How to define an everywhere defined function within a function

General Usage
1

In my package, I’d like to offer a convenience function like this:

function gaussian(σ::Real=1.0)
    @eval function (x)
        exp(-abs2(x) / $(float(4σ)))
    end
end

I want the @eval because I don’t want the 4σ to be computed at every evaluation

kernel = gaussian(3.0)
kernel(0.2)

After a long while, I realized that kernel is not defined on all workers, and seemingly neither

@everywhere kernel = gaussian(3.0)

nor putting @everywhere in the definition of gaussian helps. Something that works with ordinary variables also doesn’t work

@everywhere kernel = $kernel

because Julia claims to not know what to interpolate on the r.h.s. (UndefVarError). How can I both keep the convenience of calling a function-generating function and have the resulting function defined everywhere?

2

Have you tried just doing this normally? There is fairly aggressive constant propagation these days.

1 Like
3

I have tried with some of the @code_ macros (most of which I don’t master very well, I should say), and it did seem to me like there was the floatmul(%3, 4.0) (or something like this in the code. But maybe I was doing it wrong?

4

Have you benchmarked if this does anything to the runtime of the function? Since you have an exp call in there I would be surprised if it did.

5

Just did it.

julia> function gaussian(σ::Real=1.0)
           @eval function (x)
               exp(-abs2(x) / $(float(4σ)))
           end
       end
gaussian (generic function with 2 methods)

julia> using BenchmarkTools

julia> @benchmark $(gaussian(3.))(x) setup=(x = rand())
BenchmarkTools.Trial: 
  memory estimate:  0 bytes
  allocs estimate:  0
  --------------
  minimum time:     4.241 ns (0.00% GC)
  median time:      5.776 ns (0.00% GC)
  mean time:        6.128 ns (0.00% GC)
  maximum time:     66.726 ns (0.00% GC)
  --------------
  samples:          10000
  evals/sample:     1000

julia> function gaussian2(σ::Real=1.0)
           function (x)
               exp(-abs2(x) / float(4σ))
           end
       end
gaussian2 (generic function with 2 methods)

julia> @benchmark $(gaussian2(3.))(x) setup=(x = rand())
BenchmarkTools.Trial: 
  memory estimate:  0 bytes
  allocs estimate:  0
  --------------
  minimum time:     5.678 ns (0.00% GC)
  median time:      5.825 ns (0.00% GC)
  mean time:        6.194 ns (0.00% GC)
  maximum time:     64.749 ns (0.00% GC)
  --------------
  samples:          10000
  evals/sample:     1000

This will perhaps not be crucial in the overall process, but would be nice to avoid.

6

Why do you need @eval for this? For example:

function gaussian(σ::Real=1.0)
    let s = -1/4σ
        x -> exp(s*abs2(x))
    end
end

(Though I’m skeptical that the 3% performance difference in your benchmark is worth a specialized higher-order function here.)

1 Like
7

That was the only way I could come up with to avoid that (stupid) extra operation. I understand that this does not change the world, but it felt strange and I thought there should be a way to “precompute” this. Interestingly, when defined this way, there is no problem with kernel = gaussian(3.) being passed to different workers, without being @everywhered. So, many thanks for your help!

8

Could someone explain the use of @eval here and what the OP was trying to achieve? Suppose the OP didn’t have trouble with the parallelism, how does the @eval work? If I do

kernel = gaussian(3.0)
kernel(0.2)

kernel2 = gaussian(4.0)
kernel2(0.2)

Wouldn’t that compute the 4\sigma twice anyways?

9

Yes, but the intention is to apply the same kernel = gaussian(3.) to many different arguments, i.e., kernel(x), and precompute as much as possible that depends on σ=3.0.