Update comments in data parallel example to use sampler #7914
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will-cromar
reviewed
Aug 27, 2024
| if xr.world_size() > 1: | ||
| train_sampler = torch.utils.data.distributed.DistributedSampler( | ||
| train_dataset, | ||
| num_replicas=xr.world_size(), |
| train_sampler = torch.utils.data.distributed.DistributedSampler( | ||
| train_dataset, | ||
| num_replicas=xr.world_size(), | ||
| rank=xr.global_ordinal(), |
| # want each process to handle different parts of the data. | ||
| ''' | ||
| train_sampler = None | ||
| if xr.world_size() > 1: |
| # below code is commented out because in this example we used a fake data | ||
| # loader that does not take sampler. However this logic is needed if you | ||
| # want each process to handle different parts of the data. | ||
| ''' |
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Is it clearer to just apply this sampler to the fake dataset anyway?
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fake dataset is a XLA util https://github.com/pytorch/xla/blob/master/examples/train_resnet_base.py#L25-L29, it does not take sampler.
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Oh, I just kind of assumed that SampleGenerator was an idiomatic Dataset. Can you try just making it inherit from IterableDataset since it has __iter__ and __len__ already? You should then be able to wrap it in a standard sampler and data loader.
If that doesn't work, then one of us can follow up. Our examples should be as close to PyTorch as possible.
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fix #7904