def get_data_and_args():
parser = argparse.ArgumentParser(description='PyTorch Sentiment Discovery Classification')
parser = add_general_args(parser)
parser = add_model_args(parser)
parser = add_classifier_model_args(parser)
data_config, data_parser, run_classifier_parser, parser = add_run_classifier_args(parser)
args = parser.parse_args()
args.cuda = torch.cuda.is_available()
args.shuffle=False
if args.seed is not -1:
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
(train_data, val_data, test_data), tokenizer = data_config.apply(args)
args.data_size = tokenizer.num_tokens
args.padding_idx = tokenizer.command_name_map['pad'].Id
return (train_data, val_data, test_data), tokenizer, args
def main():
parser = argparse.ArgumentParser(
description='PyTorch Sentiment-Discovery Language Modeling')
parser = add_general_args(parser)
parser = add_model_args(parser)
data_config, data_parser = add_unsupervised_data_args(parser)
args = parser.parse_args()
torch.backends.cudnn.enabled = False
args.cuda = torch.cuda.is_available()
if args.multinode_init:
args.rank = int(os.getenv('RANK', 0))
args.world_size = int(os.getenv("WORLD_SIZE", 1))
# initialize distributed process group and set device
if args.rank > 0:
torch.cuda.set_device(args.rank % torch.cuda.device_count())
if args.world_size > 1:
init_method = 'tcp://'
if not args.multinode_init:
init_method += 'localhost:6000'
else:
master_ip = os.getenv('MASTER_ADDR', 'localhost')
master_port = os.getenv('MASTER_PORT', '6666')
init_method += master_ip + ':' + master_port
torch.distributed.init_process_group(backend=args.distributed_backend,
world_size=args.world_size,
rank=args.rank,
init_method=init_method)
# Set the random seed manually for reproducibility.
if args.seed is not None and args.seed > 0:
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
if args.loss_scale != 1 and args.dynamic_loss_scale:
raise RuntimeError(
"Static loss scale and dynamic loss scale cannot be used together."
)
(train_data, val_data, test_data), tokenizer = data_config.apply(args)
args.data_size = tokenizer.num_tokens
model, optim, LR, LR_Warmer, criterion = setup_model_and_optim(
args, train_data, tokenizer)
lr = args.lr
best_val_loss = None
# If saving process intermittently create directory for saving
if args.save_iters > 0 and not os.path.exists(
os.path.splitext(args.save)[0]) and args.rank < 1:
os.makedirs(os.path.splitext(args.save)[0])
# At any point you can hit Ctrl + C to break out of training early.
try:
total_iters = 0
elapsed_time = 0
skipped_iters = 0
if args.load_optim:
total_iters = optim_sd['iter']
skipped_iters = optim_sd['skipped_iter']
for epoch in range(1, args.epochs + 1):
if args.rank <= 0:
with open(args.save + '.train_lock', 'wb') as f:
pass
epoch_start_time = time.time()
val_loss, skipped_iters = train(epoch, model, optim, train_data,
LR, LR_Warmer, criterion, args,
total_iters, skipped_iters,
elapsed_time)
elapsed_time += time.time() - epoch_start_time
total_iters += args.train_iters
if val_data is not None:
print('entering eval')
val_loss = evaluate(val_data, model, criterion, args)
print('-' * 89)
print(
'| end of epoch {:3d} | time: {:5.2f}s | valid loss {:5.4f} | '
'valid ppl {:8.4f}'.format(epoch,
(time.time() - epoch_start_time),
val_loss,
math.exp(min(val_loss, 20))))
print('-' * 89)
# Save the model if the validation loss is the best we've seen so far.
if (not best_val_loss
or val_loss < best_val_loss) and args.rank <= 0:
torch.save(model.state_dict(), args.save)
best_val_loss = val_loss
if args.world_size == 1 or torch.distributed.get_rank() == 0:
try:
os.remove(args.save + '.train_lock')
except:
pass
# if args.world_size > 1:
# torch.distributed.barrier()
torch.cuda.synchronize()
except KeyboardInterrupt:
print('-' * 89)
print('Exiting from training early')
#while os.path.exists(args.save+'.train_lock'):
# time.sleep(1)
# Load the best saved model.
#if os.path.exists(args.save):
# model.load_state_dict(torch.load(args.save, 'cpu'))
# if not args.no_weight_norm and args.rank <= 0:
# remove_weight_norm(model)
# torch.save(model.state_dict(), args.save)
if test_data is not None:
# Run on test data.
print('entering test')
test_loss = evaluate(test_data, model, criterion, args)
print('=' * 89)
print(
'| End of training | test loss {:5.4f} | test ppl {:8.4f}'.format(
test_loss, math.exp(min(test_loss, 20))))
print('=' * 89)
