performance of captured variables in closures

[timholy]

using Images: realtype

function ifi{T<:Real,K,N}(img::AbstractArray{T,N}, kern::AbstractArray{K,N}, border::AbstractString, value)
    if border == "circular" && size(img) == size(kern)
        out = real(ifftshift(ifft(fft(img).*fft(kern))))
    elseif border != "inner"
        prepad  = [div(size(kern,i)-1, 2) for i = 1:N]
        postpad = [div(size(kern,i),   2) for i = 1:N]
        fullpad = [nextprod([2,3], size(img,i) + prepad[i] + postpad[i]) - size(img, i) - prepad[i] for i = 1:N]
        A = padarray(img, prepad, fullpad, border, convert(T, value))
        krn = zeros(typeof(one(T)*one(K)), size(A))
        indexesK = ntuple(d->[size(krn,d)-prepad[d]+1:size(krn,d);1:size(kern,d)-prepad[d]], N)::NTuple{N,Vector{Int}}
        AF = ifft(fft(A).*fft(krn))
        out = Array(realtype(eltype(AF)), size(img))
    end
    out
end

Test:

julia> @code_warntype ifi(rand(3,3), rand(3,3), "replicate", 0)
Variables:
  #self#::#ifi
  img::Array{Float64,2}
  kern::Array{Float64,2}
  border::ASCIIString
  value::Int64
  prepad::Box
...

Now comment out the indexesK = ... line (the output of which is not used at all). Suddenly prepad is inferred as Array{Int, 1}.

[JeffBezanson]

This is due to that line capturing prepad in a closure.

[andreasnoack]

Just want to add that this has quite severe consequences for @threads for. Is it the plan to change/improve the behavior of Box or is it advised not to capture arrays in clusures? The last option will make it difficult for @threads for.

[KristofferC]

This also has severe consequences for NLsolve (JuliaNLSolvers/NLsolve.jl#49). Previously things were inferred fine:

but now everything is just Box and Any and performance is pretty much garbage..

Now sure how to work around this.

This feels like a quite significant regression. Now suddenly any variable captured by a closure poisons the variable for the whole outer function. Even if it is only read from.

Edit: The example below has been fixed.

Ex:

function newton_{T}(initial_x::Vector{T})

    nn = length(initial_x)
    xold = fill(convert(T, NaN), nn)
    fvec = Array(T, nn)
    f_calls = 1

    function fo(xlin::Vector)
        if xlin != xold
            print(f_calls)
        end
        return(dot(fvec, fvec) / 2)
    end

    return
end

@code_warntype newton_(rand(5))

Here, xold, f_calls and fvec are Box for the whole function. This used to work fine on 0.4.

[davidanthoff]

Should this get a 0.5 label? For my projects this seems to cause a significant increase in runtime (~30%) for projects that worked well on 0.4.

[JeffBezanson]

Everything tagged "regression" will get attention before 0.5-final.

[davidanthoff]

Thanks!

[JeffBezanson]

Ok, I have a quick fix for many of the examples posted here. Not including @timholy 's original example in this issue, of course :/

[davidanthoff]

I'll try my cases once this has landed in a windows nightly binary.

[JeffBezanson]

#16048 should be good now, #16050 not yet but also should be easy.

[uniment]

what Jeff described only works if you are calling f inside the same function it was created in

Under what circumstance is this true? I don't see it:

julia> @btime (()->begin
           x::Int = 1
           f() = (x = x+1; x)
           for i=1:1000; f() end
           x
       end)()
  4.200 μs (490 allocations: 7.66 KiB)
1001

julia> @btime (()->begin
           x = Ref(1)
           f() = (x[] = x[] + 1; x[])
           for i=1:1000; f() end
           x[]
       end)()
  1.800 ns (0 allocations: 0 bytes)
1001

(Julia 1.9.0-beta3)

(inspired by this discourse discussion, which showed that global variables share similar performance characteristics w.r.t. typed global vs. const Ref)

[LilithHafner]

@c42f, if you happen to be rewriting lowering it would be nice to fix this issue.

[LilithHafner]

Ran into this again here