Fixed ReverseDiff stuff

Author: jonniedie

Project.toml

@@ -1,7 +1,7 @@
 name = "ComponentArrays"
 uuid = "b0b7db55-cfe3-40fc-9ded-d10e2dbeff66"
 authors = ["Jonnie Diegelman <47193959+jonniedie@users.noreply.github.com>"]
-version = "0.10.5"
+version = "0.10.6"
 
 [deps]
 ArrayInterface = "4fba245c-0d91-5ea0-9b3e-6abc04ee57a9"

src/if_required/reversediff.jl

@@ -3,11 +3,13 @@ const TrackedComponentArray{V, D, N, DA, N, A, Ax} = ReverseDiff.TrackedArray{V,
 maybe_tracked_array(val::AbstractArray, der, t) = ReverseDiff.TrackedArray(val, der, t)
 maybe_tracked_array(val, der, t) = ReverseDiff.TrackedReal(val, der, t)
 
-function Base.getindex(tca::TrackedComponentArray, inds::Union{Symbol, Val}...)
-        val = ReverseDiff.value(tca)[inds...]
-        der = ReverseDiff.deriv(tca)[inds...]
-        t = ReverseDiff.tape(tca)
-    return maybe_tracked_array(val, der, t)
+for f in [:getindex, :view]
+    @eval function Base.$f(tca::TrackedComponentArray, inds::Union{Symbol, Val}...)
+            val = $f(ReverseDiff.value(tca), inds...)
+            der = Base.maybeview(ReverseDiff.deriv(tca), inds...)
+            t = ReverseDiff.tape(tca)
+        return maybe_tracked_array(val, der, t)
+    end
 end
 
 function Base.getproperty(tca::TrackedComponentArray, s::Symbol)

test/autodiff_tests.jl

@@ -8,25 +8,27 @@ using Test
 F(x, θ, deg) = (θ[1] - x[1])^deg + θ[2] * (x[2] - x[1]^deg)^deg
 F_idx_val(ca) = F(ca[Val(:x)], ca[Val(:θ)], ca[Val(:deg)])
 F_idx_sym(ca) = F(ca[:x], ca[:θ], ca[:deg])
+F_view_val(ca) = F(@view(ca[Val(:x)]), @view(ca[Val(:θ)]), ca[Val(:deg)])
+F_view_sym(ca) = F(@view(ca[:x]), @view(ca[:θ]), ca[:deg])
 F_prop(ca) = F(ca.x, ca.θ, ca.deg)
 
 ca = ComponentArray(x = [1, 2], θ = [1.0, 100.0], deg = 2)
 truth = [-400, 200]
 
-@testset "$(nameof(F_))" for F_ in (F_idx_val, F_idx_sym, F_prop)
+@testset "$(nameof(F_))" for F_ in (F_idx_val, F_idx_sym, F_view_val, F_view_sym, F_prop)
     finite = FiniteDiff.finite_difference_gradient(ca -> F_(ca), ca).x
     @test finite ≈ truth
 
     forward = ForwardDiff.gradient(ca -> F_(ca), ca).x
     @test forward ≈ truth
 
     reverse = ReverseDiff.gradient(ca -> F_(ca), ca).x
-    if F_ in (F_idx_val, F_idx_sym)
-        @test_broken reverse ≈ truth
+    @test reverse ≈ truth
+
+    zygote_full = Zygote.gradient(ca -> F_(ca), ca)[1]
+    if F_ == F_prop && VERSION < v"1.3"
+        @test_broken zygote_full.x ≈ truth
     else
-        @test reverse ≈ truth
+        @test zygote_full.x ≈ truth
     end
-
-    zygote = Zygote.gradient(ca -> F_(ca), ca)[1].x
-    @test zygote ≈ truth
 end