working on AD. test not passing

Project.toml

@@ -8,21 +8,25 @@ BlockArrays = "8e7c35d0-a365-5155-bbbb-fb81a777f24e"
 ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
 DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
 FastDifferentiation = "eb9bf01b-bf85-4b60-bf87-ee5de06c00be"
+FiniteDiff = "6a86dc24-6348-571c-b903-95158fe2bd41"
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
 Infiltrator = "5903a43b-9cc3-4c30-8d17-598619ec4e9b"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 Symbolics = "0c5d862f-8b57-4792-8d23-62f2024744c7"
 TrajectoryGamesBase = "ac1ac542-73eb-4349-ae1b-660ab3609574"
+Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [compat]
 BlockArrays = "0.16.43"
 ChainRulesCore = "1.25.0"
 DataStructures = "0.18.20"
 FastDifferentiation = "0.4.2"
+FiniteDiff = "2.26.2"
 ForwardDiff = "0.10.38"
 Infiltrator = "1.8.3"
 LinearAlgebra = "1.11.0"
 SparseArrays = "1.11.0"
 Symbolics = "6.19.0"
 TrajectoryGamesBase = "0.3.10"
+Zygote = "0.6.73"

src/AutoDiff.jl

@@ -12,6 +12,10 @@ module AutoDiff
 using ..MCPSolver: MCPSolver
 using ChainRulesCore: ChainRulesCore
 using ForwardDiff: ForwardDiff
+using LinearAlgebra: LinearAlgebra
+using SparseArrays: SparseArrays
+
+using Infiltrator
 
 function _solve_jacobian_θ(mcp::MCPSolver.PrimalDualMCP, solution, θ)
     !isnothing(mcp.∇F_θ) || throw(
@@ -21,21 +25,34 @@ function _solve_jacobian_θ(mcp::MCPSolver.PrimalDualMCP, solution, θ)
     )
 
     (; x, y, s, ϵ) = solution
-    -mcp.∇F_z(x, y, s; θ, ϵ) \ mcp.∇F_θ(x, y, s; θ, ϵ)
+    ∂z∂θ = -mcp.∇F_z(x, y, s; θ, ϵ) \ mcp.∇F_θ(x, y, s; θ, ϵ)
+
+    SparseArrays.sparse(∂z∂θ)
 end
 
-function ChainRulesCore.rrule(::typeof(MCPSolver.solve), mcp, θ; kwargs...)
-    solution = MCPSolver.solve(mcp, θ; kwargs...)
+function ChainRulesCore.rrule(
+    ::typeof(MCPSolver.solve),
+    solver_type::MCPSolver.SolverType,
+    mcp::MCPSolver.PrimalDualMCP;
+    θ,
+    kwargs...,
+)
+    solution = MCPSolver.solve(solver_type, mcp; θ, kwargs...)
     project_to_θ = ChainRulesCore.ProjectTo(θ)
 
     function solve_pullback(∂solution)
-        no_grad_args =
-            (; ∂self = ChainRulesCore.NoTangent(), ∂problem = ChainRulesCore.NoTangent())
+        no_grad_args = (;
+            ∂self = ChainRulesCore.NoTangent(),
+            ∂solver_type = ChainRulesCore.NoTangent(),
+            ∂mcp = ChainRulesCore.NoTangent(),
+        )
 
         ∂θ = ChainRulesCore.@thunk let
             ∂z∂θ = _solve_jacobian_θ(mcp, solution, θ)
-            ∂l∂z = ∂solution.z
-            project_to_θ(∂z∂θ' * ∂l∂z)
+            ∂l∂x = ∂solution.x
+            ∂l∂y = ∂solution.y
+            ∂l∂s = ∂solution.s
+            project_to_θ(∂z∂θ' * [∂l∂x; ∂l∂y; ∂l∂s])
         end
 
         no_grad_args..., ∂θ

test/Project.toml

@@ -1,4 +1,7 @@
 [deps]
 BlockArrays = "8e7c35d0-a365-5155-bbbb-fb81a777f24e"
+FiniteDiff = "6a86dc24-6348-571c-b903-95158fe2bd41"
+ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"

test/runtests.jl

@@ -2,6 +2,8 @@ using Test: @testset, @test
 
 using MCPSolver
 using BlockArrays: BlockArray, Block, mortar, blocks
+using Zygote: Zygote
+using FiniteDiff: FiniteDiff
 
 @testset "QPTestProblem" begin
     """ Test for the following QP:
@@ -59,6 +61,28 @@ using BlockArrays: BlockArray, Block, mortar, blocks
 
         check_solution(sol)
     end
+
+    @testset "AutodifferentationTests" begin
+        mcp = MCPSolver.PrimalDualMCP(
+            G,
+            H;
+            unconstrained_dimension = size(M, 1),
+            constrained_dimension = length(b),
+            parameter_dimension = size(M, 1),
+            compute_sensitivities = true
+        )
+
+        function f(θ)
+            sol = MCPSolver.solve(MCPSolver.InteriorPoint(), mcp; θ)
+            sum(sol.x .^ 2) + sum(sol.y .^ 2)
+        end
+
+        ∇_autodiff_reverse = only(Zygote.gradient(f, θ))
+        ∇_autodiff_forward = only(Zygote.gradient(θ -> Zygote.forwarddiff(f, θ), θ))
+        ∇_finitediff = FiniteDiff.finite_difference_gradient(f, θ)
+        @test isapprox(∇_autodiff_reverse, ∇_finitediff; atol = 1e-3)
+        @test isapprox(∇_autodiff_reverse, ∇_autodiff_forward; atol = 1e-3)
+    end
 end
 
 @testset "ParametricGameTests" begin