def wrap_rnn(x,
cell_type,
nlayers,
hidden_size,
mask=None,
bidir=True,
use_ln=True,
concat=True,
dropout=0.0,
scope=None):
outputs = [x]
states = []
if mask is None:
xshp = util.shape_list(x)
mask = tf.ones([xshp[0], xshp[1]], tf.float32)
for layer in range(nlayers):
with tf.variable_scope("{}_layer_{}".format(scope or 'rnn', layer)):
with tf.variable_scope("fw_rnn"):
_, (o_fw, o_fw_s) = rnn.rnn(cell_type,
outputs[-1],
hidden_size,
mask=mask,
ln=use_ln,
sm=False)
if bidir:
with tf.variable_scope("bw_rnn"):
_, (o_bw, o_bw_s) = rnn.rnn(cell_type,
tf.reverse(outputs[-1], [1]),
hidden_size,
mask=tf.reverse(mask, [1]),
ln=use_ln,
sm=False)
o_bw = tf.reverse(o_bw, [1])
if layer != nlayers - 1:
o_fw = util.valid_apply_dropout(o_fw, dropout)
o_fw_s = util.valid_apply_dropout(o_fw_s, dropout)
if bidir:
o_bw = util.valid_apply_dropout(o_bw, dropout)
o_bw_s = util.valid_apply_dropout(o_bw_s, dropout)
if not bidir:
outputs.append(o_fw)
states.append(o_fw_s)
else:
outputs.append(tf.concat([o_fw, o_bw], -1))
states.append(tf.concat([o_fw_s, o_bw_s], -1))
if concat:
return tf.concat(outputs[1:], -1), tf.concat(states, -1)
else:
return outputs[-1], states[-1]
def highway(x,
size=None,
activation=None,
num_layers=2,
dropout=0.0,
ln=False,
scope='highway'):
with tf.variable_scope(scope or "highway"):
if size is None:
size = x.shape.as_list()[-1]
else:
x = linear(x, size, ln=ln, scope="input_projection")
for i in range(num_layers):
T = linear(x, size, ln=ln, scope='gate_%d' % i)
T = tf.nn.sigmoid(T)
H = linear(x, size, ln=ln, scope='activation_%d' % i)
if activation is not None:
H = activation(H)
H = util.valid_apply_dropout(H, dropout)
x = H * T + x * (1.0 - T)
return x
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 graph(features, params):
if params.enable_bert:
s = features['s']
bert_input = s
sequence_output = bert.bert_encoder(bert_input, params)
s_enc = tf.concat(sequence_output[0][-4:], -1)[:, 1:, :]
sb = features['sb']
sb_shp = util.shape_list(sb)
s_coord = tf.stack([util.batch_coordinates(sb_shp[0], sb_shp[1]), sb],
axis=2)
s_enc = tf.gather_nd(s_enc, s_coord)
features['bert_enc'] = util.valid_apply_dropout(s_enc, params.dropout)
if not params.use_bert_single:
features['feature'] = s_enc[:, 0, :]
else:
features['feature'] = sequence_output[1]
features = embedding_layer(features, params)
features = hierarchy_layer(features, params)
graph_output = loss_layer(features, params)
return graph_output
def graph(features, params):
if params.enable_bert:
ps = features['ps']
hs = features['hs']
bert_input = tf.concat([ps, hs], 1)
sequence_output = bert.bert_encoder(bert_input, params)
sequence_feature = bert.bert_feature(sequence_output[0])
p_len = tf.shape(ps)[1]
# 1: remove the encoding for `cls`
p_enc = sequence_feature[:, 1:p_len, :]
h_enc = sequence_feature[:, p_len:, :]
pb = features['pb']
hb = features['hb']
pb_shp = util.shape_list(pb)
hb_shp = util.shape_list(hb)
p_coord = tf.stack(
[util.batch_coordinates(pb_shp[0], pb_shp[1]), pb],
axis=2
)
p_enc = tf.gather_nd(p_enc, p_coord)
h_coord = tf.stack(
[util.batch_coordinates(hb_shp[0], hb_shp[1]), hb],
axis=2
)
h_enc = tf.gather_nd(h_enc, h_coord)
features['bert_p_enc'] = util.valid_apply_dropout(p_enc, params.dropout)
features['bert_h_enc'] = util.valid_apply_dropout(h_enc, params.dropout)
if not params.use_bert_single:
features['feature'] = sequence_feature[:, 0, :]
else:
features['feature'] = sequence_output[1]
features = embedding_layer(features, params)
features = match_layer(features, params)
features = loss_layer(features, params)
return features
def additive_attention(query, memory, mem_mask, hidden_size,
ln=False, proj_memory=None, num_heads=1,
dropout=None, att_fun="add", scope=None):
"""
additive attention model
:param query: [batch_size, dim]
:param memory: [batch_size, seq_len, mem_dim]
:param mem_mask: [batch_size, seq_len]
:param hidden_size: attention space dimension
:param ln: whether use layer normalization
:param proj_memory: this is the mapped memory for saving memory
:param num_heads: attention head number
:param dropout: attention dropout, default disable
:param scope:
:return: a value matrix, [batch_size, mem_dim]
"""
with tf.variable_scope(scope or "additive_attention",
dtype=tf.as_dtype(dtype.floatx())):
if proj_memory is None:
proj_memory = linear(memory, hidden_size, ln=ln, scope="feed_memory")
query = linear(tf.expand_dims(query, 1), hidden_size, ln=ln, scope="feed_query")
query = split_heads(query, num_heads)
proj_memory = split_heads(proj_memory, num_heads)
if att_fun == "add":
value = tf.tanh(query + proj_memory)
logits = linear(value, 1, ln=False, scope="feed_logits")
logits = tf.squeeze(logits, -1)
else:
logits = tf.matmul(query, proj_memory, transpose_b=True)
logits = tf.squeeze(logits, 2)
logits = util.mask_scale(logits, tf.expand_dims(mem_mask, 1))
weights = tf.nn.softmax(logits, -1) # [batch_size, seq_len]
dweights = util.valid_apply_dropout(weights, dropout)
memory = split_heads(memory, num_heads)
value = tf.reduce_sum(
tf.expand_dims(dweights, -1) * memory, -2, keepdims=True)
value = combine_heads(value)
value = tf.squeeze(value, 1)
results = {
'weights': weights,
'output': value,
'cache_state': proj_memory
}
return results
def ffn_layer(x, d, d_o, dropout=None, scope=None):
"""FFN layer in Transformer"""
with tf.variable_scope(scope or "ffn_layer"):
hidden = linear(x, d, scope="enlarge")
hidden = tf.nn.relu(hidden)
hidden = util.valid_apply_dropout(hidden, dropout)
output = linear(hidden, d_o, scope="output")
return output
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 residual_fn(x, y, dropout=None):
"""Residual Connection"""
y = util.valid_apply_dropout(y, dropout)
return x + y
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):
"""
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 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 fuse_mask is not None:
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'
if memory is None:
# suppose self-attention from queries alone
h = 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 = 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 = linear(memory, hidden_size, ln=ln, scope="k_map")
v = linear(memory, hidden_size, ln=ln, scope="v_map")
if cache is not None:
cache['mk'] = k
cache['mv'] = v
q = split_heads(q, num_heads)
k = split_heads(k, num_heads)
v = 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 = combine_heads(o)
if fuse_mask is not None:
# This is for AAN, the important part is sharing v_map
v_q = linear(query, hidden_size, ln=ln, scope="v_map")
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 = linear(o, hidden_size, ln=ln, scope="o_map")
results = {
'weights': weights,
'output': o,
'cache': cache
}
return results
def bert_encoder(sequence, params):
# extract sequence mask information
seq_mask = 1. - tf.to_float(tf.equal(sequence, params.bert.vocab.pad))
# extract segment information
seg_pos = tf.to_float(tf.equal(sequence, params.bert.vocab.sep))
seg_ids = tf.cumsum(seg_pos, axis=1, reverse=True)
seg_num = tf.reduce_sum(seg_pos, axis=1, keepdims=True)
seg_ids = seg_num - seg_ids
seg_ids = tf.to_int32(seg_ids * seq_mask)
# sequence length information
seq_shp = util.shape_list(sequence)
batch_size, seq_length = seq_shp[:2]
def custom_getter(getter, name, *args, **kwargs):
kwargs['trainable'] = params.tune_bert
return getter(name, *args, **kwargs)
with tf.variable_scope("bert", custom_getter=custom_getter):
# handling sequence embeddings: token_embedding pls segment embedding pls positional embedding
embed_initializer = tf.truncated_normal_initializer(stddev=params.bert.initializer_range)
with tf.variable_scope("embeddings"):
word_embedding = tf.get_variable(
name="word_embeddings",
shape=[params.bert.vocab.size, params.bert.hidden_size],
initializer=embed_initializer
)
seq_embed = tf.nn.embedding_lookup(word_embedding, sequence)
segment_embedding = tf.get_variable(
name="token_type_embeddings",
shape=[2, params.bert.hidden_size],
initializer=embed_initializer
)
seg_embed = tf.nn.embedding_lookup(segment_embedding, seg_ids)
# word embedding + segment embedding
seq_embed = seq_embed + seg_embed
# add position embedding
assert_op = tf.assert_less_equal(seq_length, params.bert.max_position_embeddings)
with tf.control_dependencies([assert_op]):
position_embedding = tf.get_variable(
name="position_embeddings",
shape=[params.bert.max_position_embeddings, params.bert.hidden_size],
initializer=embed_initializer
)
pos_embed = position_embedding[:seq_length]
seq_embed = seq_embed + tf.expand_dims(pos_embed, 0)
# post-processing, layer norm and segmentation
seq_embed = tc.layers.layer_norm(
inputs=seq_embed, begin_norm_axis=-1, begin_params_axis=-1)
seq_embed = util.valid_apply_dropout(seq_embed, params.bert.hidden_dropout_prob)
bert_outputs = []
# handling sequence encoding with transformer encoder
with tf.variable_scope("encoder"):
attention_mask = encoder.create_attention_mask_from_input_mask(
sequence, seq_mask)
# Run the stacked transformer.
# `sequence_output` shape = [batch_size, seq_length, hidden_size].
all_encoder_layers = encoder.transformer_model(
input_tensor=seq_embed,
attention_mask=attention_mask,
hidden_size=params.bert.hidden_size,
num_hidden_layers=params.bert.num_hidden_layers,
num_attention_heads=params.bert.num_attention_heads,
intermediate_size=params.bert.intermediate_size,
intermediate_act_fn=encoder.get_activation(params.bert.hidden_act),
hidden_dropout_prob=params.bert.hidden_dropout_prob,
attention_probs_dropout_prob=params.bert.attention_probs_dropout_prob,
initializer_range=params.bert.initializer_range,
do_return_all_layers=True)
sequence_output = all_encoder_layers
bert_outputs.append(sequence_output)
if params.use_bert_single:
# The "pooler" converts the encoded sequence tensor of shape
# [batch_size, seq_length, hidden_size] to a tensor of shape
# [batch_size, hidden_size]. This is necessary for segment-level
# (or segment-pair-level) classification tasks where we need a fixed
# dimensional representation of the segment.
with tf.variable_scope("pooler"):
# We "pool" the model by simply taking the hidden state corresponding
# to the first token. We assume that this has been pre-trained
first_token_tensor = tf.squeeze(sequence_output[-1][:, 0:1, :], axis=1)
pooled_output = tf.layers.dense(
first_token_tensor,
params.bert.hidden_size,
activation=tf.tanh,
kernel_initializer=embed_initializer)
bert_outputs.append(pooled_output)
return bert_outputs
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
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 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 dot_attention(query,
memory,
mem_mask,
hidden_size,
ln=False,
num_heads=1,
cache=None,
dropout=None,
use_relative_pos=True,
max_relative_position=16,
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 max_relative_position: maximum position considered for relative embedding
:param use_relative_pos: whether use relative position information
:param scope:
:return: a value matrix, [batch_size, qey_len, mem_dim]
"""
with tf.variable_scope(scope or "dot_attention"):
if memory is None:
# suppose self-attention from queries alone
h = 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 = 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:
h = linear(memory, hidden_size * 2, ln=ln, scope="kv_map")
k, v = tf.split(h, 2, -1)
if cache is not None:
cache['mk'] = k
cache['mv'] = v
q = split_heads(q, num_heads)
k = split_heads(k, num_heads)
v = 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 * k => attention weights
if use_relative_pos:
r = get_relative_positions_embeddings(
q_shp[2],
k_shp[2],
k_shp[3],
max_relative_position,
name="relative_positions_keys")
logits = 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)
weights = util.valid_apply_dropout(weights, dropout)
# weights * v => attention vectors
if use_relative_pos:
r = get_relative_positions_embeddings(
q_shp[2],
k_shp[2],
v_shp[3],
max_relative_position,
name="relative_positions_values")
o = relative_attention_inner(weights, v, r, transpose=False)
else:
o = tf.matmul(weights, v)
o = combine_heads(o)
if out_map:
o = 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
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])
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
for layer in range(params.num_decoder_layer):
with tf.variable_scope("layer_{}".format(layer)):
init_state = state["decoder_initializer"]["layer_{}".format(
layer)]
if not is_training:
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)
(_, 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)
(_, 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)
outputs, hidden_state = outputs[1]
if not is_training:
state['decoder']['state']['layer_{}'.format(
layer)] = hidden_state
y = func.linear(outputs,
params.embed_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")
if params.dl4mt_redict:
feature = func.linear(tf.concat([x, c], -1),
params.embed_size,
ln=params.layer_norm,
scope="ff")
feature = tf.nn.tanh(feature)
feature = util.valid_apply_dropout(feature, params.dropout)
else:
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])
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 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 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 encoder(source, params):
mask = dtype.tf_to_float(tf.cast(source, tf.bool))
hidden_size = params.hidden_size
source, mask = util.remove_invalid_seq(source, mask)
# extract source word embedding and apply dropout
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)
inputs = util.valid_apply_dropout(inputs, params.dropout)
# the encoder module used in the deep attention paper
with tf.variable_scope("encoder"):
# x: embedding input, h: the hidden state
x = inputs
h = 0
z = 0
for layer in range(params.num_encoder_layer + 1):
with tf.variable_scope("layer_{}".format(layer)):
if layer == 0:
# for the first layer, we perform a normal rnn layer to collect context information
outputs = rnn.rnn(params.cell,
x,
hidden_size,
mask=mask,
ln=params.layer_norm,
sm=params.swap_memory)
h = outputs[1][0]
else:
# for deeper encoder layers, we incorporate both embedding input and previous inversed hidden
# state sequence as input.
# the embedding informs current input while hidden state tells future context
is_reverse = (layer % 2 == 1)
outputs = rnn.cond_rnn(
params.cell,
tf.reverse(x, [1]) if is_reverse else x,
tf.reverse(h, [1]) if is_reverse else h,
hidden_size,
mask=tf.reverse(mask, [1]) if is_reverse else mask,
ln=params.layer_norm,
sm=params.swap_memory,
num_heads=params.num_heads,
one2one=True)
h = outputs[1][0]
h = tf.reverse(h, [1]) if is_reverse else h
# the final hidden state used for decoder state initialization
z = outputs[1][1]
with tf.variable_scope("decoder_initializer"):
decoder_cell = rnn.get_cell(params.cell,
hidden_size,
ln=params.layer_norm)
return {
"encodes": h,
"decoder_initializer": {
'layer': decoder_cell.get_init_state(x=z, scope="dec_init_state")
},
"mask": mask
}
def encoder(source, params):
mask = dtype.tf_to_float(tf.cast(source, tf.bool))
hidden_size = params.hidden_size
initializer = tf.random_normal_initializer(0.0, hidden_size**-0.5)
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],
initializer=initializer)
src_bias = tf.get_variable("bias", [params.embed_size])
inputs = tf.gather(src_emb, source) * (hidden_size**0.5)
inputs = tf.nn.bias_add(inputs, src_bias)
inputs = func.add_timing_signal(inputs)
inputs = util.valid_apply_dropout(inputs, params.dropout)
with tf.variable_scope("encoder"):
x = inputs
for layer in range(params.num_encoder_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(
mask, "masking"),
hidden_size,
num_heads=params.num_heads,
dropout=params.attention_dropout)
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)
source_encodes = x
x_shp = util.shape_list(x)
return {
"encodes": source_encodes,
"decoder_initializer": {
"layer_{}".format(l): {
# plan aan
"aan": dtype.tf_to_float(tf.zeros([x_shp[0], 1, hidden_size])),
}
for l in range(params.num_decoder_layer)
},
"mask": mask
}
def encoder(source, params):
mask = dtype.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)
inputs = util.valid_apply_dropout(inputs, params.dropout)
with tf.variable_scope("encoder"):
# forward rnn
with tf.variable_scope('forward'):
outputs = rnn.rnn(params.cell,
inputs,
hidden_size,
mask=mask,
ln=params.layer_norm,
sm=params.swap_memory)
output_fw, state_fw = outputs[1]
# backward rnn
with tf.variable_scope('backward'):
if not params.caencoder:
outputs = rnn.rnn(params.cell,
tf.reverse(inputs, [1]),
hidden_size,
mask=tf.reverse(mask, [1]),
ln=params.layer_norm,
sm=params.swap_memory)
output_bw, state_bw = outputs[1]
else:
outputs = rnn.cond_rnn(params.cell,
tf.reverse(inputs, [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:
source_encodes = tf.concat([output_fw, output_bw], -1)
source_feature = tf.concat([state_fw, state_bw], -1)
else:
source_encodes = output_bw
source_feature = state_bw
with tf.variable_scope("decoder_initializer"):
decoder_init = rnn.get_cell(
params.cell, hidden_size,
ln=params.layer_norm).get_init_state(x=source_feature)
decoder_init = tf.tanh(decoder_init)
return {
"encodes": source_encodes,
"decoder_initializer": decoder_init,
"mask": mask
}
def encoder(source, params):
mask = dtype.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)
inputs = util.valid_apply_dropout(inputs, 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)
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)
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, params.embed_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")
if params.embed_size != hidden_size:
x = func.layer_norm(func.linear(x, hidden_size, scope="x_map"))
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
}
