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Add missing type hints for PyTorch Longformer models #16244

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merged 1 commit into from
Mar 21, 2022
Merged

Add missing type hints for PyTorch Longformer models #16244

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146 changes: 73 additions & 73 deletions src/transformers/models/longformer/modeling_longformer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,7 +16,7 @@

import math
from dataclasses import dataclass
from typing import Optional, Tuple
from typing import Optional, Tuple, Union

import torch
import torch.utils.checkpoint
Expand Down Expand Up @@ -1609,17 +1609,17 @@ def _merge_to_attention_mask(self, attention_mask: torch.Tensor, global_attentio
@replace_return_docstrings(output_type=LongformerBaseModelOutputWithPooling, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids=None,
attention_mask=None,
global_attention_mask=None,
head_mask=None,
token_type_ids=None,
position_ids=None,
inputs_embeds=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
global_attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, LongformerBaseModelOutputWithPooling]:
r"""

Returns:
Expand Down Expand Up @@ -1752,18 +1752,18 @@ def set_output_embeddings(self, new_embeddings):
@replace_return_docstrings(output_type=LongformerMaskedLMOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids=None,
attention_mask=None,
global_attention_mask=None,
head_mask=None,
token_type_ids=None,
position_ids=None,
inputs_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
global_attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, LongformerMaskedLMOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
Expand Down Expand Up @@ -1858,18 +1858,18 @@ def __init__(self, config):
)
def forward(
self,
input_ids=None,
attention_mask=None,
global_attention_mask=None,
head_mask=None,
token_type_ids=None,
position_ids=None,
inputs_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
global_attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, LongformerSequenceClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
Expand Down Expand Up @@ -1979,19 +1979,19 @@ def __init__(self, config):
@replace_return_docstrings(output_type=LongformerQuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids=None,
attention_mask=None,
global_attention_mask=None,
head_mask=None,
token_type_ids=None,
position_ids=None,
inputs_embeds=None,
start_positions=None,
end_positions=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
global_attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
start_positions: Optional[torch.Tensor] = None,
end_positions: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, LongformerQuestionAnsweringModelOutput]:
r"""
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Expand Down Expand Up @@ -2124,18 +2124,18 @@ def __init__(self, config):
)
def forward(
self,
input_ids=None,
attention_mask=None,
global_attention_mask=None,
head_mask=None,
token_type_ids=None,
position_ids=None,
inputs_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
global_attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, LongformerTokenClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
Expand Down Expand Up @@ -2207,18 +2207,18 @@ def __init__(self, config):
)
def forward(
self,
input_ids=None,
token_type_ids=None,
attention_mask=None,
global_attention_mask=None,
head_mask=None,
labels=None,
position_ids=None,
inputs_embeds=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
input_ids: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
global_attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, LongformerMultipleChoiceModelOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
Expand Down