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| # Copyright 2023 The TensorFlow Authors. All Rights Reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| """Tests for BERT pretrainer model.""" | |
| import itertools | |
| from absl.testing import parameterized | |
| import tensorflow as tf, tf_keras | |
| from official.nlp.modeling import layers | |
| from official.nlp.modeling import networks | |
| from official.nlp.modeling.models import bert_pretrainer | |
| class BertPretrainerTest(tf.test.TestCase, parameterized.TestCase): | |
| def test_bert_pretrainer(self): | |
| """Validate that the Keras object can be created.""" | |
| # Build a transformer network to use within the BERT trainer. | |
| vocab_size = 100 | |
| sequence_length = 512 | |
| test_network = networks.BertEncoder( | |
| vocab_size=vocab_size, | |
| num_layers=2, | |
| max_sequence_length=sequence_length) | |
| # Create a BERT trainer with the created network. | |
| num_classes = 3 | |
| num_token_predictions = 2 | |
| bert_trainer_model = bert_pretrainer.BertPretrainer( | |
| test_network, | |
| num_classes=num_classes, | |
| num_token_predictions=num_token_predictions) | |
| # Create a set of 2-dimensional inputs (the first dimension is implicit). | |
| word_ids = tf_keras.Input(shape=(sequence_length,), dtype=tf.int32) | |
| mask = tf_keras.Input(shape=(sequence_length,), dtype=tf.int32) | |
| type_ids = tf_keras.Input(shape=(sequence_length,), dtype=tf.int32) | |
| masked_lm_positions = tf_keras.Input( | |
| shape=(num_token_predictions,), dtype=tf.int32) | |
| # Invoke the trainer model on the inputs. This causes the layer to be built. | |
| outputs = bert_trainer_model( | |
| [word_ids, mask, type_ids, masked_lm_positions]) | |
| # Validate that the outputs are of the expected shape. | |
| expected_lm_shape = [None, num_token_predictions, vocab_size] | |
| expected_classification_shape = [None, num_classes] | |
| self.assertAllEqual(expected_lm_shape, outputs['masked_lm'].shape.as_list()) | |
| self.assertAllEqual(expected_classification_shape, | |
| outputs['classification'].shape.as_list()) | |
| def test_bert_trainer_tensor_call(self): | |
| """Validate that the Keras object can be invoked.""" | |
| # Build a transformer network to use within the BERT trainer. | |
| test_network = networks.BertEncoder(vocab_size=100, num_layers=2) | |
| # Create a BERT trainer with the created network. | |
| bert_trainer_model = bert_pretrainer.BertPretrainer( | |
| test_network, num_classes=2, num_token_predictions=2) | |
| # Create a set of 2-dimensional data tensors to feed into the model. | |
| word_ids = tf.constant([[1, 1], [2, 2]], dtype=tf.int32) | |
| mask = tf.constant([[1, 1], [1, 0]], dtype=tf.int32) | |
| type_ids = tf.constant([[1, 1], [2, 2]], dtype=tf.int32) | |
| lm_mask = tf.constant([[1, 1], [1, 0]], dtype=tf.int32) | |
| # Invoke the trainer model on the tensors. In Eager mode, this does the | |
| # actual calculation. (We can't validate the outputs, since the network is | |
| # too complex: this simply ensures we're not hitting runtime errors.) | |
| _ = bert_trainer_model([word_ids, mask, type_ids, lm_mask]) | |
| def test_serialize_deserialize(self): | |
| """Validate that the BERT trainer can be serialized and deserialized.""" | |
| # Build a transformer network to use within the BERT trainer. (Here, we use | |
| # a short sequence_length for convenience.) | |
| test_network = networks.BertEncoder( | |
| vocab_size=100, num_layers=2, max_sequence_length=5) | |
| # Create a BERT trainer with the created network. (Note that all the args | |
| # are different, so we can catch any serialization mismatches.) | |
| bert_trainer_model = bert_pretrainer.BertPretrainer( | |
| test_network, num_classes=4, num_token_predictions=3) | |
| # Create another BERT trainer via serialization and deserialization. | |
| config = bert_trainer_model.get_config() | |
| new_bert_trainer_model = bert_pretrainer.BertPretrainer.from_config(config) | |
| # Validate that the config can be forced to JSON. | |
| _ = new_bert_trainer_model.to_json() | |
| # If the serialization was successful, the new config should match the old. | |
| self.assertAllEqual(bert_trainer_model.get_config(), | |
| new_bert_trainer_model.get_config()) | |
| class BertPretrainerV2Test(tf.test.TestCase, parameterized.TestCase): | |
| def test_bert_pretrainerv2(self, dict_outputs, return_all_encoder_outputs, | |
| use_customized_masked_lm, has_masked_lm_positions): | |
| """Validate that the Keras object can be created.""" | |
| # Build a transformer network to use within the BERT trainer. | |
| del dict_outputs, return_all_encoder_outputs | |
| vocab_size = 100 | |
| sequence_length = 512 | |
| hidden_size = 48 | |
| num_layers = 2 | |
| test_network = networks.BertEncoderV2( | |
| vocab_size=vocab_size, | |
| num_layers=num_layers, | |
| hidden_size=hidden_size, | |
| max_sequence_length=sequence_length) | |
| _ = test_network(test_network.inputs) | |
| # Create a BERT trainer with the created network. | |
| if use_customized_masked_lm: | |
| customized_masked_lm = layers.MaskedLM( | |
| embedding_table=test_network.get_embedding_table()) | |
| else: | |
| customized_masked_lm = None | |
| bert_trainer_model = bert_pretrainer.BertPretrainerV2( | |
| encoder_network=test_network, customized_masked_lm=customized_masked_lm) | |
| num_token_predictions = 20 | |
| # Create a set of 2-dimensional inputs (the first dimension is implicit). | |
| inputs = dict( | |
| input_word_ids=tf_keras.Input(shape=(sequence_length,), dtype=tf.int32), | |
| input_mask=tf_keras.Input(shape=(sequence_length,), dtype=tf.int32), | |
| input_type_ids=tf_keras.Input(shape=(sequence_length,), dtype=tf.int32)) | |
| if has_masked_lm_positions: | |
| inputs['masked_lm_positions'] = tf_keras.Input( | |
| shape=(num_token_predictions,), dtype=tf.int32) | |
| # Invoke the trainer model on the inputs. This causes the layer to be built. | |
| outputs = bert_trainer_model(inputs) | |
| has_encoder_outputs = True # dict_outputs or return_all_encoder_outputs | |
| expected_keys = ['sequence_output', 'pooled_output'] | |
| if has_encoder_outputs: | |
| expected_keys.append('encoder_outputs') | |
| if has_masked_lm_positions: | |
| expected_keys.append('mlm_logits') | |
| self.assertSameElements(outputs.keys(), expected_keys) | |
| # Validate that the outputs are of the expected shape. | |
| expected_lm_shape = [None, num_token_predictions, vocab_size] | |
| if has_masked_lm_positions: | |
| self.assertAllEqual(expected_lm_shape, | |
| outputs['mlm_logits'].shape.as_list()) | |
| expected_sequence_output_shape = [None, sequence_length, hidden_size] | |
| self.assertAllEqual(expected_sequence_output_shape, | |
| outputs['sequence_output'].shape.as_list()) | |
| expected_pooled_output_shape = [None, hidden_size] | |
| self.assertAllEqual(expected_pooled_output_shape, | |
| outputs['pooled_output'].shape.as_list()) | |
| def test_multiple_cls_outputs(self): | |
| """Validate that the Keras object can be created.""" | |
| # Build a transformer network to use within the BERT trainer. | |
| vocab_size = 100 | |
| sequence_length = 512 | |
| hidden_size = 48 | |
| num_layers = 2 | |
| test_network = networks.BertEncoderV2( | |
| vocab_size=vocab_size, | |
| num_layers=num_layers, | |
| hidden_size=hidden_size, | |
| max_sequence_length=sequence_length) | |
| bert_trainer_model = bert_pretrainer.BertPretrainerV2( | |
| encoder_network=test_network, | |
| classification_heads=[layers.MultiClsHeads( | |
| inner_dim=5, cls_list=[('foo', 2), ('bar', 3)])]) | |
| num_token_predictions = 20 | |
| # Create a set of 2-dimensional inputs (the first dimension is implicit). | |
| inputs = dict( | |
| input_word_ids=tf_keras.Input(shape=(sequence_length,), dtype=tf.int32), | |
| input_mask=tf_keras.Input(shape=(sequence_length,), dtype=tf.int32), | |
| input_type_ids=tf_keras.Input(shape=(sequence_length,), dtype=tf.int32), | |
| masked_lm_positions=tf_keras.Input( | |
| shape=(num_token_predictions,), dtype=tf.int32)) | |
| # Invoke the trainer model on the inputs. This causes the layer to be built. | |
| outputs = bert_trainer_model(inputs) | |
| self.assertEqual(outputs['foo'].shape.as_list(), [None, 2]) | |
| self.assertEqual(outputs['bar'].shape.as_list(), [None, 3]) | |
| def test_v2_serialize_deserialize(self): | |
| """Validate that the BERT trainer can be serialized and deserialized.""" | |
| # Build a transformer network to use within the BERT trainer. | |
| test_network = networks.BertEncoderV2(vocab_size=100, num_layers=2) | |
| # Create a BERT trainer with the created network. (Note that all the args | |
| # are different, so we can catch any serialization mismatches.) | |
| bert_trainer_model = bert_pretrainer.BertPretrainerV2( | |
| encoder_network=test_network) | |
| # Create another BERT trainer via serialization and deserialization. | |
| config = bert_trainer_model.get_config() | |
| new_bert_trainer_model = bert_pretrainer.BertPretrainerV2.from_config( | |
| config) | |
| # Validate that the config can be forced to JSON. | |
| _ = new_bert_trainer_model.to_json() | |
| # If the serialization was successful, the new config should match the old. | |
| self.assertAllEqual(bert_trainer_model.get_config(), | |
| new_bert_trainer_model.get_config()) | |
| if __name__ == '__main__': | |
| tf.test.main() | |