Learning rate free learning for SGD, AdaGrad and Adam!

by Aaron Defazio and Konstantin Mishchenko (Arxiv)

pip install dadaptation

The provided Pytorch Optimizer classes are drop-in replacements, either copy into your project or use via dadaptation.DAdaptSGD, dadaptation.DAdaptAdam or dadaptation.DAdaptAdaGrad.

• Set the LR parameter to 1.0. This parameter is not ignored, rather, setting it larger to smaller will directly scale up or down the D-Adapted learning rate.
• Use the same learning rate scheduler you would normally use on the problem.
• The Adam variant supports AdamW style weight decay, just set decouple=True. It is not turned on by default, so if you are replacing your adam implementation, make sure you use decoupled if necessary.
• It may be necessary to use larger weight decay than you would normally use, try a factor of 2 or 4 bigger if you see overfitting. D-Adaptation uses larger learning rates than people typically hand-choose, in some cases that requires more decay.
• Use the log_every setting to see the learning rate being used (d*lr) and the current D bound.
• Only the AdaGrad version supports sparse gradients.
• The Adam IP variant implements a tighter D bound, which may help on some problems. The IP variants should be considered experimental.
• If you encounter divergence early on, and are not already using learning rate warmup, try change growth_rate to match a reasonable warmup schedule rate for your problem.

See the License file.