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benchmark.yml
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name: benchmark_test
on:
workflow_dispatch:
inputs:
repo_org:
required: false
description: 'Tested repository organization name. Default is InternLM'
type: string
default: 'InternLM/lmdeploy'
repo_ref:
required: false
description: 'Set branch or tag or commit id. Default is "main"'
type: string
default: 'main'
benchmark_type:
required: true
description: 'Set benchmark type. Default is "["generation", "throughtput", "api_server"]"'
type: string
default: "['apiserver', 'generation', 'throughput']"
offline_mode:
required: true
description: 'Whether start a offline mode, if true, you should prepare code and whl package by yourself'
type: boolean
default: false
env:
HOST_PIP_CACHE_DIR: /nvme/github-actions/pip-cache
HOST_LOCALTIME: /usr/share/zoneinfo/Asia/Shanghai
OUTPUT_FOLDER: cuda11.8_dist_${{ github.run_id }}
REPORT_DIR: /nvme/qa_test_models/benchmark-reports/${{ github.run_id }}
ACTIONS_ALLOW_USE_UNSECURE_NODE_VERSION: true
FAIL_CONFIG: ${{ github.run_attempt != 1 && '--lf --lfnf none' || '--lf'}}
jobs:
linux-build:
if: ${{github.event_name == 'schedule' || (!cancelled() && !inputs.offline_mode)}}
strategy:
matrix:
pyver: [py310]
runs-on: ubuntu-latest
env:
PYTHON_VERSION: ${{ matrix.pyver }}
PLAT_NAME: manylinux2014_x86_64
DOCKER_TAG: cuda11.8
steps:
- name: Checkout repository
uses: actions/checkout@v3
with:
repository: ${{ github.event.inputs.repo_org || 'InternLM/lmdeploy' }}
ref: ${{github.event.inputs.repo_ref || 'main'}}
- name: Build
run: |
echo ${PYTHON_VERSION}
echo ${PLAT_NAME}
echo ${DOCKER_TAG}
echo ${OUTPUT_FOLDER}
echo ${GITHUB_RUN_ID}
# remove -it
sed -i 's/docker run --rm -it/docker run --rm/g' builder/manywheel/build_wheel.sh
bash builder/manywheel/build_wheel.sh ${PYTHON_VERSION} ${PLAT_NAME} ${DOCKER_TAG} ${OUTPUT_FOLDER}
- name: Upload Artifacts
uses: actions/upload-artifact@v4
with:
if-no-files-found: error
path: builder/manywheel/${{ env.OUTPUT_FOLDER }}
retention-days: 1
name: my-artifact-${{ github.run_id }}-${{ matrix.pyver }}
benchmark:
needs: linux-build
if: ${{github.event_name == 'schedule' || !cancelled()}}
runs-on: [self-hosted, linux-a100]
strategy:
fail-fast: false
matrix:
benchmark_type: ${{fromJSON(github.event.inputs.benchmark_type)}}
timeout-minutes: 480
container:
image: openmmlab/lmdeploy:latest-cu11
options: "--gpus=all --ipc=host --user root -e PIP_CACHE_DIR=/root/.cache/pip -e NVIDIA_DISABLE_REQUIRE=1 --pull never"
volumes:
- /nvme/github-actions/pip-cache:/root/.cache/pip
- /nvme/github-actions/packages:/root/packages
- /nvme/qa_test_models:/nvme/qa_test_models
- /mnt/shared:/mnt/shared
- /usr/share/zoneinfo/Asia/Shanghai:/etc/localtime:ro
steps:
- name: Clone repository
uses: actions/checkout@v3
if: ${{github.event_name == 'schedule' || !inputs.offline_mode}}
with:
repository: ${{ github.event.inputs.repo_org || 'InternLM/lmdeploy' }}
ref: ${{github.event.inputs.repo_ref || 'main'}}
- name: Copy repository - offline
if: ${{inputs.offline_mode}}
run: cp -r /nvme/qa_test_models/offline_pkg/lmdeploy/. .
- name: Download Artifacts
if: ${{github.event_name == 'schedule' || !inputs.offline_mode}}
uses: actions/download-artifact@v4
with:
name: my-artifact-${{ github.run_id }}-py310
- name: Install lmdeploy - dependency
run: |
# manually install flash attn
# the install packeage from. https://github.com/Dao-AILab/flash-attention/releases
python3 -m pip install /root/packages/flash_attn-2.6.3+cu118torch2.3cxx11abiFALSE-cp310-cp310-linux_x86_64.whl
python3 -m pip install -e /root/packages/AutoAWQ_kernels
python3 -m pip install /root/packages/autoawq-0.2.6-cp310-cp310-manylinux2014_x86_64.whl --no-deps
python3 -m pip install /root/packages/xformers-0.0.27+cu118-cp310-cp310-manylinux2014_x86_64.whl --no-deps
python3 -m pip install -r /nvme/qa_test_models/offline_pkg/requirements.txt
- name: Install lmdeploy
if: ${{github.event_name == 'schedule' || !inputs.offline_mode}}
run: |
python3 -m pip install lmdeploy-*.whl --no-deps
python3 -m pip install -r requirements/test.txt
- name: Install lmdeploy - offline
if: ${{inputs.offline_mode}}
run: |
python3 -m pip install /nvme/qa_test_models/offline_pkg/py310/lmdeploy-*.whl --no-deps
python3 -m pip install -r requirements/test.txt
- name: Check env
run: |
pip uninstall -y nvidia-nccl-cu11
python3 -m pip list
lmdeploy check_env
mkdir ${{env.REPORT_DIR}}/allure-results/.pytest_cache -p
ln -s ${{env.REPORT_DIR}}/allure-results/.pytest_cache autotest
- name: Run other benchmark
run: |
pytest autotest/benchmark/test_${{matrix.benchmark_type}}_performance.py -n 8 --run_id ${{ github.run_id }} -m gpu_num_1 ${{env.FAIL_CONFIG}} --alluredir=${{env.REPORT_DIR}}/allure-results || true
pytest autotest/benchmark/test_${{matrix.benchmark_type}}_performance.py -n 4 --run_id ${{ github.run_id }} -m gpu_num_2 ${{env.FAIL_CONFIG}} --alluredir=${{env.REPORT_DIR}}/allure-results || true
pytest autotest/benchmark/test_${{matrix.benchmark_type}}_performance.py -n 2 --run_id ${{ github.run_id }} -m gpu_num_4 ${{env.FAIL_CONFIG}} --alluredir=${{env.REPORT_DIR}}/allure-results
- name: Clear workfile
if: always()
run: |
chmod -R 777 $REPORT_DIR
export workdir=$(pwd)
cd ..
rm -rf $workdir
mkdir $workdir
chmod -R 777 $workdir
get_result_overview:
if: always() && !cancelled()
needs: [benchmark]
timeout-minutes: 5
runs-on: [self-hosted, linux-a100]
steps:
- name: Clone repository
uses: actions/checkout@v3
with:
repository: ${{ github.event.inputs.repo_org || 'InternLM/lmdeploy' }}
ref: ${{github.event.inputs.repo_ref || 'main'}}
- name: Get overview
run: |
pip install pandas fire mmengine
python3 .github/scripts/action_tools.py generate_benchmark_report $REPORT_DIR