def test_network_creation(self):
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,
num_output_classes=hidden_size,
classification_layer_initializer=tf.keras.initializers.
TruncatedNormal(stddev=0.02),
hidden_cls=ValidatedTransformerLayer,
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())
# 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 _get_network(self, vocab_size):
sequence_length = 512
hidden_size = 50
embedding_cfg = {
'vocab_size': vocab_size,
'type_vocab_size': 1,
'hidden_size': hidden_size,
'embedding_width': hidden_size,
'max_seq_length': sequence_length,
'initializer': tf.keras.initializers.TruncatedNormal(stddev=0.02),
'dropout_rate': 0.1,
}
embedding_inst = packed_sequence_embedding.PackedSequenceEmbedding(
**embedding_cfg)
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),
}
return encoder_scaffold.EncoderScaffold(
num_hidden_instances=2,
pooled_output_dim=hidden_size,
embedding_cfg=embedding_cfg,
embedding_cls=embedding_inst,
hidden_cfg=hidden_cfg,
dict_outputs=True)
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,
"dtype": "float16",
}
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),
"dtype":
"float16",
}
# Create a small EncoderScaffold for testing.
test_network = encoder_scaffold.EncoderScaffold(
num_hidden_instances=3,
num_output_classes=hidden_size,
classification_layer_initializer=tf.keras.initializers.
TruncatedNormal(stddev=0.02),
classification_layer_dtype=tf.float16,
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 output should always be float16.
self.assertAllEqual(tf.float16, data.dtype)
self.assertAllEqual(tf.float16, pooled.dtype)
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.
network = Embeddings(vocab_size, hidden_size)
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)
# 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])
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),
"dtype":
"float32",
}
# Create a small EncoderScaffold for testing.
network = encoder_scaffold.EncoderScaffold(
num_hidden_instances=3,
num_output_classes=hidden_size,
classification_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_serialize_deserialize(self, use_hidden_cls_instance):
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,
"call_class":
TestLayer
}
mask_call_list = []
mask_cfg = {"call_list": mask_call_list, "call_class": TestLayer}
# Create a small EncoderScaffold for testing. This time, we pass an already-
# instantiated layer object.
kwargs = dict(
num_hidden_instances=3,
pooled_output_dim=hidden_size,
pooler_layer_initializer=tf.keras.initializers.TruncatedNormal(
stddev=0.02),
embedding_cfg=embedding_cfg)
if use_hidden_cls_instance:
xformer = ValidatedTransformerLayer(**hidden_cfg)
xmask = ValidatedMaskLayer(**mask_cfg)
test_network = encoder_scaffold.EncoderScaffold(
hidden_cls=xformer, mask_cls=xmask, **kwargs)
else:
test_network = encoder_scaffold.EncoderScaffold(
hidden_cls=ValidatedTransformerLayer,
hidden_cfg=hidden_cfg,
mask_cls=ValidatedMaskLayer,
mask_cfg=mask_cfg,
**kwargs)
# 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_hidden_cls_list(self):
hidden_size = 32
sequence_length = 10
vocab_size = 57
embedding_network = Embeddings(vocab_size, hidden_size)
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
}
mask_call_list = []
mask_cfg = {
"call_list": mask_call_list
}
# Create a small EncoderScaffold for testing. This time, we pass an already-
# instantiated layer object.
xformer = ValidatedTransformerLayer(**hidden_cfg)
xmask = ValidatedMaskLayer(**mask_cfg)
test_network_a = 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,
mask_cls=xmask,
embedding_cls=embedding_network)
# Create a network b with same embedding and hidden layers as network a.
test_network_b = encoder_scaffold.EncoderScaffold(
num_hidden_instances=3,
pooled_output_dim=hidden_size,
pooler_layer_initializer=tf.keras.initializers.TruncatedNormal(
stddev=0.02),
mask_cls=xmask,
embedding_cls=test_network_a.embedding_network,
hidden_cls=test_network_a.hidden_layers)
# Create a network c with same embedding but fewer hidden layers compared to
# network a and b.
hidden_layers = test_network_a.hidden_layers
hidden_layers.pop()
test_network_c = encoder_scaffold.EncoderScaffold(
num_hidden_instances=2,
pooled_output_dim=hidden_size,
pooler_layer_initializer=tf.keras.initializers.TruncatedNormal(
stddev=0.02),
mask_cls=xmask,
embedding_cls=test_network_a.embedding_network,
hidden_cls=hidden_layers)
# 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)
# Create model based off of network a:
data_a, pooled_a = test_network_a([word_ids, mask])
model_a = tf.keras.Model([word_ids, mask], [data_a, pooled_a])
# Create model based off of network b:
data_b, pooled_b = test_network_b([word_ids, mask])
model_b = tf.keras.Model([word_ids, mask], [data_b, pooled_b])
# Create model based off of network b:
data_c, pooled_c = test_network_c([word_ids, mask])
model_c = tf.keras.Model([word_ids, mask], [data_c, pooled_c])
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))
output_a, _ = model_a.predict([word_id_data, mask_data])
output_b, _ = model_b.predict([word_id_data, mask_data])
output_c, _ = model_c.predict([word_id_data, mask_data])
# Outputs from model a and b should be the same since they share the same
# embedding and hidden layers.
self.assertAllEqual(output_a, output_b)
# Outputs from model a and c shouldn't be the same since they share the same
# embedding layer but different number of hidden layers.
self.assertNotAllEqual(output_a, output_c)
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
}
mask_call_list = []
mask_cfg = {
"call_list": mask_call_list
}
# Create a small EncoderScaffold for testing. This time, we pass an already-
# instantiated layer object.
xformer = ValidatedTransformerLayer(**hidden_cfg)
xmask = ValidatedMaskLayer(**mask_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,
mask_cls=xmask,
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)
attention_mask = layers.SelfAttentionMask()([word_embeddings, mask])
network = tf.keras.Model([word_ids, mask],
[word_embeddings, attention_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
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),
}
# 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,
dict_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)
outputs = test_network([word_ids, mask, type_ids])
# Create a model based off of this network:
model = tf.keras.Model([word_ids, mask, type_ids], outputs)
# 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))
preds = model.predict([word_id_data, mask_data, type_id_data])
self.assertEqual(preds["pooled_output"].shape, (3, hidden_size))
# 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)
outputs = test_network([word_ids, mask, type_ids])
model = tf.keras.Model([word_ids, mask, type_ids], outputs)
_ = model.predict([word_id_data, mask_data, type_id_data])
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,
num_output_classes=hidden_size,
classification_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())