Commit Graph

3 Commits

Author SHA1 Message Date
Ilqar Ramazanli
63219f1f9f To add Rectified Adam Algorithm to Optimizers (#58968)
Summary:
Fixes : https://github.com/pytorch/pytorch/issues/24892

In the paper : https://arxiv.org/pdf/1908.03265.pdf  Liyuan Liu et al. suggested a new optimization algorithm with an essence of similar to Adam Algorithm.

It has been discussed in the paper that, without warmup heuristic, in the early stage of adaptive optimization / learning algorithms sometimes we can get undesirable large variance which can slow overall convergence process.

Authors proposed the idea of rectification of variance of adaptive learning rate when it is expected to be high.

Differing from the paper, we selected variance tractability cut-off as 5 instead of 4. This adjustment is common practice, and could be found in the code-repository and also tensorflow swift optim library as well :

2f03dd1970/radam/radam.py (L156)

f51ee4618d/Sources/TensorFlow/Optimizers/MomentumBased.swift (L638)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/58968

Reviewed By: vincentqb

Differential Revision: D29310601

Pulled By: iramazanli

fbshipit-source-id: b7bd487f72f1074f266687fd9c0c6be264a748a9
2021-06-23 18:27:57 -07:00
Sam Estep
1abf45e37f Revert D29241736: [pytorch][PR] To add Rectified Adam Algorithm to Optimizers
Test Plan: revert-hammer

Differential Revision:
D29241736 (0d2a936176)

Original commit changeset: 288b9b1f3125

fbshipit-source-id: 56c4ec98647c6f1822b130726741a1c9ca193670
2021-06-22 12:08:31 -07:00
Ilqar Ramazanli
0d2a936176 To add Rectified Adam Algorithm to Optimizers (#58968)
Summary:
Fixes : https://github.com/pytorch/pytorch/issues/24892

In the paper : https://arxiv.org/pdf/1908.03265.pdf  Liyuan Liu et al. suggested a new optimization algorithm with an essence of similar to Adam Algorithm.

It has been discussed in the paper that, without warmup heuristic, in the early stage of adaptive optimization / learning algorithms sometimes we can get undesirable large variance which can slow overall convergence process.

Authors proposed the idea of rectification of variance of adaptive learning rate when it is expected to be high.

Differing from the paper, we selected variance tractability cut-off as 5 instead of 4. This adjustment is common practice, and could be found in the code-repository and also tensorflow swift optim library as well :

2f03dd1970/radam/radam.py (L156)

f51ee4618d/Sources/TensorFlow/Optimizers/MomentumBased.swift (L638)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/58968

Reviewed By: gchanan

Differential Revision: D29241736

Pulled By: iramazanli

fbshipit-source-id: 288b9b1f3125fdc6c7a7bb23fde1ea5c201c0448
2021-06-22 10:38:41 -07:00