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Test Enzyme on demo models #813

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0d0d87b
Test Enzyme
penelopeysm Mar 19, 2025
bb6c911
import enzyme
penelopeysm Mar 19, 2025
d6bf74a
test on all varinfos
penelopeysm Mar 19, 2025
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1 change: 1 addition & 0 deletions test/Project.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -12,6 +12,7 @@ Distributed = "8ba89e20-285c-5b6f-9357-94700520ee1b"
Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
DistributionsAD = "ced4e74d-a319-5a8a-b0ac-84af2272839c"
Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
Enzyme = "7da242da-08ed-463a-9acd-ee780be4f1d9"
EnzymeCore = "f151be2c-9106-41f4-ab19-57ee4f262869"
ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
JET = "c3a54625-cd67-489e-a8e7-0a5a0ff4e31b"
Expand Down
227 changes: 115 additions & 112 deletions test/ad.jl
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@@ -1,22 +1,25 @@
using DynamicPPL: LogDensityFunction
using EnzymeCore: set_runtime_activity, Forward, Reverse

@testset "Automatic differentiation" begin
# Used as the ground truth that others are compared against.
ref_adtype = AutoForwardDiff()
test_adtypes = [
AutoReverseDiff(; compile=false),
AutoReverseDiff(; compile=true),
AutoMooncake(; config=nothing),
# AutoReverseDiff(; compile=false),
# AutoReverseDiff(; compile=true),
# AutoMooncake(; config=nothing),
AutoEnzyme(; mode=set_runtime_activity(Forward, true)),
AutoEnzyme(; mode=set_runtime_activity(Reverse, true)),
]

@testset "Unsupported backends" begin
@model demo() = x ~ Normal()
@test_logs (:warn, r"not officially supported") LogDensityFunction(
demo(); adtype=AutoZygote()
)
end
# @testset "Unsupported backends" begin
# @model demo() = x ~ Normal()
# @test_logs (:warn, r"not officially supported") LogDensityFunction(
# demo(); adtype=AutoZygote()
# )
# end

@testset "Correctness: ForwardDiff, ReverseDiff, and Mooncake" begin
@testset "Correctness on supported AD backends" begin
@testset "$(m.f)" for m in DynamicPPL.TestUtils.DEMO_MODELS
rand_param_values = DynamicPPL.TestUtils.rand_prior_true(m)
vns = DynamicPPL.TestUtils.varnames(m)
Expand Down Expand Up @@ -66,106 +69,106 @@ using DynamicPPL: LogDensityFunction
end
end

@testset "Turing#2151: ReverseDiff compilation & eltype(vi, spl)" begin
# Failing model
t = 1:0.05:8
σ = 0.3
y = @. rand(sin(t) + Normal(0, σ))
@model function state_space(y, TT, ::Type{T}=Float64) where {T}
# Priors
α ~ Normal(y[1], 0.001)
τ ~ Exponential(1)
η ~ filldist(Normal(0, 1), TT - 1)
σ ~ Exponential(1)
# create latent variable
x = Vector{T}(undef, TT)
x[1] = α
for t in 2:TT
x[t] = x[t - 1] + η[t - 1] * τ
end
# measurement model
y ~ MvNormal(x, σ^2 * I)
return x
end
model = state_space(y, length(t))

# Dummy sampling algorithm for testing. The test case can only be replicated
# with a custom sampler, it doesn't work with SampleFromPrior(). We need to
# overload assume so that model evaluation doesn't fail due to a lack
# of implementation
struct MyEmptyAlg end
DynamicPPL.assume(
::Random.AbstractRNG, ::DynamicPPL.Sampler{MyEmptyAlg}, dist, vn, vi
) = DynamicPPL.assume(dist, vn, vi)

# Compiling the ReverseDiff tape used to fail here
spl = Sampler(MyEmptyAlg())
vi = VarInfo(model)
ldf = LogDensityFunction(
model, vi, SamplingContext(spl); adtype=AutoReverseDiff(; compile=true)
)
@test LogDensityProblems.logdensity_and_gradient(ldf, vi[:]) isa Any
end

# Test that various different ways of specifying array types as arguments work with all
# ADTypes.
@testset "Array argument types" begin
test_m = randn(2, 3)

function eval_logp_and_grad(model, m, adtype)
ldf = LogDensityFunction(model(); adtype=adtype)
return LogDensityProblems.logdensity_and_gradient(ldf, m[:])
end

@model function scalar_matrix_model(::Type{T}=Float64) where {T<:Real}
m = Matrix{T}(undef, 2, 3)
return m ~ filldist(MvNormal(zeros(2), I), 3)
end

scalar_matrix_model_reference = eval_logp_and_grad(
scalar_matrix_model, test_m, ref_adtype
)

@model function matrix_model(::Type{T}=Matrix{Float64}) where {T}
m = T(undef, 2, 3)
return m ~ filldist(MvNormal(zeros(2), I), 3)
end

matrix_model_reference = eval_logp_and_grad(matrix_model, test_m, ref_adtype)

@model function scalar_array_model(::Type{T}=Float64) where {T<:Real}
m = Array{T}(undef, 2, 3)
return m ~ filldist(MvNormal(zeros(2), I), 3)
end

scalar_array_model_reference = eval_logp_and_grad(
scalar_array_model, test_m, ref_adtype
)

@model function array_model(::Type{T}=Array{Float64}) where {T}
m = T(undef, 2, 3)
return m ~ filldist(MvNormal(zeros(2), I), 3)
end

array_model_reference = eval_logp_and_grad(array_model, test_m, ref_adtype)

@testset "$adtype" for adtype in test_adtypes
scalar_matrix_model_logp_and_grad = eval_logp_and_grad(
scalar_matrix_model, test_m, adtype
)
@test scalar_matrix_model_logp_and_grad[1] ≈ scalar_matrix_model_reference[1]
@test scalar_matrix_model_logp_and_grad[2] ≈ scalar_matrix_model_reference[2]
matrix_model_logp_and_grad = eval_logp_and_grad(matrix_model, test_m, adtype)
@test matrix_model_logp_and_grad[1] ≈ matrix_model_reference[1]
@test matrix_model_logp_and_grad[2] ≈ matrix_model_reference[2]
scalar_array_model_logp_and_grad = eval_logp_and_grad(
scalar_array_model, test_m, adtype
)
@test scalar_array_model_logp_and_grad[1] ≈ scalar_array_model_reference[1]
@test scalar_array_model_logp_and_grad[2] ≈ scalar_array_model_reference[2]
array_model_logp_and_grad = eval_logp_and_grad(array_model, test_m, adtype)
@test array_model_logp_and_grad[1] ≈ array_model_reference[1]
@test array_model_logp_and_grad[2] ≈ array_model_reference[2]
end
end
# @testset "Turing#2151: ReverseDiff compilation & eltype(vi, spl)" begin
# # Failing model
# t = 1:0.05:8
# σ = 0.3
# y = @. rand(sin(t) + Normal(0, σ))
# @model function state_space(y, TT, ::Type{T}=Float64) where {T}
# # Priors
# α ~ Normal(y[1], 0.001)
# τ ~ Exponential(1)
# η ~ filldist(Normal(0, 1), TT - 1)
# σ ~ Exponential(1)
# # create latent variable
# x = Vector{T}(undef, TT)
# x[1] = α
# for t in 2:TT
# x[t] = x[t - 1] + η[t - 1] * τ
# end
# # measurement model
# y ~ MvNormal(x, σ^2 * I)
# return x
# end
# model = state_space(y, length(t))
#
# # Dummy sampling algorithm for testing. The test case can only be replicated
# # with a custom sampler, it doesn't work with SampleFromPrior(). We need to
# # overload assume so that model evaluation doesn't fail due to a lack
# # of implementation
# struct MyEmptyAlg end
# DynamicPPL.assume(
# ::Random.AbstractRNG, ::DynamicPPL.Sampler{MyEmptyAlg}, dist, vn, vi
# ) = DynamicPPL.assume(dist, vn, vi)
#
# # Compiling the ReverseDiff tape used to fail here
# spl = Sampler(MyEmptyAlg())
# vi = VarInfo(model)
# ldf = LogDensityFunction(
# model, vi, SamplingContext(spl); adtype=AutoReverseDiff(; compile=true)
# )
# @test LogDensityProblems.logdensity_and_gradient(ldf, vi[:]) isa Any
# end
#
# # Test that various different ways of specifying array types as arguments work with all
# # ADTypes.
# @testset "Array argument types" begin
# test_m = randn(2, 3)
#
# function eval_logp_and_grad(model, m, adtype)
# ldf = LogDensityFunction(model(); adtype=adtype)
# return LogDensityProblems.logdensity_and_gradient(ldf, m[:])
# end
#
# @model function scalar_matrix_model(::Type{T}=Float64) where {T<:Real}
# m = Matrix{T}(undef, 2, 3)
# return m ~ filldist(MvNormal(zeros(2), I), 3)
# end
#
# scalar_matrix_model_reference = eval_logp_and_grad(
# scalar_matrix_model, test_m, ref_adtype
# )
#
# @model function matrix_model(::Type{T}=Matrix{Float64}) where {T}
# m = T(undef, 2, 3)
# return m ~ filldist(MvNormal(zeros(2), I), 3)
# end
#
# matrix_model_reference = eval_logp_and_grad(matrix_model, test_m, ref_adtype)
#
# @model function scalar_array_model(::Type{T}=Float64) where {T<:Real}
# m = Array{T}(undef, 2, 3)
# return m ~ filldist(MvNormal(zeros(2), I), 3)
# end
#
# scalar_array_model_reference = eval_logp_and_grad(
# scalar_array_model, test_m, ref_adtype
# )
#
# @model function array_model(::Type{T}=Array{Float64}) where {T}
# m = T(undef, 2, 3)
# return m ~ filldist(MvNormal(zeros(2), I), 3)
# end
#
# array_model_reference = eval_logp_and_grad(array_model, test_m, ref_adtype)
#
# @testset "$adtype" for adtype in test_adtypes
# scalar_matrix_model_logp_and_grad = eval_logp_and_grad(
# scalar_matrix_model, test_m, adtype
# )
# @test scalar_matrix_model_logp_and_grad[1] ≈ scalar_matrix_model_reference[1]
# @test scalar_matrix_model_logp_and_grad[2] ≈ scalar_matrix_model_reference[2]
# matrix_model_logp_and_grad = eval_logp_and_grad(matrix_model, test_m, adtype)
# @test matrix_model_logp_and_grad[1] ≈ matrix_model_reference[1]
# @test matrix_model_logp_and_grad[2] ≈ matrix_model_reference[2]
# scalar_array_model_logp_and_grad = eval_logp_and_grad(
# scalar_array_model, test_m, adtype
# )
# @test scalar_array_model_logp_and_grad[1] ≈ scalar_array_model_reference[1]
# @test scalar_array_model_logp_and_grad[2] ≈ scalar_array_model_reference[2]
# array_model_logp_and_grad = eval_logp_and_grad(array_model, test_m, adtype)
# @test array_model_logp_and_grad[1] ≈ array_model_reference[1]
# @test array_model_logp_and_grad[2] ≈ array_model_reference[2]
# end
# end
end
99 changes: 50 additions & 49 deletions test/runtests.jl
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Expand Up @@ -8,6 +8,7 @@ using DifferentiationInterface
using Distributions
using DistributionsAD
using Documenter
using Enzyme: Enzyme
using ForwardDiff
using LogDensityProblems
using MacroTools
Expand Down Expand Up @@ -45,60 +46,60 @@ include("test_util.jl")
# groups are chosen to make both groups take roughly the same amount of
# time, but beyond that there is no particular reason for the split.
if GROUP == "All" || GROUP == "Group1"
if AQUA
include("Aqua.jl")
end
include("utils.jl")
include("compiler.jl")
include("varnamedvector.jl")
include("varinfo.jl")
include("simple_varinfo.jl")
include("model.jl")
include("sampler.jl")
include("independence.jl")
include("distribution_wrappers.jl")
include("logdensityfunction.jl")
include("linking.jl")
include("serialization.jl")
include("pointwise_logdensities.jl")
include("lkj.jl")
include("contexts.jl")
include("context_implementations.jl")
include("threadsafe.jl")
include("debug_utils.jl")
include("deprecated.jl")
# if AQUA
# include("Aqua.jl")
# end
# include("utils.jl")
# include("compiler.jl")
# include("varnamedvector.jl")
# include("varinfo.jl")
# include("simple_varinfo.jl")
# include("model.jl")
# include("sampler.jl")
# include("independence.jl")
# include("distribution_wrappers.jl")
# include("logdensityfunction.jl")
# include("linking.jl")
# include("serialization.jl")
# include("pointwise_logdensities.jl")
# include("lkj.jl")
# include("contexts.jl")
# include("context_implementations.jl")
# include("threadsafe.jl")
# include("debug_utils.jl")
# include("deprecated.jl")
end

if GROUP == "All" || GROUP == "Group2"
@testset "compat" begin
include(joinpath("compat", "ad.jl"))
end
@testset "extensions" begin
include("ext/DynamicPPLMCMCChainsExt.jl")
include("ext/DynamicPPLJETExt.jl")
end
# @testset "compat" begin
# include(joinpath("compat", "ad.jl"))
# end
# @testset "extensions" begin
# include("ext/DynamicPPLMCMCChainsExt.jl")
# include("ext/DynamicPPLJETExt.jl")
# end
@testset "ad" begin
include("ext/DynamicPPLForwardDiffExt.jl")
include("ext/DynamicPPLMooncakeExt.jl")
# include("ext/DynamicPPLForwardDiffExt.jl")
# include("ext/DynamicPPLMooncakeExt.jl")
include("ad.jl")
end
@testset "prob and logprob macro" begin
@test_throws ErrorException prob"..."
@test_throws ErrorException logprob"..."
end
@testset "doctests" begin
DocMeta.setdocmeta!(
DynamicPPL,
:DocTestSetup,
:(using DynamicPPL, Distributions);
recursive=true,
)
doctestfilters = [
# Ignore the source of a warning in the doctest output, since this is dependent on host.
# This is a line that starts with "└ @ " and ends with the line number.
r"└ @ .+:[0-9]+",
]
doctest(DynamicPPL; manual=false, doctestfilters=doctestfilters)
end
# @testset "prob and logprob macro" begin
# @test_throws ErrorException prob"..."
# @test_throws ErrorException logprob"..."
# end
# @testset "doctests" begin
# DocMeta.setdocmeta!(
# DynamicPPL,
# :DocTestSetup,
# :(using DynamicPPL, Distributions);
# recursive=true,
# )
# doctestfilters = [
# # Ignore the source of a warning in the doctest output, since this is dependent on host.
# # This is a line that starts with "└ @ " and ends with the line number.
# r"└ @ .+:[0-9]+",
# ]
# doctest(DynamicPPL; manual=false, doctestfilters=doctestfilters)
# end
end
end
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