def train_model(args, encoder_seq, encoder_para, decoder_seq, decoder_para):
parser = options.get_training_parser()
args = options.parse_args_and_arch(parser)
args.ddp_backend = 'no_c10d'
args.encoder_seq = encoder_seq
args.encoder_para = encoder_para
args.decoder_seq = decoder_seq
args.decoder_para = decoder_para
#print('args.arch', args.arch)
#print('max-tokens', args.max_tokens)
if args.distributed_port > 0 or args.distributed_init_method is not None:
#print('0')
from distributed_train import main as distributed_main
distributed_main(args)
elif args.distributed_world_size > 1:
#print('1')
from multiprocessing_train import main as multiprocessing_main
multiprocessing_main(args)
else:
#print('2')
main(args)
def cli_main():
# Horrible hack, please close your eyes and don't look
cli_args = set(sys.argv)
print("Command line argumetns")
print(cli_args)
if "--arch" not in cli_args and "-a" not in cli_args:
sys.argv.append("--arch")
sys.argv.append("transformer_iwslt_de_en")
print(cli_args)
# It's over now you can look
parser = options.get_training_parser()
add_multiobj_args(parser)
args = options.parse_args_and_arch(parser)
if args.distributed_port > 0 or args.distributed_init_method is not None:
raise NotImplementedError(
"Multitask doesn't support multiprocessing yet")
from distributed_train import main as distributed_main
distributed_main(args)
elif args.distributed_world_size > 1:
raise NotImplementedError(
"Multitask doesn't support multiprocessing yet")
from multiprocessing_train import main as multiprocessing_main
multiprocessing_main(args)
else:
main(args)
def main(args):
if args.distributed_port > 0 \
or args.distributed_init_method is not None:
distributed_main(args)
elif args.distributed_world_size > 1:
multiprocessing_main(args)
else:
singleprocess_main(args)
def load_dataset_splits(args, task, splits):
for split in splits:
for k in itertools.count():
split_k = split + (str(k) if k > 0 else '')
try:
task.load_dataset(split_k)
print('| {} {} {} examples'.format(args.data, split_k,
len(task.dataset(split_k))))
except FileNotFoundError as e:
if k > 0:
break
raise e
if __name__ == '__main__':
parser = options.get_training_parser()
args = options.parse_args_and_arch(parser)
if args.distributed_port > 0 or args.distributed_init_method is not None:
from distributed_train import main as distributed_main
distributed_main(args)
elif args.distributed_world_size > 1:
from multiprocessing_train import main as multiprocessing_main
multiprocessing_main(args)
else:
main(args)
def load_checkpoint(args, trainer, epoch_itr):
"""Load a checkpoint and replay dataloader to match."""
os.makedirs(os.path.join(args.save_dir, 'checkpoints'), exist_ok=True)
checkpoint_path = os.path.join(args.save_dir, 'checkpoints',
args.restore_file)
if os.path.isfile(checkpoint_path):
extra_state = trainer.load_checkpoint(checkpoint_path)
if extra_state is not None:
# replay train iterator to match checkpoint
epoch_itr.load_state_dict(extra_state['train_iterator'])
print('| loaded checkpoint {} (epoch {} @ {} updates)'.format(
checkpoint_path, epoch_itr.epoch, trainer.get_num_updates()))
trainer.lr_step(epoch_itr.epoch)
trainer.lr_step_update(trainer.get_num_updates())
if 'best' in extra_state:
save_checkpoint.best = extra_state['best']
if __name__ == '__main__':
parser = options.get_training_parser()
ARGS = options.parse_args_and_arch(parser)
if ARGS.distributed_port > 0 or ARGS.distributed_init_method is not None:
from distributed_train import main as distributed_main
distributed_main(ARGS)
else:
main(ARGS)
