Turn @config into a dataclass_transform

tests/engine/test_arg_utils.py

@@ -3,11 +3,11 @@
 
 import json
 from argparse import ArgumentError
-from contextlib import nullcontext
-from dataclasses import dataclass, field
+from contextlib import AbstractContextManager, nullcontext
 from typing import Annotated, Literal
 
 import pytest
+from pydantic import Field
 
 from vllm.config import AttentionConfig, CompilationConfig, config
 from vllm.engine.arg_utils import (
@@ -96,46 +96,44 @@ def test_get_type(type_hints, type, expected):
     ],
 )
 def test_literal_to_kwargs(type_hints, expected):
-    context = nullcontext()
+    context: AbstractContextManager[object] = nullcontext()
     if expected is Exception:
         context = pytest.raises(expected)
     with context:
         assert literal_to_kwargs(type_hints) == expected
 
 
 @config
-@dataclass
 class NestedConfig:
     field: int = 1
     """field"""
 
 
 @config
-@dataclass
 class DummyConfig:
     regular_bool: bool = True
     """Regular bool with default True"""
     optional_bool: bool | None = None
     """Optional bool with default None"""
     optional_literal: Literal["x", "y"] | None = None
     """Optional literal with default None"""
-    tuple_n: tuple[int, ...] = field(default_factory=lambda: (1, 2, 3))
+    tuple_n: tuple[int, ...] = Field(default_factory=lambda: (1, 2, 3))
     """Tuple with variable length"""
-    tuple_2: tuple[int, int] = field(default_factory=lambda: (1, 2))
+    tuple_2: tuple[int, int] = Field(default_factory=lambda: (1, 2))
     """Tuple with fixed length"""
-    list_n: list[int] = field(default_factory=lambda: [1, 2, 3])
+    list_n: list[int] = Field(default_factory=lambda: [1, 2, 3])
     """List with variable length"""
-    list_literal: list[Literal[1, 2]] = field(default_factory=list)
+    list_literal: list[Literal[1, 2]] = Field(default_factory=list)
     """List with literal choices"""
-    list_union: list[str | type[object]] = field(default_factory=list)
+    list_union: list[str | type[object]] = Field(default_factory=list)
     """List with union type"""
-    set_n: set[int] = field(default_factory=lambda: {1, 2, 3})
+    set_n: set[int] = Field(default_factory=lambda: {1, 2, 3})
     """Set with variable length"""
     literal_literal: Literal[Literal[1], Literal[2]] = 1
     """Literal of literals with default 1"""
-    json_tip: dict = field(default_factory=dict)
+    json_tip: dict = Field(default_factory=dict)
     """Dict which will be JSON in CLI"""
-    nested_config: NestedConfig = field(default_factory=NestedConfig)
+    nested_config: NestedConfig = Field(default_factory=NestedConfig)
     """Nested config"""
 
 
@@ -195,7 +193,7 @@ def test_get_kwargs():
     json_tip = "Should either be a valid JSON string or JSON keys"
     assert json_tip in kwargs["json_tip"]["help"]
     # nested config should construct the nested config
-    assert kwargs["nested_config"]["type"]('{"field": 2}') == NestedConfig(2)
+    assert kwargs["nested_config"]["type"]('{"field": 2}') == NestedConfig(2)  # type: ignore[call-arg]
 
 
 @pytest.mark.parametrize(

tests/test_config.py

@@ -66,9 +66,6 @@ class _TestConfigFields:
 
 
 def test_get_field():
-    with pytest.raises(ValueError):
-        get_field(_TestConfigFields, "a")
-
     b = get_field(_TestConfigFields, "b")
     assert isinstance(b, Field)
     assert b.default is MISSING
@@ -188,7 +185,7 @@ def test_get_pooling_config():
 )
 def test_get_pooling_config_from_args():
     model_id = "sentence-transformers/all-MiniLM-L12-v2"
-    pooler_config = PoolerConfig(seq_pooling_type="CLS", normalize=True)
+    pooler_config = PoolerConfig(seq_pooling_type="CLS", use_activation=False)
     model_config = ModelConfig(model_id, pooler_config=pooler_config)
 
     assert asdict(model_config.pooler_config) == asdict(pooler_config)

tests/tools/test_config_validator.py

@@ -7,31 +7,22 @@
 
 from tools.pre_commit.validate_config import validate_ast
 
-_TestConfig1 = """
+_TestConfig1 = '''
 @config
 class _TestConfig1:
-    pass
-"""
-
-_TestConfig2 = '''
-@config
-@dataclass
-class _TestConfig2:
     a: int
     """docstring"""
 '''
 
-_TestConfig3 = """
+_TestConfig2 = """
 @config
-@dataclass
-class _TestConfig3:
+class _TestConfig2:
     a: int = 1
 """
 
-_TestConfig4 = '''
+_TestConfig3 = '''
 @config
-@dataclass
-class _TestConfig4:
+class _TestConfig3:
     a: Union[Literal[1], Literal[2]] = 1
     """docstring"""
 '''
@@ -40,10 +31,9 @@ class _TestConfig4:
 @pytest.mark.parametrize(
     ("test_config", "expected_error"),
     [
-        (_TestConfig1, "must be a dataclass"),
-        (_TestConfig2, "must have a default"),
-        (_TestConfig3, "must have a docstring"),
-        (_TestConfig4, "must use a single Literal"),
+        (_TestConfig1, "must have a default"),
+        (_TestConfig2, "must have a docstring"),
+        (_TestConfig3, "must use a single Literal"),
     ],
 )
 def test_config(test_config, expected_error):

tests/v1/e2e/test_spec_decode.py

@@ -766,8 +766,8 @@ def assert_draft_model_correctness(args: ArgsTest, enforce_eager: bool):
             "max_model_len": args.max_model_len,
             "enforce_eager": enforce_eager,
             "draft_tensor_parallel_size": args.draft_tensor_parallel_size,
-            "max_num_seqs": 100,  # limit cudagraph capture runtime
         },
+        max_num_seqs=100,  # limit cudagraph capture runtime
         max_model_len=args.max_model_len,
         gpu_memory_utilization=args.gpu_memory_utilization,
         tensor_parallel_size=args.target_tensor_parallel_size,

tests/v1/structured_output/test_backend_guidance.py

@@ -26,11 +26,8 @@ def test_backend_guidance_rollback_terminated():
     # guidance backend. In that case we are in a stopped state, but
     # it should be reverted in case EOS is not accepted by the target
     # model.
-    vllm_config = VllmConfig(
-        decoding_config=StructuredOutputsConfig(
-            backend="guidance",
-        )
-    )
+    structured_outputs_config = StructuredOutputsConfig(backend="guidance")
+    vllm_config = VllmConfig(structured_outputs_config=structured_outputs_config)
     tokenizer = AutoTokenizer.from_pretrained(TOKENIZER)
 
     backend = GuidanceBackend(

tools/pre_commit/validate_config.py

@@ -54,24 +54,18 @@ class ConfigValidator(ast.NodeVisitor):
     def __init__(self): ...
 
     def visit_ClassDef(self, node):
-        # Validate class with both @config and @dataclass decorators
-        decorators = [
-            id
-            for d in node.decorator_list
-            if (
-                isinstance(d, ast.Name)
-                and ((id := d.id) == "config" or id == "dataclass")
-            )
-            or (
-                isinstance(d, ast.Call)
-                and (isinstance(d.func, ast.Name) and (id := d.func.id) == "dataclass")
-            )
-        ]
-
-        if set(decorators) == {"config", "dataclass"}:
+        # Validate classes with a @config decorator
+        decorators = set()
+        for decorator in node.decorator_list:
+            if isinstance(decorator, ast.Call):
+                decorator = decorator.func
+            if isinstance(decorator, ast.Name) and decorator.id == "config":
+                decorators.add(decorator.id)
+
+        if decorators == {"config"}:
             validate_class(node)
-        elif set(decorators) == {"config"}:
-            fail(f"Class {node.name} with config decorator must be a dataclass.", node)
+        elif "config" in decorators:
+            fail(f"config decorator for {node.name} should be used alone", node)
 
         self.generic_visit(node)
 

vllm/config/__init__.py

@@ -36,6 +36,7 @@
     config,
     get_attr_docs,
     is_init_field,
+    replace,
     update_config,
 )
 from vllm.config.vllm import (
@@ -101,6 +102,7 @@
     "config",
     "get_attr_docs",
     "is_init_field",
+    "replace",
     "update_config",
     # From vllm.config.vllm
     "VllmConfig",

vllm/config/attention.py

@@ -4,14 +4,12 @@
 from typing import Any, Literal
 
 from pydantic import field_validator
-from pydantic.dataclasses import dataclass
 
 from vllm.config.utils import config
 from vllm.v1.attention.backends.registry import AttentionBackendEnum
 
 
 @config
-@dataclass
 class AttentionConfig:
     """Configuration for attention mechanisms in vLLM."""
 

vllm/config/cache.py

@@ -6,7 +6,6 @@
 from typing import TYPE_CHECKING, Any, Literal
 
 from pydantic import Field, SkipValidation, field_validator
-from pydantic.dataclasses import dataclass
 
 from vllm.config.utils import config
 from vllm.logger import init_logger
@@ -37,7 +36,6 @@
 
 
 @config
-@dataclass
 class CacheConfig:
     """Configuration for the KV cache."""
 

vllm/config/compilation.py

@@ -8,8 +8,7 @@
 from pathlib import Path
 from typing import TYPE_CHECKING, Any, ClassVar, Literal
 
-from pydantic import ConfigDict, Field, TypeAdapter, field_validator
-from pydantic.dataclasses import dataclass
+from pydantic import Field, TypeAdapter, field_validator
 
 import vllm.envs as envs
 from vllm.compilation.inductor_pass import CallableInductorPass, InductorPass
@@ -96,7 +95,6 @@ def __str__(self) -> str:
 
 
 @config
-@dataclass(config=ConfigDict(extra="forbid"))
 class PassConfig:
     """Configuration for custom Inductor passes.
 
@@ -267,7 +265,6 @@ class DynamicShapesType(str, enum.Enum):
 
 
 @config
-@dataclass(config=ConfigDict(extra="forbid"))
 class DynamicShapesConfig:
     """Configuration to control/debug torch compile dynamic shapes."""
 
@@ -311,7 +308,6 @@ def compute_hash(self) -> str:
 
 
 @config
-@dataclass(config=ConfigDict(extra="forbid"))
 class CompilationConfig:
     """Configuration for compilation.
 

vllm/config/device.py

@@ -6,16 +6,14 @@
 
 import torch
 from pydantic import ConfigDict, SkipValidation
-from pydantic.dataclasses import dataclass
 
 from vllm.config.utils import config
 from vllm.utils.hashing import safe_hash
 
 Device = Literal["auto", "cuda", "cpu", "tpu", "xpu"]
 
 
-@config
-@dataclass(config=ConfigDict(arbitrary_types_allowed=True))
+@config(config=ConfigDict(arbitrary_types_allowed=True))
 class DeviceConfig:
     """Configuration for the device to use for vLLM execution."""
 

vllm/config/ec_transfer.py

@@ -5,8 +5,6 @@
 from dataclasses import field
 from typing import Any, Literal, get_args
 
-from pydantic.dataclasses import dataclass
-
 from vllm.config.utils import config
 
 ECProducer = Literal["ec_producer"]
@@ -15,7 +13,6 @@
 
 
 @config
-@dataclass
 class ECTransferConfig:
     """Configuration for distributed EC cache transfer."""
 

vllm/config/kv_events.py

@@ -5,13 +5,11 @@
 from typing import Literal
 
 from pydantic import Field
-from pydantic.dataclasses import dataclass
 
 from vllm.config.utils import config
 
 
 @config
-@dataclass
 class KVEventsConfig:
     """Configuration for KV event publishing."""
 

vllm/config/kv_transfer.py

@@ -5,8 +5,6 @@
 from dataclasses import field
 from typing import Any, Literal, get_args
 
-from pydantic.dataclasses import dataclass
-
 from vllm.config.utils import config
 from vllm.utils.hashing import safe_hash
 
@@ -16,7 +14,6 @@
 
 
 @config
-@dataclass
 class KVTransferConfig:
     """Configuration for distributed KV cache transfer."""
 

vllm/config/load.py

@@ -4,7 +4,6 @@
 from typing import TYPE_CHECKING, Any
 
 from pydantic import Field, field_validator
-from pydantic.dataclasses import dataclass
 
 from vllm.config.utils import config
 from vllm.logger import init_logger
@@ -21,7 +20,6 @@
 
 
 @config
-@dataclass
 class LoadConfig:
     """Configuration for loading the model weights."""
 

vllm/config/lora.py

@@ -5,7 +5,6 @@
 
 import torch
 from pydantic import ConfigDict, Field, model_validator
-from pydantic.dataclasses import dataclass
 from typing_extensions import Self
 
 from vllm.config.utils import config
@@ -26,8 +25,7 @@
 LoRAExtraVocabSize = Literal[256, 512]
 
 
-@config
-@dataclass(config=ConfigDict(arbitrary_types_allowed=True))
+@config(config=ConfigDict(arbitrary_types_allowed=True))
 class LoRAConfig:
     """Configuration for LoRA."""