Use real Magnus integrators in regression test for #3232

Author: Harsh Singh

lib/OrdinaryDiffEqDifferentiation/src/alg_utils.jl

@@ -72,8 +72,6 @@ Base.@pure function determine_chunksize(u, CS)
     end
 end
 
-
-
 function DiffEqBase.prepare_alg(
         alg::Union{
             OrdinaryDiffEqAdaptiveImplicitAlgorithm{

lib/OrdinaryDiffEqDifferentiation/test/differentiation_traits_tests.jl

@@ -45,29 +45,24 @@ sol = solve(prob2, Rosenbrock23(autodiff = AutoFiniteDiff()))
 @test ≈(good_sol[:, end], sol[:, end], rtol = 1.0e-2)
 
 # Regression test for issue #3232:
-# OrdinaryDiffEqLinearExponentialAlgorithm subtypes (MagnusGL6, etc.)
-# have no `autodiff` field; _alg_autodiff and prepare_alg must not crash.
-using OrdinaryDiffEqDifferentiation: _alg_autodiff
-using OrdinaryDiffEqCore: OrdinaryDiffEqLinearExponentialAlgorithm
-using DiffEqBase: prepare_alg
-
-struct MockMagnusAlg <: OrdinaryDiffEqLinearExponentialAlgorithm
-    krylov::Bool
-    m::Int
-    iop::Int
-end
-
-@testset "LinearExponentialAlgorithm autodiff traits (issue #3232)" begin
-    mock = MockMagnusAlg(false, 30, 0)
-
-    # _alg_autodiff must return Val{false}() instead of accessing alg.autodiff
-    @test _alg_autodiff(mock) == Val{false}()
-
-    # prepare_alg must return the algorithm unchanged (no AD preparation needed)
-    u0 = ones(2)
-    mock_prob = ODEProblem((du, u, p, t) -> du .= 0, u0, (0.0, 1.0))
-    @test prepare_alg(mock, u0, nothing, mock_prob) === mock
-
-    # forwarddiffs_model must return false
-    @test SciMLBase.forwarddiffs_model(mock) == false
+# MagnusGL6 (and all OrdinaryDiffEqLinearExponentialAlgorithm subtypes)
+# have no `autodiff` field. When OrdinaryDiffEqDifferentiation is loaded,
+# _alg_autodiff must not crash by trying to access alg.autodiff.
+using OrdinaryDiffEqLinear
+using SciMLOperators: MatrixOperator
+
+@testset "MagnusGL6 solve with Differentiation loaded (issue #3232)" begin
+    function update_func!(A, u, p, t)
+        A[1, 1] = cos(t)
+        A[2, 1] = sin(t)
+        A[1, 2] = -sin(t)
+        A[2, 2] = cos(t)
+    end
+    A = MatrixOperator(ones(2, 2), update_func! = update_func!)
+    prob = ODEProblem(A, ones(2), (1.0, 6.0))
+
+    # This would crash with FieldError before the fix
+    sol = solve(prob, MagnusGL6(), dt = 1 / 10)
+    @test sol.retcode == ReturnCode.Success
+    @test length(sol.t) > 1
 end

lib/OrdinaryDiffEqLinear/test/linear_method_tests.jl

@@ -267,3 +267,21 @@ test_setup = Dict(:alg => Vern9(), :reltol => 1.0e-14, :abstol => 1.0e-14)
 
 sim = analyticless_test_convergence(dts, prob, CayleyEuler(), test_setup)
 @test sim.𝒪est[:l2] ≈ 1 atol = 0.2
+
+# Regression test for https://github.com/SciML/OrdinaryDiffEq.jl/issues/3232
+# Magnus/Linear integrators must not FieldError when OrdinaryDiffEqDifferentiation
+# is loaded (which happens transitively via DifferentialEquations.jl).
+@testset "Regression #3232: non-autonomous Magnus solve does not FieldError" begin
+    function update_func_3232!(A, u, p, t)
+        A[1, 1] = cos(t)
+        A[2, 1] = sin(t)
+        A[1, 2] = -sin(t)
+        A[2, 2] = cos(t)
+    end
+    A_3232 = MatrixOperator(ones(2, 2), update_func! = update_func_3232!)
+    prob_3232 = ODEProblem(A_3232, ones(2), (1.0, 6.0))
+    for alg in (MagnusGL6(), MagnusGL4(), MagnusGL8(), LieEuler(), CG2(), CG3(), CG4a())
+        sol = solve(prob_3232, alg, dt = 1 / 10)
+        @test sol.retcode == ReturnCode.Success
+    end
+end