Merge branch 'master' into dw/enzyme

Project.toml

@@ -1,10 +1,11 @@
 name = "Turing"
 uuid = "fce5fe82-541a-59a6-adf8-730c64b5f9a0"
-version = "0.30.7"
+version = "0.32.1"
 
 [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"
@@ -24,12 +25,12 @@ LogDensityProblems = "6fdf6af0-433a-55f7-b3ed-c6c6e0b8df7c"
 LogDensityProblemsAD = "996a588d-648d-4e1f-a8f0-a84b347e47b1"
 MCMCChains = "c7f686f2-ff18-58e9-bc7b-31028e88f75d"
 NamedArrays = "86f7a689-2022-50b4-a561-43c23ac3c673"
+OrderedCollections = "bac558e1-5e72-5ebc-8fee-abe8a469f55d"
 Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
 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"
@@ -45,32 +46,33 @@ TuringDynamicHMCExt = "DynamicHMC"
 TuringOptimExt = "Optim"
 
 [compat]
-ADTypes = "0.2"
+ADTypes = "0.2, 1"
 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"
+AdvancedPS = "0.6.0"
 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 = "0.24.7"
+DynamicPPL = "0.27.1"
 EllipticalSliceSampling = "0.5, 1, 2"
 ForwardDiff = "0.10.3"
 Libtask = "0.7, 0.8"
 LogDensityProblems = "2"
 LogDensityProblemsAD = "1.7.0"
 MCMCChains = "5, 6"
 NamedArrays = "0.9, 0.10"
+OrderedCollections = "1"
 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"

README.md

@@ -10,7 +10,7 @@
 
 Turing's home page, with links to everything you'll need to use Turing, is available at:
 
-https://turinglang.org/dev/docs/using-turing/get-started
+https://turinglang.org/docs/
 
 
 ## What's changed recently?

ext/TuringDynamicHMCExt.jl

@@ -34,7 +34,7 @@ end
 function DynamicNUTS(
     spl::DynamicHMC.NUTS = DynamicHMC.NUTS(),
     space::Tuple = ();
-    adtype::ADTypes.AbstractADType = ADTypes.AutoForwardDiff(; chunksize=0)
+    adtype::ADTypes.AbstractADType = Turing.DEFAULT_ADTYPE
 )
     return DynamicNUTS{typeof(adtype),space,typeof(spl)}(spl, adtype)
 end

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!
@@ -241,17 +241,15 @@ 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(
-        Turing.Inference.getparams(model, f.varinfo),
-        DynamicPPL.getlogp(f.varinfo)
-    )]
-    varnames = map(Symbol, first(Turing.Inference._params_to_array(model, ts)))
+    vns_vals_iter = Turing.Inference.getparams(model, f.varinfo)
+    varnames = map(Symbol ∘ first, vns_vals_iter)
+    vals = map(last, vns_vals_iter)
 
     # Store the parameters and their names in an array.
     vmat = NamedArrays.NamedArray(vals, varnames)

src/Turing.jl

@@ -11,13 +11,19 @@ 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
 
+using ADTypes: ADTypes
+
+const DEFAULT_ADTYPE = ADTypes.AutoForwardDiff()
+
 const PROGRESS = Ref(true)
 
 # TODO: remove `PROGRESS` and this function in favour of `AbstractMCMC.PROGRESS`
@@ -48,14 +54,17 @@ using .Variational
 include("optimisation/Optimisation.jl")
 using .Optimisation
 
+include("experimental/Experimental.jl")
 include("deprecated.jl") # to be removed in the next minor version release
 
 ###########
 # Exports #
 ###########
 # `using` statements for stuff to re-export
-using DynamicPPL: pointwise_loglikelihoods, generated_quantities, logprior, logjoint
+using DynamicPPL: pointwise_loglikelihoods, generated_quantities, logprior, logjoint, condition, decondition, fix, unfix, conditioned
 using StatsBase: predict
+using Bijectors: ordered
+using OrderedCollections: OrderedDict
 
 # Turing essentials - modelling macros and inference algorithms
 export  @model,                 # modelling
@@ -98,6 +107,7 @@ export  @model,                 # modelling
         AutoReverseDiff,
         AutoZygote,
         AutoTracker,
+        AutoTapir,
 
         setprogress!,           # debugging
 
@@ -107,10 +117,10 @@ export  @model,                 # modelling
         BernoulliLogit,         # Part of Distributions >= 0.25.77
         OrderedLogistic,
         LogPoisson,
-        NamedDist,
         filldist,
         arraydist,
 
+        NamedDist,              # Exports from DynamicPPL
         predict,
         pointwise_loglikelihoods,
         elementwise_loglikelihoods,
@@ -119,6 +129,15 @@ export  @model,                 # modelling
         logjoint,
         LogDensityFunction,
 
+        condition,
+        decondition,
+        fix,
+        unfix,
+        conditioned,
+        OrderedDict,
+
+        ordered,                # Exports from Bijectors
+
         constrained_space,            # optimisation interface
         MAP,
         MLE,

src/deprecated.jl

@@ -1,12 +1,12 @@
 export setadbackend, setchunksize, setadsafe
 
 function setadbackend(::Union{Symbol, Val})
-    Base.depwarn("`ADBACKEND` and `setbackend` are deprecated. Please specify the chunk size directly in the sampler constructor, e.g., `HMC(0.1, 5; adtype=AutoForwardDiff(; chunksize=0))`.\n This function has no effects.", :setbackend; force=true)
+    Base.depwarn("`ADBACKEND` and `setbackend` are deprecated. Please specify the chunk size directly in the sampler constructor, e.g., `HMC(0.1, 5; adtype=AutoForwardDiff())`.\n This function has no effects.", :setbackend; force=true)
     nothing
 end
 
 function setchunksize(::Int)
-    Base.depwarn("`CHUNKSIZE` and `setchunksize` are deprecated. Please specify the chunk size directly in the sampler constructor, e.g., `HMC(0.1, 5; adtype=AutoForwardDiff(; chunksize=0))`.\n This function has no effects.", :setchunksize; force=true)
+    Base.depwarn("`CHUNKSIZE` and `setchunksize` are deprecated. Please specify the chunk size directly in the sampler constructor, e.g., `HMC(0.1, 5; adtype=AutoForwardDiff())`.\n This function has no effects.", :setchunksize; force=true)
     nothing
 end
 

src/experimental/Experimental.jl

@@ -0,0 +1,16 @@
+module Experimental
+
+using Random: Random
+using AbstractMCMC: AbstractMCMC
+using DynamicPPL: DynamicPPL, VarName
+using Accessors: Accessors
+
+using DocStringExtensions: TYPEDFIELDS
+using Distributions
+
+using ..Turing: Turing
+using ..Turing.Inference: gibbs_rerun, InferenceAlgorithm
+
+include("gibbs.jl")
+
+end