[Multimodal] Optimize Qwen2/2.5-VL startup time

vllm/model_executor/models/qwen2_vl.py

@@ -819,6 +819,9 @@ def get_image_processor(
     def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
         return {"image": None, "video": None}
 
+    def get_max_tokens_per_item(self) -> Mapping[str, int]:
+        return {"image": 16384, "video": 98304}
+
     def _get_vision_info(
         self,
         *,

vllm/multimodal/processing.py

@@ -1100,6 +1100,18 @@ def get_allowed_mm_limits(self) -> Mapping[str, int]:
 
         return allowed_limits
 
+    def get_max_tokens_per_item(self) -> Optional[Mapping[str, int]]:
+        """Return the maximum number of tokens for each modality.
+        By default, returns `None`. When `None` is returned, vLLM will generate
+        dummy inputs (images/videos) at maximum possible sizes and process them
+        to determine the maximum token count per modality.
+        This approach works but can be very slow for certain models (e.g.,
+        Qwen2.5-VL), leading to very long startup time. For better performance,
+        each model can override this method to return pre-computed maximum token
+        counts, avoiding the need for dummy input generation and processing.
+        """
+        return None
+
 
 _I = TypeVar("_I", bound=BaseProcessingInfo)
 

vllm/multimodal/profiling.py

@@ -253,6 +253,9 @@ def get_mm_max_tokens(
         seq_len: int,
         mm_counts: Optional[Mapping[str, int]] = None,
     ) -> Mapping[str, int]:
-        mm_inputs = self._get_dummy_mm_inputs(seq_len, mm_counts)
+        max_tokens_per_item = self.processing_info.get_max_tokens_per_item()
+        if max_tokens_per_item is not None:
+            return max_tokens_per_item
 
+        mm_inputs = self._get_dummy_mm_inputs(seq_len, mm_counts)
         return self._get_mm_num_tokens(mm_inputs)