Use LogDensityProblems instead of gradient_logp

Project.toml

@@ -1,6 +1,6 @@
 name = "Turing"
 uuid = "fce5fe82-541a-59a6-adf8-730c64b5f9a0"
-version = "0.21.10"
+version = "0.21.11"
 
 [deps]
 AbstractMCMC = "80f14c24-f653-4e6a-9b94-39d6b0f70001"
@@ -11,7 +11,6 @@ AdvancedVI = "b5ca4192-6429-45e5-a2d9-87aec30a685c"
 BangBang = "198e06fe-97b7-11e9-32a5-e1d131e6ad66"
 Bijectors = "76274a88-744f-5084-9051-94815aaf08c4"
 DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
-DiffResults = "163ba53b-c6d8-5494-b064-1a9d43ac40c5"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 DistributionsAD = "ced4e74d-a319-5a8a-b0ac-84af2272839c"
 DocStringExtensions = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae"
@@ -20,6 +19,7 @@ EllipticalSliceSampling = "cad2338a-1db2-11e9-3401-43bc07c9ede2"
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
 Libtask = "6f1fad26-d15e-5dc8-ae53-837a1d7b8c9f"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
+LogDensityProblems = "6fdf6af0-433a-55f7-b3ed-c6c6e0b8df7c"
 MCMCChains = "c7f686f2-ff18-58e9-bc7b-31028e88f75d"
 NamedArrays = "86f7a689-2022-50b4-a561-43c23ac3c673"
 Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
@@ -32,7 +32,6 @@ Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 StatsFuns = "4c63d2b9-4356-54db-8cca-17b64c39e42c"
 Tracker = "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c"
-ZygoteRules = "700de1a5-db45-46bc-99cf-38207098b444"
 
 [compat]
 AbstractMCMC = "4"
@@ -43,14 +42,14 @@ AdvancedVI = "0.1"
 BangBang = "0.3"
 Bijectors = "0.8, 0.9, 0.10"
 DataStructures = "0.18"
-DiffResults = "1"
 Distributions = "0.23.3, 0.24, 0.25"
 DistributionsAD = "0.6"
 DocStringExtensions = "0.8, 0.9"
 DynamicPPL = "0.20"
 EllipticalSliceSampling = "0.5, 1"
 ForwardDiff = "0.10.3"
 Libtask = "0.6.7, 0.7"
+LogDensityProblems = "0.12"
 MCMCChains = "5"
 NamedArrays = "0.9"
 Reexport = "0.2, 1"
@@ -60,5 +59,4 @@ SpecialFunctions = "0.7.2, 0.8, 0.9, 0.10, 1, 2"
 StatsBase = "0.32, 0.33"
 StatsFuns = "0.8, 0.9, 1"
 Tracker = "0.2.3"
-ZygoteRules = "0.2"
 julia = "1.6"

docs/src/using-turing/autodiff.md

@@ -10,7 +10,7 @@ title: Automatic Differentiation
 
 Turing supports four packages of automatic differentiation (AD) in the back end during sampling. The default AD backend is [ForwardDiff](https://github.com/JuliaDiff/ForwardDiff.jl) for forward-mode AD. Three reverse-mode AD backends are also supported, namely [Tracker](https://github.com/FluxML/Tracker.jl), [Zygote](https://github.com/FluxML/Zygote.jl) and [ReverseDiff](https://github.com/JuliaDiff/ReverseDiff.jl). `Zygote` and `ReverseDiff` are supported optionally if explicitly loaded by the user with `using Zygote` or `using ReverseDiff` next to `using Turing`.
 
-To switch between the different AD backends, one can call function `Turing.setadbackend(backend_sym)`, where `backend_sym` can be `:forwarddiff` (`ForwardDiff`), `:tracker` (`Tracker`), `:zygote` (`Zygote`) or `:reversediff` (`ReverseDiff.jl`). When using `ReverseDiff`, to compile the tape only once and cache it for later use, the user needs to load [Memoization.jl](https://github.com/marius311/Memoization.jl) first with `using Memoization` then call `Turing.setrdcache(true)`. However, note that the use of caching in certain types of models can lead to incorrect results and/or errors. Models for which the compiled tape can be safely cached are models with fixed size loops and no run-time if statements. Compile-time if statements are fine. To empty the cache, you can call `Turing.emptyrdcache()`.
+To switch between the different AD backends, one can call function `Turing.setadbackend(backend_sym)`, where `backend_sym` can be `:forwarddiff` (`ForwardDiff`), `:tracker` (`Tracker`), `:zygote` (`Zygote`) or `:reversediff` (`ReverseDiff.jl`). When using `ReverseDiff`, to compile the tape only once and cache it for later use, the user has to call `Turing.setrdcache(true)`. However, note that the use of caching in certain types of models can lead to incorrect results and/or errors. Models for which the compiled tape can be safely cached are models with fixed size loops and no run-time if statements. Compile-time if statements are fine.
 
 
 ## Compositional Sampling with Differing AD Modes

src/Turing.jl

@@ -9,6 +9,7 @@ using Tracker: Tracker
 
 import AdvancedVI
 import DynamicPPL: getspace, NoDist, NamedDist
+import LogDensityProblems
 import Random
 
 const PROGRESS = Ref(true)
@@ -37,12 +38,20 @@ function (f::LogDensityFunction)(θ::AbstractVector)
     return getlogp(last(DynamicPPL.evaluate!!(f.model, VarInfo(f.varinfo, f.sampler, θ), f.sampler, f.context)))
 end
 
+# LogDensityProblems interface
+LogDensityProblems.logdensity(f::LogDensityFunction, θ::AbstractVector) = f(θ)
+LogDensityProblems.dimension(f::LogDensityFunction) = length(f.varinfo[f.sampler])
+function LogDensityProblems.capabilities(::Type{<:LogDensityFunction})
+    return LogDensityProblems.LogDensityOrder{0}()
+end
+
 # Standard tag: Improves stacktraces
 # Ref: https://www.stochasticlifestyle.com/improved-forwarddiff-jl-stacktraces-with-package-tags/
 struct TuringTag end
 
 # Allow Turing tag in gradient etc. calls of the log density function
 ForwardDiff.checktag(::Type{ForwardDiff.Tag{TuringTag, V}}, ::LogDensityFunction, ::AbstractArray{V}) where {V} = true
+ForwardDiff.checktag(::Type{ForwardDiff.Tag{TuringTag, V}}, ::Base.Fix1{typeof(LogDensityProblems.logdensity),<:LogDensityFunction}, ::AbstractArray{V}) where {V} = true
 
 # Random probability measures.
 include("stdlib/distributions.jl")

src/contrib/inference/dynamichmc.jl

@@ -19,25 +19,6 @@ DynamicNUTS{AD}(space::Symbol...) where AD = DynamicNUTS{AD, space}()
 
 DynamicPPL.getspace(::DynamicNUTS{<:Any, space}) where {space} = space
 
-# Only define traits for `DynamicNUTS` sampler to avoid type piracy and surprises
-# TODO: Implement generally with `LogDensityProblems`
-const DynamicHMCLogDensity{M<:Model,S<:Sampler{<:DynamicNUTS},V<:AbstractVarInfo} = Turing.LogDensityFunction{V,M,S,DynamicPPL.DefaultContext}
-
-function DynamicHMC.dimension(ℓ::DynamicHMCLogDensity)
-    return length(ℓ.varinfo[ℓ.sampler])
-end
-
-function DynamicHMC.capabilities(::Type{<:DynamicHMCLogDensity})
-    return DynamicHMC.LogDensityOrder{1}()
-end
-
-function DynamicHMC.logdensity_and_gradient(
-    ℓ::DynamicHMCLogDensity,
-    x::AbstractVector,
-)
-    return gradient_logp(x, ℓ.varinfo, ℓ.model, ℓ.sampler, ℓ.context)
-end
-
 """
     DynamicNUTSState
 
@@ -46,9 +27,10 @@ State of the [`DynamicNUTS`](@ref) sampler.
 # Fields
 $(TYPEDFIELDS)
 """
-struct DynamicNUTSState{V<:AbstractVarInfo,C,M,S}
+struct DynamicNUTSState{L,V<:AbstractVarInfo,C,M,S}
+    logdensity::L
     vi::V
-    "Cache of sample, log density, and gradient of log density."
+    "Cache of sample, log density, and gradient of log density evaluation."
     cache::C
     metric::M
     stepsize::S
@@ -61,10 +43,10 @@ function gibbs_state(
     state::DynamicNUTSState,
     varinfo::AbstractVarInfo,
 )
-    # Update the previous evaluation.
-    ℓ = Turing.LogDensityFunction(varinfo, model, spl, DynamicPPL.DefaultContext())
+    # Update the log density function and its cached evaluation.
+    ℓ = LogDensityProblems.ADgradient(Turing.LogDensityFunction(varinfo, model, spl, DynamicPPL.DefaultContext()))
     Q = DynamicHMC.evaluate_ℓ(ℓ, varinfo[spl])
-    return DynamicNUTSState(varinfo, Q, state.metric, state.stepsize)
+    return DynamicNUTSState(ℓ, varinfo, Q, state.metric, state.stepsize)
 end
 
 DynamicPPL.initialsampler(::Sampler{<:DynamicNUTS}) = SampleFromUniform()
@@ -82,10 +64,13 @@ function DynamicPPL.initialstep(
         model(rng, vi, spl)
     end
 
+    # Define log-density function.
+    ℓ = LogDensityProblems.ADgradient(Turing.LogDensityFunction(vi, model, spl, DynamicPPL.DefaultContext()))
+
     # Perform initial step.
     results = DynamicHMC.mcmc_keep_warmup(
         rng,
-        Turing.LogDensityFunction(vi, model, spl, DynamicPPL.DefaultContext()),
+        ℓ,
         0;
         initialization = (q = vi[spl],),
         reporter = DynamicHMC.NoProgressReport(),
@@ -99,7 +84,7 @@ function DynamicPPL.initialstep(
 
     # Create first sample and state.
     sample = Transition(vi)
-    state = DynamicNUTSState(vi, Q, steps.H.κ, steps.ϵ)
+    state = DynamicNUTSState(ℓ, vi, Q, steps.H.κ, steps.ϵ)
 
     return sample, state
 end
@@ -113,7 +98,7 @@ function AbstractMCMC.step(
 )
     # Compute next sample.
     vi = state.vi
-    ℓ = Turing.LogDensityFunction(vi, model, spl, DynamicPPL.DefaultContext())
+    ℓ = state.logdensity
     steps = DynamicHMC.mcmc_steps(
         rng,
         DynamicHMC.NUTS(),
@@ -129,7 +114,7 @@ function AbstractMCMC.step(
 
     # Create next sample and state.
     sample = Transition(vi)
-    newstate = DynamicNUTSState(vi, Q, state.metric, state.stepsize)
+    newstate = DynamicNUTSState(ℓ, vi, Q, state.metric, state.stepsize)
 
     return sample, newstate
 end

src/contrib/inference/sghmc.jl

@@ -41,7 +41,8 @@ function SGHMC{AD}(
     return SGHMC{AD,space,typeof(_learning_rate)}(_learning_rate, _momentum_decay)
 end
 
-struct SGHMCState{V<:AbstractVarInfo, T<:AbstractVector{<:Real}}
+struct SGHMCState{L,V<:AbstractVarInfo, T<:AbstractVector{<:Real}}
+    logdensity::L
     vi::V
     velocity::T
 end
@@ -61,7 +62,8 @@ function DynamicPPL.initialstep(
 
     # Compute initial sample and state.
     sample = Transition(vi)
-    state = SGHMCState(vi, zero(vi[spl]))
+    ℓ = LogDensityProblems.ADgradient(Turing.LogDensityFunction(vi, model, spl, DynamicPPL.DefaultContext()))
+    state = SGHMCState(ℓ, vi, zero(vi[spl]))
 
     return sample, state
 end
@@ -74,9 +76,10 @@ function AbstractMCMC.step(
     kwargs...
 )
     # Compute gradient of log density.
+    ℓ = state.logdensity
     vi = state.vi
     θ = vi[spl]
-    _, grad = gradient_logp(θ, vi, model, spl)
+    grad = last(LogDensityProblems.logdensity_and_gradient(ℓ, θ))
 
     # Update latent variables and velocity according to
     # equation (15) of Chen et al. (2014)
@@ -92,7 +95,7 @@ function AbstractMCMC.step(
 
     # Compute next sample and state.
     sample = Transition(vi)
-    newstate = SGHMCState(vi, newv)
+    newstate = SGHMCState(ℓ, vi, newv)
 
     return sample, newstate
 end
@@ -191,7 +194,8 @@ metadata(t::SGLDTransition) = (lp = t.lp, SGLD_stepsize = t.stepsize)
 
 DynamicPPL.getlogp(t::SGLDTransition) = t.lp
 
-struct SGLDState{V<:AbstractVarInfo}
+struct SGLDState{L,V<:AbstractVarInfo}
+    logdensity::L
     vi::V
     step::Int
 end
@@ -211,7 +215,8 @@ function DynamicPPL.initialstep(
 
     # Create first sample and state.
     sample = SGLDTransition(vi, zero(spl.alg.stepsize(0)))
-    state = SGLDState(vi, 1)
+    ℓ = LogDensityProblems.ADgradient(Turing.LogDensityFunction(vi, model, spl, DynamicPPL.DefaultContext()))
+    state = SGLDState(ℓ, vi, 1)
 
     return sample, state
 end
@@ -224,9 +229,10 @@ function AbstractMCMC.step(
     kwargs...
 )
     # Perform gradient step.
+    ℓ = state.logdensity
     vi = state.vi
     θ = vi[spl]
-    _, grad = gradient_logp(θ, vi, model, spl)
+    grad = last(LogDensityProblems.logdensity_and_gradient(ℓ, θ))
     step = state.step
     stepsize = spl.alg.stepsize(step)
     θ .+= (stepsize / 2) .* grad .+ sqrt(stepsize) .* randn(rng, eltype(θ), length(θ))
@@ -237,7 +243,7 @@ function AbstractMCMC.step(
 
     # Compute next sample and state.
     sample = SGLDTransition(vi, stepsize)
-    newstate = SGLDState(vi, state.step + 1)
+    newstate = SGLDState(ℓ, vi, state.step + 1)
 
     return sample, newstate
 end

src/essential/Essential.jl

@@ -13,22 +13,13 @@ import Bijectors: link, invlink
 using AdvancedVI
 using StatsFuns: logsumexp, softmax
 @reexport using DynamicPPL
-using Requires
 
 import AdvancedPS
-import DiffResults
-import ZygoteRules
+import LogDensityProblems
 
 include("container.jl")
 include("ad.jl")
 
-function __init__()
-    @require ReverseDiff = "37e2e3b7-166d-5795-8a7a-e32c996b4267" begin
-        include("compat/reversediff.jl")
-        export ReverseDiffAD, getrdcache, setrdcache, emptyrdcache
-    end
-end
-
 export  @model,
         @varname,
         generate_observe,
@@ -53,11 +44,13 @@ export  @model,
         ForwardDiffAD,
         TrackerAD,
         ZygoteAD,
+        ReverseDiffAD,
         value,
-        gradient_logp,
         CHUNKSIZE,
         ADBACKEND,
         setchunksize,
+        setrdcache,
+        getrdcache,
         verifygrad,
         @logprob_str,
         @prob_str