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/models/midashenglm.py

@@ -25,8 +25,8 @@
 
 import collections
 import collections.abc
-from collections.abc import Iterable, Mapping, Sequence
-from typing import Any, Callable, Optional, TypedDict, Union, cast
+from collections.abc import Callable, Iterable, Mapping, Sequence
+from typing import Any, TypeAlias, TypedDict, cast
 
 import numpy as np
 import torch
@@ -66,7 +66,7 @@ from vllm.transformers_utils.configs.midashenglm import DashengConfig
 from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
 from .utils import AutoWeightsLoader, init_vllm_registered_model, maybe_prefix
 
-_Tuple2 = Union[int, tuple[int, int], Sequence[int]]
+_Tuple2: TypeAlias = int | tuple[int, int] | Sequence[int]
 
 
 def _resolve_tuple2(x: _Tuple2) -> tuple[int, int]:
@@ -105,7 +105,7 @@ class AudioPatchEmbed(nn.Module):
         patch_stride: _Tuple2 = 16,
         in_chans: int = 1,
         embed_dim: int = 768,
-        norm_layer: Optional[Callable] = None,
+        norm_layer: Callable | None = None,
         flatten: bool = False,
     ):
         super().__init__()
@@ -151,9 +151,9 @@ class DashengMlp(nn.Module):
     def __init__(
         self,
         in_features: int,
-        hidden_features: Optional[int] = None,
-        out_features: Optional[int] = None,
-        quant_config: Optional[QuantizationConfig] = None,
+        hidden_features: int | None = None,
+        out_features: int | None = None,
+        quant_config: QuantizationConfig | None = None,
         prefix: str = "",
     ):
         super().__init__()
@@ -186,7 +186,7 @@ class DashengAttention(nn.Module):
         dim: int,
         num_heads: int = 8,
         qkv_bias: bool = False,
-        quant_config: Optional[QuantizationConfig] = None,
+        quant_config: QuantizationConfig | None = None,
         prefix: str = "",
     ):
         super().__init__()
@@ -226,7 +226,7 @@ class DashengAttention(nn.Module):
             prefix=f"{prefix}.proj",
         )
 
-    def forward(self, x: torch.Tensor, mask: Optional[torch.Tensor] = None):
+    def forward(self, x: torch.Tensor, mask: torch.Tensor | None = None):
         B, N, C = x.shape
 
         qkv, _ = self.qkv(x)
@@ -253,8 +253,8 @@ class DashengBlock(nn.Module):
         num_heads: int,
         mlp_ratio: float = 4.0,
         qkv_bias: bool = False,
-        init_values: Optional[float] = None,
-        quant_config: Optional[QuantizationConfig] = None,
+        init_values: float | None = None,
+        quant_config: QuantizationConfig | None = None,
         prefix: str = "",
     ):
         super().__init__()
@@ -285,7 +285,7 @@ class DashengBlock(nn.Module):
     def forward(
         self,
         x: torch.Tensor,
-        mask: Optional[torch.Tensor] = None,
+        mask: torch.Tensor | None = None,
     ) -> torch.Tensor:
         x = x + self.ls1(self.attn(self.norm1(x), mask))
         x = x + self.ls2(self.mlp(self.norm2(x)))
@@ -349,7 +349,7 @@ class DashengAudioTransformer(nn.Module):
     def __init__(
         self,
         config: DashengConfig,
-        quant_config: Optional[QuantizationConfig] = None,
+        quant_config: QuantizationConfig | None = None,
         prefix: str = "",
     ):
         super().__init__()
@@ -393,7 +393,7 @@ class DashengAudioTransformer(nn.Module):
     def forward_features(
         self,
         x: torch.Tensor,
-        mask: Optional[torch.Tensor] = None,
+        mask: torch.Tensor | None = None,
     ) -> torch.Tensor:
         t = x.shape[-1]
         x = x + self.time_pos_embed[:, :, :, :t]
@@ -418,8 +418,8 @@ class DashengAudioTransformer(nn.Module):
     def forward(
         self,
         x: torch.Tensor,
-        x_length: Optional[torch.Tensor] = None,
-    ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
+        x_length: torch.Tensor | None = None,
+    ) -> tuple[torch.Tensor, torch.Tensor | None]:
         x = self.front_end(x)
         x = x.to(self.time_pos_embed.dtype)
         target_length_in_patches = self.target_length // 4
@@ -462,8 +462,8 @@ class AudioProjectorSubsample(nn.Module):
         in_dim: int,
         out_dim: int,
         downsample_rate=5,
-        dtype: Optional[torch.dtype] = None,
-        quant_config: Optional[QuantizationConfig] = None,
+        dtype: torch.dtype | None = None,
+        quant_config: QuantizationConfig | None = None,
         prefix: str = "",
     ):
         super().__init__()
@@ -524,7 +524,7 @@ class MiDashengLMProcessingInfo(BaseProcessingInfo):
         feature_extractor = hf_processor.feature_extractor
         return feature_extractor
 
-    def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
+    def get_supported_mm_limits(self) -> Mapping[str, int | None]:
         return {"audio": None}
 
     def get_min_audio_len(self):
@@ -550,7 +550,7 @@ class MiDashengLMDummyInputsBuilder(BaseDummyInputsBuilder[MiDashengLMProcessing
         self,
         seq_len: int,
         mm_counts: Mapping[str, int],
-        mm_options: Optional[Mapping[str, BaseDummyOptions]] = None,
+        mm_options: Mapping[str, BaseDummyOptions] | None = None,
     ) -> MultiModalDataDict:
         num_audios = mm_counts.get("audio", 0)
 
@@ -689,7 +689,7 @@ class MiDashengLMModel(nn.Module, SupportsMultiModal, SupportsPP):
     }
 
     @classmethod
-    def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
+    def get_placeholder_str(cls, modality: str, i: int) -> str | None:
         if modality.startswith("audio"):
             return "<|audio_bos|><|AUDIO|><|audio_eos|>"
 
@@ -750,7 +750,7 @@ class MiDashengLMModel(nn.Module, SupportsMultiModal, SupportsPP):
 
     def _parse_and_validate_audio_input(
         self, **kwargs: object
-    ) -> Optional[MiDashengLMAudioInputs]:
+    ) -> MiDashengLMAudioInputs | None:
         input_values = kwargs.pop("input_values", None)
         audio_length = kwargs.pop("audio_length", None)
 
@@ -820,10 +820,10 @@ class MiDashengLMModel(nn.Module, SupportsMultiModal, SupportsPP):
         self,
         input_ids: torch.Tensor,
         positions: torch.Tensor,
-        intermediate_tensors: Optional[IntermediateTensors] = None,
-        inputs_embeds: Optional[torch.Tensor] = None,
+        intermediate_tensors: IntermediateTensors | None = None,
+        inputs_embeds: torch.Tensor | None = None,
         **kwargs: object,
-    ) -> Union[torch.Tensor, IntermediateTensors]:
+    ) -> torch.Tensor | IntermediateTensors:
         if intermediate_tensors is not None:
             inputs_embeds = None
         elif inputs_embeds is None:
@@ -845,7 +845,7 @@ class MiDashengLMModel(nn.Module, SupportsMultiModal, SupportsPP):
     def compute_logits(
         self,
         hidden_states: torch.Tensor,
-    ) -> Optional[torch.Tensor]:
+    ) -> torch.Tensor | None:
         return self.decoder.compute_logits(hidden_states)
 
     def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]: