fix: random sampling in ForgetRetainDataset #145
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molereddy
reviewed
Oct 1, 2025
src/data/unlearn.py
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| g = torch.Generator() | ||
| rank = torch.distributed.get_rank() if torch.distributed.is_initialized() else 0 | ||
| seed = int(torch.empty((), dtype=torch.int64).random_().item() + rank) | ||
| g.manual_seed(seed) |
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it would be better to use the seed from the experiment config here, rather than
int(torch.empty((), dtype=torch.int64).random_().item() to avoid introducing randomness uncontrolled by the seed.
can you try to see if you can make the experiment's cfg.seed available to this dataset class and then use seed = exp_seed + rank here?
molereddy
requested changes
Oct 1, 2025
|
Thanks for the feedback! I've updated the PR accordingly. Please let me know if there are any further adjustments required. |
|
Please fix the lint errors! |
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What does this PR do?
Fixes #139
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