【AI实战】大模型 LLM 部署推理框架的 vLLM 应用
- vLLM介绍
- 环境配置
- 环境要求
- 安装 vllm
- 算力要求
- 算力查询方法
- 算力问题
- Quickstart
- 离线批量推理
- API Server
- 兼容 OpenAI Server
- Serving
- 分布式推理和服务
- 使用 SkyPilot 运行服务
- 模型
- vLLM支持的模型
- 添加自己的模型
- 参考
vLLM介绍
vLLM is a fast and easy-to-use library for LLM inference and serving.
vLLM 速度很快:
- State-of-the-art serving throughput
- Efficient management of attention key and value memory with PagedAttention
- Continuous batching of incoming requests
- Optimized CUDA kernels
vLLM灵活且易于使用:
- Seamless integration with popular HuggingFace models
- High-throughput serving with various decoding algorithms, including parallel sampling, beam search, and more
- Tensor parallelism support for distributed inference
- Streaming outputs
- OpenAI-compatible API server
vLLM 无缝支持多数 Huggingface 模型,包括:
- BLOOM (bigscience/bloom, bigscience/bloomz, etc.)
- GPT-2 (gpt2, gpt2-xl, etc.)
- GPT BigCode (bigcode/starcoder, bigcode/gpt_bigcode-santacoder, etc.)
- GPT-J (EleutherAI/gpt-j-6b, nomic-ai/gpt4all-j, etc.)
- GPT-NeoX (EleutherAI/gpt-neox-20b, databricks/dolly-v2-12b, stabilityai/stablelm-tuned-alpha-7b, etc.)
- LLaMA (lmsys/vicuna-13b-v1.3, young-geng/koala, openlm-research/open_llama_13b, etc.)
- MPT (mosaicml/mpt-7b, mosaicml/mpt-30b, etc.)
- OPT (facebook/opt-66b, facebook/opt-iml-max-30b, etc.)
环境配置
环境要求
OS: Linux
Python: 3.8 or higher
CUDA: 11.0 – 11.8
GPU: compute capability 7.0 or higher (e.g., V100, T4, RTX20xx, A100, L4, etc.)
安装 vllm
- pip安装
pip install vllm
- 源码安装
git clone https://github.com/vllm-project/vllm.gitcd vllmpip install -e .# This may take 5-10 minutes.
算力要求
算力查询方法
- 打开bing查询地址:https://cn.bing.com/
- 查询方式选择 国际版
- 输入查询内容:
t4 GPUscompute capability
我的 GPU 是 T4,修改 t4 为你的即可
- 查询结果如下:
算力问题
vllm 对GPU 的 compute capability 要求必须大于等于 7.0,否则会报错,错误信息如下:
RuntimeError: GPUs with compute capability less than 7.0 are not supported.
Quickstart
离线批量推理
示例代码:
from vllm import LLM, SamplingParamsprompts = ["Hello, my name is","The president of the United States is","The capital of France is","The future of AI is",]sampling_params = SamplingParams(temperature=0.8, top_p=0.95)llm = LLM(model="facebook/opt-125m")outputs = llm.generate(prompts, sampling_params)# Print the outputs.for output in outputs:prompt = output.promptgenerated_text = output.outputs[0].textprint(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
API Server
以 FastAPI server
为例子, 服务使用 AsyncLLMEngine
类来支持异步请求。
- 开启服务:
python -m vllm.entrypoints.api_server
默认接口:http://localhost:8000
默认模型:OPT-125M model
- 测试:
curl http://localhost:8000/generate \-d '{"prompt": "San Francisco is a","use_beam_search": true,"n": 4,"temperature": 0}'
兼容 OpenAI Server
- 开启服务:
python -m vllm.entrypoints.openai.api_server --model facebook/opt-125m
可选参数:--host
,--port
- 查询服务:
curl http://localhost:8000/v1/models
- 测试:
curl http://localhost:8000/v1/completions \-H "Content-Type: application/json" \-d '{"model": "facebook/opt-125m","prompt": "San Francisco is a","max_tokens": 7,"temperature": 0}'
Serving
分布式推理和服务
安装依赖库:
pip install ray
- 多GPU推理
4块GPU推理:
from vllm import LLMllm = LLM("facebook/opt-13b", tensor_parallel_size=4)output = llm.generate("San Franciso is a")
使用 tensor_parallel_size 指定 GPU 数量
- 多GPU服务
python -m vllm.entrypoints.api_server \--model facebook/opt-13b \--tensor-parallel-size 4
- 扩展到多节点
运行vllm之前开启Ray runtime
:
# On head noderay start --head# On worker nodesray start --address=
使用 SkyPilot 运行服务
安装 SkyPilot :
pip install skypilotsky check
serving.yaml:
resources:accelerators: A100envs:MODEL_NAME: decapoda-research/llama-13b-hfTOKENIZER: hf-internal-testing/llama-tokenizersetup: |conda create -n vllm python=3.9 -yconda activate vllmgit clone https://github.com/vllm-project/vllm.gitcd vllmpip install .pip install gradiorun: |conda activate vllmecho 'Starting vllm api server...'python -u -m vllm.entrypoints.api_server \--model $MODEL_NAME \--tensor-parallel-size $SKYPILOT_NUM_GPUS_PER_NODE \--tokenizer $TOKENIZER 2>&1 | tee api_server.log &echo 'Waiting for vllm api server to start...'while ! `cat api_server.log | grep -q 'Uvicorn running on'`; do sleep 1; doneecho 'Starting gradio server...'python vllm/examples/gradio_webserver.py
开启服务:
sky launch serving.yaml
其他可选参数:
sky launch -c vllm-serve-new -s serve.yaml --gpus A100:8 --env MODEL_NAME=decapoda-research/llama-65b-hf
测试:
浏览器打开:https://.gradio.live
模型
vLLM支持的模型
https://vllm.readthedocs.io/en/latest/models/supported_models.html#supported-models
添加自己的模型
本文档提供了将HuggingFace Transformers模型集成到vLLM中的高级指南。
https://vllm.readthedocs.io/en/latest/models/adding_model.html
参考
1.https://vllm.readthedocs.io/en/latest/
2.https://github.com/vllm-project/vllm
3.https://vllm.ai/
4.https://github.com/vllm-project/vllm/discussions
5.https://github.com/skypilot-org/skypilot/blob/master/llm/vllm