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Rename add_start_docstrings_to_callable (#8120)

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This commit is contained in:
Sylvain Gugger
2020-10-28 13:42:31 -04:00
committed by GitHub
parent 6241c873cd
commit 378142afdf
55 changed files with 327 additions and 292 deletions
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14
templates/adding_a_new_model/modeling_tf_xxx.py
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@@ -26,7 +26,7 @@ from .file_utils import (
MULTIPLE_CHOICE_DUMMY_INPUTS,
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_callable,
add_start_docstrings_to_model_forward,
)
from .modeling_tf_outputs import (
TFBaseModelOutputWithPooling,
@@ -360,7 +360,7 @@ class TFXxxModel(TFXxxPreTrainedModel):
super().__init__(config, *inputs, **kwargs)
self.transformer = TFXxxMainLayer(config, name="transformer")
@add_start_docstrings_to_callable(XXX_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_start_docstrings_to_model_forward(XXX_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint="xxx-base-cased",
@@ -383,7 +383,7 @@ class TFXxxForMaskedLM(TFXxxPreTrainedModel, TFMaskedLanguageModelingLoss):
self.transformer = TFXxxMainLayer(config, name="transformer")
self.mlm = TFXxxMLMHead(config, self.transformer.embeddings, name="mlm")
@add_start_docstrings_to_callable(XXX_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_start_docstrings_to_model_forward(XXX_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint="xxx-base-cased",
@@ -465,7 +465,7 @@ class TFXxxForSequenceClassification(TFXxxPreTrainedModel, TFSequenceClassificat
config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
)
@add_start_docstrings_to_callable(XXX_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_start_docstrings_to_model_forward(XXX_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint="xxx-base-cased",
@@ -557,7 +557,7 @@ class TFXxxForMultipleChoice(TFXxxPreTrainedModel, TFMultipleChoiceLoss):
"""
return {"input_ids": tf.constant(MULTIPLE_CHOICE_DUMMY_INPUTS)}
@add_start_docstrings_to_callable(XXX_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
@add_start_docstrings_to_model_forward(XXX_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint="xxx-base-cased",
@@ -680,7 +680,7 @@ class TFXxxForTokenClassification(TFXxxPreTrainedModel, TFTokenClassificationLos
config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
)
@add_start_docstrings_to_callable(XXX_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_start_docstrings_to_model_forward(XXX_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint="xxx-base-cased",
@@ -761,7 +761,7 @@ class TFXxxForQuestionAnswering(TFXxxPreTrainedModel, TFQuestionAnsweringLoss):
config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="qa_outputs"
)
@add_start_docstrings_to_callable(XXX_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_start_docstrings_to_model_forward(XXX_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint="xxx-base-cased",

14
templates/adding_a_new_model/modeling_xxx.py
View File

@@ -26,7 +26,7 @@ from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from .configuration_xxx import XxxConfig
from .file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_callable
from .file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from .modeling_outputs import (
BaseModelOutputWithPooling,
MaskedLMOutput,
@@ -309,7 +309,7 @@ class XxxModel(XxxPreTrainedModel):
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_heads(heads)
@add_start_docstrings_to_callable(XXX_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_start_docstrings_to_model_forward(XXX_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint="xxx-base-uncased",
@@ -391,7 +391,7 @@ class XxxForMaskedLM(XxxPreTrainedModel):
def get_output_embeddings(self):
return self.lm_head
@add_start_docstrings_to_callable(XXX_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_start_docstrings_to_model_forward(XXX_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint="xxx-base-uncased",
@@ -468,7 +468,7 @@ class XxxForSequenceClassification(XxxPreTrainedModel):
self.init_weights()
@add_start_docstrings_to_callable(XXX_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_start_docstrings_to_model_forward(XXX_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint="xxx-base-uncased",
@@ -551,7 +551,7 @@ class XxxForMultipleChoice(XxxPreTrainedModel):
self.init_weights()
@add_start_docstrings_to_callable(XXX_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
@add_start_docstrings_to_model_forward(XXX_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint="xxx-base-uncased",
@@ -641,7 +641,7 @@ class XxxForTokenClassification(XxxPreTrainedModel):
self.init_weights()
@add_start_docstrings_to_callable(XXX_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_start_docstrings_to_model_forward(XXX_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint="xxx-base-uncased",
@@ -726,7 +726,7 @@ class XxxForQuestionAnswering(XxxPreTrainedModel):
self.init_weights()
@add_start_docstrings_to_callable(XXX_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_start_docstrings_to_model_forward(XXX_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint="xxx-base-uncased",