def prepare_decoder_input_output(tgt_words, tgt_len, vocab_table):
"""
Args:
tgt_words: tensor of word ids, [batch x max_len]
tgt_len: vector of sentence lengths, [batch]
vocab_table: instance of tf.vocab_lookup_table
Returns:
dec_input: tensor of word ids, [batch x max_len+1]
dec_input_len: vector of sentence lengths, [batch]
dec_output: tensor of word ids, [batch x max_len+1]
dec_output_len: vector of sentence lengths, [batch]
"""
start_token_id = vocab.get_token_id(vocab.START_TOKEN, vocab_table)
stop_token_id = vocab.get_token_id(vocab.STOP_TOKEN, vocab_table)
pad_token_id = vocab.get_token_id(vocab.PAD_TOKEN, vocab_table)
dec_input = decoder.prepare_decoder_inputs(tgt_words, start_token_id)
dec_input_len = seq.length_pre_embedding(dec_input)
dec_output = decoder.prepare_decoder_output(tgt_words, tgt_len,
stop_token_id, pad_token_id)
dec_output_len = seq.length_pre_embedding(dec_output)
return dec_input, dec_input_len, dec_output, dec_output_len
def test_wtf():
with tf.Graph().as_default():
V, embed_matrix = vocab.read_word_embeddings(
Path('../data') / 'word_vectors' / 'glove.6B.300d_yelp.txt',
300,
10000
)
table = vocab.create_vocab_lookup_tables(V)
vocab_s2i = table[vocab.STR_TO_INT]
vocab_i2s = table[vocab.INT_TO_STR]
dataset = input_fn('../data/yelp_dataset_large_split/train.tsv', table, 64, 1)
iter = dataset.make_initializable_iterator()
(src, tgt, iw, dw), _ = iter.get_next()
src_len = length_pre_embedding(src)
tgt_len = length_pre_embedding(tgt)
iw_len = length_pre_embedding(iw)
dw_len = length_pre_embedding(dw)
dec_inputs = decoder.prepare_decoder_inputs(tgt, vocab.get_token_id(vocab.START_TOKEN, vocab_s2i))
dec_output = decoder.prepare_decoder_output(tgt, tgt_len, vocab.get_token_id(vocab.STOP_TOKEN, vocab_s2i),
vocab.get_token_id(vocab.PAD_TOKEN, vocab_s2i))
t_src = vocab_i2s.lookup(src)
t_tgt = vocab_i2s.lookup(tgt)
t_iw = vocab_i2s.lookup(iw)
t_dw = vocab_i2s.lookup(dw)
t_do = vocab_i2s.lookup(dec_output)
t_di = vocab_i2s.lookup(dec_inputs)
with tf.Session() as sess:
sess.run([tf.global_variables_initializer(), tf.local_variables_initializer(), tf.tables_initializer()])
sess.run(iter.initializer)
while True:
try:
# src, tgt, iw, dw = sess.run([src, tgt, iw, dw])
ts, tt, tiw, tdw, tdo, tdi = sess.run([t_src, t_tgt, t_iw, t_dw, t_do, t_di])
except:
break
def fn(orig_ids):
orig_ids = tf.cast(orig_ids, tf.int64)
in_vocab_ids = tf.where(tf.less(orig_ids, vocab_size), orig_ids,
tf.ones_like(orig_ids) * vocab.OOV_TOKEN_ID)
embeds = vocab.embed_tokens(in_vocab_ids)
last_ids = tf.where(
tf.equal(orig_ids, vocab.get_token_id(vocab.START_TOKEN)),
tf.ones_like(orig_ids) * -1, orig_ids)
last_ids = tf.cast(tf.expand_dims(last_ids, 2), tf.float32)
cell_input = tf.concat([embeds, last_ids], axis=2)
return cell_input
def prepare_output_embed(
decoder_output,
temperature_starter,
decay_rate,
decay_steps,
):
# [VOCAB x word_dim]
embeddings = vocab.get_embeddings()
# Extend embedding matrix to support oov tokens
unk_id = vocab.get_token_id(vocab.UNKNOWN_TOKEN)
unk_embed = tf.expand_dims(vocab.embed_tokens(unk_id), 0)
unk_embeddings = tf.tile(unk_embed, [50, 1])
# [VOCAB+50 x word_dim]
embeddings_extended = tf.concat([embeddings, unk_embeddings], axis=0)
global_step = tf.train.get_global_step()
temperature = tf.train.exponential_decay(temperature_starter,
global_step,
decay_steps,
decay_rate,
name='temperature')
tf.summary.scalar('temper', temperature, ['extra'])
# [batch x max_len x VOCAB+50], softmax probabilities
outputs = decoder.rnn_output(decoder_output)
# substitute values less than 0 for numerical stability
outputs = tf.where(tf.less_equal(outputs, 0),
tf.ones_like(outputs) * 1e-10, outputs)
# convert softmax probabilities to one_hot vectors
dist = tfd.RelaxedOneHotCategorical(temperature, probs=outputs)
# [batch x max_len x VOCAB+50], one_hot
outputs_one_hot = dist.sample()
# [batch x max_len x word_dim], one_hot^T * embedding_matrix
outputs_embed = tf.einsum("btv,vd-> btd", outputs_one_hot,
embeddings_extended)
return outputs_embed
def editor_train(base_words,
source_words,
target_words,
insert_words,
delete_words,
embed_matrix,
vocab_table,
hidden_dim,
agenda_dim,
edit_dim,
num_encoder_layers,
num_decoder_layers,
attn_dim,
beam_width,
max_sent_length,
dropout_keep,
lamb_reg,
norm_eps,
norm_max,
kill_edit,
draw_edit,
swap_memory,
use_beam_decoder=False,
use_dropout=False,
no_insert_delete_attn=False,
enable_vae=True):
batch_size = tf.shape(source_words)[0]
# [batch]
base_len = seq.length_pre_embedding(base_words)
src_len = seq.length_pre_embedding(source_words)
tgt_len = seq.length_pre_embedding(target_words)
iw_len = seq.length_pre_embedding(insert_words)
dw_len = seq.length_pre_embedding(delete_words)
# variable of shape [vocab_size, embed_dim]
embeddings = vocab.get_embeddings()
# [batch x max_len x embed_dim]
base_word_embeds = vocab.embed_tokens(base_words)
# src_word_embeds = vocab.embed_tokens(source_words)
# tgt_word_embeds = vocab.embed_tokens(target_words)
insert_word_embeds = vocab.embed_tokens(insert_words)
delete_word_embeds = vocab.embed_tokens(delete_words)
# [batch x max_len x rnn_out_dim], [batch x rnn_out_dim]
base_sent_hidden_states, base_sent_embed = encoder.source_sent_encoder(
base_word_embeds,
base_len,
hidden_dim,
num_encoder_layers,
use_dropout=use_dropout,
dropout_keep=dropout_keep,
swap_memory=swap_memory)
# [batch x edit_dim]
if kill_edit:
edit_vector = tf.zeros(shape=(batch_size, edit_dim))
else:
if draw_edit:
edit_vector = edit_encoder.random_noise_encoder(
batch_size, edit_dim, norm_max)
else:
edit_vector = edit_encoder.accumulator_encoder(
insert_word_embeds,
delete_word_embeds,
iw_len,
dw_len,
edit_dim,
lamb_reg,
norm_eps,
norm_max,
dropout_keep,
enable_vae=enable_vae)
# [batch x agenda_dim]
input_agenda = agn.linear(base_sent_embed, edit_vector, agenda_dim)
train_dec_inp, train_dec_inp_len, \
train_dec_out, train_dec_out_len = prepare_decoder_input_output(target_words, tgt_len, vocab_table)
train_decoder = decoder.train_decoder(
input_agenda,
embeddings,
train_dec_inp,
base_sent_hidden_states,
insert_word_embeds,
delete_word_embeds,
train_dec_inp_len,
base_len,
iw_len,
dw_len,
attn_dim,
hidden_dim,
num_decoder_layers,
swap_memory,
enable_dropout=use_dropout,
dropout_keep=dropout_keep,
no_insert_delete_attn=no_insert_delete_attn)
if use_beam_decoder:
infr_decoder = decoder.beam_eval_decoder(
input_agenda,
embeddings,
vocab.get_token_id(vocab.START_TOKEN, vocab_table),
vocab.get_token_id(vocab.STOP_TOKEN, vocab_table),
base_sent_hidden_states,
insert_word_embeds,
delete_word_embeds,
base_len,
iw_len,
dw_len,
attn_dim,
hidden_dim,
num_decoder_layers,
max_sent_length,
beam_width,
swap_memory,
enable_dropout=use_dropout,
dropout_keep=dropout_keep,
no_insert_delete_attn=no_insert_delete_attn)
else:
infr_decoder = decoder.greedy_eval_decoder(
input_agenda,
embeddings,
vocab.get_token_id(vocab.START_TOKEN, vocab_table),
vocab.get_token_id(vocab.STOP_TOKEN, vocab_table),
base_sent_hidden_states,
insert_word_embeds,
delete_word_embeds,
base_len,
iw_len,
dw_len,
attn_dim,
hidden_dim,
num_decoder_layers,
max_sent_length,
swap_memory,
enable_dropout=use_dropout,
dropout_keep=dropout_keep,
no_insert_delete_attn=no_insert_delete_attn)
return train_decoder, infr_decoder, train_dec_out, train_dec_out_len
def editor_train(base_words,
extended_base_words,
output_words,
extended_output_words,
source_words,
target_words,
insert_words,
delete_words,
oov,
vocab_size,
hidden_dim,
agenda_dim,
edit_dim,
micro_edit_ev_dim,
num_heads,
num_encoder_layers,
num_decoder_layers,
attn_dim,
beam_width,
ctx_hidden_dim,
ctx_hidden_layer,
wa_hidden_dim,
wa_hidden_layer,
meve_hidden_dim,
meve_hidden_layers,
recons_dense_layers,
max_sent_length,
dropout_keep,
lamb_reg,
norm_eps,
norm_max,
kill_edit,
draw_edit,
swap_memory,
use_beam_decoder=False,
use_dropout=False,
no_insert_delete_attn=False,
enable_vae=True):
batch_size = tf.shape(source_words)[0]
# [batch]
base_len = seq.length_pre_embedding(base_words)
output_len = seq.length_pre_embedding(extended_output_words)
src_len = seq.length_pre_embedding(source_words)
tgt_len = seq.length_pre_embedding(target_words)
iw_len = seq.length_pre_embedding(insert_words)
dw_len = seq.length_pre_embedding(delete_words)
# variable of shape [vocab_size, embed_dim]
embeddings = vocab.get_embeddings()
# [batch x max_len x embed_dim]
base_word_embeds = vocab.embed_tokens(base_words)
output_word_embeds = vocab.embed_tokens(output_words)
# src_word_embeds = vocab.embed_tokens(source_words)
# tgt_word_embeds = vocab.embed_tokens(target_words)
insert_word_embeds = vocab.embed_tokens(insert_words)
delete_word_embeds = vocab.embed_tokens(delete_words)
# [batch x max_len x rnn_out_dim], [batch x rnn_out_dim]
base_sent_hidden_states, base_sent_embed = encoder.source_sent_encoder(
base_word_embeds,
base_len,
hidden_dim,
num_encoder_layers,
use_dropout=use_dropout,
dropout_keep=dropout_keep,
swap_memory=swap_memory)
assert kill_edit == False and draw_edit == False
# [batch x edit_dim]
if kill_edit:
edit_vector = tf.zeros(shape=(batch_size, edit_dim))
else:
if draw_edit:
edit_vector = random_noise_encoder(batch_size, edit_dim, norm_max)
else:
edit_vector = accumulator_encoder(insert_word_embeds,
delete_word_embeds,
iw_len,
dw_len,
edit_dim,
lamb_reg,
norm_eps,
norm_max,
dropout_keep,
enable_vae=enable_vae)
wa_inserted, wa_deleted = (tf.constant([[0]]), tf.constant(
[[0]])), (tf.constant([[0]]), tf.constant([[0]]))
# [batch x agenda_dim]
base_agenda = agn.linear(base_sent_embed, edit_vector, agenda_dim)
train_dec_inp, train_dec_inp_len, \
train_dec_out, train_dec_out_len = prepare_decoder_input_output(output_words, extended_output_words, output_len)
train_dec_inp_extended = prepare_decoder_inputs(extended_output_words,
tf.cast(-1, tf.int64))
train_decoder = decoder.train_decoder(
base_agenda,
embeddings,
extended_base_words,
oov,
train_dec_inp,
train_dec_inp_extended,
base_sent_hidden_states,
wa_inserted,
wa_deleted,
train_dec_inp_len,
base_len,
src_len,
tgt_len,
vocab_size,
attn_dim,
hidden_dim,
num_decoder_layers,
swap_memory,
enable_dropout=use_dropout,
dropout_keep=dropout_keep,
no_insert_delete_attn=no_insert_delete_attn)
if use_beam_decoder:
infr_decoder = decoder.beam_eval_decoder(
base_agenda,
embeddings,
extended_base_words,
oov,
vocab.get_token_id(vocab.START_TOKEN),
vocab.get_token_id(vocab.STOP_TOKEN),
base_sent_hidden_states,
wa_inserted,
wa_deleted,
base_len,
src_len,
tgt_len,
vocab_size,
attn_dim,
hidden_dim,
num_decoder_layers,
max_sent_length,
beam_width,
swap_memory,
enable_dropout=use_dropout,
dropout_keep=dropout_keep,
no_insert_delete_attn=no_insert_delete_attn)
else:
infr_decoder = decoder.greedy_eval_decoder(
base_agenda,
embeddings,
extended_base_words,
oov,
vocab.get_token_id(vocab.START_TOKEN),
vocab.get_token_id(vocab.STOP_TOKEN),
base_sent_hidden_states,
wa_inserted,
wa_deleted,
base_len,
src_len,
tgt_len,
vocab_size,
attn_dim,
hidden_dim,
num_decoder_layers,
max_sent_length,
swap_memory,
enable_dropout=use_dropout,
dropout_keep=dropout_keep,
no_insert_delete_attn=no_insert_delete_attn)
edit_vector_recons = output_words_to_edit_vector(
output_word_embeds, output_len, edit_dim, ctx_hidden_dim,
ctx_hidden_layer, recons_dense_layers, swap_memory)
optimizer.add_reconst_loss(edit_vector, edit_vector_recons)
return train_decoder, infr_decoder, train_dec_out, train_dec_out_len
def editor_train(base_words,
output_words,
source_words,
target_words,
insert_words,
delete_words,
hidden_dim,
agenda_dim,
edit_dim,
micro_edit_ev_dim,
num_heads,
num_encoder_layers,
num_decoder_layers,
attn_dim,
beam_width,
ctx_hidden_dim,
ctx_hidden_layer,
wa_hidden_dim,
wa_hidden_layer,
meve_hidden_dim,
meve_hidden_layers,
max_sent_length,
dropout_keep,
lamb_reg,
norm_eps,
norm_max,
kill_edit,
draw_edit,
swap_memory,
use_beam_decoder=False,
use_dropout=False,
no_insert_delete_attn=False,
enable_vae=True):
batch_size = tf.shape(source_words)[0]
# [batch]
base_len = seq.length_pre_embedding(base_words)
output_len = seq.length_pre_embedding(output_words)
src_len = seq.length_pre_embedding(source_words)
tgt_len = seq.length_pre_embedding(target_words)
iw_len = seq.length_pre_embedding(insert_words)
dw_len = seq.length_pre_embedding(delete_words)
# variable of shape [vocab_size, embed_dim]
embeddings = vocab.get_embeddings()
# [batch x max_len x embed_dim]
base_word_embeds = vocab.embed_tokens(base_words)
src_word_embeds = vocab.embed_tokens(source_words)
tgt_word_embeds = vocab.embed_tokens(target_words)
insert_word_embeds = vocab.embed_tokens(insert_words)
delete_word_embeds = vocab.embed_tokens(delete_words)
sent_encoder = tf.make_template('sent_encoder',
encoder.source_sent_encoder,
hidden_dim=hidden_dim,
num_layer=num_encoder_layers,
swap_memory=swap_memory,
use_dropout=use_dropout,
dropout_keep=dropout_keep)
# [batch x max_len x rnn_out_dim], [batch x rnn_out_dim]
base_sent_hidden_states, base_sent_embed = sent_encoder(
base_word_embeds, base_len)
assert kill_edit == False and draw_edit == False
# [batch x edit_dim]
if kill_edit:
edit_vector = tf.zeros(shape=(batch_size, edit_dim))
else:
if draw_edit:
edit_vector = random_noise_encoder(batch_size, edit_dim, norm_max)
else:
edit_vector, wa_inserted, wa_deleted = attn_encoder(
src_word_embeds,
tgt_word_embeds,
insert_word_embeds,
delete_word_embeds,
src_len,
tgt_len,
iw_len,
dw_len,
ctx_hidden_dim,
ctx_hidden_layer,
wa_hidden_dim,
wa_hidden_layer,
meve_hidden_dim,
meve_hidden_layers,
edit_dim,
micro_edit_ev_dim,
num_heads,
lamb_reg,
norm_eps,
norm_max,
sent_encoder,
use_dropout=use_dropout,
dropout_keep=dropout_keep,
swap_memory=swap_memory,
enable_vae=enable_vae)
# [batch x agenda_dim]
input_agenda = agn.linear(base_sent_embed, edit_vector, agenda_dim)
train_dec_inp, train_dec_inp_len, \
train_dec_out, train_dec_out_len = prepare_decoder_input_output(output_words, output_len, None)
train_decoder = decoder.train_decoder(
input_agenda,
embeddings,
train_dec_inp,
base_sent_hidden_states,
wa_inserted,
wa_deleted,
train_dec_inp_len,
base_len,
iw_len,
dw_len,
attn_dim,
hidden_dim,
num_decoder_layers,
swap_memory,
enable_dropout=use_dropout,
dropout_keep=dropout_keep,
no_insert_delete_attn=no_insert_delete_attn)
if use_beam_decoder:
infr_decoder = decoder.beam_eval_decoder(
input_agenda,
embeddings,
vocab.get_token_id(vocab.START_TOKEN),
vocab.get_token_id(vocab.STOP_TOKEN),
base_sent_hidden_states,
wa_inserted,
wa_deleted,
base_len,
iw_len,
dw_len,
attn_dim,
hidden_dim,
num_decoder_layers,
max_sent_length,
beam_width,
swap_memory,
enable_dropout=use_dropout,
dropout_keep=dropout_keep,
no_insert_delete_attn=no_insert_delete_attn)
else:
infr_decoder = decoder.greedy_eval_decoder(
input_agenda,
embeddings,
vocab.get_token_id(vocab.START_TOKEN),
vocab.get_token_id(vocab.STOP_TOKEN),
base_sent_hidden_states,
wa_inserted,
wa_deleted,
base_len,
iw_len,
dw_len,
attn_dim,
hidden_dim,
num_decoder_layers,
max_sent_length,
swap_memory,
enable_dropout=use_dropout,
dropout_keep=dropout_keep,
no_insert_delete_attn=no_insert_delete_attn)
return train_decoder, infr_decoder, train_dec_out, train_dec_out_len
def editor_train(base_words,
extended_base_words,
output_words,
extended_output_words,
source_words,
target_words,
insert_words,
delete_words,
oov,
vocab_size,
hidden_dim,
agenda_dim,
edit_dim,
micro_edit_ev_dim,
num_heads,
num_encoder_layers,
num_decoder_layers,
attn_dim,
beam_width,
ctx_hidden_dim,
ctx_hidden_layer,
wa_hidden_dim,
wa_hidden_layer,
meve_hidden_dim,
meve_hidden_layers,
max_sent_length,
dropout_keep,
lamb_reg,
norm_eps,
norm_max,
kill_edit,
draw_edit,
swap_memory,
use_beam_decoder=False,
use_dropout=False,
no_insert_delete_attn=False,
enable_vae=True):
# [batch]
base_len = seq.length_pre_embedding(base_words)
output_len = seq.length_pre_embedding(extended_output_words)
# variable of shape [vocab_size, embed_dim]
embeddings = vocab.get_embeddings()
# [batch x max_len x embed_dim]
base_word_embeds = vocab.embed_tokens(base_words)
# [batch x max_len x rnn_out_dim], [batch x rnn_out_dim]
base_sent_hidden_states, base_sent_embed = encoder.source_sent_encoder(
base_word_embeds,
base_len,
hidden_dim,
num_encoder_layers,
use_dropout=use_dropout,
dropout_keep=dropout_keep,
swap_memory=swap_memory)
assert kill_edit == False and draw_edit == False
# [batch x agenda_dim]
base_agenda = linear(base_sent_embed, agenda_dim)
train_dec_inp, train_dec_inp_len, \
train_dec_out, train_dec_out_len = prepare_decoder_input_output(output_words, extended_output_words, output_len)
train_dec_inp_extended = prepare_decoder_inputs(extended_output_words,
tf.cast(-1, tf.int64))
train_decoder = decoder.train_decoder(
base_agenda,
embeddings,
extended_base_words,
oov,
train_dec_inp,
train_dec_inp_extended,
base_sent_hidden_states,
train_dec_inp_len,
base_len,
vocab_size,
attn_dim,
hidden_dim,
num_decoder_layers,
swap_memory,
enable_dropout=use_dropout,
dropout_keep=dropout_keep,
no_insert_delete_attn=no_insert_delete_attn)
if use_beam_decoder:
infr_decoder = decoder.beam_eval_decoder(
base_agenda,
embeddings,
extended_base_words,
oov,
vocab.get_token_id(vocab.START_TOKEN),
vocab.get_token_id(vocab.STOP_TOKEN),
base_sent_hidden_states,
base_len,
vocab_size,
attn_dim,
hidden_dim,
num_decoder_layers,
max_sent_length,
beam_width,
swap_memory,
enable_dropout=use_dropout,
dropout_keep=dropout_keep,
no_insert_delete_attn=no_insert_delete_attn)
else:
infr_decoder = decoder.greedy_eval_decoder(
base_agenda,
embeddings,
extended_base_words,
oov,
vocab.get_token_id(vocab.START_TOKEN),
vocab.get_token_id(vocab.STOP_TOKEN),
base_sent_hidden_states,
base_len,
vocab_size,
attn_dim,
hidden_dim,
num_decoder_layers,
max_sent_length,
swap_memory,
enable_dropout=use_dropout,
dropout_keep=dropout_keep,
no_insert_delete_attn=no_insert_delete_attn)
add_decoder_attn_history_graph(infr_decoder)
return train_decoder, infr_decoder, train_dec_out, train_dec_out_len
def decoder_outputs_to_edit_vector(decoder_output, temperature_starter,
decay_rate, decay_steps, edit_dim,
enc_hidden_dim, enc_num_layers,
dense_layers, swap_memory):
with tf.variable_scope(OPS_NAME):
# [VOCAB x word_dim]
embeddings = vocab.get_embeddings()
# Extend embedding matrix to support oov tokens
unk_id = vocab.get_token_id(vocab.UNKNOWN_TOKEN)
unk_embed = tf.expand_dims(vocab.embed_tokens(unk_id), 0)
unk_embeddings = tf.tile(unk_embed, [50, 1])
# [VOCAB+50 x word_dim]
embeddings_extended = tf.concat([embeddings, unk_embeddings], axis=0)
global_step = tf.train.get_global_step()
temperature = tf.train.exponential_decay(temperature_starter,
global_step,
decay_steps,
decay_rate,
name='temperature')
tf.summary.scalar('temper', temperature, ['extra'])
# [batch x max_len x VOCAB+50], softmax probabilities
outputs = decoder.rnn_output(decoder_output)
# substitute values less than 0 for numerical stability
outputs = tf.where(tf.less_equal(outputs, 0),
tf.ones_like(outputs) * 1e-10, outputs)
# convert softmax probabilities to one_hot vectors
dist = tfd.RelaxedOneHotCategorical(temperature, probs=outputs)
# [batch x max_len x VOCAB+50], one_hot
outputs_one_hot = dist.sample()
# [batch x max_len x word_dim], one_hot^T * embedding_matrix
outputs_embed = tf.einsum("btv,vd-> btd", outputs_one_hot,
embeddings_extended)
# [batch]
outputs_length = decoder.seq_length(decoder_output)
# [batch x max_len x hidden], [batch x hidden]
hidden_states, sentence_embedding = encoder.source_sent_encoder(
outputs_embed,
outputs_length,
enc_hidden_dim,
enc_num_layers,
use_dropout=False,
dropout_keep=1.0,
swap_memory=swap_memory)
h = sentence_embedding
for l in dense_layers:
h = tf.layers.dense(h,
l,
activation='relu',
name='hidden_%s' % (l))
# [batch x edit_dim]
edit_vector = tf.layers.dense(h,
edit_dim,
activation=None,
name='linear')
return edit_vector
