Bump DynamicPPL to 0.23 (#2001)

* bump dppl test versions

* also bump bijectors

* bump AdvancedVI versions

* revert Bijectors bump

* bumped vi and bijectors too

* breaking change

* removed refernce to Bijectors.setadbackend

* make use of DynamicPPL.make_evaluate_args_and_kwargs

* bump DPPL version

* bump DPPL version for tests

* fixed bug in TracedModel

* forgot to remove some lines

* just drop the kwargs completely :(

* Update container.jl

* Update container.jl

* will now error if we're using a model with kwargs and SMC

* added reference to issue

* added test for keyword models failing

* make this a breaking change

* made error message more informative

* makde it slightly less informative

* fixed typo in checking for TRaceModel

* finally fixed the if-statement..

* Fix test error

* fixed tests maybe

* now fixed maybe

* Update test/inference/Inference.jl

---------

Co-authored-by: Hong Ge <[email protected]>

Author: torfjelde

Project.toml

@@ -1,6 +1,7 @@
 name = "Turing"
 uuid = "fce5fe82-541a-59a6-adf8-730c64b5f9a0"
-version = "0.25.3"
+version = "0.26.0"
+
 
 [deps]
 AbstractMCMC = "80f14c24-f653-4e6a-9b94-39d6b0f70001"
@@ -47,7 +48,7 @@ DataStructures = "0.18"
 Distributions = "0.23.3, 0.24, 0.25"
 DistributionsAD = "0.6"
 DocStringExtensions = "0.8, 0.9"
-DynamicPPL = "0.21.5, 0.22"
+DynamicPPL = "0.23"
 EllipticalSliceSampling = "0.5, 1"
 ForwardDiff = "0.10.3"
 Libtask = "0.7, 0.8"

src/essential/container.jl

@@ -9,36 +9,21 @@ function TracedModel(
     model::Model,
     sampler::AbstractSampler,
     varinfo::AbstractVarInfo,
-    rng::Random.AbstractRNG
-) 
+    rng::Random.AbstractRNG,
+)
     context = SamplingContext(rng, sampler, DefaultContext())
-    evaluator = _get_evaluator(model, varinfo, context)
-    return TracedModel{AbstractSampler,AbstractVarInfo,Model,Tuple}(model, sampler, varinfo, evaluator)
-end
-
-# TODO: maybe move to DynamicPPL
-@generated function _get_evaluator(
-    model::Model{_F,argnames}, varinfo, context
-) where {_F,argnames}
-    unwrap_args = [
-        :($DynamicPPL.matchingvalue(context_new, varinfo, model.args.$var)) for var in argnames
-    ]
-    # We want to give `context` precedence over `model.context` while also
-    # preserving the leaf context of `context`. We can do this by
-    # 1. Set the leaf context of `model.context` to `leafcontext(context)`.
-    # 2. Set leaf context of `context` to the context resulting from (1).
-    # The result is:
-    # `context` -> `childcontext(context)` -> ... -> `model.context`
-    #  -> `childcontext(model.context)` -> ... -> `leafcontext(context)`
-    return quote
-        context_new = DynamicPPL.setleafcontext(
-            context, DynamicPPL.setleafcontext(model.context, DynamicPPL.leafcontext(context))
-        )
-        (model.f, model, DynamicPPL.resetlogp!!(varinfo), context_new, $(unwrap_args...))
+    args, kwargs = DynamicPPL.make_evaluate_args_and_kwargs(model, varinfo, context)
+    if kwargs !== nothing && !isempty(kwargs)
+        error("Sampling with `$(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 Base.copy(model::AdvancedPS.GenericModel{<:TracedModel})
     newtask = copy(model.ctask)
     newmodel = TracedModel{AbstractSampler,AbstractVarInfo,Model,Tuple}(deepcopy(model.f.model), deepcopy(model.f.sampler), deepcopy(model.f.varinfo), deepcopy(model.f.evaluator))
@@ -73,10 +58,12 @@ function AdvancedPS.reset_logprob!(trace::TracedModel)
     return trace
 end
 
-function AdvancedPS.update_rng!(trace::AdvancedPS.Trace{AdvancedPS.GenericModel{TracedModel{M,S,V,E}, F}, R}) where {M,S,V,E,F,R} 
+function AdvancedPS.update_rng!(trace::AdvancedPS.Trace{AdvancedPS.GenericModel{TracedModel{M,S,V,E}, F}, R}) where {M,S,V,E,F,R}
+    # Extract the `args`.
     args = trace.model.ctask.args
-    _, _, container, = args
-    rng = container.rng
+    # From `args`, extract the `SamplingContext`, which contains the RNG.
+    sampling_context = args[3]
+    rng = sampling_context.rng
     trace.rng = rng
     return trace
 end

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 = "0.21.5, 0.22"
+DynamicPPL = "0.23"
 FiniteDifferences = "0.10.8, 0.11, 0.12"
 ForwardDiff = "0.10.12 - 0.10.32, 0.10"
 LogDensityProblems = "2"

test/inference/AdvancedSMC.jl

@@ -173,6 +173,12 @@ end
         @test length(unique(c[:m])) == 1
         @test length(unique(c[:s])) == 1
     end
+
+    # https://github.com/TuringLang/Turing.jl/issues/2007
+    @turing_testset "keyword arguments not supported" begin
+        @model kwarg_demo(; x = 2) = return x
+        @test_throws ErrorException sample(kwarg_demo(), PG(1), 10)
+    end
 end
 
 # @testset "pmmh.jl" begin

test/inference/Inference.jl

@@ -259,11 +259,27 @@
             return priors
         end
 
-        chain = sample(gauss2(; x=x), PG(10), 10)
-        chain = sample(gauss2(; x=x), SMC(), 10)
+        @test_throws ErrorException chain = sample(gauss2(; x=x), PG(10), 10)
+        @test_throws ErrorException chain = sample(gauss2(; x=x), SMC(), 10)
 
-        chain = sample(gauss2(Vector{Float64}; x=x), PG(10), 10)
-        chain = sample(gauss2(Vector{Float64}; x=x), SMC(), 10)
+        @test_throws ErrorException chain = sample(gauss2(Vector{Float64}; x=x), PG(10), 10)
+        @test_throws ErrorException chain = sample(gauss2(Vector{Float64}; x=x), SMC(), 10)
+
+        @model function gauss3(x, ::Type{TV}=Vector{Float64}) where {TV}
+            priors = TV(undef, 2)
+            priors[1] ~ InverseGamma(2, 3)         # s
+            priors[2] ~ Normal(0, sqrt(priors[1])) # m
+            for i in 1:length(x)
+                x[i] ~ Normal(priors[2], sqrt(priors[1]))
+            end
+            return priors
+        end
+
+        chain = sample(gauss3(x), PG(10), 10)
+        chain = sample(gauss3(x), SMC(), 10)
+
+        chain = sample(gauss3(x, Vector{Real}), PG(10), 10)
+        chain = sample(gauss3(x, Vector{Real}), SMC(), 10)
     end
     @testset "new interface" begin
         obs = [0, 1, 0, 1, 1, 1, 1, 1, 1, 1]