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:
checkpoint = None
model_opt = opt
# Peek the first dataset to determine the data_type.
# (All datasets have the same data_type).
first_dataset = next(lazily_load_dataset("train", opt))
data_type = first_dataset.data_type
# Load fields generated from preprocess phase.
fields = _load_fields(first_dataset, data_type, opt, checkpoint)
# Report src/tgt features.
src_features, tgt_features = _collect_report_features(fields)
for j, feat in enumerate(src_features):
logger.info(' * src feature %d size = %d'
% (j, len(fields[feat].vocab)))
for j, feat in enumerate(tgt_features):
logger.info(' * tgt feature %d size = %d'
% (j, len(fields[feat].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)
def train_iter_fct(): return build_dataset_iter(
lazily_load_dataset("train", opt), fields, opt)
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,
opt.valid_steps)
if opt.tensorboard:
trainer.report_manager.tensorboard_writer.close()
def main(opt):
opt = training_opt_postprocessing(opt)
# Load checkpoint if we resume from a previous training.
if opt.train_from:
print('Loading checkpoint from %s' % opt.train_from)
checkpoint = torch.load(opt.train_from,
map_location=lambda storage, loc: storage)
model_opt = checkpoint['opt']
# I don't like reassigning attributes of opt: it's not clear.
opt.start_epoch = checkpoint['epoch'] + 1
else:
checkpoint = None
model_opt = opt
# Peek the fisrt dataset to determine the data_type.
# (All datasets have the same data_type).
first_dataset = next(lazily_load_dataset("train", opt))
data_type = first_dataset.data_type
# Load fields generated from preprocess phase.
fields = _load_fields(first_dataset, data_type, opt, checkpoint)
# Report src/tgt features.
_collect_report_features(fields)
# Build model.
model = build_model(model_opt, opt, fields, checkpoint)
_tally_parameters(model)
_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,
model,
fields,
optim,
data_type,
model_saver=model_saver)
def train_iter_fct():
return build_dataset_iter(lazily_load_dataset("train", opt), fields,
opt)
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.train_steps,
opt.valid_steps, opt.save_checkpoint_steps)
if opt.tensorboard:
trainer.report_manager.tensorboard_writer.close()
def load_pre_train(path):
logger.info('Loading pre-train model from %s' % path)
checkpoint = torch.load(path, map_location=lambda storage, loc: storage)
opt = ArgumentParser.ckpt_model_opts(checkpoint["opt"])
model_opt = opt
ArgumentParser.update_model_opts(model_opt)
ArgumentParser.validate_model_opts(model_opt)
logger.info('Loading vocab from checkpoint at %s.' % opt.train_from)
fields = checkpoint['vocab']
model = build_model(model_opt, opt, fields, checkpoint)
return model
def main(opt):
ArgumentParser.validate_train_opts(opt)
ArgumentParser.update_model_opts(opt)
ArgumentParser.validate_model_opts(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)
logger.info('Loading vocab from checkpoint at %s.' % opt.train_from)
vocab = checkpoint['vocab']
else:
vocab = torch.load(opt.data + '.vocab.pt')
segment_token_idx = None
if opt.use_segments:
segment_token_idx = vocab['tgt'].base_field.vocab.stoi['.']
opt.segment_token_idx = segment_token_idx
# 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
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']
fields = vocab
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)
pdb.set_trace()
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 main(opt):
if opt.gpuid:
raise AssertionError("gpuid is deprecated \
see world_size and gpu_ranks")
assert opt.world_size <= 1, "you don't need multi-gpu for morphology"
device_id = 0 if len(opt.gpu_ranks) == 1 else -1
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 useful 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)
fields = checkpoint['vocab']
else:
checkpoint = None
model_opt = opt
fields = torch.load(opt.data + '.vocab.pt')
for key, values in fields.items():
for name, f in values:
if hasattr(f, 'use_vocab') and f.use_vocab:
logger.info(' * %s vocab size = %d' % (name, len(f.vocab)))
# Build model.
logger.info('Building model...')
model = build_model(model_opt, fields, use_gpu(opt), checkpoint)
logger.info(model)
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.
params = model.parameters()
optim_args = {"lr": opt.learning_rate}
if opt.optim == "adam":
# no need to mess with the default betas
optim_args["eps"] = 1e-9
elif opt.optim == "adagrad":
optim_args["initial_accumulator_value"] = opt.adagrad_accumulator_init
optim = getattr(torch.optim, opt.optim.title())(params, **optim_args)
print(optim)
trainer = build_trainer(opt, model_opt, device_id, model, fields, optim)
# 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()))
device = "cuda" if opt.gpu_ranks else "cpu"
train_dataset = torch.load(opt.data + '.train.pt')
train_dataset.fields = dataset_fields
train_iter = OrderedIterator(
train_dataset, opt.batch_size, sort_within_batch=True,
device=device, repeat=False, shuffle=not opt.no_shuffle)
valid_dataset = torch.load(opt.data + '.valid.pt')
valid_dataset.fields = dataset_fields
valid_iter = OrderedIterator(
valid_dataset, opt.valid_batch_size, train=False,
sort_within_batch=True, device=device)
logger.info('Starting training on {}'.format(device))
trainer.train(train_iter, valid_iter, opt.epochs)
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 main(opt,
device_id,
batch_queue=None,
semaphore=None,
train_iter=None,
passed_fields=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)
if opt.use_opt_from_trained:
model_opt = ArgumentParser.ckpt_model_opts(checkpoint["opt"])
else:
model_opt = 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)
aux_fields = None
if passed_fields is not None:
fields = passed_fields['main']
aux_fields = passed_fields['crosslingual']
elif 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,
aux_fields=aux_fields)
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.
if opt.almt_only:
almt = model.encoder.embeddings.almt_layers['mapping']
logger.info('Only training the alignment mapping.')
optim = Optimizer.from_opt(almt, opt, checkpoint=checkpoint)
else:
optim = Optimizer.from_opt(model, opt, checkpoint=checkpoint)
# Build model saver
model_saver = build_model_saver(model_opt,
opt,
model,
fields,
optim,
aux_fields=aux_fields)
trainer = build_trainer(opt,
device_id,
model,
fields,
optim,
model_saver=model_saver,
aux_fields=aux_fields)
if train_iter is not None:
pass # NOTE Use the passed one.
elif 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()
cl_valid_iter = None
if opt.crosslingual:
valid_iter = build_dataset_iter("valid",
fields,
opt,
is_train=False,
task_cls=Eat2PlainMonoTask)
if opt.crosslingual_dev_data:
# NOTE I used 'train' to prepare this in `eat_prepare.sh`, so I use 'train' here as well.
cl_valid_iter = build_dataset_iter(
'train',
fields,
opt,
is_train=False,
data_attr='crosslingual_dev_data',
task_cls=Eat2PlainCrosslingualTask)
# NOTE This is for the second eat->plain task.
aux_valid_iter = build_dataset_iter('valid',
fields,
opt,
is_train=False,
data_attr='aux_train_data',
task_cls=Eat2PlainMonoTask)
valid_iters = [valid_iter, aux_valid_iter]
else:
valid_iters = [
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_iters=valid_iters,
valid_steps=opt.valid_steps,
cl_valid_iter=cl_valid_iter)
if opt.tensorboard:
trainer.report_manager.tensorboard_writer.close()
def main(opt,
fields,
transforms_cls,
checkpoint,
device_id,
batch_queue=None,
semaphore=None):
"""Start training on `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)
model_opt = _get_model_opts(opt, checkpoint=checkpoint)
# Build model.
model = build_model(model_opt, opt, fields, checkpoint)
model.count_parameters(log=logger.info)
# 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:
_train_iter = _build_train_iter(opt, fields, transforms_cls)
train_iter = IterOnDevice(_train_iter, device_id)
else:
assert semaphore is not None, \
"Using batch_queue requires semaphore as well"
def _train_iter():
while True:
batch = batch_queue.get()
semaphore.release()
# Move batch to specified device
IterOnDevice.batch_to_device(batch, device_id)
yield batch
train_iter = _train_iter()
valid_iter = _build_valid_iter(opt, fields, transforms_cls)
if valid_iter is not None:
valid_iter = IterOnDevice(valid_iter, device_id)
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.
# 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 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 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.
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')
train_iters = OrderedDict()
valid_iters = OrderedDict()
encoders = OrderedDict()
decoders = OrderedDict()
generators = OrderedDict()
src_vocabs = OrderedDict()
tgt_vocabs = OrderedDict()
Fields_dict = OrderedDict()
# variables needed for sharing the same embedding matrix across encoders and decoders
firstTime = True
weightToShare = None
# we share the word embedding space when source lang and target lang are the same
mapLang2Emb = {}
#for (src_tgt_lang), data_path in zip(opt.src_tgt, opt.data):
for index in range(len(opt.src_tgt)):
src_tgt_lang = opt.src_tgt[index]
data_path = opt.data[index]
local_enc_dec_opts = AttrDict({
key: model_opt.__dict__[key]
for key in model_opt.__dict__.keys()
})
local_enc_dec_opts.model_type = update_to_local_attr(
model_opt.model_type, index)
#local_enc_dec_opts.audio_enc_pooling = model_opt.audio_enc_pooling[index]
local_enc_dec_opts.audio_enc_pooling = update_to_local_attr(
model_opt.audio_enc_pooling, index)
local_enc_dec_opts.enc_layers = update_to_local_attr(
model_opt.enc_layers, index)
local_enc_dec_opts.dec_layers = update_to_local_attr(
model_opt.dec_layers, index)
local_enc_dec_opts.rnn_type = update_to_local_attr(
model_opt.rnn_type, index)
local_enc_dec_opts.encoder_type = update_to_local_attr(
model_opt.encoder_type, index)
local_enc_dec_opts.batch_size = update_to_local_attr(
model_opt.batch_size, index)
local_enc_dec_opts.batch_type = update_to_local_attr(
model_opt.batch_type, index)
local_enc_dec_opts.normalization = update_to_local_attr(
model_opt.normalization, index)
#local_enc_dec_opts.dec_rnn_size = model_opt.dec_rnn_size[index]
src_lang, tgt_lang = src_tgt_lang.split('-')
vocab = torch.load(data_path + '.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[0],
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.
encoder, src_embeddings = build_embeddings_then_encoder(
local_enc_dec_opts, fields)
encoders[src_lang] = encoder
decoder, generator, tgt_embeddings = build_decoder_and_generator(
local_enc_dec_opts, fields)
decoders[tgt_lang] = decoder
# Share the embedding matrix across all the encoders and decoders - preprocess with share_vocab required.
if model_opt.share_embeddings and firstTime:
tgt_embeddings.word_lut.weight = src_embeddings.word_lut.weight
weightToShare = src_embeddings.word_lut.weight
if model_opt.share_embeddings and (not firstTime):
tgt_embeddings.word_lut.weight = weightToShare
src_embeddings.word_lut.weight = weightToShare
firstTime = False
#TEST
#if src_lang in mapLang2Emb:
if src_lang in mapLang2Emb and model_opt.model_type == "text":
encoder.embeddings.word_lut.weight = mapLang2Emb.get(src_lang)
#TEST
#else:
elif model_opt.model_type == "text":
mapLang2Emb[src_lang] = src_embeddings.word_lut.weight
if tgt_lang in mapLang2Emb:
decoder.embeddings.word_lut.weight = mapLang2Emb.get(tgt_lang)
else:
mapLang2Emb[tgt_lang] = tgt_embeddings.word_lut.weight
#TEST
if model_opt.model_type == "text":
src_vocabs[src_lang] = fields['src'].base_field.vocab
tgt_vocabs[tgt_lang] = fields['tgt'].base_field.vocab
generators[tgt_lang] = generator
# add this dataset iterator to the training iterators
train_iters[(src_lang, tgt_lang)] = build_dataset_iter_fct(
'train', fields, data_path, local_enc_dec_opts)
# add this dataset iterator to the validation iterators
valid_iters[(src_lang,
tgt_lang)] = build_dataset_iter_fct('valid',
fields,
data_path,
local_enc_dec_opts,
is_train=False)
Fields_dict[src_tgt_lang] = fields
# Build model.
model = build_model(model_opt, opt, fields, encoders, decoders, generators,
src_vocabs, tgt_vocabs, 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_dict, optim)
trainer = build_trainer(opt,
device_id,
model,
fields,
optim,
generators,
tgt_vocabs,
model_saver=model_saver)
# TODO: not implemented yet
#train_iterables = []
#if len(opt.data_ids) > 1:
# for train_id in opt.data_ids:
# shard_base = "train_" + train_id
# iterable = build_dataset_iter(shard_base, fields, opt, multi=True)
# train_iterables.append(iterable)
# train_iter = MultipleDatasetIterator(train_iterables, device_id, opt)
#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_iters, train_steps, opt.save_checkpoint_steps,
valid_iters, 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)
# 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__)
else:
checkpoint = None
model_opt = opt
# Peek the first dataset to determine the data_type.
# (All datasets have the same data_type).
first_dataset = next(lazily_load_dataset("train", opt))
data_type = first_dataset.data_type
# Load fields generated from preprocess phase.
fields = load_fields(first_dataset, opt, checkpoint)
# Report src/tgt features.
src_features, tgt_features = _collect_report_features(fields)
for j, feat in enumerate(src_features):
logger.info(' * src feature %d size = %d' %
(j, len(fields[feat].vocab)))
for j, feat in enumerate(tgt_features):
logger.info(' * tgt feature %d size = %d' %
(j, len(fields[feat].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)
def train_iter_fct():
return build_dataset_iter(lazily_load_dataset("train", opt), fields,
opt)
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):
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)
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)
# 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 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):
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')
################
# model_opt.train_steps = 0
################
# check for code where vocab is saved instead of fields
# (in the future this will be done in a smarter way)
# import pickle
# pickle.dump('\n'.join([a for a in vocab['tgt'][0][1].fields[0][1].vocab.itos]),
# open('cnndm_duc17_vocab_addtgt.vocab.itos.txt', 'w', encoding='utf-8'))
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
#################
# if len(fields['src'][0][1].fields[0][1].vocab.itos) == 30522:
# fields['src'][0][1].fields[0][1].vocab = fields['tgt'][0][1].fields[0][1].vocab
if len(fields['src'][0][1].base_field.vocab.itos) == 30801:
fields['src'][0][1].base_field.vocab.stoi.pop(
fields['src'][0][1].base_field.vocab.itos.pop(-1))
fields['src'][0][1].base_field.vocab.stoi.pop(
fields['src'][0][1].base_field.vocab.itos.pop(-1))
fields['src'][0][1].base_field.vocab.stoi.pop(
fields['src'][0][1].base_field.vocab.itos.pop(-1))
fields['src'][0][1].base_field.vocab.stoi.pop(
fields['src'][0][1].base_field.vocab.itos.pop(-1))
vocab['src'][0][1].base_field.vocab.freqs.pop('<s>')
vocab['src'][0][1].base_field.vocab.freqs.pop('</s>')
vocab['src'][0][1].base_field.vocab.freqs.pop('<t>')
vocab['src'][0][1].base_field.vocab.freqs.pop('</t>')
# if len(fields['tgt'][0][1].fields[0][1].vocab.itos) == 30529:
# fields['tgt'][0][1].fields[0][1].vocab.stoi.pop('<unk>')
# fields['tgt'][0][1].fields[0][1].vocab.stoi.pop('<blank>')
# fields['tgt'][0][1].fields[0][1].vocab.stoi.pop('<s>')
# fields['tgt'][0][1].fields[0][1].vocab.stoi.pop('</s>')
# fields['tgt'][0][1].fields[0][1].vocab.itos = fields['tgt'][0][1].fields[0][1].vocab.itos[4:]
# fields['tgt'][0][1].fields[0][1].vocab.itos += ['<s>', '</s>']
# for i, k in enumerate(fields['tgt'][0][1].fields[0][1].vocab.itos):
# fields['tgt'][0][1].fields[0][1].vocab.stoi[k] = i
# if len(fields['tgt'][0][1].fields[0][1].vocab.itos) == 30526:
# fields['tgt'][0][1].fields[0][1].vocab.stoi.pop('<unk>')
# fields['tgt'][0][1].fields[0][1].vocab.stoi.pop('<blank>')
# fields['tgt'][0][1].fields[0][1].vocab.stoi.pop('<s>')
# fields['tgt'][0][1].fields[0][1].vocab.stoi.pop('</s>')
# fields['tgt'][0][1].fields[0][1].vocab.itos += ['</s>']
# fields['tgt'][0][1].fields[0][1].vocab.stoi['</s>'] = 30526
#################
# 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)
# import pickle
# model = pickle.load(open('model.pkl', 'rb'))
# model.param = torch.nn.Parameter(torch.randn(model.encoder.total_hidden_dim * 4, dtype=torch.float32,
# device=torch.device('cuda')).view(1, -1, 1))
n_params, enc, dec = _tally_parameters(model)
# model = build_model(model_opt, opt, fields, checkpoint)
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)
# optim = Optimizer.from_opt(model, opt, checkpoint=None)
# 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')
trainer.train(train_iter,
opt.train_steps,
save_checkpoint_steps=opt.save_checkpoint_steps,
valid_iter=valid_iter,
valid_steps=opt.valid_steps,
bert=opt.bert)
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)
out_file = None
best_test_score, best_ckpt = -10000, None
dummy_parser = argparse.ArgumentParser(description='all_dev.py')
opts.model_opts(dummy_parser)
dummy_opt = dummy_parser.parse_known_args([])[0]
for i in range(0, opt.train_epochs, 10):
ckpt_path = '{}_epoch_{}.pt'.format(opt.save_model, i + 1)
logger.info('Loading checkpoint from %s' % ckpt_path)
checkpoint = torch.load(ckpt_path,
map_location=lambda storage, loc: storage)
model_opt = checkpoint['opt']
fields = load_fields_from_vocab(checkpoint['vocab'], data_type="text")
# Build model.
model = build_model(model_opt, opt, fields, checkpoint)
assert opt.train_from == '' # do not load optimizer state
optim = build_optim(model, opt, checkpoint)
# Build model saver, no need to create task dir for dev
if not os.path.exists('experiments/all_dev'):
os.mkdir('experiments/all_dev')
os.mkdir('experiments/all_dev/' + opt.meta_dev_task)
elif not os.path.exists('experiments/all_dev/' + opt.meta_dev_task):
os.mkdir('experiments/all_dev/' + opt.meta_dev_task)
model_saver = build_model_saver(
model_opt, 'experiments/all_dev/' + opt.meta_dev_task + '/model',
opt, model, fields, optim)
trainer = build_trainer(opt,
device_id,
model,
fields,
optim,
"text",
model_saver=model_saver)
train_iter = list(
build_dataset_iter(lazily_load_dataset("train", opt), fields, opt))
# do training on trainset of meta-dev task
trainer.train(train_iter, opt.inner_iterations)
# do evaluation on devset of meta-dev task
best_dev_score, best_model_path = -10000, None
for model_path in os.listdir('experiments/all_dev/' +
opt.meta_dev_task):
if model_path.find('.pt') == -1:
continue
if out_file is None:
out_file = codecs.open(opt.output, 'w+', 'utf-8')
fields, model, model_opt = onmt.model_builder.load_test_model(
opt,
dummy_opt.__dict__,
model_path='experiments/all_dev/' + opt.meta_dev_task + '/' +
model_path)
scorer = onmt.translate.GNMTGlobalScorer(opt.alpha, opt.beta,
opt.coverage_penalty,
opt.length_penalty)
kwargs = {
k: getattr(opt, k)
for k in [
"beam_size", "n_best", "max_length", "min_length",
"stepwise_penalty", "block_ngram_repeat",
"ignore_when_blocking", "dump_beam", "report_bleu",
"replace_unk", "gpu", "verbose", "fast", "mask_from"
]
}
fields['graph'] = torchtext.data.Field(sequential=False)
translator = Translator(model,
fields,
global_scorer=scorer,
out_file=out_file,
report_score=False,
copy_attn=model_opt.copy_attn,
logger=logger,
log_probs_out_file=None,
**kwargs)
# make translation and save result
all_scores, all_predictions = translator.translate(
src_path='processed_data/meta-dev/' + opt.meta_dev_task +
'/src-dev.txt',
tgt_path=None,
src_dir=None,
batch_size=opt.translate_batch_size,
attn_debug=False)
# dump predictions
f = open('experiments/all_dev/' + opt.meta_dev_task +
'/dev_predictions.csv',
'w',
encoding='utf-8')
f.write('smiles,property\n')
for n_best_mols in all_predictions:
for mol in n_best_mols:
f.write(mol.replace(' ', '') + ',0\n')
f.close()
# call chemprop to get scores
test_path = '\"' + 'experiments/all_dev/' + opt.meta_dev_task + '/dev_predictions.csv' + '\"'
checkpoint_path = '\"' + 'scorer_ckpts/' + opt.meta_dev_task + '/model.pt' + '\"'
preds_path = '\"' + 'experiments/all_dev/' + opt.meta_dev_task + '/dev_scores.csv' + '\"'
# in case of all mols are invalid (will produce not output file by chemprop)
# the predictions are copied into score file
cmd = 'cp {} {}'.format(test_path, preds_path)
result = os.popen(cmd)
result.close()
cmd = 'python chemprop/predict.py --test_path {} --checkpoint_path {} --preds_path {} --num_workers 0'.format(
test_path, checkpoint_path, preds_path)
scorer_result = os.popen(cmd)
scorer_result.close()
# read score file and get score
score = read_score_csv('experiments/all_dev/' + opt.meta_dev_task +
'/dev_scores.csv')
assert len(score) % opt.beam_size == 0
# dev_scores = []
# for i in range(0, len(score), opt.beam_size):
# dev_scores.append(sum([x[1] for x in score[i:i+opt.beam_size]]) / opt.beam_size)
# report dev score
dev_metrics = calculate_metrics(opt.meta_dev_task, 'dev', 'dev',
score)
logger.info('dev metrics: ' + str(dev_metrics))
dev_score = dev_metrics['success_rate']
if dev_score > best_dev_score:
logger.info('New best dev success rate: {:.4f} by {}'.format(
dev_score, model_path))
best_model_path = model_path
best_dev_score = dev_score
else:
logger.info('dev success rate: {:.4f} by {}'.format(
dev_score, model_path))
del fields
del model
del model_opt
del scorer
del translator
gc.collect()
assert best_model_path != None
# do testing on testset of meta-dev task
if out_file is None:
out_file = codecs.open(opt.output, 'w+', 'utf-8')
fields, model, model_opt = onmt.model_builder.load_test_model(
opt,
dummy_opt.__dict__,
model_path='experiments/all_dev/' + opt.meta_dev_task + '/' +
best_model_path)
scorer = onmt.translate.GNMTGlobalScorer(opt.alpha, opt.beta,
opt.coverage_penalty,
opt.length_penalty)
kwargs = {
k: getattr(opt, k)
for k in [
"beam_size", "n_best", "max_length", "min_length",
"stepwise_penalty", "block_ngram_repeat",
"ignore_when_blocking", "dump_beam", "report_bleu",
"replace_unk", "gpu", "verbose", "fast", "mask_from"
]
}
fields['graph'] = torchtext.data.Field(sequential=False)
translator = Translator(model,
fields,
global_scorer=scorer,
out_file=out_file,
report_score=False,
copy_attn=model_opt.copy_attn,
logger=logger,
log_probs_out_file=None,
**kwargs)
# make translation and save result
all_scores, all_predictions = translator.translate(
src_path='processed_data/meta-dev/' + opt.meta_dev_task +
'/src-test.txt',
tgt_path=None,
src_dir=None,
batch_size=opt.translate_batch_size,
attn_debug=False)
# dump predictions
f = open('experiments/all_dev/' + opt.meta_dev_task +
'/test_predictions.csv',
'w',
encoding='utf-8')
f.write('smiles,property\n')
for n_best_mols in all_predictions:
for mol in n_best_mols:
f.write(mol.replace(' ', '') + ',0\n')
f.close()
# call chemprop to get scores
test_path = '\"' + 'experiments/all_dev/' + opt.meta_dev_task + '/test_predictions.csv' + '\"'
checkpoint_path = '\"' + 'scorer_ckpts/' + opt.meta_dev_task + '/model.pt' + '\"'
preds_path = '\"' + 'experiments/all_dev/' + opt.meta_dev_task + '/test_scores.csv' + '\"'
# in case of all mols are invalid (will produce not output file by chemprop)
# the predictions are copied into score file
cmd = 'cp {} {}'.format(test_path, preds_path)
result = os.popen(cmd)
result.close()
cmd = 'python chemprop/predict.py --test_path {} --checkpoint_path {} --preds_path {} --num_workers 0'.format(
test_path, checkpoint_path, preds_path)
scorer_result = os.popen(cmd)
# logger.info('{}'.format('\n'.join(scorer_result.readlines())))
scorer_result.close()
# read score file and get score
score = read_score_csv('experiments/all_dev/' + opt.meta_dev_task +
'/test_scores.csv')
assert len(score) % opt.beam_size == 0
# test_scores = []
# for i in range(0, len(score), opt.beam_size):
# test_scores.append(sum([x[1] for x in score[i:i+opt.beam_size]]) / opt.beam_size)
# report if it is the best on test
test_metrics = calculate_metrics(opt.meta_dev_task, 'dev', 'test',
score)
logger.info('test metrics: ' + str(test_metrics))
test_score = test_metrics['success_rate']
if test_score > best_test_score:
best_ckpt = ckpt_path
logger.info('New best test success rate: {:.4f} by {}'.format(
test_score, ckpt_path))
best_test_score = test_score
else:
logger.info('test success rate: {:.4f} by {}'.format(
test_score, ckpt_path))
del model_opt
del fields
del checkpoint
del model
del optim
del model_saver
del trainer
gc.collect()
model_opt = opt
checkpoint = None
# Peek the fisrt dataset to determine the data_type.
# (All datasets have the same data_type).
first_dataset = next(lazily_load_dataset("train", opt))
data_type = first_dataset.data_type
# Load fields generated from preprocess phase.
fields = _load_fields(first_dataset, data_type, opt, checkpoint)
# Report src/tgt features.
_collect_report_features(fields)
# Build model.
model = build_model(model_opt, opt, fields, checkpoint)
remove(args.host, args.port, "OpenNMT")
probe(
args.name,
model,
args.host,
args.port,
when=lambda m, o: m._v.state == "dev",
which=lambda m, o:
True, #o._v.operation_name in ["encoder", "decoder"],
parameters=False,
forward=True,
backward=False,
batch_axis=1)
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 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):
#TODO delete all these lines related to WALS features
#begin
SimulationLanguages = [opt.wals_src, opt.wals_tgt]
print('Loading WALS features from databases...')
cwd = os.getcwd()
db = sqlite3.connect(cwd + '/onmt/WalsValues.db')
cursor = db.cursor()
cursor.execute('SELECT * FROM WalsValues')
WalsValues = cursor.fetchall()
db = sqlite3.connect(cwd + '/onmt/FeaturesList.db')
cursor = db.cursor()
cursor.execute('SELECT * FROM FeaturesList')
FeaturesList = cursor.fetchall()
db = sqlite3.connect(cwd + '/onmt/FTInfos.db')
cursor = db.cursor()
cursor.execute('SELECT * FROM FTInfos')
FTInfos = cursor.fetchall()
db = sqlite3.connect(cwd + '/onmt/FTList.db')
cursor = db.cursor()
cursor.execute('SELECT * FROM FTList')
FTList = cursor.fetchall()
ListLanguages = []
for i in WalsValues:
ListLanguages.append(i[0])
FeatureTypes = []
for i in FTList:
FeatureTypes.append((i[0], i[1].split(',')))
FeatureNames = []
for i in FeatureTypes:
FeatureNames += i[1]
FeatureTypesNames = []
for i in FeatureTypes:
FeatureTypesNames.append(i[0])
FeatureValues, FeatureTensors = get_feat_values(SimulationLanguages, WalsValues, FeaturesList, ListLanguages, FeatureTypes, FeatureNames)
print('WALS databases loaded!')
#end
#TODO: load wals features from command-line (wals.npz)
# FeatureValues: defaultdict with feature values, per language.
# FeatureTensors: tensor of possible outputs, per feature.
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:
checkpoint = None
model_opt = opt
# Peek the first dataset to determine the data_type.
# (All datasets have the same data_type).
first_dataset = next(lazily_load_dataset("train", opt))
data_type = first_dataset.data_type
# Load fields generated from preprocess phase.
fields = _load_fields(first_dataset, data_type, opt, checkpoint)
# Report src/tgt features.
src_features, tgt_features = _collect_report_features(fields)
for j, feat in enumerate(src_features):
logger.info(' * src feature %d size = %d'
% (j, len(fields[feat].vocab)))
for j, feat in enumerate(tgt_features):
logger.info(' * tgt feature %d size = %d'
% (j, len(fields[feat].vocab)))
# Build model.
#TODO: remove all parameters related to WALS features: FeatureValues, FeatureTensors, FeatureTypes, FeaturesList, FeatureNames, FTInfos, FeatureTypesNames, SimulationLanguages
#TODO: include four parameter related to WALS features: the four numpy arrays separately
model = build_model(model_opt, opt, fields, checkpoint, FeatureValues, FeatureTensors, FeatureTypes, FeaturesList, FeatureNames, FTInfos, FeatureTypesNames, SimulationLanguages)
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)
def train_iter_fct(): return build_dataset_iter(
lazily_load_dataset("train", opt), fields, opt)
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,
opt.valid_steps)
if opt.tensorboard:
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 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 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)
# 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):
# 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)
print("load weight success")
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):
print("old style vocab")
fields = load_old_vocab(vocab,
opt.model_type,
dynamic_dict=opt.copy_attn)
else:
print("not old style")
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)
# added and deleted by zhengquan
# model = torch.nn.parallel.DistributedDataParallel(model,
# device_ids=[opt.local_rank],
# output_device=opt.local_rank)
# added and deleted by zhengquan for the availability of cuda devices.
# In the DistributedDataParallel doc, it says
# "DistributedDataParallel with multi-device module only works "
# "with CUDA devices, but module parameters locate in {}."
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) #在build_dataset_iter()中会用opt中的dataset_paths来载入数据
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 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 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:
checkpoint = None
model_opt = opt
# Peek the first dataset to determine the data_type.
# (All datasets have the same data_type).
first_dataset = next(lazily_load_dataset("train", opt))
data_type = first_dataset.data_type
# Load fields generated from preprocess phase.
fields = _load_fields(first_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)
logger.info('* batch_size: %d' % opt.batch_size)
_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)
def data_iter_fct(data_stage):
"""data_stage: train / valid"""
pt_file = opt.data + '.' + data_stage + '.pt'
logger.info('Loading {} dataset'.format(data_stage))
dataset = torch.load(pt_file)
logger.info('Loaded {} dataset'.format(data_stage))
dataset.fields = fields
is_train = True if data_stage == "train" else False
batch_size = opt.batch_size if is_train else opt.valid_batch_size
repeat = True if data_stage == "train" else False
if opt.gpuid != -1:
device = "cuda"
else:
device = "cpu"
def sort_key(ex):
""" Sort using length of source sentences. """
return ex.total_tokens
return torchtext.data.Iterator(dataset=dataset,
batch_size=batch_size,
device=device,
train=is_train,
sort=False,
sort_key=sort_key,
repeat=repeat)
# Do training.
trainer.train(data_iter_fct, opt.train_steps, opt.valid_steps)
if opt.tensorboard:
trainer.report_manager.tensorboard_writer.close()
def main(opt):
opt = training_opt_postprocessing(opt)
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:
checkpoint = None
model_opt = opt
if opt.load_pretrained_selector_from:
logger.info('Loading selector checkpoint from %s' %
opt.load_pretrained_selector_from)
sel_checkpoint = torch.load(opt.load_pretrained_selector_from,
map_location=lambda storage, loc: storage)
else:
sel_checkpoint = None
if opt.load_pretrained_s2s_generator_from:
logger.info('Loading s2s generator checkpoint from %s' %
opt.load_pretrained_s2s_generator_from)
s2s_gen_checkpoint = torch.load(
opt.load_pretrained_s2s_generator_from,
map_location=lambda storage, loc: storage)
else:
s2s_gen_checkpoint = None
# Peek the fisrt dataset to determine the data_type.
# (All datasets have the same data_type).
first_dataset = next(lazily_load_dataset("train", opt))
data_type = first_dataset.data_type
# Load fields generated from preprocess phase.
fields = _load_fields(first_dataset, data_type, opt, checkpoint)
# Report src/tgt features.
src_features, tgt_features = _collect_report_features(fields)
for j, feat in enumerate(src_features):
logger.info(' * src feature %d size = %d' %
(j, len(fields[feat].vocab)))
for j, feat in enumerate(tgt_features):
logger.info(' * tgt feature %d size = %d' %
(j, len(fields[feat].vocab)))
# Build model.
model = build_model(model_opt, opt, fields, checkpoint, sel_checkpoint,
s2s_gen_checkpoint)
# Fix the pretrained selector parameters if needed
if model_opt.fix_sel_all:
assert opt.load_pretrained_selector_from
assert opt.sel_lambda == 0.0
assert not model_opt.fix_sel_classifier
for name, param in model.named_parameters():
if 'selector' in name:
param.requires_grad = False
# only fix the classifier of the selector
if model_opt.fix_sel_classifier:
assert opt.load_pretrained_selector_from
assert not model_opt.fix_sel_all
for name, param in model.named_parameters():
if 'selector' in name and 'rnn' not in name and 'embeddings' not in name:
param.requires_grad = False
n_params, sel, enc, dec, gen = _my_tally_parameters(model)
logger.info('selector: %d' % sel)
logger.info('encoder: %d' % enc)
logger.info('decoder: %d' % dec)
logger.info('generator: %d' % gen)
logger.info('* number of parameters: %d' % n_params)
print_trainable_parameters(model)
_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,
model,
fields,
optim,
data_type,
model_saver=model_saver)
def train_iter_fct():
return build_dataset_iter(lazily_load_dataset("train", opt), fields,
opt)
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.train_steps,
opt.valid_steps)
if opt.tensorboard:
trainer.report_manager.tensorboard_writer.close()
