SUMIN/HuggingFace_transformer
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Make num_train_optimization_steps int

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samuelbroscheit
2019-05-13 12:38:22 +02:00
parent 49a77ac16f
commit 94247ad6cb
3 changed files with 3 additions and 3 deletions
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2
examples/run_classifier.py
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@@ -777,7 +777,7 @@ def main():
train_sampler = DistributedSampler(train_data) train_sampler = DistributedSampler(train_data)
train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size) 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 num_train_optimization_steps = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs
if args.local_rank != -1: if args.local_rank != -1:
num_train_optimization_steps = num_train_optimization_steps // torch.distributed.get_world_size() num_train_optimization_steps = num_train_optimization_steps // torch.distributed.get_world_size()

2
examples/run_squad.py
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@@ -946,7 +946,7 @@ def main():
else: else:
train_sampler = DistributedSampler(train_data) train_sampler = DistributedSampler(train_data)
train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size) 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 num_train_optimization_steps = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs
if args.local_rank != -1: if args.local_rank != -1:
num_train_optimization_steps = num_train_optimization_steps // torch.distributed.get_world_size() num_train_optimization_steps = num_train_optimization_steps // torch.distributed.get_world_size()

2
examples/run_swag.py
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@@ -393,7 +393,7 @@ def main():
train_sampler = DistributedSampler(train_data) train_sampler = DistributedSampler(train_data)
train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size) 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 num_train_optimization_steps = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs
if args.local_rank != -1: if args.local_rank != -1:
num_train_optimization_steps = num_train_optimization_steps // torch.distributed.get_world_size() num_train_optimization_steps = num_train_optimization_steps // torch.distributed.get_world_size()