use JLArrays, add gradient test

Project.toml

@@ -17,12 +17,11 @@ Zygote = "0.6.40"
 julia = "1.6"
 
 [extras]
-Adapt = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
 CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
-GPUArrays = "0c68f7d7-f131-5f86-a1c3-88cf8149b2d7"
+JLArrays = "27aeb0d3-9eb9-45fb-866b-73c2ecf80fcb"
 StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [targets]
-test = ["Adapt", "CUDA", "GPUArrays", "StaticArrays", "Test", "Zygote"]
+test = ["CUDA", "JLArrays", "StaticArrays", "Test", "Zygote"]

test/gpuarrays.jl

@@ -1,25 +1,18 @@
 using Optimisers
-using ChainRulesCore #, Functors, StaticArrays, Zygote
-using LinearAlgebra, Statistics, Test
+using ChainRulesCore, Zygote
+using Test
 
 import CUDA
 if CUDA.functional()
   using CUDA  # exports CuArray, etc
-  @info "starting CUDA tests"
 else
-  @info "CUDA not functional, testing via GPUArrays"
-  using GPUArrays
-  GPUArrays.allowscalar(false)
+  @info "CUDA not functional, testing with JLArrays instead"
+  using JLArrays
+  JLArrays.allowscalar(false)
 
-  # GPUArrays provides a fake GPU array, for testing
-  jl_file = normpath(joinpath(pathof(GPUArrays), "..", "..", "test", "jlarray.jl"))
-  using Random, Adapt  # loaded within jl_file
-  include(jl_file)
-  using .JLArrays
-  cu = jl
+  cu = jl32
   CuArray{T,N} = JLArray{T,N}
 end
-
 @test cu(rand(3)) .+ 1 isa CuArray
 
 @testset "very basics" begin
@@ -89,6 +82,11 @@ end
   v, re = destructure(m)
   @test v isa CuArray
   @test re(2v).x isa CuArray
+
+  dm = gradient(m -> sum(abs2, destructure(m)[1]), m)[1]
+  @test dm.z isa CuArray
+  dv = gradient(v -> sum(abs2, re(v).z), cu([10, 20, 30, 40, 50.0]))[1]
+  @test dv isa CuArray
 end
 
 @testset "destructure mixed" begin