diff --git a/docs/models/supported_models.md b/docs/models/supported_models.md
index 181d684b8..45465d9c4 100644
--- a/docs/models/supported_models.md
+++ b/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+ | `CohereLabs/command-a-vision-07-2025`, etc. | | ✅︎ |
| `DeepseekVLV2ForCausalLM`^ | DeepSeek-VL2 | T + I+ | `deepseek-ai/deepseek-vl2-tiny`, `deepseek-ai/deepseek-vl2-small`, `deepseek-ai/deepseek-vl2`, etc. | | ✅︎ |
| `DeepseekOCRForCausalLM` | DeepSeek-OCR | T + I+ | `deepseek-ai/DeepSeek-OCR`, etc. | ✅︎ | ✅︎ |
+| `DeepseekOCR2ForCausalLM` | DeepSeek-OCR-2 | T + I+ | `deepseek-ai/DeepSeek-OCR-2`, etc. | ✅︎ | ✅︎ |
| `Eagle2_5_VLForConditionalGeneration` | Eagle2.5-VL | T + IE+ | `nvidia/Eagle2.5-8B`, etc. | ✅︎ | ✅︎ |
| `Ernie4_5_VLMoeForConditionalGeneration` | Ernie4.5-VL | T + I+/ V+ | `baidu/ERNIE-4.5-VL-28B-A3B-PT`, `baidu/ERNIE-4.5-VL-424B-A47B-PT` | | ✅︎ |
| `FuyuForCausalLM` | Fuyu | T + I | `adept/fuyu-8b`, etc. | | ✅︎ |
diff --git a/examples/offline_inference/vision_language.py b/examples/offline_inference/vision_language.py
index c378e6696..c22f2ab3d 100755
--- a/examples/offline_inference/vision_language.py
+++ b/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"\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: | , |
+ 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,
diff --git a/tests/models/registry.py b/tests/models/registry.py
index 1bee16c81..0e3d0d312 100644
--- a/tests/models/registry.py
+++ b/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
),
diff --git a/vllm/model_executor/models/deepencoder.py b/vllm/model_executor/models/deepencoder.py
index 651ced896..f7ae4264f 100644
--- a/vllm/model_executor/models/deepencoder.py
+++ b/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):
diff --git a/vllm/model_executor/models/deepencoder2.py b/vllm/model_executor/models/deepencoder2.py
new file mode 100644
index 000000000..b50606d47
--- /dev/null
+++ b/vllm/model_executor/models/deepencoder2.py
@@ -0,0 +1,283 @@
+# 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
diff --git a/vllm/model_executor/models/deepseek_ocr2.py b/vllm/model_executor/models/deepseek_ocr2.py
new file mode 100644
index 000000000..6541edad2
--- /dev/null
+++ b/vllm/model_executor/models/deepseek_ocr2.py
@@ -0,0 +1,444 @@
+# 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 = ""
+
+
+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 ""
+
+ 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"],
+ )
diff --git a/vllm/model_executor/models/registry.py b/vllm/model_executor/models/registry.py
index 95f6cc065..ed2a39d24 100644
--- a/vllm/model_executor/models/registry.py
+++ b/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",
diff --git a/vllm/transformers_utils/chat_templates/registry.py b/vllm/transformers_utils/chat_templates/registry.py
index fe84b6c15..0064cc6d6 100644
--- a/vllm/transformers_utils/chat_templates/registry.py
+++ b/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,
diff --git a/vllm/transformers_utils/processors/deepseek_ocr2.py b/vllm/transformers_utils/processors/deepseek_ocr2.py
new file mode 100644
index 000000000..6dbda73d4
--- /dev/null
+++ b/vllm/transformers_utils/processors/deepseek_ocr2.py
@@ -0,0 +1,320 @@
+# 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 = "",
+ 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 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)