multimodal.md

(supports-multimodal)=

# Multi-Modal Support

This document walks you through the steps to extend a basic model so that it accepts [multi-modal inputs](#multimodal-inputs).

## 1. Update the base vLLM model

It is assumed that you have already implemented the model in vLLM according to [these steps](#new-model-basic).
Further update the model as follows:

- Reserve a keyword parameter in {meth}`~torch.nn.Module.forward` for each input tensor that corresponds to a multi-modal input, as shown in the following example:

  ```diff
    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
  +     pixel_values: torch.Tensor,
    ) -> SamplerOutput:
  ```
  
  More conveniently, you can simply pass `**kwargs` to the {meth}`~torch.nn.Module.forward` method and retrieve the keyword parameters for multimodal inputs from it.

- Implement {meth}`~vllm.model_executor.models.interfaces.SupportsMultiModal.get_multimodal_embeddings` that returns the embeddings from running the multimodal inputs through the multimodal tokenizer of the model. Below we provide a boilerplate of a typical implementation pattern, but feel free to adjust it to your own needs.

    ```python
    class YourModelForImage2Seq(nn.Module):
        ...

        def _process_image_input(self, image_input: YourModelImageInputs) -> torch.Tensor:

            assert self.vision_encoder is not None
            image_features = self.vision_encoder(image_input)
            return self.multi_modal_projector(image_features)

        def get_multimodal_embeddings(
                self, **kwargs: object) -> Optional[MultiModalEmbeddings]:

            # Validate the multimodal input keyword arguments
            image_input = self._parse_and_validate_image_input(**kwargs)
            if image_input is None:
                return None

            # Run multimodal inputs through encoder and projector
            vision_embeddings = self._process_image_input(image_input)
            return vision_embeddings
    ```

    :::{important}
    The returned `multimodal_embeddings` must be either a **3D {class}`torch.Tensor`** of shape `(num_items, feature_size, hidden_size)`, or a **list / tuple of 2D {class}`torch.Tensor`'s** of shape `(feature_size, hidden_size)`, so that `multimodal_embeddings[i]` retrieves the embeddings generated from the `i`-th multimodal data item (e.g, image) of the request.
    :::

- Implement {meth}`~vllm.model_executor.models.interfaces.SupportsMultiModal.get_input_embeddings` to merge `multimodal_embeddings` with text embeddings from the `input_ids`. If input processing for the model is implemented correctly (see sections below), then you can leverage the utility function we provide to easily merge the embeddings.

    ```python
    from .utils import merge_multimodal_embeddings

    class YourModelForImage2Seq(nn.Module):
        ...

        def get_input_embeddings(
            self,
            input_ids: torch.Tensor,
            multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
        ) -> torch.Tensor:

            # `get_input_embeddings` should already be implemented for the language 
            # model as one of the requirements of basic vLLM model implementation.
            inputs_embeds = self.language_model.get_input_embeddings(input_ids)

            if multimodal_embeddings is not None:
                inputs_embeds = merge_multimodal_embeddings(
                    input_ids=input_ids, 
                    inputs_embeds=inputs_embeds, 
                    multimodal_embeddings=multimodal_embeddings,
                    placeholder_token_id=self.config.image_token_index)

            return inputs_embeds
    ```

- Once the above steps are done, update the model class with the {class}`~vllm.model_executor.models.interfaces.SupportsMultiModal` interface.

  ```diff
  + from vllm.model_executor.models.interfaces import SupportsMultiModal

  - class YourModelForImage2Seq(nn.Module):
  + class YourModelForImage2Seq(nn.Module, SupportsMultiModal):
  ```

  :::{note}
  The model class does not have to be named {code}`*ForCausalLM`.
  Check out [the HuggingFace Transformers documentation](https://huggingface.co/docs/transformers/model_doc/auto#multimodal) for some examples.
  :::

## 2. Specify processing information

Next, create a subclass of {class}`~vllm.multimodal.processing.BaseProcessingInfo`
to provide basic information related to HF processing.

### Maximum number of input items

You need to override the abstract method {meth}`~vllm.multimodal.processing.BaseProcessingInfo.get_supported_mm_limits`
to return the maximum number of input items for each modality supported by the model.

For example, if the model supports any number of images but only one video per prompt:

```python
def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
    return {"image": None, "video": 1}
```

### Maximum number of placeholder feature tokens

Also, override the abstract method {meth}`~vllm.multimodal.processing.BaseProcessingInfo.get_mm_max_tokens_per_item`
to return the maximum number of placeholder feature tokens per input item for each modality.

When calling the model, the output embeddings from the visual encoder are assigned to the input positions
containing placeholder feature tokens. Therefore, the number of placeholder feature tokens should be equal
to the size of the output embeddings.

:::::{tab-set}
::::{tab-item} Basic example: LLaVA
:sync: llava

Looking at the code of HF's `LlavaForConditionalGeneration`:

```python
# https://github.com/huggingface/transformers/blob/v4.47.1/src/transformers/models/llava/modeling_llava.py#L530-L544
n_image_tokens = (input_ids == self.config.image_token_index).sum().item()
n_image_features = image_features.shape[0] * image_features.shape[1]

if n_image_tokens != n_image_features:
    raise ValueError(
        f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {n_image_features}"
    )
special_image_mask = (
    (input_ids == self.config.image_token_index)
    .unsqueeze(-1)
    .expand_as(inputs_embeds)
    .to(inputs_embeds.device)
)
image_features = image_features.to(inputs_embeds.device, inputs_embeds.dtype)
inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features)
```

The number of placeholder feature tokens per image is `image_features.shape[1]`.
`image_features` is calculated inside the `get_image_features` method:

```python
# https://github.com/huggingface/transformers/blob/v4.47.1/src/transformers/models/llava/modeling_llava.py#L290-L300
image_outputs = self.vision_tower(pixel_values, output_hidden_states=True)

selected_image_feature = image_outputs.hidden_states[vision_feature_layer]
if vision_feature_select_strategy == "default":
    selected_image_feature = selected_image_feature[:, 1:]
elif vision_feature_select_strategy == "full":
    selected_image_feature = selected_image_feature
else:
    raise ValueError(f"Unexpected select feature strategy: {self.config.vision_feature_select_strategy}")
image_features = self.multi_modal_projector(selected_image_feature)
return image_features
```

We can infer that `image_features.shape[1]` is based on `image_outputs.hidden_states.shape[1]` from the vision tower
(`CLIPVisionModel` for the [`llava-hf/llava-1.5-7b-hf`](https://huggingface.co/llava-hf/llava-1.5-7b-hf) model).
Moreover, we only need the sequence length (the second dimension of the tensor) to get `image_features.shape[1]`.
The sequence length is determined by the initial hidden states in `CLIPVisionTransformer` since the attention
mechanism doesn't change the sequence length of the output hidden states.

```python
# https://github.com/huggingface/transformers/blob/v4.47.1/src/transformers/models/clip/modeling_clip.py#L1094-L1102
hidden_states = self.embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding)
hidden_states = self.pre_layrnorm(hidden_states)

encoder_outputs = self.encoder(
    inputs_embeds=hidden_states,
    output_attentions=output_attentions,
    output_hidden_states=output_hidden_states,
    return_dict=return_dict,
)
```

To find the sequence length, we turn to the code of `CLIPVisionEmbeddings`:

```python
# https://github.com/huggingface/transformers/blob/v4.47.1/src/transformers/models/clip/modeling_clip.py#L247-L257
target_dtype = self.patch_embedding.weight.dtype
patch_embeds = self.patch_embedding(pixel_values.to(dtype=target_dtype))  # shape = [*, width, grid, grid]
patch_embeds = patch_embeds.flatten(2).transpose(1, 2)

class_embeds = self.class_embedding.expand(batch_size, 1, -1)
embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
if interpolate_pos_encoding:
    embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width)
else:
    embeddings = embeddings + self.position_embedding(self.position_ids)
return embeddings
```

We can infer that `embeddings.shape[1] == self.num_positions`, where

```python
# https://github.com/huggingface/transformers/blob/v4.47.1/src/transformers/models/clip/modeling_clip.py#L195-L196
self.num_patches = (self.image_size // self.patch_size) ** 2
self.num_positions = self.num_patches + 1
```

Overall, the number of placeholder feature tokens for an image can be calculated as:

```python
def get_num_image_tokens(
    self,
    *,
    image_width: int,
    image_height: int,
) -> int:
    hf_config = self.get_hf_config()
    hf_processor = self.get_hf_processor()

    image_size = hf_config.vision_config.image_size
    patch_size = hf_config.vision_config.patch_size

    num_image_tokens = (image_size // patch_size) ** 2 + 1
    if hf_processor.vision_feature_select_strategy == "default":
        num_image_tokens -= 1

    return num_image_tokens
```

Notice that the number of image tokens doesn't depend on the image width and height.
So, we can calculate the maximum number of image tokens using any image size:

```python
def get_image_size_with_most_features(self) -> ImageSize:
    hf_config = self.get_hf_config()
    width = height = hf_config.image_size
    return ImageSize(width=width, height=height)

def get_max_image_tokens(self) -> int:
    target_width, target_height = self.get_image_size_with_most_features()

    return self.get_num_image_tokens(
        image_width=target_width,
        image_height=target_height,
    )
```

And thus, we can override the method as:

```python
def get_mm_max_tokens_per_item(
    self,
    seq_len: int,
    mm_counts: Mapping[str, int],
) -> Mapping[str, int]:
    return {"image": self.get_max_image_tokens()}
```

:::{note}
Our [actual code](gh-file:vllm/model_executor/models/llava.py) is more abstracted to support vision encoders other than CLIP.
:::

::::

::::{tab-item} Non-consecutive feature tokens: Fuyu
:sync: fuyu

Looking at the code of HF's `FuyuForCausalLM`:

```python
# https://github.com/huggingface/transformers/blob/v4.48.3/src/transformers/models/fuyu/modeling_fuyu.py#L311-L322
if image_patches is not None and past_key_values is None:
    patch_embeddings = [
        self.vision_embed_tokens(patch.to(self.vision_embed_tokens.weight.dtype))
        .squeeze(0)
        .to(inputs_embeds.device)
        for patch in image_patches
    ]
    inputs_embeds = self.gather_continuous_embeddings(
        word_embeddings=inputs_embeds,
        continuous_embeddings=patch_embeddings,
        image_patch_input_indices=image_patches_indices,
    )
```

The number of placeholder feature tokens for the `i`th item in the batch is `patch_embeddings[i].shape[0]`,
which is the same as `image_patches[i].shape[0]`, i.e. `num_total_patches`.

Unlike LLaVA, Fuyu does not define the number of patches inside the modeling file. Where can we get more information?
Considering that the model input comes from the output of `FuyuProcessor`, let's **look at the preprocessing files**.

The image outputs are obtained by calling `FuyuImageProcessor.preprocess` and then
`FuyuImageProcessor.preprocess_with_tokenizer_info` inside `FuyuProcessor`.

In `FuyuImageProcessor.preprocess`, the images are resized and padded to the target `FuyuImageProcessor.size`,
returning the dimensions after resizing (but before padding) as metadata.

```python
# https://github.com/huggingface/transformers/blob/v4.48.3/src/transformers/models/fuyu/processing_fuyu.py#L541-L544
image_encoding = self.image_processor.preprocess(images, **output_kwargs["images_kwargs"])
batch_images = image_encoding["images"]
image_unpadded_heights = image_encoding["image_unpadded_heights"]
image_unpadded_widths = image_encoding["image_unpadded_widths"]

# https://github.com/huggingface/transformers/blob/v4.48.3/src/transformers/models/fuyu/image_processing_fuyu.py#L480-L
if do_resize:
    batch_images = [
        [self.resize(image, size=size, input_data_format=input_data_format) for image in images]
        for images in batch_images
    ]

image_sizes = [get_image_size(images[0], channel_dim=input_data_format) for images in batch_images]
image_unpadded_heights = [[image_size[0]] for image_size in image_sizes]
image_unpadded_widths = [[image_size[1]] for image_size in image_sizes]

if do_pad:
    batch_images = [
        [
            self.pad_image(
                image,
                size=size,
                mode=padding_mode,
                constant_values=padding_value,
                input_data_format=input_data_format,
            )
            for image in images
        ]
        for images in batch_images
    ]
```

In `FuyuImageProcessor.preprocess_with_tokenizer_info`, the images are split into patches based on this metadata:

```python
# https://github.com/huggingface/transformers/blob/v4.48.3/src/transformers/models/fuyu/processing_fuyu.py#L417-L425
model_image_input = self.image_processor.preprocess_with_tokenizer_info(
    image_input=tensor_batch_images,
    image_present=image_present,
    image_unpadded_h=image_unpadded_heights,
    image_unpadded_w=image_unpadded_widths,
    image_placeholder_id=image_placeholder_id,
    image_newline_id=image_newline_id,
    variable_sized=True,
)

# https://github.com/huggingface/transformers/blob/v4.48.3/src/transformers/models/fuyu/image_processing_fuyu.py#L638-L658
image_height, image_width = image.shape[1], image.shape[2]
if variable_sized:  # variable_sized=True
    new_h = min(
        image_height,
        math.ceil(image_unpadded_h[batch_index, subseq_index] / patch_height) * patch_height,
    )
    new_w = min(
        image_width,
        math.ceil(image_unpadded_w[batch_index, subseq_index] / patch_width) * patch_width,
    )
    image = image[:, :new_h, :new_w]
    image_height, image_width = new_h, new_w

num_patches = self.get_num_patches(image_height=image_height, image_width=image_width)
tensor_of_image_ids = torch.full(
    [num_patches], image_placeholder_id, dtype=torch.int32, device=image_input.device
)
patches = self.patchify_image(image=image.unsqueeze(0)).squeeze(0)
assert num_patches == patches.shape[0]
```

The number of patches is in turn defined by `FuyuImageProcessor.get_num_patches`:

```python
# https://github.com/huggingface/transformers/blob/v4.48.3/src/transformers/models/fuyu/image_processing_fuyu.py#L552-L562
patch_size = patch_size if patch_size is not None else self.patch_size
patch_height, patch_width = self.patch_size["height"], self.patch_size["width"]

if image_height % patch_height != 0:
    raise ValueError(f"{image_height=} must be divisible by {patch_height}")
if image_width % patch_width != 0:
    raise ValueError(f"{image_width=} must be divisible by {patch_width}")

num_patches_per_dim_h = image_height // patch_height
num_patches_per_dim_w = image_width // patch_width
num_patches = num_patches_per_dim_h * num_patches_per_dim_w
```

We can calculate this in vLLM using this code:

```python
def get_num_image_patches(
    self,
    *,
    image_width: int,
    image_height: int,
) -> int:
    image_processor = self.get_image_processor()
    target_width = image_processor.size["width"]
    target_height = image_processor.size["height"]
    patch_width = image_processor.patch_size["width"]
    patch_height = image_processor.patch_size["height"]

    if not (image_width <= target_width and image_height <= target_height):
        height_scale_factor = target_height / image_height
        width_scale_factor = target_width / image_width
        optimal_scale_factor = min(height_scale_factor, width_scale_factor)

        image_height = int(image_height * optimal_scale_factor)
        image_width = int(image_width * optimal_scale_factor)

    ncols = math.ceil(image_width / patch_width)
    nrows = math.ceil(image_height / patch_height)
    return ncols * nrows
```

These image patches correspond to placeholder tokens (`|SPEAKER|`). However, the processor also
inserts newline tokens (`|NEWLINE|`) as shown here:

```python
# https://github.com/huggingface/transformers/blob/v4.48.3/src/transformers/models/fuyu/image_processing_fuyu.py#L654-L670
tensor_of_image_ids = torch.full(
    [num_patches], image_placeholder_id, dtype=torch.int32, device=image_input.device
)
patches = self.patchify_image(image=image.unsqueeze(0)).squeeze(0)
assert num_patches == patches.shape[0]

if variable_sized:
    # Now terminate each line with |NEWLINE|.
    tensor_of_image_ids = tensor_of_image_ids.reshape(-1, image_width // patch_width)
    newline_ids = torch.full(
        [tensor_of_image_ids.shape[0], 1],
        image_newline_id,
        dtype=torch.int32,
        device=image_input.device,
    )
    tensor_of_image_ids = torch.cat([tensor_of_image_ids, newline_ids], dim=1)
    tensor_of_image_ids = tensor_of_image_ids.reshape(-1)
```

So, the layout of tokens for an image is:

```
|SPEAKER||SPEAKER|...|SPEAKER||NEWLINE|
|SPEAKER||SPEAKER|...|SPEAKER||NEWLINE|
...
|SPEAKER||SPEAKER|...|SPEAKER||NEWLINE|
```

This makes the placeholder tokens non-consecutive in the prompt.
Since vLLM requires the feature tokens to be consecutive, **we also treat the newline tokens as feature tokens**.

So overall, the total number of feature tokens is

```python
def get_num_image_tokens(
    self,
    *,
    image_width: int,
    image_height: int,
) -> int:
    image_processor = self.get_image_processor()
    target_width = image_processor.size["width"]
    target_height = image_processor.size["height"]
    patch_width = image_processor.patch_size["width"]
    patch_height = image_processor.patch_size["height"]

    if not (image_width <= target_width and image_height <= target_height):
        height_scale_factor = target_height / image_height
        width_scale_factor = target_width / image_width
        optimal_scale_factor = min(height_scale_factor, width_scale_factor)

        image_height = int(image_height * optimal_scale_factor)
        image_width = int(image_width * optimal_scale_factor)

    ncols = math.ceil(image_width / patch_width)
    nrows = math.ceil(image_height / patch_height)
    return (ncols + 1) * nrows
```

To calculate the maximum number of image tokens, recall that input images are first resized
to fit within `image_processor.size`. The maximum possible dimensions of the image before
being converted into patches is therefore equal to `image_processor.size`.

```python
def get_image_size_with_most_features(self) -> ImageSize:
    image_processor = self.get_image_processor()
    return ImageSize(width=image_processor.size["width"],
                        height=image_processor.size["height"])

def get_max_image_tokens(self) -> int:
    target_width, target_height = self.get_image_size_with_most_features()

    return self.get_num_image_tokens(
        image_width=target_width,
        image_height=target_height,
    )
```

And thus, we can override the method as:

```python
def get_mm_max_tokens_per_item(
    self,
    seq_len: int,
    mm_counts: Mapping[str, int],
) -> Mapping[str, int]:
    return {"image": self.get_max_image_tokens()}
```

:::{note}
Our [actual code](gh-file:vllm/model_executor/models/fuyu.py) returns `ncols` and `nrows` directly instead of the total token count.
This is because `ncols` and `nrows` are used to specify the layout of the feature tokens (as shown in Step 4 of this guide).
:::

::::
:::::

## 3. Specify dummy inputs

Then, inherit {class}`~vllm.multimodal.profiling.BaseDummyInputsBuilder` to construct dummy inputs for
HF processing as well as memory profiling.

### For memory profiling

Override the abstract method {meth}`~vllm.multimodal.profiling.BaseDummyInputsBuilder.get_dummy_processor_inputs`
to construct dummy inputs for memory profiling. This dummy input should result in the worst-case memory usage of
the model so that vLLM can reserve the correct amount of memory for it.

Assuming that the memory usage increases with the number of tokens, the dummy input can be constructed based
on the code for {meth}`~vllm.multimodal.processing.BaseProcessingInfo.get_mm_max_tokens_per_item`.

::::{tab-set}
:::{tab-item} Basic example: LLaVA
:sync: llava

Making use of the `get_image_size_with_most_features` method implemented in Step 2:

```python
def get_dummy_processor_inputs(
    self,
    seq_len: int,
    mm_counts: Mapping[str, int],
) -> ProcessorInputs:
    num_images = mm_counts.get("image", 0)

    processor = self.info.get_hf_processor()
    image_token = processor.image_token
  
    hf_config = self.get_hf_config()
    target_width, target_height = self.info.get_image_size_with_most_features()

    mm_data = {
        "image":
        self._get_dummy_images(width=target_width,
                               height=target_height,
                               num_images=num_images)
    }

    return ProcessorInputs(
        prompt_text=image_token * num_images,
        mm_data=mm_data,
    )
```

:::

:::{tab-item} No input placeholders: Fuyu
:sync: fuyu

Fuyu does not expect image placeholders in the inputs to HF processor, so
the dummy prompt text is empty regardless of the number of images.
Otherwise, the logic of this method is very similar to LLaVA:

```python
def get_dummy_processor_inputs(
    self,
    seq_len: int,
    mm_counts: Mapping[str, int],
) -> ProcessorInputs:
    target_width, target_height = \
        self.info.get_image_size_with_most_features()
    num_images = mm_counts.get("image", 0)

    mm_data = {
        "image":
        self._get_dummy_images(width=target_width,
                                height=target_height,
                                num_images=num_images)
    }

    return ProcessorInputs(
        prompt_text="",
        mm_data=mm_data,
    )
```

:::

::::

## 4. Specify processing details

Afterwards, create a subclass of {class}`~vllm.multimodal.processing.BaseMultiModalProcessor`
to fill in the missing details about HF processing.

:::{seealso}
[Multi-Modal Data Processing](#mm-processing)
:::

### Multi-modal fields

Override {meth}`~vllm.multimodal.processing.BaseMultiModalProcessor._get_mm_fields_config` to
return a schema of the tensors outputted by the HF processor that are related to the input multi-modal items.

:::::{tab-set}
::::{tab-item} Basic example: LLaVA
:sync: llava

The output of `CLIPImageProcessor` is a simple tensor with shape
`(num_images, num_channels, image_height, image_width)`:

```python
# https://github.com/huggingface/transformers/blob/v4.47.1/src/transformers/models/clip/image_processing_clip.py#L339-L345
images = [
    to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format)
    for image in all_images
]

data = {"pixel_values": images}
return BatchFeature(data=data, tensor_type=return_tensors)
```

So, we override {meth}`~vllm.multimodal.processing.BaseMultiModalProcessor._get_mm_fields_config` as follows:

```python
def _get_mm_fields_config(
    self,
    hf_inputs: BatchFeature,
    hf_processor_mm_kwargs: Mapping[str, object],
) -> Mapping[str, MultiModalFieldConfig]:
    return dict(
        pixel_values=MultiModalFieldConfig.batched("image"),
    )
```

:::{note}
Our [actual code](gh-file:vllm/model_executor/models/llava.py) additionally supports
pre-computed image embeddings, which can be passed to be model via the `image_embeds` argument.
:::

::::

::::{tab-item} With postprocessing: Fuyu
:sync: fuyu

The `image_patches` output of `FuyuImageProcessor.preprocess_with_tokenizer_info` concatenates
the patches from each image belonging to an item in the batch:

```python
# https://github.com/huggingface/transformers/blob/v4.48.3/src/transformers/models/fuyu/image_processing_fuyu.py#L673-L679
        image_input_ids.append(tensor_of_image_ids)
        image_patches.append(patches)
    else:
        image_input_ids.append(torch.tensor([], dtype=torch.int32, device=image_input.device))

batch_image_input_ids.append(image_input_ids)
batch_image_patches.append(image_patches)
```

The shape of `image_patches` outputted by `FuyuImageProcessor` is therefore
`(1, num_images, num_patches, patch_width * patch_height * num_channels)`.

In order to support the use of {func}`MultiModalFieldConfig.batched` like in LLaVA,
we remove the extra batch dimension by overriding {meth}`BaseMultiModalProcessor._call_hf_processor`:

```python
def _call_hf_processor(
    self,
    prompt: str,
    mm_data: Mapping[str, object],
    mm_kwargs: Mapping[str, object],
) -> BatchFeature:
    processed_outputs = super()._call_hf_processor(
        prompt=prompt,
        mm_data=mm_data,
        mm_kwargs=mm_kwargs,
    )

    image_patches = processed_outputs.get("image_patches")
    if image_patches is not None:
        images = mm_data["images"]
        assert isinstance(images, list)

        # Original output: (1, num_images, Pn, Px * Py * C)
        # New output: (num_images, Pn, Px * Py * C)
        assert (isinstance(image_patches, list)
                and len(image_patches) == 1)
        assert (isinstance(image_patches[0], torch.Tensor)
                and len(image_patches[0]) == len(images))

        processed_outputs["image_patches"] = image_patches[0]

    return processed_outputs
```

:::{note}
Our [actual code](gh-file:vllm/model_executor/models/fuyu.py) has special handling
for text-only inputs to prevent unnecessary warnings from HF processor.
:::

This lets us override {meth}`~vllm.multimodal.processing.BaseMultiModalProcessor._get_mm_fields_config` as follows:

```python
def _get_mm_fields_config(
    self,
    hf_inputs: BatchFeature,
    hf_processor_mm_kwargs: Mapping[str, object],
) -> Mapping[str, MultiModalFieldConfig]:
    return dict(image_patches=MultiModalFieldConfig.batched("image"))
```

::::

:::::

### Prompt updates

Override {meth}`~vllm.multimodal.processing.BaseMultiModalProcessor._get_prompt_updates` to
return a list of {class}`~vllm.multimodal.processing.PromptUpdate` instances.

Each {class}`~vllm.multimodal.processing.PromptUpdate` instance specifies an update operation
(e.g.: insertion, replacement) performed by the HF processor.

::::{tab-set}
:::{tab-item} Basic example: LLaVA
:sync: llava

Looking at HF's `LlavaProcessor`:

```python
# https://github.com/huggingface/transformers/blob/v4.47.1/src/transformers/models/llava/processing_llava.py#L167-L170
prompt_strings = []
for sample in text:
    sample = sample.replace(self.image_token, self.image_token * num_image_tokens)
    prompt_strings.append(sample)
```

It simply repeats each input `image_token` a number of times equal to the number of placeholder feature tokens (`num_image_tokens`).
Based on this, we override {meth}`~vllm.multimodal.processing.BaseMultiModalProcessor._get_prompt_updates` as follows:

```python
def _get_prompt_updates(
    self,
    mm_items: MultiModalDataItems,
    hf_processor_mm_kwargs: Mapping[str, object],
    out_mm_kwargs: MultiModalKwargs,
) -> Sequence[PromptUpdate]:
    hf_config = self.info.get_hf_config()
    image_token_id = hf_config.image_token_index

    def get_replacement(item_idx: int):
        images = mm_items.get_items("image", ImageProcessorItems)

        image_size = images.get_image_size(item_idx)
        num_image_tokens = self.info.get_num_image_tokens(
            image_width=image_size.width,
            image_height=image_size.height,
        )

        return [image_token_id] * num_image_tokens

    return [
        PromptReplacement(
            modality="image",
            target=[image_token_id],
            replacement=get_replacement,
        ),
    ]
```

:::

:::{tab-item} Handling additional tokens: Fuyu
:sync: fuyu

Recall the layout of feature tokens from Step 2:

```
|SPEAKER||SPEAKER|...|SPEAKER||NEWLINE|
|SPEAKER||SPEAKER|...|SPEAKER||NEWLINE|
...
|SPEAKER||SPEAKER|...|SPEAKER||NEWLINE|
```

We define a helper function to return `ncols` and `nrows` directly:

```python
def get_image_feature_grid_size(
    self,
    *,
    image_width: int,
    image_height: int,
) -> tuple[int, int]:
    image_processor = self.get_image_processor()
    target_width = image_processor.size["width"]
    target_height = image_processor.size["height"]
    patch_width = image_processor.patch_size["width"]
    patch_height = image_processor.patch_size["height"]

    if not (image_width <= target_width and image_height <= target_height):
        height_scale_factor = target_height / image_height
        width_scale_factor = target_width / image_width
        optimal_scale_factor = min(height_scale_factor, width_scale_factor)

        image_height = int(image_height * optimal_scale_factor)
        image_width = int(image_width * optimal_scale_factor)

    ncols = math.ceil(image_width / patch_width)
    nrows = math.ceil(image_height / patch_height)
    return ncols, nrows
```

Based on this, we can initially define our replacement tokens as:

```python
def get_replacement(item_idx: int):
    images = mm_items.get_items("image", ImageProcessorItems)
    image_size = images.get_image_size(item_idx)

    ncols, nrows = self.info.get_image_feature_grid_size(
        image_width=image_size.width,
        image_height=image_size.height,
    )

    # `_IMAGE_TOKEN_ID` corresponds to `|SPEAKER|`
    # `_NEWLINE_TOKEN_ID` corresponds to `|NEWLINE|`
    return ([_IMAGE_TOKEN_ID] * ncols + [_NEWLINE_TOKEN_ID]) * nrows
```

However, this is not entirely correct. After `FuyuImageProcessor.preprocess_with_tokenizer_info` is called,
a BOS token (`<s>`) is also added to the promopt:

```python
# https://github.com/huggingface/transformers/blob/v4.48.3/src/transformers/models/fuyu/processing_fuyu.py#L417-L435
model_image_input = self.image_processor.preprocess_with_tokenizer_info(
    image_input=tensor_batch_images,
    image_present=image_present,
    image_unpadded_h=image_unpadded_heights,
    image_unpadded_w=image_unpadded_widths,
    image_placeholder_id=image_placeholder_id,
    image_newline_id=image_newline_id,
    variable_sized=True,
)
prompt_tokens, prompts_length = _tokenize_prompts_with_image_and_batch(
    tokenizer=self.tokenizer,
    prompts=prompts,
    scale_factors=scale_factors,
    max_tokens_to_generate=self.max_tokens_to_generate,
    max_position_embeddings=self.max_position_embeddings,
    add_BOS=True,
    add_beginning_of_answer_token=True,
)
```

To accommodate this, instead of a string you can return an instance of {class}`~vllm.multimodal.processing.PromptUpdateDetails`
with different `full` and `feature` attributes:

```python
hf_config = self.info.get_hf_config()
bos_token_id = hf_config.bos_token_id  # `<s>`
assert isinstance(bos_token_id, int)

def get_replacement_fuyu(item_idx: int):
    images = mm_items.get_items("image", ImageProcessorItems)
    image_size = images.get_image_size(item_idx)

    ncols, nrows = self.info.get_image_feature_grid_size(
        image_width=image_size.width,
        image_height=image_size.height,
    )
    image_tokens = ([_IMAGE_TOKEN_ID] * ncols +
                    [_NEWLINE_TOKEN_ID]) * nrows

    return PromptUpdateDetails(
        full=image_tokens + [bos_token_id],
        features=image_tokens,
    )
```

Finally, noticing that the HF processor removes the `|ENDOFTEXT|` token from the tokenized prompt,
we can search for it to conduct the replacement at the start of the string:

```python
def _get_prompt_updates(
    self,
    mm_items: MultiModalDataItems,
    hf_processor_mm_kwargs: Mapping[str, object],
    out_mm_kwargs: MultiModalKwargs,
) -> Sequence[PromptUpdate]:
    hf_config = self.info.get_hf_config()
    bos_token_id = hf_config.bos_token_id
    assert isinstance(bos_token_id, int)

    tokenizer = self.info.get_tokenizer()
    eot_token_id = tokenizer.bos_token_id
    assert isinstance(eot_token_id, int)

    def get_replacement_fuyu(item_idx: int):
        images = mm_items.get_items("image", ImageProcessorItems)
        image_size = images.get_image_size(item_idx)

        ncols, nrows = self.info.get_image_feature_grid_size(
            image_width=image_size.width,
            image_height=image_size.height,
        )
        image_tokens = ([_IMAGE_TOKEN_ID] * ncols +
                        [_NEWLINE_TOKEN_ID]) * nrows

        return PromptUpdateDetails(
            full=image_tokens + [bos_token_id],
            features=image_tokens,
        )

    return [
        PromptReplacement(
            modality="image",
            target=[eot_token_id],
            replacement=get_replacement_fuyu,
        )
    ]
```

:::

::::

## 5. Register processor-related classes

After you have defined {class}`~vllm.multimodal.processing.BaseProcessingInfo` (Step 2),
{class}`~vllm.multimodal.profiling.BaseDummyInputsBuilder` (Step 3),
and {class}`~vllm.multimodal.processing.BaseMultiModalProcessor` (Step 4),
decorate the model class with {meth}`MULTIMODAL_REGISTRY.register_processor <vllm.multimodal.registry.MultiModalRegistry.register_processor>`
to register them to the multi-modal registry:

```diff
  from vllm.model_executor.models.interfaces import SupportsMultiModal
+ from vllm.multimodal import MULTIMODAL_REGISTRY

+ @MULTIMODAL_REGISTRY.register_processor(YourMultiModalProcessor,
+                                         info=YourProcessingInfo,
+                                         dummy_inputs=YourDummyInputsBuilder)
  class YourModelForImage2Seq(nn.Module, SupportsMultiModal):
```

## Notes

### Inserting feature tokens without replacement

Some HF processors directly insert feature tokens without replacing anything in the original prompt. In that case, you can use {class}`~vllm.multimodal.processing.PromptInsertion` instead of {class}`~vllm.multimodal.processing.PromptReplacement` inside {meth}`~vllm.multimodal.processing.BaseMultiModalProcessor._get_prompt_updates`.

Examples:

- BLIP-2 (insert at start of prompt): <gh-file:vllm/model_executor/models/blip2.py>
- Florence2 (insert at start of prompt): <gh-file:vllm/model_executor/models/florence2.py>
- Molmo (insert after `<|endoftext|>` token): <gh-file:vllm/model_executor/models/molmo.py>

### Handling prompt updates unrelated to multi-modal data

{meth}`~vllm.multimodal.processing.BaseMultiModalProcessor._get_prompt_updates` assumes that each application of prompt update corresponds to one multi-modal item. If the HF processor performs additional processing regardless of how many multi-modal items there are, you should override {meth}`~vllm.multimodal.processing.BaseMultiModalProcessor._apply_hf_processor_tokens_only` so that the processed token inputs are consistent with the result of applying the HF processor on text inputs. This is because token inputs bypass the HF processor according to [our design](#mm-processing).

Examples:

- Chameleon (appends `sep_token`): <gh-file:vllm/model_executor/models/chameleon.py>
- Fuyu (appends `boa_token`): <gh-file:vllm/model_executor/models/fuyu.py>
- Molmo (applies chat template which is not defined elsewhere): <gh-file:vllm/model_executor/models/molmo.py>

### Custom HF processor

Some models don't define a HF processor class on HF Hub. In that case, you can define a custom HF processor that has the same call signature as HF processors and pass it to {meth}`~vllm.multimodal.processing.BaseMultiModalProcessor._call_hf_processor`.

Examples:

- DeepSeek-VL2: <gh-file:vllm/model_executor/models/deepseek_vl2.py>
- InternVL: <gh-file:vllm/model_executor/models/internvl.py>
- Qwen-VL: <gh-file:vllm/model_executor/models/qwen_vl.py>