Qwen2-VL

Qwen2-VL#

LMDeploy 支持 Qwen-VL 系列模型，具体如下：

Model

Size

Supported Inference Engine

Qwen-VL-Chat

-

TurboMind

Qwen2-VL

2B, 7B

PyTorch

本文将以Qwen2-VL-7B-Instruct为例，演示使用 LMDeploy 部署 Qwen2-VL 系列模型的方法

安装#

请参考安装文档安装 LMDeploy，并安装上游 Qwen2-VL 模型库需的依赖。

pip install qwen_vl_utils

或者，你可以为 Qwen2-VL 的推理构建 docker image。如果，宿主机器上的 CUDA 版本 >=12.4，你可以执行如下命令构建镜像：

git clone https://github.com/InternLM/lmdeploy.git
cd lmdeploy
docker build --build-arg CUDA_VERSION=cu12 -t openmmlab/lmdeploy:qwen2vl . -f ./docker/Qwen2VL_Dockerfile

否则的话，可以基于 LMDeploy cu11 的镜像来构建：

docker build --build-arg CUDA_VERSION=cu11 -t openmmlab/lmdeploy:qwen2vl . -f ./docker/Qwen2VL_Dockerfile

离线推理#

以下是使用 pipeline 进行离线推理的示例，更多用法参考VLM离线推理 pipeline

from lmdeploy import pipeline
from lmdeploy.vl import load_image

pipe = pipeline('Qwen/Qwen2-VL-2B-Instruct')

image = load_image('https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/tests/data/tiger.jpeg')
response = pipe((f'describe this image', image))
print(response)

更多例子如下：

多图多轮对话 
from lmdeploy import pipeline, GenerationConfig

pipe = pipeline('Qwen/Qwen2-VL-2B-Instruct', log_level='INFO')
messages = [
    dict(role='user', content=[
        dict(type='text', text='Describe the two images in detail.'),
        dict(type='image_url', image_url=dict(url='https://raw.githubusercontent.com/QwenLM/Qwen-VL/master/assets/mm_tutorial/Beijing_Small.jpeg')),
        dict(type='image_url', image_url=dict(url='https://raw.githubusercontent.com/QwenLM/Qwen-VL/master/assets/mm_tutorial/Chongqing_Small.jpeg'))
    ])
]
out = pipe(messages, gen_config=GenerationConfig(top_k=1))

messages.append(dict(role='assistant', content=out.text))
messages.append(dict(role='user', content='What are the similarities and differences between these two images.'))
out = pipe(messages, gen_config=GenerationConfig(top_k=1))
控制图片分辨率，加速推理 
from lmdeploy import pipeline, GenerationConfig

pipe = pipeline('Qwen/Qwen2-VL-2B-Instruct', log_level='INFO')

min_pixels = 64 * 28 * 28
max_pixels = 64 * 28 * 28
messages = [
    dict(role='user', content=[
        dict(type='text', text='Describe the two images in detail.'),
        dict(type='image_url', image_url=dict(min_pixels=min_pixels, max_pixels=max_pixels, url='https://raw.githubusercontent.com/QwenLM/Qwen-VL/master/assets/mm_tutorial/Beijing_Small.jpeg')),
        dict(type='image_url', image_url=dict(min_pixels=min_pixels, max_pixels=max_pixels, url='https://raw.githubusercontent.com/QwenLM/Qwen-VL/master/assets/mm_tutorial/Chongqing_Small.jpeg'))
    ])
]
out = pipe(messages, gen_config=GenerationConfig(top_k=1))

messages.append(dict(role='assistant', content=out.text))
messages.append(dict(role='user', content='What are the similarities and differences between these two images.'))
out = pipe(messages, gen_config=GenerationConfig(top_k=1))

在线服务#

你可以通过 lmdeploy serve api_server CLI 工具启动服务：

lmdeploy serve api_server Qwen/Qwen2-VL-2B-Instruct

也可以基于前文构建的 docker image 启动服务：

docker run --runtime nvidia --gpus all \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HUGGING_FACE_HUB_TOKEN=<secret>" \
    -p 23333:23333 \
    --ipc=host \
    openmmlab/lmdeploy:qwen2vl \
    lmdeploy serve api_server Qwen/Qwen2-VL-2B-Instruct

Docker compose 的方式也是一种选择。在 LMDeploy 代码库的根目录下创建docker-compose.yml文件，内容参考如下：

version: '3.5'

services:
  lmdeploy:
    container_name: lmdeploy
    image: openmmlab/lmdeploy:qwen2vl
    ports:
      - "23333:23333"
    environment:
      HUGGING_FACE_HUB_TOKEN: <secret>
    volumes:
      - ~/.cache/huggingface:/root/.cache/huggingface
    stdin_open: true
    tty: true
    ipc: host
    command: lmdeploy serve api_server Qwen/Qwen2-VL-2B-Instruct
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: "all"
              capabilities: [gpu]

然后，你就可以执行命令启动服务了：

docker-compose up -d

通过docker logs -f lmdeploy可以查看启动的日志信息，如果发现类似下方的日志信息，就表明服务启动成功了。

HINT:    Please open  http://0.0.0.0:23333   in a browser for detailed api usage!!!
HINT:    Please open  http://0.0.0.0:23333   in a browser for detailed api usage!!!
HINT:    Please open  http://0.0.0.0:23333   in a browser for detailed api usage!!!
INFO:     Started server process [2439]
INFO:     Waiting for application startup.
INFO:     Application startup complete.
INFO:     Uvicorn running on  http://0.0.0.0:23333  (Press CTRL+C to quit)

有关 lmdeploy serve api_server 的详细参数可以通过lmdeploy serve api_server -h查阅。

关于 api_server 更多的介绍，以及访问 api_server 的方法，请阅读此处

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