CompatHelper: bump compat for AbstractMCMC to 5, (keep existing compat)

Project.toml

@@ -1,6 +1,6 @@
 name = "DynamicPPL"
 uuid = "366bfd00-2699-11ea-058f-f148b4cae6d8"
-version = "0.23.20"
+version = "0.24.0"
 
 [deps]
 AbstractMCMC = "80f14c24-f653-4e6a-9b94-39d6b0f70001"
@@ -29,21 +29,21 @@ MCMCChains = "c7f686f2-ff18-58e9-bc7b-31028e88f75d"
 DynamicPPLMCMCChainsExt = ["MCMCChains"]
 
 [compat]
-AbstractMCMC = "2, 3.0, 4"
+AbstractMCMC = "5"
 AbstractPPL = "0.6"
 BangBang = "0.3"
 Bijectors = "0.13"
-ChainRulesCore = "0.9.7, 0.10, 1"
-ConstructionBase = "1.5.4"
+ChainRulesCore = "1"
 Compat = "4"
-Distributions = "0.23.8, 0.24, 0.25"
-DocStringExtensions = "0.8, 0.9"
+ConstructionBase = "1.5.4"
+Distributions = "0.25"
+DocStringExtensions = "0.9"
 LogDensityProblems = "2"
 MCMCChains = "6"
 MacroTools = "0.5.6"
 OrderedCollections = "1"
 Requires = "1"
-Setfield = "0.7.1, 0.8, 1"
+Setfield = "1"
 ZygoteRules = "0.2"
 julia = "1.6"
 

ext/DynamicPPLMCMCChainsExt.jl

@@ -14,6 +14,14 @@ function _check_varname_indexing(c::MCMCChains.Chains)
            error("Chains do not support indexing using $vn.")
 end
 
+# Load state from a `Chains`: By convention, it is stored in `:samplerstate` metadata
+function DynamicPPL.loadstate(chain::MCMCChains.Chains)
+    if !haskey(chain.info, :samplerstate)
+        throw(ArgumentError("The chain object does not contain the final state of the sampler: Metadata `:samplerstate` missing."))
+    end
+    return chain.info[:samplerstate]
+end
+
 # A few methods needed.
 function DynamicPPL.supports_varname_indexing(chain::MCMCChains.Chains)
     return _has_varname_to_symbol(chain.info)

src/sampler.jl

@@ -80,26 +80,33 @@ function default_varinfo(
     return VarInfo(rng, model, init_sampler, context)
 end
 
+function AbstractMCMC.sample(
+    rng::AbstractRNG,
+    model::Model,
+    sampler::Sampler,
+    N::Integer;
+    chain_type=default_chain_type(sampler),
+    resume_from=nothing,
+    kwargs...
+)
+    initial_state = loadstate(resume_from)
+    return AbstractMCMC.mcmcsample(rng, model, sampler, N; chain_type, initial_state, kwargs...)
+end
+
 # initial step: general interface for resuming and
 function AbstractMCMC.step(
     rng::Random.AbstractRNG,
     model::Model,
     spl::Sampler;
-    resume_from=nothing,
-    init_params=nothing,
+    initial_params=nothing,
     kwargs...,
 )
-    if resume_from !== nothing
-        state = loadstate(resume_from)
-        return AbstractMCMC.step(rng, model, spl, state; kwargs...)
-    end
-
     # Sample initial values.
     vi = default_varinfo(rng, model, spl)
 
     # Update the parameters if provided.
-    if init_params !== nothing
-        vi = initialize_parameters!!(vi, init_params, spl, model)
+    if initial_params !== nothing
+        vi = initialize_parameters!!(vi, initial_params, spl, model)
 
         # Update joint log probability.
         # This is a quick fix for https://github.com/TuringLang/Turing.jl/issues/1588
@@ -108,15 +115,24 @@ function AbstractMCMC.step(
         vi = last(evaluate!!(model, vi, DefaultContext()))
     end
 
-    return initialstep(rng, model, spl, vi; init_params=init_params, kwargs...)
+    return initialstep(rng, model, spl, vi; initial_params, kwargs...)
 end
 
 """
     loadstate(data)
 
 Load sampler state from `data`.
+
+By default, `data` is returned.
+"""
+loadstate(data) = data
+
+"""
+    default_chaintype(sampler)
+
+Default type of the chain of posterior samples from `sampler`.
 """
-function loadstate end
+default_chain_type(sampler::Sampler) = Any
 
 """
     initialsampler(sampler::Sampler)
@@ -129,12 +145,12 @@ By default, it returns an instance of [`SampleFromPrior`](@ref).
 initialsampler(spl::Sampler) = SampleFromPrior()
 
 function initialize_parameters!!(
-    vi::AbstractVarInfo, init_params, spl::Sampler, model::Model
+    vi::AbstractVarInfo, initial_params, spl::Sampler, model::Model
 )
-    @debug "Using passed-in initial variable values" init_params
+    @debug "Using passed-in initial variable values" initial_params
 
     # Flatten parameters.
-    init_theta = mapreduce(vcat, init_params) do x
+    init_theta = mapreduce(vcat, initial_params) do x
         vec([x;])
     end
 

test/Project.toml

@@ -22,19 +22,19 @@ Tracker = "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [compat]
-AbstractMCMC = "2.1, 3.0, 4"
+AbstractMCMC = "5"
 AbstractPPL = "0.6"
 Bijectors = "0.13"
 Compat = "4.3.0"
 Distributions = "0.25"
 DistributionsAD = "0.6.3"
-Documenter = "0.26.1, 0.27, 1"
+Documenter = "1"
 ForwardDiff = "0.10.12"
 LogDensityProblems = "2"
-MCMCChains = "4.0.4, 5, 6"
+MCMCChains = "6.0.4"
 MacroTools = "0.5.5"
-Setfield = "0.7.1, 0.8, 1"
+Setfield = "1"
 StableRNGs = "1"
 Tracker = "0.2.23"
-Zygote = "0.5.4, 0.6"
+Zygote = "0.6"
 julia = "1.6"

test/sampler.jl

@@ -84,7 +84,7 @@
             model = coinflip()
             sampler = Sampler(alg)
             lptrue = logpdf(Binomial(25, 0.2), 10)
-            chain = sample(model, sampler, 1; init_params=0.2, progress=false)
+            chain = sample(model, sampler, 1; initial_params=0.2, progress=false)
             @test chain[1].metadata.p.vals == [0.2]
             @test getlogp(chain[1]) == lptrue
 
@@ -95,7 +95,7 @@
                 MCMCThreads(),
                 1,
                 10;
-                init_params=fill(0.2, 10),
+                initial_params=fill(0.2, 10),
                 progress=false,
             )
             for c in chains
@@ -110,7 +110,7 @@
             end
             model = twovars()
             lptrue = logpdf(InverseGamma(2, 3), 4) + logpdf(Normal(0, 2), -1)
-            chain = sample(model, sampler, 1; init_params=[4, -1], progress=false)
+            chain = sample(model, sampler, 1; initial_params=[4, -1], progress=false)
             @test chain[1].metadata.s.vals == [4]
             @test chain[1].metadata.m.vals == [-1]
             @test getlogp(chain[1]) == lptrue
@@ -122,7 +122,7 @@
                 MCMCThreads(),
                 1,
                 10;
-                init_params=fill([4, -1], 10),
+                initial_params=fill([4, -1], 10),
                 progress=false,
             )
             for c in chains
@@ -132,7 +132,7 @@
             end
 
             # set only m = -1
-            chain = sample(model, sampler, 1; init_params=[missing, -1], progress=false)
+            chain = sample(model, sampler, 1; initial_params=[missing, -1], progress=false)
             @test !ismissing(chain[1].metadata.s.vals[1])
             @test chain[1].metadata.m.vals == [-1]
 
@@ -143,19 +143,19 @@
                 MCMCThreads(),
                 1,
                 10;
-                init_params=fill([missing, -1], 10),
+                initial_params=fill([missing, -1], 10),
                 progress=false,
             )
             for c in chains
                 @test !ismissing(c[1].metadata.s.vals[1])
                 @test c[1].metadata.m.vals == [-1]
             end
 
-            # specify `init_params=nothing`
+            # specify `initial_params=nothing`
             Random.seed!(1234)
             chain1 = sample(model, sampler, 1; progress=false)
             Random.seed!(1234)
-            chain2 = sample(model, sampler, 1; init_params=nothing, progress=false)
+            chain2 = sample(model, sampler, 1; initial_params=nothing, progress=false)
             @test chain1[1].metadata.m.vals == chain2[1].metadata.m.vals
             @test chain1[1].metadata.s.vals == chain2[1].metadata.s.vals
 
@@ -164,7 +164,7 @@
             chains1 = sample(model, sampler, MCMCThreads(), 1, 10; progress=false)
             Random.seed!(1234)
             chains2 = sample(
-                model, sampler, MCMCThreads(), 1, 10; init_params=nothing, progress=false
+                model, sampler, MCMCThreads(), 1, 10; initial_params=nothing, progress=false
             )
             for (c1, c2) in zip(chains1, chains2)
                 @test c1[1].metadata.m.vals == c2[1].metadata.m.vals