[Model] Support DeepSeek-OCR-2 (#33165)

Signed-off-by: liuli <ll407707@alibaba-inc.com>
Signed-off-by: Isotr0py <mozf@mail2.sysu.edu.cn>
Co-authored-by: liuli <ll407707@alibaba-inc.com>
Co-authored-by: Isotr0py <mozf@mail2.sysu.edu.cn>

docs/models/supported_models.md

@@ -672,6 +672,7 @@ These models primarily accept the [`LLM.generate`](./generative_models.md#llmgen
 | `Cohere2VisionForConditionalGeneration` | Command A Vision | T + I<sup>+</sup> | `CohereLabs/command-a-vision-07-2025`, etc. | | ✅︎ |
 | `DeepseekVLV2ForCausalLM`<sup>^</sup> | DeepSeek-VL2 | T + I<sup>+</sup> | `deepseek-ai/deepseek-vl2-tiny`, `deepseek-ai/deepseek-vl2-small`, `deepseek-ai/deepseek-vl2`, etc. | | ✅︎ |
 | `DeepseekOCRForCausalLM` | DeepSeek-OCR | T + I<sup>+</sup> | `deepseek-ai/DeepSeek-OCR`, etc. | ✅︎ | ✅︎ |
+| `DeepseekOCR2ForCausalLM` | DeepSeek-OCR-2 | T + I<sup>+</sup> | `deepseek-ai/DeepSeek-OCR-2`, etc. | ✅︎ | ✅︎ |
 | `Eagle2_5_VLForConditionalGeneration` | Eagle2.5-VL | T + I<sup>E+</sup> | `nvidia/Eagle2.5-8B`, etc. | ✅︎ | ✅︎ |
 | `Ernie4_5_VLMoeForConditionalGeneration` | Ernie4.5-VL | T + I<sup>+</sup>/ V<sup>+</sup> | `baidu/ERNIE-4.5-VL-28B-A3B-PT`, `baidu/ERNIE-4.5-VL-424B-A47B-PT` | | ✅︎ |
 | `FuyuForCausalLM` | Fuyu | T + I | `adept/fuyu-8b`, etc. | | ✅︎ |

examples/offline_inference/vision_language.py

@@ -270,6 +270,49 @@ def run_deepseek_ocr(questions: list[str], modality: str) -> ModelRequestData:
     )
 
 
+def run_deepseek_ocr2(questions: list[str], modality: str) -> ModelRequestData:
+    from vllm.model_executor.models.deepseek_ocr import NGramPerReqLogitsProcessor
+
+    assert modality == "image"
+
+    model_name = "deepseek-ai/DeepSeek-OCR-2"
+
+    engine_args = EngineArgs(
+        model=model_name,
+        limit_mm_per_prompt={modality: 1},
+        logits_processors=[NGramPerReqLogitsProcessor],
+    )
+
+    # deepseek-ocr use plain prompt template
+    prompts = [f"<image>\n{question}" for question in questions]
+
+    # The following sampling params config is taken from
+    # the official Deepseek-OCR inference example.
+    # (IMPORTANT) Use the custom logits processor and avoid skipping
+    # special tokens for this model for the optimal OCR performance.
+    sampling_params = [
+        SamplingParams(
+            temperature=0.0,
+            max_tokens=8192,
+            # ngram logit processor args
+            extra_args=dict(
+                ngram_size=30,
+                window_size=90,
+                # whitelist: <td>, </td>
+                whitelist_token_ids={128821, 128822},
+            ),
+            skip_special_tokens=False,
+        )
+        for _ in questions
+    ]
+
+    return ModelRequestData(
+        engine_args=engine_args,
+        prompts=prompts,
+        sampling_params=sampling_params,
+    )
+
+
 # Dots-OCR
 def run_dots_ocr(questions: list[str], modality: str) -> ModelRequestData:
     assert modality == "image"
@@ -2045,6 +2088,7 @@ model_example_map = {
     "command_a_vision": run_command_a_vision,
     "deepseek_vl_v2": run_deepseek_vl2,
     "deepseek_ocr": run_deepseek_ocr,
+    "deepseek_ocr2": run_deepseek_ocr2,
     "dots_ocr": run_dots_ocr,
     "eagle2_5": run_eagle2_5,
     "ernie45_vl": run_ernie45_vl,

tests/models/registry.py

@@ -687,6 +687,9 @@ _MULTIMODAL_EXAMPLE_MODELS = {
     "DeepseekOCRForCausalLM": _HfExamplesInfo(
         "deepseek-ai/DeepSeek-OCR",
     ),
+    "DeepseekOCR2ForCausalLM": _HfExamplesInfo(
+        "deepseek-ai/DeepSeek-OCR-2",
+    ),
     "DotsOCRForCausalLM": _HfExamplesInfo(
         "rednote-hilab/dots.ocr", trust_remote_code=True
     ),

vllm/model_executor/models/deepencoder.py

@@ -79,6 +79,7 @@ class ImageEncoderViT(nn.Module):
         rel_pos_zero_init: bool = True,
         window_size: int = 0,
         global_attn_indexes: tuple[int, ...] = (),
+        last_conv_output: int = 1024,
     ) -> None:
         """
         Args:
@@ -155,7 +156,7 @@ class ImageEncoderViT(nn.Module):
             256, 512, kernel_size=3, stride=2, padding=1, bias=False
         )
         self.net_3 = Conv2dLayer(
-            512, 1024, kernel_size=3, stride=2, padding=1, bias=False
+            512, last_conv_output, kernel_size=3, stride=2, padding=1, bias=False
         )
 
     def get_abs_pos(self, abs_pos: torch.Tensor, tgt_size: int):

vllm/model_executor/models/deepencoder2.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# adapted from
+# https://github.com/deepseek-ai/DeepSeek-OCR-2/blob/main/DeepSeek-OCR2-master/DeepSeek-OCR2-vllm/deepencoderv2/qwen2_d2e.py
+
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+import torch.nn as nn
+import transformers
+
+
+class CustomQwen2Decoder(nn.Module):
+    """
+    Qwen2 visual encoder
+    non-causal attention + causal attention
+    token_type_ids ：0=non-causal, 1=causal
+    """
+
+    def __init__(
+        self,
+        decoder_layer: int = 24,
+        max_position_embeddings: int = 131072,
+        hidden_dimension: int = 896,
+        num_attention_heads: int = 14,
+        num_key_value_heads: int = 2,
+        intermediate_size: int = 4864,
+        vocab_size: int = 151936,
+        attn_implementation: str = "sdpa",  # ⭐
+        rms_norm_eps: float = 1e-06,
+        rope_theta: float = 1000000.0,
+        attention_dropout: float = 0.0,
+        hidden_act: str = "silu",
+        initializer_range: float = 0.02,
+    ):
+        super().__init__()
+
+        # load
+        Qwen2Model = transformers.models.qwen2.modeling_qwen2.Qwen2Model
+        Qwen2Config = transformers.Qwen2Config
+
+        # config
+        config = Qwen2Config(
+            hidden_size=hidden_dimension,
+            num_hidden_layers=decoder_layer,
+            num_attention_heads=num_attention_heads,
+            num_key_value_heads=num_key_value_heads,
+            intermediate_size=intermediate_size,
+            max_position_embeddings=max_position_embeddings,
+            vocab_size=vocab_size,
+            rms_norm_eps=rms_norm_eps,
+            rope_theta=rope_theta,
+            attention_dropout=attention_dropout,
+            hidden_act=hidden_act,
+            initializer_range=initializer_range,
+            _attn_implementation=attn_implementation,  # ⭐
+        )
+
+        #
+        self.model = self._create_custom_model(Qwen2Model, config)
+
+        del self.model.embed_tokens
+
+    def _create_custom_model(self, Qwen2Model, config):
+        """Qwen2Model"""
+
+        class CustomQwen2ModelInner(Qwen2Model):
+            def forward(
+                self,
+                input_ids=None,
+                attention_mask=None,
+                position_ids=None,
+                past_key_values=None,
+                inputs_embeds=None,
+                token_type_ids=None,  # ⭐
+                use_cache=None,
+                output_attentions=None,
+                output_hidden_states=None,
+                return_dict=None,
+                cache_position=None,
+            ):
+                # token_type_ids
+                self._current_token_type_ids = token_type_ids
+                causal_mask_mapping = {
+                    "full_attention": self._update_causal_mask(
+                        attention_mask,
+                        inputs_embeds,
+                        cache_position,
+                        past_key_values,
+                        output_attentions,
+                    )
+                }
+                outputs = super().forward(
+                    input_ids=input_ids,
+                    attention_mask=causal_mask_mapping,
+                    position_ids=position_ids,
+                    past_key_values=past_key_values,
+                    inputs_embeds=inputs_embeds,
+                    use_cache=use_cache,
+                    output_attentions=output_attentions,
+                    output_hidden_states=output_hidden_states,
+                    return_dict=return_dict,
+                    cache_position=cache_position,
+                )
+
+                return outputs
+
+            def _update_causal_mask(
+                self,
+                attention_mask,
+                input_tensor,
+                cache_position,
+                past_key_values,
+                output_attentions,
+            ):
+                dtype, device = input_tensor.dtype, input_tensor.device
+                min_dtype = torch.finfo(dtype).min
+                batch_size, sequence_length = (
+                    input_tensor.shape[0],
+                    input_tensor.shape[1],
+                )
+
+                token_type_ids = self._current_token_type_ids
+
+                # attention mask
+                causal_mask = self._create_custom_4d_mask(
+                    sequence_length=sequence_length,
+                    dtype=dtype,
+                    device=device,
+                    batch_size=batch_size,
+                    token_type_ids=token_type_ids,
+                )
+
+                #  padding mask
+                if attention_mask is not None and attention_mask.dim() == 2:
+                    padding_mask = attention_mask[:, None, None, :].to(dtype=dtype)
+                    padding_mask = (1.0 - padding_mask) * min_dtype
+                    causal_mask = causal_mask + padding_mask
+
+                return causal_mask
+
+            def _create_custom_4d_mask(
+                self,
+                sequence_length,
+                dtype,
+                device,
+                batch_size,
+                token_type_ids,
+            ):
+                min_dtype = torch.finfo(dtype).min
+
+                masks = []
+                for b in range(batch_size):
+                    mask = torch.full(
+                        (sequence_length, sequence_length),
+                        fill_value=min_dtype,
+                        dtype=dtype,
+                        device=device,
+                    )
+
+                    type_ids = token_type_ids[b]
+
+                    image_positions = (type_ids == 0).nonzero(as_tuple=True)[0]
+                    text_positions = (type_ids == 1).nonzero(as_tuple=True)[0]
+
+                    # non-casual
+                    if len(image_positions) > 0:
+                        mask[image_positions[:, None], image_positions] = 0.0
+
+                    # causal
+                    for i, text_pos in enumerate(text_positions):
+                        if len(image_positions) > 0:
+                            mask[text_pos, image_positions] = 0.0
+                        mask[text_pos, text_positions[: i + 1]] = 0.0
+
+                    masks.append(mask)
+
+                mask = torch.stack(masks, dim=0).unsqueeze(1)
+                return mask
+
+        return CustomQwen2ModelInner(config)
+
+    def forward(
+        self,
+        inputs_embeds: torch.Tensor,
+        token_type_ids: torch.Tensor,
+        attention_mask: torch.Tensor = None,
+        **kwargs,
+    ):
+        """
+        Args:
+            inputs_embeds: [batch_size, seq_len, hidden_dim]
+            token_type_ids: [batch_size, seq_len], 0=non-causal, 1=causal
+            attention_mask: [batch_size, seq_len], optional
+        """
+        return self.model(
+            inputs_embeds=inputs_embeds,
+            token_type_ids=token_type_ids,
+            attention_mask=attention_mask,
+            **kwargs,
+        )
+
+
+class Qwen2Decoder2Encoder(nn.Module):
+    """
+    Decoder based on Multilingual BART
+    Set the initial weights and configuration with a pretrained multilingual BART model,
+    and modify the detailed configurations as a Nougat decoder
+    """
+
+    def __init__(
+        self,
+        decoder_layer: int,
+        hidden_dimension: int,
+        num_attention_heads: int,
+        num_key_value_heads: int,
+        intermediate_size: int,
+    ):
+        super().__init__()
+
+        self.model = CustomQwen2Decoder(
+            decoder_layer=decoder_layer,
+            hidden_dimension=hidden_dimension,
+            num_attention_heads=num_attention_heads,
+            num_key_value_heads=num_key_value_heads,
+            intermediate_size=intermediate_size,
+            attn_implementation="sdpa",
+        )
+        self.query_768 = nn.Embedding(144, hidden_dimension)
+        self.query_1024 = nn.Embedding(256, hidden_dimension)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = x.flatten(2).transpose(1, 2)
+
+        bs, n_query, _ = x.shape
+
+        if n_query == 144:
+            param_img = self.query_768.weight
+        elif n_query == 256:
+            param_img = self.query_1024.weight
+
+        batch_query_imgs = param_img.unsqueeze(0).expand(
+            bs, -1, -1
+        )  # (batch_size, num_queries, hidden_size)
+
+        x_combined = torch.cat([x, batch_query_imgs], dim=1)
+
+        token_type_ids = torch.cat(
+            [
+                torch.zeros(bs, n_query, dtype=torch.long),
+                torch.ones(bs, n_query, dtype=torch.long),
+            ],
+            dim=1,
+        )
+
+        y = self.model(x_combined, token_type_ids)[0]
+
+        y = y[:, n_query:, :]  # causal flow query
+
+        return y
+
+
+def build_qwen2_decoder_as_encoder(
+    decoder_layer=24,
+    hidden_dimension=896,
+    num_attention_heads=14,
+    num_key_value_heads=2,
+    intermediate_size=4864,
+):
+    decoder_as_encoder = Qwen2Decoder2Encoder(
+        decoder_layer=decoder_layer,
+        hidden_dimension=hidden_dimension,
+        num_attention_heads=num_attention_heads,
+        num_key_value_heads=num_key_value_heads,
+        intermediate_size=intermediate_size,
+    )
+
+    return decoder_as_encoder

vllm/model_executor/models/deepseek_ocr2.py

+# 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 functools import partial
+
+import torch
+import torch.nn as nn
+from transformers import BatchFeature
+
+from vllm.config import VllmConfig
+from vllm.config.multimodal import BaseDummyOptions
+from vllm.model_executor.models.interfaces import (
+    MultiModalEmbeddings,
+    SupportsLoRA,
+    SupportsMultiModal,
+    SupportsPP,
+)
+from vllm.model_executor.models.module_mapping import MultiModelKeys
+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 (
+    BaseDummyInputsBuilder,
+    BaseMultiModalProcessor,
+    BaseProcessingInfo,
+    PromptReplacement,
+    PromptUpdate,
+)
+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_ocr2 import (
+    BASE_SIZE,
+    CROP_MODE,
+    IMAGE_SIZE,
+    DeepseekOCR2Processor,
+)
+
+from ...transformers_utils.processors.deepseek_ocr import count_tiles
+from .deepencoder import ImageEncoderViT
+from .deepencoder2 import build_qwen2_decoder_as_encoder
+from .deepseek_ocr import DeepseekOCRImagePixelInputs
+from .deepseek_vl2 import MlpProjector
+
+# The image token id may be various
+_IMAGE_TOKEN = "<image>"
+
+
+class DeepseekOCR2ProcessingInfo(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(DeepseekOCR2Processor, **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 <= 768 and image_height <= 768:
+                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
+        if num_width_tiles > 1 or num_height_tiles > 1:
+            local_views_tokens = (num_height_tiles * h2) * (num_width_tiles * w2)
+        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=768 * 2, height=768 * 2)
+
+
+class DeepseekOCR2DummyInputsBuilder(
+    BaseDummyInputsBuilder[DeepseekOCR2ProcessingInfo]
+):
+    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 DeepseekOCR2MultiModalProcessor(
+    BaseMultiModalProcessor[DeepseekOCR2ProcessingInfo]
+):
+    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(
+    DeepseekOCR2MultiModalProcessor,
+    info=DeepseekOCR2ProcessingInfo,
+    dummy_inputs=DeepseekOCR2DummyInputsBuilder,
+)
+class DeepseekOCR2ForCausalLM(nn.Module, SupportsMultiModal, SupportsPP, SupportsLoRA):
+    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
+        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]
+
+        with self._mark_tower_model(vllm_config, "image"):
+            self.sam_model = ImageEncoderViT(
+                depth=12,
+                embed_dim=768,
+                img_size=1024,
+                mlp_ratio=4,
+                norm_layer=partial(torch.nn.LayerNorm, eps=1e-6),
+                num_heads=12,
+                patch_size=16,
+                qkv_bias=True,
+                use_rel_pos=True,
+                global_attn_indexes=[2, 5, 8, 11],
+                window_size=14,
+                out_chans=256,
+                last_conv_output=896,
+            )
+            self.qwen2_model = build_qwen2_decoder_as_encoder()
+
+            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":
+                # 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}"
+                )
+
+        with self._mark_language_model(vllm_config):
+            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
+
+        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,
+            },
+        )
+
+    def _encode_global_features(self, image_tensor: torch.Tensor) -> torch.Tensor:
+        global_features_1 = self.sam_model(image_tensor)
+        global_features_2 = self.qwen2_model(global_features_1)
+
+        features = self.projector(global_features_2)
+
+        _, hw, dim = features.shape
+
+        return features.view(-1, dim)
+
+    def _encode_local_features(self, patches: torch.Tensor) -> torch.Tensor | None:
+        if torch.sum(patches).item() == 0:
+            return None
+
+        local_features = self.sam_model(patches)
+        local_features = self.qwen2_model(local_features)
+
+        features = self.projector(local_features)
+
+        _, _, dim = features.shape
+
+        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]]
+
+            global_features = self._encode_global_features(image_ori)
+            local_features = self._encode_local_features(patches)
+
+            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 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
+
+    def get_mm_mapping(self) -> MultiModelKeys:
+        """
+        Get the module prefix in multimodal models
+        """
+        return MultiModelKeys.from_string_field(
+            language_model="language_model",
+            connector="projector",
+            tower_model=["sam_model", "qwen2_model"],
+        )

vllm/model_executor/models/registry.py

@@ -308,6 +308,7 @@ _MULTIMODAL_MODELS = {
     ),
     "DeepseekVLV2ForCausalLM": ("deepseek_vl2", "DeepseekVLV2ForCausalLM"),
     "DeepseekOCRForCausalLM": ("deepseek_ocr", "DeepseekOCRForCausalLM"),
+    "DeepseekOCR2ForCausalLM": ("deepseek_ocr2", "DeepseekOCR2ForCausalLM"),
     "DotsOCRForCausalLM": ("dots_ocr", "DotsOCRForCausalLM"),
     "Eagle2_5_VLForConditionalGeneration": (
         "eagle2_5_vl",

vllm/transformers_utils/chat_templates/registry.py

@@ -34,6 +34,7 @@ _MODEL_TYPE_TO_CHAT_TEMPLATE_FALLBACK: dict[str, ChatTemplatePath] = {
     "chameleon": CHAT_TEMPLATES_DIR / "template_basic.jinja",
     "clip": CHAT_TEMPLATES_DIR / "template_basic.jinja",
     "deepseek_ocr": CHAT_TEMPLATES_DIR / "template_deepseek_ocr.jinja",
+    "deepseek_ocr2": CHAT_TEMPLATES_DIR / "template_deepseek_ocr.jinja",
     "deepseek_vl_v2": CHAT_TEMPLATES_DIR / "template_deepseek_vl2.jinja",
     "fuyu": CHAT_TEMPLATES_DIR / "template_fuyu.jinja",
     "minicpmv": _get_minicpmv_chat_template_fallback,

vllm/transformers_utils/processors/deepseek_ocr2.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# adapted from https://github.com/deepseek-ai/DeepSeek-OCR/blob/main/DeepSeek-OCR-master/DeepSeek-OCR-vllm/process/image_process.py
+import math
+
+import torch
+from PIL import Image, ImageOps
+from transformers import AutoProcessor, BatchFeature, LlamaTokenizerFast
+from transformers.processing_utils import ProcessorMixin
+
+from vllm.transformers_utils.processors.deepseek_ocr import (
+    ImageTransform,
+    dynamic_preprocess,
+)
+
+BASE_SIZE = 1024
+IMAGE_SIZE = 768
+CROP_MODE = True
+MIN_CROPS = 2
+MAX_CROPS = 6
+
+
+class DeepseekOCR2Processor(ProcessorMixin):
+    tokenizer_class = ("LlamaTokenizer", "LlamaTokenizerFast")
+    attributes = ["tokenizer"]
+
+    def __init__(
+        self,
+        tokenizer: LlamaTokenizerFast,
+        patch_size: int = 16,
+        downsample_ratio: int = 4,
+        image_mean: tuple[float, float, float] = (0.5, 0.5, 0.5),
+        image_std: tuple[float, float, float] = (0.5, 0.5, 0.5),
+        normalize: bool = True,
+        image_token: str = "<image>",
+        pad_token: str = "<｜▁pad▁｜>",
+        add_special_token: bool = False,
+        sft_format: str = "deepseek",
+        mask_prompt: bool = True,
+        ignore_id: int = -100,
+        **kwargs,
+    ):
+        self.image_size = IMAGE_SIZE
+        self.base_size = BASE_SIZE
+        self.patch_size = 16
+        self.image_mean = image_mean
+        self.image_std = image_std
+        self.normalize = normalize
+        self.downsample_ratio = 4
+
+        self.image_transform = ImageTransform(
+            mean=image_mean, std=image_std, normalize=normalize
+        )
+
+        self.tokenizer = tokenizer
+        self.tokenizer.padding_side = "left"  # must set this，padding side with make a difference in batch inference # noqa: E501
+
+        # add the pad_token as special token to use 'tokenizer.pad_token'
+        # and 'tokenizer.pad_token_id'
+        if self.tokenizer.pad_token is None:
+            self.tokenizer.add_special_tokens({"pad_token": pad_token})
+
+        # add image token
+        self.image_token_id = self.tokenizer.vocab.get(image_token)
+        self.image_token = image_token
+        self.pad_token = pad_token
+        self.add_special_token = add_special_token
+        self.sft_format = sft_format
+        self.mask_prompt = mask_prompt
+        self.ignore_id = ignore_id
+
+        super().__init__(
+            tokenizer,
+            **kwargs,
+        )
+
+    @property
+    def bos_id(self):
+        return self.tokenizer.bos_token_id
+
+    @property
+    def eos_id(self):
+        return self.tokenizer.eos_token_id
+
+    @property
+    def pad_id(self):
+        return self.tokenizer.pad_token_id
+
+    def encode(self, text: str, bos: bool = True, eos: bool = False):
+        t = self.tokenizer.encode(text, add_special_tokens=False)
+        if bos:
+            t = [self.bos_id] + t
+        if eos:
+            t = t + [self.eos_id]
+        return t
+
+    def decode(self, t: list[int], **kwargs) -> str:
+        return self.tokenizer.decode(t, **kwargs)
+
+    def process_one(
+        self,
+        prompt: str,
+        images: list[Image.Image],
+        crop_mode: bool = CROP_MODE,
+    ):
+        """
+
+        Args:
+            prompt (str): the formatted prompt;
+            images (List[ImageType]): the list of images;
+            crop_mode (bool): if True, then crop the image;
+
+        Returns:
+            outputs (BaseProcessorOutput): the output of the processor,
+                - input_ids (torch.LongTensor): [N + image tokens]
+                - target_ids (torch.LongTensor): [N + image tokens]
+                - pixel_values (torch.FloatTensor): [n_patches, 3, H, W]
+                - image_id (int): the id of the image token
+                - num_image_tokens (List[int]): the number of image tokens
+        """
+
+        assert prompt is not None and images is not None, (
+            "prompt and images must be used at the same time."
+        )
+
+        sft_format = prompt
+
+        (
+            input_ids,
+            pixel_values,
+            images_crop,
+            images_seq_mask,
+            images_spatial_crop,
+            num_image_tokens,
+            _,
+        ) = self.tokenize_with_images(
+            conversation=sft_format,
+            images=images,
+            bos=True,
+            eos=True,
+            cropping=crop_mode,
+        )
+
+        prepare = BatchFeature(
+            data=dict(
+                input_ids=input_ids,
+                pixel_values=pixel_values,
+                images_crop=images_crop,
+                images_seq_mask=images_seq_mask,
+                images_spatial_crop=images_spatial_crop,
+                num_image_tokens=num_image_tokens,
+            ),
+            tensor_type="pt",
+        )
+        return prepare
+
+    def __call__(
+        self,
+        *,
+        prompt: str,
+        images: list[Image.Image],
+        crop_mode: bool = CROP_MODE,
+        **kwargs,
+    ):
+        prepare = self.process_one(
+            prompt=prompt,
+            images=images,
+            crop_mode=crop_mode,
+        )
+
+        return prepare
+
+    def tokenize_with_images(
+        self,
+        conversation: str,
+        images: list[Image.Image],
+        bos: bool = True,
+        eos: bool = True,
+        cropping: bool = True,
+    ):
+        """Tokenize text with <image> tags."""
+
+        assert conversation.count(self.image_token) == len(images)
+        text_splits = conversation.split(self.image_token)
+        images_list, images_crop_list, images_seq_mask, images_spatial_crop = (
+            [],
+            [],
+            [],
+            [],
+        )
+        image_shapes = []
+        num_image_tokens = []
+        tokenized_str = []
+        for text_sep, image in zip(text_splits, images):
+            tokenized_sep = self.encode(text_sep, bos=False, eos=False)
+            tokenized_str += tokenized_sep
+            images_seq_mask += [False] * len(tokenized_sep)
+
+            image_shapes.append(image.size)
+
+            images_crop_raw = []
+            if image.size[0] <= 768 and image.size[1] <= 768:
+                crop_ratio = [1, 1]
+            elif cropping:
+                images_crop_raw, crop_ratio = dynamic_preprocess(
+                    image, image_size=IMAGE_SIZE
+                )
+            else:
+                crop_ratio = [1, 1]
+
+            if self.image_size <= 768 and not cropping:
+                image = image.resize((self.image_size, self.image_size))
+
+            global_view = ImageOps.pad(
+                image,
+                (self.base_size, self.base_size),
+                color=tuple(int(x * 255) for x in self.image_transform.mean),
+            )
+            images_list.append(self.image_transform(global_view))
+
+            num_width_tiles, num_height_tiles = crop_ratio
+            images_spatial_crop.append([num_width_tiles, num_height_tiles])
+
+            if num_width_tiles > 1 or num_height_tiles > 1:
+                for cropped_image in images_crop_raw:
+                    images_crop_list.append(self.image_transform(cropped_image))
+
+            num_queries = math.ceil(
+                (self.image_size // self.patch_size) / self.downsample_ratio
+            )
+            num_queries_base = math.ceil(
+                (self.base_size // self.patch_size) / self.downsample_ratio
+            )
+
+            tokenized_image = (
+                [self.image_token_id] * num_queries_base
+            ) * num_queries_base
+            tokenized_image += [self.image_token_id]
+            if num_width_tiles > 1 or num_height_tiles > 1:
+                local_row = [self.image_token_id] * (num_queries * num_width_tiles)
+                tokenized_image += local_row * (num_queries * num_height_tiles)
+            tokenized_str += tokenized_image
+            images_seq_mask += [True] * len(tokenized_image)
+            num_image_tokens.append(len(tokenized_image))
+
+        """process the last text split"""
+        tokenized_sep = self.encode(text_splits[-1], bos=False, eos=False)
+        tokenized_str += tokenized_sep
+        images_seq_mask += [False] * len(tokenized_sep)
+
+        """add the bos and eos tokens"""
+        if bos:
+            tokenized_str = [self.bos_id] + tokenized_str
+            images_seq_mask = [False] + images_seq_mask
+        if eos:
+            tokenized_str = tokenized_str + [self.eos_id]
+            images_seq_mask = images_seq_mask + [False]
+
+        assert len(tokenized_str) == len(images_seq_mask), (
+            f"tokenize_with_images func: tokenized_str's length {len(tokenized_str)} "
+            f"is not equal to images_seq_mask's length {len(images_seq_mask)}."
+        )
+
+        masked_tokenized_str = []
+        for token_index in tokenized_str:
+            if token_index != self.image_token_id:
+                masked_tokenized_str.append(token_index)
+            else:
+                masked_tokenized_str.append(self.ignore_id)
+
+        assert (
+            len(tokenized_str) == len(images_seq_mask) == len(masked_tokenized_str)
+        ), (
+            f"tokenized_str's length {len(tokenized_str)}, "
+            f"input_ids' length {len(masked_tokenized_str)}, "
+            f"images_seq_mask's length {len(images_seq_mask)}, are not equal."
+        )
+
+        input_ids = torch.LongTensor(tokenized_str)
+        target_ids = torch.LongTensor(masked_tokenized_str)
+        images_seq_mask = torch.tensor(images_seq_mask, dtype=torch.bool)
+
+        # set input_ids < 0 | input_ids == self.image_token_id as ignore_id
+        target_ids[(input_ids < 0) | (input_ids == self.image_token_id)] = (
+            self.ignore_id
+        )
+        input_ids[input_ids < 0] = self.pad_id
+
+        # Remove the ending eos token
+        assert input_ids[-1] == self.eos_id
+        input_ids = input_ids[:-1]
+        target_ids = target_ids[:-1]
+        images_seq_mask = images_seq_mask[:-1]
+
+        if len(images_list) == 0:
+            pixel_values = torch.zeros((0, 3, self.base_size, self.base_size))
+            images_spatial_crop = torch.zeros((0, 2), dtype=torch.long)
+            images_crop = torch.zeros((0, 3, self.image_size, self.image_size))
+        else:
+            pixel_values = torch.stack(images_list, dim=0)
+            images_spatial_crop = torch.tensor(images_spatial_crop, dtype=torch.long)
+            if images_crop_list:
+                images_crop = torch.stack(images_crop_list, dim=0)
+            else:
+                images_crop = torch.zeros((0, 3, self.image_size, self.image_size))
+
+        input_ids = input_ids.unsqueeze(0)
+
+        return (
+            input_ids,
+            pixel_values,
+            images_crop,
+            images_seq_mask,
+            images_spatial_crop,
+            num_image_tokens,
+            image_shapes,
+        )
+
+
+AutoProcessor.register("DeepseekOCR2Processor", DeepseekOCR2Processor)