def main(opt, device_id, data):
# NOTE: It's important that ``opt`` has been validated and updated
# at this point.
configure_process(opt, device_id)
init_logger(opt.log_file)
assert len(opt.accum_count) == len(opt.accum_steps), \
'Number of accum_count values must match number of accum_steps'
checkpoint = None
# Report src and tgt vocab sizes, including for features
for side in ['src', 'tgt', 'tgt_label']:
logger.info(' * %s vocab size = %d' % (side, len(data["dict"][side])))
# Build model.
model = build_model(opt, data, checkpoint, device_id)
n_params, enc, dec = _tally_parameters(model)
logger.info('encoder: %d' % enc)
logger.info('decoder: %d' % dec)
logger.info('* number of parameters: %d' % n_params)
_check_save_model_path(opt)
# Build optimizer.
optim = Optimizer.from_opt(model, opt, checkpoint=checkpoint)
# Build model saver
model_saver = build_model_saver(opt, opt, model, data, optim)
trainer = build_trainer(
opt, device_id, model, data, optim, model_saver=model_saver)
#from IPython.core.debugger import Pdb; Pdb().set_trace()
train_iter = build_dataset_iter("train", data, opt)
valid_iter = build_dataset_iter(
"valid", data, opt, is_train=False)
if opt.gpu:
logger.info('Starting training on GPU: %s' % opt.gpu)
else:
logger.info('Starting training on CPU, could be very slow')
train_steps = opt.train_steps
trainer.train(
train_iter,
train_steps,
save_checkpoint_steps=opt.save_checkpoint_steps,
valid_iter=valid_iter,
valid_steps=opt.valid_steps)
if opt.tensorboard:
trainer.report_manager.tensorboard_writer.close()
def monitor_iter_fct():
monitor_data = dict()
for src, tgt in zip(opt.monitor_src, opt.monitor_tgt):
fname = src.split("/" if "/" in src else "\\")[-1].split(
".")[0].replace("_src", "")
monitor_data[fname] = build_dataset_iter(lazily_load_dataset(
"monitor", opt, fname),
fields,
opt,
is_train=False)
return monitor_data
def get_speech_iterator(self, name, lang1, lang2, is_train=True): # name: train or valid
"""
Create a new iterator for a dataset.
"""
key = ','.join([x for x in ['speech', name, lang1, lang2] if x is not None])
logger.info("Creating new training %s iterator ..." % key)
speech_direction = (lang1, lang2)
iterator = build_dataset_iter(
lazily_load_dataset(name, self.params.speech_dataset[speech_direction][0]),
self.speech_fields[speech_direction], self.params, is_train)
iterator = iter(iterator)
self.iterators[key] = iterator
return iterator
def get_speech_iterator(self, data_type, lang1, lang2):
"""
Create a new iterator for a dataset.
"""
assert data_type in ['valid']
speech_direction = (lang1, lang2)
lang2_id = self.params.lang2id[lang2]
iterator = build_dataset_iter(
lazily_load_dataset(data_type, self.params.speech_dataset[speech_direction][0]),
self.params.speech_fields[speech_direction], self.params, False)
iterator = iter(iterator)
for batch in iterator:
bos_index = self.params.bos_index[lang2_id]
batch.tgt[0] = bos_index
yield batch
def train_iter_fct():
return build_dataset_iter(lazily_load_dataset("train", opt), fields,
opt)
def main(opt, device_id):
# NOTE: It's important that ``opt`` has been validated and updated
# at this point.
configure_process(opt, device_id)
init_logger(opt.log_file)
# Load checkpoint if we resume from a previous training.
if opt.train_from:
logger.info('Loading checkpoint from %s' % opt.train_from)
checkpoint = torch.load(opt.train_from,
map_location=lambda storage, loc: storage)
model_opt = ArgumentParser.ckpt_model_opts(checkpoint["opt"])
ArgumentParser.update_model_opts(model_opt)
ArgumentParser.validate_model_opts(model_opt)
logger.info('Loading vocab from checkpoint at %s.' % opt.train_from)
vocab = checkpoint['vocab']
else:
checkpoint = None
model_opt = opt
vocab = torch.load(opt.data + '.vocab.pt')
# check for code where vocab is saved instead of fields
# (in the future this will be done in a smarter way)
if old_style_vocab(vocab):
fields = load_old_vocab(vocab,
opt.model_type,
dynamic_dict=opt.copy_attn)
else:
fields = vocab
# Report src and tgt vocab sizes, including for features
for side in ['src', 'tgt']:
f = fields[side]
try:
f_iter = iter(f)
except TypeError:
f_iter = [(side, f)]
for sn, sf in f_iter:
if sf.use_vocab:
logger.info(' * %s vocab size = %d' % (sn, len(sf.vocab)))
# Build model.
model = build_model(model_opt, opt, fields, checkpoint)
n_params, enc, dec = _tally_parameters(model)
logger.info('encoder: %d' % enc)
logger.info('decoder: %d' % dec)
logger.info('* number of parameters: %d' % n_params)
_check_save_model_path(opt)
# Build optimizer.
optim = Optimizer.from_opt(model, opt, checkpoint=checkpoint)
# Build model saver
model_saver = build_model_saver(model_opt, opt, model, fields, optim)
trainer = build_trainer(opt,
device_id,
model,
fields,
optim,
model_saver=model_saver)
train_iter = build_dataset_iter("train", fields, opt)
valid_iter = build_dataset_iter("valid", fields, opt, is_train=False)
if len(opt.gpu_ranks):
logger.info('Starting training on GPU: %s' % opt.gpu_ranks)
else:
logger.info('Starting training on CPU, could be very slow')
train_steps = opt.train_steps
if opt.single_pass and train_steps > 0:
logger.warning("Option single_pass is enabled, ignoring train_steps.")
train_steps = 0
trainer.train(train_iter,
train_steps,
save_checkpoint_steps=opt.save_checkpoint_steps,
valid_iter=valid_iter,
valid_steps=opt.valid_steps)
if opt.tensorboard:
trainer.report_manager.tensorboard_writer.close()
def main(opt, device_id, batch_queue=None, semaphore=None):
# NOTE: It's important that ``opt`` has been validated and updated
# at this point.
configure_process(opt, device_id)
init_logger(opt.log_file)
assert len(opt.accum_count) == len(opt.accum_steps), \
'Number of accum_count values must match number of accum_steps'
# Load checkpoint if we resume from a previous training.
if opt.train_from:
logger.info('Loading checkpoint from %s' % opt.train_from)
checkpoint = torch.load(opt.train_from,
map_location=lambda storage, loc: storage)
model_opt = ArgumentParser.ckpt_model_opts(checkpoint["opt"])
ArgumentParser.update_model_opts(model_opt)
ArgumentParser.validate_model_opts(model_opt)
logger.info('Loading vocab from checkpoint at %s.' % opt.train_from)
vocab = checkpoint['vocab']
else:
checkpoint = None
model_opt = opt
vocab = torch.load(opt.data + '.vocab.pt')
# check for code where vocab is saved instead of fields
# (in the future this will be done in a smarter way)
if old_style_vocab(vocab):
fields = load_old_vocab(vocab,
opt.model_type,
dynamic_dict=opt.copy_attn)
else:
fields = vocab
# Report src and tgt vocab sizes, including for features
for side in ['src', 'tgt']:
f = fields[side]
try:
f_iter = iter(f)
except TypeError:
f_iter = [(side, f)]
for sn, sf in f_iter:
if sf.use_vocab:
logger.info(' * %s vocab size = %d' % (sn, len(sf.vocab)))
# Build model.
model = build_model(model_opt, opt, fields, checkpoint)
n_params, enc, dec, nontrainable = _tally_parameters(model)
logger.info('encoder: %d' % enc)
logger.info('decoder: %d' % dec)
logger.info('non-trainable parameters (tgt_out_emb): %d' % nontrainable)
logger.info('* number of parameters: %d' % n_params)
_check_save_model_path(opt)
# Build optimizer.
optim = Optimizer.from_opt(model, opt, checkpoint=checkpoint)
# Build model saver
model_saver = build_model_saver(model_opt, opt, model, fields, optim)
trainer = build_trainer(opt,
device_id,
model,
fields,
optim,
model_saver=model_saver)
if batch_queue is None:
if len(opt.data_ids) > 1:
train_shards = []
for train_id in opt.data_ids:
shard_base = "train_" + train_id
train_shards.append(shard_base)
train_iter = build_dataset_iter_multiple(train_shards, fields, opt)
else:
if opt.data_ids[0] is not None:
shard_base = "train_" + opt.data_ids[0]
else:
shard_base = "train"
train_iter = build_dataset_iter(shard_base, fields, opt)
else:
assert semaphore is not None, \
"Using batch_queue requires semaphore as well"
def _train_iter():
while True:
batch = batch_queue.get()
semaphore.release()
yield batch
train_iter = _train_iter()
valid_iter = build_dataset_iter("valid", fields, opt, is_train=False)
if len(opt.gpu_ranks):
logger.info('Starting training on GPU: %s' % opt.gpu_ranks)
else:
logger.info('Starting training on CPU, could be very slow')
train_steps = opt.train_steps
if opt.single_pass and train_steps > 0:
logger.warning("Option single_pass is enabled, ignoring train_steps.")
train_steps = 0
trainer.train(train_iter,
train_steps,
save_checkpoint_steps=opt.save_checkpoint_steps,
valid_iter=valid_iter,
valid_steps=opt.valid_steps)
if trainer.report_manager.tensorboard_writer is not None:
trainer.report_manager.tensorboard_writer.close()
def train(opt):
ArgumentParser.validate_train_opts(opt)
set_random_seed(opt.seed, False)
if opt.train_from:
logger.info('Loading checkpoint from %s' % opt.train_from)
checkpoint = torch.load(opt.train_from,
map_location=lambda storage, loc: storage)
logger.info('Loading vocab from checkpoint at %s.' % opt.train_from)
vocab = checkpoint['vocab']
else:
vocab = torch.load(opt.data + '.vocab.pt')
if old_style_vocab(vocab):
fields = load_old_vocab(vocab,
opt.model_type,
dynamic_dict=opt.copy_attn)
else:
fields = vocab
patch_fields(opt, fields)
if len(opt.data_ids) > 1:
train_shards = []
for train_id in opt.data_ids:
shard_base = "train_" + train_id
train_shards.append(shard_base)
train_iter = build_dataset_iter_multiple(train_shards, fields, opt)
else:
if opt.data_ids[0] is not None:
shard_base = "train_" + opt.data_ids[0]
else:
shard_base = "train"
train_iter = build_dataset_iter(shard_base, fields, opt)
nb_gpu = len(opt.gpu_ranks)
if opt.world_size > 1:
queues = []
mp = torch.multiprocessing.get_context('spawn')
semaphore = mp.Semaphore(opt.world_size * opt.queue_size)
procs = []
for device_id in range(nb_gpu):
q = mp.Queue(opt.queue_size)
queues += [q]
procs.append(
mp.Process(target=run,
args=(opt, device_id, error_queue, q, semaphore),
daemon=True))
procs[device_id].start()
logger.info(" Starting process pid: %d " % procs[device_id].pid)
error_handler.add_child(procs[device_id].pid)
producer = mp.Process(target=batch_producer,
args=(
train_iter,
queues,
semaphore,
opt,
),
daemon=True)
producer.start()
error_handler.add_child(producer.pid)
for p in procs:
p.join()
producer.terminate()
elif nb_gpu == 1:
single_main(opt, 0)
else:
single_main(opt, -1)
def main(opt, device_id):
# opt = training_opt_postprocessing(opt, device_id)
init_logger(opt.log_file)
# Load checkpoint if we resume from a previous training.
if opt.train_from:
logger.info('Loading checkpoint from %s' % opt.train_from)
checkpoint = torch.load(opt.train_from,
map_location=lambda storage, loc: storage)
model_opt = checkpoint['opt']
else:
raise Exception('You need to load a model')
logger.info('Loading data from %s' % opt.data)
dataset = next(lazily_load_dataset("train", opt))
data_type = dataset.data_type
logger.info('Data type %s' % data_type)
# Load fields generated from preprocess phase.
fields = _load_fields(dataset, data_type, opt, checkpoint)
# Build model.
model = build_model(model_opt, opt, fields, checkpoint)
n_params, enc, dec = _tally_parameters(model)
logger.info('encoder: %d' % enc)
logger.info('decoder: %d' % dec)
logger.info('* number of parameters: %d' % n_params)
_check_save_model_path(opt)
# Build optimizer.
optim = build_optim(model, opt, checkpoint)
# Build model saver
model_saver = build_model_saver(model_opt, opt, model, fields, optim)
dataset_iter = build_dataset_iter(lazily_load_dataset("train", opt),
fields, opt)
out_file = codecs.open(opt.output, 'w+', 'utf-8')
scorer = onmt.translate.GNMTGlobalScorer(opt.alpha, opt.beta,
opt.coverage_penalty,
opt.length_penalty)
translation_builder = TranslationBuilder(dataset,
fields,
n_best=opt.n_best,
replace_unk=opt.replace_unk,
has_tgt=False)
def train_iter_fct():
return build_dataset_iter(lazily_load_dataset("train", opt), fields,
opt)
trainer = build_trainer(opt,
device_id,
model,
fields,
optim,
data_type,
model_saver=model_saver)
translator = Translator(trainer.model,
fields,
opt.beam_size,
global_scorer=scorer,
out_file=out_file,
report_score=False,
copy_attn=model_opt.copy_attn,
logger=logger)
for i, batch in enumerate(dataset_iter):
unprocessed_translations = translator.translate_batch(batch, dataset)
translations = translation_builder.from_batch(unprocessed_translations)
print "Translations: ", ' '.join(translations[0].pred_sents[0])
trainer.train_from_data(batch, train_steps=1)
if opt.tensorboard:
trainer.report_manager.tensorboard_writer.close()
def main(opt, device_id, batch_queue=None, semaphore=None):
# NOTE: It's important that ``opt`` has been validated and updated
# at this point.
configure_process(opt, device_id)
init_logger(opt.log_file)
assert len(opt.accum_count) == len(opt.accum_steps), \
'Number of accum_count values must match number of accum_steps'
# Load checkpoint if we resume from a previous training.
if opt.train_from:
logger.info('Loading checkpoint from %s' % opt.train_from)
checkpoint = torch.load(opt.train_from,
map_location=lambda storage, loc: storage)
model_opt = ArgumentParser.ckpt_model_opts(checkpoint["opt"])
ArgumentParser.update_model_opts(model_opt)
ArgumentParser.validate_model_opts(model_opt)
logger.info('Loading vocab from checkpoint at %s.' % opt.train_from)
vocab = checkpoint['vocab']
else:
checkpoint = None
model_opt = opt
vocab = torch.load(opt.data + '.vocab.pt')
if opt.teacher_model_path:
logger.info('Loading teacher model from {path}'.format(
path=opt.teacher_model_path))
teacher_model_ckpt = torch.load(
opt.teacher_model_path, map_location=lambda storage, loc: storage)
teacher_model_opt = ArgumentParser.ckpt_model_opts(
teacher_model_ckpt['opt'])
ArgumentParser.update_model_opts(teacher_model_opt)
ArgumentParser.validate_model_opts(teacher_model_opt)
logger.info('Loading vocab from checkpoint at {path}'.format(
path=opt.teacher_model_path))
teacher_vocab = teacher_model_ckpt['vocab']
# check for code where vocab is saved instead of fields
# (in the future this will be done in a smarter way)
if old_style_vocab(vocab):
fields = load_old_vocab(vocab,
opt.model_type,
dynamic_dict=opt.copy_attn)
else:
fields = vocab
teacher_fields = teacher_vocab if opt.teacher_model_path else None
# patch for fields that may be missing in old data/model
# patch_fields(opt, fields)
# Report src and tgt vocab sizes, including for features
report_vocab_size(fields)
if teacher_fields is not None:
report_vocab_size(teacher_fields)
# Build model.
fields_opt = {"original": fields, "teacher": teacher_fields}
model = custom_builder.build_model(model_opt, opt, fields_opt, checkpoint)
# model = build_model(model_opt, opt, fields, checkpoint)
teacher_model = build_model(
teacher_model_opt, teacher_model_opt, teacher_fields,
teacher_model_ckpt) if opt.teacher_model_path else None
n_params, enc, dec = _tally_parameters(model)
logger.info('encoder: %d' % enc)
logger.info('decoder: %d' % dec)
logger.info('* number of parameters: %d' % n_params)
_check_save_model_path(opt)
if teacher_model is not None:
n_params, enc, dec = _tally_parameters(teacher_model)
logger.info('encoder: %d' % enc)
logger.info('decoder: %d' % dec)
logger.info('* number of parameters: %d' % n_params)
_check_save_model_path(teacher_model_opt)
# Build optimizer.
optim = Optimizer.from_opt(model, opt, checkpoint=checkpoint)
# Build model saver
# model_saver = build_model_saver(model_opt, opt, model, fields, optim)
model_saver = custom_model_saver.build_model_saver(model_opt, opt, model,
fields_opt, optim)
tgt_field = dict(teacher_fields)["tgt"].base_field if teacher_model is not None \
else dict(fields)["tgt"].base_field
sos_id = tgt_field.vocab.stoi[tgt_field.init_token]
if teacher_model is not None and opt.word_sampling:
sampler = Emulator(teacher_model,
teacher_fields,
device_id,
max_length=50,
random_sampling_topk=5)
else:
sampler = None
if teacher_model is not None:
trainer = build_trainer(opt,
device_id,
model,
teacher_fields,
optim,
model_saver,
teacher_model=teacher_model,
emulator=sampler)
else:
trainer = build_trainer(opt,
device_id,
model,
fields,
optim,
model_saver,
teacher_model=teacher_model,
emulator=sampler)
if batch_queue is None:
if len(opt.data_ids) > 1:
train_shards = []
for train_id in opt.data_ids:
shard_base = "train_" + train_id
train_shards.append(shard_base)
train_iter = build_dataset_iter_multiple(train_shards, fields, opt)
else:
if opt.data_ids[0] is not None:
shard_base = "train_" + opt.data_ids[0]
else:
shard_base = "train"
train_iter = build_dataset_iter(shard_base, fields, opt)
else:
assert semaphore is not None, \
"Using batch_queue requires semaphore as well"
def _train_iter():
while True:
batch = batch_queue.get()
semaphore.release()
yield batch
train_iter = _train_iter()
valid_iter = build_dataset_iter("valid", fields, opt, is_train=False)
if len(opt.gpu_ranks):
logger.info('Starting training on GPU: %s' % opt.gpu_ranks)
else:
logger.info('Starting training on CPU, could be very slow')
train_steps = opt.train_steps
if opt.single_pass and train_steps > 0:
logger.warning("Option single_pass is enabled, ignoring train_steps.")
train_steps = 0
trainer.train(train_iter,
train_steps,
sos_id=sos_id,
save_checkpoint_steps=opt.save_checkpoint_steps,
valid_iter=valid_iter,
valid_steps=opt.valid_steps)
if trainer.report_manager.tensorboard_writer is not None:
trainer.report_manager.tensorboard_writer.close()
def meta_valid_iter_fct(task_id, is_log=False):
return build_dataset_iter(
lazily_load_dataset("meta_valid", opt, task_id=task_id, is_log=is_log), fields_list[task_id], opt)
def main(opt, device_id):
opt = training_opt_postprocessing(opt, device_id)
init_logger(opt.log_file)
# Load checkpoint if we resume from a previous training.
if opt.train_from:
logger.info('Loading checkpoint from %s' % opt.train_from)
checkpoint = torch.load(opt.train_from,
map_location=lambda storage, loc: storage)
# Load default opts values then overwrite it with opts from
# the checkpoint. It's usefull in order to re-train a model
# after adding a new option (not set in checkpoint)
dummy_parser = configargparse.ArgumentParser()
opts.model_opts(dummy_parser)
default_opt = dummy_parser.parse_known_args([])[0]
model_opt = default_opt
model_opt.__dict__.update(checkpoint['opt'].__dict__)
logger.info('Loading vocab from checkpoint at %s.' % opt.train_from)
vocab = checkpoint['vocab']
else:
checkpoint = None
model_opt = opt
vocab = torch.load(opt.data + '.vocab.pt')
# check for code where vocab is saved instead of fields
# (in the future this will be done in a smarter way)
if old_style_vocab(vocab):
data_type = opt.model_type
fields = load_old_vocab(vocab, data_type, dynamic_dict=opt.copy_attn)
else:
fields = vocab
# Report src and tgt vocab sizes, including for features
for side in ['src', 'tgt']:
for name, f in fields[side]:
try:
f_iter = iter(f)
except TypeError:
f_iter = [(name, f)]
for sn, sf in f_iter:
if sf.use_vocab:
logger.info(' * %s vocab size = %d' % (sn, len(sf.vocab)))
# Build model.
model = build_model(model_opt, opt, fields, checkpoint)
n_params, enc, dec = _tally_parameters(model)
logger.info('encoder: %d' % enc)
logger.info('decoder: %d' % dec)
logger.info('* number of parameters: %d' % n_params)
_check_save_model_path(opt)
# Build optimizer.
optim = Optimizer.from_opt(model, opt, checkpoint=checkpoint)
# Build model saver
model_saver = build_model_saver(model_opt, opt, model, fields, optim)
trainer = build_trainer(opt,
device_id,
model,
fields,
optim,
model_saver=model_saver)
# this line is kind of a temporary kludge because different objects expect
# fields to have a different structure
dataset_fields = dict(chain.from_iterable(fields.values()))
train_iter = build_dataset_iter("train", dataset_fields, opt)
valid_iter = build_dataset_iter("valid",
dataset_fields,
opt,
is_train=False)
if len(opt.gpu_ranks):
logger.info('Starting training on GPU: %s' % opt.gpu_ranks)
else:
logger.info('Starting training on CPU, could be very slow')
train_steps = opt.train_steps
if opt.single_pass and train_steps > 0:
logger.warning("Option single_pass is enabled, ignoring train_steps.")
train_steps = 0
trainer.train(train_iter,
train_steps,
save_checkpoint_steps=opt.save_checkpoint_steps,
valid_iter=valid_iter,
valid_steps=opt.valid_steps)
if opt.tensorboard:
trainer.report_manager.tensorboard_writer.close()
def validate(opt, device_id=0):
configure_process(opt, device_id)
configure_process
if opt.train_from:
logger.info('Loading checkpoint from %s' % opt.train_from)
checkpoint = torch.load(opt.train_from,
map_location=lambda storage, loc: storage)
model_opt = ArgumentParser.ckpt_model_opts(checkpoint["opt"])
ArgumentParser.update_model_opts(model_opt)
ArgumentParser.validate_model_opts(model_opt)
logger.info('Loading vocab from checkpoint at %s.' % opt.train_from)
vocab = checkpoint['vocab']
if old_style_vocab(vocab):
fields = load_old_vocab(vocab,
opt.model_type,
dynamic_dict=opt.copy_attn)
else:
fields = vocab
# Report src and tgt vocab sizes, including for features
for side in ['src', 'tgt']:
f = fields[side]
try:
f_iter = iter(f)
except TypeError:
f_iter = [(side, f)]
for sn, sf in f_iter:
if sf.use_vocab:
logger.info(' * %s vocab size = %d' % (sn, len(sf.vocab)))
model = build_model(model_opt, opt, fields, checkpoint)
n_params, enc, dec = _tally_parameters(model)
logger.info('encoder: %d' % enc)
logger.info('decoder: %d' % dec)
logger.info('* number of parameters: %d' % n_params)
#_check_save_model_path(opt)
valid_iter = build_dataset_iter("valid", fields, opt, is_train=False)
tgt_field = dict(fields)["tgt"].base_field
valid_loss = onmt.utils.loss.build_loss_compute(model,
tgt_field,
opt,
train=False)
model.eval()
with torch.no_grad():
stats = onmt.utils.Statistics()
for batch in valid_iter:
src, src_lengths = batch.src if isinstance(batch.src, tuple) \
else (batch.src, None)
tgt = batch.tgt
# F-prop through the model.
outputs, attns = model(src, tgt, src_lengths)
# Compute loss.
_, batch_stats = valid_loss(batch, outputs, attns)
# Update statistics.
stats.update(batch_stats)
print('n words: %d' % stats.n_words)
print('Validation perplexity: %g' % stats.ppl())
print('Validation accuracy: %g' % stats.accuracy())
print('Validation avg attention entropy: %g' % stats.attn_entropy())
def main(opt, device_id, batch_queue=None, semaphore=None):
# NOTE: It's important that ``opt`` has been validated and updated
# at this point.
configure_process(opt, device_id)
init_logger(opt.log_file)
# save training settings
if opt.log_file:
shutil.copy2(opt.config, opt.exp_dir)
logger.info(vars(opt))
assert len(opt.accum_count) == len(opt.accum_steps), \
'Number of accum_count values must match number of accum_steps'
# Load checkpoint if we resume from a previous training.
if opt.train_from:
logger.info('Loading checkpoint from %s' % opt.train_from)
checkpoint = torch.load(opt.train_from,
map_location=lambda storage, loc: storage)
model_opt = ArgumentParser.ckpt_model_opts(checkpoint["opt"])
ArgumentParser.update_model_opts(model_opt)
ArgumentParser.validate_model_opts(model_opt)
logger.info('Loading vocab from checkpoint at %s.' % opt.train_from)
vocab = checkpoint['vocab']
else:
checkpoint = None
model_opt = opt
# added by @memray for multiple datasets
if opt.vocab and opt.vocab != 'none':
vocab = torch.load(opt.vocab)
elif opt.encoder_type == 'pretrained':
vocab = None
else:
vocab = None
# check for code where vocab is saved instead of fields
# (in the future this will be done in a smarter way)
if old_style_vocab(vocab):
fields = load_old_vocab(
vocab, opt.model_type, dynamic_dict=opt.copy_attn)
else:
fields = vocab
# @memray: a temporary workaround, as well as train.py line 43
if opt.model_type == "keyphrase":
if opt.tgt_type in ["one2one", "multiple"]:
if 'sep_indices' in fields:
del fields['sep_indices']
else:
if 'sep_indices' not in fields:
sep_indices = Field(
use_vocab=False, dtype=torch.long,
postprocessing=make_tgt, sequential=False)
fields["sep_indices"] = sep_indices
if 'src_ex_vocab' not in fields:
src_ex_vocab = RawField()
fields["src_ex_vocab"] = src_ex_vocab
tokenizer = None
if opt.pretrained_tokenizer:
tokenizer = load_pretrained_tokenizer(opt.pretrained_tokenizer, opt.cache_dir, opt.special_vocab_path)
setattr(opt, 'vocab_size', len(tokenizer))
if opt.data_type == 'news':
fields = reload_news_fields(fields, opt, tokenizer)
# Report src and tgt vocab sizes, including for features
for side in ['src', 'tgt']:
f = fields[side]
try:
f_iter = iter(f)
except TypeError:
f_iter = [(side, f)]
for sn, sf in f_iter:
if sf.use_vocab:
logger.info(' * %s vocab size = %d' % (sn, len(sf.vocab)))
# Build model.
model = build_model(model_opt, opt, fields, checkpoint)
n_params, enc, dec = _tally_parameters(model)
logger.info('encoder: %d' % enc)
logger.info('decoder: %d' % dec)
logger.info('* number of parameters: %d' % n_params)
# Build optimizer.
optim = Optimizer.from_opt(model, opt, checkpoint=checkpoint)
# Build model saver
model_saver = build_model_saver(model_opt, opt, model, fields, optim)
trainer = build_trainer(
opt, device_id, model, fields, optim, model_saver=model_saver)
if batch_queue is None:
if len(opt.data_ids) > 1:
# added by @memray, for loading multiple datasets
if opt.multi_dataset:
shard_base = "train"
train_iter = build_dataset_iter(shard_base, fields, opt, tokenizer=tokenizer)
else:
train_shards = []
for train_id in opt.data_ids:
shard_base = "train_" + train_id
train_shards.append(shard_base)
train_iter = build_dataset_iter_multiple(train_shards, fields, opt, tokenizer=tokenizer)
else:
shard_base = "train"
train_iter = build_dataset_iter(shard_base, fields, opt)
else:
assert semaphore is not None, \
"Using batch_queue requires semaphore as well"
def _train_iter():
while True:
batch = batch_queue.get()
semaphore.release()
yield batch
train_iter = _train_iter()
if opt.valid:
valid_iter = build_dataset_iter(
"valid", fields, opt, is_train=False)
else:
valid_iter = None
if len(opt.gpu_ranks):
logger.info('Starting training on GPU: %s' % opt.gpu_ranks)
else:
logger.info('Starting training on CPU, could be very slow')
train_steps = opt.train_steps
if opt.single_pass and train_steps > 0:
logger.warning("Option single_pass is enabled, ignoring train_steps.")
train_steps = 0
trainer.train(
train_iter,
train_steps,
save_checkpoint_steps=opt.save_checkpoint_steps,
valid_iter=valid_iter,
valid_steps=opt.valid_steps)
if trainer.report_manager.tensorboard_writer is not None:
trainer.report_manager.tensorboard_writer.close()
def main(opt, device_id):
# NOTE: It's important that ``opt`` has been validated and updated
# at this point.
configure_process(opt, device_id)
init_logger(opt.log_file)
assert len(opt.accum_count) == len(opt.accum_steps), \
'Number of accum_count values must match number of accum_steps'
# Load checkpoint if we resume from a previous training.
if opt.train_from:
logger.info('Loading checkpoint from %s' % opt.train_from)
checkpoint = torch.load(opt.train_from,
map_location=lambda storage, loc: storage)
model_opt = ArgumentParser.ckpt_model_opts(checkpoint["opt"])
ArgumentParser.update_model_opts(model_opt)
ArgumentParser.validate_model_opts(model_opt)
logger.info('Loading vocab from checkpoint at %s.' % opt.train_from)
vocab = checkpoint['vocab']
else:
checkpoint = None
model_opt = opt
vocab = torch.load(opt.data + '.vocab.pt')
# check for code where vocab is saved instead of fields
# (in the future this will be done in a smarter way)
if old_style_vocab(vocab):
fields = load_old_vocab(
vocab, opt.model_type, dynamic_dict=opt.copy_attn)
else:
fields = vocab
# Report src and tgt vocab sizes, including for features
for side in ['src', 'tgt']:
f = fields[side]
try:
f_iter = iter(f)
except TypeError:
f_iter = [(side, f)]
for sn, sf in f_iter:
if sf.use_vocab:
logger.info(' * %s vocab size = %d' % (sn, len(sf.vocab)))
# Build model.
model = build_model(model_opt, opt, fields, checkpoint)
n_params, enc, dec = _tally_parameters(model)
logger.info('encoder: %d' % enc)
logger.info('decoder: %d' % dec)
logger.info('* number of parameters: %d' % n_params)
_check_save_model_path(opt)
# Build optimizer.
optim = Optimizer.from_opt(model, opt, checkpoint=checkpoint)
# Build model saver
model_saver = build_model_saver(model_opt, opt, model, fields, optim)
trainer = build_trainer(
opt, device_id, model, fields, optim, model_saver=model_saver)
train_iter = build_dataset_iter("train", fields, opt)
valid_iter = build_dataset_iter(
"valid", fields, opt, is_train=False)
if len(opt.gpu_ranks):
logger.info('Starting training on GPU: %s' % opt.gpu_ranks)
else:
logger.info('Starting training on CPU, could be very slow')
train_steps = opt.train_steps
if opt.single_pass and train_steps > 0:
logger.warning("Option single_pass is enabled, ignoring train_steps.")
train_steps = 0
trainer.train(
train_iter,
train_steps,
save_checkpoint_steps=opt.save_checkpoint_steps,
valid_iter=valid_iter,
valid_steps=opt.valid_steps)
if opt.tensorboard:
trainer.report_manager.tensorboard_writer.close()
def valid_iter_fct(task_id):
return build_dataset_iter(
lazily_load_dataset("valid", opt, task_id=task_id), fields_list[task_id], opt)
def main(opt, device_id):
opt = training_opt_postprocessing(opt, device_id)
init_logger(opt.log_file)
# Load checkpoint if we resume from a previous training.
if opt.train_from:
logger.info('Loading checkpoint from %s' % opt.train_from)
checkpoint = torch.load(opt.train_from,
map_location=lambda storage, loc: storage)
# Load default opts values then overwrite it with opts from
# the checkpoint. It's usefull in order to re-train a model
# after adding a new option (not set in checkpoint)
dummy_parser = configargparse.ArgumentParser()
opts.model_opts(dummy_parser)
default_opt = dummy_parser.parse_known_args([])[0]
model_opt = default_opt
model_opt.__dict__.update(checkpoint['opt'].__dict__)
logger.info('Loading vocab from checkpoint at %s.' % opt.train_from)
vocab = checkpoint['vocab']
else:
checkpoint = None
model_opt = opt
vocab = torch.load(opt.data + '.vocab.pt')
# Load a shard dataset to determine the data_type.
# (All datasets have the same data_type).
# this should be refactored out of existence reasonably soon
first_dataset = torch.load(glob.glob(opt.data + '.train*.pt')[0])
data_type = first_dataset.data_type
# check for code where vocab is saved instead of fields
# (in the future this will be done in a smarter way
if old_style_vocab(vocab):
fields = load_fields_from_vocab(vocab, data_type)
else:
fields = vocab
# Report src and tgt vocab sizes, including for features
for side in ['src', 'tgt']:
for name, f in fields[side]:
if f.use_vocab:
logger.info(' * %s vocab size = %d' % (name, len(f.vocab)))
# Build model.
model = build_model(model_opt, opt, fields, checkpoint)
n_params, enc, dec = _tally_parameters(model)
logger.info('encoder: %d' % enc)
logger.info('decoder: %d' % dec)
logger.info('* number of parameters: %d' % n_params)
_check_save_model_path(opt)
# Build optimizer.
optim = build_optim(model, opt, checkpoint)
# Build model saver
model_saver = build_model_saver(model_opt, opt, model, fields, optim)
trainer = build_trainer(opt,
device_id,
model,
fields,
optim,
data_type,
model_saver=model_saver)
# this line is kind of a temporary kludge because different objects expect
# fields to have a different structure
dataset_fields = dict(chain.from_iterable(fields.values()))
train_iter = build_dataset_iter("train", dataset_fields, opt)
valid_iter = build_dataset_iter("valid",
dataset_fields,
opt,
is_train=False)
if len(opt.gpu_ranks):
logger.info('Starting training on GPU: %s' % opt.gpu_ranks)
else:
logger.info('Starting training on CPU, could be very slow')
trainer.train(train_iter, valid_iter, opt.train_steps, opt.valid_steps)
if opt.tensorboard:
trainer.report_manager.tensorboard_writer.close()
def main(opt, device_id):
opt = training_opt_postprocessing(opt, device_id)
init_logger(opt.log_file)
# Gather information related to the training script and commit version
script_path = os.path.abspath(__file__)
script_dir = os.path.dirname(os.path.dirname(script_path))
logger.info('Train script dir: %s' % script_dir)
git_commit = str(subprocess.check_output(['bash', script_dir + '/cluster_scripts/git_version.sh']))
logger.info("Git Commit: %s" % git_commit[2:-3])
# Load checkpoint if we resume from a previous training.
if opt.train_from:
# TODO: load MTL model
logger.info('Loading checkpoint from %s' % opt.train_from)
checkpoint = torch.load(opt.train_from,
map_location=lambda storage, loc: storage)
# Load default opts values then overwrite it with opts from
# the checkpoint. It's usefull in order to re-train a model
# after adding a new option (not set in checkpoint)
dummy_parser = configargparse.ArgumentParser()
opts.model_opts(dummy_parser)
default_opt = dummy_parser.parse_known_args([])[0]
model_opt = default_opt
model_opt.__dict__.update(checkpoint['opt'].__dict__)
else:
checkpoint = None
model_opt = opt
num_tasks = len(opt.data.split(','))
opt.num_tasks = num_tasks
checkpoint_list=[]
if opt.warm_model:
base_name=opt.warm_model
for task_id in range(num_tasks):
chkpt_path=base_name.replace("X",str(task_id))
if not os.path.isfile(chkpt_path):
chkpt_path = base_name.replace("X", str(0))
logger.info('Loading a checkpoint from %s' % chkpt_path)
checkpoint = torch.load(chkpt_path,
map_location=lambda storage, loc: storage)
checkpoint_list.append(checkpoint)
else:
for task_id in range(num_tasks):
checkpoint_list.append(None)
fields_list = []
data_type=None
for task_id in range(num_tasks):
# Peek the first dataset to determine the data_type.
# (All datasets have the same data_type).
first_dataset = next(lazily_load_dataset("train", opt, task_id=task_id))
data_type = first_dataset.data_type
# Load fields generated from preprocess phase.
if opt.mtl_shared_vocab and task_id > 0:
logger.info(' * vocabulary size. Same as the main task!')
fields = fields_list[0]
else:
fields = load_fields(first_dataset, opt, checkpoint_list[task_id], task_id=task_id)
# Report src/tgt features.
src_features, tgt_features = _collect_report_features(fields)
for j, feat in enumerate(src_features):
logger.info(' * (Task %d) src feature %d size = %d'
% (task_id, j, len(fields[feat].vocab)))
for j, feat in enumerate(tgt_features):
logger.info(' * (Task %) tgt feature %d size = %d'
% (task_id, j, len(fields[feat].vocab)))
fields_list.append(fields)
if opt.epochs > -1:
total_num_batch = 0
for task_id in range(num_tasks):
train_iter = build_dataset_iter(lazily_load_dataset("train", opt, task_id=task_id), fields_list[task_id], opt)
for i, batch in enumerate(train_iter):
num_batch = i
total_num_batch+=num_batch
if opt.mtl_schedule < 10:
break
num_batch = total_num_batch
opt.train_steps = (num_batch * opt.epochs) + 1
# Do the validation and save after each epoch
opt.valid_steps = num_batch
opt.save_checkpoint_steps = 1
# logger.info(opt_to_string(opt))
logger.info(opt)
# Build model(s).
models_list = []
for task_id in range(num_tasks):
if opt.mtl_fully_share and task_id > 0:
# Since we only have one model, copy the pointer to the model for all
models_list.append(models_list[0])
else:
main_model = models_list[0] if task_id > 0 else None
model = build_model(model_opt, opt, fields_list[task_id], checkpoint_list[task_id], main_model=main_model, task_id=task_id)
n_params, enc, dec = _tally_parameters(model)
logger.info('(Task %d) encoder: %d' % (task_id, enc))
logger.info('(Task %d) decoder: %d' % (task_id, dec))
logger.info('* number of parameters: %d' % n_params)
_check_save_model_path(opt)
models_list.append(model)
# combine parameters of different models and consider shared parameters just once.
def combine_named_parameters(named_params_list):
observed_params = []
for model_named_params in named_params_list:
for name, p in model_named_params:
is_observed = False
# Check whether we observed this parameter before
for param in observed_params:
if p is param:
is_observed = True
break
if not is_observed:
observed_params.append(p)
yield name, p
# Build optimizer.
optims_list = []
all_models_params=[]
for task_id in range(num_tasks):
if not opt.mtl_shared_optimizer:
optim = build_optim(models_list[task_id], opt, checkpoint)
optims_list.append(optim)
else:
all_models_params.append(models_list[task_id].named_parameters())
# Extract the list of shared parameters among the models of all tasks.
observed_params = []
shared_params = []
for task_id in range(num_tasks):
for name, p in models_list[task_id].named_parameters():
is_observed = False
# Check whether we observed this parameter before
for param in observed_params:
if p is param:
shared_params.append(name)
is_observed = True
break
if not is_observed:
observed_params.append(p)
opt.shared_params = shared_params
if opt.mtl_shared_optimizer:
optim = build_optim_mtl_params(combine_named_parameters(all_models_params), opt, checkpoint)
optims_list.append(optim)
# Build model saver
model_saver = build_mtl_model_saver(model_opt, opt, models_list, fields_list, optims_list)
trainer = build_trainer(opt, device_id, models_list, fields_list,
optims_list, data_type, model_saver=model_saver)
def train_iter_fct(task_id):
return build_dataset_iter(
lazily_load_dataset("train", opt, task_id=task_id), fields_list[task_id], opt)
def valid_iter_fct(task_id):
return build_dataset_iter(
lazily_load_dataset("valid", opt, task_id=task_id), fields_list[task_id], opt)
def meta_valid_iter_fct(task_id, is_log=False):
return build_dataset_iter(
lazily_load_dataset("meta_valid", opt, task_id=task_id, is_log=is_log), fields_list[task_id], opt)
# Do training.
if len(opt.gpu_ranks):
logger.info('Starting training on GPU: %s' % opt.gpu_ranks)
else:
logger.info('Starting training on CPU, could be very slow')
trainer.train(train_iter_fct, valid_iter_fct, opt.train_steps,
opt.valid_steps, meta_valid_iter_fct=meta_valid_iter_fct)
if opt.tensorboard:
trainer.report_manager.tensorboard_writer.close()
def train(opt):
ArgumentParser.validate_train_opts(opt)
ArgumentParser.update_model_opts(opt)
ArgumentParser.validate_model_opts(opt)
set_random_seed(opt.seed, False)
# @Memray, check the dir existence beforehand to avoid path conflicting errors,
# and set save_model, tensorboard_log_dir, wandb_log_dir if not exist
train_single._check_save_model_path(opt)
if not os.path.exists(opt.tensorboard_log_dir):
os.makedirs(opt.tensorboard_log_dir)
# Scan previous checkpoint to resume training
latest_step = 0
latest_ckpt = None
for subdir, dirs, filenames in os.walk(opt.exp_dir):
for filename in sorted(filenames):
if not filename.endswith('.pt'):
continue
step = int(filename[filename.rfind('_') + 1:filename.rfind('.pt')])
if step > latest_step:
latest_ckpt = os.path.join(subdir, filename)
latest_step = step
# if not saved in the exp folder, check opt.save_model
if latest_ckpt is None and opt.save_model is not None:
save_model_dir = os.path.dirname(os.path.abspath(opt.save_model))
model_prefix = opt.save_model[opt.save_model.rfind(os.path.sep) + 1:]
for subdir, dirs, filenames in os.walk(save_model_dir):
for filename in sorted(filenames):
if not filename.endswith('.pt'):
continue
if not filename.startswith(model_prefix):
continue
step = int(filename[filename.rfind('_') +
1:filename.rfind('.pt')])
if step > latest_step:
latest_ckpt = os.path.join(subdir, filename)
latest_step = step
if latest_ckpt is not None:
logger.info("A previous checkpoint is found, train from it: %s" %
latest_ckpt)
setattr(opt, 'train_from', latest_ckpt)
setattr(opt, 'reset_optim', 'none')
# Load checkpoint if we resume from a previous training.
if opt.train_from:
logger.info('Loading checkpoint from %s' % opt.train_from)
checkpoint = torch.load(opt.train_from,
map_location=lambda storage, loc: storage)
logger.info('Loading vocab from checkpoint at %s.' % opt.train_from)
vocab = checkpoint['vocab']
elif opt.vocab and opt.vocab != 'none':
# added by @memray for multiple datasets
vocab = torch.load(opt.vocab)
# check for code where vocab is saved instead of fields
# (in the future this will be done in a smarter way)
if old_style_vocab(vocab):
vocab = load_old_vocab(vocab,
opt.model_type,
dynamic_dict=opt.copy_attn)
elif opt.encoder_type == 'pretrained':
vocab = None
else:
vocab = None
fields = vocab
# @memray: a temporary workaround, as well as train_single.py line 78
if fields and opt.data_type == "keyphrase":
if opt.tgt_type in ["one2one", "multiple"]:
if 'sep_indices' in fields:
del fields['sep_indices']
else:
if 'sep_indices' not in fields:
sep_indices = Field(use_vocab=False,
dtype=torch.long,
postprocessing=make_tgt,
sequential=False)
fields["sep_indices"] = sep_indices
if 'src_ex_vocab' not in fields:
src_ex_vocab = RawField()
fields["src_ex_vocab"] = src_ex_vocab
# @memray reload fields for news dataset and pretrained models
tokenizer = None
if opt.pretrained_tokenizer is not None:
tokenizer = load_pretrained_tokenizer(opt.pretrained_tokenizer,
opt.cache_dir,
opt.special_vocab_path)
setattr(opt, 'vocab_size', len(tokenizer))
if opt.data_type == 'news':
fields = reload_news_fields(opt, tokenizer=tokenizer)
# elif opt.data_type == 'keyphrase':
# fields = reload_keyphrase_fields(opt, tokenizer=tokenizer)
if len(opt.data_ids) > 1:
# added by @memray, for loading multiple datasets
if opt.multi_dataset:
shard_base = "train"
train_iter = build_dataset_iter(shard_base,
fields,
opt,
multi=True)
else:
train_shards = []
for train_id in opt.data_ids:
shard_base = "train_" + train_id
train_shards.append(shard_base)
train_iter = build_dataset_iter_multiple(train_shards, fields, opt)
else:
shard_base = "train"
train_iter = build_dataset_iter(shard_base, fields, opt)
nb_gpu = len(opt.gpu_ranks)
if opt.world_size > 1:
queues = []
mp = torch.multiprocessing.get_context('spawn')
semaphore = mp.Semaphore(opt.world_size * opt.queue_size)
# Create a thread to listen for errors in the child processes.
error_queue = mp.SimpleQueue()
error_handler = ErrorHandler(error_queue)
# Train with multiprocessing.
procs = []
for device_id in range(nb_gpu):
q = mp.Queue(opt.queue_size)
queues += [q]
procs.append(
mp.Process(target=run,
args=(opt, device_id, error_queue, q, semaphore),
daemon=True))
procs[device_id].start()
logger.info(" Starting process pid: %d " % procs[device_id].pid)
error_handler.add_child(procs[device_id].pid)
producer = mp.Process(target=batch_producer,
args=(
train_iter,
queues,
semaphore,
opt,
),
daemon=True)
producer.start()
error_handler.add_child(producer.pid)
for p in procs:
p.join()
producer.terminate()
elif nb_gpu == 1: # case 1 GPU only
single_main(opt, 0)
else: # case only CPU
single_main(opt, -1)
def main(opt, device_id):
# NOTE: It's important that ``opt`` has been validated and updated
# at this point.
if opt.local_rank != -1:
torch.cuda.set_device(opt.local_rank)
device = torch.device("cuda", opt.local_rank)
torch.distributed.init_process_group(backend='nccl')
device_id = opt.local_rank
world_size = torch.distributed.get_world_size()
else:
if device_id == -1:
device = torch.device("cpu")
else:
device = torch.device("cuda", device_id)
if opt.local_rank > 0:
logger.disabled = True
configure_process(opt, device_id)
init_logger(opt.log_file)
# Load checkpoint if we resume from a previous training.
if opt.train_from:
logger.info('Loading checkpoint from %s' % opt.train_from)
checkpoint = torch.load(opt.train_from,
map_location=lambda storage, loc: storage)
model_opt = ArgumentParser.ckpt_model_opts(checkpoint["opt"])
ArgumentParser.update_model_opts(model_opt)
ArgumentParser.validate_model_opts(model_opt)
logger.info('Loading vocab from checkpoint at %s.' % opt.train_from)
vocab = checkpoint['vocab']
else:
checkpoint = None
model_opt = opt
vocab = torch.load(opt.data + '.vocab.pt')
# check for code where vocab is saved instead of fields
# (in the future this will be done in a smarter way)
if old_style_vocab(vocab):
fields = load_old_vocab(vocab,
opt.model_type,
dynamic_dict=opt.copy_attn)
else:
fields = vocab
# Report src and tgt vocab sizes, including for features
for side in ['src', 'tgt']:
f = fields[side]
try:
f_iter = iter(f)
except TypeError:
f_iter = [(side, f)]
for sn, sf in f_iter:
if sf.use_vocab:
logger.info(' * %s vocab size = %d' % (sn, len(sf.vocab)))
# Build model.
model = build_model(model_opt, opt, fields, checkpoint)
n_params, enc, dec = _tally_parameters(model)
logger.info('encoder: %d' % enc)
logger.info('decoder: %d' % dec)
logger.info('* number of parameters: %d' % n_params)
_check_save_model_path(opt)
# Build optimizer.
optim = Optimizer.from_opt(model, opt, checkpoint=checkpoint)
# Build model saver
model_saver = build_model_saver(model_opt, opt, model, fields, optim)
trainer = build_trainer(opt,
device_id,
model,
fields,
optim,
model_saver=model_saver)
if opt.bert_kd:
src_vocab = vocab['src'].fields[0][1].vocab.stoi
tgt_vocab = vocab['tgt'].fields[0][1].vocab.stoi
assert 0 < opt.kd_topk <= 128
train_dataset = BertKdDataset(opt.data_db,
opt.bert_dump,
src_vocab,
tgt_vocab,
max_len=150,
k=opt.kd_topk)
BUCKET_SIZE = 8192
if True or opt.local_rank == -1 and opt.world_size == 1:
train_sampler = TokenBucketSampler(train_dataset.keys,
BUCKET_SIZE,
opt.batch_size,
batch_multiple=1)
else:
assert False # seems like it's handled in training loop
train_sampler = DistributedTokenBucketSampler(world_size,
device_id,
train_dataset.keys,
BUCKET_SIZE,
opt.batch_size,
batch_multiple=1)
train_loader = DataLoader(train_dataset,
batch_sampler=train_sampler,
num_workers=4,
collate_fn=BertKdDataset.pad_collate)
train_iter = cycle_loader(train_loader, device)
else:
train_iter = build_dataset_iter("train", fields, opt)
valid_iter = build_dataset_iter("valid", fields, opt, is_train=False)
if len(opt.gpu_ranks):
logger.info('Starting training on GPU: %s' % opt.gpu_ranks)
else:
logger.info('Starting training on CPU, could be very slow')
train_steps = opt.train_steps
if opt.single_pass and train_steps > 0:
logger.warning("Option single_pass is enabled, ignoring train_steps.")
train_steps = 0
trainer.train(train_iter,
train_steps,
save_checkpoint_steps=opt.save_checkpoint_steps,
valid_iter=valid_iter,
valid_steps=opt.valid_steps)
if opt.tensorboard:
if trainer.report_manager.tensorboard_writer:
trainer.report_manager.tensorboard_writer.close()
def main(opt, device_id):
# NOTE: It's important that ``opt`` has been validated and updated
# at this point.
# import pdb
# _check_ = torch.load("/home/irteam/users/kaist/ginalee/clean_data/baselines/9-domain5-185pre_step_2500.pt")
# model_encoder = [i for i in _check_['model'].keys() if "encoder" in i.split(".")]
# encoder = {}
# pdb.set_trace()
# for i, param in enumerate(model_encoder):
# if i == 0:
# encoder['embeddings.word_embeddings.weight'] = _check_['model'][param]
# continue
# param_ = ".".join(param.split(".")[1:])
# # if param.split(".")[1] == 'encoder':
# # param_ = ".".join(param.split(".")[2:])
# # else:
# # param_ = ".".join(param.split(".")[1:])
# encoder[param_] = _check_['model'][param]
# pdb.set_trace()
configure_process(opt, device_id)
init_logger(opt.log_file)
logger.info(opt)
# Load checkpoint if we resume from a previous training.
if opt.train_from:
logger.info('Loading checkpoint from %s' % opt.train_from)
checkpoint = torch.load(opt.train_from,
map_location=lambda storage, loc: storage)
model_opt = ArgumentParser.ckpt_model_opts(checkpoint["opt"])
ArgumentParser.update_model_opts(model_opt)
ArgumentParser.validate_model_opts(model_opt)
logger.info('Loading vocab from checkpoint at %s.' % opt.train_from)
load_vocab = torch.load(opt.data + '.vocab.pt')
vocab = checkpoint['vocab']
load_vocab['src'].fields[0][1].vocab = vocab['src'].fields[0][1].vocab
load_vocab['tgt'].fields[0][1].vocab = vocab['tgt'].fields[0][1].vocab
vocab = load_vocab
else:
checkpoint = None
model_opt = opt
vocab = torch.load(opt.data + '.vocab.pt')
# check for code where vocab is saved instead of fields
# (in the future this will be done in a smarter way)
if old_style_vocab(vocab):
fields = load_old_vocab(vocab,
opt.model_type,
dynamic_dict=opt.copy_attn)
else:
fields = vocab
# Report src and tgt vocab sizes, including for features
for side in ['src', 'tgt']:
f = fields[side]
try:
f_iter = iter(f)
except TypeError:
f_iter = [(side, f)]
for sn, sf in f_iter:
if sf.use_vocab:
logger.info(' * %s vocab size = %d' % (sn, len(sf.vocab)))
# Build model.
model = build_model(model_opt, opt, fields, checkpoint)
if opt.pretrain_from:
check = torch.load(opt.pretrain_from,
map_location=lambda storage, loc: storage)
model.load_state_dict(check['model'], strict=False)
model.load_state_dict(check['generator'], strict=False)
if 'dom_classifier' in check:
model.load_state_dict(check['dom_classifier'], strict=False)
n_params, enc, dec = _tally_parameters(model)
logger.info('encoder: %d' % enc)
logger.info('decoder: %d' % dec)
logger.info('* number of parameters: %d' % n_params)
_check_save_model_path(opt)
# Build optimizer.
optim = Optimizer.from_opt(model, opt, checkpoint=checkpoint)
# Build model saver
model_saver = build_model_saver(model_opt, opt, model, fields, optim)
translator = None
if opt.domain_cls_enc == False:
translator = train_build_translator(opt,
model,
model_opt,
fields,
report_score=True)
trainer = build_trainer(translator,
opt,
device_id,
model,
fields,
optim,
model_saver=model_saver)
train_iter = build_dataset_iter("train", fields, opt)
valid_iter = build_dataset_iter("valid", fields, opt, is_train=False)
if len(opt.gpu_ranks):
logger.info('Starting training on GPU: %s' % opt.gpu_ranks)
else:
logger.info('Starting training on CPU, could be very slow')
train_steps = opt.train_steps
if opt.single_pass and train_steps > 0:
logger.warning("Option single_pass is enabled, ignoring train_steps.")
train_steps = 0
trainer.train(train_iter,
train_steps,
save_checkpoint_steps=opt.save_checkpoint_steps,
valid_iter=valid_iter,
valid_steps=opt.valid_steps)
if opt.tensorboard:
trainer.report_manager.tensorboard_writer.close()
def valid_iter_fct():
return build_dataset_iter(lazily_load_dataset("valid", opt, logger),
fields, opt)
def main(opt, device_id):
# NOTE: It's important that ``opt`` has been validated and updated
# at this point.
configure_process(opt, device_id)
init_logger(opt.log_file)
# Load checkpoint if we resume from a previous training.
load_str = opt.train_from if opt.train_from else opt.load_uncond_from
if load_str:
logger.info('Loading checkpoint from %s' % load_str)
checkpoint = torch.load(load_str,
map_location=lambda storage, loc: storage)
logger.info('Loading vocab from checkpoint at %s.' % load_str)
vocab = checkpoint['vocab']
if opt.train_from:
model_opt = ArgumentParser.ckpt_model_opts(checkpoint["opt"])
ArgumentParser.update_model_opts(model_opt)
ArgumentParser.validate_model_opts(model_opt)
else:
model_opt = opt
else:
checkpoint = None
model_opt = opt
vocab = torch.load(opt.data + '.vocab.pt')
if opt.gpt2_params_path is not None:
import tensorflow as tf
import numpy as np
# Taken from pytorch-pretrained-BERT:
# Load weights from TF model
logger.info("Loading TF GPT weights...")
init_vars = tf.train.list_variables(opt.gpt2_params_path)
names = []
arrays = []
for name, shape in init_vars:
if opt.gpt_emb_only and ('wpe' not in name and 'wte' not in name):
continue
if opt.gpt_wpe_only and 'wpe' not in name:
continue
#print("Loading TF weight {} with shape {}".format(name, shape))
array = tf.train.load_variable(opt.gpt2_params_path, name)
names.append(name)
arrays.append(array.squeeze())
logger.info("Done.")
if checkpoint is None:
checkpoint = {'gpt2_params': zip(names, arrays)}
else:
checkpoint['gpt2_params'] = zip(names, arrays)
if opt.encoder_from is not None:
logger.info('Loading checkpoint with encoder from %s' %
opt.encoder_from)
enc_checkpoint = torch.load(opt.encoder_from,
map_location=lambda storage, loc: storage)
enc_vocab = enc_checkpoint['vocab']
if vocab['src'].base_field.vocab != enc_vocab['src'].base_field.vocab:
raise ValueError(
'encoder vocab and model vocab need to be identical it using pretrained encoder'
)
if checkpoint is None:
checkpoint = {}
checkpoint['enc_model'] = enc_checkpoint['model']
# check for code where vocab is saved instead of fields
# (in the future this will be done in a smarter way)
if old_style_vocab(vocab):
fields = load_old_vocab(vocab,
opt.model_type,
dynamic_dict=opt.copy_attn)
else:
fields = vocab
# Report src and tgt vocab sizes, including for features
sides = ['tgt'] if opt.model_type == 'none' else ['src', 'tgt']
for side in sides:
f = fields[side]
try:
f_iter = iter(f)
except TypeError:
f_iter = [(side, f)]
for sn, sf in f_iter:
if sf.use_vocab:
logger.info(' * %s vocab size = %d' % (sn, len(sf.vocab)))
# Build model.
model = build_model(model_opt, opt, fields, checkpoint)
n_params, enc, dec, lm_dec = _tally_parameters(model)
n_params_t, enc_t, dec_t, lm_dec_t = _tally_parameters(model,
only_trainable=True)
logger.info('encoder: %d (%d)' % (enc, enc_t))
logger.info('decoder: %d (%d)' % (dec, dec_t))
if opt.simple_fusion:
logger.info('lm decoder: %d (%d)' % (lm_dec, lm_dec_t))
logger.info('* number of parameters: %d (%d)' % (n_params, n_params_t))
_check_save_model_path(opt)
if not opt.train_from and opt.gpt2_params_path is not None:
checkpoint = None
# Build optimizer.
optim = Optimizer.from_opt(model, opt, checkpoint=checkpoint)
# Build model saver
model_saver = build_model_saver(model_opt, opt, model, fields, optim)
trainer = build_trainer(opt,
device_id,
model,
fields,
optim,
model_saver=model_saver)
train_iter = build_dataset_iter("train", fields, opt)
valid_iter = build_dataset_iter("valid", fields, opt, is_train=False)
if len(opt.gpu_ranks):
logger.info('Starting training on GPU: %s' % opt.gpu_ranks)
else:
logger.info('Starting training on CPU, could be very slow')
train_steps = opt.train_steps
if opt.single_pass and train_steps > 0:
logger.warning("Option single_pass is enabled, ignoring train_steps.")
train_steps = 0
trainer.train(train_iter,
train_steps,
save_checkpoint_steps=opt.save_checkpoint_steps,
valid_iter=valid_iter,
valid_steps=opt.valid_steps)
if opt.tensorboard:
trainer.report_manager.tensorboard_writer.close()
def valid_iter_fct(): return build_dataset_iter(
lazily_load_dataset("valid", opt), fields, opt, is_train=False)
# Do training.
trainer.train(train_iter_fct, valid_iter_fct, opt.train_steps,
def valid_iter_fct(): return build_dataset_iter(
lazily_load_dataset("valid", opt), fields, opt)
# Do training.
trainer.train(train_iter_fct, valid_iter_fct, opt.start_epoch, opt.epochs)
def train(opt):
ArgumentParser.validate_train_opts(opt)
ArgumentParser.update_model_opts(opt)
ArgumentParser.validate_model_opts(opt)
set_random_seed(opt.seed, False)
# Load checkpoint if we resume from a previous training.
if opt.train_from:
logger.info('Loading checkpoint from %s' % opt.train_from)
checkpoint = torch.load(opt.train_from,
map_location=lambda storage, loc: storage)
logger.info('Loading vocab from checkpoint at %s.' % opt.train_from)
if 'vocab' in checkpoint:
logger.info('Loading vocab from checkpoint at %s.' %
opt.train_from)
vocab = checkpoint['vocab']
else:
vocab = torch.load(opt.data + '.vocab.pt')
else:
vocab = torch.load(opt.data + '.vocab.pt')
# check for code where vocab is saved instead of fields
# (in the future this will be done in a smarter way)
if old_style_vocab(vocab):
fields = load_old_vocab(vocab,
opt.model_type,
dynamic_dict=opt.copy_attn)
else:
fields = vocab
if len(opt.data_ids) > 1:
train_shards = []
for train_id in opt.data_ids:
shard_base = "train_" + train_id
train_shards.append(shard_base)
train_iter = build_dataset_iter_multiple(train_shards, fields, opt)
else:
if opt.data_ids[0] is not None:
shard_base = "train_" + opt.data_ids[0]
else:
shard_base = "train"
train_iter = build_dataset_iter(shard_base, fields, opt)
nb_gpu = len(opt.gpu_ranks)
if opt.world_size > 1:
queues = []
mp = torch.multiprocessing.get_context('spawn')
semaphore = mp.Semaphore(opt.world_size * opt.queue_size)
# Create a thread to listen for errors in the child processes.
error_queue = mp.SimpleQueue()
error_handler = ErrorHandler(error_queue)
# Train with multiprocessing.
procs = []
for device_id in range(nb_gpu):
q = mp.Queue(opt.queue_size)
queues += [q]
procs.append(
mp.Process(target=run,
args=(opt, device_id, error_queue, q, semaphore),
daemon=True))
procs[device_id].start()
logger.info(" Starting process pid: %d " % procs[device_id].pid)
error_handler.add_child(procs[device_id].pid)
producer = mp.Process(target=batch_producer,
args=(
train_iter,
queues,
semaphore,
opt,
),
daemon=True)
producer.start()
error_handler.add_child(producer.pid)
for p in procs:
p.join()
producer.terminate()
elif nb_gpu == 1: # case 1 GPU only
single_main(opt, 0)
else: # case only CPU
single_main(opt, -1)
def train(opt):
ArgumentParser.validate_train_opts(opt)
ArgumentParser.update_model_opts(opt)
ArgumentParser.validate_model_opts(opt)
if opt.train_from != '':
raise Exception(
'train_from will be set automatically to the latest model, you should not set it manually'
)
# set gpu ranks automatically if not specified
if len(opt.gpu_ranks) == 0:
opt.gpu_ranks = list(range(opt.world_size))
# Set train_from to latest checkpoint if it exists
file_list = glob.glob(opt.save_model + '*.pt')
if len(os.listdir(os.path.dirname(
opt.save_model))) > 0 and len(file_list) == 0:
raise Exception(
'save_model directory is not empty but no pretrained models found')
if len(file_list) > 0:
ckpt_nos = list(
map(lambda x: int(x.split('_')[-1].split('.')[0]), file_list))
ckpt_no = max(ckpt_nos)
opt.train_from = opt.save_model + '_' + str(ckpt_no) + '.pt'
print(opt.train_from)
assert os.path.exists(opt.train_from)
set_random_seed(opt.seed, False)
# Load checkpoint if we resume from a previous training.
if opt.train_from:
logger.info('Loading checkpoint from %s' % opt.train_from)
checkpoint = torch.load(opt.train_from,
map_location=lambda storage, loc: storage)
logger.info('Loading vocab from checkpoint at %s.' % opt.train_from)
vocab = checkpoint['vocab']
else:
vocab = torch.load(opt.data + '.vocab.pt')
# check for code where vocab is saved instead of fields
# (in the future this will be done in a smarter way)
if old_style_vocab(vocab):
fields = load_old_vocab(vocab,
opt.model_type,
dynamic_dict=opt.copy_attn)
else:
fields = vocab
if len(opt.data_ids) > 1:
train_shards = []
for train_id in opt.data_ids:
shard_base = "train_" + train_id
train_shards.append(shard_base)
train_iter = build_dataset_iter_multiple(train_shards, fields, opt)
else:
if opt.data_ids[0] is not None:
shard_base = "train_" + opt.data_ids[0]
else:
shard_base = "train"
train_iter = build_dataset_iter(shard_base, fields, opt)
nb_gpu = len(opt.gpu_ranks)
if opt.world_size > 1:
queues = []
mp = torch.multiprocessing.get_context('spawn')
semaphore = mp.Semaphore(opt.world_size * opt.queue_size)
# Create a thread to listen for errors in the child processes.
error_queue = mp.SimpleQueue()
error_handler = ErrorHandler(error_queue)
# Train with multiprocessing.
procs = []
for device_id in range(nb_gpu):
q = mp.Queue(opt.queue_size)
queues += [q]
procs.append(
mp.Process(target=run,
args=(opt, device_id, error_queue, q, semaphore),
daemon=True))
procs[device_id].start()
logger.info(" Starting process pid: %d " % procs[device_id].pid)
error_handler.add_child(procs[device_id].pid)
producer = mp.Process(target=batch_producer,
args=(
train_iter,
queues,
semaphore,
opt,
),
daemon=True)
producer.start()
error_handler.add_child(producer.pid)
for p in procs:
p.join()
producer.terminate()
elif nb_gpu == 1: # case 1 GPU only
single_main(opt, 0)
else: # case only CPU
single_main(opt, -1)
def valid_iter_fct(): return build_dataset_iter(
lazily_load_dataset("valid", opt), fields, opt, is_train=False)
# Do training.
if len(opt.gpu_ranks):
def main(opt, device_id):
# NOTE: It's important that ``opt`` has been validated and updated
# at this point.
configure_process(opt, device_id)
init_logger(opt.log_file)
assert len(opt.accum_count) == len(opt.accum_steps), \
'Number of accum_count values must match number of accum_steps'
# Load checkpoint if we resume from a previous training.
if opt.train_from:
logger.info('Loading checkpoint from %s' % opt.train_from)
checkpoint = torch.load(opt.train_from,
map_location=lambda storage, loc: storage)
model_opt = ArgumentParser.ckpt_model_opts(checkpoint["opt"])
ArgumentParser.update_model_opts(model_opt)
ArgumentParser.validate_model_opts(model_opt)
logger.info('Loading vocab from checkpoint at %s.' % opt.train_from)
vocab = checkpoint['vocab']
else:
checkpoint = None
model_opt = opt
vocab = torch.load(opt.data + '.vocab.pt')
logger.info('Loading alignment.')
lemma_aligns = open(model_opt.lemma_align, 'rb').readlines()
src_stoi = vocab['src'].base_field.vocab.stoi
lemma_stoi = vocab['word_topic'].base_field.vocab.stoi
w2l = {}
word_to_lemma = []
for pair in lemma_aligns:
pair = pair.strip().split()
w2l[src_stoi[pair[0].decode('utf-8')]] = \
lemma_stoi[pair[1].decode('utf-8')]
w2l[src_stoi['unk']] = lemma_stoi['unk']
for index in range(len(vocab['src'].base_field.vocab.itos)):
if index in w2l:
word_to_lemma.append(w2l[index])
else:
word_to_lemma.append(w2l[lemma_stoi['unk']])
word_to_lemma = torch.tensor(word_to_lemma)
logger.info('Loading topic matrix')
if device_id >= 0:
topic_matrix = torch.load(opt.topic_matrix,
map_location=torch.device(device_id))
else:
topic_matrix = torch.load(opt.topic_matrix)
if opt.model_dtype == 'fp16':
topic_matrix = topic_matrix.half()
# check for code where vocab is saved instead of fields
# (in the future this will be done in a smarter way)
if old_style_vocab(vocab):
fields = load_old_vocab(vocab,
opt.model_type,
dynamic_dict=opt.copy_attn)
else:
fields = vocab
# Report src and tgt vocab sizes, including for features
for side in ['src', 'tgt']:
f = fields[side]
try:
f_iter = iter(f)
except TypeError:
f_iter = [(side, f)]
for sn, sf in f_iter:
if sf.use_vocab:
logger.info(' * %s vocab size = %d' % (sn, len(sf.vocab)))
# Build model.
model = build_model(model_opt, opt, fields, checkpoint)
n_params, enc, dec = _tally_parameters(model)
logger.info('encoder: %d' % enc)
logger.info('decoder: %d' % dec)
logger.info('* number of parameters: %d' % n_params)
_check_save_model_path(opt)
# Build optimizer.
optim = Optimizer.from_opt(model, opt, checkpoint=checkpoint)
# Build model saver
model_saver = build_model_saver(model_opt, opt, model, fields, optim)
trainer = build_trainer(opt,
device_id,
model,
fields,
optim,
model_saver=model_saver)
train_iter = build_dataset_iter("train", fields, opt)
valid_iter = build_dataset_iter("valid", fields, opt, is_train=False)
if len(opt.gpu_ranks):
logger.info('Starting training on GPU: %s' % opt.gpu_ranks)
else:
logger.info('Starting training on CPU, could be very slow')
train_steps = opt.train_steps
if opt.single_pass and train_steps > 0:
logger.warning("Option single_pass is enabled, ignoring train_steps.")
train_steps = 0
trainer.train(topic_matrix,
word_to_lemma,
train_iter,
train_steps,
save_checkpoint_steps=opt.save_checkpoint_steps,
valid_iter=valid_iter,
valid_steps=opt.valid_steps)
if opt.tensorboard:
trainer.report_manager.tensorboard_writer.close()
def valid_iter_fct():
return build_dataset_iter(lazily_load_dataset("valid", opt),
fields,
opt,
is_train=False)
def train_iter_fct(task_id):
return build_dataset_iter(
lazily_load_dataset("train", opt, task_id=task_id), fields_list[task_id], opt)
