def test_network_creation_with_float16_dtype(self):
tf.keras.mixed_precision.experimental.set_policy("mixed_float16")
hidden_size = 32
sequence_length = 21
embedding_cfg = {
"vocab_size": 100,
"type_vocab_size": 16,
"hidden_size": hidden_size,
"seq_length": sequence_length,
"max_seq_length": sequence_length,
"initializer": tf.keras.initializers.TruncatedNormal(stddev=0.02),
"dropout_rate": 0.1,
}
hidden_cfg = {
"num_attention_heads":
2,
"intermediate_size":
3072,
"intermediate_activation":
activations.gelu,
"dropout_rate":
0.1,
"attention_dropout_rate":
0.1,
"kernel_initializer":
tf.keras.initializers.TruncatedNormal(stddev=0.02),
}
# Create a small EncoderScaffold for testing.
test_network = encoder_scaffold.EncoderScaffold(
num_hidden_instances=3,
pooled_output_dim=hidden_size,
pooler_layer_initializer=tf.keras.initializers.TruncatedNormal(
stddev=0.02),
hidden_cfg=hidden_cfg,
embedding_cfg=embedding_cfg)
# 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_serialize_deserialize(self):
# Create a network object that sets all of its config options.
hidden_size = 32
sequence_length = 21
embedding_cfg = {
"vocab_size": 100,
"type_vocab_size": 16,
"hidden_size": hidden_size,
"seq_length": sequence_length,
"max_seq_length": sequence_length,
"initializer": tf.keras.initializers.TruncatedNormal(stddev=0.02),
"dropout_rate": 0.1,
}
hidden_cfg = {
"num_attention_heads":
2,
"intermediate_size":
3072,
"intermediate_activation":
activations.gelu,
"dropout_rate":
0.1,
"attention_dropout_rate":
0.1,
"kernel_initializer":
tf.keras.initializers.TruncatedNormal(stddev=0.02),
}
# Create a small EncoderScaffold for testing.
network = encoder_scaffold.EncoderScaffold(
num_hidden_instances=3,
pooled_output_dim=hidden_size,
pooler_layer_initializer=tf.keras.initializers.TruncatedNormal(
stddev=0.02),
hidden_cfg=hidden_cfg,
embedding_cfg=embedding_cfg)
# Create another network object from the first object's config.
new_network = encoder_scaffold.EncoderScaffold.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, return_all_layer_outputs):
hidden_size = 32
sequence_length = 21
num_hidden_instances = 3
embedding_cfg = {
"vocab_size": 100,
"type_vocab_size": 16,
"hidden_size": hidden_size,
"seq_length": sequence_length,
"max_seq_length": sequence_length,
"initializer": tf.keras.initializers.TruncatedNormal(stddev=0.02),
"dropout_rate": 0.1,
}
call_list = []
hidden_cfg = {
"num_attention_heads":
2,
"intermediate_size":
3072,
"intermediate_activation":
activations.gelu,
"dropout_rate":
0.1,
"attention_dropout_rate":
0.1,
"kernel_initializer":
tf.keras.initializers.TruncatedNormal(stddev=0.02),
"call_list":
call_list
}
# Create a small EncoderScaffold for testing.
test_network = encoder_scaffold.EncoderScaffold(
num_hidden_instances=num_hidden_instances,
pooled_output_dim=hidden_size,
pooler_layer_initializer=tf.keras.initializers.TruncatedNormal(
stddev=0.02),
hidden_cls=ValidatedTransformerLayer,
hidden_cfg=hidden_cfg,
embedding_cfg=embedding_cfg,
return_all_layer_outputs=return_all_layer_outputs)
# 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)
output_data, pooled = test_network([word_ids, mask, type_ids])
if return_all_layer_outputs:
self.assertIsInstance(output_data, list)
self.assertLen(output_data, num_hidden_instances)
data = output_data[-1]
else:
data = output_data
self.assertIsInstance(test_network.hidden_layers, list)
self.assertLen(test_network.hidden_layers, num_hidden_instances)
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)
# If call_list[0] exists and is True, the passed layer class was
# instantiated from the given config properly.
self.assertNotEmpty(call_list)
self.assertTrue(call_list[0], "The passed layer class wasn't instantiated.")
def test_serialize_deserialize(self):
hidden_size = 32
sequence_length = 21
vocab_size = 57
num_types = 7
embedding_cfg = {
"vocab_size": vocab_size,
"type_vocab_size": num_types,
"hidden_size": hidden_size,
"seq_length": sequence_length,
"max_seq_length": sequence_length,
"initializer": tf.keras.initializers.TruncatedNormal(stddev=0.02),
"dropout_rate": 0.1,
}
call_list = []
hidden_cfg = {
"num_attention_heads":
2,
"intermediate_size":
3072,
"intermediate_activation":
activations.gelu,
"dropout_rate":
0.1,
"attention_dropout_rate":
0.1,
"kernel_initializer":
tf.keras.initializers.TruncatedNormal(stddev=0.02),
"call_list":
call_list
}
# Create a small EncoderScaffold for testing. This time, we pass an already-
# instantiated layer object.
xformer = ValidatedTransformerLayer(**hidden_cfg)
test_network = encoder_scaffold.EncoderScaffold(
num_hidden_instances=3,
pooled_output_dim=hidden_size,
pooler_layer_initializer=tf.keras.initializers.TruncatedNormal(
stddev=0.02),
hidden_cls=xformer,
embedding_cfg=embedding_cfg)
# Create another network object from the first object's config.
new_network = encoder_scaffold.EncoderScaffold.from_config(
test_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(test_network.get_config(), new_network.get_config())
# Create a model based off of the old and new networks:
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 = new_network([word_ids, mask, type_ids])
new_model = tf.keras.Model([word_ids, mask, type_ids], [data, pooled])
data, pooled = test_network([word_ids, mask, type_ids])
model = tf.keras.Model([word_ids, mask, type_ids], [data, pooled])
# Copy the weights between models.
new_model.set_weights(model.get_weights())
# Invoke the models.
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))
data, cls = model.predict([word_id_data, mask_data, type_id_data])
new_data, new_cls = new_model.predict(
[word_id_data, mask_data, type_id_data])
# The output should be equal.
self.assertAllEqual(data, new_data)
self.assertAllEqual(cls, new_cls)
def test_network_invocation(self):
hidden_size = 32
sequence_length = 21
vocab_size = 57
num_types = 7
embedding_cfg = {
"vocab_size": vocab_size,
"type_vocab_size": num_types,
"hidden_size": hidden_size,
"seq_length": sequence_length,
"max_seq_length": sequence_length,
"initializer": tf.keras.initializers.TruncatedNormal(stddev=0.02),
"dropout_rate": 0.1,
}
call_list = []
hidden_cfg = {
"num_attention_heads":
2,
"intermediate_size":
3072,
"intermediate_activation":
activations.gelu,
"dropout_rate":
0.1,
"attention_dropout_rate":
0.1,
"kernel_initializer":
tf.keras.initializers.TruncatedNormal(stddev=0.02),
"call_list":
call_list
}
# Create a small EncoderScaffold for testing. This time, we pass an already-
# instantiated layer object.
xformer = ValidatedTransformerLayer(**hidden_cfg)
test_network = encoder_scaffold.EncoderScaffold(
num_hidden_instances=3,
pooled_output_dim=hidden_size,
pooler_layer_initializer=tf.keras.initializers.TruncatedNormal(
stddev=0.02),
hidden_cls=xformer,
embedding_cfg=embedding_cfg)
# 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])
# If call_list[0] exists and is True, the passed layer class was
# called as part of the graph creation.
self.assertNotEmpty(call_list)
self.assertTrue(call_list[0], "The passed layer class wasn't instantiated.")
def test_serialize_deserialize(self):
hidden_size = 32
sequence_length = 21
vocab_size = 57
# Build an embedding network to swap in for the default network. This one
# will have 2 inputs (mask and word_ids) instead of 3, and won't use
# positional embeddings.
word_ids = tf.keras.layers.Input(
shape=(sequence_length,), dtype=tf.int32, name="input_word_ids")
mask = tf.keras.layers.Input(
shape=(sequence_length,), dtype=tf.int32, name="input_mask")
embedding_layer = layers.OnDeviceEmbedding(
vocab_size=vocab_size,
embedding_width=hidden_size,
initializer=tf.keras.initializers.TruncatedNormal(stddev=0.02),
name="word_embeddings")
word_embeddings = embedding_layer(word_ids)
network = tf.keras.Model([word_ids, mask], [word_embeddings, mask])
hidden_cfg = {
"num_attention_heads":
2,
"intermediate_size":
3072,
"intermediate_activation":
activations.gelu,
"dropout_rate":
0.1,
"attention_dropout_rate":
0.1,
"kernel_initializer":
tf.keras.initializers.TruncatedNormal(stddev=0.02),
}
# Create a small EncoderScaffold for testing.
test_network = encoder_scaffold.EncoderScaffold(
num_hidden_instances=3,
pooled_output_dim=hidden_size,
pooler_layer_initializer=tf.keras.initializers.TruncatedNormal(
stddev=0.02),
hidden_cfg=hidden_cfg,
embedding_cls=network,
embedding_data=embedding_layer.embeddings)
# Create another network object from the first object's config.
new_network = encoder_scaffold.EncoderScaffold.from_config(
test_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(test_network.get_config(), new_network.get_config())
# Create a model based off of the old and new networks:
word_ids = tf.keras.Input(shape=(sequence_length,), dtype=tf.int32)
mask = tf.keras.Input(shape=(sequence_length,), dtype=tf.int32)
data, pooled = new_network([word_ids, mask])
new_model = tf.keras.Model([word_ids, mask], [data, pooled])
data, pooled = test_network([word_ids, mask])
model = tf.keras.Model([word_ids, mask], [data, pooled])
# Copy the weights between models.
new_model.set_weights(model.get_weights())
# Invoke the models.
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))
data, cls = model.predict([word_id_data, mask_data])
new_data, new_cls = new_model.predict([word_id_data, mask_data])
# The output should be equal.
self.assertAllEqual(data, new_data)
self.assertAllEqual(cls, new_cls)
# We should not be able to get a reference to the embedding data.
with self.assertRaisesRegex(RuntimeError, ".*does not have a reference.*"):
new_network.get_embedding_table()
def test_network_invocation(self):
hidden_size = 32
sequence_length = 21
vocab_size = 57
# Build an embedding network to swap in for the default network. This one
# will have 2 inputs (mask and word_ids) instead of 3, and won't use
# positional embeddings.
word_ids = tf.keras.layers.Input(
shape=(sequence_length,), dtype=tf.int32, name="input_word_ids")
mask = tf.keras.layers.Input(
shape=(sequence_length,), dtype=tf.int32, name="input_mask")
embedding_layer = layers.OnDeviceEmbedding(
vocab_size=vocab_size,
embedding_width=hidden_size,
initializer=tf.keras.initializers.TruncatedNormal(stddev=0.02),
name="word_embeddings")
word_embeddings = embedding_layer(word_ids)
network = tf.keras.Model([word_ids, mask], [word_embeddings, mask])
hidden_cfg = {
"num_attention_heads":
2,
"intermediate_size":
3072,
"intermediate_activation":
activations.gelu,
"dropout_rate":
0.1,
"attention_dropout_rate":
0.1,
"kernel_initializer":
tf.keras.initializers.TruncatedNormal(stddev=0.02),
}
# Create a small EncoderScaffold for testing.
test_network = encoder_scaffold.EncoderScaffold(
num_hidden_instances=3,
pooled_output_dim=hidden_size,
pooler_layer_initializer=tf.keras.initializers.TruncatedNormal(
stddev=0.02),
hidden_cfg=hidden_cfg,
embedding_cls=network,
embedding_data=embedding_layer.embeddings)
# 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)
data, pooled = test_network([word_ids, mask])
# Create a model based off of this network:
model = tf.keras.Model([word_ids, mask], [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))
_ = model.predict([word_id_data, mask_data])
# Test that we can get the embedding data that we passed to the object. This
# is necessary to support standard language model training.
self.assertIs(embedding_layer.embeddings,
test_network.get_embedding_table())
def test_network_invocation(self):
hidden_size = 32
sequence_length = 21
vocab_size = 57
num_types = 7
embedding_cfg = {
"vocab_size": vocab_size,
"type_vocab_size": num_types,
"hidden_size": hidden_size,
"seq_length": sequence_length,
"max_seq_length": sequence_length,
"initializer": tf.keras.initializers.TruncatedNormal(stddev=0.02),
"dropout_rate": 0.1,
}
hidden_cfg = {
"num_attention_heads":
2,
"intermediate_size":
3072,
"intermediate_activation":
activations.gelu,
"dropout_rate":
0.1,
"attention_dropout_rate":
0.1,
"kernel_initializer":
tf.keras.initializers.TruncatedNormal(stddev=0.02),
}
print(hidden_cfg)
print(embedding_cfg)
# Create a small EncoderScaffold for testing.
test_network = encoder_scaffold.EncoderScaffold(
num_hidden_instances=3,
pooled_output_dim=hidden_size,
pooler_layer_initializer=tf.keras.initializers.TruncatedNormal(
stddev=0.02),
hidden_cfg=hidden_cfg,
embedding_cfg=embedding_cfg)
# 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 EncoderScaffold with max_sequence_length != sequence_length
num_types = 7
embedding_cfg = {
"vocab_size": vocab_size,
"type_vocab_size": num_types,
"hidden_size": hidden_size,
"seq_length": sequence_length,
"max_seq_length": sequence_length * 2,
"initializer": tf.keras.initializers.TruncatedNormal(stddev=0.02),
"dropout_rate": 0.1,
}
hidden_cfg = {
"num_attention_heads":
2,
"intermediate_size":
3072,
"intermediate_activation":
activations.gelu,
"dropout_rate":
0.1,
"attention_dropout_rate":
0.1,
"kernel_initializer":
tf.keras.initializers.TruncatedNormal(stddev=0.02),
}
# Create a small EncoderScaffold for testing.
test_network = encoder_scaffold.EncoderScaffold(
num_hidden_instances=3,
pooled_output_dim=hidden_size,
pooler_layer_initializer=tf.keras.initializers.TruncatedNormal(
stddev=0.02),
hidden_cfg=hidden_cfg,
embedding_cfg=embedding_cfg)
model = tf.keras.Model([word_ids, mask, type_ids], [data, pooled])
_ = model.predict([word_id_data, mask_data, type_id_data])