多模態支援

本文將引導您完成擴充套件基礎模型以使其接受 多模態輸入 的步驟。

1. 更新基礎 vLLM 模型

假定您已按照 這些步驟 在 vLLM 中實現了該模型。請進一步更新模型，如下所示：

• 實現 get_placeholder_str 以定義在文字提示中用於表示多模態專案的佔位符字串。這應與模型的聊天模板一致。

  程式碼

  class YourModelForImage2Seq(nn.Module):
      ...
  
      @classmethod
      def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
          if modality.startswith("image"):
              return "<image>"
  
          raise ValueError("Only image modality is supported")
  

• 在 forward 中為每個對應於多模態輸入的輸入張量保留一個關鍵字引數，如以下示例所示：

  def forward(
      self,
      input_ids: torch.Tensor,
      positions: torch.Tensor,
+     pixel_values: torch.Tensor,
  ) -> SamplerOutput:

更方便地，您可以直接將 **kwargs 傳遞給 forward 方法，並從中檢索多模態輸入的關鍵字引數。

• 實現 get_multimodal_embeddings，該方法透過模型的多模態分詞器執行多模態輸入並返回其嵌入。下面我們提供了一個典型實現模式的樣板，但您可以根據自己的需要進行調整。

  程式碼

  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
  

重要

返回的 multimodal_embeddings 必須是形狀為 (num_items, feature_size, hidden_size) 的 3D torch.Tensor，或是形狀為 (feature_size, hidden_size) 的 2D torch.Tensor 的列表/元組，以便 multimodal_embeddings[i] 檢索從請求的第 i 個多模態資料項（例如影像）生成的嵌入。

• 實現 get_input_embeddings 以將 multimodal_embeddings 與來自 input_ids 的文字嵌入合併。如果模型的輸入處理實現正確（參見下面的章節），則可以利用我們提供的實用函式輕鬆合併嵌入。

  程式碼

  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
  

• 實現 get_language_model getter 以提供對底層語言模型的穩定訪問。

  class YourModelForImage2Seq(nn.Module):
      ...
  
      def get_language_model(self) -> torch.nn.Module:
          # Change `language_model` according to your implementation.
          return self.language_model
  

• 完成上述步驟後，使用 SupportsMultiModal 介面更新模型類。

+ from vllm.model_executor.models.interfaces import SupportsMultiModal

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

注意

模型類不必命名為 *ForCausalLM。檢視 HuggingFace Transformers 文件 以獲取一些示例。

2. 指定處理資訊

接下來，建立 BaseProcessingInfo 的子類，以提供與 HF 處理相關的基本資訊。

最大輸入項數

您需要覆蓋抽象方法 get_supported_mm_limits，以返回模型支援的每種模態的最大輸入項數。

例如，如果模型支援任意數量的影像，但每個提示只支援一個影片

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

3. 指定虛擬輸入

然後，繼承 BaseDummyInputsBuilder 以構建用於 HF 處理和記憶體分析的虛擬輸入。

用於記憶體分析

覆蓋抽象方法 get_dummy_text 和 get_dummy_mm_data 以構建用於記憶體分析的虛擬輸入。這些虛擬輸入應導致模型在最壞情況下的記憶體使用量，以便 vLLM 可以為其保留正確的記憶體量。

假設記憶體使用量隨令牌數量增加，則可以構建虛擬輸入以最大化輸出嵌入的數量，這與佔位符特徵令牌的數量相同。

基本示例：LLaVA無輸入佔位符：Fuyu

檢視 HF 的 LlavaForConditionalGeneration 程式碼

程式碼

# 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)

每張影像的佔位符特徵令牌數量是 image_features.shape[1]。image_features 在 get_image_features 方法內部計算。

程式碼

# 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

我們可以推斷 image_features.shape[1] 基於視覺塔（針對 llava-hf/llava-1.5-7b-hf 模型，是 CLIPVisionModel）的 image_outputs.hidden_states.shape[1] 輸出。此外，我們只需要序列長度（張量的第二個維度）即可獲取 image_features.shape[1]。序列長度由 CLIPVisionTransformer 中的初始隱藏狀態決定，因為注意力機制不改變輸出隱藏狀態的序列長度。

# 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,
)

為了找到序列長度，我們檢視 CLIPVisionEmbeddings 的程式碼

程式碼

# 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

我們可以推斷 embeddings.shape[1] == self.num_positions，其中

# 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

總的來說，一張影像的佔位符特徵令牌數量可以計算為：

程式碼

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

請注意，影像令牌的數量不依賴於影像的寬度和高度。我們可以簡單地使用虛擬的 image_size 來計算多模態分析資料。

程式碼

# NOTE: In actuality, this is usually implemented as part of the
# model's subclass of `BaseProcessingInfo`, but we show it as is
# here for simplicity.
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_dummy_mm_data(
    self,
    seq_len: int,
    mm_counts: Mapping[str, int],
) -> MultiModalDataDict:
    num_images = mm_counts.get("image", 0)

    target_width, target_height = \
        self.info.get_image_size_with_most_features()

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

對於文字，我們只需從模型配置中擴充套件多模態影像令牌，以匹配所需的影像數量。

def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
    num_images = mm_counts.get("image", 0)

    processor = self.info.get_hf_processor()
    image_token = processor.image_token

    return image_token * num_images

檢視 HF 的 FuyuForCausalLM 程式碼

程式碼

# 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,
    )

批處理中第 i 個項的佔位符特徵令牌數量是 patch_embeddings[i].shape[0]，與 image_patches[i].shape[0] 相同，即 num_total_patches。

與 LLaVA 不同，Fuyu 未在建模檔案中定義補丁數量。我們可以在哪裡獲取更多資訊？考慮到模型輸入來自 FuyuProcessor 的輸出，我們來檢視預處理檔案。

影像輸出是透過在 FuyuProcessor 內部呼叫 FuyuImageProcessor.preprocess 然後 FuyuImageProcessor.preprocess_with_tokenizer_info 獲得的。

在 FuyuImageProcessor.preprocess 中，影像被調整大小並填充到目標 FuyuImageProcessor.size，並返回調整大小後（但填充前）的尺寸作為元資料。

程式碼

# 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
    ]

在 FuyuImageProcessor.preprocess_with_tokenizer_info 中，影像根據此元資料被分割成補丁：

程式碼

# 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]

補丁的數量又由 FuyuImageProcessor.get_num_patches 定義

程式碼

# 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

這些影像補丁對應於佔位符令牌（|SPEAKER|）。因此，我們只需最大化影像補丁的數量。由於輸入影像首先被調整大小以適應 image_processor.size，我們可以透過輸入尺寸等於 image_processor.size 的影像來最大化影像補丁的數量。

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"])

Fuyu 不期望 HF 處理器輸入中包含影像佔位符，因此無論影像數量如何，虛擬提示文字都是空的。

def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
    return ""

對於多模態影像分析資料，其邏輯與 LLaVA 非常相似

程式碼

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

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

4. 指定處理詳情

之後，建立 BaseMultiModalProcessor 的子類，以補充 HF 處理中缺失的詳細資訊。

資訊

多模態資料處理

多模態欄位

覆蓋 _get_mm_fields_config，以返回 HF 處理器輸出的與輸入多模態專案相關的張量模式。

基本示例：LLaVA帶後處理：Fuyu

CLIPImageProcessor 的輸出是一個簡單張量，形狀為 (num_images, num_channels, image_height, image_width)

# 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)

因此，我們覆蓋 _get_mm_fields_config 如下所示：

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"),
    )

注意

我們的 實際程式碼 還支援預計算的影像嵌入，可以透過 image_embeds 引數傳遞給模型。

FuyuImageProcessor.preprocess_with_tokenizer_info 的 image_patches 輸出將批處理中每個專案對應的影像補丁進行拼接

# 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)

FuyuImageProcessor 輸出的 image_patches 的形狀因此是 (1, num_images, num_patches, patch_width * patch_height * num_channels)。

為了支援像 LLaVA 中使用 MultiModalFieldConfig.batched，我們透過覆蓋 BaseMultiModalProcessor._call_hf_processor 來移除額外的批處理維度

程式碼

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

注意

我們的 實際程式碼 對純文字輸入有特殊處理，以防止 HF 處理器發出不必要的警告。

注意

_call_hf_processor 方法為處理指定了 mm_kwargs 和 tok_kwargs。mm_kwargs 用於初始化和呼叫 huggingface 處理器，而 tok_kwargs 僅用於呼叫 huggingface 處理器。

這使得我們能夠如下覆蓋 _get_mm_fields_config：

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"))

提示詞更新

覆蓋 _get_prompt_updates 以返回 PromptUpdate 例項的列表。

每個 PromptUpdate 例項指定了 HF 處理器執行的更新操作（例如：插入、替換）。

基本示例：LLaVA處理額外令牌：Fuyu

檢視 HF 的 LlavaProcessor

# 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)

它簡單地將每個輸入 image_token 重複多次，次數等於佔位符特徵令牌的數量（num_image_tokens）。基於此，我們如下覆蓋 _get_prompt_updates：

程式碼

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,
        ),
    ]

回顧步驟 2 中的特徵令牌佈局

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

我們定義一個輔助函式直接返回 ncols 和 nrows

程式碼

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

基於此，我們最初可以將替換令牌定義為：

程式碼

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

然而，這並不完全正確。在呼叫 FuyuImageProcessor.preprocess_with_tokenizer_info 後，一個 BOS 令牌（<s>）也會被新增到提示中

程式碼

# 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,
)

為了僅將視覺嵌入分配給影像令牌，您可以返回一個 PromptUpdateDetails 例項，而不是字串。

程式碼

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.select_token_id(
        image_tokens + [bos_token_id],
        embed_token_id=_IMAGE_TOKEN_ID,
    )

最後，注意到 HF 處理器從分詞後的提示中移除了 |ENDOFTEXT| 令牌，我們可以搜尋它以在字串的開頭進行替換

程式碼

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.select_token_id(
            image_tokens + [bos_token_id],
            embed_token_id=_IMAGE_TOKEN_ID,
        )

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

5. 註冊處理器相關類

在您定義了 BaseProcessingInfo（步驟 2）、 BaseDummyInputsBuilder（步驟 3）和 BaseMultiModalProcessor（步驟 4）之後，使用 MULTIMODAL_REGISTRY.register_processor 裝飾模型類，將它們註冊到多模態登錄檔。

  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):

備註

不替換地插入特徵令牌

某些 HF 處理器直接插入特徵令牌，而不替換原始提示中的任何內容。在這種情況下，您可以在 _get_prompt_updates 內部使用 PromptInsertion 而不是 PromptReplacement。

示例

• BLIP-2（在提示開頭插入）： vllm/model_executor/models/blip2.py
• Florence2（在提示開頭插入）： vllm/model_executor/models/florence2.py
• Molmo（在 <|endoftext|> 令牌後插入）： vllm/model_executor/models/molmo.py

處理與多模態資料無關的提示詞更新

_get_prompt_updates 假設每次提示更新都對應一個多模態專案。如果 HF 處理器執行額外的處理，而不管有多少多模態專案，您應該覆蓋 _apply_hf_processor_tokens_only，以便處理後的令牌輸入與在文字輸入上應用 HF 處理器得到的結果一致。這是因為根據 我們的設計，令牌輸入會繞過 HF 處理器。

示例

• Chameleon（附加 sep_token）： vllm/model_executor/models/chameleon.py
• Fuyu（附加 boa_token）： vllm/model_executor/models/fuyu.py
• Molmo（應用了未在其他地方定義的聊天模板）： vllm/model_executor/models/molmo.py

自定義 HF 處理器

某些模型在 HF Hub 上沒有定義 HF 處理器類。在這種情況下，您可以定義一個與 HF 處理器具有相同呼叫簽名的自定義 HF 處理器，並將其傳遞給 _call_hf_processor。

示例

• DeepSeek-VL2: vllm/model_executor/models/deepseek_vl2.py
• InternVL: vllm/model_executor/models/internvl.py
• Qwen-VL: vllm/model_executor/models/qwen_vl.py