Support DPPL 0.37

HISTORY.md

@@ -1,6 +1,57 @@
 # 0.40.0
 
-[...]
+## Breaking changes
+
+**DynamicPPL 0.37**
+
+Turing.jl v0.40 updates DynamicPPL compatibility to 0.37.
+The summary of the changes provided here is intended for end-users of Turing.
+If you are a package developer, or would otherwise like to understand these changes in-depth, please see [the DynamicPPL changelog](https://github.com/TuringLang/DynamicPPL.jl/blob/main/HISTORY.md#0370).
+
+  - **`@submodel`** is now completely removed; please use `to_submodel`.
+
+  - **Prior and likelihood calculations** are now completely separated in Turing. Previously, the log-density used to be accumulated in a single field and thus there was no clear way to separate prior and likelihood components.
+
+      + **`@addlogprob! f`**, where `f` is a float, now adds to the likelihood by default.
+      + You can instead use **`@addlogprob! (; logprior=x, loglikelihood=y)`** to control which log-density component to add to.
+      + This means that usage of `PriorContext` and `LikelihoodContext` is no longer needed, and these have now been removed.
+  - The special **`__context__`** variable has been removed. If you still need to access the evaluation context, it is now available as `__model__.context`.
+
+**Log-density in chains**
+
+When sampling from a Turing model, the resulting `MCMCChains.Chains` object now contains not only the log-joint (accessible via `chain[:lp]`) but also the log-prior and log-likelihood (`chain[:logprior]` and `chain[:loglikelihood]` respectively).
+
+These values now correspond to the log density of the sampled variables exactly as per the model definition / user parameterisation and thus will ignore any linking (transformation to unconstrained space).
+For example, if the model is `@model f() = x ~ LogNormal()`, `chain[:lp]` would always contain the value of `logpdf(LogNormal(), x)` for each sampled value of `x`.
+Previously these values could be incorrect if linking had occurred: some samplers would return `logpdf(Normal(), log(x))` i.e. the log-density with respect to the transformed distribution.
+
+**Gibbs sampler**
+
+When using Turing's Gibbs sampler, e.g. `Gibbs(:x => MH(), :y => HMC(0.1, 20))`, the conditioned variables (for example `y` during the MH step, or `x` during the HMC step) are treated as true observations.
+Thus the log-density associated with them is added to the likelihood.
+Previously these would effectively be added to the prior (in the sense that if `LikelihoodContext` was used they would be ignored).
+This is unlikely to affect users but we mention it here to be explicit.
+This change only affects the log probabilities as the Gibbs component samplers see them; the resulting chain will include the usual log prior, likelihood, and joint, as described above.
+
+**Particle Gibbs**
+
+Previously, only 'true' observations (i.e., `x ~ dist` where `x` is a model argument or conditioned upon) would trigger resampling of particles.
+Specifically, there were two cases where resampling would not be triggered:
+
+  - Calls to `@addlogprob!`
+  - Gibbs-conditioned variables: e.g. `y` in `Gibbs(:x => PG(20), :y => MH())`
+
+Turing 0.40 changes this such that both of the above cause resampling.
+(The second case follows from the changes to the Gibbs sampler, see above.)
+
+This release also fixes a bug where, if the model ended with one of these statements, their contribution to the particle weight would be ignored, leading to incorrect results.
+
+## Other changes
+
+  - Sampling using `Prior()` should now be about twice as fast because we now avoid evaluating the model twice on every iteration.
+  - `Turing.Inference.Transition` now has different fields.
+    If `t isa Turing.Inference.Transition`, `t.stat` is always a NamedTuple, not `nothing` (if it genuinely has no information then it's an empty NamedTuple).
+    Furthermore, `t.lp` has now been split up into `t.logprior` and `t.loglikelihood` (see also 'Log-density in chains' section above).
 
 # 0.39.9
 

Project.toml

@@ -90,6 +90,3 @@ julia = "1.10.8"
 [extras]
 DynamicHMC = "bbc10e6e-7c05-544b-b16e-64fede858acb"
 Optim = "429524aa-4258-5aef-a3af-852621145aeb"
-
-[sources]
-DynamicPPL = {url = "https://github.com/TuringLang/DynamicPPL.jl", rev = "breaking"}

ext/TuringDynamicHMCExt.jl

@@ -63,7 +63,7 @@ function DynamicPPL.initialstep(
 
     # Define log-density function.
     ℓ = DynamicPPL.LogDensityFunction(
-        model, DynamicPPL.getlogjoint, vi; adtype=spl.alg.adtype
+        model, DynamicPPL.getlogjoint_internal, vi; adtype=spl.alg.adtype
     )
 
     # Perform initial step.
@@ -73,14 +73,9 @@ function DynamicPPL.initialstep(
     steps = DynamicHMC.mcmc_steps(results.sampling_logdensity, results.final_warmup_state)
     Q, _ = DynamicHMC.mcmc_next_step(steps, results.final_warmup_state.Q)
 
-    # Update the variables.
-    vi = DynamicPPL.unflatten(vi, Q.q)
-    # TODO(DPPL0.37/penelopeysm): This is obviously incorrect. Fix this.
-    vi = DynamicPPL.setloglikelihood!!(vi, Q.ℓq)
-    vi = DynamicPPL.setlogprior!!(vi, 0.0)
-
     # Create first sample and state.
-    sample = Turing.Inference.Transition(model, vi)
+    vi = DynamicPPL.unflatten(vi, Q.q)
+    sample = Turing.Inference.Transition(model, vi, nothing)
     state = DynamicNUTSState(ℓ, vi, Q, steps.H.κ, steps.ϵ)
 
     return sample, state
@@ -99,12 +94,9 @@ function AbstractMCMC.step(
     steps = DynamicHMC.mcmc_steps(rng, spl.alg.sampler, state.metric, ℓ, state.stepsize)
     Q, _ = DynamicHMC.mcmc_next_step(steps, state.cache)
 
-    # Update the variables.
-    vi = DynamicPPL.unflatten(vi, Q.q)
-    vi = DynamicPPL.setlogp!!(vi, Q.ℓq)
-
     # Create next sample and state.
-    sample = Turing.Inference.Transition(model, vi)
+    vi = DynamicPPL.unflatten(vi, Q.q)
+    sample = Turing.Inference.Transition(model, vi, nothing)
     newstate = DynamicNUTSState(ℓ, vi, Q, state.metric, state.stepsize)
 
     return sample, newstate

ext/TuringOptimExt.jl

@@ -102,7 +102,7 @@ function Optim.optimize(
     options::Optim.Options=Optim.Options();
     kwargs...,
 )
-    f = Optimisation.OptimLogDensity(model, Optimisation.getlogjoint_without_jacobian)
+    f = Optimisation.OptimLogDensity(model, DynamicPPL.getlogjoint)
     init_vals = DynamicPPL.getparams(f.ldf)
     optimizer = Optim.LBFGS()
     return _map_optimize(model, init_vals, optimizer, options; kwargs...)
@@ -124,7 +124,7 @@ function Optim.optimize(
     options::Optim.Options=Optim.Options();
     kwargs...,
 )
-    f = Optimisation.OptimLogDensity(model, Optimisation.getlogjoint_without_jacobian)
+    f = Optimisation.OptimLogDensity(model, DynamicPPL.getlogjoint)
     init_vals = DynamicPPL.getparams(f.ldf)
     return _map_optimize(model, init_vals, optimizer, options; kwargs...)
 end
@@ -140,7 +140,7 @@ function Optim.optimize(
 end
 
 function _map_optimize(model::DynamicPPL.Model, args...; kwargs...)
-    f = Optimisation.OptimLogDensity(model, Optimisation.getlogjoint_without_jacobian)
+    f = Optimisation.OptimLogDensity(model, DynamicPPL.getlogjoint)
     return _optimize(f, args...; kwargs...)
 end