Allow FiniteDiff propagation for scalar problems

Project.toml

@@ -1,7 +1,7 @@
 name = "NonlinearSolve"
 uuid = "8913a72c-1f9b-4ce2-8d82-65094dcecaec"
 authors = ["SciML"]
-version = "3.10.0"
+version = "3.10.1"
 
 [deps]
 ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"

docs/make.jl

@@ -19,7 +19,7 @@ makedocs(; sitename = "NonlinearSolve.jl",
     doctest = false,
     linkcheck = true,
     linkcheck_ignore = ["https://twitter.com/ChrisRackauckas/status/1544743542094020615",
-                        "https://link.springer.com/article/10.1007/s40096-020-00339-4"],
+        "https://link.springer.com/article/10.1007/s40096-020-00339-4"],
     checkdocs = :exports,
     warnonly = [:missing_docs],
     plugins = [bib],

src/internal/jacobian.jl

@@ -97,10 +97,17 @@ function JacobianCache(
         J, f, uf, fu, u, p, jac_cache, alg, 0, autodiff, vjp_autodiff, jvp_autodiff)
 end
 
-function JacobianCache(prob, alg, f::F, ::Number, u::Number, p; kwargs...) where {F}
+function JacobianCache(
+        prob, alg, f::F, ::Number, u::Number, p; autodiff = nothing, kwargs...) where {F}
     uf = JacobianWrapper{false}(f, p)
+    autodiff = get_concrete_forward_ad(autodiff, prob; check_reverse_mode = false)
+    if !(autodiff isa AutoForwardDiff || autodiff isa AutoFiniteDiff)
+        # Other cases are not properly supported so we fallback to finite differencing
+        @warn "Scalar AD is supported only for AutoForwardDiff and AutoFiniteDiff. \
+               Detected $(autodiff). Falling back to AutoFiniteDiff."
+    end
     return JacobianCache{false}(
-        u, f, uf, u, u, p, nothing, alg, 0, nothing, nothing, nothing)
+        u, f, uf, u, u, p, nothing, alg, 0, autodiff, nothing, nothing)
 end
 
 @inline (cache::JacobianCache)(u = cache.u) = cache(cache.J, u, cache.p)
@@ -115,7 +122,7 @@ function (cache::JacobianCache)(J::JacobianOperator, u, p = cache.p)
 end
 function (cache::JacobianCache)(::Number, u, p = cache.p) # Scalar
     cache.njacs += 1
-    J = last(__value_derivative(cache.uf, u))
+    J = last(__value_derivative(cache.autodiff, cache.uf, u))
     return J
 end
 # Compute the Jacobian
@@ -181,12 +188,16 @@ end
     end
 end
 
-@inline function __value_derivative(f::F, x::R) where {F, R}
+@inline function __value_derivative(::AutoForwardDiff, f::F, x::R) where {F, R}
     T = typeof(ForwardDiff.Tag(f, R))
     out = f(ForwardDiff.Dual{T}(x, one(x)))
     return ForwardDiff.value(out), ForwardDiff.extract_derivative(T, out)
 end
 
+@inline function __value_derivative(ad::AutoFiniteDiff, f::F, x::R) where {F, R}
+    return f(x), FiniteDiff.finite_difference_derivative(f, x, ad.fdtype)
+end
+
 @inline function __scalar_jacvec(f::F, x::R, v::V) where {F, R, V}
     T = typeof(ForwardDiff.Tag(f, R))
     out = f(ForwardDiff.Dual{T}(x, v))

src/internal/operators.jl

@@ -62,8 +62,11 @@ function JacobianOperator(prob::AbstractNonlinearProblem, fu, u; jvp_autodiff =
     elseif SciMLBase.has_vjp(f)
         f.vjp
     elseif u isa Number  # Ignore vjp directives
-        if ForwardDiff.can_dual(typeof(u))
-            @closure (v, u, p) -> last(__value_derivative(uf, u)) * v
+        if ForwardDiff.can_dual(typeof(u)) && (vjp_autodiff === nothing ||
+            vjp_autodiff isa AutoForwardDiff ||
+            vjp_autodiff isa AutoPolyesterForwardDiff)
+            # VJP is same as VJP for scalars
+            @closure (v, u, p) -> last(__scalar_jacvec(uf, u, v))
         else
             @closure (v, u, p) -> FiniteDiff.finite_difference_derivative(uf, u) * v
         end
@@ -92,8 +95,11 @@ function JacobianOperator(prob::AbstractNonlinearProblem, fu, u; jvp_autodiff =
     elseif SciMLBase.has_jvp(f)
         f.jvp
     elseif u isa Number  # Ignore jvp directives
-        if ForwardDiff.can_dual(typeof(u))
-            @closure (v, u, p) -> last(__scalar_jacvec(uf, u, v)) * v
+        # Only ForwardDiff if user didn't override
+        if ForwardDiff.can_dual(typeof(u)) && (jvp_autodiff === nothing ||
+            jvp_autodiff isa AutoForwardDiff ||
+            jvp_autodiff isa AutoPolyesterForwardDiff)
+            @closure (v, u, p) -> last(__scalar_jacvec(uf, u, v))
         else
             @closure (v, u, p) -> FiniteDiff.finite_difference_derivative(uf, u) * v
         end

test/misc/polyalg_tests.jl

@@ -86,13 +86,22 @@ end
     # Uses the `__solve` function
     @test_throws MethodError solve(probN; abstol = 1e-9)
     @test_throws MethodError solve(probN, RobustMultiNewton(); abstol = 1e-9)
-    solver = solve(probN, RobustMultiNewton(; autodiff = AutoFiniteDiff()); abstol = 1e-9)
-    @test SciMLBase.successful_retcode(solver)
-    solver = solve(
+    sol = solve(probN, RobustMultiNewton(; autodiff = AutoFiniteDiff()); abstol = 1e-9)
+    @test SciMLBase.successful_retcode(sol)
+    sol = solve(
         probN, FastShortcutNonlinearPolyalg(; autodiff = AutoFiniteDiff()); abstol = 1e-9)
-    @test SciMLBase.successful_retcode(solver)
-    solver = solve(probN, custom_polyalg; abstol = 1e-9)
-    @test SciMLBase.successful_retcode(solver)
+    @test SciMLBase.successful_retcode(sol)
+    sol = solve(probN, custom_polyalg; abstol = 1e-9)
+    @test SciMLBase.successful_retcode(sol)
+
+    quadratic_f(u::Float64, p) = u^2 - p
+
+    prob = NonlinearProblem(quadratic_f, 2.0, 4.0)
+
+    @test_throws MethodError solve(prob)
+    @test_throws MethodError solve(prob, RobustMultiNewton())
+    sol = solve(prob, RobustMultiNewton(; autodiff = AutoFiniteDiff()))
+    @test SciMLBase.successful_retcode(sol)
 end
 
 @testitem "Simple Scalar Problem #187" begin