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Your PR requires formatting changes to meet the project's style guidelines. Click here to view the suggested changes.diff --git a/lib/mkl/array.jl b/lib/mkl/array.jl
index d712081..2f46847 100644
--- a/lib/mkl/array.jl
+++ b/lib/mkl/array.jl
@@ -18,7 +18,7 @@ mutable struct oneSparseMatrixCSC{Tv, Ti} <: oneAbstractSparseMatrix{Tv, Ti}
colPtr::oneVector{Ti}
rowVal::oneVector{Ti}
nzVal::oneVector{Tv}
- dims::NTuple{2,Int}
+ dims::NTuple{2, Int}
nnz::Ti
end
@@ -46,7 +46,7 @@ SparseArrays.nnz(A::oneAbstractSparseMatrix) = A.nnz
SparseArrays.nonzeros(A::oneAbstractSparseMatrix) = A.nzVal
for (gpu, cpu) in [:oneSparseMatrixCSR => :SparseMatrixCSC,
- :oneSparseMatrixCSC => :SparseMatrixCSC,
+ :oneSparseMatrixCSC => :SparseMatrixCSC,
:oneSparseMatrixCOO => :SparseMatrixCSC]
@eval Base.show(io::IOContext, x::$gpu) =
show(io, $cpu(x))
diff --git a/lib/mkl/interfaces.jl b/lib/mkl/interfaces.jl
index 9e9100c..a9fe668 100644
--- a/lib/mkl/interfaces.jl
+++ b/lib/mkl/interfaces.jl
@@ -7,9 +7,9 @@ function LinearAlgebra.generic_matvecmul!(C::oneVector{T}, tA::AbstractChar, A::
sparse_gemv!(tA, _add.alpha, A, B, _add.beta, C)
end
-function LinearAlgebra.generic_matvecmul!(C::oneVector{T}, tA::AbstractChar, A::oneSparseMatrixCSC{T}, B::oneVector{T}, _add::MulAddMul) where T <: BlasReal
+function LinearAlgebra.generic_matvecmul!(C::oneVector{T}, tA::AbstractChar, A::oneSparseMatrixCSC{T}, B::oneVector{T}, _add::MulAddMul) where {T <: BlasReal}
tA = tA in ('S', 's', 'H', 'h') ? 'T' : (tA == 'N' ? 'T' : 'N')
- sparse_gemv!(tA, _add.alpha, A, B, _add.beta, C)
+ return sparse_gemv!(tA, _add.alpha, A, B, _add.beta, C)
end
function LinearAlgebra.generic_matmatmul!(C::oneMatrix{T}, tA, tB, A::oneSparseMatrixCSR{T}, B::oneMatrix{T}, _add::MulAddMul) where T <: BlasFloat
@@ -18,10 +18,10 @@ function LinearAlgebra.generic_matmatmul!(C::oneMatrix{T}, tA, tB, A::oneSparseM
sparse_gemm!(tA, tB, _add.alpha, A, B, _add.beta, C)
end
-function LinearAlgebra.generic_matmatmul!(C::oneMatrix{T}, tA, tB, A::oneSparseMatrixCSC{T}, B::oneMatrix{T}, _add::MulAddMul) where T <: BlasReal
+function LinearAlgebra.generic_matmatmul!(C::oneMatrix{T}, tA, tB, A::oneSparseMatrixCSC{T}, B::oneMatrix{T}, _add::MulAddMul) where {T <: BlasReal}
tA = tA in ('S', 's', 'H', 'h') ? 'T' : (tA == 'N' ? 'T' : 'N')
tB = tB in ('S', 's', 'H', 'h') ? 'N' : tB
- sparse_gemm!(tA, tB, _add.alpha, A, B, _add.beta, C)
+ return sparse_gemm!(tA, tB, _add.alpha, A, B, _add.beta, C)
end
for SparseMatrixType in (:oneSparseMatrixCSR,)
diff --git a/lib/mkl/wrappers_sparse.jl b/lib/mkl/wrappers_sparse.jl
index e4c0fa7..9dbce54 100644
--- a/lib/mkl/wrappers_sparse.jl
+++ b/lib/mkl/wrappers_sparse.jl
@@ -46,7 +46,7 @@ for (fname, elty, intty) in ((:onemklSsparse_set_csr_data , :Float32 , :Int3
nnzA = length(A.nzval)
queue = global_queue(context(nzVal), device())
$fname(sycl_queue(queue), handle_ptr[], n, m, 'O', colPtr, rowVal, nzVal) # CSC of A is CSR of Aᵀ
- dA = oneSparseMatrixCSC{$elty, $intty}(handle_ptr[], colPtr, rowVal, nzVal, (m,n), nnzA)
+ dA = oneSparseMatrixCSC{$elty, $intty}(handle_ptr[], colPtr, rowVal, nzVal, (m, n), nnzA)
finalizer(sparse_release_matrix_handle, dA)
return dA
end
@@ -122,17 +122,21 @@ for SparseMatrix in (:oneSparseMatrixCSR, :oneSparseMatrixCOO)
end
# Special handling for CSC matrices since they are stored as transposed CSR
-for (fname, elty) in ((:onemklSsparse_gemv, :Float32),
- (:onemklDsparse_gemv, :Float64),
- (:onemklCsparse_gemv, :ComplexF32),
- (:onemklZsparse_gemv, :ComplexF64))
+for (fname, elty) in (
+ (:onemklSsparse_gemv, :Float32),
+ (:onemklDsparse_gemv, :Float64),
+ (:onemklCsparse_gemv, :ComplexF32),
+ (:onemklZsparse_gemv, :ComplexF64),
+ )
@eval begin
- function sparse_gemv!(trans::Char,
- alpha::Number,
- A::oneSparseMatrixCSC{$elty},
- x::oneStridedVector{$elty},
- beta::Number,
- y::oneStridedVector{$elty})
+ function sparse_gemv!(
+ trans::Char,
+ alpha::Number,
+ A::oneSparseMatrixCSC{$elty},
+ x::oneStridedVector{$elty},
+ beta::Number,
+ y::oneStridedVector{$elty}
+ )
# CSC(A) is represented by storing CSR(A^T). Map operations accordingly:
# - trans = 'N': want A*x -> use op(S)='T' with S=A^T.
@@ -233,18 +237,22 @@ function sparse_optimize_gemm!(trans::Char, transB::Char, nrhs::Int, A::oneSpars
end
# Special handling for CSC matrices since they are stored as transposed CSR (S = A^T)
-for (fname, elty) in ((:onemklSsparse_gemm, :Float32),
- (:onemklDsparse_gemm, :Float64),
- (:onemklCsparse_gemm, :ComplexF32),
- (:onemklZsparse_gemm, :ComplexF64))
+for (fname, elty) in (
+ (:onemklSsparse_gemm, :Float32),
+ (:onemklDsparse_gemm, :Float64),
+ (:onemklCsparse_gemm, :ComplexF32),
+ (:onemklZsparse_gemm, :ComplexF64),
+ )
@eval begin
- function sparse_gemm!(transa::Char,
- transb::Char,
- alpha::Number,
- A::oneSparseMatrixCSC{$elty},
- B::oneStridedMatrix{$elty},
- beta::Number,
- C::oneStridedMatrix{$elty})
+ function sparse_gemm!(
+ transa::Char,
+ transb::Char,
+ alpha::Number,
+ A::oneSparseMatrixCSC{$elty},
+ B::oneStridedMatrix{$elty},
+ beta::Number,
+ C::oneStridedMatrix{$elty}
+ )
# Map op(A) to op(S) where S = A^T stored as CSR in the handle
# transa: 'N' -> op(S)='T'; 'T' -> op(S)='N'; 'C' ->
@@ -256,8 +264,8 @@ for (fname, elty) in ((:onemklSsparse_gemm, :Float32),
(nB != nC) && (transb == 'N') && throw(ArgumentError("B and C must have the same number of columns."))
(mB != nC) && (transb != 'N') && throw(ArgumentError("Bᵀ and C must have the same number of columns."))
nrhs = size(B, 2)
- ldb = max(1,stride(B,2))
- ldc = max(1,stride(C,2))
+ ldb = max(1, stride(B, 2))
+ ldc = max(1, stride(C, 2))
queue = global_queue(context(C), device())
diff --git a/test/onemkl.jl b/test/onemkl.jl
index ab0a620..d4bfa0b 100644
--- a/test/onemkl.jl
+++ b/test/onemkl.jl
@@ -3,7 +3,7 @@ if Sys.iswindows()
else
using oneAPI
-using oneAPI.oneMKL: band, bandex, oneSparseMatrixCSR, oneSparseMatrixCOO, oneSparseMatrixCSC
+ using oneAPI.oneMKL: band, bandex, oneSparseMatrixCSR, oneSparseMatrixCOO, oneSparseMatrixCSC
using SparseArrays
using LinearAlgebra
@@ -1088,16 +1088,16 @@ end
end
end
- @testset "oneSparseMatrixCSC" begin
- (T isa Complex) && continue
- for S in (Int32, Int64)
- A = sprand(T, 20, 10, 0.5)
- A = SparseMatrixCSC{T, S}(A)
- B = oneSparseMatrixCSC(A)
- A2 = SparseMatrixCSC(B)
- @test A == A2
+ @testset "oneSparseMatrixCSC" begin
+ (T isa Complex) && continue
+ for S in (Int32, Int64)
+ A = sprand(T, 20, 10, 0.5)
+ A = SparseMatrixCSC{T, S}(A)
+ B = oneSparseMatrixCSC(A)
+ A2 = SparseMatrixCSC(B)
+ @test A == A2
+ end
end
- end
@testset "oneSparseMatrixCOO" begin
for S in (Int32, Int64)
@@ -1110,7 +1110,7 @@ end
end
@testset "sparse gemv" begin
- @testset "$SparseMatrix" for SparseMatrix in (oneSparseMatrixCOO, oneSparseMatrixCSR, oneSparseMatrixCSC)
+ @testset "$SparseMatrix" for SparseMatrix in (oneSparseMatrixCOO, oneSparseMatrixCSR, oneSparseMatrixCSC)
@testset "transa = $transa" for (transa, opa) in [('N', identity), ('T', transpose), ('C', adjoint)]
A = sprand(T, 20, 10, 0.5)
x = transa == 'N' ? rand(T, 10) : rand(T, 20)
@@ -1130,129 +1130,134 @@ end
end
@testset "sparse gemm" begin
- @testset "$SparseMatrix" for SparseMatrix in (oneSparseMatrixCSR, oneSparseMatrixCSC)
- @testset "transa = $transa" for (transa, opa) in [('N', identity), ('T', transpose), ('C', adjoint)]
- @testset "transb = $transb" for (transb, opb) in [('N', identity), ('T', transpose), ('C', adjoint)]
- (transb == 'N') || continue
- A = sprand(T, 10, 10, 0.5)
- B = transb == 'N' ? rand(T, 10, 2) : rand(T, 2, 10)
- C = rand(T, 10, 2)
+ @testset "$SparseMatrix" for SparseMatrix in (oneSparseMatrixCSR, oneSparseMatrixCSC)
+ @testset "transa = $transa" for (transa, opa) in [('N', identity), ('T', transpose), ('C', adjoint)]
+ @testset "transb = $transb" for (transb, opb) in [('N', identity), ('T', transpose), ('C', adjoint)]
+ (transb == 'N') || continue
+ A = sprand(T, 10, 10, 0.5)
+ B = transb == 'N' ? rand(T, 10, 2) : rand(T, 2, 10)
+ C = rand(T, 10, 2)
+
+ dA = SparseMatrix(A)
+ dB = oneMatrix{T}(B)
+ dC = oneMatrix{T}(C)
- dA = SparseMatrix(A)
- dB = oneMatrix{T}(B)
- dC = oneMatrix{T}(C)
-
- alpha = rand(T)
- beta = rand(T)
- oneMKL.sparse_optimize_gemm!(transa, dA)
- oneMKL.sparse_gemm!(transa, transb, alpha, dA, dB, beta, dC)
- @test alpha * opa(A) * opb(B) + beta * C ≈ collect(dC)
- end
+ alpha = rand(T)
+ beta = rand(T)
+ oneMKL.sparse_optimize_gemm!(transa, dA)
+ oneMKL.sparse_gemm!(transa, transb, alpha, dA, dB, beta, dC)
+ @test alpha * opa(A) * opb(B) + beta * C ≈ collect(dC)
+ end
end
end
end
@testset "sparse symv" begin
- @testset "$SparseMatrix" for SparseMatrix in (oneSparseMatrixCSR, oneSparseMatrixCSC)
- @testset "uplo = $uplo" for uplo in ('L', 'U')
+ @testset "$SparseMatrix" for SparseMatrix in (oneSparseMatrixCSR, oneSparseMatrixCSC)
+ @testset "uplo = $uplo" for uplo in ('L', 'U')
A = sprand(T, 10, 10, 0.5)
- A = A + A'
+ A = A + A'
x = rand(T, 10)
y = rand(T, 10)
- dA = uplo == 'L' ? SparseMatrix(A |> tril) : SparseMatrix(A |> triu)
+ dA = uplo == 'L' ? SparseMatrix(A |> tril) : SparseMatrix(A |> triu)
dx = oneVector{T}(x)
dy = oneVector{T}(y)
alpha = rand(T)
beta = rand(T)
- oneMKL.sparse_symv!(uplo, alpha, dA, dx, beta, dy)
- # @test alpha * A * x + beta * y ≈ collect(dy)
+ oneMKL.sparse_symv!(uplo, alpha, dA, dx, beta, dy)
+ # @test alpha * A * x + beta * y ≈ collect(dy)
end
end
end
- @testset "sparse trmv" begin
- @testset "$SparseMatrix" for SparseMatrix in (oneSparseMatrixCSR, oneSparseMatrixCSC)
- @testset "transa = $transa" for (transa, opa) in [('N', identity), ('T', transpose), ('C', adjoint)]
- for (uplo, diag, wrapper) in [('L', 'N', LowerTriangular), ('L', 'U', UnitLowerTriangular),
- ('U', 'N', UpperTriangular), ('U', 'U', UnitUpperTriangular)]
- (transa == 'N') || continue
- A = sprand(T, 10, 10, 0.5)
- x = rand(T, 10)
- y = rand(T, 10)
+ @testset "sparse trmv" begin
+ @testset "$SparseMatrix" for SparseMatrix in (oneSparseMatrixCSR, oneSparseMatrixCSC)
+ @testset "transa = $transa" for (transa, opa) in [('N', identity), ('T', transpose), ('C', adjoint)]
+ for (uplo, diag, wrapper) in [
+ ('L', 'N', LowerTriangular), ('L', 'U', UnitLowerTriangular),
+ ('U', 'N', UpperTriangular), ('U', 'U', UnitUpperTriangular),
+ ]
+ (transa == 'N') || continue
+ A = sprand(T, 10, 10, 0.5)
+ x = rand(T, 10)
+ y = rand(T, 10)
- B = uplo == 'L' ? tril(A) : triu(A)
- B = diag == 'U' ? B - Diagonal(B) + I : B
- dA = SparseMatrix(B)
- dx = oneVector{T}(x)
- dy = oneVector{T}(y)
+ B = uplo == 'L' ? tril(A) : triu(A)
+ B = diag == 'U' ? B - Diagonal(B) + I : B
+ dA = SparseMatrix(B)
+ dx = oneVector{T}(x)
+ dy = oneVector{T}(y)
- alpha = rand(T)
- beta = rand(T)
+ alpha = rand(T)
+ beta = rand(T)
- oneMKL.sparse_optimize_trmv!(uplo, transa, diag, dA)
- oneMKL.sparse_trmv!(uplo, transa, diag, alpha, dA, dx, beta, dy)
- @test alpha * wrapper(opa(A)) * x + beta * y ≈ collect(dy)
- end
+ oneMKL.sparse_optimize_trmv!(uplo, transa, diag, dA)
+ oneMKL.sparse_trmv!(uplo, transa, diag, alpha, dA, dx, beta, dy)
+ @test alpha * wrapper(opa(A)) * x + beta * y ≈ collect(dy)
+ end
end
end
end
- @testset "sparse trsv" begin
- @testset "$SparseMatrix" for SparseMatrix in (oneSparseMatrixCSR, oneSparseMatrixCSC)
- @testset "transa = $transa" for (transa, opa) in [('N', identity), ('T', transpose), ('C', adjoint)]
+ @testset "sparse trsv" begin
+ @testset "$SparseMatrix" for SparseMatrix in (oneSparseMatrixCSR, oneSparseMatrixCSC)
+ @testset "transa = $transa" for (transa, opa) in [('N', identity), ('T', transpose), ('C', adjoint)]
for (uplo, diag, wrapper) in [('L', 'N', LowerTriangular), ('L', 'U', UnitLowerTriangular),
- ('U', 'N', UpperTriangular), ('U', 'U', UnitUpperTriangular)]
+ ('U', 'N', UpperTriangular), ('U', 'U', UnitUpperTriangular),
+ ]
(transa == 'N') || continue
alpha = rand(T)
A = rand(T, 10, 10) + I
A = sparse(A)
- x = rand(T, 10)
- y = rand(T, 10)
+ x = rand(T, 10)
+ y = rand(T, 10)
B = uplo == 'L' ? tril(A) : triu(A)
B = diag == 'U' ? B - Diagonal(B) + I : B
- dA = SparseMatrix(B)
- dx = oneVector{T}(x)
- dy = oneVector{T}(y)
-
- oneMKL.sparse_optimize_trsv!(uplo, transa, diag, dA)
- oneMKL.sparse_trsv!(uplo, transa, diag, alpha, dA, dx, dy)
- y = wrapper(opa(A)) \ (alpha * x)
- @test y ≈ collect(dy)
+ dA = SparseMatrix(B)
+ dx = oneVector{T}(x)
+ dy = oneVector{T}(y)
+
+ oneMKL.sparse_optimize_trsv!(uplo, transa, diag, dA)
+ oneMKL.sparse_trsv!(uplo, transa, diag, alpha, dA, dx, dy)
+ y = wrapper(opa(A)) \ (alpha * x)
+ @test y ≈ collect(dy)
+ end
end
end
end
- end
- @testset "sparse trsm" begin
- @testset "$SparseMatrix" for SparseMatrix in (oneSparseMatrixCSR, oneSparseMatrixCSC)
- @testset "transa = $transa" for (transa, opa) in [('N', identity), ('T', transpose), ('C', adjoint)]
- @testset "transx = $transx" for (transx, opx) in [('N', identity), ('T', transpose), ('C', adjoint)]
- (transx != 'N') && continue
- for (uplo, diag, wrapper) in [('L', 'N', LowerTriangular), ('L', 'U', UnitLowerTriangular),
- ('U', 'N', UpperTriangular), ('U', 'U', UnitUpperTriangular)]
- (transa == 'N') || continue
- alpha = rand(T)
- A = rand(T, 10, 10) + I
- A = sparse(A)
- X = transx == 'N' ? rand(T, 10, 4) : rand(T, 4, 10)
- Y = rand(T, 10, 4)
-
- B = uplo == 'L' ? tril(A) : triu(A)
- B = diag == 'U' ? B - Diagonal(B) + I : B
- dA = SparseMatrix(B)
- dX = oneMatrix{T}(X)
- dY = oneMatrix{T}(Y)
-
- oneMKL.sparse_optimize_trsm!(uplo, transa, diag, dA)
- oneMKL.sparse_trsm!(uplo, transa, transx, diag, alpha, dA, dX, dY)
- Y = wrapper(opa(A)) \ (alpha * opx(X))
- @test Y ≈ collect(dY)
-
- oneMKL.sparse_optimize_trsm!(uplo, transa, diag, 4, dA)
- end
+ @testset "sparse trsm" begin
+ @testset "$SparseMatrix" for SparseMatrix in (oneSparseMatrixCSR, oneSparseMatrixCSC)
+ @testset "transa = $transa" for (transa, opa) in [('N', identity), ('T', transpose), ('C', adjoint)]
+ @testset "transx = $transx" for (transx, opx) in [('N', identity), ('T', transpose), ('C', adjoint)]
+ (transx != 'N') && continue
+ for (uplo, diag, wrapper) in [
+ ('L', 'N', LowerTriangular), ('L', 'U', UnitLowerTriangular),
+ ('U', 'N', UpperTriangular), ('U', 'U', UnitUpperTriangular),
+ ]
+ (transa == 'N') || continue
+ alpha = rand(T)
+ A = rand(T, 10, 10) + I
+ A = sparse(A)
+ X = transx == 'N' ? rand(T, 10, 4) : rand(T, 4, 10)
+ Y = rand(T, 10, 4)
+
+ B = uplo == 'L' ? tril(A) : triu(A)
+ B = diag == 'U' ? B - Diagonal(B) + I : B
+ dA = SparseMatrix(B)
+ dX = oneMatrix{T}(X)
+ dY = oneMatrix{T}(Y)
+
+ oneMKL.sparse_optimize_trsm!(uplo, transa, diag, dA)
+ oneMKL.sparse_trsm!(uplo, transa, transx, diag, alpha, dA, dX, dY)
+ Y = wrapper(opa(A)) \ (alpha * opx(X))
+ @test Y ≈ collect(dY)
+
+ oneMKL.sparse_optimize_trsm!(uplo, transa, diag, 4, dA)
+ end
end
end
end |
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