Use square_view to avoid square check, instead of new hack

Project.toml

@@ -16,8 +16,8 @@ LinearAlgebra = "1.5"
 LoopVectorization = "0.10,0.11, 0.12"
 Polyester = "0.3.2,0.4.1, 0.5, 0.6, 0.7"
 PrecompileTools = "1"
-StrideArraysCore = "0.1.13, 0.2.1, 0.3, 0.4.1, 0.5"
-TriangularSolve = "0.1.1"
+StrideArraysCore = "0.5.5"
+TriangularSolve = "0.2"
 julia = "1.5"
 
 [extras]

src/lu.jl

@@ -3,11 +3,9 @@ using TriangularSolve: ldiv!
 using LinearAlgebra: BlasInt, BlasFloat, LU, UnitLowerTriangular, checknonsingular, BLAS,
                      LinearAlgebra, Adjoint, Transpose, UpperTriangular, AbstractVecOrMat
 using StrideArraysCore
+using StrideArraysCore: square_view
 using Polyester: @batch
 
-@generated function _unit_lower_triangular(B::A) where {T, A <: AbstractMatrix{T}}
-    Expr(:new, UnitLowerTriangular{T, A}, :B)
-end
 # 1.7 compat
 normalize_pivot(t::Val{T}) where {T} = t
 to_stdlib_pivot(t) = t
@@ -55,6 +53,7 @@ end
 if CUSTOMIZABLE_PIVOT
     function LinearAlgebra.ldiv!(A::LU{T, <:StridedMatrix, <:NotIPIV},
             B::StridedVecOrMat{T}) where {T <: BlasFloat}
+        tri = @inbounds square_view(A.factors, size(A.factors, 1))
         ldiv!(UpperTriangular(A.factors), ldiv!(UnitLowerTriangular(A.factors), B))
     end
 end
@@ -138,10 +137,10 @@ end
     info = reckernel!(A, Val(Pivot), m, mnmin, ipiv, info, blocksize, Val(Thread))::Int
     @inbounds if m < n # fat matrix
         # [AL AR]
-        AL = @view A[:, 1:m]
+        AL = square_view(A, m)
         AR = @view A[:, (m + 1):n]
         Pivot && apply_permutation!(ipiv, AR, Val{Thread}())
-        ldiv!(_unit_lower_triangular(AL), AR, Val{Thread}())
+        ldiv!(UnitLowerTriangular(AL), AR, Val{Thread}())
     end
     info
 end
@@ -190,7 +189,7 @@ function reckernel!(A::AbstractMatrix{T}, pivot::Val{Pivot}, m, n, ipiv, info, b
 
         # ======================================== #
         # Now, our LU process looks like this
-        # [ P1 ] [ A11 A21 ]   [ L11 0 ] [ U11 U12  ]
+        # [ P1 ] [ A11 A12 ]   [ L11 0 ] [ U11 U12  ]
         # [    ] [         ] = [       ] [          ]
         # [ P2 ] [ A21 A22 ]   [ L21 I ] [ 0   A′22 ]
         # ======================================== #
@@ -203,7 +202,7 @@ function reckernel!(A::AbstractMatrix{T}, pivot::Val{Pivot}, m, n, ipiv, info, b
         #  AL  AR
         # [A11 A12]
         # [A21 A22]
-        A11 = @view A[1:n1, 1:n1]
+        A11 = square_view(A, n1)
         A12 = @view A[1:n1, (n1 + 1):n]
         A21 = @view A[(n1 + 1):m, 1:n1]
         A22 = @view A[(n1 + 1):m, (n1 + 1):n]
@@ -223,7 +222,7 @@ function reckernel!(A::AbstractMatrix{T}, pivot::Val{Pivot}, m, n, ipiv, info, b
         # [ A22 ]    [ 0  ] [ A22 ]
         Pivot && apply_permutation!(P1, AR, thread)
         # A12 = L11 U12  =>  U12 = L11 \ A12
-        ldiv!(_unit_lower_triangular(A11), A12, thread)
+        ldiv!(UnitLowerTriangular(A11), A12, thread)
         # Schur complement:
         # We have A22 = L21 U12 + A′22, hence
         # A′22 = A22 - L21 U12