def sg_reverse_seq(tensor, opt):
r"""Reverses variable length slices.
Before applying the pure tensorflow function tf.reverse_sequence,
this function calculates sequence lengths by counting non-zeros.
For example,
```
tensor = [[1, 2, 3, 0, 0], [4, 5, 0, 0, 0]]
tensor.sg_reverse_seq()
=> [[3 2 1 0 0]
[5 4 0 0 0]]
```
Args:
tensor: A 2-D `Tensor` (automatically given by chain).
opt:
dim: Dimension to reverse. Default is 1.
name : If provided, it replaces current tensor's name.
Returns:
A `Tensor` with the same shape and type as `tensor`.
"""
# default sequence dimension
opt += tf.sg_opt(dim=1)
seq_len = tf.not_equal(tensor,
tf.zeros_like(tensor)).sg_int().sg_sum(dims=opt.dim)
return tf.reverse_sequence(tensor, seq_len, opt.dim, name=opt.name)
def sg_quasi_rnn(tensor, opt):
# Split
if opt.att:
H, Z, F, O = tf.split(tensor, 4, axis=0) # (16, 150, 320) for all
else:
Z, F, O = tf.split(tensor, 3, axis=0) # (16, 150, 320) for all
# step func
def step(z, f, o, c):
'''
Runs fo-pooling at each time step
'''
c = f * c + (1 - f) * z
if opt.att: # attention
a = tf.nn.softmax(tf.einsum("ijk,ik->ij", H,
c)) # alpha. (16, 150)
k = (a.sg_expand_dims() * H).sg_sum(
axis=1) # attentional sum. (16, 320)
h = o * (k.sg_dense(act="linear") + \
c.sg_dense(act="linear"))
else:
h = o * c
return h, c # hidden states, (new) cell memories
# Do rnn loop
c, hs = 0, []
timesteps = tensor.get_shape().as_list()[1]
for t in range(timesteps):
z = Z[:, t, :] # (16, 320)
f = F[:, t, :] # (16, 320)
o = O[:, t, :] # (16, 320)
# apply step function
h, c = step(z, f, o, c) # (16, 320), (16, 320)
# save result
hs.append(h.sg_expand_dims(axis=1))
# Concat to return
H = tf.concat(hs, 1) # (16, 150, 320)
seqlen = tf.to_int32(
tf.reduce_sum(tf.sign(tf.abs(tf.reduce_sum(H, axis=-1))),
1)) # (16,) float32
h = tf.reverse_sequence(input=H, seq_lengths=seqlen,
seq_dim=1)[:, 0, :] # last hidden state vector
if opt.is_enc:
H_z = tf.tile((h.sg_dense(act="linear").sg_expand_dims(axis=1)),
[1, timesteps, 1])
H_f = tf.tile((h.sg_dense(act="linear").sg_expand_dims(axis=1)),
[1, timesteps, 1])
H_o = tf.tile((h.sg_dense(act="linear").sg_expand_dims(axis=1)),
[1, timesteps, 1])
concatenated = tf.concat([H, H_z, H_f, H_o], 0) # (16*4, 150, 320)
return concatenated
else:
return H # (16, 150, 320)
def tower_infer_dec(chars,
scope,
rnn_cell,
dec_cell,
word_emb,
rnn_state,
out_reuse_vars=False,
dev='/cpu:0'):
with tf.device(dev):
with tf.variable_scope('embatch_size', reuse=True):
# (vocab_size, latent_dim)
emb_char = tf.sg_emb(name='emb_char',
voca_size=Hp.char_vs,
dim=Hp.hd,
dev=dev)
emb_word = tf.sg_emb(name='emb_word',
emb=word_emb,
voca_size=Hp.word_vs,
dim=300,
dev=dev)
print(chars)
ch = chars
ch = tf.reverse_sequence(input=ch,
seq_lengths=[Hp.c_maxlen] * Hp.batch_size,
seq_dim=1)
reuse_vars = reuse_vars_enc = True
# -------------------------- BYTENET ENCODER --------------------------
with tf.variable_scope('encoder'):
# embed table lookup
enc = ch.sg_lookup(emb=emb_char) #(batch, sentlen, latentdim)
# loop dilated conv block
for i in range(Hp.num_blocks):
enc = (enc.sg_res_block(size=5,
rate=1,
name="enc1_%d" % (i),
is_first=True,
reuse_vars=reuse_vars,
dev=dev).sg_res_block(
size=5,
rate=2,
name="enc2_%d" % (i),
reuse_vars=reuse_vars,
dev=dev).sg_res_block(
size=5,
rate=4,
name="enc4_%d" % (i),
reuse_vars=reuse_vars,
dev=dev).sg_res_block(
size=5,
rate=8,
name="enc8_%d" % (i),
reuse_vars=reuse_vars,
dev=dev).sg_res_block(
size=5,
rate=16,
name="enc16_%d" % (i),
reuse_vars=reuse_vars,
dev=dev))
byte_enc = enc
# -------------------------- QCNN + QPOOL ENCODER #1 --------------------------
with tf.variable_scope('quazi'):
#quasi cnn layer ZFO [batch * 3, seqlen, dim2 ]
conv = byte_enc.sg_quasi_conv1d(is_enc=True,
size=4,
name="qconv_1",
dev=dev,
reuse_vars=reuse_vars)
# c = f * c + (1 - f) * z, h = o*c [batch * 4, seqlen, hd]
pool0 = conv.sg_quasi_rnn(is_enc=False,
att=False,
name="qrnn_1",
reuse_vars=reuse_vars,
dev=dev)
qpool_last = pool0[:, -1, :]
# -------------------------- MAXPOOL along time dimension --------------------------
inpt_maxpl = tf.expand_dims(byte_enc, 1) # [batch, 1, seqlen, channels]
maxpool = tf.nn.max_pool(inpt_maxpl, [1, 1, Hp.c_maxlen, 1], [1, 1, 1, 1],
'VALID')
maxpool = tf.squeeze(maxpool, [1, 2])
# -------------------------- HIGHWAY --------------------------
concat = qpool_last + maxpool
with tf.variable_scope('highway', reuse=reuse_vars):
input_lstm = highway(concat, concat.get_shape()[-1], num_layers=1)
# -------------------------- CONTEXT LSTM --------------------------
input_lstm = tf.nn.dropout(input_lstm, Hp.keep_prob)
with tf.variable_scope('contx_lstm', reuse=reuse_vars):
output, rnn_state = rnn_cell(input_lstm, rnn_state)
beam_size = 8
reuse_vars = out_reuse_vars
greedy = False
if greedy:
dec_state = rnn_state
dec_out = []
d_out = tf.constant([1] * Hp.batch_size)
for idx in range(Hp.w_maxlen):
w_input = d_out.sg_lookup(emb=emb_word)
dec_state = tf.contrib.rnn.LSTMStateTuple(c=dec_state.c,
h=dec_state.h)
with tf.variable_scope('dec_lstm', reuse=idx > 0 or reuse_vars):
d_out, dec_state = dec_cell(w_input, dec_state)
dec_out.append(d_out)
d_out = tf.expand_dims(d_out, 1).sg_conv1d_gpus(size=1,
dim=Hp.word_vs,
name="out_conv",
act="linear",
dev=dev,
reuse=idx > 0
or reuse_vars)
d_out = tf.squeeze(d_out).sg_argmax()
dec_out = tf.stack(dec_out, 1)
dec = dec_out.sg_conv1d_gpus(size=1,
dim=Hp.word_vs,
name="out_conv",
act="linear",
dev=dev,
reuse=True)
return dec.sg_argmax(), rnn_state
else:
# ------------------ BEAM SEARCH --------------------
dec_state = tf.contrib.rnn.LSTMStateTuple(
tf.tile(tf.expand_dims(rnn_state[0], 1), [1, beam_size, 1]),
tf.tile(tf.expand_dims(rnn_state[1], 1), [1, beam_size, 1]))
initial_ids = tf.constant([1] * Hp.batch_size)
def symbols_to_logits_fn(ids, dec_state):
dec = []
dec_c, dec_h = [], []
# (batch x beam_size x decoded_seq)
ids = tf.reshape(ids, [Hp.batch_size, beam_size, -1])
print("dec_state ", dec_state[0].get_shape().as_list())
for ind in range(beam_size):
with tf.variable_scope('dec_lstm', reuse=ind > 0
or reuse_vars):
w_input = ids[:, ind, -1].sg_lookup(emb=emb_word)
dec_state0 = tf.contrib.rnn.LSTMStateTuple(
c=dec_state.c[:, ind, :], h=dec_state.h[:, ind, :])
dec_out, dec_state_i = dec_cell(w_input, dec_state0)
dec_out = tf.expand_dims(dec_out, 1)
dec_i = dec_out.sg_conv1d_gpus(size=1,
dim=Hp.word_vs,
name="out_conv",
act="linear",
dev=dev,
reuse=ind > 0 or reuse_vars)
dec.append(tf.squeeze(dec_i, 1))
dec_c.append(dec_state_i[0])
dec_h.append(dec_state_i[1])
return tf.stack(dec, 1), tf.contrib.rnn.LSTMStateTuple(
tf.stack(dec_c, 1), tf.stack(dec_h, 1))
final_ids, final_probs = beam_search.beam_search(symbols_to_logits_fn,
dec_state,
initial_ids,
beam_size,
Hp.w_maxlen - 1,
Hp.word_vs,
3.5,
eos_id=2)
return final_ids[:, 0, :], rnn_state
def rnn_body_stat(time, rnn_state):
ch = chars_sent.read(time)
ch = tf.reverse_sequence(input=ch,
seq_lengths=[Hp.c_maxlen] * Hp.batch_size,
seq_dim=1)
reuse_vars = out_reuse_vars
# -------------------------- BYTENET ENCODER --------------------------
with tf.variable_scope('encoder'):
# embed table lookup
enc = ch.sg_lookup(emb=emb_char) #(batch, sentlen, latentdim)
# loop dilated conv block
for i in range(Hp.num_blocks):
enc = (enc.sg_res_block(size=5,
rate=1,
name="enc1_%d" % (i),
is_first=True,
reuse_vars=reuse_vars,
dev=dev).sg_res_block(
size=5,
rate=2,
name="enc2_%d" % (i),
reuse_vars=reuse_vars,
dev=dev).sg_res_block(
size=5,
rate=4,
name="enc4_%d" % (i),
reuse_vars=reuse_vars,
dev=dev).sg_res_block(
size=5,
rate=8,
name="enc8_%d" % (i),
reuse_vars=reuse_vars,
dev=dev).sg_res_block(
size=5,
rate=16,
name="enc16_%d" % (i),
reuse_vars=reuse_vars,
dev=dev))
byte_enc = enc
# -------------------------- QCNN + QPOOL ENCODER #1 --------------------------
with tf.variable_scope('quazi'):
#quasi cnn layer ZFO [batch * 3, seqlen, dim2 ]
conv = byte_enc.sg_quasi_conv1d(is_enc=True,
size=4,
name="qconv_1",
dev=dev,
reuse_vars=reuse_vars)
# c = f * c + (1 - f) * z, h = o*c [batch * 4, seqlen, hd]
pool0 = conv.sg_quasi_rnn(is_enc=False,
att=False,
name="qrnn_1",
reuse_vars=reuse_vars,
dev=dev)
qpool_last = pool0[:, -1, :]
# -------------------------- MAXPOOL along time dimension --------------------------
inpt_maxpl = tf.expand_dims(byte_enc,
1) # [batch, 1, seqlen, channels]
maxpool = tf.nn.max_pool(inpt_maxpl, [1, 1, Hp.c_maxlen, 1],
[1, 1, 1, 1], 'VALID')
maxpool = tf.squeeze(maxpool, [1, 2])
# -------------------------- HIGHWAY --------------------------
concat = qpool_last + maxpool
with tf.variable_scope('highway', reuse=reuse_vars):
input_lstm = highway(concat, concat.get_shape()[-1], num_layers=1)
# -------------------------- CONTEXT LSTM --------------------------
input_lstm = tf.nn.dropout(input_lstm, Hp.keep_prob)
with tf.variable_scope('contx_lstm', reuse=reuse_vars):
output, rnn_state = rnn_cell(input_lstm, rnn_state)
return (time + 1, rnn_state)
def sg_reverse_seq(tensor, opt):
# default sequence dimension
opt += tf.sg_opt(dim=1)
seq_len = tf.not_equal(tensor,
tf.zeros_like(tensor)).sg_int().sg_sum(dims=opt.dim)
return tf.reverse_sequence(tensor, seq_len, opt.dim, name=opt.name)
def sg_lstm(tensor, opt):
r"""Applies an LSTM.
Args:
tensor: A 3-D `Tensor` (automatically passed by decorator).
opt:
in_dim: A positive `integer`. The size of input dimension.
dim: A positive `integer`. The size of output dimension.
bias: Boolean. If True, biases are added.
ln: Boolean. If True, layer normalization is applied.
init_state: A 2-D `Tensor`. If None, the initial state is set to zeros.
last_only: Boolean. If True, the outputs in the last time step are returned.
mask: Boolean 2-D `Tensor` or None(default).
For false elements values are excluded from the calculation.
As a result, the outputs for the locations become 0.
summary: If True, summaries are added. The default is True.
Returns:
A `Tensor`. If last_only is True, the output tensor has shape [batch size, dim].
Otherwise, [batch size, time steps, dim].
"""
# layer normalization
# noinspection PyPep8
ln = lambda v: _ln_rnn(v, gamma, beta) if opt.ln else v
# step func
def step(hh, cc, x):
# forget gate
f = tf.sigmoid(ln(tf.matmul(x, w_f) + tf.matmul(hh, u_f) + (b_f if opt.bias else 0)))
# input gate
ii = tf.sigmoid(ln(tf.matmul(x, w_i) + tf.matmul(hh, u_i) + (b_i if opt.bias else 0)))
# new cell value
c_new = tf.tanh(ln(tf.matmul(x, w_c) + tf.matmul(hh, u_c) + (b_c if opt.bias else 0)))
# out gate
o = tf.sigmoid(ln(tf.matmul(x, w_o) + tf.matmul(hh, u_o) + (b_o if opt.bias else 0)))
# cell update
cell = f * cc + ii * c_new
# final output
y = o * tf.tanh(cell)
return y, cell
# parameter initialize
w_i = tf.sg_initializer.orthogonal('W_i', (opt.in_dim, opt.dim), summary=opt.summary)
u_i = tf.sg_initializer.identity('U_i', opt.dim, summary=opt.summary)
w_f = tf.sg_initializer.orthogonal('W_f', (opt.in_dim, opt.dim), summary=opt.summary)
u_f = tf.sg_initializer.identity('U_f', opt.dim, summary=opt.summary)
w_o = tf.sg_initializer.orthogonal('W_o', (opt.in_dim, opt.dim), summary=opt.summary)
u_o = tf.sg_initializer.identity('U_o', opt.dim, summary=opt.summary)
w_c = tf.sg_initializer.orthogonal('W_c', (opt.in_dim, opt.dim), summary=opt.summary)
u_c = tf.sg_initializer.identity('U_c', opt.dim, summary=opt.summary)
if opt.bias:
b_i = tf.sg_initializer.constant('b_i', opt.dim, summary=opt.summary)
b_f = tf.sg_initializer.constant('b_f', opt.dim, summary=opt.summary)
b_o = tf.sg_initializer.constant('b_o', opt.dim, value=1, summary=opt.summary)
b_c = tf.sg_initializer.constant('b_c', opt.dim, summary=opt.summary)
# layer normalization parameters
if opt.ln:
# offset, scale parameter
beta = tf.sg_initializer.constant('beta', opt.dim, summary=opt.summary)
gamma = tf.sg_initializer.constant('gamma', opt.dim, value=1, summary=opt.summary)
# initial state
init_h = opt.init_state if opt.init_state is not None \
else tf.zeros((tensor.get_shape().as_list()[0], opt.dim), dtype=tf.sg_floatx)
# do rnn loop
h, c, out = init_h, init_h, []
for i in range(tensor.get_shape().as_list()[1]):
# apply step function
h, c = step(h, c, tensor[:, i, :])
# save result
out.append(h.sg_expand_dims(axis=1))
# merge tensor
out = tf.concat(out, 1)
# apply mask
if opt.mask is None:
if opt.last_only:
return out[:, -1, :]
else:
return out
else:
# apply mask
out *= opt.mask.sg_expand_dims(axis=2).sg_float()
if opt.last_only:
# calc sequence length using given mask
seq_len = opt.mask.sg_int().sg_sum(axis=1)
# get last output
rev = tf.reverse_sequence(out, seq_len, seq_axis=1)
return rev[:, 0, :]
else:
return out
def sg_gru(tensor, opt):
r"""Applies a GRU.
Args:
tensor: A 3-D `Tensor` (automatically passed by decorator).
opt:
in_dim: A positive `integer`. The size of input dimension.
dim: A positive `integer`. The size of output dimension.
bias: Boolean. If True, biases are added.
ln: Boolean. If True, layer normalization is applied.
init_state: A 2-D `Tensor`. If None, the initial state is set to zeros.
last_only: Boolean. If True, the outputs in the last time step are returned.
mask: Boolean 2-D `Tensor` or None(default).
For false elements values are excluded from the calculation.
As a result, the outputs for the locations become 0.
summary: If True, summaries are added. The default is True.
Returns:
A `Tensor`. If last_only is True, the output tensor has shape [batch size, dim].
Otherwise, [batch size, time steps, dim].
"""
# layer normalization
# noinspection PyPep8
ln = lambda v: _ln_rnn(v, gamma, beta) if opt.ln else v
# step func
def step(hh, x):
# update gate
z = tf.sigmoid(ln(tf.matmul(x, w_z) + tf.matmul(hh, u_z) + (b_z if opt.bias else 0)))
# reset gate
r = tf.sigmoid(ln(tf.matmul(x, w_r) + tf.matmul(hh, u_r) + (b_r if opt.bias else 0)))
# h_hat
h_hat = tf.tanh(ln(tf.matmul(x, w_h) + tf.matmul(r * hh, u_h) + (b_h if opt.bias else 0)))
# final output
y = (1. - z) * h_hat + z * hh
return y
# parameter initialize
w_z = tf.sg_initializer.orthogonal('W_z', (opt.in_dim, opt.dim), summary=opt.summary)
u_z = tf.sg_initializer.identity('U_z', opt.dim, summary=opt.summary)
w_r = tf.sg_initializer.orthogonal('W_r', (opt.in_dim, opt.dim), summary=opt.summary)
u_r = tf.sg_initializer.identity('U_r', opt.dim, summary=opt.summary)
w_h = tf.sg_initializer.orthogonal('W_h', (opt.in_dim, opt.dim), summary=opt.summary)
u_h = tf.sg_initializer.identity('U_h', opt.dim, summary=opt.summary)
if opt.bias:
b_z = tf.sg_initializer.constant('b_z', opt.dim, summary=opt.summary)
b_r = tf.sg_initializer.constant('b_r', opt.dim, summary=opt.summary)
b_h = tf.sg_initializer.constant('b_h', opt.dim, summary=opt.summary)
# layer normalization parameters
if opt.ln:
# offset, scale parameter
beta = tf.sg_initializer.constant('beta', opt.dim, summary=opt.summary)
gamma = tf.sg_initializer.constant('gamma', opt.dim, value=1, summary=opt.summary)
# initial state
init_h = opt.init_state if opt.init_state is not None \
else tf.zeros((tensor.get_shape().as_list()[0], opt.dim), dtype=tf.sg_floatx)
# do rnn loop
h, out = init_h, []
for i in range(tensor.get_shape().as_list()[1]):
# apply step function
h = step(h, tensor[:, i, :])
# save result
# noinspection PyUnresolvedReferences
out.append(h.sg_expand_dims(axis=1))
# merge tensor
out = tf.concat(out, 1)
# apply mask
if opt.mask is None:
if opt.last_only:
return out[:, -1, :]
else:
return out
else:
# apply mask
out *= opt.mask.sg_expand_dims(axis=2).sg_float()
if opt.last_only:
# calc sequence length using given mask
seq_len = opt.mask.sg_int().sg_sum(axis=1)
# get last output
rev = tf.reverse_sequence(out, seq_len, seq_axis=1)
return rev[:, 0, :]
else:
return out
