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/logits_processor.py

@@ -2,8 +2,6 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 """A layer that compute logits from hidden_stats."""
 
-from typing import Optional
-
 import torch
 
 from vllm.distributed import (
@@ -28,10 +26,10 @@ class LogitsProcessor(CustomOp):
     def __init__(
         self,
         vocab_size: int,
-        org_vocab_size: Optional[int] = None,
+        org_vocab_size: int | None = None,
         scale: float = 1.0,
         logits_as_input: bool = False,
-        soft_cap: Optional[float] = None,
+        soft_cap: float | None = None,
     ) -> None:
         """
         Args:
@@ -53,8 +51,8 @@ class LogitsProcessor(CustomOp):
         self,
         lm_head: VocabParallelEmbedding,
         hidden_states: torch.Tensor,
-        embedding_bias: Optional[torch.Tensor] = None,
-    ) -> Optional[torch.Tensor]:
+        embedding_bias: torch.Tensor | None = None,
+    ) -> torch.Tensor | None:
         if self.logits_as_input:
             logits = hidden_states
         else:
@@ -88,8 +86,8 @@ class LogitsProcessor(CustomOp):
         self,
         hidden_states: torch.Tensor,
         lm_head: VocabParallelEmbedding,
-        embedding_bias: Optional[torch.Tensor],
-    ) -> Optional[torch.Tensor]:
+        embedding_bias: torch.Tensor | None,
+    ) -> torch.Tensor | None:
         # Get the logits for the next tokens.
         logits = lm_head.quant_method.apply(lm_head, hidden_states, bias=embedding_bias)