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 | 1B, 4B | PyTorch |
InternVL2 | 2B, 8B-76B | TurboMind, PyTorch |
The next chapter demonstrates how to deploy an InternVL model using LMDeploy, with InternVL2-8B as an example.
Please install LMDeploy by following the installation guide, and install other packages that InternVL2 needs
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:
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
The following sample code shows the basic usage of VLM pipeline. For detailed information, please refer to VLM Offline Inference Pipeline
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:
multi-image multi-round conversation, combined images
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'<img>{IMAGE_TOKEN}{IMAGE_TOKEN}</img>\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))
multi-image multi-round conversation, separate images
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: <img>{IMAGE_TOKEN}</img>\nImage-2: <img>{IMAGE_TOKEN}</img>\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))
video multi-round conversation
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}: <img>{IMAGE_TOKEN}</img>\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))
You can launch the server by the lmdeploy serve api_server
CLI:
lmdeploy serve api_server OpenGVLab/InternVL2-8B
You can also start the service using the aforementioned built docker image:
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:
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:
docker-compose up -d
If you find the following logs after running docker logs -f lmdeploy
, it means the service launches successfully.
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