torchlogix is a PyTorch-based library for training and inference of logic neural networks. These solve machine learning tasks by learning combinations of boolean logic expressions. As the choice of boolean expressions is conventionally non-differentiable, relaxations are applied to allow training with gradient-based methods. The final model can be discretized again, resulting in a fully boolean expression with extremely efficient inference, e.g., beyond a million images of MNIST per second on a single CPU core.

Note: torchlogix is based on the difflogic package (https://github.com/Felix-Petersen/difflogic/), and extends it by new concepts such as compact parametrizations, higher-dimensional logic blocks, learnable connections and binarization as described in "WARP Logic Neural Networks" (Paper @ ArXiv). It also implements convolutions as described in "Convolutional Differentiable Logic Gate Networks (Paper @ ArXiv).

Installation

pip install torchlogix                 # basic
pip install "torchlogix[dev]"          # with dev tools

The following software stacks have validated performance: python3.12 / python3.13, cuda12.4 / cuda13.0, torch2.6 / torch2.9.

Quickstart

torchlogix provides learnable logic layers with torch.nn-like API. For example, a very simple convolutional model for MNIST can be defined like so:

import torch
from torchlogix.layers import LogicDense, LogicConv2d, OrPooling2d, GroupSum, FixedBinarization

model = torch.nn.Sequential(
    # Every pixel is False (=0) or True (>0). Standard practice on MNIST
    FixedBinarization(thresholds=[0.0]),
    # Convolution w/ 16 kernels - 4 inputs each, randomly drawn from a 3x3 receptive field
    LogicConv2d(in_dim=28, channels=1, num_kernels=16, tree_depth=2, receptive_field_size=3),
    # Reduce dimensionality with pooling operation
    OrPooling2d(kernel_size=2, stride=2, padding=0),
    torch.nn.Flatten(),
    # Two randomly connected dense layers w/ 4000 neurons
    LogicDense(16*13*13, 4_000),
    LogicDense(4_000, 4_000),
    # Output 10 logits via group sum (scaled by 1/8 for smoothness)
    GroupSum(k=10, tau=8)
)

Like ordinary PyTorch neural networks, this model may be trained, e.g., with torch.nn.CrossEntropyLoss. The Adam optimizer with a learning rate of 0.01 works well. Every layer and hence the entire model can be switched between the relaxed trainable and discrete, fully boolean version with the standard model.train() / model.eval() commands. Furthermore, the discrete model can be expressed in pure C and compiled like so

compiled_model = CompiledLogicNet(model, input_shape=(1, 28, 28))
compiled_model.compile()

all_preds = model(all_X)  #  ~15 ms for all 10000 test examples on my laptop

The full training- and evaluation of the model above is demonstrated in the example notebook experiments/mnist_example.ipynb.

Documentation

More thorough documentation is available here, including an API Reference. Some quick links:

• Installation Guide - Detailed installation instructions
• Quick Start - Get started with TorchLogix in minutes
• Concepts - Understand some of the design choices behind torchlogix

Experiments

Various experiments can be run using the script experiments/train.py. For example, the medium-sized convolutional model on CIFAR-10 from the paper "Convolutional Differentiable Logic Gate Networks", can be trained like so:

python train.py --dataset cifar-10 -a ClgnCifar10Medium --connections-init-method random-unique -lr 0.02 -wd 0.002 --device cuda --compile-model

This achieves roughly 66% discrete test accurcay, which can be increased to 68.5% with the same architecture by learning the binarization thresholds and restricting each kernel in the first layer to just a single channel:

python train.py --dataset cifar-10 -a ClgnCifar10Medium2 --connections-init-method random-unique --binarization-init distributive --binarization learnable -lr 0.02 -wd 0.002 --binarization-learning-rate 0.01 --device cuda --compile-model

The training converges within roughly 30 minutes on an A100. The accuracy can be increased further with data augmentation, and knowledge distillation but details of the training procedure are beyond the scope of this package.

Citation

If you use torchlogix in your research, please cite:

@software{torchlogix2026,
  author = {Gerlach, Lino and Gerlach, Thore and Kauffman, Elliott and Våge, Liv},
  title = {torchlogix},
  year = {2026},
  doi = {10.5281/zenodo.18800427}
}

License

torchlogix is released under the MIT license. See LICENSE for additional details about it.