Zuko - Normalizing flows in PyTorch

Zuko is a Python package that implements normalizing flows in PyTorch. It relies as much as possible on distributions and transformations already provided by PyTorch. Unfortunately, the Distribution and Transform classes of torch are not sub-classes of torch.nn.Module, which means you cannot send their internal tensors to GPU with .to('cuda') or retrieve their parameters with .parameters(). Worse, the concepts of conditional distribution and transformation, which are essential for probabilistic inference, are impossible to express.

To solve these problems, zuko defines two concepts: the LazyDistribution and LazyTransform, which are any modules whose forward pass returns a Distribution or Transform, respectively. Because the creation of the actual distribution/transformation is delayed, an eventual condition can be easily taken into account. This design enables lazy distributions, including normalizing flows, to act like distributions while retaining features inherent to modules, such as trainable parameters. It also makes the implementations easy to understand and extend.

In the Avatar cartoon, Zuko is a powerful firebender 🔥

Acknowledgements

Zuko takes significant inspiration from nflows and Stefan Webb's work in Pyro and FlowTorch.

Installation

The zuko package is available on PyPI, which means it is installable via pip.

pip install zuko

Alternatively, if you need the latest features, you can install it from the repository.

pip install git+https://github.com/probabilists/zuko

Getting started

Normalizing flows are provided in the zuko.flows module. To build one, supply the number of sample and context features as well as the transformations' hyperparameters. Then, feeding a context $c$ to the flow returns a conditional distribution $p(x | c)$ which can be evaluated and sampled from.

import torch
import zuko

# Neural spline flow (NSF) with 3 sample features and 5 context features
flow = zuko.flows.NSF(3, 5, transforms=3, hidden_features=[128] * 3)

# Train to maximize the log-likelihood
optimizer = torch.optim.Adam(flow.parameters(), lr=1e-3)

for x, c in trainset:
    loss = -flow(c).log_prob(x)  # -log p(x | c)
    loss = loss.mean()

    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

# Sample 64 points x ~ p(x | c*)
x = flow(c_star).sample((64,))

Alternatively, flows can be built as custom Flow objects.

from zuko.flows import Flow, UnconditionalDistribution, UnconditionalTransform
from zuko.flows.autoregressive import MaskedAutoregressiveTransform
from zuko.distributions import DiagNormal
from zuko.transforms import RotationTransform

flow = Flow(
    transform=[
        MaskedAutoregressiveTransform(3, 5, hidden_features=(64, 64)),
        UnconditionalTransform(RotationTransform, torch.randn(3, 3)),
        MaskedAutoregressiveTransform(3, 5, hidden_features=(64, 64)),
    ],
    base=UnconditionalDistribution(
        DiagNormal,
        loc=torch.zeros(3),
        scale=torch.ones(3),
        buffer=True,
    ),
)

For more information, check out the documentation and tutorials at zuko.readthedocs.io.

Available flows

Class	Year	Reference
GMM	-	Gaussian Mixture Model
NICE	2014	Non-linear Independent Components Estimation
RealNVP	2016	Density estimation using Real NVP
MAF	2017	Masked Autoregressive Flow for Density Estimation
NSF	2019	Neural Spline Flows
NCSF	2020	Normalizing Flows on Tori and Spheres
SOSPF	2019	Sum-of-Squares Polynomial Flow
NAF	2018	Neural Autoregressive Flows
UNAF	2019	Unconstrained Monotonic Neural Networks
CNF	2018	Neural Ordinary Differential Equations
GF	2020	Gaussianization Flows
BPF	2020	Bernstein-Polynomial Normalizing Flows
PF	2015	Planar Flows
TSF	2018	Triangular Sylvester Flows
OSF	2018	Orthogonal Sylvester Flows
HSF	2018	Householder Sylvester Flows

Contributing

If you have a question, an issue or would like to contribute, please read our contributing guidelines.