official/projects/edgetpu/nlp/modeling/pretrainer_test.py
# Copyright 2024 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.projects.edgetpu.nlp.modeling import pretrainer
class MobileBERTEdgeTPUPretrainerTest(tf.test.TestCase, parameterized.TestCase):
@parameterized.parameters(itertools.product([True, False],
[True, False],
[True, False]))
def test_mobilebert_edgetpu_pretrainer(
self,
dict_outputs,
return_all_encoder_outputs,
use_customized_masked_lm):
"""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.BertEncoder(
vocab_size=vocab_size,
num_layers=num_layers,
hidden_size=hidden_size,
max_sequence_length=sequence_length,
return_all_encoder_outputs=return_all_encoder_outputs,
dict_outputs=dict_outputs)
# 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 = pretrainer.MobileBERTEdgeTPUPretrainer(
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))
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 = dict_outputs or return_all_encoder_outputs
expected_keys = ['sequence_output', 'pooled_output']
if has_encoder_outputs:
expected_keys.append('encoder_outputs')
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]
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.BertEncoder(
vocab_size=vocab_size,
num_layers=num_layers,
hidden_size=hidden_size,
max_sequence_length=sequence_length,
dict_outputs=True)
bert_trainer_model = pretrainer.MobileBERTEdgeTPUPretrainer(
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. (Here, we use
# a short sequence_length for convenience.)
test_network = networks.BertEncoder(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 = pretrainer.MobileBERTEdgeTPUPretrainer(
encoder_network=test_network)
# Create another BERT trainer via serialization and deserialization.
config = bert_trainer_model.get_config()
new_bert_trainer_model = pretrainer.MobileBERTEdgeTPUPretrainer.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()