[CI] Fix mypy for vllm/engine and vllm/utils

tests/v1/core/test_scheduler.py

@@ -1021,7 +1021,7 @@ def test_kv_connector_unable_to_allocate():
     """
 
     # Setup Scheduler With Mock External Cache Hit.
-    BLOCK_SIZE = 4
+    BLOCK_SIZE = 8
     NUM_BLOCKS = 10
     scheduler = create_scheduler(
         enable_prefix_caching=True,
@@ -1103,7 +1103,7 @@ def test_kv_connector_handles_preemption():
     """
 
     # Setup Scheduler With Mock External Cache Hit.
-    BLOCK_SIZE = 2
+    BLOCK_SIZE = 8
     # NOTE: there is 1 null block, so this is 6 blocks.
     NUM_BLOCKS = 7
     scheduler = create_scheduler(
@@ -1124,8 +1124,8 @@ def test_kv_connector_handles_preemption():
     # Both can be scheduled at first, but the second request
     # will be preempted and re-scheduled.
     NUM_REQUESTS = 2
-    NUM_TOKENS = BLOCK_SIZE * 2 + 1
-    MAX_TOKENS = BLOCK_SIZE * 2
+    NUM_TOKENS = 3 * BLOCK_SIZE - 1
+    MAX_TOKENS = 4
     requests = create_requests(
         num_requests=NUM_REQUESTS,
         num_tokens=NUM_TOKENS,

tools/pre_commit/mypy.py

@@ -26,6 +26,7 @@
 FILES = [
     "vllm/*.py",
     "vllm/assets",
+    "vllm/engine",
     "vllm/distributed",
     "vllm/entrypoints",
     "vllm/executor",
@@ -36,6 +37,7 @@
     "vllm/transformers_utils",
     "vllm/triton_utils",
     "vllm/usage",
+    "vllm/utils",
 ]
 
 # After fixing errors resulting from changing follow_imports
@@ -44,7 +46,6 @@
     "tests",
     "vllm/attention",
     "vllm/compilation",
-    "vllm/engine",
     "vllm/inputs",
     "vllm/lora",
     "vllm/model_executor",

vllm/config/cache.py

@@ -2,10 +2,11 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import hashlib
+from collections.abc import Callable
 from dataclasses import field
 from typing import TYPE_CHECKING, Any, Literal
 
-from pydantic import Field, SkipValidation, field_validator
+from pydantic import Field, field_validator
 from pydantic.dataclasses import dataclass
 
 from vllm.config.utils import config
@@ -30,7 +31,7 @@
 class CacheConfig:
     """Configuration for the KV cache."""
 
-    block_size: SkipValidation[BlockSize] = None  # type: ignore
+    block_size: BlockSize = None
     """Size of a contiguous cache block in number of tokens. On CUDA devices,
     only block sizes up to 32 are supported.
 
@@ -150,6 +151,13 @@ def metrics_info(self):
         # metrics info
         return {key: str(value) for key, value in self.__dict__.items()}
 
+    @field_validator("block_size", mode="wrap")
+    @classmethod
+    def _skip_none_validation(cls, value: Any, handler: Callable) -> Any:
+        if value is None:
+            return value
+        return handler(value)
+
     @field_validator("cache_dtype", mode="after")
     @classmethod
     def _validate_cache_dtype(cls, cache_dtype: CacheDType) -> CacheDType:

vllm/config/model.py

@@ -10,7 +10,7 @@
 from typing import TYPE_CHECKING, Any, Literal, cast, get_args
 
 import torch
-from pydantic import ConfigDict, SkipValidation, field_validator, model_validator
+from pydantic import ConfigDict, field_validator, model_validator
 from pydantic.dataclasses import dataclass
 from safetensors.torch import _TYPES as _SAFETENSORS_TO_TORCH_DTYPE
 
@@ -120,7 +120,7 @@ class ModelConfig:
 
     Note that the model may support other tasks using the same model runner.
     """
-    tokenizer: SkipValidation[str] = None  # type: ignore
+    tokenizer: str = None
     """Name or path of the Hugging Face tokenizer to use. If unspecified, model
     name or path will be used."""
     tokenizer_mode: TokenizerMode = "auto"
@@ -171,7 +171,7 @@ 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: SkipValidation[int] = None  # type: ignore
+    max_model_len: int = None
     """Model context length (prompt and output). If unspecified, will be
     automatically derived from the model config.
 
@@ -182,7 +182,7 @@ class ModelConfig:
     - 25.6k -> 25,600"""
     spec_target_max_model_len: int | None = None
     """Specify the maximum length for spec decoding draft models."""
-    quantization: SkipValidation[QuantizationMethods | None] = None
+    quantization: str | QuantizationMethods | None = None
     """Method used to quantize the weights. If `None`, we first check the
     `quantization_config` attribute in the model config file. If that is
     `None`, we assume the model weights are not quantized and use `dtype` to
@@ -302,6 +302,13 @@ class ModelConfig:
     skip_mm_profiling: InitVar[bool | None] = None
     video_pruning_rate: InitVar[float | None] = None
 
+    @field_validator("tokenizer", "max_model_len", mode="wrap")
+    @classmethod
+    def _skip_none_validation(cls, value: Any, handler: Callable) -> Any:
+        if value is None:
+            return value
+        return handler(value)
+
     def compute_hash(self) -> str:
         """
         WARNING: Whenever a new field is added to this config,

vllm/config/parallel.py

@@ -32,6 +32,14 @@
 ExpertPlacementStrategy = Literal["linear", "round_robin"]
 DistributedExecutorBackend = Literal["ray", "mp", "uni", "external_launcher"]
 DataParallelBackend = Literal["ray", "mp"]
+All2allBackendType = Literal[
+    "naive",
+    "pplx",
+    "deepep_high_throughput",
+    "deepep_low_latency",
+    "allgather_reducescatter",
+    "flashinfer_all2allv",
+]
 
 
 @config
@@ -113,17 +121,7 @@ class ParallelConfig:
       with 4 experts and 2 ranks, rank 0 will have experts [0, 2] and rank 1
       will have experts [1, 3]. This strategy can help improve load balancing
       for grouped expert models with no redundant experts."""
-    all2all_backend: (
-        Literal[
-            "naive",
-            "pplx",
-            "deepep_high_throughput",
-            "deepep_low_latency",
-            "allgather_reducescatter",
-            "flashinfer_all2allv",
-        ]
-        | None
-    ) = None
+    all2all_backend: All2allBackendType | None = None
     """All2All backend for MoE expert parallel communication. If not set, uses
     the value from VLLM_ALL2ALL_BACKEND environment variable. Available options:
     - "naive": Naive all2all implementation using broadcasts

vllm/config/scheduler.py

@@ -2,10 +2,11 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import hashlib
+from collections.abc import Callable
 from dataclasses import InitVar, field
 from typing import Any, Literal
 
-from pydantic import SkipValidation, model_validator
+from pydantic import field_validator, model_validator
 from pydantic.dataclasses import dataclass
 from typing_extensions import Self
 
@@ -31,19 +32,19 @@ class SchedulerConfig:
     runner_type: RunnerType = "generate"
     """The runner type to launch for the model."""
 
-    max_num_batched_tokens: SkipValidation[int] = None  # type: ignore
+    max_num_batched_tokens: int = None
     """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: SkipValidation[int] = None  # type: ignore
+    max_num_seqs: int = None
     """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: SkipValidation[int] = None  # type: ignore
+    max_model_len: int = None
     """Maximum length of a sequence (including prompt and generated text). This
     is primarily set in `ModelConfig` and that value should be manually
     duplicated here."""
@@ -79,7 +80,7 @@ class SchedulerConfig:
     3. more than one value (e.g. 1 2 128) is provided, then the capture list
     will follow the provided list."""
 
-    enable_chunked_prefill: SkipValidation[bool] = None  # type: ignore
+    enable_chunked_prefill: bool = None
     """If True, prefill requests can be chunked based
     on the remaining max_num_batched_tokens."""
 
@@ -169,6 +170,19 @@ def compute_hash(self) -> str:
         hash_str = hashlib.md5(str(factors).encode(), usedforsecurity=False).hexdigest()
         return hash_str
 
+    @field_validator(
+        "max_num_batched_tokens",
+        "max_num_seqs",
+        "max_model_len",
+        "enable_chunked_prefill",
+        mode="wrap",
+    )
+    @classmethod
+    def _skip_none_validation(cls, value: Any, handler: Callable) -> Any:
+        if value is None:
+            return value
+        return handler(value)
+
     def __post_init__(self, is_encoder_decoder: bool) -> None:
         if self.max_model_len is None:
             self.max_model_len = 8192

vllm/config/utils.py

@@ -39,7 +39,7 @@ def config(cls: ConfigT) -> ConfigT:
     return cls
 
 
-def get_field(cls: ConfigType, name: str) -> Field:
+def get_field(cls: ConfigType, name: str) -> Any:
     """Get the default factory field of a dataclass by name. Used for getting
     default factory fields in `EngineArgs`."""
     if not is_dataclass(cls):

vllm/config/vllm.py

@@ -57,7 +57,7 @@ class VllmConfig:
 
     # TODO: use default_factory once default constructing ModelConfig doesn't
     # try to download a model
-    model_config: ModelConfig = Field(default=None)
+    model_config: ModelConfig = None
     """Model configuration."""
     cache_config: CacheConfig = Field(default_factory=CacheConfig)
     """Cache configuration."""
@@ -77,7 +77,9 @@ class VllmConfig:
         default_factory=StructuredOutputsConfig
     )
     """Structured outputs configuration."""
-    observability_config: ObservabilityConfig | None = None
+    observability_config: ObservabilityConfig = Field(
+        default_factory=ObservabilityConfig
+    )
     """Observability configuration."""
     quant_config: QuantizationConfig | None = None
     """Quantization configuration."""