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/deepseek_vl2.py

@@ -6,7 +6,7 @@
 
 import math
 from collections.abc import Iterable, Mapping, Sequence
-from typing import Annotated, Literal, Optional, Union
+from typing import Annotated, Literal, TypeAlias
 
 import torch
 import torch.nn as nn
@@ -88,14 +88,12 @@ class DeepseekVL2VImageEmbeddingInputs(TensorSchema):
     """
 
     type: Literal["image_embeds"]
-    data: Annotated[
-        Union[torch.Tensor, list[torch.Tensor]], TensorShape("bn", "f", "h")
-    ]
+    data: Annotated[torch.Tensor | list[torch.Tensor], TensorShape("bn", "f", "h")]
 
 
-DeepseekVL2ImageInputs = Union[
-    DeepseekVL2ImagePixelInputs, DeepseekVL2VImageEmbeddingInputs
-]
+DeepseekVL2ImageInputs: TypeAlias = (
+    DeepseekVL2ImagePixelInputs | DeepseekVL2VImageEmbeddingInputs
+)
 
 
 class MlpProjector(nn.Module):
@@ -161,7 +159,7 @@ class DeepseekVL2ProcessingInfo(BaseProcessingInfo):
     def get_hf_processor(self, **kwargs: object):
         return self.ctx.get_hf_processor(DeepseekVLV2Processor, **kwargs)
 
-    def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
+    def get_supported_mm_limits(self) -> Mapping[str, int | None]:
         return {"image": None}
 
     def get_num_image_tokens(
@@ -214,7 +212,7 @@ class DeepseekVL2DummyInputsBuilder(BaseDummyInputsBuilder[DeepseekVL2Processing
         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)
 
@@ -310,11 +308,11 @@ class DeepseekVL2MultiModalProcessor(
 
     def _cached_apply_hf_processor(
         self,
-        prompt: Union[str, list[int]],
+        prompt: str | list[int],
         mm_data_items: MultiModalDataItems,
         hf_processor_mm_kwargs: Mapping[str, object],
         tokenization_kwargs: Mapping[str, object],
-        mm_uuids: Optional[MultiModalUUIDDict] = None,
+        mm_uuids: MultiModalUUIDDict | None = None,
     ) -> tuple[list[int], MultiModalProcessingInfo, bool]:
         # The processor logic is different for len(images) <= 2 vs > 2
         # Since the processing cache assumes that the processor output is
@@ -353,7 +351,7 @@ class DeepseekVLV2ForCausalLM(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("image"):
             return "<image>"
 
@@ -454,7 +452,7 @@ class DeepseekVLV2ForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
     def _init_vision_module(
         self,
         vision_config: VisionEncoderConfig,
-        quant_config: Optional[QuantizationConfig],
+        quant_config: QuantizationConfig | None,
         prefix: str = "",
     ) -> nn.Module:
         # TODO: refactor vision model through timm wrapper from transformers
@@ -480,7 +478,7 @@ class DeepseekVLV2ForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
 
     def _parse_and_validate_image_input(
         self, **kwargs: object
-    ) -> Optional[DeepseekVL2ImageInputs]:
+    ) -> DeepseekVL2ImageInputs | None:
         pixel_values = kwargs.pop("pixel_values", None)
         images_spatial_crop = kwargs.pop("images_spatial_crop", None)
         image_embeds = kwargs.pop("image_embeds", None)
@@ -637,8 +635,8 @@ class DeepseekVLV2ForCausalLM(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,
     ):
         if intermediate_tensors is not None:
@@ -653,7 +651,7 @@ class DeepseekVLV2ForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
     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]: