8 GPU Feature Tests #66
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| name: 8 GPU Feature Tests | |
| on: | |
| push: | |
| branches: [ main ] | |
| tags: | |
| - ciflow/8gpu/* | |
| paths-ignore: | |
| - 'torchtitan/experiments/**' | |
| pull_request: | |
| types: [opened, synchronize, reopened, ready_for_review] | |
| 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: | |
| uses: ./.github/workflows/set-matrix.yaml | |
| # 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: ${DRIVER_VERSION}" | |
| pip config --user set global.progress_bar off | |
| start=$(date +%s) | |
| TORCH_SPEC="torch" | |
| if [ -n "${{ matrix.torch-version }}" ]; then | |
| TORCH_SPEC="torch==${{ matrix.torch-version }}" | |
| fi | |
| python -m pip install --force-reinstall --pre \ | |
| "${TORCH_SPEC}" --index-url ${{ matrix.index-url }} | |
| if [[ "${{ matrix.gpu-arch-type }}" == "cuda" ]]; then | |
| python -m pip install --pre torchcomms --index-url ${{ matrix.index-url }} | |
| fi | |
| end=$(date +%s) | |
| echo "pip install torch took $((end - start)) seconds" | |
| if [[ "${{ matrix.gpu-arch-type }}" == "rocm" ]]; then | |
| export HIPBLASLT_TENSILE_LIBPATH="$(python -c 'import os, torch; print(os.path.join(os.path.dirname(torch.__file__), "lib", "hipblaslt", "library"))')" | |
| echo "HIPBLASLT_TENSILE_LIBPATH=${HIPBLASLT_TENSILE_LIBPATH}" | |
| fi | |
| start=$(date +%s) | |
| USE_CPP=0 python -m pip install --pre torchao --index-url ${{ matrix.index-url }} | |
| python -m pip install torchdata==0.12.0.dev20260327 --extra-index-url https://download.pytorch.org/whl/nightly/cpu | |
| end=$(date +%s) | |
| echo "pip install torchao took $((end - start)) seconds" | |
| sudo mkdir -p "$RUNNER_TEMP/artifacts-to-be-uploaded" | |
| sudo chown -R $(id -u):$(id -g) "$RUNNER_TEMP/artifacts-to-be-uploaded" | |
| sudo mkdir -p "comm_traces" | |
| sudo chown -R $(id -u):$(id -g) "comm_traces" | |
| # 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" | |
| # Set architecture-specific parameters | |
| if [[ "${{ matrix.gpu-arch-type }}" == "cuda" ]]; then | |
| LOSS_FILE="tests/assets/losses/llama3_cuda.txt" | |
| STEPS=1 | |
| elif [[ "${{ matrix.gpu-arch-type }}" == "rocm" ]]; then | |
| # The loss results of FSDP and HSDP start to diverge after 5th | |
| # step when running with ROCm, we also need to adjust this. | |
| # But this is more an unknown issue that AMD people may want to | |
| # figure out the root cause or confirm that this is an expected | |
| # behavior. | |
| LOSS_FILE="tests/assets/losses/llama3_rocm.txt" | |
| STEPS=1 | |
| else | |
| echo "Error: Unknown GPU architecture type: ${{ matrix.gpu-arch-type }}" | |
| exit 1 | |
| fi | |
| python3 scripts/loss_compare.py . . --baseline-options="${baseline_options}" --job-dump-folder="${RUNNER_TEMP}/artifacts-to-be-uploaded/accuracy_comparison_outputs" --export-result="${RUNNER_TEMP}/artifacts-to-be-uploaded/accuracy_comparison_outputs/result.txt" --steps=100 | |
| echo "Checking FSDP8 the first tep loss is the same as FSDP2HSDP4" | |
| 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=1 | |
| rm -rf $RUNNER_TEMP/artifacts-to-be-uploaded/* | |
| echo "Checking FSDP8 loss from a new run v.s. FSDP8 loss from text file parity" | |
| python3 scripts/loss_compare.py . . --baseline-options="${baseline_options}" --job-dump-folder="${RUNNER_TEMP}/artifacts-to-be-uploaded/accuracy_comparison_outputs" --import-result="${LOSS_FILE}" --assert-equal --steps=100 | |
| 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 |