[Misc]: Performance regression between v0.11.0rc1 and v0.9.1 #4110
Description
Reproduce:
# Update DEVICE according to your device (/dev/davinci[0-7])
export DEVICE=/dev/davinci2
# Update the vllm-ascend image
#export IMAGE=swr.cn-southwest-2.myhuaweicloud.com/base_image/ascend-ci/vllm-ascend:v0.11.0rc1
export IMAGE=quay.nju.edu.cn/ascend/vllm-ascend:v0.11.0rc1
docker run --rm \
--name yikun-main \
--device $DEVICE \
--device /dev/davinci_manager \
--device /dev/devmm_svm \
--device /dev/hisi_hdc \
-v /usr/local/dcmi:/usr/local/dcmi \
-v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
-v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
-v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
-v /etc/ascend_install.info:/etc/ascend_install.info \
-v /root/.cache:/root/.cache \
-e VLLM_USE_MODELSCOPE=True \
-it $IMAGE bash
~/.cache/perf.sh
# cat ~/.cache/perf.sh
# bash fonts colors
cyan='\e[96m'
yellow='\e[33m'
red='\e[31m'
none='\e[0m'
_cyan() { echo -e "${cyan}$*${none}"; }
_yellow() { echo -e "${yellow}$*${none}"; }
_red() { echo -e "${red}$*${none}"; }
_info() { _cyan "Info: $*"; }
_warn() { _yellow "Warn: $*"; }
_err() { _red "Error: $*" && exit 1; }
CURL_TIMEOUT=1
CURL_COOLDOWN=10
CURL_MAX_TRIES=180
function install_system_packages() {
if command -v apt-get >/dev/null; then
sed -i 's|ports.ubuntu.com|mirrors.tuna.tsinghua.edu.cn|g' /etc/apt/sources.list
apt-get update -y && apt install -y curl
elif command -v yum >/dev/null; then
yum update -y && yum install -y curl
else
echo "Unknown package manager. Please install gcc, g++, numactl-devel, git, curl, and jq manually."
fi
}
function wait_url_ready() {
local serve_name="$1"
local url="$2"
i=0
while true; do
_info "===> Waiting for ${serve_name} to be ready...${i}s"
i=$((i + CURL_COOLDOWN))
set +e
curl --silent --max-time "$CURL_TIMEOUT" "${url}" >/dev/null
result=$?
set -e
if [ "$result" -eq 0 ]; then
break
fi
if [ "$i" -gt "$CURL_MAX_TRIES" ]; then
_info "===> ${CURL_MAX_TRIES}s exceeded waiting for ${serve_name} to be ready"
return 1
fi
sleep "$CURL_COOLDOWN"
done
_info "===> ${serve_name} is ready."
}
function wait_for_exit() {
local VLLM_PID="$1"
while kill -0 "$VLLM_PID"; do
_info "===> Wait for ${VLLM_PID} to exit."
sleep 1
done
_info "===> Wait for ${VLLM_PID} to exit."
}
function perf_test() {
install_system_packages
export MODEL=Qwen/Qwen3-8B
export VLLM_USE_MODELSCOPE=true
export VLLM_USE_V1=1
python3 -m vllm.entrypoints.openai.api_server --model $MODEL \
--tensor-parallel-size 1 --swap-space 16 --disable-log-stats \
--disable-log-requests --load-format dummy > /dev/null &
wait_url_ready "python3 -m vllm" "localhost:8000/v1/models"
pip config set global.index-url https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple
pip install -r /vllm-workspace/vllm-ascend/benchmarks/requirements-bench.txt
# Round 1
vllm bench serve --model $MODEL \
--dataset-name random --random-input-len 200 --num-prompts 200 --request-rate 1 \
--save-result --result-dir ./
# Round 2
vllm bench serve --model $MODEL \
--dataset-name random --random-input-len 200 --num-prompts 200 --request-rate 1 \
--save-result --result-dir ./
# Round 2
vllm bench serve --model $MODEL \
--dataset-name random --random-input-len 200 --num-prompts 200 --request-rate 1 \
--save-result --result-dir ./
VLLM_PID=$(pgrep -f "python3 -m vllm")
_info "===> Try kill -2 ${VLLM_PID} to exit."
kill -2 "$VLLM_PID"
wait_for_exit "$VLLM_PID"
}
_info "====> Start quickstart_online_test"
perf_test