TorchBench V2 nightly #11
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| name: TorchBench V2 nightly | |
| on: | |
| workflow_dispatch: | |
| schedule: | |
| - cron: '0 14 * * *' # run at 2 PM UTC | |
| jobs: | |
| run-benchmark: | |
| environment: docker-s3-upload | |
| env: | |
| TORCHBENCH_VER: "v2" | |
| CONFIG_VER: "v2" | |
| PYTHON_VER: "3.10" | |
| CUDA_VER: "12.1" | |
| MAGMA_VERSION: "magma-cuda121" | |
| CONDA_ENV_NAME: "torchbench-v2-nightly-ci" | |
| OUTPUT_DIR: ".torchbench/v2-nightly-ci" | |
| BISECTION_ROOT: ".torchbench/v2-bisection-ci" | |
| CUDA_VERSION: "cu121" | |
| SCRIBE_GRAPHQL_ACCESS_TOKEN: ${{ secrets.SCRIBE_GRAPHQL_ACCESS_TOKEN }} | |
| AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }} | |
| AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }} | |
| IS_GHA: 1 | |
| AWS_DEFAULT_REGION: us-east-1 | |
| BUILD_ENVIRONMENT: benchmark-nightly | |
| if: ${{ github.repository_owner == 'pytorch' }} | |
| runs-on: [self-hosted, bm-runner] | |
| steps: | |
| - name: Checkout | |
| uses: actions/checkout@v3 | |
| with: | |
| ref: v2.0 | |
| - name: Create conda env | |
| run: | | |
| conda create -y -q --name "${CONDA_ENV_NAME}" python=${{ env.PYTHON_VER }} | |
| - name: Install PyTorch nightly | |
| run: | | |
| . activate "${CONDA_ENV_NAME}" | |
| . /data/nvme/bin/setup_instance.sh | |
| # Install dependencies | |
| pip install requests bs4 argparse gitpython boto3 regex | |
| # Check if nightly builds are available | |
| NIGHTLIES=$(python torchbenchmark/util/torch_nightly.py --packages torch) | |
| # If failed, the script will generate empty result | |
| if [ -z $NIGHTLIES ]; then | |
| echo "Torch nightly build failed. Cancel the workflow." | |
| exit 1 | |
| fi | |
| # Install magma | |
| conda install -y -c pytorch "${MAGMA_VERSION}" | |
| # Install PyTorch nightly from pip | |
| pip install --no-cache-dir --pre torch torchvision torchaudio --index-url \ | |
| https://download.pytorch.org/whl/nightly/${CUDA_VERSION} | |
| - name: Install other TorchBench dependencies | |
| run: | | |
| . activate "${CONDA_ENV_NAME}" | |
| . /data/nvme/bin/setup_instance.sh | |
| conda install -y git-lfs | |
| python install.py | |
| - name: Run benchmark | |
| run: | | |
| . activate "${CONDA_ENV_NAME}" | |
| . /data/nvme/bin/setup_instance.sh | |
| WORKFLOW_HOME="${HOME}/${{ env.OUTPUT_DIR }}/gh${GITHUB_RUN_ID}" | |
| bash ./.github/scripts/run.sh "${WORKFLOW_HOME}" | |
| - name: Generate the bisection config | |
| run: | | |
| set -x | |
| . activate "${CONDA_ENV_NAME}" | |
| WORKFLOW_HOME="${HOME}/${{ env.OUTPUT_DIR }}/gh${GITHUB_RUN_ID}" | |
| mkdir -p benchmark-output/ | |
| # Update the self-hosted pytorch version | |
| pushd "${HOME}/pytorch" | |
| git fetch origin | |
| popd | |
| pip install gitpython pyyaml dataclasses argparse | |
| # Compare the result from yesterday and report any perf signals | |
| python ./.github/scripts/generate-abtest-config.py \ | |
| --pytorch-dir "${HOME}/pytorch" \ | |
| --github-issue "${WORKFLOW_HOME}/gh-issue.md" \ | |
| --benchmark-dir "${WORKFLOW_HOME}" \ | |
| --out "${WORKFLOW_HOME}/bisection.yaml" | |
| # Include in the GitHub artifact | |
| if [ -f "${WORKFLOW_HOME}/gh-issue.md" ]; then | |
| cp "${WORKFLOW_HOME}/bisection.yaml" ./benchmark-output/ | |
| cp "${WORKFLOW_HOME}/gh-issue.md" ./benchmark-output/ | |
| # Setup the bisection environment | |
| BISECTION_HOME="${HOME}/${{ env.BISECTION_ROOT }}/bisection-gh${GITHUB_RUN_ID}" | |
| mkdir -p "${BISECTION_HOME}" | |
| mv ./benchmark-output/gh-issue.md "${BISECTION_HOME}/gh-issue.md" | |
| cp ./benchmark-output/bisection.yaml "${BISECTION_HOME}/config.yaml" | |
| fi | |
| - name: Dispatch the bisection workflow | |
| if: env.TORCHBENCH_PERF_SIGNAL | |
| run: | | |
| # Get the workflow ID from | |
| # https://api.github.com/repos/pytorch/benchmark/actions/workflows | |
| curl -u xuzhao9:${{ secrets.TORCHBENCH_ACCESS_TOKEN }} \ | |
| -X POST \ | |
| -H "Accept: application/vnd.github.v3+json" \ | |
| https://api.github.com/repos/pytorch/benchmark/actions/workflows/16176850/dispatches \ | |
| -d '{"ref": "main", "inputs": {"issue_name": "bisection-gh'"${GITHUB_RUN_ID}"'" } }' | |
| - name: Copy artifact and upload to scribe | |
| run: | | |
| . activate "${CONDA_ENV_NAME}" | |
| TODAY=$(date "+%Y%m%d%H%M%S") | |
| LATEST_RESULT=$(find ${HOME}/${{ env.OUTPUT_DIR }}/gh${GITHUB_RUN_ID} -name "*.json" | sort -r | head -1) | |
| echo "Benchmark result file: ${LATEST_RESULT}" | |
| mkdir -p benchmark-output/ | |
| cp "${LATEST_RESULT}" ./benchmark-output/benchmark-result-${CONFIG_VER}-${TODAY}.json | |
| # Load environment variables | |
| CONFIG_DIR=torchbenchmark/score/configs/${CONFIG_VER} | |
| CONFIG_ENV=${CONFIG_DIR}/config-${CONFIG_VER}.env | |
| # Load environment variables | |
| set -a; source "${CONFIG_ENV}"; set +a | |
| SCORE_FILE="./benchmark-result-${CONFIG_VER}-score-${TODAY}.json" | |
| # Generate score file | |
| python compute_score.py --score_version "${CONFIG_VER}" --benchmark_data_file "${LATEST_RESULT}" --output-json "${SCORE_FILE}" | |
| # Upload result to Scribe | |
| python scripts/upload_scribe_${CONFIG_VER}.py --pytest_bench_json "${LATEST_RESULT}" --torchbench_score_file "${SCORE_FILE}" | |
| - name: Upload artifact | |
| uses: actions/upload-artifact@v3 | |
| with: | |
| name: Benchmark result | |
| path: benchmark-output/ | |
| - name: Destroy conda env | |
| run: | | |
| conda env remove --name "${CONDA_ENV_NAME}" |