Flux.jl/test/cuda/cudnn.jl

using Flux, CuArrays, Test

# @testset "CUDNN BatchNorm" begin
#     @testset "4D Input" begin
#         x = TrackedArray(Float64.(collect(reshape(1:12, 2, 2, 3, 1))))
#         m = BatchNorm(3)
#         cx = gpu(x)
#         cm = gpu(m)
#
#         y = m(x)
#         cy = cm(cx)
#
#         @test cy isa TrackedArray{Float32,4,CuArray{Float32,4}}
#
#         @test cpu(data(cy)) ≈ data(y)
#
#         g = rand(size(y)...)
#         Flux.back!(y, g)
#         Flux.back!(cy, gpu(g))
#
#         @test m.γ.grad ≈ cpu(cm.γ.grad)
#         @test m.β.grad ≈ cpu(cm.β.grad)
#         @test x.grad ≈ cpu(x.grad)
#     end
#
#     @testset "2D Input" begin
#         x = TrackedArray(Float64.(collect(reshape(1:12, 3, 4))))
#         m = BatchNorm(3)
#         cx = gpu(x)
#         cm = gpu(m)
#
#         y = m(x)
#         cy = cm(cx)
#
#         @test cy isa TrackedArray{Float32,2,CuArray{Float32,2}}
#
#         @test cpu(data(cy)) ≈ data(y)
#
#         g = rand(size(y)...)
#         Flux.back!(y, g)
#         Flux.back!(cy, gpu(g))
#
#         @test m.γ.grad ≈ cpu(cm.γ.grad)
#         @test m.β.grad ≈ cpu(cm.β.grad)
#         @test x.grad ≈ cpu(x.grad)
#     end
# end