[FEATURE]: Use pydantic validation in `multimodal.py` config (#26629)

Signed-off-by: Anand Roy <86306690+andycandy@users.noreply.github.com>
Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
Co-authored-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>

vllm/config/multimodal.py

@@ -3,10 +3,9 @@
 
 import hashlib
 from collections.abc import Mapping
-from dataclasses import field
 from typing import Any, Literal, TypeAlias
 
-from pydantic import ConfigDict, Field, field_validator
+from pydantic import ConfigDict, Field, field_validator, model_validator
 from pydantic.dataclasses import dataclass
 
 from vllm.config.utils import config
@@ -55,7 +54,7 @@ DummyOptions: TypeAlias = (
 class MultiModalConfig:
     """Controls the behavior of multimodal models."""
 
-    limit_per_prompt: dict[str, DummyOptions] = field(default_factory=dict)
+    limit_per_prompt: dict[str, DummyOptions] = Field(default_factory=dict)
     """The maximum number of input items and options allowed per 
         prompt for each modality.
     Defaults to 999 for each modality.
@@ -71,7 +70,7 @@ class MultiModalConfig:
         {"image": 16, "video": {"count": 1, "num_frames": 32, "width": 512, 
         "height": 512}}
     """
-    media_io_kwargs: dict[str, dict[str, Any]] = field(default_factory=dict)
+    media_io_kwargs: dict[str, dict[str, Any]] = Field(default_factory=dict)
     """Additional args passed to process media inputs, keyed by modalities.
     For example, to set num_frames for video, set
     `--media-io-kwargs '{"video": {"num_frames": 40} }'`"""
@@ -84,7 +83,7 @@ class MultiModalConfig:
 
     For example, for Phi-3-Vision:
     `{"num_crops": 4}`."""
-    mm_processor_cache_gb: float = 4
+    mm_processor_cache_gb: float = Field(default=4, ge=0)
     """The size (in GiB) of the multi-modal processor cache, which is used to
     avoid re-processing past multi-modal inputs.
 
@@ -96,7 +95,7 @@ class MultiModalConfig:
     mm_processor_cache_type: MMCacheType = "lru"
     """Type of cache to use for the multi-modal preprocessor/mapper. If `shm`,
     use shared memory FIFO cache. If `lru`, use mirrored LRU cache."""
-    mm_shm_cache_max_object_size_mb: int = 128
+    mm_shm_cache_max_object_size_mb: int = Field(default=128, ge=0)
     """Size limit (in MiB) for each object stored in the multi-modal processor
     shared memory cache. Only effective when `mm_processor_cache_type` is
     `"shm"`."""
@@ -123,7 +122,7 @@ class MultiModalConfig:
     This reduces engine startup time but shifts the responsibility to users for
     estimating the peak memory usage of the activation of multimodal encoder and
     embedding cache."""
-    video_pruning_rate: float | None = None
+    video_pruning_rate: float | None = Field(default=None, ge=0.0, lt=1.0)
     """Sets pruning rate for video pruning via Efficient Video Sampling.
     Value sits in range [0;1) and determines fraction of media tokens
     from each video to be pruned.
@@ -149,6 +148,18 @@ class MultiModalConfig:
                 value[k] = BaseDummyOptions(**v)
         return value
 
+    @model_validator(mode="after")
+    def _validate_multimodal_config(self):
+        if self.mm_processor_cache_type != "shm" and (
+            self.mm_shm_cache_max_object_size_mb
+            != MultiModalConfig.mm_shm_cache_max_object_size_mb
+        ):
+            raise ValueError(
+                "'mm_shm_cache_max_object_size_mb' should only be set when "
+                "'mm_processor_cache_type' is 'shm'."
+            )
+        return self
+
     def compute_hash(self) -> str:
         """
         WARNING: Whenever a new field is added to this config,