[Multimodal] Always enable hashing mm data (#23308)

Signed-off-by: Roger Wang <hey@rogerw.io>
Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
Co-authored-by: DarkLight1337 <tlleungac@connect.ust.hk>

vllm/config/__init__.py

@@ -1685,15 +1685,6 @@ class ModelConfig:
     def is_multimodal_model(self) -> bool:
         return self.multimodal_config is not None
 
-    @property
-    def processor_return_mm_hashes(self) -> bool:
-        """Whether the multi-modal processor should output hashes."""
-        mm_config = self.multimodal_config
-        if mm_config is None:
-            return False
-
-        return mm_config.mm_processor_cache_gb > 0
-
     @property
     def enable_mm_processor_cache(self) -> bool:
         """Whether the multi-modal processor cache should be enabled."""

vllm/inputs/preprocess.py

@@ -254,7 +254,6 @@ class InputPreprocessor:
         mm_processor_kwargs: Optional[Mapping[str, object]],
         tokenization_kwargs: Optional[dict[str, Any]] = None,
         lora_request: Optional[LoRARequest] = None,
-        return_mm_hashes: bool = False,
     ) -> MultiModalInputs:
         """
         Apply the model's multi-modal processor to a multi-modal prompt,
@@ -271,8 +270,7 @@ class InputPreprocessor:
         return mm_processor.apply(prompt,
                                   mm_data,
                                   hf_processor_mm_kwargs=mm_processor_kwargs,
-                                  tokenization_kwargs=tokenization_kwargs,
-                                  return_mm_hashes=return_mm_hashes)
+                                  tokenization_kwargs=tokenization_kwargs)
 
     async def _process_multimodal_async(
         self,
@@ -281,7 +279,6 @@ class InputPreprocessor:
         mm_processor_kwargs: Optional[Mapping[str, object]],
         tokenization_kwargs: Optional[dict[str, Any]] = None,
         lora_request: Optional[LoRARequest] = None,
-        return_mm_hashes: bool = False,
     ) -> MultiModalInputs:
         """
         Async version of
@@ -297,8 +294,7 @@ class InputPreprocessor:
         return mm_processor.apply(prompt,
                                   mm_data,
                                   hf_processor_mm_kwargs=mm_processor_kwargs,
-                                  tokenization_kwargs=tokenization_kwargs,
-                                  return_mm_hashes=return_mm_hashes)
+                                  tokenization_kwargs=tokenization_kwargs)
 
     def _process_embeds(
         self,
@@ -335,7 +331,6 @@ class InputPreprocessor:
         parsed_content: TokensPrompt,
         tokenization_kwargs: Optional[dict[str, Any]] = None,
         lora_request: Optional[LoRARequest] = None,
-        return_mm_hashes: bool = False,
     ) -> Union[TokenInputs, MultiModalInputs]:
         prompt_token_ids = parsed_content["prompt_token_ids"]
         token_type_ids = parsed_content.get("token_type_ids")
@@ -348,7 +343,6 @@ class InputPreprocessor:
                 parsed_content.get("mm_processor_kwargs"),
                 tokenization_kwargs=tokenization_kwargs,
                 lora_request=lora_request,
-                return_mm_hashes=return_mm_hashes,
             )
         else:
             inputs = token_inputs(
@@ -366,7 +360,6 @@ class InputPreprocessor:
         parsed_content: TokensPrompt,
         tokenization_kwargs: Optional[dict[str, Any]] = None,
         lora_request: Optional[LoRARequest] = None,
-        return_mm_hashes: bool = False,
     ) -> Union[TokenInputs, MultiModalInputs]:
         prompt_token_ids = parsed_content["prompt_token_ids"]
         token_type_ids = parsed_content.get("token_type_ids")
@@ -379,7 +372,6 @@ class InputPreprocessor:
                 parsed_content.get("mm_processor_kwargs"),
                 tokenization_kwargs=tokenization_kwargs,
                 lora_request=lora_request,
-                return_mm_hashes=return_mm_hashes,
             )
         else:
             inputs = token_inputs(
@@ -397,7 +389,6 @@ class InputPreprocessor:
         parsed_content: TextPrompt,
         tokenization_kwargs: Optional[dict[str, Any]] = None,
         lora_request: Optional[LoRARequest] = None,
-        return_mm_hashes: bool = False,
     ) -> Union[TokenInputs, MultiModalInputs]:
         prompt_text = parsed_content["prompt"]
 
@@ -409,7 +400,6 @@ class InputPreprocessor:
                 parsed_content.get("mm_processor_kwargs"),
                 tokenization_kwargs=tokenization_kwargs,
                 lora_request=lora_request,
-                return_mm_hashes=return_mm_hashes,
             )
         else:
             prompt_token_ids = self._tokenize_prompt(
@@ -432,7 +422,6 @@ class InputPreprocessor:
         parsed_content: TextPrompt,
         tokenization_kwargs: Optional[dict[str, Any]] = None,
         lora_request: Optional[LoRARequest] = None,
-        return_mm_hashes: bool = False,
     ) -> Union[TokenInputs, MultiModalInputs]:
         prompt_text = parsed_content["prompt"]
 
@@ -444,7 +433,6 @@ class InputPreprocessor:
                 parsed_content.get("mm_processor_kwargs"),
                 tokenization_kwargs=tokenization_kwargs,
                 lora_request=lora_request,
-                return_mm_hashes=return_mm_hashes,
             )
         else:
             prompt_token_ids = await self._tokenize_prompt_async(
@@ -467,7 +455,6 @@ class InputPreprocessor:
         prompt: SingletonPrompt,
         tokenization_kwargs: Optional[dict[str, Any]] = None,
         lora_request: Optional[LoRARequest] = None,
-        return_mm_hashes: bool = False,
     ) -> SingletonInputs:
         """
         Extract the singleton inputs from a prompt.
@@ -476,7 +463,6 @@ class InputPreprocessor:
 
         * prompt: single encoder or decoder input prompt
         * lora_request: this is only valid for decoder prompts
-        * return_mm_hashes: whether to return multimodal hashes
 
         Returns:
 
@@ -490,21 +476,18 @@ class InputPreprocessor:
             return self._process_tokens(
                 parsed["content"],
                 lora_request=lora_request,
-                return_mm_hashes=return_mm_hashes,
             )
         if parsed["type"] == "text":
             return self._process_text(
                 parsed["content"],
                 tokenization_kwargs=tokenization_kwargs,
                 lora_request=lora_request,
-                return_mm_hashes=return_mm_hashes,
             )
         if parsed["type"] == "str":
             return self._process_text(
                 TextPrompt(prompt=parsed["content"]),
                 tokenization_kwargs=tokenization_kwargs,
                 lora_request=lora_request,
-                return_mm_hashes=return_mm_hashes,
             )
 
         assert_never(parsed)
@@ -514,7 +497,6 @@ class InputPreprocessor:
         prompt: SingletonPrompt,
         tokenization_kwargs: Optional[dict[str, Any]] = None,
         lora_request: Optional[LoRARequest] = None,
-        return_mm_hashes: bool = False,
     ) -> SingletonInputs:
         """
         Async version of
@@ -528,21 +510,18 @@ class InputPreprocessor:
             return await self._process_tokens_async(
                 parsed["content"],
                 lora_request=lora_request,
-                return_mm_hashes=return_mm_hashes,
             )
         if parsed["type"] == "text":
             return await self._process_text_async(
                 parsed["content"],
                 tokenization_kwargs=tokenization_kwargs,
                 lora_request=lora_request,
-                return_mm_hashes=return_mm_hashes,
             )
         if parsed["type"] == "str":
             return await self._process_text_async(
                 TextPrompt(prompt=parsed["content"]),
                 tokenization_kwargs=tokenization_kwargs,
                 lora_request=lora_request,
-                return_mm_hashes=return_mm_hashes,
             )
 
         assert_never(parsed)
@@ -785,7 +764,6 @@ class InputPreprocessor:
         prompt: SingletonPrompt,
         tokenization_kwargs: Optional[dict[str, Any]] = None,
         lora_request: Optional[LoRARequest] = None,
-        return_mm_hashes: bool = False,
     ) -> DecoderOnlyInputs:
         """
         For decoder-only models:
@@ -796,7 +774,6 @@ class InputPreprocessor:
 
         * prompt: input prompt
         * lora_request
-        * return_mm_hashes
 
         Returns:
 
@@ -807,7 +784,6 @@ class InputPreprocessor:
             prompt,
             tokenization_kwargs=tokenization_kwargs,
             lora_request=lora_request,
-            return_mm_hashes=return_mm_hashes,
         )
 
         return self._build_decoder_only_llm_inputs(prompt_comps)
@@ -817,7 +793,6 @@ class InputPreprocessor:
         prompt: SingletonPrompt,
         tokenization_kwargs: Optional[dict[str, Any]] = None,
         lora_request: Optional[LoRARequest] = None,
-        return_mm_hashes: bool = False,
     ) -> DecoderOnlyInputs:
         """
         Async version of
@@ -827,7 +802,6 @@ class InputPreprocessor:
             prompt,
             tokenization_kwargs=tokenization_kwargs,
             lora_request=lora_request,
-            return_mm_hashes=return_mm_hashes,
         )
 
         return self._build_decoder_only_llm_inputs(prompt_comps)
@@ -837,17 +811,15 @@ class InputPreprocessor:
         prompt: PromptType,
         tokenization_kwargs: Optional[dict[str, Any]] = None,
         lora_request: Optional[LoRARequest] = None,
-        return_mm_hashes: bool = False,
     ) -> ProcessorInputs:
         """Preprocess the input prompt."""
         if self.model_config.is_encoder_decoder:
-            assert not return_mm_hashes, (
-                "Multimodal hashes for encoder-decoder models should not be ",
-                "returned until they are supported on vLLM V1.")
             # Encoder-decoder model requires special mapping of
-            # input prompts to encoder & decoder
+            # input prompts to encoder & decoder.
             return self._process_encoder_decoder_prompt(
-                prompt, tokenization_kwargs)
+                prompt,
+                tokenization_kwargs,
+            )
 
         if is_explicit_encoder_decoder_prompt(prompt):
             raise ValueError("Cannot pass encoder-decoder prompt "
@@ -858,7 +830,6 @@ class InputPreprocessor:
             prompt,
             tokenization_kwargs=tokenization_kwargs,
             lora_request=lora_request,
-            return_mm_hashes=return_mm_hashes,
         )
 
     async def preprocess_async(
@@ -866,19 +837,18 @@ class InputPreprocessor:
         prompt: PromptType,
         tokenization_kwargs: Optional[dict[str, Any]] = None,
         lora_request: Optional[LoRARequest] = None,
-        return_mm_hashes: bool = False,
     ) -> ProcessorInputs:
         """
         Async version of
         [`preprocess`][vllm.inputs.preprocess.InputPreprocessor.preprocess].
         """
         if self.model_config.is_encoder_decoder:
-            assert not return_mm_hashes, (
-                "Multimodal hashes for encoder-decoder models should not be ",
-                "returned until they are supported on vLLM V1.")
             # Encoder-decoder model requires special mapping of
-            # input prompts to encoder & decoder
-            return await self._process_encoder_decoder_prompt_async(prompt)
+            # input prompts to encoder & decoder.
+            return await self._process_encoder_decoder_prompt_async(
+                prompt,
+                tokenization_kwargs,
+            )
 
         if is_explicit_encoder_decoder_prompt(prompt):
             raise ValueError("Cannot pass encoder-decoder prompt "
@@ -889,5 +859,4 @@ class InputPreprocessor:
             prompt,
             tokenization_kwargs=tokenization_kwargs,
             lora_request=lora_request,
-            return_mm_hashes=return_mm_hashes,
         )

vllm/model_executor/models/deepseek_vl2.py

@@ -290,8 +290,6 @@ class DeepseekVL2MultiModalProcessor(
         mm_data_items: MultiModalDataItems,
         hf_processor_mm_kwargs: Mapping[str, object],
         tokenization_kwargs: Mapping[str, object],
-        *,
-        return_mm_hashes: bool,
     ) -> tuple[list[int], MultiModalProcessingInfo, bool]:
         # The processor logic is different for len(images) <= 2 vs > 2
         # Since the processing cache assumes that the processor output is
@@ -303,7 +301,6 @@ class DeepseekVL2MultiModalProcessor(
                 mm_data_items=mm_data_items,
                 hf_processor_mm_kwargs=hf_processor_mm_kwargs,
                 tokenization_kwargs=tokenization_kwargs,
-                return_mm_hashes=return_mm_hashes,
             )
 
         return super()._cached_apply_hf_processor(
@@ -311,7 +308,6 @@ class DeepseekVL2MultiModalProcessor(
             mm_data_items=mm_data_items,
             hf_processor_mm_kwargs=hf_processor_mm_kwargs,
             tokenization_kwargs=tokenization_kwargs,
-            return_mm_hashes=return_mm_hashes,
         )
 
 

vllm/model_executor/models/h2ovl.py

@@ -479,8 +479,6 @@ class H2OVLMultiModalProcessor(
         mm_data_items: MultiModalDataItems,
         hf_processor_mm_kwargs: Mapping[str, object],
         tokenization_kwargs: Mapping[str, object],
-        *,
-        return_mm_hashes: bool,
     ) -> tuple[list[int], MultiModalProcessingInfo, bool]:
         # The processor logic is different for len(images) <= 1 vs > 1
         # Since the processing cache assumes that the processor output is
@@ -492,7 +490,6 @@ class H2OVLMultiModalProcessor(
                 mm_data_items=mm_data_items,
                 hf_processor_mm_kwargs=hf_processor_mm_kwargs,
                 tokenization_kwargs=tokenization_kwargs,
-                return_mm_hashes=return_mm_hashes,
             )
 
         return super()._cached_apply_hf_processor(
@@ -500,7 +497,6 @@ class H2OVLMultiModalProcessor(
             mm_data_items=mm_data_items,
             hf_processor_mm_kwargs=hf_processor_mm_kwargs,
             tokenization_kwargs=tokenization_kwargs,
-            return_mm_hashes=return_mm_hashes,
         )
 
 

vllm/model_executor/models/llava.py

@@ -795,7 +795,6 @@ class MantisMultiModalProcessor(LlavaMultiModalProcessor):
         mm_data: MultiModalDataDict,
         hf_processor_mm_kwargs: Mapping[str, object],
         tokenization_kwargs: Optional[Mapping[str, object]] = None,
-        return_mm_hashes: bool = False,
     ) -> MultiModalInputs:
         hf_config = self.info.get_hf_config()
         image_token_id = hf_config.image_token_index
@@ -807,7 +806,7 @@ class MantisMultiModalProcessor(LlavaMultiModalProcessor):
         )
 
         result = super().apply(prompt, mm_data, hf_processor_mm_kwargs,
-                               tokenization_kwargs, return_mm_hashes)
+                               tokenization_kwargs)
 
         mm_items = self._to_mm_items(mm_data)
         mm_item_counts = mm_items.get_all_counts()

vllm/model_executor/models/mllama.py

@@ -168,10 +168,9 @@ class MllamaMultiModalProcessor(EncDecMultiModalProcessor[MllamaProcessingInfo]
         mm_data: MultiModalDataDict,
         hf_processor_mm_kwargs: Mapping[str, object],
         tokenization_kwargs: Optional[Mapping[str, object]] = None,
-        return_mm_hashes: bool = False,
     ) -> MultiModalEncDecInputs:
         mm_inputs = super().apply(prompt, mm_data, hf_processor_mm_kwargs,
-                                  tokenization_kwargs, return_mm_hashes)
+                                  tokenization_kwargs)
 
         image_token_id = self.info.get_hf_config().image_token_index
         # Check that the number of image tokens in the decoder prompt matches

vllm/model_executor/models/paligemma.py

@@ -194,10 +194,9 @@ class PaliGemmaMultiModalProcessor(
         mm_data: MultiModalDataDict,
         hf_processor_mm_kwargs: Mapping[str, object],
         tokenization_kwargs: Optional[Mapping[str, object]] = None,
-        return_mm_hashes: bool = False,
     ) -> MultiModalInputs:
         mm_inputs = super().apply(prompt, mm_data, hf_processor_mm_kwargs,
-                                  tokenization_kwargs, return_mm_hashes)
+                                  tokenization_kwargs)
         prompt_token_ids = mm_inputs["prompt_token_ids"]
 
         tokenizer = self.info.get_tokenizer()

vllm/model_executor/models/pixtral.py

@@ -308,15 +308,12 @@ class PixtralMultiModalProcessor(BaseMultiModalProcessor[PixtralProcessingInfo]
         mm_data_items: MultiModalDataItems,
         hf_processor_mm_kwargs: Mapping[str, object],
         tokenization_kwargs: Mapping[str, object],
-        *,
-        return_mm_hashes: bool,
     ) -> tuple[list[int], MultiModalProcessingInfo, bool]:
         prompt_ids, mm_info, _ = super()._cached_apply_hf_processor(
             prompt=prompt,
             mm_data_items=mm_data_items,
             hf_processor_mm_kwargs=hf_processor_mm_kwargs,
             tokenization_kwargs=tokenization_kwargs,
-            return_mm_hashes=return_mm_hashes,
         )
 
         # NOTE: The tokens are already inserted by the chat template

vllm/model_executor/models/prithvi_geospatial_mae.py

@@ -18,7 +18,7 @@
 """Inference-only IBM/NASA Prithvi Geospatial model."""
 
 from collections.abc import Iterable, Mapping, Sequence
-from typing import Optional, Union
+from typing import Any, Optional, Union
 
 import torch
 import torch.nn as nn
@@ -32,18 +32,56 @@ from vllm.model_executor.models.interfaces import (
     default_pooling_type)
 from vllm.model_executor.models.utils import AutoWeightsLoader
 from vllm.multimodal import MULTIMODAL_REGISTRY
-from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
-                                    MultiModalFieldElem, MultiModalInputs,
-                                    MultiModalKwargsItem,
-                                    MultiModalKwargsItems,
-                                    MultiModalSharedField, PlaceholderRange)
-from vllm.multimodal.parse import MultiModalDataItems
+from vllm.multimodal.inputs import (ImageItem, ModalityData,
+                                    MultiModalDataDict, MultiModalFieldConfig,
+                                    MultiModalInputs, MultiModalKwargsItems,
+                                    PlaceholderRange)
+from vllm.multimodal.parse import (DictEmbeddingItems, ModalityDataItems,
+                                   MultiModalDataItems, MultiModalDataParser)
 from vllm.multimodal.processing import (BaseMultiModalProcessor,
                                         BaseProcessingInfo, PromptUpdate)
 from vllm.multimodal.profiling import BaseDummyInputsBuilder
 from vllm.sequence import IntermediateTensors
 
 
+def _prithvi_field_config(hf_inputs: Mapping[str, torch.Tensor]):
+    # This model receives in input a multi-dimensional tensor representing
+    # a single image patch and therefore it is not to be split
+    # into multiple elements, but rather to be considered a single one.
+    # Hence, the decision of using a MultiModalSharedField.
+    # The expected shape is (num_channels, width, height).
+
+    # This model however allows the user to also submit multiple image
+    # patches as a batch, adding a further dimension to the above shape.
+    # At this stage we only support submitting one patch per request and
+    # batching is achieved via vLLM batching.
+    # TODO (christian-pinto): enable support for multi patch requests
+    # in tandem with vLLM batching.
+    return dict(
+        pixel_values=MultiModalFieldConfig.shared(batch_size=1,
+                                                  modality="image"),
+        location_coords=MultiModalFieldConfig.shared(batch_size=1,
+                                                     modality="image"),
+    )
+
+
+class PrithviGeoSpatialMAEMultiModalDataParser(MultiModalDataParser):
+
+    def _parse_image_data(
+        self,
+        data: Union[dict[str, torch.Tensor], ModalityData[ImageItem]],
+    ) -> Optional[ModalityDataItems[Any, Any]]:
+        if isinstance(data, dict):
+            return DictEmbeddingItems(
+                data,
+                modality="image",
+                required_fields={"pixel_values", "location_coords"},
+                fields_factory=_prithvi_field_config,
+            )
+
+        return super()._parse_image_data(data)
+
+
 class PrithviGeoSpatialMAEProcessingInfo(BaseProcessingInfo):
 
     def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
@@ -64,26 +102,26 @@ class PrithviGeoSpatialMAEInputBuilder(
         # This model input is fixed and is in the form of a torch Tensor.
         # The size of pixel_values might change in the cases where we resize
         # the input but never exceeds the dimensions below.
-        return {
+        image_data = {
             "pixel_values": torch.full((6, 512, 512), 1.0,
                                        dtype=torch.float16),
             "location_coords": torch.full((1, 2), 1.0, dtype=torch.float16),
         }
 
+        return {"image": image_data}
+
 
 class PrithviGeoSpatialMAEMultiModalProcessor(BaseMultiModalProcessor):
 
+    def _get_data_parser(self) -> MultiModalDataParser:
+        return PrithviGeoSpatialMAEMultiModalDataParser()
+
     def _get_mm_fields_config(
         self,
         hf_inputs: BatchFeature,
         hf_processor_mm_kwargs: Mapping[str, object],
     ) -> Mapping[str, MultiModalFieldConfig]:
-        return dict(
-            pixel_values=MultiModalFieldConfig.shared(batch_size=1,
-                                                      modality="image"),
-            location_coords=MultiModalFieldConfig.shared(batch_size=1,
-                                                         modality="image"),
-        )
+        return _prithvi_field_config(hf_inputs)
 
     def _get_prompt_updates(
         self,
@@ -99,46 +137,32 @@ class PrithviGeoSpatialMAEMultiModalProcessor(BaseMultiModalProcessor):
         mm_data: MultiModalDataDict,
         hf_processor_mm_kwargs: Mapping[str, object],
         tokenization_kwargs: Optional[Mapping[str, object]] = None,
-        return_mm_hashes: bool = False,
     ) -> MultiModalInputs:
-        mm_kwargs = {}
+        if "image" in mm_data:
+            image_data = mm_data["image"]
+        else:
+            image_data = mm_data
+            mm_data = {"image": mm_data}
 
-        for k, v in mm_data.items():
-            if isinstance(v, dict) and k == "image":
-                mm_kwargs.update(v)
-            else:
-                mm_kwargs[k] = v
+        mm_items = self._to_mm_items(mm_data)
+        mm_hashes = self._hash_mm_items(mm_items, hf_processor_mm_kwargs,
+                                        tokenization_kwargs or {})
         mm_placeholders = {"image": [PlaceholderRange(offset=0, length=0)]}
 
-        # This model receives in input a multi-dimensional tensor representing
-        # a single image patch and therefore it is not to be split
-        # into multiple elements, but rather to be considered a single one.
-        # Hence, the decision of using a MultiModalSharedField.
-        # The expected shape is (num_channels, width, height).
-
-        # This model however allows the user to also submit multiple image
-        # patches as a batch, adding a further dimension to the above shape.
-        # At this stage we only support submitting one patch per request and
-        # batching is achieved via vLLM batching.
-        # TODO (christian-pinto): enable support for multi patch requests
-        # in tandem with vLLM batching.
-        multimodal_kwargs_items = [
-            MultiModalKwargsItem.from_elems([
-                MultiModalFieldElem(
-                    modality="image",
-                    key=key,
-                    data=data,
-                    field=MultiModalSharedField(1),
-                ) for key, data in mm_kwargs.items()
-            ])
-        ]
+        mm_processed_data = BatchFeature(image_data)
+
+        mm_kwargs = MultiModalKwargsItems.from_hf_inputs(
+            mm_processed_data,
+            self._get_mm_fields_config(mm_processed_data,
+                                       hf_processor_mm_kwargs),
+        )
 
         return MultiModalInputs(
             type="multimodal",
             prompt=prompt,
             prompt_token_ids=[1],
-            mm_kwargs=MultiModalKwargsItems.from_seq(multimodal_kwargs_items),
-            mm_hashes=None,
+            mm_kwargs=mm_kwargs,
+            mm_hashes=mm_hashes,
             mm_placeholders=mm_placeholders,
         )
 

vllm/model_executor/models/transformers.py

@@ -310,7 +310,6 @@ class MultiModalProcessor(BaseMultiModalProcessor[MultiModalProcessingInfo]):
         mm_data: MultiModalDataDict,
         hf_processor_mm_kwargs: Mapping[str, object],
         tokenization_kwargs: Optional[Mapping[str, object]] = None,
-        return_mm_hashes: bool = False,
     ) -> MultiModalInputs:
         """
         Process multi-modal inputs to be used in vLLM.

vllm/model_executor/models/voxtral.py

@@ -288,15 +288,12 @@ class VoxtralMultiModalProcessor(BaseMultiModalProcessor[VoxtralProcessingInfo]
         mm_data_items: MultiModalDataItems,
         hf_processor_mm_kwargs: Mapping[str, object],
         tokenization_kwargs: Mapping[str, object],
-        *,
-        return_mm_hashes: bool,
     ) -> tuple[list[int], MultiModalProcessingInfo, bool]:
         prompt_ids, mm_info, _ = super()._cached_apply_hf_processor(
             prompt=prompt,
             mm_data_items=mm_data_items,
             hf_processor_mm_kwargs=hf_processor_mm_kwargs,
             tokenization_kwargs=tokenization_kwargs,
-            return_mm_hashes=return_mm_hashes,
         )
 
         # NOTE: The tokens are already inserted by the chat template

vllm/multimodal/hasher.py

@@ -43,7 +43,7 @@ class MultiModalHasher:
             return cls.item_to_bytes(
                 "image", np.asarray(convert_image_mode(obj, "RGBA")))
         if isinstance(obj, torch.Tensor):
-            return cls.item_to_bytes("tensor", obj.numpy())
+            return cls.item_to_bytes("tensor", obj.cpu().numpy())
         if isinstance(obj, np.ndarray):
             # If the array is non-contiguous, we need to copy it first
             arr_data = obj.data if obj.flags.c_contiguous else obj.tobytes()

vllm/multimodal/inputs.py

@@ -901,7 +901,7 @@ class MultiModalInputs(TypedDict):
     mm_kwargs: MultiModalKwargsItems
     """Keyword arguments to be directly passed to the model after batching."""
 
-    mm_hashes: Optional["MultiModalHashDict"]
+    mm_hashes: "MultiModalHashDict"
     """The hashes of the multi-modal data."""
 
     mm_placeholders: "MultiModalPlaceholderDict"

vllm/multimodal/processing.py

@@ -998,7 +998,7 @@ A collection of prompt updates with a similar structure as
 
 class MultiModalProcessingInfo(NamedTuple):
     kwargs: MultiModalKwargsItems
-    hashes: Optional[MultiModalHashes]
+    hashes: MultiModalHashes
     prompt_updates: MultiModalPromptUpdates
 
 
@@ -1399,8 +1399,6 @@ class BaseMultiModalProcessor(ABC, Generic[_I]):
         mm_data_items: MultiModalDataItems,
         hf_processor_mm_kwargs: Mapping[str, object],
         tokenization_kwargs: Mapping[str, object],
-        *,
-        return_mm_hashes: bool,
     ) -> tuple[list[int], MultiModalProcessingInfo, bool]:
         (
             prompt_ids,
@@ -1420,9 +1418,8 @@ class BaseMultiModalProcessor(ABC, Generic[_I]):
                                        hf_processor_mm_kwargs),
         )
 
-        mm_hashes = (self._hash_mm_items(mm_data_items, hf_processor_mm_kwargs,
-                                         tokenization_kwargs)
-                     if return_mm_hashes else None)
+        mm_hashes = self._hash_mm_items(mm_data_items, hf_processor_mm_kwargs,
+                                        tokenization_kwargs)
 
         unbound_prompt_updates = self._get_prompt_updates(
             mm_data_items,
@@ -1446,8 +1443,6 @@ class BaseMultiModalProcessor(ABC, Generic[_I]):
         mm_data_items: MultiModalDataItems,
         hf_processor_mm_kwargs: Mapping[str, object],
         tokenization_kwargs: Mapping[str, object],
-        *,
-        return_mm_hashes: bool,
     ) -> tuple[list[int], MultiModalProcessingInfo, bool]:
         """
         Apply the HF processor on the full prompt text,
@@ -1462,7 +1457,6 @@ class BaseMultiModalProcessor(ABC, Generic[_I]):
                 mm_data_items=mm_data_items,
                 hf_processor_mm_kwargs=hf_processor_mm_kwargs,
                 tokenization_kwargs=tokenization_kwargs,
-                return_mm_hashes=return_mm_hashes,
             )
 
         mm_hashes = self._hash_mm_items(mm_data_items, hf_processor_mm_kwargs,
@@ -1476,8 +1470,6 @@ class BaseMultiModalProcessor(ABC, Generic[_I]):
             mm_hashes=mm_hashes,
         )
 
-        mm_hashes_to_return = mm_hashes if return_mm_hashes else None
-
         # NOTE: `prompt` does not correspond to `mm_missing_data_items`,
         # so we can't apply prompt updates until the new multimodal
         # items are combined with the cached multimodal items
@@ -1515,7 +1507,7 @@ class BaseMultiModalProcessor(ABC, Generic[_I]):
 
         mm_info = MultiModalProcessingInfo(
             kwargs=mm_kwargs,
-            hashes=mm_hashes_to_return,
+            hashes=mm_hashes,
             prompt_updates=mm_prompt_updates,
         )
 
@@ -1697,7 +1689,6 @@ class BaseMultiModalProcessor(ABC, Generic[_I]):
         mm_data: MultiModalDataDict,
         hf_processor_mm_kwargs: Mapping[str, object],
         tokenization_kwargs: Optional[Mapping[str, object]] = None,
-        return_mm_hashes: bool = False,
     ) -> MultiModalInputs:
         """
         Process multi-modal inputs to be used in vLLM.
@@ -1726,7 +1717,6 @@ class BaseMultiModalProcessor(ABC, Generic[_I]):
             mm_items,
             hf_processor_mm_kwargs,
             tokenization_kwargs=tokenization_kwargs,
-            return_mm_hashes=return_mm_hashes,
         )
 
         # NOTE: tokenization_kwargs are not required to init processor
@@ -1811,7 +1801,6 @@ class EncDecMultiModalProcessor(BaseMultiModalProcessor[_I]):
         mm_data: MultiModalDataDict,
         hf_processor_mm_kwargs: Mapping[str, object],
         tokenization_kwargs: Optional[Mapping[str, object]] = None,
-        return_mm_hashes: bool = False,
     ) -> MultiModalEncDecInputs:
         """
         Process multi-modal inputs to be used in vLLM.
@@ -1826,7 +1815,6 @@ class EncDecMultiModalProcessor(BaseMultiModalProcessor[_I]):
             mm_data,
             hf_processor_mm_kwargs,
             tokenization_kwargs,
-            return_mm_hashes,
         )
 
         return self._get_enc_dec_inputs(

vllm/v1/engine/processor.py

@@ -17,7 +17,6 @@ from vllm.multimodal.utils import argsort_mm_positions
 from vllm.pooling_params import PoolingParams
 from vllm.sampling_params import SamplingParams
 from vllm.transformers_utils.tokenizer_group import TokenizerGroup
-from vllm.utils import is_list_of
 from vllm.v1.engine import EngineCoreRequest
 from vllm.v1.engine.mm_input_cache import MultiModalInputCacheClient
 from vllm.v1.structured_output.backend_guidance import (
@@ -253,13 +252,10 @@ class Processor:
         # 1. Tokenize text prompt, with LoRA request if one exists.
         # 2. For multimodal models with a merged preprocessor, preprocess
         #   multimodal data and expand prompt token ids accordingly.
-        return_mm_hashes = (self.model_config.processor_return_mm_hashes
-                            or bool(self.cache_config.enable_prefix_caching))
         processed_inputs: ProcessorInputs = self.input_preprocessor.preprocess(
             prompt,
             tokenization_kwargs=tokenization_kwargs,
             lora_request=lora_request,
-            return_mm_hashes=return_mm_hashes,
         )
         from vllm.platforms import current_platform
         current_platform.validate_request(
@@ -302,7 +298,7 @@ class Processor:
         if decoder_inputs["type"] == "multimodal":
             decoder_mm_inputs = decoder_inputs["mm_kwargs"]
             decoder_mm_positions = decoder_inputs["mm_placeholders"]
-            decoder_mm_hashes = decoder_inputs.get("mm_hashes")
+            decoder_mm_hashes = decoder_inputs["mm_hashes"]
 
             # Merge and flatten multimodal placeholders, hashes and inputs
             # from dictionaries to lists, and sort them by each item's position
@@ -317,19 +313,15 @@ class Processor:
                 decoder_mm_positions[modality][idx]
                 for modality, idx in sorted_mm_idxs
             ]
-            sorted_mm_hashes = None if decoder_mm_hashes is None else [
+            sorted_mm_hashes = [
                 decoder_mm_hashes[modality][idx]
                 for modality, idx in sorted_mm_idxs
             ]
 
-            if sorted_mm_hashes is not None:
-                sorted_mm_inputs = self.mm_input_cache_client.get_and_update(
-                    orig_sorted_mm_inputs,
-                    sorted_mm_hashes,
-                )
-            else:
-                assert is_list_of(orig_sorted_mm_inputs, MultiModalKwargsItem)
-                sorted_mm_inputs = orig_sorted_mm_inputs
+            sorted_mm_inputs = self.mm_input_cache_client.get_and_update(
+                orig_sorted_mm_inputs,
+                sorted_mm_hashes,
+            )
 
         return decoder_inputs.get("prompt"), EngineCoreRequest(
             request_id=request_id,