Opt out from generig dual number handling for SparspakFactorization

Sparspak handles sparse linear system solution for generic number
types including dual numbers

Author: j-fu

ext/LinearSolveForwardDiffExt.jl

@@ -200,6 +200,11 @@ function SciMLBase.init(prob::DualAbstractLinearProblem, alg::GenericLUFactoriza
     return __init(prob, alg, args...; kwargs...)
 end
 
+# Opt out for SparspakFactorization
+function SciMLBase.init(prob::DualAbstractLinearProblem, alg::SparspakFactorization, args...; kwargs...)
+    return __init(prob, alg, args...; kwargs...)
+end
+
 function SciMLBase.init(prob::DualAbstractLinearProblem, alg::DefaultLinearSolver, args...; kwargs...)
     if alg.alg === DefaultAlgorithmChoice.GenericLUFactorization
         return __init(prob, alg, args...; kwargs...)

test/forwarddiff_overloads.jl

@@ -3,6 +3,7 @@ using ForwardDiff
 using Test
 using SparseArrays
 using ComponentArrays
+using Sparspak
 
 function h(p)
     (A = [p[1] p[2]+1 p[2]^3;
@@ -188,7 +189,6 @@ backslash_x_p = A \ b
 
 @test ≈(overload_x_p, backslash_x_p, rtol = 1e-9)
 
-
 # Test that GenericLU doesn't create a DualLinearCache
 A, b = h([ForwardDiff.Dual(5.0, 1.0, 0.0), ForwardDiff.Dual(5.0, 0.0, 1.0)])
 
@@ -197,6 +197,12 @@ prob = LinearProblem(A, b)
 
 @test init(prob) isa LinearSolve.LinearCache
 
+# Test that SparspakFactorization doesn't create a DualLinearCache
+A, b = h([ForwardDiff.Dual(5.0, 1.0, 0.0), ForwardDiff.Dual(5.0, 0.0, 1.0)])
+
+prob = LinearProblem(sparse(A), b)
+@test init(prob, SparspakFactorization()) isa LinearSolve.LinearCache
+
 # Test ComponentArray with ForwardDiff (Issue SciML/DifferentialEquations.jl#1110)
 # This tests that ArrayInterface.restructure preserves ComponentArray structure
 
@@ -234,4 +240,4 @@ grad = ForwardDiff.gradient(component_linsolve, p_test)
 @test grad isa Vector
 @test length(grad) == 2
 @test !any(isnan, grad)
-@test !any(isinf, grad)
+@test !any(isinf, grad)