def interactive(self):
from prompt_toolkit import prompt
from prompt_toolkit.history import FileHistory
from util.nlp import NLPToolkit
nlp_toolkit = NLPToolkit()
infer_model = vanilla_helper.create_infer_model(self.config)
config_proto = model_helper.get_config_proto(self.config.log_device)
with tf.Session(graph=infer_model.graph, config=config_proto) as sess:
ckpt = tf.train.latest_checkpoint(self.config.model_dir)
loaded_infer_model = model_helper.load_model(
infer_model.model, ckpt, sess, "infer")
log.print_out("# Start decoding")
sentence = prompt(">>> ",
history=FileHistory(
os.path.join(self.config.model_dir,
".chat_history"))).strip()
while sentence:
utterance = ' '.join(nlp_toolkit.tokenize(sentence)).lower()
iterator_feed_dict = {
infer_model.src_placeholder: [utterance],
infer_model.batch_size_placeholder: 1,
}
sess.run(infer_model.iterator.initializer,
feed_dict=iterator_feed_dict)
output, _ = loaded_infer_model.decode(sess)
if self.config.beam_width > 0:
# get the top translation.
output = output[0]
resp = ncm_utils.get_translation(output, sent_id=0)
log.print_out(resp + b"\n")
sentence = prompt(">>> ",
history=FileHistory(
os.path.join(self.config.model_dir,
".chat_history"))).strip()
def train(self, target_session="", scope=None):
assert self.config.num_turns >= 2
if self.config.is_pretrain_enabled():
assert self.config.num_pretrain_turns >= 2
assert self.config.num_turns >= self.config.num_pretrain_turns
out_dir = self.config.model_dir
steps_per_stats = self.config.steps_per_stats
steps_per_eval = 20 * steps_per_stats
_helper = self._get_model_helper()
self._pre_model_creation()
train_model = _helper.create_train_model(self.config, scope)
eval_model = _helper.create_eval_model(self.config, scope)
infer_model = _helper.create_infer_model(self.config, scope)
self._post_model_creation(train_model, eval_model, infer_model)
# Preload data for sample decoding.
dev_file = self.config.dev_data
eval_data = self._load_data(dev_file, include_target=True)
summary_name = "train_log"
# Log and output files
log_file = os.path.join(out_dir, "log_%d" % time.time())
log_f = tf.gfile.GFile(log_file, mode="a")
log.print_out("# log_file=%s" % log_file, log_f)
self.config.save()
log.print_out("# Configs saved")
avg_step_time = 0.0
# TensorFlow model
config_proto = model_helper.get_config_proto(self.config.log_device)
train_sess = tf.Session(
target=target_session, config=config_proto, graph=train_model.graph)
eval_sess = tf.Session(
target=target_session, config=config_proto, graph=eval_model.graph)
infer_sess = tf.Session(
target=target_session, config=config_proto, graph=infer_model.graph)
# Pretraining
num_pretrain_steps = 0
if self.config.is_pretrain_enabled():
num_pretrain_steps = self.config.num_pretrain_steps
pretrain_model = _helper.create_pretrain_model(self.config, scope)
with tf.Session(
target=target_session, config=config_proto, graph=pretrain_model.graph) as pretrain_sess:
self.pretrain(pretrain_sess, pretrain_model, log_f)
with train_model.graph.as_default():
loaded_train_model, global_step = model_helper.create_or_load_model(
train_model.model, self.config.model_dir, train_sess, "train")
# Summary writer
summary_writer = tf.summary.FileWriter(
os.path.join(out_dir, summary_name), train_model.graph)
last_stats_step = global_step
last_eval_step = global_step
patience = self.config.patience
stats = self.init_stats()
speed, train_ppl = 0.0, 0.0
start_train_time = time.time()
log.print_out(
"# Start step %d, epoch %d, lr %g, %s" %
(global_step, self.config.epoch, loaded_train_model.learning_rate.eval(session=train_sess),
time.ctime()),
log_f)
# Initialize all of the iterators
skip_count = self.config.batch_size * self.config.epoch_step
log.print_out("# Init train iterator for %d steps, skipping %d elements" %
(self.config.num_train_steps, skip_count))
train_sess.run(
train_model.iterator.initializer,
feed_dict={train_model.skip_count_placeholder: skip_count})
while self.config.epoch < self.config.num_train_epochs and patience > 0:
### Run a step ###
start_time = time.time()
try:
step_result = loaded_train_model.train(train_sess)
self.config.epoch_step += 1
except tf.errors.OutOfRangeError:
# Finished going through the training dataset. Go to next epoch.
sw = Stopwatch()
self.run_sample_decode(infer_model, infer_sess,
self.config.model_dir, summary_writer, eval_data)
# if self.config.enable_epoch_evals:
# dev_ppl, test_ppl = self.run_full_eval(infer_model, eval_model,
# infer_sess, eval_sess,
# out_dir,
# fs.file_name(self.config.test_data) + '_' + global_step,
# summary_writer)
# log.print_out(
# "%% done epoch %d #%d step %d - dev_ppl: %.2f test_ppl: %.2f @ eval time: %ds" %
# (self.config.epoch, self.config.epoch_step, global_step, dev_ppl, test_ppl, sw.elapsed()))
# else:
log.print_out(
"## Done epoch %d in %d steps. step %d @ eval time: %ds" %
(self.config.epoch, self.config.epoch_step, global_step, sw.elapsed()))
self.config.epoch += 1
self.config.epoch_step = 0
self.config.save()
train_sess.run(
train_model.iterator.initializer,
feed_dict={train_model.skip_count_placeholder: 0})
continue
# Write step summary and accumulate statistics
global_step = self.update_stats(stats, summary_writer, start_time, step_result)
# Once in a while, we print statistics.
if global_step - last_stats_step >= steps_per_stats:
last_stats_step = global_step
train_ppl, speed, is_overflow = self.check_stats(stats, global_step, steps_per_stats, log_f)
if is_overflow:
break
# Reset statistics
stats = self.init_stats()
if global_step - last_eval_step >= steps_per_eval:
last_eval_step = global_step
log.print_out("# Save eval, global step %d" % global_step)
log.add_summary(summary_writer, global_step, "train_ppl", train_ppl)
# Save checkpoint
loaded_train_model.saver.save(train_sess,
self.config.checkpoint_file,
global_step=global_step)
# Evaluate on dev
self.run_sample_decode(infer_model, infer_sess, out_dir, summary_writer, eval_data)
dev_ppl, _ = self.run_internal_eval(eval_model, eval_sess, out_dir, summary_writer,
use_test_set=False)
if dev_ppl < self.config.best_dev_ppl:
self.config.best_dev_ppl = dev_ppl
patience = self.config.patience
log.print_out(' ** Best model thus far, ep {}|{} dev_ppl {:.3f}'.format(
self.config.epoch,
self.config.epoch_step,
dev_ppl))
elif dev_ppl > self.config.degrade_threshold * self.config.best_dev_ppl:
patience -= 1
log.print_out(
' worsened, ep {}|{} patience {} best_dev_ppl {:.3f}'.format(
self.config.epoch,
self.config.epoch_step,
patience,
self.config.best_dev_ppl))
# Save config parameters
self.config.save()
# Done training
loaded_train_model.saver.save(
train_sess,
self.config.checkpoint_file,
global_step=global_step)
if self.config.enable_final_eval:
dev_ppl, test_ppl = self.run_full_eval(infer_model, eval_model,
infer_sess, eval_sess,
out_dir,
fs.file_name(self.config.test_data) + '_final',
summary_writer)
log.print_out(
"# Final, step %d ep %d/%d lr %g "
"step-time %.2f wps %.2fK train_ppl %.2f, dev_ppl %.2f, test_ppl %.2f, %s" %
(global_step, self.config.epoch, self.config.epoch_step,
loaded_train_model.learning_rate.eval(session=train_sess),
avg_step_time, speed, train_ppl, dev_ppl, test_ppl, time.ctime()),
log_f)
else:
log.print_out(
"# Final, step %d ep %d/%d lr %g "
"step-time %.2f wps %.2fK train_ppl %.2f best_dev_ppl %.2f, %s" %
(global_step, self.config.epoch, self.config.epoch_step,
loaded_train_model.learning_rate.eval(session=train_sess),
avg_step_time, speed, train_ppl, self.config.best_dev_ppl, time.ctime()),
log_f)
log.print_time("# Done training!", start_train_time)
summary_writer.close()
eval_sess.close()
infer_sess.close()
train_sess.close()
def test(self):
start_test_time = time.time()
assert self.config.n_responses >= 1
if self.config.beam_width > 0:
assert self.config.n_responses <= self.config.beam_width
else:
assert self.config.n_responses == 1
self._pre_model_creation()
infer_model = self._get_model_helper().create_infer_model(self.config)
latest_ckpt = tf.train.latest_checkpoint(self.config.get_infer_model_dir())
with tf.Session(
config=model_helper.get_config_proto(self.config.log_device),
graph=infer_model.graph) as infer_sess:
loaded_infer_model = model_helper.load_model(
infer_model.model, latest_ckpt, infer_sess, "infer")
log.print_out("# Start decoding")
log.print_out(" beam width: {}".format(self.config.beam_width))
log.print_out(" length penalty: {}".format(self.config.length_penalty_weight))
log.print_out(" sampling temperature: {}".format(self.config.sampling_temperature))
log.print_out(" num responses per test instance: {}".format(self.config.n_responses))
feed_dict = {
infer_model.src_placeholder: self._load_data(self.config.test_data),
infer_model.batch_size_placeholder: self.config.infer_batch_size,
}
if self.config.sampling_temperature > 0:
label = "%s_t%.1f" % (
fs.file_name(self.config.test_data), self.config.sampling_temperature)
else:
label = "%s_bw%d_lp%.1f" % (
fs.file_name(self.config.test_data), self.config.beam_width, self.config.length_penalty_weight)
self._decode_and_evaluate(loaded_infer_model, infer_sess, feed_dict,
label=label,
num_responses_per_input=self.config.n_responses)
log.print_time("# Decoding done", start_test_time)
eval_model = self._get_model_helper().create_eval_model(self.config)
with tf.Session(
config=model_helper.get_config_proto(self.config.log_device),
graph=eval_model.graph) as eval_sess:
loaded_eval_model = model_helper.load_model(
eval_model.model, latest_ckpt, eval_sess, "eval")
log.print_out("# Compute Perplexity")
dev_eval_iterator_feed_dict = {
eval_model.eval_file_placeholder: self.config.test_data
}
eval_sess.run(eval_model.iterator.initializer, feed_dict=dev_eval_iterator_feed_dict)
model_helper.compute_perplexity(loaded_eval_model, eval_sess, "test")
log.print_time("# Test finished", start_test_time)
def interactive(self, scope=None):
import platform
from prompt_toolkit import prompt
from prompt_toolkit.history import FileHistory
from topic_model.lda import TopicInferer, DEFAULT_SEPARATOR
from util.nlp import NLPToolkit
nlp_toolkit = NLPToolkit()
__os = platform.system()
self.config.infer_batch_size = 1
self._pre_model_creation()
if self.config.lda_model_dir is not None:
topic_inferer = TopicInferer(self.config.lda_model_dir)
else:
topic_inferer = None
infer_model = self._get_model_helper().create_infer_model(self.config, scope)
with tf.Session(
config=model_helper.get_config_proto(self.config.log_device), graph=infer_model.graph) as sess:
latest_ckpt = tf.train.latest_checkpoint(self.config.model_dir)
loaded_infer_model = model_helper.load_model(
infer_model.model, latest_ckpt, sess, "infer")
log.print_out("# Start decoding")
if __os == 'Windows':
sentence = input("> ").strip()
else:
sentence = prompt(">>> ",
history=FileHistory(os.path.join(self.config.model_dir, ".chat_history"))).strip()
conversation = [vocab.EOS] * (self.config.num_turns - 1)
while sentence:
current_utterance = ' '.join(nlp_toolkit.tokenize(sentence)).lower()
conversation.append(current_utterance)
conversation.pop(0)
feedable_context = "\t".join(conversation)
if topic_inferer is None:
iterator_feed_dict = {
infer_model.src_placeholder: [feedable_context],
infer_model.batch_size_placeholder: 1,
}
else:
topic_words = topic_inferer.from_collection([feedable_context],
dialogue_as_doc=True,
words_per_topic=self.config.topic_words_per_utterance)
iterator_feed_dict = {
infer_model.src_placeholder:
[feedable_context + DEFAULT_SEPARATOR + " ".join(topic_words)],
infer_model.batch_size_placeholder: 1,
}
sess.run(infer_model.iterator.initializer, feed_dict=iterator_feed_dict)
output, infer_summary = loaded_infer_model.decode(sess)
if self.config.beam_width > 0 and self._consider_beam():
# get the top translation.
output = output[0]
resp = ncm_utils.get_translation(output, sent_id=0)
log.print_out(resp + b"\n")
if __os == 'Windows':
sentence = input("> ").strip()
else:
sentence = prompt(">>> ",
history=FileHistory(os.path.join(self.config.model_dir, ".chat_history"))).strip()
print("Bye!!!")
print('init config params')
args = vars()
args['mode'] = 'infer'
args['model_dir'] = 'service_model'
args['beam_width'] = 5
args['length_penalty_weight'] = 0.8
config = Config(**args)
stop_words = load_stop_words(config.model_dir)
word2label = load_keyword_label(config.model_dir)
label2topn = load_top_words(config.model_dir)
model = model_factory.create_model(config)
infer_model = model.create_infer_model_graph()
config_proto = model_helper.get_config_proto(config.log_device)
sess = tf.InteractiveSession(graph=infer_model.graph, config=config_proto)
ckpt = tf.train.latest_checkpoint(config.model_dir)
loaded_infer_model = model_helper.load_model(infer_model.model, ckpt, sess, "infer")
def extract_keyword(query):
tags = jieba.analyse.extract_tags(query, topK=10)
keyword = []
for w in tags:
if re.search('^\d+$', w):
continue
if re.search('^[a-zA-Z]+$', w):
continue
def test(self):
start_test_time = time.time()
assert self.config.n_responses >= 1
if self.config.beam_width > 0:
assert self.config.n_responses <= self.config.beam_width
else:
assert self.config.n_responses == 1
self._pre_model_creation()
infer_model = vanilla_helper.create_infer_model(self.config)
config_proto = model_helper.get_config_proto(self.config.log_device)
ckpt = tf.train.latest_checkpoint(self.config.get_infer_model_dir())
with tf.Session(graph=infer_model.graph,
config=config_proto) as infer_sess:
loaded_infer_model = model_helper.load_model(
infer_model.model, ckpt, infer_sess, "infer")
log.print_out("# Start decoding")
log.print_out(" beam width: {}".format(self.config.beam_width))
log.print_out(" length penalty: {}".format(
self.config.length_penalty_weight))
log.print_out(" sampling temperature: {}".format(
self.config.sampling_temperature))
log.print_out(" num responses per test instance: {}".format(
self.config.n_responses))
feed_dict = {
infer_model.src_placeholder:
self._load_data(self.config.test_data),
infer_model.batch_size_placeholder:
self.config.infer_batch_size,
}
infer_sess.run(infer_model.iterator.initializer,
feed_dict=feed_dict)
if self.config.sampling_temperature > 0:
label = "%s_t%.1f" % (fs.file_name(
self.config.test_data), self.config.sampling_temperature)
else:
label = "%s_bw%d_lp%.1f" % (fs.file_name(
self.config.test_data), self.config.beam_width,
self.config.length_penalty_weight)
out_file = os.path.join(self.config.model_dir,
"output_{}".format(label))
eval_metric.decode_and_evaluate(
"test",
loaded_infer_model,
infer_sess,
out_file,
ref_file=None,
metrics=self.config.metrics,
beam_width=self.config.beam_width,
num_translations_per_input=self.config.n_responses)
log.print_time("# Decoding done", start_test_time)
eval_model = vanilla_helper.create_eval_model(
vanilla_model.VanillaSeq2SeqModel, self.config)
with tf.Session(config=model_helper.get_config_proto(
self.config.log_device),
graph=eval_model.graph) as eval_sess:
loaded_eval_model = model_helper.load_model(
eval_model.model, ckpt, eval_sess, "eval")
log.print_out("# Compute Perplexity")
feed_dict = {
eval_model.eval_file_placeholder: self.config.test_data
}
eval_sess.run(eval_model.iterator.initializer, feed_dict=feed_dict)
model_helper.compute_perplexity(loaded_eval_model, eval_sess,
"test")
log.print_time("# Test finished", start_test_time)
def train(self, target_session="", scope=None):
out_dir = self.config.model_dir
model_dir = out_dir
num_train_steps = self.config.num_train_steps
steps_per_stats = self.config.steps_per_stats
# steps_per_external_eval = self.config.steps_per_external_eval
steps_per_eval = 40 * steps_per_stats
# if not steps_per_external_eval:
# steps_per_external_eval = 5 * steps_per_eval
self._pre_model_creation()
train_model = vanilla_helper.create_train_model(
vanilla_model.VanillaSeq2SeqModel, self.config, scope)
eval_model = vanilla_helper.create_eval_model(
vanilla_model.VanillaSeq2SeqModel, self.config, scope)
infer_model = vanilla_helper.create_infer_model(self.config, scope)
# Preload data for sample decoding.
dev_file = self.config.dev_data
eval_data = self._load_data(dev_file, include_target=True)
summary_name = "train_log"
# Log and output files
log_file = os.path.join(out_dir, "log_%d" % time.time())
log_f = tf.gfile.GFile(log_file, mode="a")
log.print_out("# log_file=%s" % log_file, log_f)
avg_step_time = 0.0
# TensorFlow model
config_proto = model_helper.get_config_proto(self.config.log_device)
train_sess = tf.Session(target=target_session,
config=config_proto,
graph=train_model.graph)
eval_sess = tf.Session(target=target_session,
config=config_proto,
graph=eval_model.graph)
infer_sess = tf.Session(target=target_session,
config=config_proto,
graph=infer_model.graph)
with train_model.graph.as_default():
loaded_train_model, global_step = model_helper.create_or_load_model(
train_model.model, model_dir, train_sess, "train")
# Summary writer
summary_writer = tf.summary.FileWriter(
os.path.join(out_dir, summary_name), train_model.graph)
# First evaluation
# self.run_full_eval(
# model_dir, infer_model, infer_sess,
# eval_model, eval_sess, summary_writer, eval_data)
last_stats_step = global_step
last_eval_step = global_step
# last_external_eval_step = global_step
patience = self.config.patience
# This is the training loop.
stats = self.init_stats()
speed, train_ppl = 0.0, 0.0
start_train_time = time.time()
log.print_out(
"# Start step %d, epoch %d, lr %g, %s" %
(global_step, self.config.epoch,
loaded_train_model.learning_rate.eval(session=train_sess),
time.ctime()), log_f)
self.config.save()
log.print_out("# Configs saved")
# Initialize all of the iterators
skip_count = self.config.batch_size * self.config.epoch_step
log.print_out(
"# Init train iterator for %d steps, skipping %d elements" %
(self.config.num_train_steps, skip_count))
train_sess.run(
train_model.iterator.initializer,
feed_dict={train_model.skip_count_placeholder: skip_count})
while self.config.epoch < self.config.num_train_epochs and patience > 0:
### Run a step ###
start_time = time.time()
try:
step_result = loaded_train_model.train(train_sess)
self.config.epoch_step += 1
except tf.errors.OutOfRangeError:
# Finished going through the training dataset. Go to next epoch.
sw = Stopwatch()
self.run_sample_decode(infer_model, infer_sess, model_dir,
summary_writer, eval_data)
log.print_out(
"## Done epoch %d in %d steps. step %d @ eval time: %ds" %
(self.config.epoch, self.config.epoch_step, global_step,
sw.elapsed()))
self.config.epoch += 1
self.config.epoch_step = 0
self.config.save()
# dev_scores, test_scores, _ = self.run_external_eval(infer_model, infer_sess, model_dir, summary_writer)
train_sess.run(
train_model.iterator.initializer,
feed_dict={train_model.skip_count_placeholder: 0})
continue
# Write step summary and accumulate statistics
global_step = self.update_stats(stats, summary_writer, start_time,
step_result)
# Once in a while, we print statistics.
if global_step - last_stats_step >= steps_per_stats:
last_stats_step = global_step
train_ppl, speed, is_overflow = self.check_stats(
stats, global_step, steps_per_stats, log_f)
if is_overflow:
break
# Reset statistics
stats = self.init_stats()
if global_step - last_eval_step >= steps_per_eval:
last_eval_step = global_step
log.print_out("# Save eval, global step %d" % global_step)
log.add_summary(summary_writer, global_step, "train_ppl",
train_ppl)
# Save checkpoint
loaded_train_model.saver.save(train_sess,
os.path.join(
out_dir, "vanilla.ckpt"),
global_step=global_step)
# Evaluate on dev
self.run_sample_decode(infer_model, infer_sess, model_dir,
summary_writer, eval_data)
dev_ppl, _ = self.run_internal_eval(eval_model,
eval_sess,
model_dir,
summary_writer,
use_test_set=False)
if dev_ppl < self.config.best_dev_ppl:
self.config.best_dev_ppl = dev_ppl
patience = self.config.patience
log.print_out(
' ** Best model thus far, ep {}|{} dev_ppl {:.3f}'.
format(self.config.epoch, self.config.epoch_step,
dev_ppl))
elif dev_ppl > self.config.degrade_threshold * self.config.best_dev_ppl:
patience -= 1
log.print_out(
' worsened, ep {}|{} patience {} best_dev_ppl {:.3f}'
.format(self.config.epoch, self.config.epoch_step,
patience, self.config.best_dev_ppl))
# Save config parameters
self.config.save()
# if global_step - last_external_eval_step >= steps_per_external_eval:
# last_external_eval_step = global_step
#
# # Save checkpoint
# loaded_train_model.saver.save(
# train_sess,
# os.path.join(out_dir, "vanilla.ckpt"),
# global_step=global_step)
# self.run_sample_decode(infer_model, infer_sess,
# model_dir, summary_writer, eval_data)
# dev_scores, test_scores, _ = self.run_external_eval(infer_model, infer_sess, model_dir, summary_writer)
# Done training
loaded_train_model.saver.save(train_sess,
os.path.join(out_dir, "vanilla.ckpt"),
global_step=global_step)
# result_summary, _, dev_scores, test_scores, dev_ppl, test_ppl = self.run_full_eval(
# model_dir, infer_model, infer_sess,
# eval_model, eval_sess,
# summary_writer, eval_data)
dev_scores, test_scores, dev_ppl, test_ppl = None, None, None, None
result_summary = ""
log.print_out(
"# Final, step %d lr %g "
"step-time %.2f wps %.2fK ppl %.2f, %s, %s" %
(global_step,
loaded_train_model.learning_rate.eval(session=train_sess),
avg_step_time, speed, train_ppl, result_summary, time.ctime()),
log_f)
log.print_time("# Done training!", start_train_time)
summary_writer.close()
# log.print_out("# Start evaluating saved best models.")
# for metric in self.config.metrics:
# best_model_dir = getattr(self.config, "best_" + metric + "_dir")
# summary_writer = tf.summary.FileWriter(
# os.path.join(best_model_dir, summary_name), infer_model.graph)
# result_summary, best_global_step, _, _, _, _ = self.run_full_eval(
# best_model_dir, infer_model, infer_sess, eval_model, eval_sess,
# summary_writer, eval_data)
# log.print_out("# Best %s, step %d "
# "step-time %.2f wps %.2fK, %s, %s" %
# (metric, best_global_step, avg_step_time, speed,
# result_summary, time.ctime()), log_f)
# summary_writer.close()
return (dev_scores, test_scores, dev_ppl, test_ppl, global_step)