[Perf] Validate @config in pre-commit instead of dynamically (#20200)

Signed-off-by: Lionel Villard <villard@us.ibm.com>

vllm/config.py

@@ -18,7 +18,7 @@ from functools import cached_property
 from importlib.util import find_spec
 from pathlib import Path
 from typing import (TYPE_CHECKING, Any, Callable, ClassVar, Literal, Optional,
-                    Protocol, TypeVar, Union, cast, get_args, get_origin)
+                    Protocol, TypeVar, Union, cast, get_args)
 
 import regex as re
 import torch
@@ -193,28 +193,10 @@ def config(cls: ConfigT) -> ConfigT:
     (i.e. `ConfigT(**json.loads(cli_arg))`). However, if a particular `ConfigT`
     requires custom construction from CLI (i.e. `CompilationConfig`), it can
     have a `from_cli` method, which will be called instead.
+
+    Config validation is performed by the tools/validate_config.py
+    script, which is invoked during the pre-commit checks.
     """
-    if not is_dataclass(cls):
-        raise TypeError("The decorated class must be a dataclass.")
-    attr_docs = get_attr_docs(cls)
-    for f in fields(cls):
-        if f.init and f.default is MISSING and f.default_factory is MISSING:
-            raise ValueError(
-                f"Field '{f.name}' in {cls.__name__} must have a default value."
-            )
-
-        if f.name not in attr_docs:
-            raise ValueError(
-                f"Field '{f.name}' in {cls.__name__} must have a docstring.")
-
-        if get_origin(f.type) is Union:
-            args = get_args(f.type)
-            literal_args = [arg for arg in args if get_origin(arg) is Literal]
-            if len(literal_args) > 1:
-                raise ValueError(
-                    f"Field '{f.name}' in {cls.__name__} must use a single "
-                    "Literal type. Please use 'Literal[Literal1, Literal2]' "
-                    "instead of 'Union[Literal1, Literal2]'.")
     return cls
 
 
@@ -1798,7 +1780,7 @@ class ParallelConfig:
     eplb_step_interval: int = 3000
     """
     Interval for rearranging experts in expert parallelism.
-    
+
     Note that if this is greater than the EPLB window size, only the metrics
     of the last `eplb_window_size` steps will be used for rearranging experts.
     """