Change .~ to use filldist rather than a loop

HISTORY.md

@@ -45,7 +45,9 @@ x ~ product_distribution(Normal.(y))
 x ~ MvNormal(fill(0.0, 2), I)
 ```
 
-This is often more performant as well. Note that using `~` rather than `.~` does change the internal storage format a bit: With `.~` `x[i]` are stored as separate variables, with `~` as a single multivariate variable `x`. In most cases this does not change anything for the user, but if it does cause issues, e.g. if you are dealing with `VarInfo` objects directly and need to keep the old behavior, you can always expand into a loop, such as
+This is often more performant as well.
+
+The new implementation of `x .~ ...` is just a short-hand for `x ~ filldist(...)`, which means that `x` will be seen as a single multivariate variable. In most cases this does not change anything for the user, with the one notable exception being `pointwise_loglikelihoods`, which previously treated `.~` assignments as assigning multiple univariate variables. If you _do_ want a variable to be seen as an array of univariate variables rather than a single multivariate variable, you can always expand into a loop, such as
 
 ```julia
 dists = Normal.(y)
@@ -54,7 +56,7 @@ for i in 1:length(dists)
 end
 ```
 
-Cases where the right hand side is of a different dimension than the left hand side, and neither is a scalar, must be replaced with a loop. For example,
+Cases where the right hand side is of a different dimension than the left hand side, and neither is a scalar, must always be replaced with a loop. For example,
 
 ```julia
 x = Array{Float64,3}(undef, 2, 3, 4)
@@ -70,8 +72,6 @@ for i in 1:3, j in 1:4
 end
 ```
 
-This release also completely rewrites the internal implementation of `.~`, where from now on all `.~` statements are turned into loops over `~` statements at macro time. However, the only breaking aspect of this change is the above change to what's allowed on the right hand side.
-
 ### Remove indexing by samplers
 
 This release removes the feature of `VarInfo` where it kept track of which variable was associated with which sampler. This means removing all user-facing methods where `VarInfo`s where being indexed with samplers. In particular,

src/compiler.jl

@@ -514,13 +514,10 @@ end
 Generate the expression that replaces `left .~ right` in the model body.
 """
 function generate_dot_tilde(left, right)
-    @gensym dist left_axes idx
+    @gensym dist
     return quote
         $dist = DynamicPPL.check_dot_tilde_rhs($right)
-        $left_axes = axes($left)
-        for $idx in Iterators.product($left_axes...)
-            $left[$idx...] ~ $dist
-        end
+        $left ~ DynamicPPL.product_distribution(Base.fill($dist, Base.size($left)...))
     end
 end
 

src/pointwise_logdensities.jl

@@ -157,8 +157,8 @@ y .~ Normal(μ, σ)
 y ~ MvNormal(fill(μ, n), σ^2 * I)
 ```
 
-In (1) and (2), `y` will be treated as a collection of `n` i.i.d. 1-dimensional variables,
-while in (3) `y` will be treated as a _single_ n-dimensional observation.
+In (1) `y` will be treated as a collection of `n` i.i.d. 1-dimensional variables,
+while in (2) and (3) `y` will be treated as a _single_ n-dimensional observation.
 
 This is important to keep in mind, in particular if the computation is used
 for downstream computations.
@@ -216,23 +216,17 @@ OrderedDict{VarName, Matrix{Float64}} with 6 entries:
 ```
 
 ## Broadcasting
-Note that `x .~ Dist()` will treat `x` as a collection of
-_independent_ observations rather than as a single observation.
+Note that `x .~ Dist()` will treat `x` as a single multivariate observation.
 
 ```jldoctest; setup = :(using Distributions)
 julia> @model function demo(x)
            x .~ Normal()
        end;
 
-julia> m = demo([1.0, ]);
-
-julia> ℓ = pointwise_logdensities(m, VarInfo(m)); first(ℓ[@varname(x[1])])
--1.4189385332046727
-
 julia> m = demo([1.0; 1.0]);
 
-julia> ℓ = pointwise_logdensities(m, VarInfo(m)); first.((ℓ[@varname(x[1])], ℓ[@varname(x[2])]))
-(-1.4189385332046727, -1.4189385332046727)
+julia> ℓ = pointwise_logdensities(m, VarInfo(m)); first(ℓ[@varname(x)])
+-2.8378770664093453
 ```
 
 """

src/test_utils/models.jl

@@ -208,7 +208,7 @@ function logprior_true_with_logabsdet_jacobian(
     return _demo_logprior_true_with_logabsdet_jacobian(model, s, m)
 end
 function varnames(model::Model{typeof(demo_dot_assume_observe)})
-    return [@varname(s[1]), @varname(s[2]), @varname(m)]
+    return [@varname(s), @varname(m)]
 end
 
 @model function demo_assume_index_observe(
@@ -293,7 +293,7 @@ function logprior_true_with_logabsdet_jacobian(
     return _demo_logprior_true_with_logabsdet_jacobian(model, s, m)
 end
 function varnames(model::Model{typeof(demo_dot_assume_observe_index)})
-    return [@varname(s[1]), @varname(s[2]), @varname(m)]
+    return [@varname(s), @varname(m)]
 end
 
 # Using vector of `length` 1 here so the posterior of `m` is the same
@@ -374,7 +374,7 @@ function logprior_true_with_logabsdet_jacobian(
     return _demo_logprior_true_with_logabsdet_jacobian(model, s, m)
 end
 function varnames(model::Model{typeof(demo_dot_assume_observe_index_literal)})
-    return [@varname(s[1]), @varname(s[2]), @varname(m)]
+    return [@varname(s), @varname(m)]
 end
 
 @model function demo_assume_observe_literal()
@@ -458,7 +458,7 @@ function logprior_true_with_logabsdet_jacobian(
     return _demo_logprior_true_with_logabsdet_jacobian(model, s, m)
 end
 function varnames(model::Model{typeof(demo_assume_submodel_observe_index_literal)})
-    return [@varname(s[1]), @varname(s[2]), @varname(m)]
+    return [@varname(s), @varname(m)]
 end
 
 @model function _likelihood_multivariate_observe(s, m, x)
@@ -492,7 +492,7 @@ function logprior_true_with_logabsdet_jacobian(
     return _demo_logprior_true_with_logabsdet_jacobian(model, s, m)
 end
 function varnames(model::Model{typeof(demo_dot_assume_observe_submodel)})
-    return [@varname(s[1]), @varname(s[2]), @varname(m)]
+    return [@varname(s), @varname(m)]
 end
 
 @model function demo_dot_assume_observe_matrix_index(
@@ -521,7 +521,7 @@ function logprior_true_with_logabsdet_jacobian(
     return _demo_logprior_true_with_logabsdet_jacobian(model, s, m)
 end
 function varnames(model::Model{typeof(demo_dot_assume_observe_matrix_index)})
-    return [@varname(s[1]), @varname(s[2]), @varname(m)]
+    return [@varname(s), @varname(m)]
 end
 
 @model function demo_assume_matrix_observe_matrix_index(

test/varinfo.jl

@@ -426,12 +426,7 @@ end
         # Transform only one variable
         all_vns = vcat(meta.s.vns, meta.m.vns, meta.x.vns, meta.y.vns)
         for vn in [
-            @varname(s),
-            @varname(m),
-            @varname(x),
-            @varname(y),
-            @varname(x[2]),
-            @varname(y[2])
+            @varname(s), @varname(m), @varname(x), @varname(y), @varname(x), @varname(y[2])
         ]
             target_vns = filter(x -> subsumes(vn, x), all_vns)
             other_vns = filter(x -> !subsumes(vn, x), all_vns)
@@ -874,7 +869,7 @@ end
         varinfo2 = last(
             DynamicPPL.evaluate!!(model2, deepcopy(varinfo1), SamplingContext())
         )
-        for vn in [@varname(x), @varname(y[1])]
+        for vn in [@varname(x), @varname(y)]
             @test DynamicPPL.istrans(varinfo2, vn)
         end
     end