name: LLM Whisper Models Evaluation # Cancel previous runs in the PR when you push new commits concurrency: group: ${{ github.workflow }}-llm-nightly-test-${{ github.event.pull_request.number || github.run_id }} cancel-in-progress: true permissions: contents: read # Controls when the action will run. on: schedule: - cron: "00 13 * * *" # GMT time, 13:00 GMT == 21:00 China pull_request: branches: [main] paths: - ".github/workflows/llm-whisper-evaluation.yml" # Allows you to run this workflow manually from the Actions tab workflow_dispatch: inputs: model_name: description: 'Model names, separated by comma and must be quoted.' required: true type: string precision: description: 'Precisions, separated by comma and must be quoted.' required: true type: string task: description: 'Tasks, separated by comma and must be quoted.' required: true type: string runs-on: description: 'Labels to filter the runners, separated by comma and must be quoted.' default: "accuracy" required: false type: string # A workflow run is made up of one or more jobs that can run sequentially or in parallel jobs: llm-cpp-build: # please uncomment it for PR tests uses: ./.github/workflows/llm-binary-build.yml # Set the testing matrix based on the event (schedule, PR, or manual dispatch) set-matrix: runs-on: ubuntu-latest outputs: model_name: ${{ steps.set-matrix.outputs.model_name }} precision: ${{ steps.set-matrix.outputs.precision }} task: ${{ steps.set-matrix.outputs.task }} runner: ${{ steps.set-matrix.outputs.runner }} steps: - name: set-env env: MATRIX_MODEL_NAME: '["whisper-tiny", "whisper-small", "whisper-medium", "whisper-base"]' MATRIX_TASK: '["librispeech"]' MATRIX_PRECISION: '["sym_int4", "fp8_e5m2"]' LABELS: '["self-hosted", "llm", "perf"]' run: | echo "model_name=$MATRIX_MODEL_NAME" >> $GITHUB_ENV echo "task=$MATRIX_TASK" >> $GITHUB_ENV echo "precision=$MATRIX_PRECISION" >> $GITHUB_ENV echo "runner=$LABELS" >> $GITHUB_ENV - name: set-matrix id: set-matrix run: | echo "model_name=$model_name" >> $GITHUB_OUTPUT echo "task=$task" >> $GITHUB_OUTPUT echo "precision=$precision" >> $GITHUB_OUTPUT echo "runner=$runner" >> $GITHUB_OUTPUT llm-whisper-evaluation: # if: ${{ github.event.schedule || github.event.inputs.artifact == 'llm-whisper-evaluation' || github.event.inputs.artifact == 'all' }} # please comment it for PR tests needs: [llm-cpp-build, set-matrix] # please uncomment it for PR tests # needs: [set-matrix] # please comment it for PR tests strategy: fail-fast: false matrix: python-version: ["3.9"] model_name: ${{ fromJson(needs.set-matrix.outputs.model_name) }} task: ${{ fromJson(needs.set-matrix.outputs.task) }} precision: ${{ fromJson(needs.set-matrix.outputs.precision) }} device: [xpu] runs-on: ${{ fromJson(needs.set-matrix.outputs.runner) }} env: ANALYTICS_ZOO_ROOT: ${{ github.workspace }} ORIGIN_DIR: /mnt/disk1/models steps: - uses: actions/checkout@f43a0e5ff2bd294095638e18286ca9a3d1956744 # actions/checkout@v3 - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v4 with: python-version: ${{ matrix.python-version }} - name: Install dependencies shell: bash run: | python -m pip install --upgrade pip python -m pip install --upgrade wheel python -m pip install --upgrade pandas python -m pip install --upgrade datasets python -m pip install --upgrade evaluate python -m pip install --upgrade soundfile python -m pip install --upgrade librosa python -m pip install --upgrade jiwer # please uncomment it and comment the "Install BigDL-LLM from Pypi" part for PR tests - name: Download llm binary uses: ./.github/actions/llm/download-llm-binary - name: Run LLM install (all) test uses: ./.github/actions/llm/setup-llm-env with: extra-dependency: "xpu_2.1" # - name: Install BigDL-LLM from Pypi # shell: bash # run: | # pip install --pre --upgrade bigdl-llm[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu # - name: Test installed xpu version # shell: bash # run: | # source /opt/intel/oneapi/setvars.sh # bash python/llm/test/run-llm-install-tests.sh - name: Run whisper evaluation shell: bash run: | source /opt/intel/oneapi/setvars.sh export USE_XETLA=OFF export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1 echo "MODEL_PATH=${ORIGIN_DIR}/${{ matrix.model_name }}/" >> "$GITHUB_ENV" MODEL_PATH=${ORIGIN_DIR}/${{ matrix.model_name }}/ export LIBRISPEECH_DATASET_PATH=/mnt/disk1/datasets/librispeech cd python/llm/dev/benchmark/whisper python run_whisper.py --model_path ${MODEL_PATH} --data_type other --device xpu --load_in_low_bit ${{ matrix.precision }} --save_result - uses: actions/upload-artifact@v3 with: name: whisper_results path: ${{ github.workspace }}/python/llm/dev/benchmark/whisper/results/** llm-whisper-summary: if: ${{github.event_name == 'schedule' || github.event_name == 'pull_request'}} needs: [set-matrix, llm-whisper-evaluation] runs-on: ["self-hosted", "llm", "perf"] steps: - uses: actions/checkout@f43a0e5ff2bd294095638e18286ca9a3d1956744 # actions/checkout@v3 - name: Set up Python 3.9 uses: actions/setup-python@v4 with: python-version: 3.9 - name: Set output path shell: bash run: | DATE=$(date +%Y-%m-%d) OUTPUT_PATH="results_$DATE" echo "OUTPUT_PATH=$OUTPUT_PATH" >> $GITHUB_ENV NIGHTLY_FOLDER="/mnt/disk1/whisper_nightly_gpu" echo "NIGHTLY_FOLDER=$NIGHTLY_FOLDER" >> $GITHUB_ENV PR_FOLDER="/mnt/disk1/whisper_pr_gpu" echo "PR_FOLDER=$PR_FOLDER" >> $GITHUB_ENV - name: Download all results for nightly run if: github.event_name == 'schedule' uses: actions/download-artifact@v3 with: name: whisper_results path: ${{ env.NIGHTLY_FOLDER}}/${{ env.OUTPUT_PATH }} - name: Download all results for pr run if: github.event_name == 'pull_request' uses: actions/download-artifact@v3 with: name: whisper_results path: ${{ env.PR_FOLDER}}/${{ env.OUTPUT_PATH }} - name: Summarize the results for nightly run if: github.event_name == 'schedule' shell: bash run: | cp -r /mnt/disk1/datasets/whisper_fp16_results/* /mnt/disk1/whisper_nightly_gpu/${{ env.OUTPUT_PATH }} pip install pandas==1.5.3 python ${{ github.workspace }}/python/llm/dev/benchmark/whisper/whisper_concat_csv.py -i ${{ env.NIGHTLY_FOLDER}}/${{ env.OUTPUT_PATH }} -o ${{ env.NIGHTLY_FOLDER}} python ${{ github.workspace }}/python/llm/dev/benchmark/whisper/whisper_csv_to_html.py -f ${{ env.NIGHTLY_FOLDER}} - name: Summarize the results for pull request if: github.event_name == 'pull_request' shell: bash run: | cp -r /mnt/disk1/datasets/whisper_fp16_results/* /mnt/disk1/whisper_pr_gpu/${{ env.OUTPUT_PATH }} pip install pandas==1.5.3 python ${{ github.workspace }}/python/llm/dev/benchmark/whisper/whisper_concat_csv.py -i ${{ env.PR_FOLDER}}/${{ env.OUTPUT_PATH }} -o ${{ env.PR_FOLDER}} python ${{ github.workspace }}/python/llm/dev/benchmark/whisper/whisper_csv_to_html.py -f ${{ env.PR_FOLDER}}