def prepare(config):
# process files
train_examples, train_eval = process_file(config.train_file,
lower_word=config.lower_word)
dev_examples, dev_eval = process_file(config.dev_file,
lower_word=config.lower_word)
with open(config.word_dictionary, "r") as fh:
word2idx_dict = json.load(fh)
train_meta = build_features(config, train_examples, "train",
config.train_record_file, word2idx_dict)
dev_meta = build_features(config, dev_examples, "dev",
config.dev_record_file, word2idx_dict)
save(config.train_eval_file, train_eval, message="train eval")
save(config.dev_eval_file, dev_eval, message="dev eval")
save(config.train_meta, train_meta, message="train meta")
save(config.dev_meta, dev_meta, message="dev meta")
def train_qqp_qap(config):
with open(config.word_emb_file, "r") as fh:
word_mat = np.array(json.load(fh), dtype=np.float32)
with open(config.char_emb_file, "r") as fh:
char_mat = np.array(json.load(fh), dtype=np.float32)
with open(config.pos_emb_file, "r") as fh:
pos_mat = np.array(json.load(fh), dtype=np.float32)
with open(config.ner_emb_file, "r") as fh:
ner_mat = np.array(json.load(fh), dtype=np.float32)
with open(config.label_emb_file, "r") as fh:
label_mat = np.array(json.load(fh), dtype=np.float32)
with open(config.train_eval_file, "r") as fh:
train_eval_file = json.load(fh)
with open(config.dev_eval_file, "r") as fh:
dev_eval_file = json.load(fh)
with open(config.dev_meta, "r") as fh:
meta = json.load(fh)
with open(config.word_dictionary, "r") as fh:
word_dictionary = json.load(fh)
with open(config.char_dictionary, "r") as fh:
char_dictionary = json.load(fh)
with h5py.File(config.embedding_file, 'r') as fin:
embed_weights = fin["embedding"][...]
elmo_word_mat = np.zeros(
(embed_weights.shape[0] + 1, embed_weights.shape[1]),
dtype=np.float32)
elmo_word_mat[1:, :] = embed_weights
id2word = {word_dictionary[w]: w for w in word_dictionary}
dev_total = meta["total"]
best_bleu, best_ckpt = 0., 0
print("Building model...")
parser = get_record_parser(config)
graph_qg = tf.Graph()
graph_qqp = tf.Graph()
graph_qa = tf.Graph()
with graph_qg.as_default():
train_dataset = get_batch_dataset(config.train_record_file, parser,
config)
dev_dataset = get_dataset(config.dev_record_file, parser, config)
train_iterator = train_dataset.make_one_shot_iterator()
dev_iterator = dev_dataset.make_one_shot_iterator()
with graph_qg.as_default() as g:
model_qg = QGRLModel(config, word_mat, elmo_word_mat, label_mat,
pos_mat, ner_mat)
model_qg.build_graph()
model_qg.add_train_op()
with graph_qqp.as_default() as g:
model_qqp = QPCModel(config, dev=True, trainable=False, graph=g)
with graph_qa.as_default() as g:
model_qa = BidafQA(config,
word_mat,
char_mat,
dev=True,
trainable=False)
sess_qg = tf.Session(graph=graph_qg)
sess_qqp = tf.Session(graph=graph_qqp)
sess_qa = tf.Session(graph=graph_qa)
writer = tf.summary.FileWriter(config.output_dir)
with sess_qg.as_default():
with graph_qg.as_default():
sess_qg.run(tf.global_variables_initializer())
saver_qg = tf.train.Saver(max_to_keep=1000,
var_list=[
p for p in tf.global_variables()
if "word_mat" not in p.name
])
if os.path.exists(os.path.join(config.output_dir, "checkpoint")):
print(tf.train.latest_checkpoint(config.output_dir))
saver_qg.restore(sess_qg,
tf.train.latest_checkpoint(config.output_dir))
if os.path.exists(config.best_ckpt):
with open(config.best_ckpt, "r") as fh:
best_qg_ckpt = json.load(fh)
best_bleu, best_ckpt = float(
best_qg_ckpt["best_bleu"]), int(
best_qg_ckpt["best_ckpt"])
with sess_qqp.as_default():
with graph_qqp.as_default():
sess_qqp.run(tf.global_variables_initializer())
saver_qqp = tf.train.Saver(max_to_keep=1000,
var_list=[
p for p in tf.global_variables()
if "word_mat" not in p.name
])
if os.path.exists(config.best_ckpt_qpc):
with open(config.best_ckpt_qpc, "r") as fh:
best_qpc_ckpt = json.load(fh)
best_ckpt = int(best_qpc_ckpt["best_ckpt"])
print("{}/model_{}.ckpt".format(config.output_dir_qpc,
best_ckpt))
saver_qqp.restore(
sess_qqp,
"{}/model_{}.ckpt".format(config.output_dir_qpc,
best_ckpt))
else:
print("NO the best QPC model to load!")
exit()
with sess_qa.as_default():
with graph_qa.as_default():
model_qa.build_graph()
model_qa.add_train_op()
sess_qa.run(tf.global_variables_initializer())
saver_qa = tf.train.Saver(max_to_keep=1000,
var_list=[
p for p in tf.global_variables()
if "word_mat" not in p.name
])
if os.path.exists(config.best_ckpt_qa):
with open(config.best_ckpt_qa, "r") as fh:
best_qpc_ckpt = json.load(fh)
best_ckpt = int(best_qpc_ckpt["best_ckpt"])
print("{}/model_{}.ckpt".format(config.output_dir_qa,
best_ckpt))
saver_qa.restore(
sess_qa, "{}/model_{}.ckpt".format(config.output_dir_qa,
best_ckpt))
else:
print("NO the best QA model to load!")
exit()
global_step = max(sess_qg.run(model_qg.global_step), 1)
train_next_element = train_iterator.get_next()
for _ in tqdm(range(global_step, config.num_steps + 1)):
global_step = sess_qg.run(model_qg.global_step) + 1
para, para_unk, para_char, que, que_unk, que_char, labels, pos_tags, ner_tags, \
que_labels, que_pos_tags, que_ner_tags, y1, y2, qa_id = sess_qg.run(train_next_element)
symbols, symbols_rl = sess_qg.run(
[model_qg.symbols, model_qg.symbols_rl],
feed_dict={
model_qg.para: para,
model_qg.para_unk: para_unk,
model_qg.que: que,
model_qg.labels: labels,
model_qg.pos_tags: pos_tags,
model_qg.ner_tags: ner_tags,
model_qg.qa_id: qa_id
})
# format sample for QA
que_base, que_unk_base, que_char_base, que_rl, que_unk_rl, que_char_rl = \
format_generated_ques_for_qa(train_eval_file, qa_id, symbols, symbols_rl, config.batch_size,
config.ques_limit, config.char_limit, id2word, char_dictionary)
label = np.zeros((config.batch_size, 2), dtype=np.int32)
if global_step % 4 == 0:
# QPP reward
reward_base = sess_qqp.run(model_qqp.pos_prob,
feed_dict={
model_qqp.que1: que_unk,
model_qqp.que2: que_unk_base,
model_qqp.label: label,
model_qqp.qa_id: qa_id,
})
reward_rl = sess_qqp.run(model_qqp.pos_prob,
feed_dict={
model_qqp.que1: que_unk,
model_qqp.que2: que_unk_rl,
model_qqp.label: label,
model_qqp.qa_id: qa_id,
})
reward = [rr - rb for rr, rb in zip(reward_rl, reward_base)]
mixing_ratio = 0.99
else:
# QAP reward
base_qa_loss = sess_qa.run(model_qa.batch_loss,
feed_dict={
model_qa.para: para_unk,
model_qa.para_char: para_char,
model_qa.que: que_unk_base,
model_qa.que_char: que_char_base,
model_qa.y1: y1,
model_qa.y2: y2,
model_qa.qa_id: qa_id,
})
qa_loss = sess_qa.run(model_qa.batch_loss,
feed_dict={
model_qa.para: para_unk,
model_qa.para_char: para_char,
model_qa.que: que_unk_rl,
model_qa.que_char: que_char_rl,
model_qa.y1: y1,
model_qa.y2: y2,
model_qa.qa_id: qa_id,
})
reward_base = list(map(lambda x: np.exp(-x), list(base_qa_loss)))
reward_rl = list(map(lambda x: np.exp(-x), list(qa_loss)))
reward = list(
map(lambda x: x[0] - x[1], zip(reward_rl, reward_base)))
mixing_ratio = 0.97
# train with rl
loss_ml, _ = sess_qg.run(
[model_qg.loss_ml, model_qg.train_op],
feed_dict={
model_qg.para: para,
model_qg.para_unk: para_unk,
model_qg.labels: labels,
model_qg.pos_tags: pos_tags,
model_qg.ner_tags: ner_tags,
model_qg.que: que,
model_qg.dropout: config.dropout,
model_qg.qa_id: qa_id,
model_qg.sampled_que: que_rl,
model_qg.reward: reward,
model_qg.lamda: mixing_ratio
})
if global_step % config.period == 0:
loss_sum = tf.Summary(value=[
tf.Summary.Value(tag="model/loss", simple_value=loss_ml),
])
writer.add_summary(loss_sum, global_step)
reward_base_sum = tf.Summary(value=[
tf.Summary.Value(tag="model/reward_base",
simple_value=np.mean(reward_base)),
])
writer.add_summary(reward_base_sum, global_step)
reward_rl_sum = tf.Summary(value=[
tf.Summary.Value(tag="model/reward_rl",
simple_value=np.mean(reward_rl)),
])
writer.add_summary(reward_rl_sum, global_step)
if global_step % config.checkpoint == 0:
filename = os.path.join(config.output_dir,
"model_{}.ckpt".format(global_step))
saver_qg.save(sess_qg, filename)
metrics = evaluate_batch(config,
model_qg,
config.val_num_batches,
train_eval_file,
sess_qg,
train_iterator,
id2word,
evaluate_func=evaluate_simple)
write_metrics(metrics, writer, global_step, "train")
metrics = evaluate_batch(config,
model_qg,
dev_total // config.batch_size + 1,
dev_eval_file,
sess_qg,
dev_iterator,
id2word,
evaluate_func=evaluate_simple)
write_metrics(metrics, writer, global_step, "dev")
bleu = metrics["bleu"]
if bleu > best_bleu:
best_bleu, best_ckpt = bleu, global_step
save(config.best_ckpt, {
"best_bleu": str(best_bleu),
"best_ckpt": str(best_ckpt)
}, config.best_ckpt)
def train_rl(config):
with open(config.word_emb_file, "r") as fh:
word_mat = np.array(json.load(fh), dtype=np.float32)
with open(config.pos_emb_file, "r") as fh:
pos_mat = np.array(json.load(fh), dtype=np.float32)
with open(config.ner_emb_file, "r") as fh:
ner_mat = np.array(json.load(fh), dtype=np.float32)
with open(config.label_emb_file, "r") as fh:
label_mat = np.array(json.load(fh), dtype=np.float32)
with open(config.train_eval_file, "r") as fh:
train_eval_file = json.load(fh)
with open(config.dev_eval_file, "r") as fh:
dev_eval_file = json.load(fh)
with open(config.dev_meta, "r") as fh:
meta = json.load(fh)
with open(config.word_dictionary, "r") as fh:
word_dictionary = json.load(fh)
with h5py.File(config.embedding_file, 'r') as fin:
embed_weights = fin["embedding"][...]
elmo_word_mat = np.zeros(
(embed_weights.shape[0] + 1, embed_weights.shape[1]),
dtype=np.float32)
elmo_word_mat[1:, :] = embed_weights
id2word = {word_dictionary[w]: w for w in word_dictionary}
best_bleu, best_ckpt = 0., 0
dev_total = meta["total"]
print("Building model...")
parser = get_record_parser(config)
graph_qg = tf.Graph()
with graph_qg.as_default():
train_dataset = get_batch_dataset(config.train_record_file, parser,
config.batch_size)
dev_dataset = get_dataset(config.dev_record_file, parser,
config.batch_size)
train_iterator = train_dataset.make_one_shot_iterator()
dev_iterator = dev_dataset.make_one_shot_iterator()
model_qg = QGRLModel(config, word_mat, elmo_word_mat, label_mat,
pos_mat, ner_mat)
model_qg.build_graph()
model_qg.add_train_op()
sess_qg = tf.Session(graph=graph_qg)
writer = tf.summary.FileWriter(config.output_dir)
with sess_qg.as_default():
with graph_qg.as_default():
sess_qg.run(tf.global_variables_initializer())
saver_qg = tf.train.Saver(max_to_keep=1000,
var_list=[
p for p in tf.global_variables()
if "word_mat" not in p.name
])
if os.path.exists(os.path.join(config.output_dir, "checkpoint")):
saver_qg.restore(sess_qg,
tf.train.latest_checkpoint(config.output_dir))
if os.path.exists(config.best_ckpt):
with open(config.best_ckpt, "r") as fh:
best_qg_ckpt = json.load(fh)
best_bleu, best_ckpt = float(
best_qg_ckpt["best_bleu"]), int(
best_qg_ckpt["best_ckpt"])
global_step = max(sess_qg.run(model_qg.global_step), 1)
train_next_element = train_iterator.get_next()
for _ in tqdm(range(global_step, config.num_steps + 1)):
global_step = sess_qg.run(model_qg.global_step) + 1
para, para_unk, para_char, que, que_unk, que_char, labels, pos_tags, ner_tags, \
que_labels, que_pos_tags, que_ner_tags, y1, y2, qa_id = sess_qg.run(train_next_element)
# get greedy search questions as baseline and sampled questions
symbols, symbols_rl = sess_qg.run(
[model_qg.symbols, model_qg.symbols_rl],
feed_dict={
model_qg.para: para,
model_qg.para_unk: para_unk,
model_qg.que: que,
model_qg.labels: labels,
model_qg.pos_tags: pos_tags,
model_qg.ner_tags: ner_tags,
model_qg.qa_id: qa_id
})
# get rewards and format sampled questions
reward, reward_rl, reward_base, que_rl = evaluate_rl(
train_eval_file,
qa_id,
symbols,
symbols_rl,
id2word,
metric=config.rl_metric)
# update model with policy gradient
loss_ml, _ = sess_qg.run(
[model_qg.loss_ml, model_qg.train_op],
feed_dict={
model_qg.para: para,
model_qg.para_unk: para_unk,
model_qg.que: que,
model_qg.labels: labels,
model_qg.pos_tags: pos_tags,
model_qg.ner_tags: ner_tags,
model_qg.dropout: config.dropout,
model_qg.qa_id: qa_id,
model_qg.sampled_que: que_rl,
model_qg.reward: reward
})
if global_step % config.period == 0:
loss_sum = tf.Summary(value=[
tf.Summary.Value(tag="model/loss", simple_value=loss_ml),
])
writer.add_summary(loss_sum, global_step)
reward_base_sum = tf.Summary(value=[
tf.Summary.Value(tag="model/reward_base",
simple_value=np.mean(reward_base)),
])
writer.add_summary(reward_base_sum, global_step)
reward_rl_sum = tf.Summary(value=[
tf.Summary.Value(tag="model/reward_rl",
simple_value=np.mean(reward_rl)),
])
writer.add_summary(reward_rl_sum, global_step)
if global_step % config.checkpoint == 0:
filename = os.path.join(config.output_dir,
"model_{}.ckpt".format(global_step))
saver_qg.save(sess_qg, filename)
metrics = evaluate_batch(config,
model_qg,
config.val_num_batches,
train_eval_file,
sess_qg,
train_iterator,
id2word,
evaluate_func=evaluate_simple)
write_metrics(metrics, writer, global_step, "train")
metrics = evaluate_batch(config,
model_qg,
dev_total // config.batch_size + 1,
dev_eval_file,
sess_qg,
dev_iterator,
id2word,
evaluate_func=evaluate_simple)
write_metrics(metrics, writer, global_step, "dev")
bleu = metrics["bleu"]
if bleu > best_bleu:
best_bleu, best_ckpt = bleu, global_step
save(config.best_ckpt, {
"best_bleu": str(best_bleu),
"best_ckpt": str(best_ckpt)
}, config.best_ckpt)
def train(config):
with open(config.word_emb_file, "r") as fh:
word_mat = np.array(json.load(fh), dtype=np.float32)
with open(config.pos_emb_file, "r") as fh:
pos_mat = np.array(json.load(fh), dtype=np.float32)
with open(config.ner_emb_file, "r") as fh:
ner_mat = np.array(json.load(fh), dtype=np.float32)
with open(config.label_emb_file, "r") as fh:
label_mat = np.array(json.load(fh), dtype=np.float32)
with open(config.train_eval_file, "r") as fh:
train_eval_file = json.load(fh)
with open(config.dev_eval_file, "r") as fh:
dev_eval_file = json.load(fh)
with open(config.dev_meta, "r") as fh:
meta = json.load(fh)
with open(config.word_dictionary, "r") as fh:
word_dictionary = json.load(fh)
with h5py.File(config.embedding_file, 'r') as fin:
embed_weights = fin["embedding"][...]
elmo_word_mat = np.zeros(
(embed_weights.shape[0] + 1, embed_weights.shape[1]),
dtype=np.float32)
elmo_word_mat[1:, :] = embed_weights
id2word = {word_dictionary[w]: w for w in word_dictionary}
dev_total = meta["total"]
print("Building model...")
parser = get_record_parser(config)
graph = tf.Graph()
best_bleu, best_ckpt = 0., 0
with graph.as_default() as g:
train_dataset = get_batch_dataset(config.train_record_file, parser,
config.batch_size)
dev_dataset = get_dataset(config.dev_record_file, parser,
config.batch_size)
train_iterator = train_dataset.make_one_shot_iterator()
dev_iterator = dev_dataset.make_one_shot_iterator()
model = QGModel(config, word_mat, elmo_word_mat, label_mat, pos_mat,
ner_mat)
model.build_graph()
model.add_train_op()
sess_config = tf.ConfigProto(allow_soft_placement=True)
sess_config.gpu_options.allow_growth = True
with tf.Session(config=sess_config) as sess:
writer = tf.summary.FileWriter(config.output_dir)
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(max_to_keep=1000,
var_list=[
p for p in tf.global_variables()
if "word_mat" not in p.name
])
if os.path.exists(os.path.join(config.output_dir, "checkpoint")):
saver.restore(sess,
tf.train.latest_checkpoint(config.output_dir))
if os.path.exists(config.best_ckpt):
with open(config.best_ckpt, "r") as fh:
best_qg_ckpt = json.load(fh)
best_bleu, best_ckpt = float(
best_qg_ckpt["best_bleu"]), int(
best_qg_ckpt["best_ckpt"])
global_step = max(sess.run(model.global_step), 1)
train_next_element = train_iterator.get_next()
for _ in tqdm(range(global_step, config.num_steps + 1)):
global_step = sess.run(model.global_step) + 1
para, para_unk, para_char, que, que_unk, que_char, labels, pos_tags, ner_tags, \
que_labels, que_pos_tags, que_ner_tags, y1, y2, qa_id = sess.run(train_next_element)
loss, _ = sess.run(
[model.loss, model.train_op],
feed_dict={
model.para: para,
model.para_unk: para_unk,
model.que: que,
model.labels: labels,
model.pos_tags: pos_tags,
model.ner_tags: ner_tags,
model.dropout: config.dropout,
model.qa_id: qa_id,
})
if global_step % config.period == 0:
loss_sum = tf.Summary(value=[
tf.Summary.Value(tag="model/loss", simple_value=loss),
])
writer.add_summary(loss_sum, global_step)
if global_step % config.checkpoint == 0:
filename = os.path.join(
config.output_dir, "model_{}.ckpt".format(global_step))
saver.save(sess, filename)
metrics = evaluate_batch(config,
model,
config.val_num_batches,
train_eval_file,
sess,
train_iterator,
id2word,
evaluate_func=evaluate_simple)
write_metrics(metrics, writer, global_step, "train")
metrics = evaluate_batch(config,
model,
dev_total // config.batch_size +
1,
dev_eval_file,
sess,
dev_iterator,
id2word,
evaluate_func=evaluate_simple)
write_metrics(metrics, writer, global_step, "dev")
bleu = metrics["bleu"]
if bleu > best_bleu:
best_bleu, best_ckpt = bleu, global_step
save(
config.best_ckpt, {
"best_bleu": str(best_bleu),
"best_ckpt": str(best_ckpt)
}, config.best_ckpt)
def prepare(config):
# process files
train_examples, train_eval = process_file(config.train_para_file,
config.train_question_file,
para_limit=config.para_limit)
dev_examples, dev_eval = process_file(config.dev_para_file,
config.dev_question_file,
para_limit=config.para_limit)
test_examples, test_eval = process_file(config.test_para_file,
config.test_question_file,
para_limit=config.para_limit)
with open(config.word_dictionary, "r") as fh:
word2idx_dict = json.load(fh)
print("num of words {}".format(len(word2idx_dict)))
with open(config.label_dictionary, "r") as fh:
label2idx_dict = json.load(fh)
print("num of labels {}".format(len(label2idx_dict)))
with open(config.pos_dictionary, "r") as fh:
pos2idx_dict = json.load(fh)
print("num of pos tags {}".format(len(pos2idx_dict)))
with open(config.ner_dictionary, "r") as fh:
ner2idx_dict = json.load(fh)
print("num of ner tags {}".format(len(ner2idx_dict)))
train_meta = build_features(config, train_examples, "train",
config.train_record_file, word2idx_dict,
pos2idx_dict, ner2idx_dict, label2idx_dict,
config.para_limit, config.ques_limit,
config.max_input_length)
dev_meta = build_features(config, dev_examples, "dev",
config.dev_record_file, word2idx_dict,
pos2idx_dict, ner2idx_dict, label2idx_dict,
config.para_limit, config.ques_limit,
config.max_input_length)
test_meta = build_features(config, test_examples, "test",
config.test_record_file, word2idx_dict,
pos2idx_dict, ner2idx_dict, label2idx_dict,
config.para_limit, config.ques_limit,
config.max_input_length)
save(config.train_eval_file, train_eval, message="train eval")
save(config.dev_eval_file, dev_eval, message="dev eval")
save(config.test_eval_file, test_eval, message="test eval")
save(config.train_meta, train_meta, message="train meta")
save(config.dev_meta, dev_meta, message="dev meta")
save(config.test_meta, test_meta, message="test meta")
def train(config):
with open(config.word_emb_file, "rb") as fh:
word_mat = np.array(pkl.load(fh), dtype=np.float32)
with open(config.pos_emb_file, "r") as fh:
pos_mat = np.array(json.load(fh), dtype=np.float32)
with open(config.ner_emb_file, "r") as fh:
ner_mat = np.array(json.load(fh), dtype=np.float32)
with open(config.label_emb_file, "r") as fh:
label_mat = np.array(json.load(fh), dtype=np.float32)
with open(config.word_dictionary, "r") as fh:
word_dictionary = json.load(fh)
with open(config.train_eval_file, "r") as fh:
train_eval_file = json.load(fh)
with open(config.dev_eval_file, "r") as fh:
dev_eval_file = json.load(fh)
with open(config.dev_meta, "r") as fh:
meta = json.load(fh)
with open(config.map_to_orig, "rb") as fh:
map_to_orig = pkl.load(fh)
id2word = {word_dictionary[w]: w for w in word_dictionary}
dev_total = meta["total"]
best_bleu, best_ckpt = 0., 0
print("Building model...")
parser = get_record_parser(config)
graph_para = tf.Graph()
graph_qg = tf.Graph()
with graph_para.as_default() as g:
train_dataset = get_batch_dataset(config.train_record_file, parser,
config.batch_size)
train_iterator = train_dataset.make_one_shot_iterator()
dev_dataset = get_dataset(config.dev_record_file, parser,
config.batch_size)
dev_iterator = dev_dataset.make_one_shot_iterator()
model_para = BertEmb(config, config.para_limit + 2, graph=g)
with graph_qg.as_default() as g:
model_qg = QGModel(config, word_mat, label_mat, pos_mat, ner_mat)
model_qg.build_graph()
model_qg.add_train_op()
sess_para = tf.Session(graph=graph_para)
sess_qg = tf.Session(graph=graph_qg)
with sess_para.as_default():
with graph_para.as_default():
print("init from pretrained bert..")
tvars = tf.trainable_variables()
(assignment_map, initialized_variable_names) = \
modeling.get_assignment_map_from_checkpoint(tvars, config.init_checkpoint)
tf.train.init_from_checkpoint(config.init_checkpoint,
assignment_map)
sess_para.run(tf.global_variables_initializer())
with sess_qg.as_default():
with graph_qg.as_default():
sess_qg.run(tf.global_variables_initializer())
saver_qg = tf.train.Saver(max_to_keep=1000,
var_list=[
p for p in tf.global_variables()
if "word_mat" not in p.name
])
if os.path.exists(os.path.join(config.output_dir, "checkpoint")):
print(tf.train.latest_checkpoint(config.output_dir))
saver_qg.restore(sess_qg,
tf.train.latest_checkpoint(config.output_dir))
if os.path.exists(config.best_ckpt):
with open(config.best_ckpt, "r") as fh:
best_qg_ckpt = json.load(fh)
best_bleu, best_ckpt = float(
best_qg_ckpt["best_bleu"]), int(
best_qg_ckpt["best_ckpt"])
print("best_bleu:{}, best_ckpt:{}".format(
best_bleu, best_ckpt))
writer = tf.summary.FileWriter(config.output_dir)
global_step = max(sess_qg.run(model_qg.global_step), 1)
train_next_element = train_iterator.get_next()
for _ in tqdm(range(global_step, config.num_steps + 1)):
global_step = sess_qg.run(model_qg.global_step) + 1
para, para_unk, ques, labels, pos_tags, ner_tags, qa_id = sess_para.run(
train_next_element)
para_emb = sess_para.run(model_para.bert_emb,
feed_dict={model_para.input_ids: para_unk})
loss, _ = sess_qg.run(
[model_qg.loss, model_qg.train_op],
feed_dict={
model_qg.para: para,
model_qg.bert_para: para_emb,
model_qg.que: ques,
model_qg.labels: labels,
model_qg.pos_tags: pos_tags,
model_qg.ner_tags: ner_tags,
model_qg.dropout: config.dropout,
model_qg.qa_id: qa_id
})
if global_step % config.period == 0:
loss_sum = tf.Summary(value=[
tf.Summary.Value(tag="model/loss", simple_value=loss),
])
writer.add_summary(loss_sum, global_step)
if global_step % config.checkpoint == 0:
filename = os.path.join(config.output_dir,
"model_{}.ckpt".format(global_step))
saver_qg.save(sess_qg, filename)
metrics = evaluate_batch(config,
model_para,
model_qg,
sess_para,
sess_qg,
config.val_num_batches,
train_eval_file,
train_iterator,
id2word,
map_to_orig,
evaluate_func=evaluate_simple)
write_metrics(metrics, writer, global_step, "train")
metrics = evaluate_batch(config,
model_para,
model_qg,
sess_para,
sess_qg,
dev_total // config.batch_size + 1,
dev_eval_file,
dev_iterator,
id2word,
map_to_orig,
evaluate_func=evaluate_simple)
write_metrics(metrics, writer, global_step, "dev")
bleu = metrics["bleu"]
if bleu > best_bleu:
best_bleu, best_ckpt = bleu, global_step
save(config.best_ckpt, {
"best_bleu": str(best_bleu),
"best_ckpt": str(best_ckpt)
}, config.best_ckpt)
def get_vocab(config):
print("Get the vocabulary...")
word_counter, char_counter = Counter(), Counter()
pos_counter, ner_counter, label_counter = Counter(), Counter(), Counter()
files = [(config.train_para_file, config.train_question_file),
(config.dev_para_file, config.dev_question_file)]
for para_file, que_file in files:
with open("{}.tok".format(para_file), 'r') as fp, open("{}.tok".format(que_file), 'r') as fq, \
open("{}.pos".format(para_file), 'r') as fpp, open("{}.pos".format(que_file), 'r') as fqp, \
open("{}.ner".format(para_file), 'r') as fpn, open("{}.ner".format(que_file), 'r') as fqn, \
open("{}.label".format(para_file), 'r') as fpl:
while True:
para, question = fp.readline(), fq.readline()
pos, que_pos = fpp.readline(), fqp.readline()
ner, que_ner = fpn.readline(), fqn.readline()
label = fpl.readline()
if not question or not para:
break
if config.lower_word:
para = para.lower()
question = question.lower()
para_tokens = para.strip().split(' ')
que_tokens = question.strip().split(' ')
pos_tags = pos.strip().split(' ')
ner_tags = ner.strip().split(' ')
que_pos_tags = que_pos.strip().split(' ')
que_ner_tags = que_ner.strip().split(' ')
labels = label.strip().split(' ')
for token in para_tokens + que_tokens:
word_counter[token] += 1
for char in list(token):
char_counter[char] += 1
for pos_tag in pos_tags + que_pos_tags:
pos_counter[pos_tag] += 1
for ner_tag in ner_tags + que_ner_tags:
ner_counter[ner_tag] += 1
for label in labels:
label_counter[label] += 1
word_emb_mat, word2idx_dict, unk_num = get_word_embedding(
word_counter,
emb_file=config.glove_word_file,
emb_size=config.glove_word_size,
vocab_size=config.vocab_size_limit,
vec_size=config.glove_dim,
vocab_file=config.vocab_file)
char_emb_mat, char2idx_dict = get_tag_embedding(char_counter,
"char",
vec_size=config.char_dim)
pos_emb_mat, pos2idx_dict = get_tag_embedding(pos_counter,
"pos",
vec_size=config.pos_dim)
ner_emb_mat, ner2idx_dict = get_tag_embedding(ner_counter,
"ner",
vec_size=config.ner_dim)
label_emb_mat, label2idx_dict = get_tag_embedding(
label_counter, "label", vec_size=config.label_dim)
print("{} out of {} are not in glove".format(unk_num, len(word2idx_dict)))
print("{} chars".format(char_emb_mat.shape[0]))
print("{} pos tags, {} ner tags, {} answer labels, {} chars".format(
pos_emb_mat.shape[0], ner_emb_mat.shape[0], label_emb_mat.shape[0],
char_emb_mat.shape[0]))
save(config.word_emb_file, word_emb_mat, message="word embedding")
save(config.char_emb_file, char_emb_mat, message="char embedding")
save(config.pos_emb_file, pos_emb_mat, message="pos embedding")
save(config.ner_emb_file, ner_emb_mat, message="ner embedding")
save(config.label_emb_file, label_emb_mat, message="label embedding")
save(config.word_dictionary, word2idx_dict, message="word dictionary")
save(config.char_dictionary, char2idx_dict, message="char dictionary")
save(config.pos_dictionary, pos2idx_dict, message="pos dictionary")
save(config.ner_dictionary, ner2idx_dict, message="ner dictionary")
save(config.label_dictionary, label2idx_dict, message="label dictionary")
print("Dump elmo word embedding...")
token_embedding_file = config.embedding_file
dump_token_embeddings(config.vocab_file, config.elmo_options_file,
config.elmo_weight_file, token_embedding_file)
def prepare(config):
# process files
train_examples, train_eval = process_file(config.train_para_file,
config.train_question_file,
config.train_answer_file,
lower_word=config.lower_word)
dev_examples, dev_eval = process_file(config.dev_para_file,
config.dev_question_file,
config.dev_answer_file,
lower_word=config.lower_word)
test_examples, test_eval = process_file(config.test_para_file,
config.test_question_file,
config.test_answer_file,
lower_word=config.lower_word)
with open(config.word_dictionary, "r") as fh:
word2idx_dict = json.load(fh)
with open(config.char_dictionary, "r") as fh:
char2idx_dict = json.load(fh)
with open(config.label_dictionary, "r") as fh:
label2idx_dict = json.load(fh)
with open(config.pos_dictionary, "r") as fh:
pos2idx_dict = json.load(fh)
with open(config.ner_dictionary, "r") as fh:
ner2idx_dict = json.load(fh)
train_meta = build_features(config, train_examples, "train",
config.train_record_file, word2idx_dict,
pos2idx_dict, ner2idx_dict, label2idx_dict,
char2idx_dict)
dev_meta = build_features(config, dev_examples, "dev",
config.dev_record_file, word2idx_dict,
pos2idx_dict, ner2idx_dict, label2idx_dict,
char2idx_dict)
test_meta = build_features(config,
test_examples,
"test",
config.test_record_file,
word2idx_dict,
pos2idx_dict,
ner2idx_dict,
label2idx_dict,
char2idx_dict,
is_test=True)
save(config.train_eval_file, train_eval, message="train eval")
save(config.dev_eval_file, dev_eval, message="dev eval")
save(config.test_eval_file, test_eval, message="test eval")
save(config.train_meta, train_meta, message="train meta")
save(config.dev_meta, dev_meta, message="dev meta")
save(config.test_meta, test_meta, message="test meta")
def train(config):
with open(config.train_eval_file, "r") as fh:
train_eval_file = json.load(fh)
with open(config.dev_eval_file, "r") as fh:
dev_eval_file = json.load(fh)
with open(config.dev_meta, "r") as fh:
meta = json.load(fh)
dev_total = meta["total"]
print("Building model...")
best_acc, best_ckpt = 0., 0
parser = get_record_parser_qqp(config)
graph = tf.Graph()
with graph.as_default() as g:
train_dataset = get_batch_dataset(config.train_record_file, parser,
config.batch_size)
dev_dataset = get_dataset(config.dev_record_file, parser,
config.batch_size)
train_iterator = train_dataset.make_one_shot_iterator()
dev_iterator = dev_dataset.make_one_shot_iterator()
model = QPCModel(config, graph=g)
sess_config = tf.ConfigProto(allow_soft_placement=True)
sess_config.gpu_options.allow_growth = True
with tf.Session(config=sess_config) as sess:
writer = tf.summary.FileWriter(config.output_dir)
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(max_to_keep=1000)
if os.path.exists(os.path.join(config.output_dir, "checkpoint")):
saver.restore(sess,
tf.train.latest_checkpoint(config.output_dir))
if os.path.exists(config.best_ckpt):
with open(config.best_ckpt, "r") as fh:
best_qqp_ckpt = json.load(fh)
best_acc, best_ckpt = float(
best_qqp_ckpt["best_acc"]), int(
best_qqp_ckpt["best_ckpt"])
global_step = max(sess.run(model.global_step), 1)
train_next_element = train_iterator.get_next()
for _ in tqdm(range(global_step, config.num_steps + 1)):
global_step = sess.run(model.global_step) + 1
que1, que2, label, qa_id = sess.run(train_next_element)
loss, pred_label, _ = sess.run(
[model.loss, model.pred_label, model.train_op],
feed_dict={
model.que1: que1,
model.que2: que2,
model.label: label,
model.dropout: config.dropout,
model.qa_id: qa_id,
})
if global_step % config.period == 0:
loss_sum = tf.Summary(value=[
tf.Summary.Value(tag="model/loss", simple_value=loss),
])
writer.add_summary(loss_sum, global_step)
if global_step % config.checkpoint == 0:
filename = os.path.join(
config.output_dir, "model_{}.ckpt".format(global_step))
saver.save(sess, filename)
metrics = evaluate_batch(config, model,
config.val_num_batches,
train_eval_file, sess,
train_iterator)
write_metrics(metrics, writer, global_step, "train")
metrics = evaluate_batch(
config, model, dev_total // config.batch_size + 1,
dev_eval_file, sess, dev_iterator)
write_metrics(metrics, writer, global_step, "dev")
acc = metrics["accuracy"]
if acc > best_acc:
best_acc, best_ckpt = acc, global_step
save(config.best_ckpt, {
"best_acc": str(acc),
"best_ckpt": str(best_ckpt)
}, config.best_ckpt)
def train_for_qg(config):
with open(config.word_emb_file, "r") as fh:
word_mat = np.array(json.load(fh), dtype=np.float32)
with open(config.char_emb_file, "r") as fh:
char_mat = np.array(json.load(fh), dtype=np.float32)
with open(config.train_eval_file, "r") as fh:
train_eval_file = json.load(fh)
with open(config.dev_eval_file, "r") as fh:
dev_eval_file = json.load(fh)
with open(config.dev_meta, "r") as fh:
meta = json.load(fh)
dev_total = meta["total"]
best_em, best_ckpt = 0., 0
print("Building model...")
parser = get_record_parser_qg(config)
graph = tf.Graph()
with graph.as_default() as g:
train_record_file = config.train_record_file
train_dataset = get_batch_dataset(train_record_file, parser, config.batch_size)
dev_dataset = get_dataset(config.dev_record_file, parser, config.batch_size)
train_iterator = train_dataset.make_one_shot_iterator()
dev_iterator = dev_dataset.make_one_shot_iterator()
model = BidafQA(config, word_mat, char_mat)
sess_config = tf.ConfigProto(allow_soft_placement=True)
sess_config.gpu_options.allow_growth = True
with tf.Session(config=sess_config) as sess:
# build graph
model.build_graph()
# add training operation
model.add_train_op()
writer = tf.summary.FileWriter(config.output_dir)
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(max_to_keep=1000,
var_list=[p for p in tf.global_variables() if "word_mat" not in p.name])
if os.path.exists(os.path.join(config.output_dir, "checkpoint")):
saver.restore(sess, tf.train.latest_checkpoint(config.output_dir))
if os.path.exists(config.best_ckpt):
with open(config.best_ckpt, "r") as fh:
best_qa_ckpt = json.load(fh)
best_em, best_ckpt = float(best_qa_ckpt["best_em"]), int(best_qa_ckpt["best_ckpt"])
global_step = max(sess.run(model.global_step), 1)
train_next_element = train_iterator.get_next()
for _ in tqdm(range(global_step, config.num_steps + 1)):
global_step = sess.run(model.global_step) + 1
_, para, para_char, _, que, que_char, _, _, _, _, _, _, y1, y2, qa_id = sess.run(train_next_element)
loss, _ = sess.run([model.loss, model.train_op], feed_dict={
model.para: para, model.que: que, model.para_char: para_char, model.que_char: que_char,
model.y1: y1, model.y2: y2, model.dropout: config.dropout, model.qa_id: qa_id,
})
if global_step % config.period == 0:
loss_sum = tf.Summary(value=[tf.Summary.Value(
tag="model/loss", simple_value=loss), ])
writer.add_summary(loss_sum, global_step)
if global_step % config.checkpoint == 0:
filename = os.path.join(
config.output_dir, "model_{}.ckpt".format(global_step))
saver.save(sess, filename)
metrics = evaluate_batch_qa_for_qg(model, config.val_num_batches,
train_eval_file, sess, train_iterator)
write_metrics(metrics, writer, global_step, "train")
metrics = evaluate_batch_qa_for_qg(model, dev_total // config.batch_size + 1,
dev_eval_file, sess, dev_iterator)
write_metrics(metrics, writer, global_step, "dev")
em = metrics["em"]
if em > best_em:
best_em, best_ckpt = em, global_step
save(config.best_ckpt, {"best_em": str(em), "best_ckpt": str(best_ckpt)},
config.best_ckpt)
