[Bugfix] Fix deepseek-ocr multi-image inference and add...

[Bugfix] Fix deepseek-ocr multi-image inference and add `merge_by_field_config=True` with tensor schema support (#27361)
Signed-off-by: Isotr0py <mozf@mail2.sysu.edu.cn>

examples/offline_inference/vision_language_multi_image.py

@@ -44,6 +44,7 @@ class ModelRequestData(NamedTuple):
     stop_token_ids: list[int] | None = None
     chat_template: str | None = None
     lora_requests: list[LoRARequest] | None = None
+    sampling_params: SamplingParams | None = None
 
 
 # NOTE: The default `max_num_seqs` and `max_model_len` may result in OOM on
@@ -201,6 +202,46 @@ def load_deepseek_vl2(question: str, image_urls: list[str]) -> ModelRequestData:
     )
 
 
+def load_deepseek_ocr(question: str, image_urls: list[str]) -> ModelRequestData:
+    from vllm.model_executor.models.deepseek_ocr import NGramPerReqLogitsProcessor
+
+    model_name = "deepseek-ai/DeepSeek-OCR"
+
+    engine_args = EngineArgs(
+        model=model_name,
+        max_num_seqs=2,
+        limit_mm_per_prompt={"image": len(image_urls)},
+        logits_processors=[NGramPerReqLogitsProcessor],
+    )
+
+    placeholder = "<image>\n" * len(image_urls)
+    prompt = placeholder + question
+
+    # 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,
+    )
+
+    return ModelRequestData(
+        engine_args=engine_args,
+        prompt=prompt,
+        image_data=[fetch_image(url) for url in image_urls],
+        sampling_params=sampling_params,
+    )
+
+
 def load_gemma3(question: str, image_urls: list[str]) -> ModelRequestData:
     model_name = "google/gemma-3-4b-it"
 
@@ -1253,6 +1294,7 @@ model_example_map = {
     "bee": load_bee,
     "command_a_vision": load_command_a_vision,
     "deepseek_vl_v2": load_deepseek_vl2,
+    "deepseek_ocr": load_deepseek_ocr,
     "gemma3": load_gemma3,
     "h2ovl_chat": load_h2ovl,
     "hyperclovax_seed_vision": load_hyperclovax_seed_vision,
@@ -1325,8 +1367,12 @@ def run_chat(model: str, question: str, image_urls: list[str], seed: int | None)
     engine_args = asdict(req_data.engine_args) | {"seed": seed}
     llm = LLM(**engine_args)
 
-    sampling_params = SamplingParams(
-        temperature=0.0, max_tokens=256, stop_token_ids=req_data.stop_token_ids
+    sampling_params = (
+        SamplingParams(
+            temperature=0.0, max_tokens=256, stop_token_ids=req_data.stop_token_ids
+        )
+        if req_data.sampling_params is None
+        else req_data.sampling_params
     )
     outputs = llm.chat(
         [

tests/models/multimodal/processing/test_common.py

@@ -332,6 +332,7 @@ def _test_processing_correctness_one(
         "facebook/chameleon-7b",
         "CohereLabs/command-a-vision-07-2025",
         "deepseek-ai/deepseek-vl2-tiny",
+        "deepseek-ai/DeepSeek-OCR",
         "baidu/ERNIE-4.5-VL-28B-A3B-PT",
         "adept/fuyu-8b",
         "google/gemma-3-4b-it",

vllm/model_executor/models/deepseek_ocr.py

@@ -4,6 +4,7 @@
 
 import math
 from collections.abc import Iterable, Mapping, Sequence
+from typing import Annotated, Literal
 
 import torch
 import torch.nn as nn
@@ -53,6 +54,7 @@ from vllm.transformers_utils.processors.deepseek_ocr import (
     count_tiles,
 )
 from vllm.transformers_utils.tokenizer import cached_tokenizer_from_config
+from vllm.utils.tensor_schema import TensorSchema, TensorShape
 from vllm.v1.sample.logits_processor import (
     AdapterLogitsProcessor,
     RequestLogitsProcessor,
@@ -65,6 +67,28 @@ from .deepseek_vl2 import MlpProjector
 _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,
@@ -260,10 +284,15 @@ class DeepseekOCRMultiModalProcessor(
         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.batched("image"),
+            images_crop=MultiModalFieldConfig.flat_from_sizes(
+                "image", patches_per_image
+            ),
         )
 
     def _get_prompt_updates(
@@ -302,35 +331,6 @@ class DeepseekOCRMultiModalProcessor(
             )
         ]
 
-    # TODO(Isotr0py): Check if we still need this workaround for
-    # deepseek-ocr processor.
-    # def _cached_apply_hf_processor(
-    #     self,
-    #     prompt: str | list[int],
-    #     mm_data_items: MultiModalDataItems,
-    #     hf_processor_mm_kwargs: Mapping[str, object],
-    #     tokenization_kwargs: Mapping[str, object],
-    #     mm_uuids: MultiModalUUIDDict | None = None,
-    # ) -> tuple[list[int], MultiModalKwargs, bool]:
-    #     # The processor logic is different for len(images) <= 2 vs > 2
-    #     # Since the processing cache assumes that the processor output is
-    #     # invariant of how many images are passed per prompt, we only
-    #     # perform caching for the most common case
-    #     if mm_data_items.get_count("image", strict=False) > 2:
-    #         # This code path corresponds to the cache being disabled
-    #         return self._apply_hf_processor_main(
-    #             prompt=prompt,
-    #             mm_items=mm_data_items,
-    #             hf_processor_mm_kwargs=hf_processor_mm_kwargs,
-    #             enable_hf_prompt_update=True,
-    #         )
-
-    #     return super()._cached_apply_hf_processor(
-    #         prompt=prompt,
-    #         mm_data_items=mm_data_items,
-    #         hf_processor_mm_kwargs=hf_processor_mm_kwargs,
-    #     )
-
 
 @MULTIMODAL_REGISTRY.register_processor(
     DeepseekOCRMultiModalProcessor,
@@ -338,6 +338,8 @@ class DeepseekOCRMultiModalProcessor(
     dummy_inputs=DeepseekOCRDummyInputsBuilder,
 )
 class DeepseekOCRForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
+    merge_by_field_config = True
+
     hf_to_vllm_mapper = WeightsMapper(
         orig_to_new_prefix={
             # map prefix for language backbone
@@ -389,6 +391,7 @@ class DeepseekOCRForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
         self.vision_model = DeepCLIPVisionTransformer(
             config=clip_vision_config,
             quant_config=quant_config,
+            prefix=maybe_prefix(prefix, "vision_model"),
         )
 
         self.projector = MlpProjector(self.projector_config)
@@ -426,7 +429,9 @@ class DeepseekOCRForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
             self.language_model.make_empty_intermediate_tensors
         )
 
-    def _parse_and_validate_image_input(self, **kwargs: object):
+    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)
@@ -435,23 +440,16 @@ class DeepseekOCRForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
             return None
 
         if pixel_values is not None:
-            if not isinstance(pixel_values, (torch.Tensor, list)):
-                raise ValueError(
-                    f"Incorrect type of pixel values. Got type: {type(pixel_values)}"
-                )
-
-            if not isinstance(images_spatial_crop, (torch.Tensor, list)):
-                raise ValueError(
-                    "Incorrect type of image sizes. "
-                    f"Got type: {type(images_spatial_crop)}"
-                )
-
-            if not isinstance(images_crop, (torch.Tensor, list)):
-                raise ValueError(
-                    f"Incorrect type of image crop. Got type: {type(images_crop)}"
-                )
-
-            return [pixel_values, images_crop, images_spatial_crop]
+            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.")
 
@@ -518,10 +516,13 @@ class DeepseekOCRForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
     ) -> 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][0].to(torch.bfloat16)
-            image_ori = pixel_values[jdx]
-            crop_shape = images_spatial_crop[jdx][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)
@@ -540,10 +541,12 @@ class DeepseekOCRForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
 
         return images_in_this_batch
 
-    def _process_image_input(self, image_input) -> torch.Tensor:
-        pixel_values = image_input[0].to(torch.bfloat16)
-        images_crop = image_input[1]
-        images_spatial_crop = image_input[2].to(dtype=torch.long)
+    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,

vllm/transformers_utils/processors/deepseek_ocr.py

@@ -411,20 +411,16 @@ class DeepseekOCRProcessor(ProcessorMixin):
         images_seq_mask = images_seq_mask[:-1]
 
         if len(images_list) == 0:
-            pixel_values = torch.zeros((1, 3, self.base_size, self.base_size))
-            images_spatial_crop = torch.zeros((1, 1), dtype=torch.long)
-            images_crop = torch.zeros(
-                (1, 3, self.image_size, self.image_size)
-            ).unsqueeze(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).unsqueeze(0)
+                images_crop = torch.stack(images_crop_list, dim=0)
             else:
-                images_crop = torch.zeros(
-                    (1, 3, self.image_size, self.image_size)
-                ).unsqueeze(0)
+                images_crop = torch.zeros((0, 3, self.image_size, self.image_size))
 
         input_ids = input_ids.unsqueeze(0)