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LLM Whisper Models Evaluation #63

LLM Whisper Models Evaluation

LLM Whisper Models Evaluation #63

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: ${{ matrix.python-version }}
- name: Install dependencies
shell: bash
run: |
python -m pip install --upgrade pip
python -m pip install --upgrade wheel
python -m pip install --upgrade pandas
python -m pip install --upgrade datasets
python -m pip install --upgrade evaluate
python -m pip install --upgrade soundfile
python -m pip install --upgrade librosa
python -m pip install --upgrade jiwer
# please uncomment it and comment the "Install IPEX-LLM from Pypi" part for PR tests
- name: Download llm binary
uses: ./.github/actions/llm/download-llm-binary
- name: Run LLM install (all) test
uses: ./.github/actions/llm/setup-llm-env
with:
extra-dependency: "xpu_2.1"
# - name: Install IPEX-LLM from Pypi
# shell: bash
# run: |
# pip install --pre --upgrade ipex-llm[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu
# - name: Test installed xpu version
# shell: bash
# run: |
# source /opt/intel/oneapi/setvars.sh
# bash python/llm/test/run-llm-install-tests.sh
- name: Run whisper evaluation
shell: bash
run: |
source /opt/intel/oneapi/setvars.sh
export USE_XETLA=OFF
export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
echo "MODEL_PATH=${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}}