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fixing rng sync when using custom sampler and batch_sampler #696

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merged 3 commits into from
Sep 12, 2022
Merged

fixing rng sync when using custom sampler and batch_sampler #696

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43d5247
fixing rng sync when using custom sampler and batch_sampler
pacman100 Sep 12, 2022
c823458
addressing comments
pacman100 Sep 12, 2022
9708f00
:sparkles:
sgugger Sep 12, 2022
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25 changes: 15 additions & 10 deletions src/accelerate/data_loader.py
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Original file line number Diff line number Diff line change
Expand Up @@ -364,10 +364,11 @@ def __iter__(self):

@property
def total_batch_size(self):
batch_sampler = self.sampler if isinstance(self.sampler, BatchSampler) else self.batch_sampler
return (
self.batch_sampler.batch_size
if self.batch_sampler.split_batches
else (self.batch_sampler.batch_size * self.batch_sampler.num_processes)
batch_sampler.batch_size
if batch_sampler.split_batches
else (batch_sampler.batch_size * batch_sampler.num_processes)
)

@property
Expand Down Expand Up @@ -639,14 +640,17 @@ def prepare_data_loader(
)
else:
# New batch sampler for the current process.
if hasattr(dataloader.sampler, "generator"):
if dataloader.sampler.generator is None:
dataloader.sampler.generator = torch.Generator()
generator = dataloader.sampler.generator
generator.manual_seed(int(torch.empty((), dtype=torch.int64).random_().item()))
elif getattr(dataloader.batch_sampler, "generator", None) is not None:
generator = dataloader.batch_sampler.generator
sampler_is_batch_sampler = isinstance(dataloader.sampler, BatchSampler)
if sampler_is_batch_sampler:
sampler = dataloader.sampler.sampler
else:
sampler = dataloader.batch_sampler.sampler
if hasattr(sampler, "generator"):
if sampler.generator is None:
sampler.generator = torch.Generator()
generator = sampler.generator
generator.manual_seed(int(torch.empty((), dtype=torch.int64).random_().item()))

batch_sampler = dataloader.sampler if sampler_is_batch_sampler else dataloader.batch_sampler
new_batch_sampler = BatchSamplerShard(
batch_sampler,
Expand Down Expand Up @@ -692,6 +696,7 @@ def prepare_data_loader(
new_dataset,
device=device if put_on_device and state.distributed_type != DistributedType.TPU else None,
sampler=new_batch_sampler,
batch_size=getattr(dataloader, "batch_size", _PYTORCH_DATALOADER_KWARGS["batch_size"]),
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rng_types=rng_types,
generator=generator,
**kwargs,
Expand Down