fix lu deprecation warnings on Julia nightly (#434)

Ref JuliaLang/julia#40623

Co-authored-by: Lyndon White <[email protected]>

Author: simeonschaub

Project.toml

@@ -1,6 +1,6 @@
 name = "ChainRules"
 uuid = "082447d4-558c-5d27-93f4-14fc19e9eca2"
-version = "0.8.3"
+version = "0.8.4"
 
 [deps]
 ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"

src/rulesets/LinearAlgebra/factorization.jl

@@ -15,11 +15,14 @@ using LinearAlgebra.BLAS: gemv, gemv!, gemm!, trsm!, axpy!, ger!
 # for derivations for wide and tall matrices, see
 # https://sethaxen.com/blog/2021/02/differentiating-the-lu-decomposition/
 
+const LU_RowMaximum = VERSION >= v"1.7.0-DEV.1188" ? RowMaximum : Val{true}
+const LU_NoPivot = VERSION >= v"1.7.0-DEV.1188" ? NoPivot : Val{false}
+
 function frule(
-    (_, ΔA), ::typeof(lu!), A::StridedMatrix, pivot::Union{Val{false},Val{true}}; kwargs...
+    (_, ΔA), ::typeof(lu!), A::StridedMatrix, pivot::Union{LU_RowMaximum,LU_NoPivot}; kwargs...
 )
     F = lu!(A, pivot; kwargs...)
-    ∂factors = pivot === Val(true) ? ΔA[F.p, :] : ΔA
+    ∂factors = pivot isa LU_RowMaximum ? ΔA[F.p, :] : ΔA
     m, n = size(∂factors)
     q = min(m, n)
     if m == n  # square A
@@ -72,7 +75,7 @@ function frule(
 end
 
 function rrule(
-    ::typeof(lu), A::StridedMatrix, pivot::Union{Val{false},Val{true}}; kwargs...
+    ::typeof(lu), A::StridedMatrix, pivot::Union{LU_RowMaximum,LU_NoPivot}; kwargs...
 )
     F = lu(A, pivot; kwargs...)
     function lu_pullback(ΔF::Tangent)
@@ -124,7 +127,7 @@ function rrule(
             ldiv!(L1', ∂A1)
             rdiv!(∂A, U')
         end
-        if pivot === Val(true)
+        if pivot isa LU_RowMaximum
             ∂A = ∂A[invperm(F.p), :]
         end
         return NoTangent(), ∂A, NoTangent()

test/rulesets/LinearAlgebra/factorization.jl

@@ -20,13 +20,16 @@ function FiniteDifferences.to_vec(x::Val)
     return Bool[], Val_from_vec
 end
 
+const LU_ROW_MAXIMUM = VERSION >= v"1.7.0-DEV.1188" ? RowMaximum() : Val(true)
+const LU_NO_PIVOT = VERSION >= v"1.7.0-DEV.1188" ? NoPivot() : Val(false)
+
 @testset "Factorizations" begin
     @testset "lu decomposition" begin
         n = 10
         @testset "lu! frule" begin
             @testset "lu!(A::Matrix{$T}, $pivot) for size(A)=($m, $n)" for
                 T in (Float64, ComplexF64),
-                pivot in (Val(true), Val(false)),
+                pivot in (LU_ROW_MAXIMUM, LU_NO_PIVOT),
                 m in (7, 10, 13)
 
                 test_frule(lu!, randn(T, m, n), pivot ⊢ NoTangent())
@@ -35,26 +38,26 @@ end
                 Asingular = zeros(n, n)
                 ΔAsingular = rand_tangent(Asingular)
                 @test_throws SingularException frule(
-                    (ZeroTangent(), copy(ΔAsingular)), lu!, copy(Asingular), Val(true)
+                    (ZeroTangent(), copy(ΔAsingular)), lu!, copy(Asingular), LU_ROW_MAXIMUM
                 )
-                frule((ZeroTangent(), ΔAsingular), lu!, Asingular, Val(true); check=false)
+                frule((ZeroTangent(), ΔAsingular), lu!, Asingular, LU_ROW_MAXIMUM; check=false)
                 @test true  # above line would have errored if this was not working right
             end
         end
         @testset "lu rrule" begin
             @testset "lu(A::Matrix{$T}, $pivot) for size(A)=($m, $n)" for
                 T in (Float64, ComplexF64),
-                pivot in (Val(true), Val(false)),
+                pivot in (LU_ROW_MAXIMUM, LU_NO_PIVOT),
                 m in (7, 10, 13)
 
                 test_rrule(lu, randn(T, m, n), pivot ⊢ NoTangent())
             end
             @testset "check=false passed to primal function" begin
                 Asingular = zeros(n, n)
-                F = lu(Asingular, Val(true); check=false)
+                F = lu(Asingular, LU_ROW_MAXIMUM; check=false)
                 ΔF = Tangent{typeof(F)}(; U=rand_tangent(F.U), L=rand_tangent(F.L))
-                @test_throws SingularException rrule(lu, Asingular, Val(true))
-                _, back = rrule(lu, Asingular, Val(true); check=false)
+                @test_throws SingularException rrule(lu, Asingular, LU_ROW_MAXIMUM)
+                _, back = rrule(lu, Asingular, LU_ROW_MAXIMUM; check=false)
                 back(ΔF)
                 @test true  # above line would have errored if this was not working right
             end
@@ -72,8 +75,8 @@ end
             end
             @testset "matrix inverse using LU" begin
                 @testset "inv!(lu(::LU{$T,<:StridedMatrix}))" for T in (Float64,ComplexF64)
-                    test_frule(LinearAlgebra.inv!, lu(randn(T, n, n), Val(true)))
-                    test_rrule(inv, lu(randn(T, n, n), Val(true)))
+                    test_frule(LinearAlgebra.inv!, lu(randn(T, n, n), LU_ROW_MAXIMUM))
+                    test_rrule(inv, lu(randn(T, n, n), LU_ROW_MAXIMUM))
                 end
             end
         end