zhuchen03 / mva

Maximum Variation Averaging

This repository contains the implementation of the so-called Maximum Variation Averaging (MVA) proposed in our paper, Adaptive Learning Rates with Maximum Variation Averaging . MVA aims to stabilize the adaptive step size of Adam-like optimizers by adopting an adaptive weighted average of the squared gradients, where the coordinate-wise weights are chosen to maximize the estimated gradient variance. In this repository, we provide its implementation with PyTorch on synthetic datasets, image classification, Neural Machine Translation and Natural Language Understanding tasks, as mentioned in the experiment section of our paper.

Usage

We used PyTorch v1.4.0 for the experiments. We have divided the experiments into 3 folders:

synthetic_data: You could run nonconvex.py or nqm.py to reproduce the experiments for the nonconvex function or the Noisy Quadratic Model.

image_classification: Please refer to launch.sh to launch the experiments on CIFAR10 and CIFAR100. For ImageNet, you could plug the same optimizers into the PyTorch official example code and refer to the hyper-parameters in the paper.

nmt_nlu: Please first enter the nmt_nlu directory and then run pip install --editable .. For Neural Machine Translation, please first follow the steps to download and process the data, and then refer to run-iwslt-lamadam-tristage.sh to train a transformer with our optimizers from scratch. For the GLUE benchmark, again, first follow the steps to prepare the data, download a RoBERTa-base model and put it under nmt_nlu/roberta-pretrained, and use run-glue-base.sh to fine-tune a RoBERTa-base model on the GLUE tasks.

Citation

Please cite as

@inproceedings{zhu2020mva,
  title = {Adaptive Learning Rates with Maximum Variation Averaging},
  author = {Zhu, Chen and Cheng, Yu and Gan, Zhe and Huang, Furong and Liu, Jingjing and Goldstein, Tom},
  booktitle = {arXiv: 2006.11918},
  year = {2020},
}