def test_residual_block_with_relative_attention(self,
use_pre_activation_order):
np.random.seed(1234)
batch_size = 2
seq_len = 4
hidden_size = 10
inputs = tf.constant(
np.random.normal(size=[batch_size, seq_len, hidden_size]),
tf.float32)
att_mask = tf.stack([
# Force each element in the first example to only attend to itself.
tf.eye(seq_len, dtype=tf.int32),
# The second example can attend everywhere.
tf.ones([seq_len, seq_len], dtype=tf.int32)
])
inner_layer = etc_layers.RelativeAttention(
hidden_size=hidden_size,
num_heads=2,
relative_vocab_size=2,
initializer=tf.keras.initializers.Identity())
residual_block = etc_layers.ResidualBlock(
inner_layer=inner_layer,
normalization_layer=tf.keras.layers.Lambda(lambda x: x),
dropout_probability=0.0,
use_pre_activation_order=use_pre_activation_order)
relative_att_ids1 = tf.zeros([batch_size, seq_len, seq_len],
dtype=tf.int32)
result1 = residual_block(inputs,
att_mask=att_mask,
relative_att_ids=relative_att_ids1)
self.evaluate(tf.compat.v1.global_variables_initializer())
self.assertAllClose(2 * inputs[0], result1[0])
relative_att_ids2 = tf.tile([[[0, 1, 0, 1]]], [batch_size, seq_len, 1])
result2 = residual_block(inputs,
att_mask=att_mask,
relative_att_ids=relative_att_ids2)
self.assertAllClose(result1[0], result2[0])
self.assertNotAllClose(result1[1], result2[1])
def test_relative_attention_self_attention(self, use_one_hot_lookup):
tf.compat.v1.random.set_random_seed(1234)
np.random.seed(1234)
batch_size = 3
seq_len = 16
num_heads = 5
input_hidden_size = 11
output_hidden_size = 12
total_key_size = 10
total_value_size = 15
relative_vocab_size = 21
inputs = tf.constant(
np.random.normal(size=[batch_size, seq_len, input_hidden_size]),
tf.float32)
att_mask = tf.constant(
np.random.binomial(n=1, p=0.9, size=[batch_size, seq_len,
seq_len]))
relative_att_ids = tf.constant(
np.random.randint(relative_vocab_size,
size=[batch_size, seq_len, seq_len]))
layer = etc_layers.RelativeAttention(
hidden_size=output_hidden_size,
num_heads=num_heads,
total_key_size=total_key_size,
total_value_size=total_value_size,
relative_vocab_size=relative_vocab_size,
use_one_hot_lookup=use_one_hot_lookup)
result1 = layer(inputs,
att_mask=att_mask,
relative_att_ids=relative_att_ids)
self.assertAllEqual([batch_size, seq_len, output_hidden_size],
result1.shape)
result2 = layer(from_seq=inputs,
to_seq=inputs,
att_mask=att_mask,
relative_att_ids=relative_att_ids)
self.evaluate(tf.compat.v1.global_variables_initializer())
self.assertAllEqual(result1, result2)
def test_relative_attention(self, use_one_hot_lookup):
tf.compat.v1.random.set_random_seed(1234)
np.random.seed(1234)
batch_size = 3
from_seq_len = 16
to_seq_len = 17
num_heads = 5
from_hidden_size = 11
to_hidden_size = 12
output_hidden_size = 13
total_key_size = 10
total_value_size = 15
relative_vocab_size = 21
from_seq = tf.random.normal(
[batch_size, from_seq_len, from_hidden_size])
to_seq = tf.random.normal([batch_size, to_seq_len, to_hidden_size])
att_mask = tf.constant(
np.random.binomial(n=1,
p=0.9,
size=[batch_size, from_seq_len, to_seq_len]))
relative_att_ids = tf.random.uniform(
[batch_size, from_seq_len, to_seq_len],
maxval=relative_vocab_size,
dtype=tf.int32)
layer = etc_layers.RelativeAttention(
hidden_size=output_hidden_size,
num_heads=num_heads,
total_key_size=total_key_size,
total_value_size=total_value_size,
relative_vocab_size=relative_vocab_size,
use_one_hot_lookup=use_one_hot_lookup)
result = layer(from_seq=from_seq,
to_seq=to_seq,
att_mask=att_mask,
relative_att_ids=relative_att_ids)
self.assertAllEqual([batch_size, from_seq_len, output_hidden_size],
result.shape)
def test_fused_global_local_attention_special_case_equivalence(self):
# To test for correctness, we make sure the output is equivalent to
# standard attention in the special case where `local_radius` covers the
# entire long sequence length and projection weights are shared.
# For simplicity, we don't use attention masks or relative attention ids
# in this test.
tf.compat.v1.random.set_random_seed(1234)
np.random.seed(1234)
batch_size = 3
long_seq_len = 12
global_seq_len = 6
hidden_size = 10
num_heads = 5
local_radius = 15 # Must be >= `long_seq_len - 1` to remove sparsity.
# relative_vocab_size = 9
long_input = tf.constant(
np.random.normal(size=[batch_size, long_seq_len, hidden_size]))
global_input = tf.constant(
np.random.normal(size=[batch_size, global_seq_len, hidden_size]))
fused_att_layer = etc_layers.FusedGlobalLocalAttention(
long_hidden_size=hidden_size,
global_hidden_size=hidden_size,
num_heads=num_heads,
local_radius=local_radius,
share_qkv_projections=True,
share_att_output_projection=True)
long_output, global_output = fused_att_layer(
long_input, global_input, att_implementation='sparse')
# [batch_size, long_seq_len + global_seq_len, hidden_size]
fused_output = tf.concat([long_output, global_output], axis=1)
# Create concatenated input for standard attention.
# [batch_size, long_seq_len + global_seq_len, hidden_size]
concat_input = tf.concat([long_input, global_input], axis=1)
standard_att_layer = etc_layers.RelativeAttention(
hidden_size=hidden_size,
num_heads=num_heads,
query_projection=fused_att_layer.long_query_projection,
key_projection=fused_att_layer.l2l_key_projection,
value_projection=fused_att_layer.l2l_value_projection,
output_projection=fused_att_layer.long_output_projection)
expected_output = standard_att_layer(concat_input)
self.evaluate(tf.compat.v1.global_variables_initializer())
self.assertAllClose(expected_output, fused_output)
# Make sure 'full' att_implementation gives the same output.
long_output_full_att, global_output_full_att = fused_att_layer(
long_input, global_input, att_implementation='full')
self.assertAllClose(long_output, long_output_full_att)
self.assertAllClose(global_output, global_output_full_att)
def test_relative_attention_shared_sublayers(self, use_one_hot_lookup):
tf.compat.v1.random.set_random_seed(1234)
np.random.seed(1234)
batch_size = 3
from_seq_len = 16
to_seq_len = 17
num_heads = 5
from_hidden_size = 11
to_hidden_size = 12
output_hidden_size = 13
total_key_size = 10
total_value_size = 15
relative_vocab_size = 9
from_seq = tf.constant(
np.random.random(
size=[batch_size, from_seq_len, from_hidden_size]))
to_seq = tf.constant(
np.random.random(size=[batch_size, to_seq_len, to_hidden_size]))
att_mask = tf.constant(
np.random.binomial(n=1,
p=0.9,
size=[batch_size, from_seq_len, to_seq_len]))
layer = etc_layers.RelativeAttention(
hidden_size=output_hidden_size,
num_heads=num_heads,
total_key_size=total_key_size,
total_value_size=total_value_size,
relative_vocab_size=relative_vocab_size,
use_one_hot_lookup=use_one_hot_lookup)
sharing_layer = etc_layers.RelativeAttention(
hidden_size=output_hidden_size,
num_heads=num_heads,
total_key_size=total_key_size,
total_value_size=total_value_size,
query_projection=layer.query_projection,
key_projection=layer.key_projection,
value_projection=layer.value_projection,
qkv_relative_attention=layer.qkv_relative_attention,
output_projection=layer.output_projection)
different_layer = etc_layers.RelativeAttention(
hidden_size=output_hidden_size,
num_heads=num_heads,
total_key_size=total_key_size,
total_value_size=total_value_size,
relative_vocab_size=relative_vocab_size,
use_one_hot_lookup=use_one_hot_lookup)
result1 = layer(from_seq=from_seq,
to_seq=to_seq,
att_mask=att_mask,
relative_att_ids=None)
result2 = sharing_layer(from_seq=from_seq,
to_seq=to_seq,
att_mask=att_mask,
relative_att_ids=None)
result3 = different_layer(from_seq=from_seq,
to_seq=to_seq,
att_mask=att_mask,
relative_att_ids=None)
self.evaluate(tf.compat.v1.global_variables_initializer())
self.assertAllEqual(result1, result2)
self.assertNotAllClose(result1, result3)
