Turn off DI strictness

lib/NonlinearSolveBase/src/jacobian.jl

@@ -52,9 +52,9 @@ function construct_jacobian_cache(
         end
         autodiff = construct_concrete_adtype(f, autodiff)
         di_extras = if SciMLBase.isinplace(f)
-            DI.prepare_jacobian(f, fu_cache, autodiff, u, Constant(p))
+            DI.prepare_jacobian(f, fu_cache, autodiff, u, Constant(p), strict = Val(false))
         else
-            DI.prepare_jacobian(f, autodiff, u, Constant(p))
+            DI.prepare_jacobian(f, autodiff, u, Constant(p), strict = Val(false))
         end
     else
         di_extras = nothing

lib/SciMLJacobianOperators/src/SciMLJacobianOperators.jl

@@ -307,14 +307,14 @@ function prepare_vjp(::Val{false}, prob::AbstractNonlinearProblem,
         @assert DI.check_inplace(autodiff) "Backend: $(autodiff) doesn't support in-place \
                                             problems."
         fu_cache = copy(fu)
-        di_extras = DI.prepare_pullback(f, fu_cache, autodiff, u, (fu,), Constant(prob.p))
+        di_extras = DI.prepare_pullback(f, fu_cache, autodiff, u, (fu,), Constant(prob.p), strict=Val(false))
         return @closure (vJ, v, u, p) -> begin
             DI.pullback!(f, fu_cache, (reshape(vJ, size(u)),), di_extras, autodiff,
                 u, (reshape(v, size(fu_cache)),), Constant(p))
             return
         end
     else
-        di_extras = DI.prepare_pullback(f, autodiff, u, (fu,), Constant(prob.p))
+        di_extras = DI.prepare_pullback(f, autodiff, u, (fu,), Constant(prob.p), strict=Val(false))
         return @closure (v, u, p) -> begin
             return only(DI.pullback(
                 f, di_extras, autodiff, u, (reshape(v, size(fu)),), Constant(p)))
@@ -352,14 +352,14 @@ function prepare_jvp(::Val{false}, prob::AbstractNonlinearProblem,
         @assert DI.check_inplace(autodiff) "Backend: $(autodiff) doesn't support in-place \
                                             problems."
         fu_cache = copy(fu)
-        di_extras = DI.prepare_pushforward(f, fu_cache, autodiff, u, (u,), Constant(prob.p))
+        di_extras = DI.prepare_pushforward(f, fu_cache, autodiff, u, (u,), Constant(prob.p), strict=Val(false))
         return @closure (Jv, v, u, p) -> begin
             DI.pushforward!(f, fu_cache, (reshape(Jv, size(fu_cache)),), di_extras,
                 autodiff, u, (reshape(v, size(u)),), Constant(p))
             return
         end
     else
-        di_extras = DI.prepare_pushforward(f, autodiff, u, (u,), Constant(prob.p))
+        di_extras = DI.prepare_pushforward(f, autodiff, u, (u,), Constant(prob.p), strict=Val(false))
         return @closure (v, u, p) -> begin
             return only(DI.pushforward(
                 f, di_extras, autodiff, u, (reshape(v, size(u)),), Constant(p)))
@@ -375,7 +375,7 @@ function prepare_scalar_op(::Val{false}, prob::AbstractNonlinearProblem,
 
     @assert autodiff!==nothing "`autodiff` must be provided if `f` doesn't have \
                                 analytic `vjp` or `jvp` or `jac`."
-    di_extras = DI.prepare_derivative(f, autodiff, u, Constant(prob.p))
+    di_extras = DI.prepare_derivative(f, autodiff, u, Constant(prob.p), strict=Val(false))
     return @closure (v, u, p) -> DI.derivative(f, di_extras, autodiff, u, Constant(p)) * v
 end
 

lib/SimpleNonlinearSolve/src/utils.jl

@@ -87,10 +87,10 @@ end
 function prepare_jacobian(prob, autodiff, fx, x)
     SciMLBase.has_jac(prob.f) && return AnalyticJacobian()
     if SciMLBase.isinplace(prob.f)
-        return DIExtras(DI.prepare_jacobian(prob.f, fx, autodiff, x, Constant(prob.p)))
+        return DIExtras(DI.prepare_jacobian(prob.f, fx, autodiff, x, Constant(prob.p), strict = Val(false)))
     else
         x isa SArray && return DINoPreparation()
-        return DIExtras(DI.prepare_jacobian(prob.f, autodiff, x, Constant(prob.p)))
+        return DIExtras(DI.prepare_jacobian(prob.f, autodiff, x, Constant(prob.p), strict = Val(false)))
     end
 end