TuringLang · penelopeysm · Aug 8, 2025 · Jul 9, 2025 · Jul 9, 2025 · Jul 9, 2025
diff --git a/Project.toml b/Project.toml
@@ -1,6 +1,6 @@
 name = "DynamicPPL"
 uuid = "366bfd00-2699-11ea-058f-f148b4cae6d8"
-version = "0.37.0"
+version = "0.38.0"
 
 [deps]
 ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"

diff --git a/docs/Project.toml b/docs/Project.toml
@@ -18,7 +18,7 @@ Accessors = "0.1"
 Distributions = "0.25"
 Documenter = "1"
 DocumenterMermaid = "0.1, 0.2"
-DynamicPPL = "0.37"
+DynamicPPL = "0.38"
 FillArrays = "0.13, 1"
 ForwardDiff = "0.10, 1"
 JET = "0.9, 0.10"

diff --git a/docs/src/api.md b/docs/src/api.md
@@ -8,7 +8,7 @@ Part of the API of DynamicPPL is defined in the more lightweight interface packa
 
 A core component of DynamicPPL is the [`@model`](@ref) macro.
 It can be used to define probabilistic models in an intuitive way by specifying random variables and their distributions with `~` statements.
-These statements are rewritten by `@model` as calls of [internal functions](@ref model_internal) for sampling the variables and computing their log densities.
+These statements are rewritten by `@model` as calls of internal functions for sampling the variables and computing their log densities.
 
 ```@docs
 @model
@@ -344,6 +344,13 @@ Base.empty!
 SimpleVarInfo
 ```
 
+### Tilde-pipeline
+
+```@docs
+tilde_assume!!
+tilde_observe!!
+```
+
 ### Accumulators
 
 The subtypes of [`AbstractVarInfo`](@ref) store the cumulative log prior and log likelihood, and sometimes other variables that change during executing, in what are called accumulators.
@@ -450,33 +457,45 @@ AbstractPPL.evaluate!!
 
 This method mutates the `varinfo` used for execution.
 By default, it does not perform any actual sampling: it only evaluates the model using the values of the variables that are already in the `varinfo`.
-To perform sampling, you can either wrap `model.context` in a `SamplingContext`, or use this convenience method:
-
-```@docs
-DynamicPPL.evaluate_and_sample!!
-```
+If you wish to sample new values, see the section on [VarInfo initialisation](#VarInfo-initialisation) just below this.
 
 The behaviour of a model execution can be changed with evaluation contexts, which are a field of the model.
 Contexts are subtypes of `AbstractPPL.AbstractContext`.
 
 ```@docs
-SamplingContext
 DefaultContext
-PrefixContext
 ConditionContext
+InitContext
 ```
 
-### Samplers
+### VarInfo initialisation
+
+The function `init!!` is used to initialise, or overwrite, values in a VarInfo.
+It is really a thin wrapper around using `evaluate!!` with an `InitContext`.
+
+```@docs
+DynamicPPL.init!!
+```
 
-In DynamicPPL two samplers are defined that are used to initialize unobserved random variables:
-[`SampleFromPrior`](@ref) which samples from the prior distribution, and [`SampleFromUniform`](@ref) which samples from a uniform distribution.
+To accomplish this, an initialisation _strategy_ is required, which defines how new values are to be obtained.
+There are three concrete strategies provided in DynamicPPL:
 
 ```@docs
-SampleFromPrior
-SampleFromUniform
+PriorInit
+UniformInit
+ParamsInit
 ```
 
-Additionally, a generic sampler for inference is implemented.
+If you wish to write your own, you have to subtype [`DynamicPPL.AbstractInitStrategy`](@ref) and implement the `init` method.
+
+```@docs
+DynamicPPL.AbstractInitStrategy
+DynamicPPL.init
+```
+
+### Samplers
+
+In DynamicPPL a generic sampler for inference is implemented.
 
 ```@docs
 Sampler
@@ -487,7 +506,7 @@ The default implementation of [`Sampler`](@ref) uses the following unexported fu
 ```@docs
 DynamicPPL.initialstep
 DynamicPPL.loadstate
-DynamicPPL.initialsampler
+DynamicPPL.init_strategy
 ```
 
 Finally, to specify which varinfo type a [`Sampler`](@ref) should use for a given [`Model`](@ref), this is specified by [`DynamicPPL.default_varinfo`](@ref) and can thus be overloaded for each  `model`-`sampler` combination. This can be useful in cases where one has explicit knowledge that one type of varinfo will be more performant for the given `model` and `sampler`.
@@ -502,9 +521,3 @@ There is also the _experimental_ [`DynamicPPL.Experimental.determine_suitable_va
 DynamicPPL.Experimental.determine_suitable_varinfo
 DynamicPPL.Experimental.is_suitable_varinfo
 ```
-
-### [Model-Internal Functions](@id model_internal)
-
-```@docs
-tilde_assume
-```
diff --git a/ext/DynamicPPLEnzymeCoreExt.jl b/ext/DynamicPPLEnzymeCoreExt.jl
@@ -8,8 +8,6 @@ else
     using ..EnzymeCore
 end
 
-@inline EnzymeCore.EnzymeRules.inactive_type(::Type{<:DynamicPPL.SamplingContext}) = true
-
 # Mark istrans as having 0 derivative. The `nothing` return value is not significant, Enzyme
 # only checks whether such a method exists, and never runs it.
 @inline EnzymeCore.EnzymeRules.inactive_noinl(::typeof(DynamicPPL.istrans), args...) =

diff --git a/ext/DynamicPPLJETExt.jl b/ext/DynamicPPLJETExt.jl
@@ -21,22 +21,17 @@ end
 function DynamicPPL.Experimental._determine_varinfo_jet(
     model::DynamicPPL.Model; only_ddpl::Bool=true
 )
-    # Use SamplingContext to test type stability.
-    sampling_model = DynamicPPL.contextualize(
-        model, DynamicPPL.SamplingContext(model.context)
-    )
-
     # First we try with the typed varinfo.
-    varinfo = DynamicPPL.typed_varinfo(sampling_model)
+    varinfo = DynamicPPL.typed_varinfo(model)
 
-    # Let's make sure that both evaluation and sampling doesn't result in type errors.
+    # Let's make sure that evaluation doesn't result in type errors.
     issuccess, result = DynamicPPL.Experimental.is_suitable_varinfo(
-        sampling_model, varinfo; only_ddpl
+        model, varinfo; only_ddpl
     )
 
     if !issuccess
         # Useful information for debugging.
-        @debug "Evaluaton with typed varinfo failed with the following issues:"
+        @debug "Evaluation with typed varinfo failed with the following issues:"
         @debug result
     end
 
@@ -46,7 +41,7 @@ function DynamicPPL.Experimental._determine_varinfo_jet(
     else
         # Warn the user that we can't use the type stable one.
         @warn "Model seems incompatible with typed varinfo. Falling back to untyped varinfo."
-        DynamicPPL.untyped_varinfo(sampling_model)
+        DynamicPPL.untyped_varinfo(model)
     end
 end
 

diff --git a/ext/DynamicPPLMCMCChainsExt.jl b/ext/DynamicPPLMCMCChainsExt.jl
@@ -28,7 +28,7 @@ end
 
 function _check_varname_indexing(c::MCMCChains.Chains)
     return DynamicPPL.supports_varname_indexing(c) ||
-           error("Chains do not support indexing using `VarName`s.")
+           error("This `Chains` object does not support indexing using `VarName`s.")
 end
 
 function DynamicPPL.getindex_varname(
@@ -42,6 +42,15 @@ function DynamicPPL.varnames(c::MCMCChains.Chains)
     return keys(c.info.varname_to_symbol)
 end
 
+function chain_sample_to_varname_dict(c::MCMCChains.Chains, sample_idx, chain_idx)
+    _check_varname_indexing(c)
+    d = Dict{DynamicPPL.VarName,Any}()
+    for vn in DynamicPPL.varnames(c)
+        d[vn] = DynamicPPL.getindex_varname(c, sample_idx, vn, chain_idx)
+    end
+    return d
+end
+
 """
     predict([rng::AbstractRNG,] model::Model, chain::MCMCChains.Chains; include_all=false)
 
@@ -114,9 +123,15 @@ function DynamicPPL.predict(
 
     iters = Iterators.product(1:size(chain, 1), 1:size(chain, 3))
     predictive_samples = map(iters) do (sample_idx, chain_idx)
-        DynamicPPL.setval_and_resample!(varinfo, parameter_only_chain, sample_idx, chain_idx)
-        varinfo = last(DynamicPPL.evaluate_and_sample!!(rng, model, varinfo))
-
+        # Extract values from the chain
+        values_dict = chain_sample_to_varname_dict(parameter_only_chain, sample_idx, chain_idx)
+        # Resample any variables that are not present in `values_dict`
+        _, varinfo = DynamicPPL.init!!(
+            rng,
+            model,
+            varinfo,
+            DynamicPPL.ParamsInit(values_dict, DynamicPPL.PriorInit()),
+        )
         vals = DynamicPPL.values_as_in_model(model, false, varinfo)
         varname_vals = mapreduce(
             collect,
@@ -248,13 +263,14 @@ function DynamicPPL.returned(model::DynamicPPL.Model, chain_full::MCMCChains.Cha
     varinfo = DynamicPPL.VarInfo(model)
     iters = Iterators.product(1:size(chain, 1), 1:size(chain, 3))
     return map(iters) do (sample_idx, chain_idx)
-        # TODO: Use `fix` once we've addressed https://github.com/TuringLang/DynamicPPL.jl/issues/702.
-        # Update the varinfo with the current sample and make variables not present in `chain`
-        # to be sampled.
-        DynamicPPL.setval_and_resample!(varinfo, chain, sample_idx, chain_idx)
-        # NOTE: Some of the varialbes can be a view into the `varinfo`, so we need to
-        # `deepcopy` the `varinfo` before passing it to the `model`.
-        model(deepcopy(varinfo))
+        # Extract values from the chain
+        values_dict = chain_sample_to_varname_dict(chain, sample_idx, chain_idx)
+        # Resample any variables that are not present in `values_dict`, and
+        # return the model's retval.
+        retval, _ = DynamicPPL.init!!(
+            model, varinfo, DynamicPPL.ParamsInit(values_dict, DynamicPPL.PriorInit())
+        )
+        retval
     end
 end
 

diff --git a/src/DynamicPPL.jl b/src/DynamicPPL.jl
@@ -97,18 +97,21 @@ export AbstractVarInfo,
     values_as_in_model,
     # Samplers
     Sampler,
-    SampleFromPrior,
-    SampleFromUniform,
     # LogDensityFunction
     LogDensityFunction,
     # Contexts
     contextualize,
-    SamplingContext,
     DefaultContext,
-    PrefixContext,
     ConditionContext,
-    assume,
-    tilde_assume,
+    # Tilde pipeline
+    tilde_assume!!,
+    tilde_observe!!,
+    # Initialisation
+    InitContext,
+    AbstractInitStrategy,
+    PriorInit,
+    UniformInit,
+    ParamsInit,
     # Pseudo distributions
     NamedDist,
     NoDist,
@@ -170,11 +173,13 @@ abstract type AbstractVarInfo <: AbstractModelTrace end
 # Necessary forward declarations
 include("utils.jl")
 include("chains.jl")
+include("contexts.jl")
+include("contexts/init.jl")
 include("model.jl")
+include("prefix.jl")
 include("sampler.jl")
 include("varname.jl")
 include("distribution_wrappers.jl")
-include("contexts.jl")
 include("submodel.jl")
 include("varnamedvector.jl")
 include("accumulators.jl")

diff --git a/src/compiler.jl b/src/compiler.jl
@@ -60,11 +60,14 @@ evaluates to a `VarName`, and this will be used in the subsequent checks.
 If `vn` is not specified, `AbstractPPL.varname(expr, need_concretize(expr))` will be
 used in its place.
 """
-function isassumption(expr::Union{Expr,Symbol}, vn=make_varname_expression(expr))
+function isassumption(expr::Union{Expr,Symbol}, left_vn=make_varname_expression(expr))
+    @gensym vn
     return quote
-        if $(DynamicPPL.contextual_isassumption)(
-            __model__.context, $(DynamicPPL.prefix)(__model__.context, $vn)
-        )
+        # TODO(penelopeysm): This re-prefixing seems a bit wasteful. I'd really like
+        # the whole `isassumption` thing to be simplified, though, so I'll
+        # leave it till later.
+        $vn = $(DynamicPPL.maybe_prefix)($left_vn, __model__.prefix)
+        if $(DynamicPPL.contextual_isassumption)(__model__.context, $vn)
             # Considered an assumption by `__model__.context` which means either:
             # 1. We hit the default implementation, e.g. using `DefaultContext`,
             #    which in turn means that we haven't considered if it's one of
@@ -78,8 +81,8 @@ function isassumption(expr::Union{Expr,Symbol}, vn=make_varname_expression(expr)
             #    TODO: Support by adding context to model, and use `model.args`
             #    as the default conditioning. Then we no longer need to check `inargnames`
             #    since it will all be handled by `contextual_isassumption`.
-            if !($(DynamicPPL.inargnames)($vn, __model__)) ||
-                $(DynamicPPL.inmissings)($vn, __model__)
+            if !($(DynamicPPL.inargnames)($left_vn, __model__)) ||
+                $(DynamicPPL.inmissings)($left_vn, __model__)
                 true
             else
                 $(maybe_view(expr)) === missing
@@ -99,7 +102,7 @@ isassumption(expr) = :(false)
 
 Return `true` if `vn` is considered an assumption by `context`.
 """
-function contextual_isassumption(context::AbstractContext, vn)
+function contextual_isassumption(context::AbstractContext, vn::VarName)
     if hasconditioned_nested(context, vn)
         val = getconditioned_nested(context, vn)
         # TODO: Do we even need the `>: Missing`, i.e. does it even help the compiler?
@@ -115,9 +118,7 @@ end
 
 isfixed(expr, vn) = false
 function isfixed(::Union{Symbol,Expr}, vn)
-    return :($(DynamicPPL.contextual_isfixed)(
-        __model__.context, $(DynamicPPL.prefix)(__model__.context, $vn)
-    ))
+    return :($(DynamicPPL.contextual_isfixed)(__model__.context, $vn))
 end
 
 """
@@ -413,7 +414,9 @@ function generate_assign(left, right)
     return quote
         $right_val = $right
         if $(DynamicPPL.is_extracting_values)(__varinfo__)
-            $vn = $(DynamicPPL.prefix)(__model__.context, $(make_varname_expression(left)))
+            $vn = $(DynamicPPL.maybe_prefix)(
+                $(make_varname_expression(left)), __model__.prefix
+            )
             __varinfo__ = $(map_accumulator!!)(
                 $acc -> push!($acc, $vn, $right_val), __varinfo__, Val(:ValuesAsInModel)
             )
@@ -448,24 +451,23 @@ function generate_tilde(left, right)
 
     # Otherwise it is determined by the model or its value,
     # if the LHS represents an observation
-    @gensym vn isassumption value dist
+    @gensym left_vn vn isassumption value dist
 
     return quote
         $dist = $right
-        $vn = $(DynamicPPL.resolve_varnames)($(make_varname_expression(left)), $dist)
-        $isassumption = $(DynamicPPL.isassumption(left, vn))
+        $left_vn = $(DynamicPPL.resolve_varnames)($(make_varname_expression(left)), $dist)
+        $vn = $(DynamicPPL.maybe_prefix)($left_vn, __model__.prefix)
+        $isassumption = $(DynamicPPL.isassumption(left, left_vn))
         if $(DynamicPPL.isfixed(left, vn))
-            $left = $(DynamicPPL.getfixed_nested)(
-                __model__.context, $(DynamicPPL.prefix)(__model__.context, $vn)
-            )
+            $left = $(DynamicPPL.getfixed_nested)(__model__.context, $vn)
         elseif $isassumption
             $(generate_tilde_assume(left, dist, vn))
         else
-            # If `vn` is not in `argnames`, we need to make sure that the variable is defined.
-            if !$(DynamicPPL.inargnames)($vn, __model__)
-                $left = $(DynamicPPL.getconditioned_nested)(
-                    __model__.context, $(DynamicPPL.prefix)(__model__.context, $vn)
-                )
+            # If `left_vn` is not in `argnames`, we need to make sure that the variable is defined.
+            # (Note: we use the unprefixed `left_vn` here rather than `vn` which will have had
+            # prefixes applied!)
+            if !$(DynamicPPL.inargnames)($left_vn, __model__)
+                $left = $(DynamicPPL.getconditioned_nested)(__model__.context, $vn)
             end
 
             $value, __varinfo__ = $(DynamicPPL.tilde_observe!!)(
@@ -495,6 +497,7 @@ function generate_tilde_assume(left, right, vn)
     return quote
         $value, __varinfo__ = $(DynamicPPL.tilde_assume!!)(
             __model__.context,
+            __model__.prefix,
             $(DynamicPPL.unwrap_right_vn)($(DynamicPPL.check_tilde_rhs)($right), $vn)...,
             __varinfo__,
         )