Split MTK test into separate file for macro availability at parse time

Julia macros like @parameters must be available at parse time, but
conditional `using` inside `if` blocks only executes at runtime.
Split the MTK test into a separate file that's conditionally included.

Co-Authored-By: Chris Rackauckas <accounts@chrisrackauckas.com>
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

Author: ChrisRackauckas

test/gpu_kernel_de/stiff_ode/gpu_ode_modelingtoolkit_dae.jl

@@ -110,59 +110,9 @@ end
 
 # ModelingToolkit is an optional test dependency — skip this test if not available.
 # This avoids compat conflicts in the alldeps minimum-version resolution test.
-const HAS_MTK = Base.identify_package("ModelingToolkit") !== nothing
-
-if HAS_MTK
-    using ModelingToolkit
-    using ModelingToolkit: t_nounits as t, D_nounits as D
-    # NOTE: This test is currently broken because ModelingToolkit problems with initialization
-    # data contain MTKParameters which use Vector types that cannot be stored inline in CuArrays.
-    # This is a known limitation: GPU kernels require element types that are allocated inline.
-    # Once MTK supports GPU-compatible parameter storage, this test can be re-enabled.
-    @testset "MTK Pendulum DAE with initialization" begin
-        @parameters g = 9.81 L = 1.0
-        @variables px(t) py(t) [state_priority = 10] pλ(t)
-
-        eqs = [
-            D(D(px)) ~ pλ * px / L
-            D(D(py)) ~ pλ * py / L - g
-            px^2 + py^2 ~ L^2
-        ]
-
-        @mtkcompile pendulum = ODESystem(eqs, t, [px, py, pλ], [g, L])
-
-        mtk_prob = ODEProblem(
-            pendulum, [py => 0.99], (0.0, 1.0),
-            guesses = [pλ => 0.0, px => 0.1, D(px) => 0.0, D(py) => 0.0]
-        )
-
-        # Verify it has initialization data and a mass matrix
-        @test SciMLBase.has_initialization_data(mtk_prob.f)
-        @test mtk_prob.f.mass_matrix !== LinearAlgebra.I
-
-        # Reference solution with OrdinaryDiffEq
-        ref_sol = solve(mtk_prob, Rodas5P())
-        @test ref_sol.retcode == SciMLBase.ReturnCode.Success
-
-        # GPU ensemble solve - currently broken due to MTKParameters containing non-inline types
-        # Skip actual GPU solve test until MTK supports GPU-compatible parameters
-        if backend isa CPU
-            monteprob_mtk = EnsembleProblem(mtk_prob, safetycopy = false)
-            sol_mtk = solve(
-                monteprob_mtk, GPURodas5P(), EnsembleGPUKernel(backend),
-                trajectories = 2,
-                dt = 0.01,
-                adaptive = false
-            )
-            @test length(sol_mtk.u) == 2
-            @test !any(isnan, sol_mtk.u[1][end])
-
-            # GPU solution should be close to reference (fixed step so moderate tolerance)
-            @test norm(sol_mtk.u[1][end] - ref_sol.u[end]) < 1.0
-        else
-            @test_broken false # MTK DAE with initialization not yet supported on GPU
-        end
-    end
+# The MTK test is in a separate file because macros need to be available at parse time.
+if Base.identify_package("ModelingToolkit") !== nothing
+    include("gpu_ode_modelingtoolkit_dae_mtk.jl")
 else
     @info "ModelingToolkit not available, skipping MTK DAE test"
 end

test/gpu_kernel_de/stiff_ode/gpu_ode_modelingtoolkit_dae_mtk.jl

@@ -0,0 +1,51 @@
+using ModelingToolkit
+using ModelingToolkit: t_nounits as t, D_nounits as D
+
+# NOTE: This test is currently broken because ModelingToolkit problems with initialization
+# data contain MTKParameters which use Vector types that cannot be stored inline in CuArrays.
+# This is a known limitation: GPU kernels require element types that are allocated inline.
+# Once MTK supports GPU-compatible parameter storage, this test can be re-enabled.
+@testset "MTK Pendulum DAE with initialization" begin
+    @parameters g = 9.81 L = 1.0
+    @variables px(t) py(t) [state_priority = 10] pλ(t)
+
+    eqs = [
+        D(D(px)) ~ pλ * px / L
+        D(D(py)) ~ pλ * py / L - g
+        px^2 + py^2 ~ L^2
+    ]
+
+    @mtkcompile pendulum = ODESystem(eqs, t, [px, py, pλ], [g, L])
+
+    mtk_prob = ODEProblem(
+        pendulum, [py => 0.99], (0.0, 1.0),
+        guesses = [pλ => 0.0, px => 0.1, D(px) => 0.0, D(py) => 0.0]
+    )
+
+    # Verify it has initialization data and a mass matrix
+    @test SciMLBase.has_initialization_data(mtk_prob.f)
+    @test mtk_prob.f.mass_matrix !== LinearAlgebra.I
+
+    # Reference solution with OrdinaryDiffEq
+    ref_sol = solve(mtk_prob, Rodas5P())
+    @test ref_sol.retcode == SciMLBase.ReturnCode.Success
+
+    # GPU ensemble solve - currently broken due to MTKParameters containing non-inline types
+    # Skip actual GPU solve test until MTK supports GPU-compatible parameters
+    if backend isa CPU
+        monteprob_mtk = EnsembleProblem(mtk_prob, safetycopy = false)
+        sol_mtk = solve(
+            monteprob_mtk, GPURodas5P(), EnsembleGPUKernel(backend),
+            trajectories = 2,
+            dt = 0.01,
+            adaptive = false
+        )
+        @test length(sol_mtk.u) == 2
+        @test !any(isnan, sol_mtk.u[1][end])
+
+        # GPU solution should be close to reference (fixed step so moderate tolerance)
+        @test norm(sol_mtk.u[1][end] - ref_sol.u[end]) < 1.0
+    else
+        @test_broken false # MTK DAE with initialization not yet supported on GPU
+    end
+end