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fixes the kron implementation for sparse + diagonal matrix #2804

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fixes the kron implementation for sparse + diagonal matrix #2804

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be66023
fix + tests
tam724 Jun 27, 2025
58123fa
use adapt(..)
tam724 Jun 30, 2025
5e51422
support adjoint(::Diagonal{<:CuVector{<:Complex}}) (e.g. mul! resulte…
tam724 Jul 14, 2025
c12cd91
Implement kron for {Adjoint, Transpose}(CuSparseMatrixCOO) and Diagon…
tam724 Jul 14, 2025
25b4648
Merge branch 'master' into fix_kron_cusparse_diag
tam724 Jul 14, 2025
fd04f1c
Reduce tests and test only Diagonal{Bool}
tam724 Jul 14, 2025
6195763
Merge branch 'master' into fix_kron_cusparse_diag
maleadt Jul 29, 2025
20608e3
Revert "support adjoint(::Diagonal{<:CuVector{<:Complex}}) (e.g. mul!…
tam724 Jul 29, 2025
e041932
change the type signature of kron to also match adjoint(Diagonal(.., …
tam724 Jul 29, 2025
f5baedb
populate coo-matrix manually for kron() tests
tam724 Jul 30, 2025
d675c5b
delete methods for kron with a cpu diagonal
tam724 Aug 5, 2025
54d5aec
Merge branch 'master' into fix_kron_cusparse_diag
maleadt Dec 31, 2025
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120 changes: 62 additions & 58 deletions lib/cusparse/linalg.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -33,79 +33,83 @@ function Base.reshape(A::CuSparseMatrixCOO, dims::Dims)
sparse(new_row, new_col, A.nzVal, dims[1], length(dims) == 1 ? 1 : dims[2], fmt = :coo)
end

function LinearAlgebra.kron(A::CuSparseMatrixCOO{T, Ti}, B::CuSparseMatrixCOO{T, Ti}) where {Ti, T}
mA,nA = size(A)
mB,nB = size(B)
out_shape = (mA * mB, nA * nB)
Annz = Int64(A.nnz)
Bnnz = Int64(B.nnz)

if Annz == 0 || Bnnz == 0
return CuSparseMatrixCOO(CuVector{Ti}(undef, 0), CuVector{Ti}(undef, 0), CuVector{T}(undef, 0), out_shape)
_kron_CuSparseMatrixCOO_components(A::CuSparseMatrixCOO) = A.rowInd, A.colInd, A.nzVal, identity, Int(A.nnz)
_kron_CuSparseMatrixCOO_components(At::Transpose{<:Number, <:CuSparseMatrixCOO}) = parent(At).colInd, parent(At).rowInd, parent(At).nzVal, transpose, Int(parent(At).nnz)
_kron_CuSparseMatrixCOO_components(Ah::Adjoint{<:Number, <:CuSparseMatrixCOO}) = parent(Ah).colInd, parent(Ah).rowInd, parent(Ah).nzVal, adjoint, Int(parent(Ah).nnz)

function LinearAlgebra.kron(
A::Union{CuSparseMatrixCOO{TvA, TiA}, Transpose{TvA, <:CuSparseMatrixCOO{TvA, TiA}}, Adjoint{TvA, <:CuSparseMatrixCOO{TvA, TiA}}},
B::Union{CuSparseMatrixCOO{TvB, TiB}, Transpose{TvB, <:CuSparseMatrixCOO{TvB, TiB}}, Adjoint{TvB, <:CuSparseMatrixCOO{TvB, TiB}}}
) where {TvA, TiA, TvB, TiB}
mA, nA = size(A)
mB, nB = size(B)
Ti = promote_type(TiA, TiB)
Tv = typeof(oneunit(TvA)*oneunit(TvB))

A_rowInd, A_colInd, A_nzVal, A_nzOp, A_nnz = _kron_CuSparseMatrixCOO_components(A)
B_rowInd, B_colInd, B_nzVal, B_nzOp, B_nnz = _kron_CuSparseMatrixCOO_components(B)

if A_nnz == 0 || B_nnz == 0
return CuSparseMatrixCOO{Tv, Ti}(CuVector{Ti}(undef, 0), CuVector{Ti}(undef, 0), CuVector{Tv}(undef, 0), (mA * mB, nA * nB))
end

row = (A.rowInd .- 1) .* mB
row = repeat(row, inner = Bnnz)
col = (A.colInd .- 1) .* nB
col = repeat(col, inner = Bnnz)
data = repeat(A.nzVal, inner = Bnnz)
C_nnz = A_nnz * B_nnz
C_rowInd = reshape(B_rowInd .+ Ti(mB) .* (reshape(A_rowInd, (1, A_nnz)) .- one(Ti)), C_nnz)
C_colInd = reshape(B_colInd .+ Ti(nB) .* (reshape(A_colInd, (1, A_nnz)) .- one(Ti)), C_nnz)
C_nzVal = reshape(B_nzOp.(B_nzVal) .* A_nzOp.(reshape(A_nzVal, (1, A_nnz))), C_nnz)

row .+= repeat(B.rowInd .- 1, outer = Annz) .+ 1
col .+= repeat(B.colInd .- 1, outer = Annz) .+ 1

data .*= repeat(B.nzVal, outer = Annz)

sparse(row, col, data, out_shape..., fmt = :coo)
C = CuSparseMatrixCOO{Tv, Ti}(C_rowInd, C_colInd, C_nzVal, (mA * mB, nA * nB), C_nnz)
return sort_coo(C)
end

function LinearAlgebra.kron(A::CuSparseMatrixCOO{T, Ti}, B::Diagonal) where {Ti, T}
mA,nA = size(A)
mB,nB = size(B)
out_shape = (mA * mB, nA * nB)
Annz = Int64(A.nnz)
Bnnz = nB

if Annz == 0 || Bnnz == 0
return CuSparseMatrixCOO(CuVector{Ti}(undef, 0), CuVector{Ti}(undef, 0), CuVector{T}(undef, 0), out_shape)
end
function LinearAlgebra.kron(
A::Union{CuSparseMatrixCOO{TvA, TiA}, Transpose{TvA, <:CuSparseMatrixCOO{TvA, TiA}}, Adjoint{TvA, <:CuSparseMatrixCOO{TvA, TiA}}},
B::Diagonal{TvB, <:Union{CuVector{TvB}, Base.ReshapedArray{TvB, 1, <:Adjoint{TvB, <:CuVector{TvB}}}}}
) where {TvA, TiA, TvB}
mA, nA = size(A)
mB, nB = size(B)
Ti = TiA
Tv = typeof(oneunit(TvA)*oneunit(TvB))

row = (A.rowInd .- 1) .* mB
row = repeat(row, inner = Bnnz)
col = (A.colInd .- 1) .* nB
col = repeat(col, inner = Bnnz)
data = repeat(A.nzVal, inner = Bnnz)
A_rowInd, A_colInd, A_nzVal, A_nzOp, A_nnz = _kron_CuSparseMatrixCOO_components(A)
B_rowInd, B_colInd, B_nzVal, B_nnz = one(Ti):Ti(nB), one(Ti):Ti(nB), B.diag, Int(nB)

row .+= CuVector(repeat(0:nB-1, outer = Annz)) .+ 1
col .+= CuVector(repeat(0:nB-1, outer = Annz)) .+ 1
if A_nnz == 0 || B_nnz == 0
return CuSparseMatrixCOO{Tv, Ti}(CuVector{Ti}(undef, 0), CuVector{Ti}(undef, 0), CuVector{Tv}(undef, 0), (mA * mB, nA * nB))
end

data .*= repeat(CUDA.ones(T, nB), outer = Annz)
C_nnz = A_nnz * B_nnz
C_rowInd = reshape(B_rowInd .+ Ti(mB) .* (reshape(A_rowInd, (1, A_nnz)) .- one(Ti)), C_nnz)
C_colInd = reshape(B_colInd .+ Ti(nB) .* (reshape(A_colInd, (1, A_nnz)) .- one(Ti)), C_nnz)
C_nzVal = reshape(B_nzVal .* A_nzOp.(reshape(A_nzVal, (1, A_nnz))), C_nnz)

sparse(row, col, data, out_shape..., fmt = :coo)
C = CuSparseMatrixCOO{Tv, Ti}(C_rowInd, C_colInd, C_nzVal, (mA * mB, nA * nB), C_nnz)
return sort_coo(C)
end

function LinearAlgebra.kron(A::Diagonal, B::CuSparseMatrixCOO{T, Ti}) where {Ti, T}
mA,nA = size(A)
mB,nB = size(B)
out_shape = (mA * mB, nA * nB)
Annz = nA
Bnnz = Int64(B.nnz)

if Annz == 0 || Bnnz == 0
return CuSparseMatrixCOO(CuVector{Ti}(undef, 0), CuVector{Ti}(undef, 0), CuVector{T}(undef, 0), out_shape)
end
function LinearAlgebra.kron(
A::Diagonal{TvA, <:Union{CuVector{TvA}, Base.ReshapedArray{TvA, 1, <:Adjoint{TvA, <:CuVector{TvA}}}}},
B::Union{CuSparseMatrixCOO{TvB, TiB}, Transpose{TvB, <:CuSparseMatrixCOO{TvB, TiB}}, Adjoint{TvB, <:CuSparseMatrixCOO{TvB, TiB}}}
) where {TvA, TvB, TiB}
mA, nA = size(A)
mB, nB = size(B)
Ti = TiB
Tv = typeof(oneunit(TvA)*oneunit(TvB))

row = (0:nA-1) .* mB
row = CuVector(repeat(row, inner = Bnnz))
col = (0:nA-1) .* nB
col = CuVector(repeat(col, inner = Bnnz))
data = repeat(CUDA.ones(T, nA), inner = Bnnz)
A_rowInd, A_colInd, A_nzVal, A_nnz = one(Ti):Ti(nA), one(Ti):Ti(nA), A.diag, Int(nA)
B_rowInd, B_colInd, B_nzVal, B_nzOp, B_nnz = _kron_CuSparseMatrixCOO_components(B)

row .+= repeat(B.rowInd .- 1, outer = Annz) .+ 1
col .+= repeat(B.colInd .- 1, outer = Annz) .+ 1
if A_nnz == 0 || B_nnz == 0
return CuSparseMatrixCOO{Tv, Ti}(CuVector{Ti}(undef, 0), CuVector{Ti}(undef, 0), CuVector{Tv}(undef, 0), (mA * mB, nA * nB))
end

data .*= repeat(B.nzVal, outer = Annz)
C_nnz = A_nnz * B_nnz
C_rowInd = reshape(B_rowInd .+ Ti(mB) .* (reshape(A_rowInd, (1, A_nnz)) .- one(Ti)), C_nnz)
C_colInd = reshape(B_colInd .+ Ti(nB) .* (reshape(A_colInd, (1, A_nnz)) .- one(Ti)), C_nnz)
C_nzVal = reshape(B_nzOp.(B_nzVal) .* reshape(A_nzVal, (1, A_nnz)), C_nnz)

sparse(row, col, data, out_shape..., fmt = :coo)
C = CuSparseMatrixCOO{Tv, Ti}(C_rowInd, C_colInd, C_nzVal, (mA * mB, nA * nB), C_nnz)
return sort_coo(C)
end

function LinearAlgebra.dot(y::CuVector{T}, A::CuSparseMatrixCSC{T}, x::CuVector{T}) where {T<:Union{BlasInt, BlasFloat}}
Expand Down
62 changes: 57 additions & 5 deletions test/libraries/cusparse/linalg.jl
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Original file line number Diff line number Diff line change
@@ -1,16 +1,19 @@
using CUDA.CUSPARSE
using LinearAlgebra, SparseArrays
using LinearAlgebra, SparseArrays, Adapt

m = 10
@testset "T = $T" for T in [Float32, Float64, ComplexF32, ComplexF64]
A = sprand(T, m, m, 0.2)
B = sprand(T, m, m, 0.3)
ZA = spzeros(T, m, m)
C = I(div(m, 2))
D = Diagonal(rand(T, m))
@testset "type = $typ" for typ in [CuSparseMatrixCSR, CuSparseMatrixCSC]
dA = typ(A)
dB = typ(B)
dZA = typ(ZA)
dD = adapt(CuArray, D)
dC = adapt(CuArray, C)
@testset "opnorm and norm" begin
@test opnorm(A, Inf) ≈ opnorm(dA, Inf)
@test opnorm(A, 1) ≈ opnorm(dA, 1)
Expand All @@ -35,11 +38,60 @@ m = 10
end
end
@testset "kronecker product with I opa = $opa" for opa in (identity, transpose, adjoint)
@test collect(kron(opa(dA), C)) ≈ kron(opa(A), C)
@test collect(kron(C, opa(dA))) ≈ kron(C, opa(A))
@test collect(kron(opa(dZA), C)) ≈ kron(opa(ZA), C)
@test collect(kron(C, opa(dZA))) ≈ kron(C, opa(ZA))
@test collect(kron(opa(dA), dC)) ≈ kron(opa(A), C)
@test collect(kron(dC, opa(dA))) ≈ kron(C, opa(A))
@test collect(kron(opa(dZA), dC)) ≈ kron(opa(ZA), C)
@test collect(kron(dC, opa(dZA))) ≈ kron(C, opa(ZA))
end
@testset "kronecker product with Diagonal opa = $opa" for opa in (identity, transpose, adjoint)
@test collect(kron(opa(dA), dD)) ≈ kron(opa(A), D)
@test collect(kron(dD, opa(dA))) ≈ kron(D, opa(A))
@test collect(kron(opa(dZA), dD)) ≈ kron(opa(ZA), D)
@test collect(kron(dD, opa(dZA))) ≈ kron(D, opa(ZA))
end
end
end

@testset "T = $T" for T in [Float32, Float64, ComplexF32, ComplexF64]
mat_sizes = [(2, 3), (2, 0)]
@testset "size(A) = ($(mA), $(nA)), size(B) = ($(mB), $(nB))" for (mA, nA) in mat_sizes, (mB, nB) in mat_sizes
A = sprand(T, mA, nA, 0.5)
B = sprand(T, mB, nB, 0.5)

A_I, A_J, A_V = findnz(A)
dA = CuSparseMatrixCOO{T, Cint}(adapt(CuVector{Cint}, A_I), adapt(CuVector{Cint}, A_J), adapt(CuVector{T}, A_V), size(A))
B_I, B_J, B_V = findnz(B)
dB = CuSparseMatrixCOO{T, Cint}(adapt(CuVector{Cint}, B_I), adapt(CuVector{Cint}, B_J), adapt(CuVector{T}, B_V), size(B))

@testset "kronecker (COO ⊗ COO) opa = $opa, opb = $opb" for opa in (identity, transpose, adjoint), opb in (identity, transpose, adjoint)
dC = kron(opa(dA), opb(dB))
@test collect(dC) ≈ kron(opa(A), opb(B))
@test eltype(dC) == typeof(oneunit(T) * oneunit(T))
@test dC isa CuSparseMatrixCOO
end
end
end

@testset "TA = $TA, TvB = $TvB" for TvB in [Float32, Float64, ComplexF32, ComplexF64], TA in [Bool, TvB]
A = Diagonal(rand(TA, 2))
B = sprand(TvB, 3, 4, 0.5)
dA = adapt(CuArray, A)

B_I, B_J, B_V = findnz(B)
dB = CuSparseMatrixCOO{TvB, Cint}(adapt(CuVector{Cint}, B_I), adapt(CuVector{Cint}, B_J), adapt(CuVector{TvB}, B_V), size(B))

@testset "kronecker (diagonal ⊗ COO) opa = $opa, opb = $opb" for opa in (identity, adjoint), opb in (identity, transpose, adjoint)
dC = kron(opa(dA), opb(dB))
@test collect(dC) ≈ kron(opa(A), opb(B))
@test eltype(dC) == typeof(oneunit(TA) * oneunit(TvB))
@test dC isa CuSparseMatrixCOO
end

@testset "kronecker (COO ⊗ diagonal) opa = $opa, opb = $opb" for opa in (identity, adjoint), opb in (identity, transpose, adjoint)
dC = kron(opb(dB), opa(dA))
@test collect(dC) ≈ kron(opb(B), opa(A))
@test eltype(dC) == typeof(oneunit(TvB) * oneunit(TA))
@test dC isa CuSparseMatrixCOO
end
end

Expand Down