Fix Enzyme extension (#79)

* Fix Enzyme extension

* Enable Enzyme tests

* Fix format

* Fix format

* Do not test on Julia nightly

Author: devmotion

.github/workflows/JuliaNightly.yml

@@ -1,7 +1,6 @@
 [![Stable](https://img.shields.io/badge/docs-stable-blue.svg)](https://turinglang.org/AdvancedVI.jl/stable/)
 [![Dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://turinglang.org/AdvancedVI.jl/dev/)
 [![Build Status](https://github.com/TuringLang/AdvancedVI.jl/actions/workflows/CI.yml/badge.svg?branch=master)](https://github.com/TuringLang/AdvancedVI.jl/actions/workflows/CI.yml?query=branch%3Amaster)
-[![JuliaNightly](https://github.com/TuringLang/AdvancedVI.jl/workflows/JuliaNightly/badge.svg?branch=master)](https://github.com/TuringLang/AdvancedVI.jl/actions?query=workflow%3AJuliaNightly+branch%3Amaster)
 [![Coverage](https://codecov.io/gh/TuringLang/AdvancedVI.jl/branch/master/graph/badge.svg)](https://codecov.io/gh/TuringLang/AdvancedVI.jl)
 
 # AdvancedVI.jl

README.md

@@ -11,15 +11,21 @@ else
     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
+    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))
+    _, y = Enzyme.autodiff(
+        Enzyme.ReverseWithPrimal,
+        f,
+        Enzyme.Active,
+        Enzyme.Duplicated(θ, ∇θ),
+    )
+    DiffResults.value!(out, y)
     return out
 end
 

ext/AdvancedVIEnzymeExt.jl

@@ -4,6 +4,7 @@ Bijectors = "76274a88-744f-5084-9051-94815aaf08c4"
 DiffResults = "163ba53b-c6d8-5494-b064-1a9d43ac40c5"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 DistributionsAD = "ced4e74d-a319-5a8a-b0ac-84af2272839c"
+Enzyme = "7da242da-08ed-463a-9acd-ee780be4f1d9"
 FillArrays = "1a297f60-69ca-5386-bcde-b61e274b549b"
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
 Functors = "d9f16b24-f501-4c13-a1f2-28368ffc5196"
@@ -26,6 +27,7 @@ ADTypes = "0.2.1, 1"
 Bijectors = "0.13"
 Distributions = "0.25.100"
 DistributionsAD = "0.6.45"
+Enzyme = "0.12"
 FillArrays = "1.6.1"
 ForwardDiff = "0.10.36"
 Functors = "0.4.5"

test/Project.toml

@@ -3,11 +3,11 @@ using Test
 
 @testset "ad" begin
     @testset "$(adname)" for (adname, adsymbol) ∈ Dict(
-          :ForwardDiff => AutoForwardDiff(),
-          :ReverseDiff => AutoReverseDiff(),
-          :Zygote      => AutoZygote(),
-          # :Enzyme      => AutoEnzyme() # Currently not tested against
-        )
+        :ForwardDiff => AutoForwardDiff(),
+        :ReverseDiff => AutoReverseDiff(),
+        :Zygote => AutoZygote(),
+        :Enzyme => AutoEnzyme(),
+    )
         D = 10
         A = randn(D, D)
         λ = randn(D)

test/interface/ad.jl

@@ -18,7 +18,7 @@ using DistributionsAD
 using LogDensityProblems
 using Optimisers
 using ADTypes
-using ForwardDiff, ReverseDiff, Zygote
+using Enzyme, ForwardDiff, ReverseDiff, Zygote
 
 using AdvancedVI