Add pretraining loss computation for TF Bert pretraining

src/transformers/modeling_tf_bert.py

@@ -89,6 +89,38 @@
 ]
 
 
+class TFBertPreTrainingLoss:
+    """
+    Loss function suitable for BERT-like pre-training, that is, the task of pretraining a language model by combining
+    NSP + MLM. .. note:: Any label of -100 will be ignored (along with the corresponding logits) in the loss
+    computation.
+    """
+
+    def compute_loss(self, labels, logits):
+        loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(
+            from_logits=True, reduction=tf.keras.losses.Reduction.NONE
+        )
+        # make sure only labels that are not equal to -100
+        # are taken into account as loss
+        masked_lm_active_loss = tf.not_equal(tf.reshape(labels["labels"], (-1,)), -100)
+        masked_lm_reduced_logits = tf.boolean_mask(
+            tf.reshape(logits[0], (-1, shape_list(logits[0])[2])),
+            masked_lm_active_loss,
+        )
+        masked_lm_labels = tf.boolean_mask(tf.reshape(labels["labels"], (-1,)), masked_lm_active_loss)
+        next_sentence_active_loss = tf.not_equal(tf.reshape(labels["next_sentence_label"], (-1,)), -100)
+        next_sentence_reduced_logits = tf.boolean_mask(tf.reshape(logits[1], (-1, 2)), next_sentence_active_loss)
+        next_sentence_label = tf.boolean_mask(
+            tf.reshape(labels["next_sentence_label"], (-1,)), mask=next_sentence_active_loss
+        )
+        masked_lm_loss = loss_fn(masked_lm_labels, masked_lm_reduced_logits)
+        next_sentence_loss = loss_fn(next_sentence_label, next_sentence_reduced_logits)
+        masked_lm_loss = tf.reshape(masked_lm_loss, (-1, shape_list(next_sentence_loss)[0]))
+        masked_lm_loss = tf.reduce_mean(masked_lm_loss, 0)
+
+        return masked_lm_loss + next_sentence_loss
+
+
 class TFBertEmbeddings(tf.keras.layers.Layer):
     """Construct the embeddings from word, position and token_type embeddings."""
 
@@ -688,6 +720,7 @@ class TFBertForPreTrainingOutput(ModelOutput):
             heads.
     """
 
+    loss: Optional[tf.Tensor] = None
     prediction_logits: tf.Tensor = None
     seq_relationship_logits: tf.Tensor = None
     hidden_states: Optional[Tuple[tf.Tensor]] = None
@@ -814,7 +847,7 @@ def call(self, inputs, **kwargs):
     """,
     BERT_START_DOCSTRING,
 )
-class TFBertForPreTraining(TFBertPreTrainedModel):
+class TFBertForPreTraining(TFBertPreTrainedModel, TFBertPreTrainingLoss):
     def __init__(self, config, *inputs, **kwargs):
         super().__init__(config, *inputs, **kwargs)
 
@@ -827,7 +860,21 @@ def get_output_embeddings(self):
 
     @add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @replace_return_docstrings(output_type=TFBertForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
-    def call(self, inputs, **kwargs):
+    def call(
+        self,
+        inputs=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+        labels=None,
+        next_sentence_label=None,
+        training=False,
+    ):
         r"""
         Return:
 
@@ -843,17 +890,44 @@ def call(self, inputs, **kwargs):
             >>> prediction_scores, seq_relationship_scores = outputs[:2]
 
         """
-        return_dict = kwargs.get("return_dict")
         return_dict = return_dict if return_dict is not None else self.bert.return_dict
-        outputs = self.bert(inputs, **kwargs)
+
+        if isinstance(inputs, (tuple, list)):
+            labels = inputs[9] if len(inputs) > 9 else labels
+            next_sentence_label = inputs[10] if len(inputs) > 10 else next_sentence_label
+            if len(inputs) > 9:
+                inputs = inputs[:9]
+        elif isinstance(inputs, (dict, BatchEncoding)):
+            labels = inputs.pop("labels", labels)
+            next_sentence_label = inputs.pop("next_sentence_label", next_sentence_label)
+
+        outputs = self.bert(
+            inputs,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            training=training,
+        )
         sequence_output, pooled_output = outputs[:2]
-        prediction_scores = self.mlm(sequence_output, training=kwargs.get("training", False))
+        prediction_scores = self.mlm(sequence_output, training=training)
         seq_relationship_score = self.nsp(pooled_output)
+        total_loss = None
+
+        if labels is not None and next_sentence_label is not None:
+            d_labels = {"labels": labels}
+            d_labels["next_sentence_label"] = next_sentence_label
+            total_loss = self.compute_loss(labels=d_labels, logits=(prediction_scores, seq_relationship_score))
 
         if not return_dict:
             return (prediction_scores, seq_relationship_score) + outputs[2:]
 
         return TFBertForPreTrainingOutput(
+            loss=total_loss,
             prediction_logits=prediction_scores,
             seq_relationship_logits=seq_relationship_score,
             hidden_states=outputs.hidden_states,

tests/test_modeling_tf_bert.py

@@ -26,6 +26,7 @@
 if is_tf_available():
     import tensorflow as tf
 
+    from transformers import TF_MODEL_FOR_PRETRAINING_MAPPING
     from transformers.modeling_tf_bert import (
         TFBertForMaskedLM,
         TFBertForMultipleChoice,
@@ -274,6 +275,16 @@ class TFBertModelTest(TFModelTesterMixin, unittest.TestCase):
         else ()
     )
 
+    # special case for ForPreTraining model
+    def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
+        inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
+
+        if return_labels:
+            if model_class in TF_MODEL_FOR_PRETRAINING_MAPPING.values():
+                inputs_dict["next_sentence_label"] = tf.zeros(self.model_tester.batch_size, dtype=tf.int32)
+
+        return inputs_dict
+
     def setUp(self):
         self.model_tester = TFBertModelTester(self)
         self.config_tester = ConfigTester(self, config_class=BertConfig, hidden_size=37)

tests/test_modeling_tf_common.py

@@ -36,6 +36,7 @@
         TF_MODEL_FOR_MASKED_LM_MAPPING,
         TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
         TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
+        TF_MODEL_FOR_PRETRAINING_MAPPING,
         TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
         TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
         TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
@@ -102,6 +103,7 @@ def _prepare_for_class(self, inputs_dict, model_class, return_labels=False) -> d
                 *TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.values(),
                 *TF_MODEL_FOR_CAUSAL_LM_MAPPING.values(),
                 *TF_MODEL_FOR_MASKED_LM_MAPPING.values(),
+                *TF_MODEL_FOR_PRETRAINING_MAPPING.values(),
                 *TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.values(),
             ]:
                 inputs_dict["labels"] = tf.zeros(
@@ -834,7 +836,9 @@ def test_loss_computation(self):
             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]]
+                added_label = prepared_for_class[
+                    sorted(list(prepared_for_class.keys() - inputs_dict.keys()), reverse=True)[0]
+                ]
                 loss_size = tf.size(added_label)
 
                 if model.__class__ in TF_MODEL_FOR_CAUSAL_LM_MAPPING.values():
@@ -859,23 +863,30 @@ def test_loss_computation(self):
 
                 # 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]
+                signature = inspect.signature(model.call).parameters
+                signature_names = list(signature.keys())
 
                 # Create a dictionary holding the location of the tensors in the tuple
-                tuple_index_mapping = {1: "input_ids"}
+                tuple_index_mapping = {0: "input_ids"}
                 for label_key in label_keys:
-                    label_key_index = signature.index(label_key)
+                    label_key_index = signature_names.index(label_key)
                     tuple_index_mapping[label_key_index] = label_key
                 sorted_tuple_index_mapping = sorted(tuple_index_mapping.items())
+                # Initialize a list with their default values, update the values and convert to a tuple
+                list_input = []
+
+                for name in signature_names:
+                    if name != "kwargs":
+                        list_input.append(signature[name].default)
 
-                # 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]
+                    list_input[index] = prepared_for_class[value]
+
                 tuple_input = tuple(list_input)
 
                 # Send to model
-                loss = model(tuple_input)[0]
+                loss = model(tuple_input[:-1])[0]
+
                 self.assertEqual(loss.shape, [loss_size])
 
     def _generate_random_bad_tokens(self, num_bad_tokens, model):