def testSimpleAttention(self):
x = np.random.rand(5, 7, 1, 11)
y = np.random.rand(5, 9, 1, 11)
a = common_layers.simple_attention(tf.constant(x, dtype=tf.float32),
tf.constant(y, dtype=tf.float32))
res = self.evaluate(a)
self.assertEqual(res.shape, (5, 7, 1, 11))
def testSimpleAttention(self):
x = np.random.rand(5, 7, 1, 11)
y = np.random.rand(5, 9, 1, 11)
with self.test_session() as session:
a = common_layers.simple_attention(
tf.constant(x, dtype=tf.float32), tf.constant(y, dtype=tf.float32))
session.run(tf.global_variables_initializer())
res = session.run(a)
self.assertEqual(res.shape, (5, 7, 1, 11))
def attention(targets_shifted, inputs_encoded, norm_fn, hparams, bias=None):
"""Complete attention layer with preprocessing."""
separabilities = [hparams.separability, hparams.separability]
if hparams.separability < 0:
separabilities = [hparams.separability - 1, hparams.separability]
targets_timed = common_layers.subseparable_conv_block(
common_layers.add_timing_signal(targets_shifted),
hparams.hidden_size, [((1, 1), (5, 1)), ((4, 1), (5, 1))],
normalizer_fn=norm_fn,
padding="LEFT",
separabilities=separabilities,
name="targets_time")
if hparams.attention_type == "transformer":
targets_timed = tf.squeeze(targets_timed, 2)
target_shape = tf.shape(targets_timed)
targets_segment = tf.zeros([target_shape[0], target_shape[1]])
target_attention_bias = common_attention.attention_bias(
targets_segment, targets_segment, lower_triangular=True)
inputs_attention_bias = tf.zeros([
tf.shape(inputs_encoded)[0], hparams.num_heads,
tf.shape(targets_segment)[1],
tf.shape(inputs_encoded)[1]
])
qv = common_attention.multihead_attention(targets_timed,
None,
target_attention_bias,
hparams.hidden_size,
hparams.hidden_size,
hparams.hidden_size,
hparams.num_heads,
hparams.attention_dropout,
name="self_attention")
qv = common_attention.multihead_attention(qv,
inputs_encoded,
inputs_attention_bias,
hparams.hidden_size,
hparams.hidden_size,
hparams.hidden_size,
hparams.num_heads,
hparams.attention_dropout,
name="encdec_attention")
return tf.expand_dims(qv, 2)
elif hparams.attention_type == "simple":
targets_with_attention = common_layers.simple_attention(targets_timed,
inputs_encoded,
bias=bias)
return norm_fn(targets_shifted + targets_with_attention,
name="attn_norm")
def attention(targets_shifted, inputs_encoded, norm_fn, hparams, bias=None):
"""Complete attention layer with preprocessing."""
separabilities = [hparams.separability, hparams.separability]
if hparams.separability < 0:
separabilities = [hparams.separability - 1, hparams.separability]
targets_timed = common_layers.subseparable_conv_block(
common_layers.add_timing_signal(targets_shifted),
hparams.hidden_size, [((1, 1), (5, 1)), ((4, 1), (5, 1))],
normalizer_fn=norm_fn,
padding="LEFT",
separabilities=separabilities,
name="targets_time")
if hparams.attention_type == "transformer":
targets_timed = tf.squeeze(targets_timed, 2)
target_shape = tf.shape(targets_timed)
targets_segment = tf.zeros([target_shape[0], target_shape[1]])
target_attention_bias = common_attention.attention_bias(
targets_segment, targets_segment, lower_triangular=True)
inputs_attention_bias = tf.zeros([
tf.shape(inputs_encoded)[0], hparams.num_heads,
tf.shape(targets_segment)[1],
tf.shape(inputs_encoded)[1]
])
qv = common_attention.multihead_attention(
targets_timed,
None,
target_attention_bias,
hparams.hidden_size,
hparams.hidden_size,
hparams.hidden_size,
hparams.num_heads,
hparams.attention_dropout,
name="self_attention")
qv = common_attention.multihead_attention(
qv,
inputs_encoded,
inputs_attention_bias,
hparams.hidden_size,
hparams.hidden_size,
hparams.hidden_size,
hparams.num_heads,
hparams.attention_dropout,
name="encdec_attention")
return tf.expand_dims(qv, 2)
elif hparams.attention_type == "simple":
targets_with_attention = common_layers.simple_attention(
targets_timed, inputs_encoded, bias=bias)
return norm_fn(targets_shifted + targets_with_attention, name="attn_norm")
