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/step3_vl.py

@@ -4,7 +4,7 @@ import math
 from collections.abc import Iterable, Mapping, Sequence
 from itertools import product
 from math import ceil, sqrt
-from typing import Annotated, Any, Literal, Optional, Union
+from typing import Annotated, Any, Literal, TypeAlias
 
 import numpy as np
 import torch
@@ -71,7 +71,7 @@ class Step3VLImagePixelInputs(TensorSchema):
     type: Literal["pixel_values"]
     pixel_values: Annotated[torch.Tensor, TensorShape("bn", 3, "h", "w")]
     patch_pixel_values: Annotated[
-        Optional[torch.Tensor], TensorShape("bnp", 3, "hp", "wp")
+        torch.Tensor | None, TensorShape("bnp", 3, "hp", "wp")
     ]
     num_patches: Annotated[torch.Tensor, TensorShape("bn")]
 
@@ -88,7 +88,7 @@ class Step3VLImageEmbeddingInputs(TensorSchema):
     data: Annotated[torch.Tensor, TensorShape("bn", "f", "h")]
 
 
-Step3VLImageInputs = Union[Step3VLImagePixelInputs, Step3VLImageEmbeddingInputs]
+Step3VLImageInputs: TypeAlias = Step3VLImagePixelInputs | Step3VLImageEmbeddingInputs
 
 ImageWithPatches = tuple[Image.Image, list[Image.Image], list[int] | None]
 
@@ -409,7 +409,7 @@ class Step3VLProcessor:
         self,
         num_images: int,
         num_patches: int,
-        patch_new_line_idx: Optional[list[bool]],
+        patch_new_line_idx: list[bool] | None,
     ) -> tuple[str, list[int]]:
         if num_patches > 0:
             patch_repl, patch_repl_ids = self._get_patch_repl(
@@ -438,9 +438,9 @@ class Step3VLProcessor:
 
     def __call__(
         self,
-        text: Optional[Union[str, list[str]]] = None,
-        images: Optional[Union[Image.Image, list[Image.Image]]] = None,
-        return_tensors: Optional[Union[str, TensorType]] = None,
+        text: str | list[str] | None = None,
+        images: Image.Image | list[Image.Image] | None = None,
+        return_tensors: str | TensorType | None = None,
     ) -> BatchFeature:
         if text is None:
             text = []
@@ -513,7 +513,7 @@ class Step3VLProcessingInfo(BaseProcessingInfo):
             self.get_tokenizer(),
         )
 
-    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_max_image_tokens(self) -> int:
@@ -556,7 +556,7 @@ class Step3VLDummyInputsBuilder(BaseDummyInputsBuilder[Step3VLProcessingInfo]):
         self,
         seq_len: int,
         mm_counts: Mapping[str, int],
-        mm_options: Optional[Mapping[str, BaseDummyOptions]] = None,
+        mm_options: Mapping[str, BaseDummyOptions] | None = None,
     ) -> MultiModalDataDict:
         target_width, target_height = self.info.get_image_size_with_most_features()
         num_images = mm_counts.get("image", 0)
@@ -716,7 +716,7 @@ class Step3VisionAttention(nn.Module):
     def __init__(
         self,
         config,
-        quant_config: Optional[QuantizationConfig] = None,
+        quant_config: QuantizationConfig | None = None,
         prefix: str = "",
         use_data_parallel: bool = False,
     ):
@@ -778,7 +778,7 @@ class Step3VisionMLP(nn.Module):
     def __init__(
         self,
         config,
-        quant_config: Optional[QuantizationConfig] = None,
+        quant_config: QuantizationConfig | None = None,
         prefix: str = "",
         use_data_parallel: bool = False,
     ):
@@ -813,7 +813,7 @@ class Step3VisionEncoderLayer(nn.Module):
     def __init__(
         self,
         config: Step3VisionEncoderConfig,
-        quant_config: Optional[QuantizationConfig] = None,
+        quant_config: QuantizationConfig | None = None,
         prefix: str = "",
         use_data_parallel: bool = False,
     ):
@@ -848,7 +848,7 @@ class Step3VisionEncoder(nn.Module):
     def __init__(
         self,
         config: Step3VisionEncoderConfig,
-        quant_config: Optional[QuantizationConfig] = None,
+        quant_config: QuantizationConfig | None = None,
         prefix: str = "",
         use_data_parallel: bool = False,
     ):
@@ -881,7 +881,7 @@ class Step3VisionTransformer(nn.Module):
     def __init__(
         self,
         config: Step3VisionEncoderConfig,
-        quant_config: Optional[QuantizationConfig] = None,
+        quant_config: QuantizationConfig | None = None,
         prefix: str = "",
         use_data_parallel: bool = False,
     ):
@@ -927,7 +927,7 @@ class Step3VLForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP)
     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 "<im_patch>"
 
@@ -994,7 +994,7 @@ class Step3VLForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP)
 
     def _parse_and_validate_image_input(
         self, **kwargs: object
-    ) -> Optional[Step3VLImageInputs]:
+    ) -> Step3VLImageInputs | None:
         pixel_values = kwargs.pop("pixel_values", None)
         patch_pixel_values = kwargs.pop("patch_pixel_values", None)
         num_patches = kwargs.pop("num_patches", None)
@@ -1085,9 +1085,9 @@ class Step3VLForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP)
     def get_input_embeddings(
         self,
         input_ids: torch.Tensor,
-        multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
+        multimodal_embeddings: MultiModalEmbeddings | None = None,
         *,
-        is_multimodal: Optional[torch.Tensor] = None,
+        is_multimodal: torch.Tensor | None = None,
         # Multi-modal token ID may exceed vocab size
         handle_oov_mm_token: bool = True,
     ) -> torch.Tensor:
@@ -1106,10 +1106,10 @@ class Step3VLForConditionalGeneration(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:
@@ -1130,7 +1130,7 @@ class Step3VLForConditionalGeneration(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]]):