Fix flattening bug (#52)

Author: gdalle

CITATION.bib

@@ -2,7 +2,7 @@ @misc{ImplicitDifferentiation.jl
 	author  = {Guillaume Dalle, Mohamed Tarek and contributors},
 	title   = {ImplicitDifferentiation.jl},
 	url     = {https://github.com/gdalle/ImplicitDifferentiation.jl},
-	version = {v0.4.3},
+	version = {v0.4.4},
 	year    = {2023},
 	month   = {5}
 }

Project.toml

@@ -1,7 +1,7 @@
 name = "ImplicitDifferentiation"
 uuid = "57b37032-215b-411a-8a7c-41a003a55207"
 authors = ["Guillaume Dalle", "Mohamed Tarek and contributors"]
-version = "0.4.3"
+version = "0.4.4"
 
 [deps]
 AbstractDifferentiation = "c29ec348-61ec-40c8-8164-b8c60e9d9f3d"

examples/0_basic.jl

@@ -162,25 +162,3 @@ h = rand(2)
 J_Z(t) = Zygote.jacobian(first ∘ implicit2, x .+ t .* h)[1]
 ForwardDiff.derivative(J_Z, 0) ≈ Diagonal((-0.25 .* h) ./ (x .^ 1.5))
 @test ForwardDiff.derivative(J_Z, 0) ≈ Diagonal((-0.25 .* h) ./ (x .^ 1.5))  #src
-
-# The following tests are not included in the docs  #src
-
-X = rand(2, 3, 4)  #src
-JJ = Diagonal(0.5 ./ sqrt.(vec(X)))  #src
-@test (first ∘ implicit)(X) ≈ sqrt.(X)  #src
-@test ForwardDiff.jacobian(first ∘ implicit, X) ≈ JJ  #src
-@test Zygote.jacobian(first ∘ implicit, X)[1] ≈ JJ  #src
-
-# Skipped because of https://github.com/JuliaDiff/ChainRulesTestUtils.jl/issues/232 and because it detects weird type instabilities  #src
-@testset verbose = true "ChainRulesTestUtils.jl" begin  #src
-    @test_skip test_rrule(implicit, x)  #src
-    @test_skip test_rrule(implicit, X)  #src
-end  #src
-
-x_and_dx = [ForwardDiff.Dual(x[i], (0, 0)) for i in eachindex(x)]  #src
-@inferred implicit(x_and_dx)  #src
-
-rc = Zygote.ZygoteRuleConfig()  #src
-_, pullback = @inferred rrule(rc, implicit, x)  #src
-dy, dz = zero(implicit(x)[1]), 0
-@inferred pullback((dy, dz))

ext/ImplicitDifferentiationForwardDiffExt.jl

@@ -40,9 +40,10 @@ function (implicit::ImplicitFunction)(
     end
 
     y_and_dy = let y = y, dy = dy
-        map(eachindex(y)) do i
+        y_and_dy_vec = map(eachindex(y)) do i
             Dual{T}(y[i], Partials(ntuple(k -> dy[k][i], Val(N))))
         end
+        reshape(y_and_dy_vec, size(y))
     end
     return y_and_dy, z
 end

test/misc.jl

@@ -0,0 +1,80 @@
+using ChainRulesCore
+using ChainRulesTestUtils
+using ForwardDiff
+using ImplicitDifferentiation
+using JET
+using LinearAlgebra
+using Random
+using Test
+using Zygote
+
+Random.seed!(63);
+
+function mysqrt(x::AbstractArray)
+    a = [0.0]
+    a[1] = first(x)
+    return sqrt.(x)
+end
+
+forward(x) = mysqrt(x), 0
+conditions(x, y, z) = y .^ 2 .- x
+implicit = ImplicitFunction(forward, conditions)
+
+# Skipped because of https://github.com/JuliaDiff/ChainRulesTestUtils.jl/issues/232 and because it detects weird type instabilities
+@testset verbose = true "ChainRulesTestUtils.jl" begin
+    @test_skip test_rrule(implicit, x)
+    @test_skip test_rrule(implicit, X)
+end
+
+@testset verbose = true "Vectors" begin
+    x = rand(2)
+    y, _ = implicit(x)
+    J = Diagonal(0.5 ./ sqrt.(x))
+
+    @testset "Exactness" begin
+        @test (first ∘ implicit)(x) ≈ sqrt.(x)
+        @test ForwardDiff.jacobian(first ∘ implicit, x) ≈ J
+        @test Zygote.jacobian(first ∘ implicit, x)[1] ≈ J
+    end
+
+    @testset verbose = true "Forward inference" begin
+        x_and_dx = ForwardDiff.Dual.(x, ((0, 0),))
+        @test (@inferred implicit(x_and_dx)) == implicit(x_and_dx)
+        y_and_dy, _ = implicit(x_and_dx)
+        @test size(y_and_dy) == size(y)
+    end
+    @testset "Reverse type inference" begin
+        _, pullback = @inferred rrule(Zygote.ZygoteRuleConfig(), implicit, x)
+        dy, dz = zero(implicit(x)[1]), 0
+        @test (@inferred pullback((dy, dz))) == pullback((dy, dz))
+        _, dx = pullback((dy, dz))
+        @test size(dx) == size(x)
+    end
+end
+
+@testset verbose = true "Arrays" begin
+    X = rand(2, 3, 4)
+    Y, _ = implicit(X)
+    JJ = Diagonal(0.5 ./ sqrt.(vec(X)))
+
+    @testset "Exactness" begin
+        @test (first ∘ implicit)(X) ≈ sqrt.(X)
+        @test ForwardDiff.jacobian(first ∘ implicit, X) ≈ JJ
+        @test Zygote.jacobian(first ∘ implicit, X)[1] ≈ JJ
+    end
+
+    @testset "Forward type inference" begin
+        X_and_dX = ForwardDiff.Dual.(X, ((0, 0),))
+        @test (@inferred implicit(X_and_dX)) == implicit(X_and_dX)
+        Y_and_dY, _ = implicit(X_and_dX)
+        @test size(Y_and_dY) == size(Y)
+    end
+
+    @testset "Reverse type inference" begin
+        _, pullback = @inferred rrule(Zygote.ZygoteRuleConfig(), implicit, X)
+        dY, dZ = zero(implicit(X)[1]), 0
+        @test (@inferred pullback((dY, dZ))) == pullback((dY, dZ))
+        _, dX = pullback((dY, dZ))
+        @test size(dX) == size(X)
+    end
+end

test/runtests.jl

@@ -55,6 +55,9 @@ EXAMPLES_DIR_JL = joinpath(dirname(@__DIR__), "examples")
     @testset verbose = false "Doctests (Documenter.jl)" begin
         doctest(ImplicitDifferentiation)
     end
+    @testset verbose = true "Miscellaneous" begin
+        include("misc.jl")
+    end
     for file in readdir(EXAMPLES_DIR_JL)
         path = joinpath(EXAMPLES_DIR_JL, file)
         title = markdown_title(path)