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Refactoring: Use accessor methods when manipulating sparse matrices/vectors #32953

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Aug 22, 2019
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Refactoring: Use accessor methods when manipulating sparse matrices/vectors #32953

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b4b9d3e
Use accessor methods when touching SparseMatrixCSC
tkf Nov 27, 2018
29ce841
Merge remote-tracking branch 'origin/master' into sparse-accessors
tkf Aug 18, 2019
fad304a
Fix Statistics.jl
tkf Aug 20, 2019
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48 changes: 24 additions & 24 deletions stdlib/SparseArrays/src/higherorderfns.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -9,7 +9,7 @@ import Base: map, map!, broadcast, copy, copyto!
using Base: front, tail, to_shape
using ..SparseArrays: SparseVector, SparseMatrixCSC, AbstractSparseVector,
AbstractSparseMatrix, AbstractSparseArray, indtype, nnz, nzrange,
SparseVectorUnion, AdjOrTransSparseVectorUnion, nonzeroinds, nonzeros
SparseVectorUnion, AdjOrTransSparseVectorUnion, nonzeroinds, nonzeros, rowvals, getcolptr
using Base.Broadcast: BroadcastStyle, Broadcasted, flatten
using LinearAlgebra

Expand Down Expand Up @@ -108,24 +108,24 @@ const SpBroadcasted2{Style<:SPVM,Axes,F,Args<:Tuple{SparseVecOrMat,SparseVecOrMa
# sufficient for the purposes of map[!]/broadcast[!]. This interface treats sparse vectors
# as n-by-one sparse matrices which, though technically incorrect, is how broacast[!] views
# sparse vectors in practice.
@inline numrows(A::SparseVector) = A.n
@inline numrows(A::SparseMatrixCSC) = A.m
@inline numrows(A::SparseVector) = length(A)
@inline numrows(A::SparseMatrixCSC) = size(A, 1)
@inline numcols(A::SparseVector) = 1
@inline numcols(A::SparseMatrixCSC) = A.n
@inline numcols(A::SparseMatrixCSC) = size(A, 2)
# numrows and numcols respectively yield size(A, 1) and size(A, 2), but avoid a branch
@inline columns(A::SparseVector) = 1
@inline columns(A::SparseMatrixCSC) = 1:A.n
@inline colrange(A::SparseVector, j) = 1:length(A.nzind)
@inline columns(A::SparseMatrixCSC) = 1:size(A, 2)
@inline colrange(A::SparseVector, j) = 1:length(nonzeroinds(A))
@inline colrange(A::SparseMatrixCSC, j) = nzrange(A, j)
@inline colstartind(A::SparseVector, j) = one(indtype(A))
@inline colboundind(A::SparseVector, j) = convert(indtype(A), length(A.nzind) + 1)
@inline colstartind(A::SparseMatrixCSC, j) = A.colptr[j]
@inline colboundind(A::SparseMatrixCSC, j) = A.colptr[j + 1]
@inline storedinds(A::SparseVector) = A.nzind
@inline storedinds(A::SparseMatrixCSC) = A.rowval
@inline storedvals(A::SparseVecOrMat) = A.nzval
@inline colboundind(A::SparseVector, j) = convert(indtype(A), length(nonzeroinds(A)) + 1)
@inline colstartind(A::SparseMatrixCSC, j) = getcolptr(A)[j]
@inline colboundind(A::SparseMatrixCSC, j) = getcolptr(A)[j + 1]
@inline storedinds(A::SparseVector) = nonzeroinds(A)
@inline storedinds(A::SparseMatrixCSC) = rowvals(A)
@inline storedvals(A::SparseVecOrMat) = nonzeros(A)
@inline setcolptr!(A::SparseVector, j, val) = val
@inline setcolptr!(A::SparseMatrixCSC, j, val) = A.colptr[j] = val
@inline setcolptr!(A::SparseMatrixCSC, j, val) = getcolptr(A)[j] = val
function trimstorage!(A::SparseVecOrMat, maxstored)
resize!(storedinds(A), maxstored)
resize!(storedvals(A), maxstored)
Expand Down Expand Up @@ -214,9 +214,9 @@ end
@inline _all_args_isa(t::Tuple{Broadcasted,Vararg{Any}}, ::Type{T}) where T = _all_args_isa(t[1].args, T) & _all_args_isa(tail(t), T)
@inline _densennz(shape::NTuple{1}) = shape[1]
@inline _densennz(shape::NTuple{2}) = shape[1] * shape[2]
_maxnnzfrom(shape::NTuple{1}, A) = nnz(A) * div(shape[1], A.n)
_maxnnzfrom(shape::NTuple{2}, A::SparseVector) = nnz(A) * div(shape[1], A.n) * shape[2]
_maxnnzfrom(shape::NTuple{2}, A::SparseMatrixCSC) = nnz(A) * div(shape[1], A.m) * div(shape[2], A.n)
_maxnnzfrom(shape::NTuple{1}, A::SparseVector) = nnz(A) * div(shape[1], length(A))
_maxnnzfrom(shape::NTuple{2}, A::SparseVector) = nnz(A) * div(shape[1], length(A)) * shape[2]
_maxnnzfrom(shape::NTuple{2}, A::SparseMatrixCSC) = nnz(A) * div(shape[1], size(A, 1)) * div(shape[2], size(A, 2))
@inline _maxnnzfrom_each(shape, ::Tuple{}) = ()
@inline _maxnnzfrom_each(shape, As) = (_maxnnzfrom(shape, first(As)), _maxnnzfrom_each(shape, tail(As))...)
@inline _unchecked_maxnnzbcres(shape, As::Tuple) = min(_densennz(shape), sum(_maxnnzfrom_each(shape, As)))
Expand Down Expand Up @@ -277,18 +277,18 @@ function _map_notzeropres!(f::Tf, fillvalue, C::SparseVecOrMat, A::SparseVecOrMa
end
# helper functions for these methods and some of those below
@inline _densecoloffsets(A::SparseVector) = 0
@inline _densecoloffsets(A::SparseMatrixCSC) = 0:A.m:(A.m*(A.n - 1))
@inline _densecoloffsets(A::SparseMatrixCSC) = 0:size(A, 1):(size(A, 1)*(size(A, 2) - 1))
function _densestructure!(A::SparseVector)
expandstorage!(A, A.n)
copyto!(A.nzind, 1:A.n)
expandstorage!(A, length(A))
copyto!(nonzeroinds(A), 1:length(A))
return A
end
function _densestructure!(A::SparseMatrixCSC)
nnzA = A.m * A.n
nnzA = size(A, 1) * size(A, 2)
expandstorage!(A, nnzA)
copyto!(A.colptr, 1:A.m:(nnzA + 1))
copyto!(getcolptr(A), 1:size(A, 1):(nnzA + 1))
for k in _densecoloffsets(A)
copyto!(A.rowval, k + 1, 1:A.m)
copyto!(rowvals(A), k + 1, 1:size(A, 1))
end
return A
end
Expand Down Expand Up @@ -405,7 +405,7 @@ function _map_notzeropres!(f::Tf, fillvalue, C::SparseVecOrMat, As::Vararg{Spars
# Populate values
fill!(storedvals(C), fillvalue)
# NOTE: Combining this fill! into the loop below to avoid multiple sweeps over /
# nonsequential access of C.nzval does not appear to improve performance.
# nonsequential access of nonzeros(C) does not appear to improve performance.
rowsentinel = numrows(C) + 1
stopks = _colstartind_all(1, As)
@inbounds for (j, jo) in zip(columns(C), _densecoloffsets(C))
Expand Down Expand Up @@ -812,7 +812,7 @@ function _broadcast_notzeropres!(f::Tf, fillvalue, C::SparseVecOrMat, A::SparseV
return C
end
_finishempty!(C::SparseVector) = C
_finishempty!(C::SparseMatrixCSC) = (fill!(C.colptr, 1); C)
_finishempty!(C::SparseMatrixCSC) = (fill!(getcolptr(C), 1); C)

# special case - vector outer product
_copy(f::typeof(*), x::SparseVectorUnion, y::AdjOrTransSparseVectorUnion) = _outer(x, y)
Expand Down
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