Cleaning TensorFlow models (#5229)

* Cleaning TensorFlow models

Update all classes


stylr

* Don't average loss

src/transformers/modeling_tf_albert.py

@@ -897,15 +897,15 @@ class TFAlbertForSequenceClassification(TFAlbertPreTrainedModel, TFSequenceClass
     @add_start_docstrings_to_callable(ALBERT_INPUTS_DOCSTRING)
     def call(
         self,
-        input_ids=None,
+        inputs=None,
         attention_mask=None,
         token_type_ids=None,
         position_ids=None,
         head_mask=None,
         inputs_embeds=None,
-        labels=None,
         output_attentions=None,
         output_hidden_states=None,
+        labels=None,
         training=False,
     ):
         r"""
@@ -944,9 +944,15 @@ class TFAlbertForSequenceClassification(TFAlbertPreTrainedModel, TFSequenceClass
         loss, logits = outputs[:2]
 
         """
+        if isinstance(inputs, (tuple, list)):
+            labels = inputs[8] if len(inputs) > 8 else labels
+            if len(inputs) > 8:
+                inputs = inputs[:8]
+        elif isinstance(inputs, (dict, BatchEncoding)):
+            labels = inputs.pop("labels", labels)
 
         outputs = self.albert(
-            input_ids,
+            inputs,
             attention_mask=attention_mask,
             token_type_ids=token_type_ids,
             position_ids=position_ids,
@@ -990,15 +996,15 @@ class TFAlbertForTokenClassification(TFAlbertPreTrainedModel, TFTokenClassificat
     @add_start_docstrings_to_callable(ALBERT_INPUTS_DOCSTRING)
     def call(
         self,
-        input_ids=None,
+        inputs=None,
         attention_mask=None,
         token_type_ids=None,
         position_ids=None,
         head_mask=None,
         inputs_embeds=None,
-        labels=None,
         output_attentions=None,
         output_hidden_states=None,
+        labels=None,
         training=False,
     ):
         r"""
@@ -1035,8 +1041,15 @@ class TFAlbertForTokenClassification(TFAlbertPreTrainedModel, TFTokenClassificat
         loss, scores = outputs[:2]
 
         """
+        if isinstance(inputs, (tuple, list)):
+            labels = inputs[8] if len(inputs) > 8 else labels
+            if len(inputs) > 8:
+                inputs = inputs[:8]
+        elif isinstance(inputs, (dict, BatchEncoding)):
+            labels = inputs.pop("labels", labels)
+
         outputs = self.albert(
-            input_ids,
+            inputs,
             attention_mask=attention_mask,
             token_type_ids=token_type_ids,
             position_ids=position_ids,
@@ -1078,19 +1091,16 @@ class TFAlbertForQuestionAnswering(TFAlbertPreTrainedModel, TFQuestionAnsweringL
     @add_start_docstrings_to_callable(ALBERT_INPUTS_DOCSTRING)
     def call(
         self,
-        input_ids=None,
+        inputs=None,
         attention_mask=None,
         token_type_ids=None,
         position_ids=None,
         head_mask=None,
         inputs_embeds=None,
-        start_positions=None,
-        end_positions=None,
-        cls_index=None,
-        p_mask=None,
-        is_impossible=None,
         output_attentions=None,
         output_hidden_states=None,
+        start_positions=None,
+        end_positions=None,
         training=False,
     ):
         r"""
@@ -1139,8 +1149,17 @@ class TFAlbertForQuestionAnswering(TFAlbertPreTrainedModel, TFQuestionAnsweringL
         answer = ' '.join(all_tokens[tf.math.argmax(start_scores, 1)[0] : tf.math.argmax(end_scores, 1)[0]+1])
 
         """
+        if isinstance(inputs, (tuple, list)):
+            start_positions = inputs[8] if len(inputs) > 8 else start_positions
+            end_positions = inputs[9] if len(inputs) > 9 else end_positions
+            if len(inputs) > 8:
+                inputs = inputs[:8]
+        elif isinstance(inputs, (dict, BatchEncoding)):
+            start_positions = inputs.pop("start_positions", start_positions)
+            end_positions = inputs.pop("end_positions", start_positions)
+
         outputs = self.albert(
-            input_ids,
+            inputs,
             attention_mask=attention_mask,
             token_type_ids=token_type_ids,
             position_ids=position_ids,
@@ -1202,9 +1221,9 @@ class TFAlbertForMultipleChoice(TFAlbertPreTrainedModel, TFMultipleChoiceLoss):
         position_ids=None,
         head_mask=None,
         inputs_embeds=None,
-        labels=None,
         output_attentions=None,
         output_hidden_states=None,
+        labels=None,
         training=False,
     ):
         r"""
@@ -1255,8 +1274,10 @@ class TFAlbertForMultipleChoice(TFAlbertPreTrainedModel, TFMultipleChoiceLoss):
             head_mask = inputs[4] if len(inputs) > 4 else head_mask
             inputs_embeds = inputs[5] if len(inputs) > 5 else inputs_embeds
             output_attentions = inputs[6] if len(inputs) > 6 else output_attentions
-            assert len(inputs) <= 7, "Too many inputs."
-        elif isinstance(inputs, dict):
+            output_hidden_states = inputs[7] if len(inputs) > 7 else output_hidden_states
+            labels = inputs[8] if len(inputs) > 8 else labels
+            assert len(inputs) <= 9, "Too many inputs."
+        elif isinstance(inputs, (dict, BatchEncoding)):
             input_ids = inputs.get("input_ids")
             attention_mask = inputs.get("attention_mask", attention_mask)
             token_type_ids = inputs.get("token_type_ids", token_type_ids)
@@ -1264,7 +1285,9 @@ class TFAlbertForMultipleChoice(TFAlbertPreTrainedModel, TFMultipleChoiceLoss):
             head_mask = inputs.get("head_mask", head_mask)
             inputs_embeds = inputs.get("inputs_embeds", inputs_embeds)
             output_attentions = inputs.get("output_attentions", output_attentions)
-            assert len(inputs) <= 7, "Too many inputs."
+            output_hidden_states = inputs.get("output_hidden_states", output_attentions)
+            labels = inputs.get("labels", labels)
+            assert len(inputs) <= 9, "Too many inputs."
         else:
             input_ids = inputs
 

src/transformers/modeling_tf_bert.py

@@ -932,15 +932,15 @@ class TFBertForSequenceClassification(TFBertPreTrainedModel, TFSequenceClassific
     @add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING)
     def call(
         self,
-        input_ids=None,
+        inputs=None,
         attention_mask=None,
         token_type_ids=None,
         position_ids=None,
         head_mask=None,
         inputs_embeds=None,
-        labels=None,
         output_attentions=None,
         output_hidden_states=None,
+        labels=None,
         training=False,
     ):
         r"""
@@ -979,9 +979,15 @@ class TFBertForSequenceClassification(TFBertPreTrainedModel, TFSequenceClassific
         loss, logits = outputs[:2]
 
         """
+        if isinstance(inputs, (tuple, list)):
+            labels = inputs[8] if len(inputs) > 8 else labels
+            if len(inputs) > 8:
+                inputs = inputs[:8]
+        elif isinstance(inputs, (dict, BatchEncoding)):
+            labels = inputs.pop("labels", labels)
 
         outputs = self.bert(
-            input_ids,
+            inputs,
             attention_mask=attention_mask,
             token_type_ids=token_type_ids,
             position_ids=position_ids,
@@ -1039,9 +1045,9 @@ class TFBertForMultipleChoice(TFBertPreTrainedModel, TFMultipleChoiceLoss):
         position_ids=None,
         head_mask=None,
         inputs_embeds=None,
-        labels=None,
         output_attentions=None,
         output_hidden_states=None,
+        labels=None,
         training=False,
     ):
         r"""
@@ -1092,7 +1098,9 @@ class TFBertForMultipleChoice(TFBertPreTrainedModel, TFMultipleChoiceLoss):
             head_mask = inputs[4] if len(inputs) > 4 else head_mask
             inputs_embeds = inputs[5] if len(inputs) > 5 else inputs_embeds
             output_attentions = inputs[6] if len(inputs) > 6 else output_attentions
-            assert len(inputs) <= 7, "Too many inputs."
+            output_hidden_states = inputs[7] if len(inputs) > 7 else output_hidden_states
+            labels = inputs[8] if len(inputs) > 8 else labels
+            assert len(inputs) <= 9, "Too many inputs."
         elif isinstance(inputs, (dict, BatchEncoding)):
             input_ids = inputs.get("input_ids")
             attention_mask = inputs.get("attention_mask", attention_mask)
@@ -1101,7 +1109,9 @@ class TFBertForMultipleChoice(TFBertPreTrainedModel, TFMultipleChoiceLoss):
             head_mask = inputs.get("head_mask", head_mask)
             inputs_embeds = inputs.get("inputs_embeds", inputs_embeds)
             output_attentions = inputs.get("output_attentions", output_attentions)
-            assert len(inputs) <= 7, "Too many inputs."
+            output_hidden_states = inputs.get("output_hidden_states", output_hidden_states)
+            labels = inputs.get("labels", labels)
+            assert len(inputs) <= 9, "Too many inputs."
         else:
             input_ids = inputs
 
@@ -1169,15 +1179,15 @@ class TFBertForTokenClassification(TFBertPreTrainedModel, TFTokenClassificationL
     @add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING)
     def call(
         self,
-        input_ids=None,
+        inputs=None,
         attention_mask=None,
         token_type_ids=None,
         position_ids=None,
         head_mask=None,
         inputs_embeds=None,
-        labels=None,
         output_attentions=None,
         output_hidden_states=None,
+        labels=None,
         training=False,
     ):
         r"""
@@ -1214,8 +1224,15 @@ class TFBertForTokenClassification(TFBertPreTrainedModel, TFTokenClassificationL
         loss, scores = outputs[:2]
 
         """
+        if isinstance(inputs, (tuple, list)):
+            labels = inputs[8] if len(inputs) > 8 else labels
+            if len(inputs) > 8:
+                inputs = inputs[:8]
+        elif isinstance(inputs, (dict, BatchEncoding)):
+            labels = inputs.pop("labels", labels)
+
         outputs = self.bert(
-            input_ids,
+            inputs,
             attention_mask=attention_mask,
             token_type_ids=token_type_ids,
             position_ids=position_ids,
@@ -1258,19 +1275,16 @@ class TFBertForQuestionAnswering(TFBertPreTrainedModel, TFQuestionAnsweringLoss)
     @add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING)
     def call(
         self,
-        input_ids=None,
+        inputs=None,
         attention_mask=None,
         token_type_ids=None,
         position_ids=None,
         head_mask=None,
         inputs_embeds=None,
-        start_positions=None,
-        end_positions=None,
-        cls_index=None,
-        p_mask=None,
-        is_impossible=None,
         output_attentions=None,
         output_hidden_states=None,
+        start_positions=None,
+        end_positions=None,
         training=False,
     ):
         r"""
@@ -1317,8 +1331,17 @@ class TFBertForQuestionAnswering(TFBertPreTrainedModel, TFQuestionAnsweringLoss)
         assert answer == "a nice puppet"
 
         """
+        if isinstance(inputs, (tuple, list)):
+            start_positions = inputs[8] if len(inputs) > 8 else start_positions
+            end_positions = inputs[9] if len(inputs) > 9 else end_positions
+            if len(inputs) > 8:
+                inputs = inputs[:8]
+        elif isinstance(inputs, (dict, BatchEncoding)):
+            start_positions = inputs.pop("start_positions", start_positions)
+            end_positions = inputs.pop("end_positions", start_positions)
+
         outputs = self.bert(
-            input_ids,
+            inputs,
             attention_mask=attention_mask,
             token_type_ids=token_type_ids,
             position_ids=position_ids,

src/transformers/modeling_tf_distilbert.py

@@ -715,13 +715,13 @@ class TFDistilBertForSequenceClassification(TFDistilBertPreTrainedModel, TFSeque
     @add_start_docstrings_to_callable(DISTILBERT_INPUTS_DOCSTRING)
     def call(
         self,
-        input_ids=None,
+        inputs=None,
         attention_mask=None,
         head_mask=None,
         inputs_embeds=None,
-        labels=None,
         output_attentions=None,
         output_hidden_states=None,
+        labels=None,
         training=False,
     ):
         r"""
@@ -760,8 +760,15 @@ class TFDistilBertForSequenceClassification(TFDistilBertPreTrainedModel, TFSeque
         loss, logits = outputs[:2]
 
         """
+        if isinstance(inputs, (tuple, list)):
+            labels = inputs[6] if len(inputs) > 6 else labels
+            if len(inputs) > 6:
+                inputs = inputs[:6]
+        elif isinstance(inputs, (dict, BatchEncoding)):
+            labels = inputs.pop("labels", labels)
+
         distilbert_output = self.distilbert(
-            input_ids,
+            inputs,
             attention_mask=attention_mask,
             head_mask=head_mask,
             inputs_embeds=inputs_embeds,
@@ -804,13 +811,13 @@ class TFDistilBertForTokenClassification(TFDistilBertPreTrainedModel, TFTokenCla
     @add_start_docstrings_to_callable(DISTILBERT_INPUTS_DOCSTRING)
     def call(
         self,
-        input_ids=None,
+        inputs=None,
         attention_mask=None,
         head_mask=None,
         inputs_embeds=None,
-        labels=None,
         output_attentions=None,
         output_hidden_states=None,
+        labels=None,
         training=False,
     ):
         r"""
@@ -847,8 +854,15 @@ class TFDistilBertForTokenClassification(TFDistilBertPreTrainedModel, TFTokenCla
         loss, scores = outputs[:2]
 
         """
+        if isinstance(inputs, (tuple, list)):
+            labels = inputs[6] if len(inputs) > 6 else labels
+            if len(inputs) > 6:
+                inputs = inputs[:6]
+        elif isinstance(inputs, (dict, BatchEncoding)):
+            labels = inputs.pop("labels", labels)
+
         outputs = self.distilbert(
-            input_ids,
+            inputs,
             attention_mask=attention_mask,
             head_mask=head_mask,
             inputs_embeds=inputs_embeds,
@@ -862,7 +876,7 @@ class TFDistilBertForTokenClassification(TFDistilBertPreTrainedModel, TFTokenCla
         sequence_output = self.dropout(sequence_output, training=training)
         logits = self.classifier(sequence_output)
 
-        outputs = (logits,) + outputs[2:]  # add hidden states and attention if they are here
+        outputs = (logits,) + outputs[1:]  # add hidden states and attention if they are here
 
         if labels is not None:
             loss = self.compute_loss(labels, logits)
@@ -881,7 +895,7 @@ class TFDistilBertForMultipleChoice(TFDistilBertPreTrainedModel, TFMultipleChoic
         super().__init__(config, *inputs, **kwargs)
 
         self.distilbert = TFDistilBertMainLayer(config, name="distilbert")
-        self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
+        self.dropout = tf.keras.layers.Dropout(config.seq_classif_dropout)
         self.pre_classifier = tf.keras.layers.Dense(
             config.dim,
             kernel_initializer=get_initializer(config.initializer_range),
@@ -908,9 +922,9 @@ class TFDistilBertForMultipleChoice(TFDistilBertPreTrainedModel, TFMultipleChoic
         attention_mask=None,
         head_mask=None,
         inputs_embeds=None,
-        labels=None,
         output_attentions=None,
         output_hidden_states=None,
+        labels=None,
         training=False,
     ):
         r"""
@@ -958,13 +972,19 @@ class TFDistilBertForMultipleChoice(TFDistilBertPreTrainedModel, TFMultipleChoic
             attention_mask = inputs[1] if len(inputs) > 1 else attention_mask
             head_mask = inputs[2] if len(inputs) > 2 else head_mask
             inputs_embeds = inputs[3] if len(inputs) > 3 else inputs_embeds
-            assert len(inputs) <= 4, "Too many inputs."
+            output_attentions = inputs[4] if len(inputs) > 4 else output_attentions
+            output_hidden_states = inputs[5] if len(inputs) > 5 else output_hidden_states
+            labels = inputs[6] if len(inputs) > 6 else labels
+            assert len(inputs) <= 7, "Too many inputs."
         elif isinstance(inputs, (dict, BatchEncoding)):
             input_ids = inputs.get("input_ids")
             attention_mask = inputs.get("attention_mask", attention_mask)
             head_mask = inputs.get("head_mask", head_mask)
             inputs_embeds = inputs.get("inputs_embeds", inputs_embeds)
-            assert len(inputs) <= 4, "Too many inputs."
+            output_attentions = inputs.get("output_attentions", output_attentions)
+            output_hidden_states = inputs.get("output_hidden_states", output_hidden_states)
+            labels = inputs.get("labels", labels)
+            assert len(inputs) <= 7, "Too many inputs."
         else:
             input_ids = inputs
 
@@ -977,12 +997,17 @@ class TFDistilBertForMultipleChoice(TFDistilBertPreTrainedModel, TFMultipleChoic
 
         flat_input_ids = tf.reshape(input_ids, (-1, seq_length)) if input_ids is not None else None
         flat_attention_mask = tf.reshape(attention_mask, (-1, seq_length)) if attention_mask is not None else None
+        flat_inputs_embeds = (
+            tf.reshape(inputs_embeds, (-1, seq_length, shape_list(inputs_embeds)[3]))
+            if inputs_embeds is not None
+            else None
+        )
 
         flat_inputs = [
             flat_input_ids,
             flat_attention_mask,
             head_mask,
-            inputs_embeds,
+            flat_inputs_embeds,
             output_attentions,
             output_hidden_states,
         ]
@@ -1023,17 +1048,14 @@ class TFDistilBertForQuestionAnswering(TFDistilBertPreTrainedModel, TFQuestionAn
     @add_start_docstrings_to_callable(DISTILBERT_INPUTS_DOCSTRING)
     def call(
         self,
-        input_ids=None,
+        inputs=None,
         attention_mask=None,
         head_mask=None,
         inputs_embeds=None,
-        start_positions=None,
-        end_positions=None,
-        cls_index=None,
-        p_mask=None,
-        is_impossible=None,
         output_attentions=None,
         output_hidden_states=None,
+        start_positions=None,
+        end_positions=None,
         training=False,
     ):
         r"""
@@ -1079,8 +1101,17 @@ class TFDistilBertForQuestionAnswering(TFDistilBertPreTrainedModel, TFQuestionAn
         answer = ' '.join(all_tokens[tf.math.argmax(start_scores, 1)[0] : tf.math.argmax(end_scores, 1)[0]+1])
 
         """
+        if isinstance(inputs, (tuple, list)):
+            start_positions = inputs[6] if len(inputs) > 6 else start_positions
+            end_positions = inputs[7] if len(inputs) > 7 else end_positions
+            if len(inputs) > 6:
+                inputs = inputs[:6]
+        elif isinstance(inputs, (dict, BatchEncoding)):
+            start_positions = inputs.pop("start_positions", start_positions)
+            end_positions = inputs.pop("end_positions", start_positions)
+
         distilbert_output = self.distilbert(
-            input_ids,
+            inputs,
             attention_mask=attention_mask,
             head_mask=head_mask,
             inputs_embeds=inputs_embeds,

src/transformers/modeling_tf_electra.py

@@ -613,15 +613,15 @@ class TFElectraForTokenClassification(TFElectraPreTrainedModel, TFTokenClassific
     @add_start_docstrings_to_callable(ELECTRA_INPUTS_DOCSTRING)
     def call(
         self,
-        input_ids=None,
+        inputs=None,
         attention_mask=None,
         token_type_ids=None,
         position_ids=None,
         head_mask=None,
         inputs_embeds=None,
-        labels=None,
         output_attentions=None,
         output_hidden_states=None,
+        labels=None,
         training=False,
     ):
         r"""
@@ -658,9 +658,15 @@ class TFElectraForTokenClassification(TFElectraPreTrainedModel, TFTokenClassific
         loss, scores = outputs[:2]
 
         """
+        if isinstance(inputs, (tuple, list)):
+            labels = inputs[8] if len(inputs) > 8 else labels
+            if len(inputs) > 8:
+                inputs = inputs[:8]
+        elif isinstance(inputs, (dict, BatchEncoding)):
+            labels = inputs.pop("labels", labels)
 
         discriminator_hidden_states = self.electra(
-            input_ids,
+            inputs,
             attention_mask,
             token_type_ids,
             position_ids,
@@ -701,19 +707,16 @@ class TFElectraForQuestionAnswering(TFElectraPreTrainedModel, TFQuestionAnswerin
     @add_start_docstrings_to_callable(ELECTRA_INPUTS_DOCSTRING)
     def call(
         self,
-        input_ids=None,
+        inputs=None,
         attention_mask=None,
         token_type_ids=None,
         position_ids=None,
         head_mask=None,
         inputs_embeds=None,
-        start_positions=None,
-        end_positions=None,
-        cls_index=None,
-        p_mask=None,
-        is_impossible=None,
         output_attentions=None,
         output_hidden_states=None,
+        start_positions=None,
+        end_positions=None,
         training=False,
     ):
         r"""
@@ -760,8 +763,17 @@ class TFElectraForQuestionAnswering(TFElectraPreTrainedModel, TFQuestionAnswerin
         answer = ' '.join(all_tokens[tf.math.argmax(start_scores, 1)[0] : tf.math.argmax(end_scores, 1)[0]+1])
 
         """
+        if isinstance(inputs, (tuple, list)):
+            start_positions = inputs[8] if len(inputs) > 8 else start_positions
+            end_positions = inputs[9] if len(inputs) > 9 else end_positions
+            if len(inputs) > 8:
+                inputs = inputs[:8]
+        elif isinstance(inputs, (dict, BatchEncoding)):
+            start_positions = inputs.pop("start_positions", start_positions)
+            end_positions = inputs.pop("end_positions", start_positions)
+
         discriminator_hidden_states = self.electra(
-            input_ids,
+            inputs,
             attention_mask,
             token_type_ids,
             position_ids,

src/transformers/modeling_tf_mobilebert.py

@@ -1080,15 +1080,15 @@ class TFMobileBertForSequenceClassification(TFMobileBertPreTrainedModel, TFSeque
     @add_start_docstrings_to_callable(MOBILEBERT_INPUTS_DOCSTRING)
     def call(
         self,
-        input_ids=None,
+        inputs=None,
         attention_mask=None,
         token_type_ids=None,
         position_ids=None,
         head_mask=None,
         inputs_embeds=None,
-        labels=None,
         output_attentions=None,
         output_hidden_states=None,
+        labels=None,
         training=False,
     ):
         r"""
@@ -1127,9 +1127,15 @@ class TFMobileBertForSequenceClassification(TFMobileBertPreTrainedModel, TFSeque
         loss, logits = outputs[:2]
 
         """
+        if isinstance(inputs, (tuple, list)):
+            labels = inputs[8] if len(inputs) > 8 else labels
+            if len(inputs) > 8:
+                inputs = inputs[:8]
+        elif isinstance(inputs, (dict, BatchEncoding)):
+            labels = inputs.pop("labels", labels)
 
         outputs = self.mobilebert(
-            input_ids,
+            inputs,
             attention_mask=attention_mask,
             token_type_ids=token_type_ids,
             position_ids=position_ids,
@@ -1172,19 +1178,16 @@ class TFMobileBertForQuestionAnswering(TFMobileBertPreTrainedModel, TFQuestionAn
     @add_start_docstrings_to_callable(MOBILEBERT_INPUTS_DOCSTRING)
     def call(
         self,
-        input_ids=None,
+        inputs=None,
         attention_mask=None,
         token_type_ids=None,
         position_ids=None,
         head_mask=None,
         inputs_embeds=None,
-        start_positions=None,
-        end_positions=None,
-        cls_index=None,
-        p_mask=None,
-        is_impossible=None,
         output_attentions=None,
         output_hidden_states=None,
+        start_positions=None,
+        end_positions=None,
         training=False,
     ):
         r"""
@@ -1231,8 +1234,17 @@ class TFMobileBertForQuestionAnswering(TFMobileBertPreTrainedModel, TFQuestionAn
         assert answer == "a nice puppet"
 
         """
+        if isinstance(inputs, (tuple, list)):
+            start_positions = inputs[8] if len(inputs) > 8 else start_positions
+            end_positions = inputs[9] if len(inputs) > 9 else end_positions
+            if len(inputs) > 8:
+                inputs = inputs[:8]
+        elif isinstance(inputs, (dict, BatchEncoding)):
+            start_positions = inputs.pop("start_positions", start_positions)
+            end_positions = inputs.pop("end_positions", start_positions)
+
         outputs = self.mobilebert(
-            input_ids,
+            inputs,
             attention_mask=attention_mask,
             token_type_ids=token_type_ids,
             position_ids=position_ids,
@@ -1294,9 +1306,9 @@ class TFMobileBertForMultipleChoice(TFMobileBertPreTrainedModel, TFMultipleChoic
         position_ids=None,
         head_mask=None,
         inputs_embeds=None,
-        labels=None,
         output_attentions=None,
         output_hidden_states=None,
+        labels=None,
         training=False,
     ):
         r"""
@@ -1348,7 +1360,8 @@ class TFMobileBertForMultipleChoice(TFMobileBertPreTrainedModel, TFMultipleChoic
             inputs_embeds = inputs[5] if len(inputs) > 5 else inputs_embeds
             output_attentions = inputs[6] if len(inputs) > 6 else output_attentions
             output_hidden_states = inputs[7] if len(inputs) > 7 else output_hidden_states
-            assert len(inputs) <= 8, "Too many inputs."
+            labels = inputs[8] if len(inputs) > 8 else labels
+            assert len(inputs) <= 9, "Too many inputs."
         elif isinstance(inputs, (dict, BatchEncoding)):
             input_ids = inputs.get("input_ids")
             attention_mask = inputs.get("attention_mask", attention_mask)
@@ -1358,7 +1371,8 @@ class TFMobileBertForMultipleChoice(TFMobileBertPreTrainedModel, TFMultipleChoic
             inputs_embeds = inputs.get("inputs_embeds", inputs_embeds)
             output_attentions = inputs.get("output_attentions", output_attentions)
             output_hidden_states = inputs.get("output_hidden_states", output_hidden_states)
-            assert len(inputs) <= 8, "Too many inputs."
+            labels = inputs.get("labels", labels)
+            assert len(inputs) <= 9, "Too many inputs."
         else:
             input_ids = inputs
 
@@ -1426,15 +1440,15 @@ class TFMobileBertForTokenClassification(TFMobileBertPreTrainedModel, TFTokenCla
     @add_start_docstrings_to_callable(MOBILEBERT_INPUTS_DOCSTRING)
     def call(
         self,
-        input_ids=None,
+        inputs=None,
         attention_mask=None,
         token_type_ids=None,
         position_ids=None,
         head_mask=None,
         inputs_embeds=None,
-        labels=None,
         output_attentions=None,
         output_hidden_states=None,
+        labels=None,
         training=False,
     ):
         r"""
@@ -1471,8 +1485,15 @@ class TFMobileBertForTokenClassification(TFMobileBertPreTrainedModel, TFTokenCla
         loss, scores = outputs[:2]
 
         """
+        if isinstance(inputs, (tuple, list)):
+            labels = inputs[8] if len(inputs) > 8 else labels
+            if len(inputs) > 8:
+                inputs = inputs[:8]
+        elif isinstance(inputs, (dict, BatchEncoding)):
+            labels = inputs.pop("labels", labels)
+
         outputs = self.mobilebert(
-            input_ids,
+            inputs,
             attention_mask=attention_mask,
             token_type_ids=token_type_ids,
             position_ids=position_ids,

src/transformers/modeling_tf_roberta.py

@@ -33,6 +33,7 @@ from .modeling_tf_utils import (
     keras_serializable,
     shape_list,
 )
+from .tokenization_utils_base import BatchEncoding
 
 
 logger = logging.getLogger(__name__)
@@ -359,15 +360,15 @@ class TFRobertaForSequenceClassification(TFRobertaPreTrainedModel, TFSequenceCla
     @add_start_docstrings_to_callable(ROBERTA_INPUTS_DOCSTRING)
     def call(
         self,
-        input_ids=None,
+        inputs=None,
         attention_mask=None,
         token_type_ids=None,
         position_ids=None,
         head_mask=None,
         inputs_embeds=None,
-        labels=None,
         output_attentions=None,
         output_hidden_states=None,
+        labels=None,
         training=False,
     ):
         r"""
@@ -400,8 +401,15 @@ class TFRobertaForSequenceClassification(TFRobertaPreTrainedModel, TFSequenceCla
         loss, logits = outputs[:2]
 
         """
+        if isinstance(inputs, (tuple, list)):
+            labels = inputs[8] if len(inputs) > 8 else labels
+            if len(inputs) > 8:
+                inputs = inputs[:8]
+        elif isinstance(inputs, (dict, BatchEncoding)):
+            labels = inputs.pop("labels", labels)
+
         outputs = self.roberta(
-            input_ids,
+            inputs,
             attention_mask=attention_mask,
             token_type_ids=token_type_ids,
             position_ids=position_ids,
@@ -457,9 +465,9 @@ class TFRobertaForMultipleChoice(TFRobertaPreTrainedModel, TFMultipleChoiceLoss)
         position_ids=None,
         head_mask=None,
         inputs_embeds=None,
-        labels=None,
         output_attentions=None,
         output_hidden_states=None,
+        labels=None,
         training=False,
     ):
         r"""
@@ -509,15 +517,21 @@ class TFRobertaForMultipleChoice(TFRobertaPreTrainedModel, TFMultipleChoiceLoss)
             position_ids = inputs[3] if len(inputs) > 3 else position_ids
             head_mask = inputs[4] if len(inputs) > 4 else head_mask
             inputs_embeds = inputs[5] if len(inputs) > 5 else inputs_embeds
-            assert len(inputs) <= 6, "Too many inputs."
-        elif isinstance(inputs, dict):
+            output_attentions = inputs[6] if len(inputs) > 6 else output_attentions
+            output_hidden_states = inputs[7] if len(inputs) > 7 else output_hidden_states
+            labels = inputs[8] if len(inputs) > 8 else labels
+            assert len(inputs) <= 9, "Too many inputs."
+        elif isinstance(inputs, (dict, BatchEncoding)):
             input_ids = inputs.get("input_ids")
             attention_mask = inputs.get("attention_mask", attention_mask)
             token_type_ids = inputs.get("token_type_ids", token_type_ids)
             position_ids = inputs.get("position_ids", position_ids)
             head_mask = inputs.get("head_mask", head_mask)
             inputs_embeds = inputs.get("inputs_embeds", inputs_embeds)
-            assert len(inputs) <= 6, "Too many inputs."
+            output_attentions = inputs.get("output_attentions", output_attentions)
+            output_hidden_states = inputs.get("output_hidden_states", output_attentions)
+            labels = inputs.get("labels", labels)
+            assert len(inputs) <= 9, "Too many inputs."
         else:
             input_ids = inputs
 
@@ -580,15 +594,15 @@ class TFRobertaForTokenClassification(TFRobertaPreTrainedModel, TFTokenClassific
     @add_start_docstrings_to_callable(ROBERTA_INPUTS_DOCSTRING)
     def call(
         self,
-        input_ids=None,
+        inputs=None,
         attention_mask=None,
         token_type_ids=None,
         position_ids=None,
         head_mask=None,
         inputs_embeds=None,
-        labels=None,
         output_attentions=None,
         output_hidden_states=None,
+        labels=None,
         training=False,
     ):
         r"""
@@ -625,8 +639,15 @@ class TFRobertaForTokenClassification(TFRobertaPreTrainedModel, TFTokenClassific
         loss, scores = outputs[:2]
 
         """
+        if isinstance(inputs, (tuple, list)):
+            labels = inputs[8] if len(inputs) > 8 else labels
+            if len(inputs) > 8:
+                inputs = inputs[:8]
+        elif isinstance(inputs, (dict, BatchEncoding)):
+            labels = inputs.pop("labels", labels)
+
         outputs = self.roberta(
-            input_ids,
+            inputs,
             attention_mask=attention_mask,
             token_type_ids=token_type_ids,
             position_ids=position_ids,
@@ -668,19 +689,16 @@ class TFRobertaForQuestionAnswering(TFRobertaPreTrainedModel, TFQuestionAnswerin
     @add_start_docstrings_to_callable(ROBERTA_INPUTS_DOCSTRING)
     def call(
         self,
-        input_ids=None,
+        inputs=None,
         attention_mask=None,
         token_type_ids=None,
         position_ids=None,
         head_mask=None,
         inputs_embeds=None,
-        start_positions=None,
-        end_positions=None,
-        cls_index=None,
-        p_mask=None,
-        is_impossible=None,
         output_attentions=None,
         output_hidden_states=None,
+        start_positions=None,
+        end_positions=None,
         training=False,
     ):
         r"""
@@ -729,8 +747,17 @@ class TFRobertaForQuestionAnswering(TFRobertaPreTrainedModel, TFQuestionAnswerin
         answer = ' '.join(all_tokens[tf.math.argmax(start_scores, 1)[0] : tf.math.argmax(end_scores, 1)[0]+1])
 
         """
+        if isinstance(inputs, (tuple, list)):
+            start_positions = inputs[8] if len(inputs) > 8 else start_positions
+            end_positions = inputs[9] if len(inputs) > 9 else end_positions
+            if len(inputs) > 8:
+                inputs = inputs[:8]
+        elif isinstance(inputs, (dict, BatchEncoding)):
+            start_positions = inputs.pop("start_positions", start_positions)
+            end_positions = inputs.pop("end_positions", start_positions)
+
         outputs = self.roberta(
-            input_ids,
+            inputs,
             attention_mask=attention_mask,
             token_type_ids=token_type_ids,
             position_ids=position_ids,

src/transformers/modeling_tf_xlm.py

@@ -759,7 +759,7 @@ class TFXLMForSequenceClassification(TFXLMPreTrainedModel, TFSequenceClassificat
     @add_start_docstrings_to_callable(XLM_INPUTS_DOCSTRING)
     def call(
         self,
-        input_ids,
+        inputs=None,
         attention_mask=None,
         langs=None,
         token_type_ids=None,
@@ -768,9 +768,9 @@ class TFXLMForSequenceClassification(TFXLMPreTrainedModel, TFSequenceClassificat
         cache=None,
         head_mask=None,
         inputs_embeds=None,
-        labels=None,
         output_attentions=None,
         output_hidden_states=None,
+        labels=None,
         training=False,
     ):
         r"""
@@ -809,8 +809,15 @@ class TFXLMForSequenceClassification(TFXLMPreTrainedModel, TFSequenceClassificat
         loss, logits = outputs[:2]
 
         """
+        if isinstance(inputs, (tuple, list)):
+            labels = inputs[11] if len(inputs) > 11 else labels
+            if len(inputs) > 11:
+                inputs = inputs[:11]
+        elif isinstance(inputs, (dict, BatchEncoding)):
+            labels = inputs.pop("labels", labels)
+
         transformer_outputs = self.transformer(
-            input_ids,
+            inputs,
             attention_mask=attention_mask,
             langs=langs,
             token_type_ids=token_type_ids,
@@ -1090,7 +1097,7 @@ class TFXLMForQuestionAnsweringSimple(TFXLMPreTrainedModel, TFQuestionAnsweringL
     @add_start_docstrings_to_callable(XLM_INPUTS_DOCSTRING)
     def call(
         self,
-        input_ids=None,
+        inputs=None,
         attention_mask=None,
         langs=None,
         token_type_ids=None,
@@ -1099,13 +1106,10 @@ class TFXLMForQuestionAnsweringSimple(TFXLMPreTrainedModel, TFQuestionAnsweringL
         cache=None,
         head_mask=None,
         inputs_embeds=None,
-        start_positions=None,
-        end_positions=None,
-        cls_index=None,
-        p_mask=None,
-        is_impossible=None,
         output_attentions=None,
         output_hidden_states=None,
+        start_positions=None,
+        end_positions=None,
         training=False,
     ):
         r"""
@@ -1151,9 +1155,17 @@ class TFXLMForQuestionAnsweringSimple(TFXLMPreTrainedModel, TFQuestionAnsweringL
         answer = ' '.join(all_tokens[tf.math.argmax(start_scores, 1)[0] : tf.math.argmax(end_scores, 1)[0]+1])
 
         """
+        if isinstance(inputs, (tuple, list)):
+            start_positions = inputs[11] if len(inputs) > 11 else start_positions
+            end_positions = inputs[12] if len(inputs) > 12 else end_positions
+            if len(inputs) > 11:
+                inputs = inputs[:11]
+        elif isinstance(inputs, (dict, BatchEncoding)):
+            start_positions = inputs.pop("start_positions", start_positions)
+            end_positions = inputs.pop("end_positions", start_positions)
 
         transformer_outputs = self.transformer(
-            input_ids,
+            inputs,
             attention_mask=attention_mask,
             langs=langs,
             token_type_ids=token_type_ids,

src/transformers/modeling_tf_xlnet.py

@@ -988,7 +988,7 @@ class TFXLNetForSequenceClassification(TFXLNetPreTrainedModel, TFSequenceClassif
     @add_start_docstrings_to_callable(XLNET_INPUTS_DOCSTRING)
     def call(
         self,
-        input_ids=None,
+        inputs=None,
         attention_mask=None,
         mems=None,
         perm_mask=None,
@@ -998,9 +998,9 @@ class TFXLNetForSequenceClassification(TFXLNetPreTrainedModel, TFSequenceClassif
         head_mask=None,
         inputs_embeds=None,
         use_cache=True,
-        labels=None,
         output_attentions=None,
         output_hidden_states=None,
+        labels=None,
         training=False,
     ):
         r"""
@@ -1043,8 +1043,15 @@ class TFXLNetForSequenceClassification(TFXLNetPreTrainedModel, TFSequenceClassif
         loss, logits = outputs[:2]
 
         """
+        if isinstance(inputs, (tuple, list)):
+            labels = inputs[12] if len(inputs) > 12 else labels
+            if len(inputs) > 12:
+                inputs = inputs[:12]
+        elif isinstance(inputs, (dict, BatchEncoding)):
+            labels = inputs.pop("labels", labels)
+
         transformer_outputs = self.transformer(
-            input_ids,
+            inputs,
             attention_mask=attention_mask,
             mems=mems,
             perm_mask=perm_mask,
@@ -1100,7 +1107,7 @@ class TFXLNetForMultipleChoice(TFXLNetPreTrainedModel, TFMultipleChoiceLoss):
     @add_start_docstrings_to_callable(XLNET_INPUTS_DOCSTRING)
     def call(
         self,
-        inputs,
+        inputs=None,
         token_type_ids=None,
         input_mask=None,
         attention_mask=None,
@@ -1110,9 +1117,9 @@ class TFXLNetForMultipleChoice(TFXLNetPreTrainedModel, TFMultipleChoiceLoss):
         head_mask=None,
         inputs_embeds=None,
         use_cache=True,
-        labels=None,
         output_attentions=None,
         output_hidden_states=None,
+        labels=None,
         training=False,
     ):
         r"""
@@ -1168,7 +1175,8 @@ class TFXLNetForMultipleChoice(TFXLNetPreTrainedModel, TFMultipleChoiceLoss):
             use_cache = inputs[9] if len(inputs) > 9 else use_cache
             output_attentions = inputs[10] if len(inputs) > 10 else output_attentions
             output_hidden_states = inputs[11] if len(inputs) > 11 else output_hidden_states
-            assert len(inputs) <= 12, "Too many inputs."
+            labels = inputs[12] if len(inputs) > 12 else labels
+            assert len(inputs) <= 13, "Too many inputs."
         elif isinstance(inputs, (dict, BatchEncoding)):
             input_ids = inputs.get("input_ids")
             attention_mask = inputs.get("attention_mask", attention_mask)
@@ -1181,8 +1189,9 @@ class TFXLNetForMultipleChoice(TFXLNetPreTrainedModel, TFMultipleChoiceLoss):
             inputs_embeds = inputs.get("inputs_embeds", inputs_embeds)
             use_cache = inputs.get("use_cache", use_cache)
             output_attentions = inputs.get("output_attentions", output_attentions)
-            output_hidden_states = inputs.get("output_hidden_states", output_attentions)
-            assert len(inputs) <= 12, "Too many inputs."
+            output_hidden_states = inputs.get("output_hidden_states", output_hidden_states)
+            labels = inputs.get("labels", labels)
+            assert len(inputs) <= 13, "Too many inputs."
         else:
             input_ids = inputs
 
@@ -1197,6 +1206,11 @@ class TFXLNetForMultipleChoice(TFXLNetPreTrainedModel, TFMultipleChoiceLoss):
         flat_attention_mask = tf.reshape(attention_mask, (-1, seq_length)) if attention_mask is not None else None
         flat_token_type_ids = tf.reshape(token_type_ids, (-1, seq_length)) if token_type_ids is not None else None
         flat_input_mask = tf.reshape(input_mask, (-1, seq_length)) if input_mask is not None else None
+        flat_inputs_embeds = (
+            tf.reshape(inputs_embeds, (-1, seq_length, shape_list(inputs_embeds)[3]))
+            if inputs_embeds is not None
+            else None
+        )
 
         flat_inputs = [
             flat_input_ids,
@@ -1207,7 +1221,7 @@ class TFXLNetForMultipleChoice(TFXLNetPreTrainedModel, TFMultipleChoiceLoss):
             flat_token_type_ids,
             flat_input_mask,
             head_mask,
-            inputs_embeds,
+            flat_inputs_embeds,
             use_cache,
             output_attentions,
             output_hidden_states,
@@ -1245,7 +1259,7 @@ class TFXLNetForTokenClassification(TFXLNetPreTrainedModel, TFTokenClassificatio
 
     def call(
         self,
-        input_ids=None,
+        inputs=None,
         attention_mask=None,
         mems=None,
         perm_mask=None,
@@ -1255,9 +1269,9 @@ class TFXLNetForTokenClassification(TFXLNetPreTrainedModel, TFTokenClassificatio
         head_mask=None,
         inputs_embeds=None,
         use_cache=True,
-        labels=None,
         output_attentions=None,
         output_hidden_states=None,
+        labels=None,
         training=False,
     ):
         r"""
@@ -1298,8 +1312,15 @@ class TFXLNetForTokenClassification(TFXLNetPreTrainedModel, TFTokenClassificatio
         loss, scores = outputs[:2]
 
         """
+        if isinstance(inputs, (tuple, list)):
+            labels = inputs[12] if len(inputs) > 12 else labels
+            if len(inputs) > 12:
+                inputs = inputs[:12]
+        elif isinstance(inputs, (dict, BatchEncoding)):
+            labels = inputs.pop("labels", labels)
+
         transformer_outputs = self.transformer(
-            input_ids,
+            inputs,
             attention_mask=attention_mask,
             mems=mems,
             perm_mask=perm_mask,
@@ -1342,7 +1363,7 @@ class TFXLNetForQuestionAnsweringSimple(TFXLNetPreTrainedModel, TFQuestionAnswer
     @add_start_docstrings_to_callable(XLNET_INPUTS_DOCSTRING)
     def call(
         self,
-        input_ids=None,
+        inputs=None,
         attention_mask=None,
         mems=None,
         perm_mask=None,
@@ -1352,13 +1373,10 @@ class TFXLNetForQuestionAnsweringSimple(TFXLNetPreTrainedModel, TFQuestionAnswer
         head_mask=None,
         inputs_embeds=None,
         use_cache=True,
-        start_positions=None,
-        end_positions=None,
-        cls_index=None,
-        p_mask=None,
-        is_impossible=None,
         output_attentions=None,
         output_hidden_states=None,
+        start_positions=None,
+        end_positions=None,
         training=False,
     ):
         r"""
@@ -1410,8 +1428,17 @@ class TFXLNetForQuestionAnsweringSimple(TFXLNetPreTrainedModel, TFQuestionAnswer
         answer = ' '.join(all_tokens[tf.math.argmax(start_scores, 1)[0] : tf.math.argmax(end_scores, 1)[0]+1])
 
         """
+        if isinstance(inputs, (tuple, list)):
+            start_positions = inputs[12] if len(inputs) > 12 else start_positions
+            end_positions = inputs[13] if len(inputs) > 13 else end_positions
+            if len(inputs) > 12:
+                inputs = inputs[:12]
+        elif isinstance(inputs, (dict, BatchEncoding)):
+            start_positions = inputs.pop("start_positions", start_positions)
+            end_positions = inputs.pop("end_positions", start_positions)
+
         transformer_outputs = self.transformer(
-            input_ids,
+            inputs,
             attention_mask=attention_mask,
             mems=mems,
             perm_mask=perm_mask,

tests/test_modeling_tf_common.py

@@ -15,6 +15,7 @@
 
 
 import copy
+import inspect
 import os
 import random
 import tempfile
@@ -35,6 +36,9 @@ if is_tf_available():
         TFAdaptiveEmbedding,
         TFSharedEmbeddings,
         TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
+        TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
+        TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
+        TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
     )
 
     if _tf_gpu_memory_limit is not None:
@@ -71,14 +75,25 @@ class TFModelTesterMixin:
     test_resize_embeddings = True
     is_encoder_decoder = False
 
-    def _prepare_for_class(self, inputs_dict, model_class):
+    def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
         if model_class in TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING.values():
-            return {
+            inputs_dict = {
                 k: tf.tile(tf.expand_dims(v, 1), (1, self.model_tester.num_choices, 1))
                 if isinstance(v, tf.Tensor) and v.ndim != 0
                 else v
                 for k, v in inputs_dict.items()
             }
+
+        if return_labels:
+            if model_class in TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING.values():
+                inputs_dict["labels"] = tf.ones(self.model_tester.batch_size)
+            elif model_class in TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING.values():
+                inputs_dict["start_positions"] = tf.zeros(self.model_tester.batch_size)
+                inputs_dict["end_positions"] = tf.zeros(self.model_tester.batch_size)
+            elif model_class in TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING.values():
+                inputs_dict["labels"] = tf.zeros(self.model_tester.batch_size)
+            elif model_class in TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.values():
+                inputs_dict["labels"] = tf.zeros((self.model_tester.batch_size, self.model_tester.seq_length))
         return inputs_dict
 
     def test_initialization(self):
@@ -572,6 +587,51 @@ class TFModelTesterMixin:
             generated_ids = output_tokens[:, input_ids.shape[-1] :]
             self.assertFalse(self._check_match_tokens(generated_ids.numpy().tolist(), bad_words_ids))
 
+    def test_loss_computation(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        for model_class in self.all_model_classes:
+            model = model_class(config)
+            if getattr(model, "compute_loss", None):
+                # The number of elements in the loss should be the same as the number of elements in the label
+                prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True)
+                added_label = prepared_for_class[list(prepared_for_class.keys() - inputs_dict.keys())[0]]
+                loss_size = tf.size(added_label)
+
+                # Test that model correctly compute the loss with kwargs
+                prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True)
+                input_ids = prepared_for_class.pop("input_ids")
+                loss = model(input_ids, **prepared_for_class)[0]
+                self.assertEqual(loss.shape, [loss_size])
+
+                # Test that model correctly compute the loss with a dict
+                prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True)
+                loss = model(prepared_for_class)[0]
+                self.assertEqual(loss.shape, [loss_size])
+
+                # Test that model correctly compute the loss with a tuple
+                prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True)
+
+                # Get keys that were added with the _prepare_for_class function
+                label_keys = prepared_for_class.keys() - inputs_dict.keys()
+                signature = inspect.getfullargspec(model.call)[0]
+
+                # Create a dictionary holding the location of the tensors in the tuple
+                tuple_index_mapping = {1: "input_ids"}
+                for label_key in label_keys:
+                    label_key_index = signature.index(label_key)
+                    tuple_index_mapping[label_key_index] = label_key
+                sorted_tuple_index_mapping = sorted(tuple_index_mapping.items())
+
+                # Initialize a list with None, update the values and convert to a tuple
+                list_input = [None] * sorted_tuple_index_mapping[-1][0]
+                for index, value in sorted_tuple_index_mapping:
+                    list_input[index - 1] = prepared_for_class[value]
+                tuple_input = tuple(list_input)
+
+                # Send to model
+                loss = model(tuple_input)[0]
+                self.assertEqual(loss.shape, [loss_size])
+
     def _generate_random_bad_tokens(self, num_bad_tokens, model):
         # special tokens cannot be bad tokens
         special_tokens = []

tests/test_modeling_tf_distilbert.py

@@ -24,11 +24,14 @@ from .utils import require_tf
 
 
 if is_tf_available():
+    import tensorflow as tf
     from transformers.modeling_tf_distilbert import (
         TFDistilBertModel,
         TFDistilBertForMaskedLM,
         TFDistilBertForQuestionAnswering,
         TFDistilBertForSequenceClassification,
+        TFDistilBertForTokenClassification,
+        TFDistilBertForMultipleChoice,
     )
 
 
@@ -147,6 +150,35 @@ class TFDistilBertModelTester:
         }
         self.parent.assertListEqual(list(result["logits"].shape), [self.batch_size, self.num_labels])
 
+    def create_and_check_distilbert_for_multiple_choice(
+        self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
+    ):
+        config.num_choices = self.num_choices
+        model = TFDistilBertForMultipleChoice(config)
+        multiple_choice_inputs_ids = tf.tile(tf.expand_dims(input_ids, 1), (1, self.num_choices, 1))
+        multiple_choice_input_mask = tf.tile(tf.expand_dims(input_mask, 1), (1, self.num_choices, 1))
+        inputs = {
+            "input_ids": multiple_choice_inputs_ids,
+            "attention_mask": multiple_choice_input_mask,
+        }
+        (logits,) = model(inputs)
+        result = {
+            "logits": logits.numpy(),
+        }
+        self.parent.assertListEqual(list(result["logits"].shape), [self.batch_size, self.num_choices])
+
+    def create_and_check_distilbert_for_token_classification(
+        self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
+    ):
+        config.num_labels = self.num_labels
+        model = TFDistilBertForTokenClassification(config)
+        inputs = {"input_ids": input_ids, "attention_mask": input_mask}
+        (logits,) = model(inputs)
+        result = {
+            "logits": logits.numpy(),
+        }
+        self.parent.assertListEqual(list(result["logits"].shape), [self.batch_size, self.seq_length, self.num_labels])
+
     def prepare_config_and_inputs_for_common(self):
         config_and_inputs = self.prepare_config_and_inputs()
         (config, input_ids, input_mask, sequence_labels, token_labels, choice_labels) = config_and_inputs
@@ -163,6 +195,8 @@ class TFDistilBertModelTest(TFModelTesterMixin, unittest.TestCase):
             TFDistilBertForMaskedLM,
             TFDistilBertForQuestionAnswering,
             TFDistilBertForSequenceClassification,
+            TFDistilBertForTokenClassification,
+            TFDistilBertForMultipleChoice,
         )
         if is_tf_available()
         else None
@@ -194,6 +228,14 @@ class TFDistilBertModelTest(TFModelTesterMixin, unittest.TestCase):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_distilbert_for_sequence_classification(*config_and_inputs)
 
+    def test_for_multiple_choice(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_distilbert_for_multiple_choice(*config_and_inputs)
+
+    def test_for_token_classification(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_distilbert_for_token_classification(*config_and_inputs)
+
     # @slow
     # def test_model_from_pretrained(self):
     #     for model_name in list(DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:

tests/test_modeling_tf_electra.py

@@ -29,6 +29,7 @@ if is_tf_available():
         TFElectraForMaskedLM,
         TFElectraForPreTraining,
         TFElectraForTokenClassification,
+        TFElectraForQuestionAnswering,
     )
 
 
@@ -137,6 +138,19 @@ class TFElectraModelTester:
         }
         self.parent.assertListEqual(list(result["prediction_scores"].shape), [self.batch_size, self.seq_length])
 
+    def create_and_check_electra_for_question_answering(
+        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+    ):
+        model = TFElectraForQuestionAnswering(config=config)
+        inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
+        start_logits, end_logits = model(inputs)
+        result = {
+            "start_logits": start_logits.numpy(),
+            "end_logits": end_logits.numpy(),
+        }
+        self.parent.assertListEqual(list(result["start_logits"].shape), [self.batch_size, self.seq_length])
+        self.parent.assertListEqual(list(result["end_logits"].shape), [self.batch_size, self.seq_length])
+
     def create_and_check_electra_for_token_classification(
         self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
     ):
@@ -192,6 +206,10 @@ class TFElectraModelTest(TFModelTesterMixin, unittest.TestCase):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_electra_for_pretraining(*config_and_inputs)
 
+    def test_for_question_answering(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_electra_for_question_answering(*config_and_inputs)
+
     def test_for_token_classification(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_electra_for_token_classification(*config_and_inputs)

tests/test_modeling_tf_roberta.py

@@ -32,6 +32,7 @@ if is_tf_available():
         TFRobertaForSequenceClassification,
         TFRobertaForTokenClassification,
         TFRobertaForQuestionAnswering,
+        TFRobertaForMultipleChoice,
         TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
     )
 
@@ -154,6 +155,25 @@ class TFRobertaModelTester:
         self.parent.assertListEqual(list(result["start_logits"].shape), [self.batch_size, self.seq_length])
         self.parent.assertListEqual(list(result["end_logits"].shape), [self.batch_size, self.seq_length])
 
+    def create_and_check_roberta_for_multiple_choice(
+        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+    ):
+        config.num_choices = self.num_choices
+        model = TFRobertaForMultipleChoice(config=config)
+        multiple_choice_inputs_ids = tf.tile(tf.expand_dims(input_ids, 1), (1, self.num_choices, 1))
+        multiple_choice_input_mask = tf.tile(tf.expand_dims(input_mask, 1), (1, self.num_choices, 1))
+        multiple_choice_token_type_ids = tf.tile(tf.expand_dims(token_type_ids, 1), (1, self.num_choices, 1))
+        inputs = {
+            "input_ids": multiple_choice_inputs_ids,
+            "attention_mask": multiple_choice_input_mask,
+            "token_type_ids": multiple_choice_token_type_ids,
+        }
+        (logits,) = model(inputs)
+        result = {
+            "logits": logits.numpy(),
+        }
+        self.parent.assertListEqual(list(result["logits"].shape), [self.batch_size, self.num_choices])
+
     def prepare_config_and_inputs_for_common(self):
         config_and_inputs = self.prepare_config_and_inputs()
         (
@@ -207,6 +227,10 @@ class TFRobertaModelTest(TFModelTesterMixin, unittest.TestCase):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_roberta_for_question_answering(*config_and_inputs)
 
+    def test_for_multiple_choice(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_roberta_for_multiple_choice(*config_and_inputs)
+
     @slow
     def test_model_from_pretrained(self):
         for model_name in TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:

tests/test_modeling_tf_xlnet.py

@@ -33,6 +33,7 @@ if is_tf_available():
         TFXLNetForSequenceClassification,
         TFXLNetForTokenClassification,
         TFXLNetForQuestionAnsweringSimple,
+        TFXLNetForMultipleChoice,
         TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
     )
 
@@ -66,6 +67,7 @@ class TFXLNetModelTester:
         self.bos_token_id = 1
         self.eos_token_id = 2
         self.pad_token_id = 5
+        self.num_choices = 4
 
     def prepare_config_and_inputs(self):
         input_ids_1 = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
@@ -316,6 +318,36 @@ class TFXLNetModelTester:
             [[self.seq_length, self.batch_size, self.hidden_size]] * self.num_hidden_layers,
         )
 
+    def create_and_check_xlnet_for_multiple_choice(
+        self,
+        config,
+        input_ids_1,
+        input_ids_2,
+        input_ids_q,
+        perm_mask,
+        input_mask,
+        target_mapping,
+        segment_ids,
+        lm_labels,
+        sequence_labels,
+        is_impossible_labels,
+    ):
+        config.num_choices = self.num_choices
+        model = TFXLNetForMultipleChoice(config=config)
+        multiple_choice_inputs_ids = tf.tile(tf.expand_dims(input_ids_1, 1), (1, self.num_choices, 1))
+        multiple_choice_input_mask = tf.tile(tf.expand_dims(input_mask, 1), (1, self.num_choices, 1))
+        multiple_choice_token_type_ids = tf.tile(tf.expand_dims(segment_ids, 1), (1, self.num_choices, 1))
+        inputs = {
+            "input_ids": multiple_choice_inputs_ids,
+            "attention_mask": multiple_choice_input_mask,
+            "token_type_ids": multiple_choice_token_type_ids,
+        }
+        (logits,) = model(inputs)
+        result = {
+            "logits": logits.numpy(),
+        }
+        self.parent.assertListEqual(list(result["logits"].shape), [self.batch_size, self.num_choices])
+
     def prepare_config_and_inputs_for_common(self):
         config_and_inputs = self.prepare_config_and_inputs()
         (
@@ -345,6 +377,7 @@ class TFXLNetModelTest(TFModelTesterMixin, unittest.TestCase):
             TFXLNetForSequenceClassification,
             TFXLNetForTokenClassification,
             TFXLNetForQuestionAnsweringSimple,
+            TFXLNetForMultipleChoice,
         )
         if is_tf_available()
         else ()
@@ -385,6 +418,10 @@ class TFXLNetModelTest(TFModelTesterMixin, unittest.TestCase):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_xlnet_qa(*config_and_inputs)
 
+    def test_xlnet_for_multiple_choice(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_xlnet_for_multiple_choice(*config_and_inputs)
+
     @slow
     def test_model_from_pretrained(self):
         for model_name in TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: