Update SciMLJacobianOperators.jl

Author: ChrisRackauckas

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