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Merged
ChrisRackauckas merged 1 commit into from
Jun 15, 2026
Merged

Migrate to SciMLTesting v1.2 (off ReTestItems) #70

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16 changes: 5 additions & 11 deletions Project.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -26,21 +26,18 @@ ADTypes = "1.9"
CommonSolve = "0.2.4"
ConcreteStructs = "0.2.3"
DifferentiationInterface = "0.6.2, 0.7"
ExplicitImports = "1.14.0"
FastClosures = "0.3.2"
FiniteDiff = "2.24.0"
ForwardDiff = "0.10.36, 1"
Hwloc = "3"
InteractiveUtils = "<0.0.1, 1"
LineSearches = "7.3.0"
LinearAlgebra = "1.10"
MaybeInplace = "0.1.4"
NonlinearProblemLibrary = "0.1.2"
PrecompileTools = "1.2.0"
ReTestItems = "1.28.0"
ReverseDiff = "1.15.3"
SafeTestsets = "0.1, 1"
SciMLBase = "2.54.0, 3.0"
SciMLJacobianOperators = "0.1"
SciMLTesting = "1"
StaticArraysCore = "1.4"
Test = "1.10"
Tracker = "0.2.35"
Expand All @@ -49,18 +46,15 @@ julia = "1.10"

[extras]
DifferentiationInterface = "a0c0ee7d-e4b9-4e03-894e-1c5f64a51d63"
ExplicitImports = "7d51a73a-1435-4ff3-83d9-f097790105c7"
FiniteDiff = "6a86dc24-6348-571c-b903-95158fe2bd41"
ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
Hwloc = "0e44f5e4-bd66-52a0-8798-143a42290a1d"
InteractiveUtils = "b77e0a4c-d291-57a0-90e8-8db25a27a240"
LineSearches = "d3d80556-e9d4-5f37-9878-2ab0fcc64255"
NonlinearProblemLibrary = "b7050fa9-e91f-4b37-bcee-a89a063da141"
ReTestItems = "817f1d60-ba6b-4fd5-9520-3cf149f6a823"
ReverseDiff = "37e2e3b7-166d-5795-8a7a-e32c996b4267"
SafeTestsets = "1bc83da4-3b8d-516f-aca4-4fe02f6d838f"
SciMLTesting = "09d9d899-5365-40a9-917a-5f67fddea283"
Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
Tracker = "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c"
Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"

[targets]
test = ["DifferentiationInterface", "ExplicitImports", "FiniteDiff", "ForwardDiff", "Hwloc", "InteractiveUtils", "LineSearches", "NonlinearProblemLibrary", "ReTestItems", "ReverseDiff", "Test", "Tracker", "Zygote"]
test = ["DifferentiationInterface", "FiniteDiff", "ForwardDiff", "LineSearches", "ReverseDiff", "SafeTestsets", "SciMLTesting", "Test", "Tracker", "Zygote"]
118 changes: 35 additions & 83 deletions test/custom_optimizer_tests.jl
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@@ -1,108 +1,60 @@
# Test based on https://julianlsolvers.github.io/LineSearches.jl/stable/examples/generated/customoptimizer.html
# Note: Enzyme tests are in a separate test group (test/enzyme/)
@testsetup module CustomOptimizer
using LinearAlgebra, SciMLBase, LineSearch, SciMLJacobianOperators

function gradient_descent(
prob, alg; g_atol::Real = 1.0e-5, maxiters::Int = 10000, autodiff = nothing
)
u = copy(prob.u0)
if SciMLBase.isinplace(prob)
fu = similar(u)
prob.f(fu, u, prob.p)
else
fu = prob.f(u, prob.p)
end

ls_cache = init(prob, alg, fu, u)
vjp_op = VecJacOperator(prob, fu, u; autodiff)

alphas = Float64[]
iter = 0
for _ in 1:maxiters
iter += 1
svjp_op = StatefulJacobianOperator(vjp_op, u, prob.p)
gs = svjp_op * fu .* 2
δu = -gs
# Note: Enzyme tests are in a separate test group (test/enzyme/), LineSearches.jl tests in test/linesearchesjl/
using LineSearch, Test

ls_sol = solve!(ls_cache, u, δu)

push!(alphas, ls_sol.step_size)
@. u = u + ls_sol.step_size * δu
gnorm = norm(gs)
module CustomOptimizer
using LinearAlgebra, SciMLBase, LineSearch, SciMLJacobianOperators

function gradient_descent(
prob, alg; g_atol::Real = 1.0e-5, maxiters::Int = 10000, autodiff = nothing
)
u = copy(prob.u0)
if SciMLBase.isinplace(prob)
fu = similar(u)
prob.f(fu, u, prob.p)
else
fu = prob.f(u, prob.p)
end

LineSearch.callback_into_cache!(ls_cache, fu)
ls_cache = init(prob, alg, fu, u)
vjp_op = VecJacOperator(prob, fu, u; autodiff)

gnorm < g_atol && break
end
alphas = Float64[]
iter = 0
for _ in 1:maxiters
iter += 1
svjp_op = StatefulJacobianOperator(vjp_op, u, prob.p)
gs = svjp_op * fu .* 2
δu = -gs

return fu, u, iter, alphas
end
ls_sol = solve!(ls_cache, u, δu)

export gradient_descent
push!(alphas, ls_sol.step_size)
@. u = u + ls_sol.step_size * δu
gnorm = norm(gs)

end
if SciMLBase.isinplace(prob)
fu = similar(u)
prob.f(fu, u, prob.p)
else
fu = prob.f(u, prob.p)
end

@testitem "LineSearches.jl: Custom Optimizer" tags = [:linesearchesjl] setup = [CustomOptimizer] begin
using LineSearches, SciMLBase
using ADTypes, Tracker, ForwardDiff, Zygote, ReverseDiff, FiniteDiff
LineSearch.callback_into_cache!(ls_cache, fu)

@testset "OOP Problem" begin
nlf(x, p) = [p[1] - x[1], 10.0 * (x[2] - x[1]^2)]
nlp = NonlinearProblem(nlf, [-1.0, 1.0], [1.0])

@testset for autodiff in (
AutoTracker(), AutoForwardDiff(), AutoZygote(),
AutoReverseDiff(), AutoFiniteDiff(),
)
@testset "method: $(nameof(typeof(method)))" for method in (
LineSearches.BackTracking(; order = 3),
StrongWolfe(),
HagerZhang(),
MoreThuente(),
)
linesearch = LineSearchesJL(; method, autodiff)
fu, u, iter, alphas = gradient_descent(nlp, linesearch; autodiff)

@test fu ≈ [0.0, 0.0] atol = 1.0e-2
@test u ≈ [1.0, 1.0] atol = 1.0e-2
@test !all(isone, alphas)
end
gnorm < g_atol && break
end
end

@testset "In-Place Problem" begin
nlf(dx, x, p) = (dx .= [p[1] - x[1], 10.0 * (x[2] - x[1]^2)])
nlp = NonlinearProblem(nlf, [-1.0, 1.0], [1.0])
return fu, u, iter, alphas
end

@testset for autodiff in (
AutoForwardDiff(), AutoReverseDiff(), AutoFiniteDiff(),
)
@testset "method: $(nameof(typeof(method)))" for method in (
LineSearches.BackTracking(; order = 3),
StrongWolfe(),
HagerZhang(),
MoreThuente(),
)
linesearch = LineSearchesJL(; method, autodiff)
fu, u, iter, alphas = gradient_descent(nlp, linesearch; autodiff)
export gradient_descent

@test fu ≈ [0.0, 0.0] atol = 1.0e-2
@test u ≈ [1.0, 1.0] atol = 1.0e-2
@test !all(isone, alphas)
end
end
end
end

@testitem "Native Line Search: Custom Optimizer" tags = [:core] setup = [CustomOptimizer] begin
using .CustomOptimizer

@testset "Native Line Search: Custom Optimizer" begin
using SciMLBase
using ADTypes, Tracker, ForwardDiff, Zygote, ReverseDiff, FiniteDiff

Expand Down
107 changes: 107 additions & 0 deletions test/linesearchesjl/custom_optimizer_tests.jl
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@@ -0,0 +1,107 @@
# LineSearches.jl extension: custom optimizer tests
# Based on https://julianlsolvers.github.io/LineSearches.jl/stable/examples/generated/customoptimizer.html
using LineSearch, Test

module CustomOptimizer
using LinearAlgebra, SciMLBase, LineSearch, SciMLJacobianOperators

function gradient_descent(
prob, alg; g_atol::Real = 1.0e-5, maxiters::Int = 10000, autodiff = nothing
)
u = copy(prob.u0)
if SciMLBase.isinplace(prob)
fu = similar(u)
prob.f(fu, u, prob.p)
else
fu = prob.f(u, prob.p)
end

ls_cache = init(prob, alg, fu, u)
vjp_op = VecJacOperator(prob, fu, u; autodiff)

alphas = Float64[]
iter = 0
for _ in 1:maxiters
iter += 1
svjp_op = StatefulJacobianOperator(vjp_op, u, prob.p)
gs = svjp_op * fu .* 2
δu = -gs

ls_sol = solve!(ls_cache, u, δu)

push!(alphas, ls_sol.step_size)
@. u = u + ls_sol.step_size * δu
gnorm = norm(gs)

if SciMLBase.isinplace(prob)
fu = similar(u)
prob.f(fu, u, prob.p)
else
fu = prob.f(u, prob.p)
end

LineSearch.callback_into_cache!(ls_cache, fu)

gnorm < g_atol && break
end

return fu, u, iter, alphas
end

export gradient_descent

end

using .CustomOptimizer

@testset "LineSearches.jl: Custom Optimizer" begin
using LineSearches, SciMLBase
using ADTypes, Tracker, ForwardDiff, Zygote, ReverseDiff, FiniteDiff

@testset "OOP Problem" begin
nlf(x, p) = [p[1] - x[1], 10.0 * (x[2] - x[1]^2)]
nlp = NonlinearProblem(nlf, [-1.0, 1.0], [1.0])

@testset for autodiff in (
AutoTracker(), AutoForwardDiff(), AutoZygote(),
AutoReverseDiff(), AutoFiniteDiff(),
)
@testset "method: $(nameof(typeof(method)))" for method in (
LineSearches.BackTracking(; order = 3),
StrongWolfe(),
HagerZhang(),
MoreThuente(),
)
linesearch = LineSearchesJL(; method, autodiff)
fu, u, iter, alphas = gradient_descent(nlp, linesearch; autodiff)

@test fu ≈ [0.0, 0.0] atol = 1.0e-2
@test u ≈ [1.0, 1.0] atol = 1.0e-2
@test !all(isone, alphas)
end
end
end

@testset "In-Place Problem" begin
nlf(dx, x, p) = (dx .= [p[1] - x[1], 10.0 * (x[2] - x[1]^2)])
nlp = NonlinearProblem(nlf, [-1.0, 1.0], [1.0])

@testset for autodiff in (
AutoForwardDiff(), AutoReverseDiff(), AutoFiniteDiff(),
)
@testset "method: $(nameof(typeof(method)))" for method in (
LineSearches.BackTracking(; order = 3),
StrongWolfe(),
HagerZhang(),
MoreThuente(),
)
linesearch = LineSearchesJL(; method, autodiff)
fu, u, iter, alphas = gradient_descent(nlp, linesearch; autodiff)

@test fu ≈ [0.0, 0.0] atol = 1.0e-2
@test u ≈ [1.0, 1.0] atol = 1.0e-2
@test !all(isone, alphas)
end
end
end
end
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