_pairs is None: + raise ValueError( + "--lang-pairs is required. List all the language pairs in the training objective." + ) + if isinstance(args.lang_pairs, str): + args.lang_pairs = args.lang_pairs.split(",") + if args.source_lang is not None or args.target_lang is not None: + training = False + else: + training = True + language_list = cls.load_langs(args, **kargs) + check_langs( + language_list, + ( + [p.split("-") for p in args.lang_pairs] + if training + else [(args.source_lang, args.target_lang)] + ), + ) + + def load_dictionary_and_postproc(path): + d = load_dictionary(path) + augment_dictionary( + dictionary=d, + language_list=language_list, + lang_tok_style=args.lang_tok_style, + langtoks_specs=args.langtoks_specs, + extra_data=args.extra_data, + ) + return d + + dicts = cls.load_all_dictionaries(args, language_list, load_dictionary_and_postproc, training) + return language_list, dicts, training + + @classmethod + def load_all_dictionaries(cls, args, language_list, load_dictionary, training): + dicts = OrderedDict() + if args.source_dict is not None: + dicts[SRC_DICT_NAME] = load_dictionary(args.source_dict) + if args.target_dict is not None: + dicts[TGT_DICT_NAME] = load_dictionary(args.target_dict) + + if training: + extra_lang_pairs = ( + list( + {p for _, v in args.extra_lang_pairs.items() for p in v.split(",")} + ) + if args.extra_lang_pairs + else [] + ) + src_langs_to_load_dicts = sorted( + {p.split("-")[0] for p in (args.lang_pairs + extra_lang_pairs)} + ) + tgt_langs_to_load_dicts = sorted( + {p.split("-")[1] for p in (args.lang_pairs + extra_lang_pairs)} + ) + else: + src_langs_to_load_dicts = [args.source_lang] + tgt_langs_to_load_dicts = [args.target_lang] + + paths = utils.split_paths(args.data) + assert len(paths) > 0 + + def load_dicts(langs_to_load_dicts): + for lang in langs_to_load_dicts: + dicts[lang] = load_dictionary( + os.path.join(paths[0], "dict.{}.txt".format(lang)) + ) + if len(dicts) > 0: + dict0 = next(iter(dicts.values())) + assert dicts[lang].pad() == dict0.pad() + assert dicts[lang].eos() == dict0.eos() + assert dicts[lang].unk() == dict0.unk() + logger.info("[{}] dictionary: {} types".format(lang, len(dicts[lang]))) + + if args.fixed_dictionary is not None: + fixed_dict = load_dictionary(args.fixed_dictionary) + dicts = {lang: fixed_dict for lang in src_langs_to_load_dicts + tgt_langs_to_load_dicts} + else: + if args.source_dict is None: + load_dicts(src_langs_to_load_dicts) + if args.target_dict is None: + load_dicts(tgt_langs_to_load_dicts) + return dicts + + def get_source_dictionary(self, lang): + if self.args.source_dict is not None: + return self.dicts[SRC_DICT_NAME] + else: + return self.dicts[lang] + + def get_target_dictionary(self, lang): + if self.args.target_dict is not None: + return self.dicts[TGT_DICT_NAME] + else: + return self.dicts[lang] + + @classmethod + def create_lang_dictionary(cls, langs): + unk = "<unk>" + # hack to remove symbols other than unk as they are not needed by lang dict + lang_dict = Dictionary(pad=unk, eos=unk, unk=unk, bos=unk) + for lang in langs: + lang_dict.add_symbol(lang) + return lang_dict + + @classmethod + def get_langtok_index(cls, lang_tok, dic): + idx = dic.index(lang_tok) + assert ( + idx != dic.unk_index + ), "cannot find language token {} in the dictionary".format(lang_tok) + return idx + + def get_encoder_langtok(self, src_lang, tgt_lang, spec=None): + if spec is None: + return None + if spec and spec.startswith("src"): + if src_lang is None: + return None + langtok = get_lang_tok( + lang=src_lang, lang_tok_style=self.args.lang_tok_style, spec=spec + ) + else: + if tgt_lang is None: + return None + langtok = get_lang_tok( + lang=tgt_lang, lang_tok_style=self.args.lang_tok_style, spec=spec + ) + return self.get_langtok_index( + langtok, self.get_source_dictionary(src_lang) if src_lang else self.get_target_dictionary(tgt_lang) + ) + + def get_decoder_langtok(self, tgt_lang, spec=None): + if spec is None: + return None + langtok = get_lang_tok( + lang=tgt_lang, lang_tok_style=self.args.lang_tok_style, spec=spec + ) + return self.get_langtok_index(langtok, self.get_target_dictionary(tgt_lang)) + + @classmethod + def load_data(cls, path, vdict, impl): + dataset = data_utils.load_indexed_dataset(path, vdict, impl) + return dataset + + @classmethod + def split_exists(cls, split, src, tgt, lang, data_path, dataset_impl): + filename = os.path.join(data_path, "{}.{}-{}.{}".format(split, src, tgt, lang)) + return indexed_dataset.dataset_exists(filename, impl=dataset_impl) + + def load_lang_dataset( + self, + data_path, + split, + src, + src_dict, + tgt, + tgt_dict, + combine, + dataset_impl, + upsample_primary, + max_source_positions, + prepend_bos=False, + load_alignments=False, + truncate_source=False, + ): + + src_datasets = [] + tgt_datasets = [] + + for k in itertools.count(): + split_k = split + (str(k) if k > 0 else "") + + # infer langcode + if self.split_exists(split_k, src, tgt, src, data_path, dataset_impl): + prefix = os.path.join(data_path, "{}.{}-{}.".format(split_k, src, tgt)) + elif self.split_exists(split_k, tgt, src, src, data_path, dataset_impl): + prefix = os.path.join(data_path, "{}.{}-{}.".format(split_k, tgt, src)) + else: + if k > 0: + break + else: + logger.error( + f"Dataset not found: {data_path}, {split_k}, {src}, {tgt}" + ) + raise FileNotFoundError( + "Dataset not found: {} ({})".format(split, data_path) + ) + + src_dataset = self.load_data(prefix + src, src_dict, dataset_impl) + if truncate_source: + src_dataset = AppendTokenDataset( + TruncateDataset( + StripTokenDataset(src_dataset, src_dict.eos()), + max_source_positions - 1, + ), + src_dict.eos(), + ) + src_datasets.append(src_dataset) + tgt_datasets.append(self.load_data(prefix + tgt, tgt_dict, dataset_impl)) + + logger.info( + "{} {} {}-{} {} examples".format( + data_path, split_k, src, tgt, len(src_datasets[-1]) + ) + ) + + if not combine: + break + + assert len(src_datasets) == len(tgt_datasets) + + if len(src_datasets) == 1: + src_dataset, tgt_dataset = src_datasets[0], tgt_datasets[0] + else: + sample_ratios = [1] * len(src_datasets) + sample_ratios[0] = upsample_primary + src_dataset = ConcatDataset(src_datasets, sample_ratios) + tgt_dataset = ConcatDataset(tgt_datasets, sample_ratios) + + if prepend_bos: + assert hasattr(src_dict, "bos_index") and hasattr(tgt_dict, "bos_index") + src_dataset = PrependTokenDataset(src_dataset, src_dict.bos()) + tgt_dataset = PrependTokenDataset(tgt_dataset, tgt_dict.bos()) + + align_dataset = None + if load_alignments: + align_path = os.path.join( + data_path, "{}.align.{}-{}".format(split, src, tgt) + ) + if indexed_dataset.dataset_exists(align_path, impl=dataset_impl): + align_dataset = data_utils.load_indexed_dataset( + align_path, None, dataset_impl + ) + + return src_dataset, tgt_dataset, align_dataset + + def load_langpair_dataset( + self, + data_path, + split, + src, + src_dict, + tgt, + tgt_dict, + combine, + dataset_impl, + upsample_primary, + left_pad_source, + left_pad_target, + max_source_positions, + max_target_positions, + prepend_bos=False, + load_alignments=False, + truncate_source=False, + src_dataset_transform_func=lambda dataset: dataset, + tgt_dataset_transform_func=lambda dataset: dataset, + src_lang_id=None, + tgt_lang_id=None, + langpairs_sharing_datasets=None, + ): + norm_direction = "-".join(sorted([src, tgt])) + if langpairs_sharing_datasets is not None: + src_dataset = langpairs_sharing_datasets.get( + (data_path, split, norm_direction, src), "NotInCache" + ) + tgt_dataset = langpairs_sharing_datasets.get( + (data_path, split, norm_direction, tgt), "NotInCache" + ) + align_dataset = langpairs_sharing_datasets.get( + (data_path, split, norm_direction, src, tgt), "NotInCache" + ) + + # a hack: any one is not in cache, we need to reload them + if ( + langpairs_sharing_datasets is None + or src_dataset == "NotInCache" + or tgt_dataset == "NotInCache" + or align_dataset == "NotInCache" + or split != getattr(self.args, "train_subset", None) + ): + # source and target datasets can be reused in reversed directions to save memory + # reversed directions of valid and test data will not share source and target datasets + src_dataset, tgt_dataset, align_dataset = self.load_lang_dataset( + data_path, + split, + src, + src_dict, + tgt, + tgt_dict, + combine, + dataset_impl, + upsample_primary, + max_source_positions=max_source_positions, + prepend_bos=prepend_bos, + load_alignments=load_alignments, + truncate_source=truncate_source, + ) + src_dataset = src_dataset_transform_func(src_dataset) + tgt_dataset = tgt_dataset_transform_func(tgt_dataset) + if langpairs_sharing_datasets is not None: + langpairs_sharing_datasets[ + (data_path, split, norm_direction, src) + ] = src_dataset + langpairs_sharing_datasets[ + (data_path, split, norm_direction, tgt) + ] = tgt_dataset + langpairs_sharing_datasets[ + (data_path, split, norm_direction, src, tgt) + ] = align_dataset + if align_dataset is None: + # no align data so flag the reverse direction as well in sharing + langpairs_sharing_datasets[ + (data_path, split, norm_direction, tgt, src) + ] = align_dataset + else: + logger.info( + f"Reusing source and target datasets of [{split}] {tgt}-{src} for reversed direction: " + f"[{split}] {src}-{tgt}: src length={len(src_dataset)}; tgt length={len(tgt_dataset)}" + ) + + return LanguagePairDataset( + src_dataset, + src_dataset.sizes, + src_dict, + tgt_dataset, + tgt_dataset.sizes if tgt_dataset is not None else None, + tgt_dict, + left_pad_source=left_pad_source, + left_pad_target=left_pad_target, + align_dataset=align_dataset, + src_lang_id=src_lang_id, + tgt_lang_id=tgt_lang_id, + ) + + def src_dataset_tranform_func(self, src_lang, tgt_lang, dataset, spec=None): + if self.args.lang_tok_replacing_bos_eos: + # it is handled by self.alter_dataset_langtok + # TODO: Unifiy with alter_dataset_langtok + return dataset + if spec is None: + return dataset + tok = self.get_encoder_langtok(src_lang, tgt_lang, spec) + if tok: + return PrependTokenDataset(dataset, tok) + return dataset + + def tgt_dataset_tranform_func(self, source_lang, target_lang, dataset, spec=None): + if dataset is None: + # note that target dataset can be None during inference time + return None + if self.args.lang_tok_replacing_bos_eos: + # TODO: Unifiy with alter_dataset_langtok + # It is handled by self.alter_dataset_langtok. + # The complication in self.alter_dataset_langtok + # makes a unified framework difficult. + return dataset + # if not self.args.decoder_langtok: + if not spec: + return dataset + tok = self.get_decoder_langtok(target_lang, spec) + if tok: + return PrependTokenDataset(dataset, tok) + return dataset + + def alter_dataset_langtok( + self, + lang_pair_dataset, + src_eos=None, + src_lang=None, + tgt_eos=None, + tgt_lang=None, + src_langtok_spec=None, + tgt_langtok_spec=None, + ): + if src_langtok_spec is None and tgt_langtok_spec is None: + return lang_pair_dataset + + new_src_eos = None + if ( + src_langtok_spec is not None + and src_eos is not None + and (src_lang is not None or tgt_lang is not None) + ): + new_src_eos = self.get_encoder_langtok(src_lang, tgt_lang, src_langtok_spec) + else: + src_eos = None + + new_tgt_bos = None + if tgt_langtok_spec and tgt_eos is not None and tgt_lang is not None: + new_tgt_bos = self.get_decoder_langtok(tgt_lang, tgt_langtok_spec) + else: + tgt_eos = None + + return TransformEosLangPairDataset( + lang_pair_dataset, + src_eos=src_eos, + new_src_eos=new_src_eos, + tgt_bos=tgt_eos, + new_tgt_bos=new_tgt_bos, + ) + + def load_a_dataset( + self, + split, + data_path, + src, + src_dict, + tgt, + tgt_dict, + combine, + prepend_bos=False, + langpairs_sharing_datasets=None, + data_category=None, + **extra_kwargs, + ): + dataset_impl = self.args.dataset_impl + upsample_primary = self.args.upsample_primary + left_pad_source = self.args.left_pad_source + left_pad_target = self.args.left_pad_target + max_source_positions = self.args.max_source_positions + max_target_positions = self.args.max_target_positions + load_alignments = self.args.load_alignments + truncate_source = self.args.truncate_source + src_dataset_transform_func = self.src_dataset_tranform_func + tgt_dataset_transform_func = self.tgt_dataset_tranform_func + enable_lang_ids = self.args.enable_lang_ids + lang_dictionary = self.lang_dict + src_langtok_spec, tgt_langtok_spec = extra_kwargs["langtok_spec"] + + src_langtok = self.get_encoder_langtok(src, tgt, src_langtok_spec) + tgt_langtok = self.get_decoder_langtok(tgt, tgt_langtok_spec) + logger.info( + f"{data_category}:{src}-{tgt} src_langtok: {src_langtok}; tgt_langtok: {tgt_langtok}" + ) + + langpair_ds = self.load_langpair_dataset( + data_path, + split, + src, + src_dict, + tgt, + tgt_dict, + combine, + dataset_impl, + upsample_primary, + left_pad_source, + left_pad_target, + max_source_positions, + max_target_positions, + prepend_bos, + load_alignments, + truncate_source, + src_dataset_transform_func=lambda dataset: src_dataset_transform_func( + src, tgt, dataset, src_langtok_spec + ), + tgt_dataset_transform_func=lambda dataset: tgt_dataset_transform_func( + src, tgt, dataset, tgt_langtok_spec + ), + src_lang_id=_lang_id(lang_dictionary, src) + if enable_lang_ids and lang_dictionary is not None + else None, + tgt_lang_id=_lang_id(lang_dictionary, tgt) + if enable_lang_ids and lang_dictionary is not None + else None, + langpairs_sharing_datasets=langpairs_sharing_datasets, + ) + # TODO: handle modified lang toks for mined data and dae data + if self.args.lang_tok_replacing_bos_eos: + ds = self.alter_dataset_langtok( + langpair_ds, + src_eos=self.get_source_dictionary(src).eos() if src else self.get_target_dictionary(tgt).eos(), + src_lang=src, + tgt_eos=self.get_target_dictionary(tgt).eos(), + tgt_lang=tgt, + src_langtok_spec=src_langtok_spec, + tgt_langtok_spec=tgt_langtok_spec, + ) + else: + ds = langpair_ds + return ds + + def load_split_langpair_datasets(self, split, data_param_list): + datasets = [] + langpairs_sharing_datasets = ( + {} if self.args.enable_reservsed_directions_shared_datasets else None + ) + for param in data_param_list: + ds = self.load_a_dataset( + split=split, + langpairs_sharing_datasets=langpairs_sharing_datasets, + **param, + ) + datasets.append(ds) + return datasets + + def get_data_paths_and_lang_pairs(self, split): + datapaths = {"main": self.args.data} + lang_pairs = {"main": self.lang_pairs} + if split == getattr(self.args, "train_subset", None): + # only training data can have extra data and extra language pairs + if self.args.extra_data: + extra_datapaths = self.args.extra_data + datapaths.update(extra_datapaths) + if self.args.extra_lang_pairs: + extra_lang_pairs = { + k: v.split(",") for k, v in self.args.extra_lang_pairs.items() + } + lang_pairs.update(extra_lang_pairs) + return datapaths, lang_pairs + + @classmethod + def get_dataset_key(cls, data_category, src, tgt): + return f"{data_category}:{src}-{tgt}" + + @classmethod + def _get_shard_num_dict(cls, split, paths): + shards = defaultdict(int) + for path in paths: + files = PathManager.ls(path) + directions = set() + for f in files: + if f.startswith(split) and f.endswith(".idx"): + # idx files of the form "{split}.{src}-{tgt}.{lang}.idx" + direction = f.split(".")[-3] + directions.add(direction) + for direction in directions: + shards[direction] += 1 + return shards + + def get_split_num_data_shards(self, split): + if split in self._num_shards_dict: + return self._num_shards_dict[split] + num_shards_dict = {} + data_paths, lang_pairs = self.get_data_paths_and_lang_pairs(split) + + for data_category, paths in data_paths.items(): + if data_category not in lang_pairs: + continue + paths = utils.split_paths(paths) + shards_dict = self._get_shard_num_dict(split, paths) + lang_dirs = [ + lang_pair.split("-") for lang_pair in lang_pairs[data_category] + ] + lang_dirs = [x if len(x) > 1 else (x[0], x[0]) for x in lang_dirs] + for src, tgt in lang_dirs: + key = self.get_dataset_key(data_category, src, tgt) + if "mono_" in data_category: + # monolingual data requires tgt only + assert src is None or src == tgt, ( + f"error: src={src}, " + "tgt={tgt} for data_category={data_category}" + ) + num_shards_dict[key] = shards_dict[tgt] + else: + if f"{src}-{tgt}" in shards_dict: + num_shards_dict[key] = shards_dict[f"{src}-{tgt}"] + elif f"{tgt}-{src}" in shards_dict: + # follow the fairseq tradition to use reversed direction data if it is not available + num_shards_dict[key] = shards_dict[f"{tgt}-{src}"] + self._num_shards_dict[split] = num_shards_dict + logger.info(f"[{split}] num of shards: {num_shards_dict}") + return num_shards_dict + + @classmethod + def get_shard_id(cls, num_shards, epoch, shard_epoch=None): + shard = epoch if shard_epoch is None else shard_epoch + shard = (shard - 1) % num_shards + return shard + + def get_split_data_path(self, paths, epoch, shard_epoch, num_shards): + path = paths[self.get_shard_id(num_shards, epoch, shard_epoch)] + return path + + def get_split_data_param_list(self, split, epoch, shard_epoch=None): + # TODO: to extend with extra datasets and keys and loop over different shard data paths + param_list = [] + data_paths, lang_pairs = self.get_data_paths_and_lang_pairs(split) + logger.info(f"langtoks settings: {self.args.langtoks}") + split_num_shards_dict = self.get_split_num_data_shards(split) + for data_category, paths in data_paths.items(): + if data_category not in lang_pairs: + continue + paths = utils.split_paths(paths) + assert len(paths) > 0 + if len(paths) > 1: + self._has_sharded_data = True + if split != getattr(self.args, "train_subset", None): + # if not training data set, use the first shard for valid and test + paths = paths[:1] + + if data_category in self.args.langtoks: + lang_tok_spec = self.args.langtoks[data_category] + else: + # default to None + lang_tok_spec = (None, None) + + # infer langcode + lang_dirs = [ + lang_pair.split("-") for lang_pair in lang_pairs[data_category] + ] + lang_dirs = [x if len(x) > 1 else (x[0], x[0]) for x in lang_dirs] + for src, tgt in lang_dirs: + assert src is not None or data_category == "mono_dae", ( + f"error: src={src}, " "tgt={tgt} for data_category={data_category}" + ) + # logger.info(f"preparing param for {data_category}: {src} - {tgt}") + key = self.get_dataset_key(data_category, src, tgt) + data_path = self.get_split_data_path( + paths, epoch, shard_epoch, split_num_shards_dict[key] + ) + param_list.append( + { + "key": key, + "data_path": data_path, + "split": split, + "src": src, + "src_dict": self.get_source_dictionary(src) + if src and data_category != "mono_dae" + else None, + "tgt": tgt, + "tgt_dict": self.get_target_dictionary(tgt), + "data_category": data_category, + "langtok_spec": lang_tok_spec, + } + ) + return param_list + + def get_train_dataset_sizes( + self, data_param_list, datasets, epoch, shard_epoch=None + ): + num_shards = [ + self.get_split_num_data_shards(param["split"])[param["key"]] + for param in data_param_list + ] + data_sizes = [] + for (key, d), num_shard in zip(datasets, num_shards): + my_data_sizes = self._training_data_sizes[key] + shard_ind = self.get_shard_id(num_shard, epoch, shard_epoch) + if shard_ind not in my_data_sizes: + my_data_sizes[shard_ind] = len(d) + known_size = max(my_data_sizes.values()) + data_sizes.append( + # If we don't know the data size of the shard yet, + # use the the max known data size to approximate. + # Note that we preprocess shards by a designated shard size + # and put any remaining data at the end into the last shard so + # the max shard size approximation is almost correct before loading + # the last shard; after loading the last shard, it will have the + # exact data sizes of the whole data size. + (key, sum(my_data_sizes.get(i, known_size) for i in range(num_shard))) + ) + logger.info( + f"estimated total data sizes of all shards used in sampling ratios: {data_sizes}. " + "Note that if the data a shard has not been loaded yet, use the max known data size to approximate" + ) + return [s for _, s in data_sizes] + + def get_train_sampling_ratios( + self, data_param_list, datasets, epoch=1, shard_epoch=None + ): + data_sizes = self.get_train_dataset_sizes( + data_param_list, datasets, epoch, shard_epoch + ) + sampling_func = self.sampling_method.sampling_method_selector() + sample_ratios = sampling_func(data_sizes) if sampling_func is not None else None + return sample_ratios + + def get_sampling_ratios(self, data_param_list, datasets, epoch, shard_epoch=None): + if self.args.sampling_weights_from_file: + weights = load_sampling_weights(self.args.sampling_weights_from_file) + sample_ratios = [weights[k] for k, _ in datasets] + logger.info( + "| ignoring --sampling-weights when loadding sampling weights " + f"from file {self.args.sampling_weights_from_file}" + ) + elif self.args.sampling_weights: + sample_ratios = [self.args.sampling_weights[k] for k, _ in datasets] + else: + sample_ratios = self.get_train_sampling_ratios( + data_param_list, datasets, epoch, shard_epoch + ) + + if sample_ratios is not None: + logger.info( + "| Upsample ratios: {}".format( + list(zip(map(lambda x: x["key"], data_param_list), sample_ratios)) + ) + ) + assert len(sample_ratios) == len(datasets) + return sample_ratios + + def load_split_datasets( + self, split, training, epoch=1, combine=False, shard_epoch=None, **kwargs + ): + data_param_list = self.get_split_data_param_list( + split, epoch, shard_epoch=shard_epoch + ) + langpairs_sharing_datasets = ( + {} if self.args.enable_reservsed_directions_shared_datasets else None + ) + datasets = [ + ( + param["key"], + self.load_a_dataset( + combine=combine, + langpairs_sharing_datasets=langpairs_sharing_datasets, + **param, + ), + ) + for param in data_param_list + ] + return datasets, data_param_list + + def load_into_concat_dataset(self, split, datasets, data_param_list): + if self.args.lang_tok_replacing_bos_eos: + # TODO: to investigate why TransformEosLangPairDataset doesn't work with ConcatDataset + return SampledMultiDataset( + OrderedDict(datasets), + sampling_ratios=None, + eval_key=None, + collate_format=CollateFormat.single, + virtual_size=None, + split=split, + ) + return ConcatDataset([d for _, d in datasets]) + + def load_sampled_multi_epoch_dataset( + self, split, training, epoch=0, combine=False, shard_epoch=None, **kwargs + ): + datasets, data_param_list = self.load_split_datasets( + split, training, epoch, combine, shard_epoch=shard_epoch, **kwargs + ) + if training and split == getattr(self.args, "train_subset", None): + sample_ratios = self.get_sampling_ratios(data_param_list, datasets, epoch) + return SampledMultiEpochDataset( + OrderedDict(datasets), + epoch=epoch, + shard_epoch=shard_epoch, + # valid and test datasets will be degenerate to concating datasets: + sampling_ratios=sample_ratios, + eval_key=None, + collate_format=CollateFormat.single, + virtual_size=self.args.virtual_data_size, + split=split, + virtual_epoch_size=self.args.virtual_epoch_size, + # if not using lang_tok altering, simplified to use the same collater + shared_collater=self._shared_collater(), + ) + else: + return self.load_into_concat_dataset(split, datasets, data_param_list) + + def load_sampled_multi_dataset( + self, split, training, epoch=0, combine=False, shard_epoch=None, **kwargs + ): + datasets, data_param_list = self.load_split_datasets( + split, training, epoch, combine, shard_epoch=shard_epoch, **kwargs + ) + if training and split == getattr(self.args, "train_subset", None): + sample_ratios = self.get_sampling_ratios(data_param_list, datasets, epoch) + return SampledMultiDataset( + OrderedDict(datasets), + epoch=epoch, + # valid and test datasets will be degerate to concating datasets: + sampling_ratios=sample_ratios, + eval_key=None, + collate_format=CollateFormat.single, + virtual_size=self.args.virtual_data_size, + split=split, + # if not using lang_tok altering, simplified to use the same collater + shared_collater=self._shared_collater(), + ) + else: + return self.load_into_concat_dataset(split, datasets, data_param_list) + + def load_dataset( + self, split, training, epoch=0, combine=False, shard_epoch=None, **kwargs + ): + if self.args.virtual_epoch_size is None: + return self.load_sampled_multi_dataset( + split, training, epoch, combine, shard_epoch, **kwargs + ) + else: + return self.load_sampled_multi_epoch_dataset( + split, training, epoch, combine, shard_epoch, **kwargs + ) diff --git a/fairseq/data/multilingual/multilingual_utils.py b/fairseq/data/multilingual/multilingual_utils.py new file mode 100644 index 0000000000..b4e0f9828c --- /dev/null +++ b/fairseq/data/multilingual/multilingual_utils.py @@ -0,0 +1,63 @@ +from enum import Enum +from typing import Dict, List, Optional, Sequence + +import torch +from fairseq.data import Dictionary + + +class EncoderLangtok(Enum): + """ + Prepend to the beginning of source sentence either the + source or target language token. (src/tgt). + """ + + src = "src" + tgt = "tgt" + + +class LangTokSpec(Enum): + main = "main" + mono_dae = "mono_dae" + + +class LangTokStyle(Enum): + multilingual = "multilingual" + mbart = "mbart" + + +@torch.jit.export +def get_lang_tok( + lang: str, lang_tok_style: str, spec: str = LangTokSpec.main.value +) -> str: + # TOKEN_STYLES can't be defined outside this fn since it needs to be + # TorchScriptable. + TOKEN_STYLES: Dict[str, str] = { + LangTokStyle.mbart.value: "[{}]", + LangTokStyle.multilingual.value: "__{}__", + } + + if spec.endswith("dae"): + lang = f"{lang}_dae" + elif spec.endswith("mined"): + lang = f"{lang}_mined" + style = TOKEN_STYLES[lang_tok_style] + return style.format(lang) + + +def augment_dictionary( + dictionary: Dictionary, + language_list: List[str], + lang_tok_style: str, + langtoks_specs: Sequence[str] = (LangTokSpec.main.value,), + extra_data: Optional[Dict[str, str]] = None, +) -> None: + for spec in langtoks_specs: + for language in language_list: + dictionary.add_symbol( + get_lang_tok(lang=language, lang_tok_style=lang_tok_style, spec=spec) + ) + + if lang_tok_style == LangTokStyle.mbart.value or ( + extra_data is not None and LangTokSpec.mono_dae.value in extra_data + ): + dictionary.add_symbol("<mask>") diff --git a/fairseq/data/multilingual/sampled_multi_dataset.py b/fairseq/data/multilingual/sampled_multi_dataset.py new file mode 100644 index 0000000000..b0a617424e --- /dev/null +++ b/fairseq/data/multilingual/sampled_multi_dataset.py @@ -0,0 +1,467 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import datetime +import hashlib +import logging +import time +from bisect import bisect_right +from collections import OrderedDict, defaultdict +from enum import Enum +from typing import List + +import numpy as np +import torch +from fairseq.data import FairseqDataset, data_utils +from fairseq.distributed import utils as distributed_utils + + +def get_time_gap(s, e): + return ( + datetime.datetime.fromtimestamp(e) - datetime.datetime.fromtimestamp(s) + ).__str__() + + +logger = logging.getLogger(__name__) + + +def default_virtual_size_func(datasets, ratios, max_scale_up=1.5): + sizes = [len(d) for d in datasets] + if ratios is None: + return sum(sizes) + largest_idx = np.argmax(sizes) + largest_r = ratios[largest_idx] + largest_s = sizes[largest_idx] + # set virtual sizes relative to the largest dataset + virtual_sizes = [(r / largest_r) * largest_s for r in ratios] + vsize = sum(virtual_sizes) + max_size = sum(sizes) * max_scale_up + return int(vsize if vsize < max_size else max_size) + + +class CollateFormat(Enum): + single = 1 + ordered_dict = 2 + + +class SampledMultiDataset(FairseqDataset): + """Samples from multiple sub-datasets according to given sampling ratios. + Args: + datasets ( + List[~torch.utils.data.Dataset] + or OrderedDict[str, ~torch.utils.data.Dataset] + ): datasets + sampling_ratios (List[float]): list of probability of each dataset to be sampled + (default: None, which corresponds to concatenating all dataset together). + seed (int): RNG seed to use (default: 2). + epoch (int): starting epoch number (default: 1). + eval_key (str, optional): a key used at evaluation time that causes + this instance to pass-through batches from *datasets[eval_key]*. + collate_format (CollateFormat): collater output format, either CollateFormat.ordered_dict or + CollateFormat.single (default: CollateFormat.single) where CollateFormat.single configures + the collater to output batches of data mixed from all sub-datasets, + and CollateFormat.ordered_dict configures the collater to output a dictionary of batches indexed by keys + of sub-datasets. + Note that not all sub-datasets will present in a single batch in both formats. + virtual_size (int, or callable): the expected virtual size of the dataset (default: default_virtual_size_func). + split (str): the split of the data, e.g. 'train', 'valid' or 'test'. + shared_collater (bool): whether or not to all sub-datasets have the same collater. + shuffle (bool): whether or not to shuffle data (default: True). + """ + + def __init__( + self, + datasets, + sampling_ratios=None, + seed=2, + epoch=1, + eval_key=None, + collate_format=CollateFormat.single, + virtual_size=default_virtual_size_func, + split="", + shared_collater=False, + shuffle=True, + ): + super().__init__() + self.shared_collater = shared_collater + self.shuffle = shuffle + + if isinstance(datasets, OrderedDict): + self.keys = list(datasets.keys()) + datasets = list(datasets.values()) + elif isinstance(datasets, List): + self.keys = list(range(len(datasets))) + else: + raise AssertionError() + self.datasets = datasets + self.split = split + + self.eval_key = eval_key + if self.eval_key is not None: + self.collate_format = CollateFormat.single + else: + self.collate_format = collate_format + + self.seed = seed + self._cur_epoch = None + + self.cumulated_sizes = None + # self.datasets[k][self._cur_indices[i]] is the data item i in this sampled dataset + # namely, data item i is sampled from the kth sub-dataset self.datasets[k] + # where self.cumulated_sizes[k-1] <= i < self.cumulated_sizes[k] + self._cur_indices = None + + self._sizes = None + self.virtual_size_per_dataset = None + # caching properties + self._reset_cached_properties() + self.setup_sampling(sampling_ratios, virtual_size) + self.set_epoch(epoch) + + def _clean_if_not_none(self, var_list): + for v in var_list: + if v is not None: + del v + + def _reset_cached_properties(self): + self._clean_if_not_none([self._sizes, self._cur_indices]) + self._sizes = None + self._cur_indices = None + + def setup_sampling(self, sample_ratios, virtual_size): + sizes = [len(d) for d in self.datasets] + if sample_ratios is None: + # default back to concating datasets + self.sample_ratios = None + self.virtual_size = sum(sizes) + else: + if not isinstance(sample_ratios, np.ndarray): + sample_ratios = np.array(sample_ratios) + self.sample_ratios = sample_ratios + virtual_size = ( + default_virtual_size_func if virtual_size is None else virtual_size + ) + self.virtual_size = ( + virtual_size(self.datasets, self.sample_ratios) + if callable(virtual_size) + else virtual_size + ) + + def adjust_sampling(self, epoch, sampling_ratios, virtual_size): + if sampling_ratios is not None: + sampling_ratios = self._sync_sample_ratios(sampling_ratios) + self.setup_sampling(sampling_ratios, virtual_size) + + def _sync_sample_ratios(self, ratios): + # in case the ratios are not precisely the same across processes + # also to ensure every procresses update the ratios in the same pace + ratios = torch.DoubleTensor(ratios) + if torch.distributed.is_initialized(): + if torch.cuda.is_available(): + distributed_utils.all_reduce( + ratios.cuda(), group=distributed_utils.get_data_parallel_group() + ) + else: + distributed_utils.all_reduce( + ratios, group=distributed_utils.get_data_parallel_group() + ) + ret = ratios.cpu() + ret = ret.numpy() + return ret + + def random_choice_in_dataset(self, rng, dataset, choice_size): + if hasattr(dataset, "random_choice_in_dataset"): + return dataset.random_choice_in_dataset(rng, choice_size) + dataset_size = len(dataset) + return rng.choice( + dataset_size, choice_size, replace=(choice_size > dataset_size) + ) + + def get_virtual_indices(self, rng, datasets, sample_ratios, virtual_size): + def get_counts(sample_ratios): + counts = np.array([virtual_size * r for r in sample_ratios], dtype=np.int64) + diff = virtual_size - counts.sum() + assert diff >= 0 + # due to round-offs, the size might not match the desired sizes + if diff > 0: + dataset_indices = rng.choice( + len(sample_ratios), size=diff, p=sample_ratios + ) + for i in dataset_indices: + counts[i] += 1 + return counts + + def get_in_dataset_indices(datasets, sizes, sample_ratios): + counts = get_counts(sample_ratios) + # uniformally sample desired counts for each dataset + # if the desired counts are large, sample with replacement: + indices = [ + self.random_choice_in_dataset(rng, d, c) + for c, d in zip(counts, datasets) + ] + return indices + + sizes = [len(d) for d in datasets] + if sample_ratios is None: + # default back to concating datasets + in_dataset_indices = [list(range(s)) for s in sizes] + virtual_sizes_per_dataset = sizes + else: + ratios = sample_ratios / sample_ratios.sum() + in_dataset_indices = get_in_dataset_indices(datasets, sizes, ratios) + virtual_sizes_per_dataset = [len(d) for d in in_dataset_indices] + virtual_sizes_per_dataset = np.array(virtual_sizes_per_dataset, np.int64) + cumulative_sizes = np.cumsum(virtual_sizes_per_dataset) + assert sum(virtual_sizes_per_dataset) == virtual_size + assert cumulative_sizes[-1] == virtual_size + if virtual_size < sum(sizes): + logger.warning( + f"virtual data size ({virtual_size}) is less than real data size ({sum(sizes)})." + " If virtual size << real data size, there could be data coverage issue." + ) + in_dataset_indices = np.hstack(in_dataset_indices) + return in_dataset_indices, cumulative_sizes, virtual_sizes_per_dataset + + def _get_dataset_and_index(self, index): + i = bisect_right(self.cumulated_sizes, index) + return i, self._cur_indices[index] + + def __getitem__(self, index): + # self.__getitem__(index) returns self.datasets[k][self._cur_indices[index]] + # where k satisfies self.cumulated_sizes[k - 1] <= k < self.cumulated_sizes[k] + ds_idx, ds_sample_idx = self._get_dataset_and_index(index) + ret = (ds_idx, self.datasets[ds_idx][ds_sample_idx]) + return ret + + def num_tokens(self, index): + return self.sizes[index].max() + + def num_tokens_vec(self, indices): + sizes_vec = self.sizes[np.array(indices)] + # max across all dimensions but first one + return np.amax(sizes_vec, axis=tuple(range(1, len(sizes_vec.shape)))) + + def size(self, index): + return self.sizes[index] + + def __len__(self): + return self.virtual_size + + def collater(self, samples, **extra_args): + """Merge a list of samples to form a mini-batch.""" + if len(samples) == 0: + return None + if self.collate_format == "ordered_dict": + collect_samples = [[] for _ in range(len(self.datasets))] + for (i, sample) in samples: + collect_samples[i].append(sample) + batch = OrderedDict( + [ + (self.keys[i], dataset.collater(collect_samples[i])) + for i, (key, dataset) in enumerate(zip(self.keys, self.datasets)) + if len(collect_samples[i]) > 0 + ] + ) + elif self.shared_collater: + batch = self.datasets[0].collater([s for _, s in samples]) + else: + samples_dict = defaultdict(list) + pad_to_length = ( + defaultdict(int) + if "pad_to_length" not in extra_args + else extra_args["pad_to_length"] + ) + for ds_idx, s in samples: + pad_to_length["source"] = max( + pad_to_length["source"], s["source"].size(0) + ) + if s["target"] is not None: + pad_to_length["target"] = max( + pad_to_length["target"], s["target"].size(0) + ) + samples_dict[ds_idx].append(s) + batches = [ + self.datasets[i].collater(samples_dict[i], pad_to_length=pad_to_length) + for i in range(len(self.datasets)) + if len(samples_dict[i]) > 0 + ] + + def straight_data(tensors): + batch = torch.cat(tensors, dim=0) + return batch + + src_lengths = straight_data( + [b["net_input"]["src_lengths"] for b in batches] + ) + src_lengths, sort_order = src_lengths.sort(descending=True) + + def straight_order(tensors): + batch = straight_data(tensors) + return batch.index_select(0, sort_order) + + batch = { + "id": straight_order([b["id"] for b in batches]), + "nsentences": sum(b["nsentences"] for b in batches), + "ntokens": sum(b["ntokens"] for b in batches), + "net_input": { + "src_tokens": straight_order( + [b["net_input"]["src_tokens"] for b in batches] + ), + "src_lengths": src_lengths, + }, + "target": straight_order([b["target"] for b in batches]) + if batches[0]["target"] is not None + else None, + } + if "prev_output_tokens" in batches[0]["net_input"]: + batch["net_input"]["prev_output_tokens"] = straight_order( + [b["net_input"]["prev_output_tokens"] for b in batches] + ) + if "src_lang_id" in batches[0]["net_input"]: + batch["net_input"]["src_lang_id"] = straight_order( + [b["net_input"]["src_lang_id"] for b in batches] + ) + if "tgt_lang_id" in batches[0]: + batch["tgt_lang_id"] = straight_order( + [b["tgt_lang_id"] for b in batches] + ) + return batch + + @property + def sizes(self): + if self._sizes is not None: + return self._sizes + start_time = time.time() + in_sub_dataset_indices = [ + self._cur_indices[ + 0 if i == 0 else self.cumulated_sizes[i - 1] : self.cumulated_sizes[i] + ] + for i in range(len(self.datasets)) + ] + sub_dataset_sizes = [ + d.sizes[indices] + for d, indices in zip(self.datasets, in_sub_dataset_indices) + ] + self._sizes = np.vstack(sub_dataset_sizes) + logger.info(f"sizes() calling time: {get_time_gap(start_time, time.time())}") + return self._sizes + + def ordered_indices(self): + if self.shuffle: + indices = np.random.permutation(len(self)) + else: + indices = np.arange(len(self)) + + sizes = self.sizes + tgt_sizes = sizes[:, 1] if len(sizes.shape) > 0 and sizes.shape[1] > 1 else None + src_sizes = ( + sizes[:, 0] if len(sizes.shape) > 0 and sizes.shape[1] > 1 else sizes + ) + + # sort by target length, then source length + if tgt_sizes is not None: + indices = indices[np.argsort(tgt_sizes[indices], kind="mergesort")] + sort_indices = indices[np.argsort(src_sizes[indices], kind="mergesort")] + return sort_indices + + def prefetch(self, indices): + prefetch_indices = [[] for _ in range(len(self.datasets))] + for i in indices: + ds_idx, ds_sample_idx = self._get_dataset_and_index(i) + prefetch_indices[ds_idx].append(ds_sample_idx) + for i in range(len(prefetch_indices)): + self.datasets[i].prefetch(prefetch_indices[i]) + + @property + def can_reuse_epoch_itr_across_epochs(self): + return False + + def set_epoch(self, epoch): + super().set_epoch(epoch) + if epoch == self._cur_epoch: + # re-enter so return + return + for d in self.datasets: + if hasattr(d, "set_epoch"): + d.set_epoch(epoch) + self._cur_epoch = epoch + self._establish_virtual_datasets() + + def _establish_virtual_datasets(self): + if self.sample_ratios is None and self._cur_indices is not None: + # not a samping dataset, no need to resample if indices are already established + return + self._reset_cached_properties() + + start_time = time.time() + # Generate a weighted sample of indices as a function of the + # random seed and the current epoch. + rng = np.random.RandomState( + [ + int( + hashlib.sha1( + str(self.__class__.__name__).encode("utf-8") + ).hexdigest(), + 16, + ) + % (2 ** 32), + self.seed % (2 ** 32), # global seed + self._cur_epoch, # epoch index, + ] + ) + self._clean_if_not_none( + [self.cumulated_sizes, self.virtual_size_per_dataset, self._sizes] + ) + self._sizes = None + + indices, cumulated_sizes, virtual_size_per_dataset = self.get_virtual_indices( + rng, self.datasets, self.sample_ratios, self.virtual_size + ) + self._cur_indices = indices + self.cumulated_sizes = cumulated_sizes + self.virtual_size_per_dataset = virtual_size_per_dataset + + raw_sizes = [len(d) for d in self.datasets] + sampled_sizes = self.virtual_size_per_dataset + logger.info( + f"[{self.split}] Raw sizes: {str(dict(zip(self.keys, raw_sizes)))}; " + f"raw total size: {sum(raw_sizes)}" + ) + logger.info( + f"[{self.split}] Resampled sizes: {str(dict(zip(self.keys, sampled_sizes)))}; " + f"resampled total size: {sum(sampled_sizes)}" + ) + if self.sample_ratios is not None: + logger.info( + f"[{self.split}] Upsampling ratios: {str(dict(zip(self.keys, self.sample_ratios)))}" + ) + else: + logger.info(f"[{self.split}] A concat dataset") + logger.info( + f"[{self.split}] virtual dataset established time: {get_time_gap(start_time, time.time())}" + ) + + def filter_indices_by_size(self, indices, max_sizes): + """Filter a list of sample indices. Remove those that are longer + than specified in max_sizes. + + Args: + indices (np.array): original array of sample indices + max_sizes (int or list[int] or tuple[int]): max sample size, + can be defined separately for src and tgt (then list or tuple) + + Returns: + np.array: filtered sample array + list: list of removed indices + """ + sizes = self.sizes + tgt_sizes = sizes[:, 1] if len(sizes.shape) > 0 and sizes.shape[1] > 1 else None + src_sizes = ( + sizes[:, 0] if len(sizes.shape) > 0 and sizes.shape[1] > 1 else sizes + ) + + return data_utils.filter_paired_dataset_indices_by_size( + src_sizes, tgt_sizes, indices, max_sizes + ) diff --git a/fairseq/data/multilingual/sampled_multi_epoch_dataset.py b/fairseq/data/multilingual/sampled_multi_epoch_dataset.py new file mode 100644 index 0000000000..17387b2f85 --- /dev/null +++ b/fairseq/data/multilingual/sampled_multi_epoch_dataset.py @@ -0,0 +1,199 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import hashlib +import logging +import math + +import numpy as np +from fairseq.data import SampledMultiDataset + +from .sampled_multi_dataset import CollateFormat, default_virtual_size_func + + +logger = logging.getLogger(__name__) + + +class SampledMultiEpochDataset(SampledMultiDataset): + """Samples from multiple sub-datasets according to sampling ratios + using virtual epoch sizes to speed up dataloading. + Args: + datasets ( + List[~torch.utils.data.Dataset] + or OrderedDict[str, ~torch.utils.data.Dataset] + ): datasets + sampling_ratios (List[float]): list of probability of each dataset to be sampled + (default: None, which corresponds to concating all dataset together). + seed (int): RNG seed to use (default: 2). + epoch (int): starting epoch number (default: 1). + eval_key (str, optional): a key used at evaluation time that causes + this instance to pass-through batches from *datasets[eval_key]*. + collate_format (CollateFormat): collater output format, either CollateFormat.ordered_dict or + CollateFormat.single (default: CollateFormat.single) where CollateFormat.single configures + the collater to output batches of data mixed from all sub-datasets, + and CollateFormat.ordered_dict configures the collater to output a dictionary of batches indexed by keys + of sub-datasets. + Note that not all sub-datasets will present in a single batch in both formats. + virtual_size (int, or callable): the expected virtual size of the dataset (default: default_virtual_size_func). + split (str): the split of the data, e.g. 'train', 'valid' or 'test'. + virtual_epoch_size (int): virtual epoch size, the dataset will go through the data by + this virtual epoch size one by one to speed up data loading, e.g. indicing and filtering + can be performed whenever a virtual epoch is loaded without waiting for the whole dataset to be loaded. + shared_collater (bool): whether or not to all sub-datasets have the same collater. + shard_epoch (int): the real epoch number for shard selection. + shuffle (bool): whether or not to shuffle data (default: True). + """ + + def __init__( + self, + datasets, + sampling_ratios=None, + seed=2, + epoch=1, + eval_key=None, + collate_format=CollateFormat.single, + virtual_size=default_virtual_size_func, + split="", + virtual_epoch_size=None, + shared_collater=False, + shard_epoch=1, + shuffle=True, + ): + self.virtual_epoch_size = virtual_epoch_size + self._current_epoch_start_index = None + self._random_global_indices = None + self.shard_epoch = shard_epoch if shard_epoch is not None else 1 + self.load_next_shard = None + self._epoch_sizes = None + super().__init__( + datasets=datasets, + sampling_ratios=sampling_ratios, + seed=seed, + epoch=epoch, + eval_key=eval_key, + collate_format=collate_format, + virtual_size=virtual_size, + split=split, + shared_collater=shared_collater, + shuffle=shuffle, + ) + + def _setup(self, epoch): + self.virtual_epoch_size = ( + self.virtual_epoch_size + if self.virtual_epoch_size is not None + else self.virtual_size + ) + if self.virtual_epoch_size > self.virtual_size: + logger.warning( + f"virtual epoch size {self.virtual_epoch_size} " + f"is greater than virtual dataset size {self.virtual_size}" + ) + self.virtual_epoch_size = self.virtual_size + self.num_virtual_epochs = math.ceil(self.virtual_size / self.virtual_epoch_size) + self._current_epoch_start_index = self._get_epoch_start_index(epoch) + logger.info( + f"virtual epoch size {self.virtual_epoch_size}; virtual dataset size {self.virtual_size}" + ) + + def _map_epoch_index_to_global(self, index): + index = self._current_epoch_start_index + index + # add randomness + return self._random_global_indices[index] + + @property + def sizes(self): + if self._epoch_sizes is not None: + return self._epoch_sizes + _sizes = super().sizes + indices = self._random_global_indices[ + self._current_epoch_start_index : self._current_epoch_start_index + + len(self) + ] + self._epoch_sizes = _sizes[indices] + # del super()._sizes to save memory + del self._sizes + self._sizes = None + return self._epoch_sizes + + def _get_dataset_and_index(self, index): + i = self._map_epoch_index_to_global(index) + return super()._get_dataset_and_index(i) + + def __len__(self): + return ( + self.virtual_epoch_size + if self._current_epoch_start_index + self.virtual_epoch_size + < self.virtual_size + else self.virtual_size - self._current_epoch_start_index + ) + + def set_epoch(self, epoch): + if self._current_epoch_start_index is None: + # initializing epoch idnices of a virtual dataset + self._setup(epoch) + self._next_virtual_epoch(epoch) + else: + # working on already intialized epoch indices + if epoch == self._cur_epoch: + # re-enter so return + return + self._next_virtual_epoch(epoch) + + def _get_epoch_start_index(self, epoch): + assert epoch >= 1 # fairseq is using 1-based epoch everywhere + return ((epoch - 1) % self.num_virtual_epochs) * self.virtual_epoch_size + + def _next_global_indices(self, epoch): + rng = np.random.RandomState( + [ + int( + hashlib.sha1( + str(self.__class__.__name__).encode("utf-8") + ).hexdigest(), + 16, + ) + % (2 ** 32), + self.seed % (2 ** 32), # global seed + epoch, # epoch index, + ] + ) + del self._random_global_indices + self._random_global_indices = rng.choice( + self.virtual_size, self.virtual_size, replace=False + ) + if self.load_next_shard is None: + self.load_next_shard = False + else: + # increase shard epoch for next loading + self.shard_epoch += 1 + self.load_next_shard = True + logger.info( + "to load next epoch/shard in next load_dataset: " + f"epoch={epoch}/shard_epoch={self.shard_epoch}" + ) + + def _next_virtual_epoch(self, epoch): + index = self._get_epoch_start_index(epoch) + if index == 0 or self._random_global_indices is None: + # need to start from the beginning, + # so call super().set_epoch(epoch) to establish the global virtual indices + logger.info( + "establishing a new set of global virtual indices for " + f"epoch={epoch}/shard_epoch={self.shard_epoch}" + ) + super().set_epoch(epoch) + self._next_global_indices(epoch) + else: + self._cur_epoch = epoch + + # reset cache sizes and ordered_indices for the epoch after moving to a new epoch + self._clean_if_not_none( + [ + self._epoch_sizes, + ] + ) + self._epoch_sizes = None + self._current_epoch_start_index = index diff --git a/fairseq/data/multilingual/sampling_method.py b/fairseq/data/multilingual/sampling_method.py new file mode 100644 index 0000000000..140c68f01d --- /dev/null +++ b/fairseq/data/multilingual/sampling_method.py @@ -0,0 +1,78 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +from typing import List + + +logger = logging.getLogger(__name__) + + +def uniform(dataset_sizes: List[int]): + return [1.0] * len(dataset_sizes) + + +def temperature_sampling(dataset_sizes, temp): + total_size = sum(dataset_sizes) + return [(size / total_size) ** (1.0 / temp) for size in dataset_sizes] + + +def make_temperature_sampling(temp=1.0): + def sampling_func(dataset_sizes): + return temperature_sampling(dataset_sizes, temp) + + return sampling_func + + +def make_ratio_sampling(ratios): + def sampling_func(dataset_sizes): + return ratios + + return sampling_func + + +class SamplingMethod: + @staticmethod + def add_arguments(parser): + parser.add_argument( + "--sampling-method", + choices=[ + "uniform", + "temperature", + "concat", + "RoundRobin", + ], + type=str, + default="concat", + help="The method to sample data per language pairs", + ) + parser.add_argument( + "--sampling-temperature", + default=1.5, + type=float, + help="only work with --sampling-method temperature", + ) + + @staticmethod + def build_sampler(args, task): + return SamplingMethod(args, task) + + def __init__(self, args, task): + self.args = args + self.task = task + + def is_adaptive(self): + return False + + def sampling_method_selector(self): + args = self.args + logger.info(f"selected sampler: {args.sampling_method}") + if args.sampling_method == "uniform": + return uniform + elif args.sampling_method == "temperature" or self.is_adaptive(): + return make_temperature_sampling(float(args.sampling_temperature)) + else: + # default to concating all data set together + return None diff --git a/fairseq/data/nested_dictionary_dataset.py b/fairseq/data/nested_dictionary_dataset.py index 2795f895dd..52e74abdda 100644 --- a/fairseq/data/nested_dictionary_dataset.py +++ b/fairseq/data/nested_dictionary_dataset.py @@ -15,14 +15,14 @@ def _flatten(dico, prefix=None): """Flatten a nested dictionary.""" new_dico = OrderedDict() if isinstance(dico, dict): - prefix = prefix + '.' if prefix is not None else '' + prefix = prefix + "." if prefix is not None else "" for k, v in dico.items(): if v is None: continue new_dico.update(_flatten(v, prefix + k)) elif isinstance(dico, list): for i, v in enumerate(dico): - new_dico.update(_flatten(v, prefix + '.[' + str(i) + ']')) + new_dico.update(_flatten(v, prefix + ".[" + str(i) + "]")) else: new_dico = OrderedDict({prefix: dico}) return new_dico @@ -32,10 +32,10 @@ def _unflatten(dico): """Unflatten a flattened dictionary into a nested dictionary.""" new_dico = OrderedDict() for full_k, v in dico.items(): - full_k = full_k.split('.') + full_k = full_k.split(".") node = new_dico for k in full_k[:-1]: - if k.startswith('[') and k.endswith(']'): + if k.startswith("[") and k.endswith("]"): k = int(k[1:-1]) if k not in node: node[k] = OrderedDict() @@ -45,7 +45,6 @@ def _unflatten(dico): class NestedDictionaryDataset(FairseqDataset): - def __init__(self, defn, sizes=None): super().__init__() self.defn = _flatten(defn) @@ -53,11 +52,17 @@ def __init__(self, defn, sizes=None): first = None for v in self.defn.values(): - if not isinstance(v, (FairseqDataset, torch.utils.data.Dataset, )): - raise ValueError('Expected Dataset but found: {}'.format(v.__class__)) + if not isinstance( + v, + ( + FairseqDataset, + torch.utils.data.Dataset, + ), + ): + raise ValueError("Expected Dataset but found: {}".format(v.__class__)) first = first or v if len(v) > 0: - assert len(v) == len(first), 'dataset lengths must match' + assert len(v) == len(first), "dataset lengths must match" self._len = len(first) @@ -107,9 +112,13 @@ def supports_prefetch(self): def prefetch(self, indices): """Prefetch the data required for this epoch.""" for ds in self.defn.values(): - if getattr(ds, 'supports_prefetch', False): + if getattr(ds, "supports_prefetch", False): ds.prefetch(indices) + @property + def can_reuse_epoch_itr_across_epochs(self): + return all(ds.can_reuse_epoch_itr_across_epochs for ds in self.defn.values()) + def set_epoch(self, epoch): super().set_epoch(epoch) for ds in self.defn.values(): diff --git a/fairseq/data/noising.py b/fairseq/data/noising.py index bd67e7336c..2b1cc34720 100644 --- a/fairseq/data/noising.py +++ b/fairseq/data/noising.py @@ -3,32 +3,34 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. -import torch import numpy as np - +import torch from fairseq.data import data_utils class WordNoising(object): """Generate a noisy version of a sentence, without changing words themselves.""" + def __init__(self, dictionary, bpe_cont_marker="@@", bpe_end_marker=None): self.dictionary = dictionary self.bpe_end = None if bpe_cont_marker: - self.bpe_end = np.array([ - not self.dictionary[i].endswith(bpe_cont_marker) - for i in range(len(self.dictionary)) - ]) + self.bpe_end = np.array( + [ + not self.dictionary[i].endswith(bpe_cont_marker) + for i in range(len(self.dictionary)) + ] + ) elif bpe_end_marker: - self.bpe_end = np.array([ - self.dictionary[i].endswith(bpe_end_marker) - for i in range(len(self.dictionary)) - ]) + self.bpe_end = np.array( + [ + self.dictionary[i].endswith(bpe_end_marker) + for i in range(len(self.dictionary)) + ] + ) self.get_word_idx = ( - self._get_bpe_word_idx - if self.bpe_end is not None - else self._get_token_idx + self._get_bpe_word_idx if self.bpe_end is not None else self._get_token_idx ) def noising(self, x, lengths, noising_prob=0.0): @@ -44,7 +46,7 @@ def _get_bpe_word_idx(self, x): # x: (T x B) bpe_end = self.bpe_end[x] - if (x.size(0) == 1 and x.size(1) == 1): + if x.size(0) == 1 and x.size(1) == 1: # Special case when we only have one word in x. If x = [[N]], # bpe_end is a scalar (bool) instead of a 2-dim array of bools, # which makes the sum operation below fail. @@ -70,7 +72,13 @@ class WordDropout(WordNoising): then dropped words will be removed. Otherwise, it will be replaced by the blank_idx.""" - def __init__(self, dictionary, default_dropout_prob=0.1, bpe_cont_marker="@@", bpe_end_marker=None): + def __init__( + self, + dictionary, + default_dropout_prob=0.1, + bpe_cont_marker="@@", + bpe_end_marker=None, + ): super().__init__(dictionary, bpe_cont_marker, bpe_end_marker) self.default_dropout_prob = default_dropout_prob @@ -108,13 +116,12 @@ def noising(self, x, lengths, dropout_prob=None, blank_idx=None): else: keep = np.random.rand(num_words) >= dropout_prob - words = x[:lengths[i], i].tolist() + words = x[: lengths[i], i].tolist() # TODO: speed up the following loop # drop words from the input according to keep new_s = [ - w if keep[word_idx[j, i]] else blank_idx - for j, w in enumerate(words) + w if keep[word_idx[j, i]] else blank_idx for j, w in enumerate(words) ] new_s = [w for w in new_s if w is not None] # we need to have at least one word in the sentence (more than the @@ -132,11 +139,10 @@ def noising(self, x, lengths, dropout_prob=None, blank_idx=None): # re-construct input modified_lengths = torch.LongTensor(modified_lengths) modified_x = torch.LongTensor( - modified_lengths.max(), - modified_lengths.size(0) + modified_lengths.max(), modified_lengths.size(0) ).fill_(self.dictionary.pad()) for i in range(modified_lengths.size(0)): - modified_x[:modified_lengths[i], i].copy_(torch.LongTensor(sentences[i])) + modified_x[: modified_lengths[i], i].copy_(torch.LongTensor(sentences[i])) return modified_x, modified_lengths @@ -144,7 +150,13 @@ def noising(self, x, lengths, dropout_prob=None, blank_idx=None): class WordShuffle(WordNoising): """Shuffle words by no more than k positions.""" - def __init__(self, dictionary, default_max_shuffle_distance=3, bpe_cont_marker="@@", bpe_end_marker=None): + def __init__( + self, + dictionary, + default_max_shuffle_distance=3, + bpe_cont_marker="@@", + bpe_end_marker=None, + ): super().__init__(dictionary, bpe_cont_marker, bpe_end_marker) self.default_max_shuffle_distance = 3 @@ -175,7 +187,7 @@ def noising(self, x, lengths, max_shuffle_distance=None): # generate a random permutation scores = word_idx[:length_no_eos, i] + noise[word_idx[:length_no_eos, i], i] # ensure no reordering inside a word - scores += 1e-6 * np.arange(length_no_eos) + scores += 1e-6 * np.arange(length_no_eos.item()) permutation = scores.argsort() # shuffle words x2[:length_no_eos, i].copy_( @@ -189,6 +201,7 @@ class UnsupervisedMTNoising(WordNoising): Implements the default configuration for noising in UnsupervisedMT (github.com/facebookresearch/UnsupervisedMT) """ + def __init__( self, dictionary, @@ -275,9 +288,16 @@ def __init__( self.src_dataset = src_dataset self.src_dict = src_dict self.seed = seed - self.noiser = noiser if noiser is not None else noising_class( - dictionary=src_dict, **kwargs, + self.noiser = ( + noiser + if noiser is not None + else noising_class( + dictionary=src_dict, + **kwargs, + ) ) + self.sizes = src_dataset.sizes + def __getitem__(self, index): """ diff --git a/fairseq/data/num_samples_dataset.py b/fairseq/data/num_samples_dataset.py index 9d7ea44019..99a17495c7 100644 --- a/fairseq/data/num_samples_dataset.py +++ b/fairseq/data/num_samples_dataset.py @@ -7,7 +7,6 @@ class NumSamplesDataset(FairseqDataset): - def __getitem__(self, index): return 1 diff --git a/fairseq/data/numel_dataset.py b/fairseq/data/numel_dataset.py index 50087e5857..ac86dfd2f1 100644 --- a/fairseq/data/numel_dataset.py +++ b/fairseq/data/numel_dataset.py @@ -10,7 +10,6 @@ class NumelDataset(BaseWrapperDataset): - def __init__(self, dataset, reduce=False): super().__init__(dataset) self.reduce = reduce diff --git a/fairseq/data/offset_tokens_dataset.py b/fairseq/data/offset_tokens_dataset.py index a6fd559a30..6fabbdcdaa 100644 --- a/fairseq/data/offset_tokens_dataset.py +++ b/fairseq/data/offset_tokens_dataset.py @@ -7,7 +7,6 @@ class OffsetTokensDataset(BaseWrapperDataset): - def __init__(self, dataset, offset): super().__init__(dataset) self.offset = offset diff --git a/fairseq/data/pad_dataset.py b/fairseq/data/pad_dataset.py index 4c13b549aa..8075bba6a9 100644 --- a/fairseq/data/pad_dataset.py +++ b/fairseq/data/pad_dataset.py @@ -9,7 +9,6 @@ class PadDataset(BaseWrapperDataset): - def __init__(self, dataset, pad_idx, left_pad): super().__init__(dataset) self.pad_idx = pad_idx @@ -20,12 +19,10 @@ def collater(self, samples): class LeftPadDataset(PadDataset): - def __init__(self, dataset, pad_idx): super().__init__(dataset, pad_idx, left_pad=True) class RightPadDataset(PadDataset): - def __init__(self, dataset, pad_idx): super().__init__(dataset, pad_idx, left_pad=False) diff --git a/fairseq/data/plasma_utils.py b/fairseq/data/plasma_utils.py index 33f250eea9..b9fab3b739 100644 --- a/fairseq/data/plasma_utils.py +++ b/fairseq/data/plasma_utils.py @@ -3,11 +3,23 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. + import subprocess +import json import tempfile +import hashlib +from typing import Hashable + +try: + import pyarrow.plasma as plasma + + PYARROW_AVAILABLE = True +except ImportError: + plasma = None + PYARROW_AVAILABLE = False -class PlasmaArray(object): +class PlasmaArray: """ Wrapper around numpy arrays that automatically moves the data to shared memory upon serialization. This is particularly helpful when passing numpy @@ -31,11 +43,7 @@ def __init__(self, array): @property def plasma(self): if self._plasma is None and not self.disable: - try: - import pyarrow.plasma as plasma - self._plasma = plasma - except ImportError: - self._plasma = None + self._plasma = plasma return self._plasma def start_server(self): @@ -45,34 +53,34 @@ def start_server(self): assert self.path is None self._server_tmp = tempfile.NamedTemporaryFile() self.path = self._server_tmp.name - self._server = subprocess.Popen([ - 'plasma_store', - '-m', str(int(1.05 * self.array.nbytes)), - '-s', self.path, - ]) + self._server = subprocess.Popen( + ["plasma_store", "-m", str(int(1.05 * self.array.nbytes)), "-s", self.path] + ) @property def client(self): if self._client is None: assert self.path is not None - self._client = self.plasma.connect(self.path) + self._client = self.plasma.connect(self.path, num_retries=200) return self._client def __getstate__(self): + """Called on pickle load""" if self.plasma is None: return self.__dict__ if self.object_id is None: self.start_server() self.object_id = self.client.put(self.array) state = self.__dict__.copy() - del state['array'] - state['_client'] = None - state['_server'] = None - state['_server_tmp'] = None - state['_plasma'] = None + del state["array"] + state["_client"] = None + state["_server"] = None + state["_server_tmp"] = None + state["_plasma"] = None return state def __setstate__(self, state): + """Called on pickle save""" self.__dict__.update(state) if self.plasma is None: return @@ -84,3 +92,106 @@ def __del__(self): self._server = None self._server_tmp.close() self._server_tmp = None + + +DEFAULT_PLASMA_PATH = "/tmp/plasma" + + +class PlasmaView: + """Interface to write and read from shared memory. Whereas PlasmaArray writes to plasma on serialization, + PlasmaView writes to shared memory on instantiation.""" + + def __init__(self, array, split_path: str, hash_data: Hashable, plasma_path=None): + """ + Args: + array: numpy array to store. This can be read with ``PlasmaView().array`` + split_path: the path whence the data was read, used for hashing + hash_data: other metadata about the array that can be used to create a unique key. + as of writing, the 3 callers in ``TokenBlockDataset`` use:: + + hash_data = ((block_size, document_sep_len, str(break_mode), len(dataset)), 0|1|2) + + + """ + assert PYARROW_AVAILABLE + assert split_path is not None + if plasma_path is None: + plasma_path = DEFAULT_PLASMA_PATH + + self.path = plasma_path + self.split_path = split_path + self._client = None # Initialize lazily for pickle. plasma clients should not be deep copied or serialized. + self._n = None + + self.object_id = self.get_object_id(self.split_path, hash_data) + try: + self.client.put(array, object_id=self.object_id) + except plasma.PlasmaObjectExists: + pass + + @property + def client(self): + if self._client is None: + self._client = plasma.connect(self.path, num_retries=200) + return self._client + + @property + def array(self): + """Fetch a read only view of an np.array, stored in plasma.""" + ret = self.client.get(self.object_id) + return ret + + @staticmethod + def get_object_id(split_path: str, hash_data: Hashable): + """Returns plasma.ObjectID from hashing split_path and object_num.""" + hash = hashlib.blake2b(bytes(split_path, "utf-8"), digest_size=20) + harg = json.dumps(hash_data).encode("utf-8") + hash.update(harg) + return plasma.ObjectID(hash.digest()) + + def __getstate__(self): + """Called on pickle save""" + self.disconnect() + state = self.__dict__.copy() + assert state["_client"] is None + assert "object_id" in state + return state + + def __setstate__(self, state): + """Called on pickle load""" + self.__dict__.update(state) + + def __del__(self): + self.disconnect() + + def disconnect(self): + if self._client is not None: + self._client.disconnect() + self._client = None + + def __len__(self): + """Save reads by caching len""" + if self._n is None: + self._n = len(self.array) + return self._n + + +GB100 = (1024 ** 3) * 100 + + +class PlasmaStore: + def __init__(self, path=DEFAULT_PLASMA_PATH, nbytes: int = GB100): + + self.server = self.start(path, nbytes) + + def __del__(self): + self.server.kill() + + @staticmethod + def start(path=DEFAULT_PLASMA_PATH, nbytes: int = GB100) -> subprocess.Popen: + if not PYARROW_AVAILABLE: + raise ImportError("please run pip install pyarrow to use --use_plasma_view") + # best practice is to allocate more space than we need. The limitation seems to be the size of /dev/shm + _server = subprocess.Popen(["plasma_store", "-m", str(nbytes), "-s", path]) + plasma.connect(path, num_retries=200) # If we can't connect we fail immediately + return _server diff --git a/fairseq/data/prepend_token_dataset.py b/fairseq/data/prepend_token_dataset.py index 9dac71badf..fd1331f4c4 100644 --- a/fairseq/data/prepend_token_dataset.py +++ b/fairseq/data/prepend_token_dataset.py @@ -10,7 +10,6 @@ class PrependTokenDataset(BaseWrapperDataset): - def __init__(self, dataset, token=None): super().__init__(dataset) self.token = token diff --git a/fairseq/data/raw_label_dataset.py b/fairseq/data/raw_label_dataset.py index e67170f1a5..d054904f41 100644 --- a/fairseq/data/raw_label_dataset.py +++ b/fairseq/data/raw_label_dataset.py @@ -9,7 +9,6 @@ class RawLabelDataset(FairseqDataset): - def __init__(self, labels): super().__init__() self.labels = labels diff --git a/fairseq/data/replace_dataset.py b/fairseq/data/replace_dataset.py index 3bc52f0fb5..5aac2ba96b 100644 --- a/fairseq/data/replace_dataset.py +++ b/fairseq/data/replace_dataset.py @@ -9,12 +9,12 @@ class ReplaceDataset(BaseWrapperDataset): """Replaces tokens found in the dataset by a specified replacement token - Args: - dataset (~torch.utils.data.Dataset): dataset to replace tokens in - replace_map(Dictionary[int,int]): map of token to replace -> replacement token - offsets (List[int]): do not replace tokens before (from left if pos, right if neg) this offset. should be - as many as the number of objects returned by the underlying dataset __getitem__ method. - """ + Args: + dataset (~torch.utils.data.Dataset): dataset to replace tokens in + replace_map(Dictionary[int,int]): map of token to replace -> replacement token + offsets (List[int]): do not replace tokens before (from left if pos, right if neg) this offset. should be + as many as the number of objects returned by the underlying dataset __getitem__ method. + """ def __init__(self, dataset, replace_map, offsets): super().__init__(dataset) diff --git a/fairseq/data/resampling_dataset.py b/fairseq/data/resampling_dataset.py index 2967916163..3d3b993164 100644 --- a/fairseq/data/resampling_dataset.py +++ b/fairseq/data/resampling_dataset.py @@ -3,9 +3,13 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. +import logging + import numpy as np +from fairseq.data import BaseWrapperDataset, plasma_utils + -from . import BaseWrapperDataset, plasma_utils +logger = logging.getLogger(__name__) class ResamplingDataset(BaseWrapperDataset): @@ -102,7 +106,12 @@ def ordered_indices(self): def prefetch(self, indices): self.dataset.prefetch(self._cur_indices.array[indices]) + @property + def can_reuse_epoch_itr_across_epochs(self): + return False + def set_epoch(self, epoch): + logger.debug("ResamplingDataset.set_epoch: {}".format(epoch)) super().set_epoch(epoch) if epoch == self._cur_epoch: diff --git a/fairseq/data/roll_dataset.py b/fairseq/data/roll_dataset.py index d07800d0f6..a2915eeb3e 100644 --- a/fairseq/data/roll_dataset.py +++ b/fairseq/data/roll_dataset.py @@ -9,7 +9,6 @@ class RollDataset(BaseWrapperDataset): - def __init__(self, dataset, shifts): super().__init__(dataset) self.shifts = shifts diff --git a/fairseq/data/round_robin_zip_datasets.py b/fairseq/data/round_robin_zip_datasets.py index 5bfc966ce8..2cb7447ea9 100644 --- a/fairseq/data/round_robin_zip_datasets.py +++ b/fairseq/data/round_robin_zip_datasets.py @@ -3,11 +3,15 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. +import logging from collections import OrderedDict +from typing import Dict, Sequence import numpy as np -from . import FairseqDataset +from . import FairseqDataset, LanguagePairDataset + +logger = logging.getLogger(__name__) class RoundRobinZipDatasets(FairseqDataset): @@ -25,36 +29,42 @@ class RoundRobinZipDatasets(FairseqDataset): def __init__(self, datasets, eval_key=None): super().__init__() + if isinstance(datasets, dict): + datasets = OrderedDict(datasets) assert isinstance(datasets, OrderedDict) + assert datasets, "Can't make a RoundRobinZipDatasets out of nothing" + for dataset in datasets.values(): + assert isinstance(dataset, FairseqDataset) + self.datasets = datasets self.eval_key = eval_key - self.longest_dataset = None - self.longest_dataset_key = None - for key, dataset in datasets.items(): - assert isinstance(dataset, FairseqDataset) - if self.longest_dataset is None or len(dataset) > len(self.longest_dataset): - self.longest_dataset = dataset - self.longest_dataset_key = key - - self._ordered_indices = None + self.longest_dataset_key = max(datasets, key=lambda k: len(datasets[k])) + self.longest_dataset = datasets[self.longest_dataset_key] + self._ordered_indices: Dict[str, Sequence[int]] = None def _map_index(self, key, index): - assert self._ordered_indices is not None, \ - 'Must call RoundRobinZipDatasets.ordered_indices() first' - return self._ordered_indices[key][index % len(self.datasets[key])] + assert ( + self._ordered_indices is not None + ), "Must call RoundRobinZipDatasets.ordered_indices() first" + o = self._ordered_indices[key] + return o[index % len(o)] def __getitem__(self, index): if self.eval_key is None: - return OrderedDict([ - (key, dataset[self._map_index(key, index)]) - for key, dataset in self.datasets.items() - ]) + return OrderedDict( + [ + (key, dataset[self._map_index(key, index)]) + for key, dataset in self.datasets.items() + ] + ) else: # at evaluation time it's useful to pass-through batches from a single key return self.datasets[self.eval_key][self._map_index(self.eval_key, index)] def __len__(self): + if self._ordered_indices is not None: + return len(self._ordered_indices[self.longest_dataset_key]) return len(self.longest_dataset) def collater(self, samples): @@ -62,10 +72,12 @@ def collater(self, samples): if len(samples) == 0: return None if self.eval_key is None: - return OrderedDict([ - (key, dataset.collater([sample[key] for sample in samples])) - for key, dataset in self.datasets.items() - ]) + return OrderedDict( + [ + (key, dataset.collater([sample[key] for sample in samples])) + for key, dataset in self.datasets.items() + ] + ) else: # at evaluation time it's useful to pass-through batches from a single key return self.datasets[self.eval_key].collater(samples) @@ -91,17 +103,55 @@ def ordered_indices(self): if self._ordered_indices is None: # Call the underlying dataset's ordered_indices() here, so that we # get the same random ordering as we would have from using the - # underlying dataset directly. - self._ordered_indices = OrderedDict([ - (key, dataset.ordered_indices()) - for key, dataset in self.datasets.items() - ]) + # underlying sub-datasets directly. + self._ordered_indices = OrderedDict( + [ + (key, dataset.ordered_indices()) + for key, dataset in self.datasets.items() + ] + ) return np.arange(len(self)) + def filter_indices_by_size(self, indices, max_positions=None): + """ + Filter each sub-dataset independently, then update the round robin to work + on the filtered sub-datasets. + """ + + def _deep_until_language_pair(dataset): + if isinstance(dataset, LanguagePairDataset): + return dataset + if hasattr(dataset, "tgt_dataset"): + return _deep_until_language_pair(dataset.tgt_dataset) + if hasattr(dataset, "dataset"): + return _deep_until_language_pair(dataset.dataset) + raise Exception(f"Don't know how to unwrap this dataset: {dataset}") + + if not isinstance(max_positions, dict): + max_positions = {k: max_positions for k in self.datasets.keys()} + ignored_some = False + for key, dataset in self.datasets.items(): + dataset = _deep_until_language_pair(dataset) + self._ordered_indices[key], ignored = dataset.filter_indices_by_size( + self._ordered_indices[key], max_positions[key] + ) + if len(ignored) > 0: + ignored_some = True + logger.warning( + f"{len(ignored)} samples from {key} have invalid sizes and will be skipped, " + f"max_positions={max_positions[key]}, first few sample ids={ignored[:10]}" + ) + # Since we are modifying in place the _ordered_indices, + # it's not possible anymore to return valid ignored indices. + # Hopefully the extra debug information print above should be enough to debug. + # Ideally we would receive ignore_invalid_inputs so that we could have + # a proper error message. + return (np.arange(len(self)), [0] if ignored_some else []) + @property def supports_prefetch(self): return all( - getattr(dataset, 'supports_prefetch', False) + getattr(dataset, "supports_prefetch", False) for dataset in self.datasets.values() ) diff --git a/fairseq/data/shorten_dataset.py b/fairseq/data/shorten_dataset.py new file mode 100644 index 0000000000..6ebb5d88fe --- /dev/null +++ b/fairseq/data/shorten_dataset.py @@ -0,0 +1,78 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import numpy as np +from fairseq.data import data_utils + +from . import BaseWrapperDataset + + +class TruncateDataset(BaseWrapperDataset): + """Truncate a sequence by returning the first truncation_length tokens""" + + def __init__(self, dataset, truncation_length): + super().__init__(dataset) + assert truncation_length is not None + self.truncation_length = truncation_length + self.dataset = dataset + + def __getitem__(self, index): + item = self.dataset[index] + item_len = item.size(0) + if item_len > self.truncation_length: + item = item[: self.truncation_length] + return item + + @property + def sizes(self): + return np.minimum(self.dataset.sizes, self.truncation_length) + + def __len__(self): + return len(self.dataset) + + +class RandomCropDataset(TruncateDataset): + """Truncate a sequence by returning a random crop of truncation_length tokens""" + + def __init__(self, dataset, truncation_length, seed=1): + super().__init__(dataset, truncation_length) + self.seed = seed + self.epoch = 0 + + @property + def can_reuse_epoch_itr_across_epochs(self): + return True # only the crop changes, not item sizes + + def set_epoch(self, epoch, **unused): + super().set_epoch(epoch) + self.epoch = epoch + + def __getitem__(self, index): + with data_utils.numpy_seed(self.seed, self.epoch, index): + item = self.dataset[index] + item_len = item.size(0) + excess = item_len - self.truncation_length + if excess > 0: + start_idx = np.random.randint(0, excess) + item = item[start_idx : start_idx + self.truncation_length] + return item + + +def maybe_shorten_dataset( + dataset, + split, + shorten_data_split_list, + shorten_method, + tokens_per_sample, + seed, +): + truncate_split = ( + split in shorten_data_split_list.split(",") or len(shorten_data_split_list) == 0 + ) + if shorten_method == "truncate" and truncate_split: + dataset = TruncateDataset(dataset, tokens_per_sample) + elif shorten_method == "random_crop" and truncate_split: + dataset = RandomCropDataset(dataset, tokens_per_sample, seed) + return dataset diff --git a/fairseq/data/sort_dataset.py b/fairseq/data/sort_dataset.py index 9b510b93a0..b3890e7279 100644 --- a/fairseq/data/sort_dataset.py +++ b/fairseq/data/sort_dataset.py @@ -9,7 +9,6 @@ class SortDataset(BaseWrapperDataset): - def __init__(self, dataset, sort_order): super().__init__(dataset) if not isinstance(sort_order, (list, tuple)): diff --git a/fairseq/data/strip_token_dataset.py b/fairseq/data/strip_token_dataset.py index e388db0e5f..cae39ba4d2 100644 --- a/fairseq/data/strip_token_dataset.py +++ b/fairseq/data/strip_token_dataset.py @@ -7,7 +7,6 @@ class StripTokenDataset(BaseWrapperDataset): - def __init__(self, dataset, id_to_strip): super().__init__(dataset) self.id_to_strip = id_to_strip diff --git a/fairseq/data/subsample_dataset.py b/fairseq/data/subsample_dataset.py index e395674a55..48feaf883f 100644 --- a/fairseq/data/subsample_dataset.py +++ b/fairseq/data/subsample_dataset.py @@ -16,18 +16,19 @@ class SubsampleDataset(BaseWrapperDataset): """Subsamples a given dataset by a specified ratio. Subsampling is done on the number of examples - Args: - dataset (~torch.utils.data.Dataset): dataset to subsample - size_ratio(float): the ratio to subsample to. must be between 0 and 1 (exclusive) - """ + Args: + dataset (~torch.utils.data.Dataset): dataset to subsample + size_ratio(float): the ratio to subsample to. must be between 0 and 1 (exclusive) + """ - def __init__(self, dataset, size_ratio): + def __init__(self, dataset, size_ratio, shuffle=False): super().__init__(dataset) assert size_ratio < 1 self.actual_size = np.ceil(len(dataset) * size_ratio).astype(int) self.indices = np.random.choice( list(range(len(self.dataset))), self.actual_size, replace=False ) + self.shuffle = shuffle logger.info( "subsampled dataset from {} to {} (ratio={})".format( len(self.dataset), self.actual_size, size_ratio diff --git a/fairseq/data/text_compressor.py b/fairseq/data/text_compressor.py new file mode 100644 index 0000000000..561e9ac89a --- /dev/null +++ b/fairseq/data/text_compressor.py @@ -0,0 +1,56 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from enum import Enum + + +class TextCompressionLevel(Enum): + none = 0 + low = 1 + high = 2 + + +class TextCompressor(object): + def __init__( + self, level: TextCompressionLevel, + max_input_byte_length: int = 2 ** 16 + ): + self.level = level + self.max_input_length = max_input_byte_length + + def compress(self, text: str) -> bytes: + if self.level == TextCompressionLevel.low: + import zlib + # zlib: built-in, fast + return zlib.compress(text.encode(), level=0) + elif self.level == TextCompressionLevel.high: + try: + import unishox2 + # unishox2: optimized for short text but slower + except ImportError: + raise ImportError( + "Please install unishox2 for the text compression feature: " + "pip install unishox2-py3" + ) + assert len(text.encode()) <= self.max_input_length + return unishox2.compress(text)[0] + else: + return text.encode() + + def decompress(self, compressed: bytes) -> str: + if self.level == TextCompressionLevel.low: + import zlib + return zlib.decompress(compressed).decode() + elif self.level == TextCompressionLevel.high: + try: + import unishox2 + except ImportError: + raise ImportError( + "Please install unishox2 for the text compression feature: " + "pip install unishox2-py3" + ) + return unishox2.decompress(compressed, self.max_input_length) + else: + return compressed.decode() diff --git a/fairseq/data/token_block_dataset.py b/fairseq/data/token_block_dataset.py index 4e2f5cc482..d2c65fd7e0 100644 --- a/fairseq/data/token_block_dataset.py +++ b/fairseq/data/token_block_dataset.py @@ -5,8 +5,9 @@ import numpy as np import torch - from fairseq.data import FairseqDataset, plasma_utils +from fairseq.data.indexed_dataset import best_fitting_int_dtype +from typing import Tuple class TokenBlockDataset(FairseqDataset): @@ -31,6 +32,7 @@ class TokenBlockDataset(FairseqDataset): 'complete_doc' break mode). Typically 1 if the sentences have eos and 0 otherwise. """ + def __init__( self, dataset, @@ -41,17 +43,10 @@ def __init__( break_mode=None, include_targets=False, document_sep_len=1, + use_plasma_view=False, + split_path=None, + plasma_path=None, ): - try: - from fairseq.data.token_block_utils_fast import ( - _get_slice_indices_fast, - _get_block_to_dataset_index_fast, - ) - except ImportError: - raise ImportError( - 'Please build Cython components with: `pip install --editable .` ' - 'or `python setup.py build_ext --inplace`' - ) super().__init__() self.dataset = dataset @@ -59,9 +54,46 @@ def __init__( self.eos = eos self.include_targets = include_targets - assert len(dataset) == len(sizes) assert len(dataset) > 0 + assert len(dataset) == len(sizes) + _sizes, block_to_dataset_index, slice_indices = self._build_slice_indices( + sizes, break_mode, document_sep_len, block_size + ) + if use_plasma_view: + plasma_id = (block_size, document_sep_len, str(break_mode), len(dataset)) + self._slice_indices = plasma_utils.PlasmaView( + slice_indices, split_path, (plasma_id, 0), plasma_path=plasma_path + ) + self._sizes = plasma_utils.PlasmaView( + _sizes, split_path, (plasma_id, 1), plasma_path=plasma_path + ) + self._block_to_dataset_index = plasma_utils.PlasmaView( + block_to_dataset_index, split_path, (plasma_id, 2), plasma_path=plasma_path, + ) + else: + self._slice_indices = plasma_utils.PlasmaArray(slice_indices) + self._sizes = plasma_utils.PlasmaArray(_sizes) + self._block_to_dataset_index = plasma_utils.PlasmaArray( + block_to_dataset_index + ) + + @staticmethod + def _build_slice_indices( + sizes, break_mode, document_sep_len, block_size + ) -> Tuple[np.ndarray]: + """Use token_block_utils_fast to build arrays for indexing into self.dataset""" + try: + from fairseq.data.token_block_utils_fast import ( + _get_slice_indices_fast, + _get_block_to_dataset_index_fast, + ) + except ImportError: + raise ImportError( + "Please build Cython components with: `pip install --editable .` " + "or `python setup.py build_ext --inplace`" + ) + if isinstance(sizes, list): sizes = np.array(sizes, dtype=np.int64) else: @@ -69,14 +101,16 @@ def __init__( sizes = sizes.numpy() sizes = sizes.astype(np.int64) - break_mode = break_mode if break_mode is not None else 'none' + break_mode = break_mode if break_mode is not None else "none" # For "eos" break-mode, block_size is not required parameters. if break_mode == "eos" and block_size is None: block_size = 0 - slice_indices = _get_slice_indices_fast(sizes, break_mode, block_size, document_sep_len) - self._sizes = slice_indices[:, 1] - slice_indices[:, 0] + slice_indices = _get_slice_indices_fast( + sizes, str(break_mode), block_size, document_sep_len + ) + _sizes = slice_indices[:, 1] - slice_indices[:, 0] # build index mapping block indices to the underlying dataset indices if break_mode == "eos": @@ -85,7 +119,7 @@ def __init__( [ np.arange(len(sizes)), # starting index in dataset np.zeros( - len(sizes), dtype=np.long + len(sizes), dtype=np.compat.long ), # starting offset within starting index np.arange(len(sizes)), # ending index in dataset ], @@ -93,12 +127,15 @@ def __init__( ) else: block_to_dataset_index = _get_block_to_dataset_index_fast( - sizes, - slice_indices, + sizes, slice_indices, ) - self._slice_indices = plasma_utils.PlasmaArray(slice_indices) - self._sizes = plasma_utils.PlasmaArray(self._sizes) - self._block_to_dataset_index = plasma_utils.PlasmaArray(block_to_dataset_index) + size_dtype = np.uint16 if block_size < 65535 else np.uint32 + num_tokens = slice_indices[-1].max() + slice_indices_dtype = best_fitting_int_dtype(num_tokens) + slice_indices = slice_indices.astype(slice_indices_dtype) + _sizes = _sizes.astype(size_dtype) + block_to_dataset_index = block_to_dataset_index.astype(slice_indices_dtype) + return _sizes, block_to_dataset_index, slice_indices @property def slice_indices(self): @@ -122,7 +159,6 @@ def __getitem__(self, index): buffer = torch.cat( [self.dataset[idx] for idx in range(start_ds_idx, end_ds_idx + 1)] ) - slice_s, slice_e = self.slice_indices[index] length = slice_e - slice_s s, e = start_offset, start_offset + length diff --git a/fairseq/data/token_block_utils_fast.pyx b/fairseq/data/token_block_utils_fast.pyx index 5563b973e9..08af4f3061 100644 --- a/fairseq/data/token_block_utils_fast.pyx +++ b/fairseq/data/token_block_utils_fast.pyx @@ -12,8 +12,10 @@ from libc.math cimport ceil cimport cython cimport numpy as np +from libc.stdint cimport int32_t, int64_t + DTYPE = np.int64 -ctypedef np.int64_t DTYPE_t +ctypedef int64_t DTYPE_t @cython.boundscheck(False) @@ -168,7 +170,7 @@ cdef class DatasetSearcher(object): self.current_offset += to_consume self.current_i += to_consume else: - assert remaining > 0 + assert remaining >= 0 self.current_i += remaining self.current_index += 1 self.current_offset = 0 diff --git a/fairseq/data/transform_eos_dataset.py b/fairseq/data/transform_eos_dataset.py index 4ce5ad811b..fb14ff018e 100644 --- a/fairseq/data/transform_eos_dataset.py +++ b/fairseq/data/transform_eos_dataset.py @@ -33,11 +33,11 @@ def __init__( has_target=True, ): if not isinstance(dataset, FairseqDataset): - raise ValueError('dataset must be an instance of FairseqDataset') + raise ValueError("dataset must be an instance of FairseqDataset") if append_eos_to_src and remove_eos_from_src: - raise ValueError('cannot combine append_eos_to_src and remove_eos_from_src') + raise ValueError("cannot combine append_eos_to_src and remove_eos_from_src") if append_eos_to_tgt and remove_eos_from_tgt: - raise ValueError('cannot combine append_eos_to_tgt and remove_eos_from_tgt') + raise ValueError("cannot combine append_eos_to_tgt and remove_eos_from_tgt") self.dataset = dataset self.eos = torch.LongTensor([eos]) @@ -75,24 +75,23 @@ def __len__(self): return len(self.dataset) def collater(self, samples): - def transform(item): if self.append_eos_to_src: - self.eos = self.eos.to(device=item['source'].device) - self._check_src(item['source'], expect_eos=False) - item['source'] = torch.cat([item['source'], self.eos]) + self.eos = self.eos.to(device=item["source"].device) + self._check_src(item["source"], expect_eos=False) + item["source"] = torch.cat([item["source"], self.eos]) if self.remove_eos_from_src: - self.eos = self.eos.to(device=item['source'].device) - self._check_src(item['source'], expect_eos=True) - item['source'] = item['source'][:-1] + self.eos = self.eos.to(device=item["source"].device) + self._check_src(item["source"], expect_eos=True) + item["source"] = item["source"][:-1] if self.append_eos_to_tgt: - self.eos = self.eos.to(device=item['target'].device) - self._check_tgt(item['target'], expect_eos=False) - item['target'] = torch.cat([item['target'], self.eos]) + self.eos = self.eos.to(device=item["target"].device) + self._check_tgt(item["target"], expect_eos=False) + item["target"] = torch.cat([item["target"], self.eos]) if self.remove_eos_from_tgt: - self.eos = self.eos.to(device=item['target'].device) - self._check_tgt(item['target'], expect_eos=True) - item['target'] = item['target'][:-1] + self.eos = self.eos.to(device=item["target"].device) + self._check_tgt(item["target"], expect_eos=True) + item["target"] = item["target"][:-1] return item samples = list(map(transform, samples)) @@ -115,7 +114,7 @@ def ordered_indices(self): @property def supports_prefetch(self): - return getattr(self.dataset, 'supports_prefetch', False) + return getattr(self.dataset, "supports_prefetch", False) def prefetch(self, indices): return self.dataset.prefetch(indices) diff --git a/fairseq/data/transform_eos_lang_pair_dataset.py b/fairseq/data/transform_eos_lang_pair_dataset.py index 40f77c2916..e21144a88e 100644 --- a/fairseq/data/transform_eos_lang_pair_dataset.py +++ b/fairseq/data/transform_eos_lang_pair_dataset.py @@ -4,10 +4,12 @@ # LICENSE file in the root directory of this source tree. -from . import FairseqDataset -import torch from typing import Optional +import torch + +from . import FairseqDataset + class TransformEosLangPairDataset(FairseqDataset): """A :class:`~fairseq.data.FairseqDataset` wrapper that transform bos on @@ -45,30 +47,47 @@ def __getitem__(self, index): def __len__(self): return len(self.dataset) - def collater(self, samples): - samples = self.dataset.collater(samples) + def collater(self, samples, **extra_args): + samples = self.dataset.collater(samples, **extra_args) + if len(samples) == 0: + return samples + + if 'net_input' not in samples: + return samples if self.new_src_eos is not None: if self.dataset.left_pad_source: - assert(samples['net_input']['src_tokens'][:, -1] != self.src_eos).sum() == 0 - samples['net_input']['src_tokens'][:, -1] = self.new_src_eos + assert ( + samples["net_input"]["src_tokens"][:, -1] != self.src_eos + ).sum() == 0 + samples["net_input"]["src_tokens"][:, -1] = self.new_src_eos else: - eos_idx = samples['net_input']['src_lengths'] - 1 - assert( - samples['net_input']['src_tokens'][torch.arange(eos_idx.size(0)), eos_idx] != self.src_eos + eos_idx = samples["net_input"]["src_lengths"] - 1 + assert ( + samples["net_input"]["src_tokens"][ + torch.arange(eos_idx.size(0)), eos_idx + ] + != self.src_eos ).sum() == 0 - eos_idx = eos_idx.resize_(len(samples['net_input']['src_lengths']), 1) - samples['net_input']['src_tokens'].scatter_(1, eos_idx, self.new_src_eos) + eos_idx = eos_idx.resize_(len(samples["net_input"]["src_lengths"]), 1) + samples["net_input"]["src_tokens"].scatter_( + 1, eos_idx, self.new_src_eos + ) - if self.new_tgt_bos is not None and 'prev_output_tokens' in samples['net_input']: + if ( + self.new_tgt_bos is not None + and "prev_output_tokens" in samples["net_input"] + ): if self.dataset.left_pad_target: # TODO: support different padding direction on target side raise NotImplementedError( - 'TransformEosLangPairDataset does not implement --left-pad-target True option' + "TransformEosLangPairDataset does not implement --left-pad-target True option" ) else: - assert (samples['net_input']['prev_output_tokens'][:, 0] != self.tgt_bos).sum() == 0 - samples['net_input']['prev_output_tokens'][:, 0] = self.new_tgt_bos + assert ( + samples["net_input"]["prev_output_tokens"][:, 0] != self.tgt_bos + ).sum() == 0 + samples["net_input"]["prev_output_tokens"][:, 0] = self.new_tgt_bos return samples @@ -78,12 +97,17 @@ def num_tokens(self, index): def size(self, index): return self.dataset.size(index) + @property + def sizes(self): + # dataset.sizes can be a dynamically computed sizes: + return self.dataset.sizes + def ordered_indices(self): return self.dataset.ordered_indices() @property def supports_prefetch(self): - return getattr(self.dataset, 'supports_prefetch', False) + return getattr(self.dataset, "supports_prefetch", False) def prefetch(self, indices): return self.dataset.prefetch(indices) diff --git a/fairseq/data/truncate_dataset.py b/fairseq/data/truncate_dataset.py deleted file mode 100644 index efd1c6d1cb..0000000000 --- a/fairseq/data/truncate_dataset.py +++ /dev/null @@ -1,31 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import numpy as np - -from . import BaseWrapperDataset - - -class TruncateDataset(BaseWrapperDataset): - - def __init__(self, dataset, truncation_length): - super().__init__(dataset) - assert truncation_length is not None - self.truncation_length = truncation_length - self.dataset = dataset - - def __getitem__(self, index): - item = self.dataset[index] - item_len = item.size(0) - if item_len > self.truncation_length: - item = item[:self.truncation_length] - return item - - @property - def sizes(self): - return np.minimum(self.dataset.sizes, self.truncation_length) - - def __len__(self): - return len(self.dataset) diff --git a/interactive.py b/fairseq/dataclass/__init__.py similarity index 58% rename from interactive.py rename to fairseq/dataclass/__init__.py index 0dc3da3378..25408d28ec 100644 --- a/interactive.py +++ b/fairseq/dataclass/__init__.py @@ -1,11 +1,13 @@ -#!/usr/bin/env python3 -u # Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. -from fairseq_cli.interactive import cli_main +from .configs import FairseqDataclass +from .constants import ChoiceEnum -if __name__ == '__main__': - cli_main() +__all__ = [ + "FairseqDataclass", + "ChoiceEnum", +] diff --git a/fairseq/dataclass/configs.py b/fairseq/dataclass/configs.py new file mode 100644 index 0000000000..80caa0f2da --- /dev/null +++ b/fairseq/dataclass/configs.py @@ -0,0 +1,1044 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import sys +from dataclasses import _MISSING_TYPE, dataclass, field +from typing import Any, List, Optional + +import torch + +from fairseq.dataclass.constants import ( + DATASET_IMPL_CHOICES, + DDP_BACKEND_CHOICES, + DDP_COMM_HOOK_CHOICES, + GENERATION_CONSTRAINTS_CHOICES, + GENERATION_DECODING_FORMAT_CHOICES, + LOG_FORMAT_CHOICES, + PIPELINE_CHECKPOINT_CHOICES, + PRINT_ALIGNMENT_CHOICES, + ZERO_SHARDING_CHOICES, +) + +from omegaconf import II, MISSING + + +@dataclass +class FairseqDataclass: + """fairseq base dataclass that supported fetching attributes and metas""" + + _name: Optional[str] = None + + @staticmethod + def name(): + return None + + def _get_all_attributes(self) -> List[str]: + return [k for k in self.__dataclass_fields__.keys()] + + def _get_meta( + self, attribute_name: str, meta: str, default: Optional[Any] = None + ) -> Any: + return self.__dataclass_fields__[attribute_name].metadata.get(meta, default) + + def _get_name(self, attribute_name: str) -> str: + return self.__dataclass_fields__[attribute_name].name + + def _get_default(self, attribute_name: str) -> Any: + if hasattr(self, attribute_name): + if str(getattr(self, attribute_name)).startswith("${"): + return str(getattr(self, attribute_name)) + elif str(self.__dataclass_fields__[attribute_name].default).startswith( + "${" + ): + return str(self.__dataclass_fields__[attribute_name].default) + elif ( + getattr(self, attribute_name) + != self.__dataclass_fields__[attribute_name].default + ): + return getattr(self, attribute_name) + + f = self.__dataclass_fields__[attribute_name] + if not isinstance(f.default_factory, _MISSING_TYPE): + return f.default_factory() + return f.default + + def _get_type(self, attribute_name: str) -> Any: + return self.__dataclass_fields__[attribute_name].type + + def _get_help(self, attribute_name: str) -> Any: + return self._get_meta(attribute_name, "help") + + def _get_argparse_const(self, attribute_name: str) -> Any: + return self._get_meta(attribute_name, "argparse_const") + + def _get_argparse_alias(self, attribute_name: str) -> Any: + return self._get_meta(attribute_name, "argparse_alias") + + def _get_choices(self, attribute_name: str) -> Any: + return self._get_meta(attribute_name, "choices") + + @classmethod + def from_namespace(cls, args): + if isinstance(args, cls): + return args + else: + config = cls() + for k in config.__dataclass_fields__.keys(): + if k.startswith("_"): + # private member, skip + continue + if hasattr(args, k): + setattr(config, k, getattr(args, k)) + + return config + + + +@dataclass +class CommonConfig(FairseqDataclass): + # This is the core dataclass including common parameters shared by all different jobs. Please append your params to other dataclasses if they were + # used for a particular purpose or task, such as those dedicated for `distributed training`, `optimization`, etc. + no_progress_bar: bool = field( + default=False, metadata={"help": "disable progress bar"} + ) + log_interval: int = field( + default=100, + metadata={ + "help": "log progress every N batches (when progress bar is disabled)" + }, + ) + log_format: Optional[LOG_FORMAT_CHOICES] = field( + default=None, metadata={"help": "log format to use"} + ) + log_file: Optional[str] = field( + default=None, metadata={"help": "log file to copy metrics to."} + ) + tensorboard_logdir: Optional[str] = field( + default=None, + metadata={ + "help": "path to save logs for tensorboard, should match --logdir " + "of running tensorboard (default: no tensorboard logging)" + }, + ) + wandb_project: Optional[str] = field( + default=None, + metadata={"help": "Weights and Biases project name to use for logging"}, + ) + azureml_logging: Optional[bool] = field( + default=False, metadata={"help": "Log scalars to AzureML context"}, + ) + seed: int = field( + default=1, metadata={"help": "pseudo random number generator seed"} + ) + cpu: bool = field(default=False, metadata={"help": "use CPU instead of CUDA"}) + tpu: bool = field(default=False, metadata={"help": "use TPU instead of CUDA"}) + bf16: bool = field(default=False, metadata={"help": "use bfloat16; implies --tpu"}) + memory_efficient_bf16: bool = field( + default=False, + metadata={ + "help": "use a memory-efficient version of BF16 training; implies --bf16" + }, + ) + fp16: bool = field(default=False, metadata={"help": "use FP16"}) + memory_efficient_fp16: bool = field( + default=False, + metadata={ + "help": "use a memory-efficient version of FP16 training; implies --fp16" + }, + ) + fp16_no_flatten_grads: bool = field( + default=False, metadata={"help": "don't flatten FP16 grads tensor"} + ) + fp16_init_scale: int = field( + default=2 ** 7, metadata={"help": "default FP16 loss scale"} + ) + fp16_scale_window: Optional[int] = field( + default=None, + metadata={"help": "number of updates before increasing loss scale"}, + ) + fp16_scale_tolerance: float = field( + default=0.0, + metadata={ + "help": "pct of updates that can overflow before decreasing the loss scale" + }, + ) + on_cpu_convert_precision: bool = field( + default=False, + metadata={ + "help": "if set, the floating point conversion to fp16/bf16 runs on CPU. " + "This reduces bus transfer time and GPU memory usage." + } + ) + min_loss_scale: float = field( + default=1e-4, + metadata={"help": "minimum FP16/AMP loss scale, after which training is stopped"}, + ) + threshold_loss_scale: Optional[float] = field( + default=None, metadata={"help": "threshold FP16 loss scale from below"} + ) + amp: bool = field(default=False, metadata={"help": "use automatic mixed precision"}) + amp_batch_retries: int = field( + default=2, + metadata={"help": "number of retries of same batch after reducing loss scale with AMP"}, + ) + amp_init_scale: int = field( + default=2 ** 7, metadata={"help": "default AMP loss scale"} + ) + amp_scale_window: Optional[int] = field( + default=None, + metadata={"help": "number of updates before increasing AMP loss scale"}, + ) + user_dir: Optional[str] = field( + default=None, + metadata={ + "help": "path to a python module containing custom extensions (tasks and/or architectures)" + }, + ) + empty_cache_freq: int = field( + default=0, + metadata={"help": "how often to clear the PyTorch CUDA cache (0 to disable)"}, + ) + all_gather_list_size: int = field( + default=16384, + metadata={"help": "number of bytes reserved for gathering stats from workers"}, + ) + model_parallel_size: int = field( + default=1, metadata={"help": "total number of GPUs to parallelize model over"} + ) + quantization_config_path: Optional[str] = field( + default=None, metadata={"help": "path to quantization config file"} + ) + profile: bool = field( + default=False, metadata={"help": "enable autograd profiler emit_nvtx"} + ) + reset_logging: bool = field( + default=False, + metadata={ + "help": "when using Hydra, reset the logging at the beginning of training" + }, + ) + suppress_crashes: bool = field( + default=False, + metadata={ + "help": "suppress crashes when training with the hydra_train entry point so that the " + "main method can return a value (useful for sweeps)" + }, + ) + use_plasma_view: bool = field( + default=False, metadata={"help": "Store indices and sizes in shared memory"} + ) + plasma_path: Optional[str] = field( + default="/tmp/plasma", + metadata={ + "help": "path to run plasma_store, defaults to /tmp/plasma. Paths outside /tmp tend to fail." + }, + ) + + +@dataclass +class DistributedTrainingConfig(FairseqDataclass): + distributed_world_size: int = field( + default=max(1, torch.cuda.device_count()), + metadata={ + "help": "total number of GPUs across all nodes (default: all visible GPUs)" + }, + ) + distributed_num_procs: Optional[int] = field( + default=max(1, torch.cuda.device_count()), + metadata={ + "help": "total number of processes to fork (default: all visible GPUs)" + }, + ) + distributed_rank: Optional[int] = field( + default=0, metadata={"help": "rank of the current worker"} + ) + distributed_backend: str = field( + default="nccl", metadata={"help": "distributed backend"} + ) + distributed_init_method: Optional[str] = field( + default=None, + metadata={ + "help": "typically tcp://hostname:port that will be used to " + "establish initial connetion" + }, + ) + distributed_port: int = field( + default=-1, + metadata={ + "help": "port number (not required if using --distributed-init-method)" + }, + ) + device_id: int = field( + default=0, + metadata={ + "help": "which GPU to use (usually configured automatically)", + "argparse_alias": "--local_rank", + }, + ) + distributed_no_spawn: bool = field( + default=False, + metadata={ + "help": "do not spawn multiple processes even if multiple GPUs are visible" + }, + ) + ddp_backend: DDP_BACKEND_CHOICES = field( + default="pytorch_ddp", metadata={"help": "DistributedDataParallel backend"} + ) + ddp_comm_hook: DDP_COMM_HOOK_CHOICES = field( + default="none", metadata={"help": "communication hook"} + ) + bucket_cap_mb: int = field( + default=25, metadata={"help": "bucket size for reduction"} + ) + fix_batches_to_gpus: bool = field( + default=False, + metadata={ + "help": "don't shuffle batches between GPUs; this reduces overall " + "randomness and may affect precision but avoids the cost of re-reading the data" + }, + ) + find_unused_parameters: bool = field( + default=False, + metadata={ + "help": "disable unused parameter detection (not applicable to " + "--ddp-backend=legacy_ddp)" + }, + ) + gradient_as_bucket_view: bool = field( + default=False, + metadata={ + "help": "when set to True, gradients will be views pointing to different offsets of allreduce communication buckets. This can reduce peak memory usage, where the saved memory size will be equal to the total gradients size. " + "--gradient-as-bucket-view=gradient_as_bucket_view)" + }, + ) + fast_stat_sync: bool = field( + default=False, + metadata={"help": "[deprecated] this is now defined per Criterion"}, + ) + heartbeat_timeout: int = field( + default=-1, + metadata={ + "help": "kill the job if no progress is made in N seconds; " + "set to -1 to disable" + }, + ) + broadcast_buffers: bool = field( + default=False, + metadata={ + "help": "Copy non-trainable parameters between GPUs, such as " + "batchnorm population statistics" + }, + ) + slowmo_momentum: Optional[float] = field( + default=None, + metadata={ + "help": "SlowMo momentum term; by default use 0.0 for 16 GPUs, " + "0.2 for 32 GPUs; 0.5 for 64 GPUs, 0.6 for > 64 GPUs" + }, + ) + slowmo_algorithm: str = field( + default="LocalSGD", metadata={"help": "whether to use LocalSGD or SGP"} + ) + localsgd_frequency: int = field( + default=3, metadata={"help": "Local SGD allreduce frequency"} + ) + nprocs_per_node: int = field( + default=max(1, torch.cuda.device_count()), + metadata={ + "help": "number of GPUs in each node. An allreduce operation across GPUs in " + "a node is very fast. Hence, we do allreduce across GPUs in a node, " + "and gossip across different nodes" + }, + ) + pipeline_model_parallel: bool = field( + default=False, + metadata={"help": "if set, use pipeline model parallelism across GPUs"}, + ) + pipeline_balance: Optional[str] = field( + default=None, + metadata={ + "help": "partition the model into N_K pieces, where each piece " + "contains N_i layers. The sum(args.pipeline_balance) " + "should equal the total number of layers in the model" + }, + ) + pipeline_devices: Optional[str] = field( + default=None, + metadata={ + "help": "a list of device indices indicating which device to place " + "each of the N_K partitions. The length of this list should " + "equal the length of the --pipeline-balance argument" + }, + ) + pipeline_chunks: Optional[int] = field( + default=0, metadata={"help": "microbatch count for pipeline model parallelism"} + ) + pipeline_encoder_balance: Optional[str] = field( + default=None, + metadata={ + "help": "partition the pipeline parallel encoder into N_K pieces, where each piece " + "contains N_i layers. The sum(args.pipeline_encoder_balance) " + "should equal the total number of encoder layers in the model" + }, + ) + pipeline_encoder_devices: Optional[str] = field( + default=None, + metadata={ + "help": "a list of device indices indicating which device to place " + "each of the N_K partitions. The length of this list should " + "equal the length of the --pipeline-encoder-balance argument" + }, + ) + pipeline_decoder_balance: Optional[str] = field( + default=None, + metadata={ + "help": "partition the pipeline parallel decoder into N_K pieces, where each piece " + "contains N_i layers. The sum(args.pipeline_decoder_balance) " + "should equal the total number of decoder layers in the model" + }, + ) + pipeline_decoder_devices: Optional[str] = field( + default=None, + metadata={ + "help": "a list of device indices indicating which device to place " + "each of the N_K partitions. The length of this list should " + "equal the length of the --pipeline-decoder-balance argument" + }, + ) + pipeline_checkpoint: PIPELINE_CHECKPOINT_CHOICES = field( + default="never", + metadata={"help": "checkpointing mode for pipeline model parallelism"}, + ) + zero_sharding: ZERO_SHARDING_CHOICES = field( + default="none", metadata={"help": "ZeRO sharding"} + ) + fp16: bool = II("common.fp16") + memory_efficient_fp16: bool = II("common.memory_efficient_fp16") + tpu: bool = II("common.tpu") + # configuration for --ddp-backend=fully_sharded + no_reshard_after_forward: bool = field( + default=False, metadata={"help": "don't reshard parameters after forward pass"}, + ) + fp32_reduce_scatter: bool = field( + default=False, metadata={"help": "reduce-scatter grads in FP32"}, + ) + cpu_offload: bool = field( + default=False, metadata={"help": "offload FP32 params to CPU"} + ) + use_sharded_state: bool = field( + default=False, metadata={"help": "use sharded checkpoint files"}, + ) + + +@dataclass +class DatasetConfig(FairseqDataclass): + num_workers: int = field( + default=1, metadata={"help": "how many subprocesses to use for data loading"} + ) + skip_invalid_size_inputs_valid_test: bool = field( + default=False, + metadata={"help": "ignore too long or too short lines in valid and test set"}, + ) + max_tokens: Optional[int] = field( + default=None, metadata={"help": "maximum number of tokens in a batch"} + ) + batch_size: Optional[int] = field( + default=None, + metadata={ + "help": "number of examples in a batch", + "argparse_alias": "--max-sentences", + }, + ) + required_batch_size_multiple: int = field( + default=8, metadata={"help": "batch size will be a multiplier of this value"} + ) + required_seq_len_multiple: int = field( + default=1, + metadata={ + "help": "maximum sequence length in batch will be a multiplier of this value" + }, + ) + dataset_impl: Optional[DATASET_IMPL_CHOICES] = field( + default=None, metadata={"help": "output dataset implementation"} + ) + data_buffer_size: int = field( + default=10, metadata={"help": "Number of batches to preload"} + ) + train_subset: str = field( + default="train", + metadata={"help": "data subset to use for training (e.g. train, valid, test)"}, + ) + valid_subset: str = field( + default="valid", + metadata={ + "help": "comma separated list of data subsets to use for validation" + " (e.g. train, valid, test)" + }, + ) + combine_valid_subsets: Optional[bool] = field( + default=None, + metadata={ + "help": "comma separated list of data subsets to use for validation" + " (e.g. train, valid, test)", + "argparse_alias": "--combine-val", + }, + ) + ignore_unused_valid_subsets: Optional[bool] = field( + default=False, + metadata={"help": "do not raise error if valid subsets are ignored"}, + ) + + validate_interval: int = field( + default=1, metadata={"help": "validate every N epochs"} + ) + validate_interval_updates: int = field( + default=0, metadata={"help": "validate every N updates"} + ) + validate_after_updates: int = field( + default=0, metadata={"help": "dont validate until reaching this many updates"} + ) + fixed_validation_seed: Optional[int] = field( + default=None, metadata={"help": "specified random seed for validation"} + ) + disable_validation: bool = field( + default=False, metadata={"help": "disable validation"} + ) + max_tokens_valid: Optional[int] = field( + default=II("dataset.max_tokens"), + metadata={ + "help": "maximum number of tokens in a validation batch" + " (defaults to --max-tokens)" + }, + ) + batch_size_valid: Optional[int] = field( + default=II("dataset.batch_size"), + metadata={ + "help": "batch size of the validation batch (defaults to --batch-size)", + "argparse_alias": "--max-sentences-valid", + }, + ) + max_valid_steps: Optional[int] = field(default=None, metadata={'help': 'How many batches to evaluate', + "argparse_alias": "--nval"}) + curriculum: int = field( + default=0, metadata={"help": "don't shuffle batches for first N epochs"} + ) + gen_subset: str = field( + default="test", + metadata={"help": "data subset to generate (train, valid, test)"}, + ) + num_shards: int = field( + default=1, metadata={"help": "shard generation over N shards"} + ) + shard_id: int = field( + default=0, metadata={"help": "id of the shard to generate (id < num_shards)"} + ) + + +@dataclass +class OptimizationConfig(FairseqDataclass): + max_epoch: int = field( + default=0, metadata={"help": "force stop training at specified epoch"} + ) + max_update: int = field( + default=0, metadata={"help": "force stop training at specified update"} + ) + stop_time_hours: float = field( + default=0, + metadata={ + "help": "force stop training after specified cumulative time (if >0)" + }, + ) + clip_norm: float = field( + default=0.0, metadata={"help": "clip threshold of gradients"} + ) + sentence_avg: bool = field( + default=False, + metadata={ + "help": "normalize gradients by the number of sentences in a batch" + " (default is to normalize by number of tokens)" + }, + ) + update_freq: List[int] = field( + default_factory=lambda: [1], + metadata={"help": "update parameters every N_i batches, when in epoch i"}, + ) + lr: List[float] = field( + default_factory=lambda: [0.25], + metadata={ + "help": "learning rate for the first N epochs; all epochs >N using LR_N" + " (note: this may be interpreted differently depending on --lr-scheduler)" + }, + ) + stop_min_lr: float = field( + default=-1.0, + metadata={"help": "stop training when the learning rate reaches this minimum"}, + ) + use_bmuf: bool = field( + default=False, + metadata={ + "help": "specify global optimizer for syncing models on different GPUs/shards" + }, + ) + + +@dataclass +class CheckpointConfig(FairseqDataclass): + save_dir: str = field( + default="checkpoints", metadata={"help": "path to save checkpoints"} + ) + restore_file: str = field( + default="checkpoint_last.pt", + metadata={ + "help": "filename from which to load checkpoint " + "(default: <save-dir>/checkpoint_last.pt" + }, + ) + finetune_from_model: Optional[str] = field( + default=None, + metadata={ + "help": "finetune from a pretrained model; note that meters and lr scheduler will be reset" + }, + ) + reset_dataloader: bool = field( + default=False, + metadata={ + "help": "if set, does not reload dataloader state from the checkpoint" + }, + ) + reset_lr_scheduler: bool = field( + default=False, + metadata={ + "help": "if set, does not load lr scheduler state from the checkpoint" + }, + ) + reset_meters: bool = field( + default=False, + metadata={"help": "if set, does not load meters from the checkpoint"}, + ) + reset_optimizer: bool = field( + default=False, + metadata={"help": "if set, does not load optimizer state from the checkpoint"}, + ) + optimizer_overrides: str = field( + default="{}", + metadata={ + "help": "a dictionary used to override optimizer args when loading a checkpoint" + }, + ) + save_interval: int = field( + default=1, metadata={"help": "save a checkpoint every N epochs"} + ) + save_interval_updates: int = field( + default=0, metadata={"help": "save a checkpoint (and validate) every N updates"} + ) + keep_interval_updates: int = field( + default=-1, + metadata={ + "help": "keep the last N checkpoints saved with --save-interval-updates" + }, + ) + keep_interval_updates_pattern: int = field( + default=-1, + metadata={ + "help": "when used with --keep-interval-updates, skips deleting " + "any checkpoints with update X where " + "X %% keep_interval_updates_pattern == 0" + }, + ) + keep_last_epochs: int = field( + default=-1, metadata={"help": "keep last N epoch checkpoints"} + ) + keep_best_checkpoints: int = field( + default=-1, metadata={"help": "keep best N checkpoints based on scores"} + ) + no_save: bool = field( + default=False, metadata={"help": "don't save models or checkpoints"} + ) + no_epoch_checkpoints: bool = field( + default=False, metadata={"help": "only store last and best checkpoints"} + ) + no_last_checkpoints: bool = field( + default=False, metadata={"help": "don't store last checkpoints"} + ) + no_save_optimizer_state: bool = field( + default=False, + metadata={"help": "don't save optimizer-state as part of checkpoint"}, + ) + best_checkpoint_metric: str = field( + default="loss", metadata={"help": 'metric to use for saving "best" checkpoints'} + ) + maximize_best_checkpoint_metric: bool = field( + default=False, + metadata={ + "help": 'select the largest metric value for saving "best" checkpoints' + }, + ) + patience: int = field( + default=-1, + metadata={ + "help": ( + "early stop training if valid performance doesn't " + "improve for N consecutive validation runs; note " + "that this is influenced by --validate-interval" + ) + }, + ) + checkpoint_suffix: str = field( + default="", metadata={"help": "suffix to add to the checkpoint file name"} + ) + checkpoint_shard_count: int = field( + default=1, + metadata={ + "help": "Number of shards containing the checkpoint - " + "if the checkpoint is over 300GB, it is preferable " + "to split it into shards to prevent OOM on CPU while loading " + "the checkpoint" + }, + ) + load_checkpoint_on_all_dp_ranks: bool = field( + default=False, + metadata={ + "help": "load checkpoints on all data parallel devices " + "(default: only load on rank 0 and broadcast to other devices)" + }, + ) + write_checkpoints_asynchronously: bool = field( + default=False, + metadata={ + "help": ( + "Write checkpoints asynchronously in a separate " + "thread. NOTE: This feature is currently being tested." + ), + "argparse_alias": "--save-async", + }, + ) + model_parallel_size: int = II("common.model_parallel_size") + + +@dataclass +class FairseqBMUFConfig(FairseqDataclass): + block_lr: float = field( + default=1, metadata={"help": "block learning rate for bmuf"} + ) + block_momentum: float = field( + default=0.875, metadata={"help": "block momentum for bmuf"} + ) + global_sync_iter: int = field( + default=50, metadata={"help": "Iteration for syncing global model"} + ) + warmup_iterations: int = field( + default=500, metadata={"help": "warmup iterations for model to broadcast"} + ) + use_nbm: bool = field( + default=False, + metadata={"help": "Specify whether you want to use classical BM / Nesterov BM"}, + ) + average_sync: bool = field( + default=False, + metadata={ + "help": "Specify whether you want to average the local momentum after each sync" + }, + ) + distributed_world_size: int = II("distributed_training.distributed_world_size") + + +@dataclass +class GenerationConfig(FairseqDataclass): + beam: int = field( + default=5, metadata={"help": "beam size"}, + ) + nbest: int = field( + default=1, metadata={"help": "number of hypotheses to output"}, + ) + max_len_a: float = field( + default=0, + metadata={ + "help": "generate sequences of maximum length ax + b, where x is the source length" + }, + ) + max_len_b: int = field( + default=200, + metadata={ + "help": "generate sequences of maximum length ax + b, where x is the source length" + }, + ) + min_len: int = field( + default=1, metadata={"help": "minimum generation length"}, + ) + match_source_len: bool = field( + default=False, metadata={"help": "generations should match the source length"}, + ) + unnormalized: bool = field( + default=False, metadata={"help": "compare unnormalized hypothesis scores"}, + ) + no_early_stop: bool = field( + default=False, metadata={"help": "deprecated"}, + ) + no_beamable_mm: bool = field( + default=False, metadata={"help": "don't use BeamableMM in attention layers"}, + ) + lenpen: float = field( + default=1, + metadata={ + "help": "length penalty: <1.0 favors shorter, >1.0 favors longer sentences" + }, + ) + unkpen: float = field( + default=0, + metadata={ + "help": "unknown word penalty: <0 produces more unks, >0 produces fewer" + }, + ) + replace_unk: Optional[str] = field( + default=None, + metadata={ + "help": "perform unknown replacement (optionally with alignment dictionary)", + "argparse_const": "@@ ", + }, + ) + sacrebleu: bool = field( + default=False, metadata={"help": "score with sacrebleu"}, + ) + score_reference: bool = field( + default=False, metadata={"help": "just score the reference translation"}, + ) + prefix_size: int = field( + default=0, + metadata={"help": "initialize generation by target prefix of given length"}, + ) + no_repeat_ngram_size: int = field( + default=0, + metadata={ + "help": "ngram blocking such that this size ngram cannot be repeated in the generation" + }, + ) + sampling: bool = field( + default=False, + metadata={"help": "sample hypotheses instead of using beam search"}, + ) + sampling_topk: int = field( + default=-1, + metadata={"help": "sample from top K likely next words instead of all words"}, + ) + sampling_topp: float = field( + default=-1.0, + metadata={ + "help": "sample from the smallest set whose cumulative probability mass exceeds p for next words" + }, + ) + constraints: Optional[GENERATION_CONSTRAINTS_CHOICES] = field( + default=None, + metadata={ + "help": "enables lexically constrained decoding", + "argparse_const": "ordered", + }, + ) + temperature: float = field( + default=1.0, metadata={"help": "temperature for generation"}, + ) + diverse_beam_groups: int = field( + default=-1, metadata={"help": "number of groups for Diverse Beam Search"}, + ) + diverse_beam_strength: float = field( + default=0.5, + metadata={"help": "strength of diversity penalty for Diverse Beam Search"}, + ) + diversity_rate: float = field( + default=-1.0, + metadata={"help": "strength of diversity penalty for Diverse Siblings Search"}, + ) + print_alignment: Optional[PRINT_ALIGNMENT_CHOICES] = field( + default=None, + metadata={ + "help": "if set, uses attention feedback to compute and print alignment to source tokens " + "(valid options are: hard, soft, otherwise treated as hard alignment)", + "argparse_const": "hard", + }, + ) + print_step: bool = field( + default=False, metadata={"help": "print steps"}, + ) + lm_path: Optional[str] = field( + default=None, metadata={"help": "path to lm checkpoint for lm fusion"}, + ) + lm_weight: float = field( + default=0.0, metadata={"help": "weight for lm probs for lm fusion"}, + ) + + # arguments for iterative refinement generator + iter_decode_eos_penalty: float = field( + default=0.0, + metadata={"help": "if > 0.0, it penalized early-stopping in decoding."}, + ) + iter_decode_max_iter: int = field( + default=10, metadata={"help": "maximum iterations for iterative refinement."}, + ) + iter_decode_force_max_iter: bool = field( + default=False, + metadata={ + "help": "if set, run exact the maximum number of iterations without early stop" + }, + ) + iter_decode_with_beam: int = field( + default=1, + metadata={ + "help": "if > 1, model will generate translations varying by the lengths." + }, + ) + iter_decode_with_external_reranker: bool = field( + default=False, + metadata={ + "help": "if set, the last checkpoint are assumed to be a reranker to rescore the translations" + }, + ) + retain_iter_history: bool = field( + default=False, + metadata={ + "help": "if set, decoding returns the whole history of iterative refinement" + }, + ) + retain_dropout: bool = field( + default=False, metadata={"help": "Use dropout at inference time"}, + ) + # temporarily set to Any until https://github.com/facebookresearch/hydra/issues/1117 is fixed + # retain_dropout_modules: Optional[List[str]] = field( + retain_dropout_modules: Any = field( + default=None, + metadata={ + "help": "if set, only retain dropout for the specified modules; " + "if not set, then dropout will be retained for all modules" + }, + ) + # special decoding format for advanced decoding. + decoding_format: Optional[GENERATION_DECODING_FORMAT_CHOICES] = field( + default=None, + metadata={"help": "special decoding format for advanced decoding."}, + ) + no_seed_provided: bool = field( + default=False, + metadata={"help": "if set, dont use seed for initializing random generators"}, + ) + + +@dataclass +class CommonEvalConfig(FairseqDataclass): + path: Optional[str] = field( + default=None, metadata={"help": "path(s) to model file(s), colon separated"}, + ) + post_process: Optional[str] = field( + default=None, + metadata={ + "help": ( + "post-process text by removing BPE, letter segmentation, etc. " + "Valid options can be found in fairseq.data.utils.post_process." + ), + "argparse_const": "subword_nmt", + "argparse_alias": "--remove-bpe", + }, + ) + quiet: bool = field(default=False, metadata={"help": "only print final scores"}) + model_overrides: str = field( + default="{}", + metadata={ + "help": "a dictionary used to override model args at generation that were used during model training" + }, + ) + results_path: Optional[str] = field( + default=None, metadata={"help": "path to save eval results (optional)"} + ) + + +@dataclass +class EvalLMConfig(FairseqDataclass): + output_word_probs: bool = field( + default=False, + metadata={ + "help": "if set, outputs words and their predicted log probabilities to standard output" + }, + ) + output_word_stats: bool = field( + default=False, + metadata={ + "help": "if set, outputs word statistics such as word count, average probability, etc" + }, + ) + context_window: int = field( + default=0, + metadata={ + "help": "ensures that every evaluated token has access to a context of at least this size, if possible" + }, + ) + softmax_batch: int = field( + default=sys.maxsize, + metadata={ + "help": "if BxT is more than this, will batch the softmax over vocab to this amount of tokens, in order to fit into GPU memory" + }, + ) + + +@dataclass +class InteractiveConfig(FairseqDataclass): + buffer_size: int = field( + default=0, + metadata={ + "help": "read this many sentences into a buffer before processing them" + }, + ) + input: str = field( + default="-", metadata={"help": "file to read from; use - for stdin"}, + ) + + +@dataclass +class EMAConfig(FairseqDataclass): + store_ema: bool = field( + default=False, metadata={ + help: "store exponential moving average shadow model" + } + ) + ema_decay: float = field( + default=0.9999, metadata={ + "help": 'decay for exponential moving average model' + } + ) + ema_start_update : int = field( + default=0, metadata={"help": "start EMA update after this many model updates"} + ) + ema_seed_model : Optional[str] = field( + default=None, metadata={ + "help": "Seed to load EMA model from. " + "Used to load EMA model separately from the actual model." + } + ) + ema_update_freq : int = field( + default=1, metadata={"help": "Do EMA update every this many model updates"} + ) + ema_fp32: bool = field( + default=False, + metadata={"help": "If true, store EMA model in fp32 even if model is in fp16"}, + ) + + +@dataclass +class FairseqConfig(FairseqDataclass): + common: CommonConfig = CommonConfig() + common_eval: CommonEvalConfig = CommonEvalConfig() + distributed_training: DistributedTrainingConfig = DistributedTrainingConfig() + dataset: DatasetConfig = DatasetConfig() + optimization: OptimizationConfig = OptimizationConfig() + checkpoint: CheckpointConfig = CheckpointConfig() + bmuf: FairseqBMUFConfig = FairseqBMUFConfig() + generation: GenerationConfig = GenerationConfig() + eval_lm: EvalLMConfig = EvalLMConfig() + interactive: InteractiveConfig = InteractiveConfig() + model: Any = MISSING + task: Any = None + criterion: Any = None + optimizer: Any = None + lr_scheduler: Any = None + scoring: Any = None + bpe: Any = None + tokenizer: Any = None + ema: EMAConfig = EMAConfig() diff --git a/fairseq/dataclass/constants.py b/fairseq/dataclass/constants.py new file mode 100644 index 0000000000..4f159cfe9a --- /dev/null +++ b/fairseq/dataclass/constants.py @@ -0,0 +1,54 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from enum import Enum, EnumMeta +from typing import List + + +class StrEnumMeta(EnumMeta): + # this is workaround for submitit pickling leading to instance checks failing in hydra for StrEnum, see + # https://github.com/facebookresearch/hydra/issues/1156 + @classmethod + def __instancecheck__(cls, other): + return "enum" in str(type(other)) + + +class StrEnum(Enum, metaclass=StrEnumMeta): + def __str__(self): + return self.value + + def __eq__(self, other: str): + return self.value == other + + def __repr__(self): + return self.value + + def __hash__(self): + return hash(str(self)) + + +def ChoiceEnum(choices: List[str]): + """return the Enum class used to enforce list of choices""" + return StrEnum("Choices", {k: k for k in choices}) + + +LOG_FORMAT_CHOICES = ChoiceEnum(["json", "none", "simple", "tqdm"]) +DDP_BACKEND_CHOICES = ChoiceEnum([ + "c10d", # alias for pytorch_ddp + "fully_sharded", # FullyShardedDataParallel from fairscale + "legacy_ddp", + "no_c10d", # alias for legacy_ddp + "pytorch_ddp", + "slow_mo", +]) +DDP_COMM_HOOK_CHOICES = ChoiceEnum(["none", "fp16"]) +DATASET_IMPL_CHOICES = ChoiceEnum(["raw", "lazy", "cached", "mmap", "fasta", "huffman"]) +GENERATION_CONSTRAINTS_CHOICES = ChoiceEnum(["ordered", "unordered"]) +GENERATION_DECODING_FORMAT_CHOICES = ChoiceEnum( + ["unigram", "ensemble", "vote", "dp", "bs"] +) +ZERO_SHARDING_CHOICES = ChoiceEnum(["none", "os"]) +PIPELINE_CHECKPOINT_CHOICES = ChoiceEnum(["always", "never", "except_last"]) +PRINT_ALIGNMENT_CHOICES = ChoiceEnum(["hard", "soft"]) diff --git a/fairseq/dataclass/initialize.py b/fairseq/dataclass/initialize.py new file mode 100644 index 0000000000..8f6cbafb80 --- /dev/null +++ b/fairseq/dataclass/initialize.py @@ -0,0 +1,61 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +"""isort:skip_file""" + +import logging +from hydra.core.config_store import ConfigStore +from fairseq.dataclass.configs import FairseqConfig +from omegaconf import DictConfig, OmegaConf + + +logger = logging.getLogger(__name__) + + +def hydra_init(cfg_name="config") -> None: + + cs = ConfigStore.instance() + cs.store(name=f"{cfg_name}", node=FairseqConfig) + + for k in FairseqConfig.__dataclass_fields__: + v = FairseqConfig.__dataclass_fields__[k].default + try: + cs.store(name=k, node=v) + except BaseException: + logger.error(f"{k} - {v}") + raise + + +def add_defaults(cfg: DictConfig) -> None: + """This function adds default values that are stored in dataclasses that hydra doesn't know about """ + + from fairseq.registry import REGISTRIES + from fairseq.tasks import TASK_DATACLASS_REGISTRY + from fairseq.models import ARCH_MODEL_NAME_REGISTRY, MODEL_DATACLASS_REGISTRY + from fairseq.dataclass.utils import merge_with_parent + from typing import Any + + OmegaConf.set_struct(cfg, False) + + for k, v in FairseqConfig.__dataclass_fields__.items(): + field_cfg = cfg.get(k) + if field_cfg is not None and v.type == Any: + dc = None + + if isinstance(field_cfg, str): + field_cfg = DictConfig({"_name": field_cfg}) + field_cfg.__dict__["_parent"] = field_cfg.__dict__["_parent"] + + name = getattr(field_cfg, "_name", None) + + if k == "task": + dc = TASK_DATACLASS_REGISTRY.get(name) + elif k == "model": + name = ARCH_MODEL_NAME_REGISTRY.get(name, name) + dc = MODEL_DATACLASS_REGISTRY.get(name) + elif k in REGISTRIES: + dc = REGISTRIES[k]["dataclass_registry"].get(name) + + if dc is not None: + cfg[k] = merge_with_parent(dc, field_cfg) diff --git a/fairseq/dataclass/utils.py b/fairseq/dataclass/utils.py new file mode 100644 index 0000000000..1320ec4737 --- /dev/null +++ b/fairseq/dataclass/utils.py @@ -0,0 +1,493 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import ast +import inspect +import logging +import os +import re +from argparse import ArgumentError, ArgumentParser, Namespace +from dataclasses import _MISSING_TYPE, MISSING, is_dataclass +from enum import Enum +from typing import Any, Dict, List, Optional, Tuple, Type + +from fairseq.dataclass import FairseqDataclass +from fairseq.dataclass.configs import FairseqConfig +from hydra.core.global_hydra import GlobalHydra +from hydra.experimental import compose, initialize +from omegaconf import DictConfig, OmegaConf, open_dict, _utils + +logger = logging.getLogger(__name__) + + +def eval_str_list(x, x_type=float): + if x is None: + return None + if isinstance(x, str): + if len(x) == 0: + return [] + x = ast.literal_eval(x) + try: + return list(map(x_type, x)) + except TypeError: + return [x_type(x)] + + +def interpret_dc_type(field_type): + if isinstance(field_type, str): + raise RuntimeError("field should be a type") + + if field_type == Any: + return str + + typestring = str(field_type) + if re.match( + r"(typing.|^)Union\[(.*), NoneType\]$", typestring + ) or typestring.startswith("typing.Optional"): + return field_type.__args__[0] + return field_type + + +def gen_parser_from_dataclass( + parser: ArgumentParser, + dataclass_instance: FairseqDataclass, + delete_default: bool = False, + with_prefix: Optional[str] = None, +) -> None: + """ + convert a dataclass instance to tailing parser arguments. + + If `with_prefix` is provided, prefix all the keys in the resulting parser with it. It means that we are + building a flat namespace from a structured dataclass (see transformer_config.py for example). + """ + + def argparse_name(name: str): + if name == "data" and (with_prefix is None or with_prefix == ''): + # normally data is positional args, so we don't add the -- nor the prefix + return name + if name == "_name": + # private member, skip + return None + full_name = "--" + name.replace("_", "-") + if with_prefix is not None and with_prefix != '': + # if a prefix is specified, construct the prefixed arg name + full_name = with_prefix + "-" + full_name[2:] # strip -- when composing + return full_name + + def get_kwargs_from_dc( + dataclass_instance: FairseqDataclass, k: str + ) -> Dict[str, Any]: + """k: dataclass attributes""" + + kwargs = {} + + field_type = dataclass_instance._get_type(k) + inter_type = interpret_dc_type(field_type) + + field_default = dataclass_instance._get_default(k) + + if isinstance(inter_type, type) and issubclass(inter_type, Enum): + field_choices = [t.value for t in list(inter_type)] + else: + field_choices = None + + field_help = dataclass_instance._get_help(k) + field_const = dataclass_instance._get_argparse_const(k) + + if isinstance(field_default, str) and field_default.startswith("${"): + kwargs["default"] = field_default + else: + if field_default is MISSING: + kwargs["required"] = True + if field_choices is not None: + kwargs["choices"] = field_choices + if ( + isinstance(inter_type, type) + and (issubclass(inter_type, List) or issubclass(inter_type, Tuple)) + ) or ("List" in str(inter_type) or "Tuple" in str(inter_type)): + if "int" in str(inter_type): + kwargs["type"] = lambda x: eval_str_list(x, int) + elif "float" in str(inter_type): + kwargs["type"] = lambda x: eval_str_list(x, float) + elif "str" in str(inter_type): + kwargs["type"] = lambda x: eval_str_list(x, str) + else: + raise NotImplementedError( + "parsing of type " + str(inter_type) + " is not implemented" + ) + if field_default is not MISSING: + kwargs["default"] = ( + ",".join(map(str, field_default)) + if field_default is not None + else None + ) + elif ( + isinstance(inter_type, type) and issubclass(inter_type, Enum) + ) or "Enum" in str(inter_type): + kwargs["type"] = str + if field_default is not MISSING: + if isinstance(field_default, Enum): + kwargs["default"] = field_default.value + else: + kwargs["default"] = field_default + elif inter_type is bool: + kwargs["action"] = ( + "store_false" if field_default is True else "store_true" + ) + kwargs["default"] = field_default + else: + kwargs["type"] = inter_type + if field_default is not MISSING: + kwargs["default"] = field_default + + # build the help with the hierarchical prefix + if with_prefix is not None and with_prefix != '' and field_help is not None: + field_help = with_prefix[2:] + ': ' + field_help + + kwargs["help"] = field_help + if field_const is not None: + kwargs["const"] = field_const + kwargs["nargs"] = "?" + + return kwargs + + for k in dataclass_instance._get_all_attributes(): + field_name = argparse_name(dataclass_instance._get_name(k)) + field_type = dataclass_instance._get_type(k) + if field_name is None: + continue + elif inspect.isclass(field_type) and issubclass(field_type, FairseqDataclass): + # for fields that are of type FairseqDataclass, we can recursively + # add their fields to the namespace (so we add the args from model, task, etc. to the root namespace) + prefix = None + if with_prefix is not None: + # if a prefix is specified, then we don't want to copy the subfields directly to the root namespace + # but we prefix them with the name of the current field. + prefix = field_name + gen_parser_from_dataclass(parser, field_type(), delete_default, prefix) + continue + + kwargs = get_kwargs_from_dc(dataclass_instance, k) + + field_args = [field_name] + alias = dataclass_instance._get_argparse_alias(k) + if alias is not None: + field_args.append(alias) + + if "default" in kwargs: + if isinstance(kwargs["default"], str) and kwargs["default"].startswith( + "${" + ): + if kwargs["help"] is None: + # this is a field with a name that will be added elsewhere + continue + else: + del kwargs["default"] + if delete_default and "default" in kwargs: + del kwargs["default"] + try: + parser.add_argument(*field_args, **kwargs) + except ArgumentError: + pass + + +def _set_legacy_defaults(args, cls): + """Helper to set default arguments based on *add_args*.""" + if not hasattr(cls, "add_args"): + return + + import argparse + + parser = argparse.ArgumentParser( + argument_default=argparse.SUPPRESS, allow_abbrev=False + ) + cls.add_args(parser) + # copied from argparse.py: + defaults = argparse.Namespace() + for action in parser._actions: + if action.dest is not argparse.SUPPRESS: + if not hasattr(defaults, action.dest): + if action.default is not argparse.SUPPRESS: + setattr(defaults, action.dest, action.default) + for key, default_value in vars(defaults).items(): + if not hasattr(args, key): + setattr(args, key, default_value) + + +def _override_attr( + sub_node: str, data_class: Type[FairseqDataclass], args: Namespace +) -> List[str]: + overrides = [] + + if not inspect.isclass(data_class) or not issubclass(data_class, FairseqDataclass): + return overrides + + def get_default(f): + if not isinstance(f.default_factory, _MISSING_TYPE): + return f.default_factory() + return f.default + + for k, v in data_class.__dataclass_fields__.items(): + if k.startswith("_"): + # private member, skip + continue + + val = get_default(v) if not hasattr(args, k) else getattr(args, k) + + field_type = interpret_dc_type(v.type) + if ( + isinstance(val, str) + and not val.startswith("${") # not interpolation + and field_type != str + and ( + not inspect.isclass(field_type) or not issubclass(field_type, Enum) + ) # not choices enum + ): + # upgrade old models that stored complex parameters as string + val = ast.literal_eval(val) + + if isinstance(val, tuple): + val = list(val) + + v_type = getattr(v.type, "__origin__", None) + if ( + (v_type is List or v_type is list or v_type is Optional) + # skip interpolation + and not (isinstance(val, str) and val.startswith("${")) + ): + # if type is int but val is float, then we will crash later - try to convert here + if hasattr(v.type, "__args__"): + t_args = v.type.__args__ + if len(t_args) == 1 and (t_args[0] is float or t_args[0] is int): + val = list(map(t_args[0], val)) + elif val is not None and ( + field_type is int or field_type is bool or field_type is float + ): + try: + val = field_type(val) + except: + pass # ignore errors here, they are often from interpolation args + + if val is None: + overrides.append("{}.{}=null".format(sub_node, k)) + elif val == "": + overrides.append("{}.{}=''".format(sub_node, k)) + elif isinstance(val, str): + val = val.replace("'", r"\'") + overrides.append("{}.{}='{}'".format(sub_node, k, val)) + elif isinstance(val, FairseqDataclass): + overrides += _override_attr(f"{sub_node}.{k}", type(val), args) + elif isinstance(val, Namespace): + sub_overrides, _ = override_module_args(val) + for so in sub_overrides: + overrides.append(f"{sub_node}.{k}.{so}") + else: + overrides.append("{}.{}={}".format(sub_node, k, val)) + + return overrides + + +def migrate_registry( + name, value, registry, args, overrides, deletes, use_name_as_val=False +): + if value in registry: + overrides.append("{}={}".format(name, value)) + overrides.append("{}._name={}".format(name, value)) + overrides.extend(_override_attr(name, registry[value], args)) + elif use_name_as_val and value is not None: + overrides.append("{}={}".format(name, value)) + else: + deletes.append(name) + + +def override_module_args(args: Namespace) -> Tuple[List[str], List[str]]: + """use the field in args to overrides those in cfg""" + overrides = [] + deletes = [] + + for k in FairseqConfig.__dataclass_fields__.keys(): + overrides.extend( + _override_attr(k, FairseqConfig.__dataclass_fields__[k].type, args) + ) + + if args is not None: + if hasattr(args, "task"): + from fairseq.tasks import TASK_DATACLASS_REGISTRY + + migrate_registry( + "task", args.task, TASK_DATACLASS_REGISTRY, args, overrides, deletes + ) + else: + deletes.append("task") + + # these options will be set to "None" if they have not yet been migrated + # so we can populate them with the entire flat args + CORE_REGISTRIES = {"criterion", "optimizer", "lr_scheduler"} + + from fairseq.registry import REGISTRIES + + for k, v in REGISTRIES.items(): + if hasattr(args, k): + migrate_registry( + k, + getattr(args, k), + v["dataclass_registry"], + args, + overrides, + deletes, + use_name_as_val=k not in CORE_REGISTRIES, + ) + else: + deletes.append(k) + + no_dc = True + if hasattr(args, "arch"): + from fairseq.models import ARCH_MODEL_REGISTRY, ARCH_MODEL_NAME_REGISTRY + + if args.arch in ARCH_MODEL_REGISTRY: + m_cls = ARCH_MODEL_REGISTRY[args.arch] + dc = getattr(m_cls, "__dataclass", None) + if dc is not None: + m_name = ARCH_MODEL_NAME_REGISTRY[args.arch] + overrides.append("model={}".format(m_name)) + overrides.append("model._name={}".format(args.arch)) + # override model params with those exist in args + overrides.extend(_override_attr("model", dc, args)) + no_dc = False + if no_dc: + deletes.append("model") + + return overrides, deletes + + +class omegaconf_no_object_check: + def __init__(self): + self.old_is_primitive = _utils.is_primitive_type + + def __enter__(self): + _utils.is_primitive_type = lambda _: True + + def __exit__(self, type, value, traceback): + _utils.is_primitive_type = self.old_is_primitive + + +def convert_namespace_to_omegaconf(args: Namespace) -> DictConfig: + """Convert a flat argparse.Namespace to a structured DictConfig.""" + + # Here we are using field values provided in args to override counterparts inside config object + overrides, deletes = override_module_args(args) + + # configs will be in fairseq/config after installation + config_path = os.path.join("..", "config") + + GlobalHydra.instance().clear() + + with initialize(config_path=config_path): + try: + composed_cfg = compose("config", overrides=overrides, strict=False) + except: + logger.error("Error when composing. Overrides: " + str(overrides)) + raise + + for k in deletes: + composed_cfg[k] = None + + cfg = OmegaConf.create( + OmegaConf.to_container(composed_cfg, resolve=True, enum_to_str=True) + ) + + # hack to be able to set Namespace in dict config. this should be removed when we update to newer + # omegaconf version that supports object flags, or when we migrate all existing models + from omegaconf import _utils + + with omegaconf_no_object_check(): + if cfg.task is None and getattr(args, "task", None): + cfg.task = Namespace(**vars(args)) + from fairseq.tasks import TASK_REGISTRY + + _set_legacy_defaults(cfg.task, TASK_REGISTRY[args.task]) + cfg.task._name = args.task + if cfg.model is None and getattr(args, "arch", None): + cfg.model = Namespace(**vars(args)) + from fairseq.models import ARCH_MODEL_REGISTRY + + _set_legacy_defaults(cfg.model, ARCH_MODEL_REGISTRY[args.arch]) + cfg.model._name = args.arch + if cfg.optimizer is None and getattr(args, "optimizer", None): + cfg.optimizer = Namespace(**vars(args)) + from fairseq.optim import OPTIMIZER_REGISTRY + + _set_legacy_defaults(cfg.optimizer, OPTIMIZER_REGISTRY[args.optimizer]) + cfg.optimizer._name = args.optimizer + if cfg.lr_scheduler is None and getattr(args, "lr_scheduler", None): + cfg.lr_scheduler = Namespace(**vars(args)) + from fairseq.optim.lr_scheduler import LR_SCHEDULER_REGISTRY + + _set_legacy_defaults( + cfg.lr_scheduler, LR_SCHEDULER_REGISTRY[args.lr_scheduler] + ) + cfg.lr_scheduler._name = args.lr_scheduler + if cfg.criterion is None and getattr(args, "criterion", None): + cfg.criterion = Namespace(**vars(args)) + from fairseq.criterions import CRITERION_REGISTRY + + _set_legacy_defaults(cfg.criterion, CRITERION_REGISTRY[args.criterion]) + cfg.criterion._name = args.criterion + + OmegaConf.set_struct(cfg, True) + return cfg + + +def overwrite_args_by_name(cfg: DictConfig, overrides: Dict[str, any]): + # this will be deprecated when we get rid of argparse and model_overrides logic + + from fairseq.registry import REGISTRIES + + with open_dict(cfg): + for k in cfg.keys(): + # "k in cfg" will return false if its a "mandatory value (e.g. ???)" + if k in cfg and isinstance(cfg[k], DictConfig): + if k in overrides and isinstance(overrides[k], dict): + for ok, ov in overrides[k].items(): + if isinstance(ov, dict) and cfg[k][ok] is not None: + overwrite_args_by_name(cfg[k][ok], ov) + else: + cfg[k][ok] = ov + else: + overwrite_args_by_name(cfg[k], overrides) + elif k in cfg and isinstance(cfg[k], Namespace): + for override_key, val in overrides.items(): + setattr(cfg[k], override_key, val) + elif k in overrides: + if ( + k in REGISTRIES + and overrides[k] in REGISTRIES[k]["dataclass_registry"] + ): + cfg[k] = DictConfig( + REGISTRIES[k]["dataclass_registry"][overrides[k]] + ) + overwrite_args_by_name(cfg[k], overrides) + cfg[k]._name = overrides[k] + else: + cfg[k] = overrides[k] + + +def merge_with_parent(dc: FairseqDataclass, cfg: DictConfig, remove_missing=True): + if remove_missing: + + if is_dataclass(dc): + target_keys = set(dc.__dataclass_fields__.keys()) + else: + target_keys = set(dc.keys()) + + with open_dict(cfg): + for k in list(cfg.keys()): + if k not in target_keys: + del cfg[k] + + merged_cfg = OmegaConf.merge(dc, cfg) + merged_cfg.__dict__["_parent"] = cfg.__dict__["_parent"] + OmegaConf.set_struct(merged_cfg, True) + return merged_cfg diff --git a/fairseq/distributed/__init__.py b/fairseq/distributed/__init__.py new file mode 100644 index 0000000000..d0b96b734c --- /dev/null +++ b/fairseq/distributed/__init__.py @@ -0,0 +1,21 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from .distributed_timeout_wrapper import DistributedTimeoutWrapper +from .fully_sharded_data_parallel import fsdp_enable_wrap, fsdp_wrap, FullyShardedDataParallel +from .legacy_distributed_data_parallel import LegacyDistributedDataParallel +from .module_proxy_wrapper import ModuleProxyWrapper +from .tpu_distributed_data_parallel import TPUDistributedDataParallel + + +__all__ = [ + "DistributedTimeoutWrapper", + "fsdp_enable_wrap", + "fsdp_wrap", + "FullyShardedDataParallel", + "LegacyDistributedDataParallel", + "ModuleProxyWrapper", + "TPUDistributedDataParallel", +] diff --git a/fairseq/distributed/distributed_timeout_wrapper.py b/fairseq/distributed/distributed_timeout_wrapper.py new file mode 100644 index 0000000000..18107ef27e --- /dev/null +++ b/fairseq/distributed/distributed_timeout_wrapper.py @@ -0,0 +1,94 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +import os +import signal +import threading + +from torch import nn + + +logger = logging.getLogger(__name__) + + +class DistributedTimeoutWrapper(nn.Module): + """ + A wrapper that kills the process if no progress is made within a given + *timeout*. The timer is reset every time :func:`forward` is called. + + Usage:: + + module = DistributedTimeoutWrapper(module, timeout=30) + x = module(input) + time.sleep(20) # safe + x = module(input) + time.sleep(45) # job will be killed before this returns + + Args: + module (nn.Module): module to wrap + timeout (int): number of seconds before killing the process + (set to a value <= 0 to disable the timeout) + signal (Optional): signal to send once timeout is triggered + """ + def __init__(self, module: nn.Module, timeout: int, signal=signal.SIGINT): + super().__init__() + self.module = module + self.timeout = timeout + self.signal = signal + + if timeout > 0: + self._heartbeat = threading.Event() + self._heartbeat_thread = threading.Thread( + target=self._check_heartbeat, + args=(os.getpid(),), + daemon=True, + ) + self._heartbeat_thread.start() + self._terminated = False + else: + self._heartbeat = None + self._heartbeat_thread = None + + def __del__(self): + self.stop_timeout() + + def __getattr__(self, name): + """Forward missing attributes to wrapped module.""" + try: + return super().__getattr__(name) # defer to nn.Module's logic + except AttributeError: + return getattr(self.module, name) + + def stop_timeout(self): + if self._heartbeat_thread is not None: + self._terminated = True + self._heartbeat_thread.join() + + def state_dict(self, *args, **kwargs): + return self.module.state_dict(*args, **kwargs) + + def load_state_dict(self, *args, **kwargs): + return self.module.load_state_dict(*args, **kwargs) + + def forward(self, *args, **kwargs): + if self._heartbeat is not None: + self._heartbeat.set() + return self.module(*args, **kwargs) + + def _check_heartbeat(self, parent_pid): + self._heartbeat.wait() # wait for the first forward pass + while True: + self._heartbeat.clear() + success = self._heartbeat.wait(timeout=self.timeout) + if self._terminated: + break + elif not success: + logger.error(( + "Killing job for not making progress in {} seconds. " + "Set --heartbeat-timeout=-1 to disable this timeout." + ).format(int(self.timeout))) + os.kill(parent_pid, self.signal) + return diff --git a/fairseq/distributed/fully_sharded_data_parallel.py b/fairseq/distributed/fully_sharded_data_parallel.py new file mode 100644 index 0000000000..8a96bfc765 --- /dev/null +++ b/fairseq/distributed/fully_sharded_data_parallel.py @@ -0,0 +1,135 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import contextlib +from typing import Optional + +import torch +from fairseq.dataclass.configs import DistributedTrainingConfig +from fairseq.distributed import utils as dist_utils + + +try: + from fairscale.nn.data_parallel import FullyShardedDataParallel as FSDP + + has_FSDP = True +except ImportError: + FSDP = torch.nn.Module + has_FSDP = False + + +class FullyShardedDataParallel(FSDP): + """ + A small wrapper around fairscale's FullyShardedDataParallel (FSDP) with some + fairseq-specific checkpoint saving/loading logic. + + Args: + use_sharded_state (bool): if True, then ``state_dict`` will return + ``FSDP.local_state_dict`` and ``load_state_dict`` will call + ``FSDP.load_local_state_dict``. Otherwise, ``state_dict`` will + return the full model weights on data parallel rank 0 (empty on + other ranks) and ``load_state_dict`` will broadcast model weights + from rank 0 to other ranks. + """ + + def __init__(self, *args, use_sharded_state: bool = False, **kwargs): + if not has_FSDP: + raise ImportError( + "Cannot find FullyShardedDataParallel. " + "Please install fairscale with: pip install fairscale" + ) + super().__init__(*args, **kwargs) + self.use_sharded_state = use_sharded_state + + @property + def unwrapped_module(self) -> torch.nn.Module: + if self.flatten_parameters: + return self.module.module + else: + return self.module + + def state_dict(self, destination=None, prefix="", keep_vars=False): + if self.use_sharded_state: + return super().local_state_dict( + destination=destination, prefix=prefix, keep_vars=keep_vars + ) + else: + if self.rank == 0: + return super().state_dict( + destination=destination, prefix=prefix, keep_vars=keep_vars + ) + else: + # We must call state_dict() due to use of communication + # primitives. But we don't use the result. + super().state_dict() + return destination or {} + + def load_state_dict(self, state_dict, strict=True, model_cfg=None): + if self.use_sharded_state: + return super().load_local_state_dict(state_dict, strict=strict) + else: + state_dict = dist_utils.broadcast_object( + state_dict, src_rank=0, group=self.process_group + ) + return super().load_state_dict(state_dict, strict=strict) + + +@contextlib.contextmanager +def fsdp_enable_wrap(cfg: DistributedTrainingConfig): + try: + from fairscale.nn import enable_wrap + except ImportError: + raise ImportError( + "Cannot find FullyShardedDataParallel. " + "Please install fairscale with: pip install fairscale" + ) + if cfg.memory_efficient_fp16: + assert cfg.fp16 # memory_efficient_fp16 should imply fp16 + group = dist_utils.get_data_parallel_group() + if group is None and cfg.distributed_world_size == 1: + from fairscale.utils.testing import DummyProcessGroup + + group = DummyProcessGroup(rank=0, size=1) + fsdp_config = { + "process_group": group, + "reshard_after_forward": not cfg.no_reshard_after_forward, + "mixed_precision": cfg.fp16 and not cfg.memory_efficient_fp16, + "fp32_reduce_scatter": cfg.fp32_reduce_scatter, + "flatten_parameters": True, + "cpu_offload": cfg.cpu_offload, + "compute_dtype": torch.float16 if cfg.fp16 else torch.float32, + "bucket_cap_mb": cfg.bucket_cap_mb, + "state_dict_device": torch.device("cpu"), # reduce GPU mem usage + } + with enable_wrap( + wrapper_cls=FullyShardedDataParallel, + use_sharded_state=cfg.use_sharded_state, + **fsdp_config, + ): + yield + + +def fsdp_wrap(module, min_num_params: Optional[int] = None, **kwargs): + """ + Helper to wrap layers/modules in FSDP. This falls back to a no-op if + fairscale is not available. + + Args: + module (nn.Module): module to (maybe) wrap + min_num_params (int, Optional): minimum number of layer params to wrap + """ + try: + from fairscale.nn import wrap + + if min_num_params is not None: + num_params = sum(p.numel() for p in module.parameters()) + if num_params >= min_num_params: + return wrap(module, **kwargs) + else: + return module + else: + return wrap(module, **kwargs) + except ImportError: + return module diff --git a/fairseq/distributed/legacy_distributed_data_parallel.py b/fairseq/distributed/legacy_distributed_data_parallel.py new file mode 100644 index 0000000000..f2308f87c5 --- /dev/null +++ b/fairseq/distributed/legacy_distributed_data_parallel.py @@ -0,0 +1,165 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +""" +A modified version of the legacy DistributedDataParallel module that uses c10d +communication primitives. This version is simpler than the latest PyTorch +version and is useful for debugging. Notably it does not overlap gradient +communication with the backward pass, which makes it slower but more robust +than the PyTorch version. + +This version also supports the *no_sync* context manager, which allows faster +training with `--update-freq`. +""" + +from collections import OrderedDict +from contextlib import contextmanager + +import torch +from torch import nn + +from fairseq.distributed import utils + + +class LegacyDistributedDataParallel(nn.Module): + """Implements distributed data parallelism at the module level. + + A simplified version of :class:`torch.nn.parallel.DistributedDataParallel`. + This version uses a c10d process group for communication and does not + broadcast buffers. + + Args: + module (~torch.nn.Module): module to be parallelized + process_group: the c10d process group to be used for distributed data + parallel all-reduction. + buffer_size (int, optional): number of elements to buffer before + performing all-reduce (default: 256M). + """ + + def __init__(self, module, process_group, buffer_size=2 ** 28): + super().__init__() + + self.module = module + self.process_group = process_group + self.world_size = utils.get_world_size(self.process_group) + + # Never use a bigger buffer than the number of model params + self.buffer_size = min(buffer_size, sum(p.numel() for p in module.parameters())) + self.buffer = None + + # We can also forcibly accumulate grads locally and only do the + # all-reduce at some later time + self.accumulate_grads = False + + # make per-device lists of parameters + paramlists = OrderedDict() + for param in self.module.parameters(): + device = param.device + if paramlists.get(device) is None: + paramlists[device] = [] + paramlists[device] += [param] + self.per_device_params = list(paramlists.values()) + + @contextmanager + def no_sync(self): + """A context manager to disable gradient synchronization.""" + old_accumulate_grads = self.accumulate_grads + self.accumulate_grads = True + yield + self.accumulate_grads = old_accumulate_grads + + def forward(self, *inputs, **kwargs): + return self.module(*inputs, **kwargs) + + def all_reduce_grads(self): + """ + This function must be called explicitly after backward to reduce + gradients. There is no automatic hook like c10d. + """ + + def all_reduce_params(params): + buffer = self.buffer + nonzero_buffer = False + if len(params) > 1: + offset = 0 + for p in params: + sz = p.numel() + if p.grad is not None: + buffer[offset : offset + sz].copy_(p.grad.data.view(-1)) + nonzero_buffer = True + else: + buffer[offset : offset + sz].zero_() + offset += sz + else: + # we only have a single grad to all-reduce + p = params[0] + if p.grad is not None: + buffer = p.grad.data + nonzero_buffer = True + elif p.numel() <= self.buffer.numel(): + buffer = buffer[: p.numel()] + buffer.zero_() + else: + buffer = torch.zeros_like(p) + + if nonzero_buffer: + buffer.div_(self.world_size) + + utils.all_reduce(buffer, self.process_group) + + # copy all-reduced grads back into their original place + offset = 0 + for p in params: + sz = p.numel() + if p.grad is not None: + p.grad.data.copy_(buffer[offset : offset + sz].view_as(p)) + else: + p.grad = buffer[offset : offset + sz].view_as(p).clone() + offset += sz + + def reduction_fn(): + # This function only needs to be called once + if self.accumulate_grads: + return + + if self.buffer is None: + self.buffer = next(self.module.parameters()).new(self.buffer_size) + + for params in self.per_device_params: + # All-reduce the gradients in buckets + offset = 0 + buffered_params = [] + for param in params: + if not param.requires_grad: + continue + if param.grad is None: + param.grad = torch.zeros_like(param) + + if hasattr(param, 'expert'): + # Skip gradient sync for unshared parameters + continue + + if param.grad.requires_grad: + raise RuntimeError( + "DistributedDataParallel only works " + "with gradients that don't require " + "grad" + ) + sz = param.numel() + if sz > self.buffer.numel(): + # all-reduce big params directly + all_reduce_params([param]) + else: + if offset + sz > self.buffer.numel(): + all_reduce_params(buffered_params) + offset = 0 + buffered_params.clear() + buffered_params.append(param) + offset += sz + + if len(buffered_params) > 0: + all_reduce_params(buffered_params) + + reduction_fn() diff --git a/fairseq/distributed/module_proxy_wrapper.py b/fairseq/distributed/module_proxy_wrapper.py new file mode 100644 index 0000000000..fc2c6f8c71 --- /dev/null +++ b/fairseq/distributed/module_proxy_wrapper.py @@ -0,0 +1,55 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from torch import nn + + +class ModuleProxyWrapper(nn.Module): + """ + Wrap a DistributedDataParallel module and forward requests for missing + attributes to the module wrapped by DDP (the twice-wrapped module). + Also forward calls to :func:`state_dict` and :func:`load_state_dict`. + + Usage:: + + module.xyz = "hello world" + wrapped_module = DistributedDataParallel(module, **ddp_args) + wrapped_module = ModuleProxyWrapper(wrapped_module) + assert wrapped_module.xyz == "hello world" + assert wrapped_module.state_dict().keys() == module.state_dict().keys() + + Args: + module (nn.Module): module to wrap + """ + + def __init__(self, module: nn.Module): + super().__init__() + assert hasattr(module, "module"), \ + "ModuleProxyWrapper expects input to wrap another module" + self.module = module + + def __getattr__(self, name): + """Forward missing attributes to twice-wrapped module.""" + try: + # defer to nn.Module's logic + return super().__getattr__(name) + except AttributeError: + try: + # forward to the once-wrapped module + return getattr(self.module, name) + except AttributeError: + # forward to the twice-wrapped module + return getattr(self.module.module, name) + + def state_dict(self, *args, **kwargs): + """Forward to the twice-wrapped module.""" + return self.module.module.state_dict(*args, **kwargs) + + def load_state_dict(self, *args, **kwargs): + """Forward to the twice-wrapped module.""" + return self.module.module.load_state_dict(*args, **kwargs) + + def forward(self, *args, **kwargs): + return self.module(*args, **kwargs) diff --git a/fairseq/distributed/tpu_distributed_data_parallel.py b/fairseq/distributed/tpu_distributed_data_parallel.py new file mode 100644 index 0000000000..e971cf07c5 --- /dev/null +++ b/fairseq/distributed/tpu_distributed_data_parallel.py @@ -0,0 +1,43 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +from torch import nn + +from fairseq.distributed import utils + + +class TPUDistributedDataParallel(nn.Module): + + def __init__(self, module, process_group): + super().__init__() + self.module = module + self.process_group = process_group + self.world_size = utils.get_world_size(self.process_group) + + def forward(self, *inputs, **kwargs): + return self.module(*inputs, **kwargs) + + def all_reduce_grads(self): + gradients = [] + for p in self.parameters(): + if not p.requires_grad: + continue + if p.grad is None: + p.grad = torch.zeros_like(p) + if p.grad.requires_grad: + raise RuntimeError( + "TPUDistributedDataParallel only works with gradients that don't " + "require grad" + ) + gradients.append(p.grad) + + import torch_xla.core.xla_model as xm + xm.all_reduce( + 'sum', + gradients, + scale=1. / self.world_size, + groups=self.process_group[1], + ) diff --git a/fairseq/distributed/utils.py b/fairseq/distributed/utils.py new file mode 100644 index 0000000000..b7736116f9 --- /dev/null +++ b/fairseq/distributed/utils.py @@ -0,0 +1,805 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import io +import logging +import os +import pickle +import random +import socket +import struct +import subprocess +import warnings +from argparse import Namespace +from collections import OrderedDict +from dataclasses import dataclass +from typing import Any, Dict, List, Mapping, Optional + +import torch +import torch.distributed as dist +from fairseq.dataclass.configs import DistributedTrainingConfig, FairseqConfig +from omegaconf import open_dict + +try: + import torch_xla.core.xla_model as xm +except ImportError: + xm = None + + +# Flag to indicate if we're using Megatron +# NOTE: this is a temporary hack until we move away from Megatron's model parallel init +_USE_MEGATRON = False + +# Whether to use XLA ops (e.g., on TPUs) instead of CUDA ops. +_USE_XLA = False + + +logger = logging.getLogger(__name__) + + +def is_master(cfg: DistributedTrainingConfig): + return cfg.distributed_rank == 0 + + +def infer_init_method(cfg: DistributedTrainingConfig, force_distributed=False): + if cfg.distributed_init_method is not None or cfg.tpu: + return + + num_pipelines_per_node = None + if cfg.pipeline_model_parallel: + num_pipeline_devices, num_pipelines_per_node = _pipeline_parallel_pre_init(cfg) + + if all( + key in os.environ + for key in ["MASTER_ADDR", "MASTER_PORT", "WORLD_SIZE", "RANK"] + ): + # support torch.distributed.launch + _infer_torch_distributed_launch_init(cfg) + elif cfg.distributed_port > 0: + # we can determine the init method automatically for Slurm + _infer_slurm_init(cfg, num_pipelines_per_node) + elif cfg.distributed_world_size > 1 or force_distributed: + # fallback for single node with multiple GPUs + _infer_single_node_init(cfg) + + if cfg.pipeline_model_parallel: + _pipeline_parallel_post_init(cfg, num_pipeline_devices, num_pipelines_per_node) + elif not cfg.distributed_no_spawn: + with open_dict(cfg): + cfg.distributed_num_procs = min( + torch.cuda.device_count(), cfg.distributed_world_size + ) + + +def _infer_torch_distributed_launch_init(cfg: DistributedTrainingConfig): + cfg.distributed_init_method = "env://" + cfg.distributed_world_size = int(os.environ["WORLD_SIZE"]) + cfg.distributed_rank = int(os.environ["RANK"]) + # processes are created by torch.distributed.launch + cfg.distributed_no_spawn = True + + +def _infer_slurm_init(cfg: DistributedTrainingConfig, num_pipelines_per_node): + node_list = os.environ.get("SLURM_STEP_NODELIST") + if node_list is None: + node_list = os.environ.get("SLURM_JOB_NODELIST") + if node_list is not None: + try: + hostnames = subprocess.check_output( + ["scontrol", "show", "hostnames", node_list] + ) + cfg.distributed_init_method = "tcp://{host}:{port}".format( + host=hostnames.split()[0].decode("utf-8"), + port=cfg.distributed_port, + ) + nnodes = int(os.environ.get("SLURM_NNODES")) + ntasks_per_node = os.environ.get("SLURM_NTASKS_PER_NODE") + if ntasks_per_node is not None: + ntasks_per_node = int(ntasks_per_node) + else: + ntasks = int(os.environ.get("SLURM_NTASKS")) + nnodes = int(os.environ.get("SLURM_NNODES")) + assert ntasks % nnodes == 0 + ntasks_per_node = int(ntasks / nnodes) + if ntasks_per_node == 1: + gpus_per_node = torch.cuda.device_count() + node_id = int(os.environ.get("SLURM_NODEID")) + cfg.distributed_rank = node_id * gpus_per_node + cfg.distributed_world_size = nnodes * gpus_per_node + elif cfg.pipeline_model_parallel: + assert ntasks_per_node == num_pipelines_per_node, ( + "SLURM --ntasks-per-node must match number of pipelines per " + "node (={})".format(num_pipelines_per_node) + ) + cfg.distributed_no_spawn = True + # For 4-way MP on nodes with 8 GPUs, ranks will be [0, 1] on + # the first node, [1, 2] on the second node, etc. This + # matches torch.distributed.launch. + node_id = int(os.environ.get("SLURM_NODEID")) + local_id = int(os.environ.get("SLURM_LOCALID")) + cfg.distributed_rank = node_id * num_pipelines_per_node + local_id + # In the above example, device_id will always be in [0, 1], + # which also matches torch.distributed.launch. + cfg.device_id = local_id + # We also want to set distributed_world_size to be the total + # number of pipelines across all nodes. + cfg.distributed_world_size = nnodes * num_pipelines_per_node + else: + assert ntasks_per_node == cfg.distributed_world_size // nnodes + cfg.distributed_no_spawn = True + cfg.distributed_rank = int(os.environ.get("SLURM_PROCID")) + cfg.device_id = int(os.environ.get("SLURM_LOCALID")) + except subprocess.CalledProcessError as e: # scontrol failed + raise e + except FileNotFoundError: # Slurm is not installed + pass + + +def _infer_single_node_init(cfg: DistributedTrainingConfig): + assert ( + cfg.distributed_world_size <= torch.cuda.device_count() + ), f"world size is {cfg.distributed_world_size} but have {torch.cuda.device_count()} available devices" + port = random.randint(10000, 20000) + cfg.distributed_init_method = "tcp://localhost:{port}".format(port=port) + + +def _pipeline_parallel_pre_init(cfg: DistributedTrainingConfig): + from fairseq import utils + + balance_exists = ( + cfg.pipeline_balance is not None + or cfg.pipeline_encoder_balance is not None + or cfg.pipeline_decoder_balance is not None + ) + devices_exist = ( + cfg.pipeline_devices is not None + or cfg.pipeline_encoder_devices is not None + or cfg.pipeline_decoder_devices is not None + ) + if not balance_exists: + raise ValueError( + "--pipeline-balance is currently required for pipeline model parallelism" + ) + if not devices_exist: + raise ValueError( + "--pipeline-devices is currently required for pipeline model parallelism" + ) + + cfg.pipeline_balance = utils.eval_str_list(cfg.pipeline_balance, type=int) + if cfg.pipeline_devices is not None: + cfg.pipeline_devices = utils.eval_str_list(cfg.pipeline_devices, type=int) + num_pipeline_devices = len(set(cfg.pipeline_devices)) + else: + cfg.pipeline_encoder_devices = utils.eval_str_list( + cfg.pipeline_encoder_devices, type=int + ) + cfg.pipeline_decoder_devices = utils.eval_str_list( + cfg.pipeline_decoder_devices, type=int + ) + num_pipeline_devices = len( + set(cfg.pipeline_encoder_devices + cfg.pipeline_decoder_devices) + ) + gpus_per_node = torch.cuda.device_count() + assert ( + gpus_per_node >= num_pipeline_devices + and gpus_per_node % num_pipeline_devices == 0 + ), ( + "the number of unique device IDs in --pipeline-devices must evenly divide " + "the number of GPUs per node (multi-node pipelining is not yet supported)" + ) + num_pipelines_per_node = gpus_per_node // num_pipeline_devices + return num_pipeline_devices, num_pipelines_per_node + + +def _pipeline_parallel_post_init( + cfg: DistributedTrainingConfig, num_pipeline_devices, num_pipelines_per_node +): + if not cfg.distributed_no_spawn: + # When distributed_no_spawn is False, we expect distributed_rank and + # distributed_world_size to be based on the total number of GPUs, so + # we need to correct them to be based on the number of pipelines. + assert cfg.distributed_world_size % num_pipeline_devices == 0 + cfg.distributed_world_size = ( + cfg.distributed_world_size // num_pipeline_devices + ) + # In the case of 4-way MP on nodes with 8 GPUs, we want + # distributed_rank to be the starting GPU index for each pipeline + # i.e., 0, 2, ... + gpus_per_node = torch.cuda.device_count() + assert cfg.distributed_rank % gpus_per_node == 0 + assert cfg.distributed_rank % num_pipeline_devices == 0 + + with open_dict(cfg): + cfg.distributed_rank = cfg.distributed_rank // num_pipeline_devices + # launch one process per pipeline + cfg.distributed_num_procs = num_pipelines_per_node + + # if we have 4-way MP on a node with 8 GPUs, we want device_ids to be 0 + # and 4, indicating the starting device IDs for each pipeline + cfg.device_id *= num_pipeline_devices + + if cfg.device_id > 0: + # if there's multiple pipelines on a node (e.g., 4-way MP on an 8 + # GPU node), we need to adjust pipeline_devices accordingly + logger.debug( + "setting CUDA device={} on rank {}".format( + cfg.device_id, cfg.distributed_rank + ) + ) + torch.cuda.set_device(cfg.device_id) + with open_dict(cfg): + cfg.pipeline_devices = [cfg.device_id + d for d in cfg.pipeline_devices] + logger.info( + "setting pipeline_devices={} on rank {}".format( + cfg.pipeline_devices, cfg.distributed_rank + ) + ) + + +def distributed_init(cfg: FairseqConfig): + if isinstance(cfg, Namespace): + from fairseq.dataclass.utils import convert_namespace_to_omegaconf + + cfg = convert_namespace_to_omegaconf(cfg) + + if not cfg.common.tpu: + if torch.distributed.is_available() and torch.distributed.is_initialized(): + warnings.warn( + "Distributed is already initialized, cannot initialize twice!" + ) + else: + logger.info( + "distributed init (rank {}): {}".format( + cfg.distributed_training.distributed_rank, + cfg.distributed_training.distributed_init_method, + ) + ) + dist.init_process_group( + backend=cfg.distributed_training.distributed_backend, + init_method=cfg.distributed_training.distributed_init_method, + world_size=cfg.distributed_training.distributed_world_size, + rank=cfg.distributed_training.distributed_rank, + ) + logger.info( + "initialized host {} as rank {}".format( + socket.gethostname(), + cfg.distributed_training.distributed_rank, + ) + ) + + # perform a dummy all-reduce to initialize the NCCL communicator + if torch.cuda.is_available(): + dist.all_reduce(torch.zeros(1).cuda()) + + cfg.distributed_training.distributed_rank = torch.distributed.get_rank() + else: + assert xm.xrt_world_size() == cfg.distributed_training.distributed_world_size + global _USE_XLA + _USE_XLA = True + cfg.distributed_training.device_id = xm.get_local_ordinal() + cfg.distributed_training.distributed_rank = xm.get_ordinal() + xm.rendezvous("distributed_init") # wait for all workers + + if is_master(cfg.distributed_training): + logging.getLogger().setLevel(logging.INFO) + else: + logging.getLogger().setLevel(logging.WARNING) + + if cfg.common.model_parallel_size > 1: + try: + from fairseq.model_parallel.megatron.mpu import ( + initialize_model_parallel, + model_parallel_cuda_manual_seed, + ) + except ImportError: + raise ImportError( + "\n\nPlease install the megatron submodule:" + "\n\n git submodule update --init " + "fairseq/model_parallel/megatron" + ) + global _USE_MEGATRON + _USE_MEGATRON = True + initialize_model_parallel(cfg.common.model_parallel_size) + model_parallel_cuda_manual_seed(cfg.common.seed) + model_part_number = get_model_parallel_rank() + cfg.checkpoint.checkpoint_suffix += "-model_part-{0}".format(model_part_number) + + if hasattr(cfg, "model") and getattr(cfg.model, "base_layers", 0) > 0: + cfg.checkpoint.checkpoint_suffix = f"-rank-{cfg.distributed_training.distributed_rank}" + + return cfg.distributed_training.distributed_rank + + +def distributed_main(i, main, cfg: FairseqConfig, kwargs): + cfg.distributed_training.device_id = i + if torch.cuda.is_available() and not cfg.common.cpu and not cfg.common.tpu: + torch.cuda.set_device(cfg.distributed_training.device_id) + if cfg.distributed_training.distributed_rank is None: # torch.multiprocessing.spawn + cfg.distributed_training.distributed_rank = kwargs.pop("start_rank", 0) + i + + cfg.distributed_training.distributed_rank = distributed_init(cfg) + + after_distributed_init_fn = kwargs.pop("after_distributed_init_fn", None) + if after_distributed_init_fn: + cfg = after_distributed_init_fn(cfg) + + main(cfg, **kwargs) + + if torch.distributed.is_initialized(): + torch.distributed.barrier(get_global_group()) + + +def call_main(cfg: FairseqConfig, main, **kwargs): + if cfg.distributed_training.distributed_init_method is None: + infer_init_method(cfg.distributed_training) + + if cfg.distributed_training.distributed_init_method is not None: + # distributed training + if not cfg.distributed_training.distributed_no_spawn: + start_rank = cfg.distributed_training.distributed_rank + cfg.distributed_training.distributed_rank = None # assign automatically + kwargs["start_rank"] = start_rank + torch.multiprocessing.spawn( + fn=distributed_main, + args=(main, cfg, kwargs), + nprocs=min( + torch.cuda.device_count(), + cfg.distributed_training.distributed_world_size, + ), + join=True, + ) + else: + distributed_main(cfg.distributed_training.device_id, main, cfg, kwargs) + elif cfg.common.tpu and cfg.distributed_training.distributed_world_size > 1: + import torch_xla.distributed.xla_multiprocessing as xmp + + torch.multiprocessing.set_sharing_strategy("file_system") + xmp.spawn( + fn=distributed_main, + args=(main, cfg, kwargs), + # tpu-comment: + # 8 devices in one TPU VM, is the max processes to be spawned. + # The rest is driven by xm.distributed.xla_dist + nprocs=min(cfg.distributed_training.distributed_world_size, 8), + ) + else: + # single GPU main + main(cfg, **kwargs) + + +def use_xla(): + global _USE_XLA + return _USE_XLA + + +def new_groups(grouped_ranks: List[List[int]]): + if use_xla(): + return ("tpu", grouped_ranks) + else: + groups = [dist.new_group(g) for g in grouped_ranks] + my_group_idx = _find_my_group_index(grouped_ranks) + return groups[my_group_idx] + + +def _find_my_group_index(grouped_ranks): + my_rank = get_global_rank() + for i, group in enumerate(grouped_ranks): + if my_rank in group: + return i + raise RuntimeError + + +def _find_my_group(grouped_ranks): + index = _find_my_group_index(grouped_ranks) + return grouped_ranks[index] + + +def get_rank(group): + if use_xla(): + assert group[0] == "tpu" + my_group = _find_my_group(group[1]) + return my_group.index(get_global_rank()) + else: + return dist.get_rank(group=group) + + +def get_world_size(group): + if use_xla(): + assert group[0] == "tpu" + my_group = _find_my_group(group[1]) + return len(my_group) + elif torch.distributed.is_initialized(): + return dist.get_world_size(group=group) + else: + return 1 + + +def get_global_group(): + if use_xla(): + return new_groups([list(range(get_global_world_size()))]) + elif torch.distributed.is_initialized(): + if not hasattr(get_global_group, "_global_group"): + # ideally we could use torch.distributed.group.WORLD, but it seems + # to cause random NCCL hangs in some cases + get_global_group._global_group = dist.new_group() + return get_global_group._global_group + else: + return None + + +def get_global_rank(): + if use_xla(): + return xm.get_ordinal() + elif torch.distributed.is_initialized(): + return torch.distributed.get_rank() + else: + return 0 + + +def get_global_world_size(): + if use_xla(): + return xm.xrt_world_size() + elif torch.distributed.is_initialized(): + return torch.distributed.get_world_size() + else: + return 1 + + +def get_data_parallel_group(): + """Get the data parallel group the caller rank belongs to.""" + global _USE_MEGATRON + if _USE_MEGATRON: + from fairseq.model_parallel.megatron import mpu + + return mpu.get_data_parallel_group() + else: + return get_global_group() + + +def get_data_parallel_rank(): + """Return my rank for the data parallel group.""" + return get_rank(get_data_parallel_group()) + + +def get_data_parallel_world_size(): + """Return world size for the data parallel group.""" + return get_world_size(get_data_parallel_group()) + + +def get_model_parallel_group(): + global _USE_MEGATRON + if _USE_MEGATRON: + from fairseq.model_parallel.megatron import mpu + + return mpu.get_model_parallel_group() + else: + return None + + +def get_model_parallel_rank(): + """Return my rank for the model parallel group.""" + return get_rank(get_model_parallel_group()) + + +def get_model_parallel_world_size(): + """Return world size for the model parallel group.""" + return get_world_size(get_model_parallel_group()) + + +def all_reduce(tensor, group, op="sum"): + if use_xla(): + assert isinstance(group, tuple) and group[0] == "tpu" + tensor = [tensor] # wrap in a list to make xm.all_reduce in-place + return xm.all_reduce(op, tensor, groups=group[1])[0] + else: + if op == "sum": + op = dist.ReduceOp.SUM + elif op == "max": + op = dist.ReduceOp.MAX + else: + raise NotImplementedError + dist.all_reduce(tensor, op=op, group=group) + return tensor + + +def broadcast(tensor, src, group): + if use_xla(): + # XLA doesn't support broadcast, hack it with all_reduce + if get_rank(group) != src: + tensor.zero_() + all_reduce(tensor, group) + else: + dist.broadcast(tensor, src=src, group=group) + + +def all_to_all(tensor, group): + """Perform an all-to-all operation on a 1D Tensor.""" + assert tensor.dim() == 1 + split_count = get_world_size(group=group) + assert tensor.numel() % split_count == 0 + if use_xla(): + assert isinstance(group, tuple) and group[0] == "tpu" + return xm.all_to_all( + tensor, + split_dimension=0, + concat_dimension=0, + split_count=split_count, + groups=group[1], + ) + else: + output = torch.zeros_like(tensor) + dist.all_to_all_single(output, tensor, group=group) + return output + + +def all_gather(tensor, group, return_tensor=False): + """Perform an all-gather operation.""" + if use_xla(): + result = xm.all_gather(tensor, groups=group[1]) + world_size = get_world_size(group=group) + result = result.view(world_size, *tensor.size()) + if return_tensor: + return result + else: + return [result[i] for i in range(world_size)] + else: + world_size = get_world_size(group=group) + rank = get_rank(group=group) + tensor_list = [ + tensor if i == rank else torch.empty_like(tensor) for i in range(world_size) + ] + dist.all_gather(tensor_list, tensor, group=group) + if return_tensor: + return torch.stack(tensor_list, dim=0) + else: + return tensor_list + + +def all_gather_list(data, group=None, max_size=16384): + """Gathers arbitrary data from all nodes into a list. + + Similar to :func:`~torch.distributed.all_gather` but for arbitrary Python + data. Note that *data* must be picklable and any CUDA tensors will be moved + to CPU and returned on CPU as well. + + Args: + data (Any): data from the local worker to be gathered on other workers + group: group of the collective + max_size (int, optional): maximum size of the data to be gathered + across workers + """ + from fairseq import utils + + if group is None: + group = get_global_group() + rank = get_rank(group=group) + world_size = get_world_size(group=group) + + buffer_size = max_size * world_size + if ( + not hasattr(all_gather_list, "_buffer") + or all_gather_list._buffer.numel() < buffer_size + ): + all_gather_list._buffer = torch.cuda.ByteTensor(buffer_size) + all_gather_list._cpu_buffer = torch.ByteTensor(max_size).pin_memory() + buffer = all_gather_list._buffer + buffer.zero_() + cpu_buffer = all_gather_list._cpu_buffer + + data = utils.move_to_cpu(data) + enc = pickle.dumps(data) + enc_size = len(enc) + header_size = 4 # size of header that contains the length of the encoded data + size = header_size + enc_size + if size > max_size: + raise ValueError( + "encoded data size ({}) exceeds max_size ({})".format(size, max_size) + ) + + header = struct.pack(">I", enc_size) + cpu_buffer[:size] = torch.ByteTensor(list(header + enc)) + start = rank * max_size + buffer[start : start + size].copy_(cpu_buffer[:size]) + + all_reduce(buffer, group=group) + + buffer = buffer.cpu() + try: + result = [] + for i in range(world_size): + out_buffer = buffer[i * max_size : (i + 1) * max_size] + (enc_size,) = struct.unpack(">I", bytes(out_buffer[:header_size].tolist())) + if enc_size > 0: + result.append( + pickle.loads( + bytes(out_buffer[header_size : header_size + enc_size].tolist()) + ) + ) + return result + except pickle.UnpicklingError: + raise Exception( + "Unable to unpickle data from other workers. all_gather_list requires all " + "workers to enter the function together, so this error usually indicates " + "that the workers have fallen out of sync somehow. Workers can fall out of " + "sync if one of them runs out of memory, or if there are other conditions " + "in your training script that can cause one worker to finish an epoch " + "while other workers are still iterating over their portions of the data. " + "Try rerunning with --ddp-backend=legacy_ddp and see if that helps." + ) + + +def all_reduce_dict(data: Mapping[str, Any], device, group) -> Dict[str, Any]: + """ + AllReduce a dictionary of values across workers. We separately + reduce items that are already on the device and items on CPU for + better performance. + + Args: + data (Mapping[str, Any]): dictionary of data to all-reduce, but + cannot be a nested dictionary + device (torch.device): device for the reduction + group: group of the collective + """ + data_keys = list(data.keys()) + + # We want to separately reduce items that are already on the + # device and items on CPU for performance reasons. + cpu_data = OrderedDict() + device_data = OrderedDict() + for k in data_keys: + t = data[k] + if not torch.is_tensor(t): + cpu_data[k] = torch.tensor(t, dtype=torch.double) + elif t.device.type != device.type: + cpu_data[k] = t.to(dtype=torch.double) + else: + device_data[k] = t.to(dtype=torch.double) + + def _all_reduce_dict(data: OrderedDict): + if len(data) == 0: + return data + buf = torch.cat([t.view(-1) for t in data.values()]).to(device=device) + all_reduce(buf, group=group) + split_buf = torch.split(buf, [t.numel() for t in data.values()]) + reduced_data = [t.view_as(orig) for t, orig in zip(split_buf, data.values())] + return OrderedDict(zip(data.keys(), reduced_data)) + + cpu_data = _all_reduce_dict(cpu_data) + device_data = _all_reduce_dict(device_data) + + def get_from_stack(key): + if key in cpu_data: + return cpu_data[key] + elif key in device_data: + return device_data[key] + raise KeyError + + return OrderedDict([(key, get_from_stack(key)) for key in data_keys]) + + +def broadcast_tensors( + tensors: Optional[List[torch.Tensor]], + src_rank: int, + group: object, + dist_device: Optional[torch.device] = None, +) -> List[torch.Tensor]: + """ + Broadcasts a list of tensors without other (non-src) ranks needing to know + the dtypes/shapes of the tensors. + """ + if dist_device is None: + if torch.distributed.get_backend(group) == "nccl": + dist_device = torch.device("cuda") + else: + dist_device = torch.device("cpu") + + # share metadata first to simplify transfer + is_src_rank = (get_rank(group) == src_rank) + if is_src_rank: + metadata = [ + {"size": t.size(), "dtype": t.dtype, "device": t.device} for t in tensors + ] + metadata = _broadcast_object_slow(metadata, src_rank, group, dist_device) + else: + metadata = _broadcast_object_slow(None, src_rank, group, dist_device) + + out_tensors = [] + for i, meta in enumerate(metadata): + if is_src_rank: + tensor = tensors[i] + broadcast(tensors[i].to(dist_device), src=src_rank, group=group) + else: + tensor = torch.zeros( + [meta["size"].numel()], dtype=meta["dtype"], device=dist_device + ) + broadcast(tensor, src=src_rank, group=group) + tensor = tensor.view(meta["size"]).to(meta["device"]) + out_tensors.append(tensor) + return out_tensors + + +def broadcast_object( + obj: Any, + src_rank: int, + group: object, + dist_device: Optional[torch.device] = None, +) -> Any: + """Broadcast an arbitrary Python object to other workers.""" + if dist_device is None: + if torch.distributed.get_backend(group) == "nccl": + dist_device = torch.device("cuda") + else: + dist_device = torch.device("cpu") + + if get_rank(group) == src_rank: + # split the tensors from the non-tensors so we can broadcast them + # directly, avoiding unnecessary serialization/deserialization + tensors = [] + obj = _split_tensors_from_obj(obj, tensors) + obj = _broadcast_object_slow(obj, src_rank, group, dist_device) + tensors = broadcast_tensors(tensors, src_rank, group, dist_device) + else: + obj = _broadcast_object_slow(None, src_rank, group, dist_device) + tensors = broadcast_tensors(None, src_rank, group, dist_device) + return _put_tensors_in_obj(obj, tensors) + + +def _broadcast_object_slow( + obj: Any, src_rank: int, group: object, dist_device: torch.device, +) -> Any: + if get_rank(group) == src_rank: + # Emit data + buffer = io.BytesIO() + torch.save(obj, buffer) + buffer = torch.ByteTensor(buffer.getbuffer()).to(dist_device) + length = torch.LongTensor([len(buffer)]).to(dist_device) + broadcast(length, src=src_rank, group=group) + broadcast(buffer, src=src_rank, group=group) + else: + # Fetch from the source + length = torch.LongTensor([0]).to(dist_device) + broadcast(length, src=src_rank, group=group) + buffer = torch.ByteTensor(int(length.item())).to(dist_device) + broadcast(buffer, src=src_rank, group=group) + buffer = io.BytesIO(buffer.cpu().numpy()) + obj = torch.load(buffer, map_location="cpu") + return obj + + +@dataclass(frozen=True) +class _TensorPlaceholder: + index: int + + +def _split_tensors_from_obj(obj: Any, tensors: List[torch.Tensor]) -> Any: + if torch.is_tensor(obj): + placeholder = _TensorPlaceholder(index=len(tensors)) + tensors.append(obj) + return placeholder + elif isinstance(obj, dict): + return {k: _split_tensors_from_obj(v, tensors) for k, v in obj.items()} + elif isinstance(obj, list): + return [_split_tensors_from_obj(v, tensors) for v in obj] + elif isinstance(obj, tuple): + return tuple(_split_tensors_from_obj(v, tensors) for v in obj) + elif isinstance(obj, set): + return {_split_tensors_from_obj(v, tensors) for v in obj} + else: + return obj + + +def _put_tensors_in_obj(obj: Any, tensors: List[torch.Tensor]) -> Any: + if isinstance(obj, _TensorPlaceholder): + return tensors[obj.index] + elif isinstance(obj, dict): + return {k: _put_tensors_in_obj(v, tensors) for k, v in obj.items()} + elif isinstance(obj, list): + return [_put_tensors_in_obj(v, tensors) for v in obj] + elif isinstance(obj, tuple): + return tuple(_put_tensors_in_obj(v, tensors) for v in obj) + elif isinstance(obj, set): + return {_put_tensors_in_obj(v, tensors) for v in obj} + else: + return obj diff --git a/fairseq/distributed_utils.py b/fairseq/distributed_utils.py deleted file mode 100644 index 84fcc683dc..0000000000 --- a/fairseq/distributed_utils.py +++ /dev/null @@ -1,312 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import logging -import os -import pickle -import random -import socket -import struct -import subprocess -import warnings -from collections import OrderedDict -from typing import Any, Dict, Mapping - -import torch -import torch.distributed as dist - -from fairseq import utils - - -logger = logging.getLogger(__name__) - - -def is_master(args): - return args.distributed_rank == 0 - - -def infer_init_method(args): - if args.distributed_init_method is not None or getattr(args, 'tpu', False): - return - - # support torch.distributed.launch - if all(key in os.environ for key in [ - 'MASTER_ADDR', 'MASTER_PORT', 'WORLD_SIZE', 'RANK' - ]): - args.distributed_init_method = 'env://' - args.distributed_world_size = int(os.environ['WORLD_SIZE']) - args.distributed_rank = int(os.environ['RANK']) - - # we can determine the init method automatically for Slurm - elif args.distributed_port > 0: - node_list = os.environ.get('SLURM_STEP_NODELIST') - if node_list is None: - node_list = os.environ.get('SLURM_JOB_NODELIST') - if node_list is not None: - try: - hostnames = subprocess.check_output(['scontrol', 'show', 'hostnames', node_list]) - args.distributed_init_method = 'tcp://{host}:{port}'.format( - host=hostnames.split()[0].decode('utf-8'), - port=args.distributed_port, - ) - nnodes = int(os.environ.get('SLURM_NNODES')) - ntasks_per_node = os.environ.get('SLURM_NTASKS_PER_NODE') - if ntasks_per_node is not None: - ntasks_per_node = int(ntasks_per_node) - else: - ntasks = int(os.environ.get('SLURM_NTASKS')) - nnodes = int(os.environ.get('SLURM_NNODES')) - assert ntasks % nnodes == 0 - ntasks_per_node = int(ntasks / nnodes) - if ntasks_per_node == 1: - assert args.distributed_world_size % nnodes == 0 - gpus_per_node = args.distributed_world_size // nnodes - node_id = int(os.environ.get('SLURM_NODEID')) - args.distributed_rank = node_id * gpus_per_node - else: - assert ntasks_per_node == args.distributed_world_size // nnodes - args.distributed_no_spawn = True - args.distributed_rank = int(os.environ.get('SLURM_PROCID')) - args.device_id = int(os.environ.get('SLURM_LOCALID')) - except subprocess.CalledProcessError as e: # scontrol failed - raise e - except FileNotFoundError: # Slurm is not installed - pass - - -def distributed_init(args): - if args.distributed_world_size == 1: - raise ValueError('Cannot initialize distributed with distributed_world_size=1') - - if not getattr(args, 'tpu', False): - if torch.distributed.is_initialized(): - warnings.warn('Distributed is already initialized, cannot initialize twice!') - else: - logger.info('distributed init (rank {}): {}'.format( - args.distributed_rank, args.distributed_init_method, - )) - dist.init_process_group( - backend=args.distributed_backend, - init_method=args.distributed_init_method, - world_size=args.distributed_world_size, - rank=args.distributed_rank, - ) - logger.info('initialized host {} as rank {}'.format( - socket.gethostname(), args.distributed_rank, - )) - - # perform a dummy all-reduce to initialize the NCCL communicator - if torch.cuda.is_available(): - dist.all_reduce(torch.zeros(1).cuda()) - - args.distributed_rank = torch.distributed.get_rank() - else: - import torch_xla.core.xla_model as xm - assert xm.xrt_world_size() == args.distributed_world_size - args.device_id = xm.get_local_ordinal() - args.distributed_rank = xm.get_ordinal() - xm.rendezvous('distributed_init') # wait for all workers - xm.mark_step() - - if is_master(args): - logging.getLogger().setLevel(logging.INFO) - else: - logging.getLogger().setLevel(logging.WARNING) - - if args.model_parallel_size > 1: - try: - from fairseq.model_parallel.megatron.mpu import ( - get_model_parallel_rank, - initialize_model_parallel, - model_parallel_cuda_manual_seed, - ) - except ImportError: - raise ImportError( - '\n\nPlease install the megatron submodule:' - '\n\n git submodule update --init ' - 'fairseq/model_parallel/megatron' - ) - initialize_model_parallel(args.model_parallel_size) - model_parallel_cuda_manual_seed(args.seed) - model_part_number = get_model_parallel_rank() - args.checkpoint_suffix += '-model_part-{0}'.format(model_part_number) - return args.distributed_rank - - -def _distributed_main(i, main, args, kwargs): - args.device_id = i - if torch.cuda.is_available() and not args.cpu: - torch.cuda.set_device(args.device_id) - if args.distributed_rank is None: # torch.multiprocessing.spawn - args.distributed_rank = kwargs.get('start_rank', 0) + i - - args.distributed_rank = distributed_init(args) - main(args, **kwargs) - - -def call_main(args, main, **kwargs): - if args.distributed_init_method is None: - infer_init_method(args) - - if args.distributed_init_method is not None: - # distributed main - if torch.cuda.device_count() > 1 and not args.distributed_no_spawn: - start_rank = args.distributed_rank - args.distributed_rank = None # assign automatically - kwargs['start_rank'] = start_rank - torch.multiprocessing.spawn( - fn=_distributed_main, - args=(main, args, kwargs), - nprocs=torch.cuda.device_count(), - ) - else: - _distributed_main(args.device_id, main, args, kwargs) - elif args.distributed_world_size > 1: - # fallback for single node with multiple GPUs - assert args.distributed_world_size <= torch.cuda.device_count() - port = random.randint(10000, 20000) - args.distributed_init_method = 'tcp://localhost:{port}'.format(port=port) - args.distributed_rank = None # set based on device id - torch.multiprocessing.spawn( - fn=_distributed_main, - args=(main, args, kwargs), - nprocs=args.distributed_world_size, - ) - else: - # single GPU main - main(args, **kwargs) - - -def get_rank(): - return dist.get_rank() - - -def get_world_size(): - return dist.get_world_size() - - -def get_default_group(): - return dist.group.WORLD - - -def all_reduce(tensor, group=None): - if isinstance(group, tuple) and group[0] == 'tpu': - import torch_xla.core.xla_model as xm - return xm.all_reduce('sum', [tensor], groups=group[1]) - else: - if group is None: - group = get_default_group() - return dist.all_reduce(tensor, group=group) - - -def all_gather_list(data, group=None, max_size=16384): - """Gathers arbitrary data from all nodes into a list. - - Similar to :func:`~torch.distributed.all_gather` but for arbitrary Python - data. Note that *data* must be picklable. - - Args: - data (Any): data from the local worker to be gathered on other workers - group (optional): group of the collective - max_size (int, optional): maximum size of the data to be gathered - across workers - """ - rank = get_rank() - world_size = get_world_size() - - buffer_size = max_size * world_size - if not hasattr(all_gather_list, '_buffer') or \ - all_gather_list._buffer.numel() < buffer_size: - all_gather_list._buffer = torch.cuda.ByteTensor(buffer_size) - all_gather_list._cpu_buffer = torch.ByteTensor(max_size).pin_memory() - buffer = all_gather_list._buffer - buffer.zero_() - cpu_buffer = all_gather_list._cpu_buffer - - data = utils.move_to_cpu(data) - enc = pickle.dumps(data) - enc_size = len(enc) - header_size = 4 # size of header that contains the length of the encoded data - size = header_size + enc_size - if size > max_size: - raise ValueError('encoded data size ({}) exceeds max_size ({})'.format(size, max_size)) - - header = struct.pack(">I", enc_size) - cpu_buffer[:size] = torch.ByteTensor(list(header + enc)) - start = rank * max_size - buffer[start:start + size].copy_(cpu_buffer[:size]) - - all_reduce(buffer, group=group) - - buffer = buffer.cpu() - try: - result = [] - for i in range(world_size): - out_buffer = buffer[i * max_size:(i + 1) * max_size] - enc_size, = struct.unpack(">I", bytes(out_buffer[:header_size].tolist())) - if enc_size > 0: - result.append(pickle.loads(bytes(out_buffer[header_size:header_size + enc_size].tolist()))) - return result - except pickle.UnpicklingError: - raise Exception( - 'Unable to unpickle data from other workers. all_gather_list requires all ' - 'workers to enter the function together, so this error usually indicates ' - 'that the workers have fallen out of sync somehow. Workers can fall out of ' - 'sync if one of them runs out of memory, or if there are other conditions ' - 'in your training script that can cause one worker to finish an epoch ' - 'while other workers are still iterating over their portions of the data. ' - 'Try rerunning with --ddp-backend=no_c10d and see if that helps.' - ) - - -def all_reduce_dict( - data: Mapping[str, Any], - device, - group=None, -) -> Dict[str, Any]: - """ - AllReduce a dictionary of values across workers. We separately - reduce items that are already on the device and items on CPU for - better performance. - - Args: - data (Mapping[str, Any]): dictionary of data to all-reduce, but - cannot be a nested dictionary - device (torch.device): device for the reduction - group (optional): group of the collective - """ - data_keys = list(data.keys()) - - # We want to separately reduce items that are already on the - # device and items on CPU for performance reasons. - cpu_data = OrderedDict() - device_data = OrderedDict() - for k in data_keys: - t = data[k] - if not torch.is_tensor(t): - cpu_data[k] = torch.tensor(t, dtype=torch.double) - elif t.device.type != device.type: - cpu_data[k] = t.to(dtype=torch.double) - else: - device_data[k] = t.to(dtype=torch.double) - - def _all_reduce_dict(data: OrderedDict): - if len(data) == 0: - return data - buf = torch.stack(list(data.values())).to(device=device) - all_reduce(buf, group=group) - return {k: buf[i] for i, k in enumerate(data)} - - cpu_data = _all_reduce_dict(cpu_data) - device_data = _all_reduce_dict(device_data) - - def get_from_stack(key): - if key in cpu_data: - return cpu_data[key] - elif key in device_data: - return device_data[key] - raise KeyError - - return OrderedDict([(key, get_from_stack(key)) for key in data_keys]) diff --git a/fairseq/file_chunker_utils.py b/fairseq/file_chunker_utils.py new file mode 100644 index 0000000000..443100c61a --- /dev/null +++ b/fairseq/file_chunker_utils.py @@ -0,0 +1,84 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os +import typing as tp + + +def _safe_readline(fd) -> str: + pos = fd.tell() + while True: + try: + return fd.readline() + except UnicodeDecodeError: + pos -= 1 + fd.seek(pos) # search where this character begins + + +def find_offsets(filename: str, num_chunks: int) -> tp.List[int]: + """ + given a file and a number of chuncks, find the offsets in the file + to be able to chunk around full lines. + """ + with open(filename, "r", encoding="utf-8") as f: + size = os.fstat(f.fileno()).st_size + chunk_size = size // num_chunks + offsets = [0 for _ in range(num_chunks + 1)] + for i in range(1, num_chunks): + f.seek(chunk_size * i) + _safe_readline(f) + offsets[i] = f.tell() + offsets[-1] = size + return offsets + + +class ChunkLineIterator: + """ + Iterator to properly iterate over lines of a file chunck. + """ + + def __init__(self, fd, start_offset: int, end_offset: int): + self._fd = fd + self._start_offset = start_offset + self._end_offset = end_offset + + def __iter__(self) -> tp.Iterable[str]: + self._fd.seek(self._start_offset) + # next(f) breaks f.tell(), hence readline() must be used + line = _safe_readline(self._fd) + while line: + pos = self._fd.tell() + # f.tell() does not always give the byte position in the file + # sometimes it skips to a very large number + # it is unlikely that through a normal read we go from + # end bytes to end + 2**32 bytes (4 GB) and this makes it unlikely + # that the procedure breaks by the undeterministic behavior of + # f.tell() + if ( + self._end_offset > 0 + and pos > self._end_offset + and pos < self._end_offset + 2 ** 32 + ): + break + yield line + line = self._fd.readline() + + +class Chunker: + """ + contextmanager to read a chunck of a file line by line. + """ + + def __init__(self, path: str, start_offset: int, end_offset: int): + self.path = path + self.start_offset = start_offset + self.end_offset = end_offset + + def __enter__(self) -> ChunkLineIterator: + self.fd = open(self.path, "r", encoding="utf-8") + return ChunkLineIterator(self.fd, self.start_offset, self.end_offset) + + def __exit__(self, exc_type, exc_val, exc_tb) -> None: + self.fd.close() diff --git a/fairseq/file_io.py b/fairseq/file_io.py index b15030c3c8..dba663d4aa 100644 --- a/fairseq/file_io.py +++ b/fairseq/file_io.py @@ -5,22 +5,38 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. +import logging import os import shutil from typing import List, Optional +logger = logging.getLogger(__file__) + + try: - from fvcore.common.file_io import PathManager as FVCorePathManager + from iopath.common.file_io import g_pathmgr as IOPathManager + + try: + # [FB only - for now] AWS PathHandler for PathManager + from .fb_pathhandlers import S3PathHandler + + IOPathManager.register_handler(S3PathHandler()) + except KeyError: + logging.warning("S3PathHandler already registered.") + except ImportError: + logging.debug( + "S3PathHandler couldn't be imported. Either missing fb-only files, or boto3 module." + ) except ImportError: - FVCorePathManager = None + IOPathManager = None class PathManager: """ Wrapper for insulating OSS I/O (using Python builtin operations) from - fvcore's PathManager abstraction (for transparently handling various + iopath's PathManager abstraction (for transparently handling various internal backends). """ @@ -33,8 +49,8 @@ def open( errors: Optional[str] = None, newline: Optional[str] = None, ): - if FVCorePathManager: - return FVCorePathManager.open( + if IOPathManager: + return IOPathManager.open( path=path, mode=mode, buffering=buffering, @@ -53,49 +69,126 @@ def open( @staticmethod def copy(src_path: str, dst_path: str, overwrite: bool = False) -> bool: - if FVCorePathManager: - return FVCorePathManager.copy( + if IOPathManager: + return IOPathManager.copy( src_path=src_path, dst_path=dst_path, overwrite=overwrite ) return shutil.copyfile(src_path, dst_path) @staticmethod def get_local_path(path: str, **kwargs) -> str: - if FVCorePathManager: - return FVCorePathManager.get_local_path(path, **kwargs) + if IOPathManager: + return IOPathManager.get_local_path(path, **kwargs) return path @staticmethod def exists(path: str) -> bool: - if FVCorePathManager: - return FVCorePathManager.exists(path) + if IOPathManager: + return IOPathManager.exists(path) return os.path.exists(path) @staticmethod def isfile(path: str) -> bool: - if FVCorePathManager: - return FVCorePathManager.isfile(path) + if IOPathManager: + return IOPathManager.isfile(path) return os.path.isfile(path) @staticmethod def ls(path: str) -> List[str]: - if FVCorePathManager: - return FVCorePathManager.ls(path) + if IOPathManager: + return IOPathManager.ls(path) return os.listdir(path) @staticmethod def mkdirs(path: str) -> None: - if FVCorePathManager: - return FVCorePathManager.mkdirs(path) + if IOPathManager: + return IOPathManager.mkdirs(path) os.makedirs(path, exist_ok=True) @staticmethod def rm(path: str) -> None: - if FVCorePathManager: - return FVCorePathManager.rm(path) + if IOPathManager: + return IOPathManager.rm(path) os.remove(path) + @staticmethod + def chmod(path: str, mode: int) -> None: + if not PathManager.path_requires_pathmanager(path): + os.chmod(path, mode) + @staticmethod def register_handler(handler) -> None: - if FVCorePathManager: - return FVCorePathManager.register_handler(handler=handler) + if IOPathManager: + return IOPathManager.register_handler(handler=handler) + + @staticmethod + def copy_from_local( + local_path: str, dst_path: str, overwrite: bool = False, **kwargs + ) -> None: + if IOPathManager: + return IOPathManager.copy_from_local( + local_path=local_path, dst_path=dst_path, overwrite=overwrite, **kwargs + ) + return shutil.copyfile(local_path, dst_path) + + @staticmethod + def path_requires_pathmanager(path: str) -> bool: + """Do we require PathManager to access given path?""" + if IOPathManager: + for p in IOPathManager._path_handlers.keys(): + if path.startswith(p): + return True + return False + + @staticmethod + def supports_rename(path: str) -> bool: + # PathManager doesn't yet support renames + return not PathManager.path_requires_pathmanager(path) + + @staticmethod + def rename(src: str, dst: str): + os.rename(src, dst) + + """ + ioPath async PathManager methods: + """ + @staticmethod + def opena( + path: str, + mode: str = "r", + buffering: int = -1, + encoding: Optional[str] = None, + errors: Optional[str] = None, + newline: Optional[str] = None, + ): + """ + Return file descriptor with asynchronous write operations. + """ + global IOPathManager + if not IOPathManager: + logging.info("ioPath is initializing PathManager.") + try: + from iopath.common.file_io import PathManager + IOPathManager = PathManager() + except Exception: + logging.exception("Failed to initialize ioPath PathManager object.") + return IOPathManager.opena( + path=path, + mode=mode, + buffering=buffering, + encoding=encoding, + errors=errors, + newline=newline, + ) + + @staticmethod + def async_close() -> bool: + """ + Wait for files to be written and clean up asynchronous PathManager. + NOTE: `PathManager.async_close()` must be called at the end of any + script that uses `PathManager.opena(...)`. + """ + global IOPathManager + if IOPathManager: + return IOPathManager.async_close() + return False diff --git a/fairseq/file_utils.py b/fairseq/file_utils.py index 62278b367d..d1d5ea6574 100644 --- a/fairseq/file_utils.py +++ b/fairseq/file_utils.py @@ -10,25 +10,28 @@ """ import fnmatch -from functools import wraps, partial -from hashlib import sha256 -from io import open import json import logging import os import shutil import tarfile import tempfile +from functools import partial, wraps +from hashlib import sha256 +from io import open try: from torch.hub import _get_torch_home + torch_cache_home = _get_torch_home() except ImportError: torch_cache_home = os.path.expanduser( - os.getenv('TORCH_HOME', os.path.join( - os.getenv('XDG_CACHE_HOME', '~/.cache'), 'torch'))) -default_cache_path = os.path.join(torch_cache_home, 'pytorch_fairseq') + os.getenv( + "TORCH_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "torch") + ) + ) +default_cache_path = os.path.join(torch_cache_home, "pytorch_fairseq") try: from urllib.parse import urlparse @@ -37,11 +40,10 @@ try: from pathlib import Path - PYTORCH_FAIRSEQ_CACHE = Path( - os.getenv('PYTORCH_FAIRSEQ_CACHE', default_cache_path)) + + PYTORCH_FAIRSEQ_CACHE = Path(os.getenv("PYTORCH_FAIRSEQ_CACHE", default_cache_path)) except (AttributeError, ImportError): - PYTORCH_FAIRSEQ_CACHE = os.getenv( - 'PYTORCH_FAIRSEQ_CACHE', default_cache_path) + PYTORCH_FAIRSEQ_CACHE = os.getenv("PYTORCH_FAIRSEQ_CACHE", default_cache_path) CONFIG_NAME = "config.json" WEIGHTS_NAME = "pytorch_model.bin" @@ -67,17 +69,23 @@ def load_archive_file(archive_file): if resolved_archive_file == archive_file: logger.info("loading archive file {}".format(archive_file)) else: - logger.info("loading archive file {} from cache at {}".format( - archive_file, resolved_archive_file)) + logger.info( + "loading archive file {} from cache at {}".format( + archive_file, resolved_archive_file + ) + ) # Extract archive to temp dir and replace .tar.bz2 if necessary tempdir = None if not os.path.isdir(resolved_archive_file): tempdir = tempfile.mkdtemp() - logger.info("extracting archive file {} to temp dir {}".format( - resolved_archive_file, tempdir)) + logger.info( + "extracting archive file {} to temp dir {}".format( + resolved_archive_file, tempdir + ) + ) ext = os.path.splitext(archive_file)[1][1:] - with tarfile.open(resolved_archive_file, 'r:' + ext) as archive: + with tarfile.open(resolved_archive_file, "r:" + ext) as archive: top_dir = os.path.commonprefix(archive.getnames()) archive.extractall(tempdir) os.remove(resolved_archive_file) @@ -93,14 +101,14 @@ def url_to_filename(url, etag=None): If `etag` is specified, append its hash to the URL's, delimited by a period. """ - url_bytes = url.encode('utf-8') + url_bytes = url.encode("utf-8") url_hash = sha256(url_bytes) filename = url_hash.hexdigest() if etag: - etag_bytes = etag.encode('utf-8') + etag_bytes = etag.encode("utf-8") etag_hash = sha256(etag_bytes) - filename += '.' + etag_hash.hexdigest() + filename += "." + etag_hash.hexdigest() return filename @@ -119,18 +127,31 @@ def filename_to_url(filename, cache_dir=None): if not os.path.exists(cache_path): raise EnvironmentError("file {} not found".format(cache_path)) - meta_path = cache_path + '.json' + meta_path = cache_path + ".json" if not os.path.exists(meta_path): raise EnvironmentError("file {} not found".format(meta_path)) with open(meta_path, encoding="utf-8") as meta_file: metadata = json.load(meta_file) - url = metadata['url'] - etag = metadata['etag'] + url = metadata["url"] + etag = metadata["etag"] return url, etag +def cached_path_from_pm(url_or_filename): + """ + Tries to cache the specified URL using PathManager class. + Returns the cached path if success otherwise failure. + """ + try: + from fairseq.file_io import PathManager + local_path = PathManager.get_local_path(url_or_filename) + return local_path + except Exception: + return None + + def cached_path(url_or_filename, cache_dir=None): """ Given something that might be a URL (or might be a local path), @@ -147,18 +168,23 @@ def cached_path(url_or_filename, cache_dir=None): parsed = urlparse(url_or_filename) - if parsed.scheme in ('http', 'https', 's3'): + if parsed.scheme in ("http", "https", "s3"): # URL, so get it from the cache (downloading if necessary) return get_from_cache(url_or_filename, cache_dir) elif os.path.exists(url_or_filename): # File, and it exists. return url_or_filename - elif parsed.scheme == '': + elif parsed.scheme == "": # File, but it doesn't exist. raise EnvironmentError("file {} not found".format(url_or_filename)) else: + cached_path = cached_path_from_pm(url_or_filename) + if cached_path: + return cached_path # Something unknown - raise ValueError("unable to parse {} as a URL or as a local path".format(url_or_filename)) + raise ValueError( + "unable to parse {} as a URL or as a local path".format(url_or_filename) + ) def split_s3_path(url): @@ -183,6 +209,7 @@ def s3_request(func): @wraps(func) def wrapper(url, *args, **kwargs): from botocore.exceptions import ClientError + try: return func(url, *args, **kwargs) except ClientError as exc: @@ -198,6 +225,7 @@ def wrapper(url, *args, **kwargs): def s3_etag(url): """Check ETag on S3 object.""" import boto3 + s3_resource = boto3.resource("s3") bucket_name, s3_path = split_s3_path(url) s3_object = s3_resource.Object(bucket_name, s3_path) @@ -208,6 +236,7 @@ def s3_etag(url): def s3_get(url, temp_file): """Pull a file directly from S3.""" import boto3 + s3_resource = boto3.resource("s3") bucket_name, s3_path = split_s3_path(url) s3_resource.Bucket(bucket_name).download_fileobj(s3_path, temp_file) @@ -215,12 +244,18 @@ def s3_get(url, temp_file): def request_wrap_timeout(func, url): import requests + for attempt, timeout in enumerate([10, 20, 40, 60, 60]): try: return func(timeout=timeout) except requests.exceptions.Timeout as e: - logger.warning("Request for %s timed-out (attempt %d). Retrying with a timeout of %d secs", - url, attempt, timeout, exc_info=e) + logger.warning( + "Request for %s timed-out (attempt %d). Retrying with a timeout of %d secs", + url, + attempt, + timeout, + exc_info=e, + ) continue raise RuntimeError(f"Unable to fetch file {url}") @@ -230,7 +265,7 @@ def http_get(url, temp_file): from tqdm import tqdm req = request_wrap_timeout(partial(requests.get, url, stream=True), url) - content_length = req.headers.get('Content-Length') + content_length = req.headers.get("Content-Length") total = int(content_length) if content_length is not None else None progress = tqdm(unit="B", total=total) for chunk in req.iter_content(chunk_size=1024): @@ -259,12 +294,15 @@ def get_from_cache(url, cache_dir=None): else: try: import requests - response = request_wrap_timeout(partial(requests.head, url, allow_redirects=True), url) + + response = request_wrap_timeout( + partial(requests.head, url, allow_redirects=True), url + ) if response.status_code != 200: etag = None else: etag = response.headers.get("ETag") - except EnvironmentError: + except RuntimeError: etag = None filename = url_to_filename(url, etag) @@ -275,8 +313,8 @@ def get_from_cache(url, cache_dir=None): # If we don't have a connection (etag is None) and can't identify the file # try to get the last downloaded one if not os.path.exists(cache_path) and etag is None: - matching_files = fnmatch.filter(os.listdir(cache_dir), filename + '.*') - matching_files = list(filter(lambda s: not s.endswith('.json'), matching_files)) + matching_files = fnmatch.filter(os.listdir(cache_dir), filename + ".*") + matching_files = list(filter(lambda s: not s.endswith(".json"), matching_files)) if matching_files: cache_path = os.path.join(cache_dir, matching_files[-1]) @@ -298,13 +336,13 @@ def get_from_cache(url, cache_dir=None): temp_file.seek(0) logger.info("copying %s to cache at %s", temp_file.name, cache_path) - with open(cache_path, 'wb') as cache_file: + with open(cache_path, "wb") as cache_file: shutil.copyfileobj(temp_file, cache_file) logger.info("creating metadata file for %s", cache_path) - meta = {'url': url, 'etag': etag} - meta_path = cache_path + '.json' - with open(meta_path, 'w') as meta_file: + meta = {"url": url, "etag": etag} + meta_path = cache_path + ".json" + with open(meta_path, "w") as meta_file: output_string = json.dumps(meta) meta_file.write(output_string) @@ -314,12 +352,12 @@ def get_from_cache(url, cache_dir=None): def read_set_from_file(filename): - ''' + """ Extract a de-duped collection (set) of text from a file. Expected file format is one item per line. - ''' + """ collection = set() - with open(filename, 'r', encoding='utf-8') as file_: + with open(filename, "r", encoding="utf-8") as file_: for line in file_: collection.add(line.rstrip()) return collection diff --git a/fairseq/hub_utils.py b/fairseq/hub_utils.py index 1b1091c881..d74470d2ec 100644 --- a/fairseq/hub_utils.py +++ b/fairseq/hub_utils.py @@ -8,13 +8,13 @@ import copy import logging import os -from typing import List, Dict, Iterator, Tuple, Any +from typing import Any, Dict, Iterator, List import torch -from torch import nn - from fairseq import utils from fairseq.data import encoders +from omegaconf import open_dict +from torch import nn logger = logging.getLogger(__name__) @@ -22,8 +22,8 @@ def from_pretrained( model_name_or_path, - checkpoint_file='model.pt', - data_name_or_path='.', + checkpoint_file="model.pt", + data_name_or_path=".", archive_map=None, **kwargs ): @@ -39,34 +39,36 @@ def from_pretrained( # for each model if isinstance(model_name_or_path, dict): for k, v in model_name_or_path.items(): - if k == 'checkpoint_file': + if k == "checkpoint_file": checkpoint_file = v elif ( - k != 'path' + k != "path" # only set kwargs that don't already have overrides and k not in kwargs ): kwargs[k] = v - model_name_or_path = model_name_or_path['path'] + model_name_or_path = model_name_or_path["path"] model_path = file_utils.load_archive_file(model_name_or_path) # convenience hack for loading data and BPE codes from model archive - if data_name_or_path.startswith('.'): - kwargs['data'] = os.path.abspath(os.path.join(model_path, data_name_or_path)) + if data_name_or_path.startswith("."): + kwargs["data"] = os.path.abspath(os.path.join(model_path, data_name_or_path)) else: - kwargs['data'] = file_utils.load_archive_file(data_name_or_path) + kwargs["data"] = file_utils.load_archive_file(data_name_or_path) for file, arg in { - 'code': 'bpe_codes', - 'bpecodes': 'bpe_codes', - 'sentencepiece.bpe.model': 'sentencepiece_vocab', + "code": "bpe_codes", + "bpecodes": "bpe_codes", + "sentencepiece.bpe.model": "sentencepiece_model", + "merges.txt": "bpe_merges", + "vocab.json": "bpe_vocab", }.items(): path = os.path.join(model_path, file) if os.path.exists(path): kwargs[arg] = path - if 'user_dir' in kwargs: - utils.import_user_module(argparse.Namespace(user_dir=kwargs['user_dir'])) + if "user_dir" in kwargs: + utils.import_user_module(argparse.Namespace(user_dir=kwargs["user_dir"])) models, args, task = checkpoint_utils.load_model_ensemble_and_task( [os.path.join(model_path, cpt) for cpt in checkpoint_file.split(os.pathsep)], @@ -74,9 +76,9 @@ def from_pretrained( ) return { - 'args': args, - 'task': task, - 'models': models, + "args": args, + "task": task, + "models": models, } @@ -86,9 +88,9 @@ class GeneratorHubInterface(nn.Module): translation or language model. """ - def __init__(self, args, task, models): + def __init__(self, cfg, task, models): super().__init__() - self.args = args + self.cfg = cfg self.task = task self.models = nn.ModuleList(models) self.src_dict = task.source_dictionary @@ -96,48 +98,51 @@ def __init__(self, args, task, models): # optimize model for generation for model in self.models: - model.make_generation_fast_( - beamable_mm_beam_size=( - None if getattr(args, 'no_beamable_mm', False) - else getattr(args, 'beam', 5) - ), - need_attn=getattr(args, 'print_alignment', False), - ) + model.prepare_for_inference_(cfg) # Load alignment dictionary for unknown word replacement # (None if no unknown word replacement, empty if no path to align dictionary) - self.align_dict = utils.load_align_dict(getattr(args, 'replace_unk', None)) + self.align_dict = utils.load_align_dict(cfg.generation.replace_unk) - self.tokenizer = encoders.build_tokenizer(args) - self.bpe = encoders.build_bpe(args) + self.tokenizer = encoders.build_tokenizer(cfg.tokenizer) + self.bpe = encoders.build_bpe(cfg.bpe) self.max_positions = utils.resolve_max_positions( self.task.max_positions(), *[model.max_positions() for model in models] ) # this is useful for determining the device - self.register_buffer('_float_tensor', torch.tensor([0], dtype=torch.float)) + self.register_buffer("_float_tensor", torch.tensor([0], dtype=torch.float)) @property def device(self): return self._float_tensor.device - def translate(self, sentences: List[str], beam: int = 5, verbose: bool = False, **kwargs) -> List[str]: + def translate( + self, sentences: List[str], beam: int = 5, verbose: bool = False, **kwargs + ) -> List[str]: return self.sample(sentences, beam, verbose, **kwargs) - def sample(self, sentences: List[str], beam: int = 1, verbose: bool = False, **kwargs) -> List[str]: + def sample( + self, sentences: List[str], beam: int = 1, verbose: bool = False, **kwargs + ) -> List[str]: if isinstance(sentences, str): return self.sample([sentences], beam=beam, verbose=verbose, **kwargs)[0] tokenized_sentences = [self.encode(sentence) for sentence in sentences] batched_hypos = self.generate(tokenized_sentences, beam, verbose, **kwargs) - return [self.decode(hypos[0]['tokens']) for hypos in batched_hypos] + return [self.decode(hypos[0]["tokens"]) for hypos in batched_hypos] def score(self, sentences: List[str], **kwargs): if isinstance(sentences, str): return self.score([sentences], **kwargs)[0] # NOTE: this doesn't support translation tasks currently tokenized_sentences = [self.encode(sentence) for sentence in sentences] - return [hypos[0] for hypos in self.generate(tokenized_sentences, score_reference=True, **kwargs)] + return [ + hypos[0] + for hypos in self.generate( + tokenized_sentences, score_reference=True, **kwargs + ) + ] def generate( self, @@ -146,6 +151,7 @@ def generate( verbose: bool = False, skip_invalid_size_inputs=False, inference_step_args=None, + prefix_allowed_tokens_fn=None, **kwargs ) -> List[List[Dict[str, torch.Tensor]]]: if torch.is_tensor(tokenized_sentences) and tokenized_sentences.dim() == 1: @@ -154,11 +160,16 @@ def generate( )[0] # build generator using current args as well as any kwargs - gen_args = copy.copy(self.args) - gen_args.beam = beam - for k, v in kwargs.items(): - setattr(gen_args, k, v) - generator = self.task.build_generator(self.models, gen_args) + gen_args = copy.deepcopy(self.cfg.generation) + with open_dict(gen_args): + gen_args.beam = beam + for k, v in kwargs.items(): + setattr(gen_args, k, v) + generator = self.task.build_generator( + self.models, + gen_args, + prefix_allowed_tokens_fn=prefix_allowed_tokens_fn, + ) inference_step_args = inference_step_args or {} results = [] @@ -176,21 +187,37 @@ def generate( if verbose: def getarg(name, default): - return getattr(gen_args, name, getattr(self.args, name, default)) + return getattr(gen_args, name, getattr(self.cfg, name, default)) for source_tokens, target_hypotheses in zip(tokenized_sentences, outputs): src_str_with_unk = self.string(source_tokens) - logger.info('S\t{}'.format(src_str_with_unk)) + logger.info("S\t{}".format(src_str_with_unk)) for hypo in target_hypotheses: - hypo_str = self.decode(hypo['tokens']) - logger.info('H\t{}\t{}'.format(hypo['score'], hypo_str)) - logger.info('P\t{}'.format( - ' '.join(map(lambda x: '{:.4f}'.format(x), hypo['positional_scores'].tolist())) - )) - if hypo['alignment'] is not None and getarg('print_alignment', False): - logger.info('A\t{}'.format( - ' '.join(map(lambda x: str(utils.item(x)), hypo['alignment'].int().cpu())) - )) + hypo_str = self.decode(hypo["tokens"]) + logger.info("H\t{}\t{}".format(hypo["score"], hypo_str)) + logger.info( + "P\t{}".format( + " ".join( + map( + lambda x: "{:.4f}".format(x), + hypo["positional_scores"].tolist(), + ) + ) + ) + ) + if hypo["alignment"] is not None and getarg( + "print_alignment", False + ): + logger.info( + "A\t{}".format( + " ".join( + [ + "{}-{}".format(src_idx, tgt_idx) + for src_idx, tgt_idx in hypo["alignment"] + ] + ) + ) + ) return outputs def encode(self, sentence: str) -> torch.LongTensor: @@ -235,10 +262,11 @@ def _build_batches( lengths = torch.LongTensor([t.numel() for t in tokens]) batch_iterator = self.task.get_batch_iterator( dataset=self.task.build_dataset_for_inference(tokens, lengths), - max_tokens=self.args.max_tokens, - max_sentences=self.args.max_sentences, + max_tokens=self.cfg.dataset.max_tokens, + max_sentences=self.cfg.dataset.batch_size, max_positions=self.max_positions, ignore_invalid_inputs=skip_invalid_size_inputs, + disable_iterator_cache=True, ).next_epoch_itr(shuffle=False) return batch_iterator diff --git a/fairseq/incremental_decoding_utils.py b/fairseq/incremental_decoding_utils.py index 91128e8879..b26e6cd01c 100644 --- a/fairseq/incremental_decoding_utils.py +++ b/fairseq/incremental_decoding_utils.py @@ -3,14 +3,13 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. -from typing import Dict, Optional import uuid +from typing import Dict, Optional from torch import Tensor class FairseqIncrementalState(object): - def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.init_incremental_state() @@ -46,5 +45,7 @@ def set_incremental_state( def with_incremental_state(cls): - cls.__bases__ = (FairseqIncrementalState,) + tuple(b for b in cls.__bases__ if b != FairseqIncrementalState) + cls.__bases__ = (FairseqIncrementalState,) + tuple( + b for b in cls.__bases__ if b != FairseqIncrementalState + ) return cls diff --git a/fairseq/iterative_refinement_generator.py b/fairseq/iterative_refinement_generator.py index c7a267d258..4fb0946f49 100644 --- a/fairseq/iterative_refinement_generator.py +++ b/fairseq/iterative_refinement_generator.py @@ -5,20 +5,15 @@ from collections import namedtuple -import torch import numpy as np - +import torch from fairseq import utils -DecoderOut = namedtuple('IterativeRefinementDecoderOut', [ - 'output_tokens', - 'output_scores', - 'attn', - 'step', - 'max_step', - 'history' -]) +DecoderOut = namedtuple( + "IterativeRefinementDecoderOut", + ["output_tokens", "output_scores", "attn", "step", "max_step", "history"], +) class IterativeRefinementGenerator(object): @@ -103,9 +98,12 @@ def generate_batched_itr( ref = utils.strip_pad(sample["target"][i, :], self.pad) yield id, src, ref, hypos[i] - @torch.no_grad() - def generate(self, models, sample, prefix_tokens=None): + def generate(self, models, sample, prefix_tokens=None, constraints=None): + if constraints is not None: + raise NotImplementedError( + "Constrained decoding with the IterativeRefinementGenerator is not supported" + ) # TODO: iterative refinement generator does not support ensemble for now. if not self.retain_dropout: @@ -115,13 +113,17 @@ def generate(self, models, sample, prefix_tokens=None): model, reranker = models[0], None if self.reranking: assert len(models) > 1, "Assuming the last checkpoint is the reranker" - assert self.beam_size > 1, "Reranking requires multiple translation for each example" + assert ( + self.beam_size > 1 + ), "Reranking requires multiple translation for each example" reranker = models[-1] models = models[:-1] - if len(models) > 1 and hasattr(model, 'enable_ensemble'): - assert model.allow_ensemble, "{} does not support ensembling".format(model.__class__.__name__) + if len(models) > 1 and hasattr(model, "enable_ensemble"): + assert model.allow_ensemble, "{} does not support ensembling".format( + model.__class__.__name__ + ) model.enable_ensemble(models) # TODO: better encoder inputs? @@ -134,13 +136,22 @@ def generate(self, models, sample, prefix_tokens=None): prev_decoder_out = model.initialize_output_tokens(encoder_out, src_tokens) if self.beam_size > 1: - assert model.allow_length_beam, \ - "{} does not support decoding with length beam.".format(model.__class__.__name__) + assert ( + model.allow_length_beam + ), "{} does not support decoding with length beam.".format( + model.__class__.__name__ + ) # regenerate data based on length-beam - length_beam_order = utils.new_arange(src_tokens, self.beam_size, bsz).t().reshape(-1) - encoder_out = model.encoder.reorder_encoder_out(encoder_out, length_beam_order) - prev_decoder_out = model.regenerate_length_beam(prev_decoder_out, self.beam_size) + length_beam_order = ( + utils.new_arange(src_tokens, self.beam_size, bsz).t().reshape(-1) + ) + encoder_out = model.encoder.reorder_encoder_out( + encoder_out, length_beam_order + ) + prev_decoder_out = model.regenerate_length_beam( + prev_decoder_out, self.beam_size + ) bsz = bsz * self.beam_size sent_idxs = torch.arange(bsz) @@ -204,7 +215,10 @@ def finalized_hypos(step, prev_out_token, prev_out_score, prev_out_attn): if self.adaptive: # terminate if there is a loop terminated, out_tokens, out_scores, out_attn = is_a_loop( - prev_output_tokens, decoder_out.output_tokens, decoder_out.output_scores, decoder_out.attn + prev_output_tokens, + decoder_out.output_tokens, + decoder_out.output_scores, + decoder_out.attn, ) decoder_out = decoder_out._replace( output_tokens=out_tokens, @@ -213,7 +227,9 @@ def finalized_hypos(step, prev_out_token, prev_out_score, prev_out_attn): ) else: - terminated = decoder_out.output_tokens.new_zeros(decoder_out.output_tokens.size(0)).bool() + terminated = decoder_out.output_tokens.new_zeros( + decoder_out.output_tokens.size(0) + ).bool() if step == self.max_iter: # reach last iteration, terminate terminated.fill_(1) @@ -223,7 +239,9 @@ def finalized_hypos(step, prev_out_token, prev_out_score, prev_out_attn): finalized_tokens = decoder_out.output_tokens[terminated] finalized_scores = decoder_out.output_scores[terminated] finalized_attn = ( - None if (decoder_out.attn is None or decoder_out.attn.size(0) == 0) else decoder_out.attn[terminated] + None + if (decoder_out.attn is None or decoder_out.attn.size(0) == 0) + else decoder_out.attn[terminated] ) if self.retain_history: @@ -240,13 +258,11 @@ def finalized_hypos(step, prev_out_token, prev_out_score, prev_out_attn): ] if self.retain_history: - finalized[finalized_idxs[i]][0]['history'] = [] + finalized[finalized_idxs[i]][0]["history"] = [] for j in range(len(finalized_history_tokens)): - finalized[finalized_idxs[i]][0]['history'].append( + finalized[finalized_idxs[i]][0]["history"].append( finalized_hypos( - step, - finalized_history_tokens[j][i], - None, None + step, finalized_history_tokens[j][i], None, None ) ) @@ -266,7 +282,9 @@ def finalized_hypos(step, prev_out_token, prev_out_score, prev_out_attn): if decoder_out.history is not None else None, ) - encoder_out = model.encoder.reorder_encoder_out(encoder_out, not_terminated.nonzero().squeeze()) + encoder_out = model.encoder.reorder_encoder_out( + encoder_out, not_terminated.nonzero(as_tuple=False).squeeze() + ) sent_idxs = sent_idxs[not_terminated] prev_output_tokens = prev_decoder_out.output_tokens.clone() @@ -278,38 +296,64 @@ def finalized_hypos(step, prev_out_token, prev_out_score, prev_out_attn): # aggregate information from length beam finalized = [ - finalized[np.argmax( - [finalized[self.beam_size * i + j][0]['score'] for j in range(self.beam_size)] - ) + self.beam_size * i] for i in range(len(finalized) // self.beam_size) + finalized[ + np.argmax( + [ + finalized[self.beam_size * i + j][0]["score"] + for j in range(self.beam_size) + ] + ) + + self.beam_size * i ] + for i in range(len(finalized) // self.beam_size) + ] return finalized def rerank(self, reranker, finalized, encoder_input, beam_size): - def rebuild_batch(finalized): - finalized_tokens = [f[0]['tokens'] for f in finalized] + finalized_tokens = [f[0]["tokens"] for f in finalized] finalized_maxlen = max(f.size(0) for f in finalized_tokens) - final_output_tokens = finalized_tokens[0].new_zeros(len(finalized_tokens), finalized_maxlen).fill_(self.pad) + final_output_tokens = ( + finalized_tokens[0] + .new_zeros(len(finalized_tokens), finalized_maxlen) + .fill_(self.pad) + ) for i, f in enumerate(finalized_tokens): - final_output_tokens[i, :f.size(0)] = f + final_output_tokens[i, : f.size(0)] = f return final_output_tokens final_output_tokens = rebuild_batch(finalized) - final_output_tokens[:, 0] = self.eos # autoregressive model assumes starting with EOS + final_output_tokens[ + :, 0 + ] = self.eos # autoregressive model assumes starting with EOS reranker_encoder_out = reranker.encoder(*encoder_input) - length_beam_order = utils.new_arange( - final_output_tokens, beam_size, reranker_encoder_out.encoder_out.size(1)).t().reshape(-1) - reranker_encoder_out = reranker.encoder.reorder_encoder_out(reranker_encoder_out, length_beam_order) + length_beam_order = ( + utils.new_arange( + final_output_tokens, beam_size, reranker_encoder_out.encoder_out.size(1) + ) + .t() + .reshape(-1) + ) + reranker_encoder_out = reranker.encoder.reorder_encoder_out( + reranker_encoder_out, length_beam_order + ) reranking_scores = reranker.get_normalized_probs( - reranker.decoder(final_output_tokens[:, :-1], reranker_encoder_out), True, None) + reranker.decoder(final_output_tokens[:, :-1], reranker_encoder_out), + True, + None, + ) reranking_scores = reranking_scores.gather(2, final_output_tokens[:, 1:, None]) reranking_masks = final_output_tokens[:, 1:].ne(self.pad) - reranking_scores = reranking_scores[:, :, 0].masked_fill_(~reranking_masks, 0).sum(1) - reranking_scores = reranking_scores / reranking_masks.sum(1).type_as(reranking_scores) + reranking_scores = ( + reranking_scores[:, :, 0].masked_fill_(~reranking_masks, 0).sum(1) + ) + reranking_scores = reranking_scores / reranking_masks.sum(1).type_as( + reranking_scores + ) for i in range(len(finalized)): - finalized[i][0]['score'] = reranking_scores[i] + finalized[i][0]["score"] = reranking_scores[i] return finalized diff --git a/fairseq/legacy_distributed_data_parallel.py b/fairseq/legacy_distributed_data_parallel.py deleted file mode 100644 index 2e9c82539b..0000000000 --- a/fairseq/legacy_distributed_data_parallel.py +++ /dev/null @@ -1,180 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -""" -A modified version of the legacy DistributedDataParallel module that uses c10d -communication primitives. This version is simpler than the latest PyTorch -version and is useful for debugging. Notably it does not overlap gradient -communication with the backward pass, which makes it slower but more robust -than the PyTorch version. - -This version also supports the *no_sync* context manager, which allows faster -training with `--update-freq`. -""" - -from contextlib import contextmanager -import copy - -import torch -from torch import nn -from torch.autograd import Variable - -from . import distributed_utils - - -class LegacyDistributedDataParallel(nn.Module): - """Implements distributed data parallelism at the module level. - - A simplified version of :class:`torch.nn.parallel.DistributedDataParallel`. - This version uses a c10d process group for communication and does not - broadcast buffers. - - Args: - module (~torch.nn.Module): module to be parallelized - world_size (int): number of parallel workers - process_group (optional): the c10d process group to be used for - distributed data all-reduction. If None, the default process group - will be used. - buffer_size (int, optional): number of elements to buffer before - performing all-reduce (default: 256M). - """ - - def __init__(self, module, world_size, process_group=None, buffer_size=2**28): - super().__init__() - - self.module = module - self.world_size = world_size - self.process_group = process_group - - # Never use a bigger buffer than the number of model params - self.buffer_size = min(buffer_size, sum(p.numel() for p in module.parameters())) - self.buffer = None - - # Flag used by the NCCL backend to make sure we only reduce gradients - # one time in the execution engine - self.need_reduction = False - - # We can also forcibly accumulate grads locally and only do the - # all-reduce at some later time - self.accumulate_grads = False - - # For NCCL backend, since every single NCCL call is asynchoronous, we - # therefore directly enqueue all the NCCL reduction calls to the - # default CUDA stream without spawning up other reduction threads. - # This achieves the best performance. - self._register_grad_hook() - - def __getstate__(self): - attrs = copy.copy(self.__dict__) - return attrs - - def __setstate__(self, state): - super().__setstate__(state) - self._register_grad_hook() - - @contextmanager - def no_sync(self): - """A context manager to disable gradient synchronization.""" - old_accumulate_grads = self.accumulate_grads - self.accumulate_grads = True - yield - self.accumulate_grads = old_accumulate_grads - - def forward(self, *inputs, **kwargs): - return self.module(*inputs, **kwargs) - - def _register_grad_hook(self): - """ - This function registers the callback all-reduction function for the - NCCL backend. All gradients will be all reduced in one single step. - The NCCL reduction will directly be enqueued into the default CUDA - stream. Therefore, no synchronization is needed. - """ - - def all_reduce(params): - buffer = self.buffer - nonzero_buffer = False - if len(params) > 1: - offset = 0 - for p in params: - sz = p.numel() - if p.grad is not None: - buffer[offset:offset+sz].copy_(p.grad.data.view(-1)) - nonzero_buffer = True - else: - buffer[offset:offset+sz].zero_() - offset += sz - else: - # we only have a single grad to all-reduce - p = params[0] - if p.grad is not None: - buffer = p.grad.data - nonzero_buffer = True - elif p.numel() <= self.buffer.numel(): - buffer = buffer[:p.numel()] - buffer.zero_() - else: - buffer = torch.zeros_like(p) - - if nonzero_buffer: - buffer.div_(self.world_size) - - distributed_utils.all_reduce(buffer, self.process_group) - - # copy all-reduced grads back into their original place - offset = 0 - for p in params: - sz = p.numel() - if p.grad is not None: - p.grad.data.copy_(buffer[offset:offset+sz].view_as(p)) - else: - p.grad = buffer[offset:offset+sz].view_as(p).clone() - offset += sz - - def reduction_fn(): - # This function only needs to be called once - if not self.need_reduction or self.accumulate_grads: - return - self.need_reduction = False - - if self.buffer is None: - self.buffer = next(self.module.parameters()).new(self.buffer_size) - - # All-reduce the gradients in buckets - offset = 0 - buffered_params = [] - for param in self.module.parameters(): - if not param.requires_grad: - continue - if param.grad is None: - param.grad = torch.zeros_like(param) - if param.grad.requires_grad: - raise RuntimeError("DistributedDataParallel only works " - "with gradients that don't require " - "grad") - sz = param.numel() - if sz > self.buffer.numel(): - # all-reduce big params directly - all_reduce([param]) - else: - if offset + sz > self.buffer.numel(): - all_reduce(buffered_params) - offset = 0 - buffered_params.clear() - buffered_params.append(param) - offset += sz - - if len(buffered_params) > 0: - all_reduce(buffered_params) - - # Now register the reduction hook on the parameters - for p in self.module.parameters(): - - def allreduce_hook(*unused): - self.need_reduction = True - Variable._execution_engine.queue_callback(reduction_fn) - - if p.requires_grad: - p.register_hook(allreduce_hook) diff --git a/fairseq/logging/meters.py b/fairseq/logging/meters.py index 78e6d4d224..2100b1fa0b 100644 --- a/fairseq/logging/meters.py +++ b/fairseq/logging/meters.py @@ -4,10 +4,11 @@ # LICENSE file in the root directory of this source tree. import bisect -from collections import OrderedDict import time +from collections import OrderedDict from typing import Dict, Optional + try: import torch @@ -16,6 +17,8 @@ def type_as(a, b): return a.to(b) else: return a + + except ImportError: torch = None @@ -51,11 +54,11 @@ def smoothed_value(self) -> float: def safe_round(number, ndigits): - if hasattr(number, '__round__'): + if hasattr(number, "__round__"): return round(number, ndigits) elif torch is not None and torch.is_tensor(number) and number.numel() == 1: return safe_round(number.item(), ndigits) - elif np is not None and np.ndim(number) == 0 and hasattr(number, 'item'): + elif np is not None and np.ndim(number) == 0 and hasattr(number, "item"): return safe_round(number.item(), ndigits) else: return number @@ -82,17 +85,17 @@ def update(self, val, n=1): def state_dict(self): return { - 'val': self.val, - 'sum': self.sum, - 'count': self.count, - 'round': self.round, + "val": self.val, + "sum": self.sum, + "count": self.count, + "round": self.round, } def load_state_dict(self, state_dict): - self.val = state_dict['val'] - self.sum = state_dict['sum'] - self.count = state_dict['count'] - self.round = state_dict.get('round', None) + self.val = state_dict["val"] + self.sum = state_dict["sum"] + self.count = state_dict["count"] + self.round = state_dict.get("round", None) @property def avg(self): @@ -106,6 +109,38 @@ def smoothed_value(self) -> float: return val +class SumMeter(Meter): + """Computes and stores the sum""" + + def __init__(self, round: Optional[int] = None): + self.round = round + self.reset() + + def reset(self): + self.sum = 0 # sum from all updates + + def update(self, val): + if val is not None: + self.sum = type_as(self.sum, val) + val + + def state_dict(self): + return { + "sum": self.sum, + "round": self.round, + } + + def load_state_dict(self, state_dict): + self.sum = state_dict["sum"] + self.round = state_dict.get("round", None) + + @property + def smoothed_value(self) -> float: + val = self.sum + if self.round is not None and val is not None: + val = safe_round(val, self.round) + return val + + class TimeMeter(Meter): """Computes the average occurrence of some event per second""" @@ -130,18 +165,18 @@ def update(self, val=1): def state_dict(self): return { - 'init': self.elapsed_time, - 'n': self.n, - 'round': self.round, + "init": self.elapsed_time, + "n": self.n, + "round": self.round, } def load_state_dict(self, state_dict): - if 'start' in state_dict: + if "start" in state_dict: # backwards compatibility for old state_dicts - self.reset(init=state_dict['init']) + self.reset(init=state_dict["init"]) else: - self.reset(init=state_dict['init'], n=state_dict['n']) - self.round = state_dict.get('round', None) + self.reset(init=state_dict["init"], n=state_dict["n"]) + self.round = state_dict.get("round", None) @property def avg(self): @@ -186,16 +221,16 @@ def reset(self): def state_dict(self): return { - 'sum': self.sum, - 'n': self.n, - 'round': self.round, + "sum": self.sum, + "n": self.n, + "round": self.round, } def load_state_dict(self, state_dict): - self.sum = state_dict['sum'] - self.n = state_dict['n'] + self.sum = state_dict["sum"] + self.n = state_dict["n"] self.start_time = None - self.round = state_dict.get('round', None) + self.round = state_dict.get("round", None) @property def avg(self): @@ -204,7 +239,7 @@ def avg(self): @property def elapsed_time(self): if self.start_time is None: - return 0. + return 0.0 return time.perf_counter() - self.start_time @property @@ -263,11 +298,13 @@ def get_smoothed_value(self, key: str) -> float: def get_smoothed_values(self) -> Dict[str, float]: """Get all smoothed values.""" - return OrderedDict([ - (key, self.get_smoothed_value(key)) - for key in self.keys() - if not key.startswith("_") - ]) + return OrderedDict( + [ + (key, self.get_smoothed_value(key)) + for key in self.keys() + if not key.startswith("_") + ] + ) def reset(self): """Reset Meter instances.""" diff --git a/fairseq/logging/metrics.py b/fairseq/logging/metrics.py index 6ca1d201e0..58c2fb64e1 100644 --- a/fairseq/logging/metrics.py +++ b/fairseq/logging/metrics.py @@ -11,11 +11,10 @@ :func:`aggregate` context manager for more details. """ -from collections import defaultdict, OrderedDict import contextlib -import time -from typing import Callable, Dict, List, Optional import uuid +from collections import defaultdict +from typing import Callable, List, Optional from .meters import * @@ -131,6 +130,25 @@ def log_scalar( agg.add_meter(key, AverageMeter(round=round), priority) agg[key].update(value, weight) +def log_scalar_sum( + key: str, + value: float, + priority: int = 10, + round: Optional[int] = None, +): + """Log a scalar value that is summed for reporting. + + Args: + key (str): name of the field to log + value (float): value to log + priority (int): smaller values are logged earlier in the output + round (Optional[int]): number of digits to round to when displaying + """ + for agg in get_active_aggregators(): + if key not in agg: + agg.add_meter(key, SumMeter(round=round), priority) + agg[key].update(value) + def log_derived(key: str, fn: Callable[[MetersDict], float], priority: int = 20): """Log a scalar value derived from other meters. @@ -184,7 +202,7 @@ def log_start_time(key: str, priority: int = 40, round: Optional[int] = None): agg[key].start() -def log_stop_time(key: str, weight: float = 0., prehook=None): +def log_stop_time(key: str, weight: float = 0.0, prehook=None): """Log the duration of some event in seconds. The duration will be computed since :func:`log_start_time` was called. @@ -279,13 +297,18 @@ def get_smoothed_values(name: str) -> Dict[str, float]: def state_dict(): - return OrderedDict([ - (name, agg.state_dict()) - for name, agg in _aggregators.items() - ]) + return OrderedDict([(name, agg.state_dict()) for name, agg in _aggregators.items()]) def load_state_dict(state_dict): for name, agg_state in state_dict.items(): _aggregators[name] = MetersDict() _aggregators[name].load_state_dict(agg_state) + + +def xla_metrics_report(): + try: + import torch_xla.debug.metrics as met + print(met.metrics_report()) + except ImportError: + return diff --git a/fairseq/logging/progress_bar.py b/fairseq/logging/progress_bar.py index 84c208f323..061082caef 100644 --- a/fairseq/logging/progress_bar.py +++ b/fairseq/logging/progress_bar.py @@ -29,36 +29,51 @@ def progress_bar( iterator, log_format: Optional[str] = None, log_interval: int = 100, + log_file: Optional[str] = None, epoch: Optional[int] = None, prefix: Optional[str] = None, tensorboard_logdir: Optional[str] = None, - default_log_format: str = 'tqdm', + default_log_format: str = "tqdm", + wandb_project: Optional[str] = None, + wandb_run_name: Optional[str] = None, + azureml_logging: Optional[bool] = False, ): if log_format is None: log_format = default_log_format - if log_format == 'tqdm' and not sys.stderr.isatty(): - log_format = 'simple' + if log_file is not None: + handler = logging.FileHandler(filename=log_file) + logger.addHandler(handler) - if log_format == 'json': + if log_format == "tqdm" and not sys.stderr.isatty(): + log_format = "simple" + + if log_format == "json": bar = JsonProgressBar(iterator, epoch, prefix, log_interval) - elif log_format == 'none': + elif log_format == "none": bar = NoopProgressBar(iterator, epoch, prefix) - elif log_format == 'simple': + elif log_format == "simple": bar = SimpleProgressBar(iterator, epoch, prefix, log_interval) - elif log_format == 'tqdm': + elif log_format == "tqdm": bar = TqdmProgressBar(iterator, epoch, prefix) else: - raise ValueError('Unknown log format: {}'.format(log_format)) + raise ValueError("Unknown log format: {}".format(log_format)) if tensorboard_logdir: try: # [FB only] custom wrapper for TensorBoard import palaas # noqa from .fb_tbmf_wrapper import FbTbmfWrapper + bar = FbTbmfWrapper(bar, log_interval) except ImportError: bar = TensorboardProgressBarWrapper(bar, tensorboard_logdir) + if wandb_project: + bar = WandBProgressBarWrapper(bar, wandb_project, run_name=wandb_run_name) + + if azureml_logging: + bar = AzureMLProgressBarWrapper(bar) + return bar @@ -67,14 +82,14 @@ def build_progress_bar( iterator, epoch: Optional[int] = None, prefix: Optional[str] = None, - default: str = 'tqdm', - no_progress_bar: str = 'none', + default: str = "tqdm", + no_progress_bar: str = "none", ): """Legacy wrapper that takes an argparse.Namespace.""" - if getattr(args, 'no_progress_bar', False): + if getattr(args, "no_progress_bar", False): default = no_progress_bar - if getattr(args, 'distributed_rank', 0) == 0: - tensorboard_logdir = getattr(args, 'tensorboard_logdir', None) + if getattr(args, "distributed_rank", 0) == 0: + tensorboard_logdir = getattr(args, "tensorboard_logdir", None) else: tensorboard_logdir = None return progress_bar( @@ -90,13 +105,13 @@ def build_progress_bar( def format_stat(stat): if isinstance(stat, Number): - stat = '{:g}'.format(stat) + stat = "{:g}".format(stat) elif isinstance(stat, AverageMeter): - stat = '{:.3f}'.format(stat.avg) + stat = "{:.3f}".format(stat.avg) elif isinstance(stat, TimeMeter): - stat = '{:g}'.format(round(stat.avg)) + stat = "{:g}".format(round(stat.avg)) elif isinstance(stat, StopwatchMeter): - stat = '{:g}'.format(round(stat.sum)) + stat = "{:g}".format(round(stat.sum)) elif torch.is_tensor(stat): stat = stat.tolist() return stat @@ -104,15 +119,16 @@ def format_stat(stat): class BaseProgressBar(object): """Abstract class for progress bars.""" + def __init__(self, iterable, epoch=None, prefix=None): self.iterable = iterable - self.n = getattr(iterable, 'n', 0) + self.n = getattr(iterable, "n", 0) self.epoch = epoch - self.prefix = '' + self.prefix = "" if epoch is not None: - self.prefix += 'epoch {:03d}'.format(epoch) + self.prefix += "epoch {:03d}".format(epoch) if prefix is not None: - self.prefix += ' | {}'.format(prefix) + self.prefix += (" | " if self.prefix != "" else "") + prefix def __len__(self): return len(self.iterable) @@ -134,13 +150,15 @@ def print(self, stats, tag=None, step=None): """Print end-of-epoch stats.""" raise NotImplementedError + def update_config(self, config): + """Log latest configuration.""" + pass + def _str_commas(self, stats): - return ', '.join(key + '=' + stats[key].strip() - for key in stats.keys()) + return ", ".join(key + "=" + stats[key].strip() for key in stats.keys()) def _str_pipes(self, stats): - return ' | '.join(key + ' ' + stats[key].strip() - for key in stats.keys()) + return " | ".join(key + " " + stats[key].strip() for key in stats.keys()) def _format_stats(self, stats): postfix = OrderedDict(stats) @@ -177,11 +195,7 @@ def __iter__(self): def log(self, stats, tag=None, step=None): """Log intermediate stats according to log_interval.""" step = step or self.i or 0 - if ( - step > 0 - and self.log_interval is not None - and step % self.log_interval == 0 - ): + if step > 0 and self.log_interval is not None and step % self.log_interval == 0: update = ( self.epoch - 1 + (self.i + 1) / float(self.size) if self.epoch is not None @@ -195,7 +209,9 @@ def print(self, stats, tag=None, step=None): """Print end-of-epoch stats.""" self.stats = stats if tag is not None: - self.stats = OrderedDict([(tag + '_' + k, v) for k, v in self.stats.items()]) + self.stats = OrderedDict( + [(tag + "_" + k, v) for k, v in self.stats.items()] + ) stats = self._format_stats(self.stats, epoch=self.epoch) with rename_logger(logger, tag): logger.info(json.dumps(stats)) @@ -203,9 +219,9 @@ def print(self, stats, tag=None, step=None): def _format_stats(self, stats, epoch=None, update=None): postfix = OrderedDict() if epoch is not None: - postfix['epoch'] = epoch + postfix["epoch"] = epoch if update is not None: - postfix['update'] = round(update, 3) + postfix["update"] = round(update, 3) # Preprocess stats according to datatype for key in stats.keys(): postfix[key] = format_stat(stats[key]) @@ -249,24 +265,21 @@ def __iter__(self): def log(self, stats, tag=None, step=None): """Log intermediate stats according to log_interval.""" step = step or self.i or 0 - if ( - step > 0 - and self.log_interval is not None - and step % self.log_interval == 0 - ): + if step > 0 and self.log_interval is not None and step % self.log_interval == 0: stats = self._format_stats(stats) postfix = self._str_commas(stats) with rename_logger(logger, tag): logger.info( - '{}: {:5d} / {:d} {}' - .format(self.prefix, self.i + 1, self.size, postfix) + "{}: {:5d} / {:d} {}".format( + self.prefix, self.i + 1, self.size, postfix + ) ) def print(self, stats, tag=None, step=None): """Print end-of-epoch stats.""" postfix = self._str_pipes(self._format_stats(stats)) with rename_logger(logger, tag): - logger.info('{} | {}'.format(self.prefix, postfix)) + logger.info("{} | {}".format(self.prefix, postfix)) class TqdmProgressBar(BaseProgressBar): @@ -275,7 +288,13 @@ class TqdmProgressBar(BaseProgressBar): def __init__(self, iterable, epoch=None, prefix=None): super().__init__(iterable, epoch, prefix) from tqdm import tqdm - self.tqdm = tqdm(iterable, self.prefix, leave=False) + + self.tqdm = tqdm( + iterable, + self.prefix, + leave=False, + disable=(logger.getEffectiveLevel() > logging.INFO), + ) def __iter__(self): return iter(self.tqdm) @@ -287,14 +306,18 @@ def log(self, stats, tag=None, step=None): def print(self, stats, tag=None, step=None): """Print end-of-epoch stats.""" postfix = self._str_pipes(self._format_stats(stats)) - self.tqdm.write('{} | {}'.format(self.tqdm.desc, postfix)) + with rename_logger(logger, tag): + logger.info("{} | {}".format(self.prefix, postfix)) try: _tensorboard_writers = {} - from tensorboardX import SummaryWriter + from torch.utils.tensorboard import SummaryWriter except ImportError: - SummaryWriter = None + try: + from tensorboardX import SummaryWriter + except ImportError: + SummaryWriter = None def _close_writers(): @@ -314,7 +337,7 @@ def __init__(self, wrapped_bar, tensorboard_logdir): if SummaryWriter is None: logger.warning( - "tensorboard not found, please install with: pip install tensorboardX" + "tensorboard not found, please install with: pip install tensorboard" ) def _writer(self, key): @@ -323,7 +346,7 @@ def _writer(self, key): _writers = _tensorboard_writers if key not in _writers: _writers[key] = SummaryWriter(os.path.join(self.tensorboard_logdir, key)) - _writers[key].add_text('sys.argv', " ".join(sys.argv)) + _writers[key].add_text("sys.argv", " ".join(sys.argv)) return _writers[key] def __iter__(self): @@ -339,15 +362,129 @@ def print(self, stats, tag=None, step=None): self._log_to_tensorboard(stats, tag, step) self.wrapped_bar.print(stats, tag=tag, step=step) + def update_config(self, config): + """Log latest configuration.""" + # TODO add hparams to Tensorboard + self.wrapped_bar.update_config(config) + def _log_to_tensorboard(self, stats, tag=None, step=None): - writer = self._writer(tag or '') + writer = self._writer(tag or "") if writer is None: return if step is None: - step = stats['num_updates'] - for key in stats.keys() - {'num_updates'}: + step = stats["num_updates"] + for key in stats.keys() - {"num_updates"}: if isinstance(stats[key], AverageMeter): writer.add_scalar(key, stats[key].val, step) elif isinstance(stats[key], Number): writer.add_scalar(key, stats[key], step) + elif torch.is_tensor(stats[key]) and stats[key].numel() == 1: + writer.add_scalar(key, stats[key].item(), step) writer.flush() + + +try: + import wandb +except ImportError: + wandb = None + + +class WandBProgressBarWrapper(BaseProgressBar): + """Log to Weights & Biases.""" + + def __init__(self, wrapped_bar, wandb_project, run_name=None): + self.wrapped_bar = wrapped_bar + if wandb is None: + logger.warning("wandb not found, pip install wandb") + return + + # reinit=False to ensure if wandb.init() is called multiple times + # within one process it still references the same run + wandb.init(project=wandb_project, reinit=False, name=run_name) + + def __iter__(self): + return iter(self.wrapped_bar) + + def log(self, stats, tag=None, step=None): + """Log intermediate stats to tensorboard.""" + self._log_to_wandb(stats, tag, step) + self.wrapped_bar.log(stats, tag=tag, step=step) + + def print(self, stats, tag=None, step=None): + """Print end-of-epoch stats.""" + self._log_to_wandb(stats, tag, step) + self.wrapped_bar.print(stats, tag=tag, step=step) + + def update_config(self, config): + """Log latest configuration.""" + if wandb is not None: + wandb.config.update(config) + self.wrapped_bar.update_config(config) + + def _log_to_wandb(self, stats, tag=None, step=None): + if wandb is None: + return + if step is None: + step = stats["num_updates"] + + prefix = "" if tag is None else tag + "/" + + for key in stats.keys() - {"num_updates"}: + if isinstance(stats[key], AverageMeter): + wandb.log({prefix + key: stats[key].val}, step=step) + elif isinstance(stats[key], Number): + wandb.log({prefix + key: stats[key]}, step=step) + + +try: + from azureml.core import Run +except ImportError: + Run = None + + +class AzureMLProgressBarWrapper(BaseProgressBar): + """Log to Azure ML""" + + def __init__(self, wrapped_bar): + self.wrapped_bar = wrapped_bar + if Run is None: + logger.warning("azureml.core not found, pip install azureml-core") + return + self.run = Run.get_context() + + def __exit__(self, *exc): + if Run is not None: + self.run.complete() + return False + + def __iter__(self): + return iter(self.wrapped_bar) + + def log(self, stats, tag=None, step=None): + """Log intermediate stats to AzureML""" + self._log_to_azureml(stats, tag, step) + self.wrapped_bar.log(stats, tag=tag, step=step) + + def print(self, stats, tag=None, step=None): + """Print end-of-epoch stats""" + self._log_to_azureml(stats, tag, step) + self.wrapped_bar.print(stats, tag=tag, step=step) + + def update_config(self, config): + """Log latest configuration.""" + self.wrapped_bar.update_config(config) + + def _log_to_azureml(self, stats, tag=None, step=None): + if Run is None: + return + if step is None: + step = stats["num_updates"] + + prefix = "" if tag is None else tag + "/" + + for key in stats.keys() - {"num_updates"}: + name = prefix + key + if isinstance(stats[key], AverageMeter): + self.run.log_row(name=name, **{"step": step, key: stats[key].val}) + elif isinstance(stats[key], Number): + self.run.log_row(name=name, **{"step": step, key: stats[key]}) diff --git a/fairseq/model_parallel/__init__.py b/fairseq/model_parallel/__init__.py index cc563db40b..69f2168487 100644 --- a/fairseq/model_parallel/__init__.py +++ b/fairseq/model_parallel/__init__.py @@ -3,4 +3,4 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. -from . import criterions, modules, models # noqa +from . import criterions, models, modules # noqa diff --git a/fairseq/model_parallel/criterions/__init__.py b/fairseq/model_parallel/criterions/__init__.py index b74de55982..5fae7bd4c2 100644 --- a/fairseq/model_parallel/criterions/__init__.py +++ b/fairseq/model_parallel/criterions/__init__.py @@ -8,7 +8,7 @@ # automatically import any Python files in the criterions/ directory -for file in os.listdir(os.path.dirname(__file__)): - if file.endswith('.py') and not file.startswith('_'): - module = file[:file.find('.py')] - importlib.import_module('fairseq.model_parallel.criterions.' + module) +for file in sorted(os.listdir(os.path.dirname(__file__))): + if file.endswith(".py") and not file.startswith("_"): + module = file[: file.find(".py")] + importlib.import_module("fairseq.model_parallel.criterions." + module) diff --git a/fairseq/model_parallel/criterions/vocab_parallel_cross_entropy.py b/fairseq/model_parallel/criterions/vocab_parallel_cross_entropy.py index eab8f9af4e..35c50ee152 100644 --- a/fairseq/model_parallel/criterions/vocab_parallel_cross_entropy.py +++ b/fairseq/model_parallel/criterions/vocab_parallel_cross_entropy.py @@ -8,24 +8,27 @@ from fairseq import metrics, utils from fairseq.criterions import FairseqCriterion, register_criterion + try: - from fairseq.model_parallel.megatron.mpu.cross_entropy import vocab_parallel_cross_entropy + from fairseq.model_parallel.megatron.mpu.cross_entropy import ( + vocab_parallel_cross_entropy, + ) + has_megatron_submodule = True except (ImportError, ModuleNotFoundError): has_megatron_submodule = False -@register_criterion('vocab_parallel_cross_entropy') +@register_criterion("vocab_parallel_cross_entropy") class VocabParallelCrossEntropyCriterion(FairseqCriterion): - def __init__(self, task, sentence_avg): super().__init__(task) self.sentence_avg = sentence_avg if not has_megatron_submodule: raise ImportError( - '\n\nPlease install the megatron submodule:' - '\n\n git submodule update --init ' - 'fairseq/model_parallel/megatron' + "\n\nPlease install the megatron submodule:" + "\n\n git submodule update --init " + "fairseq/model_parallel/megatron" ) def forward(self, model, sample, reduce=True): @@ -36,33 +39,43 @@ def forward(self, model, sample, reduce=True): 2) the sample size, which is used as the denominator for the gradient 3) logging outputs to display while training """ - net_output = model(**sample['net_input']) - target = sample['target'] + net_output = model(**sample["net_input"]) + target = sample["target"] loss = vocab_parallel_cross_entropy(net_output[0].float(), target) loss = (loss * (target != self.padding_idx)).sum() - sample_size = sample['target'].size(0) if self.sentence_avg else sample['ntokens'] + sample_size = ( + sample["target"].size(0) if self.sentence_avg else sample["ntokens"] + ) logging_output = { - 'loss': utils.item(loss.data) if reduce else loss.data, - 'ntokens': sample['ntokens'], - 'nsentences': sample['target'].size(0), - 'sample_size': sample_size, + "loss": utils.item(loss.data) if reduce else loss.data, + "ntokens": sample["ntokens"], + "nsentences": sample["target"].size(0), + "sample_size": sample_size, } return loss, sample_size, logging_output @staticmethod def reduce_metrics(logging_outputs) -> None: """Aggregate logging outputs from data parallel training.""" - loss_sum = sum(log.get('loss', 0) for log in logging_outputs) - ntokens = sum(log.get('ntokens', 0) for log in logging_outputs) - sample_size = sum(log.get('sample_size', 0) for log in logging_outputs) + loss_sum = sum(log.get("loss", 0) for log in logging_outputs) + ntokens = sum(log.get("ntokens", 0) for log in logging_outputs) + sample_size = sum(log.get("sample_size", 0) for log in logging_outputs) - metrics.log_scalar('loss', loss_sum / sample_size / math.log(2), sample_size, round=3) + metrics.log_scalar( + "loss", loss_sum / sample_size / math.log(2), sample_size, round=3 + ) if sample_size != ntokens: - metrics.log_scalar('nll_loss', loss_sum / ntokens / math.log(2), ntokens, round=3) - metrics.log_derived('ppl', lambda meters: utils.get_perplexity(meters['nll_loss'].avg)) + metrics.log_scalar( + "nll_loss", loss_sum / ntokens / math.log(2), ntokens, round=3 + ) + metrics.log_derived( + "ppl", lambda meters: utils.get_perplexity(meters["nll_loss"].avg) + ) else: - metrics.log_derived('ppl', lambda meters: utils.get_perplexity(meters['loss'].avg)) + metrics.log_derived( + "ppl", lambda meters: utils.get_perplexity(meters["loss"].avg) + ) @staticmethod def logging_outputs_can_be_summed() -> bool: diff --git a/fairseq/model_parallel/megatron_trainer.py b/fairseq/model_parallel/megatron_trainer.py index d1142a993c..8ab4657f73 100644 --- a/fairseq/model_parallel/megatron_trainer.py +++ b/fairseq/model_parallel/megatron_trainer.py @@ -7,57 +7,65 @@ Train a network across multiple GPUs. """ -from fairseq import distributed_utils +from fairseq.dataclass.configs import FairseqConfig +from fairseq.distributed import utils as distributed_utils from fairseq.trainer import Trainer try: from fairseq.model_parallel.megatron.mpu import ( - get_data_parallel_group, get_data_parallel_rank, get_data_parallel_world_size, - get_model_parallel_group, get_model_parallel_src_rank, + get_cuda_rng_tracker, ) + has_megatron_submodule = True except (ImportError, ModuleNotFoundError): has_megatron_submodule = False class MegatronTrainer(Trainer): - """Main class for model parallel with data parallel training. - """ - def __init__(self, args, task, model, criterion): + """Main class for model parallel with data parallel training.""" + + def __init__(self, cfg: FairseqConfig, task, model, criterion, **kwargs): if not has_megatron_submodule: raise ImportError( - '\n\nPlease install the megatron submodule:' - '\n\n git submodule update --init ' - 'fairseq/model_parallel/megatron' + "\n\nPlease install the megatron submodule:" + "\n\n git submodule update --init " + "fairseq/model_parallel/megatron" ) - super().__init__(args, task, model, criterion) - - @property - def data_parallel_world_size(self): - return get_data_parallel_world_size() - - @property - def data_parallel_process_group(self): - return get_data_parallel_group() - - @property - def data_parallel_rank(self): - return get_data_parallel_rank() - - @property - def is_data_parallel_master(self): - return get_model_parallel_src_rank() == 0 + super().__init__(cfg, task, model, criterion, **kwargs) def clip_grad_norm(self, clip_norm): def _aggregate_model_parallel_grad_norm(total_norm): total_norm = total_norm ** 2 - distributed_utils.all_reduce(total_norm, group=get_model_parallel_group()) + distributed_utils.all_reduce( + total_norm, group=distributed_utils.get_model_parallel_group() + ) total_norm = total_norm ** 0.5 return total_norm + return self.optimizer.clip_grad_norm( clip_norm, aggregate_norm_fn=_aggregate_model_parallel_grad_norm, ) + + def save_checkpoint(self, filename, extra_state): + """Save all training state in a checkpoint file.""" + extra_state['rng_tracker_states'] \ + = get_cuda_rng_tracker().get_states() + super().save_checkpoint(filename, extra_state) + + def load_checkpoint( + self, + filename, + reset_optimizer=False, + reset_lr_scheduler=False, + optimizer_overrides=None, + reset_meters=False, + ): + extra_state = super().load_checkpoint(filename, reset_optimizer=reset_optimizer, reset_lr_scheduler=reset_lr_scheduler, optimizer_overrides=optimizer_overrides, reset_meters=reset_meters) + if extra_state is not None and 'rng_tracker_states' in extra_state: + get_cuda_rng_tracker().set_states( + extra_state['rng_tracker_states']) + return extra_state diff --git a/fairseq/model_parallel/models/__init__.py b/fairseq/model_parallel/models/__init__.py index beae8afa80..3532479e52 100644 --- a/fairseq/model_parallel/models/__init__.py +++ b/fairseq/model_parallel/models/__init__.py @@ -7,7 +7,14 @@ import os -for file in os.listdir(os.path.dirname(__file__)): - if file.endswith('.py') and not file.startswith('_'): - model_name = file[:file.find('.py')] - importlib.import_module('fairseq.model_parallel.models.' + model_name) +# automatically import any Python files in the models/ directory +models_dir = os.path.dirname(__file__) +for file in os.listdir(models_dir): + path = os.path.join(models_dir, file) + if ( + not file.startswith("_") + and not file.startswith(".") + and (file.endswith(".py") or os.path.isdir(path)) + ): + model_name = file[: file.find(".py")] if file.endswith(".py") else file + module = importlib.import_module("fairseq.model_parallel.models." + model_name) diff --git a/fairseq/model_parallel/models/pipeline_parallel_transformer/__init__.py b/fairseq/model_parallel/models/pipeline_parallel_transformer/__init__.py new file mode 100644 index 0000000000..117827c3e9 --- /dev/null +++ b/fairseq/model_parallel/models/pipeline_parallel_transformer/__init__.py @@ -0,0 +1,6 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from .model import * # noqa diff --git a/fairseq/model_parallel/models/pipeline_parallel_transformer/layers.py b/fairseq/model_parallel/models/pipeline_parallel_transformer/layers.py new file mode 100644 index 0000000000..eb81ded341 --- /dev/null +++ b/fairseq/model_parallel/models/pipeline_parallel_transformer/layers.py @@ -0,0 +1,600 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math +from collections import namedtuple + +import torch +import torch.nn as nn +import torch.nn.functional as F +from fairseq import options, utils +from fairseq.modules import ( + AdaptiveSoftmax, + LayerNorm, + MultiheadAttention, + PositionalEmbedding, +) + + +EncoderOut = namedtuple( + "TransformerEncoderOut", + [ + "encoder_out", # T x B x C + "encoder_padding_mask", # B x T + "encoder_embedding", # B x T x C + "encoder_states", # List[T x B x C] + ], +) + + +class TransformerEncoderEmbedding(nn.Module): + """ Encoder Embedding + Positional Embedding """ + + def __init__(self, args, embed_tokens): + super().__init__() + self.dropout = args.dropout + self.max_source_positions = args.max_source_positions + self.embed_tokens = embed_tokens + if isinstance(embed_tokens, nn.ModuleList): + self.padding_idx = embed_tokens[0].padding_idx + embed_dim = sum(e.embedding_dim for e in embed_tokens) + else: + self.padding_idx = embed_tokens.padding_idx + embed_dim = embed_tokens.embedding_dim + self.embed_scale = math.sqrt(embed_dim) + self.embed_positions = ( + PositionalEmbedding( + args.max_source_positions, + embed_dim, + self.padding_idx, + learned=args.encoder_learned_pos, + ) + if not args.no_token_positional_embeddings + else None + ) + if getattr(args, "layernorm_embedding", False): + self.layernorm_embedding = LayerNorm(embed_dim) + else: + self.layernorm_embedding = None + + def forward(self, input): + # embed tokens and positions + src_tokens = input[0] + prev_output_tokens = input[2] + if isinstance(self.embed_tokens, nn.ModuleList): + x_embed_list = [] + for embed_tokens_part in self.embed_tokens: + x_embed_list.append(embed_tokens_part(src_tokens)) + + embedded = torch.cat(x_embed_list, dim=-1) + else: + embedded = self.embed_tokens(src_tokens) + x = embed = self.embed_scale * embedded + if self.embed_positions is not None: + x = embed + self.embed_positions(src_tokens) + if self.layernorm_embedding: + x = self.layernorm_embedding(x) + x = F.dropout(x, p=self.dropout, training=self.training) + # B x T x C -> T x B x C + x = x.transpose(0, 1) + + # compute padding mask + encoder_padding_mask = src_tokens.eq(self.padding_idx) + return (x, encoder_padding_mask, prev_output_tokens) + + +class TransformerEncoderLayerNorm(nn.Module): + """ + Layer norm at the the end of all encoder layers if + args.encoder_enormalize_before = True + """ + + def __init__(self, args, embed_dim): + super().__init__() + if args.encoder_normalize_before: + self.layer_norm = LayerNorm(embed_dim) + else: + self.layer_norm = None + + def forward(self, input): + x = input[0] + encoder_padding_mask = input[1] + prev_output_tokens = input[2] + if self.layer_norm: + x = self.layer_norm(x) + # keeping track of the incremental_state is not supported yet + return (x, encoder_padding_mask, prev_output_tokens) + + +class TransformerDecoderEmbedding(nn.Module): + """ Decoder Embedding + Positional Embedding """ + + def __init__(self, args, embed_tokens): + super().__init__() + self.dropout = args.dropout + self.share_input_output_embed = args.share_decoder_input_output_embed + input_embed_dim = ( + sum(e.embedding_dim for e in embed_tokens) + if isinstance(embed_tokens, nn.ModuleList) + else embed_tokens.embedding_dim + ) + embed_dim = args.decoder_embed_dim + self.output_embed_dim = args.decoder_output_dim + + padding_idx = ( + embed_tokens[0].padding_idx + if isinstance(embed_tokens, nn.ModuleList) + else embed_tokens.padding_idx + ) + self.max_target_positions = args.max_target_positions + + self.embed_tokens = embed_tokens + self.embed_scale = math.sqrt(embed_dim) # todo: try with input_embed_dim + + self.project_in_dim = ( + Linear(input_embed_dim, embed_dim, bias=False) + if embed_dim != input_embed_dim + else None + ) + + self.embed_positions = ( + PositionalEmbedding( + args.max_target_positions, + embed_dim, + padding_idx, + learned=args.decoder_learned_pos, + ) + if not args.no_token_positional_embeddings + else None + ) + + def forward(self, input): + mt_task = False + if isinstance(input, tuple): + if len(input) == 3: + encoder_out = input[0] + encoder_padding_mask = input[1] + prev_output_tokens = input[2] + incremental_state = None # Hardcoding to avoid passing of None objects + mt_task = True + else: + # HACK for now, need to fix (TODO sidgoyal) + prev_output_tokens = input[0] + # discard "src_lengths" + encoder_out = None + encoder_padding_mask = None + incremental_state = None + + else: + prev_output_tokens = input + encoder_out = None + encoder_padding_mask = None + incremental_state = None + + positions = ( + self.embed_positions( + prev_output_tokens, + incremental_state=incremental_state, + ) + if self.embed_positions is not None + else None + ) + + if incremental_state is not None: + prev_output_tokens = prev_output_tokens[:, -1:] + if positions is not None: + positions = positions[:, -1:] + + # embed tokens and positions + + if isinstance(self.embed_tokens, nn.ModuleList): + x_embed_list = [] + for embed_tokens_part in self.embed_tokens: + x_embed_list.append(embed_tokens_part(prev_output_tokens)) + + x = self.embed_scale * torch.cat(x_embed_list, dim=-1) + else: + x = self.embed_scale * self.embed_tokens(prev_output_tokens) + + if self.project_in_dim is not None: + x = self.project_in_dim(x) + + if positions is not None: + x += positions + x = F.dropout(x, p=self.dropout, training=self.training) + + # B x T x C -> T x B x C + x = x.transpose(0, 1) + if mt_task: + return (x, encoder_out, encoder_padding_mask) + return x + + +class TransformerDecoderOutputLayer(nn.Module): + def __init__(self, args, embed_tokens, dictionary): + super().__init__() + self.share_input_output_embed = args.share_decoder_input_output_embed + self.embed_tokens = embed_tokens + self.output_embed_dim = args.decoder_output_dim + embed_dim = args.decoder_embed_dim + + self.project_out_dim = ( + Linear(embed_dim, self.output_embed_dim, bias=False) + if embed_dim != self.output_embed_dim and not args.tie_adaptive_weights + else None + ) + self.adaptive_softmax = None + if args.adaptive_softmax_cutoff is not None: + assert not isinstance(embed_tokens, nn.ModuleList) + self.adaptive_softmax = AdaptiveSoftmax( + len(dictionary), + self.output_embed_dim, + options.eval_str_list(args.adaptive_softmax_cutoff, type=int), + dropout=args.adaptive_softmax_dropout, + adaptive_inputs=embed_tokens if args.tie_adaptive_weights else None, + factor=args.adaptive_softmax_factor, + tie_proj=args.tie_adaptive_proj, + ) + elif not self.share_input_output_embed: + self.embed_tokens = nn.Parameter( + torch.Tensor(len(dictionary), self.output_embed_dim) + ) + nn.init.normal_( + self.embed_tokens, mean=0, std=self.output_embed_dim ** -0.5 + ) + + if args.decoder_normalize_before and not getattr( + args, "no_decoder_final_norm", False + ): + self.layer_norm = LayerNorm(embed_dim) + else: + self.layer_norm = None + + def forward(self, input, apply_final_proj=True): + if isinstance(input, tuple): + x = input[0] + else: + x = input + + if self.layer_norm: + x = self.layer_norm(x) + + # T x B x C -> B x T x C + x = x.transpose(0, 1) + + if self.project_out_dim is not None: + x = self.project_out_dim(x) + if apply_final_proj: + x = self.output_layer(x) + return x + + def output_layer(self, features, **kwargs): + """Project features to the vocabulary size.""" + if self.adaptive_softmax is None: + # project back to size of vocabulary + if self.share_input_output_embed: + if isinstance(self.embed_tokens, nn.ModuleList): + output = None + for i, emb in enumerate(self.embed_tokens): + sidx = i * emb.embedding_dim + eidx = (i + 1) * emb.embedding_dim + if output is None: + output = F.linear(features[:, :, sidx:eidx], emb.weight) + else: + output += F.linear(features[:, :, sidx:eidx], emb.weight) + + return output + else: + return F.linear(features, self.embed_tokens.weight) + else: + return F.linear(features, self.embed_tokens) + else: + return features + + +class TransformerEncoderLayer(nn.Module): + """Encoder layer block. + In the original paper each operation (multi-head attention or FFN) is + postprocessed with: `dropout -> add residual -> layernorm`. In the + tensor2tensor code they suggest that learning is more robust when + preprocessing each layer with layernorm and postprocessing with: + `dropout -> add residual`. We default to the approach in the paper, but the + tensor2tensor approach can be enabled by setting + *args.encoder_normalize_before* to ``True``. + + Args: + args (argparse.Namespace): parsed command-line arguments + """ + + def __init__(self, args): + super().__init__() + self.embed_dim = args.encoder_embed_dim + self.self_attn = MultiheadAttention( + self.embed_dim, + args.encoder_attention_heads, + dropout=args.attention_dropout, + self_attention=True, + ) + self.self_attn_layer_norm = LayerNorm(self.embed_dim) + self.dropout = args.dropout + self.activation_fn = utils.get_activation_fn( + activation=getattr(args, "activation_fn", "relu") + ) + self.activation_dropout = getattr(args, "activation_dropout", 0) + if self.activation_dropout == 0: + # for backwards compatibility with models that use args.relu_dropout + self.activation_dropout = getattr(args, "relu_dropout", 0) + self.normalize_before = args.encoder_normalize_before + self.fc1 = Linear(self.embed_dim, args.encoder_ffn_embed_dim) + self.fc2 = Linear(args.encoder_ffn_embed_dim, self.embed_dim) + self.final_layer_norm = LayerNorm(self.embed_dim) + + def upgrade_state_dict_named(self, state_dict, name): + """ + Rename layer norm states from `...layer_norms.0.weight` to + `...self_attn_layer_norm.weight` and `...layer_norms.1.weight` to + `...final_layer_norm.weight` + """ + layer_norm_map = {"0": "self_attn_layer_norm", "1": "final_layer_norm"} + for old, new in layer_norm_map.items(): + for m in ("weight", "bias"): + k = "{}.layer_norms.{}.{}".format(name, old, m) + if k in state_dict: + state_dict["{}.{}.{}".format(name, new, m)] = state_dict[k] + del state_dict[k] + + def forward(self, input): + """ + Args: + input (Tuple): + input[0] (Tensor): input to the layer of shape `(seq_len, batch, embed_dim)` + input[1] (ByteTensor/FloatTensor): encoder padding mask - + binary ByteTensor of shape `(batch, src_len)` where padding elements + are indicated by ``1``. + input[2] (LongTensor): previous decoder outputs of shape + `(batch, tgt_len)`, for teacher forcing) + Returns: + output (Tuple): + output[0] (Tensor): encoded output of shape `(batch, src_len, embed_dim)` + output[1] (ByteTensor/FloatTensor): encoder padding mask + output[2] (LongTensor): previous decoder outputs + """ + x = input[0] + encoder_padding_mask = input[1] + prev_output_tokens = input[2] + residual = x + x = self.maybe_layer_norm(self.self_attn_layer_norm, x, before=True) + x, _ = self.self_attn( + query=x, key=x, value=x, key_padding_mask=encoder_padding_mask + ) + x = F.dropout(x, p=self.dropout, training=self.training) + x = residual + x + x = self.maybe_layer_norm(self.self_attn_layer_norm, x, after=True) + + residual = x + x = self.maybe_layer_norm(self.final_layer_norm, x, before=True) + x = self.activation_fn(self.fc1(x)) + x = F.dropout(x, p=self.activation_dropout, training=self.training) + x = self.fc2(x) + x = F.dropout(x, p=self.dropout, training=self.training) + x = residual + x + x = self.maybe_layer_norm(self.final_layer_norm, x, after=True) + return (x, encoder_padding_mask, prev_output_tokens) + + def maybe_layer_norm(self, layer_norm, x, before=False, after=False): + assert before ^ after + if after ^ self.normalize_before: + return layer_norm(x) + else: + return x + + +class TransformerDecoderLayer(nn.Module): + """Decoder layer block. + + In the original paper each operation (multi-head attention, encoder + attention or FFN) is postprocessed with: `dropout -> add residual -> + layernorm`. In the tensor2tensor code they suggest that learning is more + robust when preprocessing each layer with layernorm and postprocessing with: + `dropout -> add residual`. We default to the approach in the paper, but the + tensor2tensor approach can be enabled by setting + *args.decoder_normalize_before* to ``True``. + + Args: + args (argparse.Namespace): parsed command-line arguments + no_encoder_attn (bool, optional): whether to attend to encoder outputs + (default: False). + """ + + def __init__( + self, args, no_encoder_attn=False, add_bias_kv=False, add_zero_attn=False + ): + super().__init__() + self.embed_dim = args.decoder_embed_dim + self.self_attn = MultiheadAttention( + embed_dim=self.embed_dim, + num_heads=args.decoder_attention_heads, + dropout=args.attention_dropout, + add_bias_kv=add_bias_kv, + add_zero_attn=add_zero_attn, + self_attention=True, + ) + self.dropout = args.dropout + self.activation_fn = utils.get_activation_fn( + activation=getattr(args, "activation_fn", "relu") + ) + self.activation_dropout = getattr(args, "activation_dropout", 0) + if self.activation_dropout == 0: + # for backwards compatibility with models that use args.relu_dropout + self.activation_dropout = getattr(args, "relu_dropout", 0) + self.normalize_before = args.decoder_normalize_before + + # use layerNorm rather than FusedLayerNorm for exporting. + # char_inputs can be used to determint this. + # TODO remove this once we update apex with the fix + export = getattr(args, "char_inputs", False) + self.self_attn_layer_norm = LayerNorm(self.embed_dim, export=export) + + if no_encoder_attn: + self.encoder_attn = None + self.encoder_attn_layer_norm = None + else: + self.encoder_attn = MultiheadAttention( + self.embed_dim, + args.decoder_attention_heads, + kdim=getattr(args, "encoder_embed_dim", None), + vdim=getattr(args, "encoder_embed_dim", None), + dropout=args.attention_dropout, + encoder_decoder_attention=True, + ) + self.encoder_attn_layer_norm = LayerNorm(self.embed_dim, export=export) + + self.fc1 = Linear(self.embed_dim, args.decoder_ffn_embed_dim) + self.fc2 = Linear(args.decoder_ffn_embed_dim, self.embed_dim) + + self.final_layer_norm = LayerNorm(self.embed_dim, export=export) + self.need_attn = True + + self.onnx_trace = False + + def prepare_for_onnx_export_(self): + self.onnx_trace = True + + def forward(self, input): + """ + Args: + input (Tuple): + input[0] (Tensor): input to the layer of shape `(seq_len, batch, embed_dim)` + input[1] (Tensor): encoder output of shape `(batch, src_len, embed_dim)` + input[2] (ByteTensor/FloatTensor): encoder padding mask - + binary ByteTensor of shape `(batch, src_len)` where padding elements + are indicated by ``1``. + Returns: + output (Tuple): + output[0] (Tensor): encoded output of shape `(batch, src_len, embed_dim)` + output[1] (ByteTensor/FloatTensor): encoder padding mask + output[2] (LongTensor): previous decoder outputs + """ + # Note: incremental state is not yet supported + mt_task = False + if isinstance(input, tuple): + x = input[0] + encoder_out = input[1] + encoder_padding_mask = input[2] + incremental_state = None + mt_task = True + else: + x = input + encoder_out = None + encoder_padding_mask = None + incremental_state = None + + if incremental_state is None: + self_attn_mask = self.buffered_future_mask(x) + else: + self_attn_mask = None + + # TODO: add back prev_self_attn_state, prev_attn_state, + # self_attn_padding_mask + prev_self_attn_state = None + prev_attn_state = None + self_attn_padding_mask = None + + residual = x + x = self.maybe_layer_norm(self.self_attn_layer_norm, x, before=True) + if prev_self_attn_state is not None: + if incremental_state is None: + incremental_state = {} + prev_key, prev_value = prev_self_attn_state + saved_state = {"prev_key": prev_key, "prev_value": prev_value} + self.self_attn._set_input_buffer(incremental_state, saved_state) + x, attn = self.self_attn( + query=x, + key=x, + value=x, + key_padding_mask=self_attn_padding_mask, + incremental_state=incremental_state, + need_weights=False, + attn_mask=self_attn_mask, + ) + x = F.dropout(x, p=self.dropout, training=self.training) + x = residual + x + x = self.maybe_layer_norm(self.self_attn_layer_norm, x, after=True) + + if self.encoder_attn is not None: + residual = x + x = self.maybe_layer_norm(self.encoder_attn_layer_norm, x, before=True) + if prev_attn_state is not None: + if incremental_state is None: + incremental_state = {} + prev_key, prev_value = prev_attn_state + saved_state = {"prev_key": prev_key, "prev_value": prev_value} + self.encoder_attn._set_input_buffer(incremental_state, saved_state) + x, attn = self.encoder_attn( + query=x, + key=encoder_out, + value=encoder_out, + key_padding_mask=encoder_padding_mask, + incremental_state=incremental_state, + static_kv=True, + need_weights=(not self.training and self.need_attn), + ) + x = F.dropout(x, p=self.dropout, training=self.training) + x = residual + x + x = self.maybe_layer_norm(self.encoder_attn_layer_norm, x, after=True) + + residual = x + x = self.maybe_layer_norm(self.final_layer_norm, x, before=True) + x = self.activation_fn(self.fc1(x)) + x = F.dropout(x, p=self.activation_dropout, training=self.training) + x = self.fc2(x) + x = F.dropout(x, p=self.dropout, training=self.training) + x = residual + x + x = self.maybe_layer_norm(self.final_layer_norm, x, after=True) + + if mt_task: + return (x, encoder_out, encoder_padding_mask) + return x + + def buffered_future_mask(self, tensor): + dim = tensor.size(0) + if ( + not hasattr(self, "_future_mask") + or self._future_mask is None + or self._future_mask.device != tensor.device + ): + self._future_mask = torch.triu( + utils.fill_with_neg_inf(tensor.new(dim, dim)), 1 + ) + if self._future_mask.size(0) < dim: + self._future_mask = torch.triu( + utils.fill_with_neg_inf(self._future_mask.resize_(dim, dim)), 1 + ) + return self._future_mask[:dim, :dim] + + def maybe_layer_norm(self, layer_norm, x, before=False, after=False): + assert before ^ after + if after ^ self.normalize_before: + return layer_norm(x) + else: + return x + + def make_generation_fast_(self, need_attn=False, **kwargs): + self.need_attn = need_attn + + +def Embedding(num_embeddings, embedding_dim, padding_idx): + m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx) + nn.init.normal_(m.weight, mean=0, std=embedding_dim ** -0.5) + nn.init.constant_(m.weight[padding_idx], 0) + return m + + +def Linear(in_features, out_features, bias=True): + m = nn.Linear(in_features, out_features, bias) + nn.init.xavier_uniform_(m.weight) + if bias: + nn.init.constant_(m.bias, 0.0) + return m diff --git a/fairseq/model_parallel/models/pipeline_parallel_transformer/model.py b/fairseq/model_parallel/models/pipeline_parallel_transformer/model.py new file mode 100644 index 0000000000..7f30dd98bb --- /dev/null +++ b/fairseq/model_parallel/models/pipeline_parallel_transformer/model.py @@ -0,0 +1,767 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging + +import torch +import torch.nn as nn +import torch.nn.functional as F +from fairseq import utils +from fairseq.model_parallel.models.pipeline_parallel_transformer.layers import ( + Embedding, + TransformerDecoderEmbedding, + TransformerDecoderLayer, + TransformerDecoderOutputLayer, + TransformerEncoderEmbedding, + TransformerEncoderLayer, + TransformerEncoderLayerNorm, +) +from fairseq.models import ( + BaseFairseqModel, + FairseqDecoder, + FairseqEncoder, + register_model, + register_model_architecture, +) +from fairseq.models.fairseq_encoder import EncoderOut +from fairseq.models.transformer import ( + base_architecture, + transformer_iwslt_de_en, + transformer_wmt_en_de_big, +) +from fairseq.modules import SinusoidalPositionalEmbedding + + +logger = logging.getLogger(__name__) + + +DEFAULT_MAX_SOURCE_POSITIONS = 1024 +DEFAULT_MAX_TARGET_POSITIONS = 1024 +TORCH_PIPE = False +RPC_INIT = False + +def import_pipe(): + global TORCH_PIPE + global RPC_INIT + try: + from torch.distributed.pipeline.sync import Pipe # noqa + global Pipe + from torch.distributed.pipeline.sync.utils import partition_model + global partition_model + from torch.distributed import rpc + import tempfile + TORCH_PIPE = True + # Initialize single process RPC agent since TORCH_PIPE requires + # RRef. RRef depends on RPC being initialized and as a result we initialize + # RPC with a single node. + tmpfile = tempfile.NamedTemporaryFile() + if not RPC_INIT: + rpc.init_rpc( + name="worker", + rank=0, + world_size=1, + rpc_backend_options=rpc.TensorPipeRpcBackendOptions( + init_method="file://{}".format(tmpfile.name), + ) + ) + RPC_INIT = True + logger.info('Using torch pipe') + except ImportError: + try: + from fairscale.nn import Pipe # noqa + logger.info('Using fairscale pipe') + except ImportError: + raise ImportError("Please install fairscale with: pip install fairscale") + + +@register_model("pipeline_parallel_transformer") +class PipelineParallelTransformerModel(BaseFairseqModel): + def __init__(self, encoder, decoder, balance, devices, chunks, checkpoint): + import_pipe() + super().__init__() + assert isinstance(encoder, FairseqEncoder) + assert isinstance(decoder, FairseqDecoder) + encoder_module_list = ( + [encoder.embedding_layer] + + list(encoder.encoder_layers) + + [encoder.final_layer_norm] + ) + self.num_encoder_modules = len(encoder_module_list) + decoder_module_list = ( + [decoder.embedding_layer] + + list(decoder.decoder_layers) + + [decoder.decoder_output_layer] + ) + self.num_decoder_modules = len(decoder_module_list) + module_list = encoder_module_list + decoder_module_list + self.devices = devices + if TORCH_PIPE: + self.model = Pipe( + partition_model(nn.Sequential(*module_list), balance, devices), + chunks=chunks, + checkpoint=checkpoint, + ) + else: + self.model = Pipe( + nn.Sequential(*module_list), + balance=balance, + devices=devices, + chunks=chunks, + checkpoint=checkpoint, + ) + self.encoder_max_positions = self.max_positions_helper( + encoder.embedding_layer, "max_source_positions" + ) + self.decoder_max_positions = self.max_positions_helper( + decoder.embedding_layer, "max_target_positions" + ) + self.adaptive_softmax = getattr(decoder, "adaptive_softmax", None) + # Note: To be populated during inference + self.encoder = None + self.decoder = None + + def forward(self, src_tokens, src_lengths, prev_output_tokens): + if self.training: + input_lst = [src_tokens, src_lengths, prev_output_tokens] + input = tuple(i.to(self.devices[0], non_blocking=True) for i in input_lst) + if TORCH_PIPE: + return self.model(input).local_value() + else: + return self.model(input) + else: + assert self.encoder is not None and self.decoder is not None, ( + "encoder and decoder need to be initialized by " + + "calling the `prepare_for_inference_()` method" + ) + encoder_output_tuple = self.encoder(input) + return self.decoder(encoder_output_tuple) + + def prepare_for_inference_(self, cfg): + if self.encoder is not None and self.decoder is not None: + logger.info("Encoder and Decoder already initialized") + return + encoder_module_list = [] + decoder_module_list = [] + module_count = 0 + for partition in self.model.partitions: + for module in partition: + if module_count < self.num_encoder_modules: + encoder_module_list.append(module) + else: + decoder_module_list.append(module) + module_count += 1 + self.model = None + self.encoder = TransformerEncoder(cfg.distributed_training, None, None, encoder_module_list) + self.decoder = TransformerDecoder( + cfg.distributed_training, None, None, decoder_module_list=decoder_module_list + ) + + @staticmethod + def add_args(parser): + """Add model-specific arguments to the parser.""" + # fmt: off + parser.add_argument('--activation-fn', + choices=utils.get_available_activation_fns(), + help='activation function to use') + parser.add_argument('--dropout', type=float, metavar='D', + help='dropout probability') + parser.add_argument('--attention-dropout', type=float, metavar='D', + help='dropout probability for attention weights') + parser.add_argument('--activation-dropout', '--relu-dropout', type=float, metavar='D', + help='dropout probability after activation in FFN.') + parser.add_argument('--encoder-embed-path', type=str, metavar='STR', + help='path to pre-trained encoder embedding') + parser.add_argument('--encoder-embed-dim', type=int, metavar='N', + help='encoder embedding dimension') + parser.add_argument('--encoder-ffn-embed-dim', type=int, metavar='N', + help='encoder embedding dimension for FFN') + parser.add_argument('--encoder-layers', type=int, metavar='N', + help='num encoder layers') + parser.add_argument('--encoder-attention-heads', type=int, metavar='N', + help='num encoder attention heads') + parser.add_argument('--encoder-normalize-before', action='store_true', + help='apply layernorm before each encoder block') + parser.add_argument('--encoder-learned-pos', action='store_true', + help='use learned positional embeddings in the encoder') + parser.add_argument('--decoder-embed-path', type=str, metavar='STR', + help='path to pre-trained decoder embedding') + parser.add_argument('--decoder-embed-dim', type=int, metavar='N', + help='decoder embedding dimension') + parser.add_argument('--decoder-ffn-embed-dim', type=int, metavar='N', + help='decoder embedding dimension for FFN') + parser.add_argument('--decoder-layers', type=int, metavar='N', + help='num decoder layers') + parser.add_argument('--decoder-attention-heads', type=int, metavar='N', + help='num decoder attention heads') + parser.add_argument('--decoder-learned-pos', action='store_true', + help='use learned positional embeddings in the decoder') + parser.add_argument('--decoder-normalize-before', action='store_true', + help='apply layernorm before each decoder block') + parser.add_argument('--share-decoder-input-output-embed', action='store_true', + help='share decoder input and output embeddings') + parser.add_argument('--share-all-embeddings', action='store_true', + help='share encoder, decoder and output embeddings' + ' (requires shared dictionary and embed dim)') + parser.add_argument('--no-token-positional-embeddings', default=False, action='store_true', + help='if set, disables positional embeddings (outside self attention)') + parser.add_argument('--adaptive-softmax-cutoff', metavar='EXPR', + help='comma separated list of adaptive softmax cutoff points. ' + 'Must be used with adaptive_loss criterion'), + parser.add_argument('--adaptive-softmax-dropout', type=float, metavar='D', + help='sets adaptive softmax dropout for the tail projections') + parser.add_argument('--num-embedding-chunks', type=int, metavar='N', default=1, + help='Number of embedding layer chunks (enables more even distribution' + 'of optimizer states across data parallel nodes' + 'when using optimizer state sharding and' + 'a big embedding vocabulary)') + # fmt: on + + @classmethod + def build_model_base(cls, args, task): + """Build a new model instance.""" + + # make sure all arguments are present in older models + base_architecture(args) + + if not hasattr(args, "max_source_positions"): + args.max_source_positions = DEFAULT_MAX_SOURCE_POSITIONS + if not hasattr(args, "max_target_positions"): + args.max_target_positions = DEFAULT_MAX_TARGET_POSITIONS + + src_dict, tgt_dict = task.source_dictionary, task.target_dictionary + + def build_embedding(dictionary, embed_dim, path=None, num_embed_chunks=1): + assert embed_dim % num_embed_chunks == 0, ( + f"Number of embedding chunks = {num_embed_chunks} should be " + + f"divisible by the embedding dimension = {embed_dim}" + ) + assert path is None or num_embed_chunks == 1, ( + "Loading embedding from a path with number of embedding chunks > 1" + + " is not yet supported" + ) + num_embeddings = len(dictionary) + padding_idx = dictionary.pad() + # if provided, load from preloaded dictionaries + if path: + emb = Embedding(num_embeddings, embed_dim, padding_idx) + embed_dict = utils.parse_embedding(path) + utils.load_embedding(embed_dict, dictionary, emb) + else: + embed_chunk_dim = embed_dim // num_embed_chunks + emb = nn.ModuleList() + for i in range(num_embed_chunks): + emb.append(Embedding(num_embeddings, embed_chunk_dim, padding_idx)) + return emb + + num_embed_chunks = args.num_embedding_chunks + if args.share_all_embeddings: + if src_dict != tgt_dict: + raise ValueError("--share-all-embeddings requires a joined dictionary") + if args.encoder_embed_dim != args.decoder_embed_dim: + raise ValueError( + "--share-all-embeddings requires --encoder-embed-dim to match --decoder-embed-dim" + ) + if args.decoder_embed_path and ( + args.decoder_embed_path != args.encoder_embed_path + ): + raise ValueError( + "--share-all-embeddings not compatible with --decoder-embed-path" + ) + encoder_embed_tokens = build_embedding( + src_dict, + args.encoder_embed_dim, + args.encoder_embed_path, + num_embed_chunks, + ) + decoder_embed_tokens = encoder_embed_tokens + args.share_decoder_input_output_embed = True + else: + assert args.share_decoder_input_output_embed or num_embed_chunks == 1, ( + "Not sharing decoder I/O embeddings is not yet supported with number of " + + "embedding chunks > 1" + ) + encoder_embed_tokens = build_embedding( + src_dict, + args.encoder_embed_dim, + args.encoder_embed_path, + num_embed_chunks, + ) + decoder_embed_tokens = build_embedding( + tgt_dict, + args.decoder_embed_dim, + args.decoder_embed_path, + num_embed_chunks, + ) + + encoder = cls.build_encoder(args, src_dict, encoder_embed_tokens) + decoder = cls.build_decoder(args, tgt_dict, decoder_embed_tokens) + return (encoder, decoder) + + @classmethod + def build_encoder(cls, args, src_dict, embed_tokens): + return TransformerEncoder(args, src_dict, embed_tokens) + + @classmethod + def build_decoder(cls, args, tgt_dict, embed_tokens): + return TransformerDecoder(args, tgt_dict, embed_tokens) + + @classmethod + def build_model(cls, args, task): + encoder, decoder = cls.build_model_base(args, task) + return PipelineParallelTransformerModel( + encoder=encoder, + decoder=decoder, + balance=utils.eval_str_list(args.pipeline_balance, type=int), + devices=utils.eval_str_list(args.pipeline_devices, type=int), + chunks=args.pipeline_chunks, + checkpoint=args.pipeline_checkpoint, + ) + + def output_layer(self, features, **kwargs): + """Project features to the default output size (typically vocabulary size).""" + return self.decoder.output_layer(features, **kwargs) + + def max_positions(self): + """Maximum length supported by the model.""" + return (self.encoder_max_positions, self.decoder_max_positions) + + def max_positions_helper( + self, embedding_layer, max_positions_field="max_source_positions" + ): + """Maximum input length supported by the encoder or decoder.""" + if embedding_layer.embed_positions is None: + return getattr(embedding_layer, max_positions_field) + return min( + getattr(embedding_layer, max_positions_field), + embedding_layer.embed_positions.max_positions, + ) + + def get_normalized_probs(self, net_output, log_probs, sample=None): + """Get normalized probabilities (or log probs) from a net's output.""" + + if hasattr(self, "adaptive_softmax") and self.adaptive_softmax is not None: + if sample is not None: + assert "target" in sample + target = sample["target"] + else: + target = None + out = self.adaptive_softmax.get_log_prob(net_output, target=target) + return out.exp_() if not log_probs else out + + # A Pipe() module returns a tuple of tensors as the output. + # In this case, the tuple has one element - the output tensor of logits + logits = net_output if isinstance(net_output, torch.Tensor) else net_output[0] + if log_probs: + return utils.log_softmax(logits, dim=-1, onnx_trace=False) + else: + return utils.softmax(logits, dim=-1, onnx_trace=False) + + def max_decoder_positions(self): + """Maximum length supported by the decoder.""" + return self.decoder_max_positions + + def load_state_dict(self, state_dict, strict=True, model_cfg=None): + """Copies parameters and buffers from *state_dict* into this module and + its descendants. + + Overrides the method in :class:`nn.Module`. Compared with that method + this additionally "upgrades" *state_dicts* from old checkpoints. + """ + self.upgrade_state_dict(state_dict) + is_regular_transformer = not any("model.partitions" in k for k in state_dict) + if is_regular_transformer: + state_dict = self.convert_to_pipeline_parallel_state_dict(state_dict) + return super().load_state_dict(state_dict, strict) + + def convert_to_pipeline_parallel_state_dict(self, state_dict): + new_state_dict = self.state_dict() + encoder_layer_idx = 0 + decoder_layer_idx = 0 + encoder_key_suffixes = [ + "self_attn.k_proj.weight", + "self_attn.k_proj.bias", + "self_attn.v_proj.weight", + "self_attn.v_proj.bias", + "self_attn.q_proj.weight", + "self_attn.q_proj.bias", + "self_attn.out_proj.weight", + "self_attn.out_proj.bias", + "self_attn_layer_norm.weight", + "self_attn_layer_norm.bias", + "fc1.weight", + "fc1.bias", + "fc2.weight", + "fc2.bias", + "final_layer_norm.weight", + "final_layer_norm.bias", + ] + decoder_key_suffixes = [ + "self_attn.k_proj.weight", + "self_attn.k_proj.bias", + "self_attn.v_proj.weight", + "self_attn.v_proj.bias", + "self_attn.q_proj.weight", + "self_attn.q_proj.bias", + "self_attn.out_proj.weight", + "self_attn.out_proj.bias", + "self_attn_layer_norm.weight", + "self_attn_layer_norm.bias", + "encoder_attn.k_proj.weight", + "encoder_attn.k_proj.bias", + "encoder_attn.v_proj.weight", + "encoder_attn.v_proj.bias", + "encoder_attn.q_proj.weight", + "encoder_attn.q_proj.bias", + "encoder_attn.out_proj.weight", + "encoder_attn.out_proj.bias", + "encoder_attn_layer_norm.weight", + "encoder_attn_layer_norm.bias", + "fc1.weight", + "fc1.bias", + "fc2.weight", + "fc2.bias", + "final_layer_norm.weight", + "final_layer_norm.bias", + ] + for pid, partition in enumerate(self.model.partitions): + logger.info(f"Begin Partition {pid}") + for mid, module in enumerate(partition): + # fmt: off + if isinstance(module, TransformerEncoderEmbedding): + new_state_dict[f'model.partitions.{pid}.{mid}.embed_tokens.weight'] = state_dict['encoder.embed_tokens.weight'] + new_state_dict[f'model.partitions.{pid}.{mid}.embed_positions._float_tensor'] = state_dict['encoder.embed_positions._float_tensor'] + if isinstance(module, TransformerEncoderLayer): + for suffix in encoder_key_suffixes: + new_state_dict[f'model.partitions.{pid}.{mid}.{suffix}'] = state_dict[f'encoder.layers.{encoder_layer_idx}.{suffix}'] + encoder_layer_idx += 1 + if isinstance(module, TransformerDecoderLayer): + for suffix in decoder_key_suffixes: + new_state_dict[f'model.partitions.{pid}.{mid}.{suffix}'] = state_dict[f'decoder.layers.{decoder_layer_idx}.{suffix}'] + decoder_layer_idx += 1 + if isinstance(module, TransformerEncoderLayerNorm): + if 'encoder.layer_norm.weight' in state_dict: + new_state_dict[f'model.partitions.{pid}.{mid}.layer_norm.weight'] = state_dict['encoder.layer_norm.weight'] + new_state_dict[f'model.partitions.{pid}.{mid}.layer_norm.bias'] = state_dict['encoder.layer_norm.bias'] + if isinstance(module, TransformerDecoderEmbedding): + new_state_dict[f'model.partitions.{pid}.{mid}.embed_tokens.weight'] = state_dict['decoder.embed_tokens.weight'] + new_state_dict[f'model.partitions.{pid}.{mid}.embed_positions._float_tensor'] = state_dict['decoder.embed_positions._float_tensor'] + if isinstance(module, TransformerDecoderOutputLayer): + new_state_dict[f'model.partitions.{pid}.{mid}.output_projection.weight'] = state_dict['decoder.output_projection.weight'] + # fmt: on + return new_state_dict + + +class TransformerEncoder(FairseqEncoder): + """ + Transformer encoder consisting of *args.encoder_layers* layers. Each layer + is a :class:`TransformerEncoderLayer`. + + Args: + args (argparse.Namespace): parsed command-line arguments + dictionary (~fairseq.data.Dictionary): encoding dictionary + embed_tokens (torch.nn.Embedding): input embedding + """ + + def __init__(self, args, dictionary, embed_tokens, encoder_module_list=None): + super().__init__(dictionary) + self.register_buffer("version", torch.Tensor([3])) + import_pipe() + self.use_pipeline = encoder_module_list is not None + if not self.use_pipeline: + self.embedding_layer = TransformerEncoderEmbedding(args, embed_tokens) + self.encoder_layers = nn.Sequential(*[TransformerEncoderLayer(args) for i in range(args.encoder_layers)]) + if isinstance(embed_tokens, nn.ModuleList): + emb_dim = sum(e.embedding_dim for e in embed_tokens) + else: + emb_dim = embed_tokens.embedding_dim + self.final_layer_norm = TransformerEncoderLayerNorm(args, emb_dim) + else: + encoder_balance = utils.eval_str_list( + args.pipeline_encoder_balance, type=int + ) + encoder_devices = utils.eval_str_list( + args.pipeline_encoder_devices, type=int + ) + assert sum(encoder_balance) == len(encoder_module_list), ( + f"Sum of encoder_balance={encoder_balance} is not equal " + + f"to num_encoder_modules={len(encoder_module_list)}" + ) + if TORCH_PIPE: + self.model = Pipe( + module=partition_model(nn.Sequential(*encoder_module_list), encoder_balance, encoder_devices), + chunks=args.pipeline_chunks, + checkpoint=args.pipeline_checkpoint, + ) + else: + self.model = Pipe( + module=nn.Sequential(*encoder_module_list), + balance=encoder_balance, + devices=encoder_devices, + chunks=args.pipeline_chunks, + checkpoint=args.pipeline_checkpoint, + ) + + def forward(self, src_tokens, src_lengths): + """ + Args: + input_tuple( + src_tokens (LongTensor): tokens in the source language of shape + `(batch, src_len)` + src_lengths (torch.LongTensor): lengths of each source sentence of + shape `(batch)` + ) + + Returns: + output_tuple( + - **encoder_out** (Tensor): the last encoder layer's output of + shape `(src_len, batch, embed_dim)` + - **encoder_padding_mask** (ByteTensor): the positions of + padding elements of shape `(batch, src_len)` + - prev_output_tokens + - **encoder_states** (List[Tensor]): all intermediate + hidden states of shape `(src_len, batch, embed_dim)`. + Only populated if *return_all_hiddens* is True. + ) + """ + dummy_prev_output_tokens = torch.zeros( + 1, dtype=src_tokens.dtype, device=src_tokens.device + ) + input_tuple = (src_tokens, src_lengths, dummy_prev_output_tokens) + if self.use_pipeline: + input_tuple = tuple(i.to(self.model.devices[0]) for i in input_tuple) + if TORCH_PIPE: + encoder_out = self.model(input_tuple).local_value() + else: + encoder_out = self.model(input_tuple) + else: + encoder_embed_output_tuple = self.embedding_layer(input_tuple) + encoder_layers_output = self.encoder_layers(encoder_embed_output_tuple) + encoder_out = self.final_layer_norm(encoder_layers_output) + # first element is the encoder output + # second element is the encoder padding mask + # the remaining elements of EncoderOut are not computed by + # the PipelineParallelTransformer + return EncoderOut(encoder_out[0], encoder_out[1], None, None, None, None) + + def reorder_encoder_out(self, encoder_out, new_order): + """ + Reorder encoder output according to *new_order*. + + Args: + encoder_out: output from the ``forward()`` method + new_order (LongTensor): desired order + + Returns: + *encoder_out* rearranged according to *new_order* + """ + if encoder_out.encoder_out is not None: + encoder_out = encoder_out._replace( + encoder_out=encoder_out.encoder_out.index_select(1, new_order) + ) + if encoder_out.encoder_padding_mask is not None: + encoder_out = encoder_out._replace( + encoder_padding_mask=encoder_out.encoder_padding_mask.index_select( + 0, new_order + ) + ) + if encoder_out.encoder_embedding is not None: + encoder_out = encoder_out._replace( + encoder_embedding=encoder_out.encoder_embedding.index_select( + 0, new_order + ) + ) + if encoder_out.encoder_states is not None: + for idx, state in enumerate(encoder_out.encoder_states): + encoder_out.encoder_states[idx] = state.index_select(1, new_order) + return encoder_out + + def max_positions(self): + """Maximum input length supported by the encoder.""" + if self.embedding_layer.embed_positions is None: + return self.embedding_layer.max_source_positions + return min( + self.embedding_layer.max_source_positions, + self.embedding_layer.embed_positions.max_positions, + ) + + +class TransformerDecoder(FairseqDecoder): + """ + Transformer decoder consisting of *args.decoder_layers* layers. Each layer + is a :class:`TransformerDecoderLayer`. + + Args: + args (argparse.Namespace): parsed command-line arguments + dictionary (~fairseq.data.Dictionary): decoding dictionary + embed_tokens (torch.nn.Embedding): output embedding + no_encoder_attn (bool, optional): whether to attend to encoder outputs + (default: False). + """ + + def __init__( + self, + args, + dictionary, + embed_tokens, + no_encoder_attn=False, + decoder_module_list=None, + ): + super().__init__(dictionary) + self.register_buffer("version", torch.Tensor([3])) + import_pipe() + self.use_pipeline = decoder_module_list is not None + if not self.use_pipeline: + self.embedding_layer = TransformerDecoderEmbedding(args, embed_tokens) + self.decoder_layers = nn.Sequential(*[ + TransformerDecoderLayer(args, no_encoder_attn) + for _ in range(args.decoder_layers) + ]) + self.decoder_output_layer = TransformerDecoderOutputLayer( + args, embed_tokens, dictionary + ) + else: + decoder_balance = utils.eval_str_list( + args.pipeline_decoder_balance, type=int + ) + decoder_devices = utils.eval_str_list( + args.pipeline_decoder_devices, type=int + ) + assert sum(decoder_balance) == len(decoder_module_list), ( + f"Sum of decoder_balance={decoder_balance} is not equal " + + f"to num_decoder_modules={len(decoder_module_list)}" + ) + if TORCH_PIPE: + self.model = Pipe( + module=partition_model(nn.Sequential(*decoder_module_list), decoder_balance, decoder_devices), + chunks=args.pipeline_chunks, + checkpoint=args.pipeline_checkpoint, + ) + else: + self.model = Pipe( + module=nn.Sequential(*decoder_module_list), + balance=decoder_balance, + devices=decoder_devices, + chunks=args.pipeline_chunks, + checkpoint=args.pipeline_checkpoint, + ) + + def forward( + self, + prev_output_tokens, + encoder_out=None, + ): + """ + Args: + prev_output_tokens (LongTensor): previous decoder outputs of shape + `(batch, tgt_len)`, for teacher forcing + encoder_out (optional): output from the encoder, used for + encoder-side attention + incremental_state (dict): dictionary used for storing state during + :ref:`Incremental decoding` + features_only (bool, optional): only return features without + applying output layer (default: False). + + Returns: + tuple: + - the decoder's output of shape `(batch, tgt_len, vocab)` + - a dictionary with any model-specific outputs + """ + input_tuple = ( + encoder_out.encoder_out, + encoder_out.encoder_padding_mask, + prev_output_tokens, + ) + if self.use_pipeline: + input_tuple = tuple(i.to(self.model.devices[0]) for i in input_tuple) + if TORCH_PIPE: + return (self.model(input_tuple).local_value(),) + else: + return (self.model(input_tuple),) + else: + embed_layer_output = self.embedding_layer(input_tuple) + state = self.decoder_layers(embed_layer_output) + return (self.decoder_output_layer(state),) + + def output_layer(self, features, **kwargs): + """Project features to the vocabulary size.""" + if self.adaptive_softmax is None: + # project back to size of vocabulary + if self.share_input_output_embed: + return F.linear(features, self.embed_tokens.weight) + else: + return F.linear(features, self.embed_out) + else: + return features + + def max_positions(self): + """Maximum output length supported by the decoder.""" + if self.embedding_layer.embed_positions is None: + return self.embedding_layer.max_target_positions + return min( + self.embedding_layer.max_target_positions, + self.embedding_layer.embed_positions.max_positions, + ) + + def buffered_future_mask(self, tensor): + dim = tensor.size(0) + if ( + not hasattr(self, "_future_mask") + or self._future_mask is None + or self._future_mask.device != tensor.device + or self._future_mask.size(0) < dim + ): + self._future_mask = torch.triu( + utils.fill_with_neg_inf(tensor.new(dim, dim)), 1 + ) + return self._future_mask[:dim, :dim] + + def upgrade_state_dict_named(self, state_dict, name): + """Upgrade a (possibly old) state dict for new versions of fairseq.""" + if isinstance(self.embed_positions, SinusoidalPositionalEmbedding): + weights_key = "{}.embed_positions.weights".format(name) + if weights_key in state_dict: + del state_dict[weights_key] + state_dict[ + "{}.embed_positions._float_tensor".format(name) + ] = torch.FloatTensor(1) + + for i in range(len(self.layers)): + # update layer norms + layer_norm_map = { + "0": "self_attn_layer_norm", + "1": "encoder_attn_layer_norm", + "2": "final_layer_norm", + } + for old, new in layer_norm_map.items(): + for m in ("weight", "bias"): + k = "{}.layers.{}.layer_norms.{}.{}".format(name, i, old, m) + if k in state_dict: + state_dict[ + "{}.layers.{}.{}.{}".format(name, i, new, m) + ] = state_dict[k] + del state_dict[k] + + version_key = "{}.version".format(name) + if utils.item(state_dict.get(version_key, torch.Tensor([1]))[0]) <= 2: + # earlier checkpoints did not normalize after the stack of layers + self.layer_norm = None + self.normalize = False + state_dict[version_key] = torch.Tensor([1]) + + return state_dict + + +@register_model_architecture( + "pipeline_parallel_transformer", "transformer_iwslt_de_en_pipeline_parallel" +) +def transformer_iwslt_de_en_dist(args): + transformer_iwslt_de_en(args) + + +@register_model_architecture( + "pipeline_parallel_transformer", "transformer_wmt_en_de_big_pipeline_parallel" +) +def transformer_wmt_en_de_big_dist(args): + transformer_wmt_en_de_big(args) diff --git a/fairseq/model_parallel/models/roberta/__init__.py b/fairseq/model_parallel/models/roberta/__init__.py new file mode 100644 index 0000000000..117827c3e9 --- /dev/null +++ b/fairseq/model_parallel/models/roberta/__init__.py @@ -0,0 +1,6 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from .model import * # noqa diff --git a/fairseq/model_parallel/models/roberta/model.py b/fairseq/model_parallel/models/roberta/model.py new file mode 100644 index 0000000000..77a80ef720 --- /dev/null +++ b/fairseq/model_parallel/models/roberta/model.py @@ -0,0 +1,225 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +""" +RoBERTa: A Robustly Optimized BERT Pretraining Approach. +""" + +import logging + +import torch +import torch.nn as nn +import torch.nn.functional as F +from fairseq import utils +from fairseq.model_parallel.models.transformer import ModelParallelTransformerEncoder +from fairseq.models import register_model, register_model_architecture +from fairseq.models.roberta import ( + roberta_base_architecture, + roberta_prenorm_architecture, + RobertaEncoder, + RobertaModel, +) +from fairseq.modules import LayerNorm + + +try: + from fairseq.model_parallel.megatron.mpu import ( + copy_to_model_parallel_region, + gather_from_model_parallel_region, + ColumnParallelLinear, + VocabParallelEmbedding, + ) + + has_megatron_submodule = True +except (ImportError, ModuleNotFoundError): + has_megatron_submodule = False + +logger = logging.getLogger(__name__) + + +@register_model("model_parallel_roberta") +class ModelParallelRobertaModel(RobertaModel): + def __init__(self, args, encoder): + super().__init__(args, encoder) + + self.classification_heads = nn.ModuleDict() + + @staticmethod + def add_args(parser): + RobertaModel.add_args(parser) + parser.add_argument( + "--no-final-layer-norm", + action="store_true", + help=( + "don't add final layernorm (only applicable when " + "--encoder-normalize-before=True" + ), + ) + + @classmethod + def build_model(cls, args, task): + """Build a new model instance.""" + + # make sure all arguments are present + base_architecture(args) + + task.source_dictionary.pad_to_multiple_(args.model_parallel_size * 8) + task.target_dictionary.pad_to_multiple_(args.model_parallel_size * 8) + + if not hasattr(args, "max_positions"): + args.max_positions = args.tokens_per_sample + + if getattr(args, "untie_weights_roberta", False): + raise NotImplementedError( + "--untie-weights-roberta is not supported in model parallel mode" + ) + + encoder = ModelParallelRobertaEncoder(args, task.source_dictionary) + return cls(args, encoder) + + def forward( + self, + src_tokens, + features_only=False, + return_all_hiddens=False, + classification_head_name=None, + **kwargs + ): + if classification_head_name is not None: + features_only = True + + x, extra = self.encoder(src_tokens, features_only, return_all_hiddens, **kwargs) + + if classification_head_name is not None: + x = self.classification_heads[classification_head_name](x) + return x, extra + + def register_classification_head( + self, name, num_classes=None, inner_dim=None, **kwargs + ): + """Register a classification head.""" + if name in self.classification_heads: + prev_num_classes = self.classification_heads[name].out_proj.out_features + prev_inner_dim = self.classification_heads[name].dense.out_features + if num_classes != prev_num_classes or inner_dim != prev_inner_dim: + logger.warning( + 're-registering head "{}" with num_classes {} (prev: {}) ' + "and inner_dim {} (prev: {})".format( + name, num_classes, prev_num_classes, inner_dim, prev_inner_dim + ) + ) + self.classification_heads[name] = ModelParallelRobertaClassificationHead( + self.args.encoder_embed_dim, + inner_dim or self.args.encoder_embed_dim, + num_classes, + self.args.pooler_activation_fn, + self.args.pooler_dropout, + ) + + +class ModelParallelRobertaLMHead(nn.Module): + """Head for masked language modeling.""" + + def __init__(self, embed_dim, output_dim, activation_fn, weight=None): + super().__init__() + self.dense = ColumnParallelLinear(embed_dim, embed_dim, gather_output=True) + self.activation_fn = utils.get_activation_fn(activation_fn) + self.layer_norm = LayerNorm(embed_dim) + + if weight is None: + weight = nn.Linear(embed_dim, output_dim, bias=False).weight + self.weight = weight + self.bias = nn.Parameter(torch.zeros(output_dim)) + + def forward(self, features, masked_tokens=None, **kwargs): + # Only project the unmasked tokens while training, + # saves both memory and computation + if masked_tokens is not None: + features = features[masked_tokens, :] + + x = self.dense(features) + x = self.activation_fn(x) + x = self.layer_norm(x) + + x = copy_to_model_parallel_region(x) + # project back to size of vocabulary with bias + x = F.linear(x, self.weight) + x = gather_from_model_parallel_region(x).contiguous() + x = x + self.bias + return x + + +class ModelParallelRobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__( + self, input_dim, inner_dim, num_classes, activation_fn, pooler_dropout + ): + super().__init__() + self.dense = ColumnParallelLinear(input_dim, inner_dim, gather_output=True) + self.activation_fn = utils.get_activation_fn(activation_fn) + self.dropout = nn.Dropout(p=pooler_dropout) + self.out_proj = nn.Linear(inner_dim, num_classes) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take <s> token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = self.activation_fn(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +class ModelParallelRobertaEncoder(RobertaEncoder): + """RoBERTa encoder.""" + + def __init__(self, args, dictionary): + super().__init__(args, dictionary) + assert not self.args.untie_weights_roberta + + def build_embedding(self, vocab_size, embedding_dim, padding_idx): + return VocabParallelEmbedding(vocab_size, embedding_dim, padding_idx) + + def build_encoder(self, args, dictionary, embed_tokens): + return ModelParallelTransformerEncoder(args, dictionary, embed_tokens) + + def build_lm_head(self, embed_dim, output_dim, activation_fn, weight): + return ModelParallelRobertaLMHead(embed_dim, output_dim, activation_fn, weight) + + +@register_model_architecture("model_parallel_roberta", "model_parallel_roberta") +def base_architecture(args): + args.no_final_layer_norm = getattr(args, "no_final_layer_norm", False) + # model parallel RoBERTa defaults to "Pre-LN" formulation + roberta_prenorm_architecture(args) + + +# earlier versions of model parallel RoBERTa removed the final layer norm +@register_model_architecture("model_parallel_roberta", "model_parallel_roberta_v1") +def model_parallel_roberta_v1_architecture(args): + args.no_final_layer_norm = getattr(args, "no_final_layer_norm", True) + base_architecture(args) + + +@register_model_architecture( + "model_parallel_roberta", "model_parallel_roberta_postnorm" +) +def model_parallel_roberta_postnorm_architecture(args): + # the original BERT/RoBERTa uses the "Post-LN" formulation + roberta_base_architecture(args) + + +@register_model_architecture("model_parallel_roberta", "model_parallel_roberta_base") +def model_parallel_roberta_base_architecture(args): + base_architecture(args) + + +@register_model_architecture("model_parallel_roberta", "model_parallel_roberta_large") +def model_parallel_roberta_large_architecture(args): + args.encoder_layers = getattr(args, "encoder_layers", 24) + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4096) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 16) + base_architecture(args) diff --git a/fairseq/model_parallel/models/transformer.py b/fairseq/model_parallel/models/transformer.py index 0b194ad8c5..6b330ef1b7 100644 --- a/fairseq/model_parallel/models/transformer.py +++ b/fairseq/model_parallel/models/transformer.py @@ -6,22 +6,17 @@ import logging import torch.nn as nn -import torch.nn.functional as F - -from fairseq.models import ( - register_model, +from fairseq.model_parallel.modules import ( + ModelParallelTransformerDecoderLayer, + ModelParallelTransformerEncoderLayer, ) - +from fairseq.models import register_model from fairseq.models.transformer import ( TransformerDecoder, TransformerEncoder, TransformerModel, ) -from fairseq.model_parallel.modules import ( - ModelParallelTransformerDecoderLayer, - ModelParallelTransformerEncoderLayer, -) try: from fairseq.model_parallel.megatron.mpu import ( @@ -29,6 +24,7 @@ gather_from_model_parallel_region, VocabParallelEmbedding, ) + has_megatron_submodule = True except (ImportError, ModuleNotFoundError): has_megatron_submodule = False @@ -37,29 +33,36 @@ logger = logging.getLogger(__name__) -@register_model('model_parallel_transformer') +@register_model("model_parallel_transformer") class ModelParallelTransformerModel(TransformerModel): """ Model parallel Transformer model. """ + @classmethod def build_embedding(cls, args, dictionary, embed_dim, path=None): if not has_megatron_submodule: raise ImportError( - '\n\nPlease install the megatron submodule:' - '\n\n git submodule update --init ' - 'fairseq/model_parallel/megatron' + "\n\nPlease install the megatron submodule:" + "\n\n git submodule update --init " + "fairseq/model_parallel/megatron" ) + dictionary.pad_to_multiple_(args.model_parallel_size * 8) num_embeddings = len(dictionary) padding_idx = dictionary.pad() def _vocab_init(tensor, **kwargs): nn.init.normal_(tensor, mean=0, std=num_embeddings ** -0.5) nn.init.constant_(tensor[1], 0) - emb = VocabParallelEmbedding(num_embeddings, embed_dim, padding_idx, init_method=_vocab_init) + + emb = VocabParallelEmbedding( + num_embeddings, embed_dim, padding_idx, init_method=_vocab_init + ) # if provided, load from preloaded dictionaries if path: - raise NotImplementedError("Loading of embedding from path is not supported for model parallel") + raise NotImplementedError( + "Loading of embedding from path is not supported for model parallel" + ) return emb @classmethod @@ -72,7 +75,7 @@ def build_decoder(cls, args, tgt_dict, embed_tokens): args, tgt_dict, embed_tokens, - no_encoder_attn=getattr(args, 'no_cross_attention', False), + no_encoder_attn=getattr(args, "no_cross_attention", False), ) @@ -82,6 +85,12 @@ class ModelParallelTransformerEncoder(TransformerEncoder): is a :class:`ModelParallelTransformerEncoderLayer`. """ + def __init__(self, args, dictionary, embed_tokens): + super().__init__(args, dictionary, embed_tokens) + + if args.no_final_layer_norm: + self.layer_norm = None + def build_encoder_layer(self, args): return ModelParallelTransformerEncoderLayer(args) @@ -97,14 +106,16 @@ def build_decoder_layer(self, args, no_encoder_attn=False): def output_layer(self, features, **kwargs): """Project features to the vocabulary size.""" + if not self.share_input_output_embed: + raise NotImplementedError( + "Model parallel training currently requires --share-decoder-input-output-embed" + ) + features = copy_to_model_parallel_region(features) # project back to size of vocabulary - if self.share_input_output_embed: - x = F.linear(features, self.embed_tokens.weight) - else: - x = F.linear(features, self.embed_out) + x = self.output_projection(features) - if getattr(self.args, 'criterion') != 'vocab_parallel_cross_entropy': + if getattr(self.args, "criterion") != "vocab_parallel_cross_entropy": x = gather_from_model_parallel_region(x).contiguous() return x diff --git a/fairseq/model_parallel/models/transformer_lm.py b/fairseq/model_parallel/models/transformer_lm.py index 507cb02d1a..dc52f6e8dd 100644 --- a/fairseq/model_parallel/models/transformer_lm.py +++ b/fairseq/model_parallel/models/transformer_lm.py @@ -4,89 +4,171 @@ # LICENSE file in the root directory of this source tree. import torch.nn as nn +from fairseq.model_parallel.models.transformer import ModelParallelTransformerDecoder +from fairseq.models import register_model, register_model_architecture +from fairseq.models.transformer_lm import TransformerLanguageModel + -from fairseq.models import ( - register_model, - register_model_architecture, -) -from fairseq.models.transformer_lm import ( - base_lm_architecture, - TransformerLanguageModel, -) -from fairseq.model_parallel.models.transformer import ( - ModelParallelTransformerDecoder, -) try: from fairseq.model_parallel.megatron.mpu import VocabParallelEmbedding + has_megatron_submodule = True except (ImportError, ModuleNotFoundError): has_megatron_submodule = False + DEFAULT_MAX_TARGET_POSITIONS = 1024 -@register_model('model_parallel_transformer_lm') +@register_model("model_parallel_transformer_lm") class ModelParallelTransformerLanguageModel(TransformerLanguageModel): + @staticmethod + def add_args(parser): + TransformerLanguageModel.add_args(parser) + @classmethod def build_model(cls, args, task): """Build a new model instance.""" if not has_megatron_submodule: raise ImportError( - '\n\nPlease install the megatron submodule:' - '\n\n git submodule update --init ' - 'fairseq/model_parallel/megatron' + "\n\nPlease install the megatron submodule:" + "\n\n git submodule update --init " + "fairseq/model_parallel/megatron" ) # make sure all arguments are present in older models base_lm_architecture(args) + task.source_dictionary.pad_to_multiple_(args.model_parallel_size * 8) + task.target_dictionary.pad_to_multiple_(args.model_parallel_size * 8) + if args.decoder_layers_to_keep: args.decoder_layers = len(args.decoder_layers_to_keep.split(",")) - if getattr(args, 'max_target_positions', None) is None: - args.max_target_positions = getattr(args, 'tokens_per_sample', DEFAULT_MAX_TARGET_POSITIONS) + if getattr(args, "max_target_positions", None) is None: + args.max_target_positions = getattr( + args, "tokens_per_sample", DEFAULT_MAX_TARGET_POSITIONS + ) if args.character_embeddings: - raise NotImplementedError("Character embeddings is not supported for model parallel") + raise NotImplementedError( + "Character embeddings is not supported for model parallel" + ) elif args.adaptive_input: - raise NotImplementedError("Adaptive input is not supported for model parallel") + raise NotImplementedError( + "Adaptive input is not supported for model parallel" + ) else: - embed_tokens = cls.build_embedding(args, task.source_dictionary, args.decoder_input_dim) + embed_tokens = cls.build_embedding( + args, task.source_dictionary, args.decoder_input_dim + ) decoder = ModelParallelTransformerDecoder( - args, task.target_dictionary, embed_tokens, no_encoder_attn=True, + args, + task.target_dictionary, + embed_tokens, + no_encoder_attn=True, ) return cls(decoder) + @staticmethod + def add_args(parser): + TransformerLanguageModel.add_args(parser) + @classmethod def build_embedding(cls, args, dictionary, embed_dim, path=None): def _vocab_init(tensor, **kwargs): nn.init.normal_(tensor, mean=0, std=embed_dim ** -0.5) nn.init.constant_(tensor[1], 0) - embed_tokens = VocabParallelEmbedding(len(dictionary), embed_dim, dictionary.pad(), init_method=_vocab_init) + + embed_tokens = VocabParallelEmbedding( + len(dictionary), embed_dim, dictionary.pad(), init_method=_vocab_init + ) return embed_tokens -@register_model_architecture('model_parallel_transformer_lm', 'transformer_lm_megatron') +def base_lm_architecture(args): + # backward compatibility for older model checkpoints + if hasattr(args, "no_tie_adaptive_proj"): + # previous models defined --no-tie-adaptive-proj, so use the existence of + # that option to determine if this is an "old" model checkpoint + args.no_decoder_final_norm = True # old models always set this to True + if args.no_tie_adaptive_proj is False: + args.tie_adaptive_proj = True + if hasattr(args, "decoder_final_norm"): + args.no_decoder_final_norm = not args.decoder_final_norm + + args.activation_fn = getattr(args, "activation_fn", "relu") + args.dropout = getattr(args, "dropout", 0.1) + args.attention_dropout = getattr(args, "attention_dropout", 0.0) + args.activation_dropout = getattr(args, "activation_dropout", 0.0) + args.relu_dropout = getattr(args, "relu_dropout", 0.0) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512) + args.decoder_output_dim = getattr( + args, "decoder_output_dim", args.decoder_embed_dim + ) + args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim) + args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 2048) + args.decoder_layers = getattr(args, "decoder_layers", 6) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8) + # Model training is not stable without this + args.decoder_normalize_before = True + args.no_decoder_final_norm = getattr(args, "no_decoder_final_norm", False) + args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None) + args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0) + args.adaptive_softmax_factor = getattr(args, "adaptive_softmax_factor", 4) + args.no_token_positional_embeddings = getattr( + args, "no_token_positional_embeddings", False + ) + args.share_decoder_input_output_embed = getattr( + args, "share_decoder_input_output_embed", False + ) + args.character_embeddings = getattr(args, "character_embeddings", False) + args.character_filters = getattr( + args, + "character_filters", + "[(1, 64), (2, 128), (3, 192), (4, 256), (5, 256), (6, 256), (7, 256)]", + ) + args.character_embedding_dim = getattr(args, "character_embedding_dim", 4) + args.char_embedder_highway_layers = getattr(args, "char_embedder_highway_layers", 2) + args.adaptive_input = getattr(args, "adaptive_input", False) + args.adaptive_input_factor = getattr(args, "adaptive_input_factor", 4) + args.adaptive_input_cutoff = getattr(args, "adaptive_input_cutoff", None) + args.tie_adaptive_weights = getattr(args, "tie_adaptive_weights", False) + args.tie_adaptive_proj = getattr(args, "tie_adaptive_proj", False) + args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False) + args.decoder_layerdrop = getattr(args, "decoder_layerdrop", 0.0) + args.decoder_layers_to_keep = getattr(args, "decoder_layers_to_keep", None) + args.layernorm_embedding = getattr(args, "layernorm_embedding", False) + args.no_scale_embedding = getattr(args, "no_scale_embedding", False) + args.quant_noise_pq = getattr(args, "quant_noise_pq", 0.0) + args.quant_noise_pq_block_size = getattr(args, "quant_noise_pq_block_size", 8) + args.quant_noise_scalar = getattr(args, "quant_noise_scalar", 0.0) + args.add_bos_token = getattr(args, "add_bos_token", False) + + +@register_model_architecture("model_parallel_transformer_lm", "transformer_lm_megatron") def transformer_lm_megatron(args): - args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 3072) - args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', 3072 * 4) - args.decoder_layers = getattr(args, 'decoder_layers', 72) - args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 32) - args.dropout = getattr(args, 'dropout', 0.1) - args.attention_dropout = getattr(args, 'attention_dropout', 0.1) - args.activation_fn = getattr(args, 'activation_fn', 'gelu') + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 3072) + args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 3072 * 4) + args.decoder_layers = getattr(args, "decoder_layers", 72) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 32) + args.dropout = getattr(args, "dropout", 0.1) + args.attention_dropout = getattr(args, "attention_dropout", 0.1) + args.activation_fn = getattr(args, "activation_fn", "gelu") base_lm_architecture(args) -@register_model_architecture('model_parallel_transformer_lm', 'transformer_lm_megatron_11b') +@register_model_architecture( + "model_parallel_transformer_lm", "transformer_lm_megatron_11b" +) def transformer_lm_megatron_11b(args): - args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 3072) - args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', 3072 * 6) - args.decoder_layers = getattr(args, 'decoder_layers', 72) - args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 32) - args.dropout = getattr(args, 'dropout', 0.1) - args.attention_dropout = getattr(args, 'attention_dropout', 0.1) - args.activation_fn = getattr(args, 'activation_fn', 'gelu') + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 3072) + args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 3072 * 6) + args.decoder_layers = getattr(args, "decoder_layers", 72) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 32) + args.dropout = getattr(args, "dropout", 0.1) + args.attention_dropout = getattr(args, "attention_dropout", 0.1) + args.activation_fn = getattr(args, "activation_fn", "gelu") base_lm_architecture(args) diff --git a/fairseq/model_parallel/modules/__init__.py b/fairseq/model_parallel/modules/__init__.py index eb29b16bec..11603217a1 100644 --- a/fairseq/model_parallel/modules/__init__.py +++ b/fairseq/model_parallel/modules/__init__.py @@ -2,12 +2,16 @@ # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. +"""isort:skip_file""" from .multihead_attention import ModelParallelMultiheadAttention -from .transformer_layer import ModelParallelTransformerEncoderLayer, ModelParallelTransformerDecoderLayer +from .transformer_layer import ( + ModelParallelTransformerEncoderLayer, + ModelParallelTransformerDecoderLayer, +) __all__ = [ - 'ModelParallelMultiheadAttention', - 'ModelParallelTransformerEncoderLayer', - 'ModelParallelTransformerDecoderLayer', + "ModelParallelMultiheadAttention", + "ModelParallelTransformerEncoderLayer", + "ModelParallelTransformerDecoderLayer", ] diff --git a/fairseq/model_parallel/modules/multihead_attention.py b/fairseq/model_parallel/modules/multihead_attention.py index 06c7ef712a..8eb9d09dad 100644 --- a/fairseq/model_parallel/modules/multihead_attention.py +++ b/fairseq/model_parallel/modules/multihead_attention.py @@ -8,8 +8,10 @@ import torch import torch.nn.functional as F from fairseq import utils -from torch import Tensor, nn from fairseq.incremental_decoding_utils import with_incremental_state +from fairseq.modules.fairseq_dropout import FairseqDropout +from torch import Tensor, nn + try: from fairseq.model_parallel.megatron.mpu import ( @@ -18,6 +20,7 @@ ColumnParallelLinear, RowParallelLinear, ) + has_megatron_submodule = True except (ImportError, ModuleNotFoundError): has_megatron_submodule = False @@ -45,9 +48,9 @@ def __init__( super().__init__() if not has_megatron_submodule: raise ImportError( - '\n\nPlease install the megatron submodule:' - '\n\n git submodule update --init ' - 'fairseq/model_parallel/megatron' + "\n\nPlease install the megatron submodule:" + "\n\n git submodule update --init " + "fairseq/model_parallel/megatron" ) self.embed_dim = embed_dim self.kdim = kdim if kdim is not None else embed_dim @@ -59,9 +62,11 @@ def __init__( self.num_heads_partition = num_heads // self.model_parallel_size assert ( self.num_heads_partition * self.model_parallel_size == num_heads - ), "Number of heads must be divisble by model parallel size" + ), "Number of heads must be divisible by model parallel size" - self.dropout = dropout + self.dropout_module = FairseqDropout( + dropout, module_name=self.__class__.__name__ + ) self.head_dim = embed_dim // num_heads assert ( self.head_dim * num_heads == self.embed_dim @@ -71,14 +76,22 @@ def __init__( self.self_attention = self_attention self.encoder_decoder_attention = encoder_decoder_attention - assert not self.self_attention or self.qkv_same_dim, ( - "Self-attention requires query, key and value to be of the same size" - ) + assert ( + not self.self_attention or self.qkv_same_dim + ), "Self-attention requires query, key and value to be of the same size" - self.k_proj = ColumnParallelLinear(self.kdim, embed_dim, bias=bias, gather_output=False) - self.v_proj = ColumnParallelLinear(self.vdim, embed_dim, bias=bias, gather_output=False) - self.q_proj = ColumnParallelLinear(embed_dim, embed_dim, bias=bias, gather_output=False) - self.out_proj = RowParallelLinear(embed_dim, embed_dim, bias=bias, input_is_parallel=True) + self.k_proj = ColumnParallelLinear( + self.kdim, embed_dim, bias=bias, gather_output=False + ) + self.v_proj = ColumnParallelLinear( + self.vdim, embed_dim, bias=bias, gather_output=False + ) + self.q_proj = ColumnParallelLinear( + embed_dim, embed_dim, bias=bias, gather_output=False + ) + self.out_proj = RowParallelLinear( + embed_dim, embed_dim, bias=bias, input_is_parallel=True + ) def forward( self, @@ -105,6 +118,8 @@ def forward( assert embed_dim == self.embed_dim assert list(query.size()) == [tgt_len, bsz, embed_dim] + is_tpu = query.device.type == "xla" + if incremental_state is not None: saved_state = self._get_input_buffer(incremental_state) if saved_state is not None and "prev_key" in saved_state: @@ -137,7 +152,6 @@ def forward( v = self.v_proj(value) q *= self.scaling - q = ( q.contiguous() .view(tgt_len, bsz * self.num_heads_partition, self.head_dim) @@ -161,7 +175,9 @@ def forward( if "prev_key" in saved_state: _prev_key = saved_state["prev_key"] assert _prev_key is not None - prev_key = _prev_key.view(bsz * self.num_heads_partition, -1, self.head_dim) + prev_key = _prev_key.view( + bsz * self.num_heads_partition, -1, self.head_dim + ) if static_kv: k = prev_key else: @@ -170,7 +186,9 @@ def forward( if "prev_value" in saved_state: _prev_value = saved_state["prev_value"] assert _prev_value is not None - prev_value = _prev_value.view(bsz * self.num_heads_partition, -1, self.head_dim) + prev_value = _prev_value.view( + bsz * self.num_heads_partition, -1, self.head_dim + ) if static_kv: v = prev_value else: @@ -180,16 +198,22 @@ def forward( if "prev_key_padding_mask" in saved_state: prev_key_padding_mask = saved_state["prev_key_padding_mask"] assert k is not None and v is not None - key_padding_mask = ModelParallelMultiheadAttention._append_prev_key_padding_mask( - key_padding_mask=key_padding_mask, - prev_key_padding_mask=prev_key_padding_mask, - batch_size=bsz, - src_len=k.size(1), - static_kv=static_kv, + key_padding_mask = ( + ModelParallelMultiheadAttention._append_prev_key_padding_mask( + key_padding_mask=key_padding_mask, + prev_key_padding_mask=prev_key_padding_mask, + batch_size=bsz, + src_len=k.size(1), + static_kv=static_kv, + ) ) - saved_state["prev_key"] = k.view(bsz, self.num_heads_partition, -1, self.head_dim) - saved_state["prev_value"] = v.view(bsz, self.num_heads_partition, -1, self.head_dim) + saved_state["prev_key"] = k.view( + bsz, self.num_heads_partition, -1, self.head_dim + ) + saved_state["prev_value"] = v.view( + bsz, self.num_heads_partition, -1, self.head_dim + ) saved_state["prev_key_padding_mask"] = key_padding_mask # In this branch incremental_state is never None assert incremental_state is not None @@ -208,7 +232,11 @@ def forward( attn_weights = torch.bmm(q, k.transpose(1, 2)) - assert list(attn_weights.size()) == [bsz * self.num_heads_partition, tgt_len, src_len] + assert list(attn_weights.size()) == [ + bsz * self.num_heads_partition, + tgt_len, + src_len, + ] if attn_mask is not None: attn_mask = attn_mask.unsqueeze(0) @@ -216,27 +244,35 @@ def forward( if key_padding_mask is not None: # don't attend to padding symbols - attn_weights = attn_weights.view(bsz, self.num_heads_partition, tgt_len, src_len) - attn_weights = attn_weights.masked_fill( - key_padding_mask.unsqueeze(1).unsqueeze(2).to(torch.bool), float("-inf") + attn_weights = attn_weights.view( + bsz, self.num_heads_partition, tgt_len, src_len + ) + if not is_tpu: + attn_weights = attn_weights.masked_fill( + key_padding_mask.unsqueeze(1).unsqueeze(2).to(torch.bool), + float("-inf"), + ) + else: + attn_weights = attn_weights.transpose(0, 2) + attn_weights = attn_weights.masked_fill(key_padding_mask, float("-inf")) + attn_weights = attn_weights.transpose(0, 2) + attn_weights = attn_weights.view( + bsz * self.num_heads_partition, tgt_len, src_len ) - attn_weights = attn_weights.view(bsz * self.num_heads_partition, tgt_len, src_len) - attn_weights_float = utils.softmax( - attn_weights, dim=-1 - ) + attn_weights_float = utils.softmax(attn_weights, dim=-1) attn_weights = attn_weights_float.type_as(attn_weights) with get_cuda_rng_tracker().fork(): - attn_probs = F.dropout( - attn_weights_float.type_as(attn_weights), - p=self.dropout, - training=self.training, - ) + attn_probs = self.dropout_module(attn_weights) assert v is not None attn = torch.bmm(attn_probs, v) - assert list(attn.size()) == [bsz * self.num_heads_partition, tgt_len, self.head_dim] + assert list(attn.size()) == [ + bsz * self.num_heads_partition, + tgt_len, + self.head_dim, + ] embed_dim_partition = embed_dim // self.model_parallel_size attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim_partition) attn = self.out_proj(attn) diff --git a/fairseq/model_parallel/modules/transformer_layer.py b/fairseq/model_parallel/modules/transformer_layer.py index 30b23d518c..7ab53c6e5f 100644 --- a/fairseq/model_parallel/modules/transformer_layer.py +++ b/fairseq/model_parallel/modules/transformer_layer.py @@ -3,18 +3,16 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. -from fairseq.modules import ( - TransformerEncoderLayer, - TransformerDecoderLayer, -) - from fairseq.model_parallel.modules import ModelParallelMultiheadAttention +from fairseq.modules import TransformerDecoderLayer, TransformerEncoderLayer + try: from fairseq.model_parallel.megatron.mpu import ( ColumnParallelLinear, RowParallelLinear, ) + has_megatron_submodule = True except (ImportError, ModuleNotFoundError): has_megatron_submodule = False @@ -23,7 +21,7 @@ class ModelParallelTransformerEncoderLayer(TransformerEncoderLayer): """Encoder layer block over multiple gpus. - See "Megatron-LM: https://arxiv.org/pdf/1909.08053.pdf" for more details. + See "Megatron-LM: https://arxiv.org/pdf/1909.08053.pdf" for more details. """ def build_fc1(self, input_dim, output_dim, q_noise, qn_block_size): @@ -48,8 +46,9 @@ def build_self_attention(self, embed_dim, args, **unused_kwargs): class ModelParallelTransformerDecoderLayer(TransformerDecoderLayer): """Decoder layer block. - See "Megatron-LM: https://arxiv.org/pdf/1909.08053.pdf" for more details. + See "Megatron-LM: https://arxiv.org/pdf/1909.08053.pdf" for more details. """ + def build_fc1(self, input_dim, output_dim, q_noise, qn_block_size): if q_noise > 0: raise NotImplementedError diff --git a/fairseq/models/__init__.py b/fairseq/models/__init__.py index f7d8eaafad..337c77ac7b 100644 --- a/fairseq/models/__init__.py +++ b/fairseq/models/__init__.py @@ -2,53 +2,112 @@ # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. +"""isort:skip_file""" import argparse import importlib import os +from contextlib import ExitStack +from fairseq.dataclass import FairseqDataclass +from fairseq.dataclass.utils import merge_with_parent +from hydra.core.config_store import ConfigStore +from omegaconf import open_dict, OmegaConf + +from .composite_encoder import CompositeEncoder +from .distributed_fairseq_model import DistributedFairseqModel from .fairseq_decoder import FairseqDecoder from .fairseq_encoder import FairseqEncoder from .fairseq_incremental_decoder import FairseqIncrementalDecoder from .fairseq_model import ( BaseFairseqModel, - FairseqEncoderModel, FairseqEncoderDecoderModel, + FairseqEncoderModel, FairseqLanguageModel, FairseqModel, FairseqMultiModel, ) -from .composite_encoder import CompositeEncoder -from .distributed_fairseq_model import DistributedFairseqModel - MODEL_REGISTRY = {} +MODEL_DATACLASS_REGISTRY = {} ARCH_MODEL_REGISTRY = {} +ARCH_MODEL_NAME_REGISTRY = {} ARCH_MODEL_INV_REGISTRY = {} ARCH_CONFIG_REGISTRY = {} __all__ = [ - 'BaseFairseqModel', - 'CompositeEncoder', - 'DistributedFairseqModel', - 'FairseqDecoder', - 'FairseqEncoder', - 'FairseqEncoderDecoderModel', - 'FairseqEncoderModel', - 'FairseqIncrementalDecoder', - 'FairseqLanguageModel', - 'FairseqModel', - 'FairseqMultiModel', + "BaseFairseqModel", + "CompositeEncoder", + "DistributedFairseqModel", + "FairseqDecoder", + "FairseqEncoder", + "FairseqEncoderDecoderModel", + "FairseqEncoderModel", + "FairseqIncrementalDecoder", + "FairseqLanguageModel", + "FairseqModel", + "FairseqMultiModel", ] -def build_model(args, task): - return ARCH_MODEL_REGISTRY[args.arch].build_model(args, task) - - -def register_model(name): +def build_model(cfg: FairseqDataclass, task): + + model = None + model_type = getattr(cfg, "_name", None) or getattr(cfg, "arch", None) + + if not model_type and len(cfg) == 1: + # this is hit if config object is nested in directory that is named after model type + + model_type = next(iter(cfg)) + if model_type in MODEL_DATACLASS_REGISTRY: + cfg = cfg[model_type] + else: + raise Exception( + "Could not infer model type from directory. Please add _name field to indicate model type. " + "Available models: " + + str(MODEL_DATACLASS_REGISTRY.keys()) + + " Requested model type: " + + model_type + ) + + if model_type in ARCH_MODEL_REGISTRY: + # case 1: legacy models + model = ARCH_MODEL_REGISTRY[model_type] + elif model_type in MODEL_DATACLASS_REGISTRY: + # case 2: config-driven models + model = MODEL_REGISTRY[model_type] + + if model_type in MODEL_DATACLASS_REGISTRY: + # set defaults from dataclass. note that arch name and model name can be the same + dc = MODEL_DATACLASS_REGISTRY[model_type] + + if isinstance(cfg, argparse.Namespace): + cfg = dc.from_namespace(cfg) + else: + cfg = merge_with_parent(dc(), cfg) + else: + if model_type in ARCH_CONFIG_REGISTRY: + with open_dict(cfg) if OmegaConf.is_config(cfg) else ExitStack(): + # this calls the different "arch" functions (like base_architecture()) that you indicate + # if you specify --arch on the command line. this is only applicable to the old argparse based models + # hydra models should expose different architectures via different config files + # it will modify the cfg object and default parameters according to the arch + ARCH_CONFIG_REGISTRY[model_type](cfg) + + assert model is not None, ( + f"Could not infer model type from {cfg}. " + "Available models: {}".format( + MODEL_DATACLASS_REGISTRY.keys() + ) + + f" Requested model type: {model_type}" + ) + + return model.build_model(cfg, task) + + +def register_model(name, dataclass=None): """ New model types can be added to fairseq with the :func:`register_model` function decorator. @@ -70,10 +129,30 @@ class LSTM(FairseqEncoderDecoderModel): def register_model_cls(cls): if name in MODEL_REGISTRY: - raise ValueError('Cannot register duplicate model ({})'.format(name)) + raise ValueError("Cannot register duplicate model ({})".format(name)) if not issubclass(cls, BaseFairseqModel): - raise ValueError('Model ({}: {}) must extend BaseFairseqModel'.format(name, cls.__name__)) + raise ValueError( + "Model ({}: {}) must extend BaseFairseqModel".format(name, cls.__name__) + ) MODEL_REGISTRY[name] = cls + if dataclass is not None and not issubclass(dataclass, FairseqDataclass): + raise ValueError( + "Dataclass {} must extend FairseqDataclass".format(dataclass) + ) + + cls.__dataclass = dataclass + if dataclass is not None: + MODEL_DATACLASS_REGISTRY[name] = dataclass + + cs = ConfigStore.instance() + node = dataclass() + node._name = name + cs.store(name=name, group="model", node=node, provider="fairseq") + + @register_model_architecture(name, name) + def noop(_): + pass + return cls return register_model_cls @@ -89,14 +168,13 @@ def register_model_architecture(model_name, arch_name): For example:: @register_model_architecture('lstm', 'lstm_luong_wmt_en_de') - def lstm_luong_wmt_en_de(args): - args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 1000) + def lstm_luong_wmt_en_de(cfg): + args.encoder_embed_dim = getattr(cfg.model, 'encoder_embed_dim', 1000) (...) - The decorated function should take a single argument *args*, which is a - :class:`argparse.Namespace` of arguments parsed from the command-line. The - decorated function should modify these arguments in-place to match the - desired architecture. + The decorated function should take a single argument *cfg*, which is a + :class:`omegaconf.DictConfig`. The decorated function should modify these + arguments in-place to match the desired architecture. Args: model_name (str): the name of the Model (Model must already be @@ -106,12 +184,21 @@ def lstm_luong_wmt_en_de(args): def register_model_arch_fn(fn): if model_name not in MODEL_REGISTRY: - raise ValueError('Cannot register model architecture for unknown model type ({})'.format(model_name)) + raise ValueError( + "Cannot register model architecture for unknown model type ({})".format( + model_name + ) + ) if arch_name in ARCH_MODEL_REGISTRY: - raise ValueError('Cannot register duplicate model architecture ({})'.format(arch_name)) + raise ValueError( + "Cannot register duplicate model architecture ({})".format(arch_name) + ) if not callable(fn): - raise ValueError('Model architecture must be callable ({})'.format(arch_name)) + raise ValueError( + "Model architecture must be callable ({})".format(arch_name) + ) ARCH_MODEL_REGISTRY[arch_name] = MODEL_REGISTRY[model_name] + ARCH_MODEL_NAME_REGISTRY[arch_name] = model_name ARCH_MODEL_INV_REGISTRY.setdefault(model_name, []).append(arch_name) ARCH_CONFIG_REGISTRY[arch_name] = fn return fn @@ -119,23 +206,31 @@ def register_model_arch_fn(fn): return register_model_arch_fn +def import_models(models_dir, namespace): + for file in os.listdir(models_dir): + path = os.path.join(models_dir, file) + if ( + not file.startswith("_") + and not file.startswith(".") + and (file.endswith(".py") or os.path.isdir(path)) + ): + model_name = file[: file.find(".py")] if file.endswith(".py") else file + importlib.import_module(namespace + "." + model_name) + + # extra `model_parser` for sphinx + if model_name in MODEL_REGISTRY: + parser = argparse.ArgumentParser(add_help=False) + group_archs = parser.add_argument_group("Named architectures") + group_archs.add_argument( + "--arch", choices=ARCH_MODEL_INV_REGISTRY[model_name] + ) + group_args = parser.add_argument_group( + "Additional command-line arguments" + ) + MODEL_REGISTRY[model_name].add_args(group_args) + globals()[model_name + "_parser"] = parser + + # automatically import any Python files in the models/ directory models_dir = os.path.dirname(__file__) -for file in os.listdir(models_dir): - path = os.path.join(models_dir, file) - if ( - not file.startswith('_') - and not file.startswith('.') - and (file.endswith('.py') or os.path.isdir(path)) - ): - model_name = file[:file.find('.py')] if file.endswith('.py') else file - module = importlib.import_module('fairseq.models.' + model_name) - - # extra `model_parser` for sphinx - if model_name in MODEL_REGISTRY: - parser = argparse.ArgumentParser(add_help=False) - group_archs = parser.add_argument_group('Named architectures') - group_archs.add_argument('--arch', choices=ARCH_MODEL_INV_REGISTRY[model_name]) - group_args = parser.add_argument_group('Additional command-line arguments') - MODEL_REGISTRY[model_name].add_args(group_args) - globals()[model_name + '_parser'] = parser +import_models(models_dir, "fairseq.models") diff --git a/fairseq/models/bart/hub_interface.py b/fairseq/models/bart/hub_interface.py index f87291bfbd..4d47d97518 100644 --- a/fairseq/models/bart/hub_interface.py +++ b/fairseq/models/bart/hub_interface.py @@ -5,48 +5,34 @@ import copy import logging +from typing import Dict, List import numpy as np import torch import torch.nn as nn import torch.nn.functional as F - -from typing import List - from fairseq import utils from fairseq.data import encoders +from fairseq.hub_utils import GeneratorHubInterface +from omegaconf import open_dict logger = logging.getLogger(__name__) -class BARTHubInterface(nn.Module): +class BARTHubInterface(GeneratorHubInterface): """A simple PyTorch Hub interface to BART. - Usage: https://github.com/pytorch/fairseq/tree/master/examples/BART + Usage: https://github.com/pytorch/fairseq/tree/main/examples/bart """ - def __init__(self, args, task, model): - super().__init__() - self.args = args - self.task = task - self.model = model - - self.bpe = encoders.build_bpe(args) - - self.max_positions = min(utils.resolve_max_positions( - self.task.max_positions(), - self.model.max_positions(), - )) + def __init__(self, cfg, task, model): + super().__init__(cfg, task, [model]) + self.model = self.models[0] - # this is useful for determining the device - self.register_buffer('_float_tensor', torch.tensor([0], dtype=torch.float)) - - @property - def device(self): - return self._float_tensor.device - - def encode(self, sentence: str, *addl_sentences, no_separator=True) -> torch.LongTensor: + def encode( + self, sentence: str, *addl_sentences, no_separator=True + ) -> torch.LongTensor: """ BPE-encode a sentence (or multiple sentences). @@ -67,12 +53,12 @@ def encode(self, sentence: str, *addl_sentences, no_separator=True) -> torch.Lon [0, 8331, 2] """ tokens = self.bpe.encode(sentence) - if len(tokens.split(' ')) > self.max_positions - 2: - tokens = ' '.join(tokens.split(' ')[:self.max_positions - 2]) - bpe_sentence = '<s> ' + tokens + ' </s>' + if len(tokens.split(" ")) > min(self.max_positions) - 2: + tokens = " ".join(tokens.split(" ")[: min(self.max_positions) - 2]) + bpe_sentence = "<s> " + tokens + " </s>" for s in addl_sentences: - bpe_sentence += (' </s>' if not no_separator else '') - bpe_sentence += ' ' + self.bpe.encode(s) + ' </s>' + bpe_sentence += " </s>" if not no_separator else "" + bpe_sentence += " " + self.bpe.encode(s) + " </s>" tokens = self.task.source_dictionary.encode_line(bpe_sentence, append_eos=False) return tokens.long() @@ -81,10 +67,12 @@ def decode(self, tokens: torch.LongTensor): tokens = tokens.cpu().numpy() if tokens[0] == self.task.source_dictionary.bos(): tokens = tokens[1:] # remove <s> - eos_mask = (tokens == self.task.source_dictionary.eos()) + eos_mask = tokens == self.task.source_dictionary.eos() doc_mask = eos_mask[1:] & eos_mask[:-1] sentences = np.split(tokens, doc_mask.nonzero()[0] + 1) - sentences = [self.bpe.decode(self.task.source_dictionary.string(s)) for s in sentences] + sentences = [ + self.bpe.decode(self.task.source_dictionary.string(s)) for s in sentences + ] if len(sentences) == 1: return sentences[0] return sentences @@ -96,58 +84,55 @@ def _build_sample(self, src_tokens: List[torch.LongTensor]): [x.numel() for x in src_tokens], ) sample = dataset.collater(dataset) - sample = utils.apply_to_sample( - lambda tensor: tensor.to(self.device), - sample - ) + sample = utils.apply_to_sample(lambda tensor: tensor.to(self.device), sample) return sample - def sample(self, sentences: List[str], beam: int = 1, verbose: bool = False, **kwargs) -> str: - input = [self.encode(sentence) for sentence in sentences] - hypos = self.generate(input, beam, verbose, **kwargs) - return [self.decode(x['tokens']) for x in hypos] - - def generate(self, tokens: List[torch.LongTensor], beam: int = 5, verbose: bool = False, **kwargs) -> torch.LongTensor: - sample = self._build_sample(tokens) - - # build generator using current args as well as any kwargs - gen_args = copy.copy(self.args) - gen_args.beam = beam - for k, v in kwargs.items(): - setattr(gen_args, k, v) - generator = self.task.build_generator([self.model], gen_args) - translations = self.task.inference_step( - generator, - [self.model], - sample, - prefix_tokens=sample['net_input']['src_tokens'].new_zeros((len(tokens), 1)).fill_(self.task.source_dictionary.bos()), - ) - - if verbose: - src_str_with_unk = self.string(tokens) - logger.info('S\t{}'.format(src_str_with_unk)) - - def getarg(name, default): - return getattr(gen_args, name, getattr(self.args, name, default)) - - # Process top predictions - hypos = [x[0] for x in translations] - hypos = [v for _, v in sorted(zip(sample['id'].tolist(), hypos))] - return hypos - - def extract_features(self, tokens: torch.LongTensor, return_all_hiddens: bool = False) -> torch.Tensor: + def generate( + self, + tokenized_sentences: List[torch.LongTensor], + *args, + inference_step_args=None, + skip_invalid_size_inputs=False, + **kwargs + ) -> List[List[Dict[str, torch.Tensor]]]: + inference_step_args = inference_step_args or {} + if "prefix_tokens" in inference_step_args: + raise NotImplementedError("prefix generation not implemented for BART") + res = [] + for batch in self._build_batches(tokenized_sentences, skip_invalid_size_inputs): + src_tokens = batch['net_input']['src_tokens'] + inference_step_args["prefix_tokens"] =src_tokens.new_full( + (src_tokens.size(0), 1), fill_value=self.task.source_dictionary.bos() + ).to(device=self.device) + results = super().generate( + src_tokens, + *args, + inference_step_args=inference_step_args, + skip_invalid_size_inputs=skip_invalid_size_inputs, + **kwargs + ) + for id, hypos in zip(batch['id'].tolist(), results): + res.append((id, hypos)) + res = [hypos for _, hypos in sorted(res, key=lambda x: x[0])] + return res + + def extract_features( + self, tokens: torch.LongTensor, return_all_hiddens: bool = False + ) -> torch.Tensor: if tokens.dim() == 1: tokens = tokens.unsqueeze(0) if tokens.size(-1) > min(self.model.max_positions()): - raise ValueError('tokens exceeds maximum length: {} > {}'.format( - tokens.size(-1), self.model.max_positions() - )) + raise ValueError( + "tokens exceeds maximum length: {} > {}".format( + tokens.size(-1), self.model.max_positions() + ) + ) tokens.to(device=self.device), prev_output_tokens = tokens.clone() prev_output_tokens[:, 0] = tokens.gather( 1, - (tokens.ne(self.task.source_dictionary.pad()).sum(dim=1)- 1).unsqueeze(-1), + (tokens.ne(self.task.source_dictionary.pad()).sum(dim=1) - 1).unsqueeze(-1), ).squeeze() prev_output_tokens[:, 1:] = tokens[:, :-1] @@ -160,7 +145,7 @@ def extract_features(self, tokens: torch.LongTensor, return_all_hiddens: bool = ) if return_all_hiddens: # convert from T x B x C -> B x T x C - inner_states = extra['inner_states'] + inner_states = extra["inner_states"] return [inner_state.transpose(0, 1) for inner_state in inner_states] else: return features # just the last layer's features @@ -184,3 +169,40 @@ def predict(self, head: str, tokens: torch.LongTensor, return_logits: bool = Fal if return_logits: return logits return F.log_softmax(logits, dim=-1) + + def fill_mask( + self, + masked_inputs: List[str], + topk: int = 5, + match_source_len: bool = True, + **generate_kwargs + ): + masked_token = '<mask>' + batch_tokens = [] + for masked_input in masked_inputs: + assert masked_token in masked_input, \ + "please add one {} token for the input".format(masked_token) + + text_spans = masked_input.split(masked_token) + text_spans_bpe = (' {0} '.format(masked_token)).join( + [self.bpe.encode(text_span.rstrip()) for text_span in text_spans] + ).strip() + tokens = self.task.source_dictionary.encode_line( + '<s> ' + text_spans_bpe + ' </s>', + append_eos=False, + add_if_not_exist=False, + ).long() + batch_tokens.append(tokens) + + # ensure beam size is at least as big as topk + generate_kwargs['beam'] = max( + topk, + generate_kwargs.get('beam', -1), + ) + generate_kwargs['match_source_len'] = match_source_len + batch_hypos = self.generate(batch_tokens, **generate_kwargs) + + return [ + [(self.decode(hypo['tokens']), hypo['score']) for hypo in hypos[:topk]] + for hypos in batch_hypos + ] diff --git a/fairseq/models/bart/model.py b/fairseq/models/bart/model.py index 389890a31c..71d0b27cd2 100644 --- a/fairseq/models/bart/model.py +++ b/fairseq/models/bart/model.py @@ -6,17 +6,14 @@ BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension """ +from typing import Optional import logging import torch import torch.nn as nn - from fairseq import utils -from fairseq.models import ( - register_model, - register_model_architecture, -) +from fairseq.models import register_model, register_model_architecture from fairseq.models.transformer import TransformerModel from fairseq.modules.transformer_sentence_encoder import init_bert_params @@ -26,16 +23,18 @@ logger = logging.getLogger(__name__) -@register_model('bart') +@register_model("bart") class BARTModel(TransformerModel): + __jit_unused_properties__ = ["supported_targets"] @classmethod def hub_models(cls): return { - 'bart.large': 'http://dl.fbaipublicfiles.com/fairseq/models/bart.large.tar.gz', - 'bart.large.mnli': 'http://dl.fbaipublicfiles.com/fairseq/models/bart.large.mnli.tar.gz', - 'bart.large.cnn': 'http://dl.fbaipublicfiles.com/fairseq/models/bart.large.cnn.tar.gz', - 'bart.large.xsum': 'http://dl.fbaipublicfiles.com/fairseq/models/bart.large.xsum.tar.gz', + "bart.base": "http://dl.fbaipublicfiles.com/fairseq/models/bart.base.tar.gz", + "bart.large": "http://dl.fbaipublicfiles.com/fairseq/models/bart.large.tar.gz", + "bart.large.mnli": "http://dl.fbaipublicfiles.com/fairseq/models/bart.large.mnli.tar.gz", + "bart.large.cnn": "http://dl.fbaipublicfiles.com/fairseq/models/bart.large.cnn.tar.gz", + "bart.large.xsum": "http://dl.fbaipublicfiles.com/fairseq/models/bart.large.xsum.tar.gz", } def __init__(self, args, encoder, decoder): @@ -45,27 +44,44 @@ def __init__(self, args, encoder, decoder): self.apply(init_bert_params) self.classification_heads = nn.ModuleDict() + if hasattr(self.encoder, "dictionary"): + self.eos: int = self.encoder.dictionary.eos() @staticmethod def add_args(parser): super(BARTModel, BARTModel).add_args(parser) parser.add_argument( - '--pooler-dropout', type=float, metavar='D', - help='dropout probability in the masked_lm pooler layers' + "--pooler-dropout", + type=float, + metavar="D", + help="dropout probability in the masked_lm pooler layers", ) parser.add_argument( - '--pooler-activation-fn', + "--pooler-activation-fn", choices=utils.get_available_activation_fns(), - help='activation function to use for pooler layer' + help="activation function to use for pooler layer", + ) + parser.add_argument( + "--spectral-norm-classification-head", + action="store_true", + help="Apply spectral normalization on the classification head", ) @property def supported_targets(self): - return {'self'} + return {"self"} def forward( - self, src_tokens, src_lengths, prev_output_tokens, - features_only=False, classification_head_name=None, **kwargs + self, + src_tokens, + src_lengths, + prev_output_tokens, + features_only: bool = False, + classification_head_name: Optional[str] = None, + token_embeddings: Optional[torch.Tensor] = None, + return_all_hiddens: bool = True, + alignment_layer: Optional[int] = None, + alignment_heads: Optional[int] = None, ): if classification_head_name is not None: features_only = True @@ -73,34 +89,42 @@ def forward( encoder_out = self.encoder( src_tokens, src_lengths=src_lengths, - **kwargs, + token_embeddings=token_embeddings, + return_all_hiddens=return_all_hiddens ) x, extra = self.decoder( prev_output_tokens, encoder_out=encoder_out, features_only=features_only, - **kwargs, + alignment_layer=alignment_layer, + alignment_heads=alignment_heads, + src_lengths=src_lengths, + return_all_hiddens=return_all_hiddens, ) - + eos: int = self.eos if classification_head_name is not None: sentence_representation = x[ - src_tokens.eq(self.encoder.dictionary.eos()), : + src_tokens.eq(eos), : ].view(x.size(0), -1, x.size(-1))[:, -1, :] - x = self.classification_heads[classification_head_name]( - sentence_representation - ) + for k, head in self.classification_heads.items(): + # for torch script only supports iteration + if k == classification_head_name: + x = head(sentence_representation) + break return x, extra @classmethod def from_pretrained( cls, model_name_or_path, - checkpoint_file='model.pt', - data_name_or_path='.', - bpe='gpt2', + checkpoint_file="model.pt", + data_name_or_path=".", + bpe="gpt2", + sample_break_mode="eos", **kwargs, ): from fairseq import hub_utils + x = hub_utils.from_pretrained( model_name_or_path, checkpoint_file, @@ -108,11 +132,14 @@ def from_pretrained( archive_map=cls.hub_models(), bpe=bpe, load_checkpoint_heads=True, + sample_break_mode=sample_break_mode, **kwargs, ) - return BARTHubInterface(x['args'], x['task'], x['models'][0]) + return BARTHubInterface(x["args"], x["task"], x["models"][0]) - def register_classification_head(self, name, num_classes=None, inner_dim=None, **kwargs): + def register_classification_head( + self, name, num_classes=None, inner_dim=None, **kwargs + ): """Register a classification head.""" logger.info("Registering classification head: {0}".format(name)) if name in self.classification_heads: @@ -121,52 +148,66 @@ def register_classification_head(self, name, num_classes=None, inner_dim=None, * if num_classes != prev_num_classes or inner_dim != prev_inner_dim: logger.warning( 're-registering head "{}" with num_classes {} (prev: {}) ' - 'and inner_dim {} (prev: {})'.format( + "and inner_dim {} (prev: {})".format( name, num_classes, prev_num_classes, inner_dim, prev_inner_dim ) ) self.classification_heads[name] = BARTClassificationHead( - self.args.encoder_embed_dim, - inner_dim or self.args.encoder_embed_dim, - num_classes, - self.args.pooler_activation_fn, - self.args.pooler_dropout, + input_dim=self.args.encoder_embed_dim, + inner_dim=inner_dim or self.args.encoder_embed_dim, + num_classes=num_classes, + activation_fn=self.args.pooler_activation_fn, + pooler_dropout=self.args.pooler_dropout, + do_spectral_norm=getattr( + self.args, "spectral_norm_classification_head", False + ), ) def upgrade_state_dict_named(self, state_dict, name): super().upgrade_state_dict_named(state_dict, name) - prefix = name + '.' if name != '' else '' - current_head_names = [] if not hasattr(self, 'classification_heads') else \ - self.classification_heads.keys() + prefix = name + "." if name != "" else "" + current_head_names = ( + [] + if not hasattr(self, "classification_heads") + else self.classification_heads.keys() + ) # Handle new classification heads present in the state dict. keys_to_delete = [] for k in state_dict.keys(): - if not k.startswith(prefix + 'classification_heads.'): + if not k.startswith(prefix + "classification_heads."): continue - head_name = k[len(prefix + 'classification_heads.'):].split('.')[0] - num_classes = state_dict[prefix + 'classification_heads.' + head_name + '.out_proj.weight'].size(0) - inner_dim = state_dict[prefix + 'classification_heads.' + head_name + '.dense.weight'].size(0) + head_name = k[len(prefix + "classification_heads.") :].split(".")[0] + num_classes = state_dict[ + prefix + "classification_heads." + head_name + ".out_proj.weight" + ].size(0) + inner_dim = state_dict[ + prefix + "classification_heads." + head_name + ".dense.weight" + ].size(0) - if getattr(self.args, 'load_checkpoint_heads', False): + if getattr(self.args, "load_checkpoint_heads", False): if head_name not in current_head_names: self.register_classification_head(head_name, num_classes, inner_dim) else: if head_name not in current_head_names: logger.warning( - 'deleting classification head ({}) from checkpoint ' - 'not present in current model: {}'.format(head_name, k) + "deleting classification head ({}) from checkpoint " + "not present in current model: {}".format(head_name, k) ) keys_to_delete.append(k) elif ( - num_classes != self.classification_heads[head_name].out_proj.out_features - or inner_dim != self.classification_heads[head_name].dense.out_features + num_classes + != self.classification_heads[head_name].out_proj.out_features + or inner_dim + != self.classification_heads[head_name].dense.out_features ): logger.warning( - 'deleting classification head ({}) from checkpoint ' - 'with different dimensions than current model: {}'.format(head_name, k) + "deleting classification head ({}) from checkpoint " + "with different dimensions than current model: {}".format( + head_name, k + ) ) keys_to_delete.append(k) for k in keys_to_delete: @@ -178,55 +219,66 @@ def truncate_emb(key): # When finetuning on translation task, remove last row of # embedding matrix that corresponds to mask_idx token. - loaded_dict_size = state_dict['encoder.embed_tokens.weight'].size(0) - if loaded_dict_size == len(self.encoder.dictionary) + 1 and '<mask>' not in self.encoder.dictionary: - truncate_emb('encoder.embed_tokens.weight') - truncate_emb('decoder.embed_tokens.weight') - truncate_emb('encoder.output_projection.weight') - truncate_emb('decoder.output_projection.weight') + loaded_dict_size = state_dict["encoder.embed_tokens.weight"].size(0) + if ( + loaded_dict_size == len(self.encoder.dictionary) + 1 + and "<mask>" not in self.encoder.dictionary + ): + truncate_emb("encoder.embed_tokens.weight") + truncate_emb("decoder.embed_tokens.weight") + truncate_emb("encoder.output_projection.weight") + truncate_emb("decoder.output_projection.weight") # When continued pretraining on new set of languages for mbart, # add extra lang embeddings at the end of embed_tokens. # Note: newly added languages are assumed to have been added at the end. - if self.args.task == 'multilingual_denoising' and loaded_dict_size < len(self.encoder.dictionary): + if self.args.task == "multilingual_denoising" and loaded_dict_size < len( + self.encoder.dictionary + ): logger.info( - "Adding extra language embeddings not found in pretrained model for "\ + "Adding extra language embeddings not found in pretrained model for " "continued pretraining of MBART on new set of languages." ) - loaded_mask_token_embedding = state_dict['encoder.embed_tokens.weight'][-1, :] + loaded_mask_token_embedding = state_dict["encoder.embed_tokens.weight"][ + -1, : + ] num_langids_to_add = len(self.encoder.dictionary) - loaded_dict_size - embed_dim = state_dict['encoder.embed_tokens.weight'].size(1) + embed_dim = state_dict["encoder.embed_tokens.weight"].size(1) new_lang_embed_to_add = torch.zeros(num_langids_to_add, embed_dim) - nn.init.normal_( - new_lang_embed_to_add, - mean=0, - std=embed_dim ** -0.5 - ) + nn.init.normal_(new_lang_embed_to_add, mean=0, std=embed_dim ** -0.5) new_lang_embed_to_add = new_lang_embed_to_add.to( - dtype=state_dict['encoder.embed_tokens.weight'].dtype, + dtype=state_dict["encoder.embed_tokens.weight"].dtype, ) - state_dict['encoder.embed_tokens.weight'] = torch.cat([ - state_dict['encoder.embed_tokens.weight'][:loaded_dict_size-1, :], - new_lang_embed_to_add, - loaded_mask_token_embedding.unsqueeze(0)] + state_dict["encoder.embed_tokens.weight"] = torch.cat( + [ + state_dict["encoder.embed_tokens.weight"][ + : loaded_dict_size - 1, : + ], + new_lang_embed_to_add, + loaded_mask_token_embedding.unsqueeze(0), + ] ) - state_dict['decoder.embed_tokens.weight'] = torch.cat([ - state_dict['decoder.embed_tokens.weight'][:loaded_dict_size-1, :], - new_lang_embed_to_add, - loaded_mask_token_embedding.unsqueeze(0)] + state_dict["decoder.embed_tokens.weight"] = torch.cat( + [ + state_dict["decoder.embed_tokens.weight"][ + : loaded_dict_size - 1, : + ], + new_lang_embed_to_add, + loaded_mask_token_embedding.unsqueeze(0), + ] ) # Copy any newly-added classification heads into the state dict # with their current weights. - if hasattr(self, 'classification_heads'): + if hasattr(self, "classification_heads"): cur_state = self.classification_heads.state_dict() for k, v in cur_state.items(): - if prefix + 'classification_heads.' + k not in state_dict: - logger.info('Overwriting', prefix + 'classification_heads.' + k) - state_dict[prefix + 'classification_heads.' + k] = v + if prefix + "classification_heads." + k not in state_dict: + logger.info("Overwriting " + prefix + "classification_heads." + k) + state_dict[prefix + "classification_heads." + k] = v class BARTClassificationHead(nn.Module): @@ -239,6 +291,7 @@ def __init__( num_classes, activation_fn, pooler_dropout, + do_spectral_norm=False, ): super().__init__() self.dense = nn.Linear(input_dim, inner_dim) @@ -246,6 +299,9 @@ def __init__( self.dropout = nn.Dropout(p=pooler_dropout) self.out_proj = nn.Linear(inner_dim, num_classes) + if do_spectral_norm: + self.out_proj = torch.nn.utils.spectral_norm(self.out_proj) + def forward(self, features, **kwargs): x = features x = self.dropout(x) @@ -256,67 +312,73 @@ def forward(self, features, **kwargs): return x -@register_model_architecture('bart', 'bart_large') +@register_model_architecture("bart", "bart_large") def bart_large_architecture(args): - args.encoder_embed_path = getattr(args, 'encoder_embed_path', None) - args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 1024) - args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 4*1024) - args.encoder_layers = getattr(args, 'encoder_layers', 12) - args.encoder_attention_heads = getattr(args, 'encoder_attention_heads', 16) - args.encoder_normalize_before = getattr(args, 'encoder_normalize_before', False) - args.encoder_learned_pos = getattr(args, 'encoder_learned_pos', True) - args.decoder_embed_path = getattr(args, 'decoder_embed_path', None) - args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', args.encoder_embed_dim) - args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', args.encoder_ffn_embed_dim) - args.decoder_layers = getattr(args, 'decoder_layers', 12) - args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 16) - args.decoder_normalize_before = getattr(args, 'decoder_normalize_before', False) - args.decoder_learned_pos = getattr(args, 'decoder_learned_pos', True) - args.attention_dropout = getattr(args, 'attention_dropout', 0.) - args.relu_dropout = getattr(args, 'relu_dropout', 0.) - args.dropout = getattr(args, 'dropout', 0.1) - args.max_target_positions = getattr(args, 'max_target_positions', 1024) - args.max_source_positions = getattr(args, 'max_source_positions', 1024) - args.adaptive_softmax_cutoff = getattr(args, 'adaptive_softmax_cutoff', None) - args.adaptive_softmax_dropout = getattr(args, 'adaptive_softmax_dropout', 0) - args.share_decoder_input_output_embed = getattr(args, 'share_decoder_input_output_embed', True) - args.share_all_embeddings = getattr(args, 'share_all_embeddings', True) - - args.decoder_output_dim = getattr(args, 'decoder_output_dim', args.decoder_embed_dim) - args.decoder_input_dim = getattr(args, 'decoder_input_dim', args.decoder_embed_dim) - - args.no_scale_embedding = getattr(args, 'no_scale_embedding', True) - args.layernorm_embedding = getattr(args, 'layernorm_embedding', True) - - args.activation_fn = getattr(args, 'activation_fn', 'gelu') - args.pooler_activation_fn = getattr(args, 'pooler_activation_fn', 'tanh') - args.pooler_dropout = getattr(args, 'pooler_dropout', 0.0) - - -@register_model_architecture('bart', 'bart_base') + args.encoder_embed_path = getattr(args, "encoder_embed_path", None) + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4 * 1024) + args.encoder_layers = getattr(args, "encoder_layers", 12) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 16) + args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False) + args.encoder_learned_pos = getattr(args, "encoder_learned_pos", True) + args.decoder_embed_path = getattr(args, "decoder_embed_path", None) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim) + args.decoder_ffn_embed_dim = getattr( + args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim + ) + args.decoder_layers = getattr(args, "decoder_layers", 12) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 16) + args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False) + args.decoder_learned_pos = getattr(args, "decoder_learned_pos", True) + args.attention_dropout = getattr(args, "attention_dropout", 0.0) + args.relu_dropout = getattr(args, "relu_dropout", 0.0) + args.dropout = getattr(args, "dropout", 0.1) + args.max_target_positions = getattr(args, "max_target_positions", 1024) + args.max_source_positions = getattr(args, "max_source_positions", 1024) + args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None) + args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0) + args.share_decoder_input_output_embed = getattr( + args, "share_decoder_input_output_embed", True + ) + args.share_all_embeddings = getattr(args, "share_all_embeddings", True) + + args.decoder_output_dim = getattr( + args, "decoder_output_dim", args.decoder_embed_dim + ) + args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim) + + args.no_scale_embedding = getattr(args, "no_scale_embedding", True) + args.layernorm_embedding = getattr(args, "layernorm_embedding", True) + + args.activation_fn = getattr(args, "activation_fn", "gelu") + args.pooler_activation_fn = getattr(args, "pooler_activation_fn", "tanh") + args.pooler_dropout = getattr(args, "pooler_dropout", 0.0) + + +@register_model_architecture("bart", "bart_base") def bart_base_architecture(args): - args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 768) - args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 4*768) - args.encoder_layers = getattr(args, 'encoder_layers', 6) - args.encoder_attention_heads = getattr(args, 'encoder_attention_heads', 12) - args.decoder_layers = getattr(args, 'decoder_layers', 6) - args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 12) + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 768) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4 * 768) + args.encoder_layers = getattr(args, "encoder_layers", 6) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 12) + args.decoder_layers = getattr(args, "decoder_layers", 6) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 12) bart_large_architecture(args) -@register_model_architecture('bart', 'mbart_large') +@register_model_architecture("bart", "mbart_large") def mbart_large_architecture(args): - args.no_scale_embedding = getattr(args, 'no_scale_embedding', False) + args.no_scale_embedding = getattr(args, "no_scale_embedding", False) bart_large_architecture(args) -@register_model_architecture('bart', 'mbart_base') +@register_model_architecture("bart", "mbart_base") def mbart_base_architecture(args): - args.no_scale_embedding = getattr(args, 'no_scale_embedding', False) + args.no_scale_embedding = getattr(args, "no_scale_embedding", False) bart_base_architecture(args) -@register_model_architecture('bart', 'mbart_base_wmt20') +@register_model_architecture("bart", "mbart_base_wmt20") def mbart_base_wmt20_architecture(args): - args.layernorm_embedding = getattr(args, 'layernorm_embedding', False) + args.layernorm_embedding = getattr(args, "layernorm_embedding", False) mbart_base_architecture(args) diff --git a/fairseq/models/composite_encoder.py b/fairseq/models/composite_encoder.py index afef248cdc..4e20fe3a83 100644 --- a/fairseq/models/composite_encoder.py +++ b/fairseq/models/composite_encoder.py @@ -3,7 +3,7 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. -from fairseq.models import FairseqEncoder +from .fairseq_encoder import FairseqEncoder class CompositeEncoder(FairseqEncoder): @@ -43,7 +43,9 @@ def forward(self, src_tokens, src_lengths): def reorder_encoder_out(self, encoder_out, new_order): """Reorder encoder output according to new_order.""" for key in self.encoders: - encoder_out[key] = self.encoders[key].reorder_encoder_out(encoder_out[key], new_order) + encoder_out[key] = self.encoders[key].reorder_encoder_out( + encoder_out[key], new_order + ) return encoder_out def max_positions(self): diff --git a/fairseq/models/distributed_fairseq_model.py b/fairseq/models/distributed_fairseq_model.py index dd74bf1f13..5eda227640 100644 --- a/fairseq/models/distributed_fairseq_model.py +++ b/fairseq/models/distributed_fairseq_model.py @@ -3,12 +3,24 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. -import inspect +import logging +import os +import signal +import threading +import torch import torch.nn as nn +from torch.nn.parallel import DistributedDataParallel -from fairseq.legacy_distributed_data_parallel import LegacyDistributedDataParallel -from fairseq.models import BaseFairseqModel +from fairseq.distributed import ( + DistributedTimeoutWrapper, + LegacyDistributedDataParallel, + ModuleProxyWrapper, + TPUDistributedDataParallel, +) + + +logger = logging.getLogger(__name__) _GOSSIP_DISABLED = False @@ -18,7 +30,7 @@ _GOSSIP_DISABLED = True -def DistributedFairseqModel(args, model, process_group=None): +def DistributedFairseqModel(args, model, process_group, device): """ Wrap a *model* to support distributed data parallel training. @@ -30,39 +42,59 @@ def DistributedFairseqModel(args, model, process_group=None): Args: args (argparse.Namespace): fairseq args model (BaseFairseqModel): model to wrap + process_group: the c10d process group to be used for distributed data + parallel all-reduction. + device: device to move model to """ - # determine which DDP class to extend assert isinstance(model, nn.Module) - if args.distributed_wrapper == 'DDP' and args.ddp_backend == 'c10d': - ddp_class = nn.parallel.DistributedDataParallel - init_kwargs = dict( - module=model, + if args.tpu: + wrapped_model = TPUDistributedDataParallel( + module=model.to(device), + process_group=process_group, + ) + # forward missing getattr and state_dict/load_state_dict to orig model + wrapped_model = ModuleProxyWrapper(wrapped_model) + elif args.ddp_backend in {"c10d", "pytorch_ddp"}: + wrapped_model = DistributedDataParallel( + module=model.to(device), device_ids=[args.device_id], output_device=args.device_id, broadcast_buffers=args.broadcast_buffers, bucket_cap_mb=args.bucket_cap_mb, process_group=process_group, + find_unused_parameters=args.find_unused_parameters, + gradient_as_bucket_view=args.gradient_as_bucket_view, ) - # Maintain backward compatibility - if 'check_reduction' in inspect.getargspec(ddp_class)[0]: - init_kwargs['check_reduction'] = True - if 'find_unused_parameters' in inspect.getargspec(ddp_class)[0]: - init_kwargs['find_unused_parameters'] = args.find_unused_parameters - elif args.distributed_wrapper == 'DDP' and args.ddp_backend == 'no_c10d': - ddp_class = LegacyDistributedDataParallel - init_kwargs = dict( - module=model, - world_size=args.distributed_world_size, - buffer_size=2**28, + if args.ddp_comm_hook == "fp16": + logger.info("enable fp16 communication hook in DDP") + try: + from torch.distributed.algorithms.ddp_comm_hooks import ( + register_ddp_comm_hook, + DDPCommHookType, + ) + except: + logger.error( + "Could not import from torch.distributed.algorithms.ddp_comm_hooks; you may need to update your pytorch version" + ) + raise + + register_ddp_comm_hook(DDPCommHookType.FP16_COMPRESS, wrapped_model) + # forward missing getattr and state_dict/load_state_dict to orig model + wrapped_model = ModuleProxyWrapper(wrapped_model) + elif args.ddp_backend in {"no_c10d", "legacy_ddp"}: + wrapped_model = LegacyDistributedDataParallel( + module=model.to(device), + buffer_size=2 ** 28, process_group=process_group, ) - elif args.distributed_wrapper == 'SlowMo': + # forward missing getattr and state_dict/load_state_dict to orig model + wrapped_model = ModuleProxyWrapper(wrapped_model) + elif args.ddp_backend == "slow_mo": if _GOSSIP_DISABLED: raise ImportError( - 'Cannot find gossip library. Please install from: ' - 'github.com/facebookresearch/stochastic_gradient_push' + "Cannot find gossip library. Please install from: " + "github.com/facebookresearch/stochastic_gradient_push" ) - ddp_class = gossip.GossipDataParallel # The values of slowmo_momentum below were obtained by tuning on the # En-De 16 dataset by training the transformer_wmt_en_de_large model @@ -76,30 +108,39 @@ def DistributedFairseqModel(args, model, process_group=None): else: args.slowmo_momentum = 0.6 - init_kwargs = dict( - module=model, + wrapped_model = gossip.GossipDataParallel( + module=model.to(device), device_ids=[args.device_id], output_device=args.device_id, broadcast_buffers=args.broadcast_buffers, nprocs_per_node=args.nprocs_per_node, slowmo_momentum=args.slowmo_momentum, - localsgd=(args.slowmo_algorithm == 'LocalSGD'), - localsgd_frequency=args.localsgd_frequency + localsgd=(args.slowmo_algorithm == "LocalSGD"), + localsgd_frequency=args.localsgd_frequency, ) + # forward missing getattr and state_dict/load_state_dict to orig model + wrapped_model = ModuleProxyWrapper(wrapped_model) + elif args.ddp_backend == "fully_sharded": + try: + from fairscale.nn.data_parallel import FullyShardedDataParallel as FSDP + except ImportError: + raise ImportError( + "Cannot find FullyShardedDataParallel. " + "Please install fairscale with: pip install fairscale" + ) + assert isinstance(model, FSDP), "expected model to already be wrapped in FSDP" + wrapped_model = model + if args.memory_efficient_fp16: + wrapped_model = wrapped_model.half() + if not args.cpu_offload: + wrapped_model = wrapped_model.to(device=device) else: - raise ValueError('Unknown --ddp-backend: ' + args.ddp_backend) - - class _DistributedFairseqModel(ddp_class): - """Extend DistributedDataParallel to check for missing - attributes in the wrapped module.""" + raise ValueError("Unknown --ddp-backend: " + args.ddp_backend) - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - - def __getattr__(self, name): - wrapped_module = super().__getattr__('module') - if hasattr(wrapped_module, name): - return getattr(wrapped_module, name) - return super().__getattr__(name) + # kill hung distributed jobs after a timeout + if getattr(args, "heartbeat_timeout", -1) > 0: + wrapped_model = DistributedTimeoutWrapper( + wrapped_model, timeout=getattr(args, "heartbeat_timeout", -1) + ) - return _DistributedFairseqModel(**init_kwargs) + return wrapped_model diff --git a/fairseq/models/ema/__init__.py b/fairseq/models/ema/__init__.py new file mode 100644 index 0000000000..503ceaa609 --- /dev/null +++ b/fairseq/models/ema/__init__.py @@ -0,0 +1,20 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import importlib +import os + +from .ema import EMA + + +def build_ema(model, cfg, device): + return EMA(model, cfg, device) + + +# automatically import any Python files in the models/ema/ directory +for file in sorted(os.listdir(os.path.dirname(__file__))): + if file.endswith(".py") and not file.startswith("_"): + file_name = file[: file.find(".py")] + importlib.import_module("fairseq.models.ema." + file_name) diff --git a/fairseq/models/ema/ema.py b/fairseq/models/ema/ema.py new file mode 100644 index 0000000000..6c0af69325 --- /dev/null +++ b/fairseq/models/ema/ema.py @@ -0,0 +1,189 @@ +#!/usr/bin/env python3 + +""" +This module has the EMA class used to store a copy of the exponentially decayed +model params. + +Typical usage of EMA class involves initializing an object using an existing +model (random or from a seed model) and setting the config like ema_decay, +ema_start_update which determine how the EMA model is updated. After every +update of the model i.e. at the end of the train_step, the EMA should be updated +by passing the new model to the EMA.step function. The EMA model state dict +can be stored in the extra state under the key of "ema" and dumped +into a checkpoint and loaded. The EMA object can be passed to tasks +by setting task.uses_ema property. +EMA is a smoothed/ensemble model which might have better performance +when used for inference or further fine-tuning. EMA class has a +reverse function to load the EMA params into a model and use it +like a regular model. +""" + +import copy +import logging + +import torch +from fairseq import checkpoint_utils + + +class EMA(object): + """Exponential Moving Average of Fairseq Models + EMA keeps a copy of the exponentially decayed model params. + The set of params should include both gradient-descent and + non-gradient descent params, such as batch mean/var and buffers. + This is a modified implementation of + the open source code in https://github.com/zhawe01/fairseq-gec.git, + and internal source code in + fbcode/mobile-vision/projects/classification_pytorch/lib/utils/model_ema.py. + + Similar to TF EMA. + https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage. + EMA provides a averaged and smoothed set of model weights, and has been shown to + improve vision models. EMA class does all necessary functions to update, reload, + or init EMA methods. + + EMA object is initialized from an arbitrary model. By default, it is stored in + the same device (unless device specified at initialization) and with the + same precision as the model (unless ema_fp32 is True). ema_fp32 is recommended. + This stores the EMA parameters in fp32 only for the EMA update step, and + is used at the default precision otherwise. + EMA is usually enabled using EMAConfig with store_ema=True. Some important + parameters to configure EMA are + 1) ema_decay - The decay of EMA + 2) ema_update_freq - EMA is updated every this many model updates. + 3) ema_start_update - Start EMA update after this many model updates [default 0] + + Key methods: + 1) step - One update of EMA using new model + 2) restore - Update EMA from a state dict + 3) reverse - Load EMA into a model + 4) get_decay, _set_decay - Used to get or set the decay. Note _set_decay is + called from step. + 5) build_fp32_params - Used to initialize or update the fp32 copy of EMA params. + Note this is enabled only when ema_fp32=True + """ + + def __init__(self, model, config, device=None): + """ + @param model model to initialize the EMA with + @param config EMAConfig object with configuration like + ema_decay, ema_update_freq, ema_fp32 + @param device If provided, copy EMA to this device (e.g. gpu). + Otherwise EMA is in the same device as the model. + """ + + self.decay = config.ema_decay + self.model = copy.deepcopy(model) + self.model.requires_grad_(False) + self.config = config + self.fp32_params = {} + + if self.config.ema_seed_model is not None: + state = checkpoint_utils.load_ema_from_checkpoint(self.config.ema_seed_model) + self.model.load_state_dict(state["model"], strict=True) + + if device is not None: + logging.info(f"Copying EMA model to device {device}") + self.model = self.model.to(device=device) + + if self.config.ema_fp32: + self.build_fp32_params() + + self.update_freq_counter = 0 + + def get_model(self): + return self.model + + def build_fp32_params(self, state_dict=None): + """ + Store a copy of the EMA params in fp32. + If state dict is passed, the EMA params is copied from + the provided state dict. Otherwise, it is copied from the + current EMA model parameters. + """ + if not self.config.ema_fp32: + raise RuntimeError( + "build_fp32_params should not be called if ema_fp32=False. " + "Use ema_fp32=True if this is really intended." + ) + + if state_dict is None: + state_dict = self.model.state_dict() + + def _to_float(t): + return t.float() if torch.is_floating_point(t) else t + + for param_key in state_dict: + if param_key in self.fp32_params: + self.fp32_params[param_key].copy_(state_dict[param_key]) + else: + self.fp32_params[param_key] = _to_float(state_dict[param_key]) + + def restore(self, state_dict, build_fp32_params=False): + """ Load data from a model spec into EMA model """ + self.model.load_state_dict(state_dict, strict=False) + if build_fp32_params: + self.build_fp32_params(state_dict) + + def _set_decay(self, decay): + self.decay = decay + + def get_decay(self): + return self.decay + + def _step_internal(self, new_model, updates=None): + """ One update of the EMA model based on new model weights """ + decay = self.decay + + ema_state_dict = {} + ema_params = self.fp32_params if self.config.ema_fp32 else self.model.state_dict() + for key, param in new_model.state_dict().items(): + try: + ema_param = ema_params[key] + except KeyError: + ema_param = param.float().clone() if param.ndim == 1 else copy.deepcopy(param) + + if param.shape != ema_param.shape: + raise ValueError( + "incompatible tensor shapes between model param and ema param" + + "{} vs. {}".format(param.shape, ema_param.shape) + ) + if "version" in key: + # Do not decay a model.version pytorch param + continue + ema_param.mul_(decay) + ema_param.add_(param.to(dtype=ema_param.dtype), alpha=1-decay) + ema_state_dict[key] = ema_param + self.restore(ema_state_dict, build_fp32_params=False) + + def step(self, new_model, updates=None): + """ + One update of EMA which is done every self.config.ema_update_freq + updates of the model. + + @param updates The current number of model updates done. + Decay is set of 0 if model updates < ema_start_update, which means + the model will be simply copied over to the EMA. + When model updates >= ema_start_updates, then EMA is updated with + a decay of self.config.ema_decay. + """ + self._set_decay( + 0 + if updates is not None + and updates < self.config.ema_start_update + else self.config.ema_decay + ) + if updates is not None and self.config.ema_update_freq > 1: + self.update_freq_counter += 1 + if self.update_freq_counter >= self.config.ema_update_freq: + self._step_internal(new_model, updates) + self.update_freq_counter = 0 + else: + self._step_internal(new_model, updates) + + def reverse(self, model): + """ + Load the model parameters from EMA model. + Useful for inference or fine-tuning from the EMA model. + """ + model.load_state_dict(self.model.state_dict(), strict=False) + return model diff --git a/fairseq/models/fairseq_decoder.py b/fairseq/models/fairseq_decoder.py index fb6c52dc7f..4f1e8b52a2 100644 --- a/fairseq/models/fairseq_decoder.py +++ b/fairseq/models/fairseq_decoder.py @@ -17,6 +17,8 @@ def __init__(self, dictionary): super().__init__() self.dictionary = dictionary self.onnx_trace = False + self.adaptive_softmax = None + def forward(self, prev_output_tokens, encoder_out=None, **kwargs): """ @@ -62,6 +64,19 @@ def get_normalized_probs( sample: Optional[Dict[str, Tensor]] = None, ): """Get normalized probabilities (or log probs) from a net's output.""" + return self.get_normalized_probs_scriptable(net_output, log_probs, sample) + + # TorchScript doesn't support super() method so that the scriptable Subclass + # can't access the base class model in Torchscript. + # Current workaround is to add a helper function with different name and + # call the helper function from scriptable Subclass. + def get_normalized_probs_scriptable( + self, + net_output: Tuple[Tensor, Optional[Dict[str, List[Optional[Tensor]]]]], + log_probs: bool, + sample: Optional[Dict[str, Tensor]] = None, + ): + """Get normalized probabilities (or log probs) from a net's output.""" if hasattr(self, "adaptive_softmax") and self.adaptive_softmax is not None: if sample is not None: @@ -82,8 +97,8 @@ def max_positions(self): """Maximum input length supported by the decoder.""" return 1e6 # an arbitrary large number - def upgrade_state_dict(self, state_dict): - """Upgrade a (possibly old) state dict for new versions of fairseq.""" + def upgrade_state_dict_named(self, state_dict, name): + """Upgrade old state dicts to work with newer code.""" return state_dict def prepare_for_onnx_export_(self): diff --git a/fairseq/models/fairseq_encoder.py b/fairseq/models/fairseq_encoder.py index 62b0b04382..08cbde15a4 100644 --- a/fairseq/models/fairseq_encoder.py +++ b/fairseq/models/fairseq_encoder.py @@ -3,17 +3,19 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. +from typing import Dict, List, NamedTuple, Optional + import torch import torch.nn as nn -from typing import Dict, List, NamedTuple, Optional from torch import Tensor + EncoderOut = NamedTuple( "EncoderOut", [ ("encoder_out", Tensor), # T x B x C - ("encoder_padding_mask", Tensor), # B x T - ("encoder_embedding", Tensor), # B x T x C + ("encoder_padding_mask", Optional[Tensor]), # B x T + ("encoder_embedding", Optional[Tensor]), # B x T x C ("encoder_states", Optional[List[Tensor]]), # List[T x B x C] ("src_tokens", Optional[Tensor]), # B x T ("src_lengths", Optional[Tensor]), # B x 1 @@ -55,9 +57,7 @@ def forward_torchscript(self, net_input: Dict[str, Tensor]): @torch.jit.unused def forward_non_torchscript(self, net_input: Dict[str, Tensor]): encoder_input = { - k: v - for k, v in net_input.items() - if k != "prev_output_tokens" + k: v for k, v in net_input.items() if k != "prev_output_tokens" } return self.forward(**encoder_input) @@ -78,6 +78,15 @@ def max_positions(self): """Maximum input length supported by the encoder.""" return 1e6 # an arbitrary large number - def upgrade_state_dict(self, state_dict): - """Upgrade a (possibly old) state dict for new versions of fairseq.""" + def upgrade_state_dict_named(self, state_dict, name): + """Upgrade old state dicts to work with newer code.""" return state_dict + + def set_num_updates(self, num_updates): + """State from trainer to pass along to model at every update.""" + + def _apply(m): + if hasattr(m, "set_num_updates") and m != self: + m.set_num_updates(num_updates) + + self.apply(_apply) diff --git a/fairseq/models/fairseq_incremental_decoder.py b/fairseq/models/fairseq_incremental_decoder.py index 51ab577288..cc72a0f8f3 100644 --- a/fairseq/models/fairseq_incremental_decoder.py +++ b/fairseq/models/fairseq_incremental_decoder.py @@ -2,13 +2,18 @@ # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. + +import logging from typing import Dict, Optional -from fairseq.models import FairseqDecoder from fairseq.incremental_decoding_utils import with_incremental_state +from fairseq.models import FairseqDecoder from torch import Tensor +logger = logging.getLogger(__name__) + + @with_incremental_state class FairseqIncrementalDecoder(FairseqDecoder): """Base class for incremental decoders. @@ -35,7 +40,9 @@ class FairseqIncrementalDecoder(FairseqDecoder): def __init__(self, dictionary): super().__init__(dictionary) - def forward(self, prev_output_tokens, encoder_out=None, incremental_state=None, **kwargs): + def forward( + self, prev_output_tokens, encoder_out=None, incremental_state=None, **kwargs + ): """ Args: prev_output_tokens (LongTensor): shifted output tokens of shape @@ -52,7 +59,9 @@ def forward(self, prev_output_tokens, encoder_out=None, incremental_state=None, """ raise NotImplementedError - def extract_features(self, prev_output_tokens, encoder_out=None, incremental_state=None, **kwargs): + def extract_features( + self, prev_output_tokens, encoder_out=None, incremental_state=None, **kwargs + ): """ Returns: tuple: @@ -68,27 +77,40 @@ def reorder_incremental_state( ): """Reorder incremental state. - This should be called when the order of the input has changed from the + This will be called when the order of the input has changed from the previous time step. A typical use case is beam search, where the input order changes between time steps based on the selection of beams. """ - seen: Dict[int, Optional[Tensor]] = {} - for _, module in self.named_modules(): - if hasattr(module, 'reorder_incremental_state'): - if id(module) not in seen and module is not self: - seen[id(module)] = None - result = module.reorder_incremental_state(incremental_state, new_order) - if result is not None: - incremental_state = result + pass + + def reorder_incremental_state_scripting( + self, + incremental_state: Dict[str, Dict[str, Optional[Tensor]]], + new_order: Tensor, + ): + """Main entry point for reordering the incremental state. + + Due to limitations in TorchScript, we call this function in + :class:`fairseq.sequence_generator.SequenceGenerator` instead of + calling :func:`reorder_incremental_state` directly. + """ + for module in self.modules(): + if hasattr(module, "reorder_incremental_state"): + result = module.reorder_incremental_state(incremental_state, new_order) + if result is not None: + incremental_state = result def set_beam_size(self, beam_size): """Sets the beam size in the decoder and all children.""" - if getattr(self, '_beam_size', -1) != beam_size: + if getattr(self, "_beam_size", -1) != beam_size: seen = set() def apply_set_beam_size(module): - if module != self and hasattr(module, 'set_beam_size') \ - and module not in seen: + if ( + module != self + and hasattr(module, "set_beam_size") + and module not in seen + ): seen.add(module) module.set_beam_size(beam_size) diff --git a/fairseq/models/fairseq_model.py b/fairseq/models/fairseq_model.py index c9590b87a3..e55c7ba1ad 100644 --- a/fairseq/models/fairseq_model.py +++ b/fairseq/models/fairseq_model.py @@ -7,21 +7,34 @@ """ import logging +from argparse import Namespace from typing import Dict, List, Optional, Tuple import torch import torch.nn as nn import torch.nn.functional as F from fairseq import utils -from fairseq.checkpoint_utils import prune_state_dict from fairseq.data import Dictionary +from fairseq.dataclass.utils import ( + convert_namespace_to_omegaconf, + gen_parser_from_dataclass, +) from fairseq.models import FairseqDecoder, FairseqEncoder +from omegaconf import DictConfig from torch import Tensor logger = logging.getLogger(__name__) +def check_type(module, expected_type): + if hasattr(module, "unwrapped_module"): + assert isinstance(module.unwrapped_module, expected_type), \ + f"{type(module.unwrapped_module)} != {expected_type}" + else: + assert isinstance(module, expected_type), f"{type(module)} != {expected_type}" + + class BaseFairseqModel(nn.Module): """Base class for fairseq models.""" @@ -29,10 +42,13 @@ def __init__(self): super().__init__() self._is_generation_fast = False - @staticmethod - def add_args(parser): + @classmethod + def add_args(cls, parser): """Add model-specific arguments to the parser.""" - pass + dc = getattr(cls, "__dataclass", None) + if dc is not None: + # do not set defaults so that settings defaults from various architectures still works + gen_parser_from_dataclass(parser, dc(), delete_default=True) @classmethod def build_model(cls, args, task): @@ -66,6 +82,8 @@ def get_normalized_probs_scriptable( if hasattr(self, "decoder"): return self.decoder.get_normalized_probs(net_output, log_probs, sample) elif torch.is_tensor(net_output): + # syntactic sugar for simple models which don't have a decoder + # (e.g., the classification tutorial) logits = net_output.float() if log_probs: return F.log_softmax(logits, dim=-1) @@ -81,15 +99,29 @@ def max_positions(self): """Maximum length supported by the model.""" return None - def load_state_dict(self, state_dict, strict=True, args=None): + def load_state_dict( + self, + state_dict, + strict=True, + model_cfg: Optional[DictConfig] = None, + args: Optional[Namespace] = None, + ): """Copies parameters and buffers from *state_dict* into this module and its descendants. Overrides the method in :class:`nn.Module`. Compared with that method this additionally "upgrades" *state_dicts* from old checkpoints. """ + + if model_cfg is None and args is not None: + logger.warn("using 'args' is deprecated, please update your code to use dataclass config") + model_cfg = convert_namespace_to_omegaconf(args).model + self.upgrade_state_dict(state_dict) - new_state_dict = prune_state_dict(state_dict, args) + + from fairseq.checkpoint_utils import prune_state_dict + + new_state_dict = prune_state_dict(state_dict, model_cfg) return super().load_state_dict(new_state_dict, strict) def upgrade_state_dict(self, state_dict): @@ -120,16 +152,30 @@ def do_upgrade(m, prefix): do_upgrade(self, name) def set_num_updates(self, num_updates): - """ State from trainer to pass along to model at every update """ - - def _apply(m): - if hasattr(m, 'set_num_updates') and m != self: + """State from trainer to pass along to model at every update.""" + for m in self.modules(): + if hasattr(m, "set_num_updates") and m != self: m.set_num_updates(num_updates) - self.apply(_apply) + def prepare_for_inference_(self, cfg: DictConfig): + """Prepare model for inference.""" + kwargs = {} + kwargs["beamable_mm_beam_size"] = ( + None + if getattr(cfg.generation, "no_beamable_mm", False) + else getattr(cfg.generation, "beam", 5) + ) + kwargs["need_attn"] = getattr(cfg.generation, "print_alignment", False) + if getattr(cfg.generation, "retain_dropout", False): + kwargs["retain_dropout"] = cfg.generation.retain_dropout + kwargs["retain_dropout_modules"] = cfg.generation.retain_dropout_modules + self.make_generation_fast_(**kwargs) def make_generation_fast_(self, **kwargs): - """Optimize model for faster generation.""" + """ + Legacy entry point to optimize model for faster generation. + Prefer prepare_for_inference_. + """ if self._is_generation_fast: return # only apply once self._is_generation_fast = True @@ -138,23 +184,28 @@ def make_generation_fast_(self, **kwargs): def apply_remove_weight_norm(module): try: nn.utils.remove_weight_norm(module) - except ValueError: # this module didn't have weight norm + except (AttributeError, ValueError): # this module didn't have weight norm return self.apply(apply_remove_weight_norm) - seen = set() + def apply_make_generation_fast_(module, prefix): + if len(prefix) > 0: + prefix += "." - def apply_make_generation_fast_(module): - if ( - module != self - and hasattr(module, "make_generation_fast_") - and module not in seen - ): - seen.add(module) - module.make_generation_fast_(**kwargs) + base_func = BaseFairseqModel.make_generation_fast_ + for n, m in module.named_modules(): + if ( + m != self + and hasattr(m, "make_generation_fast_") + # don't call this implementation again, e.g., if + # children modules also inherit from BaseFairseqModel + and m.make_generation_fast_.__func__ is not base_func + ): + name = prefix + n + m.make_generation_fast_(name=name, **kwargs) - self.apply(apply_make_generation_fast_) + apply_make_generation_fast_(self, "") def train(mode=True): if mode: @@ -179,21 +230,6 @@ def apply_prepare_for_onnx_export_(module): self.apply(apply_prepare_for_onnx_export_) - def prepare_for_tpu_(self, **kwargs): - """Optionally modify model for use on TPUs.""" - seen = set() - - def apply_prepare_for_tpu_(module): - if ( - module != self - and hasattr(module, "prepare_for_tpu_") - and module not in seen - ): - seen.add(module) - module.prepare_for_tpu_(**kwargs) - - self.apply(apply_prepare_for_tpu_) - @classmethod def from_pretrained( cls, @@ -253,8 +289,9 @@ def __init__(self, encoder, decoder): self.encoder = encoder self.decoder = decoder - assert isinstance(self.encoder, FairseqEncoder) - assert isinstance(self.decoder, FairseqDecoder) + + check_type(self.encoder, FairseqEncoder) + check_type(self.decoder, FairseqDecoder) def forward(self, src_tokens, src_lengths, prev_output_tokens, **kwargs): """ @@ -334,8 +371,8 @@ def __init__(self, encoders, decoders): assert encoders.keys() == decoders.keys() self.keys = list(encoders.keys()) for key in self.keys: - assert isinstance(encoders[key], FairseqEncoder) - assert isinstance(decoders[key], FairseqDecoder) + check_type(encoders[key], FairseqEncoder) + check_type(decoders[key], FairseqDecoder) self.models = nn.ModuleDict( { @@ -375,13 +412,7 @@ def build_shared_embeddings( return build_embedding(shared_dict, embed_dim, pretrained_embed_path) def forward(self, src_tokens, src_lengths, prev_output_tokens, **kwargs): - decoder_outs = {} - for key in self.keys: - encoder_out = self.models[key].encoder(src_tokens, src_lengths, **kwargs) - decoder_outs[key] = self.models[key].decoder( - prev_output_tokens, encoder_out, **kwargs - ) - return decoder_outs + raise NotImplementedError def max_positions(self): """Maximum length supported by the model.""" @@ -408,15 +439,29 @@ def decoder(self): def forward_decoder(self, prev_output_tokens, **kwargs): return self.decoder(prev_output_tokens, **kwargs) - def load_state_dict(self, state_dict, strict=True, args=None): + def load_state_dict( + self, + state_dict, + strict=True, + model_cfg=None, + args: Optional[Namespace] = None, + ): """Copies parameters and buffers from *state_dict* into this module and its descendants. Overrides the method in :class:`nn.Module`. Compared with that method this additionally "upgrades" *state_dicts* from old checkpoints. """ + + if model_cfg is None and args is not None: + logger.warn("using 'args' is deprecated, please update your code to use dataclass config") + model_cfg = convert_namespace_to_omegaconf(args).model + self.upgrade_state_dict(state_dict) - new_state_dict = prune_state_dict(state_dict, args) + + from fairseq.checkpoint_utils import prune_state_dict + + new_state_dict = prune_state_dict(state_dict, model_cfg) return super().load_state_dict(new_state_dict, strict) @@ -430,7 +475,7 @@ class FairseqLanguageModel(BaseFairseqModel): def __init__(self, decoder): super().__init__() self.decoder = decoder - assert isinstance(self.decoder, FairseqDecoder) + check_type(self.decoder, FairseqDecoder) def forward(self, src_tokens, **kwargs): """ @@ -491,7 +536,7 @@ class FairseqEncoderModel(BaseFairseqModel): def __init__(self, encoder): super().__init__() self.encoder = encoder - assert isinstance(self.encoder, FairseqEncoder) + check_type(self.encoder, FairseqEncoder) def forward(self, src_tokens, src_lengths, **kwargs): """ diff --git a/fairseq/models/fconv.py b/fairseq/models/fconv.py index 38e241feb5..c99a215101 100644 --- a/fairseq/models/fconv.py +++ b/fairseq/models/fconv.py @@ -8,22 +8,25 @@ import torch import torch.nn as nn import torch.nn.functional as F - from fairseq import utils from fairseq.models import ( FairseqEncoder, - FairseqIncrementalDecoder, FairseqEncoderDecoderModel, + FairseqIncrementalDecoder, register_model, register_model_architecture, ) from fairseq.modules import ( - AdaptiveSoftmax, BeamableMM, GradMultiply, LearnedPositionalEmbedding, + AdaptiveSoftmax, + BeamableMM, + FairseqDropout, + GradMultiply, + LearnedPositionalEmbedding, LinearizedConvolution, ) -@register_model('fconv') +@register_model("fconv") class FConvModel(FairseqEncoderDecoderModel): """ A fully convolutional model, i.e. a convolutional encoder and a @@ -44,23 +47,30 @@ class FConvModel(FairseqEncoderDecoderModel): @classmethod def hub_models(cls): - def moses_subword(path): return { - 'path': path, - 'tokenizer': 'moses', - 'bpe': 'subword_nmt', + "path": path, + "tokenizer": "moses", + "bpe": "subword_nmt", } return { - 'conv.wmt14.en-fr': moses_subword('https://dl.fbaipublicfiles.com/fairseq/models/wmt14.v2.en-fr.fconv-py.tar.bz2'), - 'conv.wmt14.en-de': moses_subword('https://dl.fbaipublicfiles.com/fairseq/models/wmt14.en-de.fconv-py.tar.bz2'), - 'conv.wmt17.en-de': moses_subword('https://dl.fbaipublicfiles.com/fairseq/models/wmt17.v2.en-de.fconv-py.tar.bz2'), + "conv.wmt14.en-fr": moses_subword( + "https://dl.fbaipublicfiles.com/fairseq/models/wmt14.v2.en-fr.fconv-py.tar.bz2" + ), + "conv.wmt14.en-de": moses_subword( + "https://dl.fbaipublicfiles.com/fairseq/models/wmt14.en-de.fconv-py.tar.bz2" + ), + "conv.wmt17.en-de": moses_subword( + "https://dl.fbaipublicfiles.com/fairseq/models/wmt17.v2.en-de.fconv-py.tar.bz2" + ), } def __init__(self, encoder, decoder): super().__init__(encoder, decoder) - self.encoder.num_attention_layers = sum(layer is not None for layer in decoder.attention) + self.encoder.num_attention_layers = sum( + layer is not None for layer in decoder.attention + ) @staticmethod def add_args(parser): @@ -147,18 +157,27 @@ class FConvEncoder(FairseqEncoder): """ def __init__( - self, dictionary, embed_dim=512, embed_dict=None, max_positions=1024, - convolutions=((512, 3),) * 20, dropout=0.1, + self, + dictionary, + embed_dim=512, + embed_dict=None, + max_positions=1024, + convolutions=((512, 3),) * 20, + dropout=0.1, ): super().__init__(dictionary) - self.dropout = dropout + self.dropout_module = FairseqDropout( + dropout, module_name=self.__class__.__name__ + ) self.num_attention_layers = None num_embeddings = len(dictionary) self.padding_idx = dictionary.pad() self.embed_tokens = Embedding(num_embeddings, embed_dim, self.padding_idx) if embed_dict: - self.embed_tokens = utils.load_embedding(embed_dict, self.dictionary, self.embed_tokens) + self.embed_tokens = utils.load_embedding( + embed_dict, self.dictionary, self.embed_tokens + ) self.embed_positions = PositionalEmbedding( max_positions, @@ -179,15 +198,23 @@ def __init__( residual_dim = out_channels else: residual_dim = layer_in_channels[-residual] - self.projections.append(Linear(residual_dim, out_channels) - if residual_dim != out_channels else None) + self.projections.append( + Linear(residual_dim, out_channels) + if residual_dim != out_channels + else None + ) if kernel_size % 2 == 1: padding = kernel_size // 2 else: padding = 0 self.convolutions.append( - ConvTBC(in_channels, out_channels * 2, kernel_size, - dropout=dropout, padding=padding) + ConvTBC( + in_channels, + out_channels * 2, + kernel_size, + dropout=dropout, + padding=padding, + ) ) self.residuals.append(residual) in_channels = out_channels @@ -214,7 +241,7 @@ def forward(self, src_tokens, src_lengths): """ # embed tokens and positions x = self.embed_tokens(src_tokens) + self.embed_positions(src_tokens) - x = F.dropout(x, p=self.dropout, training=self.training) + x = self.dropout_module(x) input_embedding = x # project to size of convolution @@ -230,7 +257,9 @@ def forward(self, src_tokens, src_lengths): residuals = [x] # temporal convolutions - for proj, conv, res_layer in zip(self.projections, self.convolutions, self.residuals): + for proj, conv, res_layer in zip( + self.projections, self.convolutions, self.residuals + ): if res_layer > 0: residual = residuals[-res_layer] residual = residual if proj is None else proj(residual) @@ -240,7 +269,7 @@ def forward(self, src_tokens, src_lengths): if encoder_padding_mask is not None: x = x.masked_fill(encoder_padding_mask.unsqueeze(-1), 0) - x = F.dropout(x, p=self.dropout, training=self.training) + x = self.dropout_module(x) if conv.kernel_size[0] % 2 == 1: # padding is implicit in the conv x = conv(x) @@ -272,19 +301,20 @@ def forward(self, src_tokens, src_lengths): y = (x + input_embedding) * math.sqrt(0.5) return { - 'encoder_out': (x, y), - 'encoder_padding_mask': encoder_padding_mask, # B x T + "encoder_out": (x, y), + "encoder_padding_mask": encoder_padding_mask, # B x T } def reorder_encoder_out(self, encoder_out, new_order): - if encoder_out['encoder_out'] is not None: - encoder_out['encoder_out'] = ( - encoder_out['encoder_out'][0].index_select(0, new_order), - encoder_out['encoder_out'][1].index_select(0, new_order), + if encoder_out["encoder_out"] is not None: + encoder_out["encoder_out"] = ( + encoder_out["encoder_out"][0].index_select(0, new_order), + encoder_out["encoder_out"][1].index_select(0, new_order), ) - if encoder_out['encoder_padding_mask'] is not None: - encoder_out['encoder_padding_mask'] = \ - encoder_out['encoder_padding_mask'].index_select(0, new_order) + if encoder_out["encoder_padding_mask"] is not None: + encoder_out["encoder_padding_mask"] = encoder_out[ + "encoder_padding_mask" + ].index_select(0, new_order) return encoder_out def max_positions(self): @@ -311,10 +341,11 @@ def forward(self, x, target_embedding, encoder_out, encoder_padding_mask): # don't attend over padding if encoder_padding_mask is not None: - x = x.float().masked_fill( - encoder_padding_mask.unsqueeze(1), - float('-inf') - ).type_as(x) # FP16 support: cast to float and back + x = ( + x.float() + .masked_fill(encoder_padding_mask.unsqueeze(1), float("-inf")) + .type_as(x) + ) # FP16 support: cast to float and back # softmax over last dim sz = x.size() @@ -329,7 +360,9 @@ def forward(self, x, target_embedding, encoder_out, encoder_padding_mask): if encoder_padding_mask is None: x = x * (s * math.sqrt(1.0 / s)) else: - s = s - encoder_padding_mask.type_as(x).sum(dim=1, keepdim=True) # exclude padding + s = s - encoder_padding_mask.type_as(x).sum( + dim=1, keepdim=True + ) # exclude padding s = s.unsqueeze(-1) x = x * (s * s.rsqrt()) @@ -341,21 +374,32 @@ def make_generation_fast_(self, beamable_mm_beam_size=None, **kwargs): """Replace torch.bmm with BeamableMM.""" if beamable_mm_beam_size is not None: del self.bmm - self.add_module('bmm', BeamableMM(beamable_mm_beam_size)) + self.add_module("bmm", BeamableMM(beamable_mm_beam_size)) class FConvDecoder(FairseqIncrementalDecoder): """Convolutional decoder""" def __init__( - self, dictionary, embed_dim=512, embed_dict=None, out_embed_dim=256, - max_positions=1024, convolutions=((512, 3),) * 20, attention=True, - dropout=0.1, share_embed=False, positional_embeddings=True, - adaptive_softmax_cutoff=None, adaptive_softmax_dropout=0, + self, + dictionary, + embed_dim=512, + embed_dict=None, + out_embed_dim=256, + max_positions=1024, + convolutions=((512, 3),) * 20, + attention=True, + dropout=0.1, + share_embed=False, + positional_embeddings=True, + adaptive_softmax_cutoff=None, + adaptive_softmax_dropout=0.0, ): super().__init__(dictionary) - self.register_buffer('version', torch.Tensor([2])) - self.dropout = dropout + self.register_buffer("version", torch.Tensor([2])) + self.dropout_module = FairseqDropout( + dropout, module_name=self.__class__.__name__ + ) self.need_attn = True convolutions = extend_conv_spec(convolutions) @@ -364,20 +408,28 @@ def __init__( # expand True into [True, True, ...] and do the same with False attention = [attention] * len(convolutions) if not isinstance(attention, list) or len(attention) != len(convolutions): - raise ValueError('Attention is expected to be a list of booleans of ' - 'length equal to the number of layers.') + raise ValueError( + "Attention is expected to be a list of booleans of " + "length equal to the number of layers." + ) num_embeddings = len(dictionary) padding_idx = dictionary.pad() self.embed_tokens = Embedding(num_embeddings, embed_dim, padding_idx) if embed_dict: - self.embed_tokens = utils.load_embedding(embed_dict, self.dictionary, self.embed_tokens) + self.embed_tokens = utils.load_embedding( + embed_dict, self.dictionary, self.embed_tokens + ) - self.embed_positions = PositionalEmbedding( - max_positions, - embed_dim, - padding_idx, - ) if positional_embeddings else None + self.embed_positions = ( + PositionalEmbedding( + max_positions, + embed_dim, + padding_idx, + ) + if positional_embeddings + else None + ) self.fc1 = Linear(embed_dim, in_channels, dropout=dropout) self.projections = nn.ModuleList() @@ -391,14 +443,23 @@ def __init__( residual_dim = out_channels else: residual_dim = layer_in_channels[-residual] - self.projections.append(Linear(residual_dim, out_channels) - if residual_dim != out_channels else None) + self.projections.append( + Linear(residual_dim, out_channels) + if residual_dim != out_channels + else None + ) self.convolutions.append( - LinearizedConv1d(in_channels, out_channels * 2, kernel_size, - padding=(kernel_size - 1), dropout=dropout) + LinearizedConv1d( + in_channels, + out_channels * 2, + kernel_size, + padding=(kernel_size - 1), + dropout=dropout, + ) + ) + self.attention.append( + AttentionLayer(out_channels, embed_dim) if attention[i] else None ) - self.attention.append(AttentionLayer(out_channels, embed_dim) - if attention[i] else None) self.residuals.append(residual) in_channels = out_channels layer_in_channels.append(out_channels) @@ -408,26 +469,35 @@ def __init__( if adaptive_softmax_cutoff is not None: assert not share_embed - self.adaptive_softmax = AdaptiveSoftmax(num_embeddings, in_channels, adaptive_softmax_cutoff, - dropout=adaptive_softmax_dropout) + self.adaptive_softmax = AdaptiveSoftmax( + num_embeddings, + in_channels, + adaptive_softmax_cutoff, + dropout=adaptive_softmax_dropout, + ) else: self.fc2 = Linear(in_channels, out_embed_dim) if share_embed: - assert out_embed_dim == embed_dim, \ - "Shared embed weights implies same dimensions " \ + assert out_embed_dim == embed_dim, ( + "Shared embed weights implies same dimensions " " out_embed_dim={} vs embed_dim={}".format(out_embed_dim, embed_dim) + ) self.fc3 = nn.Linear(out_embed_dim, num_embeddings) self.fc3.weight = self.embed_tokens.weight else: self.fc3 = Linear(out_embed_dim, num_embeddings, dropout=dropout) - def forward(self, prev_output_tokens, encoder_out=None, incremental_state=None, **unused): + def forward( + self, prev_output_tokens, encoder_out=None, incremental_state=None, **unused + ): if encoder_out is not None: - encoder_padding_mask = encoder_out['encoder_padding_mask'] - encoder_out = encoder_out['encoder_out'] + encoder_padding_mask = encoder_out["encoder_padding_mask"] + encoder_out = encoder_out["encoder_out"] # split and transpose encoder outputs - encoder_a, encoder_b = self._split_encoder_out(encoder_out, incremental_state) + encoder_a, encoder_b = self._split_encoder_out( + encoder_out, incremental_state + ) if self.embed_positions is not None: pos_embed = self.embed_positions(prev_output_tokens, incremental_state) @@ -440,7 +510,7 @@ def forward(self, prev_output_tokens, encoder_out=None, incremental_state=None, # embed tokens and combine with positional embeddings x += pos_embed - x = F.dropout(x, p=self.dropout, training=self.training) + x = self.dropout_module(x) target_embedding = x # project to size of convolution @@ -453,15 +523,16 @@ def forward(self, prev_output_tokens, encoder_out=None, incremental_state=None, avg_attn_scores = None num_attn_layers = len(self.attention) residuals = [x] - for proj, conv, attention, res_layer in zip(self.projections, self.convolutions, self.attention, - self.residuals): + for proj, conv, attention, res_layer in zip( + self.projections, self.convolutions, self.attention, self.residuals + ): if res_layer > 0: residual = residuals[-res_layer] residual = residual if proj is None else proj(residual) else: residual = None - x = F.dropout(x, p=self.dropout, training=self.training) + x = self.dropout_module(x) x = conv(x, incremental_state) x = F.glu(x, dim=2) @@ -469,7 +540,9 @@ def forward(self, prev_output_tokens, encoder_out=None, incremental_state=None, if attention is not None: x = self._transpose_if_training(x, incremental_state) - x, attn_scores = attention(x, target_embedding, (encoder_a, encoder_b), encoder_padding_mask) + x, attn_scores = attention( + x, target_embedding, (encoder_a, encoder_b), encoder_padding_mask + ) if not self.training and self.need_attn: attn_scores = attn_scores / num_attn_layers @@ -491,30 +564,38 @@ def forward(self, prev_output_tokens, encoder_out=None, incremental_state=None, # project back to size of vocabulary if not using adaptive softmax if self.fc2 is not None and self.fc3 is not None: x = self.fc2(x) - x = F.dropout(x, p=self.dropout, training=self.training) + x = self.dropout_module(x) x = self.fc3(x) return x, avg_attn_scores def reorder_incremental_state(self, incremental_state, new_order): super().reorder_incremental_state(incremental_state, new_order) - encoder_out = utils.get_incremental_state(self, incremental_state, 'encoder_out') + encoder_out = utils.get_incremental_state( + self, incremental_state, "encoder_out" + ) if encoder_out is not None: encoder_out = tuple(eo.index_select(0, new_order) for eo in encoder_out) - utils.set_incremental_state(self, incremental_state, 'encoder_out', encoder_out) + utils.set_incremental_state( + self, incremental_state, "encoder_out", encoder_out + ) def max_positions(self): """Maximum output length supported by the decoder.""" - return self.embed_positions.max_positions if self.embed_positions is not None else float('inf') + return ( + self.embed_positions.max_positions + if self.embed_positions is not None + else float("inf") + ) def upgrade_state_dict(self, state_dict): - if utils.item(state_dict.get('decoder.version', torch.Tensor([1]))[0]) < 2: + if utils.item(state_dict.get("decoder.version", torch.Tensor([1]))[0]) < 2: # old models use incorrect weight norm dimension for i, conv in enumerate(self.convolutions): # reconfigure weight norm nn.utils.remove_weight_norm(conv) self.convolutions[i] = nn.utils.weight_norm(conv, dim=0) - state_dict['decoder.version'] = torch.Tensor([1]) + state_dict["decoder.version"] = torch.Tensor([1]) return state_dict def make_generation_fast_(self, need_attn=False, **kwargs): @@ -531,7 +612,9 @@ def _split_encoder_out(self, encoder_out, incremental_state): This is cached when doing incremental inference. """ - cached_result = utils.get_incremental_state(self, incremental_state, 'encoder_out') + cached_result = utils.get_incremental_state( + self, incremental_state, "encoder_out" + ) if cached_result is not None: return cached_result @@ -541,7 +624,7 @@ def _split_encoder_out(self, encoder_out, incremental_state): result = (encoder_a, encoder_b) if incremental_state is not None: - utils.set_incremental_state(self, incremental_state, 'encoder_out', result) + utils.set_incremental_state(self, incremental_state, "encoder_out", result) return result def _transpose_if_training(self, x, incremental_state): @@ -563,7 +646,11 @@ def extend_conv_spec(convolutions): elif len(spec) == 2: extended.append(spec + (1,)) else: - raise Exception('invalid number of parameters in convolution spec ' + str(spec) + '. expected 2 or 3') + raise Exception( + "invalid number of parameters in convolution spec " + + str(spec) + + ". expected 2 or 3" + ) return tuple(extended) @@ -581,7 +668,7 @@ def PositionalEmbedding(num_embeddings, embedding_dim, padding_idx): return m -def Linear(in_features, out_features, dropout=0): +def Linear(in_features, out_features, dropout=0.0): """Weight-normalized Linear layer (input: N x T x C)""" m = nn.Linear(in_features, out_features) nn.init.normal_(m.weight, mean=0, std=math.sqrt((1 - dropout) / in_features)) @@ -589,7 +676,7 @@ def Linear(in_features, out_features, dropout=0): return nn.utils.weight_norm(m) -def LinearizedConv1d(in_channels, out_channels, kernel_size, dropout=0, **kwargs): +def LinearizedConv1d(in_channels, out_channels, kernel_size, dropout=0.0, **kwargs): """Weight-normalized Conv1d layer optimized for decoding""" m = LinearizedConvolution(in_channels, out_channels, kernel_size, **kwargs) std = math.sqrt((4 * (1.0 - dropout)) / (m.kernel_size[0] * in_channels)) @@ -598,9 +685,10 @@ def LinearizedConv1d(in_channels, out_channels, kernel_size, dropout=0, **kwargs return nn.utils.weight_norm(m, dim=2) -def ConvTBC(in_channels, out_channels, kernel_size, dropout=0, **kwargs): +def ConvTBC(in_channels, out_channels, kernel_size, dropout=0.0, **kwargs): """Weight-normalized Conv1d layer""" from fairseq.modules import ConvTBC + m = ConvTBC(in_channels, out_channels, kernel_size, **kwargs) std = math.sqrt((4 * (1.0 - dropout)) / (m.kernel_size[0] * in_channels)) nn.init.normal_(m.weight, mean=0, std=std) @@ -608,61 +696,61 @@ def ConvTBC(in_channels, out_channels, kernel_size, dropout=0, **kwargs): return nn.utils.weight_norm(m, dim=2) -@register_model_architecture('fconv', 'fconv') +@register_model_architecture("fconv", "fconv") def base_architecture(args): - args.dropout = getattr(args, 'dropout', 0.1) - args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 512) - args.encoder_embed_path = getattr(args, 'encoder_embed_path', None) - args.encoder_layers = getattr(args, 'encoder_layers', '[(512, 3)] * 20') - args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 512) - args.decoder_embed_path = getattr(args, 'decoder_embed_path', None) - args.decoder_layers = getattr(args, 'decoder_layers', '[(512, 3)] * 20') - args.decoder_out_embed_dim = getattr(args, 'decoder_out_embed_dim', 256) - args.decoder_attention = getattr(args, 'decoder_attention', 'True') - args.share_input_output_embed = getattr(args, 'share_input_output_embed', False) - - -@register_model_architecture('fconv', 'fconv_iwslt_de_en') + args.dropout = getattr(args, "dropout", 0.1) + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) + args.encoder_embed_path = getattr(args, "encoder_embed_path", None) + args.encoder_layers = getattr(args, "encoder_layers", "[(512, 3)] * 20") + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512) + args.decoder_embed_path = getattr(args, "decoder_embed_path", None) + args.decoder_layers = getattr(args, "decoder_layers", "[(512, 3)] * 20") + args.decoder_out_embed_dim = getattr(args, "decoder_out_embed_dim", 256) + args.decoder_attention = getattr(args, "decoder_attention", "True") + args.share_input_output_embed = getattr(args, "share_input_output_embed", False) + + +@register_model_architecture("fconv", "fconv_iwslt_de_en") def fconv_iwslt_de_en(args): - args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 256) - args.encoder_layers = getattr(args, 'encoder_layers', '[(256, 3)] * 4') - args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 256) - args.decoder_layers = getattr(args, 'decoder_layers', '[(256, 3)] * 3') - args.decoder_out_embed_dim = getattr(args, 'decoder_out_embed_dim', 256) + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 256) + args.encoder_layers = getattr(args, "encoder_layers", "[(256, 3)] * 4") + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 256) + args.decoder_layers = getattr(args, "decoder_layers", "[(256, 3)] * 3") + args.decoder_out_embed_dim = getattr(args, "decoder_out_embed_dim", 256) base_architecture(args) -@register_model_architecture('fconv', 'fconv_wmt_en_ro') +@register_model_architecture("fconv", "fconv_wmt_en_ro") def fconv_wmt_en_ro(args): - args.decoder_out_embed_dim = getattr(args, 'decoder_out_embed_dim', 512) + args.decoder_out_embed_dim = getattr(args, "decoder_out_embed_dim", 512) base_architecture(args) -@register_model_architecture('fconv', 'fconv_wmt_en_de') +@register_model_architecture("fconv", "fconv_wmt_en_de") def fconv_wmt_en_de(args): - convs = '[(512, 3)] * 9' # first 9 layers have 512 units - convs += ' + [(1024, 3)] * 4' # next 4 layers have 1024 units - convs += ' + [(2048, 1)] * 2' # final 2 layers use 1x1 convolutions - - args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 768) - args.encoder_layers = getattr(args, 'encoder_layers', convs) - args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 768) - args.decoder_layers = getattr(args, 'decoder_layers', convs) - args.decoder_out_embed_dim = getattr(args, 'decoder_out_embed_dim', 512) + convs = "[(512, 3)] * 9" # first 9 layers have 512 units + convs += " + [(1024, 3)] * 4" # next 4 layers have 1024 units + convs += " + [(2048, 1)] * 2" # final 2 layers use 1x1 convolutions + + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 768) + args.encoder_layers = getattr(args, "encoder_layers", convs) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 768) + args.decoder_layers = getattr(args, "decoder_layers", convs) + args.decoder_out_embed_dim = getattr(args, "decoder_out_embed_dim", 512) base_architecture(args) -@register_model_architecture('fconv', 'fconv_wmt_en_fr') +@register_model_architecture("fconv", "fconv_wmt_en_fr") def fconv_wmt_en_fr(args): - convs = '[(512, 3)] * 6' # first 6 layers have 512 units - convs += ' + [(768, 3)] * 4' # next 4 layers have 768 units - convs += ' + [(1024, 3)] * 3' # next 3 layers have 1024 units - convs += ' + [(2048, 1)] * 1' # next 1 layer uses 1x1 convolutions - convs += ' + [(4096, 1)] * 1' # final 1 layer uses 1x1 convolutions - - args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 768) - args.encoder_layers = getattr(args, 'encoder_layers', convs) - args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 768) - args.decoder_layers = getattr(args, 'decoder_layers', convs) - args.decoder_out_embed_dim = getattr(args, 'decoder_out_embed_dim', 512) + convs = "[(512, 3)] * 6" # first 6 layers have 512 units + convs += " + [(768, 3)] * 4" # next 4 layers have 768 units + convs += " + [(1024, 3)] * 3" # next 3 layers have 1024 units + convs += " + [(2048, 1)] * 1" # next 1 layer uses 1x1 convolutions + convs += " + [(4096, 1)] * 1" # final 1 layer uses 1x1 convolutions + + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 768) + args.encoder_layers = getattr(args, "encoder_layers", convs) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 768) + args.decoder_layers = getattr(args, "decoder_layers", convs) + args.decoder_out_embed_dim = getattr(args, "decoder_out_embed_dim", 512) base_architecture(args) diff --git a/fairseq/models/fconv_lm.py b/fairseq/models/fconv_lm.py index f2320b1700..07391eaa29 100644 --- a/fairseq/models/fconv_lm.py +++ b/fairseq/models/fconv_lm.py @@ -3,7 +3,7 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. -from fairseq import options +from fairseq import utils from fairseq.models import ( FairseqLanguageModel, register_model, @@ -12,7 +12,7 @@ from fairseq.models.fconv import FConvDecoder -@register_model('fconv_lm') +@register_model("fconv_lm") class FConvLanguageModel(FairseqLanguageModel): def __init__(self, decoder): super().__init__(decoder) @@ -20,21 +20,45 @@ def __init__(self, decoder): @staticmethod def add_args(parser): """Add model-specific arguments to the parser.""" - parser.add_argument('--dropout', type=float, metavar='D', - help='dropout probability') - parser.add_argument('--decoder-embed-dim', type=int, metavar='N', - help='decoder embedding dimension') - parser.add_argument('--decoder-layers', type=str, metavar='EXPR', - help='decoder layers [(dim, kernel_size), ...]') - parser.add_argument('--decoder-out-embed-dim', type=int, metavar='N', - help='decoder output embedding dimension') - parser.add_argument('--adaptive-softmax-cutoff', metavar='EXPR', - help='comma separated list of adaptive softmax cutoff points. ' - 'Must be used with adaptive_loss criterion') - parser.add_argument('--adaptive-softmax-dropout', type=float, metavar='D', - help='sets adaptive softmax dropout for the tail projections') - parser.add_argument('--decoder-attention', type=str, metavar='EXPR', - help='decoder attention [True, ...]') + parser.add_argument( + "--dropout", type=float, metavar="D", help="dropout probability" + ) + parser.add_argument( + "--decoder-embed-dim", + type=int, + metavar="N", + help="decoder embedding dimension", + ) + parser.add_argument( + "--decoder-layers", + type=str, + metavar="EXPR", + help="decoder layers [(dim, kernel_size), ...]", + ) + parser.add_argument( + "--decoder-out-embed-dim", + type=int, + metavar="N", + help="decoder output embedding dimension", + ) + parser.add_argument( + "--adaptive-softmax-cutoff", + metavar="EXPR", + help="comma separated list of adaptive softmax cutoff points. " + "Must be used with adaptive_loss criterion", + ) + parser.add_argument( + "--adaptive-softmax-dropout", + type=float, + metavar="D", + help="sets adaptive softmax dropout for the tail projections", + ) + parser.add_argument( + "--decoder-attention", + type=str, + metavar="EXPR", + help="decoder attention [True, ...]", + ) @classmethod def build_model(cls, args, task): @@ -42,7 +66,9 @@ def build_model(cls, args, task): # make sure all arguments are present in older models base_lm_architecture(args) - if hasattr(args, 'max_target_positions') and not hasattr(args, 'tokens_per_sample'): + if hasattr(args, "max_target_positions") and not hasattr( + args, "tokens_per_sample" + ): args.tokens_per_sample = args.max_target_positions decoder = FConvDecoder( @@ -56,49 +82,54 @@ def build_model(cls, args, task): share_embed=False, positional_embeddings=False, adaptive_softmax_cutoff=( - options.eval_str_list(args.adaptive_softmax_cutoff, type=int) - if args.criterion == 'adaptive_loss' else None + utils.eval_str_list(args.adaptive_softmax_cutoff, type=int) + if args.criterion == "adaptive_loss" + else None ), adaptive_softmax_dropout=args.adaptive_softmax_dropout, ) return FConvLanguageModel(decoder) -@register_model_architecture('fconv_lm', 'fconv_lm') +@register_model_architecture("fconv_lm", "fconv_lm") def base_lm_architecture(args): - args.dropout = getattr(args, 'dropout', 0.1) - args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 128) - args.decoder_layers = getattr(args, 'decoder_layers', '[(1268, 4)] * 13') - args.decoder_attention = getattr(args, 'decoder_attention', 'False') - args.adaptive_softmax_cutoff = getattr(args, 'adaptive_softmax_cutoff', None) - args.adaptive_softmax_dropout = getattr(args, 'adaptive_softmax_dropout', 0) + args.dropout = getattr(args, "dropout", 0.1) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 128) + args.decoder_layers = getattr(args, "decoder_layers", "[(1268, 4)] * 13") + args.decoder_attention = getattr(args, "decoder_attention", "False") + args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None) + args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0) -@register_model_architecture('fconv_lm', 'fconv_lm_dauphin_wikitext103') +@register_model_architecture("fconv_lm", "fconv_lm_dauphin_wikitext103") def fconv_lm_dauphin_wikitext103(args): - layers = '[(850, 6)] * 3' - layers += ' + [(850, 1)] * 1' - layers += ' + [(850, 5)] * 4' - layers += ' + [(850, 1)] * 1' - layers += ' + [(850, 4)] * 3' - layers += ' + [(1024, 4)] * 1' - layers += ' + [(2048, 4)] * 1' - args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 280) - args.decoder_layers = getattr(args, 'decoder_layers', layers) - args.decoder_attention = getattr(args, 'decoder_attention', 'False') - args.adaptive_softmax_cutoff = getattr(args, 'adaptive_softmax_cutoff', '10000,20000,200000') + layers = "[(850, 6)] * 3" + layers += " + [(850, 1)] * 1" + layers += " + [(850, 5)] * 4" + layers += " + [(850, 1)] * 1" + layers += " + [(850, 4)] * 3" + layers += " + [(1024, 4)] * 1" + layers += " + [(2048, 4)] * 1" + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 280) + args.decoder_layers = getattr(args, "decoder_layers", layers) + args.decoder_attention = getattr(args, "decoder_attention", "False") + args.adaptive_softmax_cutoff = getattr( + args, "adaptive_softmax_cutoff", "10000,20000,200000" + ) base_lm_architecture(args) -@register_model_architecture('fconv_lm', 'fconv_lm_dauphin_gbw') +@register_model_architecture("fconv_lm", "fconv_lm_dauphin_gbw") def fconv_lm_dauphin_gbw(args): - layers = '[(512, 5)]' - layers += ' + [(128, 1, 0), (128, 5, 0), (512, 1, 3)] * 3' - layers += ' + [(512, 1, 0), (512, 5, 0), (1024, 1, 3)] * 3' - layers += ' + [(1024, 1, 0), (1024, 5, 0), (2048, 1, 3)] * 6' - layers += ' + [(1024, 1, 0), (1024, 5, 0), (4096, 1, 3)]' - args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 128) - args.decoder_layers = getattr(args, 'decoder_layers', layers) - args.decoder_attention = getattr(args, 'decoder_attention', 'False') - args.adaptive_softmax_cutoff = getattr(args, 'adaptive_softmax_cutoff', '10000,50000,200000') + layers = "[(512, 5)]" + layers += " + [(128, 1, 0), (128, 5, 0), (512, 1, 3)] * 3" + layers += " + [(512, 1, 0), (512, 5, 0), (1024, 1, 3)] * 3" + layers += " + [(1024, 1, 0), (1024, 5, 0), (2048, 1, 3)] * 6" + layers += " + [(1024, 1, 0), (1024, 5, 0), (4096, 1, 3)]" + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 128) + args.decoder_layers = getattr(args, "decoder_layers", layers) + args.decoder_attention = getattr(args, "decoder_attention", "False") + args.adaptive_softmax_cutoff = getattr( + args, "adaptive_softmax_cutoff", "10000,50000,200000" + ) base_lm_architecture(args) diff --git a/fairseq/models/fconv_self_att.py b/fairseq/models/fconv_self_att.py index 4f0c0a4cdc..8357ef7847 100644 --- a/fairseq/models/fconv_self_att.py +++ b/fairseq/models/fconv_self_att.py @@ -10,8 +10,8 @@ import torch import torch.nn as nn import torch.nn.functional as F - from fairseq import checkpoint_utils +from fairseq.incremental_decoding_utils import with_incremental_state from fairseq.models import ( CompositeEncoder, FairseqDecoder, @@ -22,46 +22,48 @@ ) from fairseq.modules import ( DownsampledMultiHeadAttention, + FairseqDropout, GradMultiply, LayerNorm, LearnedPositionalEmbedding, LinearizedConvolution, ) -from fairseq.incremental_decoding_utils import with_incremental_state + logger = logging.getLogger(__name__) -@register_model('fconv_self_att') +@register_model("fconv_self_att") class FConvModelSelfAtt(FairseqEncoderDecoderModel): - @classmethod def hub_models(cls): return { - 'conv.stories.pretrained': { - 'path': 'https://dl.fbaipublicfiles.com/fairseq/models/stories_checkpoint.tar.gz', - 'checkpoint_file': 'pretrained_checkpoint.pt', - 'tokenizer': 'nltk', + "conv.stories.pretrained": { + "path": "https://dl.fbaipublicfiles.com/fairseq/models/stories_checkpoint.tar.gz", + "checkpoint_file": "pretrained_checkpoint.pt", + "tokenizer": "nltk", }, - 'conv.stories': { - 'path': 'https://dl.fbaipublicfiles.com/fairseq/models/stories_checkpoint.tar.gz', - 'checkpoint_file': 'fusion_checkpoint.pt', - 'tokenizer': 'nltk', - 'pretrained': 'True', - 'pretrained_checkpoint': './pretrained_checkpoint.pt', + "conv.stories": { + "path": "https://dl.fbaipublicfiles.com/fairseq/models/stories_checkpoint.tar.gz", + "checkpoint_file": "fusion_checkpoint.pt", + "tokenizer": "nltk", + "pretrained": "True", + "pretrained_checkpoint": "./pretrained_checkpoint.pt", }, # Test set containing dictionaries - 'data.stories': 'https://dl.fbaipublicfiles.com/fairseq/data/stories_test.tar.bz2', + "data.stories": "https://dl.fbaipublicfiles.com/fairseq/data/stories_test.tar.bz2", } def __init__(self, encoder, decoder, pretrained_encoder=None): super().__init__(encoder, decoder) - self.encoder.num_attention_layers = sum(layer is not None for layer in decoder.attention) + self.encoder.num_attention_layers = sum( + layer is not None for layer in decoder.attention + ) self.pretrained_encoder = pretrained_encoder if self.pretrained_encoder is None: - encoders = {'encoder': encoder} + encoders = {"encoder": encoder} else: - encoders = {'encoder': encoder, 'pretrained': self.pretrained_encoder} + encoders = {"encoder": encoder, "pretrained": self.pretrained_encoder} # for fusion model, CompositeEncoder contains both pretrained and training encoders # these are forwarded and then combined in the decoder self.encoder = CompositeEncoder(encoders) @@ -112,9 +114,11 @@ def build_model(cls, args, task): trained_encoder, trained_decoder = None, None pretrained = eval(args.pretrained) if pretrained: - logger.info('loading pretrained model') + logger.info("loading pretrained model") if not os.path.exists(args.pretrained_checkpoint): - new_pretrained_checkpoint = os.path.join(args.data, args.pretrained_checkpoint) + new_pretrained_checkpoint = os.path.join( + args.data, args.pretrained_checkpoint + ) if os.path.exists(new_pretrained_checkpoint): args.pretrained_checkpoint = new_pretrained_checkpoint trained_model = checkpoint_utils.load_model_ensemble( @@ -137,7 +141,7 @@ def build_model(cls, args, task): dropout=args.dropout, max_positions=args.max_source_positions, attention=eval(args.encoder_attention), - attention_nheads=args.encoder_attention_nheads + attention_nheads=args.encoder_attention_nheads, ) decoder = FConvDecoder( @@ -155,7 +159,7 @@ def build_model(cls, args, task): gated_attention=eval(args.gated_attention), downsample=eval(args.downsample), pretrained=pretrained, - trained_decoder=trained_decoder + trained_decoder=trained_decoder, ) model = FConvModelSelfAtt(encoder, decoder, trained_encoder) @@ -168,13 +172,21 @@ def pretrained(self): class FConvEncoder(FairseqEncoder): """Convolutional encoder""" + def __init__( - self, dictionary, embed_dim=512, max_positions=1024, - convolutions=((512, 3),) * 20, dropout=0.1, attention=False, + self, + dictionary, + embed_dim=512, + max_positions=1024, + convolutions=((512, 3),) * 20, + dropout=0.1, + attention=False, attention_nheads=1, ): super().__init__(dictionary) - self.dropout = dropout + self.dropout_module = FairseqDropout( + dropout, module_name=self.__class__.__name__ + ) self.num_attention_layers = None num_embeddings = len(dictionary) @@ -202,14 +214,18 @@ def expand_bool_array(val): self.attproj = nn.ModuleList() for i, (out_channels, kernel_size) in enumerate(convolutions): self.projections.append( - Linear(in_channels, out_channels) if in_channels != out_channels else None + Linear(in_channels, out_channels) + if in_channels != out_channels + else None ) self.convolutions.append( ConvTBC(in_channels, out_channels * 2, kernel_size, dropout=dropout) ) self.attention.append( - SelfAttention(out_channels, embed_dim, attention_nheads) if attention[i] else None + SelfAttention(out_channels, embed_dim, attention_nheads) + if attention[i] + else None ) in_channels = out_channels @@ -218,7 +234,7 @@ def expand_bool_array(val): def forward(self, src_tokens, src_lengths): # embed tokens and positions x = self.embed_tokens(src_tokens) + self.embed_positions(src_tokens) - x = F.dropout(x, p=self.dropout, training=self.training) + x = self.dropout_module(x) input_embedding = x.transpose(0, 1) # project to size of convolution @@ -232,13 +248,15 @@ def forward(self, src_tokens, src_lengths): x = x.transpose(0, 1) # temporal convolutions - for proj, conv, attention in zip(self.projections, self.convolutions, self.attention): + for proj, conv, attention in zip( + self.projections, self.convolutions, self.attention + ): residual = x if proj is None else proj(x) if encoder_padding_mask is not None: x = x.masked_fill(encoder_padding_mask.unsqueeze(-1), 0) - x = F.dropout(x, p=self.dropout, training=self.training) + x = self.dropout_module(x) padding_l = (conv.kernel_size[0] - 1) // 2 padding_r = conv.kernel_size[0] // 2 x = F.pad(x, (0, 0, 0, 0, padding_l, padding_r)) @@ -265,23 +283,24 @@ def forward(self, src_tokens, src_lengths): y = (x + input_embedding.transpose(0, 1)) * math.sqrt(0.5) return { - 'encoder_out': (x, y), - 'encoder_padding_mask': encoder_padding_mask, # B x T + "encoder_out": (x, y), + "encoder_padding_mask": encoder_padding_mask, # B x T } def reorder_encoder_out(self, encoder_out, new_order): - encoder_out['encoder_out'] = tuple( - eo.index_select(0, new_order) for eo in encoder_out['encoder_out'] + encoder_out["encoder_out"] = tuple( + eo.index_select(0, new_order) for eo in encoder_out["encoder_out"] ) - if encoder_out['encoder_padding_mask'] is not None: - encoder_out['encoder_padding_mask'] = \ - encoder_out['encoder_padding_mask'].index_select(0, new_order) + if encoder_out["encoder_padding_mask"] is not None: + encoder_out["encoder_padding_mask"] = encoder_out[ + "encoder_padding_mask" + ].index_select(0, new_order) - if 'pretrained' in encoder_out: - encoder_out['pretrained']['encoder_out'] = tuple( + if "pretrained" in encoder_out: + encoder_out["pretrained"]["encoder_out"] = tuple( eo.index_select(0, new_order) - for eo in encoder_out['pretrained']['encoder_out'] + for eo in encoder_out["pretrained"]["encoder_out"] ) return encoder_out @@ -294,18 +313,32 @@ def max_positions(self): @with_incremental_state class FConvDecoder(FairseqDecoder): """Convolutional decoder""" + def __init__( - self, dictionary, embed_dim=512, out_embed_dim=256, max_positions=1024, - convolutions=((512, 3),) * 8, attention=True, dropout=0.1, - selfattention=False, attention_nheads=1, selfattention_nheads=1, - project_input=False, gated_attention=False, downsample=False, - pretrained=False, trained_decoder=None, + self, + dictionary, + embed_dim=512, + out_embed_dim=256, + max_positions=1024, + convolutions=((512, 3),) * 8, + attention=True, + dropout=0.1, + selfattention=False, + attention_nheads=1, + selfattention_nheads=1, + project_input=False, + gated_attention=False, + downsample=False, + pretrained=False, + trained_decoder=None, ): super().__init__(dictionary) - self.register_buffer('version', torch.Tensor([2])) + self.register_buffer("version", torch.Tensor([2])) self.pretrained = pretrained self.pretrained_decoder = trained_decoder - self.dropout = dropout + self.dropout_module = FairseqDropout( + dropout, module_name=self.__class__.__name__ + ) self.need_attn = True in_channels = convolutions[0][0] @@ -319,8 +352,10 @@ def expand_bool_array(val): selfattention = expand_bool_array(selfattention) if not isinstance(attention, list) or len(attention) != len(convolutions): - raise ValueError('Attention is expected to be a list of booleans of ' - 'length equal to the number of layers.') + raise ValueError( + "Attention is expected to be a list of booleans of " + "length equal to the number of layers." + ) num_embeddings = len(dictionary) padding_idx = dictionary.pad() @@ -340,31 +375,49 @@ def expand_bool_array(val): self.attproj = nn.ModuleList() for i, (out_channels, kernel_size) in enumerate(convolutions): self.projections.append( - Linear(in_channels, out_channels) if in_channels != out_channels else None + Linear(in_channels, out_channels) + if in_channels != out_channels + else None ) self.convolutions.append( LinearizedConv1d( - in_channels, out_channels * 2, kernel_size, - padding=(kernel_size - 1), dropout=dropout, + in_channels, + out_channels * 2, + kernel_size, + padding=(kernel_size - 1), + dropout=dropout, ) ) self.attention.append( DownsampledMultiHeadAttention( - out_channels, embed_dim, attention_nheads, - project_input=project_input, gated=False, downsample=False, - ) if attention[i] else None + out_channels, + embed_dim, + attention_nheads, + project_input=project_input, + gated=False, + downsample=False, + ) + if attention[i] + else None ) self.attproj.append( - Linear(out_channels, embed_dim, dropout=dropout) if attention[i] else None + Linear(out_channels, embed_dim, dropout=dropout) + if attention[i] + else None ) self.selfattention.append( SelfAttention( - out_channels, embed_dim, selfattention_nheads, - project_input=project_input, gated=gated_attention, + out_channels, + embed_dim, + selfattention_nheads, + project_input=project_input, + gated=gated_attention, downsample=downsample, - ) if selfattention[i] else None + ) + if selfattention[i] + else None ) in_channels = out_channels @@ -374,18 +427,22 @@ def expand_bool_array(val): # model fusion if self.pretrained: # independent gates are learned from the concatenated input - self.gate1 = nn.Sequential(Linear(out_embed_dim*2, out_embed_dim), nn.Sigmoid()) - self.gate2 = nn.Sequential(Linear(out_embed_dim*2, out_embed_dim), nn.Sigmoid()) + self.gate1 = nn.Sequential( + Linear(out_embed_dim * 2, out_embed_dim), nn.Sigmoid() + ) + self.gate2 = nn.Sequential( + Linear(out_embed_dim * 2, out_embed_dim), nn.Sigmoid() + ) # pretrained and trained models are joined self.joining = nn.Sequential( - Linear(out_embed_dim*2, out_embed_dim*2), - LayerNorm(out_embed_dim*2), + Linear(out_embed_dim * 2, out_embed_dim * 2), + LayerNorm(out_embed_dim * 2), nn.GLU(), - Linear(out_embed_dim, out_embed_dim*2), - LayerNorm(out_embed_dim*2), + Linear(out_embed_dim, out_embed_dim * 2), + LayerNorm(out_embed_dim * 2), nn.GLU(), Linear(out_embed_dim, out_embed_dim), - LayerNorm(out_embed_dim) + LayerNorm(out_embed_dim), ) # pretrained model contains an output layer that is nhid -> vocab size # but the models are combined in their hidden state @@ -395,13 +452,14 @@ def expand_bool_array(val): def save_output(): def hook(a, b, output): self.pretrained_outputs["out"] = output + return hook self.pretrained_decoder.fc2.register_forward_hook(save_output()) def forward(self, prev_output_tokens, encoder_out): - trained_encoder_out = encoder_out['pretrained'] if self.pretrained else None - encoder_out = encoder_out['encoder']['encoder_out'] + trained_encoder_out = encoder_out["pretrained"] if self.pretrained else None + encoder_out = encoder_out["encoder"]["encoder_out"] encoder_a, encoder_b = self._split_encoder_out(encoder_out) @@ -410,7 +468,7 @@ def forward(self, prev_output_tokens, encoder_out): # embed tokens and positions x = self.embed_tokens(prev_output_tokens) + positions - x = F.dropout(x, p=self.dropout, training=self.training) + x = self.dropout_module(x) target_embedding = x.transpose(0, 1) # project to size of convolution @@ -422,18 +480,24 @@ def forward(self, prev_output_tokens, encoder_out): # temporal convolutions avg_attn_scores = None for proj, conv, attention, selfattention, attproj in zip( - self.projections, self.convolutions, self.attention, self.selfattention, self.attproj + self.projections, + self.convolutions, + self.attention, + self.selfattention, + self.attproj, ): residual = x if proj is None else proj(x) - x = F.dropout(x, p=self.dropout, training=self.training) + x = self.dropout_module(x) x = conv(x) x = F.glu(x, dim=2) # attention if attention is not None: r = x - x, attn_scores = attention(attproj(x) + target_embedding, encoder_a, encoder_b) + x, attn_scores = attention( + attproj(x) + target_embedding, encoder_a, encoder_b + ) x = x + r if not self.training and self.need_attn: if avg_attn_scores is None: @@ -451,13 +515,15 @@ def forward(self, prev_output_tokens, encoder_out): # project back to size of vocabulary x = self.fc2(x) - x = F.dropout(x, p=self.dropout, training=self.training) + x = self.dropout_module(x) if not self.pretrained: x = self.fc3(x) # fusion gating if self.pretrained: - trained_x, _ = self.pretrained_decoder.forward(prev_output_tokens, trained_encoder_out) + trained_x, _ = self.pretrained_decoder.forward( + prev_output_tokens, trained_encoder_out + ) y = torch.cat([x, self.pretrained_outputs["out"]], dim=-1) gate1 = self.gate1(y) gate2 = self.gate2(y) @@ -488,12 +554,25 @@ def _split_encoder_out(self, encoder_out): class SelfAttention(nn.Module): - - def __init__(self, out_channels, embed_dim, num_heads, project_input=False, gated=False, downsample=False): + def __init__( + self, + out_channels, + embed_dim, + num_heads, + project_input=False, + gated=False, + downsample=False, + ): super().__init__() self.attention = DownsampledMultiHeadAttention( - out_channels, embed_dim, num_heads, dropout=0, bias=True, - project_input=project_input, gated=gated, downsample=downsample, + out_channels, + embed_dim, + num_heads, + dropout=0, + bias=True, + project_input=project_input, + gated=gated, + downsample=downsample, ) self.in_proj_q = Linear(out_channels, embed_dim) self.in_proj_k = Linear(out_channels, embed_dim) @@ -505,7 +584,9 @@ def forward(self, x): query = self.in_proj_q(x) key = self.in_proj_k(x) value = self.in_proj_v(x) - x, _ = self.attention(query, key, value, mask_future_timesteps=True, use_scalar_bias=True) + x, _ = self.attention( + query, key, value, mask_future_timesteps=True, use_scalar_bias=True + ) return self.ln(x + residual) @@ -521,7 +602,7 @@ def PositionalEmbedding(num_embeddings, embedding_dim, padding_idx): return m -def Linear(in_features, out_features, dropout=0.): +def Linear(in_features, out_features, dropout=0.0): """Weight-normalized Linear layer (input: N x T x C)""" m = nn.Linear(in_features, out_features) m.weight.data.normal_(mean=0, std=math.sqrt((1 - dropout) / in_features)) @@ -529,7 +610,7 @@ def Linear(in_features, out_features, dropout=0.): return m -def LinearizedConv1d(in_channels, out_channels, kernel_size, dropout=0., **kwargs): +def LinearizedConv1d(in_channels, out_channels, kernel_size, dropout=0.0, **kwargs): """Weight-normalized Conv1d layer optimized for decoding""" m = LinearizedConvolution(in_channels, out_channels, kernel_size, **kwargs) std = math.sqrt((4 * (1.0 - dropout)) / (m.kernel_size[0] * in_channels)) @@ -538,9 +619,10 @@ def LinearizedConv1d(in_channels, out_channels, kernel_size, dropout=0., **kwarg return m -def ConvTBC(in_channels, out_channels, kernel_size, dropout=0, **kwargs): +def ConvTBC(in_channels, out_channels, kernel_size, dropout=0.0, **kwargs): """Weight-normalized Conv1d layer""" from fairseq.modules import ConvTBC + m = ConvTBC(in_channels, out_channels, kernel_size, **kwargs) std = math.sqrt((4 * (1.0 - dropout)) / (m.kernel_size[0] * in_channels)) m.weight.data.normal_(mean=0, std=std) @@ -548,37 +630,45 @@ def ConvTBC(in_channels, out_channels, kernel_size, dropout=0, **kwargs): return m -@register_model_architecture('fconv_self_att', 'fconv_self_att') +@register_model_architecture("fconv_self_att", "fconv_self_att") def base_architecture(args): - args.dropout = getattr(args, 'dropout', 0.1) - args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 512) - args.encoder_layers = getattr(args, 'encoder_layers', '[(512, 3)] * 3') - args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 512) - args.decoder_layers = getattr(args, 'decoder_layers', '[(512, 3)] * 8') - args.decoder_out_embed_dim = getattr(args, 'decoder_out_embed_dim', 256) - args.decoder_attention = getattr(args, 'decoder_attention', 'True') - args.self_attention = getattr(args, 'self_attention', 'False') - args.encoder_attention = getattr(args, 'encoder_attention', 'False') - args.multihead_attention_nheads = getattr(args, 'multihead_attention_nheads', 1) - args.multihead_self_attention_nheads = getattr(args, 'multihead_self_attention_nheads', 1) - args.encoder_attention_nheads = getattr(args, 'encoder_attention_nheads', 1) - args.project_input = getattr(args, 'project_input', 'False') - args.gated_attention = getattr(args, 'gated_attention', 'False') - args.downsample = getattr(args, 'downsample', 'False') - args.pretrained_checkpoint = getattr(args, 'pretrained_checkpoint', '') - args.pretrained = getattr(args, 'pretrained', 'False') - - -@register_model_architecture('fconv_self_att', 'fconv_self_att_wp') + args.dropout = getattr(args, "dropout", 0.1) + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) + args.encoder_layers = getattr(args, "encoder_layers", "[(512, 3)] * 3") + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512) + args.decoder_layers = getattr(args, "decoder_layers", "[(512, 3)] * 8") + args.decoder_out_embed_dim = getattr(args, "decoder_out_embed_dim", 256) + args.decoder_attention = getattr(args, "decoder_attention", "True") + args.self_attention = getattr(args, "self_attention", "False") + args.encoder_attention = getattr(args, "encoder_attention", "False") + args.multihead_attention_nheads = getattr(args, "multihead_attention_nheads", 1) + args.multihead_self_attention_nheads = getattr( + args, "multihead_self_attention_nheads", 1 + ) + args.encoder_attention_nheads = getattr(args, "encoder_attention_nheads", 1) + args.project_input = getattr(args, "project_input", "False") + args.gated_attention = getattr(args, "gated_attention", "False") + args.downsample = getattr(args, "downsample", "False") + args.pretrained_checkpoint = getattr(args, "pretrained_checkpoint", "") + args.pretrained = getattr(args, "pretrained", "False") + + +@register_model_architecture("fconv_self_att", "fconv_self_att_wp") def fconv_self_att_wp(args): - args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 256) - args.encoder_layers = getattr(args, 'encoder_layers', '[(128, 3)] * 2 + [(512,3)] * 1') - args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 256) - args.decoder_layers = getattr(args, 'decoder_layers', '[(512, 4)] * 4 + [(768, 4)] * 2 + [(1024, 4)] * 1') - args.decoder_out_embed_dim = getattr(args, 'decoder_out_embed_dim', 256) - args.self_attention = getattr(args, 'self_attention', 'True') - args.multihead_self_attention_nheads = getattr(args, 'multihead_self_attention_nheads', 4) - args.project_input = getattr(args, 'project_input', 'True') - args.gated_attention = getattr(args, 'gated_attention', 'True') - args.downsample = getattr(args, 'downsample', 'True') + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 256) + args.encoder_layers = getattr( + args, "encoder_layers", "[(128, 3)] * 2 + [(512,3)] * 1" + ) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 256) + args.decoder_layers = getattr( + args, "decoder_layers", "[(512, 4)] * 4 + [(768, 4)] * 2 + [(1024, 4)] * 1" + ) + args.decoder_out_embed_dim = getattr(args, "decoder_out_embed_dim", 256) + args.self_attention = getattr(args, "self_attention", "True") + args.multihead_self_attention_nheads = getattr( + args, "multihead_self_attention_nheads", 4 + ) + args.project_input = getattr(args, "project_input", "True") + args.gated_attention = getattr(args, "gated_attention", "True") + args.downsample = getattr(args, "downsample", "True") base_architecture(args) diff --git a/fairseq/models/hubert/__init__.py b/fairseq/models/hubert/__init__.py new file mode 100644 index 0000000000..a1b0eabbdb --- /dev/null +++ b/fairseq/models/hubert/__init__.py @@ -0,0 +1,7 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from .hubert import * # noqa +from .hubert_asr import * # noqa diff --git a/fairseq/models/hubert/hubert.py b/fairseq/models/hubert/hubert.py new file mode 100644 index 0000000000..232a5e402a --- /dev/null +++ b/fairseq/models/hubert/hubert.py @@ -0,0 +1,563 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +from typing import Dict, List, Optional, Tuple + +import numpy as np + +import torch +import torch.nn as nn +from dataclasses import dataclass, field +from fairseq import utils +from fairseq.data.data_utils import compute_mask_indices +from fairseq.data.dictionary import Dictionary +from fairseq.dataclass import ChoiceEnum, FairseqDataclass +from fairseq.models import BaseFairseqModel, register_model +from fairseq.models.wav2vec.wav2vec2 import ( + ConvFeatureExtractionModel, + TransformerEncoder, +) +from fairseq.modules import GradMultiply, LayerNorm +from fairseq.tasks.hubert_pretraining import ( + HubertPretrainingConfig, + HubertPretrainingTask, +) +from omegaconf import II + +logger = logging.getLogger(__name__) + +EXTRACTOR_MODE_CHOICES = ChoiceEnum(["default", "layer_norm"]) +MASKING_DISTRIBUTION_CHOICES = ChoiceEnum( + ["static", "uniform", "normal", "poisson"] +) + + +@dataclass +class HubertConfig(FairseqDataclass): + label_rate: int = II("task.label_rate") + + extractor_mode: EXTRACTOR_MODE_CHOICES = field( + default="default", + metadata={ + "help": "mode for feature extractor. default has a single group " + "norm with d groups in the first conv block, whereas layer_norm " + "has layer norms in every block (meant to use with normalize=True)" + }, + ) + encoder_layers: int = field( + default=12, metadata={"help": "num encoder layers in the transformer"} + ) + encoder_embed_dim: int = field( + default=768, metadata={"help": "encoder embedding dimension"} + ) + encoder_ffn_embed_dim: int = field( + default=3072, metadata={"help": "encoder embedding dimension for FFN"} + ) + encoder_attention_heads: int = field( + default=12, metadata={"help": "num encoder attention heads"} + ) + activation_fn: ChoiceEnum(utils.get_available_activation_fns()) = field( + default="gelu", metadata={"help": "activation function to use"} + ) + + # dropouts + dropout: float = field( + default=0.1, + metadata={"help": "dropout probability for the transformer"}, + ) + attention_dropout: float = field( + default=0.1, + metadata={"help": "dropout probability for attention weights"}, + ) + activation_dropout: float = field( + default=0.0, + metadata={"help": "dropout probability after activation in FFN"}, + ) + encoder_layerdrop: float = field( + default=0.0, + metadata={"help": "probability of dropping a tarnsformer layer"}, + ) + dropout_input: float = field( + default=0.0, + metadata={"help": "dropout to apply to the input (after feat extr)"}, + ) + dropout_features: float = field( + default=0.0, + metadata={ + "help": "dropout to apply to the features (after feat extr)" + }, + ) + + final_dim: int = field( + default=0, + metadata={ + "help": "project final representations and targets to this many " + "dimensions. set to encoder_embed_dim is <= 0" + }, + ) + untie_final_proj: bool = field( + default=False, + metadata={"help": "use separate projection for each target"}, + ) + layer_norm_first: bool = field( + default=False, + metadata={"help": "apply layernorm first in the transformer"}, + ) + conv_feature_layers: str = field( + default="[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2", + metadata={ + "help": "string describing convolutional feature extraction " + "layers in form of a python list that contains " + "[(dim, kernel_size, stride), ...]" + }, + ) + conv_bias: bool = field( + default=False, metadata={"help": "include bias in conv encoder"} + ) + logit_temp: float = field( + default=0.1, metadata={"help": "temperature to divide logits by"} + ) + target_glu: bool = field( + default=False, metadata={"help": "adds projection + glu to targets"} + ) + feature_grad_mult: float = field( + default=1.0, + metadata={"help": "multiply feature extractor var grads by this"}, + ) + + # masking + mask_length: int = field(default=10, metadata={"help": "mask length"}) + mask_prob: float = field( + default=0.65, + metadata={"help": "probability of replacing a token with mask"}, + ) + mask_selection: MASKING_DISTRIBUTION_CHOICES = field( + default="static", metadata={"help": "how to choose mask length"} + ) + mask_other: float = field( + default=0, + metadata={ + "help": "secondary mask argument " + "(used for more complex distributions), " + "see help in compute_mask_indicesh" + }, + ) + no_mask_overlap: bool = field( + default=False, metadata={"help": "whether to allow masks to overlap"} + ) + mask_min_space: int = field( + default=1, + metadata={ + "help": "min space between spans (if no overlap is enabled)" + }, + ) + + # channel masking + mask_channel_length: int = field( + default=10, + metadata={"help": "length of the mask for features (channels)"}, + ) + mask_channel_prob: float = field( + default=0.0, + metadata={"help": "probability of replacing a feature with 0"}, + ) + mask_channel_selection: MASKING_DISTRIBUTION_CHOICES = field( + default="static", + metadata={"help": "how to choose mask length for channel masking"}, + ) + mask_channel_other: float = field( + default=0, + metadata={ + "help": "secondary mask argument " + "(used for more complex distributions), " + "see help in compute_mask_indicesh" + }, + ) + no_mask_channel_overlap: bool = field( + default=False, + metadata={"help": "whether to allow channel masks to overlap"}, + ) + mask_channel_min_space: int = field( + default=1, + metadata={ + "help": "min space between spans (if no overlap is enabled)" + }, + ) + + # positional embeddings + conv_pos: int = field( + default=128, + metadata={ + "help": "number of filters for convolutional positional embeddings" + }, + ) + conv_pos_groups: int = field( + default=16, + metadata={ + "help": "number of groups for convolutional positional embedding" + }, + ) + + latent_temp: Tuple[float, float, float] = field( + default=(2, 0.5, 0.999995), + metadata={"help": "legacy (to be removed)"}, + ) + + # loss computation + skip_masked: bool = field( + default=False, + metadata={"help": "skip computing losses over masked frames"}, + ) + skip_nomask: bool = field( + default=False, + metadata={"help": "skip computing losses over unmasked frames"}, + ) + + +@register_model("hubert", dataclass=HubertConfig) +class HubertModel(BaseFairseqModel): + def __init__( + self, + cfg: HubertConfig, + task_cfg: HubertPretrainingConfig, + dictionaries: List[Dictionary], + ) -> None: + super().__init__() + logger.info(f"HubertModel Config: {cfg}") + + feature_enc_layers = eval(cfg.conv_feature_layers) # noqa + self.embed = feature_enc_layers[-1][0] + + self.feature_extractor = ConvFeatureExtractionModel( + conv_layers=feature_enc_layers, + dropout=0.0, + mode=cfg.extractor_mode, + conv_bias=cfg.conv_bias, + ) + feature_ds_rate = np.prod([s for _, _, s in feature_enc_layers]) + self.feat2tar_ratio = ( + cfg.label_rate * feature_ds_rate / task_cfg.sample_rate + ) + + self.post_extract_proj = ( + nn.Linear(self.embed, cfg.encoder_embed_dim) + if self.embed != cfg.encoder_embed_dim + else None + ) + + self.mask_prob = cfg.mask_prob + self.mask_selection = cfg.mask_selection + self.mask_other = cfg.mask_other + self.mask_length = cfg.mask_length + self.no_mask_overlap = cfg.no_mask_overlap + self.mask_min_space = cfg.mask_min_space + + self.mask_channel_prob = cfg.mask_channel_prob + self.mask_channel_selection = cfg.mask_channel_selection + self.mask_channel_other = cfg.mask_channel_other + self.mask_channel_length = cfg.mask_channel_length + self.no_mask_channel_overlap = cfg.no_mask_channel_overlap + self.mask_channel_min_space = cfg.mask_channel_min_space + + self.dropout_input = nn.Dropout(cfg.dropout_input) + self.dropout_features = nn.Dropout(cfg.dropout_features) + + self.feature_grad_mult = cfg.feature_grad_mult + self.logit_temp = cfg.logit_temp + self.skip_masked = cfg.skip_masked + self.skip_nomask = cfg.skip_nomask + + final_dim = ( + cfg.final_dim if cfg.final_dim > 0 else cfg.encoder_embed_dim + ) + + self.mask_emb = nn.Parameter( + torch.FloatTensor(cfg.encoder_embed_dim).uniform_() + ) + + self.encoder = TransformerEncoder(cfg) + self.layer_norm = LayerNorm(self.embed) + + self.target_glu = None + if cfg.target_glu: + self.target_glu = nn.Sequential( + nn.Linear(final_dim, final_dim * 2), nn.GLU() + ) + + self.untie_final_proj = cfg.untie_final_proj + if self.untie_final_proj: + self.final_proj = nn.Linear( + cfg.encoder_embed_dim, final_dim * len(dictionaries) + ) + else: + self.final_proj = nn.Linear(cfg.encoder_embed_dim, final_dim) + + # modules below are not needed during fine-tuning + if any([d is None for d in dictionaries]): + logger.info( + "cannot find dictionary. assume will be used for fine-tuning" + ) + else: + self.num_classes = [len(d) for d in dictionaries] + self.label_embs_concat = nn.Parameter( + torch.FloatTensor(sum(self.num_classes), final_dim) + ) + nn.init.uniform_(self.label_embs_concat) + + def upgrade_state_dict_named(self, state_dict, name): + """Upgrade a (possibly old) state dict for new versions of fairseq.""" + + super().upgrade_state_dict_named(state_dict, name) + return state_dict + + @classmethod + def build_model(cls, cfg: HubertConfig, task: HubertPretrainingTask): + """Build a new model instance.""" + + model = HubertModel(cfg, task.cfg, task.dictionaries) + return model + + def apply_mask(self, x, padding_mask, target_list): + B, T, C = x.shape + if self.mask_prob > 0: + mask_indices = compute_mask_indices( + (B, T), + padding_mask, + self.mask_prob, + self.mask_length, + self.mask_selection, + self.mask_other, + min_masks=2, + no_overlap=self.no_mask_overlap, + min_space=self.mask_min_space, + ) + mask_indices = torch.from_numpy(mask_indices).to(x.device) + x[mask_indices] = self.mask_emb + else: + mask_indices = None + + if self.mask_channel_prob > 0: + mask_channel_indices = compute_mask_indices( + (B, C), + None, + self.mask_channel_prob, + self.mask_channel_length, + self.mask_channel_selection, + self.mask_channel_other, + no_overlap=self.no_mask_channel_overlap, + min_space=self.mask_channel_min_space, + ) + mask_channel_indices = ( + torch.from_numpy(mask_channel_indices) + .to(x.device) + .unsqueeze(1) + .expand(-1, T, -1) + ) + x[mask_channel_indices] = 0 + + return x, mask_indices + + def compute_nce(self, x, pos, negs): + neg_is_pos = (pos == negs).all(-1) + pos = pos.unsqueeze(0) + targets = torch.cat([pos, negs], dim=0) + + logits = torch.cosine_similarity( + x.float(), targets.float(), dim=-1 + ).type_as(x) + logits /= self.logit_temp + if neg_is_pos.any(): + logits[1:][neg_is_pos] = float("-inf") + logits = logits.transpose(0, 1) # (num_x, num_cls+1) + return logits + + def forward_features(self, source: torch.Tensor) -> torch.Tensor: + if self.feature_grad_mult > 0: + features = self.feature_extractor(source) + if self.feature_grad_mult != 1.0: + features = GradMultiply.apply(features, self.feature_grad_mult) + else: + with torch.no_grad(): + features = self.feature_extractor(source) + return features + + def forward_targets( + self, features: torch.Tensor, target_list: List[torch.Tensor], + ) -> Tuple[torch.Tensor, torch.Tensor]: + # Trim features to ensure labels exist and then get aligned labels + feat_tsz = features.size(2) + targ_tsz = min([t.size(1) for t in target_list]) + if self.feat2tar_ratio * feat_tsz > targ_tsz: + feat_tsz = int(targ_tsz / self.feat2tar_ratio) + features = features[..., :feat_tsz] + target_inds = torch.arange(feat_tsz).float() * self.feat2tar_ratio + target_list = [t[:, target_inds.long()] for t in target_list] + return features, target_list + + def forward_padding_mask( + self, features: torch.Tensor, padding_mask: torch.Tensor, + ) -> torch.Tensor: + extra = padding_mask.size(1) % features.size(1) + if extra > 0: + padding_mask = padding_mask[:, :-extra] + padding_mask = padding_mask.view( + padding_mask.size(0), features.size(1), -1 + ) + padding_mask = padding_mask.all(-1) + return padding_mask + + def forward( + self, + source: torch.Tensor, + target_list: Optional[List[torch.Tensor]] = None, + padding_mask: Optional[torch.Tensor] = None, + mask: bool = True, + features_only: bool = False, + output_layer: Optional[int] = None, + ) -> Dict[str, torch.Tensor]: + """output layer is 1-based""" + features = self.forward_features(source) + if target_list is not None: + features, target_list = self.forward_targets(features, target_list) + + features_pen = features.float().pow(2).mean() + + features = features.transpose(1, 2) + features = self.layer_norm(features) + unmasked_features = features.clone() + + if padding_mask is not None: + padding_mask = self.forward_padding_mask(features, padding_mask) + + if self.post_extract_proj is not None: + features = self.post_extract_proj(features) + + features = self.dropout_input(features) + unmasked_features = self.dropout_features(unmasked_features) + + if mask: + x, mask_indices = self.apply_mask( + features, padding_mask, target_list + ) + else: + x = features + mask_indices = None + + # feature: (B, T, D), float + # target: (B, T), long + # x: (B, T, D), float + # padding_mask: (B, T), bool + # mask_indices: (B, T), bool + x, _ = self.encoder( + x, + padding_mask=padding_mask, + layer=None if output_layer is None else output_layer - 1 + ) + + if features_only: + return {"x": x, "padding_mask": padding_mask, "features": features} + + def compute_pred(proj_x, target, label_embs): + # compute logits for the i-th label set + y = torch.index_select(label_embs, 0, target.long()) + negs = label_embs.unsqueeze(1).expand(-1, proj_x.size(0), -1) + if self.target_glu: + y = self.target_glu(y) + negs = self.target_glu(negs) + # proj_x: (S, D) + # y: (S, D) + # negs: (Neg, S, D) + return self.compute_nce(proj_x, y, negs) + + label_embs_list = self.label_embs_concat.split(self.num_classes, 0) + + if not self.skip_masked: + masked_indices = torch.logical_and(~padding_mask, mask_indices) + proj_x_m = self.final_proj(x[masked_indices]) + if self.untie_final_proj: + proj_x_m_list = proj_x_m.chunk(len(target_list), dim=-1) + else: + proj_x_m_list = [proj_x_m for _ in range(len(target_list))] + logit_m_list = [ + compute_pred(proj_x_m, t[masked_indices], label_embs_list[i]) + for i, (proj_x_m, t) in enumerate( + zip(proj_x_m_list, target_list) + ) + ] + else: + logit_m_list = [None for _ in target_list] + + if not self.skip_nomask: + nomask_indices = torch.logical_and(~padding_mask, ~mask_indices) + proj_x_u = self.final_proj(x[nomask_indices]) + if self.untie_final_proj: + proj_x_u_list = proj_x_u.chunk(len(target_list), dim=-1) + else: + proj_x_u_list = [proj_x_u for _ in range(len(target_list))] + + logit_u_list = [ + compute_pred(proj_x_u, t[nomask_indices], label_embs_list[i]) + for i, (proj_x_u, t) in enumerate( + zip(proj_x_u_list, target_list) + ) + ] + else: + logit_u_list = [None for _ in target_list] + + result = { + "logit_m_list": logit_m_list, + "logit_u_list": logit_u_list, + "padding_mask": padding_mask, + "features_pen": features_pen, + } + return result + + def extract_features( + self, + source: torch.Tensor, + padding_mask: Optional[torch.Tensor] = None, + mask: bool = False, + ret_conv: bool = False, + output_layer: Optional[int] = None, + ) -> Tuple[torch.Tensor, torch.Tensor]: + res = self.forward( + source, + padding_mask=padding_mask, + mask=mask, + features_only=True, + output_layer=output_layer, + ) + feature = res["features"] if ret_conv else res["x"] + return feature, res["padding_mask"] + + def get_logits(self, net_output, is_masked=True): + if is_masked: + logits_list = net_output["logit_m_list"] + else: + logits_list = net_output["logit_u_list"] + logits_list = [x.float() for x in logits_list if x is not None] + return logits_list + + def get_targets(self, net_output, is_masked=True): + logits_list = self.get_logits(net_output, is_masked) + targets_list = [ + x.new_zeros(x.size(0), dtype=torch.long) for x in logits_list + ] + return targets_list + + def get_extra_losses(self, net_output): + extra_losses = [] + names = [] + + if "features_pen" in net_output: + extra_losses.append(net_output["features_pen"]) + names.append("features_pen") + + return extra_losses, names + + def remove_pretraining_modules(self): + self.target_glu = None + self.final_proj = None diff --git a/fairseq/models/hubert/hubert_asr.py b/fairseq/models/hubert/hubert_asr.py new file mode 100644 index 0000000000..dce899c9de --- /dev/null +++ b/fairseq/models/hubert/hubert_asr.py @@ -0,0 +1,376 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import contextlib +from argparse import Namespace +from typing import Any + +import torch +import torch.nn as nn +from dataclasses import dataclass, field +from fairseq import checkpoint_utils, tasks, utils +from fairseq.dataclass import FairseqDataclass +from fairseq.dataclass.utils import convert_namespace_to_omegaconf +from fairseq.models import BaseFairseqModel, FairseqEncoder, register_model +from fairseq.models.hubert.hubert import MASKING_DISTRIBUTION_CHOICES +from fairseq.tasks import FairseqTask +from omegaconf import II, MISSING + + +@dataclass +class HubertAsrConfig(FairseqDataclass): + w2v_path: str = field( + default=MISSING, metadata={"help": "path to hubert model"} + ) + no_pretrained_weights: bool = field( + default=False, + metadata={"help": "if true, does not load pretrained weights"}, + ) + dropout_input: float = field( + default=0.0, + metadata={"help": "dropout to apply to the input (after feat extr)"}, + ) + final_dropout: float = field( + default=0.0, + metadata={ + "help": "dropout after transformer and before final projection" + }, + ) + dropout: float = field( + default=0.0, + metadata={"help": "dropout probability inside hubert model"}, + ) + attention_dropout: float = field( + default=0.0, + metadata={ + "help": "dropout probability for attention weights " + "inside hubert model" + }, + ) + activation_dropout: float = field( + default=0.0, + metadata={ + "help": "dropout probability after activation in FFN " + "inside hubert model" + }, + ) + + # masking + apply_mask: bool = field( + default=False, metadata={"help": "apply masking during fine-tuning"} + ) + mask_length: int = field( + default=10, metadata={"help": "repeat the mask indices multiple times"} + ) + mask_prob: float = field( + default=0.5, + metadata={ + "help": "probability of replacing a token with mask " + "(normalized by length)" + }, + ) + mask_selection: MASKING_DISTRIBUTION_CHOICES = field( + default="static", metadata={"help": "how to choose masks"} + ) + mask_other: float = field( + default=0, + metadata={ + "help": "secondary mask argument " + "(used for more complex distributions), " + "see help in compute_mask_indices" + }, + ) + no_mask_overlap: bool = field( + default=False, metadata={"help": "whether to allow masks to overlap"} + ) + + # channel masking + mask_channel_length: int = field( + default=10, + metadata={"help": "length of the mask for features (channels)"}, + ) + mask_channel_prob: float = field( + default=0.0, + metadata={"help": "probability of replacing a feature with 0"}, + ) + mask_channel_selection: MASKING_DISTRIBUTION_CHOICES = field( + default="static", + metadata={"help": "how to choose mask length for channel masking"}, + ) + mask_channel_other: float = field( + default=0, + metadata={ + "help": "secondary mask argument " + "(used for more complex distributions), " + "see help in compute_mask_indices" + }, + ) + no_mask_channel_overlap: bool = field( + default=False, + metadata={"help": "whether to allow channel masks to overlap"}, + ) + freeze_finetune_updates: int = field( + default=0, + metadata={"help": "dont finetune hubert for this many updates"}, + ) + feature_grad_mult: float = field( + default=0.0, + metadata={"help": "reset feature grad mult in hubert to this"}, + ) + layerdrop: float = field( + default=0.0, + metadata={"help": "probability of dropping a layer in hubert"}, + ) + normalize: bool = II("task.normalize") + data: str = II("task.data") + + # this holds the loaded hubert args + w2v_args: Any = None + + +@dataclass +class HubertCtcConfig(HubertAsrConfig): + pass + + +@register_model("hubert_ctc", dataclass=HubertCtcConfig) +class HubertCtc(BaseFairseqModel): + def __init__(self, cfg: HubertCtcConfig, w2v_encoder: BaseFairseqModel): + super().__init__() + self.cfg = cfg + self.w2v_encoder = w2v_encoder + + def upgrade_state_dict_named(self, state_dict, name): + super().upgrade_state_dict_named(state_dict, name) + return state_dict + + @classmethod + def build_model(cls, cfg: HubertCtcConfig, task: FairseqTask): + """Build a new model instance.""" + w2v_encoder = HubertEncoder(cfg, task.target_dictionary) + return cls(cfg, w2v_encoder) + + def get_normalized_probs(self, net_output, log_probs): + """Get normalized probabilities (or log probs) from a net's output.""" + + logits = net_output["encoder_out"] + if log_probs: + return utils.log_softmax(logits.float(), dim=-1) + else: + return utils.softmax(logits.float(), dim=-1) + + def get_logits(self, net_output): + logits = net_output["encoder_out"] + padding = net_output["encoder_padding_mask"] + if padding is not None and padding.any(): + padding = padding.T + logits[padding][..., 0] = 0 + logits[padding][..., 1:] = float("-inf") + + return logits + + def forward(self, **kwargs): + x = self.w2v_encoder(**kwargs) + return x + + +@dataclass +class HubertSeq2SeqConfig(HubertAsrConfig): + decoder_embed_dim: int = field( + default=768, metadata={"help": "decoder embedding dimension"} + ) + decoder_ffn_embed_dim: int = field( + default=3072, metadata={"help": "decoder embedding dimension for FFN"} + ) + decoder_layers: int = field( + default=6, metadata={"help": "num of decoder layers"} + ) + decoder_layerdrop: float = field( + default=0.0, metadata={"help": "decoder layerdrop chance"} + ) + decoder_attention_heads: int = field( + default=4, metadata={"help": "num decoder attention heads"} + ) + decoder_learned_pos: bool = field( + default=False, + metadata={"help": "use learned positional embeddings in the decoder"}, + ) + decoder_normalize_before: bool = field( + default=False, + metadata={"help": "apply layernorm before each decoder block"}, + ) + no_token_positional_embeddings: bool = field( + default=False, + metadata={ + "help": "if set, disables positional embeddings " + "(outside self attention)" + }, + ) + decoder_dropout: float = field( + default=0.0, metadata={"help": "dropout probability in the decoder"} + ) + decoder_attention_dropout: float = field( + default=0.0, + metadata={ + "help": "dropout probability for attention weights " + "inside the decoder" + }, + ) + decoder_activation_dropout: float = field( + default=0.0, + metadata={ + "help": "dropout probability after activation in FFN " + "inside the decoder" + }, + ) + max_target_positions: int = field( + default=2048, metadata={"help": "max target positions"} + ) + share_decoder_input_output_embed: bool = field( + default=False, + metadata={"help": "share decoder input and output embeddings"}, + ) + + +class HubertEncoder(FairseqEncoder): + def __init__(self, cfg: HubertAsrConfig, tgt_dict=None): + self.apply_mask = cfg.apply_mask + + arg_overrides = { + "dropout": cfg.dropout, + "activation_dropout": cfg.activation_dropout, + "dropout_input": cfg.dropout_input, + "attention_dropout": cfg.attention_dropout, + "mask_length": cfg.mask_length, + "mask_prob": cfg.mask_prob, + "mask_selection": cfg.mask_selection, + "mask_other": cfg.mask_other, + "no_mask_overlap": cfg.no_mask_overlap, + "mask_channel_length": cfg.mask_channel_length, + "mask_channel_prob": cfg.mask_channel_prob, + "mask_channel_selection": cfg.mask_channel_selection, + "mask_channel_other": cfg.mask_channel_other, + "no_mask_channel_overlap": cfg.no_mask_channel_overlap, + "encoder_layerdrop": cfg.layerdrop, + "feature_grad_mult": cfg.feature_grad_mult, + } + + if cfg.w2v_args is None: + state = checkpoint_utils.load_checkpoint_to_cpu( + cfg.w2v_path, arg_overrides + ) + w2v_args = state.get("cfg", None) + if w2v_args is None: + w2v_args = convert_namespace_to_omegaconf(state["args"]) + cfg.w2v_args = w2v_args + else: + state = None + w2v_args = cfg.w2v_args + if isinstance(w2v_args, Namespace): + cfg.w2v_args = w2v_args = convert_namespace_to_omegaconf( + w2v_args + ) + + assert cfg.normalize == w2v_args.task.normalize, ( + "Fine-tuning works best when data normalization is the same. " + "Please check that --normalize is set or unset for " + "both pre-training and here" + ) + + w2v_args.task.data = cfg.data + task = tasks.setup_task(w2v_args.task) + if state is not None and "task_state" in state: + # This will load the stored "dictionaries" object + task.load_state_dict(state["task_state"]) + model = task.build_model(w2v_args.model) + + if state is not None and not cfg.no_pretrained_weights: + # set strict=False because we omit some modules + model.load_state_dict(state["model"], strict=False) + + model.remove_pretraining_modules() + + super().__init__(task.source_dictionary) + + d = w2v_args.model.encoder_embed_dim + + self.w2v_model = model + + self.final_dropout = nn.Dropout(cfg.final_dropout) + self.freeze_finetune_updates = cfg.freeze_finetune_updates + self.num_updates = 0 + + if tgt_dict is not None: + self.proj = Linear(d, len(tgt_dict)) + elif getattr(cfg, "decoder_embed_dim", d) != d: + self.proj = Linear(d, cfg.decoder_embed_dim) + else: + self.proj = None + + def set_num_updates(self, num_updates): + """Set the number of parameters updates.""" + super().set_num_updates(num_updates) + self.num_updates = num_updates + + def forward(self, source, padding_mask, tbc=True, **kwargs): + + w2v_args = { + "source": source, + "padding_mask": padding_mask, + "mask": self.apply_mask and self.training, + } + + ft = self.freeze_finetune_updates <= self.num_updates + + with torch.no_grad() if not ft else contextlib.ExitStack(): + x, padding_mask = self.w2v_model.extract_features(**w2v_args) + + if tbc: + # B x T x C -> T x B x C + x = x.transpose(0, 1) + + x = self.final_dropout(x) + + if self.proj: + x = self.proj(x) + + return { + "encoder_out": x, # T x B x C + "encoder_padding_mask": padding_mask, # B x T + "padding_mask": padding_mask, + } + + def reorder_encoder_out(self, encoder_out, new_order): + if encoder_out["encoder_out"] is not None: + encoder_out["encoder_out"] = encoder_out[ + "encoder_out" + ].index_select(1, new_order) + if encoder_out["encoder_padding_mask"] is not None: + encoder_out["encoder_padding_mask"] = encoder_out[ + "encoder_padding_mask" + ].index_select(0, new_order) + return encoder_out + + def max_positions(self): + """Maximum input length supported by the encoder.""" + return None + + def upgrade_state_dict_named(self, state_dict, name): + return state_dict + + +def Embedding(num_embeddings, embedding_dim, padding_idx): + m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx) + nn.init.normal_(m.weight, mean=0, std=embedding_dim ** -0.5) + nn.init.constant_(m.weight[padding_idx], 0) + return m + + +def Linear(in_features, out_features, bias=True): + m = nn.Linear(in_features, out_features, bias) + nn.init.xavier_uniform_(m.weight) + if bias: + nn.init.constant_(m.bias, 0.0) + return m diff --git a/fairseq/models/huggingface/__init__.py b/fairseq/models/huggingface/__init__.py index e186c4b196..f7911c2c8e 100644 --- a/fairseq/models/huggingface/__init__.py +++ b/fairseq/models/huggingface/__init__.py @@ -3,4 +3,18 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. -from .hf_gpt2 import * # noqa +import importlib +import os + + +# automatically import any Python files in the models/huggingface/ directory +models_dir = os.path.dirname(__file__) +for file in os.listdir(models_dir): + path = os.path.join(models_dir, file) + if ( + not file.startswith("_") + and not file.startswith(".") + and (file.endswith(".py") or os.path.isdir(path)) + ): + model_name = file[: file.find(".py")] if file.endswith(".py") else file + module = importlib.import_module("fairseq.models.huggingface." + model_name) diff --git a/fairseq/models/huggingface/hf_gpt2.py b/fairseq/models/huggingface/hf_gpt2.py index 4107113e81..3a8eb78198 100644 --- a/fairseq/models/huggingface/hf_gpt2.py +++ b/fairseq/models/huggingface/hf_gpt2.py @@ -23,9 +23,8 @@ DEFAULT_MAX_TARGET_POSITIONS = 1024 -@register_model('hf_gpt2') +@register_model("hf_gpt2") class HuggingFaceGPT2LanguageModel(FairseqLanguageModel): - def __init__(self, decoder): super().__init__(decoder) @@ -54,27 +53,17 @@ def build_model(cls, args, task): class HuggingFaceGPT2Decoder(FairseqIncrementalDecoder): - def __init__(self, args, task): - super().__init__(task.target_dictionary) - try: - # Prepend the transformers submodule to the path, so that - # it's prioritized over other installations. This allows - # making local changes in the submodule. - sys.path.insert( - 0, os.path.join(os.path.dirname(__file__), 'transformers', 'src') - ) from transformers import GPT2Config, GPT2LMHeadModel except ImportError: raise ImportError( - '\n\nPlease install huggingface/transformers with:' - '\n\n pip install transformers' - '\n\nOr to make local edits, install the submodule:' - '\n\n git submodule update --init ' - 'fairseq/models/huggingface/transformers' + "\n\nPlease install huggingface/transformers with:" + "\n\n pip install transformers" ) + super().__init__(task.target_dictionary) + config = GPT2Config( vocab_size=len(task.target_dictionary), n_positions=args.max_target_positions + 1, @@ -103,7 +92,7 @@ def forward( ): features = self.extract_features(prev_output_tokens, incremental_state) lm_logits = self.model.lm_head(features) - return (lm_logits, ) + return (lm_logits,) def extract_features( self, @@ -142,38 +131,38 @@ def max_positions(self): return self.model.config.n_positions - 1 -@register_model_architecture('hf_gpt2', 'hf_gpt2') +@register_model_architecture("hf_gpt2", "hf_gpt2") def default_architecture(args): - if getattr(args, 'max_target_positions', None) is None: + if getattr(args, "max_target_positions", None) is None: args.max_target_positions = getattr( - args, 'tokens_per_sample', DEFAULT_MAX_TARGET_POSITIONS + args, "tokens_per_sample", DEFAULT_MAX_TARGET_POSITIONS ) - args.embed_dim = getattr(args, 'embed_dim', 768) - args.num_attention_heads = getattr(args, 'num_attention_heads', 12) - args.num_layers = getattr(args, 'num_layers', 12) - args.dropout = getattr(args, 'dropout', 0.1) - args.attention_dropout = getattr(args, 'attention_dropout', 0.1) + args.embed_dim = getattr(args, "embed_dim", 768) + args.num_attention_heads = getattr(args, "num_attention_heads", 12) + args.num_layers = getattr(args, "num_layers", 12) + args.dropout = getattr(args, "dropout", 0.1) + args.attention_dropout = getattr(args, "attention_dropout", 0.1) -@register_model_architecture('hf_gpt2', 'hf_gpt2_medium') +@register_model_architecture("hf_gpt2", "hf_gpt2_medium") def hf_gpt2_medium(args): - args.embed_dim = getattr(args, 'embed_dim', 1024) - args.num_attention_heads = getattr(args, 'num_attention_heads', 16) - args.num_layers = getattr(args, 'num_layers', 24) + args.embed_dim = getattr(args, "embed_dim", 1024) + args.num_attention_heads = getattr(args, "num_attention_heads", 16) + args.num_layers = getattr(args, "num_layers", 24) default_architecture(args) -@register_model_architecture('hf_gpt2', 'hf_gpt2_large') +@register_model_architecture("hf_gpt2", "hf_gpt2_large") def hf_gpt2_large(args): - args.embed_dim = getattr(args, 'embed_dim', 1280) - args.num_attention_heads = getattr(args, 'num_attention_heads', 20) - args.num_layers = getattr(args, 'num_layers', 36) + args.embed_dim = getattr(args, "embed_dim", 1280) + args.num_attention_heads = getattr(args, "num_attention_heads", 20) + args.num_layers = getattr(args, "num_layers", 36) default_architecture(args) -@register_model_architecture('hf_gpt2', 'hf_gpt2_xl') +@register_model_architecture("hf_gpt2", "hf_gpt2_xl") def hf_gpt2_xl(args): - args.embed_dim = getattr(args, 'embed_dim', 1600) - args.num_attention_heads = getattr(args, 'num_attention_heads', 25) - args.num_layers = getattr(args, 'num_layers', 48) + args.embed_dim = getattr(args, "embed_dim", 1600) + args.num_attention_heads = getattr(args, "num_attention_heads", 25) + args.num_layers = getattr(args, "num_layers", 48) default_architecture(args) diff --git a/fairseq/models/huggingface/transformers b/fairseq/models/huggingface/transformers deleted file mode 160000 index 839f8a563c..0000000000 --- a/fairseq/models/huggingface/transformers +++ /dev/null @@ -1 +0,0 @@ -Subproject commit 839f8a563cefcb7f2048b310024c217e7829a198 diff --git a/fairseq/models/lightconv.py b/fairseq/models/lightconv.py index 8100e37ec4..b614da3665 100644 --- a/fairseq/models/lightconv.py +++ b/fairseq/models/lightconv.py @@ -8,26 +8,26 @@ import torch import torch.nn as nn import torch.nn.functional as F - -from fairseq import options, utils +from fairseq import utils from fairseq.models import ( FairseqEncoder, - FairseqIncrementalDecoder, FairseqEncoderDecoderModel, + FairseqIncrementalDecoder, register_model, register_model_architecture, ) from fairseq.modules import ( AdaptiveSoftmax, DynamicConv, + FairseqDropout, LayerNorm, - PositionalEmbedding, LightweightConv, MultiheadAttention, + PositionalEmbedding, ) -@register_model('lightconv') +@register_model("lightconv") class LightConvModel(FairseqEncoderDecoderModel): """ LightConv and DynamicConv model from `"Pay Less Attention with Lightweight and Dynamic Convolutions" (Wu, et al, 2019) @@ -80,75 +80,175 @@ def __init__(self, encoder, decoder): @staticmethod def add_args(parser): """Add model-specific arguments to the parser.""" - parser.add_argument('--dropout', type=float, metavar='D', - help='dropout probability') - parser.add_argument('--attention-dropout', type=float, metavar='D', - help='dropout probability for attention weights') - parser.add_argument('--relu-dropout', type=float, metavar='D', - help='dropout probability after ReLU in FFN') - parser.add_argument('--input-dropout', type=float, metavar='D', - help='dropout probability of the inputs') - parser.add_argument('--encoder-embed-path', type=str, metavar='STR', - help='path to pre-trained encoder embedding') - parser.add_argument('--encoder-embed-dim', type=int, metavar='N', - help='encoder embedding dimension') - parser.add_argument('--encoder-conv-dim', type=int, metavar='N', - help='encoder embedding dimension') - parser.add_argument('--encoder-ffn-embed-dim', type=int, metavar='N', - help='encoder embedding dimension for FFN') - parser.add_argument('--encoder-layers', type=int, metavar='N', - help='num encoder layers') - parser.add_argument('--encoder-attention-heads', type=int, metavar='N', - help='num encoder attention heads or LightConv/DynamicConv heads') - parser.add_argument('--encoder-normalize-before', action='store_true', - help='apply layernorm before each encoder block') - parser.add_argument('--encoder-learned-pos', action='store_true', - help='use learned positional embeddings in the encoder') - parser.add_argument('--decoder-embed-path', type=str, metavar='STR', - help='path to pre-trained decoder embedding') - parser.add_argument('--decoder-embed-dim', type=int, metavar='N', - help='decoder embedding dimension') - parser.add_argument('--decoder-conv-dim', type=int, metavar='N', - help='decoder embedding dimension') - parser.add_argument('--decoder-ffn-embed-dim', type=int, metavar='N', - help='decoder embedding dimension for FFN') - parser.add_argument('--decoder-layers', type=int, metavar='N', - help='num decoder layers') - parser.add_argument('--decoder-attention-heads', type=int, metavar='N', - help='num decoder attention heads or LightConv/DynamicConv heads') - parser.add_argument('--decoder-learned-pos', action='store_true', - help='use learned positional embeddings in the decoder') - parser.add_argument('--decoder-normalize-before', action='store_true', - help='apply layernorm before each decoder block') - parser.add_argument('--share-decoder-input-output-embed', action='store_true', - help='share decoder input and output embeddings') - parser.add_argument('--share-all-embeddings', action='store_true', - help='share encoder, decoder and output embeddings' - ' (requires shared dictionary and embed dim)') - parser.add_argument('--adaptive-softmax-cutoff', metavar='EXPR', - help='comma separated list of adaptive softmax cutoff points. ' - 'Must be used with adaptive_loss criterion'), - parser.add_argument('--adaptive-softmax-dropout', type=float, metavar='D', - help='sets adaptive softmax dropout for the tail projections') + parser.add_argument( + "--dropout", type=float, metavar="D", help="dropout probability" + ) + parser.add_argument( + "--attention-dropout", + type=float, + metavar="D", + help="dropout probability for attention weights", + ) + parser.add_argument( + "--relu-dropout", + type=float, + metavar="D", + help="dropout probability after ReLU in FFN", + ) + parser.add_argument( + "--input-dropout", + type=float, + metavar="D", + help="dropout probability of the inputs", + ) + parser.add_argument( + "--encoder-embed-path", + type=str, + metavar="STR", + help="path to pre-trained encoder embedding", + ) + parser.add_argument( + "--encoder-embed-dim", + type=int, + metavar="N", + help="encoder embedding dimension", + ) + parser.add_argument( + "--encoder-conv-dim", + type=int, + metavar="N", + help="encoder embedding dimension", + ) + parser.add_argument( + "--encoder-ffn-embed-dim", + type=int, + metavar="N", + help="encoder embedding dimension for FFN", + ) + parser.add_argument( + "--encoder-layers", type=int, metavar="N", help="num encoder layers" + ) + parser.add_argument( + "--encoder-attention-heads", + type=int, + metavar="N", + help="num encoder attention heads or LightConv/DynamicConv heads", + ) + parser.add_argument( + "--encoder-normalize-before", + action="store_true", + help="apply layernorm before each encoder block", + ) + parser.add_argument( + "--encoder-learned-pos", + action="store_true", + help="use learned positional embeddings in the encoder", + ) + parser.add_argument( + "--decoder-embed-path", + type=str, + metavar="STR", + help="path to pre-trained decoder embedding", + ) + parser.add_argument( + "--decoder-embed-dim", + type=int, + metavar="N", + help="decoder embedding dimension", + ) + parser.add_argument( + "--decoder-conv-dim", + type=int, + metavar="N", + help="decoder embedding dimension", + ) + parser.add_argument( + "--decoder-ffn-embed-dim", + type=int, + metavar="N", + help="decoder embedding dimension for FFN", + ) + parser.add_argument( + "--decoder-layers", type=int, metavar="N", help="num decoder layers" + ) + parser.add_argument( + "--decoder-attention-heads", + type=int, + metavar="N", + help="num decoder attention heads or LightConv/DynamicConv heads", + ) + parser.add_argument( + "--decoder-learned-pos", + action="store_true", + help="use learned positional embeddings in the decoder", + ) + parser.add_argument( + "--decoder-normalize-before", + action="store_true", + help="apply layernorm before each decoder block", + ) + parser.add_argument( + "--share-decoder-input-output-embed", + action="store_true", + help="share decoder input and output embeddings", + ) + parser.add_argument( + "--share-all-embeddings", + action="store_true", + help="share encoder, decoder and output embeddings" + " (requires shared dictionary and embed dim)", + ) + parser.add_argument( + "--adaptive-softmax-cutoff", + metavar="EXPR", + help="comma separated list of adaptive softmax cutoff points. " + "Must be used with adaptive_loss criterion", + ), + parser.add_argument( + "--adaptive-softmax-dropout", + type=float, + metavar="D", + help="sets adaptive softmax dropout for the tail projections", + ) """LightConv and DynamicConv arguments""" - parser.add_argument('--encoder-kernel-size-list', type=lambda x: options.eval_str_list(x, int), - help='list of kernel size (default: "[3,7,15,31,31,31,31]")') - parser.add_argument('--decoder-kernel-size-list', type=lambda x: options.eval_str_list(x, int), - help='list of kernel size (default: "[3,7,15,31,31,31]")') - parser.add_argument('--encoder-glu', type=options.eval_bool, - help='glu after in proj') - parser.add_argument('--decoder-glu', type=options.eval_bool, - help='glu after in proj') - parser.add_argument('--encoder-conv-type', default='dynamic', type=str, - choices=['dynamic', 'lightweight'], - help='type of convolution') - parser.add_argument('--decoder-conv-type', default='dynamic', type=str, - choices=['dynamic', 'lightweight'], - help='type of convolution') - parser.add_argument('--weight-softmax', default=True, type=options.eval_bool) - parser.add_argument('--weight-dropout', type=float, metavar='D', - help='dropout probability for conv weights') + parser.add_argument( + "--encoder-kernel-size-list", + type=lambda x: utils.eval_str_list(x, int), + help='list of kernel size (default: "[3,7,15,31,31,31,31]")', + ) + parser.add_argument( + "--decoder-kernel-size-list", + type=lambda x: utils.eval_str_list(x, int), + help='list of kernel size (default: "[3,7,15,31,31,31]")', + ) + parser.add_argument( + "--encoder-glu", type=utils.eval_bool, help="glu after in proj" + ) + parser.add_argument( + "--decoder-glu", type=utils.eval_bool, help="glu after in proj" + ) + parser.add_argument( + "--encoder-conv-type", + default="dynamic", + type=str, + choices=["dynamic", "lightweight"], + help="type of convolution", + ) + parser.add_argument( + "--decoder-conv-type", + default="dynamic", + type=str, + choices=["dynamic", "lightweight"], + help="type of convolution", + ) + parser.add_argument("--weight-softmax", default=True, type=utils.eval_bool) + parser.add_argument( + "--weight-dropout", + type=float, + metavar="D", + help="dropout probability for conv weights", + ) @classmethod def build_model(cls, args, task): @@ -157,9 +257,9 @@ def build_model(cls, args, task): # make sure all arguments are present in older models base_architecture(args) - if not hasattr(args, 'max_source_positions'): + if not hasattr(args, "max_source_positions"): args.max_source_positions = 1024 - if not hasattr(args, 'max_target_positions'): + if not hasattr(args, "max_target_positions"): args.max_target_positions = 1024 src_dict, tgt_dict = task.source_dictionary, task.target_dictionary @@ -176,13 +276,19 @@ def build_embedding(dictionary, embed_dim, path=None): if args.share_all_embeddings: if src_dict != tgt_dict: - raise RuntimeError('--share-all-embeddings requires a joined dictionary') + raise RuntimeError( + "--share-all-embeddings requires a joined dictionary" + ) if args.encoder_embed_dim != args.decoder_embed_dim: raise RuntimeError( - '--share-all-embeddings requires --encoder-embed-dim to match --decoder-embed-dim') + "--share-all-embeddings requires --encoder-embed-dim to match --decoder-embed-dim" + ) if args.decoder_embed_path and ( - args.decoder_embed_path != args.encoder_embed_path): - raise RuntimeError('--share-all-embeddings not compatible with --decoder-embed-path') + args.decoder_embed_path != args.encoder_embed_path + ): + raise RuntimeError( + "--share-all-embeddings not compatible with --decoder-embed-path" + ) encoder_embed_tokens = build_embedding( src_dict, args.encoder_embed_dim, args.encoder_embed_path ) @@ -214,7 +320,9 @@ class LightConvEncoder(FairseqEncoder): def __init__(self, args, dictionary, embed_tokens): super().__init__(dictionary) - self.dropout = args.dropout + self.dropout_module = FairseqDropout( + args.dropout, module_name=self.__class__.__name__ + ) embed_dim = embed_tokens.embedding_dim self.padding_idx = embed_tokens.padding_idx @@ -222,17 +330,27 @@ def __init__(self, args, dictionary, embed_tokens): self.embed_tokens = embed_tokens self.embed_scale = math.sqrt(embed_dim) - self.embed_positions = PositionalEmbedding( - args.max_source_positions, embed_dim, self.padding_idx, - learned=args.encoder_learned_pos, - ) if not args.no_token_positional_embeddings else None + self.embed_positions = ( + PositionalEmbedding( + args.max_source_positions, + embed_dim, + self.padding_idx, + learned=args.encoder_learned_pos, + ) + if not args.no_token_positional_embeddings + else None + ) self.layers = nn.ModuleList([]) - self.layers.extend([ - LightConvEncoderLayer(args, kernel_size=args.encoder_kernel_size_list[i]) - for i in range(args.encoder_layers) - ]) - self.register_buffer('version', torch.Tensor([2])) + self.layers.extend( + [ + LightConvEncoderLayer( + args, kernel_size=args.encoder_kernel_size_list[i] + ) + for i in range(args.encoder_layers) + ] + ) + self.register_buffer("version", torch.Tensor([2])) self.normalize = args.encoder_normalize_before if self.normalize: self.layer_norm = LayerNorm(embed_dim) @@ -254,7 +372,7 @@ def forward(self, src_tokens, **unused): x = self.embed_scale * self.embed_tokens(src_tokens) if self.embed_positions is not None: x += self.embed_positions(src_tokens) - x = F.dropout(x, p=self.dropout, training=self.training) + x = self.dropout_module(x) # B x T x C -> T x B x C x = x.transpose(0, 1) @@ -272,8 +390,8 @@ def forward(self, src_tokens, **unused): x = self.layer_norm(x) return { - 'encoder_out': x, # T x B x C - 'encoder_padding_mask': encoder_padding_mask, # B x T + "encoder_out": x, # T x B x C + "encoder_padding_mask": encoder_padding_mask, # B x T } def reorder_encoder_out(self, encoder_out, new_order): @@ -287,12 +405,14 @@ def reorder_encoder_out(self, encoder_out, new_order): Returns: *encoder_out* rearranged according to *new_order* """ - if encoder_out['encoder_out'] is not None: - encoder_out['encoder_out'] = \ - encoder_out['encoder_out'].index_select(1, new_order) - if encoder_out['encoder_padding_mask'] is not None: - encoder_out['encoder_padding_mask'] = \ - encoder_out['encoder_padding_mask'].index_select(0, new_order) + if encoder_out["encoder_out"] is not None: + encoder_out["encoder_out"] = encoder_out["encoder_out"].index_select( + 1, new_order + ) + if encoder_out["encoder_padding_mask"] is not None: + encoder_out["encoder_padding_mask"] = encoder_out[ + "encoder_padding_mask" + ].index_select(0, new_order) return encoder_out def max_positions(self): @@ -315,9 +435,13 @@ class LightConvDecoder(FairseqIncrementalDecoder): Default: ``False`` """ - def __init__(self, args, dictionary, embed_tokens, no_encoder_attn=False, final_norm=True): + def __init__( + self, args, dictionary, embed_tokens, no_encoder_attn=False, final_norm=True + ): super().__init__(dictionary) - self.dropout = args.dropout + self.dropout_module = FairseqDropout( + args.dropout, module_name=self.__class__.__name__ + ) self.share_input_output_embed = args.share_decoder_input_output_embed input_embed_dim = embed_tokens.embedding_dim @@ -330,43 +454,64 @@ def __init__(self, args, dictionary, embed_tokens, no_encoder_attn=False, final_ self.embed_tokens = embed_tokens self.embed_scale = math.sqrt(embed_dim) # todo: try with input_embed_dim - self.project_in_dim = Linear(input_embed_dim, embed_dim, bias=False) if embed_dim != input_embed_dim else None - - self.embed_positions = PositionalEmbedding( - args.max_target_positions, embed_dim, padding_idx, - learned=args.decoder_learned_pos, - ) if not args.no_token_positional_embeddings else None + self.project_in_dim = ( + Linear(input_embed_dim, embed_dim, bias=False) + if embed_dim != input_embed_dim + else None + ) + + self.embed_positions = ( + PositionalEmbedding( + args.max_target_positions, + embed_dim, + padding_idx, + learned=args.decoder_learned_pos, + ) + if not args.no_token_positional_embeddings + else None + ) self.layers = nn.ModuleList([]) - self.layers.extend([ - LightConvDecoderLayer(args, no_encoder_attn, kernel_size=args.decoder_kernel_size_list[i]) - for i in range(args.decoder_layers) - ]) + self.layers.extend( + [ + LightConvDecoderLayer( + args, no_encoder_attn, kernel_size=args.decoder_kernel_size_list[i] + ) + for i in range(args.decoder_layers) + ] + ) self.adaptive_softmax = None - self.project_out_dim = Linear(embed_dim, output_embed_dim, bias=False) \ - if embed_dim != output_embed_dim and not args.tie_adaptive_weights else None + self.project_out_dim = ( + Linear(embed_dim, output_embed_dim, bias=False) + if embed_dim != output_embed_dim and not args.tie_adaptive_weights + else None + ) if args.adaptive_softmax_cutoff is not None: self.adaptive_softmax = AdaptiveSoftmax( len(dictionary), output_embed_dim, - options.eval_str_list(args.adaptive_softmax_cutoff, type=int), + utils.eval_str_list(args.adaptive_softmax_cutoff, type=int), dropout=args.adaptive_softmax_dropout, adaptive_inputs=embed_tokens if args.tie_adaptive_weights else None, factor=args.adaptive_softmax_factor, tie_proj=args.tie_adaptive_proj, ) elif not self.share_input_output_embed: - self.embed_out = nn.Parameter(torch.Tensor(len(dictionary), output_embed_dim)) + self.embed_out = nn.Parameter( + torch.Tensor(len(dictionary), output_embed_dim) + ) nn.init.normal_(self.embed_out, mean=0, std=output_embed_dim ** -0.5) - self.register_buffer('version', torch.Tensor([2])) + self.register_buffer("version", torch.Tensor([2])) self.normalize = args.decoder_normalize_before and final_norm if self.normalize: self.layer_norm = LayerNorm(embed_dim) - def forward(self, prev_output_tokens, encoder_out=None, incremental_state=None, **kwargs): + def forward( + self, prev_output_tokens, encoder_out=None, incremental_state=None, **kwargs + ): """ Args: prev_output_tokens (LongTensor): previous decoder outputs of shape @@ -384,10 +529,14 @@ def forward(self, prev_output_tokens, encoder_out=None, incremental_state=None, tgt_len, src_len)` """ # embed positions - positions = self.embed_positions( - prev_output_tokens, - incremental_state=incremental_state, - ) if self.embed_positions is not None else None + positions = ( + self.embed_positions( + prev_output_tokens, + incremental_state=incremental_state, + ) + if self.embed_positions is not None + else None + ) if incremental_state is not None: prev_output_tokens = prev_output_tokens[:, -1:] @@ -402,7 +551,7 @@ def forward(self, prev_output_tokens, encoder_out=None, incremental_state=None, if positions is not None: x += positions - x = F.dropout(x, p=self.dropout, training=self.training) + x = self.dropout_module(x) # B x T x C -> T x B x C x = x.transpose(0, 1) @@ -414,8 +563,10 @@ def forward(self, prev_output_tokens, encoder_out=None, incremental_state=None, for layer in self.layers: x, attn = layer( x, - encoder_out['encoder_out'] if encoder_out is not None else None, - encoder_out['encoder_padding_mask'] if encoder_out is not None else None, + encoder_out["encoder_out"] if encoder_out is not None else None, + encoder_out["encoder_padding_mask"] + if encoder_out is not None + else None, incremental_state, ) inner_states.append(x) @@ -436,7 +587,7 @@ def forward(self, prev_output_tokens, encoder_out=None, incremental_state=None, else: x = F.linear(x, self.embed_out) - return x, {'attn': attn, 'inner_states': inner_states} + return x, {"attn": attn, "inner_states": inner_states} def max_positions(self): """Maximum output length supported by the decoder.""" @@ -446,10 +597,18 @@ def max_positions(self): def buffered_future_mask(self, tensor): dim = tensor.size(0) - if not hasattr(self, '_future_mask') or self._future_mask is None or self._future_mask.device != tensor.device: - self._future_mask = torch.triu(utils.fill_with_neg_inf(tensor.new(dim, dim)), 1) + if ( + not hasattr(self, "_future_mask") + or self._future_mask is None + or self._future_mask.device != tensor.device + ): + self._future_mask = torch.triu( + utils.fill_with_neg_inf(tensor.new(dim, dim)), 1 + ) if self._future_mask.size(0) < dim: - self._future_mask = torch.triu(utils.fill_with_neg_inf(self._future_mask.resize_(dim, dim)), 1) + self._future_mask = torch.triu( + utils.fill_with_neg_inf(self._future_mask.resize_(dim, dim)), 1 + ) return self._future_mask[:dim, :dim] @@ -465,31 +624,49 @@ def __init__(self, args, kernel_size=0): super().__init__() self.embed_dim = args.encoder_embed_dim self.conv_dim = args.encoder_conv_dim - padding_l = kernel_size // 2 if kernel_size % 2 == 1 else ((kernel_size - 1) // 2, kernel_size // 2) + padding_l = ( + kernel_size // 2 + if kernel_size % 2 == 1 + else ((kernel_size - 1) // 2, kernel_size // 2) + ) if args.encoder_glu: - self.linear1 = Linear(self.embed_dim, 2*self.conv_dim) + self.linear1 = Linear(self.embed_dim, 2 * self.conv_dim) self.act = nn.GLU() else: self.linear1 = Linear(self.embed_dim, self.conv_dim) self.act = None - if args.encoder_conv_type == 'lightweight': - self.conv = LightweightConv(self.conv_dim, kernel_size, padding_l=padding_l, - weight_softmax=args.weight_softmax, - num_heads=args.encoder_attention_heads, - weight_dropout=args.weight_dropout) - elif args.encoder_conv_type == 'dynamic': - self.conv = DynamicConv(self.conv_dim, kernel_size, padding_l=padding_l, - weight_softmax=args.weight_softmax, - num_heads=args.encoder_attention_heads, - weight_dropout=args.weight_dropout) + if args.encoder_conv_type == "lightweight": + self.conv = LightweightConv( + self.conv_dim, + kernel_size, + padding_l=padding_l, + weight_softmax=args.weight_softmax, + num_heads=args.encoder_attention_heads, + weight_dropout=args.weight_dropout, + ) + elif args.encoder_conv_type == "dynamic": + self.conv = DynamicConv( + self.conv_dim, + kernel_size, + padding_l=padding_l, + weight_softmax=args.weight_softmax, + num_heads=args.encoder_attention_heads, + weight_dropout=args.weight_dropout, + ) else: raise NotImplementedError self.linear2 = Linear(self.conv_dim, self.embed_dim) - self.dropout = args.dropout - self.relu_dropout = args.relu_dropout - self.input_dropout = args.input_dropout + self.dropout_module = FairseqDropout( + args.dropout, module_name=self.__class__.__name__ + ) + self.relu_dropout_module = FairseqDropout( + args.relu_dropout, module_name=self.__class__.__name__ + ) + self.input_dropout_module = FairseqDropout( + args.input_dropout, module_name=self.__class__.__name__ + ) self.normalize_before = args.encoder_normalize_before self.fc1 = Linear(self.embed_dim, args.encoder_ffn_embed_dim) self.fc2 = Linear(args.encoder_ffn_embed_dim, self.embed_dim) @@ -507,7 +684,7 @@ def forward(self, x, encoder_padding_mask): """ residual = x x = self.maybe_layer_norm(0, x, before=True) - x = F.dropout(x, p=self.input_dropout, training=self.training) + x = self.input_dropout_module(x) x = self.linear1(x) if self.act is not None: x = self.act(x) @@ -515,16 +692,16 @@ def forward(self, x, encoder_padding_mask): x = x.masked_fill(encoder_padding_mask.transpose(0, 1).unsqueeze(2), 0) x = self.conv(x) x = self.linear2(x) - x = F.dropout(x, p=self.dropout, training=self.training) + x = self.dropout_module(x) x = residual + x x = self.maybe_layer_norm(0, x, after=True) residual = x x = self.maybe_layer_norm(1, x, before=True) x = F.relu(self.fc1(x)) - x = F.dropout(x, p=self.relu_dropout, training=self.training) + x = self.relu_dropout_module(x) x = self.fc2(x) - x = F.dropout(x, p=self.dropout, training=self.training) + x = self.dropout_module(x) x = residual + x x = self.maybe_layer_norm(1, x, after=True) return x @@ -537,8 +714,14 @@ def maybe_layer_norm(self, i, x, before=False, after=False): return x def extra_repr(self): - return 'dropout={}, relu_dropout={}, input_dropout={}, normalize_before={}'.format( - self.dropout, self.relu_dropout, self.input_dropout, self.normalize_before) + return ( + "dropout={}, relu_dropout={}, input_dropout={}, normalize_before={}".format( + self.dropout_module.p, + self.relu_dropout_module.p, + self.input_dropout_module.p, + self.normalize_before, + ) + ) class LightConvDecoderLayer(nn.Module): @@ -556,28 +739,42 @@ def __init__(self, args, no_encoder_attn=False, kernel_size=0): self.embed_dim = args.decoder_embed_dim self.conv_dim = args.decoder_conv_dim if args.decoder_glu: - self.linear1 = Linear(self.embed_dim, 2*self.conv_dim) + self.linear1 = Linear(self.embed_dim, 2 * self.conv_dim) self.act = nn.GLU() else: self.linear1 = Linear(self.embed_dim, self.conv_dim) self.act = None - if args.decoder_conv_type == 'lightweight': - self.conv = LightweightConv(self.conv_dim, kernel_size, padding_l=kernel_size-1, - weight_softmax=args.weight_softmax, - num_heads=args.decoder_attention_heads, - weight_dropout=args.weight_dropout) - elif args.decoder_conv_type == 'dynamic': - self.conv = DynamicConv(self.conv_dim, kernel_size, padding_l=kernel_size-1, - weight_softmax=args.weight_softmax, - num_heads=args.decoder_attention_heads, - weight_dropout=args.weight_dropout) + if args.decoder_conv_type == "lightweight": + self.conv = LightweightConv( + self.conv_dim, + kernel_size, + padding_l=kernel_size - 1, + weight_softmax=args.weight_softmax, + num_heads=args.decoder_attention_heads, + weight_dropout=args.weight_dropout, + ) + elif args.decoder_conv_type == "dynamic": + self.conv = DynamicConv( + self.conv_dim, + kernel_size, + padding_l=kernel_size - 1, + weight_softmax=args.weight_softmax, + num_heads=args.decoder_attention_heads, + weight_dropout=args.weight_dropout, + ) else: raise NotImplementedError self.linear2 = Linear(self.conv_dim, self.embed_dim) - self.dropout = args.dropout - self.relu_dropout = args.relu_dropout - self.input_dropout = args.input_dropout + self.dropout_module = FairseqDropout( + args.dropout, module_name=self.__class__.__name__ + ) + self.relu_dropout_module = FairseqDropout( + args.relu_dropout, module_name=self.__class__.__name__ + ) + self.input_dropout_module = FairseqDropout( + args.input_dropout, module_name=self.__class__.__name__ + ) self.normalize_before = args.decoder_normalize_before self.conv_layer_norm = LayerNorm(self.embed_dim) @@ -587,8 +784,10 @@ def __init__(self, args, no_encoder_attn=False, kernel_size=0): self.encoder_attn_layer_norm = None else: self.encoder_attn = MultiheadAttention( - self.embed_dim, args.decoder_attention_heads, - dropout=args.attention_dropout, encoder_decoder_attention=True + self.embed_dim, + args.decoder_attention_heads, + dropout=args.attention_dropout, + encoder_decoder_attention=True, ) self.encoder_attn_layer_norm = LayerNorm(self.embed_dim) @@ -598,9 +797,17 @@ def __init__(self, args, no_encoder_attn=False, kernel_size=0): self.final_layer_norm = LayerNorm(self.embed_dim) self.need_attn = True - def forward(self, x, encoder_out, encoder_padding_mask, incremental_state, - prev_conv_state=None, prev_attn_state=None, conv_mask=None, - conv_padding_mask=None): + def forward( + self, + x, + encoder_out, + encoder_padding_mask, + incremental_state, + prev_conv_state=None, + prev_attn_state=None, + conv_mask=None, + conv_padding_mask=None, + ): """ Args: x (Tensor): input to the layer of shape `(seq_len, batch, embed_dim)` @@ -616,13 +823,13 @@ def forward(self, x, encoder_out, encoder_padding_mask, incremental_state, if incremental_state is None: incremental_state = {} self.conv._set_input_buffer(incremental_state, prev_conv_state) - x = F.dropout(x, p=self.input_dropout, training=self.training) + x = self.input_dropout_module(x) x = self.linear1(x) if self.act is not None: x = self.act(x) x = self.conv(x, incremental_state=incremental_state) x = self.linear2(x) - x = F.dropout(x, p=self.dropout, training=self.training) + x = self.dropout_module(x) x = residual + x x = self.maybe_layer_norm(self.conv_layer_norm, x, after=True) @@ -645,16 +852,16 @@ def forward(self, x, encoder_out, encoder_padding_mask, incremental_state, static_kv=True, need_weights=(not self.training and self.need_attn), ) - x = F.dropout(x, p=self.dropout, training=self.training) + x = self.dropout_module(x) x = residual + x x = self.maybe_layer_norm(self.encoder_attn_layer_norm, x, after=True) residual = x x = self.maybe_layer_norm(self.final_layer_norm, x, before=True) x = F.relu(self.fc1(x)) - x = F.dropout(x, p=self.relu_dropout, training=self.training) + x = self.relu_dropout_module(x) x = self.fc2(x) - x = F.dropout(x, p=self.dropout, training=self.training) + x = self.dropout_module(x) x = residual + x x = self.maybe_layer_norm(self.final_layer_norm, x, after=True) return x, attn @@ -670,8 +877,14 @@ def make_generation_fast_(self, need_attn=False, **kwargs): self.need_attn = need_attn def extra_repr(self): - return 'dropout={}, relu_dropout={}, input_dropout={}, normalize_before={}'.format( - self.dropout, self.relu_dropout, self.input_dropout, self.normalize_before) + return ( + "dropout={}, relu_dropout={}, input_dropout={}, normalize_before={}".format( + self.dropout_module.p, + self.relu_dropout_module.p, + self.input_dropout_module.p, + self.normalize_before, + ) + ) def Embedding(num_embeddings, embedding_dim, padding_idx): @@ -685,101 +898,121 @@ def Linear(in_features, out_features, bias=True): m = nn.Linear(in_features, out_features, bias) nn.init.xavier_uniform_(m.weight) if bias: - nn.init.constant_(m.bias, 0.) + nn.init.constant_(m.bias, 0.0) return m -@register_model_architecture('lightconv', 'lightconv') +@register_model_architecture("lightconv", "lightconv") def base_architecture(args): - args.encoder_embed_path = getattr(args, 'encoder_embed_path', None) - args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 512) - args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 2048) - args.encoder_layers = getattr(args, 'encoder_layers', 7) - args.encoder_attention_heads = getattr(args, 'encoder_attention_heads', 8) - args.encoder_normalize_before = getattr(args, 'encoder_normalize_before', False) - args.encoder_learned_pos = getattr(args, 'encoder_learned_pos', False) - args.decoder_embed_path = getattr(args, 'decoder_embed_path', None) - args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', args.encoder_embed_dim) - args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', args.encoder_ffn_embed_dim) - args.decoder_layers = getattr(args, 'decoder_layers', 6) - args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 8) - args.decoder_normalize_before = getattr(args, 'decoder_normalize_before', False) - args.decoder_learned_pos = getattr(args, 'decoder_learned_pos', False) - args.attention_dropout = getattr(args, 'attention_dropout', 0.) - args.relu_dropout = getattr(args, 'relu_dropout', 0.) - args.dropout = getattr(args, 'dropout', 0.1) - args.adaptive_softmax_cutoff = getattr(args, 'adaptive_softmax_cutoff', None) - args.adaptive_softmax_dropout = getattr(args, 'adaptive_softmax_dropout', 0) - args.share_decoder_input_output_embed = getattr(args, 'share_decoder_input_output_embed', False) - args.share_all_embeddings = getattr(args, 'share_all_embeddings', False) - args.no_token_positional_embeddings = getattr(args, 'no_token_positional_embeddings', False) - - args.decoder_output_dim = getattr(args, 'decoder_output_dim', args.decoder_embed_dim) - args.decoder_input_dim = getattr(args, 'decoder_input_dim', args.decoder_embed_dim) - - args.encoder_conv_dim = getattr(args, 'encoder_conv_dim', args.encoder_embed_dim) - args.decoder_conv_dim = getattr(args, 'decoder_conv_dim', args.decoder_embed_dim) - - args.encoder_kernel_size_list = getattr(args, 'encoder_kernel_size_list', [3, 7, 15, 31, 31, 31, 31]) - args.decoder_kernel_size_list = getattr(args, 'decoder_kernel_size_list', [3, 7, 15, 31, 31, 31]) + args.encoder_embed_path = getattr(args, "encoder_embed_path", None) + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048) + args.encoder_layers = getattr(args, "encoder_layers", 7) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8) + args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False) + args.encoder_learned_pos = getattr(args, "encoder_learned_pos", False) + args.decoder_embed_path = getattr(args, "decoder_embed_path", None) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim) + args.decoder_ffn_embed_dim = getattr( + args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim + ) + args.decoder_layers = getattr(args, "decoder_layers", 6) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8) + args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False) + args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False) + args.attention_dropout = getattr(args, "attention_dropout", 0.0) + args.relu_dropout = getattr(args, "relu_dropout", 0.0) + args.dropout = getattr(args, "dropout", 0.1) + args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None) + args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0) + args.share_decoder_input_output_embed = getattr( + args, "share_decoder_input_output_embed", False + ) + args.share_all_embeddings = getattr(args, "share_all_embeddings", False) + args.no_token_positional_embeddings = getattr( + args, "no_token_positional_embeddings", False + ) + + args.decoder_output_dim = getattr( + args, "decoder_output_dim", args.decoder_embed_dim + ) + args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim) + + args.encoder_conv_dim = getattr(args, "encoder_conv_dim", args.encoder_embed_dim) + args.decoder_conv_dim = getattr(args, "decoder_conv_dim", args.decoder_embed_dim) + + args.encoder_kernel_size_list = getattr( + args, "encoder_kernel_size_list", [3, 7, 15, 31, 31, 31, 31] + ) + args.decoder_kernel_size_list = getattr( + args, "decoder_kernel_size_list", [3, 7, 15, 31, 31, 31] + ) if len(args.encoder_kernel_size_list) == 1: - args.encoder_kernel_size_list = args.encoder_kernel_size_list * args.encoder_layers + args.encoder_kernel_size_list = ( + args.encoder_kernel_size_list * args.encoder_layers + ) if len(args.decoder_kernel_size_list) == 1: - args.decoder_kernel_size_list = args.decoder_kernel_size_list * args.decoder_layers - assert len(args.encoder_kernel_size_list) == args.encoder_layers, "encoder_kernel_size_list doesn't match encoder_layers" - assert len(args.decoder_kernel_size_list) == args.decoder_layers, "decoder_kernel_size_list doesn't match decoder_layers" - args.encoder_glu = getattr(args, 'encoder_glu', True) - args.decoder_glu = getattr(args, 'decoder_glu', True) - args.input_dropout = getattr(args, 'input_dropout', 0.1) - args.weight_dropout = getattr(args, 'weight_dropout', args.attention_dropout) - - -@register_model_architecture('lightconv', 'lightconv_iwslt_de_en') + args.decoder_kernel_size_list = ( + args.decoder_kernel_size_list * args.decoder_layers + ) + assert ( + len(args.encoder_kernel_size_list) == args.encoder_layers + ), "encoder_kernel_size_list doesn't match encoder_layers" + assert ( + len(args.decoder_kernel_size_list) == args.decoder_layers + ), "decoder_kernel_size_list doesn't match decoder_layers" + args.encoder_glu = getattr(args, "encoder_glu", True) + args.decoder_glu = getattr(args, "decoder_glu", True) + args.input_dropout = getattr(args, "input_dropout", 0.1) + args.weight_dropout = getattr(args, "weight_dropout", args.attention_dropout) + + +@register_model_architecture("lightconv", "lightconv_iwslt_de_en") def lightconv_iwslt_de_en(args): - args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 512) - args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 1024) - args.encoder_attention_heads = getattr(args, 'encoder_attention_heads', 4) - args.encoder_layers = getattr(args, 'encoder_layers', 7) - args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 512) - args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', 1024) - args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 4) - args.decoder_layers = getattr(args, 'decoder_layers', 6) - args.attention_dropout = getattr(args, 'attention_dropout', 0.1) - args.weight_dropout = getattr(args, 'weight_dropout', 0.1) - args.encoder_glu = getattr(args, 'encoder_glu', False) - args.decoder_glu = getattr(args, 'decoder_glu', False) - args.input_dropout = getattr(args, 'input_dropout', 0.0) + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 1024) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 4) + args.encoder_layers = getattr(args, "encoder_layers", 7) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512) + args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 1024) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 4) + args.decoder_layers = getattr(args, "decoder_layers", 6) + args.attention_dropout = getattr(args, "attention_dropout", 0.1) + args.weight_dropout = getattr(args, "weight_dropout", 0.1) + args.encoder_glu = getattr(args, "encoder_glu", False) + args.decoder_glu = getattr(args, "decoder_glu", False) + args.input_dropout = getattr(args, "input_dropout", 0.0) base_architecture(args) -@register_model_architecture('lightconv', 'lightconv_wmt_en_de') +@register_model_architecture("lightconv", "lightconv_wmt_en_de") def lightconv_wmt_en_de(args): base_architecture(args) -@register_model_architecture('lightconv', 'lightconv_wmt_en_de_big') +@register_model_architecture("lightconv", "lightconv_wmt_en_de_big") def lightconv_wmt_en_de_big(args): - args.attention_dropout = getattr(args, 'attention_dropout', 0.1) - args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 1024) - args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 4096) - args.encoder_attention_heads = getattr(args, 'encoder_attention_heads', 16) - args.encoder_normalize_before = getattr(args, 'encoder_normalize_before', False) - args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 1024) - args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', 4096) - args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 16) - args.dropout = getattr(args, 'dropout', 0.3) + args.attention_dropout = getattr(args, "attention_dropout", 0.1) + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4096) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 16) + args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 1024) + args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 4096) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 16) + args.dropout = getattr(args, "dropout", 0.3) base_architecture(args) -@register_model_architecture('lightconv', 'lightconv_wmt_en_fr_big') +@register_model_architecture("lightconv", "lightconv_wmt_en_fr_big") def lightconv_wmt_en_fr_big(args): - args.dropout = getattr(args, 'dropout', 0.1) + args.dropout = getattr(args, "dropout", 0.1) lightconv_wmt_en_de_big(args) -@register_model_architecture('lightconv', 'lightconv_wmt_zh_en_big') +@register_model_architecture("lightconv", "lightconv_wmt_zh_en_big") def lightconv_wmt_zh_en_big(args): - args.dropout = getattr(args, 'dropout', 0.2) - args.attention_dropout = getattr(args, 'attention_dropout', 0.2) - args.weight_dropout = getattr(args, 'weight_dropout', 0.2) + args.dropout = getattr(args, "dropout", 0.2) + args.attention_dropout = getattr(args, "attention_dropout", 0.2) + args.weight_dropout = getattr(args, "weight_dropout", 0.2) lightconv_wmt_en_de_big(args) diff --git a/fairseq/models/lightconv_lm.py b/fairseq/models/lightconv_lm.py index a268ddd859..1d9efc4e42 100644 --- a/fairseq/models/lightconv_lm.py +++ b/fairseq/models/lightconv_lm.py @@ -3,23 +3,17 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. -from fairseq import options +from fairseq import utils from fairseq.models import ( FairseqLanguageModel, register_model, register_model_architecture, ) -from fairseq.models.lightconv import ( - Embedding, - LightConvDecoder, -) -from fairseq.modules import ( - AdaptiveInput, - CharacterTokenEmbedder, -) +from fairseq.models.lightconv import Embedding, LightConvDecoder +from fairseq.modules import AdaptiveInput, CharacterTokenEmbedder -@register_model('lightconv_lm') +@register_model("lightconv_lm") class LightConvLanguageModel(FairseqLanguageModel): def __init__(self, decoder): super().__init__(decoder) @@ -27,72 +21,182 @@ def __init__(self, decoder): @staticmethod def add_args(parser): """Add model-specific arguments to the parser.""" - parser.add_argument('--dropout', default=0.1, type=float, metavar='D', - help='dropout probability') - parser.add_argument('--attention-dropout', default=0., type=float, metavar='D', - help='dropout probability for attention weights') - parser.add_argument('--relu-dropout', default=0., type=float, metavar='D', - help='dropout probability after ReLU in FFN') - parser.add_argument('--input-dropout', type=float, metavar='D', - help='dropout probability of the inputs') - parser.add_argument('--decoder-embed-dim', type=int, metavar='N', - help='decoder embedding dimension') - parser.add_argument('--decoder-output-dim', type=int, metavar='N', - help='decoder output dimension') - parser.add_argument('--decoder-input-dim', type=int, metavar='N', - help='decoder input dimension') - parser.add_argument('--decoder-ffn-embed-dim', type=int, metavar='N', - help='decoder embedding dimension for FFN') - parser.add_argument('--decoder-layers', type=int, metavar='N', - help='num decoder layers') - parser.add_argument('--decoder-attention-heads', type=int, metavar='N', - help='num decoder attention heads or LightConv/DynamicConv heads') - parser.add_argument('--decoder-normalize-before', default=False, action='store_true', - help='apply layernorm before each decoder block') - parser.add_argument('--adaptive-softmax-cutoff', metavar='EXPR', - help='comma separated list of adaptive softmax cutoff points. ' - 'Must be used with adaptive_loss criterion') - parser.add_argument('--adaptive-softmax-dropout', type=float, metavar='D', - help='sets adaptive softmax dropout for the tail projections') - parser.add_argument('--adaptive-softmax-factor', type=float, metavar='N', - help='adaptive input factor') - parser.add_argument('--no-token-positional-embeddings', default=False, action='store_true', - help='if set, disables positional embeddings (outside self attention)') - parser.add_argument('--share-decoder-input-output-embed', default=False, action='store_true', - help='share decoder input and output embeddings') - parser.add_argument('--character-embeddings', default=False, action='store_true', - help='if set, uses character embedding convolutions to produce token embeddings') - parser.add_argument('--character-filters', type=str, metavar='LIST', - default='[(1, 64), (2, 128), (3, 192), (4, 256), (5, 256), (6, 256), (7, 256)]', - help='size of character embeddings') - parser.add_argument('--character-embedding-dim', type=int, metavar='N', default=4, - help='size of character embeddings') - parser.add_argument('--char-embedder-highway-layers', type=int, metavar='N', default=2, - help='number of highway layers for character token embeddder') - parser.add_argument('--adaptive-input', default=False, action='store_true', - help='if set, uses adaptive input') - parser.add_argument('--adaptive-input-factor', type=float, metavar='N', - help='adaptive input factor') - parser.add_argument('--adaptive-input-cutoff', metavar='EXPR', - help='comma separated list of adaptive input cutoff points.') - parser.add_argument('--tie-adaptive-weights', action='store_true', - help='if set, ties the weights of adaptive softmax and adaptive input') - parser.add_argument('--tie-adaptive-proj', action='store_true', - help='if set, ties the projection weights of adaptive softmax and adaptive input') - parser.add_argument('--decoder-learned-pos', action='store_true', - help='use learned positional embeddings in the decoder') + parser.add_argument( + "--dropout", + default=0.1, + type=float, + metavar="D", + help="dropout probability", + ) + parser.add_argument( + "--attention-dropout", + default=0.0, + type=float, + metavar="D", + help="dropout probability for attention weights", + ) + parser.add_argument( + "--relu-dropout", + default=0.0, + type=float, + metavar="D", + help="dropout probability after ReLU in FFN", + ) + parser.add_argument( + "--input-dropout", + type=float, + metavar="D", + help="dropout probability of the inputs", + ) + parser.add_argument( + "--decoder-embed-dim", + type=int, + metavar="N", + help="decoder embedding dimension", + ) + parser.add_argument( + "--decoder-output-dim", + type=int, + metavar="N", + help="decoder output dimension", + ) + parser.add_argument( + "--decoder-input-dim", type=int, metavar="N", help="decoder input dimension" + ) + parser.add_argument( + "--decoder-ffn-embed-dim", + type=int, + metavar="N", + help="decoder embedding dimension for FFN", + ) + parser.add_argument( + "--decoder-layers", type=int, metavar="N", help="num decoder layers" + ) + parser.add_argument( + "--decoder-attention-heads", + type=int, + metavar="N", + help="num decoder attention heads or LightConv/DynamicConv heads", + ) + parser.add_argument( + "--decoder-normalize-before", + default=False, + action="store_true", + help="apply layernorm before each decoder block", + ) + parser.add_argument( + "--adaptive-softmax-cutoff", + metavar="EXPR", + help="comma separated list of adaptive softmax cutoff points. " + "Must be used with adaptive_loss criterion", + ) + parser.add_argument( + "--adaptive-softmax-dropout", + type=float, + metavar="D", + help="sets adaptive softmax dropout for the tail projections", + ) + parser.add_argument( + "--adaptive-softmax-factor", + type=float, + metavar="N", + help="adaptive input factor", + ) + parser.add_argument( + "--no-token-positional-embeddings", + default=False, + action="store_true", + help="if set, disables positional embeddings (outside self attention)", + ) + parser.add_argument( + "--share-decoder-input-output-embed", + default=False, + action="store_true", + help="share decoder input and output embeddings", + ) + parser.add_argument( + "--character-embeddings", + default=False, + action="store_true", + help="if set, uses character embedding convolutions to produce token embeddings", + ) + parser.add_argument( + "--character-filters", + type=str, + metavar="LIST", + default="[(1, 64), (2, 128), (3, 192), (4, 256), (5, 256), (6, 256), (7, 256)]", + help="size of character embeddings", + ) + parser.add_argument( + "--character-embedding-dim", + type=int, + metavar="N", + default=4, + help="size of character embeddings", + ) + parser.add_argument( + "--char-embedder-highway-layers", + type=int, + metavar="N", + default=2, + help="number of highway layers for character token embeddder", + ) + parser.add_argument( + "--adaptive-input", + default=False, + action="store_true", + help="if set, uses adaptive input", + ) + parser.add_argument( + "--adaptive-input-factor", + type=float, + metavar="N", + help="adaptive input factor", + ) + parser.add_argument( + "--adaptive-input-cutoff", + metavar="EXPR", + help="comma separated list of adaptive input cutoff points.", + ) + parser.add_argument( + "--tie-adaptive-weights", + action="store_true", + help="if set, ties the weights of adaptive softmax and adaptive input", + ) + parser.add_argument( + "--tie-adaptive-proj", + action="store_true", + help="if set, ties the projection weights of adaptive softmax and adaptive input", + ) + parser.add_argument( + "--decoder-learned-pos", + action="store_true", + help="use learned positional embeddings in the decoder", + ) """LightConv and DynamicConv arguments""" - parser.add_argument('--decoder-kernel-size-list', type=lambda x: options.eval_str_list(x, int), - help='list of kernel size (default: "[3,7,15,31,31,31]")') - parser.add_argument('--decoder-glu', type=options.eval_bool, - help='glu after in proj') - parser.add_argument('--decoder-conv-type', default='dynamic', type=str, - choices=['dynamic', 'lightweight'], - help='type of convolution') - parser.add_argument('--weight-softmax', default=True, type=options.eval_bool) - parser.add_argument('--weight-dropout', type=float, metavar='D', - help='dropout probability for conv weights') + parser.add_argument( + "--decoder-kernel-size-list", + type=lambda x: utils.eval_str_list(x, int), + help='list of kernel size (default: "[3,7,15,31,31,31]")', + ) + parser.add_argument( + "--decoder-glu", type=utils.eval_bool, help="glu after in proj" + ) + parser.add_argument( + "--decoder-conv-type", + default="dynamic", + type=str, + choices=["dynamic", "lightweight"], + help="type of convolution", + ) + parser.add_argument("--weight-softmax", default=True, type=utils.eval_bool) + parser.add_argument( + "--weight-dropout", + type=float, + metavar="D", + help="dropout probability for conv weights", + ) @classmethod def build_model(cls, args, task): @@ -101,76 +205,102 @@ def build_model(cls, args, task): # make sure all arguments are present in older models base_lm_architecture(args) - if getattr(args, 'max_source_positions', None) is None: + if getattr(args, "max_source_positions", None) is None: args.max_source_positions = args.tokens_per_sample - if getattr(args, 'max_target_positions', None) is None: + if getattr(args, "max_target_positions", None) is None: args.max_target_positions = args.tokens_per_sample if args.character_embeddings: - embed_tokens = CharacterTokenEmbedder(task.dictionary, eval(args.character_filters), - args.character_embedding_dim, - args.decoder_embed_dim, - args.char_embedder_highway_layers, - ) + embed_tokens = CharacterTokenEmbedder( + task.dictionary, + eval(args.character_filters), + args.character_embedding_dim, + args.decoder_embed_dim, + args.char_embedder_highway_layers, + ) elif args.adaptive_input: - embed_tokens = AdaptiveInput(len(task.dictionary), task.dictionary.pad(), args.decoder_input_dim, - args.adaptive_input_factor, args.decoder_embed_dim, - options.eval_str_list(args.adaptive_input_cutoff, type=int)) + embed_tokens = AdaptiveInput( + len(task.dictionary), + task.dictionary.pad(), + args.decoder_input_dim, + args.adaptive_input_factor, + args.decoder_embed_dim, + utils.eval_str_list(args.adaptive_input_cutoff, type=int), + ) else: - embed_tokens = Embedding(len(task.dictionary), args.decoder_input_dim, task.dictionary.pad()) + embed_tokens = Embedding( + len(task.dictionary), args.decoder_input_dim, task.dictionary.pad() + ) if args.tie_adaptive_weights: assert args.adaptive_input assert args.adaptive_input_factor == args.adaptive_softmax_factor - assert args.adaptive_softmax_cutoff == args.adaptive_input_cutoff, '{} != {}'.format( - args.adaptive_softmax_cutoff, args.adaptive_input_cutoff) + assert ( + args.adaptive_softmax_cutoff == args.adaptive_input_cutoff + ), "{} != {}".format( + args.adaptive_softmax_cutoff, args.adaptive_input_cutoff + ) assert args.decoder_input_dim == args.decoder_output_dim - decoder = LightConvDecoder(args, task.output_dictionary, embed_tokens, no_encoder_attn=True, final_norm=False) + decoder = LightConvDecoder( + args, + task.output_dictionary, + embed_tokens, + no_encoder_attn=True, + final_norm=False, + ) return LightConvLanguageModel(decoder) -@register_model_architecture('lightconv_lm', 'lightconv_lm') +@register_model_architecture("lightconv_lm", "lightconv_lm") def base_lm_architecture(args): - args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 512) - args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', 2048) - args.decoder_layers = getattr(args, 'decoder_layers', 6) - args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 8) - args.adaptive_softmax_cutoff = getattr(args, 'adaptive_softmax_cutoff', None) - args.adaptive_softmax_dropout = getattr(args, 'adaptive_softmax_dropout', 0) - args.adaptive_softmax_factor = getattr(args, 'adaptive_softmax_factor', 4) - args.decoder_learned_pos = getattr(args, 'decoder_learned_pos', False) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512) + args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 2048) + args.decoder_layers = getattr(args, "decoder_layers", 6) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8) + args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None) + args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0) + args.adaptive_softmax_factor = getattr(args, "adaptive_softmax_factor", 4) + args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False) - args.character_embeddings = getattr(args, 'character_embeddings', False) + args.character_embeddings = getattr(args, "character_embeddings", False) - args.decoder_output_dim = getattr(args, 'decoder_output_dim', args.decoder_embed_dim) - args.decoder_input_dim = getattr(args, 'decoder_input_dim', args.decoder_embed_dim) - args.decoder_conv_dim = getattr(args, 'decoder_conv_dim', args.decoder_embed_dim) + args.decoder_output_dim = getattr( + args, "decoder_output_dim", args.decoder_embed_dim + ) + args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim) + args.decoder_conv_dim = getattr(args, "decoder_conv_dim", args.decoder_embed_dim) # The model training is not stable without this args.decoder_normalize_before = True - args.adaptive_input = getattr(args, 'adaptive_input', False) - args.adaptive_input_factor = getattr(args, 'adaptive_input_factor', 4) - args.adaptive_input_cutoff = getattr(args, 'adaptive_input_cutoff', None) + args.adaptive_input = getattr(args, "adaptive_input", False) + args.adaptive_input_factor = getattr(args, "adaptive_input_factor", 4) + args.adaptive_input_cutoff = getattr(args, "adaptive_input_cutoff", None) - args.tie_adaptive_weights = getattr(args, 'tie_adaptive_weights', False) - args.tie_adaptive_proj = getattr(args, 'tie_adaptive_proj', False) + args.tie_adaptive_weights = getattr(args, "tie_adaptive_weights", False) + args.tie_adaptive_proj = getattr(args, "tie_adaptive_proj", False) - args.decoder_kernel_size_list = getattr(args, 'decoder_kernel_size_list', [3, 7, 15, 31, 31, 31]) + args.decoder_kernel_size_list = getattr( + args, "decoder_kernel_size_list", [3, 7, 15, 31, 31, 31] + ) if len(args.decoder_kernel_size_list) == 1: - args.decoder_kernel_size_list = args.decoder_kernel_size_list * args.decoder_layers - assert len(args.decoder_kernel_size_list) == args.decoder_layers, "decoder_kernel_size_list doesn't match decoder_layers" - args.decoder_glu = getattr(args, 'decoder_glu', True) - args.input_dropout = getattr(args, 'input_dropout', 0.1) - args.weight_dropout = getattr(args, 'weight_dropout', args.attention_dropout) + args.decoder_kernel_size_list = ( + args.decoder_kernel_size_list * args.decoder_layers + ) + assert ( + len(args.decoder_kernel_size_list) == args.decoder_layers + ), "decoder_kernel_size_list doesn't match decoder_layers" + args.decoder_glu = getattr(args, "decoder_glu", True) + args.input_dropout = getattr(args, "input_dropout", 0.1) + args.weight_dropout = getattr(args, "weight_dropout", args.attention_dropout) -@register_model_architecture('lightconv_lm', 'lightconv_lm_gbw') +@register_model_architecture("lightconv_lm", "lightconv_lm_gbw") def lightconv_lm_gbw(args): - args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 512) - args.dropout = getattr(args, 'dropout', 0.1) - args.attention_dropout = getattr(args, 'attention_dropout', 0.1) - args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', 4096) - args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 16) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512) + args.dropout = getattr(args, "dropout", 0.1) + args.attention_dropout = getattr(args, "attention_dropout", 0.1) + args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 4096) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 16) base_lm_architecture(args) diff --git a/fairseq/models/lstm.py b/fairseq/models/lstm.py index 1b470d064f..e1e66a7d50 100644 --- a/fairseq/models/lstm.py +++ b/fairseq/models/lstm.py @@ -3,28 +3,28 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. +from typing import Dict, List, Optional, Tuple + import torch import torch.nn as nn import torch.nn.functional as F - -from fairseq import options, utils +from fairseq import utils from fairseq.models import ( FairseqEncoder, - FairseqIncrementalDecoder, FairseqEncoderDecoderModel, + FairseqIncrementalDecoder, register_model, register_model_architecture, ) -from fairseq.modules import AdaptiveSoftmax +from fairseq.modules import AdaptiveSoftmax, FairseqDropout from torch import Tensor -from typing import Dict, List, Optional, Tuple DEFAULT_MAX_SOURCE_POSITIONS = 1e5 DEFAULT_MAX_TARGET_POSITIONS = 1e5 -@register_model('lstm') +@register_model("lstm") class LSTMModel(FairseqEncoderDecoderModel): def __init__(self, encoder, decoder): super().__init__(encoder, decoder) @@ -89,10 +89,14 @@ def build_model(cls, args, task): base_architecture(args) if args.encoder_layers != args.decoder_layers: - raise ValueError('--encoder-layers must match --decoder-layers') + raise ValueError("--encoder-layers must match --decoder-layers") - max_source_positions = getattr(args, 'max_source_positions', DEFAULT_MAX_SOURCE_POSITIONS) - max_target_positions = getattr(args, 'max_target_positions', DEFAULT_MAX_TARGET_POSITIONS) + max_source_positions = getattr( + args, "max_source_positions", DEFAULT_MAX_SOURCE_POSITIONS + ) + max_target_positions = getattr( + args, "max_target_positions", DEFAULT_MAX_TARGET_POSITIONS + ) def load_pretrained_embedding_from_file(embed_path, dictionary, embed_dim): num_embeddings = len(dictionary) @@ -104,7 +108,8 @@ def load_pretrained_embedding_from_file(embed_path, dictionary, embed_dim): if args.encoder_embed_path: pretrained_encoder_embed = load_pretrained_embedding_from_file( - args.encoder_embed_path, task.source_dictionary, args.encoder_embed_dim) + args.encoder_embed_path, task.source_dictionary, args.encoder_embed_dim + ) else: num_embeddings = len(task.source_dictionary) pretrained_encoder_embed = Embedding( @@ -114,16 +119,17 @@ def load_pretrained_embedding_from_file(embed_path, dictionary, embed_dim): if args.share_all_embeddings: # double check all parameters combinations are valid if task.source_dictionary != task.target_dictionary: - raise ValueError('--share-all-embeddings requires a joint dictionary') + raise ValueError("--share-all-embeddings requires a joint dictionary") if args.decoder_embed_path and ( - args.decoder_embed_path != args.encoder_embed_path): + args.decoder_embed_path != args.encoder_embed_path + ): raise ValueError( - '--share-all-embed not compatible with --decoder-embed-path' + "--share-all-embed not compatible with --decoder-embed-path" ) if args.encoder_embed_dim != args.decoder_embed_dim: raise ValueError( - '--share-all-embeddings requires --encoder-embed-dim to ' - 'match --decoder-embed-dim' + "--share-all-embeddings requires --encoder-embed-dim to " + "match --decoder-embed-dim" ) pretrained_decoder_embed = pretrained_encoder_embed args.share_decoder_input_output_embed = True @@ -134,14 +140,15 @@ def load_pretrained_embedding_from_file(embed_path, dictionary, embed_dim): pretrained_decoder_embed = load_pretrained_embedding_from_file( args.decoder_embed_path, task.target_dictionary, - args.decoder_embed_dim + args.decoder_embed_dim, ) # one last double check of parameter combinations if args.share_decoder_input_output_embed and ( - args.decoder_embed_dim != args.decoder_out_embed_dim): + args.decoder_embed_dim != args.decoder_out_embed_dim + ): raise ValueError( - '--share-decoder-input-output-embeddings requires ' - '--decoder-embed-dim to match --decoder-out-embed-dim' + "--share-decoder-input-output-embeddings requires " + "--decoder-embed-dim to match --decoder-out-embed-dim" ) if args.encoder_freeze_embed: @@ -158,7 +165,7 @@ def load_pretrained_embedding_from_file(embed_path, dictionary, embed_dim): dropout_out=args.encoder_dropout_out, bidirectional=args.encoder_bidirectional, pretrained_embed=pretrained_encoder_embed, - max_source_positions=max_source_positions + max_source_positions=max_source_positions, ) decoder = LSTMDecoder( dictionary=task.target_dictionary, @@ -168,15 +175,17 @@ def load_pretrained_embedding_from_file(embed_path, dictionary, embed_dim): num_layers=args.decoder_layers, dropout_in=args.decoder_dropout_in, dropout_out=args.decoder_dropout_out, - attention=options.eval_bool(args.decoder_attention), + attention=utils.eval_bool(args.decoder_attention), encoder_output_units=encoder.output_units, pretrained_embed=pretrained_decoder_embed, share_input_output_embed=args.share_decoder_input_output_embed, adaptive_softmax_cutoff=( - options.eval_str_list(args.adaptive_softmax_cutoff, type=int) - if args.criterion == 'adaptive_loss' else None + utils.eval_str_list(args.adaptive_softmax_cutoff, type=int) + if args.criterion == "adaptive_loss" + else None ), - max_target_positions=max_target_positions + max_target_positions=max_target_positions, + residuals=False, ) return cls(encoder, decoder) @@ -189,23 +198,38 @@ def forward( ): encoder_out = self.encoder(src_tokens, src_lengths=src_lengths) decoder_out = self.decoder( - prev_output_tokens, encoder_out=encoder_out, incremental_state=incremental_state + prev_output_tokens, + encoder_out=encoder_out, + incremental_state=incremental_state, ) return decoder_out class LSTMEncoder(FairseqEncoder): """LSTM encoder.""" + def __init__( - self, dictionary, embed_dim=512, hidden_size=512, num_layers=1, - dropout_in=0.1, dropout_out=0.1, bidirectional=False, - left_pad=True, pretrained_embed=None, padding_idx=None, - max_source_positions=DEFAULT_MAX_SOURCE_POSITIONS + self, + dictionary, + embed_dim=512, + hidden_size=512, + num_layers=1, + dropout_in=0.1, + dropout_out=0.1, + bidirectional=False, + left_pad=True, + pretrained_embed=None, + padding_idx=None, + max_source_positions=DEFAULT_MAX_SOURCE_POSITIONS, ): super().__init__(dictionary) self.num_layers = num_layers - self.dropout_in = dropout_in - self.dropout_out = dropout_out + self.dropout_in_module = FairseqDropout( + dropout_in*1.0, module_name=self.__class__.__name__ + ) + self.dropout_out_module = FairseqDropout( + dropout_out*1.0, module_name=self.__class__.__name__ + ) self.bidirectional = bidirectional self.hidden_size = hidden_size self.max_source_positions = max_source_positions @@ -221,7 +245,7 @@ def __init__( input_size=embed_dim, hidden_size=hidden_size, num_layers=num_layers, - dropout=self.dropout_out if num_layers > 1 else 0., + dropout=self.dropout_out_module.p if num_layers > 1 else 0.0, bidirectional=bidirectional, ) self.left_pad = left_pad @@ -260,14 +284,14 @@ def forward( # embed tokens x = self.embed_tokens(src_tokens) - x = F.dropout(x, p=self.dropout_in, training=self.training) + x = self.dropout_in_module(x) # B x T x C -> T x B x C x = x.transpose(0, 1) # pack embedded source tokens into a PackedSequence packed_x = nn.utils.rnn.pack_padded_sequence( - x, src_lengths.data, enforce_sorted=enforce_sorted + x, src_lengths.cpu(), enforce_sorted=enforce_sorted ) # apply LSTM @@ -280,8 +304,10 @@ def forward( packed_outs, (final_hiddens, final_cells) = self.lstm(packed_x, (h0, c0)) # unpack outputs and apply dropout - x, _ = nn.utils.rnn.pad_packed_sequence(packed_outs, padding_value=self.padding_idx*1.0) - x = F.dropout(x, p=self.dropout_out, training=self.training) + x, _ = nn.utils.rnn.pad_packed_sequence( + packed_outs, padding_value=self.padding_idx * 1.0 + ) + x = self.dropout_out_module(x) assert list(x.size()) == [seqlen, bsz, self.output_units] if self.bidirectional: @@ -290,24 +316,28 @@ def forward( encoder_padding_mask = src_tokens.eq(self.padding_idx).t() - return tuple(( - x, # seq_len x batch x hidden - final_hiddens, # num_layers x batch x num_directions*hidden - final_cells, # num_layers x batch x num_directions*hidden - encoder_padding_mask, # seq_len x batch - )) + return tuple( + ( + x, # seq_len x batch x hidden + final_hiddens, # num_layers x batch x num_directions*hidden + final_cells, # num_layers x batch x num_directions*hidden + encoder_padding_mask, # seq_len x batch + ) + ) def combine_bidir(self, outs, bsz: int): out = outs.view(self.num_layers, 2, bsz, -1).transpose(1, 2).contiguous() return out.view(self.num_layers, bsz, -1) - def reorder_encoder_out(self, encoder_out, new_order): - return tuple(( - encoder_out[0].index_select(1, new_order), - encoder_out[1].index_select(1, new_order), - encoder_out[2].index_select(1, new_order), - encoder_out[3].index_select(1, new_order), - )) + def reorder_encoder_out(self, encoder_out: Tuple[Tensor, Tensor, Tensor, Tensor], new_order): + return tuple( + ( + encoder_out[0].index_select(1, new_order), + encoder_out[1].index_select(1, new_order), + encoder_out[2].index_select(1, new_order), + encoder_out[3].index_select(1, new_order), + ) + ) def max_positions(self): """Maximum input length supported by the encoder.""" @@ -319,7 +349,9 @@ def __init__(self, input_embed_dim, source_embed_dim, output_embed_dim, bias=Fal super().__init__() self.input_proj = Linear(input_embed_dim, source_embed_dim, bias=bias) - self.output_proj = Linear(input_embed_dim + source_embed_dim, output_embed_dim, bias=bias) + self.output_proj = Linear( + input_embed_dim + source_embed_dim, output_embed_dim, bias=bias + ) def forward(self, input, source_hids, encoder_padding_mask): # input: bsz x input_embed_dim @@ -333,10 +365,11 @@ def forward(self, input, source_hids, encoder_padding_mask): # don't attend over padding if encoder_padding_mask is not None: - attn_scores = attn_scores.float().masked_fill_( - encoder_padding_mask, - float('-inf') - ).type_as(attn_scores) # FP16 support: cast to float and back + attn_scores = ( + attn_scores.float() + .masked_fill_(encoder_padding_mask, float("-inf")) + .type_as(attn_scores) + ) # FP16 support: cast to float and back attn_scores = F.softmax(attn_scores, dim=0) # srclen x bsz @@ -349,20 +382,36 @@ def forward(self, input, source_hids, encoder_padding_mask): class LSTMDecoder(FairseqIncrementalDecoder): """LSTM decoder.""" + def __init__( - self, dictionary, embed_dim=512, hidden_size=512, out_embed_dim=512, - num_layers=1, dropout_in=0.1, dropout_out=0.1, attention=True, - encoder_output_units=512, pretrained_embed=None, - share_input_output_embed=False, adaptive_softmax_cutoff=None, - max_target_positions=DEFAULT_MAX_TARGET_POSITIONS + self, + dictionary, + embed_dim=512, + hidden_size=512, + out_embed_dim=512, + num_layers=1, + dropout_in=0.1, + dropout_out=0.1, + attention=True, + encoder_output_units=512, + pretrained_embed=None, + share_input_output_embed=False, + adaptive_softmax_cutoff=None, + max_target_positions=DEFAULT_MAX_TARGET_POSITIONS, + residuals=False, ): super().__init__(dictionary) - self.dropout_in = dropout_in - self.dropout_out = dropout_out + self.dropout_in_module = FairseqDropout( + dropout_in*1.0, module_name=self.__class__.__name__ + ) + self.dropout_out_module = FairseqDropout( + dropout_out*1.0, module_name=self.__class__.__name__ + ) self.hidden_size = hidden_size self.share_input_output_embed = share_input_output_embed self.need_attn = True self.max_target_positions = max_target_positions + self.residuals = residuals self.num_layers = num_layers self.adaptive_softmax = None @@ -383,17 +432,23 @@ def __init__( # disable input feeding if there is no encoder # input feeding is described in arxiv.org/abs/1508.04025 input_feed_size = 0 if encoder_output_units == 0 else hidden_size - self.layers = nn.ModuleList([ - LSTMCell( - input_size=input_feed_size + embed_dim if layer == 0 else hidden_size, - hidden_size=hidden_size, - ) - for layer in range(num_layers) - ]) + self.layers = nn.ModuleList( + [ + LSTMCell( + input_size=input_feed_size + embed_dim + if layer == 0 + else hidden_size, + hidden_size=hidden_size, + ) + for layer in range(num_layers) + ] + ) if attention: # TODO make bias configurable - self.attention = AttentionLayer(hidden_size, encoder_output_units, hidden_size, bias=False) + self.attention = AttentionLayer( + hidden_size, encoder_output_units, hidden_size, bias=False + ) else: self.attention = None @@ -403,23 +458,14 @@ def __init__( if adaptive_softmax_cutoff is not None: # setting adaptive_softmax dropout to dropout_out for now but can be redefined self.adaptive_softmax = AdaptiveSoftmax( - num_embeddings, hidden_size, adaptive_softmax_cutoff, dropout=dropout_out + num_embeddings, + hidden_size, + adaptive_softmax_cutoff, + dropout=dropout_out, ) elif not self.share_input_output_embed: self.fc_out = Linear(out_embed_dim, num_embeddings, dropout=dropout_out) - def get_cached_state(self, incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]]): - cached_state = self.get_incremental_state(incremental_state, 'cached_state') - assert cached_state is not None - prev_hiddens_ = cached_state["prev_hiddens"] - assert prev_hiddens_ is not None - prev_cells_ = cached_state["prev_cells"] - assert prev_cells_ is not None - prev_hiddens = [prev_hiddens_[i] for i in range(self.num_layers)] - prev_cells = [prev_cells_[j] for j in range(self.num_layers)] - input_feed = cached_state["input_feed"] # can be None for decoder-only language models - return prev_hiddens, prev_cells, input_feed - def forward( self, prev_output_tokens, @@ -461,14 +507,16 @@ def extract_features( # embed tokens x = self.embed_tokens(prev_output_tokens) - x = F.dropout(x, p=self.dropout_in, training=self.training) + x = self.dropout_in_module(x) # B x T x C -> T x B x C x = x.transpose(0, 1) # initialize previous states (or get from cache during incremental generation) if incremental_state is not None and len(incremental_state) > 0: - prev_hiddens, prev_cells, input_feed = self.get_cached_state(incremental_state) + prev_hiddens, prev_cells, input_feed = self.get_cached_state( + incremental_state + ) elif encoder_out is not None: # setup recurrent cells prev_hiddens = [encoder_hiddens[i] for i in range(self.num_layers)] @@ -484,9 +532,12 @@ def extract_features( prev_cells = [zero_state for i in range(self.num_layers)] input_feed = None - assert srclen > 0 or self.attention is None, \ - "attention is not supported if there are no encoder outputs" - attn_scores = x.new_zeros(srclen, seqlen, bsz) if self.attention is not None else None + assert ( + srclen > 0 or self.attention is None + ), "attention is not supported if there are no encoder outputs" + attn_scores: Optional[Tensor] = ( + x.new_zeros(srclen, seqlen, bsz) if self.attention is not None else None + ) outs = [] for j in range(seqlen): # input feeding: concatenate context vector from previous time step @@ -500,7 +551,9 @@ def extract_features( hidden, cell = rnn(input, (prev_hiddens[i], prev_cells[i])) # hidden state becomes the input to the next layer - input = F.dropout(hidden, p=self.dropout_out, training=self.training) + input = self.dropout_out_module(hidden) + if self.residuals: + input = input + prev_hiddens[i] # save state for next time step prev_hiddens[i] = hidden @@ -509,10 +562,12 @@ def extract_features( # apply attention using the last layer's hidden state if self.attention is not None: assert attn_scores is not None - out, attn_scores[:, j, :] = self.attention(hidden, encoder_outs, encoder_padding_mask) + out, attn_scores[:, j, :] = self.attention( + hidden, encoder_outs, encoder_padding_mask + ) else: out = hidden - out = F.dropout(out, p=self.dropout_out, training=self.training) + out = self.dropout_out_module(out) # input feeding if input_feed is not None: @@ -526,9 +581,13 @@ def extract_features( prev_cells_tensor = torch.stack(prev_cells) cache_state = torch.jit.annotate( Dict[str, Optional[Tensor]], - {"prev_hiddens": prev_hiddens_tensor, "prev_cells": prev_cells_tensor, "input_feed": input_feed}) - self.set_incremental_state( - incremental_state, 'cached_state', cache_state) + { + "prev_hiddens": prev_hiddens_tensor, + "prev_cells": prev_cells_tensor, + "input_feed": input_feed, + }, + ) + self.set_incremental_state(incremental_state, "cached_state", cache_state) # collect outputs across time steps x = torch.cat(outs, dim=0).view(seqlen, bsz, self.hidden_size) @@ -536,9 +595,9 @@ def extract_features( # T x B x C -> B x T x C x = x.transpose(1, 0) - if hasattr(self, 'additional_fc') and self.adaptive_softmax is None: + if hasattr(self, "additional_fc") and self.adaptive_softmax is None: x = self.additional_fc(x) - x = F.dropout(x, p=self.dropout_out, training=self.training) + x = self.dropout_out_module(x) # srclen x tgtlen x bsz -> bsz x tgtlen x srclen if not self.training and self.need_attn and self.attention is not None: assert attn_scores is not None @@ -556,24 +615,44 @@ def output_layer(self, x): x = self.fc_out(x) return x - def reorder_state(self, state: List[Tensor], new_order): - return [ - state_i.index_select(0, new_order) if state_i is not None else None - for state_i in state - ] + def get_cached_state( + self, + incremental_state: Dict[str, Dict[str, Optional[Tensor]]], + ) -> Tuple[List[Tensor], List[Tensor], Optional[Tensor]]: + cached_state = self.get_incremental_state(incremental_state, "cached_state") + assert cached_state is not None + prev_hiddens_ = cached_state["prev_hiddens"] + assert prev_hiddens_ is not None + prev_cells_ = cached_state["prev_cells"] + assert prev_cells_ is not None + prev_hiddens = [prev_hiddens_[i] for i in range(self.num_layers)] + prev_cells = [prev_cells_[j] for j in range(self.num_layers)] + input_feed = cached_state[ + "input_feed" + ] # can be None for decoder-only language models + return prev_hiddens, prev_cells, input_feed - def reorder_incremental_state(self, incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]], new_order): + def reorder_incremental_state( + self, + incremental_state: Dict[str, Dict[str, Optional[Tensor]]], + new_order: Tensor, + ): if incremental_state is None or len(incremental_state) == 0: return prev_hiddens, prev_cells, input_feed = self.get_cached_state(incremental_state) - cached_state = (prev_hiddens, prev_cells, [input_feed]) - new_state = [self.reorder_state(state, new_order) for state in cached_state] - prev_hiddens_tensor = torch.stack(new_state[0]) - prev_cells_tensor = torch.stack(new_state[1]) + prev_hiddens = [p.index_select(0, new_order) for p in prev_hiddens] + prev_cells = [p.index_select(0, new_order) for p in prev_cells] + if input_feed is not None: + input_feed = input_feed.index_select(0, new_order) cached_state_new = torch.jit.annotate( Dict[str, Optional[Tensor]], - {"prev_hiddens": prev_hiddens_tensor, "prev_cells": prev_cells_tensor, "input_feed": new_state[2][0]}) - self.set_incremental_state(incremental_state, 'cached_state', cached_state_new), + { + "prev_hiddens": torch.stack(prev_hiddens), + "prev_cells": torch.stack(prev_cells), + "input_feed": input_feed, + }, + ) + self.set_incremental_state(incremental_state, "cached_state", cached_state_new), return def max_positions(self): @@ -594,7 +673,7 @@ def Embedding(num_embeddings, embedding_dim, padding_idx): def LSTM(input_size, hidden_size, **kwargs): m = nn.LSTM(input_size, hidden_size, **kwargs) for name, param in m.named_parameters(): - if 'weight' in name or 'bias' in name: + if "weight" in name or "bias" in name: param.data.uniform_(-0.1, 0.1) return m @@ -602,12 +681,12 @@ def LSTM(input_size, hidden_size, **kwargs): def LSTMCell(input_size, hidden_size, **kwargs): m = nn.LSTMCell(input_size, hidden_size, **kwargs) for name, param in m.named_parameters(): - if 'weight' in name or 'bias' in name: + if "weight" in name or "bias" in name: param.data.uniform_(-0.1, 0.1) return m -def Linear(in_features, out_features, bias=True, dropout=0): +def Linear(in_features, out_features, bias=True, dropout=0.0): """Linear layer (input: N x T x C)""" m = nn.Linear(in_features, out_features, bias=bias) m.weight.data.uniform_(-0.1, 0.1) @@ -616,51 +695,59 @@ def Linear(in_features, out_features, bias=True, dropout=0): return m -@register_model_architecture('lstm', 'lstm') +@register_model_architecture("lstm", "lstm") def base_architecture(args): - args.dropout = getattr(args, 'dropout', 0.1) - args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 512) - args.encoder_embed_path = getattr(args, 'encoder_embed_path', None) - args.encoder_freeze_embed = getattr(args, 'encoder_freeze_embed', False) - args.encoder_hidden_size = getattr(args, 'encoder_hidden_size', args.encoder_embed_dim) - args.encoder_layers = getattr(args, 'encoder_layers', 1) - args.encoder_bidirectional = getattr(args, 'encoder_bidirectional', False) - args.encoder_dropout_in = getattr(args, 'encoder_dropout_in', args.dropout) - args.encoder_dropout_out = getattr(args, 'encoder_dropout_out', args.dropout) - args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 512) - args.decoder_embed_path = getattr(args, 'decoder_embed_path', None) - args.decoder_freeze_embed = getattr(args, 'decoder_freeze_embed', False) - args.decoder_hidden_size = getattr(args, 'decoder_hidden_size', args.decoder_embed_dim) - args.decoder_layers = getattr(args, 'decoder_layers', 1) - args.decoder_out_embed_dim = getattr(args, 'decoder_out_embed_dim', 512) - args.decoder_attention = getattr(args, 'decoder_attention', '1') - args.decoder_dropout_in = getattr(args, 'decoder_dropout_in', args.dropout) - args.decoder_dropout_out = getattr(args, 'decoder_dropout_out', args.dropout) - args.share_decoder_input_output_embed = getattr(args, 'share_decoder_input_output_embed', False) - args.share_all_embeddings = getattr(args, 'share_all_embeddings', False) - args.adaptive_softmax_cutoff = getattr(args, 'adaptive_softmax_cutoff', '10000,50000,200000') - - -@register_model_architecture('lstm', 'lstm_wiseman_iwslt_de_en') + args.dropout = getattr(args, "dropout", 0.1) + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) + args.encoder_embed_path = getattr(args, "encoder_embed_path", None) + args.encoder_freeze_embed = getattr(args, "encoder_freeze_embed", False) + args.encoder_hidden_size = getattr( + args, "encoder_hidden_size", args.encoder_embed_dim + ) + args.encoder_layers = getattr(args, "encoder_layers", 1) + args.encoder_bidirectional = getattr(args, "encoder_bidirectional", False) + args.encoder_dropout_in = getattr(args, "encoder_dropout_in", args.dropout) + args.encoder_dropout_out = getattr(args, "encoder_dropout_out", args.dropout) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512) + args.decoder_embed_path = getattr(args, "decoder_embed_path", None) + args.decoder_freeze_embed = getattr(args, "decoder_freeze_embed", False) + args.decoder_hidden_size = getattr( + args, "decoder_hidden_size", args.decoder_embed_dim + ) + args.decoder_layers = getattr(args, "decoder_layers", 1) + args.decoder_out_embed_dim = getattr(args, "decoder_out_embed_dim", 512) + args.decoder_attention = getattr(args, "decoder_attention", "1") + args.decoder_dropout_in = getattr(args, "decoder_dropout_in", args.dropout) + args.decoder_dropout_out = getattr(args, "decoder_dropout_out", args.dropout) + args.share_decoder_input_output_embed = getattr( + args, "share_decoder_input_output_embed", False + ) + args.share_all_embeddings = getattr(args, "share_all_embeddings", False) + args.adaptive_softmax_cutoff = getattr( + args, "adaptive_softmax_cutoff", "10000,50000,200000" + ) + + +@register_model_architecture("lstm", "lstm_wiseman_iwslt_de_en") def lstm_wiseman_iwslt_de_en(args): - args.dropout = getattr(args, 'dropout', 0.1) - args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 256) - args.encoder_dropout_in = getattr(args, 'encoder_dropout_in', 0) - args.encoder_dropout_out = getattr(args, 'encoder_dropout_out', 0) - args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 256) - args.decoder_out_embed_dim = getattr(args, 'decoder_out_embed_dim', 256) - args.decoder_dropout_in = getattr(args, 'decoder_dropout_in', 0) - args.decoder_dropout_out = getattr(args, 'decoder_dropout_out', args.dropout) + args.dropout = getattr(args, "dropout", 0.1) + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 256) + args.encoder_dropout_in = getattr(args, "encoder_dropout_in", 0) + args.encoder_dropout_out = getattr(args, "encoder_dropout_out", 0) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 256) + args.decoder_out_embed_dim = getattr(args, "decoder_out_embed_dim", 256) + args.decoder_dropout_in = getattr(args, "decoder_dropout_in", 0) + args.decoder_dropout_out = getattr(args, "decoder_dropout_out", args.dropout) base_architecture(args) -@register_model_architecture('lstm', 'lstm_luong_wmt_en_de') +@register_model_architecture("lstm", "lstm_luong_wmt_en_de") def lstm_luong_wmt_en_de(args): - args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 1000) - args.encoder_layers = getattr(args, 'encoder_layers', 4) - args.encoder_dropout_out = getattr(args, 'encoder_dropout_out', 0) - args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 1000) - args.decoder_layers = getattr(args, 'decoder_layers', 4) - args.decoder_out_embed_dim = getattr(args, 'decoder_out_embed_dim', 1000) - args.decoder_dropout_out = getattr(args, 'decoder_dropout_out', 0) + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1000) + args.encoder_layers = getattr(args, "encoder_layers", 4) + args.encoder_dropout_out = getattr(args, "encoder_dropout_out", 0) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 1000) + args.decoder_layers = getattr(args, "decoder_layers", 4) + args.decoder_out_embed_dim = getattr(args, "decoder_out_embed_dim", 1000) + args.decoder_dropout_out = getattr(args, "decoder_dropout_out", 0) base_architecture(args) diff --git a/fairseq/models/lstm_lm.py b/fairseq/models/lstm_lm.py index d3b6972a38..454f0ac36f 100644 --- a/fairseq/models/lstm_lm.py +++ b/fairseq/models/lstm_lm.py @@ -3,17 +3,19 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. -from fairseq import options, utils +from fairseq import utils from fairseq.models import ( - FairseqLanguageModel, register_model, register_model_architecture -) -from fairseq.models.lstm import ( - LSTMDecoder, Embedding + FairseqLanguageModel, + register_model, + register_model_architecture, ) +from fairseq.models.lstm import Embedding, LSTMDecoder + DEFAULT_MAX_TARGET_POSITIONS = 1e5 -@register_model('lstm_lm') + +@register_model("lstm_lm") class LSTMLanguageModel(FairseqLanguageModel): def __init__(self, decoder): super().__init__(decoder) @@ -39,6 +41,9 @@ def add_args(parser): parser.add_argument('--adaptive-softmax-cutoff', metavar='EXPR', help='comma separated list of adaptive softmax cutoff points. ' 'Must be used with adaptive_loss criterion') + parser.add_argument('--residuals', default=False, + action='store_true', + help='applying residuals between LSTM layers') # Granular dropout settings (if not specified these default to --dropout) parser.add_argument('--decoder-dropout-in', type=float, metavar='D', @@ -48,6 +53,7 @@ def add_args(parser): parser.add_argument('--share-decoder-input-output-embed', default=False, action='store_true', help='share decoder input and output embeddings') + # fmt: on @classmethod def build_model(cls, args, task): @@ -56,10 +62,12 @@ def build_model(cls, args, task): # make sure all arguments are present in older models base_architecture(args) - if getattr(args, 'max_target_positions', None) is not None: + if getattr(args, "max_target_positions", None) is not None: max_target_positions = args.max_target_positions else: - max_target_positions = getattr(args, 'tokens_per_sample', DEFAULT_MAX_TARGET_POSITIONS) + max_target_positions = getattr( + args, "tokens_per_sample", DEFAULT_MAX_TARGET_POSITIONS + ) def load_pretrained_embedding_from_file(embed_path, dictionary, embed_dim): num_embeddings = len(dictionary) @@ -72,21 +80,21 @@ def load_pretrained_embedding_from_file(embed_path, dictionary, embed_dim): pretrained_decoder_embed = None if args.decoder_embed_path: pretrained_decoder_embed = load_pretrained_embedding_from_file( - args.decoder_embed_path, - task.target_dictionary, - args.decoder_embed_dim + args.decoder_embed_path, task.target_dictionary, args.decoder_embed_dim ) if args.share_decoder_input_output_embed: # double check all parameters combinations are valid if task.source_dictionary != task.target_dictionary: - raise ValueError('--share-decoder-input-output-embeddings requires a joint dictionary') + raise ValueError( + "--share-decoder-input-output-embeddings requires a joint dictionary" + ) if args.decoder_embed_dim != args.decoder_out_embed_dim: raise ValueError( - '--share-decoder-input-output-embeddings requires ' - '--decoder-embed-dim to match --decoder-out-embed-dim' - ) + "--share-decoder-input-output-embeddings requires " + "--decoder-embed-dim to match --decoder-out-embed-dim" + ) decoder = LSTMDecoder( dictionary=task.dictionary, @@ -101,25 +109,34 @@ def load_pretrained_embedding_from_file(embed_path, dictionary, embed_dim): pretrained_embed=pretrained_decoder_embed, share_input_output_embed=args.share_decoder_input_output_embed, adaptive_softmax_cutoff=( - options.eval_str_list(args.adaptive_softmax_cutoff, type=int) - if args.criterion == 'adaptive_loss' else None + utils.eval_str_list(args.adaptive_softmax_cutoff, type=int) + if args.criterion == "adaptive_loss" + else None ), - max_target_positions=max_target_positions + max_target_positions=max_target_positions, + residuals=args.residuals, ) return cls(decoder) -@register_model_architecture('lstm_lm', 'lstm_lm') +@register_model_architecture("lstm_lm", "lstm_lm") def base_architecture(args): - args.dropout = getattr(args, 'dropout', 0.1) - args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 512) - args.decoder_embed_path = getattr(args, 'decoder_embed_path', None) - args.decoder_hidden_size = getattr(args, 'decoder_hidden_size', args.decoder_embed_dim) - args.decoder_layers = getattr(args, 'decoder_layers', 1) - args.decoder_out_embed_dim = getattr(args, 'decoder_out_embed_dim', 512) - args.decoder_attention = getattr(args, 'decoder_attention', '0') - args.decoder_dropout_in = getattr(args, 'decoder_dropout_in', args.dropout) - args.decoder_dropout_out = getattr(args, 'decoder_dropout_out', args.dropout) - args.share_decoder_input_output_embed = getattr(args, 'share_decoder_input_output_embed', False) - args.adaptive_softmax_cutoff = getattr(args, 'adaptive_softmax_cutoff', '10000,50000,200000') + args.dropout = getattr(args, "dropout", 0.1) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512) + args.decoder_embed_path = getattr(args, "decoder_embed_path", None) + args.decoder_hidden_size = getattr( + args, "decoder_hidden_size", args.decoder_embed_dim + ) + args.decoder_layers = getattr(args, "decoder_layers", 1) + args.decoder_out_embed_dim = getattr(args, "decoder_out_embed_dim", 512) + args.decoder_attention = getattr(args, "decoder_attention", "0") + args.decoder_dropout_in = getattr(args, "decoder_dropout_in", args.dropout) + args.decoder_dropout_out = getattr(args, "decoder_dropout_out", args.dropout) + args.share_decoder_input_output_embed = getattr( + args, "share_decoder_input_output_embed", False + ) + args.adaptive_softmax_cutoff = getattr( + args, "adaptive_softmax_cutoff", "10000,50000,200000" + ) + args.residuals = getattr(args, "residuals", False) diff --git a/fairseq/models/masked_lm.py b/fairseq/models/masked_lm.py index 0937059a4a..c786de9125 100644 --- a/fairseq/models/masked_lm.py +++ b/fairseq/models/masked_lm.py @@ -8,11 +8,10 @@ import torch import torch.nn as nn import torch.nn.functional as F - from fairseq import utils from fairseq.models import ( - BaseFairseqModel, FairseqEncoder, + FairseqEncoderModel, register_model, register_model_architecture, ) @@ -27,78 +26,125 @@ logger = logging.getLogger(__name__) -@register_model('masked_lm') -class MaskedLMModel(BaseFairseqModel): +@register_model("masked_lm") +class MaskedLMModel(FairseqEncoderModel): """ Class for training a Masked Language Model. It also supports an additional sentence level prediction if the sent-loss argument is set. """ + def __init__(self, args, encoder): - super().__init__() + super().__init__(encoder) self.args = args - self.encoder = encoder # if specified then apply bert initialization on the model. We need # to explictly call this to make sure that the output embeddings # and projection layers are also correctly initialized - if getattr(args, 'apply_bert_init', False): + if getattr(args, "apply_bert_init", False): self.apply(init_bert_params) @staticmethod def add_args(parser): """Add model-specific arguments to the parser.""" # Arguments related to dropout - parser.add_argument('--dropout', type=float, metavar='D', - help='dropout probability') - parser.add_argument('--attention-dropout', type=float, - metavar='D', help='dropout probability for' - ' attention weights') - parser.add_argument('--act-dropout', type=float, - metavar='D', help='dropout probability after' - ' activation in FFN') + parser.add_argument( + "--dropout", type=float, metavar="D", help="dropout probability" + ) + parser.add_argument( + "--attention-dropout", + type=float, + metavar="D", + help="dropout probability for" " attention weights", + ) + parser.add_argument( + "--act-dropout", + type=float, + metavar="D", + help="dropout probability after" " activation in FFN", + ) # Arguments related to hidden states and self-attention - parser.add_argument('--encoder-ffn-embed-dim', type=int, metavar='N', - help='encoder embedding dimension for FFN') - parser.add_argument('--encoder-layers', type=int, metavar='N', - help='num encoder layers') - parser.add_argument('--encoder-attention-heads', type=int, metavar='N', - help='num encoder attention heads') + parser.add_argument( + "--encoder-ffn-embed-dim", + type=int, + metavar="N", + help="encoder embedding dimension for FFN", + ) + parser.add_argument( + "--encoder-layers", type=int, metavar="N", help="num encoder layers" + ) + parser.add_argument( + "--encoder-attention-heads", + type=int, + metavar="N", + help="num encoder attention heads", + ) # Arguments related to input and output embeddings - parser.add_argument('--encoder-embed-dim', type=int, metavar='N', - help='encoder embedding dimension') - parser.add_argument('--share-encoder-input-output-embed', - action='store_true', help='share encoder input' - ' and output embeddings') - parser.add_argument('--encoder-learned-pos', action='store_true', - help='use learned positional embeddings in the encoder') - parser.add_argument('--no-token-positional-embeddings', - action='store_true', - help='if set, disables positional embeddings' - ' (outside self attention)') - parser.add_argument('--num-segment', type=int, metavar='N', - help='num segment in the input') + parser.add_argument( + "--encoder-embed-dim", + type=int, + metavar="N", + help="encoder embedding dimension", + ) + parser.add_argument( + "--share-encoder-input-output-embed", + action="store_true", + help="share encoder input" " and output embeddings", + ) + parser.add_argument( + "--encoder-learned-pos", + action="store_true", + help="use learned positional embeddings in the encoder", + ) + parser.add_argument( + "--no-token-positional-embeddings", + action="store_true", + help="if set, disables positional embeddings" " (outside self attention)", + ) + parser.add_argument( + "--num-segment", type=int, metavar="N", help="num segment in the input" + ) + parser.add_argument( + "--max-positions", type=int, help="number of positional embeddings to learn" + ) # Arguments related to sentence level prediction - parser.add_argument('--sentence-class-num', type=int, metavar='N', - help='number of classes for sentence task') - parser.add_argument('--sent-loss', action='store_true', help='if set,' - ' calculate sentence level predictions') + parser.add_argument( + "--sentence-class-num", + type=int, + metavar="N", + help="number of classes for sentence task", + ) + parser.add_argument( + "--sent-loss", + action="store_true", + help="if set," " calculate sentence level predictions", + ) # Arguments related to parameter initialization - parser.add_argument('--apply-bert-init', action='store_true', - help='use custom param initialization for BERT') + parser.add_argument( + "--apply-bert-init", + action="store_true", + help="use custom param initialization for BERT", + ) # misc params - parser.add_argument('--activation-fn', - choices=utils.get_available_activation_fns(), - help='activation function to use') - parser.add_argument('--pooler-activation-fn', - choices=utils.get_available_activation_fns(), - help='Which activation function to use for pooler layer.') - parser.add_argument('--encoder-normalize-before', action='store_true', - help='apply layernorm before each encoder block') + parser.add_argument( + "--activation-fn", + choices=utils.get_available_activation_fns(), + help="activation function to use", + ) + parser.add_argument( + "--pooler-activation-fn", + choices=utils.get_available_activation_fns(), + help="Which activation function to use for pooler layer.", + ) + parser.add_argument( + "--encoder-normalize-before", + action="store_true", + help="apply layernorm before each encoder block", + ) def forward(self, src_tokens, segment_labels=None, **kwargs): return self.encoder(src_tokens, segment_labels=segment_labels, **kwargs) @@ -112,7 +158,7 @@ def build_model(cls, args, task): # make sure all arguments are present in older models base_architecture(args) - if not hasattr(args, 'max_positions'): + if not hasattr(args, "max_positions"): args.max_positions = args.tokens_per_sample logger.info(args) @@ -159,14 +205,16 @@ def __init__(self, args, dictionary): self.lm_output_learned_bias = None # Remove head is set to true during fine-tuning - self.load_softmax = not getattr(args, 'remove_head', False) + self.load_softmax = not getattr(args, "remove_head", False) self.masked_lm_pooler = nn.Linear( args.encoder_embed_dim, args.encoder_embed_dim ) self.pooler_activation = utils.get_activation_fn(args.pooler_activation_fn) - self.lm_head_transform_weight = nn.Linear(args.encoder_embed_dim, args.encoder_embed_dim) + self.lm_head_transform_weight = nn.Linear( + args.encoder_embed_dim, args.encoder_embed_dim + ) self.activation_fn = utils.get_activation_fn(args.activation_fn) self.layer_norm = LayerNorm(args.encoder_embed_dim) @@ -176,16 +224,12 @@ def __init__(self, args, dictionary): if not self.share_input_output_embed: self.embed_out = nn.Linear( - args.encoder_embed_dim, - self.vocab_size, - bias=False + args.encoder_embed_dim, self.vocab_size, bias=False ) if args.sent_loss: self.sentence_projection_layer = nn.Linear( - args.encoder_embed_dim, - self.sentence_out_dim, - bias=False + args.encoder_embed_dim, self.sentence_out_dim, bias=False ) def forward(self, src_tokens, segment_labels=None, masked_tokens=None, **unused): @@ -226,8 +270,9 @@ def forward(self, src_tokens, segment_labels=None, masked_tokens=None, **unused) pooled_output = self.pooler_activation(self.masked_lm_pooler(sentence_rep)) # project back to size of vocabulary - if self.share_input_output_embed \ - and hasattr(self.sentence_encoder.embed_tokens, 'weight'): + if self.share_input_output_embed and hasattr( + self.sentence_encoder.embed_tokens, "weight" + ): x = F.linear(x, self.sentence_encoder.embed_tokens.weight) elif self.embed_out is not None: x = self.embed_out(x) @@ -238,9 +283,9 @@ def forward(self, src_tokens, segment_labels=None, masked_tokens=None, **unused) sentence_logits = self.sentence_projection_layer(pooled_output) return x, { - 'inner_states': inner_states, - 'pooled_output': pooled_output, - 'sentence_logits': sentence_logits + "inner_states": inner_states, + "pooled_output": pooled_output, + "sentence_logits": sentence_logits, } def max_positions(self): @@ -249,103 +294,110 @@ def max_positions(self): def upgrade_state_dict_named(self, state_dict, name): if isinstance( - self.sentence_encoder.embed_positions, - SinusoidalPositionalEmbedding + self.sentence_encoder.embed_positions, SinusoidalPositionalEmbedding ): state_dict[ - name + '.sentence_encoder.embed_positions._float_tensor' + name + ".sentence_encoder.embed_positions._float_tensor" ] = torch.FloatTensor(1) if not self.load_softmax: for k in list(state_dict.keys()): if ( - "embed_out.weight" in k or - "sentence_projection_layer.weight" in k or - "lm_output_learned_bias" in k + "embed_out.weight" in k + or "sentence_projection_layer.weight" in k + or "lm_output_learned_bias" in k ): del state_dict[k] return state_dict -@register_model_architecture('masked_lm', 'masked_lm') +@register_model_architecture("masked_lm", "masked_lm") def base_architecture(args): - args.dropout = getattr(args, 'dropout', 0.1) - args.attention_dropout = getattr(args, 'attention_dropout', 0.1) - args.act_dropout = getattr(args, 'act_dropout', 0.0) + args.dropout = getattr(args, "dropout", 0.1) + args.attention_dropout = getattr(args, "attention_dropout", 0.1) + args.act_dropout = getattr(args, "act_dropout", 0.0) - args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 4096) - args.encoder_layers = getattr(args, 'encoder_layers', 6) - args.encoder_attention_heads = getattr(args, 'encoder_attention_heads', 8) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4096) + args.encoder_layers = getattr(args, "encoder_layers", 6) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8) - args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 1024) - args.share_encoder_input_output_embed = getattr(args, 'share_encoder_input_output_embed', False) - args.encoder_learned_pos = getattr(args, 'encoder_learned_pos', False) - args.no_token_positional_embeddings = getattr(args, 'no_token_positional_embeddings', False) - args.num_segment = getattr(args, 'num_segment', 2) + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024) + args.share_encoder_input_output_embed = getattr( + args, "share_encoder_input_output_embed", False + ) + args.encoder_learned_pos = getattr(args, "encoder_learned_pos", False) + args.no_token_positional_embeddings = getattr( + args, "no_token_positional_embeddings", False + ) + args.num_segment = getattr(args, "num_segment", 2) - args.sentence_class_num = getattr(args, 'sentence_class_num', 2) - args.sent_loss = getattr(args, 'sent_loss', False) + args.sentence_class_num = getattr(args, "sentence_class_num", 2) + args.sent_loss = getattr(args, "sent_loss", False) - args.apply_bert_init = getattr(args, 'apply_bert_init', False) + args.apply_bert_init = getattr(args, "apply_bert_init", False) - args.activation_fn = getattr(args, 'activation_fn', 'relu') - args.pooler_activation_fn = getattr(args, 'pooler_activation_fn', 'tanh') - args.encoder_normalize_before = getattr(args, 'encoder_normalize_before', False) + args.activation_fn = getattr(args, "activation_fn", "relu") + args.pooler_activation_fn = getattr(args, "pooler_activation_fn", "tanh") + args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False) -@register_model_architecture('masked_lm', 'bert_base') +@register_model_architecture("masked_lm", "bert_base") def bert_base_architecture(args): - args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 768) + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 768) args.share_encoder_input_output_embed = getattr( - args, 'share_encoder_input_output_embed', True) + args, "share_encoder_input_output_embed", True + ) args.no_token_positional_embeddings = getattr( - args, 'no_token_positional_embeddings', False) - args.encoder_learned_pos = getattr(args, 'encoder_learned_pos', True) - args.num_segment = getattr(args, 'num_segment', 2) + args, "no_token_positional_embeddings", False + ) + args.encoder_learned_pos = getattr(args, "encoder_learned_pos", True) + args.num_segment = getattr(args, "num_segment", 2) - args.encoder_layers = getattr(args, 'encoder_layers', 12) + args.encoder_layers = getattr(args, "encoder_layers", 12) - args.encoder_attention_heads = getattr(args, 'encoder_attention_heads', 12) - args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 3072) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 12) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 3072) - args.sentence_class_num = getattr(args, 'sentence_class_num', 2) - args.sent_loss = getattr(args, 'sent_loss', True) + args.sentence_class_num = getattr(args, "sentence_class_num", 2) + args.sent_loss = getattr(args, "sent_loss", True) - args.apply_bert_init = getattr(args, 'apply_bert_init', True) + args.apply_bert_init = getattr(args, "apply_bert_init", True) - args.activation_fn = getattr(args, 'activation_fn', 'gelu') - args.pooler_activation_fn = getattr(args, 'pooler_activation_fn', 'tanh') - args.encoder_normalize_before = getattr(args, 'encoder_normalize_before', True) + args.activation_fn = getattr(args, "activation_fn", "gelu") + args.pooler_activation_fn = getattr(args, "pooler_activation_fn", "tanh") + args.encoder_normalize_before = getattr(args, "encoder_normalize_before", True) base_architecture(args) -@register_model_architecture('masked_lm', 'bert_large') +@register_model_architecture("masked_lm", "bert_large") def bert_large_architecture(args): - args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 1024) - args.encoder_layers = getattr(args, 'encoder_layers', 24) - args.encoder_attention_heads = getattr(args, 'encoder_attention_heads', 16) - args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 4096) + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024) + args.encoder_layers = getattr(args, "encoder_layers", 24) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 16) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4096) bert_base_architecture(args) -@register_model_architecture('masked_lm', 'xlm_base') +@register_model_architecture("masked_lm", "xlm_base") def xlm_architecture(args): - args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 1024) + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024) args.share_encoder_input_output_embed = getattr( - args, 'share_encoder_input_output_embed', True) + args, "share_encoder_input_output_embed", True + ) args.no_token_positional_embeddings = getattr( - args, 'no_token_positional_embeddings', False) - args.encoder_learned_pos = getattr(args, 'encoder_learned_pos', True) - args.num_segment = getattr(args, 'num_segment', 1) + args, "no_token_positional_embeddings", False + ) + args.encoder_learned_pos = getattr(args, "encoder_learned_pos", True) + args.num_segment = getattr(args, "num_segment", 1) - args.encoder_layers = getattr(args, 'encoder_layers', 6) + args.encoder_layers = getattr(args, "encoder_layers", 6) - args.encoder_attention_heads = getattr(args, 'encoder_attention_heads', 8) - args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 4096) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4096) - args.sent_loss = getattr(args, 'sent_loss', False) + args.sent_loss = getattr(args, "sent_loss", False) - args.activation_fn = getattr(args, 'activation_fn', 'gelu') - args.encoder_normalize_before = getattr(args, 'encoder_normalize_before', False) - args.pooler_activation_fn = getattr(args, 'pooler_activation_fn', 'tanh') - args.apply_bert_init = getattr(args, 'apply_bert_init', True) + args.activation_fn = getattr(args, "activation_fn", "gelu") + args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False) + args.pooler_activation_fn = getattr(args, "pooler_activation_fn", "tanh") + args.apply_bert_init = getattr(args, "apply_bert_init", True) base_architecture(args) diff --git a/fairseq/models/model_utils.py b/fairseq/models/model_utils.py index 46ec62f772..732d66b1d5 100644 --- a/fairseq/models/model_utils.py +++ b/fairseq/models/model_utils.py @@ -60,7 +60,9 @@ def coalesce(x: Optional[Tensor], y: Tensor) -> Tensor: @torch.jit.script -def fill_tensors(x: Optional[Tensor], mask, y: Optional[Tensor], padding_idx: int) -> Optional[Tensor]: +def fill_tensors( + x: Optional[Tensor], mask, y: Optional[Tensor], padding_idx: int +) -> Optional[Tensor]: """ Filling tensor x with y at masked positions (dim=0). """ @@ -82,9 +84,9 @@ def fill_tensors(x: Optional[Tensor], mask, y: Optional[Tensor], padding_idx: in elif x.size(1) > y.size(1): x[mask] = torch.tensor(padding_idx).type_as(x) if x.dim() == 2: - x[mask, :y.size(1)] = y + x[mask, : y.size(1)] = y else: - x[mask, :y.size(1), :] = y + x[mask, : y.size(1), :] = y else: x[mask] = y return x diff --git a/fairseq/models/multilingual_transformer.py b/fairseq/models/multilingual_transformer.py index 2f6a837805..2e1f86f36e 100644 --- a/fairseq/models/multilingual_transformer.py +++ b/fairseq/models/multilingual_transformer.py @@ -12,15 +12,15 @@ register_model_architecture, ) from fairseq.models.transformer import ( - base_architecture, Embedding, - TransformerModel, - TransformerEncoder, TransformerDecoder, + TransformerEncoder, + TransformerModel, + base_architecture, ) -@register_model('multilingual_transformer') +@register_model("multilingual_transformer") class MultilingualTransformerModel(FairseqMultiModel): """Train Transformer models for multiple language pairs simultaneously. @@ -44,31 +44,44 @@ def __init__(self, encoders, decoders): def add_args(parser): """Add model-specific arguments to the parser.""" TransformerModel.add_args(parser) - parser.add_argument('--share-encoder-embeddings', action='store_true', - help='share encoder embeddings across languages') - parser.add_argument('--share-decoder-embeddings', action='store_true', - help='share decoder embeddings across languages') - parser.add_argument('--share-encoders', action='store_true', - help='share encoders across languages') - parser.add_argument('--share-decoders', action='store_true', - help='share decoders across languages') + parser.add_argument( + "--share-encoder-embeddings", + action="store_true", + help="share encoder embeddings across languages", + ) + parser.add_argument( + "--share-decoder-embeddings", + action="store_true", + help="share decoder embeddings across languages", + ) + parser.add_argument( + "--share-encoders", + action="store_true", + help="share encoders across languages", + ) + parser.add_argument( + "--share-decoders", + action="store_true", + help="share decoders across languages", + ) @classmethod def build_model(cls, args, task): """Build a new model instance.""" from fairseq.tasks.multilingual_translation import MultilingualTranslationTask + assert isinstance(task, MultilingualTranslationTask) # make sure all arguments are present in older models base_multilingual_architecture(args) - if not hasattr(args, 'max_source_positions'): + if not hasattr(args, "max_source_positions"): args.max_source_positions = 1024 - if not hasattr(args, 'max_target_positions'): + if not hasattr(args, "max_target_positions"): args.max_target_positions = 1024 - src_langs = [lang_pair.split('-')[0] for lang_pair in task.model_lang_pairs] - tgt_langs = [lang_pair.split('-')[1] for lang_pair in task.model_lang_pairs] + src_langs = [lang_pair.split("-")[0] for lang_pair in task.model_lang_pairs] + tgt_langs = [lang_pair.split("-")[1] for lang_pair in task.model_lang_pairs] if args.share_encoders: args.share_encoder_embeddings = True @@ -90,10 +103,14 @@ def build_embedding(dictionary, embed_dim, path=None): if args.share_all_embeddings: if args.encoder_embed_dim != args.decoder_embed_dim: raise ValueError( - '--share-all-embeddings requires --encoder-embed-dim to match --decoder-embed-dim') + "--share-all-embeddings requires --encoder-embed-dim to match --decoder-embed-dim" + ) if args.decoder_embed_path and ( - args.decoder_embed_path != args.encoder_embed_path): - raise ValueError('--share-all-embeddings not compatible with --decoder-embed-path') + args.decoder_embed_path != args.encoder_embed_path + ): + raise ValueError( + "--share-all-embeddings not compatible with --decoder-embed-path" + ) shared_encoder_embed_tokens = FairseqMultiModel.build_shared_embeddings( dicts=task.dicts, langs=task.langs, @@ -105,24 +122,20 @@ def build_embedding(dictionary, embed_dim, path=None): args.share_decoder_input_output_embed = True else: if args.share_encoder_embeddings: - shared_encoder_embed_tokens = ( - FairseqMultiModel.build_shared_embeddings( - dicts=task.dicts, - langs=src_langs, - embed_dim=args.encoder_embed_dim, - build_embedding=build_embedding, - pretrained_embed_path=args.encoder_embed_path, - ) + shared_encoder_embed_tokens = FairseqMultiModel.build_shared_embeddings( + dicts=task.dicts, + langs=src_langs, + embed_dim=args.encoder_embed_dim, + build_embedding=build_embedding, + pretrained_embed_path=args.encoder_embed_path, ) if args.share_decoder_embeddings: - shared_decoder_embed_tokens = ( - FairseqMultiModel.build_shared_embeddings( - dicts=task.dicts, - langs=tgt_langs, - embed_dim=args.decoder_embed_dim, - build_embedding=build_embedding, - pretrained_embed_path=args.decoder_embed_path, - ) + shared_decoder_embed_tokens = FairseqMultiModel.build_shared_embeddings( + dicts=task.dicts, + langs=tgt_langs, + embed_dim=args.decoder_embed_dim, + build_embedding=build_embedding, + pretrained_embed_path=args.decoder_embed_path, ) # encoders/decoders for each language @@ -134,9 +147,13 @@ def get_encoder(lang): encoder_embed_tokens = shared_encoder_embed_tokens else: encoder_embed_tokens = build_embedding( - task.dicts[lang], args.encoder_embed_dim, args.encoder_embed_path + task.dicts[lang], + args.encoder_embed_dim, + args.encoder_embed_path, ) - lang_encoders[lang] = TransformerEncoder(args, task.dicts[lang], encoder_embed_tokens) + lang_encoders[lang] = cls._get_module_class( + True, args, task.dicts[lang], encoder_embed_tokens, src_langs + ) return lang_encoders[lang] def get_decoder(lang): @@ -145,9 +162,13 @@ def get_decoder(lang): decoder_embed_tokens = shared_decoder_embed_tokens else: decoder_embed_tokens = build_embedding( - task.dicts[lang], args.decoder_embed_dim, args.decoder_embed_path + task.dicts[lang], + args.decoder_embed_dim, + args.decoder_embed_path, ) - lang_decoders[lang] = TransformerDecoder(args, task.dicts[lang], decoder_embed_tokens) + lang_decoders[lang] = cls._get_module_class( + False, args, task.dicts[lang], decoder_embed_tokens, tgt_langs + ) return lang_decoders[lang] # shared encoders/decoders (if applicable) @@ -159,38 +180,49 @@ def get_decoder(lang): encoders, decoders = OrderedDict(), OrderedDict() for lang_pair, src, tgt in zip(task.model_lang_pairs, src_langs, tgt_langs): - encoders[lang_pair] = shared_encoder if shared_encoder is not None else get_encoder(src) - decoders[lang_pair] = shared_decoder if shared_decoder is not None else get_decoder(tgt) + encoders[lang_pair] = ( + shared_encoder if shared_encoder is not None else get_encoder(src) + ) + decoders[lang_pair] = ( + shared_decoder if shared_decoder is not None else get_decoder(tgt) + ) return MultilingualTransformerModel(encoders, decoders) - def load_state_dict(self, state_dict, strict=True, args=None): + @classmethod + def _get_module_class(cls, is_encoder, args, lang_dict, embed_tokens, langs): + module_class = TransformerEncoder if is_encoder else TransformerDecoder + return module_class(args, lang_dict, embed_tokens) + + def load_state_dict(self, state_dict, strict=True, model_cfg=None): state_dict_subset = state_dict.copy() for k, _ in state_dict.items(): - assert k.startswith('models.') - lang_pair = k.split('.')[1] + assert k.startswith("models.") + lang_pair = k.split(".")[1] if lang_pair not in self.models: del state_dict_subset[k] - super().load_state_dict(state_dict_subset, strict=strict, args=args) + super().load_state_dict(state_dict_subset, strict=strict, model_cfg=model_cfg) -@register_model_architecture('multilingual_transformer', 'multilingual_transformer') +@register_model_architecture("multilingual_transformer", "multilingual_transformer") def base_multilingual_architecture(args): base_architecture(args) - args.share_encoder_embeddings = getattr(args, 'share_encoder_embeddings', False) - args.share_decoder_embeddings = getattr(args, 'share_decoder_embeddings', False) - args.share_encoders = getattr(args, 'share_encoders', False) - args.share_decoders = getattr(args, 'share_decoders', False) + args.share_encoder_embeddings = getattr(args, "share_encoder_embeddings", False) + args.share_decoder_embeddings = getattr(args, "share_decoder_embeddings", False) + args.share_encoders = getattr(args, "share_encoders", False) + args.share_decoders = getattr(args, "share_decoders", False) -@register_model_architecture('multilingual_transformer', 'multilingual_transformer_iwslt_de_en') +@register_model_architecture( + "multilingual_transformer", "multilingual_transformer_iwslt_de_en" +) def multilingual_transformer_iwslt_de_en(args): - args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 512) - args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 1024) - args.encoder_attention_heads = getattr(args, 'encoder_attention_heads', 4) - args.encoder_layers = getattr(args, 'encoder_layers', 6) - args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 512) - args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', 1024) - args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 4) - args.decoder_layers = getattr(args, 'decoder_layers', 6) + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 1024) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 4) + args.encoder_layers = getattr(args, "encoder_layers", 6) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512) + args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 1024) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 4) + args.decoder_layers = getattr(args, "decoder_layers", 6) base_multilingual_architecture(args) diff --git a/fairseq/models/nat/__init__.py b/fairseq/models/nat/__init__.py index b6ca06acb9..05fe822487 100644 --- a/fairseq/models/nat/__init__.py +++ b/fairseq/models/nat/__init__.py @@ -1,3 +1,9 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +"""isort:skip_file""" + from .fairseq_nat_model import * from .nonautoregressive_transformer import * from .nat_crf_transformer import * diff --git a/fairseq/models/nat/cmlm_transformer.py b/fairseq/models/nat/cmlm_transformer.py index 86c770569d..c876e9453c 100644 --- a/fairseq/models/nat/cmlm_transformer.py +++ b/fairseq/models/nat/cmlm_transformer.py @@ -38,26 +38,34 @@ def forward( # encoding encoder_out = self.encoder(src_tokens, src_lengths=src_lengths, **kwargs) # length prediction - length_out = self.decoder.forward_length(normalize=False, encoder_out=encoder_out) - length_tgt = self.decoder.forward_length_prediction(length_out, encoder_out, tgt_tokens) + length_out = self.decoder.forward_length( + normalize=False, encoder_out=encoder_out + ) + length_tgt = self.decoder.forward_length_prediction( + length_out, encoder_out, tgt_tokens + ) # decoding word_ins_out = self.decoder( normalize=False, prev_output_tokens=prev_output_tokens, - encoder_out=encoder_out) + encoder_out=encoder_out, + ) word_ins_mask = prev_output_tokens.eq(self.unk) return { "word_ins": { - "out": word_ins_out, "tgt": tgt_tokens, - "mask": word_ins_mask, "ls": self.args.label_smoothing, - "nll_loss": True + "out": word_ins_out, + "tgt": tgt_tokens, + "mask": word_ins_mask, + "ls": self.args.label_smoothing, + "nll_loss": True, }, "length": { - "out": length_out, "tgt": length_tgt, - "factor": self.decoder.length_loss_factor - } + "out": length_out, + "tgt": length_tgt, + "factor": self.decoder.length_loss_factor, + }, } def forward_decoder(self, decoder_out, encoder_out, decoding_format=None, **kwargs): @@ -98,7 +106,7 @@ def forward_decoder(self, decoder_out, encoder_out, decoding_format=None, **kwar output_tokens=output_tokens, output_scores=output_scores, attn=None, - history=history + history=history, ) diff --git a/fairseq/models/nat/fairseq_nat_model.py b/fairseq/models/nat/fairseq_nat_model.py index d37a234ba9..b09394112f 100644 --- a/fairseq/models/nat/fairseq_nat_model.py +++ b/fairseq/models/nat/fairseq_nat_model.py @@ -4,9 +4,13 @@ # LICENSE file in the root directory of this source tree. import math -import torch -from fairseq.models.transformer import TransformerModel, TransformerEncoder, TransformerDecoder +import torch +from fairseq.models.transformer import ( + TransformerDecoder, + TransformerEncoder, + TransformerModel, +) from fairseq.modules.transformer_sentence_encoder import init_bert_params @@ -14,30 +18,50 @@ def ensemble_encoder(func): def wrapper(self, *args, **kwargs): if self.ensemble_models is None or len(self.ensemble_models) == 1: return func(self, *args, **kwargs) - encoder_outs = [func(model, *args, **kwargs) for model in self.ensemble_models] - _encoder_out = encoder_outs[0] + encoder_outs = [func(model, *args, **kwargs, return_all_hiddens=True) for model in self.ensemble_models] + _encoder_out = encoder_outs[0].copy() def stack(key): - outs = [getattr(e, key) for e in encoder_outs] - return torch.stack(outs, -1) if outs[0] is not None else None + outs = [e[key][0] for e in encoder_outs] + return [torch.stack(outs, -1) if outs[0] is not None else None] + + _encoder_out["encoder_out"] = stack("encoder_out") + _encoder_out["encoder_embedding"] = stack("encoder_embedding") + + num_layers = len(_encoder_out["encoder_states"]) + if num_layers > 0: + _encoder_out["encoder_states"] = [ + torch.stack([e["encoder_states"][i] for e in encoder_outs], -1) + for i in range(num_layers) + ] + return _encoder_out - return _encoder_out._replace( - encoder_out=stack('encoder_out'), - encoder_embedding=stack('encoder_embedding'), - encoder_states=stack('encoder_states') - ) return wrapper def ensemble_decoder(func): def wrapper(self, normalize=False, encoder_out=None, *args, **kwargs): if self.ensemble_models is None or len(self.ensemble_models) == 1: - return func(self, normalize=normalize, encoder_out=encoder_out, *args, **kwargs) + return func( + self, normalize=normalize, encoder_out=encoder_out, *args, **kwargs + ) + + def _replace(encoder_out, new_val): + new_encoder_out = encoder_out.copy() + new_encoder_out["encoder_out"] = [new_val] + return new_encoder_out action_outs = [ - func(model, normalize=normalize, encoder_out=encoder_out._replace( - encoder_out=encoder_out.encoder_out[:, :, :, i] - ), *args, **kwargs) + func( + model, + normalize=normalize, + encoder_out=_replace( + encoder_out, + encoder_out["encoder_out"][0][:, :, :, i] + ), + *args, + **kwargs + ) for i, model in enumerate(self.ensemble_models) ] @@ -51,19 +75,19 @@ def wrapper(self, normalize=False, encoder_out=None, *args, **kwargs): if i == 0 and normalize: ensembled_outs += [ torch.logsumexp( - torch.stack([a[i] for a in action_outs], -1), - dim=-1) - math.log(len(self.ensemble_models)) + torch.stack([a[i] for a in action_outs], -1), dim=-1 + ) + - math.log(len(self.ensemble_models)) ] elif action_outs[0][i] is not None: - ensembled_outs += [ - torch.stack([a[i] for a in action_outs], -1) - ] + ensembled_outs += [torch.stack([a[i] for a in action_outs], -1)] else: ensembled_outs += [None] if len(ensembled_outs) == 1: return ensembled_outs[0] return tuple(ensembled_outs) + return wrapper @@ -71,6 +95,7 @@ class FairseqNATModel(TransformerModel): """ Abstract class for all nonautoregressive-based models """ + def __init__(self, args, encoder, decoder): super().__init__(args, encoder, decoder) self.tgt_dict = decoder.dictionary diff --git a/fairseq/models/nat/insertion_transformer.py b/fairseq/models/nat/insertion_transformer.py index a5f3c1abc5..bc28000f59 100644 --- a/fairseq/models/nat/insertion_transformer.py +++ b/fairseq/models/nat/insertion_transformer.py @@ -6,17 +6,16 @@ import numpy as np import torch import torch.nn.functional as F - from fairseq.models import register_model, register_model_architecture from fairseq.models.nat import ( + FairseqNATModel, LevenshteinTransformerDecoder, LevenshteinTransformerModel, - FairseqNATModel, - ensemble_decoder + ensemble_decoder, ) from fairseq.models.transformer import Linear -from fairseq.utils import new_arange from fairseq.modules.transformer_sentence_encoder import init_bert_params +from fairseq.utils import new_arange class NegativeDistanceScore(object): @@ -58,7 +57,8 @@ def _get_ins_targets(in_tokens, out_tokens, padding_idx, unk_idx, vocab_size, ta from fairseq import libnat except ImportError as e: import sys - sys.stderr.write('ERROR: missing libnat. run `pip install --editable .`\n') + + sys.stderr.write("ERROR: missing libnat. run `pip install --editable .`\n") raise e B = in_tokens.size(0) @@ -147,7 +147,7 @@ def forward( word_ins_out = self.decoder.forward_word_ins( normalize=False, prev_output_tokens=prev_output_tokens, - encoder_out=encoder_out + encoder_out=encoder_out, ) word_ins_tgt = _get_ins_targets( @@ -162,9 +162,11 @@ def forward( return { "word_ins": { - "out": word_ins_out, "tgt": word_ins_tgt, - "mask": word_ins_masks, "ls": self.args.label_smoothing, - "nll_loss": True + "out": word_ins_out, + "tgt": word_ins_tgt, + "mask": word_ins_masks, + "ls": self.args.label_smoothing, + "nll_loss": True, } } @@ -178,9 +180,7 @@ def forward_decoder( # TODO: decoding for InsertionTransformer word_ins_score = self.decoder.forward_word_ins( - normalize=True, - prev_output_tokens=output_tokens, - encoder_out=encoder_out + normalize=True, prev_output_tokens=output_tokens, encoder_out=encoder_out ) if eos_penalty > 0.0: @@ -202,7 +202,7 @@ def forward_decoder( output_tokens=output_tokens, output_scores=output_scores, attn=None, - history=history + history=history, ) diff --git a/fairseq/models/nat/iterative_nonautoregressive_transformer.py b/fairseq/models/nat/iterative_nonautoregressive_transformer.py index dc340c387d..bc39509980 100644 --- a/fairseq/models/nat/iterative_nonautoregressive_transformer.py +++ b/fairseq/models/nat/iterative_nonautoregressive_transformer.py @@ -4,7 +4,6 @@ # LICENSE file in the root directory of this source tree. import torch - from fairseq.models import register_model, register_model_architecture from fairseq.models.nat import NATransformerModel @@ -44,8 +43,16 @@ def _sequential_poisoning(s, V, beta=0.33, bos=2, eos=3, pad=1): def gumbel_noise(input, TINY=1e-8): - return input.new_zeros(*input.size()).uniform_().add_( - TINY).log_().neg_().add_(TINY).log_().neg_() + return ( + input.new_zeros(*input.size()) + .uniform_() + .add_(TINY) + .log_() + .neg_() + .add_(TINY) + .log_() + .neg_() + ) @register_model("iterative_nonautoregressive_transformer") @@ -53,12 +60,21 @@ class IterNATransformerModel(NATransformerModel): @staticmethod def add_args(parser): NATransformerModel.add_args(parser) - parser.add_argument("--train-step", type=int, - help="number of refinement iterations during training") - parser.add_argument("--dae-ratio", type=float, - help="the probability of switching to the denoising auto-encoder loss") - parser.add_argument("--stochastic-approx", action="store_true", - help="sampling from the decoder as the inputs for next iteration") + parser.add_argument( + "--train-step", + type=int, + help="number of refinement iterations during training", + ) + parser.add_argument( + "--dae-ratio", + type=float, + help="the probability of switching to the denoising auto-encoder loss", + ) + parser.add_argument( + "--stochastic-approx", + action="store_true", + help="sampling from the decoder as the inputs for next iteration", + ) @classmethod def build_model(cls, args, task): @@ -78,14 +94,18 @@ def forward( encoder_out = self.encoder(src_tokens, src_lengths=src_lengths, **kwargs) # length prediction - length_out = self.decoder.forward_length(normalize=False, encoder_out=encoder_out) - length_tgt = self.decoder.forward_length_prediction(length_out, encoder_out, tgt_tokens) + length_out = self.decoder.forward_length( + normalize=False, encoder_out=encoder_out + ) + length_tgt = self.decoder.forward_length_prediction( + length_out, encoder_out, tgt_tokens + ) # decoding word_ins_outs, word_ins_tgts, word_ins_masks = [], [], [] for t in range(self.train_step): word_ins_out = self.decoder( - normalize=False, + normalize=False, prev_output_tokens=prev_output_tokens, encoder_out=encoder_out, step=t, @@ -133,14 +153,17 @@ def forward( return { "word_ins": { - "out": word_ins_out, "tgt": word_ins_tgt, - "mask": word_ins_mask, "ls": self.args.label_smoothing, - "nll_loss": True + "out": word_ins_out, + "tgt": word_ins_tgt, + "mask": word_ins_mask, + "ls": self.args.label_smoothing, + "nll_loss": True, }, "length": { - "out": length_out, "tgt": length_tgt, - "factor": self.decoder.length_loss_factor - } + "out": length_out, + "tgt": length_tgt, + "factor": self.decoder.length_loss_factor, + }, } diff --git a/fairseq/models/nat/levenshtein_transformer.py b/fairseq/models/nat/levenshtein_transformer.py index a015d9eb3b..d60d3c52d5 100644 --- a/fairseq/models/nat/levenshtein_transformer.py +++ b/fairseq/models/nat/levenshtein_transformer.py @@ -6,33 +6,27 @@ import torch import torch.nn as nn import torch.nn.functional as F - from fairseq.iterative_refinement_generator import DecoderOut from fairseq.models import register_model, register_model_architecture -from fairseq.models.transformer import ( - Embedding, - TransformerDecoderLayer -) - -from fairseq.models.nat import ( - FairseqNATModel, - FairseqNATDecoder, - ensemble_decoder -) - +from fairseq.models.nat import FairseqNATDecoder, FairseqNATModel, ensemble_decoder +from fairseq.models.transformer import Embedding +from fairseq.modules import TransformerDecoderLayer from fairseq.modules.transformer_sentence_encoder import init_bert_params - from .levenshtein_utils import ( - _skip, _skip_encoder_out, _fill, - _get_ins_targets, _get_del_targets, - _apply_ins_masks, _apply_ins_words, _apply_del_words + _apply_del_words, + _apply_ins_masks, + _apply_ins_words, + _fill, + _get_del_targets, + _get_ins_targets, + _skip, + _skip_encoder_out, ) @register_model("levenshtein_transformer") class LevenshteinTransformerModel(FairseqNATModel): - @property def allow_length_beam(self): return False @@ -63,8 +57,8 @@ def add_args(parser): ) parser.add_argument( "--sampling-for-deletion", - action='store_true', - help='instead of argmax, use sampling to predict the tokens' + action="store_true", + help="instead of argmax, use sampling to predict the tokens", ) @classmethod @@ -93,19 +87,19 @@ def forward( mask_ins_out, _ = self.decoder.forward_mask_ins( normalize=False, prev_output_tokens=prev_output_tokens, - encoder_out=encoder_out + encoder_out=encoder_out, ) word_ins_out, _ = self.decoder.forward_word_ins( normalize=False, prev_output_tokens=masked_tgt_tokens, - encoder_out=encoder_out + encoder_out=encoder_out, ) # make online prediction if self.decoder.sampling_for_deletion: word_predictions = torch.multinomial( - F.softmax(word_ins_out, -1).view(-1, word_ins_out.size(-1)), 1).view( - word_ins_out.size(0), -1) + F.softmax(word_ins_out, -1).view(-1, word_ins_out.size(-1)), 1 + ).view(word_ins_out.size(0), -1) else: word_predictions = F.log_softmax(word_ins_out, dim=-1).max(2)[1] @@ -118,23 +112,29 @@ def forward( word_del_out, _ = self.decoder.forward_word_del( normalize=False, prev_output_tokens=word_predictions, - encoder_out=encoder_out) + encoder_out=encoder_out, + ) word_del_masks = word_predictions.ne(self.pad) return { "mask_ins": { - "out": mask_ins_out, "tgt": mask_ins_targets, - "mask": mask_ins_masks, "ls": 0.01, + "out": mask_ins_out, + "tgt": mask_ins_targets, + "mask": mask_ins_masks, + "ls": 0.01, }, "word_ins": { - "out": word_ins_out, "tgt": tgt_tokens, - "mask": masked_tgt_masks, "ls": self.args.label_smoothing, - "nll_loss": True + "out": word_ins_out, + "tgt": tgt_tokens, + "mask": masked_tgt_masks, + "ls": self.args.label_smoothing, + "nll_loss": True, }, "word_del": { - "out": word_del_out, "tgt": word_del_targets, - "mask": word_del_masks - } + "out": word_del_out, + "tgt": word_del_targets, + "mask": word_del_masks, + }, } def forward_decoder( @@ -150,11 +150,11 @@ def forward_decoder( if max_ratio is None: max_lens = torch.zeros_like(output_tokens).fill_(255) else: - if encoder_out.encoder_padding_mask is None: - max_src_len = encoder_out.encoder_out.size(0) - src_lens = encoder_out.encoder_out.new(bsz).fill_(max_src_len) + if not encoder_out["encoder_padding_mask"]: + max_src_len = encoder_out["encoder_out"].size(0) + src_lens = encoder_out["encoder_out"].new(bsz).fill_(max_src_len) else: - src_lens = (~encoder_out.encoder_padding_mask).sum(1) + src_lens = (~encoder_out["encoder_padding_mask"][0]).sum(1) max_lens = (src_lens * max_ratio).clamp(min=10).long() # delete words @@ -164,7 +164,7 @@ def forward_decoder( word_del_score, word_del_attn = self.decoder.forward_word_del( normalize=True, prev_output_tokens=_skip(output_tokens, can_del_word), - encoder_out=_skip_encoder_out(self.encoder, encoder_out, can_del_word) + encoder_out=_skip_encoder_out(self.encoder, encoder_out, can_del_word), ) word_del_pred = word_del_score.max(-1)[1].bool() @@ -179,7 +179,7 @@ def forward_decoder( ) output_tokens = _fill(output_tokens, can_del_word, _tokens, self.pad) output_scores = _fill(output_scores, can_del_word, _scores, 0) - attn = _fill(attn, can_del_word, _attn, 0.) + attn = _fill(attn, can_del_word, _attn, 0.0) if history is not None: history.append(output_tokens.clone()) @@ -190,7 +190,7 @@ def forward_decoder( mask_ins_score, _ = self.decoder.forward_mask_ins( normalize=True, prev_output_tokens=_skip(output_tokens, can_ins_mask), - encoder_out=_skip_encoder_out(self.encoder, encoder_out, can_ins_mask) + encoder_out=_skip_encoder_out(self.encoder, encoder_out, can_ins_mask), ) if eos_penalty > 0.0: mask_ins_score[:, :, 0] = mask_ins_score[:, :, 0] - eos_penalty @@ -219,7 +219,7 @@ def forward_decoder( word_ins_score, word_ins_attn = self.decoder.forward_word_ins( normalize=True, prev_output_tokens=_skip(output_tokens, can_ins_word), - encoder_out=_skip_encoder_out(self.encoder, encoder_out, can_ins_word) + encoder_out=_skip_encoder_out(self.encoder, encoder_out, can_ins_word), ) word_ins_score, word_ins_pred = word_ins_score.max(-1) _tokens, _scores = _apply_ins_words( @@ -232,7 +232,7 @@ def forward_decoder( output_tokens = _fill(output_tokens, can_ins_word, _tokens, self.pad) output_scores = _fill(output_scores, can_ins_word, _scores, 0) - attn = _fill(attn, can_ins_word, word_ins_attn, 0.) + attn = _fill(attn, can_ins_word, word_ins_attn, 0.0) if history is not None: history.append(output_tokens.clone()) @@ -247,7 +247,7 @@ def forward_decoder( output_tokens=output_tokens, output_scores=output_scores, attn=attn, - history=history + history=history, ) def initialize_output_tokens(self, encoder_out, src_tokens): @@ -257,7 +257,7 @@ def initialize_output_tokens(self, encoder_out, src_tokens): initial_output_scores = initial_output_tokens.new_zeros( *initial_output_tokens.size() - ).type_as(encoder_out.encoder_out) + ).type_as(encoder_out["encoder_out"][0]) return DecoderOut( output_tokens=initial_output_tokens, @@ -265,7 +265,7 @@ def initialize_output_tokens(self, encoder_out, src_tokens): attn=None, step=0, max_step=0, - history=None + history=None, ) @@ -283,29 +283,40 @@ def __init__(self, args, dictionary, embed_tokens, no_encoder_attn=False): self.embed_word_del = Embedding(2, self.output_embed_dim, None) # del_word, ins_mask, ins_word - self.early_exit = [int(i) for i in args.early_exit.split(',')] + self.early_exit = [int(i) for i in args.early_exit.split(",")] assert len(self.early_exit) == 3 # copy layers for mask-predict/deletion self.layers_msk = None if getattr(args, "no_share_maskpredictor", False): - self.layers_msk = nn.ModuleList([ - TransformerDecoderLayer(args, no_encoder_attn) - for _ in range(self.early_exit[1]) - ]) + self.layers_msk = nn.ModuleList( + [ + TransformerDecoderLayer(args, no_encoder_attn) + for _ in range(self.early_exit[1]) + ] + ) self.layers_del = None if getattr(args, "no_share_discriminator", False): - self.layers_del = nn.ModuleList([ - TransformerDecoderLayer(args, no_encoder_attn) - for _ in range(self.early_exit[0]) - ]) + self.layers_del = nn.ModuleList( + [ + TransformerDecoderLayer(args, no_encoder_attn) + for _ in range(self.early_exit[0]) + ] + ) if getattr(args, "share_discriminator_maskpredictor", False): - assert getattr(args, "no_share_discriminator", False), "must set saperate discriminator" + assert getattr( + args, "no_share_discriminator", False + ), "must set saperate discriminator" self.layers_msk = self.layers_del def extract_features( - self, prev_output_tokens, encoder_out=None, early_exit=None, layers=None, **unused + self, + prev_output_tokens, + encoder_out=None, + early_exit=None, + layers=None, + **unused ): """ Similar to *forward* but only return features. @@ -333,7 +344,7 @@ def extract_features( if positions is not None: x += positions - x = F.dropout(x, p=self.dropout, training=self.training) + x = self.dropout_module(x) # B x T x C -> T x B x C x = x.transpose(0, 1) @@ -344,11 +355,18 @@ def extract_features( decoder_padding_mask = prev_output_tokens.eq(self.padding_idx) layers = self.layers if layers is None else layers early_exit = len(layers) if early_exit is None else early_exit - for _, layer in enumerate(layers[: early_exit]): + for _, layer in enumerate(layers[:early_exit]): x, attn, _ = layer( x, - encoder_out.encoder_out if encoder_out is not None else None, - encoder_out.encoder_padding_mask if encoder_out is not None else None, + encoder_out["encoder_out"][0] + if (encoder_out is not None and len(encoder_out["encoder_out"]) > 0) + else None, + encoder_out["encoder_padding_mask"][0] + if ( + encoder_out is not None + and len(encoder_out["encoder_padding_mask"]) > 0 + ) + else None, self_attn_mask=None, self_attn_padding_mask=decoder_padding_mask, ) @@ -368,33 +386,45 @@ def extract_features( @ensemble_decoder def forward_mask_ins(self, normalize, encoder_out, prev_output_tokens, **unused): features, extra = self.extract_features( - prev_output_tokens, encoder_out=encoder_out, early_exit=self.early_exit[1], layers=self.layers_msk, **unused + prev_output_tokens, + encoder_out=encoder_out, + early_exit=self.early_exit[1], + layers=self.layers_msk, + **unused ) features_cat = torch.cat([features[:, :-1, :], features[:, 1:, :]], 2) decoder_out = F.linear(features_cat, self.embed_mask_ins.weight) if normalize: - return F.log_softmax(decoder_out, -1), extra['attn'] - return decoder_out, extra['attn'] + return F.log_softmax(decoder_out, -1), extra["attn"] + return decoder_out, extra["attn"] @ensemble_decoder def forward_word_ins(self, normalize, encoder_out, prev_output_tokens, **unused): features, extra = self.extract_features( - prev_output_tokens, encoder_out=encoder_out, early_exit=self.early_exit[2], layers=self.layers, **unused + prev_output_tokens, + encoder_out=encoder_out, + early_exit=self.early_exit[2], + layers=self.layers, + **unused ) decoder_out = self.output_layer(features) if normalize: - return F.log_softmax(decoder_out, -1), extra['attn'] - return decoder_out, extra['attn'] + return F.log_softmax(decoder_out, -1), extra["attn"] + return decoder_out, extra["attn"] @ensemble_decoder def forward_word_del(self, normalize, encoder_out, prev_output_tokens, **unused): features, extra = self.extract_features( - prev_output_tokens, encoder_out=encoder_out, early_exit=self.early_exit[0], layers=self.layers_del, **unused + prev_output_tokens, + encoder_out=encoder_out, + early_exit=self.early_exit[0], + layers=self.layers_del, + **unused ) decoder_out = F.linear(features, self.embed_word_del.weight) if normalize: - return F.log_softmax(decoder_out, -1), extra['attn'] - return decoder_out, extra['attn'] + return F.log_softmax(decoder_out, -1), extra["attn"] + return decoder_out, extra["attn"] @register_model_architecture("levenshtein_transformer", "levenshtein_transformer") @@ -439,7 +469,9 @@ def levenshtein_base_architecture(args): args.early_exit = getattr(args, "early_exit", "6,6,6") args.no_share_discriminator = getattr(args, "no_share_discriminator", False) args.no_share_maskpredictor = getattr(args, "no_share_maskpredictor", False) - args.share_discriminator_maskpredictor = getattr(args, "share_discriminator_maskpredictor", False) + args.share_discriminator_maskpredictor = getattr( + args, "share_discriminator_maskpredictor", False + ) args.no_share_last_layer = getattr(args, "no_share_last_layer", False) diff --git a/fairseq/models/nat/levenshtein_utils.py b/fairseq/models/nat/levenshtein_utils.py index e29b1fa27c..375a98c2e1 100644 --- a/fairseq/models/nat/levenshtein_utils.py +++ b/fairseq/models/nat/levenshtein_utils.py @@ -9,21 +9,27 @@ # -------------- Helper Functions --------------------------------------------------- # + def load_libnat(): try: from fairseq import libnat_cuda + return libnat_cuda, True except ImportError as e: - print(str(e) + '... fall back to CPU version') + print(str(e) + "... fall back to CPU version") try: from fairseq import libnat + return libnat, False except ImportError as e: import sys - sys.stderr.write("ERROR: missing libnat_cuda. run `python setup.py build_ext --inplace`\n") + + sys.stderr.write( + "ERROR: missing libnat_cuda. run `python setup.py build_ext --inplace`\n" + ) raise e @@ -34,14 +40,18 @@ def _get_ins_targets_cuda(in_tokens, out_tokens, padding_idx, unk_idx): in_masks = in_tokens.ne(padding_idx) out_masks = out_tokens.ne(padding_idx) mask_ins_targets, masked_tgt_masks = libnat.generate_insertion_labels( - out_tokens.int(), libnat.levenshtein_distance( - in_tokens.int(), out_tokens.int(), - in_masks.sum(1).int(), out_masks.sum(1).int() - ) + out_tokens.int(), + libnat.levenshtein_distance( + in_tokens.int(), + out_tokens.int(), + in_masks.sum(1).int(), + out_masks.sum(1).int(), + ), ) masked_tgt_masks = masked_tgt_masks.bool() & out_masks - mask_ins_targets = mask_ins_targets.type_as( - in_tokens)[:, 1:in_masks.size(1)].masked_fill_(~in_masks[:, 1:], 0) + mask_ins_targets = mask_ins_targets.type_as(in_tokens)[ + :, 1 : in_masks.size(1) + ].masked_fill_(~in_masks[:, 1:], 0) masked_tgt_tokens = out_tokens.masked_fill(masked_tgt_masks, unk_idx) return masked_tgt_masks, masked_tgt_tokens, mask_ins_targets @@ -73,7 +83,8 @@ def _get_ins_targets_cpu(in_tokens, out_tokens, padding_idx, unk_idx): mask_label + [0 for _ in range(out_seq_len - len(mask_label))] ) mask_ins_targets = [ - mask_input[1:-1] + [0 for _ in range(in_seq_len - 1 - len(mask_input[1:-1]))] + mask_input[1:-1] + + [0 for _ in range(in_seq_len - 1 - len(mask_input[1:-1]))] for mask_input in mask_inputs ] @@ -100,18 +111,23 @@ def _get_del_targets_cuda(in_tokens, out_tokens, padding_idx): word_del_targets = libnat.generate_deletion_labels( in_tokens.int(), libnat.levenshtein_distance( - in_tokens.int(), out_tokens.int(), - in_masks.sum(1).int(), out_masks.sum(1).int() - ) + in_tokens.int(), + out_tokens.int(), + in_masks.sum(1).int(), + out_masks.sum(1).int(), + ), + ) + word_del_targets = word_del_targets.type_as(in_tokens).masked_fill_( + ~in_masks, 0 ) - word_del_targets = word_del_targets.type_as(in_tokens).masked_fill_(~in_masks, 0) return word_del_targets def _get_del_targets_cpu(in_tokens, out_tokens, padding_idx): out_seq_len = out_tokens.size(1) with torch.cuda.device_of(in_tokens): in_tokens_list = [ - [t for t in s if t != padding_idx] for i, s in enumerate(in_tokens.tolist()) + [t for t in s if t != padding_idx] + for i, s in enumerate(in_tokens.tolist()) ] out_tokens_list = [ [t for t in s if t != padding_idx] @@ -149,10 +165,7 @@ def _apply_ins_masks( out_lengths = in_lengths + mask_ins_pred.sum(1) out_max_len = out_lengths.max() - out_masks = ( - new_arange(out_lengths, out_max_len)[None, :] - < out_lengths[:, None] - ) + out_masks = new_arange(out_lengths, out_max_len)[None, :] < out_lengths[:, None] reordering = (mask_ins_pred + in_masks[:, 1:].long()).cumsum(1) out_tokens = ( @@ -173,9 +186,7 @@ def _apply_ins_masks( return out_tokens, out_scores -def _apply_ins_words( - in_tokens, in_scores, word_ins_pred, word_ins_scores, unk_idx -): +def _apply_ins_words(in_tokens, in_scores, word_ins_pred, word_ins_scores, unk_idx): word_ins_masks = in_tokens.eq(unk_idx) out_tokens = in_tokens.masked_scatter(word_ins_masks, word_ins_pred[word_ins_masks]) @@ -200,11 +211,7 @@ def _apply_del_words( word_del_pred.masked_fill_(~in_masks, 1) word_del_pred.masked_fill_(bos_eos_masks, 0) - reordering = ( - new_arange(in_tokens) - .masked_fill_(word_del_pred, max_len) - .sort(1)[1] - ) + reordering = new_arange(in_tokens).masked_fill_(word_del_pred, max_len).sort(1)[1] out_tokens = in_tokens.masked_fill(word_del_pred, padding_idx).gather(1, reordering) @@ -216,7 +223,7 @@ def _apply_del_words( if in_attn is not None: _mask = word_del_pred[:, :, None].expand_as(in_attn) _reordering = reordering[:, :, None].expand_as(in_attn) - out_attn = in_attn.masked_fill(_mask, 0.).gather(1, _reordering) + out_attn = in_attn.masked_fill(_mask, 0.0).gather(1, _reordering) return out_tokens, out_scores, out_attn @@ -250,7 +257,9 @@ def _skip_encoder_out(encoder, encoder_out, mask): if not mask.any(): return encoder_out else: - return encoder.reorder_encoder_out(encoder_out, mask.nonzero().squeeze()) + return encoder.reorder_encoder_out( + encoder_out, mask.nonzero(as_tuple=False).squeeze() + ) def _fill(x, mask, y, padding_idx): @@ -276,9 +285,9 @@ def _fill(x, mask, y, padding_idx): elif x.size(1) > y.size(1): x[mask] = padding_idx if x.dim() == 2: - x[mask, :y.size(1)] = y + x[mask, : y.size(1)] = y else: - x[mask, :y.size(1), :] = y + x[mask, : y.size(1), :] = y else: x[mask] = y return x diff --git a/fairseq/models/nat/nat_crf_transformer.py b/fairseq/models/nat/nat_crf_transformer.py index 8dd3a08f72..d4b3cd931c 100644 --- a/fairseq/models/nat/nat_crf_transformer.py +++ b/fairseq/models/nat/nat_crf_transformer.py @@ -4,8 +4,8 @@ # LICENSE file in the root directory of this source tree. -from fairseq.models.nat import NATransformerModel, base_architecture from fairseq.models import register_model, register_model_architecture +from fairseq.models.nat import NATransformerModel, base_architecture from fairseq.modules import DynamicCRF @@ -16,7 +16,7 @@ def __init__(self, args, encoder, decoder): self.crf_layer = DynamicCRF( num_embedding=len(self.tgt_dict), low_rank=args.crf_lowrank_approx, - beam_size=args.crf_beam_approx + beam_size=args.crf_beam_approx, ) @property @@ -26,12 +26,21 @@ def allow_ensemble(self): @staticmethod def add_args(parser): NATransformerModel.add_args(parser) - parser.add_argument("--crf-lowrank-approx", type=int, - help="the dimension of low-rank approximation of transition") - parser.add_argument("--crf-beam-approx", type=int, - help="the beam size for apporixmating the normalizing factor") - parser.add_argument("--word-ins-loss-factor", type=float, - help="weights on NAT loss used to co-training with CRF loss.") + parser.add_argument( + "--crf-lowrank-approx", + type=int, + help="the dimension of low-rank approximation of transition", + ) + parser.add_argument( + "--crf-beam-approx", + type=int, + help="the beam size for apporixmating the normalizing factor", + ) + parser.add_argument( + "--word-ins-loss-factor", + type=float, + help="weights on NAT loss used to co-training with CRF loss.", + ) def forward( self, src_tokens, src_lengths, prev_output_tokens, tgt_tokens, **kwargs @@ -40,14 +49,19 @@ def forward( encoder_out = self.encoder(src_tokens, src_lengths=src_lengths, **kwargs) # length prediction - length_out = self.decoder.forward_length(normalize=False, encoder_out=encoder_out) - length_tgt = self.decoder.forward_length_prediction(length_out, encoder_out, tgt_tokens) + length_out = self.decoder.forward_length( + normalize=False, encoder_out=encoder_out + ) + length_tgt = self.decoder.forward_length_prediction( + length_out, encoder_out, tgt_tokens + ) # decoding word_ins_out = self.decoder( normalize=False, prev_output_tokens=prev_output_tokens, - encoder_out=encoder_out) + encoder_out=encoder_out, + ) word_ins_tgt, word_ins_mask = tgt_tokens, tgt_tokens.ne(self.pad) # compute the log-likelihood of CRF @@ -56,17 +70,19 @@ def forward( return { "word_ins": { - "out": word_ins_out, "tgt": word_ins_tgt, - "mask": word_ins_mask, "ls": self.args.label_smoothing, - "nll_loss": True, "factor": self.args.word_ins_loss_factor - }, - "word_crf": { - "loss": crf_nll + "out": word_ins_out, + "tgt": word_ins_tgt, + "mask": word_ins_mask, + "ls": self.args.label_smoothing, + "nll_loss": True, + "factor": self.args.word_ins_loss_factor, }, + "word_crf": {"loss": crf_nll}, "length": { - "out": length_out, "tgt": length_tgt, - "factor": self.decoder.length_loss_factor - } + "out": length_out, + "tgt": length_tgt, + "factor": self.decoder.length_loss_factor, + }, } def forward_decoder(self, decoder_out, encoder_out, decoding_format=None, **kwargs): @@ -77,9 +93,7 @@ def forward_decoder(self, decoder_out, encoder_out, decoding_format=None, **kwar # execute the decoder and get emission scores output_masks = output_tokens.ne(self.pad) word_ins_out = self.decoder( - normalize=False, - prev_output_tokens=output_tokens, - encoder_out=encoder_out + normalize=False, prev_output_tokens=output_tokens, encoder_out=encoder_out ) # run viterbi decoding through CRF @@ -93,7 +107,7 @@ def forward_decoder(self, decoder_out, encoder_out, decoding_format=None, **kwar output_tokens=output_tokens, output_scores=output_scores, attn=None, - history=history + history=history, ) diff --git a/fairseq/models/nat/nonautoregressive_ensembles.py b/fairseq/models/nat/nonautoregressive_ensembles.py index 2ed4d956e0..705a04fb49 100644 --- a/fairseq/models/nat/nonautoregressive_ensembles.py +++ b/fairseq/models/nat/nonautoregressive_ensembles.py @@ -7,14 +7,13 @@ import torch import torch.nn.functional as F - from fairseq.models.nat import ( + _apply_del_words, + _apply_ins_masks, + _apply_ins_words, _fill, _skip, _skip_encoder_out, - _apply_ins_masks, - _apply_ins_words, - _apply_del_words, ) @@ -43,7 +42,7 @@ def __init__(self, models): self.encoder = _EnsembleModelEncoder(self.models) def has_encoder(self): - return hasattr(self.models[0], 'encoder') + return hasattr(self.models[0], "encoder") def max_decoder_positions(self): return min(m.max_decoder_positions() for m in self.models) @@ -69,7 +68,9 @@ def __init__(self, models): super().__init__(models) @torch.no_grad() - def forward_decoder(self, decoder_out, encoder_outs, eos_penalty=0.0, max_ratio=None, **kwargs): + def forward_decoder( + self, decoder_out, encoder_outs, eos_penalty=0.0, max_ratio=None, **kwargs + ): # LevT ensembling # A pipeline of three steps: deletion, placeholder, and word insertion. # We need to average scores in each step in a pipeline way because of dependence. @@ -82,10 +83,13 @@ def forward_decoder(self, decoder_out, encoder_outs, eos_penalty=0.0, max_ratio= if max_ratio is None: max_lens = output_tokens.new().fill_(255) else: - if encoder_outs[0].encoder_padding_mask is None: - src_lens = encoder_outs[0].encoder_out.new(bsz).fill_(encoder_outs[0].encoder_out.size(1)) + if not encoder_outs[0]["encoder_padding_mask"]: + src_lens = ( + encoder_outs[0]["encoder_out"][0].new(bsz) + .fill_(encoder_outs[0]["encoder_out"][0].size(1)) + ) else: - src_lens = (~encoder_outs[0].encoder_padding_mask).sum(1) + src_lens = (~encoder_outs[0]["encoder_padding_mask"][0]).sum(1) max_lens = (src_lens * max_ratio).clamp(min=10).long() # delete words @@ -104,13 +108,13 @@ def forward_decoder(self, decoder_out, encoder_outs, eos_penalty=0.0, max_ratio= can_ins_mask = output_tokens.ne(self.pad).sum(1) < max_lens if can_ins_mask.sum() != 0: output_tokens, output_scores = self.forward_mask_ins( - encoder_outs, - output_tokens, - output_scores, - can_ins_mask, - eos_penalty, - max_lens, - ) + encoder_outs, + output_tokens, + output_scores, + can_ins_mask, + eos_penalty, + max_lens, + ) # insert words can_ins_word = output_tokens.eq(self.unk).sum(1) > 0 @@ -132,10 +136,12 @@ def forward_decoder(self, decoder_out, encoder_outs, eos_penalty=0.0, max_ratio= output_tokens=output_tokens, output_scores=output_scores, attn=attn, - history=None + history=None, ) - def forward_word_del(self, encoder_outs, output_tokens, output_scores, attn, can_del_word): + def forward_word_del( + self, encoder_outs, output_tokens, output_scores, attn, can_del_word + ): word_del_score_avg = [] word_del_attn_avg = [] for model, encoder_out in zip(self.models, encoder_outs): @@ -146,10 +152,12 @@ def forward_word_del(self, encoder_outs, output_tokens, output_scores, attn, can word_del_score = F.log_softmax(word_del_out, 2) word_del_score_avg.append(word_del_score) word_del_attn_avg.append(word_del_attn) - word_del_score_avg = torch.logsumexp(torch.stack(word_del_score_avg, dim=0), dim=0) - math.log(len(self.models)) + word_del_score_avg = torch.logsumexp( + torch.stack(word_del_score_avg, dim=0), dim=0 + ) - math.log(len(self.models)) word_del_pred = word_del_score_avg.max(-1)[1].bool() if word_del_attn_avg[0] is not None: - word_del_attn_avg = torch.stack(word_del_attn_avg, dim=0)/len(self.models) + word_del_attn_avg = torch.stack(word_del_attn_avg, dim=0) / len(self.models) else: word_del_attn_avg = None @@ -164,10 +172,18 @@ def forward_word_del(self, encoder_outs, output_tokens, output_scores, attn, can ) output_tokens = _fill(output_tokens, can_del_word, _tokens, self.pad) output_scores = _fill(output_scores, can_del_word, _scores, 0) - attn = _fill(attn, can_del_word, _attn, 0.) + attn = _fill(attn, can_del_word, _attn, 0.0) return output_tokens, output_scores, attn - def forward_mask_ins(self, encoder_outs, output_tokens, output_scores, can_ins_mask, eos_penalty, max_lens): + def forward_mask_ins( + self, + encoder_outs, + output_tokens, + output_scores, + can_ins_mask, + eos_penalty, + max_lens, + ): mask_ins_score_avg = [] for model, encoder_out in zip(self.models, encoder_outs): mask_ins_out, _ = model.decoder.forward_mask_ins( @@ -178,7 +194,9 @@ def forward_mask_ins(self, encoder_outs, output_tokens, output_scores, can_ins_m if eos_penalty > 0.0: mask_ins_score[:, :, 0] -= eos_penalty mask_ins_score_avg.append(mask_ins_score) - mask_ins_score_avg = torch.logsumexp(torch.stack(mask_ins_score_avg, dim=0), dim=0) - math.log(len(self.models)) + mask_ins_score_avg = torch.logsumexp( + torch.stack(mask_ins_score_avg, dim=0), dim=0 + ) - math.log(len(self.models)) mask_ins_pred = mask_ins_score_avg.max(-1)[1] mask_ins_pred = torch.min( mask_ins_pred, max_lens[can_ins_mask, None].expand_as(mask_ins_pred) @@ -195,7 +213,9 @@ def forward_mask_ins(self, encoder_outs, output_tokens, output_scores, can_ins_m output_scores = _fill(output_scores, can_ins_mask, _scores, 0) return output_tokens, output_scores - def forward_word_ins(self, encoder_outs, output_tokens, output_scores, attn, can_ins_word): + def forward_word_ins( + self, encoder_outs, output_tokens, output_scores, attn, can_ins_word + ): word_ins_score_avg = [] word_ins_attn_avg = [] for model, encoder_out in zip(self.models, encoder_outs): @@ -206,9 +226,11 @@ def forward_word_ins(self, encoder_outs, output_tokens, output_scores, attn, can word_ins_score = F.log_softmax(word_ins_out, 2) word_ins_score_avg.append(word_ins_score) word_ins_attn_avg.append(word_ins_attn) - word_ins_score_avg = torch.logsumexp(torch.stack(word_ins_score_avg, dim=0), dim=0) - math.log(len(self.models)) + word_ins_score_avg = torch.logsumexp( + torch.stack(word_ins_score_avg, dim=0), dim=0 + ) - math.log(len(self.models)) if word_ins_attn_avg[0] is not None: - word_ins_attn_avg = torch.stack(word_ins_attn_avg, dim=0)/len(self.models) + word_ins_attn_avg = torch.stack(word_ins_attn_avg, dim=0) / len(self.models) else: word_ins_attn_avg = None word_ins_score_max, word_ins_pred = word_ins_score_avg.max(-1) @@ -223,7 +245,7 @@ def forward_word_ins(self, encoder_outs, output_tokens, output_scores, attn, can output_tokens = _fill(output_tokens, can_ins_word, _tokens, self.pad) output_scores = _fill(output_scores, can_ins_word, _scores, 0) - attn = _fill(attn, can_ins_word, word_ins_attn, 0.) + attn = _fill(attn, can_ins_word, word_ins_attn, 0.0) return output_tokens, output_scores, attn def initialize_output_tokens(self, encoder_outs, src_tokens): diff --git a/fairseq/models/nat/nonautoregressive_transformer.py b/fairseq/models/nat/nonautoregressive_transformer.py index 46459732af..d114202d25 100644 --- a/fairseq/models/nat/nonautoregressive_transformer.py +++ b/fairseq/models/nat/nonautoregressive_transformer.py @@ -5,17 +5,11 @@ import torch import torch.nn.functional as F - from fairseq import utils from fairseq.iterative_refinement_generator import DecoderOut from fairseq.models import register_model, register_model_architecture +from fairseq.models.nat import FairseqNATDecoder, FairseqNATModel, ensemble_decoder from fairseq.models.transformer import Embedding - -from fairseq.models.nat import ( - FairseqNATModel, - FairseqNATDecoder, - ensemble_decoder -) from fairseq.modules.transformer_sentence_encoder import init_bert_params @@ -48,7 +42,6 @@ def _uniform_assignment(src_lens, trg_lens): @register_model("nonautoregressive_transformer") class NATransformerModel(FairseqNATModel): - @property def allow_length_beam(self): return True @@ -58,14 +51,26 @@ def add_args(parser): FairseqNATModel.add_args(parser) # length prediction - parser.add_argument("--src-embedding-copy", action="store_true", - help="copy encoder word embeddings as the initial input of the decoder") - parser.add_argument("--pred-length-offset", action="store_true", - help="predicting the length difference between the target and source sentences") - parser.add_argument("--sg-length-pred", action="store_true", - help="stop the gradients back-propagated from the length predictor") - parser.add_argument("--length-loss-factor", type=float, - help="weights on the length prediction loss") + parser.add_argument( + "--src-embedding-copy", + action="store_true", + help="copy encoder word embeddings as the initial input of the decoder", + ) + parser.add_argument( + "--pred-length-offset", + action="store_true", + help="predicting the length difference between the target and source sentences", + ) + parser.add_argument( + "--sg-length-pred", + action="store_true", + help="stop the gradients back-propagated from the length predictor", + ) + parser.add_argument( + "--length-loss-factor", + type=float, + help="weights on the length prediction loss", + ) @classmethod def build_decoder(cls, args, tgt_dict, embed_tokens): @@ -81,25 +86,33 @@ def forward( encoder_out = self.encoder(src_tokens, src_lengths=src_lengths, **kwargs) # length prediction - length_out = self.decoder.forward_length(normalize=False, encoder_out=encoder_out) - length_tgt = self.decoder.forward_length_prediction(length_out, encoder_out, tgt_tokens) + length_out = self.decoder.forward_length( + normalize=False, encoder_out=encoder_out + ) + length_tgt = self.decoder.forward_length_prediction( + length_out, encoder_out, tgt_tokens + ) # decoding word_ins_out = self.decoder( normalize=False, prev_output_tokens=prev_output_tokens, - encoder_out=encoder_out) + encoder_out=encoder_out, + ) return { "word_ins": { - "out": word_ins_out, "tgt": tgt_tokens, - "mask": tgt_tokens.ne(self.pad), "ls": self.args.label_smoothing, - "nll_loss": True + "out": word_ins_out, + "tgt": tgt_tokens, + "mask": tgt_tokens.ne(self.pad), + "ls": self.args.label_smoothing, + "nll_loss": True, }, "length": { - "out": length_out, "tgt": length_tgt, - "factor": self.decoder.length_loss_factor - } + "out": length_out, + "tgt": length_tgt, + "factor": self.decoder.length_loss_factor, + }, } def forward_decoder(self, decoder_out, encoder_out, decoding_format=None, **kwargs): @@ -126,14 +139,14 @@ def forward_decoder(self, decoder_out, encoder_out, decoding_format=None, **kwar output_tokens=output_tokens, output_scores=output_scores, attn=None, - history=history + history=history, ) def initialize_output_tokens(self, encoder_out, src_tokens): # length prediction length_tgt = self.decoder.forward_length_prediction( self.decoder.forward_length(normalize=True, encoder_out=encoder_out), - encoder_out=encoder_out + encoder_out=encoder_out, ) max_length = length_tgt.clamp_(min=2).max() @@ -150,7 +163,7 @@ def initialize_output_tokens(self, encoder_out, src_tokens): initial_output_scores = initial_output_tokens.new_zeros( *initial_output_tokens.size() - ).type_as(encoder_out.encoder_out) + ).type_as(encoder_out["encoder_out"][0]) return DecoderOut( output_tokens=initial_output_tokens, @@ -158,13 +171,17 @@ def initialize_output_tokens(self, encoder_out, src_tokens): attn=None, step=0, max_step=0, - history=None + history=None, ) def regenerate_length_beam(self, decoder_out, beam_size): output_tokens = decoder_out.output_tokens length_tgt = output_tokens.ne(self.pad).sum(1) - length_tgt = length_tgt[:, None] + utils.new_arange(length_tgt, 1, beam_size) - beam_size // 2 + length_tgt = ( + length_tgt[:, None] + + utils.new_arange(length_tgt, 1, beam_size) + - beam_size // 2 + ) length_tgt = length_tgt.view(-1).clamp_(min=2) max_length = length_tgt.max() idx_length = utils.new_arange(length_tgt, max_length) @@ -183,8 +200,7 @@ def regenerate_length_beam(self, decoder_out, beam_size): ).type_as(decoder_out.output_scores) return decoder_out._replace( - output_tokens=initial_output_tokens, - output_scores=initial_output_scores + output_tokens=initial_output_tokens, output_scores=initial_output_scores ) @@ -217,8 +233,11 @@ def forward(self, normalize, encoder_out, prev_output_tokens, step=0, **unused): @ensemble_decoder def forward_length(self, normalize, encoder_out): - enc_feats = encoder_out.encoder_out # T x B x C - src_masks = encoder_out.encoder_padding_mask # B x T or None + enc_feats = encoder_out["encoder_out"][0] # T x B x C + if len(encoder_out["encoder_padding_mask"]) > 0: + src_masks = encoder_out["encoder_padding_mask"][0] # B x T + else: + src_masks = None enc_feats = _mean_pooling(enc_feats, src_masks) if self.sg_length_pred: enc_feats = enc_feats.detach() @@ -248,8 +267,11 @@ def extract_features( """ # embedding if embedding_copy: - src_embd = encoder_out.encoder_embedding - src_mask = encoder_out.encoder_padding_mask + src_embd = encoder_out["encoder_embedding"][0] + if len(encoder_out["encoder_padding_mask"]) > 0: + src_mask = encoder_out["encoder_padding_mask"][0] + else: + src_mask = None src_mask = ( ~src_mask if src_mask is not None @@ -281,8 +303,15 @@ def extract_features( x, attn, _ = layer( x, - encoder_out.encoder_out if encoder_out is not None else None, - encoder_out.encoder_padding_mask if encoder_out is not None else None, + encoder_out["encoder_out"][0] + if (encoder_out is not None and len(encoder_out["encoder_out"]) > 0) + else None, + encoder_out["encoder_padding_mask"][0] + if ( + encoder_out is not None + and len(encoder_out["encoder_padding_mask"]) > 0 + ) + else None, self_attn_mask=None, self_attn_padding_mask=decoder_padding_mask, ) @@ -317,7 +346,7 @@ def forward_embedding(self, prev_output_tokens, states=None): if positions is not None: x += positions - x = F.dropout(x, p=self.dropout, training=self.training) + x = self.dropout_module(x) decoder_padding_mask = prev_output_tokens.eq(self.padding_idx) return x, decoder_padding_mask @@ -337,8 +366,11 @@ def forward_copying_source(self, src_embeds, src_masks, tgt_masks): return copied_embedding def forward_length_prediction(self, length_out, encoder_out, tgt_tokens=None): - enc_feats = encoder_out.encoder_out # T x B x C - src_masks = encoder_out.encoder_padding_mask # B x T or None + enc_feats = encoder_out["encoder_out"][0] # T x B x C + if len(encoder_out["encoder_padding_mask"]) > 0: + src_masks = encoder_out["encoder_padding_mask"][0] # B x T + else: + src_masks = None if self.pred_length_offset: if src_masks is None: src_lengs = enc_feats.new_ones(enc_feats.size(1)).fill_( diff --git a/fairseq/models/roberta/__init__.py b/fairseq/models/roberta/__init__.py index 56579e5915..4cd723ae96 100644 --- a/fairseq/models/roberta/__init__.py +++ b/fairseq/models/roberta/__init__.py @@ -5,5 +5,7 @@ from .hub_interface import * # noqa from .model import * # noqa +from .enc_dec import * # noqa from .model_camembert import * # noqa +from .model_gottbert import * # noqa from .model_xlmr import * # noqa diff --git a/fairseq/models/roberta/alignment_utils.py b/fairseq/models/roberta/alignment_utils.py index 45d2e37194..ccc7f74cb9 100644 --- a/fairseq/models/roberta/alignment_utils.py +++ b/fairseq/models/roberta/alignment_utils.py @@ -29,23 +29,25 @@ def clean(text): # remove whitespaces to simplify alignment bpe_tokens = [roberta.task.source_dictionary.string([x]) for x in bpe_tokens] - bpe_tokens = [clean(roberta.bpe.decode(x) if x not in {'<s>', ''} else x) for x in bpe_tokens] + bpe_tokens = [ + clean(roberta.bpe.decode(x) if x not in {"<s>", ""} else x) for x in bpe_tokens + ] other_tokens = [clean(str(o)) for o in other_tokens] # strip leading <s> bpe_tokens = bpe_tokens[1:] - assert ''.join(bpe_tokens) == ''.join(other_tokens) + assert "".join(bpe_tokens) == "".join(other_tokens) # create alignment from every word to a list of BPE tokens alignment = [] - bpe_toks = filter(lambda item: item[1] != '', enumerate(bpe_tokens, start=1)) + bpe_toks = filter(lambda item: item[1] != "", enumerate(bpe_tokens, start=1)) j, bpe_tok = next(bpe_toks) for other_tok in other_tokens: bpe_indices = [] while True: if other_tok.startswith(bpe_tok): bpe_indices.append(j) - other_tok = other_tok[len(bpe_tok):] + other_tok = other_tok[len(bpe_tok) :] try: j, bpe_tok = next(bpe_toks) except StopIteration: @@ -53,11 +55,11 @@ def clean(text): elif bpe_tok.startswith(other_tok): # other_tok spans multiple BPE tokens bpe_indices.append(j) - bpe_tok = bpe_tok[len(other_tok):] - other_tok = '' + bpe_tok = bpe_tok[len(other_tok) :] + other_tok = "" else: raise Exception('Cannot align "{}" and "{}"'.format(other_tok, bpe_tok)) - if other_tok == '': + if other_tok == "": break assert len(bpe_indices) > 0 alignment.append(bpe_indices) @@ -96,20 +98,21 @@ def align_features_to_words(roberta, features, alignment): def spacy_nlp(): - if getattr(spacy_nlp, '_nlp', None) is None: + if getattr(spacy_nlp, "_nlp", None) is None: try: from spacy.lang.en import English + spacy_nlp._nlp = English() except ImportError: - raise ImportError('Please install spacy with: pip install spacy') + raise ImportError("Please install spacy with: pip install spacy") return spacy_nlp._nlp def spacy_tokenizer(): - if getattr(spacy_tokenizer, '_tokenizer', None) is None: + if getattr(spacy_tokenizer, "_tokenizer", None) is None: try: nlp = spacy_nlp() spacy_tokenizer._tokenizer = nlp.Defaults.create_tokenizer(nlp) except ImportError: - raise ImportError('Please install spacy with: pip install spacy') + raise ImportError("Please install spacy with: pip install spacy") return spacy_tokenizer._tokenizer diff --git a/fairseq/models/roberta/enc_dec.py b/fairseq/models/roberta/enc_dec.py new file mode 100644 index 0000000000..e538dee0aa --- /dev/null +++ b/fairseq/models/roberta/enc_dec.py @@ -0,0 +1,192 @@ +import argparse +import logging + +import torch.nn as nn +import fairseq.checkpoint_utils +from fairseq.models import ( + FairseqEncoderDecoderModel, + register_model, + register_model_architecture, +) +from fairseq.models.transformer import TransformerDecoder +from fairseq.models.roberta import model as roberta + +logger = logging.getLogger(__name__) + + +@register_model("roberta_enc_dec") +class RobertaEncDecModel(FairseqEncoderDecoderModel): + @staticmethod + def add_args(parser): + parser.add_argument( + "--pretrained-mlm-checkpoint", + default=None, + type=str, + metavar="PRETRAINED", + help="path to pretrained mlm checkpoint", + ) + parser.add_argument( + "--pretrained-decoder", action="store_true", help="reload decoder" + ) + parser.add_argument( + "--hack-layernorm-embedding", + action="store_true", + help="hack to reload old models trained with encoder-normalize-before=False (no equivalent to encoder-normalize-before=False and layernorm_embedding=False", + ) + parser.add_argument( + "--share-decoder-input-output-embed", + action="store_true", + help="share decoder input and output embeddings", + ) + parser.add_argument( + "--share-all-embeddings", + action="store_true", + help="share encoder, decoder and output embeddings" + " (requires shared dictionary and embed dim)", + ) + + @classmethod + def build_model(cls, args, task): + """Build a new model instance.""" + + # make sure all arguments are present + base_enc_dec_architecture(args) + if args.pretrained_mlm_checkpoint: + arg_overrides = None + if args.hack_layernorm_embedding: + arg_overrides = {"layernorm_embedding": False} + loaded = fairseq.checkpoint_utils.load_model_ensemble_and_task( + [args.pretrained_mlm_checkpoint], arg_overrides=arg_overrides + ) + ([roberta_enc], _cfg, _task) = loaded + else: + # Do we need to edit untie_weights here ? + share_in_out = ( + args.share_decoder_input_output_embed or args.share_all_embeddings + ) + args.untie_weights_roberta = not share_in_out + if args.hack_layernorm_embedding: + args.layernorm_embedding = False + args.encoder_normalize_before = False + roberta_enc = roberta.RobertaModel.build_model(args, task) + + return cls.from_roberta(roberta_enc, args, task.source_dictionary) + + @staticmethod + def from_roberta(roberta_enc: roberta.RobertaModel, args, dictionary): + encoder = roberta_enc.encoder.sentence_encoder + vocab_size, embed_dim = encoder.embed_tokens.weight.shape + + if args.share_all_embeddings: + lm_head = roberta_enc.encoder.lm_head + assert encoder.embed_tokens.weight is lm_head.weight, ( + "Can't use --share-all-embeddings with a model " + "that was pretraiend with --untie-weights-roberta_enc" + ) + else: + lm_head = roberta.RobertaLMHead( + embed_dim, vocab_size, roberta_enc.args.activation_fn + ) + + dec_embs = nn.Embedding(vocab_size, embed_dim, dictionary.pad()) + if args.share_all_embeddings or args.share_decoder_input_output_embed: + # Note: I wasn't able to use Embedding _weight parameter to achive this sharing. + dec_embs.weight = lm_head.weight + + decoder = TransformerDecoder( + RobertaEncDecModel.read_args_from_roberta(roberta_enc.args), + dictionary, + dec_embs, + no_encoder_attn=False, + output_projection=lm_head, + ) + if getattr(args, "pretrained_decoder", False): + decoder_dict = encoder.state_dict() + + # TODO: hide setting "encoder_attn" layers behind a flag. + for k, w in list(decoder_dict.items()): + if ".self_attn" in k: + k_enc_attn = k.replace(".self_attn", ".encoder_attn") + decoder_dict[k_enc_attn] = w.detach().clone() + + for k, w in lm_head.state_dict().items(): + decoder_dict["output_projection." + k] = w + + missing_keys, unexpected_keys = decoder.load_state_dict( + decoder_dict, strict=False + ) + # missing_keys = [m for m in missing_keys if ".encoder_attn" not in m] + assert not missing_keys and not unexpected_keys, ( + "Failed to load state dict. " + f"Missing keys: {missing_keys}. " + f"Unexpected keys: {unexpected_keys}." + ) + + if args.share_all_embeddings: + assert decoder.output_projection.weight is decoder.embed_tokens.weight + assert encoder.embed_tokens.weight is decoder.embed_tokens.weight + elif args.share_decoder_input_output_embed: + assert decoder.output_projection.weight is decoder.embed_tokens.weight + assert encoder.embed_tokens.weight is not decoder.embed_tokens.weight + else: + assert decoder.output_projection.weight is not decoder.embed_tokens.weight + assert encoder.embed_tokens.weight is not decoder.embed_tokens.weight + + return RobertaEncDecModel(encoder, decoder) + + @staticmethod + def read_args_from_roberta(roberta_args: argparse.Namespace): + # TODO: this would become easier if encoder/decoder where using a similar + # TransformerConfig object + args = argparse.Namespace(**vars(roberta_args)) + attr_map = [ + ("encoder_attention_heads", "decoder_attention_heads"), + ("encoder_embed_dim", "decoder_embed_dim"), + ("encoder_embed_dim", "decoder_output_dim"), + ("encoder_normalize_before", "decoder_normalize_before"), + ("encoder_layers_to_keep", "decoder_layers_to_keep"), + ("encoder_ffn_embed_dim", "decoder_ffn_embed_dim"), + ("encoder_layerdrop", "decoder_layerdrop"), + ("encoder_layers", "decoder_layers"), + ("encoder_learned_pos", "decoder_learned_pos"), + # should this be set from here ? + ("max_positions", "max_target_positions"), + ] + for k1, k2 in attr_map: + setattr(args, k2, getattr(roberta_args, k1)) + + args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None) + args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0) + args.share_decoder_input_output_embed = not roberta_args.untie_weights_roberta + return args + + def upgrade_state_dict_named(self, state_dict, name): + prefix = name + "." if name != "" else "" + super().upgrade_state_dict_named(state_dict, name) + old_keys = list(state_dict.keys()) + + # rename decoder -> encoder before upgrading children modules + for k in old_keys: + if k.startswith(prefix + "encoder.lm_head"): + state_dict.pop(k) + continue + new_k = k + new_k = new_k.replace(".sentence_encoder.", ".") + new_k = new_k.replace("decoder.lm_head.", "decoder.output_projection.") + if k == new_k: + continue + # print(k, "->", new_k) + state_dict[new_k] = state_dict.pop(k) + + +@register_model_architecture("roberta_enc_dec", "roberta_enc_dec") +def base_enc_dec_architecture(args): + args.hack_layernorm_embedding = getattr(args, "hack_layernorm_embedding", False) + args.pretrained_mlm_checkpoint = getattr(args, "pretrained_mlm_checkpoint", None) + args.pretrained_decoder = getattr(args, "pretrained_decoder", None) + args.share_all_embeddings = getattr(args, "share_all_embeddings", False) + args.share_decoder_input_output_embed = getattr( + args, "share_decoder_input_output_embed", False + ) + + roberta.base_architecture(args) diff --git a/fairseq/models/roberta/hub_interface.py b/fairseq/models/roberta/hub_interface.py index baf0bf28b9..ba298d63ba 100644 --- a/fairseq/models/roberta/hub_interface.py +++ b/fairseq/models/roberta/hub_interface.py @@ -7,7 +7,6 @@ import torch import torch.nn as nn import torch.nn.functional as F - from fairseq import utils from fairseq.data import encoders @@ -15,25 +14,27 @@ class RobertaHubInterface(nn.Module): """A simple PyTorch Hub interface to RoBERTa. - Usage: https://github.com/pytorch/fairseq/tree/master/examples/roberta + Usage: https://github.com/pytorch/fairseq/tree/main/examples/roberta """ - def __init__(self, args, task, model): + def __init__(self, cfg, task, model): super().__init__() - self.args = args + self.cfg = cfg self.task = task self.model = model - self.bpe = encoders.build_bpe(args) + self.bpe = encoders.build_bpe(cfg.bpe) # this is useful for determining the device - self.register_buffer('_float_tensor', torch.tensor([0], dtype=torch.float)) + self.register_buffer("_float_tensor", torch.tensor([0], dtype=torch.float)) @property def device(self): return self._float_tensor.device - def encode(self, sentence: str, *addl_sentences, no_separator=False) -> torch.LongTensor: + def encode( + self, sentence: str, *addl_sentences, no_separator=False + ) -> torch.LongTensor: """ BPE-encode a sentence (or multiple sentences). @@ -54,11 +55,13 @@ def encode(self, sentence: str, *addl_sentences, no_separator=False) -> torch.Lo >>> roberta.encode('world').tolist() [0, 8331, 2] """ - bpe_sentence = '<s> ' + self.bpe.encode(sentence) + ' </s>' + bpe_sentence = "<s> " + self.bpe.encode(sentence) + " </s>" for s in addl_sentences: - bpe_sentence += (' </s>' if not no_separator else '') - bpe_sentence += ' ' + self.bpe.encode(s) + ' </s>' - tokens = self.task.source_dictionary.encode_line(bpe_sentence, append_eos=False, add_if_not_exist=False) + bpe_sentence += " </s>" if not no_separator else "" + bpe_sentence += " " + self.bpe.encode(s) + " </s>" + tokens = self.task.source_dictionary.encode_line( + bpe_sentence, append_eos=False, add_if_not_exist=False + ) return tokens.long() def decode(self, tokens: torch.LongTensor): @@ -66,21 +69,27 @@ def decode(self, tokens: torch.LongTensor): tokens = tokens.numpy() if tokens[0] == self.task.source_dictionary.bos(): tokens = tokens[1:] # remove <s> - eos_mask = (tokens == self.task.source_dictionary.eos()) + eos_mask = tokens == self.task.source_dictionary.eos() doc_mask = eos_mask[1:] & eos_mask[:-1] sentences = np.split(tokens, doc_mask.nonzero()[0] + 1) - sentences = [self.bpe.decode(self.task.source_dictionary.string(s)) for s in sentences] + sentences = [ + self.bpe.decode(self.task.source_dictionary.string(s)) for s in sentences + ] if len(sentences) == 1: return sentences[0] return sentences - def extract_features(self, tokens: torch.LongTensor, return_all_hiddens: bool = False) -> torch.Tensor: + def extract_features( + self, tokens: torch.LongTensor, return_all_hiddens: bool = False + ) -> torch.Tensor: if tokens.dim() == 1: tokens = tokens.unsqueeze(0) if tokens.size(-1) > self.model.max_positions(): - raise ValueError('tokens exceeds maximum length: {} > {}'.format( - tokens.size(-1), self.model.max_positions() - )) + raise ValueError( + "tokens exceeds maximum length: {} > {}".format( + tokens.size(-1), self.model.max_positions() + ) + ) features, extra = self.model( tokens.to(device=self.device), features_only=True, @@ -88,7 +97,7 @@ def extract_features(self, tokens: torch.LongTensor, return_all_hiddens: bool = ) if return_all_hiddens: # convert from T x B x C -> B x T x C - inner_states = extra['inner_states'] + inner_states = extra["inner_states"] return [inner_state.transpose(0, 1) for inner_state in inner_states] else: return features # just the last layer's features @@ -107,7 +116,9 @@ def predict(self, head: str, tokens: torch.LongTensor, return_logits: bool = Fal return logits return F.log_softmax(logits, dim=-1) - def extract_features_aligned_to_words(self, sentence: str, return_all_hiddens: bool = False) -> torch.Tensor: + def extract_features_aligned_to_words( + self, sentence: str, return_all_hiddens: bool = False + ) -> torch.Tensor: """Extract RoBERTa features, aligned to spaCy's word-level tokenizer.""" from fairseq.models.roberta import alignment_utils from spacy.tokens import Doc @@ -122,40 +133,51 @@ def extract_features_aligned_to_words(self, sentence: str, return_all_hiddens: b alignment = alignment_utils.align_bpe_to_words(self, bpe_toks, spacy_toks_ws) # extract features and align them - features = self.extract_features(bpe_toks, return_all_hiddens=return_all_hiddens) + features = self.extract_features( + bpe_toks, return_all_hiddens=return_all_hiddens + ) features = features.squeeze(0) - aligned_feats = alignment_utils.align_features_to_words(self, features, alignment) + aligned_feats = alignment_utils.align_features_to_words( + self, features, alignment + ) # wrap in spaCy Doc doc = Doc( nlp.vocab, - words=['<s>'] + [x.text for x in spacy_toks] + ['</s>'], - spaces=[True] + [x.endswith(' ') for x in spacy_toks_ws[:-1]] + [True, False], + words=["<s>"] + [x.text for x in spacy_toks] + ["</s>"], + spaces=[True] + + [x.endswith(" ") for x in spacy_toks_ws[:-1]] + + [True, False], ) assert len(doc) == aligned_feats.size(0) - doc.user_token_hooks['vector'] = lambda token: aligned_feats[token.i] + doc.user_token_hooks["vector"] = lambda token: aligned_feats[token.i] return doc def fill_mask(self, masked_input: str, topk: int = 5): - masked_token = '<mask>' - assert masked_token in masked_input and masked_input.count(masked_token) == 1, \ - "Please add one {0} token for the input, eg: 'He is a {0} guy'".format(masked_token) + masked_token = "<mask>" + assert ( + masked_token in masked_input and masked_input.count(masked_token) == 1 + ), "Please add one {0} token for the input, eg: 'He is a {0} guy'".format( + masked_token + ) text_spans = masked_input.split(masked_token) - text_spans_bpe = (' {0} '.format(masked_token)).join( - [self.bpe.encode(text_span.rstrip()) for text_span in text_spans] - ).strip() + text_spans_bpe = ( + (" {0} ".format(masked_token)) + .join([self.bpe.encode(text_span.rstrip()) for text_span in text_spans]) + .strip() + ) tokens = self.task.source_dictionary.encode_line( - '<s> ' + text_spans_bpe + ' </s>', + "<s> " + text_spans_bpe + " </s>", append_eos=False, add_if_not_exist=False, ) - masked_index = (tokens == self.task.mask_idx).nonzero() + masked_index = (tokens == self.task.mask_idx).nonzero(as_tuple=False) if tokens.dim() == 1: tokens = tokens.unsqueeze(0) - with utils.eval(self.model): + with utils.model_eval(self.model): features, extra = self.model( tokens.long().to(device=self.device), features_only=False, @@ -167,25 +189,31 @@ def fill_mask(self, masked_input: str, topk: int = 5): topk_predicted_token_bpe = self.task.source_dictionary.string(index) topk_filled_outputs = [] - for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(' ')): + for index, predicted_token_bpe in enumerate( + topk_predicted_token_bpe.split(" ") + ): predicted_token = self.bpe.decode(predicted_token_bpe) # Quick hack to fix https://github.com/pytorch/fairseq/issues/1306 - if predicted_token_bpe.startswith('\u2581'): - predicted_token = ' ' + predicted_token + if predicted_token_bpe.startswith("\u2581"): + predicted_token = " " + predicted_token if " {0}".format(masked_token) in masked_input: - topk_filled_outputs.append(( - masked_input.replace( - ' {0}'.format(masked_token), predicted_token - ), - values[index].item(), - predicted_token, - )) + topk_filled_outputs.append( + ( + masked_input.replace( + " {0}".format(masked_token), predicted_token + ), + values[index].item(), + predicted_token, + ) + ) else: - topk_filled_outputs.append(( - masked_input.replace(masked_token, predicted_token), - values[index].item(), - predicted_token, - )) + topk_filled_outputs.append( + ( + masked_input.replace(masked_token, predicted_token), + values[index].item(), + predicted_token, + ) + ) return topk_filled_outputs def disambiguate_pronoun(self, sentence: str) -> bool: @@ -198,7 +226,10 @@ def disambiguate_pronoun(self, sentence: str) -> bool: >>> disambiguate_pronoun('The trophy would not fit in the brown suitcase because [it] was too big.') 'The trophy' """ - assert hasattr(self.task, 'disambiguate_pronoun'), \ - 'roberta.disambiguate_pronoun() requires a model trained with the WSC task.' - with utils.eval(self.model): - return self.task.disambiguate_pronoun(self.model, sentence, use_cuda=self.device.type == 'cuda') + assert hasattr( + self.task, "disambiguate_pronoun" + ), "roberta.disambiguate_pronoun() requires a model trained with the WSC task." + with utils.model_eval(self.model): + return self.task.disambiguate_pronoun( + self.model, sentence, use_cuda=self.device.type == "cuda" + ) diff --git a/fairseq/models/roberta/model.py b/fairseq/models/roberta/model.py index ce720db2d9..3337616be6 100644 --- a/fairseq/models/roberta/model.py +++ b/fairseq/models/roberta/model.py @@ -11,20 +11,17 @@ import torch import torch.nn as nn import torch.nn.functional as F - from fairseq import utils from fairseq.models import ( - FairseqDecoder, - FairseqLanguageModel, + FairseqEncoder, + FairseqEncoderModel, register_model, register_model_architecture, ) -from fairseq.modules import ( - LayerNorm, - TransformerSentenceEncoder, -) -from fairseq.modules.transformer_sentence_encoder import init_bert_params +from fairseq.models.transformer import DEFAULT_MIN_PARAMS_TO_WRAP, TransformerEncoder +from fairseq.modules import LayerNorm from fairseq.modules.quant_noise import quant_noise as apply_quant_noise_ +from fairseq.modules.transformer_sentence_encoder import init_bert_params from .hub_interface import RobertaHubInterface @@ -32,16 +29,15 @@ logger = logging.getLogger(__name__) -@register_model('roberta') -class RobertaModel(FairseqLanguageModel): - +@register_model("roberta") +class RobertaModel(FairseqEncoderModel): @classmethod def hub_models(cls): return { - 'roberta.base': 'http://dl.fbaipublicfiles.com/fairseq/models/roberta.base.tar.gz', - 'roberta.large': 'http://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz', - 'roberta.large.mnli': 'http://dl.fbaipublicfiles.com/fairseq/models/roberta.large.mnli.tar.gz', - 'roberta.large.wsc': 'http://dl.fbaipublicfiles.com/fairseq/models/roberta.large.wsc.tar.gz', + "roberta.base": "http://dl.fbaipublicfiles.com/fairseq/models/roberta.base.tar.gz", + "roberta.large": "http://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz", + "roberta.large.mnli": "http://dl.fbaipublicfiles.com/fairseq/models/roberta.large.mnli.tar.gz", + "roberta.large.wsc": "http://dl.fbaipublicfiles.com/fairseq/models/roberta.large.wsc.tar.gz", } def __init__(self, args, encoder): @@ -56,73 +52,191 @@ def __init__(self, args, encoder): @staticmethod def add_args(parser): """Add model-specific arguments to the parser.""" - parser.add_argument('--encoder-layers', type=int, metavar='L', - help='num encoder layers') - parser.add_argument('--encoder-embed-dim', type=int, metavar='H', - help='encoder embedding dimension') - parser.add_argument('--encoder-ffn-embed-dim', type=int, metavar='F', - help='encoder embedding dimension for FFN') - parser.add_argument('--encoder-attention-heads', type=int, metavar='A', - help='num encoder attention heads') - parser.add_argument('--activation-fn', - choices=utils.get_available_activation_fns(), - help='activation function to use') - parser.add_argument('--pooler-activation-fn', - choices=utils.get_available_activation_fns(), - help='activation function to use for pooler layer') - parser.add_argument('--encoder-normalize-before', action='store_true', - help='apply layernorm before each encoder block') - parser.add_argument('--dropout', type=float, metavar='D', - help='dropout probability') - parser.add_argument('--attention-dropout', type=float, metavar='D', - help='dropout probability for attention weights') - parser.add_argument('--activation-dropout', type=float, metavar='D', - help='dropout probability after activation in FFN') - parser.add_argument('--pooler-dropout', type=float, metavar='D', - help='dropout probability in the masked_lm pooler layers') - parser.add_argument('--max-positions', type=int, - help='number of positional embeddings to learn') - parser.add_argument('--load-checkpoint-heads', action='store_true', - help='(re-)register and load heads when loading checkpoints') + parser.add_argument( + "--encoder-layers", type=int, metavar="L", help="num encoder layers" + ) + parser.add_argument( + "--encoder-embed-dim", + type=int, + metavar="H", + help="encoder embedding dimension", + ) + parser.add_argument( + "--encoder-ffn-embed-dim", + type=int, + metavar="F", + help="encoder embedding dimension for FFN", + ) + parser.add_argument( + "--encoder-attention-heads", + type=int, + metavar="A", + help="num encoder attention heads", + ) + parser.add_argument( + "--activation-fn", + choices=utils.get_available_activation_fns(), + help="activation function to use", + ) + parser.add_argument( + "--pooler-activation-fn", + choices=utils.get_available_activation_fns(), + help="activation function to use for pooler layer", + ) + parser.add_argument( + "--encoder-normalize-before", + action="store_true", + help="apply layernorm before each encoder block", + ) + parser.add_argument( + "--layernorm-embedding", + action="store_true", + help="add layernorm to embedding", + ) + parser.add_argument( + "--dropout", type=float, metavar="D", help="dropout probability" + ) + parser.add_argument( + "--attention-dropout", + type=float, + metavar="D", + help="dropout probability for attention weights", + ) + parser.add_argument( + "--activation-dropout", + type=float, + metavar="D", + help="dropout probability after activation in FFN", + ) + parser.add_argument( + "--pooler-dropout", + type=float, + metavar="D", + help="dropout probability in the masked_lm pooler layers", + ) + parser.add_argument( + "--max-positions", type=int, help="number of positional embeddings to learn" + ) + parser.add_argument( + "--load-checkpoint-heads", + action="store_true", + help="(re-)register and load heads when loading checkpoints", + ) + parser.add_argument( + "--untie-weights-roberta", + action="store_true", + help="Untie weights between embeddings and classifiers in RoBERTa", + ) # args for "Reducing Transformer Depth on Demand with Structured Dropout" (Fan et al., 2019) - parser.add_argument('--encoder-layerdrop', type=float, metavar='D', default=0, - help='LayerDrop probability for encoder') - parser.add_argument('--encoder-layers-to-keep', default=None, - help='which layers to *keep* when pruning as a comma-separated list') + parser.add_argument( + "--encoder-layerdrop", + type=float, + metavar="D", + default=0, + help="LayerDrop probability for encoder", + ) + parser.add_argument( + "--encoder-layers-to-keep", + default=None, + help="which layers to *keep* when pruning as a comma-separated list", + ) # args for Training with Quantization Noise for Extreme Model Compression ({Fan*, Stock*} et al., 2020) - parser.add_argument('--quant-noise-pq', type=float, metavar='D', default=0, - help='iterative PQ quantization noise at training time') - parser.add_argument('--quant-noise-pq-block-size', type=int, metavar='D', default=8, - help='block size of quantization noise at training time') - parser.add_argument('--quant-noise-scalar', type=float, metavar='D', default=0, - help='scalar quantization noise and scalar quantization at training time') - parser.add_argument('--untie-weights-roberta', action='store_true', - help='Untie weights between embeddings and classifiers in RoBERTa') + parser.add_argument( + "--quant-noise-pq", + type=float, + metavar="D", + default=0, + help="iterative PQ quantization noise at training time", + ) + parser.add_argument( + "--quant-noise-pq-block-size", + type=int, + metavar="D", + default=8, + help="block size of quantization noise at training time", + ) + parser.add_argument( + "--quant-noise-scalar", + type=float, + metavar="D", + default=0, + help="scalar quantization noise and scalar quantization at training time", + ) + # args for "Better Fine-Tuning by Reducing Representational Collapse" (Aghajanyan et al. 2020) + parser.add_argument( + "--spectral-norm-classification-head", + action="store_true", + default=False, + help="Apply spectral normalization on the classification head", + ) + # args for Fully Sharded Data Parallel (FSDP) training + parser.add_argument( + "--min-params-to-wrap", + type=int, + metavar="D", + default=DEFAULT_MIN_PARAMS_TO_WRAP, + help=( + "minimum number of params for a layer to be wrapped with FSDP() when " + "training with --ddp-backend=fully_sharded. Smaller values will " + "improve memory efficiency, but may make torch.distributed " + "communication less efficient due to smaller input sizes. This option " + "is set to 0 (i.e., always wrap) when --checkpoint-activations or " + "--offload-activations are passed." + ) + ) @classmethod def build_model(cls, args, task): """Build a new model instance.""" + from omegaconf import OmegaConf + + if OmegaConf.is_config(args): + OmegaConf.set_struct(args, False) + # make sure all arguments are present base_architecture(args) - if not hasattr(args, 'max_positions'): + if not hasattr(args, "max_positions"): + if not hasattr(args, "tokens_per_sample"): + args.tokens_per_sample = task.max_positions() args.max_positions = args.tokens_per_sample encoder = RobertaEncoder(args, task.source_dictionary) + + if OmegaConf.is_config(args): + OmegaConf.set_struct(args, True) + return cls(args, encoder) - def forward(self, src_tokens, features_only=False, return_all_hiddens=False, classification_head_name=None, **kwargs): + def forward( + self, + src_tokens, + features_only=False, + return_all_hiddens=False, + classification_head_name=None, + **kwargs, + ): if classification_head_name is not None: features_only = True - x, extra = self.decoder(src_tokens, features_only, return_all_hiddens, **kwargs) + x, extra = self.encoder(src_tokens, features_only, return_all_hiddens, **kwargs) if classification_head_name is not None: x = self.classification_heads[classification_head_name](x) return x, extra - def register_classification_head(self, name, num_classes=None, inner_dim=None, **kwargs): + def get_normalized_probs(self, net_output, log_probs, sample=None): + """Get normalized probabilities (or log probs) from a net's output.""" + logits = net_output[0].float() + if log_probs: + return F.log_softmax(logits, dim=-1) + else: + return F.softmax(logits, dim=-1) + + def register_classification_head( + self, name, num_classes=None, inner_dim=None, **kwargs + ): """Register a classification head.""" if name in self.classification_heads: prev_num_classes = self.classification_heads[name].out_proj.out_features @@ -130,27 +244,36 @@ def register_classification_head(self, name, num_classes=None, inner_dim=None, * if num_classes != prev_num_classes or inner_dim != prev_inner_dim: logger.warning( 're-registering head "{}" with num_classes {} (prev: {}) ' - 'and inner_dim {} (prev: {})'.format( + "and inner_dim {} (prev: {})".format( name, num_classes, prev_num_classes, inner_dim, prev_inner_dim ) ) self.classification_heads[name] = RobertaClassificationHead( - self.args.encoder_embed_dim, - inner_dim or self.args.encoder_embed_dim, - num_classes, - self.args.pooler_activation_fn, - self.args.pooler_dropout, - self.args.quant_noise_pq, - self.args.quant_noise_pq_block_size, + input_dim=self.args.encoder_embed_dim, + inner_dim=inner_dim or self.args.encoder_embed_dim, + num_classes=num_classes, + activation_fn=self.args.pooler_activation_fn, + pooler_dropout=self.args.pooler_dropout, + q_noise=self.args.quant_noise_pq, + qn_block_size=self.args.quant_noise_pq_block_size, + do_spectral_norm=self.args.spectral_norm_classification_head, ) @property def supported_targets(self): - return {'self'} + return {"self"} @classmethod - def from_pretrained(cls, model_name_or_path, checkpoint_file='model.pt', data_name_or_path='.', bpe='gpt2', **kwargs): + def from_pretrained( + cls, + model_name_or_path, + checkpoint_file="model.pt", + data_name_or_path=".", + bpe="gpt2", + **kwargs, + ): from fairseq import hub_utils + x = hub_utils.from_pretrained( model_name_or_path, checkpoint_file, @@ -160,42 +283,70 @@ def from_pretrained(cls, model_name_or_path, checkpoint_file='model.pt', data_na load_checkpoint_heads=True, **kwargs, ) - return RobertaHubInterface(x['args'], x['task'], x['models'][0]) + + logger.info(x["args"]) + return RobertaHubInterface(x["args"], x["task"], x["models"][0]) def upgrade_state_dict_named(self, state_dict, name): + prefix = name + "." if name != "" else "" + + # rename decoder -> encoder before upgrading children modules + for k in list(state_dict.keys()): + if k.startswith(prefix + "decoder"): + new_k = prefix + "encoder" + k[len(prefix + "decoder") :] + state_dict[new_k] = state_dict[k] + del state_dict[k] + + # rename emb_layer_norm -> layernorm_embedding + for k in list(state_dict.keys()): + if ".emb_layer_norm." in k: + new_k = k.replace(".emb_layer_norm.", ".layernorm_embedding.") + state_dict[new_k] = state_dict[k] + del state_dict[k] + + # upgrade children modules super().upgrade_state_dict_named(state_dict, name) - prefix = name + '.' if name != '' else '' - current_head_names = [] if not hasattr(self, 'classification_heads') else \ - self.classification_heads.keys() - # Handle new classification heads present in the state dict. + current_head_names = ( + [] + if not hasattr(self, "classification_heads") + else self.classification_heads.keys() + ) keys_to_delete = [] for k in state_dict.keys(): - if not k.startswith(prefix + 'classification_heads.'): + if not k.startswith(prefix + "classification_heads."): continue - head_name = k[len(prefix + 'classification_heads.'):].split('.')[0] - num_classes = state_dict[prefix + 'classification_heads.' + head_name + '.out_proj.weight'].size(0) - inner_dim = state_dict[prefix + 'classification_heads.' + head_name + '.dense.weight'].size(0) + head_name = k[len(prefix + "classification_heads.") :].split(".")[0] + num_classes = state_dict[ + prefix + "classification_heads." + head_name + ".out_proj.weight" + ].size(0) + inner_dim = state_dict[ + prefix + "classification_heads." + head_name + ".dense.weight" + ].size(0) - if getattr(self.args, 'load_checkpoint_heads', False): + if getattr(self.args, "load_checkpoint_heads", False): if head_name not in current_head_names: self.register_classification_head(head_name, num_classes, inner_dim) else: if head_name not in current_head_names: logger.warning( - 'deleting classification head ({}) from checkpoint ' - 'not present in current model: {}'.format(head_name, k) + "deleting classification head ({}) from checkpoint " + "not present in current model: {}".format(head_name, k) ) keys_to_delete.append(k) elif ( - num_classes != self.classification_heads[head_name].out_proj.out_features - or inner_dim != self.classification_heads[head_name].dense.out_features + num_classes + != self.classification_heads[head_name].out_proj.out_features + or inner_dim + != self.classification_heads[head_name].dense.out_features ): logger.warning( - 'deleting classification head ({}) from checkpoint ' - 'with different dimensions than current model: {}'.format(head_name, k) + "deleting classification head ({}) from checkpoint " + "with different dimensions than current model: {}".format( + head_name, k + ) ) keys_to_delete.append(k) for k in keys_to_delete: @@ -203,12 +354,12 @@ def upgrade_state_dict_named(self, state_dict, name): # Copy any newly-added classification heads into the state dict # with their current weights. - if hasattr(self, 'classification_heads'): + if hasattr(self, "classification_heads"): cur_state = self.classification_heads.state_dict() for k, v in cur_state.items(): - if prefix + 'classification_heads.' + k not in state_dict: - logger.info('Overwriting ' + prefix + 'classification_heads.' + k) - state_dict[prefix + 'classification_heads.' + k] = v + if prefix + "classification_heads." + k not in state_dict: + logger.info("Overwriting " + prefix + "classification_heads." + k) + state_dict[prefix + "classification_heads." + k] = v class RobertaLMHead(nn.Module): @@ -242,7 +393,17 @@ def forward(self, features, masked_tokens=None, **kwargs): class RobertaClassificationHead(nn.Module): """Head for sentence-level classification tasks.""" - def __init__(self, input_dim, inner_dim, num_classes, activation_fn, pooler_dropout, q_noise=0, qn_block_size=8): + def __init__( + self, + input_dim, + inner_dim, + num_classes, + activation_fn, + pooler_dropout, + q_noise=0, + qn_block_size=8, + do_spectral_norm=False, + ): super().__init__() self.dense = nn.Linear(input_dim, inner_dim) self.activation_fn = utils.get_activation_fn(activation_fn) @@ -250,6 +411,12 @@ def __init__(self, input_dim, inner_dim, num_classes, activation_fn, pooler_drop self.out_proj = apply_quant_noise_( nn.Linear(inner_dim, num_classes), q_noise, qn_block_size ) + if do_spectral_norm: + if q_noise != 0: + raise NotImplementedError( + "Attempting to use Spectral Normalization with Quant Noise. This is not officially supported" + ) + self.out_proj = torch.nn.utils.spectral_norm(self.out_proj) def forward(self, features, **kwargs): x = features[:, 0, :] # take <s> token (equiv. to [CLS]) @@ -261,54 +428,55 @@ def forward(self, features, **kwargs): return x -class RobertaEncoder(FairseqDecoder): - """RoBERTa encoder. - - Implements the :class:`~fairseq.models.FairseqDecoder` interface required - by :class:`~fairseq.models.FairseqLanguageModel`. - """ +class RobertaEncoder(FairseqEncoder): + """RoBERTa encoder.""" def __init__(self, args, dictionary): super().__init__(dictionary) + + # set any missing default values + base_architecture(args) self.args = args - # RoBERTa is a sentence encoder model, so users will intuitively trim - # encoder layers. However, the implementation uses the fairseq decoder, - # so we fix here. if args.encoder_layers_to_keep: args.encoder_layers = len(args.encoder_layers_to_keep.split(",")) - args.decoder_layers_to_keep = args.encoder_layers_to_keep - args.encoder_layers_to_keep = None - - self.sentence_encoder = TransformerSentenceEncoder( - padding_idx=dictionary.pad(), - vocab_size=len(dictionary), - num_encoder_layers=args.encoder_layers, - embedding_dim=args.encoder_embed_dim, - ffn_embedding_dim=args.encoder_ffn_embed_dim, - num_attention_heads=args.encoder_attention_heads, - dropout=args.dropout, - attention_dropout=args.attention_dropout, - activation_dropout=args.activation_dropout, - layerdrop=args.encoder_layerdrop, - max_seq_len=args.max_positions, - num_segments=0, - encoder_normalize_before=True, - apply_bert_init=True, - activation_fn=args.activation_fn, - q_noise=args.quant_noise_pq, - qn_block_size=args.quant_noise_pq_block_size, + + embed_tokens = self.build_embedding( + len(dictionary), args.encoder_embed_dim, dictionary.pad() ) - args.untie_weights_roberta = getattr(args, 'untie_weights_roberta', False) - self.lm_head = RobertaLMHead( + self.sentence_encoder = self.build_encoder(args, dictionary, embed_tokens) + + self.lm_head = self.build_lm_head( embed_dim=args.encoder_embed_dim, output_dim=len(dictionary), activation_fn=args.activation_fn, - weight=self.sentence_encoder.embed_tokens.weight if not args.untie_weights_roberta else None, + weight=( + self.sentence_encoder.embed_tokens.weight + if not args.untie_weights_roberta + else None + ), ) - def forward(self, src_tokens, features_only=False, return_all_hiddens=False, masked_tokens=None, **unused): + def build_embedding(self, vocab_size, embedding_dim, padding_idx): + return nn.Embedding(vocab_size, embedding_dim, padding_idx) + + def build_encoder(self, args, dictionary, embed_tokens): + encoder = TransformerEncoder(args, dictionary, embed_tokens) + encoder.apply(init_bert_params) + return encoder + + def build_lm_head(self, embed_dim, output_dim, activation_fn, weight): + return RobertaLMHead(embed_dim, output_dim, activation_fn, weight) + + def forward( + self, + src_tokens, + features_only=False, + return_all_hiddens=False, + masked_tokens=None, + **unused, + ): """ Args: src_tokens (LongTensor): input tokens of shape `(batch, src_len)` @@ -325,18 +493,23 @@ def forward(self, src_tokens, features_only=False, return_all_hiddens=False, mas is a list of hidden states. Note that the hidden states have shape `(src_len, batch, vocab)`. """ - x, extra = self.extract_features(src_tokens, return_all_hiddens=return_all_hiddens) + x, extra = self.extract_features( + src_tokens, return_all_hiddens=return_all_hiddens + ) if not features_only: x = self.output_layer(x, masked_tokens=masked_tokens) return x, extra - def extract_features(self, src_tokens, return_all_hiddens=False, **unused): - inner_states, _ = self.sentence_encoder( + def extract_features(self, src_tokens, return_all_hiddens=False, **kwargs): + encoder_out = self.sentence_encoder( src_tokens, - last_state_only=not return_all_hiddens, + return_all_hiddens=return_all_hiddens, + token_embeddings=kwargs.get("token_embeddings", None), ) - features = inner_states[-1].transpose(0, 1) # T x B x C -> B x T x C - return features, {'inner_states': inner_states if return_all_hiddens else None} + # T x B x C -> B x T x C + features = encoder_out["encoder_out"][0].transpose(0, 1) + inner_states = encoder_out["encoder_states"] if return_all_hiddens else None + return features, {"inner_states": inner_states} def output_layer(self, features, masked_tokens=None, **unused): return self.lm_head(features, masked_tokens) @@ -346,42 +519,83 @@ def max_positions(self): return self.args.max_positions -@register_model_architecture('roberta', 'roberta') -def base_architecture(args): - args.encoder_layers = getattr(args, 'encoder_layers', 12) - args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 768) - args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 3072) - args.encoder_attention_heads = getattr(args, 'encoder_attention_heads', 12) +def safe_getattr(obj, k, default=None): + from omegaconf import OmegaConf + + if OmegaConf.is_config(obj): + return obj[k] if k in obj and obj[k] is not None else default - args.activation_fn = getattr(args, 'activation_fn', 'gelu') - args.pooler_activation_fn = getattr(args, 'pooler_activation_fn', 'tanh') + return getattr(obj, k, default) - args.dropout = getattr(args, 'dropout', 0.1) - args.attention_dropout = getattr(args, 'attention_dropout', 0.1) - args.activation_dropout = getattr(args, 'activation_dropout', 0.0) - args.pooler_dropout = getattr(args, 'pooler_dropout', 0.0) - args.encoder_layers_to_keep = getattr(args, 'encoder_layers_to_keep', None) - args.encoder_layerdrop = getattr(args, 'encoder_layerdrop', 0.0) +@register_model_architecture("roberta", "roberta") +def base_architecture(args): + args.encoder_layers = safe_getattr(args, "encoder_layers", 12) + args.encoder_embed_dim = safe_getattr(args, "encoder_embed_dim", 768) + args.encoder_ffn_embed_dim = safe_getattr(args, "encoder_ffn_embed_dim", 3072) + args.encoder_attention_heads = safe_getattr(args, "encoder_attention_heads", 12) + + args.dropout = safe_getattr(args, "dropout", 0.1) + args.attention_dropout = safe_getattr(args, "attention_dropout", 0.1) + args.activation_dropout = safe_getattr(args, "activation_dropout", 0.0) + args.pooler_dropout = safe_getattr(args, "pooler_dropout", 0.0) + + args.max_source_positions = safe_getattr(args, "max_positions", 512) + args.no_token_positional_embeddings = safe_getattr( + args, "no_token_positional_embeddings", False + ) + + # BERT has a few structural differences compared to the original Transformer + args.encoder_learned_pos = safe_getattr(args, "encoder_learned_pos", True) + args.layernorm_embedding = safe_getattr(args, "layernorm_embedding", True) + args.no_scale_embedding = safe_getattr(args, "no_scale_embedding", True) + args.activation_fn = safe_getattr(args, "activation_fn", "gelu") + args.encoder_normalize_before = safe_getattr(args, "encoder_normalize_before", False) + args.pooler_activation_fn = safe_getattr(args, "pooler_activation_fn", "tanh") + args.untie_weights_roberta = safe_getattr(args, "untie_weights_roberta", False) + + # Adaptive input config + args.adaptive_input = safe_getattr(args, "adaptive_input", False) + + # LayerDrop config + args.encoder_layerdrop = safe_getattr(args, "encoder_layerdrop", 0.0) + args.encoder_layers_to_keep = safe_getattr(args, "encoder_layers_to_keep", None) + + # Quantization noise config + args.quant_noise_pq = safe_getattr(args, "quant_noise_pq", 0) + args.quant_noise_pq_block_size = safe_getattr(args, "quant_noise_pq_block_size", 8) + args.quant_noise_scalar = safe_getattr(args, "quant_noise_scalar", 0) + + # R4F config + args.spectral_norm_classification_head = safe_getattr( + args, "spectral_norm_classification_head", False + ) + + +@register_model_architecture("roberta", "roberta_prenorm") +def roberta_prenorm_architecture(args): + args.layernorm_embedding = safe_getattr(args, "layernorm_embedding", False) + args.encoder_normalize_before = safe_getattr(args, "encoder_normalize_before", True) + base_architecture(args) -@register_model_architecture('roberta', 'roberta_base') +@register_model_architecture("roberta", "roberta_base") def roberta_base_architecture(args): base_architecture(args) -@register_model_architecture('roberta', 'roberta_large') +@register_model_architecture("roberta", "roberta_large") def roberta_large_architecture(args): - args.encoder_layers = getattr(args, 'encoder_layers', 24) - args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 1024) - args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 4096) - args.encoder_attention_heads = getattr(args, 'encoder_attention_heads', 16) + args.encoder_layers = safe_getattr(args, "encoder_layers", 24) + args.encoder_embed_dim = safe_getattr(args, "encoder_embed_dim", 1024) + args.encoder_ffn_embed_dim = safe_getattr(args, "encoder_ffn_embed_dim", 4096) + args.encoder_attention_heads = safe_getattr(args, "encoder_attention_heads", 16) base_architecture(args) -@register_model_architecture('roberta', 'xlm') +@register_model_architecture("roberta", "xlm") def xlm_architecture(args): - args.encoder_layers = getattr(args, 'encoder_layers', 16) - args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 1280) - args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 1280*4) - args.encoder_attention_heads = getattr(args, 'encoder_attention_heads', 16) + args.encoder_layers = safe_getattr(args, "encoder_layers", 16) + args.encoder_embed_dim = safe_getattr(args, "encoder_embed_dim", 1280) + args.encoder_ffn_embed_dim = safe_getattr(args, "encoder_ffn_embed_dim", 1280 * 4) + args.encoder_attention_heads = safe_getattr(args, "encoder_attention_heads", 16) base_architecture(args) diff --git a/fairseq/models/roberta/model_camembert.py b/fairseq/models/roberta/model_camembert.py index eb57d81d8d..46447546fa 100644 --- a/fairseq/models/roberta/model_camembert.py +++ b/fairseq/models/roberta/model_camembert.py @@ -12,25 +12,32 @@ from .model import RobertaModel -@register_model('camembert') +@register_model("camembert") class CamembertModel(RobertaModel): - @classmethod def hub_models(cls): return { - 'camembert': 'http://dl.fbaipublicfiles.com/fairseq/models/camembert-base.tar.gz', - 'camembert.v0': 'http://dl.fbaipublicfiles.com/fairseq/models/camembert-base.tar.gz', - 'camembert-base': 'http://dl.fbaipublicfiles.com/fairseq/models/camembert-base.tar.gz', - 'camembert-large': 'http://dl.fbaipublicfiles.com/fairseq/models/camembert-large.tar.gz', - 'camembert-base-ccnet': 'http://dl.fbaipublicfiles.com/fairseq/models/camembert-base-ccnet.tar.gz', - 'camembert-base-ccnet-4gb': 'http://dl.fbaipublicfiles.com/fairseq/models/camembert-base-ccnet-4gb.tar.gz', - 'camembert-base-wikipedia-4gb': 'http://dl.fbaipublicfiles.com/fairseq/models/camembert-base-wikipedia-4gb.tar.gz', - 'camembert-base-oscar-4gb': 'http://dl.fbaipublicfiles.com/fairseq/models/camembert-base-oscar-4gb.tar.gz', + "camembert": "http://dl.fbaipublicfiles.com/fairseq/models/camembert-base.tar.gz", + "camembert.v0": "http://dl.fbaipublicfiles.com/fairseq/models/camembert-base.tar.gz", + "camembert-base": "http://dl.fbaipublicfiles.com/fairseq/models/camembert-base.tar.gz", + "camembert-large": "http://dl.fbaipublicfiles.com/fairseq/models/camembert-large.tar.gz", + "camembert-base-ccnet": "http://dl.fbaipublicfiles.com/fairseq/models/camembert-base-ccnet.tar.gz", + "camembert-base-ccnet-4gb": "http://dl.fbaipublicfiles.com/fairseq/models/camembert-base-ccnet-4gb.tar.gz", + "camembert-base-wikipedia-4gb": "http://dl.fbaipublicfiles.com/fairseq/models/camembert-base-wikipedia-4gb.tar.gz", + "camembert-base-oscar-4gb": "http://dl.fbaipublicfiles.com/fairseq/models/camembert-base-oscar-4gb.tar.gz", } @classmethod - def from_pretrained(cls, model_name_or_path, checkpoint_file='model.pt', data_name_or_path='.', bpe='sentencepiece', **kwargs): + def from_pretrained( + cls, + model_name_or_path, + checkpoint_file="model.pt", + data_name_or_path=".", + bpe="sentencepiece", + **kwargs + ): from fairseq import hub_utils + x = hub_utils.from_pretrained( model_name_or_path, checkpoint_file, @@ -40,4 +47,4 @@ def from_pretrained(cls, model_name_or_path, checkpoint_file='model.pt', data_na load_checkpoint_heads=True, **kwargs, ) - return RobertaHubInterface(x['args'], x['task'], x['models'][0]) + return RobertaHubInterface(x["args"], x["task"], x["models"][0]) diff --git a/fairseq/models/roberta/model_gottbert.py b/fairseq/models/roberta/model_gottbert.py new file mode 100644 index 0000000000..2e8c66354a --- /dev/null +++ b/fairseq/models/roberta/model_gottbert.py @@ -0,0 +1,49 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +""" +GottBERT: a pure German Language Model +""" + +from fairseq.models import register_model + +from .hub_interface import RobertaHubInterface +from .model import RobertaModel + + +@register_model('gottbert') +class GottbertModel(RobertaModel): + + @classmethod + def hub_models(cls): + return { + 'gottbert-base': 'https://dl.gottbert.de/fairseq/models/gottbert-base.tar.gz', + } + + @classmethod + def from_pretrained(cls, + model_name_or_path, + checkpoint_file='model.pt', + data_name_or_path='.', + bpe='hf_byte_bpe', + bpe_vocab='vocab.json', + bpe_merges='merges.txt', + bpe_add_prefix_space=False, + **kwargs + ): + from fairseq import hub_utils + + x = hub_utils.from_pretrained( + model_name_or_path, + checkpoint_file, + data_name_or_path, + archive_map=cls.hub_models(), + bpe=bpe, + load_checkpoint_heads=True, + bpe_vocab=bpe_vocab, + bpe_merges=bpe_merges, + bpe_add_prefix_space=bpe_add_prefix_space, + **kwargs, + ) + return RobertaHubInterface(x['args'], x['task'], x['models'][0]) diff --git a/fairseq/models/roberta/model_xlmr.py b/fairseq/models/roberta/model_xlmr.py index fa71a27d12..cf6e354d53 100644 --- a/fairseq/models/roberta/model_xlmr.py +++ b/fairseq/models/roberta/model_xlmr.py @@ -12,19 +12,28 @@ from .model import RobertaModel -@register_model('xlmr') +@register_model("xlmr") class XLMRModel(RobertaModel): - @classmethod def hub_models(cls): return { - 'xlmr.base': 'http://dl.fbaipublicfiles.com/fairseq/models/xlmr.base.tar.gz', - 'xlmr.large': 'http://dl.fbaipublicfiles.com/fairseq/models/xlmr.large.tar.gz', + "xlmr.base": "http://dl.fbaipublicfiles.com/fairseq/models/xlmr.base.tar.gz", + "xlmr.large": "http://dl.fbaipublicfiles.com/fairseq/models/xlmr.large.tar.gz", + "xlmr.xl": "http://dl.fbaipublicfiles.com/fairseq/models/xlmr/xlmr.xl.tar.gz", + "xlmr.xxl": "http://dl.fbaipublicfiles.com/fairseq/models/xlmr/xlmr.xxl.tar.gz", } @classmethod - def from_pretrained(cls, model_name_or_path, checkpoint_file='model.pt', data_name_or_path='.', bpe='sentencepiece', **kwargs): + def from_pretrained( + cls, + model_name_or_path, + checkpoint_file="model.pt", + data_name_or_path=".", + bpe="sentencepiece", + **kwargs + ): from fairseq import hub_utils + x = hub_utils.from_pretrained( model_name_or_path, checkpoint_file, @@ -34,4 +43,4 @@ def from_pretrained(cls, model_name_or_path, checkpoint_file='model.pt', data_na load_checkpoint_heads=True, **kwargs, ) - return RobertaHubInterface(x['args'], x['task'], x['models'][0]) + return RobertaHubInterface(x["args"], x["task"], x["models"][0]) diff --git a/fairseq/models/speech_to_text/__init__.py b/fairseq/models/speech_to_text/__init__.py new file mode 100644 index 0000000000..1c5189c0f7 --- /dev/null +++ b/fairseq/models/speech_to_text/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from .berard import * # noqa +from .convtransformer import * # noqa +from .s2t_transformer import * # noqa +from .xm_transformer import * # noqa diff --git a/fairseq/models/speech_to_text/berard.py b/fairseq/models/speech_to_text/berard.py new file mode 100644 index 0000000000..c505e3acaa --- /dev/null +++ b/fairseq/models/speech_to_text/berard.py @@ -0,0 +1,606 @@ +#!/usr/bin/env python3 + +from ast import literal_eval +from typing import List, Tuple + +import torch +import torch.nn as nn +import torch.nn.functional as F +from fairseq import checkpoint_utils, utils +from fairseq.data.data_utils import lengths_to_padding_mask +from fairseq.models import ( + FairseqEncoder, + FairseqEncoderDecoderModel, + FairseqIncrementalDecoder, + register_model, + register_model_architecture, +) + + +@register_model("s2t_berard") +class BerardModel(FairseqEncoderDecoderModel): + """Implementation of a model similar to https://arxiv.org/abs/1802.04200 + + Paper title: End-to-End Automatic Speech Translation of Audiobooks + An implementation is available in tensorflow at + https://github.com/eske/seq2seq + Relevant files in this implementation are the config + (https://github.com/eske/seq2seq/blob/master/config/LibriSpeech/AST.yaml) + and the model code + (https://github.com/eske/seq2seq/blob/master/translate/models.py). + The encoder and decoder try to be close to the original implementation. + The attention is an MLP as in Bahdanau et al. + (https://arxiv.org/abs/1409.0473). + There is no state initialization by averaging the encoder outputs. + """ + + def __init__(self, encoder, decoder): + super().__init__(encoder, decoder) + + @staticmethod + def add_args(parser): + parser.add_argument( + "--input-layers", + type=str, + metavar="EXPR", + help="List of linear layer dimensions. These " + "layers are applied to the input features and " + "are followed by tanh and possibly dropout.", + ) + parser.add_argument( + "--dropout", + type=float, + metavar="D", + help="Dropout probability to use in the encoder/decoder. " + "Note that this parameters control dropout in various places, " + "there is no fine-grained control for dropout for embeddings " + "vs LSTM layers for example.", + ) + parser.add_argument( + "--in-channels", + type=int, + metavar="N", + help="Number of encoder input channels. " "Typically value is 1.", + ) + parser.add_argument( + "--conv-layers", + type=str, + metavar="EXPR", + help="List of conv layers " "(format: (channels, kernel, stride)).", + ) + parser.add_argument( + "--num-blstm-layers", + type=int, + metavar="N", + help="Number of encoder bi-LSTM layers.", + ) + parser.add_argument( + "--lstm-size", type=int, metavar="N", help="LSTM hidden size." + ) + parser.add_argument( + "--decoder-embed-dim", + type=int, + metavar="N", + help="Embedding dimension of the decoder target tokens.", + ) + parser.add_argument( + "--decoder-hidden-dim", + type=int, + metavar="N", + help="Decoder LSTM hidden dimension.", + ) + parser.add_argument( + "--decoder-num-layers", + type=int, + metavar="N", + help="Number of decoder LSTM layers.", + ) + parser.add_argument( + "--attention-dim", + type=int, + metavar="N", + help="Hidden layer dimension in MLP attention.", + ) + parser.add_argument( + "--output-layer-dim", + type=int, + metavar="N", + help="Hidden layer dim for linear layer prior to output projection.", + ) + parser.add_argument( + "--load-pretrained-encoder-from", + type=str, + metavar="STR", + help="model to take encoder weights from (for initialization)", + ) + parser.add_argument( + "--load-pretrained-decoder-from", + type=str, + metavar="STR", + help="model to take decoder weights from (for initialization)", + ) + + @classmethod + def build_encoder(cls, args, task): + encoder = BerardEncoder( + input_layers=literal_eval(args.input_layers), + conv_layers=literal_eval(args.conv_layers), + in_channels=args.input_channels, + input_feat_per_channel=args.input_feat_per_channel, + num_blstm_layers=args.num_blstm_layers, + lstm_size=args.lstm_size, + dropout=args.dropout, + ) + if getattr(args, "load_pretrained_encoder_from", None): + encoder = checkpoint_utils.load_pretrained_component_from_model( + component=encoder, checkpoint=args.load_pretrained_encoder_from + ) + return encoder + + @classmethod + def build_decoder(cls, args, task): + decoder = LSTMDecoder( + dictionary=task.target_dictionary, + embed_dim=args.decoder_embed_dim, + num_layers=args.decoder_num_layers, + hidden_size=args.decoder_hidden_dim, + dropout=args.dropout, + encoder_output_dim=2 * args.lstm_size, # bidirectional + attention_dim=args.attention_dim, + output_layer_dim=args.output_layer_dim, + ) + if getattr(args, "load_pretrained_decoder_from", None): + decoder = checkpoint_utils.load_pretrained_component_from_model( + component=decoder, checkpoint=args.load_pretrained_decoder_from + ) + return decoder + + @classmethod + def build_model(cls, args, task): + """Build a new model instance.""" + encoder = cls.build_encoder(args, task) + decoder = cls.build_decoder(args, task) + + return cls(encoder, decoder) + + def get_normalized_probs(self, net_output, log_probs, sample=None): + # net_output['encoder_out'] is a (B, T, D) tensor + lprobs = super().get_normalized_probs(net_output, log_probs, sample) + # lprobs is a (B, T, D) tensor + lprobs.batch_first = True + return lprobs + + +class BerardEncoder(FairseqEncoder): + def __init__( + self, + input_layers: List[int], + conv_layers: List[Tuple[int]], + in_channels: int, + input_feat_per_channel: int, + num_blstm_layers: int, + lstm_size: int, + dropout: float, + ): + """ + Args: + input_layers: list of linear layer dimensions. These layers are + applied to the input features and are followed by tanh and + possibly dropout. + conv_layers: list of conv2d layer configurations. A configuration is + a tuple (out_channels, conv_kernel_size, stride). + in_channels: number of input channels. + input_feat_per_channel: number of input features per channel. These + are speech features, typically 40 or 80. + num_blstm_layers: number of bidirectional LSTM layers. + lstm_size: size of the LSTM hidden (and cell) size. + dropout: dropout probability. Dropout can be applied after the + linear layers and LSTM layers but not to the convolutional + layers. + """ + super().__init__(None) + + self.input_layers = nn.ModuleList() + in_features = input_feat_per_channel + for out_features in input_layers: + if dropout > 0: + self.input_layers.append( + nn.Sequential( + nn.Linear(in_features, out_features), nn.Dropout(p=dropout) + ) + ) + else: + self.input_layers.append(nn.Linear(in_features, out_features)) + in_features = out_features + + self.in_channels = in_channels + self.input_dim = input_feat_per_channel + self.conv_kernel_sizes_and_strides = [] + self.conv_layers = nn.ModuleList() + lstm_input_dim = input_layers[-1] + for conv_layer in conv_layers: + out_channels, conv_kernel_size, conv_stride = conv_layer + self.conv_layers.append( + nn.Conv2d( + in_channels, + out_channels, + conv_kernel_size, + stride=conv_stride, + padding=conv_kernel_size // 2, + ) + ) + self.conv_kernel_sizes_and_strides.append((conv_kernel_size, conv_stride)) + in_channels = out_channels + lstm_input_dim //= conv_stride + + lstm_input_dim *= conv_layers[-1][0] + self.lstm_size = lstm_size + self.num_blstm_layers = num_blstm_layers + self.lstm = nn.LSTM( + input_size=lstm_input_dim, + hidden_size=lstm_size, + num_layers=num_blstm_layers, + dropout=dropout, + bidirectional=True, + ) + self.output_dim = 2 * lstm_size # bidirectional + if dropout > 0: + self.dropout = nn.Dropout(p=dropout) + else: + self.dropout = None + + def forward(self, src_tokens, src_lengths=None, **kwargs): + """ + Args + src_tokens: padded tensor (B, T, C * feat) + src_lengths: tensor of original lengths of input utterances (B,) + """ + bsz, max_seq_len, _ = src_tokens.size() + # (B, C, T, feat) + x = ( + src_tokens.view(bsz, max_seq_len, self.in_channels, self.input_dim) + .transpose(1, 2) + .contiguous() + ) + + for input_layer in self.input_layers: + x = input_layer(x) + x = torch.tanh(x) + + for conv_layer in self.conv_layers: + x = conv_layer(x) + + bsz, _, output_seq_len, _ = x.size() + + # (B, C, T, feat) -> (B, T, C, feat) -> (T, B, C, feat) -> + # (T, B, C * feat) + x = x.transpose(1, 2).transpose(0, 1).contiguous().view(output_seq_len, bsz, -1) + + input_lengths = src_lengths.clone() + for k, s in self.conv_kernel_sizes_and_strides: + p = k // 2 + input_lengths = (input_lengths.float() + 2 * p - k) / s + 1 + input_lengths = input_lengths.floor().long() + + packed_x = nn.utils.rnn.pack_padded_sequence(x, input_lengths) + + h0 = x.new(2 * self.num_blstm_layers, bsz, self.lstm_size).zero_() + c0 = x.new(2 * self.num_blstm_layers, bsz, self.lstm_size).zero_() + packed_outs, _ = self.lstm(packed_x, (h0, c0)) + + # unpack outputs and apply dropout + x, output_lengths = nn.utils.rnn.pad_packed_sequence(packed_outs) + if self.dropout is not None: + x = self.dropout(x) + + encoder_padding_mask = ( + lengths_to_padding_mask(output_lengths).to(src_tokens.device).t() + ) + + return { + "encoder_out": x, # (T, B, C) + "encoder_padding_mask": encoder_padding_mask, # (T, B) + } + + def reorder_encoder_out(self, encoder_out, new_order): + encoder_out["encoder_out"] = encoder_out["encoder_out"].index_select( + 1, new_order + ) + encoder_out["encoder_padding_mask"] = encoder_out[ + "encoder_padding_mask" + ].index_select(1, new_order) + return encoder_out + + +class MLPAttention(nn.Module): + """The original attention from Badhanau et al. (2014) + + https://arxiv.org/abs/1409.0473, based on a Multi-Layer Perceptron. + The attention score between position i in the encoder and position j in the + decoder is: alpha_ij = V_a * tanh(W_ae * enc_i + W_ad * dec_j + b_a) + """ + + def __init__(self, decoder_hidden_state_dim, context_dim, attention_dim): + super().__init__() + + self.context_dim = context_dim + self.attention_dim = attention_dim + # W_ae and b_a + self.encoder_proj = nn.Linear(context_dim, self.attention_dim, bias=True) + # W_ad + self.decoder_proj = nn.Linear( + decoder_hidden_state_dim, self.attention_dim, bias=False + ) + # V_a + self.to_scores = nn.Linear(self.attention_dim, 1, bias=False) + + def forward(self, decoder_state, source_hids, encoder_padding_mask): + """The expected input dimensions are: + decoder_state: bsz x decoder_hidden_state_dim + source_hids: src_len x bsz x context_dim + encoder_padding_mask: src_len x bsz + """ + src_len, bsz, _ = source_hids.size() + # (src_len*bsz) x context_dim (to feed through linear) + flat_source_hids = source_hids.view(-1, self.context_dim) + # (src_len*bsz) x attention_dim + encoder_component = self.encoder_proj(flat_source_hids) + # src_len x bsz x attention_dim + encoder_component = encoder_component.view(src_len, bsz, self.attention_dim) + # 1 x bsz x attention_dim + decoder_component = self.decoder_proj(decoder_state).unsqueeze(0) + # Sum with broadcasting and apply the non linearity + # src_len x bsz x attention_dim + hidden_att = torch.tanh( + (decoder_component + encoder_component).view(-1, self.attention_dim) + ) + # Project onto the reals to get attentions scores (src_len x bsz) + attn_scores = self.to_scores(hidden_att).view(src_len, bsz) + + # Mask + softmax (src_len x bsz) + if encoder_padding_mask is not None: + attn_scores = ( + attn_scores.float() + .masked_fill_(encoder_padding_mask, float("-inf")) + .type_as(attn_scores) + ) # FP16 support: cast to float and back + # srclen x bsz + normalized_masked_attn_scores = F.softmax(attn_scores, dim=0) + + # Sum weighted sources (bsz x context_dim) + attn_weighted_context = ( + source_hids * normalized_masked_attn_scores.unsqueeze(2) + ).sum(dim=0) + + return attn_weighted_context, normalized_masked_attn_scores + + +class LSTMDecoder(FairseqIncrementalDecoder): + def __init__( + self, + dictionary, + embed_dim, + num_layers, + hidden_size, + dropout, + encoder_output_dim, + attention_dim, + output_layer_dim, + ): + """ + Args: + dictionary: target text dictionary. + embed_dim: embedding dimension for target tokens. + num_layers: number of LSTM layers. + hidden_size: hidden size for LSTM layers. + dropout: dropout probability. Dropout can be applied to the + embeddings, the LSTM layers, and the context vector. + encoder_output_dim: encoder output dimension (hidden size of + encoder LSTM). + attention_dim: attention dimension for MLP attention. + output_layer_dim: size of the linear layer prior to output + projection. + """ + super().__init__(dictionary) + self.num_layers = num_layers + self.hidden_size = hidden_size + num_embeddings = len(dictionary) + padding_idx = dictionary.pad() + self.embed_tokens = nn.Embedding(num_embeddings, embed_dim, padding_idx) + if dropout > 0: + self.dropout = nn.Dropout(p=dropout) + else: + self.dropout = None + + self.layers = nn.ModuleList() + for layer_id in range(num_layers): + input_size = embed_dim if layer_id == 0 else encoder_output_dim + self.layers.append( + nn.LSTMCell(input_size=input_size, hidden_size=hidden_size) + ) + + self.context_dim = encoder_output_dim + self.attention = MLPAttention( + decoder_hidden_state_dim=hidden_size, + context_dim=encoder_output_dim, + attention_dim=attention_dim, + ) + + self.deep_output_layer = nn.Linear( + hidden_size + encoder_output_dim + embed_dim, output_layer_dim + ) + self.output_projection = nn.Linear(output_layer_dim, num_embeddings) + + def forward( + self, prev_output_tokens, encoder_out=None, incremental_state=None, **kwargs + ): + encoder_padding_mask = encoder_out["encoder_padding_mask"] + encoder_outs = encoder_out["encoder_out"] + + if incremental_state is not None: + prev_output_tokens = prev_output_tokens[:, -1:] + bsz, seqlen = prev_output_tokens.size() + + srclen = encoder_outs.size(0) + + # embed tokens + embeddings = self.embed_tokens(prev_output_tokens) + x = embeddings + if self.dropout is not None: + x = self.dropout(x) + + # B x T x C -> T x B x C + x = x.transpose(0, 1) + + # initialize previous states (or get from cache during incremental + # generation) + cached_state = utils.get_incremental_state( + self, incremental_state, "cached_state" + ) + if cached_state is not None: + prev_hiddens, prev_cells = cached_state + else: + prev_hiddens = [encoder_out["encoder_out"].mean(dim=0)] * self.num_layers + prev_cells = [x.new_zeros(bsz, self.hidden_size)] * self.num_layers + + attn_scores = x.new_zeros(bsz, srclen) + attention_outs = [] + outs = [] + for j in range(seqlen): + input = x[j, :, :] + attention_out = None + for i, layer in enumerate(self.layers): + # the previous state is one layer below except for the bottom + # layer where the previous state is the state emitted by the + # top layer + hidden, cell = layer( + input, + ( + prev_hiddens[(i - 1) % self.num_layers], + prev_cells[(i - 1) % self.num_layers], + ), + ) + if self.dropout is not None: + hidden = self.dropout(hidden) + prev_hiddens[i] = hidden + prev_cells[i] = cell + if attention_out is None: + attention_out, attn_scores = self.attention( + hidden, encoder_outs, encoder_padding_mask + ) + if self.dropout is not None: + attention_out = self.dropout(attention_out) + attention_outs.append(attention_out) + input = attention_out + + # collect the output of the top layer + outs.append(hidden) + + # cache previous states (no-op except during incremental generation) + utils.set_incremental_state( + self, incremental_state, "cached_state", (prev_hiddens, prev_cells) + ) + + # collect outputs across time steps + x = torch.cat(outs, dim=0).view(seqlen, bsz, self.hidden_size) + attention_outs_concat = torch.cat(attention_outs, dim=0).view( + seqlen, bsz, self.context_dim + ) + + # T x B x C -> B x T x C + x = x.transpose(0, 1) + attention_outs_concat = attention_outs_concat.transpose(0, 1) + + # concat LSTM output, attention output and embedding + # before output projection + x = torch.cat((x, attention_outs_concat, embeddings), dim=2) + x = self.deep_output_layer(x) + x = torch.tanh(x) + if self.dropout is not None: + x = self.dropout(x) + # project back to size of vocabulary + x = self.output_projection(x) + + # to return the full attn_scores tensor, we need to fix the decoder + # to account for subsampling input frames + # return x, attn_scores + return x, None + + def reorder_incremental_state(self, incremental_state, new_order): + super().reorder_incremental_state(incremental_state, new_order) + cached_state = utils.get_incremental_state( + self, incremental_state, "cached_state" + ) + if cached_state is None: + return + + def reorder_state(state): + if isinstance(state, list): + return [reorder_state(state_i) for state_i in state] + return state.index_select(0, new_order) + + new_state = tuple(map(reorder_state, cached_state)) + utils.set_incremental_state(self, incremental_state, "cached_state", new_state) + + +@register_model_architecture(model_name="s2t_berard", arch_name="s2t_berard") +def berard(args): + """The original version: "End-to-End Automatic Speech Translation of + Audiobooks" (https://arxiv.org/abs/1802.04200) + """ + args.input_layers = getattr(args, "input_layers", "[256, 128]") + args.conv_layers = getattr(args, "conv_layers", "[(16, 3, 2), (16, 3, 2)]") + args.num_blstm_layers = getattr(args, "num_blstm_layers", 3) + args.lstm_size = getattr(args, "lstm_size", 256) + args.dropout = getattr(args, "dropout", 0.2) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 128) + args.decoder_num_layers = getattr(args, "decoder_num_layers", 2) + args.decoder_hidden_dim = getattr(args, "decoder_hidden_dim", 512) + args.attention_dim = getattr(args, "attention_dim", 512) + args.output_layer_dim = getattr(args, "output_layer_dim", 128) + args.load_pretrained_encoder_from = getattr( + args, "load_pretrained_encoder_from", None + ) + args.load_pretrained_decoder_from = getattr( + args, "load_pretrained_decoder_from", None + ) + + +@register_model_architecture(model_name="s2t_berard", arch_name="s2t_berard_256_3_3") +def berard_256_3_3(args): + """Used in + * "Harnessing Indirect Training Data for End-to-End Automatic Speech + Translation: Tricks of the Trade" (https://arxiv.org/abs/1909.06515) + * "CoVoST: A Diverse Multilingual Speech-To-Text Translation Corpus" + (https://arxiv.org/pdf/2002.01320.pdf) + * "Self-Supervised Representations Improve End-to-End Speech Translation" + (https://arxiv.org/abs/2006.12124) + """ + args.decoder_num_layers = getattr(args, "decoder_num_layers", 3) + berard(args) + + +@register_model_architecture(model_name="s2t_berard", arch_name="s2t_berard_512_3_2") +def berard_512_3_2(args): + args.num_blstm_layers = getattr(args, "num_blstm_layers", 3) + args.lstm_size = getattr(args, "lstm_size", 512) + args.dropout = getattr(args, "dropout", 0.3) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 256) + args.decoder_num_layers = getattr(args, "decoder_num_layers", 2) + args.decoder_hidden_dim = getattr(args, "decoder_hidden_dim", 1024) + args.attention_dim = getattr(args, "attention_dim", 512) + args.output_layer_dim = getattr(args, "output_layer_dim", 256) + berard(args) + + +@register_model_architecture(model_name="s2t_berard", arch_name="s2t_berard_512_5_3") +def berard_512_5_3(args): + args.num_blstm_layers = getattr(args, "num_blstm_layers", 5) + args.lstm_size = getattr(args, "lstm_size", 512) + args.dropout = getattr(args, "dropout", 0.3) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 256) + args.decoder_num_layers = getattr(args, "decoder_num_layers", 3) + args.decoder_hidden_dim = getattr(args, "decoder_hidden_dim", 1024) + args.attention_dim = getattr(args, "attention_dim", 512) + args.output_layer_dim = getattr(args, "output_layer_dim", 256) + berard(args) diff --git a/fairseq/models/speech_to_text/convtransformer.py b/fairseq/models/speech_to_text/convtransformer.py new file mode 100644 index 0000000000..eba000d7b0 --- /dev/null +++ b/fairseq/models/speech_to_text/convtransformer.py @@ -0,0 +1,448 @@ +#!/usr/bin/env python3 + +import logging +import math +from typing import Dict, List, Optional, Tuple + +import torch +import torch.nn as nn +import torch.nn.functional as F +from fairseq import checkpoint_utils, utils +from fairseq.data.data_utils import lengths_to_padding_mask +from fairseq.models import ( + FairseqEncoder, + FairseqEncoderDecoderModel, + register_model, + register_model_architecture, +) +from fairseq.models.transformer import Embedding, TransformerDecoder +from fairseq.modules import LayerNorm, PositionalEmbedding, TransformerEncoderLayer +from torch import Tensor + +logger = logging.getLogger(__name__) + + +@register_model("convtransformer") +class ConvTransformerModel(FairseqEncoderDecoderModel): + """ + Transformer-based Speech translation model from ESPNet-ST + https://arxiv.org/abs/2004.10234 + """ + + def __init__(self, encoder, decoder): + super().__init__(encoder, decoder) + + @staticmethod + def add_args(parser): + """Add model-specific arguments to the parser.""" + parser.add_argument( + "--input-feat-per-channel", + type=int, + metavar="N", + help="encoder input dimension per input channel", + ) + parser.add_argument( + "--activation-fn", + choices=utils.get_available_activation_fns(), + help="activation function to use", + ) + parser.add_argument( + "--dropout", type=float, metavar="D", help="dropout probability" + ) + parser.add_argument( + "--attention-dropout", + type=float, + metavar="D", + help="dropout probability for attention weights", + ) + parser.add_argument( + "--activation-dropout", + "--relu-dropout", + type=float, + metavar="D", + help="dropout probability after activation in FFN.", + ) + parser.add_argument( + "--encoder-embed-dim", + type=int, + metavar="N", + help="encoder embedding dimension", + ) + parser.add_argument( + "--encoder-ffn-embed-dim", + type=int, + metavar="N", + help="encoder embedding dimension for FFN", + ) + parser.add_argument( + "--encoder-layers", type=int, metavar="N", help="num encoder layers" + ) + parser.add_argument( + "--encoder-attention-heads", + type=int, + metavar="N", + help="num encoder attention heads", + ) + parser.add_argument( + "--encoder-normalize-before", + action="store_true", + help="apply layernorm before each encoder block", + ) + parser.add_argument( + "--decoder-embed-dim", + type=int, + metavar="N", + help="decoder embedding dimension", + ) + parser.add_argument( + "--decoder-ffn-embed-dim", + type=int, + metavar="N", + help="decoder embedding dimension for FFN", + ) + parser.add_argument( + "--decoder-layers", type=int, metavar="N", help="num decoder layers" + ) + parser.add_argument( + "--decoder-attention-heads", + type=int, + metavar="N", + help="num decoder attention heads", + ) + parser.add_argument( + "--decoder-normalize-before", + action="store_true", + help="apply layernorm before each decoder block", + ) + parser.add_argument( + "--decoder-output-dim", + type=int, + metavar="N", + help="decoder output dimension (extra linear layer if different from decoder embed dim)", + ) + parser.add_argument( + "--share-decoder-input-output-embed", + action="store_true", + help="share decoder input and output embeddings", + ) + parser.add_argument( + "--layernorm-embedding", + action="store_true", + help="add layernorm to embedding", + ) + parser.add_argument( + "--no-scale-embedding", + action="store_true", + help="if True, dont scale embeddings", + ) + parser.add_argument( + "--load-pretrained-encoder-from", + type=str, + metavar="STR", + help="model to take encoder weights from (for initialization)", + ) + parser.add_argument( + "--load-pretrained-decoder-from", + type=str, + metavar="STR", + help="model to take decoder weights from (for initialization)", + ) + parser.add_argument( + "--conv-out-channels", + type=int, + metavar="INT", + help="the number of output channels of conv layer", + ) + + @classmethod + def build_encoder(cls, args): + encoder = ConvTransformerEncoder(args) + if getattr(args, "load_pretrained_encoder_from", None): + encoder = checkpoint_utils.load_pretrained_component_from_model( + component=encoder, checkpoint=args.load_pretrained_encoder_from + ) + return encoder + + @classmethod + def build_decoder(cls, args, task, embed_tokens): + decoder = TransformerDecoderNoExtra(args, task.target_dictionary, embed_tokens) + if getattr(args, "load_pretrained_decoder_from", None): + decoder = checkpoint_utils.load_pretrained_component_from_model( + component=decoder, checkpoint=args.load_pretrained_decoder_from + ) + return decoder + + @classmethod + def build_model(cls, args, task): + """Build a new model instance.""" + + # make sure all arguments are present in older models + base_architecture(args) + + def build_embedding(dictionary, embed_dim): + num_embeddings = len(dictionary) + padding_idx = dictionary.pad() + return Embedding(num_embeddings, embed_dim, padding_idx) + + decoder_embed_tokens = build_embedding( + task.target_dictionary, args.decoder_embed_dim + ) + encoder = cls.build_encoder(args) + decoder = cls.build_decoder(args, task, decoder_embed_tokens) + return cls(encoder, decoder) + + @staticmethod + @torch.jit.unused + def set_batch_first(lprobs): + lprobs.batch_first = True + + def get_normalized_probs( + self, + net_output: Tuple[Tensor, Optional[Dict[str, List[Optional[Tensor]]]]], + log_probs: bool, + sample: Optional[Dict[str, Tensor]] = None, + ): + # net_output['encoder_out'] is a (B, T, D) tensor + lprobs = self.get_normalized_probs_scriptable(net_output, log_probs, sample) + if self.training: + self.set_batch_first(lprobs) + return lprobs + + def output_layout(self): + return "BTD" + + """ + The forward method inherited from the base class has a **kwargs argument in + its input, which is not supported in torchscript. This method overrites the forward + method definition without **kwargs. + """ + + def forward(self, src_tokens, src_lengths, prev_output_tokens): + encoder_out = self.encoder(src_tokens=src_tokens, src_lengths=src_lengths) + decoder_out = self.decoder( + prev_output_tokens=prev_output_tokens, encoder_out=encoder_out + ) + return decoder_out + + +class ConvTransformerEncoder(FairseqEncoder): + """Conv + Transformer encoder""" + + def __init__(self, args): + """Construct an Encoder object.""" + super().__init__(None) + + self.dropout = args.dropout + self.embed_scale = ( + 1.0 if args.no_scale_embedding else math.sqrt(args.encoder_embed_dim) + ) + self.padding_idx = 1 + self.in_channels = 1 + self.input_dim = args.input_feat_per_channel + self.conv = torch.nn.Sequential( + torch.nn.Conv2d(1, args.conv_out_channels, 3, stride=2, padding=3 // 2), + torch.nn.ReLU(), + torch.nn.Conv2d( + args.conv_out_channels, + args.conv_out_channels, + 3, + stride=2, + padding=3 // 2, + ), + torch.nn.ReLU(), + ) + transformer_input_dim = self.infer_conv_output_dim( + self.in_channels, self.input_dim, args.conv_out_channels + ) + self.out = torch.nn.Linear(transformer_input_dim, args.encoder_embed_dim) + self.embed_positions = PositionalEmbedding( + args.max_source_positions, + args.encoder_embed_dim, + self.padding_idx, + learned=False, + ) + + self.transformer_layers = nn.ModuleList([]) + self.transformer_layers.extend( + [TransformerEncoderLayer(args) for i in range(args.encoder_layers)] + ) + if args.encoder_normalize_before: + self.layer_norm = LayerNorm(args.encoder_embed_dim) + else: + self.layer_norm = None + + def pooling_ratio(self): + return 4 + + def infer_conv_output_dim(self, in_channels, input_dim, out_channels): + sample_seq_len = 200 + sample_bsz = 10 + x = torch.randn(sample_bsz, in_channels, sample_seq_len, input_dim) + x = torch.nn.Conv2d(1, out_channels, 3, stride=2, padding=3 // 2)(x) + x = torch.nn.Conv2d(out_channels, out_channels, 3, stride=2, padding=3 // 2)(x) + x = x.transpose(1, 2) + mb, seq = x.size()[:2] + return x.contiguous().view(mb, seq, -1).size(-1) + + def forward(self, src_tokens, src_lengths): + """Encode input sequence. + :param torch.Tensor xs: input tensor + :param torch.Tensor masks: input mask + :return: position embedded tensor and mask + :rtype Tuple[torch.Tensor, torch.Tensor]: + """ + bsz, max_seq_len, _ = src_tokens.size() + x = ( + src_tokens.view(bsz, max_seq_len, self.in_channels, self.input_dim) + .transpose(1, 2) + .contiguous() + ) + x = self.conv(x) + bsz, _, output_seq_len, _ = x.size() + x = x.transpose(1, 2).transpose(0, 1).contiguous().view(output_seq_len, bsz, -1) + x = self.out(x) + x = self.embed_scale * x + + subsampling_factor = int(max_seq_len * 1.0 / output_seq_len + 0.5) + input_len_0 = (src_lengths.float() / subsampling_factor).ceil().long() + input_len_1 = x.size(0) * torch.ones([src_lengths.size(0)]).long().to( + input_len_0.device + ) + input_lengths = torch.min(input_len_0, input_len_1) + + encoder_padding_mask = lengths_to_padding_mask(input_lengths) + + positions = self.embed_positions(encoder_padding_mask).transpose(0, 1) + x += positions + x = F.dropout(x, p=self.dropout, training=self.training) + + for layer in self.transformer_layers: + x = layer(x, encoder_padding_mask) + + if not encoder_padding_mask.any(): + maybe_encoder_padding_mask = None + else: + maybe_encoder_padding_mask = encoder_padding_mask + + return { + "encoder_out": [x], + "encoder_padding_mask": [maybe_encoder_padding_mask] + if maybe_encoder_padding_mask is not None + else [], + "encoder_embedding": [], + "encoder_states": [], + "src_tokens": [], + "src_lengths": [], + } + + @torch.jit.export + def reorder_encoder_out(self, encoder_out: Dict[str, List[Tensor]], new_order): + """ + Reorder encoder output according to *new_order*. + + Args: + encoder_out: output from the ``forward()`` method + new_order (LongTensor): desired order + + Returns: + *encoder_out* rearranged according to *new_order* + """ + new_encoder_out = [encoder_out["encoder_out"][0].index_select(1, new_order)] + if len(encoder_out["encoder_padding_mask"]) == 0: + new_encoder_padding_mask = [] + else: + new_encoder_padding_mask = [ + (encoder_out["encoder_padding_mask"][0]).index_select(0, new_order) + ] + if len(encoder_out["encoder_embedding"]) == 0: + new_encoder_embedding = [] + else: + new_encoder_embedding = [ + (encoder_out["encoder_embedding"][0]).index_select(0, new_order) + ] + encoder_states = encoder_out["encoder_states"] + if len(encoder_states) > 0: + for idx, state in enumerate(encoder_states): + encoder_states[idx] = state.index_select(1, new_order) + + return { + "encoder_out": new_encoder_out, + "encoder_padding_mask": new_encoder_padding_mask, + "encoder_embedding": new_encoder_embedding, + "encoder_states": encoder_states, + "src_tokens": [], + "src_lengths": [], + } + + +class TransformerDecoderNoExtra(TransformerDecoder): + def extract_features( + self, + prev_output_tokens, + encoder_out: Optional[Dict[str, List[Tensor]]], + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, + full_context_alignment: bool = False, + alignment_layer: Optional[int] = None, + alignment_heads: Optional[int] = None, + ): + # call scriptable method from parent class + x, _ = self.extract_features_scriptable( + prev_output_tokens, + encoder_out, + incremental_state, + full_context_alignment, + alignment_layer, + alignment_heads, + ) + return x, None + + +@register_model_architecture(model_name="convtransformer", arch_name="convtransformer") +def base_architecture(args): + args.input_feat_per_channel = getattr(args, "input_feat_per_channel", 80) + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048) + args.encoder_layers = getattr(args, "encoder_layers", 6) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8) + args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim) + args.decoder_ffn_embed_dim = getattr( + args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim + ) + args.decoder_layers = getattr(args, "decoder_layers", 6) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8) + args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False) + args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False) + args.attention_dropout = getattr(args, "attention_dropout", 0.0) + args.activation_dropout = getattr(args, "activation_dropout", 0.0) + args.activation_fn = getattr(args, "activation_fn", "relu") + args.dropout = getattr(args, "dropout", 0.1) + args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None) + args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0) + args.share_decoder_input_output_embed = getattr( + args, "share_decoder_input_output_embed", False + ) + args.no_token_positional_embeddings = getattr( + args, "no_token_positional_embeddings", False + ) + args.adaptive_input = getattr(args, "adaptive_input", False) + args.decoder_layerdrop = getattr(args, "decoder_layerdrop", 0.0) + + args.decoder_output_dim = getattr( + args, "decoder_output_dim", args.decoder_embed_dim + ) + args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim) + args.no_scale_embedding = getattr(args, "no_scale_embedding", False) + args.quant_noise_pq = getattr(args, "quant_noise_pq", 0) + args.max_source_positions = getattr(args, "max_source_positions", 3000) + args.max_target_positions = getattr(args, "max_target_positions", 1024) + args.tie_adaptive_weights = getattr(args, "tie_adaptive_weights", False) + args.conv_out_channels = getattr(args, "conv_out_channels", args.encoder_embed_dim) + + +@register_model_architecture("convtransformer", "convtransformer_espnet") +def convtransformer_espnet(args): + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 256) + args.encoder_layers = getattr(args, "encoder_layers", 12) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 4) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 4) diff --git a/fairseq/models/speech_to_text/modules/augmented_memory_attention.py b/fairseq/models/speech_to_text/modules/augmented_memory_attention.py new file mode 100644 index 0000000000..e7465bc889 --- /dev/null +++ b/fairseq/models/speech_to_text/modules/augmented_memory_attention.py @@ -0,0 +1,488 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from typing import Tuple, List + +import torch +import torch.nn.functional as F +from fairseq.models import FairseqEncoder +from fairseq.models.speech_to_text import ( + ConvTransformerEncoder, +) +from fairseq.models.speech_to_text.utils import attention_suppression +from fairseq.models.speech_to_text.utils import ( + lengths_to_encoder_padding_mask, + segments_to_sequence, + sequence_to_segments, +) +from fairseq.modules import MultiheadAttention, TransformerEncoderLayer +from torch import nn, Tensor + +# ------------------------------------------------------------------------------ +# AugmentedMemoryConvTransformerEncoder +# ------------------------------------------------------------------------------ + + +class AugmentedMemoryConvTransformerEncoder(ConvTransformerEncoder): + def __init__(self, args): + super().__init__(args) + + args.encoder_stride = self.stride() + + self.left_context = args.left_context // args.encoder_stride + + self.right_context = args.right_context // args.encoder_stride + + self.left_context_after_stride = args.left_context // args.encoder_stride + self.right_context_after_stride = args.right_context // args.encoder_stride + + self.transformer_layers = nn.ModuleList([]) + self.transformer_layers.extend( + [ + AugmentedMemoryTransformerEncoderLayer(args) + for i in range(args.encoder_layers) + ] + ) + + def stride(self): + # Hard coded here. Should infer from convs in future + stride = 4 + return stride + + def forward(self, src_tokens, src_lengths, states=None): + """Encode input sequence. + :param torch.Tensor xs: input tensor + :param torch.Tensor masks: input mask + :return: position embedded tensor and mask + :rtype Tuple[torch.Tensor, torch.Tensor]: + """ + bsz, max_seq_len, _ = src_tokens.size() + x = ( + src_tokens.view(bsz, max_seq_len, self.in_channels, self.input_dim) + .transpose(1, 2) + .contiguous() + ) + x = self.conv(x) + bsz, _, output_seq_len, _ = x.size() + x = x.transpose(1, 2).transpose(0, 1).contiguous().view(output_seq_len, bsz, -1) + x = self.out(x) + x = self.embed_scale * x + + subsampling_factor = 1.0 * max_seq_len / output_seq_len + input_lengths = torch.max( + (src_lengths.float() / subsampling_factor).ceil().long(), + x.size(0) * src_lengths.new_ones([src_lengths.size(0)]).long(), + ) + + encoder_padding_mask, _ = lengths_to_encoder_padding_mask( + input_lengths, batch_first=True + ) + + # TODO: fix positional embedding + positions = self.embed_positions(encoder_padding_mask).transpose(0, 1) + + x += positions + x = F.dropout(x, p=self.dropout, training=self.training) + + # State to store memory banks etc. + if states is None: + states = [ + {"memory_banks": None, "encoder_states": None} + for i in range(len(self.transformer_layers)) + ] + + for i, layer in enumerate(self.transformer_layers): + # x size: + # (self.left_size + self.segment_size + self.right_size) + # / self.stride, num_heads, dim + # TODO: Consider mask here + x = layer(x, states[i]) + states[i]["encoder_states"] = x[ + self.left_context_after_stride : -self.right_context_after_stride + ] + + lengths = ( + ( + ~encoder_padding_mask[ + :, self.left_context_after_stride : -self.right_context_after_stride + ] + ) + .sum(dim=1, keepdim=True) + .long() + ) + + return states[-1]["encoder_states"], lengths, states + + +# ------------------------------------------------------------------------------ +# AugmentedMemoryTransformerEncoderLayer +# ------------------------------------------------------------------------------ +class AugmentedMemoryTransformerEncoderLayer(TransformerEncoderLayer): + def __init__(self, args): + super().__init__(args) + + self.left_context = args.left_context // args.encoder_stride + self.right_context = args.right_context // args.encoder_stride + + def forward(self, x, state): + + length, batch_size, x_dim = x.size() + + residual = x + + if self.normalize_before: + x = self.self_attn_layer_norm(x) + + # init_state + if state.get("memory_banks", None) is None: + state["memory_banks"] = [] + + # TODO reseach new sum_query method + seg_start = self.left_context + seg_end = length - self.right_context + if seg_start < seg_end: + summarization_query = torch.mean(x[seg_start:seg_end], keepdim=True, dim=0) + else: + summarization_query = x.new_zeros(1, batch_size, x_dim) + + x = torch.cat([x, summarization_query], dim=0) + + x = self.self_attn(input_and_summary=x, state=state) + + x = self.dropout_module(x) + x = residual + x + + if not self.normalize_before: + x = self.self_attn_layer_norm(x) + + residual = x + if self.normalize_before: + x = self.final_layer_norm(x) + + x = self.activation_fn(self.fc1(x)) + x = self.activation_dropout_module(x) + x = self.fc2(x) + x = self.dropout_module(x) + x = residual + x + if not self.normalize_before: + x = self.final_layer_norm(x) + + return x + + def build_self_attention(self, embed_dim, args): + return AugmentedMemoryMultiheadAttention( + embed_dim=embed_dim, + num_heads=args.encoder_attention_heads, + dropout=args.attention_dropout, + self_attention=True, + q_noise=self.quant_noise, + qn_block_size=self.quant_noise_block_size, + tanh_on_mem=True, + max_memory_size=args.max_memory_size, + ) + + +# ------------------------------------------------------------------------------ +# AugmentedMemoryMultiheadAttention +# ------------------------------------------------------------------------------ +class AugmentedMemoryMultiheadAttention(MultiheadAttention): + """ + Augmented Memory Attention from + Streaming Transformer-based Acoustic Models + Using Self-attention with Augmented Memory + https://arxiv.org/abs/2005.08042 + """ + + def __init__( + self, + embed_dim, + num_heads, + kdim=None, + vdim=None, + dropout=0.0, + bias=True, + add_bias_kv=False, + add_zero_attn=False, + self_attention=False, + encoder_decoder_attention=False, + q_noise=0.0, + qn_block_size=8, + tanh_on_mem=False, + memory_dim=None, + std_scale=0.5, # 0.5 based on https://arxiv.org/abs/2005.09137 + max_memory_size=-1, + disable_mem_on_mem_attn=True, + ): + super().__init__( + embed_dim, + num_heads, + kdim, + vdim, + dropout, + bias, + add_bias_kv, + add_zero_attn, + self_attention, + encoder_decoder_attention, + q_noise, + qn_block_size, + ) + + self.memory_dim = memory_dim if memory_dim is not None else embed_dim + self.std_scale = std_scale + self.disable_mem_on_mem_attn = disable_mem_on_mem_attn + + # This Operator was used for factorization in PySpeech + self.v2e = lambda x: x + + if tanh_on_mem: + self.squash_mem = torch.tanh + self.nonlinear_squash_mem = True + else: + self.squash_mem = lambda x: x + self.nonlinear_squash_mem = False + + self.max_memory_size = max_memory_size + + def forward(self, input_and_summary, state): + """ + input: Encoder states of current segment with left or right context, + plus one summarization query + + """ + + length, batch_size, _ = input_and_summary.shape + length = length - 1 # not include sum_query, last index + + memory = state["memory_banks"] + # TODO: positional embedding on memory + + if self.max_memory_size > -1 and len(memory) > self.max_memory_size: + # TODO: need to fix here + if self.max_memory_size == 0: + memory = memory.new_zeros(1, memory.size(1), self.memory_dim) + else: + memory = memory[-self.max_memory_size :] + + memory_and_input = torch.cat(memory + [input_and_summary[:-1]], dim=0) + input_and_sum_query = input_and_summary + + q = self.q_proj(self.v2e(input_and_sum_query)) + k = self.k_proj(self.v2e(memory_and_input)) + v = self.v_proj(self.v2e(memory_and_input)) + + q = ( + q.contiguous() + .view(-1, batch_size * self.num_heads, self.head_dim) + .transpose(0, 1) + * self.scaling + ) + k = ( + k.contiguous() + .view(-1, batch_size * self.num_heads, self.head_dim) + .transpose(0, 1) + ) + + v = ( + v.contiguous() + .view(-1, batch_size * self.num_heads, self.head_dim) + .transpose(0, 1) + ) + + attention_weights = torch.bmm(q, k.transpose(1, 2)) + + if self.disable_mem_on_mem_attn: + attention_weights = self.suppress_mem_on_mem_attention( + batch_size, self.num_heads, len(memory), attention_weights + ) + + if self.std_scale is not None: + attention_weights = attention_suppression(attention_weights, self.std_scale) + + assert list(attention_weights.shape) == [ + batch_size * self.num_heads, + length + 1, + length + len(memory), + ] + + attention_weights = torch.nn.functional.softmax( + attention_weights.float(), dim=-1 + ).type_as(attention_weights) + + attention_probs = self.dropout_module(attention_weights) + + # [T, T, B, n_head] + [T, B, n_head, d_head] -> [T, B, n_head, d_head] + attention = torch.bmm(attention_probs, v) + + assert list(attention.shape) == [ + batch_size * self.num_heads, + length + 1, + self.head_dim, + ] + + attention = ( + attention.transpose(0, 1) + .contiguous() + .view(length + 1, batch_size, self.embed_dim) + ) + + output_and_memory = self.out_proj(attention) + + next_m = output_and_memory[-1:] + next_m = self.squash_mem(next_m) + output = output_and_memory[:-1] + + state["memory_banks"].append(next_m) + + return output + + def suppress_mem_on_mem_attention( + self, B: int, num_heads: int, mem_size: int, attention_weight: Tensor + ): + """ + Arguments: + - B: batch size + - num_heads: number of attention heads + - mem_size: size of memory bank + - attention_weight: a [B*num_heads, T + 1, T + mem_size] vector + + Return: + modified attention_weight with [B*num_heads, -1, :mem_size] = -inf + """ + attention_weight[:, -1, :mem_size] = float("-inf") + return attention_weight + + +# ------------------------------------------------------------------------------ +# SequenceEncoder +# ------------------------------------------------------------------------------ +class SequenceEncoder(FairseqEncoder): + """ + SequenceEncoder encodes sequences. + + More specifically, `src_tokens` and `src_lengths` in `forward()` should + describe a batch of "complete" sequences rather than segments. + + Segment-by-segment inference can be triggered by `segment_size`: + 1) `segment_size` is None: + SequenceEncoder treats the input sequence as one single segment. + 2) `segment_size` is not None (some int instead): + SequenceEncoder does the following: + 1. breaks the input sequence into several segments + 2. inference on each segment and collect the outputs + 3. concatanete segment outputs into the output sequence. + Note that `segment_size` here shouldn't include additional left/right + contexts needed, for example if we wish to infer with LC-BLSTM where the + middle chunk size is 100 and right context is 20, `segment_size` should be + 100. + """ + + def __init__(self, args, module): + super().__init__(None) + + self.module = module + self.input_time_axis = 1 + self.output_time_axis = 0 + self.segment_size = args.segment_size + self.left_context = args.left_context + self.right_context = args.right_context + + def forward( + self, + src_tokens: Tensor, + src_lengths: Tensor, + states=None, + ): + + seg_src_tokens_lengths = sequence_to_segments( + sequence=src_tokens, + time_axis=self.input_time_axis, + lengths=src_lengths, + segment_size=self.segment_size, + extra_left_context=self.left_context, + extra_right_context=self.right_context, + ) + + seg_encoder_states_lengths: List[Tuple[Tensor, Tensor]] = [] + + for seg_src_tokens, seg_src_lengths in seg_src_tokens_lengths: + (seg_encoder_states, seg_enc_lengths, states) = self.module( + seg_src_tokens, + seg_src_lengths, + states=states, + ) + + seg_encoder_states_lengths.append((seg_encoder_states, seg_enc_lengths)) + + encoder_out, enc_lengths = segments_to_sequence( + segments=seg_encoder_states_lengths, time_axis=self.output_time_axis + ) + + encoder_padding_mask, _ = lengths_to_encoder_padding_mask( + enc_lengths, batch_first=True + ) + + if not encoder_padding_mask.any(): + encoder_padding_mask = None + + return { + "encoder_out": [encoder_out], + "encoder_padding_mask": [encoder_padding_mask], + "encoder_embedding": [], + "encoder_states": [states], + "src_tokens": [], + "src_lengths": [], + } + + def incremental_encode( + self, + seg_src_tokens: Tensor, + seg_src_lengths: Tensor, + states=None, + ): + """ + Different from forward function, this function takes segmented speech + as input, and append encoder states to previous states + """ + (seg_encoder_states, seg_enc_lengths, states) = self.module( + seg_src_tokens, + seg_src_lengths, + states=states, + ) + return seg_encoder_states, seg_enc_lengths, states + + +# ------------------------------------------------------------------------------ +# Augmented memory model decorator +# ------------------------------------------------------------------------------ +def augmented_memory(klass): + class StreamSeq2SeqModel(klass): + @staticmethod + def add_args(parser): + super(StreamSeq2SeqModel, StreamSeq2SeqModel).add_args(parser) + parser.add_argument( + "--segment-size", type=int, required=True, help="Length of the segment." + ) + parser.add_argument( + "--left-context", + type=int, + default=0, + help="Left context for the segment.", + ) + parser.add_argument( + "--right-context", + type=int, + default=0, + help="Right context for the segment.", + ) + parser.add_argument( + "--max-memory-size", + type=int, + default=-1, + help="Right context for the segment.", + ) + + StreamSeq2SeqModel.__name__ = klass.__name__ + return StreamSeq2SeqModel diff --git a/fairseq/models/speech_to_text/modules/emformer.py b/fairseq/models/speech_to_text/modules/emformer.py new file mode 100644 index 0000000000..6ef76bd012 --- /dev/null +++ b/fairseq/models/speech_to_text/modules/emformer.py @@ -0,0 +1,1837 @@ +#!/usr/bin/env python3 +# Copyright (c) 2017-present, Facebook, Inc. +# All rights reserved. +# +# This source code is licensed under the license found in the LICENSE file in +# the root directory of this source tree. An additional grant of patent rights +# can be found in the PATENTS file in the same directory. + + +import math +import re +from functools import partial +from typing import List, Optional, Tuple + +import torch +import torch.nn as nn +from fairseq.models import ( + FairseqEncoder, +) +from fairseq.models.speech_to_text.utils import ( + NoOp, + lengths_to_padding_mask, + segments_to_sequence, +) +from fairseq.models.speech_to_text.utils import ( + attention_suppression, + layer_norm_backward_hook, +) +from torch import Tensor, device as Device +from torch.quantization.qconfig import ( + default_dynamic_qconfig, + per_channel_dynamic_qconfig, +) + + +class RelativePositionEmbedding(nn.Module): + """ + Implementation according to https://arxiv.org/abs/1803.02155 + """ + + def __init__(self, head_dim, max_position, norm_init=True): + super().__init__() + self.head_dim = head_dim + self.max_position = max_position + self.embeddings = nn.Parameter(torch.Tensor(max_position * 2 + 1, head_dim)) + if norm_init: + nn.init.xavier_normal_(self.embeddings) + else: + nn.init.xavier_uniform_(self.embeddings) + + def forward(self, input: Tensor): + output = nn.functional.embedding(input.long(), self.embeddings) + return output + + +class Fp32LayerNorm(nn.Module): + def __init__( + self, + input_dim, + clamp_grad=True, + max_grad_value=256, + eps=1e-5, + elementwise_affine=True, + ): + super().__init__() + self.torch_module = torch.nn.LayerNorm( + input_dim, eps=eps, elementwise_affine=elementwise_affine + ) + if clamp_grad: + hook = partial(layer_norm_backward_hook, clamp_value=max_grad_value) + self.torch_module.register_backward_hook(hook) + + def forward(self, input): + output = torch.nn.functional.layer_norm( + input.float(), + self.torch_module.normalized_shape, + self.torch_module.weight.float() + if self.torch_module.weight is not None + else None, + self.torch_module.bias.float() + if self.torch_module.bias is not None + else None, + self.torch_module.eps, + ).type_as(input) + return output + + +# ------------------------------------------------------------------------------ +# PositionwiseFF +# ------------------------------------------------------------------------------ + + +class PositionwiseFF(nn.Module): + """ + FFN layer in transformer. + + Args: + input_dim: input embedding dimension + ffn_dim: FFN layer inner dimension + dropout_on_fc1: dropout for first linear layer + dropout_on_fc2: dropout fr second linear layer + activation_fn: activation function used after first linear layer. \ + Only relu or gelu is supported. + + """ + + def __init__( + self, input_dim, ffn_dim, dropout_on_fc1, dropout_on_fc2, activation_fn + ): + super(PositionwiseFF, self).__init__() + + self.input_dim = input_dim + self.ffn_dim = ffn_dim + if activation_fn == "relu": + ac = nn.ReLU() + elif activation_fn == "gelu": + ac = nn.GELU() + else: + raise ValueError("Unsupported activation_fn = ({})".format(activation_fn)) + + # fc1 -> ac -> dropout -> fc2 -> dropout + self.module = nn.Sequential( + nn.Linear(input_dim, ffn_dim), + ac, + nn.Dropout(dropout_on_fc1), + nn.Linear(ffn_dim, input_dim), + nn.Dropout(dropout_on_fc2), + ) + + self.layer_norm = Fp32LayerNorm(input_dim) + + def forward(self, input): + module_out = self.module(self.layer_norm(input)) + output = module_out + input + + return output + + def quantize_(self, params=None): + if params and "per_channel" in params and params["per_channel"]: + qconfig = per_channel_dynamic_qconfig + else: + qconfig = default_dynamic_qconfig + torch.quantization.quantize_dynamic( + self, {torch.nn.Linear: qconfig}, dtype=torch.qint8, inplace=True + ) + return self + + +# ------------------------------------------------------------------------------ +# SummarizationLayer +# ------------------------------------------------------------------------------ + + +class SummarizationLayer(nn.Module): + def __init__(self, method, segment_size, embedding_dim): + super(SummarizationLayer, self).__init__() + self.segment_size = segment_size + self.embedding_dim = embedding_dim + nonlin_match = re.match(r"nonlinear\((?P<act>[a-z]+),(?P<dim>[0-9]+)\)", method) + self.method = method + if method == "mean": + self.module = nn.AvgPool1d( + kernel_size=segment_size, + stride=segment_size, + ceil_mode=True, + ) + elif method == "max": + self.module = nn.MaxPool1d( + kernel_size=segment_size, + stride=segment_size, + ceil_mode=True, + ) + elif method == "linear": + self.module = nn.Linear(segment_size, 1) + elif nonlin_match: + nonlin_args = nonlin_match.groupdict() + act_type = nonlin_args["act"] + hid_dim = int(nonlin_args["dim"]) + if act_type == "relu": + act = nn.ReLU() + elif act_type == "gelu": + act = nn.GELU() + else: + raise ValueError("Unsupported activation_fn = ({})".format(act_type)) + self.module = nn.Sequential( + nn.Linear(segment_size, hid_dim), + act, + nn.Linear(hid_dim, 1), + ) + else: + raise ValueError("Unsupported summarization method = ({})".format(method)) + + def forward(self, input): + # T, B, D -> B, D, T + input = input.permute(1, 2, 0) + + if self.method == "mean" or self.method == "max": + output = self.module(input) + output = output.permute(2, 0, 1) + return output + + full_seg_length = input.size(2) // self.segment_size * self.segment_size + if full_seg_length > 0: + # at least one seg is full + B = input.size(0) + D = input.size(1) + input_todo = ( + input[:, :, :full_seg_length] + .contiguous() + .view(B, -1, self.segment_size) + ) + output = self.module(input_todo) + output = output.view(B, D, -1) + else: + output = input.new_zeros(input.size(0), input.size(1), 0) + left = input.size(2) - full_seg_length + if left > 0: + # when last seg is not full, use zeros as last memory placeholder + zeros = input.new_zeros(input.size(0), input.size(1), 1) + output = torch.cat([output, zeros], dim=2) + output = output.permute(2, 0, 1) + return output + + +# ------------------------------------------------------------------------------ +# NoSegAugmentedMemoryMultiheadAttentionBmm +# ------------------------------------------------------------------------------ + + +class NoSegAugmentedMemoryMultiheadAttentionBmm(nn.Module): + """ + Whole utterance augmented memory multihead attention using BMM. + + Different with previous augmented memory multihead attention where + the utterance is chunked into segments. Here we use attention mask + achieve so. The input embedding [right_context, utterance, summary] + is a concatenation of right context, utterance and summary. + + Right context block is the concatenation of all the right context for + each segments. [right_context_0, right_context_1, ..., right_context_n] + For example, if we have utterance = [v0, v1, v2, ...., v20]. segment + size 8, right_context size 4. Then the right context blocks = + [v8, v9, v10, v11, v16, v17, v18, v19, 0, 0, 0, 0], where v8, v9, v10, + and v11 are the right context for first segment. v16, v17, v18 and v19 + are the right context for second segment. 0, 0, 0 and 0 are right context + for the last segment. + + utterance is corresponding to input embedding sequence + + summary is concatenation of average of each segments. [summary_0, + summary_1, ..., ]. + + In augmented memory multihead attention, the query is [right_context, + utterance, summary], key is [memory, right_context, utterance]. Different + with AugmentedMemoryMultiheadAttentionBmm, memory here is passed from + previous attention layer. For the first attention layer, memory is average + of each segment. + + Memory is a concatenation of memory from each segments in previous attention + layer. For example, current layer is i, then memory is [m_0, m_1, ..., m_n]. + Each m_k is the output from seg_k in layer i-1. + + args: + input_dim: input embedding dimension + num_heads: number of heads in multihead self-attention + dropout: attention dropout + std_scale: if std_scale is not None. The weak attention suppression is + turned on. For std_scale = 0.5, all the attention smaller than + mean + 0.5 * std will be suppressed. + scaled_init: whether to use scaled init for linear weight + tanh_on_mem: whether to use tanh on memory output + use_mem: whether to use memory or not. When max_memory_size is 0, then + we don't have memory anymore. + layer_index: current self-attention layer index that is used in depth + initialization + max_relative_position: max relative position used in relative position + embedding + rpe_old_option: To be compatible with previous model. The previous model + was trained with attention += attention + rpe. The correct equation + should be attention = attention + rpe + + """ + + def __init__( + self, + input_dim, + num_heads, + dropout=0.0, + std_scale=None, + scaled_init=False, + tanh_on_mem=False, + use_mem=True, + mini_batches=False, + negative_inf="-inf", + layer_index=-1, + max_relative_position=0, + rpe_old_option=True, + ): + if input_dim % num_heads: + raise ValueError( + "input_dim ({}) must be divisible by num_heads ({})".format( + input_dim, num_heads + ) + ) + + super().__init__() + + embed_dim = input_dim + self.e2h_kv = torch.nn.Linear(input_dim, 2 * input_dim, bias=True) + self.e2h_q = torch.nn.Linear(input_dim, input_dim, bias=True) + self.rpe_old_option = rpe_old_option + if max_relative_position > 0: + self.use_rpe = True + self.rpe_k = RelativePositionEmbedding( + head_dim=input_dim // num_heads, + max_position=max_relative_position, + ) + self.rpe_v = RelativePositionEmbedding( + head_dim=input_dim // num_heads, + max_position=max_relative_position, + ) + else: + self.use_rpe = False + self.rpe_k = None + self.rpe_v = None + if scaled_init: + if layer_index == -1: + gain = 1.0 / math.sqrt(2) + else: + # https://arxiv.org/abs/2005.09684 depthwise initialization + # stablize the training greatly. Use depthwise initialization to + # replace incremental loss. + gain = 1.0 / math.sqrt(layer_index + 1) + torch.nn.init.xavier_uniform_(self.e2h_kv.weight, gain=gain) + torch.nn.init.xavier_uniform_(self.e2h_q.weight, gain=gain) + + self.out_proj = torch.nn.Linear(embed_dim, embed_dim, bias=True) + + self.embed_dim = embed_dim + self.num_heads = num_heads + self.dropout = dropout + + self.head_dim = embed_dim // num_heads + self.scaling = self.head_dim ** -0.5 + + self.std_scale = std_scale + self.use_mem = use_mem + self.mini_batches = mini_batches + self.negative_inf = negative_inf + + if tanh_on_mem: + self.squash_mem = torch.tanh + self.nonlinear_squash_mem = True + else: + self.squash_mem = NoOp() + self.nonlinear_squash_mem = False + + def prepare_qkv( + self, + input: Tensor, + mems: Tensor, + lengths: Tensor, + summary_length: int, + lc_length: int, + ): + # T: right_context length + utterance_length + summary_length + T, B, D = input.shape + mem_length = mems.size(0) + utterance_length = torch.max(lengths) + + right_context_blocks_length = T - utterance_length - summary_length + rc_block = input[:right_context_blocks_length, :, :] + utterance_block = input[right_context_blocks_length : T - summary_length, :, :] + + if B == 1: + padding_mask = None + else: + klengths = lengths + mem_length + right_context_blocks_length + lc_length + padding_mask = lengths_to_padding_mask(lengths=klengths) + + mem_rc_input = torch.cat([mems, rc_block, utterance_block], dim=0) + + # In training lc_length = 0 + key_length = mem_rc_input.size(0) + lc_length + rc_input_sum = input + q = self.e2h_q(rc_input_sum) + kv = self.e2h_kv(mem_rc_input) + k, v = kv.chunk(chunks=2, dim=2) + result_qkv = (q, k, v) + input_shape = (T, B, D) + result_lengths_info = ( + mem_length, + utterance_length, + right_context_blocks_length, + key_length, + ) + if padding_mask is not None: + assert padding_mask.size(0) == B + assert padding_mask.size(1) == key_length + + return result_qkv, input_shape, result_lengths_info, padding_mask + + def prepare_attention_weights( + self, + q: Tensor, + new_k: Tensor, + new_v: Tensor, + input_shape: Tuple[int, int, int], + rpe: Optional[Tensor], + ) -> Tuple[Tensor, Tensor, Tensor]: + T, B, D = input_shape + q = ( + q.contiguous().view(-1, B * self.num_heads, self.head_dim).transpose(0, 1) + * self.scaling + ) + + k = ( + new_k.contiguous() + .view(-1, B * self.num_heads, self.head_dim) + .transpose(0, 1) + ) + + v = ( + new_v.contiguous() + .view(-1, B * self.num_heads, self.head_dim) + .transpose(0, 1) + ) + + attention_weights = torch.bmm(q, k.transpose(1, 2)) + if self.use_rpe and rpe is not None and self.rpe_v is not None: + r_k = self.rpe_k(rpe) + # [q, B*h, d] * [q, k, d] -> [B*h, q, k] + attention_weights_rpe = torch.matmul( + q.transpose(0, 1), r_k.transpose(1, 2) + ).transpose(0, 1) + attention_weights = attention_weights + attention_weights_rpe + attention_weights_float = attention_weights.float() + + return attention_weights, attention_weights_float, v + + def prepare_attention_output( + self, + attention_weights: Tensor, + attention_weights_float: Tensor, + v: Tensor, + input_shape: Tuple[int, int, int], + key_length: int, + padding_mask: Optional[Tensor], + rpe: Optional[Tensor], + ) -> Tensor: + T, B, D = input_shape + if padding_mask is not None: + attention_weights_float = attention_weights_float.view( + B, self.num_heads, T, key_length + ) + attention_weights_float = attention_weights_float.masked_fill( + padding_mask.unsqueeze(1).unsqueeze(2).to(torch.bool), float("-inf") + ) + attention_weights_float = attention_weights_float.view( + B * self.num_heads, T, key_length + ) + + if self.std_scale is not None: + attention_weights_float = attention_suppression( + attention_weights_float, self.std_scale + ) + + attention_weights_float = torch.nn.functional.softmax( + attention_weights_float, dim=-1 + ) + attention_weights = attention_weights_float.type_as(attention_weights) + + attention_probs = torch.nn.functional.dropout( + attention_weights, p=self.dropout, training=self.training + ) + + # [T, key_length, B, n_head]+ [key_length, B, n_head, d_head] + # -> [T, B, n_head, d_head] + attention = torch.bmm(attention_probs, v) + if self.use_rpe and rpe is not None and self.rpe_v is not None: + r_v = self.rpe_v(rpe) + attention_rpe = torch.matmul( + attention_probs.transpose(0, 1), r_v + ).transpose(0, 1) + + if self.rpe_old_option: + attention += attention + attention_rpe + else: + attention = attention + attention_rpe + + assert list(attention.shape) == [B * self.num_heads, T, self.head_dim] + + attention = attention.transpose(0, 1).contiguous().view(T, B, self.embed_dim) + + rc_output_memory = self.out_proj(attention) + return rc_output_memory + + @torch.jit.unused + def forward( + self, + input: Tensor, + lengths: Tensor, + mems: Tensor, + attention_mask: Tensor, + pre_mems: Optional[Tensor] = None, + left_context_key: Optional[Tensor] = None, + left_context_val: Optional[Tensor] = None, + rpe: Optional[Tensor] = None, + ) -> Tuple[Tensor, Tensor, Tensor, Tensor]: + """ + forward function for NoSegAugmentedMemoryMultiheadAttentionBmm in training. + + args: + input: formed in the following way + [right_context_0, right_contex_1, ..., seg_0, seg_1, + ..., summary_0, summary_1,..] + lengths: the length of query which is [seg_0, seg_1, ....] + mems: [mem_0, mem_1, ...]. + attention_mask: attention mask for query = [right_context, query, summary] + key = [mem, right_context, query]. This is only used for traing. + + """ + if self.use_mem: + mem_length = mems.size(0) + summary_length = mem_length + 1 + if pre_mems is not None: + mems = torch.cat([pre_mems, mems], dim=0) + else: + mem_length = 0 + summary_length = 0 + + # In training, lc_length = 0 + if left_context_key is not None: + lc_length = left_context_key.size(0) + else: + lc_length = 0 + results = self.prepare_qkv( + input=input, + mems=mems, + lengths=lengths, + summary_length=summary_length, + lc_length=lc_length, + ) + result_qkv, input_shape, result_lengths_info, padding_mask = results + q, k, v = result_qkv + ( + mem_length, + utterance_length, + right_context_blocks_length, + key_length, + ) = result_lengths_info + + if left_context_key is not None: + # add the cache key and value + new_k = torch.cat( + [ + k[: mem_length + right_context_blocks_length, :, :], + left_context_key, + k[-utterance_length:, :, :], + ], + dim=0, + ) + new_v = torch.cat( + [ + v[: mem_length + right_context_blocks_length, :, :], + left_context_val, + v[-utterance_length:, :, :], + ], + dim=0, + ) + next_k = new_k[mem_length + right_context_blocks_length :, :, :] + next_v = new_v[mem_length + right_context_blocks_length :, :, :] + else: + new_k = k + new_v = v + next_k = None + next_v = None + + attention_weights, attention_weights_float, v = self.prepare_attention_weights( + q=q, + new_k=new_k, + new_v=new_v, + input_shape=input_shape, + rpe=rpe, + ) + + # mask attention + attention_mask = attention_mask.unsqueeze(0) + attention_weights_float = attention_weights_float.masked_fill( + attention_mask, float(self.negative_inf) + ) + + rc_output_memory = self.prepare_attention_output( + attention_weights=attention_weights, + attention_weights_float=attention_weights_float, + v=v, + input_shape=input_shape, + key_length=key_length, + padding_mask=padding_mask, + rpe=rpe, + ) + + if self.use_mem: + # next_m length equals to summary length - 1 + # last memory is ignored + if self.mini_batches: + next_m = rc_output_memory[-summary_length:] + else: + next_m = rc_output_memory[-summary_length:-1] + + next_m = self.squash_mem(next_m) + # rc and output + rc_output = rc_output_memory[:-summary_length] + if not self.nonlinear_squash_mem: + next_m = torch.clamp(next_m, min=-10, max=10) + else: + next_m = mems + rc_output = rc_output_memory + + return rc_output, next_m, next_k, next_v + + @torch.jit.export + def forward_jit( + self, + input: Tensor, + lengths: Tensor, + mems: Tensor, + left_context_key: Tensor, + left_context_val: Tensor, + rpe: Optional[Tensor], + ) -> Tuple[Tensor, Tensor, Tensor, Tensor]: + """ + forward function for NoSegAugmentedMemoryMultiheadAttentionBmm in decoding. + + args: + input: formed in the following way + [right_context_0, right_contex_1, ..., seg_0, seg_1, + ..., summary_0, summary_1,..] + lengths: the length of query which is [seg_0, seg_1, ....] + mems: [mem_0, mem_1, ...]. + left_context_key: left_context for key part. This is only used for online + decoding. In training, this is empty tensor + left_context_val: left_context for value part. This is only used for online + decoding. In training, this is empty tensor + + """ + lc_length = left_context_key.size(0) + + # In decoding, summary_length = 1 or 0 + if self.use_mem: + summary_length = 1 + else: + summary_length = 0 + + results = self.prepare_qkv( + input=input, + mems=mems, + lengths=lengths, + summary_length=summary_length, + lc_length=lc_length, + ) + result_qkv, input_shape, result_lengths_info, padding_mask = results + q, k, v = result_qkv + ( + mem_length, + utterance_length, + right_context_blocks_length, + key_length, + ) = result_lengths_info + + # add the cache key and value + new_k = torch.cat( + [ + k[: mem_length + right_context_blocks_length, :, :], + left_context_key, + k[-utterance_length:, :, :], + ], + dim=0, + ) + new_v = torch.cat( + [ + v[: mem_length + right_context_blocks_length, :, :], + left_context_val, + v[-utterance_length:, :, :], + ], + dim=0, + ) + next_k = new_k[mem_length + right_context_blocks_length :, :, :] + next_v = new_v[mem_length + right_context_blocks_length :, :, :] + + attention_weights, attention_weights_float, v = self.prepare_attention_weights( + q=q, + new_k=new_k, + new_v=new_v, + input_shape=input_shape, + rpe=rpe, + ) + # In online decoding, we don't have attention mask. But we still need + # to disable the attention from summary query to memory + attention_weights_float[:, -1, :mem_length] = float(self.negative_inf) + rc_output_memory = self.prepare_attention_output( + attention_weights=attention_weights, + attention_weights_float=attention_weights_float, + v=v, + input_shape=input_shape, + key_length=key_length, + padding_mask=padding_mask, + rpe=rpe, + ) + + # In decoding, summary length is 1 + if self.use_mem: + next_m = rc_output_memory[-1:] + next_m = self.squash_mem(next_m) + # rc and output + rc_output = rc_output_memory[:-1] + if not self.nonlinear_squash_mem: + next_m = torch.clamp(next_m, min=-10, max=10) + else: + rc_output = rc_output_memory + # empty tensor as input mems + next_m = mems + + return rc_output, next_m, next_k, next_v + + def quantize_(self, params=None): + if params and "per_channel" in params and params["per_channel"]: + qconfig = per_channel_dynamic_qconfig + else: + qconfig = default_dynamic_qconfig + torch.quantization.quantize_dynamic( + self, {torch.nn.Linear: qconfig}, dtype=torch.qint8, inplace=True + ) + return self + + +class NoSegAugmentedMemoryTransformer(nn.Module): + """ + Whole utterance augmented memory transformer. + + This is not pyspeech nn layer. It is used as a module in a master layer where + multiple transformers is used. + """ + + def __init__( + self, + input_dim, + num_heads, + ffn_dim, + dropout_in_attn=0.0, + dropout_on_attn=None, + dropout_on_fc1=None, + dropout_on_fc2=None, + activation_fn="relu", + tanh_on_mem=False, + std_scale=None, + scaled_init=False, + segment_size=128, + use_mem=True, + mini_batches=False, + negative_inf="-inf", + layer_index=-1, + summarization_method="mean", + max_relative_position=0, + rpe_old_option=True, + ): + super(NoSegAugmentedMemoryTransformer, self).__init__() + + self.attention = NoSegAugmentedMemoryMultiheadAttentionBmm( + input_dim=input_dim, + num_heads=num_heads, + dropout=dropout_in_attn, + scaled_init=scaled_init, + tanh_on_mem=tanh_on_mem, + std_scale=std_scale, + use_mem=use_mem, + mini_batches=mini_batches, + negative_inf=negative_inf, + layer_index=layer_index, + max_relative_position=max_relative_position, + ) + self.dropout = nn.Dropout(dropout_on_attn) + self.pos_ff = PositionwiseFF( + input_dim=input_dim, + ffn_dim=ffn_dim, + dropout_on_fc1=dropout_on_fc1, + dropout_on_fc2=dropout_on_fc2, + activation_fn=activation_fn, + ) + self.layer_norm_pre = Fp32LayerNorm(input_dim) + self.layer_norm = Fp32LayerNorm(input_dim) + self.segment_size = segment_size + self.use_mem = use_mem + + self.memory_op = SummarizationLayer( + summarization_method, segment_size, input_dim + ) + + def set_mini_batches(self, mini_batches): + self.attention.mini_batches = mini_batches + + def gen_summary_queries(self, input): + sum_input = self.memory_op(input) + return sum_input + + def pre_attention_ops(self, input, right_context_blocks): + rc_length = right_context_blocks.size(0) + input_length = input.size(0) + + rc_and_input = torch.cat([right_context_blocks, input], dim=0) + residual_input = rc_and_input + rc_and_input = self.layer_norm_pre(rc_and_input) + + query_input = rc_and_input[-input_length:, :, :] + return rc_length, input_length, residual_input, query_input, rc_and_input + + def after_attention_ops(self, attention_output, residual_input): + output = self.dropout(attention_output) + output = output + residual_input + output = self.pos_ff(output) + output = self.layer_norm(output) + return output + + @torch.jit.export + def forward_jit( + self, + input: Tensor, + lengths: Tensor, + mems: Tensor, + left_context_key: Tensor, + left_context_val: Tensor, + right_context_blocks: Tensor, + rpe: Optional[Tensor], + ) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor]: + + results = self.pre_attention_ops(input, right_context_blocks) + rc_length, input_length, residual_input, query_input, rc_and_input = results + + # In online decoding, the summary query size is always 1 or 0 + if self.use_mem: + summary_query = self.gen_summary_queries(query_input) + summary_query = summary_query[0:1, :, :] + rc_qu_su = torch.cat([rc_and_input, summary_query], dim=0) + else: + rc_qu_su = rc_and_input + + rc_output, next_m, next_k, next_v = self.attention.forward_jit( + input=rc_qu_su, + lengths=lengths, + mems=mems, + left_context_key=left_context_key, + left_context_val=left_context_val, + rpe=rpe, + ) + rc_output = self.after_attention_ops(rc_output, residual_input) + results = ( + rc_output[-input_length:, :, :], + next_m, + rc_output[0:rc_length, :, :], + next_k, + next_v, + ) + return results + + @torch.jit.unused + def forward( + self, + input, + lengths, + mems, + right_context_blocks, + attention_mask, + pre_mems, + left_context_key, + left_context_val, + rpe, + ): + + results = self.pre_attention_ops(input, right_context_blocks) + rc_length, input_length, residual_input, query_input, rc_and_input = results + if self.use_mem: + summary_query = self.gen_summary_queries(query_input) + rc_qu_su = torch.cat([rc_and_input, summary_query], dim=0) + else: + rc_qu_su = rc_and_input + + rc_output, next_m, next_k, next_v = self.attention( + input=rc_qu_su, + lengths=lengths, + mems=mems, + attention_mask=attention_mask, + pre_mems=pre_mems, + left_context_key=left_context_key, + left_context_val=left_context_val, + rpe=rpe, + ) + + # [TODO] Note memory did not go through pos_ff. What happen if we pass + # memory through the pos_ff as well? + rc_output = self.after_attention_ops(rc_output, residual_input) + results = ( + rc_output[-input_length:, :, :], + next_m, + rc_output[0:rc_length, :, :], + next_k, + next_v, + ) + + return results + + +class NoSegAugmentedMemoryTransformerEncoderLayer(FairseqEncoder): + """ + Whole utterance augmented memory transformer encoder layer. This is a master layer + where we can define multiple augmented memory transformers. There are two reasons + to setup the master layer. + 1. We only need to define once about the attention mask. All the layers in the master + layer share the same mask. + 2. pyspeech nn layer has special input and output format. Defining one master layer is + easier to passing memory between different layes inside the master layer + + args: + input_dim: input embedding dimension + num_heads: number of heads in multihead self-attention + ffn_dim: ffn dimension in FFN layer + num_layers: number of augmented memory transformer layers + dropout_in_attn: dropout used in multi-head self-attention + dropout_on_attn: dropout used for output from te multihead self-attention + dropout_on_fc1: dropout used in FFN layer for the first linear layer + dropout_on_fc2: dropout used in FFN layer for the second linear layer + segment_size: segment size for each segment + context_config: (left_context_size, right_context_size) defines the surround context size + for each segment + max_memory_size: maximum memory size used for each segment + scaled_init: whether use scaled init for weight initialization in attention layer + std_scale: if std_scale is not None. The weak attention suppression is + turned on. For std_scale = 0.5, all the attention smaller than + mean + 0.5 * std will be suppressed. + activation_fn: activation function used in FFN layer. [ReLU, GELU] supported + tanh_on_mem: whether use tanh on memory + mini_batches: use mini-btach training + negative_inf: the negative infinity value used in attention masking. default is "-inf". + For some situation, e.g. LM. it is better to use "-1e8" to avoid nan issue. + summarization_method: method to generate segment summrization embedding + max_relative_position: max relatie position for relative position embedding + rpe_old_option: To be compatible with previous model. The previous model + was trained with attention += attention + rpe. The correct equation + should be attention = attention + rpe + [TODO]: remove the rpe_old_option by the end of 2021 Q1. + + """ + + def __init__( + self, + input_dim, + num_heads, + ffn_dim, + num_layers=1, + dropout_in_attn=0.0, + dropout_on_attn=0.0, + dropout_on_fc1=0.0, + dropout_on_fc2=0.0, + segment_size=128, + context_config=(0, 0), + max_memory_size=0, + scaled_init=True, + std_scale=None, + activation_fn="relu", + tanh_on_mem=False, + mini_batches=False, + negative_inf="-inf", + deep_init=True, + summarization_method="mean", + max_relative_position=0, + rpe_old_option=True, + ): + super().__init__(None) + if input_dim % num_heads: + raise ValueError( + "input_dim ({}) must be divisible by num_heads ({})".format( + input_dim, num_heads + ) + ) + + # we used to support growing memory size. However, it will cause + # cross stream batching failure. Now we need to have exact max memory size + if max_memory_size < 0: + raise ValueError("max_memory_size must be >= 0") + + # Only assign right_context. In decoding, left context will be cached. + # No need to let the online decoder to re-assign the left context + self.left_context, self.right_context = context_config + self.segment_size = segment_size + self.memory_dim = input_dim + self.max_memory_size = max_memory_size + self.mini_batches = mini_batches + if self.max_memory_size != 0: + self.use_mem = True + else: + self.use_mem = False + + self.memory_op = SummarizationLayer( + summarization_method, segment_size, input_dim + ) + + self.layers = torch.nn.ModuleList() + self.num_layers = num_layers + self.max_relative_position = max_relative_position + if self.max_relative_position > 0: + self.use_rpe = True + else: + self.use_rpe = False + for i in range(self.num_layers): + if deep_init: + layer_index = i + else: + layer_index = -1 + + self.layers.append( + NoSegAugmentedMemoryTransformer( + num_heads=num_heads, + input_dim=input_dim, + ffn_dim=ffn_dim, + dropout_in_attn=dropout_in_attn, + dropout_on_attn=dropout_on_attn, + dropout_on_fc1=dropout_on_fc1, + dropout_on_fc2=dropout_on_fc2, + segment_size=segment_size, + std_scale=std_scale, + activation_fn=activation_fn, + tanh_on_mem=tanh_on_mem, + scaled_init=scaled_init, + use_mem=self.use_mem, + mini_batches=mini_batches, + negative_inf=negative_inf, + layer_index=layer_index, + summarization_method=summarization_method, + max_relative_position=max_relative_position, + rpe_old_option=rpe_old_option, + ) + ) + + def set_mini_batches(self, mini_batches): + # handy function only used for unit test + self.mini_batches = mini_batches + for layer in self.layers: + layer.set_mini_batches(mini_batches) + + def _get_relative_position( + self, + input: Tensor, + max_relative_position: int, + left_context_length: int, + past_length: int, + is_decoding: bool, + ): + # For training, we copy the right context to the start of the utterance + # First dimension in distance is corresponding to query. + # [right context, utterance, summary vector] + # Second dimension in distance is corresponding to key. + # [Memory bank, right context, utterance] + # For summary vector in query part, the distance with + # all other position is 2*max_position. For memory bank in key, + # the distance with all other positions is 0. + + T, B, D = input.shape + num_segs = math.ceil((T - self.right_context) / self.segment_size) + + # utterance + u_st = past_length * self.segment_size + u_ed = u_st + T + utterance_ranges = torch.arange(u_st, u_ed - self.right_context) + + # left context. Only in minibatch or decoding + left_context_ranges = torch.arange(u_st - left_context_length, u_st) + + # Right context block + # right context + utterance + right_context_blocks = [] + for i in range(0, num_segs - 1): + st = (i + 1) * self.segment_size + u_st + ed = st + self.right_context + assert ed < u_ed + temp = torch.arange(st, ed) + right_context_blocks.append(temp) + right_context_blocks.append(torch.arange(u_ed - self.right_context, u_ed)) + right_context_ranges = torch.cat(right_context_blocks) + + if self.use_mem: + # Memory bank + # The position for memory -n, .., -1 + if is_decoding: + memory_size = min(past_length, self.max_memory_size) + else: + memory_size = num_segs + past_length - 1 + memory_bank_ranges = torch.arange( + -max_relative_position - 1, -max_relative_position - 1 - memory_size, -1 + ) + + # summary vector + # The position for summary vector as the T+max_relative_position+1. + # After the clamping, the relative position is max_relative_position + summary_pos_st = u_ed + max_relative_position + 1 + summary_vector_ranges = torch.arange( + summary_pos_st, summary_pos_st + num_segs + ) + + key_ranges = torch.cat( + [ + memory_bank_ranges, + right_context_ranges, + left_context_ranges, + utterance_ranges, + ] + ) + + query_ranges = torch.cat( + [right_context_ranges, utterance_ranges, summary_vector_ranges] + ) + else: + key_ranges = torch.cat( + [right_context_ranges, left_context_ranges, utterance_ranges] + ) + + query_ranges = torch.cat([right_context_ranges, utterance_ranges]) + + distance = key_ranges[None, :] - query_ranges[:, None] + distance_clamp = ( + torch.clamp(distance, -max_relative_position, max_relative_position) + + max_relative_position + ) + distance_clamp = distance_clamp.to(input.device).long().detach() + return distance_clamp + + def _get_attention_mask(self, input, past_length=0, left_context_cache=0): + # attention mask for each query contains three parts: + # 1. memory part + # 2. left_context + segment + # 3. right_context_block + # so for each segment and its correspoinding right context block, + # the attention matrix is formed by 9 parts: + # [0, m, 0, 0, right_context, 0, 0, seg, 0] + # [before memory, memory, after memory, before right context, right_context, + # after right context, before seg, seg, after seg] + # + # Query is formed in the way as [right_context_blocks, utterance, summary] + # + # Note: put m and right_context before segment is convenient + # for padding_mask operation. + # Key lengths = m_length + right_context_block_length + lengths + utterance_length, batch_size, _ = input.shape + summary_length = math.ceil(utterance_length / self.segment_size) + num_segs = summary_length + rc_length = self.right_context * num_segs + rc = self.right_context + lc = self.left_context + + # using mini-batches, there is left context cache available for current + # sequence. + lcc = left_context_cache + + # max_memory_size is 0 then we don't have memory and summary + # past_length is the memory carry from previous sequence + if self.use_mem: + mem_length = num_segs - 1 + past_length + else: + mem_length = 0 + rc_mask = [] + query_mask = [] + summary_mask = [] + for j in range(0, num_segs): + ssize = min(self.segment_size, utterance_length - j * self.segment_size) + + rc_size = rc + rc_mat = [] + q_mat = [] + s_mat = [] + m_start = max(j + past_length - self.max_memory_size, 0) + + # max_memory_size is 0, then we don't use memory + if self.use_mem: + # part 0: before memory + rc_mat.append(input.new_zeros(rc_size, m_start)) + q_mat.append(input.new_zeros(ssize, m_start)) + s_mat.append(input.new_zeros(1, m_start)) + + # part 1: memory + col_1 = j + past_length - m_start + rc_mat.append(torch.ones(rc_size, col_1, device=input.device)) + q_mat.append(torch.ones(ssize, col_1, device=input.device)) + # based on D22875746, disable summary query attention + # on memeory is better for long form utterance + s_mat.append(input.new_zeros(1, col_1)) + + # part 2: after memory + col_2 = mem_length - (j + past_length) + rc_mat.append(input.new_zeros(rc_size, col_2)) + q_mat.append(input.new_zeros(ssize, col_2)) + s_mat.append(input.new_zeros(1, col_2)) + + # part 3: before right context + rc_start = j * rc + rc_mat.append(input.new_zeros(rc_size, rc_start)) + q_mat.append(input.new_zeros(ssize, rc_start)) + s_mat.append(input.new_zeros(1, rc_start)) + + # part 4: right context + rc_end = rc_start + rc + col_4 = rc + rc_mat.append(torch.ones(rc_size, col_4, device=input.device)) + q_mat.append(torch.ones(ssize, col_4, device=input.device)) + s_mat.append(torch.ones(1, col_4, device=input.device)) + + # part 5: after right context + col_5 = rc_length - rc_end + rc_mat.append(input.new_zeros(rc_size, col_5)) + q_mat.append(input.new_zeros(ssize, col_5)) + s_mat.append(input.new_zeros(1, col_5)) + + # part 6: before query segment + seg_start = max(j * self.segment_size + lcc - lc, 0) + rc_mat.append(input.new_zeros(rc_size, seg_start)) + q_mat.append(input.new_zeros(ssize, seg_start)) + s_mat.append(input.new_zeros(1, seg_start)) + + # part 7: query segment + # note: right context is put in right context block + # here we only need to consider about left context + seg_end = min((j + 1) * self.segment_size + lcc, utterance_length + lcc) + col_7 = seg_end - seg_start + rc_mat.append(torch.ones(rc_size, col_7, device=input.device)) + q_mat.append(torch.ones(ssize, col_7, device=input.device)) + s_mat.append(torch.ones(1, col_7, device=input.device)) + + # part 8: after query segment + col_8 = utterance_length + lcc - seg_end + rc_mat.append(input.new_zeros(rc_size, col_8)) + q_mat.append(input.new_zeros(ssize, col_8)) + s_mat.append(input.new_zeros(1, col_8)) + + rc_mask.append(torch.cat(rc_mat, dim=1)) + query_mask.append(torch.cat(q_mat, dim=1)) + summary_mask.append(torch.cat(s_mat, dim=1)) + + # no memory, then we don't need summary either + if self.use_mem: + attention_mask = ( + 1 + - torch.cat( + [ + torch.cat(rc_mask, dim=0), + torch.cat(query_mask, dim=0), + torch.cat(summary_mask, dim=0), + ], + dim=0, + ) + ).to(torch.bool) + else: + attention_mask = ( + 1 + - torch.cat( + [torch.cat(rc_mask, dim=0), torch.cat(query_mask, dim=0)], dim=0 + ) + ).to(torch.bool) + + return attention_mask + + @torch.jit.export + def init_state( + self, batch_size: int, device: Optional[Device] = None + ) -> List[Tensor]: + empty_memory = torch.zeros( + self.num_layers, + self.max_memory_size, + batch_size, + self.memory_dim, + device=device, + ) + left_context_key = torch.zeros( + self.num_layers, + self.left_context, + batch_size, + self.memory_dim, + device=device, + ) + left_context_val = torch.zeros( + self.num_layers, + self.left_context, + batch_size, + self.memory_dim, + device=device, + ) + past_length = torch.zeros(1, batch_size, dtype=torch.int32, device=device) + + return [empty_memory, left_context_key, left_context_val, past_length] + + @torch.jit.export + def batch_state(self, states: List[List[Tensor]]) -> List[Tensor]: + if len(states) == 0: + return [] + batched_m = [] + batched_lc_key = [] + batched_lc_val = [] + batched_past_length = [] + for state in states: + if len(state) == 0: + continue + m, lc_key, lc_val, past_length = state + batched_m.append(m) + batched_lc_key.append(lc_key) + batched_lc_val.append(lc_val) + batched_past_length.append(past_length) + + if ( + (len(batched_m) == 0) + or (len(batched_lc_key) == 0) + or (len(batched_lc_val) == 0) + or (len(batched_past_length) == 0) + ): + return [ + torch.tensor([]), + torch.tensor([]), + torch.tensor([]), + torch.tensor([]), + ] + + batched_m = torch.cat(batched_m, dim=2) + batched_lc_key = torch.cat(batched_lc_key, dim=2) + batched_lc_val = torch.cat(batched_lc_val, dim=2) + batched_past_length = torch.cat(batched_past_length, dim=1) + return [batched_m, batched_lc_key, batched_lc_val, batched_past_length] + + @torch.jit.export + def reorder_state(self, state: List[Tensor], indices: Tensor) -> List[Tensor]: + if len(state) == 0: + return [] + m, lc_key, lc_val, past_length = state + indices = indices.to(device=m.device) + reord_m = torch.index_select(m, 2, indices) + reord_lc_key = torch.index_select(lc_key, 2, indices) + reord_lc_val = torch.index_select(lc_val, 2, indices) + reord_past_length = torch.index_select(past_length, 1, indices) + return [reord_m, reord_lc_key, reord_lc_val, reord_past_length] + + @torch.jit.export + def reset_state(self, state: List[Tensor], indices: Tensor) -> List[Tensor]: + m, lc_key, lc_val, past_length = state + m = m.index_fill(dim=2, index=indices, value=0.0) + lc_key = lc_key.index_fill(dim=2, index=indices, value=0.0) + lc_val = lc_val.index_fill(dim=2, index=indices, value=0.0) + past_length = past_length.index_fill(dim=1, index=indices, value=0) + + return [m, lc_key, lc_val, past_length] + + @torch.jit.export + def state_size(self) -> int: + return 4 + + @torch.jit.export + def batch_size_in_state( + self, state: Optional[List[Tensor]], sloppy: bool = True + ) -> Optional[int]: + if state is None: + return None + return state[0].size(2) + + def gen_summary_queries(self, input): + sum_input = self.memory_op(input) + return sum_input + + def _gen_right_context_padded_input(self, input): + # This function deals with input that is already + # padded with right context (e.g. minibatch training) + right_context_blocks = [] + T, B, D = input.shape + num_segs = math.ceil((T - self.right_context) / self.segment_size) + for i in range(0, num_segs - 1): + st = (i + 1) * self.segment_size + ed = st + self.right_context + assert ed < T + temp = input[st:ed, :, :] + right_context_blocks.append(temp) + + # last segment right context is already available + right_context_blocks.append(input[T - self.right_context :, :, :]) + return torch.cat(right_context_blocks, dim=0) + + def _gen_segs_right_context(self, input, lengths): + segments = [] + T, B, D = input.size() + nT = T - self.right_context + + # assume input is right context padded + num_segs = math.ceil(nT / self.segment_size) + # pad zeros to the utterance to make sure each + # segment has the same right context. For the + for i in range(0, num_segs - 1): + st = i * self.segment_size + ed = min(T, st + self.segment_size + self.right_context) + temp = input[st:ed, :, :] + rest_lengths = torch.clamp( + lengths - self.segment_size, min=0, max=nT - (i + 1) * self.segment_size + ) + segments.append((temp, lengths - rest_lengths + self.right_context)) + lengths = rest_lengths + + last_seg = input[st + self.segment_size :, :, :] + segments.append((last_seg, rest_lengths + self.right_context)) + + return segments + + @torch.jit.unused + def forward( + self, input: Tensor, padding_masks: Tensor, state: Optional[List[Tensor]] = None + ) -> Tuple[Tensor, Tensor, List[Tensor], List[Tensor]]: + # Xutai: originally the second argument is lengths. + lengths = (~padding_masks).sum(dim=1).long() + # mini batch training. + if self.mini_batches: + return self.forward_mini_batches(input, lengths, state) + + # regular full sequence training. Note, assume the right context in provided + # in the input. + T, B, D = input.size() + right_context_blocks = self._gen_right_context_padded_input(input) + + # generate the relative positional embedding + if self.use_rpe: + rpe = self._get_relative_position( + input=input, + max_relative_position=self.max_relative_position, + left_context_length=0, + past_length=0, + is_decoding=False, + ) + else: + rpe = None + input = input[: T - self.right_context, :, :] + + attention_mask = self._get_attention_mask(input) + + # firt layer use each segment mean as memory + # ignore the last one seg average + if self.use_mem: + mems = self.gen_summary_queries(input)[:-1, :, :] + else: + mems = torch.zeros(0, input.size(1), input.size(2), device=input.device) + mems = mems.type_as(input) + + output = input + all_outputs = [] + + for layer in self.layers: + output, mems, right_context_blocks, _, _ = layer( + input=output, + lengths=lengths, + attention_mask=attention_mask, + mems=mems, + right_context_blocks=right_context_blocks, + pre_mems=None, + left_context_key=None, + left_context_val=None, + rpe=rpe, + ) + all_outputs.append(output) + return output, padding_masks, [], all_outputs + + def forward_jit_mini_batch_init( + self, + seg: Tensor, + state: Optional[List[Tensor]] = None, + is_decoding: bool = False, + ): + # Prepare state. In whole sequence training, state is ignored. + # For minibatch training, we need to prepare state + if state is None: + state = self.init_state(batch_size=seg.size(1), device=seg.device) + if seg.dtype == torch.half: + state = [state[0].half(), state[1].half(), state[2].half(), state[3]] + + if self.use_mem: + # note input average only on seg, not on right context + # first layer use each segmetn mean as memory. the last + # one segment average is used in state + full_mems = self.gen_summary_queries(seg) + if is_decoding: + mems = full_mems[0:1, :, :] + state_mems = torch.cat([state[0][0], mems], dim=0) + else: + mems = full_mems[:-1, :, :] + state_mems = torch.cat([state[0][0], full_mems], dim=0) + else: + mems = state[0][0] + state_mems = mems + + # track processed segment number or memory number + # the same batch as the same bumber of past length + past_length = state[3][0][0].item() + past_left_context = min(past_length * self.segment_size, self.left_context) + past_length = min(self.max_memory_size, past_length) + + return state, mems, state_mems, past_length, past_left_context + + def state_update_before( + self, layer: int, state: List[Tensor], past_length: int, past_left_context: int + ): + pre_mems = state[0][layer][self.max_memory_size - past_length :, :, :] + lc_key = state[1][layer][self.left_context - past_left_context :, :, :] + lc_val = state[2][layer][self.left_context - past_left_context :, :, :] + return pre_mems, lc_key, lc_val + + def state_update_after( + self, + layer: int, + state: List[Tensor], + mems: Tensor, + next_key: Tensor, + next_val: Tensor, + mems_list: List[Tensor], + lc_key_list: List[Tensor], + lc_val_list: List[Tensor], + ): + # mems is used for next layer + if layer < self.num_layers - 1: + state_mems = torch.cat([state[0][layer + 1], mems], dim=0) + mems_list.append(state_mems[-self.max_memory_size :, :, :]) + + # when mems pass to next sequence, we need the last memory. when mems + # use for the next layer, we can ignore the last memory + mems = mems[:-1, :, :] + + # note state[1][i] and state[2][i] original length equals to self.left_context + new_k = torch.cat([state[1][layer], next_key], dim=0) + new_v = torch.cat([state[2][layer], next_val], dim=0) + lc_key_list.append(new_k[-self.left_context :, :, :]) + lc_val_list.append(new_v[-self.left_context :, :, :]) + return mems_list, lc_key_list, lc_val_list, mems + + def state_update_after_loop( + self, + state: List[Tensor], + mems_list: List[Tensor], + lc_key_list: List[Tensor], + lc_val_list: List[Tensor], + update_length: int, + ): + state[0] = torch.stack(mems_list, dim=0) + state[1] = torch.stack(lc_key_list, dim=0) + state[2] = torch.stack(lc_val_list, dim=0) + state[3] = state[3] + update_length + return state + + @torch.jit.unused + def forward_mini_batches( + self, input: Tensor, lengths: Tensor, state: Optional[List[Tensor]] = None + ) -> Tuple[Tensor, Tensor, List[Tensor], List[Tensor]]: + T, B, D = input.size() + + # input without right context + seg = input[: T - self.right_context, :, :] + + # get right context blocks + right_context_blocks = self._gen_right_context_padded_input(input) + + mems_list = [] + lc_key_list = [] + lc_val_list = [] + results = self.forward_jit_mini_batch_init(seg, state, False) + state, mems, state_mems, past_length, past_left_context = results + + # relative position embedding + if self.use_rpe: + rpe = self._get_relative_position( + input=input, + max_relative_position=self.max_relative_position, + left_context_length=past_left_context, + past_length=past_length, + is_decoding=False, + ) + else: + rpe = None + + # get attention mask based on seg (not include right context) and available + # left context + attention_mask = self._get_attention_mask(seg, past_length, past_left_context) + mems_list.append(state_mems[-self.max_memory_size :, :, :]) + output = seg + i = 0 + all_outputs = [] + for layer in self.layers: + # In order to make cross stream batching work, mem, left context key + # and left context value in the state should always be the same shape. + # We use the past length to track the processed segment number. In this + # way, we take out the essential memory, left context key and left + # context val from the state. After finish the forward for current segment + # we add the new memory, left context key and left context value into the + # staate and trim out the oldest part to keep the shape consistent. + pre_mems, lc_key, lc_val = self.state_update_before( + i, state, past_length, past_left_context + ) + + output, mems, right_context_blocks, next_key, next_val = layer.forward( + input=output, + lengths=lengths, + attention_mask=attention_mask, + mems=mems, + right_context_blocks=right_context_blocks, + pre_mems=pre_mems, + left_context_key=lc_key, + left_context_val=lc_val, + rpe=rpe, + ) + all_outputs.append(output) + mems_list, lc_key_list, lc_val_list, mems = self.state_update_after( + layer=i, + state=state, + mems=mems, + next_key=next_key, + next_val=next_val, + mems_list=mems_list, + lc_key_list=lc_key_list, + lc_val_list=lc_val_list, + ) + + i += 1 + + # update state + update_length = math.ceil((T - self.right_context) / self.segment_size) + state = self.state_update_after_loop( + state=state, + mems_list=mems_list, + lc_key_list=lc_key_list, + lc_val_list=lc_val_list, + update_length=update_length, + ) + + return output, lengths, state, all_outputs + + def forward_jit_test( + self, input: Tensor, lengths: Tensor, state: Optional[List[Tensor]] = None + ) -> Tuple[Tensor, Tensor, List[Tensor]]: + """ + This one simulate sequence encoder forward jit. This is for unit test purpose. + It is not used in training or decoding. Note, extra_right_context is set in + the model. In unit test, input = [utterance, right_context], lengths = + [utterance_length]. + args: + input: input utterance + lengths: utterance input length + state: None here. input is whole utterance + """ + # [TODO] sequence_to_segment has bug in lengths. + seg_src_tokens_lengths = self._gen_segs_right_context(input, lengths) + + seg_enc_tokens_lengths: List[Tuple[Tensor, Tensor]] = [] + state: Optional[List[Tensor]] = None + for seg_src_tokens, seg_src_lengths in seg_src_tokens_lengths: + seg_enc_tokens, seg_enc_lengths, state = self.forward_jit( + input=seg_src_tokens, lengths=seg_src_lengths, state=state + ) + seg_enc_tokens_lengths.append((seg_enc_tokens, seg_enc_lengths)) + + enc_tokens, enc_lengths = segments_to_sequence( + segments=seg_enc_tokens_lengths, time_axis=0 + ) + + state = [] # returns trivial state + + return enc_tokens, enc_lengths, state + + @torch.jit.export + def forward_jit( + self, input: Tensor, lengths: Tensor, state: Optional[List[Tensor]] = None + ) -> Tuple[Tensor, Tensor, List[Tensor]]: + """ + Forward helper for online decoding. + + args: + input: [seg, right_context]. We assume in online we + always padding the right context to the preset right context size. + For the last segment, we may have short segment size, but right + context size is the same as other segments + lengths: utterance input length is the utterance segment length and + right context size + state: [memory, left_context_key, left_context_val]. To improve throughput, + in addition to memory, we also cache key and value for left_context in + multihead self-attention + """ + # In online decoding, input = [segment, right_context] + # Lengths = [segment_length, right_context_length] + # so we need strip right context in output + T, B, D = input.size() + rc_str = T - self.right_context + rc_end = T + right_context_blocks = input[rc_str:rc_end, :, :] + seg = input[:rc_str, :, :] + lengths = torch.clamp(lengths - self.right_context, min=0) + mems_list = [] + lc_key_list = [] + lc_val_list = [] + + results = self.forward_jit_mini_batch_init(seg, state, True) + state, mems, state_mems, past_length, past_left_context = results + + # relative position embedding + if self.use_rpe: + rpe = self._get_relative_position( + input=input, + max_relative_position=self.max_relative_position, + left_context_length=past_left_context, + past_length=past_length, + is_decoding=True, + ) + else: + rpe = None + + # memory for first layer. + mems_list.append(state_mems[-self.max_memory_size :, :, :]) + output = seg + i = 0 + for layer in self.layers: + # In order to make cross stream batching work, mem, left context key + # and left context value in the state should always be the same shape. + # We use the past length to track the processed segment number. In this + # way, we take out the essential memory, left context key and left + # context val from the state. After finish the forward for current segment + # we add the new memory, left context key and left context value into the + # staate and trim out the oldest part to keep the shape consistent. + true_mems, lc_key, lc_val = self.state_update_before( + layer=i, + state=state, + past_length=past_length, + past_left_context=past_left_context, + ) + + output, mems, right_context_blocks, next_key, next_val = layer.forward_jit( + input=output, + lengths=lengths, + mems=true_mems, + right_context_blocks=right_context_blocks, + left_context_key=lc_key, + left_context_val=lc_val, + rpe=rpe, + ) + # mems is used for next layer + mems_list, lc_key_list, lc_val_list, _ = self.state_update_after( + layer=i, + state=state, + mems_list=mems_list, + mems=mems, + next_key=next_key, + next_val=next_val, + lc_key_list=lc_key_list, + lc_val_list=lc_val_list, + ) + i += 1 + + # update state + state = self.state_update_after_loop( + state=state, + mems_list=mems_list, + lc_key_list=lc_key_list, + lc_val_list=lc_val_list, + update_length=1, + ) + + return output, lengths, state + + def quantize_(self, params=None): + if params and "per_channel" in params and params["per_channel"]: + qconfig = per_channel_dynamic_qconfig + else: + qconfig = default_dynamic_qconfig + torch.quantization.quantize_dynamic( + self, {torch.nn.Linear: qconfig}, dtype=torch.qint8, inplace=True + ) + return self + + +# ------------------------------------------------------------------------------ +# Emformer encoder for seq2seq model +# This is a wrapper over the original emformer +# ------------------------------------------------------------------------------ +def emformer_encoder(klass): + class SpeechEncoder(klass): + def __init__(self, args): + super().__init__(args) + stride = SpeechEncoder.conv_layer_stride(args) + trf_left_context = args.segment_left_context // stride + trf_right_context = args.segment_right_context // stride + context_config = [trf_left_context, trf_right_context] + self.transformer_layers = nn.ModuleList( + [ + NoSegAugmentedMemoryTransformerEncoderLayer( + input_dim=args.encoder_embed_dim, + num_heads=args.encoder_attention_heads, + ffn_dim=args.encoder_ffn_embed_dim, + num_layers=args.encoder_layers, + dropout_in_attn=args.dropout, + dropout_on_attn=args.dropout, + dropout_on_fc1=args.dropout, + dropout_on_fc2=args.dropout, + activation_fn=args.activation_fn, + context_config=context_config, + segment_size=args.segment_length, + max_memory_size=args.max_memory_size, + scaled_init=True, # TODO: use constant for now. + tanh_on_mem=args.amtrf_tanh_on_mem, + ) + ] + ) + + def forward(self, src_tokens, src_lengths): + encoder_out = super().forward(src_tokens, src_lengths) + output = encoder_out["encoder_out"][0] + encoder_padding_masks = encoder_out["encoder_padding_mask"][0] + + # This is because that in the original implementation + # the output didn't consider the last segment as right context. + encoder_padding_masks = encoder_padding_masks[:, : output.size(0)] + + return { + "encoder_out": [output], + "encoder_padding_mask": [encoder_padding_masks], + "encoder_embedding": [], + "encoder_states": [], + "src_tokens": [], + "src_lengths": [], + } + + @staticmethod + def conv_layer_stride(args): + # TODO: make it configurable from the args + return 4 + + SpeechEncoder.__name__ = klass.__name__ + return SpeechEncoder diff --git a/fairseq/models/speech_to_text/s2t_transformer.py b/fairseq/models/speech_to_text/s2t_transformer.py new file mode 100644 index 0000000000..aff9d0ffc7 --- /dev/null +++ b/fairseq/models/speech_to_text/s2t_transformer.py @@ -0,0 +1,502 @@ +#!/usr/bin/env python3 + +import logging +import math +from typing import Dict, List, Optional, Tuple +from pathlib import Path + +import torch +import torch.nn as nn +from fairseq import checkpoint_utils, utils +from fairseq.data.data_utils import lengths_to_padding_mask +from fairseq.models import ( + FairseqEncoder, + FairseqEncoderDecoderModel, + register_model, + register_model_architecture, +) +from fairseq.models.transformer import Embedding, TransformerDecoder +from fairseq.modules import ( + FairseqDropout, + LayerNorm, + PositionalEmbedding, + TransformerEncoderLayer, +) +from torch import Tensor + + +logger = logging.getLogger(__name__) + + +class Conv1dSubsampler(nn.Module): + """Convolutional subsampler: a stack of 1D convolution (along temporal + dimension) followed by non-linear activation via gated linear units + (https://arxiv.org/abs/1911.08460) + + Args: + in_channels (int): the number of input channels + mid_channels (int): the number of intermediate channels + out_channels (int): the number of output channels + kernel_sizes (List[int]): the kernel size for each convolutional layer + """ + + def __init__( + self, + in_channels: int, + mid_channels: int, + out_channels: int, + kernel_sizes: List[int] = (3, 3), + ): + super(Conv1dSubsampler, self).__init__() + self.n_layers = len(kernel_sizes) + self.conv_layers = nn.ModuleList( + nn.Conv1d( + in_channels if i == 0 else mid_channels // 2, + mid_channels if i < self.n_layers - 1 else out_channels * 2, + k, + stride=2, + padding=k // 2, + ) + for i, k in enumerate(kernel_sizes) + ) + + def get_out_seq_lens_tensor(self, in_seq_lens_tensor): + out = in_seq_lens_tensor.clone() + for _ in range(self.n_layers): + out = ((out.float() - 1) / 2 + 1).floor().long() + return out + + def forward(self, src_tokens, src_lengths): + bsz, in_seq_len, _ = src_tokens.size() # B x T x (C x D) + x = src_tokens.transpose(1, 2).contiguous() # -> B x (C x D) x T + for conv in self.conv_layers: + x = conv(x) + x = nn.functional.glu(x, dim=1) + _, _, out_seq_len = x.size() + x = x.transpose(1, 2).transpose(0, 1).contiguous() # -> T x B x (C x D) + return x, self.get_out_seq_lens_tensor(src_lengths) + + +@register_model("s2t_transformer") +class S2TTransformerModel(FairseqEncoderDecoderModel): + """Adapted Transformer model (https://arxiv.org/abs/1706.03762) for + speech-to-text tasks. The Transformer encoder/decoder remains the same. + A trainable input subsampler is prepended to the Transformer encoder to + project inputs into the encoder dimension as well as downsample input + sequence for computational efficiency.""" + + def __init__(self, encoder, decoder): + super().__init__(encoder, decoder) + + @staticmethod + def add_args(parser): + """Add model-specific arguments to the parser.""" + # input + parser.add_argument( + "--conv-kernel-sizes", + type=str, + metavar="N", + help="kernel sizes of Conv1d subsampling layers", + ) + parser.add_argument( + "--conv-channels", + type=int, + metavar="N", + help="# of channels in Conv1d subsampling layers", + ) + # Transformer + parser.add_argument( + "--activation-fn", + type=str, + default="relu", + choices=utils.get_available_activation_fns(), + help="activation function to use", + ) + parser.add_argument( + "--dropout", type=float, metavar="D", help="dropout probability" + ) + parser.add_argument( + "--attention-dropout", + type=float, + metavar="D", + help="dropout probability for attention weights", + ) + parser.add_argument( + "--activation-dropout", + "--relu-dropout", + type=float, + metavar="D", + help="dropout probability after activation in FFN.", + ) + parser.add_argument( + "--encoder-embed-dim", + type=int, + metavar="N", + help="encoder embedding dimension", + ) + parser.add_argument( + "--encoder-ffn-embed-dim", + type=int, + metavar="N", + help="encoder embedding dimension for FFN", + ) + parser.add_argument( + "--encoder-layers", type=int, metavar="N", help="num encoder layers" + ) + parser.add_argument( + "--encoder-attention-heads", + type=int, + metavar="N", + help="num encoder attention heads", + ) + parser.add_argument( + "--encoder-normalize-before", + action="store_true", + help="apply layernorm before each encoder block", + ) + parser.add_argument( + "--decoder-embed-dim", + type=int, + metavar="N", + help="decoder embedding dimension", + ) + parser.add_argument( + "--decoder-ffn-embed-dim", + type=int, + metavar="N", + help="decoder embedding dimension for FFN", + ) + parser.add_argument( + "--decoder-layers", type=int, metavar="N", help="num decoder layers" + ) + parser.add_argument( + "--decoder-attention-heads", + type=int, + metavar="N", + help="num decoder attention heads", + ) + parser.add_argument( + "--decoder-normalize-before", + action="store_true", + help="apply layernorm before each decoder block", + ) + parser.add_argument( + "--share-decoder-input-output-embed", + action="store_true", + help="share decoder input and output embeddings", + ) + parser.add_argument( + "--layernorm-embedding", + action="store_true", + help="add layernorm to embedding", + ) + parser.add_argument( + "--no-scale-embedding", + action="store_true", + help="if True, dont scale embeddings", + ) + parser.add_argument( + "--load-pretrained-encoder-from", + type=str, + metavar="STR", + help="model to take encoder weights from (for initialization)", + ) + parser.add_argument( + '--encoder-freezing-updates', + type=int, + metavar='N', + help='freeze encoder for first N updates' + ) + + @classmethod + def build_encoder(cls, args): + encoder = S2TTransformerEncoder(args) + pretraining_path = getattr(args, "load_pretrained_encoder_from", None) + if pretraining_path is not None: + if not Path(pretraining_path).exists(): + logger.warning( + f"skipped pretraining because {pretraining_path} does not exist" + ) + else: + encoder = checkpoint_utils.load_pretrained_component_from_model( + component=encoder, checkpoint=pretraining_path + ) + logger.info(f"loaded pretrained encoder from: {pretraining_path}") + return encoder + + @classmethod + def build_decoder(cls, args, task, embed_tokens): + return TransformerDecoderScriptable(args, task.target_dictionary, embed_tokens) + + @classmethod + def build_model(cls, args, task): + """Build a new model instance.""" + + # make sure all arguments are present in older models + base_architecture(args) + + def build_embedding(dictionary, embed_dim): + num_embeddings = len(dictionary) + padding_idx = dictionary.pad() + return Embedding(num_embeddings, embed_dim, padding_idx) + + decoder_embed_tokens = build_embedding( + task.target_dictionary, args.decoder_embed_dim + ) + encoder = cls.build_encoder(args) + decoder = cls.build_decoder(args, task, decoder_embed_tokens) + return cls(encoder, decoder) + + def get_normalized_probs( + self, + net_output: Tuple[Tensor, Optional[Dict[str, List[Optional[Tensor]]]]], + log_probs: bool, + sample: Optional[Dict[str, Tensor]] = None, + ): + # net_output['encoder_out'] is a (B, T, D) tensor + lprobs = self.get_normalized_probs_scriptable(net_output, log_probs, sample) + lprobs.batch_first = True + return lprobs + + def forward(self, src_tokens, src_lengths, prev_output_tokens): + """ + The forward method inherited from the base class has a **kwargs + argument in its input, which is not supported in torchscript. This + method overwrites the forward method definition without **kwargs. + """ + encoder_out = self.encoder(src_tokens=src_tokens, src_lengths=src_lengths) + decoder_out = self.decoder( + prev_output_tokens=prev_output_tokens, encoder_out=encoder_out + ) + return decoder_out + + +class S2TTransformerEncoder(FairseqEncoder): + """Speech-to-text Transformer encoder that consists of input subsampler and + Transformer encoder.""" + + def __init__(self, args): + super().__init__(None) + + self.encoder_freezing_updates = args.encoder_freezing_updates + self.num_updates = 0 + + self.dropout_module = FairseqDropout( + p=args.dropout, module_name=self.__class__.__name__ + ) + self.embed_scale = math.sqrt(args.encoder_embed_dim) + if args.no_scale_embedding: + self.embed_scale = 1.0 + self.padding_idx = 1 + + self.subsample = Conv1dSubsampler( + args.input_feat_per_channel * args.input_channels, + args.conv_channels, + args.encoder_embed_dim, + [int(k) for k in args.conv_kernel_sizes.split(",")], + ) + + self.embed_positions = PositionalEmbedding( + args.max_source_positions, args.encoder_embed_dim, self.padding_idx + ) + + self.transformer_layers = nn.ModuleList( + [TransformerEncoderLayer(args) for _ in range(args.encoder_layers)] + ) + if args.encoder_normalize_before: + self.layer_norm = LayerNorm(args.encoder_embed_dim) + else: + self.layer_norm = None + + def _forward(self, src_tokens, src_lengths, return_all_hiddens=False): + x, input_lengths = self.subsample(src_tokens, src_lengths) + x = self.embed_scale * x + + encoder_padding_mask = lengths_to_padding_mask(input_lengths) + positions = self.embed_positions(encoder_padding_mask).transpose(0, 1) + x += positions + x = self.dropout_module(x) + + encoder_states = [] + + for layer in self.transformer_layers: + x = layer(x, encoder_padding_mask) + if return_all_hiddens: + encoder_states.append(x) + + if self.layer_norm is not None: + x = self.layer_norm(x) + + return { + "encoder_out": [x], # T x B x C + "encoder_padding_mask": [encoder_padding_mask] if encoder_padding_mask.any() else [], # B x T + "encoder_embedding": [], # B x T x C + "encoder_states": encoder_states, # List[T x B x C] + "src_tokens": [], + "src_lengths": [], + } + + def forward(self, src_tokens, src_lengths, return_all_hiddens=False): + if self.num_updates < self.encoder_freezing_updates: + with torch.no_grad(): + x = self._forward(src_tokens, src_lengths, + return_all_hiddens=return_all_hiddens) + else: + x = self._forward(src_tokens, src_lengths, + return_all_hiddens=return_all_hiddens) + return x + + def reorder_encoder_out(self, encoder_out, new_order): + new_encoder_out = ( + [] if len(encoder_out["encoder_out"]) == 0 + else [x.index_select(1, new_order) for x in encoder_out["encoder_out"]] + ) + + new_encoder_padding_mask = ( + [] if len(encoder_out["encoder_padding_mask"]) == 0 + else [x.index_select(0, new_order) for x in encoder_out["encoder_padding_mask"]] + ) + + new_encoder_embedding = ( + [] if len(encoder_out["encoder_embedding"]) == 0 + else [x.index_select(0, new_order) for x in encoder_out["encoder_embedding"]] + ) + + encoder_states = encoder_out["encoder_states"] + if len(encoder_states) > 0: + for idx, state in enumerate(encoder_states): + encoder_states[idx] = state.index_select(1, new_order) + + return { + "encoder_out": new_encoder_out, # T x B x C + "encoder_padding_mask": new_encoder_padding_mask, # B x T + "encoder_embedding": new_encoder_embedding, # B x T x C + "encoder_states": encoder_states, # List[T x B x C] + "src_tokens": [], # B x T + "src_lengths": [], # B x 1 + } + + def set_num_updates(self, num_updates): + super().set_num_updates(num_updates) + self.num_updates = num_updates + + +class TransformerDecoderScriptable(TransformerDecoder): + def extract_features( + self, + prev_output_tokens, + encoder_out: Optional[Dict[str, List[Tensor]]] = None, + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, + full_context_alignment: bool = False, + alignment_layer: Optional[int] = None, + alignment_heads: Optional[int] = None, + ): + # call scriptable method from parent class + x, _ = self.extract_features_scriptable( + prev_output_tokens, + encoder_out, + incremental_state, + full_context_alignment, + alignment_layer, + alignment_heads, + ) + return x, None + + +@register_model_architecture(model_name="s2t_transformer", arch_name="s2t_transformer") +def base_architecture(args): + args.encoder_freezing_updates = getattr(args, "encoder_freezing_updates", 0) + # Convolutional subsampler + args.conv_kernel_sizes = getattr(args, "conv_kernel_sizes", "5,5") + args.conv_channels = getattr(args, "conv_channels", 1024) + # Transformer + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048) + args.encoder_layers = getattr(args, "encoder_layers", 12) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8) + args.encoder_normalize_before = getattr(args, "encoder_normalize_before", True) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim) + args.decoder_ffn_embed_dim = getattr( + args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim + ) + args.decoder_layers = getattr(args, "decoder_layers", 6) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8) + args.decoder_normalize_before = getattr(args, "decoder_normalize_before", True) + args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False) + args.dropout = getattr(args, "dropout", 0.1) + args.attention_dropout = getattr(args, "attention_dropout", args.dropout) + args.activation_dropout = getattr(args, "activation_dropout", args.dropout) + args.activation_fn = getattr(args, "activation_fn", "relu") + args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None) + args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0) + args.share_decoder_input_output_embed = getattr( + args, "share_decoder_input_output_embed", False + ) + args.no_token_positional_embeddings = getattr( + args, "no_token_positional_embeddings", False + ) + args.adaptive_input = getattr(args, "adaptive_input", False) + args.decoder_layerdrop = getattr(args, "decoder_layerdrop", 0.0) + args.decoder_output_dim = getattr( + args, "decoder_output_dim", args.decoder_embed_dim + ) + args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim) + args.no_scale_embedding = getattr(args, "no_scale_embedding", False) + args.quant_noise_pq = getattr(args, "quant_noise_pq", 0) + + +@register_model_architecture("s2t_transformer", "s2t_transformer_s") +def s2t_transformer_s(args): + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 256) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 256 * 8) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 4) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 4) + args.dropout = getattr(args, "dropout", 0.1) + base_architecture(args) + + +@register_model_architecture("s2t_transformer", "s2t_transformer_xs") +def s2t_transformer_xs(args): + args.encoder_layers = getattr(args, "encoder_layers", 6) + args.decoder_layers = getattr(args, "decoder_layers", 3) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 256 * 4) + args.dropout = getattr(args, "dropout", 0.3) + s2t_transformer_s(args) + + +@register_model_architecture("s2t_transformer", "s2t_transformer_sp") +def s2t_transformer_sp(args): + args.encoder_layers = getattr(args, "encoder_layers", 16) + s2t_transformer_s(args) + + +@register_model_architecture("s2t_transformer", "s2t_transformer_m") +def s2t_transformer_m(args): + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 512 * 4) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8) + args.dropout = getattr(args, "dropout", 0.15) + base_architecture(args) + + +@register_model_architecture("s2t_transformer", "s2t_transformer_mp") +def s2t_transformer_mp(args): + args.encoder_layers = getattr(args, "encoder_layers", 16) + s2t_transformer_m(args) + + +@register_model_architecture("s2t_transformer", "s2t_transformer_l") +def s2t_transformer_l(args): + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 1024 * 4) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 16) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 16) + args.dropout = getattr(args, "dropout", 0.2) + base_architecture(args) + + +@register_model_architecture("s2t_transformer", "s2t_transformer_lp") +def s2t_transformer_lp(args): + args.encoder_layers = getattr(args, "encoder_layers", 16) + s2t_transformer_l(args) diff --git a/fairseq/models/speech_to_text/utils.py b/fairseq/models/speech_to_text/utils.py new file mode 100644 index 0000000000..168b8bf13b --- /dev/null +++ b/fairseq/models/speech_to_text/utils.py @@ -0,0 +1,563 @@ +# Copyright (c) 2017-present, Facebook, Inc. +# All rights reserved. +# +# This source code is licensed under the license found in the LICENSE file in +# the root directory of this source tree. An additional grant of patent rights +# can be found in the PATENTS file in the same directory. + + +import logging +from collections.abc import Iterable +from itertools import repeat +from typing import List, Optional, Tuple + +import torch +from torch import Tensor + + +# ------------------------------------------------------------------------------ +# assert_equal() +# ------------------------------------------------------------------------------ + + +def assert_equal(value1, value2, name1=None, name2=None): + """Asserts two values are equal otherwise raise an error.""" + + str_name1 = "" if name1 is None else "{} ".format(name1) + str_name2 = "" if name2 is None else "{} ".format(name2) + if value1 != value2: + str_value1 = "{}" if name1 is None else "({})" + str_value1 = str_value1.format(value1) + str_value2 = "{}" if name2 is None else "({})" + str_value2 = str_value2.format(value2) + raise ValueError( + "Expected {}{} == {}{}".format(str_name1, str_value1, str_name2, str_value2) + ) + + +def fill_config(config, key, value): + if value is not None: + if key not in config or config[key] is None: + config[key] = value + assert_equal(value, config[key], "value", f'config["{key}"]') + + +# ------------------------------------------------------------------------------ +# check_and_return_expected() +# ------------------------------------------------------------------------------ + + +def check_and_return_expected(value, undefined_value, expected_value, name=None): + """ + Return the expected value while checking if the given value is undefined or + equal to the expected value. + """ + if (undefined_value is None and value is None) or (undefined_value == value): + return expected_value + if value != expected_value: + str_name = "" if name is None else "{} ".format(name) + str_value = "{}" if name is None else "({})" + str_value = str_value.format(value) + raise ValueError( + "Expected {}{} == {}".format(str_name, str_value, expected_value) + ) + return expected_value + + +# ------------------------------------------------------------------------------ +# get_time_axis() +# ------------------------------------------------------------------------------ + + +def get_time_axis(layout): + """ + Extract the time axis from the layout, for example for breaking sequence into + segments. + """ + if layout in ["TB", "TBD"]: + return 0 + if layout in ["BT", "BTD"]: + return 1 + if layout in ["BCTD"]: + return 2 + raise ValueError("Unsupported layout = {}".format(layout)) + + +# ------------------------------------------------------------------------------ +# get_batch_axis() +# ------------------------------------------------------------------------------ + + +def get_batch_axis(layout): + """ + Extract the batch axis from the layout + """ + if layout in ["TB", "TBD"]: + return 1 + if layout in ["BT", "BTD", "BCTD"]: + return 0 + raise ValueError("Unsupported layout = {}".format(layout)) + + +# ------------------------------------------------------------------------------ +# monotonically_increasing_and_bounded() +# ------------------------------------------------------------------------------ + + +def monotonically_increasing_and_bounded(iterable, min=None, max=None): + """ + Check if the elements in the given iterable are monotonically increasing and + bounded by upper/lower bounds. + """ + if not isinstance(iterable, Iterable): + raise TypeError( + "Expected iterable to be of type Iterable, got ({})".format( + iterable.__class__.__name__ + ) + ) + for i in range(len(iterable)): + if min is not None and iterable[i] < min: + return False + if max is not None and iterable[i] > max: + return False + if i > 0 and iterable[i] <= iterable[i - 1]: + return False + return True + + +# ------------------------------------------------------------------------------ +# to_pair() +# ------------------------------------------------------------------------------ + + +def to_pair(value, name): + """Make a pair (of type tuple) of given value.""" + if isinstance(value, Iterable): + if len(value) != 2: + raise ValueError( + "Expected `{}` to have exactly 2 elements, got: ({})".format( + name, value + ) + ) + return value + return tuple(repeat(value, 2)) + + +# ------------------------------------------------------------------------------ +# infer_conv_output_attrs() +# ------------------------------------------------------------------------------ + + +# TODO(cfyeh): figure out if we can get `output_dim` without calling the module. +def infer_conv_output_attrs( + module, input_channels, input_dim, batch_size=1, max_length=8 +): + """Get output attributes of a module with input.""" + input = torch.randn(batch_size, input_channels, max_length, input_dim) + output = module(input) + output_channels = output.shape[1] + output_dim = output.shape[-1] + return output_channels, output_dim + + +# ------------------------------------------------------------------------------ +# NoOp +# ------------------------------------------------------------------------------ + + +class NoOp(torch.nn.Module): + """ + NoOp simply passes the input as the output. + """ + + def __init__(self): + super().__init__() + + def forward(self, input: Tensor) -> Tensor: + return input + + +# ------------------------------------------------------------------------------ +# Permute: a torch.nn.Module applies permutation on the input tensor. +# ------------------------------------------------------------------------------ + + +class Permute(torch.nn.Module): + def __init__(self, dims): + super().__init__() + self.dims = dims + + def forward(self, input: Tensor) -> Tensor: + return input.permute(self.dims).contiguous() + + +# ------------------------------------------------------------------------------ +# lengths_to_padding_mask() +# ------------------------------------------------------------------------------ + + +def lengths_to_padding_mask(lengths: Tensor) -> Tensor: + """Convert lengths of shape (B, ) to padding mask.""" + batch_size = lengths.shape[0] + max_length = int(torch.max(lengths).item()) + padding_mask = torch.arange( # [0, ..., T-1] + max_length, device=lengths.device, dtype=lengths.dtype + ).expand(batch_size, max_length) >= lengths.unsqueeze(1) + + return padding_mask + + +# ------------------------------------------------------------------------------ +# lengths_to_attention_mask() +# ------------------------------------------------------------------------------ + + +def lengths_to_attention_mask( + lengths: Tensor, + left_context: Optional[int] = None, + right_context: Optional[int] = None, +) -> Optional[Tensor]: + """ + Generate attention mask based on (lengths, left_context, right_context). + left_context is None means unlimited left context. + right_context is None means unlimited right context. + """ + + if left_context is None and right_context is None: + return None + + max_length = int(torch.max(lengths).item()) + + # For example, with `max_length` == 5, + # indices = tensor([ + # [ 0, 1, 2, 3, 4, 5], + # [-1, 0, 1, 2, 3, 4], + # [-2, -1, 0, 1, 2, 3], + # [-3, -2, -1, 0, 1, 2], + # [-4, -3, -2, -1, 0, 1], + # [-5, -4, -3, -2, -1, 0], + # ]) + + # In some cases the second torch.arange is created on cpu which causes a + # failure. Adding the device option to guard against it. + indices = torch.arange( + max_length, device=lengths.device, dtype=lengths.dtype + ).expand(max_length, max_length) - torch.arange( + max_length, device=lengths.device + ).view( + max_length, -1 + ) + + # For example, with `max_length` == 5, + # bool_mask = tensor([ + # [True, True, True, True, True], + # [True, True, True, True, True], + # [True, True, True, True, True], + # [True, True, True, True, True], + # [True, True, True, True, True], + # ]) + bool_mask = ( + torch.tensor([True]).to(device=lengths.device).expand(max_length, max_length) + ) + + # For example, with `max_length` == 5, left_context == 2 + # left_mask = tensor([ + # [ True, True, True, True, True], + # [ True, True, True, True, True], + # [ True, True, True, True, True], + # [False, True, True, True, True], + # [False, False, True, True, True], + # ]) + if left_context is not None: + left_mask = indices >= -left_context + bool_mask = bool_mask & left_mask + + # For example, with `max_length` == 5, right_context == 1 + # right_mask = tensor([ + # [True, True, False, False, False], + # [True, True, True, False, False], + # [True, True, True, True, False], + # [True, True, True, True, True], + # [True, True, True, True, True], + # ]) + if right_context is not None: + right_mask = indices <= right_context + bool_mask = bool_mask & right_mask + + bool_mask = (~bool_mask).to(device=lengths.device) + return bool_mask + + +# ------------------------------------------------------------------------------ +# infer_output_norm() +# ------------------------------------------------------------------------------ + + +def infer_output_norm(module, output_norm=None): + """ + Infer the output norm (string and module) needed on the module gvien desired + output normalization. + """ + if output_norm == module.output_norm(): + # output_norm already matches module.output_norm(). + return (None, NoOp()) + + if output_norm is None and module.output_norm() is not None: + logger = logging.getLogger("infer_output_norm()") + logger.warning( + "trying to set output_norm ({}) ".format(output_norm) + + "but got module.output_norm() ({}), ".format(module.output_norm()) + + "the combined output_norm() will be ({})".format(module.output_norm()) + ) + return (None, NoOp()) + + if output_norm == "log_softmax": + if module.output_norm() is not None: + raise ValueError( + "incompatible output_norm ({}) ".format(output_norm) + + "and module.output_norm() ({})".format(module.output_norm()) + ) + else: + return ("log_softmax", torch.nn.LogSoftmax(dim=-1)) + + if output_norm == "softmax": + if module.output_norm() is not None: + raise ValueError( + "incompatible output_norm ({}) ".format(output_norm) + + "and module.output_norm() ({})".format(module.output_norm()) + ) + else: + return ("softmax", torch.nn.Softmax(dim=-1)) + + raise ValueError( + "output_norm ({}) not in ".format(output_norm) + + "supported list = [None, softmax, log_softmax]" + ) + + +# ------------------------------------------------------------------------------ +# infer_channels_from_layout() +# ------------------------------------------------------------------------------ + + +def infer_channels_from_layout(layout, channels): + """Extract the number of channels from the layout.""" + if layout in ("TBD", "BTD"): + if channels is not None and channels != 1: + raise ValueError( + "Expected channels ({}) to be 1 for layout = {}".format( + channels, layout + ) + ) + if channels is None: + return 1 + return channels + + +# ------------------------------------------------------------------------------ +# pad_sequence() +# ------------------------------------------------------------------------------ + + +@torch.jit.export +def pad_sequence( + sequence: Tensor, + time_axis: int, + extra_left_context: int = 0, + extra_right_context: int = 0, +) -> Tensor: + """Pad extra left/right contexts to the sequence.""" + + if extra_left_context == 0 and extra_right_context == 0: + return sequence + + tensors_to_concat = [] + + if extra_left_context: + size = (extra_left_context,) + fill_value = 0 + indices = torch.full( + size=size, + fill_value=fill_value, + dtype=torch.long, + device=sequence.device, + ) + left_padding = torch.index_select(sequence, time_axis, indices) + tensors_to_concat.append(left_padding) + + tensors_to_concat.append(sequence) + + # NOTE(cfyeh): for efficiency reason we pad 0 instead of the last frame for + # extra right contexts. + if extra_right_context: + size = list(sequence.shape) + size[time_axis] = extra_right_context + right_padding = torch.zeros(size, dtype=sequence.dtype, device=sequence.device) + tensors_to_concat.append(right_padding) + + padded_sequence = torch.cat(tensors_to_concat, dim=time_axis) + return padded_sequence + + +# ------------------------------------------------------------------------------ +# sequence_to_segments() +# ------------------------------------------------------------------------------ + + +@torch.jit.export +def sequence_to_segments( + sequence: Tensor, + time_axis: int, + lengths: Tensor, + segment_size: Optional[int] = None, + extra_left_context: int = 0, + extra_right_context: int = 0, +) -> List[Tuple[Tensor, Tensor]]: + """Breaks sequence into segments.""" + + sequence = pad_sequence( + sequence=sequence, + time_axis=time_axis, + extra_left_context=extra_left_context, + extra_right_context=extra_right_context, + ) + + lengths = lengths + extra_left_context + extra_right_context + + segments: List[Tuple[Tensor, Tensor]] = [] + + if segment_size is None: + segments.append((sequence, lengths)) + return segments + + offset = 0 + end = sequence.shape[time_axis] + step = segment_size + size = extra_left_context + segment_size + extra_right_context + + while offset + extra_left_context + extra_right_context < end: + clamped_size = min(size, end - offset) + segment_lengths = torch.clamp(lengths - offset, min=0, max=clamped_size) + indices = torch.arange( + start=offset, + end=(offset + clamped_size), + step=1, + dtype=torch.long, + device=sequence.device, + ) + segment_tensor = torch.index_select(sequence, time_axis, indices) + segments.append((segment_tensor, segment_lengths)) + offset = offset + step + + return segments + + +# ------------------------------------------------------------------------------ +# segments_to_sequence() +# ------------------------------------------------------------------------------ + + +@torch.jit.export +def segments_to_sequence( + segments: List[Tuple[Tensor, Tensor]], time_axis: int +) -> Tuple[Tensor, Tensor]: + """Concatenate segments into a full sequence.""" + if len(segments) == 1: + return segments[0] + + tensors_to_concat: List[Tensor] = [] + lengths_to_stack: List[Tensor] = [] + + for tensor, lengths in segments: + tensors_to_concat.append(tensor) + lengths_to_stack.append(lengths) + + sequence = torch.cat(tensors_to_concat, dim=time_axis) + lengths = torch.stack(lengths_to_stack, dim=0) + lengths = torch.sum(lengths, dim=0) + + return sequence, lengths + + +def lengths_to_encoder_padding_mask(lengths, batch_first: bool = False): + """ + convert lengths (a 1-D Long/Int tensor) to 2-D binary tensor + + Args: + lengths: a (B, )-shaped tensor + batch_first: whether to return a (B, T) tensor + + Return: + max_length: maximum length of B sequences + encoder_padding_mask: a (max_length, B) binary mask, where + [t, b] = False for t < lengths[b] and True otherwise + + TODO: + kernelize this function if benchmarking shows this function is slow + """ + max_lengths = torch.max(lengths).item() + bsz = lengths.size(0) + encoder_padding_mask = torch.arange( + max_lengths + ).to( # a (T, ) tensor with [0, ..., T-1] + lengths.device + ).view( # move to the right device + 1, max_lengths + ).expand( # reshape to (1, T)-shaped tensor + bsz, -1 + ) > lengths.view( # expand to (B, T)-shaped tensor + bsz, 1 + ).expand( + -1, max_lengths + ) + if not batch_first: + return encoder_padding_mask.t(), max_lengths + else: + return encoder_padding_mask, max_lengths + + +# ------------------------------------------------------------------------------ +# attention suppression +# ------------------------------------------------------------------------------ + + +def attention_suppression(attention_weights: Tensor, scale: float): + # B, H, qlen, klen -> B, H, qlen, 1 + attention_prob = torch.nn.functional.softmax(attention_weights.float(), dim=-1) + attention_nozeros = attention_prob.to(torch.bool) + nozeros_sum = torch.sum(attention_nozeros.to(torch.float), dim=-1, keepdim=True) + + # For very sparse situation, we need get round about 0s + key_sum = torch.sum(attention_prob, dim=-1, keepdim=True) + + # nozeros_sum should > 1 + key_mean = key_sum / (nozeros_sum + 1e-8) + + # std calculation + dis = (attention_prob - key_mean) * (attention_prob - key_mean) + + # if attention_prob[i] < threshold, then dis_masked[i] = 0; for all i + dis_masked = torch.where( + attention_nozeros, dis, attention_prob.new_zeros(attention_prob.size()) + ) + + key_var = torch.sum(dis_masked, dim=-1, keepdim=True) + key_var = key_var / (nozeros_sum - 1.0 + 1e-8) + key_std = torch.sqrt(key_var) + key_thread = key_mean - scale * key_std + + # if attention_prob[i] >= key_thread, then attention_prob[i] + # , otherwise "-inf" + inf_tensor = attention_prob.new_zeros(attention_prob.size()).detach() + inf_tensor[:] = float("-inf") + attention_weights_float = torch.where( + attention_prob < key_thread, + inf_tensor, + attention_weights.float(), + ) + + return attention_weights_float.type_as(attention_weights) + + +def layer_norm_backward_hook(module, grad_input, grad_output, clamp_value): + return tuple(torch.clamp(v, min=-clamp_value, max=clamp_value) for v in grad_input) diff --git a/fairseq/models/speech_to_text/xm_transformer.py b/fairseq/models/speech_to_text/xm_transformer.py new file mode 100644 index 0000000000..03c434b5ea --- /dev/null +++ b/fairseq/models/speech_to_text/xm_transformer.py @@ -0,0 +1,504 @@ +#!/usr/bin/env python3 +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +import copy +from typing import Dict, List, Optional, Tuple + +from fairseq import utils, checkpoint_utils +from fairseq.models import (FairseqEncoderDecoderModel, FairseqEncoder, + register_model, register_model_architecture) +from fairseq.models.transformer import Embedding, TransformerDecoder +from fairseq.models.wav2vec import Wav2VecEncoder +from fairseq.modules.layer_norm import LayerNorm +from fairseq.data.data_utils import lengths_to_padding_mask +from torch import Tensor +import torch.nn as nn + + +logger = logging.getLogger(__name__) + + +class Conv1dAdaptor(nn.Module): + def __init__(self, in_dim, out_dim, n_layers=3, kernel_size=3, stride=2, + add_layernorm=False): + super().__init__() + self.layers = nn.ModuleList( + nn.Conv1d(in_dim if i == 0 else out_dim, out_dim * 2, kernel_size, + stride=stride, padding=kernel_size // 2) + for i in range(n_layers) + ) + self.layernorms = None + if add_layernorm: + self.layernorms = nn.ModuleList(LayerNorm(out_dim) + for _ in range(n_layers)) + self.stride = stride + + @classmethod + def add_args(cls, parser): + parser.add_argument("--adaptor-n-layers", type=int) + parser.add_argument("--adaptor-kernel-size", type=int) + parser.add_argument("--adaptor-stride", type=int) + parser.add_argument("--adaptor-layernorm", action='store_true') + + def get_out_seq_lens_tensor(self, in_seq_lens_tensor): + out = in_seq_lens_tensor.clone() + for _ in self.layers: + out = ((out.float() - 1) / self.stride + 1).floor().long() + return out + + def forward(self, x, padding_mask): + # T x B x C -> B x C x T + x = x.transpose(0, 1).transpose(1, 2) + for i, layer in enumerate(self.layers): + x = nn.functional.glu(layer(x), dim=1) + if self.layernorms is not None: + x = self.layernorms[i](x.transpose(1, 2)).transpose(1, 2) + # B x C x T -> T x B x C + x = x.transpose(1, 2).transpose(0, 1) + + if padding_mask is None: + out_padding_mask = None + else: + out_lengths = self.get_out_seq_lens_tensor((~padding_mask).sum(1)) + out_padding_mask = lengths_to_padding_mask(out_lengths) + return x, out_padding_mask + + +def add_wav2vec_asr_args(parser): + parser.add_argument("--w2v-path", help="path to wav2vec 2.0 model") + parser.add_argument( + "--no-pretrained-weights", + action="store_true", + help="if true, does not load pretrained weights", + ) + parser.add_argument( + "--dropout-input", + type=float, + metavar="D", + help="dropout to apply to the input (after feat extr)", + ) + parser.add_argument( + "--final-dropout", + type=float, + metavar="D", + help="dropout after transformer and before final projection", + ) + parser.add_argument( + "--apply-mask", action="store_true", help="apply masking during fine-tuning" + ) + parser.add_argument( + "--dropout", + type=float, + metavar="D", + help="dropout probability inside wav2vec 2.0 model", + ) + parser.add_argument( + "--attention-dropout", + type=float, + metavar="D", + help="dropout probability for attention weights inside wav2vec 2.0 model", + ) + parser.add_argument( + "--activation-dropout", + "--relu-dropout", + type=float, + metavar="D", + help="dropout probability after activation in FFN inside wav2vec 2.0 model", + ) + + parser.add_argument( + "--mask-length", type=int, help="repeat the mask indices multiple times" + ) + + parser.add_argument( + "--mask-prob", type=float, help="probability of replacing a token with mask" + ) + + parser.add_argument( + "--mask-selection", + type=str, + choices=["static", "uniform", "normal", "poisson"], + help="how to choose masks", + ) + + parser.add_argument( + "--mask-other", + type=float, + help="stdev of the mask length in case of 'normal' selection strategy", + ) + + parser.add_argument( + "--no-mask-overlap", + action="store_true", + help="whether to allow masks to overlap", + ) + + parser.add_argument( + "--mask-channel-length", type=int, help="repeat the mask indices multiple times" + ) + + parser.add_argument( + "--mask-channel-prob", + type=float, + help="probability of replacing a token with mask", + ) + + parser.add_argument( + "--mask-channel-selection", + type=str, + choices=["static", "uniform", "normal", "poisson"], + help="how to choose masks", + ) + + parser.add_argument( + "--mask-channel-other", + type=float, + help="stdev of the mask length in case of 'normal' selection strategy", + ) + + parser.add_argument( + "--no-mask-channel-overlap", + action="store_true", + help="whether to allow masks to overlap", + ) + + parser.add_argument( + "--freeze-finetune-updates", + default=0, + type=int, + help="dont finetune wav2vec for this many updates", + ) + + parser.add_argument( + "--feature-grad-mult", + default=None, + type=float, + help="reset feature grad mult in wav2vec 2.0 to this", + ) + + parser.add_argument( + "--layerdrop", + default=0.0, + type=float, + help="probability of dropping a layer in wav2vec 2.0", + ) + parser.add_argument("--w2v-args", default=None) + + +class Wav2VecEncoderWithAdaptor(FairseqEncoder): + def __init__(self, args): + super().__init__(None) + self.w2v_encoder = Wav2VecEncoder(args) + encoder_out_dim = self.w2v_encoder.w2v_model.encoder.embedding_dim + # Projection + 8x shrinking + self.adaptor = Conv1dAdaptor( + encoder_out_dim, args.decoder_embed_dim, + n_layers=args.adaptor_n_layers, + kernel_size=args.adaptor_kernel_size, stride=args.adaptor_stride, + add_layernorm=args.adaptor_layernorm + ) + for k, p in self.w2v_encoder.w2v_model.named_parameters(): + # Freeze pretrained models by default + if hasattr(args, 'finetune_w2v_params') and XMTransformerModel.finetune_params( + args.finetune_w2v_params, k): + p.requires_grad = True + else: + p.requires_grad = False + + @classmethod + def add_args(cls, parser): + add_wav2vec_asr_args(parser) + parser.add_argument( + "--normalize", action="store_true", + help="if set, normalizes input to have 0 mean and unit variance", + ) + parser.add_argument("--finetune-w2v-params", type=str, metavar="STR", + help="comma-separated param strings to finetune.") + Conv1dAdaptor.add_args(parser) + + def forward(self, src_tokens, src_lengths=None, **kwargs): + padding_mask = lengths_to_padding_mask(src_lengths) + out = self.w2v_encoder.forward(src_tokens, padding_mask, tbc=True) + x = out["encoder_out"] + enc_padding_mask = None + if out["encoder_padding_mask"] is not None: + enc_padding_mask = out["encoder_padding_mask"].transpose(0, 1) # T X B --> B X T + + x, enc_padding_mask = self.adaptor(x, enc_padding_mask) + + return { + "encoder_out": [x], # T x B x C + "encoder_padding_mask": [enc_padding_mask] if enc_padding_mask.any() else [], # B x T + "encoder_embedding": [], # B x T x C + "encoder_states": [], # List[T x B x C] + "src_tokens": [], + "src_lengths": [], + } + + def reorder_encoder_out(self, encoder_out, new_order): + new_encoder_out = ( + [] if len(encoder_out["encoder_out"]) == 0 + else [x.index_select(1, new_order) for x in encoder_out["encoder_out"]] + ) + + new_encoder_padding_mask = ( + [] if len(encoder_out["encoder_padding_mask"]) == 0 + else [x.index_select(0, new_order) for x in + encoder_out["encoder_padding_mask"]] + ) + + new_encoder_embedding = ( + [] if len(encoder_out["encoder_embedding"]) == 0 + else [x.index_select(0, new_order) for x in + encoder_out["encoder_embedding"]] + ) + + encoder_states = encoder_out["encoder_states"] + if len(encoder_states) > 0: + for idx, state in enumerate(encoder_states): + encoder_states[idx] = state.index_select(1, new_order) + + return { + "encoder_out": new_encoder_out, # T x B x C + "encoder_padding_mask": new_encoder_padding_mask, # B x T + "encoder_embedding": new_encoder_embedding, # B x T x C + "encoder_states": encoder_states, # List[T x B x C] + "src_tokens": [], # B x T + "src_lengths": [], # B x 1 + } + + +def add_decoder_args(parser): + parser.add_argument("--activation-fn", type=str, default='relu', + choices=utils.get_available_activation_fns(), + help="activation function to use") + parser.add_argument("--decoder-dropout", type=float, metavar="D", + help="dropout probability") + parser.add_argument("--decoder-attention-dropout", type=float, + metavar="D", + help="dropout probability for attention weights") + parser.add_argument("--decoder-activation-dropout", type=float, + metavar="D", + help="dropout probability after activation in FFN.") + parser.add_argument("--decoder-embed-dim", type=int, metavar="N", + help="decoder embedding dimension") + parser.add_argument("--decoder-ffn-embed-dim", type=int, metavar="N", + help="decoder embedding dimension for FFN") + parser.add_argument("--decoder-layers", type=int, metavar="N", + help="num decoder layers") + parser.add_argument("--decoder-attention-heads", type=int, metavar="N", + help="num decoder attention heads") + parser.add_argument("--decoder-normalize-before", action="store_true", + help="apply layernorm before each decoder block") + parser.add_argument("--layernorm-embedding", action="store_true", + help="add layernorm to embedding") + parser.add_argument("--no-scale-embedding", action="store_true", + help="if True, dont scale embeddings") + parser.add_argument( + "--load-pretrained-decoder-from", type=str, metavar="STR", + help="model to take decoder weights from (for initialization)" + ) + parser.add_argument("--finetune-decoder-params", type=str, + metavar="STR", + help="comma-separated param strings to finetune.") + parser.add_argument("--checkpoint-activations", action="store_true") + + +@register_model("xm_transformer") +class XMTransformerModel(FairseqEncoderDecoderModel): + def __init__(self, encoder, decoder): + super().__init__(encoder, decoder) + + @classmethod + def add_args(cls, parser): + """Add model-specific arguments to the parser.""" + Wav2VecEncoderWithAdaptor.add_args(parser) + add_decoder_args(parser) + + @classmethod + def build_encoder(cls, args): + _args = copy.deepcopy(args) + state = checkpoint_utils.load_checkpoint_to_cpu(args.w2v_path) + if state.get("cfg") is not None: + encoder_embed_dim = state["cfg"]._content["model"]["encoder_embed_dim"] + elif state.get("args") is not None: + encoder_embed_dim = state["args"].encoder_embed_dim + else: + raise ValueError(f"Invalid config in {args.w2v_path}") + _args.decoder_embed_dim = encoder_embed_dim + encoder = Wav2VecEncoderWithAdaptor(_args) + return encoder + + @classmethod + def build_decoder(cls, args, task, embed_tokens): + _args = copy.deepcopy(args) + _args.dropout = args.decoder_dropout + _args.attention_dropout = args.decoder_attention_dropout + _args.activation_dropout = args.decoder_activation_dropout + _args.max_target_positions = 1024 + + decoder = TransformerDecoder(_args, task.target_dictionary, + embed_tokens) + if getattr(args, "load_pretrained_decoder_from", None): + decoder = checkpoint_utils.load_pretrained_component_from_model( + component=decoder, checkpoint=args.load_pretrained_decoder_from + ) + for k, p in decoder.named_parameters(): + # Freeze pretrained models by default + if hasattr(args, 'finetune_decoder_params') and XMTransformerModel.finetune_params( + args.finetune_decoder_params, k): + p.requires_grad = True + else: + p.requires_grad = False + return decoder + + @classmethod + def build_model(cls, args, task): + """Build a new model instance.""" + + # make sure all arguments are present in older models + base_architecture(args) + + def build_embedding(dictionary, embed_dim): + num_embeddings = len(dictionary) + padding_idx = dictionary.pad() + return Embedding(num_embeddings, embed_dim, padding_idx) + + decoder_embed_tokens = build_embedding(task.target_dictionary, + args.decoder_embed_dim) + encoder = cls.build_encoder(args) + decoder = cls.build_decoder(args, task, decoder_embed_tokens) + return cls(encoder, decoder) + + def get_normalized_probs( + self, + net_output: Tuple[Tensor, Optional[Dict[str, List[Optional[Tensor]]]]], + log_probs: bool, + sample: Optional[Dict[str, Tensor]] = None, + ): + # net_output['encoder_out'] is a (B, T, D) tensor + lprobs = self.get_normalized_probs_scriptable(net_output, log_probs, + sample) + lprobs.batch_first = True + return lprobs + + def forward(self, src_tokens, src_lengths, prev_output_tokens, **kwargs): + """ + The forward method inherited from the base class has a **kwargs + argument in its input, which is not supported in torchscript. This + method overrites the forward method definition without **kwargs. + """ + encoder_out = self.encoder(src_tokens=src_tokens, + src_lengths=src_lengths, **kwargs) + decoder_out = self.decoder(prev_output_tokens=prev_output_tokens, + encoder_out=encoder_out) + return decoder_out + + def upgrade_state_dict(self, state_dict): + for k, _ in state_dict.items(): + if 'adaptor.layers' in state_dict: + print(k) + new = k.replace('adaptor.layers', 'adaptor_layers') + state_dict[new] = state_dict[k] + del state_dict[k] + + @staticmethod + def finetune_params(finetune_params, param_name): + if finetune_params == "all": + return True + finetune_params_list = finetune_params.split(",") + for finetune_param in finetune_params_list: + if finetune_param in param_name: + return True + return False + + +def set_default_w2v_encoder_args(args): + args.no_pretrained_weights = getattr(args, "no_pretrained_weights", False) + args.dropout_input = getattr(args, "dropout_input", 0) + args.final_dropout = getattr(args, "final_dropout", 0) + args.apply_mask = getattr(args, "apply_mask", False) + args.dropout = getattr(args, "dropout", 0) + args.attention_dropout = getattr(args, "attention_dropout", 0) + args.activation_dropout = getattr(args, "activation_dropout", 0) + + args.mask_length = getattr(args, "mask_length", 10) + args.mask_prob = getattr(args, "mask_prob", 0.5) + args.mask_selection = getattr(args, "mask_selection", "static") + args.mask_other = getattr(args, "mask_other", 0) + args.no_mask_overlap = getattr(args, "no_mask_overlap", False) + args.mask_channel_length = getattr(args, "mask_channel_length", 10) + args.mask_channel_prob = getattr(args, "mask_channel_prob", 0.5) + args.mask_channel_before = getattr(args, "mask_channel_before", False) + args.mask_channel_selection = getattr(args, "mask_channel_selection", + "static") + args.mask_channel_other = getattr(args, "mask_channel_other", 0) + args.no_mask_channel_overlap = getattr(args, "no_mask_channel_overlap", + False) + + args.freeze_finetune_updates = getattr(args, "freeze_finetune_updates", 0) + args.feature_grad_mult = 0.1 + args.layerdrop = getattr(args, "layerdrop", 0.0) + + args.normalize = getattr(args, "normalize", False) + + +def set_default_adaptor_args(args): + args.adaptor_n_layers = getattr(args, "adaptor_n_layers", 3) + args.adaptor_kernel_size = getattr(args, "adaptor_kernel_size", 3) + args.adaptor_stride = getattr(args, "adaptor_stride", 2) + args.adaptor_layernorm = getattr(args, "adaptor_layernorm", False) + + +def set_default_mbart_decoder_args(args): + args.decoder_embed_path = getattr(args, 'decoder_embed_path', None) + args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 1024) + args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', + 4 * 1024) + args.decoder_layers = getattr(args, 'decoder_layers', 12) + args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 16) + args.decoder_normalize_before = getattr(args, 'decoder_normalize_before', + True) + args.decoder_learned_pos = getattr(args, 'decoder_learned_pos', True) + args.decoder_layerdrop = getattr(args, "decoder_layerdrop", 0.0) + args.adaptive_input = getattr(args, "adaptive_input", False) + args.decoder_attention_dropout = getattr(args, 'decoder_attention_dropout', + 0.) + args.decoder_activation_dropout = getattr(args, + 'decoder_activation_dropout', 0.) + args.decoder_dropout = getattr(args, 'decoder_dropout', 0.1) + args.adaptive_softmax_cutoff = getattr(args, 'adaptive_softmax_cutoff', + None) + args.adaptive_softmax_dropout = getattr(args, 'adaptive_softmax_dropout', 0) + args.share_decoder_input_output_embed = getattr( + args, 'share_decoder_input_output_embed', True + ) + args.no_token_positional_embeddings = getattr( + args, "no_token_positional_embeddings", False + ) + + args.decoder_output_dim = getattr(args, 'decoder_output_dim', + args.decoder_embed_dim) + args.decoder_input_dim = getattr(args, 'decoder_input_dim', + args.decoder_embed_dim) + + args.no_scale_embedding = getattr(args, 'no_scale_embedding', False) + args.quant_noise_pq = getattr(args, "quant_noise_pq", 0) + args.layernorm_embedding = getattr(args, 'layernorm_embedding', True) + + args.activation_fn = getattr(args, 'activation_fn', 'gelu') + args.pooler_activation_fn = getattr(args, 'pooler_activation_fn', 'tanh') + args.pooler_dropout = getattr(args, 'pooler_dropout', 0.0) + args.checkpoint_activations = getattr(args, "checkpoint_activations", False) + + +@register_model_architecture(model_name="xm_transformer", + arch_name="xm_transformer") +def base_architecture(args): + set_default_w2v_encoder_args(args) + set_default_adaptor_args(args) + set_default_mbart_decoder_args(args) diff --git a/fairseq/models/text_to_speech/__init__.py b/fairseq/models/text_to_speech/__init__.py new file mode 100644 index 0000000000..652fee0d68 --- /dev/null +++ b/fairseq/models/text_to_speech/__init__.py @@ -0,0 +1,8 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from .tacotron2 import * # noqa +from .tts_transformer import * # noqa +from .fastspeech2 import * # noqa diff --git a/fairseq/models/text_to_speech/fastspeech2.py b/fairseq/models/text_to_speech/fastspeech2.py new file mode 100644 index 0000000000..9c38d0917d --- /dev/null +++ b/fairseq/models/text_to_speech/fastspeech2.py @@ -0,0 +1,352 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging + +import torch +from torch import nn + +from fairseq.models import (FairseqEncoder, FairseqEncoderModel, register_model, + register_model_architecture) +from fairseq.modules import ( + LayerNorm, PositionalEmbedding, FairseqDropout, MultiheadAttention +) +from fairseq import utils +from fairseq.data.data_utils import lengths_to_padding_mask + + +logger = logging.getLogger(__name__) + + +def model_init(m): + if isinstance(m, nn.Conv1d): + nn.init.xavier_uniform_(m.weight, torch.nn.init.calculate_gain("relu")) + + +def Embedding(num_embeddings, embedding_dim, padding_idx=None): + m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx) + nn.init.normal_(m.weight, mean=0, std=embedding_dim ** -0.5) + return m + + +class PositionwiseFeedForward(nn.Module): + def __init__(self, in_dim, hidden_dim, kernel_size, dropout): + super().__init__() + self.ffn = nn.Sequential( + nn.Conv1d(in_dim, hidden_dim, kernel_size=kernel_size, + padding=(kernel_size - 1) // 2), + nn.ReLU(), + nn.Conv1d(hidden_dim, in_dim, kernel_size=kernel_size, + padding=(kernel_size - 1) // 2) + ) + self.layer_norm = LayerNorm(in_dim) + self.dropout = self.dropout_module = FairseqDropout( + p=dropout, module_name=self.__class__.__name__ + ) + + def forward(self, x): + # B x T x C + residual = x + x = self.ffn(x.transpose(1, 2)).transpose(1, 2) + x = self.dropout(x) + return self.layer_norm(x + residual) + + +class FFTLayer(torch.nn.Module): + def __init__( + self, embed_dim, n_heads, hidden_dim, kernel_size, dropout, + attention_dropout + ): + super().__init__() + self.self_attn = MultiheadAttention( + embed_dim, n_heads, dropout=attention_dropout, self_attention=True + ) + self.layer_norm = LayerNorm(embed_dim) + self.ffn = PositionwiseFeedForward( + embed_dim, hidden_dim, kernel_size, dropout=dropout + ) + + def forward(self, x, padding_mask=None): + # B x T x C + residual = x + x = x.transpose(0, 1) + x, _ = self.self_attn( + query=x, key=x, value=x, key_padding_mask=padding_mask, + need_weights=False + ) + x = x.transpose(0, 1) + x = self.layer_norm(x + residual) + return self.ffn(x) + + +class LengthRegulator(nn.Module): + def forward(self, x, durations): + # x: B x T x C + out_lens = durations.sum(dim=1) + max_len = out_lens.max() + bsz, seq_len, dim = x.size() + out = x.new_zeros((bsz, max_len, dim)) + + for b in range(bsz): + indices = [] + for t in range(seq_len): + indices.extend([t] * utils.item(durations[b, t])) + indices = torch.tensor(indices, dtype=torch.long).to(x.device) + out_len = utils.item(out_lens[b]) + out[b, :out_len] = x[b].index_select(0, indices) + + return out, out_lens + + +class VariancePredictor(nn.Module): + def __init__(self, args): + super().__init__() + self.conv1 = nn.Sequential( + nn.Conv1d( + args.encoder_embed_dim, args.var_pred_hidden_dim, + kernel_size=args.var_pred_kernel_size, + padding=(args.var_pred_kernel_size - 1) // 2 + ), + nn.ReLU() + ) + self.ln1 = nn.LayerNorm(args.var_pred_hidden_dim) + self.dropout_module = FairseqDropout( + p=args.var_pred_dropout, module_name=self.__class__.__name__ + ) + self.conv2 = nn.Sequential( + nn.Conv1d( + args.var_pred_hidden_dim, args.var_pred_hidden_dim, + kernel_size=args.var_pred_kernel_size, padding=1 + ), + nn.ReLU() + ) + self.ln2 = nn.LayerNorm(args.var_pred_hidden_dim) + self.proj = nn.Linear(args.var_pred_hidden_dim, 1) + + def forward(self, x): + # Input: B x T x C; Output: B x T + x = self.conv1(x.transpose(1, 2)).transpose(1, 2) + x = self.dropout_module(self.ln1(x)) + x = self.conv2(x.transpose(1, 2)).transpose(1, 2) + x = self.dropout_module(self.ln2(x)) + return self.proj(x).squeeze(dim=2) + + +class VarianceAdaptor(nn.Module): + def __init__(self, args): + super().__init__() + self.args = args + self.length_regulator = LengthRegulator() + self.duration_predictor = VariancePredictor(args) + self.pitch_predictor = VariancePredictor(args) + self.energy_predictor = VariancePredictor(args) + + n_bins, steps = self.args.var_pred_n_bins, self.args.var_pred_n_bins - 1 + self.pitch_bins = torch.linspace(args.pitch_min, args.pitch_max, steps) + self.embed_pitch = Embedding(n_bins, args.encoder_embed_dim) + self.energy_bins = torch.linspace(args.energy_min, args.energy_max, steps) + self.embed_energy = Embedding(n_bins, args.encoder_embed_dim) + + def get_pitch_emb(self, x, tgt=None, factor=1.0): + out = self.pitch_predictor(x) + bins = self.pitch_bins.to(x.device) + if tgt is None: + out = out * factor + emb = self.embed_pitch(torch.bucketize(out, bins)) + else: + emb = self.embed_pitch(torch.bucketize(tgt, bins)) + return out, emb + + def get_energy_emb(self, x, tgt=None, factor=1.0): + out = self.energy_predictor(x) + bins = self.energy_bins.to(x.device) + if tgt is None: + out = out * factor + emb = self.embed_energy(torch.bucketize(out, bins)) + else: + emb = self.embed_energy(torch.bucketize(tgt, bins)) + return out, emb + + def forward( + self, x, padding_mask, durations=None, pitches=None, energies=None, + d_factor=1.0, p_factor=1.0, e_factor=1.0 + ): + # x: B x T x C + log_dur_out = self.duration_predictor(x) + dur_out = torch.clamp( + torch.round((torch.exp(log_dur_out) - 1) * d_factor).long(), min=0 + ) + dur_out.masked_fill_(padding_mask, 0) + + pitch_out, pitch_emb = self.get_pitch_emb(x, pitches, p_factor) + x = x + pitch_emb + energy_out, energy_emb = self.get_energy_emb(x, energies, e_factor) + x = x + energy_emb + + x, out_lens = self.length_regulator( + x, dur_out if durations is None else durations + ) + + return x, out_lens, log_dur_out, pitch_out, energy_out + + +class FastSpeech2Encoder(FairseqEncoder): + def __init__(self, args, src_dict, embed_speaker): + super().__init__(src_dict) + self.args = args + self.padding_idx = src_dict.pad() + self.n_frames_per_step = args.n_frames_per_step + self.out_dim = args.output_frame_dim * args.n_frames_per_step + + self.embed_speaker = embed_speaker + self.spk_emb_proj = None + if embed_speaker is not None: + self.spk_emb_proj = nn.Linear( + args.encoder_embed_dim + args.speaker_embed_dim, + args.encoder_embed_dim + ) + + self.dropout_module = FairseqDropout( + p=args.dropout, module_name=self.__class__.__name__ + ) + self.embed_tokens = Embedding( + len(src_dict), args.encoder_embed_dim, padding_idx=self.padding_idx + ) + + self.embed_positions = PositionalEmbedding( + args.max_source_positions, args.encoder_embed_dim, self.padding_idx + ) + self.pos_emb_alpha = nn.Parameter(torch.ones(1)) + self.dec_pos_emb_alpha = nn.Parameter(torch.ones(1)) + + self.encoder_fft_layers = nn.ModuleList( + FFTLayer( + args.encoder_embed_dim, args.encoder_attention_heads, + args.fft_hidden_dim, args.fft_kernel_size, + dropout=args.dropout, attention_dropout=args.attention_dropout + ) + for _ in range(args.encoder_layers) + ) + + self.var_adaptor = VarianceAdaptor(args) + + self.decoder_fft_layers = nn.ModuleList( + FFTLayer( + args.decoder_embed_dim, args.decoder_attention_heads, + args.fft_hidden_dim, args.fft_kernel_size, + dropout=args.dropout, attention_dropout=args.attention_dropout + ) + for _ in range(args.decoder_layers) + ) + + self.out_proj = nn.Linear(args.decoder_embed_dim, self.out_dim) + + self.apply(model_init) + + def forward(self, src_tokens, src_lengths=None, speaker=None, + durations=None, pitches=None, energies=None, **kwargs): + x = self.embed_tokens(src_tokens) + + enc_padding_mask = src_tokens.eq(self.padding_idx) + x += self.pos_emb_alpha * self.embed_positions(enc_padding_mask) + x = self.dropout_module(x) + + for layer in self.encoder_fft_layers: + x = layer(x, enc_padding_mask) + + if self.embed_speaker is not None: + bsz, seq_len, _ = x.size() + emb = self.embed_speaker(speaker).expand(bsz, seq_len, -1) + x = self.spk_emb_proj(torch.cat([x, emb], dim=2)) + + x, out_lens, log_dur_out, pitch_out, energy_out = \ + self.var_adaptor(x, enc_padding_mask, durations, pitches, energies) + + dec_padding_mask = lengths_to_padding_mask(out_lens) + x += self.dec_pos_emb_alpha * self.embed_positions(dec_padding_mask) + for layer in self.decoder_fft_layers: + x = layer(x, dec_padding_mask) + + x = self.out_proj(x) + + return x, out_lens, log_dur_out, pitch_out, energy_out + + +@register_model("fastspeech2") +class FastSpeech2Model(FairseqEncoderModel): + """ + Implementation for https://arxiv.org/abs/2006.04558 + """ + + NON_AUTOREGRESSIVE = True + + @staticmethod + def add_args(parser): + parser.add_argument("--dropout", type=float) + parser.add_argument("--output-frame-dim", type=int) + parser.add_argument("--speaker-embed-dim", type=int) + # FFT blocks + parser.add_argument("--fft-hidden-dim", type=int) + parser.add_argument("--fft-kernel-size", type=int) + parser.add_argument("--attention-dropout", type=float) + parser.add_argument("--encoder-layers", type=int) + parser.add_argument("--encoder-embed-dim", type=int) + parser.add_argument("--encoder-attention-heads", type=int) + parser.add_argument("--decoder-layers", type=int) + parser.add_argument("--decoder-embed-dim", type=int) + parser.add_argument("--decoder-attention-heads", type=int) + # variance predictor + parser.add_argument("--var-pred-n-bins", type=int) + parser.add_argument("--var-pred-hidden-dim", type=int) + parser.add_argument("--var-pred-kernel-size", type=int) + parser.add_argument("--var-pred-dropout", type=float) + + def __init__(self, encoder, args, src_dict): + super().__init__(encoder) + self._num_updates = 0 + + out_dim = args.output_frame_dim * args.n_frames_per_step + self.ctc_proj = None + if getattr(args, "ctc_weight", 0.) > 0.: + self.ctc_proj = nn.Linear(out_dim, len(src_dict)) + + @classmethod + def build_model(cls, args, task): + embed_speaker = task.get_speaker_embeddings(args) + encoder = FastSpeech2Encoder(args, task.src_dict, embed_speaker) + return cls(encoder, args, task.src_dict) + + def set_num_updates(self, num_updates): + super().set_num_updates(num_updates) + self._num_updates = num_updates + + def get_normalized_probs(self, net_output, log_probs, sample=None): + logits = self.ctc_proj(net_output[0]) + if log_probs: + return utils.log_softmax(logits.float(), dim=-1) + else: + return utils.softmax(logits.float(), dim=-1) + + +@register_model_architecture("fastspeech2", "fastspeech2") +def base_architecture(args): + args.dropout = getattr(args, "dropout", 0.2) + args.output_frame_dim = getattr(args, "output_frame_dim", 80) + args.speaker_embed_dim = getattr(args, "speaker_embed_dim", 64) + # FFT blocks + args.fft_hidden_dim = getattr(args, "fft_hidden_dim", 1024) + args.fft_kernel_size = getattr(args, "fft_kernel_size", 9) + args.attention_dropout = getattr(args, "attention_dropout", 0.0) + args.encoder_layers = getattr(args, "encoder_layers", 4) + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 256) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 2) + args.decoder_layers = getattr(args, "decoder_layers", 4) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 256) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 2) + # variance predictor + args.var_pred_n_bins = getattr(args, "var_pred_n_bins", 256) + args.var_pred_hidden_dim = getattr(args, "var_pred_hidden_dim", 256) + args.var_pred_kernel_size = getattr(args, "var_pred_kernel_size", 3) + args.var_pred_dropout = getattr(args, "var_pred_dropout", 0.5) diff --git a/fairseq/models/text_to_speech/hifigan.py b/fairseq/models/text_to_speech/hifigan.py new file mode 100644 index 0000000000..edc7db6015 --- /dev/null +++ b/fairseq/models/text_to_speech/hifigan.py @@ -0,0 +1,173 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn import Conv1d, ConvTranspose1d +from torch.nn.utils import weight_norm, remove_weight_norm + +LRELU_SLOPE = 0.1 + + +def init_weights(m, mean=0.0, std=0.01): + classname = m.__class__.__name__ + if classname.find("Conv") != -1: + m.weight.data.normal_(mean, std) + + +def get_padding(kernel_size, dilation=1): + return (kernel_size * dilation - dilation) // 2 + + +class ResBlock(torch.nn.Module): + def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)): + super(ResBlock, self).__init__() + self.convs1 = nn.ModuleList( + [ + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[0], + padding=get_padding(kernel_size, dilation[0]), + ) + ), + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[1], + padding=get_padding(kernel_size, dilation[1]), + ) + ), + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[2], + padding=get_padding(kernel_size, dilation[2]), + ) + ), + ] + ) + self.convs1.apply(init_weights) + + self.convs2 = nn.ModuleList( + [ + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=1, + padding=get_padding(kernel_size, 1), + ) + ), + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=1, + padding=get_padding(kernel_size, 1), + ) + ), + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=1, + padding=get_padding(kernel_size, 1), + ) + ), + ] + ) + self.convs2.apply(init_weights) + + def forward(self, x): + for c1, c2 in zip(self.convs1, self.convs2): + xt = F.leaky_relu(x, LRELU_SLOPE) + xt = c1(xt) + xt = F.leaky_relu(xt, LRELU_SLOPE) + xt = c2(xt) + x = xt + x + return x + + def remove_weight_norm(self): + for layer in self.convs1: + remove_weight_norm(layer) + for layer in self.convs2: + remove_weight_norm(layer) + + +class Generator(torch.nn.Module): + def __init__(self, cfg): + super(Generator, self).__init__() + self.num_kernels = len(cfg["resblock_kernel_sizes"]) + self.num_upsamples = len(cfg["upsample_rates"]) + self.conv_pre = weight_norm( + Conv1d(80, cfg["upsample_initial_channel"], 7, 1, padding=3) + ) + + self.ups = nn.ModuleList() + for i, (u, k) in enumerate( + zip(cfg["upsample_rates"], cfg["upsample_kernel_sizes"]) + ): + self.ups.append( + weight_norm( + ConvTranspose1d( + cfg["upsample_initial_channel"] // (2 ** i), + cfg["upsample_initial_channel"] // (2 ** (i + 1)), + k, + u, + padding=(k - u) // 2, + ) + ) + ) + + self.resblocks = nn.ModuleList() + for i in range(len(self.ups)): + ch = cfg["upsample_initial_channel"] // (2 ** (i + 1)) + for k, d in zip( + cfg["resblock_kernel_sizes"], cfg["resblock_dilation_sizes"] + ): + self.resblocks.append(ResBlock(ch, k, d)) + + self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3)) + self.ups.apply(init_weights) + self.conv_post.apply(init_weights) + + def forward(self, x): + x = self.conv_pre(x) + for i in range(self.num_upsamples): + x = F.leaky_relu(x, LRELU_SLOPE) + x = self.ups[i](x) + xs = None + for j in range(self.num_kernels): + if xs is None: + xs = self.resblocks[i * self.num_kernels + j](x) + else: + xs += self.resblocks[i * self.num_kernels + j](x) + x = xs / self.num_kernels + x = F.leaky_relu(x) + x = self.conv_post(x) + x = torch.tanh(x) + + return x + + def remove_weight_norm(self): + print("Removing weight norm...") + for layer in self.ups: + remove_weight_norm(layer) + for layer in self.resblocks: + layer.remove_weight_norm() + remove_weight_norm(self.conv_pre) + remove_weight_norm(self.conv_post) diff --git a/fairseq/models/text_to_speech/tacotron2.py b/fairseq/models/text_to_speech/tacotron2.py new file mode 100644 index 0000000000..bb327e81e7 --- /dev/null +++ b/fairseq/models/text_to_speech/tacotron2.py @@ -0,0 +1,350 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging + +import torch +from torch import nn +from torch.nn import functional as F + +from fairseq.models import (FairseqEncoder, FairseqEncoderDecoderModel, + FairseqIncrementalDecoder, register_model, + register_model_architecture) +from fairseq.modules import LSTMCellWithZoneOut, LocationAttention + + +logger = logging.getLogger(__name__) + + +def encoder_init(m): + if isinstance(m, nn.Conv1d): + nn.init.xavier_uniform_(m.weight, torch.nn.init.calculate_gain("relu")) + + +class Tacotron2Encoder(FairseqEncoder): + def __init__(self, args, src_dict, embed_speaker): + super().__init__(src_dict) + self.padding_idx = src_dict.pad() + self.embed_speaker = embed_speaker + self.spk_emb_proj = None + if embed_speaker is not None: + self.spk_emb_proj = nn.Linear( + args.encoder_embed_dim + args.speaker_embed_dim, + args.encoder_embed_dim + ) + + self.embed_tokens = nn.Embedding(len(src_dict), args.encoder_embed_dim, + padding_idx=self.padding_idx) + + assert(args.encoder_conv_kernel_size % 2 == 1) + self.convolutions = nn.ModuleList( + nn.Sequential( + nn.Conv1d(args.encoder_embed_dim, args.encoder_embed_dim, + kernel_size=args.encoder_conv_kernel_size, + padding=((args.encoder_conv_kernel_size - 1) // 2)), + nn.BatchNorm1d(args.encoder_embed_dim), + nn.ReLU(), + nn.Dropout(args.encoder_dropout) + ) + for _ in range(args.encoder_conv_layers) + ) + + self.lstm = nn.LSTM(args.encoder_embed_dim, args.encoder_embed_dim // 2, + num_layers=args.encoder_lstm_layers, + batch_first=True, bidirectional=True) + + self.apply(encoder_init) + + def forward(self, src_tokens, src_lengths=None, speaker=None, **kwargs): + x = self.embed_tokens(src_tokens) + x = x.transpose(1, 2).contiguous() # B x T x C -> B x C x T + for conv in self.convolutions: + x = conv(x) + x = x.transpose(1, 2).contiguous() # B x C x T -> B x T x C + + src_lengths = src_lengths.cpu().long() + x = nn.utils.rnn.pack_padded_sequence(x, src_lengths, batch_first=True) + x = self.lstm(x)[0] + x = nn.utils.rnn.pad_packed_sequence(x, batch_first=True)[0] + + encoder_padding_mask = src_tokens.eq(self.padding_idx) + + if self.embed_speaker is not None: + seq_len, bsz, _ = x.size() + emb = self.embed_speaker(speaker).expand(seq_len, bsz, -1) + x = self.spk_emb_proj(torch.cat([x, emb], dim=2)) + + return { + "encoder_out": [x], # B x T x C + "encoder_padding_mask": encoder_padding_mask, # B x T + } + + +class Prenet(nn.Module): + def __init__(self, in_dim, n_layers, n_units, dropout): + super().__init__() + self.layers = nn.ModuleList( + nn.Sequential(nn.Linear(in_dim if i == 0 else n_units, n_units), + nn.ReLU()) + for i in range(n_layers) + ) + self.dropout = dropout + + def forward(self, x): + for layer in self.layers: + x = F.dropout(layer(x), p=self.dropout) # always applies dropout + return x + + +class Postnet(nn.Module): + def __init__(self, in_dim, n_channels, kernel_size, n_layers, dropout): + super(Postnet, self).__init__() + self.convolutions = nn.ModuleList() + assert(kernel_size % 2 == 1) + for i in range(n_layers): + cur_layers = [ + nn.Conv1d(in_dim if i == 0 else n_channels, + n_channels if i < n_layers - 1 else in_dim, + kernel_size=kernel_size, + padding=((kernel_size - 1) // 2)), + nn.BatchNorm1d(n_channels if i < n_layers - 1 else in_dim) + ] + ([nn.Tanh()] if i < n_layers - 1 else []) + [nn.Dropout(dropout)] + nn.init.xavier_uniform_( + cur_layers[0].weight, + torch.nn.init.calculate_gain( + "tanh" if i < n_layers - 1 else "linear" + ) + ) + self.convolutions.append(nn.Sequential(*cur_layers)) + + def forward(self, x): + x = x.transpose(1, 2) # B x T x C -> B x C x T + for conv in self.convolutions: + x = conv(x) + return x.transpose(1, 2) + + +def decoder_init(m): + if isinstance(m, torch.nn.Conv1d): + nn.init.xavier_uniform_(m.weight, torch.nn.init.calculate_gain("tanh")) + + +class Tacotron2Decoder(FairseqIncrementalDecoder): + def __init__(self, args, src_dict): + super().__init__(None) + self.args = args + self.n_frames_per_step = args.n_frames_per_step + self.out_dim = args.output_frame_dim * args.n_frames_per_step + + self.prenet = Prenet(self.out_dim, args.prenet_layers, args.prenet_dim, + args.prenet_dropout) + + # take prev_context, prev_frame, (speaker embedding) as input + self.attention_lstm = LSTMCellWithZoneOut( + args.zoneout, + args.prenet_dim + args.encoder_embed_dim, + args.decoder_lstm_dim + ) + + # take attention_lstm output, attention_state, encoder_out as input + self.attention = LocationAttention( + args.attention_dim, args.encoder_embed_dim, args.decoder_lstm_dim, + (1 + int(args.attention_use_cumprob)), + args.attention_conv_dim, args.attention_conv_kernel_size + ) + + # take attention_lstm output, context, (gated_latent) as input + self.lstm = nn.ModuleList( + LSTMCellWithZoneOut( + args.zoneout, + args.encoder_embed_dim + args.decoder_lstm_dim, + args.decoder_lstm_dim + ) + for i in range(args.decoder_lstm_layers) + ) + + proj_in_dim = args.encoder_embed_dim + args.decoder_lstm_dim + self.feat_proj = nn.Linear(proj_in_dim, self.out_dim) + self.eos_proj = nn.Linear(proj_in_dim, 1) + + self.postnet = Postnet(self.out_dim, args.postnet_conv_dim, + args.postnet_conv_kernel_size, + args.postnet_layers, args.postnet_dropout) + + self.ctc_proj = None + if getattr(args, "ctc_weight", 0.) > 0.: + self.ctc_proj = nn.Linear(self.out_dim, len(src_dict)) + + self.apply(decoder_init) + + def _get_states(self, incremental_state, enc_out): + bsz, in_len, _ = enc_out.size() + alstm_h = self.get_incremental_state(incremental_state, "alstm_h") + if alstm_h is None: + alstm_h = enc_out.new_zeros(bsz, self.args.decoder_lstm_dim) + alstm_c = self.get_incremental_state(incremental_state, "alstm_c") + if alstm_c is None: + alstm_c = enc_out.new_zeros(bsz, self.args.decoder_lstm_dim) + + lstm_h = self.get_incremental_state(incremental_state, "lstm_h") + if lstm_h is None: + lstm_h = [enc_out.new_zeros(bsz, self.args.decoder_lstm_dim) + for _ in range(self.args.decoder_lstm_layers)] + lstm_c = self.get_incremental_state(incremental_state, "lstm_c") + if lstm_c is None: + lstm_c = [enc_out.new_zeros(bsz, self.args.decoder_lstm_dim) + for _ in range(self.args.decoder_lstm_layers)] + + attn_w = self.get_incremental_state(incremental_state, "attn_w") + if attn_w is None: + attn_w = enc_out.new_zeros(bsz, in_len) + attn_w_cum = self.get_incremental_state(incremental_state, "attn_w_cum") + if attn_w_cum is None: + attn_w_cum = enc_out.new_zeros(bsz, in_len) + return alstm_h, alstm_c, lstm_h, lstm_c, attn_w, attn_w_cum + + def _get_init_attn_c(self, enc_out, enc_mask): + bsz = enc_out.size(0) + if self.args.init_attn_c == "zero": + return enc_out.new_zeros(bsz, self.args.encoder_embed_dim) + elif self.args.init_attn_c == "avg": + enc_w = (~enc_mask).type(enc_out.type()) + enc_w = enc_w / enc_w.sum(dim=1, keepdim=True) + return torch.sum(enc_out * enc_w.unsqueeze(2), dim=1) + else: + raise ValueError(f"{self.args.init_attn_c} not supported") + + def forward(self, prev_output_tokens, encoder_out=None, + incremental_state=None, target_lengths=None, **kwargs): + enc_mask = encoder_out["encoder_padding_mask"] + enc_out = encoder_out["encoder_out"][0] + in_len = enc_out.size(1) + + if incremental_state is not None: + prev_output_tokens = prev_output_tokens[:, -1:, :] + bsz, out_len, _ = prev_output_tokens.size() + + prenet_out = self.prenet(prev_output_tokens) + (alstm_h, alstm_c, lstm_h, lstm_c, + attn_w, attn_w_cum) = self._get_states(incremental_state, enc_out) + attn_ctx = self._get_init_attn_c(enc_out, enc_mask) + + attn_out = enc_out.new_zeros(bsz, in_len, out_len) + feat_out = enc_out.new_zeros(bsz, out_len, self.out_dim) + eos_out = enc_out.new_zeros(bsz, out_len) + for t in range(out_len): + alstm_in = torch.cat((attn_ctx, prenet_out[:, t, :]), dim=1) + alstm_h, alstm_c = self.attention_lstm(alstm_in, (alstm_h, alstm_c)) + + attn_state = attn_w.unsqueeze(1) + if self.args.attention_use_cumprob: + attn_state = torch.stack((attn_w, attn_w_cum), dim=1) + attn_ctx, attn_w = self.attention( + enc_out, enc_mask, alstm_h, attn_state + ) + attn_w_cum = attn_w_cum + attn_w + attn_out[:, :, t] = attn_w + + for i, cur_lstm in enumerate(self.lstm): + if i == 0: + lstm_in = torch.cat((attn_ctx, alstm_h), dim=1) + else: + lstm_in = torch.cat((attn_ctx, lstm_h[i - 1]), dim=1) + lstm_h[i], lstm_c[i] = cur_lstm(lstm_in, (lstm_h[i], lstm_c[i])) + + proj_in = torch.cat((attn_ctx, lstm_h[-1]), dim=1) + feat_out[:, t, :] = self.feat_proj(proj_in) + eos_out[:, t] = self.eos_proj(proj_in).squeeze(1) + self.attention.clear_cache() + + self.set_incremental_state(incremental_state, "alstm_h", alstm_h) + self.set_incremental_state(incremental_state, "alstm_c", alstm_c) + self.set_incremental_state(incremental_state, "lstm_h", lstm_h) + self.set_incremental_state(incremental_state, "lstm_c", lstm_c) + self.set_incremental_state(incremental_state, "attn_w", attn_w) + self.set_incremental_state(incremental_state, "attn_w_cum", attn_w_cum) + + post_feat_out = feat_out + self.postnet(feat_out) + eos_out = eos_out.view(bsz, out_len, 1) + return post_feat_out, eos_out, {"attn": attn_out, "feature_out": feat_out} + + +@register_model("tacotron_2") +class Tacotron2Model(FairseqEncoderDecoderModel): + """ + Implementation for https://arxiv.org/pdf/1712.05884.pdf + """ + + @staticmethod + def add_args(parser): + # encoder + parser.add_argument("--encoder-dropout", type=float) + parser.add_argument("--encoder-embed-dim", type=int) + parser.add_argument("--encoder-conv-layers", type=int) + parser.add_argument("--encoder-conv-kernel-size", type=int) + parser.add_argument("--encoder-lstm-layers", type=int) + # decoder + parser.add_argument("--attention-dim", type=int) + parser.add_argument("--attention-conv-dim", type=int) + parser.add_argument("--attention-conv-kernel-size", type=int) + parser.add_argument("--prenet-dropout", type=float) + parser.add_argument("--prenet-layers", type=int) + parser.add_argument("--prenet-dim", type=int) + parser.add_argument("--postnet-dropout", type=float) + parser.add_argument("--postnet-layers", type=int) + parser.add_argument("--postnet-conv-dim", type=int) + parser.add_argument("--postnet-conv-kernel-size", type=int) + parser.add_argument("--init-attn-c", type=str) + parser.add_argument("--attention-use-cumprob", action='store_true') + parser.add_argument("--zoneout", type=float) + parser.add_argument("--decoder-lstm-layers", type=int) + parser.add_argument("--decoder-lstm-dim", type=int) + parser.add_argument("--output-frame-dim", type=int) + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self._num_updates = 0 + + @classmethod + def build_model(cls, args, task): + embed_speaker = task.get_speaker_embeddings(args) + encoder = Tacotron2Encoder(args, task.src_dict, embed_speaker) + decoder = Tacotron2Decoder(args, task.src_dict) + return cls(encoder, decoder) + + def forward_encoder(self, src_tokens, src_lengths, **kwargs): + return self.encoder(src_tokens, src_lengths=src_lengths, **kwargs) + + def set_num_updates(self, num_updates): + super().set_num_updates(num_updates) + self._num_updates = num_updates + + +@register_model_architecture("tacotron_2", "tacotron_2") +def base_architecture(args): + # encoder + args.encoder_dropout = getattr(args, "encoder_dropout", 0.5) + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) + args.encoder_conv_layers = getattr(args, "encoder_conv_layers", 3) + args.encoder_conv_kernel_size = getattr(args, "encoder_conv_kernel_size", 5) + args.encoder_lstm_layers = getattr(args, "encoder_lstm_layers", 1) + # decoder + args.attention_dim = getattr(args, "attention_dim", 128) + args.attention_conv_dim = getattr(args, "attention_conv_dim", 32) + args.attention_conv_kernel_size = getattr(args, + "attention_conv_kernel_size", 15) + args.prenet_dropout = getattr(args, "prenet_dropout", 0.5) + args.prenet_layers = getattr(args, "prenet_layers", 2) + args.prenet_dim = getattr(args, "prenet_dim", 256) + args.postnet_dropout = getattr(args, "postnet_dropout", 0.5) + args.postnet_layers = getattr(args, "postnet_layers", 5) + args.postnet_conv_dim = getattr(args, "postnet_conv_dim", 512) + args.postnet_conv_kernel_size = getattr(args, "postnet_conv_kernel_size", 5) + args.init_attn_c = getattr(args, "init_attn_c", "zero") + args.attention_use_cumprob = getattr(args, "attention_use_cumprob", True) + args.zoneout = getattr(args, "zoneout", 0.1) + args.decoder_lstm_layers = getattr(args, "decoder_lstm_layers", 2) + args.decoder_lstm_dim = getattr(args, "decoder_lstm_dim", 1024) + args.output_frame_dim = getattr(args, "output_frame_dim", 80) diff --git a/fairseq/models/text_to_speech/tts_transformer.py b/fairseq/models/text_to_speech/tts_transformer.py new file mode 100644 index 0000000000..ff7af78bd4 --- /dev/null +++ b/fairseq/models/text_to_speech/tts_transformer.py @@ -0,0 +1,371 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +from typing import List, Optional + +import torch +from torch import nn + +from fairseq.models import (FairseqEncoder, FairseqEncoderDecoderModel, + FairseqIncrementalDecoder, register_model, + register_model_architecture) +from fairseq.modules import ( + TransformerEncoderLayer, TransformerDecoderLayer +) +from fairseq.models.text_to_speech.tacotron2 import Prenet, Postnet +from fairseq.modules import LayerNorm, PositionalEmbedding, FairseqDropout +from fairseq.data.data_utils import lengths_to_padding_mask +from fairseq import utils + +logger = logging.getLogger(__name__) + + +def encoder_init(m): + if isinstance(m, nn.Conv1d): + nn.init.xavier_uniform_(m.weight, torch.nn.init.calculate_gain("relu")) + + +def Embedding(num_embeddings, embedding_dim): + m = nn.Embedding(num_embeddings, embedding_dim) + nn.init.normal_(m.weight, mean=0, std=embedding_dim ** -0.5) + return m + + +class TTSTransformerEncoder(FairseqEncoder): + def __init__(self, args, src_dict, embed_speaker): + super().__init__(src_dict) + self.padding_idx = src_dict.pad() + self.embed_speaker = embed_speaker + self.spk_emb_proj = None + if embed_speaker is not None: + self.spk_emb_proj = nn.Linear( + args.encoder_embed_dim + args.speaker_embed_dim, + args.encoder_embed_dim + ) + + self.dropout_module = FairseqDropout( + p=args.dropout, module_name=self.__class__.__name__ + ) + self.embed_tokens = nn.Embedding(len(src_dict), args.encoder_embed_dim, + padding_idx=self.padding_idx) + assert(args.encoder_conv_kernel_size % 2 == 1) + self.prenet = nn.ModuleList( + nn.Sequential( + nn.Conv1d(args.encoder_embed_dim, args.encoder_embed_dim, + kernel_size=args.encoder_conv_kernel_size, + padding=((args.encoder_conv_kernel_size - 1) // 2)), + nn.BatchNorm1d(args.encoder_embed_dim), + nn.ReLU(), + nn.Dropout(args.encoder_dropout), + ) + for _ in range(args.encoder_conv_layers) + ) + self.prenet_proj = nn.Linear( + args.encoder_embed_dim, args.encoder_embed_dim + ) + self.embed_positions = PositionalEmbedding( + args.max_source_positions, args.encoder_embed_dim, self.padding_idx + ) + self.pos_emb_alpha = nn.Parameter(torch.ones(1)) + + self.transformer_layers = nn.ModuleList( + TransformerEncoderLayer(args) + for _ in range(args.encoder_transformer_layers) + ) + if args.encoder_normalize_before: + self.layer_norm = LayerNorm(args.encoder_embed_dim) + else: + self.layer_norm = None + + self.apply(encoder_init) + + def forward(self, src_tokens, src_lengths=None, speaker=None, **kwargs): + x = self.embed_tokens(src_tokens) + x = x.transpose(1, 2).contiguous() # B x T x C -> B x C x T + for conv in self.prenet: + x = conv(x) + x = x.transpose(1, 2).contiguous() # B x C x T -> B x T x C + x = self.prenet_proj(x) + + padding_mask = src_tokens.eq(self.padding_idx) + positions = self.embed_positions(padding_mask) + x += self.pos_emb_alpha * positions + x = self.dropout_module(x) + + # B x T x C -> T x B x C + x = x.transpose(0, 1) + + for layer in self.transformer_layers: + x = layer(x, padding_mask) + + if self.layer_norm is not None: + x = self.layer_norm(x) + + if self.embed_speaker is not None: + seq_len, bsz, _ = x.size() + emb = self.embed_speaker(speaker).transpose(0, 1) + emb = emb.expand(seq_len, bsz, -1) + x = self.spk_emb_proj(torch.cat([x, emb], dim=2)) + + return { + "encoder_out": [x], # T x B x C + "encoder_padding_mask": [padding_mask] if padding_mask.any() else [], # B x T + "encoder_embedding": [], # B x T x C + "encoder_states": [], # List[T x B x C] + "src_tokens": [], + "src_lengths": [], + } + + +def decoder_init(m): + if isinstance(m, torch.nn.Conv1d): + nn.init.xavier_uniform_(m.weight, torch.nn.init.calculate_gain("tanh")) + + +class TTSTransformerDecoder(FairseqIncrementalDecoder): + def __init__(self, args, src_dict): + super().__init__(None) + self._future_mask = torch.empty(0) + + self.args = args + self.padding_idx = src_dict.pad() + self.n_frames_per_step = args.n_frames_per_step + self.out_dim = args.output_frame_dim * args.n_frames_per_step + + self.dropout_module = FairseqDropout( + args.dropout, module_name=self.__class__.__name__ + ) + self.embed_positions = PositionalEmbedding( + args.max_target_positions, args.decoder_embed_dim, self.padding_idx + ) + self.pos_emb_alpha = nn.Parameter(torch.ones(1)) + self.prenet = nn.Sequential( + Prenet(self.out_dim, args.prenet_layers, args.prenet_dim, + args.prenet_dropout), + nn.Linear(args.prenet_dim, args.decoder_embed_dim), + ) + + self.n_transformer_layers = args.decoder_transformer_layers + self.transformer_layers = nn.ModuleList( + TransformerDecoderLayer(args) + for _ in range(self.n_transformer_layers) + ) + if args.decoder_normalize_before: + self.layer_norm = LayerNorm(args.decoder_embed_dim) + else: + self.layer_norm = None + + self.feat_proj = nn.Linear(args.decoder_embed_dim, self.out_dim) + self.eos_proj = nn.Linear(args.decoder_embed_dim, 1) + + self.postnet = Postnet(self.out_dim, args.postnet_conv_dim, + args.postnet_conv_kernel_size, + args.postnet_layers, args.postnet_dropout) + + self.ctc_proj = None + if getattr(args, "ctc_weight", 0.) > 0.: + self.ctc_proj = nn.Linear(self.out_dim, len(src_dict)) + + self.apply(decoder_init) + + def extract_features( + self, prev_outputs, encoder_out=None, incremental_state=None, + target_lengths=None, speaker=None, **kwargs + ): + alignment_layer = self.n_transformer_layers - 1 + self_attn_padding_mask = lengths_to_padding_mask(target_lengths) + positions = self.embed_positions( + self_attn_padding_mask, incremental_state=incremental_state + ) + + if incremental_state is not None: + prev_outputs = prev_outputs[:, -1:, :] + self_attn_padding_mask = self_attn_padding_mask[:, -1:] + if positions is not None: + positions = positions[:, -1:] + + x = self.prenet(prev_outputs) + x += self.pos_emb_alpha * positions + x = self.dropout_module(x) + + # B x T x C -> T x B x C + x = x.transpose(0, 1) + + if not self_attn_padding_mask.any(): + self_attn_padding_mask = None + + attn: Optional[torch.Tensor] = None + inner_states: List[Optional[torch.Tensor]] = [x] + for idx, transformer_layer in enumerate(self.transformer_layers): + if incremental_state is None: + self_attn_mask = self.buffered_future_mask(x) + else: + self_attn_mask = None + + x, layer_attn, _ = transformer_layer( + x, + encoder_out["encoder_out"][0] + if (encoder_out is not None and len(encoder_out["encoder_out"]) > 0) + else None, + encoder_out["encoder_padding_mask"][0] + if ( + encoder_out is not None + and len(encoder_out["encoder_padding_mask"]) > 0 + ) + else None, + incremental_state, + self_attn_mask=self_attn_mask, + self_attn_padding_mask=self_attn_padding_mask, + need_attn=bool((idx == alignment_layer)), + need_head_weights=bool((idx == alignment_layer)), + ) + inner_states.append(x) + if layer_attn is not None and idx == alignment_layer: + attn = layer_attn.float().to(x) + + if attn is not None: + # average probabilities over heads, transpose to + # (B, src_len, tgt_len) + attn = attn.mean(dim=0).transpose(2, 1) + + if self.layer_norm is not None: + x = self.layer_norm(x) + + # T x B x C -> B x T x C + x = x.transpose(0, 1) + + return x, {"attn": attn, "inner_states": inner_states} + + def forward(self, prev_output_tokens, encoder_out=None, + incremental_state=None, target_lengths=None, speaker=None, + **kwargs): + x, extra = self.extract_features( + prev_output_tokens, encoder_out=encoder_out, + incremental_state=incremental_state, target_lengths=target_lengths, + speaker=speaker, **kwargs + ) + attn = extra["attn"] + feat_out = self.feat_proj(x) + bsz, seq_len, _ = x.size() + eos_out = self.eos_proj(x) + post_feat_out = feat_out + self.postnet(feat_out) + return post_feat_out, eos_out, {"attn": attn, "feature_out": feat_out} + + def get_normalized_probs(self, net_output, log_probs, sample): + logits = self.ctc_proj(net_output[2]["feature_out"]) + if log_probs: + return utils.log_softmax(logits.float(), dim=-1) + else: + return utils.softmax(logits.float(), dim=-1) + + def buffered_future_mask(self, tensor): + dim = tensor.size(0) + # self._future_mask.device != tensor.device is not working in TorchScript. This is a workaround. + if ( + self._future_mask.size(0) == 0 + or (not self._future_mask.device == tensor.device) + or self._future_mask.size(0) < dim + ): + self._future_mask = torch.triu( + utils.fill_with_neg_inf(torch.zeros([dim, dim])), 1 + ) + self._future_mask = self._future_mask.to(tensor) + return self._future_mask[:dim, :dim] + + +@register_model("tts_transformer") +class TTSTransformerModel(FairseqEncoderDecoderModel): + """ + Implementation for https://arxiv.org/pdf/1809.08895.pdf + """ + + @staticmethod + def add_args(parser): + parser.add_argument("--dropout", type=float) + parser.add_argument("--output-frame-dim", type=int) + parser.add_argument("--speaker-embed-dim", type=int) + # encoder prenet + parser.add_argument("--encoder-dropout", type=float) + parser.add_argument("--encoder-conv-layers", type=int) + parser.add_argument("--encoder-conv-kernel-size", type=int) + # encoder transformer layers + parser.add_argument("--encoder-transformer-layers", type=int) + parser.add_argument("--encoder-embed-dim", type=int) + parser.add_argument("--encoder-ffn-embed-dim", type=int) + parser.add_argument("--encoder-normalize-before", action="store_true") + parser.add_argument("--encoder-attention-heads", type=int) + parser.add_argument("--attention-dropout", type=float) + parser.add_argument("--activation-dropout", "--relu-dropout", type=float) + parser.add_argument("--activation-fn", type=str, default="relu") + # decoder prenet + parser.add_argument("--prenet-dropout", type=float) + parser.add_argument("--prenet-layers", type=int) + parser.add_argument("--prenet-dim", type=int) + # decoder postnet + parser.add_argument("--postnet-dropout", type=float) + parser.add_argument("--postnet-layers", type=int) + parser.add_argument("--postnet-conv-dim", type=int) + parser.add_argument("--postnet-conv-kernel-size", type=int) + # decoder transformer layers + parser.add_argument("--decoder-transformer-layers", type=int) + parser.add_argument("--decoder-embed-dim", type=int) + parser.add_argument("--decoder-ffn-embed-dim", type=int) + parser.add_argument("--decoder-normalize-before", action="store_true") + parser.add_argument("--decoder-attention-heads", type=int) + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self._num_updates = 0 + + @classmethod + def build_model(cls, args, task): + embed_speaker = task.get_speaker_embeddings(args) + encoder = TTSTransformerEncoder(args, task.src_dict, embed_speaker) + decoder = TTSTransformerDecoder(args, task.src_dict) + return cls(encoder, decoder) + + def forward_encoder(self, src_tokens, src_lengths, speaker=None, **kwargs): + return self.encoder(src_tokens, src_lengths=src_lengths, + speaker=speaker, **kwargs) + + def set_num_updates(self, num_updates): + super().set_num_updates(num_updates) + self._num_updates = num_updates + + +@register_model_architecture("tts_transformer", "tts_transformer") +def base_architecture(args): + args.dropout = getattr(args, "dropout", 0.1) + args.output_frame_dim = getattr(args, "output_frame_dim", 80) + args.speaker_embed_dim = getattr(args, "speaker_embed_dim", 64) + # encoder prenet + args.encoder_dropout = getattr(args, "encoder_dropout", 0.5) + args.encoder_conv_layers = getattr(args, "encoder_conv_layers", 3) + args.encoder_conv_kernel_size = getattr(args, "encoder_conv_kernel_size", 5) + # encoder transformer layers + args.encoder_transformer_layers = getattr(args, "encoder_transformer_layers", 6) + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4 * args.encoder_embed_dim) + args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 4) + args.attention_dropout = getattr(args, "attention_dropout", 0.0) + args.activation_dropout = getattr(args, "activation_dropout", 0.0) + args.activation_fn = getattr(args, "activation_fn", "relu") + # decoder prenet + args.prenet_dropout = getattr(args, "prenet_dropout", 0.5) + args.prenet_layers = getattr(args, "prenet_layers", 2) + args.prenet_dim = getattr(args, "prenet_dim", 256) + # decoder postnet + args.postnet_dropout = getattr(args, "postnet_dropout", 0.5) + args.postnet_layers = getattr(args, "postnet_layers", 5) + args.postnet_conv_dim = getattr(args, "postnet_conv_dim", 512) + args.postnet_conv_kernel_size = getattr(args, "postnet_conv_kernel_size", 5) + # decoder transformer layers + args.decoder_transformer_layers = getattr(args, "decoder_transformer_layers", 6) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512) + args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 4 * args.decoder_embed_dim) + args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 4) diff --git a/fairseq/models/text_to_speech/vocoder.py b/fairseq/models/text_to_speech/vocoder.py new file mode 100644 index 0000000000..65d9f9f06b --- /dev/null +++ b/fairseq/models/text_to_speech/vocoder.py @@ -0,0 +1,197 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +import json +from typing import Dict + +import numpy as np +import torch +from torch import nn +import torch.nn.functional as F + +from fairseq.data.audio.audio_utils import ( + get_window, get_fourier_basis, get_mel_filters, TTSSpectrogram +) +from fairseq.data.audio.speech_to_text_dataset import S2TDataConfig +from fairseq.models.text_to_speech.hifigan import Generator as HiFiGANModel + +logger = logging.getLogger(__name__) + + +class PseudoInverseMelScale(torch.nn.Module): + def __init__(self, n_stft, n_mels, sample_rate, f_min, f_max) -> None: + super(PseudoInverseMelScale, self).__init__() + self.n_mels = n_mels + basis = get_mel_filters( + sample_rate, (n_stft - 1) * 2, n_mels, f_min, f_max + ) + basis = torch.pinverse(basis) # F x F_mel + self.register_buffer('basis', basis) + + def forward(self, melspec: torch.Tensor) -> torch.Tensor: + # pack batch + shape = melspec.shape # B_1 x ... x B_K x F_mel x T + n_mels, time = shape[-2], shape[-1] + melspec = melspec.view(-1, n_mels, time) + + freq, _ = self.basis.size() # F x F_mel + assert self.n_mels == n_mels, (self.n_mels, n_mels) + specgram = self.basis.matmul(melspec).clamp(min=0) + + # unpack batch + specgram = specgram.view(shape[:-2] + (freq, time)) + return specgram + + +class GriffinLim(torch.nn.Module): + def __init__( + self, n_fft: int, win_length: int, hop_length: int, n_iter: int, + window_fn=torch.hann_window + ): + super(GriffinLim, self).__init__() + self.transform = TTSSpectrogram( + n_fft, win_length, hop_length, return_phase=True + ) + + basis = get_fourier_basis(n_fft) + basis = torch.pinverse(n_fft / hop_length * basis).T[:, None, :] + basis *= get_window(window_fn, n_fft, win_length) + self.register_buffer('basis', basis) + + self.n_fft = n_fft + self.win_length = win_length + self.hop_length = hop_length + self.n_iter = n_iter + + self.tiny = 1.1754944e-38 + + @classmethod + def get_window_sum_square( + cls, n_frames, hop_length, win_length, n_fft, + window_fn=torch.hann_window + ) -> torch.Tensor: + w_sq = get_window(window_fn, n_fft, win_length) ** 2 + n = n_fft + hop_length * (n_frames - 1) + x = torch.zeros(n, dtype=torch.float32) + for i in range(n_frames): + ofst = i * hop_length + x[ofst: min(n, ofst + n_fft)] += w_sq[:max(0, min(n_fft, n - ofst))] + return x + + def inverse(self, magnitude: torch.Tensor, phase) -> torch.Tensor: + x = torch.cat( + [magnitude * torch.cos(phase), magnitude * torch.sin(phase)], + dim=1 + ) + x = F.conv_transpose1d(x, self.basis, stride=self.hop_length) + win_sum_sq = self.get_window_sum_square( + magnitude.shape[-1], hop_length=self.hop_length, + win_length=self.win_length, n_fft=self.n_fft + ).to(magnitude.device) + # remove modulation effects + approx_nonzero_indices = win_sum_sq > self.tiny + x[:, :, approx_nonzero_indices] /= win_sum_sq[approx_nonzero_indices] + x *= self.n_fft / self.hop_length + x = x[:, :, self.n_fft // 2:] + x = x[:, :, :-self.n_fft // 2:] + return x + + def forward(self, specgram: torch.Tensor) -> torch.Tensor: + angles = np.angle(np.exp(2j * np.pi * np.random.rand(*specgram.shape))) + angles = torch.from_numpy(angles).to(specgram) + _specgram = specgram.view(-1, specgram.shape[-2], specgram.shape[-1]) + waveform = self.inverse(_specgram, angles).squeeze(1) + for _ in range(self.n_iter): + _, angles = self.transform(waveform) + waveform = self.inverse(_specgram, angles).squeeze(1) + return waveform.squeeze(0) + + +class GriffinLimVocoder(nn.Module): + def __init__(self, sample_rate, win_size, hop_size, n_fft, + n_mels, f_min, f_max, window_fn, + spec_bwd_max_iter=32, + fp16=False): + super().__init__() + self.inv_mel_transform = PseudoInverseMelScale( + n_stft=n_fft // 2 + 1, n_mels=n_mels, sample_rate=sample_rate, + f_min=f_min, f_max=f_max + ) + self.gl_transform = GriffinLim( + n_fft=n_fft, win_length=win_size, hop_length=hop_size, + window_fn=window_fn, n_iter=spec_bwd_max_iter + ) + if fp16: + self.half() + self.inv_mel_transform.half() + self.gl_transform.half() + else: + self.float() + self.inv_mel_transform.float() + self.gl_transform.float() + + def forward(self, x): + # x: (B x) T x D -> (B x) 1 x T + # NOTE: batched forward produces noisier waveform. recommend running + # one utterance at a time + self.eval() + x = x.exp().transpose(-1, -2) + x = self.inv_mel_transform(x) + x = self.gl_transform(x) + return x + + @classmethod + def from_data_cfg(cls, args, data_cfg: S2TDataConfig): + feat_cfg = data_cfg.config["features"] + window_fn = getattr(torch, feat_cfg["window_fn"] + "_window") + return cls( + sample_rate=feat_cfg["sample_rate"], + win_size=int(feat_cfg["win_len_t"] * feat_cfg["sample_rate"]), + hop_size=int(feat_cfg["hop_len_t"] * feat_cfg["sample_rate"]), + n_fft=feat_cfg["n_fft"], n_mels=feat_cfg["n_mels"], + f_min=feat_cfg["f_min"], f_max=feat_cfg["f_max"], + window_fn=window_fn, spec_bwd_max_iter=args.spec_bwd_max_iter, + fp16=args.fp16 + ) + + +class HiFiGANVocoder(nn.Module): + def __init__( + self, checkpoint_path: str, model_cfg: Dict[str, str], + fp16: bool = False + ) -> None: + super().__init__() + self.model = HiFiGANModel(model_cfg) + state_dict = torch.load(checkpoint_path) + self.model.load_state_dict(state_dict["generator"]) + if fp16: + self.model.half() + logger.info(f"loaded HiFiGAN checkpoint from {checkpoint_path}") + + def forward(self, x: torch.Tensor) -> torch.Tensor: + # (B x) T x D -> (B x) 1 x T + model = self.model.eval() + if len(x.shape) == 2: + return model(x.unsqueeze(0).transpose(1, 2)).detach().squeeze(0) + else: + return model(x.transpose(-1, -2)).detach() + + @classmethod + def from_data_cfg(cls, args, data_cfg: S2TDataConfig): + vocoder_cfg = data_cfg.vocoder + assert vocoder_cfg.get("type", "griffin_lim") == "hifigan" + with open(vocoder_cfg["config"]) as f: + model_cfg = json.load(f) + return cls(vocoder_cfg["checkpoint"], model_cfg, fp16=args.fp16) + + +def get_vocoder(args, data_cfg: S2TDataConfig): + if args.vocoder == "griffin_lim": + return GriffinLimVocoder.from_data_cfg(args, data_cfg) + elif args.vocoder == "hifigan": + return HiFiGANVocoder.from_data_cfg(args, data_cfg) + else: + raise ValueError("Unknown vocoder") diff --git a/fairseq/models/transformer.py b/fairseq/models/transformer.py deleted file mode 100644 index b94a0db492..0000000000 --- a/fairseq/models/transformer.py +++ /dev/null @@ -1,979 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import math -from typing import Any, Dict, List, Optional, Tuple - -import torch -import torch.nn as nn -import torch.nn.functional as F -from fairseq import options, utils -from fairseq.models import ( - FairseqEncoder, - FairseqEncoderDecoderModel, - FairseqIncrementalDecoder, - register_model, - register_model_architecture, -) -from fairseq.models.fairseq_encoder import EncoderOut -from fairseq.modules import ( - AdaptiveSoftmax, - LayerDropModuleList, - LayerNorm, - PositionalEmbedding, - SinusoidalPositionalEmbedding, - TransformerDecoderLayer, - TransformerEncoderLayer, -) -from fairseq.modules.quant_noise import quant_noise as apply_quant_noise_ -from torch import Tensor - - -DEFAULT_MAX_SOURCE_POSITIONS = 1024 -DEFAULT_MAX_TARGET_POSITIONS = 1024 - - -@register_model("transformer") -class TransformerModel(FairseqEncoderDecoderModel): - """ - Transformer model from `"Attention Is All You Need" (Vaswani, et al, 2017) - <https://arxiv.org/abs/1706.03762>`_. - - Args: - encoder (TransformerEncoder): the encoder - decoder (TransformerDecoder): the decoder - - The Transformer model provides the following named architectures and - command-line arguments: - - .. argparse:: - :ref: fairseq.models.transformer_parser - :prog: - """ - - @classmethod - def hub_models(cls): - # fmt: off - - def moses_subword(path): - return { - 'path': path, - 'tokenizer': 'moses', - 'bpe': 'subword_nmt', - } - - def moses_fastbpe(path): - return { - 'path': path, - 'tokenizer': 'moses', - 'bpe': 'fastbpe', - } - - return { - 'transformer.wmt14.en-fr': moses_subword('https://dl.fbaipublicfiles.com/fairseq/models/wmt14.en-fr.joined-dict.transformer.tar.bz2'), - 'transformer.wmt16.en-de': 'https://dl.fbaipublicfiles.com/fairseq/models/wmt16.en-de.joined-dict.transformer.tar.bz2', - 'transformer.wmt18.en-de': moses_subword('https://dl.fbaipublicfiles.com/fairseq/models/wmt18.en-de.ensemble.tar.gz'), - 'transformer.wmt19.en-de': moses_fastbpe('https://dl.fbaipublicfiles.com/fairseq/models/wmt19.en-de.joined-dict.ensemble.tar.gz'), - 'transformer.wmt19.en-ru': moses_fastbpe('https://dl.fbaipublicfiles.com/fairseq/models/wmt19.en-ru.ensemble.tar.gz'), - 'transformer.wmt19.de-en': moses_fastbpe('https://dl.fbaipublicfiles.com/fairseq/models/wmt19.de-en.joined-dict.ensemble.tar.gz'), - 'transformer.wmt19.ru-en': moses_fastbpe('https://dl.fbaipublicfiles.com/fairseq/models/wmt19.ru-en.ensemble.tar.gz'), - 'transformer.wmt19.en-de.single_model': moses_fastbpe('https://dl.fbaipublicfiles.com/fairseq/models/wmt19.en-de.joined-dict.single_model.tar.gz'), - 'transformer.wmt19.en-ru.single_model': moses_fastbpe('https://dl.fbaipublicfiles.com/fairseq/models/wmt19.en-ru.single_model.tar.gz'), - 'transformer.wmt19.de-en.single_model': moses_fastbpe('https://dl.fbaipublicfiles.com/fairseq/models/wmt19.de-en.joined-dict.single_model.tar.gz'), - 'transformer.wmt19.ru-en.single_model': moses_fastbpe('https://dl.fbaipublicfiles.com/fairseq/models/wmt19.ru-en.single_model.tar.gz'), - } - # fmt: on - - def __init__(self, args, encoder, decoder): - super().__init__(encoder, decoder) - self.args = args - self.supports_align_args = True - - @staticmethod - def add_args(parser): - """Add model-specific arguments to the parser.""" - # fmt: off - parser.add_argument('--activation-fn', - choices=utils.get_available_activation_fns(), - help='activation function to use') - parser.add_argument('--dropout', type=float, metavar='D', - help='dropout probability') - parser.add_argument('--attention-dropout', type=float, metavar='D', - help='dropout probability for attention weights') - parser.add_argument('--activation-dropout', '--relu-dropout', type=float, metavar='D', - help='dropout probability after activation in FFN.') - parser.add_argument('--encoder-embed-path', type=str, metavar='STR', - help='path to pre-trained encoder embedding') - parser.add_argument('--encoder-embed-dim', type=int, metavar='N', - help='encoder embedding dimension') - parser.add_argument('--encoder-ffn-embed-dim', type=int, metavar='N', - help='encoder embedding dimension for FFN') - parser.add_argument('--encoder-layers', type=int, metavar='N', - help='num encoder layers') - parser.add_argument('--encoder-attention-heads', type=int, metavar='N', - help='num encoder attention heads') - parser.add_argument('--encoder-normalize-before', action='store_true', - help='apply layernorm before each encoder block') - parser.add_argument('--encoder-learned-pos', action='store_true', - help='use learned positional embeddings in the encoder') - parser.add_argument('--decoder-embed-path', type=str, metavar='STR', - help='path to pre-trained decoder embedding') - parser.add_argument('--decoder-embed-dim', type=int, metavar='N', - help='decoder embedding dimension') - parser.add_argument('--decoder-ffn-embed-dim', type=int, metavar='N', - help='decoder embedding dimension for FFN') - parser.add_argument('--decoder-layers', type=int, metavar='N', - help='num decoder layers') - parser.add_argument('--decoder-attention-heads', type=int, metavar='N', - help='num decoder attention heads') - parser.add_argument('--decoder-learned-pos', action='store_true', - help='use learned positional embeddings in the decoder') - parser.add_argument('--decoder-normalize-before', action='store_true', - help='apply layernorm before each decoder block') - parser.add_argument('--decoder-output-dim', type=int, metavar='N', - help='decoder output dimension (extra linear layer ' - 'if different from decoder embed dim') - parser.add_argument('--share-decoder-input-output-embed', action='store_true', - help='share decoder input and output embeddings') - parser.add_argument('--share-all-embeddings', action='store_true', - help='share encoder, decoder and output embeddings' - ' (requires shared dictionary and embed dim)') - parser.add_argument('--no-token-positional-embeddings', default=False, action='store_true', - help='if set, disables positional embeddings (outside self attention)') - parser.add_argument('--adaptive-softmax-cutoff', metavar='EXPR', - help='comma separated list of adaptive softmax cutoff points. ' - 'Must be used with adaptive_loss criterion'), - parser.add_argument('--adaptive-softmax-dropout', type=float, metavar='D', - help='sets adaptive softmax dropout for the tail projections') - parser.add_argument('--layernorm-embedding', action='store_true', - help='add layernorm to embedding') - parser.add_argument('--no-scale-embedding', action='store_true', - help='if True, dont scale embeddings') - # args for "Cross+Self-Attention for Transformer Models" (Peitz et al., 2019) - parser.add_argument('--no-cross-attention', default=False, action='store_true', - help='do not perform cross-attention') - parser.add_argument('--cross-self-attention', default=False, action='store_true', - help='perform cross+self-attention') - # args for "Reducing Transformer Depth on Demand with Structured Dropout" (Fan et al., 2019) - parser.add_argument('--encoder-layerdrop', type=float, metavar='D', default=0, - help='LayerDrop probability for encoder') - parser.add_argument('--decoder-layerdrop', type=float, metavar='D', default=0, - help='LayerDrop probability for decoder') - parser.add_argument('--encoder-layers-to-keep', default=None, - help='which layers to *keep* when pruning as a comma-separated list') - parser.add_argument('--decoder-layers-to-keep', default=None, - help='which layers to *keep* when pruning as a comma-separated list') - # args for Training with Quantization Noise for Extreme Model Compression ({Fan*, Stock*} et al., 2020) - parser.add_argument('--quant-noise-pq', type=float, metavar='D', default=0, - help='iterative PQ quantization noise at training time') - parser.add_argument('--quant-noise-pq-block-size', type=int, metavar='D', default=8, - help='block size of quantization noise at training time') - parser.add_argument('--quant-noise-scalar', type=float, metavar='D', default=0, - help='scalar quantization noise and scalar quantization at training time') - # fmt: on - - @classmethod - def build_model(cls, args, task): - """Build a new model instance.""" - - # make sure all arguments are present in older models - base_architecture(args) - - if args.encoder_layers_to_keep: - args.encoder_layers = len(args.encoder_layers_to_keep.split(",")) - if args.decoder_layers_to_keep: - args.decoder_layers = len(args.decoder_layers_to_keep.split(",")) - - if getattr(args, "max_source_positions", None) is None: - args.max_source_positions = DEFAULT_MAX_SOURCE_POSITIONS - if getattr(args, "max_target_positions", None) is None: - args.max_target_positions = DEFAULT_MAX_TARGET_POSITIONS - - src_dict, tgt_dict = task.source_dictionary, task.target_dictionary - - if args.share_all_embeddings: - if src_dict != tgt_dict: - raise ValueError("--share-all-embeddings requires a joined dictionary") - if args.encoder_embed_dim != args.decoder_embed_dim: - raise ValueError( - "--share-all-embeddings requires --encoder-embed-dim to match --decoder-embed-dim" - ) - if args.decoder_embed_path and ( - args.decoder_embed_path != args.encoder_embed_path - ): - raise ValueError( - "--share-all-embeddings not compatible with --decoder-embed-path" - ) - encoder_embed_tokens = cls.build_embedding( - args, src_dict, args.encoder_embed_dim, args.encoder_embed_path - ) - decoder_embed_tokens = encoder_embed_tokens - args.share_decoder_input_output_embed = True - else: - encoder_embed_tokens = cls.build_embedding( - args, src_dict, args.encoder_embed_dim, args.encoder_embed_path - ) - decoder_embed_tokens = cls.build_embedding( - args, tgt_dict, args.decoder_embed_dim, args.decoder_embed_path - ) - - encoder = cls.build_encoder(args, src_dict, encoder_embed_tokens) - decoder = cls.build_decoder(args, tgt_dict, decoder_embed_tokens) - return cls(args, encoder, decoder) - - @classmethod - def build_embedding(cls, args, dictionary, embed_dim, path=None): - num_embeddings = len(dictionary) - padding_idx = dictionary.pad() - - emb = Embedding(num_embeddings, embed_dim, padding_idx) - # if provided, load from preloaded dictionaries - if path: - embed_dict = utils.parse_embedding(path) - utils.load_embedding(embed_dict, dictionary, emb) - return emb - - @classmethod - def build_encoder(cls, args, src_dict, embed_tokens): - return TransformerEncoder(args, src_dict, embed_tokens) - - @classmethod - def build_decoder(cls, args, tgt_dict, embed_tokens): - return TransformerDecoder( - args, - tgt_dict, - embed_tokens, - no_encoder_attn=getattr(args, "no_cross_attention", False), - ) - - # TorchScript doesn't support optional arguments with variable length (**kwargs). - # Current workaround is to add union of all arguments in child classes. - def forward( - self, - src_tokens, - src_lengths, - prev_output_tokens, - return_all_hiddens: bool = True, - features_only: bool = False, - alignment_layer: Optional[int] = None, - alignment_heads: Optional[int] = None, - ): - """ - Run the forward pass for an encoder-decoder model. - - Copied from the base class, but without ``**kwargs``, - which are not supported by TorchScript. - """ - encoder_out = self.encoder( - src_tokens, - src_lengths=src_lengths, - return_all_hiddens=return_all_hiddens, - ) - decoder_out = self.decoder( - prev_output_tokens, - encoder_out=encoder_out, - features_only=features_only, - alignment_layer=alignment_layer, - alignment_heads=alignment_heads, - src_lengths=src_lengths, - return_all_hiddens=return_all_hiddens, - ) - return decoder_out - - # Since get_normalized_probs is in the Fairseq Model which is not scriptable, - # I rewrite the get_normalized_probs from Base Class to call the - # helper function in the Base Class. - @torch.jit.export - def get_normalized_probs( - self, - net_output: Tuple[Tensor, Optional[Dict[str, List[Optional[Tensor]]]]], - log_probs: bool, - sample: Optional[Dict[str, Tensor]] = None, - ): - """Get normalized probabilities (or log probs) from a net's output.""" - return self.get_normalized_probs_scriptable(net_output, log_probs, sample) - - -class TransformerEncoder(FairseqEncoder): - """ - Transformer encoder consisting of *args.encoder_layers* layers. Each layer - is a :class:`TransformerEncoderLayer`. - - Args: - args (argparse.Namespace): parsed command-line arguments - dictionary (~fairseq.data.Dictionary): encoding dictionary - embed_tokens (torch.nn.Embedding): input embedding - """ - - def __init__(self, args, dictionary, embed_tokens): - super().__init__(dictionary) - self.register_buffer("version", torch.Tensor([3])) - - self.dropout = args.dropout - self.encoder_layerdrop = args.encoder_layerdrop - - embed_dim = embed_tokens.embedding_dim - self.padding_idx = embed_tokens.padding_idx - self.max_source_positions = args.max_source_positions - - self.embed_tokens = embed_tokens - - self.embed_scale = 1.0 if args.no_scale_embedding else math.sqrt(embed_dim) - - self.embed_positions = ( - PositionalEmbedding( - args.max_source_positions, - embed_dim, - self.padding_idx, - learned=args.encoder_learned_pos, - ) - if not args.no_token_positional_embeddings - else None - ) - - if not args.adaptive_input and args.quant_noise_pq > 0: - self.quant_noise = apply_quant_noise_( - nn.Linear(embed_dim, embed_dim, bias=False), - args.quant_noise_pq, - args.quant_noise_pq_block_size, - ) - else: - self.quant_noise = None - - if self.encoder_layerdrop > 0.0: - self.layers = LayerDropModuleList(p=self.encoder_layerdrop) - else: - self.layers = nn.ModuleList([]) - self.layers.extend([ - self.build_encoder_layer(args) - for i in range(args.encoder_layers) - ]) - self.num_layers = len(self.layers) - - if args.encoder_normalize_before: - self.layer_norm = LayerNorm(embed_dim) - else: - self.layer_norm = None - if getattr(args, "layernorm_embedding", False): - self.layernorm_embedding = LayerNorm(embed_dim) - else: - self.layernorm_embedding = None - - def build_encoder_layer(self, args): - return TransformerEncoderLayer(args) - - def forward_embedding(self, src_tokens): - # embed tokens and positions - x = embed = self.embed_scale * self.embed_tokens(src_tokens) - if self.embed_positions is not None: - x = embed + self.embed_positions(src_tokens) - if self.layernorm_embedding is not None: - x = self.layernorm_embedding(x) - x = F.dropout(x, p=self.dropout, training=self.training) - if self.quant_noise is not None: - x = self.quant_noise(x) - return x, embed - - def forward( - self, - src_tokens, - src_lengths, - return_all_hiddens: bool = False, - ): - """ - Args: - src_tokens (LongTensor): tokens in the source language of shape - `(batch, src_len)` - src_lengths (torch.LongTensor): lengths of each source sentence of - shape `(batch)` - return_all_hiddens (bool, optional): also return all of the - intermediate hidden states (default: False). - - Returns: - namedtuple: - - **encoder_out** (Tensor): the last encoder layer's output of - shape `(src_len, batch, embed_dim)` - - **encoder_padding_mask** (ByteTensor): the positions of - padding elements of shape `(batch, src_len)` - - **encoder_embedding** (Tensor): the (scaled) embedding lookup - of shape `(batch, src_len, embed_dim)` - - **encoder_states** (List[Tensor]): all intermediate - hidden states of shape `(src_len, batch, embed_dim)`. - Only populated if *return_all_hiddens* is True. - """ - x, encoder_embedding = self.forward_embedding(src_tokens) - - # B x T x C -> T x B x C - x = x.transpose(0, 1) - - # compute padding mask - encoder_padding_mask = src_tokens.eq(self.padding_idx) - - encoder_states = [] if return_all_hiddens else None - - # encoder layers - for layer in self.layers: - x = layer(x, encoder_padding_mask) - if return_all_hiddens: - assert encoder_states is not None - encoder_states.append(x) - - if self.layer_norm is not None: - x = self.layer_norm(x) - - return EncoderOut( - encoder_out=x, # T x B x C - encoder_padding_mask=encoder_padding_mask, # B x T - encoder_embedding=encoder_embedding, # B x T x C - encoder_states=encoder_states, # List[T x B x C] - src_tokens=None, - src_lengths=None, - ) - - @torch.jit.export - def reorder_encoder_out(self, encoder_out: EncoderOut, new_order): - """ - Reorder encoder output according to *new_order*. - - Args: - encoder_out: output from the ``forward()`` method - new_order (LongTensor): desired order - - Returns: - *encoder_out* rearranged according to *new_order* - """ - new_encoder_out: Dict[str, Tensor] = {} - - new_encoder_out["encoder_out"] = ( - encoder_out.encoder_out - if encoder_out.encoder_out is None - else encoder_out.encoder_out.index_select(1, new_order) - ) - new_encoder_out["encoder_padding_mask"] = ( - encoder_out.encoder_padding_mask - if encoder_out.encoder_padding_mask is None - else encoder_out.encoder_padding_mask.index_select(0, new_order) - ) - new_encoder_out["encoder_embedding"] = ( - encoder_out.encoder_embedding - if encoder_out.encoder_embedding is None - else encoder_out.encoder_embedding.index_select(0, new_order) - ) - src_tokens = encoder_out.src_tokens - if src_tokens is not None: - src_tokens = src_tokens.index_select(0, new_order) - - src_lengths = encoder_out.src_lengths - if src_lengths is not None: - src_lengths = src_lengths.index_select(0, new_order) - - encoder_states = encoder_out.encoder_states - if encoder_states is not None: - for idx, state in enumerate(encoder_states): - encoder_states[idx] = state.index_select(1, new_order) - - return EncoderOut( - encoder_out=new_encoder_out["encoder_out"], # T x B x C - encoder_padding_mask=new_encoder_out["encoder_padding_mask"], # B x T - encoder_embedding=new_encoder_out["encoder_embedding"], # B x T x C - encoder_states=encoder_states, # List[T x B x C] - src_tokens=src_tokens, # B x T - src_lengths=src_lengths, # B x 1 - ) - - def max_positions(self): - """Maximum input length supported by the encoder.""" - if self.embed_positions is None: - return self.max_source_positions - return min(self.max_source_positions, self.embed_positions.max_positions) - - def upgrade_state_dict_named(self, state_dict, name): - """Upgrade a (possibly old) state dict for new versions of fairseq.""" - if isinstance(self.embed_positions, SinusoidalPositionalEmbedding): - weights_key = "{}.embed_positions.weights".format(name) - if weights_key in state_dict: - print("deleting {0}".format(weights_key)) - del state_dict[weights_key] - state_dict[ - "{}.embed_positions._float_tensor".format(name) - ] = torch.FloatTensor(1) - for i in range(self.num_layers): - # update layer norms - self.layers[i].upgrade_state_dict_named( - state_dict, "{}.layers.{}".format(name, i) - ) - - version_key = "{}.version".format(name) - if utils.item(state_dict.get(version_key, torch.Tensor([1]))[0]) < 2: - # earlier checkpoints did not normalize after the stack of layers - self.layer_norm = None - self.normalize = False - state_dict[version_key] = torch.Tensor([1]) - return state_dict - - -class TransformerDecoder(FairseqIncrementalDecoder): - """ - Transformer decoder consisting of *args.decoder_layers* layers. Each layer - is a :class:`TransformerDecoderLayer`. - - Args: - args (argparse.Namespace): parsed command-line arguments - dictionary (~fairseq.data.Dictionary): decoding dictionary - embed_tokens (torch.nn.Embedding): output embedding - no_encoder_attn (bool, optional): whether to attend to encoder outputs - (default: False). - """ - - def __init__(self, args, dictionary, embed_tokens, no_encoder_attn=False): - self.args = args - super().__init__(dictionary) - self.register_buffer("version", torch.Tensor([3])) - self._future_mask = torch.empty(0) - - self.dropout = args.dropout - self.decoder_layerdrop = args.decoder_layerdrop - self.share_input_output_embed = args.share_decoder_input_output_embed - - input_embed_dim = embed_tokens.embedding_dim - embed_dim = args.decoder_embed_dim - self.embed_dim = embed_dim - self.output_embed_dim = args.decoder_output_dim - - self.padding_idx = embed_tokens.padding_idx - self.max_target_positions = args.max_target_positions - - self.embed_tokens = embed_tokens - - self.embed_scale = 1.0 if args.no_scale_embedding else math.sqrt(embed_dim) - - if not args.adaptive_input and args.quant_noise_pq > 0: - self.quant_noise = apply_quant_noise_( - nn.Linear(embed_dim, embed_dim, bias=False), - args.quant_noise_pq, - args.quant_noise_pq_block_size, - ) - else: - self.quant_noise = None - - self.project_in_dim = ( - Linear(input_embed_dim, embed_dim, bias=False) - if embed_dim != input_embed_dim - else None - ) - - self.embed_positions = ( - PositionalEmbedding( - args.max_target_positions, - embed_dim, - self.padding_idx, - learned=args.decoder_learned_pos, - ) - if not args.no_token_positional_embeddings - else None - ) - - if getattr(args, "layernorm_embedding", False): - self.layernorm_embedding = LayerNorm(embed_dim) - else: - self.layernorm_embedding = None - - self.cross_self_attention = getattr(args, "cross_self_attention", False) - - if self.decoder_layerdrop > 0.0: - self.layers = LayerDropModuleList(p=self.decoder_layerdrop) - else: - self.layers = nn.ModuleList([]) - self.layers.extend([ - self.build_decoder_layer(args, no_encoder_attn) - for _ in range(args.decoder_layers) - ]) - self.num_layers = len(self.layers) - - if args.decoder_normalize_before and not getattr(args, "no_decoder_final_norm", False): - self.layer_norm = LayerNorm(embed_dim) - else: - self.layer_norm = None - - self.project_out_dim = ( - Linear(embed_dim, self.output_embed_dim, bias=False) - if embed_dim != self.output_embed_dim and not args.tie_adaptive_weights - else None - ) - - self.adaptive_softmax = None - self.output_projection = None - if args.adaptive_softmax_cutoff is not None: - self.adaptive_softmax = AdaptiveSoftmax( - len(dictionary), - self.output_embed_dim, - options.eval_str_list(args.adaptive_softmax_cutoff, type=int), - dropout=args.adaptive_softmax_dropout, - adaptive_inputs=embed_tokens if args.tie_adaptive_weights else None, - factor=args.adaptive_softmax_factor, - tie_proj=args.tie_adaptive_proj, - ) - elif self.share_input_output_embed: - self.output_projection = nn.Linear( - self.embed_tokens.weight.shape[1], - self.embed_tokens.weight.shape[0], - bias=False, - ) - self.output_projection.weight = self.embed_tokens.weight - else: - self.output_projection = nn.Linear( - self.output_embed_dim, len(dictionary), bias=False - ) - nn.init.normal_( - self.output_projection.weight, mean=0, std=self.output_embed_dim ** -0.5 - ) - - def build_decoder_layer(self, args, no_encoder_attn=False): - return TransformerDecoderLayer(args, no_encoder_attn) - - def forward( - self, - prev_output_tokens, - encoder_out: Optional[EncoderOut] = None, - incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, - features_only: bool = False, - alignment_layer: Optional[int] = None, - alignment_heads: Optional[int] = None, - src_lengths: Optional[Any] = None, - return_all_hiddens: bool = False, - ): - """ - Args: - prev_output_tokens (LongTensor): previous decoder outputs of shape - `(batch, tgt_len)`, for teacher forcing - encoder_out (optional): output from the encoder, used for - encoder-side attention - incremental_state (dict): dictionary used for storing state during - :ref:`Incremental decoding` - features_only (bool, optional): only return features without - applying output layer (default: False). - - Returns: - tuple: - - the decoder's output of shape `(batch, tgt_len, vocab)` - - a dictionary with any model-specific outputs - """ - x, extra = self.extract_features( - prev_output_tokens, - encoder_out=encoder_out, - incremental_state=incremental_state, - alignment_layer=alignment_layer, - alignment_heads=alignment_heads, - ) - if not features_only: - x = self.output_layer(x) - return x, extra - - def extract_features( - self, - prev_output_tokens, - encoder_out: Optional[EncoderOut] = None, - incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, - full_context_alignment: bool = False, - alignment_layer: Optional[int] = None, - alignment_heads: Optional[int] = None, - ): - """ - Similar to *forward* but only return features. - - Includes several features from "Jointly Learning to Align and - Translate with Transformer Models" (Garg et al., EMNLP 2019). - - Args: - full_context_alignment (bool, optional): don't apply - auto-regressive mask to self-attention (default: False). - alignment_layer (int, optional): return mean alignment over - heads at this layer (default: last layer). - alignment_heads (int, optional): only average alignment over - this many heads (default: all heads). - - Returns: - tuple: - - the decoder's features of shape `(batch, tgt_len, embed_dim)` - - a dictionary with any model-specific outputs - """ - if alignment_layer is None: - alignment_layer = self.num_layers - 1 - - # embed positions - positions = ( - self.embed_positions( - prev_output_tokens, incremental_state=incremental_state - ) - if self.embed_positions is not None - else None - ) - - if incremental_state is not None: - prev_output_tokens = prev_output_tokens[:, -1:] - if positions is not None: - positions = positions[:, -1:] - - # embed tokens and positions - x = self.embed_scale * self.embed_tokens(prev_output_tokens) - - if self.quant_noise is not None: - x = self.quant_noise(x) - - if self.project_in_dim is not None: - x = self.project_in_dim(x) - - if positions is not None: - x += positions - - if self.layernorm_embedding is not None: - x = self.layernorm_embedding(x) - - x = F.dropout(x, p=self.dropout, training=self.training) - - # B x T x C -> T x B x C - x = x.transpose(0, 1) - - self_attn_padding_mask: Optional[Tensor] = None - if self.cross_self_attention or prev_output_tokens.eq(self.padding_idx).any(): - self_attn_padding_mask = prev_output_tokens.eq(self.padding_idx) - - # decoder layers - attn: Optional[Tensor] = None - inner_states: List[Optional[Tensor]] = [x] - for idx, layer in enumerate(self.layers): - if incremental_state is None and not full_context_alignment: - self_attn_mask = self.buffered_future_mask(x) - else: - self_attn_mask = None - - x, layer_attn, _ = layer( - x, - encoder_out.encoder_out if encoder_out is not None else None, - encoder_out.encoder_padding_mask if encoder_out is not None else None, - incremental_state, - self_attn_mask=self_attn_mask, - self_attn_padding_mask=self_attn_padding_mask, - need_attn=bool((idx == alignment_layer)), - need_head_weights=bool((idx == alignment_layer)), - ) - inner_states.append(x) - if layer_attn is not None and idx == alignment_layer: - attn = layer_attn.float().to(x) - - if attn is not None: - if alignment_heads is not None: - attn = attn[:alignment_heads] - - # average probabilities over heads - attn = attn.mean(dim=0) - - if self.layer_norm is not None: - x = self.layer_norm(x) - - # T x B x C -> B x T x C - x = x.transpose(0, 1) - - if self.project_out_dim is not None: - x = self.project_out_dim(x) - - return x, {"attn": [attn], "inner_states": inner_states} - - def output_layer(self, features): - """Project features to the vocabulary size.""" - if self.adaptive_softmax is None: - # project back to size of vocabulary - return self.output_projection(features) - else: - return features - - def max_positions(self): - """Maximum output length supported by the decoder.""" - if self.embed_positions is None: - return self.max_target_positions - return min(self.max_target_positions, self.embed_positions.max_positions) - - def buffered_future_mask(self, tensor): - dim = tensor.size(0) - # self._future_mask.device != tensor.device is not working in TorchScript. This is a workaround. - if ( - self._future_mask.size(0) == 0 - or (not self._future_mask.device == tensor.device) - or self._future_mask.size(0) < dim - ): - self._future_mask = torch.triu( - utils.fill_with_neg_inf(torch.zeros([dim, dim])), 1 - ) - self._future_mask = self._future_mask.to(tensor) - return self._future_mask[:dim, :dim] - - def upgrade_state_dict_named(self, state_dict, name): - """Upgrade a (possibly old) state dict for new versions of fairseq.""" - if isinstance(self.embed_positions, SinusoidalPositionalEmbedding): - weights_key = "{}.embed_positions.weights".format(name) - if weights_key in state_dict: - del state_dict[weights_key] - state_dict[ - "{}.embed_positions._float_tensor".format(name) - ] = torch.FloatTensor(1) - - if self.share_input_output_embed: - embed_out_key = f"{name}.embed_tokens.weight" - else: - embed_out_key = f"{name}.embed_out" - if embed_out_key in state_dict: - state_dict[f"{name}.output_projection.weight"] = state_dict[embed_out_key] - if not self.share_input_output_embed: - del state_dict[embed_out_key] - - for i in range(self.num_layers): - # update layer norms - layer_norm_map = { - "0": "self_attn_layer_norm", - "1": "encoder_attn_layer_norm", - "2": "final_layer_norm", - } - for old, new in layer_norm_map.items(): - for m in ("weight", "bias"): - k = "{}.layers.{}.layer_norms.{}.{}".format(name, i, old, m) - if k in state_dict: - state_dict[ - "{}.layers.{}.{}.{}".format(name, i, new, m) - ] = state_dict[k] - del state_dict[k] - - version_key = "{}.version".format(name) - if utils.item(state_dict.get(version_key, torch.Tensor([1]))[0]) <= 2: - # earlier checkpoints did not normalize after the stack of layers - self.layer_norm = None - self.normalize = False - state_dict[version_key] = torch.Tensor([1]) - - return state_dict - - # Overwrite the method to temporaily support JIT scripting in Transformer - @torch.jit.export - def reorder_incremental_state( - self, - incremental_state: Dict[str, Dict[str, Optional[Tensor]]], - new_order: Tensor, - ): - """Scriptable reorder incremental state in the transformer.""" - for layer in self.layers: - layer.reorder_incremental_state(incremental_state, new_order) - - -def Embedding(num_embeddings, embedding_dim, padding_idx): - m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx) - nn.init.normal_(m.weight, mean=0, std=embedding_dim ** -0.5) - nn.init.constant_(m.weight[padding_idx], 0) - return m - - -def Linear(in_features, out_features, bias=True): - m = nn.Linear(in_features, out_features, bias) - nn.init.xavier_uniform_(m.weight) - if bias: - nn.init.constant_(m.bias, 0.0) - return m - - -@register_model_architecture("transformer", "transformer") -def base_architecture(args): - args.encoder_embed_path = getattr(args, "encoder_embed_path", None) - args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) - args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048) - args.encoder_layers = getattr(args, "encoder_layers", 6) - args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8) - args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False) - args.encoder_learned_pos = getattr(args, "encoder_learned_pos", False) - args.decoder_embed_path = getattr(args, "decoder_embed_path", None) - args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim) - args.decoder_ffn_embed_dim = getattr( - args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim - ) - args.decoder_layers = getattr(args, "decoder_layers", 6) - args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8) - args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False) - args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False) - args.attention_dropout = getattr(args, "attention_dropout", 0.0) - args.activation_dropout = getattr(args, "activation_dropout", 0.0) - args.activation_fn = getattr(args, "activation_fn", "relu") - args.dropout = getattr(args, "dropout", 0.1) - args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None) - args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0) - args.share_decoder_input_output_embed = getattr( - args, "share_decoder_input_output_embed", False - ) - args.share_all_embeddings = getattr(args, "share_all_embeddings", False) - args.no_token_positional_embeddings = getattr( - args, "no_token_positional_embeddings", False - ) - args.adaptive_input = getattr(args, "adaptive_input", False) - args.no_cross_attention = getattr(args, "no_cross_attention", False) - args.cross_self_attention = getattr(args, "cross_self_attention", False) - - args.decoder_output_dim = getattr( - args, "decoder_output_dim", args.decoder_embed_dim - ) - args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim) - - args.no_scale_embedding = getattr(args, "no_scale_embedding", False) - args.layernorm_embedding = getattr(args, "layernorm_embedding", False) - args.tie_adaptive_weights = getattr(args, "tie_adaptive_weights", False) - - -@register_model_architecture("transformer", "transformer_iwslt_de_en") -def transformer_iwslt_de_en(args): - args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) - args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 1024) - args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 4) - args.encoder_layers = getattr(args, "encoder_layers", 6) - args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512) - args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 1024) - args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 4) - args.decoder_layers = getattr(args, "decoder_layers", 6) - base_architecture(args) - - -@register_model_architecture("transformer", "transformer_wmt_en_de") -def transformer_wmt_en_de(args): - base_architecture(args) - - -# parameters used in the "Attention Is All You Need" paper (Vaswani et al., 2017) -@register_model_architecture("transformer", "transformer_vaswani_wmt_en_de_big") -def transformer_vaswani_wmt_en_de_big(args): - args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024) - args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4096) - args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 16) - args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False) - args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 1024) - args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 4096) - args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 16) - args.dropout = getattr(args, "dropout", 0.3) - base_architecture(args) - - -@register_model_architecture("transformer", "transformer_vaswani_wmt_en_fr_big") -def transformer_vaswani_wmt_en_fr_big(args): - args.dropout = getattr(args, "dropout", 0.1) - transformer_vaswani_wmt_en_de_big(args) - - -@register_model_architecture("transformer", "transformer_wmt_en_de_big") -def transformer_wmt_en_de_big(args): - args.attention_dropout = getattr(args, "attention_dropout", 0.1) - transformer_vaswani_wmt_en_de_big(args) - - -# default parameters used in tensor2tensor implementation -@register_model_architecture("transformer", "transformer_wmt_en_de_big_t2t") -def transformer_wmt_en_de_big_t2t(args): - args.encoder_normalize_before = getattr(args, "encoder_normalize_before", True) - args.decoder_normalize_before = getattr(args, "decoder_normalize_before", True) - args.attention_dropout = getattr(args, "attention_dropout", 0.1) - args.activation_dropout = getattr(args, "activation_dropout", 0.1) - transformer_vaswani_wmt_en_de_big(args) diff --git a/fairseq/models/transformer/__init__.py b/fairseq/models/transformer/__init__.py new file mode 100644 index 0000000000..681fca3d45 --- /dev/null +++ b/fairseq/models/transformer/__init__.py @@ -0,0 +1,50 @@ +# Copyright (c) Facebook Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +"""isort:skip_file""" + +from .transformer_config import ( + TransformerConfig, + DEFAULT_MAX_SOURCE_POSITIONS, + DEFAULT_MAX_TARGET_POSITIONS, + DEFAULT_MIN_PARAMS_TO_WRAP, +) +from .transformer_decoder import TransformerDecoder, TransformerDecoderBase, Linear +from .transformer_encoder import TransformerEncoder, TransformerEncoderBase +from .transformer_legacy import ( + TransformerModel, + base_architecture, + tiny_architecture, + transformer_iwslt_de_en, + transformer_wmt_en_de, + transformer_vaswani_wmt_en_de_big, + transformer_vaswani_wmt_en_fr_big, + transformer_wmt_en_de_big, + transformer_wmt_en_de_big_t2t, +) +from .transformer_base import TransformerModelBase, Embedding + + +__all__ = [ + "TransformerModelBase", + "TransformerConfig", + "TransformerDecoder", + "TransformerDecoderBase", + "TransformerEncoder", + "TransformerEncoderBase", + "TransformerModel", + "Embedding", + "Linear", + "base_architecture", + "tiny_architecture", + "transformer_iwslt_de_en", + "transformer_wmt_en_de", + "transformer_vaswani_wmt_en_de_big", + "transformer_vaswani_wmt_en_fr_big", + "transformer_wmt_en_de_big", + "transformer_wmt_en_de_big_t2t", + "DEFAULT_MAX_SOURCE_POSITIONS", + "DEFAULT_MAX_TARGET_POSITIONS", + "DEFAULT_MIN_PARAMS_TO_WRAP", +] diff --git a/fairseq/models/transformer/transformer_base.py b/fairseq/models/transformer/transformer_base.py new file mode 100644 index 0000000000..b4d5604dbb --- /dev/null +++ b/fairseq/models/transformer/transformer_base.py @@ -0,0 +1,179 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from typing import Dict, List, Optional, Tuple + +import torch +import torch.nn as nn +from fairseq import utils +from fairseq.dataclass.utils import gen_parser_from_dataclass +from fairseq.distributed import fsdp_wrap +from fairseq.models import FairseqEncoderDecoderModel +from fairseq.models.transformer import ( + TransformerEncoderBase, + TransformerDecoderBase, + TransformerConfig, +) +from torch import Tensor + + +class TransformerModelBase(FairseqEncoderDecoderModel): + """ + Transformer model from `"Attention Is All You Need" (Vaswani, et al, 2017) + <https://arxiv.org/abs/1706.03762>`_. + + Args: + encoder (TransformerEncoder): the encoder + decoder (TransformerDecoder): the decoder + + The Transformer model provides the following named architectures and + command-line arguments: + + .. argparse:: + :ref: fairseq.models.transformer_parser + :prog: + """ + + def __init__(self, cfg, encoder, decoder): + super().__init__(encoder, decoder) + self.cfg = cfg + self.supports_align_args = True + + @classmethod + def add_args(cls, parser): + """Add model-specific arguments to the parser.""" + # we want to build the args recursively in this case. + gen_parser_from_dataclass( + parser, TransformerConfig(), delete_default=False, with_prefix="" + ) + + @classmethod + def build_model(cls, cfg, task): + """Build a new model instance.""" + + # -- TODO T96535332 + # bug caused by interaction between OmegaConf II and argparsing + cfg.decoder.input_dim = int(cfg.decoder.input_dim) + cfg.decoder.output_dim = int(cfg.decoder.output_dim) + # -- + + if cfg.encoder.layers_to_keep: + cfg.encoder.layers = len(cfg.encoder.layers_to_keep.split(",")) + if cfg.decoder.layers_to_keep: + cfg.decoder.layers = len(cfg.decoder.layers_to_keep.split(",")) + + src_dict, tgt_dict = task.source_dictionary, task.target_dictionary + + if cfg.share_all_embeddings: + if src_dict != tgt_dict: + raise ValueError("--share-all-embeddings requires a joined dictionary") + if cfg.encoder.embed_dim != cfg.decoder.embed_dim: + raise ValueError( + "--share-all-embeddings requires --encoder-embed-dim to match --decoder-embed-dim" + ) + if cfg.decoder.embed_path and ( + cfg.decoder.embed_path != cfg.encoder.embed_path + ): + raise ValueError( + "--share-all-embeddings not compatible with --decoder-embed-path" + ) + encoder_embed_tokens = cls.build_embedding( + cfg, src_dict, cfg.encoder.embed_dim, cfg.encoder.embed_path + ) + decoder_embed_tokens = encoder_embed_tokens + cfg.share_decoder_input_output_embed = True + else: + encoder_embed_tokens = cls.build_embedding( + cfg, src_dict, cfg.encoder.embed_dim, cfg.encoder.embed_path + ) + decoder_embed_tokens = cls.build_embedding( + cfg, tgt_dict, cfg.decoder.embed_dim, cfg.decoder.embed_path + ) + if cfg.offload_activations: + cfg.checkpoint_activations = True # offloading implies checkpointing + encoder = cls.build_encoder(cfg, src_dict, encoder_embed_tokens) + decoder = cls.build_decoder(cfg, tgt_dict, decoder_embed_tokens) + if not cfg.share_all_embeddings: + # fsdp_wrap is a no-op when --ddp-backend != fully_sharded + encoder = fsdp_wrap(encoder, min_num_params=cfg.min_params_to_wrap) + decoder = fsdp_wrap(decoder, min_num_params=cfg.min_params_to_wrap) + return cls(cfg, encoder, decoder) + + @classmethod + def build_embedding(cls, cfg, dictionary, embed_dim, path=None): + num_embeddings = len(dictionary) + padding_idx = dictionary.pad() + + emb = Embedding(num_embeddings, embed_dim, padding_idx) + # if provided, load from preloaded dictionaries + if path: + embed_dict = utils.parse_embedding(path) + utils.load_embedding(embed_dict, dictionary, emb) + return emb + + @classmethod + def build_encoder(cls, cfg, src_dict, embed_tokens): + return TransformerEncoderBase(cfg, src_dict, embed_tokens) + + @classmethod + def build_decoder(cls, cfg, tgt_dict, embed_tokens): + return TransformerDecoderBase( + cfg, + tgt_dict, + embed_tokens, + no_encoder_attn=cfg.no_cross_attention, + ) + + # TorchScript doesn't support optional arguments with variable length (**kwargs). + # Current workaround is to add union of all arguments in child classes. + def forward( + self, + src_tokens, + src_lengths, + prev_output_tokens, + return_all_hiddens: bool = True, + features_only: bool = False, + alignment_layer: Optional[int] = None, + alignment_heads: Optional[int] = None, + ): + """ + Run the forward pass for an encoder-decoder model. + + Copied from the base class, but without ``**kwargs``, + which are not supported by TorchScript. + """ + encoder_out = self.encoder( + src_tokens, src_lengths=src_lengths, return_all_hiddens=return_all_hiddens + ) + decoder_out = self.decoder( + prev_output_tokens, + encoder_out=encoder_out, + features_only=features_only, + alignment_layer=alignment_layer, + alignment_heads=alignment_heads, + src_lengths=src_lengths, + return_all_hiddens=return_all_hiddens, + ) + return decoder_out + + # Since get_normalized_probs is in the Fairseq Model which is not scriptable, + # I rewrite the get_normalized_probs from Base Class to call the + # helper function in the Base Class. + @torch.jit.export + def get_normalized_probs( + self, + net_output: Tuple[Tensor, Optional[Dict[str, List[Optional[Tensor]]]]], + log_probs: bool, + sample: Optional[Dict[str, Tensor]] = None, + ): + """Get normalized probabilities (or log probs) from a net's output.""" + return self.get_normalized_probs_scriptable(net_output, log_probs, sample) + + +def Embedding(num_embeddings, embedding_dim, padding_idx): + m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx) + nn.init.normal_(m.weight, mean=0, std=embedding_dim ** -0.5) + nn.init.constant_(m.weight[padding_idx], 0) + return m diff --git a/fairseq/models/transformer/transformer_config.py b/fairseq/models/transformer/transformer_config.py new file mode 100644 index 0000000000..2580d20aac --- /dev/null +++ b/fairseq/models/transformer/transformer_config.py @@ -0,0 +1,318 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +import re +from dataclasses import dataclass, field, fields +from typing import List, Optional + +from fairseq import utils +from fairseq.dataclass import FairseqDataclass, ChoiceEnum +from omegaconf import II + +DEFAULT_MAX_SOURCE_POSITIONS = 1024 +DEFAULT_MAX_TARGET_POSITIONS = 1024 + +DEFAULT_MIN_PARAMS_TO_WRAP = int(1e8) + +_NAME_PARSER = r"(decoder|encoder|quant_noise)_(.*)" + + +@dataclass +class EncDecBaseConfig(FairseqDataclass): + embed_path: Optional[str] = field( + default=None, metadata={"help": "path to pre-trained embedding"} + ) + embed_dim: Optional[int] = field( + default=512, metadata={"help": "embedding dimension"} + ) + ffn_embed_dim: int = field( + default=2048, metadata={"help": "embedding dimension for FFN"} + ) + layers: int = field(default=6, metadata={"help": "number of layers"}) + attention_heads: int = field( + default=8, metadata={"help": "number of attention heads"} + ) + normalize_before: bool = field( + default=False, metadata={"help": "apply layernorm before each block"} + ) + learned_pos: bool = field( + default=False, metadata={"help": "use learned positional embeddings"} + ) + # args for "Reducing Transformer Depth on Demand with Structured Dropout" (Fan et al., 2019) + layerdrop: float = field(default=0, metadata={"help": "LayerDrop probability"}) + layers_to_keep: Optional[List[int]] = field( + default=None, metadata={"help": "which layers to *keep* when pruning"} + ) + + +@dataclass +class DecoderConfig(EncDecBaseConfig): + input_dim: int = II("model.decoder.embed_dim") + output_dim: int = field( + default=II("model.decoder.embed_dim"), + metadata={ + "help": "decoder output dimension (extra linear layer if different from decoder embed dim)" + }, + ) + + def __post_init__(self): + # II doesn't work if we are just creating the object outside of hydra so fix that + if self.input_dim == II("model.decoder.embed_dim"): + self.input_dim = self.embed_dim + if self.output_dim == II("model.decoder.embed_dim"): + self.output_dim = self.embed_dim + + +@dataclass +class QuantNoiseConfig(FairseqDataclass): + pq: float = field( + default=0.0, + metadata={"help": "iterative PQ quantization noise at training time"}, + ) + pq_block_size: int = field( + default=8, + metadata={"help": "block size of quantization noise at training time"}, + ) + scalar: float = field( + default=0.0, + metadata={ + "help": "scalar quantization noise and scalar quantization at training time" + }, + ) + + +@dataclass +class TransformerConfig(FairseqDataclass): + activation_fn: ChoiceEnum(utils.get_available_activation_fns()) = field( + default="relu", + metadata={"help": "activation function to use"}, + ) + dropout: float = field(default=0.1, metadata={"help": "dropout probability"}) + attention_dropout: float = field( + default=0.0, metadata={"help": "dropout probability for attention weights"} + ) + activation_dropout: float = field( + default=0.0, + metadata={ + "help": "dropout probability after activation in FFN.", + "alias": "--relu-dropout", + }, + ) + adaptive_input: bool = False + encoder: EncDecBaseConfig = EncDecBaseConfig() + # TODO should really be in the encoder config + max_source_positions: int = field( + default=DEFAULT_MAX_SOURCE_POSITIONS, + metadata={"help": "Maximum input length supported by the encoder"}, + ) + decoder: DecoderConfig = DecoderConfig() + # TODO should really be in the decoder config + max_target_positions: int = field( + default=DEFAULT_MAX_TARGET_POSITIONS, + metadata={"help": "Maximum output length supported by the decoder"}, + ) + share_decoder_input_output_embed: bool = field( + default=False, metadata={"help": "share decoder input and output embeddings"} + ) + share_all_embeddings: bool = field( + default=False, + metadata={ + "help": "share encoder, decoder and output embeddings (requires shared dictionary and embed dim)" + }, + ) + no_token_positional_embeddings: bool = field( + default=False, + metadata={ + "help": "if True, disables positional embeddings (outside self attention)" + }, + ) + adaptive_softmax_cutoff: Optional[List[int]] = field( + default=None, + metadata={ + "help": "list of adaptive softmax cutoff points. Must be used with adaptive_loss criterion" + }, + ) + adaptive_softmax_dropout: float = field( + default=0.0, + metadata={"help": "sets adaptive softmax dropout for the tail projections"}, + ) + adaptive_softmax_factor: float = field( + default=4, metadata={"help": "adaptive input factor"} + ) + layernorm_embedding: bool = field( + default=False, metadata={"help": "add layernorm to embedding"} + ) + tie_adaptive_weights: bool = field( + default=False, + metadata={ + "help": "if set, ties the weights of adaptive softmax and adaptive input" + }, + ) + tie_adaptive_proj: bool = field( + default=False, + metadata={ + "help": "if set, ties the projection weights of adaptive softmax and adaptive input" + }, + ) + no_scale_embedding: bool = field( + default=False, metadata={"help": "if True, dont scale embeddings"} + ) + checkpoint_activations: bool = field( + default=False, + metadata={ + "help": "checkpoint activations at each layer, which saves GPU memory usage at the cost of some additional compute" + }, + ) + offload_activations: bool = field( + default=False, + metadata={ + "help": "checkpoint activations at each layer, then save to gpu. Sets --checkpoint-activations." + }, + ) + # args for "Cross+Self-Attention for Transformer Models" (Peitz et al., 2019) + no_cross_attention: bool = field( + default=False, metadata={"help": "do not perform cross-attention"} + ) + cross_self_attention: bool = field( + default=False, metadata={"help": "perform cross+self-attention"} + ) + # args for Training with Quantization Noise for Extreme Model Compression ({Fan*, Stock*} et al., 2020) + quant_noise: QuantNoiseConfig = field(default=QuantNoiseConfig()) + min_params_to_wrap: int = field( + default=DEFAULT_MIN_PARAMS_TO_WRAP, + metadata={ + "help": "minimum number of params for a layer to be wrapped with FSDP() when " + "training with --ddp-backend=fully_sharded. Smaller values will " + "improve memory efficiency, but may make torch.distributed " + "communication less efficient due to smaller input sizes. This option " + "is set to 0 (i.e., always wrap) when --checkpoint-activations or " + "--offload-activations are passed." + }, + ) + # DEPRECATED field, but some old checkpoints might have it + char_inputs: bool = field( + default=False, metadata={"help": "if set, model takes character ids as input"} + ) + relu_dropout: float = 0.0 + # config for "BASE Layers: Simplifying Training of Large, Sparse Models" + base_layers: Optional[int] = field( + default=0, metadata={"help": "number of BASE layers in total"} + ) + base_sublayers: Optional[int] = field( + default=1, metadata={"help": "number of sublayers in each BASE layer"} + ) + base_shuffle: Optional[int] = field( + default=1, + metadata={"help": "shuffle tokens between workers before computing assignment"}, + ) + + export: bool = field( + default=False, + metadata={"help": "make the layernorm exportable with torchscript."}, + ) + + # copied from transformer_lm but expected in transformer_decoder: + no_decoder_final_norm: bool = field( + default=False, + metadata={"help": "don't add an extra layernorm after the last decoder block"}, + ) + + # We need to make this hierarchical dataclass like the flat namespace + # __getattr__ and __setattr__ here allow backward compatibility + # for subclasses of Transformer(Legacy) that depend on read/write on + # the flat namespace. + + def __getattr__(self, name): + match = re.match(_NAME_PARSER, name) + if match: + sub = getattr(self, match[1]) + return getattr(sub, match[2]) + raise AttributeError(f"invalid argument {name}.") + + def __setattr__(self, name, value): + match = re.match(_NAME_PARSER, name) + if match: + sub = getattr(self, match[1]) + setattr(sub, match[2], value) + else: + super().__setattr__(name, value) + + @staticmethod + def _copy_keys(args, cls, prefix, seen): + """ + copy the prefixed keys (decoder_embed_dim) to the DC fields: decoder.embed_dim + """ + cfg = cls() + for fld in fields(cls): + # for all the fields in the DC, find the fields (e.g. embed_dim) + # in the namespace with the prefix (e.g. decoder) + # and set it on the dc. + args_key = f"{prefix}_{fld.name}" + if hasattr(args, args_key): + seen.add(args_key) + setattr(cfg, fld.name, getattr(args, args_key)) + if hasattr(args, fld.name): + seen.add(fld.name) + setattr(cfg, fld.name, getattr(args, fld.name)) + return cfg + + @classmethod + def from_namespace(cls, args): + if args is None: + return None + if not isinstance(args, cls): + seen = set() + config = cls() + # currently, we can go generically from DC fields to args hierarchically + # but we can't easily deconstruct a flat namespace to a hierarchical + # DC. Mostly because we could have a sub-dc called `decoder-foo` that should not + # go to the sub struct called `decoder`. There are ways to go around this, but let's keep it simple + # for now. + for fld in fields(cls): + # concretelly, the transformer_config know what sub-dc it has, so we go through all the dc fields + # and if it's one that has a sub-dc, we build that sub-dc with `copy_keys()` + if fld.name == "decoder": + if hasattr(args, "decoder"): + # in some cases, the args we receive is already structured (as DictConfigs), so let's just build the correct DC + seen.add("decoder") + config.decoder = DecoderConfig(**args.decoder) + else: + config.decoder = cls._copy_keys( + args, DecoderConfig, "decoder", seen + ) + elif fld.name == "encoder": + # same but for encoder + if hasattr(args, "encoder"): + seen.add("encoder") + config.encoder = EncDecBaseConfig(**args.encoder) + else: + config.encoder = cls._copy_keys( + args, EncDecBaseConfig, "encoder", seen + ) + elif fld.name == "quant_noise": + # same but for quant_noise + if hasattr(args, "quant_noise"): + seen.add("quant_noise") + config.quant_noise = QuantNoiseConfig(**args.quant_noise) + else: + config.quant_noise = cls._copy_keys( + args, QuantNoiseConfig, "quant_noise", seen + ) + elif hasattr(args, fld.name): + # if it's not a structure field, it's just a normal field, copy it over + seen.add(fld.name) + setattr(config, fld.name, getattr(args, fld.name)) + # we got all the fields defined in the dataclass, but + # the argparse namespace might have extra args for two reasons: + # - we are in a legacy class so all the args are not declared in the dataclass. Ideally once everyone has defined a dataclass for their model, we won't need this + # - some places expect args to be there but never define them + args_dict = args._asdict() if hasattr(args, '_asdict') else vars(args) if hasattr(args, '__dict__') else {} # namedtupled doesn't have __dict__ :-/ + for key, value in args_dict.items(): + if key not in seen: + setattr(config, key, value) + return config + else: + return args diff --git a/fairseq/models/transformer/transformer_decoder.py b/fairseq/models/transformer/transformer_decoder.py new file mode 100644 index 0000000000..49e37917cc --- /dev/null +++ b/fairseq/models/transformer/transformer_decoder.py @@ -0,0 +1,482 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math +from typing import Any, Dict, List, Optional + +import torch +import torch.nn as nn +from fairseq import utils +from fairseq.distributed import fsdp_wrap +from fairseq.models import FairseqIncrementalDecoder +from fairseq.models.transformer import TransformerConfig +from fairseq.modules import ( + AdaptiveSoftmax, + BaseLayer, + FairseqDropout, + LayerDropModuleList, + LayerNorm, + PositionalEmbedding, + SinusoidalPositionalEmbedding, +) +from fairseq.modules import transformer_layer +from fairseq.modules.checkpoint_activations import checkpoint_wrapper +from fairseq.modules.quant_noise import quant_noise as apply_quant_noise_ +from torch import Tensor + + +# rewrite name for backward compatibility in `make_generation_fast_` +def module_name_fordropout(module_name: str) -> str: + if module_name == 'TransformerDecoderBase': + return 'TransformerDecoder' + else: + return module_name + + +class TransformerDecoderBase(FairseqIncrementalDecoder): + """ + Transformer decoder consisting of *cfg.decoder.layers* layers. Each layer + is a :class:`TransformerDecoderLayer`. + + Args: + args (argparse.Namespace): parsed command-line arguments + dictionary (~fairseq.data.Dictionary): decoding dictionary + embed_tokens (torch.nn.Embedding): output embedding + no_encoder_attn (bool, optional): whether to attend to encoder outputs + (default: False). + """ + + def __init__( + self, + cfg, + dictionary, + embed_tokens, + no_encoder_attn=False, + output_projection=None, + ): + self.cfg = cfg + super().__init__(dictionary) + self.register_buffer("version", torch.Tensor([3])) + self._future_mask = torch.empty(0) + + self.dropout_module = FairseqDropout( + cfg.dropout, module_name=module_name_fordropout(self.__class__.__name__) + ) + self.decoder_layerdrop = cfg.decoder.layerdrop + self.share_input_output_embed = cfg.share_decoder_input_output_embed + + input_embed_dim = embed_tokens.embedding_dim + embed_dim = cfg.decoder.embed_dim + self.embed_dim = embed_dim + self.output_embed_dim = cfg.decoder.output_dim + + self.padding_idx = embed_tokens.padding_idx + self.max_target_positions = cfg.max_target_positions + + self.embed_tokens = embed_tokens + + self.embed_scale = 1.0 if cfg.no_scale_embedding else math.sqrt(embed_dim) + + if not cfg.adaptive_input and cfg.quant_noise.pq > 0: + self.quant_noise = apply_quant_noise_( + nn.Linear(embed_dim, embed_dim, bias=False), + cfg.quant_noise.pq, + cfg.quant_noise.pq_block_size, + ) + else: + self.quant_noise = None + + self.project_in_dim = ( + Linear(input_embed_dim, embed_dim, bias=False) + if embed_dim != input_embed_dim + else None + ) + self.embed_positions = ( + PositionalEmbedding( + self.max_target_positions, + embed_dim, + self.padding_idx, + learned=cfg.decoder.learned_pos, + ) + if not cfg.no_token_positional_embeddings + else None + ) + if cfg.layernorm_embedding: + self.layernorm_embedding = LayerNorm(embed_dim, export=cfg.export) + else: + self.layernorm_embedding = None + + self.cross_self_attention = cfg.cross_self_attention + + if self.decoder_layerdrop > 0.0: + self.layers = LayerDropModuleList(p=self.decoder_layerdrop) + else: + self.layers = nn.ModuleList([]) + self.layers.extend( + [ + self.build_decoder_layer(cfg, no_encoder_attn) + for _ in range(cfg.decoder.layers) + ] + ) + self.num_layers = len(self.layers) + + if cfg.decoder.normalize_before and not cfg.no_decoder_final_norm: + self.layer_norm = LayerNorm(embed_dim, export=cfg.export) + else: + self.layer_norm = None + + self.project_out_dim = ( + Linear(embed_dim, self.output_embed_dim, bias=False) + if embed_dim != self.output_embed_dim and not cfg.tie_adaptive_weights + else None + ) + + self.adaptive_softmax = None + self.output_projection = output_projection + if self.output_projection is None: + self.build_output_projection(cfg, dictionary, embed_tokens) + + def build_output_projection(self, cfg, dictionary, embed_tokens): + if cfg.adaptive_softmax_cutoff is not None: + self.adaptive_softmax = AdaptiveSoftmax( + len(dictionary), + self.output_embed_dim, + utils.eval_str_list(cfg.adaptive_softmax_cutoff, type=int), + dropout=cfg.adaptive_softmax_dropout, + adaptive_inputs=embed_tokens if cfg.tie_adaptive_weights else None, + factor=cfg.adaptive_softmax_factor, + tie_proj=cfg.tie_adaptive_proj, + ) + elif self.share_input_output_embed: + self.output_projection = nn.Linear( + self.embed_tokens.weight.shape[1], + self.embed_tokens.weight.shape[0], + bias=False, + ) + self.output_projection.weight = self.embed_tokens.weight + else: + self.output_projection = nn.Linear( + self.output_embed_dim, len(dictionary), bias=False + ) + nn.init.normal_( + self.output_projection.weight, mean=0, std=self.output_embed_dim ** -0.5 + ) + num_base_layers = cfg.base_layers + for i in range(num_base_layers): + self.layers.insert( + ((i + 1) * cfg.decoder.layers) // (num_base_layers + 1), + BaseLayer(cfg), + ) + + def build_decoder_layer(self, cfg, no_encoder_attn=False): + layer = transformer_layer.TransformerDecoderLayerBase(cfg, no_encoder_attn) + checkpoint = cfg.checkpoint_activations + if checkpoint: + offload_to_cpu = cfg.offload_activations + layer = checkpoint_wrapper(layer, offload_to_cpu=offload_to_cpu) + # if we are checkpointing, enforce that FSDP always wraps the + # checkpointed layer, regardless of layer size + min_params_to_wrap = cfg.min_params_to_wrap if not checkpoint else 0 + layer = fsdp_wrap(layer, min_num_params=min_params_to_wrap) + return layer + + def forward( + self, + prev_output_tokens, + encoder_out: Optional[Dict[str, List[Tensor]]] = None, + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, + features_only: bool = False, + full_context_alignment: bool = False, + alignment_layer: Optional[int] = None, + alignment_heads: Optional[int] = None, + src_lengths: Optional[Any] = None, + return_all_hiddens: bool = False, + ): + """ + Args: + prev_output_tokens (LongTensor): previous decoder outputs of shape + `(batch, tgt_len)`, for teacher forcing + encoder_out (optional): output from the encoder, used for + encoder-side attention, should be of size T x B x C + incremental_state (dict): dictionary used for storing state during + :ref:`Incremental decoding` + features_only (bool, optional): only return features without + applying output layer (default: False). + full_context_alignment (bool, optional): don't apply + auto-regressive mask to self-attention (default: False). + + Returns: + tuple: + - the decoder's output of shape `(batch, tgt_len, vocab)` + - a dictionary with any model-specific outputs + """ + + x, extra = self.extract_features( + prev_output_tokens, + encoder_out=encoder_out, + incremental_state=incremental_state, + full_context_alignment=full_context_alignment, + alignment_layer=alignment_layer, + alignment_heads=alignment_heads, + ) + + if not features_only: + x = self.output_layer(x) + return x, extra + + def extract_features( + self, + prev_output_tokens, + encoder_out: Optional[Dict[str, List[Tensor]]], + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, + full_context_alignment: bool = False, + alignment_layer: Optional[int] = None, + alignment_heads: Optional[int] = None, + ): + return self.extract_features_scriptable( + prev_output_tokens, + encoder_out, + incremental_state, + full_context_alignment, + alignment_layer, + alignment_heads, + ) + + """ + A scriptable subclass of this class has an extract_features method and calls + super().extract_features, but super() is not supported in torchscript. A copy of + this function is made to be used in the subclass instead. + """ + + def extract_features_scriptable( + self, + prev_output_tokens, + encoder_out: Optional[Dict[str, List[Tensor]]], + incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, + full_context_alignment: bool = False, + alignment_layer: Optional[int] = None, + alignment_heads: Optional[int] = None, + ): + """ + Similar to *forward* but only return features. + + Includes several features from "Jointly Learning to Align and + Translate with Transformer Models" (Garg et al., EMNLP 2019). + + Args: + full_context_alignment (bool, optional): don't apply + auto-regressive mask to self-attention (default: False). + alignment_layer (int, optional): return mean alignment over + heads at this layer (default: last layer). + alignment_heads (int, optional): only average alignment over + this many heads (default: all heads). + + Returns: + tuple: + - the decoder's features of shape `(batch, tgt_len, embed_dim)` + - a dictionary with any model-specific outputs + """ + bs, slen = prev_output_tokens.size() + if alignment_layer is None: + alignment_layer = self.num_layers - 1 + + enc: Optional[Tensor] = None + padding_mask: Optional[Tensor] = None + if encoder_out is not None and len(encoder_out["encoder_out"]) > 0: + enc = encoder_out["encoder_out"][0] + assert ( + enc.size()[1] == bs + ), f"Expected enc.shape == (t, {bs}, c) got {enc.shape}" + if encoder_out is not None and len(encoder_out["encoder_padding_mask"]) > 0: + padding_mask = encoder_out["encoder_padding_mask"][0] + + # embed positions + positions = None + if self.embed_positions is not None: + positions = self.embed_positions( + prev_output_tokens, incremental_state=incremental_state + ) + + if incremental_state is not None: + prev_output_tokens = prev_output_tokens[:, -1:] + if positions is not None: + positions = positions[:, -1:] + + # embed tokens and positions + x = self.embed_scale * self.embed_tokens(prev_output_tokens) + + if self.quant_noise is not None: + x = self.quant_noise(x) + + if self.project_in_dim is not None: + x = self.project_in_dim(x) + + if positions is not None: + x += positions + + if self.layernorm_embedding is not None: + x = self.layernorm_embedding(x) + + x = self.dropout_module(x) + + # B x T x C -> T x B x C + x = x.transpose(0, 1) + + self_attn_padding_mask: Optional[Tensor] = None + if self.cross_self_attention or prev_output_tokens.eq(self.padding_idx).any(): + self_attn_padding_mask = prev_output_tokens.eq(self.padding_idx) + + # decoder layers + attn: Optional[Tensor] = None + inner_states: List[Optional[Tensor]] = [x] + for idx, layer in enumerate(self.layers): + if incremental_state is None and not full_context_alignment: + self_attn_mask = self.buffered_future_mask(x) + else: + self_attn_mask = None + + x, layer_attn, _ = layer( + x, + enc, + padding_mask, + incremental_state, + self_attn_mask=self_attn_mask, + self_attn_padding_mask=self_attn_padding_mask, + need_attn=bool((idx == alignment_layer)), + need_head_weights=bool((idx == alignment_layer)), + ) + inner_states.append(x) + if layer_attn is not None and idx == alignment_layer: + attn = layer_attn.float().to(x) + + if attn is not None: + if alignment_heads is not None: + attn = attn[:alignment_heads] + + # average probabilities over heads + attn = attn.mean(dim=0) + + if self.layer_norm is not None: + x = self.layer_norm(x) + + # T x B x C -> B x T x C + x = x.transpose(0, 1) + + if self.project_out_dim is not None: + x = self.project_out_dim(x) + + return x, {"attn": [attn], "inner_states": inner_states} + + def output_layer(self, features): + """Project features to the vocabulary size.""" + if self.adaptive_softmax is None: + # project back to size of vocabulary + return self.output_projection(features) + else: + return features + + def max_positions(self): + """Maximum output length supported by the decoder.""" + if self.embed_positions is None: + return self.max_target_positions + return min(self.max_target_positions, self.embed_positions.max_positions) + + def buffered_future_mask(self, tensor): + dim = tensor.size(0) + # self._future_mask.device != tensor.device is not working in TorchScript. This is a workaround. + if ( + self._future_mask.size(0) == 0 + or (not self._future_mask.device == tensor.device) + or self._future_mask.size(0) < dim + ): + self._future_mask = torch.triu( + utils.fill_with_neg_inf(torch.zeros([dim, dim])), 1 + ) + self._future_mask = self._future_mask.to(tensor) + return self._future_mask[:dim, :dim] + + def upgrade_state_dict_named(self, state_dict, name): + """Upgrade a (possibly old) state dict for new versions of fairseq.""" + if isinstance(self.embed_positions, SinusoidalPositionalEmbedding): + weights_key = "{}.embed_positions.weights".format(name) + if weights_key in state_dict: + del state_dict[weights_key] + state_dict[ + "{}.embed_positions._float_tensor".format(name) + ] = torch.FloatTensor(1) + + if f"{name}.output_projection.weight" not in state_dict: + if self.share_input_output_embed: + embed_out_key = f"{name}.embed_tokens.weight" + else: + embed_out_key = f"{name}.embed_out" + if embed_out_key in state_dict: + state_dict[f"{name}.output_projection.weight"] = state_dict[ + embed_out_key + ] + if not self.share_input_output_embed: + del state_dict[embed_out_key] + + for i in range(self.num_layers): + # update layer norms + layer_norm_map = { + "0": "self_attn_layer_norm", + "1": "encoder_attn_layer_norm", + "2": "final_layer_norm", + } + for old, new in layer_norm_map.items(): + for m in ("weight", "bias"): + k = "{}.layers.{}.layer_norms.{}.{}".format(name, i, old, m) + if k in state_dict: + state_dict[ + "{}.layers.{}.{}.{}".format(name, i, new, m) + ] = state_dict[k] + del state_dict[k] + + version_key = "{}.version".format(name) + if utils.item(state_dict.get(version_key, torch.Tensor([1]))[0]) <= 2: + # earlier checkpoints did not normalize after the stack of layers + self.layer_norm = None + self.normalize = False + state_dict[version_key] = torch.Tensor([1]) + + return state_dict + + +def Linear(in_features, out_features, bias=True): + m = nn.Linear(in_features, out_features, bias) + nn.init.xavier_uniform_(m.weight) + if bias: + nn.init.constant_(m.bias, 0.0) + return m + + +class TransformerDecoder(TransformerDecoderBase): + def __init__( + self, + args, + dictionary, + embed_tokens, + no_encoder_attn=False, + output_projection=None, + ): + self.args = args + super().__init__( + TransformerConfig.from_namespace(args), + dictionary, + embed_tokens, + no_encoder_attn=no_encoder_attn, + output_projection=output_projection, + ) + + def build_output_projection(self, args, dictionary, embed_tokens): + super().build_output_projection( + TransformerConfig.from_namespace(args), dictionary, embed_tokens + ) + + def build_decoder_layer(self, args, no_encoder_attn=False): + return super().build_decoder_layer( + TransformerConfig.from_namespace(args), no_encoder_attn=no_encoder_attn + ) diff --git a/fairseq/models/transformer/transformer_encoder.py b/fairseq/models/transformer/transformer_encoder.py new file mode 100644 index 0000000000..f007776a6f --- /dev/null +++ b/fairseq/models/transformer/transformer_encoder.py @@ -0,0 +1,341 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math +from typing import Dict, List, Optional + +import torch +import torch.nn as nn +from fairseq import utils +from fairseq.distributed import fsdp_wrap +from fairseq.models import FairseqEncoder +from fairseq.modules import ( + FairseqDropout, + LayerDropModuleList, + LayerNorm, + PositionalEmbedding, + SinusoidalPositionalEmbedding, +) +from fairseq.modules import transformer_layer +from fairseq.modules.checkpoint_activations import checkpoint_wrapper +from fairseq.modules.quant_noise import quant_noise as apply_quant_noise_ +from torch import Tensor +from fairseq.models.transformer import ( + TransformerConfig, +) + + +# rewrite name for backward compatibility in `make_generation_fast_` +def module_name_fordropout(module_name: str) -> str: + if module_name == 'TransformerEncoderBase': + return 'TransformerEncoder' + else: + return module_name + + +class TransformerEncoderBase(FairseqEncoder): + """ + Transformer encoder consisting of *cfg.encoder.layers* layers. Each layer + is a :class:`TransformerEncoderLayer`. + + Args: + args (argparse.Namespace): parsed command-line arguments + dictionary (~fairseq.data.Dictionary): encoding dictionary + embed_tokens (torch.nn.Embedding): input embedding + """ + + def __init__(self, cfg, dictionary, embed_tokens): + self.cfg = cfg + super().__init__(dictionary) + self.register_buffer("version", torch.Tensor([3])) + + self.dropout_module = FairseqDropout( + cfg.dropout, module_name=module_name_fordropout(self.__class__.__name__) + ) + self.encoder_layerdrop = cfg.encoder.layerdrop + + embed_dim = embed_tokens.embedding_dim + self.padding_idx = embed_tokens.padding_idx + self.max_source_positions = cfg.max_source_positions + + self.embed_tokens = embed_tokens + + self.embed_scale = 1.0 if cfg.no_scale_embedding else math.sqrt(embed_dim) + + self.embed_positions = ( + PositionalEmbedding( + cfg.max_source_positions, + embed_dim, + self.padding_idx, + learned=cfg.encoder.learned_pos, + ) + if not cfg.no_token_positional_embeddings + else None + ) + if cfg.layernorm_embedding: + self.layernorm_embedding = LayerNorm(embed_dim, export=cfg.export) + else: + self.layernorm_embedding = None + + if not cfg.adaptive_input and cfg.quant_noise.pq > 0: + self.quant_noise = apply_quant_noise_( + nn.Linear(embed_dim, embed_dim, bias=False), + cfg.quant_noise.pq, + cfg.quant_noise.pq_block_size, + ) + else: + self.quant_noise = None + + if self.encoder_layerdrop > 0.0: + self.layers = LayerDropModuleList(p=self.encoder_layerdrop) + else: + self.layers = nn.ModuleList([]) + self.layers.extend( + [self.build_encoder_layer(cfg) for i in range(cfg.encoder.layers)] + ) + self.num_layers = len(self.layers) + + if cfg.encoder.normalize_before: + self.layer_norm = LayerNorm(embed_dim, export=cfg.export) + else: + self.layer_norm = None + + def build_encoder_layer(self, cfg): + layer = transformer_layer.TransformerEncoderLayerBase(cfg) + checkpoint = cfg.checkpoint_activations + if checkpoint: + offload_to_cpu = cfg.offload_activations + layer = checkpoint_wrapper(layer, offload_to_cpu=offload_to_cpu) + # if we are checkpointing, enforce that FSDP always wraps the + # checkpointed layer, regardless of layer size + min_params_to_wrap = cfg.min_params_to_wrap if not checkpoint else 0 + layer = fsdp_wrap(layer, min_num_params=min_params_to_wrap) + return layer + + def forward_embedding( + self, src_tokens, token_embedding: Optional[torch.Tensor] = None + ): + # embed tokens and positions + if token_embedding is None: + token_embedding = self.embed_tokens(src_tokens) + x = embed = self.embed_scale * token_embedding + if self.embed_positions is not None: + x = embed + self.embed_positions(src_tokens) + if self.layernorm_embedding is not None: + x = self.layernorm_embedding(x) + x = self.dropout_module(x) + if self.quant_noise is not None: + x = self.quant_noise(x) + return x, embed + + def forward( + self, + src_tokens, + src_lengths: Optional[torch.Tensor] = None, + return_all_hiddens: bool = False, + token_embeddings: Optional[torch.Tensor] = None, + ): + """ + Args: + src_tokens (LongTensor): tokens in the source language of shape + `(batch, src_len)` + src_lengths (torch.LongTensor): lengths of each source sentence of + shape `(batch)` + return_all_hiddens (bool, optional): also return all of the + intermediate hidden states (default: False). + token_embeddings (torch.Tensor, optional): precomputed embeddings + default `None` will recompute embeddings + + Returns: + dict: + - **encoder_out** (Tensor): the last encoder layer's output of + shape `(src_len, batch, embed_dim)` + - **encoder_padding_mask** (ByteTensor): the positions of + padding elements of shape `(batch, src_len)` + - **encoder_embedding** (Tensor): the (scaled) embedding lookup + of shape `(batch, src_len, embed_dim)` + - **encoder_states** (List[Tensor]): all intermediate + hidden states of shape `(src_len, batch, embed_dim)`. + Only populated if *return_all_hiddens* is True. + """ + return self.forward_scriptable( + src_tokens, src_lengths, return_all_hiddens, token_embeddings + ) + + # TorchScript doesn't support super() method so that the scriptable Subclass + # can't access the base class model in Torchscript. + # Current workaround is to add a helper function with different name and + # call the helper function from scriptable Subclass. + def forward_scriptable( + self, + src_tokens, + src_lengths: Optional[torch.Tensor] = None, + return_all_hiddens: bool = False, + token_embeddings: Optional[torch.Tensor] = None, + ): + """ + Args: + src_tokens (LongTensor): tokens in the source language of shape + `(batch, src_len)` + src_lengths (torch.LongTensor): lengths of each source sentence of + shape `(batch)` + return_all_hiddens (bool, optional): also return all of the + intermediate hidden states (default: False). + token_embeddings (torch.Tensor, optional): precomputed embeddings + default `None` will recompute embeddings + + Returns: + dict: + - **encoder_out** (Tensor): the last encoder layer's output of + shape `(src_len, batch, embed_dim)` + - **encoder_padding_mask** (ByteTensor): the positions of + padding elements of shape `(batch, src_len)` + - **encoder_embedding** (Tensor): the (scaled) embedding lookup + of shape `(batch, src_len, embed_dim)` + - **encoder_states** (List[Tensor]): all intermediate + hidden states of shape `(src_len, batch, embed_dim)`. + Only populated if *return_all_hiddens* is True. + """ + # compute padding mask + encoder_padding_mask = src_tokens.eq(self.padding_idx) + has_pads = src_tokens.device.type == "xla" or encoder_padding_mask.any() + + x, encoder_embedding = self.forward_embedding(src_tokens, token_embeddings) + + # account for padding while computing the representation + if has_pads: + x = x * (1 - encoder_padding_mask.unsqueeze(-1).type_as(x)) + + # B x T x C -> T x B x C + x = x.transpose(0, 1) + + encoder_states = [] + + if return_all_hiddens: + encoder_states.append(x) + + # encoder layers + for layer in self.layers: + x = layer( + x, encoder_padding_mask=encoder_padding_mask if has_pads else None + ) + if return_all_hiddens: + assert encoder_states is not None + encoder_states.append(x) + + if self.layer_norm is not None: + x = self.layer_norm(x) + + # The Pytorch Mobile lite interpreter does not supports returning NamedTuple in + # `forward` so we use a dictionary instead. + # TorchScript does not support mixed values so the values are all lists. + # The empty list is equivalent to None. + src_lengths = src_tokens.ne(self.padding_idx).sum(dim=1, dtype=torch.int32).reshape(-1, 1).contiguous() + return { + "encoder_out": [x], # T x B x C + "encoder_padding_mask": [encoder_padding_mask], # B x T + "encoder_embedding": [encoder_embedding], # B x T x C + "encoder_states": encoder_states, # List[T x B x C] + "src_tokens": [], + "src_lengths": [src_lengths], + } + + @torch.jit.export + def reorder_encoder_out(self, encoder_out: Dict[str, List[Tensor]], new_order): + """ + Reorder encoder output according to *new_order*. + + Args: + encoder_out: output from the ``forward()`` method + new_order (LongTensor): desired order + + Returns: + *encoder_out* rearranged according to *new_order* + """ + if len(encoder_out["encoder_out"]) == 0: + new_encoder_out = [] + else: + new_encoder_out = [encoder_out["encoder_out"][0].index_select(1, new_order)] + if len(encoder_out["encoder_padding_mask"]) == 0: + new_encoder_padding_mask = [] + else: + new_encoder_padding_mask = [ + encoder_out["encoder_padding_mask"][0].index_select(0, new_order) + ] + if len(encoder_out["encoder_embedding"]) == 0: + new_encoder_embedding = [] + else: + new_encoder_embedding = [ + encoder_out["encoder_embedding"][0].index_select(0, new_order) + ] + + if len(encoder_out["src_tokens"]) == 0: + src_tokens = [] + else: + src_tokens = [(encoder_out["src_tokens"][0]).index_select(0, new_order)] + + if len(encoder_out["src_lengths"]) == 0: + src_lengths = [] + else: + src_lengths = [(encoder_out["src_lengths"][0]).index_select(0, new_order)] + + encoder_states = encoder_out["encoder_states"] + if len(encoder_states) > 0: + for idx, state in enumerate(encoder_states): + encoder_states[idx] = state.index_select(1, new_order) + + return { + "encoder_out": new_encoder_out, # T x B x C + "encoder_padding_mask": new_encoder_padding_mask, # B x T + "encoder_embedding": new_encoder_embedding, # B x T x C + "encoder_states": encoder_states, # List[T x B x C] + "src_tokens": src_tokens, # B x T + "src_lengths": src_lengths, # B x 1 + } + + def max_positions(self): + """Maximum input length supported by the encoder.""" + if self.embed_positions is None: + return self.max_source_positions + return min(self.max_source_positions, self.embed_positions.max_positions) + + def upgrade_state_dict_named(self, state_dict, name): + """Upgrade a (possibly old) state dict for new versions of fairseq.""" + if isinstance(self.embed_positions, SinusoidalPositionalEmbedding): + weights_key = "{}.embed_positions.weights".format(name) + if weights_key in state_dict: + print("deleting {0}".format(weights_key)) + del state_dict[weights_key] + state_dict[ + "{}.embed_positions._float_tensor".format(name) + ] = torch.FloatTensor(1) + for i in range(self.num_layers): + # update layer norms + self.layers[i].upgrade_state_dict_named( + state_dict, "{}.layers.{}".format(name, i) + ) + + version_key = "{}.version".format(name) + if utils.item(state_dict.get(version_key, torch.Tensor([1]))[0]) < 2: + # earlier checkpoints did not normalize after the stack of layers + self.layer_norm = None + self.normalize = False + state_dict[version_key] = torch.Tensor([1]) + return state_dict + + +class TransformerEncoder(TransformerEncoderBase): + def __init__(self, args, dictionary, embed_tokens): + self.args = args + super().__init__( + TransformerConfig.from_namespace(args), + dictionary, + embed_tokens, + ) + + def build_encoder_layer(self, args): + return super().build_encoder_layer( + TransformerConfig.from_namespace(args), + ) diff --git a/fairseq/models/transformer/transformer_legacy.py b/fairseq/models/transformer/transformer_legacy.py new file mode 100644 index 0000000000..af9646740a --- /dev/null +++ b/fairseq/models/transformer/transformer_legacy.py @@ -0,0 +1,275 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from fairseq.dataclass.utils import gen_parser_from_dataclass +from fairseq.models import ( + register_model, + register_model_architecture, +) +from fairseq.models.transformer.transformer_config import ( + TransformerConfig, + DEFAULT_MAX_SOURCE_POSITIONS, + DEFAULT_MAX_TARGET_POSITIONS, + DEFAULT_MIN_PARAMS_TO_WRAP, +) +from fairseq.models.transformer.transformer_base import ( + TransformerModelBase, +) + + +@register_model("transformer") +class TransformerModel(TransformerModelBase): + """ + This is the legacy implementation of the transformer model that + uses argparse for configuration. + """ + + @classmethod + def hub_models(cls): + # fmt: off + + def moses_subword(path): + return { + 'path': path, + 'tokenizer': 'moses', + 'bpe': 'subword_nmt', + } + + def moses_fastbpe(path): + return { + 'path': path, + 'tokenizer': 'moses', + 'bpe': 'fastbpe', + } + + def spm(path): + return { + 'path': path, + 'bpe': 'sentencepiece', + 'tokenizer': 'space', + } + + return { + 'transformer.wmt14.en-fr': moses_subword('https://dl.fbaipublicfiles.com/fairseq/models/wmt14.en-fr.joined-dict.transformer.tar.bz2'), + 'transformer.wmt16.en-de': 'https://dl.fbaipublicfiles.com/fairseq/models/wmt16.en-de.joined-dict.transformer.tar.bz2', + 'transformer.wmt18.en-de': moses_subword('https://dl.fbaipublicfiles.com/fairseq/models/wmt18.en-de.ensemble.tar.gz'), + 'transformer.wmt19.en-de': moses_fastbpe('https://dl.fbaipublicfiles.com/fairseq/models/wmt19.en-de.joined-dict.ensemble.tar.gz'), + 'transformer.wmt19.en-ru': moses_fastbpe('https://dl.fbaipublicfiles.com/fairseq/models/wmt19.en-ru.ensemble.tar.gz'), + 'transformer.wmt19.de-en': moses_fastbpe('https://dl.fbaipublicfiles.com/fairseq/models/wmt19.de-en.joined-dict.ensemble.tar.gz'), + 'transformer.wmt19.ru-en': moses_fastbpe('https://dl.fbaipublicfiles.com/fairseq/models/wmt19.ru-en.ensemble.tar.gz'), + 'transformer.wmt19.en-de.single_model': moses_fastbpe('https://dl.fbaipublicfiles.com/fairseq/models/wmt19.en-de.joined-dict.single_model.tar.gz'), + 'transformer.wmt19.en-ru.single_model': moses_fastbpe('https://dl.fbaipublicfiles.com/fairseq/models/wmt19.en-ru.single_model.tar.gz'), + 'transformer.wmt19.de-en.single_model': moses_fastbpe('https://dl.fbaipublicfiles.com/fairseq/models/wmt19.de-en.joined-dict.single_model.tar.gz'), + 'transformer.wmt19.ru-en.single_model': moses_fastbpe('https://dl.fbaipublicfiles.com/fairseq/models/wmt19.ru-en.single_model.tar.gz'), + 'transformer.wmt20.en-ta': spm('https://dl.fbaipublicfiles.com/fairseq/models/wmt20.en-ta.single.tar.gz'), + 'transformer.wmt20.en-iu.news': spm('https://dl.fbaipublicfiles.com/fairseq/models/wmt20.en-iu.news.single.tar.gz'), + 'transformer.wmt20.en-iu.nh': spm('https://dl.fbaipublicfiles.com/fairseq/models/wmt20.en-iu.nh.single.tar.gz'), + 'transformer.wmt20.ta-en': spm('https://dl.fbaipublicfiles.com/fairseq/models/wmt20.ta-en.single.tar.gz'), + 'transformer.wmt20.iu-en.news': spm('https://dl.fbaipublicfiles.com/fairseq/models/wmt20.iu-en.news.single.tar.gz'), + 'transformer.wmt20.iu-en.nh': spm('https://dl.fbaipublicfiles.com/fairseq/models/wmt20.iu-en.nh.single.tar.gz'), + 'transformer.flores101.mm100.615M': spm('https://dl.fbaipublicfiles.com/flores101/pretrained_models/flores101_mm100_615M.tar.gz'), + 'transformer.flores101.mm100.175M': spm('https://dl.fbaipublicfiles.com/flores101/pretrained_models/flores101_mm100_175M.tar.gz'), + } + # fmt: on + + def __init__(self, args, encoder, decoder): + cfg = TransformerConfig.from_namespace(args) + super().__init__(cfg, encoder, decoder) + self.args = args + + @classmethod + def add_args(cls, parser): + """Add model-specific arguments to the parser.""" + # we want to build the args recursively in this case. + # do not set defaults so that settings defaults from various architectures still works + gen_parser_from_dataclass( + parser, TransformerConfig(), delete_default=True, with_prefix="" + ) + + @classmethod + def build_model(cls, args, task): + """Build a new model instance.""" + + # make sure all arguments are present in older models + base_architecture(args) + + if args.encoder_layers_to_keep: + args.encoder_layers = len(args.encoder_layers_to_keep.split(",")) + if args.decoder_layers_to_keep: + args.decoder_layers = len(args.decoder_layers_to_keep.split(",")) + + if getattr(args, "max_source_positions", None) is None: + args.max_source_positions = DEFAULT_MAX_SOURCE_POSITIONS + if getattr(args, "max_target_positions", None) is None: + args.max_target_positions = DEFAULT_MAX_TARGET_POSITIONS + + src_dict, tgt_dict = task.source_dictionary, task.target_dictionary + + if args.share_all_embeddings: + if src_dict != tgt_dict: + raise ValueError("--share-all-embeddings requires a joined dictionary") + if args.encoder_embed_dim != args.decoder_embed_dim: + raise ValueError( + "--share-all-embeddings requires --encoder-embed-dim to match --decoder-embed-dim" + ) + if args.decoder_embed_path and ( + args.decoder_embed_path != args.encoder_embed_path + ): + raise ValueError( + "--share-all-embeddings not compatible with --decoder-embed-path" + ) + args.share_decoder_input_output_embed = True + + if getattr(args, "offload_activations", False): + args.checkpoint_activations = True # offloading implies checkpointing + + if not args.share_all_embeddings: + args.min_params_to_wrap = getattr( + args, "min_params_to_wrap", DEFAULT_MIN_PARAMS_TO_WRAP + ) + cfg = TransformerConfig.from_namespace(args) + return super().build_model(cfg, task) + + @classmethod + def build_embedding(cls, args, dictionary, embed_dim, path=None): + return super().build_embedding( + TransformerConfig.from_namespace(args), dictionary, embed_dim, path + ) + + @classmethod + def build_encoder(cls, args, src_dict, embed_tokens): + return super().build_encoder( + TransformerConfig.from_namespace(args), src_dict, embed_tokens + ) + + @classmethod + def build_decoder(cls, args, tgt_dict, embed_tokens): + return super().build_decoder( + TransformerConfig.from_namespace(args), tgt_dict, embed_tokens + ) + + +# architectures + + +@register_model_architecture("transformer", "transformer_tiny") +def tiny_architecture(args): + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 64) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 64) + args.encoder_layers = getattr(args, "encoder_layers", 2) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 2) + args.decoder_layers = getattr(args, "decoder_layers", 2) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 2) + return base_architecture(args) + + +@register_model_architecture("transformer", "transformer") +def base_architecture(args): + args.encoder_embed_path = getattr(args, "encoder_embed_path", None) + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048) + args.encoder_layers = getattr(args, "encoder_layers", 6) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8) + args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False) + args.encoder_learned_pos = getattr(args, "encoder_learned_pos", False) + args.decoder_embed_path = getattr(args, "decoder_embed_path", None) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim) + args.decoder_ffn_embed_dim = getattr( + args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim + ) + args.decoder_layers = getattr(args, "decoder_layers", 6) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8) + args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False) + args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False) + args.attention_dropout = getattr(args, "attention_dropout", 0.0) + args.activation_dropout = getattr(args, "activation_dropout", 0.0) + args.activation_fn = getattr(args, "activation_fn", "relu") + args.dropout = getattr(args, "dropout", 0.1) + args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None) + args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0) + args.share_decoder_input_output_embed = getattr( + args, "share_decoder_input_output_embed", False + ) + args.share_all_embeddings = getattr(args, "share_all_embeddings", False) + args.no_token_positional_embeddings = getattr( + args, "no_token_positional_embeddings", False + ) + args.adaptive_input = getattr(args, "adaptive_input", False) + args.no_cross_attention = getattr(args, "no_cross_attention", False) + args.cross_self_attention = getattr(args, "cross_self_attention", False) + + args.decoder_output_dim = getattr( + args, "decoder_output_dim", args.decoder_embed_dim + ) + args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim) + + args.no_scale_embedding = getattr(args, "no_scale_embedding", False) + args.layernorm_embedding = getattr(args, "layernorm_embedding", False) + args.tie_adaptive_weights = getattr(args, "tie_adaptive_weights", False) + args.checkpoint_activations = getattr(args, "checkpoint_activations", False) + args.offload_activations = getattr(args, "offload_activations", False) + if args.offload_activations: + args.checkpoint_activations = True + args.encoder_layers_to_keep = getattr(args, "encoder_layers_to_keep", None) + args.decoder_layers_to_keep = getattr(args, "decoder_layers_to_keep", None) + args.encoder_layerdrop = getattr(args, "encoder_layerdrop", 0) + args.decoder_layerdrop = getattr(args, "decoder_layerdrop", 0) + args.quant_noise_pq = getattr(args, "quant_noise_pq", 0) + args.quant_noise_pq_block_size = getattr(args, "quant_noise_pq_block_size", 8) + args.quant_noise_scalar = getattr(args, "quant_noise_scalar", 0) + + +@register_model_architecture("transformer", "transformer_iwslt_de_en") +def transformer_iwslt_de_en(args): + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 1024) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 4) + args.encoder_layers = getattr(args, "encoder_layers", 6) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512) + args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 1024) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 4) + args.decoder_layers = getattr(args, "decoder_layers", 6) + base_architecture(args) + + +@register_model_architecture("transformer", "transformer_wmt_en_de") +def transformer_wmt_en_de(args): + base_architecture(args) + + +# parameters used in the "Attention Is All You Need" paper (Vaswani et al., 2017) +@register_model_architecture("transformer", "transformer_vaswani_wmt_en_de_big") +def transformer_vaswani_wmt_en_de_big(args): + args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024) + args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4096) + args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 16) + args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 1024) + args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 4096) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 16) + args.dropout = getattr(args, "dropout", 0.3) + base_architecture(args) + + +@register_model_architecture("transformer", "transformer_vaswani_wmt_en_fr_big") +def transformer_vaswani_wmt_en_fr_big(args): + args.dropout = getattr(args, "dropout", 0.1) + transformer_vaswani_wmt_en_de_big(args) + + +@register_model_architecture("transformer", "transformer_wmt_en_de_big") +def transformer_wmt_en_de_big(args): + args.attention_dropout = getattr(args, "attention_dropout", 0.1) + transformer_vaswani_wmt_en_de_big(args) + + +# default parameters used in tensor2tensor implementation +@register_model_architecture("transformer", "transformer_wmt_en_de_big_t2t") +def transformer_wmt_en_de_big_t2t(args): + args.encoder_normalize_before = getattr(args, "encoder_normalize_before", True) + args.decoder_normalize_before = getattr(args, "decoder_normalize_before", True) + args.attention_dropout = getattr(args, "attention_dropout", 0.1) + args.activation_dropout = getattr(args, "activation_dropout", 0.1) + transformer_vaswani_wmt_en_de_big(args) diff --git a/fairseq/models/transformer_align.py b/fairseq/models/transformer_align.py index 4195ff3982..eaf585bd10 100644 --- a/fairseq/models/transformer_align.py +++ b/fairseq/models/transformer_align.py @@ -5,9 +5,9 @@ from fairseq.models import register_model, register_model_architecture from fairseq.models.transformer import ( + TransformerModel, base_architecture, transformer_wmt_en_de_big, - TransformerModel, ) @@ -32,7 +32,7 @@ def add_args(parser): help='Number of cross attention heads per layer to supervised with alignments') parser.add_argument('--alignment-layer', type=int, metavar='D', help='Layer number which has to be supervised. 0 corresponding to the bottommost layer.') - parser.add_argument('--full-context-alignment', type=bool, metavar='D', + parser.add_argument('--full-context-alignment', action='store_true', help='Whether or not alignment is supervised conditioned on the full target context.') # fmt: on diff --git a/fairseq/models/transformer_from_pretrained_xlm.py b/fairseq/models/transformer_from_pretrained_xlm.py index bd03c8450f..236d9942e1 100644 --- a/fairseq/models/transformer_from_pretrained_xlm.py +++ b/fairseq/models/transformer_from_pretrained_xlm.py @@ -19,7 +19,6 @@ @register_model("transformer_from_pretrained_xlm") class TransformerFromPretrainedXLMModel(TransformerModel): - @staticmethod def add_args(parser): """Add model-specific arguments to the parser.""" @@ -96,25 +95,24 @@ def upgrade_state_dict_with_xlm_weights( for search_key in ["embed_tokens", "embed_positions", "layers"]: if search_key in key: - subkey = key[key.find(search_key):] + subkey = key[key.find(search_key) :] assert subkey in state_dict, ( "{} Transformer encoder / decoder " "state_dict does not contain {}. Cannot " "load {} from pretrained XLM checkpoint " "{} into Transformer.".format( - str(state_dict.keys()), - subkey, key, pretrained_xlm_checkpoint) + str(state_dict.keys()), subkey, key, pretrained_xlm_checkpoint ) + ) state_dict[subkey] = xlm_state_dict[key] return state_dict class TransformerEncoderFromPretrainedXLM(TransformerEncoder): - def __init__(self, args, dictionary, embed_tokens): super().__init__(args, dictionary, embed_tokens) - if getattr(args, 'init_decoder_only', False): + if getattr(args, "init_decoder_only", False): # Don't load XLM weights for encoder if --init-decoder-only return @@ -130,10 +128,9 @@ def __init__(self, args, dictionary, embed_tokens): class TransformerDecoderFromPretrainedXLM(TransformerDecoder): - def __init__(self, args, dictionary, embed_tokens, no_encoder_attn=False): super().__init__(args, dictionary, embed_tokens, no_encoder_attn) - if getattr(args, 'init_encoder_only', False): + if getattr(args, "init_encoder_only", False): # Don't load XLM weights for decoder if --init-encoder-only return assert hasattr(args, "pretrained_xlm_checkpoint"), ( diff --git a/fairseq/models/transformer_lm.py b/fairseq/models/transformer_lm.py index 016e8c0743..a546776912 100644 --- a/fairseq/models/transformer_lm.py +++ b/fairseq/models/transformer_lm.py @@ -3,165 +3,287 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. + +from dataclasses import dataclass, field +from typing import Optional + from fairseq import options, utils +from fairseq.dataclass import ChoiceEnum, FairseqDataclass from fairseq.models import ( FairseqLanguageModel, register_model, register_model_architecture, ) from fairseq.models.transformer import ( - Embedding, - TransformerDecoder, -) -from fairseq.modules import ( - AdaptiveInput, - CharacterTokenEmbedder, + DEFAULT_MIN_PARAMS_TO_WRAP, Embedding, TransformerDecoder ) +from fairseq.modules import AdaptiveInput, CharacterTokenEmbedder +from omegaconf import II + DEFAULT_MAX_TARGET_POSITIONS = 1024 -@register_model('transformer_lm') +@dataclass +class TransformerLanguageModelConfig(FairseqDataclass): + activation_fn: ChoiceEnum(utils.get_available_activation_fns()) = field( + default="relu", metadata={"help": "activation function to use"} + ) + dropout: float = field(default=0.1, metadata={"help": "dropout probability"}) + attention_dropout: float = field( + default=0.0, metadata={"help": "dropout probability for attention weights"} + ) + activation_dropout: float = field( + default=0.0, metadata={"help": "dropout probability after activation in FFN."} + ) + relu_dropout: float = field( + default=0.0, metadata={"help": "dropout probability after activation in FFN."} + ) + decoder_embed_dim: int = field( + default=512, metadata={"help": "decoder embedding dimension"} + ) + decoder_output_dim: int = field( + default=512, metadata={"help": "decoder output dimension"} + ) + decoder_input_dim: int = field( + default=512, metadata={"help": "decoder input dimension"} + ) + decoder_ffn_embed_dim: int = field( + default=2048, metadata={"help": "decoder embedding dimension for FFN"} + ) + decoder_layers: int = field(default=6, metadata={"help": "num decoder layers"}) + decoder_attention_heads: int = field( + default=8, metadata={"help": "num decoder attention heads"} + ) + decoder_normalize_before: bool = field( + default=False, metadata={"help": "apply layernorm before each decoder block"} + ) + no_decoder_final_norm: bool = field( + default=False, + metadata={"help": "don't add an extra layernorm after the last decoder block"}, + ) + adaptive_softmax_cutoff: Optional[str] = field( + default=None, + metadata={ + "help": "comma separated list of adaptive softmax cutoff points. " + "Must be used with adaptive_loss criterion" + }, + ) + adaptive_softmax_dropout: float = field( + default=0, + metadata={"help": "sets adaptive softmax dropout for the tail projections"}, + ) + adaptive_softmax_factor: float = field( + default=4, metadata={"help": "adaptive input factor"} + ) + no_token_positional_embeddings: bool = field( + default=False, + metadata={ + "help": "if set, disables positional embeddings (outside self attention)" + }, + ) + share_decoder_input_output_embed: bool = field( + default=False, metadata={"help": "share decoder input and output embeddings"} + ) + character_embeddings: bool = field( + default=False, + metadata={ + "help": "if set, uses character embedding convolutions to produce token embeddings" + }, + ) + character_filters: str = field( + default="[(1, 64), (2, 128), (3, 192), (4, 256), (5, 256), (6, 256), (7, 256)]", + metadata={"help": "size of character embeddings"}, + ) + character_embedding_dim: int = field( + default=4, metadata={"help": "size of character embeddings"} + ) + char_embedder_highway_layers: int = field( + default=2, + metadata={"help": "number of highway layers for character token embeddder"}, + ) + adaptive_input: bool = field( + default=False, metadata={"help": "if set, uses adaptive input"} + ) + adaptive_input_factor: float = field( + default=4, metadata={"help": "adaptive input factor"} + ) + adaptive_input_cutoff: Optional[str] = field( + default=None, + metadata={"help": "comma separated list of adaptive input cutoff points."}, + ) + tie_adaptive_weights: bool = field( + default=False, + metadata={ + "help": "if set, ties the weights of adaptive softmax and adaptive input" + }, + ) + tie_adaptive_proj: bool = field( + default=False, + metadata={ + "help": "if set, ties the projection weights of adaptive softmax and adaptive input" + }, + ) + decoder_learned_pos: bool = field( + default=False, + metadata={"help": "use learned positional embeddings in the decoder"}, + ) + layernorm_embedding: bool = field( + default=False, metadata={"help": "add layernorm to embedding"} + ) + no_scale_embedding: bool = field( + default=False, metadata={"help": "if True, dont scale embeddings"} + ) + checkpoint_activations: bool = field( + default=False, metadata={"help": "checkpoint activations at each layer"} + ) + offload_activations: bool = field( + default=False, + metadata={"help": "move checkpointed activations to CPU after they are used."}, + ) + # config for "Reducing Transformer Depth on Demand with Structured Dropout" (Fan et al., 2019) + decoder_layerdrop: float = field( + default=0.0, metadata={"help": "LayerDrop probability for decoder"} + ) + decoder_layers_to_keep: Optional[str] = field( + default=None, + metadata={ + "help": "which layers to *keep* when pruning as a comma-separated list" + }, + ) + # config for Training with Quantization Noise for Extreme Model Compression ({Fan*, Stock*} et al., 2020) + quant_noise_pq: float = field( + default=0.0, + metadata={"help": "iterative PQ quantization noise at training time"}, + ) + quant_noise_pq_block_size: int = field( + default=8, + metadata={"help": "block size of quantization noise at training time"}, + ) + quant_noise_scalar: float = field( + default=0.0, + metadata={ + "help": "scalar quantization noise and scalar quantization at training time" + }, + ) + # config for Fully Sharded Data Parallel (FSDP) training + min_params_to_wrap: int = field( + default=DEFAULT_MIN_PARAMS_TO_WRAP, + metadata={ + "help": ( + "minimum number of params for a layer to be wrapped with FSDP() when " + "training with --ddp-backend=fully_sharded. Smaller values will " + "improve memory efficiency, but may make torch.distributed " + "communication less efficient due to smaller input sizes. This option " + "is set to 0 (i.e., always wrap) when --checkpoint-activations or " + "--offload-activations are passed." + ) + } + ) + # config for "BASE Layers: Simplifying Training of Large, Sparse Models" + base_layers: Optional[int] = field( + default=0, metadata={"help": "number of BASE layers in total"} + ) + base_sublayers: Optional[int] = field( + default=1, metadata={"help": "number of sublayers in each BASE layer"} + ) + base_shuffle: Optional[int] = field( + default=1, metadata={"help": "shuffle tokens between workers before computing assignment"} + ) + # options from other parts of the config + add_bos_token: bool = II("task.add_bos_token") + tokens_per_sample: int = II("task.tokens_per_sample") + max_target_positions: Optional[int] = II("task.max_target_positions") + tpu: bool = II("common.tpu") + + +@register_model("transformer_lm", dataclass=TransformerLanguageModelConfig) class TransformerLanguageModel(FairseqLanguageModel): - @classmethod def hub_models(cls): - def moses_fastbpe(path): - return { - 'path': path, - 'tokenizer': 'moses', - 'bpe': 'fastbpe', - } + return {"path": path, "tokenizer": "moses", "bpe": "fastbpe"} + + def spm(path): + return {"path": path, "tokenizer": "space", "bpe": "sentencepiece"} return { - 'transformer_lm.gbw.adaptive_huge': 'https://dl.fbaipublicfiles.com/fairseq/models/lm/adaptive_lm_gbw_huge.tar.bz2', - 'transformer_lm.wiki103.adaptive': 'https://dl.fbaipublicfiles.com/fairseq/models/lm/adaptive_lm_wiki103.tar.bz2', - 'transformer_lm.wmt19.en': moses_fastbpe('https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt19.en.tar.bz2'), - 'transformer_lm.wmt19.de': moses_fastbpe('https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt19.de.tar.bz2'), - 'transformer_lm.wmt19.ru': moses_fastbpe('https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt19.ru.tar.bz2'), + "transformer_lm.gbw.adaptive_huge": "https://dl.fbaipublicfiles.com/fairseq/models/lm/adaptive_lm_gbw_huge.tar.bz2", + "transformer_lm.wiki103.adaptive": "https://dl.fbaipublicfiles.com/fairseq/models/lm/adaptive_lm_wiki103.v2.tar.bz2", + "transformer_lm.wmt19.en": moses_fastbpe( + "https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt19.en.tar.bz2" + ), + "transformer_lm.wmt19.de": moses_fastbpe( + "https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt19.de.tar.bz2" + ), + "transformer_lm.wmt19.ru": moses_fastbpe( + "https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt19.ru.tar.bz2" + ), + "transformer_lm.wmt20.en": spm( + "https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt20.en.tar.gz" + ), + "transformer_lm.wmt20.ta": spm( + "https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt20.ta.tar.gz" + ), + "transformer_lm.wmt20.iu.news": spm( + "https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt20.iu.news.tar.gz" + ), + "transformer_lm.wmt20.iu.nh": spm( + "https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt20.iu.nh.tar.gz" + ), } def __init__(self, decoder): super().__init__(decoder) - @staticmethod - def add_args(parser): - """Add model-specific arguments to the parser.""" - # fmt: off - parser.add_argument('--activation-fn', - choices=utils.get_available_activation_fns(), - help='activation function to use') - parser.add_argument('--dropout', type=float, metavar='D', - help='dropout probability') - parser.add_argument('--attention-dropout', type=float, metavar='D', - help='dropout probability for attention weights') - parser.add_argument('--activation-dropout', '--relu-dropout', type=float, metavar='D', - help='dropout probability after activation in FFN.') - parser.add_argument('--decoder-embed-dim', type=int, metavar='N', - help='decoder embedding dimension') - parser.add_argument('--decoder-output-dim', type=int, metavar='N', - help='decoder output dimension') - parser.add_argument('--decoder-input-dim', type=int, metavar='N', - help='decoder input dimension') - parser.add_argument('--decoder-ffn-embed-dim', type=int, metavar='N', - help='decoder embedding dimension for FFN') - parser.add_argument('--decoder-layers', type=int, metavar='N', - help='num decoder layers') - parser.add_argument('--decoder-attention-heads', type=int, metavar='N', - help='num decoder attention heads') - parser.add_argument('--decoder-normalize-before', action='store_true', - help='apply layernorm before each decoder block') - parser.add_argument('--no-decoder-final-norm', action='store_true', - help='don\'t add an extra layernorm after the last decoder block') - parser.add_argument('--adaptive-softmax-cutoff', metavar='EXPR', - help='comma separated list of adaptive softmax cutoff points. ' - 'Must be used with adaptive_loss criterion') - parser.add_argument('--adaptive-softmax-dropout', type=float, metavar='D', - help='sets adaptive softmax dropout for the tail projections') - parser.add_argument('--adaptive-softmax-factor', type=float, metavar='N', - help='adaptive input factor') - parser.add_argument('--no-token-positional-embeddings', action='store_true', - help='if set, disables positional embeddings (outside self attention)') - parser.add_argument('--share-decoder-input-output-embed', action='store_true', - help='share decoder input and output embeddings') - parser.add_argument('--character-embeddings', action='store_true', - help='if set, uses character embedding convolutions to produce token embeddings') - parser.add_argument('--character-filters', type=str, metavar='LIST', - default='[(1, 64), (2, 128), (3, 192), (4, 256), (5, 256), (6, 256), (7, 256)]', - help='size of character embeddings') - parser.add_argument('--character-embedding-dim', default=4, type=int, metavar='N', - help='size of character embeddings') - parser.add_argument('--char-embedder-highway-layers', default=2, type=int, metavar='N', - help='number of highway layers for character token embeddder') - parser.add_argument('--adaptive-input', action='store_true', - help='if set, uses adaptive input') - parser.add_argument('--adaptive-input-factor', type=float, metavar='N', - help='adaptive input factor') - parser.add_argument('--adaptive-input-cutoff', metavar='EXPR', - help='comma separated list of adaptive input cutoff points.') - parser.add_argument('--tie-adaptive-weights', action='store_true', - help='if set, ties the weights of adaptive softmax and adaptive input') - parser.add_argument('--tie-adaptive-proj', action='store_true', - help='if set, ties the projection weights of adaptive softmax and adaptive input') - parser.add_argument('--decoder-learned-pos', action='store_true', - help='use learned positional embeddings in the decoder') - parser.add_argument('--layernorm-embedding', action='store_true', - help='add layernorm to embedding') - parser.add_argument('--no-scale-embedding', action='store_true', - help='if True, dont scale embeddings') - # args for "Reducing Transformer Depth on Demand with Structured Dropout" (Fan et al., 2019) - parser.add_argument('--decoder-layerdrop', type=float, metavar='D', default=0, - help='LayerDrop probability for decoder') - parser.add_argument('--decoder-layers-to-keep', default=None, - help='which layers to *keep* when pruning as a comma-separated list') - # args for Training with Quantization Noise for Extreme Model Compression ({Fan*, Stock*} et al., 2020) - parser.add_argument('--quant-noise-pq', type=float, metavar='D', default=0, - help='iterative PQ quantization noise at training time') - parser.add_argument('--quant-noise-pq-block-size', type=int, metavar='D', default=8, - help='block size of quantization noise at training time') - parser.add_argument('--quant-noise-scalar', type=float, metavar='D', default=0, - help='scalar quantization noise and scalar quantization at training time') - # fmt: on - @classmethod def build_model(cls, args, task): """Build a new model instance.""" - # make sure all arguments are present in older models - base_lm_architecture(args) - if args.decoder_layers_to_keep: args.decoder_layers = len(args.decoder_layers_to_keep.split(",")) - if getattr(args, 'max_target_positions', None) is None: - args.max_target_positions = getattr(args, 'tokens_per_sample', DEFAULT_MAX_TARGET_POSITIONS) + if getattr(args, "max_target_positions", None) is None: + args.max_target_positions = getattr( + args, "tokens_per_sample", DEFAULT_MAX_TARGET_POSITIONS + ) if args.character_embeddings: embed_tokens = CharacterTokenEmbedder( - task.source_dictionary, eval(args.character_filters), - args.character_embedding_dim, args.decoder_embed_dim, + task.source_dictionary, + eval(args.character_filters), + args.character_embedding_dim, + args.decoder_embed_dim, args.char_embedder_highway_layers, ) elif args.adaptive_input: embed_tokens = AdaptiveInput( - len(task.source_dictionary), task.source_dictionary.pad(), args.decoder_input_dim, - args.adaptive_input_factor, args.decoder_embed_dim, + len(task.source_dictionary), + task.source_dictionary.pad(), + args.decoder_input_dim, + args.adaptive_input_factor, + args.decoder_embed_dim, options.eval_str_list(args.adaptive_input_cutoff, type=int), - args.quant_noise_pq, args.quant_noise_pq_block_size, + args.quant_noise_pq, + args.quant_noise_pq_block_size, ) else: - embed_tokens = cls.build_embedding(args, task.source_dictionary, args.decoder_input_dim) + embed_tokens = cls.build_embedding( + args, task.source_dictionary, args.decoder_input_dim + ) if args.tie_adaptive_weights: assert args.adaptive_input assert args.adaptive_input_factor == args.adaptive_softmax_factor - assert args.adaptive_softmax_cutoff == args.adaptive_input_cutoff, '{} != {}'.format( - args.adaptive_softmax_cutoff, args.adaptive_input_cutoff) + assert ( + args.adaptive_softmax_cutoff == args.adaptive_input_cutoff + ), "{} != {}".format( + args.adaptive_softmax_cutoff, args.adaptive_input_cutoff + ) assert args.decoder_input_dim == args.decoder_output_dim decoder = TransformerDecoder( - args, task.target_dictionary, embed_tokens, no_encoder_attn=True, + args, task.target_dictionary, embed_tokens, no_encoder_attn=True ) return cls(decoder) @@ -170,134 +292,253 @@ def build_embedding(cls, args, dictionary, embed_dim, path=None): embed_tokens = Embedding(len(dictionary), embed_dim, dictionary.pad()) return embed_tokens -@register_model_architecture('transformer_lm', 'transformer_lm') + def base_lm_architecture(args): # backward compatibility for older model checkpoints - if hasattr(args, 'no_tie_adaptive_proj'): + if hasattr(args, "no_tie_adaptive_proj"): # previous models defined --no-tie-adaptive-proj, so use the existence of # that option to determine if this is an "old" model checkpoint args.no_decoder_final_norm = True # old models always set this to True if args.no_tie_adaptive_proj is False: args.tie_adaptive_proj = True - if hasattr(args, 'decoder_final_norm'): + if hasattr(args, "decoder_final_norm"): args.no_decoder_final_norm = not args.decoder_final_norm - args.dropout = getattr(args, 'dropout', 0.1) - args.attention_dropout = getattr(args, 'attention_dropout', 0.0) - - args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 512) - args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', 2048) - args.decoder_layers = getattr(args, 'decoder_layers', 6) - args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 8) - args.adaptive_softmax_cutoff = getattr(args, 'adaptive_softmax_cutoff', None) - args.adaptive_softmax_dropout = getattr(args, 'adaptive_softmax_dropout', 0) - args.adaptive_softmax_factor = getattr(args, 'adaptive_softmax_factor', 4) - args.decoder_learned_pos = getattr(args, 'decoder_learned_pos', False) - args.activation_fn = getattr(args, 'activation_fn', 'relu') - - args.add_bos_token = getattr(args, 'add_bos_token', False) - args.no_token_positional_embeddings = getattr(args, 'no_token_positional_embeddings', False) - args.share_decoder_input_output_embed = getattr(args, 'share_decoder_input_output_embed', False) - args.character_embeddings = getattr(args, 'character_embeddings', False) - - args.decoder_output_dim = getattr(args, 'decoder_output_dim', args.decoder_embed_dim) - args.decoder_input_dim = getattr(args, 'decoder_input_dim', args.decoder_embed_dim) + args.dropout = getattr(args, "dropout", 0.1) + args.attention_dropout = getattr(args, "attention_dropout", 0.0) + + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512) + args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 2048) + args.decoder_layers = getattr(args, "decoder_layers", 6) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8) + args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None) + args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0) + args.adaptive_softmax_factor = getattr(args, "adaptive_softmax_factor", 4) + args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False) + args.activation_fn = getattr(args, "activation_fn", "relu") + + args.decoder_layerdrop = getattr(args, "decoder_layerdrop", 0) + args.decoder_layers_to_keep = getattr(args, "decoder_layers_to_keep", None) + args.quant_noise_pq = getattr(args, "quant_noise_pq", 0) + args.quant_noise_pq_block_size = getattr(args, "quant_noise_pq_block_size", 8) + args.quant_noise_scalar = getattr(args, "quant_noise_scalar", 0) + + args.base_layers = getattr(args, "base_layers", 0) + args.base_sublayers = getattr(args, "base_sublayers", 1) + args.base_shuffle = getattr(args, "base_shuffle", False) + + args.add_bos_token = getattr(args, "add_bos_token", False) + args.no_token_positional_embeddings = getattr( + args, "no_token_positional_embeddings", False + ) + args.share_decoder_input_output_embed = getattr( + args, "share_decoder_input_output_embed", False + ) + args.character_embeddings = getattr(args, "character_embeddings", False) + + args.decoder_output_dim = getattr( + args, "decoder_output_dim", args.decoder_embed_dim + ) + args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim) # Model training is not stable without this args.decoder_normalize_before = True - args.no_decoder_final_norm = getattr(args, 'no_decoder_final_norm', False) + args.no_decoder_final_norm = getattr(args, "no_decoder_final_norm", False) - args.adaptive_input = getattr(args, 'adaptive_input', False) - args.adaptive_input_factor = getattr(args, 'adaptive_input_factor', 4) - args.adaptive_input_cutoff = getattr(args, 'adaptive_input_cutoff', None) + args.adaptive_input = getattr(args, "adaptive_input", False) + args.adaptive_input_factor = getattr(args, "adaptive_input_factor", 4) + args.adaptive_input_cutoff = getattr(args, "adaptive_input_cutoff", None) - args.tie_adaptive_weights = getattr(args, 'tie_adaptive_weights', False) - args.tie_adaptive_proj = getattr(args, 'tie_adaptive_proj', False) + args.tie_adaptive_weights = getattr(args, "tie_adaptive_weights", False) + args.tie_adaptive_proj = getattr(args, "tie_adaptive_proj", False) - args.no_scale_embedding = getattr(args, 'no_scale_embedding', False) - args.layernorm_embedding = getattr(args, 'layernorm_embedding', False) + args.no_scale_embedding = getattr(args, "no_scale_embedding", False) + args.layernorm_embedding = getattr(args, "layernorm_embedding", False) + args.checkpoint_activations = getattr(args, "checkpoint_activations", False) + args.offload_activations = getattr(args, "offload_activations", False) + if args.offload_activations: + args.checkpoint_activations = True -@register_model_architecture('transformer_lm', 'transformer_lm_big') +@register_model_architecture("transformer_lm", "transformer_lm_big") def transformer_lm_big(args): - args.decoder_layers = getattr(args, 'decoder_layers', 12) - args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 1024) - args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', 4096) - args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 16) + args.decoder_layers = getattr(args, "decoder_layers", 12) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 1024) + args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 4096) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 16) base_lm_architecture(args) -@register_model_architecture('transformer_lm', 'transformer_lm_wiki103') -@register_model_architecture('transformer_lm', 'transformer_lm_baevski_wiki103') +@register_model_architecture("transformer_lm", "transformer_lm_wiki103") +@register_model_architecture("transformer_lm", "transformer_lm_baevski_wiki103") def transformer_lm_baevski_wiki103(args): - args.decoder_layers = getattr(args, 'decoder_layers', 16) - args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 8) - args.dropout = getattr(args, 'dropout', 0.3) - args.adaptive_input = getattr(args, 'adaptive_input', True) - args.tie_adaptive_weights = getattr(args, 'tie_adaptive_weights', True) - args.adaptive_input_cutoff = getattr(args, 'adaptive_input_cutoff', '20000,60000') - args.adaptive_softmax_cutoff = getattr(args, 'adaptive_softmax_cutoff', '20000,60000') - args.adaptive_softmax_dropout = getattr(args, 'adaptive_softmax_dropout', 0.2) - args.attention_dropout = getattr(args, 'attention_dropout', 0.1) - args.activation_dropout = getattr(args, 'activation_dropout', 0.1) - args.no_decoder_final_norm = getattr(args, 'no_decoder_final_norm', True) - args.tie_adaptive_proj = getattr(args, 'tie_adaptive_proj', True) + args.decoder_layers = getattr(args, "decoder_layers", 16) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8) + args.dropout = getattr(args, "dropout", 0.3) + args.adaptive_input = getattr(args, "adaptive_input", True) + args.tie_adaptive_weights = getattr(args, "tie_adaptive_weights", True) + args.adaptive_input_cutoff = getattr(args, "adaptive_input_cutoff", "20000,60000") + args.adaptive_softmax_cutoff = getattr( + args, "adaptive_softmax_cutoff", "20000,60000" + ) + args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0.2) + args.attention_dropout = getattr(args, "attention_dropout", 0.1) + args.activation_dropout = getattr(args, "activation_dropout", 0.1) + args.no_decoder_final_norm = getattr(args, "no_decoder_final_norm", True) + args.tie_adaptive_proj = getattr(args, "tie_adaptive_proj", True) transformer_lm_big(args) -@register_model_architecture('transformer_lm', 'transformer_lm_gbw') -@register_model_architecture('transformer_lm', 'transformer_lm_baevski_gbw') +@register_model_architecture("transformer_lm", "transformer_lm_gbw") +@register_model_architecture("transformer_lm", "transformer_lm_baevski_gbw") def transformer_lm_baevski_gbw(args): - args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 512) - args.dropout = getattr(args, 'dropout', 0.1) - args.attention_dropout = getattr(args, 'attention_dropout', 0.1) - args.no_decoder_final_norm = getattr(args, 'no_decoder_final_norm', True) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512) + args.dropout = getattr(args, "dropout", 0.1) + args.attention_dropout = getattr(args, "attention_dropout", 0.1) + args.no_decoder_final_norm = getattr(args, "no_decoder_final_norm", True) transformer_lm_big(args) -@register_model_architecture('transformer_lm', 'transformer_lm_gpt') +@register_model_architecture("transformer_lm", "transformer_lm_gpt") def transformer_lm_gpt(args): - args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 768) - args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', 3072) - args.decoder_layers = getattr(args, 'decoder_layers', 12) - args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 12) - args.dropout = getattr(args, 'dropout', 0.1) - args.attention_dropout = getattr(args, 'attention_dropout', 0.1) - args.activation_fn = getattr(args, 'activation_fn', 'gelu') + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 768) + args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 3072) + args.decoder_layers = getattr(args, "decoder_layers", 12) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 12) + args.dropout = getattr(args, "dropout", 0.1) + args.attention_dropout = getattr(args, "attention_dropout", 0.1) + args.activation_fn = getattr(args, "activation_fn", "gelu") base_lm_architecture(args) -@register_model_architecture('transformer_lm', 'transformer_lm_gpt2_small') +@register_model_architecture("transformer_lm", "transformer_lm_gpt2_small") def transformer_lm_gpt2_small(args): - args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 1024) - args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', 4096) - args.decoder_layers = getattr(args, 'decoder_layers', 24) - args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 16) - args.dropout = getattr(args, 'dropout', 0.1) - args.attention_dropout = getattr(args, 'attention_dropout', 0.1) - args.activation_fn = getattr(args, 'activation_fn', 'gelu') + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 1024) + args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 4096) + args.decoder_layers = getattr(args, "decoder_layers", 24) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 16) + args.dropout = getattr(args, "dropout", 0.1) + args.attention_dropout = getattr(args, "attention_dropout", 0.1) + args.activation_fn = getattr(args, "activation_fn", "gelu") base_lm_architecture(args) -@register_model_architecture('transformer_lm', 'transformer_lm_gpt2_medium') +@register_model_architecture("transformer_lm", "transformer_lm_gpt2_tiny") +def transformer_lm_gpt2_tiny(args): + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 64) + args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 64) + args.decoder_layers = getattr(args, "decoder_layers", 2) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 1) + args.dropout = getattr(args, "dropout", 0.1) + args.attention_dropout = getattr(args, "attention_dropout", 0.1) + args.activation_fn = getattr(args, "activation_fn", "gelu") + base_lm_architecture(args) + + +@register_model_architecture("transformer_lm", "transformer_lm_gpt2_medium") def transformer_lm_gpt2_medium(args): - args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 1280) - args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', 5120) - args.decoder_layers = getattr(args, 'decoder_layers', 36) - args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 20) - args.dropout = getattr(args, 'dropout', 0.1) - args.attention_dropout = getattr(args, 'attention_dropout', 0.1) - args.activation_fn = getattr(args, 'activation_fn', 'gelu') + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 1280) + args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 5120) + args.decoder_layers = getattr(args, "decoder_layers", 36) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 20) + args.dropout = getattr(args, "dropout", 0.1) + args.attention_dropout = getattr(args, "attention_dropout", 0.1) + args.activation_fn = getattr(args, "activation_fn", "gelu") base_lm_architecture(args) -@register_model_architecture('transformer_lm', 'transformer_lm_gpt2_big') +@register_model_architecture("transformer_lm", "transformer_lm_gpt2_big") def transformer_lm_gpt2_big(args): - args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 1600) - args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', 6400) - args.decoder_layers = getattr(args, 'decoder_layers', 48) - args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 25) - args.dropout = getattr(args, 'dropout', 0.1) - args.attention_dropout = getattr(args, 'attention_dropout', 0.1) - args.activation_fn = getattr(args, 'activation_fn', 'gelu') + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 1600) + args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 6400) + args.decoder_layers = getattr(args, "decoder_layers", 48) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 25) + args.dropout = getattr(args, "dropout", 0.1) + args.attention_dropout = getattr(args, "attention_dropout", 0.1) + args.activation_fn = getattr(args, "activation_fn", "gelu") + base_lm_architecture(args) + + +def base_gpt3_architecture(args): + args.decoder_input_dim = args.decoder_embed_dim + args.decoder_output_dim = args.decoder_embed_dim + args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", args.decoder_embed_dim * 4) + # GPT-3 used learned positional embeddings, rather than sinusoidal + args.decoder_learned_pos = getattr(args, "decoder_learned_pos", True) + args.dropout = getattr(args, "dropout", 0.0) + args.attention_dropout = getattr(args, "attention_dropout", 0.0) + args.activation_fn = getattr(args, "activation_fn", "gelu") + args.share_decoder_input_output_embed = True base_lm_architecture(args) + + +@register_model_architecture("transformer_lm", "transformer_lm_gpt3_small") +def transformer_lm_gpt3_small(args): + # 125M params + args.decoder_layers = getattr(args, "decoder_layers", 12) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 768) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 12) + base_gpt3_architecture(args) + + +@register_model_architecture("transformer_lm", "transformer_lm_gpt3_medium") +def transformer_lm_gpt3_medium(args): + # 350M params + args.decoder_layers = getattr(args, "decoder_layers", 24) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 1024) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 16) + base_gpt3_architecture(args) + + +@register_model_architecture("transformer_lm", "transformer_lm_gpt3_large") +def transformer_lm_gpt3_large(args): + # 760M params + args.decoder_layers = getattr(args, "decoder_layers", 24) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 1536) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 16) + base_gpt3_architecture(args) + + +@register_model_architecture("transformer_lm", "transformer_lm_gpt3_xl") +def transformer_lm_gpt3_xl(args): + # 1.3B params + args.decoder_layers = getattr(args, "decoder_layers", 24) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 2048) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 32) + base_gpt3_architecture(args) + + +@register_model_architecture("transformer_lm", "transformer_lm_gpt3_2_7") +def transformer_lm_gpt3_2_7(args): + # 2.7B params + args.decoder_layers = getattr(args, "decoder_layers", 32) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 2560) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 32) + base_gpt3_architecture(args) + + +@register_model_architecture("transformer_lm", "transformer_lm_gpt3_6_7") +def transformer_lm_gpt3_6_7(args): + # 6.7B params + args.decoder_layers = getattr(args, "decoder_layers", 32) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 4096) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 32) + base_gpt3_architecture(args) + + +@register_model_architecture("transformer_lm", "transformer_lm_gpt3_13") +def transformer_lm_gpt3_13(args): + # 13B params + args.decoder_layers = getattr(args, "decoder_layers", 40) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 5120) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 40) + base_gpt3_architecture(args) + + +@register_model_architecture("transformer_lm", "transformer_lm_gpt3_175") +def transformer_lm_gpt3_175(args): + # 175B params + args.decoder_layers = getattr(args, "decoder_layers", 96) + args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 12288) + args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 96) + base_gpt3_architecture(args) diff --git a/fairseq/models/wav2vec/__init__.py b/fairseq/models/wav2vec/__init__.py new file mode 100644 index 0000000000..06cec18183 --- /dev/null +++ b/fairseq/models/wav2vec/__init__.py @@ -0,0 +1,8 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from .wav2vec import * # noqa +from .wav2vec2 import * # noqa +from .wav2vec2_asr import * # noqa diff --git a/fairseq/models/wav2vec.py b/fairseq/models/wav2vec/wav2vec.py similarity index 53% rename from fairseq/models/wav2vec.py rename to fairseq/models/wav2vec/wav2vec.py index 38a25db0a8..af6604da10 100644 --- a/fairseq/models/wav2vec.py +++ b/fairseq/models/wav2vec/wav2vec.py @@ -3,278 +3,227 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. +from dataclasses import dataclass, field import logging import math +from typing import Optional, Tuple +from omegaconf import II import sys import torch import torch.nn as nn import torch.nn.functional as F - -from fairseq.models import BaseFairseqModel, register_model, register_model_architecture +from fairseq.dataclass import ChoiceEnum, FairseqDataclass +from fairseq.models import BaseFairseqModel, register_model from fairseq.modules import ( Fp32GroupNorm, Fp32LayerNorm, GumbelVectorQuantizer, KmeansVectorQuantizer, + TransposeLast, ) +from fairseq.tasks import FairseqTask from fairseq.utils import buffered_arange -logger = logging.getLogger(__name__) - - -@register_model("wav2vec") -class Wav2VecModel(BaseFairseqModel): - @staticmethod - def add_args(parser): - """Add model-specific arguments to the parser.""" - parser.add_argument( - "--prediction-steps", - type=int, - metavar="N", - help="number of steps ahead to predict", - ) - parser.add_argument( - "--sample-distance", - type=int, - metavar="N", - help="sample distance from target. does not work properly with cross-sampling", - ) - parser.add_argument( - "--cross-sample-negatives", - type=int, - metavar="N", - help="num of cross sampled negatives", - ) - parser.add_argument( - "--num-negatives", type=int, metavar="N", help="number of negative examples" - ) - parser.add_argument( - "--conv-feature-layers", - type=str, - metavar="EXPR", - help="convolutional feature extraction layers [(dim, kernel_size, stride), ...]", - ) - parser.add_argument( - "--conv-aggregator-layers", - type=str, - metavar="EXPR", - help="convolutional feature extraction layers [(dim, kernel_size, stride), ...]", - ) - parser.add_argument( - "--dropout", - type=float, - metavar="D", - help="dropout to apply within the model", - ) - parser.add_argument( - "--dropout-features", - type=float, - metavar="D", - help="dropout to apply to the features", - ) - parser.add_argument( - "--dropout-agg", - type=float, - metavar="D", - help="dropout to apply after aggregation step", - ) - parser.add_argument( - "--encoder", type=str, choices=["cnn"], help="type of encoder to use" - ) - parser.add_argument( - "--aggregator", - type=str, - choices=["cnn", "gru"], - help="type of aggregator to use", - ) - parser.add_argument( - "--gru-dim", type=int, metavar="N", help="GRU dimensionality" - ) - - parser.add_argument( - "--no-conv-bias", - action="store_true", - help="if set, does not learn bias for conv layers", - ) - parser.add_argument( - "--agg-zero-pad", - action="store_true", - help="if set, zero pads in aggregator instead of repl pad", - ) - - parser.add_argument( - "--skip-connections-feat", - action="store_true", - help="if set, adds skip connections to the feature extractor", - ) - parser.add_argument( - "--skip-connections-agg", - action="store_true", - help="if set, adds skip connections to the aggregator", - ) - parser.add_argument( - "--residual-scale", - type=float, - metavar="D", - help="scales residual by sqrt(value)", - ) - - parser.add_argument( - "--log-compression", - action="store_true", - help="if set, adds a log compression to feature extractor", - ) - parser.add_argument( - "--balanced-classes", - action="store_true", - help="if set, loss is scaled to balance for number of negatives", - ) +logger = logging.getLogger(__name__) - parser.add_argument( - "--project-features", - choices=["none", "same", "new"], - help="if not none, features are projected using the (same or new) aggregator", - ) - parser.add_argument( - "--non-affine-group-norm", - action="store_true", - help="if set, group norm is not affine", - ) +AGGREGATOR_CHOICES = ChoiceEnum(["cnn", "gru"]) +PROJECT_FEATURES_CHOICES = ChoiceEnum(["none", "same", "new"]) +ACTIVATION_CHOICES = ChoiceEnum(["relu", "gelu"]) +VQ_TYPE_CHOICES = ChoiceEnum(["none", "gumbel", "kmeans"]) - parser.add_argument( - "--offset", - help="if set, introduces an offset from target to predictions. " - 'if set to "auto", it is computed automatically from the receptive field', - ) - parser.add_argument( - "--activation", - type=str, - choices=["relu", "gelu"], - help="which activation function to use", - ) +@dataclass +class Wav2VecConfig(FairseqDataclass): + prediction_steps: int = field( + default=12, metadata={"help": "number of steps ahead to predict"} + ) + sample_distance: Optional[int] = field( + default=None, + metadata={ + "help": "sample distance from target. does not work properly with cross-sampling" + }, + ) + cross_sample_negatives: int = field( + default=0, metadata={"help": "num of cross sampled negatives"} + ) + num_negatives: int = field( + default=10, metadata={"help": "num of sampled negatives"} + ) + conv_feature_layers: str = field( + default="[(512, 10, 5), (512, 8, 4), (512, 4, 2), (512, 4, 2), (512, 4, 2), (512, 1, 1), (512, 1, 1), (512, 1, 1)]", + metadata={ + "help": "convolutional feature extraction layers [(dim, kernel_size, stride), ...]" + }, + ) + conv_aggregator_layers: str = field( + default="[(512, 2, 1), (512, 3, 1), (512, 4, 1), (512, 5, 1), (512, 6, 1), (512, 7, 1), (512, 8, 1), (512, 9, 1), (512, 10, 1), (512, 11, 1), (512, 12, 1), (512, 13, 1)]", + metadata={ + "help": "convolutional aggregator layers [(dim, kernel_size, stride), ...]" + }, + ) + dropout: float = field( + default=0.0, metadata={"help": "dropout to apply within the model"} + ) + dropout_features: float = field( + default=0.0, metadata={"help": "dropout to apply to the features"} + ) + dropout_agg: float = field( + default=0.0, metadata={"help": "dropout to apply after aggregation step"} + ) + aggregator: AGGREGATOR_CHOICES = field( + default="cnn", metadata={"help": "type of aggregator to use"} + ) + gru_dim: int = field(default=512, metadata={"help": "GRU dimensionality"}) + no_conv_bias: bool = field( + default=False, metadata={"help": "if set, does not learn bias for conv layers"} + ) + agg_zero_pad: bool = field( + default=False, + metadata={"help": "if set, zero pads in aggregator instead of repl pad"}, + ) + skip_connections_feat: bool = field( + default=False, + metadata={"help": "if set, adds skip connections to the feature extractor"}, + ) + skip_connections_agg: bool = field( + default=True, + metadata={"help": "if set, adds skip connections to the aggregator"}, + ) + residual_scale: float = field( + default=0.5, metadata={"help": "scales residual by sqrt(value)"} + ) + log_compression: bool = field( + default=True, + metadata={"help": "if set, adds a log compression to feature extractor"}, + ) + balanced_classes: bool = field( + default=False, + metadata={"help": "if set, loss is scaled to balance for number of negatives"}, + ) + project_features: PROJECT_FEATURES_CHOICES = field( + default="none", + metadata={ + "help": "if not none, features are projected using the (same or new) aggregator" + }, + ) + non_affine_group_norm: bool = field( + default=False, metadata={"help": "if set, group norm is not affine"} + ) + offset: str = field( + default="auto", + metadata={ + "help": "if set to 'auto', it is computed automatically from the receptive field, else set to int value" + }, + ) + activation: ACTIVATION_CHOICES = field( + default="relu", + metadata={ + "help": "if set to 'auto', it is computed automatically from the receptive field, else set to int value" + }, + ) + vq_type: VQ_TYPE_CHOICES = field( + default="none", metadata={"help": "which type of quantizer to use"} + ) + vq_vars: int = field( + default=320, + metadata={"help": "project to this many vector quantized variables per group"}, + ) + vq_groups: int = field( + default=2, metadata={"help": "number of groups of latent variables"} + ) + vq_dim: int = field( + default=0, + metadata={ + "help": "uses this dimensionality for quantized vectors. 0 to use model dim // groups" + }, + ) + vq_depth: int = field( + default=1, metadata={"help": "number of layers for vq weight projection"} + ) + combine_groups: bool = field( + default=False, metadata={"help": "if set, variables are shared among groups"} + ) + vq_temp: Tuple[float, float, float] = field( + default=(2.0, 0.5, 0.999995), + metadata={ + "help": "temperature for latent variable sampling with gumbel softmax. should be a tuple of 3 values (start, end, decay)" + }, + ) + vq_gamma: float = field( + default=0.25, + metadata={"help": "gamma parameter for kmeans style vector quantization"}, + ) + infonce: bool = II("criterion.infonce") - parser.add_argument( - "--vq-type", - type=str, - choices=["none", "gumbel", "kmeans"], - help="which type of quantizer to use", - ) - parser.add_argument( - "--vq-vars", - type=int, - metavar="N", - help="if set, project to this many vector quantized variables per group", - ) - parser.add_argument( - "--vq-groups", - type=int, - metavar="N", - help="number of groups of latent variables", - ) - parser.add_argument( - "--vq-dim", - type=int, - metavar="N", - help="uses this dimensionality for quantized vectors", - ) - parser.add_argument( - "--vq-depth", - type=int, - metavar="N", - help="number of layers for vq weight projection", - ) - parser.add_argument( - "--combine-groups", - action="store_true", - help="if set, variables are shared among groups", - ) - parser.add_argument( - "--vq-temp", - type=str, - metavar="TEMP", - help="temperature for latent variable sampling with gumbel softmax. should be a tuple of 3 values (start, end, decay)", - ) - parser.add_argument( - "--vq-gamma", - type=float, - metavar="D", - help="gamma parameter for kmeans style vector quantization", - ) +@register_model("wav2vec", dataclass=Wav2VecConfig) +class Wav2VecModel(BaseFairseqModel): @classmethod - def build_model(cls, args, task): + def build_model(cls, cfg: Wav2VecConfig, task: FairseqTask): """Build a new model instance.""" - # make sure all arguments are present in older models - base_wav2vec_architecture(args) - - model = Wav2VecModel(args) + model = Wav2VecModel(cfg) logger.info(model) return model - def __init__(self, args): + def __init__(self, cfg: Wav2VecConfig): super().__init__() - self.prediction_steps = args.prediction_steps - offset = args.offset + self.prediction_steps = cfg.prediction_steps + offset = cfg.offset - if args.activation == "relu": + if cfg.activation == "relu": activation = nn.ReLU() - elif args.activation == "gelu": + elif cfg.activation == "gelu": activation = nn.GELU() else: - raise Exception("unknown activation " + args.activation) - - if args.encoder == "cnn": - feature_enc_layers = eval(args.conv_feature_layers) - self.feature_extractor = ConvFeatureExtractionModel( - conv_layers=feature_enc_layers, - dropout=0.0, - log_compression=args.log_compression, - skip_connections=args.skip_connections_feat, - residual_scale=args.residual_scale, - non_affine_group_norm=args.non_affine_group_norm, - activation=activation, - ) - embed = feature_enc_layers[-1][0] - else: - raise Exception("unknown encoder type " + args.encoder) + raise Exception("unknown activation " + cfg.activation) + + feature_enc_layers = eval(cfg.conv_feature_layers) + self.feature_extractor = ConvFeatureExtractionModel( + conv_layers=feature_enc_layers, + dropout=0.0, + log_compression=cfg.log_compression, + skip_connections=cfg.skip_connections_feat, + residual_scale=cfg.residual_scale, + non_affine_group_norm=cfg.non_affine_group_norm, + activation=activation, + ) + embed = feature_enc_layers[-1][0] self.vector_quantizer = None - if args.vq_type == "gumbel": + if cfg.vq_type == "gumbel": self.vector_quantizer = GumbelVectorQuantizer( dim=embed, - num_vars=args.vq_vars, - temp=eval(args.vq_temp), - groups=args.vq_groups, - combine_groups=args.combine_groups, - vq_dim=args.vq_dim if args.vq_dim > 0 else embed, + num_vars=cfg.vq_vars, + temp=cfg.vq_temp, + groups=cfg.vq_groups, + combine_groups=cfg.combine_groups, + vq_dim=cfg.vq_dim if cfg.vq_dim > 0 else embed, time_first=False, activation=activation, - weight_proj_depth=args.vq_depth, + weight_proj_depth=cfg.vq_depth, weight_proj_factor=2, ) - elif args.vq_type == "kmeans": + elif cfg.vq_type == "kmeans": self.vector_quantizer = KmeansVectorQuantizer( dim=embed, - num_vars=args.vq_vars, - groups=args.vq_groups, - combine_groups=args.combine_groups, - vq_dim=args.vq_dim if args.vq_dim > 0 else embed, + num_vars=cfg.vq_vars, + groups=cfg.vq_groups, + combine_groups=cfg.combine_groups, + vq_dim=cfg.vq_dim if cfg.vq_dim > 0 else embed, time_first=False, - gamma=args.vq_gamma, + gamma=cfg.vq_gamma, ) else: assert ( - args.vq_type == "none" or args.vq_type is None + cfg.vq_type == "none" or cfg.vq_type is None ), "Unknown quantizer type" - if args.offset == "auto": - assert args.encoder == "cnn" + if cfg.offset == "auto": jin = 0 rin = 0 for _, k, stride in feature_enc_layers: @@ -290,34 +239,34 @@ def __init__(self, args): offset = int(offset) def make_aggregator(): - if args.aggregator == "cnn": - agg_layers = eval(args.conv_aggregator_layers) + if cfg.aggregator == "cnn": + agg_layers = eval(cfg.conv_aggregator_layers) agg_dim = agg_layers[-1][0] feature_aggregator = ConvAggegator( conv_layers=agg_layers, embed=embed, - dropout=args.dropout, - skip_connections=args.skip_connections_agg, - residual_scale=args.residual_scale, - non_affine_group_norm=args.non_affine_group_norm, - conv_bias=not args.no_conv_bias, - zero_pad=args.agg_zero_pad, + dropout=cfg.dropout, + skip_connections=cfg.skip_connections_agg, + residual_scale=cfg.residual_scale, + non_affine_group_norm=cfg.non_affine_group_norm, + conv_bias=not cfg.no_conv_bias, + zero_pad=cfg.agg_zero_pad, activation=activation, ) - elif args.aggregator == "gru": - agg_dim = args.gru_dim + elif cfg.aggregator == "gru": + agg_dim = cfg.gru_dim feature_aggregator = nn.Sequential( TransposeLast(), nn.GRU( input_size=embed, hidden_size=agg_dim, num_layers=1, - dropout=args.dropout, + dropout=cfg.dropout, ), TransposeLast(deconstruct_idx=0), ) else: - raise Exception("unknown aggregator type " + args.aggregator) + raise Exception("unknown aggregator type " + cfg.aggregator) return feature_aggregator, agg_dim @@ -326,24 +275,24 @@ def make_aggregator(): self.wav2vec_predictions = Wav2VecPredictionsModel( in_dim=agg_dim, out_dim=embed, - prediction_steps=args.prediction_steps, - n_negatives=args.num_negatives, - cross_sample_negatives=args.cross_sample_negatives, - sample_distance=args.sample_distance, - dropout=args.dropout, + prediction_steps=cfg.prediction_steps, + n_negatives=cfg.num_negatives, + cross_sample_negatives=cfg.cross_sample_negatives, + sample_distance=cfg.sample_distance, + dropout=cfg.dropout, offset=offset, - balanced_classes=args.balanced_classes, - infonce=args.infonce, + balanced_classes=cfg.balanced_classes, + infonce=cfg.infonce, ) - self.dropout_feats = nn.Dropout(p=args.dropout_features) - self.dropout_agg = nn.Dropout(p=args.dropout_agg) + self.dropout_feats = nn.Dropout(p=cfg.dropout_features) + self.dropout_agg = nn.Dropout(p=cfg.dropout_agg) - if args.project_features == "none": + if cfg.project_features == "none": self.project_features = None - elif args.project_features == "same": + elif cfg.project_features == "same": self.project_features = self.feature_aggregator - elif args.project_features == "new": + elif cfg.project_features == "new": self.project_features, _ = make_aggregator() def forward(self, source): @@ -402,16 +351,6 @@ def get_extra_losses(self, net_output): return loss -class TransposeLast(nn.Module): - def __init__(self, deconstruct_idx=None): - super().__init__() - self.deconstruct_idx = deconstruct_idx - - def forward(self, x): - if self.deconstruct_idx is not None: - x = x[self.deconstruct_idx] - return x.transpose(-2, -1) - def norm_block(is_layer_norm, dim, affine=True): if is_layer_norm: mod = nn.Sequential( @@ -689,56 +628,3 @@ def forward(self, x, y): labels = (labels, weights) return predictions, labels - - -@register_model_architecture("wav2vec", "wav2vec") -def base_wav2vec_architecture(args): - conv_feature_layers = "[(512, 10, 5)]" - conv_feature_layers += " + [(512, 8, 4)]" - conv_feature_layers += " + [(512, 4, 2)] * 3" - args.conv_feature_layers = getattr(args, "conv_feature_layers", conv_feature_layers) - - args.conv_aggregator_layers = getattr( - args, "conv_aggregator_layers", "[(512, 3, 1)] * 9" - ) - - args.prediction_steps = getattr(args, "prediction_steps", 12) - args.num_negatives = getattr(args, "num_negatives", 1) - args.sample_distance = getattr(args, "sample_distance", None) - args.cross_sample_negatives = getattr(args, "cross_sample_negatives", 0) - - args.dropout = getattr(args, "dropout", 0.0) - args.dropout_features = getattr(args, "dropout_features", 0.0) - args.dropout_agg = getattr(args, "dropout_agg", 0.0) - args.encoder = getattr(args, "encoder", "cnn") - args.aggregator = getattr(args, "aggregator", "cnn") - - args.skip_connections_feat = getattr(args, "skip_connections_feat", False) - args.skip_connections_agg = getattr(args, "skip_connections_agg", False) - args.residual_scale = getattr(args, "residual_scale", 0.5) - - args.gru_dim = getattr(args, "gru_dim", 512) - - args.no_conv_bias = getattr(args, "no_conv_bias", False) - args.agg_zero_pad = getattr(args, "agg_zero_pad", False) - - args.log_compression = getattr(args, "log_compression", False) - - args.balanced_classes = getattr(args, "balanced_classes", False) - args.infonce = getattr(args, "infonce", False) - args.project_features = getattr(args, "project_features", "none") - - args.non_affine_group_norm = getattr(args, "non_affine_group_norm", False) - - args.offset = getattr(args, "offset", "auto") - - args.activation = getattr(args, "activation", "relu") - - args.vq_type = getattr(args, "vq_type", "none") - args.vq_vars = getattr(args, "vq_vars", 320) - args.vq_groups = getattr(args, "vq_groups", 2) - args.vq_dim = getattr(args, "vq_dim", 0) - args.vq_depth = getattr(args, "vq_depth", 1) - args.combine_groups = getattr(args, "combine_groups", False) - args.vq_temp = getattr(args, "vq_temp", "(2.0, 0.5, 0.999995)") - args.vq_gamma = getattr(args, "vq_gamma", 0.25) diff --git a/fairseq/models/wav2vec/wav2vec2.py b/fairseq/models/wav2vec/wav2vec2.py new file mode 100644 index 0000000000..714fd3ab50 --- /dev/null +++ b/fairseq/models/wav2vec/wav2vec2.py @@ -0,0 +1,1016 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math +from dataclasses import dataclass, field +from typing import List, Tuple + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from fairseq import utils +from fairseq.data.data_utils import compute_mask_indices +from fairseq.dataclass import ChoiceEnum, FairseqDataclass +from fairseq.models import BaseFairseqModel, register_model +from fairseq.modules import ( + Fp32GroupNorm, + Fp32LayerNorm, + GradMultiply, + GumbelVectorQuantizer, + LayerNorm, + MultiheadAttention, + SamePad, + TransposeLast, +) +from fairseq.modules.transformer_sentence_encoder import init_bert_params +from fairseq.utils import buffered_arange, index_put, is_xla_tensor + + +EXTRACTOR_MODE_CHOICES = ChoiceEnum(["default", "layer_norm"]) +MASKING_DISTRIBUTION_CHOICES = ChoiceEnum(["static", "uniform", "normal", "poisson"]) + + +@dataclass +class Wav2Vec2Config(FairseqDataclass): + extractor_mode: EXTRACTOR_MODE_CHOICES = field( + default="default", + metadata={ + "help": "mode for feature extractor. default has a single group norm with d " + "groups in the first conv block, whereas layer_norm has layer norms in " + "every block (meant to use with normalize=True)" + }, + ) + encoder_layers: int = field( + default=12, metadata={"help": "num encoder layers in the transformer"} + ) + encoder_embed_dim: int = field( + default=768, metadata={"help": "encoder embedding dimension"} + ) + encoder_ffn_embed_dim: int = field( + default=3072, metadata={"help": "encoder embedding dimension for FFN"} + ) + encoder_attention_heads: int = field( + default=12, metadata={"help": "num encoder attention heads"} + ) + activation_fn: ChoiceEnum(utils.get_available_activation_fns()) = field( + default="gelu", metadata={"help": "activation function to use"} + ) + + # dropouts + dropout: float = field( + default=0.1, metadata={"help": "dropout probability for the transformer"} + ) + attention_dropout: float = field( + default=0.1, metadata={"help": "dropout probability for attention weights"} + ) + activation_dropout: float = field( + default=0.0, metadata={"help": "dropout probability after activation in FFN"} + ) + encoder_layerdrop: float = field( + default=0.0, metadata={"help": "probability of dropping a tarnsformer layer"} + ) + dropout_input: float = field( + default=0.0, + metadata={"help": "dropout to apply to the input (after feat extr)"}, + ) + dropout_features: float = field( + default=0.0, + metadata={"help": "dropout to apply to the features (after feat extr)"}, + ) + + final_dim: int = field( + default=0, + metadata={ + "help": "project final representations and targets to this many dimensions." + "set to encoder_embed_dim is <= 0" + }, + ) + layer_norm_first: bool = field( + default=False, metadata={"help": "apply layernorm first in the transformer"} + ) + conv_feature_layers: str = field( + default="[(512, 10, 5)] + [(512, 3, 2)] * 4 + [(512,2,2)] + [(512,2,2)]", + metadata={ + "help": "string describing convolutional feature extraction layers in form of a python list that contains " + "[(dim, kernel_size, stride), ...]" + }, + ) + conv_bias: bool = field( + default=False, metadata={"help": "include bias in conv encoder"} + ) + logit_temp: float = field( + default=0.1, metadata={"help": "temperature to divide logits by"} + ) + quantize_targets: bool = field( + default=False, metadata={"help": "use quantized targets"} + ) + quantize_input: bool = field( + default=False, metadata={"help": "use quantized inputs"} + ) + same_quantizer: bool = field( + default=False, metadata={"help": "use same quantizer for inputs and targets"} + ) + target_glu: bool = field( + default=False, metadata={"help": "adds projection + glu to targets"} + ) + feature_grad_mult: float = field( + default=1.0, metadata={"help": "multiply feature extractor var grads by this"} + ) + quantizer_depth: int = field( + default=1, + metadata={"help": "number of quantizer layers"}, + ) + quantizer_factor: int = field( + default=3, + metadata={ + "help": "dimensionality increase for inner quantizer layers (if depth > 1)" + }, + ) + latent_vars: int = field( + default=320, + metadata={"help": "number of latent variables V in each group of the codebook"}, + ) + latent_groups: int = field( + default=2, + metadata={"help": "number of groups G of latent variables in the codebook"}, + ) + latent_dim: int = field( + default=0, + metadata={ + "help": "if > 0, uses this dimensionality for latent variables. " + "otherwise uses final_dim / latent_groups" + }, + ) + + # masking + mask_length: int = field(default=10, metadata={"help": "mask length"}) + mask_prob: float = field( + default=0.65, metadata={"help": "probability of replacing a token with mask"} + ) + mask_selection: MASKING_DISTRIBUTION_CHOICES = field( + default="static", metadata={"help": "how to choose mask length"} + ) + mask_other: float = field( + default=0, + metadata={ + "help": "secondary mask argument (used for more complex distributions), " + "see help in compute_mask_indices" + }, + ) + no_mask_overlap: bool = field( + default=False, metadata={"help": "whether to allow masks to overlap"} + ) + mask_min_space: int = field( + default=1, + metadata={"help": "min space between spans (if no overlap is enabled)"}, + ) + + # channel masking + mask_channel_length: int = field( + default=10, metadata={"help": "length of the mask for features (channels)"} + ) + mask_channel_prob: float = field( + default=0.0, metadata={"help": "probability of replacing a feature with 0"} + ) + mask_channel_before: bool = False + mask_channel_selection: MASKING_DISTRIBUTION_CHOICES = field( + default="static", + metadata={"help": "how to choose mask length for channel masking"}, + ) + mask_channel_other: float = field( + default=0, + metadata={ + "help": "secondary mask argument (used for more complex distributions), " + "see help in compute_mask_indicesh" + }, + ) + no_mask_channel_overlap: bool = field( + default=False, metadata={"help": "whether to allow channel masks to overlap"} + ) + mask_channel_min_space: int = field( + default=1, + metadata={"help": "min space between spans (if no overlap is enabled)"}, + ) + + # negative selection + num_negatives: int = field( + default=100, + metadata={"help": "number of negative examples from the same sample"}, + ) + negatives_from_everywhere: bool = field( + default=False, + metadata={"help": "sample negatives from everywhere, not just masked states"}, + ) + cross_sample_negatives: int = field( + default=0, metadata={"help": "number of negative examples from the any sample"} + ) + codebook_negatives: int = field( + default=0, metadata={"help": "number of negative examples codebook"} + ) + + # positional embeddings + conv_pos: int = field( + default=128, + metadata={"help": "number of filters for convolutional positional embeddings"}, + ) + conv_pos_groups: int = field( + default=16, + metadata={"help": "number of groups for convolutional positional embedding"}, + ) + + latent_temp: Tuple[float, float, float] = field( + default=(2, 0.5, 0.999995), + metadata={ + "help": "temperature for latent variable sampling. " + "can be tuple of 3 values (start, end, decay)" + }, + ) + + +@register_model("wav2vec2", dataclass=Wav2Vec2Config) +class Wav2Vec2Model(BaseFairseqModel): + def __init__(self, cfg: Wav2Vec2Config): + super().__init__() + self.cfg = cfg + + feature_enc_layers = eval(cfg.conv_feature_layers) + self.embed = feature_enc_layers[-1][0] + + self.feature_extractor = ConvFeatureExtractionModel( + conv_layers=feature_enc_layers, + dropout=0.0, + mode=cfg.extractor_mode, + conv_bias=cfg.conv_bias, + ) + + self.post_extract_proj = ( + nn.Linear(self.embed, cfg.encoder_embed_dim) + if self.embed != cfg.encoder_embed_dim and not cfg.quantize_input + else None + ) + + self.mask_prob = cfg.mask_prob + self.mask_selection = cfg.mask_selection + self.mask_other = cfg.mask_other + self.mask_length = cfg.mask_length + self.no_mask_overlap = cfg.no_mask_overlap + self.mask_min_space = cfg.mask_min_space + + self.mask_channel_prob = cfg.mask_channel_prob + self.mask_channel_before = cfg.mask_channel_before + self.mask_channel_selection = cfg.mask_channel_selection + self.mask_channel_other = cfg.mask_channel_other + self.mask_channel_length = cfg.mask_channel_length + self.no_mask_channel_overlap = cfg.no_mask_channel_overlap + self.mask_channel_min_space = cfg.mask_channel_min_space + + self.dropout_input = nn.Dropout(cfg.dropout_input) + self.dropout_features = nn.Dropout(cfg.dropout_features) + + self.feature_grad_mult = cfg.feature_grad_mult + + self.quantizer = None + self.input_quantizer = None + + self.n_negatives = cfg.num_negatives + self.cross_sample_negatives = cfg.cross_sample_negatives + self.codebook_negatives = cfg.codebook_negatives + self.negatives_from_everywhere = cfg.negatives_from_everywhere + + self.logit_temp = cfg.logit_temp + + final_dim = cfg.final_dim if cfg.final_dim > 0 else cfg.encoder_embed_dim + + if cfg.quantize_targets: + vq_dim = cfg.latent_dim if cfg.latent_dim > 0 else final_dim + self.quantizer = GumbelVectorQuantizer( + dim=self.embed, + num_vars=cfg.latent_vars, + temp=cfg.latent_temp, + groups=cfg.latent_groups, + combine_groups=False, + vq_dim=vq_dim, + time_first=True, + weight_proj_depth=cfg.quantizer_depth, + weight_proj_factor=cfg.quantizer_factor, + ) + self.project_q = nn.Linear(vq_dim, final_dim) + else: + self.project_q = nn.Linear(self.embed, final_dim) + + if cfg.quantize_input: + if cfg.same_quantizer and self.quantizer is not None: + vq_dim = final_dim + self.input_quantizer = self.quantizer + else: + vq_dim = cfg.latent_dim if cfg.latent_dim > 0 else cfg.encoder_embed_dim + self.input_quantizer = GumbelVectorQuantizer( + dim=self.embed, + num_vars=cfg.latent_vars, + temp=cfg.latent_temp, + groups=cfg.latent_groups, + combine_groups=False, + vq_dim=vq_dim, + time_first=True, + weight_proj_depth=cfg.quantizer_depth, + weight_proj_factor=cfg.quantizer_factor, + ) + self.project_inp = nn.Linear(vq_dim, cfg.encoder_embed_dim) + + self.mask_emb = nn.Parameter( + torch.FloatTensor(cfg.encoder_embed_dim).uniform_() + ) + + self.encoder = TransformerEncoder(cfg) + self.layer_norm = LayerNorm(self.embed) + + self.target_glu = None + if cfg.target_glu: + self.target_glu = nn.Sequential( + nn.Linear(final_dim, final_dim * 2), nn.GLU() + ) + + self.final_proj = nn.Linear(cfg.encoder_embed_dim, final_dim) + + def upgrade_state_dict_named(self, state_dict, name): + super().upgrade_state_dict_named(state_dict, name) + """Upgrade a (possibly old) state dict for new versions of fairseq.""" + return state_dict + + @classmethod + def build_model(cls, cfg: Wav2Vec2Config, task=None): + """Build a new model instance.""" + + return cls(cfg) + + def apply_mask( + self, + x, + padding_mask, + mask_indices=None, + mask_channel_indices=None, + ): + B, T, C = x.shape + + if self.mask_channel_prob > 0 and self.mask_channel_before: + mask_channel_indices = compute_mask_indices( + (B, C), + None, + self.mask_channel_prob, + self.mask_channel_length, + self.mask_channel_selection, + self.mask_channel_other, + no_overlap=self.no_mask_channel_overlap, + min_space=self.mask_channel_min_space, + ) + mask_channel_indices = ( + torch.from_numpy(mask_channel_indices) + .to(x.device) + .unsqueeze(1) + .expand(-1, T, -1) + ) + x[mask_channel_indices] = 0 + + if self.mask_prob > 0: + if mask_indices is None: + mask_indices = compute_mask_indices( + (B, T), + padding_mask, + self.mask_prob, + self.mask_length, + self.mask_selection, + self.mask_other, + min_masks=2, + no_overlap=self.no_mask_overlap, + min_space=self.mask_min_space, + ) + mask_indices = torch.from_numpy(mask_indices).to(x.device) + x = index_put(x, mask_indices, self.mask_emb) + else: + mask_indices = None + + if self.mask_channel_prob > 0 and not self.mask_channel_before: + if mask_channel_indices is None: + mask_channel_indices = compute_mask_indices( + (B, C), + None, + self.mask_channel_prob, + self.mask_channel_length, + self.mask_channel_selection, + self.mask_channel_other, + no_overlap=self.no_mask_channel_overlap, + min_space=self.mask_channel_min_space, + ) + mask_channel_indices = ( + torch.from_numpy(mask_channel_indices) + .to(x.device) + .unsqueeze(1) + .expand(-1, T, -1) + ) + x = index_put(x, mask_channel_indices, 0) + + return x, mask_indices + + def sample_negatives(self, y, num, padding_count=None): + + if self.n_negatives == 0 and self.cross_sample_negatives == 0: + return y.new(0) + + bsz, tsz, fsz = y.shape + y = y.view(-1, fsz) # BTC => (BxT)C + + # FIXME: what happens if padding_count is specified? + cross_high = tsz * bsz + high = tsz - (padding_count or 0) + with torch.no_grad(): + assert high > 1, f"{bsz,tsz,fsz}" + + if self.n_negatives > 0: + tszs = ( + buffered_arange(num) + .unsqueeze(-1) + .expand(-1, self.n_negatives) + .flatten() + ) + + neg_idxs = torch.randint( + low=0, high=high - 1, size=(bsz, self.n_negatives * num) + ) + neg_idxs[neg_idxs >= tszs] += 1 + + if self.cross_sample_negatives > 0: + tszs = ( + buffered_arange(num) + .unsqueeze(-1) + .expand(-1, self.cross_sample_negatives) + .flatten() + ) + + cross_neg_idxs = torch.randint( + low=0, + high=cross_high - 1, + size=(bsz, self.cross_sample_negatives * num), + ) + cross_neg_idxs[cross_neg_idxs >= tszs] += 1 + + if self.n_negatives > 0: + for i in range(1, bsz): + neg_idxs[i] += i * high + else: + neg_idxs = cross_neg_idxs + + if self.cross_sample_negatives > 0 and self.n_negatives > 0: + neg_idxs = torch.cat([neg_idxs, cross_neg_idxs], dim=1) + + negs = y[neg_idxs.view(-1)] + negs = negs.view( + bsz, num, self.n_negatives + self.cross_sample_negatives, fsz + ).permute( + 2, 0, 1, 3 + ) # to NxBxTxC + return negs, neg_idxs + + def compute_preds(self, x, y, negatives): + + neg_is_pos = (y == negatives).all(-1) + y = y.unsqueeze(0) + targets = torch.cat([y, negatives], dim=0) + + logits = torch.cosine_similarity(x.float(), targets.float(), dim=-1).type_as(x) + + logits = logits / self.logit_temp + + if is_xla_tensor(logits) or neg_is_pos.any(): + fillval = -float(2 ** 30) + if not hasattr(self, "_inftensor"): + self._inftensor = ( + torch.tensor(fillval).to(x.device) + if is_xla_tensor(logits) + else float("-inf") + ) + logits[1:] = index_put(logits[1:], neg_is_pos, self._inftensor) + + return logits + + def _get_feat_extract_output_lengths(self, input_lengths: torch.LongTensor): + """ + Computes the output length of the convolutional layers + """ + + def _conv_out_length(input_length, kernel_size, stride): + return torch.floor((input_length - kernel_size) / stride + 1) + + conv_cfg_list = eval(self.cfg.conv_feature_layers) + + for i in range(len(conv_cfg_list)): + input_lengths = _conv_out_length( + input_lengths, conv_cfg_list[i][1], conv_cfg_list[i][2] + ) + + return input_lengths.to(torch.long) + + def forward( + self, + source, + padding_mask=None, + mask=True, + features_only=False, + layer=None, + mask_indices=None, + mask_channel_indices=None, + padding_count=None, + ): + + if self.feature_grad_mult > 0: + features = self.feature_extractor(source) + if self.feature_grad_mult != 1.0: + features = GradMultiply.apply(features, self.feature_grad_mult) + else: + with torch.no_grad(): + features = self.feature_extractor(source) + + features_pen = features.float().pow(2).mean() + + features = features.transpose(1, 2) + features = self.layer_norm(features) + unmasked_features = features.clone() + + if padding_mask is not None and padding_mask.any(): + input_lengths = (1 - padding_mask.long()).sum(-1) + # apply conv formula to get real output_lengths + output_lengths = self._get_feat_extract_output_lengths(input_lengths) + + padding_mask = torch.zeros( + features.shape[:2], dtype=features.dtype, device=features.device + ) + + # these two operations makes sure that all values + # before the output lengths indices are attended to + padding_mask[ + ( + torch.arange(padding_mask.shape[0], device=padding_mask.device), + output_lengths - 1, + ) + ] = 1 + padding_mask = (1 - padding_mask.flip([-1]).cumsum(-1).flip([-1])).bool() + else: + padding_mask = None + + if self.post_extract_proj is not None: + features = self.post_extract_proj(features) + + features = self.dropout_input(features) + unmasked_features = self.dropout_features(unmasked_features) + + num_vars = None + code_ppl = None + prob_ppl = None + curr_temp = None + + if self.input_quantizer: + q = self.input_quantizer(features, produce_targets=False) + features = q["x"] + num_vars = q["num_vars"] + code_ppl = q["code_perplexity"] + prob_ppl = q["prob_perplexity"] + curr_temp = q["temp"] + features = self.project_inp(features) + + if mask: + x, mask_indices = self.apply_mask( + features, + padding_mask, + mask_indices=mask_indices, + mask_channel_indices=mask_channel_indices, + ) + if not is_xla_tensor(x) and mask_indices is not None: + # tpu-comment: reducing the size in a dynamic way causes + # too many recompilations on xla. + y = unmasked_features[mask_indices].view( + unmasked_features.size(0), -1, unmasked_features.size(-1) + ) + else: + y = unmasked_features + else: + x = features + y = unmasked_features + mask_indices = None + + x, layer_results = self.encoder(x, padding_mask=padding_mask, layer=layer) + + if features_only: + return { + "x": x, + "padding_mask": padding_mask, + "features": unmasked_features, + "layer_results": layer_results, + } + + if self.quantizer: + q = self.quantizer(y, produce_targets=False) + y = q["x"] + num_vars = q["num_vars"] + code_ppl = q["code_perplexity"] + prob_ppl = q["prob_perplexity"] + curr_temp = q["temp"] + + y = self.project_q(y) + + if self.negatives_from_everywhere: + neg_cands = self.quantizer(unmasked_features, produce_targets=False)[ + "x" + ] + negs, _ = self.sample_negatives( + neg_cands, + y.size(1), + padding_count=padding_count, + ) + negs = self.project_q(negs) + + else: + negs, _ = self.sample_negatives( + y, + y.size(1), + padding_count=padding_count, + ) + + if self.codebook_negatives > 0: + cb_negs = self.quantizer.sample_from_codebook( + y.size(0) * y.size(1), self.codebook_negatives + ) + cb_negs = cb_negs.view( + self.codebook_negatives, y.size(0), y.size(1), -1 + ) # order doesnt matter + cb_negs = self.project_q(cb_negs) + negs = torch.cat([negs, cb_negs], dim=0) + else: + y = self.project_q(y) + + if self.negatives_from_everywhere: + negs, _ = self.sample_negatives( + unmasked_features, + y.size(1), + padding_count=padding_count, + ) + negs = self.project_q(negs) + else: + negs, _ = self.sample_negatives( + y, + y.size(1), + padding_count=padding_count, + ) + + if not is_xla_tensor(x): + # tpu-comment: reducing the size in a dynamic way causes + # too many recompilations on xla. + x = x[mask_indices].view(x.size(0), -1, x.size(-1)) + + if self.target_glu: + y = self.target_glu(y) + negs = self.target_glu(negs) + + x = self.final_proj(x) + x = self.compute_preds(x, y, negs) + + result = { + "x": x, + "padding_mask": padding_mask, + "features_pen": features_pen, + } + + if prob_ppl is not None: + result["prob_perplexity"] = prob_ppl + result["code_perplexity"] = code_ppl + result["num_vars"] = num_vars + result["temp"] = curr_temp + + return result + + def quantize(self, x): + assert self.quantizer is not None + x = self.feature_extractor(x) + x = x.transpose(1, 2) + x = self.layer_norm(x) + return self.quantizer.forward_idx(x) + + def extract_features(self, source, padding_mask, mask=False, layer=None): + res = self.forward( + source, padding_mask, mask=mask, features_only=True, layer=layer + ) + return res + + def get_logits(self, net_output): + logits = net_output["x"] + logits = logits.transpose(0, 2) + logits = logits.reshape(-1, logits.size(-1)) + return logits + + def get_targets(self, sample, net_output, expand_steps=True): + x = net_output["x"] + return x.new_zeros(x.size(1) * x.size(2), dtype=torch.long) + + def get_extra_losses(self, net_output): + pen = [] + + if "prob_perplexity" in net_output: + pen.append( + (net_output["num_vars"] - net_output["prob_perplexity"]) + / net_output["num_vars"] + ) + + if "features_pen" in net_output: + pen.append(net_output["features_pen"]) + + return pen + + def remove_pretraining_modules(self): + self.quantizer = None + self.project_q = None + self.target_glu = None + self.final_proj = None + + +class ConvFeatureExtractionModel(nn.Module): + def __init__( + self, + conv_layers: List[Tuple[int, int, int]], + dropout: float = 0.0, + mode: str = "default", + conv_bias: bool = False, + ): + super().__init__() + + assert mode in {"default", "layer_norm"} + + def block( + n_in, + n_out, + k, + stride, + is_layer_norm=False, + is_group_norm=False, + conv_bias=False, + ): + def make_conv(): + conv = nn.Conv1d(n_in, n_out, k, stride=stride, bias=conv_bias) + nn.init.kaiming_normal_(conv.weight) + return conv + + assert ( + is_layer_norm and is_group_norm + ) == False, "layer norm and group norm are exclusive" + + if is_layer_norm: + return nn.Sequential( + make_conv(), + nn.Dropout(p=dropout), + nn.Sequential( + TransposeLast(), + Fp32LayerNorm(dim, elementwise_affine=True), + TransposeLast(), + ), + nn.GELU(), + ) + elif is_group_norm: + return nn.Sequential( + make_conv(), + nn.Dropout(p=dropout), + Fp32GroupNorm(dim, dim, affine=True), + nn.GELU(), + ) + else: + return nn.Sequential(make_conv(), nn.Dropout(p=dropout), nn.GELU()) + + in_d = 1 + self.conv_layers = nn.ModuleList() + for i, cl in enumerate(conv_layers): + assert len(cl) == 3, "invalid conv definition: " + str(cl) + (dim, k, stride) = cl + + self.conv_layers.append( + block( + in_d, + dim, + k, + stride, + is_layer_norm=mode == "layer_norm", + is_group_norm=mode == "default" and i == 0, + conv_bias=conv_bias, + ) + ) + in_d = dim + + def forward(self, x): + + # BxT -> BxCxT + x = x.unsqueeze(1) + + for conv in self.conv_layers: + x = conv(x) + + return x + + +class TransformerEncoder(nn.Module): + def __init__(self, args): + super().__init__() + + self.dropout = args.dropout + self.embedding_dim = args.encoder_embed_dim + + self.pos_conv = nn.Conv1d( + self.embedding_dim, + self.embedding_dim, + kernel_size=args.conv_pos, + padding=args.conv_pos // 2, + groups=args.conv_pos_groups, + ) + dropout = 0 + std = math.sqrt((4 * (1.0 - dropout)) / (args.conv_pos * self.embedding_dim)) + nn.init.normal_(self.pos_conv.weight, mean=0, std=std) + nn.init.constant_(self.pos_conv.bias, 0) + + self.pos_conv = nn.utils.weight_norm(self.pos_conv, name="weight", dim=2) + self.pos_conv = nn.Sequential(self.pos_conv, SamePad(args.conv_pos), nn.GELU()) + + self.layers = nn.ModuleList( + [ + TransformerSentenceEncoderLayer( + embedding_dim=self.embedding_dim, + ffn_embedding_dim=args.encoder_ffn_embed_dim, + num_attention_heads=args.encoder_attention_heads, + dropout=self.dropout, + attention_dropout=args.attention_dropout, + activation_dropout=args.activation_dropout, + activation_fn=args.activation_fn, + layer_norm_first=args.layer_norm_first, + ) + for _ in range(args.encoder_layers) + ] + ) + + self.layer_norm_first = args.layer_norm_first + self.layer_norm = LayerNorm(self.embedding_dim) + self.layerdrop = args.encoder_layerdrop + + self.apply(init_bert_params) + + def forward(self, x, padding_mask=None, layer=None): + x, layer_results = self.extract_features(x, padding_mask, layer) + + if self.layer_norm_first and layer is None: + x = self.layer_norm(x) + + return x, layer_results + + def extract_features(self, x, padding_mask=None, tgt_layer=None): + + if padding_mask is not None: + x = index_put(x, padding_mask, 0) + + x_conv = self.pos_conv(x.transpose(1, 2)) + x_conv = x_conv.transpose(1, 2) + x = x + x_conv + + if not self.layer_norm_first: + x = self.layer_norm(x) + + x = F.dropout(x, p=self.dropout, training=self.training) + + # B x T x C -> T x B x C + x = x.transpose(0, 1) + + layer_results = [] + r = None + for i, layer in enumerate(self.layers): + dropout_probability = np.random.random() + if not self.training or (dropout_probability > self.layerdrop): + x, z = layer(x, self_attn_padding_mask=padding_mask, need_weights=False) + if tgt_layer is not None: + layer_results.append((x, z)) + if i == tgt_layer: + r = x + break + + if r is not None: + x = r + + # T x B x C -> B x T x C + x = x.transpose(0, 1) + + return x, layer_results + + def max_positions(self): + """Maximum output length supported by the encoder.""" + return self.args.max_positions + + def upgrade_state_dict_named(self, state_dict, name): + """Upgrade a (possibly old) state dict for new versions of fairseq.""" + return state_dict + + +class TransformerSentenceEncoderLayer(nn.Module): + """ + Implements a Transformer Encoder Layer used in BERT/XLM style pre-trained + models. + """ + + def __init__( + self, + embedding_dim: float = 768, + ffn_embedding_dim: float = 3072, + num_attention_heads: float = 8, + dropout: float = 0.1, + attention_dropout: float = 0.1, + activation_dropout: float = 0.1, + activation_fn: str = "relu", + layer_norm_first: bool = False, + ) -> None: + + super().__init__() + # Initialize parameters + self.embedding_dim = embedding_dim + self.dropout = dropout + self.activation_dropout = activation_dropout + + # Initialize blocks + self.activation_fn = utils.get_activation_fn(activation_fn) + self.self_attn = MultiheadAttention( + self.embedding_dim, + num_attention_heads, + dropout=attention_dropout, + self_attention=True, + ) + + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(self.activation_dropout) + self.dropout3 = nn.Dropout(dropout) + + self.layer_norm_first = layer_norm_first + + # layer norm associated with the self attention layer + self.self_attn_layer_norm = LayerNorm(self.embedding_dim) + self.fc1 = nn.Linear(self.embedding_dim, ffn_embedding_dim) + self.fc2 = nn.Linear(ffn_embedding_dim, self.embedding_dim) + + # layer norm associated with the position wise feed-forward NN + self.final_layer_norm = LayerNorm(self.embedding_dim) + + def forward( + self, + x: torch.Tensor, + self_attn_mask: torch.Tensor = None, + self_attn_padding_mask: torch.Tensor = None, + need_weights: bool = False, + att_args=None, + ): + """ + LayerNorm is applied either before or after the self-attention/ffn + modules similar to the original Transformer imlementation. + """ + residual = x + + if self.layer_norm_first: + x = self.self_attn_layer_norm(x) + x, attn = self.self_attn( + query=x, + key=x, + value=x, + key_padding_mask=self_attn_padding_mask, + attn_mask=self_attn_mask, + ) + x = self.dropout1(x) + x = residual + x + + residual = x + x = self.final_layer_norm(x) + x = self.activation_fn(self.fc1(x)) + x = self.dropout2(x) + x = self.fc2(x) + x = self.dropout3(x) + x = residual + x + else: + x, attn = self.self_attn( + query=x, + key=x, + value=x, + key_padding_mask=self_attn_padding_mask, + ) + + x = self.dropout1(x) + x = residual + x + + x = self.self_attn_layer_norm(x) + + residual = x + x = self.activation_fn(self.fc1(x)) + x = self.dropout2(x) + x = self.fc2(x) + x = self.dropout3(x) + x = residual + x + x = self.final_layer_norm(x) + + return x, attn diff --git a/fairseq/models/wav2vec/wav2vec2_asr.py b/fairseq/models/wav2vec/wav2vec2_asr.py new file mode 100644 index 0000000000..eb5d819da5 --- /dev/null +++ b/fairseq/models/wav2vec/wav2vec2_asr.py @@ -0,0 +1,664 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from argparse import Namespace +import contextlib +import copy +import math +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from dataclasses import dataclass, field +from omegaconf import MISSING, II, open_dict +from typing import Any, Optional + +from fairseq import checkpoint_utils, tasks, utils +from fairseq.dataclass import FairseqDataclass +from fairseq.dataclass.utils import convert_namespace_to_omegaconf +from fairseq.tasks import FairseqTask +from fairseq.models import ( + BaseFairseqModel, + FairseqEncoder, + FairseqEncoderDecoderModel, + FairseqIncrementalDecoder, + register_model, +) +from fairseq.models.wav2vec.wav2vec2 import MASKING_DISTRIBUTION_CHOICES +from fairseq.modules import ( + LayerNorm, + PositionalEmbedding, + TransformerDecoderLayer, +) + + +@dataclass +class Wav2Vec2AsrConfig(FairseqDataclass): + w2v_path: str = field( + default=MISSING, metadata={"help": "path to wav2vec 2.0 model"} + ) + no_pretrained_weights: bool = field( + default=False, metadata={"help": "if true, does not load pretrained weights"} + ) + dropout_input: float = field( + default=0.0, + metadata={"help": "dropout to apply to the input (after feat extr)"}, + ) + final_dropout: float = field( + default=0.0, + metadata={"help": "dropout after transformer and before final projection"}, + ) + dropout: float = field( + default=0.0, metadata={"help": "dropout probability inside wav2vec 2.0 model"} + ) + attention_dropout: float = field( + default=0.0, + metadata={ + "help": "dropout probability for attention weights inside wav2vec 2.0 model" + }, + ) + activation_dropout: float = field( + default=0.0, + metadata={ + "help": "dropout probability after activation in FFN inside wav2vec 2.0 model" + }, + ) + conv_feature_layers: Optional[str] = field( + default="[(512, 10, 5)] + [(512, 3, 2)] * 4 + [(512,2,2)] + [(512,2,2)]", + metadata={ + "help": ( + "string describing convolutional feature extraction " + "layers in form of a python list that contains " + "[(dim, kernel_size, stride), ...]" + ), + }, + ) + encoder_embed_dim: Optional[int] = field( + default=768, metadata={"help": "encoder embedding dimension"} + ) + + # masking + apply_mask: bool = field( + default=False, metadata={"help": "apply masking during fine-tuning"} + ) + mask_length: int = field( + default=10, metadata={"help": "repeat the mask indices multiple times"} + ) + mask_prob: float = field( + default=0.5, + metadata={ + "help": "probability of replacing a token with mask (normalized by length)" + }, + ) + mask_selection: MASKING_DISTRIBUTION_CHOICES = field( + default="static", metadata={"help": "how to choose masks"} + ) + mask_other: float = field( + default=0, + metadata={ + "help": "secondary mask argument (used for more complex distributions), " + "see help in compute_mask_indices" + }, + ) + no_mask_overlap: bool = field( + default=False, metadata={"help": "whether to allow masks to overlap"} + ) + mask_min_space: Optional[int] = field( + default=1, + metadata={"help": "min space between spans (if no overlap is enabled)"}, + ) + + # channel masking + mask_channel_length: int = field( + default=10, metadata={"help": "length of the mask for features (channels)"} + ) + mask_channel_prob: float = field( + default=0.0, metadata={"help": "probability of replacing a feature with 0"} + ) + mask_channel_selection: MASKING_DISTRIBUTION_CHOICES = field( + default="static", + metadata={"help": "how to choose mask length for channel masking"}, + ) + mask_channel_other: float = field( + default=0, + metadata={ + "help": "secondary mask argument (used for more complex distributions), " + "see help in compute_mask_indicesh" + }, + ) + no_mask_channel_overlap: bool = field( + default=False, metadata={"help": "whether to allow channel masks to overlap"} + ) + freeze_finetune_updates: int = field( + default=0, metadata={"help": "dont finetune wav2vec for this many updates"} + ) + feature_grad_mult: float = field( + default=0.0, metadata={"help": "reset feature grad mult in wav2vec 2.0 to this"} + ) + layerdrop: float = field( + default=0.0, metadata={"help": "probability of dropping a layer in wav2vec 2.0"} + ) + mask_channel_min_space: Optional[int] = field( + default=1, + metadata={"help": "min space between spans (if no overlap is enabled)"}, + ) + mask_channel_before: bool = False + normalize: bool = II("task.normalize") + data: str = II("task.data") + # this holds the loaded wav2vec args + w2v_args: Any = None + + +@dataclass +class Wav2Vec2CtcConfig(Wav2Vec2AsrConfig): + blank_weight: float = 0 + blank_mode: str = "add" + + +@register_model("wav2vec_ctc", dataclass=Wav2Vec2CtcConfig) +class Wav2VecCtc(BaseFairseqModel): + def __init__(self, cfg: Wav2Vec2CtcConfig, w2v_encoder: BaseFairseqModel): + super().__init__() + self.cfg = cfg + self.w2v_encoder = w2v_encoder + self.blank_weight = cfg.blank_weight + self.blank_mode = cfg.blank_mode + + def upgrade_state_dict_named(self, state_dict, name): + super().upgrade_state_dict_named(state_dict, name) + return state_dict + + @classmethod + def build_model(cls, cfg: Wav2Vec2CtcConfig, task: FairseqTask): + """Build a new model instance.""" + w2v_encoder = Wav2VecEncoder(cfg, len(task.target_dictionary)) + return cls(cfg, w2v_encoder) + + def get_logits(self, net_output, normalize=False): + logits = net_output["encoder_out"] + if self.blank_weight != 0: + if self.blank_mode == "add": + logits[..., 0] += self.blank_weight + elif self.blank_mode == "set": + logits[..., 0] = self.blank_weight + else: + raise Exception(f"invalid blank mode {self.blank_mode}") + + if net_output["padding_mask"] is not None and net_output["padding_mask"].any(): + logits[net_output["padding_mask"].T][..., 0] = float("inf") + logits[net_output["padding_mask"].T][..., 1:] = float("-inf") + + if normalize: + logits = utils.log_softmax(logits.float(), dim=-1) + + return logits + + def get_normalized_probs(self, net_output, log_probs): + """Get normalized probabilities (or log probs) from a net's output.""" + + logits = self.get_logits(net_output) + + if log_probs: + return utils.log_softmax(logits.float(), dim=-1) + else: + return utils.softmax(logits.float(), dim=-1) + + def forward(self, **kwargs): + x = self.w2v_encoder(**kwargs) + return x + + +@dataclass +class Wav2Vec2Seq2SeqConfig(Wav2Vec2AsrConfig): + decoder_embed_dim: int = field( + default=768, metadata={"help": "decoder embedding dimension"} + ) + decoder_ffn_embed_dim: int = field( + default=3072, metadata={"help": "decoder embedding dimension for FFN"} + ) + decoder_layers: int = field(default=6, metadata={"help": "num of decoder layers"}) + decoder_layerdrop: float = field( + default=0.0, metadata={"help": "decoder layerdrop chance"} + ) + decoder_attention_heads: int = field( + default=4, metadata={"help": "num decoder attention heads"} + ) + decoder_learned_pos: bool = field( + default=False, + metadata={"help": "use learned positional embeddings in the decoder"}, + ) + decoder_normalize_before: bool = field( + default=False, metadata={"help": "apply layernorm before each decoder block"} + ) + no_token_positional_embeddings: bool = field( + default=False, + metadata={ + "help": "if set, disables positional embeddings (outside self attention)" + }, + ) + decoder_dropout: float = field( + default=0.0, metadata={"help": "dropout probability in the decoder"} + ) + decoder_attention_dropout: float = field( + default=0.0, + metadata={ + "help": "dropout probability for attention weights inside the decoder" + }, + ) + decoder_activation_dropout: float = field( + default=0.0, + metadata={ + "help": "dropout probability after activation in FFN inside the decoder" + }, + ) + max_target_positions: int = field( + default=2048, metadata={"help": "max target positions"} + ) + share_decoder_input_output_embed: bool = field( + default=False, metadata={"help": "share decoder input and output embeddings"} + ) + autoregressive: bool = II("task.autoregressive") + + +@register_model("wav2vec_seq2seq", dataclass=Wav2Vec2Seq2SeqConfig) +class Wav2Vec2Seq2SeqModel(FairseqEncoderDecoderModel): + def __init__(self, encoder, decoder): + super().__init__(encoder, decoder) + + @classmethod + def build_model(cls, cfg: Wav2Vec2Seq2SeqConfig, task: FairseqTask): + """Build a new model instance.""" + + assert ( + cfg.autoregressive + ), "Please set task.autoregressive=true for seq2seq asr models" + + src_dict, tgt_dict = task.source_dictionary, task.target_dictionary + + def build_embedding(dictionary, embed_dim): + num_embeddings = len(dictionary) + padding_idx = dictionary.pad() + emb = Embedding(num_embeddings, embed_dim, padding_idx) + return emb + + decoder_embed_tokens = build_embedding(tgt_dict, cfg.decoder_embed_dim) + + encoder = cls.build_encoder(cfg) + decoder = cls.build_decoder(cfg, tgt_dict, decoder_embed_tokens) + + return Wav2Vec2Seq2SeqModel(encoder, decoder) + + @classmethod + def build_encoder(cls, cfg: Wav2Vec2AsrConfig): + return Wav2VecEncoder(cfg) + + @classmethod + def build_decoder(cls, cfg: Wav2Vec2Seq2SeqConfig, tgt_dict, embed_tokens): + return TransformerDecoder(cfg, tgt_dict, embed_tokens) + + def forward(self, **kwargs): + encoder_out = self.encoder(**kwargs) + decoder_out = self.decoder(encoder_out=encoder_out, **kwargs) + return decoder_out + + def upgrade_state_dict_named(self, state_dict, name): + super().upgrade_state_dict_named(state_dict, name) + return state_dict + + +class Wav2VecEncoder(FairseqEncoder): + def __init__(self, cfg: Wav2Vec2AsrConfig, output_size=None): + self.apply_mask = cfg.apply_mask + + arg_overrides = { + "dropout": cfg.dropout, + "activation_dropout": cfg.activation_dropout, + "dropout_input": cfg.dropout_input, + "attention_dropout": cfg.attention_dropout, + "mask_length": cfg.mask_length, + "mask_prob": cfg.mask_prob, + "mask_selection": cfg.mask_selection, + "mask_other": cfg.mask_other, + "no_mask_overlap": cfg.no_mask_overlap, + "mask_channel_length": cfg.mask_channel_length, + "mask_channel_prob": cfg.mask_channel_prob, + "mask_channel_before": cfg.mask_channel_before, + "mask_channel_selection": cfg.mask_channel_selection, + "mask_channel_other": cfg.mask_channel_other, + "no_mask_channel_overlap": cfg.no_mask_channel_overlap, + "encoder_layerdrop": cfg.layerdrop, + "feature_grad_mult": cfg.feature_grad_mult, + } + + if cfg.w2v_args is None: + state = checkpoint_utils.load_checkpoint_to_cpu(cfg.w2v_path, arg_overrides) + w2v_args = state.get("cfg", None) + if w2v_args is None: + w2v_args = convert_namespace_to_omegaconf(state["args"]) + w2v_args.criterion = None + w2v_args.lr_scheduler = None + cfg.w2v_args = w2v_args + else: + state = None + w2v_args = cfg.w2v_args + if isinstance(w2v_args, Namespace): + cfg.w2v_args = w2v_args = convert_namespace_to_omegaconf(w2v_args) + + assert cfg.normalize == w2v_args.task.normalize, ( + "Fine-tuning works best when data normalization is the same. " + "Please check that --normalize is set or unset for both pre-training and here" + ) + + w2v_args.task.data = cfg.data + task = tasks.setup_task(w2v_args.task) + model = task.build_model(w2v_args.model) + + if state is not None and not cfg.no_pretrained_weights: + model.load_state_dict(state["model"], strict=True) + + model.remove_pretraining_modules() + + super().__init__(task.source_dictionary) + + d = w2v_args.model.encoder_embed_dim + + self.w2v_model = model + + self.final_dropout = nn.Dropout(cfg.final_dropout) + self.freeze_finetune_updates = cfg.freeze_finetune_updates + self.num_updates = 0 + + targ_d = None + self.proj = None + + if output_size is not None: + targ_d = output_size + elif getattr(cfg, "decoder_embed_dim", d) != d: + targ_d = cfg.decoder_embed_dim + + if targ_d is not None: + self.proj = Linear(d, targ_d) + + def set_num_updates(self, num_updates): + """Set the number of parameters updates.""" + super().set_num_updates(num_updates) + self.num_updates = num_updates + + def forward(self, source, padding_mask, **kwargs): + + w2v_args = { + "source": source, + "padding_mask": padding_mask, + "mask": self.apply_mask and self.training, + } + + ft = self.freeze_finetune_updates <= self.num_updates + + with torch.no_grad() if not ft else contextlib.ExitStack(): + res = self.w2v_model.extract_features(**w2v_args) + + x = res["x"] + padding_mask = res["padding_mask"] + + # B x T x C -> T x B x C + x = x.transpose(0, 1) + + x = self.final_dropout(x) + + if self.proj: + x = self.proj(x) + + return { + "encoder_out": x, # T x B x C + "padding_mask": padding_mask, # B x T, + "layer_results": res["layer_results"], + } + + def forward_torchscript(self, net_input): + if torch.jit.is_scripting(): + return self.forward(net_input["source"], net_input["padding_mask"]) + else: + return self.forward_non_torchscript(net_input) + + def reorder_encoder_out(self, encoder_out, new_order): + if encoder_out["encoder_out"] is not None: + encoder_out["encoder_out"] = encoder_out["encoder_out"].index_select( + 1, new_order + ) + if encoder_out["padding_mask"] is not None: + encoder_out["padding_mask"] = encoder_out[ + "padding_mask" + ].index_select(0, new_order) + return encoder_out + + def max_positions(self): + """Maximum input length supported by the encoder.""" + return None + + def upgrade_state_dict_named(self, state_dict, name): + return state_dict + + +class TransformerDecoder(FairseqIncrementalDecoder): + """ + Transformer decoder consisting of *args.decoder_layers* layers. Each layer + is a :class:`TransformerDecoderLayer`. + + Args: + args (argparse.Namespace): parsed command-line arguments + dictionary (~fairseq.data.Dictionary): decoding dictionary + embed_tokens (torch.nn.Embedding): output embedding + no_encoder_attn (bool, optional): whether to attend to encoder outputs + (default: False). + """ + + def __init__( + self, + cfg: Wav2Vec2Seq2SeqConfig, + dictionary, + embed_tokens, + no_encoder_attn=False, + ): + super().__init__(dictionary) + + self.dropout = cfg.decoder_dropout + self.share_input_output_embed = cfg.share_decoder_input_output_embed + + input_embed_dim = embed_tokens.embedding_dim + embed_dim = cfg.decoder_embed_dim + self.output_embed_dim = cfg.decoder_embed_dim + + self.layerdrop = cfg.decoder_layerdrop + + self.padding_idx = embed_tokens.padding_idx + self.max_target_positions = cfg.max_target_positions + + self.embed_tokens = embed_tokens + self.embed_scale = math.sqrt(embed_dim) # todo: try with input_embed_dim + + self.project_in_dim = ( + Linear(input_embed_dim, embed_dim, bias=False) + if embed_dim != input_embed_dim + else None + ) + + self.embed_positions = ( + PositionalEmbedding( + cfg.max_target_positions, + embed_dim, + self.padding_idx, + learned=cfg.decoder_learned_pos, + ) + if not cfg.no_token_positional_embeddings + else None + ) + + # TODO: update this when transformer gets converted to dataclass configs + transformer_cfg = copy.deepcopy(cfg) + with open_dict(transformer_cfg): + transformer_cfg.dropout = transformer_cfg.decoder_dropout + transformer_cfg.attention_dropout = ( + transformer_cfg.decoder_attention_dropout + ) + transformer_cfg.activation_dropout = ( + transformer_cfg.decoder_activation_dropout + ) + + self.layers = nn.ModuleList([]) + self.layers.extend( + [ + TransformerDecoderLayer(transformer_cfg, no_encoder_attn) + for _ in range(transformer_cfg.decoder_layers) + ] + ) + + if not self.share_input_output_embed: + self.embed_out = nn.Parameter( + torch.Tensor(len(dictionary), self.output_embed_dim) + ) + nn.init.normal_(self.embed_out, mean=0, std=self.output_embed_dim ** -0.5) + + if transformer_cfg.decoder_normalize_before: + self.layer_norm = LayerNorm(embed_dim) + else: + self.layer_norm = None + + def forward( + self, prev_output_tokens, encoder_out=None, incremental_state=None, **unused + ): + """ + Args: + prev_output_tokens (LongTensor): previous decoder outputs of shape + `(batch, tgt_len)`, for teacher forcing + encoder_out (Tensor, optional): output from the encoder, used for + encoder-side attention + incremental_state (dict): dictionary used for storing state during + :ref:`Incremental decoding` + + Returns: + tuple: + - the decoder's output of shape `(batch, tgt_len, vocab)` + - a dictionary with any model-specific outputs + """ + prev_output_tokens = prev_output_tokens.long() + x, extra = self.extract_features( + prev_output_tokens, encoder_out, incremental_state + ) + x = self.output_layer(x) + return x, extra + + def extract_features( + self, prev_output_tokens, encoder_out=None, incremental_state=None, **unused + ): + """ + Similar to *forward* but only return features. + + Returns: + tuple: + - the decoder's features of shape `(batch, tgt_len, embed_dim)` + - a dictionary with any model-specific outputs + """ + + # embed positions + positions = ( + self.embed_positions( + prev_output_tokens, incremental_state=incremental_state + ) + if self.embed_positions is not None + else None + ) + + if incremental_state is not None: + prev_output_tokens = prev_output_tokens[:, -1:] + if positions is not None: + positions = positions[:, -1:] + + # embed tokens and positions + x = self.embed_scale * self.embed_tokens(prev_output_tokens) + + if self.project_in_dim is not None: + x = self.project_in_dim(x) + + if positions is not None: + x += positions + x = F.dropout(x, p=self.dropout, training=self.training) + + # B x T x C -> T x B x C + x = x.transpose(0, 1) + attn = None + + inner_states = [x] + + # decoder layers + self_attn_padding_mask = None + if prev_output_tokens.eq(self.padding_idx).any(): + self_attn_padding_mask = prev_output_tokens.eq(self.padding_idx) + for layer in self.layers: + dropout_probability = np.random.random() + if not self.training or (dropout_probability > self.layerdrop): + x, attn, _ = layer( + x, + encoder_out["encoder_out"] if encoder_out is not None else None, + encoder_out["padding_mask"] if encoder_out is not None else None, + incremental_state, + self_attn_mask=self.buffered_future_mask(x) + if incremental_state is None + else None, + self_attn_padding_mask=self_attn_padding_mask + ) + inner_states.append(x) + + if self.layer_norm: + x = self.layer_norm(x) + + # T x B x C -> B x T x C + x = x.transpose(0, 1) + + return x, {"attn": attn, "inner_states": inner_states} + + def output_layer(self, features, **kwargs): + """Project features to the vocabulary size.""" + # project back to size of vocabulary + if self.share_input_output_embed: + return F.linear(features, self.embed_tokens.weight) + else: + return F.linear(features, self.embed_out) + + def max_positions(self): + """Maximum output length supported by the decoder.""" + if self.embed_positions is None: + return self.max_target_positions + return min(self.max_target_positions, self.embed_positions.max_positions) + + def buffered_future_mask(self, tensor): + dim = tensor.size(0) + if ( + not hasattr(self, "_future_mask") + or self._future_mask is None + or self._future_mask.device != tensor.device + or self._future_mask.size(0) < dim + ): + self._future_mask = torch.triu( + utils.fill_with_neg_inf(tensor.new(dim, dim)), 1 + ) + return self._future_mask[:dim, :dim] + + def upgrade_state_dict_named(self, state_dict, name): + return state_dict + + +def Embedding(num_embeddings, embedding_dim, padding_idx): + m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx) + nn.init.normal_(m.weight, mean=0, std=embedding_dim ** -0.5) + nn.init.constant_(m.weight[padding_idx], 0) + return m + + +def Linear(in_features, out_features, bias=True): + m = nn.Linear(in_features, out_features, bias) + nn.init.xavier_uniform_(m.weight) + if bias: + nn.init.constant_(m.bias, 0.0) + return m diff --git a/fairseq/modules/__init__.py b/fairseq/modules/__init__.py index 69b9f6962b..d7a030e2b5 100644 --- a/fairseq/modules/__init__.py +++ b/fairseq/modules/__init__.py @@ -2,9 +2,11 @@ # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. +"""isort:skip_file""" from .adaptive_input import AdaptiveInput from .adaptive_softmax import AdaptiveSoftmax +from .base_layer import BaseLayer from .beamable_mm import BeamableMM from .character_token_embedder import CharacterTokenEmbedder from .conv_tbc import ConvTBC @@ -12,6 +14,7 @@ from .downsampled_multihead_attention import DownsampledMultiHeadAttention from .dynamic_convolution import DynamicConv, DynamicConv1dTBC from .dynamic_crf_layer import DynamicCRF +from .fairseq_dropout import FairseqDropout from .fp32_group_norm import Fp32GroupNorm from .gelu import gelu, gelu_accurate from .grad_multiply import GradMultiply @@ -22,48 +25,58 @@ from .learned_positional_embedding import LearnedPositionalEmbedding from .lightweight_convolution import LightweightConv, LightweightConv1dTBC from .linearized_convolution import LinearizedConvolution +from .location_attention import LocationAttention +from .lstm_cell_with_zoneout import LSTMCellWithZoneOut from .multihead_attention import MultiheadAttention from .positional_embedding import PositionalEmbedding +from .same_pad import SamePad from .scalar_bias import ScalarBias from .sinusoidal_positional_embedding import SinusoidalPositionalEmbedding from .transformer_sentence_encoder_layer import TransformerSentenceEncoderLayer from .transformer_sentence_encoder import TransformerSentenceEncoder +from .transpose_last import TransposeLast from .unfold import unfold1d from .transformer_layer import TransformerDecoderLayer, TransformerEncoderLayer from .vggblock import VGGBlock __all__ = [ - 'AdaptiveInput', - 'AdaptiveSoftmax', - 'BeamableMM', - 'CharacterTokenEmbedder', - 'ConvTBC', - 'cross_entropy', - 'DownsampledMultiHeadAttention', - 'DynamicConv1dTBC', - 'DynamicConv', - 'DynamicCRF', - 'Fp32GroupNorm', - 'Fp32LayerNorm', - 'gelu', - 'gelu_accurate', - 'GradMultiply', - 'GumbelVectorQuantizer', - 'KmeansVectorQuantizer', - 'LayerDropModuleList', - 'LayerNorm', - 'LearnedPositionalEmbedding', - 'LightweightConv1dTBC', - 'LightweightConv', - 'LinearizedConvolution', - 'MultiheadAttention', - 'PositionalEmbedding', - 'ScalarBias', - 'SinusoidalPositionalEmbedding', - 'TransformerSentenceEncoderLayer', - 'TransformerSentenceEncoder', - 'TransformerDecoderLayer', - 'TransformerEncoderLayer', - 'VGGBlock', - 'unfold1d', + "AdaptiveInput", + "AdaptiveSoftmax", + "BaseLayer", + "BeamableMM", + "CharacterTokenEmbedder", + "ConvTBC", + "cross_entropy", + "DownsampledMultiHeadAttention", + "DynamicConv1dTBC", + "DynamicConv", + "DynamicCRF", + "FairseqDropout", + "Fp32GroupNorm", + "Fp32LayerNorm", + "gelu", + "gelu_accurate", + "GradMultiply", + "GumbelVectorQuantizer", + "KmeansVectorQuantizer", + "LayerDropModuleList", + "LayerNorm", + "LearnedPositionalEmbedding", + "LightweightConv1dTBC", + "LightweightConv", + "LinearizedConvolution", + "LocationAttention", + "LSTMCellWithZoneOut", + "MultiheadAttention", + "PositionalEmbedding", + "SamePad", + "ScalarBias", + "SinusoidalPositionalEmbedding", + "TransformerSentenceEncoderLayer", + "TransformerSentenceEncoder", + "TransformerDecoderLayer", + "TransformerEncoderLayer", + "TransposeLast", + "VGGBlock", + "unfold1d", ] diff --git a/fairseq/modules/adaptive_input.py b/fairseq/modules/adaptive_input.py index 4cfe8fca66..446534a9f8 100644 --- a/fairseq/modules/adaptive_input.py +++ b/fairseq/modules/adaptive_input.py @@ -4,15 +4,14 @@ # LICENSE file in the root directory of this source tree. +from typing import List + import torch -from torch import nn from fairseq.modules.quant_noise import quant_noise - -from typing import List +from torch import nn class AdaptiveInput(nn.Module): - def __init__( self, vocab_size: int, @@ -29,8 +28,9 @@ def __init__( if vocab_size > cutoff[-1]: cutoff = cutoff + [vocab_size] else: - assert vocab_size == cutoff[ - -1], 'cannot specify cutoff larger than vocab size' + assert ( + vocab_size == cutoff[-1] + ), "cannot specify cutoff larger than vocab size" self.cutoff = cutoff self.embedding_dim = output_dim @@ -43,7 +43,9 @@ def __init__( dim = int(initial_dim // (factor ** i)) seq = nn.Sequential( nn.Embedding(size, dim, self.padding_idx), - quant_noise(nn.Linear(dim, output_dim, bias=False), q_noise, qn_block_size), + quant_noise( + nn.Linear(dim, output_dim, bias=False), q_noise, qn_block_size + ), ) self.embeddings.append(seq) @@ -54,12 +56,12 @@ def init_weights(m): if isinstance(m, nn.Embedding): nn.init.normal_(m.weight, mean=0, std=m.weight.shape[1] ** -0.5) nn.init.constant_(m.weight[padding_idx], 0) - elif hasattr(m, 'weight'): + elif hasattr(m, "weight"): nn.init.xavier_uniform_(m.weight) self.apply(init_weights) - self.register_buffer('_float_tensor', torch.FloatTensor(1)) + self.register_buffer("_float_tensor", torch.FloatTensor(1)) def weights_for_band(self, band: int): return self.embeddings[band][0].weight, self.embeddings[band][1].weight diff --git a/fairseq/modules/adaptive_softmax.py b/fairseq/modules/adaptive_softmax.py index 1789e85d44..ae0c77ba0f 100644 --- a/fairseq/modules/adaptive_softmax.py +++ b/fairseq/modules/adaptive_softmax.py @@ -3,11 +3,12 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. -import operator import functools +import operator import torch import torch.nn.functional as F +from fairseq.modules.fairseq_dropout import FairseqDropout from fairseq.modules.quant_noise import quant_noise from torch import nn @@ -28,23 +29,29 @@ def __init__(self, weights, input_dim, num_classes, q_noise, qn_block_size): tied_emb, _ = weights self.num_words, emb_dim = tied_emb.size() - self.word_proj = quant_noise(TiedLinear(tied_emb, transpose=False), q_noise, qn_block_size) + self.word_proj = quant_noise( + TiedLinear(tied_emb, transpose=False), q_noise, qn_block_size + ) if input_dim != emb_dim: self.word_proj = nn.Sequential( - quant_noise(nn.Linear(input_dim, emb_dim, bias=False), q_noise, qn_block_size), + quant_noise( + nn.Linear(input_dim, emb_dim, bias=False), q_noise, qn_block_size + ), self.word_proj, ) - self.class_proj = quant_noise(nn.Linear(input_dim, num_classes, bias=False), q_noise, qn_block_size) + self.class_proj = quant_noise( + nn.Linear(input_dim, num_classes, bias=False), q_noise, qn_block_size + ) self.out_dim = self.num_words + num_classes - self.register_buffer('_float_tensor', torch.FloatTensor(1)) + self.register_buffer("_float_tensor", torch.FloatTensor(1)) def forward(self, input): inp_sz = functools.reduce(operator.mul, input.shape[:-1], 1) out = self._float_tensor.new(inp_sz, self.out_dim) - out[:, :self.num_words] = self.word_proj(input.view(inp_sz, -1)) - out[:, self.num_words:] = self.class_proj(input.view(inp_sz, -1)) + out[:, : self.num_words] = self.word_proj(input.view(inp_sz, -1)) + out[:, self.num_words :] = self.class_proj(input.view(inp_sz, -1)) return out @@ -55,20 +62,34 @@ class AdaptiveSoftmax(nn.Module): approximation for GPUs" (http://arxiv.org/abs/1609.04309). """ - def __init__(self, vocab_size, input_dim, cutoff, dropout, factor=4., adaptive_inputs=None, tie_proj=False, q_noise=0, qn_block_size=8): + def __init__( + self, + vocab_size, + input_dim, + cutoff, + dropout, + factor=4.0, + adaptive_inputs=None, + tie_proj=False, + q_noise=0, + qn_block_size=8, + ): super().__init__() if vocab_size > cutoff[-1]: cutoff = cutoff + [vocab_size] else: - assert vocab_size == cutoff[ - -1], 'cannot specify cutoff larger than vocab size' + assert ( + vocab_size == cutoff[-1] + ), "cannot specify cutoff larger than vocab size" output_dim = cutoff[0] + len(cutoff) - 1 self.vocab_size = vocab_size self.cutoff = cutoff - self.dropout = dropout + self.dropout_module = FairseqDropout( + dropout, module_name=self.__class__.__name__ + ) self.input_dim = input_dim self.factor = factor self.q_noise = q_noise @@ -77,53 +98,84 @@ def __init__(self, vocab_size, input_dim, cutoff, dropout, factor=4., adaptive_i self.lsm = nn.LogSoftmax(dim=1) if adaptive_inputs is not None: - self.head = TiedHeadModule(adaptive_inputs.weights_for_band(0), input_dim, len(cutoff) - 1, self.q_noise, self.qn_block_size) + self.head = TiedHeadModule( + adaptive_inputs.weights_for_band(0), + input_dim, + len(cutoff) - 1, + self.q_noise, + self.qn_block_size, + ) else: - self.head = quant_noise(nn.Linear(input_dim, output_dim, bias=False), self.q_noise, self.qn_block_size) + self.head = quant_noise( + nn.Linear(input_dim, output_dim, bias=False), + self.q_noise, + self.qn_block_size, + ) self._make_tail(adaptive_inputs, tie_proj) def init_weights(m): - if hasattr(m, 'weight') and not isinstance(m, TiedLinear) and not isinstance(m, TiedHeadModule): + if ( + hasattr(m, "weight") + and not isinstance(m, TiedLinear) + and not isinstance(m, TiedHeadModule) + ): nn.init.xavier_uniform_(m.weight) self.apply(init_weights) - self.register_buffer('version', torch.LongTensor([1])) + self.register_buffer("version", torch.LongTensor([1])) def _make_tail(self, adaptive_inputs=None, tie_proj=False): self.tail = nn.ModuleList() for i in range(len(self.cutoff) - 1): dim = int(self.input_dim // self.factor ** (i + 1)) - tied_emb, tied_proj = adaptive_inputs.weights_for_band(i + 1) \ - if adaptive_inputs is not None else (None, None) + tied_emb, tied_proj = ( + adaptive_inputs.weights_for_band(i + 1) + if adaptive_inputs is not None + else (None, None) + ) if tied_proj is not None: if tie_proj: - proj = quant_noise(TiedLinear(tied_proj, transpose=True), self.q_noise, self.qn_block_size) + proj = quant_noise( + TiedLinear(tied_proj, transpose=True), + self.q_noise, + self.qn_block_size, + ) else: - proj = quant_noise(nn.Linear(tied_proj.size(0), tied_proj.size(1), bias=False), self.q_noise, self.qn_block_size) + proj = quant_noise( + nn.Linear(tied_proj.size(0), tied_proj.size(1), bias=False), + self.q_noise, + self.qn_block_size, + ) else: - proj = quant_noise(nn.Linear(self.input_dim, dim, bias=False), self.q_noise, self.qn_block_size) + proj = quant_noise( + nn.Linear(self.input_dim, dim, bias=False), + self.q_noise, + self.qn_block_size, + ) if tied_emb is None: - out_proj = nn.Linear(dim, self.cutoff[i + 1] - self.cutoff[i], bias=False) + out_proj = nn.Linear( + dim, self.cutoff[i + 1] - self.cutoff[i], bias=False + ) else: out_proj = TiedLinear(tied_emb, transpose=False) m = nn.Sequential( proj, - nn.Dropout(self.dropout), + nn.Dropout(self.dropout_module.p), quant_noise(out_proj, self.q_noise, self.qn_block_size), ) self.tail.append(m) def upgrade_state_dict_named(self, state_dict, name): - version_name = name + '.version' + version_name = name + ".version" if version_name not in state_dict: - raise Exception('This version of the model is no longer supported') + raise Exception("This version of the model is no longer supported") def adapt_target(self, target): """ @@ -142,7 +194,7 @@ def adapt_target(self, target): new_target[0][mask] = self.cutoff[0] + i if mask.any(): - target_idxs.append(mask.nonzero().squeeze(1)) + target_idxs.append(mask.nonzero(as_tuple=False).squeeze(1)) new_target.append(target[mask].add(-self.cutoff[i])) else: target_idxs.append(None) @@ -160,7 +212,7 @@ def forward(self, input, target): """ input = input.contiguous().view(-1, input.size(-1)) - input = F.dropout(input, p=self.dropout, training=self.training) + input = self.dropout_module(input) new_target, target_idxs = self.adapt_target(target) output = [self.head(input)] @@ -192,7 +244,7 @@ def get_log_prob(self, input, target): head_sz = self.cutoff[0] + len(self.tail) log_probs[:, :head_sz] = self.lsm(head_y) - tail_priors = log_probs[:, self.cutoff[0]: head_sz].clone() + tail_priors = log_probs[:, self.cutoff[0] : head_sz].clone() for i in range(len(self.tail)): start = self.cutoff[i] @@ -201,12 +253,16 @@ def get_log_prob(self, input, target): if target_idxs is None: tail_out = log_probs[:, start:end] tail_out.copy_(self.tail[i](input)) - log_probs[:, start:end] = self.lsm(tail_out).add_(tail_priors[:, i, None]) + log_probs[:, start:end] = self.lsm(tail_out).add_( + tail_priors[:, i, None] + ) elif target_idxs[i] is not None: idxs = target_idxs[i] tail_out = log_probs[idxs, start:end] tail_out.copy_(self.tail[i](input[idxs])) - log_probs[idxs, start:end] = self.lsm(tail_out).add_(tail_priors[idxs, i, None]) + log_probs[idxs, start:end] = self.lsm(tail_out).add_( + tail_priors[idxs, i, None] + ) log_probs = log_probs.view(bsz, length, -1) return log_probs diff --git a/fairseq/modules/base_layer.py b/fairseq/modules/base_layer.py new file mode 100644 index 0000000000..e7ef155b25 --- /dev/null +++ b/fairseq/modules/base_layer.py @@ -0,0 +1,135 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch.nn as nn +import torch +import sys +from fairseq import utils +from fairseq.distributed import utils as distributed_utils +from fairseq.modules.layer_norm import LayerNorm + + +class BaseLayer(nn.Module): + + def __init__(self, args): + super().__init__() + self.num_workers = distributed_utils.get_data_parallel_world_size() + expert_centroids = torch.empty(self.num_workers, args.decoder_embed_dim) + torch.nn.init.orthogonal_(expert_centroids, gain=0.1) + self.register_parameter("expert_centroids", torch.nn.Parameter(expert_centroids)) + self.expert_network = nn.Sequential(*([BaseSublayer(args) for _ in range(args.base_sublayers)])) + self.expert_id = distributed_utils.get_data_parallel_rank() + self.shuffle = args.base_shuffle + self.cpp = self.load_assignment() + + # Add a special attribute to the expert parameters, so we know not to sync their gradients + for param in self.expert_network.parameters(): + param.expert = True + + def forward(self, input_features, *args, **kwargs): + features = input_features.reshape(-1, input_features.size(-1)) + is_training = input_features.requires_grad + + if self.shuffle and is_training: + # Send each token to a random worker, to break correlations within the batch + shuffle_sort = torch.randperm(features.size(0), device=features.device) + features = All2All.apply(features[shuffle_sort]) + + with torch.no_grad(): + # Compute similarity of each token to each expert, for routing + token_expert_affinities = features.matmul(self.expert_centroids.transpose(0, 1)) + + # Compute which token goes to which expert + sort_by_expert, input_splits, output_splits = self.balanced_assignment(token_expert_affinities) \ + if is_training else self.greedy_assignment(token_expert_affinities) + # Swap these tokens for the right ones for our expert + routed_features = All2All.apply(features[sort_by_expert], output_splits, input_splits) + + if routed_features.size(0) > 0: + # Mix in the expert network based on how appropriate it is for these tokens + alpha = torch.sigmoid(routed_features.mv(self.expert_centroids[self.expert_id])).unsqueeze(1) + routed_features = alpha * self.expert_network(routed_features) + (1 - alpha) * routed_features + # Return to original worker and ordering + result = All2All.apply(routed_features, input_splits, output_splits)[self.inverse_sort(sort_by_expert)] + + if self.shuffle and is_training: + # Undo shuffling + result = All2All.apply(result)[self.inverse_sort(shuffle_sort)] + + # Return additional Nones for compatibility with TransformerDecoderLayer + return result.view(input_features.size()), None, None + + def inverse_sort(self, order): + # Creates an index that undoes a sort: xs==xs[order][inverse_sort(order)] + return torch.empty_like(order).scatter_(0, order, torch.arange(0, order.size(0), device=order.device)) + + def balanced_assignment(self, scores): + ok = scores.isfinite() + if not ok.all(): + # NaNs here can break the assignment algorithm + scores[~ok] = scores[ok].min() + return self.cpp.balanced_assignment(scores), None, None + + # Assigns each token to the top k experts + def greedy_assignment(self, scores, k=1): + token_to_workers = torch.topk(scores, dim=1, k=k, largest=True).indices.view(-1) + token_to_workers, sort_ordering = torch.sort(token_to_workers) + worker2token = sort_ordering // k + + # Find how many tokens we're sending to each other worker (being careful for sending 0 tokens to some workers) + output_splits = torch.zeros((self.num_workers,), dtype=torch.long, device=scores.device) + workers, counts = torch.unique_consecutive(token_to_workers, return_counts=True) + output_splits[workers] = counts + # Tell other workers how many tokens to expect from us + input_splits = All2All.apply(output_splits) + return worker2token, input_splits.tolist(), output_splits.tolist() + + def load_assignment(self): + try: + from fairseq import libbase + + return libbase + + except ImportError as e: + sys.stderr.write( + "ERROR: missing libbase. run `python setup.py build_ext --inplace`\n" + ) + raise e + + +class BaseSublayer(nn.Module): + def __init__(self, args): + super().__init__() + self.activation_fn = utils.get_activation_fn( + activation=getattr(args, 'activation_fn', 'relu') or "relu" + ) + self.norm = LayerNorm(args.decoder_embed_dim, export=False) + self.ff1 = torch.nn.Linear(args.decoder_embed_dim, args.decoder_ffn_embed_dim) + self.ff2 = torch.nn.Linear(args.decoder_ffn_embed_dim, args.decoder_embed_dim) + self.ff2.weight.data.zero_() + + def forward(self, xs): + return xs + self.ff2(self.activation_fn(self.ff1(self.norm(xs)))) + + +# Wraps torch.distributed.all_to_all_single as a function that supports autograd +class All2All(torch.autograd.Function): + @staticmethod + def forward(ctx, xs, input_splits=None, output_splits=None): + ctx.input_splits = input_splits + ctx.output_splits = output_splits + + ys = torch.empty_like(xs) if output_splits is None else \ + xs.new_empty(size=[sum(output_splits)] + list(xs.size()[1:])) + torch.distributed.all_to_all_single(ys, xs, output_split_sizes=output_splits, input_split_sizes=input_splits) + return ys + + @staticmethod + def backward(ctx, grad_output): + result = torch.empty_like(grad_output) if ctx.input_splits is None else \ + grad_output.new_empty(size=[sum(ctx.input_splits)] + list(grad_output.size()[1:])) + torch.distributed.all_to_all_single(result, grad_output, + output_split_sizes=ctx.input_splits, input_split_sizes=ctx.output_splits) + return result, None, None diff --git a/fairseq/modules/beamable_mm.py b/fairseq/modules/beamable_mm.py index df77105a94..eff1a4607f 100644 --- a/fairseq/modules/beamable_mm.py +++ b/fairseq/modules/beamable_mm.py @@ -15,16 +15,18 @@ class BeamableMM(nn.Module): inference by replacing the inputs {(bsz x 1 x nhu), (bsz x sz2 x nhu)} with smaller inputs {(bsz/beam x beam x nhu), (bsz/beam x sz2 x nhu)}. """ + def __init__(self, beam_size=None): super(BeamableMM, self).__init__() self.beam_size = beam_size def forward(self, input1, input2): if ( - not self.training and # test mode - self.beam_size is not None and # beam size is set - input1.dim() == 3 and # only support batched input - input1.size(1) == 1 # single time step update + not self.training + and self.beam_size is not None # test mode + and input1.dim() == 3 # beam size is set + and input1.size(1) # only support batched input + == 1 # single time step update ): bsz, beam = input1.size(0), self.beam_size diff --git a/fairseq/modules/character_token_embedder.py b/fairseq/modules/character_token_embedder.py index 3abdaf4f28..181221b61b 100644 --- a/fairseq/modules/character_token_embedder.py +++ b/fairseq/modules/character_token_embedder.py @@ -7,10 +7,10 @@ from typing import List, Tuple import torch -from torch import nn import torch.nn.functional as F - from fairseq.data import Dictionary +from torch import nn + CHAR_PAD_IDX = 0 CHAR_EOS_IDX = 257 @@ -21,14 +21,14 @@ class CharacterTokenEmbedder(torch.nn.Module): def __init__( - self, - vocab: Dictionary, - filters: List[Tuple[int, int]], - char_embed_dim: int, - word_embed_dim: int, - highway_layers: int, - max_char_len: int = 50, - char_inputs: bool = False + self, + vocab: Dictionary, + filters: List[Tuple[int, int]], + char_embed_dim: int, + word_embed_dim: int, + highway_layers: int, + max_char_len: int = 50, + char_inputs: bool = False, ): super(CharacterTokenEmbedder, self).__init__() @@ -52,7 +52,9 @@ def __init__( self.projection = nn.Linear(last_dim, word_embed_dim) - assert vocab is not None or char_inputs, "vocab must be set if not using char inputs" + assert ( + vocab is not None or char_inputs + ), "vocab must be set if not using char inputs" self.vocab = None if vocab is not None: self.set_vocab(vocab, max_char_len) @@ -79,7 +81,11 @@ def set_vocab(self, vocab, max_char_len): word_to_char[i] = torch.LongTensor(char_idxs) if truncated > 0: - logger.info('truncated {} words longer than {} characters'.format(truncated, max_char_len)) + logger.info( + "truncated {} words longer than {} characters".format( + truncated, max_char_len + ) + ) self.vocab = vocab self.word_to_char = word_to_char @@ -93,12 +99,14 @@ def reset_parameters(self): nn.init.xavier_normal_(self.symbol_embeddings) nn.init.xavier_uniform_(self.projection.weight) - nn.init.constant_(self.char_embeddings.weight[self.char_embeddings.padding_idx], 0.) - nn.init.constant_(self.projection.bias, 0.) + nn.init.constant_( + self.char_embeddings.weight[self.char_embeddings.padding_idx], 0.0 + ) + nn.init.constant_(self.projection.bias, 0.0) def forward( - self, - input: torch.Tensor, + self, + input: torch.Tensor, ): if self.char_inputs: chars = input.view(-1, self.max_char_len) @@ -113,7 +121,9 @@ def forward( unk = None else: flat_words = input.view(-1) - chars = self.word_to_char[flat_words.type_as(self.word_to_char)].type_as(input) + chars = self.word_to_char[flat_words.type_as(self.word_to_char)].type_as( + input + ) pads = flat_words.eq(self.vocab.pad()) eos = flat_words.eq(self.vocab.eos()) unk = flat_words.eq(self.vocab.unk()) @@ -121,11 +131,17 @@ def forward( word_embs = self._convolve(chars) if self.onnx_trace: if pads.any(): - word_embs = torch.where(pads.unsqueeze(1), word_embs.new_zeros(1), word_embs) + word_embs = torch.where( + pads.unsqueeze(1), word_embs.new_zeros(1), word_embs + ) if eos.any(): - word_embs = torch.where(eos.unsqueeze(1), self.symbol_embeddings[self.eos_idx], word_embs) + word_embs = torch.where( + eos.unsqueeze(1), self.symbol_embeddings[self.eos_idx], word_embs + ) if unk is not None and unk.any(): - word_embs = torch.where(unk.unsqueeze(1), self.symbol_embeddings[self.unk_idx], word_embs) + word_embs = torch.where( + unk.unsqueeze(1), self.symbol_embeddings[self.unk_idx], word_embs + ) else: if pads.any(): word_embs[pads] = 0 @@ -137,8 +153,8 @@ def forward( return word_embs.view(input.size()[:2] + (-1,)) def _convolve( - self, - char_idxs: torch.Tensor, + self, + char_idxs: torch.Tensor, ): char_embs = self.char_embeddings(char_idxs) char_embs = char_embs.transpose(1, 2) # BTC -> BCT @@ -166,15 +182,12 @@ class Highway(torch.nn.Module): Adopted from the AllenNLP implementation. """ - def __init__( - self, - input_dim: int, - num_layers: int = 1 - ): + def __init__(self, input_dim: int, num_layers: int = 1): super(Highway, self).__init__() self.input_dim = input_dim - self.layers = nn.ModuleList([nn.Linear(input_dim, input_dim * 2) - for _ in range(num_layers)]) + self.layers = nn.ModuleList( + [nn.Linear(input_dim, input_dim * 2) for _ in range(num_layers)] + ) self.activation = nn.ReLU() self.reset_parameters() @@ -186,15 +199,12 @@ def reset_parameters(self): # setting the bias on `B(x)` to be positive, because that means `g` will be biased to # be high, so we will carry the input forward. The bias on `B(x)` is the second half # of the bias vector in each Linear layer. - nn.init.constant_(layer.bias[self.input_dim:], 1) + nn.init.constant_(layer.bias[self.input_dim :], 1) - nn.init.constant_(layer.bias[:self.input_dim], 0) + nn.init.constant_(layer.bias[: self.input_dim], 0) nn.init.xavier_normal_(layer.weight) - def forward( - self, - x: torch.Tensor - ): + def forward(self, x: torch.Tensor): for layer in self.layers: projection = layer(x) proj_x, gate = projection.chunk(2, dim=-1) diff --git a/fairseq/modules/checkpoint_activations.py b/fairseq/modules/checkpoint_activations.py new file mode 100644 index 0000000000..7489e09eb7 --- /dev/null +++ b/fairseq/modules/checkpoint_activations.py @@ -0,0 +1,236 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import functools +from typing import Any, Dict, List, Tuple, Union + +import torch +import torch.utils.checkpoint as checkpoint +from fairseq import utils + + +def checkpoint_wrapper(m, offload_to_cpu=False): + """ + A friendlier wrapper for performing activation checkpointing. + + Compared to the PyTorch version, this version: + - wraps an nn.Module, so that all subsequent calls will use checkpointing + - handles keyword arguments in the forward + - handles non-Tensor outputs from the forward + + Usage:: + + checkpointed_module = checkpoint_wrapper(my_module, offload_to_cpu=True) + a, b = checkpointed_module(x, y=3, z=torch.Tensor([1])) + """ + # should I check whether original_forward has already been set? + assert not hasattr( + m, "precheckpoint_forward" + ), "checkpoint function has already been applied?" + m.precheckpoint_forward = m.forward + m.forward = functools.partial( + _checkpointed_forward, + m.precheckpoint_forward, # original_forward + offload_to_cpu, + ) + return m + + +def unwrap_checkpoint(m: torch.nn.Module): + """ + unwrap a module and its children from checkpoint_wrapper + """ + for module in m.modules(): + if hasattr(module, "precheckpoint_forward"): + module.forward = module.precheckpoint_forward + del module.precheckpoint_forward + return m + + +def _checkpointed_forward(original_forward, offload_to_cpu, *args, **kwargs): + # Autograd Functions in PyTorch work best with positional args, since + # the backward must return gradients (or None) for every input argument. + # We can flatten keyword arguments to make this easier. + kwarg_keys, flat_args = pack_kwargs(*args, **kwargs) + parent_ctx_dict = {"offload": offload_to_cpu} + output = CheckpointFunction.apply( + original_forward, parent_ctx_dict, kwarg_keys, *flat_args + ) + if isinstance(output, torch.Tensor): + return output + else: + packed_non_tensor_outputs = parent_ctx_dict["packed_non_tensor_outputs"] + if packed_non_tensor_outputs: + output = unpack_non_tensors(output, packed_non_tensor_outputs) + return output + + +def pack_kwargs(*args, **kwargs) -> Tuple[List[str], List[Any]]: + """ + Usage:: + + kwarg_keys, flat_args = pack_kwargs(1, 2, a=3, b=4) + args, kwargs = unpack_kwargs(kwarg_keys, flat_args) + assert args == [1, 2] + assert kwargs == {"a": 3, "b": 4} + """ + kwarg_keys = [] + flat_args = list(args) + for k, v in kwargs.items(): + kwarg_keys.append(k) + flat_args.append(v) + return kwarg_keys, flat_args + + +def unpack_kwargs( + kwarg_keys: List[str], flat_args: List[Any] +) -> Tuple[List[Any], Dict[str, Any]]: + if len(kwarg_keys) == 0: + return flat_args, {} + args = flat_args[: -len(kwarg_keys)] + kwargs = {k: v for k, v in zip(kwarg_keys, flat_args[-len(kwarg_keys) :])} + return args, kwargs + + +def split_non_tensors( + mixed: Union[torch.Tensor, Tuple[Any]] +) -> Tuple[Tuple[torch.Tensor], Dict[str, List[Any]]]: + """ + Usage:: + + x = torch.Tensor([1]) + y = torch.Tensor([2]) + tensors, packed_non_tensors = split_non_tensors((x, y, None, 3)) + recon = unpack_non_tensors(tensors, packed_non_tensors) + assert recon == (x, y, None, 3) + """ + if isinstance(mixed, torch.Tensor): + return (mixed,), None + tensors = [] + packed_non_tensors = {"is_tensor": [], "objects": []} + for o in mixed: + if isinstance(o, torch.Tensor): + packed_non_tensors["is_tensor"].append(True) + tensors.append(o) + else: + packed_non_tensors["is_tensor"].append(False) + packed_non_tensors["objects"].append(o) + return tuple(tensors), packed_non_tensors + + +def unpack_non_tensors( + tensors: Tuple[torch.Tensor], + packed_non_tensors: Dict[str, List[Any]], +) -> Tuple[Any]: + if packed_non_tensors is None: + return tensors + assert isinstance(packed_non_tensors, dict) + mixed = [] + is_tensor_list = packed_non_tensors["is_tensor"] + objects = packed_non_tensors["objects"] + assert len(tensors) + len(objects) == len(is_tensor_list) + obj_i = tnsr_i = 0 + for is_tensor in is_tensor_list: + if is_tensor: + mixed.append(tensors[tnsr_i]) + tnsr_i += 1 + else: + mixed.append(objects[obj_i]) + obj_i += 1 + return tuple(mixed) + + +class CheckpointFunction(torch.autograd.Function): + """Similar to the torch version, but support non-Tensor outputs. + + The caller is expected to provide a dict (*parent_ctx_dict*) that will hold + the non-Tensor outputs. These should be combined with the Tensor *outputs* + by calling ``unpack_non_tensors``. + """ + + @staticmethod + def forward(ctx, run_function, parent_ctx_dict, kwarg_keys, *args): + if torch.is_grad_enabled(): # grad may be disabled, e.g., during validation + checkpoint.check_backward_validity(args) + + ctx.run_function = run_function + ctx.kwarg_keys = kwarg_keys + ctx.fwd_rng_state = utils.get_rng_state() + + tensor_inputs, packed_non_tensor_inputs = split_non_tensors(args) + if parent_ctx_dict["offload"]: + ctx.fwd_device = tuple(x.device for x in tensor_inputs) + ctx.grad_requirements = tuple(x.requires_grad for x in tensor_inputs) + tensor_inputs = tuple(x.to(torch.device("cpu"), non_blocking=True) for x in tensor_inputs) + + else: + ctx.fwd_device, ctx.grad_requirements = None, None + + ctx.save_for_backward(*tensor_inputs) + ctx.packed_non_tensor_inputs = packed_non_tensor_inputs + + with torch.no_grad(): + unpacked_args, unpacked_kwargs = unpack_kwargs(kwarg_keys, args) + outputs = run_function(*unpacked_args, **unpacked_kwargs) + + if isinstance(outputs, torch.Tensor): + return outputs + else: + # Autograd Functions don't like non-Tensor outputs. We can split the + # non-Tensor and Tensor outputs, returning the former by reference + # through *parent_ctx_dict* and returning the latter directly. + outputs, packed_non_tensor_outputs = split_non_tensors(outputs) + parent_ctx_dict["packed_non_tensor_outputs"] = packed_non_tensor_outputs + return outputs + + @staticmethod + def backward(ctx, *args): + if not torch.autograd._is_checkpoint_valid(): + raise RuntimeError( + "Checkpointing is not compatible with .grad(), please use .backward() if possible" + ) + + tensor_inputs: Tuple = ctx.saved_tensors + tensor_inputs = checkpoint.detach_variable(tensor_inputs) + if ctx.fwd_device is not None: + tensor_inputs = [ + t.to(ctx.fwd_device[i], non_blocking=True) for i, t in enumerate(tensor_inputs) + ] + for i, need_grad in enumerate(ctx.grad_requirements): + tensor_inputs[i].requires_grad = need_grad + inputs = unpack_non_tensors(tensor_inputs, ctx.packed_non_tensor_inputs) + + # Store the current states. + bwd_rng_state = utils.get_rng_state() + + # Set the states to what it used to be before the forward pass. + utils.set_rng_state(ctx.fwd_rng_state) + + with torch.enable_grad(): + unpacked_args, unpacked_kwargs = unpack_kwargs(ctx.kwarg_keys, inputs) + outputs = ctx.run_function(*unpacked_args, **unpacked_kwargs) + tensor_outputs, _ = split_non_tensors(outputs) + # Set the states back to what it was at the start of this function. + utils.set_rng_state(bwd_rng_state) + + # Run backward() with only Tensors that require grad + outputs_with_grad = [] + args_with_grad = [] + for i in range(len(tensor_outputs)): + if tensor_outputs[i].requires_grad: + outputs_with_grad.append(tensor_outputs[i]) + args_with_grad.append(args[i]) + if len(outputs_with_grad) == 0: + raise RuntimeError( + "None of the outputs have requires_grad=True, " + "this checkpoint() is not necessary" + ) + + torch.autograd.backward(outputs_with_grad, args_with_grad) + + grads = tuple( + inp.grad if isinstance(inp, torch.Tensor) else None for inp in inputs + ) + return (None, None, None) + grads diff --git a/fairseq/modules/conv_tbc.py b/fairseq/modules/conv_tbc.py index 1aa3eff9dc..65e17ec94f 100644 --- a/fairseq/modules/conv_tbc.py +++ b/fairseq/modules/conv_tbc.py @@ -4,7 +4,9 @@ # LICENSE file in the root directory of this source tree. import torch +from torch import nn from torch.nn.modules.utils import _single +from torch import Tensor class ConvTBC(torch.nn.Module): @@ -13,6 +15,7 @@ class ConvTBC(torch.nn.Module): The implementation uses gemm to perform the convolution. This implementation is faster than cuDNN for small kernel sizes. """ + def __init__(self, in_channels, out_channels, kernel_size, padding=0): super(ConvTBC, self).__init__() self.in_channels = in_channels @@ -20,17 +23,31 @@ def __init__(self, in_channels, out_channels, kernel_size, padding=0): self.kernel_size = _single(kernel_size) self.padding = _single(padding) - self.weight = torch.nn.Parameter(torch.Tensor( - self.kernel_size[0], in_channels, out_channels)) + self.weight = torch.nn.Parameter( + torch.Tensor(self.kernel_size[0], in_channels, out_channels) + ) self.bias = torch.nn.Parameter(torch.Tensor(out_channels)) - def forward(self, input): - return torch.conv_tbc(input.contiguous(), self.weight, self.bias, self.padding[0]) + self.reset_parameters() + + def reset_parameters(self): + nn.init.xavier_normal_(self.weight) + nn.init.zeros_(self.bias) + + def conv_tbc(self, input: Tensor): + return torch.conv_tbc( + input.contiguous(), self.weight, self.bias, self.padding[0] + ) + + def forward(self, input: Tensor): + return self.conv_tbc(input) def __repr__(self): - s = ('{name}({in_channels}, {out_channels}, kernel_size={kernel_size}' - ', padding={padding}') + s = ( + "{name}({in_channels}, {out_channels}, kernel_size={kernel_size}" + ", padding={padding}" + ) if self.bias is None: - s += ', bias=False' - s += ')' + s += ", bias=False" + s += ")" return s.format(name=self.__class__.__name__, **self.__dict__) diff --git a/fairseq/modules/cross_entropy.py b/fairseq/modules/cross_entropy.py index b46143f3af..6f33c24cb5 100644 --- a/fairseq/modules/cross_entropy.py +++ b/fairseq/modules/cross_entropy.py @@ -12,10 +12,13 @@ logger = logging.getLogger(__name__) -def _cross_entropy_pytorch(logits, target, ignore_index=None, reduction='mean'): +def _cross_entropy_pytorch(logits, target, ignore_index=None, reduction="mean"): lprobs = F.log_softmax(logits, dim=-1, dtype=torch.float32) return F.nll_loss( - lprobs, target, ignore_index=ignore_index, reduction=reduction, + lprobs, + target, + ignore_index=ignore_index, + reduction=reduction, ) @@ -23,29 +26,36 @@ def _cross_entropy_pytorch(logits, target, ignore_index=None, reduction='mean'): import xentropy_cuda from apex.contrib import xentropy - logger.info('using fused cross entropy') - - def cross_entropy(logits, target, ignore_index=-100, reduction='mean'): - if logits.device == torch.device('cpu'): + def cross_entropy(logits, target, ignore_index=-100, reduction="mean"): + if logits.device == torch.device("cpu"): return _cross_entropy_pytorch(logits, target, ignore_index, reduction) else: - half_to_float = (logits.dtype == torch.half) + if not getattr(cross_entropy, "_has_logged_once", False): + logger.info("using fused cross entropy") + cross_entropy._has_logged_once = True + + half_to_float = logits.dtype == torch.half losses = xentropy.SoftmaxCrossEntropyLoss.apply( - logits, target, 0.0, ignore_index, half_to_float, + logits, + target, + 0.0, + ignore_index, + half_to_float, ) - if reduction == 'sum': + if reduction == "sum": return losses.sum() - elif reduction == 'mean': + elif reduction == "mean": if ignore_index >= 0: return losses.sum() / target.ne(ignore_index).sum() else: return losses.mean() - elif reduction == 'none': + elif reduction == "none": return losses else: raise NotImplementedError + except ImportError: - def cross_entropy(logits, target, ignore_index=-100, reduction='mean'): + def cross_entropy(logits, target, ignore_index=-100, reduction="mean"): return _cross_entropy_pytorch(logits, target, ignore_index, reduction) diff --git a/fairseq/modules/cuda_utils.cu b/fairseq/modules/cuda_utils.cu index 516f1d9244..924f852758 100644 --- a/fairseq/modules/cuda_utils.cu +++ b/fairseq/modules/cuda_utils.cu @@ -1,20 +1,17 @@ /** * Copyright (c) Facebook, Inc. and its affiliates. - * + * * This source code is licensed under the MIT license found in the * LICENSE file in the root directory of this source tree. */ - -template <typename U, typename V> -constexpr __host__ __device__ auto divUp(U a, V b) -> decltype(a + b) { - return (a + b - 1) / b; +template <typename U, typename V> +constexpr __host__ __device__ auto divUp(U a, V b) -> decltype(a + b) { + return (a + b - 1) / b; } - -template<int FS, int SB, int padding_l, typename scalar_t> -__inline__ __device__ -void zeroSharedMem(scalar_t* data) { +template <int FS, int SB, int padding_l, typename scalar_t> +__inline__ __device__ void zeroSharedMem(scalar_t* data) { /* Given an array of length FS + SB, zero out the first padding_l and last (FS - padding_l) values in the array @@ -23,13 +20,11 @@ void zeroSharedMem(scalar_t* data) { int tid = threadIdx.x; if (FS < SB) { - // zero all if we have enough threads in a block to do all of them if (tid < padding_l || tid > SB - FS + padding_l - 1) { data[tid] = scalar_t(0.0); } } else { - // otherwise zero out one block at a time const int numIterations = divUp<int, int>(FS, SB); for (int i = 0; i < numIterations; i++) { @@ -43,9 +38,8 @@ void zeroSharedMem(scalar_t* data) { } } -template<typename scalar_t> -__inline__ __device__ -scalar_t warpReduce(scalar_t data) { +template <typename scalar_t> +__inline__ __device__ scalar_t warpReduce(scalar_t data) { /* Reduce an array within each warp. After processing all values in warp will caontain the sum of all original values in that warp. @@ -60,9 +54,8 @@ scalar_t warpReduce(scalar_t data) { return data; } -template<typename scalar_t> -__inline__ __device__ -scalar_t blockReduce(scalar_t data) { +template <typename scalar_t> +__inline__ __device__ scalar_t blockReduce(scalar_t data) { /* Reduce an entire array on the block level. After processing, the first value in the array will contain the reduced sum. @@ -82,7 +75,7 @@ scalar_t blockReduce(scalar_t data) { if (lane == 0) { warpSum[wid] = sum; } - + __syncthreads(); scalar_t v; @@ -102,21 +95,23 @@ scalar_t blockReduce(scalar_t data) { } void checkCudaStatus(cudaError_t status, int lineNumber = -1) { - if (status != cudaSuccess) { - std::cout << cudaGetErrorString(status) - << " at line " << lineNumber << std::endl; + std::cout << cudaGetErrorString(status) << " at line " << lineNumber + << std::endl; std::cout << "Exiting" << std::endl; exit(1); } } -template<int FS, int SB, int padding_l, typename scalar_t> -__device__ -void load_input_to_shared(const scalar_t* input, // global memory - int inputOffset, int sequenceLength, - int iteration, int numIterations, - bool no_prev, scalar_t* output /* shared memory */) { +template <int FS, int SB, int padding_l, typename scalar_t> +__device__ void load_input_to_shared( + const scalar_t* input, // global memory + int inputOffset, + int sequenceLength, + int iteration, + int numIterations, + bool no_prev, + scalar_t* output /* shared memory */) { /* Load a block size of input into shared memory with right and left overhang of total size FS. If previously @@ -138,19 +133,20 @@ void load_input_to_shared(const scalar_t* input, // global memory // Load the left "overhang" of input if (iteration > 0) { if (padding_l < SB) { - // load all at once if (tid < padding_l) { - output[tid] = (no_prev) ? input[inputOffset - padding_l + tid] : output[tid + SB]; + output[tid] = + (no_prev) ? input[inputOffset - padding_l + tid] : output[tid + SB]; } } else { - // load in chunks of size SB int numIterations = divUp<int, int>(padding_l, SB); for (int i = 0; i < numIterations; i++) { int offset = i * SB; if ((tid + offset) < padding_l) { - output[tid + offset] = (no_prev) ? input[inputOffset - padding_l + tid + offset] : output[tid + offset + SB]; + output[tid + offset] = (no_prev) + ? input[inputOffset - padding_l + tid + offset] + : output[tid + offset + SB]; } } } @@ -158,22 +154,25 @@ void load_input_to_shared(const scalar_t* input, // global memory // Load the right "overhang" of input if (iteration < (numIterations - 1)) { - const int elementsLeft = sequenceLength - (iteration+1) * SB; + const int elementsLeft = sequenceLength - (iteration + 1) * SB; if ((FS - padding_l) < SB) { - // load all at once if (tid < (FS - padding_l)) { - output[padding_l + SB + tid] = (tid < elementsLeft) ? input[inputOffset + SB + tid] : scalar_t(0.0); + output[padding_l + SB + tid] = (tid < elementsLeft) + ? input[inputOffset + SB + tid] + : scalar_t(0.0); } } else { - // load in chunks of size SB int numIterations = divUp<int, int>(FS - padding_l, SB); for (int i = 0; i < numIterations; i++) { int offset = i * SB; if ((tid + offset) < (FS - padding_l)) { - output[padding_l + SB + tid + offset] = ((tid + offset) < elementsLeft) ? input[inputOffset + SB + tid + offset] : scalar_t(0.0); + output[padding_l + SB + tid + offset] = + ((tid + offset) < elementsLeft) + ? input[inputOffset + SB + tid + offset] + : scalar_t(0.0); } } } @@ -182,13 +181,11 @@ void load_input_to_shared(const scalar_t* input, // global memory // We should also clear out the right "overhang" if (iteration == (numIterations - 1)) { if ((FS - padding_l) < SB) { - // clear out all at once if (tid < (FS - padding_l)) { - output[padding_l + SB + tid] = scalar_t(0.0); + output[padding_l + SB + tid] = scalar_t(0.0); } } else { - // clear in chunks of size SB int numIterations = divUp<int, int>(FS - padding_l, SB); for (int i = 0; i < numIterations; i++) { @@ -199,5 +196,7 @@ void load_input_to_shared(const scalar_t* input, // global memory } } } - output[tid + padding_l] = ((inputOffset + tid) < sequenceLength) ? input[inputOffset + tid] : scalar_t(0.0); + output[tid + padding_l] = ((inputOffset + tid) < sequenceLength) + ? input[inputOffset + tid] + : scalar_t(0.0); } diff --git a/fairseq/modules/downsampled_multihead_attention.py b/fairseq/modules/downsampled_multihead_attention.py index 5c401e4f8e..2cdece3f7f 100644 --- a/fairseq/modules/downsampled_multihead_attention.py +++ b/fairseq/modules/downsampled_multihead_attention.py @@ -9,6 +9,7 @@ import torch import torch.nn as nn import torch.nn.functional as F +from fairseq.modules.fairseq_dropout import FairseqDropout from fairseq.modules.scalar_bias import scalar_bias @@ -16,14 +17,25 @@ class SingleHeadAttention(nn.Module): """ Single-head attention that supports Gating and Downsampling """ + def __init__( - self, out_channels, embed_dim, head_dim, head_index, dropout=0., - bias=True, project_input=True, gated=False, downsample=False, + self, + out_channels, + embed_dim, + head_dim, + head_index, + dropout=0.0, + bias=True, + project_input=True, + gated=False, + downsample=False, num_heads=1, ): super().__init__() self.embed_dim = embed_dim - self.dropout = dropout + self.dropout_module = FairseqDropout( + dropout, module_name=self.__class__.__name__ + ) self.head_index = head_index self.head_dim = head_dim self.project_input = project_input @@ -57,11 +69,16 @@ def __init__( else: self.out_proj = Linear(out_proj_size, out_channels, bias=bias) - self.scaling = self.head_dim**-0.5 + self.scaling = self.head_dim ** -0.5 def forward( - self, query, key, value, mask_future_timesteps=False, - key_padding_mask=None, use_scalar_bias=False, + self, + query, + key, + value, + mask_future_timesteps=False, + key_padding_mask=None, + use_scalar_bias=False, ): """Input shape: Time x Batch x Channel Self-attention can be implemented by passing in the same arguments for @@ -105,16 +122,17 @@ def forward( attn_weights = torch.bmm(q, k.transpose(1, 2)) if mask_future_timesteps: - assert query.size() == key.size(), \ - 'mask_future_timesteps only applies to self-attention' + assert ( + query.size() == key.size() + ), "mask_future_timesteps only applies to self-attention" attn_weights *= torch.tril( attn_weights.data.new([1]).expand(tgt_len, tgt_len).clone(), diagonal=-1, - )[:, ::self.head_index + 1 if self.downsample else 1].unsqueeze(0) + )[:, :: self.head_index + 1 if self.downsample else 1].unsqueeze(0) attn_weights += torch.triu( attn_weights.data.new([-math.inf]).expand(tgt_len, tgt_len).clone(), - diagonal=0 - )[:, ::self.head_index + 1 if self.downsample else 1].unsqueeze(0) + diagonal=0, + )[:, :: self.head_index + 1 if self.downsample else 1].unsqueeze(0) tgt_size = tgt_len if use_scalar_bias: attn_weights = scalar_bias(attn_weights, 2) @@ -127,14 +145,16 @@ def forward( if self.downsample: attn_weights = attn_weights.view(bsz, 1, tgt_len, src_len) else: - attn_weights = attn_weights.view(size, self.num_heads, tgt_len, src_len) + attn_weights = attn_weights.view( + size, self.num_heads, tgt_len, src_len + ) attn_weights = attn_weights.masked_fill( key_padding_mask.unsqueeze(1).unsqueeze(2), -math.inf, ) attn_weights = attn_weights.view(size, tgt_len, src_len) attn_weights = F.softmax(attn_weights, dim=-1) - attn_weights = F.dropout(attn_weights, p=self.dropout, training=self.training) + attn_weights = self.dropout_module(attn_weights) attn = torch.bmm(attn_weights, v) if self.downsample: @@ -151,13 +171,20 @@ class DownsampledMultiHeadAttention(nn.ModuleList): """ Multi-headed attention with Gating and Downsampling """ + def __init__( - self, out_channels, embed_dim, num_heads, dropout=0., bias=True, - project_input=True, gated=False, downsample=False, + self, + out_channels, + embed_dim, + num_heads, + dropout=0.0, + bias=True, + project_input=True, + gated=False, + downsample=False, ): self.embed_dim = embed_dim self.num_heads = num_heads - self.dropout = dropout self.head_dim = embed_dim // num_heads self.downsample = downsample self.gated = gated @@ -169,9 +196,16 @@ def __init__( for index in range(self.num_heads): attention_heads.append( SingleHeadAttention( - out_channels, self.embed_dim, self.head_dim, index, - self.dropout, bias, self.project_input, self.gated, - self.downsample, self.num_heads, + out_channels, + self.embed_dim, + self.head_dim, + index, + dropout, + bias, + self.project_input, + self.gated, + self.downsample, + self.num_heads, ) ) super().__init__(modules=attention_heads) @@ -181,13 +215,26 @@ def __init__( # if not being downsampled, we can do the heads with one linear layer instead of separate ones super().__init__() self.attention_module = SingleHeadAttention( - out_channels, self.embed_dim, self.head_dim, 1, self.dropout, - bias, self.project_input, self.gated, self.downsample, self.num_heads, + out_channels, + self.embed_dim, + self.head_dim, + 1, + dropout, + bias, + self.project_input, + self.gated, + self.downsample, + self.num_heads, ) def forward( - self, query, key, value, mask_future_timesteps=False, - key_padding_mask=None, use_scalar_bias=False, + self, + query, + key, + value, + mask_future_timesteps=False, + key_padding_mask=None, + use_scalar_bias=False, ): src_len, bsz, embed_dim = key.size() tgt_len = query.size(0) @@ -205,7 +252,12 @@ def forward( for attention_head_number in range(self.num_heads): # call the forward of each attention head _attn, _attn_weight = self[attention_head_number]( - query, key, value, mask_future_timesteps, key_padding_mask, use_scalar_bias, + query, + key, + value, + mask_future_timesteps, + key_padding_mask, + use_scalar_bias, ) attn.append(_attn) attn_weights.append(_attn_weight) @@ -214,13 +266,20 @@ def forward( return full_attn, attn_weights[0].clone() else: _attn, _attn_weight = self.attention_module( - query, key, value, mask_future_timesteps, key_padding_mask, use_scalar_bias, + query, + key, + value, + mask_future_timesteps, + key_padding_mask, + use_scalar_bias, ) attn.append(_attn) attn_weights.append(_attn_weight) full_attn = torch.cat(attn, dim=2) full_attn_weights = torch.cat(attn_weights) - full_attn_weights = full_attn_weights.view(bsz, self.num_heads, tgt_size, src_len) + full_attn_weights = full_attn_weights.view( + bsz, self.num_heads, tgt_size, src_len + ) full_attn_weights = full_attn_weights.sum(dim=1) / self.num_heads return full_attn, full_attn_weights @@ -229,15 +288,16 @@ class Downsample(nn.Module): """ Selects every nth element, where n is the index """ + def __init__(self, index): super().__init__() self.index = index def forward(self, x): - return x[::self.index+1] + return x[:: self.index + 1] -def Linear(in_features, out_features, dropout=0., bias=True): +def Linear(in_features, out_features, dropout=0.0, bias=True): """Weight-normalized Linear layer (input: B x T x C)""" m = nn.Linear(in_features, out_features, bias=bias) m.weight.data.normal_(mean=0, std=math.sqrt((1 - dropout) / in_features)) @@ -245,12 +305,12 @@ def Linear(in_features, out_features, dropout=0., bias=True): return nn.utils.weight_norm(m) -def GatedLinear(in_features, out_features, dropout=0., bias=True): +def GatedLinear(in_features, out_features, dropout=0.0, bias=True): """Weight-normalized Linear layer (input: B x T x C) with interspersed GLU units""" return nn.Sequential( - Linear(in_features, out_features*4, dropout, bias), + Linear(in_features, out_features * 4, dropout, bias), nn.GLU(), - Linear(out_features*2, out_features*2, dropout, bias), + Linear(out_features * 2, out_features * 2, dropout, bias), nn.GLU(), - Linear(out_features, out_features, dropout, bias) + Linear(out_features, out_features, dropout, bias), ) diff --git a/fairseq/modules/dynamic_convolution.py b/fairseq/modules/dynamic_convolution.py index 3de931f0de..0121d453b9 100644 --- a/fairseq/modules/dynamic_convolution.py +++ b/fairseq/modules/dynamic_convolution.py @@ -6,42 +6,69 @@ import torch import torch.nn as nn import torch.nn.functional as F - from fairseq import utils -from .unfold import unfold1d from fairseq.incremental_decoding_utils import with_incremental_state +from fairseq.modules.fairseq_dropout import FairseqDropout + +from .unfold import unfold1d -def DynamicConv(input_size, kernel_size=1, padding_l=None, num_heads=1, - weight_dropout=0., weight_softmax=False, - renorm_padding=False, bias=False, conv_bias=False, - query_size=None, in_proj=False): +def DynamicConv( + input_size, + kernel_size=1, + padding_l=None, + num_heads=1, + weight_dropout=0.0, + weight_softmax=False, + renorm_padding=False, + bias=False, + conv_bias=False, + query_size=None, + in_proj=False, +): if torch.cuda.is_available(): try: from fairseq.modules.dynamicconv_layer import DynamicconvLayer - return DynamicconvLayer(input_size, kernel_size=kernel_size, - padding_l=padding_l, num_heads=num_heads, - weight_dropout=weight_dropout, - weight_softmax=weight_softmax, bias=bias) + + return DynamicconvLayer( + input_size, + kernel_size=kernel_size, + padding_l=padding_l, + num_heads=num_heads, + weight_dropout=weight_dropout, + weight_softmax=weight_softmax, + renorm_padding=renorm_padding, + bias=bias, + conv_bias=conv_bias, + query_size=query_size, + ) except ImportError as e: print(e) - return DynamicConv1dTBC(input_size, kernel_size=kernel_size, - padding_l=padding_l, num_heads=num_heads, - weight_dropout=weight_dropout, - weight_softmax=weight_softmax, bias=bias) + return DynamicConv1dTBC( + input_size, + kernel_size=kernel_size, + padding_l=padding_l, + num_heads=num_heads, + weight_dropout=weight_dropout, + weight_softmax=weight_softmax, + renorm_padding=renorm_padding, + bias=bias, + conv_bias=conv_bias, + query_size=query_size, + ) def Linear(in_features, out_features, bias=True): m = nn.Linear(in_features, out_features, bias) nn.init.xavier_uniform_(m.weight) if bias: - nn.init.constant_(m.bias, 0.) + nn.init.constant_(m.bias, 0.0) return m @with_incremental_state class DynamicConv1dTBC(nn.Module): - '''Dynamic lightweight convolution taking T x B x C inputs + """Dynamic lightweight convolution taking T x B x C inputs Args: input_size: # of channels of the input kernel_size: convolution channels @@ -63,25 +90,42 @@ class DynamicConv1dTBC(nn.Module): weight: the learnable weights of the module of shape `(num_heads, 1, kernel_size)` bias: the learnable bias of the module of shape `(input_size)` - ''' - def __init__(self, input_size, kernel_size=1, padding_l=None, num_heads=1, - weight_dropout=0., weight_softmax=False, - renorm_padding=False, bias=False, conv_bias=False, - query_size=None, in_proj=False): + """ + + def __init__( + self, + input_size, + kernel_size=1, + padding_l=None, + num_heads=1, + weight_dropout=0.0, + weight_softmax=False, + renorm_padding=False, + bias=False, + conv_bias=False, + query_size=None, + in_proj=False, + ): super().__init__() self.input_size = input_size self.query_size = input_size if query_size is None else query_size self.kernel_size = kernel_size self.padding_l = padding_l self.num_heads = num_heads - self.weight_dropout = weight_dropout + self.weight_dropout_module = FairseqDropout( + weight_dropout, module_name=self.__class__.__name__ + ) self.weight_softmax = weight_softmax self.renorm_padding = renorm_padding if in_proj: - self.weight_linear = Linear(self.input_size, self.input_size + num_heads * kernel_size * 1) + self.weight_linear = Linear( + self.input_size, self.input_size + num_heads * kernel_size * 1 + ) else: - self.weight_linear = Linear(self.query_size, num_heads * kernel_size * 1, bias=bias) + self.weight_linear = Linear( + self.query_size, num_heads * kernel_size * 1, bias=bias + ) if conv_bias: self.conv_bias = nn.Parameter(torch.Tensor(input_size)) else: @@ -90,22 +134,27 @@ def __init__(self, input_size, kernel_size=1, padding_l=None, num_heads=1, @property def in_proj(self): - return self.weight_linear.out_features == self.input_size + self.num_heads * self.kernel_size + return ( + self.weight_linear.out_features + == self.input_size + self.num_heads * self.kernel_size + ) def reset_parameters(self): self.weight_linear.reset_parameters() if self.conv_bias is not None: - nn.init.constant_(self.conv_bias, 0.) + nn.init.constant_(self.conv_bias, 0.0) def forward(self, x, incremental_state=None, query=None, unfold=None): - '''Assuming the input, x, of the shape T x B x C and producing an output in the shape T x B x C + """Assuming the input, x, of the shape T x B x C and producing an output in the shape T x B x C args: x: Input of shape T x B x C, i.e. (timesteps, batch_size, input_size) incremental_state: A dict to keep the state unfold: unfold the input or not. If not, we use the matrix trick instead query: use the specified query to predict the conv filters - ''' - unfold = x.size(0) > 512 if unfold is None else unfold # use unfold mode as default for long sequence to save memory + """ + unfold = ( + x.size(0) > 512 if unfold is None else unfold + ) # use unfold mode as default for long sequence to save memory unfold = unfold or (incremental_state is not None) assert query is None or not self.in_proj @@ -121,8 +170,8 @@ def forward(self, x, incremental_state=None, query=None, unfold=None): return output def _forward_unfolded(self, x, incremental_state, query): - '''The conventional implementation of convolutions. - Unfolding the input by having a window shifting to the right.''' + """The conventional implementation of convolutions. + Unfolding the input by having a window shifting to the right.""" T, B, C = x.size() K, H = self.kernel_size, self.num_heads R = C // H @@ -131,9 +180,11 @@ def _forward_unfolded(self, x, incremental_state, query): if self.in_proj: proj = self.weight_linear(x) x = proj.narrow(2, 0, self.input_size).contiguous() - weight = proj.narrow(2, self.input_size, H*K).contiguous().view(T*B*H, -1) + weight = ( + proj.narrow(2, self.input_size, H * K).contiguous().view(T * B * H, -1) + ) else: - weight = self.weight_linear(query).view(T*B*H, -1) + weight = self.weight_linear(query).view(T * B * H, -1) # renorm_padding is only implemented in _forward_expanded assert not self.renorm_padding or incremental_state is not None @@ -144,39 +195,41 @@ def _forward_unfolded(self, x, incremental_state, query): input_buffer = x.new() x_unfold = torch.cat([input_buffer, x.unsqueeze(3)], dim=3) if self.kernel_size > 1: - self._set_input_buffer(incremental_state, x_unfold[:, :, :, -self.kernel_size+1:]) - x_unfold = x_unfold.view(T*B*H, R, -1) + self._set_input_buffer( + incremental_state, x_unfold[:, :, :, -self.kernel_size + 1 :] + ) + x_unfold = x_unfold.view(T * B * H, R, -1) else: padding_l = self.padding_l - if K > T and padding_l == K-1: - weight = weight.narrow(1, K-T, T) - K, padding_l = T, T-1 + if K > T and padding_l == K - 1: + weight = weight.narrow(1, K - T, T) + K, padding_l = T, T - 1 # unfold the input: T x B x C --> T' x B x C x K x_unfold = unfold1d(x, K, padding_l, 0) - x_unfold = x_unfold.view(T*B*H, R, K) + x_unfold = x_unfold.view(T * B * H, R, K) if self.weight_softmax and not self.renorm_padding: weight = F.softmax(weight, dim=1) weight = weight.narrow(1, 0, K) if incremental_state is not None: - weight = weight[:, -x_unfold.size(2):] + weight = weight[:, -x_unfold.size(2) :] K = weight.size(1) if self.weight_softmax and self.renorm_padding: weight = F.softmax(weight, dim=1) - weight = F.dropout(weight, self.weight_dropout, training=self.training, inplace=False) + weight = self.weight_dropout_module(weight, inplace=False) output = torch.bmm(x_unfold, weight.unsqueeze(2)) # T*B*H x R x 1 output = output.view(T, B, C) return output def _forward_expanded(self, x, incremental_stat, query): - '''Turn the convolution filters into band matrices and do matrix multiplication. + """Turn the convolution filters into band matrices and do matrix multiplication. This is faster when the sequence is short, but less memory efficient. This is not used in the decoder during inference. - ''' + """ T, B, C = x.size() K, H = self.kernel_size, self.num_heads R = C // H @@ -184,35 +237,41 @@ def _forward_expanded(self, x, incremental_stat, query): if self.in_proj: proj = self.weight_linear(x) x = proj.narrow(2, 0, self.input_size).contiguous() - weight = proj.narrow(2, self.input_size, H*K).contiguous().view(T*B*H, -1) + weight = ( + proj.narrow(2, self.input_size, H * K).contiguous().view(T * B * H, -1) + ) else: - weight = self.weight_linear(query).view(T*B*H, -1) + weight = self.weight_linear(query).view(T * B * H, -1) if not self.renorm_padding: if self.weight_softmax: weight = F.softmax(weight, dim=1) - weight = F.dropout(weight, self.weight_dropout, training=self.training, inplace=False) + weight = self.weight_dropout_module(weight, inplace=False) weight = weight.narrow(1, 0, K).contiguous() - weight = weight.view(T, B*H, K).transpose(0, 1) + weight = weight.view(T, B * H, K).transpose(0, 1) - x = x.view(T, B*H, R).transpose(0, 1) + x = x.view(T, B * H, R).transpose(0, 1) if self.weight_softmax and self.renorm_padding: # turn the convolution filters into band matrices - weight_expanded = weight.new(B*H, T, T+K-1).fill_(float('-inf')) - weight_expanded.as_strided((B*H, T, K), (T*(T+K-1), T+K, 1)).copy_(weight) + weight_expanded = weight.new(B * H, T, T + K - 1).fill_(float("-inf")) + weight_expanded.as_strided( + (B * H, T, K), (T * (T + K - 1), T + K, 1) + ).copy_(weight) weight_expanded = weight_expanded.narrow(2, self.padding_l, T) # normalize the weight over valid positions like self-attention weight_expanded = F.softmax(weight_expanded, dim=2) - weight_expanded = F.dropout(weight_expanded, self.weight_dropout, training=self.training, inplace=False) + weight_expanded = self.weight_dropout_module(weight_expanded, inplace=False) else: P = self.padding_l - # For efficieny, we cut the kernel size and reduce the padding when the kernel is larger than the length - if K > T and P == K-1: - weight = weight.narrow(2, K-T, T) - K, P = T, T-1 + # For efficiency, we cut the kernel size and reduce the padding when the kernel is larger than the length + if K > T and P == K - 1: + weight = weight.narrow(2, K - T, T) + K, P = T, T - 1 # turn the convolution filters into band matrices - weight_expanded = weight.new_zeros(B*H, T, T+K-1, requires_grad=False) - weight_expanded.as_strided((B*H, T, K), (T*(T+K-1), T+K, 1)).copy_(weight) + weight_expanded = weight.new_zeros(B * H, T, T + K - 1, requires_grad=False) + weight_expanded.as_strided( + (B * H, T, K), (T * (T + K - 1), T + K, 1) + ).copy_(weight) weight_expanded = weight_expanded.narrow(2, P, T) # B*H x T x T output = torch.bmm(weight_expanded, x) output = output.transpose(0, 1).contiguous().view(T, B, C) @@ -225,20 +284,27 @@ def reorder_incremental_state(self, incremental_state, new_order): self._set_input_buffer(incremental_state, input_buffer) def _get_input_buffer(self, incremental_state): - return utils.get_incremental_state(self, incremental_state, 'input_buffer') + return utils.get_incremental_state(self, incremental_state, "input_buffer") def _set_input_buffer(self, incremental_state, new_buffer): - return utils.set_incremental_state(self, incremental_state, 'input_buffer', new_buffer) + return utils.set_incremental_state( + self, incremental_state, "input_buffer", new_buffer + ) def extra_repr(self): - s = '{}, kernel_size={}, padding_l={}, num_heads={}, weight_softmax={}, conv_bias={}, renorm_padding={}, in_proj={}'.format( - self.input_size, self.kernel_size, self.padding_l, - self.num_heads, self.weight_softmax, self.conv_bias is not None, self.renorm_padding, + s = "{}, kernel_size={}, padding_l={}, num_heads={}, weight_softmax={}, conv_bias={}, renorm_padding={}, in_proj={}".format( + self.input_size, + self.kernel_size, + self.padding_l, + self.num_heads, + self.weight_softmax, + self.conv_bias is not None, + self.renorm_padding, self.in_proj, ) if self.query_size != self.input_size: - s += ', query_size={}'.format(self.query_size) - if self.weight_dropout > 0.: - s += ', weight_dropout={}'.format(self.weight_dropout) + s += ", query_size={}".format(self.query_size) + if self.weight_dropout_module.p > 0.0: + s += ", weight_dropout={}".format(self.weight_dropout_module.p) return s diff --git a/fairseq/modules/dynamic_crf_layer.py b/fairseq/modules/dynamic_crf_layer.py index 6f5acf3772..8fcc6b8d26 100644 --- a/fairseq/modules/dynamic_crf_layer.py +++ b/fairseq/modules/dynamic_crf_layer.py @@ -27,16 +27,16 @@ def logsumexp(x, dim=1): class DynamicCRF(nn.Module): """Dynamic CRF layer is used to approximate the traditional - Conditional Random Fields (CRF) - $P(y | x) = 1/Z(x) exp(sum_i s(y_i, x) + sum_i t(y_{i-1}, y_i, x))$ + Conditional Random Fields (CRF) + $P(y | x) = 1/Z(x) exp(sum_i s(y_i, x) + sum_i t(y_{i-1}, y_i, x))$ - where in this function, we assume the emition scores (s) are given, - and the transition score is a |V| x |V| matrix $M$ + where in this function, we assume the emition scores (s) are given, + and the transition score is a |V| x |V| matrix $M$ - in the following two aspects: - (1) it used a low-rank approximation for the transition matrix: - $M = E_1 E_2^T$ - (2) it used a beam to estimate the normalizing factor Z(x) + in the following two aspects: + (1) it used a low-rank approximation for the transition matrix: + $M = E_1 E_2^T$ + (2) it used a beam to estimate the normalizing factor Z(x) """ def __init__(self, num_embedding, low_rank=32, beam_size=64): @@ -51,7 +51,8 @@ def __init__(self, num_embedding, low_rank=32, beam_size=64): def extra_repr(self): return "vocab_size={}, low_rank={}, beam_size={}".format( - self.vocb, self.rank, self.beam) + self.vocb, self.rank, self.beam + ) def forward(self, emissions, targets, masks, beam=None): """ @@ -104,26 +105,27 @@ def _compute_normalizer(self, emissions, targets=None, masks=None, beam=None): beam = beam if beam is not None else self.beam batch_size, seq_len = emissions.size()[:2] if targets is not None: - _emissions = emissions.scatter(2, targets[:, :, None], np.float('inf')) + _emissions = emissions.scatter(2, targets[:, :, None], np.float("inf")) beam_targets = _emissions.topk(beam, 2)[1] beam_emission_scores = emissions.gather(2, beam_targets) else: beam_emission_scores, beam_targets = emissions.topk(beam, 2) beam_transition_score1 = self.E1(beam_targets[:, :-1]) # B x (T-1) x K x D - beam_transition_score2 = self.E2(beam_targets[:, 1:]) # B x (T-1) x K x D + beam_transition_score2 = self.E2(beam_targets[:, 1:]) # B x (T-1) x K x D beam_transition_matrix = torch.bmm( beam_transition_score1.view(-1, beam, self.rank), - beam_transition_score2.view(-1, beam, self.rank).transpose(1, 2)) + beam_transition_score2.view(-1, beam, self.rank).transpose(1, 2), + ) beam_transition_matrix = beam_transition_matrix.view(batch_size, -1, beam, beam) # compute the normalizer in the log-space score = beam_emission_scores[:, 0] # B x K for i in range(1, seq_len): - next_score = score[:, :, None] + beam_transition_matrix[:, i-1] + next_score = score[:, :, None] + beam_transition_matrix[:, i - 1] next_score = logsumexp(next_score, dim=1) + beam_emission_scores[:, i] if masks is not None: - score = torch.where(masks[:, i:i+1], next_score, score) + score = torch.where(masks[:, i : i + 1], next_score, score) else: score = next_score @@ -137,10 +139,11 @@ def _viterbi_decode(self, emissions, masks=None, beam=None): batch_size, seq_len = emissions.size()[:2] beam_emission_scores, beam_targets = emissions.topk(beam, 2) beam_transition_score1 = self.E1(beam_targets[:, :-1]) # B x (T-1) x K x D - beam_transition_score2 = self.E2(beam_targets[:, 1:]) # B x (T-1) x K x D + beam_transition_score2 = self.E2(beam_targets[:, 1:]) # B x (T-1) x K x D beam_transition_matrix = torch.bmm( beam_transition_score1.view(-1, beam, self.rank), - beam_transition_score2.view(-1, beam, self.rank).transpose(1, 2)) + beam_transition_score2.view(-1, beam, self.rank).transpose(1, 2), + ) beam_transition_matrix = beam_transition_matrix.view(batch_size, -1, beam, beam) traj_tokens, traj_scores = [], [] @@ -148,17 +151,19 @@ def _viterbi_decode(self, emissions, masks=None, beam=None): # compute the normalizer in the log-space score = beam_emission_scores[:, 0] # B x K - dummy = torch.arange(beam, device=score.device).expand(*score.size()).contiguous() + dummy = ( + torch.arange(beam, device=score.device).expand(*score.size()).contiguous() + ) for i in range(1, seq_len): traj_scores.append(score) - _score = score[:, :, None] + beam_transition_matrix[:, i-1] + _score = score[:, :, None] + beam_transition_matrix[:, i - 1] _score, _index = _score.max(dim=1) _score = _score + beam_emission_scores[:, i] if masks is not None: - score = torch.where(masks[:, i: i+1], _score, score) - index = torch.where(masks[:, i: i+1], _index, dummy) + score = torch.where(masks[:, i : i + 1], _score, score) + index = torch.where(masks[:, i : i + 1], _index, dummy) else: score, index = _score, _index traj_tokens.append(index) diff --git a/fairseq/modules/dynamicconv_layer/cuda_function_gen.py b/fairseq/modules/dynamicconv_layer/cuda_function_gen.py index 926d6ca846..9304f99eb8 100644 --- a/fairseq/modules/dynamicconv_layer/cuda_function_gen.py +++ b/fairseq/modules/dynamicconv_layer/cuda_function_gen.py @@ -77,7 +77,7 @@ def gen_forward(): } """ - with open("dynamicconv_cuda_forward.cu", 'w') as forward: + with open("dynamicconv_cuda_forward.cu", "w") as forward: forward.write(head) forward.write(switch) for k in kernels: @@ -191,7 +191,7 @@ def gen_backward(): } """ - with open("dynamicconv_cuda_backward.cu", 'w') as backward: + with open("dynamicconv_cuda_backward.cu", "w") as backward: backward.write(head) for seq in seqs: backward.write(sequence_if.format(seq=seq)) diff --git a/fairseq/modules/dynamicconv_layer/dynamicconv_cuda.cpp b/fairseq/modules/dynamicconv_layer/dynamicconv_cuda.cpp index ebd4df0e96..744c363e55 100644 --- a/fairseq/modules/dynamicconv_layer/dynamicconv_cuda.cpp +++ b/fairseq/modules/dynamicconv_layer/dynamicconv_cuda.cpp @@ -8,10 +8,8 @@ #include <torch/extension.h> #include <vector> -std::vector<at::Tensor> dynamicconv_cuda_forward( - at::Tensor input, - at::Tensor filters, - int padding_l); +std::vector<at::Tensor> +dynamicconv_cuda_forward(at::Tensor input, at::Tensor filters, int padding_l); std::vector<at::Tensor> dynamicconv_cuda_backward( at::Tensor gradOutput, @@ -19,21 +17,20 @@ std::vector<at::Tensor> dynamicconv_cuda_backward( at::Tensor input, at::Tensor filters); +#define CHECK_CUDA(x) \ + AT_ASSERTM(x.type().is_cuda(), #x " must be a CUDA tensor") +#define CHECK_CONTIGUOUS(x) \ + AT_ASSERTM(x.is_contiguous(), #x " must be contiguous") +#define CHECK_INPUT(x) \ + CHECK_CUDA(x); \ + CHECK_CONTIGUOUS(x) -#define CHECK_CUDA(x) AT_ASSERTM(x.type().is_cuda(), #x " must be a CUDA tensor") -#define CHECK_CONTIGUOUS(x) AT_ASSERTM(x.is_contiguous(), #x " must be contiguous") -#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x) - -std::vector<at::Tensor> dynamicconv_forward( - at::Tensor input, - at::Tensor filters, - int padding_l) { +std::vector<at::Tensor> +dynamicconv_forward(at::Tensor input, at::Tensor filters, int padding_l) { + CHECK_INPUT(input); + CHECK_INPUT(filters); - CHECK_INPUT(input); - CHECK_INPUT(filters); - - return dynamicconv_cuda_forward(input, filters, - padding_l); + return dynamicconv_cuda_forward(input, filters, padding_l); } std::vector<at::Tensor> dynamicconv_backward( @@ -41,16 +38,14 @@ std::vector<at::Tensor> dynamicconv_backward( int padding_l, at::Tensor input, at::Tensor filters) { + CHECK_INPUT(gradOutput); + CHECK_INPUT(input); + CHECK_INPUT(filters); - CHECK_INPUT(gradOutput); - CHECK_INPUT(input); - CHECK_INPUT(filters); - - return dynamicconv_cuda_backward(gradOutput, padding_l, - input, filters); + return dynamicconv_cuda_backward(gradOutput, padding_l, input, filters); } PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { - m.def("forward", &dynamicconv_forward, "dynamicconv forward (CUDA)"); - m.def("backward", &dynamicconv_backward, "dynamicconv backward (CUDA)"); + m.def("forward", &dynamicconv_forward, "dynamicconv forward (CUDA)"); + m.def("backward", &dynamicconv_backward, "dynamicconv backward (CUDA)"); } diff --git a/fairseq/modules/dynamicconv_layer/dynamicconv_cuda.cuh b/fairseq/modules/dynamicconv_layer/dynamicconv_cuda.cuh index 2196259433..44baf21bdd 100644 --- a/fairseq/modules/dynamicconv_layer/dynamicconv_cuda.cuh +++ b/fairseq/modules/dynamicconv_layer/dynamicconv_cuda.cuh @@ -1,6 +1,6 @@ /** * Copyright (c) Facebook, Inc. and its affiliates. - * + * * This source code is licensed under the MIT license found in the * LICENSE file in the root directory of this source tree. */ @@ -19,26 +19,25 @@ #include <utility> #include <vector> -#include <stdlib.h> #include <assert.h> #include <math.h> +#include <stdlib.h> #define SHFL_MASK 0xffffffff -template<int FS, int SB, int padding_l, typename scalar_t> -__global__ -void dynamicconv_forward_kernel(const scalar_t* input, - const scalar_t* weight, - int minibatch, - int sequenceLength, - int numFeatures, - int numFiltersInBlock, - int numHeads, - scalar_t* output); +template <int FS, int SB, int padding_l, typename scalar_t> +__global__ void dynamicconv_forward_kernel( + const scalar_t* input, + const scalar_t* weight, + int minibatch, + int sequenceLength, + int numFeatures, + int numFiltersInBlock, + int numHeads, + scalar_t* output); -template<int FS, int SB, int padding_l, typename scalar_t> -__global__ -void dynamicconv_backward_kernel( +template <int FS, int SB, int padding_l, typename scalar_t> +__global__ void dynamicconv_backward_kernel( const scalar_t* gradOutput, // B * C * T const scalar_t* input, // B * C * T const scalar_t* weight, diff --git a/fairseq/modules/dynamicconv_layer/dynamicconv_cuda_kernel.cu b/fairseq/modules/dynamicconv_layer/dynamicconv_cuda_kernel.cu index 300d35b647..4630f1e982 100644 --- a/fairseq/modules/dynamicconv_layer/dynamicconv_cuda_kernel.cu +++ b/fairseq/modules/dynamicconv_layer/dynamicconv_cuda_kernel.cu @@ -1,26 +1,26 @@ /** * Copyright (c) Facebook, Inc. and its affiliates. - * + * * This source code is licensed under the MIT license found in the * LICENSE file in the root directory of this source tree. */ +#include "../cuda_utils.cu" #include "dynamicconv_cuda.cuh" -#include "dynamicconv_cuda_forward.cu" #include "dynamicconv_cuda_backward.cu" -#include "../cuda_utils.cu" +#include "dynamicconv_cuda_forward.cu" // FS is filter size and kernels are specialized for filter sizes -template<int FS, int SB, int padding_l, typename scalar_t> -__global__ -void dynamicconv_forward_kernel(const scalar_t* input, - const scalar_t* weight, - int minibatch, - int sequenceLength, - int numFeatures, - int numFiltersInBlock, - int numHeads, - scalar_t* output) { +template <int FS, int SB, int padding_l, typename scalar_t> +__global__ void dynamicconv_forward_kernel( + const scalar_t* input, + const scalar_t* weight, + int minibatch, + int sequenceLength, + int numFeatures, + int numFiltersInBlock, + int numHeads, + scalar_t* output) { assert(blockDim.x == SB); const int tid = threadIdx.x; @@ -28,8 +28,8 @@ void dynamicconv_forward_kernel(const scalar_t* input, const int featureIdx = blockIdx.y; const int head = featureIdx / numFiltersInBlock; - const int IOOffset = batchIdx * numFeatures * sequenceLength - + featureIdx * sequenceLength; + const int IOOffset = + batchIdx * numFeatures * sequenceLength + featureIdx * sequenceLength; const scalar_t* inputFeature = &input[IOOffset]; scalar_t* outputFeature = &output[IOOffset]; @@ -43,36 +43,36 @@ void dynamicconv_forward_kernel(const scalar_t* input, for (int i = 0; i < numIterations; ++i) { __syncthreads(); const int inputOffset = i * SB; - load_input_to_shared<FS, SB, padding_l>(inputFeature, inputOffset, - sequenceLength, i, - numIterations, false, tempInput); + load_input_to_shared<FS, SB, padding_l>( + inputFeature, + inputOffset, + sequenceLength, + i, + numIterations, + false, + tempInput); __syncthreads(); if (inputOffset + tid < sequenceLength) { - - #pragma unroll +#pragma unroll for (int k = 0; k < FS; ++k) { - const int filterOffset = batchIdx * numHeads * FS * sequenceLength - + head * FS * sequenceLength - + k * sequenceLength - + i * SB + tid; + const int filterOffset = batchIdx * numHeads * FS * sequenceLength + + head * FS * sequenceLength + k * sequenceLength + i * SB + tid; filter[k] = weight[filterOffset]; } scalar_t out = scalar_t(0.0); - #pragma unroll +#pragma unroll for (int k = 0; k < FS; ++k) { out += filter[k] * tempInput[tid + k]; } outputFeature[inputOffset + tid] = out; - } } } -template<int FS, int SB, int padding_l, typename scalar_t> -__global__ -void dynamicconv_backward_kernel( +template <int FS, int SB, int padding_l, typename scalar_t> +__global__ void dynamicconv_backward_kernel( const scalar_t* gradOutput, // B * C * T const scalar_t* input, // B * C * T const scalar_t* weight, @@ -111,52 +111,60 @@ void dynamicconv_backward_kernel( int idxOffset = inputOffset + tid + k - padding; if (idxOffset >= 0 && idxOffset < sequenceLength) { - int bfilterOffset = batchIdx * numHeads * FS * sequenceLength - + headIdx * FS * sequenceLength - + (FS - k - 1) * sequenceLength - + idxOffset; + int bfilterOffset = batchIdx * numHeads * FS * sequenceLength + + headIdx * FS * sequenceLength + (FS - k - 1) * sequenceLength + + idxOffset; bfilter[k] = weight[bfilterOffset]; } else { bfilter[k] = scalar_t(0.0); } } - // iterate over filter block for (int featureIdx = 0; featureIdx < numFiltersInBlock; ++featureIdx) { __syncthreads(); // load input and output gradient for this channel and chunk - const int IOOffset = batchIdx * numFeatures * sequenceLength - + (headIdx * numFiltersInBlock + featureIdx) * sequenceLength; + const int IOOffset = batchIdx * numFeatures * sequenceLength + + (headIdx * numFiltersInBlock + featureIdx) * sequenceLength; const scalar_t* inputFeature = &input[IOOffset]; const scalar_t* gradOutputFeature = &gradOutput[IOOffset]; scalar_t* gradInputFeature = &gradInput[IOOffset]; - load_input_to_shared<FS, SB, padding>(gradOutputFeature, inputOffset, - sequenceLength, chunkIdx, - numChunks, true, tempGradOutput); - load_input_to_shared<FS, SB, padding_l>(inputFeature, inputOffset, - sequenceLength, chunkIdx, - numChunks, true, tempInput); + load_input_to_shared<FS, SB, padding>( + gradOutputFeature, + inputOffset, + sequenceLength, + chunkIdx, + numChunks, + true, + tempGradOutput); + load_input_to_shared<FS, SB, padding_l>( + inputFeature, + inputOffset, + sequenceLength, + chunkIdx, + numChunks, + true, + tempInput); __syncthreads(); - + // sum input and weight gradients scalar_t out = scalar_t(0.0); - #pragma unroll +#pragma unroll for (int k = 0; k < FS; ++k) { tempGradSum[k] += tempInput[tid + k] * tempGradOutput[tid + padding]; out += bfilter[k] * tempGradOutput[tid + k]; } - + if (inputOffset + tid < sequenceLength) { gradInputFeature[inputOffset + tid] = out; } } - const int gradOffset = batchIdx * numHeads * FS * sequenceLength - + headIdx * FS * sequenceLength; - scalar_t *gradWeightFeature = &gradWeight[gradOffset]; + const int gradOffset = + batchIdx * numHeads * FS * sequenceLength + headIdx * FS * sequenceLength; + scalar_t* gradWeightFeature = &gradWeight[gradOffset]; // write weight gradient if (inputOffset + tid < sequenceLength) { diff --git a/fairseq/modules/dynamicconv_layer/dynamicconv_layer.py b/fairseq/modules/dynamicconv_layer/dynamicconv_layer.py index 975840fa73..711ed03483 100644 --- a/fairseq/modules/dynamicconv_layer/dynamicconv_layer.py +++ b/fairseq/modules/dynamicconv_layer/dynamicconv_layer.py @@ -3,19 +3,18 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. +import dynamicconv_cuda import torch -from torch import nn -from torch.autograd import Function import torch.nn.functional as F - -import dynamicconv_cuda from fairseq import utils -from fairseq.modules.unfold import unfold1d from fairseq.incremental_decoding_utils import with_incremental_state +from fairseq.modules.fairseq_dropout import FairseqDropout +from fairseq.modules.unfold import unfold1d +from torch import nn +from torch.autograd import Function class dynamicconvFunction(Function): - @staticmethod def forward(ctx, x, weights, padding_l): ctx.padding_l = padding_l @@ -27,9 +26,8 @@ def forward(ctx, x, weights, padding_l): @staticmethod def backward(ctx, grad_output): outputs = dynamicconv_cuda.backward( - grad_output.contiguous(), - ctx.padding_l, - *ctx.saved_tensors) + grad_output.contiguous(), ctx.padding_l, *ctx.saved_tensors + ) grad_input, grad_weights = outputs return grad_input, grad_weights, None @@ -37,17 +35,18 @@ def backward(ctx, grad_output): @with_incremental_state class DynamicconvLayer(nn.Module): def __init__( - self, - input_size, - kernel_size=1, - padding_l=None, - weight_softmax=False, - num_heads=1, - weight_dropout=0., - bias=False, - renorm_padding=False, - conv_bias=False, - query_size=None): + self, + input_size, + kernel_size=1, + padding_l=None, + weight_softmax=False, + num_heads=1, + weight_dropout=0.0, + bias=False, + renorm_padding=False, + conv_bias=False, + query_size=None, + ): super(DynamicconvLayer, self).__init__() self.input_size = input_size @@ -56,7 +55,9 @@ def __init__( self.padding_l = padding_l self.num_heads = num_heads self.weight_softmax = weight_softmax - self.weight_dropout = weight_dropout + self.weight_dropout_module = FairseqDropout( + weight_dropout, module_name=self.__class__.__name__ + ) self.renorm_padding = renorm_padding self.bias = bias @@ -70,8 +71,8 @@ def __init__( def reset_parameters(self): nn.init.xavier_uniform_(self.weight_linear.weight) if self.conv_bias is not None: - nn.init.constant_(self.conv_bias, 0.) - nn.init.constant_(self.weight_linaer.bias, 0.) + nn.init.constant_(self.conv_bias, 0.0) + nn.init.constant_(self.weight_linaer.bias, 0.0) def forward(self, x, incremental_state=None, query=None, unfold=None): @@ -81,7 +82,9 @@ def forward(self, x, incremental_state=None, query=None, unfold=None): # during inference time, incremental BMM is faster if incremental_state is not None: - unfold = x.size(0) > 512 if unfold is None else unfold # use unfold mode as default for long sequence to save memory + unfold = ( + x.size(0) > 512 if unfold is None else unfold + ) # use unfold mode as default for long sequence to save memory unfold = unfold or (incremental_state is not None) assert query is None @@ -102,13 +105,15 @@ def forward(self, x, incremental_state=None, query=None, unfold=None): weight = self.weight_linear(x).view(T, B, H, K) if self.weight_softmax: weight = F.softmax(weight, dim=-1) - if self.weight_dropout: - weight = F.dropout(weight, self.weight_dropout, training=self.training) + if self.weight_dropout_module.p: + weight = self.weight_dropout_module(weight) weight = weight.permute(1, 2, 3, 0).contiguous() self.filters = weight x = x.permute(1, 2, 0).contiguous() - output = dynamicconvFunction.apply(x, weight, self.padding_l).permute(2, 0, 1) + output = dynamicconvFunction.apply(x, weight, self.padding_l).permute( + 2, 0, 1 + ) if self.conv_bias is not None: output = output + self.conv_bias.view(1, 1, -1) return output @@ -120,20 +125,22 @@ def reorder_incremental_state(self, incremental_state, new_order): self._set_input_buffer(incremental_state, input_buffer) def _get_input_buffer(self, incremental_state): - return utils.get_incremental_state(self, incremental_state, 'input_buffer') + return utils.get_incremental_state(self, incremental_state, "input_buffer") def _set_input_buffer(self, incremental_state, new_buffer): - return utils.set_incremental_state(self, incremental_state, 'input_buffer', new_buffer) + return utils.set_incremental_state( + self, incremental_state, "input_buffer", new_buffer + ) def _forward_unfolded(self, x, incremental_state, query): - '''The conventional implementation of convolutions. - Unfolding the input by having a window shifting to the right.''' + """The conventional implementation of convolutions. + Unfolding the input by having a window shifting to the right.""" T, B, C = x.size() K, H = self.kernel_size, self.num_heads R = C // H assert R * H == C == self.input_size - weight = self.weight_linear(query).view(T*B*H, -1) + weight = self.weight_linear(query).view(T * B * H, -1) # renorm_padding is only implemented in _forward_expanded assert not self.renorm_padding or incremental_state is not None @@ -144,70 +151,76 @@ def _forward_unfolded(self, x, incremental_state, query): input_buffer = x.new() x_unfold = torch.cat([input_buffer, x.unsqueeze(3)], dim=3) if self.kernel_size > 1: - self._set_input_buffer(incremental_state, x_unfold[:, :, :, -self.kernel_size+1:]) - x_unfold = x_unfold.view(T*B*H, R, -1) + self._set_input_buffer( + incremental_state, x_unfold[:, :, :, -self.kernel_size + 1 :] + ) + x_unfold = x_unfold.view(T * B * H, R, -1) else: padding_l = self.padding_l - if K > T and padding_l == K-1: - weight = weight.narrow(1, K-T, T) - K, padding_l = T, T-1 + if K > T and padding_l == K - 1: + weight = weight.narrow(1, K - T, T) + K, padding_l = T, T - 1 # unfold the input: T x B x C --> T' x B x C x K x_unfold = unfold1d(x, K, padding_l, 0) - x_unfold = x_unfold.view(T*B*H, R, K) + x_unfold = x_unfold.view(T * B * H, R, K) if self.weight_softmax and not self.renorm_padding: weight = F.softmax(weight, dim=1) weight = weight.narrow(1, 0, K) if incremental_state is not None: - weight = weight[:, -x_unfold.size(2):] + weight = weight[:, -x_unfold.size(2) :] K = weight.size(1) if self.weight_softmax and self.renorm_padding: weight = F.softmax(weight, dim=1) - weight = F.dropout(weight, self.weight_dropout, training=self.training, inplace=False) + weight = self.weight_dropout_module(weight, inplace=False) output = torch.bmm(x_unfold, weight.unsqueeze(2)) # T*B*H x R x 1 output = output.view(T, B, C) return output def _forward_expanded(self, x, incremental_stat, query): - '''Turn the convolution filters into band matrices and do matrix multiplication. + """Turn the convolution filters into band matrices and do matrix multiplication. This is faster when the sequence is short, but less memory efficient. This is not used in the decoder during inference. - ''' + """ T, B, C = x.size() K, H = self.kernel_size, self.num_heads R = C // H assert R * H == C == self.input_size - weight = self.weight_linear(query).view(T*B*H, -1) + weight = self.weight_linear(query).view(T * B * H, -1) if not self.renorm_padding: if self.weight_softmax: weight = F.softmax(weight, dim=1) - weight = F.dropout(weight, self.weight_dropout, training=self.training, inplace=False) + weight = self.weight_dropout_module(weight, inplace=False) weight = weight.narrow(1, 0, K).contiguous() - weight = weight.view(T, B*H, K).transpose(0, 1) + weight = weight.view(T, B * H, K).transpose(0, 1) - x = x.view(T, B*H, R).transpose(0, 1) + x = x.view(T, B * H, R).transpose(0, 1) if self.weight_softmax and self.renorm_padding: # turn the convolution filters into band matrices - weight_expanded = weight.new(B*H, T, T+K-1).fill_(float('-inf')) - weight_expanded.as_strided((B*H, T, K), (T*(T+K-1), T+K, 1)).copy_(weight) + weight_expanded = weight.new(B * H, T, T + K - 1).fill_(float("-inf")) + weight_expanded.as_strided( + (B * H, T, K), (T * (T + K - 1), T + K, 1) + ).copy_(weight) weight_expanded = weight_expanded.narrow(2, self.padding_l, T) # normalize the weight over valid positions like self-attention weight_expanded = F.softmax(weight_expanded, dim=2) - weight_expanded = F.dropout(weight_expanded, self.weight_dropout, training=self.training, inplace=False) + weight_expanded = self.weight_dropout_module(weight_expanded, inplace=False) else: P = self.padding_l - # For efficieny, we cut the kernel size and reduce the padding when the kernel is larger than the length - if K > T and P == K-1: - weight = weight.narrow(2, K-T, T) - K, P = T, T-1 + # For efficiency, we cut the kernel size and reduce the padding when the kernel is larger than the length + if K > T and P == K - 1: + weight = weight.narrow(2, K - T, T) + K, P = T, T - 1 # turn the convolution filters into band matrices - weight_expanded = weight.new_zeros(B*H, T, T+K-1, requires_grad=False) - weight_expanded.as_strided((B*H, T, K), (T*(T+K-1), T+K, 1)).copy_(weight) + weight_expanded = weight.new_zeros(B * H, T, T + K - 1, requires_grad=False) + weight_expanded.as_strided( + (B * H, T, K), (T * (T + K - 1), T + K, 1) + ).copy_(weight) weight_expanded = weight_expanded.narrow(2, P, T) # B*H x T x T output = torch.bmm(weight_expanded, x) output = output.transpose(0, 1).contiguous().view(T, B, C) diff --git a/fairseq/modules/dynamicconv_layer/dynamiconv_cpu.cpp b/fairseq/modules/dynamicconv_layer/dynamiconv_cpu.cpp index 8a6af4285d..d7e57c8590 100644 --- a/fairseq/modules/dynamicconv_layer/dynamiconv_cpu.cpp +++ b/fairseq/modules/dynamicconv_layer/dynamiconv_cpu.cpp @@ -1,10 +1,8 @@ #include <torch/torch.h> #include <vector> -std::vector<float*> dynamicconv_cpu_forward( - float* input, - float* filters, - int padding_l); +std::vector<float*> +dynamicconv_cpu_forward(float* input, float* filters, int padding_l); std::vector<float*> dynamicconv_cpu_backward( float* gradOutput, @@ -12,12 +10,9 @@ std::vector<float*> dynamicconv_cpu_backward( float* input, float* filters); -std::vector<float*> dynamicconv_forward( - float* input, - float* filters, - int padding_l) { - - return dynamicconv_cpu_forward(input, filters, padding_l); +std::vector<float*> +dynamicconv_forward(float* input, float* filters, int padding_l) { + return dynamicconv_cpu_forward(input, filters, padding_l); } std::vector<float*> dynamicconv_backward( @@ -25,11 +20,10 @@ std::vector<float*> dynamicconv_backward( int padding_l, float* input, float* filters) { - - return dynamicconv_cpu_backward(gradOutput, padding_l, input, filters); + return dynamicconv_cpu_backward(gradOutput, padding_l, input, filters); } PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { - m.def("forward", &dynamicconv_forward, "dynamicconv forward (CPU)"); - m.def("backward", &dynamicconv_backward, "dynamicconv backward (CPU)"); + m.def("forward", &dynamicconv_forward, "dynamicconv forward (CPU)"); + m.def("backward", &dynamicconv_backward, "dynamicconv backward (CPU)"); } diff --git a/fairseq/modules/dynamicconv_layer/setup.py b/fairseq/modules/dynamicconv_layer/setup.py index 4d789c3283..6a21f7e2ee 100644 --- a/fairseq/modules/dynamicconv_layer/setup.py +++ b/fairseq/modules/dynamicconv_layer/setup.py @@ -5,19 +5,19 @@ # LICENSE file in the root directory of this source tree. from setuptools import setup -from torch.utils.cpp_extension import CUDAExtension, BuildExtension +from torch.utils.cpp_extension import BuildExtension, CUDAExtension + setup( - name='dynamicconv_layer', + name="dynamicconv_layer", ext_modules=[ CUDAExtension( - name='dynamicconv_cuda', + name="dynamicconv_cuda", sources=[ - 'dynamicconv_cuda.cpp', - 'dynamicconv_cuda_kernel.cu', + "dynamicconv_cuda.cpp", + "dynamicconv_cuda_kernel.cu", ], ), ], - cmdclass={ - 'build_ext': BuildExtension - }) + cmdclass={"build_ext": BuildExtension}, +) diff --git a/fairseq/modules/fairseq_dropout.py b/fairseq/modules/fairseq_dropout.py new file mode 100644 index 0000000000..3cddca7718 --- /dev/null +++ b/fairseq/modules/fairseq_dropout.py @@ -0,0 +1,51 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +from typing import List, Optional + +import torch.nn as nn +import torch.nn.functional as F + + +logger = logging.getLogger(__name__) + + +class FairseqDropout(nn.Module): + def __init__(self, p, module_name=None): + super().__init__() + self.p = p + self.module_name = module_name + self.apply_during_inference = False + + def forward(self, x, inplace: bool = False): + if self.p > 0 and (self.training or self.apply_during_inference): + return F.dropout(x, p=self.p, training=True, inplace=inplace) + else: + return x + + def make_generation_fast_( + self, + name: str, + retain_dropout: bool = False, + retain_dropout_modules: Optional[List[str]] = None, + **kwargs + ): + if retain_dropout: + if retain_dropout_modules is not None and self.module_name is None: + logger.warning( + "Cannot enable dropout during inference for module {} " + "because module_name was not set".format(name) + ) + elif ( + retain_dropout_modules is None # if None, apply to all modules + or self.module_name in retain_dropout_modules + ): + logger.info( + "Enabling dropout during inference for module: {}".format(name) + ) + self.apply_during_inference = True + else: + logger.info("Disabling dropout for module: {}".format(name)) diff --git a/fairseq/modules/gumbel_vector_quantizer.py b/fairseq/modules/gumbel_vector_quantizer.py index 2efc10e74b..7113438888 100644 --- a/fairseq/modules/gumbel_vector_quantizer.py +++ b/fairseq/modules/gumbel_vector_quantizer.py @@ -53,7 +53,7 @@ def __init__( num_groups = groups if not combine_groups else 1 self.vars = nn.Parameter(torch.FloatTensor(1, num_groups * num_vars, var_dim)) - nn.init.xavier_normal_(self.vars) + nn.init.uniform_(self.vars) if weight_proj_depth > 1: @@ -70,8 +70,13 @@ def block(input_dim, output_dim): ) else: self.weight_proj = nn.Linear(self.input_dim, groups * num_vars) + nn.init.normal_(self.weight_proj.weight, mean=0, std=1) + nn.init.zeros_(self.weight_proj.bias) - assert len(temp) == 3, temp + if isinstance(temp, str): + import ast + temp = ast.literal_eval(temp) + assert len(temp) == 3, f"{temp}, {len(temp)}" self.max_temp, self.min_temp, self.temp_decay = temp self.curr_temp = self.max_temp @@ -82,7 +87,7 @@ def set_num_updates(self, num_updates): self.max_temp * self.temp_decay ** num_updates, self.min_temp ) - def codebook(self): + def get_codebook_indices(self): if self.codebook_indices is None: from itertools import product @@ -99,13 +104,36 @@ def codebook(self): for b in range(1, self.groups): self.codebook_indices[:, b] += self.num_vars * b self.codebook_indices = self.codebook_indices.flatten() + return self.codebook_indices + def codebook(self): + indices = self.get_codebook_indices() return ( self.vars.squeeze(0) - .index_select(0, self.codebook_indices) + .index_select(0, indices) .view(self.num_vars ** self.groups, -1) ) + def sample_from_codebook(self, b, n): + indices = self.get_codebook_indices() + indices = indices.view(-1, self.groups) + cb_size = indices.size(0) + assert ( + n < cb_size + ), f"sample size {n} is greater than size of codebook {cb_size}" + sample_idx = torch.randint(low=0, high=cb_size, size=(b * n,)) + indices = indices[sample_idx] + + z = self.vars.squeeze(0).index_select(0, indices.flatten()).view(b, n, -1) + return z + + def to_codebook_index(self, indices): + res = indices.new_full(indices.shape[:-1], 0) + for i in range(self.groups): + exponent = self.groups - i - 1 + res += indices[..., i] * (self.num_vars ** exponent) + return res + def forward_idx(self, x): res = self.forward(x, produce_targets=True) return res["x"], res["targets"] diff --git a/fairseq/modules/kmeans_attention.py b/fairseq/modules/kmeans_attention.py new file mode 100644 index 0000000000..11a7debcf2 --- /dev/null +++ b/fairseq/modules/kmeans_attention.py @@ -0,0 +1,609 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import math +from inspect import isfunction +from operator import mul +from functools import reduce, wraps + +from aml.multimodal_video.utils.einops.lib import rearrange, repeat +from aml.multimodal_video.utils.einops.lib.layers.torch import Rearrange + +from fairseq.modules.local_attention import LocalAttention + +# constants + +TOKEN_SELF_ATTN_VALUE = -5e4 +KMEAN_INIT_ITERS = 10 + +# helper functions + + +def exists(val): + return val is not None + + +def identity(x, *args, **kwargs): + return x + + +def default(x, d): + if not exists(x): + return d if not isfunction(d) else d() + return x + + +def cast_tuple(x): + return x if isinstance(x, tuple) else (x,) + + +def cache_fn(f): + cache = None + + @wraps(f) + def cached_fn(*args, **kwargs): + nonlocal cache + if exists(cache): + return cache + cache = f(*args, **kwargs) + return cache + return cached_fn + + +def to(t): + return {'device': t.device, 'dtype': t.dtype} + + +def find_modules(nn_module, type): + return [module for module in nn_module.modules() if isinstance(module, type)] + + +def is_empty(t): + return t.nelement() == 0 + + +def max_neg_value(tensor): + return -torch.finfo(tensor.dtype).max + + +def batched_index_select(values, indices): + last_dim = values.shape[-1] + return values.gather(2, expand_dim(indices, -1, last_dim)) + + +def merge_dims(ind_from, ind_to, tensor): + shape = list(tensor.shape) + arr_slice = slice(ind_from, ind_to + 1) + shape[arr_slice] = [reduce(mul, shape[arr_slice])] + return tensor.reshape(*shape) + + +def expand_dim(t, dim, k): + t = t.unsqueeze(dim) + expand_shape = [-1] * len(t.shape) + expand_shape[dim] = k + return t.expand(*expand_shape) + + +def scatter_mean(src, t, index, dim, eps=1e-5): + numer = src.scatter_add(dim, index, t) + denom = src.scatter_add(dim, index, torch.ones_like(t)) + return numer / (denom + eps) + + +def split_at_index(dim, index, t): + pre_slices = (slice(None),) * dim + l = (*pre_slices, slice(None, index)) + r = (*pre_slices, slice(index, None)) + return t[l], t[r] + + +def reshape_dim(t, dim, split_dims): + shape = list(t.shape) + num_dims = len(shape) + dim = (dim + num_dims) % num_dims + shape[dim:dim+1] = split_dims + return t.reshape(shape) + + +def ema(old, new, decay): + if not exists(old): + return new + return old * decay + new * (1 - decay) + + +def ema_inplace(moving_avg, new, decay): + if is_empty(moving_avg): + moving_avg.data.copy_(new) + return + moving_avg.data.mul_(decay).add_(new, alpha=(1 - decay)) + +# helper classes + + +def map_first_tuple_or_el(x, fn): + if isinstance(x, tuple): + return (fn(x[0]),) + x[1:] + return fn(x) + + +class Chunk(nn.Module): + def __init__(self, chunks, fn, along_dim=-1): + super().__init__() + self.dim = along_dim + self.chunks = chunks + self.fn = fn + + def forward(self, x, **kwargs): + if self.chunks <= 1: + return self.fn(x, **kwargs) + chunks = x.chunk(self.chunks, dim=self.dim) + return torch.cat([self.fn(c, **kwargs) for c in chunks], dim=self.dim) + + +class PreNorm(nn.ModuleList): + def __init__(self, norm_class, dim, fn): + super().__init__() + self.norm = norm_class(dim) + self.fn = fn + + def forward(self, x, **kwargs): + x = self.norm(x) + return self.fn(x, **kwargs) + + +class ReZero(nn.Module): + def __init__(self, fn): + super().__init__() + self.residual_weight = nn.Parameter(torch.zeros(1)) + self.fn = fn + + def forward(self, x, **kwargs): + x = self.fn(x, **kwargs) + return map_first_tuple_or_el(x, lambda t: t * self.residual_weight) + + +class ScaleNorm(nn.Module): + def __init__(self, dim, eps=1e-5): + super().__init__() + self.g = nn.Parameter(torch.ones(1)) + self.eps = eps + + def forward(self, x): + def norm(t): + n = torch.norm(t, dim=-1, keepdim=True).clamp(min=self.eps) + return t / n * self.g + return map_first_tuple_or_el(x, norm) + + +class ProjectInOut(nn.Module): + def __init__(self, fn, dim_in, dim_out, project_out=True): + super().__init__() + self.fn = fn + self.project_in = nn.Linear(dim_in, dim_out) + self.project_out = nn.Linear(dim_out, dim_in) if project_out else identity + + def forward(self, x, **kwargs): + x = self.project_in(x) + x, loss = self.fn(x, **kwargs) + x = self.project_out(x) + return x, loss + + +class MatrixMultiply(nn.Module): + def __init__(self, tensor, transpose=False): + super().__init__() + self.tensor = tensor + self.transpose = transpose + + def forward(self, x): + tensor = self.tensor + if self.transpose: + tensor = tensor.t() + return x @ tensor + +# positional embeddings + + +class DepthWiseConv1d(nn.Module): + def __init__(self, dim_in, dim_out, kernel_size, stride=1, bias=True, causal=False): + super().__init__() + self.padding = ((kernel_size - 1), 0) if causal else (kernel_size // 2, kernel_size // 2) + + self.net = nn.Sequential( + nn.Conv1d(dim_in, dim_in, kernel_size=kernel_size, groups=dim_in, stride=stride, bias=bias), + nn.Conv1d(dim_in, dim_out, 1, bias=bias) + ) + + def forward(self, x): + x = F.pad(x, self.padding, value=0.) + return self.net(x) + + +class FixedPositionalEmbedding(nn.Module): + def __init__(self, dim, max_seq_len): + super().__init__() + inv_freq = 1. / (10000 ** (torch.arange(0, dim, 2).float() / dim)) + position = torch.arange(0, max_seq_len, dtype=torch.float) + sinusoid_inp = torch.einsum("i,j->ij", position, inv_freq) + emb = torch.cat((sinusoid_inp.sin(), sinusoid_inp.cos()), dim=-1) + self.register_buffer('emb', emb) + + def forward(self, x): + return self.emb[None, :x.shape[1], :].to(x) + + +def rotate_every_two(x): + x = rearrange(x, '... (d j) -> ... d j', j=2) + x1, x2 = x.unbind(dim=-1) + x = torch.stack((-x2, x1), dim=-1) + return rearrange(x, '... d j -> ... (d j)') + + +def apply_rotary_pos_emb(q, k, sinu_pos): + sinu_pos = rearrange(sinu_pos, '() n (j d) -> n j d', j=2) + sin, cos = sinu_pos.unbind(dim=-2) + sin, cos = map(lambda t: repeat(t, 'b n -> b (n j)', j=2), (sin, cos)) + q, k = map(lambda t: (t * cos) + (rotate_every_two(t) * sin), (q, k)) + return q, k + +# kmeans related function and class + + +def update_kmeans_on_backwards(module): + module.kmean_modules = find_modules(module, Kmeans) + + def hook(_, grad_in, grad_out): + for m in module.kmean_modules: + m.update() + + return module.register_backward_hook(hook) + + +def similarity(x, means): + return torch.einsum('bhld,hcd->bhlc', x, means) + + +def dists_and_buckets(x, means): + dists = similarity(x, means) + _, buckets = torch.max(dists, dim=-1) + return dists, buckets + + +def batched_bincount(index, num_classes, dim=-1): + shape = list(index.shape) + shape[dim] = num_classes + out = index.new_zeros(shape) + out.scatter_add_(dim, index, torch.ones_like(index, dtype=index.dtype)) + return out + + +def kmeans_iter(x, means, buckets=None): + b, h, _, d, dtype, num_clusters = *x.shape, x.dtype, means.shape[1] + + if not exists(buckets): + _, buckets = dists_and_buckets(x, means) + + bins = batched_bincount(buckets, num_clusters).sum(0, keepdim=True) + zero_mask = bins.long() == 0 + + means_ = buckets.new_zeros(b, h, num_clusters, d, dtype=dtype) + means_.scatter_add_(-2, expand_dim(buckets, -1, d), x) + means_ = F.normalize(means_.sum(0, keepdim=True), dim=-1).type(dtype) + + means = torch.where(zero_mask.unsqueeze(-1), means, means_) + means = means.squeeze(0) + return means + + +def distribution(dists, window_size): + _, topk_indices = dists.topk(k=window_size, dim=-2) + indices = topk_indices.transpose(-2, -1) + return indices.reshape(*indices.size()[:2], -1) + + +class Kmeans(nn.Module): + def __init__(self, num_heads, head_dim, num_clusters, ema_decay=0.999, commitment=1e-4): + super().__init__() + self.commitment = commitment + self.ema_decay = ema_decay + + self.register_buffer('means', torch.randn(num_heads, num_clusters, head_dim)) + self.register_buffer('initted', torch.tensor(False)) + self.num_new_means = 0 + self.new_means = None + + @torch.no_grad() + def init(self, x): + if self.initted: + return + _, h, _, d, device, _ = *x.shape, x.device, x.dtype + + num_clusters = self.means.shape[1] + + means = x.transpose(0, 1).contiguous().view(h, -1, d) + num_samples = means.shape[1] + + if num_samples >= num_clusters: + indices = torch.randperm(num_samples, device=device)[:num_clusters] + else: + indices = torch.randint(0, num_samples, (num_clusters,), device=device) + + means = means[:, indices] + + for _ in range(KMEAN_INIT_ITERS): + means = kmeans_iter(x, means) + + self.num_new_means = 0 + self.means.data.copy_(means) + self.initted.data.copy_(torch.tensor(True)) + + @torch.no_grad() + def update(self, new_means=None): + new_means = default(new_means, self.new_means) + assert exists(new_means), 'new kmeans has not been supplied' + ema_inplace(self.means, new_means, self.ema_decay) + + del self.new_means + self.new_means = None + self.num_new_means = 0 + + def forward(self, x, update_means=False): + self.init(x) + + b, dtype = x.shape[0], x.dtype + means = self.means.type(dtype) + x = F.normalize(x, 2, dim=-1).type(dtype) + + with torch.no_grad(): + dists, buckets = dists_and_buckets(x, means) + + routed_means = batched_index_select(expand_dim(means, 0, b), buckets) + loss = F.mse_loss(x, routed_means) * self.commitment + + if update_means: + with torch.no_grad(): + means = kmeans_iter(x, means, buckets) + self.new_means = ema(self.new_means, means, self.num_new_means / (self.num_new_means + 1)) + self.num_new_means += 1 + + return dists, loss + +# kmeans attention class + + +class KmeansAttention(nn.Module): + def __init__(self, num_clusters, window_size, num_heads, head_dim, causal=False, dropout=0., ema_decay=0.999, commitment=1e-4, context_window_size=None, receives_context=False, num_mem_kv=0, shared_qk=False): + super().__init__() + self.num_heads = num_heads + self.num_clusters = num_clusters + self.head_dim = head_dim + + self.window_size = window_size + self.context_window_size = default(context_window_size, window_size) + self.causal = causal + + self.shared_qk = shared_qk + self.receives_context = receives_context + self.kmeans = Kmeans(num_heads, head_dim, num_clusters, ema_decay, commitment) + self.dropout = nn.Dropout(dropout) + + self.num_mem_kv = max(num_mem_kv, 1 if causal and not shared_qk else 0) + self.mem_key = nn.Parameter(torch.randn(num_heads, num_clusters, self.num_mem_kv, head_dim)) + self.mem_value = nn.Parameter(torch.randn(num_heads, num_clusters, self.num_mem_kv, head_dim)) + + def forward(self, q, k, v, query_mask=None, key_mask=None, **kwargs): + b, h, t, d, kv_t, wsz, c_wsz, nc, device, dtype = *q.shape, k.shape[2], self.window_size, self.context_window_size, self.num_clusters, q.device, q.dtype + is_reverse = kwargs.pop('_reverse', False) + + out = torch.zeros_like(q, dtype=dtype) + + update_kmeans = self.training and not is_reverse + + key_mask = default(key_mask, query_mask) if not self.receives_context else key_mask + kv_wsz = wsz if not self.receives_context else c_wsz + + wsz = min(wsz, t) + kv_wsz = min(kv_wsz, kv_t) + + if not self.shared_qk or self.receives_context: + dists, aux_loss = self.kmeans(torch.cat((q, k), dim=2), update_kmeans) + q_dists, k_dists = split_at_index(2, t, dists) + indices = distribution(q_dists, wsz) + kv_indices = distribution(k_dists, kv_wsz) + else: + dists, aux_loss = self.kmeans(q, update_kmeans) + k = F.normalize(k, dim=-1).to(q) + indices = distribution(dists, wsz) + kv_indices = indices + + q = batched_index_select(q, indices) + k = batched_index_select(k, kv_indices) + v = batched_index_select(v, kv_indices) + + reshape_with_window = lambda x: x.reshape(b, h, nc, -1, d) + q, k, v = map(reshape_with_window, (q, k, v)) + + m_k, m_v = map(lambda x: expand_dim(x, 0, b).to(q), (self.mem_key, self.mem_value)) + k, v = map(lambda x: torch.cat(x, dim=3), ((m_k, k), (m_v, v))) + + dots = torch.einsum('bhnid,bhnjd->bhnij', q, k) * (d ** -0.5) + + mask_value = max_neg_value(dots) + + if exists(query_mask) or exists(key_mask): + query_mask = default(query_mask, lambda: torch.ones((b, t), device=device).bool()) + key_mask = default(key_mask, lambda: torch.ones((b, kv_t), device=device).bool()) + + q_mask = expand_dim(query_mask, 1, h).gather(2, indices) + kv_mask = expand_dim(key_mask, 1, h).gather(2, kv_indices) + q_mask, kv_mask = map(lambda t: t.reshape(b, h, nc, -1), (q_mask, kv_mask)) + mask = q_mask[:, :, :, :, None] * kv_mask[:, :, :, None, :] + mask = F.pad(mask, (self.num_mem_kv, 0), value=1) + dots.masked_fill_(~mask, mask_value) + del mask + + if self.causal: + q_mask, kv_mask = map(lambda t: t.reshape(b, h, nc, -1), (indices, kv_indices)) + mask = q_mask[:, :, :, :, None] >= kv_mask[:, :, :, None, :] + mask = F.pad(mask, (self.num_mem_kv, 0), value=1) + dots.masked_fill_(~mask, mask_value) + del mask + + if self.shared_qk: + q_mask, kv_mask = map(lambda t: t.reshape(b, h, nc, -1), (indices, kv_indices)) + mask = q_mask[:, :, :, :, None] == kv_mask[:, :, :, None, :] + mask = F.pad(mask, (self.num_mem_kv, 0), value=0) + dots.masked_fill_(mask, TOKEN_SELF_ATTN_VALUE) + del mask + + dots = dots.softmax(dim=-1) + dots = self.dropout(dots) + + bo = torch.einsum('bhcij,bhcjd->bhcid', dots, v) + so = torch.reshape(bo, (b, h, -1, bo.shape[-1])).type(dtype) + out = scatter_mean(out, so, indices.unsqueeze(-1).expand_as(so), -2) + return out, aux_loss + +# feedforward + + +class GELU_(nn.Module): + def forward(self, x): + return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3)))) + + +GELU = nn.GELU if hasattr(nn, 'GELU') else GELU_ + + +class FeedForward(nn.Module): + def __init__(self, dim, mult=4, dropout=0., activation=None, glu=False): + super().__init__() + activation = default(activation, GELU) + + self.glu = glu + self.w1 = nn.Linear(dim, dim * mult * (2 if glu else 1)) + self.act = activation() + self.dropout = nn.Dropout(dropout) + self.w2 = nn.Linear(dim * mult, dim) + + def forward(self, x, **kwargs): + if not self.glu: + x = self.w1(x) + x = self.act(x) + else: + x, v = self.w1(x).chunk(2, dim=-1) + x = self.act(x) * v + + x = self.dropout(x) + x = self.w2(x) + return x + +# self attention + + +class SelfAttention(nn.Module): + def __init__(self, dim, max_seq_len, heads, local_attn_heads, window_size, dim_head=None, local_attn_window_size=None, local_attn_radius_blocks=1, causal=False, attn_dropout=0., dropout=0., kmeans_ema_decay=0.999, commitment_factor=1e-4, receives_context=False, context_window_size=None, rel_pos_emb=True, num_mem_kv=0, shared_qk=False, conv_query_kernel=9): + super().__init__() + assert dim_head or (dim % heads) == 0, 'hidden dimension must be divisible by number of heads' + assert (max_seq_len % window_size) == 0, 'maximum sequence length must be divisible by the target window size' + assert local_attn_heads <= heads, 'number of local attention heads must be less than total heads' + assert not (receives_context and local_attn_heads > 0), 'local attention cannot be used for self attention with context' + assert not (receives_context and causal), 'contextual attention layer cannot be causal' + + local_attn_window_size = default(local_attn_window_size, window_size) + context_window_size = default(context_window_size, window_size) + + self.shared_qk = shared_qk + self.receives_context = receives_context + self.heads = heads + self.local_attn_heads = local_attn_heads + self.global_attn_heads = heads - local_attn_heads + + self.causal = causal + self.window_size = window_size + + dim_head = default(dim_head, dim // heads) + dim_heads = dim_head * heads + self.dim_head = dim_head + + num_clusters = max_seq_len // window_size + + # local + + local_dim_heads = dim_head * self.local_attn_heads + + if self.local_attn_heads > 0: + rel_pos_emb_config = (dim_head, local_attn_heads) if rel_pos_emb else None + self.local_attn = LocalAttention(dim=dim_head, window_size=local_attn_window_size, causal=causal, dropout=attn_dropout, rel_pos_emb_config=rel_pos_emb_config, look_backward=local_attn_radius_blocks, look_forward=0 if causal else local_attn_radius_blocks) + self.local_to_qkv = nn.Linear(dim, 3 * local_dim_heads) + + # global + + global_dim_heads = dim_head * self.global_attn_heads + + if self.global_attn_heads > 0: + self.global_attn = KmeansAttention(num_clusters, window_size, self.global_attn_heads, dim_head, causal=causal, dropout=attn_dropout, ema_decay=kmeans_ema_decay, commitment=commitment_factor, receives_context=receives_context, num_mem_kv=num_mem_kv, shared_qk=shared_qk) + + self.to_q = nn.Sequential( + Rearrange('b n c -> b c n'), + DepthWiseConv1d(dim, global_dim_heads, conv_query_kernel, causal=causal), + Rearrange('b c n -> b n c') + ) + + self.to_v = nn.Linear(dim, global_dim_heads, bias=False) + + if not self.shared_qk: + self.to_k = nn.Linear(dim, global_dim_heads, bias=False) + + # out + + self.to_out = nn.Linear(dim_heads, dim, bias=False) + self.dropout = nn.Dropout(dropout) + + def forward(self, query, key, value, context=None, key_padding_mask=None, context_mask=None, pos_emb=None, **kwargs): + assert not (self.receives_context and not exists(context)), 'context must be passed if self attention is set to receive context' + input_mask = key_padding_mask + x = query.transpose(0, 1) + b, t, _, h, dh = *x.shape, self.heads, self.dim_head + has_local, has_global = map(lambda x: x > 0, (self.local_attn_heads, self.global_attn_heads)) + + split_heads = lambda v: reshape_dim(v, -1, (-1, dh)).transpose(1, 2).contiguous() + + if has_local: + local_qkv = self.local_to_qkv(x).chunk(3, dim=-1) + lq, lk, lv = map(split_heads, local_qkv) + + if has_global: + kv_input = x if not self.receives_context else context + + q, v = self.to_q(x), self.to_v(kv_input) + + if not self.shared_qk: + k = self.to_k(kv_input) + else: + k = self.to_q(kv_input) if self.receives_context else q + + q, k, v = map(split_heads, (q, k, v)) + + out = [] + total_loss = torch.tensor(0., requires_grad=True, **to(x)) + + if has_local: + local_out = self.local_attn(lq, lk, lv, input_mask=input_mask) + out.append(local_out) + + if has_global: + if not self.receives_context and exists(pos_emb): + q, k = apply_rotary_pos_emb(q, k, pos_emb) + + global_out, loss = self.global_attn(q, k, v, query_mask=input_mask, key_mask=context_mask) + total_loss = total_loss + loss + + out.append(global_out) + + out = torch.cat(out, dim=1) + out = out.reshape(b, h, t, -1).transpose(1, 2).reshape(b, t, -1) + out = self.dropout(out.transpose(0, 1)) + # out = self.to_out(out) + return out, total_loss diff --git a/fairseq/modules/kmeans_vector_quantizer.py b/fairseq/modules/kmeans_vector_quantizer.py index be56e6081b..040db1e83e 100644 --- a/fairseq/modules/kmeans_vector_quantizer.py +++ b/fairseq/modules/kmeans_vector_quantizer.py @@ -5,7 +5,6 @@ import torch import torch.nn as nn - from fairseq.modules import Fp32GroupNorm @@ -13,17 +12,17 @@ class KmeansVectorQuantizer(nn.Module): def __init__( self, dim, num_vars, groups, combine_groups, vq_dim, time_first, gamma=0.25 ): - '''Vector quantization using straight pass-through estimator (i.e. kmeans) - - Args: - dim: input dimension (channels) - num_vars: number of quantized vectors per group - groups: number of groups for vector quantization - combine_groups: whether to use the vectors for all groups - vq_dim: dimensionality of the resulting quantized vector - time_first: if true, expect input in BxTxC format, otherwise in BxCxT - gamma: commitment loss coefficient - ''' + """Vector quantization using straight pass-through estimator (i.e. kmeans) + + Args: + dim: input dimension (channels) + num_vars: number of quantized vectors per group + groups: number of groups for vector quantization + combine_groups: whether to use the vectors for all groups + vq_dim: dimensionality of the resulting quantized vector + time_first: if true, expect input in BxTxC format, otherwise in BxCxT + gamma: commitment loss coefficient + """ super().__init__() self.groups = groups @@ -51,7 +50,7 @@ def __init__( self.mse_mean = nn.MSELoss(reduction="mean") def _pass_grad(self, x, y): - """ Manually set gradient for backward pass. + """Manually set gradient for backward pass. for y = f(x), ensure that during the backward pass, dL/dy = dL/dx regardless of f(x). Returns: @@ -102,9 +101,9 @@ def forward(self, x, produce_targets=False): x = self._pass_grad(ze, zq) hard_x = ( - idx.new_zeros(bsz*tsz*self.groups, self.num_vars) - .scatter_(-1, idx.view(-1, 1), 1.0) - .view(bsz * tsz, self.groups, -1) + idx.new_zeros(bsz * tsz * self.groups, self.num_vars) + .scatter_(-1, idx.view(-1, 1), 1.0) + .view(bsz * tsz, self.groups, -1) ) hard_probs = torch.mean(hard_x.float(), dim=0) result["code_perplexity"] = torch.exp( diff --git a/fairseq/modules/layer_norm.py b/fairseq/modules/layer_norm.py index 4fee32d4fc..234609d9e2 100644 --- a/fairseq/modules/layer_norm.py +++ b/fairseq/modules/layer_norm.py @@ -22,11 +22,14 @@ def forward(self, x): with torch.cuda.device(x.device): return super().forward(x) + except ImportError: has_fused_layernorm = False def LayerNorm(normalized_shape, eps=1e-5, elementwise_affine=True, export=False): + if torch.jit.is_scripting(): + export = True if not export and torch.cuda.is_available() and has_fused_layernorm: return FusedLayerNorm(normalized_shape, eps, elementwise_affine) return torch.nn.LayerNorm(normalized_shape, eps, elementwise_affine) diff --git a/fairseq/modules/lightconv_layer/cuda_function_gen.py b/fairseq/modules/lightconv_layer/cuda_function_gen.py index afec9e19e7..a25433dd8e 100644 --- a/fairseq/modules/lightconv_layer/cuda_function_gen.py +++ b/fairseq/modules/lightconv_layer/cuda_function_gen.py @@ -91,7 +91,7 @@ def gen_forward(): } """ - with open("lightconv_cuda_forward.cu", 'w') as forward: + with open("lightconv_cuda_forward.cu", "w") as forward: forward.write(head) for seq in seqs: forward.write(sequence_if.format(seq=seq)) @@ -261,7 +261,7 @@ def gen_backward(): thresh = [32, 32, 64, 128, 256, -1, -1, -1] max_mem = [-1, -1, -1, -1, -1, 192, 96, 64] - with open("lightconv_cuda_backward.cu", 'w') as backward: + with open("lightconv_cuda_backward.cu", "w") as backward: backward.write(head) for (k, t, mem) in zip(kernels, thresh, max_mem): backward.write(case_k.format(k=k)) diff --git a/fairseq/modules/lightconv_layer/lightconv_cuda.cpp b/fairseq/modules/lightconv_layer/lightconv_cuda.cpp index 4bf6b5ad36..ece47a8d90 100644 --- a/fairseq/modules/lightconv_layer/lightconv_cuda.cpp +++ b/fairseq/modules/lightconv_layer/lightconv_cuda.cpp @@ -8,10 +8,8 @@ #include <torch/extension.h> #include <vector> -std::vector<at::Tensor> lightconv_cuda_forward( - at::Tensor input, - at::Tensor filters, - int padding_l); +std::vector<at::Tensor> +lightconv_cuda_forward(at::Tensor input, at::Tensor filters, int padding_l); std::vector<at::Tensor> lightconv_cuda_backward( at::Tensor gradOutput, @@ -19,20 +17,20 @@ std::vector<at::Tensor> lightconv_cuda_backward( at::Tensor input, at::Tensor filters); +#define CHECK_CUDA(x) \ + AT_ASSERTM(x.type().is_cuda(), #x " must be a CUDA tensor") +#define CHECK_CONTIGUOUS(x) \ + AT_ASSERTM(x.is_contiguous(), #x " must be contiguous") +#define CHECK_INPUT(x) \ + CHECK_CUDA(x); \ + CHECK_CONTIGUOUS(x) -#define CHECK_CUDA(x) AT_ASSERTM(x.type().is_cuda(), #x " must be a CUDA tensor") -#define CHECK_CONTIGUOUS(x) AT_ASSERTM(x.is_contiguous(), #x " must be contiguous") -#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x) - -std::vector<at::Tensor> lightconv_forward( - at::Tensor input, - at::Tensor filters, - int padding_l) { +std::vector<at::Tensor> +lightconv_forward(at::Tensor input, at::Tensor filters, int padding_l) { + CHECK_INPUT(input); + CHECK_INPUT(filters); - CHECK_INPUT(input); - CHECK_INPUT(filters); - - return lightconv_cuda_forward(input, filters, padding_l); + return lightconv_cuda_forward(input, filters, padding_l); } std::vector<at::Tensor> lightconv_backward( @@ -40,15 +38,14 @@ std::vector<at::Tensor> lightconv_backward( int padding_l, at::Tensor input, at::Tensor filters) { + CHECK_INPUT(gradOutput); + CHECK_INPUT(input); + CHECK_INPUT(filters); - CHECK_INPUT(gradOutput); - CHECK_INPUT(input); - CHECK_INPUT(filters); - - return lightconv_cuda_backward(gradOutput, padding_l, input, filters); + return lightconv_cuda_backward(gradOutput, padding_l, input, filters); } PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { - m.def("forward", &lightconv_forward, "lighconv forward (CUDA)"); - m.def("backward", &lightconv_backward, "lighconv backward (CUDA)"); + m.def("forward", &lightconv_forward, "lighconv forward (CUDA)"); + m.def("backward", &lightconv_backward, "lighconv backward (CUDA)"); } diff --git a/fairseq/modules/lightconv_layer/lightconv_cuda.cuh b/fairseq/modules/lightconv_layer/lightconv_cuda.cuh index 3cae57b68f..610ab399e9 100644 --- a/fairseq/modules/lightconv_layer/lightconv_cuda.cuh +++ b/fairseq/modules/lightconv_layer/lightconv_cuda.cuh @@ -1,6 +1,6 @@ /** * Copyright (c) Facebook, Inc. and its affiliates. - * + * * This source code is licensed under the MIT license found in the * LICENSE file in the root directory of this source tree. */ @@ -18,23 +18,24 @@ #include <utility> #include <vector> -#include <stdlib.h> #include <assert.h> +#include <stdlib.h> #define SHFL_MASK 0xffffffff -template<int FS, int SB, int padding_l, typename scalar_t> -__global__ -void lightconv_forward_kernel(const scalar_t* input, - const scalar_t* filters, - int minibatch, int sequenceLength, - int numFeatures, int numFiltersInBlock, - scalar_t* output); +template <int FS, int SB, int padding_l, typename scalar_t> +__global__ void lightconv_forward_kernel( + const scalar_t* input, + const scalar_t* filters, + int minibatch, + int sequenceLength, + int numFeatures, + int numFiltersInBlock, + scalar_t* output); -template<int FS, int SB, int padding_l, typename scalar_t> -__global__ -void lightconv_grad_wrt_input_kernel( - const scalar_t* input, +template <int FS, int SB, int padding_l, typename scalar_t> +__global__ void lightconv_grad_wrt_input_kernel( + const scalar_t* input, const scalar_t* filters, int minibatch, int sequenceLength, @@ -42,9 +43,8 @@ void lightconv_grad_wrt_input_kernel( int numFiltersInBlock, scalar_t* output); -template<int FS, int SB, int padding_l, typename scalar_t> -__global__ -void lightconv_grad_wrt_weights_firstpass_short_kernel( +template <int FS, int SB, int padding_l, typename scalar_t> +__global__ void lightconv_grad_wrt_weights_firstpass_short_kernel( const scalar_t* input, const scalar_t* gradInput, int minibatch, @@ -54,17 +54,15 @@ void lightconv_grad_wrt_weights_firstpass_short_kernel( int numHeads, float* output); -template<int FS, int SB, typename scalar_t> -__global__ -void lightconv_grad_wrt_weights_secondpass_short_kernel( +template <int FS, int SB, typename scalar_t> +__global__ void lightconv_grad_wrt_weights_secondpass_short_kernel( const float* input, - const int minibatch, + const int minibatch, const int numFiltersInBlock, scalar_t* output); -template<int FS, int SB, int padding_l, typename scalar_t> -__global__ -void lightconv_grad_wrt_weights_firstpass_kernel( +template <int FS, int SB, int padding_l, typename scalar_t> +__global__ void lightconv_grad_wrt_weights_firstpass_kernel( const scalar_t* input, const scalar_t* gradInput, int minibatch, @@ -73,11 +71,9 @@ void lightconv_grad_wrt_weights_firstpass_kernel( int numFiltersInBlock, float* output); -template<int FS, int SB, typename scalar_t> -__global__ -void lightconv_grad_wrt_weights_secondpass_kernel( +template <int FS, int SB, typename scalar_t> +__global__ void lightconv_grad_wrt_weights_secondpass_kernel( const float* input, - const int minibatch, + const int minibatch, const int numFiltersInBlock, scalar_t* output); - diff --git a/fairseq/modules/lightconv_layer/lightconv_cuda_kernel.cu b/fairseq/modules/lightconv_layer/lightconv_cuda_kernel.cu index 8ee83a56c8..cdf31d5d2d 100644 --- a/fairseq/modules/lightconv_layer/lightconv_cuda_kernel.cu +++ b/fairseq/modules/lightconv_layer/lightconv_cuda_kernel.cu @@ -1,29 +1,31 @@ /** * Copyright (c) Facebook, Inc. and its affiliates. - * + * * This source code is licensed under the MIT license found in the * LICENSE file in the root directory of this source tree. */ +#include "../cuda_utils.cu" #include "lightconv_cuda.cuh" -#include "lightconv_cuda_forward.cu" #include "lightconv_cuda_backward.cu" -#include "../cuda_utils.cu" - -template<int FS, int SB, int padding_l, typename scalar_t> -__global__ -void lightconv_forward_kernel(const scalar_t* input, - const scalar_t* filters, - int minibatch, int sequenceLength, - int numFeatures, int numFiltersInBlock, - scalar_t* output) { +#include "lightconv_cuda_forward.cu" +template <int FS, int SB, int padding_l, typename scalar_t> +__global__ void lightconv_forward_kernel( + const scalar_t* input, + const scalar_t* filters, + int minibatch, + int sequenceLength, + int numFeatures, + int numFiltersInBlock, + scalar_t* output) { const int tid = threadIdx.x; const int batchIdx = blockIdx.x; const int featureIdx = blockIdx.y; const int filterIdx = featureIdx / numFiltersInBlock; - const int IOOffset = numFeatures * sequenceLength * batchIdx + featureIdx * sequenceLength; + const int IOOffset = + numFeatures * sequenceLength * batchIdx + featureIdx * sequenceLength; const scalar_t* inputFeature = &input[IOOffset]; scalar_t* outputFeature = &output[IOOffset]; const scalar_t* inputFilter = &filters[filterIdx * FS]; @@ -31,7 +33,7 @@ void lightconv_forward_kernel(const scalar_t* input, assert(blockDim.x == SB); scalar_t filter[FS]; - #pragma unroll +#pragma unroll for (int i = 0; i < FS; ++i) { filter[i] = inputFilter[i]; } @@ -45,13 +47,19 @@ void lightconv_forward_kernel(const scalar_t* input, // Read input into shared memory const int inputOffset = i * SB; - load_input_to_shared<FS, SB, padding_l>(inputFeature, inputOffset, sequenceLength, - i, numIterations, (numIterations == 1), temp); + load_input_to_shared<FS, SB, padding_l>( + inputFeature, + inputOffset, + sequenceLength, + i, + numIterations, + (numIterations == 1), + temp); __syncthreads(); scalar_t out = 0; - #pragma unroll +#pragma unroll for (int j = 0; j < FS; ++j) { out += filter[j] * temp[tid + j]; } @@ -66,9 +74,8 @@ void lightconv_forward_kernel(const scalar_t* input, } } -template<int FS, int SB, int padding_l, typename scalar_t> -__global__ -void lightconv_grad_wrt_input_kernel( +template <int FS, int SB, int padding_l, typename scalar_t> +__global__ void lightconv_grad_wrt_input_kernel( const scalar_t* input, const scalar_t* filters, int minibatch, @@ -76,14 +83,14 @@ void lightconv_grad_wrt_input_kernel( int numFeatures, int numFiltersInBlock, scalar_t* output) { - // input grad kernel is similar to forward kernel const int tid = threadIdx.x; const int batchIdx = blockIdx.x; const int featureIdx = blockIdx.y; const int filterIdx = featureIdx / numFiltersInBlock; - const int IOOffset = numFeatures * sequenceLength * batchIdx + featureIdx * sequenceLength; + const int IOOffset = + numFeatures * sequenceLength * batchIdx + featureIdx * sequenceLength; const scalar_t* inputFeature = &input[IOOffset]; scalar_t* outputFeature = &output[IOOffset]; const scalar_t* inputFilter = &filters[filterIdx * FS]; @@ -92,8 +99,8 @@ void lightconv_grad_wrt_input_kernel( scalar_t filter[FS]; - // The only change is loading the filter in reverse - #pragma unroll +// The only change is loading the filter in reverse +#pragma unroll for (int i = 0; i < FS; ++i) { filter[i] = inputFilter[FS - i - 1]; } @@ -110,13 +117,19 @@ void lightconv_grad_wrt_input_kernel( // Read input into shared memory const int inputOffset = i * SB; - load_input_to_shared<FS, SB, padding>(inputFeature, inputOffset, sequenceLength, - i, numIterations, false, temp); + load_input_to_shared<FS, SB, padding>( + inputFeature, + inputOffset, + sequenceLength, + i, + numIterations, + false, + temp); __syncthreads(); scalar_t out = 0; - #pragma unroll +#pragma unroll for (int j = 0; j < FS; ++j) { out += filter[j] * temp[tid + j]; } @@ -133,9 +146,8 @@ void lightconv_grad_wrt_input_kernel( // This is by far the most expensive kernel in terms of time taken. // Can be 16x slower than the forward or grad_wrt_input when filter size is 31 -template<int FS, int SB, int padding_l, typename scalar_t> -__global__ -void lightconv_grad_wrt_weights_firstpass_short_kernel( +template <int FS, int SB, int padding_l, typename scalar_t> +__global__ void lightconv_grad_wrt_weights_firstpass_short_kernel( const scalar_t* input, const scalar_t* gradInput, int minibatch, @@ -144,7 +156,6 @@ void lightconv_grad_wrt_weights_firstpass_short_kernel( int numFiltersInBlock, int numHeads, float* output) { - const int tid = threadIdx.x; const int batchIdx = blockIdx.x; const int filterIdx = blockIdx.y; @@ -166,52 +177,60 @@ void lightconv_grad_wrt_weights_firstpass_short_kernel( accumWeights[i] = float(0.0); } - // loop over each sequence within filterblock - for (int idxInFilterBlock = 0; idxInFilterBlock < numFiltersInBlock; ++idxInFilterBlock) { - - const int featureOffset = batchIdx * numFeatures * sequenceLength + (filterIdx * numFiltersInBlock + idxInFilterBlock) * sequenceLength; + for (int idxInFilterBlock = 0; idxInFilterBlock < numFiltersInBlock; + ++idxInFilterBlock) { + const int featureOffset = batchIdx * numFeatures * sequenceLength + + (filterIdx * numFiltersInBlock + idxInFilterBlock) * sequenceLength; const scalar_t* inputFeature = &input[featureOffset]; const scalar_t* gradInputFeature = &gradInput[featureOffset]; zeroSharedMem<FS, SB, padding_l>(tempInput); - zeroSharedMem<FS, SB, (FS/2)>(tempGradInput); + zeroSharedMem<FS, SB, (FS / 2)>(tempGradInput); __syncthreads(); for (int i = 0; i < numIterations; ++i) { - const int inputOffset = i * SB; - load_input_to_shared<FS, SB, padding_l>(inputFeature, inputOffset, sequenceLength, - i, numIterations, false, tempInput); - load_input_to_shared<FS, SB, (FS/2)>(gradInputFeature, inputOffset, sequenceLength, - i, numIterations, false, tempGradInput); + load_input_to_shared<FS, SB, padding_l>( + inputFeature, + inputOffset, + sequenceLength, + i, + numIterations, + false, + tempInput); + load_input_to_shared<FS, SB, (FS / 2)>( + gradInputFeature, + inputOffset, + sequenceLength, + i, + numIterations, + false, + tempGradInput); __syncthreads(); - const int gradIndex = (FS/2) + tid; + const int gradIndex = (FS / 2) + tid; scalar_t tempGrad = tempGradInput[gradIndex]; - #pragma unroll +#pragma unroll for (int j = 0; j < FS; j++) { const int inputIndex = tid + j; accumWeights[j] += tempInput[inputIndex] * tempGrad; } __syncthreads(); - } - } // Row-major sum for (int filterWeightIdx = 0; filterWeightIdx < FS; ++filterWeightIdx) { - float temp; if (tid < sequenceLength) { - temp = accumWeights[filterWeightIdx]; + temp = accumWeights[filterWeightIdx]; } else { - temp = float(0.0); + temp = float(0.0); } const int outputOffset = filterWeightIdx * minibatch + batchIdx; @@ -224,14 +243,12 @@ void lightconv_grad_wrt_weights_firstpass_short_kernel( } } -template<int FS, int SB, typename scalar_t> -__global__ -void lightconv_grad_wrt_weights_secondpass_short_kernel( +template <int FS, int SB, typename scalar_t> +__global__ void lightconv_grad_wrt_weights_secondpass_short_kernel( const float* input, const int minibatch, const int numFiltersInBlock, scalar_t* output) { - assert(blockDim.x == SB); const int tid = threadIdx.x; @@ -239,8 +256,8 @@ void lightconv_grad_wrt_weights_secondpass_short_kernel( const int filterIdx = blockIdx.x; const int filterWeightIdx = blockIdx.y; - const int inputOffset = filterIdx * FS * minibatch + - filterWeightIdx * minibatch; + const int inputOffset = + filterIdx * FS * minibatch + filterWeightIdx * minibatch; const float* tempInput = &input[inputOffset]; // read into shared memory for reduction @@ -261,9 +278,8 @@ void lightconv_grad_wrt_weights_secondpass_short_kernel( // This is by far the most expensive kernel in terms of time taken. // Can be 16x slower than the forward or grad_wrt_input when filter size is 31 -template<int FS, int SB, int padding_l, typename scalar_t> -__global__ -void lightconv_grad_wrt_weights_firstpass_kernel( +template <int FS, int SB, int padding_l, typename scalar_t> +__global__ void lightconv_grad_wrt_weights_firstpass_kernel( const scalar_t* input, const scalar_t* gradInput, int minibatch, @@ -271,7 +287,6 @@ void lightconv_grad_wrt_weights_firstpass_kernel( int numFeatures, int numFiltersInBlock, float* output) { - assert(blockDim.x == SB); const int tid = threadIdx.x; @@ -287,7 +302,7 @@ void lightconv_grad_wrt_weights_firstpass_kernel( __shared__ scalar_t tempInput[SB + FS]; __shared__ scalar_t tempGradInput[SB + FS]; zeroSharedMem<FS, SB, padding_l>(tempInput); - zeroSharedMem<FS, SB, (FS/2)>(tempGradInput); + zeroSharedMem<FS, SB, (FS / 2)>(tempGradInput); __syncthreads(); float accumWeights[FS]; @@ -296,23 +311,37 @@ void lightconv_grad_wrt_weights_firstpass_kernel( accumWeights[i] = float(0.0); } - const int IOOffset = batchIdx * numFeatures * sequenceLength + featureIdx * sequenceLength; + const int IOOffset = + batchIdx * numFeatures * sequenceLength + featureIdx * sequenceLength; const scalar_t* inputFeature = &input[IOOffset]; const scalar_t* gradInputFeature = &gradInput[IOOffset]; - float* tempOutputGradWeight = &output[filterIdx * FS * minibatch * numFiltersInBlock]; + float* tempOutputGradWeight = + &output[filterIdx * FS * minibatch * numFiltersInBlock]; for (int i = 0; i < numIterations; ++i) { const int inputOffset = i * SB; - load_input_to_shared<FS, SB, padding_l>(inputFeature, inputOffset, sequenceLength, - i, numIterations, false, tempInput); - load_input_to_shared<FS, SB, (FS/2)>(gradInputFeature, inputOffset, sequenceLength, - i, numIterations, false, tempGradInput); + load_input_to_shared<FS, SB, padding_l>( + inputFeature, + inputOffset, + sequenceLength, + i, + numIterations, + false, + tempInput); + load_input_to_shared<FS, SB, (FS / 2)>( + gradInputFeature, + inputOffset, + sequenceLength, + i, + numIterations, + false, + tempGradInput); __syncthreads(); - #pragma unroll +#pragma unroll for (int j = 0; j < FS; ++j) { - accumWeights[j] += tempInput[tid + j] * tempGradInput[tid + (FS/2)]; + accumWeights[j] += tempInput[tid + j] * tempGradInput[tid + (FS / 2)]; } __syncthreads(); @@ -320,7 +349,6 @@ void lightconv_grad_wrt_weights_firstpass_kernel( // Row-major sum for (int filterWeightIdx = 0; filterWeightIdx < FS; ++filterWeightIdx) { - // Write to shared memory before reduction if (tid < sequenceLength) { temp = accumWeights[filterWeightIdx]; @@ -331,8 +359,7 @@ void lightconv_grad_wrt_weights_firstpass_kernel( temp = blockReduce(temp); const int outputOffset = filterWeightIdx * minibatch * numFiltersInBlock + - batchIdx * numFiltersInBlock + - idxInFilterBlock; + batchIdx * numFiltersInBlock + idxInFilterBlock; if (tid == 0) { tempOutputGradWeight[outputOffset] = temp; @@ -340,14 +367,12 @@ void lightconv_grad_wrt_weights_firstpass_kernel( } } -template<int FS, int SB, typename scalar_t> -__global__ -void lightconv_grad_wrt_weights_secondpass_kernel( +template <int FS, int SB, typename scalar_t> +__global__ void lightconv_grad_wrt_weights_secondpass_kernel( const float* input, const int minibatch, const int numFiltersInBlock, scalar_t* output) { - assert(blockDim.x == SB); const int tid = threadIdx.x; @@ -356,7 +381,7 @@ void lightconv_grad_wrt_weights_secondpass_kernel( const int filterWeightIdx = blockIdx.y; const int inputOffset = filterIdx * FS * minibatch * numFiltersInBlock + - filterWeightIdx * minibatch * numFiltersInBlock; + filterWeightIdx * minibatch * numFiltersInBlock; const float* tempInput = &input[inputOffset]; int readIndex = tid; diff --git a/fairseq/modules/lightconv_layer/lightconv_layer.py b/fairseq/modules/lightconv_layer/lightconv_layer.py index 1b046a1611..e7e597f474 100644 --- a/fairseq/modules/lightconv_layer/lightconv_layer.py +++ b/fairseq/modules/lightconv_layer/lightconv_layer.py @@ -3,18 +3,17 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. +import lightconv_cuda import torch -from torch import nn -from torch.autograd import Function import torch.nn.functional as F - -import lightconv_cuda from fairseq import utils from fairseq.incremental_decoding_utils import with_incremental_state +from fairseq.modules.fairseq_dropout import FairseqDropout +from torch import nn +from torch.autograd import Function class lightconvFunction(Function): - @staticmethod def forward(ctx, x, weights, padding_l): ctx.padding_l = padding_l @@ -26,9 +25,8 @@ def forward(ctx, x, weights, padding_l): @staticmethod def backward(ctx, grad_output): outputs = lightconv_cuda.backward( - grad_output.contiguous(), - ctx.padding_l, - *ctx.saved_tensors) + grad_output.contiguous(), ctx.padding_l, *ctx.saved_tensors + ) grad_input, grad_weights = outputs return grad_input, grad_weights, None @@ -36,21 +34,24 @@ def backward(ctx, grad_output): @with_incremental_state class LightconvLayer(nn.Module): def __init__( - self, - input_size, - kernel_size=1, - padding_l=None, - weight_softmax=False, - num_heads=1, - weight_dropout=0., - bias=False): + self, + input_size, + kernel_size=1, + padding_l=None, + weight_softmax=False, + num_heads=1, + weight_dropout=0.0, + bias=False, + ): super(LightconvLayer, self).__init__() self.input_size = input_size self.kernel_size = kernel_size self.padding_l = padding_l self.num_heads = num_heads self.weight_softmax = weight_softmax - self.weight_dropout = weight_dropout + self.weight_dropout_module = FairseqDropout( + weight_dropout, module_name=self.__class__.__name__ + ) self.weight = nn.Parameter(torch.Tensor(num_heads, kernel_size)) if bias: @@ -60,16 +61,16 @@ def __init__( self.reset_parameters() def upgrade_state_dict_named(self, state_dict, name): - prefix = name + '.' if name != '' else '' + prefix = name + "." if name != "" else "" for k, v in state_dict.items(): - if k.endswith(prefix + 'weight'): + if k.endswith(prefix + "weight"): if v.dim() == 3 and v.size(1) == 1: state_dict[k] = v.squeeze(1) def reset_parameters(self): nn.init.xavier_uniform_(self.weight) if self.bias is not None: - nn.init.constant_(self.bias, 0.) + nn.init.constant_(self.bias, 0.0) def forward(self, x, incremental_state=None): @@ -83,20 +84,27 @@ def forward(self, x, incremental_state=None): input_buffer = x.new() x_unfold = torch.cat([input_buffer, x.unsqueeze(3)], dim=3) if self.kernel_size > 1: - self._set_input_buffer(incremental_state, x_unfold[:, :, :, -self.kernel_size+1:]) - x_unfold = x_unfold.view(T*B*H, R, -1) + self._set_input_buffer( + incremental_state, x_unfold[:, :, :, -self.kernel_size + 1 :] + ) + x_unfold = x_unfold.view(T * B * H, R, -1) weight = self.weight if self.weight_softmax: weight = F.softmax(weight.float(), dim=1).type_as(weight) - weight = weight[:, -x_unfold.size(2):] + weight = weight[:, -x_unfold.size(2) :] K = weight.size(1) - weight = weight.view(1, H, K).expand(T*B, H, K).contiguous().view(T*B*H, K, 1) + weight = ( + weight.view(1, H, K) + .expand(T * B, H, K) + .contiguous() + .view(T * B * H, K, 1) + ) - weight = F.dropout(weight, self.weight_dropout, training=self.training) + weight = self.weight_dropout_module(weight) output = torch.bmm(x_unfold, weight) # T*B*H x R x 1 output = output.view(T, B, C) return output @@ -107,8 +115,8 @@ def forward(self, x, incremental_state=None): weight = self.weight if self.weight_softmax: weight = F.softmax(self.weight, -1) - if self.weight_dropout: - weight = F.dropout(weight, self.weight_dropout, training=self.training) + if self.weight_dropout_module.p: + weight = self.weight_dropout_module(weight) return lightconvFunction.apply(x, weight, self.padding_l).permute(2, 0, 1) def reorder_incremental_state(self, incremental_state, new_order): @@ -118,10 +126,12 @@ def reorder_incremental_state(self, incremental_state, new_order): self._set_input_buffer(incremental_state, input_buffer) def _get_input_buffer(self, incremental_state): - return utils.get_incremental_state(self, incremental_state, 'input_buffer') + return utils.get_incremental_state(self, incremental_state, "input_buffer") def _set_input_buffer(self, incremental_state, new_buffer): - return utils.set_incremental_state(self, incremental_state, 'input_buffer', new_buffer) + return utils.set_incremental_state( + self, incremental_state, "input_buffer", new_buffer + ) def half(self): return self._apply(lambda t: t.half() if t.is_floating_point() else t) diff --git a/fairseq/modules/lightconv_layer/setup.py b/fairseq/modules/lightconv_layer/setup.py index 0eac1df03c..052635be79 100644 --- a/fairseq/modules/lightconv_layer/setup.py +++ b/fairseq/modules/lightconv_layer/setup.py @@ -5,16 +5,19 @@ # LICENSE file in the root directory of this source tree. from setuptools import setup -from torch.utils.cpp_extension import CUDAExtension, BuildExtension +from torch.utils.cpp_extension import BuildExtension, CUDAExtension + setup( - name='lightconv_layer', + name="lightconv_layer", ext_modules=[ - CUDAExtension('lightconv_cuda', [ - 'lightconv_cuda.cpp', - 'lightconv_cuda_kernel.cu', - ]), + CUDAExtension( + "lightconv_cuda", + [ + "lightconv_cuda.cpp", + "lightconv_cuda_kernel.cu", + ], + ), ], - cmdclass={ - 'build_ext': BuildExtension - }) + cmdclass={"build_ext": BuildExtension}, +) diff --git a/fairseq/modules/lightweight_convolution.py b/fairseq/modules/lightweight_convolution.py index 5c77843ecf..ec11a95079 100644 --- a/fairseq/modules/lightweight_convolution.py +++ b/fairseq/modules/lightweight_convolution.py @@ -6,31 +6,49 @@ import torch import torch.nn as nn import torch.nn.functional as F - from fairseq import utils -from fairseq.modules.unfold import unfold1d from fairseq.incremental_decoding_utils import with_incremental_state +from fairseq.modules.fairseq_dropout import FairseqDropout +from fairseq.modules.unfold import unfold1d -def LightweightConv(input_size, kernel_size=1, padding_l=None, num_heads=1, - weight_dropout=0., weight_softmax=False, bias=False): +def LightweightConv( + input_size, + kernel_size=1, + padding_l=None, + num_heads=1, + weight_dropout=0.0, + weight_softmax=False, + bias=False, +): if torch.cuda.is_available(): try: from fairseq.modules.lightconv_layer import LightconvLayer - return LightconvLayer(input_size, kernel_size=kernel_size, - padding_l=padding_l, num_heads=num_heads, - weight_dropout=weight_dropout, - weight_softmax=weight_softmax, bias=bias) + + return LightconvLayer( + input_size, + kernel_size=kernel_size, + padding_l=padding_l, + num_heads=num_heads, + weight_dropout=weight_dropout, + weight_softmax=weight_softmax, + bias=bias, + ) except ImportError as e: print(e) - return LightweightConv1dTBC(input_size, kernel_size=kernel_size, - padding_l=padding_l, num_heads=num_heads, - weight_dropout=weight_dropout, - weight_softmax=weight_softmax, bias=bias) + return LightweightConv1dTBC( + input_size, + kernel_size=kernel_size, + padding_l=padding_l, + num_heads=num_heads, + weight_dropout=weight_dropout, + weight_softmax=weight_softmax, + bias=bias, + ) class LightweightConv1d(nn.Module): - '''Lightweight Convolution assuming the input is BxCxT + """Lightweight Convolution assuming the input is BxCxT This is just an example that explains LightConv clearer than the TBC version. We don't use this module in the model. @@ -50,10 +68,18 @@ class LightweightConv1d(nn.Module): weight: the learnable weights of the module of shape `(num_heads, 1, kernel_size)` bias: the learnable bias of the module of shape `(input_size)` - ''' - - def __init__(self, input_size, kernel_size=1, padding=0, num_heads=1, - weight_softmax=False, bias=False, weight_dropout=0.): + """ + + def __init__( + self, + input_size, + kernel_size=1, + padding=0, + num_heads=1, + weight_softmax=False, + bias=False, + weight_dropout=0.0, + ): super().__init__() self.input_size = input_size self.kernel_size = kernel_size @@ -66,19 +92,21 @@ def __init__(self, input_size, kernel_size=1, padding=0, num_heads=1, self.bias = nn.Parameter(torch.Tensor(input_size)) else: self.bias = None - self.weight_dropout = weight_dropout + self.weight_dropout_module = FairseqDropout( + weight_dropout, module_name=self.__class__.__name__ + ) self.reset_parameters() def reset_parameters(self): nn.init.xavier_uniform_(self.weight) if self.bias is not None: - nn.init.constant_(self.bias, 0.) + nn.init.constant_(self.bias, 0.0) def forward(self, input): - ''' + """ input size: B x C x T output size: B x C x T - ''' + """ B, C, T = input.size() H = self.num_heads @@ -86,7 +114,7 @@ def forward(self, input): if self.weight_softmax: weight = F.softmax(weight, dim=-1) - weight = F.dropout(weight, self.weight_dropout, training=self.training) + weight = self.weight_dropout_module(weight) # Merge every C/H entries into the batch dimension (C = self.input_size) # B x C x T -> (B * C/H) x H x T # One can also expand the weight to C x 1 x K by a factor of C/H @@ -102,7 +130,7 @@ def forward(self, input): @with_incremental_state class LightweightConv1dTBC(nn.Module): - '''Lightweight Convolution assuming the input is TxBxC + """Lightweight Convolution assuming the input is TxBxC Args: input_size: # of channels of the input kernel_size: convolution channels @@ -120,15 +148,26 @@ class LightweightConv1dTBC(nn.Module): weight: the learnable weights of the module of shape `(num_heads, 1, kernel_size)` bias: the learnable bias of the module of shape `(input_size)` - ''' - def __init__(self, input_size, kernel_size=1, padding_l=None, num_heads=1, - weight_dropout=0., weight_softmax=False, bias=False): + """ + + def __init__( + self, + input_size, + kernel_size=1, + padding_l=None, + num_heads=1, + weight_dropout=0.0, + weight_softmax=False, + bias=False, + ): super().__init__() self.input_size = input_size self.kernel_size = kernel_size self.padding_l = padding_l self.num_heads = num_heads - self.weight_dropout = weight_dropout + self.weight_dropout_module = FairseqDropout( + weight_dropout, module_name=self.__class__.__name__ + ) self.weight_softmax = weight_softmax self.weight = nn.Parameter(torch.Tensor(num_heads, 1, kernel_size)) @@ -143,15 +182,15 @@ def __init__(self, input_size, kernel_size=1, padding_l=None, num_heads=1, def reset_parameters(self): nn.init.xavier_uniform_(self.weight) if self.bias is not None: - nn.init.constant_(self.bias, 0.) + nn.init.constant_(self.bias, 0.0) def forward(self, x, incremental_state=None, unfold=False): - '''Assuming the input, x, of the shape T x B x C and producing an output in the shape T x B x C + """Assuming the input, x, of the shape T x B x C and producing an output in the shape T x B x C args: x: Input of shape T x B x C, i.e. (timesteps, batch_size, input_size) incremental_state: A dict to keep the state unfold: unfold the input or not. If not, we use the matrix trick instead - ''' + """ unfold = unfold or (incremental_state is not None) if unfold: @@ -167,8 +206,8 @@ def prepare_for_onnx_export_(self): self.onnx_trace = True def _forward_unfolded(self, x, incremental_state): - '''The conventional implementation of convolutions. - Unfolding the input by having a window shifting to the right.''' + """The conventional implementation of convolutions. + Unfolding the input by having a window shifting to the right.""" T, B, C = x.size() K, H = self.kernel_size, self.num_heads R = C // H @@ -181,32 +220,38 @@ def _forward_unfolded(self, x, incremental_state): input_buffer = x.new() x_unfold = torch.cat([input_buffer, x.unsqueeze(3)], dim=3) if self.kernel_size > 1: - self._set_input_buffer(incremental_state, x_unfold[:, :, :, -self.kernel_size+1:]) - x_unfold = x_unfold.view(T*B*H, R, -1) + self._set_input_buffer( + incremental_state, x_unfold[:, :, :, -self.kernel_size + 1 :] + ) + x_unfold = x_unfold.view(T * B * H, R, -1) else: # unfold the input: T x B x C --> T' x B x C x K x_unfold = unfold1d(x, self.kernel_size, self.padding_l, 0) - x_unfold = x_unfold.view(T*B*H, R, K) + x_unfold = x_unfold.view(T * B * H, R, K) if self.weight_softmax: - weight = utils.softmax(weight, dim=1, onnx_trace=self.onnx_trace).type_as(weight) + weight = utils.softmax(weight, dim=1, onnx_trace=self.onnx_trace).type_as( + weight + ) if incremental_state is not None: - weight = weight[:, -x_unfold.size(2):] + weight = weight[:, -x_unfold.size(2) :] K = weight.size(1) - weight = weight.view(1, H, K).expand(T*B, H, K).contiguous().view(T*B*H, K, 1) + weight = ( + weight.view(1, H, K).expand(T * B, H, K).contiguous().view(T * B * H, K, 1) + ) - weight = F.dropout(weight, self.weight_dropout, training=self.training) + weight = self.weight_dropout_module(weight) output = torch.bmm(x_unfold, weight) # T*B*H x R x 1 output = output.view(T, B, C) return output def _forward_expanded(self, x, incremental_state): - '''Turn the convolution filters into band matrices and do matrix multiplication. + """Turn the convolution filters into band matrices and do matrix multiplication. This is faster when the sequence is short, but less memory efficient. This is not used in the decoder during inference. - ''' + """ T, B, C = x.size() K, H = self.kernel_size, self.num_heads R = C // H @@ -214,20 +259,24 @@ def _forward_expanded(self, x, incremental_state): weight = self.weight.view(H, K) if self.weight_softmax: - weight = utils.softmax(weight, dim=1, onnx_trace=self.onnx_trace).type_as(weight) - weight = weight.view(1, H, K).expand(T*B, H, K).contiguous() - weight = weight.view(T, B*H, K).transpose(0, 1) + weight = utils.softmax(weight, dim=1, onnx_trace=self.onnx_trace).type_as( + weight + ) + weight = weight.view(1, H, K).expand(T * B, H, K).contiguous() + weight = weight.view(T, B * H, K).transpose(0, 1) - x = x.view(T, B*H, R).transpose(0, 1) + x = x.view(T, B * H, R).transpose(0, 1) P = self.padding_l - if K > T and P == K-1: - weight = weight.narrow(2, K-T, T) - K, P = T, T-1 + if K > T and P == K - 1: + weight = weight.narrow(2, K - T, T) + K, P = T, T - 1 # turn the convolution filters into band matrices - weight_expanded = weight.new_zeros(B*H, T, T+K-1, requires_grad=False) - weight_expanded.as_strided((B*H, T, K), (T*(T+K-1), T+K, 1)).copy_(weight) + weight_expanded = weight.new_zeros(B * H, T, T + K - 1, requires_grad=False) + weight_expanded.as_strided((B * H, T, K), (T * (T + K - 1), T + K, 1)).copy_( + weight + ) weight_expanded = weight_expanded.narrow(2, P, T) - weight_expanded = F.dropout(weight_expanded, self.weight_dropout, training=self.training) + weight_expanded = self.weight_dropout_module(weight_expanded) output = torch.bmm(weight_expanded, x) output = output.transpose(0, 1).contiguous().view(T, B, C) @@ -240,16 +289,22 @@ def reorder_incremental_state(self, incremental_state, new_order): self._set_input_buffer(incremental_state, input_buffer) def _get_input_buffer(self, incremental_state): - return utils.get_incremental_state(self, incremental_state, 'input_buffer') + return utils.get_incremental_state(self, incremental_state, "input_buffer") def _set_input_buffer(self, incremental_state, new_buffer): - return utils.set_incremental_state(self, incremental_state, 'input_buffer', new_buffer) + return utils.set_incremental_state( + self, incremental_state, "input_buffer", new_buffer + ) def extra_repr(self): - s = '{}, kernel_size={}, padding_l={}, num_heads={}, weight_softmax={}, bias={}'.format( - self.input_size, self.kernel_size, self.padding_l, - self.num_heads, self.weight_softmax, self.bias is not None + s = "{}, kernel_size={}, padding_l={}, num_heads={}, weight_softmax={}, bias={}".format( + self.input_size, + self.kernel_size, + self.padding_l, + self.num_heads, + self.weight_softmax, + self.bias is not None, ) - if self.weight_dropout > 0.: - s += ', weight_dropout={}'.format(self.weight_dropout) + if self.weight_dropout_module.p > 0.0: + s += ", weight_dropout={}".format(self.weight_dropout_module.p) return s diff --git a/fairseq/modules/linearized_convolution.py b/fairseq/modules/linearized_convolution.py index 3dd4b151c1..f7e156cb0c 100644 --- a/fairseq/modules/linearized_convolution.py +++ b/fairseq/modules/linearized_convolution.py @@ -5,11 +5,13 @@ import torch import torch.nn.functional as F - from fairseq import utils -from .conv_tbc import ConvTBC from fairseq.incremental_decoding_utils import with_incremental_state +from .conv_tbc import ConvTBC + +from typing import Dict, Optional +from torch import Tensor @with_incremental_state class LinearizedConvolution(ConvTBC): @@ -26,19 +28,20 @@ def __init__(self, in_channels, out_channels, kernel_size, **kwargs): self._linearized_weight = None self.register_backward_hook(self._clear_linearized_weight) - def state_dict(self, destination=None, prefix='', keep_vars=False): + def state_dict(self, destination=None, prefix="", keep_vars=False): state = ConvTBC.state_dict(self, destination, prefix, keep_vars=keep_vars) # don't store redundant _linearized_weight in checkpoints - if prefix + '_linearized_weight' in state: - del state[prefix + '_linearized_weight'] + if prefix + "_linearized_weight" in state: + del state[prefix + "_linearized_weight"] return state def upgrade_state_dict_named(self, state_dict, name): - prefix = name + '.' if name != '' else '' - if prefix + '_linearized_weight' in state_dict: - del state_dict[prefix + '_linearized_weight'] + prefix = name + "." if name != "" else "" + if prefix + "_linearized_weight" in state_dict: + del state_dict[prefix + "_linearized_weight"] - def forward(self, input, incremental_state=None): + @torch.jit.export + def forward(self, input, incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None): """ Args: incremental_state: Used to buffer signal; if not None, then input is @@ -49,10 +52,10 @@ def forward(self, input, incremental_state=None): Batch x Time x Channel during inference """ if incremental_state is None: - output = super().forward(input) + output = self.conv_tbc(input) if self.kernel_size[0] > 1 and self.padding[0] > 0: # remove future timesteps added by padding - output = output[:-self.padding[0], :, :] + output = output[: -self.padding[0], :, :] return output # reshape weight @@ -76,25 +79,32 @@ def forward(self, input, incremental_state=None): output = F.linear(input.view(bsz, -1), weight, self.bias) return output.view(bsz, 1, -1) - def reorder_incremental_state(self, incremental_state, new_order): + @torch.jit.unused + def reorder_incremental_state(self, incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]], new_order): input_buffer = self._get_input_buffer(incremental_state) if input_buffer is not None: input_buffer = input_buffer.index_select(0, new_order) self._set_input_buffer(incremental_state, input_buffer) - def _get_input_buffer(self, incremental_state): - return utils.get_incremental_state(self, incremental_state, 'input_buffer') + @torch.jit.unused + def _get_input_buffer(self, incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]]): + return utils.get_incremental_state(self, incremental_state, "input_buffer") - def _set_input_buffer(self, incremental_state, new_buffer): - return utils.set_incremental_state(self, incremental_state, 'input_buffer', new_buffer) + @torch.jit.unused + def _set_input_buffer(self, incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]], new_buffer): + return utils.set_incremental_state( + self, incremental_state, "input_buffer", new_buffer + ) + @torch.jit.unused def _get_linearized_weight(self): if self._linearized_weight is None: kw = self.kernel_size[0] weight = self.weight.transpose(2, 1).transpose(1, 0).contiguous() assert weight.size() == (self.out_channels, kw, self.in_channels) - self._linearized_weight = torch.nn.Parameter(weight.view(self.out_channels, -1)) + return weight.view(self.out_channels, -1) return self._linearized_weight + @torch.jit.unused def _clear_linearized_weight(self, *args): self._linearized_weight = None diff --git a/fairseq/modules/location_attention.py b/fairseq/modules/location_attention.py new file mode 100644 index 0000000000..a970876bba --- /dev/null +++ b/fairseq/modules/location_attention.py @@ -0,0 +1,72 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch.nn as nn +import torch +import torch.nn.functional as F + + +class LocationAttention(nn.Module): + """ + Attention-Based Models for Speech Recognition + https://arxiv.org/pdf/1506.07503.pdf + + :param int encoder_dim: # projection-units of encoder + :param int decoder_dim: # units of decoder + :param int attn_dim: attention dimension + :param int conv_dim: # channels of attention convolution + :param int conv_kernel_size: filter size of attention convolution + """ + + def __init__(self, attn_dim, encoder_dim, decoder_dim, + attn_state_kernel_size, conv_dim, conv_kernel_size, + scaling=2.0): + super(LocationAttention, self).__init__() + self.attn_dim = attn_dim + self.decoder_dim = decoder_dim + self.scaling = scaling + self.proj_enc = nn.Linear(encoder_dim, attn_dim) + self.proj_dec = nn.Linear(decoder_dim, attn_dim, bias=False) + self.proj_attn = nn.Linear(conv_dim, attn_dim, bias=False) + self.conv = nn.Conv1d(attn_state_kernel_size, conv_dim, + 2 * conv_kernel_size + 1, + padding=conv_kernel_size, bias=False) + self.proj_out = nn.Sequential(nn.Tanh(), nn.Linear(attn_dim, 1)) + + self.proj_enc_out = None # cache + + def clear_cache(self): + self.proj_enc_out = None + + def forward(self, encoder_out, encoder_padding_mask, decoder_h, attn_state): + """ + :param torch.Tensor encoder_out: padded encoder hidden state B x T x D + :param torch.Tensor encoder_padding_mask: encoder padding mask + :param torch.Tensor decoder_h: decoder hidden state B x D + :param torch.Tensor attn_prev: previous attention weight B x K x T + :return: attention weighted encoder state (B, D) + :rtype: torch.Tensor + :return: previous attention weights (B x T) + :rtype: torch.Tensor + """ + bsz, seq_len, _ = encoder_out.size() + if self.proj_enc_out is None: + self.proj_enc_out = self.proj_enc(encoder_out) + + # B x K x T -> B x C x T + attn = self.conv(attn_state) + # B x C x T -> B x T x C -> B x T x D + attn = self.proj_attn(attn.transpose(1, 2)) + + if decoder_h is None: + decoder_h = encoder_out.new_zeros(bsz, self.decoder_dim) + dec_h = self.proj_dec(decoder_h).view(bsz, 1, self.attn_dim) + + out = self.proj_out(attn + self.proj_enc_out + dec_h).squeeze(2) + out.masked_fill_(encoder_padding_mask, -float("inf")) + + w = F.softmax(self.scaling * out, dim=1) + c = torch.sum(encoder_out * w.view(bsz, seq_len, 1), dim=1) + return c, w diff --git a/fairseq/modules/lstm_cell_with_zoneout.py b/fairseq/modules/lstm_cell_with_zoneout.py new file mode 100644 index 0000000000..f04e5db255 --- /dev/null +++ b/fairseq/modules/lstm_cell_with_zoneout.py @@ -0,0 +1,37 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch.nn as nn + + +class LSTMCellWithZoneOut(nn.Module): + """ + Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations + https://arxiv.org/abs/1606.01305 + """ + + def __init__(self, prob: float, input_size: int, hidden_size: int, + bias: bool = True): + super(LSTMCellWithZoneOut, self).__init__() + self.lstm_cell = nn.LSTMCell(input_size, hidden_size, bias=bias) + self.prob = prob + if prob > 1.0 or prob < 0.0: + raise ValueError("zoneout probability must be in the range from " + "0.0 to 1.0.") + + def zoneout(self, h, next_h, prob): + if isinstance(h, tuple): + return tuple( + [self.zoneout(h[i], next_h[i], prob) for i in range(len(h))] + ) + + if self.training: + mask = h.new_zeros(*h.size()).bernoulli_(prob) + return mask * h + (1 - mask) * next_h + + return prob * h + (1 - prob) * next_h + + def forward(self, x, h): + return self.zoneout(h, self.lstm_cell(x, h), self.prob) diff --git a/fairseq/modules/multihead_attention.py b/fairseq/modules/multihead_attention.py index 1b87881359..a251635611 100644 --- a/fairseq/modules/multihead_attention.py +++ b/fairseq/modules/multihead_attention.py @@ -9,10 +9,11 @@ import torch import torch.nn.functional as F from fairseq import utils -from torch import Tensor, nn -from torch.nn import Parameter from fairseq.incremental_decoding_utils import with_incremental_state +from fairseq.modules.fairseq_dropout import FairseqDropout from fairseq.modules.quant_noise import quant_noise +from torch import Tensor, nn +from torch.nn import Parameter @with_incremental_state @@ -44,7 +45,10 @@ def __init__( self.qkv_same_dim = self.kdim == embed_dim and self.vdim == embed_dim self.num_heads = num_heads - self.dropout = dropout + self.dropout_module = FairseqDropout( + dropout, module_name=self.__class__.__name__ + ) + self.head_dim = embed_dim // num_heads assert ( self.head_dim * num_heads == self.embed_dim @@ -58,11 +62,19 @@ def __init__( "Self-attention requires query, key and " "value to be of the same size" ) - self.k_proj = quant_noise(nn.Linear(self.kdim, embed_dim, bias=bias), q_noise, qn_block_size) - self.v_proj = quant_noise(nn.Linear(self.vdim, embed_dim, bias=bias), q_noise, qn_block_size) - self.q_proj = quant_noise(nn.Linear(embed_dim, embed_dim, bias=bias), q_noise, qn_block_size) + self.k_proj = quant_noise( + nn.Linear(self.kdim, embed_dim, bias=bias), q_noise, qn_block_size + ) + self.v_proj = quant_noise( + nn.Linear(self.vdim, embed_dim, bias=bias), q_noise, qn_block_size + ) + self.q_proj = quant_noise( + nn.Linear(embed_dim, embed_dim, bias=bias), q_noise, qn_block_size + ) - self.out_proj = quant_noise(nn.Linear(embed_dim, embed_dim, bias=bias), q_noise, qn_block_size) + self.out_proj = quant_noise( + nn.Linear(embed_dim, embed_dim, bias=bias), q_noise, qn_block_size + ) if add_bias_kv: self.bias_k = Parameter(torch.Tensor(1, 1, embed_dim)) @@ -75,14 +87,10 @@ def __init__( self.reset_parameters() self.onnx_trace = False - self.tpu = False def prepare_for_onnx_export_(self): self.onnx_trace = True - def prepare_for_tpu_(self, **kwargs): - self.tpu = True - def reset_parameters(self): if self.qkv_same_dim: # Empirically observed the convergence to be much better with @@ -97,7 +105,7 @@ def reset_parameters(self): nn.init.xavier_uniform_(self.out_proj.weight) if self.out_proj.bias is not None: - nn.init.constant_(self.out_proj.bias, 0.) + nn.init.constant_(self.out_proj.bias, 0.0) if self.bias_k is not None: nn.init.xavier_normal_(self.bias_k) if self.bias_v is not None: @@ -136,13 +144,22 @@ def forward( if need_head_weights: need_weights = True + is_tpu = query.device.type == "xla" + tgt_len, bsz, embed_dim = query.size() - assert embed_dim == self.embed_dim + src_len = tgt_len + assert embed_dim == self.embed_dim, f"query dim {embed_dim} != {self.embed_dim}" assert list(query.size()) == [tgt_len, bsz, embed_dim] + if key is not None: + src_len, key_bsz, _ = key.size() + if not torch.jit.is_scripting(): + assert key_bsz == bsz + assert value is not None + assert src_len, bsz == value.shape[:2] if ( not self.onnx_trace - and not self.tpu # don't use PyTorch version on TPUs + and not is_tpu # don't use PyTorch version on TPUs and incremental_state is None and not static_kv # A workaround for quantization to work. Otherwise JIT compilation @@ -161,10 +178,10 @@ def forward( self.bias_k, self.bias_v, self.add_zero_attn, - self.dropout, + self.dropout_module.p, self.out_proj.weight, self.out_proj.bias, - self.training, + self.training or self.dropout_module.apply_during_inference, key_padding_mask, need_weights, attn_mask, @@ -252,6 +269,7 @@ def forward( else: assert k is not None k = torch.cat([prev_key, k], dim=1) + src_len = k.size(1) if "prev_value" in saved_state: _prev_value = saved_state["prev_value"] assert _prev_value is not None @@ -280,7 +298,7 @@ def forward( assert incremental_state is not None incremental_state = self._set_input_buffer(incremental_state, saved_state) assert k is not None - src_len = k.size(1) + assert k.size(1) == src_len # This is part of a workaround to get around fork/join parallelism # not supporting Optional types. @@ -312,7 +330,7 @@ def forward( ) attn_weights = torch.bmm(q, k.transpose(1, 2)) - attn_weights = MultiheadAttention.apply_sparse_mask(attn_weights, tgt_len, src_len, bsz) + attn_weights = self.apply_sparse_mask(attn_weights, tgt_len, src_len, bsz) assert list(attn_weights.size()) == [bsz * self.num_heads, tgt_len, src_len] @@ -325,14 +343,14 @@ def forward( if key_padding_mask is not None: # don't attend to padding symbols attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) - if not self.tpu: + if not is_tpu: attn_weights = attn_weights.masked_fill( key_padding_mask.unsqueeze(1).unsqueeze(2).to(torch.bool), - float("-inf") + float("-inf"), ) else: attn_weights = attn_weights.transpose(0, 2) - attn_weights = attn_weights.masked_fill(key_padding_mask, float('-inf')) + attn_weights = attn_weights.masked_fill(key_padding_mask, float("-inf")) attn_weights = attn_weights.transpose(0, 2) attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) @@ -343,11 +361,8 @@ def forward( attn_weights, dim=-1, onnx_trace=self.onnx_trace ) attn_weights = attn_weights_float.type_as(attn_weights) - attn_probs = F.dropout( - attn_weights, - p=self.dropout, - training=self.training, - ) + attn_probs = self.dropout_module(attn_weights) + assert v is not None attn = torch.bmm(attn_probs, v) assert list(attn.size()) == [bsz * self.num_heads, tgt_len, self.head_dim] @@ -388,28 +403,36 @@ def _append_prev_key_padding_mask( # leaves the frame, there will be a time when prev or current # is None elif prev_key_padding_mask is not None: - filler = torch.zeros( - (batch_size, src_len - prev_key_padding_mask.size(1)), - device=prev_key_padding_mask.device, - ) - new_key_padding_mask = torch.cat( - [prev_key_padding_mask.float(), filler.float()], dim=1 - ) + if src_len > prev_key_padding_mask.size(1): + filler = torch.zeros( + (batch_size, src_len - prev_key_padding_mask.size(1)), + device=prev_key_padding_mask.device, + ) + new_key_padding_mask = torch.cat( + [prev_key_padding_mask.float(), filler.float()], dim=1 + ) + else: + new_key_padding_mask = prev_key_padding_mask.float() elif key_padding_mask is not None: - filler = torch.zeros( - (batch_size, src_len - key_padding_mask.size(1)), - device=key_padding_mask.device, - ) - new_key_padding_mask = torch.cat( - [filler.float(), key_padding_mask.float()], dim=1 - ) + if src_len > key_padding_mask.size(1): + filler = torch.zeros( + (batch_size, src_len - key_padding_mask.size(1)), + device=key_padding_mask.device, + ) + new_key_padding_mask = torch.cat( + [filler.float(), key_padding_mask.float()], dim=1 + ) + else: + new_key_padding_mask = key_padding_mask.float() else: new_key_padding_mask = prev_key_padding_mask return new_key_padding_mask @torch.jit.export def reorder_incremental_state( - self, incremental_state: Dict[str, Dict[str, Optional[Tensor]]], new_order: Tensor + self, + incremental_state: Dict[str, Dict[str, Optional[Tensor]]], + new_order: Tensor, ): """Reorder buffered internal state (for incremental generation).""" input_buffer = self._get_input_buffer(incremental_state) @@ -417,7 +440,9 @@ def reorder_incremental_state( for k in input_buffer.keys(): input_buffer_k = input_buffer[k] if input_buffer_k is not None: - if self.encoder_decoder_attention and input_buffer_k.size(0) == new_order.size(0): + if self.encoder_decoder_attention and input_buffer_k.size( + 0 + ) == new_order.size(0): break input_buffer[k] = input_buffer_k.index_select(0, new_order) incremental_state = self._set_input_buffer(incremental_state, input_buffer) @@ -440,7 +465,7 @@ def _set_input_buffer( ): return self.set_incremental_state(incremental_state, "attn_state", buffer) - def apply_sparse_mask(attn_weights, tgt_len: int, src_len: int, bsz: int): + def apply_sparse_mask(self, attn_weights, tgt_len: int, src_len: int, bsz: int): return attn_weights def upgrade_state_dict_named(self, state_dict, name): diff --git a/fairseq/modules/positional_embedding.py b/fairseq/modules/positional_embedding.py index 511460fcb7..8e94e35edb 100644 --- a/fairseq/modules/positional_embedding.py +++ b/fairseq/modules/positional_embedding.py @@ -4,15 +4,16 @@ # LICENSE file in the root directory of this source tree. import torch.nn as nn + from .learned_positional_embedding import LearnedPositionalEmbedding from .sinusoidal_positional_embedding import SinusoidalPositionalEmbedding def PositionalEmbedding( - num_embeddings: int, - embedding_dim: int, - padding_idx: int, - learned: bool = False, + num_embeddings: int, + embedding_dim: int, + padding_idx: int, + learned: bool = False, ): if learned: # if padding_idx is specified then offset the embedding ids by @@ -27,6 +28,8 @@ def PositionalEmbedding( nn.init.constant_(m.weight[padding_idx], 0) else: m = SinusoidalPositionalEmbedding( - embedding_dim, padding_idx, init_size=num_embeddings + padding_idx + 1, + embedding_dim, + padding_idx, + init_size=num_embeddings + padding_idx + 1, ) return m diff --git a/fairseq/modules/quant_noise.py b/fairseq/modules/quant_noise.py index b38ea263d3..d777dfbb6c 100644 --- a/fairseq/modules/quant_noise.py +++ b/fairseq/modules/quant_noise.py @@ -39,13 +39,17 @@ def quant_noise(module, p, block_size): # 2D matrix if not is_conv: - assert module.weight.size(1) % block_size == 0, "Input features must be a multiple of block sizes" + assert ( + module.weight.size(1) % block_size == 0 + ), "Input features must be a multiple of block sizes" # 4D matrix else: # 1x1 convolutions if module.kernel_size == (1, 1): - assert module.in_channels % block_size == 0, "Input channels must be a multiple of block sizes" + assert ( + module.in_channels % block_size == 0 + ), "Input channels must be a multiple of block sizes" # regular convolutions else: k = module.kernel_size[0] * module.kernel_size[1] @@ -61,7 +65,9 @@ def _forward_pre_hook(mod, input): out_features = weight.size(0) # split weight matrix into blocks and randomly drop selected blocks - mask = torch.zeros(in_features // block_size * out_features, device=weight.device) + mask = torch.zeros( + in_features // block_size * out_features, device=weight.device + ) mask.bernoulli_(p) mask = mask.repeat_interleave(block_size, -1).view(-1, in_features) @@ -73,16 +79,27 @@ def _forward_pre_hook(mod, input): # split weight matrix into blocks and randomly drop selected blocks if mod.kernel_size == (1, 1): - mask = torch.zeros(int(in_channels // block_size * out_channels), device=weight.device) + mask = torch.zeros( + int(in_channels // block_size * out_channels), + device=weight.device, + ) mask.bernoulli_(p) mask = mask.repeat_interleave(block_size, -1).view(-1, in_channels) else: - mask = torch.zeros(weight.size(0), weight.size(1), device=weight.device) + mask = torch.zeros( + weight.size(0), weight.size(1), device=weight.device + ) mask.bernoulli_(p) - mask = mask.unsqueeze(2).unsqueeze(3).repeat(1, 1, mod.kernel_size[0], mod.kernel_size[1]) + mask = ( + mask.unsqueeze(2) + .unsqueeze(3) + .repeat(1, 1, mod.kernel_size[0], mod.kernel_size[1]) + ) # scale weights and apply mask - mask = mask.to(torch.bool) # x.bool() is not currently supported in TorchScript + mask = mask.to( + torch.bool + ) # x.bool() is not currently supported in TorchScript s = 1 / (1 - p) mod.weight.data = s * weight.masked_fill(mask, 0) diff --git a/fairseq/modules/quantization/pq/__init__.py b/fairseq/modules/quantization/pq/__init__.py index 5b10b51b1b..c142a802e0 100644 --- a/fairseq/modules/quantization/pq/__init__.py +++ b/fairseq/modules/quantization/pq/__init__.py @@ -3,4 +3,4 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. -from .utils import SizeTracker, quantize_model_ # NOQA +from .utils import SizeTracker, get_param, attrsetter, quantize_model_ # NOQA diff --git a/fairseq/modules/quantization/pq/em.py b/fairseq/modules/quantization/pq/em.py index 420d8afda2..6f15c3e46b 100644 --- a/fairseq/modules/quantization/pq/em.py +++ b/fairseq/modules/quantization/pq/em.py @@ -3,9 +3,9 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. +import logging import os import random -import logging from collections import Counter import torch diff --git a/fairseq/modules/quantization/pq/modules/__init__.py b/fairseq/modules/quantization/pq/modules/__init__.py index f52f6f37a6..b67c8e8ad6 100644 --- a/fairseq/modules/quantization/pq/modules/__init__.py +++ b/fairseq/modules/quantization/pq/modules/__init__.py @@ -4,5 +4,5 @@ # LICENSE file in the root directory of this source tree. from .qconv import PQConv2d # NOQA -from .qlinear import PQLinear # NOQA from .qemb import PQEmbedding # NOQA +from .qlinear import PQLinear # NOQA diff --git a/fairseq/modules/quantization/pq/modules/qemb.py b/fairseq/modules/quantization/pq/modules/qemb.py index 98d856d04e..3a74ad3c4c 100644 --- a/fairseq/modules/quantization/pq/modules/qemb.py +++ b/fairseq/modules/quantization/pq/modules/qemb.py @@ -27,9 +27,19 @@ class PQEmbedding(nn.Module): the non-quantized nn.Embedding module for a standard training loop. """ - def __init__(self, centroids, assignments, num_embeddings, embedding_dim, - padding_idx=None, max_norm=None, norm_type=2., - scale_grad_by_freq=False, sparse=False, _weight=None): + def __init__( + self, + centroids, + assignments, + num_embeddings, + embedding_dim, + padding_idx=None, + max_norm=None, + norm_type=2.0, + scale_grad_by_freq=False, + sparse=False, + _weight=None, + ): super(PQEmbedding, self).__init__() self.block_size = centroids.size(1) self.n_centroids = centroids.size(0) @@ -37,9 +47,13 @@ def __init__(self, centroids, assignments, num_embeddings, embedding_dim, self.embedding_dim = embedding_dim if padding_idx is not None: if padding_idx > 0: - assert padding_idx < self.num_embeddings, 'Padding_idx must be within num_embeddings' + assert ( + padding_idx < self.num_embeddings + ), "Padding_idx must be within num_embeddings" elif padding_idx < 0: - assert padding_idx >= -self.num_embeddings, 'Padding_idx must be within num_embeddings' + assert ( + padding_idx >= -self.num_embeddings + ), "Padding_idx must be within num_embeddings" padding_idx = self.num_embeddings + padding_idx self.padding_idx = padding_idx self.max_norm = max_norm @@ -67,21 +81,27 @@ def weight(self): def forward(self, input): return F.embedding( - input, self.weight, self.padding_idx, self.max_norm, - self.norm_type, self.scale_grad_by_freq, self.sparse) + input, + self.weight, + self.padding_idx, + self.max_norm, + self.norm_type, + self.scale_grad_by_freq, + self.sparse, + ) def extra_repr(self): - s = '{num_embeddings}, {embedding_dim}' + s = "{num_embeddings}, {embedding_dim}" if self.padding_idx is not None: - s += ', padding_idx={padding_idx}' + s += ", padding_idx={padding_idx}" if self.max_norm is not None: - s += ', max_norm={max_norm}' + s += ", max_norm={max_norm}" if self.norm_type != 2: - s += ', norm_type={norm_type}' + s += ", norm_type={norm_type}" if self.scale_grad_by_freq is not False: - s += ', scale_grad_by_freq={scale_grad_by_freq}' + s += ", scale_grad_by_freq={scale_grad_by_freq}" if self.sparse is not False: - s += ', sparse=True' - s += ', n_centroids={n_centroids}, block_size={block_size}' + s += ", sparse=True" + s += ", n_centroids={n_centroids}, block_size={block_size}" return s.format(**self.__dict__) diff --git a/fairseq/modules/quantization/pq/utils.py b/fairseq/modules/quantization/pq/utils.py index 57aaa1b7a3..14c015b7c1 100644 --- a/fairseq/modules/quantization/pq/utils.py +++ b/fairseq/modules/quantization/pq/utils.py @@ -6,12 +6,12 @@ import logging import re from operator import attrgetter, itemgetter - +import torch import numpy as np -import torch.nn as nn import torch.distributed as dist +import torch.nn as nn -from .modules import PQConv2d, PQLinear, PQEmbedding +from .modules import PQConv2d, PQEmbedding, PQLinear from .pq import PQ @@ -25,7 +25,9 @@ def quantize_model_( n_iter=15, eps=1e-6, max_tentatives=100, + remove_weights=False, verbose=True, + state_dict=None, ): """ Quantize a model in-place by stages. All the targeted @@ -58,12 +60,14 @@ def quantize_model_( to layers_to_quantize[step] """ - quantized_layers = get_layers(model, layers_to_quantize[step]) + quantized_layers = get_layers(model, layers_to_quantize[step], remove_weights=remove_weights) for layer in quantized_layers: # book-keeping - is_master_process = (not dist.is_initialized()) or (dist.is_initialized() and dist.get_rank() == 0) + is_master_process = (not dist.is_initialized()) or ( + dist.is_initialized() and dist.get_rank() == 0 + ) verbose = verbose and is_master_process # get block size and centroids @@ -71,11 +75,13 @@ def quantize_model_( block_size = get_param(module, layer, block_sizes_config) n_centroids = get_param(module, layer, n_centroids_config) if verbose: - logging.info(f"Quantizing layer {layer} with block size {block_size} and {n_centroids} centroids") + logging.info( + f"Quantizing layer {layer} with block size {block_size} and {n_centroids} centroids" + ) # quantize layer weight = module.weight.data.clone() - is_bias = 'bias' in [x[0] for x in module.named_parameters()] + is_bias = "bias" in [x[0] for x in module.named_parameters()] bias = module.bias.data.clone() if is_bias else None quantizer = PQ( weight, @@ -92,6 +98,37 @@ def quantize_model_( centroids = quantizer.centroids.contiguous() assignments = quantizer.assignments.contiguous() + # If n_iter = 0 and state_dict is provided, then + # we initialize random assignments and centroids to + # random values of the appropriate dimensions + # because the quantized model parameters will + # overwritten by the state_dict later on. + if n_iter == 0 and state_dict: + # Initialize random centroids of the correct size + centroids = torch.rand(centroids.size()) + centroids.cuda() + # Get counts and assignment keys from layer in loaded checkpoint. + counts_key = layer+"."+"counts" + assignment_key = layer+"."+"assignments" + # Get number of different bins to include. + counts = list(state_dict[counts_key].shape)[0] + print(layer) + print(state_dict[counts_key]) + print(counts) + # Initialize random assignments of the correct size + # with an appropriate number of bins. + num_assignments = list(state_dict[assignment_key].shape)[0] + num_extra = num_assignments - counts + print(num_assignments) + print(num_extra) + assignments_bins = torch.arange(counts) + assignments_rand = torch.randint(0, counts-1, (num_extra, )) + assignments = torch.cat((assignments_bins, assignments_rand), 0) + # assignments = assignments.type(torch.IntTensor) + assignments.cuda() + print("assignments") + print(assignments) + # broadcast results to make sure weights are up-to-date if dist.is_initialized(): dist.broadcast(centroids, 0) @@ -148,7 +185,7 @@ def quantize_model_( return quantized_layers -def get_layers(model, filter_regexp): +def get_layers(model, filter_regexp, remove_weights=False): """ Filters out the layers according to a regexp. Note that we omit biases. @@ -177,6 +214,10 @@ def get_layers(model, filter_regexp): # remove .weight in all other names (or .weight_orig is spectral norm) all_layers = map(lambda x: x.replace(".weight_orig", ""), all_layers) + # remove weights indicates whether the weights extension should be removed, in addition to + # weight_orig and weight extension on names + if remove_weights: + all_layers = map(lambda x: x.replace(".weights", ""), all_layers) all_layers = map(lambda x: x.replace(".weight", ""), all_layers) # return filtered layers @@ -238,9 +279,7 @@ def get_param(module, layer_name, param_config): if "*" in params: feature_value = "*" else: - raise KeyError( - f"name={layer_name} not in config for {module}" - ) + raise KeyError(f"name={layer_name} not in config for {module}") else: feature_value = feature_values[0] diff --git a/fairseq/modules/quantization/scalar/modules/__init__.py b/fairseq/modules/quantization/scalar/modules/__init__.py index ead4669611..8031d9cdb2 100644 --- a/fairseq/modules/quantization/scalar/modules/__init__.py +++ b/fairseq/modules/quantization/scalar/modules/__init__.py @@ -3,7 +3,7 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. +from .qact import ActivationQuantizer # NOQA from .qconv import IntConv2d # NOQA -from .qlinear import IntLinear # NOQA from .qemb import IntEmbedding # NOQA -from .qact import ActivationQuantizer # NOQA +from .qlinear import IntLinear # NOQA diff --git a/fairseq/modules/quantization/scalar/modules/qact.py b/fairseq/modules/quantization/scalar/modules/qact.py index a9f79011c1..c5dd1d6336 100644 --- a/fairseq/modules/quantization/scalar/modules/qact.py +++ b/fairseq/modules/quantization/scalar/modules/qact.py @@ -32,8 +32,16 @@ class ActivationQuantizer: - The activations are hard-clamped in [-clamp_threshold, clamp_threshold] to prevent overflow during the backward pass """ - def __init__(self, module, p=1, update_step=1000, bits=8, - method="histogram", clamp_threshold=5): + + def __init__( + self, + module, + p=1, + update_step=1000, + bits=8, + method="histogram", + clamp_threshold=5, + ): self.module = module self.p = p self.update_step = update_step @@ -72,7 +80,7 @@ def quantize_hook(module, x, y): noise = (y_q - y).masked_fill(mask.bool(), 0) # using straight-through estimator (STE) - clamp_low = - self.scale * self.zero_point + clamp_low = -self.scale * self.zero_point clamp_high = self.scale * (2 ** self.bits - 1 - self.zero_point) return torch.clamp(y, clamp_low.item(), clamp_high.item()) + noise.detach() diff --git a/fairseq/modules/quantization/scalar/modules/qconv.py b/fairseq/modules/quantization/scalar/modules/qconv.py index d718c9b90d..83788c6f71 100644 --- a/fairseq/modules/quantization/scalar/modules/qconv.py +++ b/fairseq/modules/quantization/scalar/modules/qconv.py @@ -118,9 +118,12 @@ def forward(self, input): noise = (weight_quantized - self.weight).masked_fill(mask.bool(), 0) # using straight-through estimator (STE) - clamp_low = - self.scale * self.zero_point + clamp_low = -self.scale * self.zero_point clamp_high = self.scale * (2 ** self.bits - 1 - self.zero_point) - weight = torch.clamp(self.weight, clamp_low.item(), clamp_high.item()) + noise.detach() + weight = ( + torch.clamp(self.weight, clamp_low.item(), clamp_high.item()) + + noise.detach() + ) # return output output = self._conv_forward(input, weight) diff --git a/fairseq/modules/quantization/scalar/modules/qemb.py b/fairseq/modules/quantization/scalar/modules/qemb.py index 835b2782a7..d6cf06e587 100644 --- a/fairseq/modules/quantization/scalar/modules/qemb.py +++ b/fairseq/modules/quantization/scalar/modules/qemb.py @@ -37,7 +37,7 @@ def __init__( embedding_dim, padding_idx=None, max_norm=None, - norm_type=2., + norm_type=2.0, scale_grad_by_freq=False, sparse=False, _weight=None, @@ -51,9 +51,13 @@ def __init__( self.embedding_dim = embedding_dim if padding_idx is not None: if padding_idx > 0: - assert padding_idx < self.num_embeddings, 'Padding_idx must be within num_embeddings' + assert ( + padding_idx < self.num_embeddings + ), "Padding_idx must be within num_embeddings" elif padding_idx < 0: - assert padding_idx >= -self.num_embeddings, 'Padding_idx must be within num_embeddings' + assert ( + padding_idx >= -self.num_embeddings + ), "Padding_idx must be within num_embeddings" padding_idx = self.num_embeddings + padding_idx self.padding_idx = padding_idx self.max_norm = max_norm @@ -63,8 +67,10 @@ def __init__( self.weight = nn.Parameter(torch.Tensor(num_embeddings, embedding_dim)) self.reset_parameters() else: - assert list(_weight.shape) == [num_embeddings, embedding_dim], \ - 'Shape of weight does not match num_embeddings and embedding_dim' + assert list(_weight.shape) == [ + num_embeddings, + embedding_dim, + ], "Shape of weight does not match num_embeddings and embedding_dim" self.weight = nn.Parameter(_weight) self.sparse = sparse @@ -106,27 +112,36 @@ def forward(self, input): noise = (weight_quantized - self.weight).masked_fill(mask.bool(), 0) # using straight-through estimator (STE) - clamp_low = - self.scale * self.zero_point + clamp_low = -self.scale * self.zero_point clamp_high = self.scale * (2 ** self.bits - 1 - self.zero_point) - weight = torch.clamp(self.weight, clamp_low.item(), clamp_high.item()) + noise.detach() + weight = ( + torch.clamp(self.weight, clamp_low.item(), clamp_high.item()) + + noise.detach() + ) # return output output = F.embedding( - input, weight, self.padding_idx, self.max_norm, - self.norm_type, self.scale_grad_by_freq, self.sparse) + input, + weight, + self.padding_idx, + self.max_norm, + self.norm_type, + self.scale_grad_by_freq, + self.sparse, + ) return output def extra_repr(self): - s = '{num_embeddings}, {embedding_dim}' + s = "{num_embeddings}, {embedding_dim}" if self.padding_idx is not None: - s += ', padding_idx={padding_idx}' + s += ", padding_idx={padding_idx}" if self.max_norm is not None: - s += ', max_norm={max_norm}' + s += ", max_norm={max_norm}" if self.norm_type != 2: - s += ', norm_type={norm_type}' + s += ", norm_type={norm_type}" if self.scale_grad_by_freq is not False: - s += ', scale_grad_by_freq={scale_grad_by_freq}' + s += ", scale_grad_by_freq={scale_grad_by_freq}" if self.sparse is not False: - s += ', sparse=True' - s += 'quant_noise={p}, bits={bits}, method={method}' + s += ", sparse=True" + s += "quant_noise={p}, bits={bits}, method={method}" return s.format(**self.__dict__) diff --git a/fairseq/modules/quantization/scalar/modules/qlinear.py b/fairseq/modules/quantization/scalar/modules/qlinear.py index 2d4b27dc6c..9db1559386 100644 --- a/fairseq/modules/quantization/scalar/modules/qlinear.py +++ b/fairseq/modules/quantization/scalar/modules/qlinear.py @@ -91,9 +91,12 @@ def forward(self, input): noise = (weight_quantized - self.weight).masked_fill(mask.bool(), 0) # using straight-through estimator (STE) - clamp_low = - self.scale * self.zero_point + clamp_low = -self.scale * self.zero_point clamp_high = self.scale * (2 ** self.bits - 1 - self.zero_point) - weight = torch.clamp(self.weight, clamp_low.item(), clamp_high.item()) + noise.detach() + weight = ( + torch.clamp(self.weight, clamp_low.item(), clamp_high.item()) + + noise.detach() + ) # return output output = F.linear(input, weight, self.bias) diff --git a/fairseq/modules/quantization/scalar/ops.py b/fairseq/modules/quantization/scalar/ops.py index 90bc737cc8..c74f530380 100644 --- a/fairseq/modules/quantization/scalar/ops.py +++ b/fairseq/modules/quantization/scalar/ops.py @@ -7,41 +7,48 @@ def emulate_int(w, bits, method, scale=None, zero_point=None): - q = globals()[f"emulate_int{bits}_{method}"] - return q(w, scale=scale, zero_point=zero_point) + q = globals()[f"emulate_int8_{method}"] + return q(w, scale=scale, zero_point=zero_point, bits=bits) -def quantize(w, scale, zero_point): - return (torch.clamp(torch.round(w / scale + zero_point), 0, 255) - zero_point) * scale +def quantize(w, scale, zero_point, bits=8): + # In the default behavior, max_val = 255. + max_val = 2 ** bits - 1 + return ( + torch.clamp(torch.round(w / scale + zero_point), 0, max_val) - zero_point + ) * scale -def emulate_int8_histogram(w, scale=None, zero_point=None): +def emulate_int8_histogram(w, scale=None, zero_point=None, bits=8): if scale is None: obs = torch.quantization.observer.HistogramObserver() + obs.to(device=w.device) _ = obs(w.float()) scale, zero_point = obs.calculate_qparams() scale = scale.cuda().type_as(w) zero_point = zero_point.cuda().type_as(w) - return quantize(w, scale, zero_point), scale, zero_point + return quantize(w, scale, zero_point, bits=bits), scale, zero_point -def emulate_int8_channel(w, scale=None, zero_point=None): +def emulate_int8_channel(w, scale=None, zero_point=None, bits=8): if scale is None: obs = torch.quantization.observer.PerChannelMinMaxObserver( ch_axis=-1, qscheme=torch.per_channel_symmetric ) + obs.to(device=w.device) _ = obs(w) scale, zero_point, ch_axis = obs.get_qparams() scale = scale.cuda().type_as(w) zero_point = zero_point.cuda().type_as(w) - return quantize(w, scale, zero_point), scale, zero_point + return quantize(w, scale, zero_point, bits=bits), scale, zero_point -def emulate_int8_tensor(w, scale=None, zero_point=None): +def emulate_int8_tensor(w, scale=None, zero_point=None, bits=8): if scale is None: obs = torch.quantization.observer.MinMaxObserver() + obs.to(device=w.device) _ = obs(w) scale, zero_point = obs.calculate_qparams() scale = scale.cuda().type_as(w) zero_point = zero_point.cuda().type_as(w) - return quantize(w, scale, zero_point), scale, zero_point + return quantize(w, scale, zero_point, bits=bits), scale, zero_point diff --git a/fairseq/modules/quantization/scalar/utils.py b/fairseq/modules/quantization/scalar/utils.py index 4071f7b80a..2ec6af3fcb 100644 --- a/fairseq/modules/quantization/scalar/utils.py +++ b/fairseq/modules/quantization/scalar/utils.py @@ -6,17 +6,17 @@ import logging from operator import attrgetter -import torch.nn as nn import torch.distributed as dist +import torch.nn as nn -from ..pq.utils import get_layers, attrsetter -from .modules import IntConv2d, IntLinear, IntEmbedding, ActivationQuantizer +from ..pq.utils import attrsetter, get_layers +from .modules import ActivationQuantizer, IntConv2d, IntEmbedding, IntLinear MAPPING = {nn.Linear: IntLinear, nn.Embedding: IntEmbedding, nn.Conv2d: IntConv2d} -def quantize_model_(model, p=0.2, bits=8, update_step=3000): +def quantize_model_(model, p=0.2, bits=8, update_step=3000, method="histogram", remove_weights=False): """ Replaces all modules with their scalar quantized counterpart and registers hooks to quantize the post-ativations of those modules. @@ -27,22 +27,33 @@ def quantize_model_(model, p=0.2, bits=8, update_step=3000): - bits: number of bits - update_step: update quantization parameters every update_step steps """ - # quantize all layers - quantized_layers = get_layers(model, "(.*?)") + # remove weights indicates whether the weights extension should be removed, in addition to + # weight_orig and weight extension on names + quantized_layers = get_layers(model, "(.*?)", remove_weights=remove_weights) for layer in quantized_layers: # book-keeping - is_master_process = (not dist.is_initialized()) or (dist.is_initialized() and dist.get_rank() == 0) + is_master_process = (not dist.is_initialized()) or ( + dist.is_initialized() and dist.get_rank() == 0 + ) # recover module module = attrgetter(layer)(model) if is_master_process: - logging.info(f"Quantizing layer {layer} with bits={bits} and QuantNoise={p}") + logging.info( + f"Quantizing layer {layer} with bits={bits} and QuantNoise={p}" + ) # quantization params - q_params = {"p": p, "update_step": update_step, "bits": bits, "method": "histogram", "counter": 0} + q_params = { + "p": p, + "update_step": update_step, + "bits": bits, + "method": method, + "counter": 0, + } # instantiate the quantized counterpart if isinstance(module, tuple(MAPPING.keys())): @@ -58,7 +69,7 @@ def quantize_model_(model, p=0.2, bits=8, update_step=3000): continue # activation quantization - a_q = ActivationQuantizer(quantized_module, p=0, bits=bits, method="histogram") + a_q = ActivationQuantizer(quantized_module, p=0, bits=bits, method=method) # replace layer by its quantized counterpart attrsetter(layer)(model, quantized_module) diff --git a/fairseq/modules/same_pad.py b/fairseq/modules/same_pad.py new file mode 100644 index 0000000000..4c04990ea6 --- /dev/null +++ b/fairseq/modules/same_pad.py @@ -0,0 +1,21 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +from torch import nn + + +class SamePad(nn.Module): + def __init__(self, kernel_size, causal=False): + super().__init__() + if causal: + self.remove = kernel_size - 1 + else: + self.remove = 1 if kernel_size % 2 == 0 else 0 + + def forward(self, x): + if self.remove > 0: + x = x[:, :, : -self.remove] + return x diff --git a/fairseq/modules/sinusoidal_positional_embedding.py b/fairseq/modules/sinusoidal_positional_embedding.py index 857830faf7..4793ecfb52 100644 --- a/fairseq/modules/sinusoidal_positional_embedding.py +++ b/fairseq/modules/sinusoidal_positional_embedding.py @@ -21,7 +21,7 @@ class SinusoidalPositionalEmbedding(nn.Module): def __init__(self, embedding_dim, padding_idx, init_size=1024): super().__init__() self.embedding_dim = embedding_dim - self.padding_idx = padding_idx + self.padding_idx = padding_idx if padding_idx is not None else 0 self.weights = SinusoidalPositionalEmbedding.get_embedding( init_size, embedding_dim, padding_idx ) diff --git a/fairseq/modules/sparse_multihead_attention.py b/fairseq/modules/sparse_multihead_attention.py index 61430195c2..3cbd9d6785 100644 --- a/fairseq/modules/sparse_multihead_attention.py +++ b/fairseq/modules/sparse_multihead_attention.py @@ -4,12 +4,14 @@ # LICENSE file in the root directory of this source tree. import math + import torch + from .multihead_attention import MultiheadAttention class SparseMultiheadAttention(MultiheadAttention): - """ Sparse Multi-Headed Attention. + """Sparse Multi-Headed Attention. "Generating Long Sequences with Sparse Transformers". Implements fixed factorized self attention, where l=stride and c=expressivity. @@ -19,19 +21,40 @@ class SparseMultiheadAttention(MultiheadAttention): as in the paper. """ - def __init__(self, embed_dim, num_heads, kdim=None, vdim=None, dropout=0., bias=True, - add_bias_kv=False, add_zero_attn=False, self_attention=False, - encoder_decoder_attention=False, stride=32, expressivity=8, is_bidirectional=True): + def __init__( + self, + embed_dim, + num_heads, + kdim=None, + vdim=None, + dropout=0.0, + bias=True, + add_bias_kv=False, + add_zero_attn=False, + self_attention=False, + encoder_decoder_attention=False, + stride=32, + expressivity=8, + is_bidirectional=True, + ): super().__init__( - embed_dim, num_heads, kdim, vdim, dropout, bias, add_bias_kv, - add_zero_attn, self_attention, encoder_decoder_attention + embed_dim, + num_heads, + kdim, + vdim, + dropout, + bias, + add_bias_kv, + add_zero_attn, + self_attention, + encoder_decoder_attention, ) self.is_bidirectional = is_bidirectional self.stride = stride self.expressivity = expressivity - assert(self.stride > 0 and self.stride >= self.expressivity) + assert self.stride > 0 and self.stride >= self.expressivity # Used for Ai(2) calculations - beginning of [l-c, l] range def compute_checkpoint(self, word_index): @@ -40,7 +63,8 @@ def compute_checkpoint(self, word_index): else: checkpoint_index = ( math.floor(word_index / self.stride) * self.stride - + self.stride - self.expressivity + + self.stride + - self.expressivity ) return checkpoint_index @@ -48,12 +72,15 @@ def compute_checkpoint(self, word_index): def compute_subset_summaries(self, absolute_max): checkpoint_index = self.compute_checkpoint(0) subset_two = set() - while checkpoint_index <= absolute_max-1: - summary = set(range(checkpoint_index, min( - checkpoint_index+self.expressivity+1, absolute_max) - )) + while checkpoint_index <= absolute_max - 1: + summary = set( + range( + checkpoint_index, + min(checkpoint_index + self.expressivity + 1, absolute_max), + ) + ) subset_two = subset_two.union(summary) - checkpoint_index = self.compute_checkpoint(checkpoint_index+self.stride) + checkpoint_index = self.compute_checkpoint(checkpoint_index + self.stride) return subset_two # Sparse Transformer Fixed Attention Pattern: https://arxiv.org/pdf/1904.10509.pdf @@ -65,12 +92,19 @@ def compute_fixed_attention_subset(self, word_index, tgt_len): absolute_max = tgt_len # Subset 1 - whole window - rounded_index = math.floor((word_index + self.stride) / self.stride) * self.stride + rounded_index = ( + math.floor((word_index + self.stride) / self.stride) * self.stride + ) if word_index % self.stride == 0 and word_index != 0: - subset_one = set(range(word_index-self.stride, min(absolute_max, word_index+1))) + subset_one = set( + range(word_index - self.stride, min(absolute_max, word_index + 1)) + ) else: - subset_one = set(range(max(0, rounded_index - self.stride), min( - absolute_max, rounded_index+1)) + subset_one = set( + range( + max(0, rounded_index - self.stride), + min(absolute_max, rounded_index + 1), + ) ) # Subset 2 - summary per window @@ -83,8 +117,8 @@ def compute_fixed_attention_subset(self, word_index, tgt_len): # Compute sparse mask - if bidirectional, can pre-compute and store def buffered_sparse_mask(self, tensor, tgt_len, src_len): - assert(tgt_len > self.stride) - sparse_mask = torch.empty((tgt_len, src_len)).float().fill_(float('-inf')) + assert tgt_len > self.stride + sparse_mask = torch.empty((tgt_len, src_len)).float().fill_(float("-inf")) # If bidirectional, subset 2 is the same for every index subset_summaries = set() @@ -100,5 +134,7 @@ def buffered_sparse_mask(self, tensor, tgt_len, src_len): def apply_sparse_mask(self, attn_weights, tgt_len, src_len, bsz): sparse_mask = self.buffered_sparse_mask(attn_weights, tgt_len, src_len) - sparse_mask = sparse_mask.unsqueeze(0).expand(bsz * self.num_heads, tgt_len, src_len) + sparse_mask = sparse_mask.unsqueeze(0).expand( + bsz * self.num_heads, tgt_len, src_len + ) attn_weights += sparse_mask diff --git a/fairseq/modules/sparse_transformer_sentence_encoder.py b/fairseq/modules/sparse_transformer_sentence_encoder.py index 9589f2b41e..f41ec09327 100644 --- a/fairseq/modules/sparse_transformer_sentence_encoder.py +++ b/fairseq/modules/sparse_transformer_sentence_encoder.py @@ -5,7 +5,9 @@ import torch.nn as nn from fairseq.modules import TransformerSentenceEncoder -from fairseq.modules.sparse_transformer_sentence_encoder_layer import SparseTransformerSentenceEncoderLayer +from fairseq.modules.sparse_transformer_sentence_encoder_layer import ( + SparseTransformerSentenceEncoderLayer, +) class SparseTransformerSentenceEncoder(TransformerSentenceEncoder): @@ -43,12 +45,27 @@ def __init__( ) -> None: super().__init__( - padding_idx, vocab_size, num_encoder_layers, embedding_dim, - ffn_embedding_dim, num_attention_heads, dropout, attention_dropout, - activation_dropout, max_seq_len, num_segments, use_position_embeddings, - offset_positions_by_padding, encoder_normalize_before, apply_bert_init, - activation_fn, learned_pos_embedding, embed_scale, freeze_embeddings, - n_trans_layers_to_freeze, export + padding_idx, + vocab_size, + num_encoder_layers, + embedding_dim, + ffn_embedding_dim, + num_attention_heads, + dropout, + attention_dropout, + activation_dropout, + max_seq_len, + num_segments, + use_position_embeddings, + offset_positions_by_padding, + encoder_normalize_before, + apply_bert_init, + activation_fn, + learned_pos_embedding, + embed_scale, + freeze_embeddings, + n_trans_layers_to_freeze, + export, ) self.layers = nn.ModuleList( @@ -57,7 +74,7 @@ def __init__( embedding_dim=self.embedding_dim, ffn_embedding_dim=ffn_embedding_dim, num_attention_heads=num_attention_heads, - dropout=self.dropout, + dropout=dropout, attention_dropout=attention_dropout, activation_dropout=activation_dropout, activation_fn=activation_fn, diff --git a/fairseq/modules/sparse_transformer_sentence_encoder_layer.py b/fairseq/modules/sparse_transformer_sentence_encoder_layer.py index 21c2fe4d5a..d95da59c24 100644 --- a/fairseq/modules/sparse_transformer_sentence_encoder_layer.py +++ b/fairseq/modules/sparse_transformer_sentence_encoder_layer.py @@ -20,7 +20,7 @@ def __init__( dropout: float = 0.1, attention_dropout: float = 0.1, activation_dropout: float = 0.1, - activation_fn: str = 'relu', + activation_fn: str = "relu", export: bool = False, is_bidirectional: bool = True, stride: int = 32, @@ -28,8 +28,14 @@ def __init__( ) -> None: super().__init__( - embedding_dim, ffn_embedding_dim, num_attention_heads, dropout, - attention_dropout, activation_dropout, activation_fn, export + embedding_dim, + ffn_embedding_dim, + num_attention_heads, + dropout, + attention_dropout, + activation_dropout, + activation_fn, + export, ) self.self_attn = SparseMultiheadAttention( diff --git a/fairseq/modules/transformer_layer.py b/fairseq/modules/transformer_layer.py index cae8498315..347b8118da 100644 --- a/fairseq/modules/transformer_layer.py +++ b/fairseq/modules/transformer_layer.py @@ -7,14 +7,17 @@ import torch import torch.nn as nn -import torch.nn.functional as F from fairseq import utils from fairseq.modules import LayerNorm, MultiheadAttention +from fairseq.modules.fairseq_dropout import FairseqDropout from fairseq.modules.quant_noise import quant_noise from torch import Tensor +from fairseq.models.transformer import ( + TransformerConfig, +) -class TransformerEncoderLayer(nn.Module): +class TransformerEncoderLayerBase(nn.Module): """Encoder layer block. In the original paper each operation (multi-head attention or FFN) is @@ -23,53 +26,70 @@ class TransformerEncoderLayer(nn.Module): preprocessing each layer with layernorm and postprocessing with: `dropout -> add residual`. We default to the approach in the paper, but the tensor2tensor approach can be enabled by setting - *args.encoder_normalize_before* to ``True``. + *cfg.encoder.normalize_before* to ``True``. Args: args (argparse.Namespace): parsed command-line arguments """ - def __init__(self, args): + def __init__(self, cfg): super().__init__() - self.embed_dim = args.encoder_embed_dim - self.quant_noise = getattr(args, "quant_noise_pq", 0) - self.quant_noise_block_size = getattr(args, "quant_noise_pq_block_size", 8) - self.self_attn = self.build_self_attention(self.embed_dim, args) - self.self_attn_layer_norm = LayerNorm(self.embed_dim) - self.dropout = args.dropout - self.activation_fn = utils.get_activation_fn( - activation=getattr(args, "activation_fn", "relu") + self.cfg = cfg + self.embed_dim = cfg.encoder.embed_dim + self.quant_noise = cfg.quant_noise.pq + self.quant_noise_block_size = cfg.quant_noise.pq_block_size + self.self_attn = self.build_self_attention(self.embed_dim, cfg) + self.self_attn_layer_norm = LayerNorm(self.embed_dim, export=cfg.export) + self.dropout_module = FairseqDropout( + cfg.dropout, module_name=self.__class__.__name__ + ) + self.activation_fn = utils.get_activation_fn(activation=cfg.activation_fn) + activation_dropout_p = cfg.activation_dropout + if activation_dropout_p == 0: + # for backwards compatibility with models that use cfg.relu_dropout + activation_dropout_p = cfg.relu_dropout or 0 + self.activation_dropout_module = FairseqDropout( + float(activation_dropout_p), module_name=self.__class__.__name__ ) - self.activation_dropout = getattr(args, "activation_dropout", 0) - if self.activation_dropout == 0: - # for backwards compatibility with models that use args.relu_dropout - self.activation_dropout = getattr(args, "relu_dropout", 0) - self.normalize_before = args.encoder_normalize_before + self.normalize_before = cfg.encoder.normalize_before self.fc1 = self.build_fc1( - self.embed_dim, args.encoder_ffn_embed_dim, self.quant_noise, self.quant_noise_block_size + self.embed_dim, + cfg.encoder.ffn_embed_dim, + self.quant_noise, + self.quant_noise_block_size, ) self.fc2 = self.build_fc2( - args.encoder_ffn_embed_dim, self.embed_dim, self.quant_noise, self.quant_noise_block_size + cfg.encoder.ffn_embed_dim, + self.embed_dim, + self.quant_noise, + self.quant_noise_block_size, ) - self.final_layer_norm = LayerNorm(self.embed_dim) + self.final_layer_norm = LayerNorm(self.embed_dim, export=cfg.export) def build_fc1(self, input_dim, output_dim, q_noise, qn_block_size): - return quant_noise(nn.Linear(input_dim, output_dim), p=q_noise, block_size=qn_block_size) + return quant_noise( + nn.Linear(input_dim, output_dim), p=q_noise, block_size=qn_block_size + ) def build_fc2(self, input_dim, output_dim, q_noise, qn_block_size): - return quant_noise(nn.Linear(input_dim, output_dim), p=q_noise, block_size=qn_block_size) + return quant_noise( + nn.Linear(input_dim, output_dim), p=q_noise, block_size=qn_block_size + ) - def build_self_attention(self, embed_dim, args): + def build_self_attention(self, embed_dim, cfg): return MultiheadAttention( embed_dim, - args.encoder_attention_heads, - dropout=args.attention_dropout, + cfg.encoder.attention_heads, + dropout=cfg.attention_dropout, self_attention=True, q_noise=self.quant_noise, qn_block_size=self.quant_noise_block_size, ) + def residual_connection(self, x, residual): + return residual + x + def upgrade_state_dict_named(self, state_dict, name): """ Rename layer norm states from `...layer_norms.0.weight` to @@ -84,62 +104,80 @@ def upgrade_state_dict_named(self, state_dict, name): state_dict["{}.{}.{}".format(name, new, m)] = state_dict[k] del state_dict[k] - def forward(self, x, encoder_padding_mask, attn_mask: Optional[Tensor] = None): + def forward( + self, + x, + encoder_padding_mask: Optional[Tensor], + attn_mask: Optional[Tensor] = None, + ): """ Args: x (Tensor): input to the layer of shape `(seq_len, batch, embed_dim)` encoder_padding_mask (ByteTensor): binary ByteTensor of shape - `(batch, src_len)` where padding elements are indicated by ``1``. - attn_mask (ByteTensor): binary tensor of shape (T_tgt, T_src), where - T_tgt is the length of query, while T_src is the length of key, - though here both query and key is x here, - attn_mask[t_tgt, t_src] = 1 means when calculating embedding - for t_tgt, t_src is excluded (or masked out), =0 means it is - included in attention + `(batch, seq_len)` where padding elements are indicated by ``1``. + attn_mask (ByteTensor): binary tensor of shape `(tgt_len, src_len)`, + where `tgt_len` is the length of output and `src_len` is the + length of input, though here both are equal to `seq_len`. + `attn_mask[tgt_i, src_j] = 1` means that when calculating the + embedding for `tgt_i`, we exclude (mask out) `src_j`. This is + useful for strided self-attention. Returns: encoded output of shape `(seq_len, batch, embed_dim)` """ - residual = x - if self.normalize_before: - x = self.self_attn_layer_norm(x) - if attn_mask is not None: - attn_mask = attn_mask.masked_fill(attn_mask.to(torch.bool), -1e8) # anything in original attn_mask = 1, becomes -1e8 # anything in original attn_mask = 0, becomes 0 # Note that we cannot use -inf here, because at some edge cases, # the attention weight (before softmax) for some padded element in query # will become -inf, which results in NaN in model parameters - # TODO: to formally solve this problem, we need to change fairseq's - # MultiheadAttention. We will do this later on. + if attn_mask is not None: + attn_mask = attn_mask.masked_fill( + attn_mask.to(torch.bool), + -1e8 if x.dtype == torch.float32 else -1e4 + ) + residual = x + if self.normalize_before: + x = self.self_attn_layer_norm(x) x, _ = self.self_attn( query=x, key=x, value=x, key_padding_mask=encoder_padding_mask, + need_weights=False, attn_mask=attn_mask, ) - x = F.dropout(x, p=self.dropout, training=self.training) - x = residual + x + x = self.dropout_module(x) + x = self.residual_connection(x, residual) if not self.normalize_before: x = self.self_attn_layer_norm(x) residual = x if self.normalize_before: x = self.final_layer_norm(x) - x = self.activation_fn(self.fc1(x)) - x = F.dropout(x, p=float(self.activation_dropout), training=self.training) + x = self.activation_dropout_module(x) x = self.fc2(x) - x = F.dropout(x, p=self.dropout, training=self.training) - x = residual + x + x = self.dropout_module(x) + x = self.residual_connection(x, residual) if not self.normalize_before: x = self.final_layer_norm(x) return x -class TransformerDecoderLayer(nn.Module): +# backward compatible with the legacy argparse format +class TransformerEncoderLayer(TransformerEncoderLayerBase): + def __init__(self, args): + super().__init__(TransformerConfig.from_namespace(args)) + self.args = args + + def build_self_attention(self, embed_dim, args): + return super().build_self_attention( + embed_dim, TransformerConfig.from_namespace(args) + ) + + +class TransformerDecoderLayerBase(nn.Module): """Decoder layer block. In the original paper each operation (multi-head attention, encoder @@ -148,7 +186,7 @@ class TransformerDecoderLayer(nn.Module): robust when preprocessing each layer with layernorm and postprocessing with: `dropout -> add residual`. We default to the approach in the paper, but the tensor2tensor approach can be enabled by setting - *args.decoder_normalize_before* to ``True``. + *cfg.decoder.normalize_before* to ``True``. Args: args (argparse.Namespace): parsed command-line arguments @@ -157,52 +195,58 @@ class TransformerDecoderLayer(nn.Module): """ def __init__( - self, args, no_encoder_attn=False, add_bias_kv=False, add_zero_attn=False + self, cfg, no_encoder_attn=False, add_bias_kv=False, add_zero_attn=False ): super().__init__() - self.embed_dim = args.decoder_embed_dim - self.dropout = args.dropout - self.quant_noise = getattr(args, "quant_noise_pq", 0) - self.quant_noise_block_size = getattr(args, "quant_noise_pq_block_size", 8) + self.embed_dim = cfg.decoder.embed_dim + self.dropout_module = FairseqDropout( + cfg.dropout, module_name=self.__class__.__name__ + ) + self.quant_noise = cfg.quant_noise.pq + self.quant_noise_block_size = cfg.quant_noise.pq_block_size - self.cross_self_attention = getattr(args, "cross_self_attention", False) + self.cross_self_attention = cfg.cross_self_attention self.self_attn = self.build_self_attention( self.embed_dim, - args, + cfg, add_bias_kv=add_bias_kv, add_zero_attn=add_zero_attn, ) - self.activation_fn = utils.get_activation_fn( - activation=getattr(args, "activation_fn", "relu") + + self.activation_fn = utils.get_activation_fn(activation=cfg.activation_fn) + activation_dropout_p = cfg.activation_dropout + if activation_dropout_p == 0: + # for backwards compatibility with models that use cfg.relu_dropout + activation_dropout_p = cfg.relu_dropout or 0 + self.activation_dropout_module = FairseqDropout( + float(activation_dropout_p), module_name=self.__class__.__name__ ) - self.activation_dropout = getattr(args, "activation_dropout", 0) - if self.activation_dropout == 0: - # for backwards compatibility with models that use args.relu_dropout - self.activation_dropout = getattr(args, "relu_dropout", 0) - self.normalize_before = args.decoder_normalize_before - - # use layerNorm rather than FusedLayerNorm for exporting. - # char_inputs can be used to determint this. - # TODO remove this once we update apex with the fix - export = getattr(args, "char_inputs", False) - self.self_attn_layer_norm = LayerNorm(self.embed_dim, export=export) + self.normalize_before = cfg.decoder.normalize_before + + self.self_attn_layer_norm = LayerNorm(self.embed_dim, export=cfg.export) if no_encoder_attn: self.encoder_attn = None self.encoder_attn_layer_norm = None else: - self.encoder_attn = self.build_encoder_attention(self.embed_dim, args) - self.encoder_attn_layer_norm = LayerNorm(self.embed_dim, export=export) + self.encoder_attn = self.build_encoder_attention(self.embed_dim, cfg) + self.encoder_attn_layer_norm = LayerNorm(self.embed_dim, export=cfg.export) self.fc1 = self.build_fc1( - self.embed_dim, args.decoder_ffn_embed_dim, self.quant_noise, self.quant_noise_block_size + self.embed_dim, + cfg.decoder.ffn_embed_dim, + self.quant_noise, + self.quant_noise_block_size, ) self.fc2 = self.build_fc2( - args.decoder_ffn_embed_dim, self.embed_dim, self.quant_noise, self.quant_noise_block_size + cfg.decoder.ffn_embed_dim, + self.embed_dim, + self.quant_noise, + self.quant_noise_block_size, ) - self.final_layer_norm = LayerNorm(self.embed_dim, export=export) + self.final_layer_norm = LayerNorm(self.embed_dim, export=cfg.export) self.need_attn = True self.onnx_trace = False @@ -213,25 +257,27 @@ def build_fc1(self, input_dim, output_dim, q_noise, qn_block_size): def build_fc2(self, input_dim, output_dim, q_noise, qn_block_size): return quant_noise(nn.Linear(input_dim, output_dim), q_noise, qn_block_size) - def build_self_attention(self, embed_dim, args, add_bias_kv=False, add_zero_attn=False): + def build_self_attention( + self, embed_dim, cfg, add_bias_kv=False, add_zero_attn=False + ): return MultiheadAttention( embed_dim, - args.decoder_attention_heads, - dropout=args.attention_dropout, + cfg.decoder.attention_heads, + dropout=cfg.attention_dropout, add_bias_kv=add_bias_kv, add_zero_attn=add_zero_attn, - self_attention=not getattr(args, "cross_self_attention", False), + self_attention=not cfg.cross_self_attention, q_noise=self.quant_noise, qn_block_size=self.quant_noise_block_size, ) - def build_encoder_attention(self, embed_dim, args): + def build_encoder_attention(self, embed_dim, cfg): return MultiheadAttention( embed_dim, - args.decoder_attention_heads, - kdim=getattr(args, "encoder_embed_dim", None), - vdim=getattr(args, "encoder_embed_dim", None), - dropout=args.attention_dropout, + cfg.decoder.attention_heads, + kdim=cfg.encoder.embed_dim, + vdim=cfg.encoder.embed_dim, + dropout=cfg.attention_dropout, encoder_decoder_attention=True, q_noise=self.quant_noise, qn_block_size=self.quant_noise_block_size, @@ -240,6 +286,9 @@ def build_encoder_attention(self, embed_dim, args): def prepare_for_onnx_export_(self): self.onnx_trace = True + def residual_connection(self, x, residual): + return residual + x + def forward( self, x, @@ -316,12 +365,12 @@ def forward( need_weights=False, attn_mask=self_attn_mask, ) - x = F.dropout(x, p=self.dropout, training=self.training) - x = residual + x + x = self.dropout_module(x) + x = self.residual_connection(x, residual) if not self.normalize_before: x = self.self_attn_layer_norm(x) - if self.encoder_attn is not None: + if self.encoder_attn is not None and encoder_out is not None: residual = x if self.normalize_before: x = self.encoder_attn_layer_norm(x) @@ -346,8 +395,8 @@ def forward( need_weights=need_attn or (not self.training and self.need_attn), need_head_weights=need_head_weights, ) - x = F.dropout(x, p=self.dropout, training=self.training) - x = residual + x + x = self.dropout_module(x) + x = self.residual_connection(x, residual) if not self.normalize_before: x = self.encoder_attn_layer_norm(x) @@ -356,10 +405,10 @@ def forward( x = self.final_layer_norm(x) x = self.activation_fn(self.fc1(x)) - x = F.dropout(x, p=float(self.activation_dropout), training=self.training) + x = self.activation_dropout_module(x) x = self.fc2(x) - x = F.dropout(x, p=self.dropout, training=self.training) - x = residual + x + x = self.dropout_module(x) + x = self.residual_connection(x, residual) if not self.normalize_before: x = self.final_layer_norm(x) if self.onnx_trace and incremental_state is not None: @@ -379,22 +428,32 @@ def forward( def make_generation_fast_(self, need_attn: bool = False, **kwargs): self.need_attn = need_attn - @torch.jit.export - def reorder_incremental_state( - self, - incremental_state: Dict[str, Dict[str, Optional[Tensor]]], - new_order: Tensor, - ): - """Scriptable reorder incremental state in transformer layers.""" - self.self_attn.reorder_incremental_state(incremental_state, new_order) - if self.encoder_attn is not None: - self.encoder_attn.reorder_incremental_state(incremental_state, new_order) +# backward compatible with the legacy argparse format +class TransformerDecoderLayer(TransformerDecoderLayerBase): + def __init__( + self, args, no_encoder_attn=False, add_bias_kv=False, add_zero_attn=False + ): + super().__init__( + TransformerConfig.from_namespace(args), + no_encoder_attn=no_encoder_attn, + add_bias_kv=add_bias_kv, + add_zero_attn=add_zero_attn, + ) + self.args = args + def build_self_attention( + self, embed_dim, args, add_bias_kv=False, add_zero_attn=False + ): + return super().build_self_attention( + embed_dim, + TransformerConfig.from_namespace(args), + add_bias_kv=add_bias_kv, + add_zero_attn=add_zero_attn, + ) -def Linear(in_features, out_features, bias=True): - m = nn.Linear(in_features, out_features, bias) - nn.init.xavier_uniform_(m.weight) - if bias: - nn.init.constant_(m.bias, 0.0) - return m + def build_encoder_attention(self, embed_dim, args): + return super().build_encoder_attention( + embed_dim, + TransformerConfig.from_namespace(args), + ) diff --git a/fairseq/modules/transformer_sentence_encoder.py b/fairseq/modules/transformer_sentence_encoder.py index f8c708a7b2..d0540d6922 100644 --- a/fairseq/modules/transformer_sentence_encoder.py +++ b/fairseq/modules/transformer_sentence_encoder.py @@ -7,8 +7,8 @@ import torch import torch.nn as nn -import torch.nn.functional as F from fairseq.modules import ( + FairseqDropout, LayerDropModuleList, LayerNorm, MultiheadAttention, @@ -16,7 +16,6 @@ TransformerSentenceEncoderLayer, ) from fairseq.modules.quant_noise import quant_noise as apply_quant_noise_ -import random def init_bert_params(module): @@ -33,18 +32,25 @@ def init_bert_params(module): the normal distribution (to be validated). """ + def normal_(data): + # with FSDP, module params will be on CUDA, so we cast them back to CPU + # so that the RNG is consistent with and without FSDP + data.copy_( + data.cpu().normal_(mean=0.0, std=0.02).to(data.device) + ) + if isinstance(module, nn.Linear): - module.weight.data.normal_(mean=0.0, std=0.02) + normal_(module.weight.data) if module.bias is not None: module.bias.data.zero_() if isinstance(module, nn.Embedding): - module.weight.data.normal_(mean=0.0, std=0.02) + normal_(module.weight.data) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() if isinstance(module, MultiheadAttention): - module.q_proj.weight.data.normal_(mean=0.0, std=0.02) - module.k_proj.weight.data.normal_(mean=0.0, std=0.02) - module.v_proj.weight.data.normal_(mean=0.0, std=0.02) + normal_(module.q_proj.weight.data) + normal_(module.k_proj.weight.data) + normal_(module.v_proj.weight.data) class TransformerSentenceEncoder(nn.Module): @@ -82,7 +88,7 @@ def __init__( dropout: float = 0.1, attention_dropout: float = 0.1, activation_dropout: float = 0.1, - layerdrop : float = 0.0, + layerdrop: float = 0.0, max_seq_len: int = 256, num_segments: int = 2, use_position_embeddings: bool = True, @@ -103,7 +109,9 @@ def __init__( super().__init__() self.padding_idx = padding_idx self.vocab_size = vocab_size - self.dropout = dropout + self.dropout_module = FairseqDropout( + dropout, module_name=self.__class__.__name__ + ) self.layerdrop = layerdrop self.max_seq_len = max_seq_len self.embedding_dim = embedding_dim @@ -112,9 +120,8 @@ def __init__( self.apply_bert_init = apply_bert_init self.learned_pos_embedding = learned_pos_embedding self.traceable = traceable - self.tpu = False # whether we're on TPU - self.embed_tokens = nn.Embedding( + self.embed_tokens = self.build_embedding( self.vocab_size, self.embedding_dim, self.padding_idx ) self.embed_scale = embed_scale @@ -145,31 +152,33 @@ def __init__( else None ) - if self.layerdrop > 0.0: - self.layers = LayerDropModuleList(p=self.layerdrop) - else: - self.layers = nn.ModuleList([]) - self.layers.extend([ - TransformerSentenceEncoderLayer( - embedding_dim=self.embedding_dim, - ffn_embedding_dim=ffn_embedding_dim, - num_attention_heads=num_attention_heads, - dropout=self.dropout, - attention_dropout=attention_dropout, - activation_dropout=activation_dropout, - activation_fn=activation_fn, - export=export, - q_noise=q_noise, - qn_block_size=qn_block_size - ) - for _ in range(num_encoder_layers) - ]) - if encoder_normalize_before: self.emb_layer_norm = LayerNorm(self.embedding_dim, export=export) else: self.emb_layer_norm = None + if self.layerdrop > 0.0: + self.layers = LayerDropModuleList(p=self.layerdrop) + else: + self.layers = nn.ModuleList([]) + self.layers.extend( + [ + self.build_transformer_sentence_encoder_layer( + embedding_dim=self.embedding_dim, + ffn_embedding_dim=ffn_embedding_dim, + num_attention_heads=num_attention_heads, + dropout=self.dropout_module.p, + attention_dropout=attention_dropout, + activation_dropout=activation_dropout, + activation_fn=activation_fn, + export=export, + q_noise=q_noise, + qn_block_size=qn_block_size, + ) + for _ in range(num_encoder_layers) + ] + ) + # Apply initialization of model params after building the model if self.apply_bert_init: self.apply(init_bert_params) @@ -188,8 +197,34 @@ def freeze_module_params(m): for layer in range(n_trans_layers_to_freeze): freeze_module_params(self.layers[layer]) - def prepare_for_tpu_(self, **kwargs): - self.tpu = True + def build_embedding(self, vocab_size, embedding_dim, padding_idx): + return nn.Embedding(vocab_size, embedding_dim, padding_idx) + + def build_transformer_sentence_encoder_layer( + self, + embedding_dim, + ffn_embedding_dim, + num_attention_heads, + dropout, + attention_dropout, + activation_dropout, + activation_fn, + export, + q_noise, + qn_block_size, + ): + return TransformerSentenceEncoderLayer( + embedding_dim=embedding_dim, + ffn_embedding_dim=ffn_embedding_dim, + num_attention_heads=num_attention_heads, + dropout=dropout, + attention_dropout=attention_dropout, + activation_dropout=activation_dropout, + activation_fn=activation_fn, + export=export, + q_noise=q_noise, + qn_block_size=qn_block_size, + ) def forward( self, @@ -197,23 +232,29 @@ def forward( segment_labels: torch.Tensor = None, last_state_only: bool = False, positions: Optional[torch.Tensor] = None, + token_embeddings: Optional[torch.Tensor] = None, + attn_mask: Optional[torch.Tensor] = None, ) -> Tuple[torch.Tensor, torch.Tensor]: + is_tpu = tokens.device.type == "xla" # compute padding mask. This is needed for multi-head attention padding_mask = tokens.eq(self.padding_idx) - if not self.traceable and not self.tpu and not padding_mask.any(): + if not self.traceable and not is_tpu and not padding_mask.any(): padding_mask = None - x = self.embed_tokens(tokens) + if token_embeddings is not None: + x = token_embeddings + else: + x = self.embed_tokens(tokens) if self.embed_scale is not None: - x *= self.embed_scale + x = x * self.embed_scale if self.embed_positions is not None: - x += self.embed_positions(tokens, positions=positions) + x = x + self.embed_positions(tokens, positions=positions) if self.segment_embeddings is not None and segment_labels is not None: - x += self.segment_embeddings(segment_labels) + x = x + self.segment_embeddings(segment_labels) if self.quant_noise is not None: x = self.quant_noise(x) @@ -221,11 +262,11 @@ def forward( if self.emb_layer_norm is not None: x = self.emb_layer_norm(x) - x = F.dropout(x, p=self.dropout, training=self.training) + x = self.dropout_module(x) # account for padding while computing the representation if padding_mask is not None: - x *= 1 - padding_mask.unsqueeze(-1).type_as(x) + x = x * (1 - padding_mask.unsqueeze(-1).type_as(x)) # B x T x C -> T x B x C x = x.transpose(0, 1) @@ -235,7 +276,7 @@ def forward( inner_states.append(x) for layer in self.layers: - x, _ = layer(x, self_attn_padding_mask=padding_mask) + x, _ = layer(x, self_attn_padding_mask=padding_mask, self_attn_mask=attn_mask) if not last_state_only: inner_states.append(x) diff --git a/fairseq/modules/transformer_sentence_encoder_layer.py b/fairseq/modules/transformer_sentence_encoder_layer.py index c34ae0c724..f869c4b2f8 100644 --- a/fairseq/modules/transformer_sentence_encoder_layer.py +++ b/fairseq/modules/transformer_sentence_encoder_layer.py @@ -2,19 +2,17 @@ # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. -from typing import Optional + +from typing import Callable, Optional import torch import torch.nn as nn -import torch.nn.functional as F - from fairseq import utils -from fairseq.modules import ( - LayerNorm, - MultiheadAttention, -) +from fairseq.modules import LayerNorm, MultiheadAttention +from fairseq.modules.fairseq_dropout import FairseqDropout from fairseq.modules.quant_noise import quant_noise + class TransformerSentenceEncoderLayer(nn.Module): """ Implements a Transformer Encoder Layer used in BERT/XLM style pre-trained @@ -29,21 +27,34 @@ def __init__( dropout: float = 0.1, attention_dropout: float = 0.1, activation_dropout: float = 0.1, - activation_fn: str = 'relu', + activation_fn: str = "relu", export: bool = False, q_noise: float = 0.0, qn_block_size: int = 8, + init_fn: Callable = None, ) -> None: - super().__init__() + + if init_fn is not None: + init_fn() + # Initialize parameters self.embedding_dim = embedding_dim - self.dropout = dropout - self.activation_dropout = activation_dropout + self.num_attention_heads = num_attention_heads + self.attention_dropout = attention_dropout + self.q_noise = q_noise + self.qn_block_size = qn_block_size + + self.dropout_module = FairseqDropout( + dropout, module_name=self.__class__.__name__ + ) + self.activation_dropout_module = FairseqDropout( + activation_dropout, module_name=self.__class__.__name__ + ) # Initialize blocks self.activation_fn = utils.get_activation_fn(activation_fn) - self.self_attn = MultiheadAttention( + self.self_attn = self.build_self_attention( self.embedding_dim, num_attention_heads, dropout=attention_dropout, @@ -54,16 +65,47 @@ def __init__( # layer norm associated with the self attention layer self.self_attn_layer_norm = LayerNorm(self.embedding_dim, export=export) - self.fc1 = quant_noise( - nn.Linear(self.embedding_dim, ffn_embedding_dim), q_noise, qn_block_size + + self.fc1 = self.build_fc1( + self.embedding_dim, + ffn_embedding_dim, + q_noise=q_noise, + qn_block_size=qn_block_size, ) - self.fc2 = quant_noise( - nn.Linear(ffn_embedding_dim, self.embedding_dim), q_noise, qn_block_size + self.fc2 = self.build_fc2( + ffn_embedding_dim, + self.embedding_dim, + q_noise=q_noise, + qn_block_size=qn_block_size, ) # layer norm associated with the position wise feed-forward NN self.final_layer_norm = LayerNorm(self.embedding_dim, export=export) + def build_fc1(self, input_dim, output_dim, q_noise, qn_block_size): + return quant_noise(nn.Linear(input_dim, output_dim), q_noise, qn_block_size) + + def build_fc2(self, input_dim, output_dim, q_noise, qn_block_size): + return quant_noise(nn.Linear(input_dim, output_dim), q_noise, qn_block_size) + + def build_self_attention( + self, + embed_dim, + num_attention_heads, + dropout, + self_attention, + q_noise, + qn_block_size, + ): + return MultiheadAttention( + embed_dim, + num_attention_heads, + dropout=dropout, + self_attention=True, + q_noise=q_noise, + qn_block_size=qn_block_size, + ) + def forward( self, x: torch.Tensor, @@ -83,15 +125,15 @@ def forward( need_weights=False, attn_mask=self_attn_mask, ) - x = F.dropout(x, p=self.dropout, training=self.training) + x = self.dropout_module(x) x = residual + x x = self.self_attn_layer_norm(x) residual = x x = self.activation_fn(self.fc1(x)) - x = F.dropout(x, p=self.activation_dropout, training=self.training) + x = self.activation_dropout_module(x) x = self.fc2(x) - x = F.dropout(x, p=self.dropout, training=self.training) + x = self.dropout_module(x) x = residual + x x = self.final_layer_norm(x) return x, attn diff --git a/fairseq/modules/transpose_last.py b/fairseq/modules/transpose_last.py new file mode 100644 index 0000000000..e578b3ec50 --- /dev/null +++ b/fairseq/modules/transpose_last.py @@ -0,0 +1,20 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +""" +transpose last 2 dimensions of the input +""" + +import torch.nn as nn + + +class TransposeLast(nn.Module): + def __init__(self, deconstruct_idx=None): + super().__init__() + self.deconstruct_idx = deconstruct_idx + + def forward(self, x): + if self.deconstruct_idx is not None: + x = x[self.deconstruct_idx] + return x.transpose(-2, -1) diff --git a/fairseq/modules/unfold.py b/fairseq/modules/unfold.py index 3a142db698..138272f1ef 100644 --- a/fairseq/modules/unfold.py +++ b/fairseq/modules/unfold.py @@ -7,11 +7,13 @@ def unfold1d(x, kernel_size, padding_l, pad_value=0): - '''unfold T x B x C to T x B x C x K''' + """unfold T x B x C to T x B x C x K""" if kernel_size > 1: T, B, C = x.size() - x = F.pad(x, (0, 0, 0, 0, padding_l, kernel_size - 1 - padding_l), value=pad_value) - x = x.as_strided((T, B, C, kernel_size), (B*C, C, 1, B*C)) + x = F.pad( + x, (0, 0, 0, 0, padding_l, kernel_size - 1 - padding_l), value=pad_value + ) + x = x.as_strided((T, B, C, kernel_size), (B * C, C, 1, B * C)) else: x = x.unsqueeze(3) return x diff --git a/fairseq/nan_detector.py b/fairseq/nan_detector.py index 89ea982f69..faa8031d46 100644 --- a/fairseq/nan_detector.py +++ b/fairseq/nan_detector.py @@ -4,14 +4,16 @@ # LICENSE file in the root directory of this source tree. import logging + import torch + logger = logging.getLogger(__name__) class NanDetector: """ - Detects the first NaN or Inf in forward and/or backward pass and logs, together with the module name + Detects the first NaN or Inf in forward and/or backward pass and logs, together with the module name """ def __init__(self, model, forward=True, backward=True): @@ -19,6 +21,7 @@ def __init__(self, model, forward=True, backward=True): self.fhooks = [] self.forward = forward self.backward = backward + self.named_parameters = list(model.named_parameters()) self.reset() for name, mod in model.named_modules(): @@ -29,6 +32,20 @@ def __enter__(self): return self def __exit__(self, exc_type, exc_value, exc_traceback): + # Dump out all model gnorms to enable better debugging + norm = {} + gradients = {} + for name, param in self.named_parameters: + if param.grad is not None: + grad_norm = torch.norm(param.grad.data, p=2, dtype=torch.float32) + norm[name] = grad_norm.item() + if torch.isnan(grad_norm).any() or torch.isinf(grad_norm).any(): + gradients[name] = param.grad.data + if len(gradients) > 0: + logger.info("Detected nan/inf grad norm, dumping norms...") + logger.info(f"norms: {norm}") + logger.info(f"gradients: {gradients}") + self.close() def add_hooks(self, module): @@ -44,8 +61,10 @@ def reset(self): def _detect(self, tensor, name, backward): err = None if ( - tensor.numel() >= 2 - ): # single value tensors (like the loss) will not provide much info + torch.is_floating_point(tensor) + # single value tensors (like the loss) will not provide much info + and tensor.numel() >= 2 + ): with torch.no_grad(): if torch.isnan(tensor).any(): err = "NaN" @@ -66,7 +85,7 @@ def _apply(self, module, inp, x, backward): f" input max: {inp.max().item()}, input min: {inp.min().item()}" ) - has_printed_attr = 'has_printed_b' if backward else 'has_printed_f' + has_printed_attr = "has_printed_b" if backward else "has_printed_f" logger.warning(err) setattr(self, has_printed_attr, True) elif isinstance(x, dict): diff --git a/fairseq/ngram_repeat_block.py b/fairseq/ngram_repeat_block.py new file mode 100644 index 0000000000..8541251494 --- /dev/null +++ b/fairseq/ngram_repeat_block.py @@ -0,0 +1,150 @@ +# Originally from Microsoft Corporation. +# Licensed under the MIT License. + +""" Wrapper for ngram_repeat_block cuda extension """ +import torch +from torch import nn + +import math +from typing import Dict, List, Optional +import warnings + +try: + from fairseq import ngram_repeat_block_cuda + + EXTENSION_BUILT = True +except ImportError: + EXTENSION_BUILT = False + + +def is_cuda_extension_usable() -> bool: + """Check whether ngram_repeat_block_cuda is built properly""" + if not EXTENSION_BUILT or not torch.cuda.is_available(): + return False + bsz = 2 + tokens = torch.tensor([[4, 4, 3, 2], [1, 2, 3, 4]], dtype=torch.long, device="cuda") + lprobs = torch.rand((8, 12), device="cuda") + try: + outputs = ngram_repeat_block_cuda.forward(tokens, lprobs, bsz, 3, 4, 3) + outputs = outputs + 4 # This line breaks if the extension is built incorrectly. + return True + except RuntimeError: + warnings.warn( + "NGramRepeatBlock extension must be rebuilt." + 'Run TORCH_CUDA_ARCH_LIST="6.0;6.1;7.0" python setup.py build_ext --inplace' + ) + return False + + +class NGramRepeatBlock(nn.Module): + """ Wrapper class for calling ngram_repeat_block cuda extension """ + + def __init__(self, no_repeat_ngram_size: int, use_extension: bool = True): + super().__init__() + self.use_extension = is_cuda_extension_usable() if use_extension else False + self.no_repeat_ngram_size = no_repeat_ngram_size + + def reset_parameters(self): + pass + + @torch.jit.unused + def call_cuda_extension( + self, + tokens, + lprobs, + bsz: int, + beam_size: int, + step: int, + ): + return ngram_repeat_block_cuda.forward( + tokens, lprobs, bsz, step, beam_size, self.no_repeat_ngram_size + ) + + def forward( + self, + tokens, + lprobs, + bsz: int, + beam_size: int, + step: int, + ): + """ + Args: + tokens(Tensor): Input tokens(Bsz*beam, seq_len) + lprobs(Tensor): likelihood probability, + Expected to be updated in place.(Bsz*beam, vocab_size) + bsz(int): batch size + step(int): current step + beam_size(int): beam size + no_repeat_ngram_size(int): Ngram size + """ + msg = f"expected {bsz *beam_size} got" + assert tokens.size(0) == bsz * beam_size, f"{msg} {tokens.size(0)}" + assert lprobs.size(0) == bsz * beam_size, f"{msg} {lprobs.size(0)}" + if self.use_extension: + return self.call_cuda_extension(tokens, lprobs, bsz, beam_size, step) + + else: + return self._no_repeat_ngram( + tokens, + lprobs, + bsz, + beam_size, + step, + ) + + def _no_repeat_ngram(self, tokens, lprobs, bsz: int, beam_size: int, step: int): + """For each hypothesis generate a list of previous ngrams and set associated lprobs to -inf""" + gen_ngrams: List[Dict[str, List[int]]] = [ + torch.jit.annotate(Dict[str, List[int]], {}) + for bbsz_idx in range(bsz * beam_size) + ] + cpu_tokens = tokens.cpu() + for bbsz_idx in range(bsz * beam_size): + gen_tokens: List[int] = cpu_tokens[bbsz_idx].tolist() + for ngram in self.transpose_list( + [gen_tokens[i:] for i in range(self.no_repeat_ngram_size)] + ): + key = ",".join([str(x) for x in ngram[:-1]]) + gen_ngrams[bbsz_idx][key] = gen_ngrams[bbsz_idx].get( + key, torch.jit.annotate(List[int], []) + ) + [ngram[-1]] + if step + 2 - self.no_repeat_ngram_size >= 0: + # no banned tokens if we haven't generated no_repeat_ngram_size tokens yet + banned_tokens = [ + self.calculate_banned_tokens( + tokens, step, gen_ngrams, self.no_repeat_ngram_size, bbsz_idx + ) + for bbsz_idx in range(bsz * beam_size) + ] + else: + banned_tokens = [ + torch.jit.annotate(List[int], []) for bbsz_idx in range(bsz * beam_size) + ] + for bbsz_idx in range(bsz * beam_size): + lprobs[bbsz_idx][ + torch.tensor(banned_tokens[bbsz_idx], dtype=torch.int64) + ] = torch.tensor(-math.inf).to(lprobs) + return lprobs + + @staticmethod + def calculate_banned_tokens( + tokens, + step: int, + gen_ngrams: List[Dict[str, List[int]]], + no_repeat_ngram_size: int, + bbsz_idx: int, + ): + tokens_list: List[int] = tokens[ + bbsz_idx, step + 2 - no_repeat_ngram_size : step + 1 + ].tolist() + # before decoding the next token, prevent decoding of ngrams that have already appeared + ngram_index = ",".join([str(x) for x in tokens_list]) + return gen_ngrams[bbsz_idx].get(ngram_index, torch.jit.annotate(List[int], [])) + + @staticmethod + def transpose_list(l: List[List[int]]): + # GeneratorExp aren't supported in TS so ignoring the lint + min_len = min([len(x) for x in l]) # noqa + l2 = [[row[i] for row in l] for i in range(min_len)] + return l2 diff --git a/fairseq/optim/__init__.py b/fairseq/optim/__init__.py index 2b8334d8c2..be783be896 100644 --- a/fairseq/optim/__init__.py +++ b/fairseq/optim/__init__.py @@ -2,32 +2,47 @@ # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. +"""isort:skip_file""" import importlib import os from fairseq import registry -from fairseq.optim.fairseq_optimizer import FairseqOptimizer -from fairseq.optim.fp16_optimizer import FP16Optimizer, MemoryEfficientFP16Optimizer from fairseq.optim.bmuf import FairseqBMUF # noqa - +from fairseq.optim.fairseq_optimizer import ( # noqa + FairseqOptimizer, + LegacyFairseqOptimizer, +) +from fairseq.optim.amp_optimizer import AMPOptimizer +from fairseq.optim.fp16_optimizer import FP16Optimizer, MemoryEfficientFP16Optimizer +from fairseq.optim.shard import shard_ +from omegaconf import DictConfig __all__ = [ - 'FairseqOptimizer', - 'FP16Optimizer', - 'MemoryEfficientFP16Optimizer', + "AMPOptimizer", + "FairseqOptimizer", + "FP16Optimizer", + "MemoryEfficientFP16Optimizer", + "shard_", ] +( + _build_optimizer, + register_optimizer, + OPTIMIZER_REGISTRY, + OPTIMIZER_DATACLASS_REGISTRY, +) = registry.setup_registry("--optimizer", base_class=FairseqOptimizer, required=True) -build_optimizer, register_optimizer, OPTIMIZER_REGISTRY = registry.setup_registry( - '--optimizer', - base_class=FairseqOptimizer, - default='nag', -) + +def build_optimizer(cfg: DictConfig, params, *extra_args, **extra_kwargs): + if all(isinstance(p, dict) for p in params): + params = [t for p in params for t in p.values()] + params = list(filter(lambda p: p.requires_grad, params)) + return _build_optimizer(cfg, params, *extra_args, **extra_kwargs) # automatically import any Python files in the optim/ directory -for file in os.listdir(os.path.dirname(__file__)): - if file.endswith('.py') and not file.startswith('_'): - module = file[:file.find('.py')] - importlib.import_module('fairseq.optim.' + module) +for file in sorted(os.listdir(os.path.dirname(__file__))): + if file.endswith(".py") and not file.startswith("_"): + file_name = file[: file.find(".py")] + importlib.import_module("fairseq.optim." + file_name) diff --git a/fairseq/optim/adadelta.py b/fairseq/optim/adadelta.py index 0a76e27fe4..f1a2154977 100644 --- a/fairseq/optim/adadelta.py +++ b/fairseq/optim/adadelta.py @@ -5,11 +5,11 @@ import torch.optim -from . import FairseqOptimizer, register_optimizer +from . import LegacyFairseqOptimizer, register_optimizer -@register_optimizer('adadelta') -class Adadelta(FairseqOptimizer): +@register_optimizer("adadelta") +class Adadelta(LegacyFairseqOptimizer): def __init__(self, args, params): super().__init__(args) self._optimizer = torch.optim.Adadelta(params, **self.optimizer_config) @@ -36,10 +36,10 @@ def optimizer_config(self): different learning rate. """ return { - 'lr': self.args.lr[0], - 'rho': self.args.adadelta_rho, - 'eps': self.args.adadelta_eps, - 'weight_decay': self.args.weight_decay, + "lr": self.args.lr[0], + "rho": self.args.adadelta_rho, + "eps": self.args.adadelta_eps, + "weight_decay": self.args.weight_decay, } @property diff --git a/fairseq/optim/adafactor.py b/fairseq/optim/adafactor.py index f52ec0f139..c969b9fbc0 100644 --- a/fairseq/optim/adafactor.py +++ b/fairseq/optim/adafactor.py @@ -4,14 +4,15 @@ # LICENSE file in the root directory of this source tree. import math + import torch import torch.optim -from . import FairseqOptimizer, register_optimizer +from . import LegacyFairseqOptimizer, register_optimizer -@register_optimizer('adafactor') -class FairseqAdafactor(FairseqOptimizer): +@register_optimizer("adafactor") +class FairseqAdafactor(LegacyFairseqOptimizer): def __init__(self, args, params): super().__init__(args) self._optimizer = Adafactor(params, **self.optimizer_config) @@ -50,15 +51,15 @@ def optimizer_config(self): Might require search for appropriate configuration. """ return { - 'lr': self.args.lr[0], - 'eps': eval(self.args.adafactor_eps), - 'clip_threshold': self.args.clip_threshold, - 'decay_rate': self.args.decay_rate, - 'beta1': self.args.beta1, - 'weight_decay': self.args.weight_decay, - 'scale_parameter': self.args.scale_parameter, # defaults to False - 'relative_step': self.args.relative_step, # defaults to False - 'warmup_init': self.args.warmup_init, + "lr": self.args.lr[0], + "eps": eval(self.args.adafactor_eps), + "clip_threshold": self.args.clip_threshold, + "decay_rate": self.args.decay_rate, + "beta1": self.args.beta1, + "weight_decay": self.args.weight_decay, + "scale_parameter": self.args.scale_parameter, # defaults to False + "relative_step": self.args.relative_step, # defaults to False + "warmup_init": self.args.warmup_init, } @@ -75,7 +76,7 @@ class Adafactor(torch.optim.Optimizer): schedule you should set `scale_parameter=False` and `relative_step=False`. - Arguments: + Args: params (iterable): iterable of parameters to optimize or dicts defining parameter groups lr (float, optional): external learning rate (default: None) @@ -96,17 +97,35 @@ class Adafactor(torch.optim.Optimizer): whether warm-up initialization is being used (default: False) """ - def __init__(self, params, lr=None, eps=(1e-30, 1e-3), clip_threshold=1.0, - decay_rate=-0.8, beta1=None, weight_decay=0.0, scale_parameter=True, - relative_step=True, warmup_init=False): + def __init__( + self, + params, + lr=None, + eps=(1e-30, 1e-3), + clip_threshold=1.0, + decay_rate=-0.8, + beta1=None, + weight_decay=0.0, + scale_parameter=True, + relative_step=True, + warmup_init=False, + ): if lr is not None and relative_step: - raise ValueError('Cannot combine manual lr and relative_step options') + raise ValueError("Cannot combine manual lr and relative_step options") if warmup_init and not relative_step: - raise ValueError('warmup_init requires relative_step=True') - - defaults = dict(lr=lr, eps=eps, clip_threshold=clip_threshold, decay_rate=decay_rate, - beta1=beta1, weight_decay=weight_decay, scale_parameter=scale_parameter, - relative_step=relative_step, warmup_init=warmup_init) + raise ValueError("warmup_init requires relative_step=True") + + defaults = dict( + lr=lr, + eps=eps, + clip_threshold=clip_threshold, + decay_rate=decay_rate, + beta1=beta1, + weight_decay=weight_decay, + scale_parameter=scale_parameter, + relative_step=relative_step, + warmup_init=warmup_init, + ) super(Adafactor, self).__init__(params, defaults) @property @@ -118,18 +137,20 @@ def supports_flat_params(self): return False def _get_lr(self, param_group, param_state): - rel_step_sz = param_group['lr'] - if param_group['relative_step']: - min_step = 1e-6 * param_state['step'] if param_group['warmup_init'] else 1e-2 - rel_step_sz = min(min_step, 1.0/math.sqrt(param_state['step'])) + rel_step_sz = param_group["lr"] + if param_group["relative_step"]: + min_step = ( + 1e-6 * param_state["step"] if param_group["warmup_init"] else 1e-2 + ) + rel_step_sz = min(min_step, 1.0 / math.sqrt(param_state["step"])) param_scale = 1.0 - if param_group['scale_parameter']: - param_scale = max(param_group['eps'][1], param_state['RMS']) + if param_group["scale_parameter"]: + param_scale = max(param_group["eps"][1], param_state["RMS"]) return param_scale * rel_step_sz def _get_options(self, param_group, param_shape): factored = len(param_shape) >= 2 - use_first_moment = param_group['beta1'] is not None + use_first_moment = param_group["beta1"] is not None return factored, use_first_moment def _rms(self, tensor): @@ -137,15 +158,17 @@ def _rms(self, tensor): def _approx_sq_grad(self, exp_avg_sq_row, exp_avg_sq_col): r_factor = ( - exp_avg_sq_row / exp_avg_sq_row.mean(dim=-1, keepdim=True) - ).rsqrt_() - c_factor = exp_avg_sq_col.rsqrt() - return torch.mm(r_factor.unsqueeze(-1), c_factor.unsqueeze(0)) + (exp_avg_sq_row / exp_avg_sq_row.mean(dim=-1, keepdim=True)) + .rsqrt_() + .unsqueeze(-1) + ) + c_factor = exp_avg_sq_col.unsqueeze(-2).rsqrt() + return torch.mul(r_factor, c_factor) def step(self, closure=None): """Performs a single optimization step. - Arguments: + Args: closure (callable, optional): A closure that reevaluates the model and returns the loss. """ @@ -154,14 +177,14 @@ def step(self, closure=None): loss = closure() for group in self.param_groups: - for p in group['params']: + for p in group["params"]: if p.grad is None: continue grad = p.grad.data if grad.dtype in {torch.float16, torch.bfloat16}: grad = grad.float() if grad.is_sparse: - raise RuntimeError('Adafactor does not support sparse gradients.') + raise RuntimeError("Adafactor does not support sparse gradients.") state = self.state[p] grad_shape = grad.shape @@ -169,65 +192,73 @@ def step(self, closure=None): factored, use_first_moment = self._get_options(group, grad_shape) # State Initialization if len(state) == 0: - state['step'] = 0 + state["step"] = 0 if use_first_moment: # Exponential moving average of gradient values - state['exp_avg'] = torch.zeros_like(grad) + state["exp_avg"] = torch.zeros_like(grad) if factored: - state['exp_avg_sq_row'] = torch.zeros(grad_shape[:-1]).to(grad) - state['exp_avg_sq_col'] = torch.zeros(grad_shape[:-2] + grad_shape[-1:]).to(grad) + state["exp_avg_sq_row"] = torch.zeros(grad_shape[:-1]).to(grad) + state["exp_avg_sq_col"] = torch.zeros( + grad_shape[:-2] + grad_shape[-1:] + ).to(grad) else: - state['exp_avg_sq'] = torch.zeros_like(grad) + state["exp_avg_sq"] = torch.zeros_like(grad) - state['RMS'] = 0 + state["RMS"] = 0 else: if use_first_moment: - state['exp_avg'] = state['exp_avg'].to(grad) + state["exp_avg"] = state["exp_avg"].to(grad) if factored: - state['exp_avg_sq_row'] = state['exp_avg_sq_row'].to(grad) - state['exp_avg_sq_col'] = state['exp_avg_sq_col'].to(grad) + state["exp_avg_sq_row"] = state["exp_avg_sq_row"].to(grad) + state["exp_avg_sq_col"] = state["exp_avg_sq_col"].to(grad) else: - state['exp_avg_sq'] = state['exp_avg_sq'].to(grad) + state["exp_avg_sq"] = state["exp_avg_sq"].to(grad) p_data_fp32 = p.data if p.data.dtype in {torch.float16, torch.bfloat16}: p_data_fp32 = p_data_fp32.float() - state['step'] += 1 - state['RMS'] = self._rms(p_data_fp32) - group['lr'] = self._get_lr(group, state) + state["step"] += 1 + state["RMS"] = self._rms(p_data_fp32) + group["lr"] = self._get_lr(group, state) - beta2t = 1.0 - math.pow(state['step'], group['decay_rate']) - update = (grad**2) + group['eps'][0] + beta2t = 1.0 - math.pow(state["step"], group["decay_rate"]) + update = (grad ** 2) + group["eps"][0] if factored: - exp_avg_sq_row = state['exp_avg_sq_row'] - exp_avg_sq_col = state['exp_avg_sq_col'] + exp_avg_sq_row = state["exp_avg_sq_row"] + exp_avg_sq_col = state["exp_avg_sq_col"] - exp_avg_sq_row.mul_(beta2t).add_(1.0 - beta2t, update.mean(dim=-1)) - exp_avg_sq_col.mul_(beta2t).add_(1.0 - beta2t, update.mean(dim=-2)) + exp_avg_sq_row.mul_(beta2t).add_( + update.mean(dim=-1), alpha=1.0 - beta2t + ) + exp_avg_sq_col.mul_(beta2t).add_( + update.mean(dim=-2), alpha=1.0 - beta2t + ) # Approximation of exponential moving average of square of gradient update = self._approx_sq_grad(exp_avg_sq_row, exp_avg_sq_col) update.mul_(grad) else: - exp_avg_sq = state['exp_avg_sq'] + exp_avg_sq = state["exp_avg_sq"] - exp_avg_sq.mul_(beta2t).add_(1.0 - beta2t, update) + exp_avg_sq.mul_(beta2t).add_(update, alpha=1.0 - beta2t) update = exp_avg_sq.rsqrt().mul_(grad) update.div_( - (self._rms(update) / group['clip_threshold']).clamp_(min=1.0) + (self._rms(update) / group["clip_threshold"]).clamp_(min=1.0) ) - update.mul_(group['lr']) + update.mul_(group["lr"]) if use_first_moment: - exp_avg = state['exp_avg'] - exp_avg.mul_(group['beta1']).add_(1 - group['beta1'], update) + exp_avg = state["exp_avg"] + exp_avg.mul_(group["beta1"]).add_(update, alpha=1 - group["beta1"]) update = exp_avg - if group['weight_decay'] != 0: - p_data_fp32.add_(-group['weight_decay'] * group['lr'], p_data_fp32) + if group["weight_decay"] != 0: + p_data_fp32.add_( + p_data_fp32, alpha=-group["weight_decay"] * group["lr"] + ) p_data_fp32.add_(-update) diff --git a/fairseq/optim/adagrad.py b/fairseq/optim/adagrad.py index 57f83258cf..4f539541c1 100644 --- a/fairseq/optim/adagrad.py +++ b/fairseq/optim/adagrad.py @@ -5,11 +5,11 @@ import torch.optim -from . import FairseqOptimizer, register_optimizer +from . import LegacyFairseqOptimizer, register_optimizer -@register_optimizer('adagrad') -class Adagrad(FairseqOptimizer): +@register_optimizer("adagrad") +class Adagrad(LegacyFairseqOptimizer): def __init__(self, args, params): super().__init__(args) self._optimizer = torch.optim.Adagrad(params, **self.optimizer_config) @@ -31,10 +31,10 @@ def optimizer_config(self): different learning rate. """ return { - 'lr': self.args.lr[0], - 'weight_decay': self.args.weight_decay, + "lr": self.args.lr[0], + "weight_decay": self.args.weight_decay, } @property def supports_flat_params(self): - return True + return False diff --git a/fairseq/optim/adam.py b/fairseq/optim/adam.py index d5783b258c..d3ae9e64a7 100644 --- a/fairseq/optim/adam.py +++ b/fairseq/optim/adam.py @@ -5,19 +5,43 @@ import logging import math -import types +from collections.abc import Collection +from dataclasses import dataclass, field +from typing import Any, List import torch -import torch.optim import torch.distributed as dist - +import torch.optim +from fairseq.dataclass import FairseqDataclass from fairseq.optim import FairseqOptimizer, register_optimizer from fairseq.optim.fused_adam import get_fused_adam_class +from omegaconf import II, OmegaConf + logger = logging.getLogger(__name__) -@register_optimizer('adam') +@dataclass +class FairseqAdamConfig(FairseqDataclass): + adam_betas: Any = field( + default=(0.9, 0.999), metadata={"help": "betas for Adam optimizer"} + ) + adam_eps: float = field( + default=1e-8, metadata={"help": "epsilon for Adam optimizer"} + ) + weight_decay: float = field(default=0.0, metadata={"help": "weight decay"}) + use_old_adam: bool = field( + default=False, metadata={"help": "Use fairseq.optim.adam.Adam"} + ) + fp16_adam_stats: bool = field( + default=False, metadata={"help": "use FP16 stats (with automatic scaling)"} + ) + # TODO common vars below in parent + tpu: bool = II("common.tpu") + lr: List[float] = II("optimization.lr") + + +@register_optimizer("adam", dataclass=FairseqAdamConfig) class FairseqAdam(FairseqOptimizer): """Adam optimizer for fairseq. @@ -26,44 +50,32 @@ class FairseqAdam(FairseqOptimizer): analogous to torch.optim.AdamW from PyTorch. """ - def __init__(self, args, params): - super().__init__(args) + def __init__(self, cfg: FairseqAdamConfig, params): + super().__init__(cfg) fused_adam_cls = get_fused_adam_class() use_fused_adam = ( - not getattr(args, 'use_old_adam', False) + not getattr(cfg, "use_old_adam", False) and fused_adam_cls is not None and torch.cuda.is_available() ) - if getattr(args, 'tpu', False): + if getattr(cfg, "tpu", False): + if self.cfg.fp16_adam_stats: + raise NotImplementedError("--fp16-adam-stats is only supported on GPU") # on TPUs we use the Adam defined here, since it # automatically casts gradients to FP32 self._optimizer = Adam(params, **self.optimizer_config) elif use_fused_adam: - logger.info('using FusedAdam') - self._optimizer = fused_adam_cls(params, **self.optimizer_config) + logger.info("using FusedAdam") + self._optimizer = fused_adam_cls( + params, + use_fp16_stats=self.cfg.fp16_adam_stats, + **self.optimizer_config + ) else: + if self.cfg.fp16_adam_stats: + raise NotImplementedError("--fp16-adam-stats is only supported with FusedAdamV1") self._optimizer = Adam(params, **self.optimizer_config) - @staticmethod - def add_args(parser): - """Add optimizer-specific arguments to the parser.""" - # fmt: off - parser.add_argument('--adam-betas', default='(0.9, 0.999)', metavar='B', - help='betas for Adam optimizer') - parser.add_argument('--adam-eps', type=float, default=1e-8, metavar='D', - help='epsilon for Adam optimizer') - parser.add_argument('--weight-decay', '--wd', default=0.0, type=float, metavar='WD', - help='weight decay') - # Maintain backward compatibility with old checkpoints that have stored - # optimizer state as fairseq.optim.adam.Adam. - parser.add_argument( - "--use-old-adam", - action='store_true', - default=False, - help="Use fairseq.optim.adam.Adam", - ) - # fmt: on - @property def optimizer_config(self): """ @@ -73,10 +85,14 @@ def optimizer_config(self): different learning rate. """ return { - 'lr': self.args.lr[0], - 'betas': eval(self.args.adam_betas), - 'eps': self.args.adam_eps, - 'weight_decay': self.args.weight_decay, + "lr": self.cfg.lr[0] + if isinstance(self.cfg.lr, Collection) + else self.cfg.lr, + "betas": eval(self.cfg.adam_betas) + if isinstance(self.cfg.adam_betas, str) + else OmegaConf.to_container(self.cfg.adam_betas), + "eps": self.cfg.adam_eps, + "weight_decay": self.cfg.weight_decay, } def average_params(self): @@ -92,7 +108,7 @@ def average_params(self): class Adam(torch.optim.Optimizer): - """Implements Adam algorithm. + r"""Implements Adam algorithm. This implementation is modified from torch.optim.Adam based on: `Fixed Weight Decay Regularization in Adam` @@ -100,7 +116,7 @@ class Adam(torch.optim.Optimizer): It has been proposed in `Adam: A Method for Stochastic Optimization`_. - Arguments: + Args: params (iterable): iterable of parameters to optimize or dicts defining parameter groups lr (float, optional): learning rate (default: 1e-3) @@ -118,10 +134,18 @@ class Adam(torch.optim.Optimizer): https://openreview.net/forum?id=ryQu7f-RZ """ - def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, - weight_decay=0, amsgrad=False): - defaults = dict(lr=lr, betas=betas, eps=eps, - weight_decay=weight_decay, amsgrad=amsgrad) + def __init__( + self, + params, + lr=1e-3, + betas=(0.9, 0.999), + eps=1e-8, + weight_decay=0, + amsgrad=False, + ): + defaults = dict( + lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, amsgrad=amsgrad + ) super(Adam, self).__init__(params, defaults) @property @@ -135,7 +159,7 @@ def supports_flat_params(self): def step(self, closure=None): """Performs a single optimization step. - Arguments: + Args: closure (callable, optional): A closure that reevaluates the model and returns the loss. """ @@ -144,15 +168,17 @@ def step(self, closure=None): loss = closure() for group in self.param_groups: - for p in group['params']: + for p in group["params"]: if p.grad is None: continue grad = p.grad.data if grad.dtype in {torch.float16, torch.bfloat16}: grad = grad.float() if grad.is_sparse: - raise RuntimeError('Adam does not support sparse gradients, please consider SparseAdam instead') - amsgrad = group['amsgrad'] + raise RuntimeError( + "Adam does not support sparse gradients, please consider SparseAdam instead" + ) + amsgrad = group.get("amsgrad", False) p_data_fp32 = p.data if p.data.dtype in {torch.float16, torch.bfloat16}: @@ -162,26 +188,28 @@ def step(self, closure=None): # State initialization if len(state) == 0: - state['step'] = 0 + state["step"] = 0 # Exponential moving average of gradient values - state['exp_avg'] = torch.zeros_like(p_data_fp32) + state["exp_avg"] = torch.zeros_like(p_data_fp32) # Exponential moving average of squared gradient values - state['exp_avg_sq'] = torch.zeros_like(p_data_fp32) + state["exp_avg_sq"] = torch.zeros_like(p_data_fp32) if amsgrad: # Maintains max of all exp. moving avg. of sq. grad. values - state['max_exp_avg_sq'] = torch.zeros_like(p_data_fp32) + state["max_exp_avg_sq"] = torch.zeros_like(p_data_fp32) else: - state['exp_avg'] = state['exp_avg'].to(p_data_fp32) - state['exp_avg_sq'] = state['exp_avg_sq'].to(p_data_fp32) + state["exp_avg"] = state["exp_avg"].to(p_data_fp32) + state["exp_avg_sq"] = state["exp_avg_sq"].to(p_data_fp32) if amsgrad: - state['max_exp_avg_sq'] = state['max_exp_avg_sq'].to(p_data_fp32) + state["max_exp_avg_sq"] = state["max_exp_avg_sq"].to( + p_data_fp32 + ) - exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq'] + exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"] if amsgrad: - max_exp_avg_sq = state['max_exp_avg_sq'] - beta1, beta2 = group['betas'] + max_exp_avg_sq = state["max_exp_avg_sq"] + beta1, beta2 = group["betas"] - state['step'] += 1 + state["step"] += 1 # Decay the first and second moment running average coefficient exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1) @@ -190,16 +218,18 @@ def step(self, closure=None): # Maintains the maximum of all 2nd moment running avg. till now torch.max(max_exp_avg_sq, exp_avg_sq, out=max_exp_avg_sq) # Use the max. for normalizing running avg. of gradient - denom = max_exp_avg_sq.sqrt().add_(group['eps']) + denom = max_exp_avg_sq.sqrt().add_(group["eps"]) else: - denom = exp_avg_sq.sqrt().add_(group['eps']) + denom = exp_avg_sq.sqrt().add_(group["eps"]) - bias_correction1 = 1 - beta1 ** state['step'] - bias_correction2 = 1 - beta2 ** state['step'] - step_size = group['lr'] * math.sqrt(bias_correction2) / bias_correction1 + bias_correction1 = 1 - beta1 ** state["step"] + bias_correction2 = 1 - beta2 ** state["step"] + step_size = group["lr"] * math.sqrt(bias_correction2) / bias_correction1 - if group['weight_decay'] != 0: - p_data_fp32.add_(p_data_fp32, alpha=-group['weight_decay'] * group['lr']) + if group["weight_decay"] != 0: + p_data_fp32.add_( + p_data_fp32, alpha=-group["weight_decay"] * group["lr"] + ) p_data_fp32.addcdiv_(exp_avg, denom, value=-step_size) diff --git a/fairseq/optim/adamax.py b/fairseq/optim/adamax.py index 856215a3ba..98ff8ad7ad 100644 --- a/fairseq/optim/adamax.py +++ b/fairseq/optim/adamax.py @@ -6,11 +6,11 @@ import torch import torch.optim -from . import FairseqOptimizer, register_optimizer +from . import LegacyFairseqOptimizer, register_optimizer -@register_optimizer('adamax') -class FairseqAdamax(FairseqOptimizer): +@register_optimizer("adamax") +class FairseqAdamax(LegacyFairseqOptimizer): def __init__(self, args, params): super().__init__(args) self._optimizer = Adamax(params, **self.optimizer_config) @@ -38,11 +38,11 @@ def optimizer_config(self): different learning rate. """ return { - 'lr': self.args.lr[0], - 'betas': eval(self.args.adamax_betas), - 'eps': self.args.adamax_eps, - 'weight_decay': self.args.weight_decay, - 'bias_correction': not self.args.no_bias_correction, + "lr": self.args.lr[0], + "betas": eval(self.args.adamax_betas), + "eps": self.args.adamax_eps, + "weight_decay": self.args.weight_decay, + "bias_correction": not self.args.no_bias_correction, } @@ -53,7 +53,7 @@ class Adamax(torch.optim.Optimizer): Compared to the version in PyTorch, this version implements a fix for weight decay. - Arguments: + Args: params (iterable): iterable of parameters to optimize or dicts defining parameter groups lr (float, optional): learning rate (default: 2e-3) @@ -67,8 +67,15 @@ class Adamax(torch.optim.Optimizer): __ https://arxiv.org/abs/1412.6980 """ - def __init__(self, params, lr=2e-3, betas=(0.9, 0.999), eps=1e-8, - weight_decay=0, bias_correction=True): + def __init__( + self, + params, + lr=2e-3, + betas=(0.9, 0.999), + eps=1e-8, + weight_decay=0, + bias_correction=True, + ): if not 0.0 <= lr: raise ValueError("Invalid learning rate: {}".format(lr)) if not 0.0 <= eps: @@ -80,8 +87,13 @@ def __init__(self, params, lr=2e-3, betas=(0.9, 0.999), eps=1e-8, if not 0.0 <= weight_decay: raise ValueError("Invalid weight_decay value: {}".format(weight_decay)) - defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, - bias_correction=bias_correction) + defaults = dict( + lr=lr, + betas=betas, + eps=eps, + weight_decay=weight_decay, + bias_correction=bias_correction, + ) super(Adamax, self).__init__(params, defaults) @property @@ -95,7 +107,7 @@ def supports_flat_params(self): def step(self, closure=None): """Performs a single optimization step. - Arguments: + Args: closure (callable, optional): A closure that reevaluates the model and returns the loss. """ @@ -104,12 +116,12 @@ def step(self, closure=None): loss = closure() for group in self.param_groups: - for p in group['params']: + for p in group["params"]: if p.grad is None: continue grad = p.grad.data.float() if grad.is_sparse: - raise RuntimeError('Adamax does not support sparse gradients') + raise RuntimeError("Adamax does not support sparse gradients") p_data_fp32 = p.data if p.data.dtype in {torch.float16, torch.bfloat16}: @@ -119,18 +131,18 @@ def step(self, closure=None): # State initialization if len(state) == 0: - state['step'] = 0 - state['exp_avg'] = torch.zeros_like(p_data_fp32) - state['exp_inf'] = torch.zeros_like(p_data_fp32) + state["step"] = 0 + state["exp_avg"] = torch.zeros_like(p_data_fp32) + state["exp_inf"] = torch.zeros_like(p_data_fp32) else: - state['exp_avg'] = state['exp_avg'].to(p_data_fp32) - state['exp_inf'] = state['exp_inf'].to(p_data_fp32) + state["exp_avg"] = state["exp_avg"].to(p_data_fp32) + state["exp_inf"] = state["exp_inf"].to(p_data_fp32) - exp_avg, exp_inf = state['exp_avg'], state['exp_inf'] - beta1, beta2 = group['betas'] - eps = group['eps'] + exp_avg, exp_inf = state["exp_avg"], state["exp_inf"] + beta1, beta2 = group["betas"] + eps = group["eps"] - state['step'] += 1 + state["step"] += 1 # Update biased first moment estimate. exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1) @@ -142,13 +154,15 @@ def step(self, closure=None): out=exp_inf, ) - step_size = group['lr'] - if group['bias_correction']: - bias_correction = 1 - beta1 ** state['step'] + step_size = group["lr"] + if group["bias_correction"]: + bias_correction = 1 - beta1 ** state["step"] step_size /= bias_correction - if group['weight_decay'] != 0: - p_data_fp32.add_(p_data_fp32, alpha=-group['weight_decay'] * group['lr']) + if group["weight_decay"] != 0: + p_data_fp32.add_( + p_data_fp32, alpha=-group["weight_decay"] * group["lr"] + ) p_data_fp32.addcdiv_(exp_avg, exp_inf.add(eps), value=-step_size) diff --git a/fairseq/optim/amp_optimizer.py b/fairseq/optim/amp_optimizer.py new file mode 100644 index 0000000000..3b7958e50c --- /dev/null +++ b/fairseq/optim/amp_optimizer.py @@ -0,0 +1,105 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging + +import torch +from fairseq import optim +from omegaconf import DictConfig + +logger = logging.getLogger(__name__) + + +class AMPOptimizer(optim.FairseqOptimizer): + """ + Wrap an *optimizer* to support AMP (automatic mixed precision) training. + """ + + def __init__(self, cfg: DictConfig, params, fp32_optimizer, **kwargs): + super().__init__(cfg.optimizer) + self.fp32_optimizer = fp32_optimizer + amp_kwargs = {"init_scale": cfg.common.fp16_init_scale} + if getattr(cfg.common, "amp_scale_window", None) is not None: + amp_kwargs["growth_interval"] = cfg.common.amp_init_scale + self._grad_scaler = torch.cuda.amp.GradScaler(**amp_kwargs) + self.min_loss_scale = cfg.common.min_loss_scale + + @classmethod + def build_optimizer(cls, cfg: DictConfig, params, **kwargs): + """ + Args: + cfg (omegaconf.DictConfig): fairseq args + params (iterable): iterable of parameters to optimize + """ + fp32_optimizer = optim.build_optimizer(cfg.optimizer, params) + return cls(cfg, params, fp32_optimizer, **kwargs) + + def backward(self, loss): + """Computes the sum of gradients of the given tensor w.r.t. graph leaves. + + Compared to :func:`fairseq.optim.FairseqOptimizer.backward`, this + function additionally dynamically scales the loss to avoid gradient + underflow. + """ + self._grad_scaler.scale(loss).backward() + + def step(self): + self.scaler.step(self.fp32_optimizer) + self.scaler.update() + + def clip_grad_norm(self, max_norm, aggregate_norm_fn=None): + """Clips gradient norm.""" + self.scaler.unscale_(self.optimizer) + grad_norm = self.fp32_optimizer.clip_grad_norm(max_norm, aggregate_norm_fn) + if not torch.isfinite(grad_norm).all(): + new_loss_scale = self.next_loss_scale + if new_loss_scale <= self.min_loss_scale: + raise FloatingPointError( + ( + "AMP: Minimum loss scale reached ({}). Your loss is probably exploding. " + "Try restarting training or use fp32. {}" + ).format(self.min_loss_scale, new_loss_scale) + ) + else: + logger.info("AMP: overflow detected, setting scale to " + f"to {new_loss_scale}") + return grad_norm + + @property + def scaler(self): + return self._grad_scaler + + @property + def next_loss_scale(self): + return self.scaler.get_scale() * self.scaler.get_backoff_factor() + + @property + def optimizer(self): + return self.fp32_optimizer.optimizer + + @optimizer.setter + def optimizer(self, optimizer): + self.fp32_optimizer.optimizer = optimizer + + @property + def lr_scheduler(self): + return getattr(self.fp32_optimizer, "lr_scheduler", None) + + @property + def optimizer_config(self): + return self.fp32_optimizer.optimizer_config + + def get_lr(self): + return self.fp32_optimizer.get_lr() + + def set_lr(self, lr): + self.fp32_optimizer.set_lr(lr) + + def all_reduce_grads(self, module): + self.fp32_optimizer.all_reduce_grads(module) + + @property + def supports_flat_params(self): + return self.fp32_optimizer.supports_flat_params diff --git a/fairseq/optim/bmuf.py b/fairseq/optim/bmuf.py index be7bdd74a7..d6d0e04e86 100644 --- a/fairseq/optim/bmuf.py +++ b/fairseq/optim/bmuf.py @@ -3,10 +3,13 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. +from dataclasses import dataclass, field + import torch import torch.distributed as dist - -from . import FairseqOptimizer +from fairseq.dataclass.configs import FairseqBMUFConfig +from fairseq.dataclass.utils import gen_parser_from_dataclass +from fairseq.optim.fairseq_optimizer import FairseqOptimizer class FairseqBMUF(FairseqOptimizer): @@ -19,57 +22,24 @@ class FairseqBMUF(FairseqOptimizer): model-update filtering """ - def __init__(self, args, optimizer): - - super().__init__(args) + def __init__(self, cfg: FairseqBMUFConfig, optimizer): + super().__init__(cfg) self._optimizer = optimizer self._num_updates = 0 - self.sync_iter = self.args.global_sync_iter - self.block_momentum = self.args.block_momentum - self.block_lr = self.args.block_lr + self.sync_iter = cfg.global_sync_iter + self.block_momentum = cfg.block_momentum + self.block_lr = cfg.block_lr self._reset_local_data() - self.warmup_iteration = self.args.warmup_iterations - self.use_nbm = self.args.use_nbm + self.warmup_iteration = cfg.warmup_iterations + self.use_nbm = cfg.use_nbm self.initial_state = self._optimizer.state_dict() - self.average_sync = self.args.average_sync - self.world_size = self.args.distributed_world_size + self.average_sync = self.cfg.average_sync + self.world_size = self.cfg.distributed_world_size @staticmethod def add_args(parser): """Add optimizer-specific arguments to the parser.""" - parser.add_argument( - "--block-lr", default=1, type=float, help="block learning rate for bmuf" - ) - parser.add_argument( - "--block-momentum", - default=0.875, - type=float, - help="block momentum for bmuf", - ) - parser.add_argument( - "--global-sync-iter", - default=50, - type=int, - help="Iteration for syncing global model", - ) - parser.add_argument( - "--warmup-iterations", - default=500, - type=int, - help="warmup iterations for model to broadcast", - ) - parser.add_argument( - "--use-nbm", - default=False, - action="store_true", - help="Specify whether you want to use classical BM / Nesterov BM", - ) - parser.add_argument( - "--average-sync", - default=False, - action="store_true", - help="Specify whether you want to average the local momentum after each sync", - ) + gen_parser_from_dataclass(parser, FairseqBMUFConfig()) @property def optimizer(self): diff --git a/fairseq/optim/composite.py b/fairseq/optim/composite.py new file mode 100644 index 0000000000..a5366d6243 --- /dev/null +++ b/fairseq/optim/composite.py @@ -0,0 +1,188 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +from collections import defaultdict +from dataclasses import dataclass, field +from typing import Dict, Any, List, Optional + +import torch.optim +from fairseq.dataclass import FairseqDataclass +from fairseq.optim import FairseqOptimizer, register_optimizer, _build_optimizer +from fairseq.optim.lr_scheduler import FairseqLRScheduler, build_lr_scheduler +from omegaconf import II, open_dict + + +logger = logging.getLogger(__name__) + + +@dataclass +class OptimizerAndSchedulerConfig(FairseqDataclass): + optimizer: Any = None + lr_scheduler: Optional[Any] = None + lr: List = II("optimization.lr") + lr_float: Optional[float] = None # this makes it easier to sweep on learning rate with auto sweepers + + +@dataclass +class CompositeOptimizerConfig(FairseqDataclass): + groups: Dict[str, Any] = field( + default_factory=lambda: {}, + metadata={ + "help": "optimizer name -> optimizer OptimizerAndSchedulerConfig. " + "Configures a different optimizer and (optionally) lr scheduler for each parameter group" + }, + ) + + +@register_optimizer("composite", dataclass=CompositeOptimizerConfig) +class FairseqCompositeOptimizer(FairseqOptimizer): + + optimizers: Dict[str, FairseqOptimizer] = {} + lr_schedulers: Dict[str, FairseqLRScheduler] = {} + lr_scheduler: FairseqLRScheduler = None + _optimizer: torch.optim.Optimizer + + def __init__(self, cfg: CompositeOptimizerConfig, params): + super().__init__(cfg) + + assert ( + len(params) > 1 + ), "Composite optimizer only works when there are multiple parameter groups (try fp16_no_flatten_grads: true)" + + groupped_params = defaultdict(list) + for p in params: + group = getattr(p, "param_group", "default") + groupped_params[group].append(p) + + assert groupped_params.keys() == cfg.groups.keys(), ( + f"Parameter groups {groupped_params.keys()} and optimizer groups {cfg.groups.keys()} are not the same! " + "Try setting 'param_group' on your parameters in the model." + ) + + for group, group_params in groupped_params.items(): + group_cfg = cfg.groups[group] + with open_dict(group_cfg): + if group_cfg.lr_float is not None: + group_cfg.optimizer.lr = [group_cfg.lr_float] + group_cfg.lr_scheduler.lr = [group_cfg.lr_float] + else: + group_cfg.optimizer.lr = group_cfg.lr + group_cfg.lr_scheduler.lr = group_cfg.lr + self.optimizers[group] = _build_optimizer(group_cfg.optimizer, group_params) + if group_cfg.lr_scheduler is not None: + self.lr_schedulers[group] = build_lr_scheduler( + group_cfg.lr_scheduler, self.optimizers[group] + ) + + if len(self.lr_schedulers) > 0: + assert len(self.lr_schedulers) == len(self.optimizers), ( + f"Please provide an lr scheduler for each optimizer to use pass_through scheduler. " + f"Optimizers: {self.optimizers}; Lr scheds: {self.lr_schedulers}" + ) + self.lr_scheduler = CompositeLRScheduler(self.lr_schedulers) + + self._optimizer = CompositeOptimizer(self.optimizers) + + @property + def supports_groups(self): + return True + + @property + def param_groups(self): + for opt in self.optimizers.values(): + for group in opt.param_groups: + yield group + + def get_lr(self): + """Return the current learning rate.""" + k = ( + "default" + if "default" in self.optimizers + else next(iter(self.optimizers.keys())) + ) + return self.optimizers[k].param_groups[0]["lr"] + + def state_dict(self): + """Return the LR scheduler state dict.""" + return {k: s.state_dict() for k, s in self.optimizers.items()} + + def load_state_dict(self, state_dict, optimizer_overrides=None): + """Load an LR scheduler state dict.""" + for k, state in state_dict.items(): + if k not in self.optimizers: + # skip extra keys like "loss_scale" added by fp16 optimizer + continue + + overrides = ( + optimizer_overrides[k] + if isinstance(optimizer_overrides, dict) and k in optimizer_overrides + else None + ) + self.optimizers[k].load_state_dict(state, optimizer_overrides=overrides) + + +class CompositeOptimizer(torch.optim.Optimizer): + def __init__(self, optimizers: Dict[str, FairseqOptimizer]): + self.optimizers = optimizers + + @property + def supports_memory_efficient_fp16(self): + return all(o.supports_memory_efficient_fp16 for o in self.optimizers.values()) + + @property + def supports_flat_params(self): + return all(o.supports_flat_params for o in self.optimizers.values()) + + def step(self, closure=None, groups=None): + """Performs a single optimization step. + + Args: + closure (callable, optional): A closure that reevaluates the model + and returns the loss. + """ + loss = None + if closure is not None: + loss = closure() + + for k, opt in self.optimizers.items(): + if groups is None or k in groups: + opt.step() + + return loss + + def zero_grad(self): + for opt in self.optimizers.values(): + opt.zero_grad() + + +class CompositeLRScheduler(FairseqLRScheduler): + def __init__(self, lr_schedulers): + super().__init__(None, None) + + self.lr_schedulers = lr_schedulers + + def state_dict(self): + """Return the LR scheduler state dict.""" + return {k: s.state_dict() for k, s in self.lr_schedulers.items()} + + def load_state_dict(self, state_dict): + """Load an LR scheduler state dict.""" + for k, state in state_dict.items(): + self.lr_schedulers[k].load_state_dict(state) + + def step_begin_epoch(self, epoch): + """Update the learning rate at the beginning of the given epoch.""" + for s in self.lr_schedulers.values(): + s.step_begin_epoch(epoch) + + def step(self, epoch, val_loss=None): + """Update the learning rate at the end of the given epoch.""" + for s in self.lr_schedulers.values(): + s.step(epoch) + + def step_update(self, num_updates): + """Update the learning rate after each update.""" + return {k: s.step_update(num_updates) for k, s in self.lr_schedulers.items()} diff --git a/fairseq/optim/cpu_adam.py b/fairseq/optim/cpu_adam.py new file mode 100644 index 0000000000..b2f893aeda --- /dev/null +++ b/fairseq/optim/cpu_adam.py @@ -0,0 +1,206 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import importlib +from collections.abc import Collection +from dataclasses import dataclass, field +from typing import List + +import torch +from fairseq.dataclass import FairseqDataclass +from fairseq.optim import FairseqOptimizer, register_optimizer +from omegaconf import II, DictConfig + + +try: + import deepspeed + has_deepspeed = True +except ImportError as e: + has_deepspeed = False + + +def _get_cpu_adam(): + try: + from deepspeed.ops.op_builder import CPUAdamBuilder + return CPUAdamBuilder().load() + except ImportError: + # fbcode + from deepspeed.ops.adam import DeepSpeedCPUAdam as ds_opt_adam + return ds_opt_adam + +@dataclass +class FairseqCPUAdamConfig(FairseqDataclass): + adam_betas: str = field( + default="(0.9, 0.999)", metadata={"help": "betas for Adam optimizer"} + ) + adam_eps: float = field( + default=1e-8, metadata={"help": "epsilon for Adam optimizer"} + ) + weight_decay: float = field(default=0.0, metadata={"help": "weight decay"}) + fp16_adam_stats: bool = field( + default=False, metadata={"help": "use FP16 stats (with automatic scaling)"} + ) + # TODO common vars below in parent + lr: List[float] = II("optimization.lr") + + +@register_optimizer("cpu_adam", dataclass=FairseqCPUAdamConfig) +class FairseqCPUAdam(FairseqOptimizer): + """Adam optimizer for fairseq, optimized for CPU tensors. + + Important note: this optimizer corresponds to the "AdamW" variant of + Adam in its weight decay behavior. As such, it is most closely + analogous to torch.optim.AdamW from PyTorch. + """ + + def __init__(self, cfg: DictConfig, params): + super().__init__(cfg) + self._optimizer = CPUAdam(params, **self.optimizer_config) + + @property + def optimizer_config(self): + """ + Return a kwarg dictionary that will be used to override optimizer + args stored in checkpoints. This allows us to load a checkpoint and + resume training using a different set of optimizer args, e.g., with a + different learning rate. + """ + return { + "lr": self.cfg.lr[0] + if isinstance(self.cfg.lr, Collection) + else self.cfg.lr, + "betas": eval(self.cfg.adam_betas), + "eps": self.cfg.adam_eps, + "weight_decay": self.cfg.weight_decay, + "use_fp16_stats": self.cfg.fp16_adam_stats, + } + + +class CPUAdam(torch.optim.Optimizer): + + optimizer_id = 0 + + def __init__( + self, + params, + lr=1e-3, + bias_correction=True, + betas=(0.9, 0.999), + eps=1e-8, + weight_decay=0, + use_fp16_stats=False, + ): + defaults = { + "lr": lr, + "bias_correction": bias_correction, + "betas": betas, + "eps": eps, + "weight_decay": weight_decay, + } + super().__init__(params, defaults) + + self.use_fp16_stats = use_fp16_stats + self.FLOAT16_MAX = 65504.0 + + if not has_deepspeed: + raise ImportError("Please install DeepSpeed: pip install deepspeed") + + self.opt_id = CPUAdam.optimizer_id + CPUAdam.optimizer_id = CPUAdam.optimizer_id + 1 + + self.ds_opt_adam = _get_cpu_adam() + adamw_mode = True + self.ds_opt_adam.create_adam( + self.opt_id, lr, betas[0], betas[1], eps, weight_decay, adamw_mode + ) + + @property + def supports_memory_efficient_fp16(self): + return True + + @property + def supports_flat_params(self): + return True + + @torch.no_grad() + def step(self, closure=None): + loss = None + if closure is not None: + with torch.enable_grad(): + loss = closure() + + torch.cuda.synchronize() + + for group_id, group in enumerate(self.param_groups): + for param_id, p in enumerate(group["params"]): + if p.grad is None: + continue + + state = self.state[p] + if len(state) == 0: + state["step"] = 0 + dtype = torch.float16 if self.use_fp16_stats else p.data.dtype + # gradient momentums + state["exp_avg"] = torch.zeros_like( + p.data, dtype=dtype, device="cpu" + ) + # gradient variances + state["exp_avg_sq"] = torch.zeros_like( + p.data, dtype=dtype, device="cpu" + ) + if self.use_fp16_stats: + assert torch.is_floating_point(p.data) + state["exp_avg_scale"] = 1.0 + state["exp_avg_sq_scale"] = 1.0 + + exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"] + + p_data_bak = p.data # backup of the original data pointer + + p.data = p.data.to(dtype=torch.float32, device="cpu") + p.grad.data = p.grad.data.to(dtype=torch.float32, device="cpu") + + if self.use_fp16_stats: + exp_avg = exp_avg.float() * state["exp_avg_scale"] + exp_avg_sq = exp_avg_sq.float() * state["exp_avg_sq_scale"] + + state["step"] += 1 + beta1, beta2 = group["betas"] + + self.ds_opt_adam.adam_update( + self.opt_id, + state["step"], + group["lr"], + beta1, + beta2, + group["eps"], + group["weight_decay"], + group["bias_correction"], + p.data, + p.grad.data, + exp_avg, + exp_avg_sq, + ) + + if p_data_bak.data_ptr() != p.data.data_ptr(): + p_data_bak.copy_(p.data) + p.data = p_data_bak + + if self.use_fp16_stats: + + def inf_norm(t): + return torch.norm(t, float("inf")) + + # from github.com/openai/jukebox/blob/master/jukebox/utils/fp16.py + state["exp_avg_scale"], state["exp_avg_sq_scale"] = ( + 1e-8 + inf_norm(exp_avg) / self.FLOAT16_MAX, + 1e-8 + inf_norm(exp_avg_sq) / self.FLOAT16_MAX, + ) + state["exp_avg"], state["exp_avg_sq"] = ( + (exp_avg / state["exp_avg_scale"]).half(), + (exp_avg_sq / state["exp_avg_sq_scale"]).half(), + ) + + return loss diff --git a/fairseq/optim/dynamic_loss_scaler.py b/fairseq/optim/dynamic_loss_scaler.py new file mode 100644 index 0000000000..43f9be37b9 --- /dev/null +++ b/fairseq/optim/dynamic_loss_scaler.py @@ -0,0 +1,70 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +class DynamicLossScaler(object): + def __init__( + self, + init_scale=2.0 ** 15, + scale_factor=2.0, + scale_window=2000, + tolerance=0.0, + threshold=None, + min_loss_scale=1e-4, + ): + self.loss_scale = init_scale + self.scale_factor = scale_factor + self.scale_window = scale_window + self.tolerance = tolerance + self.threshold = threshold + self._iter = 0 + self._last_overflow_iter = -1 + self._last_rescale_iter = -1 + self._overflows_since_rescale = 0 + self.min_loss_scale = min_loss_scale + + def scale(self, outputs): + return self.loss_scale * outputs + + def update(self): + if (self._iter - self._last_overflow_iter) % self.scale_window == 0: + self.loss_scale *= self.scale_factor + self._last_rescale_iter = self._iter + self._iter += 1 + + def _decrease_loss_scale(self): + self.loss_scale /= self.scale_factor + if self.threshold is not None: + self.loss_scale = max(self.loss_scale, self.threshold) + + def check_overflow(self, grad_norm): + # detect inf and nan + if grad_norm == float("inf") or grad_norm != grad_norm: + # overflow has occured + prev_scale = self.loss_scale + iter_since_rescale = self._iter - self._last_rescale_iter + + self._last_overflow_iter = self._iter + self._overflows_since_rescale += 1 + pct_overflow = self._overflows_since_rescale / float(iter_since_rescale) + if pct_overflow >= self.tolerance: + self._decrease_loss_scale() + self._last_rescale_iter = self._iter + self._overflows_since_rescale = 0 + + if self.loss_scale <= self.min_loss_scale: + # Use FloatingPointError as an uncommon error that parent + # functions can safely catch to stop training. + self.loss_scale = prev_scale + raise FloatingPointError( + ( + "Minimum loss scale reached ({}). Your loss is probably exploding. " + "Try lowering the learning rate, using gradient clipping or " + "increasing the batch size." + ).format(self.min_loss_scale) + ) + + self._iter += 1 + raise OverflowError("setting loss scale to: " + str(self.loss_scale)) diff --git a/fairseq/optim/fairseq_optimizer.py b/fairseq/optim/fairseq_optimizer.py index cc1daa82b6..7e5411753a 100644 --- a/fairseq/optim/fairseq_optimizer.py +++ b/fairseq/optim/fairseq_optimizer.py @@ -4,30 +4,40 @@ # LICENSE file in the root directory of this source tree. import torch - from fairseq import utils +from fairseq.dataclass.utils import gen_parser_from_dataclass class FairseqOptimizer(object): - - def __init__(self, args): + def __init__(self, cfg): super().__init__() - self.args = args + self.cfg = cfg - @staticmethod - def add_args(parser): + @classmethod + def add_args(cls, parser): """Add optimizer-specific arguments to the parser.""" - pass + dc = getattr(cls, "__dataclass", None) + if dc is not None: + gen_parser_from_dataclass(parser, dc()) @property def optimizer(self): """Return a torch.optim.optimizer.Optimizer instance.""" - if not hasattr(self, '_optimizer'): + if not hasattr(self, "_optimizer"): raise NotImplementedError if not isinstance(self._optimizer, torch.optim.Optimizer): - raise ValueError('_optimizer must be an instance of torch.optim.Optimizer') + raise ValueError("_optimizer must be an instance of torch.optim.Optimizer") return self._optimizer + @optimizer.setter + def optimizer(self, optimizer): + """Reset optimizer instance.""" + if not hasattr(self, "_optimizer"): + raise NotImplementedError + if not isinstance(self._optimizer, torch.optim.Optimizer): + raise ValueError("_optimizer must be an instance of torch.optim.Optimizer") + self._optimizer = optimizer + @property def optimizer_config(self): """ @@ -41,21 +51,25 @@ def optimizer_config(self): @property def params(self): """Return an iterable of the parameters held by the optimizer.""" - for param_group in self.optimizer.param_groups: - for p in param_group['params']: + for param_group in self.param_groups: + for p in param_group["params"]: yield p + @property + def param_groups(self): + return self.optimizer.param_groups + def __getstate__(self): return self._optimizer.__getstate__() def get_lr(self): """Return the current learning rate.""" - return self.optimizer.param_groups[0]['lr'] + return self.param_groups[0]["lr"] def set_lr(self, lr): """Set the learning rate.""" - for param_group in self.optimizer.param_groups: - param_group['lr'] = lr + for param_group in self.param_groups: + param_group["lr"] = lr def state_dict(self): """Return the optimizer's state dict.""" @@ -73,26 +87,44 @@ def load_state_dict(self, state_dict, optimizer_overrides=None): if optimizer_overrides is not None and len(optimizer_overrides) > 0: # override learning rate, momentum, etc. with latest values - for group in self.optimizer.param_groups: + for group in self.param_groups: group.update(optimizer_overrides) def backward(self, loss): """Computes the sum of gradients of the given tensor w.r.t. graph leaves.""" loss.backward() + def all_reduce_grads(self, module): + """Manually all-reduce gradients (if required).""" + if hasattr(module, "all_reduce_grads"): + module.all_reduce_grads() + def multiply_grads(self, c): """Multiplies grads by a constant *c*.""" for p in self.params: if p.grad is not None: + if torch.is_tensor(c): + c = c.to(p.grad.device) p.grad.data.mul_(c) def clip_grad_norm(self, max_norm, aggregate_norm_fn=None): """Clips gradient norm.""" return utils.clip_grad_norm_(self.params, max_norm, aggregate_norm_fn) - def step(self, closure=None): + def step(self, closure=None, scale=1.0, groups=None): """Performs a single optimization step.""" - self.optimizer.step(closure) + if self.supports_step_with_scale: + if self.supports_groups: + self.optimizer.step(closure, scale=scale, groups=groups) + else: + self.optimizer.step(closure, scale=scale) + else: + if scale != 1.0: + self.multiply_grads(1.0 / scale) + if self.supports_groups: + self.optimizer.step(closure, groups=groups) + else: + self.optimizer.step(closure) def zero_grad(self): """Clears the gradients of all optimized parameters.""" @@ -102,19 +134,46 @@ def zero_grad(self): @property def supports_memory_efficient_fp16(self): - if hasattr(self.optimizer, 'supports_memory_efficient_fp16'): + if hasattr(self.optimizer, "supports_memory_efficient_fp16"): return self.optimizer.supports_memory_efficient_fp16 return False + @property + def supports_step_with_scale(self): + if hasattr(self.optimizer, "supports_step_with_scale"): + return self.optimizer.supports_step_with_scale + return False + + @property + def supports_groups(self): + if hasattr(self.optimizer, "supports_groups"): + return self.optimizer.supports_groups + return False + @property def supports_flat_params(self): """ Whether the optimizer supports collapsing of the model parameters/gradients into a single contiguous Tensor. """ - if hasattr(self.optimizer, 'supports_flat_params'): + if hasattr(self.optimizer, "supports_flat_params"): return self.optimizer.supports_flat_params return False def average_params(self): pass + + def broadcast_global_state_dict(self, state_dict): + """ + Broadcasts a global state dict to all ranks. + Useful for optimizers that shard state between ranks. + """ + if hasattr(self.optimizer, "broadcast_global_state_dict"): + return self.optimizer.broadcast_global_state_dict(state_dict) + else: + return state_dict + + +class LegacyFairseqOptimizer(FairseqOptimizer): + def __init__(self, args): + self.args = args diff --git a/fairseq/optim/fp16_optimizer.py b/fairseq/optim/fp16_optimizer.py index 277b21eb94..c59b21cf6b 100644 --- a/fairseq/optim/fp16_optimizer.py +++ b/fairseq/optim/fp16_optimizer.py @@ -3,86 +3,74 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. +from collections import defaultdict from itertools import chain import torch +from fairseq import optim +from omegaconf import DictConfig -from fairseq import optim, utils - - -class DynamicLossScaler(object): - - def __init__( - self, init_scale=2.**15, scale_factor=2., scale_window=2000, - tolerance=0.05, threshold=None, - ): - self.loss_scale = init_scale - self.scale_factor = scale_factor - self.scale_window = scale_window - self.tolerance = tolerance - self.threshold = threshold - self._iter = 0 - self._last_overflow_iter = -1 - self._last_rescale_iter = -1 - self._overflows_since_rescale = 0 - - def update_scale(self, overflow): - iter_since_rescale = self._iter - self._last_rescale_iter - if overflow: - self._last_overflow_iter = self._iter - self._overflows_since_rescale += 1 - pct_overflow = self._overflows_since_rescale / float(iter_since_rescale) - if pct_overflow >= self.tolerance: - self._decrease_loss_scale() - self._last_rescale_iter = self._iter - self._overflows_since_rescale = 0 - elif (self._iter - self._last_overflow_iter) % self.scale_window == 0: - self.loss_scale *= self.scale_factor - self._last_rescale_iter = self._iter - self._iter += 1 - - def _decrease_loss_scale(self): - self.loss_scale /= self.scale_factor - if self.threshold is not None: - self.loss_scale = max(self.loss_scale, self.threshold) - - @staticmethod - def has_overflow(grad_norm): - # detect inf and nan - if grad_norm == float('inf') or grad_norm != grad_norm: - return True - return False +from .dynamic_loss_scaler import DynamicLossScaler class _FP16OptimizerMixin(object): - def __init__(self, *args, **kwargs): # forward __init__ call to the next class in mro(method resolution order) super().__init__(*args, **kwargs) + self._multiply_factor = 1.0 @property def has_flat_params(self): - return torch.is_tensor(self.fp32_params) + return torch.is_tensor(self.fp32_params) or ( + isinstance(self.fp32_params, dict) + and all(torch.is_tensor(t) for t in self.fp32_params.values()) + ) @classmethod - def build_fp32_params(cls, params, flatten=True): + def build_fp32_params(cls, args, params, flatten=True): # create FP32 copy of parameters and grads if flatten: + is_pipeline_parallel = getattr( + args, "pipeline_model_parallel", False + ) and getattr(args, "distributed_no_spawn", False) total_param_size = sum(p.data.numel() for p in params) - fp32_params = torch.zeros(total_param_size, dtype=torch.float, device=params[0].device) - offset = 0 - for p in params: - numel = p.data.numel() - fp32_params[offset:offset+numel].copy_(p.data.view(-1)) - offset += numel - fp32_params = torch.nn.Parameter(fp32_params) - fp32_params.grad = fp32_params.data.new(total_param_size) + devices = [torch.cuda.current_device()] + if is_pipeline_parallel: + devices = list(set(args.pipeline_devices)) + fp32_params = {} + for device in devices: + if is_pipeline_parallel: + device_param_size = sum( + p.data.numel() for p in params if p.device.index == device + ) + device_params = [p for p in params if p.device.index == device] + else: + device_param_size = total_param_size + device_params = params + fp32_params[device] = ( + device_params[0].new(0).float().new(device_param_size) + ) + offset = 0 + for p in device_params: + numel = p.data.numel() + fp32_params[device][offset : offset + numel].copy_(p.data.view(-1)) + offset += numel + fp32_params[device] = torch.nn.Parameter(fp32_params[device]) + fp32_params[device].grad = fp32_params[device].data.new( + device_param_size + ) return fp32_params else: fp32_params = [] for p in params: p32 = torch.nn.Parameter(p.data.float()) + if hasattr(p, 'expert'): + p32.expert = True + elif hasattr(p, 'base_expert'): + p32.base_expert = True p32.grad = torch.zeros_like(p32.data) + if hasattr(p, "param_group"): + p32.param_group = p.param_group fp32_params.append(p32) return fp32_params @@ -90,7 +78,7 @@ def state_dict(self): """Return the optimizer's state dict.""" state_dict = self.fp32_optimizer.state_dict() if self.scaler is not None: - state_dict['loss_scale'] = self.scaler.loss_scale + state_dict["loss_scale"] = self.scaler.loss_scale return state_dict def load_state_dict(self, state_dict, optimizer_overrides=None): @@ -101,8 +89,8 @@ def load_state_dict(self, state_dict, optimizer_overrides=None): allows us to resume training from a checkpoint using a new set of optimizer args. """ - if 'loss_scale' in state_dict and self.scaler is not None: - self.scaler.loss_scale = state_dict['loss_scale'] + if "loss_scale" in state_dict and self.scaler is not None: + self.scaler.loss_scale = state_dict["loss_scale"] self.fp32_optimizer.load_state_dict(state_dict, optimizer_overrides) def backward(self, loss): @@ -113,165 +101,218 @@ def backward(self, loss): underflow. """ if self.scaler is not None: - loss = loss * self.scaler.loss_scale + loss = self.scaler.scale(loss) loss.backward() self._needs_sync = True - def _sync_fp16_grads_to_fp32(self, multiply_grads=1.): + def _sync_fp16_grads_to_fp32(self): if self._needs_sync: - if self.scaler is not None: - # correct for dynamic loss scaler - multiply_grads /= self.scaler.loss_scale - # copy FP16 grads to FP32 if self.has_flat_params: - offset = 0 + devices = list(self.fp32_params.keys()) + device_params_dict = defaultdict(list) for p in self.fp16_params: - if not p.requires_grad: - continue - grad_data = p.grad.data if p.grad is not None else p.data.new_zeros(p.data.shape) - numel = grad_data.numel() - self.fp32_params.grad.data[offset:offset+numel].copy_(grad_data.view(-1)) - offset += numel - self.fp32_params.grad.data.mul_(multiply_grads) + if p.requires_grad: + device_params_dict[p.device.index].append(p) + for device in devices: + device_params = device_params_dict[device] + offset = 0 + for p in device_params: + grad_data = ( + p.grad.data + if p.grad is not None + else p.data.new_zeros(p.data.shape) + ) + numel = grad_data.numel() + self.fp32_params[device].grad.data[ + offset : offset + numel + ].copy_(grad_data.view(-1)) + offset += numel else: for p, p32 in zip(self.fp16_params, self.fp32_params): if not p.requires_grad: continue if p.grad is not None: - p32.grad.data.copy_(p.grad.data) - p32.grad.data.mul_(multiply_grads) + if p32.grad is None: + p32.grad = p.grad.data.float() + else: + p32.grad.data.copy_(p.grad.data) else: p32.grad = torch.zeros_like(p.data, dtype=torch.float) self._needs_sync = False + def _sync_fp32_params_to_fp16(self): + # copy FP32 params back into FP16 model + if self.has_flat_params: + devices = list(self.fp32_params.keys()) + device_params_dict = defaultdict(list) + for p in self.fp16_params: + device_params_dict[p.device.index].append(p) + for device in devices: + device_params = device_params_dict[device] + offset = 0 + for p in device_params: + numel = p.data.numel() + p.data.copy_( + self.fp32_params[device] + .data[offset : offset + numel] + .view_as(p.data) + ) + offset += numel + else: + for p, p32 in zip(self.fp16_params, self.fp32_params): + if not p.requires_grad: + continue + p.data.copy_(p32.data) + + def _unscale_grads(self): + self._sync_fp16_grads_to_fp32() + if ( + # Skip the multiplication if it's a no-op (i.e., if _multiply_factor + # is 1.0). At the same time, we want to avoid the device-to-host + # transfer by comparing it to 1.0. Since _multiply_factor starts as + # a Python float, we roughly assume that if it's a tensor then it's + # probably not =1.0 anymore and we do the multiplication. Otherwise + # we can safely check the value without a D2H transfer. + torch.is_tensor(self._multiply_factor) + or self._multiply_factor != 1.0 + ): + self.fp32_optimizer.multiply_grads(self._multiply_factor) + self._multiply_factor = 1.0 + def multiply_grads(self, c): """Multiplies grads by a constant ``c``.""" - if self._needs_sync: - self._sync_fp16_grads_to_fp32(c) - elif self.has_flat_params: - self.fp32_params.grad.data.mul_(c) - else: - for p32 in self.fp32_params: - p32.grad.data.mul_(c) + self._multiply_factor *= c def clip_grad_norm(self, max_norm, aggregate_norm_fn=None): """Clips gradient norm and updates dynamic loss scaler.""" self._sync_fp16_grads_to_fp32() - grad_norm = utils.clip_grad_norm_(self.fp32_params, max_norm, aggregate_norm_fn) - # detect overflow and adjust loss scale + grad_norm = self._multiply_factor * self.fp32_optimizer.clip_grad_norm( + 0, aggregate_norm_fn + ) + if self.scaler is not None: - overflow = DynamicLossScaler.has_overflow(grad_norm) - prev_scale = self.scaler.loss_scale - self.scaler.update_scale(overflow) - if overflow: - if self.scaler.loss_scale <= self.min_loss_scale: - # Use FloatingPointError as an uncommon error that parent - # functions can safely catch to stop training. - self.scaler.loss_scale = prev_scale - raise FloatingPointError(( - 'Minimum loss scale reached ({}). Your loss is probably exploding. ' - 'Try lowering the learning rate, using gradient clipping or ' - 'increasing the batch size.' - ).format(self.min_loss_scale)) - raise OverflowError('setting loss scale to: ' + str(self.scaler.loss_scale)) + if grad_norm > max_norm > 0.0: + self._multiply_factor *= max_norm / grad_norm + + self.scaler.check_overflow(grad_norm) + elif max_norm > 0.0: + clip_coef = (max_norm / (grad_norm + 1e-6)).clamp_(max=1) + self._multiply_factor *= clip_coef return grad_norm - def step(self, closure=None): + def step(self, closure=None, groups=None): """Performs a single optimization step.""" self._sync_fp16_grads_to_fp32() - self.fp32_optimizer.step(closure) - # copy FP32 params back into FP16 model - if self.has_flat_params: - offset = 0 - for p in self.fp16_params: - if not p.requires_grad: - continue - numel = p.data.numel() - p.data.copy_(self.fp32_params.data[offset:offset+numel].view_as(p.data)) - offset += numel + if getattr(self, "supports_step_with_scale", False): + self.fp32_optimizer.step(closure, scale=(1.0 / self._multiply_factor), groups=groups) else: - for p, p32 in zip(self.fp16_params, self.fp32_params): - if not p.requires_grad: - continue - p.data.copy_(p32.data) + self._unscale_grads() + self.fp32_optimizer.step(closure, groups=groups) + + if self.scaler is not None: + self.scaler.update() + + self._sync_fp32_params_to_fp16() def zero_grad(self): """Clears the gradients of all optimized parameters.""" for p in self.fp16_params: p.grad = None if self.has_flat_params: - self.fp32_params.grad.zero_() + if torch.is_tensor(self.fp32_params): + self.fp32_params.grad.zero_() + elif isinstance(self.fp32_params, dict): + for fp32_params in self.fp32_params.values(): + fp32_params.grad.zero_() + else: + raise RuntimeError("self.fp32_params must be a tensor or dict") else: for p32 in self.fp32_params: - p32.grad.zero_() + if p32.grad is not None: + p32.grad.zero_() self._needs_sync = False + if self.scaler is not None: + self._multiply_factor = 1.0 / float(self.scaler.loss_scale) + class FP16Optimizer(_FP16OptimizerMixin, optim.FairseqOptimizer): """ Wrap an *optimizer* to support FP16 (mixed precision) training. """ - def __init__(self, args, params, fp32_optimizer, fp32_params): - super().__init__(args) + def __init__(self, cfg: DictConfig, params, fp32_optimizer, fp32_params, **kwargs): + super().__init__(cfg.optimizer) self.fp16_params = params self.fp32_optimizer = fp32_optimizer self.fp32_params = fp32_params - if getattr(args, 'fp16_scale_window', None) is None: - if len(args.update_freq) > 1: + if getattr(cfg.common, "fp16_scale_window", None) is None: + if len(cfg.optimization.update_freq) > 1: raise ValueError( - '--fp16-scale-window must be given explicitly when using a ' - 'custom --update-freq schedule' + "--fp16-scale-window must be given explicitly when using a " + "custom --update-freq schedule" ) - data_parallel_size = int(args.distributed_world_size / args.model_parallel_size) - scale_window = int(2**14 / data_parallel_size / args.update_freq[0]) + data_parallel_size = int( + cfg.distributed_training.distributed_world_size + / cfg.common.model_parallel_size + ) + scale_window = int( + 2 ** 14 / data_parallel_size / cfg.optimization.update_freq[0] + ) else: - scale_window = args.fp16_scale_window + scale_window = cfg.common.fp16_scale_window - if not getattr(args, 'bf16', False): + if not getattr(cfg.common, "bf16", False): self.scaler = DynamicLossScaler( - init_scale=args.fp16_init_scale, + init_scale=cfg.common.fp16_init_scale, scale_window=scale_window, - tolerance=args.fp16_scale_tolerance, - threshold=args.threshold_loss_scale, + tolerance=cfg.common.fp16_scale_tolerance, + threshold=cfg.common.threshold_loss_scale, + min_loss_scale=cfg.common.min_loss_scale, ) - self.min_loss_scale = self.args.min_loss_scale else: # disable loss scaling for bfloat16 self.scaler = None @classmethod - def build_optimizer(cls, args, params): + def build_optimizer(cls, cfg: DictConfig, params, **kwargs): """ Args: - args (argparse.Namespace): fairseq args + cfg (omegaconf.DictConfig): fairseq args params (iterable): iterable of parameters to optimize """ - flatten = not getattr(args, 'fp16_no_flatten_grads', False) - if getattr(args, 'bf16', False): + flatten = not getattr(cfg.common, "fp16_no_flatten_grads", False) + if getattr(cfg.common, "bf16", False): flatten = False # mixed precision is faster on TPUs without flat grads - fp32_params = cls.build_fp32_params(params, flatten=flatten) + fp32_params = cls.build_fp32_params(cfg.optimizer, params, flatten=flatten) if flatten: - fp32_optimizer = optim.build_optimizer(args, [fp32_params]) + fp32_optimizer = optim.build_optimizer(cfg.optimizer, [fp32_params]) else: - fp32_optimizer = optim.build_optimizer(args, fp32_params) + fp32_optimizer = optim.build_optimizer(cfg.optimizer, fp32_params) if flatten and not fp32_optimizer.supports_flat_params: raise RuntimeError( - 'chosen optimizer does not support flat params, ' - 'please set --fp16-no-flatten-grads' + f"chosen optimizer {fp32_optimizer.__class__.__name__} does not support flat params, please set --fp16-no-flatten-grads" ) - return cls(args, params, fp32_optimizer, fp32_params) + return cls(cfg, params, fp32_optimizer, fp32_params, **kwargs) @property def optimizer(self): return self.fp32_optimizer.optimizer + @optimizer.setter + def optimizer(self, optimizer): + self.fp32_optimizer.optimizer = optimizer + + @property + def lr_scheduler(self): + return getattr(self.fp32_optimizer, "lr_scheduler", None) + @property def optimizer_config(self): return self.fp32_optimizer.optimizer_config @@ -282,12 +323,19 @@ def get_lr(self): def set_lr(self, lr): self.fp32_optimizer.set_lr(lr) + def all_reduce_grads(self, module): + self.fp32_optimizer.all_reduce_grads(module) + + @property + def supports_flat_params(self): + return self.fp32_optimizer.supports_flat_params -class _MemoryEfficientFP16OptimizerMixin(object): +class _MemoryEfficientFP16OptimizerMixin(object): def __init__(self, *args, **kwargs): - # forward __init__ call to the next class in mro(method resolution order) + # forward __init__ call to the next class in MRO (method resolution order) super().__init__(*args, **kwargs) + self._multiply_factor = 1.0 @property def has_flat_params(self): @@ -297,7 +345,7 @@ def state_dict(self): """Return the optimizer's state dict.""" state_dict = self.wrapped_optimizer.state_dict() if self.scaler is not None: - state_dict['loss_scale'] = self.scaler.loss_scale + state_dict["loss_scale"] = self.scaler.loss_scale return state_dict def load_state_dict(self, state_dict, optimizer_overrides=None): @@ -308,8 +356,8 @@ def load_state_dict(self, state_dict, optimizer_overrides=None): allows us to resume training from a checkpoint using a new set of optimizer args. """ - if 'loss_scale' in state_dict and self.scaler is not None: - self.scaler.loss_scale = state_dict['loss_scale'] + if "loss_scale" in state_dict and self.scaler is not None: + self.scaler.loss_scale = state_dict["loss_scale"] self.wrapped_optimizer.load_state_dict(state_dict, optimizer_overrides) @@ -318,19 +366,20 @@ def load_state_dict(self, state_dict, optimizer_overrides=None): # params are FP16 while the optimizer state is FP32 and we don't want # to cast. A workaround is to manually copy back the original state # after the optimizer has been loaded. - groups = self.optimizer.param_groups - saved_groups = state_dict['param_groups'] - id_map = { - old_id: p - for old_id, p in zip( - chain(*(g['params'] for g in saved_groups)), - chain(*(g['params'] for g in groups)) - ) - } - for k, v in state_dict['state'].items(): - if k in id_map: - param = id_map[k] - self.optimizer.state[param] = v + if not getattr(self.optimizer, "disable_mem_eff_fp16_loading_hack", False): + groups = self.optimizer.param_groups + saved_groups = state_dict["param_groups"] + id_map = { + old_id: p + for old_id, p in zip( + chain(*(g["params"] for g in saved_groups)), + chain(*(g["params"] for g in groups)), + ) + } + for k, v in state_dict["state"].items(): + if k in id_map: + param = id_map[k] + self.optimizer.state[param] = v def backward(self, loss): """Computes the sum of gradients of the given tensor w.r.t. graph leaves. @@ -340,62 +389,75 @@ def backward(self, loss): underflow. """ if self.scaler is not None: - loss = loss * self.scaler.loss_scale - self._grads_are_scaled = True + loss = self.scaler.scale(loss) loss.backward() - def _unscale_grads(self, multiply_grads=1.): - if self._grads_are_scaled: - self._grads_are_scaled = False - - # correct for dynamic loss scaler - self.wrapped_optimizer.multiply_grads(multiply_grads / self.scaler.loss_scale) - else: - assert multiply_grads == 1. + def _unscale_grads(self): + if ( + # Skip the multiplication if it's a no-op (i.e., if _multiply_factor + # is 1.0). At the same time, we want to avoid the device-to-host + # transfer by comparing it to 1.0. Since _multiply_factor starts as + # a Python float, we roughly assume that if it's a tensor then it's + # probably not =1.0 anymore and we do the multiplication. Otherwise + # we can safely check the value without a D2H transfer. + torch.is_tensor(self._multiply_factor) + or self._multiply_factor != 1.0 + ): + self.wrapped_optimizer.multiply_grads(self._multiply_factor) + self._multiply_factor = 1.0 def multiply_grads(self, c): """Multiplies grads by a constant *c*.""" - if self._grads_are_scaled: - self._unscale_grads(c) - else: - self.wrapped_optimizer.multiply_grads(c) + self._multiply_factor *= c def clip_grad_norm(self, max_norm, aggregate_norm_fn=None): """Clips gradient norm and updates dynamic loss scaler.""" - self._unscale_grads() - grad_norm = self.wrapped_optimizer.clip_grad_norm(max_norm, aggregate_norm_fn) + max_norm = float(max_norm) + grad_norm = self._multiply_factor * self.wrapped_optimizer.clip_grad_norm( + 0, aggregate_norm_fn + ) - # detect overflow and adjust loss scale if self.scaler is not None: - overflow = DynamicLossScaler.has_overflow(grad_norm) - prev_scale = self.scaler.loss_scale - self.scaler.update_scale(overflow) - if overflow: - if self.scaler.loss_scale <= self.min_loss_scale: - # Use FloatingPointError as an uncommon error that parent - # functions can safely catch to stop training. - self.scaler.loss_scale = prev_scale - raise FloatingPointError(( - 'Minimum loss scale reached ({}). Your loss is probably exploding. ' - 'Try lowering the learning rate, using gradient clipping or ' - 'increasing the batch size.' - ).format(self.min_loss_scale)) - raise OverflowError('setting loss scale to: ' + str(self.scaler.loss_scale)) + grad_norm_cpu = float(grad_norm) + if grad_norm_cpu > max_norm > 0.0: + self._multiply_factor *= max_norm / grad_norm_cpu + + # detect overflow and adjust loss scale + self.scaler.check_overflow(grad_norm_cpu) + elif max_norm > 0.0: + clip_coef = (max_norm / (grad_norm + 1e-6)).clamp_(max=1) + self._multiply_factor *= clip_coef return grad_norm - def step(self, closure=None): + def step(self, closure=None, groups=None): """Performs a single optimization step.""" - self._unscale_grads() - self.wrapped_optimizer.step(closure) + if getattr(self, "supports_step_with_scale", False): + # NOTE(msb) optimizer divides by scale factor + self.wrapped_optimizer.step(closure, scale=(1.0 / self._multiply_factor), groups=groups) + else: + self._unscale_grads() + self.wrapped_optimizer.step(closure, groups=groups) + + if self.scaler is not None: + self.scaler.update() def zero_grad(self): """Clears the gradients of all optimized parameters.""" self.wrapped_optimizer.zero_grad() - self._grads_are_scaled = False + if self.scaler is not None: + self._multiply_factor = 1.0 / float(self.scaler.loss_scale) + else: + self._multiply_factor = 1.0 + + @property + def supports_flat_params(self): + return self.wrapped_optimizer.supports_flat_params -class MemoryEfficientFP16Optimizer(_MemoryEfficientFP16OptimizerMixin, optim.FairseqOptimizer): +class MemoryEfficientFP16Optimizer( + _MemoryEfficientFP16OptimizerMixin, optim.FairseqOptimizer +): """ Wrap an *optimizer* to support FP16 (mixed precision) training. @@ -411,58 +473,76 @@ class MemoryEfficientFP16Optimizer(_MemoryEfficientFP16OptimizerMixin, optim.Fai *supports_memory_efficient_fp16* property. """ - def __init__(self, args, params, optimizer): - if not optimizer.supports_memory_efficient_fp16: + def __init__( + self, cfg: DictConfig, params, optimizer, allow_unsupported=False, **kwargs + ): + if not allow_unsupported and not optimizer.supports_memory_efficient_fp16: raise ValueError( - 'Unsupported optimizer: {}'.format(optimizer.__class__.__name__) + "Unsupported optimizer: {}".format(optimizer.__class__.__name__) ) - super().__init__(args) + super().__init__(getattr(cfg, "optimizer", None)) self.wrapped_optimizer = optimizer - if getattr(args, 'fp16_scale_window', None) is None: - if len(args.update_freq) > 1: + if getattr(cfg.common, "fp16_scale_window", None) is None: + if len(cfg.optimization.update_freq) > 1: raise ValueError( - '--fp16-scale-window must be given explicitly when using a ' - 'custom --update-freq schedule' + "--fp16-scale-window must be given explicitly when using a " + "custom --update-freq schedule" ) - data_parallel_size = int(args.distributed_world_size / args.model_parallel_size) - scale_window = 2**14 / data_parallel_size / args.update_freq[0] + data_parallel_size = int( + cfg.distributed_training.distributed_world_size + / cfg.common.model_parallel_size + ) + scale_window = int( + 2 ** 14 / data_parallel_size / cfg.optimization.update_freq[0] + ) else: - scale_window = args.fp16_scale_window + scale_window = cfg.common.fp16_scale_window - if not getattr(args, 'bf16', False): + if not getattr(cfg.common, "bf16", False): self.scaler = DynamicLossScaler( - init_scale=args.fp16_init_scale, + init_scale=cfg.common.fp16_init_scale, scale_window=scale_window, - tolerance=args.fp16_scale_tolerance, - threshold=args.threshold_loss_scale, + tolerance=cfg.common.fp16_scale_tolerance, + threshold=cfg.common.threshold_loss_scale, + min_loss_scale=cfg.common.min_loss_scale, ) - self.min_loss_scale = self.args.min_loss_scale else: # disable loss scaling for bfloat16 self.scaler = None @classmethod - def build_optimizer(cls, args, params): + def build_optimizer(cls, cfg: DictConfig, params, **kwargs): """ Args: args (argparse.Namespace): fairseq args params (iterable): iterable of parameters to optimize """ - fp16_optimizer = optim.build_optimizer(args, params) - return cls(args, params, fp16_optimizer) + fp16_optimizer = optim.build_optimizer(cfg.optimizer, params) + return cls(cfg, params, fp16_optimizer, **kwargs) @property def optimizer(self): return self.wrapped_optimizer.optimizer + @optimizer.setter + def optimizer(self, optimizer): + self.wrapped_optimizer.optimizer = optimizer + @property def optimizer_config(self): return self.wrapped_optimizer.optimizer_config + @property + def lr_scheduler(self): + return getattr(self.wrapped_optimizer, "lr_scheduler", None) + def get_lr(self): return self.wrapped_optimizer.get_lr() def set_lr(self, lr): self.wrapped_optimizer.set_lr(lr) + + def all_reduce_grads(self, module): + self.wrapped_optimizer.all_reduce_grads(module) diff --git a/fairseq/optim/fused_adam.py b/fairseq/optim/fused_adam.py index 7d6c0c4a5e..7a6d1f73d5 100644 --- a/fairseq/optim/fused_adam.py +++ b/fairseq/optim/fused_adam.py @@ -21,6 +21,7 @@ def get_fused_adam_class(): # `--deprecated_fused_adam` option when building apex. global fused_adam_cuda import importlib + fused_adam_cuda = importlib.import_module("fused_adam_cuda") return FusedAdamV1 except ImportError: @@ -28,6 +29,7 @@ def get_fused_adam_class(): # fallback to the newer interface from apex.optimizers import FusedAdam as _FusedAdam # noqa from apex.multi_tensor_apply import multi_tensor_applier + if multi_tensor_applier.available: return FusedAdamV2 except ImportError: @@ -45,7 +47,7 @@ class FusedAdamV1(torch.optim.Optimizer): Compared to the original version in Apex, the fairseq version casts grads and params to FP32 internally to support ``--memory-efficient-fp16``. - Arguments: + Args: params (iterable): iterable of parameters to optimize or dicts defining parameter groups. lr (float, optional): learning rate. (default: 1e-3) @@ -67,27 +69,40 @@ class FusedAdamV1(torch.optim.Optimizer): https://openreview.net/forum?id=ryQu7f-RZ """ - def __init__(self, params, - lr=1e-3, bias_correction=True, - betas=(0.9, 0.999), eps=1e-8, eps_inside_sqrt=False, - weight_decay=0., max_grad_norm=0., amsgrad=False): + def __init__( + self, + params, + lr=1e-3, + bias_correction=True, + betas=(0.9, 0.999), + eps=1e-8, + eps_inside_sqrt=False, + weight_decay=0.0, + max_grad_norm=0.0, + amsgrad=False, + use_fp16_stats=False, + ): global fused_adam_cuda import importlib + fused_adam_cuda = importlib.import_module("fused_adam_cuda") if amsgrad: - raise RuntimeError('FusedAdam does not support the AMSGrad variant.') + raise RuntimeError("FusedAdam does not support the AMSGrad variant.") defaults = { - 'lr': lr, - 'bias_correction': bias_correction, - 'betas': betas, - 'eps': eps, - 'weight_decay': weight_decay, - 'max_grad_norm': max_grad_norm, + "lr": lr, + "bias_correction": bias_correction, + "betas": betas, + "eps": eps, + "weight_decay": weight_decay, + "max_grad_norm": max_grad_norm, } super().__init__(params, defaults) self.eps_mode = 0 if eps_inside_sqrt else 1 + self.use_fp16_stats = use_fp16_stats + self.FLOAT16_MAX = 65504.0 + @property def supports_memory_efficient_fp16(self): return True @@ -96,9 +111,13 @@ def supports_memory_efficient_fp16(self): def supports_flat_params(self): return True - def step(self, closure=None, grads=None, scale=1., grad_norms=None): + @property + def supports_step_with_scale(self): + return True + + def step(self, closure=None, grads=None, scale=1.0, grad_norms=None): """Performs a single optimization step. - Arguments: + Args: closure (callable, optional): A closure that reevaluates the model and returns the loss. grads (list of tensors, optional): weight gradient to use for the @@ -126,23 +145,25 @@ def step(self, closure=None, grads=None, scale=1., grad_norms=None): grads_group = grads if grad_norms is None: - grad_norms = [None]*len(self.param_groups) + grad_norms = [None] * len(self.param_groups) - for group, grads_this_group, grad_norm in zip(self.param_groups, grads_group, grad_norms): + for group, grads_this_group, grad_norm in zip( + self.param_groups, grads_group, grad_norms + ): if grads_this_group is None: - grads_this_group = [None]*len(group['params']) + grads_this_group = [None] * len(group["params"]) # compute combined scale factor for this group combined_scale = scale - if group.get('max_grad_norm', 0) > 0: + if group.get("max_grad_norm", 0) > 0: # norm is in fact norm*scale - clip = ((grad_norm / scale) + 1e-6) / group['max_grad_norm'] + clip = ((grad_norm / scale) + 1e-6) / group["max_grad_norm"] if clip > 1: combined_scale = clip * scale - bias_correction = 1 if group.get('bias_correction', 1) else 0 + bias_correction = 1 if group.get("bias_correction", 1) else 0 - for p, grad in zip(group['params'], grads_this_group): + for p, grad in zip(group["params"], grads_this_group): # note: p.grad should not ever be set for correct # operation of mixed precision optimizer that sometimes # sends None gradients @@ -152,47 +173,79 @@ def step(self, closure=None, grads=None, scale=1., grad_norms=None): grad = p.grad.data if grad.is_sparse: raise RuntimeError( - 'FusedAdam does not support sparse gradients, ' - 'please consider SparseAdam instead' + "FusedAdam does not support sparse gradients, " + "please consider SparseAdam instead" ) - p_data_fp32 = p.data.float() + if p.device.type == "cpu": + p_data_fp32 = p.data.cuda(non_blocking=True).float() + out_p = torch.tensor([], dtype = torch.float) + else: + p_data_fp32 = p.data.float() + out_p = p.data state = self.state[p] # State initialization + dtype = torch.float16 if self.use_fp16_stats else p_data_fp32.dtype if len(state) == 0: - state['step'] = 0 + state["step"] = 0 # Exponential moving average of gradient values - state['exp_avg'] = torch.zeros_like(p_data_fp32) + state["exp_avg"] = torch.zeros_like(p_data_fp32, dtype=dtype) # Exponential moving average of squared gradient values - state['exp_avg_sq'] = torch.zeros_like(p_data_fp32) + state["exp_avg_sq"] = torch.zeros_like(p_data_fp32, dtype=dtype) + if self.use_fp16_stats: + state["exp_avg_scale"] = 1.0 + state["exp_avg_sq_scale"] = 1.0 else: - state['exp_avg'] = state['exp_avg'].to(p_data_fp32) - state['exp_avg_sq'] = state['exp_avg_sq'].to(p_data_fp32) + device = p_data_fp32.device + state["exp_avg"] = state["exp_avg"].to(device, dtype) + state["exp_avg_sq"] = state["exp_avg_sq"].to(device, dtype) + + exp_avg = state["exp_avg"] + exp_avg_sq = state["exp_avg_sq"] + if self.use_fp16_stats: + assert exp_avg.dtype == torch.float16 + exp_avg = exp_avg.float() * state["exp_avg_scale"] + exp_avg_sq = exp_avg_sq.float() * state["exp_avg_sq_scale"] + beta1, beta2 = group["betas"] + + state["step"] += 1 + + with torch.cuda.device(p_data_fp32.device): + fused_adam_cuda.adam( + p_data_fp32, + out_p, + exp_avg, + exp_avg_sq, + grad, + group["lr"], + beta1, + beta2, + group["eps"], + combined_scale, + state["step"], + self.eps_mode, + bias_correction, + group["weight_decay"], + ) - exp_avg = state['exp_avg'] - exp_avg_sq = state['exp_avg_sq'] - beta1, beta2 = group['betas'] + if p.device.type == "cpu": + p.data.copy_(p_data_fp32, non_blocking=True) - state['step'] += 1 + if self.use_fp16_stats: + def inf_norm(t): + return torch.norm(t, float("inf")) - out_p = p.data - with torch.cuda.device(p.device): - fused_adam_cuda.adam(p_data_fp32, - out_p, - exp_avg, - exp_avg_sq, - grad, - group['lr'], - beta1, - beta2, - group['eps'], - combined_scale, - state['step'], - self.eps_mode, - bias_correction, - group['weight_decay']) + # from github.com/openai/jukebox/blob/master/jukebox/utils/fp16.py + state["exp_avg_scale"], state["exp_avg_sq_scale"] = ( + 1e-8 + inf_norm(exp_avg) / self.FLOAT16_MAX, + 1e-8 + inf_norm(exp_avg_sq) / self.FLOAT16_MAX, + ) + state["exp_avg"], state["exp_avg_sq"] = ( + (exp_avg / state["exp_avg_scale"]).half(), + (exp_avg_sq / state["exp_avg_sq_scale"]).half(), + ) return loss @@ -207,10 +260,14 @@ class FusedAdamV2(FusedAdam): and params to FP32 internally to support ``--memory-efficient-fp16``. """ - def __init__(self, *args, **kwargs): + def __init__(self, *args, use_fp16_stats=False, **kwargs): + if use_fp16_stats: + raise NotImplementedError("--fp16-adam-stats is only supported with FusedAdamV1") super().__init__(*args, **kwargs) - if not hasattr(self, 'multi_tensor_adam'): - raise Exception('Apex installation is outdated. Please install an updated version of apex.') + if not hasattr(self, "multi_tensor_adam"): + raise Exception( + "Apex installation is outdated. Please install an updated version of apex." + ) @property def supports_memory_efficient_fp16(self): @@ -220,89 +277,108 @@ def supports_memory_efficient_fp16(self): def supports_flat_params(self): return True - def step(self, closure=None, grads=None, output_params=None, scale=None, grad_norms=None): + def step( + self, + closure=None, + grads=None, + output_params=None, + scale=None, + grad_norms=None, + ): """Performs a single optimization step.""" loss = None if closure is not None: loss = closure() for group in self.param_groups: - bias_correction = 1 if group['bias_correction'] else 0 - beta1, beta2 = group['betas'] + bias_correction = 1 if group["bias_correction"] else 0 + beta1, beta2 = group["betas"] # assume same step across group now to simplify things # per parameter step can be easily support by making it tensor, or pass list into kernel - if 'step' in group: - group['step'] += 1 + if "step" in group: + group["step"] += 1 else: - group['step'] = 1 + group["step"] = 1 # create lists for multi-tensor apply g_16, p_16, orig_p_16, m_16, v_16 = [], [], [], [], [] g_32, p_32, m_32, v_32 = [], [], [], [] - for p in group['params']: + for p in group["params"]: if p.grad is None: continue if p.grad.data.is_sparse: raise RuntimeError( - 'FusedAdam does not support sparse gradients, ' - 'please consider SparseAdam instead' + "FusedAdam does not support sparse gradients, " + "please consider SparseAdam instead" ) state = self.state[p] # State initialization if len(state) == 0: # Exponential moving average of gradient values - state['exp_avg'] = torch.zeros_like(p.data, dtype=torch.float) + state["exp_avg"] = torch.zeros_like(p.data, dtype=torch.float) # Exponential moving average of squared gradient values - state['exp_avg_sq'] = torch.zeros_like(p.data, dtype=torch.float) + state["exp_avg_sq"] = torch.zeros_like( + p.data, dtype=torch.float + ) else: - state['exp_avg'] = state['exp_avg'].to(device=p.data.device, dtype=torch.float) - state['exp_avg_sq'] = state['exp_avg_sq'].to(device=p.data.device, dtype=torch.float) + state["exp_avg"] = state["exp_avg"].to( + device=p.data.device, dtype=torch.float + ) + state["exp_avg_sq"] = state["exp_avg_sq"].to( + device=p.data.device, dtype=torch.float + ) if p.dtype == torch.float16: g_16.append(p.grad.data.float()) p_16.append(p.data.float()) orig_p_16.append(p.data) - m_16.append(state['exp_avg']) - v_16.append(state['exp_avg_sq']) + m_16.append(state["exp_avg"]) + v_16.append(state["exp_avg_sq"]) elif p.dtype == torch.float32: g_32.append(p.grad.data) p_32.append(p.data) - m_32.append(state['exp_avg']) - v_32.append(state['exp_avg_sq']) + m_32.append(state["exp_avg"]) + v_32.append(state["exp_avg_sq"]) else: - raise RuntimeError('FusedAdam only support fp16 and fp32.') + raise RuntimeError("FusedAdam only support fp16 and fp32.") with torch.cuda.device(p.device): - if(len(g_16) > 0): - multi_tensor_applier(self.multi_tensor_adam, - self._dummy_overflow_buf, - [g_16, p_16, m_16, v_16], - group['lr'], - beta1, - beta2, - group['eps'], - group['step'], - self.adam_w_mode, - bias_correction, - group['weight_decay']) + if len(g_16) > 0: + multi_tensor_applier( + self.multi_tensor_adam, + self._dummy_overflow_buf, + [g_16, p_16, m_16, v_16], + group["lr"], + beta1, + beta2, + group["eps"], + group["step"], + self.adam_w_mode, + bias_correction, + group["weight_decay"], + ) for orig_p, p in zip(orig_p_16, p_16): orig_p.copy_(p.data) - if(len(g_32) > 0): - multi_tensor_applier(self.multi_tensor_adam, - self._dummy_overflow_buf, - [g_32, p_32, m_32, v_32], - group['lr'], - beta1, - beta2, - group['eps'], - group['step'], - self.adam_w_mode, - bias_correction, - group['weight_decay']) + if len(g_32) > 0: + multi_tensor_applier( + self.multi_tensor_adam, + self._dummy_overflow_buf, + [g_32, p_32, m_32, v_32], + group["lr"], + beta1, + beta2, + group["eps"], + group["step"], + self.adam_w_mode, + bias_correction, + group["weight_decay"], + ) return loss + + except ImportError: pass diff --git a/fairseq/optim/fused_lamb.py b/fairseq/optim/fused_lamb.py index f9b0409c53..f4f2bdb0c6 100644 --- a/fairseq/optim/fused_lamb.py +++ b/fairseq/optim/fused_lamb.py @@ -3,20 +3,21 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. -from fairseq.optim import FairseqOptimizer, register_optimizer +from fairseq.optim import LegacyFairseqOptimizer, register_optimizer -@register_optimizer('lamb') -class FairseqLAMB(FairseqOptimizer): +@register_optimizer("lamb") +class FairseqLAMB(LegacyFairseqOptimizer): """LAMB optimizer.""" def __init__(self, args, params): super().__init__(args) try: from apex.optimizers import FusedLAMB + self._optimizer = FusedLAMB(params, **self.optimizer_config) except ImportError: - raise ImportError('Please install apex to use LAMB optimizer') + raise ImportError("Please install apex to use LAMB optimizer") @staticmethod def add_args(parser): @@ -39,10 +40,10 @@ def optimizer_config(self): different learning rate. """ return { - 'lr': self.args.lr[0], - 'betas': eval(self.args.lamb_betas), - 'eps': self.args.lamb_eps, - 'weight_decay': self.args.weight_decay, + "lr": self.args.lr[0], + "betas": eval(self.args.lamb_betas), + "eps": self.args.lamb_eps, + "weight_decay": self.args.weight_decay, } @property diff --git a/fairseq/optim/lr_scheduler/__init__.py b/fairseq/optim/lr_scheduler/__init__.py index edd0a6a13e..5b3dbc023a 100644 --- a/fairseq/optim/lr_scheduler/__init__.py +++ b/fairseq/optim/lr_scheduler/__init__.py @@ -2,22 +2,35 @@ # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. +"""isort:skip_file""" import importlib import os from fairseq import registry -from fairseq.optim.lr_scheduler.fairseq_lr_scheduler import FairseqLRScheduler +from fairseq.optim.lr_scheduler.fairseq_lr_scheduler import ( # noqa + FairseqLRScheduler, + LegacyFairseqLRScheduler, +) +from omegaconf import DictConfig -build_lr_scheduler, register_lr_scheduler, LR_SCHEDULER_REGISTRY = registry.setup_registry( - '--lr-scheduler', - base_class=FairseqLRScheduler, - default='fixed', +( + build_lr_scheduler_, + register_lr_scheduler, + LR_SCHEDULER_REGISTRY, + LR_SCHEDULER_DATACLASS_REGISTRY, +) = registry.setup_registry( + "--lr-scheduler", base_class=FairseqLRScheduler, default="fixed" ) + +def build_lr_scheduler(cfg: DictConfig, optimizer): + return build_lr_scheduler_(cfg, optimizer) + + # automatically import any Python files in the optim/lr_scheduler/ directory -for file in os.listdir(os.path.dirname(__file__)): - if file.endswith('.py') and not file.startswith('_'): - module = file[:file.find('.py')] - importlib.import_module('fairseq.optim.lr_scheduler.' + module) +for file in sorted(os.listdir(os.path.dirname(__file__))): + if file.endswith(".py") and not file.startswith("_"): + file_name = file[: file.find(".py")] + importlib.import_module("fairseq.optim.lr_scheduler." + file_name) diff --git a/fairseq/optim/lr_scheduler/cosine_lr_scheduler.py b/fairseq/optim/lr_scheduler/cosine_lr_scheduler.py index 9137e11b78..51f58359ed 100644 --- a/fairseq/optim/lr_scheduler/cosine_lr_scheduler.py +++ b/fairseq/optim/lr_scheduler/cosine_lr_scheduler.py @@ -4,89 +4,109 @@ # LICENSE file in the root directory of this source tree. import math - -from . import FairseqLRScheduler, register_lr_scheduler - - -@register_lr_scheduler('cosine') -class CosineSchedule(FairseqLRScheduler): +from collections.abc import Collection +from dataclasses import dataclass, field +from typing import List + +from omegaconf import II + +from fairseq.dataclass import FairseqDataclass +from fairseq.optim.lr_scheduler import FairseqLRScheduler, register_lr_scheduler + + +@dataclass +class CosineLRScheduleConfig(FairseqDataclass): + warmup_updates: int = field( + default=0, + metadata={"help": "warmup the learning rate linearly for the first N updates"}, + ) + warmup_init_lr: float = field( + default=-1, + metadata={ + "help": "initial learning rate during warmup phase; default is cfg.lr" + }, + ) + lr: List[float] = field( + default=II("optimization.lr"), + metadata={"help": "max learning rate, must be more than cfg.min_lr"}, + ) + min_lr: float = field(default=0.0, metadata={"help": "min learning rate"}) + t_mult: float = field( + default=1.0, metadata={"help": "factor to grow the length of each period"} + ) + lr_period_updates: float = field( + default=-1, metadata={"help": "initial number of updates per period"} + ) + lr_shrink: float = field( + default=0.1, metadata={"help": "shrink factor for annealing"} + ) + # This is not required, but is for convenience in inferring lr_period_updates + max_update: int = II("optimization.max_update") + + +@register_lr_scheduler("cosine", dataclass=CosineLRScheduleConfig) +class CosineLRSchedule(FairseqLRScheduler): """Assign LR based on a cyclical schedule that follows the cosine function. See https://arxiv.org/pdf/1608.03983.pdf for details. We also support a warmup phase where we linearly increase the learning rate from some initial learning rate (``--warmup-init-lr``) until the configured - max learning rate (``--max-lr``). + max learning rate (``--lr``). During warmup:: - lrs = torch.linspace(args.warmup_init_lr, args.lr, args.warmup_updates) + lrs = torch.linspace(cfg.warmup_init_lr, cfg.lr, cfg.warmup_updates) lr = lrs[update_num] After warmup:: - lr = lr_min + 0.5*(lr_max - lr_min)*(1 + cos(t_curr / t_i)) + lr = cfg.min_lr + 0.5*(cfg.lr - cfg.min_lr)*(1 + cos(t_curr / t_i)) where ``t_curr`` is current percentage of updates within the current period range and ``t_i`` is the current period range, which is scaled by ``t_mul`` after every iteration. """ - def __init__(self, args, optimizer): - super().__init__(args, optimizer) - if len(args.lr) > 1: + def __init__(self, cfg: CosineLRScheduleConfig, fairseq_optimizer): + super().__init__(cfg, fairseq_optimizer) + if isinstance(cfg.lr, Collection) and len(cfg.lr) > 1: raise ValueError( - 'Cannot use a fixed learning rate schedule with cosine.' - ' Consider --lr-scheduler=fixed instead.' + "Cannot use a fixed learning rate schedule with cosine." + f" Consider --lr-scheduler=fixed instead. ({cfg.lr})" ) - warmup_end_lr = args.max_lr - if args.warmup_init_lr < 0: - args.warmup_init_lr = args.lr[0] - - self.min_lr = args.lr[0] - self.max_lr = args.max_lr + self.max_lr = cfg.lr[0] if isinstance(cfg.lr, Collection) else cfg.lr + assert ( + self.max_lr > cfg.min_lr + ), f"max_lr (={cfg.lr}) must be more than min_lr (={cfg.min_lr})" - assert self.max_lr > self.min_lr, 'max_lr must be more than lr' + warmup_end_lr = self.max_lr + if cfg.warmup_init_lr < 0: + cfg.warmup_init_lr = cfg.min_lr - self.t_mult = args.t_mult - self.period = args.lr_period_updates + self.t_mult = cfg.t_mult + self.period = cfg.lr_period_updates if self.period <= 0: - assert args.max_update >= 0, 'Either --max_update or --lr-period-updates must be set' - self.period = args.max_update - args.warmup_updates - - if args.warmup_updates > 0: - # linearly warmup for the first args.warmup_updates - self.lr_step = (warmup_end_lr - args.warmup_init_lr) / args.warmup_updates + assert ( + cfg.max_update > 0 + ), "Either --max_update or --lr-period-updates must be set" + self.period = cfg.max_update - cfg.warmup_updates + + if cfg.warmup_updates > 0: + # linearly warmup for the first cfg.warmup_updates + self.lr_step = (warmup_end_lr - cfg.warmup_init_lr) / cfg.warmup_updates else: self.lr_step = 1 - self.warmup_updates = args.warmup_updates - self.lr_shrink = args.lr_shrink + self.warmup_updates = cfg.warmup_updates + self.lr_shrink = cfg.lr_shrink # initial learning rate - self.lr = args.warmup_init_lr + self.lr = cfg.warmup_init_lr self.optimizer.set_lr(self.lr) - @staticmethod - def add_args(parser): - """Add arguments to the parser for this LR scheduler.""" - # fmt: off - parser.add_argument('--warmup-updates', default=0, type=int, metavar='N', - help='warmup the learning rate linearly for the first N updates') - parser.add_argument('--warmup-init-lr', default=-1, type=float, metavar='LR', - help='initial learning rate during warmup phase; default is args.lr') - parser.add_argument('--max-lr', type=float, metavar='LR', - help='max learning rate, must be more than args.lr') - parser.add_argument('--t-mult', default=1, type=float, metavar='LR', - help='factor to grow the length of each period') - parser.add_argument('--lr-period-updates', default=-1, type=float, metavar='LR', - help='initial number of updates per period') - parser.add_argument('--lr-shrink', default=0.1, type=float, metavar='LS', - help='shrink factor for annealing') - # fmt: on - def step(self, epoch, val_loss=None): """Update the learning rate at the end of the given epoch.""" super().step(epoch, val_loss) @@ -95,24 +115,33 @@ def step(self, epoch, val_loss=None): def step_update(self, num_updates): """Update the learning rate after each update.""" - if num_updates < self.args.warmup_updates: - self.lr = self.args.warmup_init_lr + num_updates * self.lr_step + if num_updates < self.cfg.warmup_updates: + self.lr = self.cfg.warmup_init_lr + num_updates * self.lr_step else: - curr_updates = num_updates - self.args.warmup_updates + curr_updates = num_updates - self.cfg.warmup_updates if self.t_mult != 1: - i = math.floor(math.log(1 - curr_updates / self.period * (1 - self.t_mult), self.t_mult)) + i = math.floor( + math.log( + 1 - curr_updates / self.period * (1 - self.t_mult), self.t_mult + ) + ) t_i = self.t_mult ** i * self.period - t_curr = curr_updates - (1 - self.t_mult ** i) / (1 - self.t_mult) * self.period + t_curr = ( + curr_updates + - (1 - self.t_mult ** i) / (1 - self.t_mult) * self.period + ) else: i = math.floor(curr_updates / self.period) t_i = self.period t_curr = curr_updates - (self.period * i) lr_shrink = self.lr_shrink ** i - min_lr = self.min_lr * lr_shrink + min_lr = self.cfg.min_lr * lr_shrink max_lr = self.max_lr * lr_shrink - self.lr = min_lr + 0.5 * (max_lr - min_lr) * (1 + math.cos(math.pi * t_curr / t_i)) + self.lr = min_lr + 0.5 * (max_lr - min_lr) * ( + 1 + math.cos(math.pi * t_curr / t_i) + ) self.optimizer.set_lr(self.lr) return self.lr diff --git a/fairseq/optim/lr_scheduler/fairseq_lr_scheduler.py b/fairseq/optim/lr_scheduler/fairseq_lr_scheduler.py index 8b7884829a..6c12fa56b8 100644 --- a/fairseq/optim/lr_scheduler/fairseq_lr_scheduler.py +++ b/fairseq/optim/lr_scheduler/fairseq_lr_scheduler.py @@ -3,31 +3,39 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. -from .. import FairseqOptimizer +from argparse import Namespace +from fairseq.dataclass.utils import gen_parser_from_dataclass +from fairseq.optim import FairseqOptimizer -class FairseqLRScheduler(object): - def __init__(self, args, optimizer): +class FairseqLRScheduler(object): + def __init__(self, cfg, optimizer): super().__init__() - if not isinstance(optimizer, FairseqOptimizer): - raise ValueError('optimizer must be an instance of FairseqOptimizer') - self.args = args + if optimizer is not None and not isinstance(optimizer, FairseqOptimizer): + raise ValueError("optimizer must be an instance of FairseqOptimizer") + self.cfg = cfg self.optimizer = optimizer self.best = None - @staticmethod - def add_args(parser): + @classmethod + def add_args(cls, parser): """Add arguments to the parser for this LR scheduler.""" - pass + dc = getattr(cls, "__dataclass", None) + if dc is not None: + gen_parser_from_dataclass(parser, dc()) def state_dict(self): """Return the LR scheduler state dict.""" - return {'best': self.best} + return {"best": self.best} def load_state_dict(self, state_dict): """Load an LR scheduler state dict.""" - self.best = state_dict['best'] + self.best = state_dict["best"] + + def step_begin_epoch(self, epoch): + """Update the learning rate at the beginning of the given epoch.""" + pass def step(self, epoch, val_loss=None): """Update the learning rate at the end of the given epoch.""" @@ -40,3 +48,12 @@ def step(self, epoch, val_loss=None): def step_update(self, num_updates): """Update the learning rate after each update.""" return self.optimizer.get_lr() + + +class LegacyFairseqLRScheduler(FairseqLRScheduler): + def __init__(self, args: Namespace, optimizer): + if not isinstance(optimizer, FairseqOptimizer): + raise ValueError("optimizer must be an instance of FairseqOptimizer") + self.args = args + self.optimizer = optimizer + self.best = None diff --git a/fairseq/optim/lr_scheduler/fixed_schedule.py b/fairseq/optim/lr_scheduler/fixed_schedule.py index cc10db1638..d0e7e14b7e 100644 --- a/fairseq/optim/lr_scheduler/fixed_schedule.py +++ b/fairseq/optim/lr_scheduler/fixed_schedule.py @@ -3,58 +3,73 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. -from . import FairseqLRScheduler, register_lr_scheduler +from dataclasses import dataclass, field +from typing import Optional, List +from omegaconf import II +from fairseq.dataclass import FairseqDataclass +from fairseq.optim.lr_scheduler import FairseqLRScheduler, register_lr_scheduler -@register_lr_scheduler('fixed') -class FixedSchedule(FairseqLRScheduler): - """Decay the LR on a fixed schedule.""" - def __init__(self, args, optimizer): - super().__init__(args, optimizer) +@dataclass +class FixedLRScheduleConfig(FairseqDataclass): + force_anneal: Optional[int] = field( + default=None, + metadata={"help": "force annealing at specified epoch"}, + ) + lr_shrink: float = field( + default=0.1, + metadata={"help": "shrink factor for annealing, lr_new = (lr * lr_shrink)"}, + ) + warmup_updates: int = field( + default=0, + metadata={"help": "warmup the learning rate linearly for the first N updates"}, + ) + lr: List[float] = II("optimization.lr") + - # set defaults - args.warmup_updates = getattr(args, 'warmup_updates', 0) or 0 +@register_lr_scheduler("fixed", dataclass=FixedLRScheduleConfig) +class FixedLRSchedule(FairseqLRScheduler): + """Decay the LR on a fixed schedule.""" - self.lr = args.lr[0] - if args.warmup_updates > 0: - self.warmup_factor = 1. / args.warmup_updates + def __init__(self, cfg: FixedLRScheduleConfig, optimizer): + super().__init__(cfg, optimizer) + + self.lr = cfg.lr[0] + if cfg.warmup_updates > 0: + self.warmup_factor = 1.0 / cfg.warmup_updates else: self.warmup_factor = 1 - @staticmethod - def add_args(parser): - """Add arguments to the parser for this LR scheduler.""" - # fmt: off - parser.add_argument('--force-anneal', '--fa', type=int, metavar='N', - help='force annealing at specified epoch') - parser.add_argument('--lr-shrink', default=0.1, type=float, metavar='LS', - help='shrink factor for annealing, lr_new = (lr * lr_shrink)') - parser.add_argument('--warmup-updates', default=0, type=int, metavar='N', - help='warmup the learning rate linearly for the first N updates') - # fmt: on + def state_dict(self): + return {"lr": self.lr} + + def load_state_dict(self, state_dict): + if "lr" in state_dict: + self.lr = state_dict["lr"] def get_next_lr(self, epoch): - lrs = self.args.lr - if self.args.force_anneal is None or epoch < self.args.force_anneal: + lrs = self.cfg.lr + if self.cfg.force_anneal is None or epoch < self.cfg.force_anneal: # use fixed LR schedule - next_lr = lrs[min(epoch, len(lrs) - 1)] + next_lr = lrs[min(epoch - 1, len(lrs) - 1)] else: # annneal based on lr_shrink - next_lr = lrs[-1] * self.args.lr_shrink ** (epoch + 1 - self.args.force_anneal) + next_lr = lrs[-1] * self.cfg.lr_shrink ** ( + epoch + 1 - self.cfg.force_anneal + ) return next_lr - def step(self, epoch, val_loss=None): - """Update the learning rate at the end of the given epoch.""" - super().step(epoch, val_loss) + def step_begin_epoch(self, epoch): + """Update the learning rate at the beginning of the given epoch.""" self.lr = self.get_next_lr(epoch) self.optimizer.set_lr(self.warmup_factor * self.lr) return self.optimizer.get_lr() def step_update(self, num_updates): """Update the learning rate after each update.""" - if self.args.warmup_updates > 0 and num_updates < self.args.warmup_updates: - self.warmup_factor = (num_updates + 1) / float(self.args.warmup_updates) + if self.cfg.warmup_updates > 0 and num_updates < self.cfg.warmup_updates: + self.warmup_factor = (num_updates + 1) / float(self.cfg.warmup_updates) self.optimizer.set_lr(self.warmup_factor * self.lr) else: self.optimizer.set_lr(self.lr) diff --git a/fairseq/optim/lr_scheduler/inverse_square_root_schedule.py b/fairseq/optim/lr_scheduler/inverse_square_root_schedule.py index f98a7c3b99..0f87bb5d7e 100644 --- a/fairseq/optim/lr_scheduler/inverse_square_root_schedule.py +++ b/fairseq/optim/lr_scheduler/inverse_square_root_schedule.py @@ -3,10 +3,32 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. -from . import FairseqLRScheduler, register_lr_scheduler +from collections.abc import Collection +from dataclasses import dataclass, field +from typing import List +from omegaconf import II -@register_lr_scheduler('inverse_sqrt') +from fairseq.dataclass import FairseqDataclass +from fairseq.optim.lr_scheduler import FairseqLRScheduler, register_lr_scheduler + + +@dataclass +class InverseSquareRootLRScheduleConfig(FairseqDataclass): + warmup_updates: int = field( + default=4000, + metadata={"help": "warmup the learning rate linearly for the first N updates"}, + ) + warmup_init_lr: float = field( + default=-1, + metadata={ + "help": "initial learning rate during warmup phase; default is cfg.lr" + }, + ) + lr: List[float] = II("optimization.lr") + + +@register_lr_scheduler("inverse_sqrt", dataclass=InverseSquareRootLRScheduleConfig) class InverseSquareRootSchedule(FairseqLRScheduler): """Decay the LR based on the inverse square root of the update number. @@ -17,46 +39,36 @@ class InverseSquareRootSchedule(FairseqLRScheduler): During warmup:: - lrs = torch.linspace(args.warmup_init_lr, args.lr, args.warmup_updates) + lrs = torch.linspace(cfg.warmup_init_lr, cfg.lr, cfg.warmup_updates) lr = lrs[update_num] After warmup:: - decay_factor = args.lr * sqrt(args.warmup_updates) + decay_factor = cfg.lr * sqrt(cfg.warmup_updates) lr = decay_factor / sqrt(update_num) """ - def __init__(self, args, optimizer): - super().__init__(args, optimizer) - if len(args.lr) > 1: + def __init__(self, cfg: InverseSquareRootLRScheduleConfig, optimizer): + super().__init__(cfg, optimizer) + if isinstance(cfg.lr, Collection) and len(cfg.lr) > 1: raise ValueError( - 'Cannot use a fixed learning rate schedule with inverse_sqrt.' - ' Consider --lr-scheduler=fixed instead.' + "Cannot use a fixed learning rate schedule with inverse_sqrt." + " Consider --lr-scheduler=fixed instead." ) - warmup_end_lr = args.lr[0] - if args.warmup_init_lr < 0: - args.warmup_init_lr = 0 if args.warmup_updates > 0 else warmup_end_lr + warmup_end_lr = cfg.lr[0] if isinstance(cfg.lr, Collection) else cfg.lr + if cfg.warmup_init_lr < 0: + cfg.warmup_init_lr = 0 if cfg.warmup_updates > 0 else warmup_end_lr - # linearly warmup for the first args.warmup_updates - self.lr_step = (warmup_end_lr - args.warmup_init_lr) / args.warmup_updates + # linearly warmup for the first cfg.warmup_updates + self.lr_step = (warmup_end_lr - cfg.warmup_init_lr) / cfg.warmup_updates # then, decay prop. to the inverse square root of the update number - self.decay_factor = warmup_end_lr * args.warmup_updates**0.5 + self.decay_factor = warmup_end_lr * cfg.warmup_updates ** 0.5 # initial learning rate - self.lr = args.warmup_init_lr + self.lr = cfg.warmup_init_lr self.optimizer.set_lr(self.lr) - @staticmethod - def add_args(parser): - """Add arguments to the parser for this LR scheduler.""" - # fmt: off - parser.add_argument('--warmup-updates', default=4000, type=int, metavar='N', - help='warmup the learning rate linearly for the first N updates') - parser.add_argument('--warmup-init-lr', default=-1, type=float, metavar='LR', - help='initial learning rate during warmup phase; default is args.lr') - # fmt: on - def step(self, epoch, val_loss=None): """Update the learning rate at the end of the given epoch.""" super().step(epoch, val_loss) @@ -65,9 +77,9 @@ def step(self, epoch, val_loss=None): def step_update(self, num_updates): """Update the learning rate after each update.""" - if num_updates < self.args.warmup_updates: - self.lr = self.args.warmup_init_lr + num_updates*self.lr_step + if num_updates < self.cfg.warmup_updates: + self.lr = self.cfg.warmup_init_lr + num_updates * self.lr_step else: - self.lr = self.decay_factor * num_updates**-0.5 + self.lr = self.decay_factor * num_updates ** -0.5 self.optimizer.set_lr(self.lr) return self.lr diff --git a/fairseq/optim/lr_scheduler/manual_lr_scheduler.py b/fairseq/optim/lr_scheduler/manual_lr_scheduler.py new file mode 100644 index 0000000000..0269a1e285 --- /dev/null +++ b/fairseq/optim/lr_scheduler/manual_lr_scheduler.py @@ -0,0 +1,110 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from . import LegacyFairseqLRScheduler, register_lr_scheduler +import logging +import ast + +logger = logging.getLogger(__name__) +logger.setLevel(logging.WARNING) + + +@register_lr_scheduler("manual") +class ManualSchedule(LegacyFairseqLRScheduler): + """Decay the LR on a manual schedule.""" + + def __init__(self, args, optimizer): + super().__init__(args, optimizer) + + self.epoch2lr = self.parse_manuallr_args(args.epoch2lr) + self.update2lr = self.parse_manuallr_args(args.update2lr) + logger.info("@@@ ManualSchedule epoch2lr={}".format(self.epoch2lr)) + logger.info("@@@ ManualSchedule update2lr={}".format(self.update2lr)) + + if 1 in self.epoch2lr: + self.lr = self.epoch2lr[1] + elif 1 in self.update2lr: + self.lr = self.update2lr[1] + else: + self.lr = args.lr[0] + self.optimizer.set_lr(self.lr) # Set the beginning of the epoch. + + def parse_manuallr_args(self, lr_args_str): + lr_dict = ast.literal_eval(lr_args_str.replace(' ', '')) + if not isinstance(lr_dict, dict): + raise ValueError("epoch2lr/update2lr must be abel to evaluated to a dict") + + lr_args = {} + logger.info("@@@ after parsing input dictionary lr_dict = {}".format(lr_dict)) + for key, val in lr_dict.items(): + if "," in key: + for k in key.split(","): + lr_args[int(k)] = float(val) + elif "-" in key: + s = int(key.split("-")[0]) + e = int(key.split("-")[1]) + for k in range(s, e + 1, 1): + lr_args[k] = float(val) + else: + lr_args[int(key)] = float(val) + + return lr_args + + @staticmethod + def add_args(parser): + """Add arguments to the parser for this LR scheduler.""" + # fmt: off + parser.add_argument( + "--epoch2lr", + type=str, + metavar="DICT", + default="{}", + help="a dictionary used to set lr for each epoch manually", + ) + parser.add_argument( + "--update2lr", + type=str, + metavar="DICT", + default="{}", + help="a dictionary used to set lr for each update manually", + ) + # fmt: on + + def state_dict(self): + return {"lr": self.lr} + + def load_state_dict(self, state_dict): + if "lr" in state_dict: + self.lr = state_dict["lr"] + + def get_next_lr(self, epoch): + manual_keys = [k for k in self.epoch2lr if k <= epoch] + if manual_keys: + manual_lr = self.epoch2lr[max(manual_keys)] + else: + logger.warning("@@@ epoch={} does not exist in manual lr input. epoch2lr={}...".format( + epoch, list(self.epoch2lr.items())[:min(10, len(self.epoch2lr.keys())-1)] + )) + manual_lr = self.optimizer.get_lr() + return manual_lr + + def step_begin_epoch(self, epoch): + """Update the learning rate at the beginning of the given epoch.""" + self.lr = self.get_next_lr(epoch) + self.optimizer.set_lr(self.lr) + return self.optimizer.get_lr() + + def step_update(self, num_updates): + """Update the learning rate after each update.""" + manual_keys = [k for k in self.update2lr if k <= num_updates] + if manual_keys: + manual_lr = self.update2lr[max(manual_keys)] + else: + logger.warning("epoch={} does not exist in manual lr input update2lr={}...".format( + num_updates, list(self.update2lr.items())[:min(10, len(self.update2lr.keys())-1)])) + manual_lr = self.optimizer.get_lr() + + self.optimizer.set_lr(manual_lr) + return self.optimizer.get_lr() diff --git a/fairseq/optim/lr_scheduler/pass_through.py b/fairseq/optim/lr_scheduler/pass_through.py new file mode 100644 index 0000000000..2f93db328c --- /dev/null +++ b/fairseq/optim/lr_scheduler/pass_through.py @@ -0,0 +1,39 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from dataclasses import dataclass + +from fairseq.dataclass import FairseqDataclass +from fairseq.optim.lr_scheduler import FairseqLRScheduler, register_lr_scheduler + + +@dataclass +class PassThroughScheduleConfig(FairseqDataclass): + pass + + +@register_lr_scheduler("pass_through", dataclass=PassThroughScheduleConfig) +class PassThroughScheduleSchedule(FairseqLRScheduler): + """Delegate lr scheduling to the optimizer.""" + + def __init__(self, cfg: PassThroughScheduleConfig, optimizer): + super().__init__(cfg, optimizer) + assert ( + hasattr(optimizer, "lr_scheduler") and optimizer.lr_scheduler is not None + ), "Pass-through schedule can only be used with optimizers with their own schedulers" + + def state_dict(self): + return self.optimizer.lr_scheduler.state_dict() + + def load_state_dict(self, state_dict): + self.optimizer.lr_scheduler.load_state_dict(state_dict) + + def step_begin_epoch(self, epoch): + """Update the learning rate at the beginning of the given epoch.""" + return self.optimizer.lr_scheduler.step_begin_epoch(epoch) + + def step_update(self, num_updates): + """Update the learning rate after each update.""" + return self.optimizer.lr_scheduler.step_update(num_updates) diff --git a/fairseq/optim/lr_scheduler/polynomial_decay_schedule.py b/fairseq/optim/lr_scheduler/polynomial_decay_schedule.py index aff57f9b93..b8109a7c1e 100644 --- a/fairseq/optim/lr_scheduler/polynomial_decay_schedule.py +++ b/fairseq/optim/lr_scheduler/polynomial_decay_schedule.py @@ -3,43 +3,61 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. -from . import FairseqLRScheduler, register_lr_scheduler +from dataclasses import dataclass, field +from typing import Optional, List +from omegaconf import II +from fairseq.dataclass import FairseqDataclass +from fairseq.optim.lr_scheduler import FairseqLRScheduler, register_lr_scheduler -@register_lr_scheduler('polynomial_decay') -class PolynomialDecaySchedule(FairseqLRScheduler): + +@dataclass +class PolynomialDecayLRScheduleConfig(FairseqDataclass): + warmup_updates: int = field( + default=0, + metadata={"help": "warmup the learning rate linearly for the first N updates"}, + ) + force_anneal: Optional[int] = field( + default=None, + metadata={"help": "force annealing at specified epoch"}, + ) + end_learning_rate: float = field( + default=0.0, + metadata={"help": "learning rate to decay to"}, + ) + power: float = field( + default=1.0, + metadata={"help": "decay exponent"}, + ) + total_num_update: float = field( + default=II("optimization.max_update"), + metadata={"help": "total number of updates over which to decay learning rate"}, + ) + lr: List[float] = II("optimization.lr") + + +@register_lr_scheduler("polynomial_decay", dataclass=PolynomialDecayLRScheduleConfig) +class PolynomialDecayLRSchedule(FairseqLRScheduler): """Decay the LR on a fixed schedule.""" - def __init__(self, args, optimizer): - super().__init__(args, optimizer) + def __init__(self, cfg: PolynomialDecayLRScheduleConfig, optimizer): + super().__init__(cfg, optimizer) - # set defaults - args.warmup_updates = getattr(args, 'warmup_updates', 0) or 0 + assert cfg.total_num_update > 0 - self.lr = args.lr[0] - if args.warmup_updates > 0: - self.warmup_factor = 1. / args.warmup_updates + self.lr = cfg.lr[0] + if cfg.warmup_updates > 0: + self.warmup_factor = 1.0 / cfg.warmup_updates else: self.warmup_factor = 1 - self.end_learning_rate = args.end_learning_rate - self.total_num_update = args.total_num_update - self.power = args.power + self.end_learning_rate = cfg.end_learning_rate + self.total_num_update = cfg.total_num_update + self.power = cfg.power self.optimizer.set_lr(self.warmup_factor * self.lr) - @staticmethod - def add_args(parser): - """Add arguments to the parser for this LR scheduler.""" - parser.add_argument('--force-anneal', '--fa', type=int, metavar='N', - help='force annealing at specified epoch') - parser.add_argument('--warmup-updates', default=0, type=int, metavar='N', - help='warmup the learning rate linearly for the first N updates') - parser.add_argument('--end-learning-rate', default=0.0, type=float) - parser.add_argument('--power', default=1.0, type=float) - parser.add_argument('--total-num-update', default=1000000, type=int) - def get_next_lr(self, epoch): - lrs = self.args.lr - if self.args.force_anneal is None or epoch < self.args.force_anneal: + lrs = self.cfg.lr + if self.cfg.force_anneal is None or epoch < self.cfg.force_anneal: # use fixed LR schedule next_lr = lrs[min(epoch, len(lrs) - 1)] else: @@ -47,24 +65,25 @@ def get_next_lr(self, epoch): next_lr = self.optimizer.get_lr() return next_lr - def step(self, epoch, val_loss=None): - """Update the learning rate at the end of the given epoch.""" - super().step(epoch, val_loss) + def step_begin_epoch(self, epoch): + """Update the learning rate at the beginning of the given epoch.""" self.lr = self.get_next_lr(epoch) self.optimizer.set_lr(self.warmup_factor * self.lr) return self.optimizer.get_lr() def step_update(self, num_updates): """Update the learning rate after each update.""" - if self.args.warmup_updates > 0 and num_updates <= self.args.warmup_updates: - self.warmup_factor = num_updates / float(self.args.warmup_updates) + if self.cfg.warmup_updates > 0 and num_updates <= self.cfg.warmup_updates: + self.warmup_factor = num_updates / float(self.cfg.warmup_updates) lr = self.warmup_factor * self.lr elif num_updates >= self.total_num_update: lr = self.end_learning_rate else: - warmup = self.args.warmup_updates + warmup = self.cfg.warmup_updates lr_range = self.lr - self.end_learning_rate - pct_remaining = 1 - (num_updates - warmup) / (self.total_num_update - warmup) + pct_remaining = 1 - (num_updates - warmup) / ( + self.total_num_update - warmup + ) lr = lr_range * pct_remaining ** (self.power) + self.end_learning_rate self.optimizer.set_lr(lr) return self.optimizer.get_lr() diff --git a/fairseq/optim/lr_scheduler/reduce_lr_on_plateau.py b/fairseq/optim/lr_scheduler/reduce_lr_on_plateau.py index ef9ef26195..5ee9c1be4a 100644 --- a/fairseq/optim/lr_scheduler/reduce_lr_on_plateau.py +++ b/fairseq/optim/lr_scheduler/reduce_lr_on_plateau.py @@ -3,13 +3,59 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. +from dataclasses import dataclass, field +from typing import List + import torch.optim.lr_scheduler +from omegaconf import II + +from fairseq.dataclass import FairseqDataclass +from fairseq.optim.lr_scheduler import FairseqLRScheduler, register_lr_scheduler + -from . import FairseqLRScheduler, register_lr_scheduler +@dataclass +class ReduceLROnPlateauLRScheduleConfig(FairseqDataclass): + lr_shrink: float = field( + default=0.1, metadata={"help": "shrink factor for annealing"} + ) + lr_threshold: float = field( + default=1e-4, + metadata={ + "help": ( + "threshold for measuring the new optimum, to only focus on " + "significant changes" + ) + }, + ) + lr_patience: int = field( + default=0, + metadata={ + "help": ( + "number of epochs with no improvement after which learning rate will " + "be reduced" + ) + }, + ) + warmup_updates: int = field( + default=0, + metadata={"help": "warmup the learning rate linearly for the first N updates"}, + ) + warmup_init_lr: float = field( + default=-1, + metadata={ + "help": "initial learning rate during warmup phase; default is cfg.lr" + }, + ) + lr: List[float] = II("optimization.lr") + maximize_best_checkpoint_metric: bool = II( + "checkpoint.maximize_best_checkpoint_metric" + ) -@register_lr_scheduler('reduce_lr_on_plateau') -class ReduceLROnPlateau(FairseqLRScheduler): +@register_lr_scheduler( + "reduce_lr_on_plateau", dataclass=ReduceLROnPlateauLRScheduleConfig +) +class ReduceLROnPlateauLRSchedule(FairseqLRScheduler): """ Decay the LR by a factor every time the validation loss plateaus. Also comes with optional warmup phase, where we linearly increase @@ -21,64 +67,55 @@ class ReduceLROnPlateau(FairseqLRScheduler): During warmup:: lrs = torch.linspace( - args.warmup_init_lr, args.lr, args.warmup_updates + cfg.warmup_init_lr, cfg.lr, cfg.warmup_updates ) lr = lrs[update_num] """ - def __init__(self, args, optimizer): - super().__init__(args, optimizer) - if len(args.lr) > 1: + def __init__(self, cfg: ReduceLROnPlateauLRScheduleConfig, optimizer): + super().__init__(cfg, optimizer) + if len(cfg.lr) > 1: raise ValueError( - 'Cannot use a fixed learning rate schedule with reduce_lr_on_plateau.' - ' Consider --lr-scheduler=fixed instead.' + "Cannot use a fixed learning rate schedule with reduce_lr_on_plateau." + " Consider --lr-scheduler=fixed instead." ) self.lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( - self.optimizer.optimizer, patience=0, factor=args.lr_shrink, - threshold=args.lr_threshold) - warmup_end_lr = args.lr[0] - # if no warm up, sets initial lr to be args.lr[0] - if args.warmup_init_lr < 0: - args.warmup_init_lr = 0 if args.warmup_updates > 0 else warmup_end_lr - - # linearly warmup for the first args.warmup_updates - if args.warmup_updates > 0: - self.lr_step = (warmup_end_lr - args.warmup_init_lr) / args.warmup_updates + self.optimizer.optimizer, + patience=cfg.lr_patience, + factor=cfg.lr_shrink, + mode="max" if cfg.maximize_best_checkpoint_metric else "min", + threshold=cfg.lr_threshold, + ) + warmup_end_lr = cfg.lr[0] + # if no warm up, sets initial lr to be cfg.lr[0] + if cfg.warmup_init_lr < 0: + cfg.warmup_init_lr = 0 if cfg.warmup_updates > 0 else warmup_end_lr + + # linearly warmup for the first cfg.warmup_updates + if cfg.warmup_updates > 0: + self.lr_step = (warmup_end_lr - cfg.warmup_init_lr) / cfg.warmup_updates + # this flag is either set from arg when no warm up, or set by # step_update() when warmup finishes - self.warmup_end = True if args.warmup_updates <= 0 else False + self.warmup_end = True if cfg.warmup_updates <= 0 else False + # initial learning rate # this self.lr is used only during init and/or warm up period - self.lr = args.warmup_init_lr + self.lr = warmup_end_lr if self.warmup_end else cfg.warmup_init_lr self.optimizer.set_lr(self.lr) - @staticmethod - def add_args(parser): - """Add arguments to the parser for this LR scheduler.""" - # fmt: off - parser.add_argument('--lr-shrink', default=0.1, type=float, metavar='LS', - help='shrink factor for annealing, lr_new = (lr * lr_shrink)') - parser.add_argument('--lr-threshold', default=1e-4, type=float, metavar='LT', - help='Threshold for measuring the new optimum, \ - to only focus on significant changes') - parser.add_argument('--warmup-updates', default=0, type=int, metavar='N', - help='warmup the learning rate linearly for the first N updates') - parser.add_argument('--warmup-init-lr', default=-1, type=float, metavar='LR', - help='initial learning rate during warmup phase; default is args.lr') - # fmt: on - def state_dict(self): """Return the LR scheduler state dict.""" return { - 'best': self.lr_scheduler.best, - 'last_epoch': self.lr_scheduler.last_epoch, + "best": self.lr_scheduler.best, + "last_epoch": self.lr_scheduler.last_epoch, } def load_state_dict(self, state_dict): """Load an LR scheduler state dict.""" - self.lr_scheduler.best = state_dict['best'] - if 'last_epoch' in state_dict: - self.lr_scheduler.last_epoch = state_dict['last_epoch'] + self.lr_scheduler.best = state_dict["best"] + if "last_epoch" in state_dict: + self.lr_scheduler.last_epoch = state_dict["last_epoch"] def step(self, epoch, val_loss=None): """ @@ -95,9 +132,9 @@ def step_update(self, num_updates): """ Update the learning rate after each update.""" # if there is warmup - if self.args.warmup_updates > 0: - if num_updates <= self.args.warmup_updates: - self.lr = self.args.warmup_init_lr + num_updates*self.lr_step + if self.cfg.warmup_updates > 0: + if num_updates <= self.cfg.warmup_updates: + self.lr = self.cfg.warmup_init_lr + num_updates * self.lr_step self.optimizer.set_lr(self.lr) else: if self.warmup_end is False: diff --git a/fairseq/optim/lr_scheduler/step_lr_scheduler.py b/fairseq/optim/lr_scheduler/step_lr_scheduler.py new file mode 100644 index 0000000000..8cb2006860 --- /dev/null +++ b/fairseq/optim/lr_scheduler/step_lr_scheduler.py @@ -0,0 +1,86 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from collections.abc import Collection +from dataclasses import dataclass, field +from typing import List + +from omegaconf import II + +from fairseq.dataclass import FairseqDataclass +from fairseq.optim.lr_scheduler import FairseqLRScheduler, register_lr_scheduler + + +@dataclass +class StepLRScheduleConfig(FairseqDataclass): + warmup_updates: int = field( + default=0, + metadata={"help": "warmup the learning rate linearly for the first N updates"}, + ) + warmup_init_lr: float = field( + default=-1, + metadata={ + "help": "initial learning rate during warmup phase; default is cfg.lr" + }, + ) + lr: List[float] = field( + default=II("optimization.lr"), + metadata={"help": "max learning rate, must be more than cfg.min_lr"}, + ) + min_lr: float = field(default=0.0, metadata={"help": "min learning rate"}) + lr_deacy_period: int = field(default=25000, metadata={"help": "decay period"}) + lr_decay: float = field(default=0.5, metadata={"help": "decay factor"}) + + +@register_lr_scheduler("step", dataclass=StepLRScheduleConfig) +class StepLRSchedule(FairseqLRScheduler): + """Decay learning rate every k updates by a fixed factor + """ + + def __init__(self, cfg: StepLRScheduleConfig, fairseq_optimizer): + super().__init__(cfg, fairseq_optimizer) + self.max_lr = cfg.lr[0] if isinstance(cfg.lr, Collection) else cfg.lr + self.min_lr = cfg.min_lr + self.lr_deacy_period = cfg.lr_deacy_period + self.lr_decay = cfg.lr_decay + self.warmup_updates = cfg.warmup_updates + self.warmup_init_lr = ( + cfg.warmup_init_lr if cfg.warmup_init_lr >= 0 else self.min_lr + ) + + assert(self.lr_deacy_period > 0) + assert(self.lr_decay <= 1) + assert(self.min_lr >= 0) + assert(self.max_lr > self.min_lr) + + if cfg.warmup_updates > 0: + # linearly warmup for the first cfg.warmup_updates + self.warmup_lr_step = ( + (self.max_lr - self.warmup_init_lr) / self.warmup_updates + ) + else: + self.warmup_lr_step = 1 + + # initial learning rate + self.lr = self.warmup_init_lr + self.optimizer.set_lr(self.lr) + + def step(self, epoch, val_loss=None): + """Update the learning rate at the end of the given epoch.""" + super().step(epoch, val_loss) + # we don't change the learning rate at epoch boundaries + return self.optimizer.get_lr() + + def step_update(self, num_updates): + """Update the learning rate after each update.""" + if num_updates < self.cfg.warmup_updates: + self.lr = self.warmup_init_lr + num_updates * self.warmup_lr_step + else: + curr_updates = num_updates - self.cfg.warmup_updates + lr_mult = self.lr_decay ** (curr_updates // self.lr_deacy_period) + self.lr = max(self.max_lr * lr_mult, self.min_lr) + + self.optimizer.set_lr(self.lr) + return self.lr diff --git a/fairseq/optim/lr_scheduler/tri_stage_lr_scheduler.py b/fairseq/optim/lr_scheduler/tri_stage_lr_scheduler.py index b5f99c54c7..4d5547c39b 100644 --- a/fairseq/optim/lr_scheduler/tri_stage_lr_scheduler.py +++ b/fairseq/optim/lr_scheduler/tri_stage_lr_scheduler.py @@ -3,11 +3,51 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. -from . import FairseqLRScheduler, register_lr_scheduler import math - - -@register_lr_scheduler('tri_stage') +from dataclasses import dataclass, field +from typing import Optional, List, Tuple +from omegaconf import II + +from fairseq.dataclass import FairseqDataclass +from fairseq.optim.lr_scheduler import FairseqLRScheduler, register_lr_scheduler + + +@dataclass +class TriStageLRScheduleConfig(FairseqDataclass): + warmup_steps: int = field( + default=0, + metadata={"help": "warmup the learning rate linearly for the first N updates"}, + ) + hold_steps: int = field( + default=0, + metadata={"help": "steps in hold stage"}, + ) + decay_steps: int = field( + default=0, + metadata={"help": "steps in decay stages"}, + ) + phase_ratio: Optional[Tuple[float, float, float]] = field( + default=None, + metadata={ + "help": ( + "if set, automatically sets warmup/hold/decay steps to the ratio " + "specified here from max_updates. the ratios must add up to 1.0" + ) + }, + ) + init_lr_scale: float = field( + default=0.01, + metadata={"help": "initial learning rate scale during warmup phase"}, + ) + final_lr_scale: float = field( + default=0.01, + metadata={"help": "final learning rate scale"}, + ) + max_update: float = II("optimization.max_update") + lr: List[float] = II("optimization.lr") + + +@register_lr_scheduler("tri_stage", dataclass=TriStageLRScheduleConfig) class TriStageLRSchedule(FairseqLRScheduler): """Tristage learning rate schedulr @@ -28,88 +68,63 @@ class TriStageLRSchedule(FairseqLRScheduler): During warmup:: - init_lr = args.init_lr_scale * args.lr - lrs = torch.linspace(init_lr, args.lr, args.warmup_steps) + init_lr = cfg.init_lr_scale * cfg.lr + lrs = torch.linspace(init_lr, cfg.lr, cfg.warmup_steps) lr = lrs[update_num] During hold:: - lr = args.lr + lr = cfg.lr During decay:: - decay_factor = - math.log(args.final_lr_scale) / args.decay_steps - lr = args.lr * exp(- (update_num - warmup_steps - decay_steps) * decay_factor) + decay_factor = - math.log(cfg.final_lr_scale) / cfg.decay_steps + lr = cfg.lr * exp(- (update_num - warmup_steps - decay_steps) * decay_factor) After that:: - lr = args.lr * args.final_lr_scale + lr = cfg.lr * cfg.final_lr_scale """ - def __init__(self, args, optimizer): - super().__init__(args, optimizer) - if len(args.lr) > 1: + def __init__(self, cfg: TriStageLRScheduleConfig, optimizer): + super().__init__(cfg, optimizer) + if len(cfg.lr) > 1: raise ValueError( - 'Cannot use a fixed learning rate schedule with tri-stage lr.' - ' Consider --lr-scheduler=fixed instead.' + "Cannot use a fixed learning rate schedule with tri-stage lr." + " Consider --lr-scheduler=fixed instead." ) # calculate LR at each point - self.peak_lr = args.lr[0] - self.init_lr = args.init_lr_scale * args.lr[0] - self.final_lr = args.final_lr_scale * args.lr[0] - - # remember the steps at each stage - self.warmup_steps = args.warmup_steps - self.hold_steps = args.hold_steps - self.decay_steps = args.decay_steps - - self.warmup_rate = (self.peak_lr - self.init_lr) / self.warmup_steps - self.decay_factor = -math.log(args.final_lr_scale) / args.decay_steps + self.peak_lr = cfg.lr[0] + self.init_lr = cfg.init_lr_scale * cfg.lr[0] + self.final_lr = cfg.final_lr_scale * cfg.lr[0] + + if cfg.phase_ratio is not None: + assert cfg.max_update > 0 + assert sum(cfg.phase_ratio) == 1, "phase ratios must add up to 1" + self.warmup_steps = int(cfg.max_update * cfg.phase_ratio[0]) + self.hold_steps = int(cfg.max_update * cfg.phase_ratio[1]) + self.decay_steps = int(cfg.max_update * cfg.phase_ratio[2]) + else: + self.warmup_steps = cfg.warmup_steps + self.hold_steps = cfg.hold_steps + self.decay_steps = cfg.decay_steps + + assert ( + self.warmup_steps + self.hold_steps + self.decay_steps > 0 + ), "please specify steps or phase_ratio" + + self.warmup_rate = ( + (self.peak_lr - self.init_lr) / self.warmup_steps + if self.warmup_steps != 0 + else 0 + ) + self.decay_factor = -math.log(cfg.final_lr_scale) / self.decay_steps # initial learning rate self.lr = self.init_lr self.optimizer.set_lr(self.lr) - @staticmethod - def add_args(parser): - """Add arguments to the parser for this LR scheduler.""" - # fmt: off - parser.add_argument( - '--warmup-steps', - default=4000, - type=int, - metavar='N', - help='warmup the learning rate linearly for the first N updates' - ) - parser.add_argument( - '--hold-steps', - default=20000, - type=int, - metavar='N', - help='steps in hold stage.' - ) - parser.add_argument( - '--decay-steps', - default=60000, - type=int, - metavar='N', - help='steps in decay stages' - ) - parser.add_argument( - '--init-lr-scale', - default=0.01, - type=float, - help=""" - initial learning rate scale during warmup phase; default is 0.01""") - parser.add_argument( - '--final-lr-scale', - default=0.01, - type=float, - help="final learning rate scale; default to 0.01" - ) - # fmt: on - def _decide_stage(self, update_step): """ return stage, and the corresponding steps within the current stage diff --git a/fairseq/optim/lr_scheduler/triangular_lr_scheduler.py b/fairseq/optim/lr_scheduler/triangular_lr_scheduler.py index fed0cf7ef1..bfe2a0d381 100644 --- a/fairseq/optim/lr_scheduler/triangular_lr_scheduler.py +++ b/fairseq/optim/lr_scheduler/triangular_lr_scheduler.py @@ -4,52 +4,61 @@ # LICENSE file in the root directory of this source tree. import math +from dataclasses import dataclass, field +from typing import List -from . import FairseqLRScheduler, register_lr_scheduler +from omegaconf import II +from fairseq.dataclass import FairseqDataclass +from fairseq.optim.lr_scheduler import FairseqLRScheduler, register_lr_scheduler -@register_lr_scheduler('triangular') -class TriangularSchedule(FairseqLRScheduler): + +@dataclass +class TriangularLRScheduleConfig(FairseqDataclass): + max_lr: float = field( + default="???", metadata={"help": "max learning rate, must be more than cfg.lr"} + ) + lr_period_updates: float = field( + default=5000, + metadata={"help": "initial number of updates per period (cycle length)"}, + ) + lr_shrink: float = field( + default=0.1, metadata={"help": "shrink factor for annealing"} + ) + shrink_min: bool = field( + default=False, metadata={"help": "if set, also shrinks min lr"} + ) + lr: List[float] = II("optimization.lr") + + +@register_lr_scheduler("triangular", dataclass=TriangularLRScheduleConfig) +class TriangularLRSchedule(FairseqLRScheduler): """Assign LR based on a triangular cyclical schedule. See https://arxiv.org/pdf/1506.01186.pdf for details. """ - def __init__(self, args, optimizer): - super().__init__(args, optimizer) - if len(args.lr) > 1: + def __init__(self, cfg: TriangularLRScheduleConfig, optimizer): + super().__init__(cfg, optimizer) + if len(cfg.lr) > 1: raise ValueError( - 'Cannot use a fixed learning rate schedule with triangular.' - ' Consider --lr-scheduler=fixed instead.' + "Cannot use a fixed learning rate schedule with triangular." + " Consider --lr-scheduler=fixed instead." ) - lr = args.lr[0] + lr = cfg.lr[0] - assert args.max_lr > lr, 'max_lr must be more than lr' + assert cfg.max_lr > lr, "max_lr must be more than lr" self.min_lr = lr - self.max_lr = args.max_lr - self.stepsize = args.lr_period_updates // 2 - self.lr_shrink = args.lr_shrink - self.shrink_min = args.shrink_min + self.max_lr = cfg.max_lr + self.stepsize = cfg.lr_period_updates // 2 + self.lr_shrink = cfg.lr_shrink + self.shrink_min = cfg.shrink_min # initial learning rate self.lr = self.min_lr self.optimizer.set_lr(self.lr) - @staticmethod - def add_args(parser): - """Add arguments to the parser for this LR scheduler.""" - # fmt: off - parser.add_argument('--max-lr', required=True, type=float, metavar='LR', - help='max learning rate, must be more than args.lr') - parser.add_argument('--lr-period-updates', default=5000, type=float, metavar='LR', - help='initial number of updates per period (cycle length)') - parser.add_argument('--lr-shrink', default=0.1, type=float, metavar='LS', - help='shrink factor for annealing') - parser.add_argument('--shrink-min', action='store_true', - help='if set, also shrinks min lr') - # fmt: on - def step(self, epoch, val_loss=None): """Update the learning rate at the end of the given epoch.""" super().step(epoch, val_loss) diff --git a/fairseq/optim/nag.py b/fairseq/optim/nag.py index d9b7fb8019..c30a6c0fb1 100644 --- a/fairseq/optim/nag.py +++ b/fairseq/optim/nag.py @@ -3,28 +3,32 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. +from collections.abc import Collection +from dataclasses import dataclass, field +from typing import List + import torch +from fairseq.dataclass import FairseqDataclass +from omegaconf import II, DictConfig from torch.optim.optimizer import Optimizer, required from . import FairseqOptimizer, register_optimizer -@register_optimizer('nag') +@dataclass +class FairseqNAGConfig(FairseqDataclass): + momentum: float = field(default=0.99, metadata={"help": "momentum factor"}) + weight_decay: float = field(default=0.0, metadata={"help": "weight decay"}) + # TODO common vars in parent class + lr: List[float] = II("optimization.lr") + + +@register_optimizer("nag", dataclass=FairseqNAGConfig) class FairseqNAG(FairseqOptimizer): - def __init__(self, args, params): - super().__init__(args) + def __init__(self, cfg: DictConfig, params): + super().__init__(cfg) self._optimizer = NAG(params, **self.optimizer_config) - @staticmethod - def add_args(parser): - """Add optimizer-specific arguments to the parser.""" - # fmt: off - parser.add_argument('--momentum', default=0.99, type=float, metavar='M', - help='momentum factor') - parser.add_argument('--weight-decay', '--wd', default=0.0, type=float, metavar='WD', - help='weight decay') - # fmt: on - @property def optimizer_config(self): """ @@ -34,9 +38,11 @@ def optimizer_config(self): different learning rate. """ return { - 'lr': self.args.lr[0], - 'momentum': self.args.momentum, - 'weight_decay': self.args.weight_decay, + "lr": self.cfg.lr[0] + if isinstance(self.cfg.lr, Collection) + else self.cfg.lr, + "momentum": self.cfg.momentum, + "weight_decay": self.cfg.weight_decay, } @@ -56,7 +62,7 @@ def supports_flat_params(self): def step(self, closure=None): """Performs a single optimization step. - Arguments: + Args: closure (callable, optional): A closure that reevaluates the model and returns the loss. """ @@ -65,13 +71,13 @@ def step(self, closure=None): loss = closure() for group in self.param_groups: - weight_decay = group['weight_decay'] - momentum = group['momentum'] - lr = group['lr'] - lr_old = group.get('lr_old', lr) - lr_correct = lr / lr_old + weight_decay = group["weight_decay"] + momentum = group["momentum"] + lr = group["lr"] + lr_old = group.get("lr_old", lr) + lr_correct = lr / lr_old if lr_old > 0 else lr - for p in group['params']: + for p in group["params"]: if p.grad is None: continue @@ -81,12 +87,14 @@ def step(self, closure=None): d_p = p.grad.data.float() param_state = self.state[p] - if 'momentum_buffer' not in param_state: - param_state['momentum_buffer'] = torch.zeros_like(d_p) + if "momentum_buffer" not in param_state: + param_state["momentum_buffer"] = torch.zeros_like(d_p) else: - param_state['momentum_buffer'] = param_state['momentum_buffer'].to(d_p) + param_state["momentum_buffer"] = param_state["momentum_buffer"].to( + d_p + ) - buf = param_state['momentum_buffer'] + buf = param_state["momentum_buffer"] if weight_decay != 0: p_data_fp32.mul_(1 - lr * weight_decay) @@ -98,6 +106,6 @@ def step(self, closure=None): if p.data.dtype in {torch.float16, torch.bfloat16}: p.data.copy_(p_data_fp32) - group['lr_old'] = lr + group["lr_old"] = lr return loss diff --git a/fairseq/optim/sgd.py b/fairseq/optim/sgd.py index 8c4e3e0a80..8e34fb99a1 100644 --- a/fairseq/optim/sgd.py +++ b/fairseq/optim/sgd.py @@ -5,11 +5,11 @@ import torch.optim -from . import FairseqOptimizer, register_optimizer +from . import LegacyFairseqOptimizer, register_optimizer -@register_optimizer('sgd') -class SGD(FairseqOptimizer): +@register_optimizer("sgd") +class SGD(LegacyFairseqOptimizer): def __init__(self, args, params): super().__init__(args) self._optimizer = torch.optim.SGD(params, **self.optimizer_config) @@ -33,9 +33,9 @@ def optimizer_config(self): different learning rate. """ return { - 'lr': self.args.lr[0], - 'momentum': self.args.momentum, - 'weight_decay': self.args.weight_decay, + "lr": self.args.lr[0], + "momentum": self.args.momentum, + "weight_decay": self.args.weight_decay, } @property diff --git a/fairseq/optim/shard.py b/fairseq/optim/shard.py new file mode 100644 index 0000000000..9d7f2eb9e5 --- /dev/null +++ b/fairseq/optim/shard.py @@ -0,0 +1,58 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from typing import Any, Dict + +from fairseq.distributed import utils + + +try: + from fairscale.optim import OSS + + _has_fairscale = True +except ImportError: + _has_fairscale = False + + +def shard_(optimizer, group): + if not _has_fairscale: + raise ImportError( + "\n\nPlease install the fairscale package:" "\n\n pip install fairscale" + ) + + class FairseqOSS(OSS): + @property + def disable_mem_eff_fp16_loading_hack(self): + return True + + def __getattr__(self, name): + if name.startswith("supports") and hasattr(self.optim, name): + return getattr(self.optim, name) + raise AttributeError( + "'FairseqOSS' object has no attribute {0!r}".format(name) + ) + + def broadcast_global_state_dict( + self, state_dict: Dict[str, Any] + ) -> Dict[str, Any]: + """ + Broadcasts the entire state_dict to all other ranks + each rank is responsible to load their own partition of data + """ + return utils.broadcast_object( + state_dict, + src_rank=0, + group=self.group, + ) + + torch_optimizer = optimizer.optimizer + optim_cls = type(torch_optimizer) + + optimizer.optimizer = FairseqOSS( + torch_optimizer.param_groups, + optim_cls, + group=group, + **optimizer.optimizer_config + ) diff --git a/fairseq/options.py b/fairseq/options.py index ad7d85e54e..b4d350f902 100644 --- a/fairseq/options.py +++ b/fairseq/options.py @@ -4,13 +4,28 @@ # LICENSE file in the root directory of this source tree. import argparse -import sys -from typing import Callable, List, Optional +from pathlib import Path +from typing import Callable, List, Optional, Union import torch - from fairseq import utils from fairseq.data.indexed_dataset import get_available_dataset_impl +from fairseq.dataclass.configs import ( + CheckpointConfig, + CommonConfig, + CommonEvalConfig, + DatasetConfig, + DistributedTrainingConfig, + EvalLMConfig, + GenerationConfig, + InteractiveConfig, + OptimizationConfig, + EMAConfig, +) +from fairseq.dataclass.utils import gen_parser_from_dataclass + +# this import is for backward compatibility +from fairseq.utils import csv_str_list, eval_bool, eval_str_dict, eval_str_list # noqa def get_preprocessing_parser(default_task="translation"): @@ -26,18 +41,29 @@ def get_training_parser(default_task="translation"): add_model_args(parser) add_optimization_args(parser) add_checkpoint_args(parser) + add_ema_args(parser) return parser def get_generation_parser(interactive=False, default_task="translation"): parser = get_parser("Generation", default_task) add_dataset_args(parser, gen=True) + add_distributed_training_args(parser, default_world_size=1) add_generation_args(parser) + add_checkpoint_args(parser) if interactive: add_interactive_args(parser) return parser +def get_speech_generation_parser(default_task="text_to_speech"): + parser = get_parser("Speech Generation", default_task) + add_dataset_args(parser, gen=True) + add_distributed_training_args(parser, default_world_size=1) + add_speech_generation_args(parser) + return parser + + def get_interactive_generation_parser(default_task="translation"): return get_generation_parser(interactive=True, default_task=default_task) @@ -53,31 +79,12 @@ def get_eval_lm_parser(default_task="language_modeling"): def get_validation_parser(default_task=None): parser = get_parser("Validation", default_task) add_dataset_args(parser, train=True) + add_distributed_training_args(parser, default_world_size=1) group = parser.add_argument_group("Evaluation") - add_common_eval_args(group) + gen_parser_from_dataclass(group, CommonEvalConfig()) return parser -def eval_str_list(x, type=float): - if x is None: - return None - if isinstance(x, str): - x = eval(x) - try: - return list(map(type, x)) - except TypeError: - return [type(x)] - - -def eval_bool(x, default=False): - if x is None: - return default - try: - return bool(eval(x)) - except TypeError: - return default - - def parse_args_and_arch( parser: argparse.ArgumentParser, input_args: List[str] = None, @@ -112,7 +119,7 @@ def parse_args_and_arch( **{k: v for k, v in vars(args).items() if v is not None} ) - from fairseq.models import ARCH_MODEL_REGISTRY, ARCH_CONFIG_REGISTRY + from fairseq.models import ARCH_MODEL_REGISTRY, ARCH_CONFIG_REGISTRY, MODEL_REGISTRY # Before creating the true parser, we need to import optional user module # in order to eagerly import custom tasks, optimizers, architectures, etc. @@ -138,17 +145,13 @@ def parse_args_and_arch( # arguments or which have default values. argument_default=argparse.SUPPRESS, ) - ARCH_MODEL_REGISTRY[args.arch].add_args(model_specific_group) + if args.arch in ARCH_MODEL_REGISTRY: + ARCH_MODEL_REGISTRY[args.arch].add_args(model_specific_group) + elif args.arch in MODEL_REGISTRY: + MODEL_REGISTRY[args.arch].add_args(model_specific_group) + else: + raise RuntimeError() - # Add *-specific args to parser. - from fairseq.registry import REGISTRIES - - for registry_name, REGISTRY in REGISTRIES.items(): - choice = getattr(args, registry_name, None) - if choice is not None: - cls = REGISTRY["registry"][choice] - if hasattr(cls, "add_args"): - cls.add_args(parser) if hasattr(args, "task"): from fairseq.tasks import TASK_REGISTRY @@ -159,6 +162,18 @@ def parse_args_and_arch( FairseqBMUF.add_args(parser) + # Add *-specific args to parser. + from fairseq.registry import REGISTRIES + + for registry_name, REGISTRY in REGISTRIES.items(): + choice = getattr(args, registry_name, None) + if choice is not None: + cls = REGISTRY["registry"][choice] + if hasattr(cls, "add_args"): + cls.add_args(parser) + elif hasattr(cls, "__dataclass"): + gen_parser_from_dataclass(parser, cls.__dataclass()) + # Modify the parser a second time, since defaults may have been reset if modify_parser is not None: modify_parser(parser) @@ -169,10 +184,11 @@ def parse_args_and_arch( else: args = parser.parse_args(input_args) extra = None - # Post-process args. - if hasattr(args, "max_sentences_valid") and args.max_sentences_valid is None: - args.max_sentences_valid = args.max_sentences + if ( + hasattr(args, "batch_size_valid") and args.batch_size_valid is None + ) or not hasattr(args, "batch_size_valid"): + args.batch_size_valid = args.batch_size if hasattr(args, "max_tokens_valid") and args.max_tokens_valid is None: args.max_tokens_valid = args.max_tokens if getattr(args, "memory_efficient_fp16", False): @@ -186,8 +202,14 @@ def parse_args_and_arch( if args.tpu and args.fp16: raise ValueError("Cannot combine --fp16 and --tpu, use --bf16 on TPUs") + if getattr(args, "seed", None) is None: + args.seed = 1 # default seed for training + args.no_seed_provided = True + else: + args.no_seed_provided = False + # Apply architecture configuration. - if hasattr(args, "arch"): + if hasattr(args, "arch") and args.arch in ARCH_CONFIG_REGISTRY: ARCH_CONFIG_REGISTRY[args.arch](args) if parse_known: @@ -205,64 +227,27 @@ def get_parser(desc, default_task="translation"): utils.import_user_module(usr_args) parser = argparse.ArgumentParser(allow_abbrev=False) - # fmt: off - parser.add_argument('--no-progress-bar', action='store_true', help='disable progress bar') - parser.add_argument('--log-interval', type=int, default=100, metavar='N', - help='log progress every N batches (when progress bar is disabled)') - parser.add_argument('--log-format', default=None, help='log format to use', - choices=['json', 'none', 'simple', 'tqdm']) - parser.add_argument('--tensorboard-logdir', metavar='DIR', default='', - help='path to save logs for tensorboard, should match --logdir ' - 'of running tensorboard (default: no tensorboard logging)') - parser.add_argument('--seed', default=1, type=int, metavar='N', - help='pseudo random number generator seed') - parser.add_argument('--cpu', action='store_true', help='use CPU instead of CUDA') - parser.add_argument('--tpu', action='store_true', help='use TPU instead of CUDA') - parser.add_argument('--bf16', action='store_true', help='use bfloat16; implies --tpu') - parser.add_argument('--fp16', action='store_true', help='use FP16') - parser.add_argument('--memory-efficient-bf16', action='store_true', - help='use a memory-efficient version of BF16 training; implies --bf16') - parser.add_argument('--memory-efficient-fp16', action='store_true', - help='use a memory-efficient version of FP16 training; implies --fp16') - parser.add_argument('--fp16-no-flatten-grads', action='store_true', - help='don\'t flatten FP16 grads tensor') - parser.add_argument('--fp16-init-scale', default=2 ** 7, type=int, - help='default FP16 loss scale') - parser.add_argument('--fp16-scale-window', type=int, - help='number of updates before increasing loss scale') - parser.add_argument('--fp16-scale-tolerance', default=0.0, type=float, - help='pct of updates that can overflow before decreasing the loss scale') - parser.add_argument('--min-loss-scale', default=1e-4, type=float, metavar='D', - help='minimum FP16 loss scale, after which training is stopped') - parser.add_argument('--threshold-loss-scale', type=float, - help='threshold FP16 loss scale from below') - parser.add_argument('--user-dir', default=None, - help='path to a python module containing custom extensions (tasks and/or architectures)') - parser.add_argument('--empty-cache-freq', default=0, type=int, - help='how often to clear the PyTorch CUDA cache (0 to disable)') - parser.add_argument('--all-gather-list-size', default=16384, type=int, - help='number of bytes reserved for gathering stats from workers') - parser.add_argument('--model-parallel-size', type=int, metavar='N', - default=1, - help='total number of GPUs to parallelize model over') - parser.add_argument('--checkpoint-suffix', default='', - help='suffix to add to the checkpoint file name') - parser.add_argument('--quantization-config-path', default=None, - help='path to quantization config file') + gen_parser_from_dataclass(parser, CommonConfig()) from fairseq.registry import REGISTRIES + for registry_name, REGISTRY in REGISTRIES.items(): parser.add_argument( - '--' + registry_name.replace('_', '-'), - default=REGISTRY['default'], - choices=REGISTRY['registry'].keys(), + "--" + registry_name.replace("_", "-"), + default=REGISTRY["default"], + choices=REGISTRY["registry"].keys(), ) # Task definitions can be found under fairseq/tasks/ from fairseq.tasks import TASK_REGISTRY - parser.add_argument('--task', metavar='TASK', default=default_task, - choices=TASK_REGISTRY.keys(), - help='task') + + parser.add_argument( + "--task", + metavar="TASK", + default=default_task, + choices=TASK_REGISTRY.keys(), + help="task", + ) # fmt: on return parser @@ -275,11 +260,13 @@ def add_preprocess_args(parser): group.add_argument("-t", "--target-lang", default=None, metavar="TARGET", help="target language") group.add_argument("--trainpref", metavar="FP", default=None, - help="train file prefix") + help="train file prefix (also used to build dictionaries)") group.add_argument("--validpref", metavar="FP", default=None, - help="comma separated, valid file prefixes") + help="comma separated, valid file prefixes " + "(words missing from train set are replaced with <unk>)") group.add_argument("--testpref", metavar="FP", default=None, - help="comma separated, test file prefixes") + help="comma separated, test file prefixes " + "(words missing from train set are replaced with <unk>)") group.add_argument("--align-suffix", metavar="FP", default=None, help="alignment file suffix") group.add_argument("--destdir", metavar="DIR", default="data-bin", @@ -309,315 +296,75 @@ def add_preprocess_args(parser): help="Pad dictionary size to be multiple of N") group.add_argument("--workers", metavar="N", default=1, type=int, help="number of parallel workers") + group.add_argument("--dict-only", action='store_true', + help="if true, only builds a dictionary and then exits") # fmt: on return parser def add_dataset_args(parser, train=False, gen=False): - group = parser.add_argument_group("Dataset and data loading") - # fmt: off - group.add_argument('--num-workers', default=1, type=int, metavar='N', - help='how many subprocesses to use for data loading') - group.add_argument('--skip-invalid-size-inputs-valid-test', action='store_true', - help='ignore too long or too short lines in valid and test set') - group.add_argument('--max-tokens', type=int, metavar='N', - help='maximum number of tokens in a batch') - group.add_argument('--max-sentences', '--batch-size', type=int, metavar='N', - help='maximum number of sentences in a batch') - group.add_argument('--required-batch-size-multiple', default=8, type=int, metavar='N', - help='batch size will be a multiplier of this value') - parser.add_argument('--dataset-impl', metavar='FORMAT', - choices=get_available_dataset_impl(), - help='output dataset implementation') - group.add_argument('--data-buffer-size', default=0, type=int, metavar='N', - help='Number of batches to preload') - if train: - group.add_argument('--train-subset', default='train', metavar='SPLIT', - help='data subset to use for training (e.g. train, valid, test)') - group.add_argument('--valid-subset', default='valid', metavar='SPLIT', - help='comma separated list of data subsets to use for validation' - ' (e.g. train, valid, test)') - group.add_argument('--validate-interval', type=int, default=1, metavar='N', - help='validate every N epochs') - group.add_argument('--fixed-validation-seed', default=None, type=int, metavar='N', - help='specified random seed for validation') - group.add_argument('--disable-validation', action='store_true', - help='disable validation') - group.add_argument('--max-tokens-valid', type=int, metavar='N', - help='maximum number of tokens in a validation batch' - ' (defaults to --max-tokens)') - group.add_argument('--max-sentences-valid', type=int, metavar='N', - help='maximum number of sentences in a validation batch' - ' (defaults to --max-sentences)') - group.add_argument('--curriculum', default=0, type=int, metavar='N', - help='don\'t shuffle batches for first N epochs') - if gen: - group.add_argument('--gen-subset', default='test', metavar='SPLIT', - help='data subset to generate (train, valid, test)') - group.add_argument('--num-shards', default=1, type=int, metavar='N', - help='shard generation over N shards') - group.add_argument('--shard-id', default=0, type=int, metavar='ID', - help='id of the shard to generate (id < num_shards)') + group = parser.add_argument_group("dataset_data_loading") + gen_parser_from_dataclass(group, DatasetConfig()) # fmt: on return group def add_distributed_training_args(parser, default_world_size=None): - group = parser.add_argument_group("Distributed training") - # fmt: off + group = parser.add_argument_group("distributed_training") if default_world_size is None: default_world_size = max(1, torch.cuda.device_count()) - group.add_argument('--distributed-world-size', type=int, metavar='N', - default=default_world_size, - help='total number of GPUs across all nodes (default: all visible GPUs)') - group.add_argument('--distributed-rank', default=0, type=int, - help='rank of the current worker') - group.add_argument('--distributed-backend', default='nccl', type=str, - help='distributed backend') - group.add_argument('--distributed-init-method', default=None, type=str, - help='typically tcp://hostname:port that will be used to ' - 'establish initial connetion') - group.add_argument('--distributed-port', default=-1, type=int, - help='port number (not required if using --distributed-init-method)') - group.add_argument('--device-id', '--local_rank', default=0, type=int, - help='which GPU to use (usually configured automatically)') - group.add_argument('--distributed-no-spawn', action='store_true', - help='do not spawn multiple processes even if multiple GPUs are visible') - # "c10d" is PyTorch's DDP implementation and provides the fastest - # training. "no_c10d" is a more robust, but slightly slower DDP - # implementation. Try this if you get warning messages about - # inconsistent gradients between workers, or if some of your model - # parameters are not always used. - group.add_argument('--ddp-backend', default='c10d', type=str, - choices=['c10d', 'no_c10d'], - help='DistributedDataParallel backend') - group.add_argument('--bucket-cap-mb', default=25, type=int, metavar='MB', - help='bucket size for reduction') - group.add_argument('--fix-batches-to-gpus', action='store_true', - help='don\'t shuffle batches between GPUs; this reduces overall ' - 'randomness and may affect precision but avoids the cost of ' - 're-reading the data') - group.add_argument('--find-unused-parameters', default=False, action='store_true', - help='disable unused parameter detection (not applicable to ' - 'no_c10d ddp-backend') - group.add_argument('--fast-stat-sync', default=False, action='store_true', - help='[deprecated] this is now defined per Criterion') - group.add_argument('--broadcast-buffers', default=False, action='store_true', - help='Copy non-trainable parameters between GPUs, such as ' - 'batchnorm population statistics') - - group.add_argument('--distributed-wrapper', default='DDP', type=str, - choices=['DDP', 'SlowMo'], - help='DistributedDataParallel backend') - # Add arguments for SlowMo - these will be used when SlowMo is enabled via above - group.add_argument('--slowmo-momentum', default=None, type=float, - help='SlowMo momentum term; by default use 0.0 for 16 GPUs, ' - '0.2 for 32 GPUs; 0.5 for 64 GPUs, 0.6 for > 64 GPUs') - group.add_argument('--slowmo-algorithm', default='LocalSGD', choices=['LocalSGD', 'SGP'], - help='whether to use LocalSGD or SGP') - group.add_argument('--localsgd-frequency', default=3, type=int, - help='Local SGD allreduce frequency') - group.add_argument('--nprocs-per-node', type=int, metavar='N', - default=max(1, torch.cuda.device_count()), - help='number of GPUs in each node. An allreduce operation across GPUs in ' - 'a node is very fast. Hence, we do allreduce across GPUs in a node, ' - 'and gossip across different nodes') - # fmt: on + gen_parser_from_dataclass( + group, DistributedTrainingConfig(distributed_world_size=default_world_size) + ) return group def add_optimization_args(parser): - group = parser.add_argument_group("Optimization") + group = parser.add_argument_group("optimization") # fmt: off - group.add_argument('--max-epoch', '--me', default=0, type=int, metavar='N', - help='force stop training at specified epoch') - group.add_argument('--max-update', '--mu', default=0, type=int, metavar='N', - help='force stop training at specified update') - group.add_argument('--clip-norm', default=25, type=float, metavar='NORM', - help='clip threshold of gradients') - group.add_argument('--sentence-avg', action='store_true', - help='normalize gradients by the number of sentences in a batch' - ' (default is to normalize by number of tokens)') - group.add_argument('--update-freq', default='1', metavar='N1,N2,...,N_K', - type=lambda uf: eval_str_list(uf, type=int), - help='update parameters every N_i batches, when in epoch i') - group.add_argument('--lr', '--learning-rate', default='0.25', type=eval_str_list, - metavar='LR_1,LR_2,...,LR_N', - help='learning rate for the first N epochs; all epochs >N using LR_N' - ' (note: this may be interpreted differently depending on --lr-scheduler)') - group.add_argument('--min-lr', default=-1, type=float, metavar='LR', - help='stop training when the learning rate reaches this minimum') - group.add_argument('--use-bmuf', default=False, action='store_true', - help='specify global optimizer for syncing models on different GPUs/shards') + gen_parser_from_dataclass(group, OptimizationConfig()) # fmt: on return group def add_checkpoint_args(parser): - group = parser.add_argument_group("Checkpointing") + group = parser.add_argument_group("checkpoint") # fmt: off - group.add_argument('--save-dir', metavar='DIR', default='checkpoints', - help='path to save checkpoints') - group.add_argument('--restore-file', default='checkpoint_last.pt', - help='filename from which to load checkpoint ' - '(default: <save-dir>/checkpoint_last.pt') - group.add_argument('--reset-dataloader', action='store_true', - help='if set, does not reload dataloader state from the checkpoint') - group.add_argument('--reset-lr-scheduler', action='store_true', - help='if set, does not load lr scheduler state from the checkpoint') - group.add_argument('--reset-meters', action='store_true', - help='if set, does not load meters from the checkpoint') - group.add_argument('--reset-optimizer', action='store_true', - help='if set, does not load optimizer state from the checkpoint') - group.add_argument('--optimizer-overrides', default="{}", type=str, metavar='DICT', - help='a dictionary used to override optimizer args when loading a checkpoint') - group.add_argument('--save-interval', type=int, default=1, metavar='N', - help='save a checkpoint every N epochs') - group.add_argument('--save-interval-updates', type=int, default=0, metavar='N', - help='save a checkpoint (and validate) every N updates') - group.add_argument('--keep-interval-updates', type=int, default=-1, metavar='N', - help='keep the last N checkpoints saved with --save-interval-updates') - group.add_argument('--keep-last-epochs', type=int, default=-1, metavar='N', - help='keep last N epoch checkpoints') - group.add_argument('--keep-best-checkpoints', type=int, default=-1, metavar='N', - help='keep best N checkpoints based on scores') - group.add_argument('--no-save', action='store_true', - help='don\'t save models or checkpoints') - group.add_argument('--no-epoch-checkpoints', action='store_true', - help='only store last and best checkpoints') - group.add_argument('--no-last-checkpoints', action='store_true', - help='don\'t store last checkpoints') - group.add_argument('--no-save-optimizer-state', action='store_true', - help='don\'t save optimizer-state as part of checkpoint') - group.add_argument('--best-checkpoint-metric', type=str, default='loss', - help='metric to use for saving "best" checkpoints') - group.add_argument('--maximize-best-checkpoint-metric', action='store_true', - help='select the largest metric value for saving "best" checkpoints') - group.add_argument('--patience', type=int, default=-1, metavar='N', - help=('early stop training if valid performance doesn\'t ' - 'improve for N consecutive validation runs; note ' - 'that this is influenced by --validate-interval')) + gen_parser_from_dataclass(group, CheckpointConfig()) # fmt: on return group def add_common_eval_args(group): - # fmt: off - group.add_argument('--path', metavar='FILE', - help='path(s) to model file(s), colon separated') - group.add_argument('--remove-bpe', nargs='?', const='@@ ', default=None, - help='remove BPE tokens before scoring (can be set to sentencepiece)') - group.add_argument('--quiet', action='store_true', - help='only print final scores') - group.add_argument('--model-overrides', default="{}", type=str, metavar='DICT', - help='a dictionary used to override model args at generation ' - 'that were used during model training') - group.add_argument('--results-path', metavar='RESDIR', type=str, default=None, - help='path to save eval results (optional)"') - # fmt: on + gen_parser_from_dataclass(group, CommonEvalConfig()) def add_eval_lm_args(parser): group = parser.add_argument_group("LM Evaluation") add_common_eval_args(group) - # fmt: off - group.add_argument('--output-word-probs', action='store_true', - help='if set, outputs words and their predicted log probabilities to standard output') - group.add_argument('--output-word-stats', action='store_true', - help='if set, outputs word statistics such as word count, average probability, etc') - group.add_argument('--context-window', default=0, type=int, metavar='N', - help='ensures that every evaluated token has access to a context of at least this size,' - ' if possible') - group.add_argument('--softmax-batch', default=sys.maxsize, type=int, metavar='N', - help='if BxT is more than this, will batch the softmax over vocab to this amount of tokens' - ' in order to fit into GPU memory') - # fmt: on + gen_parser_from_dataclass(group, EvalLMConfig()) def add_generation_args(parser): group = parser.add_argument_group("Generation") add_common_eval_args(group) + gen_parser_from_dataclass(group, GenerationConfig()) + return group + + +def add_speech_generation_args(parser): + group = parser.add_argument_group("Speech Generation") + add_common_eval_args(group) # NOTE: remove_bpe is not needed # fmt: off - group.add_argument('--beam', default=5, type=int, metavar='N', - help='beam size') - group.add_argument('--nbest', default=1, type=int, metavar='N', - help='number of hypotheses to output') - group.add_argument('--max-len-a', default=0, type=float, metavar='N', - help=('generate sequences of maximum length ax + b, ' - 'where x is the source length')) - group.add_argument('--max-len-b', default=200, type=int, metavar='N', - help=('generate sequences of maximum length ax + b, ' - 'where x is the source length')) - group.add_argument('--min-len', default=1, type=float, metavar='N', - help=('minimum generation length')) - group.add_argument('--match-source-len', default=False, action='store_true', - help=('generations should match the source length')) - group.add_argument('--no-early-stop', action='store_true', - help='deprecated') - group.add_argument('--unnormalized', action='store_true', - help='compare unnormalized hypothesis scores') - group.add_argument('--no-beamable-mm', action='store_true', - help='don\'t use BeamableMM in attention layers') - group.add_argument('--lenpen', default=1, type=float, - help='length penalty: <1.0 favors shorter, >1.0 favors longer sentences') - group.add_argument('--unkpen', default=0, type=float, - help='unknown word penalty: <0 produces more unks, >0 produces fewer') - group.add_argument('--replace-unk', nargs='?', const=True, default=None, - help='perform unknown replacement (optionally with alignment dictionary)') - group.add_argument('--sacrebleu', action='store_true', - help='score with sacrebleu') - group.add_argument('--score-reference', action='store_true', - help='just score the reference translation') - group.add_argument('--prefix-size', default=0, type=int, metavar='PS', - help='initialize generation by target prefix of given length') - group.add_argument('--no-repeat-ngram-size', default=0, type=int, metavar='N', - help='ngram blocking such that this size ngram cannot be repeated in the generation') - group.add_argument('--sampling', action='store_true', - help='sample hypotheses instead of using beam search') - group.add_argument('--sampling-topk', default=-1, type=int, metavar='PS', - help='sample from top K likely next words instead of all words') - group.add_argument('--sampling-topp', default=-1.0, type=float, metavar='PS', - help='sample from the smallest set whose cumulative probability mass exceeds p for next words') - group.add_argument('--temperature', default=1., type=float, metavar='N', - help='temperature for generation') - group.add_argument('--diverse-beam-groups', default=-1, type=int, metavar='N', - help='number of groups for Diverse Beam Search') - group.add_argument('--diverse-beam-strength', default=0.5, type=float, metavar='N', - help='strength of diversity penalty for Diverse Beam Search') - group.add_argument('--diversity-rate', default=-1.0, type=float, metavar='N', - help='strength of diversity penalty for Diverse Siblings Search') - group.add_argument('--print-alignment', action='store_true', - help='if set, uses attention feedback to compute and print alignment to source tokens') - group.add_argument('--print-step', action='store_true') - - # arguments for iterative refinement generator - group.add_argument('--iter-decode-eos-penalty', default=0.0, type=float, metavar='N', - help='if > 0.0, it penalized early-stopping in decoding.') - group.add_argument('--iter-decode-max-iter', default=10, type=int, metavar='N', - help='maximum iterations for iterative refinement.') - group.add_argument('--iter-decode-force-max-iter', action='store_true', - help='if set, run exact the maximum number of iterations without early stop') - group.add_argument('--iter-decode-with-beam', default=1, type=int, metavar='N', - help='if > 1, model will generate translations varying by the lengths.') - group.add_argument('--iter-decode-with-external-reranker', action='store_true', - help='if set, the last checkpoint are assumed to be a reranker to rescore the translations'), - group.add_argument('--retain-iter-history', action='store_true', - help='if set, decoding returns the whole history of iterative refinement') - - # special decoding format for advanced decoding. - group.add_argument('--decoding-format', default=None, type=str, choices=['unigram', 'ensemble', 'vote', 'dp', 'bs']) + group.add_argument('--eos_prob_threshold', default=0.5, type=float, + help='terminate when eos probability exceeds this') # fmt: on return group def add_interactive_args(parser): group = parser.add_argument_group("Interactive") - # fmt: off - group.add_argument('--buffer-size', default=0, type=int, metavar='N', - help='read this many sentences into a buffer before processing them') - group.add_argument('--input', default='-', type=str, metavar='FILE', - help='file to read from; use - for stdin') - # fmt: on + gen_parser_from_dataclass(group, InteractiveConfig()) def add_model_args(parser): @@ -632,8 +379,28 @@ def add_model_args(parser): # 2) --arch argument # 3) --encoder/decoder-* arguments (highest priority) from fairseq.models import ARCH_MODEL_REGISTRY - group.add_argument('--arch', '-a', default='fconv', metavar='ARCH', + group.add_argument('--arch', '-a', metavar='ARCH', choices=ARCH_MODEL_REGISTRY.keys(), - help='Model Architecture') + help='model architecture') # fmt: on return group + + +def get_args( + data: Union[str, Path], + task: str = "translation", + arch: str = "transformer", + **overrides +): + parser = get_training_parser(task) + args = parse_args_and_arch(parser, [str(data), "--task", task, "--arch", arch]) + + for k, v in overrides.items(): + setattr(args, k, v) + + return args + + +def add_ema_args(parser): + group = parser.add_argument_group("EMA configuration") + gen_parser_from_dataclass(group, EMAConfig()) diff --git a/fairseq/pdb.py b/fairseq/pdb.py index f1ce3c46bc..1ba6ef0d33 100644 --- a/fairseq/pdb.py +++ b/fairseq/pdb.py @@ -9,7 +9,7 @@ import sys -__all__ = ['set_trace'] +__all__ = ["set_trace"] _stdin = [None] diff --git a/fairseq/quantization_utils.py b/fairseq/quantization_utils.py index a7f5ade9b3..11fc414c85 100644 --- a/fairseq/quantization_utils.py +++ b/fairseq/quantization_utils.py @@ -6,13 +6,14 @@ import logging from fairseq.modules.quantization import pq, quantization_options, scalar +from omegaconf import DictConfig logger = logging.getLogger(__name__) -def quantize_model_scalar(model, args): - quant_noise_scalar = getattr(args, 'quant_noise_scalar', 0) +def quantize_model_scalar(model, model_cfg: DictConfig): + quant_noise_scalar = getattr(model_cfg, "quant_noise_scalar", 0) or 0 if quant_noise_scalar > 0: # quantize_model edits the model in place scalar.quantize_model_(model, p=quant_noise_scalar, bits=8, update_step=1000) @@ -20,12 +21,11 @@ def quantize_model_scalar(model, args): class Quantizer(object): - def __init__(self, config_path, max_epoch, max_update): try: import yaml except ImportError: - raise ImportError('Please install yaml with: pip install yaml') + raise ImportError("Please install yaml with: pip install yaml") # parse config if config_path: @@ -46,22 +46,23 @@ def __init__(self, config_path, max_epoch, max_update): num_iterations = len(self.layers_to_quantize) if max_epoch > 0: assert max_epoch % num_iterations == 0, ( - 'for iterative PQ, --max-epoch (={}) must be evenly divisible by ' - 'len(layers_to_quantize) (={})'.format(max_epoch, num_iterations) + "for iterative PQ, --max-epoch (={}) must be evenly divisible by " + "len(layers_to_quantize) (={})".format(max_epoch, num_iterations) ) self.epoch_schedule = max_epoch // num_iterations else: self.epoch_schedule = None if max_update > 0: assert max_update % num_iterations == 0, ( - 'for iterative PQ, --max-update (={}) must be evenly divisible by ' - 'len(layers_to_quantize) (={})'.format(max_update, num_iterations) + "for iterative PQ, --max-update (={}) must be evenly divisible by " + "len(layers_to_quantize) (={})".format(max_update, num_iterations) ) self.update_schedule = max_update // num_iterations else: self.update_schedule = None - assert (self.epoch_schedule is not None) ^ (self.update_schedule is not None), \ - 'for iterative PQ, cannot specify both --max-update and --max-epoch' + assert (self.epoch_schedule is not None) ^ ( + self.update_schedule is not None + ), "for iterative PQ, cannot specify both --max-update and --max-epoch" # 0 is a special value for quantization step, which will force # the first call to begin_epoch() to call step() @@ -80,7 +81,7 @@ def step(self): return logger.info( - 'quantizing model (step={}; layers_to_quantize[step]={})'.format( + "quantizing model (step={}; layers_to_quantize[step]={})".format( self.quantization_step, self.layers_to_quantize[self.quantization_step] ) ) @@ -92,7 +93,7 @@ def step(self): self.n_centroids_config, step=self.quantization_step, ) - logger.info('quantized layers: {}'.format(quantized_layers)) + logger.info("quantized layers: {}".format(quantized_layers)) logger.info(self.size_tracker) self.quantization_step += 1 @@ -125,18 +126,18 @@ def step_update(self, num_updates): def state_dict(self): return { - 'n_centroids_config': self.n_centroids_config, - 'block_sizes_config': self.block_sizes_config, - 'layers_to_quantize': self.layers_to_quantize, - 'epoch_schedule': self.epoch_schedule, - 'update_schedule': self.update_schedule, - 'quantization_step': self.quantization_step, + "n_centroids_config": self.n_centroids_config, + "block_sizes_config": self.block_sizes_config, + "layers_to_quantize": self.layers_to_quantize, + "epoch_schedule": self.epoch_schedule, + "update_schedule": self.update_schedule, + "quantization_step": self.quantization_step, } def load_state_dict(self, state_dict): - self.n_centroids_config = state_dict['n_centroids_config'] - self.block_sizes_config = state_dict['block_sizes_config'] - self.layers_to_quantize = state_dict['layers_to_quantize'] - self.epoch_schedule = state_dict['epoch_schedule'] - self.update_schedule = state_dict['update_schedule'] - self.quantization_step = state_dict['quantization_step'] + self.n_centroids_config = state_dict["n_centroids_config"] + self.block_sizes_config = state_dict["block_sizes_config"] + self.layers_to_quantize = state_dict["layers_to_quantize"] + self.epoch_schedule = state_dict["epoch_schedule"] + self.update_schedule = state_dict["update_schedule"] + self.quantization_step = state_dict["quantization_step"] diff --git a/fairseq/registry.py b/fairseq/registry.py index ed24258c57..f3b9406043 100644 --- a/fairseq/registry.py +++ b/fairseq/registry.py @@ -3,78 +3,98 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. -import argparse +from argparse import Namespace +from typing import Union +from fairseq.dataclass import FairseqDataclass +from fairseq.dataclass.utils import merge_with_parent +from hydra.core.config_store import ConfigStore +from omegaconf import DictConfig REGISTRIES = {} -def setup_registry( - registry_name: str, - base_class=None, - default=None, -): - assert registry_name.startswith('--') - registry_name = registry_name[2:].replace('-', '_') +def setup_registry(registry_name: str, base_class=None, default=None, required=False): + assert registry_name.startswith("--") + registry_name = registry_name[2:].replace("-", "_") REGISTRY = {} REGISTRY_CLASS_NAMES = set() + DATACLASS_REGISTRY = {} # maintain a registry of all registries if registry_name in REGISTRIES: return # registry already exists REGISTRIES[registry_name] = { - 'registry': REGISTRY, - 'default': default, + "registry": REGISTRY, + "default": default, + "dataclass_registry": DATACLASS_REGISTRY, } - def build_x(args, *extra_args, **extra_kwargs): - choice = getattr(args, registry_name, None) + def build_x(cfg: Union[DictConfig, str, Namespace], *extra_args, **extra_kwargs): + if isinstance(cfg, DictConfig): + choice = cfg._name + + if choice and choice in DATACLASS_REGISTRY: + dc = DATACLASS_REGISTRY[choice] + cfg = merge_with_parent(dc(), cfg) + elif isinstance(cfg, str): + choice = cfg + if choice in DATACLASS_REGISTRY: + cfg = DATACLASS_REGISTRY[choice]() + else: + choice = getattr(cfg, registry_name, None) + if choice in DATACLASS_REGISTRY: + cfg = DATACLASS_REGISTRY[choice].from_namespace(cfg) + if choice is None: + if required: + raise ValueError("{} is required!".format(registry_name)) return None + cls = REGISTRY[choice] - if hasattr(cls, 'build_' + registry_name): - builder = getattr(cls, 'build_' + registry_name) + if hasattr(cls, "build_" + registry_name): + builder = getattr(cls, "build_" + registry_name) else: builder = cls - set_defaults(args, cls) - return builder(args, *extra_args, **extra_kwargs) - def register_x(name): + return builder(cfg, *extra_args, **extra_kwargs) + def register_x(name, dataclass=None): def register_x_cls(cls): if name in REGISTRY: - raise ValueError('Cannot register duplicate {} ({})'.format(registry_name, name)) + raise ValueError( + "Cannot register duplicate {} ({})".format(registry_name, name) + ) if cls.__name__ in REGISTRY_CLASS_NAMES: raise ValueError( - 'Cannot register {} with duplicate class name ({})'.format( - registry_name, cls.__name__, + "Cannot register {} with duplicate class name ({})".format( + registry_name, cls.__name__ ) ) if base_class is not None and not issubclass(cls, base_class): - raise ValueError('{} must extend {}'.format(cls.__name__, base_class.__name__)) + raise ValueError( + "{} must extend {}".format(cls.__name__, base_class.__name__) + ) + + if dataclass is not None and not issubclass(dataclass, FairseqDataclass): + raise ValueError( + "Dataclass {} must extend FairseqDataclass".format(dataclass) + ) + + cls.__dataclass = dataclass + if cls.__dataclass is not None: + DATACLASS_REGISTRY[name] = cls.__dataclass + + cs = ConfigStore.instance() + node = dataclass() + node._name = name + cs.store(name=name, group=registry_name, node=node, provider="fairseq") + REGISTRY[name] = cls - REGISTRY_CLASS_NAMES.add(cls.__name__) + return cls return register_x_cls - return build_x, register_x, REGISTRY - - -def set_defaults(args, cls): - """Helper to set default arguments based on *add_args*.""" - if not hasattr(cls, 'add_args'): - return - parser = argparse.ArgumentParser(argument_default=argparse.SUPPRESS, allow_abbrev=False) - cls.add_args(parser) - # copied from argparse.py: - defaults = argparse.Namespace() - for action in parser._actions: - if action.dest is not argparse.SUPPRESS: - if not hasattr(defaults, action.dest): - if action.default is not argparse.SUPPRESS: - setattr(defaults, action.dest, action.default) - for key, default_value in vars(defaults).items(): - if not hasattr(args, key): - setattr(args, key, default_value) + return build_x, register_x, REGISTRY, DATACLASS_REGISTRY diff --git a/fairseq/scoring/__init__.py b/fairseq/scoring/__init__.py new file mode 100644 index 0000000000..58f2f563e4 --- /dev/null +++ b/fairseq/scoring/__init__.py @@ -0,0 +1,55 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +import importlib +import os +from abc import ABC, abstractmethod + +from fairseq import registry +from omegaconf import DictConfig + + +class BaseScorer(ABC): + def __init__(self, cfg): + self.cfg = cfg + self.ref = [] + self.pred = [] + + def add_string(self, ref, pred): + self.ref.append(ref) + self.pred.append(pred) + + @abstractmethod + def score(self) -> float: + pass + + @abstractmethod + def result_string(self) -> str: + pass + + +_build_scorer, register_scorer, SCORER_REGISTRY, _ = registry.setup_registry( + "--scoring", default="bleu" +) + + +def build_scorer(choice, tgt_dict): + _choice = choice._name if isinstance(choice, DictConfig) else choice + + if _choice == "bleu": + from fairseq.scoring import bleu + + return bleu.Scorer( + bleu.BleuConfig(pad=tgt_dict.pad(), eos=tgt_dict.eos(), unk=tgt_dict.unk()) + ) + return _build_scorer(choice) + + +# automatically import any Python files in the current directory +for file in sorted(os.listdir(os.path.dirname(__file__))): + if file.endswith(".py") and not file.startswith("_"): + module = file[: file.find(".py")] + importlib.import_module("fairseq.scoring." + module) diff --git a/fairseq/scoring/bleu.py b/fairseq/scoring/bleu.py new file mode 100644 index 0000000000..97de5f966e --- /dev/null +++ b/fairseq/scoring/bleu.py @@ -0,0 +1,167 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import ctypes +import math +import sys +from dataclasses import dataclass, field + +import torch +from fairseq.dataclass import FairseqDataclass +from fairseq.scoring import BaseScorer, register_scorer +from fairseq.scoring.tokenizer import EvaluationTokenizer + + +class BleuStat(ctypes.Structure): + _fields_ = [ + ("reflen", ctypes.c_size_t), + ("predlen", ctypes.c_size_t), + ("match1", ctypes.c_size_t), + ("count1", ctypes.c_size_t), + ("match2", ctypes.c_size_t), + ("count2", ctypes.c_size_t), + ("match3", ctypes.c_size_t), + ("count3", ctypes.c_size_t), + ("match4", ctypes.c_size_t), + ("count4", ctypes.c_size_t), + ] + + +@dataclass +class SacrebleuConfig(FairseqDataclass): + sacrebleu_tokenizer: EvaluationTokenizer.ALL_TOKENIZER_TYPES = field( + default="13a", metadata={"help": "tokenizer"} + ) + sacrebleu_lowercase: bool = field( + default=False, metadata={"help": "apply lowercasing"} + ) + sacrebleu_char_level: bool = field( + default=False, metadata={"help": "evaluate at character level"} + ) + + +@register_scorer("sacrebleu", dataclass=SacrebleuConfig) +class SacrebleuScorer(BaseScorer): + def __init__(self, cfg): + super(SacrebleuScorer, self).__init__(cfg) + import sacrebleu + + self.sacrebleu = sacrebleu + self.tokenizer = EvaluationTokenizer( + tokenizer_type=cfg.sacrebleu_tokenizer, + lowercase=cfg.sacrebleu_lowercase, + character_tokenization=cfg.sacrebleu_char_level, + ) + + def add_string(self, ref, pred): + self.ref.append(self.tokenizer.tokenize(ref)) + self.pred.append(self.tokenizer.tokenize(pred)) + + def score(self, order=4): + return self.result_string(order).score + + def result_string(self, order=4): + if order != 4: + raise NotImplementedError + # tokenization and lowercasing are performed by self.tokenizer instead. + return self.sacrebleu.corpus_bleu( + self.pred, [self.ref], tokenize="none" + ).format() + + +@dataclass +class BleuConfig(FairseqDataclass): + pad: int = field(default=1, metadata={"help": "padding index"}) + eos: int = field(default=2, metadata={"help": "eos index"}) + unk: int = field(default=3, metadata={"help": "unk index"}) + + +@register_scorer("bleu", dataclass=BleuConfig) +class Scorer(object): + def __init__(self, cfg): + self.stat = BleuStat() + self.pad = cfg.pad + self.eos = cfg.eos + self.unk = cfg.unk + + try: + from fairseq import libbleu + except ImportError as e: + sys.stderr.write( + "ERROR: missing libbleu.so. run `pip install --editable .`\n" + ) + raise e + + self.C = ctypes.cdll.LoadLibrary(libbleu.__file__) + + self.reset() + + def reset(self, one_init=False): + if one_init: + self.C.bleu_one_init(ctypes.byref(self.stat)) + else: + self.C.bleu_zero_init(ctypes.byref(self.stat)) + + def add(self, ref, pred): + if not isinstance(ref, torch.IntTensor): + raise TypeError("ref must be a torch.IntTensor (got {})".format(type(ref))) + if not isinstance(pred, torch.IntTensor): + raise TypeError("pred must be a torch.IntTensor(got {})".format(type(pred))) + + # don't match unknown words + rref = ref.clone() + assert not rref.lt(0).any() + rref[rref.eq(self.unk)] = -999 + + rref = rref.contiguous().view(-1) + pred = pred.contiguous().view(-1) + + self.C.bleu_add( + ctypes.byref(self.stat), + ctypes.c_size_t(rref.size(0)), + ctypes.c_void_p(rref.data_ptr()), + ctypes.c_size_t(pred.size(0)), + ctypes.c_void_p(pred.data_ptr()), + ctypes.c_int(self.pad), + ctypes.c_int(self.eos), + ) + + def score(self, order=4): + psum = sum( + math.log(p) if p > 0 else float("-Inf") for p in self.precision()[:order] + ) + return self.brevity() * math.exp(psum / order) * 100 + + def precision(self): + def ratio(a, b): + return a / b if b > 0 else 0 + + return [ + ratio(self.stat.match1, self.stat.count1), + ratio(self.stat.match2, self.stat.count2), + ratio(self.stat.match3, self.stat.count3), + ratio(self.stat.match4, self.stat.count4), + ] + + def brevity(self): + r = self.stat.reflen / self.stat.predlen + return min(1, math.exp(1 - r)) + + def result_string(self, order=4): + assert order <= 4, "BLEU scores for order > 4 aren't supported" + fmt = "BLEU{} = {:2.2f}, {:2.1f}" + for _ in range(1, order): + fmt += "/{:2.1f}" + fmt += " (BP={:.3f}, ratio={:.3f}, syslen={}, reflen={})" + bleup = [p * 100 for p in self.precision()[:order]] + return fmt.format( + order, + self.score(order=order), + *bleup, + self.brevity(), + self.stat.predlen / self.stat.reflen, + self.stat.predlen, + self.stat.reflen + ) diff --git a/fairseq/scoring/chrf.py b/fairseq/scoring/chrf.py new file mode 100644 index 0000000000..0d6cb77383 --- /dev/null +++ b/fairseq/scoring/chrf.py @@ -0,0 +1,27 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from fairseq.scoring import BaseScorer, register_scorer + + +@register_scorer("chrf") +class ChrFScorer(BaseScorer): + def __init__(self, args): + super(ChrFScorer, self).__init__(args) + import sacrebleu + + self.sacrebleu = sacrebleu + + def add_string(self, ref, pred): + self.ref.append(ref) + self.pred.append(pred) + + def score(self, order=4): + return self.result_string(order).score + + def result_string(self, order=4): + if order != 4: + raise NotImplementedError + return self.sacrebleu.corpus_chrf(self.pred, [self.ref]).format() diff --git a/fairseq/scoring/tokenizer.py b/fairseq/scoring/tokenizer.py new file mode 100644 index 0000000000..61cf6d4a7c --- /dev/null +++ b/fairseq/scoring/tokenizer.py @@ -0,0 +1,67 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import unicodedata + +from fairseq.dataclass import ChoiceEnum + + +class EvaluationTokenizer(object): + """A generic evaluation-time tokenizer, which leverages built-in tokenizers + in sacreBLEU (https://github.com/mjpost/sacrebleu). It additionally provides + lowercasing, punctuation removal and character tokenization, which are + applied after sacreBLEU tokenization. + + Args: + tokenizer_type (str): the type of sacreBLEU tokenizer to apply. + lowercase (bool): lowercase the text. + punctuation_removal (bool): remove punctuation (based on unicode + category) from text. + character_tokenization (bool): tokenize the text to characters. + """ + + SPACE = chr(32) + SPACE_ESCAPE = chr(9601) + ALL_TOKENIZER_TYPES = ChoiceEnum(["none", "13a", "intl", "zh", "ja-mecab"]) + + def __init__( + self, + tokenizer_type: str = "13a", + lowercase: bool = False, + punctuation_removal: bool = False, + character_tokenization: bool = False, + ): + from sacrebleu.tokenizers import TOKENIZERS + + assert tokenizer_type in TOKENIZERS, f"{tokenizer_type}, {TOKENIZERS}" + self.lowercase = lowercase + self.punctuation_removal = punctuation_removal + self.character_tokenization = character_tokenization + self.tokenizer = TOKENIZERS[tokenizer_type] + + @classmethod + def remove_punctuation(cls, sent: str): + """Remove punctuation based on Unicode category.""" + return cls.SPACE.join( + t + for t in sent.split(cls.SPACE) + if not all(unicodedata.category(c)[0] == "P" for c in t) + ) + + def tokenize(self, sent: str): + tokenized = self.tokenizer()(sent) + + if self.punctuation_removal: + tokenized = self.remove_punctuation(tokenized) + + if self.character_tokenization: + tokenized = self.SPACE.join( + list(tokenized.replace(self.SPACE, self.SPACE_ESCAPE)) + ) + + if self.lowercase: + tokenized = tokenized.lower() + + return tokenized diff --git a/fairseq/scoring/wer.py b/fairseq/scoring/wer.py new file mode 100644 index 0000000000..633dc47c24 --- /dev/null +++ b/fairseq/scoring/wer.py @@ -0,0 +1,58 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from dataclasses import dataclass, field + +from fairseq.dataclass import FairseqDataclass +from fairseq.scoring import BaseScorer, register_scorer +from fairseq.scoring.tokenizer import EvaluationTokenizer + + +@dataclass +class WerScorerConfig(FairseqDataclass): + wer_tokenizer: EvaluationTokenizer.ALL_TOKENIZER_TYPES = field( + default="none", metadata={"help": "sacreBLEU tokenizer to use for evaluation"} + ) + wer_remove_punct: bool = field( + default=False, metadata={"help": "remove punctuation"} + ) + wer_char_level: bool = field( + default=False, metadata={"help": "evaluate at character level"} + ) + wer_lowercase: bool = field(default=False, metadata={"help": "lowercasing"}) + + +@register_scorer("wer", dataclass=WerScorerConfig) +class WerScorer(BaseScorer): + def __init__(self, cfg): + super().__init__(cfg) + self.reset() + try: + import editdistance as ed + except ImportError: + raise ImportError("Please install editdistance to use WER scorer") + self.ed = ed + self.tokenizer = EvaluationTokenizer( + tokenizer_type=self.cfg.wer_tokenizer, + lowercase=self.cfg.wer_lowercase, + punctuation_removal=self.cfg.wer_remove_punct, + character_tokenization=self.cfg.wer_char_level, + ) + + def reset(self): + self.distance = 0 + self.ref_length = 0 + + def add_string(self, ref, pred): + ref_items = self.tokenizer.tokenize(ref).split() + pred_items = self.tokenizer.tokenize(pred).split() + self.distance += self.ed.eval(ref_items, pred_items) + self.ref_length += len(ref_items) + + def result_string(self): + return f"WER: {self.score():.2f}" + + def score(self): + return 100.0 * self.distance / self.ref_length if self.ref_length > 0 else 0 diff --git a/fairseq/search.py b/fairseq/search.py index 667a151ab0..d5ea68b4ce 100644 --- a/fairseq/search.py +++ b/fairseq/search.py @@ -4,10 +4,15 @@ # LICENSE file in the root directory of this source tree. import math -from typing import Optional, List +from typing import List, Optional import torch import torch.nn as nn +from fairseq.token_generation_constraints import ( + ConstraintState, + OrderedConstraintState, + UnorderedConstraintState, +) from torch import Tensor @@ -19,8 +24,12 @@ def __init__(self, tgt_dict): self.eos = tgt_dict.eos() self.vocab_size = len(tgt_dict) self.src_lengths = torch.tensor(-1) + self.supports_constraints = False + self.stop_on_max_len = False - def step(self, step, lprobs, scores): + def step( + self, step, lprobs, scores, prev_output_tokens=None, original_batch_idxs=None + ): """Take a single search step. Args: @@ -29,6 +38,12 @@ def step(self, step, lprobs, scores): the model's log-probabilities over the vocabulary at the current step scores: (bsz x input_beam_size x step) the historical model scores of each hypothesis up to this point + prev_output_tokens: (bsz x step) + the previously generated oputput tokens + original_batch_idxs: (bsz) + the tensor with the batch indices, in the range [0, bsz) + this is useful in case there has been applied a re-ordering + and we need to know the orignal indices Return: A tuple of (scores, indices, beams) where: scores: (bsz x output_beam_size) @@ -46,13 +61,59 @@ def step(self, step, lprobs, scores): def set_src_lengths(self, src_lengths): self.src_lengths = src_lengths + @torch.jit.export + def init_constraints(self, batch_constraints: Optional[Tensor], beam_size: int): + """Initialize constraint states for constrained decoding (if supported). + + Args: + batch_constraints: (torch.Tensor, optional) + the list of constraints, in packed form + beam_size: (int) + the beam size + Returns: + *encoder_out* rearranged according to *new_order* + """ + pass + + def prune_sentences(self, batch_idxs: Tensor): + """ + Removes constraint states for completed sentences (if supported). + This is called from sequence_generator._generate() when sentences are + deleted from the batch. + + Args: + batch_idxs: Indices of *sentences* whose constraint state should be *kept*. + """ + pass + + def update_constraints(self, active_hypos: Tensor): + """ + Updates the constraint states by selecting the beam items that are retained. + This is called at each time step of sequence_generator._generate() when + the set of 2 * {beam_size} candidate hypotheses are reduced to the beam size. + + Args: + active_hypos: (batch size, beam size) + list of integers denoting, for each sentence, which beam candidate items + should be kept. + """ + pass + class BeamSearch(Search): def __init__(self, tgt_dict): super().__init__(tgt_dict) + self.constraint_states = None @torch.jit.export - def step(self, step: int, lprobs, scores: Optional[Tensor]): + def step( + self, + step: int, + lprobs, + scores: Optional[Tensor], + prev_output_tokens: Optional[Tensor] = None, + original_batch_idxs: Optional[Tensor] = None, + ): bsz, beam_size, vocab_size = lprobs.size() if step == 0: @@ -75,14 +136,393 @@ def step(self, step: int, lprobs, scores: Optional[Tensor]): ) scores_buf = top_prediction[0] indices_buf = top_prediction[1] - if torch.__version__ < '1.6.0': - beams_buf = torch.div(indices_buf, vocab_size) + # Project back into relative indices and beams + beams_buf = indices_buf // vocab_size + indices_buf = indices_buf.fmod(vocab_size) + + # At this point, beams_buf and indices_buf are single-dim and contain relative indices + return scores_buf, indices_buf, beams_buf + + +class PrefixConstrainedBeamSearch(Search): + def __init__(self, tgt_dict, prefix_allowed_tokens_fn): + super().__init__(tgt_dict) + self.prefix_allowed_tokens_fn = prefix_allowed_tokens_fn + self.stop_on_max_len = True + + @torch.jit.export + def apply_mask(self, x, prev_output_tokens, original_batch_idxs): + beam_size = x.shape[0] // original_batch_idxs.shape[0] + original_batch_idxs = ( + original_batch_idxs.unsqueeze(-1).repeat((1, beam_size)).flatten().tolist() + ) + + mask = torch.full_like(x, -math.inf) + for sent_i, (sent, batch_i) in enumerate( + zip(prev_output_tokens, original_batch_idxs) + ): + mask[sent_i, :, self.prefix_allowed_tokens_fn(batch_i, sent)] = 0 + + return mask + + @torch.jit.export + def step( + self, + step: int, + lprobs: Tensor, + scores: Tensor, + prev_output_tokens: Tensor, + original_batch_idxs: Tensor, + ): + bsz, beam_size, vocab_size = lprobs.size() + + lprobs += self.apply_mask( + lprobs.view(bsz * beam_size, 1, vocab_size), + prev_output_tokens, + original_batch_idxs, + ).view(bsz, beam_size, vocab_size) + + if step == 0: + # at the first step all hypotheses are equally likely, so use + # only the first beam + lprobs = lprobs[:, ::beam_size, :].contiguous() else: - beams_buf = torch.floor_divide(indices_buf, vocab_size) + # make probs contain cumulative scores for each hypothesis + assert scores is not None + lprobs = lprobs + scores[:, :, step - 1].unsqueeze(-1) + + top_prediction = torch.topk( + lprobs.view(bsz, -1), + k=min( + # Take the best beam_size predictions. We'll choose the first + # beam_size of these which don't predict eos to continue with. + beam_size, + lprobs.view(bsz, -1).size(1) - 1, # -1 so we never select pad + ), + ) + scores_buf = top_prediction[0] + indices_buf = top_prediction[1] + beams_buf = indices_buf // vocab_size indices_buf = indices_buf.fmod(vocab_size) return scores_buf, indices_buf, beams_buf +class LexicallyConstrainedBeamSearch(Search): + """Implements lexically constrained beam search as described in + + Fast Lexically Constrained Decoding with Dynamic Beam + Allocation for Neural Machine Translation. Post & Vilar, + NAACL 2018. https://www.aclweb.org/anthology/N18-1119/ + + and + + Improved Lexically Constrained Decoding for Translation and + Monolingual Rewriting. Hu et al, NAACL + 2019. https://www.aclweb.org/anthology/N19-1090/ + + This is accomplished by maintaining, for each beam hypothesis, a + ConstraintState object (see constraints.py) that tracks which + constraints have been generated and using this information to + shape the beam for each input sentence. + """ + + def __init__(self, tgt_dict, representation): + super().__init__(tgt_dict) + self.representation = representation + self.vocab_size = len(tgt_dict) + self.num_cands = 0 + self.supports_constraints = True + + @torch.jit.export + def init_constraints(self, batch_constraints: Optional[Tensor], beam_size: int): + self.constraint_states = [] + for constraint_tensor in batch_constraints: + if self.representation == "ordered": + constraint_state = OrderedConstraintState.create(constraint_tensor) + elif self.representation == "unordered": + constraint_state = UnorderedConstraintState.create(constraint_tensor) + + self.constraint_states.append([constraint_state for i in range(beam_size)]) + + @torch.jit.export + def prune_sentences(self, batch_idxs: Tensor): + self.constraint_states = [ + self.constraint_states[i] for i in batch_idxs.tolist() + ] + + @torch.jit.export + def update_constraints(self, active_hypos: Tensor): + if self.constraint_states: + batch_size = active_hypos.size(0) + for sentid in range(batch_size): + self.constraint_states[sentid] = [ + self.constraint_states[sentid][i] for i in active_hypos[sentid] + ] + + @torch.jit.export + def step( + self, + step: int, + lprobs: Tensor, + scores: Optional[Tensor], + prev_output_tokens: Optional[Tensor] = None, + original_batch_idxs: Optional[Tensor] = None, + ): + """ + A constrained step builds a large candidates list from the following: + - the top 2 * {beam_size} items over the whole beam + - for each item in the beam + - the top {each_k} (default 1) + - all next constraints + We then compute the constrained state of each beam item, and assign + stripe codes: 0 to the best in each bank, 1 to the 2nd-best, and so + on. We then sort by (stripe, score), and truncate the list at + 2 * beam size. + + Args: + step: the decoder step + lprobs: (batch size, beam size, target vocab) + the target-vocab distributions for each item in the beam. + Retrun: A tuple of (scores, indices, beams, constraints) where: + scores: (batch, output beam size) + the scores of the chosen elements + indices: (batch, output beam size) + the target vocab indices of the chosen elements + beams: (batch, output beam size) + the 0-indexed hypothesis ids of the chosen elements + constraints: (batch, output beam size) + the new constraint states + """ + each_k = 1 + device = lprobs.device + + batch_size, beam_size, vocab_size = lprobs.size() + + self.num_cands = min( + # Just take the k-best. We'll get another k from the 1-best from each + # row, plus more from the constraints + beam_size * 2, + lprobs.view(batch_size, -1).size(1) - 1, # -1 so we never select pad + ) + + # STEP 0: Preliminary. Prevent EOS for unfinished hyps across all batch items + constraint_states = self.constraint_states + if constraint_states and step > 0: + not_finished_indices = [] + for sentno, sent_constraints in enumerate(constraint_states): + for beamno, state in enumerate(sent_constraints): + index = sentno * beam_size + beamno + if not state.finished: + not_finished_indices.append(index) + not_finished_indices = torch.tensor(not_finished_indices) + if not_finished_indices.numel() > 0: + lprobs.view(batch_size * beam_size, -1)[ + not_finished_indices, self.eos + ] = -math.inf + + if step == 0: + # at the first step all hypotheses are equally likely, so use + # only the first beam entry for each batch item + lprobs = lprobs[:, ::beam_size, :].contiguous() + else: + # make probs contain cumulative scores for each hypothesis + assert scores is not None + lprobs = lprobs + scores[:, :, step - 1].unsqueeze(-1) + + top_prediction = torch.topk( + lprobs.view(batch_size, -1), + self.num_cands, + ) + scores_buf, indices_buf = top_prediction + # Project back into relative indices and beams + beams_buf = indices_buf // vocab_size + indices_buf = indices_buf.fmod(vocab_size) + + # Short circuit if there are no constraints in this batch + if not constraint_states: + return scores_buf, indices_buf, beams_buf + + # STEP 1: get top-1 from each hypothesis across all sentences in the batch + if step > 0: + top_scores, top_indices = torch.topk( + lprobs.view(batch_size * beam_size, -1), + k=each_k, + dim=1, + ) + top_scores = top_scores.view(batch_size, -1) + top_indices = top_indices.view(batch_size, -1) + scores_buf = torch.cat((scores_buf, top_scores), dim=1) + indices_buf = torch.cat((indices_buf, top_indices), dim=1) + new_beams = torch.arange(0, beam_size, device=device).repeat(batch_size, 1) + beams_buf = torch.cat((beams_buf, new_beams), dim=1) + + # Now, process sentences in the batch one by one. + new_scores_buf = torch.zeros((batch_size, 2 * beam_size), device=device) + new_indices_buf = torch.zeros((batch_size, 2 * beam_size), device=device).long() + new_beams_buf = torch.zeros((batch_size, 2 * beam_size), device=device).long() + for sentno, states in enumerate(constraint_states): + scores, indices, beams, new_states = self.step_sentence( + step, + sentno, + lprobs[sentno], + constraint_states[sentno], + beams_buf[sentno].clone(), + indices_buf[sentno].clone(), + scores_buf[sentno].clone(), + ) + new_scores_buf[sentno] = scores + new_indices_buf[sentno] = indices + new_beams_buf[sentno] = beams + self.constraint_states[sentno] = new_states + + return new_scores_buf, new_indices_buf, new_beams_buf + + @torch.jit.export + def step_sentence( + self, + step: int, + sentno: int, + lprobs: Tensor, + constraint_states: List[List[ConstraintState]], + beams_buf: Tensor, + indices_buf: Tensor, + scores_buf: Tensor, + ): + """Does per-sentence processing. Adds all constraints for each + hypothesis to the list of candidates; then removes duplicates, + sorts, and dynamically stripes across the banks. All tensor inputs + are collapsed to those pertaining to a single input sentence. + """ + device = lprobs.device + + # STEP 2: Add all constraints for each beam item + for beamno, state in enumerate(constraint_states): + next_tokens = torch.tensor(list(state.next_tokens()), device=device).long() + if next_tokens.numel() != 0: + indices_buf = torch.cat((indices_buf, next_tokens)) + next_beams = ( + torch.tensor(beamno, device=device) + .repeat(next_tokens.size(0)) + .long() + ) + beams_buf = torch.cat((beams_buf, next_beams)) + next_values = lprobs[beamno].take(next_tokens.view(-1)) + scores_buf = torch.cat((scores_buf, next_values)) + + # At the 0th time step, there is just one beam item + if step == 0: + break + + # STEP 3: Compute the "bank" for each candidate. This is the + # number of constraints it's generated. We need this so that + # we can do round-robin allocation of the beam across these + # banks. If C is the number of constraints, we select the best + # item in bank C, then the best in bank C-1, etc, followed by + # the 2nd-best in bank C, the 2nd-best in bank C-1, etc, and so + # on, until the maximum beam size. We accomplish this by + # creating a sort key and striping across the banks. + + # Compute the new states for all candidates + cands_size = indices_buf.size(0) + constraint_states = [ + constraint_states[beams_buf[i]].advance(indices_buf[i]) + for i in range(cands_size) + ] + + banks = torch.tensor([state.bank for state in constraint_states], device=device) + + # STEP 4: Sort + num_constraint_tokens = len(state.tokens) + + # Sort by keys (bank, score) (i.e., sort banks together, and scores + # within banks). AFAIK pytorch doesn't support either stable sort or + # multi-key sorting, so we have to hack this. + MAX_SCORE = -100 + sort_key = (num_constraint_tokens - banks) * MAX_SCORE + scores_buf + sort_values, sort_indices = sort_key.sort(dim=0, descending=True) + scores_buf = scores_buf[sort_indices] + indices_buf = indices_buf[sort_indices] + beams_buf = beams_buf[sort_indices] + banks = banks[sort_indices] + + # Sort the constraints to follow suit + constraint_states = [constraint_states[i] for i in sort_indices] + + # STEP 5: Remove duplicates. The topk calls (overall and + # per-row) plus the per-row generation of constraints will + # produce duplicates. Here we remove them. + + def roll(t): + """Rolls a 1d tensor left by 1. + + [0, 1, 2, 3, 4] becomes [4, 0, 1, 2, 3] + """ + return torch.cat((t[-1].unsqueeze(0), t[0:-1]), dim=0) + + # We map candidates (beam, token_id) to a single dimension. + # This is then shifted by 1. We can then easily identify + # duplicates and create a mask that identifies unique + # extensions. + uniques_mask = beams_buf * (self.vocab_size + 1) + indices_buf + uniques_mask = roll(uniques_mask) != uniques_mask + + # Use the mask to pare down the data structures + scores_buf = torch.masked_select(scores_buf, uniques_mask) + indices_buf = torch.masked_select(indices_buf, uniques_mask) + beams_buf = torch.masked_select(beams_buf, uniques_mask) + banks = torch.masked_select(banks, uniques_mask) + i = 1 + for mask in uniques_mask[1:]: + if not mask: + constraint_states.pop(i) + i += mask + + # STEP 6: Assign IDs round-robin across banks, sort, and + # truncate. Now that the candidates are sorted by (bank, + # score) and uniqed, we dynamically allocate the {beam_size} + # beam by striping across the candidates. These stripes will + # be used as sort keys to do round-robin selection. This is + # accomplished in a single pass with offsets. Sorting by + # highest-banks (furthest-along hypotheses) first ensures + # progress through the constraints. + # + # e.g., BANKS: 3 3 3 2 2 2 2 1 1 1 0 0 + # OLD STRIPES: 0 1 2 0 1 2 3 0 1 2 0 1 + # NEW STRIPES: 0 1+4 2+8 0+1 1+5 2+9 3+11 0+2 1+6 2+10 0+3 1+7 + # = 0 5 10 1 6 11 13 2 7 12 3 8 + # + # Sorting by this then gives the following banks: + # + # 3 2 1 0 3 2 1 0 3 2 1 2 + # + # We'll take the top {beam_size} of these. + stripe_offsets = [offset * (len(banks) + 1) for offset in range(len(banks) + 1)] + stripes = torch.zeros_like(banks) + cur_bank_count = -1 + cur_bank = banks[0] + for i, bank in enumerate(banks): + if bank != cur_bank: + cur_bank_count = 0 + cur_bank = bank + else: + cur_bank_count += 1 + stripes[i] = num_constraint_tokens - bank + stripe_offsets[cur_bank_count] + + # STEP 7: Sort by the stripes values + sort_values, sort_indices = stripes.sort(dim=0) + scores_buf = scores_buf[sort_indices] + indices_buf = indices_buf[sort_indices] + beams_buf = beams_buf[sort_indices] + constraint_states = [constraint_states[i] for i in sort_indices] + + # STEP 8: Truncate to the candidates size! + scores_buf = scores_buf[: self.num_cands] + indices_buf = indices_buf[: self.num_cands] + beams_buf = beams_buf[: self.num_cands] + + return scores_buf, indices_buf, beams_buf, constraint_states + + class LengthConstrainedBeamSearch(Search): def __init__(self, tgt_dict, min_len_a, min_len_b, max_len_a, max_len_b): super().__init__(tgt_dict) @@ -91,8 +531,16 @@ def __init__(self, tgt_dict, min_len_a, min_len_b, max_len_a, max_len_b): self.max_len_a = max_len_a self.max_len_b = max_len_b self.beam = BeamSearch(tgt_dict) - - def step(self, step: int, lprobs, scores): + self.needs_src_lengths = True + + def step( + self, + step: int, + lprobs, + scores, + prev_output_tokens: Optional[Tensor] = None, + original_batch_idxs: Optional[Tensor] = None, + ): min_lens = self.min_len_a * self.src_lengths + self.min_len_b max_lens = self.max_len_a * self.src_lengths + self.max_len_b lprobs[step < min_lens, :, self.eos] = -math.inf @@ -117,7 +565,14 @@ def __init__(self, tgt_dict, num_groups, diversity_strength): self.beam = BeamSearch(tgt_dict) @torch.jit.export - def step(self, step: int, lprobs, scores): + def step( + self, + step: int, + lprobs, + scores, + prev_output_tokens: Optional[Tensor] = None, + original_batch_idxs: Optional[Tensor] = None, + ): bsz, beam_size, vocab_size = lprobs.size() if beam_size % self.num_groups != 0: raise ValueError( @@ -135,7 +590,9 @@ def step(self, step: int, lprobs, scores): # apply diversity penalty if g > 0: lprobs_g = torch.add( - lprobs_g, self.diversity_strength, diversity_buf.unsqueeze(1) + lprobs_g, + other=diversity_buf.unsqueeze(1), + alpha=self.diversity_strength, ) else: lprobs_g = lprobs_g.contiguous() @@ -216,7 +673,14 @@ def _sample_topp(self, lprobs): return trimed_probs, truncated_indices @torch.jit.export - def step(self, step: int, lprobs, scores): + def step( + self, + step: int, + lprobs, + scores, + prev_output_tokens: Optional[Tensor] = None, + original_batch_idxs: Optional[Tensor] = None, + ): bsz, beam_size, vocab_size = lprobs.size() if step == 0: @@ -239,7 +703,9 @@ def step(self, step: int, lprobs, scores): # sample if step == 0: indices_buf = torch.multinomial( - probs.view(bsz, -1), beam_size, replacement=True, + probs.view(bsz, -1), + beam_size, + replacement=True, ).view(bsz, beam_size) else: indices_buf = torch.multinomial( @@ -253,9 +719,7 @@ def step(self, step: int, lprobs, scores): probs = probs.expand(bsz, beam_size, -1) # gather scores - scores_buf = torch.gather( - probs, dim=2, index=indices_buf.unsqueeze(-1) - ) + scores_buf = torch.gather(probs, dim=2, index=indices_buf.unsqueeze(-1)) scores_buf = scores_buf.log_().view(bsz, -1) # remap indices if using top-k or top-P sampling @@ -298,7 +762,14 @@ def __init__(self, tgt_dict, diversity_rate): self.diversity_rate = diversity_rate self.beam = BeamSearch(tgt_dict) - def step(self, step: int, lprobs, scores): + def step( + self, + step: int, + lprobs, + scores, + prev_output_tokens: Optional[Tensor] = None, + original_batch_idxs: Optional[Tensor] = None, + ): bsz, beam_size, vocab_size = lprobs.size() k = min( # Take the best 2 x beam_size predictions. We'll choose the first @@ -335,7 +806,7 @@ def step(self, step: int, lprobs, scores): k, ) - final_beams = torch.div(final_indices, k) + final_beams = final_indices // k for i in range(bsz): final_indices[i] = indices[i][final_indices[i]] diff --git a/fairseq/sequence_generator.py b/fairseq/sequence_generator.py index 4e0ffca210..2e61140dd8 100644 --- a/fairseq/sequence_generator.py +++ b/fairseq/sequence_generator.py @@ -5,14 +5,15 @@ import math from typing import Dict, List, Optional +import sys import torch import torch.nn as nn from fairseq import search, utils from fairseq.data import data_utils from fairseq.models import FairseqIncrementalDecoder -from fairseq.models.fairseq_encoder import EncoderOut from torch import Tensor +from fairseq.ngram_repeat_block import NGramRepeatBlock class SequenceGenerator(nn.Module): @@ -23,16 +24,19 @@ def __init__( beam_size=1, max_len_a=0, max_len_b=200, + max_len=0, min_len=1, normalize_scores=True, len_penalty=1.0, unk_penalty=0.0, - retain_dropout=False, temperature=1.0, match_source_len=False, no_repeat_ngram_size=0, search_strategy=None, eos=None, + symbols_to_strip_from_output=None, + lm_model=None, + lm_weight=1.0, ): """Generates translations of a given source sentence. @@ -42,6 +46,8 @@ def __init__( beam_size (int, optional): beam width (default: 1) max_len_a/b (int, optional): generate sequences of maximum length ax + b, where x is the source length + max_len (int, optional): the maximum length of the generated output + (not including end-of-sentence) min_len (int, optional): the minimum length of the generated output (not including end-of-sentence) normalize_scores (bool, optional): normalize scores by the length @@ -50,8 +56,6 @@ def __init__( shorter, >1.0 favors longer sentences (default: 1.0) unk_penalty (float, optional): unknown word penalty, where <0 produces more unks, >0 produces fewer (default: 0.0) - retain_dropout (bool, optional): use dropout when generating - (default: False) temperature (float, optional): temperature, where values >1.0 produce more uniform samples and values <1.0 produce sharper samples (default: 1.0) @@ -63,9 +67,15 @@ def __init__( self.model = models else: self.model = EnsembleModel(models) + self.tgt_dict = tgt_dict self.pad = tgt_dict.pad() self.unk = tgt_dict.unk() self.eos = tgt_dict.eos() if eos is None else eos + self.symbols_to_strip_from_output = ( + symbols_to_strip_from_output.union({self.eos}) + if symbols_to_strip_from_output is not None + else {self.eos} + ) self.vocab_size = len(tgt_dict) self.beam_size = beam_size # the max beam size is the dictionary size - 1, since we never select pad @@ -73,21 +83,37 @@ def __init__( self.max_len_a = max_len_a self.max_len_b = max_len_b self.min_len = min_len + self.max_len = max_len or self.model.max_decoder_positions() self.normalize_scores = normalize_scores self.len_penalty = len_penalty self.unk_penalty = unk_penalty - self.retain_dropout = retain_dropout self.temperature = temperature self.match_source_len = match_source_len - self.no_repeat_ngram_size = no_repeat_ngram_size + + if no_repeat_ngram_size > 0: + self.repeat_ngram_blocker = NGramRepeatBlock(no_repeat_ngram_size) + else: + self.repeat_ngram_blocker = None + assert temperature > 0, "--temperature must be greater than 0" self.search = ( search.BeamSearch(tgt_dict) if search_strategy is None else search_strategy ) - if not self.retain_dropout: - self.model.eval() + # We only need to set src_lengths in LengthConstrainedBeamSearch. + # As a module attribute, setting it would break in multithread + # settings when the model is shared. + self.should_set_src_lengths = ( + hasattr(self.search, "needs_src_lengths") and self.search.needs_src_lengths + ) + + self.model.eval() + + self.lm_model = lm_model + self.lm_weight = lm_weight + if self.lm_model is not None: + self.lm_model.eval() def cuda(self): self.model.cuda() @@ -109,8 +135,7 @@ def forward( bos_token (int, optional): beginning of sentence token (default: self.eos) """ - self.model.reset_incremental_state() - return self._generate(sample, prefix_tokens, bos_token) + return self._generate(sample, prefix_tokens, bos_token=bos_token) # TODO(myleott): unused, deprecate after pytorch-translate migration def generate_batched_itr(self, data_itr, beam_size=None, cuda=False, timer=None): @@ -146,7 +171,7 @@ def generate_batched_itr(self, data_itr, beam_size=None, cuda=False, timer=None) yield id, src, ref, hypos[i] @torch.no_grad() - def generate(self, models, sample: Dict[str, Dict[str, Tensor]], **kwargs): + def generate(self, models, sample: Dict[str, Dict[str, Tensor]], **kwargs) -> List[List[Dict[str, Tensor]]]: """Generate translations. Match the api of other fairseq generators. Args: @@ -154,43 +179,79 @@ def generate(self, models, sample: Dict[str, Dict[str, Tensor]], **kwargs): sample (dict): batch prefix_tokens (torch.LongTensor, optional): force decoder to begin with these tokens + constraints (torch.LongTensor, optional): force decoder to include + the list of constraints bos_token (int, optional): beginning of sentence token (default: self.eos) """ - self.model.reset_incremental_state() return self._generate(sample, **kwargs) def _generate( self, sample: Dict[str, Dict[str, Tensor]], prefix_tokens: Optional[Tensor] = None, + constraints: Optional[Tensor] = None, bos_token: Optional[int] = None, ): - net_input = sample["net_input"] - src_tokens = net_input["src_tokens"] - # length of the source text being the character length except EndOfSentence and pad - src_lengths = ( - (src_tokens.ne(self.eos) & src_tokens.ne(self.pad)).long().sum(dim=1) + incremental_states = torch.jit.annotate( + List[Dict[str, Dict[str, Optional[Tensor]]]], + [ + torch.jit.annotate(Dict[str, Dict[str, Optional[Tensor]]], {}) + for i in range(self.model.models_size) + ], ) + net_input = sample["net_input"] + + if "src_tokens" in net_input: + src_tokens = net_input["src_tokens"] + # length of the source text being the character length except EndOfSentence and pad + src_lengths = ( + (src_tokens.ne(self.eos) & src_tokens.ne(self.pad)).long().sum(dim=1) + ) + elif "source" in net_input: + src_tokens = net_input["source"] + src_lengths = ( + net_input["padding_mask"].size(-1) - net_input["padding_mask"].sum(-1) + if net_input["padding_mask"] is not None + else torch.tensor(src_tokens.size(-1)).to(src_tokens) + ) + elif "features" in net_input: + src_tokens = net_input["features"] + src_lengths = ( + net_input["padding_mask"].size(-1) - net_input["padding_mask"].sum(-1) + if net_input["padding_mask"] is not None + else torch.tensor(src_tokens.size(-1)).to(src_tokens) + ) + else: + raise Exception("expected src_tokens or source in net input. input keys: " + str(net_input.keys())) + # bsz: total number of sentences in beam - input_size = src_tokens.size() - bsz, src_len = input_size[0], input_size[1] + # Note that src_tokens may have more than 2 dimensions (i.e. audio features) + bsz, src_len = src_tokens.size()[:2] beam_size = self.beam_size + if constraints is not None and not self.search.supports_constraints: + raise NotImplementedError( + "Target-side constraints were provided, but search method doesn't support them" + ) + + # Initialize constraints, when active + self.search.init_constraints(constraints, beam_size) + max_len: int = -1 if self.match_source_len: max_len = src_lengths.max().item() else: max_len = min( int(self.max_len_a * src_len + self.max_len_b), - # exclude the EOS marker - self.model.max_decoder_positions() - 1, + self.max_len - 1, ) assert ( self.min_len <= max_len ), "min_len cannot be larger than max_len, please adjust these!" # compute the encoder output for each beam - encoder_outs = self.model.forward_encoder(net_input) + with torch.autograd.profiler.record_function("EnsembleModel: forward_encoder"): + encoder_outs = self.model.forward_encoder(net_input) # placeholder of indices for bsz * beam_size to hold tokens and accumulative scores new_order = torch.arange(bsz).view(-1, 1).repeat(1, beam_size).view(-1) @@ -202,7 +263,7 @@ def _generate( # initialize buffers scores = ( torch.zeros(bsz * beam_size, max_len + 1).to(src_tokens).float() - ) # +1 for eos; pad is never choosed for scoring + ) # +1 for eos; pad is never chosen for scoring tokens = ( torch.zeros(bsz * beam_size, max_len + 2) .to(src_tokens) @@ -212,11 +273,11 @@ def _generate( tokens[:, 0] = self.eos if bos_token is None else bos_token attn: Optional[Tensor] = None - # The blacklist indicates candidates that should be ignored. + # A list that indicates candidates that should be ignored. # For example, suppose we're sampling and have already finalized 2/5 - # samples. Then the blacklist would mark 2 positions as being ignored, + # samples. Then cands_to_ignore would mark 2 positions as being ignored, # so that we only finalize the remaining 3 samples. - blacklist = ( + cands_to_ignore = ( torch.zeros(bsz, beam_size).to(src_tokens).eq(-1) ) # forward and backward-compatible False mask @@ -226,23 +287,33 @@ def _generate( [torch.jit.annotate(List[Dict[str, Tensor]], []) for i in range(bsz)], ) # contains lists of dictionaries of infomation about the hypothesis being finalized at each step - finished = [ - False for i in range(bsz) - ] # a boolean array indicating if the sentence at the index is finished or not + # a boolean array indicating if the sentence at the index is finished or not + finished = [False for i in range(bsz)] num_remaining_sent = bsz # number of sentences remaining # number of candidate hypos per step cand_size = 2 * beam_size # 2 x beam size in case half are EOS # offset arrays for converting between different indexing schemes - bbsz_offsets = (torch.arange(0, bsz) * beam_size).unsqueeze(1).type_as(tokens) - cand_offsets = torch.arange(0, cand_size).type_as(tokens) + bbsz_offsets = ( + (torch.arange(0, bsz) * beam_size) + .unsqueeze(1) + .type_as(tokens) + .to(src_tokens.device) + ) + cand_offsets = torch.arange(0, cand_size).type_as(tokens).to(src_tokens.device) reorder_state: Optional[Tensor] = None batch_idxs: Optional[Tensor] = None + + original_batch_idxs: Optional[Tensor] = None + if "id" in sample and isinstance(sample["id"], Tensor): + original_batch_idxs = sample["id"] + else: + original_batch_idxs = torch.arange(0, bsz).type_as(tokens) + for step in range(max_len + 1): # one extra step for EOS marker # reorder decoder internal states based on the prev choice of beams - # print(f'step: {step}') if reorder_state is not None: if batch_idxs is not None: # update beam indices to take into account removed sentences @@ -252,24 +323,26 @@ def _generate( reorder_state.view(-1, beam_size).add_( corr.unsqueeze(-1) * beam_size ) - self.model.reorder_incremental_state(reorder_state) + original_batch_idxs = original_batch_idxs[batch_idxs] + self.model.reorder_incremental_state(incremental_states, reorder_state) encoder_outs = self.model.reorder_encoder_out( encoder_outs, reorder_state ) + with torch.autograd.profiler.record_function("EnsembleModel: forward_decoder"): + lprobs, avg_attn_scores = self.model.forward_decoder( + tokens[:, : step + 1], + encoder_outs, + incremental_states, + self.temperature, + ) - lprobs, avg_attn_scores = self.model.forward_decoder( - tokens[:, : step + 1], encoder_outs, self.temperature - ) - lprobs[lprobs != lprobs] = torch.tensor(-math.inf).to(lprobs) - - lprobs[:, self.pad] = -math.inf # never select pad - lprobs[:, self.unk] -= self.unk_penalty # apply unk penalty - - # handle max length constraint - if step >= max_len: - lprobs[:, : self.eos] = -math.inf - lprobs[:, self.eos + 1 :] = -math.inf - + if self.lm_model is not None: + lm_out = self.lm_model(tokens[:, : step + 1]) + probs = self.lm_model.get_normalized_probs( + lm_out, log_probs=True, sample=None + ) + probs = probs[:, -1, :] * self.lm_weight + lprobs += probs # handle prefix tokens (possibly with different lengths) if ( prefix_tokens is not None @@ -283,6 +356,16 @@ def _generate( # minimum length constraint (does not apply if using prefix_tokens) lprobs[:, self.eos] = -math.inf + lprobs[lprobs != lprobs] = torch.tensor(-math.inf).to(lprobs) + + lprobs[:, self.pad] = -math.inf # never select pad + lprobs[:, self.unk] -= self.unk_penalty # apply unk penalty + + # handle max length constraint + if step >= max_len: + lprobs[:, : self.eos] = -math.inf + lprobs[:, self.eos + 1 :] = -math.inf + # Record attention scores, only support avg_attn_scores is a Tensor if avg_attn_scores is not None: if attn is None: @@ -299,15 +382,19 @@ def _generate( scores ) # scores of hypothesis ending with eos (finished sentences) - self.search.set_src_lengths(src_lengths) + if self.should_set_src_lengths: + self.search.set_src_lengths(src_lengths) - if self.no_repeat_ngram_size > 0: - lprobs = self._no_repeat_ngram(tokens, lprobs, bsz, beam_size, step) + if self.repeat_ngram_blocker is not None: + lprobs = self.repeat_ngram_blocker(tokens, lprobs, bsz, beam_size, step) + # Shape: (batch, cand_size) cand_scores, cand_indices, cand_beams = self.search.step( step, lprobs.view(bsz, -1, self.vocab_size), scores.view(bsz, beam_size, -1)[:, :, :step], + tokens[:, : step + 1], + original_batch_idxs, ) # cand_bbsz_idx contains beam indices for the top candidate @@ -316,10 +403,13 @@ def _generate( cand_bbsz_idx = cand_beams.add(bbsz_offsets) # finalize hypotheses that end in eos + # Shape of eos_mask: (batch size, beam size) eos_mask = cand_indices.eq(self.eos) & cand_scores.ne(-math.inf) - eos_mask[:, :beam_size][blacklist] = torch.tensor(0).to(eos_mask) + eos_mask[:, :beam_size][cands_to_ignore] = torch.tensor(0).to(eos_mask) # only consider eos when it's among the top beam_size indices + # Now we know what beam item(s) to finish + # Shape: 1d list of absolute-numbered eos_bbsz_idx = torch.masked_select( cand_bbsz_idx[:, :beam_size], mask=eos_mask[:, :beam_size] ) @@ -329,6 +419,7 @@ def _generate( eos_scores = torch.masked_select( cand_scores[:, :beam_size], mask=eos_mask[:, :beam_size] ) + finalized_sents = self.finalize_hypos( step, eos_bbsz_idx, @@ -347,17 +438,27 @@ def _generate( assert num_remaining_sent >= 0 if num_remaining_sent == 0: break - assert step < max_len + if self.search.stop_on_max_len and step >= max_len: + break + assert step < max_len, f"{step} < {max_len}" + # Remove finalized sentences (ones for which {beam_size} + # finished hypotheses have been generated) from the batch. if len(finalized_sents) > 0: new_bsz = bsz - len(finalized_sents) # construct batch_idxs which holds indices of batches to keep for the next pass - batch_mask = torch.ones(bsz).to(cand_indices) - batch_mask[ - torch.tensor(finalized_sents).to(cand_indices) - ] = torch.tensor(0).to(batch_mask) - batch_idxs = batch_mask.nonzero().squeeze(-1) + batch_mask = torch.ones( + bsz, dtype=torch.bool, device=cand_indices.device + ) + batch_mask[finalized_sents] = False + # TODO replace `nonzero(as_tuple=False)` after TorchScript supports it + batch_idxs = torch.arange( + bsz, device=cand_indices.device + ).masked_select(batch_mask) + + # Choose the subset of the hypothesized constraints that will continue + self.search.prune_sentences(batch_idxs) eos_mask = eos_mask[batch_idxs] cand_beams = cand_beams[batch_idxs] @@ -369,7 +470,7 @@ def _generate( if prefix_tokens is not None: prefix_tokens = prefix_tokens[batch_idxs] src_lengths = src_lengths[batch_idxs] - blacklist = blacklist[batch_idxs] + cands_to_ignore = cands_to_ignore[batch_idxs] scores = scores.view(bsz, -1)[batch_idxs].view(new_bsz * beam_size, -1) tokens = tokens.view(bsz, -1)[batch_idxs].view(new_bsz * beam_size, -1) @@ -380,28 +481,37 @@ def _generate( bsz = new_bsz else: batch_idxs = None - # set active_mask so that values > cand_size indicate eos hypos + + # Set active_mask so that values > cand_size indicate eos hypos # and values < cand_size indicate candidate active hypos. # After, the min values per row are the top candidate active hypos # Rewrite the operator since the element wise or is not supported in torchscript. - eos_mask[:, :beam_size] = ~((~blacklist) & (~eos_mask[:, :beam_size])) + eos_mask[:, :beam_size] = ~((~cands_to_ignore) & (~eos_mask[:, :beam_size])) active_mask = torch.add( eos_mask.type_as(cand_offsets) * cand_size, cand_offsets[: eos_mask.size(1)], ) - # get the top beam_size active hypotheses, which are just the hypos - # with the smallest values in active_mask - new_blacklist, active_hypos = torch.topk( + # get the top beam_size active hypotheses, which are just + # the hypos with the smallest values in active_mask. + # {active_hypos} indicates which {beam_size} hypotheses + # from the list of {2 * beam_size} candidates were + # selected. Shapes: (batch size, beam size) + new_cands_to_ignore, active_hypos = torch.topk( active_mask, k=beam_size, dim=1, largest=False ) - # update blacklist to ignore any finalized hypos - blacklist = new_blacklist.ge(cand_size)[:, :beam_size] - assert (~blacklist).any(dim=1).all() + # update cands_to_ignore to ignore any finalized hypos. + cands_to_ignore = new_cands_to_ignore.ge(cand_size)[:, :beam_size] + # Make sure there is at least one active item for each sentence in the batch. + assert (~cands_to_ignore).any(dim=1).all() + # update cands_to_ignore to ignore any finalized hypos + + # {active_bbsz_idx} denotes which beam number is continued for each new hypothesis (a beam + # can be selected more than once). active_bbsz_idx = torch.gather(cand_bbsz_idx, dim=1, index=active_hypos) active_scores = torch.gather(cand_scores, dim=1, index=active_hypos) @@ -409,9 +519,12 @@ def _generate( active_scores = active_scores.view(-1) # copy tokens and scores for active hypotheses + + # Set the tokens for each beam (can select the same row more than once) tokens[:, : step + 1] = torch.index_select( tokens[:, : step + 1], dim=0, index=active_bbsz_idx ) + # Select the next token for each of them tokens.view(bsz, beam_size, -1)[:, :, step + 1] = torch.gather( cand_indices, dim=1, index=active_hypos ) @@ -423,6 +536,9 @@ def _generate( cand_scores, dim=1, index=active_hypos ) + # Update constraints based on which candidates were selected for the next beam + self.search.update_constraints(active_hypos) + # copy attention for active hypotheses if attn is not None: attn[:, :, : step + 2] = torch.index_select( @@ -434,16 +550,14 @@ def _generate( # sort by score descending for sent in range(len(finalized)): - # make into beam container - BCList = [ - BeamContainer(elem["score"].item(), elem) for elem in finalized[sent] - ] - BCList.sort() - BCList.reverse() + scores = torch.tensor( + [float(elem["score"].item()) for elem in finalized[sent]] + ) + _, sorted_scores_indices = torch.sort(scores, descending=True) + finalized[sent] = [finalized[sent][ssi] for ssi in sorted_scores_indices] finalized[sent] = torch.jit.annotate( - List[Dict[str, Tensor]], [x.elem for x in BCList] + List[Dict[str, Tensor]], finalized[sent] ) - return finalized def _prefix_tokens( @@ -453,7 +567,7 @@ def _prefix_tokens( prefix_toks = prefix_tokens[:, step].unsqueeze(-1).repeat(1, beam_size).view(-1) prefix_lprobs = lprobs.gather(-1, prefix_toks.unsqueeze(-1)) prefix_mask = prefix_toks.ne(self.pad) - lprobs[prefix_mask] = torch.tensor(-math.inf).to(lprobs) + lprobs[prefix_mask] = torch.min(prefix_lprobs) - 1 lprobs[prefix_mask] = lprobs[prefix_mask].scatter( -1, prefix_toks[prefix_mask].unsqueeze(-1), prefix_lprobs[prefix_mask] ) @@ -495,13 +609,18 @@ def finalize_hypos( max_len: int, ): """Finalize hypothesis, store finalized information in `finalized`, and change `finished` accordingly. - Returns number of sentences being finalized. + A sentence is finalized when {beam_size} finished items have been collected for it. + + Returns number of sentences (not beam items) being finalized. + These will be removed from the batch and not processed further. Args: bbsz_idx (Tensor): """ assert bbsz_idx.numel() == eos_scores.numel() - # clone relevant token and attention tensors + # clone relevant token and attention tensors. + # tokens is (batch * beam, max_len). So the index_select + # gets the newly EOS rows, then selects cols 1..{step + 2} tokens_clone = tokens.index_select(0, bbsz_idx)[ :, 1 : step + 2 ] # skip the first index, which is EOS @@ -523,6 +642,10 @@ def finalize_hypos( if self.normalize_scores: eos_scores /= (step + 1) ** self.len_penalty + # cum_unfin records which sentences in the batch are finished. + # It helps match indexing between (a) the original sentences + # in the batch and (b) the current, possibly-reduced set of + # sentences. cum_unfin: List[int] = [] prev = 0 for f in finished: @@ -530,33 +653,38 @@ def finalize_hypos( prev += 1 else: cum_unfin.append(prev) + cum_fin_tensor = torch.tensor(cum_unfin, dtype=torch.int).to(bbsz_idx) + + unfin_idx = bbsz_idx // beam_size + sent = unfin_idx + torch.index_select(cum_fin_tensor, 0, unfin_idx) + + # Create a set of "{sent}{unfin_idx}", where + # "unfin_idx" is the index in the current (possibly reduced) + # list of sentences, and "sent" is the index in the original, + # unreduced batch + # For every finished beam item + # sentence index in the current (possibly reduced) batch + seen = (sent << 32) + unfin_idx + unique_seen: List[int] = torch.unique(seen).tolist() - # set() is not supported in script export - sents_seen: Dict[str, Optional[Tensor]] = {} + if self.match_source_len: + condition = step > torch.index_select(src_lengths, 0, unfin_idx) + eos_scores = torch.where(condition, torch.tensor(-math.inf), eos_scores) + sent_list: List[int] = sent.tolist() for i in range(bbsz_idx.size()[0]): - idx = bbsz_idx[i] - score = eos_scores[i] - unfin_idx = idx // beam_size - sent = unfin_idx + cum_unfin[unfin_idx] - # Cannot create dict for key type '(int, int)' in torchscript. - # The workaround is to cast int to string - seen = str(sent.item()) + "_" + str(unfin_idx.item()) - if seen not in sents_seen: - sents_seen[seen] = None - - if self.match_source_len and step > src_lengths[unfin_idx]: - score = torch.tensor(-math.inf).to(score) - - if len(finalized[sent]) < beam_size: + # An input sentence (among those in a batch) is finished when + # beam_size hypotheses have been collected for it + if len(finalized[sent_list[i]]) < beam_size: if attn_clone is not None: # remove padding tokens from attn scores hypo_attn = attn_clone[i] else: hypo_attn = torch.empty(0) - finalized[sent].append( + + finalized[sent_list[i]].append( { "tokens": tokens_clone[i], - "score": score, + "score": eos_scores[i], "attention": hypo_attn, # src_len x tgt_len "alignment": torch.empty(0), "positional_scores": pos_scores[i], @@ -564,15 +692,17 @@ def finalize_hypos( ) newly_finished: List[int] = [] - for seen in sents_seen.keys(): + for unique_s in unique_seen: # check termination conditions for this sentence - sent: int = int(float(seen.split("_")[0])) - unfin_idx: int = int(float(seen.split("_")[1])) - if not finished[sent] and self.is_finished( - step, unfin_idx, max_len, len(finalized[sent]), beam_size + unique_sent: int = unique_s >> 32 + unique_unfin_idx: int = unique_s - (unique_sent << 32) + + if not finished[unique_sent] and self.is_finished( + step, unique_unfin_idx, max_len, len(finalized[unique_sent]), beam_size ): - finished[sent] = True - newly_finished.append(unfin_idx) + finished[unique_sent] = True + newly_finished.append(unique_unfin_idx) + return newly_finished def is_finished( @@ -584,77 +714,19 @@ def is_finished( beam_size: int, ): """ - Check whether we've finished generation for a given sentence, by - comparing the worst score among finalized hypotheses to the best - possible score among unfinalized hypotheses. + Check whether decoding for a sentence is finished, which + occurs when the list of finalized sentences has reached the + beam size, or when we reach the maximum length. """ assert finalized_sent_len <= beam_size if finalized_sent_len == beam_size or step == max_len: return True return False - def calculate_banned_tokens( - self, - tokens, - step: int, - gen_ngrams: List[Dict[str, List[int]]], - no_repeat_ngram_size: int, - bbsz_idx: int, - ): - tokens_list: List[int] = tokens[ - bbsz_idx, step + 2 - no_repeat_ngram_size : step + 1 - ].tolist() - # before decoding the next token, prevent decoding of ngrams that have already appeared - ngram_index = ",".join([str(x) for x in tokens_list]) - return gen_ngrams[bbsz_idx].get(ngram_index, torch.jit.annotate(List[int], [])) - - def transpose_list(self, l: List[List[int]]): - # GeneratorExp aren't supported in TS so ignoring the lint - min_len = min([len(x) for x in l]) # noqa - l2 = [[row[i] for row in l] for i in range(min_len)] - return l2 - - def _no_repeat_ngram(self, tokens, lprobs, bsz: int, beam_size: int, step: int): - # for each beam and batch sentence, generate a list of previous ngrams - gen_ngrams: List[Dict[str, List[int]]] = [ - torch.jit.annotate(Dict[str, List[int]], {}) - for bbsz_idx in range(bsz * beam_size) - ] - cpu_tokens = tokens.cpu() - for bbsz_idx in range(bsz * beam_size): - gen_tokens: List[int] = cpu_tokens[bbsz_idx].tolist() - for ngram in self.transpose_list( - [gen_tokens[i:] for i in range(self.no_repeat_ngram_size)] - ): - key = ",".join([str(x) for x in ngram[:-1]]) - gen_ngrams[bbsz_idx][key] = gen_ngrams[bbsz_idx].get( - key, torch.jit.annotate(List[int], []) - ) + [ngram[-1]] - - if step + 2 - self.no_repeat_ngram_size >= 0: - # no banned tokens if we haven't generated no_repeat_ngram_size tokens yet - banned_tokens = [ - self.calculate_banned_tokens( - tokens, step, gen_ngrams, self.no_repeat_ngram_size, bbsz_idx - ) - for bbsz_idx in range(bsz * beam_size) - ] - else: - banned_tokens = [ - torch.jit.annotate(List[int], []) for bbsz_idx in range(bsz * beam_size) - ] - for bbsz_idx in range(bsz * beam_size): - lprobs[bbsz_idx][ - torch.tensor(banned_tokens[bbsz_idx]).long() - ] = torch.tensor(-math.inf, dtype=torch.float) - return lprobs - class EnsembleModel(nn.Module): """A wrapper around an ensemble of models.""" - incremental_states: List[Dict[str, Dict[str, Optional[Tensor]]]] - def __init__(self, models): super().__init__() self.models_size = len(models) @@ -662,13 +734,6 @@ def __init__(self, models): self.single_model = models[0] self.models = nn.ModuleList(models) - self.incremental_states = torch.jit.annotate( - List[Dict[str, Dict[str, Optional[Tensor]]]], - [ - torch.jit.annotate(Dict[str, Dict[str, Optional[Tensor]]], {}) - for i in range(self.models_size) - ], - ) self.has_incremental: bool = False if all( hasattr(m, "decoder") and isinstance(m.decoder, FairseqIncrementalDecoder) @@ -679,17 +744,6 @@ def __init__(self, models): def forward(self): pass - def reset_incremental_state(self): - if self.has_incremental_states(): - self.incremental_states = torch.jit.annotate( - List[Dict[str, Dict[str, Optional[Tensor]]]], - [ - torch.jit.annotate(Dict[str, Dict[str, Optional[Tensor]]], {}) - for i in range(self.models_size) - ], - ) - return - def has_encoder(self): return hasattr(self.single_model, "encoder") @@ -697,24 +751,25 @@ def has_incremental_states(self): return self.has_incremental def max_decoder_positions(self): - return min([m.max_decoder_positions() for m in self.models]) + return min([m.max_decoder_positions() for m in self.models if hasattr(m, "max_decoder_positions")] + [sys.maxsize]) @torch.jit.export def forward_encoder(self, net_input: Dict[str, Tensor]): if not self.has_encoder(): return None - return [ - model.encoder.forward_torchscript(net_input) - for model in self.models - ] + return [model.encoder.forward_torchscript(net_input) for model in self.models] @torch.jit.export def forward_decoder( - self, tokens, encoder_outs: List[EncoderOut], temperature: float = 1.0 + self, + tokens, + encoder_outs: List[Dict[str, List[Tensor]]], + incremental_states: List[Dict[str, Dict[str, Optional[Tensor]]]], + temperature: float = 1.0, ): log_probs = [] avg_attn: Optional[Tensor] = None - encoder_out: Optional[EncoderOut] = None + encoder_out: Optional[Dict[str, List[Tensor]]] = None for i, model in enumerate(self.models): if self.has_encoder(): encoder_out = encoder_outs[i] @@ -723,10 +778,13 @@ def forward_decoder( decoder_out = model.decoder.forward( tokens, encoder_out=encoder_out, - incremental_state=self.incremental_states[i], + incremental_state=incremental_states[i], ) else: - decoder_out = model.decoder.forward(tokens, encoder_out=encoder_out) + if hasattr(model, "decoder"): + decoder_out = model.decoder.forward(tokens, encoder_out=encoder_out) + else: + decoder_out = model.forward(tokens) attn: Optional[Tensor] = None decoder_len = len(decoder_out) @@ -746,7 +804,6 @@ def forward_decoder( decoder_out[0][:, -1:, :].div_(temperature), None if decoder_len <= 1 else decoder_out[1], ) - probs = model.get_normalized_probs( decoder_out_tuple, log_probs=True, sample=None ) @@ -760,15 +817,19 @@ def forward_decoder( avg_attn = attn else: avg_attn.add_(attn) + avg_probs = torch.logsumexp(torch.stack(log_probs, dim=0), dim=0) - math.log( self.models_size ) + if avg_attn is not None: avg_attn.div_(self.models_size) return avg_probs, avg_attn @torch.jit.export - def reorder_encoder_out(self, encoder_outs: Optional[List[EncoderOut]], new_order): + def reorder_encoder_out( + self, encoder_outs: Optional[List[Dict[str, List[Tensor]]]], new_order + ): """ Reorder encoder output according to *new_order*. @@ -779,7 +840,7 @@ def reorder_encoder_out(self, encoder_outs: Optional[List[EncoderOut]], new_orde Returns: *encoder_out* rearranged according to *new_order* """ - new_outs: List[EncoderOut] = [] + new_outs: List[Dict[str, List[Tensor]]] = [] if not self.has_encoder(): return new_outs for i, model in enumerate(self.models): @@ -790,17 +851,23 @@ def reorder_encoder_out(self, encoder_outs: Optional[List[EncoderOut]], new_orde return new_outs @torch.jit.export - def reorder_incremental_state(self, new_order): + def reorder_incremental_state( + self, + incremental_states: List[Dict[str, Dict[str, Optional[Tensor]]]], + new_order, + ): if not self.has_incremental_states(): return for i, model in enumerate(self.models): - model.decoder.reorder_incremental_state( - self.incremental_states[i], new_order + model.decoder.reorder_incremental_state_scripting( + incremental_states[i], new_order ) class SequenceGeneratorWithAlignment(SequenceGenerator): - def __init__(self, models, tgt_dict, left_pad_target=False, **kwargs): + def __init__( + self, models, tgt_dict, left_pad_target=False, print_alignment="hard", **kwargs + ): """Generates translations of a given source sentence. Produces alignments following "Jointly Learning to Align and @@ -814,17 +881,24 @@ def __init__(self, models, tgt_dict, left_pad_target=False, **kwargs): super().__init__(EnsembleModelWithAlignment(models), tgt_dict, **kwargs) self.left_pad_target = left_pad_target + if print_alignment == "hard": + self.extract_alignment = utils.extract_hard_alignment + elif print_alignment == "soft": + self.extract_alignment = utils.extract_soft_alignment + @torch.no_grad() def generate(self, models, sample, **kwargs): - self.model.reset_incremental_state() finalized = super()._generate(sample, **kwargs) src_tokens = sample["net_input"]["src_tokens"] bsz = src_tokens.shape[0] beam_size = self.beam_size - src_tokens, src_lengths, prev_output_tokens, tgt_tokens = self._prepare_batch_for_alignment( - sample, finalized - ) + ( + src_tokens, + src_lengths, + prev_output_tokens, + tgt_tokens, + ) = self._prepare_batch_for_alignment(sample, finalized) if any(getattr(m, "full_context_alignment", False) for m in self.model.models): attn = self.model.forward_align(src_tokens, src_lengths, prev_output_tokens) else: @@ -833,9 +907,14 @@ def generate(self, models, sample, **kwargs): for i in range(bsz * beam_size) ] + if src_tokens.device != "cpu": + src_tokens = src_tokens.to("cpu") + tgt_tokens = tgt_tokens.to("cpu") + attn = [i.to("cpu") for i in attn] + # Process the attn matrix to extract hard alignments. for i in range(bsz * beam_size): - alignment = utils.extract_hard_alignment( + alignment = self.extract_alignment( attn[i], src_tokens[i], tgt_tokens[i], self.pad, self.eos ) finalized[i // beam_size][i % beam_size]["alignment"] = alignment @@ -884,7 +963,7 @@ def forward_align(self, src_tokens, src_lengths, prev_output_tokens): avg_attn = None for model in self.models: decoder_out = model(src_tokens, src_lengths, prev_output_tokens) - attn = decoder_out[1]["attn"] + attn = decoder_out[1]["attn"][0] if avg_attn is None: avg_attn = attn else: @@ -892,17 +971,3 @@ def forward_align(self, src_tokens, src_lengths, prev_output_tokens): if len(self.models) > 1: avg_attn.div_(len(self.models)) return avg_attn - - -@torch.jit.script -class BeamContainer(object): - def __init__(self, score: float, elem: Dict[str, Tensor]): - self.score = score - self.elem = elem - - def __lt__(self, other): - # type: (BeamContainer) -> bool - # Due to https://github.com/pytorch/pytorch/issues/20388, - # this has to use old style type annotations - # Match original behavior of sorted function when two scores are equal. - return self.score <= other.score diff --git a/fairseq/sequence_scorer.py b/fairseq/sequence_scorer.py index deaf86f3ce..411d4df444 100644 --- a/fairseq/sequence_scorer.py +++ b/fairseq/sequence_scorer.py @@ -3,26 +3,38 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. -import torch import sys +import torch from fairseq import utils class SequenceScorer(object): """Scores the target for a given source sentence.""" - def __init__(self, tgt_dict, softmax_batch=None, compute_alignment=False, eos=None): + def __init__( + self, + tgt_dict, + softmax_batch=None, + compute_alignment=False, + eos=None, + symbols_to_strip_from_output=None, + ): self.pad = tgt_dict.pad() self.eos = tgt_dict.eos() if eos is None else eos self.softmax_batch = softmax_batch or sys.maxsize assert self.softmax_batch > 0 self.compute_alignment = compute_alignment + self.symbols_to_strip_from_output = ( + symbols_to_strip_from_output.union({self.eos}) + if symbols_to_strip_from_output is not None + else {self.eos} + ) @torch.no_grad() def generate(self, models, sample, **kwargs): """Score a batch of translations.""" - net_input = sample['net_input'] + net_input = sample["net_input"] def batch_for_softmax(dec_out, target): # assumes decoder_out[0] is the only thing needed (may not be correct for future models!) @@ -46,7 +58,7 @@ def gather_target_probs(probs, target): ) return probs - orig_target = sample['target'] + orig_target = sample["target"] # compute scores for each model in the ensemble avg_probs = None @@ -56,13 +68,15 @@ def gather_target_probs(probs, target): decoder_out = model(**net_input) attn = decoder_out[1] if len(decoder_out) > 1 else None if type(attn) is dict: - attn = attn.get('attn', None) + attn = attn.get("attn", None) batched = batch_for_softmax(decoder_out, orig_target) probs, idx = None, 0 for bd, tgt, is_single in batched: - sample['target'] = tgt - curr_prob = model.get_normalized_probs(bd, log_probs=len(models) == 1, sample=sample).data + sample["target"] = tgt + curr_prob = model.get_normalized_probs( + bd, log_probs=len(models) == 1, sample=sample + ).data if is_single: probs = gather_target_probs(curr_prob, orig_target) else: @@ -70,19 +84,24 @@ def gather_target_probs(probs, target): probs = curr_prob.new(orig_target.numel()) step = curr_prob.size(0) * curr_prob.size(1) end = step + idx - tgt_probs = gather_target_probs(curr_prob.view(tgt.shape + (curr_prob.size(-1),)), tgt) + tgt_probs = gather_target_probs( + curr_prob.view(tgt.shape + (curr_prob.size(-1),)), tgt + ) probs[idx:end] = tgt_probs.view(-1) idx = end - sample['target'] = orig_target + sample["target"] = orig_target - probs = probs.view(sample['target'].shape) + probs = probs.view(sample["target"].shape) if avg_probs is None: avg_probs = probs else: avg_probs.add_(probs) - if attn is not None and torch.is_tensor(attn): - attn = attn.data + if attn is not None: + if torch.is_tensor(attn): + attn = attn.data + else: + attn = attn[0] if avg_attn is None: avg_attn = attn else: @@ -95,21 +114,24 @@ def gather_target_probs(probs, target): bsz = avg_probs.size(0) hypos = [] - start_idxs = sample['start_indices'] if 'start_indices' in sample else [0] * bsz + start_idxs = sample["start_indices"] if "start_indices" in sample else [0] * bsz for i in range(bsz): # remove padding from ref - ref = utils.strip_pad(sample['target'][i, start_idxs[i]:], self.pad) \ - if sample['target'] is not None else None + ref = ( + utils.strip_pad(sample["target"][i, start_idxs[i] :], self.pad) + if sample["target"] is not None + else None + ) tgt_len = ref.numel() - avg_probs_i = avg_probs[i][start_idxs[i]:start_idxs[i] + tgt_len] + avg_probs_i = avg_probs[i][start_idxs[i] : start_idxs[i] + tgt_len] score_i = avg_probs_i.sum() / tgt_len if avg_attn is not None: avg_attn_i = avg_attn[i] if self.compute_alignment: alignment = utils.extract_hard_alignment( avg_attn_i, - sample['net_input']['src_tokens'][i], - sample['target'][i], + sample["net_input"]["src_tokens"][i], + sample["target"][i], self.pad, self.eos, ) @@ -117,11 +139,15 @@ def gather_target_probs(probs, target): alignment = None else: avg_attn_i = alignment = None - hypos.append([{ - 'tokens': ref, - 'score': score_i, - 'attention': avg_attn_i, - 'alignment': alignment, - 'positional_scores': avg_probs_i, - }]) + hypos.append( + [ + { + "tokens": ref, + "score": score_i, + "attention": avg_attn_i, + "alignment": alignment, + "positional_scores": avg_probs_i, + } + ] + ) return hypos diff --git a/fairseq/speech_generator.py b/fairseq/speech_generator.py new file mode 100644 index 0000000000..8086e34d2b --- /dev/null +++ b/fairseq/speech_generator.py @@ -0,0 +1,219 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import numpy as np + +from fairseq.data.audio.speech_to_text_dataset import S2TDataConfig + + +class SpeechGenerator(object): + def __init__(self, model, vocoder, data_cfg: S2TDataConfig): + self.model = model + self.vocoder = vocoder + stats_npz_path = data_cfg.global_cmvn_stats_npz + self.gcmvn_stats = None + if stats_npz_path is not None: + self.gcmvn_stats = np.load(stats_npz_path) + + def gcmvn_denormalize(self, x): + # x: B x T x C + if self.gcmvn_stats is None: + return x + mean = torch.from_numpy(self.gcmvn_stats["mean"]).to(x) + std = torch.from_numpy(self.gcmvn_stats["std"]).to(x) + assert len(x.shape) == 3 and mean.shape[0] == std.shape[0] == x.shape[2] + x = x * std.view(1, 1, -1).expand_as(x) + return x + mean.view(1, 1, -1).expand_as(x) + + def get_waveform(self, feat): + # T x C -> T + return None if self.vocoder is None else self.vocoder(feat).squeeze(0) + + +class AutoRegressiveSpeechGenerator(SpeechGenerator): + def __init__( + self, model, vocoder, data_cfg, max_iter: int = 6000, + eos_prob_threshold: float = 0.5, + ): + super().__init__(model, vocoder, data_cfg) + self.max_iter = max_iter + self.eos_prob_threshold = eos_prob_threshold + + @torch.no_grad() + def generate(self, model, sample, has_targ=False, **kwargs): + model.eval() + + src_tokens = sample["net_input"]["src_tokens"] + src_lengths = sample["net_input"]["src_lengths"] + bsz, src_len = src_tokens.size() + n_frames_per_step = model.decoder.n_frames_per_step + out_dim = model.decoder.out_dim + raw_dim = out_dim // n_frames_per_step + + # initialize + encoder_out = model.forward_encoder(src_tokens, src_lengths, + speaker=sample["speaker"]) + incremental_state = {} + feat, attn, eos_prob = [], [], [] + finished = src_tokens.new_zeros((bsz,)).bool() + out_lens = src_lengths.new_zeros((bsz,)).long().fill_(self.max_iter) + + prev_feat_out = encoder_out["encoder_out"][0].new_zeros(bsz, 1, out_dim) + for step in range(self.max_iter): + cur_out_lens = out_lens.clone() + cur_out_lens.masked_fill_(cur_out_lens.eq(self.max_iter), step + 1) + _, cur_eos_out, cur_extra = model.forward_decoder( + prev_feat_out, encoder_out=encoder_out, + incremental_state=incremental_state, + target_lengths=cur_out_lens, speaker=sample["speaker"], **kwargs + ) + cur_eos_prob = torch.sigmoid(cur_eos_out).squeeze(2) + feat.append(cur_extra['feature_out']) + attn.append(cur_extra['attn']) + eos_prob.append(cur_eos_prob) + + cur_finished = (cur_eos_prob.squeeze(1) > self.eos_prob_threshold) + out_lens.masked_fill_((~finished) & cur_finished, step + 1) + finished = finished | cur_finished + if finished.sum().item() == bsz: + break + prev_feat_out = cur_extra['feature_out'] + + feat = torch.cat(feat, dim=1) + feat = model.decoder.postnet(feat) + feat + eos_prob = torch.cat(eos_prob, dim=1) + attn = torch.cat(attn, dim=2) + alignment = attn.max(dim=1)[1] + + feat = feat.reshape(bsz, -1, raw_dim) + feat = self.gcmvn_denormalize(feat) + + eos_prob = eos_prob.repeat_interleave(n_frames_per_step, dim=1) + attn = attn.repeat_interleave(n_frames_per_step, dim=2) + alignment = alignment.repeat_interleave(n_frames_per_step, dim=1) + out_lens = out_lens * n_frames_per_step + + finalized = [ + { + 'feature': feat[b, :out_len], + 'eos_prob': eos_prob[b, :out_len], + 'attn': attn[b, :, :out_len], + 'alignment': alignment[b, :out_len], + 'waveform': self.get_waveform(feat[b, :out_len]), + } + for b, out_len in zip(range(bsz), out_lens) + ] + + if has_targ: + assert sample["target"].size(-1) == out_dim + tgt_feats = sample["target"].view(bsz, -1, raw_dim) + tgt_feats = self.gcmvn_denormalize(tgt_feats) + tgt_lens = sample["target_lengths"] * n_frames_per_step + for b, (f, l) in enumerate(zip(tgt_feats, tgt_lens)): + finalized[b]["targ_feature"] = f[:l] + finalized[b]["targ_waveform"] = self.get_waveform(f[:l]) + return finalized + + +class NonAutoregressiveSpeechGenerator(SpeechGenerator): + @torch.no_grad() + def generate(self, model, sample, has_targ=False, **kwargs): + model.eval() + + bsz, max_src_len = sample["net_input"]["src_tokens"].size() + n_frames_per_step = model.encoder.n_frames_per_step + out_dim = model.encoder.out_dim + raw_dim = out_dim // n_frames_per_step + + feat, out_lens, log_dur_out, _, _ = model( + src_tokens=sample["net_input"]["src_tokens"], + src_lengths=sample["net_input"]["src_lengths"], + prev_output_tokens=sample["net_input"]["prev_output_tokens"], + incremental_state=None, + target_lengths=sample["target_lengths"], + speaker=sample["speaker"] + ) + + feat = feat.view(bsz, -1, raw_dim) + feat = self.gcmvn_denormalize(feat) + + dur_out = torch.clamp( + torch.round(torch.exp(log_dur_out) - 1).long(), min=0 + ) + + def get_dur_plot_data(d): + r = [] + for i, dd in enumerate(d): + r += [i + 1] * dd.item() + return r + + out_lens = out_lens * n_frames_per_step + finalized = [ + { + 'feature': feat[b, :l] if l > 0 else feat.new_zeros([1, raw_dim]), + 'waveform': self.get_waveform( + feat[b, :l] if l > 0 else feat.new_zeros([1, raw_dim]) + ), + 'attn': feat.new_tensor(get_dur_plot_data(dur_out[b])), + } + for b, l in zip(range(bsz), out_lens) + ] + + if has_targ: + tgt_feats = sample["target"].view(bsz, -1, raw_dim) + tgt_feats = self.gcmvn_denormalize(tgt_feats) + tgt_lens = sample["target_lengths"] * n_frames_per_step + for b, (f, l) in enumerate(zip(tgt_feats, tgt_lens)): + finalized[b]["targ_feature"] = f[:l] + finalized[b]["targ_waveform"] = self.get_waveform(f[:l]) + return finalized + + +class TeacherForcingAutoRegressiveSpeechGenerator(AutoRegressiveSpeechGenerator): + @torch.no_grad() + def generate(self, model, sample, has_targ=False, **kwargs): + model.eval() + + src_tokens = sample["net_input"]["src_tokens"] + src_lens = sample["net_input"]["src_lengths"] + prev_out_tokens = sample["net_input"]["prev_output_tokens"] + tgt_lens = sample["target_lengths"] + n_frames_per_step = model.decoder.n_frames_per_step + raw_dim = model.decoder.out_dim // n_frames_per_step + bsz = src_tokens.shape[0] + + feat, eos_prob, extra = model( + src_tokens, src_lens, prev_out_tokens, incremental_state=None, + target_lengths=tgt_lens, speaker=sample["speaker"] + ) + + attn = extra["attn"] # B x T_s x T_t + alignment = attn.max(dim=1)[1] + feat = feat.reshape(bsz, -1, raw_dim) + feat = self.gcmvn_denormalize(feat) + eos_prob = eos_prob.repeat_interleave(n_frames_per_step, dim=1) + attn = attn.repeat_interleave(n_frames_per_step, dim=2) + alignment = alignment.repeat_interleave(n_frames_per_step, dim=1) + tgt_lens = sample["target_lengths"] * n_frames_per_step + + finalized = [ + { + 'feature': feat[b, :tgt_len], + 'eos_prob': eos_prob[b, :tgt_len], + 'attn': attn[b, :, :tgt_len], + 'alignment': alignment[b, :tgt_len], + 'waveform': self.get_waveform(feat[b, :tgt_len]), + } + for b, tgt_len in zip(range(bsz), tgt_lens) + ] + + if has_targ: + tgt_feats = sample["target"].view(bsz, -1, raw_dim) + tgt_feats = self.gcmvn_denormalize(tgt_feats) + for b, (f, l) in enumerate(zip(tgt_feats, tgt_lens)): + finalized[b]["targ_feature"] = f[:l] + finalized[b]["targ_waveform"] = self.get_waveform(f[:l]) + return finalized diff --git a/fairseq/tasks/__init__.py b/fairseq/tasks/__init__.py index b1bb404f1c..9a46b012c5 100644 --- a/fairseq/tasks/__init__.py +++ b/fairseq/tasks/__init__.py @@ -2,22 +2,51 @@ # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. +"""isort:skip_file""" import argparse import importlib import os -from .fairseq_task import FairseqTask +from fairseq.dataclass import FairseqDataclass +from fairseq.dataclass.utils import merge_with_parent +from hydra.core.config_store import ConfigStore +from .fairseq_task import FairseqTask, LegacyFairseqTask # noqa + + +# register dataclass +TASK_DATACLASS_REGISTRY = {} TASK_REGISTRY = {} TASK_CLASS_NAMES = set() -def setup_task(args, **kwargs): - return TASK_REGISTRY[args.task].setup_task(args, **kwargs) +def setup_task(cfg: FairseqDataclass, **kwargs): + task = None + task_name = getattr(cfg, "task", None) + + if isinstance(task_name, str): + # legacy tasks + task = TASK_REGISTRY[task_name] + if task_name in TASK_DATACLASS_REGISTRY: + dc = TASK_DATACLASS_REGISTRY[task_name] + cfg = dc.from_namespace(cfg) + else: + task_name = getattr(cfg, "_name", None) + + if task_name and task_name in TASK_DATACLASS_REGISTRY: + dc = TASK_DATACLASS_REGISTRY[task_name] + cfg = merge_with_parent(dc(), cfg) + task = TASK_REGISTRY[task_name] + assert ( + task is not None + ), f"Could not infer task type from {cfg}. Available argparse tasks: {TASK_REGISTRY.keys()}. Available hydra tasks: {TASK_DATACLASS_REGISTRY.keys()}" -def register_task(name): + return task.setup_task(cfg, **kwargs) + + +def register_task(name, dataclass=None): """ New tasks can be added to fairseq with the :func:`~fairseq.tasks.register_task` function decorator. @@ -33,21 +62,40 @@ class ClassificationTask(FairseqTask): All Tasks must implement the :class:`~fairseq.tasks.FairseqTask` interface. - Please see the - Args: name (str): the name of the task """ def register_task_cls(cls): if name in TASK_REGISTRY: - raise ValueError('Cannot register duplicate task ({})'.format(name)) + raise ValueError("Cannot register duplicate task ({})".format(name)) if not issubclass(cls, FairseqTask): - raise ValueError('Task ({}: {}) must extend FairseqTask'.format(name, cls.__name__)) + raise ValueError( + "Task ({}: {}) must extend FairseqTask".format(name, cls.__name__) + ) if cls.__name__ in TASK_CLASS_NAMES: - raise ValueError('Cannot register task with duplicate class name ({})'.format(cls.__name__)) + raise ValueError( + "Cannot register task with duplicate class name ({})".format( + cls.__name__ + ) + ) TASK_REGISTRY[name] = cls TASK_CLASS_NAMES.add(cls.__name__) + + if dataclass is not None and not issubclass(dataclass, FairseqDataclass): + raise ValueError( + "Dataclass {} must extend FairseqDataclass".format(dataclass) + ) + + cls.__dataclass = dataclass + if dataclass is not None: + TASK_DATACLASS_REGISTRY[name] = dataclass + + cs = ConfigStore.instance() + node = dataclass() + node._name = name + cs.store(name=name, group="task", node=node, provider="fairseq") + return cls return register_task_cls @@ -57,26 +105,32 @@ def get_task(name): return TASK_REGISTRY[name] +def import_tasks(tasks_dir, namespace): + for file in os.listdir(tasks_dir): + path = os.path.join(tasks_dir, file) + if ( + not file.startswith("_") + and not file.startswith(".") + and (file.endswith(".py") or os.path.isdir(path)) + ): + task_name = file[: file.find(".py")] if file.endswith(".py") else file + importlib.import_module(namespace + "." + task_name) + + # expose `task_parser` for sphinx + if task_name in TASK_REGISTRY: + parser = argparse.ArgumentParser(add_help=False) + group_task = parser.add_argument_group("Task name") + # fmt: off + group_task.add_argument('--task', metavar=task_name, + help='Enable this task with: ``--task=' + task_name + '``') + # fmt: on + group_args = parser.add_argument_group( + "Additional command-line arguments" + ) + TASK_REGISTRY[task_name].add_args(group_args) + globals()[task_name + "_parser"] = parser + + # automatically import any Python files in the tasks/ directory tasks_dir = os.path.dirname(__file__) -for file in os.listdir(tasks_dir): - path = os.path.join(tasks_dir, file) - if ( - not file.startswith('_') - and not file.startswith('.') - and (file.endswith('.py') or os.path.isdir(path)) - ): - task_name = file[:file.find('.py')] if file.endswith('.py') else file - importlib.import_module('fairseq.tasks.' + task_name) - - # expose `task_parser` for sphinx - if task_name in TASK_REGISTRY: - parser = argparse.ArgumentParser(add_help=False) - group_task = parser.add_argument_group('Task name') - # fmt: off - group_task.add_argument('--task', metavar=task_name, - help='Enable this task with: ``--task=' + task_name + '``') - # fmt: on - group_args = parser.add_argument_group('Additional command-line arguments') - TASK_REGISTRY[task_name].add_args(group_args) - globals()[task_name + '_parser'] = parser +import_tasks(tasks_dir, "fairseq.tasks") diff --git a/fairseq/tasks/audio_finetuning.py b/fairseq/tasks/audio_finetuning.py new file mode 100644 index 0000000000..4ef87c604f --- /dev/null +++ b/fairseq/tasks/audio_finetuning.py @@ -0,0 +1,346 @@ +# Copyright (c) 2017-present, Facebook, Inc. +# All rights reserved. +# +# This source code is licensed under the license found in the LICENSE file in +# the root directory of this source tree. An additional grant of patent rights +# can be found in the PATENTS file in the same directory. + +import logging +import os +import torch +import json + +from argparse import Namespace +from dataclasses import dataclass, field +from typing import Optional, Any + +from fairseq.data import AddTargetDataset, Dictionary, encoders +from fairseq.tasks.audio_pretraining import AudioPretrainingTask, AudioPretrainingConfig +from fairseq.dataclass import FairseqDataclass +from fairseq.dataclass.configs import GenerationConfig +from fairseq.data.text_compressor import TextCompressor, TextCompressionLevel + +from . import register_task +from .. import utils +from ..logging import metrics + + +logger = logging.getLogger(__name__) + + +class LabelEncoder(object): + def __init__(self, dictionary): + self.dictionary = dictionary + + def __call__(self, label): + return self.dictionary.encode_line( + label, append_eos=False, add_if_not_exist=False + ) + + +def label_len_fn(label): + return len(label.split(" ")) + + +@dataclass +class AudioFinetuningConfig(AudioPretrainingConfig): + # Options for reporting WER metrics during validation. Only applicable to + # Seq2Seq models during fine-tuning + eval_wer: bool = field( + default=False, metadata={"help": "compute WER for Seq2Seq models"} + ) + eval_wer_config: GenerationConfig = field( + default_factory=lambda: GenerationConfig(), + metadata={"help": "beam search config for evaluating wer during training"}, + ) + eval_wer_tokenizer: Any = field( + default=None, + metadata={"help": "tokenizer config for evaluating wer during training"}, + ) + eval_wer_post_process: str = field( + default="letter", + metadata={ + "help": "remove BPE tokens before scoring (can be sentencepiece, letter, and more)" + }, + ) + eval_bleu: bool = field( + default=False, metadata={"help": "evaluation with BLEU scores"} + ) + eval_bleu_detok: Optional[str] = field( + default=None, metadata={ + "help": "detokenize before computing BLEU (e.g., 'moses'); " + "required if using --eval-bleu; use 'space' to disable " + "detokenization; see fairseq.data.encoders for other options" + } + ) + eval_bleu_detok_args: str = field( + default="{}", + metadata={"help": "args for building the tokenizer, if needed"} + ) + eval_tokenized_bleu: bool = field( + default=False, + metadata={"help": "compute tokenized BLEU instead of sacrebleu"} + ) + eval_bleu_remove_bpe: Optional[str] = field( + default=None, metadata={"help": "remove BPE before computing BLEU"} + ) + eval_bleu_args: str = field( + default="{}", + metadata={"help": "generation args for BLUE scoring, e.g., " + "'{\"beam\": 4, \"lenpen\": 0.6}'"} + ) + eval_bleu_print_samples: bool = field( + default=False, + metadata={"help": "print sample generations during validation"} + ) + autoregressive: bool = field( + default=False, + metadata={ + "help": "required for autoregressive decoders (like seq2seq models); " + "adds 'prev_output_tokens' to input and appends eos to target" + }, + ) + + +@register_task("audio_finetuning", dataclass=AudioFinetuningConfig) +class AudioFinetuningTask(AudioPretrainingTask): + """ """ + + cfg: AudioFinetuningConfig + + def __init__( + self, + cfg: AudioFinetuningConfig, + ): + super().__init__(cfg) + self.blank_symbol = "<s>" + + self.state.add_factory("target_dictionary", self.load_target_dictionary) + + def load_target_dictionary(self): + if self.cfg.labels: + dict_path = os.path.join(self.cfg.data, f"dict.{self.cfg.labels}.txt") + return Dictionary.load(dict_path) + return None + + def load_dataset(self, split: str, task_cfg: AudioFinetuningConfig = None, **kwargs): + super().load_dataset(split, task_cfg, **kwargs) + + task_cfg = task_cfg or self.cfg + assert task_cfg.labels is not None + text_compression_level = getattr( + TextCompressionLevel, str(self.cfg.text_compression_level) + ) + data_path = self.cfg.data + label_path = os.path.join(data_path, f"{split}.{task_cfg.labels}") + skipped_indices = getattr(self.datasets[split], "skipped_indices", set()) + text_compressor = TextCompressor(level=text_compression_level) + with open(label_path, "r") as f: + labels = [ + text_compressor.compress(l) + for i, l in enumerate(f) if i not in skipped_indices + ] + + assert len(labels) == len(self.datasets[split]), ( + f"labels length ({len(labels)}) and dataset length " + f"({len(self.datasets[split])}) do not match" + ) + + process_label = LabelEncoder(self.target_dictionary) + + self.datasets[split] = AddTargetDataset( + self.datasets[split], + labels, + pad=self.target_dictionary.pad(), + eos=self.target_dictionary.eos(), + batch_targets=True, + process_label=process_label, + label_len_fn=label_len_fn, + add_to_input=task_cfg.get("autoregressive", False), + text_compression_level=text_compression_level + ) + + @property + def target_dictionary(self): + """Return the :class:`~fairseq.data.Dictionary` for the language + model.""" + return self.state.target_dictionary + + def valid_step(self, sample, model, criterion): + loss, sample_size, logging_output = super().valid_step(sample, model, criterion) + if self.cfg.eval_wer and self.cfg.autoregressive: + metrics = self._inference_with_wer(self.sequence_generator, sample, model) + logging_output["_num_char_errors"] = metrics["num_char_errors"] + logging_output["_num_chars"] = metrics["num_chars"] + logging_output["_num_word_errors"] = metrics["num_word_errors"] + logging_output["_num_words"] = metrics["num_words"] + if self.cfg.eval_bleu and self.cfg.autoregressive: + metrics = self._inference_with_bleu(self.sequence_generator, sample, model) + logging_output['_bleu_sys_len'] = metrics.sys_len + logging_output['_bleu_ref_len'] = metrics.ref_len + # we split counts into separate entries so that they can be + # summed efficiently across workers using fast-stat-sync + assert len(metrics.counts) == 4 + for i in range(4): + logging_output[f"_bleu_counts_{i}"] = metrics.counts[i] + logging_output[f"_bleu_totals_{i}"] = metrics.totals[i] + return loss, sample_size, logging_output + + def build_model(self, model_cfg: FairseqDataclass): + model = super().build_model(model_cfg) + + if self.cfg.eval_wer and self.cfg.autoregressive: + self.sequence_generator = self.build_generator( + [model], + self.cfg.eval_wer_config, + ) + if self.cfg.eval_wer_tokenizer: + self.tokenizer = encoders.build_tokenizer(self.cfg.eval_wer_tokenizer) + else: + self.tokenizer = None + if self.cfg.eval_bleu and self.cfg.autoregressive: + assert self.cfg.eval_bleu_detok is not None, ( + '--eval-bleu-detok is required if using --eval-bleu; ' + 'try --eval-bleu-detok=moses (or --eval-bleu-detok=space ' + 'to disable detokenization, e.g., when using sentencepiece)' + ) + detok_args = json.loads(self.cfg.eval_bleu_detok_args) + self.tokenizer = encoders.build_tokenizer( + Namespace(tokenizer=self.cfg.eval_bleu_detok, **detok_args) + ) + gen_args = json.loads(self.cfg.eval_bleu_args) + gen_args = Namespace(**gen_args) + self.sequence_generator = self.build_generator([model], gen_args) + + return model + + def _inference_with_wer(self, generator, sample, model): + import editdistance + + def decode(toks): + s = self.target_dictionary.string( + toks.int().cpu(), + self.cfg.eval_wer_post_process, + escape_unk=True, + ) + if self.tokenizer: + s = self.tokenizer.decode(s) + return s + + num_word_errors, num_char_errors = 0, 0 + num_chars, num_words = 0, 0 + gen_out = self.inference_step(generator, [model], sample, None) + for i in range(len(gen_out)): + hyp = decode(gen_out[i][0]["tokens"]) + ref = decode( + utils.strip_pad(sample["target"][i], self.target_dictionary.pad()), + ) + num_char_errors += editdistance.eval(hyp, ref) + num_chars += len(ref) + hyp_words = hyp.split() + ref_words = ref.split() + num_word_errors += editdistance.eval(hyp_words, ref_words) + num_words += len(ref_words) + + return { + "num_char_errors": num_char_errors, + "num_chars": num_chars, + "num_word_errors": num_word_errors, + "num_words": num_words, + } + + def _inference_with_bleu(self, generator, sample, model): + import sacrebleu + + def decode(toks, is_ref): + s = self.target_dictionary.string( + toks.int().cpu(), + self.cfg.eval_bleu_remove_bpe, + # The default unknown string in fairseq is `<unk>`, but + # this is tokenized by sacrebleu as `< unk >`, inflating + # BLEU scores. Instead, we use a somewhat more verbose + # alternative that is unlikely to appear in the real + # reference, but doesn't get split into multiple tokens. + unk_string=( + "UNKNOWNTOKENINREF" if is_ref else "UNKNOWNTOKENINHYP" + ), + ) + if self.tokenizer: + s = self.tokenizer.decode(s) + return s + + gen_out = self.inference_step(generator, [model], sample) + hyps, refs = [], [] + for i in range(len(gen_out)): + hyps.append(decode(gen_out[i][0]['tokens'], is_ref=False)) + refs.append( + decode( + utils.strip_pad( + sample['target'][i], + self.target_dictionary.pad() + ), + is_ref=True, # don't count <unk> as matches to the hypo + ) + ) + if self.cfg.eval_bleu_print_samples: + logger.info('H-{} {}'.format(sample["id"][0], hyps[0])) + logger.info('T-{} {}'.format(sample["id"][0], refs[0])) + + eval_tokenization = 'none' if self.cfg.eval_tokenized_bleu else '13a' + return sacrebleu.corpus_bleu(hyps, [refs], tokenize=eval_tokenization) + + def reduce_metrics(self, logging_outputs, criterion): + super().reduce_metrics(logging_outputs, criterion) + + if self.cfg.eval_wer: + zero = torch.scalar_tensor(0.0) + num_char_errors = sum( + log.get("_num_char_errors", zero) for log in logging_outputs + ) + num_chars = sum(log.get("_num_chars", zero) for log in logging_outputs) + num_word_errors = sum( + log.get("_num_word_errors", zero) for log in logging_outputs + ) + num_words = sum(log.get("_num_words", zero) for log in logging_outputs) + metrics.log_scalar("_num_char_errors", num_char_errors) + metrics.log_scalar("_num_chars", num_chars) + metrics.log_scalar("_num_word_errors", num_word_errors) + metrics.log_scalar("_num_words", num_words) + if num_chars > 0: + metrics.log_derived( + "uer", + lambda meters: meters["_num_char_errors"].sum + * 100.0 + / meters["_num_chars"].sum + if meters["_num_chars"].sum > 0 + else float("nan"), + ) + if num_words > 0: + metrics.log_derived( + "wer", + lambda meters: meters["_num_word_errors"].sum + * 100.0 + / meters["_num_words"].sum + if meters["_num_words"].sum > 0 + else float("nan"), + ) + if self.cfg.eval_bleu: + len_keys = ["_bleu_sys_len", "_bleu_ref_len"] + count_keys = [f"_bleu_counts_{i}" for i in range(4)] + total_keys = [f"_bleu_totals_{i}" for i in range(4)] + for k in len_keys + count_keys + total_keys: + metrics.log_scalar( + k, sum(log.get(k, 0) for log in logging_outputs) + ) + + import sacrebleu + metrics.log_derived( + 'bleu', + lambda meters: sacrebleu.compute_bleu( + correct=[meters[k].sum for k in count_keys], + total=[meters[k].sum for k in total_keys], + sys_len=meters['_bleu_sys_len'].sum, + ref_len=meters['_bleu_ref_len'].sum, + smooth_method="exp" + ).score + ) diff --git a/fairseq/tasks/audio_pretraining.py b/fairseq/tasks/audio_pretraining.py index e161c224e9..cc310088db 100644 --- a/fairseq/tasks/audio_pretraining.py +++ b/fairseq/tasks/audio_pretraining.py @@ -1,58 +1,206 @@ -# Copyright (c) Facebook, Inc. and its affiliates. +# Copyright (c) 2017-present, Facebook, Inc. +# All rights reserved. # -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. +# This source code is licensed under the license found in the LICENSE file in +# the root directory of this source tree. An additional grant of patent rights +# can be found in the PATENTS file in the same directory. +import logging import os +import sys + +from argparse import Namespace +from dataclasses import dataclass, field +from typing import Optional +from omegaconf import MISSING, II, OmegaConf + +from fairseq.data import BinarizedAudioDataset, FileAudioDataset +from fairseq.dataclass import FairseqDataclass, ChoiceEnum +from fairseq.data.text_compressor import TextCompressionLevel -from fairseq.data import FileAudioDataset from . import FairseqTask, register_task -@register_task('audio_pretraining') -class AudioPretrainingTask(FairseqTask): - """ +logger = logging.getLogger(__name__) + + +@dataclass +class InferredW2vConfig: + # The following are needed to precompute mask and mask channel indices + # before model's forward. + mask_length: Optional[int] = II("model.mask_length") + mask_prob: Optional[float] = II("model.mask_prob") + mask_selection: Optional[str] = II("model.mask_selection") + mask_other: Optional[float] = II("model.mask_other") + no_mask_overlap: Optional[bool] = II("model.no_mask_overlap") + mask_min_space: Optional[int] = II("model.mask_min_space") + mask_channel_length: Optional[int] = II("model.mask_channel_length") + mask_channel_prob: Optional[float] = II("model.mask_channel_prob") + mask_channel_selection: Optional[str] = II("model.mask_channel_selection") + mask_channel_other: Optional[float] = II("model.mask_channel_other") + no_mask_channel_overlap: Optional[bool] = II("model.no_mask_channel_overlap") + mask_channel_min_space: Optional[int] = II("model.mask_channel_min_space") + + conv_feature_layers: Optional[str] = II("model.conv_feature_layers") + encoder_embed_dim: Optional[int] = II("model.encoder_embed_dim") - """ - @staticmethod - def add_args(parser): - """Add task-specific arguments to the parser.""" - parser.add_argument('data', help='path to data directory') - parser.add_argument('--sample-rate', default=16000, type=int, - help='target sample rate. audio files will be up/down sampled to this rate') - parser.add_argument('--max-sample-size', default=None, type=int, - help='max sample size to crop to for batching. default = min sample length') - parser.add_argument('--min-sample-size', default=None, type=int, - help='min sample size to crop to for batching. default = same as --max-sample-size') +@dataclass +class AudioPretrainingConfig(FairseqDataclass): + data: str = field(default=MISSING, metadata={"help": "path to data directory"}) + labels: Optional[str] = field( + default=None, + metadata={ + "help": "extension of the label file to load, used for fine-tuning"}, + ) + binarized_dataset: bool = field( + default=False, + metadata={ + "help": "if true, loads binarized dataset (useful for very large datasets). " + "See examples/wav2vec/scripts/binarize_manifest.sh" + }, + ) + sample_rate: int = field( + default=16_000, + metadata={ + "help": "target sample rate. audio files will be up/down sampled to this rate" + }, + ) + normalize: bool = field( + default=False, + metadata={"help": "if set, normalizes input to have 0 mean and unit variance"}, + ) + enable_padding: bool = field( + default=False, metadata={"help": "pad shorter samples instead of cropping"} + ) + max_sample_size: Optional[int] = field( + default=None, metadata={"help": "max sample size to crop to for batching"} + ) + min_sample_size: Optional[int] = field( + default=None, metadata={"help": "min sample size to skip small examples"} + ) + num_batch_buckets: int = field( + default=0, + metadata={"help": "number of buckets"}, + ) + precompute_mask_indices: bool = field( + default=False, + metadata={ + "help": "flag to compute mask indices in data preparation.", + }, + ) - def __init__(self, args): - super().__init__(args) + inferred_w2v_config: Optional[InferredW2vConfig] = field( + default=None, + metadata={ + "help": "wav2vec 2.0 masking arguments used to pre-compute masks (required for TPU)", + }, + ) + + tpu: bool = II("common.tpu") + text_compression_level: ChoiceEnum([x.name for x in TextCompressionLevel]) = field( + default="none", + metadata={ + "help": "compression level for texts (e.g. audio filenames, " + "target texts): none/low/high (default: none). " + } + ) + + +@register_task("audio_pretraining", dataclass=AudioPretrainingConfig) +class AudioPretrainingTask(FairseqTask): + """ """ + + cfg: AudioPretrainingConfig @classmethod - def setup_task(cls, args, **kwargs): + def setup_task(cls, cfg: AudioPretrainingConfig, **kwargs): """Setup the task (e.g., load dictionaries). Args: - args (argparse.Namespace): parsed command-line arguments + cfg (AudioPretrainingConfig): configuration of this task """ - return cls(args) - def load_dataset(self, split, **kwargs): - """Load a given dataset split. + return cls(cfg) - Args: - split (str): name of the split (e.g., train, valid, test) - """ + def _get_mask_precompute_kwargs(self, cfg): + if self.cfg.precompute_mask_indices or self.cfg.tpu: + assert ( + cfg.inferred_w2v_config is not None + ), "inferred_w2v_config must be set" + return OmegaConf.to_container( + cfg.inferred_w2v_config, resolve=True, enum_to_str=True + ) + else: + return {} + + def load_dataset(self, split: str, task_cfg: FairseqDataclass = None, **kwargs): + data_path = self.cfg.data + task_cfg = task_cfg or self.cfg + + # upgrade old task + if isinstance(task_cfg, Namespace): + if not hasattr(task_cfg, "autoregressive"): + task_cfg.autoregressive = not task_cfg.criterion == "ctc" + + text_compression_level = getattr( + TextCompressionLevel, str(self.cfg.text_compression_level) + ) + if getattr(task_cfg, "binarized_dataset", False): + self.datasets[split] = BinarizedAudioDataset( + data_path, + split=split, + sample_rate=task_cfg.get("sample_rate", self.cfg.sample_rate), + max_sample_size=self.cfg.max_sample_size, + min_sample_size=self.cfg.min_sample_size, + pad=task_cfg.labels is not None or task_cfg.enable_padding, + normalize=task_cfg.normalize, + num_buckets=self.cfg.num_batch_buckets or int(self.cfg.tpu), + compute_mask_indices=(self.cfg.precompute_mask_indices or self.cfg.tpu), + **self._get_mask_precompute_kwargs(task_cfg), + ) + else: + manifest_path = os.path.join(data_path, "{}.tsv".format(split)) + + self.datasets[split] = FileAudioDataset( + manifest_path=manifest_path, + sample_rate=task_cfg.get("sample_rate", self.cfg.sample_rate), + max_sample_size=self.cfg.max_sample_size, + min_sample_size=self.cfg.min_sample_size, + pad=task_cfg.labels is not None or task_cfg.enable_padding, + normalize=task_cfg.normalize, + num_buckets=self.cfg.num_batch_buckets or int(self.cfg.tpu), + compute_mask_indices=(self.cfg.precompute_mask_indices or self.cfg.tpu), + text_compression_level=text_compression_level, + **self._get_mask_precompute_kwargs(task_cfg), + ) + + if self.cfg.tpu and task_cfg.inferred_w2v_config.mask_channel_prob == 0.0: + logger.info( + "Pretraining on TPUs may suffer convergence " + "issues when training with `mask_channel_prob` value of " + "0. You may want to set this to a low value close to 0." + ) - manifest = os.path.join(self.args.data, '{}.tsv'.format(split)) - self.datasets[split] = FileAudioDataset(manifest, - sample_rate=self.args.sample_rate, - max_sample_size=self.args.max_sample_size, - min_sample_size=self.args.min_sample_size) + @property + def source_dictionary(self): + return None @property def target_dictionary(self): - """Return the :class:`~fairseq.data.Dictionary` for the language - model.""" return None + + def max_positions(self): + """Maximum input length supported by the encoder.""" + return sys.maxsize, sys.maxsize + + def build_model(self, model_cfg: FairseqDataclass): + model = super().build_model(model_cfg) + + actualized_cfg = getattr(model, "cfg", None) + if actualized_cfg is not None: + # if "w2v_args" in actualized_cfg: + if hasattr(actualized_cfg, "w2v_args"): + model_cfg.w2v_args = actualized_cfg.w2v_args + + return model diff --git a/fairseq/tasks/cross_lingual_lm.py b/fairseq/tasks/cross_lingual_lm.py index 3589492f11..8f8fe7e2de 100644 --- a/fairseq/tasks/cross_lingual_lm.py +++ b/fairseq/tasks/cross_lingual_lm.py @@ -3,32 +3,25 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. -from collections import OrderedDict import itertools import logging import os +from collections import OrderedDict import numpy as np - -from fairseq import tokenizer -from fairseq.data.legacy.masked_lm_dictionary import MaskedLMDictionary - -from fairseq.data import ( - Dictionary, - ConcatDataset, - data_utils, - TokenBlockDataset, -) +from fairseq import tokenizer, utils +from fairseq.data import ConcatDataset, Dictionary, TokenBlockDataset, data_utils from fairseq.data.legacy.masked_lm_dataset import MaskedLMDataset +from fairseq.data.legacy.masked_lm_dictionary import MaskedLMDictionary from fairseq.data.multi_corpus_sampled_dataset import MultiCorpusSampledDataset -from fairseq.tasks import FairseqTask, register_task -from fairseq import utils +from fairseq.tasks import LegacyFairseqTask, register_task + logger = logging.getLogger(__name__) -@register_task('cross_lingual_lm') -class CrossLingualLMTask(FairseqTask): +@register_task("cross_lingual_lm") +class CrossLingualLMTask(LegacyFairseqTask): """ Task for training cross-lingual language models. @@ -41,17 +34,29 @@ class CrossLingualLMTask(FairseqTask): @staticmethod def add_args(parser): """Add task-specific arguments to the parser.""" - parser.add_argument('data', help='colon separated path to data directories list, \ - will be iterated upon during epochs in round-robin manner') - parser.add_argument('--tokens-per-sample', default=512, type=int, - help='max number of total tokens over all segments' - ' per sample') - parser.add_argument('--monolingual-langs', default='en', type=str, - help='comma separated list of languages for which we' - ' want to train XLM on') - parser.add_argument('--shuffle', action='store_true', - help='shuffle each monolingual dataset while' - ' training') + parser.add_argument( + "data", + help="colon separated path to data directories list, \ + will be iterated upon during epochs in round-robin manner", + ) + parser.add_argument( + "--tokens-per-sample", + default=512, + type=int, + help="max number of total tokens over all segments" " per sample", + ) + parser.add_argument( + "--monolingual-langs", + default="en", + type=str, + help="comma separated list of languages for which we" + " want to train XLM on", + ) + parser.add_argument( + "--shuffle", + action="store_true", + help="shuffle each monolingual dataset while" " training", + ) def __init__(self, args, dictionary): super().__init__(args) @@ -60,16 +65,13 @@ def __init__(self, args, dictionary): self.distributed_world_size = args.distributed_world_size self.langs2id = self._lang_to_id(args.monolingual_langs) - def _lang_to_id( - self, - languages: str - ): + def _lang_to_id(self, languages: str): """ Build a map from languages to ids. These ids are used as segment labels for cross-lingual LM training. """ lang2id = {} - langs = [l.strip() for l in languages.split(',')] + langs = [l.strip() for l in languages.split(",")] for id, lang in enumerate(langs): lang2id[lang] = id return lang2id @@ -79,10 +81,14 @@ def load_dictionary(cls, filename): return MaskedLMDictionary.load(filename) @classmethod - def build_dictionary(cls, filenames, workers=1, threshold=-1, nwords=-1, padding_factor=8): + def build_dictionary( + cls, filenames, workers=1, threshold=-1, nwords=-1, padding_factor=8 + ): d = MaskedLMDictionary() for filename in filenames: - Dictionary.add_file_to_dictionary(filename, d, tokenizer.tokenize_line, workers) + Dictionary.add_file_to_dictionary( + filename, d, tokenizer.tokenize_line, workers + ) d.finalize(threshold=threshold, nwords=nwords, padding_factor=padding_factor) return d @@ -93,8 +99,8 @@ def target_dictionary(self): @classmethod def setup_task(cls, args, **kwargs): """Setup the task.""" - dictionary = MaskedLMDictionary.load(os.path.join(args.data, 'dict.txt')) - logger.info('dictionary: {} types'.format(len(dictionary))) + dictionary = MaskedLMDictionary.load(os.path.join(args.data, "dict.txt")) + logger.info("dictionary: {} types".format(len(dictionary))) return cls(args, dictionary) def _load_single_lang_dataset(self, split, epoch): @@ -105,27 +111,36 @@ def _load_single_lang_dataset(self, split, epoch): data_path = paths[(epoch - 1) % len(paths)] for k in itertools.count(): - split_k = split + (str(k) if k > 0 else '') + split_k = split + (str(k) if k > 0 else "") path = os.path.join(data_path, split_k) - ds = data_utils.load_indexed_dataset(path, self.dictionary, self.args.dataset_impl) + ds = data_utils.load_indexed_dataset( + path, self.dictionary, self.args.dataset_impl + ) if ds is None: if k > 0: break else: - raise FileNotFoundError('Dataset not found: {} ({})'.format(split, data_path)) + raise FileNotFoundError( + "Dataset not found: {} ({})".format(split, data_path) + ) # Since we append each block with the classification_token, # we need to effectively create blocks of length # tokens_per_sample-1 loaded_datasets.append( TokenBlockDataset( - ds, ds.sizes, self.args.tokens_per_sample - 1, - pad=self.dictionary.pad(), eos=self.dictionary.eos(), + ds, + ds.sizes, + self.args.tokens_per_sample - 1, + pad=self.dictionary.pad(), + eos=self.dictionary.eos(), ) ) - logger.info('{} {} {} examples'.format(data_path, split_k, len(loaded_datasets[-1]))) + logger.info( + "{} {} {} examples".format(data_path, split_k, len(loaded_datasets[-1])) + ) if len(loaded_datasets) == 1: dataset = loaded_datasets[0] @@ -146,9 +161,11 @@ def load_dataset(self, split, epoch=1, combine=False, **kwargs): for lang in self.langs2id.keys(): # Datasets are expected to be in "split.lang" format (Eg: train.en) - language_split = '{}.{}'.format(split, lang) + language_split = "{}.{}".format(split, lang) - block_dataset, sizes = self._load_single_lang_dataset(split=language_split, epoch=epoch) + block_dataset, sizes = self._load_single_lang_dataset( + split=language_split, epoch=epoch + ) dataset_map[lang] = MaskedLMDataset( dataset=block_dataset, @@ -158,13 +175,17 @@ def load_dataset(self, split, epoch=1, combine=False, **kwargs): mask_idx=self.dictionary.mask(), classif_token_idx=self.dictionary.eos(), sep_token_idx=self.dictionary.eos(), - shuffle=getattr(self.args, 'shuffle', False), + shuffle=getattr(self.args, "shuffle", False), has_pairs=False, segment_id=self.langs2id[lang], seed=self.seed, ) self.datasets[split] = MultiCorpusSampledDataset(dataset_map) - logger.info('{} {} {} examples'.format( - utils.split_paths(self.args.data)[epoch - 1], split, len(self.datasets[split])) + logger.info( + "{} {} {} examples".format( + utils.split_paths(self.args.data)[epoch - 1], + split, + len(self.datasets[split]), + ) ) diff --git a/fairseq/tasks/denoising.py b/fairseq/tasks/denoising.py index 28beb517f2..d1dff26c36 100644 --- a/fairseq/tasks/denoising.py +++ b/fairseq/tasks/denoising.py @@ -6,25 +6,31 @@ import logging import os +from fairseq import utils from fairseq.data import ( - data_utils, - Dictionary, AppendTokenDataset, DenoisingDataset, + Dictionary, + IdDataset, + NestedDictionaryDataset, + NumelDataset, + PadDataset, PrependTokenDataset, StripTokenDataset, TokenBlockDataset, + data_utils, ) from fairseq.data.encoders.utils import get_whole_word_mask -from fairseq.tasks import FairseqTask, register_task -from fairseq import utils +from fairseq.data.shorten_dataset import maybe_shorten_dataset +from fairseq.tasks import LegacyFairseqTask, register_task +import numpy as np logger = logging.getLogger(__name__) -@register_task('denoising') -class DenoisingTask(FairseqTask): +@register_task("denoising") +class DenoisingTask(LegacyFairseqTask): """ Denoising task for applying sequence to sequence denoising. (ie. BART) """ @@ -32,59 +38,103 @@ class DenoisingTask(FairseqTask): @staticmethod def add_args(parser): """Add task-specific arguments to the parser.""" - parser.add_argument('data', help='path to data directory') - parser.add_argument('--tokens-per-sample', default=512, type=int, - help='max number of total tokens over all segments' - ' per sample for dataset') + parser.add_argument("data", help="path to data directory") + parser.add_argument( + "--tokens-per-sample", + default=512, + type=int, + help="max number of total tokens over all segments" + " per sample for dataset", + ) + parser.add_argument( + "--sample-break-mode", + default="complete_doc", + type=str, + help="mode for breaking sentence", + ) parser.add_argument( - '--sample-break-mode', default="complete_doc", type=str, - help='mode for breaking sentence', + "--mask", + default=0.0, + type=float, + help="fraction of words/subwords that will be masked", ) parser.add_argument( - '--mask', default=0.0, type=float, - help='fraction of words/subwords that will be masked', + "--mask-random", + default=0.0, + type=float, + help="instead of using [MASK], use random token this often", ) parser.add_argument( - '--mask-random', default=0.0, type=float, - help='instead of using [MASK], use random token this often' + "--insert", + default=0.0, + type=float, + help="insert this percentage of additional random tokens", ) parser.add_argument( - '--insert', default=0.0, type=float, - help='insert this percentage of additional random tokens', + "--permute", + default=0.0, + type=float, + help="take this proportion of subwords and permute them", ) parser.add_argument( - '--permute', default=0.0, type=float, - help='take this proportion of subwords and permute them', + "--rotate", + default=0.5, + type=float, + help="rotate this proportion of inputs", ) parser.add_argument( - '--rotate', default=0.5, type=float, - help='rotate this proportion of inputs', + "--poisson-lambda", + default=3.0, + type=float, + help="randomly shuffle sentences for this proportion of inputs", ) parser.add_argument( - '--poisson-lambda', default=3.0, type=float, - help='randomly shuffle sentences for this proportion of inputs' + "--permute-sentences", + default=0.0, + type=float, + help="shuffle this proportion of sentences in all inputs", ) parser.add_argument( - '--permute-sentences', default=0.0, type=float, - help='shuffle this proportion of sentences in all inputs' + "--mask-length", + default="subword", + type=str, + choices=["subword", "word", "span-poisson"], + help="mask length to choose", ) parser.add_argument( - '--mask-length', default="subword", type=str, - choices=['subword', 'word', 'span-poisson'], - help='mask length to choose' + "--replace-length", + default=-1, + type=int, + help="when masking N tokens, replace with 0, 1, or N tokens (use -1 for N)", ) parser.add_argument( - '--replace-length', default=-1, type=int, - help='when masking N tokens, replace with 0, 1, or N tokens (use -1 for N)' + "--max-source-positions", + default=1024, + type=int, + metavar="N", + help="max number of tokens in the source sequence", ) parser.add_argument( - '--max-source-positions', default=1024, type=int, metavar='N', - help='max number of tokens in the source sequence' + "--max-target-positions", + default=1024, + type=int, + metavar="N", + help="max number of tokens in the target sequence", ) + parser.add_argument( - '--max-target-positions', default=1024, type=int, metavar='N', - help='max number of tokens in the target sequence' + "--shorten-method", + default="none", + choices=["none", "truncate", "random_crop"], + help="if not none, shorten sequences that exceed --tokens-per-sample", ) + parser.add_argument( + "--shorten-data-split-list", + default="", + help="comma-separated list of dataset splits to apply shortening to, " + 'e.g., "train,valid" (default: all dataset splits)', + ) + def __init__(self, args, dictionary): super().__init__(args) @@ -92,15 +142,16 @@ def __init__(self, args, dictionary): self.seed = args.seed # add mask token - self.mask_idx = self.dictionary.add_symbol('<mask>') + self.mask_idx = self.dictionary.add_symbol("<mask>") @classmethod def setup_task(cls, args, **kwargs): - """Setup the task. - """ - dictionary = Dictionary.load(os.path.join(args.data, 'dict.txt')) - logger.info('dictionary: {} types'.format(len(dictionary))) - if not hasattr(args, 'shuffle_instance'): + """Setup the task.""" + paths = utils.split_paths(args.data) + assert len(paths) > 0 + dictionary = Dictionary.load(os.path.join(paths[0], "dict.txt")) + logger.info("dictionary: {} types".format(len(dictionary))) + if not hasattr(args, "shuffle_instance"): args.shuffle_instance = False return cls(args, dictionary) @@ -122,32 +173,52 @@ def load_dataset(self, split, epoch=1, combine=False, **kwargs): combine=combine, ) if dataset is None: - raise FileNotFoundError('Dataset not found: {} ({})'.format(split, split_path)) + raise FileNotFoundError( + "Dataset not found: {} ({})".format(split, split_path) + ) dataset = StripTokenDataset(dataset, self.dictionary.eos()) + dataset = maybe_shorten_dataset( + dataset, + split, + self.args.shorten_data_split_list, + self.args.shorten_method, + self.args.tokens_per_sample, + self.args.seed, + ) + # create continuous blocks of tokens dataset = TokenBlockDataset( - dataset, - dataset.sizes, - self.args.tokens_per_sample - 2, # one less for <s> and one for </s> - pad=self.dictionary.pad(), - eos=self.dictionary.eos(), - break_mode=self.args.sample_break_mode, - document_sep_len=0 + dataset, + dataset.sizes, + self.args.tokens_per_sample - 2, # one less for <s> and one for </s> + pad=self.dictionary.pad(), + eos=self.dictionary.eos(), + break_mode=self.args.sample_break_mode, + document_sep_len=0, ) + logger.info("loaded {} blocks from: {}".format(len(dataset), split_path)) # prepend beginning-of-sentence token (<s>, equiv. to [CLS] in BERT) dataset = PrependTokenDataset(dataset, self.source_dictionary.bos()) dataset = AppendTokenDataset(dataset, self.source_dictionary.eos()) - mask_whole_words = get_whole_word_mask(self.args, self.source_dictionary) \ - if self.args.mask_length != 'subword' else None + mask_whole_words = ( + get_whole_word_mask(self.args, self.source_dictionary) + if self.args.mask_length != "subword" + else None + ) self.datasets[split] = DenoisingDataset( - dataset, dataset.sizes, self.dictionary, self.mask_idx, - mask_whole_words, shuffle=self.args.shuffle_instance, - seed=self.seed, args=self.args + dataset, + dataset.sizes, + self.dictionary, + self.mask_idx, + mask_whole_words, + shuffle=self.args.shuffle_instance, + seed=self.seed, + args=self.args, ) logger.info( "Split: {0}, Loaded {1} samples of denoising_dataset".format( @@ -156,6 +227,41 @@ def load_dataset(self, split, epoch=1, combine=False, **kwargs): ) ) + def build_dataset_for_inference(self, src_tokens, src_lengths, **kwargs): + """ + Generate batches for inference. We assume that the input begins with a + bos symbol (`<s>`) and ends with an eos symbol (`</s>`). + """ + pad = self.source_dictionary.pad() + eos = self.source_dictionary.eos() + src_dataset = TokenBlockDataset( + src_tokens, + src_lengths, + block_size=self.args.tokens_per_sample - 2, # for <s> and </s> + pad=pad, + eos=eos, + break_mode=self.args.sample_break_mode, + document_sep_len=0, + ) + prev_output_tokens = PrependTokenDataset( + StripTokenDataset(src_dataset, eos), eos + ) + src_dataset = PadDataset(src_dataset, pad_idx=pad, left_pad=False) + return NestedDictionaryDataset( + { + "id": IdDataset(), + "net_input": { + "src_tokens": src_dataset, + "src_lengths": NumelDataset(src_dataset, reduce=False), + "prev_output_tokens": PadDataset( + prev_output_tokens, pad_idx=pad, left_pad=False + ), + }, + "target": src_dataset, + }, + sizes=[np.array(src_lengths)], + ) + def max_positions(self): """Return the max sentence length allowed by the task.""" return (self.args.max_source_positions, self.args.max_target_positions) diff --git a/fairseq/tasks/fairseq_task.py b/fairseq/tasks/fairseq_task.py index 5036cfe293..8148c77fe1 100644 --- a/fairseq/tasks/fairseq_task.py +++ b/fairseq/tasks/fairseq_task.py @@ -3,24 +3,72 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. +import logging +import os import warnings +from argparse import Namespace +from typing import Any, Callable, Dict, List import torch - from fairseq import metrics, search, tokenizer, utils -from fairseq.data import data_utils, FairseqDataset, iterators, Dictionary +from fairseq.data import Dictionary, FairseqDataset, data_utils, encoders, iterators +from fairseq.dataclass import FairseqDataclass +from fairseq.dataclass.utils import gen_parser_from_dataclass +from fairseq.optim.amp_optimizer import AMPOptimizer +from omegaconf import DictConfig + + +logger = logging.getLogger(__name__) + + +class StatefulContainer(object): + + def __init__(self): + self._state = dict() + self._factories = dict() + + def add_factory(self, name, factory: Callable[[], Any]): + self._factories[name] = factory + + def merge_state_dict(self, state_dict: Dict[str, Any]): + self._state.update(state_dict) + + @property + def state_dict(self) -> Dict[str, Any]: + return self._state + + def __getattr__(self, name): + if name not in self._state and name in self._factories: + self._state[name] = self._factories[name]() + + if name in self._state: + return self._state[name] + + raise AttributeError(f"Task state has no factory for attribute {name}") class FairseqTask(object): """ Tasks store dictionaries and provide helpers for loading/iterating over Datasets, initializing the Model/Criterion and calculating the loss. + + Tasks have limited statefulness. In particular, state that needs to be + saved to/loaded from checkpoints needs to be stored in the `self.state` + :class:`StatefulContainer` object. For example:: + + self.state.add_factory("dictionary", self.load_dictionary) + print(self.state.dictionary) # calls self.load_dictionary() + + This is necessary so that when loading checkpoints, we can properly + recreate the task state after initializing the task instance. """ - @staticmethod - def add_args(parser): + @classmethod + def add_args(cls, parser): """Add task-specific arguments to the parser.""" - pass + dc = getattr(cls, "__dataclass", None) + if dc is not None: + gen_parser_from_dataclass(parser, dc()) @staticmethod def logging_outputs_can_be_summed(criterion) -> bool: @@ -31,10 +79,11 @@ def logging_outputs_can_be_summed(criterion) -> bool: """ return criterion.logging_outputs_can_be_summed() - def __init__(self, args): - self.args = args - self.datasets = {} - self.dataset_to_epoch_iter = {} + def __init__(self, cfg: FairseqDataclass, **kwargs): + self.cfg = cfg + self.datasets = dict() + self.dataset_to_epoch_iter = dict() + self.state = StatefulContainer() @classmethod def load_dictionary(cls, filename): @@ -70,19 +119,31 @@ def build_dictionary( return d @classmethod - def setup_task(cls, args, **kwargs): + def setup_task(cls, cfg: DictConfig, **kwargs): """Setup the task (e.g., load dictionaries). Args: - args (argparse.Namespace): parsed command-line arguments + cfg (omegaconf.DictConfig): parsed command-line arguments """ - return cls(args, **kwargs) + return cls(cfg, **kwargs) - def load_dataset(self, split, combine=False, **kwargs): + def has_sharded_data(self, split): + return os.pathsep in getattr(self.cfg, "data", "") + + def load_dataset( + self, + split: str, + combine: bool = False, + task_cfg: FairseqDataclass = None, + **kwargs + ): """Load a given dataset split. Args: split (str): name of the split (e.g., train, valid, test) + combine (bool): combines a split segmented into pieces into one dataset + task_cfg (FairseqDataclass): optional task configuration stored in the checkpoint that can be used + to load datasets """ raise NotImplementedError @@ -104,6 +165,46 @@ def dataset(self, split): raise TypeError("Datasets are expected to be of type FairseqDataset") return self.datasets[split] + def filter_indices_by_size( + self, indices, dataset, max_positions=None, ignore_invalid_inputs=False + ): + """ + Filter examples that are too large + + Args: + indices (np.array): original array of sample indices + dataset (~fairseq.data.FairseqDataset): dataset to batch + max_positions (optional): max sentence length supported by the + model (default: None). + ignore_invalid_inputs (bool, optional): don't raise Exception for + sentences that are too long (default: False). + Returns: + np.array: array of filtered sample indices + """ + indices, ignored = dataset.filter_indices_by_size(indices, max_positions) + if len(ignored) > 0: + if not ignore_invalid_inputs: + raise Exception( + ( + "Size of sample #{} is invalid (={}) since max_positions={}, " + "skip this example with --skip-invalid-size-inputs-valid-test" + ).format(ignored[0], dataset.size(ignored[0]), max_positions) + ) + logger.warning( + ( + "{:,} samples have invalid sizes and will be skipped, " + "max_positions={}, first few sample ids={}" + ).format(len(ignored), max_positions, ignored[:10]) + ) + return indices + + def can_reuse_epoch_itr(self, dataset): + # We can reuse the epoch iterator across epochs as long as the dataset + # hasn't disabled it. We default to ``False`` here, although in practice + # this will be ``True`` for most datasets that inherit from + # ``FairseqDataset`` due to the base implementation there. + return getattr(dataset, "can_reuse_epoch_itr_across_epochs", False) + def get_batch_iterator( self, dataset, @@ -116,7 +217,9 @@ def get_batch_iterator( num_shards=1, shard_id=0, num_workers=0, - epoch=1 + epoch=1, + data_buffer_size=0, + disable_iterator_cache=False, ): """ Get an iterator that yields batches of data from the given dataset. @@ -144,14 +247,20 @@ def get_batch_iterator( (default: 0). epoch (int, optional): the epoch to start the iterator from (default: 1). + data_buffer_size (int, optional): number of batches to + preload (default: 0). + disable_iterator_cache (bool, optional): don't cache the + EpochBatchIterator (ignores `FairseqTask::can_reuse_epoch_itr`) + (default: False). Returns: ~fairseq.iterators.EpochBatchIterator: a batched iterator over the given dataset split """ - # For default fairseq task, return same iterator across epochs - # as datasets are not dynamic, can be overridden in task specific - # setting. - if dataset in self.dataset_to_epoch_iter: + can_reuse_epoch_itr = not disable_iterator_cache and self.can_reuse_epoch_itr( + dataset + ) + if can_reuse_epoch_itr and dataset in self.dataset_to_epoch_iter: + logger.debug("reusing EpochBatchIterator for epoch {}".format(epoch)) return self.dataset_to_epoch_iter[dataset] assert isinstance(dataset, FairseqDataset) @@ -165,17 +274,13 @@ def get_batch_iterator( # filter examples that are too large if max_positions is not None: - indices = data_utils.filter_by_size( - indices, - dataset, - max_positions, - raise_exception=(not ignore_invalid_inputs), + indices = self.filter_indices_by_size( + indices, dataset, max_positions, ignore_invalid_inputs ) # create mini-batches with given size constraints - batch_sampler = data_utils.batch_by_size( + batch_sampler = dataset.batch_by_size( indices, - dataset.num_tokens, max_tokens=max_tokens, max_sentences=max_sentences, required_batch_size_multiple=required_batch_size_multiple, @@ -191,45 +296,73 @@ def get_batch_iterator( shard_id=shard_id, num_workers=num_workers, epoch=epoch, - buffer_size=getattr(self.args, 'data_buffer_size', 0) + buffer_size=data_buffer_size, ) - self.dataset_to_epoch_iter[dataset] = epoch_iter + + if can_reuse_epoch_itr: + self.dataset_to_epoch_iter[dataset] = epoch_iter + return epoch_iter - def build_model(self, args): + def build_model(self, cfg: FairseqDataclass): """ Build the :class:`~fairseq.models.BaseFairseqModel` instance for this task. Args: - args (argparse.Namespace): parsed command-line arguments + cfg (FairseqDataclass): configuration object Returns: a :class:`~fairseq.models.BaseFairseqModel` instance """ from fairseq import models, quantization_utils - model = models.build_model(args, self) - if getattr(args, 'tpu', False): - model.prepare_for_tpu_() - model = quantization_utils.quantize_model_scalar(model, args) + + model = models.build_model(cfg, self) + model = quantization_utils.quantize_model_scalar(model, cfg) return model - def build_criterion(self, args): + def build_criterion(self, cfg: DictConfig): """ Build the :class:`~fairseq.criterions.FairseqCriterion` instance for this task. Args: - args (argparse.Namespace): parsed command-line arguments + cfg (omegaconf.DictConfig): configration object Returns: a :class:`~fairseq.criterions.FairseqCriterion` instance """ from fairseq import criterions - return criterions.build_criterion(args, self) + return criterions.build_criterion(cfg, self) + + def build_generator( + self, models, args, seq_gen_cls=None, extra_gen_cls_kwargs=None, prefix_allowed_tokens_fn=None, + ): + """ + Build a :class:`~fairseq.SequenceGenerator` instance for this + task. - def build_generator(self, models, args): + Args: + models (List[~fairseq.models.FairseqModel]): ensemble of models + args (fairseq.dataclass.configs.GenerationConfig): + configuration object (dataclass) for generation + extra_gen_cls_kwargs (Dict[str, Any]): extra options to pass + through to SequenceGenerator + prefix_allowed_tokens_fn (Callable[[int, torch.Tensor], List[int]]): + If provided, this function constrains the beam search to + allowed tokens only at each step. The provided function + should take 2 arguments: the batch ID (`batch_id: int`) + and a unidimensional tensor of token ids (`inputs_ids: + torch.Tensor`). It has to return a `List[int]` with the + allowed tokens for the next generation step conditioned + on the previously generated tokens (`inputs_ids`) and + the batch ID (`batch_id`). This argument is useful for + constrained generation conditioned on the prefix, as + described in "Autoregressive Entity Retrieval" + (https://arxiv.org/abs/2010.00904) and + https://github.com/facebookresearch/GENRE. + """ if getattr(args, "score_reference", False): from fairseq.sequence_scorer import SequenceScorer @@ -251,6 +384,9 @@ def build_generator(self, models, args): diverse_beam_strength = getattr(args, "diverse_beam_strength", 0.5) match_source_len = getattr(args, "match_source_len", False) diversity_rate = getattr(args, "diversity_rate", -1) + constrained = getattr(args, "constraints", False) + if prefix_allowed_tokens_fn is None: + prefix_allowed_tokens_fn = getattr(args, "prefix_allowed_tokens_fn", None) if ( sum( int(cond) @@ -290,13 +426,24 @@ def build_generator(self, models, args): search_strategy = search.DiverseSiblingsSearch( self.target_dictionary, diversity_rate ) + elif constrained: + search_strategy = search.LexicallyConstrainedBeamSearch( + self.target_dictionary, args.constraints + ) + elif prefix_allowed_tokens_fn: + search_strategy = search.PrefixConstrainedBeamSearch( + self.target_dictionary, prefix_allowed_tokens_fn + ) else: search_strategy = search.BeamSearch(self.target_dictionary) - if getattr(args, "print_alignment", False): - seq_gen_cls = SequenceGeneratorWithAlignment - else: - seq_gen_cls = SequenceGenerator + extra_gen_cls_kwargs = extra_gen_cls_kwargs or {} + if seq_gen_cls is None: + if getattr(args, "print_alignment", False): + seq_gen_cls = SequenceGeneratorWithAlignment + extra_gen_cls_kwargs["print_alignment"] = args.print_alignment + else: + seq_gen_cls = SequenceGenerator return seq_gen_cls( models, @@ -312,6 +459,7 @@ def build_generator(self, models, args): match_source_len=getattr(args, "match_source_len", False), no_repeat_ngram_size=getattr(args, "no_repeat_ngram_size", 0), search_strategy=search_strategy, + **extra_gen_cls_kwargs, ) def train_step( @@ -339,10 +487,13 @@ def train_step( """ model.train() model.set_num_updates(update_num) - loss, sample_size, logging_output = criterion(model, sample) + with torch.autograd.profiler.record_function("forward"): + with torch.cuda.amp.autocast(enabled=(isinstance(optimizer, AMPOptimizer))): + loss, sample_size, logging_output = criterion(model, sample) if ignore_grad: loss *= 0 - optimizer.backward(loss) + with torch.autograd.profiler.record_function("backward"): + optimizer.backward(loss) return loss, sample_size, logging_output def valid_step(self, sample, model, criterion): @@ -351,14 +502,30 @@ def valid_step(self, sample, model, criterion): loss, sample_size, logging_output = criterion(model, sample) return loss, sample_size, logging_output - def inference_step(self, generator, models, sample, prefix_tokens=None): + def optimizer_step(self, optimizer, model, update_num): + optimizer.step() + + def build_dataset_for_inference( + self, src_tokens: List[torch.Tensor], src_lengths: List[int], **kwargs + ) -> torch.utils.data.Dataset: + raise NotImplementedError + + def inference_step( + self, generator, models, sample, prefix_tokens=None, constraints=None + ): with torch.no_grad(): - return generator.generate(models, sample, prefix_tokens=prefix_tokens) + return generator.generate( + models, sample, prefix_tokens=prefix_tokens, constraints=constraints + ) def begin_epoch(self, epoch, model): """Hook function called before the start of each epoch.""" pass + def begin_valid_epoch(self, epoch, model): + """Hook function called before the start of each validation epoch.""" + pass + def aggregate_logging_outputs(self, logging_outputs, criterion): """[deprecated] Aggregate logging outputs from data parallel training.""" utils.deprecation_warning( @@ -405,6 +572,15 @@ def reduce_metrics(self, logging_outputs, criterion): criterion.__class__.reduce_metrics(logging_outputs) + def state_dict(self): + if self.state is not None: + return self.state.state_dict + return {} + + def load_state_dict(self, state_dict: Dict[str, Any]): + if self.state is not None: + self.state.merge_state_dict(state_dict) + def max_positions(self): """Return the max input length allowed by the task.""" return None @@ -420,3 +596,73 @@ def target_dictionary(self): """Return the target :class:`~fairseq.data.Dictionary` (if applicable for this task).""" raise NotImplementedError + + def build_tokenizer(self, args): + """Build the pre-tokenizer for this task.""" + return encoders.build_tokenizer(args) + + def build_bpe(self, args): + """Build the tokenizer for this task.""" + return encoders.build_bpe(args) + + def get_interactive_tokens_and_lengths(self, lines, encode_fn): + tokens = [ + self.source_dictionary.encode_line( + encode_fn(src_str), add_if_not_exist=False + ).long() + for src_str in lines + ] + lengths = [t.numel() for t in tokens] + return tokens, lengths + + +class LegacyFairseqTask(FairseqTask): + def __init__(self, args: Namespace): + super().__init__(None) + self.args = args + self.datasets = {} + self.dataset_to_epoch_iter = {} + + @classmethod + def setup_task(cls, args: Namespace, **kwargs): + """Setup the task (e.g., load dictionaries). + + Args: + args (argparse.Namespace): parsed command-line arguments + """ + return cls(args, **kwargs) + + def has_sharded_data(self, split): + return os.pathsep in getattr(self.args, "data", "") + + def build_model(self, args: Namespace): + """ + Build the :class:`~fairseq.models.BaseFairseqModel` instance for this + task. + + Args: + args (argparse.Namespace): parsed command-line arguments + + Returns: + a :class:`~fairseq.models.BaseFairseqModel` instance + """ + from fairseq import models, quantization_utils + + model = models.build_model(args, self) + model = quantization_utils.quantize_model_scalar(model, args) + return model + + def build_criterion(self, args: Namespace): + """ + Build the :class:`~fairseq.criterions.FairseqCriterion` instance for + this task. + + Args: + args (argparse.Namespace): parsed command-line arguments + + Returns: + a :class:`~fairseq.criterions.FairseqCriterion` instance + """ + from fairseq import criterions + + return criterions.build_criterion(args, self) diff --git a/fairseq/tasks/frm_text_to_speech.py b/fairseq/tasks/frm_text_to_speech.py new file mode 100644 index 0000000000..1fa9b0f83e --- /dev/null +++ b/fairseq/tasks/frm_text_to_speech.py @@ -0,0 +1,56 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging + +from fairseq.data.audio.frm_text_to_speech_dataset import FrmTextToSpeechDatasetCreator +from fairseq.tasks import register_task +from fairseq.tasks.text_to_speech import TextToSpeechTask + + +logging.basicConfig( + format='%(asctime)s | %(levelname)s | %(name)s | %(message)s', + datefmt='%Y-%m-%d %H:%M:%S', level=logging.INFO +) +logger = logging.getLogger(__name__) + + +@register_task('frm_text_to_speech') +class FrmTextToSpeechTask(TextToSpeechTask): + @staticmethod + def add_args(parser): + TextToSpeechTask.add_args(parser) + parser.add_argument( + "--do_chunk", action="store_true", help="train on chunks" + ) + parser.add_argument("--chunk_bound", default=-1, type=int) + parser.add_argument("--chunk_init", default=50, type=int) + parser.add_argument("--chunk_incr", default=5, type=int) + parser.add_argument("--add_eos", action="store_true") + parser.add_argument("--dedup", action="store_true") + parser.add_argument("--ref_fpu", default=-1, type=float) + + def load_dataset(self, split, **unused_kwargs): + is_train_split = split.startswith("train") + pre_tokenizer = self.build_tokenizer(self.args) + bpe_tokenizer = self.build_bpe(self.args) + self.datasets[split] = FrmTextToSpeechDatasetCreator.from_tsv( + self.args.data, + self.data_cfg, + split, + self.src_dict, + pre_tokenizer, + bpe_tokenizer, + is_train_split=is_train_split, + n_frames_per_step=self.args.n_frames_per_step, + speaker_to_id=self.speaker_to_id, + do_chunk=self.args.do_chunk, + chunk_bound=self.args.chunk_bound, + chunk_init=self.args.chunk_init, + chunk_incr=self.args.chunk_incr, + add_eos=self.args.add_eos, + dedup=self.args.dedup, + ref_fpu=self.args.ref_fpu + ) diff --git a/fairseq/tasks/hubert_pretraining.py b/fairseq/tasks/hubert_pretraining.py new file mode 100644 index 0000000000..f756080dd1 --- /dev/null +++ b/fairseq/tasks/hubert_pretraining.py @@ -0,0 +1,195 @@ +# Copyright (c) 2017-present, Facebook, Inc. +# All rights reserved. +# +# This source code is licensed under the license found in the LICENSE file in +# the root directory of this source tree. An additional grant of patent rights +# can be found in the PATENTS file in the same directory. + +import logging +import os +import sys +from typing import Dict, List, Optional, Tuple + +import numpy as np + +from dataclasses import dataclass, field +from fairseq.data import Dictionary, HubertDataset +from fairseq.dataclass.configs import FairseqDataclass +from fairseq.tasks import register_task +from fairseq.tasks.fairseq_task import FairseqTask +from omegaconf import MISSING + +logger = logging.getLogger(__name__) + + +class LabelEncoder(object): + def __init__(self, dictionary: Dictionary) -> None: + self.dictionary = dictionary + + def __call__(self, label: str) -> List[str]: + return self.dictionary.encode_line( + label, append_eos=False, add_if_not_exist=False, + ) + + +@dataclass +class HubertPretrainingConfig(FairseqDataclass): + data: str = field( + default=MISSING, metadata={"help": "path to data directory"} + ) + fine_tuning: bool = field( + default=False, metadata={"help": "set to true if fine-tuning Hubert"} + ) + labels: List[str] = field( + default_factory=lambda: ["ltr"], + metadata={ + "help": ( + "extension of the label files to load, frame-level labels for" + " pre-training, and sequence-level label for fine-tuning" + ) + }, + ) + label_dir: Optional[str] = field( + default=None, + metadata={ + "help": "if set, looks for labels in this directory instead", + }, + ) + label_rate: int = field( + default=-1, + metadata={"help": "label frame rate. -1 for sequence label"}, + ) + sample_rate: int = field( + default=16_000, + metadata={ + "help": "target sample rate. audio files will be up/down " + "sampled to this rate" + }, + ) + normalize: bool = field( + default=False, + metadata={ + "help": "if set, normalizes input to have 0 mean and unit variance" + }, + ) + enable_padding: bool = field( + default=False, + metadata={"help": "pad shorter samples instead of cropping"}, + ) + max_keep_size: Optional[int] = field( + default=None, + metadata={"help": "exclude sample longer than this"}, + ) + max_sample_size: Optional[int] = field( + default=None, + metadata={"help": "max sample size to crop to for batching"}, + ) + min_sample_size: Optional[int] = field( + default=None, + metadata={"help": "min sample size to crop to for batching"}, + ) + single_target: Optional[bool] = field( + default=False, + metadata={ + "help": "if set, AddTargetDatasets outputs same keys " + "as AddTargetDataset" + }, + ) + random_crop: Optional[bool] = field( + default=True, + metadata={"help": "always crop from the beginning if false"}, + ) + pad_audio: Optional[bool] = field( + default=False, + metadata={"help": "pad audio to the longest one in the batch if true"}, + ) + + +@register_task("hubert_pretraining", dataclass=HubertPretrainingConfig) +class HubertPretrainingTask(FairseqTask): + + cfg: HubertPretrainingConfig + + def __init__( + self, + cfg: HubertPretrainingConfig, + ) -> None: + super().__init__(cfg) + + logger.info(f"current directory is {os.getcwd()}") + logger.info(f"HubertPretrainingTask Config {cfg}") + + self.cfg = cfg + self.fine_tuning = cfg.fine_tuning + + if cfg.fine_tuning: + self.state.add_factory("target_dictionary", self.load_dictionaries) + else: + self.state.add_factory("dictionaries", self.load_dictionaries) + + self.blank_symbol = "<s>" + + @property + def source_dictionary(self) -> Optional[Dictionary]: + return None + + @property + def target_dictionary(self) -> Optional[Dictionary]: + return self.state.target_dictionary + + @property + def dictionaries(self) -> List[Dictionary]: + return self.state.dictionaries + + @classmethod + def setup_task( + cls, cfg: HubertPretrainingConfig, **kwargs + ) -> "HubertPretrainingTask": + return cls(cfg) + + def load_dictionaries(self): + label_dir = self.cfg.data if self.cfg.label_dir is None else self.cfg.label_dir + dictionaries = [Dictionary.load(f"{label_dir}/dict.{label}.txt") for label in self.cfg.labels] + return dictionaries[0] if self.cfg.fine_tuning else dictionaries + + def get_label_dir(self) -> str: + if self.cfg.label_dir is None: + return self.cfg.data + return self.cfg.label_dir + + def load_dataset(self, split: str, **kwargs) -> None: + manifest = f"{self.cfg.data}/{split}.tsv" + dicts = [self.target_dictionary] if self.cfg.fine_tuning else self.dictionaries + pad_list = [dict.pad() for dict in dicts] + eos_list = [dict.eos() for dict in dicts] + procs = [LabelEncoder(dict) for dict in dicts] + paths = [ + f"{self.get_label_dir()}/{split}.{l}" for l in self.cfg.labels + ] + + # hubert v1: pad_audio=True, random_crop=False; + self.datasets[split] = HubertDataset( + manifest, + sample_rate=self.cfg.sample_rate, + label_paths=paths, + label_rates=self.cfg.label_rate, + pad_list=pad_list, + eos_list=eos_list, + label_processors=procs, + max_keep_sample_size=self.cfg.max_keep_size, + min_keep_sample_size=self.cfg.min_sample_size, + max_sample_size=self.cfg.max_sample_size, + pad_audio=self.cfg.pad_audio, + normalize=self.cfg.normalize, + store_labels=False, + random_crop=self.cfg.random_crop, + single_target=self.cfg.single_target, + ) + + def max_positions(self) -> Tuple[int, int]: + return (sys.maxsize, sys.maxsize) + + def filter_indices_by_size( + self, indices: np.array, *args, **kwargs + ) -> np.array: + return indices diff --git a/fairseq/tasks/language_modeling.py b/fairseq/tasks/language_modeling.py index 80ec26c1b7..4b76a51c61 100644 --- a/fairseq/tasks/language_modeling.py +++ b/fairseq/tasks/language_modeling.py @@ -5,16 +5,17 @@ import logging import os +from dataclasses import dataclass, field +from typing import Optional import numpy as np import torch - from fairseq import utils from fairseq.data import ( AppendTokenDataset, - data_utils, Dictionary, IdDataset, + LMContextWindowDataset, MonolingualDataset, NestedDictionaryDataset, NumelDataset, @@ -22,18 +23,89 @@ PrependTokenDataset, StripTokenDataset, TokenBlockDataset, - TransformEosDataset, - TruncateDataset, TruncatedDictionary, + data_utils, ) -from fairseq.tasks import FairseqTask, register_task +from fairseq.data.indexed_dataset import get_available_dataset_impl +from fairseq.data.shorten_dataset import maybe_shorten_dataset +from fairseq.dataclass import ChoiceEnum, FairseqDataclass +from fairseq.tasks import LegacyFairseqTask, register_task +from omegaconf import II +SAMPLE_BREAK_MODE_CHOICES = ChoiceEnum(["none", "complete", "complete_doc", "eos"]) +SHORTEN_METHOD_CHOICES = ChoiceEnum(["none", "truncate", "random_crop"]) logger = logging.getLogger(__name__) -@register_task("language_modeling") -class LanguageModelingTask(FairseqTask): +@dataclass +class LanguageModelingConfig(FairseqDataclass): + data: Optional[str] = field( + default=None, metadata={"help": "path to data directory"} + ) + sample_break_mode: SAMPLE_BREAK_MODE_CHOICES = field( + default="none", + metadata={ + "help": 'If omitted or "none", fills each sample with tokens-per-sample ' + 'tokens. If set to "complete", splits samples only at the end ' + "of sentence, but may include multiple sentences per sample. " + '"complete_doc" is similar but respects doc boundaries. ' + 'If set to "eos", includes only one sentence per sample.' + }, + ) + tokens_per_sample: int = field( + default=1024, + metadata={"help": "max number of tokens per sample for LM dataset"}, + ) + output_dictionary_size: int = field( + default=-1, metadata={"help": "limit the size of output dictionary"} + ) + self_target: bool = field(default=False, metadata={"help": "include self target"}) + future_target: bool = field( + default=False, metadata={"help": "include future target"} + ) + past_target: bool = field(default=False, metadata={"help": "include past target"}) + add_bos_token: bool = field( + default=False, metadata={"help": "prepend beginning of sentence token (<s>)"} + ) + max_target_positions: Optional[int] = field( + default=None, metadata={"help": "max number of tokens in the target sequence"} + ) + shorten_method: SHORTEN_METHOD_CHOICES = field( + default="none", + metadata={ + "help": "if not none, shorten sequences that exceed --tokens-per-sample" + }, + ) + shorten_data_split_list: str = field( + default="", + metadata={ + "help": "comma-separated list of dataset splits to apply shortening to, " + 'e.g., "train,valid" (default: all dataset splits)' + }, + ) + pad_to_fixed_length: Optional[bool] = field( + default=False, metadata={"help": "pad to fixed length"}, + ) + pad_to_fixed_bsz: Optional[bool] = field( + default=False, metadata={"help": "boolean to pad to fixed batch size"}, + ) + + # TODO common vars below add to parent + seed: int = II("common.seed") + batch_size: Optional[int] = II("dataset.batch_size") + batch_size_valid: Optional[int] = II("dataset.batch_size_valid") + dataset_impl: Optional[ChoiceEnum(get_available_dataset_impl())] = II( + "dataset.dataset_impl" + ) + data_buffer_size: int = II("dataset.data_buffer_size") + tpu: bool = II("common.tpu") + use_plasma_view: bool = II("common.use_plasma_view") + plasma_path: str = II("common.plasma_path") + + +@register_task("language_modeling", dataclass=LanguageModelingConfig) +class LanguageModelingTask(LegacyFairseqTask): """ Train a language model. @@ -62,36 +134,6 @@ class LanguageModelingTask(FairseqTask): :prog: """ - @staticmethod - def add_args(parser): - """Add task-specific arguments to the parser.""" - # fmt: off - parser.add_argument('data', help='path to data directory') - parser.add_argument('--sample-break-mode', default='none', - choices=['none', 'complete', 'complete_doc', 'eos'], - help='If omitted or "none", fills each sample with tokens-per-sample ' - 'tokens. If set to "complete", splits samples only at the end ' - 'of sentence, but may include multiple sentences per sample. ' - '"complete_doc" is similar but respects doc boundaries. ' - 'If set to "eos", includes only one sentence per sample.') - parser.add_argument('--tokens-per-sample', default=1024, type=int, - help='max number of tokens per sample for LM dataset') - parser.add_argument('--output-dictionary-size', default=-1, type=int, - help='limit the size of output dictionary') - parser.add_argument('--self-target', action='store_true', - help='include self target') - parser.add_argument('--future-target', action='store_true', - help='include future target') - parser.add_argument('--past-target', action='store_true', - help='include past target') - parser.add_argument('--add-bos-token', action='store_true', - help='prepend beginning of sentence token (<s>)') - parser.add_argument('--max-target-positions', type=int, metavar='N', - help='max number of tokens in the target sequence') - parser.add_argument('--truncate-sequence', action='store_true', default=False, - help='truncate sequences to --tokens-per-sample') - # fmt: on - def __init__(self, args, dictionary, output_dictionary=None, targets=None): super().__init__(args) self.dictionary = dictionary @@ -102,12 +144,7 @@ def __init__(self, args, dictionary, output_dictionary=None, targets=None): self.targets = targets @classmethod - def setup_task(cls, args, **kwargs): - """Setup the task (e.g., load dictionaries). - - Args: - args (argparse.Namespace): parsed command-line arguments - """ + def setup_dictionary(cls, args, **kwargs): dictionary = None output_dictionary = None if args.data: @@ -120,10 +157,20 @@ def setup_task(cls, args, **kwargs): output_dictionary = TruncatedDictionary( dictionary, args.output_dictionary_size ) + return (dictionary, output_dictionary) + + @classmethod + def setup_task(cls, args, **kwargs): + """Setup the task (e.g., load dictionaries). + + Args: + args (argparse.Namespace): parsed command-line arguments + """ + dictionary, output_dictionary = cls.setup_dictionary(args, **kwargs) # upgrade old checkpoints - if hasattr(args, "exclude_self_target"): - args.self_target = not args.exclude_self_target + if getattr(args, "exclude_self_target", False): + args.self_target = False targets = [] if getattr(args, "self_target", False): @@ -140,7 +187,6 @@ def setup_task(cls, args, **kwargs): def build_model(self, args): model = super().build_model(args) - for target in self.targets: if target not in model.supported_targets: raise ValueError( @@ -149,11 +195,13 @@ def build_model(self, args): return model - def load_dataset(self, split, epoch=1, combine=False, **kwargs): + def load_dataset( + self, split: str, epoch=1, combine=False, **kwargs + ) -> MonolingualDataset: """Load a given dataset split. Args: - split (str): name of the split (e.g., train, valid, test) + split (str): name of the split (e.g., train, valid, valid1, test) """ paths = utils.split_paths(self.args.data) assert len(paths) > 0 @@ -161,17 +209,21 @@ def load_dataset(self, split, epoch=1, combine=False, **kwargs): data_path = paths[(epoch - 1) % len(paths)] split_path = os.path.join(data_path, split) + # each process has its own copy of the raw data (likely to be an np.memmap) dataset = data_utils.load_indexed_dataset( split_path, self.dictionary, self.args.dataset_impl, combine=combine ) if dataset is None: - raise FileNotFoundError( - "Dataset not found: {} ({})".format(split, split_path) - ) - - if self.args.truncate_sequence: - dataset = TruncateDataset(dataset, self.args.tokens_per_sample) + raise FileNotFoundError(f"Dataset not found: {split} ({split_path})") + dataset = maybe_shorten_dataset( + dataset, + split, + self.args.shorten_data_split_list, + self.args.shorten_method, + self.args.tokens_per_sample, + self.args.seed, + ) dataset = TokenBlockDataset( dataset, dataset.sizes, @@ -180,22 +232,34 @@ def load_dataset(self, split, epoch=1, combine=False, **kwargs): eos=self.dictionary.eos(), break_mode=self.args.sample_break_mode, include_targets=True, + use_plasma_view=self.args.use_plasma_view, + split_path=split_path, + plasma_path=self.args.plasma_path, ) add_eos_for_other_targets = ( self.args.sample_break_mode is not None and self.args.sample_break_mode != "none" ) + fixed_pad_length = None + if self.args.pad_to_fixed_length: + fixed_pad_length = self.args.tokens_per_sample + + pad_to_bsz = None + if self.args.pad_to_fixed_bsz: + pad_to_bsz = self.args.batch_size_valid if 'valid' in split else self.args.batch_size self.datasets[split] = MonolingualDataset( - dataset, - dataset.sizes, - self.dictionary, - self.output_dictionary, + dataset=dataset, + sizes=dataset.sizes, + src_vocab=self.dictionary, + tgt_vocab=self.output_dictionary, add_eos_for_other_targets=add_eos_for_other_targets, shuffle=True, targets=self.targets, add_bos_token=self.args.add_bos_token, + fixed_pad_length=fixed_pad_length, + pad_to_bsz=pad_to_bsz, ) def build_dataset_for_inference(self, src_tokens, src_lengths, **kwargs): @@ -224,23 +288,28 @@ def build_dataset_for_inference(self, src_tokens, src_lengths, **kwargs): else self.source_dictionary.eos() ), ) - tgt_dataset = AppendTokenDataset( - dataset, - token=self.source_dictionary.pad() - ) + tgt_dataset = AppendTokenDataset(dataset, token=self.source_dictionary.pad()) return NestedDictionaryDataset( { "id": IdDataset(), "net_input": { - "src_tokens": PadDataset(src_dataset, pad_idx=self.source_dictionary.pad(), left_pad=False), + "src_tokens": PadDataset( + src_dataset, + pad_idx=self.source_dictionary.pad(), + left_pad=False, + ), "src_lengths": NumelDataset(src_dataset, reduce=False), }, - "target": PadDataset(tgt_dataset, pad_idx=self.source_dictionary.pad(), left_pad=False), + "target": PadDataset( + tgt_dataset, pad_idx=self.source_dictionary.pad(), left_pad=False + ), }, sizes=[np.array(src_lengths)], ) - def inference_step(self, generator, models, sample, prefix_tokens=None): + def inference_step( + self, generator, models, sample, prefix_tokens=None, constraints=None + ): with torch.no_grad(): # Generation will always be conditioned on bos_token if getattr(self.args, "add_bos_token", False): @@ -248,6 +317,11 @@ def inference_step(self, generator, models, sample, prefix_tokens=None): else: bos_token = self.source_dictionary.eos() + if constraints is not None: + raise NotImplementedError( + "Constrained decoding with the language_modeling task is not supported" + ) + # SequenceGenerator doesn't use src_tokens directly, we need to # pass the `prefix_tokens` argument instead if prefix_tokens is None and sample["net_input"]["src_tokens"].nelement(): @@ -256,8 +330,41 @@ def inference_step(self, generator, models, sample, prefix_tokens=None): prefix_tokens = prefix_tokens[:, 1:] return generator.generate( - models, sample, prefix_tokens=prefix_tokens, bos_token=bos_token, + models, sample, prefix_tokens=prefix_tokens, bos_token=bos_token + ) + + def eval_lm_dataloader( + self, + dataset, + max_tokens: Optional[int] = 36000, + batch_size: Optional[int] = None, + max_positions: Optional[int] = None, + num_shards: int = 1, + shard_id: int = 0, + num_workers: int = 1, + data_buffer_size: int = 10, + # ensures that every evaluated token has access to a context of at least + # this size, if possible + context_window: int = 0, + ): + if context_window > 0: + dataset = LMContextWindowDataset( + dataset=dataset, + tokens_per_sample=self.args.tokens_per_sample, + context_window=context_window, + pad_idx=self.source_dictionary.pad(), ) + return self.get_batch_iterator( + dataset=dataset, + max_tokens=max_tokens, + max_sentences=batch_size, + max_positions=max_positions, + ignore_invalid_inputs=True, + num_shards=num_shards, + shard_id=shard_id, + num_workers=num_workers, + data_buffer_size=data_buffer_size, + ).next_epoch_itr(shuffle=False) @property def source_dictionary(self): diff --git a/fairseq/tasks/legacy_masked_lm.py b/fairseq/tasks/legacy_masked_lm.py index 40e2724953..9754976549 100644 --- a/fairseq/tasks/legacy_masked_lm.py +++ b/fairseq/tasks/legacy_masked_lm.py @@ -8,27 +8,19 @@ import os import numpy as np - -from fairseq import tokenizer -from fairseq.data import ( - ConcatDataset, - indexed_dataset, - data_utils, -) - -from fairseq.data import Dictionary +from fairseq import tokenizer, utils +from fairseq.data import ConcatDataset, Dictionary, data_utils, indexed_dataset from fairseq.data.legacy.block_pair_dataset import BlockPairDataset from fairseq.data.legacy.masked_lm_dataset import MaskedLMDataset from fairseq.data.legacy.masked_lm_dictionary import BertDictionary -from fairseq.tasks import FairseqTask, register_task -from fairseq import utils +from fairseq.tasks import LegacyFairseqTask, register_task logger = logging.getLogger(__name__) -@register_task('legacy_masked_lm') -class LegacyMaskedLMTask(FairseqTask): +@register_task("legacy_masked_lm") +class LegacyMaskedLMTask(LegacyFairseqTask): """ Task for training Masked LM (BERT) model. Args: @@ -38,13 +30,22 @@ class LegacyMaskedLMTask(FairseqTask): @staticmethod def add_args(parser): """Add task-specific arguments to the parser.""" - parser.add_argument('data', help='colon separated path to data directories list, \ - will be iterated upon during epochs in round-robin manner') - parser.add_argument('--tokens-per-sample', default=512, type=int, - help='max number of total tokens over all segments' - ' per sample for BERT dataset') - parser.add_argument('--break-mode', default="doc", type=str, help='mode for breaking sentence') - parser.add_argument('--shuffle-dataset', action='store_true', default=False) + parser.add_argument( + "data", + help="colon separated path to data directories list, \ + will be iterated upon during epochs in round-robin manner", + ) + parser.add_argument( + "--tokens-per-sample", + default=512, + type=int, + help="max number of total tokens over all segments" + " per sample for BERT dataset", + ) + parser.add_argument( + "--break-mode", default="doc", type=str, help="mode for breaking sentence" + ) + parser.add_argument("--shuffle-dataset", action="store_true", default=False) def __init__(self, args, dictionary): super().__init__(args) @@ -56,10 +57,14 @@ def load_dictionary(cls, filename): return BertDictionary.load(filename) @classmethod - def build_dictionary(cls, filenames, workers=1, threshold=-1, nwords=-1, padding_factor=8): + def build_dictionary( + cls, filenames, workers=1, threshold=-1, nwords=-1, padding_factor=8 + ): d = BertDictionary() for filename in filenames: - Dictionary.add_file_to_dictionary(filename, d, tokenizer.tokenize_line, workers) + Dictionary.add_file_to_dictionary( + filename, d, tokenizer.tokenize_line, workers + ) d.finalize(threshold=threshold, nwords=nwords, padding_factor=padding_factor) return d @@ -69,12 +74,11 @@ def target_dictionary(self): @classmethod def setup_task(cls, args, **kwargs): - """Setup the task. - """ + """Setup the task.""" paths = utils.split_paths(args.data) assert len(paths) > 0 - dictionary = BertDictionary.load(os.path.join(paths[0], 'dict.txt')) - logger.info('dictionary: {} types'.format(len(dictionary))) + dictionary = BertDictionary.load(os.path.join(paths[0], "dict.txt")) + logger.info("dictionary: {} types".format(len(dictionary))) return cls(args, dictionary) @@ -92,7 +96,7 @@ def load_dataset(self, split, epoch=1, combine=False): logger.info("data_path", data_path) for k in itertools.count(): - split_k = split + (str(k) if k > 0 else '') + split_k = split + (str(k) if k > 0 else "") path = os.path.join(data_path, split_k) ds = indexed_dataset.make_dataset( path, @@ -105,7 +109,9 @@ def load_dataset(self, split, epoch=1, combine=False): if k > 0: break else: - raise FileNotFoundError('Dataset not found: {} ({})'.format(split, data_path)) + raise FileNotFoundError( + "Dataset not found: {} ({})".format(split, data_path) + ) with data_utils.numpy_seed(self.seed + k): loaded_datasets.append( @@ -119,7 +125,9 @@ def load_dataset(self, split, epoch=1, combine=False): ) ) - logger.info('{} {} {} examples'.format(data_path, split_k, len(loaded_datasets[-1]))) + logger.info( + "{} {} {} examples".format(data_path, split_k, len(loaded_datasets[-1])) + ) if not combine: break diff --git a/fairseq/tasks/masked_lm.py b/fairseq/tasks/masked_lm.py index 8089abf7d8..0c08132fb7 100644 --- a/fairseq/tasks/masked_lm.py +++ b/fairseq/tasks/masked_lm.py @@ -3,77 +3,126 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. +from dataclasses import dataclass, field import logging import os -import numpy as np +from omegaconf import MISSING, II, OmegaConf +import numpy as np +from fairseq import utils from fairseq.data import ( - data_utils, Dictionary, IdDataset, MaskTokensDataset, NestedDictionaryDataset, NumelDataset, NumSamplesDataset, - PadDataset, PrependTokenDataset, + RightPadDataset, SortDataset, TokenBlockDataset, + data_utils, ) -from fairseq.tasks import FairseqTask, register_task from fairseq.data.encoders.utils import get_whole_word_mask -from fairseq import utils +from fairseq.data.shorten_dataset import maybe_shorten_dataset +from fairseq.dataclass import FairseqDataclass +from fairseq.tasks import FairseqTask, register_task + +from .language_modeling import SAMPLE_BREAK_MODE_CHOICES, SHORTEN_METHOD_CHOICES logger = logging.getLogger(__name__) -@register_task('masked_lm') +@dataclass +class MaskedLMConfig(FairseqDataclass): + data: str = field( + default=MISSING, + metadata={ + "help": "colon separated path to data directories list, \ + will be iterated upon during epochs in round-robin manner" + }, + ) + sample_break_mode: SAMPLE_BREAK_MODE_CHOICES = field( + default="none", + metadata={ + "help": 'If omitted or "none", fills each sample with tokens-per-sample ' + 'tokens. If set to "complete", splits samples only at the end ' + "of sentence, but may include multiple sentences per sample. " + '"complete_doc" is similar but respects doc boundaries. ' + 'If set to "eos", includes only one sentence per sample.' + }, + ) + tokens_per_sample: int = field( + default=1024, + metadata={"help": "max number of tokens per sample for LM dataset"}, + ) + mask_prob: float = field( + default=0.15, + metadata={"help": "probability of replacing a token with mask"}, + ) + leave_unmasked_prob: float = field( + default=0.1, + metadata={"help": "probability that a masked token is unmasked"}, + ) + random_token_prob: float = field( + default=0.1, + metadata={"help": "probability of replacing a token with a random token"}, + ) + freq_weighted_replacement: bool = field( + default=False, + metadata={"help": "sample random replacement words based on word frequencies"}, + ) + mask_whole_words: bool = field( + default=False, + metadata={"help": "mask whole words; you may also want to set --bpe"}, + ) + mask_multiple_length: int = field( + default=1, + metadata={"help": "repeat the mask indices multiple times"}, + ) + mask_stdev: float = field( + default=0.0, + metadata={"help": "stdev of the mask length"}, + ) + shorten_method: SHORTEN_METHOD_CHOICES = field( + default="none", + metadata={ + "help": "if not none, shorten sequences that exceed --tokens-per-sample" + }, + ) + shorten_data_split_list: str = field( + default="", + metadata={ + "help": "comma-separated list of dataset splits to apply shortening to, " + 'e.g., "train,valid" (default: all dataset splits)' + }, + ) + seed: int = II("common.seed") + + +@register_task("masked_lm", dataclass=MaskedLMConfig) class MaskedLMTask(FairseqTask): + + cfg: MaskedLMConfig + """Task for training masked language models (e.g., BERT, RoBERTa).""" - @staticmethod - def add_args(parser): - """Add task-specific arguments to the parser.""" - parser.add_argument('data', help='colon separated path to data directories list, \ - will be iterated upon during epochs in round-robin manner') - parser.add_argument('--sample-break-mode', default='complete', - choices=['none', 'complete', 'complete_doc', 'eos'], - help='If omitted or "none", fills each sample with tokens-per-sample ' - 'tokens. If set to "complete", splits samples only at the end ' - 'of sentence, but may include multiple sentences per sample. ' - '"complete_doc" is similar but respects doc boundaries. ' - 'If set to "eos", includes only one sentence per sample.') - parser.add_argument('--tokens-per-sample', default=512, type=int, - help='max number of total tokens over all segments ' - 'per sample for BERT dataset') - parser.add_argument('--mask-prob', default=0.15, type=float, - help='probability of replacing a token with mask') - parser.add_argument('--leave-unmasked-prob', default=0.1, type=float, - help='probability that a masked token is unmasked') - parser.add_argument('--random-token-prob', default=0.1, type=float, - help='probability of replacing a token with a random token') - parser.add_argument('--freq-weighted-replacement', default=False, action='store_true', - help='sample random replacement words based on word frequencies') - parser.add_argument('--mask-whole-words', default=False, action='store_true', - help='mask whole words; you may also want to set --bpe') - - def __init__(self, args, dictionary): - super().__init__(args) + def __init__(self, cfg: MaskedLMConfig, dictionary): + super().__init__(cfg) self.dictionary = dictionary - self.seed = args.seed # add mask token - self.mask_idx = dictionary.add_symbol('<mask>') + self.mask_idx = dictionary.add_symbol("<mask>") @classmethod - def setup_task(cls, args, **kwargs): - paths = utils.split_paths(args.data) + def setup_task(cls, cfg: MaskedLMConfig, **kwargs): + paths = utils.split_paths(cfg.data) assert len(paths) > 0 - dictionary = Dictionary.load(os.path.join(paths[0], 'dict.txt')) - logger.info('dictionary: {} types'.format(len(dictionary))) - return cls(args, dictionary) + dictionary = Dictionary.load(os.path.join(paths[0], "dict.txt")) + logger.info("dictionary: {} types".format(len(dictionary))) + return cls(cfg, dictionary) def load_dataset(self, split, epoch=1, combine=False, **kwargs): """Load a given dataset split. @@ -81,7 +130,7 @@ def load_dataset(self, split, epoch=1, combine=False, **kwargs): Args: split (str): name of the split (e.g., train, valid, test) """ - paths = utils.split_paths(self.args.data) + paths = utils.split_paths(self.cfg.data) assert len(paths) > 0 data_path = paths[(epoch - 1) % len(paths)] split_path = os.path.join(data_path, split) @@ -89,65 +138,78 @@ def load_dataset(self, split, epoch=1, combine=False, **kwargs): dataset = data_utils.load_indexed_dataset( split_path, self.source_dictionary, - self.args.dataset_impl, combine=combine, ) if dataset is None: - raise FileNotFoundError('Dataset not found: {} ({})'.format(split, split_path)) + raise FileNotFoundError( + "Dataset not found: {} ({})".format(split, split_path) + ) + + dataset = maybe_shorten_dataset( + dataset, + split, + self.cfg.shorten_data_split_list, + self.cfg.shorten_method, + self.cfg.tokens_per_sample, + self.cfg.seed, + ) # create continuous blocks of tokens dataset = TokenBlockDataset( dataset, dataset.sizes, - self.args.tokens_per_sample - 1, # one less for <s> + self.cfg.tokens_per_sample - 1, # one less for <s> pad=self.source_dictionary.pad(), eos=self.source_dictionary.eos(), - break_mode=self.args.sample_break_mode, + break_mode=self.cfg.sample_break_mode, ) - logger.info('loaded {} blocks from: {}'.format(len(dataset), split_path)) + logger.info("loaded {} blocks from: {}".format(len(dataset), split_path)) # prepend beginning-of-sentence token (<s>, equiv. to [CLS] in BERT) dataset = PrependTokenDataset(dataset, self.source_dictionary.bos()) # create masked input and targets - mask_whole_words = get_whole_word_mask(self.args, self.source_dictionary) \ - if self.args.mask_whole_words else None + mask_whole_words = ( + get_whole_word_mask(self.args, self.source_dictionary) + if self.cfg.mask_whole_words + else None + ) src_dataset, tgt_dataset = MaskTokensDataset.apply_mask( dataset, self.source_dictionary, pad_idx=self.source_dictionary.pad(), mask_idx=self.mask_idx, - seed=self.args.seed, - mask_prob=self.args.mask_prob, - leave_unmasked_prob=self.args.leave_unmasked_prob, - random_token_prob=self.args.random_token_prob, - freq_weighted_replacement=self.args.freq_weighted_replacement, + seed=self.cfg.seed, + mask_prob=self.cfg.mask_prob, + leave_unmasked_prob=self.cfg.leave_unmasked_prob, + random_token_prob=self.cfg.random_token_prob, + freq_weighted_replacement=self.cfg.freq_weighted_replacement, mask_whole_words=mask_whole_words, + mask_multiple_length=self.cfg.mask_multiple_length, + mask_stdev=self.cfg.mask_stdev, ) - with data_utils.numpy_seed(self.args.seed + epoch): + with data_utils.numpy_seed(self.cfg.seed): shuffle = np.random.permutation(len(src_dataset)) self.datasets[split] = SortDataset( NestedDictionaryDataset( { - 'id': IdDataset(), - 'net_input': { - 'src_tokens': PadDataset( + "id": IdDataset(), + "net_input": { + "src_tokens": RightPadDataset( src_dataset, pad_idx=self.source_dictionary.pad(), - left_pad=False, ), - 'src_lengths': NumelDataset(src_dataset, reduce=False), + "src_lengths": NumelDataset(src_dataset, reduce=False), }, - 'target': PadDataset( + "target": RightPadDataset( tgt_dataset, pad_idx=self.source_dictionary.pad(), - left_pad=False, ), - 'nsentences': NumSamplesDataset(), - 'ntokens': NumelDataset(src_dataset, reduce=True), + "nsentences": NumSamplesDataset(), + "ntokens": NumelDataset(src_dataset, reduce=True), }, sizes=[src_dataset.sizes], ), @@ -158,25 +220,24 @@ def load_dataset(self, split, epoch=1, combine=False, **kwargs): ) def build_dataset_for_inference(self, src_tokens, src_lengths, sort=True): - src_dataset = PadDataset( + src_dataset = RightPadDataset( TokenBlockDataset( src_tokens, src_lengths, - self.args.tokens_per_sample - 1, # one less for <s> + self.cfg.tokens_per_sample - 1, # one less for <s> pad=self.source_dictionary.pad(), eos=self.source_dictionary.eos(), - break_mode='eos', + break_mode="eos", ), pad_idx=self.source_dictionary.pad(), - left_pad=False, ) src_dataset = PrependTokenDataset(src_dataset, self.source_dictionary.bos()) src_dataset = NestedDictionaryDataset( { - 'id': IdDataset(), - 'net_input': { - 'src_tokens': src_dataset, - 'src_lengths': NumelDataset(src_dataset, reduce=False), + "id": IdDataset(), + "net_input": { + "src_tokens": src_dataset, + "src_lengths": NumelDataset(src_dataset, reduce=False), }, }, sizes=src_lengths, diff --git a/fairseq/tasks/multilingual_denoising.py b/fairseq/tasks/multilingual_denoising.py index f1d7068832..d1c914917f 100644 --- a/fairseq/tasks/multilingual_denoising.py +++ b/fairseq/tasks/multilingual_denoising.py @@ -7,62 +7,74 @@ import os import numpy as np - from fairseq.data import ( - data_utils, - Dictionary, AppendTokenDataset, ConcatDataset, DenoisingDataset, + Dictionary, PrependTokenDataset, ResamplingDataset, SortDataset, TokenBlockDataset, + data_utils, ) -from .denoising import DenoisingTask from fairseq.data.encoders.utils import get_whole_word_mask from fairseq.tasks import register_task +from .denoising import DenoisingTask + logger = logging.getLogger(__name__) -@register_task('multilingual_denoising') +@register_task("multilingual_denoising") class MultilingualDenoisingTask(DenoisingTask): - @staticmethod def add_args(parser): DenoisingTask.add_args(parser) - parser.add_argument('--multilang-sampling-alpha', type=float, default=1.0, - help='smoothing alpha for sample rations across multiple datasets') - parser.add_argument('--add-lang-token', default=False, action='store_true') - parser.add_argument('--langs', type=str, help="language ids we are considering", default=None) - parser.add_argument('--no-whole-word-mask-langs', type=str, default='', metavar='N', - help='languages without spacing between words dont support whole word masking') + parser.add_argument( + "--multilang-sampling-alpha", + type=float, + default=1.0, + help="smoothing alpha for sample ratios across multiple datasets", + ) + parser.add_argument("--add-lang-token", default=False, action="store_true") + parser.add_argument( + "--langs", type=str, help="language ids we are considering", default=None + ) + parser.add_argument( + "--no-whole-word-mask-langs", + type=str, + default="", + metavar="N", + help="languages without spacing between words dont support whole word masking", + ) @classmethod def setup_task(cls, args, **kwargs): - """Setup the task. - """ - paths = args.data.split(':') + """Setup the task.""" + paths = args.data.split(":") assert len(paths) > 0 - dictionary = Dictionary.load(os.path.join(paths[0], 'dict.txt')) + dictionary = Dictionary.load(os.path.join(paths[0], "dict.txt")) data_path = paths[0] if args.langs is None: - languages = sorted([ - name for name in os.listdir(data_path) - if os.path.isdir(os.path.join(data_path, name)) - ]) + languages = sorted( + [ + name + for name in os.listdir(data_path) + if os.path.isdir(os.path.join(data_path, name)) + ] + ) else: - languages = args.langs.split(',') + languages = args.langs.split(",") if args.add_lang_token: for lang in languages: - dictionary.add_symbol('[{}]'.format(lang)) + dictionary.add_symbol("[{}]".format(lang)) - logger.info("| dictionary: {} types".format(len(dictionary))) - if not hasattr(args, 'shuffle_instance'): + logger.info("dictionary: {} types".format(len(dictionary))) + if not hasattr(args, "shuffle_instance"): args.shuffle_instance = False return cls(args, dictionary) @@ -72,7 +84,7 @@ def __init__(self, args, dictionary): self.seed = args.seed # add mask token - self.mask_idx = self.dictionary.add_symbol('<mask>') + self.mask_idx = self.dictionary.add_symbol("<mask>") self.langs = args.langs self.args = args @@ -92,29 +104,32 @@ def load_dataset(self, split, epoch=1, combine=False, **kwargs): Args: split (str): name of the split (e.g., train, valid, test) """ - paths = self.args.data.split(':') + paths = self.args.data.split(":") assert len(paths) > 0 data_path = paths[(epoch - 1) % len(paths)] split_path = os.path.join(data_path, split) if self.langs is None: - languages = sorted([ - name for name in os.listdir(data_path) - if os.path.isdir(os.path.join(data_path, name)) - ]) + languages = sorted( + [ + name + for name in os.listdir(data_path) + if os.path.isdir(os.path.join(data_path, name)) + ] + ) else: - languages = self.langs.split(',') + languages = self.langs.split(",") for name in languages: - assert os.path.exists(os.path.join(data_path, name)), "all the languages must exist" + p = os.path.join(data_path, name) + assert os.path.exists(p), "data not found: {}".format(p) - logger.info("| Training on {0} languages: {1}".format(len(languages), languages)) - logger.info("| Language to id mapping: ", { - lang: id for id, lang in enumerate(languages) - } + logger.info("Training on {0} languages: {1}".format(len(languages), languages)) + logger.info( + "Language to id mapping: ", {lang: id for id, lang in enumerate(languages)} ) mask_whole_words = get_whole_word_mask(self.args, self.dictionary) - language_without_segmentations = self.args.no_whole_word_mask_langs.split(',') + language_without_segmentations = self.args.no_whole_word_mask_langs.split(",") lang_datasets = [] for language in languages: split_path = os.path.join(data_path, language, split) @@ -126,10 +141,15 @@ def load_dataset(self, split, epoch=1, combine=False, **kwargs): combine=combine, ) if dataset is None: - raise FileNotFoundError('Dataset not found: {} ({})'.format(split, split_path)) + raise FileNotFoundError( + "Dataset not found: {} ({})".format(split, split_path) + ) - end_token = self.source_dictionary.index('[{}]'.format(language)) \ - if self.args.add_lang_token else self.source_dictionary.eos() + end_token = ( + self.source_dictionary.index("[{}]".format(language)) + if self.args.add_lang_token + else self.source_dictionary.eos() + ) # create continuous blocks of tokens dataset = TokenBlockDataset( @@ -140,13 +160,17 @@ def load_dataset(self, split, epoch=1, combine=False, **kwargs): eos=end_token, break_mode=self.args.sample_break_mode, ) - logger.info('| loaded {} blocks from: {}'.format(len(dataset), split_path)) + logger.info("loaded {} blocks from: {}".format(len(dataset), split_path)) # prepend beginning-of-sentence token (<s>, equiv. to [CLS] in BERT) dataset = PrependTokenDataset(dataset, self.source_dictionary.bos()) dataset = AppendTokenDataset(dataset, end_token) - lang_mask_whole_words = mask_whole_words if language not in language_without_segmentations else None + lang_mask_whole_words = ( + mask_whole_words + if language not in language_without_segmentations + else None + ) lang_dataset = DenoisingDataset( dataset, dataset.sizes, @@ -156,7 +180,9 @@ def load_dataset(self, split, epoch=1, combine=False, **kwargs): shuffle=self.args.shuffle_instance, seed=self.seed, args=self.args, - eos=None if not self.args.add_lang_token else self.source_dictionary.index('[{}]'.format(language)), + eos=None + if not self.args.add_lang_token + else self.source_dictionary.index("[{}]".format(language)), ) lang_datasets.append(lang_dataset) @@ -165,23 +191,29 @@ def load_dataset(self, split, epoch=1, combine=False, **kwargs): dtype=float, ) logger.info( - '| loaded total {} blocks for all languages'.format( - dataset_lengths.sum(), + "loaded total {} blocks for all languages".format( + int(dataset_lengths.sum()), ) ) if split == self.args.train_subset: # For train subset, additionally up or down sample languages. sample_probs = self._get_sample_prob(dataset_lengths) - logger.info("| Sample probability by language: ", { - lang: "{0:.4f}".format(sample_probs[id]) - for id, lang in enumerate(languages) - } + logger.info( + "Sample probability by language: {}".format( + { + lang: "{0:.4f}".format(sample_probs[id]) + for id, lang in enumerate(languages) + } + ) ) size_ratio = (sample_probs * dataset_lengths.sum()) / dataset_lengths - logger.info("| Up/Down Sampling ratio by language: ", { - lang: "{0:.2f}".format(size_ratio[id]) - for id, lang in enumerate(languages) - } + logger.info( + "Up/Down Sampling ratio by language: {}".format( + { + lang: "{0:.2f}".format(size_ratio[id]) + for id, lang in enumerate(languages) + } + ) ) resampled_lang_datasets = [ @@ -201,13 +233,13 @@ def load_dataset(self, split, epoch=1, combine=False, **kwargs): dataset = ConcatDataset(lang_datasets) lang_splits = [split] for lang_id, lang_dataset in enumerate(lang_datasets): - split_name = split + '_' + languages[lang_id] + split_name = split + "_" + languages[lang_id] lang_splits.append(split_name) self.datasets[split_name] = lang_dataset if split in self.args.valid_subset: self.args.valid_subset = self.args.valid_subset.replace( - split, ','.join(lang_splits) + split, ",".join(lang_splits) ) with data_utils.numpy_seed(self.args.seed + epoch): diff --git a/fairseq/tasks/multilingual_masked_lm.py b/fairseq/tasks/multilingual_masked_lm.py index 248724bd56..9e6ce4b8a2 100644 --- a/fairseq/tasks/multilingual_masked_lm.py +++ b/fairseq/tasks/multilingual_masked_lm.py @@ -8,12 +8,10 @@ import numpy as np import torch - +from fairseq import utils from fairseq.data import ( - data_utils, - Dictionary, - encoders, ConcatDataset, + Dictionary, IdDataset, MaskTokensDataset, NestedDictionaryDataset, @@ -25,45 +23,79 @@ ResamplingDataset, SortDataset, TokenBlockDataset, + data_utils, + encoders, ) -from fairseq.tasks import FairseqTask, register_task -from fairseq import utils +from fairseq.tasks import LegacyFairseqTask, register_task logger = logging.getLogger(__name__) -@register_task('multilingual_masked_lm') -class MultiLingualMaskedLMTask(FairseqTask): +@register_task("multilingual_masked_lm") +class MultiLingualMaskedLMTask(LegacyFairseqTask): """Task for training masked language models (e.g., BERT, RoBERTa).""" @staticmethod def add_args(parser): """Add task-specific arguments to the parser.""" - parser.add_argument('data', help='colon separated path to data directories list, \ - will be iterated upon during epochs in round-robin manner') - parser.add_argument('--sample-break-mode', default='complete', - choices=['none', 'complete', 'complete_doc', 'eos'], - help='If omitted or "none", fills each sample with tokens-per-sample ' - 'tokens. If set to "complete", splits samples only at the end ' - 'of sentence, but may include multiple sentences per sample. ' - '"complete_doc" is similar but respects doc boundaries. ' - 'If set to "eos", includes only one sentence per sample.') - parser.add_argument('--tokens-per-sample', default=512, type=int, - help='max number of total tokens over all segments ' - 'per sample for BERT dataset') - parser.add_argument('--mask-prob', default=0.15, type=float, - help='probability of replacing a token with mask') - parser.add_argument('--leave-unmasked-prob', default=0.1, type=float, - help='probability that a masked token is unmasked') - parser.add_argument('--random-token-prob', default=0.1, type=float, - help='probability of replacing a token with a random token') - parser.add_argument('--freq-weighted-replacement', action='store_true', - help='sample random replacement words based on word frequencies') - parser.add_argument('--mask-whole-words', default=False, action='store_true', - help='mask whole words; you may also want to set --bpe') - parser.add_argument('--multilang-sampling-alpha', type=float, default=1.0, - help='smoothing alpha for sample rations across multiple datasets') + parser.add_argument( + "data", + help="colon separated path to data directories list, \ + will be iterated upon during epochs in round-robin manner", + ) + parser.add_argument( + "--sample-break-mode", + default="complete", + choices=["none", "complete", "complete_doc", "eos"], + help='If omitted or "none", fills each sample with tokens-per-sample ' + 'tokens. If set to "complete", splits samples only at the end ' + "of sentence, but may include multiple sentences per sample. " + '"complete_doc" is similar but respects doc boundaries. ' + 'If set to "eos", includes only one sentence per sample.', + ) + parser.add_argument( + "--tokens-per-sample", + default=512, + type=int, + help="max number of total tokens over all segments " + "per sample for BERT dataset", + ) + parser.add_argument( + "--mask-prob", + default=0.15, + type=float, + help="probability of replacing a token with mask", + ) + parser.add_argument( + "--leave-unmasked-prob", + default=0.1, + type=float, + help="probability that a masked token is unmasked", + ) + parser.add_argument( + "--random-token-prob", + default=0.1, + type=float, + help="probability of replacing a token with a random token", + ) + parser.add_argument( + "--freq-weighted-replacement", + action="store_true", + help="sample random replacement words based on word frequencies", + ) + parser.add_argument( + "--mask-whole-words", + default=False, + action="store_true", + help="mask whole words; you may also want to set --bpe", + ) + parser.add_argument( + "--multilang-sampling-alpha", + type=float, + default=1.0, + help="smoothing alpha for sample rations across multiple datasets", + ) def __init__(self, args, dictionary): super().__init__(args) @@ -71,14 +103,14 @@ def __init__(self, args, dictionary): self.seed = args.seed # add mask token - self.mask_idx = dictionary.add_symbol('<mask>') + self.mask_idx = dictionary.add_symbol("<mask>") @classmethod def setup_task(cls, args, **kwargs): paths = utils.split_paths(args.data) assert len(paths) > 0 - dictionary = Dictionary.load(os.path.join(paths[0], 'dict.txt')) - logger.info('dictionary: {} types'.format(len(dictionary))) + dictionary = Dictionary.load(os.path.join(paths[0], "dict.txt")) + logger.info("dictionary: {} types".format(len(dictionary))) return cls(args, dictionary) def _get_whole_word_mask(self): @@ -92,16 +124,16 @@ def is_beginning_of_word(i): # special elements are always considered beginnings return True tok = self.source_dictionary[i] - if tok.startswith('madeupword'): + if tok.startswith("madeupword"): return True try: return bpe.is_beginning_of_word(tok) except ValueError: return True - mask_whole_words = torch.ByteTensor(list( - map(is_beginning_of_word, range(len(self.source_dictionary))) - )) + mask_whole_words = torch.ByteTensor( + list(map(is_beginning_of_word, range(len(self.source_dictionary)))) + ) else: mask_whole_words = None return mask_whole_words @@ -127,14 +159,14 @@ def load_dataset(self, split, epoch=1, combine=False, **kwargs): data_path = paths[(epoch - 1) % len(paths)] languages = sorted( - name for name in os.listdir(data_path) + name + for name in os.listdir(data_path) if os.path.isdir(os.path.join(data_path, name)) ) logger.info("Training on {0} languages: {1}".format(len(languages), languages)) - logger.info("Language to id mapping: ", { - lang: id for id, lang in enumerate(languages) - } + logger.info( + "Language to id mapping: ", {lang: id for id, lang in enumerate(languages)} ) mask_whole_words = self._get_whole_word_mask() @@ -149,7 +181,9 @@ def load_dataset(self, split, epoch=1, combine=False, **kwargs): combine=combine, ) if dataset is None: - raise FileNotFoundError('Dataset not found: {} ({})'.format(split, split_path)) + raise FileNotFoundError( + "Dataset not found: {} ({})".format(split, split_path) + ) # create continuous blocks of tokens dataset = TokenBlockDataset( @@ -160,7 +194,7 @@ def load_dataset(self, split, epoch=1, combine=False, **kwargs): eos=self.source_dictionary.eos(), break_mode=self.args.sample_break_mode, ) - logger.info('loaded {} blocks from: {}'.format(len(dataset), split_path)) + logger.info("loaded {} blocks from: {}".format(len(dataset), split_path)) # prepend beginning-of-sentence token (<s>, equiv. to [CLS] in BERT) dataset = PrependTokenDataset(dataset, self.source_dictionary.bos()) @@ -180,50 +214,53 @@ def load_dataset(self, split, epoch=1, combine=False, **kwargs): lang_dataset = NestedDictionaryDataset( { - 'net_input': { - 'src_tokens': PadDataset( + "net_input": { + "src_tokens": PadDataset( src_dataset, pad_idx=self.source_dictionary.pad(), left_pad=False, ), - 'src_lengths': NumelDataset(src_dataset, reduce=False), + "src_lengths": NumelDataset(src_dataset, reduce=False), }, - 'target': PadDataset( + "target": PadDataset( tgt_dataset, pad_idx=self.source_dictionary.pad(), left_pad=False, ), - 'nsentences': NumSamplesDataset(), - 'ntokens': NumelDataset(src_dataset, reduce=True), - 'lang_id': RawLabelDataset([lang_id] * src_dataset.sizes.shape[0]), + "nsentences": NumSamplesDataset(), + "ntokens": NumelDataset(src_dataset, reduce=True), + "lang_id": RawLabelDataset([lang_id] * src_dataset.sizes.shape[0]), }, sizes=[src_dataset.sizes], ) lang_datasets.append(lang_dataset) - dataset_lengths = np.array( [len(d) for d in lang_datasets], dtype=float, ) logger.info( - 'loaded total {} blocks for all languages'.format( + "loaded total {} blocks for all languages".format( dataset_lengths.sum(), ) ) if split == self.args.train_subset: # For train subset, additionally up or down sample languages. sample_probs = self._get_sample_prob(dataset_lengths) - logger.info("Sample probability by language: ", { + logger.info( + "Sample probability by language: ", + { lang: "{0:.4f}".format(sample_probs[id]) for id, lang in enumerate(languages) - } + }, ) size_ratio = (sample_probs * dataset_lengths.sum()) / dataset_lengths - logger.info("Up/Down Sampling ratio by language: ", { + logger.info( + "Up/Down Sampling ratio by language: ", + { lang: "{0:.2f}".format(size_ratio[id]) for id, lang in enumerate(languages) - } + }, ) resampled_lang_datasets = [ @@ -241,7 +278,7 @@ def load_dataset(self, split, epoch=1, combine=False, **kwargs): dataset = ConcatDataset(lang_datasets) lang_splits = [split] for lang_id, lang_dataset in enumerate(lang_datasets): - split_name = split + '_' + languages[lang_id] + split_name = split + "_" + languages[lang_id] lang_splits.append(split_name) self.datasets[split_name] = lang_dataset @@ -250,7 +287,7 @@ def load_dataset(self, split, epoch=1, combine=False, **kwargs): # in more generic ways. if split in self.args.valid_subset: self.args.valid_subset = self.args.valid_subset.replace( - split, ','.join(lang_splits) + split, ",".join(lang_splits) ) with data_utils.numpy_seed(self.args.seed + epoch): @@ -272,7 +309,7 @@ def build_dataset_for_inference(self, src_tokens, src_lengths, sort=True): self.args.tokens_per_sample - 1, # one less for <s> pad=self.source_dictionary.pad(), eos=self.source_dictionary.eos(), - break_mode='eos', + break_mode="eos", ), pad_idx=self.source_dictionary.pad(), left_pad=False, @@ -280,10 +317,10 @@ def build_dataset_for_inference(self, src_tokens, src_lengths, sort=True): src_dataset = PrependTokenDataset(src_dataset, self.source_dictionary.bos()) src_dataset = NestedDictionaryDataset( { - 'id': IdDataset(), - 'net_input': { - 'src_tokens': src_dataset, - 'src_lengths': NumelDataset(src_dataset, reduce=False), + "id": IdDataset(), + "net_input": { + "src_tokens": src_dataset, + "src_lengths": NumelDataset(src_dataset, reduce=False), }, }, sizes=src_lengths, @@ -292,22 +329,6 @@ def build_dataset_for_inference(self, src_tokens, src_lengths, sort=True): src_dataset = SortDataset(src_dataset, sort_order=[src_lengths]) return src_dataset - def get_batch_iterator( - self, dataset, max_tokens=None, max_sentences=None, max_positions=None, - ignore_invalid_inputs=False, required_batch_size_multiple=1, - seed=1, num_shards=1, shard_id=0, num_workers=0, epoch=1, - ): - # Recreate epoch iterator every epoch cause the underlying - # datasets are dynamic due to sampling. - self.dataset_to_epoch_iter = {} - epoch_iter = super().get_batch_iterator( - dataset, max_tokens, max_sentences, max_positions, - ignore_invalid_inputs, required_batch_size_multiple, - seed, num_shards, shard_id, num_workers, epoch, - ) - self.dataset_to_epoch_iter = {} - return epoch_iter - @property def source_dictionary(self): return self.dictionary diff --git a/fairseq/tasks/multilingual_translation.py b/fairseq/tasks/multilingual_translation.py index 9a1315e1db..4f85ab4832 100644 --- a/fairseq/tasks/multilingual_translation.py +++ b/fairseq/tasks/multilingual_translation.py @@ -3,13 +3,14 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. -from collections import OrderedDict +import contextlib import logging import os +from collections import OrderedDict +from argparse import ArgumentError import torch - -from fairseq import metrics, options +from fairseq import metrics, options, utils from fairseq.data import ( Dictionary, LanguagePairDataset, @@ -19,26 +20,25 @@ from fairseq.models import FairseqMultiModel from fairseq.tasks.translation import load_langpair_dataset -from . import FairseqTask, register_task -from fairseq import utils +from . import LegacyFairseqTask, register_task + logger = logging.getLogger(__name__) def _lang_token(lang: str): - return '__{}__'.format(lang) + return "__{}__".format(lang) def _lang_token_index(dic: Dictionary, lang: str): """Return language token index.""" idx = dic.index(_lang_token(lang)) - assert idx != dic.unk_index, \ - 'cannot find language token for lang {}'.format(lang) + assert idx != dic.unk_index, "cannot find language token for lang {}".format(lang) return idx -@register_task('multilingual_translation') -class MultilingualTranslationTask(FairseqTask): +@register_task("multilingual_translation") +class MultilingualTranslationTask(LegacyFairseqTask): """A task for training multiple translation models simultaneously. We iterate round-robin over batches from multiple language pairs, ordered @@ -78,10 +78,14 @@ def add_args(parser): help='pad the source on the left (default: True)') parser.add_argument('--left-pad-target', default='False', type=str, metavar='BOOL', help='pad the target on the left (default: False)') - parser.add_argument('--max-source-positions', default=1024, type=int, metavar='N', - help='max number of tokens in the source sequence') - parser.add_argument('--max-target-positions', default=1024, type=int, metavar='N', - help='max number of tokens in the target sequence') + try: + parser.add_argument('--max-source-positions', default=1024, type=int, metavar='N', + help='max number of tokens in the source sequence') + parser.add_argument('--max-target-positions', default=1024, type=int, metavar='N', + help='max number of tokens in the target sequence') + except ArgumentError: + # this might have already been defined. Once we transition this to hydra it should be fine to add it here. + pass parser.add_argument('--upsample-primary', default=1, type=int, help='amount to upsample primary dataset') parser.add_argument('--encoder-langtok', default=None, type=str, choices=['src', 'tgt'], @@ -99,7 +103,7 @@ def __init__(self, args, dicts, training): if training: self.lang_pairs = args.lang_pairs else: - self.lang_pairs = ['{}-{}'.format(args.source_lang, args.target_lang)] + self.lang_pairs = ["{}-{}".format(args.source_lang, args.target_lang)] # eval_lang_pairs for multilingual translation is usually all of the # lang_pairs. However for other multitask settings or when we want to # optimize for certain languages we want to use a different subset. Thus @@ -118,15 +122,23 @@ def setup_task(cls, args, **kwargs): return cls(args, dicts, training) @classmethod - def prepare(cls, args, **kargs): - args.left_pad_source = options.eval_bool(args.left_pad_source) - args.left_pad_target = options.eval_bool(args.left_pad_target) + def update_args(cls, args): + args.left_pad_source = utils.eval_bool(args.left_pad_source) + args.left_pad_target = utils.eval_bool(args.left_pad_target) if args.lang_pairs is None: - raise ValueError('--lang-pairs is required. List all the language pairs in the training objective.') + raise ValueError( + "--lang-pairs is required. List all the language pairs in the training objective." + ) if isinstance(args.lang_pairs, str): - args.lang_pairs = args.lang_pairs.split(',') - sorted_langs = sorted(list({x for lang_pair in args.lang_pairs for x in lang_pair.split('-')})) + args.lang_pairs = args.lang_pairs.split(",") + + @classmethod + def prepare(cls, args, **kargs): + cls.update_args(args) + sorted_langs = sorted( + list({x for lang_pair in args.lang_pairs for x in lang_pair.split("-")}) + ) if args.source_lang is not None or args.target_lang is not None: training = False else: @@ -137,7 +149,9 @@ def prepare(cls, args, **kargs): for lang in sorted_langs: paths = utils.split_paths(args.data) assert len(paths) > 0 - dicts[lang] = Dictionary.load(os.path.join(paths[0], 'dict.{}.txt'.format(lang))) + dicts[lang] = cls.load_dictionary( + os.path.join(paths[0], "dict.{}.txt".format(lang)) + ) if len(dicts) > 0: assert dicts[lang].pad() == dicts[sorted_langs[0]].pad() assert dicts[lang].eos() == dicts[sorted_langs[0]].eos() @@ -145,13 +159,13 @@ def prepare(cls, args, **kargs): if args.encoder_langtok is not None or args.decoder_langtok: for lang_to_add in sorted_langs: dicts[lang].add_symbol(_lang_token(lang_to_add)) - logger.info('[{}] dictionary: {} types'.format(lang, len(dicts[lang]))) + logger.info("[{}] dictionary: {} types".format(lang, len(dicts[lang]))) return dicts, training def get_encoder_langtok(self, src_lang, tgt_lang): if self.args.encoder_langtok is None: return self.dicts[src_lang].eos() - if self.args.encoder_langtok == 'src': + if self.args.encoder_langtok == "src": return _lang_token_index(self.dicts[src_lang], src_lang) else: return _lang_token_index(self.dicts[src_lang], tgt_lang) @@ -161,14 +175,24 @@ def get_decoder_langtok(self, tgt_lang): return self.dicts[tgt_lang].eos() return _lang_token_index(self.dicts[tgt_lang], tgt_lang) - def alter_dataset_langtok(self, lang_pair_dataset, - src_eos=None, src_lang=None, tgt_eos=None, tgt_lang=None): + def alter_dataset_langtok( + self, + lang_pair_dataset, + src_eos=None, + src_lang=None, + tgt_eos=None, + tgt_lang=None, + ): if self.args.encoder_langtok is None and not self.args.decoder_langtok: return lang_pair_dataset new_src_eos = None - if self.args.encoder_langtok is not None and src_eos is not None \ - and src_lang is not None and tgt_lang is not None: + if ( + self.args.encoder_langtok is not None + and src_eos is not None + and src_lang is not None + and tgt_lang is not None + ): new_src_eos = self.get_encoder_langtok(src_lang, tgt_lang) else: src_eos = None @@ -194,10 +218,16 @@ def load_dataset(self, split, epoch=1, **kwargs): data_path = paths[(epoch - 1) % len(paths)] def language_pair_dataset(lang_pair): - src, tgt = lang_pair.split('-') + src, tgt = lang_pair.split("-") langpair_dataset = load_langpair_dataset( - data_path, split, src, self.dicts[src], tgt, self.dicts[tgt], - combine=True, dataset_impl=self.args.dataset_impl, + data_path, + split, + src, + self.dicts[src], + tgt, + self.dicts[tgt], + combine=True, + dataset_impl=self.args.dataset_impl, upsample_primary=self.args.upsample_primary, left_pad_source=self.args.left_pad_source, left_pad_target=self.args.left_pad_target, @@ -213,65 +243,132 @@ def language_pair_dataset(lang_pair): ) self.datasets[split] = RoundRobinZipDatasets( - OrderedDict([ - (lang_pair, language_pair_dataset(lang_pair)) - for lang_pair in self.lang_pairs - ]), - eval_key=None if self.training else "%s-%s" % (self.args.source_lang, self.args.target_lang), + OrderedDict( + [ + (lang_pair, language_pair_dataset(lang_pair)) + for lang_pair in self.lang_pairs + ] + ), + eval_key=None + if self.training + else "%s-%s" % (self.args.source_lang, self.args.target_lang), ) - def build_dataset_for_inference(self, src_tokens, src_lengths): + def build_dataset_for_inference(self, src_tokens, src_lengths, constraints=None): + if constraints is not None: + raise NotImplementedError( + "Constrained decoding with the multilingual_translation task is not supported" + ) + lang_pair = "%s-%s" % (self.args.source_lang, self.args.target_lang) return RoundRobinZipDatasets( - OrderedDict([( - lang_pair, - self.alter_dataset_langtok( - LanguagePairDataset( - src_tokens, src_lengths, - self.source_dictionary - ), - src_eos=self.source_dictionary.eos(), - src_lang=self.args.source_lang, - tgt_eos=self.target_dictionary.eos(), - tgt_lang=self.args.target_lang, - ), - )]), + OrderedDict( + [ + ( + lang_pair, + self.alter_dataset_langtok( + LanguagePairDataset( + src_tokens, src_lengths, self.source_dictionary + ), + src_eos=self.source_dictionary.eos(), + src_lang=self.args.source_lang, + tgt_eos=self.target_dictionary.eos(), + tgt_lang=self.args.target_lang, + ), + ) + ] + ), eval_key=lang_pair, ) def build_model(self, args): def check_args(): messages = [] - if len(set(self.args.lang_pairs).symmetric_difference(args.lang_pairs)) != 0: - messages.append('--lang-pairs should include all the language pairs {}.'.format(args.lang_pairs)) + if ( + len(set(self.args.lang_pairs).symmetric_difference(args.lang_pairs)) + != 0 + ): + messages.append( + "--lang-pairs should include all the language pairs {}.".format( + args.lang_pairs + ) + ) if self.args.encoder_langtok != args.encoder_langtok: - messages.append('--encoder-langtok should be {}.'.format(args.encoder_langtok)) + messages.append( + "--encoder-langtok should be {}.".format(args.encoder_langtok) + ) if self.args.decoder_langtok != args.decoder_langtok: - messages.append('--decoder-langtok should {} be set.'.format("" if args.decoder_langtok else "not")) + messages.append( + "--decoder-langtok should {} be set.".format( + "" if args.decoder_langtok else "not" + ) + ) if len(messages) > 0: - raise ValueError(' '.join(messages)) + raise ValueError(" ".join(messages)) + + # Update args -> the fact that the constructor here + # changes the args object doesn't mean you get the same one here + self.update_args(args) # Check if task args are consistant with model args check_args() from fairseq import models + model = models.build_model(args, self) if not isinstance(model, FairseqMultiModel): - raise ValueError('MultilingualTranslationTask requires a FairseqMultiModel architecture') + raise ValueError( + "MultilingualTranslationTask requires a FairseqMultiModel architecture" + ) return model - def train_step(self, sample, model, criterion, optimizer, update_num, ignore_grad=False): + def _per_lang_pair_train_loss( + self, lang_pair, model, update_num, criterion, sample, optimizer, ignore_grad + ): + loss, sample_size, logging_output = criterion( + model.models[lang_pair], sample[lang_pair] + ) + if ignore_grad: + loss *= 0 + optimizer.backward(loss) + return loss, sample_size, logging_output + + def train_step( + self, sample, model, criterion, optimizer, update_num, ignore_grad=False + ): model.train() from collections import defaultdict - agg_loss, agg_sample_size, agg_logging_output = 0., 0., defaultdict(float) - for lang_pair in self.model_lang_pairs: - if sample[lang_pair] is None or len(sample[lang_pair]) == 0: - continue - loss, sample_size, logging_output = criterion(model.models[lang_pair], sample[lang_pair]) - if ignore_grad: - loss *= 0 - optimizer.backward(loss) + + agg_loss, agg_sample_size, agg_logging_output = 0.0, 0.0, defaultdict(float) + curr_lang_pairs = [ + lang_pair + for lang_pair in self.model_lang_pairs + if sample[lang_pair] is not None and len(sample[lang_pair]) != 0 + ] + + for idx, lang_pair in enumerate(curr_lang_pairs): + + def maybe_no_sync(): + if ( + self.args.distributed_world_size > 1 + and hasattr(model, "no_sync") + and idx < len(curr_lang_pairs) - 1 + ): + return model.no_sync() + else: + return contextlib.ExitStack() # dummy contextmanager + + with maybe_no_sync(): + loss, sample_size, logging_output = self._per_lang_pair_train_loss( + lang_pair, + model, + update_num, + criterion, + sample, + optimizer, + ignore_grad, + ) agg_loss += loss.detach().item() # TODO make summing of the sample sizes configurable agg_sample_size += sample_size @@ -280,15 +377,25 @@ def train_step(self, sample, model, criterion, optimizer, update_num, ignore_gra agg_logging_output[f"{lang_pair}:{k}"] += logging_output[k] return agg_loss, agg_sample_size, agg_logging_output + def _per_lang_pair_valid_loss(self, lang_pair, model, criterion, sample): + return criterion(model.models[lang_pair], sample[lang_pair]) + def valid_step(self, sample, model, criterion): model.eval() with torch.no_grad(): from collections import defaultdict - agg_loss, agg_sample_size, agg_logging_output = 0., 0., defaultdict(float) + + agg_loss, agg_sample_size, agg_logging_output = 0.0, 0.0, defaultdict(float) for lang_pair in self.eval_lang_pairs: - if lang_pair not in sample or sample[lang_pair] is None or len(sample[lang_pair]) == 0: + if ( + lang_pair not in sample + or sample[lang_pair] is None + or len(sample[lang_pair]) == 0 + ): continue - loss, sample_size, logging_output = criterion(model.models[lang_pair], sample[lang_pair]) + loss, sample_size, logging_output = self._per_lang_pair_valid_loss( + lang_pair, model, criterion, sample + ) agg_loss += loss.data.item() # TODO make summing of the sample sizes configurable agg_sample_size += sample_size @@ -297,21 +404,29 @@ def valid_step(self, sample, model, criterion): agg_logging_output[f"{lang_pair}:{k}"] += logging_output[k] return agg_loss, agg_sample_size, agg_logging_output - def inference_step(self, generator, models, sample, prefix_tokens=None): + def inference_step( + self, generator, models, sample, prefix_tokens=None, constraints=None + ): with torch.no_grad(): + if self.args.decoder_langtok: + bos_token = _lang_token_index( + self.target_dictionary, self.args.target_lang + ) + else: + bos_token = self.target_dictionary.eos() return generator.generate( - models, - sample, - prefix_tokens=prefix_tokens, - bos_token=_lang_token_index(self.target_dictionary, self.args.target_lang) - if self.args.decoder_langtok else self.target_dictionary.eos(), + models, + sample, + prefix_tokens=prefix_tokens, + constraints=constraints, + bos_token=bos_token, ) def reduce_metrics(self, logging_outputs, criterion): with metrics.aggregate(): # pass 'sample_size', 'nsentences', 'ntokens' stats to fairseq_task super().reduce_metrics(logging_outputs, criterion) - for k in ['sample_size', 'nsentences', 'ntokens']: + for k in ["sample_size", "nsentences", "ntokens"]: metrics.log_scalar(k, sum(l[k] for l in logging_outputs)) @property @@ -331,10 +446,17 @@ def target_dictionary(self): def max_positions(self): """Return the max sentence length allowed by the task.""" if len(self.datasets.values()) == 0: - return {'%s-%s' % (self.args.source_lang, self.args.target_lang): - (self.args.max_source_positions, self.args.max_target_positions)} - return OrderedDict([ - (key, (self.args.max_source_positions, self.args.max_target_positions)) - for split in self.datasets.keys() - for key in self.datasets[split].datasets.keys() - ]) + return { + "%s-%s" + % (self.args.source_lang, self.args.target_lang): ( + self.args.max_source_positions, + self.args.max_target_positions, + ) + } + return OrderedDict( + [ + (key, (self.args.max_source_positions, self.args.max_target_positions)) + for split in self.datasets.keys() + for key in self.datasets[split].datasets.keys() + ] + ) diff --git a/fairseq/tasks/online_backtranslation.py b/fairseq/tasks/online_backtranslation.py new file mode 100644 index 0000000000..2e27ca237c --- /dev/null +++ b/fairseq/tasks/online_backtranslation.py @@ -0,0 +1,682 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import contextlib +import json +import logging +import math +import os +from argparse import Namespace +from collections import OrderedDict, defaultdict +from pathlib import Path +from typing import Dict, Sequence, Tuple +from argparse import ArgumentError + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F + +import fairseq +from fairseq import metrics, options, utils +from fairseq.data import ( + FairseqDataset, + LanguagePairDataset, + NoisingDataset, + PrependTokenDataset, + RoundRobinZipDatasets, + TransformEosLangPairDataset, + data_utils, + encoders, +) +from fairseq.sequence_generator import SequenceGenerator +from fairseq.tasks import register_task +from fairseq.tasks.translation import TranslationTask, load_langpair_dataset + +logger = logging.getLogger(__name__) + + +class PiecewiseLinearFn: + """Piecewise linear function. Can be configured with a string.""" + + def __init__(self, pieces: Sequence[Tuple[int, float]]): + assert pieces == sorted( + pieces + ), f"PiecewiseLinearFn configuration should be sorted, received: {pieces}" + + self.pieces = pieces + + def __call__(self, x: int) -> float: + for i, (x_a, y_a) in enumerate(self.pieces[:-1]): + x_b, y_b = self.pieces[i + 1] + if x_a <= x <= x_b: + return y_a + (x - x_a) * (y_b - y_a) / (x_b - x_a) + + return self.pieces[-1][1] + + @staticmethod + def from_string(configuration: str) -> "PiecewiseLinearFn": + """ + Parse the configuration of lambda coefficient (for scheduling). + x = "3" # lambda will be a constant equal to x + x = "0:1,1000:0" # lambda will start from 1 and linearly decrease + # to 0 during the first 1000 iterations + x = "0:0,1000:0,2000:1" # lambda will be equal to 0 for the first 1000 + # iterations, then will linearly increase to 1 until iteration 2000 + """ + if isinstance(configuration, float): + return PiecewiseLinearFn([(0, configuration)]) + + try: + parts = configuration.split(",") + if len(parts) == 1: + v = float(configuration) + return PiecewiseLinearFn([(0, v)]) + + split = [s.split(":") for s in parts] + pieces = [(int(t), float(v)) for t, v in split] + return PiecewiseLinearFn(pieces) + except Exception: + raise ValueError( + f"Invalid PiecewiseLinearFn configuration: {configuration!r}" + ) + + @staticmethod + def one() -> "PiecewiseLinearFn": + return PiecewiseLinearFn([(0, 1.0)]) + + +@register_task("online_backtranslation") +class OnlineBackTranslationTask(TranslationTask): + @staticmethod + def add_args(parser): + """Add task-specific arguments to the parser.""" + # fmt: off + # Generic translation args + parser.add_argument('data', help='colon separated path to data directories list, \ + will be iterated upon during epochs in round-robin manner; \ + however, valid and test data are always in the first directory to \ + avoid the need for repeating them in all directories') + parser.add_argument('--mono-langs', metavar='MONO_LANGS', + help='monolingual languages for training') + parser.add_argument('--valid-lang-pairs', default=None, metavar='VALID_LANG_PAIRS', + help='language pairs for validation') + parser.add_argument('--load-alignments', action='store_true', + help='load the binarized alignments') + parser.add_argument('--left-pad-source', default='False', type=str, metavar='BOOL', + help='pad the source on the left') + parser.add_argument('--left-pad-target', default='False', type=str, metavar='BOOL', + help='pad the target on the left') + parser.add_argument('--upsample-primary', default=1, type=int, + help='amount to upsample primary dataset') + try: + parser.add_argument('--max-source-positions', default=1024, type=int, metavar='N', + help='max number of tokens in the source sequence') + parser.add_argument('--max-target-positions', default=1024, type=int, metavar='N', + help='max number of tokens in the target sequence') + except ArgumentError: + # this might have already been defined. Once we transition this to hydra it should be fine to add it here. + pass + parser.add_argument('--truncate-source', action='store_true', default=False, + help='truncate source to max-source-positions') + parser.add_argument('--num-batch-buckets', default=0, type=int, metavar='N', + help='if >0, then bucket source and target lengths into N ' + 'buckets and pad accordingly; this is useful on TPUs ' + 'to minimize the number of compilations') + + # Denoising args + parser.add_argument('--max-word-shuffle-distance', default=3.0, type=float, metavar='N', + help='maximum word shuffle distance for denoising autoencoding data generation') + parser.add_argument('--word-dropout-prob', default=0.1, type=float, metavar='N', + help='word dropout probability for denoising autoencoding data generation') + parser.add_argument('--word-blanking-prob', default=0.2, type=float, metavar='N', + help='word blanking probability for denoising autoencoding data generation') + + # Backtranslation args + parser.add_argument('--lambda-bt', default="1.0", type=str, metavar='N', + help='back-translation weight') + parser.add_argument('--lambda-dae', default="1.0", type=str, metavar='N', + help='denoising auto-encoder weight') + + # Evaluation args + parser.add_argument('--generate-one-by-one', action='store_true', + help='generate one sentence at a time for backtranslation') + + parser.add_argument('--eval-bleu', action='store_true', + help='evaluation with BLEU scores') + parser.add_argument('--eval-bleu-detok', type=str, default="space", + help='detokenize before computing BLEU (e.g., "moses"); ' + 'required if using --eval-bleu; use "space" to ' + 'disable detokenization; see fairseq.data.encoders ' + 'for other options') + parser.add_argument('--eval-bleu-detok-args', type=str, metavar='JSON', + help='args for building the tokenizer, if needed') + parser.add_argument('--eval-tokenized-bleu', action='store_true', default=False, + help='compute tokenized BLEU instead of sacrebleu') + parser.add_argument('--eval-bleu-remove-bpe', nargs='?', const='@@ ', default=None, + help='remove BPE before computing BLEU') + parser.add_argument('--eval-bleu-args', type=str, metavar='JSON', + help='generation args for BLUE scoring, ' + 'e.g., \'{"beam": 4, "lenpen": 0.6}\'') + parser.add_argument('--eval-bleu-print-samples', action='store_true', + help='print sample generations during validation') + # fmt: on + + def __init__(self, args, common_dict, mono_langs, valid_lang_pairs): + super().__init__(args, common_dict, common_dict) + self.common_dict = common_dict + self.mono_langs = mono_langs + self.valid_lang_pairs = valid_lang_pairs + + self.SHOW_SAMPLES_INTERVAL = 1000 + # Start by showing samples + self._show_samples_ctr = self.SHOW_SAMPLES_INTERVAL + self.SHOW_SAMPLES_NUMBER = 5 + self.lambda_bt = PiecewiseLinearFn.from_string(args.lambda_bt) + self.lambda_dae = PiecewiseLinearFn.from_string(args.lambda_dae) + + self.args = args + self.data = utils.split_paths(self.args.data) + if len(self.data) == 1: + shards = list(Path(self.data[0]).glob("shard*")) + if len(shards) > 0: + # keep this as strings, since it can also be a manifold path + old_data = self.data + self.data = [str(shard) for shard in shards] + logging.warning(f"Expanded data directory {old_data} to {self.data}") + + @classmethod + def setup_task(cls, args, **kwargs): + """Setup the task (e.g., load dictionaries). + + Args: + args (argparse.Namespace): parsed command-line arguments + """ + args.left_pad_source = options.eval_bool(args.left_pad_source) + args.left_pad_target = options.eval_bool(args.left_pad_target) + + paths = utils.split_paths(args.data) + assert len(paths) > 0 + assert args.mono_langs is not None + + mono_langs = args.mono_langs.split(",") + valid_lang_pairs = args.valid_lang_pairs.split(",") + + # load dictionary + dict_path = os.path.join(paths[0], "dict.txt") + common_dict = cls.load_dictionary(dict_path) + + return cls(args, common_dict, mono_langs, valid_lang_pairs) + + def load_dataset(self, split, epoch=1, combine=False, **kwargs) -> FairseqDataset: + """Load a given dataset split. + + Args: + split (str): name of the split (e.g., train, valid, test) + """ + if split == "train": + data_path = self.data[(epoch - 1) % len(self.data)] + dataset = self.load_train_dataset(data_path) + else: + # valid/test should always be the same. + dataset = self.load_translation_dataset(split, self.data[0]) + + self.datasets[split] = dataset + return dataset + + def load_train_dataset(self, data_path: str) -> FairseqDataset: + """The training dataset is made of backtranslation dataset and denoising dataset.""" + data = [] + for lang in self.mono_langs: + train_path = os.path.join(data_path, lang, "train") + # TODO: could we do the BT using denoise sample ? + # this would half the data loading work + data.append((f"{lang}-BT", self.load_bt_dataset(train_path, lang))) + data.append( + (f"{lang}-DENOISE", self.load_denoise_dataset(train_path, lang)) + ) + + return RoundRobinZipDatasets(OrderedDict(data)) + + def _langpair_dataset( + self, src: FairseqDataset, tgt: FairseqDataset + ) -> LanguagePairDataset: + return LanguagePairDataset( + src, + src.sizes, + self.dictionary, + tgt=tgt, + tgt_sizes=tgt.sizes, + tgt_dict=self.dictionary, + left_pad_source=self.args.left_pad_source, + left_pad_target=self.args.left_pad_target, + # TODO: should we shuffle ? we are already sorting batch by sizes so ? + # shuffle=True, + ) + + def _prepend_lang_bos_to_target( + self, dataset: LanguagePairDataset, lang: str + ) -> LanguagePairDataset: + bos = _lang_token_index(self.dictionary, lang) + return TransformEosLangPairDataset( + dataset, + src_eos=self.dictionary.eos(), + new_src_eos=self.dictionary.eos(), + tgt_bos=self.dictionary.eos(), + new_tgt_bos=bos, + ) + + def load_bt_dataset(self, data_path: str, lang: str) -> FairseqDataset: + """The BT dataset is generated with (tgt, tgt) pairs. + The actual translation to a (generated_src, tgt) pair + is done on the fly during training. + """ + mono_dataset = data_utils.load_indexed_dataset( + data_path, self.common_dict, self.args.dataset_impl + ) + assert mono_dataset is not None, f"No dataset found for {lang}" + + mono_dataset_src = PrependTokenDataset( + mono_dataset, _lang_token_index(self.dictionary, lang) + ) + + mono_dataset_bt = self._langpair_dataset(mono_dataset_src, mono_dataset) + logger.info( + f"mono_lang = {lang} " + f"lang token index = {_lang_token_index(self.dictionary, lang)} " + f"lang token = {_lang_token(lang)}" + ) + + mono_dataset_bt = self._prepend_lang_bos_to_target(mono_dataset_bt, lang) + return mono_dataset_bt + + def load_denoise_dataset(self, data_path: str, lang: str) -> FairseqDataset: + """Classic denoising dataset""" + dataset = data_utils.load_indexed_dataset( + data_path, self.common_dict, self.args.dataset_impl + ) + noisy_dataset = NoisingDataset( + dataset, + self.dictionary, + seed=1, + max_word_shuffle_distance=self.args.max_word_shuffle_distance, + word_dropout_prob=self.args.word_dropout_prob, + word_blanking_prob=self.args.word_blanking_prob, + ) + noisy_dataset = PrependTokenDataset( + noisy_dataset, _lang_token_index(self.dictionary, lang) + ) + + clean_dataset = data_utils.load_indexed_dataset( + data_path, self.common_dict, self.args.dataset_impl + ) + denoising_dataset = self._langpair_dataset(noisy_dataset, clean_dataset) + denoising_dataset = self._prepend_lang_bos_to_target(denoising_dataset, lang) + return denoising_dataset + + def load_translation_dataset( + self, split: str, data_path: str, combine: bool = False + ): + # only judging with one language pair for the moment, + # since ConcatDataset doesn't work as expected + assert len(self.valid_lang_pairs) == 1, "For now..." + valid_lang_pair = self.valid_lang_pairs[0] + src, tgt = valid_lang_pair.split("-") + + # use the same function than TranslationTask + src_tgt_dt = load_langpair_dataset( + data_path, + split, + src, + self.common_dict, + tgt, + self.common_dict, + combine=combine, + dataset_impl=self.args.dataset_impl, + upsample_primary=self.args.upsample_primary, + left_pad_source=self.args.left_pad_source, + left_pad_target=self.args.left_pad_target, + max_source_positions=self.args.max_source_positions, + max_target_positions=self.args.max_target_positions, + load_alignments=self.args.load_alignments, + truncate_source=self.args.truncate_source, + num_buckets=self.args.num_batch_buckets, + shuffle=(split != "test"), + prepend_bos_src=_lang_token_index(self.dictionary, src), + ) + + src_tgt_eos_dt = self._prepend_lang_bos_to_target(src_tgt_dt, tgt) + src_tgt_eos_dt.args = self.args + return src_tgt_eos_dt + + def build_dataset_for_inference(self, src_tokens, src_lengths, constraints=None): + raise NotImplementedError + + def build_model(self, args): + # torch.autograd.set_detect_anomaly(True) + model = super().build_model(args) + + add_secial_tokens_to_dict_and_model(self.common_dict, model, self.mono_langs) + + self.sequence_generators = {} + for mono_lang in self.mono_langs: + self.sequence_generators[mono_lang] = SequenceGenerator( + [model], + tgt_dict=self.dictionary, + beam_size=1, + max_len_a=1.3, + max_len_b=5, + min_len=5, + # keep 1 to be able to prepend bos + max_len=model.max_decoder_positions() - 1, + ) + + if getattr(args, "eval_bleu", False): + assert getattr(args, "eval_bleu_detok", None) is not None, ( + "--eval-bleu-detok is required if using --eval-bleu; " + "try --eval-bleu-detok=moses (or --eval-bleu-detok=space " + "to disable detokenization, e.g., when using sentencepiece)" + ) + detok_args = json.loads(getattr(args, "eval_bleu_detok_args", "{}") or "{}") + self.tokenizer = encoders.build_tokenizer( + Namespace( + tokenizer=getattr(args, "eval_bleu_detok", None), **detok_args + ) + ) + + gen_args = json.loads(getattr(args, "eval_bleu_args", "{}") or "{}") + self.bleu_sequence_generator = self.build_generator( + [model], Namespace(**gen_args) + ) + + return model + + def max_positions(self): + """Return the max sentence length allowed by the task.""" + return (self.args.max_source_positions, self.args.max_target_positions) + + @property + def dictionary(self): + """Return the source :class:`~fairseq.data.Dictionary`.""" + return self.common_dict + + def display_samples_once_in_a_while(self, smp, mono_lang, other_lang): + self._show_samples_ctr += 1 + if self._show_samples_ctr < self.SHOW_SAMPLES_INTERVAL: + return + self._show_samples_ctr = 0 + + ln = smp["net_input"]["src_tokens"].shape[0] + + logger.info( + f"(r:{self.args.distributed_rank}) : " + f"{other_lang} ---> {mono_lang} " + f"({other_lang} was generated by back-translation.) {ln} samples" + ) + + for i in range(min(ln, self.SHOW_SAMPLES_NUMBER)): + src_tokens = smp["net_input"]["src_tokens"][i] + tgt_tokens = smp["target"][i] + + src_str = self.dictionary.string(src_tokens, "sentencepiece") + tgt_str = self.dictionary.string(tgt_tokens, "sentencepiece") + logger.info( + f"\n{i}\t\t[{other_lang} generated] {src_str}\n" + f"\t\t[{mono_lang} original ] {tgt_str}\n" + f"\t\t[ src tokens] {src_tokens}\n" + ) + + def backtranslate_sample(self, smp, orig_lang, other_lang) -> None: + """ + * WARNING: smp is modified in place. + * At the start of this function, `smp` has the same input and target: + |--------------------------------------------------------| + | smp['net_input']['src_tokens'] | smp['target'] | + | (from data) __en__ hello world | __en__ hello world | + |--------------------------------------------------------| + + * We call generator.generate(smp, bos_token = token("ro")), + and copy the result as input + * At the end, `smp` has the translation to other language. + |--------------------------------------------------------| + | smp['net_input']['src_tokens'] | smp['target'] | + | (generated) __ro__ salut lume | __en__ hello world | + |--------------------------------------------------------| + + """ + bos_token = _lang_token_index(self.dictionary, other_lang) + generated = self.sequence_generators[orig_lang].generate( + models=[], sample=smp, bos_token=bos_token + ) + + max_lngth = max([gn[0]["tokens"].size(0) for gn in generated]) + net_input = smp["net_input"] + n_src_tokens = torch.empty( + size=(len(generated), max_lngth + 1), dtype=net_input["src_tokens"].dtype + ) + n_src_lengths = torch.empty( + len(generated), dtype=net_input["src_lengths"].dtype + ) + + for i, gn in enumerate(generated): + tokens = gn[0]["tokens"] + tokens_size = tokens.size(0) + padding_needed = max_lngth - tokens_size + tokens = torch.cat([tokens.new([bos_token]), tokens]) + tokens = F.pad(tokens, (0, padding_needed), value=self.dictionary.pad()) + n_src_tokens[i] = tokens + n_src_lengths[i] = tokens_size + 1 + + device = net_input["src_tokens"].device + # This seems to be important + del net_input["src_tokens"] + del net_input["src_lengths"] + net_input["src_tokens"] = n_src_tokens.to(device) + net_input["src_lengths"] = n_src_lengths.to(device) + + def generate(self, smp, model): + model.eval() + orig_lang = ( + self.dictionary[smp["net_input"]["src_tokens"][0][0]] + .replace(" ", "") + .replace("_", "") + ) + bos_token = smp["net_input"]["prev_output_tokens"][0][0] + with torch.no_grad(): + generated = self.sequence_generators[orig_lang].generate( + models=[model], sample=smp, bos_token=bos_token + ) + return generated + + def get_other_lang(self, lang): + # TODO: allow more complex mapping + if lang != self.mono_langs[0]: + return self.mono_langs[0] + if len(self.mono_langs) == 2: + return self.mono_langs[1] + return self.mono_langs[np.random.randint(1, len(self.mono_langs))] + + def train_step( + self, sample, model, criterion, optimizer, update_num, ignore_grad=False + ): + + model.train() + model.set_num_updates(update_num) + + agg_loss, agg_sample_size = 0.0, 0.0 + agg_logging_output: Dict[str, float] = defaultdict(float) + + dataset_keys = self.datasets["train"].datasets.keys() + + weights = { + "BT": self.lambda_bt(update_num), + "DENOISE": self.lambda_dae(update_num), + } + log_keys = {"BT": "bt_", "DENOISE": "dae_"} + + for dataset_key in dataset_keys: + smp = sample[dataset_key] + mono_lang, task_subtype = dataset_key.split("-") + if weights[task_subtype] == 0: + continue + + if task_subtype == "BT": + with torch.autograd.profiler.record_function("backtranslation"): + model.eval() + # TODO: Could we translate to several language at once ? + # this would allow to share encoder_out and maximize GPU usage. + other_lang = self.get_other_lang(mono_lang) + self.backtranslate_sample(smp, mono_lang, other_lang) + self.display_samples_once_in_a_while(smp, mono_lang, other_lang) + model.train() + + # Like in FairseqTask.train_step + with torch.autograd.profiler.record_function("forward"): + loss, sample_size, logging_output = criterion(model, smp) + loss *= weights[task_subtype] + if ignore_grad: + loss *= 0 + with torch.autograd.profiler.record_function("backward"): + optimizer.backward(loss) + + agg_loss += loss.item() + agg_sample_size += sample_size + for k in logging_output: + agg_logging_output[log_keys[task_subtype] + k] += logging_output[k] + agg_logging_output[k] += logging_output[k] + + return agg_loss, agg_sample_size, agg_logging_output + + def get_bos_token_from_sample(self, sample): + net_input = sample["net_input"] + source_lang_token_id = torch.unique(net_input["src_tokens"][:, 0]).item() + source_lang_token = self.dictionary[source_lang_token_id].replace("_", "") + target_lang_token_id = _lang_token_index( + self.dictionary, self.get_other_lang(source_lang_token) + ) + + return target_lang_token_id + + def reduce_metrics(self, logging_outputs, criterion): + super().reduce_metrics(logging_outputs, criterion) + bt_sample_size = sum(x.get("bt_sample_size", 0) for x in logging_outputs) + if bt_sample_size: + bt_loss_sum = sum(x.get("bt_loss", 0) for x in logging_outputs) + bt_loss_sum *= 1 / bt_sample_size / math.log(2) + metrics.log_scalar("bt_loss", bt_loss_sum, bt_sample_size, round=3) + + bt_nll_loss_sum = sum(x.get("bt_nll_loss", 0) for x in logging_outputs) + bt_ntokens = sum(x.get("bt_ntokens", 0) for x in logging_outputs) + bt_nll_loss_sum *= 1 / bt_ntokens / math.log(2) + metrics.log_scalar("bt_nll_loss", bt_nll_loss_sum, bt_ntokens, round=3) + metrics.log_derived( + "bt_ppl", lambda meters: utils.get_perplexity(meters["bt_nll_loss"].avg) + ) + + dae_sample_size = sum(x.get("dae_sample_size", 0) for x in logging_outputs) + if dae_sample_size: + dae_loss_sum = sum(x.get("dae_loss", 0) for x in logging_outputs) + dae_loss_sum *= 1 / dae_sample_size / math.log(2) + metrics.log_scalar("dae_loss", dae_loss_sum, dae_sample_size, round=3) + + dae_nll_loss_sum = sum(x.get("dae_nll_loss", 0) for x in logging_outputs) + dae_ntokens = sum(x.get("dae_ntokens", 0) for x in logging_outputs) + dae_nll_loss_sum *= 1 / dae_ntokens / math.log(2) + metrics.log_scalar("dae_nll_loss", dae_nll_loss_sum, dae_ntokens, round=3) + metrics.log_derived( + "dae_ppl", + lambda meters: utils.get_perplexity(meters["dae_nll_loss"].avg), + ) + + +@torch.no_grad() +def extend_embedding( + emb: nn.Module, new_vocab_size: int, copy_from_token_id: int +) -> None: + old_emb_data = emb.weight.data + (old_vocab_size, dim) = old_emb_data.shape + assert new_vocab_size >= old_vocab_size + + if new_vocab_size > old_vocab_size: + emb.weight.data = torch.zeros((new_vocab_size, dim)) + emb.weight.data[:old_vocab_size, :] = old_emb_data + # initialize new embeddings + emb.weight.data[old_vocab_size:, :] = old_emb_data[copy_from_token_id] + if hasattr(emb, "num_embeddings"): + emb.num_embeddings = new_vocab_size + if hasattr(emb, "out_features"): + emb.out_features = new_vocab_size + + if getattr(emb, "bias", None) is None: + return + + # Fix the bias. + # Bias shape can be different from the previous vocab size + # if the weight matrix was shared and alread extended but not the bias. + (old_vocab_size,) = emb.bias.shape + assert new_vocab_size >= old_vocab_size + if new_vocab_size > old_vocab_size: + old_bias = emb.bias.data + new_bias = torch.zeros( + (new_vocab_size,), dtype=old_bias.dtype, device=old_bias.device + ) + new_bias[:old_vocab_size] = old_bias + emb.bias.data = new_bias + + +def add_secial_tokens_to_dict_and_model( + dictionary: "fairseq.data.Dictionary", + model: nn.Module, + mono_langs: Sequence[str], +) -> None: + embs = model.encoder.embed_tokens + vocab_size, embedding_dim = embs.weight.shape + + # The model may or may not have a '<mask>' embedding yet + assert ( + len(dictionary) <= vocab_size <= len(dictionary) + 1 + ), f"Dictionary len ({len(dictionary)}) doesn't match embs shape ({embs.weight.shape})" + # TODO: we should reuse the pretrained model dict which already has <mask> + dictionary.add_symbol("<mask>") + + for lang in mono_langs: + lang_token = _lang_token(lang) + dictionary.add_symbol(lang_token) + logger.info( + f"dictionary: {len(dictionary)} -> {vocab_size} tokens " + f"after adding {len(mono_langs)} lang tokens." + ) + + if len(dictionary) <= vocab_size: + return + + extend_embedding(embs, len(dictionary), dictionary.bos()) + dec_embs = model.decoder.embed_tokens + extend_embedding(dec_embs, len(dictionary), dictionary.bos()) + lm_head = model.decoder.output_projection + extend_embedding(lm_head, len(dictionary), dictionary.bos()) + assert lm_head.weight.shape == (len(dictionary), embedding_dim) + + +def _lang_token(lang: str) -> str: + return f"__{lang}__" + + +def _lang_token_index(dictionary, lang: str) -> int: + return dictionary.index(_lang_token(lang)) + + +@contextlib.contextmanager +def assert_weights_have_changed(model: nn.Module): + def checksum(model: nn.Module) -> float: + return sum(p.sum().item() for p in model.parameters()) + + initial_checksum = checksum(model) + yield model + final_checksum = checksum(model) + logger.info( + f"initial_checksum={initial_checksum} -> final_checksum={final_checksum}" + ) + assert initial_checksum != final_checksum, "Model hasn't changed !" diff --git a/fairseq/tasks/semisupervised_translation.py b/fairseq/tasks/semisupervised_translation.py index bf770bfe15..b2f9bf9a73 100644 --- a/fairseq/tasks/semisupervised_translation.py +++ b/fairseq/tasks/semisupervised_translation.py @@ -3,27 +3,28 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. -from collections import OrderedDict import logging import os +from collections import OrderedDict +from fairseq import utils from fairseq.data import ( BacktranslationDataset, - data_utils, - indexed_dataset, IndexedCachedDataset, IndexedDataset, IndexedRawTextDataset, LanguagePairDataset, NoisingDataset, RoundRobinZipDatasets, + data_utils, + indexed_dataset, ) from fairseq.models import FairseqMultiModel from fairseq.sequence_generator import SequenceGenerator -from .multilingual_translation import MultilingualTranslationTask from . import register_task -from fairseq import utils +from .multilingual_translation import MultilingualTranslationTask + logger = logging.getLogger(__name__) @@ -46,18 +47,20 @@ def parse_lambda_config(x): x = "0:0,1000:0,2000:1" # lambda will be equal to 0 for the first 1000 # iterations, then will linearly increase to 1 until iteration 2000 """ - split = x.split(',') + split = x.split(",") if len(split) == 1: return float(x), None else: split = [s.split(os.pathsep) for s in split] assert all(len(s) == 2 for s in split) assert all(k.isdigit() for k, _ in split) - assert all(int(split[i][0]) < int(split[i + 1][0]) for i in range(len(split) - 1)) + assert all( + int(split[i][0]) < int(split[i + 1][0]) for i in range(len(split) - 1) + ) return float(split[0][1]), [(int(k), float(v)) for k, v in split] -@register_task('semisupervised_translation') +@register_task("semisupervised_translation") class SemisupervisedTranslationTask(MultilingualTranslationTask): """A task for training multiple translation models simultaneously. @@ -119,13 +122,19 @@ def add_args(parser): def __init__(self, args, dicts, training): super().__init__(args, dicts, training) - self.lambda_parallel, self.lambda_parallel_steps = parse_lambda_config(args.lambda_parallel_config) - self.lambda_otf_bt, self.lambda_otf_bt_steps = parse_lambda_config(args.lambda_otf_bt_config) - self.lambda_denoising, self.lambda_denoising_steps = parse_lambda_config(args.lambda_denoising_config) - if (self.lambda_denoising > 0.0 or self.lambda_denoising_steps is not None): + self.lambda_parallel, self.lambda_parallel_steps = parse_lambda_config( + args.lambda_parallel_config + ) + self.lambda_otf_bt, self.lambda_otf_bt_steps = parse_lambda_config( + args.lambda_otf_bt_config + ) + self.lambda_denoising, self.lambda_denoising_steps = parse_lambda_config( + args.lambda_denoising_config + ) + if self.lambda_denoising > 0.0 or self.lambda_denoising_steps is not None: denoising_lang_pairs = [ "%s-%s" % (tgt, tgt) - for tgt in {lang_pair.split('-')[1] for lang_pair in args.lang_pairs} + for tgt in {lang_pair.split("-")[1] for lang_pair in args.lang_pairs} ] self.model_lang_pairs = self.model_lang_pairs + denoising_lang_pairs self.backtranslate_datasets = {} @@ -144,39 +153,71 @@ def load_dataset(self, split, epoch=1, **kwargs): def split_exists(split, src, tgt, lang): if src is not None: - filename = os.path.join(data_path, '{}.{}-{}.{}'.format(split, src, tgt, lang)) + filename = os.path.join( + data_path, "{}.{}-{}.{}".format(split, src, tgt, lang) + ) else: - filename = os.path.join(data_path, '{}.{}-None.{}'.format(split, src, tgt)) + filename = os.path.join( + data_path, "{}.{}-None.{}".format(split, src, tgt) + ) return indexed_dataset.dataset_exists(filename, impl=self.args.dataset_impl) def load_indexed_dataset(path, dictionary): - return data_utils.load_indexed_dataset(path, dictionary, self.args.dataset_impl) + return data_utils.load_indexed_dataset( + path, dictionary, self.args.dataset_impl + ) # load parallel datasets src_datasets, tgt_datasets = {}, {} - if (self.lambda_parallel > 0.0 or self.lambda_parallel_steps is not None or not split.startswith("train")): + if ( + self.lambda_parallel > 0.0 + or self.lambda_parallel_steps is not None + or not split.startswith("train") + ): for lang_pair in self.lang_pairs: - src, tgt = lang_pair.split('-') + src, tgt = lang_pair.split("-") if split_exists(split, src, tgt, src): - prefix = os.path.join(data_path, '{}.{}-{}.'.format(split, src, tgt)) + prefix = os.path.join( + data_path, "{}.{}-{}.".format(split, src, tgt) + ) elif split_exists(split, tgt, src, src): - prefix = os.path.join(data_path, '{}.{}-{}.'.format(split, tgt, src)) + prefix = os.path.join( + data_path, "{}.{}-{}.".format(split, tgt, src) + ) else: continue - src_datasets[lang_pair] = load_indexed_dataset(prefix + src, self.dicts[src]) - tgt_datasets[lang_pair] = load_indexed_dataset(prefix + tgt, self.dicts[tgt]) - logger.info('parallel-{} {} {} examples'.format(data_path, split, len(src_datasets[lang_pair]))) + src_datasets[lang_pair] = load_indexed_dataset( + prefix + src, self.dicts[src] + ) + tgt_datasets[lang_pair] = load_indexed_dataset( + prefix + tgt, self.dicts[tgt] + ) + logger.info( + "parallel-{} {} {} examples".format( + data_path, split, len(src_datasets[lang_pair]) + ) + ) if len(src_datasets) == 0: - raise FileNotFoundError('Dataset not found: {} ({})'.format(split, data_path)) + raise FileNotFoundError( + "Dataset not found: {} ({})".format(split, data_path) + ) # back translation datasets backtranslate_datasets = {} - if (self.lambda_otf_bt > 0.0 or self.lambda_otf_bt_steps is not None) and split.startswith("train"): + if ( + self.lambda_otf_bt > 0.0 or self.lambda_otf_bt_steps is not None + ) and split.startswith("train"): for lang_pair in self.lang_pairs: - src, tgt = lang_pair.split('-') + src, tgt = lang_pair.split("-") if not split_exists(split, tgt, None, tgt): - raise FileNotFoundError('Dataset not found: backtranslation {} ({})'.format(split, data_path)) - filename = os.path.join(data_path, '{}.{}-None.{}'.format(split, tgt, tgt)) + raise FileNotFoundError( + "Dataset not found: backtranslation {} ({})".format( + split, data_path + ) + ) + filename = os.path.join( + data_path, "{}.{}-None.{}".format(split, tgt, tgt) + ) dataset = load_indexed_dataset(filename, self.dicts[tgt]) lang_pair_dataset_tgt = LanguagePairDataset( dataset, @@ -203,7 +244,8 @@ def load_indexed_dataset(path, dictionary): tgt_lang=src, ), backtranslation_fn=self.backtranslators[lang_pair], - src_dict=self.dicts[src], tgt_dict=self.dicts[tgt], + src_dict=self.dicts[src], + tgt_dict=self.dicts[tgt], output_collater=self.alter_dataset_langtok( lang_pair_dataset=lang_pair_dataset, src_eos=self.dicts[src].eos(), @@ -212,19 +254,30 @@ def load_indexed_dataset(path, dictionary): tgt_lang=tgt, ).collater, ) - logger.info('backtranslate-{}: {} {} {} examples'.format( - tgt, data_path, split, len(backtranslate_datasets[lang_pair]), - )) - self.backtranslate_datasets[lang_pair] = backtranslate_datasets[lang_pair] + logger.info( + "backtranslate-{}: {} {} {} examples".format( + tgt, + data_path, + split, + len(backtranslate_datasets[lang_pair]), + ) + ) + self.backtranslate_datasets[lang_pair] = backtranslate_datasets[ + lang_pair + ] # denoising autoencoder noising_datasets = {} - if (self.lambda_denoising > 0.0 or self.lambda_denoising_steps is not None) and split.startswith("train"): + if ( + self.lambda_denoising > 0.0 or self.lambda_denoising_steps is not None + ) and split.startswith("train"): for lang_pair in self.lang_pairs: - _, tgt = lang_pair.split('-') + _, tgt = lang_pair.split("-") if not split_exists(split, tgt, None, tgt): continue - filename = os.path.join(data_path, '{}.{}-None.{}'.format(split, tgt, tgt)) + filename = os.path.join( + data_path, "{}.{}-None.{}".format(split, tgt, tgt) + ) tgt_dataset1 = load_indexed_dataset(filename, self.dicts[tgt]) tgt_dataset2 = load_indexed_dataset(filename, self.dicts[tgt]) noising_dataset = NoisingDataset( @@ -251,21 +304,28 @@ def load_indexed_dataset(path, dictionary): tgt_eos=self.dicts[tgt].eos(), tgt_lang=tgt, ) - logger.info('denoising-{}: {} {} {} examples'.format( - tgt, data_path, split, len(noising_datasets[lang_pair]), - )) + logger.info( + "denoising-{}: {} {} {} examples".format( + tgt, + data_path, + split, + len(noising_datasets[lang_pair]), + ) + ) def language_pair_dataset(lang_pair): - src, tgt = lang_pair.split('-') + src, tgt = lang_pair.split("-") src_dataset, tgt_dataset = src_datasets[lang_pair], tgt_datasets[lang_pair] return self.alter_dataset_langtok( LanguagePairDataset( - src_dataset, src_dataset.sizes, self.dicts[src], - tgt_dataset, tgt_dataset.sizes, self.dicts[tgt], + src_dataset, + src_dataset.sizes, + self.dicts[src], + tgt_dataset, + tgt_dataset.sizes, + self.dicts[tgt], left_pad_source=self.args.left_pad_source, left_pad_target=self.args.left_pad_target, - max_source_positions=self.args.max_source_positions, - max_target_positions=self.args.max_target_positions, ), self.dicts[src].eos(), src, @@ -274,32 +334,44 @@ def language_pair_dataset(lang_pair): ) self.datasets[split] = RoundRobinZipDatasets( - OrderedDict([ - (lang_pair, language_pair_dataset(lang_pair)) - for lang_pair in src_datasets.keys() - ] + [ - (_get_bt_dataset_key(lang_pair), dataset) - for lang_pair, dataset in backtranslate_datasets.items() - ] + [ - (_get_denoising_dataset_key(lang_pair), dataset) - for lang_pair, dataset in noising_datasets.items() - ]), - eval_key=None if self.training else "%s-%s" % (self.args.source_lang, self.args.target_lang), + OrderedDict( + [ + (lang_pair, language_pair_dataset(lang_pair)) + for lang_pair in src_datasets.keys() + ] + + [ + (_get_bt_dataset_key(lang_pair), dataset) + for lang_pair, dataset in backtranslate_datasets.items() + ] + + [ + (_get_denoising_dataset_key(lang_pair), dataset) + for lang_pair, dataset in noising_datasets.items() + ] + ), + eval_key=None + if self.training + else "%s-%s" % (self.args.source_lang, self.args.target_lang), ) def build_model(self, args): from fairseq import models + model = models.build_model(args, self) if not isinstance(model, FairseqMultiModel): - raise ValueError('SemisupervisedTranslationTask requires a FairseqMultiModel architecture') + raise ValueError( + "SemisupervisedTranslationTask requires a FairseqMultiModel architecture" + ) # create SequenceGenerator for each model that has backtranslation dependency on it self.sequence_generators = {} - if (self.lambda_otf_bt > 0.0 or self.lambda_otf_bt_steps is not None) and self.training: + if ( + self.lambda_otf_bt > 0.0 or self.lambda_otf_bt_steps is not None + ) and self.training: for lang_pair in self.lang_pairs: - src, tgt = lang_pair.split('-') - key = '{}-{}'.format(tgt, src) + src, tgt = lang_pair.split("-") + key = "{}-{}".format(tgt, src) self.sequence_generators[key] = SequenceGenerator( + [model.models[key]], tgt_dict=self.dicts[src], beam_size=args.bt_beam_size, max_len_a=args.bt_max_len_a, @@ -308,7 +380,8 @@ def build_model(self, args): decoder_lang_tok_idx = self.get_decoder_langtok(src) def backtranslate_fn( - sample, model=model.models[key], + sample, + model=model.models[key], bos_token=decoder_lang_tok_idx, sequence_generator=self.sequence_generators[key], ): @@ -317,17 +390,20 @@ def backtranslate_fn( sample, bos_token=bos_token, ) + self.backtranslators[lang_pair] = backtranslate_fn return model - def train_step(self, sample, model, criterion, optimizer, update_num, ignore_grad=False): + def train_step( + self, sample, model, criterion, optimizer, update_num, ignore_grad=False + ): model.train() if update_num > 0: self.update_step(update_num) - agg_loss, agg_sample_size, agg_logging_output = 0., 0., {} + agg_loss, agg_sample_size, agg_logging_output = 0.0, 0.0, {} def forward_backward(model, samples, logging_output_key, weight): nonlocal agg_loss, agg_sample_size, agg_logging_output @@ -342,22 +418,39 @@ def forward_backward(model, samples, logging_output_key, weight): agg_loss += loss.detach().item() # TODO make summing of the sample sizes configurable agg_sample_size += sample_size - agg_logging_output[logging_output_key] = logging_output + for k in logging_output: + agg_logging_output[k] += logging_output[k] + agg_logging_output[logging_output_key] += logging_output[k] if self.lambda_parallel > 0.0: for lang_pair in self.lang_pairs: - forward_backward(model.models[lang_pair], sample[lang_pair], lang_pair, self.lambda_parallel) + forward_backward( + model.models[lang_pair], + sample[lang_pair], + lang_pair, + self.lambda_parallel, + ) if self.lambda_otf_bt > 0.0: for lang_pair in self.lang_pairs: sample_key = _get_bt_dataset_key(lang_pair) - forward_backward(model.models[lang_pair], sample[sample_key], sample_key, self.lambda_otf_bt) + forward_backward( + model.models[lang_pair], + sample[sample_key], + sample_key, + self.lambda_otf_bt, + ) if self.lambda_denoising > 0.0: for lang_pair in self.lang_pairs: - _, tgt = lang_pair.split('-') + _, tgt = lang_pair.split("-") sample_key = _get_denoising_dataset_key(lang_pair) - forward_backward(model.models['{0}-{0}'.format(tgt)], sample[sample_key], sample_key, self.lambda_denoising) + forward_backward( + model.models["{0}-{0}".format(tgt)], + sample[sample_key], + sample_key, + self.lambda_denoising, + ) return agg_loss, agg_sample_size, agg_logging_output @@ -366,7 +459,11 @@ def lambda_step_func(config, n_iter): """ Update a lambda value according to its schedule configuration. """ - ranges = [i for i in range(len(config) - 1) if config[i][0] <= n_iter < config[i + 1][0]] + ranges = [ + i + for i in range(len(config) - 1) + if config[i][0] <= n_iter < config[i + 1][0] + ] if len(ranges) == 0: assert n_iter >= config[-1][0] return config[-1][1] @@ -377,25 +474,12 @@ def lambda_step_func(config, n_iter): return y_a + (n_iter - x_a) * float(y_b - y_a) / float(x_b - x_a) if self.lambda_parallel_steps is not None: - self.lambda_parallel = lambda_step_func(self.lambda_parallel_steps, num_updates) + self.lambda_parallel = lambda_step_func( + self.lambda_parallel_steps, num_updates + ) if self.lambda_denoising_steps is not None: - self.lambda_denoising = lambda_step_func(self.lambda_denoising_steps, num_updates) + self.lambda_denoising = lambda_step_func( + self.lambda_denoising_steps, num_updates + ) if self.lambda_otf_bt_steps is not None: self.lambda_otf_bt = lambda_step_func(self.lambda_otf_bt_steps, num_updates) - - def aggregate_logging_outputs(self, logging_outputs, criterion): - # aggregate logging outputs for each language pair - logging_output_keys = { - key - for logging_output in logging_outputs - for key in logging_output - } - lang_pair_keys = set(self.lang_pairs + [ - _get_bt_dataset_key(lang_pair) - for lang_pair in self.lang_pairs - ] + [ - _get_denoising_dataset_key(lang_pair) - for lang_pair in self.lang_pairs - ]) - logging_output_keys = logging_output_keys.intersection(lang_pair_keys) - return super().aggregate_logging_outputs(logging_outputs, criterion, logging_output_keys) diff --git a/fairseq/tasks/sentence_prediction.py b/fairseq/tasks/sentence_prediction.py index 471baf428a..d5f9302c10 100644 --- a/fairseq/tasks/sentence_prediction.py +++ b/fairseq/tasks/sentence_prediction.py @@ -6,16 +6,19 @@ import logging import os -import numpy as np +import contextlib +from dataclasses import dataclass, field +from typing import Optional +from omegaconf import MISSING, II, open_dict, OmegaConf +import numpy as np from fairseq.data import ( ConcatSentencesDataset, - data_utils, Dictionary, IdDataset, NestedDictionaryDataset, - NumSamplesDataset, NumelDataset, + NumSamplesDataset, OffsetTokensDataset, PrependTokenDataset, RawLabelDataset, @@ -23,15 +26,65 @@ RollDataset, SortDataset, StripTokenDataset, - TruncateDataset, + data_utils, ) -from fairseq.tasks import FairseqTask, register_task +from fairseq.data.shorten_dataset import maybe_shorten_dataset +from fairseq.tasks import FairseqDataclass, FairseqTask, register_task +from fairseq.dataclass import ChoiceEnum logger = logging.getLogger(__name__) - - -@register_task('sentence_prediction') +SHORTEN_METHOD_CHOICES = ChoiceEnum(["none", "truncate", "random_crop"]) + + +@dataclass +class SentencePredictionConfig(FairseqDataclass): + data: str = field(default=MISSING, metadata={"help": "path to data directory"}) + num_classes: int = field( + default=-1, + metadata={"help": "number of classes or regression targets"}, + ) + init_token: Optional[int] = field( + default=None, + metadata={"help": "add token at the beginning of each batch item"}, + ) + separator_token: Optional[int] = field( + default=None, + metadata={"help": "add separator token between inputs"}, + ) + no_shuffle: bool = field( + default=False, + ) + shorten_method: SHORTEN_METHOD_CHOICES = field( + default="none", + metadata={ + "help": "if not none, shorten sequences that exceed tokens_per_sample" + }, + ) + shorten_data_split_list: str = field( + default="", + metadata={ + "help": "comma-separated list of dataset splits to apply shortening to, " + 'e.g., "train,valid" (default: all dataset splits)' + }, + ) + add_prev_output_tokens: bool = field( + default=False, + metadata={ + "help": "add prev_output_tokens to sample, used for encoder-decoder arch" + }, + ) + max_positions: int = field( + default=512, + metadata={"help": "max tokens per example"}, + ) + + regression_target: bool = II("criterion.regression_target") + classification_head_name: str = II("criterion.classification_head_name") + seed: int = II("common.seed") + + +@register_task("sentence_prediction", dataclass=SentencePredictionConfig) class SentencePredictionTask(FairseqTask): """ Sentence (or sentence pair) prediction (classification or regression) task. @@ -40,134 +93,118 @@ class SentencePredictionTask(FairseqTask): dictionary (Dictionary): the dictionary for the input of the task """ - @staticmethod - def add_args(parser): - """Add task-specific arguments to the parser.""" - parser.add_argument('data', metavar='FILE', - help='file prefix for data') - parser.add_argument('--num-classes', type=int, default=-1, - help='number of classes or regression targets') - parser.add_argument('--init-token', type=int, default=None, - help='add token at the beginning of each batch item') - parser.add_argument('--separator-token', type=int, default=None, - help='add separator token between inputs') - parser.add_argument('--regression-target', action='store_true', default=False) - parser.add_argument('--no-shuffle', action='store_true', default=False) - parser.add_argument('--truncate-sequence', action='store_true', default=False, - help='truncate sequence to max-positions') - parser.add_argument('--add-prev-output-tokens', action='store_true', default=False, - help='add prev_output_tokens to sample, used for encoder-decoder arch') - - def __init__(self, args, data_dictionary, label_dictionary): - super().__init__(args) + def __init__(self, cfg, data_dictionary, label_dictionary): + super().__init__(cfg) self.dictionary = data_dictionary self._label_dictionary = label_dictionary - if not hasattr(args, 'max_positions'): - self._max_positions = ( - args.max_source_positions, - args.max_target_positions, - ) - else: - self._max_positions = args.max_positions - args.tokens_per_sample = self._max_positions @classmethod - def load_dictionary(cls, args, filename, source=True): + def load_dictionary(cls, filename): """Load the dictionary from the filename Args: filename (str): the filename """ dictionary = Dictionary.load(filename) - dictionary.add_symbol('<mask>') + dictionary.add_symbol("<mask>") return dictionary @classmethod - def setup_task(cls, args, **kwargs): - assert args.num_classes > 0, 'Must set --num-classes' + def setup_task(cls, cfg, **kwargs): + assert cfg.num_classes > 0, "Must set task.num_classes" # load data dictionary data_dict = cls.load_dictionary( - args, - os.path.join(args.data, 'input0', 'dict.txt'), - source=True, + os.path.join(cfg.data, "input0", "dict.txt"), ) - logger.info('[input] dictionary: {} types'.format(len(data_dict))) + logger.info("[input] dictionary: {} types".format(len(data_dict))) - label_dict = None - if not args.regression_target: - # load label dictionary + # load label dictionary + if not cfg.regression_target: label_dict = cls.load_dictionary( - args, - os.path.join(args.data, 'label', 'dict.txt'), - source=False, + os.path.join(cfg.data, "label", "dict.txt"), ) - logger.info('[label] dictionary: {} types'.format(len(label_dict))) + logger.info("[label] dictionary: {} types".format(len(label_dict))) else: label_dict = data_dict - return SentencePredictionTask(args, data_dict, label_dict) + return cls(cfg, data_dict, label_dict) def load_dataset(self, split, combine=False, **kwargs): """Load a given dataset split (e.g., train, valid, test).""" - def get_path(type, split): - return os.path.join(self.args.data, type, split) - def make_dataset(type, dictionary): - split_path = get_path(type, split) + def get_path(key, split): + return os.path.join(self.cfg.data, key, split) - dataset = data_utils.load_indexed_dataset( - split_path, - dictionary, - self.args.dataset_impl, - combine=combine, - ) + def make_dataset(key, dictionary): + split_path = get_path(key, split) + + try: + dataset = data_utils.load_indexed_dataset( + split_path, + dictionary, + combine=combine, + ) + except Exception as e: + if "StorageException: [404] Path not found" in str(e): + logger.warning(f"dataset {e} not found") + dataset = None + else: + raise e return dataset - input0 = make_dataset('input0', self.source_dictionary) - assert input0 is not None, 'could not find dataset: {}'.format(get_path(type, split)) - input1 = make_dataset('input1', self.source_dictionary) + input0 = make_dataset("input0", self.source_dictionary) + assert input0 is not None, "could not find dataset: {}".format( + get_path("input0", split) + ) + input1 = make_dataset("input1", self.source_dictionary) - if self.args.init_token is not None: - input0 = PrependTokenDataset(input0, self.args.init_token) + if self.cfg.init_token is not None: + input0 = PrependTokenDataset(input0, self.cfg.init_token) if input1 is None: src_tokens = input0 else: - if self.args.separator_token is not None: - input1 = PrependTokenDataset(input1, self.args.separator_token) + if self.cfg.separator_token is not None: + input1 = PrependTokenDataset(input1, self.cfg.separator_token) src_tokens = ConcatSentencesDataset(input0, input1) - with data_utils.numpy_seed(self.args.seed): + with data_utils.numpy_seed(self.cfg.seed): shuffle = np.random.permutation(len(src_tokens)) - if self.args.truncate_sequence: - src_tokens = TruncateDataset(src_tokens, self.args.max_positions) + src_tokens = maybe_shorten_dataset( + src_tokens, + split, + self.cfg.shorten_data_split_list, + self.cfg.shorten_method, + self.max_positions(), + self.cfg.seed, + ) dataset = { - 'id': IdDataset(), - 'net_input': { - 'src_tokens': RightPadDataset( + "id": IdDataset(), + "net_input": { + "src_tokens": RightPadDataset( src_tokens, pad_idx=self.source_dictionary.pad(), ), - 'src_lengths': NumelDataset(src_tokens, reduce=False), + "src_lengths": NumelDataset(src_tokens, reduce=False), }, - 'nsentences': NumSamplesDataset(), - 'ntokens': NumelDataset(src_tokens, reduce=True), + "nsentences": NumSamplesDataset(), + "ntokens": NumelDataset(src_tokens, reduce=True), } - if self.args.add_prev_output_tokens: + if self.cfg.add_prev_output_tokens: prev_tokens_dataset = RightPadDataset( RollDataset(src_tokens, 1), pad_idx=self.dictionary.pad(), ) - dataset['net_input'].update( + dataset["net_input"].update( prev_output_tokens=prev_tokens_dataset, ) - if not self.args.regression_target: - label_dataset = make_dataset('label', self.label_dictionary) + if not self.cfg.regression_target: + label_dataset = make_dataset("label", self.label_dictionary) if label_dataset is not None: dataset.update( target=OffsetTokensDataset( @@ -179,25 +216,32 @@ def make_dataset(type, dictionary): ) ) else: - label_path = "{0}.label".format(get_path('label', split)) + label_path = "{0}.label".format(get_path("label", split)) if os.path.exists(label_path): + def parse_regression_target(i, line): values = line.split() - assert len(values) == self.args.num_classes, \ - f'expected num_classes={self.args.num_classes} regression target values on line {i}, found: "{line}"' + assert ( + len(values) == self.cfg.num_classes + ), f'expected num_classes={self.cfg.num_classes} regression target values on line {i}, found: "{line}"' return [float(x) for x in values] - dataset.update( - target=RawLabelDataset([ - parse_regression_target(i, line.strip()) for i, line in enumerate(open(label_path).readlines()) - ]) - ) + + with open(label_path) as h: + dataset.update( + target=RawLabelDataset( + [ + parse_regression_target(i, line.strip()) + for i, line in enumerate(h.readlines()) + ] + ) + ) nested_dataset = NestedDictionaryDataset( dataset, sizes=[src_tokens.sizes], ) - if self.args.no_shuffle: + if self.cfg.no_shuffle: dataset = nested_dataset else: dataset = SortDataset( @@ -211,19 +255,23 @@ def parse_regression_target(i, line): self.datasets[split] = dataset return self.datasets[split] - def build_model(self, args): + def build_model(self, cfg): from fairseq import models - model = models.build_model(args, self) + + with open_dict(cfg) if OmegaConf.is_config(cfg) else contextlib.ExitStack(): + cfg.max_positions = self.cfg.max_positions + + model = models.build_model(cfg, self) model.register_classification_head( - getattr(args, 'classification_head_name', 'sentence_classification_head'), - num_classes=self.args.num_classes, + self.cfg.classification_head_name, + num_classes=self.cfg.num_classes, ) return model def max_positions(self): - return self._max_positions + return self.cfg.max_positions @property def source_dictionary(self): diff --git a/fairseq/tasks/sentence_ranking.py b/fairseq/tasks/sentence_ranking.py index 2438ab7404..bed44f34e5 100644 --- a/fairseq/tasks/sentence_ranking.py +++ b/fairseq/tasks/sentence_ranking.py @@ -7,29 +7,30 @@ import os import numpy as np - +from fairseq import utils from fairseq.data import ( ConcatSentencesDataset, - data_utils, Dictionary, IdDataset, NestedDictionaryDataset, - NumSamplesDataset, NumelDataset, + NumSamplesDataset, PrependTokenDataset, RawLabelDataset, RightPadDataset, SortDataset, TruncateDataset, + data_utils, ) -from fairseq.tasks import FairseqTask, register_task +from fairseq.data.shorten_dataset import maybe_shorten_dataset +from fairseq.tasks import LegacyFairseqTask, register_task logger = logging.getLogger(__name__) -@register_task('sentence_ranking') -class SentenceRankingTask(FairseqTask): +@register_task("sentence_ranking") +class SentenceRankingTask(LegacyFairseqTask): """ Ranking task on multiple sentences. @@ -40,19 +41,34 @@ class SentenceRankingTask(FairseqTask): @staticmethod def add_args(parser): """Add task-specific arguments to the parser.""" - parser.add_argument('data', metavar='FILE', - help='file prefix for data') - parser.add_argument('--num-classes', type=int, - help='number of sentences to be ranked') - parser.add_argument('--init-token', type=int, - help='add token at the beginning of each batch item') - parser.add_argument('--separator-token', type=int, - help='add separator token between inputs') - parser.add_argument('--no-shuffle', action='store_true') - parser.add_argument('--truncate-sequence', action='store_true', - help='Truncate sequence to max_positions') - parser.add_argument('--max-option-length', type=int, - help='max length for each option') + parser.add_argument("data", metavar="FILE", help="file prefix for data") + parser.add_argument( + "--num-classes", type=int, help="number of sentences to be ranked" + ) + parser.add_argument( + "--init-token", + type=int, + help="add token at the beginning of each batch item", + ) + parser.add_argument( + "--separator-token", type=int, help="add separator token between inputs" + ) + parser.add_argument("--no-shuffle", action="store_true") + parser.add_argument( + "--shorten-method", + default="none", + choices=["none", "truncate", "random_crop"], + help="if not none, shorten sequences that exceed --tokens-per-sample", + ) + parser.add_argument( + "--shorten-data-split-list", + default="", + help="comma-separated list of dataset splits to apply shortening to, " + 'e.g., "train,valid" (default: all dataset splits)', + ) + parser.add_argument( + "--max-option-length", type=int, help="max length for each option" + ) def __init__(self, args, dictionary): super().__init__(args) @@ -66,21 +82,22 @@ def load_dictionary(cls, args, filename, source=True): filename (str): the filename """ dictionary = Dictionary.load(filename) - dictionary.add_symbol('<mask>') + dictionary.add_symbol("<mask>") return dictionary @classmethod def setup_task(cls, args, **kwargs): - assert args.criterion == 'sentence_ranking', \ - 'Must set --criterion=sentence_ranking' + assert ( + args.criterion == "sentence_ranking" + ), "Must set --criterion=sentence_ranking" # load data dictionary data_dict = cls.load_dictionary( args, - os.path.join(args.data, 'input0', 'dict.txt'), + os.path.join(args.data, "input0", "dict.txt"), source=True, ) - logger.info('[input] dictionary: {} types'.format(len(data_dict))) + logger.info("[input] dictionary: {} types".format(len(data_dict))) return SentenceRankingTask(args, data_dict) def load_dataset(self, split, combine=False, **kwargs): @@ -100,12 +117,9 @@ def make_dataset(type, dictionary): ) return dataset - input0 = make_dataset('input0', self.source_dictionary) + input0 = make_dataset("input0", self.source_dictionary) input_options = [ - make_dataset( - 'input{idx}'.format(idx=idx + 1), - self.source_dictionary - ) + make_dataset("input{idx}".format(idx=idx + 1), self.source_dictionary) for idx in range(self.args.num_classes) ] @@ -117,41 +131,49 @@ def make_dataset(type, dictionary): if self.args.init_token is not None: input_option = PrependTokenDataset(input_option, self.args.init_token) if self.args.max_option_length is not None: - input_option = TruncateDataset(input_option, self.args.max_option_length) + input_option = TruncateDataset( + input_option, self.args.max_option_length + ) src_token = ConcatSentencesDataset(input_option, input0) - if self.args.truncate_sequence: - src_token = TruncateDataset(src_token, self.args.max_positions) + src_token = maybe_shorten_dataset( + src_token, + split, + self.args.shorten_data_split_list, + self.args.shorten_method, + self.args.max_positions, + self.args.seed, + ) src_tokens.append(src_token) with data_utils.numpy_seed(self.args.seed): shuffle = np.random.permutation(len(src_tokens[0])) dataset = { - 'id': IdDataset(), - 'nsentences': NumSamplesDataset(), - 'ntokens': NumelDataset(src_tokens[0], reduce=True), + "id": IdDataset(), + "nsentences": NumSamplesDataset(), + "ntokens": NumelDataset(src_tokens[0], reduce=True), } for src_token_idx in range(len(src_tokens)): dataset.update( { - 'net_input{idx}'.format(idx=src_token_idx+1): { - 'src_tokens': RightPadDataset( + "net_input{idx}".format(idx=src_token_idx + 1): { + "src_tokens": RightPadDataset( src_tokens[src_token_idx], pad_idx=self.source_dictionary.pad(), ), - 'src_lengths': NumelDataset(src_tokens[src_token_idx], reduce=False), + "src_lengths": NumelDataset( + src_tokens[src_token_idx], reduce=False + ), } } ) - label_path = '{}.label'.format(get_path('label', split)) + label_path = "{}.label".format(get_path("label", split)) if os.path.exists(label_path): with open(label_path) as h: dataset.update( - target=RawLabelDataset([ - int(x.strip()) for x in h.readlines() - ]) + target=RawLabelDataset([int(x.strip()) for x in h.readlines()]) ) nested_dataset = NestedDictionaryDataset( @@ -175,10 +197,11 @@ def make_dataset(type, dictionary): def build_model(self, args): from fairseq import models + model = models.build_model(args, self) model.register_classification_head( - getattr(args, 'ranking_head_name', 'sentence_classification_head'), + getattr(args, "ranking_head_name", "sentence_classification_head"), num_classes=1, ) diff --git a/fairseq/tasks/simultaneous_translation.py b/fairseq/tasks/simultaneous_translation.py new file mode 100644 index 0000000000..11c7dc1ea9 --- /dev/null +++ b/fairseq/tasks/simultaneous_translation.py @@ -0,0 +1,42 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +from fairseq.tasks import register_task +from fairseq.tasks.speech_to_text import SpeechToTextTask +from fairseq.tasks.translation import ( + TranslationTask, TranslationConfig +) + +try: + import examples.simultaneous_translation # noqa + import_successful = True +except BaseException: + import_successful = False + + +logger = logging.getLogger(__name__) + + +def check_import(flag): + if not flag: + raise ImportError( + "'examples.simultaneous_translation' is not correctly imported. " + "Please considering `pip install -e $FAIRSEQ_DIR`." + ) + + +@register_task("simul_speech_to_text") +class SimulSpeechToTextTask(SpeechToTextTask): + def __init__(self, args, tgt_dict): + check_import(import_successful) + super().__init__(args, tgt_dict) + + +@register_task("simul_text_to_text", dataclass=TranslationConfig) +class SimulTextToTextTask(TranslationTask): + def __init__(self, cfg, src_dict, tgt_dict): + check_import(import_successful) + super().__init__(cfg, src_dict, tgt_dict) diff --git a/fairseq/tasks/speech_to_text.py b/fairseq/tasks/speech_to_text.py new file mode 100644 index 0000000000..06e292103e --- /dev/null +++ b/fairseq/tasks/speech_to_text.py @@ -0,0 +1,165 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +from pathlib import Path +from argparse import Namespace + +from fairseq.data import Dictionary, encoders +from fairseq.data.audio.speech_to_text_dataset import ( + S2TDataConfig, + SpeechToTextDataset, + SpeechToTextDatasetCreator, + get_features_or_waveform +) +from fairseq.tasks import LegacyFairseqTask, register_task + + +logger = logging.getLogger(__name__) + + +@register_task("speech_to_text") +class SpeechToTextTask(LegacyFairseqTask): + @classmethod + def add_args(cls, parser): + parser.add_argument("data", help="manifest root path") + parser.add_argument( + "--config-yaml", + type=str, + default="config.yaml", + help="Configuration YAML filename (under manifest root)", + ) + parser.add_argument( + "--max-source-positions", + default=6000, + type=int, + metavar="N", + help="max number of tokens in the source sequence", + ) + parser.add_argument( + "--max-target-positions", + default=1024, + type=int, + metavar="N", + help="max number of tokens in the target sequence", + ) + + def __init__(self, args, tgt_dict): + super().__init__(args) + self.tgt_dict = tgt_dict + self.data_cfg = S2TDataConfig(Path(args.data) / args.config_yaml) + self.speaker_to_id = self._get_speaker_to_id() + + def _get_speaker_to_id(self): + speaker_to_id = None + speaker_set_filename = self.data_cfg.config.get("speaker_set_filename") + if speaker_set_filename is not None: + speaker_set_path = Path(self.args.data) / speaker_set_filename + with open(speaker_set_path) as f: + speaker_to_id = {r.strip(): i for i, r in enumerate(f)} + return speaker_to_id + + @classmethod + def setup_task(cls, args, **kwargs): + data_cfg = S2TDataConfig(Path(args.data) / args.config_yaml) + dict_path = Path(args.data) / data_cfg.vocab_filename + if not dict_path.is_file(): + raise FileNotFoundError(f"Dict not found: {dict_path.as_posix()}") + tgt_dict = Dictionary.load(dict_path.as_posix()) + logger.info( + f"dictionary size ({data_cfg.vocab_filename}): " f"{len(tgt_dict):,}" + ) + + if getattr(args, "train_subset", None) is not None: + if not all(s.startswith("train") for s in args.train_subset.split(",")): + raise ValueError('Train splits should be named like "train*".') + return cls(args, tgt_dict) + + def build_criterion(self, args): + from fairseq import criterions + + if self.data_cfg.prepend_tgt_lang_tag and args.ignore_prefix_size != 1: + raise ValueError( + 'Please set "--ignore-prefix-size 1" since ' + "target language ID token is prepended as BOS." + ) + return criterions.build_criterion(args, self) + + def load_dataset(self, split, epoch=1, combine=False, **kwargs): + is_train_split = split.startswith("train") + pre_tokenizer = self.build_tokenizer(self.args) + bpe_tokenizer = self.build_bpe(self.args) + self.datasets[split] = SpeechToTextDatasetCreator.from_tsv( + self.args.data, + self.data_cfg, + split, + self.tgt_dict, + pre_tokenizer, + bpe_tokenizer, + is_train_split=is_train_split, + epoch=epoch, + seed=self.args.seed, + speaker_to_id=self.speaker_to_id + ) + + @property + def target_dictionary(self): + return self.tgt_dict + + @property + def source_dictionary(self): + return None + + def max_positions(self): + return self.args.max_source_positions, self.args.max_target_positions + + def build_model(self, args): + args.input_feat_per_channel = self.data_cfg.input_feat_per_channel + args.input_channels = self.data_cfg.input_channels + args.speaker_to_id = self.speaker_to_id + return super(SpeechToTextTask, self).build_model(args) + + def build_generator( + self, + models, + args, + seq_gen_cls=None, + extra_gen_cls_kwargs=None, + ): + if self.data_cfg.prepend_tgt_lang_tag and args.prefix_size != 1: + raise ValueError( + 'Please set "--prefix-size 1" since ' + "target language ID token is prepended as BOS." + ) + lang_token_ids = { + i + for s, i in self.tgt_dict.indices.items() + if SpeechToTextDataset.is_lang_tag(s) + } + + if extra_gen_cls_kwargs is None: + extra_gen_cls_kwargs = {} + extra_gen_cls_kwargs["symbols_to_strip_from_output"] = lang_token_ids + return super().build_generator( + models, args, seq_gen_cls=None, + extra_gen_cls_kwargs=extra_gen_cls_kwargs + ) + + def build_tokenizer(self, args): + logger.info(f"pre-tokenizer: {self.data_cfg.pre_tokenizer}") + return encoders.build_tokenizer(Namespace(**self.data_cfg.pre_tokenizer)) + + def build_bpe(self, args): + logger.info(f"tokenizer: {self.data_cfg.bpe_tokenizer}") + return encoders.build_bpe(Namespace(**self.data_cfg.bpe_tokenizer)) + + def get_interactive_tokens_and_lengths(self, lines, encode_fn): + n_frames = [get_features_or_waveform(p).shape[0] for p in lines] + return lines, n_frames + + def build_dataset_for_inference(self, src_tokens, src_lengths, **kwargs): + return SpeechToTextDataset( + "interactive", False, self.data_cfg, src_tokens, src_lengths + ) diff --git a/fairseq/tasks/text_to_speech.py b/fairseq/tasks/text_to_speech.py new file mode 100644 index 0000000000..5646e41d39 --- /dev/null +++ b/fairseq/tasks/text_to_speech.py @@ -0,0 +1,467 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +import os +import os.path as op + +import torch +import torch.nn.functional as F +import numpy as np + +from fairseq.data.audio.text_to_speech_dataset import TextToSpeechDatasetCreator +from fairseq.tasks import register_task +from fairseq.tasks.speech_to_text import SpeechToTextTask +from fairseq.speech_generator import ( + AutoRegressiveSpeechGenerator, NonAutoregressiveSpeechGenerator, + TeacherForcingAutoRegressiveSpeechGenerator +) + +logging.basicConfig( + format='%(asctime)s | %(levelname)s | %(name)s | %(message)s', + datefmt='%Y-%m-%d %H:%M:%S', level=logging.INFO +) +logger = logging.getLogger(__name__) + + +try: + from tensorboardX import SummaryWriter +except ImportError: + logger.info("Please install tensorboardX: pip install tensorboardX") + SummaryWriter = None + + +@register_task('text_to_speech') +class TextToSpeechTask(SpeechToTextTask): + @staticmethod + def add_args(parser): + parser.add_argument('data', help='manifest root path') + parser.add_argument( + '--config-yaml', type=str, default='config.yaml', + help='Configuration YAML filename (under manifest root)' + ) + parser.add_argument('--max-source-positions', default=1024, type=int, + metavar='N', + help='max number of tokens in the source sequence') + parser.add_argument('--max-target-positions', default=1200, type=int, + metavar='N', + help='max number of tokens in the target sequence') + parser.add_argument("--n-frames-per-step", type=int, default=1) + parser.add_argument("--eos-prob-threshold", type=float, default=0.5) + parser.add_argument("--eval-inference", action="store_true") + parser.add_argument("--eval-tb-nsample", type=int, default=8) + parser.add_argument("--vocoder", type=str, default="griffin_lim") + parser.add_argument("--spec-bwd-max-iter", type=int, default=8) + + def __init__(self, args, src_dict): + super().__init__(args, src_dict) + self.src_dict = src_dict + self.sr = self.data_cfg.config.get("features").get("sample_rate") + + self.tensorboard_writer = None + self.tensorboard_dir = "" + if args.tensorboard_logdir and SummaryWriter is not None: + self.tensorboard_dir = os.path.join(args.tensorboard_logdir, + "valid_extra") + + def load_dataset(self, split, epoch=1, combine=False, **kwargs): + is_train_split = split.startswith('train') + pre_tokenizer = self.build_tokenizer(self.args) + bpe_tokenizer = self.build_bpe(self.args) + self.datasets[split] = TextToSpeechDatasetCreator.from_tsv( + self.args.data, self.data_cfg, split, self.src_dict, + pre_tokenizer, bpe_tokenizer, is_train_split=is_train_split, + epoch=epoch, seed=self.args.seed, + n_frames_per_step=self.args.n_frames_per_step, + speaker_to_id=self.speaker_to_id + ) + + @property + def target_dictionary(self): + return None + + @property + def source_dictionary(self): + return self.src_dict + + def get_speaker_embeddings_path(self): + speaker_emb_path = None + if self.data_cfg.config.get("speaker_emb_filename") is not None: + speaker_emb_path = op.join( + self.args.data, self.data_cfg.config.get("speaker_emb_filename") + ) + return speaker_emb_path + + @classmethod + def get_speaker_embeddings(cls, args): + embed_speaker = None + if args.speaker_to_id is not None: + if args.speaker_emb_path is None: + embed_speaker = torch.nn.Embedding( + len(args.speaker_to_id), args.speaker_embed_dim + ) + else: + speaker_emb_mat = np.load(args.speaker_emb_path) + assert speaker_emb_mat.shape[1] == args.speaker_embed_dim + embed_speaker = torch.nn.Embedding.from_pretrained( + torch.from_numpy(speaker_emb_mat), freeze=True, + ) + logger.info( + f"load speaker embeddings from {args.speaker_emb_path}. " + f"train embedding? {embed_speaker.weight.requires_grad}\n" + f"embeddings:\n{speaker_emb_mat}" + ) + return embed_speaker + + def build_model(self, cfg): + cfg.pitch_min = self.data_cfg.config["features"].get("pitch_min", None) + cfg.pitch_max = self.data_cfg.config["features"].get("pitch_max", None) + cfg.energy_min = self.data_cfg.config["features"].get("energy_min", None) + cfg.energy_max = self.data_cfg.config["features"].get("energy_max", None) + cfg.speaker_emb_path = self.get_speaker_embeddings_path() + model = super().build_model(cfg) + self.generator = None + if getattr(cfg, "eval_inference", False): + self.generator = self.build_generator([model], cfg) + return model + + def build_generator(self, models, cfg, vocoder=None, **unused): + if vocoder is None: + vocoder = self.build_default_vocoder() + model = models[0] + if getattr(model, "NON_AUTOREGRESSIVE", False): + return NonAutoregressiveSpeechGenerator( + model, vocoder, self.data_cfg + ) + else: + generator = AutoRegressiveSpeechGenerator + if getattr(cfg, "teacher_forcing", False): + generator = TeacherForcingAutoRegressiveSpeechGenerator + logger.info("Teacher forcing mode for generation") + return generator( + model, vocoder, self.data_cfg, + max_iter=self.args.max_target_positions, + eos_prob_threshold=self.args.eos_prob_threshold + ) + + def build_default_vocoder(self): + from fairseq.models.text_to_speech.vocoder import get_vocoder + vocoder = get_vocoder(self.args, self.data_cfg) + if torch.cuda.is_available() and not self.args.cpu: + vocoder = vocoder.cuda() + else: + vocoder = vocoder.cpu() + return vocoder + + def valid_step(self, sample, model, criterion): + loss, sample_size, logging_output = super().valid_step( + sample, model, criterion + ) + + if getattr(self.args, "eval_inference", False): + hypos, inference_losses = self.valid_step_with_inference( + sample, model, self.generator + ) + for k, v in inference_losses.items(): + assert(k not in logging_output) + logging_output[k] = v + + picked_id = 0 + if self.tensorboard_dir and (sample["id"] == picked_id).any(): + self.log_tensorboard( + sample, + hypos[:self.args.eval_tb_nsample], + model._num_updates, + is_na_model=getattr(model, "NON_AUTOREGRESSIVE", False) + ) + return loss, sample_size, logging_output + + def valid_step_with_inference(self, sample, model, generator): + hypos = generator.generate(model, sample, has_targ=True) + + losses = { + "mcd_loss": 0., + "targ_frames": 0., + "pred_frames": 0., + "nins": 0., + "ndel": 0., + } + rets = batch_mel_cepstral_distortion( + [hypo["targ_waveform"] for hypo in hypos], + [hypo["waveform"] for hypo in hypos], + self.sr, + normalize_type=None + ) + for d, extra in rets: + pathmap = extra[-1] + losses["mcd_loss"] += d.item() + losses["targ_frames"] += pathmap.size(0) + losses["pred_frames"] += pathmap.size(1) + losses["nins"] += (pathmap.sum(dim=1) - 1).sum().item() + losses["ndel"] += (pathmap.sum(dim=0) - 1).sum().item() + + return hypos, losses + + def log_tensorboard(self, sample, hypos, num_updates, is_na_model=False): + if self.tensorboard_writer is None: + self.tensorboard_writer = SummaryWriter(self.tensorboard_dir) + tb_writer = self.tensorboard_writer + for b in range(len(hypos)): + idx = sample["id"][b] + text = sample["src_texts"][b] + targ = hypos[b]["targ_feature"] + pred = hypos[b]["feature"] + attn = hypos[b]["attn"] + + if is_na_model: + data = plot_tts_output( + [targ.transpose(0, 1), pred.transpose(0, 1)], + [f"target (idx={idx})", "output"], attn, + "alignment", ret_np=True, suptitle=text, + ) + else: + eos_prob = hypos[b]["eos_prob"] + data = plot_tts_output( + [targ.transpose(0, 1), pred.transpose(0, 1), attn], + [f"target (idx={idx})", "output", "alignment"], eos_prob, + "eos prob", ret_np=True, suptitle=text, + ) + + tb_writer.add_image( + f"inference_sample_{b}", data, num_updates, + dataformats="HWC" + ) + + if hypos[b]["waveform"] is not None: + targ_wave = hypos[b]["targ_waveform"].detach().cpu().float() + pred_wave = hypos[b]["waveform"].detach().cpu().float() + tb_writer.add_audio( + f"inference_targ_{b}", + targ_wave, + num_updates, + sample_rate=self.sr + ) + tb_writer.add_audio( + f"inference_pred_{b}", + pred_wave, + num_updates, + sample_rate=self.sr + ) + + +def save_figure_to_numpy(fig): + data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='') + data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,)) + return data + + +DEFAULT_V_MIN = np.log(1e-5) + + +def plot_tts_output( + data_2d, title_2d, data_1d, title_1d, figsize=(24, 4), + v_min=DEFAULT_V_MIN, v_max=3, ret_np=False, suptitle="" +): + try: + import matplotlib.pyplot as plt + from mpl_toolkits.axes_grid1 import make_axes_locatable + except ImportError: + raise ImportError("Please install Matplotlib: pip install matplotlib") + + data_2d = [ + x.detach().cpu().float().numpy() + if isinstance(x, torch.Tensor) else x for x in data_2d + ] + fig, axes = plt.subplots(1, len(data_2d) + 1, figsize=figsize) + if suptitle: + fig.suptitle(suptitle[:400]) # capped at 400 chars + axes = [axes] if len(data_2d) == 0 else axes + for ax, x, name in zip(axes, data_2d, title_2d): + ax.set_title(name) + divider = make_axes_locatable(ax) + cax = divider.append_axes('right', size='5%', pad=0.05) + im = ax.imshow( + x, origin="lower", aspect="auto", vmin=max(x.min(), v_min), + vmax=min(x.max(), v_max) + ) + fig.colorbar(im, cax=cax, orientation='vertical') + + if isinstance(data_1d, torch.Tensor): + data_1d = data_1d.detach().cpu().numpy() + axes[-1].plot(data_1d) + axes[-1].set_title(title_1d) + plt.tight_layout() + + if ret_np: + fig.canvas.draw() + data = save_figure_to_numpy(fig) + plt.close(fig) + return data + + +def antidiag_indices(offset, min_i=0, max_i=None, min_j=0, max_j=None): + """ + for a (3, 4) matrix with min_i=1, max_i=3, min_j=1, max_j=4, outputs + + offset=2 (1, 1), + offset=3 (2, 1), (1, 2) + offset=4 (2, 2), (1, 3) + offset=5 (2, 3) + + constraints: + i + j = offset + min_j <= j < max_j + min_i <= offset - j < max_i + """ + if max_i is None: + max_i = offset + 1 + if max_j is None: + max_j = offset + 1 + min_j = max(min_j, offset - max_i + 1, 0) + max_j = min(max_j, offset - min_i + 1, offset + 1) + j = torch.arange(min_j, max_j) + i = offset - j + return torch.stack([i, j]) + + +def batch_dynamic_time_warping(distance, shapes=None): + """full batched DTW without any constraints + + distance: (batchsize, max_M, max_N) matrix + shapes: (batchsize,) vector specifying (M, N) for each entry + """ + # ptr: 0=left, 1=up-left, 2=up + ptr2dij = {0: (0, -1), 1: (-1, -1), 2: (-1, 0)} + + bsz, m, n = distance.size() + cumdist = torch.zeros_like(distance) + backptr = torch.zeros_like(distance).type(torch.int32) - 1 + + # initialize + cumdist[:, 0, :] = distance[:, 0, :].cumsum(dim=-1) + cumdist[:, :, 0] = distance[:, :, 0].cumsum(dim=-1) + backptr[:, 0, :] = 0 + backptr[:, :, 0] = 2 + + # DP with optimized anti-diagonal parallelization, O(M+N) steps + for offset in range(2, m + n - 1): + ind = antidiag_indices(offset, 1, m, 1, n) + c = torch.stack( + [cumdist[:, ind[0], ind[1] - 1], cumdist[:, ind[0] - 1, ind[1] - 1], + cumdist[:, ind[0] - 1, ind[1]], ], + dim=2 + ) + v, b = c.min(axis=-1) + backptr[:, ind[0], ind[1]] = b.int() + cumdist[:, ind[0], ind[1]] = v + distance[:, ind[0], ind[1]] + + # backtrace + pathmap = torch.zeros_like(backptr) + for b in range(bsz): + i = m - 1 if shapes is None else (shapes[b][0] - 1).item() + j = n - 1 if shapes is None else (shapes[b][1] - 1).item() + dtwpath = [(i, j)] + while (i != 0 or j != 0) and len(dtwpath) < 10000: + assert (i >= 0 and j >= 0) + di, dj = ptr2dij[backptr[b, i, j].item()] + i, j = i + di, j + dj + dtwpath.append((i, j)) + dtwpath = dtwpath[::-1] + indices = torch.from_numpy(np.array(dtwpath)) + pathmap[b, indices[:, 0], indices[:, 1]] = 1 + + return cumdist, backptr, pathmap + + +def compute_l2_dist(x1, x2): + """compute an (m, n) L2 distance matrix from (m, d) and (n, d) matrices""" + return torch.cdist(x1.unsqueeze(0), x2.unsqueeze(0), p=2).squeeze(0).pow(2) + + +def compute_rms_dist(x1, x2): + l2_dist = compute_l2_dist(x1, x2) + return (l2_dist / x1.size(1)).pow(0.5) + + +def get_divisor(pathmap, normalize_type): + if normalize_type is None: + return 1 + elif normalize_type == "len1": + return pathmap.size(0) + elif normalize_type == "len2": + return pathmap.size(1) + elif normalize_type == "path": + return pathmap.sum().item() + else: + raise ValueError(f"normalize_type {normalize_type} not supported") + + +def batch_compute_distortion(y1, y2, sr, feat_fn, dist_fn, normalize_type): + d, s, x1, x2 = [], [], [], [] + for cur_y1, cur_y2 in zip(y1, y2): + assert (cur_y1.ndim == 1 and cur_y2.ndim == 1) + cur_x1 = feat_fn(cur_y1) + cur_x2 = feat_fn(cur_y2) + x1.append(cur_x1) + x2.append(cur_x2) + + cur_d = dist_fn(cur_x1, cur_x2) + d.append(cur_d) + s.append(d[-1].size()) + max_m = max(ss[0] for ss in s) + max_n = max(ss[1] for ss in s) + d = torch.stack( + [F.pad(dd, (0, max_n - dd.size(1), 0, max_m - dd.size(0))) for dd in d] + ) + s = torch.LongTensor(s).to(d.device) + cumdists, backptrs, pathmaps = batch_dynamic_time_warping(d, s) + + rets = [] + itr = zip(s, x1, x2, d, cumdists, backptrs, pathmaps) + for (m, n), cur_x1, cur_x2, dist, cumdist, backptr, pathmap in itr: + cumdist = cumdist[:m, :n] + backptr = backptr[:m, :n] + pathmap = pathmap[:m, :n] + divisor = get_divisor(pathmap, normalize_type) + + distortion = cumdist[-1, -1] / divisor + ret = distortion, (cur_x1, cur_x2, dist, cumdist, backptr, pathmap) + rets.append(ret) + return rets + + +def batch_mel_cepstral_distortion( + y1, y2, sr, normalize_type="path", mfcc_fn=None +): + """ + https://arxiv.org/pdf/2011.03568.pdf + + The root mean squared error computed on 13-dimensional MFCC using DTW for + alignment. MFCC features are computed from an 80-channel log-mel + spectrogram using a 50ms Hann window and hop of 12.5ms. + + y1: list of waveforms + y2: list of waveforms + sr: sampling rate + """ + + try: + import torchaudio + except ImportError: + raise ImportError("Please install torchaudio: pip install torchaudio") + + if mfcc_fn is None or mfcc_fn.sample_rate != sr: + melkwargs = { + "n_fft": int(0.05 * sr), "win_length": int(0.05 * sr), + "hop_length": int(0.0125 * sr), "f_min": 20, + "n_mels": 80, "window_fn": torch.hann_window + } + mfcc_fn = torchaudio.transforms.MFCC( + sr, n_mfcc=13, log_mels=True, melkwargs=melkwargs + ).to(y1[0].device) + return batch_compute_distortion( + y1, y2, sr, lambda y: mfcc_fn(y).transpose(-1, -2), compute_rms_dist, + normalize_type + ) diff --git a/fairseq/tasks/translation.py b/fairseq/tasks/translation.py index 6e6ea5596c..8647360867 100644 --- a/fairseq/tasks/translation.py +++ b/fairseq/tasks/translation.py @@ -3,29 +3,33 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. -from argparse import Namespace -import json +from dataclasses import dataclass, field import itertools +import json import logging import os +from typing import Optional +from argparse import Namespace +from omegaconf import II import numpy as np - -from fairseq import metrics, options, utils +from fairseq import metrics, utils from fairseq.data import ( AppendTokenDataset, ConcatDataset, - data_utils, - encoders, - indexed_dataset, LanguagePairDataset, PrependTokenDataset, StripTokenDataset, TruncateDataset, + data_utils, + encoders, + indexed_dataset, ) - +from fairseq.data.indexed_dataset import get_available_dataset_impl +from fairseq.dataclass import ChoiceEnum, FairseqDataclass from fairseq.tasks import FairseqTask, register_task + EVAL_BLEU_ORDER = 4 @@ -33,37 +37,54 @@ def load_langpair_dataset( - data_path, split, - src, src_dict, - tgt, tgt_dict, - combine, dataset_impl, upsample_primary, - left_pad_source, left_pad_target, max_source_positions, - max_target_positions, prepend_bos=False, load_alignments=False, - truncate_source=False, append_source_id=False + data_path, + split, + src, + src_dict, + tgt, + tgt_dict, + combine, + dataset_impl, + upsample_primary, + left_pad_source, + left_pad_target, + max_source_positions, + max_target_positions, + prepend_bos=False, + load_alignments=False, + truncate_source=False, + append_source_id=False, + num_buckets=0, + shuffle=True, + pad_to_multiple=1, + prepend_bos_src=None, ): - def split_exists(split, src, tgt, lang, data_path): - filename = os.path.join(data_path, '{}.{}-{}.{}'.format(split, src, tgt, lang)) + filename = os.path.join(data_path, "{}.{}-{}.{}".format(split, src, tgt, lang)) return indexed_dataset.dataset_exists(filename, impl=dataset_impl) src_datasets = [] tgt_datasets = [] for k in itertools.count(): - split_k = split + (str(k) if k > 0 else '') + split_k = split + (str(k) if k > 0 else "") # infer langcode if split_exists(split_k, src, tgt, src, data_path): - prefix = os.path.join(data_path, '{}.{}-{}.'.format(split_k, src, tgt)) + prefix = os.path.join(data_path, "{}.{}-{}.".format(split_k, src, tgt)) elif split_exists(split_k, tgt, src, src, data_path): - prefix = os.path.join(data_path, '{}.{}-{}.'.format(split_k, tgt, src)) + prefix = os.path.join(data_path, "{}.{}-{}.".format(split_k, tgt, src)) else: if k > 0: break else: - raise FileNotFoundError('Dataset not found: {} ({})'.format(split, data_path)) + raise FileNotFoundError( + "Dataset not found: {} ({})".format(split, data_path) + ) - src_dataset = data_utils.load_indexed_dataset(prefix + src, src_dict, dataset_impl) + src_dataset = data_utils.load_indexed_dataset( + prefix + src, src_dict, dataset_impl + ) if truncate_source: src_dataset = AppendTokenDataset( TruncateDataset( @@ -74,13 +95,17 @@ def split_exists(split, src, tgt, lang, data_path): ) src_datasets.append(src_dataset) - tgt_dataset = data_utils.load_indexed_dataset(prefix + tgt, tgt_dict, dataset_impl) + tgt_dataset = data_utils.load_indexed_dataset( + prefix + tgt, tgt_dict, dataset_impl + ) if tgt_dataset is not None: tgt_datasets.append(tgt_dataset) - logger.info('{} {} {}-{} {} examples'.format( - data_path, split_k, src, tgt, len(src_datasets[-1]) - )) + logger.info( + "{} {} {}-{} {} examples".format( + data_path, split_k, src, tgt, len(src_datasets[-1]) + ) + ) if not combine: break @@ -104,33 +129,142 @@ def split_exists(split, src, tgt, lang, data_path): src_dataset = PrependTokenDataset(src_dataset, src_dict.bos()) if tgt_dataset is not None: tgt_dataset = PrependTokenDataset(tgt_dataset, tgt_dict.bos()) + elif prepend_bos_src is not None: + logger.info(f"prepending src bos: {prepend_bos_src}") + src_dataset = PrependTokenDataset(src_dataset, prepend_bos_src) eos = None if append_source_id: - src_dataset = AppendTokenDataset(src_dataset, src_dict.index('[{}]'.format(src))) + src_dataset = AppendTokenDataset( + src_dataset, src_dict.index("[{}]".format(src)) + ) if tgt_dataset is not None: - tgt_dataset = AppendTokenDataset(tgt_dataset, tgt_dict.index('[{}]'.format(tgt))) - eos = tgt_dict.index('[{}]'.format(tgt)) + tgt_dataset = AppendTokenDataset( + tgt_dataset, tgt_dict.index("[{}]".format(tgt)) + ) + eos = tgt_dict.index("[{}]".format(tgt)) align_dataset = None if load_alignments: - align_path = os.path.join(data_path, '{}.align.{}-{}'.format(split, src, tgt)) + align_path = os.path.join(data_path, "{}.align.{}-{}".format(split, src, tgt)) if indexed_dataset.dataset_exists(align_path, impl=dataset_impl): - align_dataset = data_utils.load_indexed_dataset(align_path, None, dataset_impl) + align_dataset = data_utils.load_indexed_dataset( + align_path, None, dataset_impl + ) tgt_dataset_sizes = tgt_dataset.sizes if tgt_dataset is not None else None return LanguagePairDataset( - src_dataset, src_dataset.sizes, src_dict, - tgt_dataset, tgt_dataset_sizes, tgt_dict, + src_dataset, + src_dataset.sizes, + src_dict, + tgt_dataset, + tgt_dataset_sizes, + tgt_dict, left_pad_source=left_pad_source, left_pad_target=left_pad_target, - max_source_positions=max_source_positions, - max_target_positions=max_target_positions, - align_dataset=align_dataset, eos=eos + align_dataset=align_dataset, + eos=eos, + num_buckets=num_buckets, + shuffle=shuffle, + pad_to_multiple=pad_to_multiple, + ) + + +@dataclass +class TranslationConfig(FairseqDataclass): + data: Optional[str] = field( + default=None, + metadata={ + "help": "colon separated path to data directories list, will be iterated upon during epochs " + "in round-robin manner; however, valid and test data are always in the first directory " + "to avoid the need for repeating them in all directories" + }, + ) + source_lang: Optional[str] = field( + default=None, + metadata={ + "help": "source language", + "argparse_alias": "-s", + }, + ) + target_lang: Optional[str] = field( + default=None, + metadata={ + "help": "target language", + "argparse_alias": "-t", + }, + ) + load_alignments: bool = field( + default=False, metadata={"help": "load the binarized alignments"} + ) + left_pad_source: bool = field( + default=True, metadata={"help": "pad the source on the left"} + ) + left_pad_target: bool = field( + default=False, metadata={"help": "pad the target on the left"} + ) + max_source_positions: int = field( + default=1024, metadata={"help": "max number of tokens in the source sequence"} + ) + max_target_positions: int = field( + default=1024, metadata={"help": "max number of tokens in the target sequence"} + ) + upsample_primary: int = field( + default=-1, metadata={"help": "the amount of upsample primary dataset"} + ) + truncate_source: bool = field( + default=False, metadata={"help": "truncate source to max-source-positions"} + ) + num_batch_buckets: int = field( + default=0, + metadata={ + "help": "if >0, then bucket source and target lengths into " + "N buckets and pad accordingly; this is useful on TPUs to minimize the number of compilations" + }, + ) + train_subset: str = II("dataset.train_subset") + dataset_impl: Optional[ChoiceEnum(get_available_dataset_impl())] = II( + "dataset.dataset_impl" + ) + required_seq_len_multiple: int = II("dataset.required_seq_len_multiple") + + # options for reporting BLEU during validation + eval_bleu: bool = field( + default=False, metadata={"help": "evaluation with BLEU scores"} + ) + eval_bleu_args: Optional[str] = field( + default="{}", + metadata={ + "help": 'generation args for BLUE scoring, e.g., \'{"beam": 4, "lenpen": 0.6}\', as JSON string' + }, + ) + eval_bleu_detok: str = field( + default="space", + metadata={ + "help": "detokenize before computing BLEU (e.g., 'moses'); required if using --eval-bleu; " + "use 'space' to disable detokenization; see fairseq.data.encoders for other options" + }, + ) + eval_bleu_detok_args: Optional[str] = field( + default="{}", + metadata={"help": "args for building the tokenizer, if needed, as JSON string"}, + ) + eval_tokenized_bleu: bool = field( + default=False, metadata={"help": "compute tokenized BLEU instead of sacrebleu"} + ) + eval_bleu_remove_bpe: Optional[str] = field( + default=None, + metadata={ + "help": "remove BPE before computing BLEU", + "argparse_const": "@@ ", + }, + ) + eval_bleu_print_samples: bool = field( + default=False, metadata={"help": "print sample generations during validation"} ) -@register_task('translation') +@register_task("translation", dataclass=TranslationConfig) class TranslationTask(FairseqTask): """ Translate from one (source) language to another (target) language. @@ -143,94 +277,47 @@ class TranslationTask(FairseqTask): The translation task is compatible with :mod:`fairseq-train`, :mod:`fairseq-generate` and :mod:`fairseq-interactive`. - - The translation task provides the following additional command-line - arguments: - - .. argparse:: - :ref: fairseq.tasks.translation_parser - :prog: """ - @staticmethod - def add_args(parser): - """Add task-specific arguments to the parser.""" - # fmt: off - parser.add_argument('data', help='colon separated path to data directories list, \ - will be iterated upon during epochs in round-robin manner') - parser.add_argument('-s', '--source-lang', default=None, metavar='SRC', - help='source language') - parser.add_argument('-t', '--target-lang', default=None, metavar='TARGET', - help='target language') - parser.add_argument('--load-alignments', action='store_true', - help='load the binarized alignments') - parser.add_argument('--left-pad-source', default='True', type=str, metavar='BOOL', - help='pad the source on the left') - parser.add_argument('--left-pad-target', default='False', type=str, metavar='BOOL', - help='pad the target on the left') - parser.add_argument('--max-source-positions', default=1024, type=int, metavar='N', - help='max number of tokens in the source sequence') - parser.add_argument('--max-target-positions', default=1024, type=int, metavar='N', - help='max number of tokens in the target sequence') - parser.add_argument('--upsample-primary', default=1, type=int, - help='amount to upsample primary dataset') - parser.add_argument('--truncate-source', action='store_true', default=False, - help='truncate source to max-source-positions') - - # options for reporting BLEU during validation - parser.add_argument('--eval-bleu', action='store_true', - help='evaluation with BLEU scores') - parser.add_argument('--eval-bleu-detok', type=str, default="space", - help='detokenize before computing BLEU (e.g., "moses"); ' - 'required if using --eval-bleu; use "space" to ' - 'disable detokenization; see fairseq.data.encoders ' - 'for other options') - parser.add_argument('--eval-bleu-detok-args', type=str, metavar='JSON', - help='args for building the tokenizer, if needed') - parser.add_argument('--eval-tokenized-bleu', action='store_true', default=False, - help='compute tokenized BLEU instead of sacrebleu') - parser.add_argument('--eval-bleu-remove-bpe', nargs='?', const='@@ ', default=None, - help='remove BPE before computing BLEU') - parser.add_argument('--eval-bleu-args', type=str, metavar='JSON', - help='generation args for BLUE scoring, ' - 'e.g., \'{"beam": 4, "lenpen": 0.6}\'') - parser.add_argument('--eval-bleu-print-samples', action='store_true', - help='print sample generations during validation') - # fmt: on - - def __init__(self, args, src_dict, tgt_dict): - super().__init__(args) + cfg: TranslationConfig + + def __init__(self, cfg: TranslationConfig, src_dict, tgt_dict): + super().__init__(cfg) self.src_dict = src_dict self.tgt_dict = tgt_dict @classmethod - def setup_task(cls, args, **kwargs): + def setup_task(cls, cfg: TranslationConfig, **kwargs): """Setup the task (e.g., load dictionaries). Args: args (argparse.Namespace): parsed command-line arguments """ - args.left_pad_source = options.eval_bool(args.left_pad_source) - args.left_pad_target = options.eval_bool(args.left_pad_target) - paths = utils.split_paths(args.data) + paths = utils.split_paths(cfg.data) assert len(paths) > 0 # find language pair automatically - if args.source_lang is None or args.target_lang is None: - args.source_lang, args.target_lang = data_utils.infer_language_pair(paths[0]) - if args.source_lang is None or args.target_lang is None: - raise Exception('Could not infer language pair, please provide it explicitly') + if cfg.source_lang is None or cfg.target_lang is None: + cfg.source_lang, cfg.target_lang = data_utils.infer_language_pair(paths[0]) + if cfg.source_lang is None or cfg.target_lang is None: + raise Exception( + "Could not infer language pair, please provide it explicitly" + ) # load dictionaries - src_dict = cls.load_dictionary(os.path.join(paths[0], 'dict.{}.txt'.format(args.source_lang))) - tgt_dict = cls.load_dictionary(os.path.join(paths[0], 'dict.{}.txt'.format(args.target_lang))) + src_dict = cls.load_dictionary( + os.path.join(paths[0], "dict.{}.txt".format(cfg.source_lang)) + ) + tgt_dict = cls.load_dictionary( + os.path.join(paths[0], "dict.{}.txt".format(cfg.target_lang)) + ) assert src_dict.pad() == tgt_dict.pad() assert src_dict.eos() == tgt_dict.eos() assert src_dict.unk() == tgt_dict.unk() - logger.info('[{}] dictionary: {} types'.format(args.source_lang, len(src_dict))) - logger.info('[{}] dictionary: {} types'.format(args.target_lang, len(tgt_dict))) + logger.info("[{}] dictionary: {} types".format(cfg.source_lang, len(src_dict))) + logger.info("[{}] dictionary: {} types".format(cfg.target_lang, len(tgt_dict))) - return cls(args, src_dict, tgt_dict) + return cls(cfg, src_dict, tgt_dict) def load_dataset(self, split, epoch=1, combine=False, **kwargs): """Load a given dataset split. @@ -238,101 +325,126 @@ def load_dataset(self, split, epoch=1, combine=False, **kwargs): Args: split (str): name of the split (e.g., train, valid, test) """ - paths = utils.split_paths(self.args.data) + paths = utils.split_paths(self.cfg.data) assert len(paths) > 0 + if split != self.cfg.train_subset: + # if not training data set, use the first shard for valid and test + paths = paths[:1] data_path = paths[(epoch - 1) % len(paths)] # infer langcode - src, tgt = self.args.source_lang, self.args.target_lang + src, tgt = self.cfg.source_lang, self.cfg.target_lang self.datasets[split] = load_langpair_dataset( - data_path, split, src, self.src_dict, tgt, self.tgt_dict, - combine=combine, dataset_impl=self.args.dataset_impl, - upsample_primary=self.args.upsample_primary, - left_pad_source=self.args.left_pad_source, - left_pad_target=self.args.left_pad_target, - max_source_positions=self.args.max_source_positions, - max_target_positions=self.args.max_target_positions, - load_alignments=self.args.load_alignments, - truncate_source=self.args.truncate_source, + data_path, + split, + src, + self.src_dict, + tgt, + self.tgt_dict, + combine=combine, + dataset_impl=self.cfg.dataset_impl, + upsample_primary=self.cfg.upsample_primary, + left_pad_source=self.cfg.left_pad_source, + left_pad_target=self.cfg.left_pad_target, + max_source_positions=self.cfg.max_source_positions, + max_target_positions=self.cfg.max_target_positions, + load_alignments=self.cfg.load_alignments, + truncate_source=self.cfg.truncate_source, + num_buckets=self.cfg.num_batch_buckets, + shuffle=(split != "test"), + pad_to_multiple=self.cfg.required_seq_len_multiple, + ) + + def build_dataset_for_inference(self, src_tokens, src_lengths, constraints=None): + return LanguagePairDataset( + src_tokens, + src_lengths, + self.source_dictionary, + tgt_dict=self.target_dictionary, + constraints=constraints, ) - def build_dataset_for_inference(self, src_tokens, src_lengths): - return LanguagePairDataset(src_tokens, src_lengths, self.source_dictionary) + def build_model(self, cfg): + model = super().build_model(cfg) + if self.cfg.eval_bleu: + detok_args = json.loads(self.cfg.eval_bleu_detok_args) + self.tokenizer = encoders.build_tokenizer( + Namespace(tokenizer=self.cfg.eval_bleu_detok, **detok_args) + ) - def build_model(self, args): - model = super().build_model(args) - if getattr(args, 'eval_bleu', False): - assert getattr(args, 'eval_bleu_detok', None) is not None, ( - '--eval-bleu-detok is required if using --eval-bleu; ' - 'try --eval-bleu-detok=moses (or --eval-bleu-detok=space ' - 'to disable detokenization, e.g., when using sentencepiece)' + gen_args = json.loads(self.cfg.eval_bleu_args) + self.sequence_generator = self.build_generator( + [model], Namespace(**gen_args) ) - detok_args = json.loads(getattr(args, 'eval_bleu_detok_args', '{}') or '{}') - self.tokenizer = encoders.build_tokenizer(Namespace( - tokenizer=getattr(args, 'eval_bleu_detok', None), - **detok_args - )) - - gen_args = json.loads(getattr(args, 'eval_bleu_args', '{}') or '{}') - self.sequence_generator = self.build_generator([model], Namespace(**gen_args)) return model def valid_step(self, sample, model, criterion): loss, sample_size, logging_output = super().valid_step(sample, model, criterion) - if self.args.eval_bleu: + if self.cfg.eval_bleu: bleu = self._inference_with_bleu(self.sequence_generator, sample, model) - logging_output['_bleu_sys_len'] = bleu.sys_len - logging_output['_bleu_ref_len'] = bleu.ref_len + logging_output["_bleu_sys_len"] = bleu.sys_len + logging_output["_bleu_ref_len"] = bleu.ref_len # we split counts into separate entries so that they can be # summed efficiently across workers using fast-stat-sync assert len(bleu.counts) == EVAL_BLEU_ORDER for i in range(EVAL_BLEU_ORDER): - logging_output['_bleu_counts_' + str(i)] = bleu.counts[i] - logging_output['_bleu_totals_' + str(i)] = bleu.totals[i] + logging_output["_bleu_counts_" + str(i)] = bleu.counts[i] + logging_output["_bleu_totals_" + str(i)] = bleu.totals[i] return loss, sample_size, logging_output def reduce_metrics(self, logging_outputs, criterion): super().reduce_metrics(logging_outputs, criterion) - if self.args.eval_bleu: + if self.cfg.eval_bleu: def sum_logs(key): - return sum(log.get(key, 0) for log in logging_outputs) + import torch + result = sum(log.get(key, 0) for log in logging_outputs) + if torch.is_tensor(result): + result = result.cpu() + return result counts, totals = [], [] for i in range(EVAL_BLEU_ORDER): - counts.append(sum_logs('_bleu_counts_' + str(i))) - totals.append(sum_logs('_bleu_totals_' + str(i))) + counts.append(sum_logs("_bleu_counts_" + str(i))) + totals.append(sum_logs("_bleu_totals_" + str(i))) if max(totals) > 0: # log counts as numpy arrays -- log_scalar will sum them correctly - metrics.log_scalar('_bleu_counts', np.array(counts)) - metrics.log_scalar('_bleu_totals', np.array(totals)) - metrics.log_scalar('_bleu_sys_len', sum_logs('_bleu_sys_len')) - metrics.log_scalar('_bleu_ref_len', sum_logs('_bleu_ref_len')) + metrics.log_scalar("_bleu_counts", np.array(counts)) + metrics.log_scalar("_bleu_totals", np.array(totals)) + metrics.log_scalar("_bleu_sys_len", sum_logs("_bleu_sys_len")) + metrics.log_scalar("_bleu_ref_len", sum_logs("_bleu_ref_len")) def compute_bleu(meters): import inspect - import sacrebleu - fn_sig = inspect.getfullargspec(sacrebleu.compute_bleu)[0] - if 'smooth_method' in fn_sig: - smooth = {'smooth_method': 'exp'} + try: + from sacrebleu.metrics import BLEU + comp_bleu = BLEU.compute_bleu + except ImportError: + # compatibility API for sacrebleu 1.x + import sacrebleu + comp_bleu = sacrebleu.compute_bleu + + fn_sig = inspect.getfullargspec(comp_bleu)[0] + if "smooth_method" in fn_sig: + smooth = {"smooth_method": "exp"} else: - smooth = {'smooth': 'exp'} - bleu = sacrebleu.compute_bleu( - correct=meters['_bleu_counts'].sum, - total=meters['_bleu_totals'].sum, - sys_len=meters['_bleu_sys_len'].sum, - ref_len=meters['_bleu_ref_len'].sum, + smooth = {"smooth": "exp"} + bleu = comp_bleu( + correct=meters["_bleu_counts"].sum, + total=meters["_bleu_totals"].sum, + sys_len=meters["_bleu_sys_len"].sum, + ref_len=meters["_bleu_ref_len"].sum, **smooth ) return round(bleu.score, 2) - metrics.log_derived('bleu', compute_bleu) + metrics.log_derived("bleu", compute_bleu) def max_positions(self): """Return the max sentence length allowed by the task.""" - return (self.args.max_source_positions, self.args.max_target_positions) + return (self.cfg.max_source_positions, self.cfg.max_target_positions) @property def source_dictionary(self): @@ -350,32 +462,32 @@ def _inference_with_bleu(self, generator, sample, model): def decode(toks, escape_unk=False): s = self.tgt_dict.string( toks.int().cpu(), - self.args.eval_bleu_remove_bpe, + self.cfg.eval_bleu_remove_bpe, # The default unknown string in fairseq is `<unk>`, but # this is tokenized by sacrebleu as `< unk >`, inflating # BLEU scores. Instead, we use a somewhat more verbose # alternative that is unlikely to appear in the real # reference, but doesn't get split into multiple tokens. - unk_string=( - "UNKNOWNTOKENINREF" if escape_unk else "UNKNOWNTOKENINHYP" - ), + unk_string=("UNKNOWNTOKENINREF" if escape_unk else "UNKNOWNTOKENINHYP"), ) if self.tokenizer: s = self.tokenizer.decode(s) return s - gen_out = self.inference_step(generator, [model], sample, None) + gen_out = self.inference_step(generator, [model], sample, prefix_tokens=None) hyps, refs = [], [] for i in range(len(gen_out)): - hyps.append(decode(gen_out[i][0]['tokens'])) - refs.append(decode( - utils.strip_pad(sample['target'][i], self.tgt_dict.pad()), - escape_unk=True, # don't count <unk> as matches to the hypo - )) - if self.args.eval_bleu_print_samples: - logger.info('example hypothesis: ' + hyps[0]) - logger.info('example reference: ' + refs[0]) - if self.args.eval_tokenized_bleu: - return sacrebleu.corpus_bleu(hyps, [refs], tokenize='none') + hyps.append(decode(gen_out[i][0]["tokens"])) + refs.append( + decode( + utils.strip_pad(sample["target"][i], self.tgt_dict.pad()), + escape_unk=True, # don't count <unk> as matches to the hypo + ) + ) + if self.cfg.eval_bleu_print_samples: + logger.info("example hypothesis: " + hyps[0]) + logger.info("example reference: " + refs[0]) + if self.cfg.eval_tokenized_bleu: + return sacrebleu.corpus_bleu(hyps, [refs], tokenize="none") else: return sacrebleu.corpus_bleu(hyps, [refs]) diff --git a/fairseq/tasks/translation_from_pretrained_bart.py b/fairseq/tasks/translation_from_pretrained_bart.py index 00eaf4830b..0fd7a5b29f 100644 --- a/fairseq/tasks/translation_from_pretrained_bart.py +++ b/fairseq/tasks/translation_from_pretrained_bart.py @@ -4,14 +4,14 @@ # LICENSE file in the root directory of this source tree. import torch - +from fairseq import utils from fairseq.data import LanguagePairDataset -from .translation import load_langpair_dataset, TranslationTask from . import register_task +from .translation import TranslationTask, load_langpair_dataset -@register_task('translation_from_pretrained_bart') +@register_task("translation_from_pretrained_bart") class TranslationFromPretrainedBARTTask(TranslationTask): """ Translate from source language to target language with a model initialized with a multilingual pretrain. @@ -38,20 +38,24 @@ def add_args(parser): """Add task-specific arguments to the parser.""" # fmt: off TranslationTask.add_args(parser) - parser.add_argument('--langs', required=True, metavar='LANG', - help='comma-separated list of monolingual language, for example, "en,de,fr"' - 'be careful these langs are what you used for pretraining (the same order),' - 'not for finetuning.' - 'you should always add all pretraining language idx during finetuning.') + parser.add_argument('--langs', type=str, metavar='LANG', + help='comma-separated list of monolingual language, ' + 'for example, "en,de,fr". These should match the ' + 'langs from pretraining (and be in the same order). ' + 'You should always add all pretraining language idx ' + 'during finetuning.') + parser.add_argument('--prepend-bos', action='store_true', + help='prepend bos token to each sentence, which matches ' + 'mBART pretraining') # fmt: on def __init__(self, args, src_dict, tgt_dict): super().__init__(args, src_dict, tgt_dict) - self.langs = args.langs.split(',') + self.langs = args.langs.split(",") for d in [src_dict, tgt_dict]: for l in self.langs: - d.add_symbol('[{}]'.format(l)) - d.add_symbol('<mask>') + d.add_symbol("[{}]".format(l)) + d.add_symbol("<mask>") def load_dataset(self, split, epoch=1, combine=False, **kwargs): """Load a given dataset split. @@ -59,7 +63,7 @@ def load_dataset(self, split, epoch=1, combine=False, **kwargs): Args: split (str): name of the split (e.g., train, valid, test) """ - paths = self.args.data.split(':') + paths = utils.split_paths(self.args.data) assert len(paths) > 0 data_path = paths[(epoch - 1) % len(paths)] @@ -67,48 +71,62 @@ def load_dataset(self, split, epoch=1, combine=False, **kwargs): src, tgt = self.args.source_lang, self.args.target_lang self.datasets[split] = load_langpair_dataset( - data_path, split, src, self.src_dict, tgt, self.tgt_dict, - combine=combine, dataset_impl=self.args.dataset_impl, + data_path, + split, + src, + self.src_dict, + tgt, + self.tgt_dict, + combine=combine, + dataset_impl=self.args.dataset_impl, upsample_primary=self.args.upsample_primary, left_pad_source=self.args.left_pad_source, left_pad_target=self.args.left_pad_target, - max_source_positions=getattr(self.args, 'max_source_positions', 1024), - max_target_positions=getattr(self.args, 'max_target_positions', 1024), + max_source_positions=getattr(self.args, "max_source_positions", 1024), + max_target_positions=getattr(self.args, "max_target_positions", 1024), load_alignments=self.args.load_alignments, - prepend_bos=getattr(self.args, 'preprend_bos', False), - append_source_id=True - ) + prepend_bos=getattr(self.args, "prepend_bos", False), + append_source_id=True, + ) - def build_generator(self, models, args): - if getattr(args, 'score_reference', False): + def build_generator(self, models, args, **unused): + if getattr(args, "score_reference", False): from fairseq.sequence_scorer import SequenceScorer + return SequenceScorer( self.target_dictionary, - eos=self.tgt_dict.index('[{}]'.format(self.args.target_lang)) + eos=self.tgt_dict.index("[{}]".format(self.args.target_lang)), ) else: from fairseq.sequence_generator import SequenceGenerator + return SequenceGenerator( models, self.target_dictionary, - beam_size=getattr(args, 'beam', 5), - max_len_a=getattr(args, 'max_len_a', 0), - max_len_b=getattr(args, 'max_len_b', 200), - min_len=getattr(args, 'min_len', 1), - normalize_scores=(not getattr(args, 'unnormalized', False)), - len_penalty=getattr(args, 'lenpen', 1), - unk_penalty=getattr(args, 'unkpen', 0), - temperature=getattr(args, 'temperature', 1.), - match_source_len=getattr(args, 'match_source_len', False), - no_repeat_ngram_size=getattr(args, 'no_repeat_ngram_size', 0), - eos=self.tgt_dict.index('[{}]'.format(self.args.target_lang)) + beam_size=getattr(args, "beam", 5), + max_len_a=getattr(args, "max_len_a", 0), + max_len_b=getattr(args, "max_len_b", 200), + min_len=getattr(args, "min_len", 1), + normalize_scores=(not getattr(args, "unnormalized", False)), + len_penalty=getattr(args, "lenpen", 1), + unk_penalty=getattr(args, "unkpen", 0), + temperature=getattr(args, "temperature", 1.0), + match_source_len=getattr(args, "match_source_len", False), + no_repeat_ngram_size=getattr(args, "no_repeat_ngram_size", 0), + eos=self.tgt_dict.index("[{}]".format(self.args.target_lang)), ) - def build_dataset_for_inference(self, src_tokens, src_lengths): - src_lang_id = self.source_dictionary.index('[{}]'.format(self.args.source_lang)) + def build_dataset_for_inference(self, src_tokens, src_lengths, constraints=None): + src_lang_id = self.source_dictionary.index("[{}]".format(self.args.source_lang)) source_tokens = [] for s_t in src_tokens: s_t = torch.cat([s_t, s_t.new(1).fill_(src_lang_id)]) source_tokens.append(s_t) - dataset = LanguagePairDataset(source_tokens, src_lengths, self.source_dictionary) + dataset = LanguagePairDataset( + source_tokens, + src_lengths, + self.source_dictionary, + tgt_dict=self.target_dictionary, + constraints=constraints, + ) return dataset diff --git a/fairseq/tasks/translation_from_pretrained_xlm.py b/fairseq/tasks/translation_from_pretrained_xlm.py index 347a6eccb7..a05f289152 100644 --- a/fairseq/tasks/translation_from_pretrained_xlm.py +++ b/fairseq/tasks/translation_from_pretrained_xlm.py @@ -3,13 +3,21 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. +from dataclasses import dataclass from fairseq.data.legacy.masked_lm_dictionary import MaskedLMDictionary -from fairseq.tasks.translation import TranslationTask +from fairseq.tasks.translation import TranslationConfig, TranslationTask from . import register_task -@register_task("translation_from_pretrained_xlm") +@dataclass +class TranslationFromPretrainedXLMConfig(TranslationConfig): + pass + + +@register_task( + "translation_from_pretrained_xlm", dataclass=TranslationFromPretrainedXLMConfig +) class TranslationFromPretrainedXLMTask(TranslationTask): """ Same as TranslationTask except use the MaskedLMDictionary class so that diff --git a/fairseq/tasks/translation_lev.py b/fairseq/tasks/translation_lev.py index d07c271569..041279305d 100644 --- a/fairseq/tasks/translation_lev.py +++ b/fairseq/tasks/translation_lev.py @@ -3,31 +3,35 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. -import os - +from dataclasses import dataclass, field import torch - -from fairseq.utils import new_arange -from fairseq.tasks import register_task -from fairseq.tasks.translation import TranslationTask, load_langpair_dataset from fairseq import utils +from fairseq.data import LanguagePairDataset +from fairseq.dataclass import ChoiceEnum +from fairseq.tasks import register_task +from fairseq.tasks.translation import TranslationConfig, TranslationTask, load_langpair_dataset +from fairseq.utils import new_arange + + +NOISE_CHOICES = ChoiceEnum(["random_delete", "random_mask", "no_noise", "full_mask"]) -@register_task('translation_lev') +@dataclass +class TranslationLevenshteinConfig(TranslationConfig): + noise: NOISE_CHOICES = field( + default="random_delete", + metadata={ + "help": "type of noise" + }, + ) + +@register_task("translation_lev", dataclass=TranslationLevenshteinConfig) class TranslationLevenshteinTask(TranslationTask): """ Translation (Sequence Generation) task for Levenshtein Transformer See `"Levenshtein Transformer" <https://arxiv.org/abs/1905.11006>`_. """ - @staticmethod - def add_args(parser): - """Add task-specific arguments to the parser.""" - # fmt: off - TranslationTask.add_args(parser) - parser.add_argument( - '--noise', - default='random_delete', - choices=['random_delete', 'random_mask', 'no_noise', 'full_mask']) + cfg: TranslationLevenshteinConfig def load_dataset(self, split, epoch=1, combine=False, **kwargs): """Load a given dataset split. @@ -35,21 +39,27 @@ def load_dataset(self, split, epoch=1, combine=False, **kwargs): Args: split (str): name of the split (e.g., train, valid, test) """ - paths = utils.split_paths(self.args.data) + paths = utils.split_paths(self.cfg.data) assert len(paths) > 0 data_path = paths[(epoch - 1) % len(paths)] # infer langcode - src, tgt = self.args.source_lang, self.args.target_lang + src, tgt = self.cfg.source_lang, self.cfg.target_lang self.datasets[split] = load_langpair_dataset( - data_path, split, src, self.src_dict, tgt, self.tgt_dict, - combine=combine, dataset_impl=self.args.dataset_impl, - upsample_primary=self.args.upsample_primary, - left_pad_source=self.args.left_pad_source, - left_pad_target=self.args.left_pad_target, - max_source_positions=self.args.max_source_positions, - max_target_positions=self.args.max_target_positions, + data_path, + split, + src, + self.src_dict, + tgt, + self.tgt_dict, + combine=combine, + dataset_impl=self.cfg.dataset_impl, + upsample_primary=self.cfg.upsample_primary, + left_pad_source=self.cfg.left_pad_source, + left_pad_target=self.cfg.left_pad_target, + max_source_positions=self.cfg.max_source_positions, + max_target_positions=self.cfg.max_target_positions, prepend_bos=True, ) @@ -63,24 +73,32 @@ def _random_delete(target_tokens): target_mask = target_tokens.eq(pad) target_score = target_tokens.clone().float().uniform_() target_score.masked_fill_( - target_tokens.eq(bos) | target_tokens.eq(eos), 0.0) + target_tokens.eq(bos) | target_tokens.eq(eos), 0.0 + ) target_score.masked_fill_(target_mask, 1) target_score, target_rank = target_score.sort(1) target_length = target_mask.size(1) - target_mask.float().sum( - 1, keepdim=True) + 1, keepdim=True + ) # do not delete <bos> and <eos> (we assign 0 score for them) - target_cutoff = 2 + ((target_length - 2) * target_score.new_zeros( - target_score.size(0), 1).uniform_()).long() + target_cutoff = ( + 2 + + ( + (target_length - 2) + * target_score.new_zeros(target_score.size(0), 1).uniform_() + ).long() + ) target_cutoff = target_score.sort(1)[1] >= target_cutoff - prev_target_tokens = target_tokens.gather( - 1, target_rank).masked_fill_(target_cutoff, pad).gather( - 1, - target_rank.masked_fill_(target_cutoff, - max_len).sort(1)[1]) - prev_target_tokens = prev_target_tokens[:, :prev_target_tokens. - ne(pad).sum(1).max()] + prev_target_tokens = ( + target_tokens.gather(1, target_rank) + .masked_fill_(target_cutoff, pad) + .gather(1, target_rank.masked_fill_(target_cutoff, max_len).sort(1)[1]) + ) + prev_target_tokens = prev_target_tokens[ + :, : prev_target_tokens.ne(pad).sum(1).max() + ] return prev_target_tokens @@ -90,9 +108,9 @@ def _random_mask(target_tokens): eos = self.tgt_dict.eos() unk = self.tgt_dict.unk() - target_masks = target_tokens.ne(pad) & \ - target_tokens.ne(bos) & \ - target_tokens.ne(eos) + target_masks = ( + target_tokens.ne(pad) & target_tokens.ne(bos) & target_tokens.ne(eos) + ) target_score = target_tokens.clone().float().uniform_() target_score.masked_fill_(~target_masks, 2.0) target_length = target_masks.sum(1).float() @@ -102,7 +120,8 @@ def _random_mask(target_tokens): _, target_rank = target_score.sort(1) target_cutoff = new_arange(target_rank) < target_length[:, None].long() prev_target_tokens = target_tokens.masked_fill( - target_cutoff.scatter(1, target_rank, target_cutoff), unk) + target_cutoff.scatter(1, target_rank, target_cutoff), unk + ) return prev_target_tokens def _full_mask(target_tokens): @@ -111,43 +130,53 @@ def _full_mask(target_tokens): eos = self.tgt_dict.eos() unk = self.tgt_dict.unk() - target_mask = target_tokens.eq(bos) | target_tokens.eq( - eos) | target_tokens.eq(pad) + target_mask = ( + target_tokens.eq(bos) | target_tokens.eq(eos) | target_tokens.eq(pad) + ) return target_tokens.masked_fill(~target_mask, unk) - if self.args.noise == 'random_delete': + if self.cfg.noise == "random_delete": return _random_delete(target_tokens) - elif self.args.noise == 'random_mask': + elif self.cfg.noise == "random_mask": return _random_mask(target_tokens) - elif self.args.noise == 'full_mask': + elif self.cfg.noise == "full_mask": return _full_mask(target_tokens) - elif self.args.noise == 'no_noise': + elif self.cfg.noise == "no_noise": return target_tokens else: raise NotImplementedError - def build_generator(self, models, args): + def build_generator(self, models, args, **unused): # add models input to match the API for SequenceGenerator from fairseq.iterative_refinement_generator import IterativeRefinementGenerator + return IterativeRefinementGenerator( self.target_dictionary, - eos_penalty=getattr(args, 'iter_decode_eos_penalty', 0.0), - max_iter=getattr(args, 'iter_decode_max_iter', 10), - beam_size=getattr(args, 'iter_decode_with_beam', 1), - reranking=getattr(args, 'iter_decode_with_external_reranker', False), - decoding_format=getattr(args, 'decoding_format', None), - adaptive=not getattr(args, 'iter_decode_force_max_iter', False), - retain_history=getattr(args, 'retain_iter_history', False)) - - def train_step(self, - sample, - model, - criterion, - optimizer, - update_num, - ignore_grad=False): + eos_penalty=getattr(args, "iter_decode_eos_penalty", 0.0), + max_iter=getattr(args, "iter_decode_max_iter", 10), + beam_size=getattr(args, "iter_decode_with_beam", 1), + reranking=getattr(args, "iter_decode_with_external_reranker", False), + decoding_format=getattr(args, "decoding_format", None), + adaptive=not getattr(args, "iter_decode_force_max_iter", False), + retain_history=getattr(args, "retain_iter_history", False), + ) + + def build_dataset_for_inference(self, src_tokens, src_lengths, constraints=None): + if constraints is not None: + # Though see Susanto et al. (ACL 2020): https://www.aclweb.org/anthology/2020.acl-main.325/ + raise NotImplementedError( + "Constrained decoding with the translation_lev task is not supported" + ) + + return LanguagePairDataset( + src_tokens, src_lengths, self.source_dictionary, append_bos=True + ) + + def train_step( + self, sample, model, criterion, optimizer, update_num, ignore_grad=False + ): model.train() - sample['prev_target'] = self.inject_noise(sample['target']) + sample["prev_target"] = self.inject_noise(sample["target"]) loss, sample_size, logging_output = criterion(model, sample) if ignore_grad: loss *= 0 @@ -157,6 +186,6 @@ def train_step(self, def valid_step(self, sample, model, criterion): model.eval() with torch.no_grad(): - sample['prev_target'] = self.inject_noise(sample['target']) + sample["prev_target"] = self.inject_noise(sample["target"]) loss, sample_size, logging_output = criterion(model, sample) return loss, sample_size, logging_output diff --git a/fairseq/tasks/translation_multi_simple_epoch.py b/fairseq/tasks/translation_multi_simple_epoch.py new file mode 100644 index 0000000000..6f36e5b93e --- /dev/null +++ b/fairseq/tasks/translation_multi_simple_epoch.py @@ -0,0 +1,430 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import datetime +import logging +import time + +import torch +from fairseq.data import ( + FairseqDataset, + LanguagePairDataset, + ListDataset, + data_utils, + iterators, +) +from fairseq.data.multilingual.multilingual_data_manager import ( + MultilingualDatasetManager, +) +from fairseq.data.multilingual.sampling_method import SamplingMethod +from fairseq.tasks import LegacyFairseqTask, register_task +from fairseq.utils import FileContentsAction + + +### +def get_time_gap(s, e): + return ( + datetime.datetime.fromtimestamp(e) - datetime.datetime.fromtimestamp(s) + ).__str__() + + +### + + +logger = logging.getLogger(__name__) + + +@register_task("translation_multi_simple_epoch") +class TranslationMultiSimpleEpochTask(LegacyFairseqTask): + """ + Translate from one (source) language to another (target) language. + + Args: + langs (List[str]): a list of languages that are being supported + dicts (Dict[str, fairseq.data.Dictionary]): mapping from supported languages to their dictionaries + training (bool): whether the task should be configured for training or not + + .. note:: + + The translation task is compatible with :mod:`fairseq-train`, + :mod:`fairseq-generate` and :mod:`fairseq-interactive`. + + The translation task provides the following additional command-line + arguments: + + .. argparse:: + :ref: fairseq.tasks.translation_parser + :prog: + """ + + @staticmethod + def add_args(parser): + """Add task-specific arguments to the parser.""" + # fmt: off + parser.add_argument('-s', '--source-lang', default=None, metavar='SRC', + help='inference source language') + parser.add_argument('-t', '--target-lang', default=None, metavar='TARGET', + help='inference target language') + parser.add_argument('--lang-pairs', default=None, metavar='PAIRS', + help='comma-separated list of language pairs (in training order): en-de,en-fr,de-fr', + action=FileContentsAction) + parser.add_argument('--keep-inference-langtok', action='store_true', + help='keep language tokens in inference output (e.g. for analysis or debugging)') + + SamplingMethod.add_arguments(parser) + MultilingualDatasetManager.add_args(parser) + # fmt: on + + def __init__(self, args, langs, dicts, training): + super().__init__(args) + self.langs = langs + self.dicts = dicts + self.training = training + if training: + self.lang_pairs = args.lang_pairs + else: + self.lang_pairs = ["{}-{}".format(args.source_lang, args.target_lang)] + # eval_lang_pairs for multilingual translation is usually all of the + # lang_pairs. However for other multitask settings or when we want to + # optimize for certain languages we want to use a different subset. Thus + # the eval_lang_pairs class variable is provided for classes that extend + # this class. + self.eval_lang_pairs = self.lang_pairs + # model_lang_pairs will be used to build encoder-decoder model pairs in + # models.build_model(). This allows multitask type of sub-class can + # build models other than the input lang_pairs + self.model_lang_pairs = self.lang_pairs + self.source_langs = [d.split("-")[0] for d in self.lang_pairs] + self.target_langs = [d.split("-")[1] for d in self.lang_pairs] + self.check_dicts(self.dicts, self.source_langs, self.target_langs) + + self.sampling_method = SamplingMethod.build_sampler(args, self) + self.data_manager = MultilingualDatasetManager.setup_data_manager( + args, self.lang_pairs, langs, dicts, self.sampling_method + ) + + def check_dicts(self, dicts, source_langs, target_langs): + if self.args.source_dict is not None or self.args.target_dict is not None: + # no need to check whether the source side and target side are sharing dictionaries + return + src_dict = dicts[source_langs[0]] + tgt_dict = dicts[target_langs[0]] + for src_lang in source_langs: + assert ( + src_dict == dicts[src_lang] + ), "Diffrent dictionary are specified for different source languages; " + "TranslationMultiSimpleEpochTask only supports one shared dictionary across all source languages" + for tgt_lang in target_langs: + assert ( + tgt_dict == dicts[tgt_lang] + ), "Diffrent dictionary are specified for different target languages; " + "TranslationMultiSimpleEpochTask only supports one shared dictionary across all target languages" + + @classmethod + def setup_task(cls, args, **kwargs): + langs, dicts, training = MultilingualDatasetManager.prepare( + cls.load_dictionary, args, **kwargs + ) + return cls(args, langs, dicts, training) + + def has_sharded_data(self, split): + return self.data_manager.has_sharded_data(split) + + def load_dataset(self, split, epoch=1, combine=False, **kwargs): + """Load a given dataset split. + + Args: + split (str): name of the split (e.g., train, valid, test) + """ + if split in self.datasets: + dataset = self.datasets[split] + if self.has_sharded_data(split): + if self.args.virtual_epoch_size is not None: + if dataset.load_next_shard: + shard_epoch = dataset.shard_epoch + else: + # no need to load next shard so skip loading + # also this avoid always loading from beginning of the data + return + else: + shard_epoch = epoch + else: + # estimate the shard epoch from virtual data size and virtual epoch size + shard_epoch = self.data_manager.estimate_global_pass_epoch(epoch) + logger.info(f"loading data for {split} epoch={epoch}/{shard_epoch}") + logger.info(f"mem usage: {data_utils.get_mem_usage()}") + if split in self.datasets: + del self.datasets[split] + logger.info("old dataset deleted manually") + logger.info(f"mem usage: {data_utils.get_mem_usage()}") + self.datasets[split] = self.data_manager.load_dataset( + split, + self.training, + epoch=epoch, + combine=combine, + shard_epoch=shard_epoch, + **kwargs, + ) + + def build_dataset_for_inference(self, src_tokens, src_lengths, constraints=None): + if constraints is not None: + raise NotImplementedError( + "Constrained decoding with the multilingual_translation task is not supported" + ) + + src_data = ListDataset(src_tokens, src_lengths) + dataset = LanguagePairDataset(src_data, src_lengths, self.source_dictionary) + src_langtok_spec, tgt_langtok_spec = self.args.langtoks["main"] + if self.args.lang_tok_replacing_bos_eos: + dataset = self.data_manager.alter_dataset_langtok( + dataset, + src_eos=self.source_dictionary.eos(), + src_lang=self.args.source_lang, + tgt_eos=self.target_dictionary.eos(), + tgt_lang=self.args.target_lang, + src_langtok_spec=src_langtok_spec, + tgt_langtok_spec=tgt_langtok_spec, + ) + else: + dataset.src = self.data_manager.src_dataset_tranform_func( + self.args.source_lang, + self.args.target_lang, + dataset=dataset.src, + spec=src_langtok_spec, + ) + return dataset + + def build_generator( + self, + models, + args, + seq_gen_cls=None, + extra_gen_cls_kwargs=None, + ): + if not getattr(args, "keep_inference_langtok", False): + _, tgt_langtok_spec = self.args.langtoks["main"] + if tgt_langtok_spec: + tgt_lang_tok = self.data_manager.get_decoder_langtok( + self.args.target_lang, tgt_langtok_spec + ) + extra_gen_cls_kwargs = extra_gen_cls_kwargs or {} + extra_gen_cls_kwargs["symbols_to_strip_from_output"] = {tgt_lang_tok} + + return super().build_generator( + models, args, seq_gen_cls=None, extra_gen_cls_kwargs=extra_gen_cls_kwargs + ) + + def build_model(self, args): + return super().build_model(args) + + def valid_step(self, sample, model, criterion): + loss, sample_size, logging_output = super().valid_step(sample, model, criterion) + return loss, sample_size, logging_output + + def inference_step( + self, generator, models, sample, prefix_tokens=None, constraints=None + ): + with torch.no_grad(): + _, tgt_langtok_spec = self.args.langtoks["main"] + if not self.args.lang_tok_replacing_bos_eos: + if prefix_tokens is None and tgt_langtok_spec: + tgt_lang_tok = self.data_manager.get_decoder_langtok( + self.args.target_lang, tgt_langtok_spec + ) + src_tokens = sample["net_input"]["src_tokens"] + bsz = src_tokens.size(0) + prefix_tokens = ( + torch.LongTensor([[tgt_lang_tok]]).expand(bsz, 1).to(src_tokens) + ) + return generator.generate( + models, + sample, + prefix_tokens=prefix_tokens, + constraints=constraints, + ) + else: + return generator.generate( + models, + sample, + prefix_tokens=prefix_tokens, + bos_token=self.data_manager.get_decoder_langtok( + self.args.target_lang, tgt_langtok_spec + ) + if tgt_langtok_spec + else self.target_dictionary.eos(), + ) + + def reduce_metrics(self, logging_outputs, criterion): + super().reduce_metrics(logging_outputs, criterion) + + def max_positions(self): + """Return the max sentence length allowed by the task.""" + return (self.args.max_source_positions, self.args.max_target_positions) + + @property + def source_dictionary(self): + return self.data_manager.get_source_dictionary(self.source_langs[0]) + + @property + def target_dictionary(self): + return self.data_manager.get_target_dictionary(self.target_langs[0]) + + def create_batch_sampler_func( + self, + max_positions, + ignore_invalid_inputs, + max_tokens, + max_sentences, + required_batch_size_multiple=1, + seed=1, + ): + def construct_batch_sampler(dataset, epoch): + splits = [ + s for s, _ in self.datasets.items() if self.datasets[s] == dataset + ] + split = splits[0] if len(splits) > 0 else None + # NEW implementation + if epoch is not None: + # initialize the dataset with the correct starting epoch + dataset.set_epoch(epoch) + + # get indices ordered by example size + start_time = time.time() + logger.info(f"start batch sampler: mem usage: {data_utils.get_mem_usage()}") + + with data_utils.numpy_seed(seed): + indices = dataset.ordered_indices() + logger.info( + f"[{split}] @batch_sampler order indices time: {get_time_gap(start_time, time.time())}" + ) + logger.info(f"mem usage: {data_utils.get_mem_usage()}") + + # filter examples that are too large + if max_positions is not None: + my_time = time.time() + indices = self.filter_indices_by_size( + indices, dataset, max_positions, ignore_invalid_inputs + ) + logger.info( + f"[{split}] @batch_sampler filter_by_size time: {get_time_gap(my_time, time.time())}" + ) + logger.info(f"mem usage: {data_utils.get_mem_usage()}") + + # create mini-batches with given size constraints + my_time = time.time() + batch_sampler = dataset.batch_by_size( + indices, + max_tokens=max_tokens, + max_sentences=max_sentences, + required_batch_size_multiple=required_batch_size_multiple, + ) + + logger.info( + f"[{split}] @batch_sampler batch_by_size time: {get_time_gap(my_time, time.time())}" + ) + logger.info( + f"[{split}] per epoch batch_sampler set-up time: {get_time_gap(start_time, time.time())}" + ) + logger.info(f"mem usage: {data_utils.get_mem_usage()}") + + return batch_sampler + + return construct_batch_sampler + + # we need to override get_batch_iterator because we want to reset the epoch iterator each time + def get_batch_iterator( + self, + dataset, + max_tokens=None, + max_sentences=None, + max_positions=None, + ignore_invalid_inputs=False, + required_batch_size_multiple=1, + seed=1, + num_shards=1, + shard_id=0, + num_workers=0, + epoch=1, + data_buffer_size=0, + disable_iterator_cache=False, + ): + """ + Get an iterator that yields batches of data from the given dataset. + + Args: + dataset (~fairseq.data.FairseqDataset): dataset to batch + max_tokens (int, optional): max number of tokens in each batch + (default: None). + max_sentences (int, optional): max number of sentences in each + batch (default: None). + max_positions (optional): max sentence length supported by the + model (default: None). + ignore_invalid_inputs (bool, optional): don't raise Exception for + sentences that are too long (default: False). + required_batch_size_multiple (int, optional): require batch size to + be a multiple of N (default: 1). + seed (int, optional): seed for random number generator for + reproducibility (default: 1). + num_shards (int, optional): shard the data iterator into N + shards (default: 1). + shard_id (int, optional): which shard of the data iterator to + return (default: 0). + num_workers (int, optional): how many subprocesses to use for data + loading. 0 means the data will be loaded in the main process + (default: 0). + epoch (int, optional): the epoch to start the iterator from + (default: 0). + data_buffer_size (int, optional): number of batches to + preload (default: 0). + disable_iterator_cache (bool, optional): don't cache the + EpochBatchIterator (ignores `FairseqTask::can_reuse_epoch_itr`) + (default: False). + Returns: + ~fairseq.iterators.EpochBatchIterator: a batched iterator over the + given dataset split + """ + # initialize the dataset with the correct starting epoch + assert isinstance(dataset, FairseqDataset) + if dataset in self.dataset_to_epoch_iter: + return self.dataset_to_epoch_iter[dataset] + if self.args.sampling_method == "RoundRobin": + batch_iter = super().get_batch_iterator( + dataset, + max_tokens=max_tokens, + max_sentences=max_sentences, + max_positions=max_positions, + ignore_invalid_inputs=ignore_invalid_inputs, + required_batch_size_multiple=required_batch_size_multiple, + seed=seed, + num_shards=num_shards, + shard_id=shard_id, + num_workers=num_workers, + epoch=epoch, + data_buffer_size=data_buffer_size, + disable_iterator_cache=disable_iterator_cache, + ) + self.dataset_to_epoch_iter[dataset] = batch_iter + return batch_iter + + construct_batch_sampler = self.create_batch_sampler_func( + max_positions, + ignore_invalid_inputs, + max_tokens, + max_sentences, + required_batch_size_multiple=required_batch_size_multiple, + seed=seed, + ) + + epoch_iter = iterators.EpochBatchIterator( + dataset=dataset, + collate_fn=dataset.collater, + batch_sampler=construct_batch_sampler, + seed=seed, + num_shards=num_shards, + shard_id=shard_id, + num_workers=num_workers, + epoch=epoch, + ) + return epoch_iter diff --git a/fairseq/token_generation_constraints.py b/fairseq/token_generation_constraints.py new file mode 100644 index 0000000000..e708dc51bc --- /dev/null +++ b/fairseq/token_generation_constraints.py @@ -0,0 +1,506 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +"""Implements tracking of constraints for a beam item. + +A list of constraints is given as a list of one or more token +sequences, each of length at least one token. For example, for an input sentence + +> Die maschinelle Übersetzung ist schwer zu kontrollieren. + +We could have the constraints: +* to influence +* hard + +There are two implementations: +* OrderedConstraintState: Tracks progress through an ordered list of multitoken constraints. +* UnorderedConstraintState: Tracks progress through an unordered list of multitoken constraints. + +The difference is that in the first, the constraints are assumed to be +in order; the algorithm will permit zero or more tokens between them. +In the second, the constraints are not ordered, so many orderings will +be explored. + +The same sequence can be present any number of times, and will appear +that many times in the output. +""" + +from collections import Counter +from typing import List, Optional, Set, Tuple + +import torch + + +class ConstraintState: + def __init__(self): + pass + + +def pack_constraints(batch_constraints: List[List[torch.Tensor]]) -> torch.Tensor: + """Takes a list of list of constraints in tensor form (a list of + tensor constraints for each sentence) and transforms it into a + packed Tensor. For example, here is a batch of size 3 with 3, 0, + and 1 constraints: + + [ [ [3 1 2], [3], [4 5 6 7], ] + [], + [ [1 8 9 10 1 4 11 12], ] + ] + + Its corresponding packed structure is: + + [ [ 3 3 1 2 0 3 0 4 5 6 7 0], + [ 0 0 0 0 0 0 0 0 0 0 0 0], + [ 1 1 8 9 10 1 4 11 12 0 0 0] ] + + The packed tensor has shape (batch size, maxlen), where + maxlen is defined below. Each row contains concatenated + constraint tokens for that sentence, with 0 appended after + each constraint. The first item in each row is the number + of constraints for that sentence. So maxlen is the maximum + of + + (number of constraints) + (sum length of constraints) + 1. + + across all sentences in the batch. + """ + # The maximum word length of concatenated constraints for any sentence + max_constraints_len = 1 + for sentence_constraints in batch_constraints: + if len(sentence_constraints): + # number of constraints, plus sum of constrain lens, plus a zero after each + constraints_len = ( + 1 + + sum([c.size(0) for c in sentence_constraints]) + + len(sentence_constraints) + ) + max_constraints_len = max(max_constraints_len, constraints_len) + + batch_size = len(batch_constraints) + constraints_tensor = torch.zeros((batch_size, max_constraints_len)).long() + for i, sentence_constraints in enumerate(batch_constraints): + constraints_tensor[i, 0] = len(sentence_constraints) + offset = 1 + for j, constraint in enumerate(sentence_constraints): + this_len = constraint.size(0) + constraints_tensor[i, offset : offset + this_len] = constraint + offset += this_len + 1 + + return constraints_tensor.long() + + +def unpack_constraints(constraint_tensor: torch.Tensor) -> List[torch.Tensor]: + """ + Transforms *one row* of a packed constraint tensor (e.g., for one + sentence in the batch) into a list of constraint tensors. + """ + constraint_list = [] + num_constraints = constraint_tensor[0] + constraints = constraint_tensor.tolist() + offset = 1 + for i in range(num_constraints): + where = constraints.index(0, offset) + constraint_list.append(constraint_tensor[offset:where]) + offset = where + 1 + + return constraint_list + + +class ConstraintNode: + """ + Represents a node in a trie managing unordered constraints. + """ + + def __init__(self, token: int = None, parent=None): + # The token associate with this node (None for the root) + self.token = int(token) if token is not None else None + # The parent (None at the root) + self.parent = parent + # Whether this node is a completed constraint + self.terminal = 0 + # List of child nodes + self.children = {} + + # The cumulative number of constraints from this point in the + # trie forward + self.num_constraints = 0 + + @property + def id(self): + return self.token + + def __str__(self): + term = self.terminal != 0 + return f"[{self.token}].{term}#{self.num_constraints}" + + def __getitem__(self, key: int): + return self.children.get(key, None) + + def next_tokens(self) -> Set[int]: + """The set of child labels.""" + return set(self.children.keys()) + + @staticmethod + def create(constraints: List[List[int]]): + root = ConstraintNode() + for sequence in constraints: + root.add_sequence(sequence) + + return root + + @staticmethod + def print_graph(node: "ConstraintNode"): + if len(node.children) == 0: + return str(node) + else: + s = f"({node}" + for child in node.children.values(): + s += " " + ConstraintNode.print_graph(child) + s += ")" + return s + + def token_counts(self) -> Counter: + """Returns a counter of the number of times each token is used + in a constraint. + """ + token_counts = Counter() + kids = list(self.children.values()) + while len(kids) > 0: + kid = kids.pop() + token_counts[kid.id] += kid.num_constraints + kids += list(kid.children.values()) + + return token_counts + + def tokens(self) -> Set[int]: + """Returns the set of tokens in constraints.""" + return set(self.token_counts().keys()) + + def add_sequence(self, sequence: List[int]): + """Adds a constraint, represented as a list of integers, to + the trie.""" + assert len(sequence) > 0 + + token = int(sequence[0]) + if token not in self.children: + self.children[token] = ConstraintNode(token, parent=self) + + node = self.children[token] + if len(sequence) == 1: + node.terminal += 1 + node.num_constraints += 1 + parent = node.parent + while parent is not None: + parent.num_constraints += 1 + parent = parent.parent + else: + node.add_sequence(sequence[1:]) + + +class UnorderedConstraintState(ConstraintState): + """ + Records progress through the set of constraints for each item in the beam + using a trie. + """ + + def __init__(self, node: ConstraintNode, copy_from: "ConstraintState" = None): + self.node = node + + if copy_from is None: + # The root node + self.root = node + # The set of states in the graph that have been completed + self.completed = Counter() + # The... + self.generated = Counter() + # The list of tokens we need to generate + self.needed_tokens = self.root.tokens() + else: + self.completed = Counter(copy_from.completed) + self.generated = Counter(copy_from.generated) + self.root = copy_from.root + + # Mark the node as generated + if self.node != self.root: + self.generated[node] += 1 + + @staticmethod + def create(constraint_tensor: torch.Tensor): + constraint_list = unpack_constraints(constraint_tensor) + constraint_trie_root = ConstraintNode.create(constraint_list) + return UnorderedConstraintState(constraint_trie_root) + + def __str__(self): + gen_str = ",".join([str(node) for node in self.generated]) + return f"{self.name}/{self.bank}({gen_str})x{self.num_completed}" + + def __copy__(self): + copied_state = UnorderedConstraintState(self.node, copy_from=self) + return copied_state + + def copy(self): + return self.__copy__() + + @property + def name(self): + if self.node.id is None: + return "ROOT" + else: + return str(self.node.id) + + @property + def is_root(self): + return self.node == self.root + + @property + def bank(self): + return sum(self.generated.values()) + + @property + def num_completed(self): + """The number of constraints (not constraint tokens) that are completed. + In addition to the already-completed states, we need to account for the + current state, which might get marked as completed when another token + is generated. + """ + in_final = self.node.terminal and self.completed[self.node] < self.node.terminal + return sum(self.completed.values()) + in_final + + @property + def finished(self): + return self.root.num_constraints - self.num_completed == 0 + + @property + def token_counts(self): + return self.root.token_counts() + + @property + def tokens(self): + return self.root.tokens() + + @property + def num_constraint_tokens(self): + return sum(self.token_counts.values()) + + def next_tokens(self) -> Set[int]: + """Returns the list of tokens that could come next. + These are (a) all tokens extending the root state and, for + non-root states, additionally all tokens extending the current + state.""" + + if self.node != self.root: + return self.root.next_tokens().union(self.node.next_tokens()) + else: + return self.root.next_tokens() + + def advance(self, token: int): + """Reads in a token and advances the state. Here's how it works. + + We can advance to the next state if: + - there is a matching child + - its path isn't blocked + + A path is blocked when all constraints that are descendants of + that node have already been generated, in the current state. + + If we are not able to advance from the current state, we "fall + off the graph" and return to the root state. There, we again + try to advance, checking the same criteria. + + In any case, when falling off the graph, we need to do some + bookkeeping. We: + - check whether any constraints were met (all prefixes of + current state) + - if one is found, mark it as completed + - adjust visited nodes accordingly + """ + token = int(token) + + next_state = None + child = self.node[token] + if child is not None and self.generated[child] < child.num_constraints: + next_state = UnorderedConstraintState(child, copy_from=self) + + def rewind(): + """If we're mid-trie and an "illegal" token is chosen next, we need + to reset our state to the root state. However, along the way, we need + to check whether a prefix of the current trie state represents a state + we could mark as completed. + """ + node = self.node + while node != self.root: + if node.terminal and self.completed[node] < node.terminal: + next_state.completed[node] += 1 + return + + next_state.generated[node] -= 1 + node = node.parent + + # Fall off the graph, check the root + if next_state is None and token in self.root.next_tokens(): + child = self.root[token] + # We can only traverse this edge if it's not saturated + if self.generated[child] < child.num_constraints: + next_state = UnorderedConstraintState(child, copy_from=self) + else: + next_state = UnorderedConstraintState(self.root, copy_from=self) + + # Rewind + rewind() + + elif next_state is None: + next_state = UnorderedConstraintState(self.root, copy_from=self) + # Rewind + rewind() + + return next_state + + +class ConstraintSequence: + def __init__(self, sequences: List[List[int]]): + """Represents a set of possibly multitoken constraints by + concatenating them and internally recording the end points. + """ + self.sequences = [] + self.endpoints = [] + self.num_tokens = 0 + self.tokens = set() + for sequence in sequences: + for token in sequence: + self.tokens.add(token) + self.num_tokens += len(sequence) + self.endpoints += [False for x in range(len(sequence) - 1)] + [True] + self.sequences += sequence + + def __getitem__(self, key: int): + return self.sequences[key] + + def __len__(self): + return len(self.sequences) + + def __str__(self): + return str(self.sequences) + + +class OrderedConstraintState(ConstraintState): + """ + Records progress through the set of linear nonbranching constraints with gaps. + """ + + def __init__(self, sequence: ConstraintSequence, state: int = -1): + self.sequence = sequence + self.state = state + + @staticmethod + def create(constraint_tensor: torch.Tensor): + constraint_list = unpack_constraints(constraint_tensor) + return OrderedConstraintState(ConstraintSequence(constraint_list), -1) + + def __str__(self): + return f"{self.state}/{self.bank}x{self.num_completed}" + + def __copy__(self): + return OrderedConstraintState(self.sequence, self.state) + + def copy(self): + return self.__copy__() + + @property + def num_completed(self): + if self.state == -1: + return 0 + count = len( + list(filter(lambda x: x, self.sequence.endpoints[0 : self.state + 1])) + ) + return count + + @property + def is_root(self): + return self.state == -1 + + @property + def name(self): + if self.state == -1: + return "ROOT" + else: + return str(self.sequence[self.state]) + + @property + def bank(self) -> int: + return self.state + 1 + + @property + def finished(self): + return self.state + 1 == len(self.sequence) + + @property + def token_counts(self): + return self.sequence.token_counts() + + @property + def tokens(self): + return self.sequence.tokens + + @property + def num_constraint_tokens(self): + return sum(self.token_counts.values()) + + def next_tokens(self) -> Set[int]: + """Returns the list of tokens that could come next. + These are (a) all tokens extending the root state and, for + non-root states, additionally all tokens extending the current + state.""" + + tokens = set() + if self.state > 0: + tokens.add(self.sequence[0]) + if not self.finished: + tokens.add(self.sequence[self.state + 1]) + return tokens + + def advance(self, token: int): + """Reads in a token and advances the state. Here's how it works. + + We can advance to the next state if: + - there is a matching child + - its path isn't blocked + + A path is blocked when all constraints that are descendants of + that node have already been generated, in the current state. + + If we are not able to advance from the current state, we "fall + off the graph" and return to the root state. There, we again + try to advance, checking the same criteria. + + In any case, when falling off the graph, we need to do some + bookkeeping. We: + - check whether any constraints were met (all prefixes of + current state) + - if one is found, mark it as completed + - adjust visited nodes accordingly + """ + token = int(token) + # print(f"{self} ADVANCE({token}) {self.sequence} -> ", end="") + + if self.finished: + # Accept anything + next_state = self.copy() + + elif self.sequence[self.state + 1] == token: + # Advance to the next token + next_state = OrderedConstraintState(self.sequence, self.state + 1) + + elif self.sequence.endpoints[self.state]: + # Accept anything between constraints (*) + next_state = self.copy() + + elif token == self.sequence[0]: + # Start over having generated the first token + next_state = OrderedConstraintState(self.sequence, 0) + else: + # Start over from the root + next_state = OrderedConstraintState(self.sequence, -1) + + return next_state diff --git a/fairseq/tokenizer.py b/fairseq/tokenizer.py index 8c4d694aa0..42131f7b1d 100644 --- a/fairseq/tokenizer.py +++ b/fairseq/tokenizer.py @@ -5,6 +5,7 @@ import re + SPACE_NORMALIZER = re.compile(r"\s+") diff --git a/fairseq/trainer.py b/fairseq/trainer.py index 377ce99b44..e46ccfe0b8 100644 --- a/fairseq/trainer.py +++ b/fairseq/trainer.py @@ -8,19 +8,24 @@ """ import contextlib -from itertools import chain import logging import sys +import time +from argparse import Namespace +from itertools import chain from typing import Any, Dict, List import torch - -from fairseq import checkpoint_utils, distributed_utils, models, optim, utils +from fairseq import checkpoint_utils, models, optim, utils +from fairseq.dataclass.configs import FairseqConfig +from fairseq.dataclass.utils import convert_namespace_to_omegaconf +from fairseq.distributed import utils as distributed_utils from fairseq.file_io import PathManager from fairseq.logging import meters, metrics +from fairseq.models.ema import build_ema from fairseq.nan_detector import NanDetector from fairseq.optim import lr_scheduler - +from omegaconf import OmegaConf logger = logging.getLogger(__name__) @@ -35,47 +40,90 @@ class Trainer(object): communication of the gradients across workers. """ - def __init__(self, args, task, model, criterion, quantizer=None): - self.args = args + def __init__(self, cfg: FairseqConfig, task, model, criterion, quantizer=None): + + if isinstance(cfg, Namespace): + logger.warning( + "argparse.Namespace configuration is deprecated! Automatically converting to OmegaConf" + ) + cfg = convert_namespace_to_omegaconf(cfg) + + self.cfg = cfg self.task = task # catalog shared parameters shared_params = _catalog_shared_params(model) - - self.tpu = getattr(args, 'tpu', False) - self.cuda = torch.cuda.is_available() and not args.cpu and not self.tpu + self.tpu = cfg.common.tpu + self.cuda = torch.cuda.is_available() and not cfg.common.cpu and not self.tpu if self.cuda: - self.device = torch.device('cuda') + self.device = torch.device("cuda") elif self.tpu: - self.device = utils.get_tpu_device(args) + self.device = utils.get_tpu_device() else: - self.device = torch.device('cpu') + self.device = torch.device("cpu") + + if self.is_fsdp: + import fairscale + if self.cfg.common.bf16: + raise ValueError( + "FullyShardedDataParallel is not compatible with --bf16 or " + "--memory-efficient-bf16" + ) + if self.cfg.distributed_training.zero_sharding != "none": + raise ValueError( + "FullyShardedDataParallel is not compatible with --zero-sharding " + "option (it's already built in)" + ) + if max(self.cfg.optimization.update_freq) > 1 and fairscale.__version__ < "0.4.0": + raise RuntimeError( + "Please update to fairscale 0.4.0 or newer when combining " + "--update-freq with FullyShardedDataParallel" + ) + else: + if ( + hasattr(self.cfg.distributed_training, "cpu_offload") + and self.cfg.distributed_training.cpu_offload + ): + raise ValueError("--cpu-offload requires --ddp-backend=fully_sharded") # copy model and criterion to current device/dtype self._criterion = criterion self._model = model - if self.tpu: - import torch_xla.core.xla_model as xm - self._model = xm.send_cpu_data_to_device(self._model, self.device) - if args.fp16: - self._criterion = self._criterion.half() - self._model = self._model.half() - elif args.bf16: - self._criterion = self._criterion.to(dtype=torch.bfloat16) - self._model = self._model.to(dtype=torch.bfloat16) - self._criterion = self._criterion.to(device=self.device) - self._model = self._model.to(device=self.device) + if not self.is_fsdp: + if cfg.common.fp16: + assert not cfg.common.amp, "Cannot use fp16 and AMP together" + self._criterion = self._criterion.half() + self._model = self._model.half() + elif cfg.common.bf16: + self._criterion = self._criterion.to(dtype=torch.bfloat16) + self._model = self._model.to(dtype=torch.bfloat16) + elif cfg.common.amp: + self._amp_retries = 0 + if ( + not cfg.distributed_training.pipeline_model_parallel + # the DistributedFairseqModel wrapper will handle moving to device, + # so only handle cases which don't use the wrapper + and not self.use_distributed_wrapper + ): + self._criterion = self._criterion.to(device=self.device) + self._model = self._model.to(device=self.device) + self.pipeline_model_parallel = cfg.distributed_training.pipeline_model_parallel + self.last_device = None + if self.cuda and self.pipeline_model_parallel: + self.last_device = torch.device( + cfg.distributed_training.pipeline_devices[-1] + ) # check that shared parameters are preserved after device transfer for shared_param in shared_params: ref = _get_module_by_path(self._model, shared_param[0]) for path in shared_param[1:]: logger.info( - 'detected shared parameter: {} <- {}'.format(shared_param[0], path) + "detected shared parameter: {} <- {}".format(shared_param[0], path) ) _set_module_by_path(self._model, path, ref) - self._dummy_batch = "DUMMY" # indicates we don't have a dummy batch at first + self._dummy_batch = None # indicates we don't have a dummy batch at first self._lr_scheduler = None self._num_updates = 0 self._num_xla_compiles = 0 # for TPUs @@ -84,6 +132,7 @@ def __init__(self, args, task, model, criterion, quantizer=None): self._warn_once = set() self._wrapped_criterion = None self._wrapped_model = None + self._ema = None # TODO(myleott): support tpu if self.cuda and self.data_parallel_world_size > 1: @@ -95,8 +144,27 @@ def __init__(self, args, task, model, criterion, quantizer=None): if self.quantizer is not None: self.quantizer.set_trainer(self) + # get detailed cuda environment + if self.cuda: + self.cuda_env = utils.CudaEnvironment() + if self.data_parallel_world_size > 1: + self.cuda_env_arr = distributed_utils.all_gather_list( + self.cuda_env, group=distributed_utils.get_global_group() + ) + else: + self.cuda_env_arr = [self.cuda_env] + if self.data_parallel_rank == 0: + utils.CudaEnvironment.pretty_print_cuda_env_list(self.cuda_env_arr) + else: + self.cuda_env = None + self.cuda_env_arr = None + metrics.log_start_time("wall", priority=790, round=0) + self._start_time = time.time() + self._previous_training_time = 0 + self._cumulative_training_time = None + def reinitialize(self): """Reinitialize the Trainer, typically after model params change.""" self._lr_scheduler = None @@ -106,35 +174,71 @@ def reinitialize(self): @property def data_parallel_world_size(self): - return self.args.distributed_world_size + if self.cfg.distributed_training.distributed_world_size == 1: + return 1 + return distributed_utils.get_data_parallel_world_size() @property def data_parallel_process_group(self): - if self.tpu: - return ('tpu', None) - else: - return None + return distributed_utils.get_data_parallel_group() @property def data_parallel_rank(self): - return self.args.distributed_rank + if self.cfg.distributed_training.distributed_world_size == 1: + return 0 + return distributed_utils.get_data_parallel_rank() @property def is_data_parallel_master(self): - return distributed_utils.is_master(self.args) + # NOTE: this returns true for all model parallel replicas with data + # parallel rank 0 + return self.data_parallel_rank == 0 + + @property + def use_distributed_wrapper(self) -> bool: + return ( + self.data_parallel_world_size > 1 and not self.cfg.optimization.use_bmuf + ) or ( + self.is_fsdp and self.cfg.distributed_training.cpu_offload + ) + + @property + def should_save_checkpoint_on_current_rank(self) -> bool: + """Indicates whether to save checkpoints on the current DDP rank.""" + if ( + self.is_fsdp and self.cfg.distributed_training.use_sharded_state + ) or getattr(self.cfg.model, "base_layers", 0) > 0: + return True + else: + return self.is_data_parallel_master + + @property + def always_call_state_dict_during_save_checkpoint(self) -> bool: + if self.is_fsdp and not self.cfg.distributed_training.use_sharded_state: + # FSDP calls communication collective when consolidating checkpoints + return True + else: + return False + + @property + def checkpoint_suffix(self) -> str: + """Suffix to add to the checkpoint file name.""" + if self.is_fsdp and self.cfg.distributed_training.use_sharded_state: + return self.cfg.checkpoint.checkpoint_suffix + "-shard{0}".format( + self.data_parallel_rank + ) + else: + return self.cfg.checkpoint.checkpoint_suffix or "" @property def criterion(self): if self._wrapped_criterion is None: - if ( - utils.has_parameters(self._criterion) - and self.data_parallel_world_size > 1 - and not self.args.use_bmuf - and not self.tpu - ): + if utils.has_parameters(self._criterion) and self.use_distributed_wrapper: self._wrapped_criterion = models.DistributedFairseqModel( - self.args, self._criterion, - process_group=self.data_parallel_process_group + self.cfg.distributed_training, + self._criterion, + process_group=self.data_parallel_process_group, + device=self.device, ) else: self._wrapped_criterion = self._criterion @@ -143,19 +247,30 @@ def criterion(self): @property def model(self): if self._wrapped_model is None: - if ( - self.data_parallel_world_size > 1 - and not self.args.use_bmuf - and not self.tpu - ): + if self.use_distributed_wrapper: self._wrapped_model = models.DistributedFairseqModel( - self.args, self._model, - process_group=self.data_parallel_process_group + self.cfg.distributed_training, + self._model, + process_group=self.data_parallel_process_group, + device=self.device, ) else: self._wrapped_model = self._model return self._wrapped_model + @property + def ema(self): + if self._ema is None: + self._build_ema() + return self._ema + + def _build_ema(self): + if self.cfg.ema.store_ema: + self._ema = build_ema(self._model, self.cfg.ema, self.device) + logger.info( + "Exponential Moving Average Shadow Model is initialized." + ) + @property def optimizer(self): if self._optimizer is None: @@ -176,46 +291,152 @@ def _build_optimizer(self): ) ) - if self.args.fp16 or self.args.bf16: + if self.is_fsdp and self.cfg.common.fp16: + # FullyShardedDataParallel always uses MemoryEfficientFP16 wrapper, + # mostly for the grad scaling. But if we don't have the + # --memory-efficient-fp16 flag set, then we're effectively doing + # regular --fp16 and can allow the use of optimizers that would + # otherwise be unsupported by MemoryEfficientFP16Optimizer. + allow_unsupported = not self.cfg.common.memory_efficient_fp16 + self._optimizer = optim.MemoryEfficientFP16Optimizer.build_optimizer( + self.cfg, params, allow_unsupported=allow_unsupported + ) + elif self.cfg.common.fp16 or self.cfg.common.bf16 or self.cfg.common.amp: if self.cuda and torch.cuda.get_device_capability(0)[0] < 7: logger.info( - "NOTE: your device does NOT support faster training with --fp16, " + "NOTE: your device does NOT support faster training with --fp16 or --amp, " "please switch to FP32 which is likely to be faster" ) - if self.args.memory_efficient_fp16 or self.args.memory_efficient_bf16: + if ( + self.cfg.common.memory_efficient_fp16 + or self.cfg.common.memory_efficient_bf16 + ): self._optimizer = optim.MemoryEfficientFP16Optimizer.build_optimizer( - self.args, params + self.cfg, params ) + elif self.cfg.common.amp: + self._optimizer = optim.AMPOptimizer.build_optimizer(self.cfg, params) else: - self._optimizer = optim.FP16Optimizer.build_optimizer(self.args, params) + self._optimizer = optim.FP16Optimizer.build_optimizer(self.cfg, params) else: if self.cuda and torch.cuda.get_device_capability(0)[0] >= 7: - logger.info("NOTE: your device may support faster training with --fp16") - self._optimizer = optim.build_optimizer(self.args, params) + logger.info("NOTE: your device may support faster training with --fp16 or --amp") + self._optimizer = optim.build_optimizer(self.cfg.optimizer, params) - if self.args.use_bmuf: - self._optimizer = optim.FairseqBMUF(self.args, self._optimizer) + if self.is_fsdp: + assert ( + not self.cfg.optimization.use_bmuf + ), "--ddp-backend=fully_sharded is not compatible with BMUF" + assert self._optimizer.supports_flat_params, ( + "--ddp-backend=fully_sharded is only compatible with pointwise " + "optimizers (e.g., Adam, AdamW, Adadelta, Adamax, SGD, etc.). " + "However, the sharding will result in slightly different results when " + "using non-pointwise optimizers (e.g., Adagrad, Adafactor, LAMB)" + ) + + if self.cfg.optimization.use_bmuf: + self._optimizer = optim.FairseqBMUF( + self.cfg.bmuf, + self._optimizer, + ) + + if self.cfg.distributed_training.zero_sharding == "os": + if ( + self.cfg.common.fp16 + and not self.cfg.common.memory_efficient_fp16 + and not self.cfg.common.memory_efficient_bf16 + ) and not self.cfg.common.fp16_no_flatten_grads: + raise ValueError( + "ZeRO is incomptabile with fp16 and flattened grads. " + "Please use --fp16-no-flatten-grads" + ) + else: + optim.shard_(self._optimizer, self.data_parallel_process_group) # We should initialize the learning rate scheduler immediately after # building the optimizer, so that the initial learning rate is set. - self._lr_scheduler = lr_scheduler.build_lr_scheduler(self.args, self.optimizer) + self._lr_scheduler = lr_scheduler.build_lr_scheduler( + self.cfg.lr_scheduler, + self.optimizer, + ) self._lr_scheduler.step_update(0) + @property + def is_fsdp(self): + return self.cfg.distributed_training.ddp_backend == "fully_sharded" + + def consolidate_optimizer(self): + """For OSS, we need to consolidate the state dict.""" + if self.cfg.checkpoint.no_save_optimizer_state: + return + self._gathered_optim_state = None + if hasattr(self.optimizer.optimizer, "consolidate_state_dict"): + self.optimizer.optimizer.consolidate_state_dict() + elif self.is_fsdp and not self.model.use_sharded_state: + st = self.model.gather_full_optim_state_dict( + self.optimizer + ) # only returns on rank 0 + self._gathered_optim_state = st + + def state_dict(self): + state_dict = { + "args": None, # legacy + "cfg": ( + OmegaConf.to_container(self.cfg, resolve=True, enum_to_str=True) + if OmegaConf.is_config(self.cfg) + else self.cfg + ), + "model": self.model.state_dict(), + "criterion": ( + self.criterion.state_dict() + if utils.has_parameters(self.criterion) + else None + ), + "optimizer_history": (self._optim_history or []) + + [ + { + "criterion_name": self.get_criterion().__class__.__name__, + "optimizer_name": self.optimizer.__class__.__name__, + "lr_scheduler_state": self.lr_scheduler.state_dict(), + "num_updates": self.get_num_updates(), + } + ], + "task_state": self.task.state_dict() if self.task is not None else {}, + "extra_state": { + "metrics": metrics.state_dict(), + "previous_training_time": self.cumulative_training_time(), + }, + } + if self.cfg.ema.store_ema: + # Save EMA model state as extra state + state_dict["extra_state"]["ema"] = self.ema.get_model().state_dict() + if self.cfg.ema.ema_fp32: + # Save EMA params in fp32 + state_dict["extra_state"]["ema_fp32_params"] = self.ema.fp32_params + if not self.cfg.checkpoint.no_save_optimizer_state: + if self._gathered_optim_state is not None: + state_dict["last_optimizer_state"] = self._gathered_optim_state + self._gathered_optim_state = None + else: + state_dict["last_optimizer_state"] = self.optimizer.state_dict() + if self.is_fsdp: + # save meta data for recombining checkpoint upon loading + state_dict["fsdp_metadata"] = self.model.local_metadata_dict() + return state_dict + def save_checkpoint(self, filename, extra_state): """Save all training state in a checkpoint file.""" - if self.is_data_parallel_master: # only save one checkpoint - extra_state["metrics"] = metrics.state_dict() - checkpoint_utils.save_state( + logger.info(f"Saving checkpoint to {filename}") + # call state_dict on all ranks in case it needs internal communication + state_dict = utils.move_to_cpu(self.state_dict()) + state_dict["extra_state"].update(extra_state) + if self.should_save_checkpoint_on_current_rank: + checkpoint_utils.torch_persistent_save( + state_dict, filename, - self.args, - self.get_model().state_dict(), - self.get_criterion(), - self.optimizer, - self.lr_scheduler, - self.get_num_updates(), - self._optim_history, - extra_state, + async_write=self.cfg.checkpoint.write_checkpoints_asynchronously, ) + logger.info(f"Finished saving checkpoint to {filename}") def load_checkpoint( self, @@ -225,31 +446,77 @@ def load_checkpoint( optimizer_overrides=None, reset_meters=False, ): - """Load all training state from a checkpoint file.""" + """ + Load all training state from a checkpoint file. + rank = 0 will load the checkpoint, and then broadcast it to all + other ranks. + """ extra_state, self._optim_history, last_optim_state = None, [], None + logger.info(f"Preparing to load checkpoint {filename}") + is_distributed = self.data_parallel_world_size > 1 bexists = PathManager.isfile(filename) if bexists: - state = checkpoint_utils.load_checkpoint_to_cpu(filename) + load_on_all_ranks = ( + self.cfg.checkpoint.load_checkpoint_on_all_dp_ranks + # TPUs don't support broadcast yet, so load checkpoints + # on every worker for now + or self.tpu + # FSDP requires loading checkpoint shards on all ranks + or (self.is_fsdp and self.cfg.distributed_training.use_sharded_state) + or getattr(self.cfg.model, "base_layers", 0) > 0 + ) + + if load_on_all_ranks or self.data_parallel_rank == 0: + state = checkpoint_utils.load_checkpoint_to_cpu( + filename, load_on_all_ranks=load_on_all_ranks + ) + last_optim_state = state.get("last_optimizer_state", None) + + # If doing zero_sharding, do not broadcast global optimizer + # state. Later we will broadcast sharded states to each rank + # to avoid memory from exploding. + if ( + not load_on_all_ranks + and self.cfg.distributed_training.zero_sharding == "os" + and "last_optimizer_state" in state + and is_distributed + ): + state["last_optimizer_state"] = "SHARDED" + else: + last_optim_state = None + state = None + + if is_distributed and not load_on_all_ranks: + state = distributed_utils.broadcast_object( + state, + src_rank=0, + group=self.data_parallel_process_group, + dist_device=self.device, + ) + if self.data_parallel_rank > 0: + last_optim_state = state.get("last_optimizer_state", None) # load model parameters try: - self.get_model().load_state_dict( - state["model"], strict=True, args=self.args + self.model.load_state_dict( + state["model"], strict=True, model_cfg=self.cfg.model ) + # save memory for later steps + del state["model"] if utils.has_parameters(self.get_criterion()): self.get_criterion().load_state_dict( state["criterion"], strict=True ) + del state["criterion"] + except Exception: raise Exception( "Cannot load model parameters from checkpoint {}; " "please ensure that the architectures match.".format(filename) ) - extra_state = state["extra_state"] self._optim_history = state["optimizer_history"] - last_optim_state = state.get("last_optimizer_state", None) if last_optim_state is not None and not reset_optimizer: # rebuild optimizer after loading model, since params may have changed @@ -259,27 +526,45 @@ def load_checkpoint( last_optim = self._optim_history[-1] assert ( last_optim["criterion_name"] == self.get_criterion().__class__.__name__ - ), "Criterion does not match; please reset the optimizer (--reset-optimizer)." + ), f"Criterion does not match; please reset the optimizer (--reset-optimizer). {last_optim['criterion_name']} vs {self.get_criterion().__class__.__name__}" assert ( last_optim["optimizer_name"] == self.optimizer.__class__.__name__ - ), "Optimizer does not match; please reset the optimizer (--reset-optimizer)." + ), f"Optimizer does not match; please reset the optimizer (--reset-optimizer). {last_optim['optimizer_name']} vs {self.optimizer.__class__.__name__}" if not reset_lr_scheduler: self.lr_scheduler.load_state_dict(last_optim["lr_scheduler_state"]) + + if self.is_fsdp and not self.model.use_sharded_state: + # if use_sharded_state, the last_optim_state is already sharded, skip this + last_optim_state = self.model.get_shard_from_optim_state_dict( + last_optim_state + ) + elif not load_on_all_ranks and is_distributed: + last_optim_state = self.optimizer.broadcast_global_state_dict( + last_optim_state + ) + self.optimizer.load_state_dict(last_optim_state, optimizer_overrides) self.set_num_updates(last_optim["num_updates"]) if extra_state is not None: - epoch = extra_state["train_iterator"]["epoch"] - logger.info( - "loaded checkpoint {} (epoch {} @ {} updates)".format( - filename, epoch, self.get_num_updates() - ) - ) + itr_state = extra_state["train_iterator"] + epoch = itr_state["epoch"] + + if "previous_training_time" in extra_state: + self._previous_training_time = extra_state["previous_training_time"] + self._start_time = time.time() self.lr_step(epoch) + if ( + itr_state.get("version", 1) >= 2 + and itr_state["iterations_in_epoch"] == 0 + ): + # reset meters at start of epoch + reset_meters = True + if "metrics" in extra_state and not reset_meters: metrics.load_state_dict(extra_state["metrics"]) @@ -287,8 +572,40 @@ def load_checkpoint( for meter in metrics.get_meters("default"): if isinstance(meter, meters.TimeMeter): meter.reset() + + if self.cfg.ema.store_ema: + if "ema" not in extra_state: + logger.warn( + "EMA not found in checkpoint. But store_ema is True. " + "EMA is re-initialized from checkpoint." + ) + self.ema.restore(state["model"], build_fp32_params=self.cfg.ema.ema_fp32) + else: + logger.info( + "Loading EMA from checkpoint" + ) + self.ema.restore(extra_state["ema"], build_fp32_params=False) + + if self.cfg.ema.ema_fp32: + if "ema_fp32_params" in extra_state: + logger.info( + "Loading EMA fp32 params from checkpoint" + ) + self.ema.build_fp32_params(extra_state["ema_fp32_params"]) + else: + logger.info( + "Building EMA fp32 params from EMA model in checkpoint" + ) + self.ema.build_fp32_params() + + logger.info( + "Loaded checkpoint {} (epoch {} @ {} updates)".format( + filename, epoch, self.get_num_updates() + ) + ) + else: - logger.info("no existing checkpoint found {}".format(filename)) + logger.info("No existing checkpoint found {}".format(filename)) return extra_state @@ -299,69 +616,99 @@ def get_train_iterator( load_dataset=True, data_selector=None, shard_batch_itr=True, + disable_iterator_cache=False, ): """Return an EpochBatchIterator over the training set for a given epoch.""" if load_dataset: logger.info("loading train data for epoch {}".format(epoch)) self.task.load_dataset( - self.args.train_subset, + self.cfg.dataset.train_subset, epoch=epoch, combine=combine, data_selector=data_selector, + tpu=self.tpu, ) - return self.task.get_batch_iterator( - dataset=self.task.dataset(self.args.train_subset), - max_tokens=self.args.max_tokens, - max_sentences=self.args.max_sentences, + batch_iterator = self.task.get_batch_iterator( + dataset=self.task.dataset(self.cfg.dataset.train_subset), + max_tokens=self.cfg.dataset.max_tokens, + max_sentences=self.cfg.dataset.batch_size, max_positions=utils.resolve_max_positions( self.task.max_positions(), self.model.max_positions(), - self.args.max_tokens, + self.cfg.dataset.max_tokens, ), ignore_invalid_inputs=True, - required_batch_size_multiple=self.args.required_batch_size_multiple, - seed=self.args.seed, + required_batch_size_multiple=self.cfg.dataset.required_batch_size_multiple, + seed=self.cfg.common.seed, num_shards=self.data_parallel_world_size if shard_batch_itr else 1, shard_id=self.data_parallel_rank if shard_batch_itr else 0, - num_workers=self.args.num_workers, - epoch=epoch + num_workers=self.cfg.dataset.num_workers, + epoch=epoch, + data_buffer_size=self.cfg.dataset.data_buffer_size, + disable_iterator_cache=disable_iterator_cache, ) + self.reset_dummy_batch(batch_iterator.first_batch) + return batch_iterator def get_valid_iterator( self, subset, + disable_iterator_cache=False, ): """Return an EpochBatchIterator over given validation subset for a given epoch.""" - return self.task.get_batch_iterator( + batch_iterator = self.task.get_batch_iterator( dataset=self.task.dataset(subset), - max_tokens=self.args.max_tokens_valid, - max_sentences=self.args.max_sentences_valid, + max_tokens=self.cfg.dataset.max_tokens_valid, + max_sentences=self.cfg.dataset.batch_size_valid, max_positions=utils.resolve_max_positions( self.task.max_positions(), self.model.max_positions(), ), - ignore_invalid_inputs=self.args.skip_invalid_size_inputs_valid_test, - required_batch_size_multiple=self.args.required_batch_size_multiple, - seed=self.args.seed, + ignore_invalid_inputs=self.cfg.dataset.skip_invalid_size_inputs_valid_test, + required_batch_size_multiple=self.cfg.dataset.required_batch_size_multiple, + seed=self.cfg.common.seed, num_shards=self.data_parallel_world_size, shard_id=self.data_parallel_rank, - num_workers=self.args.num_workers + num_workers=self.cfg.dataset.num_workers, + # always pass a fixed "epoch" to keep validation data consistent + # across training epochs + epoch=1, + data_buffer_size=self.cfg.dataset.data_buffer_size, + disable_iterator_cache=disable_iterator_cache, ) + self.reset_dummy_batch(batch_iterator.first_batch) + return batch_iterator def begin_epoch(self, epoch): """Called at the beginning of each epoch.""" + logger.info("begin training epoch {}".format(epoch)) + + self.lr_step_begin_epoch(epoch) + if self.quantizer is not None: self.quantizer.begin_epoch(epoch) # task specific setup per epoch self.task.begin_epoch(epoch, self.get_model()) + if self.tpu: + import torch_xla.core.xla_model as xm + + xm.rendezvous("begin_epoch") # wait for all workers + xm.mark_step() + + def begin_valid_epoch(self, epoch): + """Called at the beginning of each validation epoch.""" + + # task specific setup per validation epoch + self.task.begin_valid_epoch(epoch, self.get_model()) + + def reset_dummy_batch(self, batch): + self._dummy_batch = batch + @metrics.aggregate("train") def train_step(self, samples, raise_oom=False): """Do forward, backward and parameter update.""" - if self._dummy_batch == "DUMMY": - self._dummy_batch = samples[0] - self._set_seed() self.model.train() self.criterion.train() @@ -369,17 +716,17 @@ def train_step(self, samples, raise_oom=False): metrics.log_start_time("train_wall", priority=800, round=0) + # If EMA is enabled through store_ema=True + # and task.uses_ema is True, pass the EMA model as a keyword + # argument to the task. + extra_kwargs = {} + if self.cfg.ema.store_ema and getattr(self.task, "uses_ema", False): + extra_kwargs["ema_model"] = self.ema.get_model() + # forward and backward pass logging_outputs, sample_size, ooms = [], 0, 0 - for i, sample in enumerate(samples): - sample = self._prepare_sample(sample) - if sample is None: - # when sample is None, run forward/backward on a dummy batch - # and ignore the resulting gradients - sample = self._prepare_sample(self._dummy_batch) - is_dummy_batch = True - else: - is_dummy_batch = False + for i, sample in enumerate(samples): # delayed update loop + sample, is_dummy_batch = self._prepare_sample(sample) def maybe_no_sync(): """ @@ -391,6 +738,11 @@ def maybe_no_sync(): self.data_parallel_world_size > 1 and hasattr(self.model, "no_sync") and i < len(samples) - 1 + # The no_sync context manager results in increased memory + # usage with FSDP, since full-size gradients will be + # accumulated on each GPU. It's typically a better tradeoff + # to do the extra communication with FSDP. + and not self.is_fsdp ): return self.model.no_sync() else: @@ -406,6 +758,7 @@ def maybe_no_sync(): optimizer=self.optimizer, update_num=self.get_num_updates(), ignore_grad=is_dummy_batch, + **extra_kwargs, ) del loss @@ -426,6 +779,10 @@ def maybe_no_sync(): ) ooms += 1 self.zero_grad() + if self.cuda: + torch.cuda.empty_cache() + if self.cfg.distributed_training.distributed_world_size == 1: + return None else: raise e @@ -435,14 +792,13 @@ def maybe_no_sync(): # before marking step can lead to OOM errors. # To handle gradient accumulation use case, we explicitly # mark step here for every forward pass without a backward pass - import torch_xla.core.xla_model as xm - xm.mark_step() + self._xla_markstep_and_send_to_cpu() if is_dummy_batch: if torch.is_tensor(sample_size): sample_size.zero_() else: - sample_size *= 0. + sample_size *= 0.0 if torch.is_tensor(sample_size): sample_size = sample_size.float() @@ -451,51 +807,102 @@ def maybe_no_sync(): # gather logging outputs from all replicas if self._sync_stats(): - logging_outputs, (sample_size, ooms) = self._aggregate_logging_outputs( - logging_outputs, sample_size, ooms, ignore=is_dummy_batch, + train_time = self._local_cumulative_training_time() + logging_outputs, ( + sample_size, + ooms, + total_train_time, + ) = self._aggregate_logging_outputs( + logging_outputs, sample_size, ooms, train_time, ignore=is_dummy_batch + ) + self._cumulative_training_time = ( + total_train_time / self.data_parallel_world_size ) overflow = False try: - if self.tpu and self.data_parallel_world_size > 1: - import torch_xla.core.xla_model as xm - gradients = xm._fetch_gradients(self.optimizer.optimizer) - xm.all_reduce('sum', gradients, scale=1.0 / self.data_parallel_world_size) - - # multiply gradients by (# GPUs / sample_size) since DDP - # already normalizes by the number of GPUs. Thus we get - # (sum_of_gradients / sample_size). - if not self.args.use_bmuf: - self.optimizer.multiply_grads(self.data_parallel_world_size / sample_size) - elif sample_size > 0: # BMUF needs to check sample size - num = self.data_parallel_world_size if self._sync_stats() else 1 - self.optimizer.multiply_grads(num / sample_size) + with torch.autograd.profiler.record_function("reduce-grads"): + # reduce gradients across workers + self.optimizer.all_reduce_grads(self.model) + if utils.has_parameters(self.criterion): + self.optimizer.all_reduce_grads(self.criterion) + + with torch.autograd.profiler.record_function("multiply-grads"): + # multiply gradients by (data_parallel_size / sample_size) since + # DDP normalizes by the number of data parallel workers for + # improved fp16 precision. + # Thus we get (sum_of_gradients / sample_size) at the end. + # In case of fp16, this step also undoes loss scaling. + # (Debugging note: Some optimizers perform this scaling on the + # fly, so inspecting model.parameters() or optimizer.params may + # still show the original, unscaled gradients.) + numer = ( + self.data_parallel_world_size + if not self.cfg.optimization.use_bmuf or self._sync_stats() + else 1 + ) + self.optimizer.multiply_grads(numer / (sample_size or 1.0)) + # Note: (sample_size or 1.0) handles the case of a zero gradient, in a + # way that avoids CPU/device transfers in case sample_size is a GPU or + # TPU object. The assumption is that the gradient itself is also 0. - # clip grads - grad_norm = self.clip_grad_norm(self.args.clip_norm) + with torch.autograd.profiler.record_function("clip-grads"): + # clip grads + grad_norm = self.clip_grad_norm(self.cfg.optimization.clip_norm) # check that grad norms are consistent across workers - if ( - not self.args.use_bmuf - and self.args.distributed_wrapper != 'SlowMo' - and not self.tpu - ): - self._check_grad_norms(grad_norm) + # on tpu check tensor is slow + if not self.tpu: + if ( + not self.cfg.optimization.use_bmuf + and self.cfg.distributed_training.ddp_backend != "slow_mo" + ): + self._check_grad_norms(grad_norm) + if not torch.isfinite(grad_norm).all(): + # in case of AMP, if gradients are Nan/Inf then + # optimizer step is still required + if self.cfg.common.amp: + overflow = True + else: + # check local gradnorm single GPU case, trigger NanDetector + raise FloatingPointError("gradients are Nan/Inf") + + with torch.autograd.profiler.record_function("optimizer"): + # take an optimization step + self.task.optimizer_step( + self.optimizer, model=self.model, update_num=self.get_num_updates() + ) + if self.cfg.common.amp and overflow: + if self._amp_retries == self.cfg.common.amp_batch_retries: + logger.info("AMP: skipping this batch.") + self._amp_retries = 0 + else: + self._amp_retries += 1 + return self.train_step(samples, raise_oom) # recursion to feed in same batch - # take an optimization step - self.optimizer.step() except FloatingPointError: - # re-run the forward and backward pass with hooks attached to print out where it fails - with NanDetector(self.model): - self.task.train_step( - sample, self.model, self.criterion, self.optimizer, self.get_num_updates(), - ignore_grad=False - ) + # re-run the forward and backward pass with hooks attached to print + # out where it fails + self.zero_grad() + with NanDetector(self.get_model()): + for _, sample in enumerate(samples): + sample, _ = self._prepare_sample(sample) + self.task.train_step( + sample, + self.model, + self.criterion, + self.optimizer, + self.get_num_updates(), + ignore_grad=False, + **extra_kwargs, + ) raise except OverflowError as e: overflow = True - logger.info("NOTE: overflow detected, " + str(e)) - grad_norm = torch.tensor(0.).cuda() + logger.info( + f"NOTE: gradient overflow detected, ignoring gradient, {str(e)}" + ) + grad_norm = torch.tensor(0.0).cuda() self.zero_grad() except RuntimeError as e: if "out of memory" in str(e): @@ -503,27 +910,61 @@ def maybe_no_sync(): logger.error("OOM during optimization, irrecoverable") raise e - # Some distributed wrappers (e.g., SlowMo) need access to the optimizer after the step - if hasattr(self.model, 'perform_additional_optimizer_actions'): - if hasattr(self.optimizer, 'fp32_params'): - self.model.perform_additional_optimizer_actions(self.optimizer.optimizer, self.optimizer.fp32_params) + # Some distributed wrappers (e.g., SlowMo) need access to the optimizer + # after the step + if hasattr(self.model, "perform_additional_optimizer_actions"): + if hasattr(self.optimizer, "fp32_params"): + self.model.perform_additional_optimizer_actions( + self.optimizer.optimizer, self.optimizer.fp32_params + ) else: - self.model.perform_additional_optimizer_actions(self.optimizer.optimizer) + self.model.perform_additional_optimizer_actions( + self.optimizer.optimizer + ) - if not overflow or self.args.distributed_wrapper == 'SlowMo': + logging_output = None + if not overflow or self.cfg.distributed_training.ddp_backend == "slow_mo": self.set_num_updates(self.get_num_updates() + 1) + if self.cfg.ema.store_ema: + # Step EMA forward with new model. + self.ema.step( + self.get_model(), + self.get_num_updates(), + ) + metrics.log_scalar( + "ema_decay", + self.ema.get_decay(), + priority=10000, + round=5, + weight=0, + ) + if self.tpu: - # mark step on TPUs import torch_xla.core.xla_model as xm - xm.mark_step() + + # mark step on TPUs + self._xla_markstep_and_send_to_cpu() # only log stats every log_interval steps # this causes wps to be misreported when log_interval > 1 logging_output = {} - if self.get_num_updates() % self.args.log_interval == 0: + if self.get_num_updates() % self.cfg.common.log_interval == 0: + # log memory usage + mem_info = xm.get_memory_info(self.device) + gb_free = mem_info["kb_free"] / 1024 / 1024 + gb_total = mem_info["kb_total"] / 1024 / 1024 + metrics.log_scalar( + "gb_free", gb_free, priority=1500, round=1, weight=0 + ) + metrics.log_scalar( + "gb_total", gb_total, priority=1600, round=1, weight=0 + ) + logging_outputs = self._xla_markstep_and_send_to_cpu( + logging_outputs + ) logging_output = self._reduce_and_log_stats( - logging_outputs, sample_size, grad_norm, + logging_outputs, sample_size, grad_norm ) # log whenever there's an XLA compilation, since these @@ -531,53 +972,72 @@ def maybe_no_sync(): # optimization self._check_xla_compilation() else: + if self.cuda and self.cuda_env is not None: + # log minimum free memory over the iteration + gb_used = torch.cuda.max_memory_allocated() / 1024 / 1024 / 1024 + torch.cuda.reset_peak_memory_stats() + gb_free = self.cuda_env.total_memory_in_GB - gb_used + metrics.log_scalar( + "gb_free", gb_free, priority=1500, round=1, weight=0 + ) + # log stats logging_output = self._reduce_and_log_stats( - logging_outputs, sample_size, grad_norm, + logging_outputs, sample_size, grad_norm ) # clear CUDA cache to reduce memory fragmentation if ( self.cuda - and self.args.empty_cache_freq > 0 + and self.cfg.common.empty_cache_freq > 0 and ( - (self.get_num_updates() + self.args.empty_cache_freq - 1) - % self.args.empty_cache_freq - ) == 0 + (self.get_num_updates() + self.cfg.common.empty_cache_freq - 1) + % self.cfg.common.empty_cache_freq + ) + == 0 ): torch.cuda.empty_cache() - if self.args.fp16: - metrics.log_scalar("loss_scale", self.optimizer.scaler.loss_scale, priority=700, round=0) + if self.cfg.common.fp16 or self.cfg.common.amp: + metrics.log_scalar( + "loss_scale", + ( + self.optimizer.scaler.loss_scale + if self.cfg.common.fp16 + else self.optimizer.scaler.get_scale() + ), + priority=700, + round=4, + weight=0, + ) metrics.log_stop_time("train_wall") - return logging_output @metrics.aggregate("valid") def valid_step(self, sample, raise_oom=False): """Do forward pass in evaluation mode.""" - if self._dummy_batch == "DUMMY": - self._dummy_batch = sample if self.tpu: import torch_xla.core.xla_model as xm - xm.rendezvous('valid_step') # wait for all workers - xm.mark_step() + + xm.rendezvous("valid_step") # wait for all workers + + # If EMA is enabled through store_ema=True + # and task.uses_ema is True, pass the EMA model as a keyword + # argument to the task. + extra_kwargs = {} + if self.cfg.ema.store_ema and getattr(self.task, "uses_ema", False): + extra_kwargs["ema_model"] = self.ema.get_model() with torch.no_grad(): self.model.eval() self.criterion.eval() - sample = self._prepare_sample(sample) - if sample is None: - sample = self._prepare_sample(self._dummy_batch) - is_dummy_batch = True - else: - is_dummy_batch = False + sample, is_dummy_batch = self._prepare_sample(sample) try: _loss, sample_size, logging_output = self.task.valid_step( - sample, self.model, self.criterion + sample, self.model, self.criterion, **extra_kwargs ) except RuntimeError as e: if "out of memory" in str(e): @@ -599,15 +1059,19 @@ def valid_step(self, sample, raise_oom=False): if torch.is_tensor(sample_size): sample_size.zero_() else: - sample_size *= 0. + sample_size *= 0.0 # gather logging outputs from all replicas if self.data_parallel_world_size > 1: - logging_outputs, (sample_size, ) = self._aggregate_logging_outputs( - logging_outputs, sample_size, ignore=is_dummy_batch, + logging_outputs, (sample_size,) = self._aggregate_logging_outputs( + logging_outputs, + sample_size, + ignore=is_dummy_batch, ) # log validation stats + if self.tpu: + logging_outputs = self._xla_markstep_and_send_to_cpu(logging_outputs) logging_output = self._reduce_and_log_stats(logging_outputs, sample_size) return logging_output @@ -615,6 +1079,12 @@ def valid_step(self, sample, raise_oom=False): def zero_grad(self): self.optimizer.zero_grad() + def lr_step_begin_epoch(self, epoch): + """Adjust the learning rate at the beginning of the epoch.""" + self.lr_scheduler.step_begin_epoch(epoch) + # prefer updating the LR based on the number of steps + return self.lr_step_update() + def lr_step(self, epoch, val_loss=None): """Adjust the learning rate at the end of the epoch.""" self.lr_scheduler.step(epoch, val_loss) @@ -624,7 +1094,12 @@ def lr_step(self, epoch, val_loss=None): def lr_step_update(self): """Update the learning rate after each update.""" new_lr = self.lr_scheduler.step_update(self.get_num_updates()) - metrics.log_scalar("lr", new_lr, weight=0, priority=300) + if isinstance(new_lr, dict): + for k, v in new_lr.items(): + metrics.log_scalar(f"lr_{k}", v, weight=0, priority=300) + new_lr = new_lr.get("default", next(iter(new_lr.values()))) + else: + metrics.log_scalar("lr", new_lr, weight=0, priority=300) return new_lr def get_lr(self): @@ -643,10 +1118,10 @@ def get_meter(self, name): """[deprecated] Get a specific meter by name.""" from fairseq import meters - if 'get_meter' not in self._warn_once: - self._warn_once.add('get_meter') + if "get_meter" not in self._warn_once: + self._warn_once.add("get_meter") utils.deprecation_warning( - 'Trainer.get_meter is deprecated. Please use fairseq.metrics instead.' + "Trainer.get_meter is deprecated. Please use fairseq.metrics instead." ) train_meters = metrics.get_meters("train") @@ -671,7 +1146,7 @@ def get_meter(self, name): elif name in {"valid_loss", "valid_nll_loss"}: # support for legacy train.py, which assumed these meters # are always initialized - k = name[len("valid_"):] + k = name[len("valid_") :] m = metrics.get_meter("valid", k) return m or meters.AverageMeter() elif name == "oom": @@ -693,25 +1168,39 @@ def set_num_updates(self, num_updates): metrics.log_scalar("num_updates", self._num_updates, weight=0, priority=200) def clip_grad_norm(self, clip_norm): - return self.optimizer.clip_grad_norm(clip_norm, aggregate_norm_fn=None) + def agg_norm_fn(total_norm): + total_norm = total_norm.cuda().float() ** 2 + total_norm = distributed_utils.all_reduce( + total_norm, group=self.data_parallel_process_group + ) + return total_norm ** 0.5 - def _prepare_sample(self, sample): - if sample == "DUMMY": - raise Exception( - "Trying to use an uninitialized 'dummy' batch. This usually indicates " - "that the total number of batches is smaller than the number of " - "participating GPUs. Try reducing the batch size or using fewer GPUs." + should_agg_norm = ( + self.is_fsdp + and ( + self.data_parallel_process_group is not None + or torch.distributed.is_initialized() ) + ) + return self.optimizer.clip_grad_norm( + clip_norm, aggregate_norm_fn=agg_norm_fn if should_agg_norm else None + ) - if sample is None or len(sample) == 0: - return None + def cumulative_training_time(self): + if self._cumulative_training_time is None: + # single GPU + return self._local_cumulative_training_time() + else: + return self._cumulative_training_time - if self.cuda: - sample = utils.move_to_cuda(sample) + def _local_cumulative_training_time(self): + """Aggregate training time in seconds.""" + return time.time() - self._start_time + self._previous_training_time + def _fp_convert_sample(self, sample): def apply_half(t): if t.dtype is torch.float32: - return t.half() + return t.to(dtype=torch.half) return t def apply_bfloat16(t): @@ -719,18 +1208,58 @@ def apply_bfloat16(t): return t.to(dtype=torch.bfloat16) return t - if self.args.fp16: + if self.cfg.common.fp16: sample = utils.apply_to_sample(apply_half, sample) - if self.args.bf16: + if self.cfg.common.bf16: sample = utils.apply_to_sample(apply_bfloat16, sample) return sample + def _prepare_sample(self, sample, is_dummy=False): + if sample == "DUMMY": + raise Exception( + "Trying to use an uninitialized 'dummy' batch. This usually indicates " + "that the total number of batches is smaller than the number of " + "participating GPUs. Try reducing the batch size or using fewer GPUs." + ) + + if sample is None or len(sample) == 0: + assert ( + self._dummy_batch is not None and len(self._dummy_batch) > 0 + ), "Invalid dummy batch: {}".format(self._dummy_batch) + sample, _ = self._prepare_sample(self._dummy_batch, is_dummy=True) + return sample, True + + # Given that PCIe/NVLink bandwidth is significantly smaller than DRAM bandwidth + # it makes sense to do the format conversion on the CPU and then transfer + # a smaller buffer to the device. This also saves GPU memory capacity. + + if self.cfg.common.on_cpu_convert_precision: + sample = self._fp_convert_sample(sample) + + if self.cuda: + if self.pipeline_model_parallel: + if 'target' in sample: + sample['target'] = utils.move_to_cuda(sample['target'], device=self.last_device) + else: + sample = utils.move_to_cuda(sample) + elif self.tpu and is_dummy: + # the dummy batch may not be on the appropriate device + sample = utils.move_to_cuda(sample, device=self.device) + + if not self.cfg.common.on_cpu_convert_precision: + sample = self._fp_convert_sample(sample) + + if self._dummy_batch == "DUMMY": + self._dummy_batch = sample + + return sample, False + def _set_seed(self): # Set seed based on args.seed and the update number so that we get # reproducible results when resuming from checkpoints - seed = self.args.seed + self.get_num_updates() + seed = self.cfg.common.seed + self.get_num_updates() utils.set_torch_seed(seed) def _sync_stats(self): @@ -738,11 +1267,12 @@ def _sync_stats(self): # BMUF and it's a bmuf sync with warmup iterations completed before. if self.data_parallel_world_size == 1: return False - elif self.args.use_bmuf: + elif self.cfg.optimization.use_bmuf: return ( - (self.get_num_updates() + 1) % self.args.global_sync_iter == 0 - and (self.get_num_updates() + 1) > self.args.warmup_iterations - ) + self.get_num_updates() + 1 + ) % self.cfg.bmuf.global_sync_iter == 0 and ( + self.get_num_updates() + 1 + ) > self.cfg.bmuf.warmup_iterations else: return True @@ -783,13 +1313,15 @@ def _all_gather_list_sync( raise NotImplementedError if ignore: logging_outputs = [] - results = list(zip( - *distributed_utils.all_gather_list( - [logging_outputs] + list(extra_stats_to_sum), - max_size=getattr(self.args, 'all_gather_list_size', 16384), - group=self.data_parallel_process_group, + results = list( + zip( + *distributed_utils.all_gather_list( + [logging_outputs] + list(extra_stats_to_sum), + max_size=getattr(self.cfg.common, "all_gather_list_size", 16384), + group=self.data_parallel_process_group, + ) ) - )) + ) logging_outputs, extra_stats_to_sum = results[0], results[1:] logging_outputs = list(chain.from_iterable(logging_outputs)) extra_stats_to_sum = [sum(s) for s in extra_stats_to_sum] @@ -809,7 +1341,7 @@ def _fast_stat_sync_sum( """ data = {} for i, stat in enumerate(extra_stats_to_sum): - data['extra_stats_' + str(i)] = stat + data["extra_stats_" + str(i)] = stat if len(logging_outputs) > 0: log_keys = list(logging_outputs[0].keys()) for k in log_keys: @@ -818,21 +1350,19 @@ def _fast_stat_sync_sum( else: v = logging_outputs[0][k] v = torch.zeros_like(v) if torch.is_tensor(v) else 0 - data['logging_outputs_' + k] = v + data["logging_outputs_" + k] = v else: log_keys = None data = distributed_utils.all_reduce_dict( - data, - device=self.device, - group=self.data_parallel_process_group + data, device=self.device, group=self.data_parallel_process_group ) extra_stats_to_sum = [ - data['extra_stats_' + str(i)] for i in range(len(extra_stats_to_sum)) + data["extra_stats_" + str(i)] for i in range(len(extra_stats_to_sum)) ] if log_keys is not None: - logging_outputs = [{k: data['logging_outputs_' + k] for k in log_keys}] + logging_outputs = [{k: data["logging_outputs_" + k] for k in log_keys}] else: logging_outputs = [] return logging_outputs, extra_stats_to_sum @@ -842,22 +1372,50 @@ def _check_grad_norms(self, grad_norm): if self._grad_norm_buf is not None: self._grad_norm_buf.zero_() self._grad_norm_buf[self.data_parallel_rank] = grad_norm - distributed_utils.all_reduce(self._grad_norm_buf, group=self.data_parallel_process_group) - if not (self._grad_norm_buf == self._grad_norm_buf[0]).all(): - raise RuntimeError( + distributed_utils.all_reduce( + self._grad_norm_buf, group=self.data_parallel_process_group + ) + + def is_consistent(tensor): + max_abs_diff = torch.max(torch.abs(tensor - tensor[0])) + return ( + (torch.isfinite(tensor).all() + and (max_abs_diff / (tensor[0] + 1e-6) < 1e-6).all()) + or + (self.cfg.common.amp and not torch.isfinite(tensor).all()) + # in case of amp non-finite grads are fine + ) + + if not is_consistent(self._grad_norm_buf): + pretty_detail = "\n".join( + "rank {:3d} = {:.8f}".format(r, n) + for r, n in enumerate(self._grad_norm_buf.tolist()) + ) + error_detail = "grad_norm across the workers:\n{}\n".format( + pretty_detail + ) + # use FloatingPointError to trigger NanDetector + raise FloatingPointError( "Fatal error: gradients are inconsistent between workers. " - "Try --ddp-backend=no_c10d." + "Try --ddp-backend=legacy_ddp. " + "Or are you mixing up different generation of GPUs in training?" + + "\n" + + "-" * 80 + + "\n{}\n".format(error_detail) + + "-" * 80 ) def _reduce_and_log_stats(self, logging_outputs, sample_size, grad_norm=None): - if grad_norm is not None: - metrics.log_speed("ups", 1., priority=100, round=2) + if grad_norm is not None and ( + not torch.is_tensor(grad_norm) or torch.isfinite(grad_norm) + ): + metrics.log_speed("ups", 1.0, priority=100, round=2) metrics.log_scalar("gnorm", grad_norm, priority=400, round=3) - if self.args.clip_norm > 0: + if self.cfg.optimization.clip_norm > 0: metrics.log_scalar( "clip", torch.where( - grad_norm > self.args.clip_norm, + grad_norm > self.cfg.optimization.clip_norm, grad_norm.new_tensor(100), grad_norm.new_tensor(0), ), @@ -870,6 +1428,16 @@ def _reduce_and_log_stats(self, logging_outputs, sample_size, grad_norm=None): self.task.reduce_metrics(logging_outputs, self.get_criterion()) del logging_outputs + # extra warning for criterions that don't properly log a loss value + if "loss" not in agg: + if "loss" not in self._warn_once: + self._warn_once.add("loss") + logger.warning( + "Criterion.reduce_metrics did not log a 'loss' value, " + "which may break some functionality" + ) + metrics.log_scalar("loss", -1) + # support legacy interface if self.tpu: logging_output = {} @@ -881,51 +1449,61 @@ def _reduce_and_log_stats(self, logging_outputs, sample_size, grad_norm=None): del logging_output[key_to_delete] return logging_output - def _check_xla_compilation(self, message=None): + def _check_xla_compilation(self): import torch_xla.debug.metrics as met + compile_stats = met.metric_data("CompileTime") if compile_stats is None: return num_xla_compiles = compile_stats[0] if num_xla_compiles > self._num_xla_compiles: - if message is None: - message = ( - "too many of these can lead to slow training, " - "but we expect a few in the beginning" + logger.warning( + "XLA compilation detected on device #{}; too many of these can lead " + "to slow training, but we expect a few in the beginning".format( + self.cfg.distributed_training.distributed_rank ) - logging.info("NOTE: XLA compilation detected; {}".format(message)) + ) self._num_xla_compiles = num_xla_compiles + def _xla_markstep_and_send_to_cpu(self, data=None): + import torch_xla.core.xla_model as xm + + xm.mark_step() + if data is not None: + from fairseq.utils import xla_device_to_cpu + + return xla_device_to_cpu(data) + -def _catalog_shared_params(module, memo=None, prefix=''): +def _catalog_shared_params(module, memo=None, prefix=""): if memo is None: first_call = True memo = {} else: first_call = False for name, param in module._parameters.items(): - param_prefix = prefix + ('.' if prefix else '') + name + param_prefix = prefix + ("." if prefix else "") + name if param not in memo: memo[param] = [] memo[param].append(param_prefix) for name, m in module._modules.items(): if m is None: continue - submodule_prefix = prefix + ('.' if prefix else '') + name + submodule_prefix = prefix + ("." if prefix else "") + name _catalog_shared_params(m, memo, submodule_prefix) if first_call: return [x for x in memo.values() if len(x) > 1] def _get_module_by_path(module, path): - path = path.split('.') + path = path.split(".") for name in path: module = getattr(module, name) return module def _set_module_by_path(module, path, value): - path = path.split('.') + path = path.split(".") for name in path[:-1]: module = getattr(module, name) setattr(module, path[-1], value) diff --git a/fairseq/utils.py b/fairseq/utils.py index 22008012f5..623ff87a8c 100644 --- a/fairseq/utils.py +++ b/fairseq/utils.py @@ -3,32 +3,65 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. +import argparse import contextlib import copy -import importlib.util +import importlib import logging -import math import os import sys import warnings -from collections import defaultdict from itertools import accumulate -from typing import Callable, Dict, List, Optional +from typing import Callable, Dict, List, Optional, TYPE_CHECKING -import numpy as np import torch import torch.nn.functional as F -from fairseq.logging.meters import safe_round -from fairseq.modules import gelu, gelu_accurate -from fairseq.modules.multihead_attention import MultiheadAttention from torch import Tensor +import collections + +if TYPE_CHECKING: + from fairseq.modules.multihead_attention import MultiheadAttention + +try: + from amp_C import multi_tensor_l2norm + + multi_tensor_l2norm_available = True +except ImportError: + multi_tensor_l2norm_available = False + +try: + import torch_xla.core.xla_model as xm +except ImportError: + xm = None logger = logging.getLogger(__name__) -def split_paths(paths: str) -> List[str]: - return paths.split(os.pathsep) if "://" not in paths else paths.split("|") +MANIFOLD_PATH_SEP = "|" + + +class FileContentsAction(argparse.Action): + def __init__(self, option_strings, dest, nargs=None, **kwargs): + if nargs is not None: + raise ValueError("nargs not allowed") + super(FileContentsAction, self).__init__(option_strings, dest, **kwargs) + + def __call__(self, parser, namespace, values, option_string=None): + from fairseq.file_io import PathManager + + if PathManager.isfile(values): + with PathManager.open(values) as f: + argument = f.read().strip() + else: + argument = values + setattr(namespace, self.dest, argument) + + +def split_paths(paths: str, separator=os.pathsep) -> List[str]: + return ( + paths.split(separator) if "://" not in paths else paths.split(MANIFOLD_PATH_SEP) + ) def load_ensemble_for_inference(filenames, task, model_arg_overrides=None): @@ -44,12 +77,17 @@ def load_ensemble_for_inference(filenames, task, model_arg_overrides=None): def apply_to_sample(f, sample): - if hasattr(sample, '__len__') and len(sample) == 0: + if hasattr(sample, "__len__") and len(sample) == 0: return {} def _apply(x): if torch.is_tensor(x): return f(x) + elif isinstance(x, collections.OrderedDict): + # OrderedDict has attributes that needs to be preserved + od = collections.OrderedDict((key, _apply(value)) for key, value in x.items()) + od.__dict__ = x.__dict__ + return od elif isinstance(x, dict): return {key: _apply(value) for key, value in x.items()} elif isinstance(x, list): @@ -64,9 +102,13 @@ def _apply(x): return _apply(sample) -def move_to_cuda(sample): +def move_to_cuda(sample, device=None): + device = device or torch.cuda.current_device() + def _move_to_cuda(tensor): - return tensor.cuda() + # non_blocking is ignored if tensor is not pinned, so we can always set + # to True (see github.com/PyTorchLightning/pytorch-lightning/issues/620) + return tensor.to(device=device, non_blocking=True) return apply_to_sample(_move_to_cuda, sample) @@ -82,8 +124,20 @@ def _move_to_cpu(tensor): return apply_to_sample(_move_to_cpu, sample) +def move_to_tpu(sample): + + import torch_xla.core.xla_model as xm + + device = xm.xla_device() + + def _move_to_tpu(tensor): + return tensor.to(device) + + return apply_to_sample(_move_to_tpu, sample) + + def get_incremental_state( - module: MultiheadAttention, + module: "MultiheadAttention", incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]], key: str, ) -> Optional[Dict[str, Optional[Tensor]]]: @@ -92,7 +146,7 @@ def get_incremental_state( def set_incremental_state( - module: MultiheadAttention, + module: "MultiheadAttention", incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]], key: str, value: Dict[str, Optional[Tensor]], @@ -175,9 +229,17 @@ def replace_unk(hypo_str, src_str, alignment, align_dict, unk): def post_process_prediction( - hypo_tokens, src_str, alignment, align_dict, tgt_dict, remove_bpe=None, extra_symbols_to_ignore=None + hypo_tokens, + src_str, + alignment, + align_dict, + tgt_dict, + remove_bpe=None, + extra_symbols_to_ignore=None, ): - hypo_str = tgt_dict.string(hypo_tokens, remove_bpe, extra_symbols_to_ignore=extra_symbols_to_ignore) + hypo_str = tgt_dict.string( + hypo_tokens, remove_bpe, extra_symbols_to_ignore=extra_symbols_to_ignore + ) if align_dict is not None: hypo_str = replace_unk( hypo_str, src_str, alignment, align_dict, tgt_dict.unk_string() @@ -243,6 +305,9 @@ def convert_padding_direction( def item(tensor): + # tpu-comment: making this a no-op for xla devices. + if torch.is_tensor(tensor) and tensor.device.type == "xla": + return tensor.detach() if hasattr(tensor, "item"): return tensor.item() if hasattr(tensor, "__getitem__"): @@ -250,21 +315,74 @@ def item(tensor): return tensor +def multi_tensor_total_norm(grads, chunk_size=2048 * 32) -> torch.Tensor: + per_device_grads = {} + norms = [] + for grad in grads: + device = grad.device + cur_device_grads = per_device_grads.get(device) + if cur_device_grads is None: + cur_device_grads = [] + per_device_grads[device] = cur_device_grads + cur_device_grads.append(grad) + for device in per_device_grads.keys(): + cur_device_grads = per_device_grads[device] + if device.type == "cuda": + # TODO(msb) return has_inf + has_inf = torch.zeros((1, 1), dtype=torch.int, device=device) + with torch.cuda.device(device): + norm = multi_tensor_l2norm( + chunk_size, has_inf, [cur_device_grads], False + ) + norms.append(norm[0].to(torch.cuda.current_device())) + else: + norms += [torch.norm(g, p=2, dtype=torch.float32) for g in cur_device_grads] + total_norm = torch.norm(torch.stack(norms)) + return total_norm + + +@torch.no_grad() def clip_grad_norm_(params, max_norm, aggregate_norm_fn=None) -> torch.Tensor: + def grad_exists(p): + return p is not None and getattr(p, "grad", None) is not None + if isinstance(params, torch.Tensor): params = [params] params = list(params) - grads = [p.grad.detach() for p in filter(lambda p: p.grad is not None, params)] + grads = [ + p.grad.detach() for p in params if grad_exists(p) and not hasattr(p, "expert") + ] + expert_grads = [ + p.grad.detach() for p in params if grad_exists(p) and hasattr(p, "expert") + ] + if len(grads) == 0: if len(params) > 0: - return params[0].new_tensor(0.) + return params[0].new_tensor(0.0) else: - return torch.tensor(0.) + return torch.tensor(0.0) if len(grads) == 1: - total_norm = torch.norm(grads[0]) + total_norm = torch.norm(grads[0], p=2, dtype=torch.float32) else: - total_norm = torch.norm(torch.stack([torch.norm(g) for g in grads])) + if multi_tensor_l2norm_available: + total_norm = multi_tensor_total_norm(grads) + else: + if torch.cuda.is_available(): + warnings.warn( + "amp_C fused kernels unavailable, disabling multi_tensor_l2norm; " + "you may get better performance by installing NVIDIA's apex library" + ) + device = torch.cuda.current_device() + elif grads[0].device.type == "xla": + device = grads[0].device + else: + device = torch.device("cpu") + total_norm = torch.norm( + torch.stack( + [torch.norm(g, p=2, dtype=torch.float32).to(device) for g in grads] + ) + ) if aggregate_norm_fn is not None: total_norm = aggregate_norm_fn(total_norm) @@ -272,7 +390,7 @@ def clip_grad_norm_(params, max_norm, aggregate_norm_fn=None) -> torch.Tensor: if max_norm > 0: max_norm = float(max_norm) clip_coef = (max_norm / (total_norm + 1e-6)).clamp_(max=1) - for g in grads: + for g in grads + expert_grads: g.mul_(clip_coef) return total_norm @@ -345,18 +463,48 @@ def import_user_module(args): module_path = getattr(args, "user_dir", None) if module_path is not None: module_path = os.path.abspath(args.user_dir) - if not os.path.exists(module_path): - fairseq_rel_path = os.path.join( - os.path.dirname(__file__), "..", args.user_dir - ) + if not os.path.exists(module_path) and not os.path.isfile( + os.path.dirname(module_path) + ): + fairseq_rel_path = os.path.join(os.path.dirname(__file__), args.user_dir) if os.path.exists(fairseq_rel_path): module_path = fairseq_rel_path - module_parent, module_name = os.path.split(module_path) + else: + fairseq_rel_path = os.path.join( + os.path.dirname(__file__), "..", args.user_dir + ) + if os.path.exists(fairseq_rel_path): + module_path = fairseq_rel_path + else: + raise FileNotFoundError(module_path) + + # ensure that user modules are only imported once + import_user_module.memo = getattr(import_user_module, "memo", set()) + if module_path not in import_user_module.memo: + import_user_module.memo.add(module_path) - if module_name not in sys.modules: - sys.path.insert(0, module_parent) - importlib.import_module(module_name) - sys.path.pop(0) + module_parent, module_name = os.path.split(module_path) + if module_name not in sys.modules: + sys.path.insert(0, module_parent) + importlib.import_module(module_name) + + tasks_path = os.path.join(module_path, "tasks") + if os.path.exists(tasks_path): + from fairseq.tasks import import_tasks + + import_tasks(tasks_path, f"{module_name}.tasks") + + models_path = os.path.join(module_path, "models") + if os.path.exists(models_path): + from fairseq.models import import_models + + import_models(models_path, f"{module_name}.models") + else: + raise ImportError( + "Failed to import --user-dir={} because the corresponding module name " + "({}) is not globally unique. Please rename the directory to " + "something unique and try again.".format(module_path, module_name) + ) def softmax(x, dim: int, onnx_trace: bool = False): @@ -374,12 +522,14 @@ def log_softmax(x, dim: int, onnx_trace: bool = False): def get_perplexity(loss, round=2, base=2): + from fairseq.logging.meters import safe_round + if loss is None: - return 0. + return 0.0 try: return safe_round(base ** loss, round) except OverflowError: - return float('inf') + return float("inf") def deprecation_warning(message, stacklevel=3): @@ -388,7 +538,9 @@ def deprecation_warning(message, stacklevel=3): def get_activation_fn(activation: str) -> Callable: - """ Returns the activation function corresponding to `activation` """ + """Returns the activation function corresponding to `activation`""" + from fairseq.modules import gelu, gelu_accurate + if activation == "relu": return F.relu elif activation == "gelu": @@ -420,7 +572,7 @@ def get_available_activation_fns() -> List: @contextlib.contextmanager -def eval(model): +def model_eval(model): is_training = model.training model.eval() yield @@ -435,23 +587,39 @@ def has_parameters(module): return False -def set_torch_seed(seed): - # Set seed based on args.seed and the update number so that we get - # reproducible results when resuming from checkpoints - assert isinstance(seed, int) - torch.manual_seed(seed) - torch.cuda.manual_seed(seed) +def get_rng_state(): + state = {"torch_rng_state": torch.get_rng_state()} + if xm is not None: + state["xla_rng_state"] = xm.get_rng_state() + if torch.cuda.is_available(): + state["cuda_rng_state"] = torch.cuda.get_rng_state() + return state -@contextlib.contextmanager -def with_torch_seed(seed): - assert isinstance(seed, int) - rng_state = torch.get_rng_state() - cuda_rng_state = torch.cuda.get_rng_state() - set_torch_seed(seed) - yield - torch.set_rng_state(rng_state) - torch.cuda.set_rng_state(cuda_rng_state) +def set_rng_state(state): + torch.set_rng_state(state["torch_rng_state"]) + if xm is not None: + xm.set_rng_state(state["xla_rng_state"]) + if torch.cuda.is_available(): + torch.cuda.set_rng_state(state["cuda_rng_state"]) + + +class set_torch_seed(object): + def __init__(self, seed): + assert isinstance(seed, int) + self.rng_state = get_rng_state() + + torch.manual_seed(seed) + if xm is not None: + xm.set_rng_state(seed) + if torch.cuda.is_available(): + torch.cuda.manual_seed(seed) + + def __enter__(self): + return self + + def __exit__(self, *exc): + set_rng_state(self.rng_state) def parse_alignment(line): @@ -484,8 +652,12 @@ def get_token_to_word_mapping(tokens, exclude_list): def extract_hard_alignment(attn, src_sent, tgt_sent, pad, eos): - tgt_valid = ((tgt_sent != pad) & (tgt_sent != eos)).nonzero().squeeze(dim=-1) - src_invalid = ((src_sent == pad) | (src_sent == eos)).nonzero().squeeze(dim=-1) + tgt_valid = ( + ((tgt_sent != pad) & (tgt_sent != eos)).nonzero(as_tuple=False).squeeze(dim=-1) + ) + src_invalid = ( + ((src_sent == pad) | (src_sent == eos)).nonzero(as_tuple=False).squeeze(dim=-1) + ) src_token_to_word = get_token_to_word_mapping(src_sent, [eos, pad]) tgt_token_to_word = get_token_to_word_mapping(tgt_sent, [eos, pad]) alignment = [] @@ -503,6 +675,18 @@ def extract_hard_alignment(attn, src_sent, tgt_sent, pad, eos): return alignment +def extract_soft_alignment(attn, src_sent, tgt_sent, pad, eos): + tgt_valid = ((tgt_sent != pad)).nonzero(as_tuple=False) + src_valid = ((src_sent != pad)).nonzero(as_tuple=False).squeeze(dim=-1) + alignment = [] + if len(tgt_valid) != 0 and len(src_valid) != 0: + attn_valid = attn[tgt_valid, src_valid] + alignment = [ + ["{:.6f}".format(p) for p in src_probs.tolist()] for src_probs in attn_valid + ] + return alignment + + def new_arange(x, *size): """ Return a Tensor of `size` filled with a range function on the device of x. @@ -513,6 +697,118 @@ def new_arange(x, *size): return torch.arange(size[-1], device=x.device).expand(*size).contiguous() -def get_tpu_device(args): - import torch_xla.core.xla_model as xm +def get_tpu_device(): return xm.xla_device() + + +def tpu_data_loader(itr): + import torch_xla.core.xla_model as xm + import torch_xla.distributed.parallel_loader as pl + from fairseq.data import iterators + + xm.rendezvous("tpu_data_loader") # wait for all workers + xm.mark_step() + device = xm.xla_device() + return iterators.CountingIterator( + pl.ParallelLoader(itr, [device]).per_device_loader(device), + start=getattr(itr, "n", 0), + total=len(itr), + ) + + +def is_xla_tensor(tensor): + return torch.is_tensor(tensor) and tensor.device.type == "xla" + + +def index_put(tensor, indices, value): + if is_xla_tensor(tensor): + for _ in range(indices.dim(), tensor.dim()): + indices = indices.unsqueeze(-1) + if indices.size(-1) < tensor.size(-1): + indices = indices.expand_as(tensor) + tensor = torch.mul(tensor, ~indices) + torch.mul(value, indices) + else: + tensor[indices] = value + return tensor + + +def xla_device_to_cpu(dat): + import torch_xla.core.xla_model as xm + + return xm._maybe_convert_to_cpu(dat) + + +class CudaEnvironment(object): + def __init__(self): + cur_device = torch.cuda.current_device() + prop = torch.cuda.get_device_properties("cuda:{}".format(cur_device)) + self.name = prop.name + self.major = prop.major + self.minor = prop.minor + self.total_memory_in_GB = prop.total_memory / 1024 / 1024 / 1024 + + @staticmethod + def pretty_print_cuda_env_list(cuda_env_list): + """ + Given a list of CudaEnviorments, pretty print them + """ + num_workers = len(cuda_env_list) + center = "CUDA enviroments for all {} workers".format(num_workers) + banner_len = 40 - len(center) // 2 + first_line = "*" * banner_len + center + "*" * banner_len + logger.info(first_line) + for r, env in enumerate(cuda_env_list): + logger.info( + "rank {:3d}: ".format(r) + + "capabilities = {:2d}.{:<2d} ; ".format(env.major, env.minor) + + "total memory = {:.3f} GB ; ".format(env.total_memory_in_GB) + + "name = {:40s}".format(env.name) + ) + logger.info(first_line) + + +def csv_str_list(x): + return x.split(",") + + +def eval_str_list(x, type=float): + if x is None: + return None + if isinstance(x, str): + x = eval(x) + try: + return list(map(type, x)) + except TypeError: + return [type(x)] + + +def eval_str_dict(x, type=dict): + if x is None: + return None + if isinstance(x, str): + x = eval(x) + return x + + +def eval_bool(x, default=False): + if x is None: + return default + try: + return bool(eval(x)) + except TypeError: + return default + + +def reset_logging(): + root = logging.getLogger() + for handler in root.handlers: + root.removeHandler(handler) + root.setLevel(os.environ.get("LOGLEVEL", "INFO").upper()) + handler = logging.StreamHandler(sys.stdout) + handler.setFormatter( + logging.Formatter( + fmt="%(asctime)s | %(levelname)s | %(name)s | %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", + ) + ) + root.addHandler(handler) diff --git a/fairseq/version.txt b/fairseq/version.txt new file mode 100644 index 0000000000..41432f00d9 --- /dev/null +++ b/fairseq/version.txt @@ -0,0 +1 @@ +1.0.0a0 diff --git a/fairseq_cli/eval_lm.py b/fairseq_cli/eval_lm.py index be529253a4..ab6e77029e 100644 --- a/fairseq_cli/eval_lm.py +++ b/fairseq_cli/eval_lm.py @@ -11,145 +11,92 @@ import logging import math import os +import sys +from argparse import Namespace +from typing import Iterable, List, Optional import torch - -from fairseq import checkpoint_utils, options, tasks, utils -from fairseq.data import LMContextWindowDataset +import fairseq +from fairseq import checkpoint_utils, distributed_utils, options, tasks, utils +from fairseq.dataclass.utils import convert_namespace_to_omegaconf from fairseq.logging import progress_bar -from fairseq.logging.meters import StopwatchMeter, TimeMeter +from fairseq.logging.meters import StopwatchMeter from fairseq.sequence_scorer import SequenceScorer -from fairseq.options import add_distributed_training_args -from fairseq import distributed_utils +from omegaconf import DictConfig logging.basicConfig( - format='%(asctime)s | %(levelname)s | %(name)s | %(message)s', - datefmt='%Y-%m-%d %H:%M:%S', - level=logging.INFO, + format="%(asctime)s | %(levelname)s | %(name)s | %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", + level=os.environ.get("LOGLEVEL", "INFO").upper(), + stream=sys.stdout, ) -logger = logging.getLogger('fairseq_cli.eval_lm') - - -class WordStat(object): - def __init__(self, word, is_bpe): - self.word = word - self.is_bpe = is_bpe - self.log_prob = 0 - self.next_word_prob = 0 - self.count = 0 - self.missing_next_words = 0 - - def add(self, log_prob, next_word_prob): - """ increments counters for the sum of log probs of current word and next - word (given context ending at current word). Since the next word might be at the end of the example, - or it might be not counted because it is not an ending subword unit, - also keeps track of how many of those we have seen """ - if next_word_prob is not None: - self.next_word_prob += next_word_prob - else: - self.missing_next_words += 1 - self.log_prob += log_prob - self.count += 1 - - def __str__(self): - return '{}\t{}\t{}\t{}\t{}\t{}'.format(self.word, self.count, self.log_prob, self.is_bpe, - self.next_word_prob, self.count - self.missing_next_words) - - -def main(parsed_args, **unused_kwargs): - assert parsed_args.path is not None, '--path required for evaluation!' - - if torch.cuda.is_available() and not parsed_args.cpu: - torch.cuda.set_device(parsed_args.device_id) - - utils.import_user_module(parsed_args) - - logger.info(parsed_args) - - use_cuda = torch.cuda.is_available() and not parsed_args.cpu - - task = tasks.setup_task(parsed_args) - - # Load ensemble - logger.info('loading model(s) from {}'.format(parsed_args.path)) - models, args = checkpoint_utils.load_model_ensemble( - parsed_args.path.split(os.pathsep), - arg_overrides=eval(parsed_args.model_overrides), - task=task, - suffix=getattr(parsed_args, "checkpoint_suffix", ""), - ) - - for arg in vars(parsed_args).keys(): - if arg not in { - 'self_target', 'future_target', 'past_target', 'tokens_per_sample', - 'output_size_dictionary', 'add_bos_token', - }: - setattr(args, arg, getattr(parsed_args, arg)) - - # reduce tokens per sample by the required context window size - args.tokens_per_sample -= args.context_window - task = tasks.setup_task(args) - - # Load dataset splits - task.load_dataset(args.gen_subset) - dataset = task.dataset(args.gen_subset) - if args.context_window > 0: - dataset = LMContextWindowDataset( - dataset=dataset, - tokens_per_sample=args.tokens_per_sample, - context_window=args.context_window, - pad_idx=task.source_dictionary.pad(), - ) - logger.info('{} {} {} examples'.format(args.data, args.gen_subset, len(dataset))) - - # Optimize ensemble for generation and set the source and dest dicts on the model (required by scorer) - for model in models: - model.make_generation_fast_() - if args.fp16: - model.half() - if use_cuda: - model.cuda() - - assert len(models) > 0 - - logger.info('num. model params: {}'.format(sum(p.numel() for p in models[0].parameters()))) - - itr = task.get_batch_iterator( - dataset=dataset, - max_tokens=args.max_tokens or 36000, - max_sentences=args.max_sentences, - max_positions=utils.resolve_max_positions(*[ - model.max_positions() for model in models - ]), - ignore_invalid_inputs=True, - num_shards=args.num_shards, - shard_id=args.shard_id, - num_workers=args.num_workers, - ).next_epoch_itr(shuffle=False) - progress = progress_bar.progress_bar( - itr, - log_format=args.log_format, - log_interval=args.log_interval, - default_log_format=('tqdm' if not args.no_progress_bar else 'none'), - ) +logger = logging.getLogger("fairseq_cli.eval_lm") + + +def eval_lm( + models: List[fairseq.models.FairseqModel], + source_dictionary: fairseq.data.Dictionary, + batch_iterator: Iterable, + post_process: Optional[str] = None, + output_word_probs: bool = False, + output_word_stats: bool = False, + target_dictionary: Optional[fairseq.data.Dictionary] = None, + softmax_batch: int = 0, + remove_bos_token: bool = False, + device: Optional[torch.device] = None, +): + """ + Args: + models (List[~fairseq.models.FairseqModel]): list of models to + evaluate. Models are essentially `nn.Module` instances, but + must be compatible with fairseq's `SequenceScorer`. + source_dictionary (~fairseq.data.Dictionary): dictionary for + applying any relevant post processing or outputing word + probs/stats. + batch_iterator (Iterable): yield batches of data + post_process (Optional[str]): post-process text by removing BPE, + letter segmentation, etc. Valid options can be found in + fairseq.data.utils.post_process, although not all options + are implemented here. + output_word_probs (Optional[bool]): output words and their + predicted log probabilities + output_word_stats (Optional[bool]): output word statistics such + as word count and average probability + target_dictionary (Optional[~fairseq.data.Dictionary]): output + dictionary (defaults to *source_dictionary*) + softmax_batch (Optional[bool]): if BxT is more than this, will + batch the softmax over vocab to this amount of tokens, in + order to fit into GPU memory + remove_bos_token (Optional[bool]): if True, confirm that the + first token is the beginning-of-sentence symbol (according + to the relevant dictionary) and remove it from the output + device (Optional[torch.device]): device to use for evaluation + (defaults to device of first model parameter) + """ + if target_dictionary is None: + target_dictionary = source_dictionary + if device is None: + device = next(models[0].parameters()).device gen_timer = StopwatchMeter() - scorer = SequenceScorer(task.target_dictionary, args.softmax_batch) + scorer = SequenceScorer(target_dictionary, softmax_batch) - score_sum = 0. + score_sum = 0.0 count = 0 - if args.remove_bpe is not None: - if args.remove_bpe == 'sentencepiece': - raise NotImplementedError - else: - bpe_cont = args.remove_bpe.rstrip() + if post_process is not None: + if post_process in {"subword_nmt", "@@ "}: + bpe_cont = post_process.rstrip() bpe_toks = { i - for i in range(len(task.source_dictionary)) - if task.source_dictionary[i].endswith(bpe_cont) + for i in range(len(source_dictionary)) + if source_dictionary[i].endswith(bpe_cont) } + else: + raise NotImplementedError( + "--post-process={post_process} is not implemented" + ) bpe_len = len(bpe_cont) else: bpe_toks = None @@ -157,28 +104,26 @@ def main(parsed_args, **unused_kwargs): word_stats = dict() - wps_meter = TimeMeter() - - for sample in progress: - if 'net_input' not in sample: + for sample in batch_iterator: + if "net_input" not in sample: continue - sample = utils.move_to_cuda(sample) if use_cuda else sample + sample = utils.move_to_cuda(sample, device=device) gen_timer.start() hypos = scorer.generate(models, sample) - gen_timer.stop(sample['ntokens']) + gen_timer.stop(sample["ntokens"]) for i, hypos_i in enumerate(hypos): hypo = hypos_i[0] - sample_id = sample['id'][i] + sample_id = sample["id"][i] - tokens = hypo['tokens'] + tokens = hypo["tokens"] tgt_len = tokens.numel() - pos_scores = hypo['positional_scores'].float() + pos_scores = hypo["positional_scores"].float() - if args.add_bos_token: - assert hypo['tokens'][0].item() == task.target_dictionary.bos() + if remove_bos_token: + assert hypo["tokens"][0].item() == target_dictionary.bos() tokens = tokens[1:] pos_scores = pos_scores[1:] @@ -190,23 +135,23 @@ def main(parsed_args, **unused_kwargs): pos_scores[i + 1] += pos_scores[i] pos_scores[i] = 0 - inf_scores = pos_scores.eq(float('inf')) | pos_scores.eq(float('-inf')) + inf_scores = pos_scores.eq(float("inf")) | pos_scores.eq(float("-inf")) if inf_scores.any(): logger.info( - 'skipping tokens with inf scores:', - task.target_dictionary.string(tokens[inf_scores.nonzero()]) + "skipping tokens with inf scores:", + target_dictionary.string(tokens[inf_scores.nonzero()]), ) pos_scores = pos_scores[(~inf_scores).nonzero()] score_sum += pos_scores.sum().cpu() count += pos_scores.numel() - skipped_toks - if args.output_word_probs or args.output_word_stats: - w = '' + if output_word_probs or output_word_stats: + w = "" word_prob = [] is_bpe = False for i in range(len(tokens)): w_ind = tokens[i].item() - w += task.source_dictionary[w_ind] + w += source_dictionary[w_ind] if bpe_toks is not None and w_ind in bpe_toks: w = w[:-bpe_len] is_bpe = True @@ -221,36 +166,182 @@ def main(parsed_args, **unused_kwargs): break ind += 1 - word_stats.setdefault(w, WordStat(w, is_bpe)).add(pos_scores[i].item(), next_prob) + word_stats.setdefault(w, WordStat(w, is_bpe)).add( + pos_scores[i].item(), next_prob + ) is_bpe = False - w = '' - if args.output_word_probs: + w = "" + if output_word_probs: logger.info( - str(int(sample_id)) + " " - + ('\t'.join('{} [{:2f}]'.format(x[0], x[1]) for x in word_prob)) + str(int(sample_id)) + + " " + + ( + "\t".join( + "{} [{:2f}]".format(x[0], x[1]) for x in word_prob + ) + ) ) - wps_meter.update(sample['ntokens']) - progress.log({'wps': round(wps_meter.avg)}) - - avg_nll_loss = -score_sum / count / math.log(2) # convert to base 2 - logger.info('Evaluated {} tokens in {:.1f}s ({:.2f} tokens/s)'.format( - gen_timer.n, gen_timer.sum, 1. / gen_timer.avg - )) - logger.info('Loss (base 2): {:.4f}, Perplexity: {:.2f}'.format( - avg_nll_loss, 2**avg_nll_loss - )) + avg_nll_loss = ( + -score_sum / count / math.log(2) if count > 0 else 0 + ) # convert to base 2 + logger.info( + "Evaluated {:,} tokens in {:.1f}s ({:.2f} tokens/s)".format( + gen_timer.n, gen_timer.sum, 1.0 / gen_timer.avg if gen_timer.avg > 0 else 0 + ) + ) - if args.output_word_stats: + if output_word_stats: for ws in sorted(word_stats.values(), key=lambda x: x.count, reverse=True): logger.info(ws) + return { + "loss": avg_nll_loss, + "perplexity": 2 ** avg_nll_loss, + } + + +class WordStat(object): + def __init__(self, word, is_bpe): + self.word = word + self.is_bpe = is_bpe + self.log_prob = 0 + self.next_word_prob = 0 + self.count = 0 + self.missing_next_words = 0 + + def add(self, log_prob, next_word_prob): + """increments counters for the sum of log probs of current word and next + word (given context ending at current word). Since the next word might be at the end of the example, + or it might be not counted because it is not an ending subword unit, + also keeps track of how many of those we have seen""" + if next_word_prob is not None: + self.next_word_prob += next_word_prob + else: + self.missing_next_words += 1 + self.log_prob += log_prob + self.count += 1 + + def __str__(self): + return "{}\t{}\t{}\t{}\t{}\t{}".format( + self.word, + self.count, + self.log_prob, + self.is_bpe, + self.next_word_prob, + self.count - self.missing_next_words, + ) + + +def main(cfg: DictConfig, **unused_kwargs): + if isinstance(cfg, Namespace): + cfg = convert_namespace_to_omegaconf(cfg) + + utils.import_user_module(cfg.common) + + logger.info(cfg) + + if cfg.eval_lm.context_window > 0: + # reduce tokens per sample by the required context window size + cfg.task.tokens_per_sample -= cfg.eval_lm.context_window + + # Initialize the task using the current *cfg* + task = tasks.setup_task(cfg.task) + + # Load ensemble + logger.info("loading model(s) from {}".format(cfg.common_eval.path)) + models, model_args, task = checkpoint_utils.load_model_ensemble_and_task( + [cfg.common_eval.path], + arg_overrides=eval(cfg.common_eval.model_overrides), + suffix=cfg.checkpoint.checkpoint_suffix, + strict=(cfg.checkpoint.checkpoint_shard_count == 1), + num_shards=cfg.checkpoint.checkpoint_shard_count, + task=task, + ) + + use_fp16 = cfg.common.fp16 + use_cuda = torch.cuda.is_available() and not cfg.common.cpu + if use_cuda: + torch.cuda.set_device(cfg.distributed_training.device_id) + + # Optimize ensemble for generation and set the source and dest dicts on the model + # (required by scorer) + for model in models: + if use_fp16: + model.half() + if use_cuda and not cfg.distributed_training.pipeline_model_parallel: + model.cuda() + model.prepare_for_inference_(cfg) + + assert len(models) > 0 + + logger.info( + "num. model params: {:,}".format(sum(p.numel() for p in models[0].parameters())) + ) + + # Load dataset splits + task.load_dataset(cfg.dataset.gen_subset) + dataset = task.dataset(cfg.dataset.gen_subset) + logger.info( + "{} {} {:,} examples".format( + cfg.task.data, cfg.dataset.gen_subset, len(dataset) + ) + ) + + itr = task.eval_lm_dataloader( + dataset=dataset, + max_tokens=cfg.dataset.max_tokens or 36000, + batch_size=cfg.dataset.batch_size, + max_positions=utils.resolve_max_positions( + *[model.max_positions() for model in models] + ), + num_shards=max( + cfg.dataset.num_shards, + cfg.distributed_training.distributed_world_size, + ), + shard_id=max( + cfg.dataset.shard_id, + cfg.distributed_training.distributed_rank, + ), + num_workers=cfg.dataset.num_workers, + data_buffer_size=cfg.dataset.data_buffer_size, + context_window=cfg.eval_lm.context_window, + ) + + itr = progress_bar.progress_bar( + itr, + log_format=cfg.common.log_format, + log_interval=cfg.common.log_interval, + default_log_format=("tqdm" if not cfg.common.no_progress_bar else "simple"), + ) + + results = eval_lm( + models=models, + source_dictionary=task.source_dictionary, + batch_iterator=itr, + post_process=cfg.common_eval.post_process, + output_word_probs=cfg.eval_lm.output_word_probs, + output_word_stats=cfg.eval_lm.output_word_stats, + target_dictionary=task.target_dictionary, + softmax_batch=cfg.eval_lm.softmax_batch, + remove_bos_token=getattr(cfg.task, "add_bos_token", False), + ) + + logger.info( + "Loss (base 2): {:.4f}, Perplexity: {:.2f}".format( + results["loss"], results["perplexity"] + ) + ) + + return results + def cli_main(): parser = options.get_eval_lm_parser() args = options.parse_args_and_arch(parser) - distributed_utils.call_main(args, main) + + distributed_utils.call_main(convert_namespace_to_omegaconf(args), main) -if __name__ == '__main__': +if __name__ == "__main__": cli_main() diff --git a/fairseq_cli/generate.py b/fairseq_cli/generate.py index 70fed512c8..7e887e8864 100644 --- a/fairseq_cli/generate.py +++ b/fairseq_cli/generate.py @@ -7,115 +7,170 @@ Translate pre-processed data with a trained model. """ +import ast import logging import math import os import sys +from argparse import Namespace +from itertools import chain +import numpy as np import torch - -from fairseq import bleu, checkpoint_utils, options, tasks, utils +from fairseq import checkpoint_utils, options, scoring, tasks, utils +from fairseq.dataclass.utils import convert_namespace_to_omegaconf from fairseq.logging import progress_bar from fairseq.logging.meters import StopwatchMeter, TimeMeter -from fairseq.data import encoders +from omegaconf import DictConfig + + +def main(cfg: DictConfig): + if isinstance(cfg, Namespace): + cfg = convert_namespace_to_omegaconf(cfg) -def main(args): - assert args.path is not None, '--path required for generation!' - assert not args.sampling or args.nbest == args.beam, \ - '--sampling requires --nbest to be equal to --beam' - assert args.replace_unk is None or args.dataset_impl == 'raw', \ - '--replace-unk requires a raw text dataset (--dataset-impl=raw)' + assert cfg.common_eval.path is not None, "--path required for generation!" + assert ( + not cfg.generation.sampling or cfg.generation.nbest == cfg.generation.beam + ), "--sampling requires --nbest to be equal to --beam" + assert ( + cfg.generation.replace_unk is None or cfg.dataset.dataset_impl == "raw" + ), "--replace-unk requires a raw text dataset (--dataset-impl=raw)" - if args.results_path is not None: - os.makedirs(args.results_path, exist_ok=True) - output_path = os.path.join(args.results_path, 'generate-{}.txt'.format(args.gen_subset)) - with open(output_path, 'w', buffering=1) as h: - return _main(args, h) + if cfg.common_eval.results_path is not None: + os.makedirs(cfg.common_eval.results_path, exist_ok=True) + output_path = os.path.join( + cfg.common_eval.results_path, + "generate-{}.txt".format(cfg.dataset.gen_subset), + ) + with open(output_path, "w", buffering=1, encoding="utf-8") as h: + return _main(cfg, h) else: - return _main(args, sys.stdout) + return _main(cfg, sys.stdout) + +def get_symbols_to_strip_from_output(generator): + if hasattr(generator, "symbols_to_strip_from_output"): + return generator.symbols_to_strip_from_output + else: + return {generator.eos} -def _main(args, output_file): + +def _main(cfg: DictConfig, output_file): logging.basicConfig( - format='%(asctime)s | %(levelname)s | %(name)s | %(message)s', - datefmt='%Y-%m-%d %H:%M:%S', - level=logging.INFO, + format="%(asctime)s | %(levelname)s | %(name)s | %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", + level=os.environ.get("LOGLEVEL", "INFO").upper(), stream=output_file, ) - logger = logging.getLogger('fairseq_cli.generate') + logger = logging.getLogger("fairseq_cli.generate") + + utils.import_user_module(cfg.common) - utils.import_user_module(args) + if cfg.dataset.max_tokens is None and cfg.dataset.batch_size is None: + cfg.dataset.max_tokens = 12000 + logger.info(cfg) - if args.max_tokens is None and args.max_sentences is None: - args.max_tokens = 12000 - logger.info(args) + # Fix seed for stochastic decoding + if cfg.common.seed is not None and not cfg.generation.no_seed_provided: + np.random.seed(cfg.common.seed) + utils.set_torch_seed(cfg.common.seed) - use_cuda = torch.cuda.is_available() and not args.cpu + use_cuda = torch.cuda.is_available() and not cfg.common.cpu # Load dataset splits - task = tasks.setup_task(args) - task.load_dataset(args.gen_subset) + task = tasks.setup_task(cfg.task) + # Set dictionaries try: - src_dict = getattr(task, 'source_dictionary', None) + src_dict = getattr(task, "source_dictionary", None) except NotImplementedError: src_dict = None tgt_dict = task.target_dictionary + overrides = ast.literal_eval(cfg.common_eval.model_overrides) + # Load ensemble - logger.info('loading model(s) from {}'.format(args.path)) - models, _model_args = checkpoint_utils.load_model_ensemble( - utils.split_paths(args.path), - arg_overrides=eval(args.model_overrides), + logger.info("loading model(s) from {}".format(cfg.common_eval.path)) + models, saved_cfg = checkpoint_utils.load_model_ensemble( + utils.split_paths(cfg.common_eval.path), + arg_overrides=overrides, task=task, + suffix=cfg.checkpoint.checkpoint_suffix, + strict=(cfg.checkpoint.checkpoint_shard_count == 1), + num_shards=cfg.checkpoint.checkpoint_shard_count, ) + # loading the dataset should happen after the checkpoint has been loaded so we can give it the saved task config + task.load_dataset(cfg.dataset.gen_subset, task_cfg=saved_cfg.task) + + if cfg.generation.lm_path is not None: + overrides["data"] = cfg.task.data + + try: + lms, _ = checkpoint_utils.load_model_ensemble( + [cfg.generation.lm_path], arg_overrides=overrides, task=None + ) + except: + logger.warning( + f"Failed to load language model! Please make sure that the language model dict is the same " + f"as target dict and is located in the data dir ({cfg.task.data})" + ) + raise + + assert len(lms) == 1 + else: + lms = [None] + # Optimize ensemble for generation - for model in models: - model.make_generation_fast_( - beamable_mm_beam_size=None if args.no_beamable_mm else args.beam, - need_attn=args.print_alignment, - ) - if args.fp16: + for model in chain(models, lms): + if model is None: + continue + if cfg.common.fp16: model.half() - if use_cuda: + if use_cuda and not cfg.distributed_training.pipeline_model_parallel: model.cuda() + model.prepare_for_inference_(cfg) # Load alignment dictionary for unknown word replacement # (None if no unknown word replacement, empty if no path to align dictionary) - align_dict = utils.load_align_dict(args.replace_unk) + align_dict = utils.load_align_dict(cfg.generation.replace_unk) # Load dataset (possibly sharded) itr = task.get_batch_iterator( - dataset=task.dataset(args.gen_subset), - max_tokens=args.max_tokens, - max_sentences=args.max_sentences, + dataset=task.dataset(cfg.dataset.gen_subset), + max_tokens=cfg.dataset.max_tokens, + max_sentences=cfg.dataset.batch_size, max_positions=utils.resolve_max_positions( - task.max_positions(), - *[model.max_positions() for model in models] + task.max_positions(), *[m.max_positions() for m in models] ), - ignore_invalid_inputs=args.skip_invalid_size_inputs_valid_test, - required_batch_size_multiple=args.required_batch_size_multiple, - num_shards=args.num_shards, - shard_id=args.shard_id, - num_workers=args.num_workers, + ignore_invalid_inputs=cfg.dataset.skip_invalid_size_inputs_valid_test, + required_batch_size_multiple=cfg.dataset.required_batch_size_multiple, + seed=cfg.common.seed, + num_shards=cfg.distributed_training.distributed_world_size, + shard_id=cfg.distributed_training.distributed_rank, + num_workers=cfg.dataset.num_workers, + data_buffer_size=cfg.dataset.data_buffer_size, ).next_epoch_itr(shuffle=False) progress = progress_bar.progress_bar( itr, - log_format=args.log_format, - log_interval=args.log_interval, - default_log_format=('tqdm' if not args.no_progress_bar else 'none'), + log_format=cfg.common.log_format, + log_interval=cfg.common.log_interval, + default_log_format=("tqdm" if not cfg.common.no_progress_bar else "simple"), ) # Initialize generator gen_timer = StopwatchMeter() - generator = task.build_generator(models, args) + + extra_gen_cls_kwargs = {"lm_model": lms[0], "lm_weight": cfg.generation.lm_weight} + generator = task.build_generator( + models, cfg.generation, extra_gen_cls_kwargs=extra_gen_cls_kwargs + ) # Handle tokenization and BPE - tokenizer = encoders.build_tokenizer(args) - bpe = encoders.build_bpe(args) + tokenizer = task.build_tokenizer(cfg.tokenizer) + bpe = task.build_bpe(cfg.bpe) def decode_fn(x): if bpe is not None: @@ -124,150 +179,236 @@ def decode_fn(x): x = tokenizer.decode(x) return x - # Generate and compute BLEU score - if args.sacrebleu: - scorer = bleu.SacrebleuScorer() - else: - scorer = bleu.Scorer(tgt_dict.pad(), tgt_dict.eos(), tgt_dict.unk()) + scorer = scoring.build_scorer(cfg.scoring, tgt_dict) + num_sentences = 0 has_target = True wps_meter = TimeMeter() for sample in progress: sample = utils.move_to_cuda(sample) if use_cuda else sample - if 'net_input' not in sample: + if "net_input" not in sample: continue prefix_tokens = None - if args.prefix_size > 0: - prefix_tokens = sample['target'][:, :args.prefix_size] + if cfg.generation.prefix_size > 0: + prefix_tokens = sample["target"][:, : cfg.generation.prefix_size] + + constraints = None + if "constraints" in sample: + constraints = sample["constraints"] gen_timer.start() - hypos = task.inference_step(generator, models, sample, prefix_tokens) - num_generated_tokens = sum(len(h[0]['tokens']) for h in hypos) + hypos = task.inference_step( + generator, + models, + sample, + prefix_tokens=prefix_tokens, + constraints=constraints, + ) + num_generated_tokens = sum(len(h[0]["tokens"]) for h in hypos) gen_timer.stop(num_generated_tokens) - for i, sample_id in enumerate(sample['id'].tolist()): - has_target = sample['target'] is not None + for i, sample_id in enumerate(sample["id"].tolist()): + has_target = sample["target"] is not None # Remove padding - src_tokens = utils.strip_pad(sample['net_input']['src_tokens'][i, :], tgt_dict.pad()) + if "src_tokens" in sample["net_input"]: + src_tokens = utils.strip_pad( + sample["net_input"]["src_tokens"][i, :], tgt_dict.pad() + ) + else: + src_tokens = None + target_tokens = None if has_target: - target_tokens = utils.strip_pad(sample['target'][i, :], tgt_dict.pad()).int().cpu() + target_tokens = ( + utils.strip_pad(sample["target"][i, :], tgt_dict.pad()).int().cpu() + ) # Either retrieve the original sentences or regenerate them from tokens. if align_dict is not None: - src_str = task.dataset(args.gen_subset).src.get_original_text(sample_id) - target_str = task.dataset(args.gen_subset).tgt.get_original_text(sample_id) + src_str = task.dataset(cfg.dataset.gen_subset).src.get_original_text( + sample_id + ) + target_str = task.dataset(cfg.dataset.gen_subset).tgt.get_original_text( + sample_id + ) else: if src_dict is not None: - src_str = src_dict.string(src_tokens, args.remove_bpe) + src_str = src_dict.string(src_tokens, cfg.common_eval.post_process) else: src_str = "" if has_target: target_str = tgt_dict.string( target_tokens, - args.remove_bpe, + cfg.common_eval.post_process, escape_unk=True, - extra_symbols_to_ignore={ - generator.eos, - } + extra_symbols_to_ignore=get_symbols_to_strip_from_output( + generator + ), ) src_str = decode_fn(src_str) if has_target: target_str = decode_fn(target_str) - if not args.quiet: + if not cfg.common_eval.quiet: if src_dict is not None: - print('S-{}\t{}'.format(sample_id, src_str), file=output_file) + print("S-{}\t{}".format(sample_id, src_str), file=output_file) if has_target: - print('T-{}\t{}'.format(sample_id, target_str), file=output_file) + print("T-{}\t{}".format(sample_id, target_str), file=output_file) # Process top predictions - for j, hypo in enumerate(hypos[i][:args.nbest]): + for j, hypo in enumerate(hypos[i][: cfg.generation.nbest]): hypo_tokens, hypo_str, alignment = utils.post_process_prediction( - hypo_tokens=hypo['tokens'].int().cpu(), + hypo_tokens=hypo["tokens"].int().cpu(), src_str=src_str, - alignment=hypo['alignment'], + alignment=hypo["alignment"], align_dict=align_dict, tgt_dict=tgt_dict, - remove_bpe=args.remove_bpe, - extra_symbols_to_ignore={ - generator.eos, - } + remove_bpe=cfg.common_eval.post_process, + extra_symbols_to_ignore=get_symbols_to_strip_from_output(generator), ) detok_hypo_str = decode_fn(hypo_str) - if not args.quiet: - score = hypo['score'] / math.log(2) # convert to base 2 + if not cfg.common_eval.quiet: + score = hypo["score"] / math.log(2) # convert to base 2 # original hypothesis (after tokenization and BPE) - print('H-{}\t{}\t{}'.format(sample_id, score, hypo_str), file=output_file) + print( + "H-{}\t{}\t{}".format(sample_id, score, hypo_str), + file=output_file, + ) # detokenized hypothesis - print('D-{}\t{}\t{}'.format(sample_id, score, detok_hypo_str), file=output_file) - print('P-{}\t{}'.format( - sample_id, - ' '.join(map( - lambda x: '{:.4f}'.format(x), - # convert from base e to base 2 - hypo['positional_scores'].div_(math.log(2)).tolist(), - )) - ), file=output_file) - - if args.print_alignment: - print('A-{}\t{}'.format( + print( + "D-{}\t{}\t{}".format(sample_id, score, detok_hypo_str), + file=output_file, + ) + print( + "P-{}\t{}".format( sample_id, - ' '.join(['{}-{}'.format(src_idx, tgt_idx) for src_idx, tgt_idx in alignment]) - ), file=output_file) - - if args.print_step: - print('I-{}\t{}'.format(sample_id, hypo['steps']), file=output_file) + " ".join( + map( + lambda x: "{:.4f}".format(x), + # convert from base e to base 2 + hypo["positional_scores"] + .div_(math.log(2)) + .tolist(), + ) + ), + ), + file=output_file, + ) - if getattr(args, 'retain_iter_history', False): - for step, h in enumerate(hypo['history']): + if cfg.generation.print_alignment == "hard": + print( + "A-{}\t{}".format( + sample_id, + " ".join( + [ + "{}-{}".format(src_idx, tgt_idx) + for src_idx, tgt_idx in alignment + ] + ), + ), + file=output_file, + ) + if cfg.generation.print_alignment == "soft": + print( + "A-{}\t{}".format( + sample_id, + " ".join( + [ + ",".join(src_probs) + for src_probs in alignment + ] + ), + ), + file=output_file, + ) + + if cfg.generation.print_step: + print( + "I-{}\t{}".format(sample_id, hypo["steps"]), + file=output_file, + ) + + if cfg.generation.retain_iter_history: + for step, h in enumerate(hypo["history"]): _, h_str, _ = utils.post_process_prediction( - hypo_tokens=h['tokens'].int().cpu(), + hypo_tokens=h["tokens"].int().cpu(), src_str=src_str, alignment=None, align_dict=None, tgt_dict=tgt_dict, remove_bpe=None, ) - print('E-{}_{}\t{}'.format(sample_id, step, h_str), file=output_file) + print( + "E-{}_{}\t{}".format(sample_id, step, h_str), + file=output_file, + ) # Score only the top hypothesis if has_target and j == 0: - if align_dict is not None or args.remove_bpe is not None: + if align_dict is not None or cfg.common_eval.post_process is not None: # Convert back to tokens for evaluation with unk replacement and/or without BPE - target_tokens = tgt_dict.encode_line(target_str, add_if_not_exist=True) - hypo_tokens = tgt_dict.encode_line(detok_hypo_str, add_if_not_exist=True) - if hasattr(scorer, 'add_string'): + target_tokens = tgt_dict.encode_line( + target_str, add_if_not_exist=True + ) + hypo_tokens = tgt_dict.encode_line( + detok_hypo_str, add_if_not_exist=True + ) + if hasattr(scorer, "add_string"): scorer.add_string(target_str, detok_hypo_str) else: scorer.add(target_tokens, hypo_tokens) wps_meter.update(num_generated_tokens) - progress.log({'wps': round(wps_meter.avg)}) - num_sentences += sample['nsentences'] + progress.log({"wps": round(wps_meter.avg)}) + num_sentences += ( + sample["nsentences"] if "nsentences" in sample else sample["id"].numel() + ) - logger.info('NOTE: hypothesis and token scores are output in base 2') - logger.info('Translated {} sentences ({} tokens) in {:.1f}s ({:.2f} sentences/s, {:.2f} tokens/s)'.format( - num_sentences, gen_timer.n, gen_timer.sum, num_sentences / gen_timer.sum, 1. / gen_timer.avg)) + logger.info("NOTE: hypothesis and token scores are output in base 2") + logger.info( + "Translated {:,} sentences ({:,} tokens) in {:.1f}s ({:.2f} sentences/s, {:.2f} tokens/s)".format( + num_sentences, + gen_timer.n, + gen_timer.sum, + num_sentences / gen_timer.sum, + 1.0 / gen_timer.avg, + ) + ) if has_target: - if args.bpe and not args.sacrebleu: - if args.remove_bpe: - logger.warning("BLEU score is being computed by splitting detokenized string on spaces, this is probably not what you want. Use --sacrebleu for standard 13a BLEU tokenization") + if cfg.bpe and not cfg.generation.sacrebleu: + if cfg.common_eval.post_process: + logger.warning( + "BLEU score is being computed by splitting detokenized string on spaces, this is probably not what you want. Use --sacrebleu for standard 13a BLEU tokenization" + ) else: - logger.warning("If you are using BPE on the target side, the BLEU score is computed on BPE tokens, not on proper words. Use --sacrebleu for standard 13a BLEU tokenization") - logger.info('Generate {} with beam={}: {}'.format(args.gen_subset, args.beam, scorer.result_string())) + logger.warning( + "If you are using BPE on the target side, the BLEU score is computed on BPE tokens, not on proper words. Use --sacrebleu for standard 13a BLEU tokenization" + ) + # use print to be consistent with other main outputs: S-, H-, T-, D- and so on + print( + "Generate {} with beam={}: {}".format( + cfg.dataset.gen_subset, cfg.generation.beam, scorer.result_string() + ), + file=output_file, + ) return scorer def cli_main(): parser = options.get_generation_parser() + # TODO: replace this workaround with refactoring of `AudioPretraining` + parser.add_argument( + '--arch', '-a', metavar='ARCH', default="wav2vec2", + help='Model architecture. For constructing tasks that rely on ' + 'model args (e.g. `AudioPretraining`)' + ) args = options.parse_args_and_arch(parser) main(args) -if __name__ == '__main__': +if __name__ == "__main__": cli_main() diff --git a/fairseq_cli/hydra_train.py b/fairseq_cli/hydra_train.py new file mode 100644 index 0000000000..6555ab415e --- /dev/null +++ b/fairseq_cli/hydra_train.py @@ -0,0 +1,88 @@ +#!/usr/bin/env python3 -u +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +import os + +from fairseq.dataclass.initialize import add_defaults, hydra_init +from fairseq_cli.train import main as pre_main +from fairseq import distributed_utils, metrics +from fairseq.dataclass.configs import FairseqConfig +from fairseq.dataclass.utils import omegaconf_no_object_check +from fairseq.utils import reset_logging + +import hydra +from hydra.core.hydra_config import HydraConfig +import torch +from omegaconf import OmegaConf, open_dict + + +logger = logging.getLogger("fairseq_cli.hydra_train") + + +@hydra.main(config_path=os.path.join("..", "fairseq", "config"), config_name="config") +def hydra_main(cfg: FairseqConfig) -> float: + _hydra_main(cfg) + + +def _hydra_main(cfg: FairseqConfig, **kwargs) -> float: + add_defaults(cfg) + + if cfg.common.reset_logging: + reset_logging() # Hydra hijacks logging, fix that + else: + # check if directly called or called through hydra_main + if HydraConfig.initialized(): + with open_dict(cfg): + # make hydra logging work with ddp (see # see https://github.com/facebookresearch/hydra/issues/1126) + cfg.job_logging_cfg = OmegaConf.to_container(HydraConfig.get().job_logging, resolve=True) + + with omegaconf_no_object_check(): + cfg = OmegaConf.create(OmegaConf.to_container(cfg, resolve=True, enum_to_str=True)) + OmegaConf.set_struct(cfg, True) + + try: + if cfg.common.profile: + with torch.cuda.profiler.profile(): + with torch.autograd.profiler.emit_nvtx(): + distributed_utils.call_main(cfg, pre_main, **kwargs) + else: + distributed_utils.call_main(cfg, pre_main, **kwargs) + except BaseException as e: + if not cfg.common.suppress_crashes: + raise + else: + logger.error("Crashed! " + str(e)) + + # get best val and return - useful for sweepers + try: + best_val = metrics.get_smoothed_value( + "valid", cfg.checkpoint.best_checkpoint_metric + ) + except: + best_val = None + + if best_val is None: + best_val = float("inf") + + return best_val + + +def cli_main(): + try: + from hydra._internal.utils import get_args + + cfg_name = get_args().config_name or "config" + except: + logger.warning("Failed to get config name from hydra args") + cfg_name = "config" + + hydra_init(cfg_name) + hydra_main() + + +if __name__ == "__main__": + cli_main() diff --git a/fairseq_cli/interactive.py b/fairseq_cli/interactive.py index cfcd2c535b..cadef2821a 100644 --- a/fairseq_cli/interactive.py +++ b/fairseq_cli/interactive.py @@ -7,30 +7,36 @@ Translate raw text with a trained model. Batches data on-the-fly. """ -from collections import namedtuple +import ast import fileinput import logging import math -import sys import os +import sys +import time +from argparse import Namespace +from collections import namedtuple +import numpy as np import torch - -from fairseq import checkpoint_utils, options, tasks, utils -from fairseq.data import encoders +from fairseq import checkpoint_utils, distributed_utils, options, tasks, utils +from fairseq.dataclass.configs import FairseqConfig +from fairseq.dataclass.utils import convert_namespace_to_omegaconf +from fairseq.token_generation_constraints import pack_constraints, unpack_constraints +from fairseq_cli.generate import get_symbols_to_strip_from_output logging.basicConfig( - format='%(asctime)s | %(levelname)s | %(name)s | %(message)s', - datefmt='%Y-%m-%d %H:%M:%S', - level=logging.INFO, + format="%(asctime)s | %(levelname)s | %(name)s | %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", + level=os.environ.get("LOGLEVEL", "INFO").upper(), stream=sys.stdout, ) -logger = logging.getLogger('fairseq_cli.interactive') +logger = logging.getLogger("fairseq_cli.interactive") -Batch = namedtuple('Batch', 'ids src_tokens src_lengths') -Translation = namedtuple('Translation', 'src_str hypos pos_scores alignments') +Batch = namedtuple("Batch", "ids src_tokens src_lengths constraints") +Translation = namedtuple("Translation", "src_str hypos pos_scores alignments") def buffered_read(input, buffer_size): @@ -46,54 +52,103 @@ def buffered_read(input, buffer_size): yield buffer -def make_batches(lines, args, task, max_positions, encode_fn): - tokens = [ - task.source_dictionary.encode_line( - encode_fn(src_str), add_if_not_exist=False - ).long() - for src_str in lines - ] - lengths = [t.numel() for t in tokens] +def make_batches(lines, cfg, task, max_positions, encode_fn): + def encode_fn_target(x): + return encode_fn(x) + + if cfg.generation.constraints: + # Strip (tab-delimited) contraints, if present, from input lines, + # store them in batch_constraints + batch_constraints = [list() for _ in lines] + for i, line in enumerate(lines): + if "\t" in line: + lines[i], *batch_constraints[i] = line.split("\t") + + # Convert each List[str] to List[Tensor] + for i, constraint_list in enumerate(batch_constraints): + batch_constraints[i] = [ + task.target_dictionary.encode_line( + encode_fn_target(constraint), + append_eos=False, + add_if_not_exist=False, + ) + for constraint in constraint_list + ] + + if cfg.generation.constraints: + constraints_tensor = pack_constraints(batch_constraints) + else: + constraints_tensor = None + + tokens, lengths = task.get_interactive_tokens_and_lengths(lines, encode_fn) + itr = task.get_batch_iterator( - dataset=task.build_dataset_for_inference(tokens, lengths), - max_tokens=args.max_tokens, - max_sentences=args.max_sentences, + dataset=task.build_dataset_for_inference( + tokens, lengths, constraints=constraints_tensor + ), + max_tokens=cfg.dataset.max_tokens, + max_sentences=cfg.dataset.batch_size, max_positions=max_positions, - ignore_invalid_inputs=args.skip_invalid_size_inputs_valid_test + ignore_invalid_inputs=cfg.dataset.skip_invalid_size_inputs_valid_test, ).next_epoch_itr(shuffle=False) for batch in itr: + ids = batch["id"] + src_tokens = batch["net_input"]["src_tokens"] + src_lengths = batch["net_input"]["src_lengths"] + constraints = batch.get("constraints", None) + yield Batch( - ids=batch['id'], - src_tokens=batch['net_input']['src_tokens'], src_lengths=batch['net_input']['src_lengths'], + ids=ids, + src_tokens=src_tokens, + src_lengths=src_lengths, + constraints=constraints, ) -def main(args): - utils.import_user_module(args) +def main(cfg: FairseqConfig): + if isinstance(cfg, Namespace): + cfg = convert_namespace_to_omegaconf(cfg) + + start_time = time.time() + total_translate_time = 0 - if args.buffer_size < 1: - args.buffer_size = 1 - if args.max_tokens is None and args.max_sentences is None: - args.max_sentences = 1 + utils.import_user_module(cfg.common) - assert not args.sampling or args.nbest == args.beam, \ - '--sampling requires --nbest to be equal to --beam' - assert not args.max_sentences or args.max_sentences <= args.buffer_size, \ - '--max-sentences/--batch-size cannot be larger than --buffer-size' + if cfg.interactive.buffer_size < 1: + cfg.interactive.buffer_size = 1 + if cfg.dataset.max_tokens is None and cfg.dataset.batch_size is None: + cfg.dataset.batch_size = 1 - logger.info(args) + assert ( + not cfg.generation.sampling or cfg.generation.nbest == cfg.generation.beam + ), "--sampling requires --nbest to be equal to --beam" + assert ( + not cfg.dataset.batch_size + or cfg.dataset.batch_size <= cfg.interactive.buffer_size + ), "--batch-size cannot be larger than --buffer-size" - use_cuda = torch.cuda.is_available() and not args.cpu + logger.info(cfg) + + # Fix seed for stochastic decoding + if cfg.common.seed is not None and not cfg.generation.no_seed_provided: + np.random.seed(cfg.common.seed) + utils.set_torch_seed(cfg.common.seed) + + use_cuda = torch.cuda.is_available() and not cfg.common.cpu # Setup task, e.g., translation - task = tasks.setup_task(args) + task = tasks.setup_task(cfg.task) # Load ensemble - logger.info('loading model(s) from {}'.format(args.path)) + overrides = ast.literal_eval(cfg.common_eval.model_overrides) + logger.info("loading model(s) from {}".format(cfg.common_eval.path)) models, _model_args = checkpoint_utils.load_model_ensemble( - args.path.split(os.pathsep), - arg_overrides=eval(args.model_overrides), + utils.split_paths(cfg.common_eval.path), + arg_overrides=overrides, task=task, + suffix=cfg.checkpoint.checkpoint_suffix, + strict=(cfg.checkpoint.checkpoint_shard_count == 1), + num_shards=cfg.checkpoint.checkpoint_shard_count, ) # Set dictionaries @@ -102,21 +157,20 @@ def main(args): # Optimize ensemble for generation for model in models: - model.make_generation_fast_( - beamable_mm_beam_size=None if args.no_beamable_mm else args.beam, - need_attn=args.print_alignment, - ) - if args.fp16: + if model is None: + continue + if cfg.common.fp16: model.half() - if use_cuda: + if use_cuda and not cfg.distributed_training.pipeline_model_parallel: model.cuda() + model.prepare_for_inference_(cfg) # Initialize generator - generator = task.build_generator(models, args) + generator = task.build_generator(models, cfg.generation) # Handle tokenization and BPE - tokenizer = encoders.build_tokenizer(args) - bpe = encoders.build_bpe(args) + tokenizer = task.build_tokenizer(cfg.tokenizer) + bpe = task.build_bpe(cfg.bpe) def encode_fn(x): if tokenizer is not None: @@ -134,84 +188,129 @@ def decode_fn(x): # Load alignment dictionary for unknown word replacement # (None if no unknown word replacement, empty if no path to align dictionary) - align_dict = utils.load_align_dict(args.replace_unk) + align_dict = utils.load_align_dict(cfg.generation.replace_unk) max_positions = utils.resolve_max_positions( - task.max_positions(), - *[model.max_positions() for model in models] + task.max_positions(), *[model.max_positions() for model in models] ) - if args.buffer_size > 1: - logger.info('Sentence buffer size: %s', args.buffer_size) - logger.info('NOTE: hypothesis and token scores are output in base 2') - logger.info('Type the input sentence and press return:') + if cfg.generation.constraints: + logger.warning( + "NOTE: Constrained decoding currently assumes a shared subword vocabulary." + ) + + if cfg.interactive.buffer_size > 1: + logger.info("Sentence buffer size: %s", cfg.interactive.buffer_size) + logger.info("NOTE: hypothesis and token scores are output in base 2") + logger.info("Type the input sentence and press return:") start_id = 0 - for inputs in buffered_read(args.input, args.buffer_size): + for inputs in buffered_read(cfg.interactive.input, cfg.interactive.buffer_size): results = [] - for batch in make_batches(inputs, args, task, max_positions, encode_fn): + for batch in make_batches(inputs, cfg, task, max_positions, encode_fn): + bsz = batch.src_tokens.size(0) src_tokens = batch.src_tokens src_lengths = batch.src_lengths + constraints = batch.constraints if use_cuda: src_tokens = src_tokens.cuda() src_lengths = src_lengths.cuda() + if constraints is not None: + constraints = constraints.cuda() sample = { - 'net_input': { - 'src_tokens': src_tokens, - 'src_lengths': src_lengths, + "net_input": { + "src_tokens": src_tokens, + "src_lengths": src_lengths, }, } - translations = task.inference_step(generator, models, sample) + translate_start_time = time.time() + translations = task.inference_step( + generator, models, sample, constraints=constraints + ) + translate_time = time.time() - translate_start_time + total_translate_time += translate_time + list_constraints = [[] for _ in range(bsz)] + if cfg.generation.constraints: + list_constraints = [unpack_constraints(c) for c in constraints] for i, (id, hypos) in enumerate(zip(batch.ids.tolist(), translations)): src_tokens_i = utils.strip_pad(src_tokens[i], tgt_dict.pad()) - results.append((start_id + id, src_tokens_i, hypos)) + constraints = list_constraints[i] + results.append( + ( + start_id + id, + src_tokens_i, + hypos, + { + "constraints": constraints, + "time": translate_time / len(translations), + }, + ) + ) # sort output to match input order - for id, src_tokens, hypos in sorted(results, key=lambda x: x[0]): + for id_, src_tokens, hypos, info in sorted(results, key=lambda x: x[0]): + src_str = '' if src_dict is not None: - src_str = src_dict.string(src_tokens, args.remove_bpe) - print('S-{}\t{}'.format(id, src_str)) + src_str = src_dict.string(src_tokens, cfg.common_eval.post_process) + print("S-{}\t{}".format(id_, src_str)) + print("W-{}\t{:.3f}\tseconds".format(id_, info["time"])) + for constraint in info["constraints"]: + print( + "C-{}\t{}".format( + id_, tgt_dict.string(constraint, cfg.common_eval.post_process) + ) + ) # Process top predictions - for hypo in hypos[:min(len(hypos), args.nbest)]: + for hypo in hypos[: min(len(hypos), cfg.generation.nbest)]: hypo_tokens, hypo_str, alignment = utils.post_process_prediction( - hypo_tokens=hypo['tokens'].int().cpu(), + hypo_tokens=hypo["tokens"].int().cpu(), src_str=src_str, - alignment=hypo['alignment'], + alignment=hypo["alignment"], align_dict=align_dict, tgt_dict=tgt_dict, - remove_bpe=args.remove_bpe, + remove_bpe=cfg.common_eval.post_process, + extra_symbols_to_ignore=get_symbols_to_strip_from_output(generator), ) detok_hypo_str = decode_fn(hypo_str) - score = hypo['score'] / math.log(2) # convert to base 2 + score = hypo["score"] / math.log(2) # convert to base 2 # original hypothesis (after tokenization and BPE) - print('H-{}\t{}\t{}'.format(id, score, hypo_str)) + print("H-{}\t{}\t{}".format(id_, score, hypo_str)) # detokenized hypothesis - print('D-{}\t{}\t{}'.format(id, score, detok_hypo_str)) - print('P-{}\t{}'.format( - id, - ' '.join(map( - lambda x: '{:.4f}'.format(x), - # convert from base e to base 2 - hypo['positional_scores'].div_(math.log(2)).tolist(), - )) - )) - if args.print_alignment: - alignment_str = " ".join(["{}-{}".format(src, tgt) for src, tgt in alignment]) - print('A-{}\t{}'.format( - id, - alignment_str - )) - - # update running id counter + print("D-{}\t{}\t{}".format(id_, score, detok_hypo_str)) + print( + "P-{}\t{}".format( + id_, + " ".join( + map( + lambda x: "{:.4f}".format(x), + # convert from base e to base 2 + hypo["positional_scores"].div_(math.log(2)).tolist(), + ) + ), + ) + ) + if cfg.generation.print_alignment: + alignment_str = " ".join( + ["{}-{}".format(src, tgt) for src, tgt in alignment] + ) + print("A-{}\t{}".format(id_, alignment_str)) + + # update running id_ counter start_id += len(inputs) + logger.info( + "Total time: {:.3f} seconds; translation time: {:.3f}".format( + time.time() - start_time, total_translate_time + ) + ) + def cli_main(): - parser = options.get_generation_parser(interactive=True) + parser = options.get_interactive_generation_parser() args = options.parse_args_and_arch(parser) - main(args) + distributed_utils.call_main(convert_namespace_to_omegaconf(args), main) -if __name__ == '__main__': +if __name__ == "__main__": cli_main() diff --git a/fairseq_cli/preprocess.py b/fairseq_cli/preprocess.py index 5a60d2c611..4ee9a1e3ba 100644 --- a/fairseq_cli/preprocess.py +++ b/fairseq_cli/preprocess.py @@ -7,26 +7,26 @@ Data pre-processing: build vocabularies and binarize training data. """ -from collections import Counter -from itertools import zip_longest import logging -from multiprocessing import Pool import os import shutil import sys +from collections import Counter +from itertools import zip_longest +from multiprocessing import Pool from fairseq import options, tasks, utils -from fairseq.data import indexed_dataset from fairseq.binarizer import Binarizer - +from fairseq.data import indexed_dataset +from fairseq.file_chunker_utils import find_offsets logging.basicConfig( - format='%(asctime)s | %(levelname)s | %(name)s | %(message)s', - datefmt='%Y-%m-%d %H:%M:%S', - level=logging.INFO, + format="%(asctime)s | %(levelname)s | %(name)s | %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", + level=os.environ.get("LOGLEVEL", "INFO").upper(), stream=sys.stdout, ) -logger = logging.getLogger('fairseq_cli.preprocess') +logger = logging.getLogger("fairseq_cli.preprocess") def main(args): @@ -34,11 +34,15 @@ def main(args): os.makedirs(args.destdir, exist_ok=True) - logger.addHandler(logging.FileHandler( - filename=os.path.join(args.destdir, 'preprocess.log'), - )) + logger.addHandler( + logging.FileHandler( + filename=os.path.join(args.destdir, "preprocess.log"), + ) + ) logger.info(args) + assert args.dataset_impl != "huffman", "preprocessing.py doesn't support Huffman yet, use HuffmanCodeBuilder directly." + task = tasks.get_task(args.task) def train_path(lang): @@ -74,31 +78,39 @@ def build_dictionary(filenames, src=False, tgt=False): raise FileExistsError(dict_path(args.target_lang)) if args.joined_dictionary: - assert not args.srcdict or not args.tgtdict, \ - "cannot use both --srcdict and --tgtdict with --joined-dictionary" + assert ( + not args.srcdict or not args.tgtdict + ), "cannot use both --srcdict and --tgtdict with --joined-dictionary" if args.srcdict: src_dict = task.load_dictionary(args.srcdict) elif args.tgtdict: src_dict = task.load_dictionary(args.tgtdict) else: - assert args.trainpref, "--trainpref must be set if --srcdict is not specified" + assert ( + args.trainpref + ), "--trainpref must be set if --srcdict is not specified" src_dict = build_dictionary( - {train_path(lang) for lang in [args.source_lang, args.target_lang]}, src=True + {train_path(lang) for lang in [args.source_lang, args.target_lang]}, + src=True, ) tgt_dict = src_dict else: if args.srcdict: src_dict = task.load_dictionary(args.srcdict) else: - assert args.trainpref, "--trainpref must be set if --srcdict is not specified" + assert ( + args.trainpref + ), "--trainpref must be set if --srcdict is not specified" src_dict = build_dictionary([train_path(args.source_lang)], src=True) if target: if args.tgtdict: tgt_dict = task.load_dictionary(args.tgtdict) else: - assert args.trainpref, "--trainpref must be set if --tgtdict is not specified" + assert ( + args.trainpref + ), "--trainpref must be set if --tgtdict is not specified" tgt_dict = build_dictionary([train_path(args.target_lang)], tgt=True) else: tgt_dict = None @@ -107,8 +119,11 @@ def build_dictionary(filenames, src=False, tgt=False): if target and tgt_dict is not None: tgt_dict.save(dict_path(args.target_lang)) + if args.dict_only: + return + def make_binary_dataset(vocab, input_prefix, output_prefix, lang, num_workers): - logger.info("[{}] Dictionary: {} types".format(lang, len(vocab) - 1)) + logger.info("[{}] Dictionary: {} types".format(lang, len(vocab))) n_seq_tok = [0, 0] replaced = Counter() @@ -120,11 +135,14 @@ def merge_result(worker_result): input_file = "{}{}".format( input_prefix, ("." + lang) if lang is not None else "" ) - offsets = Binarizer.find_offsets(input_file, num_workers) + offsets = find_offsets(input_file, num_workers) + (first_chunk, *more_chunks) = zip(offsets, offsets[1:]) pool = None if num_workers > 1: pool = Pool(processes=num_workers - 1) - for worker_id in range(1, num_workers): + for worker_id, (start_offset, end_offset) in enumerate( + more_chunks, start=1 + ): prefix = "{}{}".format(output_prefix, worker_id) pool.apply_async( binarize, @@ -134,19 +152,25 @@ def merge_result(worker_result): vocab, prefix, lang, - offsets[worker_id], - offsets[worker_id + 1] + start_offset, + end_offset, ), - callback=merge_result + callback=merge_result, ) pool.close() - ds = indexed_dataset.make_builder(dataset_dest_file(args, output_prefix, lang, "bin"), - impl=args.dataset_impl, vocab_size=len(vocab)) + ds = indexed_dataset.make_builder( + dataset_dest_file(args, output_prefix, lang, "bin"), + impl=args.dataset_impl, + vocab_size=len(vocab), + ) merge_result( Binarizer.binarize( - input_file, vocab, lambda t: ds.add_item(t), - offset=0, end=offsets[1] + input_file, + vocab, + lambda t: ds.add_item(t), + offset=first_chunk[0], + end=first_chunk[1], ) ) if num_workers > 1: @@ -175,14 +199,17 @@ def make_binary_alignment_dataset(input_prefix, output_prefix, num_workers): nseq = [0] def merge_result(worker_result): - nseq[0] += worker_result['nseq'] + nseq[0] += worker_result["nseq"] input_file = input_prefix - offsets = Binarizer.find_offsets(input_file, num_workers) + offsets = find_offsets(input_file, num_workers) + (first_chunk, *more_chunks) = zip(offsets, offsets[1:]) pool = None if num_workers > 1: pool = Pool(processes=num_workers - 1) - for worker_id in range(1, num_workers): + for worker_id, (start_offset, end_offset) in enumerate( + more_chunks, start=1 + ): prefix = "{}{}".format(output_prefix, worker_id) pool.apply_async( binarize_alignments, @@ -191,20 +218,24 @@ def merge_result(worker_result): input_file, utils.parse_alignment, prefix, - offsets[worker_id], - offsets[worker_id + 1] + start_offset, + end_offset, ), - callback=merge_result + callback=merge_result, ) pool.close() - ds = indexed_dataset.make_builder(dataset_dest_file(args, output_prefix, None, "bin"), - impl=args.dataset_impl) + ds = indexed_dataset.make_builder( + dataset_dest_file(args, output_prefix, None, "bin"), impl=args.dataset_impl + ) merge_result( Binarizer.binarize_alignments( - input_file, utils.parse_alignment, lambda t: ds.add_item(t), - offset=0, end=offsets[1] + input_file, + utils.parse_alignment, + lambda t: ds.add_item(t), + offset=first_chunk[0], + end=first_chunk[1], ) ) if num_workers > 1: @@ -218,12 +249,7 @@ def merge_result(worker_result): ds.finalize(dataset_dest_file(args, output_prefix, None, "idx")) - logger.info( - "[alignments] {}: parsed {} alignments".format( - input_file, - nseq[0] - ) - ) + logger.info("[alignments] {}: parsed {} alignments".format(input_file, nseq[0])) def make_dataset(vocab, input_prefix, output_prefix, lang, num_workers=1): if args.dataset_impl == "raw": @@ -242,7 +268,9 @@ def make_all(lang, vocab): if args.validpref: for k, validpref in enumerate(args.validpref.split(",")): outprefix = "valid{}".format(k) if k > 0 else "valid" - make_dataset(vocab, validpref, outprefix, lang, num_workers=args.workers) + make_dataset( + vocab, validpref, outprefix, lang, num_workers=args.workers + ) if args.testpref: for k, testpref in enumerate(args.testpref.split(",")): outprefix = "test{}".format(k) if k > 0 else "test" @@ -250,11 +278,23 @@ def make_all(lang, vocab): def make_all_alignments(): if args.trainpref and os.path.exists(args.trainpref + "." + args.align_suffix): - make_binary_alignment_dataset(args.trainpref + "." + args.align_suffix, "train.align", num_workers=args.workers) + make_binary_alignment_dataset( + args.trainpref + "." + args.align_suffix, + "train.align", + num_workers=args.workers, + ) if args.validpref and os.path.exists(args.validpref + "." + args.align_suffix): - make_binary_alignment_dataset(args.validpref + "." + args.align_suffix, "valid.align", num_workers=args.workers) + make_binary_alignment_dataset( + args.validpref + "." + args.align_suffix, + "valid.align", + num_workers=args.workers, + ) if args.testpref and os.path.exists(args.testpref + "." + args.align_suffix): - make_binary_alignment_dataset(args.testpref + "." + args.align_suffix, "test.align", num_workers=args.workers) + make_binary_alignment_dataset( + args.testpref + "." + args.align_suffix, + "test.align", + num_workers=args.workers, + ) make_all(args.source_lang, src_dict) if target: @@ -269,9 +309,9 @@ def make_all_alignments(): src_file_name = train_path(args.source_lang) tgt_file_name = train_path(args.target_lang) freq_map = {} - with open(args.alignfile, "r", encoding='utf-8') as align_file: - with open(src_file_name, "r", encoding='utf-8') as src_file: - with open(tgt_file_name, "r", encoding='utf-8') as tgt_file: + with open(args.alignfile, "r", encoding="utf-8") as align_file: + with open(src_file_name, "r", encoding="utf-8") as src_file: + with open(tgt_file_name, "r", encoding="utf-8") as tgt_file: for a, s, t in zip_longest(align_file, src_file, tgt_file): si = src_dict.encode_line(s, add_if_not_exist=False) ti = tgt_dict.encode_line(t, add_if_not_exist=False) @@ -297,38 +337,47 @@ def make_all_alignments(): align_dict[srcidx] = max(freq_map[srcidx], key=freq_map[srcidx].get) with open( - os.path.join( - args.destdir, - "alignment.{}-{}.txt".format(args.source_lang, args.target_lang), - ), - "w", encoding='utf-8' + os.path.join( + args.destdir, + "alignment.{}-{}.txt".format(args.source_lang, args.target_lang), + ), + "w", + encoding="utf-8", ) as f: for k, v in align_dict.items(): print("{} {}".format(src_dict[k], tgt_dict[v]), file=f) def binarize(args, filename, vocab, output_prefix, lang, offset, end, append_eos=True): - ds = indexed_dataset.make_builder(dataset_dest_file(args, output_prefix, lang, "bin"), - impl=args.dataset_impl, vocab_size=len(vocab)) + ds = indexed_dataset.make_builder( + dataset_dest_file(args, output_prefix, lang, "bin"), + impl=args.dataset_impl, + vocab_size=len(vocab), + ) def consumer(tensor): ds.add_item(tensor) - res = Binarizer.binarize(filename, vocab, consumer, append_eos=append_eos, - offset=offset, end=end) + res = Binarizer.binarize( + filename, vocab, consumer, append_eos=append_eos, offset=offset, end=end + ) ds.finalize(dataset_dest_file(args, output_prefix, lang, "idx")) return res def binarize_alignments(args, filename, parse_alignment, output_prefix, offset, end): - ds = indexed_dataset.make_builder(dataset_dest_file(args, output_prefix, None, "bin"), - impl=args.dataset_impl, vocab_size=None) + ds = indexed_dataset.make_builder( + dataset_dest_file(args, output_prefix, None, "bin"), + impl=args.dataset_impl, + vocab_size=None, + ) def consumer(tensor): ds.add_item(tensor) - res = Binarizer.binarize_alignments(filename, parse_alignment, consumer, offset=offset, - end=end) + res = Binarizer.binarize_alignments( + filename, parse_alignment, consumer, offset=offset, end=end + ) ds.finalize(dataset_dest_file(args, output_prefix, None, "idx")) return res @@ -350,10 +399,6 @@ def dataset_dest_file(args, output_prefix, lang, extension): return "{}.{}".format(base, extension) -def get_offsets(input_file, num_workers): - return Binarizer.find_offsets(input_file, num_workers) - - def cli_main(): parser = options.get_preprocessing_parser() args = parser.parse_args() diff --git a/fairseq_cli/score.py b/fairseq_cli/score.py index f7c3dc42b9..0b207be959 100644 --- a/fairseq_cli/score.py +++ b/fairseq_cli/score.py @@ -11,12 +11,14 @@ import os import sys -from fairseq import bleu from fairseq.data import dictionary +from fairseq.scoring import bleu def get_parser(): - parser = argparse.ArgumentParser(description='Command-line script for BLEU scoring.') + parser = argparse.ArgumentParser( + description="Command-line script for BLEU scoring." + ) # fmt: off parser.add_argument('-s', '--sys', default='-', help='system output') parser.add_argument('-r', '--ref', required=True, help='references') @@ -37,10 +39,10 @@ def cli_main(): args = parser.parse_args() print(args) - assert args.sys == '-' or os.path.exists(args.sys), \ - "System output file {} does not exist".format(args.sys) - assert os.path.exists(args.ref), \ - "Reference file {} does not exist".format(args.ref) + assert args.sys == "-" or os.path.exists( + args.sys + ), "System output file {} does not exist".format(args.sys) + assert os.path.exists(args.ref), "Reference file {} does not exist".format(args.ref) dict = dictionary.Dictionary() @@ -56,33 +58,45 @@ def readlines(fd): def score(fdsys): with open(args.ref) as fdref: - print(sacrebleu.corpus_bleu(fdsys, [fdref])) + print(sacrebleu.corpus_bleu(fdsys, [fdref]).format()) + elif args.sentence_bleu: + def score(fdsys): with open(args.ref) as fdref: scorer = bleu.Scorer(dict.pad(), dict.eos(), dict.unk()) - for i, (sys_tok, ref_tok) in enumerate(zip(readlines(fdsys), readlines(fdref))): + for i, (sys_tok, ref_tok) in enumerate( + zip(readlines(fdsys), readlines(fdref)) + ): scorer.reset(one_init=True) sys_tok = dict.encode_line(sys_tok) ref_tok = dict.encode_line(ref_tok) scorer.add(ref_tok, sys_tok) print(i, scorer.result_string(args.order)) + else: + def score(fdsys): with open(args.ref) as fdref: - scorer = bleu.Scorer(dict.pad(), dict.eos(), dict.unk()) + scorer = bleu.Scorer( + bleu.BleuConfig( + pad=dict.pad(), + eos=dict.eos(), + unk=dict.unk(), + ) + ) for sys_tok, ref_tok in zip(readlines(fdsys), readlines(fdref)): sys_tok = dict.encode_line(sys_tok) ref_tok = dict.encode_line(ref_tok) scorer.add(ref_tok, sys_tok) print(scorer.result_string(args.order)) - if args.sys == '-': + if args.sys == "-": score(sys.stdin) else: - with open(args.sys, 'r') as f: + with open(args.sys, "r") as f: score(f) -if __name__ == '__main__': +if __name__ == "__main__": cli_main() diff --git a/fairseq_cli/train.py b/fairseq_cli/train.py index ee08d6febd..8347587313 100644 --- a/fairseq_cli/train.py +++ b/fairseq_cli/train.py @@ -7,119 +7,178 @@ Train a new model on one or across multiple GPUs. """ +import argparse import logging import math import os -import random import sys +from typing import Dict, Optional, Any, List, Tuple, Callable + +# We need to setup root logger before importing any fairseq libraries. +logging.basicConfig( + format="%(asctime)s | %(levelname)s | %(name)s | %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", + level=os.environ.get("LOGLEVEL", "INFO").upper(), + stream=sys.stdout, +) +logger = logging.getLogger("fairseq_cli.train") import numpy as np import torch - from fairseq import ( checkpoint_utils, - distributed_utils, options, quantization_utils, tasks, utils, ) -from fairseq.data import iterators +from fairseq.data import iterators, data_utils +from fairseq.data.plasma_utils import PlasmaStore +from fairseq.dataclass.configs import FairseqConfig +from fairseq.dataclass.utils import convert_namespace_to_omegaconf +from fairseq.distributed import fsdp_enable_wrap, fsdp_wrap, utils as distributed_utils +from fairseq.file_io import PathManager from fairseq.logging import meters, metrics, progress_bar -from fairseq.trainer import Trainer from fairseq.model_parallel.megatron_trainer import MegatronTrainer +from fairseq.trainer import Trainer +from omegaconf import DictConfig, OmegaConf -logging.basicConfig( - format='%(asctime)s | %(levelname)s | %(name)s | %(message)s', - datefmt='%Y-%m-%d %H:%M:%S', - level=logging.INFO, - stream=sys.stdout, -) -logger = logging.getLogger('fairseq_cli.train') -def main(args, init_distributed=False): - utils.import_user_module(args) +def main(cfg: FairseqConfig) -> None: + if isinstance(cfg, argparse.Namespace): + cfg = convert_namespace_to_omegaconf(cfg) - assert args.max_tokens is not None or args.max_sentences is not None, \ - 'Must specify batch size either with --max-tokens or --max-sentences' + utils.import_user_module(cfg.common) + + if distributed_utils.is_master(cfg.distributed_training) and "job_logging_cfg" in cfg: + # make hydra logging work with ddp (see # see https://github.com/facebookresearch/hydra/issues/1126) + logging.config.dictConfig(OmegaConf.to_container(cfg.job_logging_cfg)) + + assert ( + cfg.dataset.max_tokens is not None or cfg.dataset.batch_size is not None + ), "Must specify batch size either with --max-tokens or --batch-size" metrics.reset() - # Initialize CUDA and distributed training - if torch.cuda.is_available() and not args.cpu and not getattr(args, 'tpu', False): - torch.cuda.set_device(args.device_id) - np.random.seed(args.seed) - utils.set_torch_seed(args.seed) - if init_distributed: - args.distributed_rank = distributed_utils.distributed_init(args) + if cfg.common.log_file is not None: + handler = logging.FileHandler(filename=cfg.common.log_file) + logger.addHandler(handler) + + np.random.seed(cfg.common.seed) + utils.set_torch_seed(cfg.common.seed) - if distributed_utils.is_master(args): - checkpoint_utils.verify_checkpoint_directory(args.save_dir) + if distributed_utils.is_master(cfg.distributed_training): + checkpoint_utils.verify_checkpoint_directory(cfg.checkpoint.save_dir) # Print args - logger.info(args) + logger.info(cfg) + + if cfg.checkpoint.write_checkpoints_asynchronously: + try: + import iopath # noqa: F401 + except ImportError: + logging.exception( + "Asynchronous checkpoint writing is specified but iopath is " + "not installed: `pip install iopath`" + ) + return # Setup task, e.g., translation, language modeling, etc. - task = tasks.setup_task(args) + task = tasks.setup_task(cfg.task) - # Load valid dataset (we load training data below, based on the latest checkpoint) - for valid_sub_split in args.valid_subset.split(','): - task.load_dataset(valid_sub_split, combine=False, epoch=1) + assert cfg.criterion, "Please specify criterion to train a model" # Build model and criterion - model = task.build_model(args) - criterion = task.build_criterion(args) + if cfg.distributed_training.ddp_backend == "fully_sharded": + with fsdp_enable_wrap(cfg.distributed_training): + model = fsdp_wrap(task.build_model(cfg.model)) + else: + model = task.build_model(cfg.model) + criterion = task.build_criterion(cfg.criterion) logger.info(model) - logger.info('model {}, criterion {}'.format(args.arch, criterion.__class__.__name__)) - logger.info('num. model params: {} (num. trained: {})'.format( - sum(p.numel() for p in model.parameters()), - sum(p.numel() for p in model.parameters() if p.requires_grad), - )) + logger.info("task: {}".format(task.__class__.__name__)) + logger.info("model: {}".format(model.__class__.__name__)) + logger.info("criterion: {}".format(criterion.__class__.__name__)) + logger.info( + "num. shared model params: {:,} (num. trained: {:,})".format( + sum(p.numel() for p in model.parameters() if not getattr(p, "expert", False)), + sum(p.numel() for p in model.parameters() if not getattr(p, "expert", False) and p.requires_grad) + ) + ) + + logger.info( + "num. expert model params: {} (num. trained: {})".format( + sum(p.numel() for p in model.parameters() if getattr(p, "expert", False)), + sum(p.numel() for p in model.parameters() if getattr(p, "expert", False) and p.requires_grad), + ) + ) + + # Load valid dataset (we load training data below, based on the latest checkpoint) + # We load the valid dataset AFTER building the model + data_utils.raise_if_valid_subsets_unintentionally_ignored(cfg) + if cfg.dataset.combine_valid_subsets: + task.load_dataset("valid", combine=True, epoch=1) + else: + for valid_sub_split in cfg.dataset.valid_subset.split(","): + task.load_dataset(valid_sub_split, combine=False, epoch=1) # (optionally) Configure quantization - if args.quantization_config_path is not None: + if cfg.common.quantization_config_path is not None: quantizer = quantization_utils.Quantizer( - config_path=args.quantization_config_path, - max_epoch=args.max_epoch, - max_update=args.max_update, + config_path=cfg.common.quantization_config_path, + max_epoch=cfg.optimization.max_epoch, + max_update=cfg.optimization.max_update, ) else: quantizer = None # Build trainer - if args.model_parallel_size == 1: - trainer = Trainer(args, task, model, criterion, quantizer) + if cfg.common.model_parallel_size == 1: + trainer = Trainer(cfg, task, model, criterion, quantizer) else: - trainer = MegatronTrainer(args, task, model, criterion) - - logger.info('training on {} devices (GPUs/TPUs)'.format(args.distributed_world_size)) - logger.info('max tokens per GPU = {} and max sentences per GPU = {}'.format( - args.max_tokens, - args.max_sentences, - )) + trainer = MegatronTrainer(cfg, task, model, criterion) + logger.info( + "training on {} devices (GPUs/TPUs)".format( + cfg.distributed_training.distributed_world_size + ) + ) + logger.info( + "max tokens per device = {} and max sentences per device = {}".format( + cfg.dataset.max_tokens, + cfg.dataset.batch_size, + ) + ) # Load the latest checkpoint if one is available and restore the # corresponding train iterator - extra_state, epoch_itr = checkpoint_utils.load_checkpoint(args, trainer) - if args.tpu: + extra_state, epoch_itr = checkpoint_utils.load_checkpoint( + cfg.checkpoint, + trainer, + # don't cache epoch iterators for sharded datasets + disable_iterator_cache=task.has_sharded_data("train"), + ) + if cfg.common.tpu: import torch_xla.core.xla_model as xm - xm.rendezvous('load_checkpoint') # wait for all workers - xm.mark_step() + xm.rendezvous("load_checkpoint") # wait for all workers - # Train until the learning rate gets too small - max_epoch = args.max_epoch or math.inf - max_update = args.max_update or math.inf + max_epoch = cfg.optimization.max_epoch or math.inf lr = trainer.get_lr() + train_meter = meters.StopwatchMeter() train_meter.start() - while ( - lr > args.min_lr - and epoch_itr.next_epoch_idx <= max_epoch - ): + while epoch_itr.next_epoch_idx <= max_epoch: + if lr <= cfg.optimization.stop_min_lr: + logger.info( + f"stopping training because current learning rate ({lr}) is smaller " + "than or equal to minimum learning rate " + f"(--stop-min-lr={cfg.optimization.stop_min_lr})" + ) + break + # train for one epoch - valid_losses = train(args, trainer, task, epoch_itr, max_update) - if should_stop_early(args, valid_losses[0]) or trainer.get_num_updates() >= max_update: + valid_losses, should_stop = train(cfg, trainer, task, epoch_itr) + if should_stop: break # only use first validation loss to update the learning rate @@ -128,259 +187,328 @@ def main(args, init_distributed=False): epoch_itr = trainer.get_train_iterator( epoch_itr.next_epoch_idx, # sharded data: get train iterator for next epoch - load_dataset=(os.pathsep in getattr(args, 'data', '')), + load_dataset=task.has_sharded_data("train"), + # don't cache epoch iterators for sharded datasets + disable_iterator_cache=task.has_sharded_data("train"), ) train_meter.stop() - logger.info('done training in {:.1f} seconds'.format(train_meter.sum)) + logger.info("done training in {:.1f} seconds".format(train_meter.sum)) + + # ioPath implementation to wait for all asynchronous file writes to complete. + if cfg.checkpoint.write_checkpoints_asynchronously: + logger.info( + "ioPath PathManager waiting for all asynchronous checkpoint " + "writes to finish." + ) + PathManager.async_close() + logger.info("ioPath PathManager finished waiting.") -def should_stop_early(args, valid_loss): +def should_stop_early(cfg: DictConfig, valid_loss: float) -> bool: # skip check if no validation was done in the current epoch if valid_loss is None: return False - if args.patience <= 0: + if cfg.checkpoint.patience <= 0: return False def is_better(a, b): - return a > b if args.maximize_best_checkpoint_metric else a < b + return a > b if cfg.checkpoint.maximize_best_checkpoint_metric else a < b - prev_best = getattr(should_stop_early, 'best', None) + prev_best = getattr(should_stop_early, "best", None) if prev_best is None or is_better(valid_loss, prev_best): should_stop_early.best = valid_loss should_stop_early.num_runs = 0 return False else: should_stop_early.num_runs += 1 - if should_stop_early.num_runs >= args.patience: - logger.info('early stop since valid performance hasn\'t improved for last {} runs'.format(args.patience)) + if should_stop_early.num_runs >= cfg.checkpoint.patience: + logger.info( + "early stop since valid performance hasn't improved for last {} runs".format( + cfg.checkpoint.patience + ) + ) return True else: return False -def tpu_data_loader(args, itr): - import torch_xla.core.xla_model as xm - import torch_xla.distributed.parallel_loader as pl - xm.rendezvous('tpu_data_loader') # wait for all workers - xm.mark_step() - device = utils.get_tpu_device(args) - return iterators.CountingIterator( - pl.ParallelLoader(itr, [device]).per_device_loader(device), - start=getattr(itr, 'n', 0), - total=len(itr), - ) - - -@metrics.aggregate('train') -def train(args, trainer, task, epoch_itr, max_update=math.inf): +@metrics.aggregate("train") +def train( + cfg: DictConfig, trainer: Trainer, task: tasks.FairseqTask, epoch_itr +) -> Tuple[List[Optional[float]], bool]: """Train the model for one epoch and return validation losses.""" # Initialize data iterator itr = epoch_itr.next_epoch_itr( - fix_batches_to_gpus=args.fix_batches_to_gpus, - shuffle=(epoch_itr.next_epoch_idx > args.curriculum), + fix_batches_to_gpus=cfg.distributed_training.fix_batches_to_gpus, + shuffle=(epoch_itr.next_epoch_idx > cfg.dataset.curriculum), ) update_freq = ( - args.update_freq[epoch_itr.epoch - 1] - if epoch_itr.epoch <= len(args.update_freq) - else args.update_freq[-1] + cfg.optimization.update_freq[epoch_itr.epoch - 1] + if epoch_itr.epoch <= len(cfg.optimization.update_freq) + else cfg.optimization.update_freq[-1] ) itr = iterators.GroupedIterator(itr, update_freq) - if getattr(args, 'tpu', False): - itr = tpu_data_loader(args, itr) + if cfg.common.tpu: + itr = utils.tpu_data_loader(itr) progress = progress_bar.progress_bar( itr, - log_format=args.log_format, - log_interval=args.log_interval, + log_format=cfg.common.log_format, + log_file=cfg.common.log_file, + log_interval=cfg.common.log_interval, epoch=epoch_itr.epoch, tensorboard_logdir=( - args.tensorboard_logdir if distributed_utils.is_master(args) else None + cfg.common.tensorboard_logdir + if distributed_utils.is_master(cfg.distributed_training) + else None + ), + default_log_format=("tqdm" if not cfg.common.no_progress_bar else "simple"), + wandb_project=( + cfg.common.wandb_project + if distributed_utils.is_master(cfg.distributed_training) + else None + ), + wandb_run_name=os.environ.get( + "WANDB_NAME", os.path.basename(cfg.checkpoint.save_dir) + ), + azureml_logging=( + cfg.common.azureml_logging + if distributed_utils.is_master(cfg.distributed_training) + else False ), - default_log_format=('tqdm' if not args.no_progress_bar else 'simple'), ) + progress.update_config(_flatten_config(cfg)) trainer.begin_epoch(epoch_itr.epoch) - valid_subsets = args.valid_subset.split(',') - for samples in progress: - with metrics.aggregate('train_inner'): + valid_subsets = cfg.dataset.valid_subset.split(",") + should_stop = False + num_updates = trainer.get_num_updates() + logger.info("Start iterating over samples") + for i, samples in enumerate(progress): + with metrics.aggregate("train_inner"), torch.autograd.profiler.record_function( + "train_step-%d" % i + ): log_output = trainer.train_step(samples) - if log_output is None: # OOM, overflow, ... - continue - # log mid-epoch stats - num_updates = trainer.get_num_updates() - if num_updates % args.log_interval == 0: - stats = get_training_stats(metrics.get_smoothed_values('train_inner')) - progress.log(stats, tag='train_inner', step=num_updates) + if log_output is not None: # not OOM, overflow, ... + # log mid-epoch stats + num_updates = trainer.get_num_updates() + if num_updates % cfg.common.log_interval == 0: + stats = get_training_stats(metrics.get_smoothed_values("train_inner")) + progress.log(stats, tag="train_inner", step=num_updates) - # reset mid-epoch stats after each log interval - # the end-of-epoch stats will still be preserved - metrics.reset_meters('train_inner') + # reset mid-epoch stats after each log interval + # the end-of-epoch stats will still be preserved + metrics.reset_meters("train_inner") end_of_epoch = not itr.has_next() - valid_losses = validate_and_save( - args, trainer, task, epoch_itr, valid_subsets, end_of_epoch + valid_losses, should_stop = validate_and_save( + cfg, trainer, task, epoch_itr, valid_subsets, end_of_epoch ) - if should_stop_early(args, valid_losses[0]) or num_updates >= max_update: + + if should_stop: break # log end-of-epoch stats - stats = get_training_stats(metrics.get_smoothed_values('train')) - progress.print(stats, tag='train', step=num_updates) + logger.info("end of epoch {} (average epoch stats below)".format(epoch_itr.epoch)) + stats = get_training_stats(metrics.get_smoothed_values("train")) + progress.print(stats, tag="train", step=num_updates) # reset epoch-level meters - metrics.reset_meters('train') - return valid_losses + metrics.reset_meters("train") + return valid_losses, should_stop + + +def _flatten_config(cfg: DictConfig): + config = OmegaConf.to_container(cfg) + # remove any legacy Namespaces and replace with a single "args" + namespace = None + for k, v in list(config.items()): + if isinstance(v, argparse.Namespace): + namespace = v + del config[k] + if namespace is not None: + config["args"] = vars(namespace) + return config + + +def validate_and_save( + cfg: DictConfig, + trainer: Trainer, + task: tasks.FairseqTask, + epoch_itr, + valid_subsets: List[str], + end_of_epoch: bool, +) -> Tuple[List[Optional[float]], bool]: + num_updates = trainer.get_num_updates() + max_update = cfg.optimization.max_update or math.inf + + # Stopping conditions (and an additional one based on validation loss later + # on) + should_stop = False + if num_updates >= max_update: + should_stop = True + logger.info( + f"Stopping training due to " + f"num_updates: {num_updates} >= max_update: {max_update}" + ) + training_time_hours = trainer.cumulative_training_time() / (60 * 60) + if ( + cfg.optimization.stop_time_hours > 0 + and training_time_hours > cfg.optimization.stop_time_hours + ): + should_stop = True + logger.info( + f"Stopping training due to " + f"cumulative_training_time: {training_time_hours} > " + f"stop_time_hours: {cfg.optimization.stop_time_hours} hour(s)" + ) -def validate_and_save(args, trainer, task, epoch_itr, valid_subsets, end_of_epoch): - num_updates = trainer.get_num_updates() do_save = ( - ( - args.save_interval_updates > 0 + (end_of_epoch and epoch_itr.epoch % cfg.checkpoint.save_interval == 0) + or should_stop + or ( + cfg.checkpoint.save_interval_updates > 0 and num_updates > 0 - and num_updates % args.save_interval_updates == 0 + and num_updates % cfg.checkpoint.save_interval_updates == 0 + and num_updates >= cfg.dataset.validate_after_updates ) - or (end_of_epoch and epoch_itr.epoch % args.save_interval == 0) ) do_validate = ( - ( - do_save # saving requires validation - or (end_of_epoch and epoch_itr.epoch % args.validate_interval == 0) + (not end_of_epoch and do_save) # validate during mid-epoch saves + or (end_of_epoch and epoch_itr.epoch % cfg.dataset.validate_interval == 0) + or should_stop + or ( + cfg.dataset.validate_interval_updates > 0 + and num_updates > 0 + and num_updates % cfg.dataset.validate_interval_updates == 0 ) - and not args.disable_validation - ) + ) and not cfg.dataset.disable_validation and num_updates >= cfg.dataset.validate_after_updates # Validate valid_losses = [None] if do_validate: - valid_losses = validate(args, trainer, task, epoch_itr, valid_subsets) - # Save - if do_save: - checkpoint_utils.save_checkpoint(args, trainer, epoch_itr, valid_losses[0]) - return valid_losses + valid_losses = validate(cfg, trainer, task, epoch_itr, valid_subsets) + + should_stop |= should_stop_early(cfg, valid_losses[0]) + + # Save checkpoint + if do_save or should_stop: + checkpoint_utils.save_checkpoint( + cfg.checkpoint, trainer, epoch_itr, valid_losses[0] + ) + + return valid_losses, should_stop -def get_training_stats(stats): - stats['wall'] = round(metrics.get_meter('default', 'wall').elapsed_time, 0) +def get_training_stats(stats: Dict[str, Any]) -> Dict[str, Any]: + stats["wall"] = round(metrics.get_meter("default", "wall").elapsed_time, 0) return stats -def validate(args, trainer, task, epoch_itr, subsets): +def validate( + cfg: DictConfig, + trainer: Trainer, + task: tasks.FairseqTask, + epoch_itr, + subsets: List[str], +) -> List[Optional[float]]: """Evaluate the model on the validation set(s) and return the losses.""" - if args.fixed_validation_seed is not None: + if cfg.dataset.fixed_validation_seed is not None: # set fixed seed for every validation - utils.set_torch_seed(args.fixed_validation_seed) + utils.set_torch_seed(cfg.dataset.fixed_validation_seed) + trainer.begin_valid_epoch(epoch_itr.epoch) valid_losses = [] for subset in subsets: + logger.info('begin validation on "{}" subset'.format(subset)) + # Initialize data iterator - itr = task.get_batch_iterator( - dataset=task.dataset(subset), - max_tokens=args.max_tokens_valid, - max_sentences=args.max_sentences_valid, - max_positions=utils.resolve_max_positions( - task.max_positions(), - trainer.get_model().max_positions(), - ), - ignore_invalid_inputs=args.skip_invalid_size_inputs_valid_test, - required_batch_size_multiple=args.required_batch_size_multiple, - seed=args.seed, - num_shards=args.distributed_world_size, - shard_id=args.distributed_rank, - num_workers=args.num_workers, - ).next_epoch_itr(shuffle=False) - if getattr(args, 'tpu', False): - itr = tpu_data_loader(args, itr) + itr = trainer.get_valid_iterator(subset).next_epoch_itr( + shuffle=False, set_dataset_epoch=False # use a fixed valid set + ) + if cfg.common.tpu: + itr = utils.tpu_data_loader(itr) progress = progress_bar.progress_bar( itr, - log_format=args.log_format, - log_interval=args.log_interval, + log_format=cfg.common.log_format, + log_interval=cfg.common.log_interval, epoch=epoch_itr.epoch, prefix=f"valid on '{subset}' subset", tensorboard_logdir=( - args.tensorboard_logdir if distributed_utils.is_master(args) else None + cfg.common.tensorboard_logdir + if distributed_utils.is_master(cfg.distributed_training) + else None + ), + default_log_format=("tqdm" if not cfg.common.no_progress_bar else "simple"), + wandb_project=( + cfg.common.wandb_project + if distributed_utils.is_master(cfg.distributed_training) + else None + ), + wandb_run_name=os.environ.get( + "WANDB_NAME", os.path.basename(cfg.checkpoint.save_dir) ), - default_log_format=('tqdm' if not args.no_progress_bar else 'simple'), ) # create a new root metrics aggregator so validation metrics # don't pollute other aggregators (e.g., train meters) with metrics.aggregate(new_root=True) as agg: - for sample in progress: + for i, sample in enumerate(progress): + if cfg.dataset.max_valid_steps is not None and i > cfg.dataset.max_valid_steps: + break trainer.valid_step(sample) # log validation stats - stats = get_valid_stats(args, trainer, agg.get_smoothed_values()) + stats = get_valid_stats(cfg, trainer, agg.get_smoothed_values()) + + if hasattr(task, "post_validate"): + task.post_validate(trainer.get_model(), stats, agg) + progress.print(stats, tag=subset, step=trainer.get_num_updates()) - valid_losses.append(stats[args.best_checkpoint_metric]) + valid_losses.append(stats[cfg.checkpoint.best_checkpoint_metric]) return valid_losses -def get_valid_stats(args, trainer, stats): - stats['num_updates'] = trainer.get_num_updates() - if hasattr(checkpoint_utils.save_checkpoint, 'best'): - key = 'best_{0}'.format(args.best_checkpoint_metric) - best_function = max if args.maximize_best_checkpoint_metric else min +def get_valid_stats( + cfg: DictConfig, trainer: Trainer, stats: Dict[str, Any] +) -> Dict[str, Any]: + stats["num_updates"] = trainer.get_num_updates() + if hasattr(checkpoint_utils.save_checkpoint, "best"): + key = "best_{0}".format(cfg.checkpoint.best_checkpoint_metric) + best_function = max if cfg.checkpoint.maximize_best_checkpoint_metric else min stats[key] = best_function( checkpoint_utils.save_checkpoint.best, - stats[args.best_checkpoint_metric], + stats[cfg.checkpoint.best_checkpoint_metric], ) return stats -def distributed_main(i, args, start_rank=0): - args.device_id = i - if args.distributed_rank is None: # torch.multiprocessing.spawn - args.distributed_rank = start_rank + i - main(args, init_distributed=True) - - -def cli_main(modify_parser=None): +def cli_main( + modify_parser: Optional[Callable[[argparse.ArgumentParser], None]] = None +) -> None: parser = options.get_training_parser() args = options.parse_args_and_arch(parser, modify_parser=modify_parser) - if args.distributed_init_method is None: - distributed_utils.infer_init_method(args) - - if args.distributed_init_method is not None: - # distributed training - if torch.cuda.device_count() > 1 and not args.distributed_no_spawn: - start_rank = args.distributed_rank - args.distributed_rank = None # assign automatically - torch.multiprocessing.spawn( - fn=distributed_main, - args=(args, start_rank), - nprocs=torch.cuda.device_count(), - ) - else: - distributed_main(args.device_id, args) - elif args.distributed_world_size > 1: - if not getattr(args, 'tpu', False): - # fallback for single node with multiple GPUs - assert args.distributed_world_size <= torch.cuda.device_count() - port = random.randint(10000, 20000) - args.distributed_init_method = 'tcp://localhost:{port}'.format(port=port) - args.distributed_rank = None # set based on device id - torch.multiprocessing.spawn( - fn=distributed_main, - args=(args, ), - nprocs=args.distributed_world_size, - ) - else: - import torch_xla.distributed.xla_multiprocessing as xmp - torch.multiprocessing.set_sharing_strategy('file_system') - xmp.spawn( - fn=distributed_main, - args=(args, ), - nprocs=8, # use all 8 TPU cores - ) + cfg = convert_namespace_to_omegaconf(args) + + if cfg.common.use_plasma_view: + server = PlasmaStore(path=cfg.common.plasma_path) + logger.info(f"Started plasma server pid {server.server.pid} {cfg.common.plasma_path}") + + if args.profile: + with torch.cuda.profiler.profile(): + with torch.autograd.profiler.emit_nvtx(): + distributed_utils.call_main(cfg, main) else: - # single GPU training - main(args) + distributed_utils.call_main(cfg, main) + + # if cfg.common.use_plasma_view: + # server.server.kill() -if __name__ == '__main__': +if __name__ == "__main__": cli_main() diff --git a/fairseq_cli/validate.py b/fairseq_cli/validate.py index 580ac7b8b3..22b93e9a6a 100644 --- a/fairseq_cli/validate.py +++ b/fairseq_cli/validate.py @@ -1,93 +1,117 @@ #!/usr/bin/env python3 -u -#!/usr/bin/env python3 -u # Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. import logging +import os import sys +from argparse import Namespace +from itertools import chain import torch - from fairseq import checkpoint_utils, distributed_utils, options, utils +from fairseq.dataclass.utils import convert_namespace_to_omegaconf from fairseq.logging import metrics, progress_bar -from fairseq.options import add_distributed_training_args +from fairseq.utils import reset_logging +from omegaconf import DictConfig + logging.basicConfig( - format='%(asctime)s | %(levelname)s | %(name)s | %(message)s', - datefmt='%Y-%m-%d %H:%M:%S', - level=logging.INFO, + format="%(asctime)s | %(levelname)s | %(name)s | %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", + level=os.environ.get("LOGLEVEL", "INFO").upper(), stream=sys.stdout, ) -logger = logging.getLogger('fairseq_cli.validate') +logger = logging.getLogger("fairseq_cli.validate") + + +def main(cfg: DictConfig, override_args=None): + if isinstance(cfg, Namespace): + cfg = convert_namespace_to_omegaconf(cfg) + + utils.import_user_module(cfg.common) + reset_logging() -def main(args, override_args=None): - utils.import_user_module(args) + assert ( + cfg.dataset.max_tokens is not None or cfg.dataset.batch_size is not None + ), "Must specify batch size either with --max-tokens or --batch-size" - assert args.max_tokens is not None or args.max_sentences is not None, \ - 'Must specify batch size either with --max-tokens or --max-sentences' + use_fp16 = cfg.common.fp16 + use_cuda = torch.cuda.is_available() and not cfg.common.cpu - use_fp16 = args.fp16 - use_cuda = torch.cuda.is_available() and not args.cpu + if use_cuda: + torch.cuda.set_device(cfg.distributed_training.device_id) + + if cfg.distributed_training.distributed_world_size > 1: + data_parallel_world_size = distributed_utils.get_data_parallel_world_size() + data_parallel_rank = distributed_utils.get_data_parallel_rank() + else: + data_parallel_world_size = 1 + data_parallel_rank = 0 if override_args is not None: overrides = vars(override_args) - overrides.update(eval(getattr(override_args, 'model_overrides', '{}'))) + overrides.update(eval(getattr(override_args, "model_overrides", "{}"))) else: overrides = None # Load ensemble - logger.info('loading model(s) from {}'.format(args.path)) - models, model_args, task = checkpoint_utils.load_model_ensemble_and_task( - [args.path], + logger.info("loading model(s) from {}".format(cfg.common_eval.path)) + models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task( + [cfg.common_eval.path], arg_overrides=overrides, - suffix=getattr(args, "checkpoint_suffix", ""), + suffix=cfg.checkpoint.checkpoint_suffix, ) model = models[0] # Move models to GPU for model in models: + model.eval() if use_fp16: model.half() if use_cuda: model.cuda() # Print args - logger.info(model_args) + logger.info(saved_cfg) # Build criterion - criterion = task.build_criterion(model_args) + criterion = task.build_criterion(saved_cfg.criterion) criterion.eval() - for subset in args.valid_subset.split(','): + for subset in cfg.dataset.valid_subset.split(","): try: - task.load_dataset(subset, combine=False, epoch=1) + task.load_dataset(subset, combine=False, epoch=1, task_cfg=saved_cfg.task) dataset = task.dataset(subset) except KeyError: - raise Exception('Cannot find dataset: ' + subset) + raise Exception("Cannot find dataset: " + subset) # Initialize data iterator itr = task.get_batch_iterator( dataset=dataset, - max_tokens=args.max_tokens, - max_sentences=args.max_sentences, + max_tokens=cfg.dataset.max_tokens, + max_sentences=cfg.dataset.batch_size, max_positions=utils.resolve_max_positions( task.max_positions(), *[m.max_positions() for m in models], ), - ignore_invalid_inputs=args.skip_invalid_size_inputs_valid_test, - required_batch_size_multiple=args.required_batch_size_multiple, - seed=args.seed, - num_workers=args.num_workers, + ignore_invalid_inputs=cfg.dataset.skip_invalid_size_inputs_valid_test, + required_batch_size_multiple=cfg.dataset.required_batch_size_multiple, + seed=cfg.common.seed, + num_shards=data_parallel_world_size, + shard_id=data_parallel_rank, + num_workers=cfg.dataset.num_workers, + data_buffer_size=cfg.dataset.data_buffer_size, ).next_epoch_itr(shuffle=False) progress = progress_bar.progress_bar( itr, - log_format=args.log_format, - log_interval=args.log_interval, + log_format=cfg.common.log_format, + log_interval=cfg.common.log_interval, prefix=f"valid on '{subset}' subset", - default_log_format=('tqdm' if not args.no_progress_bar else 'simple'), + default_log_format=("tqdm" if not cfg.common.no_progress_bar else "simple"), ) log_outputs = [] @@ -97,6 +121,14 @@ def main(args, override_args=None): progress.log(log_output, step=i) log_outputs.append(log_output) + if data_parallel_world_size > 1: + log_outputs = distributed_utils.all_gather_list( + log_outputs, + max_size=cfg.common.all_gather_list_size, + group=distributed_utils.get_data_parallel_group(), + ) + log_outputs = list(chain.from_iterable(log_outputs)) + with metrics.aggregate() as agg: task.reduce_metrics(log_outputs, criterion) log_output = agg.get_smoothed_values() @@ -106,16 +138,18 @@ def main(args, override_args=None): def cli_main(): parser = options.get_validation_parser() - add_distributed_training_args(parser) args = options.parse_args_and_arch(parser) # only override args that are explicitly given on the command line override_parser = options.get_validation_parser() - add_distributed_training_args(override_parser) - override_args = options.parse_args_and_arch(override_parser, suppress_defaults=True) + override_args = options.parse_args_and_arch( + override_parser, suppress_defaults=True + ) - distributed_utils.call_main(args, main, override_args=override_args) + distributed_utils.call_main( + convert_namespace_to_omegaconf(args), main, override_args=override_args + ) -if __name__ == '__main__': +if __name__ == "__main__": cli_main() diff --git a/hubconf.py b/hubconf.py index 2a0c9c097e..5949e274ed 100644 --- a/hubconf.py +++ b/hubconf.py @@ -2,47 +2,72 @@ # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. +"""isort:skip_file""" import functools - -from fairseq.hub_utils import BPEHubInterface as bpe # noqa -from fairseq.hub_utils import TokenizerHubInterface as tokenizer # noqa -from fairseq.models import MODEL_REGISTRY +import importlib dependencies = [ - 'numpy', - 'regex', - 'requests', - 'torch', + "dataclasses", + "hydra", + "numpy", + "omegaconf", + "regex", + "requests", + "torch", ] +# Check for required dependencies and raise a RuntimeError if any are missing. +missing_deps = [] +for dep in dependencies: + try: + importlib.import_module(dep) + except ImportError: + # Hack: the hydra package is provided under the "hydra-core" name in + # pypi. We don't want the user mistakenly calling `pip install hydra` + # since that will install an unrelated package. + if dep == "hydra": + dep = "hydra-core" + missing_deps.append(dep) +if len(missing_deps) > 0: + raise RuntimeError("Missing dependencies: {}".format(", ".join(missing_deps))) + + +# only do fairseq imports after checking for dependencies +from fairseq.hub_utils import ( # noqa; noqa + BPEHubInterface as bpe, + TokenizerHubInterface as tokenizer, +) +from fairseq.models import MODEL_REGISTRY # noqa + + # torch.hub doesn't build Cython components, so if they are not found then try # to build them here try: - import fairseq.data.token_block_utils_fast -except (ImportError, ModuleNotFoundError): + import fairseq.data.token_block_utils_fast # noqa +except ImportError: try: - import cython + import cython # noqa import os from setuptools import sandbox + sandbox.run_setup( - os.path.join(os.path.dirname(__file__), 'setup.py'), - ['build_ext', '--inplace'], + os.path.join(os.path.dirname(__file__), "setup.py"), + ["build_ext", "--inplace"], ) - except (ImportError, ModuleNotFoundError): + except ImportError: print( - 'Unable to build Cython components. Please make sure Cython is ' - 'installed if the torch.hub model you are loading depends on it.' + "Unable to build Cython components. Please make sure Cython is " + "installed if the torch.hub model you are loading depends on it." ) +# automatically expose models defined in FairseqModel::hub_models for _model_type, _cls in MODEL_REGISTRY.items(): for model_name in _cls.hub_models().keys(): globals()[model_name] = functools.partial( _cls.from_pretrained, model_name, ) - # to simplify the interface we only expose named models - # globals()[_model_type] = _cls.from_pretrained diff --git a/scripts/average_checkpoints.py b/scripts/average_checkpoints.py index 7890516154..c512f802bc 100644 --- a/scripts/average_checkpoints.py +++ b/scripts/average_checkpoints.py @@ -6,10 +6,10 @@ import argparse import collections -import torch import os import re +import torch from fairseq.file_io import PathManager @@ -30,26 +30,26 @@ def average_checkpoints(inputs): num_models = len(inputs) for fpath in inputs: - with PathManager.open(fpath, 'rb') as f: + with PathManager.open(fpath, "rb") as f: state = torch.load( f, map_location=( - lambda s, _: torch.serialization.default_restore_location(s, 'cpu') + lambda s, _: torch.serialization.default_restore_location(s, "cpu") ), ) # Copies over the settings from the first checkpoint if new_state is None: new_state = state - model_params = state['model'] + model_params = state["model"] model_params_keys = list(model_params.keys()) if params_keys is None: params_keys = model_params_keys elif params_keys != model_params_keys: raise KeyError( - 'For checkpoint {}, expected list of params: {}, ' - 'but found: {}'.format(f, params_keys, model_params_keys) + "For checkpoint {}, expected list of params: {}, " + "but found: {}".format(f, params_keys, model_params_keys) ) for k in params_keys: @@ -65,8 +65,11 @@ def average_checkpoints(inputs): averaged_params = collections.OrderedDict() for k, v in params_dict.items(): averaged_params[k] = v - averaged_params[k].div_(num_models) - new_state['model'] = averaged_params + if averaged_params[k].is_floating_point(): + averaged_params[k].div_(num_models) + else: + averaged_params[k] //= num_models + new_state["model"] = averaged_params return new_state @@ -74,9 +77,9 @@ def last_n_checkpoints(paths, n, update_based, upper_bound=None): assert len(paths) == 1 path = paths[0] if update_based: - pt_regexp = re.compile(r'checkpoint_\d+_(\d+)\.pt') + pt_regexp = re.compile(r"checkpoint_\d+_(\d+)\.pt") else: - pt_regexp = re.compile(r'checkpoint(\d+)\.pt') + pt_regexp = re.compile(r"checkpoint(\d+)\.pt") files = PathManager.ls(path) entries = [] @@ -87,14 +90,16 @@ def last_n_checkpoints(paths, n, update_based, upper_bound=None): if upper_bound is None or sort_key <= upper_bound: entries.append((sort_key, m.group(0))) if len(entries) < n: - raise Exception('Found {} checkpoint files but need at least {}', len(entries), n) + raise Exception( + "Found {} checkpoint files but need at least {}", len(entries), n + ) return [os.path.join(path, x[1]) for x in sorted(entries, reverse=True)[:n]] def main(): parser = argparse.ArgumentParser( - description='Tool to average the params of input checkpoints to ' - 'produce a new checkpoint', + description="Tool to average the params of input checkpoints to " + "produce a new checkpoint", ) # fmt: off parser.add_argument('--inputs', required=True, nargs='+', @@ -126,22 +131,28 @@ def main(): elif args.num_epoch_checkpoints is not None: num = args.num_epoch_checkpoints - assert args.checkpoint_upper_bound is None or (args.num_epoch_checkpoints is not None or args.num_update_checkpoints is not None), \ - '--checkpoint-upper-bound requires --num-epoch-checkpoints or --num-update-checkpoints' - assert args.num_epoch_checkpoints is None or args.num_update_checkpoints is None, \ - 'Cannot combine --num-epoch-checkpoints and --num-update-checkpoints' + assert args.checkpoint_upper_bound is None or ( + args.num_epoch_checkpoints is not None + or args.num_update_checkpoints is not None + ), "--checkpoint-upper-bound requires --num-epoch-checkpoints or --num-update-checkpoints" + assert ( + args.num_epoch_checkpoints is None or args.num_update_checkpoints is None + ), "Cannot combine --num-epoch-checkpoints and --num-update-checkpoints" if num is not None: args.inputs = last_n_checkpoints( - args.inputs, num, is_update_based, upper_bound=args.checkpoint_upper_bound, + args.inputs, + num, + is_update_based, + upper_bound=args.checkpoint_upper_bound, ) - print('averaging checkpoints: ', args.inputs) + print("averaging checkpoints: ", args.inputs) new_state = average_checkpoints(args.inputs) - with PathManager.open(args.output, 'wb') as f: + with PathManager.open(args.output, "wb") as f: torch.save(new_state, f) - print('Finished writing averaged checkpoint to {}.'.format(args.output)) + print("Finished writing averaged checkpoint to {}".format(args.output)) -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/scripts/build_sym_alignment.py b/scripts/build_sym_alignment.py index bb0cac09dd..0ca5c18f7b 100644 --- a/scripts/build_sym_alignment.py +++ b/scripts/build_sym_alignment.py @@ -27,7 +27,7 @@ def main(): - parser = argparse.ArgumentParser(description='symmetric alignment builer') + parser = argparse.ArgumentParser(description="symmetric alignment builer") # fmt: off parser.add_argument('--fast_align_dir', help='path to fast_align build directory') @@ -47,40 +47,40 @@ def main(): # fmt: on args = parser.parse_args() - fast_align_bin = os.path.join(args.fast_align_dir, 'fast_align') - symal_bin = os.path.join(args.mosesdecoder_dir, 'bin', 'symal') + fast_align_bin = os.path.join(args.fast_align_dir, "fast_align") + symal_bin = os.path.join(args.mosesdecoder_dir, "bin", "symal") sym_fast_align_bin = os.path.join( - args.mosesdecoder_dir, 'scripts', 'ems', - 'support', 'symmetrize-fast-align.perl') + args.mosesdecoder_dir, "scripts", "ems", "support", "symmetrize-fast-align.perl" + ) # create joined file - joined_file = os.path.join(args.output_dir, 'text.joined') - with open(args.source_file, 'r', encoding='utf-8') as src, open(args.target_file, 'r', encoding='utf-8') as tgt: - with open(joined_file, 'w', encoding='utf-8') as joined: + joined_file = os.path.join(args.output_dir, "text.joined") + with open(args.source_file, "r", encoding="utf-8") as src, open( + args.target_file, "r", encoding="utf-8" + ) as tgt: + with open(joined_file, "w", encoding="utf-8") as joined: for s, t in zip_longest(src, tgt): - print('{} ||| {}'.format(s.strip(), t.strip()), file=joined) + print("{} ||| {}".format(s.strip(), t.strip()), file=joined) - bwd_align_file = os.path.join(args.output_dir, 'align.backward') + bwd_align_file = os.path.join(args.output_dir, "align.backward") # run forward alignment - fwd_align_file = os.path.join(args.output_dir, 'align.forward') - fwd_fast_align_cmd = '{FASTALIGN} -i {JOINED} -d -o -v > {FWD}'.format( - FASTALIGN=fast_align_bin, - JOINED=joined_file, - FWD=fwd_align_file) + fwd_align_file = os.path.join(args.output_dir, "align.forward") + fwd_fast_align_cmd = "{FASTALIGN} -i {JOINED} -d -o -v > {FWD}".format( + FASTALIGN=fast_align_bin, JOINED=joined_file, FWD=fwd_align_file + ) assert os.system(fwd_fast_align_cmd) == 0 # run backward alignment - bwd_align_file = os.path.join(args.output_dir, 'align.backward') - bwd_fast_align_cmd = '{FASTALIGN} -i {JOINED} -d -o -v -r > {BWD}'.format( - FASTALIGN=fast_align_bin, - JOINED=joined_file, - BWD=bwd_align_file) + bwd_align_file = os.path.join(args.output_dir, "align.backward") + bwd_fast_align_cmd = "{FASTALIGN} -i {JOINED} -d -o -v -r > {BWD}".format( + FASTALIGN=fast_align_bin, JOINED=joined_file, BWD=bwd_align_file + ) assert os.system(bwd_fast_align_cmd) == 0 # run symmetrization - sym_out_file = os.path.join(args.output_dir, 'aligned') - sym_cmd = '{SYMFASTALIGN} {FWD} {BWD} {SRC} {TGT} {OUT} {HEURISTIC} {SYMAL}'.format( + sym_out_file = os.path.join(args.output_dir, "aligned") + sym_cmd = "{SYMFASTALIGN} {FWD} {BWD} {SRC} {TGT} {OUT} {HEURISTIC} {SYMAL}".format( SYMFASTALIGN=sym_fast_align_bin, FWD=fwd_align_file, BWD=bwd_align_file, @@ -88,10 +88,10 @@ def main(): TGT=args.target_file, OUT=sym_out_file, HEURISTIC=args.sym_heuristic, - SYMAL=symal_bin + SYMAL=symal_bin, ) assert os.system(sym_cmd) == 0 -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/scripts/compare_namespaces.py b/scripts/compare_namespaces.py index db5121189a..bc24db624f 100644 --- a/scripts/compare_namespaces.py +++ b/scripts/compare_namespaces.py @@ -6,13 +6,13 @@ def main(): - ns1 = eval(input('Namespace 1: ')) - ns2 = eval(input('Namespace 2: ')) + ns1 = eval(input("Namespace 1: ")) + ns2 = eval(input("Namespace 2: ")) def keys(ns): ks = set() for k in dir(ns): - if not k.startswith('_'): + if not k.startswith("_"): ks.add(k) return ks @@ -22,23 +22,25 @@ def keys(ns): def print_keys(ks, ns1, ns2=None): for k in ks: if ns2 is None: - print('{}\t{}'.format(k, getattr(ns1, k, None))) + print("{}\t{}".format(k, getattr(ns1, k, None))) else: - print('{}\t{}\t{}'.format(k, getattr(ns1, k, None), getattr(ns2, k, None))) + print( + "{}\t{}\t{}".format(k, getattr(ns1, k, None), getattr(ns2, k, None)) + ) - print('Keys unique to namespace 1:') + print("Keys unique to namespace 1:") print_keys(k1 - k2, ns1) print() - print('Keys unique to namespace 2:') + print("Keys unique to namespace 2:") print_keys(k2 - k1, ns2) print() - print('Overlapping keys with different values:') - ks = [k for k in k1 & k2 if getattr(ns1, k, 'None') != getattr(ns2, k, 'None')] + print("Overlapping keys with different values:") + ks = [k for k in k1 & k2 if getattr(ns1, k, "None") != getattr(ns2, k, "None")] print_keys(ks, ns1, ns2) print() -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/scripts/constraints/extract.py b/scripts/constraints/extract.py new file mode 100755 index 0000000000..f6155d0a05 --- /dev/null +++ b/scripts/constraints/extract.py @@ -0,0 +1,92 @@ +#!/usr/bin/env python3 +# +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +"""Extracts random constraints from reference files.""" + +import argparse +import random +import sys + +from sacrebleu import extract_ngrams + + +def get_phrase(words, index, length): + assert index < len(words) - length + 1 + phr = " ".join(words[index : index + length]) + for i in range(index, index + length): + words.pop(index) + return phr + + +def main(args): + + if args.seed: + random.seed(args.seed) + + for line in sys.stdin: + constraints = [] + + def add_constraint(constraint): + constraints.append(constraint) + + source = line.rstrip() + if "\t" in line: + source, target = line.split("\t") + if args.add_sos: + target = f"<s> {target}" + if args.add_eos: + target = f"{target} </s>" + + if len(target.split()) >= args.len: + words = [target] + + num = args.number + + choices = {} + for i in range(num): + if len(words) == 0: + break + segmentno = random.choice(range(len(words))) + segment = words.pop(segmentno) + tokens = segment.split() + phrase_index = random.choice(range(len(tokens))) + choice = " ".join( + tokens[phrase_index : min(len(tokens), phrase_index + args.len)] + ) + for j in range( + phrase_index, min(len(tokens), phrase_index + args.len) + ): + tokens.pop(phrase_index) + if phrase_index > 0: + words.append(" ".join(tokens[0:phrase_index])) + if phrase_index + 1 < len(tokens): + words.append(" ".join(tokens[phrase_index:])) + choices[target.find(choice)] = choice + + # mask out with spaces + target = target.replace(choice, " " * len(choice), 1) + + for key in sorted(choices.keys()): + add_constraint(choices[key]) + + print(source, *constraints, sep="\t") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--number", "-n", type=int, default=1, help="number of phrases") + parser.add_argument("--len", "-l", type=int, default=1, help="phrase length") + parser.add_argument( + "--add-sos", default=False, action="store_true", help="add <s> token" + ) + parser.add_argument( + "--add-eos", default=False, action="store_true", help="add </s> token" + ) + parser.add_argument("--seed", "-s", default=0, type=int) + args = parser.parse_args() + + main(args) diff --git a/scripts/constraints/validate.py b/scripts/constraints/validate.py new file mode 100755 index 0000000000..d531ad9f39 --- /dev/null +++ b/scripts/constraints/validate.py @@ -0,0 +1,34 @@ +#!/usr/bin/env python3 +# +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import sys + + +"""Reads in a fairseq output file, and verifies that the constraints +(C- lines) are present in the output (the first H- line). Assumes that +constraints are listed prior to the first hypothesis. +""" + +constraints = [] +found = 0 +total = 0 +for line in sys.stdin: + if line.startswith("C-"): + constraints.append(line.rstrip().split("\t")[1]) + elif line.startswith("H-"): + text = line.split("\t")[2] + + for constraint in constraints: + total += 1 + if constraint in text: + found += 1 + else: + print(f"No {constraint} in {text}", file=sys.stderr) + + constraints = [] + +print(f"Found {found} / {total} = {100 * found / total:.1f}%") diff --git a/scripts/count_docs.py b/scripts/count_docs.py index 8d185398a7..58d85af85e 100644 --- a/scripts/count_docs.py +++ b/scripts/count_docs.py @@ -17,15 +17,15 @@ def main(): parser = argparse.ArgumentParser() - parser.add_argument('input') - parser.add_argument('--gzip', action='store_true') + parser.add_argument("input") + parser.add_argument("--gzip", action="store_true") args = parser.parse_args() def gopen(): if args.gzip: - return gzip.open(args.input, 'r') + return gzip.open(args.input, "r") else: - return open(args.input, 'r', encoding='utf-8') + return open(args.input, "r", encoding="utf-8") num_lines = [] num_toks = [] @@ -54,5 +54,5 @@ def gopen(): print("average num toks per doc: {}".format(np.mean(num_toks))) -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/scripts/read_binarized.py b/scripts/read_binarized.py index f48409beb4..a414095d03 100644 --- a/scripts/read_binarized.py +++ b/scripts/read_binarized.py @@ -6,12 +6,13 @@ import argparse -from fairseq.data import data_utils, Dictionary, indexed_dataset +from fairseq.data import Dictionary, data_utils, indexed_dataset def get_parser(): parser = argparse.ArgumentParser( - description='writes text from binarized file to stdout') + description="writes text from binarized file to stdout" + ) # fmt: off parser.add_argument('--dataset-impl', help='dataset implementation', choices=indexed_dataset.get_available_dataset_impl()) @@ -31,17 +32,17 @@ def main(): args.input, dictionary, dataset_impl=args.dataset_impl, - default='lazy', + default="lazy", ) for tensor_line in dataset: if dictionary is None: - line = ' '.join([str(int(x)) for x in tensor_line]) + line = " ".join([str(int(x)) for x in tensor_line]) else: line = dictionary.string(tensor_line) print(line) -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/scripts/rm_pt.py b/scripts/rm_pt.py index 21976cee4f..6cd063d21f 100644 --- a/scripts/rm_pt.py +++ b/scripts/rm_pt.py @@ -11,9 +11,9 @@ import sys -pt_regexp = re.compile(r'checkpoint(\d+|_\d+_\d+|_[a-z]+)\.pt') -pt_regexp_epoch_based = re.compile(r'checkpoint(\d+)\.pt') -pt_regexp_update_based = re.compile(r'checkpoint_\d+_(\d+)\.pt') +pt_regexp = re.compile(r"checkpoint(\d+|_\d+_\d+|_[a-z]+)\.pt") +pt_regexp_epoch_based = re.compile(r"checkpoint(\d+)\.pt") +pt_regexp_update_based = re.compile(r"checkpoint_\d+_(\d+)\.pt") def parse_checkpoints(files): @@ -42,18 +42,31 @@ def every_n_checkpoints(files, n): def main(): parser = argparse.ArgumentParser( description=( - 'Recursively delete checkpoint files from `root_dir`, ' - 'but preserve checkpoint_best.pt and checkpoint_last.pt' + "Recursively delete checkpoint files from `root_dir`, " + "but preserve checkpoint_best.pt and checkpoint_last.pt" ) ) - parser.add_argument('root_dirs', nargs='*') - parser.add_argument('--save-last', type=int, default=0, help='number of last checkpoints to save') - parser.add_argument('--save-every', type=int, default=0, help='interval of checkpoints to save') - parser.add_argument('--preserve-test', action='store_true', - help='preserve checkpoints in dirs that start with test_ prefix (default: delete them)') - parser.add_argument('--delete-best', action='store_true', help='delete checkpoint_best.pt') - parser.add_argument('--delete-last', action='store_true', help='delete checkpoint_last.pt') - parser.add_argument('--no-dereference', action='store_true', help='don\'t dereference symlinks') + parser.add_argument("root_dirs", nargs="*") + parser.add_argument( + "--save-last", type=int, default=0, help="number of last checkpoints to save" + ) + parser.add_argument( + "--save-every", type=int, default=0, help="interval of checkpoints to save" + ) + parser.add_argument( + "--preserve-test", + action="store_true", + help="preserve checkpoints in dirs that start with test_ prefix (default: delete them)", + ) + parser.add_argument( + "--delete-best", action="store_true", help="delete checkpoint_best.pt" + ) + parser.add_argument( + "--delete-last", action="store_true", help="delete checkpoint_last.pt" + ) + parser.add_argument( + "--no-dereference", action="store_true", help="don't dereference symlinks" + ) args = parser.parse_args() files_to_desymlink = [] @@ -72,15 +85,11 @@ def main(): continue full_path = os.path.join(root, file) if ( - ( - not os.path.basename(root).startswith('test_') - or args.preserve_test - ) - and ( - (file == 'checkpoint_last.pt' and not args.delete_last) - or (file == 'checkpoint_best.pt' and not args.delete_best) - or file in to_save - ) + not os.path.basename(root).startswith("test_") or args.preserve_test + ) and ( + (file == "checkpoint_last.pt" and not args.delete_last) + or (file == "checkpoint_best.pt" and not args.delete_best) + or file in to_save ): if os.path.islink(full_path) and not args.no_dereference: files_to_desymlink.append(full_path) @@ -90,43 +99,43 @@ def main(): files_to_delete.append(full_path) if len(files_to_desymlink) == 0 and len(files_to_delete) == 0: - print('Nothing to do.') + print("Nothing to do.") sys.exit(0) files_to_desymlink = sorted(files_to_desymlink) files_to_preserve = sorted(files_to_preserve) files_to_delete = sorted(files_to_delete) - print('Operations to perform (in order):') + print("Operations to perform (in order):") if len(files_to_desymlink) > 0: for file in files_to_desymlink: - print(' - preserve (and dereference symlink): ' + file) + print(" - preserve (and dereference symlink): " + file) if len(files_to_preserve) > 0: for file in files_to_preserve: - print(' - preserve: ' + file) + print(" - preserve: " + file) if len(files_to_delete) > 0: for file in files_to_delete: - print(' - delete: ' + file) + print(" - delete: " + file) while True: - resp = input('Continue? (Y/N): ') - if resp.strip().lower() == 'y': + resp = input("Continue? (Y/N): ") + if resp.strip().lower() == "y": break - elif resp.strip().lower() == 'n': + elif resp.strip().lower() == "n": sys.exit(0) - print('Executing...') + print("Executing...") if len(files_to_desymlink) > 0: for file in files_to_desymlink: realpath = os.path.realpath(file) - print('rm ' + file) + print("rm " + file) os.remove(file) - print('cp {} {}'.format(realpath, file)) + print("cp {} {}".format(realpath, file)) shutil.copyfile(realpath, file) if len(files_to_delete) > 0: for file in files_to_delete: - print('rm ' + file) + print("rm " + file) os.remove(file) -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/scripts/sacrebleu_pregen.sh b/scripts/sacrebleu.sh similarity index 55% rename from scripts/sacrebleu_pregen.sh rename to scripts/sacrebleu.sh index 6fd3dd3c04..c10bf2b76e 100644 --- a/scripts/sacrebleu_pregen.sh +++ b/scripts/sacrebleu.sh @@ -11,18 +11,17 @@ TGTLANG=$3 GEN=$4 -echo 'Cloning Moses github repository (for tokenization scripts)...' -git clone https://github.com/moses-smt/mosesdecoder.git - -SCRIPTS=mosesdecoder/scripts -DETOKENIZER=$SCRIPTS/tokenizer/detokenizer.perl +if ! command -v sacremoses &> /dev/null +then + echo "sacremoses could not be found, please install with: pip install sacremoses" + exit +fi grep ^H $GEN \ | sed 's/^H\-//' \ | sort -n -k 1 \ | cut -f 3 \ -| perl $DETOKENIZER -l $TGTLANG \ -| sed "s/ - /-/g" \ +| sacremoses detokenize \ > $GEN.sorted.detok sacrebleu --test-set $TESTSET --language-pair "${SRCLANG}-${TGTLANG}" < $GEN.sorted.detok diff --git a/scripts/shard_docs.py b/scripts/shard_docs.py index 87d7c22d4f..97232c3c84 100644 --- a/scripts/shard_docs.py +++ b/scripts/shard_docs.py @@ -14,21 +14,23 @@ def main(): parser = argparse.ArgumentParser() - parser.add_argument('input') - parser.add_argument('--num-shards', type=int) + parser.add_argument("input") + parser.add_argument("--num-shards", type=int) args = parser.parse_args() assert args.num_shards is not None and args.num_shards > 1 - with open(args.input, 'r', encoding='utf-8') as h: + with open(args.input, "r", encoding="utf-8") as h: with contextlib.ExitStack() as stack: outputs = [ - stack.enter_context(open(args.input + ".shard" + str(i), "w", encoding="utf-8")) + stack.enter_context( + open(args.input + ".shard" + str(i), "w", encoding="utf-8") + ) for i in range(args.num_shards) ] doc = [] - first_doc = [True]*args.num_shards + first_doc = [True] * args.num_shards def output_doc(i): if not first_doc[i]: @@ -48,5 +50,5 @@ def output_doc(i): output_doc(num_docs % args.num_shards) -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/scripts/split_train_valid_docs.py b/scripts/split_train_valid_docs.py index 9adf99634c..ff15978528 100644 --- a/scripts/split_train_valid_docs.py +++ b/scripts/split_train_valid_docs.py @@ -15,12 +15,13 @@ def main(): parser = argparse.ArgumentParser() - parser.add_argument('input') - parser.add_argument('sample_output', help='train output file') - parser.add_argument('remainder_output', help='valid output file') - parser.add_argument('-k', type=int, help="remainder size") - parser.add_argument('--lines', action='store_true', - help='split lines instead of docs') + parser.add_argument("input") + parser.add_argument("sample_output", help="train output file") + parser.add_argument("remainder_output", help="valid output file") + parser.add_argument("-k", type=int, help="remainder size") + parser.add_argument( + "--lines", action="store_true", help="split lines instead of docs" + ) args = parser.parse_args() assert args.k is not None @@ -43,7 +44,7 @@ def update_sample(doc): num_docs[0] += 1 doc.clear() - with open(args.input, 'r', encoding='utf-8') as h: + with open(args.input, "r", encoding="utf-8") as h: doc = [] for i, line in enumerate(h): if line.strip() == "": # empty line indicates new document @@ -62,7 +63,7 @@ def update_sample(doc): assert len(sample) == args.k - with open(args.sample_output, 'w', encoding='utf-8') as out: + with open(args.sample_output, "w", encoding="utf-8") as out: first = True for doc in sample: if not first and not args.lines: @@ -71,7 +72,7 @@ def update_sample(doc): for line in doc: out.write(line) - with open(args.remainder_output, 'w', encoding='utf-8') as out: + with open(args.remainder_output, "w", encoding="utf-8") as out: first = True for doc in remainder: if not first and not args.lines: @@ -81,5 +82,5 @@ def update_sample(doc): out.write(line) -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/scripts/spm_decode.py b/scripts/spm_decode.py index bd3961ab97..1c18b1d2a7 100644 --- a/scripts/spm_decode.py +++ b/scripts/spm_decode.py @@ -14,8 +14,9 @@ def main(): parser = argparse.ArgumentParser() - parser.add_argument("--model", required=True, - help="sentencepiece model to use for decoding") + parser.add_argument( + "--model", required=True, help="sentencepiece model to use for decoding" + ) parser.add_argument("--input", required=True, help="input file to decode") parser.add_argument("--input_format", choices=["piece", "id"], default="piece") args = parser.parse_args() @@ -24,11 +25,15 @@ def main(): sp.Load(args.model) if args.input_format == "piece": + def decode(l): return "".join(sp.DecodePieces(l)) + elif args.input_format == "id": + def decode(l): return "".join(sp.DecodeIds(l)) + else: raise NotImplementedError @@ -43,5 +48,6 @@ def tok2int(tok): elif args.input_format == "piece": print(decode(line.rstrip().split())) + if __name__ == "__main__": main() diff --git a/scripts/spm_encode.py b/scripts/spm_encode.py index e1cb54192a..83facfb3b1 100644 --- a/scripts/spm_encode.py +++ b/scripts/spm_encode.py @@ -16,53 +16,73 @@ def main(): parser = argparse.ArgumentParser() - parser.add_argument("--model", required=True, - help="sentencepiece model to use for encoding") - parser.add_argument("--inputs", nargs="+", default=['-'], - help="input files to filter/encode") - parser.add_argument("--outputs", nargs="+", default=['-'], - help="path to save encoded outputs") + parser.add_argument( + "--model", required=True, help="sentencepiece model to use for encoding" + ) + parser.add_argument( + "--inputs", nargs="+", default=["-"], help="input files to filter/encode" + ) + parser.add_argument( + "--outputs", nargs="+", default=["-"], help="path to save encoded outputs" + ) parser.add_argument("--output_format", choices=["piece", "id"], default="piece") - parser.add_argument("--min-len", type=int, metavar="N", - help="filter sentence pairs with fewer than N tokens") - parser.add_argument("--max-len", type=int, metavar="N", - help="filter sentence pairs with more than N tokens") + parser.add_argument( + "--min-len", + type=int, + metavar="N", + help="filter sentence pairs with fewer than N tokens", + ) + parser.add_argument( + "--max-len", + type=int, + metavar="N", + help="filter sentence pairs with more than N tokens", + ) args = parser.parse_args() - assert len(args.inputs) == len(args.outputs), \ - "number of input and output paths should match" + assert len(args.inputs) == len( + args.outputs + ), "number of input and output paths should match" sp = spm.SentencePieceProcessor() sp.Load(args.model) if args.output_format == "piece": + def encode(l): return sp.EncodeAsPieces(l) + elif args.output_format == "id": + def encode(l): return list(map(str, sp.EncodeAsIds(l))) + else: raise NotImplementedError if args.min_len is not None or args.max_len is not None: + def valid(line): - return ( - (args.min_len is None or len(line) >= args.min_len) - and (args.max_len is None or len(line) <= args.max_len) + return (args.min_len is None or len(line) >= args.min_len) and ( + args.max_len is None or len(line) <= args.max_len ) + else: + def valid(lines): return True with contextlib.ExitStack() as stack: inputs = [ - stack.enter_context(open(input, "r", encoding="utf-8")) \ - if input != "-" else sys.stdin + stack.enter_context(open(input, "r", encoding="utf-8")) + if input != "-" + else sys.stdin for input in args.inputs ] outputs = [ - stack.enter_context(open(output, "w", encoding="utf-8")) \ - if output != "-" else sys.stdout + stack.enter_context(open(output, "w", encoding="utf-8")) + if output != "-" + else sys.stdout for output in args.outputs ] diff --git a/scripts/test_fsdp.sh b/scripts/test_fsdp.sh new file mode 100755 index 0000000000..1f428a035e --- /dev/null +++ b/scripts/test_fsdp.sh @@ -0,0 +1,24 @@ +#!/usr/bin/env bash +rm -rf fsdp_dummy +mkdir -p fsdp_dummy +CUDA_VISIBLE_DEVICES=0,1,2,3 fairseq-train /private/home/sshleifer/data-bin/stories_mmap \ + --ddp-backend fully_sharded --fp16 --fp16-init-scale 4 \ + --cpu-offload --checkpoint-activations \ + --task language_modeling --tokens-per-sample 256 --batch-size 8 \ + --arch transformer_lm_gpt2_tiny \ + --optimizer cpu_adam --adam-betas "(0.9,0.98)" \ + --lr 0.0001 --lr-scheduler polynomial_decay --warmup-updates 5 --total-num-update 10 \ + --max-update 5 --log-format json --log-interval 1 \ + --save-interval-updates 5 --save-dir fsdp_dummy --disable-validation \ + --restore-file x.pt "$@" + +# Now we try to load the checkpoint +CUDA_VISIBLE_DEVICES=0,1 fairseq-train /private/home/sshleifer/data-bin/stories_mmap \ + --ddp-backend fully_sharded --fp16 --fp16-init-scale 4 \ + --cpu-offload --checkpoint-activations \ + --task language_modeling --tokens-per-sample 256 --batch-size 8 \ + --arch transformer_lm_gpt2_tiny \ + --optimizer cpu_adam --adam-betas "(0.9,0.98)" \ + --lr 0.0001 --lr-scheduler polynomial_decay --warmup-updates 5 --total-num-update 10 \ + --max-update 2 --log-format json --log-interval 1 \ + --save-interval-updates 2 --save-dir fsdp_dummy diff --git a/setup.py b/setup.py index 7e2358c7a4..1f3998de80 100644 --- a/setup.py +++ b/setup.py @@ -5,22 +5,49 @@ # LICENSE file in the root directory of this source tree. import os -from setuptools import setup, find_packages, Extension +import subprocess import sys +from setuptools import setup, find_packages, Extension + +from setuptools import Extension, find_packages, setup if sys.version_info < (3, 6): - sys.exit('Sorry, Python >= 3.6 is required for fairseq.') + sys.exit("Sorry, Python >= 3.6 is required for fairseq.") + + +def write_version_py(): + with open(os.path.join("fairseq", "version.txt")) as f: + version = f.read().strip() + + # append latest commit hash to version string + try: + sha = ( + subprocess.check_output(["git", "rev-parse", "HEAD"]) + .decode("ascii") + .strip() + ) + version += "+" + sha[:7] + except Exception: + pass + + # write version info to fairseq/version.py + with open(os.path.join("fairseq", "version.py"), "w") as f: + f.write('__version__ = "{}"\n'.format(version)) + return version -with open('README.md') as f: +version = write_version_py() + + +with open("README.md") as f: readme = f.read() -if sys.platform == 'darwin': - extra_compile_args = ['-stdlib=libc++', '-O3'] +if sys.platform == "darwin": + extra_compile_args = ["-stdlib=libc++", "-O3"] else: - extra_compile_args = ['-std=c++11', '-O3'] + extra_compile_args = ["-std=c++11", "-O3"] class NumpyExtension(Extension): @@ -33,6 +60,7 @@ def __init__(self, *args, **kwargs): @property def include_dirs(self): import numpy + return self.__include_dirs + [numpy.get_include()] @include_dirs.setter @@ -42,23 +70,23 @@ def include_dirs(self, dirs): extensions = [ Extension( - 'fairseq.libbleu', + "fairseq.libbleu", sources=[ - 'fairseq/clib/libbleu/libbleu.cpp', - 'fairseq/clib/libbleu/module.cpp', + "fairseq/clib/libbleu/libbleu.cpp", + "fairseq/clib/libbleu/module.cpp", ], extra_compile_args=extra_compile_args, ), NumpyExtension( - 'fairseq.data.data_utils_fast', - sources=['fairseq/data/data_utils_fast.pyx'], - language='c++', + "fairseq.data.data_utils_fast", + sources=["fairseq/data/data_utils_fast.pyx"], + language="c++", extra_compile_args=extra_compile_args, ), NumpyExtension( - 'fairseq.data.token_block_utils_fast', - sources=['fairseq/data/token_block_utils_fast.pyx'], - language='c++', + "fairseq.data.token_block_utils_fast", + sources=["fairseq/data/token_block_utils_fast.pyx"], + language="c++", extra_compile_args=extra_compile_args, ), ] @@ -70,93 +98,176 @@ def include_dirs(self, dirs): try: # torch is not available when generating docs from torch.utils import cpp_extension - extensions.extend([ - cpp_extension.CppExtension( - 'fairseq.libnat', - sources=[ - 'fairseq/clib/libnat/edit_dist.cpp', - ], - ) - ]) - if 'CUDA_HOME' in os.environ: - extensions.extend([ + extensions.extend( + [ + cpp_extension.CppExtension( + "fairseq.libbase", + sources=[ + "fairseq/clib/libbase/balanced_assignment.cpp", + ], + ) + ] + ) + + extensions.extend( + [ cpp_extension.CppExtension( - 'fairseq.libnat_cuda', + "fairseq.libnat", sources=[ - 'fairseq/clib/libnat_cuda/edit_dist.cu', - 'fairseq/clib/libnat_cuda/binding.cpp' + "fairseq/clib/libnat/edit_dist.cpp", ], - )]) - cmdclass['build_ext'] = cpp_extension.BuildExtension + ) + ] + ) + if "CUDA_HOME" in os.environ: + extensions.extend( + [ + cpp_extension.CppExtension( + "fairseq.libnat_cuda", + sources=[ + "fairseq/clib/libnat_cuda/edit_dist.cu", + "fairseq/clib/libnat_cuda/binding.cpp", + ], + ), + cpp_extension.CppExtension( + "fairseq.ngram_repeat_block_cuda", + sources=[ + "fairseq/clib/cuda/ngram_repeat_block_cuda.cpp", + "fairseq/clib/cuda/ngram_repeat_block_cuda_kernel.cu", + ], + ), + ] + ) + cmdclass["build_ext"] = cpp_extension.BuildExtension except ImportError: pass -if 'READTHEDOCS' in os.environ: +if "READTHEDOCS" in os.environ: # don't build extensions when generating docs extensions = [] - if 'build_ext' in cmdclass: - del cmdclass['build_ext'] + if "build_ext" in cmdclass: + del cmdclass["build_ext"] # use CPU build of PyTorch dependency_links = [ - 'https://download.pytorch.org/whl/cpu/torch-1.3.0%2Bcpu-cp36-cp36m-linux_x86_64.whl' + "https://download.pytorch.org/whl/cpu/torch-1.7.0%2Bcpu-cp36-cp36m-linux_x86_64.whl" ] else: dependency_links = [] -if 'clean' in sys.argv[1:]: +if "clean" in sys.argv[1:]: # Source: https://bit.ly/2NLVsgE print("deleting Cython files...") import subprocess - subprocess.run(['rm -f fairseq/*.so fairseq/**/*.so fairseq/*.pyd fairseq/**/*.pyd'], shell=True) - - -setup( - name='fairseq', - version='0.9.0', - description='Facebook AI Research Sequence-to-Sequence Toolkit', - url='https://github.com/pytorch/fairseq', - classifiers=[ - 'Intended Audience :: Science/Research', - 'License :: OSI Approved :: MIT License', - 'Programming Language :: Python :: 3.6', - 'Topic :: Scientific/Engineering :: Artificial Intelligence', - ], - long_description=readme, - long_description_content_type='text/markdown', - setup_requires=[ - 'cython', - 'numpy', - 'setuptools>=18.0', - ], - install_requires=[ - 'cffi', - 'cython', - 'numpy', - 'regex', - 'sacrebleu', - 'torch', - 'tqdm', - ], - dependency_links=dependency_links, - packages=find_packages(exclude=['scripts', 'tests']), - ext_modules=extensions, - test_suite='tests', - entry_points={ - 'console_scripts': [ - 'fairseq-eval-lm = fairseq_cli.eval_lm:cli_main', - 'fairseq-generate = fairseq_cli.generate:cli_main', - 'fairseq-interactive = fairseq_cli.interactive:cli_main', - 'fairseq-preprocess = fairseq_cli.preprocess:cli_main', - 'fairseq-score = fairseq_cli.score:cli_main', - 'fairseq-train = fairseq_cli.train:cli_main', - 'fairseq-validate = fairseq_cli.validate:cli_main', + + subprocess.run( + ["rm -f fairseq/*.so fairseq/**/*.so fairseq/*.pyd fairseq/**/*.pyd"], + shell=True, + ) + + +extra_packages = [] +if os.path.exists(os.path.join("fairseq", "model_parallel", "megatron", "mpu")): + extra_packages.append("fairseq.model_parallel.megatron.mpu") + + +def do_setup(package_data): + setup( + name="fairseq", + version=version, + description="Facebook AI Research Sequence-to-Sequence Toolkit", + url="https://github.com/pytorch/fairseq", + classifiers=[ + "Intended Audience :: Science/Research", + "License :: OSI Approved :: MIT License", + "Programming Language :: Python :: 3.6", + "Programming Language :: Python :: 3.7", + "Programming Language :: Python :: 3.8", + "Topic :: Scientific/Engineering :: Artificial Intelligence", + ], + long_description=readme, + long_description_content_type="text/markdown", + setup_requires=[ + "cython", + 'numpy<1.20.0; python_version<"3.7"', + 'numpy; python_version>="3.7"', + "setuptools>=18.0", ], - }, - cmdclass=cmdclass, - zip_safe=False, -) + install_requires=[ + "cffi", + "cython", + 'dataclasses; python_version<"3.7"', + "hydra-core>=1.0.7,<1.1", + "omegaconf<2.1", + 'numpy<1.20.0; python_version<"3.7"', + 'numpy; python_version>="3.7"', + "regex", + "sacrebleu>=1.4.12", + "torch", + "tqdm", + "bitarray", + "torchaudio>=0.8.0", + ], + dependency_links=dependency_links, + packages=find_packages( + exclude=[ + "examples", + "examples.*", + "scripts", + "scripts.*", + "tests", + "tests.*", + ] + ) + + extra_packages, + package_data=package_data, + ext_modules=extensions, + test_suite="tests", + entry_points={ + "console_scripts": [ + "fairseq-eval-lm = fairseq_cli.eval_lm:cli_main", + "fairseq-generate = fairseq_cli.generate:cli_main", + "fairseq-hydra-train = fairseq_cli.hydra_train:cli_main", + "fairseq-interactive = fairseq_cli.interactive:cli_main", + "fairseq-preprocess = fairseq_cli.preprocess:cli_main", + "fairseq-score = fairseq_cli.score:cli_main", + "fairseq-train = fairseq_cli.train:cli_main", + "fairseq-validate = fairseq_cli.validate:cli_main", + ], + }, + cmdclass=cmdclass, + zip_safe=False, + ) + + +def get_files(path, relative_to="fairseq"): + all_files = [] + for root, _dirs, files in os.walk(path, followlinks=True): + root = os.path.relpath(root, relative_to) + for file in files: + if file.endswith(".pyc"): + continue + all_files.append(os.path.join(root, file)) + return all_files + + +if __name__ == "__main__": + try: + # symlink examples into fairseq package so package_data accepts them + fairseq_examples = os.path.join("fairseq", "examples") + if "build_ext" not in sys.argv[1:] and not os.path.exists(fairseq_examples): + os.symlink(os.path.join("..", "examples"), fairseq_examples) + + package_data = { + "fairseq": ( + get_files(fairseq_examples) + get_files(os.path.join("fairseq", "config")) + ) + } + do_setup(package_data) + finally: + if "build_ext" not in sys.argv[1:] and os.path.islink(fairseq_examples): + os.unlink(fairseq_examples) diff --git a/tests/distributed/__init__.py b/tests/distributed/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/tests/test_bmuf.py b/tests/distributed/test_bmuf.py similarity index 59% rename from tests/test_bmuf.py rename to tests/distributed/test_bmuf.py index 30563bdb50..8b7cadb094 100644 --- a/tests/test_bmuf.py +++ b/tests/distributed/test_bmuf.py @@ -4,14 +4,16 @@ # LICENSE file in the root directory of this source tree. import argparse -from multiprocessing import Manager +import functools import random import unittest +from multiprocessing import Manager import torch import torch.nn as nn - -from fairseq import distributed_utils, optim +from fairseq import optim +from fairseq.distributed import utils as distributed_utils +from omegaconf import OmegaConf class Model(nn.Module): @@ -24,13 +26,14 @@ def forward(self, input): return output -def setup_model_loss_criterion(args, rank, is_cuda): +def setup_model_loss_criterion(cfg, args, rank, is_cuda): """ setup model, criterion and optimizer based on input args """ args.distributed_rank = rank - if args.distributed_world_size > 1: - distributed_utils.distributed_init(args) + cfg.distributed_training.distributed_rank = args.distributed_rank + if cfg.distributed_training.distributed_world_size > 1: + distributed_utils.distributed_init(cfg) torch.manual_seed(1) model = Model(args.input_size, args.nb_classes) loss_fn = nn.CrossEntropyLoss() @@ -39,7 +42,10 @@ def setup_model_loss_criterion(args, rank, is_cuda): loss_fn = loss_fn.cuda() optimizer = optim.sgd.SGD(args, model.parameters()) - optimizer = optim.FairseqBMUF(args, optimizer) + optimizer = optim.FairseqBMUF( + cfg=cfg.bmuf, + optimizer=optimizer + ) return model, loss_fn, optimizer @@ -53,13 +59,13 @@ def train_step(input, target, model, loss_fn, optimizer, **unused): optimizer.step() -def single_gpu_training(args, rank, iterations, shared_results): +def single_gpu_training(cfg, args, rank, iterations, shared_results): is_cuda = torch.cuda.is_available() if is_cuda: torch.cuda.set_device(rank) - model, loss_fn, optimizer = setup_model_loss_criterion(args, rank, is_cuda) + model, loss_fn, optimizer = setup_model_loss_criterion(cfg, args, rank, is_cuda) for _ in range(iterations): input = torch.randn(1, args.input_size) @@ -104,41 +110,64 @@ def setup_args(): args.distributed_init_host = "localhost" args.distributed_port = port + 1 args.local_world_size = args.distributed_world_size - return args + + cfg = OmegaConf.create() + cfg.optimization = OmegaConf.create() + cfg.common = OmegaConf.create() + cfg.distributed_training = OmegaConf.create() + cfg.dataset = OmegaConf.create() + cfg.bmuf = OmegaConf.create() + cfg.optimizer = OmegaConf.create() + + cfg.bmuf.global_sync_iter = args.global_sync_iter + cfg.bmuf.block_momentum = args.block_momentum + cfg.bmuf.block_lr = args.block_lr + cfg.dataset.batch_size = args.batch_size + cfg.optimization.lr = args.lr + cfg.optimizer.momentum = args.momentum + cfg.optimizer.weight_decay = args.weight_decay + cfg.bmuf.warmup_iterations = args.warmup_iterations + cfg.bmuf.use_nbm = args.use_nbm + cfg.bmuf.average_sync = args.average_sync + cfg.common.model_parallel_size = args.model_parallel_size + cfg.distributed_training.distributed_backend = args.distributed_backend + cfg.distributed_training.distributed_world_size = args.distributed_world_size + cfg.bmuf.distributed_world_size = args.distributed_world_size + cfg.distributed_training.distributed_init_method = args.distributed_init_method + cfg.distributed_training.distributed_port = args.distributed_port + + return cfg, args @unittest.skipIf(torch.cuda.device_count() < 2, "test requires 2 GPUs") class TestBMUF(unittest.TestCase): - def bmuf_process(self, args, iterations): + def bmuf_process(self, cfg, args, iterations): processes = [] results = Manager().dict() - ctx = torch.multiprocessing.get_context("spawn") - for rank in range(args.distributed_world_size): - p = ctx.Process( - target=single_gpu_training, args=(args, rank, iterations, results) - ) - p.start() - processes.append(p) - - for p in processes: - p.join() + torch.multiprocessing.spawn( + fn=functools.partial(single_gpu_training, cfg, args), + args=(iterations, results), + nprocs=args.distributed_world_size, + join=True, + ) return results def test_bmuf_sync(self): # Train model for 1 iteration and do bmuf sync without doing warmup - args = setup_args() + cfg, args = setup_args() iterations = 1 - results = self.bmuf_process(args, iterations) + results = self.bmuf_process(cfg, args, iterations) # Make sure params in both machines are same assert len(results) == 2 self.assertAlmostEqual(results[0], results[1]) def test_warmup_sync(self): # Train model for 20 iteration and do warmup sync without doing bmuf sync - args = setup_args() + cfg, args = setup_args() args.warmup_iterations = 20 + cfg.bmuf.warmup_iterations = args.warmup_iterations iterations = 20 - results = self.bmuf_process(args, iterations) + results = self.bmuf_process(cfg, args, iterations) # Make sure params in both machines are same assert len(results) == 2 self.assertAlmostEqual(results[0], results[1]) @@ -146,22 +175,27 @@ def test_warmup_sync(self): def test_warmup_sync_bmuf_sync(self): # Train model for 25 iteration and do warmup sync after 20 iteration # and bmuf sync after 25 iteration - args = setup_args() + cfg, args = setup_args() args.warmup_iterations = 20 args.global_sync_iter = 5 + cfg.bmuf.warmup_iterations = args.warmup_iterations + cfg.bmuf.global_sync_iter = args.global_sync_iter iterations = 25 - results = self.bmuf_process(args, iterations) + results = self.bmuf_process(cfg, args, iterations) # Make sure params in both machines are same assert len(results) == 2 self.assertAlmostEqual(results[0], results[1]) def test_single_gpu_bmuf(self): # Train model for 5 iterations and use GPU 1 - args = setup_args() + cfg, args = setup_args() args.distributed_world_size = 1 args.warmup_iterations = 5 + cfg.distributed_training.distributed_world_size = args.distributed_world_size + cfg.bmuf.distributed_world_size = args.distributed_world_size + cfg.bmuf.warmup_iterations = args.warmup_iterations iterations = 20 - results = self.bmuf_process(args, iterations) + results = self.bmuf_process(cfg, args, iterations) assert len(results) == 1 def assertAlmostEqual(self, t1, t2): @@ -169,5 +203,5 @@ def assertAlmostEqual(self, t1, t2): self.assertLess((t1 - t2).abs().max(), 1e-4) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/tests/distributed/test_distributed_timeout_wrapper.py b/tests/distributed/test_distributed_timeout_wrapper.py new file mode 100644 index 0000000000..27908b9d3f --- /dev/null +++ b/tests/distributed/test_distributed_timeout_wrapper.py @@ -0,0 +1,54 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +import signal +import time +import unittest + +import torch +from torch import nn + +from fairseq.distributed import DistributedTimeoutWrapper + + +class ModuleWithDelay(nn.Module): + + def __init__(self, delay): + super().__init__() + self.delay = delay + + def forward(self, x): + time.sleep(self.delay) + return x + + +class TestDistributedTimeoutWrapper(unittest.TestCase): + + def setUp(self): + logging.disable(logging.CRITICAL) + + def tearDown(self): + logging.disable(logging.NOTSET) + + def test_no_timeout(self): + module = DistributedTimeoutWrapper(ModuleWithDelay(1), 0, signal.SIGINT) + module(torch.rand(5)) + module.stop_timeout() + + def test_timeout_safe(self): + module = DistributedTimeoutWrapper(ModuleWithDelay(1), 10, signal.SIGINT) + module(torch.rand(5)) + module.stop_timeout() + + def test_timeout_killed(self): + with self.assertRaises(KeyboardInterrupt): + module = DistributedTimeoutWrapper(ModuleWithDelay(5), 1, signal.SIGINT) + module(torch.rand(5)) + module.stop_timeout() + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/distributed/test_module_proxy_wrapper.py b/tests/distributed/test_module_proxy_wrapper.py new file mode 100644 index 0000000000..2803a044cd --- /dev/null +++ b/tests/distributed/test_module_proxy_wrapper.py @@ -0,0 +1,75 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import unittest + +import torch +from torch import nn + +from fairseq.distributed import ModuleProxyWrapper + +from .utils import objects_are_equal + + +class MockDDPWrapper(nn.Module): + """A simple wrapper with an interface similar to DistributedDataParallel.""" + + def __init__(self, module): + super().__init__() + self.module = module + + def forward(self, x): + return self.module(x) + + +class Model(nn.Module): + def __init__(self): + super().__init__() + self.linear = nn.Linear(5, 10) + self.xyz = "hello" + + def forward(self, x): + return self.linear(x) + + def get_xyz(self): + return self.xyz + + +class TestModuleProxyWrapper(unittest.TestCase): + + def _get_module(self): + module = Model() + wrapped_module = MockDDPWrapper(module) + wrapped_module = ModuleProxyWrapper(wrapped_module) + return wrapped_module, module + + def test_getattr_forwarding(self): + wrapped_module, module = self._get_module() + assert module.xyz == "hello" + assert module.get_xyz() == "hello" + assert wrapped_module.xyz == "hello" + + wrapped_module.xyz = "world" + assert wrapped_module.xyz == "world" + assert module.get_xyz() == "hello" + + def test_state_dict(self): + wrapped_module, module = self._get_module() + assert objects_are_equal(wrapped_module.state_dict(), module.state_dict()) + + def test_load_state_dict(self): + wrapped_module, module = self._get_module() + wrapped_module.load_state_dict(module.state_dict()) + input = torch.rand(4, 5) + torch.testing.assert_allclose(wrapped_module(input), module(input)) + + def test_forward(self): + wrapped_module, module = self._get_module() + input = torch.rand(4, 5) + torch.testing.assert_allclose(wrapped_module(input), module(input)) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/distributed/test_utils.py b/tests/distributed/test_utils.py new file mode 100644 index 0000000000..30f995b67a --- /dev/null +++ b/tests/distributed/test_utils.py @@ -0,0 +1,124 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import functools +import sys +import unittest + +import torch + +from fairseq.distributed import utils as dist_utils + +from .utils import objects_are_equal, spawn_and_init + + +class DistributedTest(unittest.TestCase): + def setUp(self): + if not torch.cuda.is_available(): + raise unittest.SkipTest("CUDA not available, skipping test") + if sys.platform == "win32": + raise unittest.SkipTest("NCCL doesn't support Windows, skipping test") + if torch.cuda.device_count() < 2: + raise unittest.SkipTest("distributed tests require 2+ GPUs, skipping") + + +class TestBroadcastObject(DistributedTest): + def test_str(self): + spawn_and_init( + functools.partial( + TestBroadcastObject._test_broadcast_object, "hello world" + ), + world_size=2, + ) + + def test_tensor(self): + spawn_and_init( + functools.partial( + TestBroadcastObject._test_broadcast_object, + torch.rand(5), + ), + world_size=2, + ) + + def test_complex(self): + spawn_and_init( + functools.partial( + TestBroadcastObject._test_broadcast_object, + { + "a": "1", + "b": [2, torch.rand(2, 3), 3], + "c": (torch.rand(2, 3), 4), + "d": {5, torch.rand(5)}, + "e": torch.rand(5), + "f": torch.rand(5).int().cuda(), + }, + ), + world_size=2, + ) + + @staticmethod + def _test_broadcast_object(ref_obj, rank, group): + obj = dist_utils.broadcast_object( + ref_obj if rank == 0 else None, src_rank=0, group=group + ) + assert objects_are_equal(ref_obj, obj) + + +class TestAllGatherList(DistributedTest): + def test_str_equality(self): + spawn_and_init( + functools.partial( + TestAllGatherList._test_all_gather_list_equality, + "hello world", + ), + world_size=2, + ) + + def test_tensor_equality(self): + spawn_and_init( + functools.partial( + TestAllGatherList._test_all_gather_list_equality, + torch.rand(5), + ), + world_size=2, + ) + + def test_complex_equality(self): + spawn_and_init( + functools.partial( + TestAllGatherList._test_all_gather_list_equality, + { + "a": "1", + "b": [2, torch.rand(2, 3), 3], + "c": (torch.rand(2, 3), 4), + "d": {5, torch.rand(5)}, + "e": torch.rand(5), + "f": torch.rand(5).int(), + }, + ), + world_size=2, + ) + + @staticmethod + def _test_all_gather_list_equality(ref_obj, rank, group): + objs = dist_utils.all_gather_list(ref_obj, group) + for obj in objs: + assert objects_are_equal(ref_obj, obj) + + def test_rank_tensor(self): + spawn_and_init( + TestAllGatherList._test_all_gather_list_rank_tensor, world_size=2 + ) + + @staticmethod + def _test_all_gather_list_rank_tensor(rank, group): + obj = torch.tensor([rank]) + objs = dist_utils.all_gather_list(obj, group) + for i, obj in enumerate(objs): + assert obj.item() == i + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/distributed/utils.py b/tests/distributed/utils.py new file mode 100644 index 0000000000..c8040392a8 --- /dev/null +++ b/tests/distributed/utils.py @@ -0,0 +1,62 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import functools +import tempfile + +import torch + + +def spawn_and_init(fn, world_size, args=None): + if args is None: + args = () + with tempfile.NamedTemporaryFile(delete=False) as tmp_file: + torch.multiprocessing.spawn( + fn=functools.partial(init_and_run, fn, args), + args=(world_size, tmp_file.name,), + nprocs=world_size, + join=True, + ) + + +def distributed_init(rank, world_size, tmp_file): + torch.distributed.init_process_group( + backend="nccl", + init_method="file://{}".format(tmp_file), + world_size=world_size, + rank=rank, + ) + torch.cuda.set_device(rank) + + +def init_and_run(fn, args, rank, world_size, tmp_file): + distributed_init(rank, world_size, tmp_file) + group = torch.distributed.new_group() + fn(rank, group, *args) + + +def objects_are_equal(a, b) -> bool: + if type(a) is not type(b): + return False + if isinstance(a, dict): + if set(a.keys()) != set(b.keys()): + return False + for k in a.keys(): + if not objects_are_equal(a[k], b[k]): + return False + return True + elif isinstance(a, (list, tuple, set)): + if len(a) != len(b): + return False + return all(objects_are_equal(x, y) for x, y in zip(a, b)) + elif torch.is_tensor(a): + return ( + a.size() == b.size() + and a.dtype == b.dtype + and a.device == b.device + and torch.all(a == b) + ) + else: + return a == b diff --git a/tests/gpu/__init__.py b/tests/gpu/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/tests/gpu/test_binaries_gpu.py b/tests/gpu/test_binaries_gpu.py new file mode 100644 index 0000000000..de8c242613 --- /dev/null +++ b/tests/gpu/test_binaries_gpu.py @@ -0,0 +1,449 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import contextlib +import logging +import json +import os +import tempfile +import unittest +from io import StringIO + +import torch +from fairseq import options +from fairseq_cli import train +from tests.utils import ( + create_dummy_data, + generate_main, + preprocess_lm_data, + preprocess_translation_data, + train_translation_model, +) + + +@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU") +class TestTranslationGPU(unittest.TestCase): + def setUp(self): + logging.disable(logging.CRITICAL) + + def tearDown(self): + logging.disable(logging.NOTSET) + + def test_fp16_multigpu(self): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_fp16") as data_dir: + log = os.path.join(data_dir, "train.log") + create_dummy_data(data_dir) + preprocess_translation_data(data_dir) + train_translation_model( + data_dir, + "fconv_iwslt_de_en", + ["--fp16", "--log-file", log], + world_size=min(torch.cuda.device_count(), 2), + ) + generate_main(data_dir) + assert os.path.exists(log) + + @staticmethod + def parse_logs(logfile): + logs = [] + for ln in open(logfile, "r").readlines(): + try: + logs.append(json.loads(ln)) + except json.JSONDecodeError: + continue + return logs + + def test_resume_training_fsdp(self): + self._test_resume_training(["--ddp-backend", "fully_sharded"]) + + def test_resume_training_fsdp_sharded_state(self): + self._test_resume_training(["--ddp-backend", "fully_sharded", "--use-sharded-state"]) + + def test_resume_training_noc10d(self): + self._test_resume_training([]) + + def _test_resume_training(self, extra_clargs, arch="fconv_iwslt_de_en"): + flags = [ + "--fp16", + "--log-format", + "json", + "--max-update", + "10", + "--save-interval-updates", + "2", + "--log-interval", + "1", + ] + extra_clargs + world_size = min(torch.cuda.device_count(), 2) + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_fp16") as data_dir: + log = os.path.join(data_dir, "train.log") + create_dummy_data(data_dir) + preprocess_translation_data(data_dir) + train_translation_model( + data_dir, arch, flags + ["--log-file", log], world_size=world_size, + ) + log2 = os.path.join(data_dir, "resume.log") + restore_file = os.path.join(data_dir, "checkpoint_1_2.pt") + train_translation_model( + data_dir, + arch, + flags + ["--log-file", log2, "--restore-file", restore_file], + world_size=world_size, + ) + + l1 = self.parse_logs(log) + l2 = self.parse_logs(log2) + assert int(l2[0]["num_updates"]) == 3, f"{l1}\n\n {l2}" + for k in [ + "train_loss", + "train_num_updates", + "train_ppl", + "train_gnorm", + ]: + from_scratch, resumed = l1[-1][k], l2[-1][k] + assert ( + from_scratch == resumed + ), f"difference at {k} {from_scratch} != {resumed}" + + def test_memory_efficient_fp16(self): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_memory_efficient_fp16") as data_dir: + create_dummy_data(data_dir) + preprocess_translation_data(data_dir) + train_translation_model( + data_dir, "fconv_iwslt_de_en", ["--memory-efficient-fp16"] + ) + generate_main(data_dir) + + def test_transformer_fp16(self): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_transformer") as data_dir: + create_dummy_data(data_dir) + preprocess_translation_data(data_dir) + train_translation_model( + data_dir, + "transformer_iwslt_de_en", + [ + "--encoder-layers", + "2", + "--decoder-layers", + "2", + "--encoder-embed-dim", + "64", + "--decoder-embed-dim", + "64", + "--fp16", + ], + run_validation=True, + ) + generate_main(data_dir) + + @unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU") + def test_amp(self): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_amp") as data_dir: + create_dummy_data(data_dir) + preprocess_translation_data(data_dir) + train_translation_model(data_dir, "fconv_iwslt_de_en", ["--amp"]) + generate_main(data_dir) + + @unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU") + def test_transformer_amp(self): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_transformer") as data_dir: + create_dummy_data(data_dir) + preprocess_translation_data(data_dir) + train_translation_model( + data_dir, + "transformer_iwslt_de_en", + [ + "--encoder-layers", + "2", + "--decoder-layers", + "2", + "--encoder-embed-dim", + "64", + "--decoder-embed-dim", + "64", + "--amp", + ], + run_validation=True, + ) + generate_main(data_dir) + + @unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU") + def test_levenshtein_transformer(self): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory( + "test_levenshtein_transformer" + ) as data_dir: + create_dummy_data(data_dir) + preprocess_translation_data(data_dir, ["--joined-dictionary"]) + train_translation_model( + data_dir, + "levenshtein_transformer", + [ + "--apply-bert-init", + "--early-exit", + "6,6,6", + "--criterion", + "nat_loss", + ], + task="translation_lev", + ) + gen_config = [ + "--task", + "translation_lev", + "--iter-decode-max-iter", + "9", + "--iter-decode-eos-penalty", + "0", + "--print-step", + ] + # non-ensemble generation + generate_main(data_dir, gen_config) + # ensemble generation + generate_main( + data_dir, + gen_config, + path=os.pathsep.join( + [ + os.path.join(data_dir, "checkpoint_last.pt"), + os.path.join(data_dir, "checkpoint_last.pt"), + ] + ), + ) + + def test_fsdp_checkpoint_generate(self): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_fsdp_sharded") as data_dir: + log = os.path.join(data_dir, "train.log") + create_dummy_data(data_dir) + preprocess_translation_data(data_dir) + world_size = min(torch.cuda.device_count(), 2) + train_translation_model( + data_dir, + "fconv_iwslt_de_en", + ["--log-file", log, "--ddp-backend", "fully_sharded"], + world_size=world_size, + ) + generate_main(data_dir) + assert os.path.exists(log) + + def test_fsdp_sharded_checkpoint_generate(self): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_fsdp_sharded") as data_dir: + log = os.path.join(data_dir, "train.log") + create_dummy_data(data_dir) + preprocess_translation_data(data_dir) + world_size = min(torch.cuda.device_count(), 2) + train_translation_model( + data_dir, + "fconv_iwslt_de_en", + ["--log-file", log, "--ddp-backend", "fully_sharded", "--use-sharded-state"], + world_size=world_size, + ) + generate_main(data_dir, ["--checkpoint-shard-count", str(world_size)]) + assert os.path.exists(log) + + +def _quantize_language_model(data_dir, arch, extra_flags=None, run_validation=False): + train_parser = options.get_training_parser() + train_args = options.parse_args_and_arch( + train_parser, + [ + "--task", + "language_modeling", + data_dir, + "--arch", + arch, + "--optimizer", + "adam", + "--lr", + "0.0001", + "--criterion", + "adaptive_loss", + "--adaptive-softmax-cutoff", + "5,10,15", + "--max-tokens", + "500", + "--tokens-per-sample", + "500", + "--save-dir", + data_dir, + "--max-epoch", + "1", + "--no-progress-bar", + "--distributed-world-size", + "1", + "--ddp-backend", + "no_c10d", + "--num-workers", + "0", + ] + + (extra_flags or []), + ) + train.main(train_args) + + # try scalar quantization + scalar_quant_train_parser = options.get_training_parser() + scalar_quant_train_args = options.parse_args_and_arch( + scalar_quant_train_parser, + [ + "--task", + "language_modeling", + data_dir, + "--arch", + arch, + "--optimizer", + "adam", + "--lr", + "0.0001", + "--criterion", + "adaptive_loss", + "--adaptive-softmax-cutoff", + "5,10,15", + "--max-tokens", + "500", + "--tokens-per-sample", + "500", + "--save-dir", + data_dir, + "--max-update", + "3", + "--no-progress-bar", + "--distributed-world-size", + "1", + "--ddp-backend", + "no_c10d", + "--num-workers", + "0", + "--quant-noise-scalar", + "0.5", + ] + + (extra_flags or []), + ) + train.main(scalar_quant_train_args) + + # try iterative PQ quantization + quantize_parser = options.get_training_parser() + quantize_args = options.parse_args_and_arch( + quantize_parser, + [ + "--task", + "language_modeling", + data_dir, + "--arch", + arch, + "--optimizer", + "adam", + "--lr", + "0.0001", + "--criterion", + "adaptive_loss", + "--adaptive-softmax-cutoff", + "5,10,15", + "--max-tokens", + "50", + "--tokens-per-sample", + "50", + "--max-update", + "6", + "--no-progress-bar", + "--distributed-world-size", + "1", + "--ddp-backend", + "no_c10d", + "--num-workers", + "0", + "--restore-file", + os.path.join(data_dir, "checkpoint_last.pt"), + "--reset-optimizer", + "--quantization-config-path", + os.path.join( + os.path.dirname(__file__), "transformer_quantization_config.yaml" + ), + ] + + (extra_flags or []), + ) + train.main(quantize_args) + + +@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU") +class TestQuantization(unittest.TestCase): + def setUp(self): + logging.disable(logging.CRITICAL) + + def tearDown(self): + logging.disable(logging.NOTSET) + + def test_quantization(self): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_quantization") as data_dir: + create_dummy_data(data_dir) + preprocess_lm_data(data_dir) + # tests both scalar and iterative PQ quantization + _quantize_language_model(data_dir, "transformer_lm") + + +@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU") +class TestOptimizersGPU(unittest.TestCase): + def setUp(self): + logging.disable(logging.CRITICAL) + + def tearDown(self): + logging.disable(logging.NOTSET) + + def test_flat_grads(self): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_flat_grads") as data_dir: + # Use just a bit of data and tiny model to keep this test runtime reasonable + create_dummy_data(data_dir, num_examples=10, maxlen=5) + preprocess_translation_data(data_dir) + with self.assertRaises(RuntimeError): + # adafactor isn't compatible with flat grads, which + # are used by default with --fp16 + train_translation_model( + data_dir, + "lstm", + [ + "--required-batch-size-multiple", + "1", + "--encoder-layers", + "1", + "--encoder-hidden-size", + "32", + "--decoder-layers", + "1", + "--optimizer", + "adafactor", + "--fp16", + ], + ) + # but it should pass once we set --fp16-no-flatten-grads + train_translation_model( + data_dir, + "lstm", + [ + "--required-batch-size-multiple", + "1", + "--encoder-layers", + "1", + "--encoder-hidden-size", + "32", + "--decoder-layers", + "1", + "--optimizer", + "adafactor", + "--fp16", + "--fp16-no-flatten-grads", + ], + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/gpu/test_ema_gpu.py b/tests/gpu/test_ema_gpu.py new file mode 100644 index 0000000000..337107d69a --- /dev/null +++ b/tests/gpu/test_ema_gpu.py @@ -0,0 +1,200 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import unittest +from copy import deepcopy +from dataclasses import dataclass +from typing import Optional + +import torch +from fairseq.models.ema import EMA + + +class DummyModule(torch.nn.Module): + def __init__(self) -> None: + """LightningModule for testing purposes + + Args: + epoch_min_loss_override (int, optional): Pass in an epoch that will be set to the minimum + validation loss for testing purposes (zero based). If None this is ignored. Defaults to None. + """ + super().__init__() + self.layer = torch.nn.Linear(in_features=32, out_features=2) + self.another_layer = torch.nn.Linear(in_features=2, out_features=2) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.layer(x) + return self.another_layer(x) + + +@dataclass +class EMAConfig(object): + ema_decay: float = 0.99 + ema_start_update: int = 0 + ema_fp32: bool = False + ema_seed_model: Optional[str] = None + + +@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU") +class TestEMAGPU(unittest.TestCase): + def assertTorchAllClose(self, x, y, atol=1e-8, rtol=1e-5, msg=None): + diff = x.float() - y.float() + diff_norm = torch.norm(diff) + other_norm = torch.norm(y.float()) + + if msg is None: + msg = "|input - other| > {} + {} * |other|".format( + atol, rtol + ) + + self.assertLessEqual( + diff_norm, + atol + rtol * other_norm, + msg=msg, + ) + + def test_ema(self): + model = DummyModule().cuda() + optimizer = torch.optim.SGD(model.parameters(), lr=0.01) + state = deepcopy(model.state_dict()) + config = EMAConfig() + ema = EMA(model, config) + + # set decay + ema._set_decay(config.ema_decay) + self.assertEqual(ema.get_decay(), config.ema_decay) + + # get model + self.assertEqual(ema.get_model(), ema.model) + + # Since fp32 params is not used, it should be of size 0 + self.assertEqual(len(ema.fp32_params), 0) + + # EMA step + x = torch.randn(32).cuda() + y = model(x) + loss = y.sum() + loss.backward() + optimizer.step() + + ema.step(model) + + ema_state_dict = ema.get_model().state_dict() + + for key, param in model.state_dict().items(): + prev_param = state[key] + ema_param = ema_state_dict[key] + + if "version" in key: + # Do not decay a model.version pytorch param + continue + self.assertTorchAllClose( + ema_param, + config.ema_decay * prev_param + (1 - config.ema_decay) * param, + ) + + # Since fp32 params is not used, it should be of size 0 + self.assertEqual(len(ema.fp32_params), 0) + + # Load EMA into model + model2 = DummyModule().cuda() + ema.reverse(model2) + + for key, param in model2.state_dict().items(): + ema_param = ema_state_dict[key] + self.assertTrue( + torch.allclose(ema_param, param) + ) + + def test_ema_fp32(self): + model = DummyModule().cuda().half() + optimizer = torch.optim.SGD(model.parameters(), lr=0.01) + state = deepcopy(model.state_dict()) + config = EMAConfig(ema_fp32=True) + ema = EMA(model, config) + + x = torch.randn(32).cuda() + y = model(x.half()) + loss = y.sum() + loss.backward() + optimizer.step() + + ema.step(model) + + for key, param in model.state_dict().items(): + prev_param = state[key] + ema_param = ema.get_model().state_dict()[key] + + if "version" in key: + # Do not decay a model.version pytorch param + continue + self.assertIn(key, ema.fp32_params) + + # EMA update is done in fp32, and hence the EMA param must be + # closer to the EMA update done in fp32 than in fp16. + self.assertLessEqual( + torch.norm( + ema_param.float() - + (config.ema_decay * prev_param.float() + (1 - config.ema_decay) * param.float()).half().float() + ), + torch.norm( + ema_param.float() - + (config.ema_decay * prev_param + (1 - config.ema_decay) * param).float() + ), + ) + self.assertTorchAllClose( + ema_param, + (config.ema_decay * prev_param.float() + (1 - config.ema_decay) * param.float()).half(), + ) + + def test_ema_fp16(self): + model = DummyModule().cuda().half() + optimizer = torch.optim.SGD(model.parameters(), lr=0.01) + state = deepcopy(model.state_dict()) + config = EMAConfig(ema_fp32=False) + ema = EMA(model, config) + + # Since fp32 params is not used, it should be of size 0 + self.assertEqual(len(ema.fp32_params), 0) + + x = torch.randn(32).cuda() + y = model(x.half()) + loss = y.sum() + loss.backward() + optimizer.step() + + ema.step(model) + + for key, param in model.state_dict().items(): + prev_param = state[key] + ema_param = ema.get_model().state_dict()[key] + + if "version" in key: + # Do not decay a model.version pytorch param + continue + + # EMA update is done in fp16, and hence the EMA param must be + # closer to the EMA update done in fp16 than in fp32. + self.assertLessEqual( + torch.norm( + ema_param.float() - + (config.ema_decay * prev_param + (1 - config.ema_decay) * param).float() + ), + torch.norm( + ema_param.float() - + (config.ema_decay * prev_param.float() + (1 - config.ema_decay) * param.float()).half().float() + ), + ) + self.assertTorchAllClose( + ema_param, + config.ema_decay * prev_param + (1 - config.ema_decay) * param, + ) + + # Since fp32 params is not used, it should be of size 0 + self.assertEqual(len(ema.fp32_params), 0) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/transformer_quantization_config.yaml b/tests/gpu/transformer_quantization_config.yaml similarity index 100% rename from tests/transformer_quantization_config.yaml rename to tests/gpu/transformer_quantization_config.yaml diff --git a/tests/speech_recognition/asr_test_base.py b/tests/speech_recognition/asr_test_base.py index 7482858ffc..8c5d414e7b 100644 --- a/tests/speech_recognition/asr_test_base.py +++ b/tests/speech_recognition/asr_test_base.py @@ -7,6 +7,7 @@ import numpy as np import torch +from examples.speech_recognition.data.data_utils import lengths_to_encoder_padding_mask from fairseq.data import data_utils as fairseq_data_utils from fairseq.data.dictionary import Dictionary from fairseq.models import ( @@ -17,8 +18,7 @@ FairseqEncoderModel, FairseqModel, ) -from fairseq.tasks.fairseq_task import FairseqTask -from examples.speech_recognition.data.data_utils import lengths_to_encoder_padding_mask +from fairseq.tasks.fairseq_task import LegacyFairseqTask DEFAULT_TEST_VOCAB_SIZE = 100 @@ -37,7 +37,7 @@ def get_dummy_dictionary(vocab_size=DEFAULT_TEST_VOCAB_SIZE): return dummy_dict -class DummyTask(FairseqTask): +class DummyTask(LegacyFairseqTask): def __init__(self, args): super().__init__(args) self.dictionary = get_dummy_dictionary() @@ -172,9 +172,8 @@ def check_encoder_output(encoder_output, batch_size=None): "encoder_padding_mask must be a torch.Tensor" + _current_postion_info() ) return False, msg - if ( - mask.dtype != torch.uint8 - and (not hasattr(torch, 'bool') or mask.dtype != torch.bool) + if mask.dtype != torch.uint8 and ( + not hasattr(torch, "bool") or mask.dtype != torch.bool ): msg = ( "encoder_padding_mask must have dtype of uint8" @@ -273,6 +272,7 @@ def setUpModel(self, model_cls, extra_args_setters=None): model_cls.add_args(parser) args = parser.parse_args([]) + if extra_args_setters is not None: for args_setter in extra_args_setters: args_setter(args) @@ -516,14 +516,14 @@ def setUpArgs(self): def setUp(self): args = self.setUpArgs() self.model = DummyEncoderModel(encoder=DummyEncoder()) - self.criterion = self.criterion_cls.build_criterion(args=args, task=DummyTask(args)) + self.criterion = self.criterion_cls.build_criterion(args, task=DummyTask(args)) def get_src_tokens(self, correct_prediction, aggregate): """ - correct_prediction: True if the net_output (src_tokens) should - predict the correct target - aggregate: True if the criterion expects net_output (src_tokens) - aggregated across time axis + correct_prediction: True if the net_output (src_tokens) should + predict the correct target + aggregate: True if the criterion expects net_output (src_tokens) + aggregated across time axis """ predicted_idx = 0 if correct_prediction else 1 if aggregate: diff --git a/tests/speech_recognition/test_cross_entropy.py b/tests/speech_recognition/test_cross_entropy.py index 508d490e01..b05400ed95 100644 --- a/tests/speech_recognition/test_cross_entropy.py +++ b/tests/speech_recognition/test_cross_entropy.py @@ -4,7 +4,10 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. -from examples.speech_recognition.criterions.cross_entropy_acc import CrossEntropyWithAccCriterion +from examples.speech_recognition.criterions.cross_entropy_acc import ( + CrossEntropyWithAccCriterion, +) + from .asr_test_base import CrossEntropyCriterionTestBase diff --git a/tests/speech_recognition/test_data_utils.py b/tests/speech_recognition/test_data_utils.py new file mode 100644 index 0000000000..a72e0b6694 --- /dev/null +++ b/tests/speech_recognition/test_data_utils.py @@ -0,0 +1,62 @@ +#!/usr/bin/env python3 +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +import unittest + +import torch +from examples.speech_recognition.data import data_utils + + +class DataUtilsTest(unittest.TestCase): + def test_normalization(self): + sample_len1 = torch.tensor( + [ + [ + -0.7661, + -1.3889, + -2.0972, + -0.9134, + -0.7071, + -0.9765, + -0.8700, + -0.8283, + 0.7512, + 1.3211, + 2.1532, + 2.1174, + 1.2800, + 1.2633, + 1.6147, + 1.6322, + 2.0723, + 3.1522, + 3.2852, + 2.2309, + 2.5569, + 2.2183, + 2.2862, + 1.5886, + 0.8773, + 0.8725, + 1.2662, + 0.9899, + 1.1069, + 1.3926, + 1.2795, + 1.1199, + 1.1477, + 1.2687, + 1.3843, + 1.1903, + 0.8355, + 1.1367, + 1.2639, + 1.4707, + ] + ] + ) + out = data_utils.apply_mv_norm(sample_len1) + assert not torch.isnan(out).any() + assert (out == sample_len1).all() diff --git a/tests/test_activation_checkpointing.py b/tests/test_activation_checkpointing.py new file mode 100644 index 0000000000..647a957288 --- /dev/null +++ b/tests/test_activation_checkpointing.py @@ -0,0 +1,79 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import unittest + +import torch +import torch.nn as nn +from fairseq.modules.checkpoint_activations import checkpoint_wrapper +from torch.utils.checkpoint import checkpoint + + +class Model(nn.Module): + def __init__( + self, use_pytorch_checkpoint=False, use_fairseq_checkpoint=False, **kwargs + ): + super().__init__() + torch.manual_seed(0) + self.use_pytorch_checkpoint = use_pytorch_checkpoint + self.ffn = nn.Sequential( + nn.Linear(32, 128), + # add a Dropout layer to test RNG save/restore + nn.Dropout(p=0.5), + nn.Linear(128, 32), + ) + if use_fairseq_checkpoint: + self.ffn = checkpoint_wrapper(self.ffn, **kwargs) + self.out = nn.Linear(32, 1) + + def forward(self, x): + if self.use_pytorch_checkpoint: + x = checkpoint(self.ffn, x) + else: + x = self.ffn(x) + return self.out(x) + + +class TestActivationCheckpointing(unittest.TestCase): + def _test_checkpoint_wrapper(self, device, log_memory_usage=False): + def get_loss_and_gnorm(model): + torch.manual_seed(1) + input = torch.rand(2, 16, 32).requires_grad_(True).to(device) + model.zero_grad() + loss = model(input).sum() + loss.backward() + gnorm = torch.norm( + torch.stack([torch.norm(p.grad.detach()) for p in model.parameters()]) + ) + return {"loss": loss, "gnorm": gnorm} + + model = Model().to(device) + no_cpt = get_loss_and_gnorm(model) + + model = Model(use_pytorch_checkpoint=True).to(device) + pyt_cpt = get_loss_and_gnorm(model) + torch.testing.assert_allclose(no_cpt["loss"], pyt_cpt["loss"]) + torch.testing.assert_allclose(no_cpt["gnorm"], pyt_cpt["gnorm"]) + + model = Model(use_fairseq_checkpoint=True).to(device) + fairseq_cpt = get_loss_and_gnorm(model) + torch.testing.assert_allclose(no_cpt["loss"], fairseq_cpt["loss"]) + torch.testing.assert_allclose(no_cpt["gnorm"], fairseq_cpt["gnorm"]) + + model = Model(use_fairseq_checkpoint=True, offload_to_cpu=True).to(device) + fairseq_cpt_offload = get_loss_and_gnorm(model) + torch.testing.assert_allclose(no_cpt["loss"], fairseq_cpt_offload["loss"]) + torch.testing.assert_allclose(no_cpt["gnorm"], fairseq_cpt_offload["gnorm"]) + + def test_checkpoint_wrapper_cpu(self): + self._test_checkpoint_wrapper(device=torch.device("cpu")) + + @unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU") + def test_checkpoint_wrapper_cuda(self): + self._test_checkpoint_wrapper(device=torch.device("cuda")) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_amp_optimizer.py b/tests/test_amp_optimizer.py new file mode 100644 index 0000000000..3a785e1830 --- /dev/null +++ b/tests/test_amp_optimizer.py @@ -0,0 +1,78 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import copy +import unittest + +import torch +from torch.cuda.amp import autocast, GradScaler +from fairseq.optim import build_optimizer + + +@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU") +class TestGradientScalingAMP(unittest.TestCase): + def setUp(self): + self.x = torch.tensor([2.0]).cuda().half() + weight = 3.0 + bias = 5.0 + self.error = 1.0 + self.target = torch.tensor([self.x * weight + bias + self.error]).cuda() + self.loss_fn = torch.nn.L1Loss() + + self.model = torch.nn.Linear(1, 1) + self.model.weight.data = torch.tensor([[weight]]) + self.model.bias.data = torch.tensor([bias]) + self.model.cuda() + self.params = list(self.model.parameters()) + + self.namespace_dls = argparse.Namespace( + optimizer="adam", + lr=[0.1], + adam_betas="(0.9, 0.999)", + adam_eps=1e-8, + weight_decay=0.0, + threshold_loss_scale=1, + min_loss_scale=1e-4, + ) + self.scaler = GradScaler( + init_scale=1, + growth_interval=1, + ) + + def run_iter(self, model, params, optimizer): + optimizer.zero_grad() + with autocast(): + y = model(self.x) + loss = self.loss_fn(y, self.target) + self.scaler.scale(loss).backward() + self.assertEqual(loss, torch.tensor(1.0, device="cuda:0", dtype=torch.float16)) + + self.scaler.unscale_(optimizer) + grad_norm = optimizer.clip_grad_norm(0) + self.assertAlmostEqual(grad_norm.item(), 2.2361, 4) + + self.scaler.step(optimizer) + self.scaler.update() + self.assertEqual( + model.weight, + torch.tensor( + [[3.1]], device="cuda:0", requires_grad=True + ), + ) + self.assertEqual( + model.bias, + torch.tensor( + [5.1], device="cuda:0", requires_grad=True + ), + ) + self.assertEqual(self.scaler.get_scale(), 2.0) + + def test_automatic_mixed_precision(self): + model = copy.deepcopy(self.model) + params = list(model.parameters()) + optimizer = build_optimizer(self.namespace_dls, params) + + self.run_iter(model, params, optimizer) diff --git a/tests/test_average_checkpoints.py b/tests/test_average_checkpoints.py index 8ed298c3c9..f348b56b86 100644 --- a/tests/test_average_checkpoints.py +++ b/tests/test_average_checkpoints.py @@ -5,16 +5,14 @@ import collections import os +import shutil import tempfile import unittest -import shutil import numpy as np import torch -from torch import nn - - from scripts.average_checkpoints import average_checkpoints +from torch import nn class ModelWithSharedParameter(nn.Module): @@ -37,33 +35,33 @@ class TestAverageCheckpoints(unittest.TestCase): def test_average_checkpoints(self): params_0 = collections.OrderedDict( [ - ('a', torch.DoubleTensor([100.0])), - ('b', torch.FloatTensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])), - ('c', torch.IntTensor([7, 8, 9])), + ("a", torch.DoubleTensor([100.0])), + ("b", torch.FloatTensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])), + ("c", torch.IntTensor([7, 8, 9])), ] ) params_1 = collections.OrderedDict( [ - ('a', torch.DoubleTensor([1.0])), - ('b', torch.FloatTensor([[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]])), - ('c', torch.IntTensor([2, 2, 2])), + ("a", torch.DoubleTensor([1.0])), + ("b", torch.FloatTensor([[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]])), + ("c", torch.IntTensor([2, 2, 2])), ] ) params_avg = collections.OrderedDict( [ - ('a', torch.DoubleTensor([50.5])), - ('b', torch.FloatTensor([[1.0, 1.5, 2.0], [2.5, 3.0, 3.5]])), + ("a", torch.DoubleTensor([50.5])), + ("b", torch.FloatTensor([[1.0, 1.5, 2.0], [2.5, 3.0, 3.5]])), # We expect truncation for integer division - ('c', torch.IntTensor([4, 5, 5])), + ("c", torch.IntTensor([4, 5, 5])), ] ) fd_0, path_0 = tempfile.mkstemp() fd_1, path_1 = tempfile.mkstemp() - torch.save(collections.OrderedDict([('model', params_0)]), path_0) - torch.save(collections.OrderedDict([('model', params_1)]), path_1) + torch.save(collections.OrderedDict([("model", params_0)]), path_0) + torch.save(collections.OrderedDict([("model", params_1)]), path_1) - output = average_checkpoints([path_0, path_1])['model'] + output = average_checkpoints([path_0, path_1])["model"] os.close(fd_0) os.remove(path_0) @@ -71,28 +69,27 @@ def test_average_checkpoints(self): os.remove(path_1) for (k_expected, v_expected), (k_out, v_out) in zip( - params_avg.items(), output.items()): + params_avg.items(), output.items() + ): self.assertEqual( - k_expected, k_out, 'Key mismatch - expected {} but found {}. ' - '(Expected list of keys: {} vs actual list of keys: {})'.format( + k_expected, + k_out, + "Key mismatch - expected {} but found {}. " + "(Expected list of keys: {} vs actual list of keys: {})".format( k_expected, k_out, params_avg.keys(), output.keys() - ) + ), ) np.testing.assert_allclose( v_expected.numpy(), v_out.numpy(), - err_msg='Tensor value mismatch for key {}'.format(k_expected) + err_msg="Tensor value mismatch for key {}".format(k_expected), ) def test_average_checkpoints_with_shared_parameters(self): - def _construct_model_with_shared_parameters(path, value): m = ModelWithSharedParameter() nn.init.constant_(m.FC1.weight, value) - torch.save( - {'model': m.state_dict()}, - path - ) + torch.save({"model": m.state_dict()}, path) return m tmpdir = tempfile.mkdtemp() @@ -112,32 +109,26 @@ def _construct_model_with_shared_parameters(path, value): new_model = average_checkpoints(paths) self.assertTrue( torch.equal( - new_model['model']['embedding.weight'], - (m1.embedding.weight + - m2.embedding.weight + - m3.embedding.weight) / 3.0 + new_model["model"]["embedding.weight"], + (m1.embedding.weight + m2.embedding.weight + m3.embedding.weight) / 3.0, ) ) self.assertTrue( torch.equal( - new_model['model']['FC1.weight'], - (m1.FC1.weight + - m2.FC1.weight + - m3.FC1.weight) / 3.0 + new_model["model"]["FC1.weight"], + (m1.FC1.weight + m2.FC1.weight + m3.FC1.weight) / 3.0, ) ) self.assertTrue( torch.equal( - new_model['model']['FC2.weight'], - (m1.FC2.weight + - m2.FC2.weight + - m3.FC2.weight) / 3.0 + new_model["model"]["FC2.weight"], + (m1.FC2.weight + m2.FC2.weight + m3.FC2.weight) / 3.0, ) ) shutil.rmtree(tmpdir) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/tests/test_backtranslation_dataset.py b/tests/test_backtranslation_dataset.py index 23ae333761..dffc3b4938 100644 --- a/tests/test_backtranslation_dataset.py +++ b/tests/test_backtranslation_dataset.py @@ -5,8 +5,8 @@ import unittest +import tests.utils as test_utils import torch - from fairseq.data import ( BacktranslationDataset, LanguagePairDataset, @@ -14,15 +14,17 @@ ) from fairseq.sequence_generator import SequenceGenerator -import tests.utils as test_utils - class TestBacktranslationDataset(unittest.TestCase): - def setUp(self): - self.tgt_dict, self.w1, self.w2, self.src_tokens, self.src_lengths, self.model = ( - test_utils.sequence_generator_setup() - ) + ( + self.tgt_dict, + self.w1, + self.w2, + self.src_tokens, + self.src_lengths, + self.model, + ) = test_utils.sequence_generator_setup() dummy_src_samples = self.src_tokens @@ -30,7 +32,9 @@ def setUp(self): self.cuda = torch.cuda.is_available() def _backtranslation_dataset_helper( - self, remove_eos_from_input_src, remove_eos_from_output_src, + self, + remove_eos_from_input_src, + remove_eos_from_output_src, ): tgt_dataset = LanguagePairDataset( src=self.tgt_dataset, @@ -94,17 +98,20 @@ def _backtranslation_dataset_helper( def test_backtranslation_dataset_no_eos_in_output_src(self): self._backtranslation_dataset_helper( - remove_eos_from_input_src=False, remove_eos_from_output_src=True, + remove_eos_from_input_src=False, + remove_eos_from_output_src=True, ) def test_backtranslation_dataset_with_eos_in_output_src(self): self._backtranslation_dataset_helper( - remove_eos_from_input_src=False, remove_eos_from_output_src=False, + remove_eos_from_input_src=False, + remove_eos_from_output_src=False, ) def test_backtranslation_dataset_no_eos_in_input_src(self): self._backtranslation_dataset_helper( - remove_eos_from_input_src=True, remove_eos_from_output_src=False, + remove_eos_from_input_src=True, + remove_eos_from_output_src=False, ) def assertTensorEqual(self, t1, t2): diff --git a/tests/test_binaries.py b/tests/test_binaries.py index e1f037bcb0..4e20774262 100644 --- a/tests/test_binaries.py +++ b/tests/test_binaries.py @@ -4,27 +4,39 @@ # LICENSE file in the root directory of this source tree. import contextlib -from io import StringIO import logging +import json import os import random import sys import tempfile import unittest - +from io import StringIO +from typing import List, Dict import torch - from fairseq import options -from fairseq_cli import preprocess -from fairseq_cli import train -from fairseq_cli import generate -from fairseq_cli import interactive -from fairseq_cli import eval_lm -from fairseq_cli import validate +from fairseq_cli import eval_lm, train +from tests.utils import ( + create_dummy_data, + generate_main, + preprocess_lm_data, + preprocess_summarization_data, + preprocess_translation_data, + create_laser_data_and_config_json, + train_translation_model, + train_language_model, +) -class TestTranslation(unittest.TestCase): +try: + import transformers # noqa + has_hf_transformers = True +except ImportError: + has_hf_transformers = False + + +class TestTranslation(unittest.TestCase): def setUp(self): logging.disable(logging.CRITICAL) @@ -33,302 +45,737 @@ def tearDown(self): def test_fconv(self): with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory('test_fconv') as data_dir: + with tempfile.TemporaryDirectory("test_fconv") as data_dir: create_dummy_data(data_dir) preprocess_translation_data(data_dir) - train_translation_model(data_dir, 'fconv_iwslt_de_en') + train_translation_model(data_dir, "fconv_iwslt_de_en") generate_main(data_dir) def test_raw(self): with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory('test_fconv_raw') as data_dir: + with tempfile.TemporaryDirectory("test_fconv_raw") as data_dir: create_dummy_data(data_dir) - preprocess_translation_data(data_dir, ['--dataset-impl', 'raw']) - train_translation_model(data_dir, 'fconv_iwslt_de_en', ['--dataset-impl', 'raw']) - generate_main(data_dir, ['--dataset-impl', 'raw']) - - @unittest.skipIf(not torch.cuda.is_available(), 'test requires a GPU') - def test_fp16(self): - with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory('test_fp16') as data_dir: - create_dummy_data(data_dir) - preprocess_translation_data(data_dir) - train_translation_model(data_dir, 'fconv_iwslt_de_en', ['--fp16']) - generate_main(data_dir) - - @unittest.skipIf(not torch.cuda.is_available(), 'test requires a GPU') - def test_memory_efficient_fp16(self): - with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory('test_memory_efficient_fp16') as data_dir: - create_dummy_data(data_dir) - preprocess_translation_data(data_dir) - train_translation_model(data_dir, 'fconv_iwslt_de_en', ['--memory-efficient-fp16']) - generate_main(data_dir) + preprocess_translation_data(data_dir, ["--dataset-impl", "raw"]) + train_translation_model( + data_dir, "fconv_iwslt_de_en", ["--dataset-impl", "raw"] + ) + generate_main(data_dir, ["--dataset-impl", "raw"]) def test_update_freq(self): with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory('test_update_freq') as data_dir: + with tempfile.TemporaryDirectory("test_update_freq") as data_dir: create_dummy_data(data_dir) preprocess_translation_data(data_dir) - train_translation_model(data_dir, 'fconv_iwslt_de_en', ['--update-freq', '3']) + train_translation_model( + data_dir, "fconv_iwslt_de_en", ["--update-freq", "3"] + ) generate_main(data_dir) def test_max_positions(self): with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory('test_max_positions') as data_dir: + with tempfile.TemporaryDirectory("test_max_positions") as data_dir: create_dummy_data(data_dir) preprocess_translation_data(data_dir) with self.assertRaises(Exception) as context: train_translation_model( - data_dir, 'fconv_iwslt_de_en', ['--max-target-positions', '5'], + data_dir, + "fconv_iwslt_de_en", + ["--max-target-positions", "5"], ) self.assertTrue( - 'skip this example with --skip-invalid-size-inputs-valid-test' in str(context.exception) + "skip this example with --skip-invalid-size-inputs-valid-test" + in str(context.exception) ) train_translation_model( - data_dir, 'fconv_iwslt_de_en', - ['--max-target-positions', '5', '--skip-invalid-size-inputs-valid-test'], + data_dir, + "fconv_iwslt_de_en", + [ + "--max-target-positions", + "5", + "--skip-invalid-size-inputs-valid-test", + ], ) with self.assertRaises(Exception) as context: generate_main(data_dir) - generate_main(data_dir, ['--skip-invalid-size-inputs-valid-test']) + generate_main(data_dir, ["--skip-invalid-size-inputs-valid-test"]) def test_generation(self): with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory('test_sampling') as data_dir: + with tempfile.TemporaryDirectory("test_sampling") as data_dir: create_dummy_data(data_dir) preprocess_translation_data(data_dir) - train_translation_model(data_dir, 'fconv_iwslt_de_en') - generate_main(data_dir, [ - '--sampling', - '--temperature', '2', - '--beam', '2', - '--nbest', '2', - ]) - generate_main(data_dir, [ - '--sampling', - '--sampling-topk', '3', - '--beam', '2', - '--nbest', '2', - ]) - generate_main(data_dir, [ - '--sampling', - '--sampling-topp', '0.2', - '--beam', '2', - '--nbest', '2', - ]) - generate_main(data_dir, [ - '--diversity-rate', '0.5', - '--beam', '6', - ]) + train_translation_model(data_dir, "fconv_iwslt_de_en") + generate_main( + data_dir, + [ + "--sampling", + "--temperature", + "2", + "--beam", + "2", + "--nbest", + "2", + ], + ) + generate_main( + data_dir, + [ + "--sampling", + "--sampling-topk", + "3", + "--beam", + "2", + "--nbest", + "2", + ], + ) + generate_main( + data_dir, + [ + "--sampling", + "--sampling-topp", + "0.2", + "--beam", + "2", + "--nbest", + "2", + ], + ) + generate_main( + data_dir, + [ + "--diversity-rate", + "0.5", + "--beam", + "6", + ], + ) with self.assertRaises(ValueError): - generate_main(data_dir, [ - '--diverse-beam-groups', '4', - '--match-source-len', - ]) - generate_main(data_dir, ['--prefix-size', '2']) + generate_main( + data_dir, + [ + "--diverse-beam-groups", + "4", + "--match-source-len", + ], + ) + generate_main(data_dir, ["--prefix-size", "2"]) + generate_main(data_dir, ["--retain-dropout"]) def test_eval_bleu(self): with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory('test_eval_bleu') as data_dir: + with tempfile.TemporaryDirectory("test_eval_bleu") as data_dir: create_dummy_data(data_dir) preprocess_translation_data(data_dir) - train_translation_model(data_dir, 'fconv_iwslt_de_en', [ - '--eval-bleu', - '--eval-bleu-print-samples', - '--eval-bleu-remove-bpe', - '--eval-bleu-detok', 'space', - '--eval-bleu-args', '{"beam": 4, "min_len": 10}', - ]) + train_translation_model( + data_dir, + "fconv_iwslt_de_en", + [ + "--eval-bleu", + "--eval-bleu-print-samples", + "--eval-bleu-remove-bpe", + "--eval-bleu-detok", + "space", + "--eval-bleu-args", + '{"beam": 4, "min_len": 10}', + ], + ) def test_lstm(self): with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory('test_lstm') as data_dir: + with tempfile.TemporaryDirectory("test_lstm") as data_dir: create_dummy_data(data_dir) preprocess_translation_data(data_dir) - train_translation_model(data_dir, 'lstm_wiseman_iwslt_de_en', [ - '--encoder-layers', '2', - '--decoder-layers', '2', - '--encoder-embed-dim', '8', - '--decoder-embed-dim', '8', - '--decoder-out-embed-dim', '8', - ]) + train_translation_model( + data_dir, + "lstm_wiseman_iwslt_de_en", + [ + "--encoder-layers", + "2", + "--decoder-layers", + "2", + "--encoder-embed-dim", + "8", + "--decoder-embed-dim", + "8", + "--decoder-out-embed-dim", + "8", + ], + ) generate_main(data_dir) def test_lstm_bidirectional(self): with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory('test_lstm_bidirectional') as data_dir: + with tempfile.TemporaryDirectory("test_lstm_bidirectional") as data_dir: create_dummy_data(data_dir) preprocess_translation_data(data_dir) - train_translation_model(data_dir, 'lstm', [ - '--encoder-layers', '2', - '--encoder-bidirectional', - '--encoder-hidden-size', '16', - '--encoder-embed-dim', '8', - '--decoder-embed-dim', '8', - '--decoder-out-embed-dim', '8', - '--decoder-layers', '2', - ]) + train_translation_model( + data_dir, + "lstm", + [ + "--encoder-layers", + "2", + "--encoder-bidirectional", + "--encoder-hidden-size", + "16", + "--encoder-embed-dim", + "8", + "--decoder-embed-dim", + "8", + "--decoder-out-embed-dim", + "8", + "--decoder-layers", + "2", + ], + ) generate_main(data_dir) def test_transformer(self): with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory('test_transformer') as data_dir: + with tempfile.TemporaryDirectory("test_transformer") as data_dir: create_dummy_data(data_dir) preprocess_translation_data(data_dir) - train_translation_model(data_dir, 'transformer_iwslt_de_en', [ - '--encoder-layers', '2', - '--decoder-layers', '2', - '--encoder-embed-dim', '8', - '--decoder-embed-dim', '8', - ], run_validation=True) - generate_main(data_dir) - - @unittest.skipIf(not torch.cuda.is_available(), 'test requires a GPU') - def test_transformer_fp16(self): - with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory('test_transformer') as data_dir: - create_dummy_data(data_dir) - preprocess_translation_data(data_dir) - train_translation_model(data_dir, 'transformer_iwslt_de_en', [ - '--encoder-layers', '2', - '--decoder-layers', '2', - '--encoder-embed-dim', '8', - '--decoder-embed-dim', '8', - '--fp16', - ], run_validation=True) + train_translation_model( + data_dir, + "transformer_iwslt_de_en", + [ + "--encoder-layers", + "2", + "--decoder-layers", + "2", + "--encoder-embed-dim", + "8", + "--decoder-embed-dim", + "8", + ], + run_validation=True, + ) generate_main(data_dir) def test_multilingual_transformer(self): # test with all combinations of encoder/decoder lang tokens - encoder_langtok_flags = [[], ['--encoder-langtok', 'src'], ['--encoder-langtok', 'tgt']] - decoder_langtok_flags = [[], ['--decoder-langtok']] + encoder_langtok_flags = [ + [], + ["--encoder-langtok", "src"], + ["--encoder-langtok", "tgt"], + ] + decoder_langtok_flags = [[], ["--decoder-langtok"]] with contextlib.redirect_stdout(StringIO()): for i in range(len(encoder_langtok_flags)): for j in range(len(decoder_langtok_flags)): enc_ltok_flag = encoder_langtok_flags[i] dec_ltok_flag = decoder_langtok_flags[j] - with tempfile.TemporaryDirectory(f'test_multilingual_transformer_{i}_{j}') as data_dir: + with tempfile.TemporaryDirectory( + f"test_multilingual_transformer_{i}_{j}" + ) as data_dir: create_dummy_data(data_dir) preprocess_translation_data(data_dir) train_translation_model( data_dir, - arch='multilingual_transformer', - task='multilingual_translation', + arch="multilingual_transformer", + task="multilingual_translation", + extra_flags=[ + "--encoder-layers", + "2", + "--decoder-layers", + "2", + "--encoder-embed-dim", + "8", + "--decoder-embed-dim", + "8", + ] + + enc_ltok_flag + + dec_ltok_flag, + lang_flags=["--lang-pairs", "in-out,out-in"], + run_validation=True, + extra_valid_flags=enc_ltok_flag + dec_ltok_flag, + ) + generate_main( + data_dir, + extra_flags=[ + "--task", + "multilingual_translation", + "--lang-pairs", + "in-out,out-in", + "--source-lang", + "in", + "--target-lang", + "out", + ] + + enc_ltok_flag + + dec_ltok_flag, + ) + + @unittest.skipIf( + sys.platform.lower() == "darwin", "skip latent depth test on MacOS" + ) + def test_multilingual_translation_latent_depth(self): + # test with latent depth in encoder, decoder, or both + encoder_latent_layer = [[], ["--encoder-latent-layer"]] + decoder_latent_layer = [[], ["--decoder-latent-layer"]] + with contextlib.redirect_stdout(StringIO()): + for i in range(len(encoder_latent_layer)): + for j in range(len(decoder_latent_layer)): + if i == 0 and j == 0: + continue + enc_ll_flag = encoder_latent_layer[i] + dec_ll_flag = decoder_latent_layer[j] + with tempfile.TemporaryDirectory( + f"test_multilingual_translation_latent_depth_{i}_{j}" + ) as data_dir: + create_dummy_data(data_dir) + preprocess_translation_data( + data_dir, extra_flags=["--joined-dictionary"] + ) + train_translation_model( + data_dir, + arch="latent_multilingual_transformer", + task="multilingual_translation_latent_depth", + extra_flags=[ + "--user-dir", + "examples/latent_depth/latent_depth_src", + "--encoder-layers", + "2", + "--decoder-layers", + "2", + "--encoder-embed-dim", + "8", + "--decoder-embed-dim", + "8", + "--share-encoders", + "--share-decoders", + "--sparsity-weight", + "0.1", + ] + + enc_ll_flag + + dec_ll_flag, + lang_flags=["--lang-pairs", "in-out,out-in"], + run_validation=True, + extra_valid_flags=[ + "--user-dir", + "examples/latent_depth/latent_depth_src", + ] + + enc_ll_flag + + dec_ll_flag, + ) + generate_main( + data_dir, + extra_flags=[ + "--user-dir", + "examples/latent_depth/latent_depth_src", + "--task", + "multilingual_translation_latent_depth", + "--lang-pairs", + "in-out,out-in", + "--source-lang", + "in", + "--target-lang", + "out", + ] + + enc_ll_flag + + dec_ll_flag, + ) + + def test_translation_multi_simple_epoch(self): + # test with all combinations of encoder/decoder lang tokens + encoder_langtok_flags = [ + [], + ["--encoder-langtok", "src"], + ["--encoder-langtok", "tgt"], + ] + decoder_langtok_flags = [[], ["--decoder-langtok"]] + with contextlib.redirect_stdout(StringIO()): + for i in range(len(encoder_langtok_flags)): + for j in range(len(decoder_langtok_flags)): + enc_ltok_flag = encoder_langtok_flags[i] + dec_ltok_flag = decoder_langtok_flags[j] + with tempfile.TemporaryDirectory( + f"test_translation_multi_simple_epoch_{i}_{j}" + ) as data_dir: + create_dummy_data(data_dir) + preprocess_translation_data( + data_dir, extra_flags=["--joined-dictionary"] + ) + train_translation_model( + data_dir, + arch="transformer", + task="translation_multi_simple_epoch", extra_flags=[ - '--encoder-layers', '2', - '--decoder-layers', '2', - '--encoder-embed-dim', '8', - '--decoder-embed-dim', '8', - ] + enc_ltok_flag + dec_ltok_flag, - lang_flags=['--lang-pairs', 'in-out,out-in'], + "--encoder-layers", + "2", + "--decoder-layers", + "2", + "--encoder-embed-dim", + "8", + "--decoder-embed-dim", + "8", + "--sampling-method", + "temperature", + "--sampling-temperature", + "1.5", + "--virtual-epoch-size", + "1000", + ] + + enc_ltok_flag + + dec_ltok_flag, + lang_flags=["--lang-pairs", "in-out,out-in"], run_validation=True, extra_valid_flags=enc_ltok_flag + dec_ltok_flag, ) generate_main( data_dir, extra_flags=[ - '--task', 'multilingual_translation', - '--lang-pairs', 'in-out,out-in', - '--source-lang', 'in', - '--target-lang', 'out', - ] + enc_ltok_flag + dec_ltok_flag, + "--task", + "translation_multi_simple_epoch", + "--lang-pairs", + "in-out,out-in", + "--source-lang", + "in", + "--target-lang", + "out", + ] + + enc_ltok_flag + + dec_ltok_flag, ) + def test_translation_multi_simple_epoch_no_vepoch(self): + # test with all combinations of encoder/decoder lang tokens + with contextlib.redirect_stdout(StringIO()): + enc_ltok_flag = ["--encoder-langtok", "src"] + dec_ltok_flag = ["--decoder-langtok"] + with tempfile.TemporaryDirectory( + "test_translation_multi_simple_epoch_dict" + ) as data_dir: + create_dummy_data(data_dir) + preprocess_translation_data(data_dir, extra_flags=[]) + train_translation_model( + data_dir, + arch="transformer", + task="translation_multi_simple_epoch", + extra_flags=[ + "--encoder-layers", + "2", + "--decoder-layers", + "2", + "--encoder-embed-dim", + "8", + "--decoder-embed-dim", + "8", + "--sampling-method", + "temperature", + "--sampling-temperature", + "1.5", + ] + + enc_ltok_flag + + dec_ltok_flag, + lang_flags=["--lang-pairs", "in-out"], + run_validation=True, + extra_valid_flags=enc_ltok_flag + dec_ltok_flag, + ) + generate_main( + data_dir, + extra_flags=[ + "--task", + "translation_multi_simple_epoch", + "--lang-pairs", + "in-out", + "--source-lang", + "in", + "--target-lang", + "out", + ] + + enc_ltok_flag + + dec_ltok_flag, + ) + + def test_translation_multi_simple_epoch_dicts(self): + # test with all combinations of encoder/decoder lang tokens + with contextlib.redirect_stdout(StringIO()): + enc_ltok_flag = ["--encoder-langtok", "src"] + dec_ltok_flag = ["--decoder-langtok"] + with tempfile.TemporaryDirectory( + "test_translation_multi_simple_epoch_dict" + ) as data_dir: + create_dummy_data(data_dir) + preprocess_translation_data(data_dir, extra_flags=[]) + train_translation_model( + data_dir, + arch="transformer", + task="translation_multi_simple_epoch", + extra_flags=[ + "--encoder-layers", + "2", + "--decoder-layers", + "2", + "--encoder-embed-dim", + "8", + "--decoder-embed-dim", + "8", + "--sampling-method", + "temperature", + "--sampling-temperature", + "1.5", + "--virtual-epoch-size", + "1000", + ] + + enc_ltok_flag + + dec_ltok_flag, + lang_flags=["--lang-pairs", "in-out"], + run_validation=True, + extra_valid_flags=enc_ltok_flag + dec_ltok_flag, + ) + generate_main( + data_dir, + extra_flags=[ + "--task", + "translation_multi_simple_epoch", + "--lang-pairs", + "in-out", + "--source-lang", + "in", + "--target-lang", + "out", + ] + + enc_ltok_flag + + dec_ltok_flag, + ) + + def test_translation_multi_simple_epoch_src_tgt_dict_spec(self): + # test the specification of explicit --src-dict and --tgt-dict + with contextlib.redirect_stdout(StringIO()): + enc_ltok_flag = ["--encoder-langtok", "src"] + dec_ltok_flag = ["--decoder-langtok"] + with tempfile.TemporaryDirectory( + "test_translation_multi_simple_epoch_dict" + ) as data_dir: + create_dummy_data(data_dir) + preprocess_translation_data(data_dir, extra_flags=[]) + train_translation_model( + data_dir, + arch="transformer", + task="translation_multi_simple_epoch", + extra_flags=[ + "--source-dict", + f"{data_dir}/dict.in.txt", + "--target-dict", + f"{data_dir}/dict.out.txt", + "--encoder-layers", + "2", + "--decoder-layers", + "2", + "--encoder-embed-dim", + "8", + "--decoder-embed-dim", + "8", + "--sampling-method", + "temperature", + "--sampling-temperature", + "1.5", + "--virtual-epoch-size", + "1000", + ] + + enc_ltok_flag + + dec_ltok_flag, + lang_flags=["--lang-pairs", "in-out"], + run_validation=True, + extra_valid_flags=enc_ltok_flag + dec_ltok_flag, + ) + generate_main( + data_dir, + extra_flags=[ + "--task", + "translation_multi_simple_epoch", + "--lang-pairs", + "in-out", + "--source-lang", + "in", + "--target-lang", + "out", + ] + + enc_ltok_flag + + dec_ltok_flag, + ) + def test_transformer_cross_self_attention(self): with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory('test_transformer_cross_self_attention') as data_dir: + with tempfile.TemporaryDirectory( + "test_transformer_cross_self_attention" + ) as data_dir: create_dummy_data(data_dir) preprocess_translation_data(data_dir) - train_translation_model(data_dir, 'transformer_iwslt_de_en', [ - '--encoder-layers', '2', - '--decoder-layers', '2', - '--encoder-embed-dim', '8', - '--decoder-embed-dim', '8', - '--decoder-embed-dim', '8', - '--no-cross-attention', - '--cross-self-attention', - ], run_validation=True) + train_translation_model( + data_dir, + "transformer_iwslt_de_en", + [ + "--encoder-layers", + "2", + "--decoder-layers", + "2", + "--encoder-embed-dim", + "8", + "--decoder-embed-dim", + "8", + "--decoder-embed-dim", + "8", + "--no-cross-attention", + "--cross-self-attention", + ], + run_validation=True, + ) generate_main(data_dir, extra_flags=[]) + def test_transformer_pointer_generator(self): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory( + "test_transformer_pointer_generator" + ) as data_dir: + create_dummy_data(data_dir) + preprocess_summarization_data(data_dir) + train_translation_model( + data_dir, + "transformer_pointer_generator", + extra_flags=[ + "--user-dir", + "examples/pointer_generator/pointer_generator_src", + "--encoder-layers", + "2", + "--decoder-layers", + "2", + "--encoder-embed-dim", + "8", + "--decoder-embed-dim", + "8", + "--alignment-layer", + "-1", + "--alignment-heads", + "1", + "--source-position-markers", + "0", + ], + run_validation=True, + extra_valid_flags=[ + "--user-dir", + "examples/pointer_generator/pointer_generator_src", + ], + ) + generate_main( + data_dir, + extra_flags=[ + "--user-dir", + "examples/pointer_generator/pointer_generator_src", + ], + ) + def test_lightconv(self): with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory('test_lightconv') as data_dir: + with tempfile.TemporaryDirectory("test_lightconv") as data_dir: create_dummy_data(data_dir) preprocess_translation_data(data_dir) - train_translation_model(data_dir, 'lightconv_iwslt_de_en', [ - '--encoder-conv-type', 'lightweight', - '--decoder-conv-type', 'lightweight', - '--encoder-embed-dim', '8', - '--decoder-embed-dim', '8', - ]) + train_translation_model( + data_dir, + "lightconv_iwslt_de_en", + [ + "--encoder-conv-type", + "lightweight", + "--decoder-conv-type", + "lightweight", + "--encoder-embed-dim", + "8", + "--decoder-embed-dim", + "8", + ], + ) generate_main(data_dir) def test_dynamicconv(self): with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory('test_dynamicconv') as data_dir: + with tempfile.TemporaryDirectory("test_dynamicconv") as data_dir: create_dummy_data(data_dir) preprocess_translation_data(data_dir) - train_translation_model(data_dir, 'lightconv_iwslt_de_en', [ - '--encoder-conv-type', 'dynamic', - '--decoder-conv-type', 'dynamic', - '--encoder-embed-dim', '8', - '--decoder-embed-dim', '8', - ]) + train_translation_model( + data_dir, + "lightconv_iwslt_de_en", + [ + "--encoder-conv-type", + "dynamic", + "--decoder-conv-type", + "dynamic", + "--encoder-embed-dim", + "8", + "--decoder-embed-dim", + "8", + ], + ) generate_main(data_dir) def test_cmlm_transformer(self): with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory('test_cmlm_transformer') as data_dir: - create_dummy_data(data_dir) - preprocess_translation_data(data_dir, ['--joined-dictionary']) - train_translation_model(data_dir, 'cmlm_transformer', [ - '--apply-bert-init', - '--criterion', 'nat_loss', - '--noise', 'full_mask', - '--pred-length-offset', - '--length-loss-factor', '0.1' - ], task='translation_lev') - generate_main(data_dir, [ - '--task', 'translation_lev', - '--iter-decode-max-iter', '9', - '--iter-decode-eos-penalty', '0', - '--print-step', - ]) - - @unittest.skipIf(not torch.cuda.is_available(), 'test requires a GPU') - def test_levenshtein_transformer(self): - with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory('test_levenshtein_transformer') as data_dir: + with tempfile.TemporaryDirectory("test_cmlm_transformer") as data_dir: create_dummy_data(data_dir) - preprocess_translation_data(data_dir, ['--joined-dictionary']) - train_translation_model(data_dir, 'levenshtein_transformer', [ - '--apply-bert-init', '--early-exit', '6,6,6', - '--criterion', 'nat_loss' - ], task='translation_lev') - generate_main(data_dir, [ - '--task', 'translation_lev', - '--iter-decode-max-iter', '9', - '--iter-decode-eos-penalty', '0', - '--print-step', - ]) + preprocess_translation_data(data_dir, ["--joined-dictionary"]) + train_translation_model( + data_dir, + "cmlm_transformer", + [ + "--apply-bert-init", + "--criterion", + "nat_loss", + "--noise", + "full_mask", + "--pred-length-offset", + "--length-loss-factor", + "0.1", + ], + task="translation_lev", + ) + generate_main( + data_dir, + [ + "--task", + "translation_lev", + "--iter-decode-max-iter", + "9", + "--iter-decode-eos-penalty", + "0", + "--print-step", + ], + ) def test_nonautoregressive_transformer(self): with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory('test_nonautoregressive_transformer') as data_dir: + with tempfile.TemporaryDirectory( + "test_nonautoregressive_transformer" + ) as data_dir: create_dummy_data(data_dir) - preprocess_translation_data(data_dir, ['--joined-dictionary']) - train_translation_model(data_dir, 'nonautoregressive_transformer', [ - '--apply-bert-init', '--src-embedding-copy', '--criterion', - 'nat_loss', '--noise', 'full_mask', '--pred-length-offset', - '--length-loss-factor', '0.1' - ], task='translation_lev') - generate_main(data_dir, [ - '--task', 'translation_lev', - '--iter-decode-max-iter', '0', - '--iter-decode-eos-penalty', '0', - '--print-step', - ]) + preprocess_translation_data(data_dir, ["--joined-dictionary"]) + train_translation_model( + data_dir, + "nonautoregressive_transformer", + [ + "--apply-bert-init", + "--src-embedding-copy", + "--criterion", + "nat_loss", + "--noise", + "full_mask", + "--pred-length-offset", + "--length-loss-factor", + "0.1", + ], + task="translation_lev", + ) + generate_main( + data_dir, + [ + "--task", + "translation_lev", + "--iter-decode-max-iter", + "0", + "--iter-decode-eos-penalty", + "0", + "--print-step", + ], + ) # def test_nat_crf_transformer(self): # with contextlib.redirect_stdout(StringIO()): @@ -352,86 +799,265 @@ def test_nonautoregressive_transformer(self): def test_iterative_nonautoregressive_transformer(self): with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory('test_iterative_nonautoregressive_transformer') as data_dir: + with tempfile.TemporaryDirectory( + "test_iterative_nonautoregressive_transformer" + ) as data_dir: create_dummy_data(data_dir) - preprocess_translation_data(data_dir, ['--joined-dictionary']) - train_translation_model(data_dir, 'iterative_nonautoregressive_transformer', [ - '--apply-bert-init', '--src-embedding-copy', '--criterion', - 'nat_loss', '--noise', 'full_mask', '--stochastic-approx', - '--dae-ratio', '0.5', '--train-step', '3' - ], task='translation_lev') - generate_main(data_dir, [ - '--task', 'translation_lev', - '--iter-decode-max-iter', '9', - '--iter-decode-eos-penalty', '0', - '--print-step', - ]) + preprocess_translation_data(data_dir, ["--joined-dictionary"]) + train_translation_model( + data_dir, + "iterative_nonautoregressive_transformer", + [ + "--apply-bert-init", + "--src-embedding-copy", + "--criterion", + "nat_loss", + "--noise", + "full_mask", + "--stochastic-approx", + "--dae-ratio", + "0.5", + "--train-step", + "3", + ], + task="translation_lev", + ) + generate_main( + data_dir, + [ + "--task", + "translation_lev", + "--iter-decode-max-iter", + "9", + "--iter-decode-eos-penalty", + "0", + "--print-step", + ], + ) def test_insertion_transformer(self): with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory('test_insertion_transformer') as data_dir: + with tempfile.TemporaryDirectory("test_insertion_transformer") as data_dir: create_dummy_data(data_dir) - preprocess_translation_data(data_dir, ['--joined-dictionary']) - train_translation_model(data_dir, 'insertion_transformer', [ - '--apply-bert-init', '--criterion', 'nat_loss', '--noise', - 'random_mask' - ], task='translation_lev') - generate_main(data_dir, [ - '--task', 'translation_lev', - '--iter-decode-max-iter', '9', - '--iter-decode-eos-penalty', '0', - '--print-step', - ]) + preprocess_translation_data(data_dir, ["--joined-dictionary"]) + train_translation_model( + data_dir, + "insertion_transformer", + [ + "--apply-bert-init", + "--criterion", + "nat_loss", + "--noise", + "random_mask", + ], + task="translation_lev", + ) + generate_main( + data_dir, + [ + "--task", + "translation_lev", + "--iter-decode-max-iter", + "9", + "--iter-decode-eos-penalty", + "0", + "--print-step", + ], + ) def test_mixture_of_experts(self): with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory('test_moe') as data_dir: + with tempfile.TemporaryDirectory("test_moe") as data_dir: create_dummy_data(data_dir) preprocess_translation_data(data_dir) - train_translation_model(data_dir, 'transformer_iwslt_de_en', [ - '--task', 'translation_moe', - '--user-dir', 'examples/translation_moe/src', - '--method', 'hMoElp', - '--mean-pool-gating-network', - '--num-experts', '3', - '--encoder-layers', '2', - '--decoder-layers', '2', - '--encoder-embed-dim', '8', - '--decoder-embed-dim', '8', - ]) - generate_main(data_dir, [ - '--task', 'translation_moe', - '--user-dir', 'examples/translation_moe/src', - '--method', 'hMoElp', - '--mean-pool-gating-network', - '--num-experts', '3', - '--gen-expert', '0' - ]) + train_translation_model( + data_dir, + "transformer_iwslt_de_en", + [ + "--task", + "translation_moe", + "--user-dir", + "examples/translation_moe/translation_moe_src", + "--method", + "hMoElp", + "--mean-pool-gating-network", + "--num-experts", + "3", + "--encoder-layers", + "2", + "--decoder-layers", + "2", + "--encoder-embed-dim", + "8", + "--decoder-embed-dim", + "8", + ], + ) + generate_main( + data_dir, + [ + "--task", + "translation_moe", + "--user-dir", + "examples/translation_moe/translation_moe_src", + "--method", + "hMoElp", + "--mean-pool-gating-network", + "--num-experts", + "3", + "--gen-expert", + "0", + ], + ) def test_alignment(self): with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory('test_alignment') as data_dir: + with tempfile.TemporaryDirectory("test_alignment") as data_dir: create_dummy_data(data_dir, alignment=True) - preprocess_translation_data(data_dir, ['--align-suffix', 'align']) + preprocess_translation_data(data_dir, ["--align-suffix", "align"]) train_translation_model( data_dir, - 'transformer_align', + "transformer_align", [ - '--encoder-layers', '2', - '--decoder-layers', '2', - '--encoder-embed-dim', '8', - '--decoder-embed-dim', '8', - '--load-alignments', - '--alignment-layer', '1', - '--criterion', 'label_smoothed_cross_entropy_with_alignment' + "--encoder-layers", + "2", + "--decoder-layers", + "2", + "--encoder-embed-dim", + "8", + "--decoder-embed-dim", + "8", + "--load-alignments", + "--alignment-layer", + "1", + "--criterion", + "label_smoothed_cross_entropy_with_alignment", ], run_validation=True, ) generate_main(data_dir) + def test_laser_lstm(self): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_laser_lstm") as data_dir: + laser_config_file = create_laser_data_and_config_json(data_dir) + train_translation_model( + laser_config_file.name, + "laser_lstm", + [ + "--user-dir", + "examples/laser/laser_src", + "--weighting-alpha", + "0.3", + "--encoder-bidirectional", + "--encoder-hidden-size", + "512", + "--encoder-layers", + "5", + "--decoder-layers", + "1", + "--encoder-embed-dim", + "320", + "--decoder-embed-dim", + "320", + "--decoder-lang-embed-dim", + "32", + "--save-dir", + data_dir, + "--disable-validation", + ], + task="laser", + lang_flags=[], + ) -class TestStories(unittest.TestCase): + def test_laser_transformer(self): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_laser_transformer") as data_dir: + laser_config_file = create_laser_data_and_config_json(data_dir) + train_translation_model( + laser_config_file.name, + "laser_transformer", + [ + "--user-dir", + "examples/laser/laser_src", + "--weighting-alpha", + "0.3", + "--encoder-embed-dim", + "320", + "--decoder-embed-dim", + "320", + "--decoder-lang-embed-dim", + "32", + "--save-dir", + data_dir, + "--disable-validation", + ], + task="laser", + lang_flags=[], + ) + + def test_alignment_full_context(self): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_alignment") as data_dir: + create_dummy_data(data_dir, alignment=True) + preprocess_translation_data(data_dir, ["--align-suffix", "align"]) + train_translation_model( + data_dir, + "transformer_align", + [ + "--encoder-layers", + "2", + "--decoder-layers", + "2", + "--encoder-embed-dim", + "8", + "--decoder-embed-dim", + "8", + "--load-alignments", + "--alignment-layer", + "1", + "--criterion", + "label_smoothed_cross_entropy_with_alignment", + "--full-context-alignment", + ], + run_validation=True, + ) + generate_main(data_dir) + def test_transformer_layerdrop(self): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_transformer_layerdrop") as data_dir: + create_dummy_data(data_dir) + preprocess_translation_data(data_dir) + train_translation_model( + data_dir, + "transformer_iwslt_de_en", + [ + "--encoder-layers", + "3", + "--decoder-layers", + "3", + "--encoder-embed-dim", + "8", + "--decoder-embed-dim", + "8", + "--encoder-layerdrop", + "0.01", + "--decoder-layerdrop", + "0.01", + ], + ) + generate_main(data_dir) + generate_main( + data_dir, + [ + "--model-overrides", + "{'encoder_layers_to_keep':'0,2','decoder_layers_to_keep':'1'}", + ], + ) + + +class TestStories(unittest.TestCase): def setUp(self): logging.disable(logging.CRITICAL) @@ -440,109 +1066,268 @@ def tearDown(self): def test_fconv_self_att_wp(self): with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory('test_fconv_self_att_wp') as data_dir: + with tempfile.TemporaryDirectory("test_fconv_self_att_wp") as data_dir: create_dummy_data(data_dir) preprocess_translation_data(data_dir) config = [ - '--encoder-layers', '[(128, 3)] * 2', - '--decoder-layers', '[(128, 3)] * 2', - '--decoder-attention', 'True', - '--encoder-attention', 'False', - '--gated-attention', 'True', - '--self-attention', 'True', - '--project-input', 'True', - '--encoder-embed-dim', '8', - '--decoder-embed-dim', '8', - '--decoder-out-embed-dim', '8', - '--multihead-self-attention-nheads', '2' + "--encoder-layers", + "[(128, 3)] * 2", + "--decoder-layers", + "[(128, 3)] * 2", + "--decoder-attention", + "True", + "--encoder-attention", + "False", + "--gated-attention", + "True", + "--self-attention", + "True", + "--project-input", + "True", + "--encoder-embed-dim", + "8", + "--decoder-embed-dim", + "8", + "--decoder-out-embed-dim", + "8", + "--multihead-self-attention-nheads", + "2", ] - train_translation_model(data_dir, 'fconv_self_att_wp', config) + train_translation_model(data_dir, "fconv_self_att_wp", config) generate_main(data_dir) # fusion model - os.rename(os.path.join(data_dir, 'checkpoint_last.pt'), os.path.join(data_dir, 'pretrained.pt')) - config.extend([ - '--pretrained', 'True', - '--pretrained-checkpoint', os.path.join(data_dir, 'pretrained.pt'), - '--save-dir', os.path.join(data_dir, 'fusion_model'), - ]) - train_translation_model(data_dir, 'fconv_self_att_wp', config) + os.rename( + os.path.join(data_dir, "checkpoint_last.pt"), + os.path.join(data_dir, "pretrained.pt"), + ) + config.extend( + [ + "--pretrained", + "True", + "--pretrained-checkpoint", + os.path.join(data_dir, "pretrained.pt"), + "--save-dir", + os.path.join(data_dir, "fusion_model"), + ] + ) + train_translation_model(data_dir, "fconv_self_att_wp", config) class TestLanguageModeling(unittest.TestCase): - def setUp(self): logging.disable(logging.CRITICAL) def tearDown(self): logging.disable(logging.NOTSET) - def test_fconv_lm(self): + def test_fconv_lm(self): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_fconv_lm") as data_dir: + create_dummy_data(data_dir) + preprocess_lm_data(data_dir) + train_language_model( + data_dir, + "fconv_lm", + [ + "--decoder-layers", + "[(850, 3)] * 2 + [(1024,4)]", + "--decoder-embed-dim", + "280", + "--optimizer", + "nag", + "--lr", + "0.1", + ], + ) + eval_lm_main(data_dir) + generate_main( + data_dir, + [ + "--task", + "language_modeling", + "--sample-break-mode", + "eos", + "--tokens-per-sample", + "500", + ], + ) + + def test_transformer_lm(self): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_transformer_lm") as data_dir: + create_dummy_data(data_dir) + preprocess_lm_data(data_dir) + train_language_model( + data_dir, + "transformer_lm", + ["--add-bos-token", '--nval', '1'], + run_validation=True, + ) + eval_lm_main(data_dir) + eval_lm_main(data_dir, extra_flags=["--context-window", "25"]) + generate_main( + data_dir, + [ + "--task", + "language_modeling", + "--sample-break-mode", + "eos", + "--tokens-per-sample", + "500", + ], + ) + + def test_transformer_lm_with_adaptive_softmax(self): with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory('test_fconv_lm') as data_dir: + with tempfile.TemporaryDirectory( + "test_transformer_lm_with_adaptive_softmax" + ) as data_dir: create_dummy_data(data_dir) preprocess_lm_data(data_dir) - train_language_model(data_dir, 'fconv_lm', [ - '--decoder-layers', '[(850, 3)] * 2 + [(1024,4)]', - '--decoder-embed-dim', '280', - '--optimizer', 'nag', - '--lr', '0.1', - ]) + train_language_model( + data_dir, + "transformer_lm", + [ + "--add-bos-token", + "--criterion", + "adaptive_loss", + "--adaptive-softmax-cutoff", + "5,10,15", + ], + run_validation=True, + ) eval_lm_main(data_dir) - generate_main(data_dir, [ - '--task', 'language_modeling', - '--sample-break-mode', 'eos', - '--tokens-per-sample', '500', - ]) + generate_main( + data_dir, + [ + "--task", + "language_modeling", + "--sample-break-mode", + "eos", + "--tokens-per-sample", + "500", + ], + ) - def test_transformer_lm(self): + def test_lightconv_lm(self): with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory('test_transformer_lm') as data_dir: + with tempfile.TemporaryDirectory("test_lightconv_lm") as data_dir: create_dummy_data(data_dir) preprocess_lm_data(data_dir) train_language_model( - data_dir, 'transformer_lm', ['--add-bos-token'], run_validation=True, + data_dir, + "lightconv_lm", + ["--add-bos-token"], + run_validation=True, ) eval_lm_main(data_dir) - generate_main(data_dir, [ - '--task', 'language_modeling', - '--sample-break-mode', 'eos', - '--tokens-per-sample', '500', - ]) + generate_main( + data_dir, + [ + "--task", + "language_modeling", + "--sample-break-mode", + "eos", + "--tokens-per-sample", + "500", + ], + ) - def test_lightconv_lm(self): + def test_lstm_lm(self): with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory('test_lightconv_lm') as data_dir: + with tempfile.TemporaryDirectory("test_lstm_lm") as data_dir: create_dummy_data(data_dir) preprocess_lm_data(data_dir) train_language_model( - data_dir, 'lightconv_lm', ['--add-bos-token'], run_validation=True, + data_dir, + "lstm_lm", + ["--add-bos-token"], + run_validation=True, ) eval_lm_main(data_dir) - generate_main(data_dir, [ - '--task', 'language_modeling', - '--sample-break-mode', 'eos', - '--tokens-per-sample', '500', - ]) + generate_main( + data_dir, + [ + "--task", + "language_modeling", + "--sample-break-mode", + "eos", + "--tokens-per-sample", + "500", + ], + ) - def test_lstm_lm(self): + def test_lstm_lm_residuals(self): with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory('test_lstm_lm') as data_dir: + with tempfile.TemporaryDirectory("test_lstm_lm_residuals") as data_dir: create_dummy_data(data_dir) preprocess_lm_data(data_dir) train_language_model( - data_dir, 'lstm_lm', ['--add-bos-token'], run_validation=True, + data_dir, + "lstm_lm", + ["--add-bos-token", "--residuals"], + run_validation=True, ) eval_lm_main(data_dir) - generate_main(data_dir, [ - '--task', 'language_modeling', - '--sample-break-mode', 'eos', - '--tokens-per-sample', '500', - ]) + generate_main( + data_dir, + [ + "--task", + "language_modeling", + "--sample-break-mode", + "eos", + "--tokens-per-sample", + "500", + ], + ) + + @unittest.skipIf(not has_hf_transformers, "skip test if transformers is missing") + def test_transformer_xl_bptt_lm(self): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_transformer_xl_bptt_lm") as data_dir: + create_dummy_data(data_dir) + preprocess_lm_data(data_dir) + task_flags = [ + "--user-dir", + "examples/truncated_bptt", + "--task", + "truncated_bptt_lm", + "--batch-size", + "2", + "--tokens-per-sample", + "50", + ] + train_language_model( + data_dir=data_dir, + arch="transformer_xl", + extra_flags=task_flags + + [ + "--n-layer", + "2", + ], + task="truncated_bptt_lm", + run_validation=True, + extra_valid_flags=task_flags, + ) + eval_lm_main(data_dir, extra_flags=task_flags) + # Train with activation offloading + train_language_model( + data_dir=data_dir, + arch="transformer_xl", + extra_flags=task_flags + + [ + "--n-layer", + "2", + "--offload-activations", + ], + task="truncated_bptt_lm", + run_validation=True, + extra_valid_flags=task_flags, + ) class TestMaskedLanguageModel(unittest.TestCase): - def setUp(self): logging.disable(logging.CRITICAL) @@ -561,32 +1346,124 @@ def test_roberta_masked_lm(self): with tempfile.TemporaryDirectory("test_roberta_mlm") as data_dir: create_dummy_data(data_dir) preprocess_lm_data(data_dir) - train_masked_lm(data_dir, "roberta_base") + train_masked_lm( + data_dir, "roberta_base", extra_flags=["--encoder-layers", "2"] + ) def test_roberta_sentence_prediction(self): num_classes = 3 with contextlib.redirect_stdout(StringIO()): with tempfile.TemporaryDirectory("test_roberta_head") as data_dir: create_dummy_roberta_head_data(data_dir, num_classes=num_classes) - preprocess_lm_data(os.path.join(data_dir, 'input0')) - preprocess_lm_data(os.path.join(data_dir, 'label')) + preprocess_lm_data(os.path.join(data_dir, "input0")) + preprocess_lm_data(os.path.join(data_dir, "label")) train_roberta_head(data_dir, "roberta_base", num_classes=num_classes) def test_roberta_regression_single(self): num_classes = 1 with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory("test_roberta_regression_single") as data_dir: - create_dummy_roberta_head_data(data_dir, num_classes=num_classes, regression=True) - preprocess_lm_data(os.path.join(data_dir, 'input0')) - train_roberta_head(data_dir, "roberta_base", num_classes=num_classes, extra_flags=['--regression-target']) + with tempfile.TemporaryDirectory( + "test_roberta_regression_single" + ) as data_dir: + create_dummy_roberta_head_data( + data_dir, num_classes=num_classes, regression=True + ) + preprocess_lm_data(os.path.join(data_dir, "input0")) + train_roberta_head( + data_dir, + "roberta_base", + num_classes=num_classes, + extra_flags=["--regression-target"], + ) def test_roberta_regression_multiple(self): num_classes = 3 with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory("test_roberta_regression_multiple") as data_dir: - create_dummy_roberta_head_data(data_dir, num_classes=num_classes, regression=True) - preprocess_lm_data(os.path.join(data_dir, 'input0')) - train_roberta_head(data_dir, "roberta_base", num_classes=num_classes, extra_flags=['--regression-target']) + with tempfile.TemporaryDirectory( + "test_roberta_regression_multiple" + ) as data_dir: + create_dummy_roberta_head_data( + data_dir, num_classes=num_classes, regression=True + ) + preprocess_lm_data(os.path.join(data_dir, "input0")) + train_roberta_head( + data_dir, + "roberta_base", + num_classes=num_classes, + extra_flags=["--regression-target"], + ) + + def test_linformer_roberta_masked_lm(self): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_linformer_roberta_mlm") as data_dir: + create_dummy_data(data_dir) + preprocess_lm_data(data_dir) + train_masked_lm( + data_dir, + "linformer_roberta_base", + extra_flags=[ + "--user-dir", + "examples/linformer/linformer_src", + "--encoder-layers", + "2", + ], + ) + + def test_linformer_roberta_sentence_prediction(self): + num_classes = 3 + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_linformer_roberta_head") as data_dir: + create_dummy_roberta_head_data(data_dir, num_classes=num_classes) + preprocess_lm_data(os.path.join(data_dir, "input0")) + preprocess_lm_data(os.path.join(data_dir, "label")) + train_roberta_head( + data_dir, + "linformer_roberta_base", + num_classes=num_classes, + extra_flags=["--user-dir", "examples/linformer/linformer_src"], + ) + + def test_linformer_roberta_regression_single(self): + num_classes = 1 + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory( + "test_linformer_roberta_regression_single" + ) as data_dir: + create_dummy_roberta_head_data( + data_dir, num_classes=num_classes, regression=True + ) + preprocess_lm_data(os.path.join(data_dir, "input0")) + train_roberta_head( + data_dir, + "linformer_roberta_base", + num_classes=num_classes, + extra_flags=[ + "--regression-target", + "--user-dir", + "examples/linformer/linformer_src", + ], + ) + + def test_linformer_roberta_regression_multiple(self): + num_classes = 3 + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory( + "test_linformer_roberta_regression_multiple" + ) as data_dir: + create_dummy_roberta_head_data( + data_dir, num_classes=num_classes, regression=True + ) + preprocess_lm_data(os.path.join(data_dir, "input0")) + train_roberta_head( + data_dir, + "linformer_roberta_base", + num_classes=num_classes, + extra_flags=[ + "--regression-target", + "--user-dir", + "examples/linformer/linformer_src", + ], + ) def _test_pretrained_masked_lm_for_translation(self, learned_pos_emb, encoder_only): with contextlib.redirect_stdout(StringIO()): @@ -596,7 +1473,7 @@ def _test_pretrained_masked_lm_for_translation(self, learned_pos_emb, encoder_on train_legacy_masked_language_model( data_dir, arch="masked_lm", - extra_args=('--encoder-learned-pos',) if learned_pos_emb else () + extra_args=("--encoder-learned-pos",) if learned_pos_emb else (), ) with tempfile.TemporaryDirectory( "test_mlm_translation" @@ -634,10 +1511,13 @@ def _test_pretrained_masked_lm_for_translation(self, learned_pos_emb, encoder_on "500", "--max-target-positions", "500", - ] + ( + ] + + ( ["--encoder-learned-pos", "--decoder-learned-pos"] - if learned_pos_emb else [] - ) + (['--init-encoder-only'] if encoder_only else []), + if learned_pos_emb + else [] + ) + + (["--init-encoder-only"] if encoder_only else []), task="translation_from_pretrained_xlm", ) @@ -650,6 +1530,26 @@ def test_pretrained_masked_lm_for_translation_sinusoidal_pos_emb(self): def test_pretrained_masked_lm_for_translation_encoder_only(self): self._test_pretrained_masked_lm_for_translation(True, True) + def test_r4f_roberta(self): + num_classes = 3 + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_r4f_roberta_head") as data_dir: + create_dummy_roberta_head_data(data_dir, num_classes=num_classes) + preprocess_lm_data(os.path.join(data_dir, "input0")) + preprocess_lm_data(os.path.join(data_dir, "label")) + train_roberta_head( + data_dir, + "roberta_base", + num_classes=num_classes, + extra_flags=[ + "--user-dir", + "examples/rxf/rxf_src", + "--criterion", + "sentence_prediction_r3f", + "--spectral-norm-classification-head", + ], + ) + def train_legacy_masked_language_model(data_dir, arch, extra_args=()): train_parser = options.get_training_parser() @@ -671,7 +1571,7 @@ def train_legacy_masked_language_model(data_dir, arch, extra_args=()): "0.5", "--lr", "0.0001", - "--min-lr", + "--stop-min-lr", "1e-09", # dropout, attention args "--dropout", @@ -709,30 +1609,15 @@ def train_legacy_masked_language_model(data_dir, arch, extra_args=()): "1", "--dataset-impl", "raw", - ] + list(extra_args), + "--num-workers", + "0", + ] + + list(extra_args), ) train.main(train_args) -class TestQuantization(unittest.TestCase): - def setUp(self): - logging.disable(logging.CRITICAL) - - def tearDown(self): - logging.disable(logging.NOTSET) - - @unittest.skipIf(not torch.cuda.is_available(), 'test requires a GPU') - def test_quantization(self): - with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory('test_quantization') as data_dir: - create_dummy_data(data_dir) - preprocess_lm_data(data_dir) - # tests both scalar and iterative PQ quantization - quantize_language_model(data_dir, 'transformer_lm') - - class TestOptimizers(unittest.TestCase): - def setUp(self): logging.disable(logging.CRITICAL) @@ -741,95 +1626,123 @@ def tearDown(self): def test_optimizers(self): with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory('test_optimizers') as data_dir: + with tempfile.TemporaryDirectory("test_optimizers") as data_dir: # Use just a bit of data and tiny model to keep this test runtime reasonable create_dummy_data(data_dir, num_examples=10, maxlen=5) preprocess_translation_data(data_dir) - optimizers = ['adafactor', 'adam', 'nag', 'adagrad', 'sgd', 'adadelta'] - last_checkpoint = os.path.join(data_dir, 'checkpoint_last.pt') + optimizers = ["adafactor", "adam", "nag", "adagrad", "sgd", "adadelta"] + last_checkpoint = os.path.join(data_dir, "checkpoint_last.pt") for optimizer in optimizers: if os.path.exists(last_checkpoint): os.remove(last_checkpoint) - train_translation_model(data_dir, 'lstm', [ - '--required-batch-size-multiple', '1', - '--encoder-layers', '1', - '--encoder-hidden-size', '32', - '--decoder-layers', '1', - '--optimizer', optimizer, - ]) + train_translation_model( + data_dir, + "lstm", + [ + "--required-batch-size-multiple", + "1", + "--encoder-layers", + "1", + "--encoder-hidden-size", + "32", + "--decoder-layers", + "1", + "--optimizer", + optimizer, + ], + ) generate_main(data_dir) - @unittest.skipIf(not torch.cuda.is_available(), 'test requires a GPU') - def test_flat_grads(self): - with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory('test_flat_grads') as data_dir: - # Use just a bit of data and tiny model to keep this test runtime reasonable - create_dummy_data(data_dir, num_examples=10, maxlen=5) + +def read_last_log_entry( + logs: List[logging.LogRecord], logger_name: str +) -> Dict[str, float]: + for x in reversed(logs): + if x.name == logger_name: + return json.loads(x.message) + raise ValueError(f"No entries from {logger_name} found in captured logs") + + +class TestActivationCheckpointing(unittest.TestCase): + base_flags = [ + "--encoder-layers", + "2", + "--decoder-layers", + "2", + "--encoder-embed-dim", + "8", + "--decoder-embed-dim", + "8", + "--restore-file", + "x.pt", + "--log-format", + "json", + "--log-interval", + "1", + "--max-update", + "2", + ] + + def _train(self, data_dir, extra_flags): + with self.assertLogs() as logs: + train_translation_model( + data_dir, + "transformer_iwslt_de_en", + self.base_flags + extra_flags, + run_validation=True, + extra_valid_flags=["--log-format", "json"], + ) + return logs.records + + def test_activation_offloading_does_not_change_metrics(self): + """Neither ----checkpoint-activations nor --offload-activations should change loss""" + with tempfile.TemporaryDirectory("test_transformer_with_act_cpt") as data_dir: + + with self.assertLogs(): + create_dummy_data(data_dir, num_examples=20) preprocess_translation_data(data_dir) - with self.assertRaises(RuntimeError): - # adafactor isn't compatible with flat grads, which - # are used by default with --fp16 - train_translation_model(data_dir, 'lstm', [ - '--required-batch-size-multiple', '1', - '--encoder-layers', '1', - '--encoder-hidden-size', '32', - '--decoder-layers', '1', - '--optimizer', 'adafactor', - '--fp16', - ]) - # but it should pass once we set --fp16-no-flatten-grads - train_translation_model(data_dir, 'lstm', [ - '--required-batch-size-multiple', '1', - '--encoder-layers', '1', - '--encoder-hidden-size', '32', - '--decoder-layers', '1', - '--optimizer', 'adafactor', - '--fp16', - '--fp16-no-flatten-grads', - ]) - - -def create_dummy_data(data_dir, num_examples=100, maxlen=20, alignment=False): - def _create_dummy_data(filename): - data = torch.rand(num_examples * maxlen) - data = 97 + torch.floor(26 * data).int() - with open(os.path.join(data_dir, filename), 'w') as h: - offset = 0 - for _ in range(num_examples): - ex_len = random.randint(1, maxlen) - ex_str = ' '.join(map(chr, data[offset:offset+ex_len])) - print(ex_str, file=h) - offset += ex_len - - def _create_dummy_alignment_data(filename_src, filename_tgt, filename): - with open(os.path.join(data_dir, filename_src), 'r') as src_f, \ - open(os.path.join(data_dir, filename_tgt), 'r') as tgt_f, \ - open(os.path.join(data_dir, filename), 'w') as h: - for src, tgt in zip(src_f, tgt_f): - src_len = len(src.split()) - tgt_len = len(tgt.split()) - avg_len = (src_len + tgt_len) // 2 - num_alignments = random.randint(avg_len // 2, 2 * avg_len) - src_indices = torch.floor(torch.rand(num_alignments) * src_len).int() - tgt_indices = torch.floor(torch.rand(num_alignments) * tgt_len).int() - ex_str = ' '.join(["{}-{}".format(src, tgt) for src, tgt in zip(src_indices, tgt_indices)]) - print(ex_str, file=h) - - _create_dummy_data('train.in') - _create_dummy_data('train.out') - _create_dummy_data('valid.in') - _create_dummy_data('valid.out') - _create_dummy_data('test.in') - _create_dummy_data('test.out') - - if alignment: - _create_dummy_alignment_data('train.in', 'train.out', 'train.align') - _create_dummy_alignment_data('valid.in', 'valid.out', 'valid.align') - _create_dummy_alignment_data('test.in', 'test.out', 'test.align') - - -def create_dummy_roberta_head_data(data_dir, num_examples=100, maxlen=10, num_classes=2, regression=False): - input_dir = 'input0' + offload_logs = self._train(data_dir, ["--offload-activations"]) + baseline_logs = self._train(data_dir, []) + + assert len(baseline_logs) == len(offload_logs) + + baseline_valid_stats = read_last_log_entry(baseline_logs, "valid") + offload_valid_stats = read_last_log_entry(offload_logs, "valid") + baseline_train_stats = read_last_log_entry(baseline_logs, "train") + offload_train_stats = read_last_log_entry(offload_logs, "train") + + assert ( + baseline_train_stats["train_loss"] == offload_train_stats["train_loss"] + ) + assert ( + baseline_valid_stats["valid_loss"] == offload_valid_stats["valid_loss"] + ) + + def test_activation_checkpointing_does_not_change_metrics(self): + """--checkpoint-activations should not change loss""" + + with tempfile.TemporaryDirectory("test_transformer_with_act_cpt") as data_dir: + with self.assertLogs(): + create_dummy_data(data_dir, num_examples=20) + preprocess_translation_data(data_dir) + ckpt_logs = self._train(data_dir, ["--checkpoint-activations"]) + baseline_logs = self._train(data_dir, []) + assert len(baseline_logs) == len(ckpt_logs) + + baseline_train_stats = read_last_log_entry(baseline_logs, "train") + ckpt_train_stats = read_last_log_entry(ckpt_logs, "train") + assert baseline_train_stats["train_loss"] == ckpt_train_stats["train_loss"] + + baseline_valid_stats = read_last_log_entry(baseline_logs, "valid") + ckpt_valid_stats = read_last_log_entry(ckpt_logs, "valid") + assert baseline_valid_stats["valid_loss"] == ckpt_valid_stats["valid_loss"] + + +def create_dummy_roberta_head_data( + data_dir, num_examples=100, maxlen=10, num_classes=2, regression=False +): + input_dir = "input0" + def _create_dummy_data(filename): random_data = torch.rand(num_examples * maxlen) input_data = 97 + torch.floor(26 * random_data).int() @@ -837,132 +1750,29 @@ def _create_dummy_data(filename): output_data = torch.rand((num_examples, num_classes)) else: output_data = 1 + torch.floor(num_classes * torch.rand(num_examples)).int() - with open(os.path.join(data_dir, input_dir, filename+'.out'), 'w') as f_in: - label_filename = filename+'.label' if regression else filename+'.out' - with open(os.path.join(data_dir, 'label', label_filename), 'w') as f_out: + with open(os.path.join(data_dir, input_dir, filename + ".out"), "w") as f_in: + label_filename = filename + ".label" if regression else filename + ".out" + with open(os.path.join(data_dir, "label", label_filename), "w") as f_out: offset = 0 for i in range(num_examples): # write example input ex_len = random.randint(1, maxlen) - ex_str = ' '.join(map(chr, input_data[offset:offset+ex_len])) + ex_str = " ".join(map(chr, input_data[offset : offset + ex_len])) print(ex_str, file=f_in) # write example label if regression: - class_str = ' '.join(map(str, output_data[i].numpy())) + class_str = " ".join(map(str, output_data[i].numpy())) print(class_str, file=f_out) else: - class_str = 'class{}'.format(output_data[i]) + class_str = "class{}".format(output_data[i]) print(class_str, file=f_out) offset += ex_len os.mkdir(os.path.join(data_dir, input_dir)) - os.mkdir(os.path.join(data_dir, 'label')) - _create_dummy_data('train') - _create_dummy_data('valid') - _create_dummy_data('test') - - -def preprocess_translation_data(data_dir, extra_flags=None): - preprocess_parser = options.get_preprocessing_parser() - preprocess_args = preprocess_parser.parse_args( - [ - '--source-lang', 'in', - '--target-lang', 'out', - '--trainpref', os.path.join(data_dir, 'train'), - '--validpref', os.path.join(data_dir, 'valid'), - '--testpref', os.path.join(data_dir, 'test'), - '--thresholdtgt', '0', - '--thresholdsrc', '0', - '--destdir', data_dir, - ] + (extra_flags or []), - ) - preprocess.main(preprocess_args) - - -def train_translation_model(data_dir, arch, extra_flags=None, task='translation', run_validation=False, - lang_flags=None, extra_valid_flags=None): - if lang_flags is None: - lang_flags = [ - '--source-lang', 'in', - '--target-lang', 'out', - ] - train_parser = options.get_training_parser() - train_args = options.parse_args_and_arch( - train_parser, - [ - '--task', task, - data_dir, - '--save-dir', data_dir, - '--arch', arch, - '--lr', '0.05', - '--max-tokens', '500', - '--max-epoch', '1', - '--no-progress-bar', - '--distributed-world-size', '1', - '--num-workers', 0, - ] + lang_flags + (extra_flags or []), - ) - train.main(train_args) - - if run_validation: - # test validation - validate_parser = options.get_validation_parser() - validate_args = options.parse_args_and_arch( - validate_parser, - [ - '--task', task, - data_dir, - '--path', os.path.join(data_dir, 'checkpoint_last.pt'), - '--valid-subset', 'valid', - '--max-tokens', '500', - '--no-progress-bar', - ] + lang_flags + (extra_valid_flags or []) - ) - validate.main(validate_args) - - -def generate_main(data_dir, extra_flags=None): - if extra_flags is None: - extra_flags = [ - '--print-alignment', - ] - generate_parser = options.get_generation_parser() - generate_args = options.parse_args_and_arch( - generate_parser, - [ - data_dir, - '--path', os.path.join(data_dir, 'checkpoint_last.pt'), - '--beam', '3', - '--batch-size', '64', - '--max-len-b', '5', - '--gen-subset', 'valid', - '--no-progress-bar', - ] + (extra_flags or []), - ) - - # evaluate model in batch mode - generate.main(generate_args) - - # evaluate model interactively - generate_args.buffer_size = 0 - generate_args.input = '-' - generate_args.max_sentences = None - orig_stdin = sys.stdin - sys.stdin = StringIO('h e l l o\n') - interactive.main(generate_args) - sys.stdin = orig_stdin - - -def preprocess_lm_data(data_dir): - preprocess_parser = options.get_preprocessing_parser() - preprocess_args = preprocess_parser.parse_args([ - '--only-source', - '--trainpref', os.path.join(data_dir, 'train.out'), - '--validpref', os.path.join(data_dir, 'valid.out'), - '--testpref', os.path.join(data_dir, 'test.out'), - '--destdir', data_dir, - ]) - preprocess.main(preprocess_args) + os.mkdir(os.path.join(data_dir, "label")) + _create_dummy_data("train") + _create_dummy_data("valid") + _create_dummy_data("test") def train_masked_lm(data_dir, arch, extra_flags=None): @@ -970,151 +1780,61 @@ def train_masked_lm(data_dir, arch, extra_flags=None): train_args = options.parse_args_and_arch( train_parser, [ - '--task', 'masked_lm', - data_dir, - '--arch', arch, - '--optimizer', 'adam', - '--lr', '0.0001', - '--criterion', 'masked_lm', - '--max-sentences', '500', - '--save-dir', data_dir, - '--max-epoch', '1', - '--no-progress-bar', - '--distributed-world-size', '1', - '--ddp-backend', 'no_c10d', - '--num-workers', 0, - ] + (extra_flags or []), - ) - train.main(train_args) - - -def train_roberta_head(data_dir, arch, num_classes=2, extra_flags=None): - train_parser = options.get_training_parser() - train_args = options.parse_args_and_arch( - train_parser, - [ - '--task', 'sentence_prediction', + "--task", + "masked_lm", data_dir, - '--arch', arch, - '--num-classes', str(num_classes), - '--optimizer', 'adam', - '--lr', '0.0001', - '--criterion', 'sentence_prediction', - '--max-tokens', '500', - '--max-positions', '500', - '--max-sentences', '500', - '--save-dir', data_dir, - '--max-epoch', '1', - '--no-progress-bar', - '--distributed-world-size', '1', - '--ddp-backend', 'no_c10d', - '--num-workers', 0, - ] + (extra_flags or []), - ) - train.main(train_args) - - -def train_language_model(data_dir, arch, extra_flags=None, run_validation=False): - train_parser = options.get_training_parser() - train_args = options.parse_args_and_arch( - train_parser, - [ - '--task', 'language_modeling', + "--arch", + arch, + "--optimizer", + "adam", + "--lr", + "0.0001", + "--criterion", + "masked_lm", + "--batch-size", + "500", + "--save-dir", data_dir, - '--arch', arch, - '--optimizer', 'adam', - '--lr', '0.0001', - '--criterion', 'adaptive_loss', - '--adaptive-softmax-cutoff', '5,10,15', - '--max-tokens', '500', - '--tokens-per-sample', '500', - '--save-dir', data_dir, - '--max-epoch', '1', - '--no-progress-bar', - '--distributed-world-size', '1', - '--ddp-backend', 'no_c10d', - ] + (extra_flags or []), + "--max-epoch", + "1", + "--no-progress-bar", + "--distributed-world-size", + "1", + "--ddp-backend", + "no_c10d", + "--num-workers", + "0", + ] + + (extra_flags or []), ) train.main(train_args) - if run_validation: - # test validation - validate_parser = options.get_validation_parser() - validate_args = options.parse_args_and_arch( - validate_parser, - [ - '--task', 'language_modeling', - data_dir, - '--path', os.path.join(data_dir, 'checkpoint_last.pt'), - '--valid-subset', 'valid', - '--max-tokens', '500', - '--no-progress-bar', - ] - ) - validate.main(validate_args) - - -def eval_lm_main(data_dir): - eval_lm_parser = options.get_eval_lm_parser() - eval_lm_args = options.parse_args_and_arch( - eval_lm_parser, - [ - data_dir, - '--path', os.path.join(data_dir, 'checkpoint_last.pt'), - '--no-progress-bar', - ], - ) - eval_lm.main(eval_lm_args) - -def train_masked_language_model(data_dir, arch, extra_args=()): +def train_roberta_head(data_dir, arch, num_classes=2, extra_flags=None): train_parser = options.get_training_parser() - # TODO: langs should be in and out right? train_args = options.parse_args_and_arch( train_parser, [ "--task", - "cross_lingual_lm", + "sentence_prediction", data_dir, "--arch", arch, - # Optimizer args + "--encoder-layers", + "2", + "--num-classes", + str(num_classes), "--optimizer", "adam", - "--lr-scheduler", - "reduce_lr_on_plateau", - "--lr-shrink", - "0.5", "--lr", "0.0001", - "--min-lr", - "1e-09", - # dropout, attention args - "--dropout", - "0.1", - "--attention-dropout", - "0.1", - # MLM args "--criterion", - "masked_lm_loss", - "--masked-lm-only", - "--monolingual-langs", - "in,out", - "--num-segment", - "5", - # Transformer args: use a small transformer model for fast training - "--encoder-layers", - "1", - "--encoder-embed-dim", - "32", - "--encoder-attention-heads", - "1", - "--encoder-ffn-embed-dim", - "32", - # Other training args + "sentence_prediction", "--max-tokens", "500", - "--tokens-per-sample", + "--max-positions", + "500", + "--batch-size", "500", "--save-dir", data_dir, @@ -1123,87 +1843,32 @@ def train_masked_language_model(data_dir, arch, extra_args=()): "--no-progress-bar", "--distributed-world-size", "1", - "--dataset-impl", - "raw", - ] + list(extra_args), + "--ddp-backend", + "no_c10d", + "--num-workers", + "0", + ] + + (extra_flags or []), ) train.main(train_args) -def quantize_language_model(data_dir, arch, extra_flags=None, run_validation=False): - train_parser = options.get_training_parser() - train_args = options.parse_args_and_arch( - train_parser, - [ - '--task', 'language_modeling', - data_dir, - '--arch', arch, - '--optimizer', 'adam', - '--lr', '0.0001', - '--criterion', 'adaptive_loss', - '--adaptive-softmax-cutoff', '5,10,15', - '--max-tokens', '500', - '--tokens-per-sample', '500', - '--save-dir', data_dir, - '--max-epoch', '1', - '--no-progress-bar', - '--distributed-world-size', '1', - '--ddp-backend', 'no_c10d', - '--num-workers', 0, - ] + (extra_flags or []), - ) - train.main(train_args) - - # try scalar quantization - scalar_quant_train_parser = options.get_training_parser() - scalar_quant_train_args = options.parse_args_and_arch( - scalar_quant_train_parser, +def eval_lm_main(data_dir, extra_flags=None): + eval_lm_parser = options.get_eval_lm_parser() + eval_lm_args = options.parse_args_and_arch( + eval_lm_parser, [ - '--task', 'language_modeling', data_dir, - '--arch', arch, - '--optimizer', 'adam', - '--lr', '0.0001', - '--criterion', 'adaptive_loss', - '--adaptive-softmax-cutoff', '5,10,15', - '--max-tokens', '500', - '--tokens-per-sample', '500', - '--save-dir', data_dir, - '--max-update', '3', - '--no-progress-bar', - '--distributed-world-size', '1', - '--ddp-backend', 'no_c10d', - '--num-workers', 0, - '--quant-noise-scalar', '0.5', - ] + (extra_flags or []), + "--path", + os.path.join(data_dir, "checkpoint_last.pt"), + "--no-progress-bar", + "--num-workers", + "0", + ] + + (extra_flags or []), ) - train.main(scalar_quant_train_args) + eval_lm.main(eval_lm_args) - # try iterative PQ quantization - quantize_parser = options.get_training_parser() - quantize_args = options.parse_args_and_arch( - quantize_parser, - [ - '--task', 'language_modeling', - data_dir, - '--arch', arch, - '--optimizer', 'adam', - '--lr', '0.0001', - '--criterion', 'adaptive_loss', - '--adaptive-softmax-cutoff', '5,10,15', - '--max-tokens', '50', - '--tokens-per-sample', '50', - '--max-update', '6', - '--no-progress-bar', - '--distributed-world-size', '1', - '--ddp-backend', 'no_c10d', - '--num-workers', 0, - '--restore-file', os.path.join(data_dir, 'checkpoint_last.pt'), - '--reset-optimizer', - '--quantization-config-path', os.path.join(os.path.dirname(__file__), 'transformer_quantization_config.yaml'), - ] + (extra_flags or []), - ) - train.main(quantize_args) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/tests/test_character_token_embedder.py b/tests/test_character_token_embedder.py index 81042c2a3f..24940ebd21 100644 --- a/tests/test_character_token_embedder.py +++ b/tests/test_character_token_embedder.py @@ -3,9 +3,9 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. -import torch import unittest +import torch from fairseq.data import Dictionary from fairseq.modules import CharacterTokenEmbedder @@ -13,12 +13,14 @@ class TestCharacterTokenEmbedder(unittest.TestCase): def test_character_token_embedder(self): vocab = Dictionary() - vocab.add_symbol('hello') - vocab.add_symbol('there') + vocab.add_symbol("hello") + vocab.add_symbol("there") - embedder = CharacterTokenEmbedder(vocab, [(2, 16), (4, 32), (8, 64), (16, 2)], 64, 5, 2) + embedder = CharacterTokenEmbedder( + vocab, [(2, 16), (4, 32), (8, 64), (16, 2)], 64, 5, 2 + ) - test_sents = [['hello', 'unk', 'there'], ['there'], ['hello', 'there']] + test_sents = [["hello", "unk", "there"], ["there"], ["hello", "there"]] max_len = max(len(s) for s in test_sents) input = torch.LongTensor(len(test_sents), max_len + 2).fill_(vocab.pad()) for i in range(len(test_sents)): @@ -42,5 +44,5 @@ def assertAlmostEqual(self, t1, t2): self.assertLess((t1 - t2).abs().max(), 1e-6) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/tests/test_checkpoint_utils.py b/tests/test_checkpoint_utils.py new file mode 100644 index 0000000000..0f28222633 --- /dev/null +++ b/tests/test_checkpoint_utils.py @@ -0,0 +1,106 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import contextlib +import logging +import os +import tempfile +import unittest +from io import StringIO +from unittest.mock import patch + +from fairseq import checkpoint_utils +from omegaconf import OmegaConf + +from tests.utils import ( + create_dummy_data, + preprocess_translation_data, + train_translation_model, +) + + +class TestCheckpointUtils(unittest.TestCase): + def setUp(self): + logging.disable(logging.CRITICAL) + + def tearDown(self): + logging.disable(logging.NOTSET) + + @contextlib.contextmanager + def _train_transformer(self, seed, extra_args=None): + if extra_args is None: + extra_args = [] + with tempfile.TemporaryDirectory(f"_train_transformer_seed{seed}") as data_dir: + create_dummy_data(data_dir) + preprocess_translation_data(data_dir) + train_translation_model( + data_dir, + "transformer_iwslt_de_en", + [ + "--encoder-layers", + "3", + "--decoder-layers", + "3", + "--encoder-embed-dim", + "8", + "--decoder-embed-dim", + "8", + "--seed", + str(seed), + ] + + extra_args, + ) + yield os.path.join(data_dir, "checkpoint_last.pt") + + def test_load_model_ensemble_and_task(self): + # with contextlib.redirect_stdout(StringIO()): + with self._train_transformer(seed=123) as model1: + with self._train_transformer(seed=456) as model2: + ensemble, cfg, task = checkpoint_utils.load_model_ensemble_and_task( + filenames=[model1, model2] + ) + self.assertEqual(len(ensemble), 2) + + # after Transformer has been migrated to Hydra, this will probably + # become cfg.common.seed + self.assertEqual(ensemble[0].args.seed, 123) + self.assertEqual(ensemble[1].args.seed, 456) + + # the task from the first model should be returned + self.assertTrue("seed123" in task.cfg.data) + + # last cfg is saved + self.assertEqual(cfg.common.seed, 456) + + def test_prune_state_dict(self): + with contextlib.redirect_stdout(StringIO()): + extra_args = ["--encoder-layerdrop", "0.01", "--decoder-layerdrop", "0.01"] + with self._train_transformer(seed=1, extra_args=extra_args) as model: + ensemble, cfg, task = checkpoint_utils.load_model_ensemble_and_task( + filenames=[model], + arg_overrides={ + "encoder_layers_to_keep": "0,2", + "decoder_layers_to_keep": "1", + }, + ) + self.assertEqual(len(ensemble), 1) + self.assertEqual(len(ensemble[0].encoder.layers), 2) + self.assertEqual(len(ensemble[0].decoder.layers), 1) + + def test_torch_persistent_save_async(self): + state_dict = {} + filename = "async_checkpoint.pt" + + with patch(f"{checkpoint_utils.__name__}.PathManager.opena") as mock_opena: + with patch(f"{checkpoint_utils.__name__}._torch_persistent_save") as mock_save: + checkpoint_utils.torch_persistent_save( + state_dict, filename, async_write=True + ) + mock_opena.assert_called_with(filename, "wb") + mock_save.assert_called() + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_concat_dataset.py b/tests/test_concat_dataset.py index dbdb2ac518..d94aeffd48 100644 --- a/tests/test_concat_dataset.py +++ b/tests/test_concat_dataset.py @@ -40,25 +40,19 @@ def setUp(self): ) def test_concat_dataset_basics(self): - d = ConcatDataset( - [self.dataset_1, self.dataset_2] - ) - assert(len(d) == 2) - assert(d[0]['source'][0] == 1) - assert(d[1]['source'][0] == 2) + d = ConcatDataset([self.dataset_1, self.dataset_2]) + assert len(d) == 2 + assert d[0]["source"][0] == 1 + assert d[1]["source"][0] == 2 - d = ConcatDataset( - [self.dataset_1, self.dataset_2], sample_ratios=[1, 2] - ) - assert(len(d) == 3) - assert(d[0]['source'][0] == 1) - assert(d[1]['source'][0] == 2) - assert(d[2]['source'][0] == 2) + d = ConcatDataset([self.dataset_1, self.dataset_2], sample_ratios=[1, 2]) + assert len(d) == 3 + assert d[0]["source"][0] == 1 + assert d[1]["source"][0] == 2 + assert d[2]["source"][0] == 2 - d = ConcatDataset( - [self.dataset_1, self.dataset_2], sample_ratios=[2, 1] - ) - assert(len(d) == 3) - assert(d[0]['source'][0] == 1) - assert(d[1]['source'][0] == 1) - assert(d[2]['source'][0] == 2) + d = ConcatDataset([self.dataset_1, self.dataset_2], sample_ratios=[2, 1]) + assert len(d) == 3 + assert d[0]["source"][0] == 1 + assert d[1]["source"][0] == 1 + assert d[2]["source"][0] == 2 diff --git a/tests/test_constraints.py b/tests/test_constraints.py new file mode 100755 index 0000000000..1c37f7e1fb --- /dev/null +++ b/tests/test_constraints.py @@ -0,0 +1,269 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import sys +import unittest + +import torch +from fairseq.token_generation_constraints import * + + +def tensorize(constraints: List[List[int]]) -> torch.Tensor: + return [torch.tensor(x) for x in constraints] + + +class TestHelperRoutines(unittest.TestCase): + def setUp(self): + self.examples = [ + ([[]], torch.tensor([[0]])), + ([[], []], torch.tensor([[0], [0]])), + ([[torch.tensor([1, 2])], []], torch.tensor([[1, 1, 2, 0], [0, 0, 0, 0]])), + ( + [ + [ + torch.tensor([3, 1, 2]), + torch.tensor([3]), + torch.tensor([4, 5, 6, 7]), + ], + [], + [torch.tensor([1, 8, 9, 10, 1, 4, 11, 12])], + ], + torch.tensor( + [ + [3, 3, 1, 2, 0, 3, 0, 4, 5, 6, 7, 0], + [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], + [1, 1, 8, 9, 10, 1, 4, 11, 12, 0, 0, 0], + ] + ), + ), + ] + + def test_packing(self): + """Ensures the list of lists of tensors gets packed correctly.""" + for batch_constraints, expected_tensor in self.examples: + packed = pack_constraints(batch_constraints) + assert torch.equal(packed, expected_tensor) + + +class TestUnorderedConstraintState(unittest.TestCase): + def setUp(self): + # Tuples of (contraint set, expected printed graph, token counts per node) + self.examples = [ + ( + tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]), + "([None].False#6 ([1].True#4 ([2].False#1 [3].True#1) [3].True#1 [4].True#1) ([4].False#2 ([5].True#2 ([6].False#1 [7].True#1))))", + {1: 4, 2: 1, 3: 2, 4: 3, 5: 2, 6: 1, 7: 1}, + ), + ([], "[None].False#0", {}), + (tensorize([[0]]), "([None].False#1 [0].True#1)", {0: 1}), + ( + tensorize([[100000, 1, 2, 3, 4, 5]]), + "([None].False#1 ([100000].False#1 ([1].False#1 ([2].False#1 ([3].False#1 ([4].False#1 [5].True#1))))))", + {100000: 1, 1: 1, 2: 1, 3: 1, 4: 1, 5: 1}, + ), + ( + tensorize([[1, 2], [1, 2]]), + "([None].False#2 ([1].False#2 [2].True#2))", + {1: 2, 2: 2}, + ), + ( + tensorize([[1, 2], [3, 4]]), + "([None].False#2 ([1].False#1 [2].True#1) ([3].False#1 [4].True#1))", + {1: 1, 2: 1, 3: 1, 4: 1}, + ), + ] + + self.sequences = [ + ( + self.examples[0][0], + [], + {"bank": 0, "num_completed": 0, "finished": False, "is_root": True}, + ), + ( + self.examples[0][0], + [1, 2], + {"bank": 2, "num_completed": 0, "finished": False, "is_root": False}, + ), + ( + self.examples[0][0], + [1, 2, 94], + {"bank": 1, "num_completed": 1, "finished": False, "is_root": True}, + ), + ( + self.examples[0][0], + [1, 3, 999, 1, 4], + {"bank": 4, "num_completed": 2, "finished": False, "is_root": False}, + ), + ( + self.examples[0][0], + [1, 3, 999, 1, 4, 999], + {"bank": 4, "num_completed": 2, "finished": False, "is_root": True}, + ), + ( + self.examples[0][0], + [4, 5, 6, 8], + {"bank": 2, "num_completed": 1, "finished": False, "is_root": True}, + ), + ( + self.examples[0][0], + # Tricky, because in last three, goes down [1->4] branch, could miss [1] and [4->5] + # [[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]], + [1, 2, 3, 1, 3, 1, 4, 4, 5, 6, 7, 1, 4, 5], + {"bank": 14, "num_completed": 6, "finished": True, "is_root": False}, + ), + ( + self.examples[0][0], + [1, 2, 3, 999, 1, 3, 1, 4, 4, 5, 6, 7, 1, 4, 5, 117], + {"bank": 14, "num_completed": 6, "finished": True, "is_root": True}, + ), + ( + tensorize([[1], [2, 3]]), + # Should not be able to get credit for entering 1 a second time + [1, 1], + {"bank": 1, "num_completed": 1, "finished": False, "is_root": True}, + ), + ( + self.examples[4][0], + [1, 2, 1, 2], + {"bank": 4, "num_completed": 2, "finished": True, "is_root": False}, + ), + ( + self.examples[4][0], + [1, 2, 1, 2, 1], + {"bank": 4, "num_completed": 2, "finished": True, "is_root": True}, + ), + ( + self.examples[5][0], + [1, 2, 3, 4, 5], + {"bank": 4, "num_completed": 2, "finished": True, "is_root": True}, + ), + ] + + def test_graphs(self): + """ + Test whether unordered graph systems are created correctly. + """ + for example in self.examples: + constraints, expected, gold_counts = example + c = ConstraintNode.create(constraints) + assert ( + ConstraintNode.print_graph(c) == expected + ), f"got {ConstraintNode.print_graph(c)}, expected {expected}" + assert ( + c.token_counts() == gold_counts + ), f"{c} got {c.token_counts()} wanted {gold_counts}" + + def test_next_tokens(self): + """ + Tests that the set of next tokens is correct. + """ + for example in self.examples: + constraints, expected, gold_counts = example + root = ConstraintNode.create(constraints) + + root_tokens = set(root.children.keys()) + for sequence in constraints: + state = UnorderedConstraintState(root) + for token in sequence: + all_tokens = root_tokens.union(state.node.children.keys()) + assert ( + all_tokens == state.next_tokens() + ), f"ALL {all_tokens} NEXT {state.next_tokens()}" + state = state.advance(token) + + def test_sequences(self): + for constraints, tokens, expected in self.sequences: + state = UnorderedConstraintState.create(pack_constraints([constraints])[0]) + for token in tokens: + state = state.advance(token) + result = {} + for attr in expected.keys(): + result[attr] = getattr(state, attr) + + assert ( + result == expected + ), f"TEST({tokens}) GOT: {result} WANTED: {expected}" + + +class TestOrderedConstraintState(unittest.TestCase): + def setUp(self): + self.sequences = [ + ( + tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]), + [], + {"bank": 0, "num_completed": 0, "finished": False, "is_root": True}, + ), + ( + tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]), + [1, 2], + {"bank": 2, "num_completed": 0, "finished": False, "is_root": False}, + ), + ( + tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]), + [1, 2, 94], + {"bank": 0, "num_completed": 0, "finished": False, "is_root": True}, + ), + ( + tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]), + [1, 3, 999, 1, 4], + {"bank": 0, "num_completed": 0, "finished": False, "is_root": True}, + ), + ( + tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]), + [1, 2, 3, 999, 999], + {"bank": 3, "num_completed": 1, "finished": False, "is_root": False}, + ), + ( + tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]), + [1, 2, 3, 77, 1, 3, 1], + {"bank": 6, "num_completed": 2, "finished": False, "is_root": False}, + ), + ( + tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]), + [1, 2, 3, 1, 3, 1, 4, 4, 5, 6, 7, 1, 4, 5], + {"bank": 14, "num_completed": 6, "finished": True, "is_root": False}, + ), + ( + tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]), + [1, 2, 999, 1, 2, 3, 999, 1, 3, 1, 4, 4, 5, 6, 7, 1, 4, 5, 117], + {"bank": 14, "num_completed": 6, "finished": True, "is_root": False}, + ), + ( + tensorize([[1], [2, 3]]), + [1, 1], + {"bank": 1, "num_completed": 1, "finished": False, "is_root": False}, + ), + ( + tensorize([[1, 2], [1, 2]]), + [1, 2, 1, 2], + {"bank": 4, "num_completed": 2, "finished": True, "is_root": False}, + ), + ( + tensorize([[1, 2], [1, 2]]), + [1, 2, 1, 2, 1], + {"bank": 4, "num_completed": 2, "finished": True, "is_root": False}, + ), + ( + tensorize([[1, 2], [3, 4]]), + [1, 2, 3, 4, 5], + {"bank": 4, "num_completed": 2, "finished": True, "is_root": False}, + ), + ] + + def test_sequences(self): + for i, (constraints, tokens, expected) in enumerate(self.sequences): + state = OrderedConstraintState.create(pack_constraints([constraints])[0]) + for token in tokens: + state = state.advance(token) + result = {} + for attr in expected.keys(): + result[attr] = getattr(state, attr) + assert ( + result == expected + ), f"TEST({tokens}) GOT: {result} WANTED: {expected}" + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_convtbc.py b/tests/test_convtbc.py index fc2ac0b5dc..3a3c9b91e7 100644 --- a/tests/test_convtbc.py +++ b/tests/test_convtbc.py @@ -3,14 +3,14 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. -import torch import unittest -from fairseq.modules import ConvTBC + +import torch import torch.nn as nn +from fairseq.modules import ConvTBC class TestConvTBC(unittest.TestCase): - def test_convtbc(self): # ksz, in_channels, out_channels conv_tbc = ConvTBC(4, 5, kernel_size=3, padding=1) @@ -27,7 +27,9 @@ def test_convtbc(self): output_tbc = conv_tbc(input_tbc) output1d = conv1d(input1d) - self.assertAlmostEqual(output_tbc.data.transpose(0, 1).transpose(1, 2), output1d.data) + self.assertAlmostEqual( + output_tbc.data.transpose(0, 1).transpose(1, 2), output1d.data + ) grad_tbc = torch.randn(output_tbc.size()) grad1d = grad_tbc.transpose(0, 1).transpose(1, 2).contiguous() @@ -35,14 +37,18 @@ def test_convtbc(self): output_tbc.backward(grad_tbc) output1d.backward(grad1d) - self.assertAlmostEqual(conv_tbc.weight.grad.data.transpose(0, 2), conv1d.weight.grad.data) + self.assertAlmostEqual( + conv_tbc.weight.grad.data.transpose(0, 2), conv1d.weight.grad.data + ) self.assertAlmostEqual(conv_tbc.bias.grad.data, conv1d.bias.grad.data) - self.assertAlmostEqual(input_tbc.grad.data.transpose(0, 1).transpose(1, 2), input1d.grad.data) + self.assertAlmostEqual( + input_tbc.grad.data.transpose(0, 1).transpose(1, 2), input1d.grad.data + ) def assertAlmostEqual(self, t1, t2): self.assertEqual(t1.size(), t2.size(), "size mismatch") self.assertLess((t1 - t2).abs().max(), 1e-4) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/tests/test_data_utils.py b/tests/test_data_utils.py new file mode 100644 index 0000000000..2acfc8dc18 --- /dev/null +++ b/tests/test_data_utils.py @@ -0,0 +1,136 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import unittest + +import numpy as np +from fairseq.data.data_utils_fast import batch_by_size_fn +from fairseq.data.data_utils_fast import batch_by_size_vec + + +class TestBatchBySize(unittest.TestCase): + @classmethod + def batch_by_size_baseline( + cls, + indices, + num_tokens_vec, + max_tokens, + max_sentences, + bsz_mult, + ): + """Simple, reliable and slow implementation of batch by size """ + batches = [] + start = 0 + while start < len(indices): + for end in range(start + 1, len(indices) + 1): + max_val = max(num_tokens_vec[pos] for pos in range(start, end)) + sent_count = end - start + num_tokens = max_val * sent_count + overflow = num_tokens > max_tokens > 0 or sent_count > max_sentences > 0 + terminate = overflow or end == len(indices) + if overflow: + sent_count -= 1 + if terminate: + if sent_count > bsz_mult: + sent_count = sent_count - sent_count % bsz_mult + batches.append(indices[start : start + sent_count]) + start = start + sent_count + break + return batches + + @classmethod + def _get_error_message( + cls, max_sentences, max_tokens, bsz_mult, num_tokens_vec, validation, results + ): + return f"""Reference batch_by_size implementation should produce + same output as the baseline method. + Params: + max_sentences={max_sentences}, + max_tokens={max_tokens}, + bsz_mult={bsz_mult}, + num_tokens_vec={num_tokens_vec}, + expected_batches={validation}, + returned_batches={results}""" + + def _compare_results( + self, + indices_len, + batch_by_size_impl, + max_sentences, + max_tokens, + bsz_mult, + num_tokens_vec, + ): + indices = np.array(list(range(indices_len))) + validation = self.batch_by_size_baseline( + indices, + num_tokens_vec, + max_tokens=max_tokens, + max_sentences=max_sentences, + bsz_mult=bsz_mult, + ) + results = batch_by_size_impl( + indices, + num_tokens_vec, + max_tokens=max_tokens, + max_sentences=max_sentences, + bsz_mult=bsz_mult, + ) + error_msg = self._get_error_message( + max_sentences, max_tokens, bsz_mult, num_tokens_vec, validation, results + ) + self.assertEqual(len(validation), len(results), error_msg) + for first, second in zip(validation, results): + self.assertTrue(np.array_equal(first, second), error_msg) + + def _run_compare_with_baseline_sweep(self, batch_by_size_impl): + """Compare reference batch_by_size implementation with batch_by_size_baseline + across a dense grid of hyperparam values""" + MAX_MAX_TOKENS = 10 + NUM_TOKENS_VECS_COUNT = 5 + for indices_len in [10, 11]: # try odd and even len of indices + for max_sentences in range(0, indices_len + 2): + for max_tokens in range(0, MAX_MAX_TOKENS): + for bsz_mult in range(1, max(MAX_MAX_TOKENS, indices_len) + 2): + for _ in range(NUM_TOKENS_VECS_COUNT): + num_tokens_vec = np.random.randint( + 0, max_tokens + 1, size=indices_len + ) + self._compare_results( + indices_len, + batch_by_size_impl, + max_sentences, + max_tokens, + bsz_mult, + num_tokens_vec, + ) + + +class TestBatchBySizeVec(TestBatchBySize): + def test_compare_with_baseline(self): + self._run_compare_with_baseline_sweep(batch_by_size_vec) + + +class TestBatchBySizeFn(TestBatchBySize): + def test_compare_with_baseline(self): + def batch_by_size_fn_wrapper( + indices, + num_tokens_vec, + max_tokens, + max_sentences, + bsz_mult, + ): + def num_tokens_fn(idx): + return num_tokens_vec[idx] + + return batch_by_size_fn( + indices, num_tokens_fn, max_tokens, max_sentences, bsz_mult + ) + + self._run_compare_with_baseline_sweep(batch_by_size_fn_wrapper) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_dataclass_utils.py b/tests/test_dataclass_utils.py new file mode 100644 index 0000000000..45fc391a97 --- /dev/null +++ b/tests/test_dataclass_utils.py @@ -0,0 +1,87 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import unittest +from argparse import ArgumentParser +from dataclasses import dataclass, field + +from fairseq.dataclass import FairseqDataclass +from fairseq.dataclass.utils import gen_parser_from_dataclass + + +@dataclass +class A(FairseqDataclass): + data: str = field(default="test", metadata={"help": "the data input"}) + num_layers: int = field(default=200, metadata={"help": "more layers is better?"}) + + +@dataclass +class B(FairseqDataclass): + bar: A = field(default=A()) + foo: int = field(default=0, metadata={"help": "not a bar"}) + + +@dataclass +class D(FairseqDataclass): + arch: A = field(default=A()) + foo: int = field(default=0, metadata={"help": "not a bar"}) + + +@dataclass +class C(FairseqDataclass): + data: str = field(default="test", metadata={"help": "root level data input"}) + encoder: D = field(default=D()) + decoder: A = field(default=A()) + lr: int = field(default=0, metadata={"help": "learning rate"}) + + +class TestDataclassUtils(unittest.TestCase): + def test_argparse_convert_basic(self): + parser = ArgumentParser() + gen_parser_from_dataclass(parser, A(), True) + args = parser.parse_args(["--num-layers", '10', "the/data/path"]) + self.assertEqual(args.num_layers, 10) + self.assertEqual(args.data, "the/data/path") + + def test_argparse_recursive(self): + parser = ArgumentParser() + gen_parser_from_dataclass(parser, B(), True) + args = parser.parse_args(["--num-layers", "10", "--foo", "10", "the/data/path"]) + self.assertEqual(args.num_layers, 10) + self.assertEqual(args.foo, 10) + self.assertEqual(args.data, "the/data/path") + + def test_argparse_recursive_prefixing(self): + self.maxDiff = None + parser = ArgumentParser() + gen_parser_from_dataclass(parser, C(), True, "") + args = parser.parse_args( + [ + "--encoder-arch-data", + "ENCODER_ARCH_DATA", + "--encoder-arch-num-layers", + "10", + "--encoder-foo", + "10", + "--decoder-data", + "DECODER_DATA", + "--decoder-num-layers", + "10", + "--lr", + "10", + "the/data/path", + ] + ) + self.assertEqual(args.encoder_arch_data, "ENCODER_ARCH_DATA") + self.assertEqual(args.encoder_arch_num_layers, 10) + self.assertEqual(args.encoder_foo, 10) + self.assertEqual(args.decoder_data, "DECODER_DATA") + self.assertEqual(args.decoder_num_layers, 10) + self.assertEqual(args.lr, 10) + self.assertEqual(args.data, "the/data/path") + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_dataset.py b/tests/test_dataset.py new file mode 100644 index 0000000000..a3e3970028 --- /dev/null +++ b/tests/test_dataset.py @@ -0,0 +1,66 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +import unittest +from typing import Sequence + +from fairseq.data import LanguagePairDataset, ListDataset, RoundRobinZipDatasets +from tests.test_train import mock_dict + + +def lang_pair_dataset(lengths: Sequence[int]) -> LanguagePairDataset: + tokens = [[i] * l for i, l in enumerate(lengths)] + return LanguagePairDataset(ListDataset(tokens), lengths, mock_dict()) + + +def sample(id: int, length: int): + return {"id": id, "source": [id] * length, "target": None} + + +class TestDataset(unittest.TestCase): + def setUp(self): + logging.disable(logging.CRITICAL) + + def tearDown(self): + logging.disable(logging.NOTSET) + + def test_round_robin_zip_datasets(self): + long_dataset = lang_pair_dataset([10, 9, 8, 11]) + short_dataset = lang_pair_dataset([11, 9]) + + dataset = RoundRobinZipDatasets({"a": long_dataset, "b": short_dataset}) + # Dataset is now sorted by sentence length + dataset.ordered_indices() + assert dataset.longest_dataset is long_dataset + self.assertEqual(dict(dataset[0]), {"a": sample(2, 8), "b": sample(1, 9)}) + # The item 2 of dataset 'a' is with item (2 % 2 = 0) of dataset 'b' + self.assertEqual(dict(dataset[2]), {"a": sample(0, 10), "b": sample(1, 9)}) + + def test_round_robin_zip_datasets_filtered(self): + long_dataset = lang_pair_dataset([10, 20, 8, 11, 1000, 7, 12]) + short_dataset = lang_pair_dataset([11, 20, 9, 1000]) + + dataset = RoundRobinZipDatasets({"a": long_dataset, "b": short_dataset}) + # Dataset is now sorted by sentence length + idx = dataset.ordered_indices() + idx, _ = dataset.filter_indices_by_size(idx, {"a": 19, "b": 900}) + self.assertEqual(list(idx), [0, 1, 2, 3, 4]) + self.assertEqual(dict(dataset[0]), {"a": sample(5, 7), "b": sample(2, 9)}) + self.assertEqual(dict(dataset[2]), {"a": sample(0, 10), "b": sample(1, 20)}) + self.assertEqual(dict(dataset[4]), {"a": sample(6, 12), "b": sample(0, 11)}) + + def test_round_robin_zip_datasets_filtered_with_tuple(self): + long_dataset = lang_pair_dataset([10, 20, 8, 11, 1000, 7, 12]) + short_dataset = lang_pair_dataset([11, 20, 9, 1000]) + + dataset = RoundRobinZipDatasets({"a": long_dataset, "b": short_dataset}) + # Dataset is now sorted by sentence length + idx = dataset.ordered_indices() + idx, _ = dataset.filter_indices_by_size(idx, 19) + self.assertEqual(list(idx), [0, 1, 2, 3, 4]) + self.assertEqual(dict(dataset[0]), {"a": sample(5, 7), "b": sample(2, 9)}) + self.assertEqual(dict(dataset[2]), {"a": sample(0, 10), "b": sample(2, 9)}) + self.assertEqual(dict(dataset[4]), {"a": sample(6, 12), "b": sample(2, 9)}) diff --git a/tests/test_dictionary.py b/tests/test_dictionary.py index d9a1ec72c8..dc9d71b3c7 100644 --- a/tests/test_dictionary.py +++ b/tests/test_dictionary.py @@ -4,35 +4,46 @@ # LICENSE file in the root directory of this source tree. import io +import os +import string import tempfile import unittest import torch - +from fairseq import tokenizer from fairseq.data import Dictionary class TestDictionary(unittest.TestCase): - def test_finalize(self): txt = [ - 'A B C D', - 'B C D', - 'C D', - 'D', + "A B C D", + "B C D", + "C D", + "D", ] - ref_ids1 = list(map(torch.IntTensor, [ - [4, 5, 6, 7, 2], - [5, 6, 7, 2], - [6, 7, 2], - [7, 2], - ])) - ref_ids2 = list(map(torch.IntTensor, [ - [7, 6, 5, 4, 2], - [6, 5, 4, 2], - [5, 4, 2], - [4, 2], - ])) + ref_ids1 = list( + map( + torch.IntTensor, + [ + [4, 5, 6, 7, 2], + [5, 6, 7, 2], + [6, 7, 2], + [7, 2], + ], + ) + ) + ref_ids2 = list( + map( + torch.IntTensor, + [ + [7, 6, 5, 4, 2], + [6, 5, 4, 2], + [5, 4, 2], + [4, 2], + ], + ) + ) # build dictionary d = Dictionary() @@ -59,7 +70,7 @@ def assertMatch(ids, ref_ids): assertMatch(finalized_ids, ref_ids2) # write to disk and reload - with tempfile.NamedTemporaryFile(mode='w') as tmp_dict: + with tempfile.NamedTemporaryFile(mode="w") as tmp_dict: d.save(tmp_dict.name) d = Dictionary.load(tmp_dict.name) reload_ids = get_ids(d) @@ -77,40 +88,58 @@ def test_overwrite(self): ) d = Dictionary() d.add_from_file(dict_file) - self.assertEqual(d.index('<pad>'), 1) - self.assertEqual(d.index('foo'), 3) - self.assertEqual(d.index('<unk>'), 4) - self.assertEqual(d.index('<s>'), 5) - self.assertEqual(d.index('</s>'), 6) - self.assertEqual(d.index(','), 7) - self.assertEqual(d.index('▁de'), 8) + self.assertEqual(d.index("<pad>"), 1) + self.assertEqual(d.index("foo"), 3) + self.assertEqual(d.index("<unk>"), 4) + self.assertEqual(d.index("<s>"), 5) + self.assertEqual(d.index("</s>"), 6) + self.assertEqual(d.index(","), 7) + self.assertEqual(d.index("▁de"), 8) def test_no_overwrite(self): # for example, Camembert overwrites <unk>, <s> and </s> dict_file = io.StringIO( - "<unk> 999\n" - "<s> 999\n" - "</s> 999\n" - ", 999\n" - "▁de 999\n" + "<unk> 999\n" "<s> 999\n" "</s> 999\n" ", 999\n" "▁de 999\n" ) d = Dictionary() - with self.assertRaisesRegex(RuntimeError, 'Duplicate'): + with self.assertRaisesRegex(RuntimeError, "Duplicate"): d.add_from_file(dict_file) def test_space(self): # for example, character models treat space as a symbol - dict_file = io.StringIO( - " 999\n" - "a 999\n" - "b 999\n" - ) + dict_file = io.StringIO(" 999\n" "a 999\n" "b 999\n") d = Dictionary() d.add_from_file(dict_file) - self.assertEqual(d.index(' '), 4) - self.assertEqual(d.index('a'), 5) - self.assertEqual(d.index('b'), 6) - - -if __name__ == '__main__': + self.assertEqual(d.index(" "), 4) + self.assertEqual(d.index("a"), 5) + self.assertEqual(d.index("b"), 6) + + def test_add_file_to_dict(self): + counts = {} + num_lines = 100 + per_line = 10 + with tempfile.TemporaryDirectory("test_sampling") as data_dir: + filename = os.path.join(data_dir, "dummy.txt") + with open(filename, "w", encoding="utf-8") as data: + for c in string.ascii_letters: + line = f"{c} " * per_line + for _ in range(num_lines): + data.write(f"{line}\n") + counts[c] = per_line * num_lines + per_line += 5 + + dict = Dictionary() + Dictionary.add_file_to_dictionary( + filename, dict, tokenizer.tokenize_line, 10 + ) + dict.finalize(threshold=0, nwords=-1, padding_factor=8) + + for c in string.ascii_letters: + count = dict.get_count(dict.index(c)) + self.assertEqual( + counts[c], count, f"{c} count is {count} but should be {counts[c]}" + ) + + +if __name__ == "__main__": unittest.main() diff --git a/tests/test_ema.py b/tests/test_ema.py new file mode 100644 index 0000000000..88ea65a434 --- /dev/null +++ b/tests/test_ema.py @@ -0,0 +1,199 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import unittest +from copy import deepcopy +from dataclasses import dataclass +from typing import Optional + +import torch +from fairseq.models.ema import EMA + + +class DummyModule(torch.nn.Module): + def __init__(self) -> None: + """LightningModule for testing purposes + + Args: + epoch_min_loss_override (int, optional): Pass in an epoch that will be set to the minimum + validation loss for testing purposes (zero based). If None this is ignored. Defaults to None. + """ + super().__init__() + self.layer = torch.nn.Linear(in_features=32, out_features=2) + self.another_layer = torch.nn.Linear(in_features=2, out_features=2) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.layer(x) + return self.another_layer(x) + + +@dataclass +class EMAConfig(object): + ema_decay: float = 0.99 + ema_start_update: int = 0 + ema_fp32: bool = False + ema_seed_model: Optional[str] = None + + +class TestEMAGPU(unittest.TestCase): + def assertTorchAllClose(self, x, y, atol=1e-8, rtol=1e-5, msg=None): + diff = x.float() - y.float() + diff_norm = torch.norm(diff) + other_norm = torch.norm(y.float()) + + if msg is None: + msg = "|input - other| > {} + {} * |other|".format( + atol, rtol + ) + + self.assertLessEqual( + diff_norm, + atol + rtol * other_norm, + msg=msg, + ) + + def test_ema(self): + model = DummyModule() + optimizer = torch.optim.SGD(model.parameters(), lr=0.01) + state = deepcopy(model.state_dict()) + config = EMAConfig() + ema = EMA(model, config) + + # set decay + ema._set_decay(config.ema_decay) + self.assertEqual(ema.get_decay(), config.ema_decay) + + # get model + self.assertEqual(ema.get_model(), ema.model) + + # Since fp32 params is not used, it should be of size 0 + self.assertEqual(len(ema.fp32_params), 0) + + # EMA step + x = torch.randn(32) + y = model(x) + loss = y.sum() + loss.backward() + optimizer.step() + + ema.step(model) + + ema_state_dict = ema.get_model().state_dict() + + for key, param in model.state_dict().items(): + prev_param = state[key] + ema_param = ema_state_dict[key] + + if "version" in key: + # Do not decay a model.version pytorch param + continue + self.assertTorchAllClose( + ema_param, + config.ema_decay * prev_param + (1 - config.ema_decay) * param, + ) + + # Since fp32 params is not used, it should be of size 0 + self.assertEqual(len(ema.fp32_params), 0) + + # Load EMA into model + model2 = DummyModule() + ema.reverse(model2) + + for key, param in model2.state_dict().items(): + ema_param = ema_state_dict[key] + self.assertTrue( + torch.allclose(ema_param, param) + ) + + def test_ema_fp32(self): + model = DummyModule().half() + optimizer = torch.optim.SGD(model.parameters(), lr=0.01) + state = deepcopy(model.state_dict()) + config = EMAConfig(ema_fp32=True) + ema = EMA(model, config) + + x = torch.randn(32) + y = model(x.half()) + loss = y.sum() + loss.backward() + optimizer.step() + + ema.step(model) + + for key, param in model.state_dict().items(): + prev_param = state[key] + ema_param = ema.get_model().state_dict()[key] + + if "version" in key: + # Do not decay a model.version pytorch param + continue + self.assertIn(key, ema.fp32_params) + + # EMA update is done in fp32, and hence the EMA param must be + # closer to the EMA update done in fp32 than in fp16. + self.assertLessEqual( + torch.norm( + ema_param.float() - + (config.ema_decay * prev_param.float() + (1 - config.ema_decay) * param.float()).half().float() + ), + torch.norm( + ema_param.float() - + (config.ema_decay * prev_param + (1 - config.ema_decay) * param).float() + ), + ) + self.assertTorchAllClose( + ema_param, + (config.ema_decay * prev_param.float() + (1 - config.ema_decay) * param.float()).half(), + ) + + def test_ema_fp16(self): + model = DummyModule().half() + optimizer = torch.optim.SGD(model.parameters(), lr=0.01) + state = deepcopy(model.state_dict()) + config = EMAConfig(ema_fp32=False) + ema = EMA(model, config) + + # Since fp32 params is not used, it should be of size 0 + self.assertEqual(len(ema.fp32_params), 0) + + x = torch.randn(32) + y = model(x.half()) + loss = y.sum() + loss.backward() + optimizer.step() + + ema.step(model) + + for key, param in model.state_dict().items(): + prev_param = state[key] + ema_param = ema.get_model().state_dict()[key] + + if "version" in key: + # Do not decay a model.version pytorch param + continue + + # EMA update is done in fp16, and hence the EMA param must be + # closer to the EMA update done in fp16 than in fp32. + self.assertLessEqual( + torch.norm( + ema_param.float() - + (config.ema_decay * prev_param + (1 - config.ema_decay) * param).float() + ), + torch.norm( + ema_param.float() - + (config.ema_decay * prev_param.float() + (1 - config.ema_decay) * param.float()).half().float() + ), + ) + self.assertTorchAllClose( + ema_param, + config.ema_decay * prev_param + (1 - config.ema_decay) * param, + ) + + # Since fp32 params is not used, it should be of size 0 + self.assertEqual(len(ema.fp32_params), 0) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_export.py b/tests/test_export.py index 7b0e7fcf1d..b380697b9a 100644 --- a/tests/test_export.py +++ b/tests/test_export.py @@ -12,13 +12,13 @@ from fairseq.data.dictionary import Dictionary from fairseq.models.transformer import TransformerModel from fairseq.modules import multihead_attention, sinusoidal_positional_embedding -from fairseq.tasks.fairseq_task import FairseqTask +from fairseq.tasks.fairseq_task import LegacyFairseqTask DEFAULT_TEST_VOCAB_SIZE = 100 -class DummyTask(FairseqTask): +class DummyTask(LegacyFairseqTask): def __init__(self, args): super().__init__(args) self.dictionary = get_dummy_dictionary() @@ -103,6 +103,19 @@ def test_export_transformer(self): scripted = torch.jit.script(model) _test_save_and_load(scripted) + @unittest.skipIf( + torch.__version__ < "1.6.0", "Targeting OSS scriptability for the 1.6 release" + ) + def test_export_transformer_no_token_pos_emb(self): + task, parser = get_dummy_task_and_parser() + TransformerModel.add_args(parser) + args = parser.parse_args([]) + args.no_token_positional_embeddings = True + model = TransformerModel.build_model(args, task) + scripted = torch.jit.script(model) + _test_save_and_load(scripted) + + if __name__ == "__main__": unittest.main() diff --git a/tests/test_file_chunker_utils.py b/tests/test_file_chunker_utils.py new file mode 100644 index 0000000000..5cded04572 --- /dev/null +++ b/tests/test_file_chunker_utils.py @@ -0,0 +1,63 @@ +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os +import shutil +import tempfile +import unittest +from typing import Optional + + +class TestFileChunker(unittest.TestCase): + _tmpdir: Optional[str] = None + _tmpfile: Optional[str] = None + _line_content = "Hello, World\n" + _num_bytes = None + _num_lines = 200 + _num_splits = 20 + + @classmethod + def setUpClass(cls) -> None: + cls._num_bytes = len(cls._line_content.encode("utf-8")) + cls._tmpdir = tempfile.mkdtemp() + with open(os.path.join(cls._tmpdir, "test.txt"), "w") as f: + cls._tmpfile = f.name + for _i in range(cls._num_lines): + f.write(cls._line_content) + f.flush() + + @classmethod + def tearDownClass(cls) -> None: + # Cleanup temp working dir. + if cls._tmpdir is not None: + shutil.rmtree(cls._tmpdir) # type: ignore + + def test_find_offsets(self): + from fairseq.file_chunker_utils import find_offsets + + offsets = find_offsets(self._tmpfile, self._num_splits) + self.assertEqual(len(offsets), self._num_splits + 1) + (zero, *real_offsets, last) = offsets + self.assertEqual(zero, 0) + for i, o in enumerate(real_offsets): + self.assertEqual( + o, + self._num_bytes + + ((i + 1) * self._num_bytes * self._num_lines / self._num_splits), + ) + self.assertEqual(last, self._num_bytes * self._num_lines) + + def test_readchunks(self): + from fairseq.file_chunker_utils import Chunker, find_offsets + + offsets = find_offsets(self._tmpfile, self._num_splits) + for start, end in zip(offsets, offsets[1:]): + with Chunker(self._tmpfile, start, end) as lines: + all_lines = list(lines) + num_lines = self._num_lines / self._num_splits + self.assertAlmostEqual( + len(all_lines), num_lines, delta=1 + ) # because we split on the bites, we might end up with one more/less line in a chunk + self.assertListEqual( + all_lines, [self._line_content for _ in range(len(all_lines))] + ) diff --git a/tests/test_file_io.py b/tests/test_file_io.py index ffcc6a3eef..425812bf16 100644 --- a/tests/test_file_io.py +++ b/tests/test_file_io.py @@ -1,14 +1,12 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. -import sys -import tempfile import os import shutil - -from typing import Optional - +import sys +import tempfile import unittest +from typing import Optional from unittest.mock import MagicMock @@ -34,14 +32,27 @@ def tearDownClass(cls) -> None: def test_file_io(self): from fairseq.file_io import PathManager + with PathManager.open(os.path.join(self._tmpdir, "test.txt"), "r") as f: s = f.read() self.assertEqual(s, self._tmpfile_contents) def test_file_io_oss(self): - # Mock fvcore to simulate oss environment. - sys.modules['fvcore'] = MagicMock() + # Mock iopath to simulate oss environment. + sys.modules["iopath"] = MagicMock() from fairseq.file_io import PathManager + with PathManager.open(os.path.join(self._tmpdir, "test.txt"), "r") as f: s = f.read() self.assertEqual(s, self._tmpfile_contents) + + def test_file_io_async(self): + # ioPath `PathManager` is initialized after the first `opena` call. + try: + from fairseq.file_io import IOPathManager, PathManager + _asyncfile = os.path.join(self._tmpdir, "async.txt") + f = PathManager.opena(_asyncfile, "wb") + f.close() + + finally: + self.assertTrue(PathManager.async_close()) diff --git a/tests/test_fp16_optimizer.py b/tests/test_fp16_optimizer.py new file mode 100644 index 0000000000..ce4f1c055c --- /dev/null +++ b/tests/test_fp16_optimizer.py @@ -0,0 +1,112 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import copy +import logging +import unittest + +import torch +from fairseq.optim.fp16_optimizer import FP16Optimizer, MemoryEfficientFP16Optimizer +from omegaconf import OmegaConf + + +@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU") +class TestGradientScaling(unittest.TestCase): + def setUp(self): + self.x = torch.tensor([2.0]).cuda().half() + weight = 3.0 + bias = 5.0 + self.error = 1.0 + self.target = torch.tensor([self.x * weight + bias + self.error]).cuda().half() + self.loss_fn = torch.nn.L1Loss() + + self.model = torch.nn.Linear(1, 1) + self.model.weight.data = torch.tensor([[weight]]) + self.model.bias.data = torch.tensor([bias]) + self.model.cuda().half() + self.params = list(self.model.parameters()) + + self.cfg_dls = OmegaConf.create( + { + "optimization": { + "lr": [0.1], + }, + "optimizer": { + "_name": "adam", + "lr": [0.1], + "adam_betas": "(0.9, 0.999)", + "adam_eps": 1e-8, + "weight_decay": 0.0, + }, + "common": { + "fp16_init_scale": 1, + "fp16_scale_window": 1, + "fp16_scale_tolerance": 1, + "threshold_loss_scale": 1, + "min_loss_scale": 1e-4, + "tpu": False, + }, + } + ) + logging.disable(logging.CRITICAL) + + def tearDown(self): + logging.disable(logging.NOTSET) + + def run_iter(self, model, params, optimizer): + optimizer.zero_grad() + y = model(self.x) + loss = self.loss_fn(y, self.target) + optimizer.backward(loss) + self.assertEqual(loss, torch.tensor(1.0, device="cuda:0", dtype=torch.float16)) + + grad_norm = optimizer.clip_grad_norm(0) + self.assertAlmostEqual(grad_norm.item(), 2.2361, 4) + + optimizer.step() + self.assertEqual( + model.weight, + torch.tensor( + [[3.0996]], device="cuda:0", dtype=torch.float16, requires_grad=True + ), + ) + self.assertEqual( + model.bias, + torch.tensor( + [5.1016], device="cuda:0", dtype=torch.float16, requires_grad=True + ), + ) + self.assertEqual(optimizer.scaler.loss_scale, 2.0) + + def test_mixed_precision(self): + model = copy.deepcopy(self.model) + params = list(model.parameters()) + optimizer = FP16Optimizer.build_optimizer(self.cfg_dls, params) + + self.run_iter(model, params, optimizer) + self.assertTrue( + all( + torch.all( + fp32_params.eq( + torch.tensor( + [3.1000, 5.1000], device="cuda:0", requires_grad=True + ) + ) + ) + for fp32_params in optimizer.fp32_params.values() + ) + ) + + def test_memory_efficient(self): + model = copy.deepcopy(self.model) + params = list(model.parameters()) + optimizer = MemoryEfficientFP16Optimizer.build_optimizer(self.cfg_dls, params) + + self.run_iter(model, params, optimizer) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_huffman.py b/tests/test_huffman.py new file mode 100644 index 0000000000..a8cd5222b4 --- /dev/null +++ b/tests/test_huffman.py @@ -0,0 +1,201 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import os +import random +import string +import typing as tp +import unittest +from collections import Counter +from tempfile import NamedTemporaryFile, TemporaryDirectory + +from fairseq.data import Dictionary, indexed_dataset +from fairseq.data.huffman import ( + HuffmanCodeBuilder, + HuffmanCoder, + HuffmanMMapIndexedDataset, + HuffmanMMapIndexedDatasetBuilder, +) + +POPULATION = string.ascii_letters + string.digits + + +def make_sentence() -> tp.List[str]: + length = random.randint(10, 50) + return random.choices( + population=POPULATION, k=length, weights=range(1, len(POPULATION) + 1) + ) + + +def make_data(length=1000) -> tp.List[tp.List[str]]: + return ( + [make_sentence() for _ in range(0, length)] + # add all the symbols at least once + + [list(string.ascii_letters), list(string.digits)] + ) + + +def make_counts(data: tp.List[tp.List[str]]) -> Counter: + return Counter([symbol for sentence in data for symbol in sentence]) + + +def make_code_builder(data: tp.List[tp.List[str]]) -> HuffmanCodeBuilder: + builder = HuffmanCodeBuilder() + for sentence in data: + builder.add_symbols(*sentence) + return builder + + +class TestCodeBuilder(unittest.TestCase): + def test_code_builder_can_count(self): + data = make_data() + counts = make_counts(data) + builder = make_code_builder(data) + + self.assertEqual(builder.symbols, counts) + + def test_code_builder_can_add(self): + data = make_data() + counts = make_counts(data) + builder = make_code_builder(data) + + new_builder = builder + builder + + self.assertEqual(new_builder.symbols, counts + counts) + + def test_code_builder_can_io(self): + data = make_data() + builder = make_code_builder(data) + + with NamedTemporaryFile() as tmp_fp: + builder.to_file(tmp_fp.name) + other_builder = HuffmanCodeBuilder.from_file(tmp_fp.name) + + self.assertEqual(builder.symbols, other_builder.symbols) + + +class TestCoder(unittest.TestCase): + def test_coder_can_io(self): + data = make_data() + builder = make_code_builder(data) + coder = builder.build_code() + + with NamedTemporaryFile() as tmp_fp: + coder.to_file(tmp_fp.name) + other_coder = HuffmanCoder.from_file(tmp_fp.name) + + self.assertEqual(coder, other_coder) + + def test_coder_can_encode_decode(self): + data = make_data() + builder = make_code_builder(data) + coder = builder.build_code() + + encoded = [coder.encode(sentence) for sentence in data] + decoded = [[n.symbol for n in coder.decode(enc)] for enc in encoded] + + self.assertEqual(decoded, data) + + unseen_data = make_data() + unseen_encoded = [coder.encode(sentence) for sentence in unseen_data] + unseen_decoded = [ + [n.symbol for n in coder.decode(enc)] for enc in unseen_encoded + ] + self.assertEqual(unseen_decoded, unseen_data) + + +def build_dataset(prefix, data, coder): + with HuffmanMMapIndexedDatasetBuilder(prefix, coder) as builder: + for sentence in data: + builder.add_item(sentence) + + +def sizes(data): + return [len(sentence) for sentence in data] + + +class TestHuffmanDataset(unittest.TestCase): + def test_huffman_can_encode_decode(self): + data = make_data() + builder = make_code_builder(data) + coder = builder.build_code() + + with TemporaryDirectory() as dirname: + prefix = os.path.join(dirname, "test1") + build_dataset(prefix, data, coder) + dataset = HuffmanMMapIndexedDataset(prefix) + + self.assertEqual(len(dataset), len(data)) + decoded = [list(dataset.get_symbols(i)) for i in range(0, len(dataset))] + + self.assertEqual(decoded, data) + data_sizes = [i.item() for i in dataset.sizes] + self.assertEqual(data_sizes, sizes(data)) + + def test_huffman_compresses(self): + data = make_data() + builder = make_code_builder(data) + coder = builder.build_code() + + with TemporaryDirectory() as dirname: + prefix = os.path.join(dirname, "huffman") + build_dataset(prefix, data, coder) + + prefix_mmap = os.path.join(dirname, "mmap") + mmap_builder = indexed_dataset.make_builder( + indexed_dataset.data_file_path(prefix_mmap), + "mmap", + vocab_size=len(POPULATION), + ) + dictionary = Dictionary() + for c in POPULATION: + dictionary.add_symbol(c) + dictionary.finalize() + for sentence in data: + mmap_builder.add_item(dictionary.encode_line(" ".join(sentence))) + mmap_builder.finalize(indexed_dataset.index_file_path(prefix_mmap)) + + huff_size = os.stat(indexed_dataset.data_file_path(prefix)).st_size + mmap_size = os.stat(indexed_dataset.data_file_path(prefix_mmap)).st_size + self.assertLess(huff_size, mmap_size) + + def test_huffman_can_append(self): + data1 = make_data() + builder = make_code_builder(data1) + coder = builder.build_code() + + with TemporaryDirectory() as dirname: + prefix1 = os.path.join(dirname, "test1") + build_dataset(prefix1, data1, coder) + + data2 = make_data() + prefix2 = os.path.join(dirname, "test2") + build_dataset(prefix2, data2, coder) + + prefix3 = os.path.join(dirname, "test3") + + with HuffmanMMapIndexedDatasetBuilder(prefix3, coder) as builder: + builder.append(prefix1) + builder.append(prefix2) + + dataset = HuffmanMMapIndexedDataset(prefix3) + + self.assertEqual(len(dataset), len(data1) + len(data2)) + + decoded1 = [list(dataset.get_symbols(i)) for i in range(0, len(data1))] + self.assertEqual(decoded1, data1) + + decoded2 = [ + list(dataset.get_symbols(i)) for i in range(len(data1), len(dataset)) + ] + self.assertEqual(decoded2, data2) + + data_sizes = [i.item() for i in dataset.sizes] + self.assertEqual(data_sizes[: len(data1)], sizes(data1)) + self.assertEqual(data_sizes[len(data1) : len(dataset)], sizes(data2)) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_inference_dropout.py b/tests/test_inference_dropout.py new file mode 100644 index 0000000000..353ac67478 --- /dev/null +++ b/tests/test_inference_dropout.py @@ -0,0 +1,70 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +import unittest + +from fairseq.dataclass.utils import convert_namespace_to_omegaconf +from fairseq.models.transformer import TransformerModel +from tests.test_sequence_generator import get_dummy_task_and_parser + + +class TestInferenceDropout(unittest.TestCase): + def setUp(self): + self.task, self.parser = get_dummy_task_and_parser() + TransformerModel.add_args(self.parser) + self.args = self.parser.parse_args([]) + self.args.encoder_layers = 2 + self.args.decoder_layers = 1 + logging.disable(logging.CRITICAL) + + def tearDown(self): + logging.disable(logging.NOTSET) + + def test_sets_inference_dropout_to_true(self): + self.args.retain_dropout = True + self.transformer_model = TransformerModel.build_model(self.args, self.task) + cfg = convert_namespace_to_omegaconf(self.args) + self.transformer_model.prepare_for_inference_(cfg) + assert self.transformer_model.encoder.dropout_module.apply_during_inference + assert self.transformer_model.decoder.dropout_module.apply_during_inference + for layer in self.transformer_model.encoder.layers: + assert layer.dropout_module.apply_during_inference + + def test_inference_dropout_false_by_default(self): + self.transformer_model = TransformerModel.build_model(self.args, self.task) + cfg = convert_namespace_to_omegaconf(self.args) + self.transformer_model.prepare_for_inference_(cfg) + assert not self.transformer_model.encoder.dropout_module.apply_during_inference + assert not self.transformer_model.decoder.dropout_module.apply_during_inference + for layer in self.transformer_model.encoder.layers: + assert not layer.dropout_module.apply_during_inference + for layer in self.transformer_model.decoder.layers: + assert not layer.dropout_module.apply_during_inference + + def test_applies_training_mode(self): + self.transformer_model = TransformerModel.build_model(self.args, self.task) + assert self.transformer_model.encoder.dropout_module.training + for layer in self.transformer_model.encoder.layers: + assert layer.dropout_module.training + + self.transformer_model.eval() + assert not self.transformer_model.decoder.dropout_module.training + for layer in self.transformer_model.encoder.layers: + assert not layer.dropout_module.training + + def test_retain_modules(self): + self.args.retain_dropout = True + self.args.retain_dropout_modules = [ + "TransformerEncoder", + "TransformerEncoderLayer", + ] + self.transformer_model = TransformerModel.build_model(self.args, self.task) + cfg = convert_namespace_to_omegaconf(self.args) + self.transformer_model.prepare_for_inference_(cfg) + assert self.transformer_model.encoder.dropout_module.apply_during_inference + assert not self.transformer_model.decoder.dropout_module.apply_during_inference + for layer in self.transformer_model.decoder.layers: + assert not layer.dropout_module.apply_during_inference diff --git a/tests/test_iopath.py b/tests/test_iopath.py new file mode 100644 index 0000000000..908261a661 --- /dev/null +++ b/tests/test_iopath.py @@ -0,0 +1,29 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import unittest +from unittest import mock + + +class TestIOPath(unittest.TestCase): + + def test_no_iopath(self): + from .test_reproducibility import TestReproducibility + + with mock.patch.dict("sys.modules", {"iopath": None}): + # reuse reproducibility tests, which are e2e tests that should cover + # most checkpoint related functionality + TestReproducibility._test_reproducibility(self, "test_reproducibility") + + def test_no_supports_rename(self): + from .test_reproducibility import TestReproducibility + + with mock.patch("fairseq.file_io.PathManager.supports_rename") as mock_fn: + mock_fn.return_value = False + TestReproducibility._test_reproducibility(self, "test_reproducibility") + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_iterators.py b/tests/test_iterators.py index 6e935ffc55..7b3dd48485 100644 --- a/tests/test_iterators.py +++ b/tests/test_iterators.py @@ -9,8 +9,8 @@ class TestIterators(unittest.TestCase): - - def test_counting_iterator(self, ref=None, itr=None): + def test_counting_iterator_index(self, ref=None, itr=None): + # Test the indexing functionality of CountingIterator if ref is None: assert itr is None ref = list(range(10)) @@ -18,6 +18,7 @@ def test_counting_iterator(self, ref=None, itr=None): else: assert len(ref) == 10 assert itr is not None + self.assertTrue(itr.has_next()) self.assertEqual(itr.n, 0) self.assertEqual(next(itr), ref[0]) @@ -27,9 +28,36 @@ def test_counting_iterator(self, ref=None, itr=None): itr.skip(3) self.assertEqual(itr.n, 5) self.assertEqual(next(itr), ref[5]) - itr.skip(3) - self.assertEqual(itr.n, 9) - self.assertEqual(next(itr), ref[9]) + itr.skip(2) + self.assertEqual(itr.n, 8) + self.assertEqual(list(itr), [ref[8], ref[9]]) + self.assertFalse(itr.has_next()) + + def test_counting_iterator_length_mismatch(self): + ref = list(range(10)) + # When the underlying iterable is longer than the CountingIterator, + # the remaining items in the iterable should be ignored + itr = iterators.CountingIterator(ref, total=8) + self.assertEqual(list(itr), ref[:8]) + # When the underlying iterable is shorter than the CountingIterator, + # raise an IndexError when the underlying iterable is exhausted + itr = iterators.CountingIterator(ref, total=12) + self.assertRaises(IndexError, list, itr) + + def test_counting_iterator_take(self): + # Test the "take" method of CountingIterator + ref = list(range(10)) + itr = iterators.CountingIterator(ref) + itr.take(5) + self.assertEqual(len(itr), len(list(iter(itr)))) + self.assertEqual(len(itr), 5) + + itr = iterators.CountingIterator(ref) + itr.take(5) + self.assertEqual(next(itr), ref[0]) + self.assertEqual(next(itr), ref[1]) + itr.skip(2) + self.assertEqual(next(itr), ref[4]) self.assertFalse(itr.has_next()) def test_grouped_iterator(self): @@ -42,11 +70,11 @@ def test_grouped_iterator(self): itr = iterators.GroupedIterator(x, 5) self.assertEqual(list(itr), [[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]]) - # test CountingIterator functionality + # test the GroupIterator also works correctly as a CountingIterator x = list(range(30)) ref = list(iterators.GroupedIterator(x, 3)) itr = iterators.GroupedIterator(x, 3) - self.test_counting_iterator(ref, itr) + self.test_counting_iterator_index(ref, itr) def test_sharded_iterator(self): # test correctness @@ -68,8 +96,42 @@ def test_sharded_iterator(self): x = list(range(30)) ref = list(iterators.ShardedIterator(x, num_shards=3, shard_id=0)) itr = iterators.ShardedIterator(x, num_shards=3, shard_id=0) - self.test_counting_iterator(ref, itr) + self.test_counting_iterator_index(ref, itr) + + def test_counting_iterator_buffered_iterator_take(self): + ref = list(range(10)) + buffered_itr = iterators.BufferedIterator(2, ref) + itr = iterators.CountingIterator(buffered_itr) + itr.take(5) + self.assertEqual(len(itr), len(list(iter(itr)))) + self.assertEqual(len(itr), 5) + + buffered_itr = iterators.BufferedIterator(2, ref) + itr = iterators.CountingIterator(buffered_itr) + itr.take(5) + self.assertEqual(len(buffered_itr), 5) + self.assertEqual(len(list(iter(buffered_itr))), 5) + + buffered_itr = iterators.BufferedIterator(2, ref) + itr = iterators.CountingIterator(buffered_itr) + itr.take(5) + self.assertEqual(next(itr), ref[0]) + self.assertEqual(next(itr), ref[1]) + itr.skip(2) + self.assertEqual(next(itr), ref[4]) + self.assertFalse(itr.has_next()) + self.assertRaises(StopIteration, next, buffered_itr) + + ref = list(range(4, 10)) + buffered_itr = iterators.BufferedIterator(2, ref) + itr = iterators.CountingIterator(buffered_itr, start=4) + itr.take(5) + self.assertEqual(len(itr), 5) + self.assertEqual(len(buffered_itr), 1) + self.assertEqual(next(itr), ref[0]) + self.assertFalse(itr.has_next()) + self.assertRaises(StopIteration, next, buffered_itr) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/tests/test_label_smoothing.py b/tests/test_label_smoothing.py index 8432d3c7bf..04c0f974ac 100644 --- a/tests/test_label_smoothing.py +++ b/tests/test_label_smoothing.py @@ -7,16 +7,15 @@ import copy import unittest +import tests.utils as test_utils import torch - from fairseq.criterions.cross_entropy import CrossEntropyCriterion -from fairseq.criterions.label_smoothed_cross_entropy import LabelSmoothedCrossEntropyCriterion - -import tests.utils as test_utils +from fairseq.criterions.label_smoothed_cross_entropy import ( + LabelSmoothedCrossEntropyCriterion, +) class TestLabelSmoothing(unittest.TestCase): - def setUp(self): # build dictionary self.d = test_utils.dummy_dictionary(3) @@ -30,31 +29,50 @@ def setUp(self): # build dataset self.data = [ # the first batch item has padding - {'source': torch.LongTensor([w1, eos]), 'target': torch.LongTensor([w1, eos])}, - {'source': torch.LongTensor([w1, eos]), 'target': torch.LongTensor([w1, w1, eos])}, + { + "source": torch.LongTensor([w1, eos]), + "target": torch.LongTensor([w1, eos]), + }, + { + "source": torch.LongTensor([w1, eos]), + "target": torch.LongTensor([w1, w1, eos]), + }, ] self.sample = next(test_utils.dummy_dataloader(self.data)) # build model self.args = argparse.Namespace() self.args.sentence_avg = False - self.args.probs = torch.FloatTensor([ - # pad eos unk w1 w2 w3 - [0.05, 0.05, 0.1, 0.05, 0.3, 0.4, 0.05], - [0.05, 0.10, 0.2, 0.05, 0.2, 0.3, 0.10], - [0.05, 0.15, 0.3, 0.05, 0.1, 0.2, 0.15], - ]).unsqueeze(0).expand(2, 3, 7) # add batch dimension + self.args.report_accuracy = False + self.args.probs = ( + torch.FloatTensor( + [ + # pad eos unk w1 w2 w3 + [0.05, 0.05, 0.1, 0.05, 0.3, 0.4, 0.05], + [0.05, 0.10, 0.2, 0.05, 0.2, 0.3, 0.10], + [0.05, 0.15, 0.3, 0.05, 0.1, 0.2, 0.15], + ] + ) + .unsqueeze(0) + .expand(2, 3, 7) + ) # add batch dimension self.task = test_utils.TestTranslationTask.setup_task(self.args, self.d, self.d) self.model = self.task.build_model(self.args) def test_nll_loss(self): self.args.label_smoothing = 0.1 nll_crit = CrossEntropyCriterion.build_criterion(self.args, self.task) - smooth_crit = LabelSmoothedCrossEntropyCriterion.build_criterion(self.args, self.task) - nll_loss, nll_sample_size, nll_logging_output = nll_crit(self.model, self.sample) - smooth_loss, smooth_sample_size, smooth_logging_output = smooth_crit(self.model, self.sample) - self.assertLess(abs(nll_loss - nll_logging_output['loss']), 1e-6) - self.assertLess(abs(nll_loss - smooth_logging_output['nll_loss']), 1e-6) + smooth_crit = LabelSmoothedCrossEntropyCriterion.build_criterion( + self.args, self.task + ) + nll_loss, nll_sample_size, nll_logging_output = nll_crit( + self.model, self.sample + ) + smooth_loss, smooth_sample_size, smooth_logging_output = smooth_crit( + self.model, self.sample + ) + self.assertLess(abs(nll_loss - nll_logging_output["loss"]), 1e-6) + self.assertLess(abs(nll_loss - smooth_logging_output["nll_loss"]), 1e-6) def test_padding(self): self.args.label_smoothing = 0.1 @@ -85,9 +103,15 @@ def test_reduction(self): def test_zero_eps(self): self.args.label_smoothing = 0.0 nll_crit = CrossEntropyCriterion.build_criterion(self.args, self.task) - smooth_crit = LabelSmoothedCrossEntropyCriterion.build_criterion(self.args, self.task) - nll_loss, nll_sample_size, nll_logging_output = nll_crit(self.model, self.sample) - smooth_loss, smooth_sample_size, smooth_logging_output = smooth_crit(self.model, self.sample) + smooth_crit = LabelSmoothedCrossEntropyCriterion.build_criterion( + self.args, self.task + ) + nll_loss, nll_sample_size, nll_logging_output = nll_crit( + self.model, self.sample + ) + smooth_loss, smooth_sample_size, smooth_logging_output = smooth_crit( + self.model, self.sample + ) self.assertAlmostEqual(nll_loss, smooth_loss) def assertAlmostEqual(self, t1, t2): @@ -95,5 +119,5 @@ def assertAlmostEqual(self, t1, t2): self.assertLess((t1 - t2).abs().max(), 1e-6) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/tests/test_lm_context_window.py b/tests/test_lm_context_window.py new file mode 100644 index 0000000000..7415e86abd --- /dev/null +++ b/tests/test_lm_context_window.py @@ -0,0 +1,51 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import unittest + +import torch +from fairseq.data import MonolingualDataset +from fairseq.tasks.language_modeling import LanguageModelingTask, LanguageModelingConfig +from tests import utils as test_utils + + +class TestLMContextWindow(unittest.TestCase): + + def test_eval_dataloader(self): + dictionary = test_utils.dummy_dictionary(10) + assert len(dictionary) == 14 # 4 extra special symbols + assert dictionary.pad() == 1 + + dataset = test_utils.TestDataset([ + torch.tensor([4, 5, 6, 7], dtype=torch.long), + torch.tensor([8, 9, 10, 11], dtype=torch.long), + torch.tensor([12, 13], dtype=torch.long), + ]) + dataset = MonolingualDataset(dataset, sizes=[4, 4, 2], src_vocab=dictionary) + + config = LanguageModelingConfig(tokens_per_sample=4) + task = LanguageModelingTask(config, dictionary) + + eval_dataloader = task.eval_lm_dataloader( + dataset=dataset, + batch_size=1, + context_window=2, + ) + + batch = next(eval_dataloader) + assert batch["net_input"]["src_tokens"][0].tolist() == [4, 5, 6, 7, 1, 1] + assert batch["target"][0].tolist() == [4, 5, 6, 7, 1, 1] + + batch = next(eval_dataloader) + assert batch["net_input"]["src_tokens"][0].tolist() == [6, 7, 8, 9, 10, 11] + assert batch["target"][0].tolist() == [1, 1, 8, 9, 10, 11] + + batch = next(eval_dataloader) + assert batch["net_input"]["src_tokens"][0].tolist() == [10, 11, 12, 13] + assert batch["target"][0].tolist() == [1, 1, 12, 13] + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_lstm_jitable.py b/tests/test_lstm_jitable.py index d0d812ceac..38f79d1793 100644 --- a/tests/test_lstm_jitable.py +++ b/tests/test_lstm_jitable.py @@ -10,13 +10,13 @@ import torch from fairseq.data.dictionary import Dictionary from fairseq.models.lstm import LSTMModel -from fairseq.tasks.fairseq_task import FairseqTask +from fairseq.tasks.fairseq_task import LegacyFairseqTask DEFAULT_TEST_VOCAB_SIZE = 100 -class DummyTask(FairseqTask): +class DummyTask(LegacyFairseqTask): def __init__(self, args): super().__init__(args) self.dictionary = get_dummy_dictionary() @@ -92,19 +92,21 @@ def test_assert_jit_vs_nonjit_(self): idx = len(task.source_dictionary) iter = 100 # Inject random input and check output - seq_len_tensor = torch.randint(1, 10, (iter, )) - num_samples_tensor = torch.randint(1, 10, (iter, )) + seq_len_tensor = torch.randint(1, 10, (iter,)) + num_samples_tensor = torch.randint(1, 10, (iter,)) for i in range(iter): seq_len = seq_len_tensor[i] num_samples = num_samples_tensor[i] - src_token = torch.randint(0, idx, (num_samples, seq_len)), - src_lengths = torch.randint(1, seq_len+1, (num_samples,)) + src_token = (torch.randint(0, idx, (num_samples, seq_len)),) + src_lengths = torch.randint(1, seq_len + 1, (num_samples,)) src_lengths, _ = torch.sort(src_lengths, descending=True) # Force the first sample to have seq_len src_lengths[0] = seq_len - prev_output_token = torch.randint(0, idx, (num_samples, 1)), + prev_output_token = (torch.randint(0, idx, (num_samples, 1)),) result = model(src_token[0], src_lengths, prev_output_token[0], None) - scripted_result = scripted_model(src_token[0], src_lengths, prev_output_token[0], None) + scripted_result = scripted_model( + src_token[0], src_lengths, prev_output_token[0], None + ) self.assertTensorEqual(result[0], scripted_result[0]) self.assertTensorEqual(result[1], scripted_result[1]) diff --git a/tests/test_memory_efficient_fp16.py b/tests/test_memory_efficient_fp16.py index bd2b8faeb4..2bf2f29888 100644 --- a/tests/test_memory_efficient_fp16.py +++ b/tests/test_memory_efficient_fp16.py @@ -8,14 +8,13 @@ import unittest import torch - from fairseq.optim.adam import FairseqAdam from fairseq.optim.fp16_optimizer import MemoryEfficientFP16Optimizer +from omegaconf import OmegaConf -@unittest.skipIf(not torch.cuda.is_available(), 'test requires a GPU') +@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU") class TestMemoryEfficientFP16(unittest.TestCase): - def setUp(self): logging.disable(logging.CRITICAL) @@ -28,25 +27,36 @@ def test_load_state_dict(self): params = list(model.parameters()) # initialize memory efficient FP16 optimizer + # with pseudo DictConfigs optimizer = FairseqAdam( - argparse.Namespace( - lr=[0.00001], - adam_betas='(0.9, 0.999)', - adam_eps=1e-8, - weight_decay=0.0, + cfg=OmegaConf.create( + vars( + argparse.Namespace( + adam_betas="(0.9, 0.999)", + adam_eps=1e-8, + weight_decay=0.0, + lr=[0.00001], + ) + ) ), - params, + params=params, ) me_optimizer = MemoryEfficientFP16Optimizer( - argparse.Namespace( - fp16_init_scale=1, - fp16_scale_window=1, - fp16_scale_tolerance=1, - threshold_loss_scale=1, - min_loss_scale=1e-4, + cfg=OmegaConf.create( + { + "common": vars( + argparse.Namespace( + fp16_init_scale=1, + fp16_scale_window=1, + fp16_scale_tolerance=1, + threshold_loss_scale=1, + min_loss_scale=1e-4, + ) + ) + } ), - params, - optimizer, + params=params, + optimizer=optimizer, ) # optimizer state is created in the first step @@ -64,5 +74,5 @@ def test_load_state_dict(self): self.assertTrue(v_i.dtype == torch.float32) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/tests/test_metrics.py b/tests/test_metrics.py index 060291808e..2de6969cf4 100644 --- a/tests/test_metrics.py +++ b/tests/test_metrics.py @@ -10,69 +10,68 @@ class TestMetrics(unittest.TestCase): - def test_nesting(self): with metrics.aggregate() as a: - metrics.log_scalar('loss', 1) + metrics.log_scalar("loss", 1) with metrics.aggregate() as b: - metrics.log_scalar('loss', 2) + metrics.log_scalar("loss", 2) - self.assertEqual(a.get_smoothed_values()['loss'], 1.5) - self.assertEqual(b.get_smoothed_values()['loss'], 2) + self.assertEqual(a.get_smoothed_values()["loss"], 1.5) + self.assertEqual(b.get_smoothed_values()["loss"], 2) def test_new_root(self): with metrics.aggregate() as a: - metrics.log_scalar('loss', 1) + metrics.log_scalar("loss", 1) with metrics.aggregate(new_root=True) as b: - metrics.log_scalar('loss', 2) + metrics.log_scalar("loss", 2) - self.assertEqual(a.get_smoothed_values()['loss'], 1) - self.assertEqual(b.get_smoothed_values()['loss'], 2) + self.assertEqual(a.get_smoothed_values()["loss"], 1) + self.assertEqual(b.get_smoothed_values()["loss"], 2) def test_nested_new_root(self): with metrics.aggregate() as layer1: - metrics.log_scalar('loss', 1) + metrics.log_scalar("loss", 1) with metrics.aggregate(new_root=True) as layer2: - metrics.log_scalar('loss', 2) + metrics.log_scalar("loss", 2) with metrics.aggregate() as layer3: - metrics.log_scalar('loss', 3) + metrics.log_scalar("loss", 3) with metrics.aggregate(new_root=True) as layer4: - metrics.log_scalar('loss', 4) - metrics.log_scalar('loss', 1.5) + metrics.log_scalar("loss", 4) + metrics.log_scalar("loss", 1.5) - self.assertEqual(layer4.get_smoothed_values()['loss'], 4) - self.assertEqual(layer3.get_smoothed_values()['loss'], 3) - self.assertEqual(layer2.get_smoothed_values()['loss'], 2.5) - self.assertEqual(layer1.get_smoothed_values()['loss'], 1.25) + self.assertEqual(layer4.get_smoothed_values()["loss"], 4) + self.assertEqual(layer3.get_smoothed_values()["loss"], 3) + self.assertEqual(layer2.get_smoothed_values()["loss"], 2.5) + self.assertEqual(layer1.get_smoothed_values()["loss"], 1.25) def test_named(self): name = str(uuid.uuid4()) metrics.reset_meters(name) with metrics.aggregate(name): - metrics.log_scalar('loss', 1) + metrics.log_scalar("loss", 1) - metrics.log_scalar('loss', 3) + metrics.log_scalar("loss", 3) with metrics.aggregate(name): - metrics.log_scalar('loss', 2) + metrics.log_scalar("loss", 2) - self.assertEqual(metrics.get_smoothed_values(name)['loss'], 1.5) + self.assertEqual(metrics.get_smoothed_values(name)["loss"], 1.5) def test_nested_duplicate_names(self): name = str(uuid.uuid4()) metrics.reset_meters(name) with metrics.aggregate(name): - metrics.log_scalar('loss', 1) + metrics.log_scalar("loss", 1) with metrics.aggregate() as other: with metrics.aggregate(name): - metrics.log_scalar('loss', 2) - metrics.log_scalar('loss', 6) + metrics.log_scalar("loss", 2) + metrics.log_scalar("loss", 6) - self.assertEqual(metrics.get_smoothed_values(name)['loss'], 3) - self.assertEqual(other.get_smoothed_values()['loss'], 2) + self.assertEqual(metrics.get_smoothed_values(name)["loss"], 3) + self.assertEqual(other.get_smoothed_values()["loss"], 2) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/tests/test_multi_corpus_dataset.py b/tests/test_multi_corpus_dataset.py new file mode 100644 index 0000000000..5a79f4b680 --- /dev/null +++ b/tests/test_multi_corpus_dataset.py @@ -0,0 +1,79 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import unittest +from collections import OrderedDict + +import torch +from fairseq.data import LanguagePairDataset, TokenBlockDataset +from fairseq.data.multi_corpus_dataset import MultiCorpusDataset +from tests.test_train import mock_dict + + +class TestMultiCorpusDataset(unittest.TestCase): + def setUp(self): + d = mock_dict() + tokens_1 = torch.LongTensor([i for i in range(1, 5000, 2)]).view(1, -1) + tokens_ds1 = TokenBlockDataset( + tokens_1, + sizes=[tokens_1.size(-1)], + block_size=1, + pad=0, + eos=1, + include_targets=False, + ) + self.dataset_1 = LanguagePairDataset( + tokens_ds1, tokens_ds1.sizes, d, shuffle=False + ) + tokens_2 = torch.LongTensor([i for i in range(0, 5000, 2)]).view(1, -1) + tokens_ds2 = TokenBlockDataset( + tokens_2, + sizes=[tokens_2.size(-1)], + block_size=1, + pad=0, + eos=1, + include_targets=False, + ) + self.dataset_2 = LanguagePairDataset( + tokens_ds2, tokens_ds2.sizes, d, shuffle=False + ) + + def _test_sample_helper( + self, + distribution, + ): + m = MultiCorpusDataset( + OrderedDict({0: self.dataset_1, 1: self.dataset_2}), + distribution=distribution, + seed=0, + sort_indices=True, + ) + m.set_epoch(1) + indices = m.ordered_indices() + count_sample_from_first_dataset = 0 + items = set() + for i in indices: + item = m[i]["source"].item() + if item % 2 == 1: + count_sample_from_first_dataset += 1 + + items.add(item) + sample_from_first_ds_percentage = ( + 1.0 * count_sample_from_first_dataset / len(indices) + ) + self.assertLess( + abs(sample_from_first_ds_percentage - distribution[0]), + 0.01, + ) + self.assertEqual( + len(items), + int(min(len(self.dataset_1), len(indices) * distribution[0]) + + min(len(self.dataset_1), len(indices) * distribution[1])) + ) + print(distribution) + + def test_multi_corpus_dataset(self): + for distribution in [[0.5, 0.5], [0.1, 0.9], [0.9, 0.1]]: + self._test_sample_helper(distribution=distribution) diff --git a/tests/test_multihead_attention.py b/tests/test_multihead_attention.py index 324d8e3eb5..620a2d6791 100644 --- a/tests/test_multihead_attention.py +++ b/tests/test_multihead_attention.py @@ -3,8 +3,9 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. -import torch import unittest + +import torch from fairseq.modules.multihead_attention import MultiheadAttention @@ -34,6 +35,18 @@ def test_append_prev_key_padding_mask(self): torch.tensor([[0, 1, 0]]).bool(), torch.tensor([[0, 1, 0, 1]]).bool(), ), + # prev_key_padding_mask already full + ( + torch.tensor([[0, 1, 0, 1]]).bool(), + None, + torch.tensor([[0, 1, 0, 1]]).bool(), + ), + # key_padding_mask already full + ( + None, + torch.tensor([[0, 1, 0, 1]]).bool(), + torch.tensor([[0, 1, 0, 1]]).bool(), + ), ] for c in cases: key_padding_mask = MultiheadAttention._append_prev_key_padding_mask( @@ -47,8 +60,8 @@ def test_append_prev_key_padding_mask(self): if key_padding_mask is not None: self.assertTrue( torch.all(torch.eq(key_padding_mask, c[2])), - f'Unexpected resultant key padding mask: {key_padding_mask}' - f' given current: {c[0]} and previous: {c[1]}', + f"Unexpected resultant key padding mask: {key_padding_mask}" + f" given current: {c[0]} and previous: {c[1]}", ) self.assertEqual(key_padding_mask.size(0), bsz) self.assertEqual(key_padding_mask.size(1), src_len) @@ -56,5 +69,5 @@ def test_append_prev_key_padding_mask(self): self.assertIsNone(c[2]) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/tests/test_noising.py b/tests/test_noising.py index da792a1826..b3d0d123c4 100644 --- a/tests/test_noising.py +++ b/tests/test_noising.py @@ -408,7 +408,10 @@ def test_word_blank_without_eos(self): self.assert_no_eos_at_end(x=x_noised, x_len=l_noised, eos=vocab.eos()) def _get_noising_dataset_batch( - self, src_tokens_no_pad, src_dict, append_eos_to_tgt=False, + self, + src_tokens_no_pad, + src_dict, + append_eos_to_tgt=False, ): """ Constructs a NoisingDataset and the corresponding @@ -433,7 +436,8 @@ def _get_noising_dataset_batch( src=noising_dataset, tgt=tgt, src_sizes=None, src_dict=src_dict ) language_pair_dataset = TransformEosDataset( - language_pair_dataset, src_dict.eos(), + language_pair_dataset, + src_dict.eos(), append_eos_to_tgt=append_eos_to_tgt, ) diff --git a/tests/test_online_backtranslation.py b/tests/test_online_backtranslation.py new file mode 100644 index 0000000000..0ae7e773da --- /dev/null +++ b/tests/test_online_backtranslation.py @@ -0,0 +1,206 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import tempfile +import unittest +from pathlib import Path +from typing import Any, Dict, Sequence + +import fairseq.data.indexed_dataset as indexed_dataset +import fairseq.options +import fairseq.tasks.online_backtranslation as obt +import torch +from tests import utils + + +def mk_sample(tokens: Sequence[int], batch_size: int = 2) -> Dict[str, Any]: + batch = torch.stack([torch.tensor(tokens, dtype=torch.long)] * batch_size) + sample = { + "net_input": { + "src_tokens": batch, + "prev_output_tokens": batch, + "src_lengths": torch.tensor([len(tokens)] * batch_size, dtype=torch.long), + }, + "target": batch[:, 1:], + } + return sample + + +def mk_dataset(num_samples: int, max_len: int, output: Path): + output.parent.mkdir(exist_ok=True) + idx = indexed_dataset.IndexedDatasetBuilder(str(output)) + data = torch.randint(5, 100, (num_samples, max_len)) + lengths = torch.randint(3, max_len, (num_samples,)) + for d, l in zip(data, lengths): + d[0] = 0 + idx.add_item(d[:l]) + idx.finalize(output.with_suffix(".idx")) + assert output.exists() + assert output.with_suffix(".idx").exists() + + +class OnlineBacktranslationTest(unittest.TestCase): + + tmp_dir = Path(tempfile.mkdtemp(suffix="OnlineBacktranslationTest")) + + @classmethod + def obt_task( + cls, languages: Sequence[str], data: Path = None, language_mapping: str = None + ): + dict_path = cls.tmp_dir / "dict.txt" + if not dict_path.exists(): + dictionary = utils.dummy_dictionary(100) + dictionary.save(str(dict_path)) + + if data is not None: + (data / "dict.txt").write_text(dict_path.read_text()) + else: + data = cls.tmp_dir + assert len(languages) >= 2 + + kwargs = { + "arch": "transformer", + # --max-sentences=1 for better predictability of batches + "max_sentences": 1, + # Use characteristics dimensions + "encoder_layers": 3, + "encoder_embed_dim": 12, + "encoder_ffn_embed_dim": 14, + "encoder_attention_heads": 4, + "decoder_layers": 3, + "decoder_embed_dim": 12, + "decoder_output_dim": 12, + "decoder_ffn_embed_dim": 14, + "decoder_attention_heads": 4, + # Disable dropout so we have comparable tests. + "dropout": 0, + "attention_dropout": 0, + "activation_dropout": 0, + "encoder_layerdrop": 0, + } + + args = fairseq.options.get_args( + data, + task="online_backtranslation", + mono_langs=",".join(languages), + valid_lang_pairs=f"{languages[0]}-{languages[1]}", + tokens_per_sample=256, + language_mapping=language_mapping, + **kwargs, + ) + task = obt.OnlineBackTranslationTask.setup_task(args) + # we need to build the model to have the correct dictionary + model = task.build_model(task.args) + return task, model + + def tmp_path(self, test_case: str) -> Path: + return Path(tempfile.mkdtemp(test_case, dir=self.tmp_dir)) + + def test_lang_tokens(self): + task, model = self.obt_task(["en", "ro", "zh"]) + assert obt._lang_token("en") in task.dictionary + assert obt._lang_token("ro") in task.dictionary + assert obt._lang_token("zh") in task.dictionary + + en_bos = obt._lang_token_index(task.common_dict, "en") + assert "en" == task.common_dict[en_bos].strip("_") + zh_bos = obt._lang_token_index(task.common_dict, "zh") + assert "zh" == task.common_dict[zh_bos].strip("_") + zh_sample = mk_sample([zh_bos, 16, 14, 12, 10]) + + # we expect to receive the bos token for translation + assert task.get_bos_token_from_sample(zh_sample) == en_bos + + def test_backtranslate_sample(self): + task, model = self.obt_task(["en", "ro", "zh"]) + + en_bos = obt._lang_token_index(task.common_dict, "en") + zh_bos = obt._lang_token_index(task.common_dict, "zh") + sample = mk_sample([zh_bos, 16, 14, 12, 10]) + + task.backtranslate_sample(sample, "zh", "en") + target_zh = list(sample["target"][0]) + assert target_zh == [16, 14, 12, 10] # original zh sentence + generated_en = sample["net_input"]["src_tokens"][0] + assert generated_en[0] == en_bos + + def test_train_dataset(self): + data = self.tmp_path("test_train_dataset") + mk_dataset(20, 10, data / "en" / "train.bin") + mk_dataset(10, 10, data / "zh" / "train.bin") + task, model = self.obt_task(["en", "zh"], data) + task.load_dataset("train") + + en_bos = obt._lang_token_index(task.common_dict, "en") + zh_bos = obt._lang_token_index(task.common_dict, "zh") + + train = task.datasets["train"] + train.ordered_indices() + train.prefetch([0, 19]) + sample_0 = train[0] + sample_19 = train[19] + self.assertEqual( + set(sample_0.keys()), {"en-BT", "en-DENOISE", "zh-BT", "zh-DENOISE"} + ) + for sample in (sample_0, sample_19): + self.assertEqual(sample["en-BT"]["source"][0], en_bos) + # bt target isn't ready to look at. + self.assertEqual(sample["en-DENOISE"]["source"][0], en_bos) + # TODO What could we check on the target side ? + + for i in range(10): + # Zh dataset is shorter, and is wrapped around En dataset. + train.prefetch([i, i + 10]) + self.assertEqual( + list(train[i]["zh-DENOISE"]["source"]), + list(train[i + 10]["zh-DENOISE"]["source"]), + ) + self.assertEqual(train[i]["zh-DENOISE"]["source"][0].item(), zh_bos) + + # Sorted by increasing len + self.assertLess( + len(sample_0["en-BT"]["source"]), len(sample_19["en-BT"]["source"]) + ) + + def test_valid_dataset(self): + data = self.tmp_path("test_valid_dataset") + mk_dataset(10, 21, data / "valid.en-zh.en.bin") + mk_dataset(10, 21, data / "valid.en-zh.zh.bin") + + task, model = self.obt_task(["en", "zh"], data) + valid = task.load_dataset("valid") + en_bos = obt._lang_token_index(task.common_dict, "en") + + assert valid is not None + valid.prefetch(range(10)) + sample_0 = valid[0] + sample_9 = valid[9] + self.assertEqual(sample_0["id"], 0) + self.assertEqual(sample_9["id"], 9) + self.assertEqual(sample_0["source"][0], en_bos) + self.assertEqual(sample_9["source"][0], en_bos) + # TODO: could we test the target side ? + + def assertFnMatch(self, fn, values): + for x, y in values.items(): + fn_x = fn(x) + self.assertEqual(fn_x, y, f"Fn has wrong value: fn({x}) = {fn_x} != {y}") + + def test_piecewise_linear_fn(self): + self.assertFnMatch( + obt.PiecewiseLinearFn.from_string("1.0"), {0: 1, 100: 1, 500: 1, 1000: 1} + ) + self.assertFnMatch( + obt.PiecewiseLinearFn.from_string("0:1,1000:0"), + {0: 1, 500: 0.5, 1000: 0, 2000: 0}, + ) + self.assertFnMatch( + obt.PiecewiseLinearFn.from_string("0:0,1000:1"), + {0: 0, 500: 0.5, 1000: 1, 2000: 1}, + ) + self.assertFnMatch( + obt.PiecewiseLinearFn.from_string("0:0,1000:1,2000:0"), + {0: 0, 500: 0.5, 1000: 1, 1500: 0.5, 2000: 0, 3000: 0}, + ) diff --git a/tests/test_plasma_utils.py b/tests/test_plasma_utils.py new file mode 100644 index 0000000000..e6344c2a5a --- /dev/null +++ b/tests/test_plasma_utils.py @@ -0,0 +1,126 @@ +import contextlib +import unittest +import tempfile +from io import StringIO + +import numpy as np + +from tests.utils import create_dummy_data, preprocess_lm_data, train_language_model + +try: + from pyarrow import plasma + from fairseq.data.plasma_utils import PlasmaView, PlasmaStore + + PYARROW_AVAILABLE = True +except ImportError: + PYARROW_AVAILABLE = False + +dummy_path = "dummy" + + +@unittest.skipUnless(PYARROW_AVAILABLE, "") +class TestPlasmaView(unittest.TestCase): + def setUp(self) -> None: + self.tmp_file = tempfile.NamedTemporaryFile() # noqa: P201 + self.path = self.tmp_file.name + self.server = PlasmaStore.start(path=self.path, nbytes=10000) + self.client = plasma.connect(self.path, num_retries=10) + + def tearDown(self) -> None: + self.client.disconnect() + self.tmp_file.close() + self.server.kill() + + def test_two_servers_do_not_share_object_id_space(self): + data_server_1 = np.array([0, 1]) + data_server_2 = np.array([2, 3]) + server_2_path = self.path + with tempfile.NamedTemporaryFile() as server_1_path: + server = PlasmaStore.start(path=server_1_path.name, nbytes=10000) + arr1 = PlasmaView( + data_server_1, dummy_path, 1, plasma_path=server_1_path.name + ) + assert len(arr1.client.list()) == 1 + assert (arr1.array == data_server_1).all() + arr2 = PlasmaView(data_server_2, dummy_path, 1, plasma_path=server_2_path) + assert (arr2.array == data_server_2).all() + assert (arr1.array == data_server_1).all() + server.kill() + + def test_hash_collision(self): + data_server_1 = np.array([0, 1]) + data_server_2 = np.array([2, 3]) + arr1 = PlasmaView(data_server_1, dummy_path, 1, plasma_path=self.path) + assert len(arr1.client.list()) == 1 + arr2 = PlasmaView(data_server_2, dummy_path, 1, plasma_path=self.path) + assert len(arr1.client.list()) == 1 + assert len(arr2.client.list()) == 1 + assert (arr2.array == data_server_1).all() + # New hash key based on tuples + arr3 = PlasmaView( + data_server_2, dummy_path, (1, 12312312312, None), plasma_path=self.path + ) + assert ( + len(arr2.client.list()) == 2 + ), "No new object was created by using a novel hash key" + assert ( + arr3.object_id in arr2.client.list() + ), "No new object was created by using a novel hash key" + assert ( + arr3.object_id in arr3.client.list() + ), "No new object was created by using a novel hash key" + del arr3, arr2, arr1 + + @staticmethod + def _assert_view_equal(pv1, pv2): + np.testing.assert_array_equal(pv1.array, pv2.array) + + def test_putting_same_array_twice(self): + data = np.array([4, 4, 4]) + arr1 = PlasmaView(data, dummy_path, 1, plasma_path=self.path) + assert len(self.client.list()) == 1 + arr1b = PlasmaView( + data, dummy_path, 1, plasma_path=self.path + ) # should not change contents of store + arr1c = PlasmaView( + None, dummy_path, 1, plasma_path=self.path + ) # should not change contents of store + + assert len(self.client.list()) == 1 + self._assert_view_equal(arr1, arr1b) + self._assert_view_equal(arr1, arr1c) + PlasmaView( + data, dummy_path, 2, plasma_path=self.path + ) # new object id, adds new entry + assert len(self.client.list()) == 2 + + new_client = plasma.connect(self.path) + assert len(new_client.list()) == 2 # new client can access same objects + assert isinstance(arr1.object_id, plasma.ObjectID) + del arr1b + del arr1c + + def test_plasma_store_full_raises(self): + with tempfile.NamedTemporaryFile() as new_path: + server = PlasmaStore.start(path=new_path.name, nbytes=10000) + with self.assertRaises(plasma.PlasmaStoreFull): + # 2000 floats is more than 2000 bytes + PlasmaView( + np.random.rand(10000, 1), dummy_path, 1, plasma_path=new_path.name + ) + server.kill() + + def test_object_id_overflow(self): + PlasmaView.get_object_id("", 2 ** 21) + + def test_training_lm_plasma(self): + with contextlib.redirect_stdout(StringIO()): + with tempfile.TemporaryDirectory("test_transformer_lm") as data_dir: + create_dummy_data(data_dir) + preprocess_lm_data(data_dir) + train_language_model( + data_dir, + "transformer_lm", + ["--use-plasma-view", "--plasma-path", self.path], + run_validation=True, + ) diff --git a/tests/test_reproducibility.py b/tests/test_reproducibility.py index 80f2948250..94931b2a07 100644 --- a/tests/test_reproducibility.py +++ b/tests/test_reproducibility.py @@ -4,11 +4,11 @@ # LICENSE file in the root directory of this source tree. import contextlib -from io import StringIO import json import os import tempfile import unittest +from io import StringIO import torch @@ -16,18 +16,17 @@ class TestReproducibility(unittest.TestCase): - def _test_reproducibility( self, name, extra_flags=None, delta=0.0001, - resume_checkpoint='checkpoint1.pt', + resume_checkpoint="checkpoint1.pt", max_epoch=3, ): def get_last_log_stats_containing_string(log_records, search_string): for log_record in logs.records[::-1]: - if search_string in log_record.msg: + if isinstance(log_record.msg, str) and search_string in log_record.msg: return json.loads(log_record.msg) if extra_flags is None: @@ -41,63 +40,111 @@ def get_last_log_stats_containing_string(log_records, search_string): # train epochs 1 and 2 together with self.assertLogs() as logs: test_binaries.train_translation_model( - data_dir, 'fconv_iwslt_de_en', [ - '--dropout', '0.0', - '--log-format', 'json', - '--log-interval', '1', - '--max-epoch', str(max_epoch), - ] + extra_flags, + data_dir, + "fconv_iwslt_de_en", + [ + "--dropout", + "0.0", + "--log-format", + "json", + "--log-interval", + "1", + "--max-epoch", + str(max_epoch), + ] + + extra_flags, ) - train_log = get_last_log_stats_containing_string(logs.records, 'train_loss') - valid_log = get_last_log_stats_containing_string(logs.records, 'valid_loss') + train_log = get_last_log_stats_containing_string(logs.records, "train_loss") + valid_log = get_last_log_stats_containing_string(logs.records, "valid_loss") # train epoch 2, resuming from previous checkpoint 1 os.rename( os.path.join(data_dir, resume_checkpoint), - os.path.join(data_dir, 'checkpoint_last.pt'), + os.path.join(data_dir, "checkpoint_last.pt"), ) with self.assertLogs() as logs: test_binaries.train_translation_model( - data_dir, 'fconv_iwslt_de_en', [ - '--dropout', '0.0', - '--log-format', 'json', - '--log-interval', '1', - '--max-epoch', str(max_epoch), - ] + extra_flags, + data_dir, + "fconv_iwslt_de_en", + [ + "--dropout", + "0.0", + "--log-format", + "json", + "--log-interval", + "1", + "--max-epoch", + str(max_epoch), + ] + + extra_flags, ) - train_res_log = get_last_log_stats_containing_string(logs.records, 'train_loss') - valid_res_log = get_last_log_stats_containing_string(logs.records, 'valid_loss') + train_res_log = get_last_log_stats_containing_string( + logs.records, "train_loss" + ) + valid_res_log = get_last_log_stats_containing_string( + logs.records, "valid_loss" + ) - for k in ['train_loss', 'train_ppl', 'train_num_updates', 'train_gnorm']: - self.assertAlmostEqual(float(train_log[k]), float(train_res_log[k]), delta=delta) - for k in ['valid_loss', 'valid_ppl', 'valid_num_updates', 'valid_best_loss']: - self.assertAlmostEqual(float(valid_log[k]), float(valid_res_log[k]), delta=delta) + for k in ["train_loss", "train_ppl", "train_num_updates", "train_gnorm"]: + self.assertAlmostEqual( + float(train_log[k]), float(train_res_log[k]), delta=delta + ) + for k in [ + "valid_loss", + "valid_ppl", + "valid_num_updates", + "valid_best_loss", + ]: + self.assertAlmostEqual( + float(valid_log[k]), float(valid_res_log[k]), delta=delta + ) def test_reproducibility(self): - self._test_reproducibility('test_reproducibility') + self._test_reproducibility("test_reproducibility") - @unittest.skipIf(not torch.cuda.is_available(), 'test requires a GPU') + @unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU") def test_reproducibility_fp16(self): - self._test_reproducibility('test_reproducibility_fp16', [ - '--fp16', - '--fp16-init-scale', '4096', - ], delta=0.011) + self._test_reproducibility( + "test_reproducibility_fp16", + [ + "--fp16", + "--fp16-init-scale", + "4096", + ], + delta=0.011, + ) - @unittest.skipIf(not torch.cuda.is_available(), 'test requires a GPU') + @unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU") def test_reproducibility_memory_efficient_fp16(self): - self._test_reproducibility('test_reproducibility_memory_efficient_fp16', [ - '--memory-efficient-fp16', - '--fp16-init-scale', '4096', - ]) + self._test_reproducibility( + "test_reproducibility_memory_efficient_fp16", + [ + "--memory-efficient-fp16", + "--fp16-init-scale", + "4096", + ], + ) + + @unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU") + def test_reproducibility_amp(self): + self._test_reproducibility( + "test_reproducibility_amp", + [ + "--amp", + "--fp16-init-scale", + "4096", + ], + delta=0.011, + ) def test_mid_epoch_reproducibility(self): self._test_reproducibility( - 'test_mid_epoch_reproducibility', - ['--save-interval-updates', '3'], - resume_checkpoint='checkpoint_1_3.pt', + "test_mid_epoch_reproducibility", + ["--save-interval-updates", "3"], + resume_checkpoint="checkpoint_1_3.pt", max_epoch=1, ) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/tests/test_resampling_dataset.py b/tests/test_resampling_dataset.py index 0d142f5a8d..ccb53a253c 100644 --- a/tests/test_resampling_dataset.py +++ b/tests/test_resampling_dataset.py @@ -7,7 +7,6 @@ import unittest import numpy as np - from fairseq.data import ListDataset, ResamplingDataset diff --git a/tests/test_roberta.py b/tests/test_roberta.py new file mode 100644 index 0000000000..b0b9cfd31e --- /dev/null +++ b/tests/test_roberta.py @@ -0,0 +1,314 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import functools +import unittest +from typing import Any, Dict, Sequence + +import fairseq +import fairseq.options +import fairseq.tasks +import torch +from tests.utils import dummy_dictionary + +VOCAB_SIZE = 100 + + +@fairseq.tasks.register_task("fake_task") +class FakeTask(fairseq.tasks.LegacyFairseqTask): + def __init__(self, args): + super().__init__(args) + self.dictionary = dummy_dictionary(VOCAB_SIZE - 4) + assert len(self.dictionary) == VOCAB_SIZE + + @property + def source_dictionary(self): + return self.dictionary + + @property + def target_dictionary(self): + return self.dictionary + + +@functools.lru_cache() +def get_toy_model( + device: str, + architecture: str = "roberta_enc_dec", + **extra_args: Any, +): + assert device in ("gpu", "cpu") + kwargs = { + "arch": architecture, + # Use characteristics dimensions + "encoder_layers": 3, + "encoder_embed_dim": 12, + "encoder_ffn_embed_dim": 14, + "encoder_attention_heads": 4, + "decoder_layers": 3, + "decoder_embed_dim": 12, + "decoder_ffn_embed_dim": 14, + "decoder_attention_heads": 4, + # Disable dropout so we have comparable tests. + "dropout": 0, + "attention_dropout": 0, + "activation_dropout": 0, + "encoder_layerdrop": 0, + # required args + "tokens_per_sample": 256, + "data": "/tmp/test_roberta", + } + kwargs.update(extra_args) + fake_task = FakeTask(kwargs) + args = fairseq.options.get_args( + task="online_backtranslation", + mono_langs="en,ro", + valid_lang_pairs="en-ro", + **kwargs, + ) + torch.manual_seed(0) + model = fake_task.build_model(args) + if device == "gpu": + model.cuda() + return fake_task, model + + +def mk_sample( + lang: str, device: str, tok: Sequence[int] = None, batch_size: int = 2 +) -> Dict[str, Any]: + assert device in ("gpu", "cpu") + if not tok: + if lang == "en": + tok = [10, 11, 12, 13, 14, 15, 2] + else: + tok = [20, 21, 22, 23, 24, 25, 26, 27, 2] + + batch = torch.stack([torch.tensor(tok, dtype=torch.long)] * batch_size) + if device == "gpu": + batch = batch.cuda() + sample = { + "net_input": { + "src_tokens": batch, + "prev_output_tokens": batch, + "src_lengths": torch.tensor( + [len(tok)] * batch_size, dtype=torch.long, device=batch.device + ), + }, + "target": batch[:, 1:], + } + return sample + + +def cpu_gpu(fn): + def helper(self): + fn(self, "cpu") + if torch.cuda.is_available(): + fn(self, "gpu") + + return helper + + +def architectures(fn): + def helper(self): + for arch in ["roberta_enc_dec", "transformer"]: + fn(self, arch) + + return helper + + +class RobertaTest(unittest.TestCase): + def assertTensorEqual(self, t1, t2, delta: float = 1e-6): + self.assertEqual(t1.size(), t2.size(), "size mismatch") + if delta == 0.0: + self.assertEqual(t1.ne(t2).long().sum(), 0) + else: + self.assertEqual(((t2 - t1).abs() > delta).long().sum(), 0) + + def assertSharing(self, model, link_groups: Sequence[Sequence[str]]): + ids = {} + for group in link_groups: + group_ids = {name: id(params(model, name)) for name in group} + shared_id = group_ids[group[0]] + self.assertEqual(group_ids, {name: shared_id for name in group}) + self.assertNotIn(shared_id, ids) + ids[shared_id] = group + + def test_roberta_shared_params(self): + _, roberta = get_toy_model("cpu", architecture="roberta") + self.assertSharing( + roberta, + [ + [ + "encoder.sentence_encoder.embed_tokens.weight", + "encoder.lm_head.weight", + ] + ], + ) + + _, roberta = get_toy_model( + "cpu", architecture="roberta", untie_weights_roberta=True + ) + self.assertSharing( + roberta, + [ + ["encoder.sentence_encoder.embed_tokens.weight"], + ["encoder.lm_head.weight"], + ], + ) + + def test_roberta_enc_dec_shared_params(self): + # 3 distinct embeddings + _, enc_dec = get_toy_model("cpu", architecture="roberta_enc_dec") + self.assertSharing( + enc_dec, + [ + ["encoder.embed_tokens.weight"], + ["decoder.embed_tokens.weight"], + ["decoder.output_projection.weight"], + ], + ) + + # 2 distinct embeddings, one for encoder, one for decoder + _, enc_dec = get_toy_model( + "cpu", architecture="roberta_enc_dec", share_decoder_input_output_embed=True + ) + self.assertSharing( + enc_dec, + [ + ["encoder.embed_tokens.weight"], + [ + "decoder.embed_tokens.weight", + "decoder.output_projection.weight", + ], + ], + ) + + # shared embeddings + _, enc_dec = get_toy_model( + "cpu", architecture="roberta_enc_dec", share_all_embeddings=True + ) + self.assertSharing( + enc_dec, + [ + [ + "encoder.embed_tokens.weight", + "decoder.embed_tokens.weight", + "decoder.output_projection.weight", + ] + ], + ) + + def test_roberta_max_positions_is_correctly_set(self): + device = "cpu" + task, model = get_toy_model(device) + max_pos = model.max_decoder_positions() + self.assertEqual(max_pos, 256) + self.assertEqual(max_pos, model.decoder.max_positions()) + self.assertEqual(max_pos, model.encoder.max_positions()) + self.assertEqual(max_pos, model.encoder.embed_positions.max_positions) + + sentence = [31 for _ in range(max_pos)] + sample = mk_sample("en", device, sentence, batch_size=1) + self.assertEqual(list(sample["net_input"]["src_lengths"]), [max_pos]) + self.assertEqual(len(sample["net_input"]["src_tokens"][0]), max_pos) + x, _ = model.forward(**sample["net_input"]) + self.assertEqual(x.shape, (1, max_pos, VOCAB_SIZE)) + + @cpu_gpu + def test_roberta_forward_backward(self, device: str): + _, model = get_toy_model(device) + sample = mk_sample("en", device) + en_tokens = sample["net_input"]["src_tokens"] + (bs, l) = en_tokens.shape + # Forward + logits, _ = model(**sample["net_input"]) + self.assertEqual(logits.shape, (bs, l, VOCAB_SIZE)) + + # Backward + loss = logits.sum() + loss.backward() + + @cpu_gpu + def test_roberta_forward_backward_bs1(self, device: str): + _, model = get_toy_model(device) + sample = mk_sample("en", device, batch_size=1) + o, _ = model.forward(**sample["net_input"]) + loss = o.sum() + sample2 = mk_sample("ro", device, batch_size=1) + o, _ = model.forward(**sample2["net_input"]) + loss += o.sum() + loss.backward() + + @cpu_gpu + def test_roberta_batching(self, device: str): + """ + Checks that the batch of size 2 give twice the same results than the batch of size 1. + """ + _, model = get_toy_model(device) + sample = mk_sample("en", device, batch_size=1) + slen = sample["net_input"]["src_lengths"][0] + sample2 = mk_sample("en", device, batch_size=2) + with torch.no_grad(): + z = model.encoder.forward( + sample["net_input"]["src_tokens"], sample["net_input"]["src_lengths"] + ) + z = z["encoder_out"][-1] + logits, _ = model.forward(**sample["net_input"]) + + z2 = model.encoder.forward( + sample2["net_input"]["src_tokens"], sample["net_input"]["src_lengths"] + ) + z2 = z2["encoder_out"][-1] + logits2, _ = model.forward(**sample2["net_input"]) + + self.assertEqual(z.shape, (slen, 1, 12)) + self.assertEqual(z2.shape, (slen, 2, 12)) + self.assertTensorEqual(logits2[0], logits2[1]) + self.assertTensorEqual(logits[0], logits2[0]) + + @cpu_gpu + def test_roberta_incremental_decoder(self, device: str): + """ + Checks that incremental decoding yields the same result than non incremental one. + """ + task, model = get_toy_model(device) + + en_sample = mk_sample("en", device) + en_tokens = en_sample["net_input"]["src_tokens"] + ro_sample = mk_sample("ro", device) + ro_tokens = ro_sample["net_input"]["src_tokens"] + + en_enc = model.encoder.forward( + en_tokens, src_lengths=en_sample["net_input"]["src_lengths"] + ) + (bs, tgt_len) = ro_tokens.shape + + # Decode without incremental state + ro_dec, _ = model.decoder.forward(ro_tokens, encoder_out=en_enc) + self.assertEqual(ro_dec.shape, (bs, tgt_len, VOCAB_SIZE)) + self.assertTensorEqual(ro_dec[0], ro_dec[1]) + + # Decode with incremental state + inc_state = {} + ro_dec_inc = [] + for l in range(tgt_len): + ro, _ = model.decoder.forward( + ro_tokens[:, : l + 1], encoder_out=en_enc, incremental_state=inc_state + ) + self.assertEqual(ro.shape, (bs, 1, VOCAB_SIZE)) + ro_dec_inc.append(ro) + + for l in range(tgt_len): + # Intra-batch + self.assertTensorEqual(ro_dec_inc[l][0], ro_dec_inc[l][1]) + # Incremental vs non-incremental + self.assertTensorEqual(ro_dec_inc[l][:, 0], ro_dec[:, l]) + + +def params(model, name): + if "." not in name: + return getattr(model, name) + + prefix, name = name.split(".", 1) + return params(getattr(model, prefix), name) diff --git a/tests/test_sequence_generator.py b/tests/test_sequence_generator.py index 36560bcca6..9273191962 100644 --- a/tests/test_sequence_generator.py +++ b/tests/test_sequence_generator.py @@ -6,21 +6,24 @@ import argparse import tempfile import unittest +import math +import numpy as np + import tests.utils as test_utils import torch from fairseq import search from fairseq.data.dictionary import Dictionary - from fairseq.models.transformer import TransformerModel -from fairseq.sequence_generator import SequenceGenerator, EnsembleModel -from fairseq.tasks.fairseq_task import FairseqTask +from fairseq.sequence_generator import EnsembleModel, SequenceGenerator +from fairseq.ngram_repeat_block import NGramRepeatBlock +from fairseq.tasks.fairseq_task import LegacyFairseqTask DEFAULT_TEST_VOCAB_SIZE = 100 -class DummyTask(FairseqTask): +class DummyTask(LegacyFairseqTask): def __init__(self, args): super().__init__(args) self.dictionary = get_dummy_dictionary() @@ -42,7 +45,7 @@ def get_dummy_dictionary(vocab_size=DEFAULT_TEST_VOCAB_SIZE): dummy_dict = Dictionary() # add dummy symbol to satisfy vocab size for id, _ in enumerate(range(vocab_size)): - dummy_dict.add_symbol("{}".format(id), 1000) + dummy_dict.add_symbol("{}".format(id), n=1000) return dummy_dict @@ -108,32 +111,27 @@ def _test_save_and_load(self, scripted_module): torch.jit.load(f.name) -class TestJitSequeneceGenerator(TestJitSequenceGeneratorBase): +JIT_MSG = "Targeting OSS scriptability for the 1.6 release" - @unittest.skipIf( - torch.__version__ < "1.6.0", "Targeting OSS scriptability for the 1.6 release" - ) + +@unittest.skipIf(torch.__version__ < "1.6.0", JIT_MSG) +class TestJitSequenceGenerator(TestJitSequenceGeneratorBase): def test_export_transformer(self): model = self.transformer_model torch.jit.script(model) - @unittest.skipIf( - torch.__version__ < "1.6.0", "Targeting OSS scriptability for the 1.6 release" - ) def test_ensemble_sequence_generator(self): model = self.transformer_model generator = SequenceGenerator( - [model], self.task.tgt_dict, beam_size=2, no_repeat_ngram_size=2 + [model], + self.task.tgt_dict, + beam_size=2, + no_repeat_ngram_size=2, + max_len_b=10, ) scripted_model = torch.jit.script(generator) self._test_save_and_load(scripted_model) - -class TestJitEnsemble(TestJitSequenceGeneratorBase): - - @unittest.skipIf( - torch.__version__ < "1.6.0", "Targeting OSS scriptability for the 1.6 release" - ) def test_export_ensemble_model(self): model = self.transformer_model ensemble_models = EnsembleModel([model]) @@ -188,11 +186,16 @@ def assertTensorEqual(self, t1, t2): self.assertEqual(t1.ne(t2).long().sum(), 0) -class TestSequeneceGenerator(TestSequenceGeneratorBase): +class TestSequenceGenerator(TestSequenceGeneratorBase): def setUp(self): - self.tgt_dict, self.w1, self.w2, src_tokens, src_lengths, self.model = ( - test_utils.sequence_generator_setup() - ) + ( + self.tgt_dict, + self.w1, + self.w2, + src_tokens, + src_lengths, + self.model, + ) = test_utils.sequence_generator_setup() self.sample = { "net_input": {"src_tokens": src_tokens, "src_lengths": src_lengths} } @@ -276,7 +279,9 @@ def test_with_lenpen_favoring_long_hypos(self): self.assertHypoScore(hypos[1][1], [0.7, 0.4, 0.6], lenpen=lenpen) def test_maxlen(self): - generator = SequenceGenerator([self.model], self.tgt_dict, beam_size=2, max_len_b=2) + generator = SequenceGenerator( + [self.model], self.tgt_dict, beam_size=2, max_len_b=2 + ) hypos = generator.forward(self.sample) eos, w1, w2 = self.tgt_dict.eos(), self.w1, self.w2 # sentence 1, beam 1 @@ -294,21 +299,27 @@ def test_maxlen(self): def test_encoder_with_different_output_len(self): args = self.model.encoder.args - task = test_utils.TestTranslationTask.setup_task(args, self.tgt_dict, self.tgt_dict) + task = test_utils.TestTranslationTask.setup_task( + args, self.tgt_dict, self.tgt_dict + ) reshaping_model = test_utils.TestReshapingModel.build_model(args, task) - generator = SequenceGenerator([reshaping_model], self.tgt_dict, beam_size=2, max_len_b=2) + generator = SequenceGenerator( + [reshaping_model], self.tgt_dict, beam_size=2, max_len_b=2 + ) hypos = generator.forward(self.sample) for sent in [0, 1]: for beam in [0, 1]: - assert hypos[sent][beam]['attention'] is not None + assert hypos[sent][beam]["attention"] is not None def test_generation_with_additional_input(self): args = self.model.encoder.args - task = test_utils.TestTranslationTask.setup_task(args, self.tgt_dict, self.tgt_dict) + task = test_utils.TestTranslationTask.setup_task( + args, self.tgt_dict, self.tgt_dict + ) add_input_model = test_utils.TestAdditionalInputModel.build_model(args, task) generator = SequenceGenerator([add_input_model], self.tgt_dict, beam_size=2) sample = self.sample.copy() - sample['net_input']['fancy_other_input'] = sample['net_input']['src_tokens'] + sample["net_input"]["fancy_other_input"] = sample["net_input"]["src_tokens"] hypos = generator.forward(self.sample) eos, w1, w2 = self.tgt_dict.eos(), self.w1, self.w2 # sentence 1, beam 1 @@ -316,8 +327,104 @@ def test_generation_with_additional_input(self): self.assertHypoScore(hypos[0][0], [0.9, 1.0]) -class TestDiverseBeamSearch(TestSequenceGeneratorBase): +@unittest.skipUnless(torch.cuda.is_available(), "") +class TestRepeatNgramBlocking(TestSequenceGeneratorBase): + @classmethod + def setUpClass(cls): + ( + cls.tgt_dict, + cls.w1, + cls.w2, + src_tokens, + src_lengths, + cls.model, + ) = test_utils.sequence_generator_setup() + return cls + + def test_finds_repetitive_tokens(self): + bsz, vocab_size, beam_size, step = 2, 4, 1, 3 + generated_tok = torch.tensor( + [[2, 2, 2, 2], [3, 3, 3, 3]], dtype=torch.long, device="cuda" + ) + lprobs = torch.zeros((beam_size * bsz, vocab_size), device="cuda") + desired_result = lprobs.new_tensor( + [[0.0, 0.0, -math.inf, 0.0], [0.0, 0.0, 0.0, -math.inf]] + ) + + cuda_ext_result, baseline_result = self._compare_cuda_ext_to_default_implem( + bsz, beam_size, generated_tok, lprobs, step, 2 + ) + self.assertTensorEqual(cuda_ext_result, desired_result) + self.assertTensorEqual(baseline_result, desired_result) + + @unittest.skipIf(torch.__version__ < "1.6.0", JIT_MSG) + def test_jit_no_extension(self): + bsz, vocab_size, beam_size, step = 2, 4, 1, 3 + generated_tok = torch.tensor( + [[2, 2, 2, 2], [3, 3, 3, 3]], dtype=torch.long, device="cuda" + ) + lprobs = torch.zeros((beam_size * bsz, vocab_size), device="cuda") + blocker = NGramRepeatBlock(2, use_extension=False) + base_result = blocker(generated_tok, lprobs.clone(), bsz, beam_size, step) + scripted_blocker = torch.jit.script(blocker) + jit_result = scripted_blocker( + generated_tok, lprobs.clone(), bsz, beam_size, step + ) + self.assertTensorEqual(base_result, jit_result) + + def test_ngram_blocking_same_as_default_implem(self): + """Test that cuda extension returns same things as default impl in many settings.""" + vocab_size = 4 + step = 6 + for _ in range(2): + block_param = np.random.choice([1, 2, 3, 4]) + batch_size = np.random.randint(1, 8) + beam_size = np.random.choice([1, 2, 4, 8]) + lprobs = torch.zeros((beam_size * batch_size, vocab_size), device="cuda") + + generated_tok = torch.tensor( + np.random.randint( + 0, vocab_size, size=(batch_size * beam_size, step + 1) + ), + device="cuda", + dtype=torch.long, + ) + self._compare_cuda_ext_to_default_implem( + batch_size, + beam_size, + generated_tok, + lprobs, + step, + block_param, + ) + + def _compare_cuda_ext_to_default_implem( + self, bsz, beam_size, generated_tok, lprobs, step, block_param + ): + """Assert that cuda extension and default implem return the same thing.""" + blocker = NGramRepeatBlock(block_param) + assert blocker.use_extension, "Extension not compiled" + cuda_ext_result = blocker( + generated_tok, + lprobs.clone(), + bsz, + beam_size, + step, + ) + blocker.use_extension = False + baseline_result = blocker( + generated_tok, + lprobs.clone(), + bsz, + beam_size, + step, + ) + self.assertTensorEqual(cuda_ext_result, baseline_result) + blocker.use_extension = True + return cuda_ext_result, baseline_result + +class TestDiverseBeamSearch(TestSequenceGeneratorBase): def setUp(self): # construct dummy dictionary d = test_utils.dummy_dictionary(vocab_size=2) @@ -329,45 +436,53 @@ def setUp(self): self.w2 = 5 # construct source data - self.src_tokens = torch.LongTensor([ - [self.w1, self.w2, self.eos], - [self.w1, self.w2, self.eos], - ]) + self.src_tokens = torch.LongTensor( + [ + [self.w1, self.w2, self.eos], + [self.w1, self.w2, self.eos], + ] + ) self.src_lengths = torch.LongTensor([2, 2]) args = argparse.Namespace() - unk = 0. + unk = 0.0 args.beam_probs = [ # step 0: - torch.FloatTensor([ - # eos w1 w2 - # sentence 1: - [0.0, unk, 0.9, 0.1], # beam 1 - [0.0, unk, 0.9, 0.1], # beam 2 - # sentence 2: - [0.0, unk, 0.7, 0.3], - [0.0, unk, 0.7, 0.3], - ]), + torch.FloatTensor( + [ + # eos w1 w2 + # sentence 1: + [0.0, unk, 0.9, 0.1], # beam 1 + [0.0, unk, 0.9, 0.1], # beam 2 + # sentence 2: + [0.0, unk, 0.7, 0.3], + [0.0, unk, 0.7, 0.3], + ] + ), # step 1: - torch.FloatTensor([ - # eos w1 w2 - # sentence 1: - [0.0, unk, 0.6, 0.4], - [0.0, unk, 0.6, 0.4], - # sentence 2: - [0.25, unk, 0.35, 0.4], - [0.25, unk, 0.35, 0.4], - ]), + torch.FloatTensor( + [ + # eos w1 w2 + # sentence 1: + [0.0, unk, 0.6, 0.4], + [0.0, unk, 0.6, 0.4], + # sentence 2: + [0.25, unk, 0.35, 0.4], + [0.25, unk, 0.35, 0.4], + ] + ), # step 2: - torch.FloatTensor([ - # eos w1 w2 - # sentence 1: - [1.0, unk, 0.0, 0.0], - [1.0, unk, 0.0, 0.0], - # sentence 2: - [0.9, unk, 0.1, 0.0], - [0.9, unk, 0.1, 0.0], - ]), + torch.FloatTensor( + [ + # eos w1 w2 + # sentence 1: + [1.0, unk, 0.0, 0.0], + [1.0, unk, 0.0, 0.0], + # sentence 2: + [0.9, unk, 0.1, 0.0], + [0.9, unk, 0.1, 0.0], + ] + ), ] task = test_utils.TestTranslationTask.setup_task(args, d, d) @@ -375,11 +490,21 @@ def setUp(self): self.tgt_dict = task.target_dictionary def test_diverse_beam_search(self): - search_strategy = search.DiverseBeamSearch(self.tgt_dict, num_groups=2, diversity_strength=0.) + search_strategy = search.DiverseBeamSearch( + self.tgt_dict, num_groups=2, diversity_strength=0.0 + ) generator = SequenceGenerator( - [self.model], self.tgt_dict, beam_size=2, search_strategy=search_strategy, + [self.model], + self.tgt_dict, + beam_size=2, + search_strategy=search_strategy, ) - sample = {'net_input': {'src_tokens': self.src_tokens, 'src_lengths': self.src_lengths}} + sample = { + "net_input": { + "src_tokens": self.src_tokens, + "src_lengths": self.src_lengths, + } + } hypos = generator.forward(sample) eos, w1, w2 = self.eos, self.w1, self.w2 # sentence 1, beam 1 @@ -438,8 +563,61 @@ def test_diverse_beam_search(self): self.assertHypoScore(hypos[1][1], [0.7, 0.35, 0.9], [0, 2, 1], 0.5) -class TestTopPSamplingSearch(TestSequenceGeneratorBase): +class TestPrefixBeamSearch(TestSequenceGeneratorBase): + def setUp(self): + # construct dummy dictionary + vocab_size = 10 + d = test_utils.dummy_dictionary(vocab_size=vocab_size) + self.assertEqual(d.pad(), 1) + self.assertEqual(d.eos(), 2) + self.assertEqual(d.unk(), 3) + self.eos = d.eos() + self.w1 = 4 + self.w2 = 5 + self.beam_size = 3 + + # construct prefix data + self.tokens = torch.LongTensor( + [ + [self.w1, self.w2, self.eos], + ] + ) + self.token_lengths = torch.LongTensor([2]) + + args = argparse.Namespace() + unk = 0.0 + args.beam_probs = [ + # prefix step 0: + torch.FloatTensor( + [ + # eos + [0.0, unk] + [1.0 / vocab_size] * vocab_size # beam 1 + ] * self.beam_size + ), + ] * vocab_size + + task = test_utils.TestTranslationTask.setup_task(args, d, d) + self.model = task.build_model(args) + self.tgt_dict = task.target_dictionary + def test_prefix_beam_search(self): + search_strategy = search.BeamSearch(self.tgt_dict) + generator = SequenceGenerator( + [self.model], + self.tgt_dict, + beam_size=self.beam_size, + search_strategy=search_strategy, + ) + sample = { + "net_input": { + "src_tokens": self.tokens, + "src_lengths": self.token_lengths, + } + } + # make sure test sample doesn't break any assertion + generator.forward(sample, prefix_tokens=self.tokens[:, :-1]) + +class TestTopPSamplingSearch(TestSequenceGeneratorBase): def setUp(self): # construct dummy dictionary d = test_utils.dummy_dictionary(vocab_size=2) @@ -451,14 +629,16 @@ def setUp(self): self.w2 = 5 # construct source data - self.src_tokens = torch.LongTensor([ - [self.w1, self.w2, self.eos], - [self.w1, self.w2, self.eos], - ]) + self.src_tokens = torch.LongTensor( + [ + [self.w1, self.w2, self.eos], + [self.w1, self.w2, self.eos], + ] + ) self.src_lengths = torch.LongTensor([2, 2]) args = argparse.Namespace() - unk = 0. + unk = 0.0 # The minimal probability of top 2 tokens. self.min_top2_prob = 0.75 # The minimal probability of the top 1 token. @@ -470,29 +650,35 @@ def setUp(self): args.beam_probs = [ # step 0: - torch.FloatTensor([ - # eos w1 w2 - [0.0, unk, 1.0, 0.0], - [0.0, unk, 1.0, 0.0], - [0.0, unk, 1.0, 0.0], - [0.0, unk, 1.0, 0.0], - ]), + torch.FloatTensor( + [ + # eos w1 w2 + [0.0, unk, 1.0, 0.0], + [0.0, unk, 1.0, 0.0], + [0.0, unk, 1.0, 0.0], + [0.0, unk, 1.0, 0.0], + ] + ), # step 1: - torch.FloatTensor([ - # eos w1 w2 - [eos_prob, unk, w1_prob, w2_prob], - [eos_prob, unk, w1_prob, w2_prob], - [eos_prob, unk, w1_prob, w2_prob], - [eos_prob, unk, w1_prob, w2_prob], - ]), + torch.FloatTensor( + [ + # eos w1 w2 + [eos_prob, unk, w1_prob, w2_prob], + [eos_prob, unk, w1_prob, w2_prob], + [eos_prob, unk, w1_prob, w2_prob], + [eos_prob, unk, w1_prob, w2_prob], + ] + ), # step 2: - torch.FloatTensor([ - # eos w1 w2 - [1.0, unk, 0.0, 0.0], - [1.0, unk, 0.0, 0.0], - [1.0, unk, 0.0, 0.0], - [1.0, unk, 0.0, 0.0], - ]), + torch.FloatTensor( + [ + # eos w1 w2 + [1.0, unk, 0.0, 0.0], + [1.0, unk, 0.0, 0.0], + [1.0, unk, 0.0, 0.0], + [1.0, unk, 0.0, 0.0], + ] + ), ] task = test_utils.TestTranslationTask.setup_task(args, d, d) @@ -502,14 +688,17 @@ def setUp(self): def test_topp_sampling_search_low_prob(self): # Given a prob low enough to top-P sampling, we expect only the top # 1 token to be sampled, which always results in the same output. - low_sampling_topp = self.min_top1_prob/2.0 - search_strategy = search.Sampling(self.tgt_dict, sampling_topp=low_sampling_topp) + low_sampling_topp = self.min_top1_prob / 2.0 + search_strategy = search.Sampling( + self.tgt_dict, sampling_topp=low_sampling_topp + ) generator = SequenceGenerator( - [self.model], self.tgt_dict, beam_size=2, search_strategy=search_strategy) + [self.model], self.tgt_dict, beam_size=2, search_strategy=search_strategy + ) sample = { - 'net_input': { - 'src_tokens': self.src_tokens, - 'src_lengths': self.src_lengths + "net_input": { + "src_tokens": self.src_tokens, + "src_lengths": self.src_lengths, } } hypos = generator.forward(sample) @@ -530,55 +719,74 @@ def test_topp_sampling_search_low_prob(self): def test_topp_sampling_search_high_prob(self): # Given a prob high enough to top-P sampling, any of the top 2 # tokens could be sampled. This can cause different outputs. - high_sampling_topp = (self.min_top1_prob+self.min_top2_prob)/2.0 - search_strategy = search.Sampling(self.tgt_dict, sampling_topp=high_sampling_topp) + high_sampling_topp = (self.min_top1_prob + self.min_top2_prob) / 2.0 + search_strategy = search.Sampling( + self.tgt_dict, sampling_topp=high_sampling_topp + ) generator = SequenceGenerator( - [self.model], self.tgt_dict, beam_size=2, search_strategy=search_strategy) + [self.model], self.tgt_dict, beam_size=2, search_strategy=search_strategy + ) sample = { - 'net_input': { - 'src_tokens': self.src_tokens, - 'src_lengths': self.src_lengths + "net_input": { + "src_tokens": self.src_tokens, + "src_lengths": self.src_lengths, } } hypos = generator.forward(sample) eos, w1, w2 = self.eos, self.w1, self.w2 # sentence 1, beam 1 - self.assertTrue(self.hypoTokens(hypos[0][0], [w1, w1, eos]) or - self.hypoTokens(hypos[0][0], [w1, w2, eos])) - self.assertTrue(self.hypoScore(hypos[0][0], [1.0, 0.4, 1.0]) or - self.hypoScore(hypos[0][0], [1.0, 0.35, 1.0])) + self.assertTrue( + self.hypoTokens(hypos[0][0], [w1, w1, eos]) + or self.hypoTokens(hypos[0][0], [w1, w2, eos]) + ) + self.assertTrue( + self.hypoScore(hypos[0][0], [1.0, 0.4, 1.0]) + or self.hypoScore(hypos[0][0], [1.0, 0.35, 1.0]) + ) # sentence 1, beam 2 - self.assertTrue(self.hypoTokens(hypos[0][1], [w1, w1, eos]) or - self.hypoTokens(hypos[0][1], [w1, w2, eos])) - self.assertTrue(self.hypoScore(hypos[0][1], [1.0, 0.4, 1.0]) or - self.hypoScore(hypos[0][1], [1.0, 0.35, 1.0])) + self.assertTrue( + self.hypoTokens(hypos[0][1], [w1, w1, eos]) + or self.hypoTokens(hypos[0][1], [w1, w2, eos]) + ) + self.assertTrue( + self.hypoScore(hypos[0][1], [1.0, 0.4, 1.0]) + or self.hypoScore(hypos[0][1], [1.0, 0.35, 1.0]) + ) # sentence 2, beam 1 - self.assertTrue(self.hypoTokens(hypos[1][0], [w1, w1, eos]) or - self.hypoTokens(hypos[1][0], [w1, w2, eos])) - self.assertTrue(self.hypoScore(hypos[1][0], [1.0, 0.4, 1.0]) or - self.hypoScore(hypos[1][0], [1.0, 0.35, 1.0])) + self.assertTrue( + self.hypoTokens(hypos[1][0], [w1, w1, eos]) + or self.hypoTokens(hypos[1][0], [w1, w2, eos]) + ) + self.assertTrue( + self.hypoScore(hypos[1][0], [1.0, 0.4, 1.0]) + or self.hypoScore(hypos[1][0], [1.0, 0.35, 1.0]) + ) # sentence 2, beam 2 - self.assertTrue(self.hypoTokens(hypos[1][1], [w1, w1, eos]) or - self.hypoTokens(hypos[1][1], [w1, w2, eos])) - self.assertTrue(self.hypoScore(hypos[1][1], [1.0, 0.4, 1.0]) or - self.hypoScore(hypos[1][1], [1.0, 0.35, 1.0])) + self.assertTrue( + self.hypoTokens(hypos[1][1], [w1, w1, eos]) + or self.hypoTokens(hypos[1][1], [w1, w2, eos]) + ) + self.assertTrue( + self.hypoScore(hypos[1][1], [1.0, 0.4, 1.0]) + or self.hypoScore(hypos[1][1], [1.0, 0.35, 1.0]) + ) def hypoTokens(self, hypo, tokens): - return self.tensorEqual(hypo['tokens'], torch.LongTensor(tokens)) + return self.tensorEqual(hypo["tokens"], torch.LongTensor(tokens)) - def hypoScore(self, hypo, pos_probs, normalized=True, lenpen=1.): + def hypoScore(self, hypo, pos_probs, normalized=True, lenpen=1.0): pos_scores = torch.FloatTensor(pos_probs).log() - if not self.almostEqual(hypo['positional_scores'], pos_scores): + if not self.almostEqual(hypo["positional_scores"], pos_scores): return False - if pos_scores.numel() != hypo['tokens'].numel(): + if pos_scores.numel() != hypo["tokens"].numel(): return False score = pos_scores.sum() if normalized: score /= pos_scores.numel() ** lenpen - return abs(score - hypo['score']) < 1e-6 + return abs(score - hypo["score"]) < 1e-6 def almostEqual(self, t1, t2): return t1.size() == t2.size() and (t1 - t2).abs().max() < 1e-4 diff --git a/tests/test_sequence_scorer.py b/tests/test_sequence_scorer.py index a7c2a53a90..42f9447b59 100644 --- a/tests/test_sequence_scorer.py +++ b/tests/test_sequence_scorer.py @@ -6,15 +6,12 @@ import argparse import unittest +import tests.utils as test_utils import torch - from fairseq.sequence_scorer import SequenceScorer -import tests.utils as test_utils - class TestSequenceScorer(unittest.TestCase): - def test_sequence_scorer(self): # construct dummy dictionary d = test_utils.dummy_dictionary(vocab_size=2) @@ -28,52 +25,60 @@ def test_sequence_scorer(self): # construct dataloader data = [ { - 'source': torch.LongTensor([w1, w2, eos]), - 'target': torch.LongTensor([w1, w2, w1, eos]), + "source": torch.LongTensor([w1, w2, eos]), + "target": torch.LongTensor([w1, w2, w1, eos]), }, { - 'source': torch.LongTensor([w2, eos]), - 'target': torch.LongTensor([w2, w1, eos]), + "source": torch.LongTensor([w2, eos]), + "target": torch.LongTensor([w2, w1, eos]), }, { - 'source': torch.LongTensor([w2, eos]), - 'target': torch.LongTensor([w2, eos]), + "source": torch.LongTensor([w2, eos]), + "target": torch.LongTensor([w2, eos]), }, ] data_itr = test_utils.dummy_dataloader(data) # specify expected output probabilities args = argparse.Namespace() - unk = 0. + unk = 0.0 args.beam_probs = [ # step 0: - torch.FloatTensor([ - # eos w1 w2 - [0.0, unk, 0.6, 0.4], # sentence 1 - [0.0, unk, 0.4, 0.6], # sentence 2 - [0.0, unk, 0.7, 0.3], # sentence 3 - ]), + torch.FloatTensor( + [ + # eos w1 w2 + [0.0, unk, 0.6, 0.4], # sentence 1 + [0.0, unk, 0.4, 0.6], # sentence 2 + [0.0, unk, 0.7, 0.3], # sentence 3 + ] + ), # step 1: - torch.FloatTensor([ - # eos w1 w2 - [0.0, unk, 0.2, 0.7], # sentence 1 - [0.0, unk, 0.8, 0.2], # sentence 2 - [0.7, unk, 0.1, 0.2], # sentence 3 - ]), + torch.FloatTensor( + [ + # eos w1 w2 + [0.0, unk, 0.2, 0.7], # sentence 1 + [0.0, unk, 0.8, 0.2], # sentence 2 + [0.7, unk, 0.1, 0.2], # sentence 3 + ] + ), # step 2: - torch.FloatTensor([ - # eos w1 w2 - [0.10, unk, 0.50, 0.4], # sentence 1 - [0.15, unk, 0.15, 0.7], # sentence 2 - [0.00, unk, 0.00, 0.0], # sentence 3 - ]), + torch.FloatTensor( + [ + # eos w1 w2 + [0.10, unk, 0.50, 0.4], # sentence 1 + [0.15, unk, 0.15, 0.7], # sentence 2 + [0.00, unk, 0.00, 0.0], # sentence 3 + ] + ), # step 3: - torch.FloatTensor([ - # eos w1 w2 - [0.9, unk, 0.05, 0.05], # sentence 1 - [0.0, unk, 0.00, 0.0], # sentence 2 - [0.0, unk, 0.00, 0.0], # sentence 3 - ]), + torch.FloatTensor( + [ + # eos w1 w2 + [0.9, unk, 0.05, 0.05], # sentence 1 + [0.0, unk, 0.00, 0.0], # sentence 2 + [0.0, unk, 0.00, 0.0], # sentence 3 + ] + ), ] expected_scores = [ [0.6, 0.7, 0.5, 0.9], # sentence 1 @@ -86,21 +91,21 @@ def test_sequence_scorer(self): scorer = SequenceScorer(task.target_dictionary) for sample in data_itr: hypos = task.inference_step(scorer, [model], sample) - for id, hypos_id in zip(sample['id'].tolist(), hypos): - self.assertHypoTokens(hypos_id[0], data[id]['target']) + for id, hypos_id in zip(sample["id"].tolist(), hypos): + self.assertHypoTokens(hypos_id[0], data[id]["target"]) self.assertHypoScore(hypos_id[0], expected_scores[id]) def assertHypoTokens(self, hypo, tokens): - self.assertTensorEqual(hypo['tokens'], torch.LongTensor(tokens)) + self.assertTensorEqual(hypo["tokens"], torch.LongTensor(tokens)) - def assertHypoScore(self, hypo, pos_probs, normalized=True, lenpen=1.): + def assertHypoScore(self, hypo, pos_probs, normalized=True, lenpen=1.0): pos_scores = torch.FloatTensor(pos_probs).log() - self.assertAlmostEqual(hypo['positional_scores'], pos_scores) - self.assertEqual(pos_scores.numel(), hypo['tokens'].numel()) + self.assertAlmostEqual(hypo["positional_scores"], pos_scores) + self.assertEqual(pos_scores.numel(), hypo["tokens"].numel()) score = pos_scores.sum() if normalized: - score /= pos_scores.numel()**lenpen - self.assertLess(abs(score - hypo['score']), 1e-6) + score /= pos_scores.numel() ** lenpen + self.assertLess(abs(score - hypo["score"]), 1e-6) def assertAlmostEqual(self, t1, t2): self.assertEqual(t1.size(), t2.size(), "size mismatch") @@ -111,5 +116,5 @@ def assertTensorEqual(self, t1, t2): self.assertEqual(t1.ne(t2).long().sum(), 0) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/tests/test_sparse_multihead_attention.py b/tests/test_sparse_multihead_attention.py index eaf9742cdf..3e32b25a7f 100644 --- a/tests/test_sparse_multihead_attention.py +++ b/tests/test_sparse_multihead_attention.py @@ -3,46 +3,112 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. -import torch import unittest + +import torch from fairseq.modules.sparse_multihead_attention import SparseMultiheadAttention class TestSparseMultiheadAttention(unittest.TestCase): def test_sparse_multihead_attention(self): attn_weights = torch.randn(1, 8, 8) - bidirectional_sparse_mask = torch.tensor([ - [0, 0, 0, 0, 0, float('-inf'), float('-inf'), 0], - [0, 0, 0, 0, 0, float('-inf'), float('-inf'), 0], - [0, 0, 0, 0, 0, float('-inf'), float('-inf'), 0], - [0, 0, 0, 0, 0, float('-inf'), float('-inf'), 0], - [float('-inf'), float('-inf'), float('-inf'), 0, 0, 0, 0, 0], - [float('-inf'), float('-inf'), float('-inf'), 0, 0, 0, 0, 0], - [float('-inf'), float('-inf'), float('-inf'), 0, 0, 0, 0, 0], - [float('-inf'), float('-inf'), float('-inf'), 0, 0, 0, 0, 0] - ]) - - bidirectional_attention = SparseMultiheadAttention(16, 1, stride=4, expressivity=1, is_bidirectional=True) - bidirectional_attention_sparse_mask = bidirectional_attention.buffered_sparse_mask(attn_weights, 8, 8) - torch.all(torch.eq(bidirectional_attention_sparse_mask, bidirectional_sparse_mask)) - - sparse_mask = torch.tensor([ - [0, float('-inf'), float('-inf'), float('-inf'), float('-inf'), float('-inf'), - float('-inf'), float('-inf')], - [0, 0, float('-inf'), float('-inf'), float('-inf'), float('-inf'), float('-inf'), float('-inf')], - [0, 0, 0, float('-inf'), float('-inf'), float('-inf'), float('-inf'), float('-inf')], - [0, 0, 0, 0, float('-inf'), float('-inf'), float('-inf'), float('-inf')], - [0, 0, 0, 0, 0, float('-inf'), float('-inf'), float('-inf')], - [float('-inf'), float('-inf'), float('-inf'), 0, 0, 0, float('-inf'), float('-inf')], - [float('-inf'), float('-inf'), float('-inf'), 0, 0, 0, 0, float('-inf')], - [float('-inf'), float('-inf'), float('-inf'), 0, 0, 0, 0, 0], - ]) - - attention = SparseMultiheadAttention(16, 1, stride=4, expressivity=1, is_bidirectional=False) + bidirectional_sparse_mask = torch.tensor( + [ + [0, 0, 0, 0, 0, float("-inf"), float("-inf"), 0], + [0, 0, 0, 0, 0, float("-inf"), float("-inf"), 0], + [0, 0, 0, 0, 0, float("-inf"), float("-inf"), 0], + [0, 0, 0, 0, 0, float("-inf"), float("-inf"), 0], + [float("-inf"), float("-inf"), float("-inf"), 0, 0, 0, 0, 0], + [float("-inf"), float("-inf"), float("-inf"), 0, 0, 0, 0, 0], + [float("-inf"), float("-inf"), float("-inf"), 0, 0, 0, 0, 0], + [float("-inf"), float("-inf"), float("-inf"), 0, 0, 0, 0, 0], + ] + ) + + bidirectional_attention = SparseMultiheadAttention( + 16, 1, stride=4, expressivity=1, is_bidirectional=True + ) + bidirectional_attention_sparse_mask = ( + bidirectional_attention.buffered_sparse_mask(attn_weights, 8, 8) + ) + torch.all( + torch.eq(bidirectional_attention_sparse_mask, bidirectional_sparse_mask) + ) + + sparse_mask = torch.tensor( + [ + [ + 0, + float("-inf"), + float("-inf"), + float("-inf"), + float("-inf"), + float("-inf"), + float("-inf"), + float("-inf"), + ], + [ + 0, + 0, + float("-inf"), + float("-inf"), + float("-inf"), + float("-inf"), + float("-inf"), + float("-inf"), + ], + [ + 0, + 0, + 0, + float("-inf"), + float("-inf"), + float("-inf"), + float("-inf"), + float("-inf"), + ], + [ + 0, + 0, + 0, + 0, + float("-inf"), + float("-inf"), + float("-inf"), + float("-inf"), + ], + [0, 0, 0, 0, 0, float("-inf"), float("-inf"), float("-inf")], + [ + float("-inf"), + float("-inf"), + float("-inf"), + 0, + 0, + 0, + float("-inf"), + float("-inf"), + ], + [ + float("-inf"), + float("-inf"), + float("-inf"), + 0, + 0, + 0, + 0, + float("-inf"), + ], + [float("-inf"), float("-inf"), float("-inf"), 0, 0, 0, 0, 0], + ] + ) + + attention = SparseMultiheadAttention( + 16, 1, stride=4, expressivity=1, is_bidirectional=False + ) attention_sparse_mask = attention.buffered_sparse_mask(attn_weights, 8, 8) torch.all(torch.eq(attention_sparse_mask, sparse_mask)) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/tests/test_token_block_dataset.py b/tests/test_token_block_dataset.py index 41abb194da..c4d7b76dcd 100644 --- a/tests/test_token_block_dataset.py +++ b/tests/test_token_block_dataset.py @@ -5,15 +5,12 @@ import unittest +import tests.utils as test_utils import torch - from fairseq.data import TokenBlockDataset -import tests.utils as test_utils - class TestTokenBlockDataset(unittest.TestCase): - def _build_dataset(self, data, **kwargs): sizes = [len(x) for x in data] underlying_ds = test_utils.TestDataset(data) @@ -25,7 +22,7 @@ def test_eos_break_mode(self): torch.tensor([1], dtype=torch.long), torch.tensor([8, 7, 6, 1], dtype=torch.long), ] - ds = self._build_dataset(data, block_size=None, pad=0, eos=1, break_mode='eos') + ds = self._build_dataset(data, block_size=None, pad=0, eos=1, break_mode="eos") self.assertEqual(ds[0].tolist(), [5, 4, 3, 2, 1]) self.assertEqual(ds[1].tolist(), [1]) self.assertEqual(ds[2].tolist(), [8, 7, 6, 1]) @@ -35,7 +32,7 @@ def test_eos_break_mode(self): torch.tensor([8, 7, 6, 1], dtype=torch.long), torch.tensor([1], dtype=torch.long), ] - ds = self._build_dataset(data, block_size=None, pad=0, eos=1, break_mode='eos') + ds = self._build_dataset(data, block_size=None, pad=0, eos=1, break_mode="eos") self.assertEqual(ds[0].tolist(), [5, 4, 3, 2, 1]) self.assertEqual(ds[1].tolist(), [8, 7, 6, 1]) self.assertEqual(ds[2].tolist(), [1]) @@ -46,7 +43,7 @@ def test_block_break_mode(self): torch.tensor([8, 7, 6, 1], dtype=torch.long), torch.tensor([9, 1], dtype=torch.long), ] - ds = self._build_dataset(data, block_size=3, pad=0, eos=1, break_mode='none') + ds = self._build_dataset(data, block_size=3, pad=0, eos=1, break_mode="none") self.assertEqual(ds[0].tolist(), [5, 4, 3]) self.assertEqual(ds[1].tolist(), [2, 1, 8]) self.assertEqual(ds[2].tolist(), [7, 6, 1]) @@ -58,7 +55,9 @@ def test_complete_break_mode(self): torch.tensor([8, 7, 6, 1], dtype=torch.long), torch.tensor([9, 1], dtype=torch.long), ] - ds = self._build_dataset(data, block_size=6, pad=0, eos=1, break_mode='complete') + ds = self._build_dataset( + data, block_size=6, pad=0, eos=1, break_mode="complete" + ) self.assertEqual(ds[0].tolist(), [5, 4, 3, 2, 1]) self.assertEqual(ds[1].tolist(), [8, 7, 6, 1, 9, 1]) @@ -68,11 +67,26 @@ def test_complete_break_mode(self): torch.tensor([1], dtype=torch.long), torch.tensor([6, 1], dtype=torch.long), ] - ds = self._build_dataset(data, block_size=3, pad=0, eos=1, break_mode='complete') + ds = self._build_dataset( + data, block_size=3, pad=0, eos=1, break_mode="complete" + ) self.assertEqual(ds[0].tolist(), [4, 3, 2, 1]) self.assertEqual(ds[1].tolist(), [5, 1, 1]) self.assertEqual(ds[2].tolist(), [6, 1]) + def test_4billion_tokens(self): + """Regression test for numpy type promotion issue https://github.com/numpy/numpy/issues/5745""" + data = [torch.tensor(list(range(10000)), dtype=torch.long)] * 430000 + ds = self._build_dataset( + data, block_size=6, pad=0, eos=1, break_mode="complete" + ) + ds[-1] # __getitem__ works + start, end = ds.slice_indices[-1] + assert end > 4294967295 # data must be sufficiently large to overflow uint32 + assert not isinstance( + end + 1, float + ) # this would also raise, since np.uint64(1) + 1 => 2.0 + if __name__ == "__main__": unittest.main() diff --git a/tests/test_train.py b/tests/test_train.py index 734d0e8601..02ef94cc5b 100644 --- a/tests/test_train.py +++ b/tests/test_train.py @@ -4,22 +4,23 @@ # LICENSE file in the root directory of this source tree. import contextlib -from io import StringIO +import logging import unittest +from io import StringIO from unittest.mock import MagicMock, patch import torch - -from fairseq import data, checkpoint_utils +from fairseq import checkpoint_utils, data +from omegaconf import OmegaConf def mock_trainer(epoch, num_updates, iterations_in_epoch): trainer = MagicMock() trainer.load_checkpoint.return_value = { - 'train_iterator': { - 'epoch': epoch, - 'iterations_in_epoch': iterations_in_epoch, - 'shuffle': False, + "train_iterator": { + "epoch": epoch, + "iterations_in_epoch": iterations_in_epoch, + "shuffle": False, }, } trainer.get_num_updates.return_value = num_updates @@ -37,10 +38,17 @@ def mock_dict(): def get_trainer_and_epoch_itr(epoch, epoch_size, num_updates, iterations_in_epoch): tokens = torch.LongTensor(list(range(epoch_size))).view(1, -1) tokens_ds = data.TokenBlockDataset( - tokens, sizes=[tokens.size(-1)], block_size=1, pad=0, eos=1, include_targets=False, + tokens, + sizes=[tokens.size(-1)], + block_size=1, + pad=0, + eos=1, + include_targets=False, ) trainer = mock_trainer(epoch, num_updates, iterations_in_epoch) - dataset = data.LanguagePairDataset(tokens_ds, tokens_ds.sizes, mock_dict(), shuffle=False) + dataset = data.LanguagePairDataset( + tokens_ds, tokens_ds.sizes, mock_dict(), shuffle=False + ) epoch_itr = data.EpochBatchIterator( dataset=dataset, collate_fn=dataset.collater, @@ -49,29 +57,54 @@ def get_trainer_and_epoch_itr(epoch, epoch_size, num_updates, iterations_in_epoc return trainer, epoch_itr -class TestLoadCheckpoint(unittest.TestCase): +def get_mock_cfg(finetune_from_model): + cfg_mock = OmegaConf.create( + { + "checkpoint": { + "save_dir": None, + "optimizer_overrides": "{}", + "reset_dataloader": False, + "reset_meters": False, + "reset_optimizer": False, + "reset_lr_scheduler": False, + "finetune_from_model": finetune_from_model, + "model_parallel_size": 1, + "restore_file": "checkpoint_last.pt", + }, + "common": { + "model_parallel_size": 1, + }, + } + ) + return cfg_mock + +class TestLoadCheckpoint(unittest.TestCase): def setUp(self): - self.args_mock = MagicMock() - self.args_mock.optimizer_overrides = '{}' - self.args_mock.reset_dataloader = False - self.args_mock.reset_meters = False - self.args_mock.reset_optimizer = False + self.cfg_mock = get_mock_cfg(None) self.patches = { - 'os.makedirs': MagicMock(), - 'os.path.join': MagicMock(), - 'os.path.isfile': MagicMock(return_value=True), - 'os.path.isabs': MagicMock(return_value=False), + "os.makedirs": MagicMock(), + "os.path.join": MagicMock(), + "os.path.isfile": MagicMock(return_value=True), + "os.path.isabs": MagicMock(return_value=False), + "fairseq.file_io.PathManager.exists": MagicMock(return_value=False), } self.applied_patches = [patch(p, d) for p, d in self.patches.items()] [p.start() for p in self.applied_patches] + logging.disable(logging.CRITICAL) + + def tearDown(self): + patch.stopall() + logging.disable(logging.NOTSET) def test_load_partial_checkpoint(self): with contextlib.redirect_stdout(StringIO()): trainer, epoch_itr = get_trainer_and_epoch_itr(2, 150, 200, 50) trainer.get_train_iterator = MagicMock(return_value=epoch_itr) - _, epoch_itr = checkpoint_utils.load_checkpoint(self.args_mock, trainer) + _, epoch_itr = checkpoint_utils.load_checkpoint( + self.cfg_mock.checkpoint, trainer + ) self.assertEqual(epoch_itr.epoch, 2) self.assertEqual(epoch_itr.iterations_in_epoch, 50) @@ -80,7 +113,7 @@ def test_load_partial_checkpoint(self): self.assertEqual(epoch_itr.epoch, 2) self.assertEqual(epoch_itr.iterations_in_epoch, 50) - self.assertEqual(next(itr)['net_input']['src_tokens'][0].item(), 50) + self.assertEqual(next(itr)["net_input"]["src_tokens"][0].item(), 50) self.assertEqual(epoch_itr.iterations_in_epoch, 51) for _ in range(150 - 52): @@ -100,29 +133,115 @@ def test_load_full_checkpoint(self): trainer, epoch_itr = get_trainer_and_epoch_itr(2, 150, 300, 150) trainer.get_train_iterator = MagicMock(return_value=epoch_itr) - _, epoch_itr = checkpoint_utils.load_checkpoint(self.args_mock, trainer) + _, epoch_itr = checkpoint_utils.load_checkpoint( + self.cfg_mock.checkpoint, trainer + ) itr = epoch_itr.next_epoch_itr(shuffle=False) self.assertEqual(epoch_itr.epoch, 3) self.assertEqual(epoch_itr.iterations_in_epoch, 0) - self.assertEqual(next(itr)['net_input']['src_tokens'][0].item(), 0) + self.assertEqual(next(itr)["net_input"]["src_tokens"][0].item(), 0) def test_load_no_checkpoint(self): with contextlib.redirect_stdout(StringIO()): trainer, epoch_itr = get_trainer_and_epoch_itr(1, 150, 0, 0) trainer.get_train_iterator = MagicMock(return_value=epoch_itr) - self.patches['os.path.isfile'].return_value = False + self.patches["os.path.isfile"].return_value = False - _, epoch_itr = checkpoint_utils.load_checkpoint(self.args_mock, trainer) + _, epoch_itr = checkpoint_utils.load_checkpoint( + self.cfg_mock.checkpoint, trainer + ) itr = epoch_itr.next_epoch_itr(shuffle=False) self.assertEqual(epoch_itr.epoch, 1) self.assertEqual(epoch_itr.iterations_in_epoch, 0) - self.assertEqual(next(itr)['net_input']['src_tokens'][0].item(), 0) - - def tearDown(self): - patch.stopall() + self.assertEqual(next(itr)["net_input"]["src_tokens"][0].item(), 0) + def test_finetune_from_model_args_conflict(self): + with contextlib.redirect_stdout(StringIO()): + trainer, epoch_itr = get_trainer_and_epoch_itr(1, 150, 0, 0) + trainer.get_train_iterator = MagicMock(return_value=epoch_itr) -if __name__ == '__main__': + for arg in [ + "reset_optimizer", + "reset_lr_scheduler", + "reset_meters", + "reset_dataloader", + ]: + with self.subTest(arg=arg): + cfg_mock = get_mock_cfg("/temp/checkpoint_pretrained.pt") + cfg_mock["checkpoint"][arg] = True + with self.assertRaises(Exception) as context: + _, _ = checkpoint_utils.load_checkpoint( + cfg_mock.checkpoint, trainer + ) + + self.assertTrue( + "--finetune-from-model can not be set together with either --reset-optimizer" + " or reset_lr_scheduler or reset_meters or reset_dataloader" + in str(context.exception) + ) + + def test_finetune_from_model(self): + with contextlib.redirect_stdout(StringIO()): + trainer, epoch_itr = get_trainer_and_epoch_itr(1, 150, 0, 0) + trainer.get_train_iterator = MagicMock(return_value=epoch_itr) + from_model_path = "/temp/checkpoint_pretrained.pt" + + def mock_finetune_exist(path): + if path == from_model_path: + return True + else: + return False + + self.patches[ + "fairseq.file_io.PathManager.exists" + ].side_effect = mock_finetune_exist + cfg_mock = get_mock_cfg(from_model_path) + cfg_mock.checkpoint.restore_file = "checkpoint_last.pt" + _, _ = checkpoint_utils.load_checkpoint(cfg_mock.checkpoint, trainer) + ( + checkpoint_path, + reset_optimizer, + reset_lr_scheduler, + optimizer_overrides, + ) = trainer.load_checkpoint.call_args[0] + reset_meters = trainer.load_checkpoint.call_args[1]["reset_meters"] + self.assertTrue(reset_optimizer) + self.assertTrue(reset_lr_scheduler) + self.assertTrue(reset_meters) + + def test_finetune_from_model_resume(self): + with contextlib.redirect_stdout(StringIO()): + trainer, epoch_itr = get_trainer_and_epoch_itr(1, 150, 0, 0) + trainer.get_train_iterator = MagicMock(return_value=epoch_itr) + from_model_path = "/temp/checkpoint_pretrained.pt" + + # launch second time + # both restore_file=checkpoint_last.pt and finetune_from_model are set + def mock_finetune_exist(path): + if path == from_model_path or path.endsWith("checkpoint_last.pt"): + return True + else: + return False + + self.patches[ + "fairseq.file_io.PathManager.exists" + ].side_effect = mock_finetune_exist + cfg_mock = get_mock_cfg(from_model_path) + cfg_mock.checkpoint.restore_file = "checkpoint_last.pt" + _, _ = checkpoint_utils.load_checkpoint(cfg_mock.checkpoint, trainer) + ( + checkpoint_path, + reset_optimizer, + reset_lr_scheduler, + optimizer_overrides, + ) = trainer.load_checkpoint.call_args[0] + reset_meters = trainer.load_checkpoint.call_args[1]["reset_meters"] + self.assertFalse(reset_optimizer) + self.assertFalse(reset_lr_scheduler) + self.assertFalse(reset_meters) + + +if __name__ == "__main__": unittest.main() diff --git a/tests/test_transformer.py b/tests/test_transformer.py new file mode 100644 index 0000000000..de5c5bdbd4 --- /dev/null +++ b/tests/test_transformer.py @@ -0,0 +1,65 @@ +import argparse +import unittest +from typing import Any, Dict, Sequence + +import torch +from fairseq.models import transformer + +from tests.test_roberta import FakeTask + + +def mk_sample(tok: Sequence[int] = None, batch_size: int = 2) -> Dict[str, Any]: + if not tok: + tok = [10, 11, 12, 13, 14, 15, 2] + + batch = torch.stack([torch.tensor(tok, dtype=torch.long)] * batch_size) + sample = { + "net_input": { + "src_tokens": batch, + "prev_output_tokens": batch, + "src_lengths": torch.tensor( + [len(tok)] * batch_size, dtype=torch.long, device=batch.device + ), + }, + "target": batch[:, 1:], + } + return sample + + +def mk_transformer(**extra_args: Any): + overrides = { + # Use characteristics dimensions + "encoder_embed_dim": 12, + "encoder_ffn_embed_dim": 14, + "decoder_embed_dim": 12, + "decoder_ffn_embed_dim": 14, + # Disable dropout so we have comparable tests. + "dropout": 0, + "attention_dropout": 0, + "activation_dropout": 0, + "encoder_layerdrop": 0, + } + overrides.update(extra_args) + # Overrides the defaults from the parser + args = argparse.Namespace(**overrides) + transformer.tiny_architecture(args) + + torch.manual_seed(0) + task = FakeTask(args) + return transformer.TransformerModel.build_model(args, task) + + +class TransformerTestCase(unittest.TestCase): + def test_forward_backward(self): + model = mk_transformer(encoder_embed_dim=12, decoder_embed_dim=12) + sample = mk_sample() + o, _ = model.forward(**sample["net_input"]) + loss = o.sum() + loss.backward() + + def test_different_encoder_decoder_embed_dim(self): + model = mk_transformer(encoder_embed_dim=12, decoder_embed_dim=16) + sample = mk_sample() + o, _ = model.forward(**sample["net_input"]) + loss = o.sum() + loss.backward() diff --git a/tests/test_utils.py b/tests/test_utils.py index 8d070a4758..79195903e0 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -6,24 +6,26 @@ import unittest import torch - from fairseq import utils class TestUtils(unittest.TestCase): - def test_convert_padding_direction(self): pad = 1 - left_pad = torch.LongTensor([ - [2, 3, 4, 5, 6], - [1, 7, 8, 9, 10], - [1, 1, 1, 11, 12], - ]) - right_pad = torch.LongTensor([ - [2, 3, 4, 5, 6], - [7, 8, 9, 10, 1], - [11, 12, 1, 1, 1], - ]) + left_pad = torch.LongTensor( + [ + [2, 3, 4, 5, 6], + [1, 7, 8, 9, 10], + [1, 1, 1, 11, 12], + ] + ) + right_pad = torch.LongTensor( + [ + [2, 3, 4, 5, 6], + [7, 8, 9, 10, 1], + [11, 12, 1, 1, 1], + ] + ) self.assertAlmostEqual( right_pad, @@ -44,26 +46,34 @@ def test_convert_padding_direction(self): def test_make_positions(self): pad = 1 - left_pad_input = torch.LongTensor([ - [9, 9, 9, 9, 9], - [1, 9, 9, 9, 9], - [1, 1, 1, 9, 9], - ]) - left_pad_output = torch.LongTensor([ - [2, 3, 4, 5, 6], - [1, 2, 3, 4, 5], - [1, 1, 1, 2, 3], - ]) - right_pad_input = torch.LongTensor([ - [9, 9, 9, 9, 9], - [9, 9, 9, 9, 1], - [9, 9, 1, 1, 1], - ]) - right_pad_output = torch.LongTensor([ - [2, 3, 4, 5, 6], - [2, 3, 4, 5, 1], - [2, 3, 1, 1, 1], - ]) + left_pad_input = torch.LongTensor( + [ + [9, 9, 9, 9, 9], + [1, 9, 9, 9, 9], + [1, 1, 1, 9, 9], + ] + ) + left_pad_output = torch.LongTensor( + [ + [2, 3, 4, 5, 6], + [1, 2, 3, 4, 5], + [1, 1, 1, 2, 3], + ] + ) + right_pad_input = torch.LongTensor( + [ + [9, 9, 9, 9, 9], + [9, 9, 9, 9, 1], + [9, 9, 1, 1, 1], + ] + ) + right_pad_output = torch.LongTensor( + [ + [2, 3, 4, 5, 6], + [2, 3, 4, 5, 1], + [2, 3, 1, 1, 1], + ] + ) self.assertAlmostEqual( left_pad_output, @@ -82,9 +92,9 @@ def test_clip_grad_norm_(self): params = [torch.nn.Parameter(torch.zeros(5)) for i in range(3)] for p in params: - p.grad = torch.full((5,), fill_value=2) + p.grad = torch.full((5,), fill_value=2.0) grad_norm = utils.clip_grad_norm_(params, 1.0) - exp_grad_norm = torch.full((15,), fill_value=2).norm() + exp_grad_norm = torch.full((15,), fill_value=2.0).norm() self.assertTrue(torch.is_tensor(grad_norm)) self.assertEqual(grad_norm, exp_grad_norm) @@ -100,5 +110,5 @@ def assertAlmostEqual(self, t1, t2): self.assertLess(utils.item((t1 - t2).abs().max()), 1e-4) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/tests/test_valid_subset_checks.py b/tests/test_valid_subset_checks.py new file mode 100644 index 0000000000..3e9191bda6 --- /dev/null +++ b/tests/test_valid_subset_checks.py @@ -0,0 +1,138 @@ +import os +import shutil +import tempfile +import unittest + +from fairseq import options +from fairseq.dataclass.utils import convert_namespace_to_omegaconf +from fairseq.data.data_utils import raise_if_valid_subsets_unintentionally_ignored +from .utils import create_dummy_data, preprocess_lm_data, train_language_model + + +def make_lm_config( + data_dir=None, + extra_flags=None, + task="language_modeling", + arch="transformer_lm_gpt2_tiny", +): + task_args = [task] + if data_dir is not None: + task_args += [data_dir] + train_parser = options.get_training_parser() + train_args = options.parse_args_and_arch( + train_parser, + [ + "--task", + *task_args, + "--arch", + arch, + "--optimizer", + "adam", + "--lr", + "0.0001", + "--max-tokens", + "500", + "--tokens-per-sample", + "500", + "--save-dir", + data_dir, + "--max-epoch", + "1", + ] + + (extra_flags or []), + ) + cfg = convert_namespace_to_omegaconf(train_args) + return cfg + + +def write_empty_file(path): + with open(path, "w"): + pass + assert os.path.exists(path) + + +class TestValidSubsetsErrors(unittest.TestCase): + """Test various filesystem, clarg combinations and ensure that error raising happens as expected""" + + def _test_case(self, paths, extra_flags): + with tempfile.TemporaryDirectory() as data_dir: + [ + write_empty_file(os.path.join(data_dir, f"{p}.bin")) + for p in paths + ["train"] + ] + cfg = make_lm_config(data_dir, extra_flags=extra_flags) + raise_if_valid_subsets_unintentionally_ignored(cfg) + + def test_default_raises(self): + with self.assertRaises(ValueError): + self._test_case(["valid", "valid1"], []) + with self.assertRaises(ValueError): + self._test_case( + ["valid", "valid1", "valid2"], ["--valid-subset", "valid,valid1"] + ) + + def partially_specified_valid_subsets(self): + with self.assertRaises(ValueError): + self._test_case( + ["valid", "valid1", "valid2"], ["--valid-subset", "valid,valid1"] + ) + # Fix with ignore unused + self._test_case( + ["valid", "valid1", "valid2"], + ["--valid-subset", "valid,valid1", "--ignore-unused-valid-subsets"], + ) + + def test_legal_configs(self): + self._test_case(["valid"], []) + self._test_case(["valid", "valid1"], ["--ignore-unused-valid-subsets"]) + self._test_case(["valid", "valid1"], ["--combine-val"]) + self._test_case(["valid", "valid1"], ["--valid-subset", "valid,valid1"]) + self._test_case(["valid", "valid1"], ["--valid-subset", "valid1"]) + self._test_case( + ["valid", "valid1"], ["--combine-val", "--ignore-unused-valid-subsets"] + ) + self._test_case( + ["valid1"], ["--valid-subset", "valid1"] + ) # valid.bin doesn't need to be ignored. + + def test_disable_validation(self): + self._test_case([], ["--disable-validation"]) + self._test_case(["valid", "valid1"], ["--disable-validation"]) + + def test_dummy_task(self): + cfg = make_lm_config(task="dummy_lm") + raise_if_valid_subsets_unintentionally_ignored(cfg) + + def test_masked_dummy_task(self): + cfg = make_lm_config(task="dummy_masked_lm") + raise_if_valid_subsets_unintentionally_ignored(cfg) + + +class TestCombineValidSubsets(unittest.TestCase): + def _train(self, extra_flags): + with self.assertLogs() as logs: + with tempfile.TemporaryDirectory("test_transformer_lm") as data_dir: + create_dummy_data(data_dir, num_examples=20) + preprocess_lm_data(data_dir) + + shutil.copyfile(f"{data_dir}/valid.bin", f"{data_dir}/valid1.bin") + shutil.copyfile(f"{data_dir}/valid.idx", f"{data_dir}/valid1.idx") + train_language_model( + data_dir, + "transformer_lm", + ["--max-update", "0", "--log-format", "json"] + extra_flags, + run_validation=False, + ) + return [x.message for x in logs.records] + + def test_combined(self): + flags = ["--combine-valid-subsets"] + logs = self._train(flags) + assert any(["valid1" in x for x in logs]) # loaded 100 examples from valid1 + assert not any(["valid1_ppl" in x for x in logs]) # metrics are combined + + def test_subsets(self): + flags = ["--valid-subset", "valid,valid1"] + logs = self._train(flags) + assert any(["valid_ppl" in x for x in logs]) # loaded 100 examples from valid1 + assert any(["valid1_ppl" in x for x in logs]) # metrics are combined diff --git a/tests/utils.py b/tests/utils.py index f908e5e74a..6e0c709517 100644 --- a/tests/utils.py +++ b/tests/utils.py @@ -4,10 +4,15 @@ # LICENSE file in the root directory of this source tree. import argparse +import json +import os +import random +import sys +from io import StringIO + import torch import torch.nn.functional as F - -from fairseq import utils +from fairseq import options, utils from fairseq.data import Dictionary from fairseq.data.language_pair_dataset import collate from fairseq.models import ( @@ -16,10 +21,13 @@ FairseqIncrementalDecoder, ) from fairseq.models.fairseq_encoder import EncoderOut -from fairseq.tasks import FairseqTask +from fairseq.tasks import LegacyFairseqTask +from fairseq_cli import generate, interactive, preprocess, train, validate +import fairseq.distributed.utils as distributed_utils +from fairseq.dataclass.utils import convert_namespace_to_omegaconf -def dummy_dictionary(vocab_size, prefix='token_'): +def dummy_dictionary(vocab_size, prefix="token_"): d = Dictionary() for i in range(vocab_size): token = prefix + str(i) @@ -29,18 +37,15 @@ def dummy_dictionary(vocab_size, prefix='token_'): def dummy_dataloader( - samples, - padding_idx=1, - eos_idx=2, - batch_size=None, + samples, padding_idx=1, eos_idx=2, batch_size=None, ): if batch_size is None: batch_size = len(samples) # add any missing data to samples for i, sample in enumerate(samples): - if 'id' not in sample: - sample['id'] = i + if "id" not in sample: + sample["id"] = i # create dataloader dataset = TestDataset(samples) @@ -65,48 +70,86 @@ def sequence_generator_setup(): src_lengths = torch.LongTensor([2, 2]) args = argparse.Namespace() - unk = 0. + unk = 0.0 args.beam_probs = [ # step 0: - torch.FloatTensor([ - # eos w1 w2 - # sentence 1: - [0.0, unk, 0.9, 0.1], # beam 1 - [0.0, unk, 0.9, 0.1], # beam 2 - # sentence 2: - [0.0, unk, 0.7, 0.3], - [0.0, unk, 0.7, 0.3], - ]), + torch.FloatTensor( + [ + # eos w1 w2 + # sentence 1: + [0.0, unk, 0.9, 0.1], # beam 1 + [0.0, unk, 0.9, 0.1], # beam 2 + # sentence 2: + [0.0, unk, 0.7, 0.3], + [0.0, unk, 0.7, 0.3], + ] + ), # step 1: - torch.FloatTensor([ - # eos w1 w2 prefix - # sentence 1: - [1.0, unk, 0.0, 0.0], # w1: 0.9 (emit: w1 <eos>: 0.9*1.0) - [0.0, unk, 0.9, 0.1], # w2: 0.1 - # sentence 2: - [0.25, unk, 0.35, 0.4], # w1: 0.7 (don't emit: w1 <eos>: 0.7*0.25) - [0.00, unk, 0.10, 0.9], # w2: 0.3 - ]), + torch.FloatTensor( + [ + # eos w1 w2 prefix + # sentence 1: + [1.0, unk, 0.0, 0.0], # w1: 0.9 (emit: w1 <eos>: 0.9*1.0) + [0.0, unk, 0.9, 0.1], # w2: 0.1 + # sentence 2: + [0.25, unk, 0.35, 0.4], # w1: 0.7 (don't emit: w1 <eos>: 0.7*0.25) + [0.00, unk, 0.10, 0.9], # w2: 0.3 + ] + ), # step 2: - torch.FloatTensor([ - # eos w1 w2 prefix - # sentence 1: - [0.0, unk, 0.1, 0.9], # w2 w1: 0.1*0.9 - [0.6, unk, 0.2, 0.2], # w2 w2: 0.1*0.1 (emit: w2 w2 <eos>: 0.1*0.1*0.6) - # sentence 2: - [0.60, unk, 0.4, 0.00], # w1 w2: 0.7*0.4 (emit: w1 w2 <eos>: 0.7*0.4*0.6) - [0.01, unk, 0.0, 0.99], # w2 w2: 0.3*0.9 - ]), + torch.FloatTensor( + [ + # eos w1 w2 prefix + # sentence 1: + [0.0, unk, 0.1, 0.9], # w2 w1: 0.1*0.9 + [ + 0.6, + unk, + 0.2, + 0.2, + ], # w2 w2: 0.1*0.1 (emit: w2 w2 <eos>: 0.1*0.1*0.6) + # sentence 2: + [ + 0.60, + unk, + 0.4, + 0.00, + ], # w1 w2: 0.7*0.4 (emit: w1 w2 <eos>: 0.7*0.4*0.6) + [0.01, unk, 0.0, 0.99], # w2 w2: 0.3*0.9 + ] + ), # step 3: - torch.FloatTensor([ - # eos w1 w2 prefix - # sentence 1: - [1.0, unk, 0.0, 0.0], # w2 w1 w2: 0.1*0.9*0.9 (emit: w2 w1 w2 <eos>: 0.1*0.9*0.9*1.0) - [1.0, unk, 0.0, 0.0], # w2 w1 w1: 0.1*0.9*0.1 (emit: w2 w1 w1 <eos>: 0.1*0.9*0.1*1.0) - # sentence 2: - [0.1, unk, 0.5, 0.4], # w2 w2 w2: 0.3*0.9*0.99 (emit: w2 w2 w2 <eos>: 0.3*0.9*0.99*0.1) - [1.0, unk, 0.0, 0.0], # w1 w2 w1: 0.7*0.4*0.4 (emit: w1 w2 w1 <eos>: 0.7*0.4*0.4*1.0) - ]), + torch.FloatTensor( + [ + # eos w1 w2 prefix + # sentence 1: + [ + 1.0, + unk, + 0.0, + 0.0, + ], # w2 w1 w2: 0.1*0.9*0.9 (emit: w2 w1 w2 <eos>: 0.1*0.9*0.9*1.0) + [ + 1.0, + unk, + 0.0, + 0.0, + ], # w2 w1 w1: 0.1*0.9*0.1 (emit: w2 w1 w1 <eos>: 0.1*0.9*0.1*1.0) + # sentence 2: + [ + 0.1, + unk, + 0.5, + 0.4, + ], # w2 w2 w2: 0.3*0.9*0.99 (emit: w2 w2 w2 <eos>: 0.3*0.9*0.99*0.1) + [ + 1.0, + unk, + 0.0, + 0.0, + ], # w1 w2 w1: 0.7*0.4*0.4 (emit: w1 w2 w1 <eos>: 0.7*0.4*0.4*1.0) + ] + ), ] task = TestTranslationTask.setup_task(args, d, d) @@ -116,8 +159,275 @@ def sequence_generator_setup(): return tgt_dict, w1, w2, src_tokens, src_lengths, model -class TestDataset(torch.utils.data.Dataset): +def create_dummy_data(data_dir, num_examples=100, maxlen=20, alignment=False): + def _create_dummy_data(filename): + data = torch.rand(num_examples * maxlen) + data = 97 + torch.floor(26 * data).int() + with open(os.path.join(data_dir, filename), "w") as h: + offset = 0 + for _ in range(num_examples): + ex_len = random.randint(1, maxlen) + ex_str = " ".join(map(chr, data[offset : offset + ex_len])) + print(ex_str, file=h) + offset += ex_len + + def _create_dummy_alignment_data(filename_src, filename_tgt, filename): + with open(os.path.join(data_dir, filename_src), "r") as src_f, open( + os.path.join(data_dir, filename_tgt), "r" + ) as tgt_f, open(os.path.join(data_dir, filename), "w") as h: + for src, tgt in zip(src_f, tgt_f): + src_len = len(src.split()) + tgt_len = len(tgt.split()) + avg_len = (src_len + tgt_len) // 2 + num_alignments = random.randint(avg_len // 2, 2 * avg_len) + src_indices = torch.floor(torch.rand(num_alignments) * src_len).int() + tgt_indices = torch.floor(torch.rand(num_alignments) * tgt_len).int() + ex_str = " ".join( + [ + "{}-{}".format(src, tgt) + for src, tgt in zip(src_indices, tgt_indices) + ] + ) + print(ex_str, file=h) + + _create_dummy_data("train.in") + _create_dummy_data("train.out") + _create_dummy_data("valid.in") + _create_dummy_data("valid.out") + _create_dummy_data("test.in") + _create_dummy_data("test.out") + + if alignment: + _create_dummy_alignment_data("train.in", "train.out", "train.align") + _create_dummy_alignment_data("valid.in", "valid.out", "valid.align") + _create_dummy_alignment_data("test.in", "test.out", "test.align") + + +def preprocess_lm_data(data_dir): + preprocess_parser = options.get_preprocessing_parser() + preprocess_args = preprocess_parser.parse_args( + [ + "--only-source", + "--trainpref", + os.path.join(data_dir, "train.out"), + "--validpref", + os.path.join(data_dir, "valid.out"), + "--testpref", + os.path.join(data_dir, "test.out"), + "--destdir", + data_dir, + ] + ) + preprocess.main(preprocess_args) + + +def preprocess_translation_data(data_dir, extra_flags=None): + preprocess_parser = options.get_preprocessing_parser() + preprocess_args = preprocess_parser.parse_args( + [ + "--source-lang", + "in", + "--target-lang", + "out", + "--trainpref", + os.path.join(data_dir, "train"), + "--validpref", + os.path.join(data_dir, "valid"), + "--testpref", + os.path.join(data_dir, "test"), + "--thresholdtgt", + "0", + "--thresholdsrc", + "0", + "--destdir", + data_dir, + ] + + (extra_flags or []), + ) + preprocess.main(preprocess_args) + + +def preprocess_summarization_data(data_dir, extra_flags=None): + preprocess_parser = options.get_preprocessing_parser() + preprocess_args = preprocess_parser.parse_args( + [ + "--source-lang", + "in", + "--target-lang", + "out", + "--trainpref", + os.path.join(data_dir, "train"), + "--validpref", + os.path.join(data_dir, "valid"), + "--testpref", + os.path.join(data_dir, "test"), + "--thresholdtgt", + "0", + "--thresholdsrc", + "0", + "--joined-dictionary", + "--destdir", + data_dir, + ] + + (extra_flags or []), + ) + preprocess.main(preprocess_args) + + +def create_laser_data_and_config_json(data_dir): + src_langs = ["de", "fr", "ru", "tr", "zh"] + tgt_langs = ["en", "es"] + config_json = {} + config_train_json = [] + src_vocab = None + tgt_vocab = None + + for src_lang in src_langs: + for tgt_lang in tgt_langs: + langpair_folder = f"{src_lang}-{tgt_lang}" + + langpair_path = os.path.join(data_dir, langpair_folder) + os.mkdir(langpair_path) + create_dummy_data(langpair_path) + preprocess_translation_data(langpair_path, ["--dataset-impl", "cached"]) + + src_vocab = os.path.join(langpair_path, "dict.in.txt") + tgt_vocab = os.path.join(langpair_path, "dict.out.txt") + config_train_json.append( + { + "id": 0 if tgt_lang == "en" else 1, + "src": os.path.join(langpair_path, "train.in-out.in"), + "tgt": os.path.join(langpair_path, "train.in-out.out"), + } + ) + + config_json["src_vocab"] = src_vocab + config_json["tgt_vocab"] = tgt_vocab + config_json["train"] = config_train_json + + with open(os.path.join(data_dir, "laserconfig.json"), "w") as config_file: + json.dump(config_json, config_file) + + return config_file + + +def train_translation_model( + data_dir, + arch, + extra_flags=None, + task="translation", + run_validation=False, + lang_flags=None, + extra_valid_flags=None, + world_size=1, +): + if lang_flags is None: + lang_flags = [ + "--source-lang", + "in", + "--target-lang", + "out", + ] + train_parser = options.get_training_parser() + train_args = options.parse_args_and_arch( + train_parser, + [ + "--task", + task, + data_dir, + "--save-dir", + data_dir, + "--arch", + arch, + "--optimizer", + "nag", + "--lr", + "0.05", + "--max-tokens", + "500", + "--max-epoch", + "1", + "--no-progress-bar", + "--distributed-world-size", + str(world_size), + "--num-workers", + "0", + ] + + lang_flags + + (extra_flags or []), + ) + + cfg = convert_namespace_to_omegaconf(train_args) + distributed_utils.call_main(cfg, train.main) + + if run_validation: + # test validation + validate_parser = options.get_validation_parser() + validate_args = options.parse_args_and_arch( + validate_parser, + [ + "--task", + task, + data_dir, + "--path", + os.path.join(data_dir, "checkpoint_last.pt"), + "--valid-subset", + "valid", + "--max-tokens", + "500", + "--no-progress-bar", + "--num-workers", + "0", + ] + + lang_flags + + (extra_valid_flags or []), + ) + validate.main(validate_args) + + +def generate_main(data_dir, extra_flags=None, path=None): + if extra_flags is None: + extra_flags = [ + "--print-alignment", + ] + if path is None: + path = os.path.join(data_dir, "checkpoint_last.pt") + generate_parser = options.get_generation_parser() + generate_args = options.parse_args_and_arch( + generate_parser, + [ + data_dir, + "--path", + path, + "--beam", + "3", + "--batch-size", + "64", + "--max-len-b", + "5", + "--gen-subset", + "valid", + "--no-progress-bar", + "--num-workers", + "0", + ] + + (extra_flags or []), + ) + # evaluate model in batch mode + generate.main(generate_args) + + # evaluate model interactively + generate_args.buffer_size = 0 + generate_args.input = "-" + generate_args.batch_size = None + orig_stdin = sys.stdin + sys.stdin = StringIO("h e l l o\n") + interactive.main(generate_args) + sys.stdin = orig_stdin + + +class TestDataset(torch.utils.data.Dataset): def __init__(self, data): super().__init__() self.data = data @@ -130,8 +440,7 @@ def __len__(self): return len(self.data) -class TestTranslationTask(FairseqTask): - +class TestTranslationTask(LegacyFairseqTask): def __init__(self, args, src_dict, tgt_dict, model): super().__init__(args) self.src_dict = src_dict @@ -194,8 +503,8 @@ def reorder_encoder_out(self, encoder_out, new_order): class TestIncrementalDecoder(FairseqIncrementalDecoder): def __init__(self, args, dictionary): super().__init__(dictionary) - assert hasattr(args, 'beam_probs') or hasattr(args, 'probs') - args.max_decoder_positions = getattr(args, 'max_decoder_positions', 100) + assert hasattr(args, "beam_probs") or hasattr(args, "probs") + args.max_decoder_positions = getattr(args, "max_decoder_positions", 100) self.args = args def forward(self, prev_output_tokens, encoder_out=None, incremental_state=None): @@ -209,18 +518,19 @@ def forward(self, prev_output_tokens, encoder_out=None, incremental_state=None): # determine number of steps if incremental_state is not None: # cache step number - step = utils.get_incremental_state(self, incremental_state, 'step') + step = utils.get_incremental_state(self, incremental_state, "step") if step is None: step = 0 - utils.set_incremental_state(self, incremental_state, 'step', step + 1) + utils.set_incremental_state(self, incremental_state, "step", step + 1) steps = [step] else: steps = list(range(tgt_len)) # define output in terms of raw probs - if hasattr(self.args, 'probs'): - assert self.args.probs.dim() == 3, \ - 'expected probs to have size bsz*steps*vocab' + if hasattr(self.args, "probs"): + assert ( + self.args.probs.dim() == 3 + ), "expected probs to have size bsz*steps*vocab" probs = self.args.probs.index_select(1, torch.LongTensor(steps)) else: probs = torch.FloatTensor(bbsz, len(steps), vocab).zero_() @@ -228,7 +538,7 @@ def forward(self, prev_output_tokens, encoder_out=None, incremental_state=None): # args.beam_probs gives the probability for every vocab element, # starting with eos, then unknown, and then the rest of the vocab if step < len(self.args.beam_probs): - probs[:, i, self.dictionary.eos():] = self.args.beam_probs[step] + probs[:, i, self.dictionary.eos() :] = self.args.beam_probs[step] else: probs[:, i, self.dictionary.eos()] = 1.0 @@ -300,8 +610,8 @@ def __init__(self, args, dictionary): self.args = args def forward(self, src_tokens, src_lengths=None, **kwargs): - assert 'fancy_other_input' in kwargs - assert kwargs['fancy_other_input'] is not None + assert "fancy_other_input" in kwargs + assert kwargs["fancy_other_input"] is not None return EncoderOut( encoder_out=src_tokens, encoder_padding_mask=None, @@ -333,8 +643,75 @@ def build_model(cls, args, task): return cls(encoder, decoder) def forward(self, src_tokens, src_lengths, prev_output_tokens, **kwargs): - encoder_out = self.encoder( - src_tokens, src_lengths=src_lengths, **kwargs) + encoder_out = self.encoder(src_tokens, src_lengths=src_lengths, **kwargs) decoder_out = self.decoder( - prev_output_tokens, encoder_out=encoder_out, **kwargs) + prev_output_tokens, encoder_out=encoder_out, **kwargs + ) return decoder_out + + +def train_language_model( + data_dir, + arch, + extra_flags=None, + run_validation=False, + extra_valid_flags=None, + task="language_modeling", + world_size=1, +): + train_parser = options.get_training_parser() + train_args = options.parse_args_and_arch( + train_parser, + [ + "--task", + task, + data_dir, + "--arch", + arch, + "--optimizer", + "adam", + "--lr", + "0.0001", + "--max-tokens", + "500", + "--tokens-per-sample", + "500", + "--save-dir", + data_dir, + "--max-epoch", + "1", + "--no-progress-bar", + "--distributed-world-size", + str(world_size), + "--ddp-backend", + "no_c10d", + "--num-workers", + "0", + ] + + (extra_flags or []), + ) + cfg = convert_namespace_to_omegaconf(train_args) + distributed_utils.call_main(cfg, train.main) + + if run_validation: + # test validation + validate_parser = options.get_validation_parser() + validate_args = options.parse_args_and_arch( + validate_parser, + [ + "--task", + task, + data_dir, + "--path", + os.path.join(data_dir, "checkpoint_last.pt"), + "--valid-subset", + "valid", + "--max-tokens", + "500", + "--no-progress-bar", + "--num-workers", + "0", + ] + + (extra_valid_flags or []), + ) + validate.main(validate_args) diff --git a/train.py b/train.py index 8e7d1115fd..321de3d9b5 100644 --- a/train.py +++ b/train.py @@ -3,9 +3,12 @@ # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. +""" +Legacy entry point. Use fairseq_cli/train.py or fairseq-train instead. +""" from fairseq_cli.train import cli_main -if __name__ == '__main__': +if __name__ == "__main__": cli_main()