[Frontend] Use engine argument to control MM cache size (#22441)

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

docs/configuration/conserving_memory.md

@@ -86,7 +86,7 @@ llm = LLM(model="meta-llama/Llama-3.1-8B-Instruct",
 
 If you run out of CPU RAM, try the following options:
 
-- (Multi-modal models only) you can set the size of multi-modal processor cache using `VLLM_MM_INPUT_CACHE_GIB` environment variable (default 4 GiB per API process + 4 GiB per engine core process)
+- (Multi-modal models only) you can set the size of multi-modal processor cache by setting `mm_processor_cache_gb` engine argument (default 4 GiB per API process + 4 GiB per engine core process)
 - (CPU backend only) you can set the size of KV cache using `VLLM_CPU_KVCACHE_SPACE` environment variable (default 4 GiB).
 
 ## Multi-modal input limits

docs/configuration/optimization.md

@@ -161,12 +161,18 @@ By default, the multi-modal processor cache is enabled to avoid repeatedly proce
 the same multi-modal inputs via Hugging Face `AutoProcessor`,
 which commonly occurs in multi-turn conversations.
 
-You can adjust the size of the cache via `VLLM_MM_INPUT_CACHE_GIB` environment variable
+You can adjust the size of the cache by setting the value of `mm_processor_cache_gb`
 (default 4 GiB per API process + 4 GiB per engine core process).
+If you do not benefit much from the cache, you can disable it completely via `mm_processor_cache_gb=0`.
 
-If you do not benefit much from the cache, you can disable it completely via `disable_mm_preprocessor_cache`:
+Examples:
 
 ```python
+# Use a larger cache
 llm = LLM(model="Qwen/Qwen2.5-VL-3B-Instruct",
-          disable_mm_preprocessor_cache=True)
+          mm_processor_cache_gb=8)
+
+# Disable the cache
+llm = LLM(model="Qwen/Qwen2.5-VL-3B-Instruct",
+          mm_processor_cache_gb=0)
 ```

examples/offline_inference/mistral-small.py

@@ -68,7 +68,7 @@ def run_simple_demo(args: argparse.Namespace):
         max_model_len=4096,
         max_num_seqs=2,
         tensor_parallel_size=2,
-        disable_mm_preprocessor_cache=args.disable_mm_preprocessor_cache,
+        mm_processor_cache_gb=0 if args.disable_mm_processor_cache else 4,
     )
 
     prompt = "Describe this image in one sentence."
@@ -105,7 +105,7 @@ def run_advanced_demo(args: argparse.Namespace):
         limit_mm_per_prompt={"image": max_img_per_msg},
         max_model_len=max_img_per_msg * max_tokens_per_img,
         tensor_parallel_size=2,
-        disable_mm_preprocessor_cache=args.disable_mm_preprocessor_cache,
+        mm_processor_cache_gb=0 if args.disable_mm_processor_cache else 4,
     )
 
     prompt = "Describe the following image."
@@ -164,7 +164,7 @@ def parse_args():
     )
 
     parser.add_argument(
-        "--disable-mm-preprocessor-cache",
+        "--disable-mm-processor-cache",
         action="store_true",
         help="If True, disables caching of multi-modal processor.",
     )

examples/offline_inference/vision_language.py

@@ -1563,7 +1563,7 @@ def parse_args():
     )
 
     parser.add_argument(
-        "--disable-mm-preprocessor-cache",
+        "--disable-mm-processor-cache",
         action="store_true",
         help="If True, disables caching of multi-modal processor.",
     )
@@ -1603,7 +1603,7 @@ def main(args):
 
     engine_args = asdict(req_data.engine_args) | {
         "seed": args.seed,
-        "disable_mm_preprocessor_cache": args.disable_mm_preprocessor_cache,
+        "mm_processor_cache_gb": 0 if args.disable_mm_processor_cache else 4,
     }
     llm = LLM(**engine_args)
 

tests/models/multimodal/generation/vlm_utils/core.py

@@ -62,9 +62,7 @@ def run_test(
     # if we run HF first, the cuda initialization will be done and it
     # will hurt multiprocessing backend with fork method (the default method).
 
-    vllm_runner_kwargs_: dict[str, Any] = {
-        "disable_mm_preprocessor_cache": True,
-    }
+    vllm_runner_kwargs_: dict[str, Any] = {"mm_processor_cache_gb": 0}
     if model_info.tokenizer:
         vllm_runner_kwargs_["tokenizer_name"] = model_info.tokenizer
     if model_info.tokenizer_mode:

tests/models/multimodal/processing/test_llama4.py

@@ -15,14 +15,14 @@ from ...utils import build_model_context
                          ["meta-llama/Llama-4-Scout-17B-16E-Instruct"])
 @pytest.mark.parametrize("mm_processor_kwargs", [{}])
 @pytest.mark.parametrize("num_imgs", [1, 5])
-@pytest.mark.parametrize("disable_mm_preprocessor_cache", [True, False])
+@pytest.mark.parametrize("mm_processor_cache_gb", [0, 4])
 @pytest.mark.parametrize("tokenized_prompt", [True, False])
 def test_processor_override(
     image_assets: ImageTestAssets,
     model_id: str,
     mm_processor_kwargs: dict,
     num_imgs: int,
-    disable_mm_preprocessor_cache: bool,
+    mm_processor_cache_gb: int,
     tokenized_prompt: bool,
 ):
     """Ensure llama4 processor works properly."""
@@ -30,7 +30,7 @@ def test_processor_override(
         model_id,
         mm_processor_kwargs=mm_processor_kwargs,
         limit_mm_per_prompt={"image": num_imgs},
-        disable_mm_preprocessor_cache=disable_mm_preprocessor_cache,
+        mm_processor_cache_gb=mm_processor_cache_gb,
     )
     processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
     config = processor.info.get_hf_config()

tests/models/utils.py

@@ -261,7 +261,7 @@ def build_model_context(
     model_config_kwargs: Optional[dict[str, Any]] = None,
     mm_processor_kwargs: Optional[dict[str, Any]] = None,
     limit_mm_per_prompt: Optional[dict[str, int]] = None,
-    disable_mm_preprocessor_cache: bool = True,
+    mm_processor_cache_gb: int = 0,
 ):
     """Creates an InputContext for a given model.
 
@@ -291,7 +291,7 @@ def build_model_context(
         seed=0,
         mm_processor_kwargs=mm_processor_kwargs,
         limit_mm_per_prompt=limit_mm_per_prompt,
-        disable_mm_preprocessor_cache=disable_mm_preprocessor_cache,
+        mm_processor_cache_gb=mm_processor_cache_gb,
         hf_overrides=model_info.hf_overrides,
         **model_config_kwargs,
     )

vllm/config.py

@@ -443,8 +443,15 @@ class ModelConfig:
     from `AutoProcessor.from_pretrained`. The available overrides depend on the
     model that is being run. For example, for Phi-3-Vision: `{"num_crops": 4}`.
     """
-    disable_mm_preprocessor_cache: bool = False
-    """If `True`, disable caching of the multi-modal processor."""
+    mm_processor_cache_gb: int = 4
+    """The size (in GiB) of the multi-modal processor cache, which is used to
+    avoid re-processing past multi-modal inputs.
+
+    This cache is duplicated for each API process and engine core process,
+    resulting in a total memory usage of
+    `mm_processor_cache_gb * (api_server_count + data_parallel_size)`.
+
+    Set to `0` to disable this cache completely (not recommended)."""
     override_neuron_config: dict[str, Any] = field(default_factory=dict)
     """Initialize non-default neuron config or override default neuron config
     that are specific to Neuron devices, this argument will be used to
@@ -881,17 +888,16 @@ class ModelConfig:
                 limit_per_prompt=self.limit_mm_per_prompt,
                 media_io_kwargs=self.media_io_kwargs,
                 mm_processor_kwargs=self.mm_processor_kwargs,
-                disable_mm_preprocessor_cache=self.
-                disable_mm_preprocessor_cache,
+                mm_processor_cache_gb=self.mm_processor_cache_gb,
                 interleave_mm_strings=self.interleave_mm_strings)
 
         return None
 
-    def set_disable_mm_preprocessor_cache(self, value: bool) -> None:
+    def set_mm_processor_cache_gb(self, value: int) -> None:
         mm_config = self.get_multimodal_config()
 
-        self.disable_mm_preprocessor_cache = value
-        mm_config.disable_mm_preprocessor_cache = value
+        self.mm_processor_cache_gb = value
+        mm_config.mm_processor_cache_gb = value
 
     def _get_encoder_config(self):
         return get_sentence_transformer_tokenizer_config(
@@ -1698,7 +1704,16 @@ class ModelConfig:
         if mm_config is None:
             return False
 
-        return not mm_config.disable_mm_preprocessor_cache
+        return mm_config.mm_processor_cache_gb > 0
+
+    @property
+    def enable_mm_processor_cache(self) -> bool:
+        """Whether the multi-modal processor cache should be enabled."""
+        mm_config = self.multimodal_config
+        if mm_config is None:
+            return False
+
+        return mm_config.mm_processor_cache_gb > 0
 
     @property
     def enable_mm_input_cache(self) -> bool:
@@ -1707,7 +1722,7 @@ class ModelConfig:
         if mm_config is None:
             return False
 
-        return not mm_config.disable_mm_preprocessor_cache
+        return mm_config.mm_processor_cache_gb > 0
 
     def get_mm_input_cache_gb(self) -> int:
         mm_config = self.multimodal_config
@@ -3391,9 +3406,15 @@ class MultiModalConfig:
     `{"num_crops": 4}`.
     """
 
-    disable_mm_preprocessor_cache: bool = False
+    mm_processor_cache_gb: int = 4
     """
-    If `True`, disable caching of the multi-modal processor.
+    The size (in GiB) of the multi-modal processor cache, which is used to
+
+    This cache is duplicated for each API process and engine core process,
+    resulting in a total memory usage of
+    `mm_processor_cache_gb * (api_server_count + data_parallel_size)`.
+
+    Set to `0` to disable this cache completely (not recommended).
     """
 
     interleave_mm_strings: bool = False

vllm/engine/arg_utils.py

@@ -358,8 +358,8 @@ class EngineArgs:
                                                       "media_io_kwargs")
     mm_processor_kwargs: Optional[Dict[str, Any]] = \
         MultiModalConfig.mm_processor_kwargs
-    disable_mm_preprocessor_cache: bool = \
-        MultiModalConfig.disable_mm_preprocessor_cache
+    disable_mm_preprocessor_cache: bool = False  # DEPRECATED
+    mm_processor_cache_gb: int = MultiModalConfig.mm_processor_cache_gb
     # LoRA fields
     enable_lora: bool = False
     enable_lora_bias: bool = LoRAConfig.bias_enabled
@@ -720,8 +720,11 @@ class EngineArgs:
             "--mm-processor-kwargs",
             **multimodal_kwargs["mm_processor_kwargs"])
         multimodal_group.add_argument(
-            "--disable-mm-preprocessor-cache",
-            **multimodal_kwargs["disable_mm_preprocessor_cache"])
+            "--mm-processor-cache-gb",
+            **multimodal_kwargs["mm_processor_cache_gb"])
+        multimodal_group.add_argument("--disable-mm-preprocessor-cache",
+                                      type=bool,
+                                      deprecated=True)
         multimodal_group.add_argument(
             "--interleave-mm-strings",
             **multimodal_kwargs["interleave_mm_strings"])
@@ -886,6 +889,23 @@ class EngineArgs:
             self.model = f"{MODEL_WEIGHTS_S3_BUCKET}/{self.model}"
             self.load_format = "runai_streamer"
 
+        if self.disable_mm_preprocessor_cache:
+            logger.warning(
+                "`--disable-mm-preprocessor-cache` is deprecated "
+                "and will be removed in v0.13. "
+                "Please use `--mm-processor-cache-gb 0` instead.", )
+
+            self.mm_processor_cache_gb = 0
+        elif envs.VLLM_MM_INPUT_CACHE_GIB != 4:
+            logger.warning(
+                "VLLM_MM_INPUT_CACHE_GIB` is deprecated "
+                "and will be removed in v0.13. "
+                "Please use `--mm-processor-cache-gb %d` instead.",
+                envs.VLLM_MM_INPUT_CACHE_GIB,
+            )
+
+            self.mm_processor_cache_gb = envs.VLLM_MM_INPUT_CACHE_GIB
+
         return ModelConfig(
             model=self.model,
             hf_config_path=self.hf_config_path,
@@ -922,7 +942,7 @@ class EngineArgs:
             use_async_output_proc=not self.disable_async_output_proc,
             config_format=self.config_format,
             mm_processor_kwargs=self.mm_processor_kwargs,
-            disable_mm_preprocessor_cache=self.disable_mm_preprocessor_cache,
+            mm_processor_cache_gb=self.mm_processor_cache_gb,
             override_neuron_config=self.override_neuron_config,
             override_pooler_config=self.override_pooler_config,
             logits_processor_pattern=self.logits_processor_pattern,
@@ -1234,13 +1254,13 @@ class EngineArgs:
             dp_supports_mm_processor_cache = (self.data_parallel_size == 1
                                               or data_parallel_external_lb)
             if (not dp_supports_mm_processor_cache
-                    and not model_config.disable_mm_preprocessor_cache):
+                    and model_config.mm_processor_cache_gb > 0):
                 logger.warning(
                     "Multi-modal processor cache is disabled because "
                     "it is not compatible with data parallelism when "
                     "there does not exist a one-to-one correspondance "
                     "between API and engine core processes.")
-                model_config.set_disable_mm_preprocessor_cache(True)
+                model_config.set_mm_processor_cache_gb(0)
 
         speculative_config = self.create_speculative_config(
             target_model_config=model_config,

vllm/entrypoints/cli/serve.py

@@ -138,13 +138,13 @@ def run_multi_api_server(args: argparse.Namespace):
     num_api_servers = args.api_server_count
     assert num_api_servers > 0
 
-    orig_disable_mm_preprocessor_cache = args.disable_mm_preprocessor_cache
+    orig_mm_processor_cache_gb = args.mm_processor_cache_gb
 
     if num_api_servers > 1:
         setup_multiprocess_prometheus()
 
         # Not compatible with API server scale-out
-        args.disable_mm_preprocessor_cache = True
+        args.mm_processor_cache_gb = 0
 
     listen_address, sock = setup_server(args)
 
@@ -161,8 +161,7 @@ def run_multi_api_server(args: argparse.Namespace):
             raise ValueError("VLLM_ALLOW_RUNTIME_LORA_UPDATING cannot be used "
                              "with api_server_count > 1")
 
-        if model_config.is_multimodal_model and not (
-                orig_disable_mm_preprocessor_cache):
+        if model_config.is_multimodal_model and orig_mm_processor_cache_gb > 0:
             logger.warning("Multi-modal processor cache is disabled because "
                            "it is not compatible with `api_server_count > 1`.")
 

vllm/envs.py

@@ -561,7 +561,7 @@ environment_variables: dict[str, Callable[[], Any]] = {
     "VLLM_VIDEO_LOADER_BACKEND":
     lambda: os.getenv("VLLM_VIDEO_LOADER_BACKEND", "opencv"),
 
-    # Cache size (in GiB per process) for multimodal input cache
+    # [DEPRECATED] Cache size (in GiB per process) for multimodal input cache
     # Default is 4 GiB per API process + 4 GiB per engine core process
     "VLLM_MM_INPUT_CACHE_GIB":
     lambda: int(os.getenv("VLLM_MM_INPUT_CACHE_GIB", "4")),

vllm/multimodal/registry.py

@@ -6,7 +6,6 @@ from typing import TYPE_CHECKING, Generic, Optional, Protocol, TypeVar
 
 import torch.nn as nn
 
-from vllm.envs import VLLM_MM_INPUT_CACHE_GIB
 from vllm.inputs import InputProcessingContext
 from vllm.logger import init_logger
 from vllm.transformers_utils.tokenizer import (AnyTokenizer,
@@ -96,11 +95,22 @@ class MultiModalRegistry:
         self._processor_factories = ClassRegistry[nn.Module,
                                                   _ProcessorFactories]()
 
-        self._processing_cache = ProcessingCache(VLLM_MM_INPUT_CACHE_GIB)
+        self._processor_cache: Optional[ProcessingCache] = None
+
+    def _get_processor_cache(self, model_config: "ModelConfig"):
+        capacity_gb = model_config.mm_processor_cache_gb
+        if capacity_gb is None:
+            return None  # Overrides `disable_cache` argument
+
+        if self._processor_cache is None:
+            self._processor_cache = ProcessingCache(capacity_gb)
+
+        return self._processor_cache
 
     def reset_processor_cache(self) -> bool:
         """Reset the multi-modal processing cache."""
-        self._processing_cache.reset()
+        if self._processor_cache:
+            self._processor_cache.reset()
 
         return True  # Success
 
@@ -244,14 +254,14 @@ class MultiModalRegistry:
         if tokenizer is None and not model_config.skip_tokenizer_init:
             tokenizer = cached_tokenizer_from_config(model_config)
         if disable_cache is None:
-            mm_config = model_config.get_multimodal_config()
-            disable_cache = mm_config.disable_mm_preprocessor_cache
+            disable_cache = not model_config.enable_mm_processor_cache
 
         model_cls = self._get_model_cls(model_config)
         factories = self._processor_factories[model_cls]
 
         ctx = InputProcessingContext(model_config, tokenizer)
-        cache = None if disable_cache else self._processing_cache
+        cache = None if disable_cache else self._get_processor_cache(
+            model_config)
 
         return factories.build_processor(ctx, cache=cache)
 

vllm/v1/core/kv_cache_utils.py

@@ -430,7 +430,7 @@ def _gen_mm_extra_hash_keys(request: Request, start_token_idx: int,
         raise ValueError(
             "The number of multi-modal positions and hashes must match. This "
             "is likely because you did not enable MM hashing. "
-            "Please set `disable_mm_preprocessor_cache=False`.")
+            "Please set `mm_processor_cache_gb > 0`.")
 
     # Note that we assume mm_positions is sorted by offset.
     # We do not need to check all mm inputs if the start token index is out of