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Unverified Commit c6f7ea19 authored by Mowaninuola Osifeso's avatar Mowaninuola Osifeso Committed by GitHub
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Add type hints to xlnet (#16214) 

* added type hints to xlnet PT

* added type hints to xlnet TF

* added type hints to xlnet TF
parent abf3cc70
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Inline Side-by-side
Showing with 194 additions and 192 deletions
src/transformers/models/xlnet/modeling_tf_xlnet.py
View file @ c6f7ea19
......@@ -19,8 +19,9 @@
import warnings
from dataclasses import dataclass
from typing import List, Optional, Tuple
from typing import List, Optional, Tuple, Union
import numpy as np
import tensorflow as tf
from ...activations_tf import get_tf_activation
......@@ -34,6 +35,7 @@ from ...file_utils import (
)
from ...modeling_tf_utils import (
TFCausalLanguageModelingLoss,
TFModelInputType,
TFMultipleChoiceLoss,
TFPreTrainedModel,
TFQuestionAnsweringLoss,
......@@ -1245,23 +1247,23 @@ class TFXLNetLMHeadModel(TFXLNetPreTrainedModel, TFCausalLanguageModelingLoss):
@replace_return_docstrings(output_type=TFXLNetLMHeadModelOutput, config_class=_CONFIG_FOR_DOC)
def call(
self,
input_ids=None,
attention_mask=None,
mems=None,
perm_mask=None,
target_mapping=None,
token_type_ids=None,
input_mask=None,
head_mask=None,
inputs_embeds=None,
use_mems=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
labels=None,
training=False,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
mems: Optional[Union[np.ndarray, tf.Tensor]] = None,
perm_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
target_mapping: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
input_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
use_mems: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[Union[np.ndarray, tf.Tensor]] = None,
training: Optional[bool] = False,
**kwargs,
):
) -> Union[TFXLNetLMHeadModelOutput, Tuple[tf.Tensor]]:
r"""
labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the cross entropy classification loss. Indices should be in `[0, ...,
......@@ -1377,23 +1379,23 @@ class TFXLNetForSequenceClassification(TFXLNetPreTrainedModel, TFSequenceClassif
)
def call(
self,
input_ids=None,
attention_mask=None,
mems=None,
perm_mask=None,
target_mapping=None,
token_type_ids=None,
input_mask=None,
head_mask=None,
inputs_embeds=None,
use_mems=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
labels=None,
training=False,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
mems: Optional[Union[np.ndarray, tf.Tensor]] = None,
perm_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
target_mapping: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
input_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
use_mems: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[Union[np.ndarray, tf.Tensor]] = None,
training: Optional[bool] = False,
**kwargs,
):
) -> Union[TFXLNetForSequenceClassificationOutput, Tuple[tf.Tensor]]:
r"""
labels (`tf.Tensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
......@@ -1484,23 +1486,23 @@ class TFXLNetForMultipleChoice(TFXLNetPreTrainedModel, TFMultipleChoiceLoss):
)
def call(
self,
input_ids=None,
token_type_ids=None,
input_mask=None,
attention_mask=None,
mems=None,
perm_mask=None,
target_mapping=None,
head_mask=None,
inputs_embeds=None,
use_mems=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
labels=None,
training=False,
input_ids: Optional[TFModelInputType] = None,
token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
input_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
mems: Optional[Union[np.ndarray, tf.Tensor]] = None,
perm_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
target_mapping: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
use_mems: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[Union[np.ndarray, tf.Tensor]] = None,
training: Optional[bool] = False,
**kwargs,
):
) -> Union[TFXLNetForMultipleChoiceOutput, Tuple[tf.Tensor]]:
r"""
labels (`tf.Tensor` of shape `(batch_size,)`, *optional*):
Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices]`
......@@ -1606,23 +1608,23 @@ class TFXLNetForTokenClassification(TFXLNetPreTrainedModel, TFTokenClassificatio
)
def call(
self,
input_ids=None,
attention_mask=None,
mems=None,
perm_mask=None,
target_mapping=None,
token_type_ids=None,
input_mask=None,
head_mask=None,
inputs_embeds=None,
use_mems=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
labels=None,
training=False,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
mems: Optional[Union[np.ndarray, tf.Tensor]] = None,
perm_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
target_mapping: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
input_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
use_mems: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[Union[np.ndarray, tf.Tensor]] = None,
training: Optional[bool] = False,
**kwargs,
):
) -> Union[TFXLNetForTokenClassificationOutput, Tuple[tf.Tensor]]:
r"""
labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
......@@ -1693,24 +1695,24 @@ class TFXLNetForQuestionAnsweringSimple(TFXLNetPreTrainedModel, TFQuestionAnswer
)
def call(
self,
input_ids=None,
attention_mask=None,
mems=None,
perm_mask=None,
target_mapping=None,
token_type_ids=None,
input_mask=None,
head_mask=None,
inputs_embeds=None,
use_mems=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
start_positions=None,
end_positions=None,
training=False,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
mems: Optional[Union[np.ndarray, tf.Tensor]] = None,
perm_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
target_mapping: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
input_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
use_mems: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
start_positions: Optional[Union[np.ndarray, tf.Tensor]] = None,
end_positions: Optional[Union[np.ndarray, tf.Tensor]] = None,
training: Optional[bool] = False,
**kwargs,
):
) -> Union[TFXLNetForQuestionAnsweringSimpleOutput, Tuple[tf.Tensor]]:
r"""
start_positions (`tf.Tensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the start of the labelled span for computing the token classification loss.
......
src/transformers/models/xlnet/modeling_xlnet.py
View file @ c6f7ea19
......@@ -18,7 +18,7 @@
"""
import warnings
from dataclasses import dataclass
from typing import List, Optional, Tuple
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
......@@ -1074,21 +1074,21 @@ class XLNetModel(XLNetPreTrainedModel):
)
def forward(
self,
input_ids=None,
attention_mask=None,
mems=None,
perm_mask=None,
target_mapping=None,
token_type_ids=None,
input_mask=None,
head_mask=None,
inputs_embeds=None,
use_mems=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
mems: Optional[torch.Tensor] = None,
perm_mask: Optional[torch.Tensor] = None,
target_mapping: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
input_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
use_mems: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
**kwargs, # delete after depreciation warning is removed
):
) -> Union[Tuple, XLNetModelOutput]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
......@@ -1364,22 +1364,22 @@ class XLNetLMHeadModel(XLNetPreTrainedModel):
@replace_return_docstrings(output_type=XLNetLMHeadModelOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids=None,
attention_mask=None,
mems=None,
perm_mask=None,
target_mapping=None,
token_type_ids=None,
input_mask=None,
head_mask=None,
inputs_embeds=None,
labels=None,
use_mems=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
mems: Optional[torch.Tensor] = None,
perm_mask: Optional[torch.Tensor] = None,
target_mapping: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
input_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
use_mems: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
**kwargs, # delete when `use_cache` is removed in XLNetModel
):
) -> Union[Tuple, XLNetLMHeadModelOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, num_predict)`, *optional*):
Labels for masked language modeling. `num_predict` corresponds to `target_mapping.shape[1]`. If
......@@ -1526,22 +1526,22 @@ class XLNetForSequenceClassification(XLNetPreTrainedModel):
)
def forward(
self,
input_ids=None,
attention_mask=None,
mems=None,
perm_mask=None,
target_mapping=None,
token_type_ids=None,
input_mask=None,
head_mask=None,
inputs_embeds=None,
labels=None,
use_mems=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
mems: Optional[torch.Tensor] = None,
perm_mask: Optional[torch.Tensor] = None,
target_mapping: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
input_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
use_mems: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
**kwargs, # delete when `use_cache` is removed in XLNetModel
):
) -> Union[Tuple, XLNetForSequenceClassificationOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
......@@ -1634,22 +1634,22 @@ class XLNetForTokenClassification(XLNetPreTrainedModel):
)
def forward(
self,
input_ids=None,
attention_mask=None,
mems=None,
perm_mask=None,
target_mapping=None,
token_type_ids=None,
input_mask=None,
head_mask=None,
inputs_embeds=None,
labels=None,
use_mems=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
mems: Optional[torch.Tensor] = None,
perm_mask: Optional[torch.Tensor] = None,
target_mapping: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
input_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
use_mems: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
**kwargs, # delete when `use_cache` is removed in XLNetModel
):
) -> Union[Tuple, XLNetForTokenClassificationOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices]`
......@@ -1722,22 +1722,22 @@ class XLNetForMultipleChoice(XLNetPreTrainedModel):
)
def forward(
self,
input_ids=None,
token_type_ids=None,
input_mask=None,
attention_mask=None,
mems=None,
perm_mask=None,
target_mapping=None,
head_mask=None,
inputs_embeds=None,
labels=None,
use_mems=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
input_ids: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
input_mask: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
mems: Optional[torch.Tensor] = None,
perm_mask: Optional[torch.Tensor] = None,
target_mapping: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
use_mems: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
**kwargs, # delete when `use_cache` is removed in XLNetModel
):
) -> Union[Tuple, XLNetForMultipleChoiceOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
......@@ -1826,23 +1826,23 @@ class XLNetForQuestionAnsweringSimple(XLNetPreTrainedModel):
)
def forward(
self,
input_ids=None,
attention_mask=None,
mems=None,
perm_mask=None,
target_mapping=None,
token_type_ids=None,
input_mask=None,
head_mask=None,
inputs_embeds=None,
start_positions=None,
end_positions=None,
use_mems=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
mems: Optional[torch.Tensor] = None,
perm_mask: Optional[torch.Tensor] = None,
target_mapping: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
input_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
start_positions: Optional[torch.Tensor] = None,
end_positions: Optional[torch.Tensor] = None,
use_mems: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
**kwargs, # delete when `use_cache` is removed in XLNetModel
):
) -> Union[Tuple, XLNetForQuestionAnsweringSimpleOutput]:
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.
......@@ -1935,26 +1935,26 @@ class XLNetForQuestionAnswering(XLNetPreTrainedModel):
@replace_return_docstrings(output_type=XLNetForQuestionAnsweringOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids=None,
attention_mask=None,
mems=None,
perm_mask=None,
target_mapping=None,
token_type_ids=None,
input_mask=None,
head_mask=None,
inputs_embeds=None,
start_positions=None,
end_positions=None,
is_impossible=None,
cls_index=None,
p_mask=None,
use_mems=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
mems: Optional[torch.Tensor] = None,
perm_mask: Optional[torch.Tensor] = None,
target_mapping: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
input_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
start_positions: Optional[torch.Tensor] = None,
end_positions: Optional[torch.Tensor] = None,
is_impossible: Optional[torch.Tensor] = None,
cls_index: Optional[torch.Tensor] = None,
p_mask: Optional[torch.Tensor] = None,
use_mems: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
**kwargs, # delete when `use_cache` is removed in XLNetModel
):
) -> Union[Tuple, XLNetForQuestionAnsweringOutput]:
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.
......
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