def test_embeddings_as_inputs(self):
hidden_size = 32
sequence_length = 21
dense_sequence_length = 20
# Create a small BertEncoder for testing.
test_network = bert_dense_encoder.BertDenseEncoder(
vocab_size=100,
hidden_size=hidden_size,
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)
dense_inputs = tf.keras.Input(shape=(dense_sequence_length,
hidden_size),
dtype=tf.float32)
dense_mask = tf.keras.Input(shape=(dense_sequence_length, ),
dtype=tf.int32)
dense_type_ids = tf.keras.Input(shape=(dense_sequence_length, ),
dtype=tf.int32)
test_network.build(
dict(input_word_ids=word_ids,
input_mask=mask,
input_type_ids=type_ids,
dense_inputs=dense_inputs,
dense_mask=dense_mask,
dense_type_ids=dense_type_ids))
embeddings = test_network.get_embedding_layer()(word_ids)
# Calls with the embeddings.
dict_outputs = test_network(
dict(input_word_embeddings=embeddings,
input_mask=mask,
input_type_ids=type_ids,
dense_inputs=dense_inputs,
dense_mask=dense_mask,
dense_type_ids=dense_type_ids))
all_encoder_outputs = dict_outputs["encoder_outputs"]
pooled = dict_outputs["pooled_output"]
expected_data_shape = [
None, sequence_length + dense_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_dict_outputs_network_creation(self):
hidden_size = 32
sequence_length = 21
dense_sequence_length = 20
# Create a small dense BertDenseEncoder for testing.
kwargs = {}
test_network = bert_dense_encoder.BertDenseEncoder(
vocab_size=100,
hidden_size=hidden_size,
num_attention_heads=2,
num_layers=3,
**kwargs)
# 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)
dense_inputs = tf.keras.Input(shape=(dense_sequence_length,
hidden_size),
dtype=tf.float32)
dense_mask = tf.keras.Input(shape=(dense_sequence_length, ),
dtype=tf.int32)
dense_type_ids = tf.keras.Input(shape=(dense_sequence_length, ),
dtype=tf.int32)
dict_outputs = test_network(
dict(input_word_ids=word_ids,
input_mask=mask,
input_type_ids=type_ids,
dense_inputs=dense_inputs,
dense_mask=dense_mask,
dense_type_ids=dense_type_ids))
data = dict_outputs["sequence_output"]
pooled = dict_outputs["pooled_output"]
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 + dense_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 test_dict_outputs_network_creation_with_float16_dtype(self):
hidden_size = 32
sequence_length = 21
dense_sequence_length = 20
tf.keras.mixed_precision.set_global_policy("mixed_float16")
# Create a small BertEncoder for testing.
test_network = bert_dense_encoder.BertDenseEncoder(
vocab_size=100,
hidden_size=hidden_size,
num_attention_heads=2,
num_layers=3,
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)
dense_inputs = tf.keras.Input(shape=(dense_sequence_length,
hidden_size),
dtype=tf.float32)
dense_mask = tf.keras.Input(shape=(dense_sequence_length, ),
dtype=tf.int32)
dense_type_ids = tf.keras.Input(shape=(dense_sequence_length, ),
dtype=tf.int32)
dict_outputs = test_network(
dict(input_word_ids=word_ids,
input_mask=mask,
input_type_ids=type_ids,
dense_inputs=dense_inputs,
dense_mask=dense_mask,
dense_type_ids=dense_type_ids))
data = dict_outputs["sequence_output"]
pooled = dict_outputs["pooled_output"]
expected_data_shape = [
None, sequence_length + dense_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_dict_outputs_network_invocation(self, encoder_cls, output_range,
out_seq_len):
hidden_size = 32
sequence_length = 21
dense_sequence_length = 20
vocab_size = 57
num_types = 7
# Create a small BertEncoder for testing.
test_network = encoder_cls(vocab_size=vocab_size,
hidden_size=hidden_size,
num_attention_heads=2,
num_layers=3,
type_vocab_size=num_types,
output_range=output_range,
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)
dense_inputs = tf.keras.Input(shape=(dense_sequence_length,
hidden_size),
dtype=tf.float32)
dense_mask = tf.keras.Input(shape=(dense_sequence_length, ),
dtype=tf.int32)
dense_type_ids = tf.keras.Input(shape=(dense_sequence_length, ),
dtype=tf.int32)
dict_outputs = test_network(
dict(input_word_ids=word_ids,
input_mask=mask,
input_type_ids=type_ids,
dense_inputs=dense_inputs,
dense_mask=dense_mask,
dense_type_ids=dense_type_ids))
data = dict_outputs["sequence_output"]
pooled = dict_outputs["pooled_output"]
# Create a model based off of this network:
model = tf.keras.Model([
word_ids, mask, type_ids, dense_inputs, dense_mask, dense_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))
dense_input_data = np.random.rand(batch_size, dense_sequence_length,
hidden_size)
dense_mask_data = np.random.randint(2,
size=(batch_size,
dense_sequence_length))
dense_type_ids_data = np.random.randint(num_types,
size=(batch_size,
dense_sequence_length))
outputs = model.predict([
word_id_data, mask_data, type_id_data, dense_input_data,
dense_mask_data, dense_type_ids_data
])
self.assertEqual(outputs[0].shape[1], out_seq_len)
# Creates a BertEncoder with max_sequence_length != sequence_length
max_sequence_length = 128
test_network = encoder_cls(vocab_size=vocab_size,
hidden_size=hidden_size,
max_sequence_length=max_sequence_length,
num_attention_heads=2,
num_layers=3,
type_vocab_size=num_types,
dict_outputs=True)
dict_outputs = test_network(
dict(input_word_ids=word_ids,
input_mask=mask,
input_type_ids=type_ids,
dense_inputs=dense_inputs,
dense_mask=dense_mask,
dense_type_ids=dense_type_ids))
data = dict_outputs["sequence_output"]
pooled = dict_outputs["pooled_output"]
model = tf.keras.Model([
word_ids, mask, type_ids, dense_inputs, dense_mask, dense_type_ids
], [data, pooled])
outputs = model.predict([
word_id_data, mask_data, type_id_data, dense_input_data,
dense_mask_data, dense_type_ids_data
])
self.assertEqual(outputs[0].shape[1],
sequence_length + dense_sequence_length)
# Creates a BertEncoder with embedding_width != hidden_size
embedding_width = 16
test_network = bert_dense_encoder.BertDenseEncoder(
vocab_size=vocab_size,
hidden_size=hidden_size,
max_sequence_length=max_sequence_length,
num_attention_heads=2,
num_layers=3,
type_vocab_size=num_types,
embedding_width=embedding_width,
dict_outputs=True)
dense_inputs = tf.keras.Input(shape=(dense_sequence_length,
embedding_width),
dtype=tf.float32)
dense_input_data = np.zeros(
(batch_size, dense_sequence_length, embedding_width), dtype=float)
dict_outputs = test_network(
dict(input_word_ids=word_ids,
input_mask=mask,
input_type_ids=type_ids,
dense_inputs=dense_inputs,
dense_mask=dense_mask,
dense_type_ids=dense_type_ids))
data = dict_outputs["sequence_output"]
pooled = dict_outputs["pooled_output"]
model = tf.keras.Model([
word_ids, mask, type_ids, dense_inputs, dense_mask, dense_type_ids
], [data, pooled])
outputs = model.predict([
word_id_data, mask_data, type_id_data, dense_input_data,
dense_mask_data, dense_type_ids_data
])
self.assertEqual(outputs[0].shape[-1], hidden_size)
self.assertTrue(hasattr(test_network, "_embedding_projection"))