ForwardDiff Overload Fixes

ext/LinearSolveForwardDiffExt.jl

@@ -1,6 +1,7 @@
 module LinearSolveForwardDiffExt
 
 using LinearSolve
+using LinearSolve: SciMLLinearSolveAlgorithm
 using LinearAlgebra
 using ForwardDiff
 using ForwardDiff: Dual, Partials
@@ -36,8 +37,14 @@ const DualAbstractLinearProblem = Union{
 LinearSolve.@concrete mutable struct DualLinearCache
     linear_cache
     dual_type
+
     partials_A
     partials_b
+    partials_u
+
+    dual_A
+    dual_b
+    dual_u
 end
 
 function linearsolve_forwarddiff_solve(cache::DualLinearCache, alg, args...; kwargs...)
@@ -55,16 +62,15 @@ function linearsolve_forwarddiff_solve(cache::DualLinearCache, alg, args...; kwa
 
     rhs_list = xp_linsolve_rhs(uu, ∂_A, ∂_b)
 
-    partial_cache = cache.linear_cache
-    partial_cache.u = dual_u0
-
+    cache.linear_cache.u = dual_u0
+    # We can reuse the linear cache, because the same factorization will work for the partials.
     for i in eachindex(rhs_list)
-        partial_cache.b = rhs_list[i]
-        rhs_list[i] = copy(solve!(partial_cache, alg, args...; kwargs...).u)
+        cache.linear_cache.b = rhs_list[i]
+        rhs_list[i] = copy(solve!(cache.linear_cache, alg, args...; kwargs...).u)
     end
 
-    # Reset to the original `b`, users will expect that `b` doesn't change if they don't tell it to
-    partial_cache.b = primal_b
+    # Reset to the original `b` and `u`, users will expect that `b` doesn't change if they don't tell it to
+    cache.linear_cache.b = primal_b
 
     partial_sols = rhs_list
 
@@ -96,35 +102,25 @@ function xp_linsolve_rhs(
     b_list
 end
 
-#=
-function SciMLBase.solve(prob::DualAbstractLinearProblem, args...; kwargs...)
-    return solve(prob, nothing, args...; kwargs...)
-end
-
-function SciMLBase.solve(prob::DualAbstractLinearProblem, ::Nothing, args...;
-        assump = OperatorAssumptions(issquare(prob.A)), kwargs...)
-    return solve(prob, LinearSolve.defaultalg(prob.A, prob.b, assump), args...; kwargs...)
-end
-
-function SciMLBase.solve(prob::DualAbstractLinearProblem,
-        alg::LinearSolve.SciMLLinearSolveAlgorithm, args...; kwargs...)
-    solve!(init(prob, alg, args...; kwargs...))
-end
-=#
-
 function linearsolve_dual_solution(
         u::Number, partials, dual_type)
     return dual_type(u, partials)
 end
 
-function linearsolve_dual_solution(
-        u::AbstractArray, partials, dual_type)
+function linearsolve_dual_solution(u::Number, partials,
+        dual_type::Type{<:Dual{T, V, P}}) where {T, V, P}
+    # Handle single-level duals
+    return dual_type(u, partials)
+end
+
+function linearsolve_dual_solution(u::AbstractArray, partials,
+        dual_type::Type{<:Dual{T, V, P}}) where {T, V, P}
+    # Handle single-level duals for arrays
     partials_list = RecursiveArrayTools.VectorOfArray(partials)
     return map(((uᵢ, pᵢ),) -> dual_type(uᵢ, Partials(Tuple(pᵢ))),
-        zip(u, partials_list[i, :] for i in 1:length(partials_list[1])))
+        zip(u, partials_list[i, :] for i in 1:length(partials_list.u[1])))
 end
 
-#=
 function SciMLBase.init(
         prob::DualAbstractLinearProblem, alg::LinearSolve.SciMLLinearSolveAlgorithm,
         args...;
@@ -138,7 +134,6 @@ function SciMLBase.init(
         assumptions = OperatorAssumptions(issquare(prob.A)),
         sensealg = LinearSolveAdjoint(),
         kwargs...)
-
     (; A, b, u0, p) = prob
     new_A = nodual_value(A)
     new_b = nodual_value(b)
@@ -147,7 +142,6 @@ function SciMLBase.init(
     ∂_A = partial_vals(A)
     ∂_b = partial_vals(b)
 
-    #primal_prob = LinearProblem(new_A, new_b, u0 = new_u0)
     primal_prob = remake(prob; A = new_A, b = new_b, u0 = new_u0)
 
     if get_dual_type(prob.A) !== nothing
@@ -156,48 +150,71 @@ function SciMLBase.init(
         dual_type = get_dual_type(prob.b)
     end
 
+    alg isa LinearSolve.DefaultLinearSolver ? real_alg = LinearSolve.defaultalg(primal_prob.A, primal_prob.b) : real_alg = alg
+
     non_partial_cache = init(
-        primal_prob, alg, args...; alias = alias, abstol = abstol, reltol = reltol,
+        primal_prob, real_alg, assumptions, args...;
+        alias = alias, abstol = abstol, reltol = reltol,
         maxiters = maxiters, verbose = verbose, Pl = Pl, Pr = Pr, assumptions = assumptions,
         sensealg = sensealg, u0 = new_u0, kwargs...)
-    return DualLinearCache(non_partial_cache, dual_type, ∂_A, ∂_b)
+    return DualLinearCache(non_partial_cache, dual_type, ∂_A, ∂_b, !isnothing(∂_b) ? zero.(∂_b) : ∂_b, A, b, zeros(dual_type, length(b)))
 end
 
 function SciMLBase.solve!(cache::DualLinearCache, args...; kwargs...)
+   solve!(cache, cache.alg, args...; kwargs...)
+end
+
+function SciMLBase.solve!(cache::DualLinearCache, alg::SciMLLinearSolveAlgorithm, args...; kwargs...)
     sol,
     partials = linearsolve_forwarddiff_solve(
         cache::DualLinearCache, cache.alg, args...; kwargs...)
-
     dual_sol = linearsolve_dual_solution(sol.u, partials, cache.dual_type)
+
+    if cache.dual_u isa AbstractArray
+        cache.dual_u[:] = dual_sol
+    else
+        cache.dual_u = dual_sol
+    end
+
     return SciMLBase.build_linear_solution(
         cache.alg, dual_sol, sol.resid, cache; sol.retcode, sol.iters, sol.stats
     )
 end
-=#
 
 # If setting A or b for DualLinearCache, put the Dual-stripped versions in the LinearCache
-# Also "forwards" setproperty so that 
 function Base.setproperty!(dc::DualLinearCache, sym::Symbol, val)
     # If the property is A or b, also update it in the LinearCache
     if sym === :A || sym === :b || sym === :u
         setproperty!(dc.linear_cache, sym, nodual_value(val))
+    elseif hasfield(DualLinearCache, sym)
+        setfield!(dc, sym, val)
     elseif hasfield(LinearSolve.LinearCache, sym)
         setproperty!(dc.linear_cache, sym, val)
     end
 
+
     # Update the partials if setting A or b
     if sym === :A
+        setfield!(dc, :dual_A, val)
         setfield!(dc, :partials_A, partial_vals(val))
-    elseif  sym === :b
+    elseif sym === :b
+        setfield!(dc, :dual_b, val)
         setfield!(dc, :partials_b, partial_vals(val))
-    else
-        setfield!(dc, sym, val)
+    elseif sym === :u
+        setfield!(dc, :dual_u, val)
+        setfield!(dc, :partials_u, partial_vals(val))
     end
 end
 
 # "Forwards" getproperty to LinearCache if necessary
 function Base.getproperty(dc::DualLinearCache, sym::Symbol)
-    if hasfield(LinearSolve.LinearCache, sym)
+    if sym === :A
+        dc.dual_A
+    elseif sym === :b
+        dc.dual_b
+    elseif sym === :u
+        dc.dual_u
+    elseif hasfield(LinearSolve.LinearCache, sym)
         return getproperty(dc.linear_cache, sym)
     else
         return getfield(dc, sym)
@@ -206,31 +223,36 @@ end
 
 
 
-# Helper functions for Dual numbers
-get_dual_type(x::Dual) = typeof(x)
+# Enhanced helper functions for Dual numbers to handle recursion
+get_dual_type(x::Dual{T, V, P}) where {T, V <: AbstractFloat, P} = typeof(x)
+get_dual_type(x::Dual{T, V, P}) where {T, V <: Dual, P} = typeof(x)
 get_dual_type(x::AbstractArray{<:Dual}) = eltype(x)
 get_dual_type(x) = nothing
 
-partial_vals(x::Dual) = ForwardDiff.partials(x)
+# Add recursive handling for nested dual partials
+partial_vals(x::Dual{T, V, P}) where {T, V <: AbstractFloat, P} = ForwardDiff.partials(x)
+partial_vals(x::Dual{T, V, P}) where {T, V <: Dual, P} = ForwardDiff.partials(x)
 partial_vals(x::AbstractArray{<:Dual}) = map(ForwardDiff.partials, x)
 partial_vals(x) = nothing
 
+# Add recursive handling for nested dual values
 nodual_value(x) = x
-nodual_value(x::Dual) = ForwardDiff.value(x)
-nodual_value(x::AbstractArray{<:Dual}) = map(ForwardDiff.value, x)
+nodual_value(x::Dual{T, V, P}) where {T, V <: AbstractFloat, P} = ForwardDiff.value(x)
+nodual_value(x::Dual{T, V, P}) where {T, V <: Dual, P} = x.value  # Keep the inner dual intact
+nodual_value(x::AbstractArray{<:Dual}) = map(nodual_value, x)
 
 
-function partials_to_list(partial_matrix::Vector)
+function partials_to_list(partial_matrix::AbstractVector{T}) where {T}
     p = eachindex(first(partial_matrix))
     [[partial[i] for partial in partial_matrix] for i in p]
 end
 
 function partials_to_list(partial_matrix)
     p = length(first(partial_matrix))
     m, n = size(partial_matrix)
-    res_list = fill(zeros(m, n), p)
+    res_list = fill(zeros(typeof(partial_matrix[1, 1][1]), m, n), p)
     for k in 1:p
-        res = zeros(m, n)
+        res = zeros(typeof(partial_matrix[1, 1][1]), m, n)
         for i in 1:m
             for j in 1:n
                 res[i, j] = partial_matrix[i, j][k]
@@ -243,3 +265,4 @@ end
 
 
 end
+

test/forwarddiff_overloads.jl

@@ -1,6 +1,7 @@
 using LinearSolve
 using ForwardDiff
 using Test
+using SparseArrays
 
 function h(p)
     (A = [p[1] p[2]+1 p[2]^3;
@@ -23,12 +24,11 @@ krylov_u0_sol = solve(krylov_prob, KrylovJL_GMRES())
 
 @test ≈(krylov_u0_sol, backslash_x_p, rtol = 1e-9)
 
-
 A, _ = h([ForwardDiff.Dual(5.0, 1.0, 0.0), ForwardDiff.Dual(5.0, 0.0, 1.0)])
 backslash_x_p = A \ [6.0, 10.0, 25.0]
 prob = LinearProblem(A, [6.0, 10.0, 25.0])
 
-@test ≈(solve(prob).u, backslash_x_p, rtol = 1e-9) 
+@test ≈(solve(prob).u, backslash_x_p, rtol = 1e-9)
 @test ≈(solve(prob, KrylovJL_GMRES()).u, backslash_x_p, rtol = 1e-9)
 
 _, b = h([ForwardDiff.Dual(5.0, 1.0, 0.0), ForwardDiff.Dual(5.0, 0.0, 1.0)])
@@ -48,6 +48,9 @@ new_A, new_b = h([ForwardDiff.Dual(5.0, 1.0, 0.0), ForwardDiff.Dual(5.0, 0.0, 1.
 cache.A = new_A
 cache.b = new_b
 
+@test cache.A == new_A
+@test cache.b == new_b
+
 x_p = solve!(cache)
 backslash_x_p = new_A \ new_b
 
@@ -61,6 +64,7 @@ cache = init(prob)
 
 new_A, _ = h([ForwardDiff.Dual(5.0, 1.0, 0.0), ForwardDiff.Dual(5.0, 0.0, 1.0)])
 cache.A = new_A
+@test cache.A == new_A
 
 x_p = solve!(cache)
 backslash_x_p = new_A \ b
@@ -75,8 +79,115 @@ cache = init(prob)
 
 _, new_b = h([ForwardDiff.Dual(5.0, 1.0, 0.0), ForwardDiff.Dual(5.0, 0.0, 1.0)])
 cache.b = new_b
+@test cache.b == new_b
 
 x_p = solve!(cache)
 backslash_x_p = A \ new_b
 
-@test ≈(x_p, backslash_x_p, rtol = 1e-9)
+@test ≈(x_p, backslash_x_p, rtol = 1e-9)
+
+# Nested Duals
+function h(p)
+    (A = [p[1] p[2]+1 p[2]^3;
+          3*p[1] p[1]+5 p[2] * p[1]-4;
+          p[2]^2 9*p[1] p[2]],
+        b = [p[1] + 1, p[2] * 2, p[1]^2])
+end
+
+A, b = h([ForwardDiff.Dual(ForwardDiff.Dual(5.0, 1.0, 0.0), 1.0, 0.0),
+    ForwardDiff.Dual(ForwardDiff.Dual(5.0, 1.0, 0.0), 0.0, 1.0)])
+
+prob = LinearProblem(A, b)
+overload_x_p = solve(prob)
+
+original_x_p = A \ b
+
+@test ≈(overload_x_p, original_x_p, rtol = 1e-9)
+
+prob = LinearProblem(A, b)
+cache = init(prob)
+
+new_A, new_b = h([ForwardDiff.Dual(ForwardDiff.Dual(10.0, 1.0, 0.0), 1.0, 0.0),
+    ForwardDiff.Dual(ForwardDiff.Dual(10.0, 1.0, 0.0), 0.0, 1.0)])
+
+cache.A = new_A
+cache.b = new_b
+
+@test cache.A == new_A
+@test cache.b == new_b
+
+function linprob_f(p)
+    A, b = h(p)
+    prob = LinearProblem(A, b)
+    solve(prob)
+end
+
+function slash_f(p)
+    A, b = h(p)
+    A \ b
+end
+
+@test ≈(
+    ForwardDiff.jacobian(slash_f, [5.0, 5.0]), ForwardDiff.jacobian(linprob_f, [5.0, 5.0]))
+
+@test ≈(ForwardDiff.jacobian(p -> ForwardDiff.jacobian(slash_f, [5.0, p[1]]), [5.0]),
+    ForwardDiff.jacobian(p -> ForwardDiff.jacobian(linprob_f, [5.0, p[1]]), [5.0]))
+
+function g(p)
+    (A = [p[1] p[1]+1 p[1]^3;
+          3*p[1] p[1]+5 p[1] * p[1]-4;
+          p[1]^2 9*p[1] p[1]],
+        b = [p[1] + 1, p[1] * 2, p[1]^2])
+end
+
+function slash_f_hes(p)
+    A, b = g(p)
+    x = A \ b
+    sum(x)
+end
+
+function linprob_f_hes(p)
+    A, b = g(p)
+    prob = LinearProblem(A, b)
+    x = solve(prob)
+    sum(x)
+end
+
+@test ≈(ForwardDiff.hessian(slash_f_hes, [5.0]),
+    ForwardDiff.hessian(linprob_f_hes, [5.0]))
+
+# Test aliasing
+A, b = h([ForwardDiff.Dual(5.0, 1.0, 0.0), ForwardDiff.Dual(5.0, 0.0, 1.0)])
+
+prob = LinearProblem(A, b)
+cache = init(prob)
+
+new_A, new_b = h([ForwardDiff.Dual(5.0, 1.0, 0.0), ForwardDiff.Dual(5.0, 0.0, 1.0)])
+cache.A = new_A
+cache.b = new_b
+
+linu = [ForwardDiff.Dual(0.0, 0.0, 0.0), ForwardDiff.Dual(0.0, 0.0, 0.0),
+    ForwardDiff.Dual(0.0, 0.0, 0.0)]
+cache.u = linu
+x_p = solve!(cache)
+backslash_x_p = new_A \ new_b
+
+@test linu == cache.u
+
+# Test Float Only solvers
+
+A, b = h([ForwardDiff.Dual(5.0, 1.0, 0.0), ForwardDiff.Dual(5.0, 0.0, 1.0)])
+
+prob = LinearProblem(sparse(A), sparse(b))
+overload_x_p = solve(prob, KLUFactorization())
+backslash_x_p = A \ b
+
+@test ≈(overload_x_p, backslash_x_p, rtol = 1e-9)
+
+A, b = h([ForwardDiff.Dual(5.0, 1.0, 0.0), ForwardDiff.Dual(5.0, 0.0, 1.0)])
+
+prob = LinearProblem(sparse(A), sparse(b))
+overload_x_p = solve(prob, UMFPACKFactorization())
+backslash_x_p = A \ b
+
+@test ≈(overload_x_p, backslash_x_p, rtol = 1e-9)