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. (Here,
# we use # a short sequence_length for convenience.)
sequence_length = 2
test_network = networks.BertEncoder(vocab_size=100,
num_layers=2,
sequence_length=sequence_length)
# 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_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,
sequence_length=sequence_length,
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())
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())