Use SimpleDistributedPerLayerClipping optimizer in hooks mode #750
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…-pytorch#720) Summary: Pull Request resolved: meta-pytorch#720 register_backward_hook is deprecated and may lead to errors in gradient calculation. We switch to the supported register_full_backward_hook. Differential Revision: D68562558 Reviewed By: HuanyuZhang
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…ytorch#750) Summary: We use SimpleDistributedPerLayerOptimizer instead of DistributedPerLayerOptimizer. The latter causes an issue when switching to `register_full_backward_hook`. The issue arises because DistributedPerLayerOptimizer uses per-parameter hooks on top of the per-module hooks. During the backward pass, the per-parameter hooks fire before the per-module hooks. Per-sample gradients are computed when the per-module hooks fire, and an error occurs when the per-parameter hooks try to access the per-sample gradients before they are computed. Forcing the order in which hooks are called is not possible with PyTorch. Differential Revision: D72420168
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…ytorch#750) Summary: Pull Request resolved: meta-pytorch#750 We use SimpleDistributedPerLayerOptimizer instead of DistributedPerLayerOptimizer. The latter causes an issue when switching to `register_full_backward_hook`. The issue arises because DistributedPerLayerOptimizer uses per-parameter hooks on top of the per-module hooks. During the backward pass, the per-parameter hooks fire before the per-module hooks. Per-sample gradients are computed when the per-module hooks fire, and an error occurs when the per-parameter hooks try to access the per-sample gradients before they are computed. Forcing the order in which hooks are called is not possible with PyTorch. Differential Revision: D72420168
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Summary:
We use SimpleDistributedPerLayerOptimizer instead of DistributedPerLayerOptimizer.
The latter causes an issue when switching to
register_full_backward_hook.The issue arises because DistributedPerLayerOptimizer uses per-parameter hooks on top of the per-module hooks. During the backward pass, the per-parameter hooks fire before the per-module hooks. Per-sample gradients are computed when the per-module hooks fire, and an error occurs when the per-parameter hooks try to access the per-sample gradients before they are computed. Forcing the order in which hooks are called is not possible with PyTorch.
Differential Revision: D72420168