Run CheckInit after Initialization and Fix Core1 tests

Project.toml

@@ -77,7 +77,7 @@ LinearAlgebra = "1.10"
 LinearSolve = "2, 3"
 Lux = "1"
 Markdown = "1.10"
-ModelingToolkit = "9.74"
+ModelingToolkit = "9.77"
 ModelingToolkitStandardLibrary = "2"
 Mooncake = "0.4.52"
 NLsolve = "4.5.1"

src/SciMLSensitivity.jl

@@ -33,7 +33,7 @@ using SciMLBase: SciMLBase, AbstractOverloadingSensitivityAlgorithm,
                  AbstractNonlinearProblem, AbstractSensitivityAlgorithm,
                  AbstractDiffEqFunction, AbstractODEFunction, unwrapped_f, CallbackSet,
                  ContinuousCallback, DESolution, NonlinearFunction, NonlinearProblem,
-                 DiscreteCallback, LinearProblem, ODEFunction, ODEProblem,
+                 DiscreteCallback, LinearProblem, ODEFunction, ODEProblem, DAEProblem,
                  RODEFunction, RODEProblem, ReturnCode, SDEFunction,
                  SDEProblem, VectorContinuousCallback, deleteat!,
                  get_tmp_cache, has_adjoint, isinplace, reinit!, remake,

src/adjoint_common.jl

@@ -761,4 +761,4 @@ if !hasmethod(Zygote.adjoint,
         end
         sol.u, solu_adjoint
     end
-end
+end

src/concrete_solve.jl

@@ -408,7 +408,7 @@ function DiffEqBase._concrete_solve_adjoint(
     end
 
     # Remove callbacks, saveat, etc. from kwargs since it's handled separately
-    kwargs_fwd = NamedTuple{Base.diff_names(Base._nt_names(values(kwargs)), (:callback,))}(values(kwargs))
+    kwargs_fwd = NamedTuple{Base.diff_names(Base._nt_names(values(kwargs)), (:callback, :initializealg))}(values(kwargs))
 
     # Capture the callback_adj for the reverse pass and remove both callbacks
     kwargs_adj = NamedTuple{
@@ -454,10 +454,11 @@ function DiffEqBase._concrete_solve_adjoint(
         end
         igs = back(one(iy))[1] .- one(eltype(tunables))
 
-        igs, new_u0, new_p, SciMLBase.NoInit()
+        igs, new_u0, new_p, SciMLBase.CheckInit()
     else
         nothing, u0, p, initializealg
     end
+
     _prob = remake(_prob, u0 = new_u0, p = new_p)
 
     if sensealg isa BacksolveAdjoint
@@ -672,18 +673,20 @@ function DiffEqBase._concrete_solve_adjoint(
         else
             cb2 = cb
         end
-        if ArrayInterface.ismutable(eltype(state_values(sol)))
+
+        if prob isa Union{ODEProblem, DAEProblem}
             du0, dp = adjoint_sensitivities(sol, alg, args...; t = ts,
-                dgdu_discrete = df_iip,
-                sensealg = sensealg,
-                callback = cb2,
-                kwargs_init...)
+            dgdu_discrete = ArrayInterface.ismutable(eltype(state_values(sol))) ? df_iip : df_oop,
+            sensealg = sensealg,
+            callback = cb2,
+            initializealg = BrownFullBasicInit(),
+            kwargs_init...)
         else
             du0, dp = adjoint_sensitivities(sol, alg, args...; t = ts,
-                dgdu_discrete = df_oop,
-                sensealg = sensealg,
-                callback = cb2,
-                kwargs_init...)
+            dgdu_discrete = ArrayInterface.ismutable(eltype(state_values(sol))) ? df_iip : df_oop,
+            sensealg = sensealg,
+            callback = cb2,
+            kwargs_init...)
         end
 
         du0 = reshape(du0, size(u0))
@@ -1581,6 +1584,8 @@ function DiffEqBase._concrete_solve_adjoint(
             Array(ybar)
         elseif eltype(ybar) <: AbstractArray
             Array(VectorOfArray(ybar))
+        elseif ybar isa Tangent
+            Array(VectorOfArray(ybar.u))
         else
             ybar
         end
@@ -1769,7 +1774,8 @@ function DiffEqBase._concrete_solve_adjoint(
                 @. _out[_save_idxs] = Δ.u[_save_idxs]
             end
         end
-        dp = adjoint_sensitivities(sol, alg; sensealg = sensealg, dgdu = df, initializealg = BrownFullBasicInit())
+
+        dp = adjoint_sensitivities(sol, alg; sensealg = sensealg, dgdu = df)
 
         dp, Δtunables = if Δ isa AbstractArray || Δ isa Number
             # if Δ isa AbstractArray, the gradients correspond to `u`

test/desauty_dae_mwe.jl

@@ -36,7 +36,7 @@ desauty_model = create_model()
 sys = structural_simplify(desauty_model)
 
 
-prob = ODEProblem(sys, [], (0.0, 0.1), guesses = [sys.resistor1.v => 1.])
+prob = ODEProblem(sys, [sys.resistor1.v => 1.], (0.0, 0.1))
 iprob = prob.f.initialization_data.initializeprob
 isys = iprob.f.sys
 

test/mtk.jl

@@ -70,34 +70,34 @@ tspan = (0.0, 100.0)
 # and with the initialization corrected to satisfy the algebraic equation
 prob_incorrectu0 = ODEProblem(sys, u0_incorrect, tspan, p, jac = true, guesses = [w2 => 0.0])
 mtkparams_incorrectu0 = SciMLSensitivity.parameter_values(prob_incorrectu0)
+test_sol = solve(prob_incorrectu0, Rodas5P(), abstol = 1e-6, reltol = 1e-3)
 
 u0_timedep = [D(x) => 2.0,
     x => 1.0,
     y => t,
-    z => 0.0,
-    w2 => 0.0,]
+    z => 0.0]
 # this ensures that `y => t` is not applied in the adjoint equation
 # If the MTK init is called for the reverse, then `y0` in the backwards
 # pass will be extremely far off and cause an incorrect gradient
 prob_timedepu0 = ODEProblem(sys, u0_timedep, tspan, p, jac = true, guesses = [w2 => 0.0])
 mtkparams_timedepu0 = SciMLSensitivity.parameter_values(prob_incorrectu0)
+test_sol = solve(prob_timedepu0, Rodas5P(), abstol = 1e-6, reltol = 1e-3)
 
 u0_correct = [D(x) => 2.0,
     x => 1.0,
     y => 0.0,
-    z => 0.0,
-    w2 => -1.0,]
+    z => 0.0,]
 prob_correctu0 = ODEProblem(sys, u0_correct, tspan, p, jac = true, guesses = [w2 => -1.0])
 mtkparams_correctu0 = SciMLSensitivity.parameter_values(prob_correctu0)
-prob_correctu0.u0[5] = -1.0
-
+test_sol = solve(prob_correctu0, Rodas5P(), abstol = 1e-6, reltol = 1e-3)
 u0_overdetermined = [D(x) => 2.0,
     x => 1.0,
     y => 0.0,
     z => 0.0,
     w2 => -1.0,]
 prob_overdetermined = ODEProblem(sys, u0_overdetermined, tspan, p, jac = true)
 mtkparams_overdetermined = SciMLSensitivity.parameter_values(prob_overdetermined)
+test_sol = solve(prob_overdetermined, Rodas5P(), abstol = 1e-6, reltol = 1e-3)
 
 sensealg = GaussAdjoint(; autojacvec = SciMLSensitivity.ZygoteVJP())
 
@@ -115,25 +115,26 @@ setups = [
           (prob_correctu0, mtkparams_correctu0, BrownFullBasicInit()),
           (prob_correctu0, mtkparams_correctu0, OrdinaryDiffEqCore.DefaultInit()),
 
-          (prob_correctu0, mtkparams_correctu0, NoInit()), 
+        (prob_correctu0, mtkparams_correctu0, NoInit()),
           (prob_correctu0, mtkparams_correctu0, nothing),
 
           (prob_overdetermined, mtkparams_overdetermined, BrownFullBasicInit()),
           (prob_overdetermined, mtkparams_overdetermined, OrdinaryDiffEq.OrdinaryDiffEqCore.DefaultInit()),
 
           (prob_overdetermined, mtkparams_overdetermined, NoInit()),
           (prob_overdetermined, mtkparams_overdetermined, nothing),
-]
+];
 
 grads = map(setups) do setup
     prob, ps, init = setup
     @show init
     u0 = prob.u0
     Zygote.gradient(u0, ps) do u0,p
+        new_prob = remake(prob, u0 = u0, p = p)
         if init === nothing
-            new_sol = solve(prob, Rodas5P(); u0 = u0, p = ps, sensealg, abstol = 1e-6, reltol = 1e-3)
+            new_sol = solve(new_prob, Rodas5P(); sensealg, abstol = 1e-6, reltol = 1e-3)
         else
-            new_sol = solve(prob, Rodas5P(); u0 = u0, p = ps, initializealg = init, sensealg, abstol = 1e-6, reltol = 1e-3)
+            new_sol = solve(new_prob, Rodas5P(); initializealg = init, sensealg, abstol = 1e-6, reltol = 1e-3)
         end
         gt = Zygote.ChainRules.ChainRulesCore.ignore_derivatives() do
             @test new_sol.retcode == SciMLBase.ReturnCode.Success
@@ -148,5 +149,5 @@ end
 
 u0grads = getindex.(grads,1)
 pgrads = getproperty.(getindex.(grads, 2), (:tunable,))
-@test all(x ≈ u0grads[1] for x in grads)
-@test all(x ≈ pgrads[1] for x in grads)
+@test all(x ≈ u0grads[1] for x in u0grads)
+@test all(x ≈ pgrads[1] for x in pgrads)