def test_multi_dim_with_key(self):
# Query tensor of shape [1, 1, 1]
q = np.array([[[1.1]]], dtype=np.float32)
# Value tensor of shape [1, 3, 1]
v = np.array([[[0.5], [0.8], [-0.3]]], dtype=np.float32)
# Key tensor of shape [1, 3, 1]
k = np.array([[[1.6], [0.7], [-0.8]]], dtype=np.float32)
# Value mask tensor of shape [1, 3]
v_mask = np.array([[True, True, False]], dtype=np.bool_)
attention_layer = dense_attention.AdditiveAttention()
attention_layer.build(input_shape=([1, 1, 1], [1, 3, 1]))
# Scale tensor of shape [1]
attention_layer.scale = np.array([[[0.5]]], dtype=np.float32)
actual = attention_layer([q, v, k], mask=[None, v_mask])
# pylint:disable=line-too-long
# Expected scores of shape [1, 1, 3]
# scores = [[[0.5 * tanh(1.1 + 1.6), 0.5 * tanh(1.1 + 0.7), 0.5 * tanh(1.1 - 0.8)]]]
# = [[[0.49550372683, 0.47340300642, 0.14565630622]]]
# Expected attention distribution = softmax(scores) with zeros in
# positions where v_mask == False.
# => attention_distribution000
# = exp(0.49550372683)/(exp(0.49550372683) + exp(0.47340300642))
# = 0.50552495521
# attention_distribution001
# = exp(0.47340300642)/(exp(0.49550372683) + exp(0.47340300642))
# = 0.49447504478
# attention_distribution002 = 0
#
# Expected tensor of shape [1, 1, 1].
# expected000 = 0.50552495521 * 0.5 + 0.49447504478 * 0.8 - 0 * 0.3
# = 0.64834251342
# pylint:enable=line-too-long
expected = np.array([[[0.64834251342]]], dtype=np.float32)
self.assertAllClose(expected, actual)
def test_calculate_scores_multi_dim(self):
# Query tensor of shape [1, 2, 4]
q = np.array([[[1., 1.1, 1.2, 1.3], [2., 2.1, 2.2, 2.3]]],
dtype=np.float32)
# Key tensor of shape [1, 3, 4]
k = np.array([[[1.5, 1.6, 1.7, 1.8], [2.5, 2.6, 2.7, 2.8],
[3.5, 3.6, 3.7, 3.8]]],
dtype=np.float32)
attention_layer = dense_attention.AdditiveAttention()
attention_layer.build(input_shape=([1, 2, 4], [1, 3, 4]))
# Scale tensor of shape [4]
attention_layer.scale = np.array([[[0.5, 0.6, 0.7, 0.8]]],
dtype=np.float32)
actual = attention_layer._calculate_scores(query=q, key=k)
# pylint:disable=line-too-long
# expected000 = 0.5*tanh(1.+1.5) + 0.6*tanh(1.1+1.6) + 0.7*tanh(1.2+1.7) + 0.8*tanh(1.3+1.8) = 2.58044532581
# expected001 = 0.5*tanh(1.+2.5) + 0.6*tanh(1.1+2.6) + 0.7*tanh(1.2+2.7) + 0.8*tanh(1.3+2.8) = 2.59734317449
# expected002 = 0.5*tanh(1.+3.5) + 0.6*tanh(1.1+3.6) + 0.7*tanh(1.2+3.7) + 0.8*tanh(1.3+3.8) = 2.59964024652
# expected010 = 0.5*tanh(2.+1.5) + 0.6*tanh(2.1+1.6) + 0.7*tanh(2.2+1.7) + 0.8*tanh(2.3+1.8) = 2.59734317449
# expected011 = 0.5*tanh(2.+2.5) + 0.6*tanh(2.1+2.6) + 0.7*tanh(2.2+2.7) + 0.8*tanh(2.3+2.8) = 2.59964024652
# expected012 = 0.5*tanh(2.+3.5) + 0.6*tanh(2.1+3.6) + 0.7*tanh(2.2+3.7) + 0.8*tanh(2.3+3.8) = 2.59995130916
# pylint:enable=line-too-long
expected = np.array([[[2.58044532581, 2.59734317449, 2.59964024652],
[2.59734317449, 2.59964024652, 2.59995130916]]],
dtype=np.float32)
self.assertAllClose(expected, actual)
def test_serialization(self):
# Test serialization with use_scale
layer = dense_attention.AdditiveAttention(use_scale=True)
config = keras.layers.serialize(layer)
new_layer = keras.layers.deserialize(config)
self.assertEqual(new_layer.use_scale, True)
config = layer.get_config()
new_layer = dense_attention.AdditiveAttention.from_config(config)
self.assertEqual(new_layer.use_scale, True)
def test_mixed_float16_policy(self):
# Test case for GitHub issue:
# https://github.com/tensorflow/tensorflow/issues/46064
with policy.policy_scope('mixed_float16'):
q = math_ops.cast(random_ops.random_uniform((2, 3, 4), seed=1),
'float16')
v = math_ops.cast(random_ops.random_uniform((2, 3, 4), seed=2),
'float16')
k = math_ops.cast(random_ops.random_uniform((2, 3, 4), seed=3),
'float16')
layer = dense_attention.AdditiveAttention(causal=True)
_ = layer([q, v, k])
def test_shape_no_scale(self):
# Query tensor of shape [1, 2, 4]
q = np.array([[[1., 1.1, 1.2, 1.3], [2., 2.1, 2.2, 2.3]]],
dtype=np.float32)
# Value tensor of shape [1, 3, 4]
v = np.array([[[1.5, 1.6, 1.7, 1.8], [2.5, 2.6, 2.7, 2.8],
[3.5, 3.6, 3.7, 3.8]]],
dtype=np.float32)
# Value mask tensor of shape [1, 3]
v_mask = np.array([[True, True, False]], dtype=np.bool_)
attention_layer = dense_attention.AdditiveAttention(use_scale=False)
actual = attention_layer([q, v], mask=[None, v_mask])
expected_shape = [1, 2, 4]
self.assertAllEqual(expected_shape, array_ops.shape(actual))
def test_calculate_scores_one_dim(self):
# Query tensor of shape [1, 1, 1]
q = np.array([[[1.1]]], dtype=np.float32)
# Key tensor of shape [1, 1, 1]
k = np.array([[[1.6]]], dtype=np.float32)
attention_layer = dense_attention.AdditiveAttention()
attention_layer.build(input_shape=([1, 1, 1], [1, 1, 1]))
# Scale tensor of shape [1]
attention_layer.scale = np.array([[[0.5]]], dtype=np.float32)
actual = attention_layer._calculate_scores(query=q, key=k)
# Expected tensor of shape [1, 1, 1].
# expected000 = 0.5 * tanh(1.1 + 1.6) = 0.49550372683
expected = np.array([[[0.49550372683]]], dtype=np.float32)
self.assertAllClose(expected, actual)
def test_multi_dim_with_query_mask(self):
# Query tensor of shape [1, 2, 1]
q = np.array([[[1.1], [-0.5]]], dtype=np.float32)
# Value tensor of shape [1, 3, 1]
v = np.array([[[1.6], [0.7], [-0.8]]], dtype=np.float32)
# Query mask tensor of shape [1, 2]
q_mask = np.array([[True, False]], dtype=np.bool_)
# Value mask tensor of shape [1, 3]
v_mask = np.array([[True, True, False]], dtype=np.bool_)
attention_layer = dense_attention.AdditiveAttention()
attention_layer.build(input_shape=([1, 1, 1], [1, 3, 1]))
# Scale tensor of shape [1]
attention_layer.scale = np.array([[[0.5]]], dtype=np.float32)
actual = attention_layer([q, v], mask=[q_mask, v_mask])
# pylint:disable=line-too-long
# Expected scores of shape [1, 2, 3]
# scores = [[[0.5 * tanh(1.1 + 1.6), 0.5 * tanh(1.1 + 0.7), 0.5 * tanh(1.1 - 0.8)],
# [0.5 * tanh(-0.5 + 1.6), 0.5 * tanh(-0.5 + 0.7), 0.5 * tanh(-0.5 - 0.8)]]]
# = [[[0.49550372683, 0.47340300642, 0.14565630622],
# [0.40024951088, 0.09868766011, -0.43086157965]]]
# Expected attention distribution = softmax(scores) with zeros in
# positions where v_mask == False.
# => attention_distribution000
# = exp(0.49550372683)/(exp(0.49550372683) + exp(0.47340300642))
# = 0.50552495521
# attention_distribution001
# = exp(0.47340300642)/(exp(0.49550372683) + exp(0.47340300642))
# = 0.49447504478
# attention_distribution002 = 0
# => attention_distribution010
# = exp(0.40024951088)/(exp(0.40024951088) + exp(0.09868766011))
# = 0.57482427975
# attention_distribution011
# = exp(0.09868766011)/(exp(0.40024951088) + exp(0.09868766011))
# = 0.42517572025
# attention_distribution012 = 0
#
# Expected tensor of shape [1, 2, 1] with zeros where q_mask == False.
# expected000 = 0.50552495521 * 1.6 + 0.49447504478 * 0.7 - 0 * 0.8
# = 1.15497245968
# expected000 = 0
# pylint:enable=line-too-long
expected = np.array([[[1.15497245968], [0.]]], dtype=np.float32)
self.assertAllClose(expected, actual)
class LayerCorrectnessTest(keras_parameterized.TestCase):
def setUp(self):
super(LayerCorrectnessTest, self).setUp()
# Set two virtual CPUs to test MirroredStrategy with multiple devices
cpus = config_module.list_physical_devices('CPU')
config_module.set_logical_device_configuration(cpus[0], [
context.LogicalDeviceConfiguration(),
context.LogicalDeviceConfiguration(),
])
def _create_model_from_layer(self, layer, input_shapes):
inputs = [layers.Input(batch_input_shape=s) for s in input_shapes]
if len(inputs) == 1:
inputs = inputs[0]
y = layer(inputs)
model = models.Model(inputs, y)
model.compile('sgd', 'mse')
return model
@parameterized.named_parameters(
('LeakyReLU', advanced_activations.LeakyReLU, (2, 2)),
('PReLU', advanced_activations.PReLU, (2, 2)),
('ELU', advanced_activations.ELU, (2, 2)),
('ThresholdedReLU', advanced_activations.ThresholdedReLU, (2, 2)),
('Softmax', advanced_activations.Softmax, (2, 2)),
('ReLU', advanced_activations.ReLU, (2, 2)),
('Conv1D', lambda: convolutional.Conv1D(2, 2), (2, 2, 1)),
('Conv2D', lambda: convolutional.Conv2D(2, 2), (2, 2, 2, 1)),
('Conv3D', lambda: convolutional.Conv3D(2, 2), (2, 2, 2, 2, 1)),
('Conv2DTranspose', lambda: convolutional.Conv2DTranspose(2, 2),
(2, 2, 2, 2)),
('SeparableConv2D', lambda: convolutional.SeparableConv2D(2, 2),
(2, 2, 2, 1)),
('DepthwiseConv2D', lambda: convolutional.DepthwiseConv2D(2, 2),
(2, 2, 2, 1)),
('UpSampling2D', convolutional.UpSampling2D, (2, 2, 2, 1)),
('ZeroPadding2D', convolutional.ZeroPadding2D, (2, 2, 2, 1)),
('Cropping2D', convolutional.Cropping2D, (2, 3, 3, 1)),
('ConvLSTM2D',
lambda: convolutional_recurrent.ConvLSTM2D(4, kernel_size=(2, 2)),
(4, 4, 4, 4, 4)),
('Dense', lambda: core.Dense(2), (2, 2)),
('Dropout', lambda: core.Dropout(0.5), (2, 2)),
('SpatialDropout2D', lambda: core.SpatialDropout2D(0.5), (2, 2, 2, 2)),
('Activation', lambda: core.Activation('sigmoid'), (2, 2)),
('Reshape', lambda: core.Reshape((1, 4, 1)), (2, 2, 2)),
('Permute', lambda: core.Permute((2, 1)), (2, 2, 2)),
('Attention', dense_attention.Attention, [(2, 2, 3), (2, 3, 3),
(2, 3, 3)]),
('AdditiveAttention', dense_attention.AdditiveAttention, [(2, 2, 3),
(2, 3, 3),
(2, 3, 3)]),
('Embedding', lambda: embeddings.Embedding(4, 4),
(2, 4), 2e-3, 2e-3, np.random.randint(4, size=(2, 4))),
('LocallyConnected1D', lambda: local.LocallyConnected1D(2, 2), (2, 2, 1)),
('LocallyConnected2D', lambda: local.LocallyConnected2D(2, 2),
(2, 2, 2, 1)),
('Add', merge.Add, [(2, 2), (2, 2)]),
('Subtract', merge.Subtract, [(2, 2), (2, 2)]),
('Multiply', merge.Multiply, [(2, 2), (2, 2)]),
('Average', merge.Average, [(2, 2), (2, 2)]),
('Maximum', merge.Maximum, [(2, 2), (2, 2)]),
('Minimum', merge.Minimum, [(2, 2), (2, 2)]),
('Concatenate', merge.Concatenate, [(2, 2), (2, 2)]),
('Dot', lambda: merge.Dot(1), [(2, 2), (2, 2)]),
('GaussianNoise', lambda: noise.GaussianNoise(0.5), (2, 2)),
('GaussianDropout', lambda: noise.GaussianDropout(0.5), (2, 2)),
('AlphaDropout', lambda: noise.AlphaDropout(0.5), (2, 2)),
('BatchNormalization', normalization_v2.BatchNormalization,
(2, 2), 1e-2, 1e-2),
('LayerNormalization', normalization.LayerNormalization, (2, 2)),
('LayerNormalizationUnfused',
lambda: normalization.LayerNormalization(axis=1), (2, 2, 2)),
('MaxPooling2D', pooling.MaxPooling2D, (2, 2, 2, 1)),
('AveragePooling2D', pooling.AveragePooling2D, (2, 2, 2, 1)),
('GlobalMaxPooling2D', pooling.GlobalMaxPooling2D, (2, 2, 2, 1)),
('GlobalAveragePooling2D', pooling.GlobalAveragePooling2D, (2, 2, 2, 1)),
('SimpleRNN', lambda: recurrent.SimpleRNN(units=4),
(4, 4, 4), 1e-2, 1e-2),
('GRU', lambda: recurrent.GRU(units=4), (4, 4, 4)),
('LSTM', lambda: recurrent.LSTM(units=4), (4, 4, 4)),
('GRUV2', lambda: recurrent_v2.GRU(units=4), (4, 4, 4)),
('LSTMV2', lambda: recurrent_v2.LSTM(units=4), (4, 4, 4)),
('TimeDistributed', lambda: wrappers.TimeDistributed(core.Dense(2)),
(2, 2, 2)),
('Bidirectional',
lambda: wrappers.Bidirectional(recurrent.SimpleRNN(units=4)), (2, 2, 2)),
('AttentionLayerCausal', lambda: dense_attention.Attention(causal=True), [
(2, 2, 3), (2, 3, 3), (2, 3, 3)
]),
('AdditiveAttentionLayerCausal',
lambda: dense_attention.AdditiveAttention(causal=True), [(2, 3, 4),
(2, 3, 4),
(2, 3, 4)]),
)
def test_layer(self, f32_layer_fn, input_shape, rtol=2e-3, atol=2e-3,
input_data=None):
"""Tests a layer by comparing the float32 and mixed precision weights.
A float32 layer, a mixed precision layer, and a distributed mixed precision
layer are run. The three layers are identical other than their dtypes and
distribution strategies. The outputs after predict() and weights after fit()
are asserted to be close.
Args:
f32_layer_fn: A function returning a float32 layer. The other two layers
will automatically be created from this
input_shape: The shape of the input to the layer, including the batch
dimension. Or a list of shapes if the layer takes multiple inputs.
rtol: The relative tolerance to be asserted.
atol: The absolute tolerance to be asserted.
input_data: A Numpy array with the data of the input. If None, input data
will be randomly generated
"""
if f32_layer_fn == convolutional.ZeroPadding2D and \
test.is_built_with_rocm():
return
if isinstance(input_shape[0], int):
input_shapes = [input_shape]
else:
input_shapes = input_shape
strategy = create_mirrored_strategy()
f32_layer = f32_layer_fn()
# Create the layers
assert f32_layer.dtype == f32_layer._compute_dtype == 'float32'
config = f32_layer.get_config()
config['dtype'] = policy.Policy('mixed_float16')
mp_layer = f32_layer.__class__.from_config(config)
distributed_mp_layer = f32_layer.__class__.from_config(config)
# Compute per_replica_input_shapes for the distributed model
global_batch_size = input_shapes[0][0]
assert global_batch_size % strategy.num_replicas_in_sync == 0, (
'The number of replicas, %d, does not divide the global batch size of '
'%d' % (strategy.num_replicas_in_sync, global_batch_size))
per_replica_batch_size = (
global_batch_size // strategy.num_replicas_in_sync)
per_replica_input_shapes = [(per_replica_batch_size,) + s[1:]
for s in input_shapes]
# Create the models
f32_model = self._create_model_from_layer(f32_layer, input_shapes)
mp_model = self._create_model_from_layer(mp_layer, input_shapes)
with strategy.scope():
distributed_mp_model = self._create_model_from_layer(
distributed_mp_layer, per_replica_input_shapes)
# Set all model weights to the same values
f32_weights = f32_model.get_weights()
mp_model.set_weights(f32_weights)
distributed_mp_model.set_weights(f32_weights)
# Generate input data
if input_data is None:
# Cast inputs to float16 to avoid measuring error from having f16 layers
# cast to float16.
input_data = [np.random.normal(size=s).astype('float16')
for s in input_shapes]
if len(input_data) == 1:
input_data = input_data[0]
# Assert all models have close outputs.
f32_output = f32_model.predict(input_data)
mp_output = mp_model.predict(input_data)
self.assertAllClose(
mp_output, f32_output, rtol=rtol, atol=atol)
self.assertAllClose(
distributed_mp_model.predict(input_data), f32_output, rtol=rtol,
atol=atol)
# Run fit() on models
output = np.random.normal(size=f32_model.outputs[0].shape).astype('float16')
for model in f32_model, mp_model, distributed_mp_model:
model.fit(input_data, output, batch_size=global_batch_size)
# Assert all models have close weights
f32_weights = f32_model.get_weights()
self.assertAllClose(
mp_model.get_weights(), f32_weights, rtol=rtol, atol=atol)
self.assertAllClose(
distributed_mp_model.get_weights(), f32_weights, rtol=rtol, atol=atol)
