def main(args):
toker = GPT2Tokenizer.from_pretrained('gpt2')
attrs = []
if args.reverse:
attrs.append('reverse')
if args.two_turn:
attrs.append('2turn')
if attrs:
temp = str(".".join(attrs))
# str(args.corpus[:-4]) + '.' + str(args.max_seq_len) + 'len.'
db_path = (str(args.corpus[:-4]) + '.' + str(args.max_seq_len) + 'len.' + temp + '.db/db')
else:
db_path = str(args.corpus[:-4]) + '.' + str(args.max_seq_len) + 'len.db/db'
if exists(dirname(db_path)):
raise ValueError('Found existing DB, please backup')
else:
os.makedirs(dirname(db_path))
with open(args.corpus, "r", encoding="utf-8") as reader, \
shelve.open(db_path, 'n') as db:
chunk = []
n_chunk = 0
n_example = 0
for line in tqdm(reader, total=_get_file_len(args.corpus)):
try:
if len(chunk) >= args.chunk_size:
# save and renew chunk
db['chunk_' + str(n_chunk)] = gzip.compress(
json.dumps(chunk[:args.chunk_size]).encode('utf-8'))
chunk = chunk[args.chunk_size:]
n_chunk += 1
weights, inputs = _get_inputs_from_text(line, toker)
if args.reverse:
weights = list(reversed(weights))
inputs = list(reversed(inputs))
if args.two_turn:
weights = weights[:2]
inputs = inputs[:2]
if len(weights) < 2:
continue
features = _make_features(n_example, weights, inputs,
toker, args.max_seq_len)
for feature in features:
chunk.append(vars(feature))
n_example += 1
except Exception as e:
print('!!! prepro exception !!!', e)
continue
# save last chunk
db['chunk_' + str(n_chunk)] = gzip.compress(
json.dumps(chunk).encode('utf-8'))
# save relevant information to reproduce
meta = {'n_example': n_example,
'chunk_size': args.chunk_size,
'max_seq_len': args.max_seq_len,
'reverse': args.reverse,
'two_turn': args.two_turn}
with open(join(dirname(db_path), 'meta.json'), 'w') as writer:
json.dump(meta, writer, indent=4)
torch.save(toker, join(dirname(db_path), 'tokenizer.pt'))
'GPT2.{}.{}.{}gpu.{}'.format(args.learning_rate, args.train_batch_size,
n_gpu, timestamp))
log_dir = args.log_dir if args.log_dir is not None and len(
args.log_dir) > 0 else output_dir
if args.local_rank == -1:
os.makedirs(output_dir, exist_ok=True)
logger.info('Input Argument Information')
args_dict = vars(args)
for a in args_dict:
logger.info('%-28s %s' % (a, args_dict[a]))
#########################################################################
# Prepare Data Set
##########################################################################
enc = GPT2Tokenizer.from_pretrained(args.model_name_or_path)
config = GPT2Config.from_json_file(join(args.model_name_or_path,
'config.json'))
if args.local_rank == -1:
train_dataloader = BucketingDataLoader(args.train_input_file,
args.train_batch_size,
args.max_seq_length)
else:
pass
# train_dataloader = DistributedBucketingDataLoader(
# get_rank(), get_world_size(),
# args.train_input_file, args.train_batch_size,
# args.max_seq_length)
args.train_batch_size, n_gpu,
timestamp))
log_dir = args.log_dir if args.log_dir is not None and len(args.log_dir) > 0 else output_dir
if args.local_rank == -1 or get_rank() == 0:
os.makedirs(output_dir, exist_ok=True)
logger.info('Input Argument Information')
args_dict = vars(args)
for a in args_dict:
logger.info('%-28s %s' % (a, args_dict[a]))
#########################################################################
# Prepare Data Set
##########################################################################
enc = GPT2Tokenizer.from_pretrained('gpt2')
config = GPT2Config.from_json_file(
join(args.model_name_or_path, 'config.json'))
if args.local_rank == -1:
train_dataloader = BucketingDataLoader(args.train_input_file,
args.train_batch_size,
args.max_seq_length)
else:
train_dataloader = DistributedBucketingDataLoader(
get_rank(), get_world_size(),
args.train_input_file, args.train_batch_size,
args.max_seq_length)
eval_dataloader_loss = DynamicBatchingLoader(
