AdvancedVI.jl

Implementation of variational Bayes inference algorithms
Author TuringLang
Sub Category Numerical Analysis
Category Mathematics
Website Github
Popularity
78 Stars
Updated Last
3 Months Ago
Started In
October 2019

Stable Dev Build Status Coverage

AdvancedVI.jl

AdvancedVI provides implementations of variational inference (VI) algorithms, which is a family of algorithms aiming for scalable approximate Bayesian inference by leveraging optimization. AdvancedVI is part of the Turing probabilistic programming ecosystem. The purpose of this package is to provide a common accessible interface for various VI algorithms and utilities so that other packages, e.g. Turing, only need to write a light wrapper for integration. For example, integrating Turing with AdvancedVI.ADVI only involves converting a Turing.Model into a LogDensityProblem and extracting a corresponding Bijectors.bijector.

Examples

AdvancedVI works with differentiable models specified as a LogDensityProblem. For example, for the normal-log-normal model:

$$ \begin{aligned} x &\sim \mathrm{LogNormal}\left(\mu_x, \sigma_x^2\right) \\ y &\sim \mathcal{N}\left(\mu_y, \sigma_y^2\right), \end{aligned} $$

a LogDensityProblem can be implemented as

using LogDensityProblems
using SimpleUnPack

struct NormalLogNormal{MX,SX,MY,SY}
    μ_x::MX
    σ_x::SX
    μ_y::MY
    Σ_y::SY
end

function LogDensityProblems.logdensity(model::NormalLogNormal, θ)
    (; μ_x, σ_x, μ_y, Σ_y) = model
    logpdf(LogNormal(μ_x, σ_x), θ[1]) + logpdf(MvNormal(μ_y, Σ_y), θ[2:end])
end

function LogDensityProblems.dimension(model::NormalLogNormal)
    length(model.μ_y) + 1
end

function LogDensityProblems.capabilities(::Type{<:NormalLogNormal})
    LogDensityProblems.LogDensityOrder{0}()
end

Since the support of x is constrained to be positive and VI is best done in the unconstrained Euclidean space, we need to use a bijector to transform x into unconstrained Euclidean space. We will use the Bijectors.jl package for this purpose. This corresponds to the automatic differentiation variational inference (ADVI) formulation1.

using Bijectors

function Bijectors.bijector(model::NormalLogNormal)
    (; μ_x, σ_x, μ_y, Σ_y) = model
    Bijectors.Stacked(
        Bijectors.bijector.([LogNormal(μ_x, σ_x), MvNormal(μ_y, Σ_y)]),
        [1:1, 2:1+length(μ_y)])
end

A simpler approach is to use Turing, where a Turing.Model can be automatically be converted into a LogDensityProblem and a corresponding bijector is automatically generated.

Let us instantiate a random normal-log-normal model.

using LinearAlgebra

n_dims = 10
μ_x    = randn()
σ_x    = exp.(randn())
μ_y    = randn(n_dims)
σ_y    = exp.(randn(n_dims))
model  = NormalLogNormal(μ_x, σ_x, μ_y, Diagonal(σ_y.^2))

We can perform VI with stochastic gradient descent (SGD) using reparameterization gradient estimates of the ELBO234 as follows:

using Optimisers
using ADTypes, ForwardDiff
using AdvancedVI

# ELBO objective with the reparameterization gradient
n_montecarlo = 10
elbo         = AdvancedVI.RepGradELBO(n_montecarlo)

# Mean-field Gaussian variational family
d = LogDensityProblems.dimension(model)
μ = zeros(d)
L = Diagonal(ones(d))
q = AdvancedVI.MeanFieldGaussian(μ, L)

# Match support by applying the `model`'s inverse bijector
b             = Bijectors.bijector(model)
binv          = inverse(b)
q_transformed = Bijectors.TransformedDistribution(q, binv)


# Run inference
max_iter = 10^3
q_avg, _, stats, _ = AdvancedVI.optimize(
    model,
    elbo,
    q_transformed,
    max_iter;
    adtype    = ADTypes.AutoForwardDiff(),
    optimizer = Optimisers.Adam(1e-3)
)

# Evaluate final ELBO with 10^3 Monte Carlo samples
estimate_objective(elbo, q_avg, model; n_samples=10^4)

For more examples and details, please refer to the documentation.

Footnotes

  1. Kucukelbir, A., Tran, D., Ranganath, R., Gelman, A., & Blei, D. M. (2017). Automatic differentiation variational inference. Journal of machine learning research. ↩

  2. Titsias, M., & Lázaro-Gredilla, M. (2014, June). Doubly stochastic variational Bayes for non-conjugate inference. In International Conference on Machine Learning. PMLR. ↩

  3. Rezende, D. J., Mohamed, S., & Wierstra, D. (2014, June). Stochastic backpropagation and approximate inference in deep generative models. In International Conference on Machine Learning. PMLR. ↩

  4. Kingma, D. P., & Welling, M. (2014). Auto-encoding variational bayes. In International Conference on Learning Representations. ↩

Required Packages

View all packages

Used By Packages