def __call__(self, h_, x):
# h_: the concatenation of previous hidden state
# and memory cell state
# x_i/x: the current input state for input gate
# x_f/x: the current input state for forget gate
# x_o/x: the current input state for output gate
# x_c/x: the current input state for candidate cell
"""
f = sigmoid(h_, x)
i = sigmoid(h_, x)
o = sigmoid(h_, x)
c' = tanh(h_, x)
c = f * c_ + i * c'
h = o * tanh(c)
"""
with tf.variable_scope("cell_{}".format(self.scope or "lstm")):
x_g, x_c = x
h_, c_ = tf.split(h_, 2, -1)
h_g = linear(h_, self.d * 3, ln=self.ln, scope="gate_h")
i, f, o = tf.split(tf.sigmoid(x_g + h_g), 3, -1)
h_c = linear(h_, self.d, ln=self.ln, scope="hide_h")
h_c = tf.tanh(x_c + h_c)
c = i * h_c + f * c_
h = o * tf.tanh(c)
return tf.concat([h, c], -1)
def tensor2vector(tensor,
hidden_size,
mask=None,
init=None,
use_ln=False,
dropout=0.1,
scope="vecatt"):
with tf.variable_scope(scope):
if util.valid_dropout(dropout):
tensor = tf.nn.dropout(tensor, 1. - dropout)
if init is None:
m = tf.nn.tanh(
func.linear(tensor, hidden_size, ln=use_ln, scope="m_tensor"))
else:
init = util.expand_tile_dims(init, tf.shape(tensor)[1], 1)
if util.valid_dropout(dropout):
init = tf.nn.dropout(init, 1. - dropout)
m = tf.nn.tanh(
func.linear(tensor, hidden_size, ln=use_ln, scope="m_tensor") +
func.linear(init, hidden_size, scope="m_init"))
s = func.linear(m, 1, bias=False, scope="sore")
if mask is None:
mask = tf.ones(
[tf.shape(tensor)[0], tf.shape(tensor)[1]], tf.float32)
s = tf.squeeze(s, -1) + (1. - mask) * (-1e9)
w = tf.nn.softmax(s)
return tf.reduce_sum(tf.expand_dims(w, 2) * tensor, axis=1), s
def ffn_layer(x, d, d_o, dropout=None, scope=None, numblocks=None):
"""
FFN layer in Transformer
:param numblocks: size of 'L' in fixup paper
:param scope:
"""
with tf.variable_scope(scope or "ffn_layer",
dtype=tf.as_dtype(dtype.floatx())) as scope:
assert numblocks is not None, 'Fixup requires the total model depth L'
in_initializer = initializer.scale_initializer(
math.pow(numblocks, -1. / 2.), scope.initializer)
x = shift_layer(x)
hidden = func.linear(x,
d,
scope="enlarge",
weight_initializer=in_initializer,
bias=False)
hidden = shift_layer(hidden)
hidden = tf.nn.relu(hidden)
hidden = util.valid_apply_dropout(hidden, dropout)
hidden = shift_layer(hidden)
output = func.linear(hidden,
d_o,
scope="output",
bias=False,
weight_initializer=tf.zeros_initializer())
output = scale_layer(output)
return output
def loss_layer(features, params):
t_enc = features['t_enc']
feature = [t_enc]
if params.enable_bert:
s_mask = tf.to_float(
tf.cast(tf.reduce_sum(features['t_mask'], 1), tf.bool))
batch_size = tf.shape(features['l'])[0]
s_mask = tf.reshape(s_mask, [batch_size, -1])
bert_feature = features['feature']
bert_feature = tf.reshape(
bert_feature,
[batch_size, -1,
bert_feature.get_shape().as_list()[-1]])
bert_vec, _ = tensor2vector(bert_feature,
params.hidden_size,
mask=s_mask,
use_ln=params.layer_norm,
dropout=params.dropout,
scope="bert_att")
feature.append(bert_vec)
feature = tf.concat(feature, -1)
label_logits = func.linear(feature,
params.label_size,
ln=params.layer_norm,
scope="label")
# multi-label classification-based objective
def mlceloss(logits, labels):
soft_label, normalizer = util.label_smooth(labels,
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(labels))
return tf.reduce_mean(centropy)
loss = mlceloss(label_logits, features['l'])
if params.weight_decay > 0:
with tf.variable_scope('l2_loss'):
l2_loss = tf.add_n([
tf.nn.l2_loss(v) for v in tf.trainable_variables()
if 'bias' not in v.name
])
loss += params.weight_decay * l2_loss
features.update({'loss': loss})
prediction = tf.argmax(label_logits, -1)
label_output = tf.nn.softmax(label_logits, -1)
return features, prediction, label_output
def __call__(self, h_, x):
# h_: the previous hidden state
# x: the current input state
"""
p = W x
q = U h_
i = sigmoid(p + q)
f = sigmoid(p - q)
h = i * p + f * h_
"""
if isinstance(x, (list, tuple)):
x = x[0]
with tf.variable_scope("cell_{}".format(self.scope or "atr")):
q = linear(h_, self.d, ln=self.ln, scope="hide_h")
p = x
f = tf.sigmoid(p - q)
if self.twin:
i = tf.sigmoid(p + q)
# we empirically find that the following simple form is more stable.
else:
i = 1. - f
h = i * p + f * h_
return h
def __call__(self, h_, x):
# h_: the previous hidden state
# x_g/x: the current input state for gate
# x_h/x: the current input state for hidden
"""
z = sigmoid(h_, x)
r = sigmoid(h_, x)
h' = tanh(x, r * h_)
h = z * h_ + (1. - z) * h'
"""
with tf.variable_scope("cell_{}".format(self.scope or "gru")):
x_g, x_h = x
h_g = linear(h_, self.d * 2, ln=self.ln, scope="gate_h")
z, r = tf.split(tf.sigmoid(x_g + h_g), 2, -1)
h_h = linear(h_ * r, self.d, ln=self.ln, scope="hide_h")
h = tf.tanh(x_h + h_h)
h = z * h_ + (1. - z) * h
return h
def _get_init_state(self, d, shape=None, x=None, scope=None):
# gen init state vector
# if no evidence x is provided, use zero initialization
if x is None:
assert shape is not None, "you should provide shape"
if not isinstance(shape, (tuple, list)):
shape = [shape]
shape = shape + [d]
return dtype.tf_to_float(tf.zeros(shape))
else:
return linear(x,
d,
bias=True,
ln=self.ln,
scope="{}_init".format(scope or self.scope))
def loss_layer(features, params):
p_enc = features['p_enc']
h_enc = features['h_enc']
p_mask = features['p_mask']
h_mask = features['h_mask']
feature_list = [
tensor2vector(p_enc, params.hidden_size, mask=p_mask, scope="p_att")[0],
tensor2vector(h_enc, params.hidden_size, mask=h_mask, scope="h_att")[0],
]
if params.enable_bert:
feature_list.append(features['feature'])
feature = tf.concat(feature_list, -1)
label_logits = func.linear(feature, params.label_size, ln=params.layer_norm, scope="label")
def celoss(logits, labels):
soft_label, normalizer = util.label_smooth(
labels,
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(labels))
return tf.reduce_mean(centropy)
loss = celoss(label_logits, features['l'])
if params.weight_decay > 0:
with tf.variable_scope('l2_loss'):
l2_loss = tf.add_n([tf.nn.l2_loss(v) for v in tf.trainable_variables() if 'bias' not in v.name])
loss += params.weight_decay * l2_loss
features.update({
'loss': loss
})
return features, tf.argmax(label_logits, -1)
def __call__(self, h_, x):
# h_: the previous hidden state
# x: the current input state
"""
p = W x
q = U h_
i = sigmoid(p + q)
f = sigmoid(p - q)
h = i * p + f * h_
"""
if isinstance(x, (list, tuple)):
x = x[0]
with tf.variable_scope("cell_{}".format(self.scope or "atr")):
q = linear(h_, self.d, ln=self.ln, scope="hide_h")
p = x
f = tf.sigmoid(p - q)
i = tf.sigmoid(p + q)
h = tf.tanh(i * p + f * h_)
return h
def fetch_states(self, x):
with tf.variable_scope("fetch_state_{}".format(self.scope or "lstm")):
g = linear(x, self.d * 3, bias=True, ln=self.ln, scope="gate_x")
c = linear(x, self.d, bias=True, ln=self.ln, scope="hide_x")
return g, c
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 deep_att_dec_rnn(cell_name,
x,
memory,
d,
init_state=None,
mask=None,
mem_mask=None,
ln=False,
sm=True,
depth=1,
num_heads=1):
"""Self implemented conditional-RNN procedure, supporting mask trick"""
# cell_name: gru, lstm or atr
# x: input sequence embedding matrix, [batch, seq_len, dim]
# memory: the conditional part
# d: hidden dimension for rnn
# mask: mask matrix, [batch, seq_len]
# mem_mask: memory mask matrix, [batch, mem_seq_len]
# ln: whether use layer normalization
# init_state: the initial hidden states, for cache purpose
# sm: whether apply swap memory during rnn scan
# depth: depth for the decoder in deep attention
# num_heads: number of attention heads, multi-head attention
# dp: variational dropout
in_shape = util.shape_list(x)
batch_size, time_steps = in_shape[:2]
mem_shape = util.shape_list(memory)
cell_lower = rnn.get_cell(cell_name,
d,
ln=ln,
scope="{}_lower".format(cell_name))
cells_higher = []
for layer in range(depth):
cell_higher = rnn.get_cell(cell_name,
d,
ln=ln,
scope="{}_higher_{}".format(
cell_name, layer))
cells_higher.append(cell_higher)
if init_state is None:
init_state = cell_lower.get_init_state(shape=[batch_size])
if mask is None:
mask = dtype.tf_to_float(tf.ones([batch_size, time_steps]))
if mem_mask is None:
mem_mask = dtype.tf_to_float(tf.ones([batch_size, mem_shape[1]]))
# prepare projected encodes and inputs
cache_inputs = cell_lower.fetch_states(x)
cache_inputs = [tf.transpose(v, [1, 0, 2]) for v in list(cache_inputs)]
proj_memories = func.linear(memory,
mem_shape[-1],
bias=False,
ln=ln,
scope="context_att")
mask_ta = tf.transpose(tf.expand_dims(mask, -1), [1, 0, 2])
init_context = dtype.tf_to_float(
tf.zeros([batch_size, depth, mem_shape[-1]]))
init_weight = dtype.tf_to_float(
tf.zeros([batch_size, depth, num_heads, mem_shape[1]]))
mask_pos = len(cache_inputs)
def _step_fn(prev, x):
t, h_, c_, a_ = prev
m, v = x[mask_pos], x[:mask_pos]
# the first decoder rnn subcell, composing previous hidden state with the current word embedding
s_ = cell_lower(h_, v)
s_ = m * s_ + (1. - m) * h_
atts, att_ctxs = [], []
for layer in range(depth):
# perform attention
prev_cell = cell_lower if layer == 0 else cells_higher[layer - 1]
vle = func.additive_attention(
prev_cell.get_hidden(s_),
memory,
mem_mask,
mem_shape[-1],
ln=ln,
num_heads=num_heads,
proj_memory=proj_memories,
scope="deep_attention_{}".format(layer))
a, c = vle['weights'], vle['output']
atts.append(tf.expand_dims(a, 1))
att_ctxs.append(tf.expand_dims(c, 1))
# perform next-level recurrence
c_c = cells_higher[layer].fetch_states(c)
ss_ = cells_higher[layer](s_, c_c)
s_ = m * ss_ + (1. - m) * s_
h = s_
a = tf.concat(atts, axis=1)
c = tf.concat(att_ctxs, axis=1)
return t + 1, h, c, a
time = tf.constant(0, dtype=tf.int32, name="time")
step_states = (time, init_state, init_context, init_weight)
step_vars = cache_inputs + [mask_ta]
outputs = tf.scan(_step_fn,
step_vars,
initializer=step_states,
parallel_iterations=32,
swap_memory=sm)
output_ta = outputs[1]
context_ta = outputs[2]
attention_ta = outputs[3]
outputs = tf.transpose(output_ta, [1, 0, 2])
output_states = outputs[:, -1]
# batch x target length x depth x mem-dimension
contexts = tf.transpose(context_ta, [1, 0, 2, 3])
# batch x num_heads x depth x target length x source length
attentions = tf.transpose(attention_ta, [1, 3, 2, 0, 4])
return (outputs, output_states), \
(cells_higher[-1].get_hidden(outputs), cells_higher[-1].get_hidden(output_states)), \
contexts, attentions
def embedding_layer(features, params):
t = features['t']
t_mask = tf.to_float(tf.cast(t, tf.bool))
with tf.device('/cpu:0'):
symbol_embeddings = tf.get_variable('special_symbol_embeddings',
shape=(3, params.embed_size),
trainable=True)
embedding_initializer = tf.glorot_uniform_initializer()
if params.word_vocab.pretrained_embedding is not None:
pretrain_embedding = params.word_vocab.pretrained_embedding
embedding_initializer = tf.constant_initializer(pretrain_embedding)
general_embeddings = tf.get_variable(
'general_symbol_embeddings',
shape=(params.word_vocab.size() - 3, params.embed_size),
initializer=embedding_initializer,
trainable=params.word_vocab.pretrained_embedding is None)
word_embeddings = tf.concat([symbol_embeddings, general_embeddings], 0)
# apply word dropout
wd_mask = util.valid_apply_dropout(t_mask, params.word_dropout)
wd_mask = tf.to_float(tf.cast(wd_mask, tf.bool))
t_emb = tf.nn.embedding_lookup(word_embeddings,
t * tf.to_int32(wd_mask))
t_emb = t_emb * tf.expand_dims(t_mask, -1)
embed_features = [t_emb]
if params.enable_bert:
embed_features.append(features['bert_enc'])
if params.use_char:
c = features['c']
c_mask = tf.to_float(tf.cast(c, tf.bool))
c = tf.reshape(c, [-1, tf.shape(c)[-1]])
c_mask = tf.reshape(c_mask, [-1, tf.shape(c_mask)[-1]])
with tf.device('/cpu:0'):
char_embeddings = tf.get_variable(
'char_embeddings',
shape=(params.char_vocab.size(), params.char_embed_size),
initializer=tf.glorot_uniform_initializer(),
trainable=True)
with tf.variable_scope('char_embedding'):
c_emb = tf.nn.embedding_lookup(char_embeddings, c)
c_emb = util.valid_apply_dropout(c_emb, 0.5 * params.dropout)
with tf.variable_scope("char_encoding", reuse=tf.AUTO_REUSE):
c_emb = c_emb * tf.expand_dims(c_mask, -1)
c_shp = util.shape_list(features['c'])
c_emb = tf.reshape(
c_emb, [c_shp[0], c_shp[1], c_shp[2], params.char_embed_size])
c_state = func.linear(tf.reduce_max(c_emb, 2),
params.char_embed_size,
scope="cmap")
embed_features.append(c_state)
t_emb = tf.concat(embed_features, axis=2) * tf.expand_dims(t_mask, -1)
features.update({
't_emb': t_emb,
't_mask': t_mask,
})
return features
def fetch_states(self, x):
with tf.variable_scope("fetch_state_{}".format(self.scope or "lrn")):
h = linear(x, self.d * 3, bias=True, ln=self.ln, scope="hide_x")
return (h, )
def decoder(target, state, params):
mask = dtype.tf_to_float(tf.cast(target, tf.bool))
hidden_size = params.hidden_size
is_training = ('decoder' not in state)
# handling target-side word embedding, including shift-padding for training
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 is_training:
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)
inputs = util.valid_apply_dropout(inputs, params.dropout)
with tf.variable_scope("decoder"):
x = inputs
init_state = state["decoder_initializer"]["layer"]
if not is_training:
init_state = state["decoder"]["state"]["layer"]
returns = deep_att_dec_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,
depth=params.num_decoder_layer)
(_, hidden_state), (outputs, _), contexts, attentions = returns
if not is_training:
state['decoder']['state']['layer'] = hidden_state
x = outputs
cshp = util.shape_list(contexts)
c = tf.reshape(contexts, [cshp[0], cshp[1], cshp[2] * cshp[3]])
feature = func.linear(tf.concat([x, c, inputs], -1),
params.embed_size,
ln=params.layer_norm,
scope="ff")
feature = tf.nn.tanh(feature)
feature = util.valid_apply_dropout(feature, params.dropout)
if 'dev_decode' in state:
feature = feature[:, -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)
logits = tf.cast(logits, tf.float32)
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))
mask = tf.cast(mask, tf.float32)
per_sample_loss = tf.reduce_sum(centropy * mask, -1) / tf.reduce_sum(
mask, -1)
loss = tf.reduce_mean(per_sample_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, per_sample_loss
def fetch_states(self, x):
with tf.variable_scope("fetch_state_{}".format(self.scope or "gru")):
g = linear(x, self.d * 2, bias=False, ln=self.ln, scope="gate_x")
h = linear(x, self.d, bias=False, ln=self.ln, scope="hide_x")
return g, h
def dot_attention(query, memory, mem_mask, hidden_size,
ln=False, num_heads=1, cache=None, dropout=None,
out_map=True, scope=None):
"""
dotted attention model
:param query: [batch_size, qey_len, dim]
:param memory: [batch_size, seq_len, mem_dim] or None
:param mem_mask: [batch_size, seq_len]
:param hidden_size: attention space dimension
:param ln: whether use layer normalization
:param num_heads: attention head number
:param dropout: attention dropout, default disable
:param out_map: output additional mapping
:param cache: cache-based decoding
:param scope:
:return: a value matrix, [batch_size, qey_len, mem_dim]
"""
with tf.variable_scope(scope or "dot_attention", reuse=tf.AUTO_REUSE,
dtype=tf.as_dtype(dtype.floatx())):
if memory is None:
# suppose self-attention from queries alone
h = func.linear(query, hidden_size * 3, ln=ln, scope="qkv_map")
q, k, v = tf.split(h, 3, -1)
if cache is not None:
k = tf.concat([cache['k'], k], axis=1)
v = tf.concat([cache['v'], v], axis=1)
cache = {
'k': k,
'v': v,
}
else:
q = func.linear(query, hidden_size, ln=ln, scope="q_map")
if cache is not None and ('mk' in cache and 'mv' in cache):
k, v = cache['mk'], cache['mv']
else:
k = func.linear(memory, hidden_size, ln=ln, scope="k_map")
v = func.linear(memory, hidden_size, ln=ln, scope="v_map")
if cache is not None:
cache['mk'] = k
cache['mv'] = v
q = func.split_heads(q, num_heads)
k = func.split_heads(k, num_heads)
v = func.split_heads(v, num_heads)
q *= (hidden_size // num_heads) ** (-0.5)
# q * k => attention weights
logits = tf.matmul(q, k, transpose_b=True)
# convert the mask to 0-1 form and multiply to logits
if mem_mask is not None:
zero_one_mask = tf.to_float(tf.equal(mem_mask, 0.0))
logits *= zero_one_mask
# replace softmax with relu
# weights = tf.nn.softmax(logits)
weights = tf.nn.relu(logits)
dweights = util.valid_apply_dropout(weights, dropout)
# weights * v => attention vectors
o = tf.matmul(dweights, v)
o = func.combine_heads(o)
# perform RMSNorm to stabilize running
o = gated_rms_norm(o, scope="post")
if out_map:
o = func.linear(o, hidden_size, ln=ln, scope="o_map")
results = {
'weights': weights,
'output': o,
'cache': cache
}
return results
def decoder(target, state, params):
mask = dtype.tf_to_float(tf.cast(target, tf.bool))
hidden_size = params.hidden_size
initializer = tf.random_normal_initializer(0.0, hidden_size**-0.5)
is_training = ('decoder' not in state)
if is_training:
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],
initializer=initializer)
tgt_bias = tf.get_variable("bias", [params.embed_size])
inputs = tf.gather(tgt_emb, target) * (hidden_size**0.5)
inputs = tf.nn.bias_add(inputs, tgt_bias)
# shift
if is_training:
inputs = tf.pad(inputs, [[0, 0], [1, 0], [0, 0]])
inputs = inputs[:, :-1, :]
inputs = func.add_timing_signal(inputs)
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)
inputs = func.add_timing_signal(inputs,
time=dtype.tf_to_float(state['time']))
inputs = util.valid_apply_dropout(inputs, params.dropout)
with tf.variable_scope("decoder"):
x = inputs
for layer in range(params.num_decoder_layer):
if params.deep_transformer_init:
layer_initializer = tf.variance_scaling_initializer(
params.initializer_gain * (layer + 1)**-0.5,
mode="fan_avg",
distribution="uniform")
else:
layer_initializer = None
with tf.variable_scope("layer_{}".format(layer),
initializer=layer_initializer):
with tf.variable_scope("average_attention"):
x_fwds = []
for strategy in params.strategies:
with tf.variable_scope(strategy):
x_fwd = average_attention_strategy(
strategy, x, mask, state, layer, params)
x_fwds.append(x_fwd)
x_fwd = tf.add_n(x_fwds) / len(x_fwds)
# FFN activation
if params.use_ffn:
y = func.ffn_layer(
x_fwd,
params.filter_size,
hidden_size,
dropout=params.relu_dropout,
)
else:
y = x_fwd
# Gating layer
z = func.linear(tf.concat([x, y], axis=-1),
hidden_size * 2,
scope="z_project")
i, f = tf.split(z, 2, axis=-1)
y = tf.sigmoid(i) * x + tf.sigmoid(f) * y
x = func.residual_fn(x, y, dropout=params.residual_dropout)
x = func.layer_norm(x)
with tf.variable_scope("cross_attention"):
y = func.dot_attention(
x,
state['encodes'],
func.attention_bias(state['mask'], "masking"),
hidden_size,
num_heads=params.num_heads,
dropout=params.attention_dropout,
cache=None if is_training else
state['decoder']['state']['layer_{}'.format(layer)])
if not is_training:
# mk, mv
state['decoder']['state']['layer_{}'.format(layer)]\
.update(y['cache'])
y = y['output']
x = func.residual_fn(x, y, dropout=params.residual_dropout)
x = func.layer_norm(x)
with tf.variable_scope("feed_forward"):
y = func.ffn_layer(
x,
params.filter_size,
hidden_size,
dropout=params.relu_dropout,
)
x = func.residual_fn(x, y, dropout=params.residual_dropout)
x = func.layer_norm(x)
feature = x
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],
initializer=initializer)
feature = tf.reshape(feature, [-1, params.embed_size])
logits = tf.matmul(feature, softmax_emb, False, True)
logits = tf.cast(logits, tf.float32)
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))
mask = tf.cast(mask, tf.float32)
per_sample_loss = tf.reduce_sum(centropy * mask, -1) / tf.reduce_sum(
mask, -1)
loss = tf.reduce_mean(per_sample_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, per_sample_loss
def embedding_layer(features, params):
p = features['p']
h = features['h']
p_mask = tf.to_float(tf.cast(p, tf.bool))
h_mask = tf.to_float(tf.cast(h, tf.bool))
with tf.device('/cpu:0'):
symbol_embeddings = tf.get_variable('special_symbol_embeddings',
shape=(3, params.embed_size),
trainable=True)
embedding_initializer = tf.glorot_uniform_initializer()
if tf.gfile.Exists(params.pretrain_word_embedding_file):
pretrain_embedding = np.load(params.pretrain_word_embedding_file)['data']
embedding_initializer = tf.constant_initializer(pretrain_embedding)
general_embeddings = tf.get_variable('general_symbol_embeddings',
shape=(params.word_vocab.size() - 3, params.embed_size),
initializer=embedding_initializer,
trainable=False)
word_embeddings = tf.concat([symbol_embeddings, general_embeddings], 0)
p_emb = tf.nn.embedding_lookup(word_embeddings, p)
h_emb = tf.nn.embedding_lookup(word_embeddings, h)
p_features = [p_emb]
h_features = [h_emb]
if params.enable_bert:
p_features.append(features['bert_p_enc'])
h_features.append(features['bert_h_enc'])
if params.use_char:
pc = features['pc']
hc = features['hc']
pc_mask = tf.to_float(tf.cast(pc, tf.bool))
hc_mask = tf.to_float(tf.cast(hc, tf.bool))
pc = tf.reshape(pc, [-1, tf.shape(pc)[-1]])
hc = tf.reshape(hc, [-1, tf.shape(hc)[-1]])
pc_mask = tf.reshape(pc_mask, [-1, tf.shape(pc_mask)[-1]])
hc_mask = tf.reshape(hc_mask, [-1, tf.shape(hc_mask)[-1]])
with tf.device('/cpu:0'):
char_embeddings = tf.get_variable('char_embeddings',
shape=(params.char_vocab.size(), params.char_embed_size),
initializer=tf.glorot_uniform_initializer(),
trainable=True)
with tf.variable_scope('char_embedding'):
pc_emb = tf.nn.embedding_lookup(char_embeddings, pc)
hc_emb = tf.nn.embedding_lookup(char_embeddings, hc)
if util.valid_dropout(params.dropout):
pc_emb = tf.nn.dropout(pc_emb, 1. - 0.5 * params.dropout)
hc_emb = tf.nn.dropout(hc_emb, 1. - 0.5 * params.dropout)
with tf.variable_scope("char_encoding", reuse=tf.AUTO_REUSE):
pc_emb = pc_emb * tf.expand_dims(pc_mask, -1)
hc_emb = hc_emb * tf.expand_dims(hc_mask, -1)
pc_shp = util.shape_list(features['pc'])
pc_emb = tf.reshape(pc_emb, [pc_shp[0], pc_shp[1], pc_shp[2], params.char_embed_size])
hc_shp = util.shape_list(features['hc'])
hc_emb = tf.reshape(hc_emb, [hc_shp[0], hc_shp[1], hc_shp[2], params.char_embed_size])
pc_state = func.linear(tf.reduce_max(pc_emb, 2), params.char_embed_size, scope="cmap")
hc_state = func.linear(tf.reduce_max(hc_emb, 2), params.char_embed_size, scope="cmap")
p_features.append(pc_state)
h_features.append(hc_state)
'''
p_emb = func.highway(tf.concat(p_features, axis=2),
size=params.hidden_size, dropout=params.dropout, num_layers=2, scope='highway')
h_emb = func.highway(tf.concat(h_features, axis=2),
size=params.hidden_size, dropout=params.dropout, num_layers=2, scope='highway')
'''
p_emb = tf.concat(p_features, axis=2)
h_emb = tf.concat(h_features, axis=2)
p_emb = p_emb * tf.expand_dims(p_mask, -1)
h_emb = h_emb * tf.expand_dims(h_mask, -1)
features.update({'p_emb': p_emb,
'h_emb': h_emb,
'p_mask': p_mask,
'h_mask': h_mask,
})
return features
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 dot_attention(query,
memory,
mem_mask,
hidden_size,
ln=False,
num_heads=1,
cache=None,
dropout=None,
use_relative_pos=False,
max_relative_position=16,
out_map=True,
scope=None,
fuse_mask=None,
decode_step=None,
numblocks=None):
"""
dotted attention model
:param query: [batch_size, qey_len, dim]
:param memory: [batch_size, seq_len, mem_dim] or None
:param mem_mask: [batch_size, seq_len]
:param hidden_size: attention space dimension
:param ln: whether use layer normalization
:param num_heads: attention head number
:param dropout: attention dropout, default disable
:param out_map: output additional mapping
:param cache: cache-based decoding
:param fuse_mask: aan mask during training, and timestep for testing
:param max_relative_position: maximum position considered for relative embedding
:param use_relative_pos: whether use relative position information
:param decode_step: the time step of current decoding, 0-based
:param numblocks: size of 'L' in fixup paper
:param scope:
:return: a value matrix, [batch_size, qey_len, mem_dim]
"""
with tf.variable_scope(scope or "dot_attention",
reuse=tf.AUTO_REUSE,
dtype=tf.as_dtype(dtype.floatx())) as scope:
if fuse_mask:
assert memory is not None, 'Fuse mechanism only applied with cross-attention'
if cache and use_relative_pos:
assert decode_step is not None, 'Decode Step must provide when use relative position encoding'
assert numblocks is not None, 'Fixup requires the total model depth L'
scale_base = 6. if fuse_mask is None else 8.
in_initializer = initializer.scale_initializer(
math.pow(numblocks, -1. / scale_base), scope.initializer)
if memory is None:
# suppose self-attention from queries alone
h = func.linear(query,
hidden_size * 3,
ln=ln,
scope="qkv_map",
weight_initializer=in_initializer,
bias=False)
q, k, v = tf.split(h, 3, -1)
if cache is not None:
k = tf.concat([cache['k'], k], axis=1)
v = tf.concat([cache['v'], v], axis=1)
cache = {
'k': k,
'v': v,
}
else:
q = func.linear(query,
hidden_size,
ln=ln,
scope="q_map",
weight_initializer=in_initializer,
bias=False)
if cache is not None and ('mk' in cache and 'mv' in cache):
k, v = cache['mk'], cache['mv']
else:
k = func.linear(memory,
hidden_size,
ln=ln,
scope="k_map",
weight_initializer=in_initializer,
bias=False)
v = func.linear(memory,
hidden_size,
ln=ln,
scope="v_map",
weight_initializer=in_initializer,
bias=False)
if cache is not None:
cache['mk'] = k
cache['mv'] = v
q = func.split_heads(q, num_heads)
k = func.split_heads(k, num_heads)
v = func.split_heads(v, num_heads)
q *= (hidden_size // num_heads)**(-0.5)
q_shp = util.shape_list(q)
k_shp = util.shape_list(k)
v_shp = util.shape_list(v)
q_len = q_shp[2] if decode_step is None else decode_step + 1
r_lst = None if decode_step is None else 1
# q * k => attention weights
if use_relative_pos:
r = rpr.get_relative_positions_embeddings(q_len,
k_shp[2],
k_shp[3],
max_relative_position,
name="rpr_keys",
last=r_lst)
logits = rpr.relative_attention_inner(q, k, r, transpose=True)
else:
logits = tf.matmul(q, k, transpose_b=True)
if mem_mask is not None:
logits += mem_mask
weights = tf.nn.softmax(logits)
dweights = util.valid_apply_dropout(weights, dropout)
# weights * v => attention vectors
if use_relative_pos:
r = rpr.get_relative_positions_embeddings(q_len,
k_shp[2],
v_shp[3],
max_relative_position,
name="rpr_values",
last=r_lst)
o = rpr.relative_attention_inner(dweights, v, r, transpose=False)
else:
o = tf.matmul(dweights, v)
o = func.combine_heads(o)
if fuse_mask is not None:
# This is for AAN, the important part is sharing v_map
v_q = func.linear(query,
hidden_size,
ln=ln,
scope="v_map",
weight_initializer=in_initializer,
bias=False)
if cache is not None and 'aan' in cache:
aan_o = (v_q + cache['aan']) / dtype.tf_to_float(fuse_mask + 1)
else:
# Simplified Average Attention Network
aan_o = tf.matmul(fuse_mask, v_q)
if cache is not None:
if 'aan' not in cache:
cache['aan'] = v_q
else:
cache['aan'] = v_q + cache['aan']
# Directly sum both self-attention and cross attention
o = o + aan_o
if out_map:
o = func.linear(o,
hidden_size,
ln=ln,
scope="o_map",
weight_initializer=tf.zeros_initializer(),
bias=False)
results = {'weights': weights, 'output': o, 'cache': cache}
return results
def dot_attention(query,
memory,
mem_mask,
hidden_size,
ln=False,
num_heads=1,
cache=None,
dropout=None,
out_map=True,
scope=None,
count_mask=None):
"""
dotted attention model with l0drop
:param query: [batch_size, qey_len, dim]
:param memory: [batch_size, seq_len, mem_dim] or None
:param mem_mask: [batch_size, seq_len]
:param hidden_size: attention space dimension
:param ln: whether use layer normalization
:param num_heads: attention head number
:param dropout: attention dropout, default disable
:param out_map: output additional mapping
:param cache: cache-based decoding
:param count_mask: counting vector for l0drop
:param scope:
:return: a value matrix, [batch_size, qey_len, mem_dim]
"""
with tf.variable_scope(scope or "dot_attention",
reuse=tf.AUTO_REUSE,
dtype=tf.as_dtype(dtype.floatx())):
if memory is None:
# suppose self-attention from queries alone
h = func.linear(query, hidden_size * 3, ln=ln, scope="qkv_map")
q, k, v = tf.split(h, 3, -1)
if cache is not None:
k = tf.concat([cache['k'], k], axis=1)
v = tf.concat([cache['v'], v], axis=1)
cache = {
'k': k,
'v': v,
}
else:
q = func.linear(query, hidden_size, ln=ln, scope="q_map")
if cache is not None and ('mk' in cache and 'mv' in cache):
k, v = cache['mk'], cache['mv']
else:
k = func.linear(memory, hidden_size, ln=ln, scope="k_map")
v = func.linear(memory, hidden_size, ln=ln, scope="v_map")
if cache is not None:
cache['mk'] = k
cache['mv'] = v
q = func.split_heads(q, num_heads)
k = func.split_heads(k, num_heads)
v = func.split_heads(v, num_heads)
q *= (hidden_size // num_heads)**(-0.5)
# q * k => attention weights
logits = tf.matmul(q, k, transpose_b=True)
if mem_mask is not None:
logits += mem_mask
# modifying 'weights = tf.nn.softmax(logits)' to include the counting information.
# --------
logits = logits - tf.reduce_max(logits, -1, keepdims=True)
exp_logits = tf.exp(logits)
# basically, the count considers how many states are dropped (i.e. gate value 0s)
if count_mask is not None:
exp_logits *= count_mask
exp_sum_logits = tf.reduce_sum(exp_logits, -1, keepdims=True)
weights = exp_logits / exp_sum_logits
# --------
dweights = util.valid_apply_dropout(weights, dropout)
# weights * v => attention vectors
o = tf.matmul(dweights, v)
o = func.combine_heads(o)
if out_map:
o = func.linear(o, hidden_size, ln=ln, scope="o_map")
results = {'weights': weights, 'output': o, 'cache': cache}
return results
def decoder(target, state, params):
mask = dtype.tf_to_float(tf.cast(target, tf.bool))
hidden_size = params.hidden_size
initializer = tf.random_normal_initializer(0.0, hidden_size**-0.5)
is_training = ('decoder' not in state)
if is_training:
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],
initializer=initializer)
tgt_bias = tf.get_variable("bias", [params.embed_size])
inputs = tf.gather(tgt_emb, target) * (hidden_size**0.5)
inputs = tf.nn.bias_add(inputs, tgt_bias)
# shift
if is_training:
inputs = tf.pad(inputs, [[0, 0], [1, 0], [0, 0]])
inputs = inputs[:, :-1, :]
inputs = func.add_timing_signal(inputs)
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)
inputs = func.add_timing_signal(inputs,
time=dtype.tf_to_float(state['time']))
inputs = util.valid_apply_dropout(inputs, params.dropout)
# Applying L0Drop
# --------
source_memory = state["encodes"]
source_mask = state["mask"]
# source_pruning: log alpha_i = x_i w^T
source_pruning = func.linear(source_memory, 1, scope="source_pruning")
if is_training: # training
source_memory, l0_mask = l0norm.var_train(
(source_memory, source_pruning))
l0_norm_loss = tf.squeeze(l0norm.l0_norm(source_pruning), -1)
l0_norm_loss = tf.reduce_sum(l0_norm_loss * source_mask,
-1) / tf.reduce_sum(source_mask, -1)
l0_norm_loss = tf.reduce_mean(l0_norm_loss)
l0_norm_loss = l0norm.l0_regularization_loss(
l0_norm_loss,
reg_scalar=params.l0_norm_reg_scalar,
start_reg_ramp_up=params.l0_norm_start_reg_ramp_up,
end_reg_ramp_up=params.l0_norm_end_reg_ramp_up,
warm_up=params.l0_norm_warm_up,
)
# force the model to only attend to unmasked position
source_mask = dtype.tf_to_float(
tf.cast(tf.squeeze(l0_mask, -1), tf.bool)) * source_mask
else: # evaluation
source_memory, l0_mask = l0norm.var_eval(
(source_memory, source_pruning))
l0_norm_loss = 0.0
source_memory, source_mask, count_mask = extract_encodes(
source_memory, source_mask, l0_mask)
count_mask = tf.expand_dims(tf.expand_dims(count_mask, 1), 1)
# --------
with tf.variable_scope("decoder"):
x = inputs
for layer in range(params.num_decoder_layer):
if params.deep_transformer_init:
layer_initializer = tf.variance_scaling_initializer(
params.initializer_gain * (layer + 1)**-0.5,
mode="fan_avg",
distribution="uniform")
else:
layer_initializer = None
with tf.variable_scope("layer_{}".format(layer),
initializer=layer_initializer):
with tf.variable_scope("self_attention"):
y = func.dot_attention(
x,
None,
func.attention_bias(tf.shape(mask)[1], "causal"),
hidden_size,
num_heads=params.num_heads,
dropout=params.attention_dropout,
cache=None if is_training else
state['decoder']['state']['layer_{}'.format(layer)])
if not is_training:
# k, v
state['decoder']['state']['layer_{}'.format(layer)] \
.update(y['cache'])
y = y['output']
x = func.residual_fn(x, y, dropout=params.residual_dropout)
x = func.layer_norm(x)
with tf.variable_scope("cross_attention"):
if is_training:
y = func.dot_attention(
x,
source_memory,
func.attention_bias(source_mask, "masking"),
hidden_size,
num_heads=params.num_heads,
dropout=params.attention_dropout,
)
else:
y = dot_attention(x,
source_memory,
func.attention_bias(
source_mask, "masking"),
hidden_size,
count_mask=count_mask,
num_heads=params.num_heads,
dropout=params.attention_dropout,
cache=state['decoder']['state'][
'layer_{}'.format(layer)])
# mk, mv
state['decoder']['state']['layer_{}'.format(layer)] \
.update(y['cache'])
y = y['output']
x = func.residual_fn(x, y, dropout=params.residual_dropout)
x = func.layer_norm(x)
with tf.variable_scope("feed_forward"):
y = func.ffn_layer(
x,
params.filter_size,
hidden_size,
dropout=params.relu_dropout,
)
x = func.residual_fn(x, y, dropout=params.residual_dropout)
x = func.layer_norm(x)
feature = x
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],
initializer=initializer)
feature = tf.reshape(feature, [-1, params.embed_size])
logits = tf.matmul(feature, softmax_emb, False, True)
logits = tf.cast(logits, tf.float32)
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))
mask = tf.cast(mask, tf.float32)
per_sample_loss = tf.reduce_sum(centropy * mask, -1) / tf.reduce_sum(
mask, -1)
loss = tf.reduce_mean(per_sample_loss)
loss = loss + l0_norm_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, tf.float32), lambda: loss)
return loss, logits, state, per_sample_loss
def cond_rnn(cell_name,
x,
memory,
d,
init_state=None,
mask=None,
mem_mask=None,
ln=False,
sm=True,
one2one=False):
"""Self implemented conditional-RNN procedure, supporting mask trick"""
# cell_name: gru, lstm or atr
# x: input sequence embedding matrix, [batch, seq_len, dim]
# memory: the conditional part
# d: hidden dimension for rnn
# mask: mask matrix, [batch, seq_len]
# mem_mask: memory mask matrix, [batch, mem_seq_len]
# ln: whether use layer normalization
# init_state: the initial hidden states, for cache purpose
# sm: whether apply swap memory during rnn scan
# one2one: whether the memory is one-to-one mapping for x
in_shape = util.shape_list(x)
batch_size, time_steps = in_shape[:2]
mem_shape = util.shape_list(memory)
cell_lower = get_cell(cell_name,
d,
ln=ln,
scope="{}_lower".format(cell_name))
cell_higher = get_cell(cell_name,
d,
ln=ln,
scope="{}_higher".format(cell_name))
if init_state is None:
init_state = cell_lower.get_init_state(shape=[batch_size])
if mask is None:
mask = tf.ones([batch_size, time_steps], tf.float32)
if mem_mask is None:
mem_mask = tf.ones([batch_size, mem_shape[1]], tf.float32)
# prepare projected encodes and inputs
cache_inputs = cell_lower.fetch_states(x)
cache_inputs = [tf.transpose(v, [1, 0, 2]) for v in list(cache_inputs)]
if not one2one:
proj_memories = linear(memory,
mem_shape[-1],
bias=False,
ln=ln,
scope="context_att")
else:
cache_memories = cell_higher.fetch_states(memory)
cache_memories = [
tf.transpose(v, [1, 0, 2]) for v in list(cache_memories)
]
mask_ta = tf.transpose(tf.expand_dims(mask, -1), [1, 0, 2])
init_context = tf.zeros([batch_size, mem_shape[-1]], tf.float32)
init_weight = tf.zeros([batch_size, mem_shape[1]], tf.float32)
mask_pos = len(cache_inputs)
def _step_fn(prev, x):
t, h_, c_, a_ = prev
if not one2one:
m, v = x[mask_pos], x[:mask_pos]
else:
c, c_c, m, v = x[-1], x[mask_pos + 1:-1], x[mask_pos], x[:mask_pos]
s = cell_lower(h_, v)
s = m * s + (1. - m) * h_
if not one2one:
a, c = additive_attention(cell_lower.get_hidden(s),
memory,
mem_mask,
mem_shape[-1],
ln=ln,
proj_memory=proj_memories,
scope="attention")
c_c = cell_higher.fetch_states(c)
else:
a = tf.tile(tf.expand_dims(tf.range(time_steps), 0),
[batch_size, 1])
a = tf.to_float(a == t)
a = tf.reshape(a, tf.shape(init_weight))
h = cell_higher(s, c_c)
h = m * h + (1. - m) * s
return t + 1, h, c, a
time = tf.constant(0, dtype=tf.int32, name="time")
step_states = (time, init_state, init_context, init_weight)
step_vars = cache_inputs + [mask_ta]
if one2one:
step_vars += cache_memories + [memory]
outputs = tf.scan(_step_fn,
step_vars,
initializer=step_states,
parallel_iterations=32,
swap_memory=sm)
output_ta = outputs[1]
context_ta = outputs[2]
attention_ta = outputs[3]
outputs = tf.transpose(output_ta, [1, 0, 2])
output_states = outputs[:, -1]
contexts = tf.transpose(context_ta, [1, 0, 2])
attentions = tf.transpose(attention_ta, [1, 0, 2])
return (outputs, output_states), \
(cell_higher.get_hidden(outputs), cell_higher.get_hidden(output_states)), \
contexts, attentions
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"):
init_state = state["decoder_initializer"]
if 'decoder' in state:
init_state = state["decoder"]["state"]
returns = rnn.cond_rnn(params.cell, inputs, state["encodes"], hidden_size,
init_state=init_state, mask=mask,
mem_mask=state["mask"], ln=params.layer_norm,
sm=params.swap_memory, one2one=False)
(hidden_states, _), (outputs, _), contexts, attentions = returns
feature = linear([outputs, contexts, inputs], params.embed_size,
ln=params.layer_norm, scope="pre_logits")
feature = tf.tanh(feature)
if util.valid_dropout(params.dropout):
feature = tf.nn.dropout(feature, 1. - params.dropout)
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)
centropy = tf.nn.softmax_cross_entropy_with_logits(
logits=logits,
labels=util.label_smooth(target,
util.shape_list(logits)[-1],
factor=params.label_smooth)
)
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)
if 'decoder' in state:
state['decoder']['state'] = hidden_states
return loss, logits, state
