Flux.jl/test/cuda/cuda.jl

using Flux, Flux.Tracker, CuArrays, Test
using Flux: gpu

# @info "Testing GPU Support"
#
# @testset "CuArrays" begin
#
# CuArrays.allowscalar(false)
#
# x = param(randn(5, 5))
# cx = gpu(x)
# @test cx isa TrackedArray && cx.data isa CuArray
#
# x = Flux.onehotbatch([1, 2, 3], 1:3)
# cx = gpu(x)
# @test cx isa Flux.OneHotMatrix && cx.data isa CuArray
# @test (cx .+ 1) isa CuArray
#
# m = Chain(Dense(10, 5, tanh), Dense(5, 2), softmax)
# cm = gpu(m)
#
# @test all(p isa TrackedArray && p.data isa CuArray for p in params(cm))
# @test cm(gpu(rand(10, 10))) isa TrackedArray{Float32,2,CuArray{Float32,2}}
#
# x = [1,2,3]
# cx = gpu(x)
# @test Flux.crossentropy(x,x) ≈ Flux.crossentropy(cx,cx)
#
# xs = param(rand(5,5))
# ys = Flux.onehotbatch(1:5,1:5)
# @test collect(cu(xs) .+ cu(ys)) ≈ collect(xs .+ ys)
#
# c = gpu(Conv((2,2),3=>4))
# l = c(gpu(rand(10,10,3,2)))
# Flux.back!(sum(l))
#
# end

if CuArrays.cudnn_available()
    @info "Testing Flux/CUDNN BatchNorm"
    include("cudnn.jl")
    @info "Testing Flux/CUDNN RNN"
    include("curnn.jl")
end