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v0.2 step 5: GitHub Actions CI for platform-neutral test suite
Adds .github/workflows/ci.yaml that runs on every push to main and every PR. Two jobs: unit-tests Runs pytest on the platform-neutral test surface with --cov-fail- under=100 enforcing 100% line coverage on every shipping lib module: inference_engine.server / memory / scheduler / pipeline / proposer training.repr_align Test paths: tests/inference_engine/server/ (deterministic engine doubles) tests/inference_engine/memory/ (pure torch tensors) tests/inference_engine/scheduler/ (deterministic engine doubles) tests/inference_engine/pipeline/ (pure asyncio) tests/training/repr_align/ (Stage 1 surgery, pure torch) tests/backends/mlx/test_env.py (importorskip — runs because env detection is platform- neutral, all other MLX tests skip cleanly on Linux) Excluded (local-only, run on hosts with HF cache or MLX/CUDA): tests/core/ — needs HF Qwen3 weights tests/system/ — same tests/inference_engine/proposer/ — uses real Qwen3 sparse path tests/backends/mlx/test_{verifier,proposer,cache,torch_bridge}.py — Apple-Silicon only Uploads coverage.xml + junit.xml as artifacts for diagnostic. package-import-smoke Imports every shipping subpackage on a fresh interpreter so an accidentally-broken __init__.py is caught even before pytest collection runs. Concurrency group cancels superseded runs on the same branch so rapid-fire pushes don't queue up CI minutes. Linux VM verification of the exact CI invocation: pytest <CI test paths> --cov-fail-under=100 -> 410 passed, 100.00% coverage on 1059 statements Also adds a CI badge + a 'Continuous integration' section to README so contributors can see at a glance which test surface CI guards versus which stays local. Co-authored-by: FluffyAIcode <FluffyAIcode@users.noreply.github.com>
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‎.github/workflows/ci.yaml‎

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name: CI
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# Runs on every push to main and on every PR targeting main.
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#
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# Scope: platform-neutral unit tests with 100% line coverage on the
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# library modules we actually ship for this commit. We deliberately
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# DO NOT run:
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#
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# * tests/core/ — needs HuggingFace weights
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# * tests/system/ — same, plus is slow
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# * tests/inference_engine/proposer/ — uses real Qwen3 sparse
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# proposer; HF-cache-bound
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# * tests/backends/mlx/test_{verifier,proposer,cache,torch_bridge}.py
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# — Apple-Silicon only
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#
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# Mac and CUDA contributors run the full suite locally via
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# scripts/run_platform_tests.sh and push the platform-test reports to
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# the PR branch as evidence; this CI workflow guards the platform-
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# neutral surface so a regression there cannot land on main.
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on:
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push:
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branches: [main]
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pull_request:
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branches: [main]
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workflow_dispatch: {}
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# Cancel superseded runs on the same branch — saves CI time on
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# rapid-fire pushes.
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concurrency:
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group: ${{ github.workflow }}-${{ github.ref }}
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cancel-in-progress: true
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jobs:
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unit-tests:
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name: unit tests + 100% coverage
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runs-on: ubuntu-latest
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strategy:
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fail-fast: false
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matrix:
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python-version: ["3.12"]
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steps:
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- name: Check out
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uses: actions/checkout@v4
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- name: Set up Python ${{ matrix.python-version }}
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uses: actions/setup-python@v5
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with:
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python-version: ${{ matrix.python-version }}
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cache: pip
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- name: Install dependencies
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run: |
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python -m pip install --upgrade pip
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pip install -r requirements.txt
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- name: Show installed key versions
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run: |
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python -c "import torch, fastapi, pydantic, prometheus_client, transformers; \
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print('torch', torch.__version__); \
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print('fastapi', fastapi.__version__); \
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print('pydantic', pydantic.VERSION); \
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print('transformers', transformers.__version__); \
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print('prometheus_client', __import__('importlib.metadata', fromlist=['version']).version('prometheus_client'))"
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- name: Run platform-neutral test suite with 100% coverage
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env:
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PYTHONPATH: .
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run: |
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pytest \
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tests/inference_engine/server/ \
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tests/inference_engine/memory/ \
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tests/inference_engine/scheduler/ \
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tests/inference_engine/pipeline/ \
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tests/training/repr_align/ \
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tests/backends/mlx/test_env.py \
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--cov=inference_engine.server \
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--cov=inference_engine.memory \
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--cov=inference_engine.scheduler \
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--cov=inference_engine.pipeline \
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--cov=training.repr_align \
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--cov-report=term \
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--cov-report=xml:coverage.xml \
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--cov-fail-under=100 \
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--junitxml=junit.xml \
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-v
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- name: Upload coverage artifact
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if: always()
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uses: actions/upload-artifact@v4
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with:
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name: coverage-py${{ matrix.python-version }}
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path: |
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coverage.xml
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junit.xml
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if-no-files-found: warn
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retention-days: 14
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package-import-smoke:
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name: package import smoke
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runs-on: ubuntu-latest
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steps:
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- name: Check out
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uses: actions/checkout@v4
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- name: Set up Python 3.12
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uses: actions/setup-python@v5
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with:
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python-version: "3.12"
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cache: pip
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- name: Install dependencies
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run: |
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python -m pip install --upgrade pip
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pip install -r requirements.txt
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- name: Import every shipping subpackage
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env:
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PYTHONPATH: .
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run: |
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python -c "import inference_engine; \
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import inference_engine.server; \
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import inference_engine.server.app; \
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import inference_engine.server.config; \
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import inference_engine.server.engine; \
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import inference_engine.server.metrics; \
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import inference_engine.server.errors; \
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import inference_engine.server.auth; \
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import inference_engine.server.tokenizer; \
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import inference_engine.server.streaming; \
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import inference_engine.server.schemas; \
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import inference_engine.memory; \
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import inference_engine.memory.slab; \
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import inference_engine.memory.pool; \
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import inference_engine.scheduler; \
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import inference_engine.scheduler.config; \
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import inference_engine.scheduler.scheduler; \
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import inference_engine.scheduler.session; \
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import inference_engine.pipeline; \
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import inference_engine.pipeline.coordinator; \
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import inference_engine.proposer; \
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import inference_engine.proposer.sparse_logits; \
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import inference_engine.backends.mlx.env; \
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import training.repr_align; \
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print('all imports succeeded')"

‎README.md‎

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# DLM Proposer + AR Verifier — runnable KV-cache-saving framework
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[![CI](https://github.com/FluffyAIcode/Kakeya-LLM-Inference-engine/actions/workflows/ci.yaml/badge.svg?branch=main)](https://github.com/FluffyAIcode/Kakeya-LLM-Inference-engine/actions/workflows/ci.yaml)
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[![Release](https://img.shields.io/badge/release-v0.1.0-blue)](https://github.com/FluffyAIcode/Kakeya-LLM-Inference-engine/releases/tag/v0.1.0)
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[![Platform](https://img.shields.io/badge/platform-Apple%20Silicon-lightgrey)](docs/local-inference-engine.md)
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[![ADRs](https://img.shields.io/badge/ADRs-0001%20%7C%200002-green)](docs/adr/)
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Runs the speculative-decoding architecture designed in the prior product
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discussion using **real, public** weights:
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Configuration is via env vars (all prefixed `KAKEYA_*`): see the
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docstring of [`inference_engine/server/config.py`](inference_engine/server/config.py).
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## Continuous integration
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Every push to `main` and every PR runs the platform-neutral test
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suite on GitHub Actions ([`.github/workflows/ci.yaml`](.github/workflows/ci.yaml)),
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enforcing **100% line coverage** on the shipping library modules:
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```
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inference_engine.server inference_engine.memory inference_engine.scheduler
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inference_engine.pipeline inference_engine.proposer training.repr_align
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```
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Tests that need real Qwen3 weights (`tests/core/`, `tests/system/`,
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`tests/inference_engine/proposer/`) are run locally on hosts with the
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HuggingFace cache populated; backend-specific suites
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(`tests/backends/mlx/test_{verifier,proposer,cache,torch_bridge}.py`)
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run on Apple Silicon contributors' machines via
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`scripts/run_platform_tests.sh --backend mlx`. The CI workflow
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guards the platform-neutral surface so a regression there cannot
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land on `main`.
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## Architecture Decision Records
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Design decisions that the rest of the codebase depends on are recorded

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