vllm.model_executor.models.fuyu ¶

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

    def get_dummy_mm_data(
        self,
        seq_len: int,
        mm_counts: Mapping[str, int],
        mm_options: Optional[Mapping[str, BaseDummyOptions]] = None,
    ) -> MultiModalDataDict:
        target_width, target_height = self.info.get_image_size_with_most_features()
        num_images = mm_counts.get("image", 0)

        image_overrides = mm_options.get("image") if mm_options else None

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

@MULTIMODAL_REGISTRY.register_processor(
    FuyuMultiModalProcessor,
    info=FuyuProcessingInfo,
    dummy_inputs=FuyuDummyInputsBuilder,
)
class FuyuForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
    merge_by_field_config = True

    hf_to_vllm_mapper = WeightsMapper(
        orig_to_new_prefix={
            "model.vision_embed_tokens.": "vision_embed_tokens.",
            "model.language_model.": "language_model.model.",
            "lm_head.": "language_model.lm_head.",
        }
    )

    @classmethod
    def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
        if modality.startswith("image"):
            return None

        raise ValueError("Only image modality is supported")

    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__()
        config = vllm_config.model_config.hf_config
        quant_config = vllm_config.quant_config
        multimodal_config = vllm_config.model_config.multimodal_config
        self.config = config
        self.multimodal_config = multimodal_config

        self.vocab_size = config.text_config.vocab_size
        self.image_token_id = _IMAGE_TOKEN_ID
        self.image_feature_size = config.patch_size**2 * config.num_channels

        self.vision_embed_tokens = ColumnParallelLinear(
            self.image_feature_size,
            config.hidden_size,
            quant_config=quant_config,
            gather_output=True,
        )
        self.language_model = PersimmonForCausalLM(
            vllm_config=vllm_config.with_hf_config(config.text_config),
            prefix=maybe_prefix(prefix, "language_model"),
        )
        self.make_empty_intermediate_tensors = (
            self.language_model.make_empty_intermediate_tensors
        )

    def _parse_and_validate_image_input(
        self, **kwargs: object
    ) -> Optional[FuyuImagePatchInputs]:
        image_patches = kwargs.pop("image_patches", None)
        patches_per_image = kwargs.pop("patches_per_image", None)

        if image_patches is None:
            return None

        return FuyuImagePatchInputs(
            type="image_patches",
            image_patches_flat=image_patches,
            patches_per_image=patches_per_image,
            resolve_bindings={"fn": self.image_feature_size},
        )

    def _process_image_input(
        self, image_input: FuyuImagePatchInputs
    ) -> MultiModalEmbeddings:
        image_patches_flat = image_input["image_patches_flat"]
        patches_per_image = image_input["patches_per_image"]

        assert self.vision_embed_tokens is not None
        vision_embeddings_flat, _ = self.vision_embed_tokens(image_patches_flat)

        return vision_embeddings_flat.split(patches_per_image.tolist(), dim=0)

    def get_language_model(self) -> torch.nn.Module:
        return self.language_model

    def get_multimodal_embeddings(self, **kwargs: object) -> MultiModalEmbeddings:
        image_input = self._parse_and_validate_image_input(**kwargs)
        if image_input is None:
            return []

        return self._process_image_input(image_input)

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        intermediate_tensors: Optional[IntermediateTensors] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        **kwargs: object,
    ):
        if intermediate_tensors is not None:
            inputs_embeds = None

        hidden_states = self.language_model(
            input_ids=input_ids,
            positions=positions,
            intermediate_tensors=intermediate_tensors,
            inputs_embeds=inputs_embeds,
        )
        return hidden_states

    def compute_logits(
        self,
        hidden_states: torch.Tensor,
    ) -> Optional[torch.Tensor]:
        logits = self.language_model.logits_processor(
            self.language_model.lm_head, hidden_states
        )
        return logits

    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
        loader = AutoWeightsLoader(self)
        return loader.load_weights(weights)

class FuyuImagePatchInputs(TensorSchema):
    """
    Dimensions:
        - bn: Batch size * number of images
        - bnp: Batch size * number of images * number of patches
        - fn: patch_size_x * patch_size_y * num_channels
    """

    type: Literal["image_patches"] = "image_patches"

    image_patches_flat: Annotated[torch.Tensor, TensorShape("bnp", "fn")]

    patches_per_image: Annotated[list[int], TensorShape("bn")]
    """
    The number of total patches for each image in the batch.

    This is used to split the embeddings which has the first two dimensions
    flattened just like `image_patches_flat`.
    """

class FuyuMultiModalProcessor(BaseMultiModalProcessor[FuyuProcessingInfo]):
    def _call_hf_processor(
        self,
        prompt: str,
        mm_data: Mapping[str, object],
        mm_kwargs: Mapping[str, object],
        tok_kwargs: Mapping[str, object],
    ) -> BatchFeature:
        if not mm_data:
            # Avoid warning from HF logger for text-only input
            prompt_ids = self.info.get_tokenizer().encode(prompt)
            prompt_ids = self._apply_hf_processor_tokens_only(prompt_ids)
            return BatchFeature(dict(input_ids=[prompt_ids]), tensor_type="pt")

        processed_outputs = super()._call_hf_processor(
            prompt=prompt,
            mm_data=mm_data,
            mm_kwargs=mm_kwargs,
            tok_kwargs=tok_kwargs,
        )

        image_patches = processed_outputs["image_patches"]
        processed_outputs["image_patches"] = flatten_bn(image_patches)
        processed_outputs["patches_per_image"] = torch.tensor(
            [len(p) for p in image_patches]
        )

        return processed_outputs

    def _apply_hf_processor_tokens_only(
        self,
        prompt_tokens: list[int],
    ) -> list[int]:
        # HF processor adds boa_token_id
        tokenizer = self.info.get_tokenizer()
        vocab = tokenizer.get_vocab()

        boa_token_id = vocab["<0x04>"]
        if prompt_tokens[-1] != boa_token_id:
            prompt_tokens.append(boa_token_id)

        return prompt_tokens

    def _get_mm_fields_config(
        self,
        hf_inputs: BatchFeature,
        hf_processor_mm_kwargs: Mapping[str, object],
    ) -> Mapping[str, MultiModalFieldConfig]:
        patches_per_image = hf_inputs.get("patches_per_image", torch.empty(0))

        return dict(
            image_patches=MultiModalFieldConfig.flat_from_sizes(
                "image", patches_per_image
            ),
            patches_per_image=MultiModalFieldConfig.batched("image"),
        )

    def _get_prompt_updates(
        self,
        mm_items: MultiModalDataItems,
        hf_processor_mm_kwargs: Mapping[str, object],
        out_mm_kwargs: MultiModalKwargsItems,
    ) -> 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,
            )
        ]

class FuyuProcessingInfo(BaseProcessingInfo):
    def get_hf_config(self):
        return self.ctx.get_hf_config(FuyuConfig)

    def get_hf_processor(self, **kwargs: object):
        return self.ctx.get_hf_processor(FuyuProcessor, **kwargs)

    def get_image_processor(self, **kwargs: object) -> FuyuImageProcessor:
        return self.get_hf_processor(**kwargs).image_processor

    def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
        return {"image": 1}

    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

    def get_num_image_tokens(
        self,
        *,
        image_width: int,
        image_height: int,
    ) -> int:
        ncols, nrows = self.get_image_feature_grid_size(
            image_width=image_width,
            image_height=image_height,
        )

        return ncols * nrows

    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"]
        )