Add check keyword argument to generic calls

`lu!(fact, A, check=false)` works for CPU but not for GPU because this keyword argument is not allowed. This makes it hard to use the generic interface in generic code because it has slightly different arguments. This adds the `check` keyword argument to match the original argument interface.

src/generic.jl

@@ -12,7 +12,7 @@ The type `T` can be `Float32`, `Float64`, `ComplexF32` or `ComplexF64`.
 
 * `solver`: an opaque structure [`CudssSolver`](@ref) that stores the factors of the LU decomposition.
 """
-function LinearAlgebra.lu(A::CuSparseMatrixCSR{T,Cint}) where T <: BlasFloat
+function LinearAlgebra.lu(A::CuSparseMatrixCSR{T,Cint}; check = false) where T <: BlasFloat
   n = checksquare(A)
   solver = CudssSolver(A, "G", 'F')
   x = CudssMatrix(T, n)
@@ -28,7 +28,7 @@ end
 Compute the LU factorization of a sparse matrix `A` on an NVIDIA GPU, reusing the symbolic factorization stored in `solver`.
 The type `T` can be `Float32`, `Float64`, `ComplexF32` or `ComplexF64`.
 """
-function LinearAlgebra.lu!(solver::CudssSolver{T}, A::CuSparseMatrixCSR{T,Cint}) where T <: BlasFloat
+function LinearAlgebra.lu!(solver::CudssSolver{T}, A::CuSparseMatrixCSR{T,Cint}; check = false) where T <: BlasFloat
   n = checksquare(A)
   cudss_set(solver, A)
   x = CudssMatrix(T, n)
@@ -55,7 +55,7 @@ The type `T` can be `Float32`, `Float64`, `ComplexF32` or `ComplexF64`.
 
 * `solver`: Opaque structure [`CudssSolver`](@ref) that stores the factors of the LDLᴴ decomposition.
 """
-function LinearAlgebra.ldlt(A::CuSparseMatrixCSR{T,Cint}; view::Char='F') where T <: BlasFloat
+function LinearAlgebra.ldlt(A::CuSparseMatrixCSR{T,Cint}; view::Char='F', check = false) where T <: BlasFloat
   n = checksquare(A)
   structure = T <: Real ? "S" : "H"
   solver = CudssSolver(A, structure, view)
@@ -76,7 +76,7 @@ LinearAlgebra.ldlt(A::Hermitian{T,<:CuSparseMatrixCSR{T,Cint}}) where T <: BlasF
 Compute the LDLᴴ factorization of a sparse matrix `A` on an NVIDIA GPU, reusing the symbolic factorization stored in `solver`.
 The type `T` can be `Float32`, `Float64`, `ComplexF32` or `ComplexF64`.
 """
-function LinearAlgebra.ldlt!(solver::CudssSolver{T}, A::CuSparseMatrixCSR{T,Cint}) where T <: BlasFloat
+function LinearAlgebra.ldlt!(solver::CudssSolver{T}, A::CuSparseMatrixCSR{T,Cint}; check = false) where T <: BlasFloat
   n = checksquare(A)
   cudss_set(solver, A)
   x = CudssMatrix(T, n)
@@ -103,7 +103,7 @@ The type `T` can be `Float32`, `Float64`, `ComplexF32` or `ComplexF64`.
 
 * `solver`: Opaque structure [`CudssSolver`](@ref) that stores the factors of the LLᴴ decomposition.
 """
-function LinearAlgebra.cholesky(A::CuSparseMatrixCSR{T,Cint}; view::Char='F') where T <: BlasFloat
+function LinearAlgebra.cholesky(A::CuSparseMatrixCSR{T,Cint}; view::Char='F', check = false) where T <: BlasFloat
   n = checksquare(A)
   structure = T <: Real ? "SPD" : "HPD"
   solver = CudssSolver(A, structure, view)
@@ -114,16 +114,16 @@ function LinearAlgebra.cholesky(A::CuSparseMatrixCSR{T,Cint}; view::Char='F') wh
   return solver
 end
 
-LinearAlgebra.cholesky(A::Symmetric{T,<:CuSparseMatrixCSR{T,Cint}}) where T <: BlasReal = LinearAlgebra.cholesky(A.data, view=A.uplo)
-LinearAlgebra.cholesky(A::Hermitian{T,<:CuSparseMatrixCSR{T,Cint}}) where T <: BlasFloat = LinearAlgebra.cholesky(A.data, view=A.uplo)
+LinearAlgebra.cholesky(A::Symmetric{T,<:CuSparseMatrixCSR{T,Cint}}; check = false) where T <: BlasReal = LinearAlgebra.cholesky(A.data, view=A.uplo)
+LinearAlgebra.cholesky(A::Hermitian{T,<:CuSparseMatrixCSR{T,Cint}}; check = false) where T <: BlasFloat = LinearAlgebra.cholesky(A.data, view=A.uplo)
 
 """
     solver = cholesky!(solver::CudssSolver{T}, A::CuSparseMatrixCSR{T,Cint})
 
 Compute the LLᴴ factorization of a sparse matrix `A` on an NVIDIA GPU, reusing the symbolic factorization stored in `solver`.
 The type `T` can be `Float32`, `Float64`, `ComplexF32` or `ComplexF64`.
 """
-function LinearAlgebra.cholesky!(solver::CudssSolver{T}, A::CuSparseMatrixCSR{T,Cint}) where T <: BlasFloat
+function LinearAlgebra.cholesky!(solver::CudssSolver{T}, A::CuSparseMatrixCSR{T,Cint}; check = false) where T <: BlasFloat
   n = checksquare(A)
   cudss_set(solver, A)
   x = CudssMatrix(T, n)