From 4f7022d68d4bae4b5e6a748b7a7323515c6fdcd3 Mon Sep 17 00:00:00 2001 From: Julien Plu Date: Sun, 10 Jan 2021 15:10:15 +0100 Subject: [PATCH] Reformat (#9482) --- .../models/albert/modeling_tf_albert.py | 35 ++++-------- .../models/bert/modeling_tf_bert.py | 41 +++----------- .../models/ctrl/modeling_tf_ctrl.py | 21 +++----- .../distilbert/modeling_tf_distilbert.py | 40 ++++---------- .../models/dpr/modeling_tf_dpr.py | 12 +---- .../models/electra/modeling_tf_electra.py | 47 +++++----------- .../models/flaubert/modeling_tf_flaubert.py | 13 ++--- .../models/funnel/modeling_tf_funnel.py | 54 ++++++------------- .../models/gpt2/modeling_tf_gpt2.py | 17 ++---- .../longformer/modeling_tf_longformer.py | 21 ++------ .../mobilebert/modeling_tf_mobilebert.py | 42 +++++---------- .../models/mpnet/modeling_tf_mpnet.py | 35 ++++-------- .../models/openai/modeling_tf_openai.py | 26 +++------ .../models/roberta/modeling_tf_roberta.py | 35 ++++-------- src/transformers/models/t5/modeling_tf_t5.py | 7 +-- .../transfo_xl/modeling_tf_transfo_xl.py | 5 +- .../models/xlm/modeling_tf_xlm.py | 40 ++++---------- .../models/xlnet/modeling_tf_xlnet.py | 31 ++--------- ...tf_{{cookiecutter.lowercase_modelname}}.py | 44 ++++++--------- 19 files changed, 151 insertions(+), 415 deletions(-) diff --git a/src/transformers/models/albert/modeling_tf_albert.py b/src/transformers/models/albert/modeling_tf_albert.py index fd0c752f37..3913fa70bb 100644 --- a/src/transformers/models/albert/modeling_tf_albert.py +++ b/src/transformers/models/albert/modeling_tf_albert.py @@ -803,6 +803,7 @@ class TFAlbertModel(TFAlbertPreTrainedModel): return outputs + # Copied from transformers.models.bert.modeling_tf_bert.TFBertModel.serving_output def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None @@ -1080,15 +1081,12 @@ class TFAlbertForMaskedLM(TFAlbertPreTrainedModel, TFMaskedLanguageModelingLoss) attentions=outputs.attentions, ) + # Copied from transformers.models.bert.modeling_tf_bert.TFBertForMaskedLM.serving_output def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None - return TFMaskedLMOutput( - logits=output.logits, - hidden_states=hs, - attentions=attns, - ) + return TFMaskedLMOutput(logits=output.logits, hidden_states=hs, attentions=attns) @add_start_docstrings( @@ -1186,15 +1184,12 @@ class TFAlbertForSequenceClassification(TFAlbertPreTrainedModel, TFSequenceClass attentions=outputs.attentions, ) + # Copied from transformers.models.bert.modeling_tf_bert.TFBertForSequenceClassification.serving_output def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None - return TFSequenceClassifierOutput( - logits=output.logits, - hidden_states=hs, - attentions=attns, - ) + return TFSequenceClassifierOutput(logits=output.logits, hidden_states=hs, attentions=attns) @add_start_docstrings( @@ -1291,15 +1286,12 @@ class TFAlbertForTokenClassification(TFAlbertPreTrainedModel, TFTokenClassificat attentions=outputs.attentions, ) + # Copied from transformers.models.bert.modeling_tf_bert.TFBertForTokenClassification.serving_output def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None - return TFTokenClassifierOutput( - logits=output.logits, - hidden_states=hs, - attentions=attns, - ) + return TFTokenClassifierOutput(logits=output.logits, hidden_states=hs, attentions=attns) @add_start_docstrings( @@ -1409,15 +1401,13 @@ class TFAlbertForQuestionAnswering(TFAlbertPreTrainedModel, TFQuestionAnsweringL attentions=outputs.attentions, ) + # Copied from transformers.models.bert.modeling_tf_bert.TFBertForQuestionAnswering.serving_output def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None return TFQuestionAnsweringModelOutput( - start_logits=output.start_logits, - end_logits=output.end_logits, - hidden_states=hs, - attentions=attns, + start_logits=output.start_logits, end_logits=output.end_logits, hidden_states=hs, attentions=attns ) @@ -1564,12 +1554,9 @@ class TFAlbertForMultipleChoice(TFAlbertPreTrainedModel, TFMultipleChoiceLoss): return self.serving_output(output) + # Copied from transformers.models.bert.modeling_tf_bert.TFBertForMultipleChoice.serving_output def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None - return TFMultipleChoiceModelOutput( - logits=output.logits, - hidden_states=hs, - attentions=attns, - ) + return TFMultipleChoiceModelOutput(logits=output.logits, hidden_states=hs, attentions=attns) diff --git a/src/transformers/models/bert/modeling_tf_bert.py b/src/transformers/models/bert/modeling_tf_bert.py index 2549868f64..634fbc1903 100644 --- a/src/transformers/models/bert/modeling_tf_bert.py +++ b/src/transformers/models/bert/modeling_tf_bert.py @@ -1128,11 +1128,7 @@ class TFBertForMaskedLM(TFBertPreTrainedModel, TFMaskedLanguageModelingLoss): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None - return TFMaskedLMOutput( - logits=output.logits, - hidden_states=hs, - attentions=attns, - ) + return TFMaskedLMOutput(logits=output.logits, hidden_states=hs, attentions=attns) class TFBertLMHeadModel(TFBertPreTrainedModel, TFCausalLanguageModelingLoss): @@ -1241,11 +1237,7 @@ class TFBertLMHeadModel(TFBertPreTrainedModel, TFCausalLanguageModelingLoss): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None - return TFCausalLMOutput( - logits=output.logits, - hidden_states=hs, - attentions=attns, - ) + return TFCausalLMOutput(logits=output.logits, hidden_states=hs, attentions=attns) @add_start_docstrings( @@ -1348,11 +1340,7 @@ class TFBertForNextSentencePrediction(TFBertPreTrainedModel, TFNextSentencePredi hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None - return TFNextSentencePredictorOutput( - logits=output.logits, - hidden_states=hs, - attentions=attns, - ) + return TFNextSentencePredictorOutput(logits=output.logits, hidden_states=hs, attentions=attns) @add_start_docstrings( @@ -1453,11 +1441,7 @@ class TFBertForSequenceClassification(TFBertPreTrainedModel, TFSequenceClassific hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None - return TFSequenceClassifierOutput( - logits=output.logits, - hidden_states=hs, - attentions=attns, - ) + return TFSequenceClassifierOutput(logits=output.logits, hidden_states=hs, attentions=attns) @add_start_docstrings( @@ -1605,11 +1589,7 @@ class TFBertForMultipleChoice(TFBertPreTrainedModel, TFMultipleChoiceLoss): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None - return TFMultipleChoiceModelOutput( - logits=output.logits, - hidden_states=hs, - attentions=attns, - ) + return TFMultipleChoiceModelOutput(logits=output.logits, hidden_states=hs, attentions=attns) @add_start_docstrings( @@ -1715,11 +1695,7 @@ class TFBertForTokenClassification(TFBertPreTrainedModel, TFTokenClassificationL hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None - return TFTokenClassifierOutput( - logits=output.logits, - hidden_states=hs, - attentions=attns, - ) + return TFTokenClassifierOutput(logits=output.logits, hidden_states=hs, attentions=attns) @add_start_docstrings( @@ -1839,8 +1815,5 @@ class TFBertForQuestionAnswering(TFBertPreTrainedModel, TFQuestionAnsweringLoss) attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None return TFQuestionAnsweringModelOutput( - start_logits=output.start_logits, - end_logits=output.end_logits, - hidden_states=hs, - attentions=attns, + start_logits=output.start_logits, end_logits=output.end_logits, hidden_states=hs, attentions=attns ) diff --git a/src/transformers/models/ctrl/modeling_tf_ctrl.py b/src/transformers/models/ctrl/modeling_tf_ctrl.py index 452cdaff17..fdd1e1f443 100644 --- a/src/transformers/models/ctrl/modeling_tf_ctrl.py +++ b/src/transformers/models/ctrl/modeling_tf_ctrl.py @@ -594,16 +594,14 @@ class TFCTRLModel(TFCTRLPreTrainedModel): ) return outputs + # Copied from transformers.models.gpt2.modeling_tf_gpt2.TFGPT2Model.serving_output def serving_output(self, output): pkv = tf.convert_to_tensor(output.past_key_values) if self.config.use_cache else None hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None return TFBaseModelOutputWithPast( - last_hidden_state=output.last_hidden_state, - past_key_values=pkv, - hidden_states=hs, - attentions=attns, + last_hidden_state=output.last_hidden_state, past_key_values=pkv, hidden_states=hs, attentions=attns ) @@ -741,17 +739,13 @@ class TFCTRLLMHeadModel(TFCTRLPreTrainedModel, TFCausalLanguageModelingLoss): attentions=transformer_outputs.attentions, ) + # Copied from transformers.models.gpt2.modeling_tf_gpt2.TFGPT2LMHeadModel.serving_output def serving_output(self, output): pkv = tf.convert_to_tensor(output.past_key_values) if self.config.use_cache else None hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None - return TFCausalLMOutputWithPast( - logits=output.logits, - past_key_values=pkv, - hidden_states=hs, - attentions=attns, - ) + return TFCausalLMOutputWithPast(logits=output.logits, past_key_values=pkv, hidden_states=hs, attentions=attns) @add_start_docstrings( @@ -910,12 +904,9 @@ class TFCTRLForSequenceClassification(TFCTRLPreTrainedModel, TFSequenceClassific attentions=transformer_outputs.attentions, ) + # Copied from transformers.models.bert.modeling_tf_bert.TFBertForSequenceClassification.serving_output def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None - return TFSequenceClassifierOutput( - logits=output.logits, - hidden_states=hs, - attentions=attns, - ) + return TFSequenceClassifierOutput(logits=output.logits, hidden_states=hs, attentions=attns) diff --git a/src/transformers/models/distilbert/modeling_tf_distilbert.py b/src/transformers/models/distilbert/modeling_tf_distilbert.py index 175127976e..cad6427cac 100644 --- a/src/transformers/models/distilbert/modeling_tf_distilbert.py +++ b/src/transformers/models/distilbert/modeling_tf_distilbert.py @@ -632,11 +632,7 @@ class TFDistilBertModel(TFDistilBertPreTrainedModel): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None - return TFBaseModelOutput( - last_hidden_state=output.last_hidden_state, - hidden_states=hs, - attentions=attns, - ) + return TFBaseModelOutput(last_hidden_state=output.last_hidden_state, hidden_states=hs, attentions=attns) class TFDistilBertLMHead(tf.keras.layers.Layer): @@ -753,15 +749,12 @@ class TFDistilBertForMaskedLM(TFDistilBertPreTrainedModel, TFMaskedLanguageModel attentions=distilbert_output.attentions, ) + # Copied from transformers.models.bert.modeling_tf_bert.TFBertForMaskedLM.serving_output def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None - return TFMaskedLMOutput( - logits=output.logits, - hidden_states=hs, - attentions=attns, - ) + return TFMaskedLMOutput(logits=output.logits, hidden_states=hs, attentions=attns) @add_start_docstrings( @@ -857,15 +850,12 @@ class TFDistilBertForSequenceClassification(TFDistilBertPreTrainedModel, TFSeque attentions=distilbert_output.attentions, ) + # Copied from transformers.models.bert.modeling_tf_bert.TFBertForSequenceClassification.serving_output def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None - return TFSequenceClassifierOutput( - logits=output.logits, - hidden_states=hs, - attentions=attns, - ) + return TFSequenceClassifierOutput(logits=output.logits, hidden_states=hs, attentions=attns) @add_start_docstrings( @@ -951,15 +941,12 @@ class TFDistilBertForTokenClassification(TFDistilBertPreTrainedModel, TFTokenCla attentions=outputs.attentions, ) + # Copied from transformers.models.bert.modeling_tf_bert.TFBertForTokenClassification.serving_output def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None - return TFTokenClassifierOutput( - logits=output.logits, - hidden_states=hs, - attentions=attns, - ) + return TFTokenClassifierOutput(logits=output.logits, hidden_states=hs, attentions=attns) @add_start_docstrings( @@ -1097,15 +1084,12 @@ class TFDistilBertForMultipleChoice(TFDistilBertPreTrainedModel, TFMultipleChoic return self.serving_output(output) + # Copied from transformers.models.bert.modeling_tf_bert.TFBertForMultipleChoice.serving_output def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None - return TFMultipleChoiceModelOutput( - logits=output.logits, - hidden_states=hs, - attentions=attns, - ) + return TFMultipleChoiceModelOutput(logits=output.logits, hidden_states=hs, attentions=attns) @add_start_docstrings( @@ -1207,13 +1191,11 @@ class TFDistilBertForQuestionAnswering(TFDistilBertPreTrainedModel, TFQuestionAn attentions=distilbert_output.attentions, ) + # Copied from transformers.models.bert.modeling_tf_bert.TFBertForQuestionAnswering.serving_output def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None return TFQuestionAnsweringModelOutput( - start_logits=output.start_logits, - end_logits=output.end_logits, - hidden_states=hs, - attentions=attns, + start_logits=output.start_logits, end_logits=output.end_logits, hidden_states=hs, attentions=attns ) diff --git a/src/transformers/models/dpr/modeling_tf_dpr.py b/src/transformers/models/dpr/modeling_tf_dpr.py index 79bd4384d1..6975953bf6 100644 --- a/src/transformers/models/dpr/modeling_tf_dpr.py +++ b/src/transformers/models/dpr/modeling_tf_dpr.py @@ -658,11 +658,7 @@ class TFDPRContextEncoder(TFDPRPretrainedContextEncoder): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None - return TFDPRContextEncoderOutput( - pooler_output=output.pooler_output, - hidden_states=hs, - attentions=attns, - ) + return TFDPRContextEncoderOutput(pooler_output=output.pooler_output, hidden_states=hs, attentions=attns) @add_start_docstrings( @@ -755,11 +751,7 @@ class TFDPRQuestionEncoder(TFDPRPretrainedQuestionEncoder): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None - return TFDPRQuestionEncoderOutput( - pooler_output=output.pooler_output, - hidden_states=hs, - attentions=attns, - ) + return TFDPRQuestionEncoderOutput(pooler_output=output.pooler_output, hidden_states=hs, attentions=attns) @add_start_docstrings( diff --git a/src/transformers/models/electra/modeling_tf_electra.py b/src/transformers/models/electra/modeling_tf_electra.py index c494ab062b..92c236167c 100644 --- a/src/transformers/models/electra/modeling_tf_electra.py +++ b/src/transformers/models/electra/modeling_tf_electra.py @@ -800,15 +800,12 @@ class TFElectraModel(TFElectraPreTrainedModel): return outputs + # Copied from transformers.models.distilbert.modeling_tf_distilbert.TFDistilBertModel.serving_output def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None - return TFBaseModelOutput( - last_hidden_state=output.last_hidden_state, - hidden_states=hs, - attentions=attns, - ) + return TFBaseModelOutput(last_hidden_state=output.last_hidden_state, hidden_states=hs, attentions=attns) @add_start_docstrings( @@ -900,11 +897,7 @@ class TFElectraForPreTraining(TFElectraPreTrainedModel): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None - return TFElectraForPreTrainingOutput( - logits=output.logits, - hidden_states=hs, - attentions=attns, - ) + return TFElectraForPreTrainingOutput(logits=output.logits, hidden_states=hs, attentions=attns) class TFElectraMaskedLMHead(tf.keras.layers.Layer): @@ -1032,15 +1025,12 @@ class TFElectraForMaskedLM(TFElectraPreTrainedModel, TFMaskedLanguageModelingLos attentions=generator_hidden_states.attentions, ) + # Copied from transformers.models.bert.modeling_tf_bert.TFBertForMaskedLM.serving_output def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None - return TFMaskedLMOutput( - logits=output.logits, - hidden_states=hs, - attentions=attns, - ) + return TFMaskedLMOutput(logits=output.logits, hidden_states=hs, attentions=attns) class TFElectraClassificationHead(tf.keras.layers.Layer): @@ -1153,15 +1143,12 @@ class TFElectraForSequenceClassification(TFElectraPreTrainedModel, TFSequenceCla attentions=outputs.attentions, ) + # Copied from transformers.models.bert.modeling_tf_bert.TFBertForSequenceClassification.serving_output def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None - return TFSequenceClassifierOutput( - logits=output.logits, - hidden_states=hs, - attentions=attns, - ) + return TFSequenceClassifierOutput(logits=output.logits, hidden_states=hs, attentions=attns) @add_start_docstrings( @@ -1303,15 +1290,12 @@ class TFElectraForMultipleChoice(TFElectraPreTrainedModel, TFMultipleChoiceLoss) return self.serving_output(output) + # Copied from transformers.models.bert.modeling_tf_bert.TFBertForMultipleChoice.serving_output def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None - return TFMultipleChoiceModelOutput( - logits=output.logits, - hidden_states=hs, - attentions=attns, - ) + return TFMultipleChoiceModelOutput(logits=output.logits, hidden_states=hs, attentions=attns) @add_start_docstrings( @@ -1404,15 +1388,12 @@ class TFElectraForTokenClassification(TFElectraPreTrainedModel, TFTokenClassific attentions=discriminator_hidden_states.attentions, ) + # Copied from transformers.models.bert.modeling_tf_bert.TFBertForTokenClassification.serving_output def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None - return TFTokenClassifierOutput( - logits=output.logits, - hidden_states=hs, - attentions=attns, - ) + return TFTokenClassifierOutput(logits=output.logits, hidden_states=hs, attentions=attns) @add_start_docstrings( @@ -1522,13 +1503,11 @@ class TFElectraForQuestionAnswering(TFElectraPreTrainedModel, TFQuestionAnswerin attentions=discriminator_hidden_states.attentions, ) + # Copied from transformers.models.bert.modeling_tf_bert.TFBertForQuestionAnswering.serving_output def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None return TFQuestionAnsweringModelOutput( - start_logits=output.start_logits, - end_logits=output.end_logits, - hidden_states=hs, - attentions=attns, + start_logits=output.start_logits, end_logits=output.end_logits, hidden_states=hs, attentions=attns ) diff --git a/src/transformers/models/flaubert/modeling_tf_flaubert.py b/src/transformers/models/flaubert/modeling_tf_flaubert.py index 09e42b3830..819bbb15b3 100644 --- a/src/transformers/models/flaubert/modeling_tf_flaubert.py +++ b/src/transformers/models/flaubert/modeling_tf_flaubert.py @@ -288,15 +288,12 @@ class TFFlaubertModel(TFFlaubertPreTrainedModel): return outputs + # Copied from transformers.models.distilbert.modeling_tf_distilbert.TFDistilBertModel.serving_output def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None - return TFBaseModelOutput( - last_hidden_state=output.last_hidden_state, - hidden_states=hs, - attentions=attns, - ) + return TFBaseModelOutput(last_hidden_state=output.last_hidden_state, hidden_states=hs, attentions=attns) # Copied from transformers.models.xlm.modeling_tf_xlm.TFXLMMultiHeadAttention with XLM->Flaubert @@ -864,11 +861,7 @@ class TFFlaubertWithLMHeadModel(TFFlaubertPreTrainedModel): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None - return TFFlaubertWithLMHeadModelOutput( - logits=output.logits, - hidden_states=hs, - attentions=attns, - ) + return TFFlaubertWithLMHeadModelOutput(logits=output.logits, hidden_states=hs, attentions=attns) @add_start_docstrings( diff --git a/src/transformers/models/funnel/modeling_tf_funnel.py b/src/transformers/models/funnel/modeling_tf_funnel.py index bf3540b7be..cf9ebb3dfd 100644 --- a/src/transformers/models/funnel/modeling_tf_funnel.py +++ b/src/transformers/models/funnel/modeling_tf_funnel.py @@ -1189,15 +1189,12 @@ class TFFunnelBaseModel(TFFunnelPreTrainedModel): training=inputs["training"], ) + # Copied from transformers.models.distilbert.modeling_tf_distilbert.TFDistilBertModel.serving_output def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None - return TFBaseModelOutput( - last_hidden_state=output.last_hidden_state, - hidden_states=hs, - attentions=attns, - ) + return TFBaseModelOutput(last_hidden_state=output.last_hidden_state, hidden_states=hs, attentions=attns) @add_start_docstrings( @@ -1253,15 +1250,12 @@ class TFFunnelModel(TFFunnelPreTrainedModel): training=inputs["training"], ) + # Copied from transformers.models.distilbert.modeling_tf_distilbert.TFDistilBertModel.serving_output def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None - return TFBaseModelOutput( - last_hidden_state=output.last_hidden_state, - hidden_states=hs, - attentions=attns, - ) + return TFBaseModelOutput(last_hidden_state=output.last_hidden_state, hidden_states=hs, attentions=attns) @add_start_docstrings( @@ -1344,11 +1338,7 @@ class TFFunnelForPreTraining(TFFunnelPreTrainedModel): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None - return TFFunnelForPreTrainingOutput( - logits=output.logits, - hidden_states=hs, - attentions=attns, - ) + return TFFunnelForPreTrainingOutput(logits=output.logits, hidden_states=hs, attentions=attns) @add_start_docstrings("""Funnel Model with a `language modeling` head on top. """, FUNNEL_START_DOCSTRING) @@ -1434,15 +1424,12 @@ class TFFunnelForMaskedLM(TFFunnelPreTrainedModel, TFMaskedLanguageModelingLoss) attentions=outputs.attentions, ) + # Copied from transformers.models.bert.modeling_tf_bert.TFBertForMaskedLM.serving_output def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None - return TFMaskedLMOutput( - logits=output.logits, - hidden_states=hs, - attentions=attns, - ) + return TFMaskedLMOutput(logits=output.logits, hidden_states=hs, attentions=attns) @add_start_docstrings( @@ -1527,15 +1514,12 @@ class TFFunnelForSequenceClassification(TFFunnelPreTrainedModel, TFSequenceClass attentions=outputs.attentions, ) + # Copied from transformers.models.bert.modeling_tf_bert.TFBertForSequenceClassification.serving_output def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None - return TFSequenceClassifierOutput( - logits=output.logits, - hidden_states=hs, - attentions=attns, - ) + return TFSequenceClassifierOutput(logits=output.logits, hidden_states=hs, attentions=attns) @add_start_docstrings( @@ -1666,15 +1650,12 @@ class TFFunnelForMultipleChoice(TFFunnelPreTrainedModel, TFMultipleChoiceLoss): return self.serving_output(output) + # Copied from transformers.models.bert.modeling_tf_bert.TFBertForMultipleChoice.serving_output def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None - return TFMultipleChoiceModelOutput( - logits=output.logits, - hidden_states=hs, - attentions=attns, - ) + return TFMultipleChoiceModelOutput(logits=output.logits, hidden_states=hs, attentions=attns) @add_start_docstrings( @@ -1762,15 +1743,12 @@ class TFFunnelForTokenClassification(TFFunnelPreTrainedModel, TFTokenClassificat attentions=outputs.attentions, ) + # Copied from transformers.models.bert.modeling_tf_bert.TFBertForTokenClassification.serving_output def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None - return TFTokenClassifierOutput( - logits=output.logits, - hidden_states=hs, - attentions=attns, - ) + return TFTokenClassifierOutput(logits=output.logits, hidden_states=hs, attentions=attns) @add_start_docstrings( @@ -1870,13 +1848,11 @@ class TFFunnelForQuestionAnswering(TFFunnelPreTrainedModel, TFQuestionAnsweringL attentions=outputs.attentions, ) + # Copied from transformers.models.bert.modeling_tf_bert.TFBertForQuestionAnswering.serving_output def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None return TFQuestionAnsweringModelOutput( - start_logits=output.start_logits, - end_logits=output.end_logits, - hidden_states=hs, - attentions=attns, + start_logits=output.start_logits, end_logits=output.end_logits, hidden_states=hs, attentions=attns ) diff --git a/src/transformers/models/gpt2/modeling_tf_gpt2.py b/src/transformers/models/gpt2/modeling_tf_gpt2.py index cf68bc0913..d8d82e79ba 100644 --- a/src/transformers/models/gpt2/modeling_tf_gpt2.py +++ b/src/transformers/models/gpt2/modeling_tf_gpt2.py @@ -636,10 +636,7 @@ class TFGPT2Model(TFGPT2PreTrainedModel): attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None return TFBaseModelOutputWithPast( - last_hidden_state=output.last_hidden_state, - past_key_values=pkv, - hidden_states=hs, - attentions=attns, + last_hidden_state=output.last_hidden_state, past_key_values=pkv, hidden_states=hs, attentions=attns ) @@ -753,12 +750,7 @@ class TFGPT2LMHeadModel(TFGPT2PreTrainedModel, TFCausalLanguageModelingLoss): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None - return TFCausalLMOutputWithPast( - logits=output.logits, - past_key_values=pkv, - hidden_states=hs, - attentions=attns, - ) + return TFCausalLMOutputWithPast(logits=output.logits, past_key_values=pkv, hidden_states=hs, attentions=attns) @add_start_docstrings( @@ -1086,8 +1078,5 @@ class TFGPT2ForSequenceClassification(TFGPT2PreTrainedModel, TFSequenceClassific attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None return TFSequenceClassifierOutputWithPast( - logits=output.logits, - past_key_values=pkv, - hidden_states=hs, - attentions=attns, + logits=output.logits, past_key_values=pkv, hidden_states=hs, attentions=attns ) diff --git a/src/transformers/models/longformer/modeling_tf_longformer.py b/src/transformers/models/longformer/modeling_tf_longformer.py index 4a1a294091..8fd5cce52e 100644 --- a/src/transformers/models/longformer/modeling_tf_longformer.py +++ b/src/transformers/models/longformer/modeling_tf_longformer.py @@ -2128,11 +2128,7 @@ class TFLongformerForMaskedLM(TFLongformerPreTrainedModel, TFMaskedLanguageModel g_attns = tf.convert_to_tensor(output.global_attentions) if self.config.output_attentions else None return TFLongformerMaskedLMOutput( - loss=None, - logits=output.logits, - hidden_states=hs, - attentions=attns, - global_attentions=g_attns, + logits=output.logits, hidden_states=hs, attentions=attns, global_attentions=g_attns ) @@ -2407,10 +2403,7 @@ class TFLongformerForSequenceClassification(TFLongformerPreTrainedModel, TFSeque g_attns = tf.convert_to_tensor(output.global_attentions) if self.config.output_attentions else None return TFLongformerSequenceClassifierOutput( - logits=output.logits, - hidden_states=hs, - attentions=attns, - global_attentions=g_attns, + logits=output.logits, hidden_states=hs, attentions=attns, global_attentions=g_attns ) @@ -2567,10 +2560,7 @@ class TFLongformerForMultipleChoice(TFLongformerPreTrainedModel, TFMultipleChoic g_attns = tf.convert_to_tensor(output.global_attentions) if self.config.output_attentions else None return TFLongformerMultipleChoiceModelOutput( - logits=output.logits, - hidden_states=hs, - attentions=attns, - global_attentions=g_attns, + logits=output.logits, hidden_states=hs, attentions=attns, global_attentions=g_attns ) @@ -2674,8 +2664,5 @@ class TFLongformerForTokenClassification(TFLongformerPreTrainedModel, TFTokenCla g_attns = tf.convert_to_tensor(output.global_attentions) if self.config.output_attentions else None return TFLongformerTokenClassifierOutput( - logits=output.logits, - hidden_states=hs, - attentions=attns, - global_attentions=g_attns, + logits=output.logits, hidden_states=hs, attentions=attns, global_attentions=g_attns ) diff --git a/src/transformers/models/mobilebert/modeling_tf_mobilebert.py b/src/transformers/models/mobilebert/modeling_tf_mobilebert.py index ceaba93905..7d37097bca 100644 --- a/src/transformers/models/mobilebert/modeling_tf_mobilebert.py +++ b/src/transformers/models/mobilebert/modeling_tf_mobilebert.py @@ -1012,6 +1012,7 @@ class TFMobileBertModel(TFMobileBertPreTrainedModel): return outputs + # Copied from transformers.models.bert.modeling_tf_bert.TFBertModel.serving_output def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None @@ -1229,15 +1230,12 @@ class TFMobileBertForMaskedLM(TFMobileBertPreTrainedModel, TFMaskedLanguageModel attentions=outputs.attentions, ) + # Copied from transformers.models.bert.modeling_tf_bert.TFBertForMaskedLM.serving_output def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None - return TFMaskedLMOutput( - logits=output.logits, - hidden_states=hs, - attentions=attns, - ) + return TFMaskedLMOutput(logits=output.logits, hidden_states=hs, attentions=attns) class TFMobileBertOnlyNSPHead(tf.keras.layers.Layer): @@ -1346,15 +1344,12 @@ class TFMobileBertForNextSentencePrediction(TFMobileBertPreTrainedModel, TFNextS attentions=outputs.attentions, ) + # Copied from transformers.models.bert.modeling_tf_bert.TFBertForNextSentencePrediction.serving_output def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None - return TFNextSentencePredictorOutput( - logits=output.logits, - hidden_states=hs, - attentions=attns, - ) + return TFNextSentencePredictorOutput(logits=output.logits, hidden_states=hs, attentions=attns) @add_start_docstrings( @@ -1458,15 +1453,12 @@ class TFMobileBertForSequenceClassification(TFMobileBertPreTrainedModel, TFSeque attentions=outputs.attentions, ) + # Copied from transformers.models.bert.modeling_tf_bert.TFBertForSequenceClassification.serving_output def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None - return TFSequenceClassifierOutput( - logits=output.logits, - hidden_states=hs, - attentions=attns, - ) + return TFSequenceClassifierOutput(logits=output.logits, hidden_states=hs, attentions=attns) @add_start_docstrings( @@ -1582,15 +1574,13 @@ class TFMobileBertForQuestionAnswering(TFMobileBertPreTrainedModel, TFQuestionAn attentions=outputs.attentions, ) + # Copied from transformers.models.bert.modeling_tf_bert.TFBertForQuestionAnswering.serving_output def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None return TFQuestionAnsweringModelOutput( - start_logits=output.start_logits, - end_logits=output.end_logits, - hidden_states=hs, - attentions=attns, + start_logits=output.start_logits, end_logits=output.end_logits, hidden_states=hs, attentions=attns ) @@ -1743,15 +1733,12 @@ class TFMobileBertForMultipleChoice(TFMobileBertPreTrainedModel, TFMultipleChoic return self.serving_output(output) + # Copied from transformers.models.bert.modeling_tf_bert.TFBertForMultipleChoice.serving_output def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None - return TFMultipleChoiceModelOutput( - logits=output.logits, - hidden_states=hs, - attentions=attns, - ) + return TFMultipleChoiceModelOutput(logits=output.logits, hidden_states=hs, attentions=attns) @add_start_docstrings( @@ -1855,12 +1842,9 @@ class TFMobileBertForTokenClassification(TFMobileBertPreTrainedModel, TFTokenCla attentions=outputs.attentions, ) + # Copied from transformers.models.bert.modeling_tf_bert.TFBertForTokenClassification.serving_output def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None - return TFTokenClassifierOutput( - logits=output.logits, - hidden_states=hs, - attentions=attns, - ) + return TFTokenClassifierOutput(logits=output.logits, hidden_states=hs, attentions=attns) diff --git a/src/transformers/models/mpnet/modeling_tf_mpnet.py b/src/transformers/models/mpnet/modeling_tf_mpnet.py index 70a7a29e21..9ef16555e9 100644 --- a/src/transformers/models/mpnet/modeling_tf_mpnet.py +++ b/src/transformers/models/mpnet/modeling_tf_mpnet.py @@ -805,6 +805,7 @@ class TFMPNetModel(TFMPNetPreTrainedModel): ) return outputs + # Copied from transformers.models.bert.modeling_tf_bert.TFBertModel.serving_output def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None @@ -942,15 +943,12 @@ class TFMPNetForMaskedLM(TFMPNetPreTrainedModel, TFMaskedLanguageModelingLoss): attentions=outputs.attentions, ) + # Copied from transformers.models.bert.modeling_tf_bert.TFBertForMaskedLM.serving_output def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None - return TFMaskedLMOutput( - logits=output.logits, - hidden_states=hs, - attentions=attns, - ) + return TFMaskedLMOutput(logits=output.logits, hidden_states=hs, attentions=attns) class TFMPNetClassificationHead(tf.keras.layers.Layer): @@ -1069,15 +1067,12 @@ class TFMPNetForSequenceClassification(TFMPNetPreTrainedModel, TFSequenceClassif attentions=outputs.attentions, ) + # Copied from transformers.models.bert.modeling_tf_bert.TFBertForSequenceClassification.serving_output def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None - return TFSequenceClassifierOutput( - logits=output.logits, - hidden_states=hs, - attentions=attns, - ) + return TFSequenceClassifierOutput(logits=output.logits, hidden_states=hs, attentions=attns) @add_start_docstrings( @@ -1216,15 +1211,12 @@ class TFMPNetForMultipleChoice(TFMPNetPreTrainedModel, TFMultipleChoiceLoss): return self.serving_output(output) + # Copied from transformers.models.bert.modeling_tf_bert.TFBertForMultipleChoice.serving_output def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None - return TFMultipleChoiceModelOutput( - logits=output.logits, - hidden_states=hs, - attentions=attns, - ) + return TFMultipleChoiceModelOutput(logits=output.logits, hidden_states=hs, attentions=attns) @add_start_docstrings( @@ -1321,15 +1313,12 @@ class TFMPNetForTokenClassification(TFMPNetPreTrainedModel, TFTokenClassificatio attentions=outputs.attentions, ) + # Copied from transformers.models.bert.modeling_tf_bert.TFBertForTokenClassification.serving_output def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None - return TFTokenClassifierOutput( - logits=output.logits, - hidden_states=hs, - attentions=attns, - ) + return TFTokenClassifierOutput(logits=output.logits, hidden_states=hs, attentions=attns) @add_start_docstrings( @@ -1439,13 +1428,11 @@ class TFMPNetForQuestionAnswering(TFMPNetPreTrainedModel, TFQuestionAnsweringLos attentions=outputs.attentions, ) + # Copied from transformers.models.bert.modeling_tf_bert.TFBertForQuestionAnswering.serving_output def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None return TFQuestionAnsweringModelOutput( - start_logits=output.start_logits, - end_logits=output.end_logits, - hidden_states=hs, - attentions=attns, + start_logits=output.start_logits, end_logits=output.end_logits, hidden_states=hs, attentions=attns ) diff --git a/src/transformers/models/openai/modeling_tf_openai.py b/src/transformers/models/openai/modeling_tf_openai.py index 4cd689dad8..e07d360c83 100644 --- a/src/transformers/models/openai/modeling_tf_openai.py +++ b/src/transformers/models/openai/modeling_tf_openai.py @@ -556,15 +556,12 @@ class TFOpenAIGPTModel(TFOpenAIGPTPreTrainedModel): ) return outputs + # Copied from transformers.models.distilbert.modeling_tf_distilbert.TFDistilBertModel.serving_output def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None - return TFBaseModelOutput( - last_hidden_state=output.last_hidden_state, - hidden_states=hs, - attentions=attns, - ) + return TFBaseModelOutput(last_hidden_state=output.last_hidden_state, hidden_states=hs, attentions=attns) @add_start_docstrings( @@ -659,15 +656,12 @@ class TFOpenAIGPTLMHeadModel(TFOpenAIGPTPreTrainedModel, TFCausalLanguageModelin attentions=transformer_outputs.attentions, ) + # Copied from transformers.models.bert.modeling_tf_bert.TFBertLMHeadModel.serving_output def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None - return TFCausalLMOutput( - logits=output.logits, - hidden_states=hs, - attentions=attns, - ) + return TFCausalLMOutput(logits=output.logits, hidden_states=hs, attentions=attns) @add_start_docstrings( @@ -816,10 +810,7 @@ class TFOpenAIGPTDoubleHeadsModel(TFOpenAIGPTPreTrainedModel): attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None return TFOpenAIGPTDoubleHeadsModelOutput( - logits=output.logits, - mc_logits=output.mc_logits, - hidden_states=hs, - attentions=attns, + logits=output.logits, mc_logits=output.mc_logits, hidden_states=hs, attentions=attns ) @@ -973,12 +964,9 @@ class TFOpenAIGPTForSequenceClassification(TFOpenAIGPTPreTrainedModel, TFSequenc attentions=transformer_outputs.attentions, ) + # Copied from transformers.models.bert.modeling_tf_bert.TFBertForSequenceClassification.serving_output def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None - return TFSequenceClassifierOutput( - logits=output.logits, - hidden_states=hs, - attentions=attns, - ) + return TFSequenceClassifierOutput(logits=output.logits, hidden_states=hs, attentions=attns) diff --git a/src/transformers/models/roberta/modeling_tf_roberta.py b/src/transformers/models/roberta/modeling_tf_roberta.py index 042ec47f48..da955aabf5 100644 --- a/src/transformers/models/roberta/modeling_tf_roberta.py +++ b/src/transformers/models/roberta/modeling_tf_roberta.py @@ -792,6 +792,7 @@ class TFRobertaModel(TFRobertaPreTrainedModel): return outputs + # Copied from transformers.models.bert.modeling_tf_bert.TFBertModel.serving_output def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None @@ -930,15 +931,12 @@ class TFRobertaForMaskedLM(TFRobertaPreTrainedModel, TFMaskedLanguageModelingLos attentions=outputs.attentions, ) + # Copied from transformers.models.bert.modeling_tf_bert.TFBertForMaskedLM.serving_output def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None - return TFMaskedLMOutput( - logits=output.logits, - hidden_states=hs, - attentions=attns, - ) + return TFMaskedLMOutput(logits=output.logits, hidden_states=hs, attentions=attns) class TFRobertaClassificationHead(tf.keras.layers.Layer): @@ -1056,15 +1054,12 @@ class TFRobertaForSequenceClassification(TFRobertaPreTrainedModel, TFSequenceCla attentions=outputs.attentions, ) + # Copied from transformers.models.bert.modeling_tf_bert.TFBertForSequenceClassification.serving_output def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None - return TFSequenceClassifierOutput( - logits=output.logits, - hidden_states=hs, - attentions=attns, - ) + return TFSequenceClassifierOutput(logits=output.logits, hidden_states=hs, attentions=attns) @add_start_docstrings( @@ -1203,15 +1198,12 @@ class TFRobertaForMultipleChoice(TFRobertaPreTrainedModel, TFMultipleChoiceLoss) return self.serving_output(output) + # Copied from transformers.models.bert.modeling_tf_bert.TFBertForMultipleChoice.serving_output def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None - return TFMultipleChoiceModelOutput( - logits=output.logits, - hidden_states=hs, - attentions=attns, - ) + return TFMultipleChoiceModelOutput(logits=output.logits, hidden_states=hs, attentions=attns) @add_start_docstrings( @@ -1309,15 +1301,12 @@ class TFRobertaForTokenClassification(TFRobertaPreTrainedModel, TFTokenClassific attentions=outputs.attentions, ) + # Copied from transformers.models.bert.modeling_tf_bert.TFBertForTokenClassification.serving_output def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None - return TFTokenClassifierOutput( - logits=output.logits, - hidden_states=hs, - attentions=attns, - ) + return TFTokenClassifierOutput(logits=output.logits, hidden_states=hs, attentions=attns) @add_start_docstrings( @@ -1427,13 +1416,11 @@ class TFRobertaForQuestionAnswering(TFRobertaPreTrainedModel, TFQuestionAnswerin attentions=outputs.attentions, ) + # Copied from transformers.models.bert.modeling_tf_bert.TFBertForQuestionAnswering.serving_output def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None return TFQuestionAnsweringModelOutput( - start_logits=output.start_logits, - end_logits=output.end_logits, - hidden_states=hs, - attentions=attns, + start_logits=output.start_logits, end_logits=output.end_logits, hidden_states=hs, attentions=attns ) diff --git a/src/transformers/models/t5/modeling_tf_t5.py b/src/transformers/models/t5/modeling_tf_t5.py index b70976cf39..83a8c22a92 100644 --- a/src/transformers/models/t5/modeling_tf_t5.py +++ b/src/transformers/models/t5/modeling_tf_t5.py @@ -1571,12 +1571,9 @@ class TFT5EncoderModel(TFT5PreTrainedModel): attentions=encoder_outputs.attentions, ) + # Copied from transformers.models.distilbert.modeling_tf_distilbert.TFDistilBertModel.serving_output def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None - return TFBaseModelOutput( - last_hidden_state=output.last_hidden_state, - hidden_states=hs, - attentions=attns, - ) + return TFBaseModelOutput(last_hidden_state=output.last_hidden_state, hidden_states=hs, attentions=attns) diff --git a/src/transformers/models/transfo_xl/modeling_tf_transfo_xl.py b/src/transformers/models/transfo_xl/modeling_tf_transfo_xl.py index 9aec7949bd..f3b228ce59 100644 --- a/src/transformers/models/transfo_xl/modeling_tf_transfo_xl.py +++ b/src/transformers/models/transfo_xl/modeling_tf_transfo_xl.py @@ -1196,8 +1196,5 @@ class TFTransfoXLForSequenceClassification(TFTransfoXLPreTrainedModel, TFSequenc attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None return TFTransfoXLSequenceClassifierOutputWithPast( - logits=output.logits, - mems=tf.convert_to_tensor(output.mems), - hidden_states=hs, - attentions=attns, + logits=output.logits, mems=tf.convert_to_tensor(output.mems), hidden_states=hs, attentions=attns ) diff --git a/src/transformers/models/xlm/modeling_tf_xlm.py b/src/transformers/models/xlm/modeling_tf_xlm.py index 2c08d26850..4bd04bb3bb 100644 --- a/src/transformers/models/xlm/modeling_tf_xlm.py +++ b/src/transformers/models/xlm/modeling_tf_xlm.py @@ -749,15 +749,12 @@ class TFXLMModel(TFXLMPreTrainedModel): return outputs + # Copied from transformers.models.distilbert.modeling_tf_distilbert.TFDistilBertModel.serving_output def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None - return TFBaseModelOutput( - last_hidden_state=output.last_hidden_state, - hidden_states=hs, - attentions=attns, - ) + return TFBaseModelOutput(last_hidden_state=output.last_hidden_state, hidden_states=hs, attentions=attns) class TFXLMPredLayer(tf.keras.layers.Layer): @@ -905,11 +902,7 @@ class TFXLMWithLMHeadModel(TFXLMPreTrainedModel): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None - return TFXLMWithLMHeadModelOutput( - logits=output.logits, - hidden_states=hs, - attentions=attns, - ) + return TFXLMWithLMHeadModelOutput(logits=output.logits, hidden_states=hs, attentions=attns) @add_start_docstrings( @@ -1009,15 +1002,12 @@ class TFXLMForSequenceClassification(TFXLMPreTrainedModel, TFSequenceClassificat attentions=transformer_outputs.attentions, ) + # Copied from transformers.models.bert.modeling_tf_bert.TFBertForSequenceClassification.serving_output def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None - return TFSequenceClassifierOutput( - logits=output.logits, - hidden_states=hs, - attentions=attns, - ) + return TFSequenceClassifierOutput(logits=output.logits, hidden_states=hs, attentions=attns) @add_start_docstrings( @@ -1173,15 +1163,12 @@ class TFXLMForMultipleChoice(TFXLMPreTrainedModel, TFMultipleChoiceLoss): return self.serving_output(output) + # Copied from transformers.models.bert.modeling_tf_bert.TFBertForMultipleChoice.serving_output def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None - return TFMultipleChoiceModelOutput( - logits=output.logits, - hidden_states=hs, - attentions=attns, - ) + return TFMultipleChoiceModelOutput(logits=output.logits, hidden_states=hs, attentions=attns) @add_start_docstrings( @@ -1284,15 +1271,12 @@ class TFXLMForTokenClassification(TFXLMPreTrainedModel, TFTokenClassificationLos attentions=transformer_outputs.attentions, ) + # Copied from transformers.models.bert.modeling_tf_bert.TFBertForTokenClassification.serving_output def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None - return TFTokenClassifierOutput( - logits=output.logits, - hidden_states=hs, - attentions=attns, - ) + return TFTokenClassifierOutput(logits=output.logits, hidden_states=hs, attentions=attns) @add_start_docstrings( @@ -1406,13 +1390,11 @@ class TFXLMForQuestionAnsweringSimple(TFXLMPreTrainedModel, TFQuestionAnsweringL attentions=transformer_outputs.attentions, ) + # Copied from transformers.models.bert.modeling_tf_bert.TFBertForQuestionAnswering.serving_output def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None return TFQuestionAnsweringModelOutput( - start_logits=output.start_logits, - end_logits=output.end_logits, - hidden_states=hs, - attentions=attns, + start_logits=output.start_logits, end_logits=output.end_logits, hidden_states=hs, attentions=attns ) diff --git a/src/transformers/models/xlnet/modeling_tf_xlnet.py b/src/transformers/models/xlnet/modeling_tf_xlnet.py index c93ed3124e..66e41bbf11 100644 --- a/src/transformers/models/xlnet/modeling_tf_xlnet.py +++ b/src/transformers/models/xlnet/modeling_tf_xlnet.py @@ -1211,10 +1211,7 @@ class TFXLNetModel(TFXLNetPreTrainedModel): mems = tf.convert_to_tensor(output.mems) if output.mems is not None else None return TFXLNetModelOutput( - last_hidden_state=output.last_hidden_state, - mems=mems, - hidden_states=hs, - attentions=attns, + last_hidden_state=output.last_hidden_state, mems=mems, hidden_states=hs, attentions=attns ) @@ -1393,12 +1390,7 @@ class TFXLNetLMHeadModel(TFXLNetPreTrainedModel, TFCausalLanguageModelingLoss): attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None mems = tf.convert_to_tensor(output.mems) if output.mems is not None else None - return TFXLNetLMHeadModelOutput( - logits=output.logits, - mems=mems, - hidden_states=hs, - attentions=attns, - ) + return TFXLNetLMHeadModelOutput(logits=output.logits, mems=mems, hidden_states=hs, attentions=attns) @add_start_docstrings( @@ -1514,10 +1506,7 @@ class TFXLNetForSequenceClassification(TFXLNetPreTrainedModel, TFSequenceClassif mems = tf.convert_to_tensor(output.mems) if output.mems is not None else None return TFXLNetForSequenceClassificationOutput( - logits=output.logits, - mems=mems, - hidden_states=hs, - attentions=attns, + logits=output.logits, mems=mems, hidden_states=hs, attentions=attns ) @@ -1679,12 +1668,7 @@ class TFXLNetForMultipleChoice(TFXLNetPreTrainedModel, TFMultipleChoiceLoss): attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None mems = tf.convert_to_tensor(output.mems) if output.mems is not None else None - return TFXLNetForMultipleChoiceOutput( - logits=output.logits, - mems=mems, - hidden_states=hs, - attentions=attns, - ) + return TFXLNetForMultipleChoiceOutput(logits=output.logits, mems=mems, hidden_states=hs, attentions=attns) @add_start_docstrings( @@ -1793,12 +1777,7 @@ class TFXLNetForTokenClassification(TFXLNetPreTrainedModel, TFTokenClassificatio attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None mems = tf.convert_to_tensor(output.mems) if output.mems is not None else None - return TFXLNetForTokenClassificationOutput( - logits=output.logits, - mems=mems, - hidden_states=hs, - attentions=attns, - ) + return TFXLNetForTokenClassificationOutput(logits=output.logits, mems=mems, hidden_states=hs, attentions=attns) @add_start_docstrings( diff --git a/templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/modeling_tf_{{cookiecutter.lowercase_modelname}}.py b/templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/modeling_tf_{{cookiecutter.lowercase_modelname}}.py index 50b5329211..90b554574c 100644 --- a/templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/modeling_tf_{{cookiecutter.lowercase_modelname}}.py +++ b/templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/modeling_tf_{{cookiecutter.lowercase_modelname}}.py @@ -777,6 +777,7 @@ class TF{{cookiecutter.camelcase_modelname}}Model(TF{{cookiecutter.camelcase_mod return outputs + # Copied from transformers.models.distilbert.modeling_tf_distilbert.TFDistilBertModel.serving_output def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None @@ -885,15 +886,12 @@ class TF{{cookiecutter.camelcase_modelname}}ForMaskedLM(TF{{cookiecutter.camelca attentions=outputs.attentions, ) + # Copied from transformers.models.bert.modeling_tf_bert.TFBertForMaskedLM.serving_output def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None - return TFMaskedLMOutput( - logits=output.logits, - hidden_states=hs, - attentions=attns, - ) + return TFMaskedLMOutput(logits=output.logits, hidden_states=hs, attentions=attns) @add_start_docstrings( """{{cookiecutter.modelname}} Model with a `language modeling` head on top for CLM fine-tuning. """, {{cookiecutter.uppercase_modelname}}_START_DOCSTRING @@ -993,15 +991,12 @@ class TF{{cookiecutter.camelcase_modelname}}ForCausalLM(TF{{cookiecutter.camelca attentions=outputs.attentions, ) + # Copied from transformers.models.bert.modeling_tf_bert.TFBertLMHeadModel.serving_output def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None - return TFCausalLMOutput( - logits=output.logits, - hidden_states=hs, - attentions=attns, - ) + return TFCausalLMOutput(logits=output.logits, hidden_states=hs, attentions=attns) class TF{{cookiecutter.camelcase_modelname}}ClassificationHead(tf.keras.layers.Layer): """Head for sentence-level classification tasks.""" @@ -1114,15 +1109,12 @@ class TF{{cookiecutter.camelcase_modelname}}ForSequenceClassification(TF{{cookie attentions=outputs.attentions, ) + # Copied from transformers.models.bert.modeling_tf_bert.TFBertForSequenceClassification.serving_output def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None - return TFSequenceClassifierOutput( - logits=output.logits, - hidden_states=hs, - attentions=attns, - ) + return TFSequenceClassifierOutput(logits=output.logits, hidden_states=hs, attentions=attns) @add_start_docstrings( @@ -1258,15 +1250,12 @@ class TF{{cookiecutter.camelcase_modelname}}ForMultipleChoice(TF{{cookiecutter.c return self.serving_output(output) + # Copied from transformers.models.bert.modeling_tf_bert.TFBertForMultipleChoice.serving_output def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None - return TFMultipleChoiceModelOutput( - logits=output.logits, - hidden_states=hs, - attentions=attns, - ) + return TFMultipleChoiceModelOutput(logits=output.logits, hidden_states=hs, attentions=attns) @add_start_docstrings( @@ -1357,15 +1346,12 @@ class TF{{cookiecutter.camelcase_modelname}}ForTokenClassification(TF{{cookiecut attentions=outputs.attentions, ) + # Copied from transformers.models.bert.modeling_tf_bert.TFBertForTokenClassification.serving_output def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None - return TFTokenClassifierOutput( - logits=output.logits, - hidden_states=hs, - attentions=attns, - ) + return TFTokenClassifierOutput(logits=output.logits, hidden_states=hs, attentions=attns) @add_start_docstrings( @@ -1470,15 +1456,13 @@ class TF{{cookiecutter.camelcase_modelname}}ForQuestionAnswering(TF{{cookiecutte attentions=outputs.attentions, ) + # Copied from transformers.models.bert.modeling_tf_bert.TFBertForQuestionAnswering.serving_output def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None return TFQuestionAnsweringModelOutput( - start_logits=output.start_logits, - end_logits=output.end_logits, - hidden_states=hs, - attentions=attns, + start_logits=output.start_logits, end_logits=output.end_logits, hidden_states=hs, attentions=attns ) {% else %} @@ -2454,6 +2438,7 @@ class TF{{cookiecutter.camelcase_modelname}}Model(TF{{cookiecutter.camelcase_mod encoder_attentions=inputs["encoder_outputs"].attentions, ) + # Copied from transformers.models.bart.modeling_tf_bart.TFBartModel.serving_output def serving_output(self, output): pkv = tf.tuple(output.past_key_values)[1] if self.config.use_cache else None, dec_hs = tf.convert_to_tensor(output.decoder_hidden_states) if self.config.output_hidden_states else None @@ -2616,6 +2601,7 @@ class TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration(TF{{cookiec encoder_attentions=outputs.encoder_attentions, # 2 of e out ) + # Copied from transformers.models.bart.modeling_tf_bart.TFBartForConditionalGeneration.serving_output def serving_output(self, output): pkv = tf.tuple(output.past_key_values)[1] if self.config.use_cache else None, dec_hs = tf.convert_to_tensor(output.decoder_hidden_states) if self.config.output_hidden_states else None