def MoS(x, hidden_size, vocab_size, n_experts=10):
'''
a Mixture-of-Softamx operator, as specified in arXiv:1711.03953
relatively untested.
x - tensor, dtype = tf.float32 shape = [..., hidden_size]
hidden_size - the final dimension of x
vocab_size - the vocab_size to calculate
n_experts - the number of experts, int
'''
batch_size = tf.shape(x)[0]
sequence_size = tf.shape(x)[1]
with tf.variable_scope('latent'):
latent = utils.dense(x,
output_dim=n_experts * hidden_size,
name='latent')
latent = tf.nn.tanh(latent)
with tf.variable_scope('decoder'):
latent = tf.reshape(latent, [-1, hidden_size])
logit = utils.dense(latent, output_dim=vocab_size, name='decoder')
with tf.variable_scope('prior'):
prior_logit = utils.dense(x, output_dim=n_experts, name='prior')
prior_logit = tf.reshape(prior_logit, [-1, n_experts])
prior = tf.nn.softmax(prior_logit, axis=-1)
prob = tf.reshape(
tf.nn.softmax(tf.reshape(logit, [-1, vocab_size]), axis=-1),
[-1, n_experts, vocab_size])
prior = tf.expand_dims(prior, axis=2)
prior = tf.tile(prior, [1, 1, vocab_size])
prob = (prob * prior)
prob = tf.reduce_sum(prob, axis=1)
prob = tf.reshape(prob, [batch_size, sequence_size, vocab_size])
return prob
def transformer_ffd(self, x):
x = utils.dense(x,
output_dim=self.arg.filter_size,
use_bias=True,
name='ffd_1')
x = self.dropout_fn(x)
if self.arg.use_relu:
x = tf.nn.relu(x)
else:
x = utils.gelu(x)
return utils.dense(x,
output_dim=self.arg.hidden_size,
use_bias=True,
name='ffd_2')
def __call__(self, inputs, state, scope=None, *args, **kwargs):
with tf.variable_scope('attention'):
hidden_with_time_axis = tf.expand_dims(state, axis=1)
score = utils.dense(tf.nn.tanh(
utils.dense(
self.encoder_output, output_dim=self.state_size, name='W1')
+ utils.dense(hidden_with_time_axis,
output_dim=self.state_size,
name='W2')),
output_dim=1,
name='V')
attention_weights = tf.nn.softmax(score, axis=1)
context_vector = attention_weights * self.encoder_output
context_vector = tf.reduce_sum(context_vector, axis=1)
inputs = tf.concat([inputs, context_vector], axis=1)
return self.cell.__call__(inputs, state, scope=scope, *args, **kwargs)
def body(x, i, halting_probability, remainders, n_updates):
with tf.variable_scope('decoder_layer'):
state = x
x += self.timing_position(x)
pondering = utils.dense(x, output_dim=1, name='pondering')
pondering = tf.squeeze(pondering, axis=-1)
pondering = tf.nn.sigmoid(pondering)
update_weights, halting_probability, remainders, n_updates = act(
pondering, halt_threshold, halting_probability, remainders,
n_updates)
with tf.variable_scope('attention'):
y = utils.layer_norm(x)
y = utils.multihead_attention(
query=y,
memory=None,
bias=decoder_self_attention_bias,
total_key_depth=self.arg.head_size *
self.arg.num_heads,
total_value_depth=self.arg.head_size *
self.arg.num_heads,
output_depth=self.arg.hidden_size,
num_heads=self.arg.num_heads,
deparameterize=self.arg.deparameterize,
dropout_keep_prob=self.keep_prob,
dropout_type=self.arg.dropout_type,
relative_attention=self.arg.relative_attention,
max_relative_position=self.arg.max_relative_position)
y = self.dropout_fn(y)
x += y
with tf.variable_scope('encoder_attention'):
y = utils.layer_norm(x)
y = utils.multihead_attention(
query=y,
memory=memory,
bias=encoder_decoder_attention_bias,
total_key_depth=self.arg.head_size *
self.arg.num_heads,
total_value_depth=self.arg.head_size *
self.arg.num_heads,
output_depth=self.arg.hidden_size,
num_heads=self.arg.num_heads,
dropout_keep_prob=self.keep_prob,
dropout_type=self.arg.dropout_type,
relative_attention=False,
max_relative_position=self.arg.max_relative_position)
y = self.dropout_fn(y)
x += y
with tf.variable_scope('ffd'):
y = utils.layer_norm(x)
y = self.ffd(y)
y = self.dropout_fn(y)
x += y
x = (x * update_weights) + (state * (1 - update_weights))
return x, i + 1, halting_probability, remainders, n_updates
def SRU(x,
num_layers=2,
activation=None,
initial_state=None,
name=None,
reuse=None,
reuse_layer=False):
'''
SRU introduced in arXiv:1709.02755
code based on tensor2tensor
x - tensor, dtype = tf.float32 shape = [batch_size, sequence_size, hidden_size]
'''
with tf.variable_scope(name, default_name='SRU', reuse=reuse):
tf_x_shape = tf.shape(x)
x_shape = x.shape.as_list()
x = tf.transpose(x, perm=[1, 0, 2], name='input_transpose')
if initial_state is None:
initial_state = tf.zeros(
[tf.shape(x)[1], tf.shape(x)[2]], dtype=x.dtype)
for i in range(num_layers):
with tf.variable_scope('layer_{}'.format(i + 1),
reuse=(i != 0 and reuse_layer)):
x_orig = x
x, f, r = tf.split(utils.dense(x,
output_dim=3 * x_shape[-1],
name='dense'),
num_or_size_splits=3,
axis=-1)
f, r = tf.sigmoid(f), tf.sigmoid(r)
x_times_one_minus_f = x * (1.0 - f)
c_states = tf.scan(next_state, (x_times_one_minus_f, f),
initializer=initial_state,
parallel_iterations=2,
name='scan_{}'.format(i))
if activation is not None:
c_states = activation(c_states)
h = c_states * r + (1.0 - r) * x_orig
x = h
x = tf.transpose(x, perm=[1, 0, 2])
return tf.reshape(x, tf_x_shape)
def decoder(self, inputs, memory, decoder_self_attention_bias,
encoder_decoder_attention_bias):
x = inputs
if self.arg.adaptive_mask:
self.decoder_l0 = []
for layer in range(1, self.arg.decoder_layers + 1):
with tf.variable_scope('layer_{}'.format(layer)):
with tf.variable_scope('16_head_self_attention'):
y = utils.layer_norm(x)
left_state = utils.multihead_attention(
query=y,
memory=None,
bias=self.decoder_self_attention_bias,
total_key_depth=self.arg.head_size *
max(min(self.arg.num_heads * 2, 16),
self.arg.num_heads),
total_value_depth=self.arg.head_size *
max(min(self.arg.num_heads * 2, 16),
self.arg.num_heads),
output_depth=self.arg.hidden_size,
num_heads=max(min(self.arg.num_heads * 2, 16),
self.arg.num_heads),
dropout_keep_prob=self.keep_prob,
dropout_type=self.arg.dropout_type,
name='self_attention',
relative_attention=self.arg.relative_attention,
max_relative_position=self.arg.max_relative_position,
adaptive_mask=self.arg.adaptive_mask,
dynamic_attention_span=self.arg.dynamic_attention_span)
if self.arg.adaptive_mask:
self.decoder_l0.append(left_state[1])
left_state = left_state[0]
right_state = utils.multihead_attention(
query=y,
memory=memory,
bias=self.encoder_decoder_attention_bias,
total_key_depth=self.arg.head_size *
self.arg.num_heads,
total_value_depth=self.arg.head_size *
self.arg.num_heads,
output_depth=self.arg.hidden_size,
num_heads=self.arg.num_heads,
dropout_keep_prob=self.keep_prob,
dropout_type=self.arg.dropout_type,
name='encoder_attention',
relative_attention=False,
max_relative_position=self.arg.max_relative_position,
adaptive_mask=self.arg.adaptive_mask,
dynamic_attention_span=self.arg.dynamic_attention_span)
if self.arg.adaptive_mask:
self.decoder_l0.append(right_state[1])
right_state = right_state[0]
x += self.dropout_fn(left_state) + self.dropout_fn(
right_state)
with tf.variable_scope('conv_branches'):
y = utils.layer_norm(x)
if self.arg.unidirectional_decoder:
left_state = tf.concat([
tf.zeros(
[self.batch_size, 10, self.arg.hidden_size]), y
],
axis=1)
padding = 'VALID'
else:
padding = 'SAME'
left_state = y
left_state = utils.separable_conv(
left_state,
filters=self.arg.hidden_size * 2,
kernel_size=11,
padding=padding,
name='separable_11x1')
if self.arg.use_relu:
left_state = tf.nn.relu(left_state)
else:
left_state = utils.gelu(left_state)
left_state = self.dropout_fn(left_state)
if self.arg.unidirectional_decoder:
right_state = tf.concat([
tf.zeros(
[self.batch_size, 6, self.arg.hidden_size]), y
],
axis=1)
padding = 'VALID'
else:
padding = 'SAME'
right_state = y
right_state = utils.separable_conv(
right_state,
filters=int(self.arg.hidden_size / 2),
kernel_size=7,
padding=padding,
name='separable_7x1')
right_state = tf.pad(
right_state,
paddings=[[0, 0], [0, 0],
[0, int(self.arg.hidden_size * 1.5)]],
constant_values=0)
y = left_state + right_state
y = utils.layer_norm(y)
if self.arg.unidirectional_decoder:
y = tf.concat([
tf.zeros([
self.batch_size, 6, self.arg.hidden_size * 2
]), y
],
axis=1)
padding = 'VALID'
else:
padding = 'SAME'
y = utils.separable_conv(y,
filters=self.arg.hidden_size,
kernel_size=7,
padding=padding,
name='separable_7x1_2')
x += self.dropout_fn(y)
with tf.variable_scope('self_attention'):
y = utils.layer_norm(x)
y = utils.multihead_attention(
query=y,
memory=None,
bias=self.decoder_self_attention_bias,
total_key_depth=self.arg.head_size *
self.arg.num_heads,
total_value_depth=self.arg.head_size *
self.arg.num_heads,
output_depth=self.arg.hidden_size,
num_heads=self.arg.num_heads,
dropout_keep_prob=self.keep_prob,
dropout_type=self.arg.dropout_type,
relative_attention=self.arg.relative_attention,
max_relative_position=self.arg.max_relative_position,
adaptive_mask=self.arg.adaptive_mask,
dynamic_attention_span=self.arg.dynamic_attention_span)
if self.arg.adaptive_mask:
self.decoder_l0.append(y[1])
y = y[0]
x += self.dropout_fn(y)
with tf.variable_scope('encoder_attention'):
y = utils.layer_norm(x)
y = utils.multihead_attention(
query=y,
memory=memory,
bias=self.encoder_decoder_attention_bias,
total_key_depth=self.arg.head_size *
self.arg.num_heads,
total_value_depth=self.arg.head_size *
self.arg.num_heads,
output_depth=self.arg.hidden_size,
num_heads=self.arg.num_heads,
dropout_keep_prob=self.keep_prob,
dropout_type=self.arg.dropout_type,
relative_attention=False,
max_relative_position=self.arg.max_relative_position,
adaptive_mask=self.arg.adaptive_mask,
dynamic_attention_span=self.arg.dynamic_attention_span)
if self.arg.adaptive_mask:
self.decoder_l0.append(y[1])
y = y[0]
x += self.dropout_fn(y)
with tf.variable_scope('dense_layers'):
y = utils.layer_norm(x)
y = utils.dense(y,
output_dim=self.arg.hidden_size * 4,
name='dense_1')
y = tf.nn.swish(y)
y = utils.layer_norm(y)
y = utils.dense(y,
output_dim=self.arg.hidden_size,
name='dense_2')
x += self.dropout_fn(y)
return utils.layer_norm(x)
def encoder(self, inputs, encoder_self_attention_bias):
x = inputs
if self.arg.adaptive_mask:
self.encoder_l0 = []
for layer in range(1, self.arg.encoder_layers + 1):
with tf.variable_scope('layer_{}'.format(layer)):
with tf.variable_scope('gated_linear_unit'):
y = utils.layer_norm(x)
y = utils.convolution_gating(
y,
kernel_size=1,
input_dim=y.shape.as_list()[-1],
output_dim=y.shape.as_list()[-1])
y = self.dropout_fn(y)
x += y
with tf.variable_scope('conv_branches'):
y = utils.layer_norm(x)
if self.arg.use_relu:
left_state = tf.nn.relu(
utils.dense(y,
output_dim=int(self.arg.hidden_size *
4),
name='left_branch'))
else:
left_state = utils.gelu(
utils.dense(y,
output_dim=int(self.arg.hidden_size *
4),
name='right_branch'))
left_state = self.dropout_fn(left_state)
with tf.variable_scope('right_branch'):
kernel = tf.get_variable('kernel',
shape=[
3,
y.shape.as_list()[-1],
int(self.arg.hidden_size /
2)
],
dtype=tf.float32)
'''
given that the tensor, at this point, is of shape [batch_size, sequence_size, hidden_size],
and the kernel size of the convolution is 3,
then an unmoderated convolution, at time-step t, would analyze the time-steps (t-1, t, t+1) for the output of time-step t
If the anaylsis is unidirectional, and that the analysis at time-step t cannot see 'ahead through time', this form of analysis if invalid.
Therefore, in order to avoid this illegal analysis, a zeros vector is concatenated to the left of the tensor.
Therefore, at time-step t, the time-steps (t-2, t-1, t) is analyzed, where the tokens at -2 and -1 are 0
If the analysis is bidirectional, analyzing the time-steps (t-1, t, t+1) is a legal move
'''
if self.arg.unidirectional_encoder:
padding = 'VALID'
y = tf.concat([
tf.zeros([
self.batch_size, 2, self.arg.hidden_size
]), y
],
axis=1)
else:
padding = 'SAME'
right_state = tf.nn.convolution(
y,
kernel,
padding=padding,
name='convolution_conv_3x1')
if self.arg.use_relu:
right_state = tf.nn.relu(right_state)
else:
right_state = utils.gelu(right_state)
right_state = self.dropout_fn(right_state)
right_state = tf.pad(right_state,
[[0, 0], [0, 0],
[
0,
int(self.arg.hidden_size * 4) -
int(self.arg.hidden_size / 2)
]],
constant_values=0)
y = left_state + right_state
y = utils.layer_norm(y)
if self.arg.unidirectional_encoder:
padding = 'VALID'
y = tf.concat([
tf.zeros([
self.batch_size, 8, self.arg.hidden_size * 4
]), y
],
axis=1)
else:
padding = 'SAME'
y = utils.separable_conv(y,
filters=int(self.arg.hidden_size /
2),
kernel_size=9,
padding=padding,
name='separable_9x1')
y = tf.pad(
y,
[[0, 0], [0, 0], [0, int(self.arg.hidden_size / 2)]],
constant_values=0)
x += self.dropout_fn(y)
with tf.variable_scope('self_attention'):
y = utils.layer_norm(x)
y = utils.multihead_attention(
query=y,
memory=None,
bias=self.encoder_self_attention_bias,
total_key_depth=self.arg.head_size *
self.arg.num_heads,
total_value_depth=self.arg.head_size *
self.arg.num_heads,
output_depth=self.arg.hidden_size,
num_heads=self.arg.num_heads,
dropout_keep_prob=self.keep_prob,
dropout_type=self.arg.dropout_type,
relative_attention=self.arg.relative_attention,
max_relative_position=self.arg.max_relative_position,
adaptive_mask=self.arg.adaptive_mask,
dynamic_attention_span=self.arg.dynamic_attention_span)
if self.arg.adaptive_mask:
self.encoder_l0.append(y[1])
y = y[0]
x += self.dropout_fn(y)
with tf.variable_scope('dense_layers'):
y = utils.layer_norm(x)
y = utils.dense(y,
output_dim=int(self.arg.hidden_size * 4),
name='dense_1')
if self.arg.use_relu:
y = tf.nn.relu(y)
else:
y = utils.gelu(y)
y = self.dropout_fn(y)
y = utils.dense(y,
output_dim=int(self.arg.hidden_size),
name='dense_2')
x += self.dropout_fn(y)
return utils.layer_norm(x)
def encoder(self, inputs, encoder_self_attention_bias):
with tf.variable_scope('positional_embedding'):
pos_seq = tf.range(
tf.shape(self.encoder_self_attention_bias)[-1] - 1, -1, -1.0)
inv_freq = 1 / (10000**(tf.range(0, self.arg.hidden_size, 2.0) /
self.arg.hidden_size))
sinusoid_inp = tf.einsum('i,j->ij', pos_seq, inv_freq)
pos_emb = tf.concat([tf.sin(sinusoid_inp),
tf.cos(sinusoid_inp)],
axis=-1)
pos_emb = tf.tile(pos_emb[None, :, :], [self.batch_size, 1, 1])
if self.arg.tie_weights:
r_w_bias = tf.get_variable(
'r_w_bias',
shape=[1, self.arg.num_heads, 1, self.arg.head_size],
dtype=tf.float32)
r_r_bias = tf.get_variable(
'r_r_bias',
shape=[1, self.arg.num_heads, 1, self.arg.head_size],
dtype=tf.float32)
else:
r_w_bias = tf.get_variable('r_w_bias',
shape=[
self.arg.encoder_layers, 1,
self.arg.num_heads, 1,
self.arg.head_size
],
dtype=tf.float32)
r_r_bias = tf.get_variable('r_r_bias',
shape=[
self.arg.encoder_layers, 1,
self.arg.num_heads, 1,
self.arg.head_size
],
dtype=tf.float32)
x = inputs
for layer in range(1, self.arg.encoder_layers + 1):
with tf.variable_scope('layer_{}'.format(layer)):
x = self.timing_position(x)
with tf.variable_scope('attention'):
self.new_mems.append(
self._cache_mem(x, self.memory[layer - 1]))
memory = tf.concat([self.memory[layer - 1], x], axis=1)
y = utils.layer_norm(x)
memory = utils.layer_norm(memory)
q, k, v = utils.compute_qkv(
query=y,
memory=memory,
total_key_depth=self.arg.head_size *
self.arg.num_heads,
total_value_depth=self.arg.head_size *
self.arg.num_heads,
deparameterize=self.arg.deparameterize)
r = utils.dense(pos_emb,
output_dim=self.arg.head_size *
self.arg.num_heads,
use_bias=False,
name='pos_emb')
r = tf.reshape(r, [
self.batch_size, self.arg.num_heads, -1,
self.arg.head_size
])
q = utils.split_heads(q, self.arg.num_heads)
k = utils.split_heads(k, self.arg.num_heads)
v = utils.split_heads(v, self.arg.num_heads)
if self.arg.tie_weights:
AD = tf.matmul(q + r_w_bias, k, transpose_b=True)
BD = tf.matmul(q + r_r_bias, r, transpose_b=True)
else:
AD = tf.matmul(q + r_w_bias[layer - 1],
k,
transpose_b=True)
BD = tf.matmul(q + r_r_bias[layer - 1],
r,
transpose_b=True)
BD = self.rel_shift(BD)
logits = AD + BD
logits /= k.shape.as_list()[-1]
logits += self.encoder_self_attention_bias
weights = tf.nn.softmax(logits, name='attention_weights')
y = tf.matmul(weights, v)
y = utils.combine_heads(y)
y.set_shape(y.shape.as_list()[:-1] +
[self.arg.head_size * self.arg.num_heads])
with tf.variable_scope('output'):
y = utils.dense(y,
output_dim=self.arg.hidden_size,
use_bias=False,
name='output_transform')
y = self.dropout_fn(y)
x += y
with tf.variable_scope('ffd'):
y = utils.layer_norm(x)
y = self.ffd(y)
y = self.dropout_fn(y)
x += y
with tf.variable_scope('output'):
return utils.layer_norm(x)
def __init__(self, arg, name=None):
'''
a Seq2Seq model based on the model described in arXiv:1804.00946
the stop-feature mechanism, in particular, was taken from these mechanisms
'''
if name:
self.name = name
else:
self.name = 'Seq2Seq'
batch_size = 32
input_sequence_size = 10
output_sequence_size = 12
if __name__ != '__main__':
batch_size = input_sequence_size = output_sequence_size = None
self.arg = arg
self.inputs = tf.placeholder(tf.int32,
shape=[batch_size, input_sequence_size],
name='inputs')
self.targets = tf.placeholder(tf.int32,
shape=[batch_size, output_sequence_size],
name='targets')
self.training = tf.placeholder(tf.bool, name='training')
self.learning_rate = tf.placeholder(tf.float32, name='learning_rate')
self.keep_prob = tf.placeholder(tf.float32, name='keep_prob')
self.input_stop_feature = tf.placeholder(
tf.float32,
shape=[batch_size, input_sequence_size, 1],
name='input_stop_feature')
self.target_stop_feature = tf.placeholder(
tf.float32,
shape=[batch_size, output_sequence_size, 1],
name='target_stop_feature')
self.batch_size = tf.shape(self.inputs)[0]
self.input_sequence_size = tf.shape(self.inputs)[1]
self.target_sequence_size = tf.shape(self.targets)[1]
if self.arg.mask_loss:
self.loss_mask = tf.placeholder(
tf.float32,
shape=[batch_size, output_sequence_size
], # (batch_size, output_sequence_size)
name='loss_mask')
else:
self.loss_mask = None
with tf.variable_scope('embedding'):
embedded_inputs, embedded_targets = self.embedding()
embedded_inputs = tf.concat(
[embedded_inputs, self.input_stop_feature], axis=2)
embedded_targets = tf.concat(
[embedded_targets, self.target_stop_feature], axis=2)
with tf.variable_scope('encode'):
encoder_output, encoder_state = self.encode(embedded_inputs)
encoder_output = self.dropout_fn(encoder_output)
with tf.variable_scope('decode'):
decoder_output, _ = self.decode(encoder_output, encoder_state,
embedded_targets)
decoder_output = self.dropout_fn(decoder_output)
with tf.variable_scope('output'):
self.logits = utils.dense(decoder_output,
output_dim=self.arg.target_vocab_size,
name='logits')
with tf.variable_scope('loss'):
self.loss_cl = loss.Loss(self.logits,
self.targets,
self.arg.loss,
vocab_size=self.arg.target_vocab_size,
label_smoothing=self.arg.label_smoothing)
cost = self.loss_cl.loss
if self.arg.mask_loss:
self.cost = tf.reduce_mean(cost * self.loss_mask)
else:
self.cost = tf.reduce_mean(cost)
if self.arg.weight_decay_regularization:
l2_loss = self.loss_cl.l2_loss(tf.trainable_variables())
l2_loss *= self.arg.weight_decay_hyperparameter
self.cost += l2_loss
self.optimizer = optimize.Optimizer(
arg, loss=self.cost, learning_rate=self.learning_rate)
self.optimizer.accuracy(self.logits,
self.targets,
mask=self.loss_mask)
self.train_op = self.optimizer.train_op
self.predict = self.optimizer.predict
self.correct_prediction = self.optimizer.correct_prediction
self.accuracy = self.optimizer.accuracy
self.optimizer.sequential_accuracy(self.logits,
self.targets,
mask=self.loss_mask)
self.sequential_accuracy = self.optimizer.sequential_accuracy
self.fetches = [embedded_inputs, encoder_output, self.logits]
