[Misc] Simplify max tokens in multimodal registry (#27500)

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

vllm/multimodal/profiling.py

@@ -355,7 +355,11 @@ class MultiModalProfiler(Generic[_I]):
             mm_counts=mm_counts,
         )
         if max_tokens_per_item is not None:
-            return max_tokens_per_item
+            return {
+                modality: max_tokens
+                for modality, max_tokens in max_tokens_per_item.items()
+                if mm_counts.get(modality, 0) > 0
+            }
 
         mm_inputs = self._get_dummy_mm_inputs(seq_len, mm_counts)
         return self._get_mm_num_tokens(mm_inputs, mm_embeddings_only=mm_embeddings_only)
@@ -375,5 +379,4 @@ class MultiModalProfiler(Generic[_I]):
         This is important to take into account when profiling and
         initializing the encoder cache size.
         """
-
         return self._get_mm_max_tokens(seq_len, mm_counts, mm_embeddings_only=False)

vllm/multimodal/registry.py

@@ -152,6 +152,7 @@ class MultiModalRegistry:
         model_config: "ModelConfig",
         *,
         cache: BaseMultiModalProcessorCache | None = None,
+        profiler_limits: Mapping[str, int] | None = None,
     ) -> Mapping[str, int]:
         """
         Get the maximum number of tokens per data item from each modality based
@@ -164,40 +165,15 @@ class MultiModalRegistry:
         profiler: MultiModalProfiler = MultiModalProfiler(processor)
 
         seq_len = model_config.max_model_len
-        mm_limits = self.get_mm_limits_per_prompt(model_config, cache=cache)
+        profiler_limits = (
+            profiler.get_mm_limits() if profiler_limits is None else profiler_limits
+        )
 
         return profiler.get_mm_max_contiguous_tokens(
             seq_len,
-            {modality: 1 for modality, limit in mm_limits.items() if limit > 0},
+            {modality: 1 for modality, limit in profiler_limits.items() if limit > 0},
         )
 
-    def get_max_tokens_per_item_by_nonzero_modality(
-        self,
-        model_config: "ModelConfig",
-        *,
-        cache: BaseMultiModalProcessorCache | None = None,
-    ) -> Mapping[str, int]:
-        """
-        Get the maximum number of tokens per data item from each modality based
-        on underlying model configuration, excluding modalities that user
-        explicitly disabled via `limit_mm_per_prompt`.
-
-        Note:
-            This is currently directly used only in V1 for profiling the memory
-            usage of a model.
-        """
-        mm_limits = self.get_mm_limits_per_prompt(model_config, cache=cache)
-        max_tokens_per_item = self.get_max_tokens_per_item_by_modality(
-            model_config,
-            cache=cache,
-        )
-
-        return {
-            key: max_tokens_per_mm_item
-            for key, max_tokens_per_mm_item in max_tokens_per_item.items()
-            if mm_limits[key] > 0
-        }
-
     def get_mm_limits_per_prompt(
         self,
         model_config: "ModelConfig",
@@ -369,7 +345,7 @@ class MultiModalRegistry:
         """
         if not model_config.is_encoder_decoder:
             return 0
-        max_tokens = self.get_max_tokens_per_item_by_nonzero_modality(model_config)
+        max_tokens = self.get_max_tokens_per_item_by_modality(model_config)
         if not max_tokens:
             # TODO - this function assumes encoder-decoder models are
             # multimodal. This will need to change when adding support for more

vllm/v1/core/encoder_cache_manager.py

@@ -264,8 +264,8 @@ def compute_encoder_budget(
             from the input sequence.
     """
     if mm_registry.supports_multimodal_inputs(model_config):
-        max_tokens_by_modality = (
-            mm_registry.get_max_tokens_per_item_by_nonzero_modality(model_config)
+        max_tokens_by_modality = mm_registry.get_max_tokens_per_item_by_modality(
+            model_config
         )
 
         return compute_mm_encoder_budget(

vllm/v1/worker/utils.py

@@ -42,10 +42,10 @@ class MultiModalBudget:
 
         self.mm_limits = mm_registry.get_mm_limits_per_prompt(model_config, cache=cache)
 
-        max_tokens_by_modality = (
-            mm_registry.get_max_tokens_per_item_by_nonzero_modality(
-                model_config, cache=cache
-            )
+        max_tokens_by_modality = mm_registry.get_max_tokens_per_item_by_modality(
+            model_config,
+            cache=cache,
+            profiler_limits=self.mm_limits,
         )
 
         encoder_compute_budget, encoder_cache_size = compute_mm_encoder_budget(