Remove unused token_type_ids in MPNet (#9564)

* Add warning

* Remove unused import

* Fix missing call

* Fix missing call

* Completely remove token_type_ids

* Apply style

* Remove unused import

* Update src/transformers/models/mpnet/modeling_tf_mpnet.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

src/transformers/models/mpnet/modeling_mpnet.py

@@ -597,7 +597,6 @@ class MPNetForMaskedLM(MPNetPreTrainedModel):
         self,
         input_ids=None,
         attention_mask=None,
-        token_type_ids=None,
         position_ids=None,
         head_mask=None,
         inputs_embeds=None,
@@ -617,7 +616,6 @@ class MPNetForMaskedLM(MPNetPreTrainedModel):
         outputs = self.mpnet(
             input_ids,
             attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
             position_ids=position_ids,
             head_mask=head_mask,
             inputs_embeds=inputs_embeds,
@@ -701,7 +699,6 @@ class MPNetForSequenceClassification(MPNetPreTrainedModel):
         self,
         input_ids=None,
         attention_mask=None,
-        token_type_ids=None,
         position_ids=None,
         head_mask=None,
         inputs_embeds=None,
@@ -716,12 +713,12 @@ class MPNetForSequenceClassification(MPNetPreTrainedModel):
             config.num_labels - 1]`. If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
             If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
         """
+
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
         outputs = self.mpnet(
             input_ids,
             attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
             position_ids=position_ids,
             head_mask=head_mask,
             inputs_embeds=inputs_embeds,
@@ -783,7 +780,6 @@ class MPNetForMultipleChoice(MPNetPreTrainedModel):
     def forward(
         self,
         input_ids=None,
-        token_type_ids=None,
         attention_mask=None,
         position_ids=None,
         head_mask=None,
@@ -799,12 +795,12 @@ class MPNetForMultipleChoice(MPNetPreTrainedModel):
             num_choices-1]`` where :obj:`num_choices` is the size of the second dimension of the input tensors. (See
             :obj:`input_ids` above)
         """
+
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
         num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
 
         flat_input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
         flat_position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
-        flat_token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
         flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
         flat_inputs_embeds = (
             inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
@@ -815,7 +811,6 @@ class MPNetForMultipleChoice(MPNetPreTrainedModel):
         outputs = self.mpnet(
             flat_input_ids,
             position_ids=flat_position_ids,
-            token_type_ids=flat_token_type_ids,
             attention_mask=flat_attention_mask,
             head_mask=head_mask,
             inputs_embeds=flat_inputs_embeds,
@@ -878,7 +873,6 @@ class MPNetForTokenClassification(MPNetPreTrainedModel):
         self,
         input_ids=None,
         attention_mask=None,
-        token_type_ids=None,
         position_ids=None,
         head_mask=None,
         inputs_embeds=None,
@@ -892,12 +886,12 @@ class MPNetForTokenClassification(MPNetPreTrainedModel):
             Labels for computing the token classification loss. Indices should be in ``[0, ..., config.num_labels -
             1]``.
         """
+
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
         outputs = self.mpnet(
             input_ids,
             attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
             position_ids=position_ids,
             head_mask=head_mask,
             inputs_embeds=inputs_embeds,
@@ -987,7 +981,6 @@ class MPNetForQuestionAnswering(MPNetPreTrainedModel):
         self,
         input_ids=None,
         attention_mask=None,
-        token_type_ids=None,
         position_ids=None,
         head_mask=None,
         inputs_embeds=None,
@@ -1013,7 +1006,6 @@ class MPNetForQuestionAnswering(MPNetPreTrainedModel):
         outputs = self.mpnet(
             input_ids,
             attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
             position_ids=position_ids,
             head_mask=head_mask,
             inputs_embeds=inputs_embeds,

src/transformers/models/mpnet/modeling_tf_mpnet.py

@@ -147,7 +147,6 @@ class TFMPNetEmbeddings(tf.keras.layers.Layer):
         self,
         input_ids=None,
         position_ids=None,
-        token_type_ids=None,
         inputs_embeds=None,
         mode="embedding",
         training=False,
@@ -156,7 +155,7 @@ class TFMPNetEmbeddings(tf.keras.layers.Layer):
         Get token embeddings of inputs
 
         Args:
-            inputs: list of three int64 tensors with shape [batch_size, length]: (input_ids, position_ids, token_type_ids)
+            inputs: list of two int64 tensors with shape [batch_size, length]: (input_ids, position_ids)
             mode: string, a valid value is one of "embedding" and "linear"
 
         Returns:
@@ -169,13 +168,13 @@ class TFMPNetEmbeddings(tf.keras.layers.Layer):
             https://github.com/tensorflow/models/blob/a009f4fb9d2fc4949e32192a944688925ef78659/official/transformer/v2/embedding_layer.py#L24
         """
         if mode == "embedding":
-            return self._embedding(input_ids, position_ids, token_type_ids, inputs_embeds, training=training)
+            return self._embedding(input_ids, position_ids, inputs_embeds, training=training)
         elif mode == "linear":
             return self._linear(input_ids)
         else:
             raise ValueError("mode {} is not valid.".format(mode))
 
-    def _embedding(self, input_ids, position_ids, token_type_ids, inputs_embeds, training=False):
+    def _embedding(self, input_ids, position_ids, inputs_embeds, training=False):
         """Applies embedding based on inputs tensor."""
         assert not (input_ids is None and inputs_embeds is None)
 
@@ -552,12 +551,10 @@ class TFMPNetMainLayer(tf.keras.layers.Layer):
         """
         raise NotImplementedError
 
-    # Copied from transformers.models.bert.modeling_tf_bert.TFBertMainLayer.call
     def call(
         self,
         input_ids=None,
         attention_mask=None,
-        token_type_ids=None,
         position_ids=None,
         head_mask=None,
         inputs_embeds=None,
@@ -572,7 +569,6 @@ class TFMPNetMainLayer(tf.keras.layers.Layer):
             config=self.config,
             input_ids=input_ids,
             attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
             position_ids=position_ids,
             head_mask=head_mask,
             inputs_embeds=inputs_embeds,
@@ -582,6 +578,7 @@ class TFMPNetMainLayer(tf.keras.layers.Layer):
             training=training,
             kwargs_call=kwargs,
         )
+
         if inputs["input_ids"] is not None and inputs["inputs_embeds"] is not None:
             raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
         elif inputs["input_ids"] is not None:
@@ -594,13 +591,9 @@ class TFMPNetMainLayer(tf.keras.layers.Layer):
         if inputs["attention_mask"] is None:
             inputs["attention_mask"] = tf.fill(input_shape, 1)
 
-        if inputs["token_type_ids"] is None:
-            inputs["token_type_ids"] = tf.fill(input_shape, 0)
-
         embedding_output = self.embeddings(
             inputs["input_ids"],
             inputs["position_ids"],
-            inputs["token_type_ids"],
             inputs["inputs_embeds"],
             training=inputs["training"],
         )
@@ -682,9 +675,9 @@ MPNET_START_DOCSTRING = r"""
 
         - a single Tensor with :obj:`input_ids` only and nothing else: :obj:`model(inputs_ids)`
         - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
-          :obj:`model([input_ids, attention_mask])` or :obj:`model([input_ids, attention_mask, token_type_ids])`
+          :obj:`model([input_ids, attention_mask])`
         - a dictionary with one or several input Tensors associated to the input names given in the docstring:
-          :obj:`model({"input_ids": input_ids, "token_type_ids": token_type_ids})`
+          :obj:`model({"input_ids": input_ids, "attention_mask": attention_mask})`
 
     Args:
         config (:class:`~transformers.MPNetConfig`): Model configuration class with all the parameters of the model.
@@ -710,14 +703,6 @@ MPNET_INPUTS_DOCSTRING = r"""
             - 0 for tokens that are **masked**.
 
             `What are attention masks? <../glossary.html#attention-mask>`__
-        token_type_ids (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`({0})`, `optional`):
-            Segment token indices to indicate first and second portions of the inputs. Indices are selected in ``[0,
-            1]``:
-
-            - 0 corresponds to a `sentence A` token,
-            - 1 corresponds to a `sentence B` token.
-
-            `What are token type IDs? <../glossary.html#token-type-ids>`__
         position_ids (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`({0})`, `optional`):
             Indices of positions of each input sequence tokens in the position embeddings. Selected in the range ``[0,
             config.max_position_embeddings - 1]``.
@@ -767,7 +752,6 @@ class TFMPNetModel(TFMPNetPreTrainedModel):
         self,
         input_ids=None,
         attention_mask=None,
-        token_type_ids=None,
         position_ids=None,
         head_mask=None,
         inputs_embeds=None,
@@ -782,7 +766,6 @@ class TFMPNetModel(TFMPNetPreTrainedModel):
             config=self.config,
             input_ids=input_ids,
             attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
             position_ids=position_ids,
             head_mask=head_mask,
             inputs_embeds=inputs_embeds,
@@ -795,7 +778,6 @@ class TFMPNetModel(TFMPNetPreTrainedModel):
         outputs = self.mpnet(
             input_ids=inputs["input_ids"],
             attention_mask=inputs["attention_mask"],
-            token_type_ids=inputs["token_type_ids"],
             position_ids=inputs["position_ids"],
             head_mask=inputs["head_mask"],
             inputs_embeds=inputs["inputs_embeds"],
@@ -895,7 +877,6 @@ class TFMPNetForMaskedLM(TFMPNetPreTrainedModel, TFMaskedLanguageModelingLoss):
         self,
         input_ids=None,
         attention_mask=None,
-        token_type_ids=None,
         position_ids=None,
         head_mask=None,
         inputs_embeds=None,
@@ -912,12 +893,12 @@ class TFMPNetForMaskedLM(TFMPNetPreTrainedModel, TFMaskedLanguageModelingLoss):
             config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are ignored
             (masked), the loss is only computed for the tokens with labels in ``[0, ..., config.vocab_size]``
         """
+
         inputs = input_processing(
             func=self.call,
             config=self.config,
             input_ids=input_ids,
             attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
             position_ids=position_ids,
             head_mask=head_mask,
             inputs_embeds=inputs_embeds,
@@ -931,7 +912,6 @@ class TFMPNetForMaskedLM(TFMPNetPreTrainedModel, TFMaskedLanguageModelingLoss):
         outputs = self.mpnet(
             inputs["input_ids"],
             attention_mask=inputs["attention_mask"],
-            token_type_ids=inputs["token_type_ids"],
             position_ids=inputs["position_ids"],
             head_mask=inputs["head_mask"],
             inputs_embeds=inputs["inputs_embeds"],
@@ -1018,7 +998,6 @@ class TFMPNetForSequenceClassification(TFMPNetPreTrainedModel, TFSequenceClassif
         self,
         input_ids=None,
         attention_mask=None,
-        token_type_ids=None,
         position_ids=None,
         head_mask=None,
         inputs_embeds=None,
@@ -1035,12 +1014,12 @@ class TFMPNetForSequenceClassification(TFMPNetPreTrainedModel, TFSequenceClassif
             config.num_labels - 1]`. If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
             If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
         """
+
         inputs = input_processing(
             func=self.call,
             config=self.config,
             input_ids=input_ids,
             attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
             position_ids=position_ids,
             head_mask=head_mask,
             inputs_embeds=inputs_embeds,
@@ -1054,7 +1033,6 @@ class TFMPNetForSequenceClassification(TFMPNetPreTrainedModel, TFSequenceClassif
         outputs = self.mpnet(
             inputs["input_ids"],
             attention_mask=inputs["attention_mask"],
-            token_type_ids=inputs["token_type_ids"],
             position_ids=inputs["position_ids"],
             head_mask=inputs["head_mask"],
             inputs_embeds=inputs["inputs_embeds"],
@@ -1126,7 +1104,6 @@ class TFMPNetForMultipleChoice(TFMPNetPreTrainedModel, TFMultipleChoiceLoss):
         self,
         input_ids=None,
         attention_mask=None,
-        token_type_ids=None,
         position_ids=None,
         head_mask=None,
         inputs_embeds=None,
@@ -1148,7 +1125,6 @@ class TFMPNetForMultipleChoice(TFMPNetPreTrainedModel, TFMultipleChoiceLoss):
             config=self.config,
             input_ids=input_ids,
             attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
             position_ids=position_ids,
             head_mask=head_mask,
             inputs_embeds=inputs_embeds,
@@ -1171,9 +1147,6 @@ class TFMPNetForMultipleChoice(TFMPNetPreTrainedModel, TFMultipleChoiceLoss):
         flat_attention_mask = (
             tf.reshape(inputs["attention_mask"], (-1, seq_length)) if inputs["attention_mask"] is not None else None
         )
-        flat_token_type_ids = (
-            tf.reshape(inputs["token_type_ids"], (-1, seq_length)) if inputs["token_type_ids"] is not None else None
-        )
         flat_position_ids = (
             tf.reshape(inputs["position_ids"], (-1, seq_length)) if inputs["position_ids"] is not None else None
         )
@@ -1185,7 +1158,6 @@ class TFMPNetForMultipleChoice(TFMPNetPreTrainedModel, TFMultipleChoiceLoss):
         outputs = self.mpnet(
             flat_input_ids,
             flat_attention_mask,
-            flat_token_type_ids,
             flat_position_ids,
             inputs["head_mask"],
             flat_inputs_embeds,
@@ -1264,7 +1236,6 @@ class TFMPNetForTokenClassification(TFMPNetPreTrainedModel, TFTokenClassificatio
         self,
         input_ids=None,
         attention_mask=None,
-        token_type_ids=None,
         position_ids=None,
         head_mask=None,
         inputs_embeds=None,
@@ -1280,12 +1251,12 @@ class TFMPNetForTokenClassification(TFMPNetPreTrainedModel, TFTokenClassificatio
             Labels for computing the token classification loss. Indices should be in ``[0, ..., config.num_labels -
             1]``.
         """
+
         inputs = input_processing(
             func=self.call,
             config=self.config,
             input_ids=input_ids,
             attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
             position_ids=position_ids,
             head_mask=head_mask,
             inputs_embeds=inputs_embeds,
@@ -1299,7 +1270,6 @@ class TFMPNetForTokenClassification(TFMPNetPreTrainedModel, TFTokenClassificatio
         outputs = self.mpnet(
             input_ids=inputs["input_ids"],
             attention_mask=inputs["attention_mask"],
-            token_type_ids=inputs["token_type_ids"],
             position_ids=inputs["position_ids"],
             head_mask=inputs["head_mask"],
             inputs_embeds=inputs["inputs_embeds"],
@@ -1365,7 +1335,6 @@ class TFMPNetForQuestionAnswering(TFMPNetPreTrainedModel, TFQuestionAnsweringLos
         self,
         input_ids=None,
         attention_mask=None,
-        token_type_ids=None,
         position_ids=None,
         head_mask=None,
         inputs_embeds=None,
@@ -1387,12 +1356,12 @@ class TFMPNetForQuestionAnswering(TFMPNetPreTrainedModel, TFQuestionAnsweringLos
             Positions are clamped to the length of the sequence (:obj:`sequence_length`). Position outside of the
             sequence are not taken into account for computing the loss.
         """
+
         inputs = input_processing(
             func=self.call,
             config=self.config,
             input_ids=input_ids,
             attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
             position_ids=position_ids,
             head_mask=head_mask,
             inputs_embeds=inputs_embeds,
@@ -1407,7 +1376,6 @@ class TFMPNetForQuestionAnswering(TFMPNetPreTrainedModel, TFQuestionAnsweringLos
         outputs = self.mpnet(
             inputs["input_ids"],
             attention_mask=inputs["attention_mask"],
-            token_type_ids=inputs["token_type_ids"],
             position_ids=inputs["position_ids"],
             head_mask=inputs["head_mask"],
             inputs_embeds=inputs["inputs_embeds"],

src/transformers/models/mpnet/tokenization_mpnet.py

@@ -122,6 +122,7 @@ class MPNetTokenizer(PreTrainedTokenizer):
     pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
     pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
     max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
+    model_input_names = ["attention_mask"]
 
     def __init__(
         self,

src/transformers/models/mpnet/tokenization_mpnet_fast.py

@@ -102,6 +102,7 @@ class MPNetTokenizerFast(PreTrainedTokenizerFast):
     pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
     max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     slow_tokenizer_class = MPNetTokenizer
+    model_input_names = ["attention_mask"]
 
     def __init__(
         self,