[Feature] Use pydantic validation in lora.py and load.py configs (#26413)

Signed-off-by: simondanielsson <simon.danielsson99@hotmail.com>

vllm/config/load.py

@@ -2,9 +2,9 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import hashlib
-from dataclasses import field
 from typing import TYPE_CHECKING, Any, Optional, Union
 
+from pydantic import Field, field_validator
 from pydantic.dataclasses import dataclass
 
 from vllm.config.utils import config
@@ -64,7 +64,7 @@ class LoadConfig:
       was quantized using torchao and saved using safetensors.
       Needs torchao >= 0.14.0
     """
-    model_loader_extra_config: Union[dict, TensorizerConfig] = field(
+    model_loader_extra_config: Union[dict, TensorizerConfig] = Field(
         default_factory=dict
     )
     """Extra config for model loader. This will be passed to the model loader
@@ -72,7 +72,9 @@ class LoadConfig:
     device: Optional[str] = None
     """Device to which model weights will be loaded, default to
     device_config.device"""
-    ignore_patterns: Optional[Union[list[str], str]] = None
+    ignore_patterns: Union[list[str], str] = Field(
+        default_factory=lambda: ["original/**/*"]
+    )
     """The list of patterns to ignore when loading the model. Default to
     "original/**/*" to avoid repeated loading of llama's checkpoints."""
     use_tqdm_on_load: bool = True
@@ -107,12 +109,18 @@ class LoadConfig:
         hash_str = hashlib.md5(str(factors).encode(), usedforsecurity=False).hexdigest()
         return hash_str
 
-    def __post_init__(self):
-        self.load_format = self.load_format.lower()
-        if self.ignore_patterns is not None and len(self.ignore_patterns) > 0:
+    @field_validator("load_format", mode="after")
+    def _lowercase_load_format(cls, load_format: str) -> str:
+        return load_format.lower()
+
+    @field_validator("ignore_patterns", mode="after")
+    def _validate_ignore_patterns(
+        cls, ignore_patterns: Union[list[str], str]
+    ) -> Union[list[str], str]:
+        if ignore_patterns != ["original/**/*"] and len(ignore_patterns) > 0:
             logger.info(
                 "Ignoring the following patterns when downloading weights: %s",
-                self.ignore_patterns,
+                ignore_patterns,
             )
-        else:
-            self.ignore_patterns = ["original/**/*"]
+
+        return ignore_patterns

vllm/config/lora.py

@@ -5,8 +5,9 @@ import hashlib
 from typing import TYPE_CHECKING, Any, ClassVar, Literal, Optional, Union
 
 import torch
-from pydantic import ConfigDict
+from pydantic import ConfigDict, Field, model_validator
 from pydantic.dataclasses import dataclass
+from typing_extensions import Self
 
 import vllm.envs as envs
 from vllm.config.utils import config
@@ -23,6 +24,8 @@ else:
 logger = init_logger(__name__)
 
 LoRADType = Literal["auto", "float16", "bfloat16"]
+MaxLoRARanks = Literal[1, 8, 16, 32, 64, 128, 256, 320, 512]
+LoRAExtraVocabSize = Literal[256, 512]
 
 
 @config
@@ -30,9 +33,9 @@ LoRADType = Literal["auto", "float16", "bfloat16"]
 class LoRAConfig:
     """Configuration for LoRA."""
 
-    max_lora_rank: int = 16
+    max_lora_rank: MaxLoRARanks = 16
     """Max LoRA rank."""
-    max_loras: int = 1
+    max_loras: int = Field(default=1, ge=1)
     """Max number of LoRAs in a single batch."""
     fully_sharded_loras: bool = False
     """By default, only half of the LoRA computation is sharded with tensor
@@ -44,7 +47,14 @@ class LoRAConfig:
     `max_loras`."""
     lora_dtype: Union[torch.dtype, LoRADType] = "auto"
     """Data type for LoRA. If auto, will default to base model dtype."""
-    lora_extra_vocab_size: int = 256
+    lora_extra_vocab_size: LoRAExtraVocabSize = Field(
+        default=256,
+        deprecated=(
+            "`lora_extra_vocab_size` is deprecated and will be removed "
+            "in v0.12.0. Additional vocabulary support for "
+            "LoRA adapters is being phased out."
+        ),
+    )
     """(Deprecated) Maximum size of extra vocabulary that can be present in a 
     LoRA adapter. Will be removed in v0.12.0."""
     lora_vocab_padding_size: ClassVar[int] = (
@@ -60,7 +70,10 @@ class LoRAConfig:
     per prompt. When run in offline mode, the lora IDs for n modalities
     will be automatically assigned to 1-n with the names of the modalities
     in alphabetic order."""
-    bias_enabled: bool = False
+    bias_enabled: bool = Field(
+        default=False,
+        deprecated="`bias_enabled` is deprecated and will be removed in v0.12.0.",
+    )
     """[DEPRECATED] Enable bias for LoRA adapters. This option will be
     removed in v0.12.0."""
 
@@ -87,36 +100,8 @@ class LoRAConfig:
         hash_str = hashlib.md5(str(factors).encode(), usedforsecurity=False).hexdigest()
         return hash_str
 
-    def __post_init__(self):
-        # Deprecation warning for lora_extra_vocab_size
-        logger.warning(
-            "`lora_extra_vocab_size` is deprecated and will be removed "
-            "in v0.12.0. Additional vocabulary support for "
-            "LoRA adapters is being phased out."
-        )
-
-        # Deprecation warning for enable_lora_bias
-        if self.bias_enabled:
-            logger.warning(
-                "`enable_lora_bias` is deprecated and will be removed in v0.12.0."
-            )
-
-        # Setting the maximum rank to 512 should be able to satisfy the vast
-        # majority of applications.
-        possible_max_ranks = (1, 8, 16, 32, 64, 128, 256, 320, 512)
-        possible_lora_extra_vocab_size = (256, 512)
-        if self.max_lora_rank not in possible_max_ranks:
-            raise ValueError(
-                f"max_lora_rank ({self.max_lora_rank}) must be one of "
-                f"{possible_max_ranks}."
-            )
-        if self.lora_extra_vocab_size not in possible_lora_extra_vocab_size:
-            raise ValueError(
-                f"lora_extra_vocab_size ({self.lora_extra_vocab_size}) "
-                f"must be one of {possible_lora_extra_vocab_size}."
-            )
-        if self.max_loras < 1:
-            raise ValueError(f"max_loras ({self.max_loras}) must be >= 1.")
+    @model_validator(mode="after")
+    def _validate_lora_config(self) -> Self:
         if self.max_cpu_loras is None:
             self.max_cpu_loras = self.max_loras
         elif self.max_cpu_loras < self.max_loras:
@@ -125,6 +110,8 @@ class LoRAConfig:
                 f"max_loras ({self.max_loras})"
             )
 
+        return self
+
     def verify_with_cache_config(self, cache_config: CacheConfig):
         if cache_config.cpu_offload_gb > 0 and not envs.VLLM_USE_V1:
             raise ValueError("V0 LoRA does not support CPU offload, please use V1.")

vllm/config/utils.py

@@ -11,6 +11,7 @@ from itertools import pairwise
 from typing import TYPE_CHECKING, Any, Protocol, TypeVar
 
 import regex as re
+from pydantic.fields import FieldInfo
 from typing_extensions import runtime_checkable
 
 if TYPE_CHECKING:
@@ -50,7 +51,14 @@ def get_field(cls: ConfigType, name: str) -> Field:
     if (default_factory := named_field.default_factory) is not MISSING:
         return field(default_factory=default_factory)
     if (default := named_field.default) is not MISSING:
+        if isinstance(default, FieldInfo):
+            # Handle pydantic.Field defaults
+            if default.default_factory is not None:
+                return field(default_factory=default.default_factory)
+            else:
+                default = default.default
         return field(default=default)
+
     raise ValueError(
         f"{cls.__name__}.{name} must have a default value or default factory."
     )

vllm/engine/arg_utils.py

@@ -452,7 +452,7 @@ class EngineArgs:
     num_gpu_blocks_override: Optional[int] = CacheConfig.num_gpu_blocks_override
     num_lookahead_slots: int = SchedulerConfig.num_lookahead_slots
     model_loader_extra_config: dict = get_field(LoadConfig, "model_loader_extra_config")
-    ignore_patterns: Optional[Union[str, list[str]]] = LoadConfig.ignore_patterns
+    ignore_patterns: Union[str, list[str]] = get_field(LoadConfig, "ignore_patterns")
 
     enable_chunked_prefill: Optional[bool] = SchedulerConfig.enable_chunked_prefill
     disable_chunked_mm_input: bool = SchedulerConfig.disable_chunked_mm_input