To add Rectified Adam Algorithm to Optimizers #58968
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💊 CI failures summary and remediationsAs of commit d8ac387 (more details on the Dr. CI page and at hud.pytorch.org/pr/58968):
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discussed offline: we'll keep the name as RAdam instead of PlainRAdam. If we need another version, we could explore calling it something else (ModifiedRAdam/ApproximateRAdam for this one?) or have a toggle? |
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This pull request has been merged in 0ff3634. |
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Summary: Fixes : #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 : https://github.com/LiyuanLucasLiu/RAdam/blob/2f03dd197022da442c6a15c47321f4335d113a3f/radam/radam.py#L156 https://github.com/tensorflow/swift-apis/blob/f51ee4618d652a2419e998bf9418ad80bda67454/Sources/TensorFlow/Optimizers/MomentumBased.swift#L638 Pull Request resolved: #58968 Reviewed By: gchanan Differential Revision: D29241736 Pulled By: iramazanli fbshipit-source-id: 288b9b1f3125fdc6c7a7bb23fde1ea5c201c0448
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This pull request has been reverted by 57967dc498dee032dc189f9ab4fc264ab905581e. |
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This pull request has been reverted by 1abf45e. |
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Summary: Fixes : #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 : https://github.com/LiyuanLucasLiu/RAdam/blob/2f03dd197022da442c6a15c47321f4335d113a3f/radam/radam.py#L156 https://github.com/tensorflow/swift-apis/blob/f51ee4618d652a2419e998bf9418ad80bda67454/Sources/TensorFlow/Optimizers/MomentumBased.swift#L638 Pull Request resolved: #58968 Reviewed By: vincentqb Differential Revision: D29310601 Pulled By: iramazanli fbshipit-source-id: b7bd487f72f1074f266687fd9c0c6be264a748a9
facebook-github-bot
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Summary: Previously in the PR: #58968 we added RAdam to Optimizers. Here in this PR we are proposing multi-tensor version of RAdam for PyTorch. Radam has been proposed in the paper https://arxiv.org/pdf/1908.03265.pdf Liyuan Liu et al. It has been one of the most used algorithm in Deep Learning community. Differing from the paper, we selected variance tractability cut-off as 5 instead of 4 as it is the common practice. Pull Request resolved: #59161 Reviewed By: vincentqb Differential Revision: D29360576 Pulled By: iramazanli fbshipit-source-id: 7ccdbf12b1ee7f12e66f7d7992123a70cc818b6b
asuhan
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…rch#59161) Summary: Previously in the PR: pytorch#58968 we added RAdam to Optimizers. Here in this PR we are proposing multi-tensor version of RAdam for PyTorch. Radam has been proposed in the paper https://arxiv.org/pdf/1908.03265.pdf Liyuan Liu et al. It has been one of the most used algorithm in Deep Learning community. Differing from the paper, we selected variance tractability cut-off as 5 instead of 4 as it is the common practice. Pull Request resolved: pytorch#59161 Reviewed By: vincentqb Differential Revision: D29360576 Pulled By: iramazanli fbshipit-source-id: 7ccdbf12b1ee7f12e66f7d7992123a70cc818b6b
asuhan
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Jun 30, 2021
Summary: Previously in the PR: #58968 we added RAdam to Optimizers. Here in this PR we are proposing multi-tensor version of RAdam for PyTorch. Radam has been proposed in the paper https://arxiv.org/pdf/1908.03265.pdf Liyuan Liu et al. It has been one of the most used algorithm in Deep Learning community. Differing from the paper, we selected variance tractability cut-off as 5 instead of 4 as it is the common practice. Pull Request resolved: #59161 Reviewed By: vincentqb Differential Revision: D29360576 Pulled By: iramazanli fbshipit-source-id: 7ccdbf12b1ee7f12e66f7d7992123a70cc818b6b
xuruiyang
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Summary: Fixes : pytorch/pytorch#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 : https://github.com/LiyuanLucasLiu/RAdam/blob/2f03dd197022da442c6a15c47321f4335d113a3f/radam/radam.py#L156 https://github.com/tensorflow/swift-apis/blob/f51ee4618d652a2419e998bf9418ad80bda67454/Sources/TensorFlow/Optimizers/MomentumBased.swift#L638 Pull Request resolved: pytorch/pytorch#58968 Reviewed By: gchanan Differential Revision: D29241736 Pulled By: iramazanli fbshipit-source-id: 288b9b1f3125fdc6c7a7bb23fde1ea5c201c0448
xuruiyang
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Summary: Fixes : pytorch/pytorch#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 : https://github.com/LiyuanLucasLiu/RAdam/blob/2f03dd197022da442c6a15c47321f4335d113a3f/radam/radam.py#L156 https://github.com/tensorflow/swift-apis/blob/f51ee4618d652a2419e998bf9418ad80bda67454/Sources/TensorFlow/Optimizers/MomentumBased.swift#L638 Pull Request resolved: pytorch/pytorch#58968 Reviewed By: vincentqb Differential Revision: D29310601 Pulled By: iramazanli fbshipit-source-id: b7bd487f72f1074f266687fd9c0c6be264a748a9
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Fixes : #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 :
https://github.com/LiyuanLucasLiu/RAdam/blob/2f03dd197022da442c6a15c47321f4335d113a3f/radam/radam.py#L156
https://github.com/tensorflow/swift-apis/blob/f51ee4618d652a2419e998bf9418ad80bda67454/Sources/TensorFlow/Optimizers/MomentumBased.swift#L638