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constructors to fix 321 #348

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merged 2 commits into from
Dec 6, 2023
Merged

constructors to fix 321 #348

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6a7527e
some utility functions
willow-ahrens Dec 6, 2023
91eb280
should be fixed now
willow-ahrens Dec 6, 2023
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46 changes: 45 additions & 1 deletion ext/SparseArraysExt.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -3,7 +3,7 @@
using Finch
using Finch: AbstractCompiler, DefaultStyle, Extent
using Finch: Unfurled, Furlable, Stepper, Jumper, Run, Fill, Lookup, Simplify, Sequence, Phase, Thunk, Spike
using Finch: virtual_size, virtual_default, getstart, getstop, freshen
using Finch: virtual_size, virtual_default, getstart, getstop, freshen, SwizzleArray
using Finch.FinchNotation

using Base: @kwdef
Expand Down Expand Up @@ -34,6 +34,36 @@
return Fiber(SparseList{Ti}(Element{zero(Tv)}(arr.nzval), n, [1, length(arr.nzind) + 1], arr.nzind))
end

"""
SparseMatrixCSC(arr::Union{Fiber, SwizzleArray})

Construct a sparse matrix from a fiber or swizzle. May reuse the underlying storage if possible.
"""
function SparseArrays.SparseMatrixCSC(arr::Union{Fiber, SwizzleArray})
default(arr) === zero(eltype(arr)) || throw(ArgumentError("SparseArrays, a Julia stdlib, only supports zero default values, was given $(default(arr)) as default"))
return SparseMatrixCSC(Fiber!(Dense(SparseList(Element(0.0))), arr))
end

function SparseArrays.SparseMatrixCSC(arr::Fiber{<:Dense{Ti, <:SparseList{Ti, Ptr, Idx, <:Element{D, Tv}}}}) where {D, Ti, Ptr, Idx, Tv}
D === zero(Tv) || throw(ArgumentError("SparseArrays, a Julia stdlib, only supports zero default values, was given $D as default"))
return SparseMatrixCSC{Tv, Ti}(size(arr)..., arr.lvl.lvl.ptr, arr.lvl.lvl.idx, arr.lvl.lvl.lvl.val)
end

"""
sparse(arr::Union{Fiber, SwizzleArray})

Construct a SparseArray from a Fiber or Swizzle. May reuse the underlying storage if possible.
"""
function SparseArrays.sparse(fbr::Union{Fiber, SwizzleArray})
if ndims(fbr) == 1
return SparseVector(fbr)
elseif ndims(fbr) == 2
return SparseMatrixCSC(fbr)
else
throw(ArgumentError("SparseArrays, a Julia stdlib, only supports 1-D and 2-D arrays, was given a $(ndims(fbr))-D array"))

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end
end

@kwdef mutable struct VirtualSparseMatrixCSC
ex
Tv
Expand Down Expand Up @@ -131,6 +161,20 @@
Finch.virtual_default(arr::VirtualSparseMatrixCSC, ctx) = zero(arr.Tv)
Finch.virtual_eltype(tns::VirtualSparseMatrixCSC, ctx) = tns.Tv

"""
SparseVector(arr::Union{Fiber, SwizzleArray})

Construct a sparse matrix from a fiber or swizzle. May reuse the underlying storage if possible.
"""
function SparseArrays.SparseVector(arr::Union{Fiber, SwizzleArray})
default(arr) === zero(eltype(arr)) || throw(ArgumentError("SparseArrays, a Julia stdlib, only supports zero default values, was given $(default(arr)) as default"))
return SparseVector(Fiber!(SparseList(Element(0.0)), arr))
end

function SparseArrays.SparseVector(arr::Fiber{<:SparseList{Ti, Ptr, Idx, <:Element{D, Tv}}}) where {Ti, Ptr, Idx, Tv, D}
D === zero(Tv) || throw(ArgumentError("SparseArrays, a Julia stdlib, only supports zero default values, was given $D as default"))
return SparseVector{Tv, Ti}(size(arr)..., arr.lvl.idx, arr.lvl.lvl.val)
end
@kwdef mutable struct VirtualSparseVector
ex
Tv
Expand Down
10 changes: 10 additions & 0 deletions src/interface/abstractarrays.jl
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Expand Up @@ -53,6 +53,16 @@ end
default(a::AbstractArray) = default(typeof(a))
default(T::Type{<:AbstractArray}) = zero(eltype(T))

"""
Array(arr::Union{Fiber, SwizzleArray})

Construct an array from a fiber or swizzle. May reuse memory, will usually densify the fiber.
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seems like the implementation here can't ever re-use memory; is the "may reuse memory" here so that this could be optimized later?

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Finch stores multidimensional dense arrays as vectors, so without adding a reshaped array operator, only vectors can share memory. However, in the future I'm not sure if it will be possible to reshape arrays without creating a reshapedarray, so I thought I'd leave the possibility open. At the very least, I could add a case for dense vectors.

"""
function Base.Array(fbr::Union{Fiber, SwizzleArray})
arr = Array{eltype(fbr)}(undef, size(fbr)...)
return copyto!(arr, fbr)
end

struct AsArray{T, N, Fbr} <: AbstractArray{T, N}
fbr::Fbr
function AsArray{T, N, Fbr}(fbr::Fbr) where {T, N, Fbr}
Expand Down
22 changes: 22 additions & 0 deletions test/test_issues.jl
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Expand Up @@ -489,4 +489,26 @@ using CIndices

@finch (output_tensor .=0; for j=_,i=_,k=_; output_tensor[i,k] += a_fiber[i,j] * b_fiber[k,j]; end)
end

#https://github.com/willow-ahrens/Finch.jl/issues/321
let
A = fsprand((10, 10), 0.1)
B = sparse(A)
@test B isa SparseMatrixCSC
@test B == A
B = SparseMatrixCSC(A)
@test B isa SparseMatrixCSC
@test B == A
A = fsprand((10,), 0.1)
B = sparse(A)
@test B isa SparseVector
@test B == A
B = SparseVector(A)
@test B isa SparseVector
@test B == A
A = fsprand((10, 10), 0.1)
B = Array(A)
@test B isa Array
@test B == A
end
end
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