Sync from v0.13

2026-01-19 10:38:50 +08:00
parent b2ef04d792
commit 5aef6c175a
3714 changed files with 854317 additions and 89342 deletions
--- a/vllm/model_executor/models/deepseek_ocr.py
+++ b/vllm/model_executor/models/deepseek_ocr.py
@@ -0,0 +1,591 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Inference-only Deepseek-OCR model compatible with HuggingFace weights."""
+
+import math
+from collections.abc import Iterable, Mapping, Sequence
+from typing import Annotated, Literal
+
+import torch
+import torch.nn as nn
+from transformers import BatchFeature, CLIPVisionConfig
+
+from vllm.config import VllmConfig
+from vllm.config.multimodal import BaseDummyOptions
+from vllm.model_executor.models.interfaces import (
+    MultiModalEmbeddings,
+    SupportsMultiModal,
+    SupportsPP,
+)
+from vllm.model_executor.models.utils import (
+    AutoWeightsLoader,
+    WeightsMapper,
+    init_vllm_registered_model,
+    maybe_prefix,
+)
+from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.inputs import (
+    MultiModalDataDict,
+    MultiModalFieldConfig,
+    MultiModalKwargsItems,
+    NestedTensors,
+)
+from vllm.multimodal.parse import (
+    ImageEmbeddingItems,
+    ImageProcessorItems,
+    ImageSize,
+    MultiModalDataItems,
+)
+from vllm.multimodal.processing import (
+    BaseMultiModalProcessor,
+    BaseProcessingInfo,
+    PromptReplacement,
+    PromptUpdate,
+)
+from vllm.multimodal.profiling import BaseDummyInputsBuilder
+from vllm.sampling_params import SamplingParams
+from vllm.sequence import IntermediateTensors
+from vllm.tokenizers import cached_tokenizer_from_config
+from vllm.transformers_utils.configs.deepseek_vl2 import DeepseekVLV2Config
+from vllm.transformers_utils.processors.deepseek_ocr import (
+    BASE_SIZE,
+    CROP_MODE,
+    IMAGE_SIZE,
+    DeepseekOCRProcessor,
+    count_tiles,
+)
+from vllm.utils.tensor_schema import TensorSchema, TensorShape
+from vllm.v1.sample.logits_processor import (
+    AdapterLogitsProcessor,
+    RequestLogitsProcessor,
+)
+
+from .deepencoder import DeepCLIPVisionTransformer, build_sam_vit_b
+from .deepseek_vl2 import MlpProjector
+
+# The image token id may be various
+_IMAGE_TOKEN = "<image>"
+
+
+class DeepseekOCRImagePixelInputs(TensorSchema):
+    """
+    Dimensions:
+        - b: Batch size
+        - n: Number of images
+        - p: Number of patches
+        - base_size: Base size of the processor
+        - image_size: Image size of the processor
+    """
+
+    type: Literal["pixel_values"]
+    data: Annotated[
+        torch.Tensor,
+        TensorShape("bn", 3, "base_size", "base_size", dynamic_dims={"bnp"}),
+    ]
+    images_crop: Annotated[
+        torch.Tensor,
+        TensorShape("bnp", 3, "image_size", "image_size", dynamic_dims={"bnp"}),
+    ]
+    images_spatial_crop: Annotated[torch.Tensor, TensorShape("bn", 2)]
+
+
+class NoRepeatNGramLogitsProcessor:
+    def __init__(
+        self,
+        ngram_size: int,
+        window_size: int,
+        whitelist_token_ids: set[int] | None = None,
+    ):
+        self.ngram_size = ngram_size
+        self.window_size = window_size
+        self.whitelist_token_ids = whitelist_token_ids or set()
+
+    def __call__(
+        self,
+        output_ids: list[int],
+        logits: torch.Tensor,
+    ) -> torch.Tensor:
+        if len(output_ids) < self.ngram_size:
+            return logits
+
+        current_prefix = tuple(output_ids[-(self.ngram_size - 1) :])
+
+        search_start = max(0, len(output_ids) - self.window_size)
+        search_end = len(output_ids) - self.ngram_size + 1
+
+        banned_tokens = set()
+        for i in range(search_start, search_end):
+            ngram = tuple(output_ids[i : i + self.ngram_size])
+            if ngram[:-1] == current_prefix:
+                banned_tokens.add(ngram[-1])
+
+        banned_tokens = banned_tokens - self.whitelist_token_ids
+
+        if banned_tokens:
+            logits[list(banned_tokens)] = -float("inf")
+
+        return logits
+
+
+class NGramPerReqLogitsProcessor(AdapterLogitsProcessor):
+    """Example of overriding the wrapper class `__init__()` in order to utilize
+    info about the device type"""
+
+    @classmethod
+    def validate_params(cls, params: SamplingParams):
+        ngram_size = params.extra_args and params.extra_args.get("ngram_size")
+        window_size = params.extra_args and params.extra_args.get("window_size", 100)
+        whitelist_token_ids = params.extra_args and params.extra_args.get(
+            "whitelist_token_ids", None
+        )
+        # if ngram_size is not provided, skip validation because the processor
+        # will not be used.
+        if ngram_size is None:
+            return None
+
+        if not isinstance(ngram_size, int) or ngram_size <= 0:
+            raise ValueError(
+                f"`ngram_size` has to be a strictly positive integer, got {ngram_size}."
+            )
+        if not isinstance(window_size, int) or window_size <= 0:
+            raise ValueError(
+                "`window_size` has to be a strictly positive integer, "
+                f"got {window_size}."
+            )
+        if whitelist_token_ids is not None and not isinstance(
+            whitelist_token_ids, Iterable
+        ):
+            raise ValueError(
+                "`whitelist_token_ids` has to be a sequence of integers, "
+                f"got {whitelist_token_ids}."
+            )
+
+    def is_argmax_invariant(self) -> bool:
+        return False
+
+    def new_req_logits_processor(
+        self,
+        params: SamplingParams,
+    ) -> RequestLogitsProcessor | None:
+        ngram_size = params.extra_args and params.extra_args.get("ngram_size")
+        window_size = params.extra_args and params.extra_args.get("window_size", 100)
+        whitelist_token_ids = params.extra_args and params.extra_args.get(
+            "whitelist_token_ids", None
+        )
+        if ngram_size is None:
+            return None
+
+        whitelist_token_ids = set(whitelist_token_ids) if whitelist_token_ids else None
+        return NoRepeatNGramLogitsProcessor(
+            ngram_size=ngram_size,
+            window_size=window_size,
+            whitelist_token_ids=whitelist_token_ids,
+        )
+
+
+class DeepseekOCRProcessingInfo(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(DeepseekOCRProcessor, **kwargs)
+
+    def get_supported_mm_limits(self) -> Mapping[str, int | None]:
+        return {"image": None}
+
+    def get_num_image_tokens(
+        self, *, image_width: int, image_height: int, cropping: bool = True
+    ) -> int:
+        image_size = IMAGE_SIZE
+        base_size = BASE_SIZE
+        patch_size = 16
+        downsample_ratio = 4
+
+        if CROP_MODE:
+            if image_width <= 640 and image_height <= 640:
+                crop_ratio = [1, 1]
+            else:
+                # find the closest aspect ratio to the target
+                crop_ratio = count_tiles(
+                    image_width, image_height, image_size=IMAGE_SIZE
+                )
+
+            num_width_tiles, num_height_tiles = crop_ratio
+        else:
+            num_width_tiles = num_height_tiles = 1
+
+        h = w = math.ceil((base_size // patch_size) / downsample_ratio)
+
+        h2 = w2 = math.ceil((image_size // patch_size) / downsample_ratio)
+
+        global_views_tokens = h * (w + 1)
+        if num_width_tiles > 1 or num_height_tiles > 1:
+            local_views_tokens = (num_height_tiles * h2) * (num_width_tiles * w2 + 1)
+        else:
+            local_views_tokens = 0
+
+        return global_views_tokens + local_views_tokens + 1
+
+    def get_image_size_with_most_features(self) -> ImageSize:
+        if IMAGE_SIZE == 1024 and BASE_SIZE == 1280:
+            return ImageSize(width=1024 * 2, height=1024 * 2)
+        return ImageSize(width=640 * 2, height=640 * 2)
+
+
+class DeepseekOCRDummyInputsBuilder(BaseDummyInputsBuilder[DeepseekOCRProcessingInfo]):
+    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: Mapping[str, BaseDummyOptions] | None = None,
+    ) -> MultiModalDataDict:
+        num_images = mm_counts.get("image", 0)
+
+        max_image_size = self.info.get_image_size_with_most_features()
+
+        return {
+            "image": self._get_dummy_images(
+                width=max_image_size.width,
+                height=max_image_size.height,
+                num_images=num_images,
+            )
+        }
+
+
+class DeepseekOCRMultiModalProcessor(
+    BaseMultiModalProcessor[DeepseekOCRProcessingInfo]
+):
+    def _call_hf_processor(
+        self,
+        prompt: str,
+        mm_data: Mapping[str, object],
+        mm_kwargs: Mapping[str, object],
+        tok_kwargs: Mapping[str, object],
+    ) -> BatchFeature:
+        if mm_data:
+            processed_outputs = self.info.ctx.call_hf_processor(
+                self.info.get_hf_processor(**mm_kwargs),
+                dict(prompt=prompt, **mm_data),
+                mm_kwargs,
+            )
+
+        else:
+            tokenizer = self.info.get_tokenizer()
+            processed_outputs = tokenizer(
+                prompt, add_special_tokens=True, return_tensors="pt"
+            )
+
+        return processed_outputs
+
+    def _get_mm_fields_config(
+        self,
+        hf_inputs: BatchFeature,
+        hf_processor_mm_kwargs: Mapping[str, object],
+    ) -> Mapping[str, MultiModalFieldConfig]:
+        images_spatial_crop = hf_inputs.get("images_spatial_crop", torch.empty((0, 2)))
+        is_tiled = (images_spatial_crop[:, 0] > 1) | (images_spatial_crop[:, 1] > 1)
+        patches_per_image = torch.where(is_tiled, images_spatial_crop.prod(dim=-1), 0)
+        return dict(
+            pixel_values=MultiModalFieldConfig.batched("image"),
+            images_spatial_crop=MultiModalFieldConfig.batched("image"),
+            images_crop=MultiModalFieldConfig.flat_from_sizes(
+                "image", patches_per_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:
+                size = images.get_image_size(item_idx)
+
+                num_image_tokens = self.info.get_num_image_tokens(
+                    image_width=size.width,
+                    image_height=size.height,
+                    cropping=CROP_MODE,
+                )
+            return [image_token_id] * num_image_tokens
+
+        return [
+            PromptReplacement(
+                modality="image",
+                target=[image_token_id],
+                replacement=get_replacement_deepseek_vl2,
+            )
+        ]
+
+
+@MULTIMODAL_REGISTRY.register_processor(
+    DeepseekOCRMultiModalProcessor,
+    info=DeepseekOCRProcessingInfo,
+    dummy_inputs=DeepseekOCRDummyInputsBuilder,
+)
+class DeepseekOCRForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
+    hf_to_vllm_mapper = WeightsMapper(
+        orig_to_new_prefix={
+            # map prefix for language backbone
+            "model.embed_tokens.": "language_model.model.embed_tokens.",
+            "model.layers.": "language_model.model.layers.",
+            "model.norm.": "language_model.model.norm.",
+            "lm_head.": "language_model.lm_head.",
+            # remove "model." prefix for other components
+            "model.": "",
+        }
+    )
+
+    @classmethod
+    def get_placeholder_str(cls, modality: str, i: int) -> str | None:
+        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 = tokenizer.vocab[_IMAGE_TOKEN]
+
+        self.sam_model = build_sam_vit_b()
+        clip_vision_config = CLIPVisionConfig(
+            hidden_size=1024,
+            intermediate_size=4096,
+            num_attention_heads=16,
+            num_hidden_layers=24,
+            image_size=224,
+            patch_size=14,
+            projection_dim=512,
+            layer_norm_eps=1e-5,
+        )
+        self.vision_model = DeepCLIPVisionTransformer(
+            config=clip_vision_config,
+            quant_config=quant_config,
+            prefix=maybe_prefix(prefix, "vision_model"),
+        )
+
+        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
+        n_embed = self.projector_config.n_embed
+        embed_std = 1 / torch.sqrt(torch.tensor(n_embed, dtype=torch.float32))
+        if self.tile_tag == "2D":
+            # <|view_separator|>, <|\n|>
+            self.image_newline = nn.Parameter(torch.randn(n_embed) * embed_std)
+            # This is a typo in original implementation
+            self.view_seperator = nn.Parameter(torch.randn(n_embed) * embed_std)
+        else:
+            raise ValueError(
+                f"Only 2D tile_tag is supported currently, got: {self.tile_tag}"
+            )
+
+        self.language_model = init_vllm_registered_model(
+            vllm_config=vllm_config,
+            hf_config=self.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
+    ) -> DeepseekOCRImagePixelInputs | None:
+        pixel_values = kwargs.pop("pixel_values", None)
+        images_spatial_crop = kwargs.pop("images_spatial_crop", None)
+        images_crop = kwargs.pop("images_crop", None)
+
+        if pixel_values is None or torch.sum(pixel_values).item() == 0:
+            return None
+
+        if pixel_values is not None:
+            base_size = self.vision_config.image_size
+            return DeepseekOCRImagePixelInputs(
+                type="pixel_values",
+                data=pixel_values,
+                images_crop=images_crop,
+                images_spatial_crop=images_spatial_crop,
+                resolve_bindings={
+                    "base_size": base_size,
+                },
+            )
+
+        raise AssertionError("This line should be unreachable.")
+
+    def _encode_global_features(self, image_tensor: torch.Tensor) -> torch.Tensor:
+        global_features_1 = self.sam_model(image_tensor)
+        global_features_2 = self.vision_model(image_tensor, global_features_1)
+        features = torch.cat(
+            (
+                global_features_2[:, 1:],
+                global_features_1.flatten(2).permute(0, 2, 1),
+            ),
+            dim=-1,
+        )
+        features = self.projector(features)
+
+        _, hw, dim = features.shape
+        side = int(hw**0.5)
+
+        features = features.view(side, side, dim)
+        newline = self.image_newline[None, None, :].expand(side, 1, dim)
+        features = torch.cat([features, newline], dim=1)
+        return features.view(-1, dim)
+
+    def _encode_local_features(
+        self, patches: torch.Tensor, crop_shape: torch.Tensor
+    ) -> torch.Tensor | None:
+        if torch.sum(patches).item() == 0:
+            return None
+
+        local_features_1 = self.sam_model(patches)
+        local_features_2 = self.vision_model(patches, local_features_1)
+        features = torch.cat(
+            (
+                local_features_2[:, 1:],
+                local_features_1.flatten(2).permute(0, 2, 1),
+            ),
+            dim=-1,
+        )
+        features = self.projector(features)
+
+        _, hw, dim = features.shape
+        patch_side = int(hw**0.5)
+
+        width_tiles = int(crop_shape[0].item())
+        height_tiles = int(crop_shape[1].item())
+
+        features = (
+            features.view(height_tiles, width_tiles, patch_side, patch_side, dim)
+            .permute(0, 2, 1, 3, 4)
+            .reshape(height_tiles * patch_side, width_tiles * patch_side, dim)
+        )
+        newline = self.image_newline[None, None, :].expand(
+            height_tiles * patch_side, 1, dim
+        )
+        features = torch.cat([features, newline], dim=1)
+
+        return features.view(-1, dim)
+
+    def _pixel_values_to_embedding(
+        self,
+        pixel_values: torch.Tensor,
+        images_crop: torch.Tensor,
+        images_spatial_crop: torch.Tensor,
+    ) -> NestedTensors:
+        images_in_this_batch = []
+
+        is_tiled = (images_spatial_crop[:, 0] > 1) | (images_spatial_crop[:, 1] > 1)
+        patches_per_image = torch.where(is_tiled, images_spatial_crop.prod(dim=-1), 0)
+        images_crop = images_crop.split(patches_per_image.tolist())
+        for jdx in range(images_spatial_crop.size(0)):
+            patches = images_crop[jdx]
+            image_ori = pixel_values[[jdx]]
+            crop_shape = images_spatial_crop[jdx]
+
+            global_features = self._encode_global_features(image_ori)
+            local_features = self._encode_local_features(patches, crop_shape)
+
+            if local_features is not None:
+                combined = torch.cat(
+                    [local_features, global_features, self.view_seperator[None, :]],
+                    dim=0,
+                )
+            else:
+                combined = torch.cat(
+                    [global_features, self.view_seperator[None, :]], dim=0
+                )
+
+            images_in_this_batch.append(combined)
+
+        return images_in_this_batch
+
+    def _process_image_input(
+        self, image_input: DeepseekOCRImagePixelInputs
+    ) -> torch.Tensor:
+        pixel_values = image_input.data
+        images_crop = image_input.images_crop
+        images_spatial_crop = image_input.images_spatial_crop.to(dtype=torch.long)
+
+        vision_features = self._pixel_values_to_embedding(
+            pixel_values=pixel_values,
+            images_crop=images_crop,
+            images_spatial_crop=images_spatial_crop,
+        )
+
+        return vision_features
+
+    def get_language_model(self) -> torch.nn.Module:
+        return self.language_model
+
+    def embed_multimodal(self, **kwargs: object) -> MultiModalEmbeddings | None:
+        image_input = self._parse_and_validate_image_input(**kwargs)
+        if image_input is None:
+            return None
+        vision_embeddings = self._process_image_input(image_input)
+        return vision_embeddings
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: IntermediateTensors | None = None,
+        inputs_embeds: torch.Tensor | None = 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,
+    ) -> torch.Tensor | None:
+        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