diff --git a/.vscode/launch.json b/.vscode/launch.json new file mode 100644 index 0000000..ad7d763 --- /dev/null +++ b/.vscode/launch.json @@ -0,0 +1,18 @@ +{ + // Use IntelliSense to learn about possible attributes. + // Hover to view descriptions of existing attributes. + // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387 + "version": "0.2.0", + "configurations": [ + { + "name": "Python Debugger: MobileNetV2", + "type": "debugpy", + "request": "launch", + "program": "${workspaceFolder}/models/icl/mobilenet", + "cwd": "${workspaceFolder}/models/icl/mobilenet", + "justMyCode": false, + "args": ["--config", "workflow.json"], + "console": "integratedTerminal" + } + ] +} diff --git a/models/icl/mobilenet/.gitignore b/models/icl/mobilenet/.gitignore new file mode 100644 index 0000000..92f76cc --- /dev/null +++ b/models/icl/mobilenet/.gitignore @@ -0,0 +1,3 @@ +data/** +outputs/** +temp/** \ No newline at end of file diff --git a/models/icl/mobilenet/README.md b/models/icl/mobilenet/README.md new file mode 100644 index 0000000..0bd5d3b --- /dev/null +++ b/models/icl/mobilenet/README.md @@ -0,0 +1,68 @@ +# MobileNet + +Quantize [MobileNet V2](https://huggingface.co/google/mobilenet_v2_1.4_224) with [Olive](https://github.com/microsoft/Olive) using [ONNX QDQ](https://onnxruntime.ai/docs/performance/model-optimizations/quantization.html#onnx-quantization-representation-format). + +Dependencies: + +- [Olive](https://github.com/microsoft/Olive) +- [onnxruntime-qnn](https://onnxruntime.ai/docs/execution-providers/QNN-ExecutionProvider.html#pre-built-packages-windows-only) + +## Set up Environment with Conda and Poetry + +```bash +# create a new conda environment with environment.yml +conda env create -f environment.yml -n {env_name} +conda activate {env_name} + +# install dependencies with poetry +cd /path/to/repo/models/icl/mobilenet +poetry install +``` + +### Login Hugging Face + +```bash +poetry run huggingface-cli login +``` + +## Quantize MobileNet V2 + +### Run the whole workflow + +```bash +cd /path/to/repo/models/icl/mobilenet + +# or `python . --config workflow.json` +olive run --config workflow.json +``` + +### Debugging + +Set breakpoints in `__main__.py`, select `Python Debugger: MobileNetV2` in the debug panel then press `F5`. + +> Debug Configuration +> +> ```json +> { +> "name": "Python Debugger: MobileNetV2", +> "type": "debugpy", +> "request": "launch", +> "program": "${workspaceFolder}/models/icl/mobilenet", +> "cwd": "${workspaceFolder}/models/icl/mobilenet", +> "justMyCode": false, +> "args": ["--config", "workflow.json"], +> "console": "integratedTerminal" +> } +> ``` + +## TODO + +- [] Packaging +- [] Support QNN profiling +- [] Enable `evaluate_input_model` with multiple `accelerator` +- [] Generate EPContext model, aka "JIT" +- [] Resume from checkpoint +- [] Adopt `IsolatedORTSystem` +- [] Implement ImagenetDataset +- [] Add NPU support in `OrtSessionParamsTuning` +- [] auto-opt diff --git a/models/icl/mobilenet/__main__.py b/models/icl/mobilenet/__main__.py new file mode 100644 index 0000000..4f64153 --- /dev/null +++ b/models/icl/mobilenet/__main__.py @@ -0,0 +1,28 @@ +import argparse +from pathlib import Path + +from olive.workflows import run + + +def parse_args(): + arg_parser = argparse.ArgumentParser() + arg_parser.add_argument("--config", type=str, required=True) + return arg_parser.parse_args() + + +if __name__ == "__main__": + arg_parser = argparse.ArgumentParser() + arg_parser.add_argument("--config", type=str, required=True) + args = arg_parser.parse_args() + + workflow_path = Path(args.config).resolve() + if not workflow_path.exists(): + cwd = Path(__file__).resolve().parent + workflow_path = cwd / args.config + if not workflow_path.exists(): + raise FileNotFoundError( + f"Workflow configuration file not found: {workflow_path}") + + print(f"QDQ MobileNetV2 with config: {workflow_path}") + + run(run_config=workflow_path) diff --git a/models/icl/mobilenet/environment.yml b/models/icl/mobilenet/environment.yml new file mode 100644 index 0000000..e40791b --- /dev/null +++ b/models/icl/mobilenet/environment.yml @@ -0,0 +1,10 @@ +channels: + - conda-forge + - pytorch + - huggingface + - defaults +dependencies: + - python=3.11 + - pip + - pip: + - poetry>=1.8.0,<1.9.0 diff --git a/models/icl/mobilenet/imagenet.py b/models/icl/mobilenet/imagenet.py new file mode 100644 index 0000000..ee88381 --- /dev/null +++ b/models/icl/mobilenet/imagenet.py @@ -0,0 +1,136 @@ + +from pathlib import Path +from logging import getLogger + +from datasets import load_dataset, Split +import polars as pl + +import numpy as np +from PIL import Image + +import torch +from torchvision import transforms +from torch.utils.data import DataLoader, Dataset +from transformers import AutoModelForImageClassification + +from olive.data.registry import Registry + +logger = getLogger(__name__) + + +class MobileNetDataset(Dataset): + def __init__(self, data_dir: str): + with np.load(Path(data_dir) / 'data.npz') as data: + self.images = torch.from_numpy(data['images']) + self.labels = torch.from_numpy(data['labels']) + + def __len__(self): + return min(len(self.images), len(self.labels)) + + def __getitem__(self, idx): + # data = torch.unsqueeze(self.data[idx], dim=0) + label = self.labels[idx] if self.labels is not None else -1 + return {'input_image': self.images[idx]}, label + + +@Registry.register_dataset() +def qnn_evaluation_dataset(data_dir, **kwargs): + return MobileNetDataset(data_dir) + + +@Registry.register_post_process() +def qnn_post_process(output): + return output.argmax(axis=-1) + + +@Registry.register_dataloader() +def mobilenet_calibration_reader(dataset, batch_size, data_dir, **kwargs): + dataset = MobileNetDataset(data_dir) + return DataLoader(dataset, batch_size=batch_size, shuffle=False) + + +def preprocess_image(image): + src_img = Image.open(image) + + # If black and white image, convert to rgb (all 3 channels the same) + if len(np.shape(src_img)) == 2: + src_img = src_img.convert(mode='RGB') + + transformations = transforms.Compose( + [ + transforms.Resize(256), + transforms.CenterCrop(224), + transforms.ToTensor(), + transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), + ] + ) + return transformations(src_img).numpy().astype(np.float32) + + +def download_dataset(data_dir: Path, split: Split, size: int) -> None: + data_dir = data_dir / str(split) + data_dir.mkdir(parents=True, exist_ok=True) + + # Load the dataset with a filtering function + dataset = load_dataset( + 'imagenet-1k', + split=split, + streaming=True, # Enable streaming to avoid full download + trust_remote_code=True, + # cache_dir=data_dir, + ) + + model = AutoModelForImageClassification.from_pretrained( + "google/mobilenet_v2_1.4_224") + model.eval() + + # Iterate over the dataset to extract a subset of the specified size + records = [] + images = [] + for i, sample in enumerate(dataset): + if i >= size: + break + + # Save the image file and its metadata + img_path = data_dir / f"img_{i}.jpg" + sample['image'].save(img_path) + logger.debug("Saved image to %s" % (img_path)) + + # Process the image and store it as a tensor + image = preprocess_image(img_path) + images.append(image) + + # Run the model on the image to get the predicted label + inputs = {"pixel_values": from_numpy(image).unsqueeze(dim=0)} + outputs = model(**inputs) + label = outputs['logits'][0, :].argmax(axis=-1).item() + + # Save the image and label as a record + records.append({'filename': str(img_path), 'label': label}) + + # Save the images and labels as a NumPy array + np.savez_compressed( + data_dir / 'data.npz', + allow_pickle=False, + **dict( + images=np.array(images), + labels=np.array([record['label'] for record in records]) + ), + ) + + # Save the ground truth labels as CSV and Parquet files + df = pl.DataFrame(records, schema=['filename', 'label']) + df.write_csv(data_dir / 'ground_truth.csv') + 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