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make examples consistent, revert error in num_train_steps calculation

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This commit is contained in:
Matej Svejda
2019-01-30 11:47:25 +01:00
parent 9c6a48c8c3
commit 5169069997
5 changed files with 21 additions and 17 deletions
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14
examples/run_lm_finetuning.py
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@@ -497,7 +497,7 @@ def main():
raise ValueError("Invalid gradient_accumulation_steps parameter: {}, should be >= 1".format(
args.gradient_accumulation_steps))
args.train_batch_size = int(args.train_batch_size / args.gradient_accumulation_steps)
args.train_batch_size = args.train_batch_size // args.gradient_accumulation_steps
random.seed(args.seed)
np.random.seed(args.seed)
@@ -520,8 +520,8 @@ def main():
print("Loading Train Dataset", args.train_file)
train_dataset = BERTDataset(args.train_file, tokenizer, seq_len=args.max_seq_length,
corpus_lines=None, on_memory=args.on_memory)
num_train_steps = int(
len(train_dataset) / args.train_batch_size * args.num_train_epochs)
num_train_steps =
len(train_dataset) // args.train_batch_size // args.gradient_accumulation_steps * args.num_train_epochs
# Prepare model
model = BertForPreTraining.from_pretrained(args.bert_model)
@@ -544,6 +544,10 @@ def main():
{'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01},
{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
]
t_total = num_train_steps
if args.local_rank != -1:
t_total = t_total // torch.distributed.get_world_size()
if args.fp16:
try:
from apex.optimizers import FP16_Optimizer
@@ -564,7 +568,7 @@ def main():
optimizer = BertAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
warmup=args.warmup_proportion,
t_total=num_train_steps)
t_total=t_total)
global_step = 0
if args.do_train:
@@ -604,7 +608,7 @@ 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_steps, args.warmup_proportion)
lr_this_step = args.learning_rate * warmup_linear(global_step/t_total, args.warmup_proportion)
for param_group in optimizer.param_groups:
param_group['lr'] = lr_this_step
optimizer.step()