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Update Optional[x] -> x | None and Union[x, y] to x | y (#26633)

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Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
This commit is contained in:
Harry Mellor
2025-10-12 17:51:31 +01:00
committed by GitHub
parent 9bb38130cb
commit 8fcaaf6a16
944 changed files with 9490 additions and 10121 deletions
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51
vllm/model_executor/models/swin.py
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@@ -2,7 +2,6 @@
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from collections.abc import Iterable
from typing import Optional
import torch
import torch.nn as nn
@@ -28,7 +27,7 @@ class SwinSelfAttention(nn.Module):
dim: int,
num_heads: int,
window_size: int,
quant_config: Optional[QuantizationConfig] = None,
quant_config: QuantizationConfig | None = None,
prefix: str = "",
) -> None:
super().__init__()
@@ -102,9 +101,9 @@ class SwinSelfAttention(nn.Module):
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = False,
attention_mask: torch.FloatTensor | None = None,
head_mask: torch.FloatTensor | None = None,
output_attentions: bool | None = False,
) -> tuple[torch.Tensor, ...]:
batch_size, dim, num_channels = hidden_states.shape
@@ -155,7 +154,7 @@ class SwinSelfOutput(nn.Module):
self,
config: SwinConfig,
dim: int,
quant_config: Optional[QuantizationConfig] = None,
quant_config: QuantizationConfig | None = None,
prefix: str = "",
) -> None:
super().__init__()
@@ -181,7 +180,7 @@ class SwinAttention(nn.Module):
dim: int,
num_heads: int,
window_size: int,
quant_config: Optional[QuantizationConfig] = None,
quant_config: QuantizationConfig | None = None,
prefix: str = "",
) -> None:
super().__init__()
@@ -201,9 +200,9 @@ class SwinAttention(nn.Module):
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = False,
attention_mask: torch.FloatTensor | None = None,
head_mask: torch.FloatTensor | None = None,
output_attentions: bool | None = False,
) -> tuple[torch.Tensor]:
self_outputs = self.self(
hidden_states, attention_mask, head_mask, output_attentions
@@ -218,7 +217,7 @@ class SwinIntermediate(nn.Module):
self,
config: SwinConfig,
dim: int,
quant_config: Optional[QuantizationConfig] = None,
quant_config: QuantizationConfig | None = None,
prefix: str = "",
) -> None:
super().__init__()
@@ -241,7 +240,7 @@ class SwinOutput(nn.Module):
self,
config: SwinConfig,
dim: int,
quant_config: Optional[QuantizationConfig] = None,
quant_config: QuantizationConfig | None = None,
prefix: str = "",
) -> None:
super().__init__()
@@ -266,7 +265,7 @@ class SwinLayer(HFSwinLayer):
num_heads: int,
drop_path_rate: float = 0.0,
shift_size: int = 0,
quant_config: Optional[QuantizationConfig] = None,
quant_config: QuantizationConfig | None = None,
prefix: str = "",
) -> None:
super().__init__(
@@ -303,8 +302,8 @@ class SwinStage(nn.Module):
depth: int,
num_heads: int,
drop_path: list[float],
downsample: Optional[SwinPatchMerging] = None,
quant_config: Optional[QuantizationConfig] = None,
downsample: SwinPatchMerging | None = None,
quant_config: QuantizationConfig | None = None,
prefix: str = "",
) -> None:
super().__init__()
@@ -340,9 +339,9 @@ class SwinStage(nn.Module):
self,
hidden_states: torch.Tensor,
input_dimensions: tuple[int, int],
head_mask: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = False,
always_partition: Optional[bool] = False,
head_mask: torch.FloatTensor | None = None,
output_attentions: bool | None = False,
always_partition: bool | None = False,
) -> tuple[torch.Tensor]:
height, width = input_dimensions
for i, layer_module in enumerate(self.blocks):
@@ -384,7 +383,7 @@ class SwinEncoder(nn.Module):
self,
config: SwinConfig,
grid_size: int,
quant_config: Optional[QuantizationConfig] = None,
quant_config: QuantizationConfig | None = None,
prefix: str = "",
) -> None:
super().__init__()
@@ -426,9 +425,9 @@ class SwinEncoder(nn.Module):
self,
hidden_states: torch.Tensor,
input_dimensions: tuple[int, int],
head_mask: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = False,
always_partition: Optional[bool] = False,
head_mask: torch.FloatTensor | None = None,
output_attentions: bool | None = False,
always_partition: bool | None = False,
) -> tuple[torch.Tensor]:
for i, layer_module in enumerate(self.layers):
layer_head_mask = head_mask[i] if head_mask is not None else None
@@ -455,7 +454,7 @@ class SwinModel(nn.Module):
def __init__(
self,
config: SwinConfig,
quant_config: Optional[QuantizationConfig] = None,
quant_config: QuantizationConfig | None = None,
prefix: str = "",
) -> None:
super().__init__()
@@ -473,9 +472,9 @@ class SwinModel(nn.Module):
def forward(
self,
pixel_values: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
pixel_values: torch.FloatTensor | None = None,
head_mask: torch.FloatTensor | None = None,
output_attentions: bool | None = None,
) -> tuple[torch.Tensor]:
embedding_output, input_dimensions = self.embeddings(pixel_values)