Support DPPL 0.37

Project.toml

@@ -62,7 +62,7 @@ Distributions = "0.25.77"
 DistributionsAD = "0.6"
 DocStringExtensions = "0.8, 0.9"
 DynamicHMC = "3.4"
-DynamicPPL = "0.36"
+DynamicPPL = "0.37"
 EllipticalSliceSampling = "0.5, 1, 2"
 ForwardDiff = "0.10.3"
 Libtask = "0.8.8"

ext/TuringOptimExt.jl

@@ -34,8 +34,7 @@ function Optim.optimize(
     options::Optim.Options=Optim.Options();
     kwargs...,
 )
-    ctx = Optimisation.OptimizationContext(DynamicPPL.LikelihoodContext())
-    f = Optimisation.OptimLogDensity(model, ctx)
+    f = Optimisation.OptimLogDensity(model, DynamicPPL.getloglikelihood)
     init_vals = DynamicPPL.getparams(f.ldf)
     optimizer = Optim.LBFGS()
     return _mle_optimize(model, init_vals, optimizer, options; kwargs...)
@@ -57,8 +56,7 @@ function Optim.optimize(
     options::Optim.Options=Optim.Options();
     kwargs...,
 )
-    ctx = Optimisation.OptimizationContext(DynamicPPL.LikelihoodContext())
-    f = Optimisation.OptimLogDensity(model, ctx)
+    f = Optimisation.OptimLogDensity(model, DynamicPPL.getloglikelihood)
     init_vals = DynamicPPL.getparams(f.ldf)
     return _mle_optimize(model, init_vals, optimizer, options; kwargs...)
 end
@@ -74,8 +72,8 @@ function Optim.optimize(
 end
 
 function _mle_optimize(model::DynamicPPL.Model, args...; kwargs...)
-    ctx = Optimisation.OptimizationContext(DynamicPPL.LikelihoodContext())
-    return _optimize(Optimisation.OptimLogDensity(model, ctx), args...; kwargs...)
+    f = Optimisation.OptimLogDensity(model, DynamicPPL.getloglikelihood)
+    return _optimize(f, args...; kwargs...)
 end
 
 """
@@ -104,8 +102,7 @@ function Optim.optimize(
     options::Optim.Options=Optim.Options();
     kwargs...,
 )
-    ctx = Optimisation.OptimizationContext(DynamicPPL.DefaultContext())
-    f = Optimisation.OptimLogDensity(model, ctx)
+    f = Optimisation.OptimLogDensity(model, Optimisation.getlogjoint_without_jacobian)
     init_vals = DynamicPPL.getparams(f.ldf)
     optimizer = Optim.LBFGS()
     return _map_optimize(model, init_vals, optimizer, options; kwargs...)
@@ -127,8 +124,7 @@ function Optim.optimize(
     options::Optim.Options=Optim.Options();
     kwargs...,
 )
-    ctx = Optimisation.OptimizationContext(DynamicPPL.DefaultContext())
-    f = Optimisation.OptimLogDensity(model, ctx)
+    f = Optimisation.OptimLogDensity(model, Optimisation.getlogjoint_without_jacobian)
     init_vals = DynamicPPL.getparams(f.ldf)
     return _map_optimize(model, init_vals, optimizer, options; kwargs...)
 end
@@ -144,9 +140,10 @@ function Optim.optimize(
 end
 
 function _map_optimize(model::DynamicPPL.Model, args...; kwargs...)
-    ctx = Optimisation.OptimizationContext(DynamicPPL.DefaultContext())
-    return _optimize(Optimisation.OptimLogDensity(model, ctx), args...; kwargs...)
+    f = Optimisation.OptimLogDensity(model, Optimisation.getlogjoint_without_jacobian)
+    return _optimize(f, args...; kwargs...)
 end
+
 """
     _optimize(f::OptimLogDensity, optimizer=Optim.LBFGS(), args...; kwargs...)
 
@@ -166,7 +163,9 @@ function _optimize(
     # whether initialisation is really necessary at all
     vi = DynamicPPL.unflatten(f.ldf.varinfo, init_vals)
     vi = DynamicPPL.link(vi, f.ldf.model)
-    f = Optimisation.OptimLogDensity(f.ldf.model, vi, f.ldf.context; adtype=f.ldf.adtype)
+    f = Optimisation.OptimLogDensity(
+        f.ldf.model, f.ldf.getlogdensity, vi; adtype=f.ldf.adtype
+    )
     init_vals = DynamicPPL.getparams(f.ldf)
 
     # Optimize!
@@ -184,7 +183,7 @@ function _optimize(
     vi = f.ldf.varinfo
     vi_optimum = DynamicPPL.unflatten(vi, M.minimizer)
     logdensity_optimum = Optimisation.OptimLogDensity(
-        f.ldf.model, vi_optimum, f.ldf.context
+        f.ldf.model, f.ldf.getlogdensity, vi_optimum; adtype=f.ldf.adtype
     )
     vns_vals_iter = Turing.Inference.getparams(f.ldf.model, vi_optimum)
     varnames = map(Symbol ∘ first, vns_vals_iter)

src/essential/container.jl

@@ -28,7 +28,8 @@ function AdvancedPS.advance!(
     trace::AdvancedPS.Trace{<:AdvancedPS.LibtaskModel{<:TracedModel}}, isref::Bool=false
 )
     # Make sure we load/reset the rng in the new replaying mechanism
-    DynamicPPL.increment_num_produce!(trace.model.f.varinfo)
+    # TODO(mhauru) Stop ignoring the return value.
+    DynamicPPL.increment_num_produce!!(trace.model.f.varinfo)
     isref ? AdvancedPS.load_state!(trace.rng) : AdvancedPS.save_state!(trace.rng)
     score = consume(trace.model.ctask)
     if score === nothing
@@ -44,11 +45,13 @@ function AdvancedPS.delete_retained!(trace::TracedModel)
 end
 
 function AdvancedPS.reset_model(trace::TracedModel)
-    DynamicPPL.reset_num_produce!(trace.varinfo)
+    new_vi = DynamicPPL.reset_num_produce!!(trace.varinfo)
+    trace = TracedModel(trace.model, trace.sampler, new_vi, trace.evaluator)
     return trace
 end
 
 function AdvancedPS.reset_logprob!(trace::TracedModel)
+    # TODO(mhauru) Stop ignoring the return value.
     DynamicPPL.resetlogp!!(trace.model.varinfo)
     return trace
 end

src/mcmc/Inference.jl

@@ -14,6 +14,7 @@ using DynamicPPL:
     push!!,
     setlogp!!,
     getlogp,
+    getlogjoint,
     VarName,
     getsym,
     getdist,
@@ -22,8 +23,6 @@ using DynamicPPL:
     SampleFromPrior,
     SampleFromUniform,
     DefaultContext,
-    PriorContext,
-    LikelihoodContext,
     set_flag!,
     unset_flag!
 using Distributions, Libtask, Bijectors
@@ -75,7 +74,6 @@ export InferenceAlgorithm,
     RepeatSampler,
     Prior,
     assume,
-    observe,
     predict,
     externalsampler
 
@@ -182,12 +180,10 @@ function AbstractMCMC.step(
     state=nothing;
     kwargs...,
 )
+    vi = VarInfo()
+    vi = DynamicPPL.setaccs!!(vi, (DynamicPPL.LogPriorAccumulator(),))
     vi = last(
-        DynamicPPL.evaluate!!(
-            model,
-            VarInfo(),
-            SamplingContext(rng, DynamicPPL.SampleFromPrior(), DynamicPPL.PriorContext()),
-        ),
+        DynamicPPL.evaluate!!(model, vi, SamplingContext(rng, DynamicPPL.SampleFromPrior()))
     )
     return vi, nothing
 end
@@ -228,7 +224,7 @@ end
 Transition(θ, lp) = Transition(θ, lp, nothing)
 function Transition(model::DynamicPPL.Model, vi::AbstractVarInfo, t)
     θ = getparams(model, vi)
-    lp = getlogp(vi)
+    lp = getlogjoint(vi)
     return Transition(θ, lp, getstats(t))
 end
 
@@ -241,10 +237,10 @@ function metadata(t::Transition)
     end
 end
 
-DynamicPPL.getlogp(t::Transition) = t.lp
+DynamicPPL.getlogjoint(t::Transition) = t.lp
 
 # Metadata of VarInfo object
-metadata(vi::AbstractVarInfo) = (lp=getlogp(vi),)
+metadata(vi::AbstractVarInfo) = (lp=getlogjoint(vi),)
 
 # TODO: Implement additional checks for certain samplers, e.g.
 # HMC not supporting discrete parameters.
@@ -381,7 +377,7 @@ function _params_to_array(model::DynamicPPL.Model, ts::Vector)
 end
 
 function get_transition_extras(ts::AbstractVector{<:VarInfo})
-    valmat = reshape([getlogp(t) for t in ts], :, 1)
+    valmat = reshape([getlogjoint(t) for t in ts], :, 1)
     return [:lp], valmat
 end
 
@@ -594,7 +590,7 @@ julia> chain = Chains(randn(2, 1, 1), ["m"]); # 2 samples of `m`
 
 julia> transitions = Turing.Inference.transitions_from_chain(m, chain);
 
-julia> [Turing.Inference.getlogp(t) for t in transitions] # extract the logjoints
+julia> [Turing.Inference.getlogjoint(t) for t in transitions] # extract the logjoints
 2-element Array{Float64,1}:
  -3.6294991938628374
  -2.5697948166987845

src/mcmc/ess.jl

@@ -49,7 +49,7 @@ function AbstractMCMC.step(
         rng,
         EllipticalSliceSampling.ESSModel(
             ESSPrior(model, spl, vi),
-            DynamicPPL.LogDensityFunction(
+            DynamicPPL.LogDensityFunction{:LogLikelihood}(
                 model, vi, DynamicPPL.SamplingContext(spl, DynamicPPL.DefaultContext())
             ),
         ),
@@ -59,7 +59,7 @@ function AbstractMCMC.step(
 
     # update sample and log-likelihood
     vi = DynamicPPL.unflatten(vi, sample)
-    vi = setlogp!!(vi, state.loglikelihood)
+    vi = setloglikelihood!!(vi, state.loglikelihood)
 
     return Transition(model, vi), vi
 end
@@ -108,20 +108,14 @@ end
 # Mean of prior distribution
 Distributions.mean(p::ESSPrior) = p.μ
 
-# Evaluate log-likelihood of proposals
-const ESSLogLikelihood{M<:Model,S<:Sampler{<:ESS},V<:AbstractVarInfo} =
-    DynamicPPL.LogDensityFunction{M,V,<:DynamicPPL.SamplingContext{<:S},AD} where {AD}
-
-(ℓ::ESSLogLikelihood)(f::AbstractVector) = LogDensityProblems.logdensity(ℓ, f)
-
 function DynamicPPL.tilde_assume(
-    rng::Random.AbstractRNG, ::DefaultContext, ::Sampler{<:ESS}, right, vn, vi
+    rng::Random.AbstractRNG, ctx::DefaultContext, ::Sampler{<:ESS}, right, vn, vi
 )
-    return DynamicPPL.tilde_assume(
-        rng, LikelihoodContext(), SampleFromPrior(), right, vn, vi
-    )
+    return DynamicPPL.tilde_assume(rng, ctx, SampleFromPrior(), right, vn, vi)
 end
 
-function DynamicPPL.tilde_observe(ctx::DefaultContext, ::Sampler{<:ESS}, right, left, vi)
-    return DynamicPPL.tilde_observe(ctx, SampleFromPrior(), right, left, vi)
+function DynamicPPL.tilde_observe!!(
+    ctx::DefaultContext, ::Sampler{<:ESS}, right, left, vn, vi
+)
+    return DynamicPPL.tilde_observe!!(ctx, SampleFromPrior(), right, left, vn, vi)
 end

src/mcmc/gibbs.jl

@@ -32,7 +32,7 @@ can_be_wrapped(ctx::DynamicPPL.PrefixContext) = can_be_wrapped(ctx.context)
 #
 # Purpose: avoid triggering resampling of variables we're conditioning on.
 # - Using standard `DynamicPPL.condition` results in conditioned variables being treated
-#   as observations in the truest sense, i.e. we hit `DynamicPPL.tilde_observe`.
+#   as observations in the truest sense, i.e. we hit `DynamicPPL.tilde_observe!!`.
 # - But `observe` is overloaded by some samplers, e.g. `CSMC`, which can lead to
 #   undesirable behavior, e.g. `CSMC` triggering a resampling for every conditioned variable
 #   rather than only for the "true" observations.
@@ -177,24 +177,26 @@ function DynamicPPL.tilde_assume(context::GibbsContext, right, vn, vi)
         DynamicPPL.tilde_assume(child_context, right, vn, vi)
     elseif has_conditioned_gibbs(context, vn)
         # Short-circuit the tilde assume if `vn` is present in `context`.
-        value, lp, _ = DynamicPPL.tilde_assume(
+        # TODO(mhauru) Fix accumulation here. In this branch anything that gets
+        # accumulated just gets discarded with `_`.
+        value, _ = DynamicPPL.tilde_assume(
             child_context, right, vn, get_global_varinfo(context)
         )
-        value, lp, vi
+        value, vi
     else
         # If the varname has not been conditioned on, nor is it a target variable, its
         # presumably a new variable that should be sampled from its prior. We need to add
         # this new variable to the global `varinfo` of the context, but not to the local one
         # being used by the current sampler.
-        value, lp, new_global_vi = DynamicPPL.tilde_assume(
+        value, new_global_vi = DynamicPPL.tilde_assume(
             child_context,
             DynamicPPL.SampleFromPrior(),
             right,
             vn,
             get_global_varinfo(context),
         )
         set_global_varinfo!(context, new_global_vi)
-        value, lp, vi
+        value, vi
     end
 end
 

src/mcmc/hmc.jl

@@ -199,7 +199,7 @@ function DynamicPPL.initialstep(
     end
 
     # Cache current log density.
-    log_density_old = getlogp(vi)
+    log_density_old = getloglikelihood(vi)
 
     # Find good eps if not provided one
     if iszero(spl.alg.ϵ)
@@ -227,10 +227,12 @@ function DynamicPPL.initialstep(
     # Update `vi` based on acceptance
     if t.stat.is_accept
         vi = DynamicPPL.unflatten(vi, t.z.θ)
-        vi = setlogp!!(vi, t.stat.log_density)
+        # TODO(mhauru) Is setloglikelihood! the right thing here?
+        vi = setloglikelihood!!(vi, t.stat.log_density)
     else
         vi = DynamicPPL.unflatten(vi, theta)
-        vi = setlogp!!(vi, log_density_old)
+        # TODO(mhauru) Is setloglikelihood! the right thing here?
+        vi = setloglikelihood!!(vi, log_density_old)
     end
 
     transition = Transition(model, vi, t)
@@ -275,7 +277,8 @@ function AbstractMCMC.step(
     vi = state.vi
     if t.stat.is_accept
         vi = DynamicPPL.unflatten(vi, t.z.θ)
-        vi = setlogp!!(vi, t.stat.log_density)
+        # TODO(mhauru) Is setloglikelihood! the right thing here?
+        vi = setloglikelihood!!(vi, t.stat.log_density)
     end
 
     # Compute next transition and state.
@@ -501,10 +504,6 @@ function DynamicPPL.assume(
     return DynamicPPL.assume(dist, vn, vi)
 end
 
-function DynamicPPL.observe(::Sampler{<:Hamiltonian}, d::Distribution, value, vi)
-    return DynamicPPL.observe(d, value, vi)
-end
-
 ####
 #### Default HMC stepsize and mass matrix adaptor
 ####

src/mcmc/is.jl

@@ -55,7 +55,3 @@ function DynamicPPL.assume(rng, ::Sampler{<:IS}, dist::Distribution, vn::VarName
     end
     return r, 0, vi
 end
-
-function DynamicPPL.observe(::Sampler{<:IS}, dist::Distribution, value, vi)
-    return logpdf(dist, value), vi
-end

src/mcmc/mh.jl

@@ -390,7 +390,3 @@ function DynamicPPL.assume(
     retval = DynamicPPL.assume(rng, SampleFromPrior(), dist, vn, vi)
     return retval
 end
-
-function DynamicPPL.observe(spl::Sampler{<:MH}, d::Distribution, value, vi)
-    return DynamicPPL.observe(SampleFromPrior(), d, value, vi)
-end