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vllm-v0.6.2/docs/source/models/vlm.rst
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vllm-v0.6.2/docs/source/models/vlm.rst
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.. _vlm:
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Using VLMs
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==========
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vLLM provides experimental support for Vision Language Models (VLMs). See the :ref:`list of supported VLMs here <supported_vlms>`.
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This document shows you how to run and serve these models using vLLM.
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.. note::
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We are actively iterating on VLM support. See `this RFC <https://github.com/vllm-project/vllm/issues/4194>`_ for upcoming changes,
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and `open an issue on GitHub <https://github.com/vllm-project/vllm/issues/new/choose>`_ if you have any feedback or feature requests.
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Offline Inference
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-----------------
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Single-image input
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^^^^^^^^^^^^^^^^^^
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The :class:`~vllm.LLM` class can be instantiated in much the same way as language-only models.
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.. code-block:: python
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llm = LLM(model="llava-hf/llava-1.5-7b-hf")
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To pass an image to the model, note the following in :class:`vllm.inputs.PromptType`:
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* ``prompt``: The prompt should follow the format that is documented on HuggingFace.
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* ``multi_modal_data``: This is a dictionary that follows the schema defined in :class:`vllm.multimodal.MultiModalDataDict`.
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.. code-block:: python
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# Refer to the HuggingFace repo for the correct format to use
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prompt = "USER: <image>\nWhat is the content of this image?\nASSISTANT:"
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# Load the image using PIL.Image
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image = PIL.Image.open(...)
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# Single prompt inference
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outputs = llm.generate({
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"prompt": prompt,
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"multi_modal_data": {"image": image},
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})
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for o in outputs:
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generated_text = o.outputs[0].text
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print(generated_text)
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# Inference with image embeddings as input
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image_embeds = torch.load(...) # torch.Tensor of shape (1, image_feature_size, hidden_size of LM)
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outputs = llm.generate({
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"prompt": prompt,
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"multi_modal_data": {"image": image_embeds},
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})
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for o in outputs:
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generated_text = o.outputs[0].text
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print(generated_text)
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# Inference with image embeddings as input with additional parameters
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# Specifically, we are conducting a trial run of Qwen2VL and MiniCPM-V with the new input format, which utilizes additional parameters.
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mm_data = {}
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image_embeds = torch.load(...) # torch.Tensor of shape (num_images, image_feature_size, hidden_size of LM)
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# For Qwen2VL, image_grid_thw is needed to calculate positional encoding.
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mm_data['image'] = {
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"image_embeds": image_embeds,
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"image_grid_thw": torch.load(...) # torch.Tensor of shape (1, 3),
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}
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# For MiniCPM-V, image_size_list is needed to calculate details of the sliced image.
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mm_data['image'] = {
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"image_embeds": image_embeds,
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"image_size_list": [image.size] # list of image sizes
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}
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outputs = llm.generate({
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"prompt": prompt,
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"multi_modal_data": mm_data,
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})
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for o in outputs:
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generated_text = o.outputs[0].text
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print(generated_text)
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# Batch inference
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image_1 = PIL.Image.open(...)
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image_2 = PIL.Image.open(...)
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outputs = llm.generate(
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[
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{
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"prompt": "USER: <image>\nWhat is the content of this image?\nASSISTANT:",
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"multi_modal_data": {"image": image_1},
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},
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{
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"prompt": "USER: <image>\nWhat's the color of this image?\nASSISTANT:",
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"multi_modal_data": {"image": image_2},
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}
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]
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)
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for o in outputs:
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generated_text = o.outputs[0].text
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print(generated_text)
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A code example can be found in `examples/offline_inference_vision_language.py <https://github.com/vllm-project/vllm/blob/main/examples/offline_inference_vision_language.py>`_.
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Multi-image input
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^^^^^^^^^^^^^^^^^
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Multi-image input is only supported for a subset of VLMs, as shown :ref:`here <supported_vlms>`.
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To enable multiple multi-modal items per text prompt, you have to set ``limit_mm_per_prompt`` for the :class:`~vllm.LLM` class.
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.. code-block:: python
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llm = LLM(
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model="microsoft/Phi-3.5-vision-instruct",
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trust_remote_code=True, # Required to load Phi-3.5-vision
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max_model_len=4096, # Otherwise, it may not fit in smaller GPUs
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limit_mm_per_prompt={"image": 2}, # The maximum number to accept
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)
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Instead of passing in a single image, you can pass in a list of images.
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.. code-block:: python
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# Refer to the HuggingFace repo for the correct format to use
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prompt = "<|user|>\n<|image_1|>\n<|image_2|>\nWhat is the content of each image?<|end|>\n<|assistant|>\n"
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# Load the images using PIL.Image
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image1 = PIL.Image.open(...)
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image2 = PIL.Image.open(...)
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outputs = llm.generate({
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"prompt": prompt,
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"multi_modal_data": {
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"image": [image1, image2]
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},
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})
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for o in outputs:
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generated_text = o.outputs[0].text
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print(generated_text)
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A code example can be found in `examples/offline_inference_vision_language_multi_image.py <https://github.com/vllm-project/vllm/blob/main/examples/offline_inference_vision_language_multi_image.py>`_.
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Multi-image input can be extended to perform video captioning. We show this with `Qwen2-VL <https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct>`_ as it supports videos:
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.. code-block:: python
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# Specify the maximum number of frames per video to be 4. This can be changed.
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llm = LLM("Qwen/Qwen2-VL-2B-Instruct", limit_mm_per_prompt={"image": 4})
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# Create the request payload.
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video_frames = ... # load your video making sure it only has the number of frames specified earlier.
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message = {
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"role": "user",
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"content": [
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{"type": "text", "text": "Describe this set of frames. Consider the frames to be a part of the same video."},
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],
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}
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for i in range(len(video_frames)):
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base64_image = encode_image(video_frames[i]) # base64 encoding.
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new_image = {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{base64_image}"}}
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message["content"].append(new_image)
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# Perform inference and log output.
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outputs = llm.chat([message])
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for o in outputs:
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generated_text = o.outputs[0].text
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print(generated_text)
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Online Inference
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----------------
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OpenAI Vision API
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^^^^^^^^^^^^^^^^^
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You can serve vision language models with vLLM's HTTP server that is compatible with `OpenAI Vision API <https://platform.openai.com/docs/guides/vision>`_.
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Below is an example on how to launch the same ``microsoft/Phi-3.5-vision-instruct`` with vLLM's OpenAI-compatible API server.
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.. code-block:: bash
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vllm serve microsoft/Phi-3.5-vision-instruct --task generate \
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--trust-remote-code --max-model-len 4096 --limit-mm-per-prompt image=2
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.. important::
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Since OpenAI Vision API is based on `Chat Completions API <https://platform.openai.com/docs/api-reference/chat>`_,
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a chat template is **required** to launch the API server.
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Although Phi-3.5-Vision comes with a chat template, for other models you may have to provide one if the model's tokenizer does not come with it.
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The chat template can be inferred based on the documentation on the model's HuggingFace repo.
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For example, LLaVA-1.5 (``llava-hf/llava-1.5-7b-hf``) requires a chat template that can be found `here <https://github.com/vllm-project/vllm/blob/main/examples/template_llava.jinja>`_.
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To consume the server, you can use the OpenAI client like in the example below:
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.. code-block:: python
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from openai import OpenAI
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openai_api_key = "EMPTY"
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openai_api_base = "http://localhost:8000/v1"
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client = OpenAI(
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api_key=openai_api_key,
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base_url=openai_api_base,
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)
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# Single-image input inference
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image_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
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chat_response = client.chat.completions.create(
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model="microsoft/Phi-3.5-vision-instruct",
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messages=[{
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"role": "user",
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"content": [
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# NOTE: The prompt formatting with the image token `<image>` is not needed
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# since the prompt will be processed automatically by the API server.
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{"type": "text", "text": "What’s in this image?"},
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{"type": "image_url", "image_url": {"url": image_url}},
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],
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}],
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)
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print("Chat completion output:", chat_response.choices[0].message.content)
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# Multi-image input inference
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image_url_duck = "https://upload.wikimedia.org/wikipedia/commons/d/da/2015_Kaczka_krzy%C5%BCowka_w_wodzie_%28samiec%29.jpg"
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image_url_lion = "https://upload.wikimedia.org/wikipedia/commons/7/77/002_The_lion_king_Snyggve_in_the_Serengeti_National_Park_Photo_by_Giles_Laurent.jpg"
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chat_response = client.chat.completions.create(
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model="microsoft/Phi-3.5-vision-instruct",
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messages=[{
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"role": "user",
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"content": [
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{"type": "text", "text": "What are the animals in these images?"},
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{"type": "image_url", "image_url": {"url": image_url_duck}},
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{"type": "image_url", "image_url": {"url": image_url_lion}},
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],
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}],
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)
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print("Chat completion output:", chat_response.choices[0].message.content)
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A full code example can be found in `examples/openai_chat_completion_client_for_multimodal.py <https://github.com/vllm-project/vllm/blob/main/examples/openai_chat_completion_client_for_multimodal.py>`_.
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.. tip::
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Loading from local file paths is also supported on vLLM: You can specify the allowed local media path via ``--allowed-local-media-path`` when launching the API server/engine,
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and pass the file path as ``url`` in the API request.
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.. tip::
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There is no need to place image placeholders in the text content of the API request - they are already represented by the image content.
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In fact, you can place image placeholders in the middle of the text by interleaving text and image content.
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.. note::
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By default, the timeout for fetching images through http url is ``5`` seconds. You can override this by setting the environment variable:
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.. code-block:: console
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$ export VLLM_IMAGE_FETCH_TIMEOUT=<timeout>
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Chat Embeddings API
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^^^^^^^^^^^^^^^^^^^
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vLLM's Chat Embeddings API is a superset of OpenAI's `Embeddings API <https://platform.openai.com/docs/api-reference/embeddings>`_,
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where a list of ``messages`` can be passed instead of batched ``inputs``. This enables multi-modal inputs to be passed to embedding models.
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.. tip::
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The schema of ``messages`` is exactly the same as in Chat Completions API.
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In this example, we will serve the ``TIGER-Lab/VLM2Vec-Full`` model.
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.. code-block:: bash
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vllm serve TIGER-Lab/VLM2Vec-Full --task embedding \
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--trust-remote-code --max-model-len 4096 --chat-template examples/template_vlm2vec.jinja
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.. important::
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Since VLM2Vec has the same model architecture as Phi-3.5-Vision, we have to explicitly pass ``--task embedding``
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to run this model in embedding mode instead of text generation mode.
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.. important::
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VLM2Vec does not expect chat-based input. We use a `custom chat template <https://github.com/vllm-project/vllm/blob/main/examples/template_vlm2vec.jinja>`_
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to combine the text and images together.
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Since the request schema is not defined by OpenAI client, we post a request to the server using the lower-level ``requests`` library:
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.. code-block:: python
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import requests
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image_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
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response = requests.post(
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"http://localhost:8000/v1/embeddings",
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json={
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"model": "TIGER-Lab/VLM2Vec-Full",
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"messages": [{
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"role": "user",
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"content": [
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{"type": "image_url", "image_url": {"url": image_url}},
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{"type": "text", "text": "Represent the given image."},
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],
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}],
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"encoding_format": "float",
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},
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)
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response.raise_for_status()
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response_json = response.json()
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print("Embedding output:", response_json["data"][0]["embedding"])
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Here is an example for serving the ``MrLight/dse-qwen2-2b-mrl-v1`` model.
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.. code-block:: bash
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vllm serve MrLight/dse-qwen2-2b-mrl-v1 --task embedding \
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--trust-remote-code --max-model-len 8192 --chat-template examples/template_dse_qwen2_vl.jinja
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.. important::
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Like with VLM2Vec, we have to explicitly pass ``--task embedding``. Additionally, ``MrLight/dse-qwen2-2b-mrl-v1`` requires an EOS token for embeddings,
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which is handled by the jinja template.
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.. important::
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Also important, ``MrLight/dse-qwen2-2b-mrl-v1`` requires a placeholder image of the minimum image size for text query embeddings. See the full code
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example below for details.
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A full code example can be found in `examples/openai_chat_embedding_client_for_multimodal.py <https://github.com/vllm-project/vllm/blob/main/examples/openai_chat_embedding_client_for_multimodal.py>`_.
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