[Refactor] Get prompt updates earlier (#23097)

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

vllm/model_executor/models/deepseek_vl2.py

@@ -25,7 +25,8 @@ from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
 from vllm.multimodal.parse import (ImageEmbeddingItems, ImageProcessorItems,
                                    ImageSize, MultiModalDataItems)
 from vllm.multimodal.processing import (BaseMultiModalProcessor,
-                                        BaseProcessingInfo, MultiModalHashes,
+                                        BaseProcessingInfo,
+                                        MultiModalProcessingInfo,
                                         PromptReplacement, PromptUpdate)
 from vllm.multimodal.profiling import BaseDummyInputsBuilder
 from vllm.sequence import IntermediateTensors
@@ -291,8 +292,7 @@ class DeepseekVL2MultiModalProcessor(
         tokenization_kwargs: Mapping[str, object],
         *,
         return_mm_hashes: bool,
-    ) -> tuple[list[int], MultiModalKwargsItems, Optional[MultiModalHashes],
-               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
         # invariant of how many images are passed per prompt, we only

vllm/model_executor/models/h2ovl.py

@@ -20,8 +20,9 @@ from vllm.multimodal import MULTIMODAL_REGISTRY
 from vllm.multimodal.inputs import MultiModalKwargsItems
 from vllm.multimodal.parse import (ImageEmbeddingItems, ImageProcessorItems,
                                    MultiModalDataItems)
-from vllm.multimodal.processing import (MultiModalHashes, PromptReplacement,
-                                        PromptUpdate, PromptUpdateDetails)
+from vllm.multimodal.processing import (MultiModalProcessingInfo,
+                                        PromptReplacement, PromptUpdate,
+                                        PromptUpdateDetails)
 from vllm.transformers_utils.tokenizer import AnyTokenizer
 
 from .intern_vit import InternVisionModel
@@ -480,8 +481,7 @@ class H2OVLMultiModalProcessor(
         tokenization_kwargs: Mapping[str, object],
         *,
         return_mm_hashes: bool,
-    ) -> tuple[list[int], MultiModalKwargsItems, Optional[MultiModalHashes],
-               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
         # invariant of how many images are passed per prompt, we only

vllm/model_executor/models/pixtral.py

@@ -39,7 +39,8 @@ from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
 from vllm.multimodal.parse import (ImageProcessorItems, ImageSize,
                                    MultiModalDataItems)
 from vllm.multimodal.processing import (BaseMultiModalProcessor,
-                                        BaseProcessingInfo, MultiModalHashes,
+                                        BaseProcessingInfo,
+                                        MultiModalProcessingInfo,
                                         PromptReplacement, PromptUpdate,
                                         PromptUpdateDetails)
 from vllm.multimodal.profiling import BaseDummyInputsBuilder, ProcessorInputs
@@ -309,14 +310,8 @@ class PixtralMultiModalProcessor(BaseMultiModalProcessor[PixtralProcessingInfo]
         tokenization_kwargs: Mapping[str, object],
         *,
         return_mm_hashes: bool,
-    ) -> tuple[list[int], MultiModalKwargsItems, Optional[MultiModalHashes],
-               bool]:
-        (
-            prompt_ids,
-            mm_kwargs,
-            mm_hashes,
-            _,
-        ) = super()._cached_apply_hf_processor(
+    ) -> 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,
@@ -325,7 +320,7 @@ class PixtralMultiModalProcessor(BaseMultiModalProcessor[PixtralProcessingInfo]
         )
 
         # NOTE: The tokens are already inserted by the chat template
-        return prompt_ids, mm_kwargs, mm_hashes, True
+        return prompt_ids, mm_info, True
 
 
 @MULTIMODAL_REGISTRY.register_processor(PixtralMultiModalProcessor,

vllm/model_executor/models/qwen2_5_omni_thinker.py

@@ -59,6 +59,7 @@ from vllm.multimodal.parse import (AudioProcessorItems, DictEmbeddingItems,
                                    ModalityDataItems, MultiModalDataItems,
                                    MultiModalDataParser)
 from vllm.multimodal.processing import (BaseMultiModalProcessor,
+                                        MultiModalPromptUpdates,
                                         PlaceholderFeaturesInfo,
                                         PromptReplacement, PromptUpdate)
 from vllm.multimodal.profiling import BaseDummyInputsBuilder
@@ -88,10 +89,7 @@ def _qwen2_5_omni_thinker_field_config(hf_inputs: Mapping[str, torch.Tensor]):
     video_grid_thw = hf_inputs.get("video_grid_thw", torch.empty((0, 3)))
     video_grid_sizes = video_grid_thw.prod(-1)
 
-    # vllm use `second_per_grid_ts` to compute multimodal rotary embedding
-    video_second_per_grid = hf_inputs.get("video_second_per_grid", None)
-    if video_second_per_grid is not None:
-        hf_inputs["second_per_grid_ts"] = video_second_per_grid
+    num_videos = len(video_grid_sizes)
 
     return dict(
         input_audio_features=MultiModalFieldConfig.flat_from_sizes(
@@ -109,6 +107,7 @@ def _qwen2_5_omni_thinker_field_config(hf_inputs: Mapping[str, torch.Tensor]):
             "video", video_grid_sizes),
         video_grid_thw=MultiModalFieldConfig.batched("video"),
         second_per_grid_ts=MultiModalFieldConfig.batched("video"),
+        use_audio_in_video=MultiModalFieldConfig.shared("video", num_videos),
     )
 
 
@@ -251,6 +250,14 @@ class Qwen2_5OmniThinkerMultiModalProcessor(
         if ('audio_feature_lengths' not in hf_inputs
                 and feature_attention_mask is not None):
             hf_inputs['audio_feature_lengths'] = feature_attention_mask.sum(-1)
+
+        video_second_per_grid = hf_inputs.get("video_second_per_grid", None)
+        if video_second_per_grid is not None:
+            hf_inputs["second_per_grid_ts"] = video_second_per_grid
+
+        use_audio_in_video = mm_kwargs.get("use_audio_in_video", False)
+        hf_inputs["use_audio_in_video"] = torch.tensor(use_audio_in_video)
+
         return hf_inputs
 
     def _get_mm_fields_config(
@@ -263,27 +270,20 @@ class Qwen2_5OmniThinkerMultiModalProcessor(
     def _maybe_apply_prompt_updates(
         self,
         mm_items: MultiModalDataItems,
-        hf_processor_mm_kwargs: Mapping[str, object],
         prompt_ids: list[int],
         mm_kwargs: MultiModalKwargsItems,
+        mm_prompt_updates: MultiModalPromptUpdates,
         is_update_applied: bool,
     ) -> tuple[list[int], str, Mapping[str, list[PlaceholderFeaturesInfo]]]:
         """
         Qwen2.5-Omni reimplements this function to handle `use_audio_in_video`.
         """
-        unbound_prompt_updates = self._get_prompt_updates(
-            mm_items,
-            hf_processor_mm_kwargs,
-            mm_kwargs,
-        )
-        mm_prompt_updates = self._bind_and_group_updates(
-            unbound_prompt_updates)
-
         mm_item_counts = mm_items.get_all_counts()
         self._validate_mm_kwargs(mm_kwargs, mm_item_counts)
 
-        use_audio_in_video = hf_processor_mm_kwargs.get(
-            "use_audio_in_video", False)
+        use_audio_in_video = (all(
+            item["use_audio_in_video"].data
+            for item in mm_kwargs["video"]) if "video" in mm_kwargs else False)
 
         if is_update_applied:
             mm_placeholders = self._find_mm_placeholders(
@@ -316,9 +316,6 @@ class Qwen2_5OmniThinkerMultiModalProcessor(
         tokenizer = self.info.get_tokenizer()
         prompt = decode_tokens(tokenizer, prompt_ids)
 
-        if use_audio_in_video:
-            mm_kwargs["use_audio_in_video"] = True
-
         return prompt_ids, prompt, mm_placeholders
 
     def _get_prompt_updates(

vllm/model_executor/models/voxtral.py

@@ -35,7 +35,8 @@ from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
 from vllm.multimodal.parse import (AudioProcessorItems, MultiModalDataItems,
                                    MultiModalDataParser)
 from vllm.multimodal.processing import (BaseMultiModalProcessor,
-                                        BaseProcessingInfo, MultiModalHashes,
+                                        BaseProcessingInfo,
+                                        MultiModalProcessingInfo,
                                         PromptReplacement, PromptUpdate)
 from vllm.multimodal.profiling import BaseDummyInputsBuilder, ProcessorInputs
 from vllm.sequence import IntermediateTensors
@@ -289,10 +290,8 @@ class VoxtralMultiModalProcessor(BaseMultiModalProcessor[VoxtralProcessingInfo]
         tokenization_kwargs: Mapping[str, object],
         *,
         return_mm_hashes: bool,
-    ) -> tuple[list[int], MultiModalKwargsItems, Optional[MultiModalHashes],
-               bool]:
-        prompt_ids, mm_kwargs, mm_hashes, _ = super(
-        )._cached_apply_hf_processor(
+    ) -> 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,
@@ -301,7 +300,7 @@ class VoxtralMultiModalProcessor(BaseMultiModalProcessor[VoxtralProcessingInfo]
         )
 
         # NOTE: The tokens are already inserted by the chat template
-        return prompt_ids, mm_kwargs, mm_hashes, True
+        return prompt_ids, mm_info, True
 
     def _get_data_parser(self) -> MultiModalDataParser:
         sampling_rate = self.info.get_hf_processor().sampling_rate

vllm/multimodal/processing.py

@@ -989,6 +989,18 @@ A collection of hashes with a similar structure as
 [`MultiModalKwargsItems`][vllm.multimodal.inputs.MultiModalKwargsItems].
 """
 
+MultiModalPromptUpdates = dict[str, Sequence[BoundPromptUpdate]]
+"""
+A collection of prompt updates with a similar structure as
+[`MultiModalKwargsItems`][vllm.multimodal.inputs.MultiModalKwargsItems].
+"""
+
+
+class MultiModalProcessingInfo(NamedTuple):
+    kwargs: MultiModalKwargsItems
+    hashes: Optional[MultiModalHashes]
+    prompt_updates: MultiModalPromptUpdates
+
 
 class BaseMultiModalProcessor(ABC, Generic[_I]):
     """
@@ -1363,7 +1375,7 @@ class BaseMultiModalProcessor(ABC, Generic[_I]):
         cache: ProcessingCache,
         mm_cache_items_or_hashes: dict[str, list[_CacheItemOrHash]],
         mm_missing_kwargs: MultiModalKwargsItems,
-    ) -> dict[str, list[MultiModalKwargsItem]]:
+    ) -> MultiModalKwargsItems:
         mm_missing_next_idx = defaultdict[str, int](lambda: 0)
 
         merged_items = defaultdict[str, list[MultiModalKwargsItem]](list)
@@ -1379,7 +1391,7 @@ class BaseMultiModalProcessor(ABC, Generic[_I]):
 
                 merged_items[modality].append(kw_item)
 
-        return dict(merged_items)
+        return MultiModalKwargsItems(merged_items)
 
     def _apply_hf_processor(
         self,
@@ -1389,8 +1401,7 @@ class BaseMultiModalProcessor(ABC, Generic[_I]):
         tokenization_kwargs: Mapping[str, object],
         *,
         return_mm_hashes: bool,
-    ) -> tuple[list[int], MultiModalKwargsItems, Optional[MultiModalHashes],
-               bool]:
+    ) -> tuple[list[int], MultiModalProcessingInfo, bool]:
         (
             prompt_ids,
             mm_processed_data,
@@ -1413,7 +1424,21 @@ class BaseMultiModalProcessor(ABC, Generic[_I]):
                                          tokenization_kwargs)
                      if return_mm_hashes else None)
 
-        return prompt_ids, mm_kwargs, mm_hashes, is_update_applied
+        unbound_prompt_updates = self._get_prompt_updates(
+            mm_data_items,
+            hf_processor_mm_kwargs,
+            mm_kwargs,
+        )
+        mm_prompt_updates = self._bind_and_group_updates(
+            unbound_prompt_updates)
+
+        mm_info = MultiModalProcessingInfo(
+            kwargs=mm_kwargs,
+            hashes=mm_hashes,
+            prompt_updates=mm_prompt_updates,
+        )
+
+        return prompt_ids, mm_info, is_update_applied
 
     def _cached_apply_hf_processor(
         self,
@@ -1423,8 +1448,7 @@ class BaseMultiModalProcessor(ABC, Generic[_I]):
         tokenization_kwargs: Mapping[str, object],
         *,
         return_mm_hashes: bool,
-    ) -> tuple[list[int], MultiModalKwargsItems, Optional[MultiModalHashes],
-               bool]:
+    ) -> tuple[list[int], MultiModalProcessingInfo, bool]:
         """
         Apply the HF processor on the full prompt text,
         caching the results and reusing cached results.
@@ -1475,18 +1499,27 @@ class BaseMultiModalProcessor(ABC, Generic[_I]):
                                        hf_processor_mm_kwargs),
         )
 
-        mm_cache_items_merged = self._merge_mm_kwargs(
+        mm_kwargs = self._merge_mm_kwargs(
             cache,
             mm_cache_items_or_hashes=mm_cache_items_or_hashes,
             mm_missing_kwargs=mm_missing_kwargs,
         )
 
-        mm_kwargs = MultiModalKwargsItems.from_seq([
-            item for cache_items in mm_cache_items_merged.values()
-            for item in cache_items
-        ])
+        unbound_prompt_updates = self._get_prompt_updates(
+            mm_data_items,
+            hf_processor_mm_kwargs,
+            mm_kwargs,
+        )
+        mm_prompt_updates = self._bind_and_group_updates(
+            unbound_prompt_updates)
 
-        return prompt_ids, mm_kwargs, mm_hashes_to_return, is_update_applied
+        mm_info = MultiModalProcessingInfo(
+            kwargs=mm_kwargs,
+            hashes=mm_hashes_to_return,
+            prompt_updates=mm_prompt_updates,
+        )
+
+        return prompt_ids, mm_info, is_update_applied
 
     def _bind_and_group_updates(
         self,
@@ -1626,19 +1659,11 @@ class BaseMultiModalProcessor(ABC, Generic[_I]):
     def _maybe_apply_prompt_updates(
         self,
         mm_items: MultiModalDataItems,
-        hf_processor_mm_kwargs: Mapping[str, object],
         prompt_ids: list[int],
         mm_kwargs: MultiModalKwargsItems,
+        mm_prompt_updates: MultiModalPromptUpdates,
         is_update_applied: bool,
     ) -> tuple[list[int], str, Mapping[str, list[PlaceholderFeaturesInfo]]]:
-        unbound_prompt_updates = self._get_prompt_updates(
-            mm_items,
-            hf_processor_mm_kwargs,
-            mm_kwargs,
-        )
-        mm_prompt_updates = self._bind_and_group_updates(
-            unbound_prompt_updates)
-
         mm_item_counts = mm_items.get_all_counts()
         self._validate_mm_kwargs(mm_kwargs, mm_item_counts)
 
@@ -1694,8 +1719,7 @@ class BaseMultiModalProcessor(ABC, Generic[_I]):
 
         (
             prompt_ids,
-            mm_kwargs,
-            mm_hashes,
+            mm_info,
             is_update_applied,
         ) = self._cached_apply_hf_processor(
             prompt,
@@ -1708,9 +1732,9 @@ class BaseMultiModalProcessor(ABC, Generic[_I]):
         # NOTE: tokenization_kwargs are not required to init processor
         prompt_ids, prompt, mm_placeholders = self._maybe_apply_prompt_updates(
             mm_items=mm_items,
-            hf_processor_mm_kwargs=hf_processor_mm_kwargs,
             prompt_ids=prompt_ids,
-            mm_kwargs=mm_kwargs,
+            mm_kwargs=mm_info.kwargs,
+            mm_prompt_updates=mm_info.prompt_updates,
             is_update_applied=is_update_applied,
         )
 
@@ -1723,8 +1747,8 @@ class BaseMultiModalProcessor(ABC, Generic[_I]):
             type="multimodal",
             prompt=prompt,
             prompt_token_ids=prompt_ids,
-            mm_kwargs=mm_kwargs,
-            mm_hashes=mm_hashes,
+            mm_kwargs=mm_info.kwargs,
+            mm_hashes=mm_info.hashes,
             mm_placeholders=mm_placeholder_ranges,
         )