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Drop Turing test dep (#267)
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* Remove Turing from Project

* Tidy up testing

* Bump DynamicPPL dep

* Remove ReverseDiff from test deps

* Swap out Turing for DynamicPPL + Distributions in benchmarking

* Bump patch
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@willtebbutt
willtebbutt authored Sep 26, 2024
1 parent b31cb44 commit f4e3ca2
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Showing 4 changed files with 28 additions and 91 deletions.
9 changes: 3 additions & 6 deletions Project.toml
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Original file line number Diff line number Diff line change
@@ -1,7 +1,7 @@
name = "Mooncake"
uuid = "da2b9cff-9c12-43a0-ae48-6db2b0edb7d6"
authors = ["Will Tebbutt, Hong Ge, and contributors"]
version = "0.4.1"
version = "0.4.2"

[deps]
ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"
Expand Down Expand Up @@ -40,7 +40,7 @@ DiffRules = "1"
DiffTests = "0.1"
Distributions = "0.25"
Documenter = "1"
DynamicPPL = "0.28"
DynamicPPL = "0.29"
ExprTools = "0.1"
FillArrays = "1"
Graphs = "1"
Expand All @@ -52,7 +52,6 @@ Setfield = "1"
SpecialFunctions = "2"
StableRNGs = "1"
TemporalGPs = "0.6"
Turing = "0.34"
julia = "1.10"

[extras]
Expand All @@ -68,12 +67,10 @@ JET = "c3a54625-cd67-489e-a8e7-0a5a0ff4e31b"
KernelFunctions = "ec8451be-7e33-11e9-00cf-bbf324bd1392"
LogDensityProblemsAD = "996a588d-648d-4e1f-a8f0-a84b347e47b1"
PDMats = "90014a1f-27ba-587c-ab20-58faa44d9150"
ReverseDiff = "37e2e3b7-166d-5795-8a7a-e32c996b4267"
SpecialFunctions = "276daf66-3868-5448-9aa4-cd146d93841b"
StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
TemporalGPs = "e155a3c4-0841-43e1-8b83-a0e4f03cc18f"
Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0"

[targets]
test = ["AbstractGPs", "BenchmarkTools", "CUDA", "DiffTests", "Distributions", "Documenter", "DynamicPPL", "FillArrays", "KernelFunctions", "JET", "LogDensityProblemsAD", "PDMats", "ReverseDiff", "SpecialFunctions", "StableRNGs", "Test", "Turing", "TemporalGPs"]
test = ["AbstractGPs", "BenchmarkTools", "CUDA", "DiffTests", "Distributions", "Documenter", "DynamicPPL", "FillArrays", "KernelFunctions", "JET", "LogDensityProblemsAD", "PDMats", "SpecialFunctions", "StableRNGs", "Test", "TemporalGPs"]
5 changes: 3 additions & 2 deletions bench/Project.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -3,14 +3,15 @@ AbstractGPs = "99985d1d-32ba-4be9-9821-2ec096f28918"
CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
Chairmarks = "0ca39b1e-fe0b-4e98-acfc-b1656634c4de"
DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
DynamicPPL = "366bfd00-2699-11ea-058f-f148b4cae6d8"
Enzyme = "7da242da-08ed-463a-9acd-ee780be4f1d9"
KernelFunctions = "ec8451be-7e33-11e9-00cf-bbf324bd1392"
Mooncake = "da2b9cff-9c12-43a0-ae48-6db2b0edb7d6"
Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
PrettyTables = "08abe8d2-0d0c-5749-adfa-8a2ac140af0d"
Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
ReverseDiff = "37e2e3b7-166d-5795-8a7a-e32c996b4267"
Mooncake = "da2b9cff-9c12-43a0-ae48-6db2b0edb7d6"
Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0"
UnicodePlots = "b8865327-cd53-5732-bb35-84acbb429228"
Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
19 changes: 10 additions & 9 deletions bench/run_benchmarks.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -6,6 +6,8 @@ using
Chairmarks,
CSV,
DataFrames,
Distributions,
DynamicPPL,
Enzyme,
KernelFunctions,
LinearAlgebra,
Expand All @@ -15,7 +17,6 @@ using
ReverseDiff,
Mooncake,
Test,
Turing,
Zygote

using Mooncake:
Expand Down Expand Up @@ -106,22 +107,22 @@ end
function build_turing_problem()
rng = Xoshiro(123)
model = broadcast_demo(rand(LogNormal(1.5, 0.5), 100_000))
ctx = Turing.DefaultContext()
vi = Turing.SimpleVarInfo(model)
vi_linked = Turing.link(vi, model)
ldp = Turing.LogDensityFunction(vi_linked, model, ctx)
test_function = Base.Fix1(Turing.LogDensityProblems.logdensity, ldp)
d = Turing.LogDensityProblems.dimension(ldp)
ctx = DynamicPPL.DefaultContext()
vi = DynamicPPL.SimpleVarInfo(model)
vi_linked = DynamicPPL.link(vi, model)
ldp = DynamicPPL.LogDensityFunction(vi_linked, model, ctx)
test_function = Base.Fix1(DynamicPPL.LogDensityProblems.logdensity, ldp)
d = DynamicPPL.LogDensityProblems.dimension(ldp)
return test_function, randn(rng, d)
end

run_turing_problem(f::F, x::X) where {F, X} = f(x)

should_run_benchmark(
::Val{:zygote}, ::Base.Fix1{<:typeof(Turing.LogDensityProblems.logdensity)}, x...
::Val{:zygote}, ::Base.Fix1{<:typeof(DynamicPPL.LogDensityProblems.logdensity)}, x...
) = false
should_run_benchmark(
::Val{:enzyme}, ::Base.Fix1{<:typeof(Turing.LogDensityProblems.logdensity)}, x...
::Val{:enzyme}, ::Base.Fix1{<:typeof(DynamicPPL.LogDensityProblems.logdensity)}, x...
) = false

@inline g(x, a, ::Val{N}) where {N} = N > 0 ? g(x * a, a, Val(N-1)) : x
Expand Down
86 changes: 12 additions & 74 deletions test/integration_testing/turing.jl
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Original file line number Diff line number Diff line change
@@ -1,13 +1,4 @@
using Turing

using ReverseDiff
# using CSV, DataFrames, ReverseDiff
# turing_bench_results = DataFrame(
# :name => String[],
# :primal => [],
# :gradient => [],
# :reversediff => [],
# )
using Distributions, DynamicPPL

@model function simple_model()
y ~ Normal()
Expand All @@ -27,7 +18,6 @@ end
@model broadcast_demo(x) = begin
μ ~ truncated(Normal(1, 2), 0.1, 10)
σ ~ truncated(Normal(1, 2), 0.1, 10)

x .~ LogNormal(μ, σ)
end

Expand Down Expand Up @@ -68,7 +58,7 @@ end
θ ~ filldist(Dirichlet(α), D)

log_product = log.(β * θ)
Turing.@addlogprob! sum(log_product[CartesianIndex.(w, doc)])
DynamicPPL.@addlogprob! sum(log_product[CartesianIndex.(w, doc)])
# Above is equivalent to below
#product = β * θ
#dist = [Categorical(product[:,i]) for i in 1:D]
Expand All @@ -88,12 +78,12 @@ end
make_large_model()

function build_turing_problem(rng, model, example=nothing)
ctx = Turing.DefaultContext()
vi = example === nothing ? Turing.SimpleVarInfo(model) : Turing.SimpleVarInfo(example)
vi_linked = Turing.link(vi, model)
ldp = Turing.LogDensityFunction(vi_linked, model, ctx)
test_function = Base.Fix1(Turing.LogDensityProblems.logdensity, ldp)
d = Turing.LogDensityProblems.dimension(ldp)
ctx = DynamicPPL.DefaultContext()
vi = DynamicPPL.SimpleVarInfo(example === nothing ? model : example)
vi_linked = DynamicPPL.link(vi, model)
ldp = DynamicPPL.LogDensityFunction(vi_linked, model, ctx)
test_function = Base.Fix1(DynamicPPL.LogDensityProblems.logdensity, ldp)
d = DynamicPPL.LogDensityProblems.dimension(ldp)
return test_function, randn(rng, d)
end

Expand All @@ -118,64 +108,12 @@ end
# ), doesn't currently work with SimpleVarInfo
],
Any[
(false, "demo_$n", m, Turing.DynamicPPL.TestUtils.rand_prior_true(m)) for
(n, m) in enumerate(Turing.DynamicPPL.TestUtils.DEMO_MODELS)
(false, "demo_$n", m, DynamicPPL.TestUtils.rand_prior_true(m)) for
(n, m) in enumerate(DynamicPPL.TestUtils.DEMO_MODELS)
],
)
@info name
rng = sr(123)
f, x = build_turing_problem(rng, model, ex)
test_rule(sr(123456), f, x; interface_only=true, is_primitive=false, debug_mode=true)

# rule = build_rrule(interp, _typeof((f, x)))
# codualed_args = map(zero_codual, (f, x))
# TestUtils.to_benchmark(rule, codualed_args...)

# primal = @benchmark $f($x)
# gradient = @benchmark(TestUtils.to_benchmark($rule, $codualed_args...))

# println("primal")
# display(primal)
# println()

# println("gradient")
# display(gradient)
# println()

# try
# tape = ReverseDiff.GradientTape(f, x);
# ReverseDiff.gradient!(tape, x);
# result = zeros(size(x));
# ReverseDiff.gradient!(result, tape, x)

# revdiff = @benchmark ReverseDiff.gradient!($result, $tape, $x)
# println("ReverseDiff")
# display(revdiff)
# println()
# @show time(revdiff) / time(primal)
# catch
# display("revdiff failed")
# end

# @show time(gradient) / time(primal)

# @profview run_many_times(10_000, TestUtils.to_benchmark, rule, codualed_args...)

# Profile.clear()
# @profile run_many_times(10_000, TestUtils.to_benchmark, rule, codualed_args...)
# pprof()

# push!(turing_bench_results, (name, primal, gradient, revdiff))
f, x = build_turing_problem(sr(123), model, ex)
test_rule(sr(123456), f, x; interface_only=true, is_primitive=false)
end
end

# function process_turing_bench_results(df::DataFrame)
# out_df = DataFrame(
# :name => df.name,
# :primal => map(time, df.primal),
# :gradient => map(time, df.gradient),
# :reversediff => map(time, df.reversediff),
# )
# CSV.write("turing_benchmarks.csv", out_df)
# end
# process_turing_bench_results(turing_bench_results)

2 comments on commit f4e3ca2

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Registration pull request created: JuliaRegistries/General/116081

Tip: Release Notes

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Release notes:

## Breaking changes

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To add them here just re-invoke and the PR will be updated.

Tagging

After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.

This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:

git tag -a v0.4.2 -m "<description of version>" f4e3ca2ac9e7c8fdd3d881f5ae6df101d875ea76
git push origin v0.4.2

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