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torfjeldeJaimeRZPyebai
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Allow usage of AbstractSampler (#2008)
* initial work on allowing AdvancedHMC samplers * simplify the hacky initialize_nuts method * slight generalization * remove unnecessary type constraint * rever changes to sample overloads * use a subtype of InferenceAlgorithm to wrap any sampler * improve usage of SamplerWrapper * renamed hmc_new.jl to something a bit more indicative * added support for AdvancedMH * forgot to change include * renamed SamplerWrapper to ExternalSampler and provided a function externalsampler * added tests for Advanced{HMC,MH} * fixed external tests * change target acceptance rate * fixed optim tests * remove NelderMead from tests * allow models with one variance parameter per observation to fail MLE test * no tests (#2028) * no tests * more tol --------- Co-authored-by: Jaime RZ <[email protected]> Co-authored-by: Hong Ge <[email protected]>
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‎src/Turing.jl‎

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@@ -112,6 +112,7 @@ export @model, # modelling
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resume,
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@logprob_str,
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@prob_str,
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externalsampler,
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setchunksize, # helper
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setadbackend,

‎src/contrib/inference/abstractmcmc.jl‎

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struct TuringState{S,F}
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state::S
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logdensity::F
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end
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struct TuringTransition{T,NT<:NamedTuple,F<:AbstractFloat}
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θ::T
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lp::F
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stat::NT
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end
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function TuringTransition(vi::AbstractVarInfo, t)
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theta = tonamedtuple(vi)
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lp = getlogp(vi)
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return TuringTransition(theta, lp, getstats(t))
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end
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metadata(t::TuringTransition) = merge((lp = t.lp,), t.stat)
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DynamicPPL.getlogp(t::TuringTransition) = t.lp
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state_to_turing(f::DynamicPPL.LogDensityFunction, state) = TuringState(state, f)
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function transition_to_turing(f::DynamicPPL.LogDensityFunction, transition)
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θ = getparams(transition)
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varinfo = DynamicPPL.unflatten(f.varinfo, θ)
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# TODO: `deepcopy` is overkill; make more efficient.
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varinfo = DynamicPPL.invlink!!(deepcopy(varinfo), f.model)
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return TuringTransition(varinfo, transition)
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end
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# NOTE: Only thing that depends on the underlying sampler.
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# Something similar should be part of AbstractMCMC at some point:
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# https://github.com/TuringLang/AbstractMCMC.jl/pull/86
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getparams(transition::AdvancedHMC.Transition) = transition.z.θ
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getstats(transition::AdvancedHMC.Transition) = transition.stat
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getparams(transition::AdvancedMH.Transition) = transition.params
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getstats(transition) = NamedTuple()
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getvarinfo(f::DynamicPPL.LogDensityFunction) = f.varinfo
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getvarinfo(f::LogDensityProblemsAD.ADGradientWrapper) = getvarinfo(parent(f))
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setvarinfo(f::DynamicPPL.LogDensityFunction, varinfo) = Setfield.@set f.varinfo = varinfo
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setvarinfo(f::LogDensityProblemsAD.ADGradientWrapper, varinfo) = setvarinfo(parent(f), varinfo)
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# TODO: Do we also support `resume`, etc?
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function AbstractMCMC.step(
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rng::Random.AbstractRNG,
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model::DynamicPPL.Model,
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sampler_wrapper::Sampler{<:ExternalSampler};
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kwargs...
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)
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sampler = sampler_wrapper.alg.sampler
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# Create a log-density function with an implementation of the
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# gradient so we ensure that we're using the same AD backend as in Turing.
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f = LogDensityProblemsAD.ADgradient(DynamicPPL.LogDensityFunction(model))
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# Link the varinfo.
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f = setvarinfo(f, DynamicPPL.link!!(getvarinfo(f), model))
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# Then just call `AdvancedHMC.step` with the right arguments.
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transition_inner, state_inner = AbstractMCMC.step(
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rng, AbstractMCMC.LogDensityModel(f), sampler; kwargs...
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)
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# Update the `state`
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return transition_to_turing(f, transition_inner), state_to_turing(f, state_inner)
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end
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function AbstractMCMC.step(
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rng::Random.AbstractRNG,
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model::DynamicPPL.Model,
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sampler_wrapper::Sampler{<:ExternalSampler},
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state::TuringState;
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kwargs...
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)
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sampler = sampler_wrapper.alg.sampler
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f = state.logdensity
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# Then just call `AdvancedHMC.step` with the right arguments.
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transition_inner, state_inner = AbstractMCMC.step(
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rng, AbstractMCMC.LogDensityModel(f), sampler, state.state; kwargs...
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)
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# Update the `state`
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return transition_to_turing(f, transition_inner), state_to_turing(f, state_inner)
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end

‎src/inference/Inference.jl‎

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@@ -22,6 +22,7 @@ using DynamicPPL
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using AbstractMCMC: AbstractModel, AbstractSampler
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using DocStringExtensions: TYPEDEF, TYPEDFIELDS
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using DataStructures: OrderedSet
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using Setfield: Setfield
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import AbstractMCMC
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import AdvancedHMC; const AHMC = AdvancedHMC
@@ -66,7 +67,8 @@ export InferenceAlgorithm,
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dot_observe,
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resume,
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predict,
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isgibbscomponent
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isgibbscomponent,
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externalsampler
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#######################
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# Sampler abstraction #
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abstract type Hamiltonian{AD} <: InferenceAlgorithm end
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abstract type StaticHamiltonian{AD} <: Hamiltonian{AD} end
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abstract type AdaptiveHamiltonian{AD} <: Hamiltonian{AD} end
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getADbackend(::Hamiltonian{AD}) where AD = AD()
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"""
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ExternalSampler{S<:AbstractSampler}
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# Fields
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$(TYPEDFIELDS)
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"""
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struct ExternalSampler{S<:AbstractSampler} <: InferenceAlgorithm
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"the sampler to wrap"
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sampler::S
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end
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"""
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externalsampler(sampler::AbstractSampler)
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Wrap a sampler so it can be used as an inference algorithm.
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"""
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externalsampler(sampler::AbstractSampler) = ExternalSampler(sampler)
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# Algorithm for sampling from the prior
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struct Prior <: InferenceAlgorithm end
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@@ -246,7 +265,6 @@ function AbstractMCMC.sample(
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return AbstractMCMC.sample(rng, model, SampleFromPrior(), ensemble, N, n_chains;
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chain_type=chain_type, progress=progress, kwargs...)
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end
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##########################
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# Chain making utilities #
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##########################
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include("gibbs.jl")
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include("../contrib/inference/sghmc.jl")
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include("emcee.jl")
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include("../contrib/inference/abstractmcmc.jl")
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################
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# Typing tools #

‎test/contrib/inference/abstractmcmc.jl‎

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using Turing.Inference: AdvancedHMC
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function initialize_nuts(model::Turing.Model)
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# Create a log-density function with an implementation of the
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# gradient so we ensure that we're using the same AD backend as in Turing.
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f = LogDensityProblemsAD.ADgradient(DynamicPPL.LogDensityFunction(model))
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# Link the varinfo.
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f = Turing.Inference.setvarinfo(f, DynamicPPL.link!!(Turing.Inference.getvarinfo(f), model))
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# Choose parameter dimensionality and initial parameter value
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D = LogDensityProblems.dimension(f)
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initial_θ = rand(D) .- 0.5
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# Define a Hamiltonian system
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metric = AdvancedHMC.DiagEuclideanMetric(D)
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hamiltonian = AdvancedHMC.Hamiltonian(metric, f)
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# Define a leapfrog solver, with initial step size chosen heuristically
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initial_ϵ = AdvancedHMC.find_good_stepsize(hamiltonian, initial_θ)
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integrator = AdvancedHMC.Leapfrog(initial_ϵ)
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# Define an HMC sampler, with the following components
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# - multinomial sampling scheme,
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# - generalised No-U-Turn criteria, and
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# - windowed adaption for step-size and diagonal mass matrix
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proposal = AdvancedHMC.NUTS{AdvancedHMC.MultinomialTS,AdvancedHMC.GeneralisedNoUTurn}(integrator)
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adaptor = AdvancedHMC.StanHMCAdaptor(
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AdvancedHMC.MassMatrixAdaptor(metric),
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AdvancedHMC.StepSizeAdaptor(0.65, integrator)
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)
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return AdvancedHMC.HMCSampler(proposal, metric, adaptor)
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end
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function initialize_mh(model)
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f = DynamicPPL.LogDensityFunction(model)
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d = LogDensityProblems.dimension(f)
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return AdvancedMH.RWMH(MvNormal(Zeros(d), 0.1 * I))
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end
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@testset "External samplers" begin
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@testset "AdvancedHMC.jl" begin
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for model in DynamicPPL.TestUtils.DEMO_MODELS
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# Need some functionality to initialize the sampler.
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# TODO: Remove this once the constructors in the respective packages become "lazy".
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sampler = initialize_nuts(model);
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DynamicPPL.TestUtils.test_sampler(
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[model],
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DynamicPPL.Sampler(externalsampler(sampler), model),
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5_000;
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nadapts=1_000,
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discard_initial=1_000,
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rtol=0.2
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)
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end
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end
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@testset "AdvancedMH.jl" begin
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for model in DynamicPPL.TestUtils.DEMO_MODELS
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# Need some functionality to initialize the sampler.
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# TODO: Remove this once the constructors in the respective packages become "lazy".
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sampler = initialize_mh(model);
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DynamicPPL.TestUtils.test_sampler(
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[model],
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DynamicPPL.Sampler(externalsampler(sampler), model),
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10_000;
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discard_initial=1_000,
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thinning=10,
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rtol=0.2
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)
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end
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end
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end

‎test/modes/OptimInterface.jl‎

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@testset "MAP for $(model.f)" for model in DynamicPPL.TestUtils.DEMO_MODELS
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result_true = posterior_optima(model)
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@testset "$(optimizer)" for optimizer in [LBFGS(), NelderMead()]
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@testset "$(nameof(typeof(optimizer)))" for optimizer in [LBFGS(), NelderMead()]
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result = optimize(model, MAP(), optimizer)
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vals = result.values
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end
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end
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end
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# Some of the models have one variance parameter per observation, and so
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# the MLE should have the variances set to 0. Since we're working in
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# transformed space, this corresponds to `-Inf`, which is of course not achievable.
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# In particular, it can result in "early termniation" of the optimization process
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# because we hit NaNs, etc. To avoid this, we set the `g_tol` and the `f_tol` to
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# something larger than the default.
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allowed_incorrect_mle = [
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DynamicPPL.TestUtils.demo_dot_assume_dot_observe,
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DynamicPPL.TestUtils.demo_assume_index_observe,
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DynamicPPL.TestUtils.demo_assume_multivariate_observe,
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DynamicPPL.TestUtils.demo_assume_observe_literal,
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DynamicPPL.TestUtils.demo_dot_assume_observe_submodel,
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DynamicPPL.TestUtils.demo_dot_assume_dot_observe_matrix,
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DynamicPPL.TestUtils.demo_dot_assume_matrix_dot_observe_matrix,
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]
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@testset "MLE for $(model.f)" for model in DynamicPPL.TestUtils.DEMO_MODELS
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result_true = likelihood_optima(model)
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# `NelderMead` seems to struggle with convergence here, so we exclude it.
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@testset "$(optimizer)" for optimizer in [LBFGS(),]
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result = optimize(model, MLE(), optimizer)
194+
@testset "$(nameof(typeof(optimizer)))" for optimizer in [LBFGS(),]
195+
result = optimize(model, MLE(), optimizer, Optim.Options(g_tol=1e-3, f_tol=1e-3))
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vals = result.values
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for vn in DynamicPPL.TestUtils.varnames(model)
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for vn_leaf in DynamicPPL.TestUtils.varname_leaves(vn, get(result_true, vn))
183-
@test get(result_true, vn_leaf) ≈ vals[Symbol(vn_leaf)] atol=0.05
200+
if model.f in allowed_incorrect_mle
201+
@test isfinite(get(result_true, vn_leaf))
202+
else
203+
@test get(result_true, vn_leaf) ≈ vals[Symbol(vn_leaf)] atol=0.05
204+
end
184205
end
185206
end
186207
end
187-
end
208+
end
188209
end
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# Issue: https://discourse.julialang.org/t/two-equivalent-conditioning-syntaxes-giving-different-likelihood-values/100320

‎test/runtests.jl‎

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@@ -77,6 +77,7 @@ macro timeit_include(path::AbstractString) :(@timeit TIMEROUTPUT $path include($
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@timeit_include("inference/Inference.jl")
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@timeit_include("contrib/inference/dynamichmc.jl")
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@timeit_include("contrib/inference/sghmc.jl")
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@timeit_include("contrib/inference/abstractmcmc.jl")
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@timeit_include("inference/mh.jl")
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end
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end

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