riaz.somc/transformers
1
0
Fork 0
mirror of https://github.com/huggingface/transformers.git synced 2025-07-31 02:02:21 +06:00
Code Issues Actions 23 Packages Projects Releases Wiki Activity

fix run_glue test

Browse Source
This commit is contained in:
thomwolf 2019-07-11 22:20:10 +02:00
parent ccb6947dc1
commit 92a782b108
2 changed files with 17 additions and 11 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

18
examples/run_glue.py
View File

@ -53,6 +53,15 @@ MODEL_CLASSES = {
'xlm': (XLMConfig, XLMForSequenceClassification, XLMTokenizer),
}
def set_seed(args):
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if args.n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
def train(args, train_dataset, model, tokenizer):
""" Train the model """
if args.local_rank in [-1, 0]:
@ -97,6 +106,7 @@ def train(args, train_dataset, model, tokenizer):
tr_loss, logging_loss = 0.0, 0.0
model.zero_grad()
train_iterator = trange(int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0])
set_seed(args) # Added here for reproductibility (even between python 2 and 3)
for _ in train_iterator:
epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])
for step, batch in enumerate(epoch_iterator):
@ -371,12 +381,8 @@ def main():
logger.warning("Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
args.local_rank, device, args.n_gpu, bool(args.local_rank != -1), args.fp16)
# Setup seeds
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if args.n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
# Set seed
set_seed(args)
# Prepare GLUE task
args.task_name = args.task_name.lower()

10
pytorch_transformers/optimization.py
View File

@ -167,14 +167,14 @@ class AdamW(Optimizer):
# Decay the first and second moment running average coefficient
# In-place operations to update the averages at the same time
exp_avg.mul_(beta1).add_(1 - beta1, grad)
exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
exp_avg.mul_(beta1).add_(1.0 - beta1, grad)
exp_avg_sq.mul_(beta2).addcmul_(1.0 - beta2, grad, grad)
denom = exp_avg_sq.sqrt().add_(group['eps'])
step_size = group['lr']
if group['correct_bias']: # No bias correction for Bert
bias_correction1 = 1 - beta1 ** state['step']
bias_correction2 = 1 - beta2 ** state['step']
bias_correction1 = 1.0 - beta1 ** state['step']
bias_correction2 = 1.0 - beta2 ** state['step']
step_size = step_size * math.sqrt(bias_correction2) / bias_correction1
p.data.addcdiv_(-step_size, exp_avg, denom)
@ -187,7 +187,7 @@ class AdamW(Optimizer):
# with the m/v parameters. This is equivalent to adding the square
# of the weights to the loss with plain (non-momentum) SGD.
# Add weight decay at the end (fixed version)
if group['weight_decay'] > 0:
if group['weight_decay'] > 0.0:
p.data.add_(-group['lr'] * group['weight_decay'], p.data)
return loss