# 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](https://huggingface.co/Qwen/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](../get_started/installation.md)安装 LMDeploy，并安装上游 Qwen2-VL 模型库需的依赖。

```shell
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：

```shell
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](./vl_pipeline.md)

```python
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：

<details>
  <summary>
    <b>多图多轮对话</b>
  </summary>

```python
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))
```

</details>

<details>
  <summary>
    <b>控制图片分辨率，加速推理</b>
  </summary>

```python
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))
```

</details>

## 在线服务

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

```shell
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：

```shell
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：

```yaml
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：

```shell
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。

```text
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](api_server_vl.md)