An EinsumNetworks Implementation

This repository contains code for my personal EinsumNetworks implementation.

Notebooks

The notebooks directory contains Jupyter notebooks that demonstrate the usage of this library.

• Training a discriminative Einet the iris dataset
• Training a generative Einet on MNIST
• Training an Einet on synthetic multivariate Normal data

PyTorch Lightning Training

The main_pl.py script offers PyTorch-Lightning based training for discriminative and generative Einets.

Classification on MNIST examples:

python main_pl.py dataset=mnist batch_size=128 epochs=100 dist=normal D=5 I=32 S=32 R=8 lr=0.001 gpu=0 classification=true 

Generative training on MNIST:

python main_pl.py dataset=mnist D=5 I=16 R=10 S=16 lr=0.1 dist=binomial epochs=10 batch_size=128

Installation

You can install simple-einet as a dependency in your project as follows:

pip install git+https://github.com/braun-steven/simple-einet

If you want to additionally install the dependencies requires to launch the provided scripts such as main.py, main_pl.py or the notebooks, run

pip install "git+https://github.com/braun-steven/simple-einet#egg=simple-einet[app]"

If you plan to edit the files after installation:

git clone [email protected]:braun-steven/simple-einet.git
cd simple-einet
pip install -e .

Usage Example

The following is a simple usage example of how to create, optimize, and sample from an Einet.

import torch
from simple_einet.layers.distributions.normal import Normal
from simple_einet.einet import Einet
from simple_einet.einet import EinetConfig


if __name__ == "__main__":
    torch.manual_seed(0)

    # Input dimensions
    in_features = 4
    batchsize = 5

    # Create input sample
    x = torch.randn(batchsize, in_features)

    # Construct Einet
    cfg = EinetConfig(
        num_features=in_features,
        depth=2,
        num_sums=2,
        num_channels=1,
        num_leaves=3,
        num_repetitions=3,
        num_classes=1,
        dropout=0.0,
        leaf_type=Normal,
    )
    einet = Einet(cfg)

    # Compute log-likelihoods
    lls = einet(x)
    print(f"lls.shape: {lls.shape}")
    print(f"lls: \n{lls}")

    # Optimize Einet parameters (weights and leaf params)
    optim = torch.optim.Adam(einet.parameters(), lr=0.001)

    for _ in range(1000):
        optim.zero_grad()

        # Forward pass: compute log-likelihoods
        lls = einet(x)

        # Backprop negative log-likelihood loss
        nlls = -1 * lls.sum()
        nlls.backward()

        # Update weights
        optim.step()

    # Construct samples
    samples = einet.sample(2)
    print(f"samples.shape: {samples.shape}")
    print(f"samples: \n{samples}")

Citing EinsumNetworks

If you use this software, please cite it as below.

@software{braun2021simple-einet,
author = {Braun, Steven},
title = {{Simple-einet: An EinsumNetworks Implementation}},
url = {https://github.com/braun-steven/simple-einet},
version = {0.0.1},
}

If you use EinsumNetworks as a model in your publications, please cite our official EinsumNetworks paper.

@inproceedings{pmlr-v119-peharz20a,
  title = {Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits},
  author = {Peharz, Robert and Lang, Steven and Vergari, Antonio and Stelzner, Karl and Molina, Alejandro and Trapp, Martin and Van Den Broeck, Guy and Kersting, Kristian and Ghahramani, Zoubin},
  booktitle = {Proceedings of the 37th International Conference on Machine Learning},
  pages = {7563--7574},
  year = {2020},
  editor = {III, Hal Daumé and Singh, Aarti},
  volume = {119},
  series = {Proceedings of Machine Learning Research},
  month = {13--18 Jul},
  publisher = {PMLR},
  pdf = {http://proceedings.mlr.press/v119/peharz20a/peharz20a.pdf},
  url = {http://proceedings.mlr.press/v119/peharz20a.html},
  code = {https://github.com/cambridge-mlg/EinsumNetworks},
}