Tuning is not robust to highly variable runtimes

[LilithHafner]

julia> f(::Int) = nothing
f (generic function with 1 method)

julia> f(::Float64) = sleep(.01)
f (generic function with 2 methods)

julia> using StableRNGs

julia> rng = StableRNG(0)
StableRNGs.LehmerRNG(state=0x00000000000000000000000000000001)

julia> @be rand(rng, (1, 2.0)) f
[ Info: Loading Chairmarks ...
Benchmark: 17 samples with 1 evaluation
min    0 ns
median 42.000 ns
mean   5.206 ms (1.88 allocs: 52.706 bytes)
max    11.079 ms (4 allocs: 112 bytes)

julia> rng = StableRNG(1)
StableRNGs.LehmerRNG(state=0x00000000000000000000000000000003)

julia> @be rand(rng, (1, 2.0)) f
[hangs for 5 minutes]

A less reliably reproducing variant was originally reported by @mbauman here.

Proposed fix:

When reporting final results (or maybe half way through the runtime budget) check to see if evals is actually reasonable. If not, rerun or warn that auto-tuning failed and prompt the user to manually tune the benchmark.

When choosing a high number of evals, increase the number of evals run by at most a factor of 10x at a time and make each of those trials a new sample (with new setup & teardown).

This will not cover the @be rand() < .01 if _ sleep(10) end case, but that case is nearly impossible to cover, and this will cover all reasonable cases (I hope).