Merge pull request #35 from christiangnrd/splittests

[NFC] Split up tests into different files

Author: anicusan

test/accumulate.jl

@@ -0,0 +1,296 @@
+@testset "accumulate_1d" begin
+
+    Random.seed!(0)
+
+    # Single block exlusive scan (each block processes two elements)
+    for num_elems in 1:256
+        x = array_from_host(ones(Int32, num_elems))
+        y = copy(x)
+        AK.accumulate!(+, y; init=0, inclusive=false, block_size=128)
+        yh = Array(y)
+        @test all(yh .== 0:length(yh) - 1)
+    end
+
+    # Single block inclusive scan
+    for num_elems in 1:256
+        x = array_from_host(rand(1:1000, num_elems), Int32)
+        y = copy(x)
+        AK.accumulate!(+, y; init=0, block_size=128)
+        @test all(Array(y) .== accumulate(+, Array(x)))
+    end
+
+    # Large exclusive scan
+    for _ in 1:1000
+        num_elems = rand(1:100_000)
+        x = array_from_host(ones(Int32, num_elems))
+        y = copy(x)
+        AK.accumulate!(+, y; init=0, inclusive=false)
+        yh = Array(y)
+        @test all(yh .== 0:length(yh) - 1)
+    end
+
+    # Large inclusive scan
+    for _ in 1:1000
+        num_elems = rand(1:100_000)
+        x = array_from_host(rand(1:1000, num_elems), Int32)
+        y = copy(x)
+        AK.accumulate!(+, y; init=0)
+        @test all(Array(y) .== accumulate(+, Array(x)))
+    end
+
+    # Stress-testing small block sizes -> many blocks
+    for _ in 1:100
+        num_elems = rand(1:100_000)
+        x = array_from_host(rand(1:1000, num_elems), Int32)
+        y = copy(x)
+        AK.accumulate!(+, y; init=0, block_size=16)
+        @test all(Array(y) .== accumulate(+, Array(x)))
+    end
+
+    # Allowing N-dimensional arrays, still reduced as 1D
+    for _ in 1:100
+        n1 = rand(1:100)
+        n2 = rand(1:100)
+        n3 = rand(1:100)
+        vh = rand(Float32, n1, n2, n3)
+        v = array_from_host(vh)
+        AK.accumulate!(+, v; init=0)
+        @test all(Array(v) .≈ accumulate(+, vh))
+    end
+
+    # Ensuring the init value is respected
+    for _ in 1:100
+        num_elems = rand(1:100_000)
+        x = array_from_host(rand(1:1000, num_elems), Int32)
+        y = similar(x)
+        init = rand(-1000:1000)
+        AK.accumulate!(+, y, x; init=Int32(init))
+        @test all(Array(y) .== accumulate(+, Array(x), init=init))
+    end
+
+    # Exclusive scan
+    x = array_from_host(ones(Int32, 10))
+    y = copy(x)
+    AK.accumulate!(+, y; init=0, inclusive=false)
+    @test all(Array(y) .== 0:9)
+
+    # Test init value is respected with exclusive scan too
+    x = array_from_host(ones(Int32, 10))
+    y = copy(x)
+    init = 10
+    AK.accumulate!(+, y; init=Int32(init), inclusive=false)
+    @test all(Array(y) .== 10:19)
+
+    # Testing different settings
+    AK.accumulate!(+, array_from_host(ones(Int32, 1000)), init=0, inclusive=false,
+                block_size=128,
+                temp=array_from_host(zeros(Int32, 1000)),
+                temp_flags=array_from_host(zeros(Int8, 1000)))
+    AK.accumulate(+, array_from_host(ones(Int32, 1000)), init=0, inclusive=false,
+                block_size=128,
+                temp=array_from_host(zeros(Int64, 1000)),
+                temp_flags=array_from_host(zeros(Int8, 1000)))
+end
+
+
+@testset "accumulate_nd" begin
+    Random.seed!(0)
+
+    # Test all possible corner cases against Base.accumulate
+    for dims in 1:4
+        for isize in 0:3
+            for jsize in 0:3
+                for ksize in 0:3
+                    sh = rand(Int32(1):Int32(100), isize, jsize, ksize)
+                    s = array_from_host(sh)
+                    d = AK.accumulate(+, s; init=Int32(0), dims=dims)
+
+                    dh = Array(d)
+                    dhres = accumulate(+, sh, init=Int32(0), dims=dims)
+                    @test dh == dhres
+                    @test eltype(dh) == eltype(dhres)
+                end
+            end
+        end
+    end
+
+    # Fuzzy correctness testing
+    for _ in 1:100
+        for dims in 1:3
+            n1 = rand(1:100)
+            n2 = rand(1:100)
+            n3 = rand(1:100)
+            vh = rand(Int32(1):Int32(100), n1, n2, n3)
+            v = array_from_host(vh)
+
+            s = AK.accumulate(+, v; init=Int32(0), dims=dims)
+            sh = Array(s)
+            @test sh == accumulate(+, vh, init=Int32(0), dims=dims)
+        end
+    end
+
+    for _ in 1:100
+        for dims in 1:3
+            n1 = rand(1:100)
+            n2 = rand(1:100)
+            n3 = rand(1:100)
+            vh = rand(UInt32(1):UInt32(100), n1, n2, n3)
+            v = array_from_host(vh)
+
+            s = AK.accumulate(+, v; init=UInt32(0), dims=dims)
+            sh = Array(s)
+            @test sh == accumulate(+, vh, init=UInt32(0), dims=dims)
+        end
+    end
+
+    for _ in 1:100
+        for dims in 1:3
+            n1 = rand(1:100)
+            n2 = rand(1:100)
+            n3 = rand(1:100)
+            vh = rand(Float32, n1, n2, n3)
+            v = array_from_host(vh)
+
+            s = AK.accumulate(+, v; init=Float32(0), dims=dims)
+            sh = Array(s)
+            @test all(sh .≈ accumulate(+, vh, init=Float32(0), dims=dims))
+        end
+    end
+
+    # Ensure the init value is respected
+    for _ in 1:100
+        for dims in 1:3
+            n1 = rand(1:100)
+            n2 = rand(1:100)
+            n3 = rand(1:100)
+            vh = rand(Float32, n1, n2, n3)
+            v = array_from_host(vh)
+            init = rand(-1000:1000)
+            s = AK.accumulate(+, v; init=Float32(init), dims=dims)
+            sh = Array(s)
+            @test all(sh .≈ accumulate(+, vh, init=Float32(init), dims=dims))
+        end
+    end
+
+    # Exclusive scan
+    vh = ones(Int32, 10, 10)
+    v = array_from_host(vh)
+    s = AK.accumulate(+, v; init=0, dims=2, inclusive=false)
+    sh = Array(s)
+    @test all([sh[i, :] == 0:9 for i in 1:10])
+
+    # Test init value is respected with exclusive scan too
+    vh = ones(Int32, 10, 10)
+    v = array_from_host(vh)
+    s = AK.accumulate(+, v; init=10, dims=2, inclusive=false)
+    sh = Array(s)
+    @test all([sh[i, :] == 10:19 for i in 1:10])
+
+    # Testing different settings
+    AK.accumulate(
+        (x, y) -> x + 1,
+        array_from_host(rand(Int32, 3, 4, 5)),
+        init=Int32(0),
+        neutral=Int32(0),
+        dims=2,
+        block_size=64,
+        temp=array_from_host(zeros(Int32, 3, 1, 5)),
+    )
+    AK.accumulate(
+        (x, y) -> x + 1,
+        array_from_host(rand(Int32, 3, 4, 5)),
+        init=Int32(0),
+        neutral=Int32(0),
+        dims=3,
+        block_size=64,
+        temp=array_from_host(zeros(Int32, 3, 4, 1)),
+    )
+end
+@testset "cumsum" begin
+
+    Random.seed!(0)
+
+    # Simple correctness tests
+    v = array_from_host(1:100)
+    vh = Array(v)
+    @test Array(AK.cumsum(v)) == cumsum(vh)
+
+    # Fuzzy testing
+    for _ in 1:100
+        num_elems = rand(1:100_000)
+        vh = rand(Float32, num_elems)
+        v = array_from_host(vh)
+        @test all(Array(AK.cumsum(v)) .≈ cumsum(vh))
+    end
+
+    for _ in 1:100
+        for dims in 1:3
+            n1 = rand(1:10)
+            n2 = rand(1:10)
+            n3 = rand(1:10)
+            vh = rand(Int32(-5):Int32(5), n1, n2, n3)
+            v = array_from_host(vh)
+
+            # Indexing into array as if linear; not supported in Base
+            # @test all(Array(AK.cumsum(v)) .== cumsum(vh))
+
+            # Along dimensions
+            r = Array(AK.cumsum(v, dims=dims))
+            rh = cumsum(vh, dims=dims)
+
+            @test r == rh
+        end
+    end
+
+    # Test promotion to op-dictated type
+    xh = rand(Bool, 16)
+    x = array_from_host(xh)
+    @test Array(AK.cumsum(x)) == cumsum(xh)
+
+    # Testing different settings
+    v = array_from_host(rand(-5:5, 100_000))
+    AK.cumsum(v, block_size=64)
+
+    # The other settings are stress-tested in reduce
+end
+
+
+@testset "cumprod" begin
+
+    Random.seed!(0)
+
+    # Simple correctness tests
+    v = array_from_host(1:100)
+    vh = Array(v)
+    @test Array(AK.cumprod(v)) == cumprod(vh)
+
+    vh = ones(Float32, 100_000)
+    v = array_from_host(vh)
+    @test Array(AK.cumprod(v)) == vh
+
+    # Fuzzy testing
+    for _ in 1:100
+        for dims in 1:3
+            n1 = rand(1:10)
+            n2 = rand(1:10)
+            n3 = rand(1:10)
+            vh = rand(Int32(-5):Int32(5), n1, n2, n3)
+            v = array_from_host(vh)
+
+            # Indexing into array as if linear; not supported in Base
+            # @test all(Array(AK.cumprod(v)) .== cumprod(vh))
+
+            # Along dimensions
+            r = Array(AK.cumprod(v, dims=dims))
+            rh = cumprod(vh, dims=dims)
+
+            @test r == rh
+        end
+    end
+
+    # Testing different settings
+    v = array_from_host(rand(-5:5, 100_000))
+    AK.cumprod(v, block_size=64)
+
+    # The other settings are stress-tested in reduce
+end

test/binarysearch.jl

@@ -0,0 +1,87 @@
+@testset "searchsorted" begin
+
+    Random.seed!(0)
+
+    # Fuzzy correctness testing of searchsortedfirst
+    for _ in 1:100
+        num_elems_v = rand(1:100_000)
+        num_elems_x = rand(1:100_000)
+
+        # Ints
+        v = array_from_host(sort(rand(Int32, num_elems_v)))
+        x = array_from_host(rand(Int32, num_elems_x))
+        ix = similar(x, Int32)
+        AK.searchsortedfirst!(ix, v, x)
+
+        vh = Array(v)
+        xh = Array(x)
+        ixh = AK.searchsortedfirst(vh, xh)
+        ixh_base = [searchsortedfirst(vh, e) for e in xh]
+
+        @test all(Array(ix) .== ixh .== ixh_base)
+
+        # Floats
+        v = array_from_host(sort(rand(Float32, num_elems_v)))
+        x = array_from_host(rand(Float32, num_elems_x))
+        ix = similar(x, Int32)
+        AK.searchsortedfirst!(ix, v, x)
+
+        vh = Array(v)
+        xh = Array(x)
+        ixh = AK.searchsortedfirst(vh, xh)
+        ixh_base = [searchsortedfirst(vh, e) for e in xh]
+
+        @test all(Array(ix) .== ixh .== ixh_base)
+    end
+
+    # Fuzzy correctness testing of searchsortedlast
+    for _ in 1:100
+        num_elems_v = rand(1:100_000)
+        num_elems_x = rand(1:100_000)
+
+        # Ints
+        v = array_from_host(sort(rand(Int32, num_elems_v)))
+        x = array_from_host(rand(Int32, num_elems_x))
+        ix = similar(x, Int32)
+        AK.searchsortedlast!(ix, v, x)
+
+        vh = Array(v)
+        xh = Array(x)
+        ixh = AK.searchsortedlast(vh, xh)
+        ixh_base = [searchsortedlast(vh, e) for e in xh]
+
+        @test all(Array(ix) .== ixh .== ixh_base)
+
+        # Floats
+        v = array_from_host(sort(rand(Float32, num_elems_v)))
+        x = array_from_host(rand(Float32, num_elems_x))
+        ix = similar(x, Int32)
+        AK.searchsortedlast!(ix, v, x)
+
+        vh = Array(v)
+        xh = Array(x)
+        ixh = AK.searchsortedlast(vh, xh)
+        ixh_base = [searchsortedlast(vh, e) for e in xh]
+
+        @test all(Array(ix) .== ixh .== ixh_base)
+    end
+
+    # Testing different settings
+    v = array_from_host(sort(rand(Int32, 100_000)))
+    x = array_from_host(rand(Int32, 10_000))
+    ix = similar(x, Int32)
+
+    AK.searchsortedfirst!(ix, v, x, by=abs, lt=(>), rev=true, block_size=64)
+    AK.searchsortedfirst(v, x, by=abs, lt=(>), rev=true, block_size=64)
+    AK.searchsortedlast!(ix, v, x, by=abs, lt=(>), rev=true, block_size=64)
+    AK.searchsortedlast(v, x, by=abs, lt=(>), rev=true, block_size=64)
+
+    vh = Array(v)
+    xh = Array(x)
+    ixh = similar(xh, Int32)
+
+    AK.searchsortedfirst!(ixh, vh, xh, by=abs, lt=(>), rev=true, max_tasks=10, min_elems=100)
+    AK.searchsortedfirst(vh, xh, by=abs, lt=(>), rev=true, max_tasks=10, min_elems=100)
+    AK.searchsortedlast!(ixh, vh, xh, by=abs, lt=(>), rev=true, max_tasks=10, min_elems=100)
+    AK.searchsortedlast(vh, xh, by=abs, lt=(>), rev=true, max_tasks=10, min_elems=100)
+end