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Controlled Sparsity via Constrained Optimization

About

This repository contains the official implementation for the paper Controlled Sparsity via Constrained Optimization or: How I Learned to Stop Tuning Penalties and Love Constraints. This code enables the training of sparse neural networks using constrained $L_0$ regularization.

We use the Cooper library for implementing and solving the constrained optimization problems.

Setup

Create an environment using your favorite tool and install the required packages.

pip install -r requirements.txt

Dataset paths

The utils/paths.json file must be set up with the location of datasets before running. See Utils below for details.

Configs

We provide YAML files containing all the hyper-parameters choices made for each of the experiments presented in the paper. You can find these files under the configs folder. In the Examples section, we demonstrate how to use these configs to trigger runs.

Weights and Biases

If you don't have a Weights and Biases account, or prefer not to log metrics to their servers, you can use the flag -wboff.

Examples

Constrained experiments can be triggered by providing the --target_density (or -tdst) arg, along with a) 1 number for model-wise constraints, or b) 1 number for each of the sparsifiable layers of the network. The target densities are expected to be floats between 0 and 1. Penalized experiments are triggered in a similar way using the --lmbdas arg.

Constrained WideResNet-28-10 on CIFAR-10

python core_exp.py --yaml_file configs/defaults/cifar_defaults.yml -tdst 0.7

Penalized ResNet18 on TinyImageNet

python core_exp.py --yaml_file configs/defaults/tiny_imagenet_r18_defaults.yml --lmbdas 1e-3

Project structure

constrained_l0
├── bash_scripts
├── configs
├── custom_experiments
├── get_results
├── sparse
├── tests
├── utils
├── core_exp.py         # Script used for running experiments
├── l0cmp.py            # Constrained minimization problem
├── README.md
├── requirements.txt

Sparse

The sparse folder contains the main components used to construct $L_0$ sparsifiable networks.

Our implementation of $L_0$-sparse models is based on the code by Louizos et al. for the paper Learning Sparse Neural Networks through L0 Regularization, ICLR, 2018.

Fully connected and convolutional $L_0$ layers are found inside of l0_layers.py, as well as L0BatchNorm, a batch normalization layer compatible with output sparsity layers. The modules models.py, resnet_models.py and wresnet_models.py implement various models composed both of standard Pytorch layers and $L_0$ layers. These include MLPs, LeNet5s, WideResNet28-10s, ResNet18s and ResNet50s. The code is general enough to be easily extensible to variations of these architectures.

├── sparse
│   ├── l0_layers.py
│   ├── models.py
│   ├── resnet_models.py
│   ├── wresnet_models.py
│   ├── purged_models.py
│   ├── purged_resnet_models.py
│   ├── utils.py
│   ...

Utils

utils contains various project utils.

├── utils
│   ├── imagenet            # Utils for ImageNet dataloader
│   ├── basic_utils.py
│   ├── constraints.py      # Constraint schedulers
│   ├── datasets.py         # Dataloaders
│   ├── exp_utils.py        # Utils for core_exp.py, e.g. train and val loops
│   ├── paths.json

The paths.json file must be setup by the user to indicate the location of folders associated with different datasets. For instance,

{
    "mnist": "~/data/mnist",
    "cifar10": "~/data/cifar10",
    "cifar100": "~/data/cifar100",
    "tiny_imagenet": "~/data/tiny-imagenet-200",
    "imagenet": "~/data/imagenet"
}

Configs

configs contains YAML files with basic configurations for the experiments presented throughout our paper. For instance, mnist_defaults.yml indicates the batch size, learning rates, optimizers and other details used for our MNIST experiments.

These YAML files were designed to be used in conjunction with the scripts in the bash_scripts folder. Arguments that are required to trigger a run, but were not specified in the YAML file are marked explicitly. You can find these values in the corresponding bash_scripts file, as well as the appendix in the paper.

├── configs
│   ├── defaults
│   │   ├── mnist_defaults.yml
│   │   ├── cifar_defaults.yml
│   │   ├── imagenet_defaults.yml
│   │   ...

Custom Experiments

We implement two experiments which serve as baselines for comparison with our constrained $L_0$ approach. These are:

• bisection: training a model to achieve a pre-defined sparsity target via the penalized approach. The search over penalization coefficients $\lambda_{pen}$ is done via the bisection method.
• pretrained: comparison with magnitude pruning starting from a Pytorch-pretrained torchvision.models.resnet50. Also includes a loop for
  fine-tuning the remaining weights.

    ├── custom_experiments
    │   ├── bisection
    │   ├── pretrained
    │   │   ├── resnet_magnitude_pruning.py

Tests

Automated tests implemented on Pytest are included in the tests folder. Besides the automated tests (runnable from the root folder with python -m pytest), we provide YAML files under the configs folder to test the core_exp.py script for different model architectures for 2 epochs. These must be triggered manually, as described in the Examples section.

├── test
│   ├── helpers
│   ├── configs
│   ...

Bash scripts

We executed our experiments on a computer cluster with Slurm job management. The bash scripts used for triggering these runs are contained in the bash_scripts folder.

The experiments are split across different subfolders. For instance, the tiny_imagenet folder contains scripts for the TinyImagenet control table and plots.

├── bash_scripts
│   ├── bash_helpers.sh
│   ├── run_basic_exp.sh
│   ├── mnist
│   ├── cifar
│   ├── tiny_imagenet
│   ├── imagenet
│   ├── detach_gates
│   ├── magnitude_pruning

Results: plots and tables

The get_results folder includes scripts for producing the plots and tables found in the paper. These scripts depend on calls to the Weights and Biases API for retrieving logged metrics.

├── get_results
│   ├── neurips             # Scripts for producing tables and plots
│   ├── saved_dataframes    # Tables saved as csv
│   ├── wandb_utils.py      # Utils to access WandB API