Bump DynamicPPL to v0.25 (TuringLang#2197)

* reeclpad Setfield with Accessors to bump up to DPPL v0.25

* bump DPPL version

* use Accessors

* replaced usages of `@set!` with `BangBang.@set!!`

* fixed Project.toml

* reverted accidental change

* import BangBang in Turing

* replace `BangBang.@set!!` with `Accessors.@set`

* bump minor version since this is a breaking change

* makke failing test conditional on Julia version >1.7

* fixed references to Setfield.jl in Experimental module

* disabled another test due to the same issue

---------

Co-authored-by: Xianda Sun <sunxdt@gmail.com>
Co-authored-by: Xianda Sun <5433119+sunxd3@users.noreply.github.com>

Project.toml

@@ -1,10 +1,11 @@
 name = "Turing"
 uuid = "fce5fe82-541a-59a6-adf8-730c64b5f9a0"
-version = "0.30.9"
+version = "0.31.0"
 
 [deps]
 ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"
 AbstractMCMC = "80f14c24-f653-4e6a-9b94-39d6b0f70001"
+Accessors = "7d9f7c33-5ae7-4f3b-8dc6-eff91059b697"
 AdvancedHMC = "0bf59076-c3b1-5ca4-86bd-e02cd72cde3d"
 AdvancedMH = "5b7e9947-ddc0-4b3f-9b55-0d8042f74170"
 AdvancedPS = "576499cb-2369-40b2-a588-c64705576edc"
@@ -29,7 +30,6 @@ Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
 Requires = "ae029012-a4dd-5104-9daa-d747884805df"
 SciMLBase = "0bca4576-84f4-4d90-8ffe-ffa030f20462"
-Setfield = "efcf1570-3423-57d1-acb7-fd33fddbac46"
 SpecialFunctions = "276daf66-3868-5448-9aa4-cd146d93841b"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 StatsAPI = "82ae8749-77ed-4fe6-ae5f-f523153014b0"
@@ -47,18 +47,19 @@ TuringOptimExt = "Optim"
 [compat]
 ADTypes = "0.2"
 AbstractMCMC = "5.2"
+Accessors = "0.1"
 AdvancedHMC = "0.3.0, 0.4.0, 0.5.2, 0.6"
 AdvancedMH = "0.8"
 AdvancedPS = "0.5.4"
 AdvancedVI = "0.2"
-BangBang = "0.3"
+BangBang = "0.4"
 Bijectors = "0.13.6"
 DataStructures = "0.18"
 Distributions = "0.23.3, 0.24, 0.25"
 DistributionsAD = "0.6"
 DocStringExtensions = "0.8, 0.9"
 DynamicHMC = "3.4"
-DynamicPPL = "0.24.10"
+DynamicPPL = "0.25.1"
 EllipticalSliceSampling = "0.5, 1, 2"
 ForwardDiff = "0.10.3"
 Libtask = "0.7, 0.8"
@@ -70,7 +71,6 @@ Optim = "1"
 Reexport = "0.2, 1"
 Requires = "0.5, 1.0"
 SciMLBase = "1.37.1, 2"
-Setfield = "0.8, 1"
 SpecialFunctions = "0.7.2, 0.8, 0.9, 0.10, 1, 2"
 Statistics = "1.6"
 StatsAPI = "1.6"

ext/TuringOptimExt.jl

@@ -2,11 +2,11 @@ module TuringOptimExt
 
 if isdefined(Base, :get_extension)
     import Turing
-    import Turing: Distributions, DynamicPPL, ForwardDiff, NamedArrays, Printf, Setfield, Statistics, StatsAPI, StatsBase 
+    import Turing: Distributions, DynamicPPL, ForwardDiff, NamedArrays, Printf, Accessors, Statistics, StatsAPI, StatsBase 
     import Optim
 else
     import ..Turing
-    import ..Turing: Distributions, DynamicPPL, ForwardDiff, NamedArrays, Printf, Setfield, Statistics, StatsAPI, StatsBase
+    import ..Turing: Distributions, DynamicPPL, ForwardDiff, NamedArrays, Printf, Accessors, Statistics, StatsAPI, StatsBase
     import ..Optim
 end
 
@@ -80,7 +80,7 @@ function StatsBase.informationmatrix(m::ModeResult; hessian_function=ForwardDiff
     # Hessian is computed with respect to the untransformed parameters.
     linked = DynamicPPL.istrans(m.f.varinfo)
     if linked
-        Setfield.@set! m.f.varinfo = DynamicPPL.invlink!!(m.f.varinfo, m.f.model)
+        m = Accessors.@set m.f.varinfo = DynamicPPL.invlink!!(m.f.varinfo, m.f.model)
     end
 
     # Calculate the Hessian, which is the information matrix because the negative of the log likelihood was optimized
@@ -89,7 +89,7 @@ function StatsBase.informationmatrix(m::ModeResult; hessian_function=ForwardDiff
 
     # Link it back if we invlinked it.
     if linked
-        Setfield.@set! m.f.varinfo = DynamicPPL.link!!(m.f.varinfo, m.f.model)
+        m = Accessors.@set m.f.varinfo = DynamicPPL.link!!(m.f.varinfo, m.f.model)
     end
 
     return NamedArrays.NamedArray(info, (varnames, varnames))
@@ -227,8 +227,8 @@ function _optimize(
 )
     # Convert the initial values, since it is assumed that users provide them
     # in the constrained space.
-    Setfield.@set! f.varinfo = DynamicPPL.unflatten(f.varinfo, init_vals)
-    Setfield.@set! f.varinfo = DynamicPPL.link(f.varinfo, model)
+    f = Accessors.@set f.varinfo = DynamicPPL.unflatten(f.varinfo, init_vals)
+    f = Accessors.@set f.varinfo = DynamicPPL.link(f.varinfo, model)
     init_vals = DynamicPPL.getparams(f)
 
     # Optimize!
@@ -241,10 +241,10 @@ function _optimize(
 
     # Get the VarInfo at the MLE/MAP point, and run the model to ensure
     # correct dimensionality.
-    Setfield.@set! f.varinfo = DynamicPPL.unflatten(f.varinfo, M.minimizer)
-    Setfield.@set! f.varinfo = DynamicPPL.invlink(f.varinfo, model)
+    f = Accessors.@set f.varinfo = DynamicPPL.unflatten(f.varinfo, M.minimizer)
+    f = Accessors.@set f.varinfo = DynamicPPL.invlink(f.varinfo, model)
     vals = DynamicPPL.getparams(f)
-    Setfield.@set! f.varinfo = DynamicPPL.link(f.varinfo, model)
+    f = Accessors.@set f.varinfo = DynamicPPL.link(f.varinfo, model)
 
     # Make one transition to get the parameter names.
     ts = [Turing.Inference.Transition(

src/Turing.jl

@@ -11,10 +11,12 @@ using DynamicPPL: DynamicPPL, LogDensityFunction
 import DynamicPPL: getspace, NoDist, NamedDist
 import LogDensityProblems
 import NamedArrays
-import Setfield
+import Accessors
 import StatsAPI
 import StatsBase
 
+using Accessors: Accessors
+
 import Printf
 import Random
 

src/experimental/Experimental.jl

@@ -3,7 +3,7 @@ module Experimental
 using Random: Random
 using AbstractMCMC: AbstractMCMC
 using DynamicPPL: DynamicPPL, VarName
-using Setfield: Setfield
+using Accessors: Accessors
 
 using DocStringExtensions: TYPEDFIELDS
 using Distributions

src/experimental/gibbs.jl

@@ -107,9 +107,9 @@ Returns the preferred value type for a variable with the given `varinfo`.
 preferred_value_type(::DynamicPPL.AbstractVarInfo) = DynamicPPL.OrderedDict
 preferred_value_type(::DynamicPPL.SimpleVarInfo{<:NamedTuple}) = NamedTuple
 function preferred_value_type(varinfo::DynamicPPL.TypedVarInfo)
-    # We can only do this in the scenario where all the varnames are `Setfield.IdentityLens`.
+    # We can only do this in the scenario where all the varnames are `Accessors.IdentityLens`.
     namedtuple_compatible = all(varinfo.metadata) do md
-        eltype(md.vns) <: VarName{<:Any,Setfield.IdentityLens}
+        eltype(md.vns) <: VarName{<:Any,typeof(identity)}
     end
     return namedtuple_compatible ? NamedTuple : DynamicPPL.OrderedDict
 end
@@ -321,8 +321,8 @@ function AbstractMCMC.step(
         )
 
         # Update the `states` and `varinfos`.
-        states = Setfield.setindex(states, new_state_local, index)
-        varinfos = Setfield.setindex(varinfos, new_varinfo_local, index)
+        states = Accessors.setindex(states, new_state_local, index)
+        varinfos = Accessors.setindex(varinfos, new_varinfo_local, index)
     end
 
     # Combine the resulting varinfo objects.
@@ -349,7 +349,7 @@ function make_rerun_sampler(model::DynamicPPL.Model, sampler::DynamicPPL.Sampler
     # NOTE: This is different from the implementation used in the old `Gibbs` sampler, where we specifically provide
     # a `gid`. Here, because `model` only contains random variables to be sampled by `sampler`, we just use the exact
     # same `selector` as before but now with `rerun` set to `true` if needed.
-    return Setfield.@set sampler.selector.rerun = true
+    return Accessors.@set sampler.selector.rerun = true
 end
 
 # Interface we need a sampler to implement to work as a component in a Gibbs sampler.

src/mcmc/Inference.jl

@@ -21,14 +21,14 @@ using DynamicPPL
 using AbstractMCMC: AbstractModel, AbstractSampler
 using DocStringExtensions: TYPEDEF, TYPEDFIELDS
 using DataStructures: OrderedSet
-using Setfield: Setfield
+using Accessors: Accessors
 
 import ADTypes
 import AbstractMCMC
 import AdvancedHMC; const AHMC = AdvancedHMC
 import AdvancedMH; const AMH = AdvancedMH
 import AdvancedPS
-import BangBang
+import Accessors
 import EllipticalSliceSampling
 import LogDensityProblems
 import LogDensityProblemsAD

src/mcmc/abstractmcmc.jl

@@ -23,7 +23,7 @@ getparams(::DynamicPPL.Model, transition::AdvancedMH.Transition) = transition.pa
 getvarinfo(f::DynamicPPL.LogDensityFunction) = f.varinfo
 getvarinfo(f::LogDensityProblemsAD.ADGradientWrapper) = getvarinfo(parent(f))
 
-setvarinfo(f::DynamicPPL.LogDensityFunction, varinfo) = Setfield.@set f.varinfo = varinfo
+setvarinfo(f::DynamicPPL.LogDensityFunction, varinfo) = Accessors.@set f.varinfo = varinfo
 setvarinfo(f::LogDensityProblemsAD.ADGradientWrapper, varinfo) = setvarinfo(parent(f), varinfo)
 
 # TODO: Do we also support `resume`, etc?

src/optimisation/Optimisation.jl

@@ -5,7 +5,7 @@ using Bijectors
 using Random
 using SciMLBase: OptimizationFunction, OptimizationProblem, AbstractADType, NoAD
 
-using Setfield
+using Accessors: Accessors
 using DynamicPPL
 using DynamicPPL: Model, AbstractContext, VarInfo, VarName,
     _getindex, getsym, getfield,  setorder!,
@@ -150,7 +150,7 @@ function transform!!(f::OptimLogDensity)
     linked = DynamicPPL.istrans(f.varinfo)
 
     ## transform into constrained or unconstrained space depending on current state of vi
-    @set! f.varinfo = if !linked
+    f = Accessors.@set f.varinfo = if !linked
         DynamicPPL.link!!(f.varinfo, f.model)
     else
         DynamicPPL.invlink!!(f.varinfo, f.model)

test/Project.toml

@@ -40,7 +40,7 @@ Clustering = "0.14, 0.15"
 Distributions = "0.25"
 DistributionsAD = "0.6.3"
 DynamicHMC = "2.1.6, 3.0"
-DynamicPPL = "0.24"
+DynamicPPL = "0.25.1"
 FiniteDifferences = "0.10.8, 0.11, 0.12"
 ForwardDiff = "0.10.12 - 0.10.32, 0.10"
 LogDensityProblems = "2"

test/mcmc/hmc.jl

@@ -247,14 +247,18 @@
         end
     end
 
-    @turing_testset "(partially) issue: #2095" begin
-        @model function vector_of_dirichlet(::Type{TV}=Vector{Float64}) where {TV}
-            xs = Vector{TV}(undef, 2)
-            xs[1] ~ Dirichlet(ones(5))
-            xs[2] ~ Dirichlet(ones(5))
+    # Disable on Julia <1.8 due to https://github.com/TuringLang/Turing.jl/pull/2197.
+    # TODO: Remove this block once https://github.com/JuliaFolds2/BangBang.jl/pull/22 has been released.
+    if VERSION ≥ v"1.8"
+        @turing_testset "(partially) issue: #2095" begin
+            @model function vector_of_dirichlet(::Type{TV}=Vector{Float64}) where {TV}
+                xs = Vector{TV}(undef, 2)
+                xs[1] ~ Dirichlet(ones(5))
+                xs[2] ~ Dirichlet(ones(5))
+            end
+            model = vector_of_dirichlet()
+            chain = sample(model, NUTS(), 1000)
+            @test mean(Array(chain)) ≈ 0.2
         end
-        model = vector_of_dirichlet()
-        chain = sample(model, NUTS(), 1000)
-        @test mean(Array(chain)) ≈ 0.2
     end
 end

test/mcmc/mh.jl

@@ -162,24 +162,28 @@
         # @test v1 < v2
     end
 
-    @turing_testset "vector of multivariate distributions" begin
-        @model function test(k)
-            T = Vector{Vector{Float64}}(undef, k)
-            for i in 1:k
-                T[i] ~ Dirichlet(5, 1.0)
+    # Disable on Julia <1.8 due to https://github.com/TuringLang/Turing.jl/pull/2197.
+    # TODO: Remove this block once https://github.com/JuliaFolds2/BangBang.jl/pull/22 has been released.
+    if VERSION ≥ v"1.8"
+        @turing_testset "vector of multivariate distributions" begin
+            @model function test(k)
+                T = Vector{Vector{Float64}}(undef, k)
+                for i in 1:k
+                    T[i] ~ Dirichlet(5, 1.0)
+                end
             end
-        end
 
-        Random.seed!(100)
-        chain = sample(test(1), MH(), 5_000)
-        for i in 1:5
-            @test mean(chain, "T[1][$i]") ≈ 0.2 atol=0.01
-        end
+            Random.seed!(100)
+            chain = sample(test(1), MH(), 5_000)
+            for i in 1:5
+                @test mean(chain, "T[1][$i]") ≈ 0.2 atol = 0.01
+            end
 
-        Random.seed!(100)
-        chain = sample(test(10), MH(), 5_000)
-        for j in 1:10, i in 1:5
-            @test mean(chain, "T[$j][$i]") ≈ 0.2 atol=0.01
+            Random.seed!(100)
+            chain = sample(test(10), MH(), 5_000)
+            for j in 1:10, i in 1:5
+                @test mean(chain, "T[$j][$i]") ≈ 0.2 atol = 0.01
+            end
         end
     end