Fix multiple-chain method ambiguity

HISTORY.md

@@ -1,3 +1,12 @@
+# 0.40.3
+
+This patch makes the `resume_from` keyword argument work correctly when sampling multiple chains.
+
+In the process this also fixes a method ambiguity caused by a bugfix in DynamicPPL 0.37.2.
+
+This patch means that if you are using `RepeatSampler()` to sample from a model, and you want to obtain `MCMCChains.Chains` from it, you need to specify `sample(...; chain_type=MCMCChains.Chains)`.
+This only applies if the sampler itself is a `RepeatSampler`; it doesn't apply if you are using `RepeatSampler` _within_ another sampler like Gibbs.
+
 # 0.40.2
 
 `sample(model, NUTS(), N; verbose=false)` now suppresses the 'initial step size' message.

Project.toml

@@ -1,6 +1,6 @@
 name = "Turing"
 uuid = "fce5fe82-541a-59a6-adf8-730c64b5f9a0"
-version = "0.40.2"
+version = "0.40.3"
 
 [deps]
 ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"
@@ -64,7 +64,7 @@ Distributions = "0.25.77"
 DistributionsAD = "0.6"
 DocStringExtensions = "0.8, 0.9"
 DynamicHMC = "3.4"
-DynamicPPL = "0.37"
+DynamicPPL = "0.37.2"
 EllipticalSliceSampling = "0.5, 1, 2"
 ForwardDiff = "0.10.3, 1"
 Libtask = "0.9.3"

src/mcmc/abstractmcmc.jl

@@ -57,27 +57,3 @@ function AbstractMCMC.sample(
     check_model && _check_model(model, alg)
     return AbstractMCMC.sample(rng, model, Sampler(alg), ensemble, N, n_chains; kwargs...)
 end
-
-function AbstractMCMC.sample(
-    rng::AbstractRNG,
-    model::AbstractModel,
-    sampler::Union{Sampler{<:InferenceAlgorithm},RepeatSampler},
-    ensemble::AbstractMCMC.AbstractMCMCEnsemble,
-    N::Integer,
-    n_chains::Integer;
-    chain_type=MCMCChains.Chains,
-    progress=PROGRESS[],
-    kwargs...,
-)
-    return AbstractMCMC.mcmcsample(
-        rng,
-        model,
-        sampler,
-        ensemble,
-        N,
-        n_chains;
-        chain_type=chain_type,
-        progress=progress,
-        kwargs...,
-    )
-end

src/mcmc/repeat_sampler.jl

@@ -28,11 +28,6 @@ function RepeatSampler(alg::InferenceAlgorithm, num_repeat::Int)
     return RepeatSampler(Sampler(alg), num_repeat)
 end
 
-getADType(spl::RepeatSampler) = getADType(spl.sampler)
-DynamicPPL.default_chain_type(sampler::RepeatSampler) = default_chain_type(sampler.sampler)
-# TODO(mhauru) Remove the below once DynamicPPL has removed all its Selector stuff.
-DynamicPPL.inspace(vn::VarName, spl::RepeatSampler) = inspace(vn, spl.sampler)
-
 function setparams_varinfo!!(model::DynamicPPL.Model, sampler::RepeatSampler, state, params)
     return setparams_varinfo!!(model, sampler.sampler, state, params)
 end

test/Project.toml

@@ -53,14 +53,14 @@ Combinatorics = "1"
 Distributions = "0.25"
 DistributionsAD = "0.6.3"
 DynamicHMC = "2.1.6, 3.0"
-DynamicPPL = "0.37"
+DynamicPPL = "0.37.2"
 FiniteDifferences = "0.10.8, 0.11, 0.12"
 ForwardDiff = "0.10.12 - 0.10.32, 0.10, 1"
 HypothesisTests = "0.11"
 LinearAlgebra = "1"
 LogDensityProblems = "2"
 LogDensityProblemsAD = "1.4"
-MCMCChains = "5, 6, 7"
+MCMCChains = "7.3.0"
 NamedArrays = "0.9.4, 0.10"
 Optim = "1"
 Optimization = "3, 4"

test/mcmc/Inference.jl

@@ -4,6 +4,7 @@ using ..Models: gdemo_d, gdemo_default
 using ..NumericalTests: check_gdemo, check_numerical
 using Distributions: Bernoulli, Beta, InverseGamma, Normal
 using Distributions: sample
+using AbstractMCMC: AbstractMCMC
 import DynamicPPL
 using DynamicPPL: Sampler
 import ForwardDiff
@@ -72,7 +73,48 @@ using Turing
         end
     end
 
-    @testset "chain save/resume" begin
+    @testset "save/resume correctly reloads state" begin
+        struct StaticSampler <: Turing.Inference.InferenceAlgorithm end
+        function DynamicPPL.initialstep(
+            rng, model, ::DynamicPPL.Sampler{<:StaticSampler}, vi; kwargs...
+        )
+            return Turing.Inference.Transition(model, vi, nothing), vi
+        end
+        function AbstractMCMC.step(
+            rng,
+            model,
+            ::DynamicPPL.Sampler{<:StaticSampler},
+            vi::DynamicPPL.AbstractVarInfo;
+            kwargs...,
+        )
+            return Turing.Inference.Transition(model, vi, nothing), vi
+        end
+
+        @model demo() = x ~ Normal()
+
+        @testset "single-chain" begin
+            chn1 = sample(demo(), StaticSampler(), 10; save_state=true)
+            @test chn1.info.samplerstate isa DynamicPPL.AbstractVarInfo
+            chn2 = sample(demo(), StaticSampler(), 10; resume_from=chn1)
+            xval = chn1[:x][1]
+            @test all(chn2[:x] .== xval)
+        end
+
+        @testset "multiple-chain" for nchains in [1, 3]
+            chn1 = sample(
+                demo(), StaticSampler(), MCMCThreads(), 10, nchains; save_state=true
+            )
+            @test chn1.info.samplerstate isa AbstractVector{<:DynamicPPL.AbstractVarInfo}
+            @test length(chn1.info.samplerstate) == nchains
+            chn2 = sample(
+                demo(), StaticSampler(), MCMCThreads(), 10, nchains; resume_from=chn1
+            )
+            xval = chn1[:x][1, :]
+            @test all(i -> chn2[:x][i, :] == xval, 1:10)
+        end
+    end
+
+    @testset "single-chain save/resume numerical accuracy" begin
         alg1 = HMCDA(1000, 0.65, 0.15)
         alg2 = PG(20)
         alg3 = Gibbs(:s => PG(30), :m => HMC(0.2, 4))

test/mcmc/repeat_sampler.jl

@@ -2,6 +2,7 @@ module RepeatSamplerTests
 
 using ..Models: gdemo_default
 using DynamicPPL: Sampler
+using MCMCChains: Chains
 using StableRNGs: StableRNG
 using Test: @test, @testset
 using Turing
@@ -26,7 +27,13 @@ using Turing
         )
         repeat_sampler = RepeatSampler(sampler, num_repeats)
         chn2 = sample(
-            copy(rng), gdemo_default, repeat_sampler, MCMCThreads(), num_samples, num_chains
+            copy(rng),
+            gdemo_default,
+            repeat_sampler,
+            MCMCThreads(),
+            num_samples,
+            num_chains;
+            chain_type=Chains,
         )
         @test chn1.value == chn2.value
     end