def test_masked_attention(self):
"""Test with a mask tensor."""
test_layer = attention.Attention(num_heads=2, head_size=2)
# Create a 3-dimensional input (the first dimension is implicit).
from_tensor = tf.keras.Input(shape=(4, 8))
to_tensor = tf.keras.Input(shape=(2, 8))
mask_tensor = tf.keras.Input(shape=(4, 2))
output = test_layer([from_tensor, to_tensor, mask_tensor])
# Create a model containing the test layer.
model = tf.keras.Model([from_tensor, to_tensor, mask_tensor], output)
# Generate data for the input (non-mask) tensors.
from_data = 10 * np.random.random_sample((3, 4, 8))
to_data = 10 * np.random.random_sample((3, 2, 8))
# Invoke the data with a random set of mask data. This should mask at least
# one element.
mask_data = np.random.randint(2, size=(3, 4, 2))
masked_output_data = model.predict([from_data, to_data, mask_data])
# Invoke the same data, but with a null mask (where no elements are masked).
null_mask_data = np.ones((3, 4, 2))
unmasked_output_data = model.predict(
[from_data, to_data, null_mask_data])
# Because one data is masked and one is not, the outputs should not be the
# same.
self.assertNotAllClose(masked_output_data, unmasked_output_data)
def test_non_masked_self_attention(self):
"""Test with one input (self-attenntion) and no mask tensor."""
test_layer = attention.Attention(num_heads=12, head_size=64)
# Create a 3-dimensional input (the first dimension is implicit).
from_tensor = tf.keras.Input(shape=(40, 80))
output = test_layer([from_tensor, from_tensor])
self.assertEqual(output.shape.as_list(), [None, 40, 12, 64])
def test_initializer(self):
"""Test with a specified initializer."""
test_layer = attention.Attention(
num_heads=12,
head_size=64,
kernel_initializer=tf.keras.initializers.TruncatedNormal(
stddev=0.02))
# Create a 3-dimensional input (the first dimension is implicit).
from_tensor = tf.keras.Input(shape=(40, 80))
output = test_layer([from_tensor, from_tensor])
self.assertEqual(output.shape.as_list(), [None, 40, 12, 64])
def build(self, input_shape):
input_tensor = input_shape[0] if len(input_shape) == 2 else input_shape
input_tensor_shape = tf.TensorShape(input_tensor)
if len(input_tensor_shape) != 3:
raise ValueError(
"TransformerLayer expects a three-dimensional input of "
"shape [batch, sequence, width].")
batch_size, sequence_length, hidden_size = input_tensor_shape
if len(input_shape) == 2:
mask_tensor_shape = tf.TensorShape(input_shape[1])
expected_mask_tensor_shape = tf.TensorShape(
[batch_size, sequence_length, sequence_length])
if not expected_mask_tensor_shape.is_compatible_with(
mask_tensor_shape):
raise ValueError(
"When passing a mask tensor to TransformerLayer, the "
"mask tensor must be of shape [batch, "
"sequence_length, sequence_length] (here %s). Got a "
"mask tensor of shape %s." %
(expected_mask_tensor_shape, mask_tensor_shape))
if hidden_size % self._num_heads != 0:
raise ValueError(
"The input size (%d) is not a multiple of the number of attention "
"heads (%d)" % (hidden_size, self._num_heads))
self._attention_head_size = int(hidden_size // self._num_heads)
self._attention_layer = attention.Attention(
num_heads=self._num_heads,
head_size=self._attention_head_size,
dropout_rate=self._attention_dropout_rate,
kernel_initializer=self._kernel_initializer,
bias_initializer=self._bias_initializer,
kernel_regularizer=self._kernel_regularizer,
bias_regularizer=self._bias_regularizer,
activity_regularizer=self._activity_regularizer,
kernel_constraint=self._kernel_constraint,
bias_constraint=self._bias_constraint,
name="self_attention")
self._attention_output_dense = dense_einsum.DenseEinsum(
output_shape=hidden_size,
num_summed_dimensions=2,
kernel_initializer=self._kernel_initializer,
bias_initializer=self._bias_initializer,
kernel_regularizer=self._kernel_regularizer,
bias_regularizer=self._bias_regularizer,
activity_regularizer=self._activity_regularizer,
kernel_constraint=self._kernel_constraint,
bias_constraint=self._bias_constraint,
name="self_attention_output")
self._attention_dropout = tf.keras.layers.Dropout(
rate=self._dropout_rate)
# Use float32 in layernorm for numeric stability.
# It is probably safe in mixed_float16, but we haven't validated this yet.
self._attention_layer_norm = (tf.keras.layers.LayerNormalization(
name="self_attention_layer_norm",
axis=-1,
epsilon=1e-12,
dtype=tf.float32))
self._intermediate_dense = dense_einsum.DenseEinsum(
output_shape=self._intermediate_size,
activation=None,
kernel_initializer=self._kernel_initializer,
bias_initializer=self._bias_initializer,
kernel_regularizer=self._kernel_regularizer,
bias_regularizer=self._bias_regularizer,
activity_regularizer=self._activity_regularizer,
kernel_constraint=self._kernel_constraint,
bias_constraint=self._bias_constraint,
name="intermediate")
# Use float32 in intermediate gelu activation for numeric stability.
# TODO(b/149117297): investigate gelu numeric stability.
self._intermediate_activation_layer = tf.keras.layers.Activation(
self._intermediate_activation, dtype=tf.float32)
self._output_dense = dense_einsum.DenseEinsum(
output_shape=hidden_size,
kernel_initializer=self._kernel_initializer,
bias_initializer=self._bias_initializer,
kernel_regularizer=self._kernel_regularizer,
bias_regularizer=self._bias_regularizer,
activity_regularizer=self._activity_regularizer,
kernel_constraint=self._kernel_constraint,
bias_constraint=self._bias_constraint,
name="output")
self._output_dropout = tf.keras.layers.Dropout(rate=self._dropout_rate)
# Use float32 in layernorm for numeric stability.
self._output_layer_norm = tf.keras.layers.LayerNormalization(
name="output_layer_norm", axis=-1, epsilon=1e-12, dtype=tf.float32)
super(Transformer, self).build(input_shape)
