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thomvetthomvet
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needs clean up
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‎.buildkite/pipeline.yml‎

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steps:
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- label: "Julia 1"
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plugins:
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- JuliaCI/julia#v1:
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version: "1"
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- JuliaCI/julia-test#v1:
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coverage: false # 1000x slowdown
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agents:
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queue: "juliagpu"
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cuda: "*"
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timeout_in_minutes: 30
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# Don't run Buildkite if the commit message includes the text [skip tests]
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if: build.message !~ /\[skip tests\]/
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env:
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GROUP: GPU
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JULIA_PKG_SERVER: "" # it often struggles with our large artifacts
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# SECRET_CODECOV_TOKEN: "..."

‎.github/workflows/CI.yml‎

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matrix:
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group:
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- Core
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- GPU
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steps:
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- uses: actions/checkout@v2
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- uses: julia-actions/setup-julia@v1

‎Project.toml‎

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julia = "1.6"
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[extras]
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CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
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LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
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OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
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Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
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RecursiveArrayTools = "731186ca-8d62-57ce-b412-fbd966d074cd"
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Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
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SafeTestsets = "1bc83da4-3b8d-516f-aca4-4fe02f6d838f"
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[targets]
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test = ["LinearAlgebra", "OrdinaryDiffEq", "Test", "CUDA", "RecursiveArrayTools"]
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test = ["LinearAlgebra", "OrdinaryDiffEq", "Test", "RecursiveArrayTools", "Pkg", "SafeTestsets"]

‎test/GPU/Project.toml‎

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[deps]
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CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
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PreallocationTools = "d236fae5-4411-538c-8e31-a6e3d9e00b46"

‎test/core_dispatch.jl‎

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using LinearAlgebra, OrdinaryDiffEq, Test, PreallocationTools, ForwardDiff, LabelledArrays, RecursiveArrayTools
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#Base Array tests
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chunk_size = 5
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u0_B = ones(5, 5)
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dual_B = zeros(ForwardDiff.Dual{ForwardDiff.Tag{typeof(something), Float64}, Float64, chunk_size}, 2, 2)
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cache_B = dualcache(u0_B, chunk_size)
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tmp_du_BA = get_tmp(cache_B, u0_B)
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tmp_dual_du_BA = get_tmp(cache_B, dual_B)
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tmp_du_BN = get_tmp(cache_B, u0_B[1])
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tmp_dual_du_BN = get_tmp(cache_B, dual_B[1])
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@test size(tmp_du_BA) == size(u0_B)
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@test typeof(tmp_du_BA) == typeof(u0_B)
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@test eltype(tmp_du_BA) == eltype(u0_B)
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@test size(tmp_dual_du_BA) == size(u0_B)
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@test typeof(tmp_dual_du_BA) == typeof(dual_B)
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@test eltype(tmp_dual_du_BA) == eltype(dual_B)
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@test size(tmp_du_BN) == size(u0_B)
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@test typeof(tmp_du_BN) == typeof(u0_B)
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@test eltype(tmp_du_BN) == eltype(u0_B)
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@test size(tmp_dual_du_BN) == size(u0_B)
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@test typeof(tmp_dual_du_BN) == typeof(dual_B)
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@test eltype(tmp_dual_du_BN) == eltype(dual_B)
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#LArray tests
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chunk_size = 4
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u0_L = LArray((2,2); a=1.0, b=1.0, c=1.0, d=1.0)
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zerodual = zero(ForwardDiff.Dual{ForwardDiff.Tag{typeof(something), Float64}, Float64, chunk_size})
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dual_L = LArray((2,2); a=zerodual, b=zerodual, c=zerodual, d=zerodual)
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cache_L = dualcache(u0_L, chunk_size)
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tmp_du_LA = get_tmp(cache_L, u0_L)
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tmp_dual_du_LA = get_tmp(cache_L, dual_L)
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tmp_du_LN = get_tmp(cache_L, u0_L[1])
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tmp_dual_du_LN = get_tmp(cache_L, dual_L[1])
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@test size(tmp_du_LA) == size(u0_L)
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@test typeof(tmp_du_LA) == typeof(u0_L)
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@test eltype(tmp_du_LA) == eltype(u0_L)
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@test size(tmp_dual_du_LA) == size(u0_L)
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@test typeof(tmp_dual_du_LA) == typeof(dual_L)
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@test eltype(tmp_dual_du_LA) == eltype(dual_L)
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@test size(tmp_du_LN) == size(u0_L)
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@test typeof(tmp_du_LN) == typeof(u0_L)
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@test eltype(tmp_du_LN) == eltype(u0_L)
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@test size(tmp_dual_du_LN) == size(u0_L)
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@test typeof(tmp_dual_du_LN) == typeof(dual_L)
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@test eltype(tmp_dual_du_LN) == eltype(dual_L)
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#ArrayPartition tests
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u0_AP = ArrayPartition(ones(2,2), ones(3,3))
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dual_a = zeros(ForwardDiff.Dual{ForwardDiff.Tag{typeof(something), Float64}, Float64, chunk_size}, 2, 2)
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dual_b = zeros(ForwardDiff.Dual{ForwardDiff.Tag{typeof(something), Float64}, Float64, chunk_size}, 3, 3)
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dual_AP = ArrayPartition(dual_a, dual_b)
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cache_AP = dualcache(u0_AP, chunk_size)
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tmp_du_APA = get_tmp(cache_AP, u0_AP)
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tmp_dual_du_APA = get_tmp(cache_AP, dual_AP)
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tmp_du_APN = get_tmp(cache_AP, u0_AP[1])
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tmp_dual_du_APN = get_tmp(cache_AP, dual_AP[1])
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@test size(tmp_du_APA) == size(u0_AP)
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@test typeof(tmp_du_APA) == typeof(u0_AP)
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@test eltype(tmp_du_APA) == eltype(u0_AP)
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@test size(tmp_dual_du_APA) == size(u0_AP)
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@test typeof(tmp_dual_du_APA) == typeof(dual_AP)
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@test eltype(tmp_dual_du_APA) == eltype(dual_AP)
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@test size(tmp_du_APN) == size(u0_AP)
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@test typeof(tmp_du_APN) == typeof(u0_AP)
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@test eltype(tmp_du_APN) == eltype(u0_AP)
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@test size(tmp_dual_du_APN) == size(u0_AP)
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@test typeof(tmp_dual_du_APN) == typeof(dual_AP)
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@test eltype(tmp_dual_du_APN) == eltype(dual_AP)

‎test/core_odes.jl‎

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using LinearAlgebra, OrdinaryDiffEq, Test, PreallocationTools, LabelledArrays, RecursiveArrayTools
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#Base array
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function foo(du, u, (A, tmp), t)
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tmp = get_tmp(tmp, u)
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mul!(tmp, A, u)
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@. du = u + tmp
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nothing
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end
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#with defined chunk_size
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chunk_size = 5
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u0 = ones(5, 5)
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A = ones(5,5)
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cache = dualcache(zeros(5,5), chunk_size)
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prob = ODEProblem(foo, u0, (0., 1.0), (A, cache))
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sol = solve(prob, TRBDF2(chunk_size=chunk_size))
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@test sol.retcode == :Success
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#with auto-detected chunk_size
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prob = ODEProblem(foo, ones(5, 5), (0., 1.0), (ones(5,5), dualcache(zeros(5,5))))
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sol = solve(prob, TRBDF2())
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@test sol.retcode == :Success
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#Base array with LBC
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function foo(du, u, (A, lbc), t)
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tmp = lbc[u]
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mul!(tmp, A, u)
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@. du = u + tmp
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nothing
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end
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prob = ODEProblem(foo, ones(5, 5), (0., 1.0), (ones(5,5), LazyBufferCache()))
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sol = solve(prob, TRBDF2())
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@test sol.retcode == :Success
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#LArray
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A = LArray((2,2); a=1.0, b=1.0, c=1.0, d=1.0)
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c = LArray((2,2); a=0.0, b=0.0, c=0.0, d=0.0)
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u0 = LArray((2,2); a=1.0, b=1.0, c=1.0, d=1.0)
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function foo(du, u, (A, tmp), t)
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tmp = get_tmp(tmp, u)
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mul!(tmp, A, u)
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@. du = u + tmp
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nothing
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end
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#with specified chunk_size
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chunk_size = 4
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prob = ODEProblem(foo, u0, (0., 1.0), (A, dualcache(c, chunk_size)))
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sol = solve(prob, TRBDF2(chunk_size = chunk_size))
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@test sol.retcode == :Success
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#with auto-detected chunk_size
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prob = ODEProblem(foo, u0, (0., 1.0), (A, dualcache(c)))
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sol = solve(prob, TRBDF2())
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@test sol.retcode == :Success

‎test/core_resizing.jl‎

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using Test, PreallocationTools, ForwardDiff
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randmat = rand(5, 3)
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sto = similar(randmat)
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stod = dualcache(sto)
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function claytonsample!(sto, τ, α; randmat=randmat)
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sto = get_tmp(sto, τ)
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sto .= randmat
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τ == 0 && return sto
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n = size(sto, 1)
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for i in 1:n
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v = sto[i, 2]
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u = sto[i, 1]
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sto[i, 1] = (1 - u^(-τ) + u^(-τ)*v^(-(τ/(1 + τ))))^(-1/τ)*α
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sto[i, 2] = (1 - u^(-τ) + u^(-τ)*v^(-(τ/(1 + τ))))^(-1/τ)
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end
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return sto
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end
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#taking the derivative of claytonsample! with respect to τ only
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df1 = ForwardDiff.derivative(τ -> claytonsample!(stod, τ, 0.0), 0.3)
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@test size(randmat) == size(df1)
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#calculating the jacobian of claytonsample! with respect to τ and α
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df2 = ForwardDiff.jacobian(x -> claytonsample!(stod, x[1], x[2]), [0.3; 0.0]) #should give a 15x2 array,
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#because ForwardDiff flattens the output of jacobian, see: https://juliadiff.org/ForwardDiff.jl/stable/user/api/#ForwardDiff.jacobian
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@test (length(randmat), 2) == size(df2)
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@test df1[1:5,2] ≈ df2[6:10,1]

‎test/gpu_all.jl‎

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using LinearAlgebra, OrdinaryDiffEq, Test, PreallocationTools, CUDA
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#Dispatch tests
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u0_CU = cu(ones(5,5))
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dual_CU = cu(zeros(ForwardDiff.Dual{ForwardDiff.Tag{typeof(something), Float64}, Float64, chunk_size}, 2, 2))
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cache_CU = dualcache(u0_CU, chunk_size)
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tmp_du_CUA = get_tmp(cache_CU, u0_CU)
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tmp_dual_du_CUA = get_tmp(cache_CU, dual_CU)
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tmp_du_CUN = get_tmp(cache_CU, u0_CU[1])
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tmp_dual_du_CUN = get_tmp(cache_CU, dual_CU[1])
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@test typeof(cache_CU.dual_du) == typeof(u0_CU) #check that dual cache array is a GPU array for performance reasons.
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@test size(tmp_du_CUA) == size(u0_CU)
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@test typeof(tmp_du_CUA) == typeof(u0_CU)
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@test eltype(tmp_du_CUA) == eltype(u0_CU)
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@test size(tmp_dual_du_CUA) == size(u0_CU)
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@test typeof(tmp_dual_du_CUA) == typeof(dual_CU)
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@test eltype(tmp_dual_du_CUA) == eltype(dual_CU)
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@test size(tmp_du_CUN) == size(u0_CU)
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@test typeof(tmp_du_CUN) == typeof(u0_CU)
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@test eltype(tmp_du_CUN) == eltype(u0_CU)
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@test size(tmp_dual_du_CUN) == size(u0_CU)
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@test typeof(tmp_dual_du_CUN) == typeof(dual_CU)
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@test eltype(tmp_dual_du_CUN) == eltype(dual_CU)
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#ODE tests
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function foo(du, u, (A, tmp), t)
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tmp = get_tmp(tmp, u)
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mul!(tmp, A, u)
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@. du = u + tmp
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nothing
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end
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#with specified chunk_size
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chunk_size = 10
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u0 = cu(rand(10,10)) #example kept small for test purposes.
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A = cu(-randn(10,10))
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cache = dualcache(A, chunk_size)
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prob = ODEProblem(foo, u0, (0.0f0,1.0f0), (A, cache))
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sol = solve(prob, TRBDF2(chunk_size = chunk_size))
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@test sol.retcode == :Success
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#with auto-detected chunk_size
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u0 = cu(rand(10,10)) #example kept small for test purposes.
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A = cu(-randn(10,10))
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cache = dualcache(A)
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prob = ODEProblem(foo, u0, (0.0f0,1.0f0), (A, cache))
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sol = solve(prob, TRBDF2())
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@test sol.retcode == :Success
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randmat = cu(rand(5, 3))
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sto = similar(randmat)
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stod = dualcache(sto)
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function claytonsample!(sto, τ, α; randmat=randmat)
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sto = get_tmp(sto, τ)
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sto .= randmat
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τ == 0 && return sto
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n = size(sto, 1)
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for i in 1:n
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v = sto[i, 2]
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u = sto[i, 1]
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sto[i, 1] = (1 - u^(-τ) + u^(-τ)*v^(-(τ/(1 + τ))))^(-1/τ)*α
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sto[i, 2] = (1 - u^(-τ) + u^(-τ)*v^(-(τ/(1 + τ))))^(-1/τ)
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end
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return sto
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end
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#resizing tests
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#taking the derivative of claytonsample! with respect to τ only
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df1 = ForwardDiff.derivative(τ -> claytonsample!(stod, τ, 0.0), 0.3)
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@test size(randmat) == size(df1)
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#calculating the jacobian of claytonsample! with respect to τ and α
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df2 = ForwardDiff.jacobian(x -> claytonsample!(stod, x[1], x[2]), [0.3; 0.0]) #should give a 15x2 array,
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#because ForwardDiff flattens the output of jacobian, see: https://juliadiff.org/ForwardDiff.jl/stable/user/api/#ForwardDiff.jacobian
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@test (length(randmat), 2) == size(df2)
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@test df1[1:5,2] ≈ df2[6:10,1]

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