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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()
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