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import os
import sys
import torch
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__),
os.path.pardir)))
from tqdm import tqdm
from megatron import get_args
from megatron import get_tokenizer
from megatron.initialize import initialize_megatron
from pretrain_gpt import train_valid_test_datasets_provider
from megatron.training import build_train_valid_test_data_iterators
def get_detok_args(parser):
group = parser.add_argument_group(title='detokenizer')
group.add_argument('--detokenizer_output',
type=str,
required=True,
help='detokenizer output path')
return parser
def process_split(split, dataset, out_path):
print(f'Processing {split}')
tokenizer = get_tokenizer()
full_text = []
for batch in tqdm(dataset, total=len(dataset)):
tokens = batch['text'].reshape(-1).tolist()
text = tokenizer.detokenize(tokens)
full_text.append(text)
out_name = os.path.join(out_path, f'{split}.text')
print(f'Writing to {out_name}')
with open(out_name, 'w') as f:
f.writelines(full_text)
def main():
# below arguments are to force the full dataset according to the
# train/valid/test split based on args.split
forced_args = {
"micro_batch_size": 1,
"train_samples": None,
"train_iters": 1,
"eval_iters": 1,
"eval_interval": 2,
"use_seq_len_plus_one_tokens": False
}
initialize_megatron(extra_args_provider=get_detok_args, args_defaults=forced_args)
torch.distributed.barrier()
# after parsing, we have to force again the required args
args = get_args()
for name, value in forced_args.items():
setattr(args, name, value)
# create train/valid/test split based on args.split
args.iteration = 0
train_iter, valid_iter, test_iter = build_train_valid_test_data_iterators(
train_valid_test_datasets_provider)
os.makedirs(args.detokenizer_output, exist_ok=True)
process_split('test', test_iter._dataset, args.detokenizer_output)
process_split('valid', valid_iter._dataset, args.detokenizer_output)
process_split('train', train_iter._dataset, args.detokenizer_output)
if __name__ == '__main__':
main()
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