Use new DeviceMesh unflatten to rewrite parallel_dims #1808
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| name: 8 GPU Feature Tests | |
| on: | |
| push: | |
| branches: [ main ] | |
| paths-ignore: | |
| - 'torchtitan/experiments/**' | |
| pull_request: | |
| paths-ignore: | |
| - 'torchtitan/experiments/**' | |
| schedule: | |
| # Runs every 6 hours | |
| - cron: '0 */6 * * *' | |
| concurrency: | |
| group: unit-test${{ github.workflow }}-${{ github.ref == 'refs/heads/main' && github.run_number || github.ref }} | |
| cancel-in-progress: true | |
| defaults: | |
| run: | |
| shell: bash -l -eo pipefail {0} | |
| permissions: | |
| id-token: write | |
| contents: read | |
| jobs: | |
| # Step 1: Dynamically compute the matrix based on conditions | |
| set-matrix: | |
| runs-on: ubuntu-latest | |
| outputs: | |
| matrix: ${{ steps.set.outputs.matrix }} | |
| steps: | |
| - id: set | |
| run: | | |
| # Decide which matrix entries to include based on event type | |
| if [[ "${{ github.event_name }}" == "push" && "${{ github.ref }}" == "refs/heads/main" ]] || [[ "${{ github.event_name }}" == "schedule" ]]; then | |
| # Include both CUDA and ROCm | |
| echo '{"include":[ | |
| {"name":"cuda","runner":"linux.g5.48xlarge.nvidia.gpu","gpu-arch-type":"cuda","gpu-arch-version":"12.6","docker-image":"torchtitan-ubuntu-20.04-clang12","index-url":"https://download.pytorch.org/whl/nightly/cu126"}, | |
| {"name":"rocm","runner":"linux.rocm.gpu.gfx942.8","gpu-arch-type":"rocm","gpu-arch-version":"7.0","docker-image":"torchtitan-rocm-ubuntu-22.04-clang12","index-url":"https://download.pytorch.org/whl/nightly/rocm7.0"} | |
| ]}' > matrix.json | |
| else | |
| # Include only CUDA | |
| echo '{"include":[ | |
| {"name":"cuda","runner":"linux.g5.48xlarge.nvidia.gpu","gpu-arch-type":"cuda","gpu-arch-version":"12.6","docker-image":"torchtitan-ubuntu-20.04-clang12","index-url":"https://download.pytorch.org/whl/nightly/cu126"} | |
| ]}' > matrix.json | |
| fi | |
| # Export matrix to job outputs | |
| { | |
| echo 'matrix<<EOF' | |
| cat matrix.json | |
| echo 'EOF' | |
| } >> $GITHUB_OUTPUT | |
| # Step 2: Use the dynamic matrix in the build-test job | |
| build-test: | |
| needs: set-matrix | |
| uses: pytorch/test-infra/.github/workflows/linux_job_v2.yml@main | |
| strategy: | |
| fail-fast: false | |
| matrix: ${{ fromJSON(needs.set-matrix.outputs.matrix) }} | |
| with: | |
| runner: ${{ matrix.runner }} | |
| gpu-arch-type: ${{ matrix.gpu-arch-type }} | |
| gpu-arch-version: ${{ matrix.gpu-arch-version }} | |
| docker-image: ${{ matrix.docker-image }} | |
| repository: pytorch/torchtitan | |
| upload-artifact: outputs | |
| timeout: 45 | |
| script: | | |
| set -eux | |
| # The generic Linux job chooses to use base env, not the one setup by the image | |
| CONDA_ENV=$(conda env list --json | jq -r ".envs | .[-1]") | |
| conda activate "${CONDA_ENV}" | |
| # Log CUDA driver version for debugging. | |
| DRIVER_VERSION=$(nvidia-smi --query-gpu=driver_version --format=csv,noheader | head -n 1 || true) | |
| echo "CUDA driver version: ${DRIVER_VERSION}" | |
| pip config --user set global.progress_bar off | |
| python -m pip install --force-reinstall --pre torch --index-url ${{ matrix.index-url }} | |
| USE_CPP=0 python -m pip install --pre torchao --index-url ${{ matrix.index-url }} | |
| sudo mkdir -p "$RUNNER_TEMP/artifacts-to-be-uploaded" | |
| sudo chown -R $(id -u):$(id -g) "$RUNNER_TEMP/artifacts-to-be-uploaded" | |
| # Verify the accuracy first. | |
| echo "Checking FSDP8 v.s. HSDP (4, 2) accuracy parity" | |
| export baseline_options="--parallelism.data_parallel_replicate_degree=1" | |
| export test_options="--parallelism.data_parallel_replicate_degree=4" | |
| python3 scripts/loss_compare.py . . --baseline-options="${baseline_options}" --test-options="${test_options}" --job-dump-folder="${RUNNER_TEMP}/artifacts-to-be-uploaded/accuracy_comparison_outputs" --assert-equal --steps=10 --import-result tests/assets/losses/llama3.txt | |
| rm -rf $RUNNER_TEMP/artifacts-to-be-uploaded/* | |
| python -m tests.integration_tests.run_tests --gpu_arch_type ${{ matrix.gpu-arch-type }} --test_suite features $RUNNER_TEMP/artifacts-to-be-uploaded --ngpu 8 | |
| # Cleanup the checkpoints so that we don't waste network bandwidth and time. | |
| rm -rf $RUNNER_TEMP/artifacts-to-be-uploaded/*/checkpoint | |
| rm -rf artifacts-to-be-uploaded/*/checkpoint |