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fix performance #39

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merged 4 commits into from
Dec 10, 2024
Merged

fix performance #39

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28b028c
fix performance
mbauman Dec 3, 2024
fab9c0d
optimize collect
mbauman Dec 3, 2024
335416c
fix for v1.11's cartesian logical arrays
mbauman Dec 10, 2024
53c1f5c
Update test/runtests.jl
mbauman Dec 10, 2024
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42 changes: 37 additions & 5 deletions src/InvertedIndices.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -101,17 +101,49 @@ end
InvertedIndexIterator(skips, picks) = InvertedIndexIterator{eltype(picks), typeof(skips), typeof(picks)}(skips, picks)
Base.size(III::InvertedIndexIterator) = (length(III.picks) - length(III.skips),)

@inline Base.iterate(I::InvertedIndexIterator) = iterate(I, (iterate(I.skips), iterate(I.picks)))
Base.iterate(I::InvertedIndexIterator, ::Tuple{Any, Nothing}) = nothing
@inline function Base.iterate(I::InvertedIndexIterator, (skipitr, pickitr))
@inline function Base.iterate(I::InvertedIndexIterator)
skipitr = iterate(I.skips)
pickitr = iterate(I.picks)
pickitr === nothing && return nothing
while should_skip(skipitr, pickitr)
skipitr = iterate(I.skips, skipitr[2])
pickitr = iterate(I.picks, pickitr[2])
pickitr === nothing && return nothing
end
# This is a little silly, but splitting the tuple here allows inference to normalize
# Tuple{Union{Nothing, Tuple}, Tuple} to Union{Tuple{Nothing, Tuple}, Tuple{Tuple, Tuple}}
Comment on lines +113 to +114
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Out of curiosity, have you noticed this across all supported versions or just in e.g. 1.11?

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The type stability unit tests fail without this manual union-split ternary, but pass with it. It's the difference between putting a type-unstable value in a tuple v.s. returning two different tuples... and those fundamentals hold true going back to 1.0:

▶ julia +1.0 -q
julia> function f()
           x = rand([nothing, 1.0])
           return (x,)
       end
f (generic function with 1 method)

julia> @code_warntype f()
Body::Tuple{Union{Nothing, Float64}}
# ...

julia> function g()
           x = rand([nothing, 1.0])
           return x===nothing ? (nothing,) : (x,)
       end
g (generic function with 1 method)

julia> @code_warntype g()
Body::Union{Tuple{Nothing}, Tuple{Float64}}

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Oh interesting, I don't think I knew that. Thanks, I appreciate the explanation!

return skipitr === nothing ?
(pickitr[1], (nothing, pickitr[2])) :
(pickitr[1], (skipitr, pickitr[2]))
end
@inline function Base.iterate(I::InvertedIndexIterator, (_, pickstate)::Tuple{Nothing, Any})
pickitr = iterate(I.picks, pickstate)
pickitr === nothing && return nothing
return (pickitr[1], (nothing, pickitr[2]))
end
@inline function Base.iterate(I::InvertedIndexIterator, (skipitr, pickstate)::Tuple)
pickitr = iterate(I.picks, pickstate)
pickitr === nothing && return nothing
while should_skip(skipitr, pickitr)
skipitr = iterate(I.skips, tail(skipitr)...)
pickitr = iterate(I.picks, tail(pickitr)...)
pickitr === nothing && return nothing
end
return (pickitr[1], (skipitr, iterate(I.picks, tail(pickitr)...)))
return skipitr === nothing ?
(pickitr[1], (nothing, pickitr[2])) :
(pickitr[1], (skipitr, pickitr[2]))
end
function Base.collect(III::InvertedIndexIterator{T}) where {T}
!isconcretetype(T) && return [i for i in III] # use widening if T is not concrete
v = Vector{T}(undef, length(III))
i = 0
for elt in III
i += 1
@inbounds v[i] = elt
end
i != length(v) && throw(AssertionError("length of inverted index does not match iterated count"))
return v
end
Base.collect(III::InvertedIndexIterator) = [i for i in III]

should_skip(::Nothing, ::Any) = false
should_skip(s::Tuple, p::Tuple) = _should_skip(s[1], p[1])
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18 changes: 18 additions & 0 deletions test/runtests.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -185,3 +185,21 @@ end
@test x isa InvertedIndex{InvertedIndices.NotMultiIndex}
@test_throws ArgumentError v[x]
end

returns(val) = _->val
@testset "type stability" begin
for arr in (
1:5,
reshape(1:5*3, 5, 3),
reshape(1:5*3*7, 5, 3, 7),
reshape(1:5*3*7*11, 5, 3, 7, 11),
)
I = to_indices(arr, (Not(iseven.(arr)),))[1]
@test all(isodd, arr[I])
@test all(isodd, LinearIndices(arr)[I])
@test all(isodd, LinearIndices(arr)[collect(I)])
@allocated(foreach(returns(nothing), I))
@test @allocated(foreach(returns(nothing), I)) == 0
@test @inferred(LinearIndices(arr)[collect(I)]) == vec(filter(!iseven, arr))
end
end
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