Specify default chain type in Turing

Author: penelopeysm

src/mcmc/Inference.jl

@@ -80,6 +80,8 @@ export InferenceAlgorithm,
 # Abstract interface for inference algorithms #
 ###############################################
 
+const TURING_CHAIN_TYPE = MCMCChains.Chains
+
 include("algorithm.jl")
 
 ####################

src/mcmc/algorithm.jl

@@ -11,8 +11,6 @@ this wrapping occurs automatically.
 """
 abstract type InferenceAlgorithm end
 
-DynamicPPL.default_chain_type(sampler::Sampler{<:InferenceAlgorithm}) = MCMCChains.Chains
-
 function DynamicPPL.init_strategy(sampler::Sampler{<:InferenceAlgorithm})
     return DynamicPPL.InitFromPrior()
 end

src/mcmc/hmc.jl

@@ -88,7 +88,7 @@ function AbstractMCMC.sample(
     model::DynamicPPL.Model,
     sampler::Sampler{<:AdaptiveHamiltonian},
     N::Integer;
-    chain_type=DynamicPPL.default_chain_type(sampler),
+    chain_type=TURING_CHAIN_TYPE,
     resume_from=nothing,
     initial_params=DynamicPPL.init_strategy(sampler),
     initial_state=DynamicPPL.loadstate(resume_from),

src/mcmc/particle_mcmc.jl

@@ -142,7 +142,7 @@ function AbstractMCMC.sample(
     model::DynamicPPL.Model,
     sampler::Sampler{<:SMC},
     N::Integer;
-    chain_type=DynamicPPL.default_chain_type(sampler),
+    chain_type=TURING_CHAIN_TYPE,
     resume_from=nothing,
     initial_params=DynamicPPL.init_strategy(sampler),
     initial_state=DynamicPPL.loadstate(resume_from),

src/mcmc/repeat_sampler.jl

@@ -95,7 +95,7 @@ function AbstractMCMC.sample(
     sampler::RepeatSampler{<:Sampler},
     N::Integer;
     initial_params=DynamicPPL.init_strategy(sampler),
-    chain_type=MCMCChains.Chains,
+    chain_type=TURING_CHAIN_TYPE,
     progress=PROGRESS[],
     kwargs...,
 )
@@ -119,7 +119,7 @@ function AbstractMCMC.sample(
     N::Integer,
     n_chains::Integer;
     initial_params=fill(DynamicPPL.init_strategy(sampler), n_chains),
-    chain_type=MCMCChains.Chains,
+    chain_type=TURING_CHAIN_TYPE,
     progress=PROGRESS[],
     kwargs...,
 )