def main():
"""Main training program."""
print('Generate Samples')
# Disable CuDNN.
torch.backends.cudnn.enabled = False
# Arguments.
args = get_args()
args.mem_length = args.seq_length + args.mem_length - 1
# Pytorch distributed.
initialize_distributed(args)
# Random seeds for reproducability.
set_random_seed(args.seed)
# get the tokenizer
tokenizer = prepare_tokenizer(args)
# Model, optimizer, and learning rate.
model = setup_model(args)
# setting default batch size to 1
args.batch_size = 1
# generate samples
generate_samples(model, tokenizer, args, torch.cuda.current_device())
print_rank_0('done :-)')
if __name__ == '__main__':
# Disable CuDNN.
torch.backends.cudnn.enabled = False
# Arguments.
args = get_args()
assert args.finetune
# Pytorch distributed.
initialize_distributed(args)
# Random seeds for reproducability.
set_random_seed(args.seed)
from tasks.superglue.dataset import PROCESSORS
superglue_tasks = list(PROCESSORS.keys())
if args.task.lower() in superglue_tasks:
from tasks.superglue.finetune import main
elif args.task.lower() in ['lambda', 'wikitext', 'language_model']:
from tasks.language_model.finetune import main
elif args.task.lower() in [
'cnn_dm', 'cnn_dm_original', 'gigaword', 'blank',
'squad_generation', 'squad', 'squad_v1', 'xsum', 'extraction',
'cmrc'
]:
from tasks.seq2seq.finetune import main
else:
raise NotImplementedError('Task {} is not implemented.'.format(