LLM Harness Evaluation #253
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name: LLM Harness Evalution | |
# 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 | |
# Controls when the action will run. | |
on: | |
schedule: | |
- cron: "00 16 * * *" # GMT time, 16:00 GMT == 00:00 China | |
pull_request: | |
branches: [main] | |
paths: | |
- ".github/workflows/llm-harness-evaluation.yml" | |
# Allows you to run this workflow manually from the Actions tab | |
workflow_dispatch: | |
inputs: | |
model_name: | |
description: 'Model names, seperated by comma and must be quoted.' | |
required: true | |
type: string | |
precision: | |
description: 'Precisions, seperated by comma and must be quoted.' | |
required: true | |
type: string | |
task: | |
description: 'Tasks, seperated by comma and must be quoted.' | |
required: true | |
type: string | |
runs-on: | |
description: 'Labels to filter the runners, seperated 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: | |
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-nightly-env | |
if: ${{github.event_name == 'schedule'}} | |
env: | |
NIGHTLY_MATRIX_MODEL_NAME: '["Llama2-7b-guanaco-dolphin-500", "falcon-7b-instruct-with-patch", | |
"Mistral-7B-v0.1", "mpt-7b-chat", "Baichuan2-7B-Chat-LLaMAfied", "stablelm-3b"]' | |
NIGHTLY_MATRIX_TASK: '["arc", "truthfulqa", "winogrande"]' | |
NIGHTLY_MATRIX_PRECISION: '["sym_int4", "fp8"]' | |
NIGHTLY_LABELS: '["self-hosted", "llm", "accuracy-nightly"]' | |
run: | | |
echo "model_name=$NIGHTLY_MATRIX_MODEL_NAME" >> $GITHUB_ENV | |
echo "precision=$NIGHTLY_MATRIX_PRECISION" >> $GITHUB_ENV | |
echo "task=$NIGHTLY_MATRIX_TASK" >> $GITHUB_ENV | |
echo "runner=$NIGHTLY_LABELS" >> $GITHUB_ENV | |
- name: set-pr-env | |
if: ${{github.event_name == 'pull_request'}} | |
env: | |
PR_MATRIX_MODEL_NAME: '["stablelm-3b-4e1t", "Mistral-7B-v0.1"]' | |
PR_MATRIX_TASK: '["truthfulqa"]' | |
PR_MATRIX_PRECISION: '["fp8"]' | |
PR_LABELS: '["self-hosted", "llm", "temp-arc01"]' | |
run: | | |
echo "model_name=$PR_MATRIX_MODEL_NAME" >> $GITHUB_ENV | |
echo "precision=$PR_MATRIX_PRECISION" >> $GITHUB_ENV | |
echo "task=$PR_MATRIX_TASK" >> $GITHUB_ENV | |
echo "runner=$PR_LABELS" >> $GITHUB_ENV | |
- name: set-manual-env | |
if: ${{github.event_name == 'workflow_dispatch'}} | |
env: | |
MANUAL_MATRIX_MODEL_NAME: ${{format('[ {0} ]', inputs.model_name)}} | |
MANUAL_MATRIX_TASK: ${{format('[ {0} ]', inputs.task)}} | |
MANUAL_MATRIX_PRECISION: ${{format('[ {0} ]', inputs.precision)}} | |
MANUAL_LABELS: ${{format('["self-hosted", "llm", {0}]', inputs.runs-on)}} | |
run: | | |
echo "model_name=$MANUAL_MATRIX_MODEL_NAME" >> $GITHUB_ENV | |
echo "precision=$MANUAL_MATRIX_PRECISION" >> $GITHUB_ENV | |
echo "task=$MANUAL_MATRIX_TASK" >> $GITHUB_ENV | |
echo "runner=$MANUAL_LABELS" >> $GITHUB_ENV | |
- name: set-matrix | |
id: set-matrix | |
run: | | |
echo "model_name=$model_name" >> $GITHUB_OUTPUT | |
echo "precision=$precision" >> $GITHUB_OUTPUT | |
echo "task=$task" >> $GITHUB_OUTPUT | |
echo "runner=$runner" >> $GITHUB_OUTPUT | |
llm-harness-evalution: | |
timeout-minutes: 1000 | |
needs: [llm-cpp-build, set-matrix] | |
strategy: | |
fail-fast: false | |
matrix: | |
# include: | |
# python-version: "3.9" | |
# model_name: "stablelm-3b-4e1t" | |
# task: "arc" | |
# precision: "sym_int4" #options: sym_int4, fp4, mixed_fp4, sym_int8, fp8, mixed_fp8 | |
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 | |
HARNESS_HF_HOME: /mnt/disk1/harness_home | |
steps: | |
- uses: 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: | | |
set -e | |
python -m pip install --upgrade pip | |
python -m pip install --upgrade setuptools==58.0.4 | |
python -m pip install --upgrade wheel | |
- 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 harness | |
working-directory: ${{ github.workspace }}/python/llm/dev/benchmark/harness/ | |
shell: bash | |
run: | | |
pip install git+https://github.com/EleutherAI/lm-evaluation-harness.git@e81d3cc | |
- name: Download models and datasets | |
shell: bash | |
run: | | |
echo "MODEL_PATH=${ORIGIN_DIR}/${{ matrix.model_name }}/" >> "$GITHUB_ENV" | |
MODEL_PATH=${ORIGIN_DIR}/${{ matrix.model_name }}/ | |
if [ ! -d $HARNESS_HF_HOME ]; then | |
mkdir -p $HARNESS_HF_HOME | |
fi | |
wget -r -nH -nc -l inf --no-verbose --cut-dirs=2 ${LLM_FTP_URL}/llm/LeaderBoard_Datasets/ -P $HARNESS_HF_HOME/ | |
wget -r -nH -nc --no-verbose --cut-dirs=1 ${LLM_FTP_URL}/llm/${{ matrix.model_name }} -P ${ORIGIN_DIR} | |
- name: Upgrade packages | |
shell: bash | |
run: | | |
pip install --upgrade datasets==2.14.6 | |
if [ "${{ matrix.model_name }}" = "Mistral-7B-v0.1" ]; then | |
pip install --upgrade transformers==4.36 | |
else | |
pip install --upgrade transformers==4.31 | |
fi | |
- name: Run harness | |
shell: bash | |
working-directory: ${{ github.workspace }}/python/llm/dev/benchmark/harness | |
env: | |
USE_XETLA: OFF | |
# SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS: 1 | |
run: | | |
export HF_HOME=${HARNESS_HF_HOME} | |
export HF_DATASETS=$HARNESS_HF_HOME/datasets | |
export HF_DATASETS_CACHE=$HARNESS_HF_HOME/datasets | |
source /opt/intel/oneapi/setvars.sh | |
python run_llb.py \ | |
--model bigdl-llm \ | |
--pretrained ${MODEL_PATH} \ | |
--precision ${{ matrix.precision }} \ | |
--device ${{ matrix.device }} \ | |
--tasks ${{ matrix.task }} \ | |
--batch_size 1 --no_cache --output_path results | |
- name: Compare with golden accuracy | |
shell: bash | |
if: ${{github.event_name == 'schedule'}} | |
working-directory: ${{ github.workspace }}/python/llm | |
run: | | |
python test/benchmark/harness_nightly/accuracy_regression.py \ | |
dev/benchmark/harness/results/${{ matrix.model_name }}/${{ matrix.device }}/${{ matrix.precision }}/${{ matrix.task }}/result.json \ | |
test/benchmark/harness_nightly/golden_results.json | |
- uses: actions/upload-artifact@v3 | |
with: | |
name: harness_results | |
path: | |
${{ github.workspace }}/python/llm/dev/benchmark/harness/results/** | |
- name: echo single result | |
shell: bash | |
working-directory: ${{ github.workspace }}/python/llm/dev/benchmark/harness/results/ | |
run: | | |
cat ${{ matrix.model_name }}/${{ matrix.device }}/${{ matrix.precision }}/${{ matrix.task }}/result.json | |
llm-harness-summary: | |
if: ${{ always() }} | |
needs: llm-harness-evalution | |
runs-on: ubuntu-latest | |
steps: | |
- uses: actions/checkout@v3 | |
- name: Set up Python 3.9 | |
uses: actions/setup-python@v4 | |
with: | |
python-version: 3.9 | |
- name: Install dependencies | |
shell: bash | |
run: | | |
pip install --upgrade pip | |
pip install jsonlines pytablewriter regex | |
- name: Download all results | |
uses: actions/download-artifact@v3 | |
with: | |
name: harness_results | |
path: results | |
- name: Summarize the results | |
shell: bash | |
run: | | |
ls results | |
python ${{ github.workspace }}/python/llm/dev/benchmark/harness/make_table_results.py results |