multimodal_inputs.md

(multimodal-inputs)=

# Multimodal Inputs

This page teaches you how to pass multi-modal inputs to [multi-modal models](#supported-mm-models) in vLLM.

:::{note}
We are actively iterating on multi-modal support. See [this RFC](gh-issue:4194) for upcoming changes,
and [open an issue on GitHub](https://github.com/vllm-project/vllm/issues/new/choose) if you have any feedback or feature requests.
:::

## Offline Inference

To input multi-modal data, follow this schema in {class}`vllm.inputs.PromptType`:

- `prompt`: The prompt should follow the format that is documented on HuggingFace.
- `multi_modal_data`: This is a dictionary that follows the schema defined in {class}`vllm.multimodal.inputs.MultiModalDataDict`.

### Image Inputs

You can pass a single image to the `'image'` field of the multi-modal dictionary, as shown in the following examples:

```python
from vllm import LLM

llm = LLM(model="llava-hf/llava-1.5-7b-hf")

# Refer to the HuggingFace repo for the correct format to use
prompt = "USER: <image>\nWhat is the content of this image?\nASSISTANT:"

# Load the image using PIL.Image
image = PIL.Image.open(...)

# Single prompt inference
outputs = llm.generate({
    "prompt": prompt,
    "multi_modal_data": {"image": image},
})

for o in outputs:
    generated_text = o.outputs[0].text
    print(generated_text)

# Batch inference
image_1 = PIL.Image.open(...)
image_2 = PIL.Image.open(...)
outputs = llm.generate(
    [
        {
            "prompt": "USER: <image>\nWhat is the content of this image?\nASSISTANT:",
            "multi_modal_data": {"image": image_1},
        },
        {
            "prompt": "USER: <image>\nWhat's the color of this image?\nASSISTANT:",
            "multi_modal_data": {"image": image_2},
        }
    ]
)

for o in outputs:
    generated_text = o.outputs[0].text
    print(generated_text)
```

Full example: <gh-file:examples/offline_inference/vision_language.py>

To substitute multiple images inside the same text prompt, you can pass in a list of images instead:

```python
from vllm import LLM

llm = LLM(
    model="microsoft/Phi-3.5-vision-instruct",
    trust_remote_code=True,  # Required to load Phi-3.5-vision
    max_model_len=4096,  # Otherwise, it may not fit in smaller GPUs
    limit_mm_per_prompt={"image": 2},  # The maximum number to accept
)

# Refer to the HuggingFace repo for the correct format to use
prompt = "<|user|>\n<|image_1|>\n<|image_2|>\nWhat is the content of each image?<|end|>\n<|assistant|>\n"

# Load the images using PIL.Image
image1 = PIL.Image.open(...)
image2 = PIL.Image.open(...)

outputs = llm.generate({
    "prompt": prompt,
    "multi_modal_data": {
        "image": [image1, image2]
    },
})

for o in outputs:
    generated_text = o.outputs[0].text
    print(generated_text)
```

Full example: <gh-file:examples/offline_inference/vision_language_multi_image.py>

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:

```python
from vllm import LLM

# Specify the maximum number of frames per video to be 4. This can be changed.
llm = LLM("Qwen/Qwen2-VL-2B-Instruct", limit_mm_per_prompt={"image": 4})

# Create the request payload.
video_frames = ... # load your video making sure it only has the number of frames specified earlier.
message = {
    "role": "user",
    "content": [
        {"type": "text", "text": "Describe this set of frames. Consider the frames to be a part of the same video."},
    ],
}
for i in range(len(video_frames)):
    base64_image = encode_image(video_frames[i]) # base64 encoding.
    new_image = {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{base64_image}"}}
    message["content"].append(new_image)

# Perform inference and log output.
outputs = llm.chat([message])

for o in outputs:
    generated_text = o.outputs[0].text
    print(generated_text)
```

### Video Inputs

You can pass a list of NumPy arrays directly to the `'video'` field of the multi-modal dictionary
instead of using multi-image input.

Full example: <gh-file:examples/offline_inference/vision_language.py>

### Audio Inputs

You can pass a tuple `(array, sampling_rate)` to the `'audio'` field of the multi-modal dictionary.

Full example: <gh-file:examples/offline_inference/audio_language.py>

### Embedding Inputs

To input pre-computed embeddings belonging to a data type (i.e. image, video, or audio) directly to the language model,
pass a tensor of shape `(num_items, feature_size, hidden_size of LM)` to the corresponding field of the multi-modal dictionary.

```python
from vllm import LLM

# Inference with image embeddings as input
llm = LLM(model="llava-hf/llava-1.5-7b-hf")

# Refer to the HuggingFace repo for the correct format to use
prompt = "USER: <image>\nWhat is the content of this image?\nASSISTANT:"

# Embeddings for single image
# torch.Tensor of shape (1, image_feature_size, hidden_size of LM)
image_embeds = torch.load(...)

outputs = llm.generate({
    "prompt": prompt,
    "multi_modal_data": {"image": image_embeds},
})

for o in outputs:
    generated_text = o.outputs[0].text
    print(generated_text)
```

For Qwen2-VL and MiniCPM-V, we accept additional parameters alongside the embeddings:

```python
# Construct the prompt based on your model
prompt = ...

# Embeddings for multiple images
# torch.Tensor of shape (num_images, image_feature_size, hidden_size of LM)
image_embeds = torch.load(...)

# Qwen2-VL
llm = LLM("Qwen/Qwen2-VL-2B-Instruct", limit_mm_per_prompt={"image": 4})
mm_data = {
    "image": {
        "image_embeds": image_embeds,
        # image_grid_thw is needed to calculate positional encoding.
        "image_grid_thw": torch.load(...),  # torch.Tensor of shape (1, 3),
    }
}

# MiniCPM-V
llm = LLM("openbmb/MiniCPM-V-2_6", trust_remote_code=True, limit_mm_per_prompt={"image": 4})
mm_data = {
    "image": {
        "image_embeds": image_embeds,
        # image_sizes is needed to calculate details of the sliced image.
        "image_sizes": [image.size for image in images],  # list of image sizes
    }
}

outputs = llm.generate({
    "prompt": prompt,
    "multi_modal_data": mm_data,
})

for o in outputs:
    generated_text = o.outputs[0].text
    print(generated_text)
```

## Online Serving

Our OpenAI-compatible server accepts multi-modal data via the [Chat Completions API](https://platform.openai.com/docs/api-reference/chat).

:::{important}
A chat template is **required** to use Chat Completions API.

Although most models come with a chat template, for others you have to define one yourself.
The chat template can be inferred based on the documentation on the model's HuggingFace repo.
For example, LLaVA-1.5 (`llava-hf/llava-1.5-7b-hf`) requires a chat template that can be found here: <gh-file:examples/template_llava.jinja>
:::

### Image Inputs

Image input is supported according to [OpenAI Vision API](https://platform.openai.com/docs/guides/vision).
Here is a simple example using Phi-3.5-Vision.

First, launch the OpenAI-compatible server:

```bash
vllm serve microsoft/Phi-3.5-vision-instruct --task generate \
  --trust-remote-code --max-model-len 4096 --limit-mm-per-prompt image=2
```

Then, you can use the OpenAI client as follows:

```python
from openai import OpenAI

openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"

client = OpenAI(
    api_key=openai_api_key,
    base_url=openai_api_base,
)

# Single-image input inference
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"

chat_response = client.chat.completions.create(
    model="microsoft/Phi-3.5-vision-instruct",
    messages=[{
        "role": "user",
        "content": [
            # NOTE: The prompt formatting with the image token `<image>` is not needed
            # since the prompt will be processed automatically by the API server.
            {"type": "text", "text": "What’s in this image?"},
            {"type": "image_url", "image_url": {"url": image_url}},
        ],
    }],
)
print("Chat completion output:", chat_response.choices[0].message.content)

# Multi-image input inference
image_url_duck = "https://upload.wikimedia.org/wikipedia/commons/d/da/2015_Kaczka_krzy%C5%BCowka_w_wodzie_%28samiec%29.jpg"
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"

chat_response = client.chat.completions.create(
    model="microsoft/Phi-3.5-vision-instruct",
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "What are the animals in these images?"},
            {"type": "image_url", "image_url": {"url": image_url_duck}},
            {"type": "image_url", "image_url": {"url": image_url_lion}},
        ],
    }],
)
print("Chat completion output:", chat_response.choices[0].message.content)
```

Full example: <gh-file:examples/online_serving/openai_chat_completion_client_for_multimodal.py>

:::{tip}
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,
and pass the file path as `url` in the API request.
:::

:::{tip}
There is no need to place image placeholders in the text content of the API request - they are already represented by the image content.
In fact, you can place image placeholders in the middle of the text by interleaving text and image content.
:::

:::{note}
By default, the timeout for fetching images through HTTP URL is `5` seconds.
You can override this by setting the environment variable:

```console
export VLLM_IMAGE_FETCH_TIMEOUT=<timeout>
```

:::

### Video Inputs

Instead of `image_url`, you can pass a video file via `video_url`. Here is a simple example using [LLaVA-OneVision](https://huggingface.co/llava-hf/llava-onevision-qwen2-0.5b-ov-hf).

First, launch the OpenAI-compatible server:

```bash
vllm serve llava-hf/llava-onevision-qwen2-0.5b-ov-hf --task generate --max-model-len 8192
```

Then, you can use the OpenAI client as follows:

```python
from openai import OpenAI

openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"

client = OpenAI(
    api_key=openai_api_key,
    base_url=openai_api_base,
)

video_url = "http://commondatastorage.googleapis.com/gtv-videos-bucket/sample/ForBiggerFun.mp4"

## Use video url in the payload
chat_completion_from_url = client.chat.completions.create(
    messages=[{
        "role":
        "user",
        "content": [
            {
                "type": "text",
                "text": "What's in this video?"
            },
            {
                "type": "video_url",
                "video_url": {
                    "url": video_url
                },
            },
        ],
    }],
    model=model,
    max_completion_tokens=64,
)

result = chat_completion_from_url.choices[0].message.content
print("Chat completion output from image url:", result)
```

Full example: <gh-file:examples/online_serving/openai_chat_completion_client_for_multimodal.py>

:::{note}
By default, the timeout for fetching videos through HTTP URL is `30` seconds.
You can override this by setting the environment variable:

```console
export VLLM_VIDEO_FETCH_TIMEOUT=<timeout>
```

:::

### Audio Inputs

Audio input is supported according to [OpenAI Audio API](https://platform.openai.com/docs/guides/audio?audio-generation-quickstart-example=audio-in).
Here is a simple example using Ultravox-v0.5-1B.

First, launch the OpenAI-compatible server:

```bash
vllm serve fixie-ai/ultravox-v0_5-llama-3_2-1b
```

Then, you can use the OpenAI client as follows:

```python
import base64
import requests
from openai import OpenAI
from vllm.assets.audio import AudioAsset

def encode_base64_content_from_url(content_url: str) -> str:
    """Encode a content retrieved from a remote url to base64 format."""

    with requests.get(content_url) as response:
        response.raise_for_status()
        result = base64.b64encode(response.content).decode('utf-8')

    return result

openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"

client = OpenAI(
    api_key=openai_api_key,
    base_url=openai_api_base,
)

# Any format supported by librosa is supported
audio_url = AudioAsset("winning_call").url
audio_base64 = encode_base64_content_from_url(audio_url)

chat_completion_from_base64 = client.chat.completions.create(
    messages=[{
        "role": "user",
        "content": [
            {
                "type": "text",
                "text": "What's in this audio?"
            },
            {
                "type": "input_audio",
                "input_audio": {
                    "data": audio_base64,
                    "format": "wav"
                },
            },
        ],
    }],
    model=model,
    max_completion_tokens=64,
)

result = chat_completion_from_base64.choices[0].message.content
print("Chat completion output from input audio:", result)
```

Alternatively, you can pass `audio_url`, which is the audio counterpart of `image_url` for image input:

```python
chat_completion_from_url = client.chat.completions.create(
    messages=[{
        "role": "user",
        "content": [
            {
                "type": "text",
                "text": "What's in this audio?"
            },
            {
                "type": "audio_url",
                "audio_url": {
                    "url": audio_url
                },
            },
        ],
    }],
    model=model,
    max_completion_tokens=64,
)

result = chat_completion_from_url.choices[0].message.content
print("Chat completion output from audio url:", result)
```

Full example: <gh-file:examples/online_serving/openai_chat_completion_client_for_multimodal.py>

:::{note}
By default, the timeout for fetching audios through HTTP URL is `10` seconds.
You can override this by setting the environment variable:

```console
export VLLM_AUDIO_FETCH_TIMEOUT=<timeout>
```

:::

### Embedding Inputs

To input pre-computed embeddings belonging to a data type (i.e. image, video, or audio) directly to the language model,
pass a tensor of shape to the corresponding field of the multi-modal dictionary.
#### Image Embedding Inputs
For image embeddings, you can pass the base64-encoded tensor to the `image_embeds` field.
The following example demonstrates how to pass image embeddings to the OpenAI server:

```python
image_embedding = torch.load(...)
grid_thw = torch.load(...) # Required by Qwen/Qwen2-VL-2B-Instruct

buffer = io.BytesIO()
torch.save(image_embedding, buffer)
buffer.seek(0)
binary_data = buffer.read()
base64_image_embedding = base64.b64encode(binary_data).decode('utf-8')

client = OpenAI(
    # defaults to os.environ.get("OPENAI_API_KEY")
    api_key=openai_api_key,
    base_url=openai_api_base,
)

# Basic usage - this is equivalent to the LLaVA example for offline inference
model = "llava-hf/llava-1.5-7b-hf"
embeds =  {
    "type": "image_embeds",
    "image_embeds": f"{base64_image_embedding}" 
}

# Pass additional parameters (available to Qwen2-VL and MiniCPM-V)
model = "Qwen/Qwen2-VL-2B-Instruct"
embeds =  {
    "type": "image_embeds",
    "image_embeds": {
        "image_embeds": f"{base64_image_embedding}" , # Required
        "image_grid_thw": f"{base64_image_grid_thw}"  # Required by Qwen/Qwen2-VL-2B-Instruct
    },
}
model = "openbmb/MiniCPM-V-2_6"
embeds =  {
    "type": "image_embeds",
    "image_embeds": {
        "image_embeds": f"{base64_image_embedding}" , # Required
        "image_sizes": f"{base64_image_sizes}"  # Required by openbmb/MiniCPM-V-2_6
    },
}
chat_completion = client.chat.completions.create(
    messages=[
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": [
        {
            "type": "text",
            "text": "What's in this image?",
        },
        embeds,
        ],
    },
],
    model=model,
)
```

:::{note}
Only one message can contain `{"type": "image_embeds"}`.
If used with a model that requires additional parameters, you must also provide a tensor for each of them, e.g. `image_grid_thw`, `image_sizes`, etc.
:::