def scale_by_grad_accum_steps_wrapper_hook(
hook_state, bucket: dist.GradBucket
) -> torch.futures.Future[torch.Tensor]:
bucket.set_buffer(bucket.buffer().div_(
args.gradient_accumulation_steps))
fut = hook(hook_state, bucket)
return fut
def bf16_compress_wrapper_hook(
hook_state,
bucket: dist.GradBucket) -> torch.futures.Future[torch.Tensor]:
# Cast bucket tensor to BF16.
bucket.set_buffer(bucket.buffer().to(torch.bfloat16))
fut = hook(hook_state, bucket)
def decompress(fut):
decompressed_tensor = bucket.buffer()
# Decompress in place to reduce the peak memory.
# See: https://github.com/pytorch/pytorch/issues/45968
decompressed_tensor.copy_(fut.value())
return decompressed_tensor
# Decompress after hook has run.
return fut.then(decompress)
