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Allow dims::Tuple in sum #909

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Allow dims::Tuple in sum #909

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28202e3
allow dims::Tuple in sum
mcabbott May 8, 2021
7a66328
multiple dimensions for reduce only, not mapreduce
mcabbott May 8, 2021
1a4aa62
fix tests?
mcabbott May 8, 2021
4a986db
one isapprox
mcabbott May 9, 2021
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11 changes: 11 additions & 0 deletions src/mapreduce.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -158,6 +158,9 @@ end
end
end

@inline _mapreduce(f, op, D::Tuple{<:Any}, init, sz::Size{S}, a::StaticArray) where {S} =
_mapreduce(f, op, first(D), init, sz, a)
Comment on lines +161 to +162
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Things like sum(abs2, SA[1 2; 3 4], dims = (1,2)) still won't work, that seems like a bigger project. It would be easy to add a method here to make them give a friendly "not yet supported" error, instead of an "internal" error. But opinions vary as to whether that's a good idea.

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The change needed is not large though, we can use Base.mapreducedim! to generated the code we want:

@generated function _mapfoldl(f, op, dims::Val{D}, init,
                               ::Size{S}, a::StaticArray) where {S,D}
    _init = init === _InitialValue ? :default : :init
    iter = CartesianIndices(S)
    exprs = fill!(similar(iter, Any, Base.reduced_indices(iter, D)), _init)
    rf(ex, nex) = ex === :default ? :(Base.reduce_first(op, $nex)) : :(op($ex,$nex))
    Base.mapreducedim!(I->:(f(a[$(I.I...)])), rf, exprs, iter)
    return quote
        @_inline_meta
        @inbounds elements = tuple($(exprs...))
        @inbounds return similar_type(a, eltype(elements), Size($(size(exprs))))(elements)
    end
end

The current test passed locally, and the influence on compile time seems negligible.


@generated function _mapfoldl(f, op, dims::Val{D}, init,
::Size{S}, a::StaticArray) where {S,D}
N = length(S)
Expand Down Expand Up @@ -209,6 +212,14 @@ reduce(::typeof(hcat), A::StaticArray{<:Tuple,<:StaticVecOrMatLike}) =
@inline _reduce(op, a::StaticArray, dims, init = _InitialValue()) =
_mapreduce(identity, op, dims, init, Size(a), a)

@inline function _reduce(op, a::StaticArray, dims::Tuple, init = _InitialValue())
b = _reduce(op, a, first(dims))
return _reduce(op, b, Base.tail(dims), init)
end
_reduce(op, a::StaticArray, dims::Tuple{}, ::_InitialValue) = a
_reduce(op, a::StaticArray, dims::Tuple{}, init) = op.(init, a)


################
## (map)foldl ##
################
Expand Down
12 changes: 12 additions & 0 deletions test/mapreduce.jl
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Expand Up @@ -37,16 +37,24 @@ using Statistics: mean
v2 = [4,3,2,1]; sv2 = SVector{4}(v2)
@test reduce(+, sv1) === reduce(+, v1)
@test reduce(+, sv1; init=0) === reduce(+, v1; init=0)
@test reduce(+, sv1; init=99) === reduce(+, v1; init=99)
@test reduce(max, sa; dims=Val(1), init=-1.) === SMatrix{1,J}(reduce(max, a, dims=1, init=-1.))
@test reduce(max, sa; dims=1, init=-1.) === SMatrix{1,J}(reduce(max, a, dims=1, init=-1.))
@test reduce(max, sa; dims=2, init=-1.) === SMatrix{I,1}(reduce(max, a, dims=2, init=-1.))
@test reduce(*, sa; dims=(1,2), init=2.0) ≈ SMatrix{1,1}(reduce(*, a, dims=(1,2), init=2.0))
@test reduce(*, sa; dims=(), init=(1.0+im)) === SMatrix{I,J}(reduce(*, a, dims=(), init=(1.0+im)))
@test mapreduce(-, +, sv1) === mapreduce(-, +, v1)
@test mapreduce(-, +, sv1; init=0) === mapreduce(-, +, v1, init=0)
@test mapreduce(*, +, sv1, sv2) === 40
@test mapreduce(*, +, sv1, sv2; init=0) === 40
@test mapreduce(x->x^2, max, sa; dims=Val(1), init=-1.) == SMatrix{1,J}(mapreduce(x->x^2, max, a, dims=1, init=-1.))
@test mapreduce(x->x^2, max, sa; dims=1, init=-1.) == SMatrix{1,J}(mapreduce(x->x^2, max, a, dims=1, init=-1.))
@test mapreduce(x->x^2, max, sa; dims=2, init=-1.) == SMatrix{I,1}(mapreduce(x->x^2, max, a, dims=2, init=-1.))

# Zero-dim array
a0 = fill(rand()); sa0 = SArray{Tuple{}}(a0)
@test reduce(+, sa0) === reduce(+, a0)
@test reduce(/, sa0, dims=(), init=1.2) === SArray{Tuple{}}(reduce(/, a0, dims=(), init=1.2))
end

@testset "[map]foldl" begin
Expand Down Expand Up @@ -102,6 +110,7 @@ using Statistics: mean
RSArray1 = SArray{Tuple{1,J,K}} # reduced in dimension 1
RSArray2 = SArray{Tuple{I,1,K}} # reduced in dimension 2
RSArray3 = SArray{Tuple{I,J,1}} # reduced in dimension 3
RSArray13 = SArray{Tuple{1,J,1}} # reduced in dimension 1 and 3
a = randn(I,J,K); sa = OSArray(a)
b = rand(Bool,I,J,K); sb = OSArray(b)
z = zeros(I,J,K); sz = OSArray(z)
Expand All @@ -111,8 +120,11 @@ using Statistics: mean
@test sum(sa) === sum(a)
@test sum(abs2, sa) === sum(abs2, a)
@test sum(sa, dims=2) === RSArray2(sum(a, dims=2))
@test sum(sa, dims=(2,)) === RSArray2(sum(a, dims=2))
@test sum(sa, dims=Val(2)) === RSArray2(sum(a, dims=2))
@test sum(sa, dims=(1,3)) === RSArray13(sum(a, dims=(1,3)))
@test sum(abs2, sa; dims=2) === RSArray2(sum(abs2, a, dims=2))
@test sum(abs2, sa; dims=(2,)) === RSArray2(sum(abs2, a, dims=2))
@test sum(abs2, sa; dims=Val(2)) === RSArray2(sum(abs2, a, dims=2))

@test prod(sa) === prod(a)
Expand Down