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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 ELECTRA pre trainer network.""" | |
| import tensorflow as tf, tf_keras | |
| from official.nlp.modeling import networks | |
| from official.nlp.modeling.models import electra_pretrainer | |
| class ElectraPretrainerTest(tf.test.TestCase): | |
| def test_electra_pretrainer(self): | |
| """Validate that the Keras object can be created.""" | |
| # Build a transformer network to use within the ELECTRA trainer. | |
| vocab_size = 100 | |
| sequence_length = 512 | |
| test_generator_network = networks.BertEncoder( | |
| vocab_size=vocab_size, | |
| num_layers=2, | |
| max_sequence_length=sequence_length, | |
| dict_outputs=True) | |
| test_discriminator_network = networks.BertEncoder( | |
| vocab_size=vocab_size, | |
| num_layers=2, | |
| max_sequence_length=sequence_length, | |
| dict_outputs=True) | |
| # Create a ELECTRA trainer with the created network. | |
| num_classes = 3 | |
| num_token_predictions = 2 | |
| eletrca_trainer_model = electra_pretrainer.ElectraPretrainer( | |
| generator_network=test_generator_network, | |
| discriminator_network=test_discriminator_network, | |
| vocab_size=vocab_size, | |
| num_classes=num_classes, | |
| num_token_predictions=num_token_predictions, | |
| disallow_correct=True) | |
| # 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) | |
| lm_positions = tf_keras.Input( | |
| shape=(num_token_predictions,), dtype=tf.int32) | |
| lm_ids = tf_keras.Input(shape=(num_token_predictions,), dtype=tf.int32) | |
| inputs = { | |
| 'input_word_ids': word_ids, | |
| 'input_mask': mask, | |
| 'input_type_ids': type_ids, | |
| 'masked_lm_positions': lm_positions, | |
| 'masked_lm_ids': lm_ids | |
| } | |
| # Invoke the trainer model on the inputs. This causes the layer to be built. | |
| outputs = eletrca_trainer_model(inputs) | |
| lm_outs = outputs['lm_outputs'] | |
| cls_outs = outputs['sentence_outputs'] | |
| disc_logits = outputs['disc_logits'] | |
| disc_label = outputs['disc_label'] | |
| # Validate that the outputs are of the expected shape. | |
| expected_lm_shape = [None, num_token_predictions, vocab_size] | |
| expected_classification_shape = [None, num_classes] | |
| expected_disc_logits_shape = [None, sequence_length] | |
| expected_disc_label_shape = [None, sequence_length] | |
| self.assertAllEqual(expected_lm_shape, lm_outs.shape.as_list()) | |
| self.assertAllEqual(expected_classification_shape, cls_outs.shape.as_list()) | |
| self.assertAllEqual(expected_disc_logits_shape, disc_logits.shape.as_list()) | |
| self.assertAllEqual(expected_disc_label_shape, disc_label.shape.as_list()) | |
| def test_electra_trainer_tensor_call(self): | |
| """Validate that the Keras object can be invoked.""" | |
| # Build a transformer network to use within the ELECTRA trainer. (Here, we | |
| # use a short sequence_length for convenience.) | |
| test_generator_network = networks.BertEncoder( | |
| vocab_size=100, num_layers=4, max_sequence_length=3, dict_outputs=True) | |
| test_discriminator_network = networks.BertEncoder( | |
| vocab_size=100, num_layers=4, max_sequence_length=3, dict_outputs=True) | |
| # Create a ELECTRA trainer with the created network. | |
| eletrca_trainer_model = electra_pretrainer.ElectraPretrainer( | |
| generator_network=test_generator_network, | |
| discriminator_network=test_discriminator_network, | |
| vocab_size=100, | |
| 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, 1], [2, 2, 2]], dtype=tf.int32) | |
| mask = tf.constant([[1, 1, 1], [1, 0, 0]], dtype=tf.int32) | |
| type_ids = tf.constant([[1, 1, 1], [2, 2, 2]], dtype=tf.int32) | |
| lm_positions = tf.constant([[0, 1], [0, 2]], dtype=tf.int32) | |
| lm_ids = tf.constant([[10, 20], [20, 30]], dtype=tf.int32) | |
| inputs = { | |
| 'input_word_ids': word_ids, | |
| 'input_mask': mask, | |
| 'input_type_ids': type_ids, | |
| 'masked_lm_positions': lm_positions, | |
| 'masked_lm_ids': lm_ids | |
| } | |
| # 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.) | |
| _ = eletrca_trainer_model(inputs) | |
| def test_serialize_deserialize(self): | |
| """Validate that the ELECTRA 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_generator_network = networks.BertEncoder( | |
| vocab_size=100, num_layers=4, max_sequence_length=3) | |
| test_discriminator_network = networks.BertEncoder( | |
| vocab_size=100, num_layers=4, max_sequence_length=3) | |
| # Create a ELECTRA trainer with the created network. (Note that all the args | |
| # are different, so we can catch any serialization mismatches.) | |
| electra_trainer_model = electra_pretrainer.ElectraPretrainer( | |
| generator_network=test_generator_network, | |
| discriminator_network=test_discriminator_network, | |
| vocab_size=100, | |
| num_classes=2, | |
| num_token_predictions=2) | |
| # Create another BERT trainer via serialization and deserialization. | |
| config = electra_trainer_model.get_config() | |
| new_electra_trainer_model = electra_pretrainer.ElectraPretrainer.from_config( | |
| config) | |
| # Validate that the config can be forced to JSON. | |
| _ = new_electra_trainer_model.to_json() | |
| # If the serialization was successful, the new config should match the old. | |
| self.assertAllEqual(electra_trainer_model.get_config(), | |
| new_electra_trainer_model.get_config()) | |
| if __name__ == '__main__': | |
| tf.test.main() | |