From 7de5c6aa5e222ad99402f70cf7258b49a5cf7fe5 Mon Sep 17 00:00:00 2001 From: Matthew Carrigan Date: Wed, 20 Mar 2019 16:44:04 +0000 Subject: [PATCH] PEP8 and formatting cleanups --- examples/lm_finetuning/finetune_on_pregenerated.py | 11 +++++++---- examples/lm_finetuning/pregenerate_training_data.py | 1 - 2 files changed, 7 insertions(+), 5 deletions(-) diff --git a/examples/lm_finetuning/finetune_on_pregenerated.py b/examples/lm_finetuning/finetune_on_pregenerated.py index 26003045fc..a0d393568a 100644 --- a/examples/lm_finetuning/finetune_on_pregenerated.py +++ b/examples/lm_finetuning/finetune_on_pregenerated.py @@ -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() diff --git a/examples/lm_finetuning/pregenerate_training_data.py b/examples/lm_finetuning/pregenerate_training_data.py index e37d9ba822..b9e7410af2 100644 --- a/examples/lm_finetuning/pregenerate_training_data.py +++ b/examples/lm_finetuning/pregenerate_training_data.py @@ -269,6 +269,5 @@ def main(): metrics_file.write(json.dumps(metrics)) - if __name__ == '__main__': main()