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Update for newer AbstractMCMC/Turing interface #49

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4 changes: 2 additions & 2 deletions .gitignore
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Expand Up @@ -4,5 +4,5 @@
/docs/Manifest.toml
/docs/build/
**/*~
Maniest.toml
test/Manifest.toml
Manifest.toml
test/Manifest.toml
8 changes: 5 additions & 3 deletions Project.toml
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@@ -1,9 +1,10 @@
name = "SliceSampling"
uuid = "43f4d3e8-9711-4a8c-bd1b-03ac73a255cf"
version = "0.7.10"
version = "0.7.11"

[deps]
AbstractMCMC = "80f14c24-f653-4e6a-9b94-39d6b0f70001"
Accessors = "7d9f7c33-5ae7-4f3b-8dc6-eff91059b697"
Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
LogDensityProblems = "6fdf6af0-433a-55f7-b3ed-c6c6e0b8df7c"
Expand All @@ -16,12 +17,13 @@ Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0"
SliceSamplingTuringExt = ["Turing"]

[compat]
AbstractMCMC = "4, 5"
AbstractMCMC = "5.9"
Accessors = "0.1"
Distributions = "0.25"
LinearAlgebra = "1"
LogDensityProblems = "2"
Random = "1"
Turing = "0.41"
Turing = "0.42"
julia = "1.10"

[extras]
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4 changes: 2 additions & 2 deletions docs/Project.toml
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Expand Up @@ -15,7 +15,7 @@ Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0"

[compat]
AbstractMCMC = "5"
AbstractMCMC = "5.9"
Distributions = "0.25"
Documenter = "1"
FillArrays = "1"
Expand All @@ -28,5 +28,5 @@ Random = "1"
SliceSampling = "0.7.1"
StableRNGs = "1"
Statistics = "1"
Turing = "0.41, 0.42"
Turing = "0.42"
julia = "1.10"
29 changes: 0 additions & 29 deletions ext/SliceSamplingTuringExt.jl
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Expand Up @@ -6,35 +6,6 @@ using Random
using SliceSampling
using Turing

# Required for using the slice samplers as `externalsampler`s in Turing
# begin
function Turing.Inference.getparams(
::Turing.DynamicPPL.Model, sample::SliceSampling.Transition
)
return sample.params
end
# end
Comment on lines -9 to -16
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@penelopeysm penelopeysm Dec 14, 2025

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The changes to the external sampler interface are described in https://github.com/TuringLang/Turing.jl/releases/tag/v0.42.0 -- the general aim is that you should not need to overload Turing internal functions (getparams was actually not exported afaik) and shifting this to AbstractMCMC means that it's easier for other packages to make use of this info.

Turing.Inference.getparams is gone now, it's replaced with AbstractMCMC.getparams (but called on the state instead of the transition).


# Required for using the slice samplers as `Gibbs` samplers in Turing
# begin
Turing.Inference.isgibbscomponent(::SliceSampling.RandPermGibbs) = true
Turing.Inference.isgibbscomponent(::SliceSampling.HitAndRun) = true
Turing.Inference.isgibbscomponent(::SliceSampling.Slice) = true
Turing.Inference.isgibbscomponent(::SliceSampling.SliceSteppingOut) = true
Turing.Inference.isgibbscomponent(::SliceSampling.SliceDoublingOut) = true
Comment on lines -20 to -24
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@penelopeysm penelopeysm Dec 14, 2025

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isgibbscomponent now defaults to true so it no longer needs to be overridden


const SliceSamplingStates = Union{
SliceSampling.UnivariateSliceState,
SliceSampling.GibbsState,
SliceSampling.HitAndRunState,
SliceSampling.LatentSliceState,
SliceSampling.GibbsPolarSliceState,
}
function Turing.Inference.getparams(::Turing.DynamicPPL.Model, sample::SliceSamplingStates)
return sample.transition.params
end
# end

function SliceSampling.initial_sample(rng::Random.AbstractRNG, ℓ::Turing.LogDensityFunction)
n_max_attempts = 1000

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17 changes: 17 additions & 0 deletions src/SliceSampling.jl
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Expand Up @@ -2,6 +2,7 @@
module SliceSampling

using AbstractMCMC
using Accessors: Accessors
using Distributions
using LinearAlgebra
using LogDensityProblems
Expand Down Expand Up @@ -37,6 +38,22 @@ struct Transition{P,L<:Real,I<:NamedTuple}
info::I
end

"""
abstract type AbstractStateWithTransition

Base type for MCMC states that contain a `Transition` stored in the `transition` field.
"""
abstract type AbstractStateWithTransition end
AbstractMCMC.getparams(state::AbstractStateWithTransition) = state.transition.params
AbstractMCMC.getstats(state::AbstractStateWithTransition) = state.transition.info
function AbstractMCMC.setparams!!(
model::AbstractMCMC.LogDensityModel, state::AbstractStateWithTransition, params
)
new_lp = LogDensityProblems.logdensity(model.logdensity, params)
new_transition = Transition(params, new_lp, NamedTuple())
return Accessors.@set state.transition = new_transition
end
Comment on lines +46 to +55
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@penelopeysm penelopeysm Dec 14, 2025

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Since the definitions of these functions are the same for all states in this package, I thought it would be cleaner to just define the behaviour on an abstract type. It does necessitate an extra dep on Accessors, but that's fairly lightweight.


"""
initial_sample(rng, model)

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3 changes: 2 additions & 1 deletion src/multivariate/gibbspolar.jl
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Expand Up @@ -29,7 +29,8 @@ function GibbsPolarSlice(w::Real; max_proposals::Int=DEFAULT_MAX_PROPOSALS)
return GibbsPolarSlice(w, max_proposals)
end

struct GibbsPolarSliceState{T<:Transition,R<:Real,D<:AbstractVector}
struct GibbsPolarSliceState{T<:Transition,R<:Real,D<:AbstractVector} <:
AbstractStateWithTransition
"Current [`Transition`](@ref)."
transition::T

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9 changes: 1 addition & 8 deletions src/multivariate/hitandrun.jl
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Expand Up @@ -12,18 +12,11 @@ struct HitAndRun{S<:AbstractUnivariateSliceSampling} <: AbstractMultivariateSlic
unislice::S
end

struct HitAndRunState{T<:Transition}
struct HitAndRunState{T<:Transition} <: AbstractStateWithTransition
"Current [`Transition`](@ref)."
transition::T
end

function AbstractMCMC.setparams!!(
model::AbstractMCMC.LogDensityModel, state::HitAndRunState, params
)
lp = LogDensityProblems.logdensity(model.logdensity, params)
return HitAndRunState(Transition(params, lp, NamedTuple()))
end

struct HitAndRunTarget{Model,Vec<:AbstractVector}
model :: Model
direction :: Vec
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2 changes: 1 addition & 1 deletion src/multivariate/latent.jl
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Expand Up @@ -20,7 +20,7 @@ function LatentSlice(beta::Real; max_proposals::Int=DEFAULT_MAX_PROPOSALS)
return LatentSlice(beta, max_proposals)
end

struct LatentSliceState{T<:Transition,S<:AbstractVector}
struct LatentSliceState{T<:Transition,S<:AbstractVector} <: AbstractStateWithTransition
"Current [`Transition`](@ref)."
transition::T

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7 changes: 1 addition & 6 deletions src/multivariate/randpermgibbs.jl
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Expand Up @@ -20,16 +20,11 @@ struct RandPermGibbs{
unislice::S
end

struct GibbsState{T<:Transition}
struct GibbsState{T<:Transition} <: AbstractStateWithTransition
"Current [`Transition`](@ref)."
transition::T
end

function AbstractMCMC.setparams!!(model::AbstractMCMC.LogDensityModel, ::GibbsState, params)
lp = LogDensityProblems.logdensity(model.logdensity, params)
return GibbsState(Transition(params, lp, NamedTuple()))
end

struct GibbsTarget{Model,Idx<:Integer,Vec<:AbstractVector}
model :: Model
idx :: Idx
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9 changes: 1 addition & 8 deletions src/univariate/univariate.jl
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Expand Up @@ -24,18 +24,11 @@ function slice_sampling_univariate(
return exceeded_max_prop(max_prop)
end

struct UnivariateSliceState{T<:Transition}
struct UnivariateSliceState{T<:Transition} <: AbstractStateWithTransition
"Current [`Transition`](@ref)."
transition::T
end

function AbstractMCMC.setparams!!(
model::AbstractMCMC.LogDensityModel, state::UnivariateSliceState, params
)
lp = LogDensityProblems.logdensity(model.logdensity, params)
return UnivariateSliceState(Transition(params, lp, NamedTuple()))
end

function AbstractMCMC.step(
rng::Random.AbstractRNG,
model::AbstractMCMC.LogDensityModel,
Expand Down
4 changes: 2 additions & 2 deletions test/Project.toml
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Expand Up @@ -10,13 +10,13 @@ Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0"

[compat]
AbstractMCMC = "4, 5"
AbstractMCMC = "5.9"
Accessors = "0.1"
Distributions = "0.25"
LogDensityProblems = "2"
MCMCTesting = "0.3"
Random = "1"
StableRNGs = "1"
Test = "1"
Turing = "0.41, 0.42"
Turing = "0.42"
julia = "1.10"
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