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array.jl
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array.jl
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using LinearAlgebra
import Adapt
@testset "constructors" begin
xs = CuArray{Int}(undef, 2, 3)
@test collect(CuArray([1 2; 3 4])) == [1 2; 3 4]
@test collect(cu[1, 2, 3]) == [1, 2, 3]
@test collect(cu([1, 2, 3])) == [1, 2, 3]
@test testf(vec, rand(5,3))
@test cu(1:3) === 1:3
@test Base.elsize(xs) == sizeof(Int)
@test CuArray{Int, 2}(xs) === xs
# test aggressive conversion to Float32, but only for floats, and only with `cu`
@test cu([1]) isa AbstractArray{Int}
@test cu(Float64[1]) isa AbstractArray{Float32}
@test cu(ComplexF64[1+1im]) isa AbstractArray{ComplexF32}
@test Adapt.adapt(CuArray, Float64[1]) isa AbstractArray{Float64}
@test Adapt.adapt(CuArray, ComplexF64[1]) isa AbstractArray{ComplexF64}
@test Adapt.adapt(CuArray{Float16}, Float64[1]) isa AbstractArray{Float16}
@test_throws ArgumentError Base.unsafe_convert(Ptr{Int}, xs)
@test_throws ArgumentError Base.unsafe_convert(Ptr{Float32}, xs)
# unsafe_wrap
@test Base.unsafe_wrap(CuArray, CU_NULL, 1; own=false).state == CUDA.ARRAY_UNMANAGED
## compare structs without using mapreduce
structeq(a,b) = false
structeq(a::T,b::T) where T = all(field->getfield(a,field) == getfield(b,field), fieldnames(T))
@test structeq(Base.unsafe_wrap(CuArray, CU_NULL, 2), CuArray{Nothing,1}(CU_NULL, (2,)))
@test structeq(Base.unsafe_wrap(CuArray{Nothing}, CU_NULL, 2), CuArray{Nothing,1}(CU_NULL, (2,)))
@test structeq(Base.unsafe_wrap(CuArray{Nothing,1}, CU_NULL, 2), CuArray{Nothing,1}(CU_NULL, (2,)))
@test structeq(Base.unsafe_wrap(CuArray, CU_NULL, (1,2)), CuArray{Nothing,2}(CU_NULL, (1,2)))
@test structeq(Base.unsafe_wrap(CuArray{Nothing}, CU_NULL, (1,2)), CuArray{Nothing,2}(CU_NULL, (1,2)))
@test structeq(Base.unsafe_wrap(CuArray{Nothing,2}, CU_NULL, (1,2)), CuArray{Nothing,2}(CU_NULL, (1,2)))
@test collect(CUDA.zeros(2, 2)) == zeros(Float32, 2, 2)
@test collect(CUDA.ones(2, 2)) == ones(Float32, 2, 2)
@test collect(CUDA.fill(0, 2, 2)) == zeros(Float32, 2, 2)
@test collect(CUDA.fill(1, 2, 2)) == ones(Float32, 2, 2)
end
@testset "adapt" begin
A = rand(Float32, 3, 3)
dA = CuArray(A)
@test Adapt.adapt(Array, dA) == A
@test Adapt.adapt(CuArray, A) isa CuArray
@test Array(Adapt.adapt(CuArray, A)) == A
end
@testset "view" begin
@test testf(rand(5)) do x
y = x[2:4]
y .= 1
x
end
@test testf(rand(5)) do x
y = view(x, 2:4)
y .= 1
x
end
@test testf(x->view(x, :, 1:4, 3), rand(Float32, 5, 4, 3))
let x = CUDA.rand(Float32, 5, 4, 3)
@test_throws BoundsError view(x, :, :, 1:10)
end
# bug in parentindices conversion
let x = CuArray{Int}(undef, 1, 1)
x[1,:] .= 42
@test Array(x)[1,1] == 42
end
# bug in conversion of indices (#506)
show(devnull, cu(view(ones(1), [1])))
# performance loss due to Array indices
let x = CuArray{Int}(undef, 1)
i = [1]
y = view(x, i)
@test parent(y) isa CuArray
@test parentindices(y) isa Tuple{CuArray}
end
@testset "GPU array source" begin
a = rand(3)
i = rand(1:3, 2)
@test testf(view, a, i)
@test testf(view, a, view(i, 2:2))
end
@testset "CPU array source" begin
a = rand(3)
i = rand(1:3, 2)
@test testf(view, a, i)
@test testf(view, a, view(i, 2:2))
end
end
@testset "reshape" begin
A = [1 2 3 4
5 6 7 8]
gA = reshape(CuArray(A),1,8)
_A = reshape(A,1,8)
_gA = Array(gA)
@test all(_A .== _gA)
A = [1,2,3,4]
gA = reshape(CuArray(A),4)
end
@testset "Dense derivatives" begin
a = CUDA.rand(Int64, 5, 4, 3)
@test a isa CuArray
# Contiguous views should return new CuArray
@test view(a, :, 1, 2) isa CuVector{Int64}
@test view(a, 1:4, 1, 2) isa CuVector{Int64}
@test view(a, :, 1:4, 3) isa CuMatrix{Int64}
@test view(a, :, :, 1) isa CuMatrix{Int64}
@test view(a, :, :, :) isa CuArray{Int64,3}
@test view(a, :) isa CuVector{Int64}
@test view(a, 1:3) isa CuVector{Int64}
@test view(a, 1, 1, 1) isa CuArray{Int64}
# Non-contiguous views should fall back to base's SubArray
@test view(a, 1:3, 1:3, 3) isa SubArray
@test view(a, 1, :, 3) isa SubArray
@test view(a, 1, 1:4, 3) isa SubArray
@test view(a, :, 1, 1:3) isa SubArray
@test view(a, :, 1:2:4, 1) isa SubArray
@test view(a, 1:2:5, 1, 1) isa SubArray
b = reshape(a, (6,10))
@test b isa CuArray
@test b isa StridedCuArray
@test view(b, :, :, 1) isa DenseCuArray
b = reshape(a, :)
@test b isa CuArray
b = reinterpret(Float64, a)
@test b isa CuArray
@test b isa StridedCuArray
@test view(b, :, :, 1) isa DenseCuArray
end
@testset "StridedArray" begin
a = CUDA.rand(Int64, 2,2,2)
@test a isa StridedCuArray
@test view(a, :, :, 1) isa StridedCuArray
@test view(a, :, 1, :) isa StridedCuArray
@test view(a, 1, :, :) isa StridedCuArray
b = reshape(a, (2,4))
@test b isa CuArray
@test b isa StridedCuArray
@test view(b, :, 1, :) isa StridedCuArray
b = reinterpret(Float64, a)
@test b isa CuArray
@test b isa StridedCuArray
@test view(b, :, 1, :) isa StridedCuArray
end
@testset "accumulate" begin
for n in (0, 1, 2, 3, 10, 10_000, 16384, 16384+1) # small, large, odd & even, pow2 and not
@test testf(x->accumulate(+, x), rand(n))
end
# multidimensional
for (sizes, dims) in ((2,) => 2,
(3,4,5) => 2,
(1, 70, 50, 20) => 3)
@test testf(x->accumulate(+, x; dims=dims), rand(Int, sizes))
end
# using initializer
for (sizes, dims) in ((2,) => 2,
(3,4,5) => 2,
(1, 70, 50, 20) => 3)
@test testf(x->accumulate(+, x; dims=dims, init=100.), rand(Int, sizes))
end
# in place
@test testf(x->(accumulate!(+, x, copy(x)); x), rand(2))
# specialized
@test testf(cumsum, rand(2))
@test testf(cumprod, rand(2))
end
@testset "logical indexing" begin
@test CuArray{Int}(undef, 2)[CuArray{Bool}(undef, 2)] isa CuArray
@test CuArray{Int}(undef, 2, 2)[CuArray{Bool}(undef, 2, 2)] isa CuArray
@test CuArray{Int}(undef, 2, 2, 2)[CuArray{Bool}(undef, 2, 2, 2)] isa CuArray
@test CuArray{Int}(undef, 2)[Array{Bool}(undef, 2)] isa CuArray
@test CuArray{Int}(undef, 2, 2)[Array{Bool}(undef, 2, 2)] isa CuArray
@test CuArray{Int}(undef, 2, 2, 2)[Array{Bool}(undef, 2, 2, 2)] isa CuArray
@test testf((x,y)->x[y], rand(2), rand(Bool, 2))
@test testf((x,y)->x[y], rand(2, 2), rand(Bool, 2, 2))
@test testf((x,y)->x[y], rand(2, 2, 2), rand(Bool, 2, 2, 2))
@test testf(x -> x[x .> 0.5], rand(2))
@test testf(x -> x[x .> 0.5], rand(2,2))
@test testf(x -> x[x .> 0.5], rand(2,2,2))
@test testf(x -> filter(y->y .> 0.5, x), rand(2))
@test testf(x -> filter(y->y .> 0.5, x), rand(2,2))
@test testf(x -> filter(y->y .> 0.5, x), rand(2,2,2))
end
@testset "reverse" begin
# 1-d out-of-place
@test testf(x->reverse(x), rand(1000))
@test testf(x->reverse(x, 10), rand(1000))
@test testf(x->reverse(x, 10, 90), rand(1000))
# 1-d in-place
@test testf(x->reverse!(x), rand(1000))
@test testf(x->reverse!(x, 10), rand(1000))
@test testf(x->reverse!(x, 10, 90), rand(1000))
# n-d out-of-place
for shape in ([1, 2, 4, 3], [4, 2], [5], [2^5, 2^5, 2^5]),
dim in 1:length(shape)
@test testf(x->reverse(x; dims=dim), rand(shape...))
cpu = rand(shape...)
gpu = CuArray(cpu)
reverse!(gpu; dims=dim)
@test Array(gpu) == reverse(cpu; dims=dim)
end
# wrapped array
@test testf(x->reverse(x), reshape(rand(2,2), 4))
end
@testset "findall" begin
# 1D
@test testf(x->findall(x), rand(Bool, 100))
@test testf(x->findall(y->y>0.5, x), rand(100))
# ND
let x = rand(Bool, 10, 10)
@test findall(x) == Array(findall(CuArray(x)))
end
let x = rand(10, 10)
@test findall(y->y>0.5, x) == Array(findall(y->y>0.5, CuArray(x)))
end
end
@testset "findfirst" begin
# 1D
@test testf(x->findfirst(x), rand(Bool, 100))
@test testf(x->findfirst(y->y>0.5, x), rand(100))
let x = fill(false, 10)
@test findfirst(x) == findfirst(CuArray(x))
end
# ND
let x = rand(Bool, 10, 10)
@test findfirst(x) == findfirst(CuArray(x))
end
let x = rand(10, 10)
@test findfirst(y->y>0.5, x) == findfirst(y->y>0.5, CuArray(x))
end
end
@testset "findmax & findmin" begin
let x = rand(Float32, 100)
@test findmax(x) == findmax(CuArray(x))
@test findmax(x; dims=1) == Array.(findmax(CuArray(x); dims=1))
x[32] = x[33] = x[55] = x[66] = NaN32
@test isequal(findmax(x), findmax(CuArray(x)))
@test isequal(findmax(x; dims=1), Array.(findmax(CuArray(x); dims=1)))
end
let x = rand(Float32, 10, 10)
@test findmax(x) == findmax(CuArray(x))
@test findmax(x; dims=1) == Array.(findmax(CuArray(x); dims=1))
@test findmax(x; dims=2) == Array.(findmax(CuArray(x); dims=2))
x[rand(CartesianIndices((10, 10)), 10)] .= NaN
@test isequal(findmax(x), findmax(CuArray(x)))
@test isequal(findmax(x; dims=1), Array.(findmax(CuArray(x); dims=1)))
end
let x = rand(Float32, 10, 10, 10)
@test findmax(x) == findmax(CuArray(x))
@test findmax(x; dims=1) == Array.(findmax(CuArray(x); dims=1))
@test findmax(x; dims=2) == Array.(findmax(CuArray(x); dims=2))
@test findmax(x; dims=3) == Array.(findmax(CuArray(x); dims=3))
x[rand(CartesianIndices((10, 10, 10)), 20)] .= NaN
@test isequal(findmax(x), findmax(CuArray(x)))
@test isequal(findmax(x; dims=1), Array.(findmax(CuArray(x); dims=1)))
@test isequal(findmax(x; dims=2), Array.(findmax(CuArray(x); dims=2)))
@test isequal(findmax(x; dims=3), Array.(findmax(CuArray(x); dims=3)))
end
let x = rand(Float32, 100)
@test findmin(x) == findmin(CuArray(x))
@test findmin(x; dims=1) == Array.(findmin(CuArray(x); dims=1))
x[32] = x[33] = x[55] = x[66] = NaN32
@test isequal(findmin(x), findmin(CuArray(x)))
@test isequal(findmin(x; dims=1), Array.(findmin(CuArray(x); dims=1)))
end
let x = rand(Float32, 10, 10)
@test findmin(x) == findmin(CuArray(x))
@test findmin(x; dims=1) == Array.(findmin(CuArray(x); dims=1))
@test findmin(x; dims=2) == Array.(findmin(CuArray(x); dims=2))
x[rand(CartesianIndices((10, 10)), 10)] .= NaN
@test isequal(findmin(x), findmin(CuArray(x)))
@test isequal(findmin(x; dims=1), Array.(findmin(CuArray(x); dims=1)))
@test isequal(findmin(x; dims=2), Array.(findmin(CuArray(x); dims=2)))
@test isequal(findmin(x; dims=3), Array.(findmin(CuArray(x); dims=3)))
end
let x = rand(Float32, 10, 10, 10)
@test findmin(x) == findmin(CuArray(x))
@test findmin(x; dims=1) == Array.(findmin(CuArray(x); dims=1))
@test findmin(x; dims=2) == Array.(findmin(CuArray(x); dims=2))
@test findmin(x; dims=3) == Array.(findmin(CuArray(x); dims=3))
x[rand(CartesianIndices((10, 10, 10)), 20)] .= NaN
@test isequal(findmin(x), findmin(CuArray(x)))
@test isequal(findmin(x; dims=1), Array.(findmin(CuArray(x); dims=1)))
@test isequal(findmin(x; dims=2), Array.(findmin(CuArray(x); dims=2)))
@test isequal(findmin(x; dims=3), Array.(findmin(CuArray(x); dims=3)))
end
end
@testset "argmax & argmin" begin
@test testf(argmax, rand(Int, 10))
@test testf(argmax, -rand(Int, 10))
@test testf(argmin, rand(Int, 10))
@test testf(argmin, -rand(Int, 10))
end
@testset "issue #543" begin
x = CUDA.rand(ComplexF32, 1)
@test x isa CuArray{Complex{Float32}}
y = exp.(x)
@test y isa CuArray{Complex{Float32}}
end
@testset "resizing" begin
a = CuArray([1,2,3])
resize!(a, 3)
@test length(a) == 3
@test Array(a) == [1,2,3]
resize!(a, 5)
@test length(a) == 5
@test Array(a)[1:3] == [1,2,3]
resize!(a, 2)
@test length(a) == 2
@test Array(a)[1:2] == [1,2]
GC.@preserve a begin
b = unsafe_wrap(CuArray{Int}, pointer(a), 2)
@test_throws ArgumentError resize!(b, 3)
end
end
@testset "aliasing" begin
x = CuArray([1,2])
y = view(x, 2:2)
@test Base.mightalias(x, x)
@test Base.mightalias(x, y)
z = view(x, 1:1)
@test Base.mightalias(x, z)
@test !Base.mightalias(y, z)
a = copy(y)::typeof(x)
@test !Base.mightalias(x, a)
a .= 3
@test Array(y) == [2]
b = Base.unaliascopy(y)::typeof(y)
@test !Base.mightalias(x, b)
b .= 3
@test Array(y) == [2]
end