Rename add_start_docstrings_to_callable (#8120)

src/transformers/modeling_layoutlm.py

@@ -23,7 +23,7 @@ from torch.nn import CrossEntropyLoss
 
 from .activations import ACT2FN
 from .configuration_layoutlm import LayoutLMConfig
-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 BaseModelOutput, BaseModelOutputWithPooling, MaskedLMOutput, TokenClassifierOutput
 from .modeling_utils import (
     PreTrainedModel,
@@ -607,7 +607,7 @@ class LayoutLMModel(LayoutLMPreTrainedModel):
         for layer, heads in heads_to_prune.items():
             self.encoder.layer[layer].attention.prune_heads(heads)
 
-    @add_start_docstrings_to_callable(LAYOUTLM_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
+    @add_start_docstrings_to_model_forward(LAYOUTLM_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,
         checkpoint="layoutlm-base-uncased",
@@ -744,7 +744,7 @@ class LayoutLMForMaskedLM(LayoutLMPreTrainedModel):
     def get_output_embeddings(self):
         return self.cls.predictions.decoder
 
-    @add_start_docstrings_to_callable(LAYOUTLM_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
+    @add_start_docstrings_to_model_forward(LAYOUTLM_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,
         checkpoint="layoutlm-base-uncased",
@@ -832,7 +832,7 @@ class LayoutLMForTokenClassification(LayoutLMPreTrainedModel):
     def get_input_embeddings(self):
         return self.layoutlm.embeddings.word_embeddings
 
-    @add_start_docstrings_to_callable(LAYOUTLM_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
+    @add_start_docstrings_to_model_forward(LAYOUTLM_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,
         checkpoint="layoutlm-base-uncased",

src/transformers/modeling_longformer.py

@@ -27,7 +27,7 @@ from .configuration_longformer import LongformerConfig
 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_outputs import (
@@ -1181,7 +1181,7 @@ class LongformerModel(LongformerPreTrainedModel):
             attention_mask = global_attention_mask + 1
         return attention_mask
 
-    @add_start_docstrings_to_callable(LONGFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_start_docstrings_to_model_forward(LONGFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=_CONFIG_FOR_DOC)
     def forward(
         self,
@@ -1308,7 +1308,7 @@ class LongformerForMaskedLM(LongformerPreTrainedModel):
     def get_output_embeddings(self):
         return self.lm_head.decoder
 
-    @add_start_docstrings_to_callable(LONGFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_start_docstrings_to_model_forward(LONGFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @replace_return_docstrings(output_type=MaskedLMOutput, config_class=_CONFIG_FOR_DOC)
     def forward(
         self,
@@ -1412,7 +1412,7 @@ class LongformerForSequenceClassification(LongformerPreTrainedModel):
 
         self.init_weights()
 
-    @add_start_docstrings_to_callable(LONGFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_start_docstrings_to_model_forward(LONGFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,
         checkpoint="allenai/longformer-base-4096",
@@ -1521,7 +1521,7 @@ class LongformerForQuestionAnswering(LongformerPreTrainedModel):
 
         self.init_weights()
 
-    @add_start_docstrings_to_callable(LONGFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_start_docstrings_to_model_forward(LONGFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @replace_return_docstrings(output_type=QuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC)
     def forward(
         self,
@@ -1655,7 +1655,7 @@ class LongformerForTokenClassification(LongformerPreTrainedModel):
 
         self.init_weights()
 
-    @add_start_docstrings_to_callable(LONGFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_start_docstrings_to_model_forward(LONGFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,
         checkpoint="allenai/longformer-base-4096",
@@ -1742,7 +1742,9 @@ class LongformerForMultipleChoice(LongformerPreTrainedModel):
 
         self.init_weights()
 
-    @add_start_docstrings_to_callable(LONGFORMER_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
+    @add_start_docstrings_to_model_forward(
+        LONGFORMER_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")
+    )
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,
         checkpoint="allenai/longformer-base-4096",

src/transformers/modeling_lxmert.py

@@ -30,7 +30,7 @@ from .file_utils import (
     ModelOutput,
     add_code_sample_docstrings,
     add_start_docstrings,
-    add_start_docstrings_to_callable,
+    add_start_docstrings_to_model_forward,
     replace_return_docstrings,
 )
 from .modeling_utils import PreTrainedModel
@@ -893,7 +893,7 @@ class LxmertModel(LxmertPreTrainedModel):
     def set_input_embeddings(self, new_embeddings):
         self.embeddings.word_embeddings = new_embeddings
 
-    @add_start_docstrings_to_callable(LXMERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_start_docstrings_to_model_forward(LXMERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,
         checkpoint="unc-nlp/lxmert-base-uncased",
@@ -1145,7 +1145,7 @@ class LxmertForPreTraining(LxmertPreTrainedModel):
 
         return new_qa_logit_layer
 
-    @add_start_docstrings_to_callable(LXMERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_start_docstrings_to_model_forward(LXMERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @replace_return_docstrings(output_type=LxmertForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
     def forward(
         self,
@@ -1368,7 +1368,7 @@ class LxmertForQuestionAnswering(LxmertPreTrainedModel):
 
         return new_qa_logit_layer
 
-    @add_start_docstrings_to_callable(LXMERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_start_docstrings_to_model_forward(LXMERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,
         checkpoint="unc-nlp/lxmert-base-uncased",

src/transformers/modeling_mmbt.py

@@ -20,7 +20,7 @@ import torch
 import torch.nn as nn
 from torch.nn import CrossEntropyLoss, MSELoss
 
-from .file_utils import add_start_docstrings, add_start_docstrings_to_callable, replace_return_docstrings
+from .file_utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
 from .modeling_outputs import BaseModelOutputWithPooling, SequenceClassifierOutput
 from .modeling_utils import ModuleUtilsMixin
 from .utils import logging
@@ -187,7 +187,7 @@ class MMBTModel(nn.Module, ModuleUtilsMixin):
         self.transformer = transformer
         self.modal_encoder = ModalEmbeddings(config, encoder, transformer.embeddings)
 
-    @add_start_docstrings_to_callable(MMBT_INPUTS_DOCSTRING)
+    @add_start_docstrings_to_model_forward(MMBT_INPUTS_DOCSTRING)
     @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=_CONFIG_FOR_DOC)
     def forward(
         self,

src/transformers/modeling_mobilebert.py

@@ -37,7 +37,7 @@ from .file_utils import (
     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 (
@@ -837,7 +837,7 @@ class MobileBertModel(MobileBertPreTrainedModel):
         for layer, heads in heads_to_prune.items():
             self.encoder.layer[layer].attention.prune_heads(heads)
 
-    @add_start_docstrings_to_callable(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_start_docstrings_to_model_forward(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,
         checkpoint="google/mobilebert-uncased",
@@ -970,7 +970,7 @@ class MobileBertForPreTraining(MobileBertPreTrainedModel):
         if output_embeddings is not None and self.config.tie_word_embeddings:
             self._tie_or_clone_weights(output_embeddings, self.get_input_embeddings())
 
-    @add_start_docstrings_to_callable(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_start_docstrings_to_model_forward(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @replace_return_docstrings(output_type=MobileBertForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
     def forward(
         self,
@@ -1088,7 +1088,7 @@ class MobileBertForMaskedLM(MobileBertPreTrainedModel):
         if output_embeddings is not None and self.config.tie_word_embeddings:
             self._tie_or_clone_weights(output_embeddings, self.get_input_embeddings())
 
-    @add_start_docstrings_to_callable(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_start_docstrings_to_model_forward(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,
         checkpoint="google/mobilebert-uncased",
@@ -1184,7 +1184,7 @@ class MobileBertForNextSentencePrediction(MobileBertPreTrainedModel):
 
         self.init_weights()
 
-    @add_start_docstrings_to_callable(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_start_docstrings_to_model_forward(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @replace_return_docstrings(output_type=NextSentencePredictorOutput, config_class=_CONFIG_FOR_DOC)
     def forward(
         self,
@@ -1276,7 +1276,7 @@ class MobileBertForSequenceClassification(MobileBertPreTrainedModel):
 
         self.init_weights()
 
-    @add_start_docstrings_to_callable(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_start_docstrings_to_model_forward(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,
         checkpoint="google/mobilebert-uncased",
@@ -1361,7 +1361,7 @@ class MobileBertForQuestionAnswering(MobileBertPreTrainedModel):
 
         self.init_weights()
 
-    @add_start_docstrings_to_callable(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_start_docstrings_to_model_forward(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,
         checkpoint="google/mobilebert-uncased",
@@ -1460,7 +1460,9 @@ class MobileBertForMultipleChoice(MobileBertPreTrainedModel):
 
         self.init_weights()
 
-    @add_start_docstrings_to_callable(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
+    @add_start_docstrings_to_model_forward(
+        MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")
+    )
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,
         checkpoint="google/mobilebert-uncased",
@@ -1555,7 +1557,7 @@ class MobileBertForTokenClassification(MobileBertPreTrainedModel):
 
         self.init_weights()
 
-    @add_start_docstrings_to_callable(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_start_docstrings_to_model_forward(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,
         checkpoint="google/mobilebert-uncased",

src/transformers/modeling_openai.py

@@ -33,7 +33,7 @@ from .file_utils import (
     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 BaseModelOutput, CausalLMOutput, SequenceClassifierOutput
@@ -427,7 +427,7 @@ class OpenAIGPTModel(OpenAIGPTPreTrainedModel):
         for layer, heads in heads_to_prune.items():
             self.h[layer].attn.prune_heads(heads)
 
-    @add_start_docstrings_to_callable(OPENAI_GPT_INPUTS_DOCSTRING)
+    @add_start_docstrings_to_model_forward(OPENAI_GPT_INPUTS_DOCSTRING)
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,
         checkpoint="openai-gpt",
@@ -543,7 +543,7 @@ class OpenAIGPTLMHeadModel(OpenAIGPTPreTrainedModel):
     def get_output_embeddings(self):
         return self.lm_head
 
-    @add_start_docstrings_to_callable(OPENAI_GPT_INPUTS_DOCSTRING)
+    @add_start_docstrings_to_model_forward(OPENAI_GPT_INPUTS_DOCSTRING)
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,
         checkpoint="openai-gpt",
@@ -629,7 +629,7 @@ class OpenAIGPTDoubleHeadsModel(OpenAIGPTPreTrainedModel):
     def get_output_embeddings(self):
         return self.lm_head
 
-    @add_start_docstrings_to_callable(OPENAI_GPT_INPUTS_DOCSTRING)
+    @add_start_docstrings_to_model_forward(OPENAI_GPT_INPUTS_DOCSTRING)
     @replace_return_docstrings(output_type=OpenAIGPTDoubleHeadsModelOutput, config_class=_CONFIG_FOR_DOC)
     def forward(
         self,
@@ -754,7 +754,7 @@ class OpenAIGPTForSequenceClassification(OpenAIGPTPreTrainedModel):
 
         self.init_weights()
 
-    @add_start_docstrings_to_callable(OPENAI_GPT_INPUTS_DOCSTRING)
+    @add_start_docstrings_to_model_forward(OPENAI_GPT_INPUTS_DOCSTRING)
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,
         checkpoint="openai-gpt",

src/transformers/modeling_prophetnet.py

@@ -25,7 +25,12 @@ from torch import Tensor, nn
 
 from .activations import ACT2FN
 from .configuration_prophetnet import ProphetNetConfig
-from .file_utils import ModelOutput, add_start_docstrings, add_start_docstrings_to_callable, replace_return_docstrings
+from .file_utils import (
+    ModelOutput,
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    replace_return_docstrings,
+)
 from .modeling_outputs import BaseModelOutput
 from .modeling_utils import PreTrainedModel
 from .utils import logging
@@ -1138,7 +1143,7 @@ class ProphetNetEncoder(ProphetNetPreTrainedModel):
     def set_input_embeddings(self, value):
         self.word_embeddings = value
 
-    @add_start_docstrings_to_callable(PROPHETNET_STANDALONE_INPUTS_DOCSTRING)
+    @add_start_docstrings_to_model_forward(PROPHETNET_STANDALONE_INPUTS_DOCSTRING)
     @replace_return_docstrings(output_type=BaseModelOutput, config_class=_CONFIG_FOR_DOC)
     def forward(
         self,
@@ -1254,7 +1259,7 @@ class ProphetNetDecoder(ProphetNetPreTrainedModel):
     def set_input_embeddings(self, value):
         self.word_embeddings = value
 
-    @add_start_docstrings_to_callable(PROPHETNET_STANDALONE_INPUTS_DOCSTRING)
+    @add_start_docstrings_to_model_forward(PROPHETNET_STANDALONE_INPUTS_DOCSTRING)
     @replace_return_docstrings(output_type=ProphetNetDecoderModelOutput, config_class=_CONFIG_FOR_DOC)
     def forward(
         self,
@@ -1570,7 +1575,7 @@ class ProphetNetModel(ProphetNetPreTrainedModel):
     def get_decoder(self):
         return self.decoder
 
-    @add_start_docstrings_to_callable(PROPHETNET_INPUTS_DOCSTRING)
+    @add_start_docstrings_to_model_forward(PROPHETNET_INPUTS_DOCSTRING)
     @replace_return_docstrings(output_type=ProphetNetSeq2SeqModelOutput, config_class=_CONFIG_FOR_DOC)
     def forward(
         self,
@@ -1674,7 +1679,7 @@ class ProphetNetForConditionalGeneration(ProphetNetPreTrainedModel):
     def get_input_embeddings(self):
         return self.prophetnet.word_embeddings
 
-    @add_start_docstrings_to_callable(PROPHETNET_INPUTS_DOCSTRING)
+    @add_start_docstrings_to_model_forward(PROPHETNET_INPUTS_DOCSTRING)
     @replace_return_docstrings(output_type=ProphetNetSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
     def forward(
         self,
@@ -1865,7 +1870,7 @@ class ProphetNetForCausalLM(ProphetNetPreTrainedModel):
     def get_output_embeddings(self):
         return self.lm_head
 
-    @add_start_docstrings_to_callable(PROPHETNET_STANDALONE_INPUTS_DOCSTRING)
+    @add_start_docstrings_to_model_forward(PROPHETNET_STANDALONE_INPUTS_DOCSTRING)
     @replace_return_docstrings(output_type=ProphetNetDecoderLMOutput, config_class=_CONFIG_FOR_DOC)
     def forward(
         self,

src/transformers/modeling_rag.py

@@ -21,7 +21,7 @@ import torch
 
 from .configuration_rag import RagConfig
 from .configuration_utils import PretrainedConfig
-from .file_utils import add_start_docstrings_to_callable, replace_return_docstrings
+from .file_utils import add_start_docstrings_to_model_forward, replace_return_docstrings
 from .modeling_outputs import ModelOutput
 from .modeling_utils import PreTrainedModel
 from .retrieval_rag import RagRetriever
@@ -459,7 +459,7 @@ RAG_FORWARD_INPUTS_DOCSTRING = r"""
 """
 
 
-@add_start_docstrings_to_callable(RAG_START_DOCSTRING)
+@add_start_docstrings_to_model_forward(RAG_START_DOCSTRING)
 class RagModel(RagPreTrainedModel):
     def __init__(
         self,
@@ -502,7 +502,7 @@ class RagModel(RagPreTrainedModel):
         self.question_encoder = question_encoder
         self.generator = generator
 
-    @add_start_docstrings_to_callable(RAG_FORWARD_INPUTS_DOCSTRING)
+    @add_start_docstrings_to_model_forward(RAG_FORWARD_INPUTS_DOCSTRING)
     @replace_return_docstrings(output_type=RetrievAugLMOutput, config_class=_CONFIG_FOR_DOC)
     def forward(
         self,
@@ -658,7 +658,7 @@ class RagModel(RagPreTrainedModel):
         )
 
 
-@add_start_docstrings_to_callable(
+@add_start_docstrings_to_model_forward(
     """
     A RAG-sequence model impementation. It performs RAG-sequence specific marginalization in the forward pass.
     """,
@@ -687,7 +687,7 @@ class RagSequenceForGeneration(RagPreTrainedModel):
     def set_retriever(self, retriever: RagRetriever):
         self.rag.retriever = retriever
 
-    @add_start_docstrings_to_callable(RAG_FORWARD_INPUTS_DOCSTRING)
+    @add_start_docstrings_to_model_forward(RAG_FORWARD_INPUTS_DOCSTRING)
     @replace_return_docstrings(output_type=RetrievAugLMMarginOutput, config_class=_CONFIG_FOR_DOC)
     def forward(
         self,
@@ -984,7 +984,7 @@ class RagSequenceForGeneration(RagPreTrainedModel):
         return output
 
 
-@add_start_docstrings_to_callable(
+@add_start_docstrings_to_model_forward(
     """
     A RAG-token model impementation. It performs RAG-token specific marginalization in the forward pass.
     """,
@@ -1080,7 +1080,7 @@ class RagTokenForGeneration(RagPreTrainedModel):
         log_prob_sum = seq_logprobs + doc_logprobs.unsqueeze(-1).unsqueeze(-1)
         return torch.logsumexp(log_prob_sum, dim=1)
 
-    @add_start_docstrings_to_callable(RAG_FORWARD_INPUTS_DOCSTRING)
+    @add_start_docstrings_to_model_forward(RAG_FORWARD_INPUTS_DOCSTRING)
     @replace_return_docstrings(output_type=RetrievAugLMMarginOutput, config_class=_CONFIG_FOR_DOC)
     def forward(
         self,

src/transformers/modeling_reformer.py

@@ -36,7 +36,7 @@ from .file_utils import (
     ModelOutput,
     add_code_sample_docstrings,
     add_start_docstrings,
-    add_start_docstrings_to_callable,
+    add_start_docstrings_to_model_forward,
 )
 from .modeling_outputs import CausalLMOutput, MaskedLMOutput, QuestionAnsweringModelOutput, SequenceClassifierOutput
 from .modeling_utils import PreTrainedModel, apply_chunking_to_forward
@@ -1991,7 +1991,7 @@ class ReformerModel(ReformerPreTrainedModel):
         for layer, heads in heads_to_prune.items():
             self.encoder.layer[layer].attention.prune_heads(heads)
 
-    @add_start_docstrings_to_callable(REFORMER_INPUTS_DOCSTRING)
+    @add_start_docstrings_to_model_forward(REFORMER_INPUTS_DOCSTRING)
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,
         checkpoint="google/reformer-crime-and-punishment",
@@ -2195,7 +2195,7 @@ class ReformerModelWithLMHead(ReformerPreTrainedModel):
     def get_output_embeddings(self):
         return self.lm_head.decoder
 
-    @add_start_docstrings_to_callable(REFORMER_INPUTS_DOCSTRING)
+    @add_start_docstrings_to_model_forward(REFORMER_INPUTS_DOCSTRING)
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,
         checkpoint="google/reformer-crime-and-punishment",
@@ -2309,7 +2309,7 @@ class ReformerForMaskedLM(ReformerPreTrainedModel):
     def get_output_embeddings(self):
         return self.lm_head.decoder
 
-    @add_start_docstrings_to_callable(REFORMER_INPUTS_DOCSTRING)
+    @add_start_docstrings_to_model_forward(REFORMER_INPUTS_DOCSTRING)
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,
         checkpoint="google/reformer-crime-and-punishment",
@@ -2389,7 +2389,7 @@ class ReformerForSequenceClassification(ReformerPreTrainedModel):
 
         self.init_weights()
 
-    @add_start_docstrings_to_callable(REFORMER_INPUTS_DOCSTRING)
+    @add_start_docstrings_to_model_forward(REFORMER_INPUTS_DOCSTRING)
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,
         checkpoint="google/reformer-crime-and-punishment",
@@ -2491,7 +2491,7 @@ class ReformerForQuestionAnswering(ReformerPreTrainedModel):
 
         self.init_weights()
 
-    @add_start_docstrings_to_callable(REFORMER_INPUTS_DOCSTRING)
+    @add_start_docstrings_to_model_forward(REFORMER_INPUTS_DOCSTRING)
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,
         checkpoint="google/reformer-crime-and-punishment",

src/transformers/modeling_roberta.py

@@ -27,7 +27,7 @@ from .configuration_roberta import RobertaConfig
 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_outputs import (
@@ -595,7 +595,7 @@ class RobertaModel(RobertaPreTrainedModel):
         for layer, heads in heads_to_prune.items():
             self.encoder.layer[layer].attention.prune_heads(heads)
 
-    @add_start_docstrings_to_callable(ROBERTA_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
+    @add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,
         checkpoint="roberta-base",
@@ -718,7 +718,7 @@ class RobertaForCausalLM(RobertaPreTrainedModel):
     def get_output_embeddings(self):
         return self.lm_head.decoder
 
-    @add_start_docstrings_to_callable(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @replace_return_docstrings(output_type=CausalLMOutput, config_class=_CONFIG_FOR_DOC)
     def forward(
         self,
@@ -838,7 +838,7 @@ class RobertaForMaskedLM(RobertaPreTrainedModel):
     def get_output_embeddings(self):
         return self.lm_head.decoder
 
-    @add_start_docstrings_to_callable(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,
         checkpoint="roberta-base",
@@ -956,7 +956,7 @@ class RobertaForSequenceClassification(RobertaPreTrainedModel):
 
         self.init_weights()
 
-    @add_start_docstrings_to_callable(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,
         checkpoint="roberta-base",
@@ -1039,7 +1039,7 @@ class RobertaForMultipleChoice(RobertaPreTrainedModel):
 
         self.init_weights()
 
-    @add_start_docstrings_to_callable(ROBERTA_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
+    @add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,
         checkpoint="roberta-base",
@@ -1133,7 +1133,7 @@ class RobertaForTokenClassification(RobertaPreTrainedModel):
 
         self.init_weights()
 
-    @add_start_docstrings_to_callable(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,
         checkpoint="roberta-base",
@@ -1242,7 +1242,7 @@ class RobertaForQuestionAnswering(RobertaPreTrainedModel):
 
         self.init_weights()
 
-    @add_start_docstrings_to_callable(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,
         checkpoint="roberta-base",

src/transformers/modeling_squeezebert.py

@@ -23,7 +23,7 @@ from torch.nn import CrossEntropyLoss, MSELoss
 
 from .activations import ACT2FN
 from .configuration_squeezebert import SqueezeBertConfig
-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 (
     BaseModelOutput,
     BaseModelOutputWithPooling,
@@ -518,7 +518,7 @@ class SqueezeBertModel(SqueezeBertPreTrainedModel):
         for layer, heads in heads_to_prune.items():
             self.encoder.layer[layer].attention.prune_heads(heads)
 
-    @add_start_docstrings_to_callable(SQUEEZEBERT_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
+    @add_start_docstrings_to_model_forward(SQUEEZEBERT_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,
         checkpoint="squeezebert/squeezebert-mnli-headless",
@@ -605,7 +605,7 @@ class SqueezeBertForMaskedLM(SqueezeBertPreTrainedModel):
     def get_output_embeddings(self):
         return self.lm_head
 
-    @add_start_docstrings_to_callable(SQUEEZEBERT_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
+    @add_start_docstrings_to_model_forward(SQUEEZEBERT_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,
         checkpoint="squeezebert/squeezebert-uncased",
@@ -683,7 +683,7 @@ class SqueezeBertForSequenceClassification(SqueezeBertPreTrainedModel):
 
         self.init_weights()
 
-    @add_start_docstrings_to_callable(SQUEEZEBERT_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
+    @add_start_docstrings_to_model_forward(SQUEEZEBERT_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,
         checkpoint="squeezebert/squeezebert-mnli-headless",
@@ -767,7 +767,7 @@ class SqueezeBertForMultipleChoice(SqueezeBertPreTrainedModel):
 
         self.init_weights()
 
-    @add_start_docstrings_to_callable(
+    @add_start_docstrings_to_model_forward(
         SQUEEZEBERT_INPUTS_DOCSTRING.format("(batch_size, num_choices, sequence_length)")
     )
     @add_code_sample_docstrings(
@@ -861,7 +861,7 @@ class SqueezeBertForTokenClassification(SqueezeBertPreTrainedModel):
 
         self.init_weights()
 
-    @add_start_docstrings_to_callable(SQUEEZEBERT_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
+    @add_start_docstrings_to_model_forward(SQUEEZEBERT_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,
         checkpoint="squeezebert/squeezebert-mnli-headless",
@@ -948,7 +948,7 @@ class SqueezeBertForQuestionAnswering(SqueezeBertPreTrainedModel):
 
         self.init_weights()
 
-    @add_start_docstrings_to_callable(SQUEEZEBERT_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
+    @add_start_docstrings_to_model_forward(SQUEEZEBERT_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,
         checkpoint="squeezebert/squeezebert-mnli-headless",

src/transformers/modeling_t5.py

@@ -30,7 +30,7 @@ from .file_utils import (
     DUMMY_INPUTS,
     DUMMY_MASK,
     add_start_docstrings,
-    add_start_docstrings_to_callable,
+    add_start_docstrings_to_model_forward,
     replace_return_docstrings,
 )
 from .modeling_outputs import BaseModelOutput, BaseModelOutputWithPast, Seq2SeqLMOutput, Seq2SeqModelOutput
@@ -943,7 +943,7 @@ class T5Model(T5PreTrainedModel):
         for layer, heads in heads_to_prune.items():
             self.encoder.layer[layer].attention.prune_heads(heads)
 
-    @add_start_docstrings_to_callable(T5_INPUTS_DOCSTRING)
+    @add_start_docstrings_to_model_forward(T5_INPUTS_DOCSTRING)
     @replace_return_docstrings(output_type=Seq2SeqModelOutput, config_class=_CONFIG_FOR_DOC)
     def forward(
         self,
@@ -1086,7 +1086,7 @@ class T5ForConditionalGeneration(T5PreTrainedModel):
     def get_decoder(self):
         return self.decoder
 
-    @add_start_docstrings_to_callable(T5_INPUTS_DOCSTRING)
+    @add_start_docstrings_to_model_forward(T5_INPUTS_DOCSTRING)
     @replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
     def forward(
         self,

src/transformers/modeling_tf_albert.py

@@ -28,7 +28,7 @@ from .file_utils import (
     ModelOutput,
     add_code_sample_docstrings,
     add_start_docstrings,
-    add_start_docstrings_to_callable,
+    add_start_docstrings_to_model_forward,
     replace_return_docstrings,
 )
 from .modeling_tf_outputs import (
@@ -747,7 +747,7 @@ class TFAlbertModel(TFAlbertPreTrainedModel):
         super().__init__(config, *inputs, **kwargs)
         self.albert = TFAlbertMainLayer(config, name="albert")
 
-    @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",
@@ -778,7 +778,7 @@ class TFAlbertForPreTraining(TFAlbertPreTrainedModel):
     def get_output_embeddings(self):
         return self.albert.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=TFAlbertForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
     def call(self, inputs, **kwargs):
         r"""
@@ -847,7 +847,7 @@ class TFAlbertForMaskedLM(TFAlbertPreTrainedModel, TFMaskedLanguageModelingLoss)
     def get_output_embeddings(self):
         return self.albert.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",
@@ -930,7 +930,7 @@ class TFAlbertForSequenceClassification(TFAlbertPreTrainedModel, TFSequenceClass
             config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
         )
 
-    @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",
@@ -1018,7 +1018,7 @@ class TFAlbertForTokenClassification(TFAlbertPreTrainedModel, TFTokenClassificat
             config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
         )
 
-    @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",
@@ -1104,7 +1104,7 @@ class TFAlbertForQuestionAnswering(TFAlbertPreTrainedModel, TFQuestionAnsweringL
             config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="qa_outputs"
         )
 
-    @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",
@@ -1212,7 +1212,7 @@ class TFAlbertForMultipleChoice(TFAlbertPreTrainedModel, TFMultipleChoiceLoss):
         """
         return {"input_ids": tf.constant(MULTIPLE_CHOICE_DUMMY_INPUTS)}
 
-    @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",

src/transformers/modeling_tf_bart.py

@@ -25,7 +25,7 @@ from tensorflow.keras.layers import Dense, LayerNormalization
 
 from .activations_tf import ACT2FN
 from .configuration_bart import BartConfig
-from .file_utils import add_start_docstrings, add_start_docstrings_to_callable, replace_return_docstrings
+from .file_utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
 from .modeling_tf_outputs import TFBaseModelOutput, TFBaseModelOutputWithPast, TFSeq2SeqLMOutput, TFSeq2SeqModelOutput
 
 # Public API
@@ -827,7 +827,7 @@ class TFBartModel(TFPretrainedBartModel):
         causal_lm_mask = causal_attention_mask(tgt_len, tgt_len, mask_dtype)
         return decoder_input_ids, decoder_padding_mask, causal_lm_mask
 
-    @add_start_docstrings_to_callable(BART_INPUTS_DOCSTRING)
+    @add_start_docstrings_to_model_forward(BART_INPUTS_DOCSTRING)
     @replace_return_docstrings(output_type=TFSeq2SeqModelOutput, config_class=_CONFIG_FOR_DOC)
     def call(
         self,
@@ -961,7 +961,7 @@ class TFBartForConditionalGeneration(TFPretrainedBartModel):
         self.model = TFBartModel(config, name="model")
         self.use_cache = config.use_cache
 
-    @add_start_docstrings_to_callable(BART_INPUTS_DOCSTRING)
+    @add_start_docstrings_to_model_forward(BART_INPUTS_DOCSTRING)
     @replace_return_docstrings(output_type=TFSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
     def call(
         self,

src/transformers/modeling_tf_bert.py

@@ -28,7 +28,7 @@ from .file_utils import (
     ModelOutput,
     add_code_sample_docstrings,
     add_start_docstrings,
-    add_start_docstrings_to_callable,
+    add_start_docstrings_to_model_forward,
     replace_return_docstrings,
 )
 from .modeling_tf_outputs import (
@@ -793,7 +793,7 @@ class TFBertModel(TFBertPreTrainedModel):
 
         self.bert = TFBertMainLayer(config, name="bert")
 
-    @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-cased",
@@ -824,7 +824,7 @@ class TFBertForPreTraining(TFBertPreTrainedModel):
     def get_output_embeddings(self):
         return self.bert.embeddings
 
-    @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=TFBertForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
     def call(self, inputs, **kwargs):
         r"""
@@ -881,7 +881,7 @@ class TFBertForMaskedLM(TFBertPreTrainedModel, TFMaskedLanguageModelingLoss):
     def get_output_embeddings(self):
         return self.bert.embeddings
 
-    @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-cased",
@@ -1043,7 +1043,7 @@ class TFBertForNextSentencePrediction(TFBertPreTrainedModel):
         self.bert = TFBertMainLayer(config, name="bert")
         self.nsp = TFBertNSPHead(config, name="nsp___cls")
 
-    @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=TFNextSentencePredictorOutput, config_class=_CONFIG_FOR_DOC)
     def call(self, inputs, **kwargs):
         r"""
@@ -1098,7 +1098,7 @@ class TFBertForSequenceClassification(TFBertPreTrainedModel, TFSequenceClassific
             config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
         )
 
-    @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-cased",
@@ -1191,7 +1191,7 @@ class TFBertForMultipleChoice(TFBertPreTrainedModel, TFMultipleChoiceLoss):
         """
         return {"input_ids": tf.constant(MULTIPLE_CHOICE_DUMMY_INPUTS)}
 
-    @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-cased",
@@ -1315,7 +1315,7 @@ class TFBertForTokenClassification(TFBertPreTrainedModel, TFTokenClassificationL
             config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
         )
 
-    @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-cased",
@@ -1400,7 +1400,7 @@ class TFBertForQuestionAnswering(TFBertPreTrainedModel, TFQuestionAnsweringLoss)
             config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="qa_outputs"
         )
 
-    @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-cased",

src/transformers/modeling_tf_ctrl.py

@@ -20,7 +20,7 @@ import numpy as np
 import tensorflow as tf
 
 from .configuration_ctrl import CTRLConfig
-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_tf_outputs import TFBaseModelOutputWithPast, TFCausalLMOutputWithPast
 from .modeling_tf_utils import (
     TFCausalLanguageModelingLoss,
@@ -547,7 +547,7 @@ class TFCTRLModel(TFCTRLPreTrainedModel):
         super().__init__(config, *inputs, **kwargs)
         self.transformer = TFCTRLMainLayer(config, name="transformer")
 
-    @add_start_docstrings_to_callable(CTRL_INPUTS_DOCSTRING)
+    @add_start_docstrings_to_model_forward(CTRL_INPUTS_DOCSTRING)
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,
         checkpoint="ctrl",
@@ -602,7 +602,7 @@ class TFCTRLLMHeadModel(TFCTRLPreTrainedModel, TFCausalLanguageModelingLoss):
 
         return {"inputs": inputs, "past": past, "use_cache": kwargs["use_cache"]}
 
-    @add_start_docstrings_to_callable(CTRL_INPUTS_DOCSTRING)
+    @add_start_docstrings_to_model_forward(CTRL_INPUTS_DOCSTRING)
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,
         checkpoint="ctrl",

src/transformers/modeling_tf_distilbert.py

@@ -25,7 +25,7 @@ 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 (
     TFBaseModelOutput,
@@ -579,7 +579,7 @@ class TFDistilBertModel(TFDistilBertPreTrainedModel):
         super().__init__(config, *inputs, **kwargs)
         self.distilbert = TFDistilBertMainLayer(config, name="distilbert")  # Embeddings
 
-    @add_start_docstrings_to_callable(DISTILBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_start_docstrings_to_model_forward(DISTILBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,
         checkpoint="distilbert-base-uncased",
@@ -630,7 +630,7 @@ class TFDistilBertForMaskedLM(TFDistilBertPreTrainedModel, TFMaskedLanguageModel
     def get_output_embeddings(self):
         return self.vocab_projector.input_embeddings
 
-    @add_start_docstrings_to_callable(DISTILBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_start_docstrings_to_model_forward(DISTILBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,
         checkpoint="distilbert-base-uncased",
@@ -718,7 +718,7 @@ class TFDistilBertForSequenceClassification(TFDistilBertPreTrainedModel, TFSeque
         )
         self.dropout = tf.keras.layers.Dropout(config.seq_classif_dropout)
 
-    @add_start_docstrings_to_callable(DISTILBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_start_docstrings_to_model_forward(DISTILBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,
         checkpoint="distilbert-base-uncased",
@@ -800,7 +800,7 @@ class TFDistilBertForTokenClassification(TFDistilBertPreTrainedModel, TFTokenCla
             config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
         )
 
-    @add_start_docstrings_to_callable(DISTILBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_start_docstrings_to_model_forward(DISTILBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,
         checkpoint="distilbert-base-uncased",
@@ -895,7 +895,9 @@ class TFDistilBertForMultipleChoice(TFDistilBertPreTrainedModel, TFMultipleChoic
         """
         return {"input_ids": tf.constant(MULTIPLE_CHOICE_DUMMY_INPUTS)}
 
-    @add_start_docstrings_to_callable(DISTILBERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
+    @add_start_docstrings_to_model_forward(
+        DISTILBERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")
+    )
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,
         checkpoint="distilbert-base-uncased",
@@ -1007,7 +1009,7 @@ class TFDistilBertForQuestionAnswering(TFDistilBertPreTrainedModel, TFQuestionAn
         assert config.num_labels == 2, f"Incorrect number of labels {config.num_labels} instead of 2"
         self.dropout = tf.keras.layers.Dropout(config.qa_dropout)
 
-    @add_start_docstrings_to_callable(DISTILBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_start_docstrings_to_model_forward(DISTILBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,
         checkpoint="distilbert-base-uncased",

src/transformers/modeling_tf_electra.py

@@ -11,7 +11,7 @@ from .file_utils import (
     ModelOutput,
     add_code_sample_docstrings,
     add_start_docstrings,
-    add_start_docstrings_to_callable,
+    add_start_docstrings_to_model_forward,
     replace_return_docstrings,
 )
 from .modeling_tf_outputs import (
@@ -719,7 +719,7 @@ class TFElectraModel(TFElectraPreTrainedModel):
 
         self.electra = TFElectraMainLayer(config, name="electra")
 
-    @add_start_docstrings_to_callable(ELECTRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_start_docstrings_to_model_forward(ELECTRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,
         checkpoint="google/electra-small-discriminator",
@@ -749,7 +749,7 @@ class TFElectraForPreTraining(TFElectraPreTrainedModel):
         self.electra = TFElectraMainLayer(config, name="electra")
         self.discriminator_predictions = TFElectraDiscriminatorPredictions(config, name="discriminator_predictions")
 
-    @add_start_docstrings_to_callable(ELECTRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_start_docstrings_to_model_forward(ELECTRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @replace_return_docstrings(output_type=TFElectraForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
     def call(
         self,
@@ -858,7 +858,7 @@ class TFElectraForMaskedLM(TFElectraPreTrainedModel, TFMaskedLanguageModelingLos
     def get_output_embeddings(self):
         return self.generator_lm_head
 
-    @add_start_docstrings_to_callable(ELECTRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_start_docstrings_to_model_forward(ELECTRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,
         checkpoint="google/electra-small-generator",
@@ -971,7 +971,7 @@ class TFElectraForSequenceClassification(TFElectraPreTrainedModel, TFSequenceCla
         self.electra = TFElectraMainLayer(config, name="electra")
         self.classifier = TFElectraClassificationHead(config, name="classifier")
 
-    @add_start_docstrings_to_callable(ELECTRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_start_docstrings_to_model_forward(ELECTRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,
         checkpoint="google/electra-small-discriminator",
@@ -1072,7 +1072,7 @@ class TFElectraForMultipleChoice(TFElectraPreTrainedModel, TFMultipleChoiceLoss)
         """
         return {"input_ids": tf.constant(MULTIPLE_CHOICE_DUMMY_INPUTS)}
 
-    @add_start_docstrings_to_callable(ELECTRA_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
+    @add_start_docstrings_to_model_forward(ELECTRA_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,
         checkpoint="google/electra-small-discriminator",
@@ -1192,7 +1192,7 @@ class TFElectraForTokenClassification(TFElectraPreTrainedModel, TFTokenClassific
             config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
         )
 
-    @add_start_docstrings_to_callable(ELECTRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_start_docstrings_to_model_forward(ELECTRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,
         checkpoint="google/electra-small-discriminator",
@@ -1275,7 +1275,7 @@ class TFElectraForQuestionAnswering(TFElectraPreTrainedModel, TFQuestionAnswerin
             config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="qa_outputs"
         )
 
-    @add_start_docstrings_to_callable(ELECTRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_start_docstrings_to_model_forward(ELECTRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,
         checkpoint="google/electra-small-discriminator",

src/transformers/modeling_tf_flaubert.py

@@ -25,7 +25,12 @@ import tensorflow as tf
 from transformers.activations_tf import get_tf_activation
 
 from .configuration_flaubert import FlaubertConfig
-from .file_utils import ModelOutput, add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_callable
+from .file_utils import (
+    ModelOutput,
+    add_code_sample_docstrings,
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+)
 from .modeling_tf_outputs import TFBaseModelOutput
 from .modeling_tf_utils import TFPreTrainedModel, TFSharedEmbeddings, get_initializer, keras_serializable, shape_list
 from .modeling_tf_xlm import (
@@ -217,7 +222,7 @@ class TFFlaubertModel(TFFlaubertPreTrainedModel):
         super().__init__(config, *inputs, **kwargs)
         self.transformer = TFFlaubertMainLayer(config, name="transformer")
 
-    @add_start_docstrings_to_callable(FLAUBERT_INPUTS_DOCSTRING)
+    @add_start_docstrings_to_model_forward(FLAUBERT_INPUTS_DOCSTRING)
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,
         checkpoint="jplu/tf-flaubert-small-cased",
@@ -721,7 +726,7 @@ class TFFlaubertWithLMHeadModel(TFFlaubertPreTrainedModel):
             langs = None
         return {"inputs": inputs, "langs": langs}
 
-    @add_start_docstrings_to_callable(FLAUBERT_INPUTS_DOCSTRING)
+    @add_start_docstrings_to_model_forward(FLAUBERT_INPUTS_DOCSTRING)
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,
         checkpoint="jplu/tf-flaubert-small-cased",

src/transformers/modeling_tf_funnel.py

@@ -27,7 +27,7 @@ from .file_utils import (
     ModelOutput,
     add_code_sample_docstrings,
     add_start_docstrings,
-    add_start_docstrings_to_callable,
+    add_start_docstrings_to_model_forward,
     replace_return_docstrings,
 )
 from .modeling_tf_outputs import (
@@ -1148,7 +1148,7 @@ class TFFunnelBaseModel(TFFunnelPreTrainedModel):
         super().__init__(config, *inputs, **kwargs)
         self.funnel = TFFunnelBaseLayer(config, name="funnel")
 
-    @add_start_docstrings_to_callable(FUNNEL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_start_docstrings_to_model_forward(FUNNEL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,
         checkpoint="funnel-transformer/small-base",
@@ -1168,7 +1168,7 @@ class TFFunnelModel(TFFunnelPreTrainedModel):
         super().__init__(config, *inputs, **kwargs)
         self.funnel = TFFunnelMainLayer(config, name="funnel")
 
-    @add_start_docstrings_to_callable(FUNNEL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_start_docstrings_to_model_forward(FUNNEL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,
         checkpoint="funnel-transformer/small",
@@ -1192,7 +1192,7 @@ class TFFunnelForPreTraining(TFFunnelPreTrainedModel):
         self.funnel = TFFunnelMainLayer(config, name="funnel")
         self.discriminator_predictions = TFFunnelDiscriminatorPredictions(config, name="discriminator_predictions")
 
-    @add_start_docstrings_to_callable(FUNNEL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_start_docstrings_to_model_forward(FUNNEL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @replace_return_docstrings(output_type=TFFunnelForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
     def call(
         self,
@@ -1259,7 +1259,7 @@ class TFFunnelForMaskedLM(TFFunnelPreTrainedModel, TFMaskedLanguageModelingLoss)
         self.funnel = TFFunnelMainLayer(config, name="funnel")
         self.lm_head = TFFunnelMaskedLMHead(config, self.funnel.embeddings, name="lm_head")
 
-    @add_start_docstrings_to_callable(FUNNEL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_start_docstrings_to_model_forward(FUNNEL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,
         checkpoint="funnel-transformer/small",
@@ -1335,7 +1335,7 @@ class TFFunnelForSequenceClassification(TFFunnelPreTrainedModel, TFSequenceClass
         self.funnel = TFFunnelBaseLayer(config, name="funnel")
         self.classifier = TFFunnelClassificationHead(config, config.num_labels, name="classifier")
 
-    @add_start_docstrings_to_callable(FUNNEL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_start_docstrings_to_model_forward(FUNNEL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,
         checkpoint="funnel-transformer/small-base",
@@ -1421,7 +1421,7 @@ class TFFunnelForMultipleChoice(TFFunnelPreTrainedModel, TFMultipleChoiceLoss):
         """
         return {"input_ids": tf.constant(MULTIPLE_CHOICE_DUMMY_INPUTS)}
 
-    @add_start_docstrings_to_callable(FUNNEL_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
+    @add_start_docstrings_to_model_forward(FUNNEL_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,
         checkpoint="funnel-transformer/small-base",
@@ -1534,7 +1534,7 @@ class TFFunnelForTokenClassification(TFFunnelPreTrainedModel, TFTokenClassificat
             config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
         )
 
-    @add_start_docstrings_to_callable(FUNNEL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_start_docstrings_to_model_forward(FUNNEL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,
         checkpoint="funnel-transformer/small",
@@ -1613,7 +1613,7 @@ class TFFunnelForQuestionAnswering(TFFunnelPreTrainedModel, TFQuestionAnsweringL
             config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="qa_outputs"
         )
 
-    @add_start_docstrings_to_callable(FUNNEL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_start_docstrings_to_model_forward(FUNNEL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,
         checkpoint="funnel-transformer/small",