[Core][Frontend] Add Support for Inference Time mm_processor_kwargs (#9131)

Signed-off-by: Alex-Brooks <Alex.Brooks@ibm.com>

vllm/multimodal/video.py

@@ -1,5 +1,5 @@
 from functools import lru_cache
-from typing import List, Union
+from typing import Any, Dict, List, Optional, Union
 
 import numpy as np
 
@@ -36,11 +36,13 @@ class VideoPlugin(ImagePlugin):
     def get_data_key(self) -> str:
         return "video"
 
-    def _get_hf_video_processor(self, model_config: ModelConfig):
-        mm_processor_kwargs = ({} if model_config.mm_processor_kwargs is None
-                               else model_config.mm_processor_kwargs)
-        # We don't explicitly check kwarg overrides to the HF class
-        # since the automodel just takes kwargs, so we can't inspect it
+    def _get_hf_video_processor(
+        self,
+        model_config: ModelConfig,
+        mm_processor_kwargs: Optional[Dict[str, Any]] = None,
+    ):
+        if mm_processor_kwargs is None:
+            mm_processor_kwargs = {}
         return cached_get_video_processor(
             model_config.model,
             trust_remote_code=model_config.trust_remote_code,
@@ -50,16 +52,24 @@ class VideoPlugin(ImagePlugin):
         self,
         ctx: InputContext,
         data: MultiModalData[object],
+        **mm_processor_kwargs,
     ) -> MultiModalInputs:
         model_config = ctx.model_config
 
         # single video input as np.ndarray
         if isinstance(data, np.ndarray):
-            video_processor = self._get_hf_video_processor(model_config)
+            video_processor = self._get_hf_video_processor(
+                model_config,
+                mm_processor_kwargs,
+            )
             if video_processor is None:
                 raise RuntimeError("No HuggingFace processor is available "
                                    "to process the image object")
             try:
+                # NOTE: Similar to image; it may be a good idea to filter and
+                # pass mm_processor_kwargs here too, but for now we don't to
+                # avoid extra complexity if the initializer and preprocess
+                # signatures of the processor don't align
                 batch_data = video_processor(data, return_tensors="pt").data
             except Exception:
                 logger.error("Failed to process image (%s)", data)