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Fixing the issues reported in https://github.com/huggingface/pytorch-pretrained-BERT/issues/556

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Reason for issue was that optimzation steps where computed from example size, which is different from actual size of dataloader when an example is chunked into multiple instances.

Solution in this pull request is to compute num_optimization_steps directly from len(data_loader).
This commit is contained in:
samuel.broscheit
2019-05-12 00:13:45 +02:00
parent 3fc63f126d
commit 3bf3f9596f
4 changed files with 66 additions and 66 deletions
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28
examples/run_swag.py
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@@ -362,8 +362,20 @@ def main():
num_train_optimization_steps = None
if args.do_train:
train_examples = read_swag_examples(os.path.join(args.data_dir, 'train.csv'), is_training = True)
num_train_optimization_steps = int(
len(train_examples) / args.train_batch_size / args.gradient_accumulation_steps) * args.num_train_epochs
train_features = convert_examples_to_features(
train_examples, tokenizer, args.max_seq_length, True)
all_input_ids = torch.tensor(select_field(train_features, 'input_ids'), dtype=torch.long)
all_input_mask = torch.tensor(select_field(train_features, 'input_mask'), dtype=torch.long)
all_segment_ids = torch.tensor(select_field(train_features, 'segment_ids'), dtype=torch.long)
all_label = torch.tensor([f.label for f in train_features], dtype=torch.long)
train_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label)
if args.local_rank == -1:
train_sampler = RandomSampler(train_data)
else:
train_sampler = DistributedSampler(train_data)
train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size)
num_train_optimization_steps = len(train_dataloader) / args.gradient_accumulation_steps * args.num_train_epochs
if args.local_rank != -1:
num_train_optimization_steps = num_train_optimization_steps // torch.distributed.get_world_size()
@@ -422,22 +434,10 @@ def main():
global_step = 0
if args.do_train:
train_features = convert_examples_to_features(
train_examples, tokenizer, args.max_seq_length, True)
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_examples))
logger.info(" Batch size = %d", args.train_batch_size)
logger.info(" Num steps = %d", num_train_optimization_steps)
all_input_ids = torch.tensor(select_field(train_features, 'input_ids'), dtype=torch.long)
all_input_mask = torch.tensor(select_field(train_features, 'input_mask'), dtype=torch.long)
all_segment_ids = torch.tensor(select_field(train_features, 'segment_ids'), dtype=torch.long)
all_label = torch.tensor([f.label for f in train_features], dtype=torch.long)
train_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label)
if args.local_rank == -1:
train_sampler = RandomSampler(train_data)
else:
train_sampler = DistributedSampler(train_data)
train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size)
model.train()
for _ in trange(int(args.num_train_epochs), desc="Epoch"):