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Unverified Commit 7643b1ca authored by Johnny Greco's avatar Johnny Greco Committed by GitHub
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Added type hints to PyTorch Longformer models (#16244)

parent c77092a5
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Showing with 73 additions and 73 deletions
src/transformers/models/longformer/modeling_longformer.py
View file @ 7643b1ca
...@@ -16,7 +16,7 @@ ...@@ -16,7 +16,7 @@
import math import math
from dataclasses import dataclass from dataclasses import dataclass
from typing import Optional, Tuple from typing import Optional, Tuple, Union
import torch import torch
import torch.utils.checkpoint import torch.utils.checkpoint
...@@ -1609,17 +1609,17 @@ class LongformerModel(LongformerPreTrainedModel): ...@@ -1609,17 +1609,17 @@ class LongformerModel(LongformerPreTrainedModel):
@replace_return_docstrings(output_type=LongformerBaseModelOutputWithPooling, config_class=_CONFIG_FOR_DOC) @replace_return_docstrings(output_type=LongformerBaseModelOutputWithPooling, config_class=_CONFIG_FOR_DOC)
def forward( def forward(
self, self,
input_ids=None, input_ids: Optional[torch.Tensor] = None,
attention_mask=None, attention_mask: Optional[torch.Tensor] = None,
global_attention_mask=None, global_attention_mask: Optional[torch.Tensor] = None,
head_mask=None, head_mask: Optional[torch.Tensor] = None,
token_type_ids=None, token_type_ids: Optional[torch.Tensor] = None,
position_ids=None, position_ids: Optional[torch.Tensor] = None,
inputs_embeds=None, inputs_embeds: Optional[torch.Tensor] = None,
output_attentions=None, output_attentions: Optional[bool] = None,
output_hidden_states=None, output_hidden_states: Optional[bool] = None,
return_dict=None, return_dict: Optional[bool] = None,
): ) -> Union[Tuple, LongformerBaseModelOutputWithPooling]:
r""" r"""
Returns: Returns:
...@@ -1752,18 +1752,18 @@ class LongformerForMaskedLM(LongformerPreTrainedModel): ...@@ -1752,18 +1752,18 @@ class LongformerForMaskedLM(LongformerPreTrainedModel):
@replace_return_docstrings(output_type=LongformerMaskedLMOutput, config_class=_CONFIG_FOR_DOC) @replace_return_docstrings(output_type=LongformerMaskedLMOutput, config_class=_CONFIG_FOR_DOC)
def forward( def forward(
self, self,
input_ids=None, input_ids: Optional[torch.Tensor] = None,
attention_mask=None, attention_mask: Optional[torch.Tensor] = None,
global_attention_mask=None, global_attention_mask: Optional[torch.Tensor] = None,
head_mask=None, head_mask: Optional[torch.Tensor] = None,
token_type_ids=None, token_type_ids: Optional[torch.Tensor] = None,
position_ids=None, position_ids: Optional[torch.Tensor] = None,
inputs_embeds=None, inputs_embeds: Optional[torch.Tensor] = None,
labels=None, labels: Optional[torch.Tensor] = None,
output_attentions=None, output_attentions: Optional[bool] = None,
output_hidden_states=None, output_hidden_states: Optional[bool] = None,
return_dict=None, return_dict: Optional[bool] = None,
): ) -> Union[Tuple, LongformerMaskedLMOutput]:
r""" r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
...@@ -1858,18 +1858,18 @@ class LongformerForSequenceClassification(LongformerPreTrainedModel): ...@@ -1858,18 +1858,18 @@ class LongformerForSequenceClassification(LongformerPreTrainedModel):
) )
def forward( def forward(
self, self,
input_ids=None, input_ids: Optional[torch.Tensor] = None,
attention_mask=None, attention_mask: Optional[torch.Tensor] = None,
global_attention_mask=None, global_attention_mask: Optional[torch.Tensor] = None,
head_mask=None, head_mask: Optional[torch.Tensor] = None,
token_type_ids=None, token_type_ids: Optional[torch.Tensor] = None,
position_ids=None, position_ids: Optional[torch.Tensor] = None,
inputs_embeds=None, inputs_embeds: Optional[torch.Tensor] = None,
labels=None, labels: Optional[torch.Tensor] = None,
output_attentions=None, output_attentions: Optional[bool] = None,
output_hidden_states=None, output_hidden_states: Optional[bool] = None,
return_dict=None, return_dict: Optional[bool] = None,
): ) -> Union[Tuple, LongformerSequenceClassifierOutput]:
r""" r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
...@@ -1979,19 +1979,19 @@ class LongformerForQuestionAnswering(LongformerPreTrainedModel): ...@@ -1979,19 +1979,19 @@ class LongformerForQuestionAnswering(LongformerPreTrainedModel):
@replace_return_docstrings(output_type=LongformerQuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC) @replace_return_docstrings(output_type=LongformerQuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC)
def forward( def forward(
self, self,
input_ids=None, input_ids: Optional[torch.Tensor] = None,
attention_mask=None, attention_mask: Optional[torch.Tensor] = None,
global_attention_mask=None, global_attention_mask: Optional[torch.Tensor] = None,
head_mask=None, head_mask: Optional[torch.Tensor] = None,
token_type_ids=None, token_type_ids: Optional[torch.Tensor] = None,
position_ids=None, position_ids: Optional[torch.Tensor] = None,
inputs_embeds=None, inputs_embeds: Optional[torch.Tensor] = None,
start_positions=None, start_positions: Optional[torch.Tensor] = None,
end_positions=None, end_positions: Optional[torch.Tensor] = None,
output_attentions=None, output_attentions: Optional[bool] = None,
output_hidden_states=None, output_hidden_states: Optional[bool] = None,
return_dict=None, return_dict: Optional[bool] = None,
): ) -> Union[Tuple, LongformerQuestionAnsweringModelOutput]:
r""" r"""
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): 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. Labels for position (index) of the start of the labelled span for computing the token classification loss.
...@@ -2124,18 +2124,18 @@ class LongformerForTokenClassification(LongformerPreTrainedModel): ...@@ -2124,18 +2124,18 @@ class LongformerForTokenClassification(LongformerPreTrainedModel):
) )
def forward( def forward(
self, self,
input_ids=None, input_ids: Optional[torch.Tensor] = None,
attention_mask=None, attention_mask: Optional[torch.Tensor] = None,
global_attention_mask=None, global_attention_mask: Optional[torch.Tensor] = None,
head_mask=None, head_mask: Optional[torch.Tensor] = None,
token_type_ids=None, token_type_ids: Optional[torch.Tensor] = None,
position_ids=None, position_ids: Optional[torch.Tensor] = None,
inputs_embeds=None, inputs_embeds: Optional[torch.Tensor] = None,
labels=None, labels: Optional[torch.Tensor] = None,
output_attentions=None, output_attentions: Optional[bool] = None,
output_hidden_states=None, output_hidden_states: Optional[bool] = None,
return_dict=None, return_dict: Optional[bool] = None,
): ) -> Union[Tuple, LongformerTokenClassifierOutput]:
r""" r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): 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]`. Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
...@@ -2207,18 +2207,18 @@ class LongformerForMultipleChoice(LongformerPreTrainedModel): ...@@ -2207,18 +2207,18 @@ class LongformerForMultipleChoice(LongformerPreTrainedModel):
) )
def forward( def forward(
self, self,
input_ids=None, input_ids: Optional[torch.Tensor] = None,
token_type_ids=None, token_type_ids: Optional[torch.Tensor] = None,
attention_mask=None, attention_mask: Optional[torch.Tensor] = None,
global_attention_mask=None, global_attention_mask: Optional[torch.Tensor] = None,
head_mask=None, head_mask: Optional[torch.Tensor] = None,
labels=None, labels: Optional[torch.Tensor] = None,
position_ids=None, position_ids: Optional[torch.Tensor] = None,
inputs_embeds=None, inputs_embeds: Optional[torch.Tensor] = None,
output_attentions=None, output_attentions: Optional[bool] = None,
output_hidden_states=None, output_hidden_states: Optional[bool] = None,
return_dict=None, return_dict: Optional[bool] = None,
): ) -> Union[Tuple, LongformerMultipleChoiceModelOutput]:
r""" r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
......
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