[V1] Consolidate MM cache size to vllm.envs

vllm/envs.py

@@ -55,6 +55,7 @@
     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 @@ def maybe_convert_int(value: Optional[str]) -> Optional[int]:
     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":

vllm/multimodal/registry.py

@@ -8,6 +8,7 @@
 
 import torch.nn as nn
 
+from vllm.envs import VLLM_MM_INPUT_CACHE_SIZE
 from vllm.inputs import InputProcessingContext
 from vllm.logger import init_logger
 from vllm.transformers_utils.tokenizer import AnyTokenizer
@@ -28,9 +29,6 @@
 
 logger = init_logger(__name__)
 
-# TODO: Tune the MM cache size
-MM_CACHE_SIZE = 256
-
 N = TypeVar("N", bound=Type[nn.Module])
 _I = TypeVar("_I", bound=BaseProcessingInfo)
 _I_co = TypeVar("_I_co", bound=BaseProcessingInfo, covariant=True)
@@ -121,7 +119,7 @@ def __init__(
 
         self._limits_by_model = _MultiModalLimits()
 
-        self._processing_cache = ProcessingCache(MM_CACHE_SIZE)
+        self._processing_cache = ProcessingCache(VLLM_MM_INPUT_CACHE_SIZE)
 
     def register_plugin(self, plugin: MultiModalPlugin) -> None:
         """

vllm/v1/engine/mm_input_cache.py

@@ -3,6 +3,7 @@
 from typing import Any, Dict, List, Optional
 
 from vllm.config import ModelConfig
+from vllm.envs import VLLM_MM_INPUT_CACHE_SIZE
 from vllm.logger import init_logger
 from vllm.multimodal import (MULTIMODAL_REGISTRY, MultiModalDataDict,
                              MultiModalKwargs, MultiModalRegistry)
@@ -28,9 +29,8 @@
 # client (=P0) and server (=P1) processes.
 
 # Both Client and Server must use the same cache size
-# (to perform mirrored caching)
-# TODO: Tune the MM cache size
-MM_CACHE_SIZE = 256
+# (to perform mirrored caching). This cache size is set by the environment
+# variable VLLM_MM_INPUT_CACHE_SIZE.
 
 
 # TODO(ywang96): Deprecate this class once all multimodal models migrate to use
@@ -50,7 +50,8 @@ def __init__(
 
         # Init cache
         self.use_cache = not model_config.disable_mm_preprocessor_cache
-        self.mm_cache = LRUCache[str, MultiModalKwargs](MM_CACHE_SIZE)
+        self.mm_cache = LRUCache[str,
+                                 MultiModalKwargs](VLLM_MM_INPUT_CACHE_SIZE)
 
         # DEBUG: Set to None to disable
         self.mm_debug_cache_hit_ratio_steps = None
@@ -127,7 +128,8 @@ class MMInputCacheServer:
 
     def __init__(self, model_config):
         self.use_cache = not model_config.disable_mm_preprocessor_cache
-        self.mm_cache = LRUCache[str, MultiModalKwargs](MM_CACHE_SIZE)
+        self.mm_cache = LRUCache[str,
+                                 MultiModalKwargs](VLLM_MM_INPUT_CACHE_SIZE)
 
     def get_and_update(
         self,