Enable Pydantic mypy checks and convert configs to Pydantic dataclasses (#17599)

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

.pre-commit-config.yaml

@@ -58,7 +58,7 @@ repos:
     entry: tools/mypy.sh 0 "local"
     language: python
     types: [python]
-    additional_dependencies: &mypy_deps [mypy==1.11.1, types-cachetools, types-setuptools, types-PyYAML, types-requests]
+    additional_dependencies: &mypy_deps [mypy==1.11.1, types-cachetools, types-setuptools, types-PyYAML, types-requests, pydantic]
     stages: [pre-commit] # Don't run in CI
   - id: mypy-3.9 # TODO: Use https://github.com/pre-commit/mirrors-mypy when mypy setup is less awkward
     name: Run mypy for Python 3.9

pyproject.toml

@@ -110,6 +110,7 @@ ignore = [
 ]
 
 [tool.mypy]
+plugins = ['pydantic.mypy']
 ignore_missing_imports = true
 check_untyped_defs = true
 follow_imports = "silent"

tests/lora/test_quant_model.py

@@ -24,16 +24,16 @@ if current_platform.is_rocm():
     MODELS = [
         ModelWithQuantization(
             model_path="TheBloke/TinyLlama-1.1B-Chat-v0.3-GPTQ",
-            quantization="GPTQ"),
+            quantization="gptq"),
     ]
 else:
     MODELS = [
         ModelWithQuantization(
             model_path="TheBloke/TinyLlama-1.1B-Chat-v0.3-AWQ",
-            quantization="AWQ"),
+            quantization="awq"),
         ModelWithQuantization(
             model_path="TheBloke/TinyLlama-1.1B-Chat-v0.3-GPTQ",
-            quantization="GPTQ"),
+            quantization="gptq"),
     ]
 
 
@@ -100,7 +100,7 @@ def test_quant_model_lora(tinyllama_lora_files, model):
             "#ff8050",
             "#ff8080",
         ]
-    elif model.quantization == "AWQ":
+    elif model.quantization == "awq":
         expected_no_lora_output = [
             "I'm sorry, I don't understand",
             "I'm sorry, I don't understand",
@@ -109,7 +109,7 @@ def test_quant_model_lora(tinyllama_lora_files, model):
             "#f07700: A v",
             "#f00000: A v",
         ]
-    elif model.quantization == "GPTQ":
+    elif model.quantization == "gptq":
         expected_no_lora_output = [
             "I'm sorry, I don't have",
             "I'm sorry, I don't have",
@@ -122,7 +122,7 @@ def test_quant_model_lora(tinyllama_lora_files, model):
     def expect_match(output, expected_output):
         # HACK: GPTQ lora outputs are just incredibly unstable.
         # Assert that the outputs changed.
-        if (model.quantization == "GPTQ"
+        if (model.quantization == "gptq"
                 and expected_output is expected_lora_output):
             assert output != expected_no_lora_output
             for i, o in enumerate(output):
@@ -172,7 +172,7 @@ def test_quant_model_tp_equality(tinyllama_lora_files, num_gpus_available,
                                  model):
     if num_gpus_available < 2:
         pytest.skip(f"Not enough GPUs for tensor parallelism {2}")
-    if model.quantization == "GPTQ":
+    if model.quantization == "gptq":
         pytest.skip("GPTQ lora outputs are just incredibly unstable")
     llm_tp1 = vllm.LLM(
         model=model.model_path,

tests/tracing/test_tracing.py

@@ -173,7 +173,7 @@ def test_traces_with_detailed_steps(
         llm = LLM(
             model=model,
             otlp_traces_endpoint=FAKE_TRACE_SERVER_ADDRESS,
-            collect_detailed_traces="all",
+            collect_detailed_traces=["all"],
         )
         prompts = ["This is a short prompt"]
         outputs = llm.generate(prompts, sampling_params=sampling_params)

vllm/config.py

@@ -11,8 +11,8 @@ import uuid
 import warnings
 from collections import Counter
 from contextlib import contextmanager
-from dataclasses import (MISSING, Field, asdict, dataclass, field, fields,
-                         is_dataclass, replace)
+from dataclasses import (MISSING, Field, asdict, field, fields, is_dataclass,
+                         replace)
 from functools import cached_property
 from importlib.util import find_spec
 from pathlib import Path
@@ -21,9 +21,12 @@ from typing import (TYPE_CHECKING, Any, Callable, ClassVar, Literal, Optional,
 
 import regex as re
 import torch
+from pydantic import (ConfigDict, SkipValidation, TypeAdapter, field_validator,
+                      model_validator)
+from pydantic.dataclasses import dataclass
 from torch.distributed import ProcessGroup, ReduceOp
 from transformers import PretrainedConfig
-from typing_extensions import deprecated
+from typing_extensions import deprecated, runtime_checkable
 
 import vllm.envs as envs
 from vllm import version
@@ -57,10 +60,15 @@ if TYPE_CHECKING:
     from vllm.model_executor.layers.quantization.base_config import (
         QuantizationConfig)
     from vllm.model_executor.model_loader import BaseModelLoader
+    from vllm.model_executor.model_loader.tensorizer import TensorizerConfig
 
     ConfigType = type[DataclassInstance]
 else:
+    PlacementGroup = Any
+    ExecutorBase = Any
     QuantizationConfig = Any
+    BaseModelLoader = Any
+    TensorizerConfig = Any
     ConfigType = type
 
 logger = init_logger(__name__)
@@ -92,6 +100,7 @@ HfOverrides = Union[dict[str, Any], Callable[[PretrainedConfig],
                                              PretrainedConfig]]
 
 
+@runtime_checkable
 class SupportsHash(Protocol):
 
     def compute_hash(self) -> str:
@@ -223,7 +232,7 @@ ModelDType = Literal["auto", "half", "float16", "bfloat16", "float", "float32"]
 
 
 @config
-@dataclass
+@dataclass(config=ConfigDict(arbitrary_types_allowed=True))
 class ModelConfig:
     """Configuration for the model."""
 
@@ -236,7 +245,7 @@ class ModelConfig:
     task, even if the same model can be used for multiple tasks. When the model
     only supports one task, "auto" can be used to select it; otherwise, you
     must specify explicitly which task to use."""
-    tokenizer: str = None  # type: ignore
+    tokenizer: SkipValidation[str] = None  # type: ignore
     """Name or path of the Hugging Face tokenizer to use. If unspecified, model
     name or path will be used."""
     tokenizer_mode: TokenizerMode = "auto"
@@ -284,7 +293,7 @@ class ModelConfig:
     """The specific revision to use for the tokenizer on the Hugging Face Hub.
     It can be a branch name, a tag name, or a commit id. If unspecified, will
     use the default version."""
-    max_model_len: int = None  # type: ignore
+    max_model_len: SkipValidation[int] = None  # type: ignore
     """Model context length (prompt and output). If unspecified, will be
     automatically derived from the model config.
 
@@ -602,6 +611,22 @@ class ModelConfig:
         self._verify_cuda_graph()
         self._verify_bnb_config()
 
+    @field_validator("quantization", mode="before")
+    @classmethod
+    def validate_quantization_before(cls, value: Any) -> Any:
+        if isinstance(value, str):
+            return value.lower()
+        return value
+
+    @model_validator(mode="after")
+    def validate_model_config_after(self: "ModelConfig") -> "ModelConfig":
+        if not isinstance(self.tokenizer, str):
+            raise ValueError("tokenizer must be a string after __post_init__.")
+        if not isinstance(self.max_model_len, int):
+            raise ValueError(
+                "max_model_len must be an integer after __post_init__.")
+        return self
+
     @property
     def registry(self):
         return ModelRegistry
@@ -823,8 +848,7 @@ class ModelConfig:
             "quark", "modelopt_fp4", "bitblas", "gptq_bitblas"
         ]
         if self.quantization is not None:
-            self.quantization = cast(QuantizationMethods,
-                                     self.quantization.lower())
+            self.quantization = cast(QuantizationMethods, self.quantization)
 
         # Parse quantization method from the HF model config, if available.
         quant_cfg = self._parse_quant_hf_config()
@@ -1397,7 +1421,7 @@ PrefixCachingHashAlgo = Literal["builtin", "sha256"]
 class CacheConfig:
     """Configuration for the KV cache."""
 
-    block_size: BlockSize = None  # type: ignore
+    block_size: SkipValidation[BlockSize] = None  # type: ignore
     """Size of a contiguous cache block in number of tokens. This is ignored on
     neuron devices and set to `--max-model-len`. On CUDA devices, only block
     sizes up to 32 are supported. On HPU devices, block size defaults to 128.
@@ -1619,7 +1643,8 @@ class LoadConfig:
     download_dir: Optional[str] = None
     """Directory to download and load the weights, default to the default
     cache directory of Hugging Face."""
-    model_loader_extra_config: dict = field(default_factory=dict)
+    model_loader_extra_config: Union[dict, TensorizerConfig] = field(
+        default_factory=dict)
     """Extra config for model loader. This will be passed to the model loader
     corresponding to the chosen load_format."""
     ignore_patterns: Optional[Union[list[str], str]] = None
@@ -1929,19 +1954,19 @@ class SchedulerConfig:
     runner_type: RunnerType = "generate"
     """The runner type to launch for the model."""
 
-    max_num_batched_tokens: int = None  # type: ignore
+    max_num_batched_tokens: SkipValidation[int] = None  # type: ignore
     """Maximum number of tokens to be processed in a single iteration.
 
     This config has no static default. If left unspecified by the user, it will
     be set in `EngineArgs.create_engine_config` based on the usage context."""
 
-    max_num_seqs: int = None  # type: ignore
+    max_num_seqs: SkipValidation[int] = None  # type: ignore
     """Maximum number of sequences to be processed in a single iteration.
 
     This config has no static default. If left unspecified by the user, it will
     be set in `EngineArgs.create_engine_config` based on the usage context."""
 
-    max_model_len: int = None  # type: ignore
+    max_model_len: SkipValidation[int] = None  # type: ignore
     """Maximum length of a sequence (including prompt and generated text). This
     is primarily set in `ModelConfig` and that value should be manually
     duplicated here."""
@@ -1980,7 +2005,7 @@ class SchedulerConfig:
     """Apply a delay (of delay factor multiplied by previous
     prompt latency) before scheduling next prompt."""
 
-    enable_chunked_prefill: bool = None  # type: ignore
+    enable_chunked_prefill: SkipValidation[bool] = None  # type: ignore
     """If True, prefill requests can be chunked based
     on the remaining max_num_batched_tokens."""
 
@@ -2202,7 +2227,7 @@ Device = Literal["auto", "cuda", "neuron", "cpu", "tpu", "xpu", "hpu"]
 
 
 @config
-@dataclass
+@dataclass(config=ConfigDict(arbitrary_types_allowed=True))
 class DeviceConfig:
     """Configuration for the device to use for vLLM execution."""
 
@@ -2260,8 +2285,8 @@ class DeviceConfig:
             self.device = torch.device(self.device_type)
 
 
-SpeculativeMethod = Literal["ngram", "eagle", "medusa", "mlp_speculator",
-                            "draft_model", "deepseek_mtp"]
+SpeculativeMethod = Literal["ngram", "eagle", "eagle3", "medusa",
+                            "mlp_speculator", "draft_model", "deepseek_mtp"]
 SpeculativeAcceptanceMethod = Literal["rejection_sampler",
                                       "typical_acceptance_sampler"]
 
@@ -2272,8 +2297,7 @@ class SpeculativeConfig:
     """Configuration for speculative decoding."""
 
     # General speculative decoding control
-    num_speculative_tokens: int = field(default=None,
-                                        init=True)  # type: ignore
+    num_speculative_tokens: SkipValidation[int] = None  # type: ignore
     """The number of speculative tokens, if provided. It will default to the
     number in the draft model config if present, otherwise, it is required."""
     model: Optional[str] = None
@@ -2349,26 +2373,23 @@ class SpeculativeConfig:
     """Specifies the tree structure for speculative token generation.
     """
     # required configuration params passed from engine
-    target_model_config: ModelConfig = field(default=None,
-                                             init=True)  # type: ignore
+    target_model_config: SkipValidation[ModelConfig] = None  # type: ignore
     """The configuration of the target model."""
-    target_parallel_config: ParallelConfig = field(default=None,
-                                                   init=True)  # type: ignore
+    target_parallel_config: SkipValidation[
+        ParallelConfig] = None  # type: ignore
     """The parallel configuration for the target model."""
-    enable_chunked_prefill: bool = field(default=None,
-                                         init=True)  # type: ignore
+    enable_chunked_prefill: SkipValidation[bool] = None  # type: ignore
     """Whether vLLM is configured to use chunked prefill or not. Used for
     raising an error since it's not yet compatible with speculative decode."""
-    disable_log_stats: bool = field(default=None, init=True)  # type: ignore
+    disable_log_stats: SkipValidation[bool] = None  # type: ignore
     """Whether to disable the periodic printing of stage times in speculative
     decoding."""
 
     # params generated in the post-init stage
-    draft_model_config: ModelConfig = field(default=None,
-                                            init=True)  # type: ignore
+    draft_model_config: SkipValidation[ModelConfig] = None  # type: ignore
     """The configuration of the draft model initialized internal."""
-    draft_parallel_config: ParallelConfig = field(default=None,
-                                                  init=True)  # type: ignore
+    draft_parallel_config: SkipValidation[
+        ParallelConfig] = None  # type: ignore
     """The parallel configuration for the draft model initialized internal."""
 
     def compute_hash(self) -> str:
@@ -2766,7 +2787,7 @@ LoRADType = Literal["auto", "float16", "bfloat16"]
 
 
 @config
-@dataclass
+@dataclass(config=ConfigDict(arbitrary_types_allowed=True))
 class LoRAConfig:
     """Configuration for LoRA."""
 
@@ -2863,7 +2884,7 @@ class LoRAConfig:
 
 
 @config
-@dataclass
+@dataclass(config=ConfigDict(arbitrary_types_allowed=True))
 class PromptAdapterConfig:
     """Configuration for PromptAdapters."""
 
@@ -3892,17 +3913,11 @@ class CompilationConfig:
             "pass_config",
             "traced_files",
         }
-        include = dict()
-        for k, v in asdict(self).items():
-            if k in exclude:
-                continue
-            f = get_field(CompilationConfig, k)
-            if (d := f.default) is not MISSING and d == v:
-                continue
-            if (df := f.default_factory) is not MISSING and df() == v:
-                continue
-            include[k] = v
-        return json.dumps(include)
+        # The cast to string is necessary because Pydantic is mocked in docs
+        # builds and sphinx-argparse doesn't know the return type of decode()
+        return str(
+            TypeAdapter(CompilationConfig).dump_json(
+                self, exclude=exclude, exclude_unset=True).decode())
 
     __str__ = __repr__
 
@@ -3911,7 +3926,7 @@ class CompilationConfig:
         """Parse the CLI value for the compilation config."""
         if cli_value in ["0", "1", "2", "3"]:
             return cls(level=int(cli_value))
-        return cls(**json.loads(cli_value))
+        return TypeAdapter(CompilationConfig).validate_json(cli_value)
 
     def __post_init__(self) -> None:
         count_none = self.custom_ops.count("none")
@@ -4037,7 +4052,7 @@ class CompilationConfig:
 
 
 @config
-@dataclass
+@dataclass(config=ConfigDict(arbitrary_types_allowed=True))
 class VllmConfig:
     """Dataclass which contains all vllm-related configuration. This
     simplifies passing around the distinct configurations in the codebase.
@@ -4294,9 +4309,6 @@ class VllmConfig:
                 "To workaround this limitation, vLLM will set 'ieee' input "
                 "precision for chunked prefill triton kernels.")
 
-        if self.compilation_config is None:
-            self.compilation_config = CompilationConfig()
-
         # async tp is built on top of sequence parallelism
         # and requires it to be enabled.
         if self.compilation_config.pass_config.enable_async_tp:

vllm/engine/arg_utils.py

@@ -14,6 +14,7 @@ from typing import (Annotated, Any, Callable, Dict, List, Literal, Optional,
 
 import regex as re
 import torch
+from pydantic import SkipValidation, TypeAdapter, ValidationError
 from typing_extensions import TypeIs, deprecated
 
 import vllm.envs as envs
@@ -38,7 +39,7 @@ from vllm.test_utils import MODEL_WEIGHTS_S3_BUCKET, MODELS_ON_S3
 from vllm.transformers_utils.utils import check_gguf_file
 from vllm.usage.usage_lib import UsageContext
 from vllm.utils import (STR_DUAL_CHUNK_FLASH_ATTN_VAL, FlexibleArgumentParser,
-                        GiB_bytes, is_in_doc_build, is_in_ray_actor)
+                        GiB_bytes, is_in_ray_actor)
 
 # yapf: enable
 
@@ -156,7 +157,8 @@ def get_kwargs(cls: ConfigType) -> dict[str, Any]:
         # Get the set of possible types for the field
         type_hints: set[TypeHint] = set()
         if get_origin(field.type) in {Union, Annotated}:
-            type_hints.update(get_args(field.type))
+            predicate = lambda arg: not isinstance(arg, SkipValidation)
+            type_hints.update(filter(predicate, get_args(field.type)))
         else:
             type_hints.add(field.type)
 
@@ -168,9 +170,6 @@ def get_kwargs(cls: ConfigType) -> dict[str, Any]:
         if field.default is not MISSING:
             default = field.default
         elif field.default_factory is not MISSING:
-            if is_dataclass(field.default_factory) and is_in_doc_build():
-                default = {}
-            else:
             default = field.default_factory()
 
         # Get the help text for the field
@@ -189,12 +188,16 @@ def get_kwargs(cls: ConfigType) -> dict[str, Any]:
         - `--json-arg '{"key1": "value1", "key2": {"key3": "value2"}}'`\n
         - `--json-arg.key1 value1 --json-arg.key2.key3 value2`\n\n"""
         if dataclass_cls is not None:
-            dataclass_init = lambda x, f=dataclass_cls: f(**json.loads(x))
-            # Special case for configs with a from_cli method
-            if hasattr(dataclass_cls, "from_cli"):
-                from_cli = dataclass_cls.from_cli
-                dataclass_init = lambda x, f=from_cli: f(x)
-            kwargs[name]["type"] = dataclass_init
+
+            def parse_dataclass(val: str, cls=dataclass_cls) -> Any:
+                try:
+                    if hasattr(cls, "from_cli"):
+                        return cls.from_cli(val)
+                    return TypeAdapter(cls).validate_json(val)
+                except ValidationError as e:
+                    raise argparse.ArgumentTypeError(repr(e)) from e
+
+            kwargs[name]["type"] = parse_dataclass
             kwargs[name]["help"] += json_tip
         elif contains_type(type_hints, bool):
             # Creates --no-<name> and --<name> flags
@@ -225,12 +228,11 @@ def get_kwargs(cls: ConfigType) -> dict[str, Any]:
                 kwargs[name]["type"] = human_readable_int
         elif contains_type(type_hints, float):
             kwargs[name]["type"] = float
-        elif contains_type(type_hints,
-                           dict) and (contains_type(type_hints, str) or any(
-                               is_not_builtin(th) for th in type_hints)):
+        elif (contains_type(type_hints, dict)
+              and (contains_type(type_hints, str)
+                   or any(is_not_builtin(th) for th in type_hints))):
             kwargs[name]["type"] = union_dict_and_str
         elif contains_type(type_hints, dict):
-            # Dict arguments will always be optional
             kwargs[name]["type"] = parse_type(json.loads)
             kwargs[name]["help"] += json_tip
         elif (contains_type(type_hints, str)
@@ -317,8 +319,7 @@ class EngineArgs:
     rope_scaling: dict[str, Any] = get_field(ModelConfig, "rope_scaling")
     rope_theta: Optional[float] = ModelConfig.rope_theta
     hf_token: Optional[Union[bool, str]] = ModelConfig.hf_token
-    hf_overrides: Optional[HfOverrides] = \
-        get_field(ModelConfig, "hf_overrides")
+    hf_overrides: HfOverrides = get_field(ModelConfig, "hf_overrides")
     tokenizer_revision: Optional[str] = ModelConfig.tokenizer_revision
     quantization: Optional[QuantizationMethods] = ModelConfig.quantization
     enforce_eager: bool = ModelConfig.enforce_eager
@@ -398,7 +399,8 @@ class EngineArgs:
         get_field(ModelConfig, "override_neuron_config")
     override_pooler_config: Optional[Union[dict, PoolerConfig]] = \
         ModelConfig.override_pooler_config
-    compilation_config: Optional[CompilationConfig] = None
+    compilation_config: CompilationConfig = \
+        get_field(VllmConfig, "compilation_config")
     worker_cls: str = ParallelConfig.worker_cls
     worker_extension_cls: str = ParallelConfig.worker_extension_cls
 
@@ -413,7 +415,8 @@ class EngineArgs:
 
     calculate_kv_scales: bool = CacheConfig.calculate_kv_scales
 
-    additional_config: Optional[Dict[str, Any]] = None
+    additional_config: dict[str, Any] = \
+        get_field(VllmConfig, "additional_config")
     enable_reasoning: Optional[bool] = None  # DEPRECATED
     reasoning_parser: str = DecodingConfig.reasoning_backend
 

vllm/entrypoints/llm.py

@@ -207,6 +207,9 @@ class LLM:
             if isinstance(worker_cls, type):
                 kwargs["worker_cls"] = cloudpickle.dumps(worker_cls)
 
+        if hf_overrides is None:
+            hf_overrides = {}
+
         if compilation_config is not None:
             if isinstance(compilation_config, int):
                 compilation_config_instance = CompilationConfig(
@@ -218,7 +221,7 @@ class LLM:
             else:
                 compilation_config_instance = compilation_config
         else:
-            compilation_config_instance = None
+            compilation_config_instance = CompilationConfig()
 
         engine_args = EngineArgs(
             model=model,

vllm/entrypoints/openai/protocol.py

@@ -175,11 +175,15 @@ class ChatCompletionNamedToolChoiceParam(OpenAIBaseModel):
     type: Literal["function"] = "function"
 
 
+# extra="forbid" is a workaround to have kwargs as a field,
+# see https://github.com/pydantic/pydantic/issues/3125
 class LogitsProcessorConstructor(BaseModel):
     qualname: str
     args: Optional[list[Any]] = None
     kwargs: Optional[dict[str, Any]] = None
 
+    model_config = ConfigDict(extra="forbid")
+
 
 LogitsProcessors = list[Union[str, LogitsProcessorConstructor]]
 
@@ -234,7 +238,7 @@ class ChatCompletionRequest(OpenAIBaseModel):
     presence_penalty: Optional[float] = 0.0
     response_format: Optional[AnyResponseFormat] = None
     seed: Optional[int] = Field(None, ge=_LONG_INFO.min, le=_LONG_INFO.max)
-    stop: Optional[Union[str, list[str]]] = Field(default_factory=list)
+    stop: Optional[Union[str, list[str]]] = []
     stream: Optional[bool] = False
     stream_options: Optional[StreamOptions] = None
     temperature: Optional[float] = None
@@ -258,7 +262,7 @@ class ChatCompletionRequest(OpenAIBaseModel):
     min_p: Optional[float] = None
     repetition_penalty: Optional[float] = None
     length_penalty: float = 1.0
-    stop_token_ids: Optional[list[int]] = Field(default_factory=list)
+    stop_token_ids: Optional[list[int]] = []
     include_stop_str_in_output: bool = False
     ignore_eos: bool = False
     min_tokens: int = 0
@@ -756,7 +760,7 @@ class CompletionRequest(OpenAIBaseModel):
     n: int = 1
     presence_penalty: Optional[float] = 0.0
     seed: Optional[int] = Field(None, ge=_LONG_INFO.min, le=_LONG_INFO.max)
-    stop: Optional[Union[str, list[str]]] = Field(default_factory=list)
+    stop: Optional[Union[str, list[str]]] = []
     stream: Optional[bool] = False
     stream_options: Optional[StreamOptions] = None
     suffix: Optional[str] = None
@@ -770,7 +774,7 @@ class CompletionRequest(OpenAIBaseModel):
     min_p: Optional[float] = None
     repetition_penalty: Optional[float] = None
     length_penalty: float = 1.0
-    stop_token_ids: Optional[list[int]] = Field(default_factory=list)
+    stop_token_ids: Optional[list[int]] = []
     include_stop_str_in_output: bool = False
     ignore_eos: bool = False
     min_tokens: int = 0

vllm/entrypoints/openai/serving_engine.py

@@ -134,11 +134,9 @@ class RequestProcessingMixin(BaseModel):
     Mixin for request processing, 
     handling prompt preparation and engine input.
     """
-    request_prompts: Optional[Sequence[RequestPrompt]] = \
-                            Field(default_factory=list)
+    request_prompts: Optional[Sequence[RequestPrompt]] = []
     engine_prompts: Optional[Union[list[EngineTokensPrompt],
-                                   list[EngineEmbedsPrompt]]] = Field(
-                                       default_factory=list)
+                                   list[EngineEmbedsPrompt]]] = []
 
     model_config = ConfigDict(arbitrary_types_allowed=True)
 
@@ -528,12 +526,14 @@ class OpenAIServing:
         if isinstance(request,
                       (EmbeddingChatRequest, EmbeddingCompletionRequest,
                        ScoreRequest, RerankRequest, ClassificationRequest)):
-            operation = {
-                ScoreRequest: "score",
-                ClassificationRequest: "classification"
-            }.get(type(request), "embedding generation")
 
             if token_num > self.max_model_len:
+                operations: dict[type[AnyRequest], str] = {
+                    ScoreRequest: "score",
+                    ClassificationRequest: "classification"
+                }
+                operation = operations.get(type(request),
+                                           "embedding generation")
                 raise ValueError(
                     f"This model's maximum context length is "
                     f"{self.max_model_len} tokens. However, you requested "

vllm/model_executor/guided_decoding/guided_fields.py

@@ -3,12 +3,10 @@
 from dataclasses import dataclass
 from typing import Optional, TypedDict, Union
 
-from pydantic import BaseModel
-
 
 # These classes are deprecated, see SamplingParams
 class LLMGuidedOptions(TypedDict, total=False):
-    guided_json: Union[dict, BaseModel, str]
+    guided_json: Union[dict, str]
     guided_regex: str
     guided_choice: list[str]
     guided_grammar: str
@@ -20,7 +18,7 @@ class LLMGuidedOptions(TypedDict, total=False):
 @dataclass
 class GuidedDecodingRequest:
     """One of the fields will be used to retrieve the logit processor."""
-    guided_json: Optional[Union[dict, BaseModel, str]] = None
+    guided_json: Optional[Union[dict, str]] = None
     guided_regex: Optional[str] = None
     guided_choice: Optional[list[str]] = None
     guided_grammar: Optional[str] = None

vllm/utils.py

@@ -1878,14 +1878,6 @@ def get_cuda_view_from_cpu_tensor(cpu_tensor: torch.Tensor) -> torch.Tensor:
     return torch.ops._C.get_cuda_view_from_cpu_tensor(cpu_tensor)
 
 
-def is_in_doc_build() -> bool:
-    try:
-        from sphinx.ext.autodoc.mock import _MockModule
-        return isinstance(zmq, _MockModule)
-    except ModuleNotFoundError:
-        return False
-
-
 def import_from_path(module_name: str, file_path: Union[str, os.PathLike]):
     """
     Import a Python file according to its file path.