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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,54 @@ | ||
| module DynamicPPLForwardDiffExt | ||
|
|
||
| if isdefined(Base, :get_extension) | ||
| using DynamicPPL: ADTypes, DynamicPPL, LogDensityProblems, LogDensityProblemsAD | ||
| using ForwardDiff | ||
| else | ||
| using ..DynamicPPL: ADTypes, DynamicPPL, LogDensityProblems, LogDensityProblemsAD | ||
| using ..ForwardDiff | ||
| end | ||
|
|
||
| getchunksize(::ADTypes.AutoForwardDiff{chunk}) where {chunk} = chunk | ||
|
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| standardtag(::ADTypes.AutoForwardDiff{<:Any,Nothing}) = true | ||
| standardtag(::ADTypes.AutoForwardDiff) = false | ||
|
|
||
| function LogDensityProblemsAD.ADgradient( | ||
| ad::ADTypes.AutoForwardDiff, ℓ::DynamicPPL.LogDensityFunction | ||
| ) | ||
| θ = DynamicPPL.getparams(ℓ) | ||
| f = Base.Fix1(LogDensityProblems.logdensity, ℓ) | ||
|
|
||
| # Define configuration for ForwardDiff. | ||
| tag = if standardtag(ad) | ||
| ForwardDiff.Tag(DynamicPPL.DynamicPPLTag(), eltype(θ)) | ||
| else | ||
| ForwardDiff.Tag(f, eltype(θ)) | ||
| end | ||
| chunk_size = getchunksize(ad) | ||
| chunk = if chunk_size == 0 | ||
| ForwardDiff.Chunk(θ) | ||
| else | ||
| ForwardDiff.Chunk(length(θ), chunk_size) | ||
| end | ||
|
|
||
| return LogDensityProblemsAD.ADgradient(Val(:ForwardDiff), ℓ; chunk, tag, x=θ) | ||
| end | ||
|
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||
| # Allow Turing tag in gradient etc. calls of the log density function | ||
| function ForwardDiff.checktag( | ||
| ::Type{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag,V}}, | ||
| ::DynamicPPL.LogDensityFunction, | ||
| ::AbstractArray{W}, | ||
| ) where {V,W} | ||
| return true | ||
| end | ||
| function ForwardDiff.checktag( | ||
| ::Type{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag,V}}, | ||
| ::Base.Fix1{typeof(LogDensityProblems.logdensity),<:DynamicPPL.LogDensityFunction}, | ||
| ::AbstractArray{W}, | ||
| ) where {V,W} | ||
| return true | ||
| end | ||
|
|
||
| end # module |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,26 @@ | ||
| module DynamicPPLReverseDiffExt | ||
|
|
||
| if isdefined(Base, :get_extension) | ||
| using DynamicPPL: ADTypes, DynamicPPL, LogDensityProblems, LogDensityProblemsAD | ||
| using ReverseDiff | ||
| else | ||
| using ..DynamicPPL: ADTypes, DynamicPPL, LogDensityProblems, LogDensityProblemsAD | ||
| using ..ReverseDiff | ||
| end | ||
|
|
||
| function LogDensityProblemsAD.ADgradient( | ||
| ad::ADTypes.AutoReverseDiff, ℓ::DynamicPPL.LogDensityFunction | ||
| ) | ||
| return LogDensityProblemsAD.ADgradient( | ||
| Val(:ReverseDiff), | ||
| ℓ; | ||
| compile=Val(ad.compile), | ||
| # `getparams` can return `Vector{Real}`, in which case, `ReverseDiff` will initialize the gradients to Integer 0 | ||
| # because at https://github.com/JuliaDiff/ReverseDiff.jl/blob/c982cde5494fc166965a9d04691f390d9e3073fd/src/tracked.jl#L473 | ||
| # `zero(D)` will return 0 when D is Real. | ||
| # here we use `identity` to possibly concretize the type to `Vector{Float64}` in the case of `Vector{Real}`. | ||
| x=map(identity, DynamicPPL.getparams(ℓ)), | ||
| ) | ||
| end | ||
|
|
||
| end # module |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,28 @@ | ||
| @testset "AD: ForwardDiff and ReverseDiff" begin | ||
| @testset "$(m.f)" for m in DynamicPPL.TestUtils.DEMO_MODELS | ||
| f = DynamicPPL.LogDensityFunction(m) | ||
| rand_param_values = DynamicPPL.TestUtils.rand_prior_true(m) | ||
| vns = DynamicPPL.TestUtils.varnames(m) | ||
| varinfos = DynamicPPL.TestUtils.setup_varinfos(m, rand_param_values, vns) | ||
|
|
||
| @testset "$(short_varinfo_name(varinfo))" for varinfo in varinfos | ||
| f = DynamicPPL.LogDensityFunction(m, varinfo) | ||
|
|
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| # use ForwardDiff result as reference | ||
| ad_forwarddiff_f = LogDensityProblemsAD.ADgradient( | ||
| ADTypes.AutoForwardDiff(; chunksize=0), f | ||
| ) | ||
| # convert to `Vector{Float64}` to avoid `ReverseDiff` initializing the gradients to Integer 0 | ||
| # reference: https://github.com/TuringLang/DynamicPPL.jl/pull/571#issuecomment-1924304489 | ||
| θ = convert(Vector{Float64}, varinfo[:]) | ||
| logp, ref_grad = LogDensityProblems.logdensity_and_gradient(ad_forwarddiff_f, θ) | ||
|
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| @testset "ReverseDiff with compile=$compile" for compile in (false, true) | ||
| adtype = ADTypes.AutoReverseDiff(; compile=compile) | ||
| ad_f = LogDensityProblemsAD.ADgradient(adtype, f) | ||
| _, grad = LogDensityProblems.logdensity_and_gradient(ad_f, θ) | ||
| @test grad ≈ ref_grad | ||
| end | ||
| end | ||
| end | ||
| end |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,14 @@ | ||
| @testset "tag" begin | ||
| for chunksize in (0, 1, 10) | ||
| ad = ADTypes.AutoForwardDiff(; chunksize=chunksize) | ||
| standardtag = if !isdefined(Base, :get_extension) | ||
| DynamicPPL.DynamicPPLForwardDiffExt.standardtag | ||
| else | ||
| Base.get_extension(DynamicPPL, :DynamicPPLForwardDiffExt).standardtag | ||
| end | ||
| @test standardtag(ad) | ||
| for tag in (false, 0, 1) | ||
| @test !standardtag(AutoForwardDiff(; chunksize=chunksize, tag=tag)) | ||
| end | ||
| end | ||
| end |
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