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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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76
vllm/model_executor/models/phi4mm_audio.py
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@@ -7,7 +7,7 @@
#!/usr/bin/env python3
import abc
import math
from typing import Any, Literal, Optional, Union
from typing import Any, Literal
import numpy as np
import torch
@@ -221,7 +221,7 @@ class ConformerEncoderLayer(nn.Module):
pos_k: torch.Tensor,
pos_v: torch.Tensor,
mask: torch.Tensor,
relative_attention_bias: Optional[Tensor] = None,
relative_attention_bias: Tensor | None = None,
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
"""ConformerEncoder forward.
@@ -329,8 +329,8 @@ class TransformerEncoderBase(abc.ABC, nn.Module):
def __init__(
self,
input_size: int,
chunk_size: Union[int, list[int]],
left_chunk: Union[int, list[int]],
chunk_size: int | list[int],
left_chunk: int | list[int],
attention_dim: int = 256,
attention_heads: int = 4,
input_layer: str = "nemo_conv",
@@ -339,12 +339,12 @@ class TransformerEncoderBase(abc.ABC, nn.Module):
time_reduction: int = 4,
dropout_rate: float = 0.0,
padding_idx: int = -1,
relative_attention_bias_args: Optional[dict[str, Any]] = None,
relative_attention_bias_args: dict[str, Any] | None = None,
positional_dropout_rate: float = 0.0,
nemo_conv_settings: Optional[dict[str, Any]] = None,
nemo_conv_settings: dict[str, Any] | None = None,
conv2d_extra_padding: Literal["feat", "feat_time", "none", True] = "none",
attention_group_size: int = 1,
encoder_embedding_config: Optional[dict[str, Any]] = None,
encoder_embedding_config: dict[str, Any] | None = None,
) -> None:
super().__init__()
self.input_size = input_size
@@ -411,8 +411,8 @@ class TransformerEncoderBase(abc.ABC, nn.Module):
)
def compute_lens_change(
self, feature_lens: Union[int, torch.Tensor]
) -> Union[int, torch.Tensor]:
self, feature_lens: int | torch.Tensor
) -> int | torch.Tensor:
"""feature_lens: int
return updated feature lens.
@@ -452,8 +452,8 @@ class TransformerEncoderBase(abc.ABC, nn.Module):
def _chunk_size_selection(
self,
chunk_size: Optional[Union[int, list[int]]] = None,
left_chunk: Optional[Union[int, list[int]]] = None,
chunk_size: int | list[int] | None = None,
left_chunk: int | list[int] | None = None,
) -> tuple[int, int]:
"""If chunk size is a list, we will randomly select a chunk size."""
@@ -503,7 +503,7 @@ class TransformerEncoderBase(abc.ABC, nn.Module):
def _position_embedding(
self, input_tensor: torch.Tensor
) -> tuple[Optional[torch.Tensor], Optional[torch.Tensor]]:
) -> tuple[torch.Tensor | None, torch.Tensor | None]:
pos_k = None
pos_v = None
if self.relative_attention_bias_layer is None:
@@ -516,8 +516,8 @@ class TransformerEncoderBase(abc.ABC, nn.Module):
self,
seq_len: int,
batch_size: int,
chunk_size: Union[int, list[int]],
left_chunk: Union[int, list[int]],
chunk_size: int | list[int],
left_chunk: int | list[int],
) -> torch.Tensor:
chunk_size_train_eff, left_chunk_train_eff = self._chunk_size_selection(
chunk_size, left_chunk
@@ -540,25 +540,25 @@ class TransformerEncoderBase(abc.ABC, nn.Module):
self,
xs_pad: torch.Tensor,
masks: torch.Tensor,
chunk_size_nc: Optional[Union[int, list[int]]] = None,
left_chunk_nc: Optional[Union[int, list[int]]] = None,
) -> Union[
chunk_size_nc: int | list[int] | None = None,
left_chunk_nc: int | list[int] | None = None,
) -> (
tuple[
torch.Tensor,
Optional[torch.Tensor],
Optional[torch.Tensor],
torch.Tensor | None,
torch.Tensor | None,
torch.Tensor,
torch.Tensor,
],
tuple[
]
| tuple[
torch.Tensor,
Optional[torch.Tensor],
Optional[torch.Tensor],
torch.Tensor | None,
torch.Tensor | None,
torch.Tensor,
torch.Tensor,
torch.Tensor,
],
]:
]
):
"""Forwarding the inputs through the top embedding layers
Args:
@@ -803,9 +803,9 @@ class ConformerEncoder(TransformerEncoderBase):
def __init__( # pylint: disable-all
self,
input_size: int,
chunk_size: Union[int, list[int]],
left_chunk: Union[int, list[int]],
num_lang: Optional[int] = None,
chunk_size: int | list[int],
left_chunk: int | list[int],
num_lang: int | None = None,
attention_dim: int = 256,
attention_heads: int = 4,
linear_units: int = 2048,
@@ -832,14 +832,14 @@ class ConformerEncoder(TransformerEncoderBase):
extra_layer_output_idx: int = -1,
extra_multi_layer_output_idxs: list[int] = [], # noqa
activation_checkpointing: str = "",
relative_attention_bias_args: Optional[dict[str, Any]] = None,
relative_attention_bias_args: dict[str, Any] | None = None,
time_reduction: int = 4,
use_pt_scaled_dot_product_attention: bool = False,
nemo_conv_settings: Optional[dict[str, Any]] = None,
nemo_conv_settings: dict[str, Any] | None = None,
conv2d_extra_padding: Literal["feat", "feat_time", "none", True] = "none",
replication_pad_for_subsample_embedding: bool = False,
attention_group_size: int = 1,
encoder_embedding_config: Optional[dict[str, Any]] = None,
encoder_embedding_config: dict[str, Any] | None = None,
) -> None:
super().__init__(
input_size,
@@ -908,12 +908,12 @@ class ConformerEncoder(TransformerEncoderBase):
def init_relative_attention_bias(
self, input_tensor: torch.Tensor
) -> Optional[torch.Tensor]:
) -> torch.Tensor | None:
if self.relative_attention_bias_layer:
return self.relative_attention_bias_layer(input_tensor)
def calculate_hs_mask(
self, xs_pad: torch.Tensor, device: torch.device, mask: Optional[torch.Tensor]
self, xs_pad: torch.Tensor, device: torch.device, mask: torch.Tensor | None
) -> torch.Tensor:
max_audio_length = xs_pad.shape[1]
batch_size = xs_pad.shape[0]
@@ -1066,9 +1066,9 @@ class WindowQformer(nn.Module):
def forward(
self,
audio_embed: torch.Tensor,
mask: Optional[torch.Tensor],
embed_len: Optional[int] = None,
) -> tuple[torch.Tensor, Optional[int]]:
mask: torch.Tensor | None,
embed_len: int | None = None,
) -> tuple[torch.Tensor, int | None]:
"""forward decoder"""
# audio_embed: N x T x D => N x D x T
@@ -1224,7 +1224,7 @@ class AudioEmbedding(nn.Module):
def get_audio_features(
self,
input_embeds: torch.Tensor,
audio_attention_mask: Optional[torch.Tensor] = None,
audio_attention_mask: torch.Tensor | None = None,
audio_projection_mode: str = "speech",
) -> torch.Tensor:
"""
@@ -1278,7 +1278,7 @@ class AudioEmbedding(nn.Module):
def forward(
self,
audio_features: torch.Tensor,
audio_attention_mask: Optional[torch.Tensor] = None,
audio_attention_mask: torch.Tensor | None = None,
audio_projection_mode: str = "speech",
) -> torch.Tensor:
"""