feat: experimental python packaging and interface #6
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| # based on https://github.com/Dao-AILab/flash-attention/blob/main/.github/workflows/publish.yml | |
| name: Build wheels and deploy | |
| on: | |
| push: | |
| branches: | |
| - main | |
| tags: | |
| - "*" | |
| pull_request: | |
| workflow_dispatch: | |
| jobs: | |
| build_wheels: | |
| name: Build Wheel | |
| runs-on: ${{ matrix.os }} | |
| strategy: | |
| fail-fast: false | |
| matrix: | |
| # Using ubuntu-20.04 instead of 22.04 for more compatibility (glibc). Ideally we'd use the | |
| # manylinux docker image, but I haven't figured out how to install CUDA on manylinux. | |
| os: [ubuntu-20.04] | |
| python-version: [ | |
| # "3.7", "3.8", "3.9", "3.10", | |
| "3.11", | |
| ] | |
| torch-version: [ | |
| # "1.12.1", "1.13.1", "2.0.1", "2.1.2", "2.2.2", | |
| "2.3.0", | |
| ] | |
| cuda-version: [ | |
| # "11.8.0", | |
| "12.2.2", | |
| ] | |
| # We need separate wheels that either uses C++11 ABI (-D_GLIBCXX_USE_CXX11_ABI) or not. | |
| # Pytorch wheels currently don't use it, but nvcr images have Pytorch compiled with C++11 ABI. | |
| # Without this we get import error (undefined symbol: _ZN3c105ErrorC2ENS_14SourceLocationESs) | |
| # when building without C++11 ABI and using it on nvcr images. | |
| cxx11_abi: [ | |
| # "FALSE", | |
| "TRUE", | |
| ] | |
| exclude: | |
| # see https://github.com/pytorch/pytorch/blob/main/RELEASE.md#release-compatibility-matrix | |
| # Pytorch <= 1.12 does not support Python 3.11 | |
| - torch-version: "1.12.1" | |
| python-version: "3.11" | |
| # Pytorch >= 2.0 only supports Python >= 3.8 | |
| - torch-version: "2.0.1" | |
| python-version: "3.7" | |
| - torch-version: "2.1.2" | |
| python-version: "3.7" | |
| - torch-version: "2.2.2" | |
| python-version: "3.7" | |
| - torch-version: "2.3.0" | |
| python-version: "3.7" | |
| # Pytorch <= 2.0 only supports CUDA <= 11.8 | |
| - torch-version: "1.12.1" | |
| cuda-version: "12.2.2" | |
| - torch-version: "1.13.1" | |
| cuda-version: "12.2.2" | |
| - torch-version: "2.0.1" | |
| cuda-version: "12.2.2" | |
| steps: | |
| - name: Checkout | |
| uses: actions/checkout@v3 | |
| - name: Set up Python | |
| uses: actions/setup-python@v4 | |
| with: | |
| python-version: ${{ matrix.python-version }} | |
| - name: Set CUDA and PyTorch versions | |
| run: | | |
| echo "MATRIX_CUDA_VERSION=$(echo ${{ matrix.cuda-version }} | awk -F \. {'print $1 $2'})" >> $GITHUB_ENV | |
| echo "MATRIX_TORCH_VERSION=$(echo ${{ matrix.torch-version }} | awk -F \. {'print $1 "." $2'})" >> $GITHUB_ENV | |
| echo "MATRIX_PYTHON_VERSION=$(echo ${{ matrix.python-version }} | awk -F \. {'print $1 $2'})" >> $GITHUB_ENV | |
| - name: Free up disk space | |
| if: ${{ runner.os == 'Linux' }} | |
| # https://github.com/easimon/maximize-build-space/blob/master/action.yml | |
| # https://github.com/easimon/maximize-build-space/tree/test-report | |
| run: | | |
| sudo rm -rf /usr/share/dotnet | |
| sudo rm -rf /opt/ghc | |
| sudo rm -rf /opt/hostedtoolcache/CodeQL | |
| - name: Set up swap space | |
| if: runner.os == 'Linux' | |
| uses: pierotofy/set-swap-space@v1.0 | |
| with: | |
| swap-size-gb: 10 | |
| - name: Install CUDA ${{ matrix.cuda-version }} | |
| if: ${{ matrix.cuda-version != 'cpu' }} | |
| uses: Jimver/cuda-toolkit@v0.2.14 | |
| id: cuda-toolkit | |
| with: | |
| cuda: ${{ matrix.cuda-version }} | |
| linux-local-args: '["--toolkit"]' | |
| # default method is "local", and we're hitting some error with caching for CUDA 11.8 and 12.1 | |
| # method: ${{ (matrix.cuda-version == '11.8.0' || matrix.cuda-version == '12.1.0') && 'network' || 'local' }} | |
| method: "network" | |
| # We need the cuda libraries (e.g. cuSparse, cuSolver) for compiling PyTorch extensions, | |
| # not just nvcc | |
| # sub-packages: '["nvcc"]' | |
| - name: Install PyTorch ${{ matrix.torch-version }}+cu${{ matrix.cuda-version }} | |
| run: | | |
| pip install --upgrade pip | |
| # If we don't install before installing Pytorch, we get error for torch 2.0.1 | |
| # ERROR: Could not find a version that satisfies the requirement setuptools>=40.8.0 (from versions: none) | |
| pip install lit | |
| # We want to figure out the CUDA version to download pytorch | |
| # e.g. we can have system CUDA version being 11.7 but if torch==1.12 then we need to download the wheel from cu116 | |
| # see https://github.com/pytorch/pytorch/blob/main/RELEASE.md#release-compatibility-matrix | |
| # This code is ugly, maybe there's a better way to do this. | |
| export TORCH_CUDA_VERSION=$(python -c "from os import environ as env; \ | |
| minv = {'1.12': 113, '1.13': 116, '2.0': 117, '2.1': 118, '2.2': 118, '2.3': 118}[env['MATRIX_TORCH_VERSION']]; \ | |
| maxv = {'1.12': 116, '1.13': 117, '2.0': 118, '2.1': 121, '2.2': 121, '2.3': 121}[env['MATRIX_TORCH_VERSION']]; \ | |
| print(max(min(int(env['MATRIX_CUDA_VERSION']), maxv), minv))" \ | |
| ) | |
| if [[ ${{ matrix.torch-version }} == *"dev"* ]]; then | |
| if [[ ${MATRIX_TORCH_VERSION} == "2.2" ]]; then | |
| # --no-deps because we can't install old versions of pytorch-triton | |
| pip install typing-extensions jinja2 | |
| pip install --no-cache-dir --no-deps --pre https://download.pytorch.org/whl/nightly/cu${TORCH_CUDA_VERSION}/torch-${{ matrix.torch-version }}%2Bcu${TORCH_CUDA_VERSION}-cp${MATRIX_PYTHON_VERSION}-cp${MATRIX_PYTHON_VERSION}-linux_x86_64.whl | |
| else | |
| pip install --no-cache-dir --pre torch==${{ matrix.torch-version }} --index-url https://download.pytorch.org/whl/nightly/cu${TORCH_CUDA_VERSION} | |
| fi | |
| else | |
| pip install --no-cache-dir torch==${{ matrix.torch-version }} --index-url https://download.pytorch.org/whl/cu${TORCH_CUDA_VERSION} | |
| fi | |
| nvcc --version | |
| python --version | |
| python -c "import torch; print('PyTorch:', torch.__version__)" | |
| python -c "import torch; print('CUDA:', torch.version.cuda)" | |
| python -c "from torch.utils import cpp_extension; print (cpp_extension.CUDA_HOME)" | |
| shell: bash | |
| - name: Build wheel | |
| run: | | |
| # Ensure we have the correct version of setuptools to avoid compatibility issues with CUDA | |
| pip install setuptools==68.0.0 | |
| pip install ninja packaging wheel | |
| # Set up the environment for CUDA | |
| export PATH=/usr/local/nvidia/bin:/usr/local/nvidia/lib64:$PATH | |
| export LD_LIBRARY_PATH=/usr/local/nvidia/lib64:/usr/local/cuda/lib64:$LD_LIBRARY_PATH | |
| # Clone the repository at the specific commit | |
| git clone https://github.com/HazyResearch/flash-attention.git | |
| cd flash-attention | |
| git checkout 3a9bfd076f98746c73362328958dbc68d145fbec | |
| # Build the wheel with limited jobs to prevent OOM issues on the GitHub runner | |
| MAX_JOBS=2 FLASH_ATTENTION_FORCE_BUILD="TRUE" FLASH_ATTENTION_FORCE_CXX11_ABI=${{ matrix.cxx11_abi }} python setup.py bdist_wheel --dist-dir=dist | |
| # Generate a custom name for the wheel to include CUDA and Torch versions | |
| tmpname=cu${MATRIX_CUDA_VERSION}torch${MATRIX_TORCH_VERSION}cxx11abi${{ matrix.cxx11_abi }} | |
| wheel_name=$(ls dist/*whl | xargs -n 1 basename | sed "s/-/+$tmpname-/2") | |
| # Rename the wheel with the custom name | |
| ls dist/*whl |xargs -I {} mv {} dist/${wheel_name} | |
| # Save the wheel name to the GitHub environment | |
| echo "wheel_name=${wheel_name}" >> $GITHUB_ENV | |
| - name: Log Built Wheels | |
| run: | | |
| ls flash-attention/dist | |
| # - name: Get the tag version | |
| # id: extract_branch | |
| # run: echo ::set-output name=branch::${GITHUB_REF#refs/tags/} | |
| # - name: Get Release with tag | |
| # id: get_current_release | |
| # uses: joutvhu/get-release@v1 | |
| # with: | |
| # tag_name: ${{ steps.extract_branch.outputs.branch }} | |
| # env: | |
| # GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} | |
| # - name: Upload Release Asset | |
| # id: upload_release_asset | |
| # uses: actions/upload-release-asset@v1 | |
| # env: | |
| # GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} | |
| # with: | |
| # upload_url: ${{ steps.get_current_release.outputs.upload_url }} | |
| # asset_path: ./dist/${{env.wheel_name}} | |
| # asset_name: ${{env.wheel_name}} | |
| # asset_content_type: application/* |