lfprop 1.0.0

:octopus: Flexibility

LFP is highly flexible w.r.t. the models and objective functions it can be used with, as it does not require differentiability. Consequently, it can be applied in non-differentiable architectures (e.g., Spiking Neural Networks) without requiring further adaptations, and naturally handles discrete objectives, such as feedback directly obtained from humans.

:gear: Efficiency

LFP applies an implicit weight-scaling of updates and only propagates feedback through nonzero connections and activations. This leads to sparsity of updates and the final model, while not sacrificing performance or convergence speed meaningfully compared to gradient descent. The obtained models can be pruned more easily since they represent information more efficiently.

:page_with_curl: Paper

For more details, refer to our Paper.

If you use this package in your research, please cite

@article{weber2025efficient,
  title={Efficient and Flexible Neural Network Training through Layer-wise Feedback Propagation},
  author={Leander Weber and Jim Berend and Moritz Weckbecker and Alexander Binder and Thomas Wiegand and Wojciech Samek and Sebastian Lapuschkin},
  journal={Transactions on Machine Learning Research},
  issn={2835-8856},
  year={2025},
  url={https://openreview.net/forum?id=9oToxYVOSW},
}

:scroll: License

This project is licensed under the BSD-3 Clause License, since LRP (which LFP is based on) is a patented technology that can only be used free of charge for personal and scientific purposes.

:rocket: Getting Started

:fire: Installation

Using PyPI (Recommended)

LFP is available from PyPI, and we recommend this installation if you simply want to use LFP or run any of the notebooks or experiments in this repository.

pip install lfprop

If you would like to check out the minimal example.ipynb notebook, first clone the repository, and then install the necessary dependencies:

git clone https://github.com/leanderweber/layerwise-feedback-propagation
cd layerwise-feedback-propagation
pip install lfprop[quickstart]

Similarly, if you would like to run the scripts and notebooks for reproducing the paper experiments, you can run

git clone https://github.com/leanderweber/layerwise-feedback-propagation
cd layerwise-feedback-propagation
pip install lfprop[full]

instead to install the full dependencies.

Using Poetry

If you would like to contribute to the repository, or extend the code in some way, we recommend the installation via Poetry:

git clone https://github.com/leanderweber/layerwise-feedback-propagation
cd layerwise-feedback-propagation
poetry install

This requires poetry-core>=2.0.0,<3.0.0.

:brain: How it works

Our implementation of LFP is based the LRP-implementation of the LXT library which is based on PyTorch and modifies the backward pass to return relevances instead of gradients. Note: We deprecated the implementation based on zennit in LFP version 1.0.0 due to LXT being much faster and extensible to more architectures. Some experiments in the paper were based on the zennit implementation (cf. Figure 9), however, and can be found under commit https://github.com/leanderweber/layerwise-feedback-propagation/commit/8dcf5131fc66aa20480917b4cfe08e54b2945aa8

lfprop extends LXT to return relevances not only w.r.t. activations, but also w.r.t. parameters. Similar to LXT, this requires registering a composite to modify the backward pass.

Standard

from lfprop.propagation import propagator_lxt
propagation_composite = propagator_lxt.LFPEpsilonComposite()

Some architectures may use special layer types that require the definition of dedicated rules for LFP. Similarly, non-classification tasks may require redefinition of the default LFP rules. Several examples are already implemented under lfprop/propagation:

LIF SNNs

from lfprop.propagation import propagator_snn
propagation_composite = propagator_snn.LFPSNNEpsilonComposite()

ViT

from lfprop.propagation import propagator_vit
propagation_composite = propagator_vit.LFPEpsilonComposite()

Simple Regression

from lfprop.propagation import propagator_regression
propagation_composite = propagator_regression.LFPEpsilonRegressionComposite()

Instead of an initial relevance, LFP requires an initial reward at the output, to be decomposed throughout the model. We implement several reward functions, with a similar signature to torch.nn.Loss functions.

from lfprop.rewards import reward_functions as rewards
reward_func = rewards.SoftmaxLossReward(device)

To apply the modified backward pass, the composite simply needs to be registered.

After the backward pass is finished, the computed LFP-feedback can then be accessed via the (newly added) .feedback attribute of each parameter.

The model can simply be optimized using any torch.nn.Optimizer, by first overwriting the .grad attribute by the corresponding (negative) feedback.

This results in the following training step:

optimizer = torch.optim.SGD(model.parameters(), lr=lr, momentum=momentum)
optimizer.zero_grad()

with propagation_composite.context(model) as modified:
    inputs = inputs.detach().requires_grad_(True)
    outputs = modified(inputs)

    # Calculate reward
    reward = torch.from_numpy(
        reward_func(outputs, labels).detach().cpu().numpy()
    ).to(device)

    # Calculate LFP
    input_reward = torch.autograd.grad(
        (outputs,), (inputs,), grad_outputs=(reward,), retain_graph=False
    )[0]

    # Write LFP Values into .grad attributes.
    for name, param in model.named_parameters():
        param.grad = -param.feedback

    # Optimization step
    optimizer.step()

:mag: Examples

A simple, full example of how to train a LeNet model on MNIST can be found under minimal_example.ipynb. An example using SNNs can be found under minimal_example_spiking_nets.ipynb.Note that to run these notebooks, you need to install the necessary dependencies using lfprop[quickstart], as described under Installation.

:test_tube: Reproducing Experiments

To reproduce experiments from the paper, you first need to install the necessary dependencies with lfprop[full], as described under Installation.

Most toy data experiments can then be reproduced by simply running the corresponding notebooks under nbs/. You can find the used hyperparameters for the notebooks within the first two cells.

For reproducing the experiments that require training on more complex data and models (LFP for Non-ReLU, Pruning experiments, SNN experiments, ViT training), the training script is implemented in run_experiment.py. Hyperparameters for these experiments can be generated using the scripts under configs/.

For reproducing these experiments first run

# 1. generate the config files
python configs/<experimentname>/config_generator<somesuffix>.py
# 2. run training script
python run_experiment.py --config_file=configs/<experimentname>/cluster/<selected-config-name>

For the pruning experiments, you can then run the nbs/*eval-clusterresults-pruning.ipynb notebooks using the obtained models.

:bell: Roadmap

This is a first release of LFP, which does not work with all types of data or models, but we are actively working on extending the package. You can check this Roadmap to get an overview over features planned to the future.

• LFP for CNNs and Fully-Connected Models
• LFP for SNNs
• LFP for ViT
• LFP for Classification Tasks
• LFP for LLMs and larger Transformer-based Architectures

:pencil2: Contributing

Feel free to contribute to the code, experiment with different models and datasets, and raise any suggestions or encountered problems as Issues or create a Pull Request.

For contributing, we recommend the Installation via Poetry.

Note that we use Ruff for formatting and linting.