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/glm4_1v.py

@@ -27,9 +27,9 @@
 """Inference-only GLM-4V model compatible with HuggingFace weights."""
 
 import math
-from collections.abc import Iterable, Mapping, Sequence
+from collections.abc import Callable, Iterable, Mapping, Sequence
 from functools import partial
-from typing import Annotated, Any, Callable, Literal, Optional, Union
+from typing import Annotated, Any, Literal, TypeAlias
 
 import numpy as np
 import torch
@@ -140,7 +140,7 @@ class Glm4vImageEmbeddingInputs(TensorSchema):
     image_grid_thw: Annotated[torch.Tensor, TensorShape("n", 3)]
 
 
-Glm4vImageInputs = Union[Glm4vImagePixelInputs, Glm4vImageEmbeddingInputs]
+Glm4vImageInputs: TypeAlias = Glm4vImagePixelInputs | Glm4vImageEmbeddingInputs
 
 
 class Glm4vVideoPixelInputs(TensorSchema):
@@ -176,7 +176,7 @@ class Glm4vVideoEmbeddingInputs(TensorSchema):
     video_grid_thw: Annotated[torch.Tensor, TensorShape("f", 3)]
 
 
-Glm4vVideoInputs = Union[Glm4vVideoPixelInputs, Glm4vVideoEmbeddingInputs]
+Glm4vVideoInputs: TypeAlias = Glm4vVideoPixelInputs | Glm4vVideoEmbeddingInputs
 
 # ==== Vision Encoder ==== #
 
@@ -187,7 +187,7 @@ class Glm4vVisionMLP(nn.Module):
         in_features: int,
         hidden_features: int,
         bias: bool = False,
-        quant_config: Optional[QuantizationConfig] = None,
+        quant_config: QuantizationConfig | None = None,
         prefix: str = "",
         use_data_parallel: bool = False,
     ):
@@ -244,7 +244,7 @@ class Glm4vVisionAttention(nn.Module):
         embed_dim: int,
         num_heads: int,
         projection_size: int,
-        quant_config: Optional[QuantizationConfig] = None,
+        quant_config: QuantizationConfig | None = None,
         prefix: str = "",
         use_data_parallel: bool = False,
     ) -> None:
@@ -334,8 +334,8 @@ class Glm4vVisionAttention(nn.Module):
         x: torch.Tensor,
         cu_seqlens: torch.Tensor,
         rotary_pos_emb: torch.Tensor,
-        max_seqlen: Optional[int] = None,  # Only used for Flash Attention
-        seqlens: Optional[list[int]] = None,  # Only used for xFormers
+        max_seqlen: int | None = None,  # Only used for Flash Attention
+        seqlens: list[int] | None = None,  # Only used for xFormers
     ) -> torch.Tensor:
         # [s, b, c] --> [s, b, head * 3 * head_dim]
         x, _ = self.qkv(x)
@@ -413,8 +413,8 @@ class Glm4vVisionBlock(nn.Module):
         dim: int,
         num_heads: int,
         mlp_hidden_dim: int,
-        norm_layer: Optional[Callable[[int], nn.Module]] = None,
-        quant_config: Optional[QuantizationConfig] = None,
+        norm_layer: Callable[[int], nn.Module] | None = None,
+        quant_config: QuantizationConfig | None = None,
         prefix: str = "",
         use_data_parallel: bool = False,
     ) -> None:
@@ -445,8 +445,8 @@ class Glm4vVisionBlock(nn.Module):
         x: torch.Tensor,
         cu_seqlens: torch.Tensor,
         rotary_pos_emb: torch.Tensor,
-        max_seqlen: Optional[int] = None,  # Only used for Flash Attention
-        seqlens: Optional[list[int]] = None,  # Only used for xFormers
+        max_seqlen: int | None = None,  # Only used for Flash Attention
+        seqlens: list[int] | None = None,  # Only used for xFormers
     ) -> torch.Tensor:
         x_attn = self.attn(
             self.norm1(x),
@@ -495,7 +495,7 @@ class Glm4vPatchMerger(nn.Module):
         self,
         d_model: int,
         context_dim: int,
-        quant_config: Optional[QuantizationConfig] = None,
+        quant_config: QuantizationConfig | None = None,
         bias: bool = False,
         prefix: str = "",
         use_data_parallel: bool = False,
@@ -693,7 +693,7 @@ class Glm4vVisionTransformer(nn.Module):
         self,
         vision_config: Glm4vVisionConfig,
         norm_eps: float = 1e-6,
-        quant_config: Optional[QuantizationConfig] = None,
+        quant_config: QuantizationConfig | None = None,
         prefix: str = "",
         use_data_parallel: bool = False,
     ) -> None:
@@ -809,7 +809,7 @@ class Glm4vVisionTransformer(nn.Module):
     def compute_attn_mask_seqlen(
         self,
         cu_seqlens: torch.Tensor,
-    ) -> tuple[Optional[int], Optional[list[int]]]:
+    ) -> tuple[int | None, list[int] | None]:
         max_seqlen, seqlens = None, None
         seqlens = (cu_seqlens[1:] - cu_seqlens[:-1]).tolist()
         if (
@@ -904,7 +904,7 @@ class Glm4vProcessingInfo(BaseProcessingInfo):
     def get_tokenizer(self):
         return self.ctx.tokenizer
 
-    def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
+    def get_supported_mm_limits(self) -> Mapping[str, int | None]:
         return {"image": None, "video": 1}
 
     def get_image_processor(self, **kwargs: object) -> Glm4vImageProcessor:
@@ -1141,7 +1141,7 @@ class Glm4vDummyInputsBuilder(BaseDummyInputsBuilder[Glm4vProcessingInfo]):
         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_images = mm_counts.get("image", 0)
         num_videos = mm_counts.get("video", 0)
@@ -1177,7 +1177,7 @@ class Glm4vDummyInputsBuilder(BaseDummyInputsBuilder[Glm4vProcessingInfo]):
         height: int,
         num_frames: int,
         num_videos: int,
-        overrides: Optional[VideoDummyOptions] = None,
+        overrides: VideoDummyOptions | None = None,
     ) -> list[VideoItem]:
         if overrides:
             if overrides.num_frames:
@@ -1419,7 +1419,7 @@ class Glm4vForConditionalGeneration(
     supports_encoder_tp_data = True
 
     @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("image"):
             return "<|begin_of_image|><|image|><|end_of_image|>"
         if modality.startswith("video"):
@@ -1465,7 +1465,7 @@ class Glm4vForConditionalGeneration(
 
     def _parse_and_validate_image_input(
         self, **kwargs: object
-    ) -> Optional[Glm4vImageInputs]:
+    ) -> Glm4vImageInputs | None:
         pixel_values = kwargs.pop("pixel_values", None)
         image_embeds = kwargs.pop("image_embeds", None)
         image_grid_thw = kwargs.pop("image_grid_thw", None)
@@ -1489,7 +1489,7 @@ class Glm4vForConditionalGeneration(
 
     def _parse_and_validate_video_input(
         self, **kwargs: object
-    ) -> Optional[Glm4vVideoInputs]:
+    ) -> Glm4vVideoInputs | None:
         pixel_values_videos = kwargs.pop("pixel_values_videos", None)
         video_embeds = kwargs.pop("video_embeds", None)
         video_grid_thw = kwargs.pop("video_grid_thw", None)
@@ -1594,7 +1594,7 @@ class Glm4vForConditionalGeneration(
 
     def get_multimodal_embeddings(
         self, **kwargs: object
-    ) -> Optional[MultiModalEmbeddings]:
+    ) -> MultiModalEmbeddings | None:
         mm_input_by_modality = self._parse_and_validate_multimodal_inputs(**kwargs)
         if not mm_input_by_modality:
             return None
@@ -1619,10 +1619,10 @@ class Glm4vForConditionalGeneration(
         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:
         """Run forward pass for GLM-4V.
 
         Args:
@@ -1652,7 +1652,7 @@ class Glm4vForConditionalGeneration(
     def compute_logits(
         self,
         hidden_states: torch.Tensor,
-    ) -> Optional[torch.Tensor]:
+    ) -> torch.Tensor | None:
         return self.language_model.compute_logits(hidden_states)
 
     def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]: