More lenient iterative linear solver (#119)

* More lenient iterative linear solver

* Typo

* Fix bools

* Fix tests and add docs

Project.toml

@@ -1,7 +1,7 @@
 name = "ImplicitDifferentiation"
 uuid = "57b37032-215b-411a-8a7c-41a003a55207"
 authors = ["Guillaume Dalle", "Mohamed Tarek and contributors"]
-version = "0.5.0"
+version = "0.5.1"
 
 [deps]
 AbstractDifferentiation = "c29ec348-61ec-40c8-8164-b8c60e9d9f3d"

src/linear_solver.jl

@@ -16,22 +16,36 @@ abstract type AbstractLinearSolver end
 """
     IterativeLinearSolver
 
-An implementation of `AbstractLinearSolver` using `Krylov.gmres`.
+An implementation of `AbstractLinearSolver` using `Krylov.gmres`, set as the default for `ImplicitFunction`.
 
 # Fields
 
-- `verbose::Bool`: Whether to throw a warning when the solver fails (defaults to `true`)
+- `verbose::Bool`: Whether to display a warning when the solver fails and returns `NaN`s (defaults to `true`)
+- `accept_inconsistent::Bool`: Whether to accept approximate least squares solutions for inconsistent systems, or fail and return `NaN`s (defaults to `false`)
+
+!!! note
+    If you find that your implicit gradients contains `NaN`s, try using this solver with `accept_inconsistent=true`.
+    However, beware that the implicit function theorem does not cover the case of inconsistent linear systems `AJ = B`, so it is unclear what the result will mean.
 """
 Base.@kwdef struct IterativeLinearSolver <: AbstractLinearSolver
     verbose::Bool = true
+    accept_inconsistent::Bool = false
 end
 
 presolve(::IterativeLinearSolver, A, y) = A
 
 function solve(sol::IterativeLinearSolver, A, b)
     x, stats = gmres(A, b)
-    if !stats.solved || stats.inconsistent
-        sol.verbose && @warn "IterativeLinearSolver failed, result contains NaNs"
+    if sol.accept_inconsistent
+        success = stats.solved || stats.inconsistent
+    else
+        success = stats.solved && !stats.inconsistent
+    end
+    if !success
+        if sol.verbose
+            @warn "IterativeLinearSolver failed, result contains NaNs"
+            @show stats
+        end
         x .= NaN
     end
     return x

test/errors.jl

@@ -26,8 +26,13 @@ end
 @testset verbose = true "Derivative NaNs" begin
     x = zeros(Float32, 2)
     linear_solvers = (
-        IterativeLinearSolver(; verbose=false), DirectLinearSolver(; verbose=false)
+        IterativeLinearSolver(; verbose=false),  #
+        IterativeLinearSolver(; verbose=false, accept_inconsistent=true),  #
+        DirectLinearSolver(; verbose=false),  #
     )
+    function should_give_nan(linear_solver)
+        return linear_solver isa DirectLinearSolver || !linear_solver.accept_inconsistent
+    end
 
     @testset "Infinite derivative" begin
         f = x -> sqrt.(x)  # nondifferentiable at 0
@@ -37,8 +42,10 @@ end
                 implicit = ImplicitFunction(f, c; linear_solver)
                 J1 = ForwardDiff.jacobian(implicit, x)
                 J2 = Zygote.jacobian(implicit, x)[1]
-                @test all(isnan, J1) && eltype(J1) == Float32
-                @test all(isnan, J2) && eltype(J2) == Float32
+                @test all(isnan, J1) == should_give_nan(linear_solver)
+                @test all(isnan, J2) == should_give_nan(linear_solver)
+                @test eltype(J1) == Float32
+                @test eltype(J2) == Float32
             end
         end
     end
@@ -51,8 +58,10 @@ end
                 implicit = ImplicitFunction(f, c; linear_solver)
                 J1 = ForwardDiff.jacobian(implicit, x)
                 J2 = Zygote.jacobian(implicit, x)[1]
-                @test all(isnan, J1) && eltype(J1) == Float32
-                @test all(isnan, J2) && eltype(J2) == Float32
+                @test all(isnan, J1) == should_give_nan(linear_solver)
+                @test all(isnan, J2) == should_give_nan(linear_solver)
+                @test eltype(J1) == Float32
+                @test eltype(J2) == Float32
             end
         end
     end