基于 SWIFT 的 VLLM 推理加速与部署实战

基于 SWIFT 的 VLLM 推理加速与部署实战 | 极客日志

基于 SWIFT 的 VLLM 推理加速与部署实战

1. 环境准备

支持 GPU 设备：A10, 3090, V100, A100 等。

# 设置 pip 全局镜像 (加速下载)
pip config set global.index-url https://mirrors.aliyun.com/pypi/simple/

# 安装 ms-swift
pip install 'ms-swift[llm]' -U

# vllm 与 cuda 版本有对应关系，请按照官方文档选择版本
pip install vllm -U
pip install openai -U

# 环境对齐 (通常不需要运行。如果你运行错误，可以跑下面的代码，仓库使用最新环境测试)
pip install -r requirements/framework.txt -U
pip install -r requirements/llm.txt -U

2. 推理加速

2.1 Qwen-7B-Chat

import os
os.environ['CUDA_VISIBLE_DEVICES'] = '0'

from swift.llm import (
    ModelType, get_vllm_engine, get_default_template_type,
    get_template, inference_vllm
)

model_type = ModelType.qwen_7b_chat
llm_engine = get_vllm_engine(model_type)
template_type = get_default_template_type(model_type)
template = get_template(template_type, llm_engine.hf_tokenizer)
# 与 `transformers.GenerationConfig` 类似的接口
llm_engine.generation_config.max_new_tokens = 256

request_list = [{'query': '你好!'}, {'query': '浙江的省会在哪？'}]
resp_list = inference_vllm(llm_engine, template, request_list)
for request, resp in zip(request_list, resp_list):
    print(f"query: {request['query']}")
    print(f"response: {resp['response']}")

history1 = resp_list[1]['history']
request_list = [{'query': '这有什么好吃的', 'history': history1}]
resp_list = inference_vllm(llm_engine, template, request_list)
for request, resp in zip(request_list, resp_list):
    print(f"query: {request['query']}")
    print(f"response: {resp['response']}")
    print(f"history: {resp['history']}")

2.2 流式输出

import os
os.environ['CUDA_VISIBLE_DEVICES'] = '0'

from swift.llm import (
    ModelType, get_vllm_engine, get_default_template_type,
    get_template, inference_stream_vllm
)

model_type = ModelType.qwen_7b_chat
llm_engine = get_vllm_engine(model_type)
template_type = get_default_template_type(model_type)
template = get_template(template_type, llm_engine.hf_tokenizer)
llm_engine.generation_config.max_new_tokens = 256

request_list = [{'query': '你好!'}, {'query': '浙江的省会在哪？'}]
gen = inference_stream_vllm(llm_engine, template, request_list)
query_list = [request['query'] for request in request_list]
print(f"query_list: {query_list}")
for resp_list in gen:
    response_list = [resp['response'] for resp in resp_list]
    print(f'response_list: {response_list}')

history1 = resp_list[1]['history']
request_list = [{'query': '这有什么好吃的', 'history': history1}]
gen = inference_stream_vllm(llm_engine, template, request_list)
query = request_list[0]['query']
print(f"query: {query}")
for resp_list in gen:
    response = resp_list[0]['response']
    print(f'response: {response}')

history = resp_list[0]['history']
print()

2.3 ChatGLM3

import os
os.environ['CUDA_VISIBLE_DEVICES'] = '0'

from swift.llm import (
    ModelType, get_vllm_engine, get_default_template_type,
    get_template, inference_vllm
)

model_type = ModelType.chatglm3_6b
llm_engine = get_vllm_engine(model_type)
template_type = get_default_template_type(model_type)
template = get_template(template_type, llm_engine.hf_tokenizer)
llm_engine.generation_config.max_new_tokens = 256

request_list = [{'query': '你好!'}, {'query': '浙江的省会在哪？'}]
resp_list = inference_vllm(llm_engine, template, request_list)
for request, resp in zip(request_list, resp_list):
    print(f"query: {request['query']}")
    print(f"response: {resp['response']}")

history1 = resp_list[1]['history']
request_list = [{'query': '这有什么好吃的', 'history': history1}]
resp_list = inference_vllm(llm_engine, template, request_list)
for request, resp in zip(request_list, resp_list):
    print(f"query: {request['query']}")
    print(f"response: {resp['response']}")
    print(f"history: {resp['history']}")

2.4 使用 CLI

# qwen
CUDA_VISIBLE_DEVICES=0 swift infer --model_type qwen-7b-chat --infer_backend vllm
# yi
CUDA_VISIBLE_DEVICES=0 swift infer --model_type yi-6b-chat --infer_backend vllm
# gptq
CUDA_VISIBLE_DEVICES=0 swift infer --model_type qwen1half-7b-chat-int4 --infer_backend vllm

2.5 微调后的模型

单样本推理: 使用 LoRA 进行微调的模型你需要先 merge-lora，产生完整的 checkpoint 目录。使用全参数微调的模型可以无缝使用 VLLM 进行推理加速。

import os
os.environ['CUDA_VISIBLE_DEVICES'] = '0'

from swift.llm import (
    ModelType, get_vllm_engine, get_default_template_type,
    get_template, inference_vllm
)

ckpt_dir = 'vx-xxx/checkpoint-100-merged'
model_type = ModelType.qwen_7b_chat
template_type = get_default_template_type(model_type)

llm_engine = get_vllm_engine(model_type, model_id_or_path=ckpt_dir)
tokenizer = llm_engine.hf_tokenizer
template = get_template(template_type, tokenizer)
query = '你好'
resp = inference_vllm(llm_engine, template, [{'query': query}])[0]
print(f"response: {resp['response']}")
print(f"history: {resp['history']}")

使用 CLI:

# merge LoRA 增量权重并使用 vllm 进行推理加速
# 如果你需要量化，可以指定 `--quant_bits 4`.
CUDA_VISIBLE_DEVICES=0 swift export \
    --ckpt_dir 'xxx/vx-xxx/checkpoint-xxx' --merge_lora true

# 使用数据集评估
CUDA_VISIBLE_DEVICES=0 swift infer \
    --ckpt_dir 'xxx/vx-xxx/checkpoint-xxx-merged' \
    --infer_backend vllm \
    --load_dataset_config true

# 人工评估
CUDA_VISIBLE_DEVICES=0 swift infer \
    --ckpt_dir 'xxx/vx-xxx/checkpoint-xxx-merged' \
    --infer_backend vllm

3. Web-UI 加速

3.1 原始模型

CUDA_VISIBLE_DEVICES=0 swift app-ui --model_type qwen-7b-chat --infer_backend vllm

3.2 微调后模型

# merge LoRA 增量权重并使用 vllm 作为 backend 构建 app-ui
# 如果你需要量化，可以指定 `--quant_bits 4`.
CUDA_VISIBLE_DEVICES=0 swift export \
    --ckpt_dir 'xxx/vx-xxx/checkpoint-xxx' --merge_lora true

CUDA_VISIBLE_DEVICES=0 swift app-ui --ckpt_dir 'xxx/vx-xxx/checkpoint-xxx-merged' --infer_backend vllm

4. 部署

swift 使用 VLLM 作为推理后端，并兼容 OpenAI 的 API 样式。服务端的部署命令行参数可以参考相关文档。客户端的 OpenAI 的 API 参数可以参考 OpenAI 官方文档。

4.1 原始模型

Qwen-7B-Chat

服务端:

CUDA_VISIBLE_DEVICES=0 swift deploy --model_type qwen-7b-chat
# 多卡部署
RAY_memory_monitor_refresh_ms=0 CUDA_VISIBLE_DEVICES=0,1,2,3 swift deploy --model_type qwen-7b-chat --tensor_parallel_size 4

客户端:

测试:

curl http://localhost:8000/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "qwen-7b-chat",
"messages": [{"role": "user", "content": "晚上睡不着觉怎么办？"}],
"max_tokens": 256,
"temperature": 0
}'

使用 Swift:

from swift.llm import get_model_list_client, XRequestConfig, inference_client

model_list = get_model_list_client()
model_type = model_list.data[0].id
print(f'model_type: {model_type}')

query = '浙江的省会在哪里?'
request_config = XRequestConfig(seed=42)
resp = inference_client(model_type, query, request_config=request_config)
response = resp.choices[0].message.content
print(f'query: {query}')
print(f'response: {response}')

history = [(query, response)]
query = '这有什么好吃的?'
request_config = XRequestConfig(stream=True, seed=42)
stream_resp = inference_client(model_type, query, history, request_config=request_config)
print(f'query: {query}')
print('response: ', end='')
for chunk in stream_resp:
    print(chunk.choices[0].delta.content, end='', flush=True)
print()

使用 OpenAI:

from openai import OpenAI
client = OpenAI(
    api_key='EMPTY',
    base_url='http://localhost:8000/v1',
)
model_type = client.models.list().data[0].id
print(f'model_type: {model_type}')

query = '浙江的省会在哪里?'
messages = [{
    'role': 'user',
    'content': query
}]
resp = client.chat.completions.create(
    model=model_type,
    messages=messages,
    seed=42)
response = resp.choices[0].message.content
print(f'query: {query}')
print(f'response: {response}')

# 流式
messages.append({'role': 'assistant', 'content': response})
query = '这有什么好吃的?'
messages.append({'role': 'user', 'content': query})
stream_resp = client.chat.completions.create(
    model=model_type,
    messages=messages,
    stream=True,
    seed=42)

print(f'query: {query}')
print('response: ', end='')
for chunk in stream_resp:
    print(chunk.choices[0].delta.content, end='', flush=True)
print()

Qwen-7B

服务端:

CUDA_VISIBLE_DEVICES=0 swift deploy --model_type qwen-7b
# 多卡部署
RAY_memory_monitor_refresh_ms=0 CUDA_VISIBLE_DEVICES=0,1,2,3 swift deploy --model_type qwen-7b --tensor_parallel_size 4

客户端:

测试:

curl http://localhost:8000/v1/completions \
-H "Content-Type: application/json" \
-d '{
"model": "qwen-7b",
"prompt": "浙江 -> 杭州\n安徽 -> 合肥\n四川 ->",
"max_tokens": 32,
"temperature": 0.1,
"seed": 42
}'

使用 Swift:

from swift.llm import get_model_list_client, XRequestConfig, inference_client

model_list = get_model_list_client()
model_type = model_list.data[0].id
print(f'model_type: {model_type}')

query = '浙江 -> 杭州\n安徽 -> 合肥\n四川 ->'
request_config = XRequestConfig(max_tokens=32, temperature=0.1, seed=42)
resp = inference_client(model_type, query, request_config=request_config)
response = resp.choices[0].text
print(f'query: {query}')
print(f'response: {response}')

request_config.stream = True
stream_resp = inference_client(model_type, query, request_config=request_config)
print(f'query: {query}')
print('response: ', end='')
for chunk in stream_resp:
    print(chunk.choices[0].text, end='', flush=True)
print()

使用 OpenAI:

from openai import OpenAI
client = OpenAI(
    api_key='EMPTY',
    base_url='http://localhost:8000/v1',
)
model_type = client.models.list().data[0].id
print(f'model_type: {model_type}')

query = '浙江 -> 杭州\n安徽 -> 合肥\n四川 ->'
kwargs = {'model': model_type, 'prompt': query, 'seed': 42, 'temperature': 0.1, 'max_tokens': 32}

resp = client.completions.create(**kwargs)
response = resp.choices[0].text
print(f'query: {query}')
print(f'response: {response}')

# 流式
stream_resp = client.completions.create(stream=True, **kwargs)
response = resp.choices[0].text
print(f'query: {query}')
print('response: ', end='')
for chunk in stream_resp:
    print(chunk.choices[0].text, end='', flush=True)
print()

4.2 微调后模型

服务端:

# merge LoRA 增量权重并部署
# 如果你需要量化，可以指定 `--quant_bits 4`.
CUDA_VISIBLE_DEVICES=0 swift export \
    --ckpt_dir 'xxx/vx-xxx/checkpoint-xxx' --merge_lora true

CUDA_VISIBLE_DEVICES=0 swift deploy --ckpt_dir 'xxx/vx-xxx/checkpoint-xxx-merged'

客户端示例代码同原始模型。

4.3 多 LoRA 部署

目前 pt 方式部署模型已经支持 peft>=0.10.0 进行多 LoRA 部署，具体方法为：

确保部署时 merge_lora 为 False
使用 --lora_modules 参数，可以查看命令行文档
推理时指定 lora tuner 的名字到模型字段

举例：

# 假设从 llama3-8b-instruct 训练了一个名字叫卡卡罗特的 LoRA 模型
# 服务端
swift deploy --ckpt_dir /mnt/ckpt-1000 --infer_backend pt --lora_modules my_tuner=/mnt/my-tuner
# 会加载起来两个 tuner，一个是 `/mnt/ckpt-1000` 的 `default-lora`，一个是 `/mnt/my-tuner` 的 `my_tuner`

# 客户端
curl http://localhost:8000/v1/chat/completions -H "Content-Type: application/json" -d '{
"model": "my-tuner",
"messages": [{"role": "user", "content": "who are you?"}],
"max_tokens": 256,
"temperature": 0
}'
# resp: 我是卡卡罗特...
# 如果指定 mode='llama3-8b-instruct'，则返回 I'm llama3...，即原模型的返回值

注意：--ckpt_dir 参数如果是个 lora 路径，则原来的 default 会被加载到 default-lora 的 tuner 上，其他的 tuner 需要通过 lora_modules 自行加载。

5. VLLM & LoRA

VLLM & LoRA 支持的模型可以查看官方文档。

5.1 准备 LoRA

# Experimental environment: 4 * A100
# 4 * 30GB GPU memory
CUDA_VISIBLE_DEVICES=0,1,2,3 \
NPROC_PER_NODE=4 \
swift sft \
    --model_type llama2-7b-chat \
    --dataset sharegpt-gpt4-mini \
    --train_dataset_sample 1000 \
    --logging_steps 5 \
    --max_length 4096 \
    --learning_rate 5e-5 \
    --warmup_ratio 0.4 \
    --output_dir output \
    --lora_target_modules ALL \
    --self_cognition_sample 500 \
    --model_name 小黄 'Xiao Huang' \
    --model_author 魔搭 ModelScope

将 lora 从 swift 格式转换成 peft 格式：

CUDA_VISIBLE_DEVICES=0 swift export \
    --ckpt_dir output/llama2-7b-chat/vx-xxx/checkpoint-xxx \
    --to_peft_format true

5.2 VLLM 推理加速

推理:

CUDA_VISIBLE_DEVICES=0 swift infer \
    --ckpt_dir output/llama2-7b-chat/vx-xxx/checkpoint-xxx-peft \
    --infer_backend vllm \
    --vllm_enable_lora true

单样本推理:

import os
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
import torch
from swift.llm import (
    ModelType, get_vllm_engine, get_default_template_type,
    get_template, inference_stream_vllm, LoRARequest, inference_vllm
)

lora_checkpoint = 'output/llama2-7b-chat/vx-xxx/checkpoint-xxx-peft'
lora_request = LoRARequest('default-lora', 1, lora_checkpoint)

model_type = ModelType.llama2_7b_chat
llm_engine = get_vllm_engine(model_type, torch.float16, enable_lora=True,
                             max_loras=1, max_lora_rank=16)
template_type = get_default_template_type(model_type)
template = get_template(template_type, llm_engine.hf_tokenizer)
llm_engine.generation_config.max_new_tokens = 256

# use lora
request_list = [{'query': 'who are you?'}]
query = request_list[0]['query']
resp_list = inference_vllm(llm_engine, template, request_list, lora_request=lora_request)
response = resp_list[0]['response']
print(f'query: {query}')
print(f'response: {response}')

# no lora
gen = inference_stream_vllm(llm_engine, template, request_list)
query = request_list[0]['query']
print(f'query: {query}\nresponse: ', end='')
print_idx = 0
for resp_list in gen:
    response = resp_list[0]['response']
    print(response[print_idx:], end='', flush=True)
    print_idx = (response)
()

5.3 部署

服务端:

CUDA_VISIBLE_DEVICES=0 swift deploy \
    --ckpt_dir output/llama2-7b-chat/vx-xxx/checkpoint-xxx-peft \
    --infer_backend vllm \
    --vllm_enable_lora true

客户端:

测试:

curl http://localhost:8000/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "default-lora",
"messages": [{"role": "user", "content": "who are you?"}],
"max_tokens": 256,
"temperature": 0
}'

curl http://localhost:8000/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "llama2-7b-chat",
"messages": [{"role": "user", "content": "who are you?"}],
"max_tokens": 256,
"temperature": 0
}'

使用 OpenAI:

from openai import OpenAI
client = OpenAI(
    api_key='EMPTY',
    base_url='http://localhost:8000/v1',
)
model_type_list = [model.id for model in client.models.list().data]
print(f'model_type_list: {model_type_list}')

query = 'who are you?'
messages = [{
    'role': 'user',
    'content': query
}]
resp = client.chat.completions.create(
    model='default-lora',
    messages=messages,
    seed=42)
response = resp.choices[0].message.content
print(f'query: {query}')
print(f'response: {response}')

# 流式
stream_resp = client.chat.completions.create(
    model='llama2-7b-chat',
    messages=messages,
    stream=True,
    seed=42)

print(f'query: {query}')
print('response: ', end='')
for chunk in stream_resp:
    print(chunk.choices[0].delta.content, end='', flush=True)
print()

总结

本文详细介绍了如何使用 MS-SWIFT 框架结合 VLLM 引擎进行大语言模型的推理加速与生产部署。通过上述步骤，开发者可以实现从环境配置、基础推理、流式输出到多卡部署及 LoRA 微调模型集成的完整工作流。VLLM 的高吞吐量特性显著降低了推理延迟，而 SWIFT 提供的统一接口简化了不同模型（如 Qwen、ChatGLM）的适配过程。在生产环境中，建议根据硬件资源合理配置 Tensor Parallelism，并利用 LoRA 技术实现高效的多租户模型管理。

基于 SWIFT 的 VLLM 推理加速与部署实战