Avoid reevaluating model where unnecessary

[penelopeysm]

Closes #2639

Quoting from the changelog:

Avoid reevaluating the model on MCMC iterations where the transition is not saved to the chain (e.g. in initial burn-in, or when using thinning).
Also avoid each component sampler of Gibbs unnecessarily evaluating the model once per iteration.

This makes use of the discard_sample keyword argument to step / step_warmup TuringLang/AbstractMCMC.jl#194

Note that this is still type stable as the value of the discard_sample kwarg can be constant propagated (I've checked this with code_warntype etc).

[github-actions]

Turing.jl documentation for PR #2759 is available at:
https://TuringLang.github.io/Turing.jl/previews/PR2759/

[penelopeysm]

Assuming this passes CI, @joelkandiah, you might find this a little bit useful. It doesn't help with compilation, but here's a demonstration:

using Turing

modelevals = Ref(0)

@model function f()
    modelevals[] += 1
    a1 ~ Normal()
    a2 ~ Normal()
    a3 ~ Normal()
    a4 ~ Normal()
    a5 ~ Normal()
    a6 ~ Normal()
    a7 ~ Normal()
    a8 ~ Normal()
end

modelevals[] = 0
@time sample(f(), Gibbs(
        @varname(a1) => MH(),
        @varname(a2) => MH(),
        @varname(a3) => MH(),
        @varname(a4) => MH(),
        @varname(a5) => MH(),
        @varname(a6) => MH(),
        @varname(a7) => MH(),
        @varname(a8) => MH()
    ),
    1000;
    chain_type=Any, progress=false
);
@show modelevals[]

# this PR: 0.274875 seconds (2.26 M allocations: 169.478 MiB, 4.95% gc time)
# modelevals[] = 17003
#
# main: 0.354257 seconds (2.88 M allocations: 208.250 MiB, 4.81% gc time)
# modelevals[] = 25003

If you're using discard_initial and/or thinning, the benefits should scale accordingly. For example, adding thinning=10 to the above gives:

# With thinning=10
#
# this PR: 2.419929 seconds (21.36 M allocations: 1.582 GiB, 4.45% gc time)
# modelevals[] = 160859
#
# main: 3.395153 seconds (28.70 M allocations: 2.029 GiB, 4.05% gc time)
# modelevals[] = 249778

[penelopeysm]

(I definitely think it should in theory be possible to cut the model evaluations down to 8000, or maybe 9000 (each component sampler goes once and Gibbs itself has to do it once), but that really requires deeper refactoring that would follow on from #2756. The extra 8000 evaluations are coming from this setparams_varinfo!! stuff that complicates matters.)