MiniCPM-V

LMDeploy 支持以下 MiniCPM-V 系列模型，具体如下表所示：

模型	支持的推理引擎
MiniCPM-Llama3-V-2_5	TurboMind
MiniCPM-V-2_6	TurboMind

下一章将演示如何使用LMDeploy部署MiniCPM-V模型，以MiniCPM-V-2_6为例。

安装

请按照安装指南安装LMDeploy。

离线推理

以下示例代码展示了VLM管道的基本用法。有关详细信息，请参阅VLM离线推理管道

from lmdeploy import pipeline
from lmdeploy.vl import load_image

pipe = pipeline('openbmb/MiniCPM-V-2_6')

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

更多示例如下：

Chat with multiple images

from lmdeploy import pipeline, GenerationConfig

pipe = pipeline('openbmb/MiniCPM-V-2_6', 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(max_slice_nums=9, url='https://raw.githubusercontent.com/OpenGVLab/InternVL/main/internvl_chat/examples/image1.jpg')),
        dict(type='image_url', image_url=dict(max_slice_nums=9, url='https://raw.githubusercontent.com/OpenGVLab/InternVL/main/internvl_chat/examples/image2.jpg'))
    ])
]
out = pipe(messages, gen_config=GenerationConfig(top_k=1))
print(out.text)

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))
print(out.text)

In-context few-shot learning

from lmdeploy import pipeline, GenerationConfig

pipe = pipeline('openbmb/MiniCPM-V-2_6', log_level='INFO')

question = "production date"
messages = [
    dict(role='user', content=[
        dict(type='text', text=question),
        dict(type='image_url', image_url=dict(url='example1.jpg')),
    ]),
    dict(role='assistant', content='2023.08.04'),
    dict(role='user', content=[
        dict(type='text', text=question),
        dict(type='image_url', image_url=dict(url='example2.jpg')),
    ]),
    dict(role='assistant', content='2007.04.24'),
    dict(role='user', content=[
        dict(type='text', text=question),
        dict(type='image_url', image_url=dict(url='test.jpg')),
    ])
]
out = pipe(messages, gen_config=GenerationConfig(top_k=1))
print(out.text)

Chat with video

from lmdeploy import pipeline, GenerationConfig
from lmdeploy.vl.utils import encode_image_base64
import torch
from PIL import Image
from transformers import AutoModel, AutoTokenizer
from decord import VideoReader, cpu    # pip install decord

pipe = pipeline('openbmb/MiniCPM-V-2_6', log_level='INFO')

MAX_NUM_FRAMES=64 # if cuda OOM set a smaller number
def encode_video(video_path):
    def uniform_sample(l, n):
        gap = len(l) / n
        idxs = [int(i * gap + gap / 2) for i in range(n)]
        return [l[i] for i in idxs]
    vr = VideoReader(video_path, ctx=cpu(0))
    sample_fps = round(vr.get_avg_fps() / 1)  # FPS
    frame_idx = [i for i in range(0, len(vr), sample_fps)]
    if len(frame_idx) > MAX_NUM_FRAMES:
        frame_idx = uniform_sample(frame_idx, MAX_NUM_FRAMES)
    frames = vr.get_batch(frame_idx).asnumpy()
    frames = [Image.fromarray(v.astype('uint8')) for v in frames]
    print('num frames:', len(frames))
    return frames

video_path="video_test.mp4"
frames = encode_video(video_path)
question = "Describe the video"

content=[dict(type='text', text=question)]
for frame in frames:
    content.append(dict(type='image_url', image_url=dict(use_image_id=False, max_slice_nums=2,
        url=f'data:image/jpeg;base64,{encode_image_base64(frame)}')))

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

在线服务

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

lmdeploy serve api_server openbmb/MiniCPM-V-2_6

你也可以使用官方的 lmdeploy docker 镜像来启动服务：

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:latest \
    lmdeploy serve api_server openbmb/MiniCPM-V-2_6

Docker compose 是另一种选择。在 lmdeploy 项目的根目录下创建一个 docker-compose.yml 配置文件，如下所示：

version: '3.5'

services:
  lmdeploy:
    container_name: lmdeploy
    image: openmmlab/lmdeploy:latest
    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 openbmb/MiniCPM-V-2_6
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: "all"
              capabilities: [gpu]

然后，您可以执行如下启动命令：

docker-compose up -d

如果你在运行docker logs -f lmdeploy后看到以下日志，这意味着服务已成功启动。

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)

lmdeploy serve api_server 的参数可以通过 lmdeploy serve api_server -h 详细查看。

有关api_server的更多信息以及如何访问该服务，可以从这里找到