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Oct 28, 2020
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Rename add_start_docstrings_to_callable #8120

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6 changes: 3 additions & 3 deletions examples/bert-loses-patience/pabee/modeling_pabee_albert.py
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Original file line number Diff line number Diff line change
Expand Up @@ -20,7 +20,7 @@
import torch.nn as nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_callable
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.modeling_albert import (
ALBERT_INPUTS_DOCSTRING,
ALBERT_START_DOCSTRING,
Expand Down Expand Up @@ -87,7 +87,7 @@ def log_stats(self):
message = f"*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up = {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***"
print(message)

@add_start_docstrings_to_callable(ALBERT_INPUTS_DOCSTRING)
@add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING)
def forward(
self,
input_ids=None,
Expand Down Expand Up @@ -230,7 +230,7 @@ def __init__(self, config):

self.init_weights()

@add_start_docstrings_to_callable(ALBERT_INPUTS_DOCSTRING)
@add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING)
def forward(
self,
input_ids=None,
Expand Down
6 changes: 3 additions & 3 deletions examples/bert-loses-patience/pabee/modeling_pabee_bert.py
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Expand Up @@ -22,7 +22,7 @@
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_callable
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
Expand Down Expand Up @@ -92,7 +92,7 @@ def log_stats(self):
message = f"*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up = {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***"
print(message)

@add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING)
@add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING)
def forward(
self,
input_ids=None,
Expand Down Expand Up @@ -254,7 +254,7 @@ def __init__(self, config):

self.init_weights()

@add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING)
@add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING)
def forward(
self,
input_ids=None,
Expand Down
6 changes: 3 additions & 3 deletions examples/deebert/src/modeling_highway_bert.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,7 +2,7 @@
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_callable
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
Expand Down Expand Up @@ -134,7 +134,7 @@ def _prune_heads(self, heads_to_prune):
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_heads(heads)

@add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING)
@add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING)
def forward(
self,
input_ids=None,
Expand Down Expand Up @@ -288,7 +288,7 @@ def __init__(self, config):

self.init_weights()

@add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING)
@add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING)
def forward(
self,
input_ids=None,
Expand Down
4 changes: 2 additions & 2 deletions examples/deebert/src/modeling_highway_roberta.py
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Expand Up @@ -4,7 +4,7 @@
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.configuration_roberta import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_callable
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.modeling_roberta import ROBERTA_INPUTS_DOCSTRING, ROBERTA_START_DOCSTRING, RobertaEmbeddings

from .modeling_highway_bert import BertPreTrainedModel, DeeBertModel, HighwayException, entropy
Expand Down Expand Up @@ -45,7 +45,7 @@ def __init__(self, config):
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, self.config.num_labels)

@add_start_docstrings_to_callable(ROBERTA_INPUTS_DOCSTRING)
@add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING)
def forward(
self,
input_ids=None,
Expand Down
12 changes: 6 additions & 6 deletions examples/movement-pruning/emmental/modeling_bert_masked.py
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Expand Up @@ -28,7 +28,7 @@

from emmental import MaskedBertConfig
from emmental.modules import MaskedLinear
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_callable
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.modeling_bert import ACT2FN, BertLayerNorm, load_tf_weights_in_bert
from transformers.modeling_utils import PreTrainedModel, prune_linear_layer

Expand Down Expand Up @@ -498,7 +498,7 @@ def _prune_heads(self, heads_to_prune):
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_heads(heads)

@add_start_docstrings_to_callable(MASKED_BERT_INPUTS_DOCSTRING)
@add_start_docstrings_to_model_forward(MASKED_BERT_INPUTS_DOCSTRING)
def forward(
self,
input_ids=None,
Expand Down Expand Up @@ -671,7 +671,7 @@ def __init__(self, config):

self.init_weights()

@add_start_docstrings_to_callable(MASKED_BERT_INPUTS_DOCSTRING)
@add_start_docstrings_to_model_forward(MASKED_BERT_INPUTS_DOCSTRING)
def forward(
self,
input_ids=None,
Expand Down Expand Up @@ -756,7 +756,7 @@ def __init__(self, config):

self.init_weights()

@add_start_docstrings_to_callable(MASKED_BERT_INPUTS_DOCSTRING)
@add_start_docstrings_to_model_forward(MASKED_BERT_INPUTS_DOCSTRING)
def forward(
self,
input_ids=None,
Expand Down Expand Up @@ -846,7 +846,7 @@ def __init__(self, config):

self.init_weights()

@add_start_docstrings_to_callable(MASKED_BERT_INPUTS_DOCSTRING)
@add_start_docstrings_to_model_forward(MASKED_BERT_INPUTS_DOCSTRING)
def forward(
self,
input_ids=None,
Expand Down Expand Up @@ -932,7 +932,7 @@ def __init__(self, config):

self.init_weights()

@add_start_docstrings_to_callable(MASKED_BERT_INPUTS_DOCSTRING)
@add_start_docstrings_to_model_forward(MASKED_BERT_INPUTS_DOCSTRING)
def forward(
self,
input_ids=None,
Expand Down
2 changes: 1 addition & 1 deletion src/transformers/file_utils.py
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Expand Up @@ -425,7 +425,7 @@ def docstring_decorator(fn):
return docstring_decorator


def add_start_docstrings_to_callable(*docstr):
def add_start_docstrings_to_model_forward(*docstr):
def docstring_decorator(fn):
class_name = ":class:`~transformers.{}`".format(fn.__qualname__.split(".")[0])
intro = " The {} forward method, overrides the :func:`__call__` special method.".format(class_name)
Expand Down
16 changes: 8 additions & 8 deletions src/transformers/modeling_albert.py
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Expand Up @@ -30,7 +30,7 @@
ModelOutput,
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_callable,
add_start_docstrings_to_model_forward,
replace_return_docstrings,
)
from .modeling_outputs import (
Expand Down Expand Up @@ -631,7 +631,7 @@ def _prune_heads(self, heads_to_prune):
inner_group_idx = int(layer - group_idx * self.config.inner_group_num)
self.encoder.albert_layer_groups[group_idx].albert_layers[inner_group_idx].attention.prune_heads(heads)

@add_start_docstrings_to_callable(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint="albert-base-v2",
Expand Down Expand Up @@ -727,7 +727,7 @@ def get_output_embeddings(self):
def get_input_embeddings(self):
return self.albert.embeddings.word_embeddings

@add_start_docstrings_to_callable(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=AlbertForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
Expand Down Expand Up @@ -879,7 +879,7 @@ def get_output_embeddings(self):
def get_input_embeddings(self):
return self.albert.embeddings.word_embeddings

@add_start_docstrings_to_callable(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint="albert-base-v2",
Expand Down Expand Up @@ -967,7 +967,7 @@ def __init__(self, config):

self.init_weights()

@add_start_docstrings_to_callable(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint="albert-base-v2",
Expand Down Expand Up @@ -1055,7 +1055,7 @@ def __init__(self, config):

self.init_weights()

@add_start_docstrings_to_callable(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint="albert-base-v2",
Expand Down Expand Up @@ -1143,7 +1143,7 @@ def __init__(self, config):

self.init_weights()

@add_start_docstrings_to_callable(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint="albert-base-v2",
Expand Down Expand Up @@ -1242,7 +1242,7 @@ def __init__(self, config):

self.init_weights()

@add_start_docstrings_to_callable(ALBERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
@add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint="albert-base-v2",
Expand Down
10 changes: 5 additions & 5 deletions src/transformers/modeling_bart.py
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Expand Up @@ -30,7 +30,7 @@
add_code_sample_docstrings,
add_end_docstrings,
add_start_docstrings,
add_start_docstrings_to_callable,
add_start_docstrings_to_model_forward,
replace_return_docstrings,
)
from .modeling_outputs import (
Expand Down Expand Up @@ -846,7 +846,7 @@ def __init__(self, config: BartConfig):

self.init_weights()

@add_start_docstrings_to_callable(BART_INPUTS_DOCSTRING)
@add_start_docstrings_to_model_forward(BART_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint="facebook/bart-large",
Expand Down Expand Up @@ -981,7 +981,7 @@ def _resize_final_logits_bias(self, new_num_tokens: int, old_num_tokens: int) ->
new_bias = torch.cat([self.final_logits_bias, extra_bias], dim=1)
self.register_buffer("final_logits_bias", new_bias)

@add_start_docstrings_to_callable(BART_INPUTS_DOCSTRING)
@add_start_docstrings_to_model_forward(BART_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
@add_end_docstrings(BART_GENERATION_EXAMPLE)
def forward(
Expand Down Expand Up @@ -1147,7 +1147,7 @@ def __init__(self, config: BartConfig, **kwargs):
self.model._init_weights(self.classification_head.dense)
self.model._init_weights(self.classification_head.out_proj)

@add_start_docstrings_to_callable(BART_INPUTS_DOCSTRING)
@add_start_docstrings_to_model_forward(BART_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint="facebook/bart-large",
Expand Down Expand Up @@ -1234,7 +1234,7 @@ def __init__(self, config):

self.model._init_weights(self.qa_outputs)

@add_start_docstrings_to_callable(BART_INPUTS_DOCSTRING)
@add_start_docstrings_to_model_forward(BART_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint="facebook/bart-large",
Expand Down
20 changes: 10 additions & 10 deletions src/transformers/modeling_bert.py
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Expand Up @@ -33,7 +33,7 @@
ModelOutput,
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_callable,
add_start_docstrings_to_model_forward,
replace_return_docstrings,
)
from .modeling_outputs import (
Expand Down Expand Up @@ -748,7 +748,7 @@ class PreTrainedModel
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_heads(heads)

@add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint="bert-base-uncased",
Expand Down Expand Up @@ -870,7 +870,7 @@ def __init__(self, config):
def get_output_embeddings(self):
return self.cls.predictions.decoder

@add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=BertForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
Expand Down Expand Up @@ -983,7 +983,7 @@ def __init__(self, config):
def get_output_embeddings(self):
return self.cls.predictions.decoder

@add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=CausalLMOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
Expand Down Expand Up @@ -1103,7 +1103,7 @@ def __init__(self, config):
def get_output_embeddings(self):
return self.cls.predictions.decoder

@add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint="bert-base-uncased",
Expand Down Expand Up @@ -1206,7 +1206,7 @@ def __init__(self, config):

self.init_weights()

@add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=NextSentencePredictorOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
Expand Down Expand Up @@ -1300,7 +1300,7 @@ def __init__(self, config):

self.init_weights()

@add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint="bert-base-uncased",
Expand Down Expand Up @@ -1384,7 +1384,7 @@ def __init__(self, config):

self.init_weights()

@add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
@add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint="bert-base-uncased",
Expand Down Expand Up @@ -1479,7 +1479,7 @@ def __init__(self, config):

self.init_weights()

@add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint="bert-base-uncased",
Expand Down Expand Up @@ -1569,7 +1569,7 @@ def __init__(self, config):

self.init_weights()

@add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint="bert-base-uncased",
Expand Down
6 changes: 3 additions & 3 deletions src/transformers/modeling_bert_generation.py
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Expand Up @@ -24,7 +24,7 @@
from .file_utils import (
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_callable,
add_start_docstrings_to_model_forward,
replace_return_docstrings,
)
from .modeling_bert import BertEncoder
Expand Down Expand Up @@ -293,7 +293,7 @@ class PreTrainedModel
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_heads(heads)

@add_start_docstrings_to_callable(BERT_GENERATION_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_start_docstrings_to_model_forward(BERT_GENERATION_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint="google/bert_for_seq_generation_L-24_bbc_encoder",
Expand Down Expand Up @@ -421,7 +421,7 @@ def __init__(self, config):
def get_output_embeddings(self):
return self.lm_head.decoder

@add_start_docstrings_to_callable(BERT_GENERATION_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_start_docstrings_to_model_forward(BERT_GENERATION_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=CausalLMOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
Expand Down
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