Move external sampler interface to AbstractMCMC

[penelopeysm]

Note

This PR is pretty much ready to go, but requires TuringLang/AbstractMCMC.jl#182 before it can be properly merged, hence marking as draft.

This PR moves external sampler interface functions like getparams and getstats out of Turing. It's really a proper continuation of #2616, which I messed up quite badly by merging into a patch release and had to revert. This PR therefore targets breaking.

The twin PR above adds them to AbstractMCMC. Once this is done, anyone who defines an external sampler will only need to depend on AbstractMCMC (which they presumably already do, because it is a sampler) and LogDensityProblems (which is already a dep of AbstractMCMC). No need for a Turing extension.

For an example implementation, see the test mock in this PR (note no Turing dependencies required):

Turing.jl/test/mcmc/external_sampler.jl

Lines 20 to 74 in 06752c4

	# Turing declares an interface for external samplers (see docstring for
	# ExternalSampler). We should check that implementing this interface
	# and only this interface allows us to use the sampler in Turing.
	struct MyState{V<:AbstractVector}
	params::V
	end
	AbstractMCMC.getparams(s::MyState) = s.params
	AbstractMCMC.getstats(s::MyState) = (param_length=length(s.params),)
	# externalsamplers must accept LogDensityModel inside their step function.
	# By default Turing gives the externalsampler a LDF constructed with
	# adtype=ForwardDiff, so we should expect that inside the sampler we can
	# call both `logdensity` and `logdensity_and_gradient`.
	#
	# The behaviour of this sampler is to simply calculate logp and its
	# gradient, and then return the same values.
	#
	# TODO: Do we also want to run ADTypeCheckContext to make sure that it is
	# indeed using the adtype provided from Turing?
	struct MySampler <: AbstractMCMC.AbstractSampler end
	function AbstractMCMC.step(
	rng::Random.AbstractRNG,
	model::AbstractMCMC.LogDensityModel,
	sampler::MySampler;
	# This initial_params should be an AbstractVector because the model is just a
	# LogDensityModel, not a DynamicPPL.Model
	initial_params::AbstractVector,
	kwargs...,
	)
	# Step 1
	ldf = model.logdensity
	lp = LogDensityProblems.logdensity(ldf, initial_params)
	@test lp isa Real
	lp, grad = LogDensityProblems.logdensity_and_gradient(ldf, initial_params)
	@test lp isa Real
	@test grad isa AbstractVector{<:Real}
	return nothing, MyState(initial_params)
	end
	function AbstractMCMC.step(
	rng::Random.AbstractRNG,
	model::AbstractMCMC.LogDensityModel,
	sampler::MySampler,
	state::MyState;
	kwargs...,
	)
	# Step >= 1
	params = state.params
	ldf = model.logdensity
	lp = LogDensityProblems.logdensity(ldf, params)
	@test lp isa Real
	lp, grad = LogDensityProblems.logdensity_and_gradient(ldf, params)
	@test lp isa Real
	@test grad isa AbstractVector{<:Real}
	return nothing, MyState(params)
	end

[github-actions]

Turing.jl documentation for PR #2704 is available at:
https://TuringLang.github.io/Turing.jl/previews/PR2704/

[codecov]

Codecov Report

❌ Patch coverage is 63.15789% with 7 lines in your changes missing coverage. Please review.
✅ Project coverage is 85.52%. Comparing base (be007f3) to head (0ebf0e8).

Files with missing lines	Patch %	Lines
src/mcmc/gibbs.jl	0.00%	7 Missing ⚠️

Additional details and impacted files

@@             Coverage Diff              @@
##           breaking    #2704      +/-   ##
============================================
- Coverage     86.45%   85.52%   -0.94%     
============================================
  Files            21       21              
  Lines          1418     1409       -9     
============================================
- Hits           1226     1205      -21     
- Misses          192      204      +12     

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[penelopeysm]

Moved this type parameter earlier so that we can dispatch on it more easily.