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| 1 | +using Optimisers |
| 2 | +using ChainRulesCore #, Functors, StaticArrays, Zygote |
| 3 | +using LinearAlgebra, Statistics, Test |
| 4 | + |
| 5 | +import CUDA |
| 6 | +if CUDA.functional() |
| 7 | + using CUDA # exports CuArray, etc |
| 8 | + @info "starting CUDA tests" |
| 9 | +else |
| 10 | + @info "CUDA not functional, testing via GPUArrays" |
| 11 | + using GPUArrays |
| 12 | + GPUArrays.allowscalar(false) |
| 13 | + |
| 14 | + # GPUArrays provides a fake GPU array, for testing |
| 15 | + jl_file = normpath(joinpath(pathof(GPUArrays), "..", "..", "test", "jlarray.jl")) |
| 16 | + using Random, Adapt # loaded within jl_file |
| 17 | + include(jl_file) |
| 18 | + using .JLArrays |
| 19 | + cu = jl |
| 20 | + CuArray{T,N} = JLArray{T,N} |
| 21 | +end |
| 22 | + |
| 23 | +@test cu(rand(3)) .+ 1 isa CuArray |
| 24 | + |
| 25 | +@testset "very basics" begin |
| 26 | + m = (cu([1.0, 2.0]),) |
| 27 | + mid = objectid(m[1]) |
| 28 | + g = (cu([25, 33]),) |
| 29 | + o = Descent(0.1f0) |
| 30 | + s = Optimisers.setup(o, m) |
| 31 | + |
| 32 | + s2, m2 = Optimisers.update(s, m, g) |
| 33 | + @test Array(m[1]) == 1:2 # not mutated |
| 34 | + @test m2[1] isa CuArray |
| 35 | + @test Array(m2[1]) ≈ [1,2] .- 0.1 .* [25, 33] atol=1e-6 |
| 36 | + |
| 37 | + s3, m3 = Optimisers.update!(s, m, g) |
| 38 | + @test objectid(m3[1]) == mid |
| 39 | + @test Array(m3[1]) ≈ [1,2] .- 0.1 .* [25, 33] atol=1e-6 |
| 40 | + |
| 41 | + g4 = Tangent{typeof(m)}(g...) |
| 42 | + s4, m4 = Optimisers.update!(s, (cu([1.0, 2.0]),), g4) |
| 43 | + @test Array(m4[1]) ≈ [1,2] .- 0.1 .* [25, 33] atol=1e-6 |
| 44 | +end |
| 45 | + |
| 46 | +@testset "basic mixed" begin |
| 47 | + # Works trivially as every element of the tree is either here or there |
| 48 | + m = (device = cu([1.0, 2.0]), host = [3.0, 4.0], neither = (5, 6, sin)) |
| 49 | + s = Optimisers.setup(ADAM(0.1), m) |
| 50 | + @test s.device.state[1] isa CuArray |
| 51 | + @test s.host.state[1] isa Array |
| 52 | + |
| 53 | + g = (device = cu([1, 0.1]), host = [1, 10], neither = nothing) |
| 54 | + s2, m2 = Optimisers.update(s, m, g) |
| 55 | + |
| 56 | + @test m2.device isa CuArray |
| 57 | + @test Array(m2.device) ≈ [0.9, 1.9] atol=1e-6 |
| 58 | + |
| 59 | + @test m2.host isa Array |
| 60 | + @test m2.host ≈ [2.9, 3.9] |
| 61 | +end |
| 62 | + |
| 63 | +RULES = [ |
| 64 | + # Just a selection: |
| 65 | + Descent(), ADAM(), RMSProp(), NADAM(), |
| 66 | + # A few chained combinations: |
| 67 | + OptimiserChain(WeightDecay(), ADAM(0.001)), |
| 68 | + OptimiserChain(ClipNorm(), ADAM(0.001)), |
| 69 | + OptimiserChain(ClipGrad(0.5), Momentum()), |
| 70 | +] |
| 71 | + |
| 72 | +name(o) = typeof(o).name.name # just for printing testset headings |
| 73 | +name(o::OptimiserChain) = join(name.(o.opts), " → ") |
| 74 | + |
| 75 | +@testset "rules: simple sum" begin |
| 76 | + @testset "$(name(o))" for o in RULES |
| 77 | + m = cu(shuffle!(reshape(1:64, 8, 8) .+ 0.0)) |
| 78 | + s = Optimisers.setup(o, m) |
| 79 | + for _ in 1:10 |
| 80 | + g = Zygote.gradient(x -> sum(abs2, x + x'), m)[1] |
| 81 | + s, m = Optimisers.update!(s, m, g) |
| 82 | + end |
| 83 | + @test sum(m) < sum(1:64) |
| 84 | + end |
| 85 | +end |
| 86 | + |
| 87 | +@testset "destructure GPU" begin |
| 88 | + m = (x = cu(Float32[1,2,3]), y = (0, 99), z = cu(Float32[4,5])) |
| 89 | + v, re = destructure(m) |
| 90 | + @test v isa CuArray |
| 91 | + @test re(2v).x isa CuArray |
| 92 | +end |
| 93 | + |
| 94 | +@testset "destructure mixed" begin |
| 95 | + # Not sure what should happen here! |
| 96 | + m_c1 = (x = cu(Float32[1,2,3]), y = Float32[4,5]) |
| 97 | + v, re = destructure(m_c1) |
| 98 | + @test re(2v).x isa CuArray |
| 99 | + @test_broken re(2v).y isa Array |
| 100 | + |
| 101 | + m_c2 = (x = Float32[1,2,3], y = cu(Float32[4,5])) |
| 102 | + @test_skip destructure(m_c2) # ERROR: Scalar indexing |
| 103 | +end |
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