This repository contains code for my personal EinsumNetworks implementation.
For a speed benchmark comparison against the official EinsumNetworks implementation, check out benchmark.md (TLDR: simple-einet is faster in all dimensions except the input-channel size in which it scales similar to the official EinsumNetworks implementation).
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
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
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 git@github.com:braun-steven/simple-einet.git
cd simple-einet
pip install -e .
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}")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},
}