print("device %s n_gpu %d" % (device, n_gpu))
print("device: {} n_gpu: {} 16-bits training: {}".format(
device, n_gpu, args.float16))
# load the bert setting
bert_config = BertConfig.from_json_file(args.bert_config_file)
# load data
print('loading data...')
tokenizer = tokenization.BertTokenizer(vocab_file=args.vocab_file,
do_lower_case=True)
assert args.vocab_size == len(tokenizer.vocab)
if not os.path.exists(args.train_dir):
json2features(args.train_file, [
args.train_dir.replace('_features_', '_examples_'), args.train_dir
],
tokenizer,
is_training=True,
max_seq_length=bert_config.max_position_embeddings)
if not os.path.exists(args.dev_dir1) or not os.path.exists(args.dev_dir2):
json2features(args.dev_file, [args.dev_dir1, args.dev_dir2],
tokenizer,
is_training=False,
max_seq_length=bert_config.max_position_embeddings)
train_features = json.load(open(args.train_dir, 'r'))
dev_examples = json.load(open(args.dev_dir1, 'r'))
dev_features = json.load(open(args.dev_dir2, 'r'))
if os.path.exists(args.log_file):
os.remove(args.log_file)
args.checkpoint_dir += (
'/epoch{}_batch{}_lr{}_warmup{}_anslen{}_tf/'.format(
args.train_epochs, args.n_batch, args.lr, args.warmup_rate,
args.max_ans_length))
args = utils.check_args(args, mpi_rank)
print_rank0('######## generating data ########')
if mpi_rank == 0:
tokenizer = BertTokenizer(vocab_file=args.vocab_file,
do_lower_case=True)
assert args.vocab_size == len(tokenizer.vocab)
if not os.path.exists(args.train_dir):
json2features(args.train_file, [
args.train_dir.replace('_features_', '_examples_'),
args.train_dir
],
tokenizer,
is_training=True)
if not os.path.exists(args.dev_dir1) or not os.path.exists(
args.dev_dir2):
json2features(args.dev_file, [args.dev_dir1, args.dev_dir2],
tokenizer,
is_training=False)
train_data = json.load(open(args.train_dir, 'r'))
dev_examples = json.load(open(args.dev_dir1, 'r'))
dev_data = json.load(open(args.dev_dir2, 'r'))
if mpi_rank == 0:
if os.path.exists(args.log_file):
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_ids
n_gpu = len(args.gpu_ids.split(','))
args.checkpoint_dir += (
'/epoch{}_batch{}_lr{}_warmup{}_anslen{}_tf/'.format(
args.train_epochs, args.n_batch, args.lr, args.warmup_rate,
args.max_ans_length))
mpi_rank = 0
args = utils.check_args(args, mpi_rank)
tokenizer = BertTokenizer(vocab_file=args.vocab_file, do_lower_case=True)
if not os.path.exists(args.eval_dir1) or not os.path.exists(
args.eval_dir2):
json2features(args.eval_file, [args.eval_dir1, args.eval_dir2],
tokenizer,
is_training=False)
eval_examples = json.load(open(args.eval_dir1, 'r'))
eval_data = json.load(open(args.eval_dir2, 'r'))
eval_steps_per_epoch = len(eval_data) // (args.n_batch * n_gpu)
eval_gen = data_generator(eval_data,
args.n_batch * n_gpu,
shuffle=False,
drop_last=False)
if len(eval_data) % (args.n_batch * n_gpu) != 0:
eval_steps_per_epoch += 1
with tf.device("/gpu:0"):
