Depreciate@submodel l ~ m in favour of l ~ to_submodel(m); rename generated_quantities to returned

Project.toml

@@ -1,6 +1,6 @@
 name = "DynamicPPL"
 uuid = "366bfd00-2699-11ea-058f-f148b4cae6d8"
-version = "0.31.0"
+version = "0.31.1"
 
 [deps]
 ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"

docs/src/api.md

@@ -14,12 +14,6 @@ These statements are rewritten by `@model` as calls of [internal functions](@ref
 @model
 ```
 
-One can nest models and call another model inside the model function with [`@submodel`](@ref).
-
-```@docs
-@submodel
-```
-
 ### Type
 
 A [`Model`](@ref) can be created by calling the model function, as defined by [`@model`](@ref).
@@ -110,6 +104,34 @@ Similarly, we can [`unfix`](@ref) variables, i.e. return them to their original
 unfix
 ```
 
+## Models within models
+
+One can include models and call another model inside the model function with `left ~ to_submodel(model)`.
+
+```@docs
+to_submodel
+```
+
+Note that a `[to_submodel](@ref)` is only sampleable; one cannot compute `logpdf` for its realizations.
+
+In the past, one would instead embed sub-models using [`@submodel`](@ref), which has been deprecated since the introduction of [`to_submodel(model)`](@ref)
+
+```@docs
+@submodel
+```
+
+In the context of including models within models, it's also useful to prefix the variables in sub-models to avoid variable names clashing:
+
+```@docs
+prefix
+```
+
+Under the hood, [`to_submodel`](@ref) makes use of the following method to indicate that the model it's wrapping is a model over its return-values rather than something else
+
+```@docs
+returned(::Model)
+```
+
 ## Utilities
 
 It is possible to manually increase (or decrease) the accumulated log density from within a model function.
@@ -118,10 +140,10 @@ It is possible to manually increase (or decrease) the accumulated log density fr
 @addlogprob!
 ```
 
-Return values of the model function for a collection of samples can be obtained with [`generated_quantities`](@ref).
+Return values of the model function for a collection of samples can be obtained with [`returned(model, chain)`](@ref).
 
 ```@docs
-generated_quantities
+returned(::DynamicPPL.Model, ::NamedTuple)
 ```
 
 For a chain of samples, one can compute the pointwise log-likelihoods of each observed random variable with [`pointwise_loglikelihoods`](@ref). Similarly, the log-densities of the priors using

ext/DynamicPPLMCMCChainsExt.jl

@@ -43,7 +43,7 @@ function DynamicPPL.varnames(c::MCMCChains.Chains)
 end
 
 """
-    generated_quantities(model::Model, chain::MCMCChains.Chains)
+    returned(model::Model, chain::MCMCChains.Chains)
 
 Execute `model` for each of the samples in `chain` and return an array of the values
 returned by the `model` for each sample.
@@ -63,12 +63,12 @@ m = demo(data)
 chain = sample(m, alg, n)
 # To inspect the `interesting_quantity(θ, x)` where `θ` is replaced by samples
 # from the posterior/`chain`:
-generated_quantities(m, chain) # <= results in a `Vector` of returned values
+returned(m, chain) # <= results in a `Vector` of returned values
                                #    from `interesting_quantity(θ, x)`
 ```
 ## Concrete (and simple)
 ```julia
-julia> using DynamicPPL, Turing
+julia> using Turing
 
 julia> @model function demo(xs)
            s ~ InverseGamma(2, 3)
@@ -87,7 +87,7 @@ julia> model = demo(randn(10));
 
 julia> chain = sample(model, MH(), 10);
 
-julia> generated_quantities(model, chain)
+julia> returned(model, chain)
 10×1 Array{Tuple{Float64},2}:
  (2.1964758025119338,)
  (2.1964758025119338,)
@@ -101,9 +101,7 @@ julia> generated_quantities(model, chain)
  (-0.16489786710222099,)
 ```
 """
-function DynamicPPL.generated_quantities(
-    model::DynamicPPL.Model, chain_full::MCMCChains.Chains
-)
+function DynamicPPL.returned(model::DynamicPPL.Model, chain_full::MCMCChains.Chains)
     chain = MCMCChains.get_sections(chain_full, :parameters)
     varinfo = DynamicPPL.VarInfo(model)
     iters = Iterators.product(1:size(chain, 1), 1:size(chain, 3))

src/DynamicPPL.jl

@@ -86,7 +86,6 @@ export AbstractVarInfo,
     Model,
     getmissings,
     getargnames,
-    generated_quantities,
     extract_priors,
     values_as_in_model,
     # Samplers
@@ -122,6 +121,9 @@ export AbstractVarInfo,
     decondition,
     fix,
     unfix,
+    prefix,
+    returned,
+    to_submodel,
     # Convenience macros
     @addlogprob!,
     @submodel,
@@ -130,7 +132,8 @@ export AbstractVarInfo,
     check_model_and_trace,
     # Deprecated.
     @logprob_str,
-    @prob_str
+    @prob_str,
+    generated_quantities
 
 # Reexport
 using Distributions: loglikelihood
@@ -196,6 +199,8 @@ include("values_as_in_model.jl")
 include("debug_utils.jl")
 using .DebugUtils
 
+include("deprecated.jl")
+
 if !isdefined(Base, :get_extension)
     using Requires
 end

src/compiler.jl

@@ -178,6 +178,11 @@ function check_tilde_rhs(@nospecialize(x))
 end
 check_tilde_rhs(x::Distribution) = x
 check_tilde_rhs(x::AbstractArray{<:Distribution}) = x
+check_tilde_rhs(x::ReturnedModelWrapper) = x
+function check_tilde_rhs(x::Sampleable{<:Any,AutoPrefix}) where {AutoPrefix}
+    model = check_tilde_rhs(x.model)
+    return Sampleable{typeof(model),AutoPrefix}(model)
+end
 
 """
     unwrap_right_vn(right, vn)

src/context_implementations.jl

@@ -103,8 +103,17 @@ By default, calls `tilde_assume(context, right, vn, vi)` and accumulates the log
 probability of `vi` with the returned value.
 """
 function tilde_assume!!(context, right, vn, vi)
-    value, logp, vi = tilde_assume(context, right, vn, vi)
-    return value, acclogp_assume!!(context, vi, logp)
+    return if is_rhs_model(right)
+        # Prefix the variables using the `vn`.
+        rand_like!!(
+            right,
+            should_auto_prefix(right) ? PrefixContext{Symbol(vn)}(context) : context,
+            vi,
+        )
+    else
+        value, logp, vi = tilde_assume(context, right, vn, vi)
+        value, acclogp_assume!!(context, vi, logp)
+    end
 end
 
 # observe
@@ -159,6 +168,11 @@ Falls back to `tilde_observe!!(context, right, left, vi)` ignoring the informati
 and indices; if needed, these can be accessed through this function, though.
 """
 function tilde_observe!!(context, right, left, vname, vi)
+    is_rhs_model(right) && throw(
+        ArgumentError(
+            "`~` with a model on the right-hand side of an observe statement is not supported",
+        ),
+    )
     return tilde_observe!!(context, right, left, vi)
 end
 
@@ -172,6 +186,11 @@ By default, calls `tilde_observe(context, right, left, vi)` and accumulates the
 probability of `vi` with the returned value.
 """
 function tilde_observe!!(context, right, left, vi)
+    is_rhs_model(right) && throw(
+        ArgumentError(
+            "`~` with a model on the right-hand side of an observe statement is not supported",
+        ),
+    )
     logp, vi = tilde_observe(context, right, left, vi)
     return left, acclogp_observe!!(context, vi, logp)
 end
@@ -321,8 +340,13 @@ model inputs), accumulate the log probability, and return the sampled value and
 Falls back to `dot_tilde_assume(context, right, left, vn, vi)`.
 """
 function dot_tilde_assume!!(context, right, left, vn, vi)
+    is_rhs_model(right) && throw(
+        ArgumentError(
+            "`.~` with a model on the right-hand side is not supported; please use `~`"
+        ),
+    )
     value, logp, vi = dot_tilde_assume(context, right, left, vn, vi)
-    return value, acclogp_assume!!(context, vi, logp), vi
+    return value, acclogp_assume!!(context, vi, logp)
 end
 
 # `dot_assume`
@@ -573,6 +597,11 @@ Falls back to `dot_tilde_observe!!(context, right, left, vi)` ignoring the infor
 name and indices; if needed, these can be accessed through this function, though.
 """
 function dot_tilde_observe!!(context, right, left, vn, vi)
+    is_rhs_model(right) && throw(
+        ArgumentError(
+            "`~` with a model on the right-hand side of an observe statement is not supported",
+        ),
+    )
     return dot_tilde_observe!!(context, right, left, vi)
 end
 
@@ -585,6 +614,11 @@ probability, and return the observed value and updated `vi`.
 Falls back to `dot_tilde_observe(context, right, left, vi)`.
 """
 function dot_tilde_observe!!(context, right, left, vi)
+    is_rhs_model(right) && throw(
+        ArgumentError(
+            "`~` with a model on the right-hand side of an observe statement is not supported",
+        ),
+    )
     logp, vi = dot_tilde_observe(context, right, left, vi)
     return left, acclogp_observe!!(context, vi, logp)
 end

src/contexts.jl

@@ -281,6 +281,34 @@ function prefix(::PrefixContext{Prefix}, vn::VarName{Sym}) where {Prefix,Sym}
     end
 end
 
+"""
+    prefix(model::Model, x)
+
+Return `model` but with all random variables prefixed by `x`.
+
+If `x` is known at compile-time, use `Val{x}()` to avoid runtime overheads for prefixing.
+
+# Examples
+
+```jldoctest
+julia> using DynamicPPL: prefix
+
+julia> @model demo() = x ~ Dirac(1)
+demo (generic function with 2 methods)
+
+julia> rand(prefix(demo(), :my_prefix))
+(var"my_prefix.x" = 1,)
+
+julia> # One can also use `Val` to avoid runtime overheads.
+       rand(prefix(demo(), Val(:my_prefix)))
+(var"my_prefix.x" = 1,)
+```
+"""
+prefix(model::Model, x) = contextualize(model, PrefixContext{Symbol(x)}(model.context))
+function prefix(model::Model, ::Val{x}) where {x}
+    return contextualize(model, PrefixContext{Symbol(x)}(model.context))
+end
+
 struct ConditionContext{Values,Ctx<:AbstractContext} <: AbstractContext
     values::Values
     context::Ctx

src/deprecated.jl

@@ -0,0 +1 @@
+@deprecate generated_quantities(model, params) returned(model, params)