Metadata-Version: 2.1
Name: madgrad
Version: 1.0
Summary: A general purpose PyTorch Optimizer
Home-page: https://github.com/facebookresearch/madgrad
Author: Aaron Defazio
Author-email: adefazio@fb.com
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown


# MADGRAD Optimization method

A Momentumized, Adaptive, Dual Averaged Gradient Method for Stochastic Optimization

Try it out! A best-of-both-worlds optimizer with the generalization performance of SGD and at least as fast convergence as that of Adam, often faster.

The madgrad.py file containing the optimizer can be directly dropped into any PyTorch project. If you are using fairseq, you need the acompanying fairseq_madgrad.py file as well, which you can use with `--optimizer madgrad` command line option.

Documentation availiable at https://madgrad.readthedocs.io/en/latest/.

## Things to note:
 - You may need to use a lower weight decay than you are accustomed to. Often 0.
 - You should do a full learning rate sweep as the optimal learning rate will be different from SGD or Adam. Best LR values we found were 2.5e-4 for 152 layer PreActResNet on CIFAR10, 0.001 for ResNet-50 on ImageNet, 0.025 for IWSLT14 using `transformer_iwslt_de_en` and 0.005 for RoBERTa training on BookWiki using `BERT_BASE`. On NLP models gradient clipping also helped.

# Tech Report

[Adaptivity without Compromise: A Momentumized, Adaptive, Dual Averaged Gradient Method for Stochastic Optimization](https://arxiv.org/abs/2101.11075)

We introduce MADGRAD, a novel optimization method in the family of AdaGrad adaptive gradient methods. MADGRAD shows excellent performance on deep learning optimization problems from multiple fields, including classification and image-to-image tasks in vision, and recurrent and bidirectionally-masked models in natural language processing. For each of these tasks, MADGRAD matches or outperforms both SGD and ADAM in test set performance, even on problems for which adaptive methods normally perform poorly.


```BibTeX
@misc{defazio2021adaptivity,
      title={Adaptivity without Compromise: A Momentumized, Adaptive, Dual Averaged Gradient Method for Stochastic Optimization}, 
      author={Aaron Defazio and Samy Jelassi},
      year={2021},
      eprint={2101.11075},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}
```


# License

MADGRAD is licensed under the [MIT License](LICENSE).


