[Merged by Bors] - Sibling PR of introduction of Setfield.jl in AbstractPPL.jl

[torfjelde]

This is a sibling PR to TuringLang/AbstractPPL.jl#26 fixing some issues + allowing us to do neat stuff.

We also finally drop the passing of the inds around in the tilde-pipeline, which is not very useful now that we have the more general lenses in VarName.

TODOs:

• Deprecate *tilde_* with inds argument appropriately. EDIT: On second thought, let's not. See comment for reason.
• It seems like the prob macro is now somehow broken 😕
• (Maybe) Rewrite @model to not escape the entire expression. Deferred to Turning around escape in model macro #311
• Figure out performance degradation.
  • Answer: hash for Tuple vs. hash for immutable struct 😕

Sample fields of structs

julia> @model function demo(x, y)
           s ~ InverseGamma(2, 3)
           m ~ Normal(0, √s)
           for i in 2:length(x.a) - 1
               x.a[i] ~ Normal(m, √s)
           end

           # Dynamic indexing
           x.a[begin] ~ Normal(-100.0, 1.0)
           x.a[end] ~ Normal(100.0, 1.0)
           
           # Immutable set
           y.a ~ Normal()
           
           # Dotted
           z = Vector{Float64}(undef, 3)
           z[1:2] .~ Normal()
           z[end:end] .~ Normal()
           
           return (; s, m, x, y, z)
       end

julia> struct MyCoolStruct{T}
           a::T
       end

julia> m = demo(MyCoolStruct([missing, missing]), MyCoolStruct(missing));

julia> m()
(s = 3.483799020996254, m = -0.35566330762328, x = MyCoolStruct{Vector{Union{Missing, Float64}}}(Union{Missing, Float64}[-100.75592540694562, 98.61295291877542]), y = MyCoolStruct{Float64}(-2.1107980419121546), z = [-2.2868359094832584, -1.1378866583607443, 1.172250491861777])

Sample fields of DataFrame

julia> using DataFrames

julia> using Setfield: ConstructionBase

julia> function ConstructionBase.setproperties(df::DataFrame, patch::NamedTuple)
           # Only need `copy` because we'll replace entire columns
           columns = copy(DataFrames._columns(df))
           colindex = DataFrames.index(df)
           for k in keys(patch)
               columns[colindex[k]] = patch[k]
           end
           return DataFrame(columns, colindex)
       end

julia> @model function demo(x)
           s ~ InverseGamma(2, 3)
           m ~ Normal(0, √s)
           for i in 1:length(x.a) - 1
               x.a[i] ~ Normal(m, √s)
           end

           x.a[end] ~ Normal(100.0, 1.0)
           
           return x
       end
demo (generic function with 1 method)

julia> m = demo(df, (a = missing, ));

julia> m()
3×1 DataFrame
 Row │ a        
     │ Float64? 
─────┼──────────
   1 │   1.0
   2 │   2.0
   3 │  99.8838

julia> df
3×1 DataFrame
 Row │ a         
     │ Float64?  
─────┼───────────
   1 │       1.0
   2 │       2.0
   3 │ missing   

Benchmarks

Unfortunately there does seem to be performance regression when using a very large number of varnames in a loop in the model (for broadcasting which uses the same number of varnames but does so "internally", there is no difference):

The weird thing is that we're using less memory, indicating that type-inference might better?

0.31.1

0.31.1

Setup

using BenchmarkTools, DynamicPPL, Distributions, Serialization

import DynamicPPLBenchmarks: time_model_def, make_suite, typed_code, weave_child

Models

demo1

@model function demo1(x)
    m ~ Normal()
    x ~ Normal(m, 1)

    return (m = m, x = x)
end

model_def = demo1;
data = 1.0;

@time model_def(data)();

0.059594 seconds (115.76 k allocations: 6.982 MiB, 99.91% compilation tim
e)

m = time_model_def(model_def, data);

0.000004 seconds (2 allocations: 48 bytes)

suite = make_suite(m);
results = run(suite);

results["evaluation_untyped"]

BenchmarkTools.Trial: 10000 samples with 1 evaluation.
 Range (min … max):  619.000 ns …  19.678 μs  ┊ GC (min … max): 0.00% … 0.0
0%
 Time  (median):     654.000 ns               ┊ GC (median):    0.00%
 Time  (mean ± σ):   677.650 ns ± 333.145 ns  ┊ GC (mean ± σ):  0.00% ± 0.0
0%

    ▅▆▇█▅▄▃                                                      
  ▃▅███████▇▆▅▄▃▄▄▃▃▃▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▁▂▂▂▂ ▃
  619 ns           Histogram: frequency by time          945 ns <

 Memory estimate: 480 bytes, allocs estimate: 13.

results["evaluation_typed"]

BenchmarkTools.Trial: 10000 samples with 1 evaluation.
 Range (min … max):  249.000 ns …  11.048 μs  ┊ GC (min … max): 0.00% … 0.0
0%
 Time  (median):     264.000 ns               ┊ GC (median):    0.00%
 Time  (mean ± σ):   267.650 ns ± 137.452 ns  ┊ GC (mean ± σ):  0.00% ± 0.0
0%

                ▂▄ ▆▇ █▇ ▇▄ ▂▂                                   
  ▂▂▂▁▂▂▁▃▃▁▅▅▁███▁██▁██▁██▁██▁▇▇▅▁▄▄▁▃▃▁▃▃▁▃▂▁▂▂▂▁▂▂▁▂▂▁▂▂▁▂▂▂ ▃
  249 ns           Histogram: frequency by time          291 ns <

 Memory estimate: 0 bytes, allocs estimate: 0.

if WEAVE_ARGS[:include_typed_code]
    typed = typed_code(m)
end

demo2

@model function demo2(y) 
    # Our prior belief about the probability of heads in a coin.
    p ~ Beta(1, 1)

    # The number of observations.
    N = length(y)
    for n in 1:N
        # Heads or tails of a coin are drawn from a Bernoulli distribution.
        y[n] ~ Bernoulli(p)
    end
end

model_def = demo2;
data = rand(0:1, 10);

@time model_def(data)();

0.067078 seconds (143.91 k allocations: 8.544 MiB, 99.91% compilation tim
e)

m = time_model_def(model_def, data);

0.000002 seconds (1 allocation: 32 bytes)

suite = make_suite(m);
results = run(suite);

results["evaluation_untyped"]

BenchmarkTools.Trial: 10000 samples with 1 evaluation.
 Range (min … max):  1.637 μs …  48.917 μs  ┊ GC (min … max): 0.00% … 0.00%
 Time  (median):     1.694 μs               ┊ GC (median):    0.00%
 Time  (mean ± σ):   1.746 μs ± 550.372 ns  ┊ GC (mean ± σ):  0.00% ± 0.00%

    ▂█▇▃                                                       
  ▁▄████▇▄▄▅▅▅▄▃▃▃▂▂▂▂▂▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁ ▂
  1.64 μs         Histogram: frequency by time        2.23 μs <

 Memory estimate: 1.66 KiB, allocs estimate: 47.

results["evaluation_typed"]

BenchmarkTools.Trial: 10000 samples with 1 evaluation.
 Range (min … max):  506.000 ns …  10.733 μs  ┊ GC (min … max): 0.00% … 0.0
0%
 Time  (median):     546.000 ns               ┊ GC (median):    0.00%
 Time  (mean ± σ):   553.478 ns ± 118.542 ns  ┊ GC (mean ± σ):  0.00% ± 0.0
0%

    ▃█  ▆▅                                                       
  ▂▃██▇▇██▅▃▂▂▂▂▂▂▂▂▂▂▂▂▁▂▂▂▁▁▁▂▂▁▁▁▁▁▂▂▁▂▂▁▁▂▂▁▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ ▃
  506 ns           Histogram: frequency by time          933 ns <

 Memory estimate: 0 bytes, allocs estimate: 0.

if WEAVE_ARGS[:include_typed_code]
    typed = typed_code(m)
end

demo3

@model function demo3(x)
    D, N = size(x)

    # Draw the parameters for cluster 1.
    μ1 ~ Normal()

    # Draw the parameters for cluster 2.
    μ2 ~ Normal()

    μ = [μ1, μ2]

    # Comment out this line if you instead want to draw the weights.
    w = [0.5, 0.5]

    # Draw assignments for each datum and generate it from a multivariate normal.
    k = Vector{Int}(undef, N)
    for i in 1:N
        k[i] ~ Categorical(w)
        x[:,i] ~ MvNormal([μ[k[i]], μ[k[i]]], 1.)
    end
    return k
end

model_def = demo3

# Construct 30 data points for each cluster.
N = 30

# Parameters for each cluster, we assume that each cluster is Gaussian distributed in the example.
μs = [-3.5, 0.0]

# Construct the data points.
data = mapreduce(c -> rand(MvNormal([μs[c], μs[c]], 1.), N), hcat, 1:2);

@time model_def(data)();

0.097628 seconds (224.06 k allocations: 13.410 MiB, 99.79% compilation ti
me)

m = time_model_def(model_def, data);

0.000002 seconds (1 allocation: 32 bytes)

suite = make_suite(m);
results = run(suite);

results["evaluation_untyped"]

BenchmarkTools.Trial: 10000 samples with 1 evaluation.
 Range (min … max):  48.200 μs …  16.129 ms  ┊ GC (min … max): 0.00% … 99.5
3%
 Time  (median):     51.017 μs               ┊ GC (median):    0.00%
 Time  (mean ± σ):   60.128 μs ± 265.008 μs  ┊ GC (mean ± σ):  7.61% ±  1.7
2%

  ▂▆█                                                           
  ████▂▂▂▁▂▃▄▅▇▅▃▂▂▂▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁ ▂
  48.2 μs         Histogram: frequency by time          101 μs <

 Memory estimate: 48.20 KiB, allocs estimate: 1042.

results["evaluation_typed"]

BenchmarkTools.Trial: 10000 samples with 1 evaluation.
 Range (min … max):  22.210 μs …  13.796 ms  ┊ GC (min … max): 0.00% … 99.7
0%
 Time  (median):     25.882 μs               ┊ GC (median):    0.00%
 Time  (mean ± σ):   27.536 μs ± 137.815 μs  ┊ GC (mean ± σ):  5.00% ±  1.0
0%

  █▇▆▄▂ ▁▇▆▇▆▅▄▂   ▂▂▂▁                                        ▂
  ████████████████████████▆▆▃▅▅▅▅▅▅▁▆▇▆▅▅▅▆▆▅▆▇▇▇▇▆▆▅▆▆▅▅▇█▇█▇ █
  22.2 μs       Histogram: log(frequency) by time        51 μs <

 Memory estimate: 17.62 KiB, allocs estimate: 183.

if WEAVE_ARGS[:include_typed_code]
    typed = typed_code(m)
end

demo4: loads of indexing

@model function demo4(n, ::Type{TV}=Vector{Float64}) where {TV}
    m ~ Normal()
    x = TV(undef, n)
    for i in eachindex(x)
        x[i] ~ Normal(m, 1.0)
    end
end

model_def = demo4
data = (100_000, );

@time model_def(data)();

0.435154 seconds (3.12 M allocations: 192.275 MiB, 8.73% gc time, 1.84% c
ompilation time)

m = time_model_def(model_def, data);

0.000002 seconds (2 allocations: 64 bytes)

suite = make_suite(m);
results = run(suite);

results["evaluation_untyped"]

BenchmarkTools.Trial: 62 samples with 1 evaluation.
 Range (min … max):  61.601 ms … 101.432 ms  ┊ GC (min … max): 0.00% … 25.0
2%
 Time  (median):     76.902 ms               ┊ GC (median):    0.00%
 Time  (mean ± σ):   77.276 ms ±  11.445 ms  ┊ GC (mean ± σ):  6.48% ± 10.7
7%

     ▂              ▂    █ ▆                                    
  ▆▆██▄▄▁▄█▄▁▁▁▁▁▁▆▁█▁█▁▄████▁▄▁▁▁▁▁▁▄▁▁▁▁▁▁▁▁▄▁▁▆▁▁▄▆▄▁▄▁▆▄▁▄ ▁
  61.6 ms         Histogram: frequency by time          101 ms <

 Memory estimate: 44.37 MiB, allocs estimate: 1357727.

results["evaluation_typed"]

BenchmarkTools.Trial: 189 samples with 1 evaluation.
 Range (min … max):  23.796 ms … 40.845 ms  ┊ GC (min … max): 0.00% … 0.00%
 Time  (median):     24.838 ms              ┊ GC (median):    0.00%
 Time  (mean ± σ):   25.162 ms ±  1.434 ms  ┊ GC (mean ± σ):  0.00% ± 0.00%

        ▁  ▂▂▃█▂ ▃▂  ▁                                         
  ▃▅█▃▇▅█▇██████▇█████▇▄▅▃▅▆▇█▃▆▃▃▄▅▁▄▁▃▁▆▅▄▁▁▁▃▁▃▄▁▁▃▃▁▁▁▁▃▄ ▃
  23.8 ms         Histogram: frequency by time        27.8 ms <

 Memory estimate: 781.70 KiB, allocs estimate: 6.

if WEAVE_ARGS[:include_typed_code]
    typed = typed_code(m)
end

@model function demo4_dotted(n, ::Type{TV}=Vector{Float64}) where {TV}
    m ~ Normal()
    x = TV(undef, n)
    x .~ Normal(m, 1.0)
end

model_def = demo4_dotted
data = (100_000, );

@time model_def(data)();

1.476057 seconds (5.08 M allocations: 375.205 MiB, 5.02% gc time, 0.62% c
ompilation time)

m = time_model_def(model_def, data);

0.000002 seconds (2 allocations: 64 bytes)

suite = make_suite(m);
results = run(suite);

results["evaluation_untyped"]

BenchmarkTools.Trial: 39 samples with 1 evaluation.
 Range (min … max):  112.078 ms … 350.311 ms  ┊ GC (min … max): 11.20% … 4.
74%
 Time  (median):     115.686 ms               ┊ GC (median):    12.93%
 Time  (mean ± σ):   122.722 ms ±  37.638 ms  ┊ GC (mean ± σ):  12.96% ± 2.
85%

  █▅ ▁                                                           
  ██▅█▅▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▅ ▁
  112 ms        Histogram: log(frequency) by time        350 ms <

 Memory estimate: 347.71 MiB, allocs estimate: 964550.

results["evaluation_typed"]

BenchmarkTools.Trial: 59 samples with 1 evaluation.
 Range (min … max):  69.420 ms … 407.970 ms  ┊ GC (min … max): 12.25% … 6.3
0%
 Time  (median):     71.514 ms               ┊ GC (median):    12.41%
 Time  (mean ± σ):   78.481 ms ±  43.867 ms  ┊ GC (mean ± σ):  12.80% ± 2.8
4%

   ▅▂█ █▅                                                       
  ▇██████▅▅▄▁▅▁▁▁▁▁▁▁▁▁▁▁▁▁▅▁▁▁▄▄▁▁▁▄▁▁▁▁▄▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▄ ▁
  69.4 ms         Histogram: frequency by time         94.2 ms <

 Memory estimate: 337.55 MiB, allocs estimate: 399306.

if WEAVE_ARGS[:include_typed_code]
    typed = typed_code(m)
end

This PR

This PR

Setup

using BenchmarkTools, DynamicPPL, Distributions, Serialization

import DynamicPPLBenchmarks: time_model_def, make_suite, typed_code, weave_child

Models

demo1

@model function demo1(x)
    m ~ Normal()
    x ~ Normal(m, 1)

    return (m = m, x = x)
end

model_def = demo1;
data = 1.0;

@time model_def(data)();

1.063017 seconds (2.88 M allocations: 180.745 MiB, 4.19% gc time, 99.90% 
compilation time)

m = time_model_def(model_def, data);

0.000004 seconds (2 allocations: 48 bytes)

suite = make_suite(m);
results = run(suite);

results["evaluation_untyped"]

BenchmarkTools.Trial: 10000 samples with 1 evaluation.
 Range (min … max):  615.000 ns …  13.280 ms  ┊ GC (min … max): 0.00% … 0.0
0%
 Time  (median):     650.000 ns               ┊ GC (median):    0.00%
 Time  (mean ± σ):     2.037 μs ± 132.793 μs  ┊ GC (mean ± σ):  0.00% ± 0.0
0%

  ▅█▇▅▄▄▃▂▁▁                                                    ▁
  ███████████▇▇▇▆▆▆▆▃▄▆▆▅▆▇▆▆▇▆▆▇▆▆▆▆▅▆▆▅▅▅▅▄▄▅▅▃▅▅▃▅▄▅▅▅▅▅▄▅▆▅ █
  615 ns        Histogram: log(frequency) by time        1.7 μs <

 Memory estimate: 480 bytes, allocs estimate: 13.

results["evaluation_typed"]

BenchmarkTools.Trial: 10000 samples with 1 evaluation.
 Range (min … max):  272.000 ns …   9.093 μs  ┊ GC (min … max): 0.00% … 0.0
0%
 Time  (median):     284.000 ns               ┊ GC (median):    0.00%
 Time  (mean ± σ):   310.535 ns ± 156.251 ns  ┊ GC (mean ± σ):  0.00% ± 0.0
0%

  ▅█▆▄▃▃▂▁▁                                                     ▁
  ███████████▇▇▆▄▄▃▃▄▅▆▅▆▅▆▆▆▆▆▆▆▇▇▆▆▆▆▆▇▆▆▆▆▇▆▇▇▇▇▆▆▆▆▆▅▆▆▅▄▅▅ █
  272 ns        Histogram: log(frequency) by time        643 ns <

 Memory estimate: 0 bytes, allocs estimate: 0.

if WEAVE_ARGS[:include_typed_code]
    typed = typed_code(m)
end

demo2

@model function demo2(y) 
    # Our prior belief about the probability of heads in a coin.
    p ~ Beta(1, 1)

    # The number of observations.
    N = length(y)
    for n in 1:N
        # Heads or tails of a coin are drawn from a Bernoulli distribution.
        y[n] ~ Bernoulli(p)
    end
end

model_def = demo2;
data = rand(0:1, 10);

@time model_def(data)();

0.401535 seconds (863.20 k allocations: 51.771 MiB, 2.88% gc time, 99.90%
 compilation time)

m = time_model_def(model_def, data);

0.000003 seconds (1 allocation: 32 bytes)

suite = make_suite(m);
results = run(suite);

results["evaluation_untyped"]

BenchmarkTools.Trial: 10000 samples with 1 evaluation.
 Range (min … max):  1.672 μs …  9.849 ms  ┊ GC (min … max): 0.00% … 0.00%
 Time  (median):     1.754 μs              ┊ GC (median):    0.00%
 Time  (mean ± σ):   2.835 μs ± 98.472 μs  ┊ GC (mean ± σ):  0.00% ± 0.00%

  ▅██▇▆▆▅▄▄▃▂▂▁▁                        ▁▁▁ ▁                ▂
  ██████████████████▇▇▇▇▇▆▇▆▅▆▄▄▁▄▄▄▆▇██████████▆▆▇▇▇▇▆▇▆▆▆▆ █
  1.67 μs      Histogram: log(frequency) by time     3.19 μs <

 Memory estimate: 1.50 KiB, allocs estimate: 37.

results["evaluation_typed"]

BenchmarkTools.Trial: 10000 samples with 1 evaluation.
 Range (min … max):  544.000 ns …  19.704 μs  ┊ GC (min … max): 0.00% … 0.0
0%
 Time  (median):     567.000 ns               ┊ GC (median):    0.00%
 Time  (mean ± σ):   578.671 ns ± 222.201 ns  ┊ GC (mean ± σ):  0.00% ± 0.0
0%

   ▄█▇▅▂▃                                                        
  ▃███████▅▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▁▂▂▂▂▂▂▂▂▁▁▁▁▁▂▁▂▁▂▁▁▁▁▁▂▂▂▂▂▂▂▁▂▂▂▂▂ ▃
  544 ns           Histogram: frequency by time          888 ns <

 Memory estimate: 0 bytes, allocs estimate: 0.

if WEAVE_ARGS[:include_typed_code]
    typed = typed_code(m)
end

demo3

@model function demo3(x)
    D, N = size(x)

    # Draw the parameters for cluster 1.
    μ1 ~ Normal()

    # Draw the parameters for cluster 2.
    μ2 ~ Normal()

    μ = [μ1, μ2]

    # Comment out this line if you instead want to draw the weights.
    w = [0.5, 0.5]

    # Draw assignments for each datum and generate it from a multivariate normal.
    k = Vector{Int}(undef, N)
    for i in 1:N
        k[i] ~ Categorical(w)
        x[:,i] ~ MvNormal([μ[k[i]], μ[k[i]]], 1.)
    end
    return k
end

model_def = demo3

# Construct 30 data points for each cluster.
N = 30

# Parameters for each cluster, we assume that each cluster is Gaussian distributed in the example.
μs = [-3.5, 0.0]

# Construct the data points.
data = mapreduce(c -> rand(MvNormal([μs[c], μs[c]], 1.), N), hcat, 1:2);

@time model_def(data)();

1.031824 seconds (2.34 M allocations: 139.934 MiB, 3.16% gc time, 99.96% 
compilation time)

m = time_model_def(model_def, data);

0.000004 seconds (1 allocation: 32 bytes)

suite = make_suite(m);
results = run(suite);

results["evaluation_untyped"]

BenchmarkTools.Trial: 10000 samples with 1 evaluation.
 Range (min … max):  52.509 μs …   9.913 ms  ┊ GC (min … max): 0.00% … 0.00
%
 Time  (median):     53.706 μs               ┊ GC (median):    0.00%
 Time  (mean ± σ):   61.948 μs ± 210.490 μs  ┊ GC (mean ± σ):  9.84% ± 3.27
%

  ▂▆██▇▆▅▄▄▃▃▂▂▁▁▁▁                ▁                           ▂
  █████████████████████▇█▇▇▇█████████▇▇▇▇▅▆▆▅▆▅▅▆▇▅▅▄▅▅▄▄▄▄▂▄▃ █
  52.5 μs       Histogram: log(frequency) by time      71.3 μs <

 Memory estimate: 47.66 KiB, allocs estimate: 1007.

results["evaluation_typed"]

BenchmarkTools.Trial: 10000 samples with 1 evaluation.
 Range (min … max):  25.046 μs …   7.474 ms  ┊ GC (min … max): 0.00% … 99.4
0%
 Time  (median):     25.591 μs               ┊ GC (median):    0.00%
 Time  (mean ± σ):   29.101 μs ± 105.160 μs  ┊ GC (mean ± σ):  6.84% ±  1.9
8%

  ▇█▆▄▃▂▂▁  ▃▄▂▃▃▂▂▁                                           ▂
  █████████▇█████████▇▆▅▆▆▇▇▇▇▅▆▆▅▃▅▄▃▂▂▃▂▄▄▅▄▄▅▄▅▅▅▆▆▅▅▅▅▆▇▇█ █
  25 μs         Histogram: log(frequency) by time        46 μs <

 Memory estimate: 17.62 KiB, allocs estimate: 183.

if WEAVE_ARGS[:include_typed_code]
    typed = typed_code(m)
end

demo4: lots of univariate random variables

@model function demo4(n, ::Type{TV}=Vector{Float64}) where {TV}
    m ~ Normal()
    x = TV(undef, n)
    for i in eachindex(x)
        x[i] ~ Normal(m, 1.0)
    end
end

model_def = demo4
data = (100_000, );

@time model_def(data)();

0.835503 seconds (3.93 M allocations: 244.654 MiB, 10.38% gc time, 9.43% 
compilation time)

m = time_model_def(model_def, data);

0.000004 seconds (2 allocations: 64 bytes)

suite = make_suite(m);
results = run(suite);

results["evaluation_untyped"]

BenchmarkTools.Trial: 60 samples with 1 evaluation.
 Range (min … max):  68.149 ms … 104.358 ms  ┊ GC (min … max): 0.00% … 0.00
%
 Time  (median):     77.456 ms               ┊ GC (median):    0.00%
 Time  (mean ± σ):   80.173 ms ±   9.858 ms  ┊ GC (mean ± σ):  6.67% ± 8.31
%

    ▆█                █▄                              ▂▄        
  █▆██▁▁▄▁▁▁▁▁▁▁▁▁▁▆▆▁██▆▁▄▁▁▁▁▄▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▆████▄▁▄▁▄ ▁
  68.1 ms         Histogram: frequency by time         94.8 ms <

 Memory estimate: 42.78 MiB, allocs estimate: 1253404.

results["evaluation_typed"]

BenchmarkTools.Trial: 145 samples with 1 evaluation.
 Range (min … max):  29.232 ms … 139.283 ms  ┊ GC (min … max): 0.00% … 0.00
%
 Time  (median):     30.997 ms               ┊ GC (median):    0.00%
 Time  (mean ± σ):   32.506 ms ±   9.228 ms  ┊ GC (mean ± σ):  0.23% ± 1.93
%

    ▁▆█▇▆▃▁                                                     
  ▃▆███████▅▄▃▃▃▃▃▃▁▅▅▄▃▁▁▃▁▃▃▃▁▁▁▃▃▁▃▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▃ ▃
  29.2 ms         Histogram: frequency by time         46.4 ms <

 Memory estimate: 781.86 KiB, allocs estimate: 7.

if WEAVE_ARGS[:include_typed_code]
    typed = typed_code(m)
end

@model function demo4_dotted(n, ::Type{TV}=Vector{Float64}) where {TV}
    m ~ Normal()
    x = TV(undef, n)
    x .~ Normal(m, 1.0)
end

model_def = demo4_dotted
data = (100_000, );

@time model_def(data)();

1.421197 seconds (5.08 M allocations: 375.131 MiB, 6.23% gc time, 0.62% c
ompilation time)

m = time_model_def(model_def, data);

0.000002 seconds (2 allocations: 64 bytes)

suite = make_suite(m);
results = run(suite);

results["evaluation_untyped"]

BenchmarkTools.Trial: 39 samples with 1 evaluation.
 Range (min … max):  108.605 ms … 348.289 ms  ┊ GC (min … max):  9.70% … 9.
23%
 Time  (median):     118.470 ms               ┊ GC (median):    15.38%
 Time  (mean ± σ):   121.407 ms ±  37.585 ms  ┊ GC (mean ± σ):  13.35% ± 3.
15%

  ▆ █                                                            
  █▁█▄▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▃ ▁
  109 ms           Histogram: frequency by time          348 ms <

 Memory estimate: 347.69 MiB, allocs estimate: 963583.

results["evaluation_typed"]

BenchmarkTools.Trial: 61 samples with 1 evaluation.
 Range (min … max):  66.380 ms … 350.632 ms  ┊ GC (min … max):  9.01% … 4.7
7%
 Time  (median):     73.635 ms               ┊ GC (median):    16.29%
 Time  (mean ± σ):   75.751 ms ±  35.996 ms  ┊ GC (mean ± σ):  12.78% ± 3.8
9%

   █                      ▄  ▃                                  
  ▇█▆▆▄▄▁▁▁▃▁▁▁▁▁▁▁▁▁▁▁▁▁▆████▄▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▃ ▁
  66.4 ms         Histogram: frequency by time         84.5 ms <

 Memory estimate: 337.55 MiB, allocs estimate: 399306.

if WEAVE_ARGS[:include_typed_code]
    typed = typed_code(m)
end

[torfjelde]

Because we do the wrong thing and escape the entire body of the method in @model, we cannot handle nested escaping. And since Setfield.lensmacro does the right thing, only escaping what it needs to, we run into issues.

This is a hack to essentially ensure that any escaping will be removed. Note that this doesn't break anything because before we couldn't even use escaped expressions within @model, and so I think it's fine for this PR alone. BUT we should really address this properly, i.e. rewrite @model to only escape what it needs to.

[torfjelde]

Same as above. setmacro correctly escapes, we don't, and so we hack.

[torfjelde]

This is essentially copy-paste from Setfields' implementation, but we add this in here.

I believe this will also be sorted out if we fix the "escape EVERYTHING" in @model since then lens should automatically be assigned a unique symbol expanded.

If not we should just make a PR to Setfield.jl. Using gensym seems like it loses nothing.

[phipsgabler]

Regarding my above comment about setmacro: if we have copied the function to here anyway, we might as well rename it to something more descriptive.

[torfjelde]

Agree with that, though my intention is to make a PR to Setfield.jl now that we seem to be going in the direction of using it.

[torfjelde]

jw3126/Setfield.jl#156

[torfjelde]

@phipsgabler :)

[phipsgabler]

expr -> setmacro(idendity, expr; overwrite=true) deserves its own function name... This is pretty opaque, IMHO, if you haven't just before read the source of Setfield.jl.

[phipsgabler]

Regarding my above comment about setmacro: if we have copied the function to here anyway, we might as well rename it to something more descriptive.

[torfjelde]

@views is now needed since we can have property-access, etc. in x.

[torfjelde]

I just did some benchmarks and it's a bit inconclusive, though the variations are small.

• UntypedVarInfo on demo2 is faster and with fewer allocations on this PR
• TypedVarInfo on demo3 is faster on 0.31.1.

We're talking miniscule differences though, so uncertain if we even care or can conclude anything from this 🤷
But I'm struggling to understand exactly why we get more allocations on the last problem by looking at lowered code 😕 So I think we to investigate a bit more.

EDIT: I figured it out. It was because x[i] were going through Setfield.setindex rather than Setfield.setindex!. Now we're using BangBang.prefermutation and so we'll use setindex! whenever possible. This is also the behavior we currently have.

EDIT 2: See the description of the PR for up-to-date benchmarks.

[torfjelde]

bors r+

[bors]

Build failed:

• test (1.3, ubuntu-latest, x64, 1)

[torfjelde]

bors r+

[bors]

Build failed:

• test (1.3, ubuntu-latest, x64, 2)

[torfjelde]

bors r+

[bors]

Build failed:

• test (1.3, ubuntu-latest, x64, 2)

[devmotion]

I guess this should be

Suggested change

	global sampler_ = __context__.s2ampler
	global sampler_ = __context__.sampler

[torfjelde]

Nono, that was fully intended. I forgot to mention that while you were gone we renamed the sampler field for SamplingContext to s2ampler. I thought it looked cooler 😎

[torfjelde]

bors r+

[bors]

Build failed:

• test (1, ubuntu-latest, x64, 1)

[torfjelde]

bors r+

[bors]

Pull request successfully merged into master.

Build succeeded:

• format
• test (1.3, ubuntu-latest, x64, 1)
• test (1.3, ubuntu-latest, x64, 2)
• test (1, macOS-latest, x64, 2)
• test (1, ubuntu-latest, x64, 1)
• test (1, ubuntu-latest, x64, 2)
• test (1, ubuntu-latest, x86, 2)
• test (1, windows-latest, x64, 2)
• Turing.jl