Move external sampler interface to AbstractMCMC

[penelopeysm]

Following on from:

• Add AbstractMCMC.getstats and AbstractMCMC.requires_unconstrained_space AbstractMCMC.jl#182 Adding the interface function AbstractMCMC.getstats
• Implement AbstractMCMC.getstats AdvancedHMC.jl#471 and implement AbstractMCMC.getstats AdvancedMH.jl#119 Implementing them for AdvancedHMC and AdvancedMH

this PR changes Turing's external sampler interface to exclusively use AbstractMCMC functions. With this PR, 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.

To be precise, it makes the following changes:

• Previously where one had to define Turing.Inference.getparams, now one has to define AbstractMCMC.getparams.
• Previously there was no way to include sampler stats in the resulting chain, now one can define AbstractMCMC.getstats.
• The default for Turing.Inference.isgibbscomponent is changed to true, so that external sampler packages don't need to override it (unless absolutely necessary).

As an example implementation, this PR contains a test mock (note how it doesn't require a Turing dep):

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 88.23529% with 2 lines in your changes missing coverage. Please review.
✅ Project coverage is 86.52%. Comparing base (be007f3) to head (d8dcd11).
⚠️ Report is 1 commits behind head on breaking.

Files with missing lines	Patch %	Lines
src/mcmc/gibbs.jl	71.42%	2 Missing ⚠️

Additional details and impacted files

@@             Coverage Diff              @@
##           breaking    #2704      +/-   ##
============================================
+ Coverage     86.45%   86.52%   +0.06%     
============================================
  Files            21       21              
  Lines          1418     1410       -8     
============================================
- Hits           1226     1220       -6     
+ Misses          192      190       -2     

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[penelopeysm]

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

[penelopeysm]

I'll write proper docs about this separately (in the docs repo).

[sunxd3]

I see no issue