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lm_train.py
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lm_train.py
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import argparse
import pprint
import torch
from torch import optim
import torch.nn as nn
from simple_nmt.data_loader import DataLoader
import simple_nmt.data_loader as data_loader
from simple_nmt.models.rnnlm import LanguageModel
from simple_nmt.lm_trainer import LanguageModelTrainer as LMTrainer
from dual_train import get_crits
def define_argparser(is_continue=False):
p = argparse.ArgumentParser()
if is_continue:
p.add_argument(
'--load_fn',
required=True,
help='Model file name to continue.'
)
p.add_argument(
'--model_fn',
required=not is_continue,
help='Model file name to save. Additional information would be annotated to the file name.'
)
p.add_argument(
'--train',
required=not is_continue,
help='Training set file name except the extention. (ex: train.en --> train)'
)
p.add_argument(
'--valid',
required=not is_continue,
help='Validation set file name except the extention. (ex: valid.en --> valid)'
)
p.add_argument(
'--lang',
required=not is_continue,
help='Set of extention represents language pair. (ex: en + ko --> enko)'
)
p.add_argument(
'--gpu_id',
type=int,
default=-1,
help='GPU ID to train. Currently, GPU parallel is not supported. -1 for CPU. Default=%(default)s'
)
p.add_argument(
'--off_autocast',
action='store_true',
help='Turn-off Automatic Mixed Precision (AMP), which speed-up training.',
)
p.add_argument(
'--batch_size',
type=int,
default=32,
help='Mini batch size for gradient descent. Default=%(default)s'
)
p.add_argument(
'--n_epochs',
type=int,
default=20,
help='Number of epochs to train. Default=%(default)s'
)
p.add_argument(
'--verbose',
type=int,
default=2,
help='VERBOSE_SILENT, VERBOSE_EPOCH_WISE, VERBOSE_BATCH_WISE = 0, 1, 2. Default=%(default)s'
)
p.add_argument(
'--max_length',
type=int,
default=100,
help='Maximum length of the training sequence. Default=%(default)s'
)
p.add_argument(
'--dropout',
type=float,
default=.2,
help='Dropout rate. Default=%(default)s'
)
p.add_argument(
'--word_vec_size',
type=int,
default=512,
help='Word embedding vector dimension. Default=%(default)s'
)
p.add_argument(
'--hidden_size',
type=int,
default=768,
help='Hidden size of LSTM. Default=%(default)s'
)
p.add_argument(
'--n_layers',
type=int,
default=4,
help='Number of layers in LSTM. Default=%(default)s'
)
p.add_argument(
'--max_grad_norm',
type=float,
default=1e+8,
help='Threshold for gradient clipping. Default=%(default)s'
)
config = p.parse_args()
return config
def get_models(src_vocab_size, tgt_vocab_size, config):
language_models = [
LanguageModel(
tgt_vocab_size,
config.word_vec_size,
config.hidden_size,
n_layers=config.n_layers,
dropout_p=config.dropout,
),
LanguageModel(
src_vocab_size,
config.word_vec_size,
config.hidden_size,
n_layers=config.n_layers,
dropout_p=config.dropout,
),
]
return language_models
def main(config):
def print_config(config):
pp = pprint.PrettyPrinter(indent=4)
pp.pprint(vars(config))
print_config(config)
loader = DataLoader(
config.train,
config.valid,
(config.lang[:2], config.lang[-2:]),
batch_size=config.batch_size,
device=-1,
max_length=config.max_length,
dsl=True,
)
src_vocab_size = len(loader.src.vocab)
tgt_vocab_size = len(loader.tgt.vocab)
models = get_models(
src_vocab_size,
tgt_vocab_size,
config
)
crits = get_crits(
src_vocab_size,
tgt_vocab_size,
pad_index=data_loader.PAD
)
if config.gpu_id >= 0:
for model, crit in zip(models, crits):
model.cuda(config.gpu_id)
crit.cuda(config.gpu_id)
if config.verbose >= 2:
print(models)
for model, crit in zip(models, crits):
optimizer = optim.Adam(model.parameters())
lm_trainer = LMTrainer(config)
model = lm_trainer.train(
model, crit, optimizer,
train_loader=loader.train_iter,
valid_loader=loader.valid_iter,
src_vocab=loader.src.vocab if model.vocab_size == src_vocab_size else None,
tgt_vocab=loader.tgt.vocab if model.vocab_size == tgt_vocab_size else None,
n_epochs=config.n_epochs,
)
torch.save(
{
'model': [
models[0].state_dict(),
models[1].state_dict(),
],
'config': config,
'src_vocab': loader.src.vocab,
'tgt_vocab': loader.tgt.vocab,
}, config.model_fn
)
if __name__ == '__main__':
config = define_argparser()
main(config)