Add check_args and drop 1.3

[theogf]

Summary
Similarly to Distributions.jl we do not necessarily want to check for argument correctness all the time.

Proposed changes

This adds a check_args keyword to all constructors that require it to deactivate the check for the correctness for the arguments.
Additionally this drops 1.3 for 1.6 because I am lazy to write kwarg=kwarg (appears in 1.5 or 1.6 can't remember0

• ...

What alternatives have you considered?
We could define a reparametrization of the parameters but we already discussed that it should be the duty of the user to do this.

Breaking changes
None! Crazy right?

[willtebbutt]

Approved subject to CI etc. I'd also like an approval from either @devmotion or @st-- before merging

[codecov]

Codecov Report

Patch coverage: 73.07% and project coverage change: -1.42 ⚠️

Comparison is base (8746034) 94.25% compared to head (0c8185a) 92.83%.

Additional details and impacted files

@@            Coverage Diff             @@
##           master     #499      +/-   ##
==========================================
- Coverage   94.25%   92.83%   -1.42%     
==========================================
  Files          52       52              
  Lines        1374     1396      +22     
==========================================
+ Hits         1295     1296       +1     
- Misses         79      100      +21     

Impacted Files	Coverage Δ
src/utils.jl	73.07% <12.50%> (-18.39%)	⬇️
src/basekernels/constant.jl	100.00% <100.00%> (ø)
src/basekernels/exponential.jl	100.00% <100.00%> (ø)
src/basekernels/fbm.jl	100.00% <100.00%> (ø)
src/basekernels/matern.jl	100.00% <100.00%> (ø)
src/basekernels/periodic.jl	100.00% <100.00%> (ø)
src/basekernels/polynomial.jl	100.00% <100.00%> (ø)
src/basekernels/rational.jl	100.00% <100.00%> (ø)
src/basekernels/wiener.jl	92.85% <100.00%> (ø)
src/kernels/scaledkernel.jl	88.23% <100.00%> (ø)
... and 3 more

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[devmotion]

I'm a bit worried that this might cause performance regressions, or at least is suboptimal, based on the fixes that were needed in Distributions: JuliaStats/Distributions.jl#1492

[willtebbutt]

Good point @devmotion . @theogf could you check some simple examples with Zygote?

[theogf]

I'm a bit worried that this might cause performance regressions, or at least is suboptimal, based on the fixes that were needed in Distributions: JuliaStats/Distributions.jl#1492

Then shouldn't I just reuse the @check_args from Distributions.jl ?

[theogf]

@devmotion do you think that would be enough?

[devmotion]

Did you do compare performance with Zygote?

[theogf]

Here are the benchmarks:

using Zygote
using BenchmarkTools
using KernelFunctions

x = [3.0]

macro old_check_args(K, param, cond, desc=string(cond))
    quote
        if !($(esc(cond)))
            throw(
                ArgumentError(
                    string(
                        $(string(K)),
                        ": ",
                        $(string(param)),
                        " = ",
                        $(esc(param)),
                        " does not ",
                        "satisfy the constraint ",
                        $(string(desc)),
                        ".",
                    ),
                ),
            )
        end
    end
end


struct OldLinearKernel{Tc<:Real} <: KernelFunctions.SimpleKernel
    c::Vector{Tc}

    function OldLinearKernel(c::Real)
        @old_check_args(LinearKernel, c, c >= zero(c), "c ≥ 0")
        return new{typeof(c)}([c])
    end
end

function f(x)
    k = LinearKernel(;c = x[1])
    sum(k.c)
end
function g(x)
    k = LinearKernel(;c = x[1], check_args=false)
    sum(k.c)
end
function h(x)
    k = OldLinearKernel(x[1])
    sum(k.c)
end

@btime Zygote.gradient($f, $x) # 15.980 μs (150 allocations: 5.89 KiB)
@btime Zygote.gradient($g, $x) #  13.853 μs (142 allocations: 5.72 KiB)
@btime Zygote.gradient($h, $x)  #  4.700 μs (51 allocations: 1.89 KiB)

[devmotion]

That's a quite noticeable regression. Do we know what exactly causes it?

[theogf]

For completeness I added the constructor

    function OldLinearKernel(c::Real; check_args=true)
        check_args && @old_check_args(LinearKernel, c, c >= zero(c), "c ≥ 0")
        return new{typeof(c)}([c])
    end

And these are the results

function h(x)
    k = OldLinearKernel(x[1])
    sum(k.c)
end
function i(x)
    k = OldLinearKernel(x[1]; check_args=false)
    sum(k.c)
end

@btime Zygote.gradient($h, $x)  # 6.086 μs (65 allocations: 2.58 KiB)
@btime Zygote.gradient($i, $x) # 10.404 μs (91 allocations: 3.38 KiB)

[theogf]

Looking back at this, how about using CheckArgs.jl ?