Update Optional[x] -> x | None and Union[x, y] to x | y (#26633)

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

vllm/model_executor/layers/quantization/mxfp4.py

@@ -1,7 +1,8 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from collections.abc import Callable
 from enum import Enum
-from typing import Callable, Optional, Union
+from typing import Optional
 
 import torch
 from torch.nn.parameter import Parameter
@@ -137,7 +138,7 @@ def get_mxfp4_backend():
 
 
 class Mxfp4Config(QuantizationConfig):
-    def __init__(self, ignored_layers: Optional[list[str]] = None):
+    def __init__(self, ignored_layers: list[str] | None = None):
         super().__init__()
         self.ignored_layers = ignored_layers
 
@@ -756,7 +757,7 @@ class Mxfp4MoEMethod(FusedMoEMethodBase):
 
     def get_fused_moe_quant_config(
         self, layer: torch.nn.Module
-    ) -> Optional[FusedMoEQuantConfig]:
+    ) -> FusedMoEQuantConfig | None:
         if self.mxfp4_backend == Mxfp4Backend.MARLIN:
             return mxfp4_w4a16_moe_quant_config(
                 w1_bias=layer.w13_bias,
@@ -824,19 +825,19 @@ class Mxfp4MoEMethod(FusedMoEMethodBase):
         top_k: int,
         renormalize: bool,
         use_grouped_topk: bool = False,
-        topk_group: Optional[int] = None,
-        num_expert_group: Optional[int] = None,
+        topk_group: int | None = None,
+        num_expert_group: int | None = None,
         global_num_experts: int = -1,
-        expert_map: Optional[torch.Tensor] = None,
-        custom_routing_function: Optional[Callable] = None,
+        expert_map: torch.Tensor | None = None,
+        custom_routing_function: Callable | None = None,
         scoring_func: str = "softmax",
-        e_score_correction_bias: Optional[torch.Tensor] = None,
+        e_score_correction_bias: torch.Tensor | None = None,
         apply_router_weight_on_input: bool = False,
         activation: str = "silu",
         enable_eplb: bool = False,
-        expert_load_view: Optional[torch.Tensor] = None,
-        logical_to_physical_map: Optional[torch.Tensor] = None,
-        logical_replica_count: Optional[torch.Tensor] = None,
+        expert_load_view: torch.Tensor | None = None,
+        logical_to_physical_map: torch.Tensor | None = None,
+        logical_replica_count: torch.Tensor | None = None,
     ) -> torch.Tensor:
         assert isinstance(self.fused_experts, mk.FusedMoEModularKernel)
 
@@ -890,21 +891,21 @@ class Mxfp4MoEMethod(FusedMoEMethodBase):
         top_k: int,
         renormalize: bool,
         use_grouped_topk: bool = False,
-        topk_group: Optional[int] = None,
-        num_expert_group: Optional[int] = None,
+        topk_group: int | None = None,
+        num_expert_group: int | None = None,
         global_num_experts: int = -1,
-        expert_map: Optional[torch.Tensor] = None,
-        custom_routing_function: Optional[Callable] = None,
+        expert_map: torch.Tensor | None = None,
+        custom_routing_function: Callable | None = None,
         scoring_func: str = "softmax",
         routed_scaling_factor: float = 1.0,
-        e_score_correction_bias: Optional[torch.Tensor] = None,
+        e_score_correction_bias: torch.Tensor | None = None,
         apply_router_weight_on_input: bool = False,
         activation: str = "silu",
         enable_eplb: bool = False,
-        expert_load_view: Optional[torch.Tensor] = None,
-        logical_to_physical_map: Optional[torch.Tensor] = None,
-        logical_replica_count: Optional[torch.Tensor] = None,
-    ) -> Union[torch.Tensor, tuple[torch.Tensor, torch.Tensor]]:
+        expert_load_view: torch.Tensor | None = None,
+        logical_to_physical_map: torch.Tensor | None = None,
+        logical_replica_count: torch.Tensor | None = None,
+    ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
         if enable_eplb:
             raise NotImplementedError("EPLB is not supported for mxfp4")