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NTK-SAP: Improving neural network pruning by aligning training dynamics

Yite Wang, Dawei Li, Ruoyu Sun

In ICLR 2023.

Overview

This is the PyTorch implementation of NTK-SAP: Improving neural network pruning by aligning training dynamics.

Installation

To run our code, then install all dependencies

pip install -r requirements.txt

Running

Below is a description of the major sections of the code base. Run python main.py --help for a complete description of flags and hyperparameters.

1. Prepare the datasets

MNIST, CIFAR-10, CIFAR-100, Tiny ImageNet will be downloaded automatically. For ImageNet experiment, please download it to Data/imagenet_raw/, or change corresponding path in Utils/load.py.

2. Run foresight pruning experiments

Note experiments of ImageNet requires running code to prune and train separately, see the argument experiment. For other experiments, models will be trained right after pruning. We include a few important arguments:

  • --experiment: For CIFAR-10, CIFAR-100, and Tiny-ImageNet experiments, you can either use singleshot or multishot. For ImageNet experiment, please use multishot_ddp_prune to get mask then train with multishot_ddp_train.
  • --dataset: Which dataset to use, to reproduce our results, use cifar10, cifar100, tiny-imagenet, and imagenet.
  • --model-class: For CIFAR-10 and CIFAR-100 experiments, please use lottery. For Tiny-imagenet and ImageNet experiments, please use imagenet.
  • --model: Which model architecture to use. In our experiments, we use resnet20, vgg16-bn, resnet18, and resnet50.
  • --pruner: Which pruning algorithms to use, choose from: rand, mag, snip, grasp, synflow, itersnip, NTKSAP.
  • --prune-batch-size: Batch size of pruning datasets.
  • --compression: You can use this argument to change sparsity for singleshot experiments. Specifically, the target density will be $0.8^{\text{compression}}$. For multishot experiments, please refer to --compression-list.
  • --prune-train-mode: Set this to True if you use pruning algorithms except Synflow.
  • --prune-epochs: Number of pruning iterations $T$.
  • --ntksap_R: Number of resampling procedures, only change this for CIFAR-10 experiment.
  • --ntk_epsilon: Perturbation hyper-parameter used in NTK-SAP.

A sample script can be found in scripts/run.sh.

Acknowledgement

Our code is developed based on the Synflow code: https://github.com/ganguli-lab/Synaptic-Flow.