Basic rewrite of the package 2023 edition Part I: ADVI

Project.toml

@@ -1,37 +1,67 @@
 name = "AdvancedVI"
 uuid = "b5ca4192-6429-45e5-a2d9-87aec30a685c"
-version = "0.2.4"
+version = "0.3.0"
 
 [deps]
+ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"
+Accessors = "7d9f7c33-5ae7-4f3b-8dc6-eff91059b697"
 Bijectors = "76274a88-744f-5084-9051-94815aaf08c4"
+ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
+DiffResults = "163ba53b-c6d8-5494-b064-1a9d43ac40c5"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
-DistributionsAD = "ced4e74d-a319-5a8a-b0ac-84af2272839c"
 DocStringExtensions = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae"
-ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
+FillArrays = "1a297f60-69ca-5386-bcde-b61e274b549b"
+Functors = "d9f16b24-f501-4c13-a1f2-28368ffc5196"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
+LogDensityProblems = "6fdf6af0-433a-55f7-b3ed-c6c6e0b8df7c"
+Optimisers = "3bd65402-5787-11e9-1adc-39752487f4e2"
 ProgressMeter = "92933f4c-e287-5a05-a399-4b506db050ca"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Requires = "ae029012-a4dd-5104-9daa-d747884805df"
+SimpleUnPack = "ce78b400-467f-4804-87d8-8f486da07d0a"
 StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
-StatsFuns = "4c63d2b9-4356-54db-8cca-17b64c39e42c"
-Tracker = "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c"
+
+[weakdeps]
+Enzyme = "7da242da-08ed-463a-9acd-ee780be4f1d9"
+ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
+ReverseDiff = "37e2e3b7-166d-5795-8a7a-e32c996b4267"
+Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
+
+[extensions]
+AdvancedVIEnzymeExt = "Enzyme"
+AdvancedVIForwardDiffExt = "ForwardDiff"
+AdvancedVIReverseDiffExt = "ReverseDiff"
+AdvancedVIZygoteExt = "Zygote"
 
 [compat]
-Bijectors = "0.11, 0.12, 0.13"
-Distributions = "0.21, 0.22, 0.23, 0.24, 0.25"
-DistributionsAD = "0.2, 0.3, 0.4, 0.5, 0.6"
+ADTypes = "0.1, 0.2"
+Accessors = "0.1"
+Bijectors = "0.12, 0.13"
+ChainRulesCore = "1.16"
+DiffResults = "1"
+Distributions = "0.25.87"
 DocStringExtensions = "0.8, 0.9"
-ForwardDiff = "0.10.3"
-ProgressMeter = "1.0.0"
-Requires = "0.5, 1.0"
+Enzyme = "0.11.7"
+FillArrays = "1.3"
+ForwardDiff = "0.10.36"
+Functors = "0.4"
+LogDensityProblems = "2"
+Optimisers = "0.2.16, 0.3"
+ProgressMeter = "1.6"
+Requires = "1.0"
+ReverseDiff = "1.15.1"
+SimpleUnPack = "1.1.0"
 StatsBase = "0.32, 0.33, 0.34"
-StatsFuns = "0.8, 0.9, 1"
-Tracker = "0.2.3"
+Zygote = "0.6.63"
 julia = "1.6"
 
 [extras]
+Enzyme = "7da242da-08ed-463a-9acd-ee780be4f1d9"
+ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
 Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
+ReverseDiff = "37e2e3b7-166d-5795-8a7a-e32c996b4267"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [targets]
 test = ["Pkg", "Test"]

ext/AdvancedVIEnzymeExt.jl

@@ -0,0 +1,26 @@
+
+module AdvancedVIEnzymeExt
+
+if isdefined(Base, :get_extension)
+    using Enzyme
+    using AdvancedVI
+    using AdvancedVI: ADTypes, DiffResults
+else
+    using ..Enzyme
+    using ..AdvancedVI
+    using ..AdvancedVI: ADTypes, DiffResults
+end
+
+# Enzyme doesn't support f::Bijectors (see https://github.com/EnzymeAD/Enzyme.jl/issues/916)
+function AdvancedVI.value_and_gradient!(
+    ad::ADTypes.AutoEnzyme, f, θ::AbstractVector{T}, out::DiffResults.MutableDiffResult
+) where {T<:Real}
+    y = f(θ)
+    DiffResults.value!(out, y)
+    ∇θ = DiffResults.gradient(out)
+    fill!(∇θ, zero(T))
+    Enzyme.autodiff(Enzyme.ReverseWithPrimal, f, Enzyme.Active, Enzyme.Duplicated(θ, ∇θ))
+    return out
+end
+
+end

ext/AdvancedVIForwardDiffExt.jl

@@ -0,0 +1,29 @@
+
+module AdvancedVIForwardDiffExt
+
+if isdefined(Base, :get_extension)
+    using ForwardDiff
+    using AdvancedVI
+    using AdvancedVI: ADTypes, DiffResults
+else
+    using ..ForwardDiff
+    using ..AdvancedVI
+    using ..AdvancedVI: ADTypes, DiffResults
+end
+
+getchunksize(::ADTypes.AutoForwardDiff{chunksize}) where {chunksize} = chunksize
+
+function AdvancedVI.value_and_gradient!(
+    ad::ADTypes.AutoForwardDiff, f, θ::AbstractVector{T}, out::DiffResults.MutableDiffResult
+) where {T<:Real}
+    chunk_size = getchunksize(ad)
+    config = if isnothing(chunk_size)
+        ForwardDiff.GradientConfig(f, θ)
+    else
+        ForwardDiff.GradientConfig(f, θ, ForwardDiff.Chunk(length(θ), chunk_size))
+    end
+    ForwardDiff.gradient!(out, f, θ, config)
+    return out
+end
+
+end

ext/AdvancedVIReverseDiffExt.jl

@@ -0,0 +1,23 @@
+
+module AdvancedVIReverseDiffExt
+
+if isdefined(Base, :get_extension)
+    using AdvancedVI
+    using AdvancedVI: ADTypes, DiffResults
+    using ReverseDiff
+else
+    using ..AdvancedVI
+    using ..AdvancedVI: ADTypes, DiffResults
+    using ..ReverseDiff
+end
+
+# ReverseDiff without compiled tape
+function AdvancedVI.value_and_gradient!(
+    ad::ADTypes.AutoReverseDiff, f, θ::AbstractVector{<:Real}, out::DiffResults.MutableDiffResult
+)
+    tp = ReverseDiff.GradientTape(f, θ)
+    ReverseDiff.gradient!(out, tp, θ)
+    return out
+end
+
+end

ext/AdvancedVIZygoteExt.jl

@@ -0,0 +1,24 @@
+
+module AdvancedVIZygoteExt
+
+if isdefined(Base, :get_extension)
+    using AdvancedVI
+    using AdvancedVI: ADTypes, DiffResults
+    using Zygote
+else
+    using ..AdvancedVI
+    using ..AdvancedVI: ADTypes, DiffResults
+    using ..Zygote
+end
+
+function AdvancedVI.value_and_gradient!(
+    ad::ADTypes.AutoZygote, f, θ::AbstractVector{<:Real}, out::DiffResults.MutableDiffResult
+)
+    y, back = Zygote.pullback(f, θ)
+    ∇θ = back(one(y))
+    DiffResults.value!(out, y)
+    DiffResults.gradient!(out, only(∇θ))
+    return out
+end
+
+end