[Model] Pass mm_features directly into get_mrope_input_positions (#28399)

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

vllm/model_executor/models/transformers/multimodal.py

@@ -27,6 +27,7 @@ from vllm.model_executor.models.utils import WeightsMapper
 from vllm.multimodal import MultiModalKwargsItems
 from vllm.multimodal.inputs import (
     MultiModalDataDict,
+    MultiModalFeatureSpec,
     MultiModalFieldConfig,
     MultiModalInputs,
     MultiModalUUIDDict,
@@ -38,7 +39,7 @@ from vllm.multimodal.profiling import BaseDummyInputsBuilder
 from vllm.sequence import IntermediateTensors
 
 if TYPE_CHECKING:
-    from transformers import BatchFeature, PretrainedConfig
+    from transformers import BatchFeature
 
     from vllm.config import VllmConfig
     from vllm.config.multimodal import BaseDummyOptions
@@ -367,20 +368,34 @@ class MultiModalMixin(SupportsMultiModal, SupportsMRoPE):
     def get_mrope_input_positions(
         self,
         input_tokens: list[int],
-        hf_config: "PretrainedConfig",
-        image_grid_thw: list[list[int]] | torch.Tensor | None,
-        video_grid_thw: list[list[int]] | torch.Tensor | None,
-        second_per_grid_ts: list[float] | None = None,
-        audio_feature_lengths: torch.Tensor | None = None,
-        use_audio_in_video: bool = False,
+        mm_features: list[MultiModalFeatureSpec],
     ) -> tuple[torch.Tensor, int]:
-        if any((second_per_grid_ts, audio_feature_lengths, use_audio_in_video)):
+        kwargs = MultiModalFeatureSpec.gather_kwargs(
+            mm_features,
+            {
+                "image_grid_thw",
+                "video_grid_thw",
+                "second_per_grid_ts",
+                "audio_feature_lengths",
+                "use_audio_in_video",
+            },
+        )
+        if any(
+            v
+            for k, v in kwargs.items()
+            if k not in {"image_grid_thw", "video_grid_thw"}
+        ):
             raise NotImplementedError("Transformers backend only supports images.")
 
-        if isinstance(image_grid_thw, list):
-            image_grid_thw = torch.tensor(image_grid_thw)
-        if isinstance(video_grid_thw, list):
-            video_grid_thw = torch.tensor(video_grid_thw)
+        image_grid_thw = kwargs.get("image_grid_thw", [])
+        video_grid_thw = kwargs.get("video_grid_thw", [])
+
+        image_grid_thw = (torch.stack if image_grid_thw else torch.tensor)(
+            image_grid_thw
+        )
+        video_grid_thw = (torch.stack if video_grid_thw else torch.tensor)(
+            video_grid_thw
+        )
 
         mrope_positions, mrope_position_delta = self.model.get_rope_index(
             input_ids=torch.tensor(input_tokens).unsqueeze(0),