fix bug

Signed-off-by: bk-201 <joy25810@foxmail.com>

vllm/v1/worker/gpu_model_runner.py

@@ -593,11 +593,13 @@ class GPUModelRunner(
         )
 
         # Multimodal LoRA support
-        if self.supports_mm_inputs:
+        self.enable_tower_connector_lora = False
+        if self.supports_mm_inputs and self.lora_config:
             self.info = self.mm_registry.create_processor(self.model_config).info
-            self.supports_mm_lora = hasattr(self.info, "get_num_mm_encoder_tokens")
-        else:
-            self.supports_mm_lora = False
+            self.enable_tower_connector_lora = (
+                hasattr(self.info, "get_num_mm_encoder_tokens")
+                and self.lora_config.enable_tower_connector_lora
+            )
 
         # Pre-allocated tensor for copying valid sampled token counts to CPU,
         # with dedicated stream for overlapping and event for coordination.
@@ -2148,7 +2150,7 @@ class GPUModelRunner(
         # encoder outputs.
         model = cast(SupportsMultiModal, self.model)
 
-        if self.lora_config and self.supports_mm_lora:
+        if self.enable_tower_connector_lora:
             # Build LoRA mappings independently for encoder inputs
             # (encoder batch structure is different from main batch)
             prompt_lora_mapping = []
@@ -2371,37 +2373,6 @@ class GPUModelRunner(
 
         return mm_embeds, is_mm_embed
 
-    def _extract_encoder_inputs(
-        self,
-        scheduler_output: "SchedulerOutput",
-    ) -> dict[str, torch.Tensor]:
-        """Extract encoder inputs for encoder-decoder models.
-
-        This method extracts multimodal input features from scheduled encoder
-        inputs and formats them for the encoder-decoder model forward pass.
-        """
-        # Batch the multi-modal inputs using the helper method.
-        mm_kwargs, _, _ = self._batch_mm_kwargs_from_scheduler(scheduler_output)
-
-        if not mm_kwargs:
-            return {}
-
-        # Group MM kwargs by modality and extract features
-        model = cast(SupportsMultiModal, self.model)
-        encoder_features = {}
-        for _, _, mm_kwargs_group in group_mm_kwargs_by_modality(
-            mm_kwargs,
-            device=self.device,
-            pin_memory=self.pin_memory,
-            merge_by_field_config=model.merge_by_field_config,
-        ):
-            # Add the grouped features to encoder_features dict
-            # This allows the model to receive them as kwargs (e.g.,
-            # input_features=...)
-            encoder_features.update(mm_kwargs_group)
-
-        return encoder_features
-
     def get_model(self) -> nn.Module:
         # get raw model out of the cudagraph wrapper.
         if isinstance(self.model, (CUDAGraphWrapper, UBatchWrapper)):