[Misc] Replace Optional[X] with X | None syntax (#33332)

Signed-off-by: carlory <baofa.fan@daocloud.io>
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>

vllm/beam_search.py

@@ -2,7 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 from dataclasses import dataclass
-from typing import TYPE_CHECKING, Any, Optional
+from typing import TYPE_CHECKING, Any
 
 from vllm.logprobs import Logprob
 from vllm.lora.request import LoRARequest
@@ -27,7 +27,7 @@ class BeamSearchSequence:
     text: str | None = None
     finish_reason: str | None = None
     stop_reason: int | str | None = None
-    multi_modal_data: Optional["MultiModalDataDict"] = None
+    multi_modal_data: "MultiModalDataDict | None" = None
     mm_processor_kwargs: dict[str, Any] | None = None
 
 

vllm/distributed/kv_transfer/kv_connector/factory.py

@@ -3,7 +3,7 @@
 
 import importlib
 from collections.abc import Callable
-from typing import TYPE_CHECKING, Optional, cast
+from typing import TYPE_CHECKING, cast
 
 from vllm.distributed.kv_transfer.kv_connector.base import (
     KVConnectorBase,
@@ -44,7 +44,7 @@ class KVConnectorFactory:
         cls,
         config: "VllmConfig",
         role: KVConnectorRole,
-        kv_cache_config: Optional["KVCacheConfig"] = None,
+        kv_cache_config: "KVCacheConfig | None" = None,
     ) -> KVConnectorBase:
         kv_transfer_config = config.kv_transfer_config
         if kv_transfer_config is None:

vllm/distributed/kv_transfer/kv_connector/v1/base.py

@@ -41,7 +41,7 @@ The class provides the following primitives:
 import enum
 from abc import ABC, abstractmethod
 from collections.abc import Callable, Iterable
-from typing import TYPE_CHECKING, Any, Literal, Optional
+from typing import TYPE_CHECKING, Any, Literal
 
 import torch
 
@@ -161,7 +161,7 @@ class KVConnectorBase_V1(ABC):
         self,
         vllm_config: "VllmConfig",
         role: KVConnectorRole,
-        kv_cache_config: Optional["KVCacheConfig"] = None,
+        kv_cache_config: "KVCacheConfig | None" = None,
     ):
         logger.warning(
             "Initializing KVConnectorBase_V1. This API is experimental and "
@@ -383,13 +383,13 @@ class KVConnectorBase_V1(ABC):
         """
         return None
 
-    def get_kv_connector_stats(self) -> Optional["KVConnectorStats"]:
+    def get_kv_connector_stats(self) -> "KVConnectorStats | None":
         """
         Get the KV connector stats collected during the last interval.
         """
         return None
 
-    def get_kv_connector_kv_cache_events(self) -> Optional["KVConnectorKVEvents"]:
+    def get_kv_connector_kv_cache_events(self) -> "KVConnectorKVEvents | None":
         """
         Get the KV connector kv cache events collected during the last interval.
         This function should be called by the model runner every time after the
@@ -558,7 +558,7 @@ class KVConnectorBase_V1(ABC):
     @classmethod
     def build_kv_connector_stats(
         cls, data: dict[str, Any] | None = None
-    ) -> Optional["KVConnectorStats"]:
+    ) -> "KVConnectorStats | None":
         """
         KVConnectorStats resolution method. This method allows dynamically
         registered connectors to return their own KVConnectorStats object,
@@ -584,7 +584,7 @@ class KVConnectorBase_V1(ABC):
         metric_types: dict[type["PromMetric"], type["PromMetricT"]],
         labelnames: list[str],
         per_engine_labelvalues: dict[int, list[object]],
-    ) -> Optional["KVConnectorPromMetrics"]:
+    ) -> "KVConnectorPromMetrics | None":
         """
         Create a KVConnectorPromMetrics subclass which should register
         per-connector Prometheus metrics and implement observe() to

vllm/distributed/kv_transfer/kv_connector/v1/decode_bench_connector.py

@@ -32,7 +32,7 @@ Usage:
 """
 
 from dataclasses import dataclass
-from typing import TYPE_CHECKING, Any, Optional
+from typing import TYPE_CHECKING, Any
 
 import torch
 
@@ -84,7 +84,7 @@ class DecodeBenchConnector(KVConnectorBase_V1):
         self,
         vllm_config: "VllmConfig",
         role: KVConnectorRole,
-        kv_cache_config: Optional["KVCacheConfig"] = None,
+        kv_cache_config: "KVCacheConfig | None" = None,
     ):
         super().__init__(vllm_config, role, kv_cache_config)
 

vllm/distributed/kv_transfer/kv_connector/v1/example_connector.py

@@ -2,7 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import os
 from dataclasses import dataclass, field
-from typing import TYPE_CHECKING, Any, Optional
+from typing import TYPE_CHECKING, Any
 
 import safetensors
 import torch
@@ -91,7 +91,7 @@ class ExampleConnector(KVConnectorBase_V1):
         self,
         vllm_config: "VllmConfig",
         role: KVConnectorRole,
-        kv_cache_config: Optional["KVCacheConfig"] = None,
+        kv_cache_config: "KVCacheConfig | None" = None,
     ):
         super().__init__(
             vllm_config=vllm_config,

vllm/distributed/kv_transfer/kv_connector/v1/lmcache_integration/vllm_v1_adapter.py

@@ -5,7 +5,7 @@ import os
 import uuid
 from collections.abc import Generator
 from dataclasses import dataclass, field
-from typing import TYPE_CHECKING, Any, Optional
+from typing import TYPE_CHECKING, Any
 
 import torch
 from lmcache import utils
@@ -274,7 +274,7 @@ class ReqMeta:
         load_spec: LoadSpec | None = None,
         discard_partial_chunks: bool = True,
         save_decode_cache: bool = False,
-    ) -> Optional["ReqMeta"]:
+    ) -> "ReqMeta | None":
         """Create the request metadata from a request tracker.
 
         Args:

vllm/distributed/kv_transfer/kv_connector/v1/lmcache_mp_connector.py

@@ -3,7 +3,7 @@
 import enum
 from collections.abc import Iterable
 from dataclasses import dataclass, field
-from typing import TYPE_CHECKING, Any, Literal, Optional, cast
+from typing import TYPE_CHECKING, Any, Literal, cast
 
 import torch
 import zmq
@@ -385,7 +385,7 @@ class LMCacheMPConnector(KVConnectorBase_V1):
         self,
         vllm_config: "VllmConfig",
         role: KVConnectorRole,
-        kv_cache_config: Optional["KVCacheConfig"] = None,
+        kv_cache_config: "KVCacheConfig | None" = None,
     ):
         super().__init__(vllm_config, role, kv_cache_config)
 
@@ -595,7 +595,7 @@ class LMCacheMPConnector(KVConnectorBase_V1):
             self.worker_adapter.shutdown()
         return None
 
-    def get_kv_connector_stats(self) -> Optional["KVConnectorStats"]:
+    def get_kv_connector_stats(self) -> "KVConnectorStats | None":
         """
         Get the KV connector stats collected during the last interval.
         """
@@ -810,7 +810,7 @@ class LMCacheMPConnector(KVConnectorBase_V1):
     @classmethod
     def build_kv_connector_stats(
         cls, data: dict[str, Any] | None = None
-    ) -> Optional["KVConnectorStats"]:
+    ) -> "KVConnectorStats | None":
         """
         KVConnectorStats resolution method. This method allows dynamically
         registered connectors to return their own KVConnectorStats object,
@@ -825,7 +825,7 @@ class LMCacheMPConnector(KVConnectorBase_V1):
         metric_types: dict[type["PromMetric"], type["PromMetricT"]],
         labelnames: list[str],
         per_engine_labelvalues: dict[int, list[object]],
-    ) -> Optional["KVConnectorPromMetrics"]:
+    ) -> "KVConnectorPromMetrics | None":
         """
         Create a KVConnectorPromMetrics subclass which should register
         per-connector Prometheus metrics and implement observe() to

vllm/distributed/kv_transfer/kv_connector/v1/mooncake_connector.py

@@ -6,7 +6,7 @@ import time
 from collections import defaultdict
 from concurrent.futures import ThreadPoolExecutor
 from dataclasses import dataclass
-from typing import TYPE_CHECKING, Any, Optional
+from typing import TYPE_CHECKING, Any
 
 import msgspec
 import numpy as np
@@ -115,7 +115,7 @@ class MooncakeConnector(KVConnectorBase_V1):
         self,
         vllm_config: VllmConfig,
         role: KVConnectorRole,
-        kv_cache_config: Optional["KVCacheConfig"] = None,
+        kv_cache_config: "KVCacheConfig | None" = None,
     ):
         super().__init__(vllm_config, role, kv_cache_config)
 

vllm/distributed/kv_transfer/kv_connector/v1/moriio/moriio_common.py

@@ -5,7 +5,7 @@ import threading
 import time
 from collections.abc import Iterator
 from dataclasses import dataclass
-from typing import TYPE_CHECKING, Any, Optional
+from typing import TYPE_CHECKING, Any
 
 import msgspec
 import torch
@@ -101,7 +101,7 @@ class MoRIIOAgentMetadata(
 class RoleManager:
     """Manages role state across the connector."""
 
-    _instance: Optional["RoleManager"] = None
+    _instance: "RoleManager | None" = None
     _lock = threading.Lock()
 
     def __init__(self) -> None:

vllm/distributed/kv_transfer/kv_connector/v1/moriio/moriio_connector.py

@@ -7,7 +7,7 @@ import threading
 import time
 from collections import defaultdict
 from concurrent.futures import Future, ThreadPoolExecutor
-from typing import TYPE_CHECKING, Any, Optional
+from typing import TYPE_CHECKING, Any
 
 import msgpack
 import msgspec
@@ -90,7 +90,7 @@ class MoRIIOConnector(KVConnectorBase_V1):
         self,
         vllm_config: VllmConfig,
         role: KVConnectorRole,
-        kv_cache_config: Optional["KVCacheConfig"] = None,
+        kv_cache_config: "KVCacheConfig | None" = None,
     ):
         super().__init__(vllm_config, role)
         assert vllm_config.kv_transfer_config is not None, (
@@ -333,7 +333,7 @@ class MoRIIOConnectorScheduler:
         request: "Request",
         blocks: "KVCacheBlocks",
         num_external_tokens: int,
-        connector_worker: Optional["MoRIIOConnectorWorker"] = None,
+        connector_worker: "MoRIIOConnectorWorker | None" = None,
     ):
         params = request.kv_transfer_params
         if not params:

vllm/distributed/kv_transfer/kv_connector/v1/moriio/moriio_engine.py

@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import threading
-from typing import TYPE_CHECKING, Any, Optional
+from typing import TYPE_CHECKING, Any
 from weakref import ref as weakref_ref
 
 import msgpack
@@ -340,7 +340,7 @@ class MoRIIOWrapper:
 
     def __init__(
         self,
-        moriio_engine: Optional["IOEngine"] = None,
+        moriio_engine: "IOEngine | None" = None,
         tp_rank: int = 0,
         dp_rank: int = 0,
     ):

vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py

@@ -14,7 +14,7 @@ from collections import defaultdict
 from collections.abc import Iterator
 from concurrent.futures import Future, ThreadPoolExecutor
 from dataclasses import dataclass
-from typing import TYPE_CHECKING, Any, Optional
+from typing import TYPE_CHECKING, Any
 
 import msgspec
 import numpy as np
@@ -302,7 +302,7 @@ class NixlConnector(KVConnectorBase_V1):
         self,
         vllm_config: VllmConfig,
         role: KVConnectorRole,
-        kv_cache_config: Optional["KVCacheConfig"] = None,
+        kv_cache_config: "KVCacheConfig | None" = None,
     ):
         super().__init__(vllm_config, role, kv_cache_config)
 

vllm/distributed/kv_transfer/kv_connector/v1/p2p/p2p_nccl_connector.py

@@ -2,7 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 from dataclasses import dataclass
-from typing import TYPE_CHECKING, Any, Optional
+from typing import TYPE_CHECKING, Any
 
 import regex as re
 import torch
@@ -76,7 +76,7 @@ class P2pNcclConnector(KVConnectorBase_V1):
         self,
         vllm_config: "VllmConfig",
         role: KVConnectorRole,
-        kv_cache_config: Optional["KVCacheConfig"] = None,
+        kv_cache_config: "KVCacheConfig | None" = None,
     ):
         super().__init__(
             vllm_config=vllm_config,

vllm/distributed/kv_transfer/kv_transfer_state.py

@@ -1,6 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-from typing import TYPE_CHECKING, Optional
+from typing import TYPE_CHECKING
 
 from vllm.distributed.kv_transfer.kv_connector.base import KVConnectorBaseType
 from vllm.distributed.kv_transfer.kv_connector.factory import KVConnectorFactory
@@ -49,7 +49,7 @@ def is_v1_kv_transfer_group(connector: KVConnectorBaseType | None = None) -> boo
 
 
 def ensure_kv_transfer_initialized(
-    vllm_config: "VllmConfig", kv_cache_config: Optional["KVCacheConfig"] = None
+    vllm_config: "VllmConfig", kv_cache_config: "KVCacheConfig | None" = None
 ) -> None:
     """
     Initialize KV cache transfer parallel group.

vllm/distributed/parallel_state.py

@@ -33,7 +33,7 @@ from contextlib import contextmanager, nullcontext
 from dataclasses import dataclass
 from datetime import timedelta
 from multiprocessing import shared_memory
-from typing import Any, Optional
+from typing import Any
 from unittest.mock import patch
 
 import torch
@@ -106,7 +106,7 @@ def _get_unique_name(name: str) -> str:
     return newname
 
 
-_groups: dict[str, Callable[[], Optional["GroupCoordinator"]]] = {}
+_groups: dict[str, Callable[[], "GroupCoordinator | None"]] = {}
 
 
 def _register_group(group: "GroupCoordinator") -> None:
@@ -784,7 +784,7 @@ class GroupCoordinator:
         self,
         tensor_dict: dict[str, torch.Tensor | Any],
         dst: int | None = None,
-        all_gather_group: Optional["GroupCoordinator"] = None,
+        all_gather_group: "GroupCoordinator | None" = None,
         all_gather_tensors: dict[str, bool] | None = None,
     ) -> dict[str, torch.Tensor | Any] | None:
         """Send the input tensor dictionary.
@@ -871,7 +871,7 @@ class GroupCoordinator:
     def recv_tensor_dict(
         self,
         src: int | None = None,
-        all_gather_group: Optional["GroupCoordinator"] = None,
+        all_gather_group: "GroupCoordinator | None" = None,
         all_gather_tensors: dict[str, bool] | None = None,
     ) -> dict[str, torch.Tensor | Any] | None:
         """Recv the input tensor dictionary.

vllm/entrypoints/anthropic/protocol.py

@@ -3,7 +3,7 @@
 """Pydantic models for Anthropic API protocol"""
 
 import time
-from typing import Any, Literal, Optional
+from typing import Any, Literal
 
 from pydantic import BaseModel, field_validator
 
@@ -135,7 +135,7 @@ class AnthropicStreamEvent(BaseModel):
         "ping",
         "error",
     ]
-    message: Optional["AnthropicMessagesResponse"] = None
+    message: "AnthropicMessagesResponse | None" = None
     delta: AnthropicDelta | None = None
     content_block: AnthropicContentBlock | None = None
     index: int | None = None

vllm/lora/lora_weights.py

@@ -2,7 +2,6 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 from collections.abc import Sequence as GenericSequence
-from typing import Optional
 
 import torch
 import torch.types
@@ -126,7 +125,7 @@ class PackedLoRALayerWeights(LoRALayerWeights):
 
     @classmethod
     def pack(
-        cls, loras: GenericSequence[Optional["LoRALayerWeights"]]
+        cls, loras: GenericSequence["LoRALayerWeights | None"]
     ) -> "PackedLoRALayerWeights":
         """Pack a list of LoRAs into a single LoRA.
 
@@ -155,7 +154,7 @@ class PackedLoRALayerWeights(LoRALayerWeights):
     @classmethod
     def pack_moe(
         cls,
-        loras: GenericSequence[Optional["LoRALayerWeights"]],
+        loras: GenericSequence["LoRALayerWeights | None"],
         module_name: str,
         is_non_gated_moe: bool = False,
     ) -> "PackedLoRALayerWeights":

vllm/lora/utils.py

@@ -2,7 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import os
-from typing import TYPE_CHECKING, Optional
+from typing import TYPE_CHECKING
 
 import huggingface_hub
 from huggingface_hub.utils import HfHubHTTPError, HFValidationError
@@ -131,7 +131,7 @@ def replace_submodule(
 
 
 def parse_fine_tuned_lora_name(
-    name: str, weights_mapper: Optional["WeightsMapper"] = None
+    name: str, weights_mapper: "WeightsMapper | None" = None
 ) -> tuple[str, bool]:
     """Parse the name of lora weights.
 

vllm/model_executor/layers/fused_moe/config.py

@@ -2,7 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 from dataclasses import dataclass
 from enum import IntEnum
-from typing import Optional, Union
+from typing import Union
 
 import torch
 
@@ -284,7 +284,7 @@ class FusedMoEQuantConfig:
         return self._w1.bias
 
     @property
-    def w1_precision(self) -> Optional["PrecisionConfig"]:
+    def w1_precision(self) -> "PrecisionConfig | None":
         assert self._w1.scale is None or isinstance(self._w1.scale, PrecisionConfig)
         return self._w1.scale
 
@@ -306,7 +306,7 @@ class FusedMoEQuantConfig:
         return self._w2.bias
 
     @property
-    def w2_precision(self) -> Optional["PrecisionConfig"]:
+    def w2_precision(self) -> "PrecisionConfig | None":
         assert self._w2.scale is None or isinstance(self._w2.scale, PrecisionConfig)
         return self._w2.scale
 

vllm/model_executor/layers/quantization/awq_marlin.py

@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-from typing import TYPE_CHECKING, Any, Optional
+from typing import TYPE_CHECKING, Any
 
 import torch
 from safetensors.torch import _TYPES as _SAFETENSORS_TO_TORCH_DTYPE
@@ -148,7 +148,7 @@ class AWQMarlinConfig(QuantizationConfig):
     @classmethod
     def override_quantization_method(
         cls, hf_quant_cfg, user_quant
-    ) -> Optional["QuantizationMethods"]:
+    ) -> "QuantizationMethods | None":
         can_convert = cls.is_awq_marlin_compatible(hf_quant_cfg)
         is_valid_user_quant = (
             user_quant is None or user_quant == "marlin" or user_quant == "awq_marlin"
@@ -173,7 +173,7 @@ class AWQMarlinConfig(QuantizationConfig):
 
     def get_quant_method(
         self, layer: torch.nn.Module, prefix: str
-    ) -> Optional["QuantizeMethodBase"]:
+    ) -> "QuantizeMethodBase | None":
         if isinstance(layer, LinearBase) or (
             isinstance(layer, ParallelLMHead) and self.lm_head_quantized
         ):

vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py

@@ -3,7 +3,7 @@
 
 from contextlib import suppress
 from functools import partial
-from typing import TYPE_CHECKING, Any, Literal, Optional, cast
+from typing import TYPE_CHECKING, Any, Literal, cast
 
 import torch
 from compressed_tensors.config import (
@@ -160,7 +160,7 @@ class CompressedTensorsConfig(QuantizationConfig):
         self,
         layer: torch.nn.Module,
         prefix: str,
-    ) -> Optional["QuantizeMethodBase"]:
+    ) -> "QuantizeMethodBase | None":
         if isinstance(layer, LinearBase):
             # collect schemes
             quant_scheme = self.get_scheme(layer=layer, layer_name=prefix)
@@ -691,7 +691,7 @@ class CompressedTensorsConfig(QuantizationConfig):
 
     def get_scheme(
         self, layer: torch.nn.Module, layer_name: str | None = None
-    ) -> Optional["CompressedTensorsScheme"]:
+    ) -> "CompressedTensorsScheme | None":
         """
         compressed-tensors supports non uniform in the following way:
 

vllm/model_executor/layers/quantization/cpu_wna16.py

@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-from typing import Any, Optional
+from typing import Any
 
 import torch
 from safetensors.torch import _TYPES as _SAFETENSORS_TO_TORCH_DTYPE
@@ -105,7 +105,7 @@ class CPUAWQConfig(QuantizationConfig):
     @classmethod
     def override_quantization_method(
         cls, hf_quant_cfg, user_quant
-    ) -> Optional["QuantizationMethods"]:
+    ) -> "QuantizationMethods | None":
         quant_method = hf_quant_cfg.get("quant_method", "").lower()
         if current_platform.is_cpu() and (quant_method == "awq"):
             return cls.get_name()
@@ -113,7 +113,7 @@ class CPUAWQConfig(QuantizationConfig):
 
     def get_quant_method(
         self, layer: torch.nn.Module, prefix: str
-    ) -> Optional["QuantizeMethodBase"]:
+    ) -> "QuantizeMethodBase | None":
         if isinstance(layer, LinearBase) or (
             isinstance(layer, ParallelLMHead) and self.lm_head_quantized
         ):

vllm/model_executor/layers/quantization/experts_int8.py

@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-from typing import Any, Optional
+from typing import Any
 
 import torch
 
@@ -52,7 +52,7 @@ class ExpertsInt8Config(QuantizationConfig):
 
     def get_quant_method(
         self, layer: torch.nn.Module, prefix: str
-    ) -> Optional["QuantizeMethodBase"]:
+    ) -> "QuantizeMethodBase | None":
         if isinstance(layer, LinearBase):
             return UnquantizedLinearMethod()
         elif isinstance(layer, FusedMoE):

vllm/model_executor/layers/quantization/fbgemm_fp8.py

@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-from typing import Any, Optional
+from typing import Any
 
 import torch
 from torch.nn import Module
@@ -78,7 +78,7 @@ class FBGEMMFp8Config(QuantizationConfig):
 
     def get_quant_method(
         self, layer: torch.nn.Module, prefix: str
-    ) -> Optional["QuantizeMethodBase"]:
+    ) -> "QuantizeMethodBase | None":
         if isinstance(layer, LinearBase):
             if is_layer_skipped(
                 prefix=prefix,

vllm/model_executor/layers/quantization/fp8.py

@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-from typing import TYPE_CHECKING, Any, Optional
+from typing import TYPE_CHECKING, Any
 
 import torch
 from torch.nn import Module
@@ -182,7 +182,7 @@ class Fp8Config(QuantizationConfig):
 
     def get_xpu_quant_method(
         self, layer: torch.nn.Module, prefix: str
-    ) -> Optional["QuantizeMethodBase"]:
+    ) -> "QuantizeMethodBase | None":
         from vllm.model_executor.layers.quantization.ipex_quant import (
             XPUFp8LinearMethod,
             XPUFp8MoEMethod,
@@ -218,7 +218,7 @@ class Fp8Config(QuantizationConfig):
 
     def get_quant_method(
         self, layer: torch.nn.Module, prefix: str
-    ) -> Optional["QuantizeMethodBase"]:
+    ) -> "QuantizeMethodBase | None":
         if current_platform.is_xpu():
             return self.get_xpu_quant_method(layer, prefix)
         if isinstance(layer, LinearBase):

vllm/model_executor/layers/quantization/gguf.py

@@ -3,7 +3,7 @@
 
 from collections.abc import Mapping
 from types import MappingProxyType
-from typing import Any, Optional
+from typing import Any
 
 import gguf
 import torch
@@ -77,7 +77,7 @@ class GGUFConfig(QuantizationConfig):
 
     def get_quant_method(
         self, layer: torch.nn.Module, prefix: str
-    ) -> Optional["QuantizeMethodBase"]:
+    ) -> "QuantizeMethodBase | None":
         if isinstance(layer, LinearBase):
             if is_layer_skipped_gguf(
                 prefix, self.unquantized_modules, self.packed_modules_mapping

vllm/model_executor/layers/quantization/gptq_marlin.py

@@ -2,7 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 from copy import deepcopy
-from typing import Any, Optional
+from typing import Any
 
 import torch
 from safetensors.torch import _TYPES as _SAFETENSORS_TO_TORCH_DTYPE
@@ -240,7 +240,7 @@ class GPTQMarlinConfig(QuantizationConfig):
 
     def get_quant_method(
         self, layer: torch.nn.Module, prefix: str
-    ) -> Optional["QuantizeMethodBase"]:
+    ) -> "QuantizeMethodBase | None":
         if isinstance(layer, FusedMoE):
             from vllm.model_executor.layers.quantization.moe_wna16 import MoeWNA16Config
 

vllm/model_executor/layers/quantization/inc.py

@@ -2,7 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 from fractions import Fraction
-from typing import TYPE_CHECKING, Any, Optional
+from typing import TYPE_CHECKING, Any
 
 import regex as re
 import torch
@@ -456,7 +456,7 @@ class INCConfig(QuantizationConfig):
     @classmethod
     def override_quantization_method(
         cls, hf_quant_cfg, user_quant
-    ) -> Optional["QuantizationMethods"]:
+    ) -> "QuantizationMethods | None":
         """Override the `auto-round` method to `inc`."""
         is_auto_round_format = hf_quant_cfg.get("quant_method", None) == "auto-round"
         if is_auto_round_format:

vllm/model_executor/layers/quantization/ipex_quant.py

@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-from typing import Any, Optional
+from typing import Any
 
 import torch
 from packaging import version
@@ -144,7 +144,7 @@ class IPEXConfig(QuantizationConfig):
 
     def get_quant_method(
         self, layer: torch.nn.Module, prefix: str
-    ) -> Optional["LinearMethodBase"]:
+    ) -> "LinearMethodBase | None":
         if isinstance(layer, LinearBase):
             if self.method == "awq":
                 if is_layer_skipped(

vllm/model_executor/layers/quantization/modelopt.py

@@ -2,7 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 from fnmatch import fnmatch
-from typing import TYPE_CHECKING, Any, Optional
+from typing import TYPE_CHECKING, Any
 
 import torch
 from torch.nn import Module
@@ -181,7 +181,7 @@ class ModelOptQuantConfigBase(QuantizationConfig):
 
     def get_quant_method(
         self, layer: torch.nn.Module, prefix: str
-    ) -> Optional["QuantizeMethodBase"]:
+    ) -> "QuantizeMethodBase | None":
         # handle kv-cache first so we can focus only on weight quantization thereafter
         if isinstance(layer, Attention):
             return self.KVCacheMethodCls(self)

vllm/model_executor/layers/quantization/moe_wna16.py

@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-from typing import Any, Optional
+from typing import Any
 
 import torch
 
@@ -163,7 +163,7 @@ class MoeWNA16Config(QuantizationConfig):
 
     def get_quant_method(
         self, layer: torch.nn.Module, prefix: str
-    ) -> Optional["QuantizeMethodBase"]:
+    ) -> "QuantizeMethodBase | None":
         if is_layer_skipped_quant(prefix, self.modules_to_not_convert):
             if isinstance(layer, FusedMoE):
                 return UnquantizedFusedMoEMethod(layer.moe_config)

vllm/model_executor/layers/quantization/mxfp4.py

@@ -1,7 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 from enum import Enum
-from typing import Optional
 
 import torch
 from torch.nn.parameter import Parameter
@@ -197,7 +196,7 @@ class Mxfp4Config(QuantizationConfig):
 
     def get_quant_method(
         self, layer: torch.nn.Module, prefix: str
-    ) -> Optional["QuantizeMethodBase"]:
+    ) -> "QuantizeMethodBase | None":
         if isinstance(layer, LinearBase):
             if self.ignored_layers and is_layer_skipped(
                 prefix=prefix,

vllm/model_executor/layers/quantization/petit.py

@@ -2,7 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 # Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/layers/quantization/modelopt.py
 
-from typing import Any, Optional
+from typing import Any
 
 import regex as re
 import torch
@@ -159,7 +159,7 @@ class PetitNvFp4Config(QuantizationConfig):
 
     def get_quant_method(
         self, layer: torch.nn.Module, prefix: str
-    ) -> Optional["QuantizeMethodBase"]:
+    ) -> "QuantizeMethodBase | None":
         exclude = self.require_exclude_modules()
 
         if isinstance(layer, LinearBase):

vllm/model_executor/layers/quantization/ptpc_fp8.py

@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-from typing import Any, Optional
+from typing import Any
 
 import torch
 from torch.nn.parameter import Parameter
@@ -67,7 +67,7 @@ class PTPCFp8Config(Fp8Config):
 
     def get_quant_method(
         self, layer: torch.nn.Module, prefix: str
-    ) -> Optional["QuantizeMethodBase"]:
+    ) -> "QuantizeMethodBase | None":
         if isinstance(layer, LinearBase):
             if is_layer_skipped(prefix, self.ignored_layers):
                 return UnquantizedLinearMethod()

vllm/model_executor/layers/quantization/quark/quark.py

@@ -2,7 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import fnmatch
-from typing import TYPE_CHECKING, Any, Optional, cast
+from typing import TYPE_CHECKING, Any, cast
 
 import torch
 
@@ -102,7 +102,7 @@ class QuarkConfig(QuantizationConfig):
 
     def get_quant_method(
         self, layer: torch.nn.Module, prefix: str
-    ) -> Optional["QuantizeMethodBase"]:
+    ) -> "QuantizeMethodBase | None":
         # Check if the layer is skipped for quantization.
         exclude_layers = cast(list[str], self.quant_config.get("exclude"))
         if should_ignore_layer(

vllm/model_executor/layers/quantization/torchao.py

@@ -4,7 +4,7 @@ import importlib
 import json
 import types
 from importlib.util import find_spec
-from typing import Any, Optional
+from typing import Any
 
 import regex as re
 import torch
@@ -209,7 +209,7 @@ class TorchAOConfig(QuantizationConfig):
 
     def get_quant_method(
         self, layer: torch.nn.Module, prefix: str
-    ) -> Optional["QuantizeMethodBase"]:
+    ) -> "QuantizeMethodBase | None":
         if not isinstance(layer, LinearBase):
             return None
 

vllm/model_executor/layers/quantization/utils/petit_utils.py

@@ -1,6 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-from typing import TYPE_CHECKING, Optional
+from typing import TYPE_CHECKING
 
 import torch
 
@@ -9,7 +9,7 @@ if TYPE_CHECKING:
     from types import ModuleType
 
 # 1. Create a global variable as a placeholder for the module
-_petit_kernel: Optional["ModuleType"] = None
+_petit_kernel: "ModuleType | None" = None
 
 _PETIT_INSTALL_MSG = (
     "Petit is not installed. Please install it with `pip install petit-kernel`."

vllm/model_executor/model_loader/tensorizer.py

@@ -12,7 +12,7 @@ import threading
 import time
 from collections.abc import Generator, MutableMapping
 from dataclasses import asdict, dataclass, field, fields
-from typing import TYPE_CHECKING, Any, ClassVar, Optional
+from typing import TYPE_CHECKING, Any, ClassVar
 
 import regex as re
 import torch
@@ -323,7 +323,7 @@ class TensorizerConfig(MutableMapping):
                 " is unstable and may lead to errors."
             )
 
-    def open_stream(self, tensorizer_args: Optional["TensorizerArgs"] = None):
+    def open_stream(self, tensorizer_args: "TensorizerArgs | None" = None):
         if tensorizer_args is None:
             tensorizer_args = self._construct_tensorizer_args()
 

vllm/multimodal/inputs.py

@@ -11,7 +11,6 @@ from typing import (
     TYPE_CHECKING,
     Any,
     Literal,
-    Optional,
     TypeAlias,
     TypedDict,
     Union,
@@ -186,7 +185,7 @@ class PlaceholderRange:
     length: int
     """The length of the placeholder."""
 
-    is_embed: Optional["torch.Tensor"] = None
+    is_embed: "torch.Tensor | None" = None
     """
     A boolean mask of shape `(length,)` indicating which positions
     between `offset` and `offset + length` to assign embeddings to.
@@ -341,7 +340,7 @@ class MultiModalFeatureSpec:
     `MultiModalFeatureSpec` per item.
     """
 
-    data: Optional["MultiModalKwargsItem"]
+    data: "MultiModalKwargsItem | None"
     """
     Represents multimodal data for this feature.
 

vllm/platforms/cuda.py

@@ -7,7 +7,7 @@ pynvml. However, it should not initialize cuda context.
 import os
 from collections.abc import Callable
 from functools import cache, wraps
-from typing import TYPE_CHECKING, Optional, TypeVar
+from typing import TYPE_CHECKING, TypeVar
 
 import torch
 from typing_extensions import ParamSpec
@@ -382,7 +382,7 @@ class CudaPlatformBase(Platform):
         cls,
         head_size: int,
         dtype: torch.dtype,
-        backend: Optional["AttentionBackendEnum"] = None,
+        backend: "AttentionBackendEnum | None" = None,
     ) -> "AttentionBackendEnum":
         if backend is not None:
             assert backend in cls.get_supported_vit_attn_backends(), (

vllm/platforms/interface.py

@@ -7,7 +7,7 @@ import platform
 import random
 import sys
 from datetime import timedelta
-from typing import TYPE_CHECKING, Any, NamedTuple, Optional
+from typing import TYPE_CHECKING, Any, NamedTuple
 
 import numpy as np
 import torch
@@ -243,7 +243,7 @@ class Platform:
         cls,
         head_size: int,
         dtype: torch.dtype,
-        backend: Optional["AttentionBackendEnum"] = None,
+        backend: "AttentionBackendEnum | None" = None,
     ) -> "AttentionBackendEnum":
         """
         Get the vision attention backend class of a device.

vllm/platforms/rocm.py

@@ -3,7 +3,7 @@
 
 import os
 from functools import cache, lru_cache, wraps
-from typing import TYPE_CHECKING, Optional
+from typing import TYPE_CHECKING
 
 import torch
 
@@ -356,7 +356,7 @@ class RocmPlatform(Platform):
         cls,
         head_size: int,
         dtype: torch.dtype,
-        backend: Optional["AttentionBackendEnum"] = None,
+        backend: "AttentionBackendEnum | None" = None,
     ) -> "AttentionBackendEnum":
         if backend is not None:
             assert backend in cls.get_supported_vit_attn_backends(), (

vllm/platforms/xpu.py

@@ -3,7 +3,7 @@
 
 import contextlib
 import os
-from typing import TYPE_CHECKING, Optional
+from typing import TYPE_CHECKING
 
 import torch
 
@@ -88,7 +88,7 @@ class XPUPlatform(Platform):
         cls,
         head_size: int,
         dtype: torch.dtype,
-        backend: Optional["AttentionBackendEnum"] = None,
+        backend: "AttentionBackendEnum | None" = None,
     ) -> "AttentionBackendEnum":
         if backend is not None:
             assert backend in cls.get_supported_vit_attn_backends(), (

vllm/pooling_params.py

@@ -2,7 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 from copy import deepcopy
-from typing import Annotated, Any, Optional
+from typing import Annotated, Any
 
 import msgspec
 
@@ -80,7 +80,7 @@ class PoolingParams(
         return deepcopy(self)
 
     def verify(
-        self, task: PoolingTask, model_config: Optional["ModelConfig"] = None
+        self, task: PoolingTask, model_config: "ModelConfig | None" = None
     ) -> None:
         if self.task is None:
             self.task = task
@@ -106,7 +106,7 @@ class PoolingParams(
         self._verify_valid_parameters()
 
     def _merge_default_parameters(
-        self, model_config: Optional["ModelConfig"] = None
+        self, model_config: "ModelConfig | None" = None
     ) -> None:
         if model_config is None:
             return
@@ -160,7 +160,7 @@ class PoolingParams(
                 if getattr(self, k, None) is None:
                     setattr(self, k, getattr(pooler_config, k))
 
-    def _set_default_parameters(self, model_config: Optional["ModelConfig"]):
+    def _set_default_parameters(self, model_config: "ModelConfig | None"):
         if self.task in ["embed", "token_embed"]:
             if self.use_activation is None:
                 self.use_activation = True

vllm/profiler/layerwise_profile.py

@@ -5,7 +5,7 @@ import copy
 from collections import defaultdict
 from collections.abc import Callable
 from dataclasses import asdict, dataclass, field
-from typing import Any, Optional, TypeAlias
+from typing import Any, TypeAlias
 
 from torch._C._autograd import DeviceType, _KinetoEvent, _ProfilerResult
 from torch._C._profiler import _EventType, _ExperimentalConfig, _ProfilerEvent
@@ -31,7 +31,7 @@ except ImportError:
 @dataclass
 class _ModuleTreeNode:
     event: _ProfilerEvent
-    parent: Optional["_ModuleTreeNode"] = None
+    parent: "_ModuleTreeNode | None" = None
     children: list["_ModuleTreeNode"] = field(default_factory=list)
     trace: str = ""
 

vllm/transformers_utils/s3_utils.py

@@ -2,7 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import fnmatch
-from typing import TYPE_CHECKING, Optional
+from typing import TYPE_CHECKING
 
 from vllm.utils.import_utils import PlaceholderModule
 
@@ -32,7 +32,7 @@ def _filter_ignore(paths: list[str], patterns: list[str]) -> list[str]:
 
 
 def glob(
-    s3: Optional["BaseClient"] = None,
+    s3: "BaseClient | None" = None,
     path: str = "",
     allow_pattern: list[str] | None = None,
 ) -> list[str]:

vllm/v1/attention/backends/mla/flashmla_sparse.py

@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 from dataclasses import dataclass
-from typing import TYPE_CHECKING, ClassVar, Optional
+from typing import TYPE_CHECKING, ClassVar
 
 import numpy as np
 import torch
@@ -707,7 +707,7 @@ class FlashMLASparseImpl(MLACommonBaseImpl[FlashMLASparseMetadata]):
         kv_sharing_target_layer_name: str | None,
         # MLA Specific Arguments
         topk_indice_buffer: torch.Tensor | None = None,
-        indexer: Optional["Indexer"] = None,
+        indexer: "Indexer | None" = None,
         **mla_args,
     ) -> None:
         super().__init__(

vllm/v1/attention/backends/mla/rocm_aiter_mla_sparse.py

@@ -2,7 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 from dataclasses import dataclass
-from typing import TYPE_CHECKING, ClassVar, Optional
+from typing import TYPE_CHECKING, ClassVar
 
 import numpy as np
 import torch
@@ -284,7 +284,7 @@ class ROCMAiterMLASparseImpl(MLACommonBaseImpl[ROCMAiterMLASparseMetadata]):
         kv_sharing_target_layer_name: str | None,
         # MLA Specific Arguments
         topk_indice_buffer: torch.Tensor | None = None,
-        indexer: Optional["Indexer"] = None,
+        indexer: "Indexer | None" = None,
         **mla_args,
     ) -> None:
         super().__init__(

vllm/v1/attention/backends/tree_attn.py

@@ -4,7 +4,7 @@
 
 import ast
 from dataclasses import dataclass
-from typing import ClassVar, Optional
+from typing import ClassVar
 
 import torch
 
@@ -87,11 +87,11 @@ class TreeAttentionMetadata:
     tree_attn_bias: torch.Tensor | None = None
 
     # Cached Prefill/decode metadata.
-    _cached_prefill_metadata: Optional["TreeAttentionMetadata"] = None
-    _cached_decode_metadata: Optional["TreeAttentionMetadata"] = None
+    _cached_prefill_metadata: "TreeAttentionMetadata | None" = None
+    _cached_decode_metadata: "TreeAttentionMetadata | None" = None
 
     @property
-    def prefill_metadata(self) -> Optional["TreeAttentionMetadata"]:
+    def prefill_metadata(self) -> "TreeAttentionMetadata | None":
         if self.num_prefills == 0:
             return None
 
@@ -116,7 +116,7 @@ class TreeAttentionMetadata:
         return self._cached_prefill_metadata
 
     @property
-    def decode_metadata(self) -> Optional["TreeAttentionMetadata"]:
+    def decode_metadata(self) -> "TreeAttentionMetadata | None":
         if self.num_decode_tokens == 0:
             return None
 

vllm/v1/core/sched/interface.py

@@ -2,7 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 from abc import ABC, abstractmethod
 from collections.abc import Iterable
-from typing import TYPE_CHECKING, Optional
+from typing import TYPE_CHECKING
 
 from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalRegistry
 
@@ -189,7 +189,7 @@ class SchedulerInterface(ABC):
         raise NotImplementedError
 
     @abstractmethod
-    def make_stats(self) -> Optional["SchedulerStats"]:
+    def make_stats(self) -> "SchedulerStats | None":
         """Make a SchedulerStats object for logging.
 
         The SchedulerStats object is created for every scheduling step.
@@ -201,5 +201,5 @@ class SchedulerInterface(ABC):
         """Shutdown the scheduler."""
         raise NotImplementedError
 
-    def get_kv_connector(self) -> Optional["KVConnectorBase_V1"]:
+    def get_kv_connector(self) -> "KVConnectorBase_V1 | None":
         return None

vllm/v1/engine/parallel_sampling.py

@@ -2,7 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 from copy import copy
-from typing import Optional, cast
+from typing import cast
 
 from vllm.outputs import CompletionOutput
 from vllm.sampling_params import RequestOutputKind, SamplingParams
@@ -133,7 +133,7 @@ class ParentRequest:
 
     @staticmethod
     def observe_finished_request(
-        parent_req: Optional["ParentRequest"],
+        parent_req: "ParentRequest | None",
         iteration_stats: IterationStats,
         num_generation_tokens: int,
     ):

vllm/v1/request.py

@@ -7,7 +7,7 @@ from collections import deque
 from collections.abc import Callable, Mapping
 from dataclasses import dataclass
 from functools import partial
-from typing import TYPE_CHECKING, Any, Optional
+from typing import TYPE_CHECKING, Any
 
 import torch
 
@@ -68,7 +68,7 @@ class Request:
         arrival_time: float | None = None,
         prompt_embeds: torch.Tensor | None = None,
         mm_features: list[MultiModalFeatureSpec] | None = None,
-        lora_request: Optional["LoRARequest"] = None,
+        lora_request: "LoRARequest | None" = None,
         cache_salt: str | None = None,
         priority: int = 0,
         trace_headers: Mapping[str, str] | None = None,

vllm/v1/sample/logits_processor/interface.py

@@ -4,7 +4,7 @@ from abc import ABC, abstractmethod
 from collections.abc import Sequence
 from dataclasses import dataclass
 from enum import Enum, auto
-from typing import TYPE_CHECKING, Optional
+from typing import TYPE_CHECKING
 
 import torch
 
@@ -94,7 +94,7 @@ class LogitsProcessor(ABC):
     @abstractmethod
     def update_state(
         self,
-        batch_update: Optional["BatchUpdate"],
+        batch_update: "BatchUpdate | None",
     ) -> None:
         """Called when there are new output tokens, prior
         to each forward pass.

vllm/v1/utils.py

@@ -14,7 +14,6 @@ from typing import (
     TYPE_CHECKING,
     Any,
     Generic,
-    Optional,
     TypeVar,
     Union,
     overload,
@@ -229,7 +228,7 @@ def wait_for_completion_or_failure(
     api_server_manager: APIServerProcessManager,
     engine_manager: Union["CoreEngineProcManager", "CoreEngineActorManager"]
     | None = None,
-    coordinator: Optional["DPCoordinator"] = None,
+    coordinator: "DPCoordinator | None" = None,
 ) -> None:
     """Wait for all processes to complete or detect if any fail.
 

vllm/v1/worker/ubatching.py

@@ -1,7 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import threading
-from typing import Optional
 
 import torch
 
@@ -15,7 +14,7 @@ logger = init_logger(__name__)
 _THREAD_ID_TO_CONTEXT: dict = {}
 # Here we hardcode the number of microbatches to 2 for default.
 _NUM_UBATCHES: int = 2
-_CURRENT_CONTEXTS: list[Optional["UBatchContext"]] = []
+_CURRENT_CONTEXTS: list["UBatchContext | None"] = []
 
 
 class UBatchContext:

vllm/v1/worker/workspace.py

@@ -5,7 +5,6 @@ import inspect
 import os
 from itertools import accumulate
 from math import prod
-from typing import Optional
 
 import torch
 
@@ -26,7 +25,7 @@ _MB = 1024**2
 _GiB = 1024**3
 
 # Global workspace manager instance
-_manager: Optional["WorkspaceManager"] = None
+_manager: "WorkspaceManager | None" = None
 
 
 class WorkspaceManager: