def train(FLAGS):
"""
FLAGS:
saveto: str
reload: store_true
config_path: str
pretrain_path: str, default=""
model_name: str
log_path: str
"""
# write log of training to file.
write_log_to_file(
os.path.join(FLAGS.log_path,
"%s.log" % time.strftime("%Y%m%d-%H%M%S")))
GlobalNames.USE_GPU = FLAGS.use_gpu
if GlobalNames.USE_GPU:
CURRENT_DEVICE = "cpu"
else:
CURRENT_DEVICE = "cuda:0"
config_path = os.path.abspath(FLAGS.config_path)
with open(config_path.strip()) as f:
configs = yaml.load(f)
INFO(pretty_configs(configs))
# Add default configs
configs = default_configs(configs)
data_configs = configs['data_configs']
model_configs = configs['model_configs']
optimizer_configs = configs['optimizer_configs']
training_configs = configs['training_configs']
GlobalNames.SEED = training_configs['seed']
set_seed(GlobalNames.SEED)
best_model_prefix = os.path.join(
FLAGS.saveto, FLAGS.model_name + GlobalNames.MY_BEST_MODEL_SUFFIX)
timer = Timer()
# ================================================================================== #
# Load Data
INFO('Loading data...')
timer.tic()
# Generate target dictionary
vocab_tgt = Vocabulary(**data_configs["vocabularies"][0])
train_batch_size = training_configs["batch_size"] * max(
1, training_configs["update_cycle"])
train_buffer_size = training_configs["buffer_size"] * max(
1, training_configs["update_cycle"])
train_bitext_dataset = ZipDataset(TextLineDataset(
data_path=data_configs['train_data'][0],
vocabulary=vocab_tgt,
max_len=data_configs['max_len'][0],
),
shuffle=training_configs['shuffle'])
valid_bitext_dataset = ZipDataset(
TextLineDataset(
data_path=data_configs['valid_data'][0],
vocabulary=vocab_tgt,
))
training_iterator = DataIterator(
dataset=train_bitext_dataset,
batch_size=train_batch_size,
use_bucket=training_configs['use_bucket'],
buffer_size=train_buffer_size,
batching_func=training_configs['batching_key'])
valid_iterator = DataIterator(
dataset=valid_bitext_dataset,
batch_size=training_configs['valid_batch_size'],
use_bucket=True,
buffer_size=100000,
numbering=True)
INFO('Done. Elapsed time {0}'.format(timer.toc()))
lrate = optimizer_configs['learning_rate']
is_early_stop = False
# ================================ Begin ======================================== #
# Build Model & Optimizer
# We would do steps below on after another
# 1. build models & criterion
# 2. move models & criterion to gpu if needed
# 3. load pre-trained model if needed
# 4. build optimizer
# 5. build learning rate scheduler if needed
# 6. load checkpoints if needed
# 0. Initial
model_collections = Collections()
checkpoint_saver = Saver(
save_prefix="{0}.ckpt".format(
os.path.join(FLAGS.saveto, FLAGS.model_name)),
num_max_keeping=training_configs['num_kept_checkpoints'])
best_model_saver = Saver(
save_prefix=best_model_prefix,
num_max_keeping=training_configs['num_kept_best_model'])
# 1. Build Model & Criterion
INFO('Building model...')
timer.tic()
lm_model = build_model(n_tgt_vocab=vocab_tgt.max_n_words, **model_configs)
INFO(lm_model)
params_total = sum([p.numel() for n, p in lm_model.named_parameters()])
params_with_embedding = sum([
p.numel() for n, p in lm_model.named_parameters()
if n.find('embedding') == -1
])
INFO('Total parameters: {}'.format(params_total))
INFO('Total parameters (excluding word embeddings): {}'.format(
params_with_embedding))
critic = NMTCriterion(label_smoothing=model_configs['label_smoothing'])
INFO(critic)
INFO('Done. Elapsed time {0}'.format(timer.toc()))
# 2. Move to GPU
if GlobalNames.USE_GPU:
lm_model = lm_model.cuda()
critic = critic.cuda()
# 3. Load pretrained model if needed
lm_model.init_parameters(FLAGS.pretrain_path, device=CURRENT_DEVICE)
# 4. Build optimizer
INFO('Building Optimizer...')
optim = Optimizer(name=optimizer_configs['optimizer'],
model=lm_model,
lr=lrate,
grad_clip=optimizer_configs['grad_clip'],
optim_args=optimizer_configs['optimizer_params'])
# 5. Build scheduler for optimizer if needed
if optimizer_configs['schedule_method'] is not None:
if optimizer_configs['schedule_method'] == "loss":
scheduler = ReduceOnPlateauScheduler(
optimizer=optim, **optimizer_configs["scheduler_configs"])
elif optimizer_configs['schedule_method'] == "noam":
scheduler = NoamScheduler(optimizer=optim,
**optimizer_configs['scheduler_configs'])
else:
WARN(
"Unknown scheduler name {0}. Do not use lr_scheduling.".format(
optimizer_configs['schedule_method']))
scheduler = None
else:
scheduler = None
# 6. build moving average
if training_configs['moving_average_method'] is not None:
ma = MovingAverage(
moving_average_method=training_configs['moving_average_method'],
named_params=lm_model.named_parameters(),
alpha=training_configs['moving_average_alpha'])
else:
ma = None
INFO('Done. Elapsed time {0}'.format(timer.toc()))
# Reload from latest checkpoint
if FLAGS.reload:
checkpoint_saver.load_latest(model=lm_model,
optim=optim,
lr_scheduler=scheduler,
collections=model_collections,
ma=ma)
# ================================================================================== #
# Prepare training
eidx = model_collections.get_collection("eidx", [0])[-1]
uidx = model_collections.get_collection("uidx", [0])[-1]
bad_count = model_collections.get_collection("bad_count", [0])[-1]
oom_count = model_collections.get_collection("oom_count", [0])[-1]
summary_writer = SummaryWriter(log_dir=FLAGS.log_path)
cum_samples = 0
cum_words = 0
valid_loss = best_valid_loss = float('inf') # Max Float
saving_files = []
# Timer for computing speed
timer_for_speed = Timer()
timer_for_speed.tic()
INFO('Begin training...')
while True:
summary_writer.add_scalar("Epoch", (eidx + 1), uidx)
# Build iterator and progress bar
training_iter = training_iterator.build_generator()
training_progress_bar = tqdm(desc=' - (Epc {}, Upd {}) '.format(
eidx, uidx),
total=len(training_iterator),
unit="sents")
for batch in training_iter:
uidx += 1
if optimizer_configs[
"schedule_method"] is not None and optimizer_configs[
"schedule_method"] != "loss":
scheduler.step(global_step=uidx)
seqs_y = batch
n_samples_t = len(seqs_y)
n_words_t = sum(len(s) for s in seqs_y)
cum_samples += n_samples_t
cum_words += n_words_t
train_loss = 0.
optim.zero_grad()
try:
# Prepare data
for (seqs_y_t, ) in split_shard(
seqs_y, split_size=training_configs['update_cycle']):
y = prepare_data(seqs_y_t, cuda=GlobalNames.USE_GPU)
loss = compute_forward(
model=lm_model,
critic=critic,
# seqs_x=x,
seqs_y=y,
eval=False,
normalization=n_samples_t,
norm_by_words=training_configs["norm_by_words"])
train_loss += loss / y.size(
1) if not training_configs["norm_by_words"] else loss
optim.step()
except RuntimeError as e:
if 'out of memory' in str(e):
print('| WARNING: ran out of memory, skipping batch')
oom_count += 1
optim.zero_grad()
else:
raise e
if ma is not None and eidx >= training_configs[
'moving_average_start_epoch']:
ma.step()
training_progress_bar.update(n_samples_t)
training_progress_bar.set_description(
' - (Epc {}, Upd {}) '.format(eidx, uidx))
training_progress_bar.set_postfix_str(
'TrainLoss: {:.2f}, ValidLoss(best): {:.2f} ({:.2f})'.format(
train_loss, valid_loss, best_valid_loss))
summary_writer.add_scalar("train_loss",
scalar_value=train_loss,
global_step=uidx)
# ================================================================================== #
# Display some information
if should_trigger_by_steps(
uidx, eidx, every_n_step=training_configs['disp_freq']):
# words per second and sents per second
words_per_sec = cum_words / (timer.toc(return_seconds=True))
sents_per_sec = cum_samples / (timer.toc(return_seconds=True))
lrate = list(optim.get_lrate())[0]
summary_writer.add_scalar("Speed(words/sec)",
scalar_value=words_per_sec,
global_step=uidx)
summary_writer.add_scalar("Speed(sents/sen)",
scalar_value=sents_per_sec,
global_step=uidx)
summary_writer.add_scalar("lrate",
scalar_value=lrate,
global_step=uidx)
summary_writer.add_scalar("oom_count",
scalar_value=oom_count,
global_step=uidx)
# Reset timer
timer.tic()
cum_words = 0
cum_samples = 0
# ================================================================================== #
# Saving checkpoints
if should_trigger_by_steps(
uidx,
eidx,
every_n_step=training_configs['save_freq'],
debug=FLAGS.debug):
model_collections.add_to_collection("uidx", uidx)
model_collections.add_to_collection("eidx", eidx)
model_collections.add_to_collection("bad_count", bad_count)
if not is_early_stop:
checkpoint_saver.save(global_step=uidx,
model=lm_model,
optim=optim,
lr_scheduler=scheduler,
collections=model_collections,
ma=ma)
# ================================================================================== #
# Loss Validation & Learning rate annealing
if should_trigger_by_steps(
global_step=uidx,
n_epoch=eidx,
every_n_step=training_configs['loss_valid_freq'],
debug=FLAGS.debug):
if ma is not None:
origin_state_dict = deepcopy(lm_model.state_dict())
lm_model.load_state_dict(ma.export_ma_params(),
strict=False)
valid_loss = loss_validation(
model=lm_model,
critic=critic,
valid_iterator=valid_iterator,
norm_by_words=training_configs["norm_by_words"])
model_collections.add_to_collection("history_losses",
valid_loss)
min_history_loss = np.array(
model_collections.get_collection("history_losses")).min()
summary_writer.add_scalar("loss", valid_loss, global_step=uidx)
summary_writer.add_scalar("best_loss",
min_history_loss,
global_step=uidx)
if ma is not None:
lm_model.load_state_dict(origin_state_dict)
del origin_state_dict
if optimizer_configs["schedule_method"] == "loss":
scheduler.step(metric=best_valid_loss)
# If model get new best valid loss
if valid_loss < best_valid_loss:
bad_count = 0
if is_early_stop is False:
# 1. save the best model
torch.save(lm_model.state_dict(),
best_model_prefix + ".final")
# 2. record all several best models
best_model_saver.save(global_step=uidx, model=lm_model)
else:
bad_count += 1
# At least one epoch should be traversed
if bad_count >= training_configs[
'early_stop_patience'] and eidx > 0:
is_early_stop = True
WARN("Early Stop!")
best_valid_loss = min_history_loss
summary_writer.add_scalar("bad_count", bad_count, uidx)
INFO("{0} Loss: {1:.2f} lrate: {2:6f} patience: {3}".format(
uidx, valid_loss, lrate, bad_count))
training_progress_bar.close()
eidx += 1
if eidx > training_configs["max_epochs"]:
break
def tune(flags):
"""
flags:
saveto: str
reload: store_true
config_path: str
pretrain_path: str, default=""
model_name: str
log_path: str
"""
# ================================================================================== #
# Initialization for training on different devices
# - CPU/GPU
# - Single/Distributed
Constants.USE_GPU = flags.use_gpu
if flags.multi_gpu:
dist.distributed_init(flags.shared_dir)
world_size = dist.get_world_size()
rank = dist.get_rank()
local_rank = dist.get_local_rank()
else:
world_size = 1
rank = 0
local_rank = 0
if Constants.USE_GPU:
torch.cuda.set_device(local_rank)
Constants.CURRENT_DEVICE = "cuda:{0}".format(local_rank)
else:
Constants.CURRENT_DEVICE = "cpu"
# If not root_rank, close logging
# else write log of training to file.
if rank == 0:
write_log_to_file(
os.path.join(flags.log_path,
"%s.log" % time.strftime("%Y%m%d-%H%M%S")))
else:
close_logging()
# ================================================================================== #
# Parsing configuration files
# - Load default settings
# - Load pre-defined settings
# - Load user-defined settings
configs = prepare_configs(flags.config_path, flags.predefined_config)
data_configs = configs['data_configs']
model_configs = configs['model_configs']
optimizer_configs = configs['optimizer_configs']
training_configs = configs['training_configs']
INFO(pretty_configs(configs))
Constants.SEED = training_configs['seed']
set_seed(Constants.SEED)
timer = Timer()
# ================================================================================== #
# Load Data
INFO('Loading data...')
timer.tic()
# Generate target dictionary
vocab_src = Vocabulary.build_from_file(**data_configs['vocabularies'][0])
vocab_tgt = Vocabulary.build_from_file(**data_configs['vocabularies'][1])
Constants.EOS = vocab_src.eos
Constants.PAD = vocab_src.pad
Constants.BOS = vocab_src.bos
# bt tag dataset
train_bitext_dataset = ZipDataset(
TextLineDataset(data_path=data_configs['train_data'][0],
vocabulary=vocab_src,
max_len=data_configs['max_len'][0],
is_train_dataset=True),
TextLineDataset(data_path=data_configs['train_data'][1],
vocabulary=vocab_tgt,
max_len=data_configs['max_len'][1],
is_train_dataset=True))
training_iterator = DataIterator(
dataset=train_bitext_dataset,
batch_size=training_configs["batch_size"],
use_bucket=training_configs['use_bucket'],
buffer_size=training_configs['buffer_size'],
batching_func=training_configs['batching_key'],
world_size=world_size,
rank=rank)
INFO('Done. Elapsed time {0}'.format(timer.toc()))
# ================================ Begin ======================================== #
# Build Model & Optimizer
# We would do steps below on after another
# 1. build models & criterion
# 2. move models & criterion to gpu if needed
# 3. load pre-trained model if needed
# 4. build optimizer
# 5. build learning rate scheduler if needed
# 6. load checkpoints if needed
# 0. Initial
lrate = optimizer_configs['learning_rate']
model_collections = Collections()
checkpoint_saver = Saver(
save_prefix="{0}.ckpt".format(
os.path.join(flags.saveto, flags.model_name)),
num_max_keeping=training_configs['num_kept_checkpoints'])
best_model_prefix = os.path.join(
flags.saveto, flags.model_name + Constants.MY_BEST_MODEL_SUFFIX)
best_model_saver = Saver(
save_prefix=best_model_prefix,
num_max_keeping=training_configs['num_kept_best_model'])
# 1. Build Model & Criterion
INFO('Building model...')
timer.tic()
nmt_model = build_model(n_src_vocab=vocab_src.max_n_words,
n_tgt_vocab=vocab_tgt.max_n_words,
padding_idx=vocab_src.pad,
vocab_src=vocab_src,
vocab_tgt=vocab_tgt,
**model_configs)
INFO(nmt_model)
critic = NMTCriterion(label_smoothing=model_configs['label_smoothing'],
padding_idx=vocab_tgt.pad)
INFO(critic)
# 2. Move to GPU
if Constants.USE_GPU:
nmt_model = nmt_model.cuda()
critic = critic.cuda()
# 3. Load pretrained model if needed
load_pretrained_model(nmt_model,
flags.pretrain_path,
exclude_prefix=flags.pretrain_exclude_prefix,
device=Constants.CURRENT_DEVICE)
# froze_parameters
froze_params(nmt_model, flags.froze_config)
INFO('Done. Elapsed time {0}'.format(timer.toc()))
# 4. Build optimizer
INFO('Building Optimizer...')
if not flags.multi_gpu:
optim = Optimizer(name=optimizer_configs['optimizer'],
model=nmt_model,
lr=lrate,
grad_clip=optimizer_configs['grad_clip'],
optim_args=optimizer_configs['optimizer_params'],
update_cycle=training_configs['update_cycle'])
else:
optim = dist.DistributedOptimizer(
name=optimizer_configs['optimizer'],
model=nmt_model,
lr=lrate,
grad_clip=optimizer_configs['grad_clip'],
optim_args=optimizer_configs['optimizer_params'],
device_id=local_rank)
# 5. Build scheduler for optimizer if needed
scheduler = build_scheduler(
schedule_method=optimizer_configs['schedule_method'],
optimizer=optim,
scheduler_configs=optimizer_configs['scheduler_configs'])
# 6. build moving average
if training_configs['moving_average_method'] is not None:
ma = MovingAverage(
moving_average_method=training_configs['moving_average_method'],
named_params=nmt_model.named_parameters(),
alpha=training_configs['moving_average_alpha'])
else:
ma = None
INFO('Done. Elapsed time {0}'.format(timer.toc()))
# Reload from latest checkpoint
if flags.reload:
checkpoint_saver.load_latest(model=nmt_model,
optim=optim,
lr_scheduler=scheduler,
collections=model_collections,
ma=ma,
device=Constants.CURRENT_DEVICE)
# broadcast parameters and optimizer states
if world_size > 1:
INFO("Broadcasting model parameters...")
dist.broadcast_parameters(params=nmt_model.state_dict())
INFO("Broadcasting optimizer states...")
dist.broadcast_optimizer_state(optimizer=optim.optim)
INFO('Done.')
# ================================================================================== #
# Prepare training
eidx = model_collections.get_collection("eidx", [0])[-1]
uidx = model_collections.get_collection("uidx", [1])[-1]
bad_count = model_collections.get_collection("bad_count", [0])[-1]
oom_count = model_collections.get_collection("oom_count", [0])[-1]
is_early_stop = model_collections.get_collection("is_early_stop", [
False,
])[-1]
train_loss_meter = AverageMeter()
sent_per_sec_meter = TimeMeter()
tok_per_sec_meter = TimeMeter()
update_cycle = training_configs['update_cycle']
grad_denom = 0
train_loss = 0.0
cum_n_words = 0
valid_loss = best_valid_loss = float('inf')
if rank == 0:
summary_writer = SummaryWriter(log_dir=flags.log_path)
else:
summary_writer = None
sent_per_sec_meter.start()
tok_per_sec_meter.start()
INFO('Begin training...')
while True:
if summary_writer is not None:
summary_writer.add_scalar("Epoch", (eidx + 1), uidx)
# Build iterator and progress bar
training_iter = training_iterator.build_generator()
if rank == 0:
training_progress_bar = tqdm(desc=' - (Epc {}, Upd {}) '.format(
eidx, uidx),
total=len(training_iterator),
unit="sents")
else:
training_progress_bar = None
# INFO(Constants.USE_BT)
for batch in training_iter:
# bt attrib data
seqs_x, seqs_y = batch
batch_size = len(seqs_x)
cum_n_words += sum(len(s) for s in seqs_y)
try:
# Prepare data
x, y = prepare_data(seqs_x, seqs_y, cuda=Constants.USE_GPU)
loss = compute_forward(
model=nmt_model,
critic=critic,
seqs_x=x,
seqs_y=y,
eval=False,
normalization=1.0,
norm_by_words=training_configs["norm_by_words"])
update_cycle -= 1
grad_denom += batch_size
train_loss += loss
except RuntimeError as e:
if 'out of memory' in str(e):
print('| WARNING: ran out of memory, skipping batch')
oom_count += 1
else:
raise e
# When update_cycle becomes 0, it means end of one batch. Several things will be done:
# - update parameters
# - reset update_cycle and grad_denom, update uidx
# - learning rate scheduling
# - update moving average
if update_cycle == 0:
# 0. reduce variables
if world_size > 1:
grad_denom = dist.all_reduce_py(grad_denom)
train_loss = dist.all_reduce_py(train_loss)
cum_n_words = dist.all_reduce_py(cum_n_words)
# 1. update parameters
optim.step(denom=grad_denom)
optim.zero_grad()
if training_progress_bar is not None:
training_progress_bar.update(grad_denom)
training_progress_bar.set_description(
' - (Epc {}, Upd {}) '.format(eidx, uidx))
postfix_str = 'TrainLoss: {:.2f}, ValidLoss(best): {:.2f} ({:.2f}), '.format(
train_loss, valid_loss, best_valid_loss)
training_progress_bar.set_postfix_str(postfix_str)
# 2. learning rate scheduling
if scheduler is not None and optimizer_configs[
"schedule_method"] != "loss":
scheduler.step(global_step=uidx)
# 3. update moving average
if ma is not None and eidx >= training_configs[
'moving_average_start_epoch']:
ma.step()
# 4. update meters
train_loss_meter.update(train_loss, grad_denom)
sent_per_sec_meter.update(grad_denom)
tok_per_sec_meter.update(cum_n_words)
# 5. reset accumulated variables, update uidx
update_cycle = training_configs['update_cycle']
grad_denom = 0
uidx += 1
cum_n_words = 0.0
train_loss = 0.0
else:
continue
# ================================================================================== #
# Display some information
if should_trigger_by_steps(
uidx, eidx, every_n_step=training_configs['disp_freq']):
lrate = list(optim.get_lrate())[0]
if summary_writer is not None:
summary_writer.add_scalar(
"Speed(sents/sec)",
scalar_value=sent_per_sec_meter.ave,
global_step=uidx)
summary_writer.add_scalar(
"Speed(words/sec)",
scalar_value=tok_per_sec_meter.ave,
global_step=uidx)
summary_writer.add_scalar(
"train_loss",
scalar_value=train_loss_meter.ave,
global_step=uidx)
summary_writer.add_scalar("lrate",
scalar_value=lrate,
global_step=uidx)
summary_writer.add_scalar("oom_count",
scalar_value=oom_count,
global_step=uidx)
# Reset Meters
sent_per_sec_meter.reset()
tok_per_sec_meter.reset()
train_loss_meter.reset()
# ================================================================================== #
# Saving checkpoints
# if should_trigger_by_steps(uidx, eidx, every_n_step=training_configs['save_freq'], debug=flags.debug):
# model_collections.add_to_collection("uidx", uidx)
# model_collections.add_to_collection("eidx", eidx)
# model_collections.add_to_collection("bad_count", bad_count)
#
# if not is_early_stop:
# if rank == 0:
# checkpoint_saver.save(global_step=uidx,
# model=nmt_model,
# optim=optim,
# lr_scheduler=scheduler,
# collections=model_collections,
# ma=ma)
torch.save(nmt_model.state_dict(), best_model_prefix + ".final")
if training_progress_bar is not None:
training_progress_bar.close()
eidx += 1
if eidx > training_configs["max_epochs"]:
break
def train(FLAGS):
"""
FLAGS:
saveto: str
reload: store_true
config_path: str
pretrain_path: str, default=""
model_name: str
log_path: str
"""
# write log of training to file.
write_log_to_file(os.path.join(FLAGS.log_path, "%s.log" % time.strftime("%Y%m%d-%H%M%S")))
GlobalNames.USE_GPU = FLAGS.use_gpu
if GlobalNames.USE_GPU:
CURRENT_DEVICE = "cpu"
else:
CURRENT_DEVICE = "cuda:0"
config_path = os.path.abspath(FLAGS.config_path)
with open(config_path.strip()) as f:
configs = yaml.load(f)
INFO(pretty_configs(configs))
# Add default configs
configs = default_configs(configs)
data_configs = configs['data_configs']
model_configs = configs['model_configs']
optimizer_configs = configs['optimizer_configs']
training_configs = configs['training_configs']
GlobalNames.SEED = training_configs['seed']
set_seed(GlobalNames.SEED)
best_model_prefix = os.path.join(FLAGS.saveto, FLAGS.model_name + GlobalNames.MY_BEST_MODEL_SUFFIX)
timer = Timer()
# ================================================================================== #
# Load Data
INFO('Loading data...')
timer.tic()
# Generate target dictionary
vocab_src = Vocabulary(**data_configs["vocabularies"][0])
vocab_tgt = Vocabulary(**data_configs["vocabularies"][1])
train_batch_size = training_configs["batch_size"] * max(1, training_configs["update_cycle"])
train_buffer_size = training_configs["buffer_size"] * max(1, training_configs["update_cycle"])
train_bitext_dataset = ZipDataset(
TextLineDataset(data_path=data_configs['train_data'][0],
vocabulary=vocab_src,
max_len=data_configs['max_len'][0],
),
TextLineDataset(data_path=data_configs['train_data'][1],
vocabulary=vocab_tgt,
max_len=data_configs['max_len'][1],
),
shuffle=training_configs['shuffle']
)
valid_bitext_dataset = ZipDataset(
TextLineDataset(data_path=data_configs['valid_data'][0],
vocabulary=vocab_src,
),
TextLineDataset(data_path=data_configs['valid_data'][1],
vocabulary=vocab_tgt,
)
)
training_iterator = DataIterator(dataset=train_bitext_dataset,
batch_size=train_batch_size,
use_bucket=training_configs['use_bucket'],
buffer_size=train_buffer_size,
batching_func=training_configs['batching_key'])
valid_iterator = DataIterator(dataset=valid_bitext_dataset,
batch_size=training_configs['valid_batch_size'],
use_bucket=True, buffer_size=100000, numbering=True)
bleu_scorer = SacreBLEUScorer(reference_path=data_configs["bleu_valid_reference"],
num_refs=data_configs["num_refs"],
lang_pair=data_configs["lang_pair"],
sacrebleu_args=training_configs["bleu_valid_configs"]['sacrebleu_args'],
postprocess=training_configs["bleu_valid_configs"]['postprocess']
)
INFO('Done. Elapsed time {0}'.format(timer.toc()))
lrate = optimizer_configs['learning_rate']
is_early_stop = False
# ================================ Begin ======================================== #
# Build Model & Optimizer
# We would do steps below on after another
# 1. build models & criterion
# 2. move models & criterion to gpu if needed
# 3. load pre-trained model if needed
# 4. build optimizer
# 5. build learning rate scheduler if needed
# 6. load checkpoints if needed
# 0. Initial
model_collections = Collections()
checkpoint_saver = Saver(save_prefix="{0}.ckpt".format(os.path.join(FLAGS.saveto, FLAGS.model_name)),
num_max_keeping=training_configs['num_kept_checkpoints']
)
best_model_saver = Saver(save_prefix=best_model_prefix, num_max_keeping=training_configs['num_kept_best_model'])
# 1. Build Model & Criterion
INFO('Building model...')
timer.tic()
nmt_model = build_model(n_src_vocab=vocab_src.max_n_words,
n_tgt_vocab=vocab_tgt.max_n_words, **model_configs)
INFO(nmt_model)
critic = NMTCriterion(label_smoothing=model_configs['label_smoothing'])
INFO(critic)
INFO('Done. Elapsed time {0}'.format(timer.toc()))
# 2. Move to GPU
if GlobalNames.USE_GPU:
nmt_model = nmt_model.cuda()
critic = critic.cuda()
# 3. Load pretrained model if needed
load_pretrained_model(nmt_model, FLAGS.pretrain_path, exclude_prefix=None, device=CURRENT_DEVICE)
# 4. Build optimizer
INFO('Building Optimizer...')
optim = Optimizer(name=optimizer_configs['optimizer'],
model=nmt_model,
lr=lrate,
grad_clip=optimizer_configs['grad_clip'],
optim_args=optimizer_configs['optimizer_params']
)
# 5. Build scheduler for optimizer if needed
if optimizer_configs['schedule_method'] is not None:
if optimizer_configs['schedule_method'] == "loss":
scheduler = ReduceOnPlateauScheduler(optimizer=optim,
**optimizer_configs["scheduler_configs"]
)
elif optimizer_configs['schedule_method'] == "noam":
scheduler = NoamScheduler(optimizer=optim, **optimizer_configs['scheduler_configs'])
else:
WARN("Unknown scheduler name {0}. Do not use lr_scheduling.".format(optimizer_configs['schedule_method']))
scheduler = None
else:
scheduler = None
# 6. build EMA
if training_configs['ema_decay'] > 0.0:
ema = ExponentialMovingAverage(named_params=nmt_model.named_parameters(), decay=training_configs['ema_decay'])
else:
ema = None
INFO('Done. Elapsed time {0}'.format(timer.toc()))
# Reload from latest checkpoint
if FLAGS.reload:
checkpoint_saver.load_latest(model=nmt_model, optim=optim, lr_scheduler=scheduler,
collections=model_collections)
# ================================================================================== #
# Prepare training
eidx = model_collections.get_collection("eidx", [0])[-1]
uidx = model_collections.get_collection("uidx", [0])[-1]
bad_count = model_collections.get_collection("bad_count", [0])[-1]
summary_writer = SummaryWriter(log_dir=FLAGS.log_path)
cum_samples = 0
cum_words = 0
best_valid_loss = 1.0 * 1e10 # Max Float
saving_files = []
# Timer for computing speed
timer_for_speed = Timer()
timer_for_speed.tic()
INFO('Begin training...')
while True:
summary_writer.add_scalar("Epoch", (eidx + 1), uidx)
# Build iterator and progress bar
training_iter = training_iterator.build_generator()
training_progress_bar = tqdm(desc=' - (Epoch %d) ' % eidx,
total=len(training_iterator),
unit="sents"
)
for batch in training_iter:
uidx += 1
if scheduler is None:
pass
elif optimizer_configs["schedule_method"] == "loss":
scheduler.step(metric=best_valid_loss)
else:
scheduler.step(global_step=uidx)
seqs_x, seqs_y = batch
n_samples_t = len(seqs_x)
n_words_t = sum(len(s) for s in seqs_y)
cum_samples += n_samples_t
cum_words += n_words_t
training_progress_bar.update(n_samples_t)
optim.zero_grad()
# Prepare data
for seqs_x_t, seqs_y_t in split_shard(seqs_x, seqs_y, split_size=training_configs['update_cycle']):
x, y = prepare_data(seqs_x_t, seqs_y_t, cuda=GlobalNames.USE_GPU)
loss = compute_forward(model=nmt_model,
critic=critic,
seqs_x=x,
seqs_y=y,
eval=False,
normalization=n_samples_t,
norm_by_words=training_configs["norm_by_words"])
optim.step()
if ema is not None:
ema.step()
# ================================================================================== #
# Display some information
if should_trigger_by_steps(uidx, eidx, every_n_step=training_configs['disp_freq']):
# words per second and sents per second
words_per_sec = cum_words / (timer.toc(return_seconds=True))
sents_per_sec = cum_samples / (timer.toc(return_seconds=True))
lrate = list(optim.get_lrate())[0]
summary_writer.add_scalar("Speed(words/sec)", scalar_value=words_per_sec, global_step=uidx)
summary_writer.add_scalar("Speed(sents/sen)", scalar_value=sents_per_sec, global_step=uidx)
summary_writer.add_scalar("lrate", scalar_value=lrate, global_step=uidx)
# Reset timer
timer.tic()
cum_words = 0
cum_samples = 0
# ================================================================================== #
# Saving checkpoints
if should_trigger_by_steps(uidx, eidx, every_n_step=training_configs['save_freq'], debug=FLAGS.debug):
model_collections.add_to_collection("uidx", uidx)
model_collections.add_to_collection("eidx", eidx)
model_collections.add_to_collection("bad_count", bad_count)
if not is_early_stop:
checkpoint_saver.save(global_step=uidx, model=nmt_model, optim=optim, lr_scheduler=scheduler,
collections=model_collections, ema=ema)
# ================================================================================== #
# Loss Validation & Learning rate annealing
if should_trigger_by_steps(global_step=uidx, n_epoch=eidx, every_n_step=training_configs['loss_valid_freq'],
debug=FLAGS.debug):
if ema is not None:
origin_state_dict = deepcopy(nmt_model.state_dict())
nmt_model.load_state_dict(ema.state_dict(), strict=False)
valid_loss = loss_validation(model=nmt_model,
critic=critic,
valid_iterator=valid_iterator,
)
model_collections.add_to_collection("history_losses", valid_loss)
min_history_loss = np.array(model_collections.get_collection("history_losses")).min()
summary_writer.add_scalar("loss", valid_loss, global_step=uidx)
summary_writer.add_scalar("best_loss", min_history_loss, global_step=uidx)
best_valid_loss = min_history_loss
if ema is not None:
nmt_model.load_state_dict(origin_state_dict)
del origin_state_dict
# ================================================================================== #
# BLEU Validation & Early Stop
if should_trigger_by_steps(global_step=uidx, n_epoch=eidx,
every_n_step=training_configs['bleu_valid_freq'],
min_step=training_configs['bleu_valid_warmup'],
debug=FLAGS.debug):
if ema is not None:
origin_state_dict = deepcopy(nmt_model.state_dict())
nmt_model.load_state_dict(ema.state_dict(), strict=False)
valid_bleu = bleu_validation(uidx=uidx,
valid_iterator=valid_iterator,
batch_size=training_configs["bleu_valid_batch_size"],
model=nmt_model,
bleu_scorer=bleu_scorer,
vocab_tgt=vocab_tgt,
valid_dir=FLAGS.valid_path,
max_steps=training_configs["bleu_valid_configs"]["max_steps"],
beam_size=training_configs["bleu_valid_configs"]["beam_size"],
alpha=training_configs["bleu_valid_configs"]["alpha"]
)
model_collections.add_to_collection(key="history_bleus", value=valid_bleu)
best_valid_bleu = float(np.array(model_collections.get_collection("history_bleus")).max())
summary_writer.add_scalar("bleu", valid_bleu, uidx)
summary_writer.add_scalar("best_bleu", best_valid_bleu, uidx)
# If model get new best valid bleu score
if valid_bleu >= best_valid_bleu:
bad_count = 0
if is_early_stop is False:
# 1. save the best model
torch.save(nmt_model.state_dict(), best_model_prefix + ".final")
# 2. record all several best models
best_model_saver.save(global_step=uidx, model=nmt_model)
else:
bad_count += 1
# At least one epoch should be traversed
if bad_count >= training_configs['early_stop_patience'] and eidx > 0:
is_early_stop = True
WARN("Early Stop!")
summary_writer.add_scalar("bad_count", bad_count, uidx)
if ema is not None:
nmt_model.load_state_dict(origin_state_dict)
del origin_state_dict
INFO("{0} Loss: {1:.2f} BLEU: {2:.2f} lrate: {3:6f} patience: {4}".format(
uidx, valid_loss, valid_bleu, lrate, bad_count
))
training_progress_bar.close()
eidx += 1
if eidx > training_configs["max_epochs"]:
break
def train(FLAGS):
"""
FLAGS:
saveto: str
reload: store_true
config_path: str
pretrain_path: str, default=""
model_name: str
log_path: str
"""
# ================================================================================== #
# Initialization for training on different devices
# - CPU/GPU
# - Single/Distributed
GlobalNames.USE_GPU = FLAGS.use_gpu
if FLAGS.multi_gpu:
if hvd is None or distributed is None:
ERROR("Distributed training is disable. Please check the installation of Horovod.")
hvd.init()
world_size = hvd.size()
rank = hvd.rank()
local_rank = hvd.local_rank()
else:
world_size = 1
rank = 0
local_rank = 0
if GlobalNames.USE_GPU:
torch.cuda.set_device(local_rank)
CURRENT_DEVICE = "cuda:{0}".format(local_rank)
else:
CURRENT_DEVICE = "cpu"
# If not root_rank, close logging
if rank != 0:
close_logging()
# write log of training to file.
if rank == 0:
write_log_to_file(os.path.join(FLAGS.log_path, "%s.log" % time.strftime("%Y%m%d-%H%M%S")))
# ================================================================================== #
# Parsing configuration files
config_path = os.path.abspath(FLAGS.config_path)
with open(config_path.strip()) as f:
configs = yaml.load(f)
INFO(pretty_configs(configs))
# Add default configs
configs = default_baseline_configs(configs)
data_configs = configs['data_configs']
model_configs = configs['model_configs']
optimizer_configs = configs['optimizer_configs']
training_configs = configs['training_configs']
GlobalNames.SEED = training_configs['seed']
set_seed(GlobalNames.SEED)
timer = Timer()
# ================================================================================== #
# Load Data
INFO('Loading data...')
timer.tic()
# Generate target dictionary
vocab_src = Vocabulary(**data_configs["vocabularies"][0])
vocab_tgt = Vocabulary(**data_configs["vocabularies"][1])
actual_buffer_size = training_configs["buffer_size"] * max(1, training_configs["update_cycle"])
train_bitext_dataset = ZipDataset(
TextLineDataset(data_path=data_configs['train_data'][0],
vocabulary=vocab_src,
max_len=data_configs['max_len'][0],
),
TextLineDataset(data_path=data_configs['train_data'][1],
vocabulary=vocab_tgt,
max_len=data_configs['max_len'][1],
)
)
valid_bitext_dataset = ZipDataset(
TextLineDataset(data_path=data_configs['valid_data'][0],
vocabulary=vocab_src,
),
TextLineDataset(data_path=data_configs['valid_data'][1],
vocabulary=vocab_tgt,
)
)
training_iterator = DataIterator(dataset=train_bitext_dataset,
batch_size=training_configs["batch_size"],
use_bucket=training_configs['use_bucket'],
buffer_size=actual_buffer_size,
batching_func=training_configs['batching_key'],
world_size=world_size,
rank=rank)
valid_iterator = DataIterator(dataset=valid_bitext_dataset,
batch_size=training_configs['valid_batch_size'],
use_bucket=True, buffer_size=100000, numbering=True,
world_size=world_size, rank=rank)
bleu_scorer = SacreBLEUScorer(reference_path=data_configs["bleu_valid_reference"],
num_refs=data_configs["num_refs"],
lang_pair=data_configs["lang_pair"],
sacrebleu_args=training_configs["bleu_valid_configs"]['sacrebleu_args'],
postprocess=training_configs["bleu_valid_configs"]['postprocess']
)
INFO('Done. Elapsed time {0}'.format(timer.toc()))
lrate = optimizer_configs['learning_rate']
is_early_stop = False
# ================================ Begin ======================================== #
# Build Model & Optimizer
# We would do steps below on after another
# 1. build models & criterion
# 2. move models & criterion to gpu if needed
# 3. load pre-trained model if needed
# 4. build optimizer
# 5. build learning rate scheduler if needed
# 6. load checkpoints if needed
# 0. Initial
model_collections = Collections()
best_model_prefix = os.path.join(FLAGS.saveto, FLAGS.model_name + GlobalNames.MY_BEST_MODEL_SUFFIX)
checkpoint_saver = Saver(save_prefix="{0}.ckpt".format(os.path.join(FLAGS.saveto, FLAGS.model_name)),
num_max_keeping=training_configs['num_kept_checkpoints']
)
best_model_saver = Saver(save_prefix=best_model_prefix, num_max_keeping=training_configs['num_kept_best_model'])
INFO('Building model...')
timer.tic()
nmt_model = build_model(n_src_vocab=vocab_src.max_n_words,
n_tgt_vocab=vocab_tgt.max_n_words, **model_configs)
INFO(nmt_model)
critic = NMTCriterion(label_smoothing=model_configs['label_smoothing'])
INFO(critic)
INFO('Done. Elapsed time {0}'.format(timer.toc()))
# 2. Move to GPU
if GlobalNames.USE_GPU:
nmt_model = nmt_model.cuda()
critic = critic.cuda()
# 3. Load pretrained model if needed
load_pretrained_model(nmt_model, FLAGS.pretrain_path, exclude_prefix=None, device=CURRENT_DEVICE)
# 4. Build optimizer
INFO('Building Optimizer...')
optim = Optimizer(name=optimizer_configs['optimizer'],
model=nmt_model,
lr=lrate,
grad_clip=optimizer_configs['grad_clip'],
optim_args=optimizer_configs['optimizer_params'],
distributed=True if world_size > 1 else False,
update_cycle=training_configs['update_cycle']
)
# 5. Build scheduler for optimizer if needed
if optimizer_configs['schedule_method'] is not None:
if optimizer_configs['schedule_method'] == "loss":
scheduler = ReduceOnPlateauScheduler(optimizer=optim,
**optimizer_configs["scheduler_configs"]
)
elif optimizer_configs['schedule_method'] == "noam":
scheduler = NoamScheduler(optimizer=optim, **optimizer_configs['scheduler_configs'])
else:
WARN("Unknown scheduler name {0}. Do not use lr_scheduling.".format(optimizer_configs['schedule_method']))
scheduler = None
else:
scheduler = None
# 6. build moving average
if training_configs['moving_average_method'] is not None:
ma = MovingAverage(moving_average_method=training_configs['moving_average_method'],
named_params=nmt_model.named_parameters(),
alpha=training_configs['moving_average_alpha'])
else:
ma = None
INFO('Done. Elapsed time {0}'.format(timer.toc()))
# Reload from latest checkpoint
if FLAGS.reload:
checkpoint_saver.load_latest(model=nmt_model, optim=optim, lr_scheduler=scheduler,
collections=model_collections, ma=ma)
# broadcast parameters and optimizer states
if world_size > 1:
hvd.broadcast_parameters(params=nmt_model.state_dict(), root_rank=0)
hvd.broadcast_optimizer_state(optimizer=optim.optim, root_rank=0)
# ================================================================================== #
# Prepare training
eidx = model_collections.get_collection("eidx", [0])[-1]
uidx = model_collections.get_collection("uidx", [1])[-1]
bad_count = model_collections.get_collection("bad_count", [0])[-1]
oom_count = model_collections.get_collection("oom_count", [0])[-1]
cum_n_samples = 0
cum_n_words = 0
best_valid_loss = 1.0 * 1e10 # Max Float
update_cycle = training_configs['update_cycle']
grad_denom = 0
if rank == 0:
summary_writer = SummaryWriter(log_dir=FLAGS.log_path)
else:
summary_writer = None
# Timer for computing speed
timer_for_speed = Timer()
timer_for_speed.tic()
INFO('Begin training...')
while True:
if summary_writer is not None:
summary_writer.add_scalar("Epoch", (eidx + 1), uidx)
# Build iterator and progress bar
training_iter = training_iterator.build_generator()
if rank == 0:
training_progress_bar = tqdm(desc=' - (Epoch %d) ' % eidx,
total=len(training_iterator),
unit="sents"
)
else:
training_progress_bar = None
for batch in training_iter:
seqs_x, seqs_y = batch
batch_size = len(seqs_x)
cum_n_samples += batch_size
cum_n_words += sum(len(s) for s in seqs_y)
try:
# Prepare data
x, y = prepare_data(seqs_x, seqs_y, cuda=GlobalNames.USE_GPU)
loss = compute_forward(model=nmt_model,
critic=critic,
seqs_x=x,
seqs_y=y,
eval=False,
normalization=1.0,
norm_by_words=training_configs["norm_by_words"])
update_cycle -= 1
grad_denom += batch_size
except RuntimeError as e:
if 'out of memory' in str(e):
print('| WARNING: ran out of memory, skipping batch')
oom_count += 1
else:
raise e
# When update_cycle becomes 0, it means end of one batch. Several things will be done:
# - update parameters
# - reset update_cycle and grad_denom
# - update uidx
# - update moving average
if update_cycle == 0:
if world_size > 1:
grad_denom = distributed.all_reduce(grad_denom)
optim.step(denom=grad_denom)
optim.zero_grad()
if training_progress_bar is not None:
training_progress_bar.update(grad_denom)
update_cycle = training_configs['update_cycle']
grad_denom = 0
uidx += 1
if scheduler is None:
pass
elif optimizer_configs["schedule_method"] == "loss":
scheduler.step(metric=best_valid_loss)
else:
scheduler.step(global_step=uidx)
if ma is not None and eidx >= training_configs['moving_average_start_epoch']:
ma.step()
else:
continue
# ================================================================================== #
# Display some information
if should_trigger_by_steps(uidx, eidx, every_n_step=training_configs['disp_freq']):
if world_size > 1:
cum_n_words = sum(distributed.all_gather(cum_n_words))
cum_n_samples = sum(distributed.all_gather(cum_n_samples))
# words per second and sents per second
words_per_sec = cum_n_words / (timer.toc(return_seconds=True))
sents_per_sec = cum_n_samples / (timer.toc(return_seconds=True))
lrate = list(optim.get_lrate())[0]
if summary_writer is not None:
summary_writer.add_scalar("Speed(words/sec)", scalar_value=words_per_sec, global_step=uidx)
summary_writer.add_scalar("Speed(sents/sen)", scalar_value=sents_per_sec, global_step=uidx)
summary_writer.add_scalar("lrate", scalar_value=lrate, global_step=uidx)
summary_writer.add_scalar("oom_count", scalar_value=oom_count, global_step=uidx)
# Reset timer
timer.tic()
cum_n_words = 0
cum_n_samples = 0
# ================================================================================== #
# Loss Validation & Learning rate annealing
if should_trigger_by_steps(global_step=uidx, n_epoch=eidx, every_n_step=training_configs['loss_valid_freq'],
debug=FLAGS.debug):
valid_loss = loss_validation(model=nmt_model,
critic=critic,
valid_iterator=valid_iterator,
rank=rank,
world_size=world_size
)
model_collections.add_to_collection("history_losses", valid_loss)
min_history_loss = np.array(model_collections.get_collection("history_losses")).min()
best_valid_loss = min_history_loss
if summary_writer is not None:
summary_writer.add_scalar("loss", valid_loss, global_step=uidx)
summary_writer.add_scalar("best_loss", min_history_loss, global_step=uidx)
# ================================================================================== #
# BLEU Validation & Early Stop
if should_trigger_by_steps(global_step=uidx, n_epoch=eidx,
every_n_step=training_configs['bleu_valid_freq'],
min_step=training_configs['bleu_valid_warmup'],
debug=FLAGS.debug):
valid_bleu = bleu_validation(uidx=uidx,
valid_iterator=valid_iterator,
batch_size=training_configs["bleu_valid_batch_size"],
model=nmt_model,
bleu_scorer=bleu_scorer,
vocab_tgt=vocab_tgt,
valid_dir=FLAGS.valid_path,
max_steps=training_configs["bleu_valid_configs"]["max_steps"],
beam_size=training_configs["bleu_valid_configs"]["beam_size"],
alpha=training_configs["bleu_valid_configs"]["alpha"],
world_size=world_size,
rank=rank,
)
model_collections.add_to_collection(key="history_bleus", value=valid_bleu)
best_valid_bleu = float(np.array(model_collections.get_collection("history_bleus")).max())
if summary_writer is not None:
summary_writer.add_scalar("bleu", valid_bleu, uidx)
summary_writer.add_scalar("best_bleu", best_valid_bleu, uidx)
# If model get new best valid bleu score
if valid_bleu >= best_valid_bleu:
bad_count = 0
if is_early_stop is False:
if rank == 0:
# 1. save the best model
torch.save(nmt_model.state_dict(), best_model_prefix + ".final")
# 2. record all several best models
best_model_saver.save(global_step=uidx, model=nmt_model, ma=ma)
else:
bad_count += 1
# At least one epoch should be traversed
if bad_count >= training_configs['early_stop_patience'] and eidx > 0:
is_early_stop = True
WARN("Early Stop!")
if summary_writer is not None:
summary_writer.add_scalar("bad_count", bad_count, uidx)
INFO("{0} Loss: {1:.2f} BLEU: {2:.2f} lrate: {3:6f} patience: {4}".format(
uidx, valid_loss, valid_bleu, lrate, bad_count
))
# ================================================================================== #
# Saving checkpoints
if should_trigger_by_steps(uidx, eidx, every_n_step=training_configs['save_freq'], debug=FLAGS.debug):
model_collections.add_to_collection("uidx", uidx)
model_collections.add_to_collection("eidx", eidx)
model_collections.add_to_collection("bad_count", bad_count)
if not is_early_stop:
if rank == 0:
checkpoint_saver.save(global_step=uidx,
model=nmt_model,
optim=optim,
lr_scheduler=scheduler,
collections=model_collections,
ma=ma)
if training_progress_bar is not None:
training_progress_bar.close()
eidx += 1
if eidx > training_configs["max_epochs"]:
break
def train(flags):
"""
flags:
saveto: str
reload: store_true
config_path: str
pretrain_path: str, default=""
model_name: str
log_path: str
"""
# ================================================================================== #
# Initialization for training on different devices
# - CPU/GPU
# - Single/Distributed
Constants.USE_GPU = flags.use_gpu
if flags.multi_gpu:
dist.distributed_init(flags.shared_dir)
world_size = dist.get_world_size()
rank = dist.get_rank()
local_rank = dist.get_local_rank()
else:
world_size = 1
rank = 0
local_rank = 0
if Constants.USE_GPU:
torch.cuda.set_device(local_rank)
Constants.CURRENT_DEVICE = "cuda:{0}".format(local_rank)
else:
Constants.CURRENT_DEVICE = "cpu"
# If not root_rank, close logging
# else write log of training to file.
if rank == 0:
write_log_to_file(
os.path.join(flags.log_path,
"%s.log" % time.strftime("%Y%m%d-%H%M%S")))
else:
close_logging()
# ================================================================================== #
# Parsing configuration files
# - Load default settings
# - Load pre-defined settings
# - Load user-defined settings
configs = prepare_configs(flags.config_path, flags.predefined_config)
data_configs = configs['data_configs']
model_configs = configs['model_configs']
optimizer_configs = configs['optimizer_configs']
training_configs = configs['training_configs']
INFO(pretty_configs(configs))
# use odc
if training_configs['use_odc'] is True:
ave_best_k = check_odc_config(training_configs)
else:
ave_best_k = 0
Constants.SEED = training_configs['seed']
set_seed(Constants.SEED)
timer = Timer()
# ================================================================================== #
# Load Data
INFO('Loading data...')
timer.tic()
# Generate target dictionary
vocab_src = Vocabulary.build_from_file(**data_configs['vocabularies'][0])
vocab_tgt = Vocabulary.build_from_file(**data_configs['vocabularies'][1])
Constants.EOS = vocab_src.eos
Constants.PAD = vocab_src.pad
Constants.BOS = vocab_src.bos
train_bitext_dataset = ZipDataset(
TextLineDataset(data_path=data_configs['train_data'][0],
vocabulary=vocab_src,
max_len=data_configs['max_len'][0],
is_train_dataset=True),
TextLineDataset(data_path=data_configs['train_data'][1],
vocabulary=vocab_tgt,
max_len=data_configs['max_len'][1],
is_train_dataset=True))
valid_bitext_dataset = ZipDataset(
TextLineDataset(
data_path=data_configs['valid_data'][0],
vocabulary=vocab_src,
is_train_dataset=False,
),
TextLineDataset(data_path=data_configs['valid_data'][1],
vocabulary=vocab_tgt,
is_train_dataset=False))
training_iterator = DataIterator(
dataset=train_bitext_dataset,
batch_size=training_configs["batch_size"],
use_bucket=training_configs['use_bucket'],
buffer_size=training_configs['buffer_size'],
batching_func=training_configs['batching_key'],
world_size=world_size,
rank=rank)
valid_iterator = DataIterator(
dataset=valid_bitext_dataset,
batch_size=training_configs['valid_batch_size'],
use_bucket=True,
buffer_size=100000,
numbering=True,
world_size=world_size,
rank=rank)
bleu_scorer = SacreBLEUScorer(
reference_path=data_configs["bleu_valid_reference"],
num_refs=data_configs["num_refs"],
lang_pair=data_configs["lang_pair"],
sacrebleu_args=training_configs["bleu_valid_configs"]
['sacrebleu_args'],
postprocess=training_configs["bleu_valid_configs"]['postprocess'])
INFO('Done. Elapsed time {0}'.format(timer.toc()))
# ================================ Begin ======================================== #
# Build Model & Optimizer
# We would do steps below on after another
# 1. build models & criterion
# 2. move models & criterion to gpu if needed
# 3. load pre-trained model if needed
# 4. build optimizer
# 5. build learning rate scheduler if needed
# 6. load checkpoints if needed
# 0. Initial
lrate = optimizer_configs['learning_rate']
model_collections = Collections()
checkpoint_saver = Saver(
save_prefix="{0}.ckpt".format(
os.path.join(flags.saveto, flags.model_name)),
num_max_keeping=training_configs['num_kept_checkpoints'])
best_model_prefix = os.path.join(
flags.saveto, flags.model_name + Constants.MY_BEST_MODEL_SUFFIX)
best_k_saver = BestKSaver(
save_prefix="{0}.best_k_ckpt".format(
os.path.join(flags.saveto, flags.model_name)),
num_max_keeping=training_configs['num_kept_best_k_checkpoints'])
# 1. Build Model & Criterion
INFO('Building model...')
timer.tic()
nmt_model = build_model(n_src_vocab=vocab_src.max_n_words,
n_tgt_vocab=vocab_tgt.max_n_words,
padding_idx=vocab_src.pad,
vocab_src=vocab_src,
**model_configs)
INFO(nmt_model)
# build teacher model
teacher_model, teacher_model_path = get_teacher_model(
training_configs, model_configs, vocab_src, vocab_tgt, flags)
# build critic
critic = CombinationCriterion(model_configs['loss_configs'],
padding_idx=vocab_tgt.pad,
teacher=teacher_model)
# INFO(critic)
critic.INFO()
# 2. Move to GPU
if Constants.USE_GPU:
nmt_model = nmt_model.cuda()
critic = critic.cuda()
# 3. Load pretrained model if needed
load_pretrained_model(nmt_model,
flags.pretrain_path,
exclude_prefix=None,
device=Constants.CURRENT_DEVICE)
INFO('Done. Elapsed time {0}'.format(timer.toc()))
# 4. Build optimizer
INFO('Building Optimizer...')
if not flags.multi_gpu:
optim = Optimizer(name=optimizer_configs['optimizer'],
model=nmt_model,
lr=lrate,
grad_clip=optimizer_configs['grad_clip'],
optim_args=optimizer_configs['optimizer_params'],
update_cycle=training_configs['update_cycle'])
else:
optim = dist.DistributedOptimizer(
name=optimizer_configs['optimizer'],
model=nmt_model,
lr=lrate,
grad_clip=optimizer_configs['grad_clip'],
optim_args=optimizer_configs['optimizer_params'],
device_id=local_rank)
# 5. Build scheduler for optimizer if needed
scheduler = build_scheduler(
schedule_method=optimizer_configs['schedule_method'],
optimizer=optim,
scheduler_configs=optimizer_configs['scheduler_configs'])
# 6. build moving average
ma = build_ma(training_configs, nmt_model.named_parameters())
INFO('Done. Elapsed time {0}'.format(timer.toc()))
# Reload from latest checkpoint
if flags.reload:
checkpoint_saver.load_latest(model=nmt_model,
optim=optim,
lr_scheduler=scheduler,
collections=model_collections,
ma=ma,
device=Constants.CURRENT_DEVICE)
# broadcast parameters and optimizer states
if world_size > 1:
INFO("Broadcasting model parameters...")
dist.broadcast_parameters(params=nmt_model.state_dict())
INFO("Broadcasting optimizer states...")
dist.broadcast_optimizer_state(optimizer=optim.optim)
INFO('Done.')
# ================================================================================== #
# Prepare training
eidx = model_collections.get_collection("eidx", [0])[-1]
uidx = model_collections.get_collection("uidx", [1])[-1]
bad_count = model_collections.get_collection("bad_count", [0])[-1]
oom_count = model_collections.get_collection("oom_count", [0])[-1]
is_early_stop = model_collections.get_collection("is_early_stop", [
False,
])[-1]
teacher_patience = model_collections.get_collection(
"teacher_patience", [training_configs['teacher_patience']])[-1]
train_loss_meter = AverageMeter()
train_loss_dict_meter = AverageMeterDict(critic.get_critic_name())
sent_per_sec_meter = TimeMeter()
tok_per_sec_meter = TimeMeter()
update_cycle = training_configs['update_cycle']
grad_denom = 0
train_loss = 0.0
cum_n_words = 0
train_loss_dict = dict()
valid_loss = best_valid_loss = float('inf')
if rank == 0:
summary_writer = SummaryWriter(log_dir=flags.log_path)
else:
summary_writer = None
sent_per_sec_meter.start()
tok_per_sec_meter.start()
INFO('Begin training...')
while True:
if summary_writer is not None:
summary_writer.add_scalar("Epoch", (eidx + 1), uidx)
# Build iterator and progress bar
training_iter = training_iterator.build_generator()
if rank == 0:
training_progress_bar = tqdm(desc=' - (Epc {}, Upd {}) '.format(
eidx, uidx),
total=len(training_iterator),
unit="sents")
else:
training_progress_bar = None
for batch in training_iter:
seqs_x, seqs_y = batch
batch_size = len(seqs_x)
cum_n_words += sum(len(s) for s in seqs_y)
try:
# Prepare data
x, y = prepare_data(seqs_x, seqs_y, cuda=Constants.USE_GPU)
loss, loss_dict = compute_forward(
model=nmt_model,
critic=critic,
seqs_x=x,
seqs_y=y,
eval=False,
normalization=1.0,
norm_by_words=training_configs["norm_by_words"])
update_cycle -= 1
grad_denom += batch_size
train_loss += loss
train_loss_dict = add_dict_value(train_loss_dict, loss_dict)
except RuntimeError as e:
if 'out of memory' in str(e):
print('| WARNING: ran out of memory, skipping batch')
oom_count += 1
else:
raise e
# When update_cycle becomes 0, it means end of one batch. Several things will be done:
# - update parameters
# - reset update_cycle and grad_denom, update uidx
# - learning rate scheduling
# - update moving average
if update_cycle == 0:
# 0. reduce variables
if world_size > 1:
grad_denom = dist.all_reduce_py(grad_denom)
train_loss = dist.all_reduce_py(train_loss)
train_loss_dict = dist.all_reduce_py(train_loss_dict)
cum_n_words = dist.all_reduce_py(cum_n_words)
# 1. update parameters
optim.step(denom=grad_denom)
optim.zero_grad()
if training_progress_bar is not None:
training_progress_bar.update(grad_denom)
training_progress_bar.set_description(
' - (Epc {}, Upd {}) '.format(eidx, uidx))
postfix_str = 'TrainLoss: {:.2f}, ValidLoss(best): {:.2f} ({:.2f}), '.format(
train_loss, valid_loss, best_valid_loss)
for critic_name, loss_value in train_loss_dict.items():
postfix_str += (critic_name +
': {:.2f}, ').format(loss_value)
training_progress_bar.set_postfix_str(postfix_str)
# 2. learning rate scheduling
if scheduler is not None and optimizer_configs[
"schedule_method"] != "loss":
scheduler.step(global_step=uidx)
# 3. update moving average
if ma is not None and eidx >= training_configs[
'moving_average_start_epoch']:
ma.step()
# 4. update meters
train_loss_meter.update(train_loss, grad_denom)
train_loss_dict_meter.update(train_loss_dict, grad_denom)
sent_per_sec_meter.update(grad_denom)
tok_per_sec_meter.update(cum_n_words)
# 5. reset accumulated variables, update uidx
update_cycle = training_configs['update_cycle']
grad_denom = 0
uidx += 1
cum_n_words = 0.0
train_loss = 0.0
train_loss_dict = dict()
else:
continue
# ================================================================================== #
# Display some information
if should_trigger_by_steps(
uidx, eidx, every_n_step=training_configs['disp_freq']):
lrate = list(optim.get_lrate())[0]
if summary_writer is not None:
summary_writer.add_scalar(
"Speed(sents/sec)",
scalar_value=sent_per_sec_meter.ave,
global_step=uidx)
summary_writer.add_scalar(
"Speed(words/sec)",
scalar_value=tok_per_sec_meter.ave,
global_step=uidx)
summary_writer.add_scalar(
"train_loss",
scalar_value=train_loss_meter.ave,
global_step=uidx)
# add loss for every critic
if flags.display_loss_detail:
combination_loss = train_loss_dict_meter.value
for key, value in combination_loss.items():
summary_writer.add_scalar(key,
scalar_value=value,
global_step=uidx)
summary_writer.add_scalar("lrate",
scalar_value=lrate,
global_step=uidx)
summary_writer.add_scalar("oom_count",
scalar_value=oom_count,
global_step=uidx)
# Reset Meters
sent_per_sec_meter.reset()
tok_per_sec_meter.reset()
train_loss_meter.reset()
train_loss_dict_meter.reset()
# ================================================================================== #
# Loss Validation & Learning rate annealing
if should_trigger_by_steps(
global_step=uidx,
n_epoch=eidx,
every_n_step=training_configs['loss_valid_freq'],
debug=flags.debug):
with cache_parameters(nmt_model):
valid_loss, valid_loss_dict = loss_evaluation(
model=nmt_model,
critic=critic,
valid_iterator=valid_iterator,
rank=rank,
world_size=world_size)
if scheduler is not None and optimizer_configs[
"schedule_method"] == "loss":
scheduler.step(metric=valid_loss)
model_collections.add_to_collection("history_losses",
valid_loss)
min_history_loss = np.array(
model_collections.get_collection("history_losses")).min()
best_valid_loss = min_history_loss
if summary_writer is not None:
summary_writer.add_scalar("loss",
valid_loss,
global_step=uidx)
summary_writer.add_scalar("best_loss",
min_history_loss,
global_step=uidx)
# ================================================================================== #
# BLEU Validation & Early Stop
if should_trigger_by_steps(
global_step=uidx,
n_epoch=eidx,
every_n_step=training_configs['bleu_valid_freq'],
min_step=training_configs['bleu_valid_warmup'],
debug=flags.debug):
with cache_parameters(nmt_model):
valid_bleu = bleu_evaluation(
uidx=uidx,
valid_iterator=valid_iterator,
batch_size=training_configs["bleu_valid_batch_size"],
model=nmt_model,
bleu_scorer=bleu_scorer,
vocab_src=vocab_src,
vocab_tgt=vocab_tgt,
valid_dir=flags.valid_path,
max_steps=training_configs["bleu_valid_configs"]
["max_steps"],
beam_size=training_configs["bleu_valid_configs"]
["beam_size"],
alpha=training_configs["bleu_valid_configs"]["alpha"],
world_size=world_size,
rank=rank,
)
model_collections.add_to_collection(key="history_bleus",
value=valid_bleu)
best_valid_bleu = float(
np.array(model_collections.get_collection(
"history_bleus")).max())
if summary_writer is not None:
summary_writer.add_scalar("bleu", valid_bleu, uidx)
summary_writer.add_scalar("best_bleu", best_valid_bleu,
uidx)
# If model get new best valid bleu score
if valid_bleu >= best_valid_bleu:
bad_count = 0
if is_early_stop is False:
if rank == 0:
# 1. save the best model
torch.save(nmt_model.state_dict(),
best_model_prefix + ".final")
else:
bad_count += 1
# At least one epoch should be traversed
if bad_count >= training_configs[
'early_stop_patience'] and eidx > 0:
is_early_stop = True
WARN("Early Stop!")
exit(0)
if rank == 0:
best_k_saver.save(global_step=uidx,
metric=valid_bleu,
model=nmt_model,
optim=optim,
lr_scheduler=scheduler,
collections=model_collections,
ma=ma)
# ODC
if training_configs['use_odc'] is True:
if valid_bleu >= best_valid_bleu:
pass
# choose method to generate teachers from checkpoints
# - best
# - ave_k_best
# - ma
if training_configs['teacher_choice'] == 'ma':
teacher_params = ma.export_ma_params()
elif training_configs['teacher_choice'] == 'best':
teacher_params = nmt_model.state_dict()
elif "ave_best" in training_configs['teacher_choice']:
if best_k_saver.num_saved >= ave_best_k:
teacher_params = average_checkpoints(
best_k_saver.get_all_ckpt_path()
[-ave_best_k:])
else:
teacher_params = nmt_model.state_dict()
else:
raise ValueError(
"can not support teacher choice %s" %
training_configs['teacher_choice'])
torch.save(teacher_params, teacher_model_path)
del teacher_params
teacher_patience = 0
critic.set_use_KD(False)
else:
teacher_patience += 1
if teacher_patience >= training_configs[
'teacher_refresh_warmup']:
teacher_params = torch.load(
teacher_model_path,
map_location=Constants.CURRENT_DEVICE)
teacher_model.load_state_dict(teacher_params,
strict=False)
del teacher_params
critic.reset_teacher(teacher_model)
critic.set_use_KD(True)
if summary_writer is not None:
summary_writer.add_scalar("bad_count", bad_count, uidx)
info_str = "{0} Loss: {1:.2f} BLEU: {2:.2f} lrate: {3:6f} patience: {4} ".format(
uidx, valid_loss, valid_bleu, lrate, bad_count)
for key, value in valid_loss_dict.items():
info_str += (key + ': {0:.2f} '.format(value))
INFO(info_str)
# ================================================================================== #
# Saving checkpoints
if should_trigger_by_steps(
uidx,
eidx,
every_n_step=training_configs['save_freq'],
debug=flags.debug):
model_collections.add_to_collection("uidx", uidx)
model_collections.add_to_collection("eidx", eidx)
model_collections.add_to_collection("bad_count", bad_count)
model_collections.add_to_collection("teacher_patience",
teacher_patience)
if not is_early_stop:
if rank == 0:
checkpoint_saver.save(global_step=uidx,
model=nmt_model,
optim=optim,
lr_scheduler=scheduler,
collections=model_collections,
ma=ma)
if training_progress_bar is not None:
training_progress_bar.close()
eidx += 1
if eidx > training_configs["max_epochs"]:
break