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dirkgr
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Feb 4, 2025
| compression_topk=cfg.optimizer.compression_topk, | ||
| compression_chunk=cfg.optimizer.compression_chunk, | ||
| weight_decay=cfg.optimizer.weight_decay, | ||
| process_group=None, # TODO: fix for hybrid sharding |
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This seems important? Hybrid is necessary for big models.
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| ### DeMo parameters | ||
| compression_decay: float = 0.999 | ||
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| compression_topk: int = 32 | ||
| """ | ||
| How many numbers of topk to transmit per chunk, if dynamic is enabled, this is the initial topk | ||
| """ | ||
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| compression_chunk: int = 64 | ||
| """ | ||
| Size of the chunk of the gradients, note that 2D gradients are chunked in 2D, which the topk sparsity is squared compared to 1D | ||
| """ |
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| disable_grad_sync: bool = False | ||
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I see this setting twice, once here, and once in DDPGradSyncMode?
| return metrics | ||
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| class DeMo(torch.optim.SGD, Optimizer): |
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It seems like the organization would make more sense if this class, and demo_utils.py, were in their own file together, and then we use __all__ to make this optimizer appear the same as the others.
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Oh, I see. You put a reference in the description 🙈. Paper says you pushed this to 1B/100B tokens. Can you go further? Experience says, things like this stop working if you go really big. |
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Cleaned-up version of https://github.com/bloc97/DeMo for integrating efficient distributed training a la Decoupled Monentum Optimization (https://arxiv.org/abs/2411.19870)