mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-12-24 13:28:13 +08:00
b9bdf82ce3
* Enhance run_ci_xpu.sh with caching and prefill options * Update model path and configuration in run_ci_xpu.sh * Add '北朝' keyword to assertion in run_45vl.py * Enhance process termination logic in run_ci_xpu.sh * Set timeout for CI_XPU job to 60 minutes * Remove extra newline in stop_processes function * Update paddlepaddle-xpu installation command Comment out the previous paddlepaddle-xpu installation command and replace it with a specific version installation due to EP parallel error.
571 lines
17 KiB
Bash
571 lines
17 KiB
Bash
#!/bin/bash
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DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
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echo "$DIR"
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#安装lsof工具
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apt install -y lsof
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#先kill一遍
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function stop_processes() {
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ps -efww | grep -E 'cache_transfer_manager.py' | grep -v grep | awk '{print $2}' | xargs kill -9 || true
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ps -efww | grep -E 'api_server' | grep -v grep | awk '{print $2}' | xargs kill -9 || true
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ps -efww | grep -E "$((8188 + XPU_ID * 100))" | grep -v grep | awk '{print $2}' | xargs kill -9 || true
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lsof -t -i :$((8188 + XPU_ID * 100)) | xargs kill -9 || true
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for port in $(seq $((8188 + XPU_ID * 100 + 10)) $((8188 + XPU_ID * 100 + 40))); do
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lsof -t -i :${port} | xargs kill -9 || true
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done
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netstat -tunlp 2>/dev/null | grep $((8190 + XPU_ID * 100)) | awk '{print $NF}' | awk -F'/' '{print $1}' | xargs -r kill -9
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netstat -tunlp 2>/dev/null | grep $((8190 + XPU_ID * 100)) | awk '{print $(NF-1)}' | cut -d/ -f1 | grep -E '^[0-9]+$' | xargs -r kill -9
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}
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stop_processes >kill.log 2>&1
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# 由于机器原因，需重启使用的卡，以保障没有问题
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if [[ "$XPU_ID" == "0" ]]; then
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export XPU_VISIBLE_DEVICES="0,1,2,3"
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else
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export XPU_VISIBLE_DEVICES="4,5,6,7"
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fi
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mkdir -p /workspace/deps
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cd /workspace/deps
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wget -q https://klx-sdk-release-public.su.bcebos.com/xre/kl3-release/5.0.21.21/xre-Linux-x86_64-5.0.21.21.tar.gz
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tar -zxf xre-Linux-x86_64-5.0.21.21.tar.gz && mv xre-Linux-x86_64-5.0.21.21 xre
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cd -
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export PATH=/workspace/deps/xre/bin:$PATH
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xpu-smi -r -i $XPU_VISIBLE_DEVICES
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xpu-smi
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echo "pip requirements"
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python -m pip install -r requirements.txt
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echo "uninstall org"
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python -m pip uninstall paddlepaddle-xpu -y
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python -m pip uninstall fastdeploy-xpu -y
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# python -m pip install paddlepaddle-xpu -i https://www.paddlepaddle.org.cn/packages/nightly/xpu-p800/
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# 由于ep并行报错暂时锁死paddle版本
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python -m pip install https://paddle-whl.bj.bcebos.com/nightly/xpu-p800/paddlepaddle-xpu/paddlepaddle_xpu-3.3.0.dev20251123-cp310-cp310-linux_x86_64.whl
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echo "build whl"
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bash custom_ops/xpu_ops/download_dependencies.sh develop
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export CLANG_PATH=$(pwd)/custom_ops/xpu_ops/third_party/xtdk
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export XVLLM_PATH=$(pwd)/custom_ops/xpu_ops/third_party/xvllm
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bash build.sh || exit 1
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echo "pip others"
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python -m pip install openai -U
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python -m pip uninstall -y triton
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python -m pip install triton==3.3.0
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python -m pip install pytest
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python -m pip install pytest-timeout
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unset http_proxy
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unset https_proxy
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unset no_proxy
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stop_processes >kill.log 2>&1
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# 起服务
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rm -rf log/*
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rm -f core*
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# pkill -9 python #流水线不执行这个
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#清空消息队列
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ipcrm --all=msg
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echo "============================开始V1模式测试!============================"
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if [[ "$XPU_ID" == "0" ]]; then
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export XPU_VISIBLE_DEVICES="0,1,2,3"
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else
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export XPU_VISIBLE_DEVICES="4,5,6,7"
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fi
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export port_num=$((8188 + XPU_ID * 100))
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python -m fastdeploy.entrypoints.openai.api_server \
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--model ${MODEL_PATH}/ERNIE-4.5-300B-A47B-Paddle \
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--port $port_num \
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--engine-worker-queue-port $((port_num + 1)) \
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--metrics-port $((port_num + 2)) \
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--cache-queue-port $((port_num + 47873)) \
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--tensor-parallel-size 4 \
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--num-gpu-blocks-override 16384 \
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--max-model-len 32768 \
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--max-num-seqs 128 \
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--quantization wint4 \
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--enable-prefix-caching \
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--enable-chunked-prefill > server.log 2>&1 &
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sleep 60
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# 探活
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TIMEOUT=$((15 * 60))
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INTERVAL=10 # 检查间隔（秒）
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ENDPOINT="http://0.0.0.0:${port_num}/health"
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START_TIME=$(date +%s) # 记录开始时间戳
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echo "开始服务健康检查，最长等待时间：${TIMEOUT}秒"
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while true; do
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# 计算已耗时
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CURRENT_TIME=$(date +%s)
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ELAPSED=$((CURRENT_TIME - START_TIME))
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# 超时判断
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if [ $ELAPSED -ge $TIMEOUT ]; then
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echo -e "\n服务启动超时：经过 $((TIMEOUT/60)) 分钟服务仍未启动！"
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stop_processes
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echo "server.log"
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cat server.log
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echo "log/workerlog.0"
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cat log/workerlog.0
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exit 1
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fi
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HTTP_CODE=$(curl -s -o /dev/null -w "%{http_code}" -m 2 "$ENDPOINT" || true)
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echo -e "\r服务健康检查中... 已等待 ${ELAPSED} 秒，当前状态码：${HTTP_CODE}"
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if [ "$HTTP_CODE" = "200" ]; then
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echo -e "\n服务启动成功！耗时 ${ELAPSED} 秒"
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break
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else
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sleep $INTERVAL
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fi
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done
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# 执行服务化推理
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python -m pytest -s tests/ci_use/XPU_45T/run_45T.py
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kv_block_test_exit_code=$?
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echo kv_block_test_exit_code is ${kv_block_test_exit_code}
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stop_processes >kill.log 2>&1
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if [ ${kv_block_test_exit_code} -ne 0 ]; then
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echo "server.log"
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cat server.log
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echo "log/workerlog.0"
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cat log/workerlog.0
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echo "kv block相关测试失败，请检查pr代码"
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exit 1
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fi
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sleep 5
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# 起服务
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rm -rf log/*
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rm -f core*
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# pkill -9 python #流水线不执行这个
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#清空消息队列
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ipcrm --all=msg
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echo "============================开始W4A8测试!============================"
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if [[ "$XPU_ID" == "0" ]]; then
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export XPU_VISIBLE_DEVICES="0,1,2,3"
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else
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export XPU_VISIBLE_DEVICES="4,5,6,7"
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fi
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export port_num=$((8188 + XPU_ID * 100))
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python -m fastdeploy.entrypoints.openai.api_server \
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--model ${MODEL_PATH}/ERNIE-4.5-300B-A47B-W4A8C8-TP4-Paddle \
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--port $port_num \
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--engine-worker-queue-port $((port_num + 1)) \
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--metrics-port $((port_num + 2)) \
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--cache-queue-port $((port_num + 47873)) \
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--tensor-parallel-size 4 \
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--num-gpu-blocks-override 16384 \
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--max-model-len 32768 \
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--max-num-seqs 64 \
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--quantization "W4A8" > server.log 2>&1 &
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sleep 60
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# 探活
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TIMEOUT=$((15 * 60))
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INTERVAL=10 # 检查间隔（秒）
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ENDPOINT="http://0.0.0.0:${port_num}/health"
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START_TIME=$(date +%s) # 记录开始时间戳
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echo "开始服务健康检查，最长等待时间：${TIMEOUT}秒"
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while true; do
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# 计算已耗时
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CURRENT_TIME=$(date +%s)
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ELAPSED=$((CURRENT_TIME - START_TIME))
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# 超时判断
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if [ $ELAPSED -ge $TIMEOUT ]; then
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echo -e "\n服务启动超时：经过 $((TIMEOUT/60)) 分钟服务仍未启动！"
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stop_processes
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echo "server.log"
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cat server.log
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echo "log/workerlog.0"
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cat log/workerlog.0
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exit 1
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fi
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HTTP_CODE=$(curl -s -o /dev/null -w "%{http_code}" -m 2 "$ENDPOINT" || true)
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echo -e "\r服务健康检查中... 已等待 ${ELAPSED} 秒，当前状态码：${HTTP_CODE}"
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if [ "$HTTP_CODE" = "200" ]; then
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echo -e "\n服务启动成功！耗时 ${ELAPSED} 秒"
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break
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else
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sleep $INTERVAL
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fi
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done
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# 执行服务化推理
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python -m pytest -s tests/ci_use/XPU_45T/run_w4a8.py
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w4a8_test_exit_code=$?
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echo w4a8_test_exit_code is ${w4a8_test_exit_code}
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stop_processes >kill.log 2>&1
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if [ ${w4a8_test_exit_code} -ne 0 ]; then
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echo "server.log"
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cat server.log
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echo "log/workerlog.0"
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cat log/workerlog.0
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echo "w4a8 测试失败，请检查pr代码"
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exit 1
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fi
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sleep 5
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# 起服务
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rm -rf log/*
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rm -f core*
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# pkill -9 python #流水线不执行这个
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#清空消息队列
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ipcrm --all=msg
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echo "============================开始vl模型测试!============================"
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if [[ "$XPU_ID" == "0" ]]; then
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export XPU_VISIBLE_DEVICES="0,1,2,3"
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else
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export XPU_VISIBLE_DEVICES="4,5,6,7"
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fi
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export port_num=$((8188 + XPU_ID * 100))
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python -m fastdeploy.entrypoints.openai.api_server \
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--model ${MODEL_PATH}/ERNIE-4.5-VL-28B-A3B-Thinking \
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--port $port_num \
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--engine-worker-queue-port $((port_num + 1)) \
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--metrics-port $((port_num + 2)) \
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--cache-queue-port $((port_num + 47873)) \
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--tensor-parallel-size 4 \
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--max-model-len 32768 \
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--max-num-seqs 32 \
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--quantization wint8 \
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--reasoning-parser ernie-45-vl-thinking \
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--tool-call-parser ernie-45-vl-thinking \
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--mm-processor-kwargs '{"image_max_pixels": 12845056 }' \
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--enable-chunked-prefill > server.log 2>&1 &
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sleep 60
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# 探活
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TIMEOUT=$((15 * 60))
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INTERVAL=10 # 检查间隔（秒）
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ENDPOINT="http://0.0.0.0:${port_num}/health"
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START_TIME=$(date +%s) # 记录开始时间戳
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echo "开始服务健康检查，最长等待时间：${TIMEOUT}秒"
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while true; do
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# 计算已耗时
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CURRENT_TIME=$(date +%s)
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ELAPSED=$((CURRENT_TIME - START_TIME))
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# 超时判断
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if [ $ELAPSED -ge $TIMEOUT ]; then
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echo -e "\n服务启动超时：经过 $((TIMEOUT/60)) 分钟服务仍未启动！"
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stop_processes
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echo "server.log"
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cat server.log
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echo "log/workerlog.0"
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cat log/workerlog.0
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exit 1
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fi
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HTTP_CODE=$(curl -s -o /dev/null -w "%{http_code}" -m 2 "$ENDPOINT" || true)
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echo -e "\r服务健康检查中... 已等待 ${ELAPSED} 秒，当前状态码：${HTTP_CODE}"
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if [ "$HTTP_CODE" = "200" ]; then
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echo -e "\n服务启动成功！耗时 ${ELAPSED} 秒"
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break
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else
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sleep $INTERVAL
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fi
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done
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# 执行服务化推理
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python -m pytest -s tests/ci_use/XPU_45T/run_45vl.py
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vl_test_exit_code=$?
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echo vl_test_exit_code is ${vl_test_exit_code}
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stop_processes >kill.log 2>&1
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if [ ${vl_test_exit_code} -ne 0 ]; then
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echo "server.log"
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cat server.log
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echo "log/workerlog.0"
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cat log/workerlog.0
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echo " vl模型 测试失败，请检查pr代码"
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exit 1
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fi
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echo "============================开始 EP4TP4 在线服务测试!============================"
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sleep 5
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rm -rf log/*
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rm -f core*
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# pkill -9 python #流水线不执行这个
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ipcrm --all=msg
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xpu-smi
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if [[ "$XPU_ID" == "0" ]]; then
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export XPU_VISIBLE_DEVICES="0,1,2,3"
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else
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export XPU_VISIBLE_DEVICES="4,5,6,7"
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fi
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export BKCL_ENABLE_XDR=1
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export BKCL_RDMA_NICS=xgbe1,xgbe2,xgbe3,xgbe4
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export BKCL_TRACE_TOPO=1
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export BKCL_PCIE_RING=1
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export XSHMEM_MODE=1
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export XSHMEM_QP_NUM_PER_RANK=32
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export BKCL_RDMA_VERBS=1
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wget -q https://paddle-qa.bj.bcebos.com/xpu_third_party/xDeepEP.tar.gz
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tar -xzf xDeepEP.tar.gz
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cd xDeepEP
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bash build.sh
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cd -
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export port_num=$((8188 + XPU_ID * 100))
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# 启动服务
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python -m fastdeploy.entrypoints.openai.api_server \
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--model ${MODEL_PATH}/ERNIE-4.5-300B-A47B-Paddle \
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--port $port_num \
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--tensor-parallel-size 4 \
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--enable-expert-parallel \
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--data-parallel-size 1 \
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--max-model-len 32768 \
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--max-num-seqs 64 \
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--quantization "wint4" \
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--engine-worker-queue-port $((port_num + 10)) \
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--metrics-port $((port_num + 2)) \
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--cache-queue-port $((port_num + 47873)) \
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--disable-sequence-parallel-moe \
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--gpu-memory-utilization 0.9 \
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--load-choices "default" > server.log 2>&1 &
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sleep 60
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# 探活
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TIMEOUT=$((15 * 60))
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INTERVAL=10
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ENDPOINT="http://0.0.0.0:${port_num}/health"
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START_TIME=$(date +%s)
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echo "开始服务健康检查，最长等待时间：${TIMEOUT}秒"
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while true; do
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CURRENT_TIME=$(date +%s)
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ELAPSED=$((CURRENT_TIME - START_TIME))
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if [ $ELAPSED -ge $TIMEOUT ]; then
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echo -e "\n服务启动超时：经过 $((TIMEOUT/60)) 分钟服务仍未启动！"
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stop_processes
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cat server.log
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echo "log/workerlog.0"
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cat log/workerlog.0
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exit 1
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fi
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HTTP_CODE=$(curl -s -o /dev/null -w "%{http_code}" -m 2 "$ENDPOINT" || true)
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echo -e "\r服务健康检查中... 已等待 ${ELAPSED} 秒，当前状态码：${HTTP_CODE}"
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if [ "$HTTP_CODE" = "200" ]; then
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echo -e "\n服务启动成功！耗时 ${ELAPSED} 秒"
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break
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else
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sleep $INTERVAL
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fi
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done
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# 执行在线推理验证脚本
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python -m pytest -s tests/ci_use/XPU_45T/run_ep_online.py
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ep_online_exit_code=$?
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echo ep_online_exit_code is ${ep_online_exit_code}
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unset BKCL_ENABLE_XDR
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unset BKCL_RDMA_NICS
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unset BKCL_TRACE_TOPO
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unset BKCL_PCIE_RING
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unset XSHMEM_MODE
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unset XSHMEM_QP_NUM_PER_RANK
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unset BKCL_RDMA_VERBS
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stop_processes >kill.log 2>&1
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if [ ${ep_online_exit_code} -ne 0 ]; then
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echo "server.log"
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cat server.log
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cat log/workerlog.0
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echo "EP4TP4 在线服务相关测试失败，请检查pr代码"
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exit 1
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fi
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echo "============================开始 EP4TP1 在线服务测试!============================"
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sleep 5
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rm -rf log/*
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rm -f core*
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# pkill -9 python #流水线不执行这个
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ipcrm --all=msg
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xpu-smi
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if [[ "$XPU_ID" == "0" ]]; then
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export XPU_VISIBLE_DEVICES="0,1,2,3"
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else
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export XPU_VISIBLE_DEVICES="4,5,6,7"
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fi
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export BKCL_ENABLE_XDR=1
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export BKCL_RDMA_NICS=xgbe1,xgbe2,xgbe3,xgbe4
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export BKCL_TRACE_TOPO=1
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export BKCL_PCIE_RING=1
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export XSHMEM_MODE=1
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export XSHMEM_QP_NUM_PER_RANK=32
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export BKCL_RDMA_VERBS=1
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export port_num=$((8188 + XPU_ID * 100))
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# 启动服务
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python -m fastdeploy.entrypoints.openai.api_server \
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--model ${MODEL_PATH}/ERNIE-4.5-300B-A47B-Paddle \
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--port $port_num \
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--tensor-parallel-size 1 \
|
||
--enable-expert-parallel \
|
||
--data-parallel-size 4 \
|
||
--max-model-len 32768 \
|
||
--max-num-seqs 64 \
|
||
--quantization "wint4" \
|
||
--engine-worker-queue-port "$((port_num + 10)),$((port_num + 20)),$((port_num + 30)),$((port_num + 40))" \
|
||
--metrics-port $((port_num + 2)) \
|
||
--cache-queue-port $((port_num + 47873)) \
|
||
--gpu-memory-utilization 0.9 \
|
||
--load-choices "default" > server.log 2>&1 &
|
||
|
||
sleep 60
|
||
# 探活（同上）
|
||
TIMEOUT=$((15 * 60))
|
||
INTERVAL=10
|
||
ENDPOINT="http://0.0.0.0:${port_num}/health"
|
||
START_TIME=$(date +%s)
|
||
while true; do
|
||
CURRENT_TIME=$(date +%s)
|
||
ELAPSED=$((CURRENT_TIME - START_TIME))
|
||
if [ $ELAPSED -ge $TIMEOUT ]; then
|
||
echo -e "\n服务启动超时：经过 $((TIMEOUT/60)) 分钟服务仍未启动！"
|
||
stop_processes
|
||
cat server.log
|
||
cat log/workerlog.0
|
||
exit 1
|
||
fi
|
||
HTTP_CODE=$(curl -s -o /dev/null -w "%{http_code}" -m 2 "$ENDPOINT" || true)
|
||
echo -e "\r服务健康检查中... 已等待 ${ELAPSED} 秒，当前状态码：${HTTP_CODE}"
|
||
if [ "$HTTP_CODE" = "200" ]; then
|
||
echo -e "\n服务启动成功！耗时 ${ELAPSED} 秒"
|
||
break
|
||
else
|
||
sleep $INTERVAL
|
||
fi
|
||
done
|
||
|
||
|
||
# 执行在线推理验证脚本
|
||
python -m pytest -s tests/ci_use/XPU_45T/run_ep_online.py
|
||
ep_online_exit_code=$?
|
||
echo ep_online_exit_code is ${ep_online_exit_code}
|
||
|
||
unset BKCL_ENABLE_XDR
|
||
unset BKCL_RDMA_NICS
|
||
unset BKCL_TRACE_TOPO
|
||
unset BKCL_PCIE_RING
|
||
unset XSHMEM_MODE
|
||
unset XSHMEM_QP_NUM_PER_RANK
|
||
unset BKCL_RDMA_VERBS
|
||
stop_processes >kill.log 2>&1
|
||
|
||
if [ ${ep_online_exit_code} -ne 0 ]; then
|
||
echo "server.log"
|
||
cat server.log
|
||
cat log/workerlog.0
|
||
echo "EP4TP1 在线服务相关测试失败，请检查pr代码"
|
||
exit 1
|
||
fi
|
||
|
||
echo "============================开始 EP4TP4 all2all 测试!============================"
|
||
sleep 5
|
||
rm -rf log/*
|
||
rm -f core*
|
||
# pkill -9 python #流水线不执行这个
|
||
ipcrm --all=msg
|
||
xpu-smi
|
||
if [[ "$XPU_ID" == "0" ]]; then
|
||
export XPU_VISIBLE_DEVICES="0,1,2,3"
|
||
else
|
||
export XPU_VISIBLE_DEVICES="4,5,6,7"
|
||
fi
|
||
|
||
export BKCL_ENABLE_XDR=1
|
||
export BKCL_RDMA_NICS=xgbe1,xgbe2,xgbe3,xgbe4
|
||
export BKCL_TRACE_TOPO=1
|
||
export BKCL_PCIE_RING=1
|
||
export XSHMEM_MODE=1
|
||
export XSHMEM_QP_NUM_PER_RANK=32
|
||
export BKCL_RDMA_VERBS=1
|
||
|
||
export port_num=$((8188 + XPU_ID * 100))
|
||
# 启动服务
|
||
python -m fastdeploy.entrypoints.openai.api_server \
|
||
--model ${MODEL_PATH}/ERNIE-4.5-300B-A47B-Paddle \
|
||
--port $port_num \
|
||
--tensor-parallel-size 4 \
|
||
--enable-expert-parallel \
|
||
--data-parallel-size 1 \
|
||
--max-model-len 32768 \
|
||
--max-num-seqs 64 \
|
||
--quantization "wint4" \
|
||
--engine-worker-queue-port $((port_num + 10)) \
|
||
--metrics-port $((port_num + 2)) \
|
||
--cache-queue-port $((port_num + 47873)) \
|
||
--gpu-memory-utilization 0.9 \
|
||
--load-choices "default" > server.log 2>&1 &
|
||
|
||
sleep 60
|
||
# 探活
|
||
TIMEOUT=$((15 * 60))
|
||
INTERVAL=10
|
||
ENDPOINT="http://0.0.0.0:${port_num}/health"
|
||
START_TIME=$(date +%s)
|
||
echo "开始服务健康检查，最长等待时间：${TIMEOUT}秒"
|
||
while true; do
|
||
CURRENT_TIME=$(date +%s)
|
||
ELAPSED=$((CURRENT_TIME - START_TIME))
|
||
if [ $ELAPSED -ge $TIMEOUT ]; then
|
||
echo -e "\n服务启动超时：经过 $((TIMEOUT/60)) 分钟服务仍未启动！"
|
||
stop_processes
|
||
cat server.log
|
||
echo "log/workerlog.0"
|
||
cat log/workerlog.0
|
||
exit 1
|
||
fi
|
||
HTTP_CODE=$(curl -s -o /dev/null -w "%{http_code}" -m 2 "$ENDPOINT" || true)
|
||
echo -e "\r服务健康检查中... 已等待 ${ELAPSED} 秒，当前状态码：${HTTP_CODE}"
|
||
if [ "$HTTP_CODE" = "200" ]; then
|
||
echo -e "\n服务启动成功！耗时 ${ELAPSED} 秒"
|
||
break
|
||
else
|
||
sleep $INTERVAL
|
||
fi
|
||
done
|
||
|
||
|
||
# 执行在线推理验证脚本
|
||
python -m pytest -s tests/ci_use/XPU_45T/run_ep_online.py
|
||
ep_online_exit_code=$?
|
||
echo ep_online_exit_code is ${ep_online_exit_code}
|
||
|
||
unset BKCL_ENABLE_XDR
|
||
unset BKCL_RDMA_NICS
|
||
unset BKCL_TRACE_TOPO
|
||
unset BKCL_PCIE_RING
|
||
unset XSHMEM_MODE
|
||
unset XSHMEM_QP_NUM_PER_RANK
|
||
unset BKCL_RDMA_VERBS
|
||
stop_processes >kill.log 2>&1
|
||
|
||
if [ ${ep_online_exit_code} -ne 0 ]; then
|
||
echo "server.log"
|
||
cat server.log
|
||
cat log/workerlog.0
|
||
echo "EP4TP4 all2all 在线服务相关测试失败，请检查pr代码"
|
||
exit 1
|
||
fi
|