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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 dual encoder network."""
from absl.testing import parameterized
import tensorflow as tf, tf_keras
from official.nlp.modeling import networks
from official.nlp.modeling.models import dual_encoder
class DualEncoderTest(tf.test.TestCase, parameterized.TestCase):
@parameterized.parameters((192, 'logits'), (768, 'predictions'))
def test_dual_encoder(self, hidden_size, output):
"""Validate that the Keras object can be created."""
# Build a transformer network to use within the dual encoder model.
vocab_size = 100
sequence_length = 512
test_network = networks.BertEncoder(
vocab_size=vocab_size,
num_layers=2,
hidden_size=hidden_size,
dict_outputs=True)
# Create a dual encoder model with the created network.
dual_encoder_model = dual_encoder.DualEncoder(
test_network, max_seq_length=sequence_length, output=output)
# Create a set of 2-dimensional inputs (the first dimension is implicit).
left_word_ids = tf_keras.Input(shape=(sequence_length,), dtype=tf.int32)
left_mask = tf_keras.Input(shape=(sequence_length,), dtype=tf.int32)
left_type_ids = tf_keras.Input(shape=(sequence_length,), dtype=tf.int32)
right_word_ids = tf_keras.Input(shape=(sequence_length,), dtype=tf.int32)
right_mask = tf_keras.Input(shape=(sequence_length,), dtype=tf.int32)
right_type_ids = tf_keras.Input(shape=(sequence_length,), dtype=tf.int32)
if output == 'logits':
outputs = dual_encoder_model([
left_word_ids, left_mask, left_type_ids, right_word_ids, right_mask,
right_type_ids
])
_ = outputs['left_logits']
elif output == 'predictions':
outputs = dual_encoder_model([left_word_ids, left_mask, left_type_ids])
# Validate that the outputs are of the expected shape.
expected_sequence_shape = [None, sequence_length, 768]
self.assertAllEqual(expected_sequence_shape,
outputs['sequence_output'].shape.as_list())
left_encoded = outputs['pooled_output']
expected_encoding_shape = [None, 768]
self.assertAllEqual(expected_encoding_shape, left_encoded.shape.as_list())
@parameterized.parameters((192, 'logits'), (768, 'predictions'))
def test_dual_encoder_tensor_call(self, hidden_size, output):
"""Validate that the Keras object can be invoked."""
# Build a transformer network to use within the dual encoder model.
del hidden_size
sequence_length = 2
test_network = networks.BertEncoder(vocab_size=100, num_layers=2)
# Create a dual encoder model with the created network.
dual_encoder_model = dual_encoder.DualEncoder(
test_network, max_seq_length=sequence_length, output=output)
# 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)
# Invoke the model 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.)
if output == 'logits':
_ = dual_encoder_model(
[word_ids, mask, type_ids, word_ids, mask, type_ids])
elif output == 'predictions':
_ = dual_encoder_model([word_ids, mask, type_ids])
def test_serialize_deserialize(self):
"""Validate that the dual encoder model can be serialized / deserialized."""
# Build a transformer network to use within the dual encoder model.
sequence_length = 32
test_network = networks.BertEncoder(vocab_size=100, num_layers=2)
# Create a dual encoder model with the created network. (Note that all the
# args are different, so we can catch any serialization mismatches.)
dual_encoder_model = dual_encoder.DualEncoder(
test_network, max_seq_length=sequence_length, output='predictions')
# Create another dual encoder moel via serialization and deserialization.
config = dual_encoder_model.get_config()
new_dual_encoder = dual_encoder.DualEncoder.from_config(config)
# Validate that the config can be forced to JSON.
_ = new_dual_encoder.to_json()
# If the serialization was successful, the new config should match the old.
self.assertAllEqual(dual_encoder_model.get_config(),
new_dual_encoder.get_config())
if __name__ == '__main__':
tf.test.main()
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