vLLM后端部署大模型
部署步骤
去https://catalog.ngc.nvidia.com/orgs/nvidia/containers/tritonserver/tags拉取 vllm 后端的镜像 进入容器
docker run -it --name triton_vllm_25.01 --ipc=host --network host --entrypoint /bin/bash --gpus all -v /data/:/models/ tritonserver:25.01-vllm-python-py3
准备目录
./models/
└── vllm_ai
├── 1
│ └── model.json
└── config.pbtxt
# model.json
{
"model":"/Qwen2.5-7B-Instruct",
"disable_log_requests":true,
"gpu_memory_utilization": 0.9,
"enforce_eager":true,
"max_model_len": 8196,
"tensor_parallel_size": 4
}
# config.pbtxt
backend:"vllm"
instance_group [
{
count: 1
kind: KIND_MODEL
}
]
启动服务
tritonserver --model-repository=./models/
发送请求
curl -X POST localhost:8000/v2/models/vllm_ai/generate \
-d'{"text_input": "What is Triton Inference Server?用中文回答我", "parameters": {"stream": false, "temperature": 0, "exclude_input_in_output": true, "max_tokens": 450}}'
输出：
{"model_name":"vllm_ai","model_version":"1",
"text_output":"。\nTriton Inference Server 是一个高性能的机器学习推理服务器，由 NVIDIA 开发。它能够支持多种深度学习框架生成的模型，并提供统一的接口来执行这些模型的推理任务。Triton Inference Server 可以在多种硬件平台上运行，包括 NVIDIA GPU、CPU 和其他加速器。它支持多种模型格式，包括 ONNX、TensorFlow、PyTorch 等，并且可以进行模型优化和并行化处理，以提高推理性能。此外，Triton Inference Server 还提供了灵活的调度策略和负载均衡功能，可以满足不同应用场景的需求。总之，Triton Inference Server 是一个功能强大、灵活且高效的机器学习推理解决方案。"}
查看性能指标
curl localhost:8002/metrics
输出：
nv_inference_count{model="vllm_ai",version="1"} 4
# HELP nv_inference_exec_count Number of model executions performed (does not include cached requests)
# TYPE nv_inference_exec_count counter
nv_inference_exec_count{model="vllm_ai",version="1"} 4
# HELP nv_inference_request_duration_us Cumulative inference request duration in microseconds (includes cached requests)
# TYPE nv_inference_request_duration_us counter
nv_inference_request_duration_us{model="vllm_ai",version="1"} 4406
# HELP nv_inference_queue_duration_us Cumulative inference queuing duration in microseconds (includes cached requests)
# TYPE nv_inference_queue_duration_us counter
nv_inference_queue_duration_us{model="vllm_ai",version="1"} 463
# HELP nv_inference_compute_input_duration_us Cumulative compute input duration in microseconds (does not include cached requests)
# TYPE nv_inference_compute_input_duration_us counter
nv_inference_compute_input_duration_us{model="vllm_ai",version="1"} 668
# HELP nv_inference_compute_infer_duration_us Cumulative compute inference duration in microseconds (does not include cached requests)
# TYPE nv_inference_compute_infer_duration_us counter
nv_inference_compute_infer_duration_us{model="vllm_ai",version="1"} 3197
# HELP nv_inference_compute_output_duration_us Cumulative inference compute output duration in microseconds (does not include cached requests)
# TYPE nv_inference_compute_output_duration_us counter
nv_inference_compute_output_duration_us{model="vllm_ai",version="1"} 43
从这些指标可以看出：
推理总耗时： 4.4 毫秒（nv_inference_request_duration_us）。
主要耗时阶段：
推理计算（nv_inference_compute_infer_duration_us）： 3.2 毫秒，占总耗时的 72.7%。
输入处理（nv_inference_compute_input_duration_us）： 0.67 毫秒，占总耗时的 15.2%。
队列等待（nv_inference_queue_duration_us）： 0.46 毫秒，占总耗时的 10.5%。
输出处理（nv_inference_compute_output_duration_us）： 0.04 毫秒，占总耗时的 0.9%。
瓶颈分析：
推理计算阶段（nv_inference_compute_infer_duration_us）是主要的性能瓶颈，占总耗时的 72.7%。
部署 OpenAI 兼容接口的服务
需要用到https://github.com/triton-inference-server/server/tree/main/python/openai的代码
git clone 下来
启动服务
python3 /server/python/openai/openai_frontend/main.py \
--model-repository ./models/ \
--tokenizer /Qwen2.5-0.5B-Instruct/
curl 调用
MODEL="vllm_ai"
curl -s http://localhost:9000/v1/chat/completions -H'Content-Type: application/json'-d'{
"model": "'${MODEL}'",
"messages": [{"role": "user", "content": "你好，你是谁!"}]
}'
输出：
{"id":"cmpl-dd951084-f40c-11ef-add2-3fb38431bf72",
"choices":[{"finish_reason":"stop","index":0,
"message":{"content":"你好！我是Qwen，由阿里云开发的大型语言模型。我",
"tool_calls":null,"role":"assistant","function_call":null},"logprobs":null}],
"model":"vllm_ai", "system_fingerprint":null,"object":"chat.completion","usage":null}
代码调用
fromopenaiimportOpenAI
client = OpenAI(
base_url="http://localhost:9000/v1",
api_key="EMPTY",
)
model ="vllm_ai"
completion = client.chat.completions.create(
model=model,
messages=[
{
"role":"system",
"content":"You are a helpful assistant.",
},
{"role":"user","content":"你是deepseek吗?"},
],
max_tokens=256,
)
print(completion.choices[0].message.content)
# 不，我不是DeepSeek。我是阿里云开发的超大规模语言模型“通义千问”，与DeepSeek无关。
# 如果您有任何问题或需要帮助，可以随时告诉我，我会尽力提供支持。
性能测试
使用genai-perf测试，pip install genai-perf
genai-perf profile \
-m vllm_ai \
--tokenizer /Qwen2.5-0.5B-Instruct \
--service-kind openai \
--endpoint-type chat \
--url localhost:9000 \
--synthetic-input-tokens-mean 200 \
--synthetic-input-tokens-stddev 0 \
--output-tokens-mean 100 \
--output-tokens-stddev 0 \
--streaming \
--request-count 50 \
--warmup-request-count 10 \
--concurrency 1
输出：
[INFO] genai_perf.parser:115 - Profiling these models: vllm_ai
[INFO] genai_perf.subcommand.common:208 - Running Perf Analyzer :'perf_analyzer -m vllm_ai --async --input-data artifacts/vllm_ai-openai-chat-concurrency1/inputs.json -i http --concurrency-range 1 --endpoint v1/chat/completions --service-kind openai -u localhost:9000 --request-count 50 --warmup-request-count 10 --profile-export-file artifacts/vllm_ai-openai-chat-concurrency1/profile_export.json --measurement-interval 10000 --stability-percentage 999'
并发 = 1 输出：并发 = 10 输出：
可以获得：首token响应时间、吞吐量等指标