Remove src/essential and deprecated function stubs

Author: penelopeysm

HISTORY.md

@@ -1,3 +1,10 @@
+# Release 0.39.0
+
+## Removal of Turing.Essential
+
+The Turing.Essential module has been removed.
+Anything exported from there can be imported from either `Turing` or `DynamicPPL`.
+
 # Release 0.38.4
 
 The minimum Julia version was increased to 1.10.2 (from 1.10.0).

src/Turing.jl

@@ -47,8 +47,6 @@ end
 # Random probability measures.
 include("stdlib/distributions.jl")
 include("stdlib/RandomMeasures.jl")
-include("essential/Essential.jl")
-using .Essential
 include("mcmc/Inference.jl")  # inference algorithms
 using .Inference
 include("variational/VariationalInference.jl")
@@ -57,8 +55,6 @@ using .Variational
 include("optimisation/Optimisation.jl")
 using .Optimisation
 
-include("deprecated.jl") # to be removed in the next minor version release
-
 ###########
 # Exports #
 ###########

src/deprecated.jl

@@ -1,6 +1,5 @@
 module Inference
 
-using ..Essential
 using DynamicPPL:
     Metadata,
     VarInfo,

src/essential/Essential.jl

@@ -2,6 +2,79 @@
 ### Particle Filtering and Particle MCMC Samplers.
 ###
 
+### AdvancedPS models and interface
+
+struct TracedModel{S<:AbstractSampler,V<:AbstractVarInfo,M<:Model,E<:Tuple} <:
+       AdvancedPS.AbstractGenericModel
+    model::M
+    sampler::S
+    varinfo::V
+    evaluator::E
+end
+
+function TracedModel(
+    model::Model,
+    sampler::AbstractSampler,
+    varinfo::AbstractVarInfo,
+    rng::Random.AbstractRNG,
+)
+    context = SamplingContext(rng, sampler, DefaultContext())
+    args, kwargs = DynamicPPL.make_evaluate_args_and_kwargs(model, varinfo, context)
+    if kwargs !== nothing && !isempty(kwargs)
+        error(
+            "Sampling with `$(sampler.alg)` does not support models with keyword arguments. See issue #2007 for more details.",
+        )
+    end
+    return TracedModel{AbstractSampler,AbstractVarInfo,Model,Tuple}(
+        model, sampler, varinfo, (model.f, args...)
+    )
+end
+
+function AdvancedPS.advance!(
+    trace::AdvancedPS.Trace{<:AdvancedPS.LibtaskModel{<:TracedModel}}, isref::Bool=false
+)
+    # Make sure we load/reset the rng in the new replaying mechanism
+    DynamicPPL.increment_num_produce!(trace.model.f.varinfo)
+    isref ? AdvancedPS.load_state!(trace.rng) : AdvancedPS.save_state!(trace.rng)
+    score = consume(trace.model.ctask)
+    if score === nothing
+        return nothing
+    else
+        return score + DynamicPPL.getlogp(trace.model.f.varinfo)
+    end
+end
+
+function AdvancedPS.delete_retained!(trace::TracedModel)
+    DynamicPPL.set_retained_vns_del!(trace.varinfo)
+    return trace
+end
+
+function AdvancedPS.reset_model(trace::TracedModel)
+    DynamicPPL.reset_num_produce!(trace.varinfo)
+    return trace
+end
+
+function AdvancedPS.reset_logprob!(trace::TracedModel)
+    DynamicPPL.resetlogp!!(trace.model.varinfo)
+    return trace
+end
+
+function AdvancedPS.update_rng!(
+    trace::AdvancedPS.Trace{<:AdvancedPS.LibtaskModel{<:TracedModel}}
+)
+    # Extract the `args`.
+    args = trace.model.ctask.args
+    # From `args`, extract the `SamplingContext`, which contains the RNG.
+    sampling_context = args[3]
+    rng = sampling_context.rng
+    trace.rng = rng
+    return trace
+end
+
+function Libtask.TapedTask(model::TracedModel, ::Random.AbstractRNG, args...; kwargs...) # RNG ?
+    return Libtask.TapedTask(model.evaluator[1], model.evaluator[2:end]...; kwargs...)
+end
+
 ####
 #### Generic Sequential Monte Carlo sampler.
 ####
@@ -408,7 +481,7 @@ function AdvancedPS.Trace(
     newvarinfo = deepcopy(varinfo)
     DynamicPPL.reset_num_produce!(newvarinfo)
 
-    tmodel = Turing.Essential.TracedModel(model, sampler, newvarinfo, rng)
+    tmodel = TracedModel(model, sampler, newvarinfo, rng)
     newtrace = AdvancedPS.Trace(tmodel, rng)
     AdvancedPS.addreference!(newtrace.model.ctask.task, newtrace)
     return newtrace