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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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56
vllm/model_executor/models/qwen_vl.py
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@@ -9,9 +9,9 @@
import copy
import math
import unicodedata
from collections.abc import Collection, Mapping, Sequence, Set
from collections.abc import Callable, Collection, Mapping, Sequence, Set
from functools import lru_cache, partial
from typing import Annotated, Callable, Literal, Optional, Union
from typing import Annotated, Literal, TypeAlias
import regex as re
import torch
@@ -93,7 +93,7 @@ class QwenImageEmbeddingInputs(TensorSchema):
data: Annotated[torch.Tensor, TensorShape("bn", 256, "hs")]
QwenImageInputs = Union[QwenImagePixelInputs, QwenImageEmbeddingInputs]
QwenImageInputs: TypeAlias = QwenImagePixelInputs | QwenImageEmbeddingInputs
class VisualAttention(nn.Module):
@@ -107,8 +107,8 @@ class VisualAttention(nn.Module):
embed_dim: int,
num_heads: int,
bias: bool = True,
kdim: Optional[int] = None,
vdim: Optional[int] = None,
kdim: int | None = None,
vdim: int | None = None,
):
super().__init__()
self.embed_dim = embed_dim
@@ -135,7 +135,7 @@ class VisualAttention(nn.Module):
def forward(
self,
x: torch.Tensor,
attn_mask: Optional[torch.Tensor] = None,
attn_mask: torch.Tensor | None = None,
) -> torch.Tensor:
# query/key/value: [sq, b, h]
sq, b, _ = x.size()
@@ -213,7 +213,7 @@ class QwenVLMLP(nn.Module):
self,
hidden_size: int,
intermediate_size: int,
quant_config: Optional[QuantizationConfig] = None,
quant_config: QuantizationConfig | None = None,
):
super().__init__()
self.c_fc = ColumnParallelLinear(
@@ -241,7 +241,7 @@ class VisualAttentionBlock(nn.Module):
n_head: int,
mlp_ratio: float = 4.0,
norm_layer: Callable[[int], nn.Module] = nn.LayerNorm,
quant_config: Optional[QuantizationConfig] = None,
quant_config: QuantizationConfig | None = None,
):
super().__init__()
@@ -258,7 +258,7 @@ class VisualAttentionBlock(nn.Module):
def attention(
self,
x: torch.Tensor,
attn_mask: Optional[torch.Tensor] = None,
attn_mask: torch.Tensor | None = None,
) -> torch.Tensor:
attn_mask = attn_mask.to(x.dtype) if attn_mask is not None else None
return self.attn(x, attn_mask=attn_mask)
@@ -266,7 +266,7 @@ class VisualAttentionBlock(nn.Module):
def forward(
self,
x: torch.Tensor,
attn_mask: Optional[torch.Tensor] = None,
attn_mask: torch.Tensor | None = None,
) -> torch.Tensor:
x = x + self.attention(self.ln_1(x), attn_mask=attn_mask)
x = x + self.mlp(self.ln_2(x))
@@ -281,7 +281,7 @@ class TransformerBlock(nn.Module):
heads: int,
mlp_ratio: float = 4.0,
norm_layer: Callable[[int], nn.Module] = nn.LayerNorm,
quant_config: Optional[QuantizationConfig] = None,
quant_config: QuantizationConfig | None = None,
):
super().__init__()
self.width = width
@@ -307,7 +307,7 @@ class TransformerBlock(nn.Module):
return self.resblocks[0].mlp.c_fc.weight.device
def forward(
self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None
self, x: torch.Tensor, attn_mask: torch.Tensor | None = None
) -> torch.Tensor:
for r in self.resblocks:
x = r(x, attn_mask=attn_mask)
@@ -326,7 +326,7 @@ class VisionTransformer(nn.Module):
n_queries: int = 256,
output_dim: int = 512,
image_start_id: int = 151857,
quant_config: Optional[QuantizationConfig] = None,
quant_config: QuantizationConfig | None = None,
**kwargs,
):
super().__init__()
@@ -434,10 +434,10 @@ def _get_tokenizer_without_image_pad(
def tokenize(
self,
text: str,
allowed_special: Union[Set[str], str] = "all",
disallowed_special: Union[Collection[str], str] = (),
allowed_special: Set[str] | str = "all",
disallowed_special: Collection[str] | str = (),
**kwargs,
) -> list[Union[bytes, str]]:
) -> list[bytes | str]:
text = unicodedata.normalize("NFC", text)
return [
@@ -451,9 +451,9 @@ def _get_tokenizer_without_image_pad(
def _decode(
self,
token_ids: Union[int, list[int]],
token_ids: int | list[int],
skip_special_tokens: bool = False,
errors: Optional[str] = None,
errors: str | None = None,
**kwargs,
) -> str:
if isinstance(token_ids, int):
@@ -523,9 +523,9 @@ class QwenVLProcessor:
def __call__(
self,
text: Optional[Union[TextInput, list[TextInput]]] = None,
images: Optional[Union[ImageInput, list[ImageInput]]] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
text: TextInput | list[TextInput] | None = None,
images: ImageInput | list[ImageInput] | None = None,
return_tensors: str | TensorType | None = None,
) -> BatchFeature:
if text is None:
text = []
@@ -568,7 +568,7 @@ class QwenVLProcessingInfo(BaseProcessingInfo):
**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(self) -> int:
@@ -597,7 +597,7 @@ class QwenVLDummyInputsBuilder(BaseDummyInputsBuilder[QwenVLProcessingInfo]):
self,
seq_len: int,
mm_counts: Mapping[str, int],
mm_options: Optional[Mapping[str, BaseDummyOptions]] = None,
mm_options: Mapping[str, BaseDummyOptions] | None = None,
) -> MultiModalDataDict:
hf_config = self.info.get_hf_config()
vision_config = hf_config.visual
@@ -722,7 +722,7 @@ class QwenVLForConditionalGeneration(
)
@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 f"Picture {i}: <img></img>"
@@ -745,7 +745,7 @@ class QwenVLForConditionalGeneration(
def _parse_and_validate_image_input(
self, **kwargs: object
) -> Optional[QwenImageInputs]:
) -> QwenImageInputs | None:
pixel_values = kwargs.pop("pixel_values", None)
image_embeds = kwargs.pop("image_embeds", None)
@@ -799,10 +799,10 @@ class QwenVLForConditionalGeneration(
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