Lib sparse

.github/workflows/CI.yml

@@ -18,7 +18,7 @@ jobs:
         os:
           - ubuntu-latest
           - windows-latest
-          - macOS-13 # intel
+          - macOS-15-intel
         arch:
           - x64
         include:

Project.toml

@@ -1,10 +1,11 @@
 name = "AppleAccelerate"
 uuid = "13e28ba4-7ad8-5781-acae-3021b1ed3924"
-version = "0.4.5"
+version = "0.5.0"
 
 [deps]
 Libdl = "8f399da3-3557-5675-b5ff-fb832c97cbdb"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
+SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 
 [compat]
 julia = "1.9"

src/AppleAccelerate.jl

@@ -141,6 +141,11 @@ if Sys.isapple()
     include("Util.jl")
     include("Array.jl")
     include("DSP.jl")
+    include("libSparse/wrappers.jl")
+    include("libSparse/AASparseMatrices.jl")
+    include("libSparse/AAFactorizations.jl")
+    export AASparseMatrix, muladd!
+    export AAFactorization, solve, solve!, factor!
 end
 
 end # module

src/libSparse/AAFactorizations.jl

@@ -0,0 +1,89 @@
+@doc """Factorization object.
+
+Create via `f = AAFactorization(A::SparseMatrixCSC{T, Int64})`. Calls to `solve`,
+`ldiv`, and their in-place versions require explicitly passing in the
+factorization object as the first argument. On construction, the struct stores a
+placeholder yet-to-be-factored object: the factorization is computed upon the first call
+to `solve`, or by explicitly calling `factor!`. If the matrix is symmetric, it defaults to
+a Cholesky factorization; otherwise, it defaults to QR.
+"""
+mutable struct AAFactorization{T<:vTypes} <: LinearAlgebra.Factorization{T}
+    matrixObj::AASparseMatrix{T}
+    _factorization::SparseOpaqueFactorization{T}
+end
+
+# returns an AAFactorization containing A and a dummy "yet-to-be-factored" factorization.
+function AAFactorization(A::AASparseMatrix{T}) where T<:vTypes
+    obj = AAFactorization(A, SparseOpaqueFactorization(T))
+    function cleanup(aa_fact)
+        # If it's yet-to-be-factored, then there's nothing to release
+        if !(aa_fact._factorization.status in (SparseYetToBeFactored,
+                                                            SparseStatusReleased))
+            SparseCleanup(aa_fact._factorization)
+        end
+    end
+    return finalizer(cleanup, obj)
+end
+
+LinearAlgebra.factorize(A::AASparseMatrix{T}) where T<:vTypes = AAFactorization(A)
+
+# julia's LinearAlgebra module doesn't provide similar constructors.
+AAFactorization(M::SparseMatrixCSC{T, Int64}) where T<:vTypes =
+                                        AAFactorization(AASparseMatrix(M))
+
+# easiest way to make this follow the defaults and naming conventions of LinearAlgebra?
+# TODO: add tests for the different kinds of factorizations, beyond QR.
+function factor!(aa_fact::AAFactorization{T},
+            kind::SparseFactorization_t = SparseFactorizationTBD) where T<:vTypes
+    if aa_fact._factorization.status == SparseYetToBeFactored
+        if kind == SparseFactorizationTBD
+            # so far I'm only dealing with ordinary and symmetric
+            kind = issymmetric(aa_fact.matrixObj) ? SparseFactorizationCholesky :
+                            SparseFactorizationQR
+        end
+        aa_fact._factorization = SparseFactor(kind, aa_fact.matrixObj.matrix)
+        if aa_fact._factorization.status == SparseMatrixIsSingular
+            # throw a SingularException? Factoring a singular matrix usually does not
+            # make it this far: the call to SparseFactor throws an error.
+            throw(ErrorException("The matrix is singular."))
+        elseif aa_fact._factorization.status == SparseStatusFailed
+            throw(ErrorException("Factorization failed: check that the matrix"
+                        * " has the correct properties for the factorization."))
+        elseif aa_fact._factorization.status != SparseStatusOk
+            throw(ErrorException("Something went wrong internally. Error type: "
+                                * String(aa_fact._factorization.status)))
+        end
+    end
+end
+
+function solve(aa_fact::AAFactorization{T}, b::StridedVecOrMat{T}) where T<:vTypes
+    @assert size(aa_fact.matrixObj)[2] == size(b, 1)
+    factor!(aa_fact)
+    x = Array{T}(undef, size(aa_fact.matrixObj)[2], size(b)[2:end]...)
+    SparseSolve(aa_fact._factorization, b, x)
+    return x
+end
+
+function solve!(aa_fact::AAFactorization{T}, xb::StridedVecOrMat{T}) where T<:vTypes
+    @assert (xb isa StridedVector) ||
+            (size(aa_fact.matrixObj)[1] == size(aa_fact.matrixObj)[2]) "Can't in-place " *
+            "solve: x and b are different sizes and Julia cannot resize a matrix."
+    factor!(aa_fact)
+    SparseSolve(aa_fact._factorization, xb)
+    return xb # written in imitation of KLU.jl, which also returns
+end
+
+LinearAlgebra.ldiv!(aa_fact::AAFactorization{T}, xb::StridedVecOrMat{T}) where T<:vTypes =
+        solve!(aa_fact, xb)
+
+function LinearAlgebra.ldiv!(x::StridedVecOrMat{T},
+                            aa_fact::AAFactorization{T},
+                            b::StridedVecOrMat{T}) where T<:vTypes
+    @assert size(aa_fact.matrixObj)[2] == size(b, 1)
+    factor!(aa_fact)
+    SparseSolve(aa_fact._factorization, b, x)
+    return x
+end
+
+
+

src/libSparse/AASparseMatrices.jl

@@ -0,0 +1,116 @@
+
+@doc """Matrix wrapper, containing the Apple sparse matrix struct
+and the pointed-to data. Construct from a `SparseMatrixCSC`.
+
+Multiplication (`*`) and multiply-add (`muladd!`) with both
+`Vector` and `Matrix` objects are working.
+`transpose` creates a new matrix structure with the opposite
+transpose flag, that references the same CSC data.
+"""
+mutable struct AASparseMatrix{T<:vTypes} <: AbstractMatrix{T}
+    matrix::SparseMatrix{T}
+    _colptr::Vector{Clong}
+    _rowval::Vector{Cint}
+    _nzval::Vector{T}
+end
+
+# I use StridedVector here because it allows for views/references,
+# so you can do shallow copies: same pointed-to data. Better way?
+"""Constructor for advanced usage: col and row here are 0-indexed CSC data.
+Could allow for shared  `_colptr`, `_rowval`, `_nzval` between multiple
+structs via views or references. Currently unused."""
+function AASparseMatrix(n::Int, m::Int,
+            col::StridedVector{Clong}, row::StridedVector{Cint},
+            data::StridedVector{T},
+            attributes::att_type = ATT_ORDINARY) where T<:vTypes
+    @assert stride(col, 1) == 1 && stride(row, 1) == 1 && stride(data, 1) == 1
+    # I'm assuming here that pointer(row) == pointer(_row_inds),
+    # ie that col, row, and data are passed by reference, not by value.
+    s = SparseMatrixStructure(n, m, pointer(col),
+                    pointer(row), attributes, 1)
+    m = SparseMatrix(s, pointer(data))
+    return AASparseMatrix(m, col, row, data)
+end
+
+function AASparseMatrix(sparseM::SparseMatrixCSC{T, Int64},
+                        attributes::att_type = ATT_ORDINARY) where T<:vTypes
+    if issymmetric(sparseM) && attributes == ATT_ORDINARY
+        return AASparseMatrix(tril(sparseM), ATT_SYMMETRIC | ATT_LOWER_TRIANGLE)
+    elseif (istril(sparseM) || istriu(sparseM)) && attributes == ATT_ORDINARY
+        attributes = istril(sparseM) ? ATT_TRI_LOWER : ATT_TRI_UPPER
+    end
+    if attributes in (ATT_TRI_LOWER, ATT_TRI_UPPER) &&
+                    all(diag(sparseM) .== one(eltype(sparseM)))
+        attributes |= ATT_UNIT_TRIANGULAR
+    end
+    c = Clong.(sparseM.colptr .+ -1)
+    r = Cint.(sparseM.rowval .+ -1)
+    vals = copy(sparseM.nzval)
+    return AASparseMatrix(size(sparseM)..., c, r, vals, attributes)
+end
+
+Base.size(M::AASparseMatrix) = (M.matrix.structure.rowCount,
+                                    M.matrix.structure.columnCount)
+Base.eltype(M::AASparseMatrix) = eltype(M._nzval)
+LinearAlgebra.issymmetric(M::AASparseMatrix) = (M.matrix.structure.attributes &
+                                                ATT_KIND_MASK) == ATT_SYMMETRIC
+istri(M::AASparseMatrix) = (M.matrix.structure.attributes
+                                    & ATT_KIND_MASK) == ATT_TRIANGULAR
+LinearAlgebra.istriu(M::AASparseMatrix) = istri(M) && (MM.matrix.structure.attributes
+                                        & ATT_TRIANGLE_MASK == ATT_LOWER_TRIANGLE)
+LinearAlgebra.istril(M::AASparseMatrix) = istri(M) && (MM.matrix.structure.attributes
+                                        & ATT_TRIANGLE_MASK == ATT_UPPER_TRIANGLE)
+
+function Base.getindex(M::AASparseMatrix, i::Int, j::Int)
+    @assert all((1, 1) .<= (i,j) .<= size(M))
+    (startCol, endCol) = (M._colptr[j], M._colptr[j+1]-1) .+ 1
+    rowsInCol = @view M._rowval[startCol:endCol]
+    ind = searchsortedfirst(rowsInCol, i-1)
+    if ind <= length(rowsInCol) && rowsInCol[ind] == i-1
+        return M._nzval[startCol+ind-1]
+    end
+    return zero(eltype(M))
+end
+
+function Base.getindex(M::AASparseMatrix, i::Int)
+    @assert 1 <= i <= size(M)[1]*size(M)[2]
+    return M[(i-1) % size(M)[1] + 1, div(i-1, size(M)[1]) + 1]
+end
+# Creates a new structure, referring to the same data,
+# but with the transpose flag (in attributes) flipped.
+# TODO: untested
+Base.transpose(M::AASparseMatrix) = AASparseMatrix(SparseGetTranspose(M.matrix),
+                        M._colptr, M._rowval, M._nzval)
+
+function Base.:(*)(A::AASparseMatrix{T}, x::StridedVecOrMat{T}) where T<:vTypes
+    @assert size(x)[1] == size(A)[2]
+    y = Array{T}(undef, size(A)[1], size(x)[2:end]...)
+    SparseMultiply(A.matrix, x, y)
+    return y
+end
+
+function Base.:(*)(alpha::T, A::AASparseMatrix{T},
+                            x::StridedVecOrMat{T}) where T<:vTypes
+    @assert size(x)[1] == size(A)[2]
+    y = Array{T}(undef, size(A)[1], size(x)[2:end]...)
+    SparseMultiply(alpha, A.matrix, x, y)
+    return y
+end
+
+"""
+Computes y += A*x in place. Note that this modifies its LAST argument.
+"""
+function muladd!(A::AASparseMatrix{T}, x::StridedVecOrMat{T},
+                    y::StridedVecOrMat{T}) where T<:vTypes
+    @assert size(x) == size(y) && size(x)[1] == size(A)[2]
+    SparseMultiplyAdd(A.matrix, x, y)
+end
+
+"""
+Computes y += alpha*A*x in place. Note that this modifies its LAST argument.
+"""
+function muladd!(alpha::T, A::AASparseMatrix{T},
+                x::StridedVecOrMat{T}, y::StridedVecOrMat{T}) where T<:vTypes
+    @assert size(x) == size(y) && size(x)[1] == size(A)[2]
+    SparseMultiplyAdd(alpha, A.matrix, x, y)
+end