Create a workflow to run benchmarks #108
Workflow file for this run
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| name: Benchmarks | |
| on: | |
| pull_request: | |
| branches: | |
| - main | |
| workflow_dispatch: | |
| inputs: | |
| halt-for-connection: | |
| description: 'Should this workflow run wait for a remote connection?' | |
| type: choice | |
| required: true | |
| default: 'no' | |
| options: | |
| - 'yes' | |
| - 'no' | |
| jobs: | |
| build-xla-gpu-and-test: | |
| runs-on: linux-x86-g2-48-l4-4gpu # Use a GPU-enabled runner | |
| container: | |
| image: "gcr.io/tensorflow-testing/nosla-cuda12.3-cudnn9.1-ubuntu20.04-manylinux2014-multipython:latest" | |
| options: --gpus all --privileged # Might need privileged mode, use with caution | |
| steps: | |
| # - name: Checkout XLA | |
| # uses: actions/checkout@v3 | |
| # with: | |
| # repository: openxla/xla # Replace with your fork if needed | |
| # path: xla | |
| - name: Checkout repository | |
| uses: actions/checkout@v3 | |
| with: | |
| repository: juliagmt-google/xla | |
| path: xla | |
| - name: Create results directory | |
| working-directory: xla | |
| run: mkdir -p results | |
| # - name: Get GPU spec | |
| # working-directory: xla | |
| # continue-on-error: true | |
| # run: nvidia-smi | |
| - name: Configure XLA | |
| working-directory: xla | |
| run: ./configure.py --backend CUDA --nccl | |
| - name: Set TF_CPP_MAX_VLOG_LEVEL | |
| working-directory: xla | |
| run: echo "TF_CPP_MAX_VLOG_LEVEL=1" >> $GITHUB_ENV # Use GITHUB_ENV to persist across steps | |
| - name: Check TF_CPP_MAX_VLOG_LEVEL | |
| working-directory: xla | |
| run: echo "$TF_CPP_MAX_VLOG_LEVEL" | |
| - name: Wait For Connection | |
| uses: google-ml-infra/actions/ci_connection@main | |
| with: | |
| halt-dispatch-input: ${{ inputs.halt-for-connection }} | |
| - name: Wait For Connection | |
| uses: google-ml-infra/actions/ci_connection@main | |
| with: | |
| halt-dispatch-input: ${{ inputs.halt-for-connection }} | |
| - name: Build hlo_runner_main | |
| working-directory: xla | |
| run: bazel build -c opt --config=cuda --dynamic_mode=off //xla/tools/multihost_hlo_runner:hlo_runner_main | |
| # - name: Wait For Connection | |
| # uses: google-ml-infra/actions/ci_connection@main | |
| # with: | |
| # halt-dispatch-input: ${{ inputs.halt-for-connection }} | |
| - name: Create gpu_hlo_backend.hlo | |
| working-directory: xla | |
| run: | | |
| cat << EOF > gpu_hlo_backend.hlo | |
| HloModule module | |
| // CHECK: is_scheduled=true | |
| ENTRY computation { | |
| p = f32[5000,6000]{1,0} parameter(0) | |
| e = f32[5000,6000]{1,0} sqrt(p) | |
| c = f32[6000,5000] transpose(p), dimensions={1,0} | |
| r = f32[300,20,5000] reshape(c) | |
| ROOT out = (f32[5000,6000], f32[300,20,5000]) tuple(e,r) | |
| } | |
| EOF | |
| # - name: Wait For Connection | |
| # uses: google-ml-infra/actions/ci_connection@main | |
| # with: | |
| # halt-dispatch-input: ${{ inputs.halt-for-connection }} | |
| - name: Run an HLO file | |
| working-directory: xla | |
| run: | | |
| nvidia-smi --query-gpu=utilization.gpu --format=csv -l 1 > results/gpu_utilization_gpu_backend.log & ./bazel-bin/xla/tools/multihost_hlo_runner/hlo_runner_main --device_type=gpu --log_output=True --use_spmd_partitioning gpu_hlo_backend.hlo | |
| cd results | |
| ls | |
| cat gpu_hlo_backend.hlo.log | |
| - name: Wait For Connection | |
| uses: google-ml-infra/actions/ci_connection@main | |
| with: | |
| halt-dispatch-input: ${{ inputs.halt-for-connection }} | |
| - name: Download parse_xla_logs.py | |
| working-directory: xla | |
| run: wget https://raw.githubusercontent.com/juliagmt-google/xla/main/.github/workflows/parse_xla_logs.py | |
| - name: Parse XLA logs | |
| working-directory: xla | |
| run: python parse_xla_logs.py results/gpu_hlo_backend.hlo.log | |
| - name: Upload Results | |
| uses: actions/upload-artifact@v4 | |
| with: | |
| name: gpu-xla-benchmarks | |
| path: xla/results | |
| # # jax-build-and-test: | |
| # # runs-on: linux-x86-g2-48-l4-4gpu # Use a GPU-enabled runner | |
| # # container: | |
| # # image: "gcr.io/tensorflow-testing/nosla-cuda12.3-cudnn9.1-ubuntu20.04-manylinux2014-multipython:latest" | |
| # # env: | |
| # # JAXCI_HERMETIC_PYTHON_VERSION: 3.11 | |
| # # steps: | |
| # # - name: Checkout JAX Fork | |
| # # uses: actions/checkout@v3 | |
| # # with: | |
| # # repository: 'google-ml-infra/jax-fork' | |
| # # path: jax-fork | |
| # # - name: Install JAX Dependencies | |
| # # working-directory: jax-fork | |
| # # run: | | |
| # # python -m pip install --upgrade pip | |
| # # pip install pytest | |
| # # pip install absl-py | |
| # # pip install "jax[cuda12_pip]" # Adjust CUDA version if needed | |
| # # pip install google-benchmark | |
| # # - name: Run JAX Multiprocess GPU Test | |
| # # working-directory: jax-fork | |
| # # continue-on-error: true | |
| # # run: python -m pytest tests/multiprocess_gpu_test.py | |
| # # - name: Run HLO Module Benchmarks withg GPU in xla/tests/fuzz | |
| # # working-directory: xla | |
| # # continue-on-error: true | |
| # # run: | | |
| # # for file in xla/tests/fuzz/*.hlo; do | |
| # # filename=$(basename "$file") | |
| # # # Skip expected failed hlo files. | |
| # # if [[ "$filename" == "rand_000060.hlo" || "$filename" == "rand_000067.hlo" || "$filename" == "rand_000072.hlo" ]]; then | |
| # # echo "Skipping benchmark on $file" | |
| # # continue | |
| # # fi | |
| # # echo "Running benchmark on $file" &> results/"$filename".log | |
| # # ./bazel-bin/xla/tools/multihost_hlo_runner/hlo_runner_main --device_type=gpu --use_spmd_partitioning "$file" &> results/"$filename".log | |
| # # done | |
| # # - name: Upload Results | |
| # # uses: actions/upload-artifact@v4 | |
| # # with: | |
| # # name: gpu-xla-benchmarks | |
| # # path: xla/results |