Rename Turing.Core to Turing.Essential (#1762)

* Rename `Turing.Core` to `Turing.Essential`

* Deprecate Turing.Core

Co-authored-by: Tor Erlend Fjelde <[email protected]>

Co-authored-by: Tor Erlend Fjelde <[email protected]>

Author: devmotion

Project.toml

@@ -1,6 +1,6 @@
 name = "Turing"
 uuid = "fce5fe82-541a-59a6-adf8-730c64b5f9a0"
-version = "0.19.3"
+version = "0.19.4"
 
 [deps]
 AbstractMCMC = "80f14c24-f653-4e6a-9b94-39d6b0f70001"

docs/src/library/api.md

@@ -10,7 +10,7 @@ CurrentModule = Turing
 ## Index
 
 ```@index
-Modules = [Turing, Turing.Core, Turing.Inference, Libtask]
+Modules = [Turing, Turing.Essential, Turing.Inference, Libtask]
 ```
 
 ## Modelling

src/Turing.jl

@@ -30,8 +30,9 @@ include("stdlib/distributions.jl")
 include("stdlib/RandomMeasures.jl")
 include("utilities/Utilities.jl")
 using .Utilities
-include("core/Core.jl")
-using .Core
+include("essential/Essential.jl")
+Base.@deprecate_binding Core Essential false
+using .Essential
 include("inference/Inference.jl")  # inference algorithms
 using .Inference
 include("variational/VariationalInference.jl")
@@ -140,7 +141,7 @@ export  @model,                 # modelling
         MAP,
         MLE,
         get_parameter_bounds,
-        optim_objective, 
+        optim_objective,
         optim_function,
         optim_problem
 end

src/core/Core.jl renamed to src/essential/Essential.jl

@@ -1,4 +1,4 @@
-module Core
+module Essential
 
 using DistributionsAD, Bijectors
 using Libtask, ForwardDiff, Random

src/core/ad.jl renamed to src/essential/ad.jl

@@ -365,6 +365,6 @@ function AdvancedPS.Trace(
 )
     newvarinfo = deepcopy(varinfo)
     DynamicPPL.reset_num_produce!(newvarinfo)
-    f = Turing.Core.TracedModel(model, sampler, newvarinfo)
+    f = Turing.Essential.TracedModel(model, sampler, newvarinfo)
     return AdvancedPS.Trace(f)
 end

src/core/compat/reversediff.jl renamed to src/essential/compat/reversediff.jl

@@ -1,8 +1,8 @@
 module Inference
 
-using ..Core
+using ..Essential
 using ..Utilities
-using DynamicPPL: Metadata, VarInfo, TypedVarInfo, 
+using DynamicPPL: Metadata, VarInfo, TypedVarInfo,
     islinked, invlink!, link!,
     setindex!!, push!!,
     setlogp!!, getlogp,
@@ -28,7 +28,7 @@ import AdvancedHMC; const AHMC = AdvancedHMC
 import AdvancedMH; const AMH = AdvancedMH
 import AdvancedPS
 import BangBang
-import ..Core: getchunksize, getADbackend
+import ..Essential: getchunksize, getADbackend
 import EllipticalSliceSampling
 import Random
 import MCMCChains
@@ -260,7 +260,7 @@ function _params_to_array(ts::Vector)
         return Dict(nms[j] => vs[j] for j in 1:length(vs))
     end
     names = collect(names_set)
-    vals = [get(dicts[i], key, missing) for i in eachindex(dicts), 
+    vals = [get(dicts[i], key, missing) for i in eachindex(dicts),
         (j, key) in enumerate(names)]
 
     return names, vals
@@ -444,7 +444,7 @@ for alg in (:HMC, :HMCDA, :NUTS, :SGLD, :SGHMC)
 end
 
 function DynamicPPL.get_matching_type(
-    spl::Sampler{<:Union{PG, SMC}}, 
+    spl::Sampler{<:Union{PG, SMC}},
     vi,
     ::Type{TV},
 ) where {T, N, TV <: Array{T, N}}
@@ -566,8 +566,8 @@ end
 
     transitions_from_chain(
         [rng::AbstractRNG,]
-        model::Model, 
-        chain::MCMCChains.Chains; 
+        model::Model,
+        chain::MCMCChains.Chains;
         sampler = DynamicPPL.SampleFromPrior()
     )
 

src/core/container.jl renamed to src/essential/container.jl

@@ -77,11 +77,11 @@
         sample(dir(), HMC(0.01, 1), 1000);
         Turing.setrdcache(true)
         sample(dir(), HMC(0.01, 1), 1000);
-        caches = Memoization.find_caches(Turing.Core.memoized_taperesult)
+        caches = Memoization.find_caches(Turing.Essential.memoized_taperesult)
         @test length(caches) == 1
         @test !isempty(first(values(caches)))
         Turing.emptyrdcache()
-        caches = Memoization.find_caches(Turing.Core.memoized_taperesult)
+        caches = Memoization.find_caches(Turing.Essential.memoized_taperesult)
         @test length(caches) == 1
         @test isempty(first(values(caches)))
     end
@@ -111,35 +111,35 @@
         @model function tst(x, ::Type{TV}=Vector{Float64}) where {TV}
             params = TV(undef, 2)
             @. params ~ Normal(0, 1)
-        
+
             x ~ MvNormal(params, I)
         end
-        
+
         function make_logjoint(model::DynamicPPL.Model, ctx::DynamicPPL.AbstractContext)
             # setup
             varinfo_init = Turing.VarInfo(model)
-            spl = DynamicPPL.SampleFromPrior()    
+            spl = DynamicPPL.SampleFromPrior()
             DynamicPPL.link!(varinfo_init, spl)
-        
+
             function logπ(z; unlinked = false)
                 varinfo = DynamicPPL.VarInfo(varinfo_init, spl, z)
-        
+
                 unlinked && DynamicPPL.invlink!(varinfo_init, spl)
                 model(varinfo, spl, ctx)
                 unlinked && DynamicPPL.link!(varinfo_init, spl)
-        
+
                 return -DynamicPPL.getlogp(varinfo)
             end
-        
+
             return logπ
         end
-        
+
         data = [0.5, -0.5]
         model = tst(data)
-        
+
         likelihood = make_logjoint(model, DynamicPPL.LikelihoodContext())
         target(x) = likelihood(x, unlinked=true)
-        
+
         H_f = ForwardDiff.hessian(target, zeros(2))
         H_r = ReverseDiff.hessian(target, zeros(2))
         @test H_f == [1.0 0.0; 0.0 1.0]