README.md

mL-BFGS codebase

This doc provide necessary guides for training models (CNNs, Transformers) with mL-BFGS optimizer.

Paper can be found at: https://openreview.net/pdf?id=9jnsPp8DP3

Quick Start

---

Run run-SLIM.sh to quickly train a model with mL-BFGS optimizer. For running args, please refer to run-SLIM.sh for further information.

ResNet-18 on CIFAR-100 (1 GPU needed)

bash run-SLIM.sh 1 127.0.0.1 11113 resnet18 cifar100 0.1 0.0002 0.9 150 4 0.999 0.9 50

ResNet-50 on ImageNet (4 GPUs needed)

bash run-SLIM.sh 4 127.0.0.1 11113 resnet50 imagenet 0.1 0.0002 0.9 100 "0,1,2,3" 0.999 0.9 50

DeiT on CIFAR-100 (1 GPU needed)

bash run-SLIM.sh 1 127.0.0.1 11113 deit cifar100 0.1 0.0005 0.9 50 1 0.999 0.9 50

Running parameters are explained in run-SLIM.sh.

Package Dependency

---

Below is my running environment:

python=3.8
torch=1.9
numpy=1.17
tensorboard=2.6

Further Details

---

To train a model, the code follows a standard machine learning training flow. If using mL-BFGS optimizer, we only need to call the slimblock.py to define the optimizer. The rest of training is the same as the standard training with SGD.

Please go to ./opt/slimblock.py for implementation details.

While ./opt/slimblock.py is a block-wise optimization, we also provide ./opt/slim.py, a full-model Hessian estimation version.

Report Issues

---

If you have any issues/questions, you can either create an issue in the repo, or directly reach me via yueniu@usc.edu.