[Optim] Compute multimodal hash only once per item (vllm-project#17314)

Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>

Author: DarkLight1337

vllm/model_executor/models/deepseek_vl2.py

@@ -22,8 +22,8 @@
 from vllm.multimodal.parse import (ImageEmbeddingItems, ImageProcessorItems,
                                    ImageSize, MultiModalDataItems)
 from vllm.multimodal.processing import (BaseMultiModalProcessor,
-                                        BaseProcessingInfo, PromptReplacement,
-                                        PromptUpdate)
+                                        BaseProcessingInfo, MultiModalHashes,
+                                        PromptReplacement, PromptUpdate)
 from vllm.multimodal.profiling import BaseDummyInputsBuilder
 from vllm.sequence import IntermediateTensors
 from vllm.transformers_utils.configs.deepseek_vl2 import (DeepseekVLV2Config,
@@ -279,24 +279,26 @@ def _cached_apply_hf_processor(
         prompt: Union[str, list[int]],
         mm_data_items: MultiModalDataItems,
         hf_processor_mm_kwargs: Mapping[str, object],
-    ) -> tuple[list[int], MultiModalKwargs, bool]:
+        *,
+        return_mm_hashes: bool,
+    ) -> tuple[list[int], MultiModalKwargs, Optional[MultiModalHashes], 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(
+            return self._apply_hf_processor(
                 prompt=prompt,
-                mm_items=mm_data_items,
+                mm_data_items=mm_data_items,
                 hf_processor_mm_kwargs=hf_processor_mm_kwargs,
-                enable_hf_prompt_update=True,
+                return_mm_hashes=return_mm_hashes,
             )
 
         return super()._cached_apply_hf_processor(
             prompt=prompt,
             mm_data_items=mm_data_items,
             hf_processor_mm_kwargs=hf_processor_mm_kwargs,
+            return_mm_hashes=return_mm_hashes,
         )
 
 

vllm/model_executor/models/h2ovl.py

@@ -19,8 +19,8 @@
 from vllm.multimodal.inputs import MultiModalKwargs
 from vllm.multimodal.parse import (ImageEmbeddingItems, ImageProcessorItems,
                                    MultiModalDataItems)
-from vllm.multimodal.processing import (PromptReplacement, PromptUpdate,
-                                        PromptUpdateDetails)
+from vllm.multimodal.processing import (MultiModalHashes, PromptReplacement,
+                                        PromptUpdate, PromptUpdateDetails)
 from vllm.transformers_utils.tokenizer import AnyTokenizer
 
 from .intern_vit import InternVisionModel
@@ -488,24 +488,26 @@ def _cached_apply_hf_processor(
         prompt: Union[str, list[int]],
         mm_data_items: MultiModalDataItems,
         hf_processor_mm_kwargs: Mapping[str, object],
-    ) -> tuple[list[int], MultiModalKwargs, bool]:
+        *,
+        return_mm_hashes: bool,
+    ) -> tuple[list[int], MultiModalKwargs, Optional[MultiModalHashes], bool]:
         # The processor logic is different for len(images) <= 1 vs > 1
         # 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) > 1:
-            # This code path corresponds to the cache being disabled
-            return self._apply_hf_processor_main(
+            return self._apply_hf_processor(
                 prompt=prompt,
-                mm_items=mm_data_items,
+                mm_data_items=mm_data_items,
                 hf_processor_mm_kwargs=hf_processor_mm_kwargs,
-                enable_hf_prompt_update=True,
+                return_mm_hashes=return_mm_hashes,
             )
 
         return super()._cached_apply_hf_processor(
             prompt=prompt,
             mm_data_items=mm_data_items,
             hf_processor_mm_kwargs=hf_processor_mm_kwargs,
+            return_mm_hashes=return_mm_hashes,
         )
 
 

vllm/model_executor/models/llava.py

@@ -396,22 +396,19 @@ def _build_llava_or_pixtral_hf_processor(
     dummy_inputs: BaseDummyInputsBuilder[_I],
     *,
     cache: Optional[ProcessingCache] = None,
-    enable_sanity_checks: bool = True,
 ) -> BaseMultiModalProcessor:
     if isinstance(info, PixtralHFProcessingInfo):
         return PixtralHFMultiModalProcessor(
             info,
             dummy_inputs,  # type: ignore
             cache=cache,
-            enable_sanity_checks=enable_sanity_checks,
         )
 
     if isinstance(info, LlavaProcessingInfo):
         return LlavaMultiModalProcessor(
             info,
             dummy_inputs,  # type: ignore
             cache=cache,
-            enable_sanity_checks=enable_sanity_checks,
         )
 
     raise NotImplementedError(type(info))

vllm/model_executor/models/mistral3.py

@@ -312,14 +312,12 @@ def _build_mistral3_processor(
     dummy_inputs: BaseDummyInputsBuilder[_I],
     *,
     cache: Optional[ProcessingCache] = None,
-    enable_sanity_checks: bool = True,
 ) -> BaseMultiModalProcessor:
     assert isinstance(info, Mistral3ProcessingInfo)
     return Mistral3MultiModalProcessor(
         info,
         dummy_inputs,  # type: ignore
         cache=cache,
-        enable_sanity_checks=enable_sanity_checks,
     )
 
 

vllm/model_executor/models/pixtral.py

@@ -36,8 +36,9 @@
 from vllm.multimodal.parse import (ImageProcessorItems, ImageSize,
                                    MultiModalDataItems)
 from vllm.multimodal.processing import (BaseMultiModalProcessor,
-                                        BaseProcessingInfo, PromptReplacement,
-                                        PromptUpdate, PromptUpdateDetails)
+                                        BaseProcessingInfo, MultiModalHashes,
+                                        PromptReplacement, PromptUpdate,
+                                        PromptUpdateDetails)
 from vllm.multimodal.profiling import BaseDummyInputsBuilder
 from vllm.sequence import IntermediateTensors
 from vllm.transformers_utils.tokenizer import (MistralTokenizer,
@@ -271,15 +272,19 @@ def _cached_apply_hf_processor(
         prompt: Union[str, list[int]],
         mm_data_items: MultiModalDataItems,
         hf_processor_mm_kwargs: Mapping[str, object],
-    ) -> tuple[list[int], MultiModalKwargs, bool]:
-        prompt_ids, mm_kwargs, _ = super()._cached_apply_hf_processor(
+        *,
+        return_mm_hashes: bool,
+    ) -> tuple[list[int], MultiModalKwargs, Optional[MultiModalHashes], bool]:
+        prompt_ids, mm_kwargs, mm_hashes, _ = super(
+        )._cached_apply_hf_processor(
             prompt=prompt,
             mm_data_items=mm_data_items,
             hf_processor_mm_kwargs=hf_processor_mm_kwargs,
+            return_mm_hashes=return_mm_hashes,
         )
 
         # NOTE: The tokens are already inserted by the chat template
-        return prompt_ids, mm_kwargs, True
+        return prompt_ids, mm_kwargs, mm_hashes, True
 
 
 @MULTIMODAL_REGISTRY.register_processor(PixtralMultiModalProcessor,