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deepnmt.py
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deepnmt.py
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# coding: utf-8
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import copy
import tensorflow as tf
import func
from utils import util
from rnns import rnn
def encoder(source, params):
mask = tf.to_float(tf.cast(source, tf.bool))
hidden_size = params.hidden_size
source, mask = util.remove_invalid_seq(source, mask)
embed_name = "embedding" if params.shared_source_target_embedding \
else "src_embedding"
src_emb = tf.get_variable(embed_name,
[params.src_vocab.size(), params.embed_size])
src_bias = tf.get_variable("bias", [params.embed_size])
inputs = tf.gather(src_emb, source)
inputs = tf.nn.bias_add(inputs, src_bias)
if util.valid_dropout(params.dropout):
inputs = tf.nn.dropout(inputs, 1. - params.dropout)
with tf.variable_scope("encoder"):
x = inputs
for layer in range(params.num_encoder_layer):
with tf.variable_scope("layer_{}".format(layer)):
# forward rnn
with tf.variable_scope('forward'):
outputs = rnn.rnn(params.cell, x, hidden_size, mask=mask,
ln=params.layer_norm, sm=params.swap_memory,
dp=params.dropout)
output_fw, state_fw = outputs[1]
if layer == 0:
# backward rnn
with tf.variable_scope('backward'):
if not params.caencoder:
outputs = rnn.rnn(params.cell, tf.reverse(x, [1]),
hidden_size, mask=tf.reverse(mask, [1]),
ln=params.layer_norm, sm=params.swap_memory,
dp=params.dropout)
output_bw, state_bw = outputs[1]
else:
outputs = rnn.cond_rnn(params.cell, tf.reverse(x, [1]),
tf.reverse(output_fw, [1]), hidden_size,
mask=tf.reverse(mask, [1]),
ln=params.layer_norm,
sm=params.swap_memory,
num_heads=params.num_heads,
one2one=True)
output_bw, state_bw = outputs[1]
output_bw = tf.reverse(output_bw, [1])
if not params.caencoder:
y = tf.concat([output_fw, output_bw], -1)
z = tf.concat([state_fw, state_bw], -1)
else:
y = output_bw
z = state_bw
else:
y = output_fw
z = state_fw
y = func.linear(y, hidden_size, ln=False, scope="ff")
# short cut via residual connection
if x.get_shape()[-1].value == y.get_shape()[-1].value:
x = func.residual_fn(x, y, dropout=params.dropout)
else:
x = y
if params.layer_norm:
x = func.layer_norm(x, scope="ln")
with tf.variable_scope("decoder_initializer"):
decoder_cell = rnn.get_cell(
params.cell, hidden_size, ln=params.layer_norm
)
return {
"encodes": x,
"decoder_initializer": {
"layer_{}".format(l):
decoder_cell.get_init_state(
x=z, scope="layer_{}".format(l))
for l in range(params.num_decoder_layer)
},
"mask": mask
}
def decoder(target, state, params):
mask = tf.to_float(tf.cast(target, tf.bool))
hidden_size = params.hidden_size
if 'decoder' not in state:
target, mask = util.remove_invalid_seq(target, mask)
embed_name = "embedding" if params.shared_source_target_embedding \
else "tgt_embedding"
tgt_emb = tf.get_variable(embed_name,
[params.tgt_vocab.size(), params.embed_size])
tgt_bias = tf.get_variable("bias", [params.embed_size])
inputs = tf.gather(tgt_emb, target)
inputs = tf.nn.bias_add(inputs, tgt_bias)
# shift
if 'decoder' not in state:
inputs = tf.pad(inputs, [[0, 0], [1, 0], [0, 0]])
inputs = inputs[:, :-1, :]
else:
inputs = tf.cond(tf.reduce_all(tf.equal(target, params.tgt_vocab.pad())),
lambda: tf.zeros_like(inputs),
lambda: inputs)
mask = tf.ones_like(mask)
if util.valid_dropout(params.dropout):
inputs = tf.nn.dropout(inputs, 1. - params.dropout)
with tf.variable_scope("decoder"):
x = inputs
for layer in range(params.num_decoder_layer):
with tf.variable_scope("layer_{}".format(layer)):
init_state = state["decoder_initializer"]["layer_{}".format(layer)]
if 'decoder' in state:
init_state = state["decoder"]["state"]["layer_{}".format(layer)]
if layer == 0 or params.use_deep_att:
returns = rnn.cond_rnn(params.cell, x, state["encodes"], hidden_size,
init_state=init_state, mask=mask,
num_heads=params.num_heads,
mem_mask=state["mask"], ln=params.layer_norm,
sm=params.swap_memory, one2one=False,
dp=params.dropout)
(_, hidden_state), (outputs, _), contexts, attentions = returns
c = contexts
else:
if params.caencoder:
returns = rnn.cond_rnn(params.cell, x, c,
hidden_size, init_state=init_state,
mask=mask, mem_mask=mask,
ln=params.layer_norm,
sm=params.swap_memory,
num_heads=params.num_heads,
one2one=True, dp=params.dropout)
(_, hidden_state), (outputs, _), contexts, attentions = returns
else:
outputs = rnn.rnn(params.cell, tf.concat([x, c], -1),
hidden_size, mask=mask, init_state=init_state,
ln=params.layer_norm, sm=params.swap_memory,
dp=params.dropout)
outputs, hidden_state = outputs[1]
if 'decoder' in state:
state['decoder']['state']['layer_{}'.format(layer)] = hidden_state
y = func.linear(outputs, hidden_size, ln=False, scope="ff")
# short cut via residual connection
if x.get_shape()[-1].value == y.get_shape()[-1].value:
x = func.residual_fn(x, y, dropout=params.dropout)
else:
x = y
if params.layer_norm:
x = func.layer_norm(x, scope="ln")
feature = func.linear(tf.concat([x, c], -1), params.embed_size, ln=params.layer_norm, scope="ff")
feature = tf.nn.tanh(feature)
if util.valid_dropout(params.dropout):
feature = tf.nn.dropout(feature, 1. - params.dropout)
if 'dev_decode' in state:
feature = x[:, -1, :]
embed_name = "tgt_embedding" if params.shared_target_softmax_embedding \
else "softmax_embedding"
embed_name = "embedding" if params.shared_source_target_embedding \
else embed_name
softmax_emb = tf.get_variable(embed_name,
[params.tgt_vocab.size(), params.embed_size])
feature = tf.reshape(feature, [-1, params.embed_size])
logits = tf.matmul(feature, softmax_emb, False, True)
soft_label, normalizer = util.label_smooth(
target,
util.shape_list(logits)[-1],
factor=params.label_smooth)
centropy = tf.nn.softmax_cross_entropy_with_logits_v2(
logits=logits,
labels=soft_label
)
centropy -= normalizer
centropy = tf.reshape(centropy, tf.shape(target))
loss = tf.reduce_sum(centropy * mask, -1) / tf.reduce_sum(mask, -1)
loss = tf.reduce_mean(loss)
# these mask tricks mainly used to deal with zero shapes, such as [0, 1]
loss = tf.cond(tf.equal(tf.shape(target)[0], 0),
lambda: tf.constant(0, dtype=tf.float32),
lambda: loss)
return loss, logits, state
def train_fn(features, params, initializer=None):
with tf.variable_scope(params.model_name or "model",
initializer=initializer,
reuse=tf.AUTO_REUSE):
state = encoder(features['source'], params)
loss, logits, state = decoder(features['target'], state, params)
return {
"loss": loss
}
def infer_fn(params):
params = copy.copy(params)
params = util.closing_dropout(params)
def encoding_fn(source):
with tf.variable_scope(params.model_name or "model",
reuse=tf.AUTO_REUSE):
state = encoder(source, params)
state["decoder"] = {
"state": state["decoder_initializer"]
}
return state
def decoding_fn(target, state, time):
with tf.variable_scope(params.model_name or "model",
reuse=tf.AUTO_REUSE):
if params.search_mode == "cache":
step_loss, step_logits, step_state = decoder(
target, state, params)
else:
estate = encoder(state, params)
estate['dev_decode'] = True
_, step_logits, _ = decoder(target, estate, params)
step_state = state
return step_logits, step_state
return encoding_fn, decoding_fn