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TuringLang / NormalizingFlows.jl

https://turinglang.org/NormalizingFlows.jl/
MIT License
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NormalizingFlows.jl

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Last updated: 2023-Aug-23

A normalizing flow library for Julia.

The purpose of this package is to provide a simple and flexible interface for variational inference (VI) and normalizing flows (NF) for Bayesian computation or generative modeling. The key focus is to ensure modularity and extensibility, so that users can easily construct (e.g., define customized flow layers) and combine various components (e.g., choose different VI objectives or gradient estimates) for variational approximation of general target distributions, without being tied to specific probabilistic programming frameworks or applications.

See the documentation for more.

Installation

To install the package, run the following command in the Julia REPL:

]  # enter Pkg mode
(@v1.9) pkg> add git@github.com:TuringLang/NormalizingFlows.jl.git

Then simply run the following command to use the package:

using NormalizingFlows

Quick recap of normalizing flows

Normalizing flows transform a simple reference distribution $q_0$ (sometimes known as base distribution) to a complex distribution $q$ using invertible functions.

In more details, given the base distribution, usually a standard Gaussian distribution, i.e., $q0 = \mathcal{N}(0, I)$, we apply a series of parameterized invertible transformations (called flow layers), $T{1, \theta1}, \cdots, T{N, \theta_k}$, yielding that

Z_N = T_{N, \theta_N} \circ \cdots \circ T_{1, \theta_1} (Z_0) , \quad Z_0 \sim q_0,\quad  Z_N \sim q_{\theta},

where $\theta = (\theta_1, \dots, \thetaN)$ is the parameter to be learned, and $q{\theta}$ is the variational distribution (flow distribution). This describes sampling procedure of normalizing flows, which requires sending draws through a forward pass of these flow layers.

Since all the transformations are invertible (technically diffeomorphic), we can evaluate the density of a normalizing flow distribution $q_{\theta}$ by the change of variable formula:

q_\theta(x)=\frac{q_0\left(T_1^{-1} \circ \cdots \circ
T_N^{-1}(x)\right)}{\prod_{n=1}^N J_n\left(T_n^{-1} \circ \cdots \circ
T_N^{-1}(x)\right)} \quad J_n(x)=\left|\text{det} \nabla_x
T_n(x)\right|.

Here we drop the subscript $\theta_n, n = 1, \dots, N$ for simplicity. Density evaluation of normalizing flow requires computing the inverse and the Jacobian determinant of each flow layer.

Given the feasibility of i.i.d. sampling and density evaluation, normalizing flows can be trained by minimizing some statistical distances to the target distribution $p$. The typical choice of the statistical distance is the forward and backward Kullback-Leibler (KL) divergence, which leads to the following optimization problems:

\begin{aligned}
\text{Reverse KL:}\quad
&\arg\min _{\theta} \mathbb{E}_{q_{\theta}}\left[\log q_{\theta}(Z)-\log p(Z)\right] \\
&= \arg\min _{\theta} \mathbb{E}_{q_0}\left[\log \frac{q_\theta(T_N\circ \cdots \circ T_1(Z_0))}{p(T_N\circ \cdots \circ T_1(Z_0))}\right] \\
&= \arg\max _{\theta} \mathbb{E}_{q_0}\left[ \log p\left(T_N \circ \cdots \circ T_1(Z_0)\right)-\log q_0(X)+\sum_{n=1}^N \log J_n\left(F_n \circ \cdots \circ F_1(X)\right)\right]
\end{aligned}

and 

\begin{aligned}
\text{Forward KL:}\quad
&\arg\min _{\theta} \mathbb{E}_{p}\left[\log q_{\theta}(Z)-\log p(Z)\right] \\
&= \arg\min _{\theta} \mathbb{E}_{p}\left[\log q_\theta(Z)\right] 
\end{aligned}

Both problems can be solved via standard stochastic optimization algorithms, such as stochastic gradient descent (SGD) and its variants.

Reverse KL minimization is typically used for Bayesian computation, where one wants to approximate a posterior distribution $p$ that is only known up to a normalizing constant. In contrast, forward KL minimization is typically used for generative modeling, where one wants to learn the underlying distribution of some data.

Current status and TODOs

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