def joey_translate(self, message_text, model, src_vocab, trg_vocab, logger,
beam_size, beam_alpha, level, lowercase,
max_output_length, use_cuda, nbest, cuda_device):
sentence = message_text.strip()
if lowercase:
sentence = sentence.lower()
# load the data which consists only of this sentence
test_data, src_vocab, trg_vocab = self.load_line_as_data(
lowercase=lowercase,
line=sentence,
src_vocab=src_vocab,
trg_vocab=trg_vocab,
level=level)
# generate outputs
score, loss, ppl, sources, sources_raw, references, hypotheses, \
hypotheses_raw, attention_scores = validate_on_data(
model=model, data=test_data, batch_size=1, level=level,
max_output_length=max_output_length, eval_metric=None,
use_cuda=use_cuda, beam_size=beam_size,
beam_alpha=beam_alpha, n_best=nbest, cuda_device=cuda_device, n_gpu=1)
return hypotheses[0] if nbest == 1 else hypotheses
def translate(message_text, model, src_vocab, trg_vocab, preprocess,
postprocess, logger, beam_size, beam_alpha, level, lowercase,
max_output_length, use_cuda):
"""
Describes how to translate a text message.
:param message_text: Slack command, could be text.
:param model: The Joey NMT model.
:param src_vocab: Source vocabulary.
:param trg_vocab: Target vocabulary.
:param preprocess: Preprocessing pipeline (a list).
:param postprocess: Postprocessing pipeline (a list).
:param beam_size: Beam size for decoding.
:param beam_alpha: Beam alpha for decoding.
:param level: Segmentation level.
:param lowercase: Lowercasing.
:param max_output_length: Maximum output length.
:param use_cuda: Using CUDA or not.
:return:
"""
sentence = message_text.strip()
# remove emojis
emoji_pattern = re.compile("\:[a-zA-Z]+\:")
sentence = re.sub(emoji_pattern, "", sentence)
sentence = sentence.strip()
if lowercase:
sentence = sentence.lower()
for p in preprocess:
sentence = p(sentence)
# load the data which consists only of this sentence
test_data, src_vocab, trg_vocab = load_line_as_data(lowercase=lowercase,
line=sentence,
src_vocab=src_vocab,
trg_vocab=trg_vocab,
level=level)
# generate outputs
score, loss, ppl, sources, sources_raw, references, hypotheses, \
hypotheses_raw, attention_scores = validate_on_data(
model, data=test_data, batch_size=1, level=level,
max_output_length=max_output_length, eval_metric=None,
use_cuda=use_cuda, loss_function=None, beam_size=beam_size,
beam_alpha=beam_alpha, logger=logger)
# post-process
if level == "char":
response = "".join(hypotheses)
else:
response = " ".join(hypotheses)
for p in postprocess:
response = p(response)
return response
def _translate(self, n_best):
(batch_size, batch_type, use_cuda, device, n_gpu, level, eval_metric,
max_output_length, beam_size, beam_alpha, postprocess, bpe_type,
sacrebleu, _, _) = self.parsed_cfg
(score, loss, ppl, sources, sources_raw, references, hypotheses,
hypotheses_raw, attention_scores) = validate_on_data(
self.model, data=self.test_data, batch_size=batch_size,
batch_type=batch_type, level=level, use_cuda=use_cuda,
max_output_length=max_output_length, eval_metric=None,
compute_loss=False, beam_size=beam_size, beam_alpha=beam_alpha,
postprocess=postprocess, bpe_type=bpe_type, sacrebleu=sacrebleu,
n_gpu=n_gpu, n_best=n_best)
return sources, hypotheses
def train_and_validate(self, train_data: Dataset, valid_data: Dataset) \
-> None:
"""
Train the model and validate it from time to time on the validation set.
:param train_data: training data
:param valid_data: validation data
"""
train_iter = make_data_iter(train_data,
batch_size=self.batch_size,
batch_type=self.batch_type,
train=True,
shuffle=self.shuffle)
# For last batch in epoch batch_multiplier needs to be adjusted
# to fit the number of leftover training examples
leftover_batch_size = len(train_data) % (self.batch_multiplier *
self.batch_size)
for epoch_no in range(self.epochs):
self.logger.info("EPOCH %d", epoch_no + 1)
if self.scheduler is not None and self.scheduler_step_at == "epoch":
self.scheduler.step(epoch=epoch_no)
self.model.train()
# Reset statistics for each epoch.
start = time.time()
total_valid_duration = 0
start_tokens = self.total_tokens
self.current_batch_multiplier = self.batch_multiplier
self.optimizer.zero_grad()
count = self.current_batch_multiplier - 1
epoch_loss = 0
for i, batch in enumerate(iter(train_iter)):
# reactivate training
self.model.train()
# create a Batch object from torchtext batch
batch = Batch(batch, self.pad_index, use_cuda=self.use_cuda)
# only update every batch_multiplier batches
# see https://medium.com/@davidlmorton/
# increasing-mini-batch-size-without-increasing-
# memory-6794e10db672
# Set current_batch_mutliplier to fit
# number of leftover examples for last batch in epoch
# Only works if batch_type == sentence
if self.batch_type == "sentence":
if self.batch_multiplier > 1 and i == len(train_iter) - \
math.ceil(leftover_batch_size / self.batch_size):
self.current_batch_multiplier = math.ceil(
leftover_batch_size / self.batch_size)
count = self.current_batch_multiplier - 1
update = count == 0
# print(count, update, self.steps)
batch_loss = self._train_batch(batch,
update=update,
count=count)
# Only save finaly computed batch_loss of full batch
if update:
self.tb_writer.add_scalar("train/train_batch_loss",
batch_loss, self.steps)
count = self.batch_multiplier if update else count
count -= 1
# Only add complete batch_loss of full mini-batch to epoch_loss
if update:
epoch_loss += batch_loss.detach().cpu().numpy()
if self.scheduler is not None and \
self.scheduler_step_at == "step" and update:
self.scheduler.step()
# log learning progress
if self.steps % self.logging_freq == 0 and update:
elapsed = time.time() - start - total_valid_duration
elapsed_tokens = self.total_tokens - start_tokens
self.logger.info(
"Epoch %3d Step: %8d Batch Loss: %12.6f "
"Tokens per Sec: %8.0f, Lr: %.6f", epoch_no + 1,
self.steps, batch_loss, elapsed_tokens / elapsed,
self.optimizer.param_groups[0]["lr"])
start = time.time()
total_valid_duration = 0
start_tokens = self.total_tokens
# validate on the entire dev set
if self.steps % self.validation_freq == 0 and update:
valid_start_time = time.time()
valid_score, valid_loss, valid_ppl, valid_sources, \
valid_sources_raw, valid_references, valid_hypotheses, \
valid_hypotheses_raw, valid_attention_scores = \
validate_on_data(
logger=self.logger,
batch_size=self.eval_batch_size,
data=valid_data,
eval_metric=self.eval_metric,
level=self.level, model=self.model,
use_cuda=self.use_cuda,
max_output_length=self.max_output_length,
loss_function=self.loss,
beam_size=1, # greedy validations
batch_type=self.eval_batch_type,
postprocess=True # always remove BPE for validation
)
self.tb_writer.add_scalar("valid/valid_loss", valid_loss,
self.steps)
self.tb_writer.add_scalar("valid/valid_score", valid_score,
self.steps)
self.tb_writer.add_scalar("valid/valid_ppl", valid_ppl,
self.steps)
if self.early_stopping_metric == "loss":
ckpt_score = valid_loss
elif self.early_stopping_metric in ["ppl", "perplexity"]:
ckpt_score = valid_ppl
else:
ckpt_score = valid_score
new_best = False
if self.is_best(ckpt_score):
self.best_ckpt_score = ckpt_score
self.best_ckpt_iteration = self.steps
self.logger.info(
'Hooray! New best validation result [%s]!',
self.early_stopping_metric)
if self.ckpt_queue.maxsize > 0:
self.logger.info("Saving new checkpoint.")
new_best = True
self._save_checkpoint()
if self.scheduler is not None \
and self.scheduler_step_at == "validation":
self.scheduler.step(ckpt_score)
# append to validation report
self._add_report(valid_score=valid_score,
valid_loss=valid_loss,
valid_ppl=valid_ppl,
eval_metric=self.eval_metric,
new_best=new_best)
self._log_examples(
sources_raw=[v for v in valid_sources_raw],
sources=valid_sources,
hypotheses_raw=valid_hypotheses_raw,
hypotheses=valid_hypotheses,
references=valid_references)
valid_duration = time.time() - valid_start_time
total_valid_duration += valid_duration
self.logger.info(
'Validation result (greedy) at epoch %3d, '
'step %8d: %s: %6.2f, loss: %8.4f, ppl: %8.4f, '
'duration: %.4fs', epoch_no + 1, self.steps,
self.eval_metric, valid_score, valid_loss, valid_ppl,
valid_duration)
# store validation set outputs
self._store_outputs(valid_hypotheses)
# store attention plots for selected valid sentences
if valid_attention_scores:
store_attention_plots(
attentions=valid_attention_scores,
targets=valid_hypotheses_raw,
sources=[s for s in valid_data.src],
indices=self.log_valid_sents,
output_prefix="{}/att.{}".format(
self.model_dir, self.steps),
tb_writer=self.tb_writer,
steps=self.steps)
if self.stop:
break
if self.stop:
self.logger.info(
'Training ended since minimum lr %f was reached.',
self.learning_rate_min)
break
self.logger.info('Epoch %3d: total training loss %.2f',
epoch_no + 1, epoch_loss)
else:
self.logger.info('Training ended after %3d epochs.', epoch_no + 1)
self.logger.info(
'Best validation result (greedy) at step '
'%8d: %6.2f %s.', self.best_ckpt_iteration, self.best_ckpt_score,
self.early_stopping_metric)
self.tb_writer.close() # close Tensorboard writer
def translate(self, message_text, model, src_vocab, trg_vocab, preprocess, postprocess,
logger, beam_size, beam_alpha, level, lowercase,
max_output_length, use_cuda):
"""
Describes how to translate a text message.
:param message_text: Slack command, could be text.
:param model: The Joey NMT model.
:param src_vocab: Source vocabulary.
:param trg_vocab: Target vocabulary.
:param preprocess: Preprocessing pipeline (a list).
:param postprocess: Postprocessing pipeline (a list).
:param beam_size: Beam size for decoding.
:param beam_alpha: Beam alpha for decoding.
:param level: Segmentation level.
:param lowercase: Lowercasing.
:param max_output_length: Maximum output length.
:param use_cuda: Using CUDA or not.
:return:
"""
# ipdb.set_trace()
sentence = message_text.strip()
# remove emojis
emoji_pattern = re.compile("\:[a-zA-Z]+\:")
sentence = re.sub(emoji_pattern, "", sentence)
sentence = sentence.strip()
if lowercase:
sentence = sentence.lower()
for p in preprocess:
sentence = p(sentence)
# load the data which consists only of this sentence
test_data, src_vocab, trg_vocab = load_line_as_data(lowercase=lowercase,
line=sentence, src_vocab=src_vocab, trg_vocab=trg_vocab, level=level)
# generate outputs
score, loss, ppl, sources, sources_raw, references, hypotheses, \
hypotheses_raw, attention_scores = validate_on_data(
model, data=test_data, batch_size=1, level=level,
max_output_length=max_output_length, eval_metric=None,
use_cuda=use_cuda, beam_size=beam_size,
beam_alpha=beam_alpha, n_gpu=0)
# validate_on_data(model: Model, data: Dataset,
# batch_size: int,
# use_cuda: bool, max_output_length: int,
# level: str, eval_metric: Optional[str],
# n_gpu: int,
# batch_class: Batch = Batch,
# compute_loss: bool = False,
# beam_size: int = 1, beam_alpha: int = -1,
# batch_type: str = "sentence",
# postprocess: bool = True,
# bpe_type: str = "subword-nmt",
# sacrebleu: dict = None) \
# post-process
if level == "char":
response = "".join(hypotheses)
else:
response = " ".join(hypotheses)
for p in postprocess:
response = p(response)
return response
def train(cfg_file):
"""
Main training function. After training, also test on test data if given.
:param cfg_file:
:return:
"""
cfg = load_config(cfg_file)
# set the random seed
# torch.backends.cudnn.deterministic = True
seed = cfg["training"].get("random_seed", 42)
torch.manual_seed(seed)
np.random.seed(seed)
random.seed(seed)
# load the data
train_data, dev_data, test_data, src_vocab, trg_vocab = \
load_data(cfg=cfg)
# build an encoder-decoder model
model = build_model(cfg["model"], src_vocab=src_vocab, trg_vocab=trg_vocab)
# for training management, e.g. early stopping and model selection
trainer = TrainManager(model=model, config=cfg)
# store copy of original training config in model dir
shutil.copy2(cfg_file, trainer.model_dir + "/config.yaml")
# print config
log_cfg(cfg, trainer.logger)
log_data_info(train_data=train_data,
valid_data=dev_data,
test_data=test_data,
src_vocab=src_vocab,
trg_vocab=trg_vocab,
logging_function=trainer.logger.info)
model.log_parameters_list(logging_function=trainer.logger.info)
logging.info(model)
# store the vocabs
src_vocab_file = "{}/src_vocab.txt".format(cfg["training"]["model_dir"])
src_vocab.to_file(src_vocab_file)
trg_vocab_file = "{}/trg_vocab.txt".format(cfg["training"]["model_dir"])
trg_vocab.to_file(trg_vocab_file)
# train the model
trainer.train_and_validate(train_data=train_data, valid_data=dev_data)
if test_data is not None:
trainer.load_checkpoint("{}/{}.ckpt".format(
trainer.model_dir, trainer.best_ckpt_iteration))
# test model
if "testing" in cfg.keys():
beam_size = cfg["testing"].get("beam_size", 0)
beam_alpha = cfg["testing"].get("alpha", -1)
else:
beam_size = 0
beam_alpha = -1
# pylint: disable=unused-variable
score, loss, ppl, sources, sources_raw, references, hypotheses, \
hypotheses_raw, attention_scores = validate_on_data(
data=test_data, batch_size=trainer.batch_size,
eval_metric=trainer.eval_metric, level=trainer.level,
max_output_length=trainer.max_output_length,
model=model, use_cuda=trainer.use_cuda, criterion=None,
beam_size=beam_size, beam_alpha=beam_alpha)
if "trg" in test_data.fields:
decoding_description = "Greedy decoding" if beam_size == 0 else \
"Beam search decoding with beam size = {} and alpha = {}"\
.format(beam_size, beam_alpha)
trainer.logger.info("Test data result: %f %s [%s]", score,
trainer.eval_metric, decoding_description)
else:
trainer.logger.info(
"No references given for %s.%s -> no evaluation.",
cfg["data"]["test"], cfg["data"]["src"])
output_path_set = "{}/{}.{}".format(trainer.model_dir, "test",
cfg["data"]["trg"])
with open(output_path_set, mode="w", encoding="utf-8") as f:
for h in hypotheses:
f.write(h + "\n")
trainer.logger.info("Test translations saved to: %s", output_path_set)
def train_and_validate(self, train_data, valid_data):
"""
Train the model and validate it from time to time on the validation set.
:param train_data:
:param valid_data:
:return:
"""
train_iter = make_data_iter(train_data,
batch_size=self.batch_size,
train=True,
shuffle=self.shuffle)
for epoch_no in range(self.epochs):
self.logger.info("EPOCH %d", epoch_no + 1)
self.model.train()
start = time.time()
total_valid_duration = 0
processed_tokens = self.total_tokens
count = 0
for batch in iter(train_iter):
# reactivate training
self.model.train()
batch = Batch(batch, self.pad_index, use_cuda=self.use_cuda)
# only update every batch_multiplier batches
# see https://medium.com/@davidlmorton/
# increasing-mini-batch-size-without-increasing-
# memory-6794e10db672
update = count == 0
# print(count, update, self.steps)
batch_loss = self._train_batch(batch, update=update)
count = self.batch_multiplier if update else count
count -= 1
# log learning progress
if self.model.training and self.steps % self.logging_freq == 0 \
and update:
elapsed = time.time() - start - total_valid_duration
elapsed_tokens = self.total_tokens - processed_tokens
self.logger.info(
"Epoch %d Step: %d Loss: %f Tokens per Sec: %f",
epoch_no + 1, self.steps, batch_loss,
elapsed_tokens / elapsed)
start = time.time()
total_valid_duration = 0
# validate on the entire dev set
if self.steps % self.validation_freq == 0 and update:
valid_start_time = time.time()
valid_score, valid_loss, valid_ppl, valid_sources, \
valid_sources_raw, valid_references, valid_hypotheses, \
valid_hypotheses_raw, valid_attention_scores = \
validate_on_data(
batch_size=self.batch_size, data=valid_data,
eval_metric=self.eval_metric,
level=self.level, model=self.model,
use_cuda=self.use_cuda,
max_output_length=self.max_output_length,
criterion=self.criterion)
if self.ckpt_metric == "loss":
ckpt_score = valid_loss
elif self.ckpt_metric in ["ppl", "perplexity"]:
ckpt_score = valid_ppl
else:
ckpt_score = valid_score
new_best = False
if self.is_best(ckpt_score):
self.best_ckpt_score = ckpt_score
self.best_ckpt_iteration = self.steps
self.logger.info(
'Hooray! New best validation result [%s]!',
self.ckpt_metric)
new_best = True
self.save_checkpoint()
# pass validation score or loss or ppl to scheduler
if self.schedule_metric == "loss":
# schedule based on loss
schedule_score = valid_loss
elif self.schedule_metric in ["ppl", "perplexity"]:
# schedule based on perplexity
schedule_score = valid_ppl
else:
# schedule based on evaluation score
schedule_score = valid_score
if self.scheduler is not None:
self.scheduler.step(schedule_score)
# append to validation report
self._add_report(valid_score=valid_score,
valid_loss=valid_loss,
valid_ppl=valid_ppl,
eval_metric=self.eval_metric,
new_best=new_best)
# always print first x sentences
for p in range(self.print_valid_sents):
self.logger.debug("Example #%d", p)
self.logger.debug("\tRaw source: %s",
valid_sources_raw[p])
self.logger.debug("\tSource: %s", valid_sources[p])
self.logger.debug("\tReference: %s",
valid_references[p])
self.logger.debug("\tRaw hypothesis: %s",
valid_hypotheses_raw[p])
self.logger.debug("\tHypothesis: %s",
valid_hypotheses[p])
valid_duration = time.time() - valid_start_time
total_valid_duration += valid_duration
self.logger.info(
'Validation result at epoch %d, step %d: %s: %f, '
'loss: %f, ppl: %f, duration: %.4fs', epoch_no + 1,
self.steps, self.eval_metric, valid_score, valid_loss,
valid_ppl, valid_duration)
# store validation set outputs
self.store_outputs(valid_hypotheses)
# store attention plots for first three sentences of
# valid data and one randomly chosen example
store_attention_plots(attentions=valid_attention_scores,
targets=valid_hypotheses_raw,
sources=[s for s in valid_data.src],
idx=[
0, 1, 2,
np.random.randint(
0, len(valid_hypotheses))
],
output_prefix="{}/att.{}".format(
self.model_dir, self.steps))
if self.stop:
break
if self.stop:
self.logger.info(
'Training ended since minimum lr %f was reached.',
self.learning_rate_min)
break
else:
self.logger.info('Training ended after %d epochs.', epoch_no + 1)
self.logger.info('Best validation result at step %d: %f %s.',
self.best_ckpt_iteration, self.best_ckpt_score,
self.ckpt_metric)
def _validate(self, valid_data, epoch_no):
valid_start_time = time.time()
valid_score, valid_loss, valid_ppl, valid_sources, \
valid_sources_raw, valid_references, valid_hypotheses, \
valid_hypotheses_raw, valid_attention_scores = \
validate_on_data(
batch_size=self.eval_batch_size,
batch_class=self.batch_class,
data=valid_data,
eval_metric=self.eval_metric,
level=self.level, model=self.model,
use_cuda=self.use_cuda,
max_output_length=self.max_output_length,
compute_loss=True,
beam_size=1, # greedy validations
batch_type=self.eval_batch_type,
postprocess=True, # always remove BPE for validation
bpe_type=self.bpe_type, # "subword-nmt" or "sentencepiece"
sacrebleu=self.sacrebleu, # sacrebleu options
n_gpu=self.n_gpu
)
self.tb_writer.add_scalar("valid/valid_loss", valid_loss,
self.stats.steps)
self.tb_writer.add_scalar("valid/valid_score", valid_score,
self.stats.steps)
self.tb_writer.add_scalar("valid/valid_ppl", valid_ppl,
self.stats.steps)
if self.early_stopping_metric == "loss":
ckpt_score = valid_loss
elif self.early_stopping_metric in ["ppl", "perplexity"]:
ckpt_score = valid_ppl
else:
ckpt_score = valid_score
if self.scheduler is not None \
and self.scheduler_step_at == "validation":
self.scheduler.step(ckpt_score)
new_best = False
if self.stats.is_best(ckpt_score):
self.stats.best_ckpt_score = ckpt_score
self.stats.best_ckpt_iter = self.stats.steps
logger.info('Hooray! New best validation result [%s]!',
self.early_stopping_metric)
if self.ckpt_queue.maxlen > 0:
logger.info("Saving new checkpoint.")
new_best = True
self._save_checkpoint(new_best)
elif self.save_latest_checkpoint:
self._save_checkpoint(new_best)
# append to validation report
self._add_report(valid_score=valid_score,
valid_loss=valid_loss,
valid_ppl=valid_ppl,
eval_metric=self.eval_metric,
new_best=new_best)
self._log_examples(sources_raw=[v for v in valid_sources_raw],
sources=valid_sources,
hypotheses_raw=valid_hypotheses_raw,
hypotheses=valid_hypotheses,
references=valid_references)
valid_duration = time.time() - valid_start_time
logger.info(
'Validation result (greedy) at epoch %3d, '
'step %8d: %s: %6.2f, loss: %8.4f, ppl: %8.4f, '
'duration: %.4fs', epoch_no + 1, self.stats.steps,
self.eval_metric, valid_score, valid_loss, valid_ppl,
valid_duration)
# store validation set outputs
self._store_outputs(valid_hypotheses)
# store attention plots for selected valid sentences
if valid_attention_scores:
store_attention_plots(attentions=valid_attention_scores,
targets=valid_hypotheses_raw,
sources=[s for s in valid_data.src],
indices=self.log_valid_sents,
output_prefix="{}/att.{}".format(
self.model_dir, self.stats.steps),
tb_writer=self.tb_writer,
steps=self.stats.steps)
return valid_duration
def Q_learning(cfg_file: str) -> None:
"""
Main training function. After training, also test on test data if given.
:param cfg_file: path to configuration yaml file
"""
cfg = load_config(cfg_file) # config is a dict
# make logger
model_dir = make_model_dir(cfg["training"]["model_dir"],
overwrite=cfg["training"].get(
"overwrite", False))
_ = make_logger(model_dir, mode="train") # version string returned
# TODO: save version number in model checkpoints
# set the random seed
set_seed(seed=cfg["training"].get("random_seed", 42))
# load the data
print("loadding data here")
train_data, dev_data, test_data, src_vocab, trg_vocab = load_data(
data_cfg=cfg["data"])
# The training data is filtered to include sentences up to `max_sent_length`
# on source and target side.
# training config:
train_config = cfg["training"]
shuffle = train_config.get("shuffle", True)
batch_size = train_config["batch_size"]
mini_BATCH_SIZE = train_config["mini_batch_size"]
batch_type = train_config.get("batch_type", "sentence")
outer_epochs = train_config.get("outer_epochs", 10)
inner_epochs = train_config.get("inner_epochs", 10)
TARGET_UPDATE = train_config.get("target_update", 10)
Gamma = train_config.get("Gamma", 0.999)
use_cuda = train_config["use_cuda"] and torch.cuda.is_available()
# validation part config
# validation
validation_freq = train_config.get("validation_freq", 1000)
ckpt_queue = queue.Queue(maxsize=train_config.get("keep_last_ckpts", 5))
eval_batch_size = train_config.get("eval_batch_size", batch_size)
level = cfg["data"]["level"]
eval_metric = train_config.get("eval_metric", "bleu")
n_gpu = torch.cuda.device_count() if use_cuda else 0
eval_batch_type = train_config.get("eval_batch_type", batch_type)
# eval options
test_config = cfg["testing"]
bpe_type = test_config.get("bpe_type", "subword-nmt")
sacrebleu = {"remove_whitespace": True, "tokenize": "13a"}
max_output_length = train_config.get("max_output_length", None)
minimize_metric = True
# initialize training statistics
stats = TrainStatistics(
steps=0,
stop=False,
total_tokens=0,
best_ckpt_iter=0,
best_ckpt_score=np.inf if minimize_metric else -np.inf,
minimize_metric=minimize_metric)
early_stopping_metric = train_config.get("early_stopping_metric",
"eval_metric")
if early_stopping_metric in ["ppl", "loss"]:
stats.minimize_metric = True
stats.best_ckpt_score = np.inf
elif early_stopping_metric == "eval_metric":
if eval_metric in [
"bleu", "chrf", "token_accuracy", "sequence_accuracy"
]:
stats.minimize_metric = False
stats.best_ckpt_score = -np.inf
# eval metric that has to get minimized (not yet implemented)
else:
stats.minimize_metric = True
# data loader(modified from train_and_validate function
# Returns a torchtext iterator for a torchtext dataset.
# param dataset: torchtext dataset containing src and optionally trg
train_iter = make_data_iter(train_data,
batch_size=batch_size,
batch_type=batch_type,
train=True,
shuffle=shuffle)
# initialize the Replay Memory D with capacity N
memory = ReplayMemory(10000)
steps_done = 0
# initialize two DQN networks
policy_net = build_model(cfg["model"],
src_vocab=src_vocab,
trg_vocab=trg_vocab) # Q_network
target_net = build_model(cfg["model"],
src_vocab=src_vocab,
trg_vocab=trg_vocab) # Q_hat_network
#logger.info(policy_net.src_vocab.stoi)
#print("###############trg vocab: ", len(target_net.trg_vocab.stoi))
#print("trg embed: ", target_net.trg_embed.vocab_size)
if use_cuda:
policy_net.cuda()
target_net.cuda()
target_net.load_state_dict(policy_net.state_dict())
# Initialize target net Q_hat with weights equal to policy_net
target_net.eval() # target_net not update the parameters, test mode
# Optimizer
optimizer = build_optimizer(config=cfg["training"],
parameters=policy_net.parameters())
# Loss function
mse_loss = torch.nn.MSELoss()
pad_index = policy_net.pad_index
# print('!!!'*10, pad_index)
cross_entropy_loss = XentLoss(pad_index=pad_index)
policy_net.loss_function = cross_entropy_loss
# learning rate scheduling
scheduler, scheduler_step_at = build_scheduler(
config=train_config,
scheduler_mode="min" if minimize_metric else "max",
optimizer=optimizer,
hidden_size=cfg["model"]["encoder"]["hidden_size"])
# model parameters
if "load_model" in train_config.keys():
load_model_path = train_config["load_model"]
reset_best_ckpt = train_config.get("reset_best_ckpt", False)
reset_scheduler = train_config.get("reset_scheduler", False)
reset_optimizer = train_config.get("reset_optimizer", False)
reset_iter_state = train_config.get("reset_iter_state", False)
print('settings', reset_best_ckpt, reset_iter_state, reset_optimizer,
reset_scheduler)
logger.info("Loading model from %s", load_model_path)
model_checkpoint = load_checkpoint(path=load_model_path,
use_cuda=use_cuda)
# restore model and optimizer parameters
policy_net.load_state_dict(model_checkpoint["model_state"])
if not reset_optimizer:
optimizer.load_state_dict(model_checkpoint["optimizer_state"])
else:
logger.info("Reset optimizer.")
if not reset_scheduler:
if model_checkpoint["scheduler_state"] is not None and \
scheduler is not None:
scheduler.load_state_dict(model_checkpoint["scheduler_state"])
else:
logger.info("Reset scheduler.")
if not reset_best_ckpt:
stats.best_ckpt_score = model_checkpoint["best_ckpt_score"]
stats.best_ckpt_iter = model_checkpoint["best_ckpt_iteration"]
print('stats.best_ckpt_score', stats.best_ckpt_score)
print('stats.best_ckpt_iter', stats.best_ckpt_iter)
else:
logger.info("Reset tracking of the best checkpoint.")
if (not reset_iter_state and model_checkpoint.get(
'train_iter_state', None) is not None):
train_iter_state = model_checkpoint["train_iter_state"]
# move parameters to cuda
target_net.load_state_dict(policy_net.state_dict())
# Initialize target net Q_hat with weights equal to policy_net
target_net.eval()
if use_cuda:
policy_net.cuda()
target_net.cuda()
for i_episode in range(outer_epochs):
# Outer loop
# get batch
for i, batch in enumerate(iter(train_iter)): # joeynmt training.py 377
# create a Batch object from torchtext batch
# ( use class Batch from batch.py)
# return the sentences same length (with padding) in one batch
batch = Batch(batch, policy_net.pad_index, use_cuda=use_cuda)
# we want to get batch.src and batch.trg
# the shape of batch.src: (batch_size * length of the sentence)
# source here is represented by the word index not word embedding.
encoder_output_batch, _, _, _ = policy_net(
return_type="encode",
src=batch.src,
src_length=batch.src_length,
src_mask=batch.src_mask,
)
trans_output_batch, _ = transformer_greedy(
src_mask=batch.src_mask,
max_output_length=max_output_length,
model=policy_net,
encoder_output=encoder_output_batch,
steps_done=steps_done,
use_cuda=use_cuda)
#print('steps_done',steps_done)
steps_done += 1
#print('trans_output_batch.shape is:', trans_output_batch.shape)
# batch_size * max_translation_sentence_length
#print('batch.src', batch.src)
#print('batch.trg', batch.trg)
print('batch.trg.shape is:', batch.trg.shape)
print('trans_output_batch', trans_output_batch)
reward_batch = [
] # Get the reward_batch (Get the bleu score of the sentences in a batch)
for i in range(int(batch.src.shape[0])):
all_outputs = [(trans_output_batch[i])[1:]]
all_ref = [batch.trg[i]]
sentence_score = calculate_bleu(model=policy_net,
level=level,
raw_hypo=all_outputs,
raw_ref=all_ref)
reward_batch.append(sentence_score)
print('reward batch is', reward_batch)
reward_batch = torch.tensor(reward_batch, dtype=torch.float)
# reward_batch = bleu(hypotheses, references, tokenize="13a")
# print('reward_batch.shape', reward_batch.shape)
# make prefix and push tuples into memory
push_sample_to_memory(model=policy_net,
level=level,
eos_index=policy_net.eos_index,
memory=memory,
src_batch=batch.src,
trg_batch=batch.trg,
trans_output_batch=trans_output_batch,
reward_batch=reward_batch,
max_output_length=max_output_length)
print(memory.capacity, len(memory.memory))
if len(memory.memory) == memory.capacity:
# inner loop
for t in range(inner_epochs):
# Sample mini-batch from the memory
transitions = memory.sample(mini_BATCH_SIZE)
# transition = [Transition(source=array([]), prefix=array([]), next_word= int, reward= int),
# Transition(source=array([]), prefix=array([]), next_word= int, reward= int,...]
# Each Transition is what we push into memory for one sentence: memory.push(source, prefix, next_word, reward_batch[i])
mini_batch = Transition(*zip(*transitions))
# merge the same class in transition together
# mini_batch = Transition(source=(array([]), array([]),...), prefix=(array([],...),
# next_word=array([...]), reward=array([...]))
# mini_batch.reward is tuple: length is mini_BATCH_SIZE.
#print('mini_batch', mini_batch)
#concatenate together into a tensor.
words = []
for word in mini_batch.next_word:
new_word = word.unsqueeze(0)
words.append(new_word)
mini_next_word = torch.cat(
words) # shape (mini_BATCH_SIZE,)
mini_reward = torch.tensor(
mini_batch.reward) # shape (mini_BATCH_SIZE,)
#print('mini_batch.finish', mini_batch.finish)
mini_is_eos = torch.Tensor(mini_batch.finish)
#print(mini_is_eos)
mini_src_length = [
len(item) for item in mini_batch.source_sentence
]
mini_src_length = torch.Tensor(mini_src_length)
mini_src = pad_sequence(mini_batch.source_sentence,
batch_first=True,
padding_value=float(pad_index))
# shape (mini_BATCH_SIZE, max_length_src)
length_prefix = [len(item) for item in mini_batch.prefix]
mini_prefix_length = torch.Tensor(length_prefix)
prefix_list = []
for prefix_ in mini_batch.prefix:
prefix_ = torch.from_numpy(prefix_)
prefix_list.append(prefix_)
mini_prefix = pad_sequence(prefix_list,
batch_first=True,
padding_value=pad_index)
# shape (mini_BATCH_SIZE, max_length_prefix)
mini_src_mask = (mini_src != pad_index).unsqueeze(1)
mini_trg_mask = (mini_prefix != pad_index).unsqueeze(1)
#print('mini_src', mini_src)
#print('mini_src_length', mini_src_length)
#print('mini_src_mask', mini_src_mask)
#print('mini_prefix', mini_prefix)
#print('mini_trg_mask', mini_trg_mask)
#print('mini_reward', mini_reward)
# max_length_src = torch.max(mini_src_length) #max([len(item) for item in mini_batch.source_sentence])
if use_cuda:
mini_src = mini_src.cuda()
mini_prefix = mini_prefix.cuda()
mini_src_mask = mini_src_mask.cuda()
mini_src_length = mini_src_length.cuda()
mini_trg_mask = mini_trg_mask.cuda()
mini_next_word = mini_next_word.cuda()
# print(next(policy_net.parameters()).is_cuda)
# print(mini_trg_mask.get_device())
# calculate the Q_value
logits_Q, _, _, _ = policy_net._encode_decode(
src=mini_src,
trg_input=mini_prefix,
src_mask=mini_src_mask,
src_length=mini_src_length,
trg_mask=
mini_trg_mask # trg_mask = (self.trg_input != pad_index).unsqueeze(1)
)
#print('mini_prefix_length', mini_prefix_length)
#print('logits_Q.shape', logits_Q.shape) # torch.Size([64, 99, 31716])
#print('logits_Q', logits_Q)
# length_prefix = max([len(item) for item in mini_batch.prefix])
# logits_Q shape: batch_size * length of the sentence * total number of words in corpus.
logits_Q = logits_Q[range(mini_BATCH_SIZE),
mini_prefix_length.long() - 1, :]
#print('logits_Q_.shape', logits_Q.shape) #shape(mini_batch_size, num_words)
# logits shape: mini_batch_size * total number of words in corpus
Q_value = logits_Q[range(mini_BATCH_SIZE), mini_next_word]
#print('mini_next_word', mini_next_word)
#print("Q_value", Q_value)
mini_prefix_add = torch.cat(
[mini_prefix, mini_next_word.unsqueeze(1)], dim=1)
#print('mini_prefix_add', mini_prefix_add)
mini_trg_mask_add = (mini_prefix_add !=
pad_index).unsqueeze(1)
#print('mini_trg_mask_add', mini_trg_mask_add)
if use_cuda:
mini_prefix_add = mini_prefix_add.cuda()
mini_trg_mask_add = mini_trg_mask_add.cuda()
logits_Q_hat, _, _, _ = target_net._encode_decode(
src=mini_src,
trg_input=mini_prefix_add,
src_mask=mini_src_mask,
src_length=mini_src_length,
trg_mask=mini_trg_mask_add)
#print('mini_prefix_add.shape', mini_prefix_add.shape)
#print('logits_Q_hat.shape', logits_Q_hat.shape)
#print('mini_prefix_length.long()', mini_prefix_length.long())
logits_Q_hat = logits_Q_hat[range(mini_BATCH_SIZE),
mini_prefix_length.long(), :]
Q_hat_value, _ = torch.max(logits_Q_hat, dim=1)
#print('Q_hat_value', Q_hat_value)
if use_cuda:
Q_hat_value = Q_hat_value.cuda()
mini_reward = mini_reward.cuda()
mini_is_eos = mini_is_eos.cuda()
yj = mini_reward.float() + Gamma * Q_hat_value
#print('yj', yj)
index = mini_is_eos.long()
#print('mini_is_eos', mini_is_eos)
yj[index] = mini_reward[index]
#print('yj', yj)
#print('Q_value1', Q_value)
yj.detach()
# Optimize the model
policy_net.zero_grad()
# Compute loss
loss = mse_loss(yj, Q_value)
print('loss', loss)
logger.info("step = {}, loss = {}".format(
stats.steps, loss.item()))
loss.backward()
#for param in policy_net.parameters():
# param.grad.data.clamp_(-1, 1)
optimizer.step()
stats.steps += 1
#print('step', stats.steps)
if stats.steps % TARGET_UPDATE == 0:
#print('update the parameters in target_net.')
target_net.load_state_dict(policy_net.state_dict())
if stats.steps % validation_freq == 0: # Validation
print('Start validation')
valid_score, valid_loss, valid_ppl, valid_sources, \
valid_sources_raw, valid_references, valid_hypotheses, \
valid_hypotheses_raw, valid_attention_scores = \
validate_on_data(
model=policy_net,
data=dev_data,
batch_size=eval_batch_size,
use_cuda=use_cuda,
level=level,
eval_metric=eval_metric,
n_gpu=n_gpu,
compute_loss=True,
beam_size=1,
beam_alpha=-1,
batch_type=eval_batch_type,
postprocess=True,
bpe_type=bpe_type,
sacrebleu=sacrebleu,
max_output_length=max_output_length
)
print(
'validation_loss: {}, validation_score: {}'.format(
valid_loss, valid_score))
logger.info(valid_loss)
print('average loss: total_loss/n_tokens:', valid_ppl)
if early_stopping_metric == "loss":
ckpt_score = valid_loss
elif early_stopping_metric in ["ppl", "perplexity"]:
ckpt_score = valid_ppl
else:
ckpt_score = valid_score
if stats.is_best(ckpt_score):
stats.best_ckpt_score = ckpt_score
stats.best_ckpt_iter = stats.steps
logger.info(
'Hooray! New best validation result [%s]!',
early_stopping_metric)
if ckpt_queue.maxsize > 0:
logger.info("Saving new checkpoint.")
# def _save_checkpoint(self) -> None:
"""
Save the model's current parameters and the training state to a
checkpoint.
The training state contains the total number of training steps,
the total number of training tokens,
the best checkpoint score and iteration so far,
and optimizer and scheduler states.
"""
model_path = "{}/{}.ckpt".format(
model_dir, stats.steps)
model_state_dict = policy_net.module.state_dict() \
if isinstance(policy_net, torch.nn.DataParallel) \
else policy_net.state_dict()
state = {
"steps": stats.steps,
"total_tokens": stats.total_tokens,
"best_ckpt_score": stats.best_ckpt_score,
"best_ckpt_iteration":
stats.best_ckpt_iter,
"model_state": model_state_dict,
"optimizer_state": optimizer.state_dict(),
# "scheduler_state": scheduler.state_dict() if
# self.scheduler is not None else None,
# 'amp_state': amp.state_dict() if self.fp16 else None
}
torch.save(state, model_path)
if ckpt_queue.full():
to_delete = ckpt_queue.get(
) # delete oldest ckpt
try:
os.remove(to_delete)
except FileNotFoundError:
logger.warning(
"Wanted to delete old checkpoint %s but "
"file does not exist.", to_delete)
ckpt_queue.put(model_path)
best_path = "{}/best.ckpt".format(model_dir)
try:
# create/modify symbolic link for best checkpoint
symlink_update(
"{}.ckpt".format(stats.steps),
best_path)
except OSError:
# overwrite best.ckpt
torch.save(state, best_path)
def train_and_validate(self, train_data: Dataset, valid_data: Dataset):
"""
Train the model and validate it on the validation set.
:param train_data: training data
:param valid_data: validation data
"""
train_iter = make_data_iter(
train_data,
batch_size=self.batch_size,
batch_type=self.batch_type,
train=True,
shuffle=self.shuffle)
for epoch_no in range(1, self.epochs + 1):
self.logger.info("EPOCH %d", epoch_no)
if self.scheduler is not None and self.scheduler_step_at == "epoch":
self.scheduler.step(epoch=epoch_no - 1) # 0-based indexing
self.model.train()
start = time.time()
total_valid_duration = 0
processed_tokens = self.total_tokens
epoch_loss = 0
for i, batch in enumerate(iter(train_iter), 1):
# reactivate training
self.model.train()
# create a Batch object from torchtext batch
batch = Batch(batch, self.pad_index, use_cuda=self.use_cuda)
# only update every batch_multiplier batches
# see https://medium.com/@davidlmorton/
# increasing-mini-batch-size-without-increasing-
# memory-6794e10db672
update = i % self.batch_multiplier == 0
batch_loss = self._train_batch(batch, update=update)
self.log_tensorboard("train", batch_loss=batch_loss)
epoch_loss += batch_loss.detach().cpu().numpy()
if self.scheduler is not None and \
self.scheduler_step_at == "step" and update:
self.scheduler.step()
# log learning progress
if self.steps % self.logging_freq == 0 and update:
elapsed = time.time() - start - total_valid_duration
elapsed_tokens = self.total_tokens - processed_tokens
self.logger.info(
"Epoch %3d Step: %8d Batch Loss: %12.6f "
"Tokens per Sec: %8.0f, Lr: %.6f",
epoch_no, self.steps, batch_loss,
elapsed_tokens / elapsed,
self.optimizer.param_groups[0]["lr"])
start = time.time()
total_valid_duration = 0
processed_tokens = self.total_tokens
# validate on the entire dev set
if self.steps % self.validation_freq == 0 and update:
valid_start_time = time.time()
# it would be nice to include loss and ppl in valid_scores
valid_scores, valid_sources, valid_sources_raw, \
valid_references, valid_hypotheses, \
valid_hypotheses_raw, valid_attention_scores, \
scores_by_lang, by_lang = validate_on_data(
batch_size=self.eval_batch_size,
data=valid_data,
eval_metrics=self.eval_metrics,
attn_metrics=self.attn_metrics,
src_level=self.src_level,
trg_level=self.trg_level,
model=self.model,
use_cuda=self.use_cuda,
max_output_length=self.max_output_length,
loss_function=self.loss,
beam_size=0, # greedy validations
batch_type=self.eval_batch_type,
save_attention=self.plot_attention,
log_sparsity=self.log_sparsity,
apply_mask=self.valid_apply_mask
)
ckpt_score = valid_scores[self.early_stopping_metric]
self.log_tensorboard("valid", **valid_scores)
new_best = False
if self.is_best(ckpt_score):
self.best_ckpt_score = ckpt_score
self.best_ckpt_iteration = self.steps
self.logger.info(
'Hooray! New best validation result [%s]!',
self.early_stopping_metric)
if self.ckpt_queue.maxsize > 0:
self.logger.info("Saving new checkpoint.")
new_best = True
self._save_checkpoint()
if self.scheduler is not None \
and self.scheduler_step_at == "validation":
self.scheduler.step(ckpt_score)
# append to validation report
self._add_report(
valid_scores=valid_scores,
eval_metrics=self.eval_metrics,
new_best=new_best)
self._log_examples(
sources_raw=valid_sources_raw,
sources=valid_sources,
hypotheses_raw=valid_hypotheses_raw,
hypotheses=valid_hypotheses,
references=valid_references
)
labeled_scores = sorted(valid_scores.items())
eval_report = ", ".join("{}: {:.5f}".format(n, v)
for n, v in labeled_scores)
valid_duration = time.time() - valid_start_time
total_valid_duration += valid_duration
self.logger.info(
'Validation result at epoch %3d, step %8d: %s, '
'duration: %.4fs',
epoch_no, self.steps, eval_report, valid_duration)
if scores_by_lang is not None:
for metric, scores in scores_by_lang.items():
# make a report
lang_report = [metric]
numbers = sorted(scores.items())
lang_report.extend(["{}: {:.5f}".format(k, v)
for k, v in numbers])
self.logger.info("\n\t".join(lang_report))
# store validation set outputs
self._store_outputs(valid_hypotheses)
# store attention plots for selected valid sentences
if valid_attention_scores and self.plot_attention:
store_attention_plots(
attentions=valid_attention_scores,
sources=[s for s in valid_data.src],
targets=valid_hypotheses_raw,
indices=self.log_valid_sents,
model_dir=self.model_dir,
tb_writer=self.tb_writer,
steps=self.steps)
if self.stop:
break
if self.stop:
self.logger.info(
'Training ended since minimum lr %f was reached.',
self.learning_rate_min)
break
self.logger.info(
'Epoch %3d: total training loss %.2f', epoch_no, epoch_loss)
else:
self.logger.info('Training ended after %3d epochs.', epoch_no)
self.logger.info('Best validation result at step %8d: %6.2f %s.',
self.best_ckpt_iteration, self.best_ckpt_score,
self.early_stopping_metric)
self.tb_writer.close() # close Tensorboard writer
def train(cfg_file: str) -> None:
"""
Main training function. After training, also test on test data if given.
:param cfg_file: path to configuration yaml file
"""
cfg = load_config(cfg_file)
# set the random seed
set_seed(seed=cfg["training"].get("random_seed", 42))
# load the data
train_data, dev_data, test_data, src_vocab, trg_vocab = load_data(
data_cfg=cfg["data"])
# build an encoder-decoder model
model = build_model(cfg["model"], src_vocab=src_vocab, trg_vocab=trg_vocab)
# for training management, e.g. early stopping and model selection
trainer = TrainManager(model=model, config=cfg)
# store copy of original training config in model dir
shutil.copy2(cfg_file, trainer.model_dir + "/config.yaml")
# log all entries of config
log_cfg(cfg, trainer.logger)
log_data_info(train_data=train_data,
valid_data=dev_data,
test_data=test_data,
src_vocab=src_vocab,
trg_vocab=trg_vocab,
logging_function=trainer.logger.info)
# store the vocabs
src_vocab_file = "{}/src_vocab.txt".format(cfg["training"]["model_dir"])
src_vocab.to_file(src_vocab_file)
trg_vocab_file = "{}/trg_vocab.txt".format(cfg["training"]["model_dir"])
trg_vocab.to_file(trg_vocab_file)
# train the model
trainer.train_and_validate(train_data=train_data, valid_data=dev_data)
# test the model with the best checkpoint
if test_data is not None:
# load checkpoint
if trainer.best_ckpt_iteration > 0:
checkpoint_path = "{}/{}.ckpt".format(trainer.model_dir,
trainer.best_ckpt_iteration)
else:
## For save_checkpoint by save_freq
checkpoint_path = get_latest_checkpoint(trainer.model_dir)
try:
trainer.init_from_checkpoint(checkpoint_path)
except AssertionError:
trainer.logger.warning(
"Checkpoint %s does not exist. "
"Skipping testing.", checkpoint_path)
if trainer.best_ckpt_iteration == 0 \
and trainer.best_ckpt_score in [np.inf, -np.inf]:
trainer.logger.warning(
"It seems like no checkpoint was written, "
"since no improvement was obtained over the initial model."
)
return
# generate hypotheses for test data
if "testing" in cfg.keys():
beam_size = cfg["testing"].get("beam_size", 0)
beam_alpha = cfg["testing"].get("alpha", -1)
return_logp = cfg["testing"].get("return_logp", False)
else:
beam_size = 0
beam_alpha = -1
return_logp = False
# pylint: disable=unused-variable
score, loss, ppl, sources, sources_raw, references, hypotheses, \
hypotheses_raw, attention_scores, log_probs = validate_on_data(
data=test_data, batch_size=trainer.batch_size,
eval_metric=trainer.eval_metric, level=trainer.level,
max_output_length=trainer.max_output_length,
model=model, use_cuda=trainer.use_cuda, loss_function=None,
beam_size=beam_size, beam_alpha=beam_alpha,
return_logp=return_logp)
if "trg" in test_data.fields:
decoding_description = "Greedy decoding" if beam_size == 0 else \
"Beam search decoding with beam size = {} and alpha = {}"\
.format(beam_size, beam_alpha)
trainer.logger.info("Test data result: %f %s [%s]", score,
trainer.eval_metric, decoding_description)
else:
trainer.logger.info(
"No references given for %s.%s -> no evaluation.",
cfg["data"]["test"], cfg["data"]["src"])
output_path_set = "{}/{}.{}".format(trainer.model_dir, "test",
cfg["data"]["trg"])
with open(output_path_set, mode="w", encoding="utf-8") as f:
for h in hypotheses:
f.write("{}\n".format(h))
trainer.logger.info("Test translations saved to: %s", output_path_set)
if return_logp:
output_path_set_logp = output_path_set + ".logp"
with open(output_path_set_logp, mode="w", encoding="utf-8") as f:
for l in log_probs:
f.write("{}\n".format(l))
trainer.logger.info("Test log probs saved to: %s",
output_path_set_logp)
def train_and_validate(self, train_data: Dataset, valid_data: Dataset) \
-> None:
"""
Train the model and validate it from time to time on the validation set.
:param train_data: training data
:param valid_data: validation data
"""
train_iter = make_data_iter(train_data,
batch_size=self.batch_size,
train=True,
shuffle=self.shuffle)
for epoch_no in range(self.epochs):
self.logger.info("EPOCH %d", epoch_no + 1)
if self.scheduler is not None and self.scheduler_step_at == "epoch":
self.scheduler.step(epoch=epoch_no)
self.model.train()
start = time.time()
total_valid_duration = 0
processed_tokens = self.total_tokens
count = 0
epoch_loss = 0
for batch in iter(train_iter):
# reactivate training
self.model.train()
# create a Batch object from torchtext batch
batch = Batch(batch, self.pad_index, use_cuda=self.use_cuda)
# only update every batch_multiplier batches
# see https://medium.com/@davidlmorton/
# increasing-mini-batch-size-without-increasing-
# memory-6794e10db672
update = count == 0
# print(count, update, self.steps)
batch_loss = self._train_batch(batch, update=update)
self.tb_writer.add_scalar("train/train_batch_loss", batch_loss,
self.steps)
count = self.batch_multiplier if update else count
count -= 1
epoch_loss += batch_loss.detach().cpu().numpy()
# log learning progress
if self.steps % self.logging_freq == 0 and update:
elapsed = time.time() - start - total_valid_duration
elapsed_tokens = self.total_tokens - processed_tokens
self.logger.info(
"Epoch %d Step: %d Batch Loss: %f Tokens per Sec: %f",
epoch_no + 1, self.steps, batch_loss,
elapsed_tokens / elapsed)
start = time.time()
total_valid_duration = 0
# validate on the entire dev set
if valid_data is not None and \
self.steps % self.validation_freq == 0 and update:
valid_start_time = time.time()
valid_score, valid_loss, valid_ppl, valid_sources, \
valid_sources_raw, valid_references, valid_hypotheses, \
valid_hypotheses_raw, valid_attention_scores, \
valid_logps = validate_on_data(
batch_size=self.batch_size, data=valid_data,
eval_metric=self.eval_metric,
level=self.level, model=self.model,
use_cuda=self.use_cuda,
max_output_length=self.max_output_length,
loss_function=self.loss,
return_logp=self.return_logp)
self.tb_writer.add_scalar("valid/valid_loss", valid_loss,
self.steps)
self.tb_writer.add_scalar("valid/valid_score", valid_score,
self.steps)
self.tb_writer.add_scalar("valid/valid_ppl", valid_ppl,
self.steps)
if self.early_stopping_metric == "loss":
ckpt_score = valid_loss
elif self.early_stopping_metric in ["ppl", "perplexity"]:
ckpt_score = valid_ppl
else:
ckpt_score = valid_score
new_best = False
if self.is_best(ckpt_score):
self.best_ckpt_score = ckpt_score
self.best_ckpt_iteration = self.steps
self.logger.info(
'Hooray! New best validation result [%s]!',
self.early_stopping_metric)
if self.ckpt_queue.maxsize > 0:
self.logger.info("Saving new checkpoint.")
new_best = True
self._save_checkpoint()
if self.scheduler is not None \
and self.scheduler_step_at == "validation":
self.scheduler.step(ckpt_score)
# append to validation report
self._add_report(valid_score=valid_score,
valid_loss=valid_loss,
valid_ppl=valid_ppl,
eval_metric=self.eval_metric,
new_best=new_best)
self._log_examples(sources_raw=valid_sources_raw,
sources=valid_sources,
hypotheses_raw=valid_hypotheses_raw,
hypotheses=valid_hypotheses,
references=valid_references)
valid_duration = time.time() - valid_start_time
total_valid_duration += valid_duration
self.logger.info(
'Validation result at epoch %d, step %d: %s: %f, '
'loss: %f, ppl: %f, duration: %.4fs', epoch_no + 1,
self.steps, self.eval_metric, valid_score, valid_loss,
valid_ppl, valid_duration)
# store validation set outputs
self._store_outputs(
valid_hypotheses if self.post_process else
[" ".join(v) for v in valid_hypotheses_raw],
valid_logps if self.return_logp else None)
# store attention plots for selected valid sentences
store_attention_plots(attentions=valid_attention_scores,
targets=valid_hypotheses_raw,
sources=[s for s in valid_data.src],
indices=self.log_valid_sents,
output_prefix="{}/att.{}".format(
self.model_dir, self.steps),
tb_writer=self.tb_writer,
steps=self.steps)
if self.save_freq > 0 and self.steps % self.save_freq == 0:
## Drop checkpoint by number of batches
## Take care of batch multipler in to description
self.logger.info("Saving new checkpoint!"
"Batches passed:{}"
"Number of updates:{}".format(
self.batch_multiplier * self.steps,
self.steps))
self._save_checkpoint()
if self.stop:
break
if self.stop:
self.logger.info(
'Training ended since minimum lr %f was reached.',
self.learning_rate_min)
break
self.logger.info('Epoch %d: total training loss %.2f',
epoch_no + 1, epoch_loss)
else:
self.logger.info('Training ended after %d epochs.', epoch_no + 1)
if valid_data is not None:
self.logger.info('Best validation result at step %d: %f %s.',
self.best_ckpt_iteration, self.best_ckpt_score,
self.early_stopping_metric)
def train_and_validate(self, train_data: Dataset, valid_data: Dataset, kb_task=None, train_kb: TranslationDataset =None,\
train_kb_lkp: list = [], train_kb_lens: list = [], train_kb_truvals: TranslationDataset=None, valid_kb: Tuple=None, \
valid_kb_lkp: list=[], valid_kb_lens: list = [], valid_kb_truvals:list=[],
valid_data_canon: list=[]) \
-> None:
"""
Train the model and validate it from time to time on the validation set.
:param train_data: training data
:param valid_data: validation data
:param kb_task: is not None if kb_task should be executed
:param train_kb: TranslationDataset holding the loaded train kb data
:param train_kb_lkp: List with train example index to corresponding kb indices
:param train_kb_len: List with num of triples per kb
:param valid_kb: TranslationDataset holding the loaded valid kb data
:param valid_kb_lkp: List with valid example index to corresponding kb indices
:param valid_kb_len: List with num of triples per kb
:param valid_kb_truvals: FIXME TODO
:param valid_data_canon: required to report loss
"""
if kb_task:
train_iter = make_data_iter_kb(train_data,
train_kb,
train_kb_lkp,
train_kb_lens,
train_kb_truvals,
batch_size=self.batch_size,
batch_type=self.batch_type,
train=True,
shuffle=self.shuffle,
canonize=self.model.canonize)
else:
train_iter = make_data_iter(train_data,
batch_size=self.batch_size,
batch_type=self.batch_type,
train=True,
shuffle=self.shuffle)
with torch.autograd.set_detect_anomaly(True):
for epoch_no in range(self.epochs):
self.logger.info("EPOCH %d", epoch_no + 1)
if self.scheduler is not None and self.scheduler_step_at == "epoch":
self.scheduler.step(epoch=epoch_no)
self.model.train()
start = time.time()
total_valid_duration = 0
processed_tokens = self.total_tokens
count = self.batch_multiplier - 1
epoch_loss = 0
for batch in iter(train_iter):
# reactivate training
self.model.train()
# create a Batch object from torchtext batch
batch = Batch(batch, self.pad_index, use_cuda=self.use_cuda) if not kb_task else \
Batch_with_KB(batch, self.pad_index, use_cuda=self.use_cuda)
if kb_task:
assert hasattr(batch, "kbsrc"), dir(batch)
assert hasattr(batch, "kbtrg"), dir(batch)
assert hasattr(batch, "kbtrv"), dir(batch)
# only update every batch_multiplier batches
# see https://medium.com/@davidlmorton/
# increasing-mini-batch-size-without-increasing-
# memory-6794e10db672
update = count == 0
batch_loss = self._train_batch(batch, update=update)
if update:
self.tb_writer.add_scalar("train/train_batch_loss",
batch_loss, self.steps)
count = self.batch_multiplier if update else count
count -= 1
epoch_loss += batch_loss.detach().cpu().numpy()
if self.scheduler is not None and \
self.scheduler_step_at == "step" and update:
self.scheduler.step()
# log learning progress
if self.steps % self.logging_freq == 0 and update:
elapsed = time.time() - start - total_valid_duration
elapsed_tokens = self.total_tokens - processed_tokens
self.logger.info(
"Epoch %3d Step: %8d Batch Loss: %12.6f "
"Tokens per Sec: %8.0f, Lr: %.6f", epoch_no + 1,
self.steps, batch_loss, elapsed_tokens / elapsed,
self.optimizer.param_groups[0]["lr"])
start = time.time()
total_valid_duration = 0
# validate on the entire dev set
if self.steps % self.validation_freq == 0 and update:
if self.manage_decoder_timer:
self._log_decoder_timer_stats("train")
self.decoder_timer.reset()
valid_start_time = time.time()
valid_score, valid_loss, valid_ppl, valid_sources, \
valid_sources_raw, valid_references, valid_hypotheses, \
valid_hypotheses_raw, valid_attention_scores, valid_kb_att_scores, \
valid_ent_f1, valid_ent_mcc = \
validate_on_data(
batch_size=self.eval_batch_size,
data=valid_data,
eval_metric=self.eval_metric,
level=self.level,
model=self.model,
use_cuda=self.use_cuda,
max_output_length=self.max_output_length,
loss_function=self.loss,
beam_size=0, # greedy validations #FIXME XXX NOTE TODO BUG set to 0 again!
batch_type=self.eval_batch_type,
kb_task=kb_task,
valid_kb=valid_kb,
valid_kb_lkp=valid_kb_lkp,
valid_kb_lens=valid_kb_lens,
valid_kb_truvals=valid_kb_truvals,
valid_data_canon=valid_data_canon,
report_on_canonicals=self.report_entf1_on_canonicals
)
if self.manage_decoder_timer:
self._log_decoder_timer_stats("valid")
self.decoder_timer.reset()
self.tb_writer.add_scalar("valid/valid_loss",
valid_loss, self.steps)
self.tb_writer.add_scalar("valid/valid_score",
valid_score, self.steps)
self.tb_writer.add_scalar("valid/valid_ppl", valid_ppl,
self.steps)
if self.early_stopping_metric == "loss":
ckpt_score = valid_loss
elif self.early_stopping_metric in [
"ppl", "perplexity"
]:
ckpt_score = valid_ppl
else:
ckpt_score = valid_score
new_best = False
if self.is_best(ckpt_score):
self.best_ckpt_score = ckpt_score
self.best_ckpt_iteration = self.steps
self.logger.info(
'Hooray! New best validation result [%s]!',
self.early_stopping_metric)
if self.ckpt_queue.maxsize > 0:
self.logger.info("Saving new checkpoint.")
new_best = True
self._save_checkpoint()
if self.scheduler is not None \
and self.scheduler_step_at == "validation":
self.scheduler.step(ckpt_score)
# append to validation report
self._add_report(valid_score=valid_score,
valid_loss=valid_loss,
valid_ppl=valid_ppl,
eval_metric=self.eval_metric,
valid_ent_f1=valid_ent_f1,
valid_ent_mcc=valid_ent_mcc,
new_best=new_best)
# pylint: disable=unnecessary-comprehension
self._log_examples(
sources_raw=[v for v in valid_sources_raw],
sources=valid_sources,
hypotheses_raw=valid_hypotheses_raw,
hypotheses=valid_hypotheses,
references=valid_references)
valid_duration = time.time() - valid_start_time
total_valid_duration += valid_duration
self.logger.info(
'Validation result at epoch %3d, step %8d: %s: %6.2f, '
'loss: %8.4f, ppl: %8.4f, duration: %.4fs',
epoch_no + 1, self.steps, self.eval_metric,
valid_score, valid_loss, valid_ppl, valid_duration)
# store validation set outputs
self._store_outputs(valid_hypotheses)
valid_src = list(valid_data.src)
# store attention plots for selected valid sentences
if valid_attention_scores:
plot_success_ratio = store_attention_plots(
attentions=valid_attention_scores,
targets=valid_hypotheses_raw,
sources=valid_src,
indices=self.log_valid_sents,
output_prefix="{}/att.{}".format(
self.model_dir, self.steps),
tb_writer=self.tb_writer,
steps=self.steps)
self.logger.info(
f"stored {plot_success_ratio} valid att scores!"
)
if valid_kb_att_scores:
plot_success_ratio = store_attention_plots(
attentions=valid_kb_att_scores,
targets=valid_hypotheses_raw,
sources=list(valid_kb.kbsrc),
indices=self.log_valid_sents,
output_prefix="{}/kbatt.{}".format(
self.model_dir, self.steps),
tb_writer=self.tb_writer,
steps=self.steps,
kb_info=(valid_kb_lkp, valid_kb_lens,
valid_kb_truvals),
on_the_fly_info=(valid_src, valid_kb,
self.model.canonize,
self.model.trg_vocab))
self.logger.info(
f"stored {plot_success_ratio} valid kb att scores!"
)
else:
self.logger.info(
"theres no valid kb att scores...")
if self.stop:
break
if self.stop:
self.logger.info(
'Training ended since minimum lr %f was reached.',
self.learning_rate_min)
break
self.logger.info('Epoch %3d: total training loss %.2f',
epoch_no + 1, epoch_loss)
else:
self.logger.info('Training ended after %3d epochs.',
epoch_no + 1)
self.logger.info('Best validation result at step %8d: %6.2f %s.',
self.best_ckpt_iteration, self.best_ckpt_score,
self.early_stopping_metric)
self.tb_writer.close() # close Tensorboard writer
