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

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

tests/quantization/test_register_quantization_config.py

@@ -7,7 +7,7 @@ See https://github.com/vllm-project/vllm/issues/11926 for more details.
 Run `pytest tests/quantization/test_register_quantization_config.py`.
 """
 
-from typing import Any, Optional
+from typing import Any
 
 import pytest
 import torch
@@ -37,10 +37,10 @@ class FakeQuantLinearMethod(UnquantizedLinearMethod):
 
     def apply(
         self,
-        layer: "torch.nn.Module",
-        x: "torch.Tensor",
-        bias: Optional["torch.Tensor"] = None,
-    ) -> "torch.Tensor":
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        bias: torch.Tensor | None = None,
+    ) -> torch.Tensor:
         """Perform fake quantization before the linear layer."""
 
         # Calculate the scales dynamically
@@ -72,7 +72,7 @@ class CustomQuantConfig(QuantizationConfig):
         """Name of the quantization method."""
         return "custom_quant"
 
-    def get_supported_act_dtypes(self) -> list["torch.dtype"]:
+    def get_supported_act_dtypes(self) -> list[torch.dtype]:
         """List of supported activation dtypes."""
         return [torch.float16, torch.bfloat16]
 
@@ -92,8 +92,8 @@ class CustomQuantConfig(QuantizationConfig):
         return CustomQuantConfig(num_bits=config.get("num_bits", 8))
 
     def get_quant_method(
-        self, layer: "torch.nn.Module", prefix: str
-    ) -> Optional["FakeQuantLinearMethod"]:
+        self, layer: torch.nn.Module, prefix: str
+    ) -> FakeQuantLinearMethod | None:
         """Get the quantize method to use for the quantized layer."""
         if isinstance(layer, LinearBase):
             return FakeQuantLinearMethod(num_bits=self.num_bits)