def test_network_invocation(self, output_range, out_seq_len):
hidden_size = 32
sequence_length = 21
vocab_size = 57
num_types = 7
# Create a small TransformerEncoder for testing.
test_network = transformer_encoder.TransformerEncoder(
vocab_size=vocab_size,
hidden_size=hidden_size,
sequence_length=sequence_length,
num_attention_heads=2,
num_layers=3,
type_vocab_size=num_types,
output_range=output_range)
self.assertTrue(
test_network._position_embedding_layer._use_dynamic_slicing)
# Create the inputs (note that 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)
data, pooled = test_network([word_ids, mask, type_ids])
# Create a model based off of this network:
model = tf.keras.Model([word_ids, mask, type_ids], [data, pooled])
# Invoke the model. We can't validate the output data here (the model is too
# complex) but this will catch structural runtime errors.
batch_size = 3
word_id_data = np.random.randint(vocab_size,
size=(batch_size, sequence_length))
mask_data = np.random.randint(2, size=(batch_size, sequence_length))
type_id_data = np.random.randint(num_types,
size=(batch_size, sequence_length))
_ = model.predict([word_id_data, mask_data, type_id_data])
# Creates a TransformerEncoder with max_sequence_length != sequence_length
max_sequence_length = 128
test_network = transformer_encoder.TransformerEncoder(
vocab_size=vocab_size,
hidden_size=hidden_size,
sequence_length=sequence_length,
max_sequence_length=max_sequence_length,
num_attention_heads=2,
num_layers=3,
type_vocab_size=num_types)
self.assertTrue(
test_network._position_embedding_layer._use_dynamic_slicing)
model = tf.keras.Model([word_ids, mask, type_ids], [data, pooled])
outputs = model.predict([word_id_data, mask_data, type_id_data])
self.assertEqual(outputs[0].shape[1], out_seq_len)
def test_network_creation_with_float16_dtype(self):
hidden_size = 32
sequence_length = 21
tf.keras.mixed_precision.experimental.set_policy("mixed_float16")
# Create a small TransformerEncoder for testing.
test_network = transformer_encoder.TransformerEncoder(
vocab_size=100,
hidden_size=hidden_size,
sequence_length=sequence_length,
num_attention_heads=2,
num_layers=3)
# Create the inputs (note that 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)
data, pooled = test_network([word_ids, mask, type_ids])
expected_data_shape = [None, sequence_length, hidden_size]
expected_pooled_shape = [None, hidden_size]
self.assertAllEqual(expected_data_shape, data.shape.as_list())
self.assertAllEqual(expected_pooled_shape, pooled.shape.as_list())
# If float_dtype is set to float16, the data output is float32 (from a layer
# norm) and pool output should be float16.
self.assertAllEqual(tf.float32, data.dtype)
self.assertAllEqual(tf.float16, pooled.dtype)
def test_all_encoder_outputs_network_creation(self):
hidden_size = 32
sequence_length = 21
# Create a small TransformerEncoder for testing.
test_network = transformer_encoder.TransformerEncoder(
vocab_size=100,
hidden_size=hidden_size,
sequence_length=sequence_length,
num_attention_heads=2,
num_layers=3,
return_all_encoder_outputs=True)
# Create the inputs (note that 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)
all_encoder_outputs, pooled = test_network([word_ids, mask, type_ids])
expected_data_shape = [None, sequence_length, hidden_size]
expected_pooled_shape = [None, hidden_size]
self.assertLen(all_encoder_outputs, 3)
for data in all_encoder_outputs:
self.assertAllEqual(expected_data_shape, data.shape.as_list())
self.assertAllEqual(expected_pooled_shape, pooled.shape.as_list())
# The default output dtype is float32.
self.assertAllEqual(tf.float32, all_encoder_outputs[-1].dtype)
self.assertAllEqual(tf.float32, pooled.dtype)
def test_serialize_deserialize(self):
tf.keras.mixed_precision.experimental.set_policy("mixed_float16")
# Create a network object that sets all of its config options.
kwargs = dict(vocab_size=100,
hidden_size=32,
num_layers=3,
num_attention_heads=2,
sequence_length=21,
max_sequence_length=21,
type_vocab_size=12,
intermediate_size=1223,
activation="relu",
dropout_rate=0.05,
attention_dropout_rate=0.22,
initializer="glorot_uniform",
return_all_encoder_outputs=False,
output_range=-1)
network = transformer_encoder.TransformerEncoder(**kwargs)
expected_config = dict(kwargs)
expected_config["activation"] = tf.keras.activations.serialize(
tf.keras.activations.get(expected_config["activation"]))
expected_config["initializer"] = tf.keras.initializers.serialize(
tf.keras.initializers.get(expected_config["initializer"]))
self.assertEqual(network.get_config(), expected_config)
# Create another network object from the first object's config.
new_network = transformer_encoder.TransformerEncoder.from_config(
network.get_config())
# Validate that the config can be forced to JSON.
_ = new_network.to_json()
# If the serialization was successful, the new config should match the old.
self.assertAllEqual(network.get_config(), new_network.get_config())
def test_network_creation(self):
hidden_size = 32
sequence_length = 21
# Create a small TransformerEncoder for testing.
test_network = transformer_encoder.TransformerEncoder(
vocab_size=100,
hidden_size=hidden_size,
<<<<<<< HEAD
sequence_length=sequence_length,
=======
>>>>>>> a811a3b7e640722318ad868c99feddf3f3063e36
num_attention_heads=2,
num_layers=3)
# Create the inputs (note that 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)
data, pooled = test_network([word_ids, mask, type_ids])
self.assertIsInstance(test_network.transformer_layers, list)
self.assertLen(test_network.transformer_layers, 3)
self.assertIsInstance(test_network.pooler_layer, tf.keras.layers.Dense)
expected_data_shape = [None, sequence_length, hidden_size]
expected_pooled_shape = [None, hidden_size]
self.assertAllEqual(expected_data_shape, data.shape.as_list())
self.assertAllEqual(expected_pooled_shape, pooled.shape.as_list())
# The default output dtype is float32.
self.assertAllEqual(tf.float32, data.dtype)
self.assertAllEqual(tf.float32, pooled.dtype)
def create_network(self,
vocab_size,
sequence_length,
hidden_size,
num_predictions,
output='predictions',
xformer_stack=None):
# First, create a transformer stack that we can use to get the LM's
# vocabulary weight.
if xformer_stack is None:
xformer_stack = transformer_encoder.TransformerEncoder(
vocab_size=vocab_size,
num_layers=1,
sequence_length=sequence_length,
hidden_size=hidden_size,
num_attention_heads=4,
)
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_outputs, _ = xformer_stack([word_ids, mask, type_ids])
# Create a maskedLM from the transformer stack.
test_network = masked_lm.MaskedLM(num_predictions=num_predictions,
input_width=lm_outputs.shape[-1],
source_network=xformer_stack,
output=output)
return test_network
def test_layer_invocation_with_external_logits(self):
vocab_size = 100
sequence_length = 32
hidden_size = 64
num_predictions = 21
xformer_stack = transformer_encoder.TransformerEncoder(
vocab_size=vocab_size,
num_layers=1,
sequence_length=sequence_length,
hidden_size=hidden_size,
num_attention_heads=4,
)
test_layer = self.create_layer(vocab_size=vocab_size,
sequence_length=sequence_length,
hidden_size=hidden_size,
xformer_stack=xformer_stack,
output='predictions')
logit_layer = self.create_layer(vocab_size=vocab_size,
sequence_length=sequence_length,
hidden_size=hidden_size,
xformer_stack=xformer_stack,
output='logits')
# Create a model from the masked LM layer.
lm_input_tensor = tf.keras.Input(shape=(sequence_length, hidden_size))
masked_positions = tf.keras.Input(shape=(num_predictions, ),
dtype=tf.int32)
output = test_layer(lm_input_tensor, masked_positions)
logit_output = logit_layer(lm_input_tensor, masked_positions)
logit_output = tf.keras.layers.Activation(
tf.nn.log_softmax)(logit_output)
logit_layer.set_weights(test_layer.get_weights())
model = tf.keras.Model([lm_input_tensor, masked_positions], output)
logits_model = tf.keras.Model(([lm_input_tensor, masked_positions]),
logit_output)
# Invoke the masked LM on some fake data to make sure there are no runtime
# errors in the code.
batch_size = 3
lm_input_data = 10 * np.random.random_sample(
(batch_size, sequence_length, hidden_size))
masked_position_data = np.random.randint(sequence_length,
size=(batch_size,
num_predictions))
# ref_outputs = model.predict([lm_input_data, masked_position_data])
# outputs = logits_model.predict([lm_input_data, masked_position_data])
ref_outputs = model([lm_input_data, masked_position_data])
outputs = logits_model([lm_input_data, masked_position_data])
# Ensure that the tensor shapes are correct.
expected_output_shape = (batch_size, num_predictions, vocab_size)
self.assertEqual(expected_output_shape, ref_outputs.shape)
self.assertEqual(expected_output_shape, outputs.shape)
self.assertAllClose(ref_outputs, outputs)
def create_layer(self,
vocab_size,
hidden_size,
output='predictions',
xformer_stack=None):
# First, create a transformer stack that we can use to get the LM's
# vocabulary weight.
if xformer_stack is None:
xformer_stack = transformer_encoder.TransformerEncoder(
vocab_size=vocab_size,
num_layers=1,
hidden_size=hidden_size,
num_attention_heads=4,
)
# Create a maskedLM from the transformer stack.
test_layer = masked_lm.MaskedLM(
embedding_table=xformer_stack.get_embedding_table(), output=output)
return test_layer
class MaskedLMTest(keras_parameterized.TestCase):
def create_layer(self,
vocab_size,
<<<<<<< HEAD
sequence_length,
=======
>>>>>>> a811a3b7e640722318ad868c99feddf3f3063e36
hidden_size,
output='predictions',
xformer_stack=None):
# First, create a transformer stack that we can use to get the LM's
# vocabulary weight.
if xformer_stack is None:
xformer_stack = transformer_encoder.TransformerEncoder(
vocab_size=vocab_size,
num_layers=1,
<<<<<<< HEAD
sequence_length=sequence_length,
=======
>>>>>>> a811a3b7e640722318ad868c99feddf3f3063e36
hidden_size=hidden_size,
num_attention_heads=4,
)
# Create a maskedLM from the transformer stack.
test_layer = masked_lm.MaskedLM(
embedding_table=xformer_stack.get_embedding_table(),
output=output)
return test_layer