Merge remote-tracking branch 'origin/master' into mhauru/tapir-tests

Author: mhauru

Project.toml

@@ -1,6 +1,6 @@
 name = "Turing"
 uuid = "fce5fe82-541a-59a6-adf8-730c64b5f9a0"
-version = "0.33.3"
+version = "0.34.0"
 
 [deps]
 ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"

src/mcmc/Inference.jl

@@ -213,7 +213,9 @@ end
 # Extended in contrib/inference/abstractmcmc.jl
 getstats(t) = nothing
 
-struct Transition{T, F<:AbstractFloat, S<:Union{NamedTuple, Nothing}}
+abstract type AbstractTransition end
+
+struct Transition{T, F<:AbstractFloat, S<:Union{NamedTuple, Nothing}} <: AbstractTransition
     θ     :: T
     lp    :: F # TODO: merge `lp` with `stat`
     stat  :: S
@@ -409,7 +411,7 @@ getlogevidence(transitions, sampler, state) = missing
 # Default MCMCChains.Chains constructor.
 # This is type piracy (at least for SampleFromPrior).
 function AbstractMCMC.bundle_samples(
-    ts::Vector,
+    ts::Vector{<:Union{AbstractTransition,AbstractVarInfo}},
     model::AbstractModel,
     spl::Union{Sampler{<:InferenceAlgorithm},SampleFromPrior},
     state,
@@ -472,7 +474,7 @@ end
 
 # This is type piracy (for SampleFromPrior).
 function AbstractMCMC.bundle_samples(
-    ts::Vector,
+    ts::Vector{<:Union{AbstractTransition,AbstractVarInfo}},
     model::AbstractModel,
     spl::Union{Sampler{<:InferenceAlgorithm},SampleFromPrior},
     state,

src/mcmc/hmc.jl

@@ -563,58 +563,3 @@ end
 function AHMCAdaptor(::Hamiltonian, ::AHMC.AbstractMetric; kwargs...)
     return AHMC.Adaptation.NoAdaptation()
 end
-
-##########################
-# HMC State Constructors #
-##########################
-
-function HMCState(
-    rng::AbstractRNG,
-    model::Model,
-    spl::Sampler{<:Hamiltonian},
-    vi::AbstractVarInfo;
-    kwargs...,
-)
-    # Link everything if needed.
-    waslinked = islinked(vi, spl)
-    if !waslinked
-        vi = link!!(vi, spl, model)
-    end
-
-    # Get the initial log pdf and gradient functions.
-    ∂logπ∂θ = gen_∂logπ∂θ(vi, spl, model)
-    logπ = Turing.LogDensityFunction(
-        vi,
-        model,
-        DynamicPPL.SamplingContext(rng, spl, DynamicPPL.leafcontext(model.context)),
-    )
-
-    # Get the metric type.
-    metricT = getmetricT(spl.alg)
-
-    # Create a Hamiltonian.
-    θ_init = Vector{Float64}(spl.state.vi[spl])
-    metric = metricT(length(θ_init))
-    h = AHMC.Hamiltonian(metric, logπ, ∂logπ∂θ)
-
-    # Find good eps if not provided one
-    if iszero(spl.alg.ϵ)
-        ϵ = AHMC.find_good_stepsize(rng, h, θ_init)
-        @info "Found initial step size" ϵ
-    else
-        ϵ = spl.alg.ϵ
-    end
-
-    # Generate a kernel.
-    kernel = make_ahmc_kernel(spl.alg, ϵ)
-
-    # Generate a phasepoint. Replaced during sample_init!
-    h, t = AHMC.sample_init(rng, h, θ_init) # this also ensure AHMC has the same dim as θ.
-
-    # Unlink everything, if it was indeed linked before.
-    if waslinked
-        vi = invlink!!(vi, spl, model)
-    end
-
-    return HMCState(vi, 0, 0, kernel.τ, h, AHMCAdaptor(spl.alg, metric; ϵ=ϵ), t.z)
-end

src/mcmc/particle_mcmc.jl

@@ -45,7 +45,7 @@ end
 SMC(space::Symbol...) = SMC(space)
 SMC(space::Tuple) = SMC(AdvancedPS.ResampleWithESSThreshold(), space)
 
-struct SMCTransition{T,F<:AbstractFloat}
+struct SMCTransition{T,F<:AbstractFloat} <: AbstractTransition
     "The parameters for any given sample."
     θ::T
     "The joint log probability of the sample (NOTE: does not work, always set to zero)."
@@ -222,7 +222,7 @@ end
 
 const CSMC = PG # type alias of PG as Conditional SMC
 
-struct PGTransition{T,F<:AbstractFloat}
+struct PGTransition{T,F<:AbstractFloat} <: AbstractTransition
     "The parameters for any given sample."
     θ::T
     "The joint log probability of the sample (NOTE: does not work, always set to zero)."

src/mcmc/sghmc.jl

@@ -193,7 +193,7 @@ function SGLD(
     return SGLD{typeof(adtype),space,typeof(stepsize)}(stepsize, adtype)
 end
 
-struct SGLDTransition{T,F<:Real}
+struct SGLDTransition{T,F<:Real} <: AbstractTransition
     "The parameters for any given sample."
     θ::T
     "The joint log probability of the sample."

src/optimisation/Optimisation.jl

@@ -277,13 +277,13 @@ StatsBase.loglikelihood(m::ModeResult) = m.lp
 
 """
     Base.get(m::ModeResult, var_symbol::Symbol)
-    Base.get(m::ModeResult, var_symbols)
+    Base.get(m::ModeResult, var_symbols::AbstractVector{Symbol})
 
 Return the values of all the variables with the symbol(s) `var_symbol` in the mode result
 `m`. The return value is a `NamedTuple` with `var_symbols` as the key(s). The second
-argument should be either a `Symbol` or an iterator of `Symbol`s.
+argument should be either a `Symbol` or a vector of `Symbol`s.
 """
-function Base.get(m::ModeResult, var_symbols)
+function Base.get(m::ModeResult, var_symbols::AbstractVector{Symbol})
     log_density = m.f
     # Get all the variable names in the model. This is the same as the list of keys in
     # m.values, but they are more convenient to filter when they are VarNames rather than
@@ -304,7 +304,7 @@ function Base.get(m::ModeResult, var_symbols)
     return (; zip(var_symbols, value_vectors)...)
 end
 
-Base.get(m::ModeResult, var_symbol::Symbol) = get(m, (var_symbol,))
+Base.get(m::ModeResult, var_symbol::Symbol) = get(m, [var_symbol])
 
 """
     ModeResult(log_density::OptimLogDensity, solution::SciMLBase.OptimizationSolution)

test/Aqua.jl

@@ -3,8 +3,9 @@ module AquaTests
 using Aqua: Aqua
 using Turing
 
-# TODO(mhauru) We skip testing for method ambiguities because it catches a lot of problems
-# in dependencies. Would like to check it for just Turing.jl itself though.
+# We test ambiguities separately because it catches a lot of problems
+# in dependencies but we test it for Turing.
+Aqua.test_ambiguities([Turing])
 Aqua.test_all(Turing; ambiguities=false)
 
 end

test/mcmc/hmc.jl

@@ -1,6 +1,7 @@
 module HMCTests
 
 using ..Models: gdemo_default
+using ..ADUtils: ADTypeCheckContext
 #using ..Models: gdemo
 using ..NumericalTests: check_gdemo, check_numerical
 import ..ADUtils
@@ -324,6 +325,15 @@ ADUtils.install_tapir && import Tapir
         # KS will compare the empirical CDFs, which seems like a reasonable thing to do here.
         @test pvalue(ApproximateTwoSampleKSTest(vec(results), vec(results_prior))) > 0.001
     end
+
+    @testset "Check ADType" begin
+        alg = HMC(0.1, 10; adtype=adbackend)
+        m = DynamicPPL.contextualize(
+            gdemo_default, ADTypeCheckContext(adbackend, gdemo_default.context)
+        )
+        # These will error if the adbackend being used is not the one set.
+        sample(rng, m, alg, 10)
+    end
 end
 
 end

test/optimisation/Optimisation.jl

@@ -1,6 +1,7 @@
 module OptimisationTests
 
 using ..Models: gdemo, gdemo_default
+using ..ADUtils: ADTypeCheckContext
 using Distributions
 using Distributions.FillArrays: Zeros
 using DynamicPPL: DynamicPPL
@@ -140,7 +141,6 @@ using Turing
                 gdemo_default, OptimizationOptimJL.LBFGS(); initial_params=true_value
             )
             m3 = maximum_likelihood(gdemo_default, OptimizationOptimJL.Newton())
-            # TODO(mhauru) How can we check that the adtype is actually AutoReverseDiff?
             m4 = maximum_likelihood(
                 gdemo_default, OptimizationOptimJL.BFGS(); adtype=AutoReverseDiff()
             )
@@ -616,6 +616,18 @@ using Turing
         @assert vcat(get_a[:a], get_b[:b]) == result.values.array
         @assert get(result, :c) == (; :c => Array{Float64}[])
     end
+
+    @testset "ADType" begin
+        Random.seed!(222)
+        for adbackend in (AutoReverseDiff(), AutoForwardDiff(), AutoTracker())
+            m = DynamicPPL.contextualize(
+                gdemo_default, ADTypeCheckContext(adbackend, gdemo_default.context)
+            )
+            # These will error if the adbackend being used is not the one set.
+            maximum_likelihood(m; adtype=adbackend)
+            maximum_a_posteriori(m; adtype=adbackend)
+        end
+    end
 end
 
 end