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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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49
vllm/model_executor/models/bert.py
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@@ -2,7 +2,6 @@
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from collections.abc import Iterable, Set
from typing import Optional, Union
import torch
from torch import nn
@@ -66,7 +65,7 @@ class BertEmbedding(nn.Module):
self,
input_ids: torch.Tensor,
position_ids: torch.Tensor,
inputs_embeds: Optional[torch.Tensor] = None,
inputs_embeds: torch.Tensor | None = None,
) -> torch.Tensor:
token_type_ids = _decode_token_type_ids(input_ids)
@@ -103,9 +102,9 @@ class BertPooler(Pooler):
def forward(
self,
hidden_states: Union[torch.Tensor, list[torch.Tensor]],
hidden_states: torch.Tensor | list[torch.Tensor],
pooling_metadata: PoolingMetadata,
) -> Union[torch.Tensor, list[torch.Tensor]]:
) -> torch.Tensor | list[torch.Tensor]:
pooled_output = self.pooling(hidden_states, pooling_metadata)
if isinstance(pooled_output, list):
@@ -147,8 +146,8 @@ class BertLayer(nn.Module):
def __init__(
self,
config: BertConfig,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
cache_config: CacheConfig | None = None,
quant_config: QuantizationConfig | None = None,
prefix: str = "",
):
super().__init__()
@@ -191,8 +190,8 @@ class BertAttention(nn.Module):
hidden_size: int,
num_attention_heads: int,
layer_norm_eps: float,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
cache_config: CacheConfig | None = None,
quant_config: QuantizationConfig | None = None,
prefix: str = "",
):
super().__init__()
@@ -225,8 +224,8 @@ class BertSelfAttention(nn.Module):
self,
hidden_size: int,
num_attention_heads: int,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
cache_config: CacheConfig | None = None,
quant_config: QuantizationConfig | None = None,
prefix: str = "",
):
super().__init__()
@@ -281,7 +280,7 @@ class BertSelfOutput(nn.Module):
self,
hidden_size: int,
layer_norm_eps: float,
quant_config: Optional[QuantizationConfig] = None,
quant_config: QuantizationConfig | None = None,
prefix: str = "",
):
super().__init__()
@@ -308,7 +307,7 @@ class BertIntermediate(nn.Module):
hidden_size: int,
intermediate_size: int,
hidden_act: str,
quant_config: Optional[QuantizationConfig] = None,
quant_config: QuantizationConfig | None = None,
prefix: str = "",
):
super().__init__()
@@ -333,7 +332,7 @@ class BertOutput(nn.Module):
hidden_size: int,
intermediate_size: int,
layer_norm_eps: float,
quant_config: Optional[QuantizationConfig] = None,
quant_config: QuantizationConfig | None = None,
prefix: str = "",
):
super().__init__()
@@ -383,8 +382,8 @@ class BertModel(nn.Module, SupportsQuant):
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,
) -> torch.Tensor:
hidden_states = self.embeddings(
input_ids=input_ids,
@@ -494,8 +493,8 @@ class BertEmbeddingModel(nn.Module, SupportsQuant):
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,
) -> torch.Tensor:
return self.model(
input_ids=input_ids,
@@ -636,11 +635,11 @@ class BertForSequenceClassification(nn.Module, SupportsCrossEncoding, SupportsQu
def forward(
self,
input_ids: Optional[torch.Tensor],
input_ids: torch.Tensor | None,
positions: torch.Tensor,
intermediate_tensors: Optional[IntermediateTensors] = None,
inputs_embeds: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
intermediate_tensors: IntermediateTensors | None = None,
inputs_embeds: torch.Tensor | None = None,
token_type_ids: torch.Tensor | None = None,
) -> torch.Tensor:
if token_type_ids is not None:
assert self.bert.config.vocab_size < (1 << TOKEN_TYPE_SHIFT)
@@ -692,11 +691,11 @@ class BertForTokenClassification(nn.Module):
def forward(
self,
input_ids: Optional[torch.Tensor],
input_ids: torch.Tensor | None,
positions: torch.Tensor,
intermediate_tensors: Optional[IntermediateTensors] = None,
inputs_embeds: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
intermediate_tensors: IntermediateTensors | None = None,
inputs_embeds: torch.Tensor | None = None,
token_type_ids: torch.Tensor | None = None,
) -> torch.Tensor:
if token_type_ids is not None:
assert self.bert.config.vocab_size < (1 << TOKEN_TYPE_SHIFT)