fix: regression in non-fast scalar indexing support

Author: avik-pal

Project.toml

@@ -1,6 +1,6 @@
 name = "ForwardDiff"
 uuid = "f6369f11-7733-5829-9624-2563aa707210"
-version = "1.0.1"
+version = "1.0.2"
 
 [deps]
 CommonSubexpressions = "bbf7d656-a473-5ed7-a52c-81e309532950"
@@ -16,9 +16,11 @@ SpecialFunctions = "276daf66-3868-5448-9aa4-cd146d93841b"
 StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
 
 [weakdeps]
+GPUArraysCore = "46192b85-c4d5-4398-a991-12ede77f4527"
 StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
 
 [extensions]
+ForwardDiffGPUArraysCoreExt = "GPUArraysCore"
 ForwardDiffStaticArraysExt = "StaticArrays"
 
 [compat]
@@ -27,6 +29,7 @@ CommonSubexpressions = "0.3"
 DiffResults = "1.1"
 DiffRules = "1.4"
 DiffTests = "0.1"
+GPUArraysCore = "0.2"
 IrrationalConstants = "0.1, 0.2"
 LogExpFunctions = "0.3"
 NaNMath = "1"
@@ -40,9 +43,10 @@ Calculus = "49dc2e85-a5d0-5ad3-a950-438e2897f1b9"
 DiffTests = "de460e47-3fe3-5279-bb4a-814414816d5d"
 InteractiveUtils = "b77e0a4c-d291-57a0-90e8-8db25a27a240"
 IrrationalConstants = "92d709cd-6900-40b7-9082-c6be49f344b6"
+JLArrays = "27aeb0d3-9eb9-45fb-866b-73c2ecf80fcb"
 SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["Calculus", "DiffTests", "IrrationalConstants", "SparseArrays", "StaticArrays", "Test", "InteractiveUtils"]
+test = ["Calculus", "DiffTests", "IrrationalConstants", "SparseArrays", "StaticArrays", "Test", "InteractiveUtils", "JLArrays"]

ext/ForwardDiffGPUArraysCoreExt.jl

@@ -0,0 +1,36 @@
+module ForwardDiffGPUArraysCoreExt
+
+using GPUArraysCore: AbstractGPUArray
+using ForwardDiff: ForwardDiff, Dual, Partials
+
+function ForwardDiff.seed!(duals::AbstractGPUArray{Dual{T,V,N}}, x,
+                           seed::Partials{N,V} = zero(Partials{N,V})) where {T,V,N}
+    idxs = ForwardDiff.structural_eachindex(duals, x)
+    duals[idxs] .= Dual{T,V,N}.(x[idxs], seed)
+    return duals
+end
+
+function ForwardDiff.seed!(duals::AbstractGPUArray{Dual{T,V,N}}, x,
+                           seeds::NTuple{N,Partials{N,V}}) where {T,V,N}
+    idxs = ForwardDiff.structural_eachindex(duals, x)
+    duals[idxs] .= Dual{T,V,N}.(x[idxs], seeds[1:N])
+    return duals
+end
+
+function ForwardDiff.seed!(duals::AbstractGPUArray{Dual{T,V,N}}, x, index,
+                           seed::Partials{N,V} = zero(Partials{N,V})) where {T,V,N}
+    offset = index - 1
+    idxs = Iterators.drop(ForwardDiff.structural_eachindex(duals, x), offset)
+    duals[idxs] .= Dual{T,V,N}.(x[idxs], seed)
+    return duals
+end
+
+function ForwardDiff.seed!(duals::AbstractGPUArray{Dual{T,V,N}}, x, index,
+                           seeds::NTuple{N,Partials{N,V}}, chunksize = N) where {T,V,N}
+    offset = index - 1
+    idxs = Iterators.drop(ForwardDiff.structural_eachindex(duals, x), offset)
+    duals[idxs] .= Dual{T,V,N}.(x[idxs], seeds[1:chunksize])
+    return duals
+end
+
+end

test/JacobianTest.jl

@@ -8,6 +8,7 @@ using ForwardDiff: Dual, Tag, JacobianConfig
 using StaticArrays
 using DiffTests
 using LinearAlgebra
+using JLArrays
 
 include(joinpath(dirname(@__FILE__), "utils.jl"))
 
@@ -279,4 +280,17 @@ end
     end
 end
 
+@testset "GPUArraysCore" begin
+    f(x) = x .^ 2 ./ 2
+
+    x = [1.0, 2.0, 3.0]
+    x_jl = JLArray(x)
+
+    jac = ForwardDiff.jacobian(f, x)
+    jac_jl = ForwardDiff.jacobian(f, x_jl)
+
+    @test jac_jl isa JLArray
+    @test Array(jac_jl) ≈ jac
+end
+
 end # module