Enable Pydantic mypy checks and convert configs to Pydantic dataclasses

.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 @@ class ModelWithQuantization:
     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 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 @@
 
 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 @@
     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 @@
                                              PretrainedConfig]]
 
 
+@runtime_checkable
 class SupportsHash(Protocol):
 
     def compute_hash(self) -> str:
@@ -223,7 +232,7 @@ def is_init_field(cls: ConfigType, name: str) -> bool:
 
 
 @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.
 
@@ -607,6 +616,22 @@ def __post_init__(self) -> None:
         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
@@ -833,8 +858,7 @@ def _verify_quantization(self) -> None:
             "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 @@ def matryoshka_dimensions(self):
 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 @@ def is_multi_step(self) -> bool:
 
 
 @config
-@dataclass
+@dataclass(config=ConfigDict(arbitrary_types_allowed=True))
 class DeviceConfig:
     """Configuration for the device to use for vLLM execution."""
 
@@ -2260,8 +2285,8 @@ def __post_init__(self):
             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 @@ def __repr__(self) -> str:
 
 
 @config
-@dataclass
+@dataclass(config=ConfigDict(arbitrary_types_allowed=True))
 class LoRAConfig:
     """Configuration for LoRA."""
 
@@ -2863,7 +2884,7 @@ def verify_lora_support(self):
 
 
 @config
-@dataclass
+@dataclass(config=ConfigDict(arbitrary_types_allowed=True))
 class PromptAdapterConfig:
     """Configuration for PromptAdapters."""
 
@@ -3888,17 +3909,11 @@ def __repr__(self) -> str:
             "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__
 
@@ -3907,7 +3922,7 @@ def from_cli(cls, cli_value: str) -> "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")
@@ -4033,7 +4048,7 @@ def set_splitting_ops_for_v1(self):
 
 
 @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.
@@ -4290,9 +4305,6 @@ def __post_init__(self):
                 "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: