Improve-mm-and-pooler-and-decoding-configs (#16789)

Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>

vllm/config.py

@@ -17,7 +17,7 @@ from dataclasses import (MISSING, dataclass, field, fields, is_dataclass,
 from importlib.util import find_spec
 from pathlib import Path
 from typing import (TYPE_CHECKING, Any, Callable, ClassVar, Final, Literal,
-                    Optional, Protocol, TypeVar, Union)
+                    Optional, Protocol, TypeVar, Union, get_args)
 
 import torch
 from pydantic import BaseModel, Field, PrivateAttr
@@ -2725,6 +2725,7 @@ class PromptAdapterConfig:
                                                 self.prompt_adapter_dtype)
 
 
+@config
 @dataclass
 class MultiModalConfig:
     """Controls the behavior of multimodal models."""
@@ -2732,6 +2733,8 @@ class MultiModalConfig:
     limit_per_prompt: Mapping[str, int] = field(default_factory=dict)
     """
     The maximum number of input items allowed per prompt for each modality.
+    This should be a JSON string that will be parsed into a dictionary.
+    Defaults to 1 (V0) or 999 (V1) for each modality.
     """
 
     def compute_hash(self) -> str:
@@ -2753,24 +2756,20 @@ class MultiModalConfig:
                                usedforsecurity=False).hexdigest()
         return hash_str
 
-    def get_default_limit_per_prompt(self) -> int:
-        """
-        Return the default number of input items allowed per prompt
-        for any modality if not specified by the user.
-        """
-        return 999 if envs.VLLM_USE_V1 else 1
-
     def get_limit_per_prompt(self, modality: str) -> int:
         """
         Get the maximum number of input items allowed per prompt
         for the given modality.
         """
-        default = self.get_default_limit_per_prompt()
-        return self.limit_per_prompt.get(modality, default)
+        return self.limit_per_prompt.get(
+            modality,
+            999 if envs.VLLM_USE_V1 else 1,
+        )
 
     # TODO: Add configs to init vision tower or not.
 
 
+@config
 @dataclass
 class PoolerConfig:
     """Controls the behavior of output pooling in pooling models."""
@@ -3095,15 +3094,28 @@ def get_served_model_name(model: str,
     return served_model_name
 
 
+GuidedDecodingBackendV0 = Literal["auto", "outlines", "lm-format-enforcer",
+                                  "xgrammar"]
+GuidedDecodingBackendV1 = Literal["auto", "xgrammar", "guidance"]
+
+
+@config
 @dataclass
 class DecodingConfig:
-    """Dataclass which contains the decoding strategy of the engine"""
+    """Dataclass which contains the decoding strategy of the engine."""
 
-    # Which guided decoding algo to use.
-    # 'outlines' / 'lm-format-enforcer' / 'xgrammar'
-    guided_decoding_backend: str = "auto" if envs.VLLM_USE_V1 else "xgrammar"
+    guided_decoding_backend: Union[
+        GuidedDecodingBackendV0,
+        GuidedDecodingBackendV1] = "auto" if envs.VLLM_USE_V1 else "xgrammar"
+    """Which engine will be used for guided decoding (JSON schema / regex etc)
+    by default. With "auto", we will make opinionated choices based on request
+    contents and what the backend libraries currently support, so the behavior
+    is subject to change in each release."""
 
     reasoning_backend: Optional[str] = None
+    """Select the reasoning parser depending on the model that you're using.
+    This is used to parse the reasoning content into OpenAI API format.
+    Required for `--enable-reasoning`."""
 
     def compute_hash(self) -> str:
         """
@@ -3125,17 +3137,12 @@ class DecodingConfig:
         return hash_str
 
     def __post_init__(self):
-        v0_valid_guided_backends = [
-            'outlines', 'lm-format-enforcer', 'xgrammar', 'auto'
-        ]
-        v1_valid_guided_backends = ['xgrammar', 'guidance', 'auto']
-
         backend = GuidedDecodingParams(
             backend=self.guided_decoding_backend).backend_name
         if envs.VLLM_USE_V1:
-            valid_guided_backends = v1_valid_guided_backends
+            valid_guided_backends = get_args(GuidedDecodingBackendV1)
         else:
-            valid_guided_backends = v0_valid_guided_backends
+            valid_guided_backends = get_args(GuidedDecodingBackendV0)
         if backend not in valid_guided_backends:
             raise ValueError(f"Invalid guided_decoding_backend '{backend}',"
                              f" must be one of {valid_guided_backends}")