[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/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

 # 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

 # 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

 # 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

 # 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

 # 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

 # 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

 # 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

 # 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

 # 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(), (