def test_layer_creation_with_mask(self):
sequence_length = 21
width = 80
call_list = []
attention_layer_cfg = {
'num_heads': 10,
'head_size': 8,
'call_list': call_list,
}
test_layer = transformer_scaffold.TransformerScaffold(
attention_cls=ValidatedAttentionLayer,
attention_cfg=attention_layer_cfg,
num_attention_heads=10,
intermediate_size=2048,
intermediate_activation='relu')
# Create a 3-dimensional input (the first dimension is implicit).
data_tensor = tf.keras.Input(shape=(sequence_length, width))
# Create a 2-dimensional input (the first dimension is implicit).
mask_tensor = tf.keras.Input(shape=(sequence_length, sequence_length))
output_tensor = test_layer([data_tensor, mask_tensor])
# The default output of a transformer layer should be the same as the input.
self.assertEqual(data_tensor.shape.as_list(),
output_tensor.shape.as_list())
# 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_layer_restoration_from_config(self):
sequence_length = 21
width = 80
call_list = []
attention_layer_cfg = {
'num_heads': 10,
'head_size': 8,
'call_list': call_list,
'name': 'test_layer',
}
test_layer = transformer_scaffold.TransformerScaffold(
attention_cls=ValidatedAttentionLayer,
attention_cfg=attention_layer_cfg,
num_attention_heads=10,
intermediate_size=2048,
intermediate_activation='relu')
# Create a 3-dimensional input (the first dimension is implicit).
data_tensor = tf.keras.Input(shape=(sequence_length, width))
# Create a 2-dimensional input (the first dimension is implicit).
mask_tensor = tf.keras.Input(shape=(sequence_length, sequence_length))
output_tensor = test_layer([data_tensor, mask_tensor])
# Create a model from the test layer.
model = tf.keras.Model([data_tensor, mask_tensor], output_tensor)
# Invoke the model on test data. We can't validate the output data itself
# (the NN is too complex) but this will rule out structural runtime errors.
batch_size = 6
input_data = 10 * np.random.random_sample(
(batch_size, sequence_length, width))
# The attention mask should be of shape (batch, from_seq_len, to_seq_len),
# which here is (batch, sequence_length, sequence_length)
mask_data = np.random.randint(2,
size=(batch_size, sequence_length,
sequence_length))
pre_serialization_output = model.predict([input_data, mask_data])
# Serialize the model config. Pass the serialized data through json to
# ensure that we can serialize this layer to disk.
serialized_data = json.dumps(model.get_config())
post_string_serialized_data = json.loads(serialized_data)
# Create a new model from the old config, and copy the weights. These models
# should have identical outputs.
new_model = tf.keras.Model.from_config(post_string_serialized_data)
new_model.set_weights(model.get_weights())
output = new_model.predict([input_data, mask_data])
self.assertAllClose(pre_serialization_output, output)
# If the layer was configured correctly, it should have a list attribute
# (since it should have the custom class and config passed to it).
new_model.summary()
new_call_list = new_model.get_layer(
name='transformer_scaffold')._attention_layer.list
self.assertNotEmpty(new_call_list)
self.assertTrue(new_call_list[0],
"The passed layer class wasn't instantiated.")
def test_layer_invocation(self):
sequence_length = 21
width = 80
call_list = []
attention_layer_cfg = {
'num_heads': 10,
'head_size': 8,
'call_list': call_list,
}
test_layer = transformer_scaffold.TransformerScaffold(
attention_cls=ValidatedAttentionLayer,
attention_cfg=attention_layer_cfg,
num_attention_heads=10,
intermediate_size=2048,
intermediate_activation='relu')
# Create a 3-dimensional input (the first dimension is implicit).
data_tensor = tf.keras.Input(shape=(sequence_length, width))
output_tensor = test_layer(data_tensor)
# Create a model from the test layer.
model = tf.keras.Model(data_tensor, output_tensor)
# Invoke the model on test data. We can't validate the output data itself
# (the NN is too complex) but this will rule out structural runtime errors.
batch_size = 6
input_data = 10 * np.random.random_sample(
(batch_size, sequence_length, width))
_ = model.predict(input_data)
# 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_layer_creation_with_incorrect_mask_fails(self):
sequence_length = 21
width = 80
call_list = []
attention_layer_cfg = {
'num_heads': 10,
'head_size': 8,
'call_list': call_list,
}
test_layer = transformer_scaffold.TransformerScaffold(
attention_cls=ValidatedAttentionLayer,
attention_cfg=attention_layer_cfg,
num_attention_heads=10,
intermediate_size=2048,
intermediate_activation='relu')
# Create a 3-dimensional input (the first dimension is implicit).
data_tensor = tf.keras.Input(shape=(sequence_length, width))
# Create a 2-dimensional input (the first dimension is implicit).
mask_tensor = tf.keras.Input(shape=(sequence_length,
sequence_length - 3))
with self.assertRaisesRegex(ValueError,
'When passing a mask tensor.*'):
_ = test_layer([data_tensor, mask_tensor])
def test_layer_invocation_with_float16_dtype(self):
sequence_length = 21
width = 80
call_list = []
attention_layer_cfg = {
'num_heads': 10,
'head_size': 8,
'call_list': call_list,
}
test_layer = transformer_scaffold.TransformerScaffold(
attention_cls=ValidatedAttentionLayer,
attention_cfg=attention_layer_cfg,
num_attention_heads=10,
intermediate_size=2048,
intermediate_activation='relu',
dtype='float16')
# Create a 3-dimensional input (the first dimension is implicit).
data_tensor = tf.keras.Input(shape=(sequence_length, width),
dtype=tf.float16)
# Create a 2-dimensional input (the first dimension is implicit).
mask_tensor = tf.keras.Input(shape=(sequence_length, sequence_length))
output_tensor = test_layer([data_tensor, mask_tensor])
# Create a model from the test layer.
model = tf.keras.Model([data_tensor, mask_tensor], output_tensor)
# Invoke the model on test data. We can't validate the output data itself
# (the NN is too complex) but this will rule out structural runtime errors.
batch_size = 6
input_data = (10 * np.random.random_sample(
(batch_size, sequence_length, width))).astype(np.float16)
# The attention mask should be of shape (batch, from_seq_len, to_seq_len),
# which here is (batch, sequence_length, sequence_length)
mask_data = np.random.randint(2,
size=(batch_size, sequence_length,
sequence_length))
_ = model.predict([input_data, mask_data])
# 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.")