fix: fix get_updated_symbolic_problem

Respect `u0` and `p` passed as kwargs

Author: AayushSabharwal

src/systems/nonlinear/initializesystem.jl

@@ -772,18 +772,18 @@ function SciMLBase.late_binding_update_u0_p(
     return newu0, newp
 end
 
-function DiffEqBase.get_updated_symbolic_problem(sys::AbstractSystem, prob; kw...)
+function DiffEqBase.get_updated_symbolic_problem(
+        sys::AbstractSystem, prob; u0 = state_values(prob),
+        p = parameter_values(prob), kw...)
     supports_initialization(sys) || return prob
     initdata = prob.f.initialization_data
     initdata isa SciMLBase.OverrideInitData || return prob
     meta = initdata.metadata
     meta isa InitializationMetadata || return prob
     meta.get_updated_u0 === nothing && return prob
 
-    u0 = state_values(prob)
-    u0 === nothing && return prob
+    u0 === nothing && return remake(prob; p)
 
-    p = parameter_values(prob)
     t0 = is_time_dependent(prob) ? current_time(prob) : nothing
 
     if p isa MTKParameters
@@ -800,7 +800,7 @@ function DiffEqBase.get_updated_symbolic_problem(sys::AbstractSystem, prob; kw..
         T = StaticArrays.similar_type(u0)
     end
 
-    return remake(prob; u0 = T(meta.get_updated_u0(prob, initdata.initializeprob)))
+    return remake(prob; u0 = T(meta.get_updated_u0(prob, initdata.initializeprob)), p)
 end
 
 """

test/extensions/Project.toml

@@ -24,6 +24,7 @@ Pyomo = "0e8e1daf-01b5-4eba-a626-3897743a3816"
 SciMLSensitivity = "1ed8b502-d754-442c-8d5d-10ac956f44a1"
 SciMLStructures = "53ae85a6-f571-4167-b2af-e1d143709226"
 SimpleDiffEq = "05bca326-078c-5bf0-a5bf-ce7c7982d7fd"
+StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
 SymbolicIndexingInterface = "2efcf032-c050-4f8e-a9bb-153293bab1f5"
 Symbolics = "0c5d862f-8b57-4792-8d23-62f2024744c7"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"

test/extensions/ad.jl

@@ -8,6 +8,7 @@ using OrdinaryDiffEqNonlinearSolve
 using NonlinearSolve
 using SciMLSensitivity
 using ForwardDiff
+using StableRNGs
 using ChainRulesCore
 using ChainRulesCore: NoTangent
 using ChainRulesTestUtils: test_rrule, rand_tangent
@@ -136,3 +137,46 @@ end
         prob[sys.x]
     end
 end
+
+@testset "`p` provided to `solve` is respected" begin
+    @mtkmodel Linear begin
+        @variables begin
+            x(t) = 1.0, [description = "Prey"]
+        end
+        @parameters begin
+            α = 1.5
+        end
+        @equations begin
+            D(x) ~ -α * x
+        end
+    end
+
+    @mtkcompile linear = Linear()
+    problem = ODEProblem(linear, [], (0.0, 1.0))
+    solution = solve(problem, Tsit5(), saveat = 0.1)
+    rng = StableRNG(42)
+    data = (;
+        t = solution.t,
+        # [[y, x], :]
+        measurements = Array(solution)
+    )
+    data.measurements .+= 0.05 * randn(rng, size(data.measurements))
+
+    p0, repack, _ = SciMLStructures.canonicalize(SciMLStructures.Tunable(), problem.p)
+
+    objective = let repack = repack, problem = problem
+        (p, data) -> begin
+            pnew = repack(p)
+            sol = solve(problem, Tsit5(), p = pnew, saveat = data.t)
+            sum(abs2, sol .- data.measurements) / size(data.t, 1)
+        end
+    end
+
+    # Check 0.0031677344878386607 
+    @test_nowarn objective(p0, data)
+
+    fd = ForwardDiff.gradient(Base.Fix2(objective, data), p0)
+    zg = Zygote.gradient(Base.Fix2(objective, data), p0)
+
+    @test fd≈zg[1] atol=1e-6
+end