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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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38
vllm/model_executor/models/radio.py
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@@ -11,7 +11,7 @@
import math
from collections.abc import Iterable
from itertools import repeat
from typing import Optional, Union
from typing import TypeAlias
import torch
import torch.nn as nn
@@ -23,8 +23,8 @@ from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from vllm.model_executor.models.intern_vit import InternVisionEncoder
input_dim_t = Union[int, tuple[int, int]]
norm_t = Union[tuple[float, float, float], torch.Tensor]
input_dim_t: TypeAlias = int | tuple[int, int]
norm_t: TypeAlias = tuple[float, float, float] | torch.Tensor
def _ntuple(n):
@@ -75,8 +75,8 @@ class ClsToken(nn.Module):
ndim: int,
num_tokens: int = 1,
enabled: bool = True,
register_multiple: Optional[int] = None,
num_registers: Optional[int] = None,
register_multiple: int | None = None,
num_registers: int | None = None,
):
super().__init__()
@@ -128,12 +128,12 @@ class ViTPatchGenerator(nn.Module):
abs_pos: bool = True,
normalize_patches: bool = False,
cls_token: bool = False,
max_input_dims: Optional[input_dim_t] = None,
max_input_dims: input_dim_t | None = None,
pos_dropout: float = 0.0,
return_pos_enc: bool = False,
num_cls_tokens: int = 1,
register_multiple: Optional[int] = None,
num_registers: Optional[int] = None,
register_multiple: int | None = None,
num_registers: int | None = None,
patch_bias: bool = False,
device=None,
dtype=None,
@@ -275,8 +275,8 @@ class ViTPatchGenerator(nn.Module):
def apply_pos_enc(
self,
patches: torch.Tensor,
patch_idxs: Optional[torch.Tensor] = None,
input_size: Optional[tuple[int, int]] = None,
patch_idxs: torch.Tensor | None = None,
input_size: tuple[int, int] | None = None,
) -> torch.Tensor:
if not self.abs_pos:
return patches
@@ -299,8 +299,8 @@ class ViTPatchGenerator(nn.Module):
def get_pos_enc(
self,
batch_size: int,
patch_idxs: Optional[torch.Tensor] = None,
input_size: Optional[tuple[int, int]] = None,
patch_idxs: torch.Tensor | None = None,
input_size: tuple[int, int] | None = None,
) -> torch.Tensor:
if input_size is None:
input_dims = self.input_dims
@@ -440,9 +440,9 @@ class RadioInternVisionModel(nn.Module):
def __init__(
self,
config: PretrainedConfig = None,
quant_config: Optional[QuantizationConfig] = None,
quant_config: QuantizationConfig | None = None,
*,
num_hidden_layers_override: Optional[int] = None,
num_hidden_layers_override: int | None = None,
num_dummy_heads: int = 0,
prefix: str = "",
) -> None:
@@ -472,7 +472,7 @@ class RadioInternVisionModel(nn.Module):
prefix=f"{prefix}.encoder",
)
def _init_img_size(self, patch_size, img_size: Union[int, tuple[int, int]]):
def _init_img_size(self, patch_size, img_size: int | tuple[int, int]):
if img_size is None:
return None, None, None
img_size = to_2tuple(img_size)
@@ -498,9 +498,9 @@ class RadioModel(nn.Module):
def __init__(
self,
config: PretrainedConfig,
quant_config: Optional[QuantizationConfig] = None,
quant_config: QuantizationConfig | None = None,
*,
num_hidden_layers_override: Optional[int] = None,
num_hidden_layers_override: int | None = None,
num_dummy_heads: int = 0,
prefix: str = "",
) -> None:
@@ -522,8 +522,8 @@ class RadioModel(nn.Module):
def forward(
self,
pixel_values: Optional[torch.Tensor] = None,
pixel_embeds: Optional[torch.Tensor] = None,
pixel_values: torch.Tensor | None = None,
pixel_embeds: torch.Tensor | None = None,
) -> torch.FloatTensor:
x = self.input_conditioner(pixel_values)
y = self.model(x)