Disable Enzyme on Julia v1.12 and enable v1.11 testing

.github/workflows/CI.yml

@@ -31,6 +31,7 @@ jobs:
           - SDE3
         version:
           - '1'
+          - '1.11'
           - 'lts'
     steps:
       - uses: actions/checkout@v4

src/SciMLSensitivity.jl

@@ -45,7 +45,9 @@ using OrdinaryDiffEqCore: OrdinaryDiffEqCore, BrownFullBasicInit, DefaultInit,
 # AD Backends
 using ChainRulesCore: unthunk, @thunk, NoTangent, @not_implemented, Tangent, ZeroTangent,
                       AbstractThunk, AbstractTangent
-using Enzyme: Enzyme
+@static if VERSION < v"1.12"
+    using Enzyme: Enzyme
+end
 using FiniteDiff: FiniteDiff
 using ForwardDiff: ForwardDiff
 using Tracker: Tracker, TrackedArray

src/concrete_solve.jl

@@ -1285,105 +1285,107 @@ function DiffEqBase._concrete_solve_adjoint(
         p)
 end
 
-function DiffEqBase._concrete_solve_adjoint(
-        prob::Union{SciMLBase.AbstractDiscreteProblem,
-            SciMLBase.AbstractODEProblem,
-            SciMLBase.AbstractDAEProblem,
-            SciMLBase.AbstractDDEProblem,
-            SciMLBase.AbstractSDEProblem,
-            SciMLBase.AbstractSDDEProblem,
-            SciMLBase.AbstractRODEProblem
-        },
-        alg, sensealg::EnzymeAdjoint,
-        u0, p, originator::SciMLBase.ADOriginator,
-        args...; kwargs...)
-    kwargs_filtered = NamedTuple(filter(x -> x[1] != :sensealg, kwargs))
-    du0 = Enzyme.make_zero(u0)
-    dp = Enzyme.make_zero(p)
-    mode = sensealg.mode
-
-    # Force no FunctionWrappers for Enzyme
-    _prob = remake(prob, f =  f = ODEFunction{isinplace(prob), SciMLBase.FullSpecialize}(unwrapped_f(prob.f)) )
-
-    diff_func = (u0,
-        p) -> solve(_prob, alg, args...; u0 = u0, p = p,
-        sensealg = SensitivityADPassThrough(),
-        kwargs_filtered...)
-
-    splitmode = if mode isa Enzyme.ForwardMode
-        error("EnzymeAdjoint currently only allows mode=Reverse. File an issue if this is necessary.")
-    elseif mode === nothing || mode isa Enzyme.ReverseMode
-        Enzyme.set_runtime_activity(Enzyme.ReverseSplitWithPrimal)
-    end
+@static if VERSION < v"1.12"
+    function DiffEqBase._concrete_solve_adjoint(
+            prob::Union{SciMLBase.AbstractDiscreteProblem,
+                SciMLBase.AbstractODEProblem,
+                SciMLBase.AbstractDAEProblem,
+                SciMLBase.AbstractDDEProblem,
+                SciMLBase.AbstractSDEProblem,
+                SciMLBase.AbstractSDDEProblem,
+                SciMLBase.AbstractRODEProblem
+            },
+            alg, sensealg::EnzymeAdjoint,
+            u0, p, originator::SciMLBase.ADOriginator,
+            args...; kwargs...)
+        kwargs_filtered = NamedTuple(filter(x -> x[1] != :sensealg, kwargs))
+        du0 = Enzyme.make_zero(u0)
+        dp = Enzyme.make_zero(p)
+        mode = sensealg.mode
 
-    forward,
-    reverse = Enzyme.autodiff_thunk(
-        splitmode, Enzyme.Const{typeof(diff_func)}, Enzyme.Duplicated,
-        Enzyme.Duplicated{typeof(u0)}, Enzyme.Duplicated{typeof(p)})
-    tape, result,
-    shadow_result = forward(
-        Enzyme.Const(diff_func), Enzyme.Duplicated(copy(u0), du0), Enzyme.Duplicated(copy(p), dp))
-
-    function enzyme_sensitivity_backpass(Δ)
-        if (Δ isa AbstractArray{<:AbstractArray} || Δ isa AbstractVectorOfArray)
-            for (x, y) in zip(shadow_result.u, Δ.u)
-                x .= y
-            end
-        else
-            error("typeof(Δ) = $(typeof(Δ)) is not currently handled in EnzymeAdjoint. Please open an issue with an MWE to add support")
+        # Force no FunctionWrappers for Enzyme
+        _prob = remake(prob, f =  f = ODEFunction{isinplace(prob), SciMLBase.FullSpecialize}(unwrapped_f(prob.f)) )
+
+        diff_func = (u0,
+            p) -> solve(_prob, alg, args...; u0 = u0, p = p,
+            sensealg = SensitivityADPassThrough(),
+            kwargs_filtered...)
+
+        splitmode = if mode isa Enzyme.ForwardMode
+            error("EnzymeAdjoint currently only allows mode=Reverse. File an issue if this is necessary.")
+        elseif mode === nothing || mode isa Enzyme.ReverseMode
+            Enzyme.set_runtime_activity(Enzyme.ReverseSplitWithPrimal)
         end
-        reverse(Enzyme.Const(diff_func), Enzyme.Duplicated(u0, du0), Enzyme.Duplicated(p, dp), tape)
-        if originator isa SciMLBase.TrackerOriginator ||
-           originator isa SciMLBase.ReverseDiffOriginator
-            (NoTangent(), NoTangent(), du0, dp, NoTangent(),
-                ntuple(_ -> NoTangent(), length(args))...)
-        else
-            (NoTangent(), NoTangent(), NoTangent(), du0, dp, NoTangent(),
-                ntuple(_ -> NoTangent(), length(args))...)
+
+        forward,
+        reverse = Enzyme.autodiff_thunk(
+            splitmode, Enzyme.Const{typeof(diff_func)}, Enzyme.Duplicated,
+            Enzyme.Duplicated{typeof(u0)}, Enzyme.Duplicated{typeof(p)})
+        tape, result,
+        shadow_result = forward(
+            Enzyme.Const(diff_func), Enzyme.Duplicated(copy(u0), du0), Enzyme.Duplicated(copy(p), dp))
+
+        function enzyme_sensitivity_backpass(Δ)
+            if (Δ isa AbstractArray{<:AbstractArray} || Δ isa AbstractVectorOfArray)
+                for (x, y) in zip(shadow_result.u, Δ.u)
+                    x .= y
+                end
+            else
+                error("typeof(Δ) = $(typeof(Δ)) is not currently handled in EnzymeAdjoint. Please open an issue with an MWE to add support")
+            end
+            reverse(Enzyme.Const(diff_func), Enzyme.Duplicated(u0, du0), Enzyme.Duplicated(p, dp), tape)
+            if originator isa SciMLBase.TrackerOriginator ||
+               originator isa SciMLBase.ReverseDiffOriginator
+                (NoTangent(), NoTangent(), du0, dp, NoTangent(),
+                    ntuple(_ -> NoTangent(), length(args))...)
+            else
+                (NoTangent(), NoTangent(), NoTangent(), du0, dp, NoTangent(),
+                    ntuple(_ -> NoTangent(), length(args))...)
+            end
         end
+        result, enzyme_sensitivity_backpass
     end
-    result, enzyme_sensitivity_backpass
-end
 
-# NOTE: This is needed to prevent a method ambiguity error
-function DiffEqBase._concrete_solve_adjoint(
-        prob::AbstractNonlinearProblem, alg, sensealg::EnzymeAdjoint,
-        u0, p, originator::SciMLBase.ADOriginator,
-        args...; kwargs...)
-    kwargs_filtered = NamedTuple(filter(x -> x[1] != :sensealg, kwargs))
-
-    du0 = make_zero(u0)
-    dp = make_zero(p)
-    mode = sensealg.mode
+    # NOTE: This is needed to prevent a method ambiguity error
+    function DiffEqBase._concrete_solve_adjoint(
+            prob::AbstractNonlinearProblem, alg, sensealg::EnzymeAdjoint,
+            u0, p, originator::SciMLBase.ADOriginator,
+            args...; kwargs...)
+        kwargs_filtered = NamedTuple(filter(x -> x[1] != :sensealg, kwargs))
 
-    f = (u0,
-        p) -> solve(prob, alg, args...; u0 = u0, p = p,
-        sensealg = SensitivityADPassThrough(),
-        kwargs_filtered...)
+        du0 = make_zero(u0)
+        dp = make_zero(p)
+        mode = sensealg.mode
 
-    splitmode = if mode isa Forward
-        error("EnzymeAdjoint currently only allows mode=Reverse. File an issue if this is necessary.")
-    elseif mode === nothing || mode === Reverse
-        ReverseSplitWithPrimal
-    end
+        f = (u0,
+            p) -> solve(prob, alg, args...; u0 = u0, p = p,
+            sensealg = SensitivityADPassThrough(),
+            kwargs_filtered...)
 
-    forward,
-    reverse = autodiff_thunk(splitmode, Const{typeof(f)}, Duplicated,
-        Duplicated{typeof(u0)}, Duplicated{typeof(p)})
-    tape, result, shadow_result = forward(Const(f), Duplicated(u0, du0), Duplicated(p, dp))
+        splitmode = if mode isa Forward
+            error("EnzymeAdjoint currently only allows mode=Reverse. File an issue if this is necessary.")
+        elseif mode === nothing || mode === Reverse
+            ReverseSplitWithPrimal
+        end
 
-    function enzyme_sensitivity_backpass(Δ)
-        reverse(Const(f), Duplicated(u0, du0), Duplicated(p, dp), Δ, tape)
-        if originator isa SciMLBase.TrackerOriginator ||
-           originator isa SciMLBase.ReverseDiffOriginator
-            (NoTangent(), NoTangent(), du0, dp, NoTangent(),
-                ntuple(_ -> NoTangent(), length(args))...)
-        else
-            (NoTangent(), NoTangent(), NoTangent(), du0, dp, NoTangent(),
-                ntuple(_ -> NoTangent(), length(args))...)
+        forward,
+        reverse = autodiff_thunk(splitmode, Const{typeof(f)}, Duplicated,
+            Duplicated{typeof(u0)}, Duplicated{typeof(p)})
+        tape, result, shadow_result = forward(Const(f), Duplicated(u0, du0), Duplicated(p, dp))
+
+        function enzyme_sensitivity_backpass(Δ)
+            reverse(Const(f), Duplicated(u0, du0), Duplicated(p, dp), Δ, tape)
+            if originator isa SciMLBase.TrackerOriginator ||
+               originator isa SciMLBase.ReverseDiffOriginator
+                (NoTangent(), NoTangent(), du0, dp, NoTangent(),
+                    ntuple(_ -> NoTangent(), length(args))...)
+            else
+                (NoTangent(), NoTangent(), NoTangent(), du0, dp, NoTangent(),
+                    ntuple(_ -> NoTangent(), length(args))...)
+            end
         end
+        sol, enzyme_sensitivity_backpass
     end
-    sol, enzyme_sensitivity_backpass
 end
 
 const ENZYME_TRACKED_REAL_ERROR_MESSAGE = """

src/sensitivity_algorithms.jl

@@ -801,10 +801,21 @@ EnzymeAdjoint(mode = nothing)
 
 Currently fails on almost every solver.
 """
-struct EnzymeAdjoint{M <: Union{Nothing, Enzyme.EnzymeCore.Mode}} <:
-       AbstractAdjointSensitivityAlgorithm{nothing, true, nothing}
-    mode::M
-    EnzymeAdjoint(mode = nothing) = new{typeof(mode)}(mode)
+@static if VERSION < v"1.12"
+    struct EnzymeAdjoint{M <: Union{Nothing, Enzyme.EnzymeCore.Mode}} <:
+           AbstractAdjointSensitivityAlgorithm{nothing, true, nothing}
+        mode::M
+        EnzymeAdjoint(mode = nothing) = new{typeof(mode)}(mode)
+    end
+else
+    # Dummy type for Julia 1.12+ - Enzyme is not loaded on this version
+    struct EnzymeAdjoint{M <: Nothing} <:
+           AbstractAdjointSensitivityAlgorithm{nothing, true, nothing}
+        mode::M
+        function EnzymeAdjoint(mode = nothing)
+            error("EnzymeAdjoint is not supported on Julia 1.12+. Please use a different sensitivity algorithm.")
+        end
+    end
 end
 
 """

test/adjoint.jl

@@ -139,16 +139,18 @@ easy_res11 = adjoint_sensitivities(solb, Tsit5(), t = t, dgdu_discrete = dg,
     abstol = 1e-14,
     reltol = 1e-14,
     sensealg = InterpolatingAdjoint(autojacvec = SciMLSensitivity.ReverseDiffVJP(true)))
-_,
-easy_res12 = adjoint_sensitivities(solb, Tsit5(), t = t, dgdu_discrete = dg,
-    abstol = 1e-14,
-    reltol = 1e-14,
-    sensealg = InterpolatingAdjoint(autojacvec = SciMLSensitivity.EnzymeVJP()))
-_,
-easy_res13 = adjoint_sensitivities(solb, Tsit5(), t = t, dgdu_discrete = dg,
-    abstol = 1e-14,
-    reltol = 1e-14,
-    sensealg = QuadratureAdjoint(autojacvec = SciMLSensitivity.EnzymeVJP()))
+@static if VERSION < v"1.12"
+    _,
+    easy_res12 = adjoint_sensitivities(solb, Tsit5(), t = t, dgdu_discrete = dg,
+        abstol = 1e-14,
+        reltol = 1e-14,
+        sensealg = InterpolatingAdjoint(autojacvec = SciMLSensitivity.EnzymeVJP()))
+    _,
+    easy_res13 = adjoint_sensitivities(solb, Tsit5(), t = t, dgdu_discrete = dg,
+        abstol = 1e-14,
+        reltol = 1e-14,
+        sensealg = QuadratureAdjoint(autojacvec = SciMLSensitivity.EnzymeVJP()))
+end
 _,
 easy_res14 = adjoint_sensitivities(solb, Tsit5(), t = t, dgdu_discrete = dg,
     abstol = 1e-14,
@@ -179,11 +181,13 @@ easy_res143 = adjoint_sensitivities(solb, Tsit5(), t = t, dgdu_discrete = dg,
     abstol = 1e-14,
     reltol = 1e-14,
     sensealg = GaussAdjoint(autojacvec = ReverseDiffVJP(true)))
-_,
-easy_res144 = adjoint_sensitivities(solb, Tsit5(), t = t, dgdu_discrete = dg,
-    abstol = 1e-14,
-    reltol = 1e-14,
-    sensealg = GaussAdjoint(autojacvec = SciMLSensitivity.EnzymeVJP()))
+@static if VERSION < v"1.12"
+    _,
+    easy_res144 = adjoint_sensitivities(solb, Tsit5(), t = t, dgdu_discrete = dg,
+        abstol = 1e-14,
+        reltol = 1e-14,
+        sensealg = GaussAdjoint(autojacvec = SciMLSensitivity.EnzymeVJP()))
+end
 _,
 easy_res145 = adjoint_sensitivities(sol_nodense, Tsit5(), t = t, dgdu_discrete = dg,
     abstol = 1e-14,
@@ -212,11 +216,13 @@ easy_res143k = adjoint_sensitivities(solb, Tsit5(), t = t, dgdu_discrete = dg,
     abstol = 1e-14,
     reltol = 1e-14,
     sensealg = GaussKronrodAdjoint(autojacvec = ReverseDiffVJP(true)))
-_,
-easy_res144k = adjoint_sensitivities(solb, Tsit5(), t = t, dgdu_discrete = dg,
-    abstol = 1e-14,
-    reltol = 1e-14,
-    sensealg = GaussKronrodAdjoint(autojacvec = SciMLSensitivity.EnzymeVJP()))
+@static if VERSION < v"1.12"
+    _,
+    easy_res144k = adjoint_sensitivities(solb, Tsit5(), t = t, dgdu_discrete = dg,
+        abstol = 1e-14,
+        reltol = 1e-14,
+        sensealg = GaussKronrodAdjoint(autojacvec = SciMLSensitivity.EnzymeVJP()))
+end
 _,
 easy_res145k = adjoint_sensitivities(sol_nodense, Tsit5(), t = t, dgdu_discrete = dg,
     abstol = 1e-14,
@@ -1049,34 +1055,36 @@ function dynamics!(du, u, p, t)
     du[2] = -u[2] + tanh(p[3] * u[1] + p[4] * u[2])
 end
 
-function backsolve_grad(sol, lqr_params, checkpointing)
-    bwd_sol = solve(
-        ODEAdjointProblem(sol,
-            BacksolveAdjoint(autojacvec = EnzymeVJP(),
-                checkpointing = checkpointing),
+@static if VERSION < v"1.12"
+    function backsolve_grad(sol, lqr_params, checkpointing)
+        bwd_sol = solve(
+            ODEAdjointProblem(sol,
+                BacksolveAdjoint(autojacvec = EnzymeVJP(),
+                    checkpointing = checkpointing),
+                Tsit5(),
+                nothing, nothing, nothing, nothing, nothing,
+                (x, lqr_params, t) -> cost(x, lqr_params)),
             Tsit5(),
-            nothing, nothing, nothing, nothing, nothing,
-            (x, lqr_params, t) -> cost(x, lqr_params)),
-        Tsit5(),
+            dense = false,
+            save_everystep = false)
+
+        bwd_sol.u[end][1:(end - x_dim)]
+        #fwd_sol, bwd_sol
+    end
+
+    x0 = ones(x_dim)
+    fwd_sol = solve(ODEProblem(dynamics!, x0, (0, T), params),
+        Tsit5(), abstol = 1e-9, reltol = 1e-9,
+        u0 = x0,
+        p = params,
         dense = false,
-        save_everystep = false)
+        save_everystep = true)
 
-    bwd_sol.u[end][1:(end - x_dim)]
-    #fwd_sol, bwd_sol
-end
+    backsolve_results = backsolve_grad(fwd_sol, params, false)
+    backsolve_checkpointing_results = backsolve_grad(fwd_sol, params, true)
 
-x0 = ones(x_dim)
-fwd_sol = solve(ODEProblem(dynamics!, x0, (0, T), params),
-    Tsit5(), abstol = 1e-9, reltol = 1e-9,
-    u0 = x0,
-    p = params,
-    dense = false,
-    save_everystep = true)
-
-backsolve_results = backsolve_grad(fwd_sol, params, false)
-backsolve_checkpointing_results = backsolve_grad(fwd_sol, params, true)
-
-@test backsolve_results != backsolve_checkpointing_results
+    @test backsolve_results != backsolve_checkpointing_results
+end
 
 int_u0,
 int_p = adjoint_sensitivities(fwd_sol, Tsit5(),

test/autodiff_events.jl

@@ -1,5 +1,5 @@
 using SciMLSensitivity
-using OrdinaryDiffEq, Calculus, Test
+using OrdinaryDiffEq, OrdinaryDiffEqCore, Calculus, Test
 using Zygote
 
 function f(du, u, p, t)
@@ -56,11 +56,11 @@ g4 = Zygote.gradient(θ -> test_f2(θ, ReverseDiffAdjoint(), PIController(7 // 5
     p)
 g6 = Zygote.gradient(
     θ -> test_f2(θ, ForwardDiffSensitivity(),
-        OrdinaryDiffEq.PredictiveController(), TRBDF2()),
+        OrdinaryDiffEqCore.PredictiveController(), TRBDF2()),
     p)
 @test_broken g7 = Zygote.gradient(
     θ -> test_f2(θ, ReverseDiffAdjoint(),
-        OrdinaryDiffEq.PredictiveController(),
+        OrdinaryDiffEqCore.PredictiveController(),
         TRBDF2()),
     p)