`to_submodel` type instabilities

[penelopeysm]

Reported via Julia Slack: https://julialang.slack.com/archives/CCYDC34A0/p1738281740089319

using DynamicPPL, Distributions

@model function inner()
   x ~ Normal()
end

@model function outer1(nms)
   for i in 1:2
       a ~ to_submodel(prefix(inner(), nms[i]), false)
   end
end
m1 = outer1(["1","2"])

@model function outer2(nms)
   for i in 1:2
       @submodel prefix = nms[i] inner()
   end
end
m2 = outer2(["1", "2"])

@code_warntype m1.f(m1, VarInfo(m1), SamplingContext(), m1.args...)
@code_warntype m2.f(m2, VarInfo(m2), SamplingContext(), m2.args...)

@code_warntype DynamicPPL.evaluate!!(m1, VarInfo(m1), SamplingContext())
@code_warntype DynamicPPL.evaluate!!(m2, VarInfo(m2), SamplingContext())

There is a fairly small (10%) performance regression:

julia> using Chairmarks

julia> @be m1.f(m1, v1, SamplingContext(), m1.args...) # to_submodel
Benchmark: 3273 samples with 9 evaluations
 min    2.759 μs (42 allocs: 2.812 KiB)
 median 2.866 μs (42 allocs: 2.812 KiB)
 mean   3.158 μs (42 allocs: 2.812 KiB, 0.03% gc time)
 max    805.736 μs (42 allocs: 2.812 KiB, 98.86% gc time)

julia> @be m2.f(m2, v2, SamplingContext(), m2.args...) # @submodel
Benchmark: 7396 samples with 4 evaluations
 min    2.510 μs (41 allocs: 2.656 KiB)
 median 2.615 μs (41 allocs: 2.656 KiB)
 mean   2.844 μs (41 allocs: 2.656 KiB, 0.01% gc time)
 max    1.465 ms (41 allocs: 2.656 KiB, 98.93% gc time)

julia> @be DynamicPPL.evaluate!!(m1, v1, SamplingContext()) # to_submodel
Benchmark: 3117 samples with 10 evaluations
 min    2.633 μs (41 allocs: 2.750 KiB)
 median 2.750 μs (41 allocs: 2.750 KiB)
 mean   2.982 μs (41 allocs: 2.750 KiB, 0.03% gc time)
 max    636.521 μs (41 allocs: 2.750 KiB, 98.77% gc time)

julia> @be DynamicPPL.evaluate!!(m2, v2, SamplingContext()) # @submodel
Benchmark: 2848 samples with 12 evaluations
 min    2.392 μs (40 allocs: 2.594 KiB)
 median 2.512 μs (40 allocs: 2.594 KiB)
 mean   2.723 μs (40 allocs: 2.594 KiB, 0.03% gc time)
 max    540.396 μs (40 allocs: 2.594 KiB, 98.44% gc time)

[yebai]

An alternative way of writing the outer model is

@model function outer1(nms)
   N = length(nms)
   a = Vector(undef, N)
   for i in 1:N
       a[i] ~ to_submodel(inner())
   end
end

This is arguably better than looping over to_submodel with the same variable name a. If one writes

@model function outer1(nms)
   for i in 1:2
       a ~ Normal()
   end
end

An error will be thrown during inference due to a clash of variable names. Maybe we should also throw an error if users try to loop over to_submodel with the same variable name.