mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-09-26 20:41:53 +08:00
[Iluvatar GPU] Add CI scripts (#2876)
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84
.github/workflows/ci_iluvatar.yml
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84
.github/workflows/ci_iluvatar.yml
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name: CI_ILUVATAR
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on:
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pull_request:
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branches: [ develop ]
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workflow_dispatch:
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concurrency:
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group: ${{ github.event.pull_request.number }}-iluvatar-ci
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cancel-in-progress: true
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jobs:
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CI_ILUVATAR:
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runs-on: [self-hosted, IXUCA]
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steps:
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- name: Print current runner name
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run: |
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echo "Current runner name: ${{ runner.name }}"
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# Because the system version is lower than 2.23, the checkout cannot be used.
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# - name: Checkout code
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# uses: actions/checkout@v4
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- name: Code Checkout
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env:
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docker_image: ccr-2vdh3abv-pub.cnc.bj.baidubce.com/device/paddle-ixuca:latest
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run: |
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REPO="https://github.com/${{ github.repository }}.git"
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FULL_REPO="${{ github.repository }}"
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REPO_NAME="${FULL_REPO##*/}"
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# Clean the repository directory before starting
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docker run --rm --net=host -v $(pwd):/workspace -w /workspace \
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-e "REPO_NAME=${REPO_NAME}" \
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${docker_image} /bin/bash -c '
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if [ -d ${REPO_NAME} ]; then
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echo "Directory ${REPO_NAME} exists, removing it..."
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rm -rf ${REPO_NAME}
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fi
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'
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git config --global user.name "FastDeployCI"
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git config --global user.email "fastdeploy_ci@example.com"
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git clone ${REPO} ${REPO_NAME}
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cd FastDeploy
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if [ "${{ github.event_name }}" = "pull_request" ]; then
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git fetch origin pull/${{ github.event.pull_request.number }}/head:pr/${{ github.event.pull_request.number }}
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git merge pr/${{ github.event.pull_request.number }}
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git log -n 3 --oneline
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else
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git checkout ${{ github.sha }}
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git log -n 3 --oneline
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fi
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- name: Run CI unittest
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env:
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docker_image: ccr-2vdh3abv-pub.cnc.bj.baidubce.com/device/paddle-ixuca:latest
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run: |
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runner_name="${{ runner.name }}"
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last_char="${runner_name: -1}"
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if [[ "$last_char" =~ [0-3] ]]; then
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gpu_id="$last_char"
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else
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gpu_id="0"
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fi
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FD_API_PORT=$((9180 + gpu_id * 100))
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FD_ENGINE_QUEUE_PORT=$((9150 + gpu_id * 100))
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FD_METRICS_PORT=$((9170 + gpu_id * 100))
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PARENT_DIR=$(dirname "$WORKSPACE")
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echo "PARENT_DIR:$PARENT_DIR"
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docker run --rm --net=host --pid=host --cap-add=ALL --privileged --shm-size=64G \
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-v /usr/src:/usr/src -v /lib/modules:/lib/modules -v /dev:/dev \
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-v $(pwd):/workspace -w /workspace \
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-v "/data1/fastdeploy:/data1/fastdeploy" \
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-e "MODEL_PATH=/ssd3/model" \
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-e "http_proxy=$(git config --global --get http.proxy)" \
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-e "https_proxy=$(git config --global --get https.proxy)" \
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-e "FD_API_PORT=${FD_API_PORT}" \
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-e "FD_ENGINE_QUEUE_PORT=${FD_ENGINE_QUEUE_PORT}" \
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-e "FD_METRICS_PORT=${FD_METRICS_PORT}" \
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${docker_image} /bin/bash -c "
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git config --global --add safe.directory /workspace/FastDeploy
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cd FastDeploy
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bash scripts/run_ci_iluvatar.sh
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"
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@@ -1,4 +1,4 @@
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setuptools>=62.3.0,<80.0
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setuptools>=79.0.1,<80.0
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pre-commit
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yapf
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flake8
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43
scripts/run_ci_iluvatar.sh
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43
scripts/run_ci_iluvatar.sh
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#!/bin/bash
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DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
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echo "$DIR"
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#先kill一遍
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ps -efww | grep -E 'run_ernie300B_4layer' | grep -v grep | awk '{print $2}' | xargs kill -9 || true
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ixsmi -r
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export LD_PRELOAD=/usr/local/corex/lib64/libcuda.so.1
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ln -sf /usr/local/bin/python3 /usr/local/bin/python
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echo "pip requirements"
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python -m pip install -r requirements_iluvatar.txt
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echo "uninstall org"
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python -m pip uninstall paddlepaddle -y
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python -m pip uninstall paddle-iluvatar-gpu -y
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python -m pip install paddlepaddle==3.1.0a0 -i https://www.paddlepaddle.org.cn/packages/stable/cpu/
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# TODO: Change to open access URL
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# python -m pip install --pre paddle-iluvatar-gpu -i https://www.paddlepaddle.org.cn/packages/nightly/ixuca/
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python -m pip install /data1/fastdeploy/packages/paddle_iluvatar_gpu-0.0.0-cp310-cp310-linux_x86_64.whl
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# Patch, remove if image updated
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cp /data1/fastdeploy/packages/cusolver.h /usr/local/lib/python3.10/site-packages/paddle/include/paddle/phi/backends/dynload/cusolver.h
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echo "build whl"
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bash build.sh || exit 1
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unset http_proxy
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unset https_proxy
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unset no_proxy
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rm -rf log/*
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export INFERENCE_MSG_QUEUE_ID=232132
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export FD_DEBUG=1
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export PADDLE_XCCL_BACKEND=iluvatar_gpu
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python test/ci_use/iluvatar_UT/run_ernie300B_4layer.py
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exit_code=$?
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echo exit_code is ${exit_code}
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ps -efww | grep -E 'run_ernie300B_4layer' | grep -v grep | awk '{print $2}' | xargs kill -9 || true
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if [ ${exit_code} -ne 0 ]; then
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echo "log/workerlog.0"
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cat log/workerlog.0
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exit 1
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fi
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16
test/ci_use/iluvatar_UT/run_ernie300B_4layer.py
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16
test/ci_use/iluvatar_UT/run_ernie300B_4layer.py
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from fastdeploy import LLM, SamplingParams
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prompts = [
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"Hello, my name is",
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]
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# 采样参数
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sampling_params = SamplingParams(temperature=0.8, top_p=0.00001, max_tokens=16)
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# 加载模型
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llm = LLM(model="/data1/fastdeploy/ERNIE_300B_4L", tensor_parallel_size=16, max_model_len=8192, static_decode_blocks=0, quantization='wint8', block_size=16)
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# 批量进行推理(llm内部基于资源情况进行请求排队、动态插入处理)
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outputs = llm.generate(prompts, sampling_params)
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assert outputs[0].outputs.token_ids==[23768, 97000, 47814, 59335, 68170, 183, 49080, 94717, 82966, 99140, 31615, 51497, 94851, 60764, 10889, 2]
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