Simplify GTPSA tests - compare GTPSA vs ForwardDiff only

GTPSA is itself an AD engine, so these tests are regression tests
comparing GTPSA jacobian/hessian results against ForwardDiff as
a reference implementation. No need for multi-backend testing.

Author: claude

test/downstream/gtpsa.jl

@@ -1,50 +1,10 @@
 using OrdinaryDiffEq, GTPSA, Test
-using SciMLSensitivity  # Required for reverse-mode AD
 using DifferentiationInterface
-using ADTypes: AutoForwardDiff, AutoMooncake
-
-# Load backends for all versions (required for DifferentiationInterface extensions)
+using ADTypes: AutoForwardDiff
 using ForwardDiff
-using Mooncake
-
-# Version-dependent imports
-if VERSION <= v"1.11"
-    using Zygote
-    using ADTypes: AutoZygote
-end
-if VERSION <= v"1.11"
-    using Enzyme
-    using ADTypes: AutoEnzyme
-end
-
-# Define backends based on Julia version
-# ForwardDiff: All versions
-# Mooncake: All versions
-# Zygote: Julia <= 1.11
-# Enzyme: Julia <= 1.11
-function get_test_backends()
-    backends = Pair{String, Any}[]
-    # ForwardDiff on all versions
-    push!(backends, "ForwardDiff" => AutoForwardDiff())
-    # Mooncake on all versions
-    push!(backends, "Mooncake" => AutoMooncake(; config = nothing))
-    # Zygote only on Julia <= 1.11
-    if VERSION <= v"1.11"
-        push!(backends, "Zygote" => AutoZygote())
-    end
-    # Enzyme only on Julia <= 1.11
-    if VERSION <= v"1.11"
-        push!(backends, "Enzyme" => AutoEnzyme())
-    end
-    return backends
-end
 
-backends = get_test_backends()
-# Note: All backends except ForwardDiff are excluded due to issues with GTPSA differentiation
-# Mooncake: issues with GTPSA jacobian/hessian compilation
-# Zygote: Jacobian test fails, Hessian tests error
-# Enzyme: All tests error (missing rules for GTPSA types)
-backends_for_gtpsa = filter(b -> b[1] == "ForwardDiff", backends)
+# GTPSA is itself an AD engine - these tests compare GTPSA jacobian/hessian
+# results against ForwardDiff as a reference implementation
 
 # ODEProblem 1 =======================
 
@@ -74,28 +34,19 @@ function sol_end_problem1(t)
 end
 
 @testset "GTPSA Problem 1 Jacobian tests" begin
-    for (name, backend) in backends_for_gtpsa
-        @testset "Jacobian comparison with $name" begin
-            J_AD = DifferentiationInterface.jacobian(sol_end_problem1, backend, [x..., p...])
-            @test J_AD ≈ GTPSA.jacobian(sol_GTPSA.u[end], include_params = true)
-        end
-    end
+    J_AD = DifferentiationInterface.jacobian(sol_end_problem1, AutoForwardDiff(), [x..., p...])
+    @test J_AD ≈ GTPSA.jacobian(sol_GTPSA.u[end], include_params = true)
 end
 
-# Compare Hessians against AD backends using DifferentiationInterface
 @testset "GTPSA Problem 1 Hessian tests" begin
-    for (name, backend) in backends_for_gtpsa
-        @testset "Hessian comparison with $name" begin
-            for i in 1:3
-                function sol_end_i_problem1(t)
-                    prob = ODEProblem(f!, t[1:3], (0.0, 1.0), t[4:6])
-                    sol = solve(prob, Tsit5(), reltol = 1.0e-16, abstol = 1.0e-16)
-                    return sol.u[end][i]
-                end
-                Hi_AD = DifferentiationInterface.hessian(sol_end_i_problem1, backend, [x..., p...])
-                @test Hi_AD ≈ GTPSA.hessian(sol_GTPSA.u[end][i], include_params = true)
-            end
+    for i in 1:3
+        function sol_end_i_problem1(t)
+            prob = ODEProblem(f!, t[1:3], (0.0, 1.0), t[4:6])
+            sol = solve(prob, Tsit5(), reltol = 1.0e-16, abstol = 1.0e-16)
+            return sol.u[end][i]
         end
+        Hi_AD = DifferentiationInterface.hessian(sol_end_i_problem1, AutoForwardDiff(), [x..., p...])
+        @test Hi_AD ≈ GTPSA.hessian(sol_GTPSA.u[end][i], include_params = true)
     end
 end
 
@@ -127,27 +78,18 @@ function sol_end_problem2(t)
 end
 
 @testset "GTPSA Problem 2 Jacobian tests" begin
-    for (name, backend) in backends_for_gtpsa
-        @testset "Jacobian comparison with $name" begin
-            J_AD = DifferentiationInterface.jacobian(sol_end_problem2, backend, zeros(6))
-            @test J_AD ≈ GTPSA.jacobian(sol_GTPSA2.u[end], include_params = true)
-        end
-    end
+    J_AD = DifferentiationInterface.jacobian(sol_end_problem2, AutoForwardDiff(), zeros(6))
+    @test J_AD ≈ GTPSA.jacobian(sol_GTPSA2.u[end], include_params = true)
 end
 
-# Compare Hessians against AD backends using DifferentiationInterface
 @testset "GTPSA Problem 2 Hessian tests" begin
-    for (name, backend) in backends_for_gtpsa
-        @testset "Hessian comparison with $name" begin
-            for i in 1:6
-                function sol_end_i_problem2(t)
-                    prob = DynamicalODEProblem(pdot!, qdot!, t[1:3], t[4:6], (0.0, 25.0))
-                    sol = solve(prob, Yoshida6(), dt = 1.0, reltol = 1.0e-16, abstol = 1.0e-16)
-                    return sol.u[end][i]
-                end
-                Hi_AD = DifferentiationInterface.hessian(sol_end_i_problem2, backend, zeros(6))
-                @test Hi_AD ≈ GTPSA.hessian(sol_GTPSA2.u[end][i], include_params = true)
-            end
+    for i in 1:6
+        function sol_end_i_problem2(t)
+            prob = DynamicalODEProblem(pdot!, qdot!, t[1:3], t[4:6], (0.0, 25.0))
+            sol = solve(prob, Yoshida6(), dt = 1.0, reltol = 1.0e-16, abstol = 1.0e-16)
+            return sol.u[end][i]
         end
+        Hi_AD = DifferentiationInterface.hessian(sol_end_i_problem2, AutoForwardDiff(), zeros(6))
+        @test Hi_AD ≈ GTPSA.hessian(sol_GTPSA2.u[end][i], include_params = true)
     end
 end