def test_embeddings_as_inputs(self):
hidden_size = 32
sequence_length = 21
# Create a small BertEncoder for testing.
test_network = bert_encoder.BertEncoderV2(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)
test_network.build(
dict(input_word_ids=word_ids,
input_mask=mask,
input_type_ids=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))
all_encoder_outputs = dict_outputs["encoder_outputs"]
pooled = dict_outputs["pooled_output"]
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_weights_forward_compatible(self):
batch_size = 3
hidden_size = 32
sequence_length = 21
vocab_size = 57
num_types = 7
kwargs = dict(
vocab_size=vocab_size,
hidden_size=hidden_size,
num_attention_heads=2,
num_layers=3,
type_vocab_size=num_types,
output_range=None)
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 = dict(
input_word_ids=word_id_data,
input_mask=mask_data,
input_type_ids=type_id_data)
# Create small BertEncoders for testing.
new_net = bert_encoder.BertEncoderV2(**kwargs)
_ = new_net(data)
kwargs["dict_outputs"] = True
old_net = bert_encoder.BertEncoder(**kwargs)
_ = old_net(data)
new_net._embedding_layer.set_weights(old_net._embedding_layer.get_weights())
new_net._position_embedding_layer.set_weights(
old_net._position_embedding_layer.get_weights())
new_net._type_embedding_layer.set_weights(
old_net._type_embedding_layer.get_weights())
new_net._embedding_norm_layer.set_weights(
old_net._embedding_norm_layer.get_weights())
# embedding_dropout has no weights.
if hasattr(old_net, "_embedding_projection"):
new_net._embedding_projection.set_weights(
old_net._embedding_projection.get_weights())
# attention_mask_layer has no weights.
new_net._pooler_layer.set_weights(old_net._pooler_layer.get_weights())
for otl, ntl in zip(old_net._transformer_layers,
new_net._transformer_layers):
ntl.set_weights(otl.get_weights())
def check_output_close(data, net1, net2):
output1 = net1(data)
output2 = net2(data)
for key in output1:
self.assertAllClose(output1[key], output2[key])
check_output_close(data, old_net, new_net)
def test_dict_outputs_network_creation(self):
hidden_size = 32
sequence_length = 21
dense_sequence_length = 20
# Create a small dense BertEncoderV2 for testing.
kwargs = {}
test_network = bert_encoder.BertEncoderV2(
vocab_size=100,
hidden_size=hidden_size,
num_attention_heads=2,
num_layers=3,
with_dense_inputs=True,
**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_keras_model_checkpoint_forward_compatible(self):
batch_size = 3
hidden_size = 32
sequence_length = 21
vocab_size = 57
num_types = 7
kwargs = dict(
vocab_size=vocab_size,
hidden_size=hidden_size,
num_attention_heads=2,
num_layers=3,
type_vocab_size=num_types,
output_range=None)
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 = dict(
input_word_ids=word_id_data,
input_mask=mask_data,
input_type_ids=type_id_data)
kwargs["dict_outputs"] = True
old_net = bert_encoder.BertEncoder(**kwargs)
inputs = old_net.inputs
outputs = old_net(inputs)
old_model = tf.keras.Model(inputs=inputs, outputs=outputs)
old_model_outputs = old_model(data)
ckpt = tf.train.Checkpoint(net=old_model)
path = ckpt.save(self.get_temp_dir())
del kwargs["dict_outputs"]
new_net = bert_encoder.BertEncoderV2(**kwargs)
inputs = new_net.inputs
outputs = new_net(inputs)
new_model = tf.keras.Model(inputs=inputs, outputs=outputs)
new_ckpt = tf.train.Checkpoint(net=new_model)
status = new_ckpt.restore(path)
status.assert_existing_objects_matched()
new_model_outputs = new_model(data)
self.assertAllEqual(old_model_outputs.keys(), new_model_outputs.keys())
for key in old_model_outputs:
self.assertAllClose(old_model_outputs[key], new_model_outputs[key])
def test_dict_outputs_all_encoder_outputs_network_creation(self):
hidden_size = 32
sequence_length = 21
dense_sequence_length = 20
# Create a small BertEncoder for testing.
test_network = bert_encoder.BertEncoderV2(
vocab_size=100,
hidden_size=hidden_size,
num_attention_heads=2,
num_layers=3,
dict_outputs=True,
with_dense_inputs=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))
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_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_encoder.BertEncoderV2(
vocab_size=100,
hidden_size=hidden_size,
num_attention_heads=2,
num_layers=3,
dict_outputs=True,
with_dense_inputs=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_checkpoint_forward_compatible(self):
batch_size = 3
hidden_size = 32
sequence_length = 21
vocab_size = 57
num_types = 7
kwargs = dict(vocab_size=vocab_size,
hidden_size=hidden_size,
num_attention_heads=2,
num_layers=3,
type_vocab_size=num_types,
output_range=None)
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 = dict(input_word_ids=word_id_data,
input_mask=mask_data,
input_type_ids=type_id_data)
old_net = bert_encoder.BertEncoderV2(**kwargs)
old_net_outputs = old_net(data)
ckpt = tf.train.Checkpoint(net=old_net)
path = ckpt.save(self.get_temp_dir())
new_net = encoder.TokenDropBertEncoder(token_keep_k=sequence_length,
token_allow_list=(),
token_deny_list=(),
**kwargs)
new_ckpt = tf.train.Checkpoint(net=new_net)
status = new_ckpt.restore(path)
status.assert_existing_objects_matched()
# assert_consumed will fail because the old model has redundant nodes.
new_net_outputs = new_net(data)
self.assertAllEqual(old_net_outputs.keys(), new_net_outputs.keys())
for key in old_net_outputs:
self.assertAllClose(old_net_outputs[key], new_net_outputs[key])
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,
with_dense_inputs=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_encoder.BertEncoderV2(
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"))