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PEP8 and formatting cleanups

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This commit is contained in:
Matthew Carrigan 2019-03-20 16:44:04 +00:00
parent 1798e98e5a
commit 7de5c6aa5e
2 changed files with 7 additions and 5 deletions
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11
examples/lm_finetuning/finetune_on_pregenerated.py
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@ -9,7 +9,7 @@ from collections import namedtuple
from torch.utils.data import DataLoader, Dataset, RandomSampler
from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm, trange
from tqdm import tqdm
from pytorch_pretrained_bert.modeling import BertForPreTraining
from pytorch_pretrained_bert.tokenization import BertTokenizer
@ -149,7 +149,8 @@ def main():
help="random seed for initialization")
args = parser.parse_args()
assert args.pregenerated_data.is_dir(), "--pregenerated_data should point to the folder of files made by pregenerate_training_data.py!"
assert args.pregenerated_data.is_dir(), \
"--pregenerated_data should point to the folder of files made by pregenerate_training_data.py!"
samples_per_epoch = []
for i in range(args.epochs):
@ -237,7 +238,8 @@ def main():
from apex.optimizers import FP16_Optimizer
from apex.optimizers import FusedAdam
except ImportError:
raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training.")
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training.")
optimizer = FusedAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
@ -293,7 +295,8 @@ def main():
if args.fp16:
# modify learning rate with special warm up BERT uses
# if args.fp16 is False, BertAdam is used that handles this automatically
lr_this_step = args.learning_rate * warmup_linear(global_step/num_train_optimization_steps, args.warmup_proportion)
lr_this_step = args.learning_rate * warmup_linear(global_step/num_train_optimization_steps,
args.warmup_proportion)
for param_group in optimizer.param_groups:
param_group['lr'] = lr_this_step
optimizer.step()

1
examples/lm_finetuning/pregenerate_training_data.py
View File

@ -269,6 +269,5 @@ def main():
metrics_file.write(json.dumps(metrics))
if __name__ == '__main__':
main()