# InternVL

LMDeploy supports the following InternVL series of models, which are detailed in the table below:

|         Model         |     Size      | Supported Inference Engine |
| :-------------------: | :-----------: | :------------------------: |
|       InternVL        |    13B-19B    |         TurboMind          |
|      InternVL1.5      |    2B-26B     |     TurboMind, PyTorch     |
|       InternVL2       |      4B       |          PyTorch           |
|       InternVL2       | 1B-2B, 8B-76B |     TurboMind, PyTorch     |
| InternVL2.5/2.5-MPO/3 |    1B-78B     |     TurboMind, PyTorch     |
|     Mono-InternVL     |      2B       |          PyTorch           |

The next chapter demonstrates how to deploy an InternVL model using LMDeploy, with [InternVL2-8B](https://huggingface.co/OpenGVLab/InternVL2-8B) as an example.

## Installation

Please install LMDeploy by following the [installation guide](../get_started/installation.md), and install other packages that InternVL2 needs

```shell
pip install timm
# It is recommended to find the whl package that matches the environment from the releases on https://github.com/Dao-AILab/flash-attention.
pip install flash-attn
```

Or, you can build a docker image to set up the inference environment. If the CUDA version on your host machine is `>=12.4`, you can run:

```
docker build --build-arg CUDA_VERSION=cu12 -t openmmlab/lmdeploy:internvl . -f ./docker/InternVL_Dockerfile
```

Otherwise, you can go with:

```shell
git clone https://github.com/InternLM/lmdeploy.git
cd lmdeploy
docker build --build-arg CUDA_VERSION=cu11 -t openmmlab/lmdeploy:internvl . -f ./docker/InternVL_Dockerfile
```

## Offline inference

The following sample code shows the basic usage of VLM pipeline. For detailed information, please refer to [VLM Offline Inference Pipeline](./vl_pipeline.md)

```python
from lmdeploy import pipeline
from lmdeploy.vl import load_image

pipe = pipeline('OpenGVLab/InternVL2-8B')

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

More examples are listed below:

<details>
  <summary>
    <b>multi-image multi-round conversation, combined images</b>
  </summary>

```python
from lmdeploy import pipeline, GenerationConfig
from lmdeploy.vl.constants import IMAGE_TOKEN

pipe = pipeline('OpenGVLab/InternVL2-8B', log_level='INFO')
messages = [
    dict(role='user', content=[
        dict(type='text', text=f'{IMAGE_TOKEN}{IMAGE_TOKEN}\nDescribe the two images in detail.'),
        dict(type='image_url', image_url=dict(max_dynamic_patch=12, url='https://raw.githubusercontent.com/OpenGVLab/InternVL/main/internvl_chat/examples/image1.jpg')),
        dict(type='image_url', image_url=dict(max_dynamic_patch=12, url='https://raw.githubusercontent.com/OpenGVLab/InternVL/main/internvl_chat/examples/image2.jpg'))
    ])
]
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>multi-image multi-round conversation, separate images</b>
  </summary>

```python
from lmdeploy import pipeline, GenerationConfig
from lmdeploy.vl.constants import IMAGE_TOKEN

pipe = pipeline('OpenGVLab/InternVL2-8B', log_level='INFO')
messages = [
    dict(role='user', content=[
        dict(type='text', text=f'Image-1: {IMAGE_TOKEN}\nImage-2: {IMAGE_TOKEN}\nDescribe the two images in detail.'),
        dict(type='image_url', image_url=dict(max_dynamic_patch=12, url='https://raw.githubusercontent.com/OpenGVLab/InternVL/main/internvl_chat/examples/image1.jpg')),
        dict(type='image_url', image_url=dict(max_dynamic_patch=12, url='https://raw.githubusercontent.com/OpenGVLab/InternVL/main/internvl_chat/examples/image2.jpg'))
    ])
]
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>video multi-round conversation</b>
  </summary>

```python
import numpy as np
from lmdeploy import pipeline, GenerationConfig
from decord import VideoReader, cpu
from lmdeploy.vl.constants import IMAGE_TOKEN
from lmdeploy.vl.utils import encode_image_base64
from PIL import Image
pipe = pipeline('OpenGVLab/InternVL2-8B', log_level='INFO')


def get_index(bound, fps, max_frame, first_idx=0, num_segments=32):
    if bound:
        start, end = bound[0], bound[1]
    else:
        start, end = -100000, 100000
    start_idx = max(first_idx, round(start * fps))
    end_idx = min(round(end * fps), max_frame)
    seg_size = float(end_idx - start_idx) / num_segments
    frame_indices = np.array([
        int(start_idx + (seg_size / 2) + np.round(seg_size * idx))
        for idx in range(num_segments)
    ])
    return frame_indices


def load_video(video_path, bound=None, num_segments=32):
    vr = VideoReader(video_path, ctx=cpu(0), num_threads=1)
    max_frame = len(vr) - 1
    fps = float(vr.get_avg_fps())
    pixel_values_list, num_patches_list = [], []
    frame_indices = get_index(bound, fps, max_frame, first_idx=0, num_segments=num_segments)
    imgs = []
    for frame_index in frame_indices:
        img = Image.fromarray(vr[frame_index].asnumpy()).convert('RGB')
        imgs.append(img)
    return imgs


video_path = 'red-panda.mp4'
imgs = load_video(video_path, num_segments=8)

question = ''
for i in range(len(imgs)):
    question = question + f'Frame{i+1}: {IMAGE_TOKEN}\n'

question += 'What is the red panda doing?'

content = [{'type': 'text', 'text': question}]
for img in imgs:
    content.append({'type': 'image_url', 'image_url': {'max_dynamic_patch': 1, 'url': f'data:image/jpeg;base64,{encode_image_base64(img)}'}})

messages = [dict(role='user', content=content)]
out = pipe(messages, gen_config=GenerationConfig(top_k=1))

messages.append(dict(role='assistant', content=out.text))
messages.append(dict(role='user', content='Describe this video in detail. Don\'t repeat.'))
out = pipe(messages, gen_config=GenerationConfig(top_k=1))
```

</details>

## Online serving

You can launch the server by the `lmdeploy serve api_server` CLI:

```shell
lmdeploy serve api_server OpenGVLab/InternVL2-8B
```

You can also start the service using the aforementioned built docker image:

```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:internvl \
    lmdeploy serve api_server OpenGVLab/InternVL2-8B
```

The docker compose is another option. Create a `docker-compose.yml` configuration file in the root directory of the lmdeploy project as follows:

```yaml
version: '3.5'

services:
  lmdeploy:
    container_name: lmdeploy
    image: openmmlab/lmdeploy:internvl
    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 OpenGVLab/InternVL2-8B
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: "all"
              capabilities: [gpu]
```

Then, you can execute the startup command as below:

```shell
docker-compose up -d
```

If you find the following logs after running `docker logs -f lmdeploy`, it means the service launches successfully.

```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)
```

The arguments of `lmdeploy serve api_server` can be reviewed in detail by `lmdeploy serve api_server -h`.

More information about `api_server` as well as how to access the service can be found from [here](api_server_vl.md)