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Test generic sparse map[!]/broadcast[!] for sparse vectors/matrices.
Condense and systematize existing tests for generic sparse map[!]/broadcast[!], and extend to sparse vectors and vector/matrix combinations. Relocate new test code to a separate file test/sparse/higherorderfns.jl corresponding to base/sparse/higherorderfns.jl. (Test/sparse/sparsevector.jl is hypothetically confined to SparseVectors, and test/sparse/sparse.jl mostly dedicated to SparseMatrixCSCs.) Move older tests of sparse broadcast[!] into that new file as well.
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‎test/choosetests.jl

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@@ -102,7 +102,7 @@ function choosetests(choices = [])
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end
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sparsetests = ["sparse/sparse", "sparse/sparsevector"]
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sparsetests = ["sparse/sparse", "sparse/sparsevector", "sparse/higherorderfns"]
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if Base.USE_GPL_LIBS
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append!(sparsetests, ["sparse/umfpack", "sparse/cholmod", "sparse/spqr"])
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end

‎test/sparse/higherorderfns.jl

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# This file is a part of Julia. License is MIT: http://julialang.org/license
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#
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# These tests cover the higher order functions specialized for sparse arrays defined in
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# base/sparse/higherorderfns.jl, particularly map[!]/broadcast[!] for SparseVectors and
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# SparseMatrixCSCs at present.
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@testset "map[!] implementation specialized for a single (input) sparse vector/matrix" begin
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N, M = 10, 12
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# (also the implementation for broadcast[!] over a single (input) sparse vector/matrix)
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for shapeA in ((N,), (N, M))
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A = sprand(shapeA..., 0.4); fA = Array(A)
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# --> test map entry point
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@test map(sin, A) == sparse(map(sin, fA))
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@test map(cos, A) == sparse(map(cos, fA))
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# --> test map! entry point
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fX = copy(fA); X = sparse(fX)
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map!(sin, X, A); X = sparse(fX) # warmup for @allocated
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@test (@allocated map!(sin, X, A)) == 0
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@test map!(sin, X, A) == sparse(map!(sin, fX, fA))
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@test map!(cos, X, A) == sparse(map!(cos, fX, fA))
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@test_throws DimensionMismatch map!(sin, X, spzeros((shapeA .- 1)...))
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end
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end
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@testset "map[!] implementation specialized for a pair of (input) sparse vectors/matrices" begin
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N, M = 10, 12
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f(x, y) = x + y + 1
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for shapeA in ((N,), (N, M))
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A, Bo = sprand(shapeA..., 0.3), sprand(shapeA..., 0.3)
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B = ndims(Bo) == 1 ? SparseVector{Float32, Int32}(Bo) : SparseMatrixCSC{Float32,Int32}(Bo)
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# use different types to check internal type stability via allocation tests below
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fA, fB = map(Array, (A, B))
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# --> test map entry point
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@test map(+, A, B) == sparse(map(+, fA, fB))
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@test map(*, A, B) == sparse(map(*, fA, fB))
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@test map(f, A, B) == sparse(map(f, fA, fB))
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@test_throws DimensionMismatch map(+, A, spzeros((shapeA .- 1)...))
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# --> test map! entry point
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fX = map(+, fA, fB); X = sparse(fX)
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map!(+, X, A, B); X = sparse(fX) # warmup for @allocated
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@test (@allocated map!(+, X, A, B)) == 0
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@test map!(+, X, A, B) == sparse(map!(+, fX, fA, fB))
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fX = map(*, fA, fB); X = sparse(fX)
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map!(*, X, A, B); X = sparse(fX) # warmup for @allocated
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@test (@allocated map!(*, X, A, B)) == 0
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@test map!(*, X, A, B) == sparse(map!(*, fX, fA, fB))
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@test map!(f, X, A, B) == sparse(map!(f, fX, fA, fB))
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@test_throws DimensionMismatch map!(f, X, A, spzeros((shapeA .- 1)...))
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end
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end
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@testset "map[!] implementation capable of handling >2 (input) sparse vectors/matrices" begin
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N, M = 10, 12
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f(x, y, z) = x + y + z + 1
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for shapeA in ((N,), (N, M))
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A, B, Co = sprand(shapeA..., 0.2), sprand(shapeA..., 0.2), sprand(shapeA..., 0.2)
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C = ndims(Co) == 1 ? SparseVector{Float32,Int32}(Co) : SparseMatrixCSC{Float32,Int32}(Co)
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# use different types to check internal type stability via allocation tests below
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fA, fB, fC = map(Array, (A, B, C))
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# --> test map entry point
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@test map(+, A, B, C) == sparse(map(+, fA, fB, fC))
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@test map(*, A, B, C) == sparse(map(*, fA, fB, fC))
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@test map(f, A, B, C) == sparse(map(f, fA, fB, fC))
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@test_throws DimensionMismatch map(+, A, B, spzeros(N, M - 1))
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# --> test map! entry point
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fX = map(+, fA, fB, fC); X = sparse(fX)
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map!(+, X, A, B, C); X = sparse(fX) # warmup for @allocated
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@test (@allocated map!(+, X, A, B, C)) == 0
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@test map!(+, X, A, B, C) == sparse(map!(+, fX, fA, fB, fC))
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fX = map(*, fA, fB, fC); X = sparse(fX)
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map!(*, X, A, B, C); X = sparse(fX) # warmup for @allocated
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@test (@allocated map!(*, X, A, B, C)) == 0
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@test map!(*, X, A, B, C) == sparse(map!(*, fX, fA, fB, fC))
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@test map!(f, X, A, B, C) == sparse(map!(f, fX, fA, fB, fC))
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@test_throws DimensionMismatch map!(f, X, A, B, spzeros((shapeA .- 1)...))
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end
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end
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@testset "broadcast[!] implementation specialized for a single (input) sparse vector/matrix" begin
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# broadcast[!] for a single sparse vector/matrix falls back to map[!], tested extensively
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# above. here we simply lightly exercise the relevant broadcast[!] entry points.
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N, M, p = 10, 12, 0.4
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a, A = sprand(N, p), sprand(N, M, p)
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fa, fA = Array(a), Array(A)
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@test broadcast(sin, a) == sparse(broadcast(sin, fa))
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@test broadcast(sin, A) == sparse(broadcast(sin, fA))
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@test broadcast!(sin, copy(a), a) == sparse(broadcast!(sin, copy(fa), fa))
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@test broadcast!(sin, copy(A), A) == sparse(broadcast!(sin, copy(fA), fA))
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end
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@testset "broadcast[!] implementation specialized for pairs of (input) sparse vectors/matrices" begin
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N, M, p = 10, 12, 0.3
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f(x, y) = x + y + 1
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mats = (sprand(N, M, p), sprand(N, 1, p), sprand(1, M, p), sprand(1, 1, 1.0), spzeros(1, 1))
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vecs = (sprand(N, p), sprand(1, 1.0), spzeros(1))
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tens = (mats..., vecs...)
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for Xo in tens
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X = ndims(Xo) == 1 ? SparseVector{Float32,Int32}(Xo) : SparseMatrixCSC{Float32,Int32}(Xo)
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# use different types to check internal type stability via allocation tests below
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shapeX, fX = size(X), Array(X)
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for Y in tens
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fY = Array(Y)
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# --> test broadcast entry point
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@test broadcast(+, X, Y) == sparse(broadcast(+, fX, fY))
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@test broadcast(*, X, Y) == sparse(broadcast(*, fX, fY))
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@test broadcast(f, X, Y) == sparse(broadcast(f, fX, fY))
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try Base.Broadcast.broadcast_indices(spzeros((shapeX .- 1)...), Y)
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catch @test_throws DimensionMismatch broadcast(+, spzeros((shapeX .- 1)...), Y) end
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# --> test broadcast! entry point / +-like zero-preserving op
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fZ = broadcast(+, fX, fY); Z = sparse(fZ)
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broadcast!(+, Z, X, Y); Z = sparse(fZ) # warmup for @allocated
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@test (@allocated broadcast!(+, Z, X, Y)) == 0
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@test broadcast!(+, Z, X, Y) == sparse(broadcast!(+, fZ, fX, fY))
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# --> test broadcast! entry point / *-like zero-preserving op
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fZ = broadcast(*, fX, fY); Z = sparse(fZ)
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broadcast!(*, Z, X, Y); Z = sparse(fZ) # warmup for @allocated
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@test (@allocated broadcast!(*, Z, X, Y)) == 0
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@test broadcast!(*, Z, X, Y) == sparse(broadcast!(*, fZ, fX, fY))
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# --> test broadcast! entry point / not zero-preserving op
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fZ = broadcast(f, fX, fY); Z = sparse(fZ)
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broadcast!(f, Z, X, Y); Z = sparse(fZ) # warmup for @allocated
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@test (@allocated broadcast!(f, Z, X, Y)) == 0
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@test broadcast!(f, Z, X, Y) == sparse(broadcast!(f, fZ, fX, fY))
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# --> test shape checks for both braodcast and broadcast! entry points
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try Base.Broadcast.check_broadcast_indices(indices(Z), spzeros((shapeX .- 1)...), Y)
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catch @test_throws DimensionMismatch broadcast!(f, Z, spzeros((shapeX .- 1)...), Y) end
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end
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end
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end
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@testset "broadcast[!] implementation capable of handling >2 (input) sparse vectors/matrices" begin
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N, M, p = 10, 12, 0.3
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f(x, y, z) = x + y + z + 1
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mats = (sprand(N, M, p), sprand(N, 1, p), sprand(1, M, p), sprand(1, 1, 1.0), spzeros(1, 1))
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vecs = (sprand(N, p), sprand(1, 1.0), spzeros(1))
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tens = (mats..., vecs...)
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for Xo in tens
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X = ndims(Xo) == 1 ? SparseVector{Float32,Int32}(Xo) : SparseMatrixCSC{Float32,Int32}(Xo)
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# use different types to check internal type stability via allocation tests below
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shapeX, fX = size(X), Array(X)
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for Y in tens, Z in tens
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fY, fZ = Array(Y), Array(Z)
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# --> test broadcast entry point
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@test broadcast(+, X, Y, Z) == sparse(broadcast(+, fX, fY, fZ))
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@test broadcast(*, X, Y, Z) == sparse(broadcast(*, fX, fY, fZ))
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@test broadcast(f, X, Y, Z) == sparse(broadcast(f, fX, fY, fZ))
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try Base.Broadcast.broadcast_indices(spzeros((shapeX .- 1)...), Y, Z)
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catch @test_throws DimensionMismatch broadcast(+, spzeros((shapeX .- 1)...), Y, Z) end
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# --> test broadcast! entry point / +-like zero-preserving op
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fQ = broadcast(+, fX, fY, fZ); Q = sparse(fQ)
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broadcast!(+, Q, X, Y, Z); Q = sparse(fQ) # warmup for @allocated
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@test (@allocated broadcast!(+, Q, X, Y, Z)) == 0
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@test broadcast!(+, Q, X, Y, Z) == sparse(broadcast!(+, fQ, fX, fY, fZ))
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# --> test broadcast! entry point / *-like zero-preserving op
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fQ = broadcast(*, fX, fY, fZ); Q = sparse(fQ)
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broadcast!(*, Q, X, Y, Z); Q = sparse(fQ) # warmup for @allocated
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@test (@allocated broadcast!(*, Q, X, Y, Z)) == 0
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@test broadcast!(*, Q, X, Y, Z) == sparse(broadcast!(*, fQ, fX, fY, fZ))
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# --> test broadcast! entry point / not zero-preserving op
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fQ = broadcast(f, fX, fY, fZ); Q = sparse(fQ)
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broadcast!(f, Q, X, Y, Z); Q = sparse(fQ) # warmup for @allocated
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@test_broken (@allocated broadcast!(f, Z, X, Y)) == 0
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# the preceding test allocates 16 bytes in the entry point for broadcast!, but
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# none of the earlier tests of the same code path allocate. no allocation shows
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# up with --track-allocation=user. allocation shows up on the first line of the
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# entry point for broadcast! with --track-allocation=all, but that first line
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# almost certainly should not allocate. so not certain what's going on.
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@test broadcast!(f, Q, X, Y, Z) == sparse(broadcast!(f, fQ, fX, fY, fZ))
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# --> test shape checks for both braodcast and broadcast! entry points
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try Base.Broadcast.check_broadcast_indices(indices(Q), spzeros((shapeX .- 1)...), Y, Z)
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catch @test_throws DimensionMismatch broadcast!(f, Q, spzeros((shapeX .- 1)...), Y, Z) end
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end
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end
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end
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# Older tests of sparse broadcast, now largely covered by the tests above
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@testset "assorted tests of sparse broadcast over two input arguments" begin
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N, p = 10, 0.3
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A, B, CF = sprand(N, N, p), sprand(N, N, p), rand(N, N)
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AF, BF, C = Array(A), Array(B), sparse(CF)
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@test A .* B == AF .* BF
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@test A[1,:] .* B == AF[1,:] .* BF
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@test A[:,1] .* B == AF[:,1] .* BF
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@test A .* B[1,:] == AF .* BF[1,:]
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@test A .* B[:,1] == AF .* BF[:,1]
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@test A .* B == AF .* BF
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@test A[1,:] .* BF == AF[1,:] .* BF
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@test A[:,1] .* BF == AF[:,1] .* BF
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@test A .* BF[1,:] == AF .* BF[1,:]
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@test A .* BF[:,1] == AF .* BF[:,1]
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@test A .* B == AF .* BF
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@test AF[1,:] .* B == AF[1,:] .* BF
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@test AF[:,1] .* B == AF[:,1] .* BF
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@test AF .* B[1,:] == AF .* BF[1,:]
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@test AF .* B[:,1] == AF .* BF[:,1]
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@test A .* B == AF .* BF
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@test A[1,:] .* B == AF[1,:] .* BF
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@test A[:,1] .* B == AF[:,1] .* BF
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@test A .* B[1,:] == AF .* BF[1,:]
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@test A .* B[:,1] == AF .* BF[:,1]
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@test A .* 3 == AF .* 3
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@test 3 .* A == 3 .* AF
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#@test A[1,:] .* 3 == AF[1,:] .* 3
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@test all(A[1,:] .* 3 .== AF[1,:] .* 3)
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#@test A[:,1] .* 3 == AF[:,1] .* 3
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@test all(A[:,1] .* 3 .== AF[:,1] .* 3)
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#TODO: simple comparation with == returns false because the left side is a (two-dimensional) SparseMatrixCSC
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# while the right side is a Vector
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@test A .- 3 == AF .- 3
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@test 3 .- A == 3 .- AF
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@test A .- B == AF .- BF
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@test A - AF == zeros(AF)
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@test AF - A == zeros(AF)
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@test A[1,:] .- B == AF[1,:] .- BF
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@test A[:,1] .- B == AF[:,1] .- BF
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@test A .- B[1,:] == AF .- BF[1,:]
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@test A .- B[:,1] == AF .- BF[:,1]
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@test A .+ 3 == AF .+ 3
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@test 3 .+ A == 3 .+ AF
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@test A .+ B == AF .+ BF
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@test A + AF == AF + A
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@test (A .< B) == (AF .< BF)
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@test (A .!= B) == (AF .!= BF)
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@test A ./ 3 == AF ./ 3
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@test A .\ 3 == AF .\ 3
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@test 3 ./ A == 3 ./ AF
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@test 3 .\ A == 3 .\ AF
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@test A .\ C == AF .\ CF
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@test A ./ C == AF ./ CF
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@test A ./ CF[:,1] == AF ./ CF[:,1]
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@test A .\ CF[:,1] == AF .\ CF[:,1]
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@test BF ./ C == BF ./ CF
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@test BF .\ C == BF .\ CF
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@test A .^ 3 == AF .^ 3
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@test 3 .^ A == 3 .^ AF
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@test A .^ BF[:,1] == AF .^ BF[:,1]
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@test BF[:,1] .^ A == BF[:,1] .^ AF
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@test spzeros(0,0) + spzeros(0,0) == zeros(0,0)
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@test spzeros(0,0) * spzeros(0,0) == zeros(0,0)
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@test spzeros(1,0) .+ spzeros(2,1) == zeros(2,0)
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@test spzeros(1,0) .* spzeros(2,1) == zeros(2,0)
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@test spzeros(1,2) .+ spzeros(0,1) == zeros(0,2)
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@test spzeros(1,2) .* spzeros(0,1) == zeros(0,2)
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end

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