[V1] Consolidate MM cache size to vllm.envs (#13239)

vllm/envs.py

@@ -55,6 +55,7 @@ if TYPE_CHECKING:
     VLLM_IMAGE_FETCH_TIMEOUT: int = 5
     VLLM_VIDEO_FETCH_TIMEOUT: int = 30
     VLLM_AUDIO_FETCH_TIMEOUT: int = 10
+    VLLM_MM_INPUT_CACHE_SIZE: int = 256
     VLLM_TARGET_DEVICE: str = "cuda"
     MAX_JOBS: Optional[str] = None
     NVCC_THREADS: Optional[str] = None
@@ -401,15 +402,21 @@ environment_variables: Dict[str, Callable[[], Any]] = {
     lambda: int(os.getenv("VLLM_IMAGE_FETCH_TIMEOUT", "5")),
 
     # Timeout for fetching videos when serving multimodal models
-    # Default is 15 seconds
+    # Default is 30 seconds
     "VLLM_VIDEO_FETCH_TIMEOUT":
-    lambda: int(os.getenv("VLLM_VIDEO_FETCH_TIMEOUT", "15")),
+    lambda: int(os.getenv("VLLM_VIDEO_FETCH_TIMEOUT", "30")),
 
     # Timeout for fetching audio when serving multimodal models
     # Default is 10 seconds
     "VLLM_AUDIO_FETCH_TIMEOUT":
     lambda: int(os.getenv("VLLM_AUDIO_FETCH_TIMEOUT", "10")),
 
+    # Cache size for multimodal feature/input cache for multimodal models
+    # in unit of number of multimodal data items (e.g. image, video, audio).
+    # Default is 256 multimodal data items.
+    "VLLM_MM_INPUT_CACHE_SIZE":
+    lambda: int(os.getenv("VLLM_MM_INPUT_CACHE_SIZE", "256")),
+
     # Path to the XLA persistent cache directory.
     # Only used for XLA devices such as TPUs.
     "VLLM_XLA_CACHE_PATH":