Use TestSuite for QR and LQ

Author: kshyatt

Project.toml

@@ -33,10 +33,11 @@ GenericSchur = "0.5.6"
 JET = "0.9, 0.10"
 LinearAlgebra = "1"
 Mooncake = "0.4.183"
+Random = "1"
 SafeTestsets = "0.1"
 StableRNGs = "1"
 Test = "1"
-TestExtras = "0.2,0.3"
+TestExtras = "0.3.2"
 Zygote = "0.7"
 julia = "1.10"
 
@@ -47,11 +48,12 @@ ChainRulesTestUtils = "cdddcdb0-9152-4a09-a978-84456f9df70a"
 CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
 JET = "c3a54625-cd67-489e-a8e7-0a5a0ff4e31b"
 Mooncake = "da2b9cff-9c12-43a0-ae48-6db2b0edb7d6"
+Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 SafeTestsets = "1bc83da4-3b8d-516f-aca4-4fe02f6d838f"
 StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 TestExtras = "5ed8adda-3752-4e41-b88a-e8b09835ee3a"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [targets]
-test = ["Aqua", "JET", "SafeTestsets", "Test", "TestExtras", "ChainRulesCore", "ChainRulesTestUtils", "StableRNGs", "Zygote", "CUDA", "AMDGPU", "GenericLinearAlgebra", "GenericSchur", "Mooncake"]
+test = ["Aqua", "JET", "SafeTestsets", "Test", "TestExtras", "ChainRulesCore", "ChainRulesTestUtils", "Random", "StableRNGs", "Zygote", "CUDA", "AMDGPU", "GenericLinearAlgebra", "GenericSchur", "Mooncake"]

ext/MatrixAlgebraKitGenericLinearAlgebraExt.jl

@@ -56,7 +56,7 @@ function MatrixAlgebraKit.eigh_vals!(A::AbstractMatrix, D, ::GLA_QRIteration)
     return eigvals!(Hermitian(A); sortby = real)
 end
 
-function MatrixAlgebraKit.default_qr_algorithm(::Type{T}; kwargs...) where {T <: StridedMatrix{<:Union{BigFloat, Complex{BigFloat}}}}
+function MatrixAlgebraKit.default_qr_algorithm(::Type{T}; kwargs...) where {T <: StridedMatrix{<:Union{Float16, ComplexF16, BigFloat, Complex{BigFloat}}}}
     return GLA_HouseholderQR(; kwargs...)
 end
 
@@ -109,7 +109,7 @@ function _gla_householder_qr!(A::AbstractMatrix, Q, R; positive = false, blocksi
     return Q, R
 end
 
-function MatrixAlgebraKit.default_lq_algorithm(::Type{T}; kwargs...) where {T <: StridedMatrix{<:Union{BigFloat, Complex{BigFloat}}}}
+function MatrixAlgebraKit.default_lq_algorithm(::Type{T}; kwargs...) where {T <: StridedMatrix{<:Union{Float16, ComplexF16, BigFloat, Complex{BigFloat}}}}
     return MatrixAlgebraKit.LQViaTransposedQR(GLA_HouseholderQR(; kwargs...))
 end
 

src/implementations/lq.jl

@@ -153,8 +153,8 @@ function _lapack_lq!(
     computeL = length(L) > 0
     inplaceQ = Q === A
 
-    if pivoted
-        throw(ArgumentError("LAPACK does not provide an implementation for a pivoted LQ decomposition"))
+    if pivoted && (blocksize > 1)
+        throw(ArgumentError("LAPACK does not provide a blocked implementation for a pivoted LQ decomposition"))
     end
     if inplaceQ && (computeL || positive || blocksize > 1 || n < m)
         throw(ArgumentError("inplace Q only supported if matrix is wide (`m <= n`), L is not required, and using the unblocked algorithm (`blocksize=1`) with `positive=false`"))

src/implementations/qr.jl

@@ -270,10 +270,12 @@ function _gpu_unmqr!(
 end
 
 function _gpu_qr!(
-        A::AbstractMatrix, Q::AbstractMatrix, R::AbstractMatrix; positive = false, blocksize = 1
+        A::AbstractMatrix, Q::AbstractMatrix, R::AbstractMatrix; positive = false, blocksize = 1, pivoted = false
     )
     blocksize > 1 &&
         throw(ArgumentError("CUSOLVER/ROCSOLVER does not provide a blocked implementation for a QR decomposition"))
+    pivoted &&
+        throw(ArgumentError("CUSOLVER/ROCSOLVER does not provide a pivoted implementation for a QR decomposition"))
     m, n = size(A)
     minmn = min(m, n)
     computeR = length(R) > 0
@@ -309,10 +311,12 @@ function _gpu_qr!(
 end
 
 function _gpu_qr_null!(
-        A::AbstractMatrix, N::AbstractMatrix; positive = false, blocksize = 1
+        A::AbstractMatrix, N::AbstractMatrix; positive = false, blocksize = 1, pivoted = false
     )
     blocksize > 1 &&
         throw(ArgumentError("CUSOLVER/ROCSOLVER does not provide a blocked implementation for a QR decomposition"))
+    pivoted &&
+        throw(ArgumentError("CUSOLVER/ROCSOLVER does not provide a pivoted implementation for a QR decomposition"))
     m, n = size(A)
     minmn = min(m, n)
     fill!(N, zero(eltype(N)))