def encode(self,
inputs,
sequence_length=None,
mode=tf.estimator.ModeKeys.TRAIN):
if self.position_encoder is not None:
inputs = self.position_encoder(inputs,
sequence_length=sequence_length)
inputs = tf.layers.dropout(
inputs,
rate=self.dropout,
training=mode == tf.estimator.ModeKeys.TRAIN)
mask = transformer.build_sequence_mask(
sequence_length,
num_heads=self.num_heads,
maximum_length=tf.shape(inputs)[1],
dtype=inputs.dtype)
state = ()
for l in range(self.num_layers):
with tf.variable_scope("layer_{}".format(l)):
with tf.variable_scope("multi_head"):
inputs_norm = transformer.norm(inputs)
context = transformer.multi_head_attention(
self.num_heads,
inputs_norm,
inputs_norm,
mode,
num_units=self.num_units,
mask=mask,
dropout=self.attention_dropout)
context = transformer.drop_and_add(inputs,
context,
mode,
dropout=self.dropout)
with tf.variable_scope("ffn"):
transformed = transformer.feed_forward(
transformer.norm(context),
self.ffn_inner_dim,
mode,
dropout=self.relu_dropout)
transformed = transformer.drop_and_add(
context, transformed, mode, dropout=self.dropout)
inputs = transformed
state += (tf.reduce_mean(inputs, axis=1), )
outputs = transformer.norm(inputs)
return (outputs, state, sequence_length)
def testBuildSequenceMask(self):
num_heads = 4
length = [5, 3, 7]
expected = [[1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0],
[1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0],
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]]
mask = transformer.build_sequence_mask(tf.constant(length),
num_heads=num_heads)
with self.test_session() as sess:
mask = sess.run(mask)
mask = np.transpose(mask, (1, 0, 2))
for b in range(len(length)):
self.assertAllEqual(expected, mask[b])
def testScaledDotAttention(self):
batch_size = 3
num_heads = 8
values_length = [5, 3, 7]
queries_length = [8, 6, 10]
depth = 20
queries = tf.placeholder_with_default(
np.random.randn(batch_size, num_heads, max(queries_length),
depth).astype(np.float32),
shape=(None, num_heads, None, depth))
values = tf.placeholder_with_default(
np.random.randn(batch_size, num_heads, max(values_length),
depth).astype(np.float32),
shape=(None, num_heads, None, depth))
keys = values
mask = transformer.build_sequence_mask(values_length,
num_heads=num_heads)
context, attn = transformer.scaled_dot_attention(
queries, keys, values, tf.estimator.ModeKeys.PREDICT, mask=mask)
with self.test_session() as sess:
context, attn = sess.run([context, attn])
self.assertTupleEqual(
(batch_size, num_heads, max(queries_length), depth),
context.shape)
self.assertTupleEqual((batch_size, num_heads, max(queries_length),
max(values_length)), attn.shape)
for i in range(batch_size):
length = values_length[i]
padding_length = max(values_length) - length
if padding_length > 0:
self.assertEqual(
0.0, np.sum(attn[i, :, :, length:max(values_length)]))
def _self_attention_stack(self,
inputs,
sequence_length=None,
mode=tf.estimator.ModeKeys.TRAIN,
cache=None,
memory=None,
memory_sequence_length=None):
inputs = tf.layers.dropout(
inputs,
rate=self.dropout,
training=mode == tf.estimator.ModeKeys.TRAIN)
decoder_mask = None
memory_mask = None
if sequence_length is not None:
decoder_mask = transformer.build_future_mask(
sequence_length, num_heads=self.num_heads, dtype=inputs.dtype)
if memory_sequence_length is not None:
memory_mask = transformer.build_sequence_mask(
memory_sequence_length,
num_heads=self.num_heads,
dtype=memory.dtype)
for l in range(self.num_layers):
layer_name = "layer_{}".format(l)
layer_cache = cache[layer_name] if cache is not None else None
with tf.variable_scope(layer_name):
with tf.variable_scope("masked_multi_head"):
inputs_norm = transformer.norm(inputs)
encoded = transformer.multi_head_attention(
self.num_heads,
inputs_norm,
inputs_norm,
mode,
num_units=self.num_units,
mask=decoder_mask,
cache=layer_cache,
dropout=self.attention_dropout)
encoded = transformer.drop_and_add(inputs,
encoded,
mode,
dropout=self.dropout)
if memory is not None:
with tf.variable_scope("multi_head"):
context = transformer.multi_head_attention(
self.num_heads,
transformer.norm(encoded),
memory,
mode,
mask=memory_mask,
dropout=self.attention_dropout)
context = transformer.drop_and_add(
encoded, context, mode, dropout=self.dropout)
with tf.variable_scope("ffn"):
transformed = transformer.feed_forward(
transformer.norm(context),
self.ffn_inner_dim,
mode,
dropout=self.relu_dropout)
transformed = transformer.drop_and_add(
context, transformed, mode, dropout=self.dropout)
inputs = transformed
outputs = transformer.norm(inputs)
return outputs