def cnn_for_context_fusion(rep_tensor,
rep_mask,
filter_sizes=(3, 4, 5),
num_filters=200,
scope=None,
is_train=None,
keep_prob=1.,
wd=0.):
bs, sl, vec = tf.shape(rep_tensor)[0], tf.shape(rep_tensor)[1], tf.shape(
rep_tensor)[2]
ivec = rep_tensor.get_shape().as_list()[2]
with tf.variable_scope(scope or 'cnn_for_sentence_encoding'):
rep_tensor = mask_for_high_rank(rep_tensor, rep_mask)
rep_tensor_expand = tf.expand_dims(rep_tensor, 3) # bs, sl,
rep_tensor_expand_dp = dropout(rep_tensor_expand, keep_prob, is_train)
# Create a convolution + maxpool layer for each filter size
pooled_outputs = []
for i, filter_size in enumerate(filter_sizes):
with tf.variable_scope("conv-maxpool-%s" % filter_size):
# Convolution Layer
filter_shape = [filter_size, ivec, 1, num_filters]
W = tf.get_variable('W', filter_shape, tf.float32)
b = tf.get_variable('b', [num_filters], tf.float32)
# # pading in the sequence
if filter_size % 2 == 1:
padding_front = padding_back = int((filter_size - 1) / 2)
else:
padding_front = (filter_size - 1) // 2
padding_back = padding_front + 1
padding = [[0, 0], [padding_front, padding_back], [0, 0],
[0, 0]]
rep_tensor_expand_dp_pad = tf.pad(rep_tensor_expand_dp,
padding)
conv = tf.nn.conv2d(rep_tensor_expand_dp_pad,
W,
strides=[1, 1, 1, 1],
padding="VALID",
name="conv")
# Apply nonlinearity
h = tf.nn.relu(tf.nn.bias_add(conv, b),
name="relu") # bs, sl, 1, fn
h_squeeze = tf.squeeze(h, [2]) # bs, sl, fn
pooled_outputs.append(h_squeeze)
# Combine all the pooled features
result = tf.concat(pooled_outputs, 2) # bs, sl, 3 * fn
if wd > 0.:
add_reg_without_bias()
return result
def pooling_with_mask(rep_tensor, rep_mask, method='max', scope=None):
# rep_tensor have one more rank than rep_mask
with tf.name_scope(scope or '%s_pooling' % method):
if method == 'max':
rep_tensor_masked = exp_mask_for_high_rank(rep_tensor, rep_mask)
output = tf.reduce_max(rep_tensor_masked, -2)
elif method == 'mean':
rep_tensor_masked = mask_for_high_rank(rep_tensor,
rep_mask) # [...,sl,hn]
rep_sum = tf.reduce_sum(rep_tensor_masked, -2) #[..., hn]
denominator = tf.reduce_sum(tf.cast(rep_mask, tf.int32), -1,
True) # [..., 1]
denominator = tf.where(
tf.equal(denominator, tf.zeros_like(denominator, tf.int32)),
tf.ones_like(denominator, tf.int32), denominator)
output = rep_sum / tf.cast(denominator, tf.float32)
else:
raise AttributeError('No Pooling method name as %s' % method)
return output
def cnn_for_sentence_encoding( # kim
rep_tensor,
rep_mask,
filter_sizes=(3, 4, 5),
num_filters=200,
scope=None,
is_train=None,
keep_prob=1.,
wd=0.):
"""
:param rep_tensor:
:param rep_mask:
:param filter_sizes:
:param num_filters:
:param scope:
:param is_train:
:param keep_prob:
:param wd:
:return:
"""
bs, sl, vec = tf.shape(rep_tensor)[0], tf.shape(rep_tensor)[1], tf.shape(
rep_tensor)[2]
ivec = rep_tensor.get_shape().as_list()[2]
with tf.variable_scope(scope or 'cnn_for_sentence_encoding'):
rep_tensor = mask_for_high_rank(rep_tensor, rep_mask)
rep_tensor_expand = tf.expand_dims(rep_tensor, 3)
rep_tensor_expand_dp = dropout(rep_tensor_expand, keep_prob, is_train)
# Create a convolution + maxpool layer for each filter size
pooled_outputs = []
for i, filter_size in enumerate(filter_sizes):
with tf.variable_scope("conv-maxpool-%s" % filter_size):
# Convolution Layer
filter_shape = [filter_size, ivec, 1, num_filters]
W = tf.get_variable('W', filter_shape, tf.float32)
b = tf.get_variable('b', [num_filters], tf.float32)
conv = tf.nn.conv2d(rep_tensor_expand_dp,
W,
strides=[1, 1, 1, 1],
padding="VALID",
name="conv")
# Apply nonlinearity
h = tf.nn.relu(tf.nn.bias_add(conv, b),
name="relu") # bs, sl-fs+1, 1, fn
# Maxpooling over the outputs
# pooled = tf.nn.max_pool(
# h,
# ksize=[1, sl - filter_size + 1, 1, 1],
# strides=[1, 1, 1, 1],
# padding='VALID',
# name="pool")
pooled = tf.reduce_max(h, 1, True) # bs, 1, 1, fn
pooled_outputs.append(pooled)
# Combine all the pooled features
num_filters_total = num_filters * len(filter_sizes)
h_pool = tf.concat(pooled_outputs, 3)
h_pool_flat = tf.reshape(h_pool, [-1, num_filters_total])
if wd > 0.:
add_reg_without_bias()
return h_pool_flat
def hierarchical_cnn_res_gate(rep_tensor,
rep_mask,
n_gram=5,
layer_num=5,
hn=None,
scope=None,
is_train=None,
keep_prob=1.,
wd=0.):
# padding
if n_gram % 2 == 1:
padding_front = padding_back = int((n_gram - 1) / 2)
else:
padding_front = (n_gram - 1) // 2
padding_back = padding_front + 1
padding = [[0, 0], [padding_front, padding_back], [0, 0], [0, 0]]
# lengths
bs, sl, vec = tf.shape(rep_tensor)[0], tf.shape(rep_tensor)[1], tf.shape(
rep_tensor)[2]
org_ivec = rep_tensor.get_shape().as_list()[2]
ivec = hn or org_ivec
with tf.variable_scope(scope or 'cnn_for_sentence_encoding'):
rep_tensor = mask_for_high_rank(rep_tensor, rep_mask) # bs, sl, hn
iter_rep = rep_tensor
layer_res_list = []
for layer_idx in range(layer_num):
with tf.variable_scope("conv_maxpool_%s" % layer_idx):
iter_rep_etd = tf.expand_dims(iter_rep, 3) # bs,sl,hn,1
iter_rep_etd_dp = dropout(iter_rep_etd, keep_prob, is_train)
# Convolution Layer
feature_size = org_ivec if layer_idx == 0 else ivec
filter_shape = [n_gram, feature_size, 1, 2 * ivec]
W = tf.get_variable('W', filter_shape, tf.float32)
b = tf.get_variable('b', [2 * ivec], tf.float32)
iter_rep_etd_pad = tf.pad(iter_rep_etd_dp, padding)
conv = tf.nn.conv2d(iter_rep_etd_pad,
W,
strides=[1, 1, 1, 1],
padding="VALID",
name="conv")
map_res = tf.nn.relu(tf.nn.bias_add(conv, b),
name="relu") # bs,sl,1,2hn
map_res = tf.squeeze(map_res, [2]) # bs,sl,2*hn
# gate
map_res_a, map_res_b = tf.split(map_res,
num_or_size_splits=2,
axis=2)
iter_rep = map_res_a * tf.nn.sigmoid(map_res_b)
# res
if len(layer_res_list) > 0:
iter_rep = iter_rep + layer_res_list[-1]
layer_res_list.append(iter_rep)
if wd > 0.:
add_reg_without_bias()
return iter_rep
def directional_attention_with_dense(rep_tensor,
rep_mask,
direction=None,
scope=None,
keep_prob=1.,
is_train=None,
wd=0.,
activation='elu',
tensor_dict=None,
name=None,
hn=None):
def scaled_tanh(x, scale=5.):
return scale * tf.nn.tanh(1. / scale * x)
bs, sl, vec = tf.shape(rep_tensor)[0], tf.shape(rep_tensor)[1], tf.shape(
rep_tensor)[2]
ivec = rep_tensor.get_shape().as_list()[2]
ivec = hn or ivec
with tf.variable_scope(scope or 'directional_attention_%s' % direction
or 'diag'):
# mask generation
sl_indices = tf.range(sl, dtype=tf.int32)
sl_col, sl_row = tf.meshgrid(sl_indices, sl_indices)
if direction is None:
direct_mask = tf.cast(
tf.diag(-tf.ones([sl], tf.int32)) + 1, tf.bool)
else:
if direction == 'forward':
direct_mask = tf.greater(sl_row, sl_col)
else:
direct_mask = tf.greater(sl_col, sl_row)
direct_mask_tile = tf.tile(tf.expand_dims(direct_mask, 0),
[bs, 1, 1]) # bs,sl,sl
rep_mask_tile = tf.tile(tf.expand_dims(rep_mask, 1),
[1, sl, 1]) # bs,sl,sl
attn_mask = tf.logical_and(direct_mask_tile, rep_mask_tile) # bs,sl,sl
# non-linear
rep_map = bn_dense_layer(rep_tensor, ivec, True, 0., 'bn_dense_map',
activation, False, wd, keep_prob, is_train)
rep_map_tile = tf.tile(tf.expand_dims(rep_map, 1),
[1, sl, 1, 1]) # bs,sl,sl,vec
rep_map_dp = dropout(rep_map, keep_prob, is_train)
# attention
with tf.variable_scope('attention'): # bs,sl,sl,vec
f_bias = tf.get_variable('f_bias', [ivec], tf.float32,
tf.constant_initializer(0.))
dependent = linear(rep_map_dp,
ivec,
False,
scope='linear_dependent',
is_train=is_train) # bs,sl,vec
dependent_etd = tf.expand_dims(dependent, 1) # bs,1,sl,vec
head = linear(rep_map_dp,
ivec,
False,
scope='linear_head',
is_train=is_train) # bs,sl,vec
head_etd = tf.expand_dims(head, 2) # bs,sl,1,vec
logits = scaled_tanh(dependent_etd + head_etd + f_bias,
5.0) # bs,sl,sl,vec
logits_masked = exp_mask_for_high_rank(logits, attn_mask)
attn_score = tf.nn.softmax(logits_masked, 2) # bs,sl,sl,vec
attn_score = mask_for_high_rank(attn_score, attn_mask)
attn_result = tf.reduce_sum(attn_score * rep_map_tile,
2) # bs,sl,vec
with tf.variable_scope('output'):
o_bias = tf.get_variable('o_bias', [ivec], tf.float32,
tf.constant_initializer(0.))
# input gate
fusion_gate = tf.nn.sigmoid(
linear(rep_map, ivec, True, 0., 'linear_fusion_i', False, wd,
keep_prob, is_train) +
linear(attn_result, ivec, True, 0., 'linear_fusion_a', False,
wd, keep_prob, is_train) + o_bias)
output = fusion_gate * rep_map + (1 - fusion_gate) * attn_result
output = mask_for_high_rank(output, rep_mask)
# save attn
if tensor_dict is not None and name is not None:
tensor_dict[name + '_dependent'] = dependent
tensor_dict[name + '_head'] = head
tensor_dict[name] = attn_score
tensor_dict[name + '_gate'] = fusion_gate
return output
def multi_head_attention(rep_tensor,
rep_mask,
head_num=8,
hidden_units_num=64,
scope=None,
is_train=None,
keep_prob=1.,
wd=0.):
bs, sl, vec = tf.shape(rep_tensor)[0], tf.shape(rep_tensor)[1], tf.shape(
rep_tensor)[2]
ivec = rep_tensor.get_shape().as_list()[2]
with tf.variable_scope(scope or 'multi_head_attention'):
with tf.variable_scope('positional_encoding'):
seq_idxs = tf.tile(tf.expand_dims(tf.range(sl), 1),
[1, ivec]) # sl, ivec
feature_idxs = tf.tile(tf.expand_dims(tf.range(ivec), 0),
[sl, 1]) # sl, ivec
pos_enc = tf.where(
tf.equal(tf.mod(feature_idxs, 2), 0),
tf.sin(
tf.cast(seq_idxs, tf.float32) / tf.pow(
10000., 2.0 * tf.cast(feature_idxs, tf.float32) /
(1.0 * ivec))),
tf.cos(
tf.cast(seq_idxs, tf.float32) / tf.pow(
10000., 2.0 * tf.cast(feature_idxs - 1, tf.float32) /
(1.0 * ivec))),
)
rep_tensor_pos = mask_for_high_rank(rep_tensor + pos_enc,
rep_mask) # bs, sl, ivec
with tf.variable_scope('multi_head_attention'):
W = tf.get_variable('W', [3, head_num, ivec, hidden_units_num],
tf.float32)
rep_tile = tf.tile(
tf.expand_dims(tf.expand_dims(rep_tensor_pos, 0), 0),
[3, head_num, 1, 1, 1]) # 3,head_num,bs,sl,ivec
rep_tile_reshape = tf.reshape(
rep_tile, [3, head_num, bs * sl, ivec]) # head_num,bs*sl,ivec
maps = tf.reshape( # 3,head_num,bs*sl,hn -> 3,head_num,bs,sl,hn
tf.matmul(dropout(rep_tile_reshape, keep_prob, is_train), W),
[3, head_num, bs, sl, hidden_units_num])
Q_map, K_map, V_map = tf.split(maps, 3, 0)
Q_map = tf.squeeze(Q_map, [0]) # head_num,bs,sl,hn
K_map = tf.squeeze(K_map, [0]) # head_num,bs,sl,hn
V_map = tf.squeeze(V_map, [0]) # head_num,bs,sl,hn
# head_num,bs,sl,sl
# similarity_mat = tf.reduce_sum(Q_map_tile * K_map_tile, -1) / math.sqrt(1. * hidden_units_num)
similarity_mat = tf.matmul(Q_map, tf.transpose(
K_map, [0, 1, 3, 2])) / math.sqrt(1. * hidden_units_num)
# mask: bs,sl -> head_num,bs,sl
multi_mask = tf.tile(tf.expand_dims(rep_mask, 0),
[head_num, 1, 1]) # head_num,bs,sl
multi_mask_tile_1 = tf.expand_dims(multi_mask,
2) # head_num,bs,1,sl
multi_mask_tile_2 = tf.expand_dims(multi_mask,
3) # head_num,bs,sl,1
multi_mask_tile = tf.logical_and(
multi_mask_tile_1, multi_mask_tile_2) # head_num,bs,sl,sl
similarity_mat_masked = exp_mask(
similarity_mat, multi_mask_tile) # head_num,bs,sl,sl
prob_dist = tf.nn.softmax(
similarity_mat_masked) # head_num,bs,sl,sl
prob_dist_dp = dropout(prob_dist, keep_prob, is_train)
attn_res = tf.matmul(prob_dist_dp, V_map) # head_num,bs,sl,hn
attn_res_tran = tf.transpose(attn_res, [1, 2, 0, 3])
output = tf.reshape(attn_res_tran,
[bs, sl, head_num * hidden_units_num])
if wd > 0.:
add_reg_without_bias()
return output
def simple_block_attention(rep_tensor,
rep_mask,
block_len=5,
scope=None,
direction=None,
keep_prob=1.,
is_train=None,
wd=0.,
activation='elu',
hn=None):
assert direction is not None
def scaled_tanh(x, scale=5.):
return scale * tf.nn.tanh(1. / scale * x)
bs, sl, vec = tf.shape(rep_tensor)[0], tf.shape(rep_tensor)[1], tf.shape(
rep_tensor)[2]
org_ivec = rep_tensor.get_shape().as_list()[2]
ivec = hn or org_ivec
with tf.variable_scope(scope or 'block_simple'):
# @1. split sequence
with tf.variable_scope('split_seq'):
block_num = tf.cast(
tf.ceil(
tf.divide(tf.cast(sl, tf.float32),
tf.cast(block_len, tf.float32))), tf.int32)
comp_len = block_num * block_len - sl
rep_tensor_comp = tf.concat(
[rep_tensor,
tf.zeros([bs, comp_len, org_ivec], tf.float32)], 1)
rep_mask_comp = tf.concat([
rep_mask,
tf.cast(tf.zeros([bs, comp_len], tf.int32), tf.bool)
], 1)
rep_tensor_split = tf.reshape(
rep_tensor_comp,
[bs, block_num, block_len, org_ivec]) # bs,bn,bl,d
rep_mask_split = tf.reshape(rep_mask_comp,
[bs, block_num, block_len]) # bs,bn,bl
# non-linear
rep_map = bn_dense_layer(rep_tensor_split, ivec, True, 0.,
'bn_dense_map', activation, False, wd,
keep_prob, is_train) # bs,bn,bl,vec
rep_map_tile = tf.tile(tf.expand_dims(rep_map, 2),
[1, 1, block_len, 1, 1]) # bs,bn,bl,bl,vec
# rep_map_dp = dropout(rep_map, keep_prob, is_train)
bn = block_num
bl = block_len
with tf.variable_scope('self_attention'):
# @2.self-attention in block
# mask generation
sl_indices = tf.range(block_len, dtype=tf.int32)
sl_col, sl_row = tf.meshgrid(sl_indices, sl_indices)
if direction == 'forward':
direct_mask = tf.greater(sl_row, sl_col) # bl,bl
else:
direct_mask = tf.greater(sl_col, sl_row) # bl,bl
direct_mask_tile = tf.tile(
tf.expand_dims(tf.expand_dims(direct_mask, 0), 0),
[bs, bn, 1, 1]) # bs,bn,bl,bl
rep_mask_tile_1 = tf.tile(tf.expand_dims(rep_mask_split, 2),
[1, 1, bl, 1]) # bs,bn,bl,bl
rep_mask_tile_2 = tf.tile(tf.expand_dims(rep_mask_split, 3),
[1, 1, 1, bl]) # bs,bn,bl,bl
rep_mask_tile = tf.logical_and(rep_mask_tile_1, rep_mask_tile_2)
attn_mask = tf.logical_and(direct_mask_tile,
rep_mask_tile,
name='attn_mask') # bs,bn,bl,bl
# attention
f_bias = tf.get_variable('f_bias', [ivec], tf.float32,
tf.constant_initializer(0.))
dependent_head = linear(rep_map, 2 * ivec, False, 0.,
'linear_dependent_head', False, wd,
keep_prob, is_train) # bs,bn,bl,2vec
dependent, head = tf.split(dependent_head, 2, 3)
dependent_etd = tf.expand_dims(dependent, 2) # bs,bn,1,bl,vec
head_etd = tf.expand_dims(head, 3) # bs,bn,bl,1,vec
logits = scaled_tanh(dependent_etd + head_etd + f_bias,
5.0) # bs,bn,bl,bl,vec
logits_masked = exp_mask_for_high_rank(logits, attn_mask)
attn_score = tf.nn.softmax(logits_masked, 3) # bs,bn,bl,bl,vec
attn_score = mask_for_high_rank(attn_score,
attn_mask) # bs,bn,bl,bl,vec
self_attn_result = tf.reduce_sum(attn_score * rep_map_tile,
3) # bs,bn,bl,vec
with tf.variable_scope('source2token_self_attn'):
inter_block_logits = bn_dense_layer(self_attn_result, ivec, True,
0., 'bn_dense_map', 'linear',
False, wd, keep_prob,
is_train) # bs,bn,bl,vec
inter_block_logits_masked = exp_mask_for_high_rank(
inter_block_logits, rep_mask_split) # bs,bn,bl,vec
inter_block_soft = tf.nn.softmax(inter_block_logits_masked,
2) # bs,bn,bl,vec
inter_block_attn_output = tf.reduce_sum(
self_attn_result * inter_block_soft, 2) # bs,bn,vec
with tf.variable_scope('self_attn_inter_block'):
inter_block_attn_output_mask = tf.cast(tf.ones([bs, bn], tf.int32),
tf.bool)
block_ct_res = directional_attention_with_dense(
inter_block_attn_output, inter_block_attn_output_mask,
direction, 'disa', keep_prob, is_train, wd,
activation) # [bs,bn,vec]
block_ct_res_tile = tf.tile(tf.expand_dims(
block_ct_res, 2), [1, 1, bl, 1]) #[bs,bn,vec]->[bs,bn,bl,vec]
with tf.variable_scope('combination'):
# input:1.rep_map[bs,bn,bl,vec]; 2.self_attn_result[bs,bn,bl,vec]; 3.rnn_res_tile[bs,bn,bl,vec]
rep_tensor_with_ct = tf.concat(
[rep_map, self_attn_result, block_ct_res_tile],
-1) # [bs,bn,bl,3vec]
new_context_and_gate = linear(rep_tensor_with_ct, 2 * ivec, True,
0., 'linear_new_context_and_gate',
False, wd, keep_prob,
is_train) # [bs,bn,bl,2vec]
new_context, gate = tf.split(new_context_and_gate, 2,
3) # bs,bn,bl,vec
if activation == "relu":
new_context_act = tf.nn.relu(new_context)
elif activation == "elu":
new_context_act = tf.nn.elu(new_context)
elif activation == "linear":
new_context_act = tf.identity(new_context)
else:
raise RuntimeError
gate_sig = tf.nn.sigmoid(gate)
combination_res = gate_sig * new_context_act + (
1 - gate_sig) * rep_map # bs,bn,bl,vec
with tf.variable_scope('restore_original_length'):
combination_res_reshape = tf.reshape(
combination_res, [bs, bn * bl, ivec]) # bs,bn*bl,vec
output = combination_res_reshape[:, :sl, :]
return output
