Remove getmodel and setmodel after transferred to DynamicPPL (#2292)

* remove `getmodel` and `setmodel` after transferred to DynamicPPL

* bump version

Author: sunxd3

Project.toml

@@ -1,6 +1,6 @@
 name = "Turing"
 uuid = "fce5fe82-541a-59a6-adf8-730c64b5f9a0"
-version = "0.33.2"
+version = "0.33.3"
 
 [deps]
 ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"
@@ -63,7 +63,7 @@ Distributions = "0.23.3, 0.24, 0.25"
 DistributionsAD = "0.6"
 DocStringExtensions = "0.8, 0.9"
 DynamicHMC = "3.4"
-DynamicPPL = "0.28.1"
+DynamicPPL = "0.28.2"
 Compat = "4.15.0"
 EllipticalSliceSampling = "0.5, 1, 2"
 ForwardDiff = "0.10.3"

src/mcmc/abstractmcmc.jl

@@ -17,50 +17,13 @@ function transition_to_turing(f::LogDensityProblemsAD.ADGradientWrapper, transit
     return transition_to_turing(parent(f), transition)
 end
 
-"""
-    getmodel(f)
-
-Return the `DynamicPPL.Model` wrapped in the given log-density function `f`.
-"""
-getmodel(f::LogDensityProblemsAD.ADGradientWrapper) = getmodel(parent(f))
-getmodel(f::DynamicPPL.LogDensityFunction) = f.model
-
-"""
-    setmodel(f, model[, adtype])
-
-Set the `DynamicPPL.Model` in the given log-density function `f` to `model`.
-
-!!! warning
-    Note that if `f` is a `LogDensityProblemsAD.ADGradientWrapper` wrapping a
-    `DynamicPPL.LogDensityFunction`, performing an update of the `model` in `f`
-    might require recompilation of the gradient tape, depending on the AD backend.
-"""
-function setmodel(
-    f::LogDensityProblemsAD.ADGradientWrapper,
-    model::DynamicPPL.Model,
-    adtype::ADTypes.AbstractADType
-)
-    # TODO: Should we handle `SciMLBase.NoAD`?
-    # For an `ADGradientWrapper` we do the following:
-    # 1. Update the `Model` in the underlying `LogDensityFunction`.
-    # 2. Re-construct the `ADGradientWrapper` using `ADgradient` using the provided `adtype`
-    #    to ensure that the recompilation of gradient tapes, etc. also occur. For example,
-    #    ReverseDiff.jl in compiled mode will cache the compiled tape, which means that just
-    #    replacing the corresponding field with the new model won't be sufficient to obtain
-    #    the correct gradients.
-    return LogDensityProblemsAD.ADgradient(adtype, setmodel(parent(f), model))
-end
-function setmodel(f::DynamicPPL.LogDensityFunction, model::DynamicPPL.Model)
-    return Accessors.@set f.model = model
-end
-
 function varinfo_from_logdensityfn(f::LogDensityProblemsAD.ADGradientWrapper)
     return varinfo_from_logdensityfn(parent(f))
 end
 varinfo_from_logdensityfn(f::DynamicPPL.LogDensityFunction) = f.varinfo
 
 function varinfo(state::TuringState)
-    θ = getparams(getmodel(state.logdensity), state.state)
+    θ = getparams(DynamicPPL.getmodel(state.logdensity), state.state)
     # TODO: Do we need to link here first?
     return DynamicPPL.unflatten(varinfo_from_logdensityfn(state.logdensity), θ)
 end
@@ -97,7 +60,7 @@ function recompute_logprob!!(
 )
     # Re-using the log-density function from the `state` and updating only the `model` field,
     # since the `model` might now contain different conditioning values.
-    f = setmodel(state.logdensity, model, sampler.alg.adtype)
+    f = DynamicPPL.setmodel(state.logdensity, model, sampler.alg.adtype)
     # Recompute the log-probability with the new `model`.
     state_inner = recompute_logprob!!(
         rng, AbstractMCMC.LogDensityModel(f), sampler.alg.sampler, state.state