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Distributed sampling parity between Lite and PyTorch #16101
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Borda
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LGTM !
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What does this PR do?
Restores parity between pure PyTorch dataloader and Lite dataloader when using DistributedSampler (DDP).
This PR avoids the DistributedSamplerWrapper for the two standard samplers, RandomSampler and SequentialSampler. The DistributedSamplerWrapper should only be used for custom samplers. A note was added to the documentation of this wrapper.
We also ensure that
.set_epoch()
gets called automatically for the user.Fixes #14636
Does your PR introduce any breaking changes? If yes, please list them.
The order of samples when shuffling is enabled. But this should be considered a bugfix.
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Did you have fun?
I made sure I had fun coding 🙃
cc @Borda @carmocca @justusschock @awaelchli