TorchBench V3 nightly (A100) #232
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| name: TorchBench V3 nightly (A100) | |
| on: | |
| workflow_dispatch: | |
| schedule: | |
| - cron: '0 16 * * *' # run at 4 PM UTC | |
| jobs: | |
| run-benchmark: | |
| environment: docker-s3-upload | |
| env: | |
| BASE_CONDA_ENV: "torchbench" | |
| CONDA_ENV: "torchbench-v3-nightly" | |
| PLATFORM_NAME: "gcp_a100" | |
| SETUP_SCRIPT: "/workspace/setup_instance.sh" | |
| TORCHBENCH_USERBENCHMARK_SCRIBE_GRAPHQL_ACCESS_TOKEN: ${{ secrets.TORCHBENCH_USERBENCHMARK_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 | |
| BUILD_ENVIRONMENT: benchmark-nightly | |
| if: ${{ github.repository_owner == 'pytorch' }} | |
| runs-on: [self-hosted, a100-runner] | |
| steps: | |
| - name: Checkout TorchBench v3.0 branch | |
| uses: actions/checkout@v3 | |
| with: | |
| ref: v3.0 | |
| path: benchmark | |
| - name: Tune Nvidia GPU | |
| run: | | |
| sudo nvidia-smi -pm 1 | |
| sudo nvidia-smi -ac 1215,1410 | |
| nvidia-smi | |
| - name: Check PyTorch nightly if scheduled | |
| if: ${{ github.event_name == 'schedule' }} | |
| run: | | |
| CONDA_ENV=${BASE_CONDA_ENV} . "${SETUP_SCRIPT}" | |
| pushd benchmark | |
| TODAY_STR=$(date +'%Y%m%d') | |
| python utils/cuda_utils.py --check-torch-nightly-version --force-date ${TODAY_STR} | |
| - name: Clone and setup conda env | |
| run: | | |
| CONDA_ENV=${BASE_CONDA_ENV} . "${SETUP_SCRIPT}" | |
| conda create --name "${CONDA_ENV}" --clone "${BASE_CONDA_ENV}" | |
| - name: Install TorchBench | |
| run: | | |
| set -x | |
| . "${SETUP_SCRIPT}" | |
| pushd benchmark | |
| python install.py | |
| - name: Run the torch-nightly userbenchmark | |
| run: | | |
| . "${SETUP_SCRIPT}" | |
| # remove old results | |
| if [ -d benchmark-output ]; then rm -Rf benchmark-output; fi | |
| pushd benchmark | |
| if [ -d .userbenchmark ]; then rm -Rf .userbenchmark; fi | |
| python run_benchmark.py torch-nightly -c v3-cuda-tests.yaml | |
| cp -r ./.userbenchmark/torch-nightly ../benchmark-output | |
| - name: Detect potential regressions | |
| continue-on-error: true | |
| run: | | |
| . "${SETUP_SCRIPT}" | |
| pushd benchmark | |
| RESULTS=($(find ${PWD}/../benchmark-output -name "metrics-*.json" -maxdepth 2 | sort -r)) | |
| # TODO: the following assumes only one metrics-*.json is found. It will keep | |
| # overwriting gh-issue.md if multiple are found. | |
| for r in ${RESULTS[@]}; do | |
| python regression_detector.py --platform "${PLATFORM_NAME}" --treatment "${r}" --owner @xuzhao9 \ | |
| --gh-issue-path gh-issue.md --errors-path errors.txt | |
| done | |
| rm -r ../benchmark-output || true | |
| cp -r ./.userbenchmark/torch-nightly ../benchmark-output | |
| - name: Create the github issue | |
| continue-on-error: true | |
| if: env.TORCHBENCH_REGRESSION_DETECTED | |
| uses: peter-evans/create-issue-from-file@v4 | |
| with: | |
| title: V3 Performance Signal Detected by TorchBench Userbenchmark "torch-nightly" on ${{ env.TORCHBENCH_REGRESSION_DETECTED }} | |
| content-filepath: ./benchmark/gh-issue.md | |
| labels: | | |
| torchbench-perf-report | |
| - name: Copy artifact and upload to scribe and Amazon S3 | |
| run: | | |
| . "${SETUP_SCRIPT}" | |
| pushd benchmark | |
| LATEST_RESULT=$(find ../benchmark-output/ -name "metrics-*.json" | sort -r | head -1) | |
| echo "Benchmark result file: ${LATEST_RESULT}" | |
| # Upload the result json to Scribe | |
| python ./scripts/userbenchmark/upload_scribe.py --userbenchmark_json "${LATEST_RESULT}" --userbenchmark_platform "${PLATFORM_NAME}" | |
| # Upload the result json to Amazon S3 | |
| python ./scripts/userbenchmark/upload_s3.py --upload-file "${LATEST_RESULT}" --userbenchmark_platform "${PLATFORM_NAME}" | |
| - name: Copy regression results to Amazon S3 | |
| if: env.TORCHBENCH_REGRESSION_DETECTED | |
| run: | | |
| . "${SETUP_SCRIPT}" | |
| pushd benchmark | |
| LATEST_REGRESSION_RESULT=$(find ../benchmark-output/ -name "regression-*.yaml" | sort -r | head -1) | |
| # Upload the regression json to Amazon S3 | |
| python ./scripts/userbenchmark/upload_s3.py --upload-file "${LATEST_REGRESSION_RESULT}" --userbenchmark_platform "${PLATFORM_NAME}" | |
| - name: Upload result to GH Actions Artifact | |
| uses: actions/upload-artifact@v3 | |
| with: | |
| name: TorchBench V3 result | |
| path: benchmark-output/ | |
| - name: Clean up Conda env | |
| if: always() | |
| run: | | |
| . "${SETUP_SCRIPT}" | |
| conda deactivate && conda deactivate | |
| conda remove -n "${CONDA_ENV}" --all |