def test_high_dim_attention(self, q_dims, v_dims, mask_dims,
attention_axes):
"""Test with a mask tensor."""
test_layer = multi_head_attention.MultiHeadAttention(
num_heads=2, key_dim=2, attention_axes=attention_axes)
batch_size, hidden_size = 3, 8
# Generate data for the input (non-mask) tensors.
query_shape = [batch_size] + q_dims + [hidden_size]
value_shape = [batch_size] + v_dims + [hidden_size]
mask_shape = [batch_size] + mask_dims
query = 10 * np.random.random_sample(query_shape)
value = 10 * np.random.random_sample(value_shape)
# Invoke the data with a random set of mask data. This should mask at least
# one element.
mask_data = np.random.randint(2, size=mask_shape).astype("bool")
# Invoke the same data, but with a null mask (where no elements are masked).
null_mask_data = np.ones(mask_shape)
# Because one data is masked and one is not, the outputs should not be the
# same.
query_tensor = keras.Input(query_shape[1:], name="query")
value_tensor = keras.Input(value_shape[1:], name="value")
mask_tensor = keras.Input(mask_shape[1:], name="mask")
output = test_layer(query=query_tensor,
value=value_tensor,
attention_mask=mask_tensor)
model = keras.Model([query_tensor, value_tensor, mask_tensor], output)
self.assertNotAllClose(model.predict([query, value, mask_data]),
model.predict([query, value, null_mask_data]))
def test_non_masked_self_attention(self):
"""Test with one input (self-attenntion) and no mask tensor."""
test_layer = multi_head_attention.MultiHeadAttention(num_heads=12,
key_dim=64)
# Create a 3-dimensional input (the first dimension is implicit).
query = keras.Input(shape=(40, 80))
output = test_layer(query, query)
self.assertEqual(output.shape.as_list(), [None, 40, 80])
def test_masked_attention(self, use_bias):
"""Test with a mask tensor."""
test_layer = multi_head_attention.MultiHeadAttention(num_heads=2,
key_dim=2,
use_bias=use_bias)
# Create a 3-dimensional input (the first dimension is implicit).
batch_size = 3
query = keras.Input(shape=(4, 8))
value = keras.Input(shape=(2, 8))
mask_tensor = keras.Input(shape=(4, 2))
output = test_layer(query=query,
value=value,
attention_mask=mask_tensor)
# Create a model containing the test layer.
model = keras.Model([query, value, mask_tensor], output)
# Generate data for the input (non-mask) tensors.
from_data = 10 * np.random.random_sample((batch_size, 4, 8))
to_data = 10 * np.random.random_sample((batch_size, 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=(batch_size, 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((batch_size, 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)
# Tests the layer with three inputs: Q, K, V.
key = keras.Input(shape=(2, 8))
output = test_layer(query,
value=value,
key=key,
attention_mask=mask_tensor)
model = keras.Model([query, value, key, mask_tensor], output)
masked_output_data = model.predict(
[from_data, to_data, to_data, mask_data])
unmasked_output_data = model.predict(
[from_data, to_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)
if use_bias:
self.assertLen(test_layer._query_dense.trainable_variables, 2)
self.assertLen(test_layer._output_dense.trainable_variables, 2)
else:
self.assertLen(test_layer._query_dense.trainable_variables, 1)
self.assertLen(test_layer._output_dense.trainable_variables, 1)
def test_attention_scores(self):
"""Test attention outputs with coefficients."""
test_layer = multi_head_attention.MultiHeadAttention(num_heads=12,
key_dim=64)
# Create a 3-dimensional input (the first dimension is implicit).
query = keras.Input(shape=(40, 80))
output, coef = test_layer(query, query, return_attention_scores=True)
self.assertEqual(output.shape.as_list(), [None, 40, 80])
self.assertEqual(coef.shape.as_list(), [None, 12, 40, 40])
def __init__(self):
super(TestModel, self).__init__()
self.attention = multi_head_attention.MultiHeadAttention(
num_heads=3,
key_dim=4,
value_dim=4,
use_bias=True,
dropout=0.0,
output_shape=[12])
def test_initializer(self):
"""Test with a specified initializer."""
test_layer = multi_head_attention.MultiHeadAttention(
num_heads=12,
key_dim=64,
kernel_initializer=keras.initializers.TruncatedNormal(stddev=0.02))
# Create a 3-dimensional input (the first dimension is implicit).
query = keras.Input(shape=(40, 80))
output = test_layer(query, query)
self.assertEqual(output.shape.as_list(), [None, 40, 80])
def test_non_masked_attention(self, value_dim, output_shape, output_dims):
"""Test that the attention layer can be created without a mask tensor."""
test_layer = multi_head_attention.MultiHeadAttention(
num_heads=12,
key_dim=64,
value_dim=value_dim,
output_shape=output_shape)
# Create a 3-dimensional input (the first dimension is implicit).
query = keras.Input(shape=(40, 80))
value = keras.Input(shape=(20, 80))
output = test_layer(query=query, value=value)
self.assertEqual(output.shape.as_list(), [None] + output_dims)
def test_masked_attention_with_scores(self):
"""Test with a mask tensor."""
test_layer = multi_head_attention.MultiHeadAttention(num_heads=2,
key_dim=2)
# Create a 3-dimensional input (the first dimension is implicit).
batch_size = 3
query = keras.Input(shape=(4, 8))
value = keras.Input(shape=(2, 8))
mask_tensor = keras.Input(shape=(4, 2))
output = test_layer(query=query,
value=value,
attention_mask=mask_tensor)
# Create a model containing the test layer.
model = keras.Model([query, value, mask_tensor], output)
# Generate data for the input (non-mask) tensors.
from_data = 10 * np.random.random_sample((batch_size, 4, 8))
to_data = 10 * np.random.random_sample((batch_size, 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=(batch_size, 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((batch_size, 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)
# Create a model containing attention scores.
output, scores = test_layer(query=query,
value=value,
attention_mask=mask_tensor,
return_attention_scores=True)
model = keras.Model([query, value, mask_tensor], [output, scores])
masked_output_data_score, masked_score = model.predict(
[from_data, to_data, mask_data])
unmasked_output_data_score, unmasked_score = model.predict(
[from_data, to_data, null_mask_data])
self.assertNotAllClose(masked_output_data_score,
unmasked_output_data_score)
self.assertAllClose(masked_output_data, masked_output_data_score)
self.assertAllClose(unmasked_output_data, unmasked_output_data_score)
self.assertNotAllClose(masked_score, unmasked_score)
def test_dropout(self):
test_layer = multi_head_attention.MultiHeadAttention(
num_heads=2, key_dim=2, dropout=0.5)
# Generate data for the input (non-mask) tensors.
from_data = keras.backend.ones(shape=(32, 4, 8))
to_data = keras.backend.ones(shape=(32, 2, 8))
train_out = test_layer(from_data, to_data, None, None, None, True)
test_out = test_layer(from_data, to_data, None, None, None, False)
# Output should be close when not in training mode,
# and should not be close when enabling dropout in training mode.
self.assertNotAllClose(
keras.backend.eval(train_out),
keras.backend.eval(test_out))
def test_keras_saving_functional(self, save_format):
model = TestModel()
query = keras.Input(shape=(40, 80))
output = multi_head_attention.MultiHeadAttention(
num_heads=3,
key_dim=4,
value_dim=4,
use_bias=True,
dropout=0.0)(query, query)
model = keras.Model(inputs=query, outputs=output)
model_path = self.get_temp_dir() + "/tmp_model"
keras.models.save_model(model, model_path, save_format=save_format)
reloaded_model = keras.models.load_model(model_path)
self.assertEqual(
len(model.trainable_variables), len(reloaded_model.trainable_variables))
for src_v, loaded_v in zip(model.trainable_variables,
reloaded_model.trainable_variables):
self.assertAllEqual(src_v, loaded_v)
def test_create_without_build(self):
not_intialized_layer = multi_head_attention.MultiHeadAttention(
num_heads=3, key_dim=4, value_dim=4)
multi_head_attention.MultiHeadAttention.from_config(
not_intialized_layer.get_config())
