f315c7f93a
* move harness nightly files to test folder * change workflow file path accordingly * use arc01 when pr * fix path * fix fp16 csv path
317 lines
No EOL
12 KiB
YAML
317 lines
No EOL
12 KiB
YAML
name: LLM Harness Evaluation
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# Cancel previous runs in the PR when you push new commits
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concurrency:
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group: ${{ github.workflow }}-llm-nightly-test-${{ github.event.pull_request.number || github.run_id }}
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cancel-in-progress: true
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permissions:
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contents: read
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# Controls when the action will run.
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on:
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schedule:
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- cron: "30 12 * * *" # GMT time, 12:30 GMT == 20:30 China
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pull_request:
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branches: [main]
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paths:
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- ".github/workflows/llm-harness-evaluation.yml"
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# Allows you to run this workflow manually from the Actions tab
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workflow_dispatch:
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inputs:
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model_name:
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description: 'Model names, separated by comma and must be quoted.'
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required: true
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type: string
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precision:
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description: 'Precisions, separated by comma and must be quoted.'
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required: true
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type: string
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task:
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description: 'Tasks, separated by comma and must be quoted.'
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required: true
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type: string
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runs-on:
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description: 'Labels to filter the runners, separated by comma and must be quoted.'
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default: "accuracy"
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required: false
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type: string
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# A workflow run is made up of one or more jobs that can run sequentially or in parallel
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jobs:
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llm-cpp-build:
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uses: ./.github/workflows/llm-binary-build.yml
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# Set the testing matrix based on the event (schedule, PR, or manual dispatch)
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set-matrix:
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runs-on: ubuntu-latest
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outputs:
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model_name: ${{ steps.set-matrix.outputs.model_name }}
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precision: ${{ steps.set-matrix.outputs.precision }}
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task: ${{ steps.set-matrix.outputs.task }}
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runner: ${{ steps.set-matrix.outputs.runner }}
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steps:
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- name: set-nightly-env
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if: ${{github.event_name == 'schedule'}}
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env:
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NIGHTLY_MATRIX_MODEL_NAME: '["Llama2-7b-guanaco-dolphin-500", "falcon-7b-instruct-with-patch",
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"Mistral-7B-v0.1", "mpt-7b-chat", "Baichuan2-7B-Chat-LLaMAfied"]'
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NIGHTLY_MATRIX_TASK: '["arc", "truthfulqa", "winogrande"]'
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NIGHTLY_MATRIX_PRECISION: '["sym_int4", "fp8"]'
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NIGHTLY_LABELS: '["self-hosted", "llm", "accuracy-nightly"]'
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run: |
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echo "model_name=$NIGHTLY_MATRIX_MODEL_NAME" >> $GITHUB_ENV
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echo "precision=$NIGHTLY_MATRIX_PRECISION" >> $GITHUB_ENV
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echo "task=$NIGHTLY_MATRIX_TASK" >> $GITHUB_ENV
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echo "runner=$NIGHTLY_LABELS" >> $GITHUB_ENV
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- name: set-pr-env
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if: ${{github.event_name == 'pull_request'}}
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env:
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PR_MATRIX_MODEL_NAME: '["Mistral-7B-v0.1"]'
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PR_MATRIX_TASK: '["arc", "truthfulqa", "winogrande"]'
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PR_MATRIX_PRECISION: '["fp8"]'
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PR_LABELS: '["self-hosted", "llm", "temp-arc01"]'
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run: |
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echo "model_name=$PR_MATRIX_MODEL_NAME" >> $GITHUB_ENV
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echo "precision=$PR_MATRIX_PRECISION" >> $GITHUB_ENV
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echo "task=$PR_MATRIX_TASK" >> $GITHUB_ENV
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echo "runner=$PR_LABELS" >> $GITHUB_ENV
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- name: set-manual-env
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if: ${{github.event_name == 'workflow_dispatch'}}
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env:
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MANUAL_MATRIX_MODEL_NAME: ${{format('[ {0} ]', inputs.model_name)}}
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MANUAL_MATRIX_TASK: ${{format('[ {0} ]', inputs.task)}}
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MANUAL_MATRIX_PRECISION: ${{format('[ {0} ]', inputs.precision)}}
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MANUAL_LABELS: ${{format('["self-hosted", "llm", {0}]', inputs.runs-on)}}
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run: |
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echo "model_name=$MANUAL_MATRIX_MODEL_NAME" >> $GITHUB_ENV
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echo "precision=$MANUAL_MATRIX_PRECISION" >> $GITHUB_ENV
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echo "task=$MANUAL_MATRIX_TASK" >> $GITHUB_ENV
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echo "runner=$MANUAL_LABELS" >> $GITHUB_ENV
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- name: set-matrix
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id: set-matrix
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run: |
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echo "model_name=$model_name" >> $GITHUB_OUTPUT
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echo "precision=$precision" >> $GITHUB_OUTPUT
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echo "task=$task" >> $GITHUB_OUTPUT
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echo "runner=$runner" >> $GITHUB_OUTPUT
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llm-harness-evaluation:
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timeout-minutes: 1000
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needs: [llm-cpp-build, set-matrix]
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strategy:
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fail-fast: false
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matrix:
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python-version: ["3.9"]
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model_name: ${{ fromJson(needs.set-matrix.outputs.model_name) }}
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task: ${{ fromJson(needs.set-matrix.outputs.task) }}
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precision: ${{ fromJson(needs.set-matrix.outputs.precision) }}
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device: [xpu]
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runs-on: ${{ fromJson(needs.set-matrix.outputs.runner) }}
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outputs:
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output_path: ${{ steps.run_harness.outputs.output_path }}
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env:
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ANALYTICS_ZOO_ROOT: ${{ github.workspace }}
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ORIGIN_DIR: /mnt/disk1/models
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HARNESS_HF_HOME: /mnt/disk1/harness_home
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steps:
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- uses: actions/checkout@f43a0e5ff2bd294095638e18286ca9a3d1956744 # actions/checkout@v3
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- name: Set up Python ${{ matrix.python-version }}
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uses: actions/setup-python@v4
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with:
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python-version: ${{ matrix.python-version }}
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- name: Install dependencies
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shell: bash
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run: |
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set -e
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python -m pip install --upgrade pip
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python -m pip install --upgrade setuptools==58.0.4
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python -m pip install --upgrade wheel
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- name: Download llm binary
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uses: ./.github/actions/llm/download-llm-binary
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- name: Run LLM install (all) test
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uses: ./.github/actions/llm/setup-llm-env
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with:
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extra-dependency: "xpu_2.1"
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- name: Install harness
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working-directory: ${{ github.workspace }}/python/llm/dev/benchmark/harness/
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shell: bash
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run: |
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pip install git+https://github.com/EleutherAI/lm-evaluation-harness.git@b281b09
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- name: Download models and datasets
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shell: bash
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run: |
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echo "MODEL_PATH=${ORIGIN_DIR}/${{ matrix.model_name }}/" >> "$GITHUB_ENV"
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MODEL_PATH=${ORIGIN_DIR}/${{ matrix.model_name }}/
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if [ ! -d $HARNESS_HF_HOME ]; then
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mkdir -p $HARNESS_HF_HOME
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fi
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wget -r -nH -nc -l inf --no-verbose --cut-dirs=2 ${LLM_FTP_URL}/llm/LeaderBoard_Datasets/ -P $HARNESS_HF_HOME/
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wget -r -nH -nc --no-verbose --cut-dirs=1 ${LLM_FTP_URL}/llm/${{ matrix.model_name }} -P ${ORIGIN_DIR}
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- name: Upgrade packages
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shell: bash
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run: |
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pip install --upgrade datasets==2.14.6
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if [ "${{ matrix.model_name }}" = "Mistral-7B-v0.1" ]; then
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pip install --upgrade transformers==4.36
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else
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pip install --upgrade transformers==4.31
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fi
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- name: Run harness nightly
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if: ${{github.event_name == 'schedule'}}
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shell: bash
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working-directory: ${{ github.workspace }}/python/llm/dev/benchmark/harness
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env:
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USE_XETLA: OFF
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# SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS: 1
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run: |
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export HF_HOME=${HARNESS_HF_HOME}
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export HF_DATASETS=$HARNESS_HF_HOME/datasets
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export HF_DATASETS_CACHE=$HARNESS_HF_HOME/datasets
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source /opt/intel/oneapi/setvars.sh
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python run_llb.py \
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--model bigdl-llm \
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--pretrained ${MODEL_PATH} \
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--precision ${{ matrix.precision }} \
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--device ${{ matrix.device }} \
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--tasks ${{ matrix.task }} \
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--batch_size 1 --no_cache --output_path results \
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- name: Run harness pr
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if: ${{github.event_name == 'pull_request'}}
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shell: bash
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working-directory: ${{ github.workspace }}/python/llm/dev/benchmark/harness
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env:
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USE_XETLA: OFF
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# SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS: 1
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run: |
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export HF_HOME=${HARNESS_HF_HOME}
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export HF_DATASETS=$HARNESS_HF_HOME/datasets
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export HF_DATASETS_CACHE=$HARNESS_HF_HOME/datasets
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source /opt/intel/oneapi/setvars.sh
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python run_llb.py \
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--model bigdl-llm \
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--pretrained ${MODEL_PATH} \
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--precision ${{ matrix.precision }} \
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--device ${{ matrix.device }} \
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--tasks ${{ matrix.task }} \
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--batch_size 1 --no_cache --output_path results \
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--limit 3 \
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- uses: actions/upload-artifact@v3
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with:
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name: harness_results
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path:
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${{ github.workspace }}/python/llm/dev/benchmark/harness/results/**
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- name: echo single result
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shell: bash
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working-directory: ${{ github.workspace }}/python/llm/dev/benchmark/harness/results/
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run: |
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cat ${{ matrix.model_name }}/${{ matrix.device }}/${{ matrix.precision }}/${{ matrix.task }}/result.json
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llm-harness-summary:
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if: ${{ always() }}
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needs: llm-harness-evaluation
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@f43a0e5ff2bd294095638e18286ca9a3d1956744 # actions/checkout@v3
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- name: Set up Python 3.9
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uses: actions/setup-python@v4
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with:
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python-version: 3.9
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- name: Install dependencies
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shell: bash
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run: |
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pip install --upgrade pip
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pip install jsonlines pytablewriter regex
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- name: Download all results
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uses: actions/download-artifact@v3
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with:
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name: harness_results
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path: results
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- name: Summarize the results
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shell: bash
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run: |
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ls results
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python ${{ github.workspace }}/python/llm/dev/benchmark/harness/make_table_and_csv.py results
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# TODO: change machine to store the results later
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llm-harness-summary-html:
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if: ${{github.event_name == 'schedule' || github.event_name == 'pull_request'}}
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needs: [set-matrix, llm-harness-evaluation]
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runs-on: ["self-hosted", "llm", "accuracy1", "accuracy-nightly"]
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steps:
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- uses: actions/checkout@f43a0e5ff2bd294095638e18286ca9a3d1956744 # actions/checkout@v3
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- name: Set up Python 3.9
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uses: actions/setup-python@v4
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with:
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python-version: 3.9
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- name: Install dependencies
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shell: bash
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run: |
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pip install --upgrade pip
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pip install jsonlines pytablewriter regex
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- name: Set output path
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shell: bash
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run: |
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DATE=$(date +%Y-%m-%d)
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OUTPUT_PATH="results_$DATE"
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echo "OUTPUT_PATH=$OUTPUT_PATH" >> $GITHUB_ENV
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NIGHTLY_FOLDER="/home/arda/harness-action-runners/nightly-accuracy-data"
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echo "NIGHTLY_FOLDER=$NIGHTLY_FOLDER" >> $GITHUB_ENV
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PR_FOLDER="/home/arda/harness-action-runners/pr-accuracy-data"
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echo "PR_FOLDER=$PR_FOLDER" >> $GITHUB_ENV
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- name: Download all results for nightly run
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if: github.event_name == 'schedule'
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uses: actions/download-artifact@v3
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with:
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name: harness_results
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path: ${{ env.NIGHTLY_FOLDER}}/${{ env.OUTPUT_PATH }}
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- name: Download all results for pr run
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if: github.event_name == 'pull_request'
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uses: actions/download-artifact@v3
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with:
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name: harness_results
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path: ${{ env.PR_FOLDER}}/${{ env.OUTPUT_PATH }}
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# Save fp16.csv in the parent folder of env.nightly_folder
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- name: Download fp16.csv for summary
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shell: bash
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run: |
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wget https://raw.githubusercontent.com/intel-analytics/BigDL/main/python/llm/test/benchmark/harness/fp16.csv -O ${{ env.NIGHTLY_FOLDER}}/../fp16.csv
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ls ${{ env.NIGHTLY_FOLDER}}/..
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- name: Summarize the results for nightly run
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if: github.event_name == 'schedule'
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shell: bash
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run: |
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ls /home/arda/harness-action-runners/nightly-accuracy-data/${{ env.OUTPUT_PATH }}
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pip install pandas==1.5.3
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python ${{ github.workspace }}/python/llm/dev/benchmark/harness/make_table_and_csv.py ${{ env.NIGHTLY_FOLDER}}/${{ env.OUTPUT_PATH }} ${{ env.NIGHTLY_FOLDER}}
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python ${{ github.workspace }}/python/llm/test/benchmark/harness/harness_csv_to_html.py -f ${{ env.NIGHTLY_FOLDER}}
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python ${{ github.workspace }}/python/llm/test/benchmark/harness/update_html_in_parent_folder.py -f ${{ env.NIGHTLY_FOLDER }}
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- name: Summarize the results for pull request
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if: github.event_name == 'pull_request'
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shell: bash
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run: |
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ls /home/arda/harness-action-runners/pr-accuracy-data/${{ env.OUTPUT_PATH }}
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pip install pandas==1.5.3
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python ${{ github.workspace }}/python/llm/dev/benchmark/harness/make_table_and_csv.py ${{ env.PR_FOLDER}}/${{ env.OUTPUT_PATH }} ${{ env.PR_FOLDER}}
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python ${{ github.workspace }}/python/llm/test/benchmark/harness/harness_csv_to_html.py -f ${{ env.PR_FOLDER}} |