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Basic rewrite of the package 2023 edition Part II: Location-scale variational families (#51)
* add location scale family * refactor switch bijector tests to use locscale, enable ReverseDiff * fix test file name for location-scale plus bijector inference test * fix wrong testset names, add interface test for VILocationScale * fix test parameters for `LocationScale` * fix test for LocationScale with Bijectors * add tests to improve coverage, fix bug for `rand!` with vectors * rename location scale, fix type ambiguity for `rand` * remove duplicate type tests for `LocationScale`
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‎src/AdvancedVI.jl

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@@ -141,6 +141,16 @@ export
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include("objectives/elbo/entropy.jl")
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include("objectives/elbo/repgradelbo.jl")
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# Variational Families
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export
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VILocationScale,
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MeanFieldGaussian,
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FullRankGaussian
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include("families/location_scale.jl")
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# Optimization Routine
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function optimize end

‎src/families/location_scale.jl

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"""
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MvLocationScale(location, scale, dist) <: ContinuousMultivariateDistribution
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The location scale variational family broadly represents various variational
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families using `location` and `scale` variational parameters.
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It generally represents any distribution for which the sampling path can be
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represented as follows:
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```julia
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d = length(location)
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u = rand(dist, d)
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z = scale*u + location
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```
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"""
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struct MvLocationScale{
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S, D <: ContinuousDistribution, L
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} <: ContinuousMultivariateDistribution
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location::L
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scale ::S
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dist ::D
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end
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Functors.@functor MvLocationScale (location, scale)
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# Specialization of `Optimisers.destructure` for mean-field location-scale families.
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# These are necessary because we only want to extract the diagonal elements of
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# `scale <: Diagonal`, which is not the default behavior. Otherwise, forward-mode AD
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# is very inefficient.
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# begin
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struct RestructureMeanField{S <: Diagonal, D, L}
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q::MvLocationScale{S, D, L}
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end
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function (re::RestructureMeanField)(flat::AbstractVector)
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n_dims = div(length(flat), 2)
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location = first(flat, n_dims)
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scale = Diagonal(last(flat, n_dims))
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MvLocationScale(location, scale, re.q.dist)
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end
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function Optimisers.destructure(
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q::MvLocationScale{<:Diagonal, D, L}
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) where {D, L}
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@unpack location, scale, dist = q
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flat = vcat(location, diag(scale))
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flat, RestructureMeanField(q)
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end
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# end
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Base.length(q::MvLocationScale) = length(q.location)
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Base.size(q::MvLocationScale) = size(q.location)
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Base.eltype(::Type{<:MvLocationScale{S, D, L}}) where {S, D, L} = eltype(D)
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function StatsBase.entropy(q::MvLocationScale)
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@unpack location, scale, dist = q
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n_dims = length(location)
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n_dims*convert(eltype(location), entropy(dist)) + first(logabsdet(scale))
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end
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function Distributions.logpdf(q::MvLocationScale, z::AbstractVector{<:Real})
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@unpack location, scale, dist = q
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sum(Base.Fix1(logpdf, dist), scale \ (z - location)) - first(logabsdet(scale))
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end
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function Distributions._logpdf(q::MvLocationScale, z::AbstractVector{<:Real})
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@unpack location, scale, dist = q
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sum(Base.Fix1(logpdf, dist), scale \ (z - location)) - first(logabsdet(scale))
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end
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function Distributions.rand(q::MvLocationScale)
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@unpack location, scale, dist = q
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n_dims = length(location)
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scale*rand(dist, n_dims) + location
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end
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function Distributions.rand(
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rng::AbstractRNG, q::MvLocationScale{S, D, L}, num_samples::Int
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) where {S, D, L}
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@unpack location, scale, dist = q
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n_dims = length(location)
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scale*rand(rng, dist, n_dims, num_samples) .+ location
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end
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# This specialization improves AD performance of the sampling path
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function Distributions.rand(
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rng::AbstractRNG, q::MvLocationScale{<:Diagonal, D, L}, num_samples::Int
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) where {L, D}
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@unpack location, scale, dist = q
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n_dims = length(location)
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scale_diag = diag(scale)
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scale_diag.*rand(rng, dist, n_dims, num_samples) .+ location
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end
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function Distributions._rand!(rng::AbstractRNG, q::MvLocationScale, x::AbstractVecOrMat{<:Real})
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@unpack location, scale, dist = q
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rand!(rng, dist, x)
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x[:] = scale*x
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return x .+= location
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end
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Distributions.mean(q::MvLocationScale) = q.location
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function Distributions.var(q::MvLocationScale)
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C = q.scale
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Diagonal(C*C')
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end
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function Distributions.cov(q::MvLocationScale)
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C = q.scale
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Hermitian(C*C')
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end
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"""
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FullRankGaussian(location, scale; check_args = true)
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Construct a Gaussian variational approximation with a dense covariance matrix.
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# Arguments
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- `location::AbstractVector{T}`: Mean of the Gaussian.
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- `scale::LinearAlgebra.AbstractTriangular{T}`: Cholesky factor of the covariance of the Gaussian.
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# Keyword Arguments
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- `check_args`: Check the conditioning of the initial scale (default: `true`).
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"""
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function FullRankGaussian(
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μ::AbstractVector{T},
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L::LinearAlgebra.AbstractTriangular{T};
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check_args::Bool = true
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) where {T <: Real}
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@assert minimum(diag(L)) > eps(eltype(L)) "Scale must be positive definite"
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if check_args && (minimum(diag(L)) < sqrt(eps(eltype(L))))
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@warn "Initial scale is too small (minimum eigenvalue is $(minimum(diag(L)))). This might result in unstable optimization behavior."
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end
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q_base = Normal{T}(zero(T), one(T))
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MvLocationScale(μ, L, q_base)
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end
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"""
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MeanFieldGaussian(location, scale; check_args = true)
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Construct a Gaussian variational approximation with a diagonal covariance matrix.
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# Arguments
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- `location::AbstractVector{T}`: Mean of the Gaussian.
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- `scale::Diagonal{T}`: Diagonal Cholesky factor of the covariance of the Gaussian.
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# Keyword Arguments
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- `check_args`: Check the conditioning of the initial scale (default: `true`).
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"""
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function MeanFieldGaussian(
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μ::AbstractVector{T},
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L::Diagonal{T};
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check_args::Bool = true
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) where {T <: Real}
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@assert minimum(diag(L)) > eps(eltype(L)) "Scale must be a Cholesky factor"
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if check_args && (minimum(diag(L)) < sqrt(eps(eltype(L))))
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@warn "Initial scale is too small (minimum eigenvalue is $(minimum(diag(L)))). This might result in unstable optimization behavior."
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end
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q_base = Normal{T}(zero(T), one(T))
163+
MvLocationScale(μ, L, q_base)
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end

‎test/inference/repgradelbo_locationscale.jl

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@@ -0,0 +1,82 @@
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const PROGRESS = length(ARGS) > 0 && ARGS[1] == "--progress" ? true : false
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using Test
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@testset "inference RepGradELBO VILocationScale" begin
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@testset "$(modelname) $(objname) $(realtype) $(adbackname)" for
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realtype ∈ [Float64, Float32],
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(modelname, modelconstr) ∈ Dict(
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:Normal=> normal_meanfield,
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:Normal=> normal_fullrank,
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),
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(objname, objective) ∈ Dict(
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:RepGradELBOClosedFormEntropy => RepGradELBO(10),
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:RepGradELBOStickingTheLanding => RepGradELBO(10, entropy = StickingTheLandingEntropy()),
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),
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(adbackname, adbackend) ∈ Dict(
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:ForwarDiff => AutoForwardDiff(),
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:ReverseDiff => AutoReverseDiff(),
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:Zygote => AutoZygote(),
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#:Enzyme => AutoEnzyme(),
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)
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seed = (0x38bef07cf9cc549d)
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rng = StableRNG(seed)
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modelstats = modelconstr(rng, realtype)
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@unpack model, μ_true, L_true, n_dims, is_meanfield = modelstats
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T, η = is_meanfield ? (5_000, 1e-2) : (30_000, 1e-3)
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q0 = if is_meanfield
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MeanFieldGaussian(zeros(realtype, n_dims), Diagonal(ones(realtype, n_dims)))
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else
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L0 = Matrix{realtype}(I, n_dims, n_dims) |> LowerTriangular
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FullRankGaussian(zeros(realtype, n_dims), L0)
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end
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39+
@testset "convergence" begin
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Δλ₀ = sum(abs2, q0.location - μ_true) + sum(abs2, q0.scale - L_true)
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q, stats, _ = optimize(
42+
rng, model, objective, q0, T;
43+
optimizer = Optimisers.Adam(realtype(η)),
44+
show_progress = PROGRESS,
45+
adbackend = adbackend,
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)
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μ = q.location
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L = q.scale
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Δλ = sum(abs2, μ - μ_true) + sum(abs2, L - L_true)
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@test Δλ ≤ Δλ₀/T^(1/4)
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@test eltype(μ) == eltype(μ_true)
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@test eltype(L) == eltype(L_true)
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end
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@testset "determinism" begin
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rng = StableRNG(seed)
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q, stats, _ = optimize(
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rng, model, objective, q0, T;
61+
optimizer = Optimisers.Adam(realtype(η)),
62+
show_progress = PROGRESS,
63+
adbackend = adbackend,
64+
)
65+
μ = q.location
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L = q.scale
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rng_repl = StableRNG(seed)
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q, stats, _ = optimize(
70+
rng_repl, model, objective, q0, T;
71+
optimizer = Optimisers.Adam(realtype(η)),
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show_progress = PROGRESS,
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adbackend = adbackend,
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)
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μ_repl = q.location
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L_repl = q.scale
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@test μ == μ_repl
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@test L == L_repl
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end
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end
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end
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‎test/inference/repgradelbo_distributionsad_bijectors.jl renamed to ‎test/inference/repgradelbo_locationscale_bijectors.jl

+21-16
Original file line numberDiff line numberDiff line change
@@ -3,7 +3,7 @@ const PROGRESS = length(ARGS) > 0 && ARGS[1] == "--progress" ? true : false
33

44
using Test
55

6-
@testset "inference RepGradELBO DistributionsAD Bijectors" begin
6+
@testset "inference RepGradELBO VILocationScale Bijectors" begin
77
@testset "$(modelname) $(objname) $(realtype) $(adbackname)" for
88
realtype ∈ [Float64, Float32],
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(modelname, modelconstr) ∈ Dict(
@@ -15,7 +15,7 @@ using Test
1515
),
1616
(adbackname, adbackend) ∈ Dict(
1717
:ForwarDiff => AutoForwardDiff(),
18-
#:ReverseDiff => AutoReverseDiff(),
18+
:ReverseDiff => AutoReverseDiff(),
1919
#:Zygote => AutoZygote(),
2020
#:Enzyme => AutoEnzyme(),
2121
)
@@ -30,23 +30,28 @@ using Test
3030

3131
b = Bijectors.bijector(model)
3232
b⁻¹ = inverse(b)
33-
μ₀ = Zeros(realtype, n_dims)
34-
L₀ = Diagonal(Ones(realtype, n_dims))
33+
μ0 = Zeros(realtype, n_dims)
34+
L0 = Diagonal(Ones(realtype, n_dims))
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36-
q₀_η = TuringDiagMvNormal(μ₀, diag(L₀))
37-
q₀_z = Bijectors.transformed(q₀_η, b⁻¹)
36+
q0_η = if is_meanfield
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MeanFieldGaussian(zeros(realtype, n_dims), Diagonal(ones(realtype, n_dims)))
38+
else
39+
L0 = Matrix{realtype}(I, n_dims, n_dims) |> LowerTriangular
40+
FullRankGaussian(zeros(realtype, n_dims), L0)
41+
end
42+
q0_z = Bijectors.transformed(q0_η, b⁻¹)
3843

3944
@testset "convergence" begin
40-
Δλ₀ = sum(abs2, μ₀ - μ_true) + sum(abs2, L₀ - L_true)
45+
Δλ₀ = sum(abs2, μ0 - μ_true) + sum(abs2, L0 - L_true)
4146
q, stats, _ = optimize(
42-
rng, model, objective, q₀_z, T;
47+
rng, model, objective, q0_z, T;
4348
optimizer = Optimisers.Adam(realtype(η)),
4449
show_progress = PROGRESS,
4550
adbackend = adbackend,
4651
)
4752

48-
μ = mean(q.dist)
49-
L = sqrt(cov(q.dist))
53+
μ = q.dist.location
54+
L = q.dist.scale
5055
Δλ = sum(abs2, μ - μ_true) + sum(abs2, L - L_true)
5156

5257
@test Δλ ≤ Δλ₀/T^(1/4)
@@ -57,23 +62,23 @@ using Test
5762
@testset "determinism" begin
5863
rng = StableRNG(seed)
5964
q, stats, _ = optimize(
60-
rng, model, objective, q₀_z, T;
65+
rng, model, objective, q0_z, T;
6166
optimizer = Optimisers.Adam(realtype(η)),
6267
show_progress = PROGRESS,
6368
adbackend = adbackend,
6469
)
65-
μ = mean(q.dist)
66-
L = sqrt(cov(q.dist))
70+
μ = q.dist.location
71+
L = q.dist.scale
6772

6873
rng_repl = StableRNG(seed)
6974
q, stats, _ = optimize(
70-
rng_repl, model, objective, q₀_z, T;
75+
rng_repl, model, objective, q0_z, T;
7176
optimizer = Optimisers.Adam(realtype(η)),
7277
show_progress = PROGRESS,
7378
adbackend = adbackend,
7479
)
75-
μ_repl = mean(q.dist)
76-
L_repl = sqrt(cov(q.dist))
80+
μ_repl = q.dist.location
81+
L_repl = q.dist.scale
7782
@test μ == μ_repl
7883
@test L == L_repl
7984
end

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