vllm.model_executor.models.deepseek_vl2 ¶

class DeepseekVL2DummyInputsBuilder(BaseDummyInputsBuilder[DeepseekVL2ProcessingInfo]):
    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

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

        max_image_size = self.info.get_image_size_with_most_features()

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

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

class DeepseekVL2ImagePixelInputs(TensorSchema):
    """
    Dimensions:
        - bnp: Batch size * number of images * number of patches
        - p: Number of patches
        - c: Number of channels (3)
        - h: Height of each image
        - w: Width of each image
    """

    type: Literal["pixel_values"]
    data: Annotated[torch.Tensor, TensorShape("bnp", 3, "h", "w", dynamic_dims={"bnp"})]
    images_spatial_crop: Annotated[torch.Tensor, TensorShape("bn", 2)]

class DeepseekVL2MultiModalProcessor(
    BaseMultiModalProcessor[DeepseekVL2ProcessingInfo]
):
    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:
            tokenizer = self.info.get_tokenizer()
            return tokenizer(prompt, add_special_tokens=True, return_tensors="pt")

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

        processed_outputs["num_patches"] = (
            processed_outputs["images_spatial_crop"].prod(-1) + 1
        )

        return processed_outputs

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

        return dict(
            pixel_values=MultiModalFieldConfig.flat_from_sizes("image", num_patches),
            images_spatial_crop=MultiModalFieldConfig.batched("image"),
            image_embeds=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_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)

        image_token_id = hf_processor.image_token_id
        assert isinstance(image_token_id, int)

        def get_replacement_deepseek_vl2(item_idx: int):
            images = mm_items.get_items(
                "image", (ImageEmbeddingItems, ImageProcessorItems)
            )

            if isinstance(images, ImageEmbeddingItems):
                num_image_tokens = images.get_feature_size(item_idx)
            else:
                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,
                    cropping=len(images) <= 2,
                )
            return [image_token_id] * num_image_tokens

        return [
            PromptReplacement(
                modality="image",
                target=[image_token_id],
                replacement=get_replacement_deepseek_vl2,
            )
        ]

    def _cached_apply_hf_processor(
        self,
        prompt: Union[str, list[int]],
        mm_data_items: MultiModalDataItems,
        hf_processor_mm_kwargs: Mapping[str, object],
        tokenization_kwargs: Mapping[str, object],
        mm_uuids: Optional[MultiModalUUIDDict] = None,
    ) -> tuple[list[int], MultiModalProcessingInfo, bool]:
        # The processor logic is different for len(images) <= 2 vs > 2
        # Since the processing cache assumes that the processor output is
        # invariant of how many images are passed per prompt, we only
        # perform caching for the most common case
        if mm_data_items.get_count("image", strict=False) > 2:
            return self._apply_hf_processor(
                prompt=prompt,
                mm_data_items=mm_data_items,
                hf_processor_mm_kwargs=hf_processor_mm_kwargs,
                tokenization_kwargs=tokenization_kwargs,
                mm_uuids=mm_uuids,
            )

        return super()._cached_apply_hf_processor(
            prompt=prompt,
            mm_data_items=mm_data_items,
            hf_processor_mm_kwargs=hf_processor_mm_kwargs,
            tokenization_kwargs=tokenization_kwargs,
            mm_uuids=mm_uuids,
        )

class DeepseekVL2ProcessingInfo(BaseProcessingInfo):
    def get_hf_config(self):
        return self.ctx.get_hf_config(DeepseekVLV2Config)

    def get_hf_processor(self, **kwargs: object):
        return self.ctx.get_hf_processor(DeepseekVLV2Processor, **kwargs)

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

    def get_num_image_tokens(
        self, *, image_width: int, image_height: int, cropping: bool = True
    ) -> int:
        hf_processor = self.get_hf_processor()
        image_size = hf_processor.image_size
        patch_size = hf_processor.patch_size
        downsample_ratio = hf_processor.downsample_ratio

        if cropping:
            best_width, best_height = hf_processor.select_best_resolution(
                (image_width, image_height)
            )
            num_width_tiles, num_height_tiles = (
                best_width // image_size,
                best_height // image_size,
            )
        else:
            num_width_tiles = num_height_tiles = 1

        h = w = math.ceil((image_size // patch_size) / downsample_ratio)

        global_views_tokens = h * (w + 1)
        local_views_tokens = (num_height_tiles * h) * (num_width_tiles * w + 1)
        return global_views_tokens + local_views_tokens + 1

    def get_image_size_with_most_features(self) -> ImageSize:
        hf_config = self.get_hf_config()
        candidate_resolutions = hf_config.candidate_resolutions
        height, width = max(
            candidate_resolutions,
            key=lambda x: self.get_num_image_tokens(
                image_width=x[1], image_height=x[0]
            ),
        )
        return ImageSize(width=width, height=height)

class DeepseekVL2VImageEmbeddingInputs(TensorSchema):
    """
    Dimensions:
        - bn: Batch size * number of images
        - f: Image feature size
        - h: Hidden size (must match language model backbone)
    """

    type: Literal["image_embeds"]
    data: Annotated[
        Union[torch.Tensor, list[torch.Tensor]], TensorShape("bn", "f", "h")
    ]

@MULTIMODAL_REGISTRY.register_processor(
    DeepseekVL2MultiModalProcessor,
    info=DeepseekVL2ProcessingInfo,
    dummy_inputs=DeepseekVL2DummyInputsBuilder,
)
class DeepseekVLV2ForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
    merge_by_field_config = True

    hf_to_vllm_mapper = WeightsMapper(
        orig_to_new_prefix={
            "language.": "language_model.",
        }
    )

    @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")

    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__()
        config: DeepseekVLV2Config = 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.vision_config = config.vision_config
        self.projector_config = config.projector_config
        self.text_config = config.text_config

        model_config = vllm_config.model_config
        tokenizer = cached_tokenizer_from_config(model_config)
        self.image_token_id: int = tokenizer.vocab[_IMAGE_TOKEN]

        self.vision = self._init_vision_module(
            self.vision_config, quant_config, maybe_prefix(prefix, "vision")
        )

        self.projector = MlpProjector(self.projector_config)
        self.tile_tag = config.tile_tag
        self.global_view_pos = config.global_view_pos

        # special token for image token sequence format
        embed_std = 1 / torch.sqrt(
            torch.tensor(self.projector_config.n_embed, dtype=torch.float32)
        )
        if self.tile_tag == "2D":
            # <|view_seperator|>, <|\n|>
            self.image_newline = nn.Parameter(
                torch.randn(self.projector_config.n_embed) * embed_std
            )
            # This is a typo in original implementation
            self.view_seperator = nn.Parameter(
                torch.randn(self.projector_config.n_embed) * embed_std
            )
        else:
            raise ValueError(
                f"Only 2D tile_tag is supported currently, got: {self.tile_tag}"
            )

        if self.text_config.topk_method == "noaux_tc":
            architectures = ["DeepseekV3ForCausalLM"]
        elif not self.text_config.use_mla:
            architectures = ["DeepseekForCausalLM"]
        else:
            architectures = ["DeepseekV2ForCausalLM"]

        self.language_model = init_vllm_registered_model(
            vllm_config=vllm_config,
            hf_config=self.text_config,
            prefix=maybe_prefix(prefix, "language"),
            architectures=architectures,
        )

        self.make_empty_intermediate_tensors = (
            self.language_model.make_empty_intermediate_tensors
        )

    def _get_parent_and_attr(self, root: torch.nn.Module, dotted_name: str):
        """Return (parent_module, final_attr_name) for a dotted module path."""
        names = dotted_name.split(".")
        parent = root
        for n in names[:-1]:
            parent = getattr(parent, n)
        return parent, names[-1]

    # patch for timm ViT instance to support tensor parallel
    def patch_vit_for_tp(self, vit: torch.nn.Module, quant_config: QuantizationConfig):
        try:
            import timm
        except ImportError as e:
            raise ImportError("Please install timm") from e

        for name, module in vit.named_modules():
            if isinstance(module, nn.Linear):
                parent, attr_name = self._get_parent_and_attr(vit, name)
                if isinstance(parent, timm.layers.Mlp) and attr_name == "fc1":
                    new_linear = replace_linear_class(
                        module, "colwise", quant_config, prefix=name
                    )
                    setattr(parent, attr_name, new_linear)
                elif isinstance(parent, timm.layers.Mlp) and attr_name == "fc2":
                    new_linear = replace_linear_class(
                        module, "rowwise", quant_config, prefix=name
                    )
                    setattr(parent, attr_name, new_linear)

        return vit

    def _init_vision_module(
        self,
        vision_config: VisionEncoderConfig,
        quant_config: Optional[QuantizationConfig],
        prefix: str = "",
    ) -> nn.Module:
        # TODO: refactor vision model through timm wrapper from transformers
        try:
            import timm
        except ImportError as e:
            raise ImportError("Please install timm") from e

        with set_default_torch_dtype(torch.float16):
            model = timm.create_model(
                "vit_so400m_patch14_siglip_384.webli",
                pretrained=False,
                num_classes=0,
                dynamic_img_size=True,
                dynamic_img_pad=True,
            )

        if get_tensor_model_parallel_world_size() > 1:
            model = self.patch_vit_for_tp(model, quant_config)

        model = model.to(dtype=torch.get_default_dtype())
        return model

    def _parse_and_validate_image_input(
        self, **kwargs: object
    ) -> Optional[DeepseekVL2ImageInputs]:
        pixel_values = kwargs.pop("pixel_values", None)
        images_spatial_crop = kwargs.pop("images_spatial_crop", None)
        image_embeds = kwargs.pop("image_embeds", None)

        if pixel_values is None and image_embeds is None:
            return None

        if pixel_values is not None:
            expected_h = expected_w = self.vision_config.image_size
            return DeepseekVL2ImagePixelInputs(
                type="pixel_values",
                data=pixel_values,
                images_spatial_crop=images_spatial_crop,
                resolve_bindings={
                    "h": expected_h,
                    "w": expected_w,
                },
            )

        if image_embeds is not None:
            return DeepseekVL2VImageEmbeddingInputs(
                type="image_embeds",
                data=image_embeds,
            )

        raise AssertionError("This line should be unreachable.")

    def _pixel_values_to_embedding(
        self,
        pixel_values: torch.Tensor,
        images_spatial_crop: torch.Tensor,
    ) -> list[torch.Tensor]:
        # [batch_all_tiles, vit_seq_len, c]
        images_feature = self.vision.forward_features(pixel_values)

        # [batch_all_tiles, hw, D]
        images_embeds = self.projector(images_feature)

        _, hw, n_dim = images_embeds.shape
        h = w = int(hw**0.5)

        # fill image token based on self.tile_tag & self.global_view_pos
        tile_index = 0
        vision_embeddings = []
        for jdx in range(images_spatial_crop.size(0)):
            # extra global & local features
            num_width_tiles, num_height_tiles = images_spatial_crop[jdx]
            if num_width_tiles == 0 or num_height_tiles == 0:
                break
            num_tiles_in_image = num_width_tiles * num_height_tiles

            # [hw, D]
            global_features = images_embeds[tile_index]

            # [num_height_tiles * num_width_tiles, hw, D]
            local_features = images_embeds[
                tile_index + 1 : tile_index + 1 + num_tiles_in_image
            ]
            tile_index += num_tiles_in_image + 1

            # format global and local features
            # ----------------- global view add newline -----------------
            # [hw, D] -> [h, w, D]
            global_features = global_features.view(h, w, n_dim)

            # [D]     -> [h, 1, D]
            new_lines_in_global = repeat(self.image_newline, "d -> h 1 d", h=h)

            # cat([h, w, D], [h, 1, D], dim=1) -> [h, w + 1, D]
            global_features = torch.cat([global_features, new_lines_in_global], dim=1)

            # [h, w + 1, D] -> [h * (w + 1), D]
            global_features = global_features.view(-1, n_dim)

            # ----------------- local view add newline -----------------
            # [num_height_tiles * num_width_tiles, h * w, D] ->
            # [num_height_tiles * h, num_width_tiles * w, D]
            local_features = rearrange(
                local_features,
                "(th tw) (h w) d -> (th h) (tw w) d",
                th=num_height_tiles,
                tw=num_width_tiles,
                h=h,
                w=w,
            )

            # [D] -> [num_height_tiles * h, 1, D]
            new_lines_in_local = repeat(
                self.image_newline, "d -> (th h) 1 d", th=num_height_tiles, h=h
            )

            # [num_height_tiles * h, num_width_tiles * w + 1, D]
            local_features = torch.cat([local_features, new_lines_in_local], dim=1)

            # [num_height_tiles * h, num_width_tiles * w + 1, D]
            #   --> [(num_height_tiles * h) * (num_width_tiles * w + 1), D]
            local_features = local_features.view(-1, n_dim)

            # merge global and local tiles
            if self.global_view_pos == "head":
                global_local_features = torch.cat(
                    [
                        global_features,
                        self.view_seperator[None, :],
                        local_features,
                    ]
                )
            else:
                global_local_features = torch.cat(
                    [
                        local_features,
                        self.view_seperator[None, :],
                        global_features,
                    ]
                )

            vision_embeddings.append(global_local_features)
        return vision_embeddings

    def _process_image_input(
        self, image_input: DeepseekVL2ImageInputs
    ) -> list[torch.Tensor]:
        if image_input["type"] == "image_embeds":
            image_data = image_input["data"]
            if is_list_of(image_data, torch.Tensor):
                # it's already a list of tensors
                return image_data
            if len(image_data.shape) == 3:
                # 3D tensor
                return list(torch.unbind(image_data, dim=0))
            raise ValueError(
                "We expect batched 2D tensors; "
                "this can be either a list of 2D tensors or a single 3D tensor."
            )

        pixel_values = image_input["data"]
        images_spatial_crop = image_input["images_spatial_crop"]

        return self._pixel_values_to_embedding(
            pixel_values=pixel_values, images_spatial_crop=images_spatial_crop
        )

    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 []
        vision_embeddings = self._process_image_input(image_input)
        return vision_embeddings

    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, positions, intermediate_tensors, inputs_embeds=inputs_embeds
        )

        return hidden_states

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

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