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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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20
vllm/model_executor/layers/resampler.py
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@@ -34,8 +34,8 @@ Example models: Qwen (Qwen-VL), MiniCPM-V 2.0
"""
import math
from collections.abc import Callable
from functools import partial
from typing import Callable, Optional, Union
import numpy as np
import torch
@@ -48,9 +48,7 @@ from vllm.model_executor.layers.quantization import QuantizationConfig
DEFAULT_LN = partial(nn.LayerNorm, eps=1e-6)
def get_abs_pos(
abs_pos: torch.Tensor, tgt_size: Union[torch.Tensor, int]
) -> torch.Tensor:
def get_abs_pos(abs_pos: torch.Tensor, tgt_size: torch.Tensor | int) -> torch.Tensor:
# abs_pos: L, C
# tgt_size: (H, W)
# return: M, C
@@ -124,7 +122,7 @@ def get_2d_sincos_pos_embed_from_grid(
def get_2d_sincos_pos_embed(
embed_dim: int,
grid_size: Union[int, tuple[int, int]],
grid_size: int | tuple[int, int],
cls_token: bool = False,
version: tuple[int, int] = (2, 0),
) -> torch.Tensor:
@@ -168,10 +166,10 @@ class BaseResampler(nn.Module):
num_queries: int,
embed_dim: int,
num_heads: int,
kv_dim: Optional[int] = None,
kv_dim: int | None = None,
norm_layer: Callable[[int], nn.LayerNorm] = DEFAULT_LN,
do_post_projection: bool = True,
quant_config: Optional[QuantizationConfig] = None,
quant_config: QuantizationConfig | None = None,
prefix: str = "",
) -> None:
super().__init__()
@@ -222,11 +220,11 @@ class Resampler2(BaseResampler):
grid_size: int,
embed_dim: int,
num_heads: int,
kv_dim: Optional[int] = None,
kv_dim: int | None = None,
norm_layer: Callable[[int], nn.LayerNorm] = DEFAULT_LN,
adaptive: bool = False,
do_post_projection: bool = True,
quant_config: Optional[QuantizationConfig] = None,
quant_config: QuantizationConfig | None = None,
prefix: str = "",
) -> None:
super().__init__(
@@ -250,8 +248,8 @@ class Resampler2(BaseResampler):
def forward(
self,
x: torch.Tensor,
tgt_sizes: Optional[torch.Tensor] = None,
attn_mask: Optional[torch.Tensor] = None,
tgt_sizes: torch.Tensor | None = None,
attn_mask: torch.Tensor | None = None,
) -> torch.Tensor:
if tgt_sizes is None:
tgt_sizes = int(math.sqrt(x.size(1)))