ADTypes + ADgradient Performance

Project.toml

@@ -30,15 +30,13 @@ ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
 EnzymeCore = "f151be2c-9106-41f4-ab19-57ee4f262869"
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
 MCMCChains = "c7f686f2-ff18-58e9-bc7b-31028e88f75d"
-ReverseDiff = "37e2e3b7-166d-5795-8a7a-e32c996b4267"
 ZygoteRules = "700de1a5-db45-46bc-99cf-38207098b444"
 
 [extensions]
 DynamicPPLChainRulesCoreExt = ["ChainRulesCore"]
 DynamicPPLEnzymeCoreExt = ["EnzymeCore"]
 DynamicPPLForwardDiffExt = ["ForwardDiff"]
 DynamicPPLMCMCChainsExt = ["MCMCChains"]
-DynamicPPLReverseDiffExt = ["ReverseDiff"]
 DynamicPPLZygoteRulesExt = ["ZygoteRules"]
 
 [compat]
@@ -63,15 +61,6 @@ MacroTools = "0.5.6"
 OrderedCollections = "1"
 Random = "1.6"
 Requires = "1"
-ReverseDiff = "1"
 Test = "1.6"
 ZygoteRules = "0.2"
 julia = "1.10"
-
-[extras]
-ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
-EnzymeCore = "f151be2c-9106-41f4-ab19-57ee4f262869"
-ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
-MCMCChains = "c7f686f2-ff18-58e9-bc7b-31028e88f75d"
-ReverseDiff = "37e2e3b7-166d-5795-8a7a-e32c996b4267"
-ZygoteRules = "700de1a5-db45-46bc-99cf-38207098b444"

src/logdensityfunction.jl

@@ -144,3 +144,19 @@ function LogDensityProblems.capabilities(::Type{<:LogDensityFunction})
 end
 # TODO: should we instead implement and call on `length(f.varinfo)` (at least in the cases where no sampler is involved)?
 LogDensityProblems.dimension(f::LogDensityFunction) = length(getparams(f))
+
+# This is important for performance -- one needs to provide `ADGradient` with a vector of
+# parameters, or DifferentiationInterface will not have sufficient information to e.g.
+# compile a rule for Mooncake (because it won't know the type of the input), or pre-allocate
+# a tape when using ReverseDiff.jl.
+function _make_ad_gradient(ad::ADTypes.AbstractADType, ℓ::LogDensityFunction)
+    x = map(identity, getparams(ℓ)) # ensure we concretise the elements of the params
+    return LogDensityProblemsAD.ADgradient(ad, ℓ; x)
+end
+
+function LogDensityProblemsAD.ADgradient(ad::ADTypes.AutoMooncake, f::LogDensityFunction)
+    return _make_ad_gradient(ad, f)
+end
+function LogDensityProblemsAD.ADgradient(ad::ADTypes.AutoReverseDiff, f::LogDensityFunction)
+    return _make_ad_gradient(ad, f)
+end

test/Project.toml

@@ -6,6 +6,7 @@ Accessors = "7d9f7c33-5ae7-4f3b-8dc6-eff91059b697"
 Bijectors = "76274a88-744f-5084-9051-94815aaf08c4"
 Combinatorics = "861a8166-3701-5b0c-9a16-15d98fcdc6aa"
 Compat = "34da2185-b29b-5c13-b0c7-acf172513d20"
+DifferentiationInterface = "a0c0ee7d-e4b9-4e03-894e-1c5f64a51d63"
 Distributed = "8ba89e20-285c-5b6f-9357-94700520ee1b"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 DistributionsAD = "ced4e74d-a319-5a8a-b0ac-84af2272839c"
@@ -17,6 +18,7 @@ LogDensityProblems = "6fdf6af0-433a-55f7-b3ed-c6c6e0b8df7c"
 LogDensityProblemsAD = "996a588d-648d-4e1f-a8f0-a84b347e47b1"
 MCMCChains = "c7f686f2-ff18-58e9-bc7b-31028e88f75d"
 MacroTools = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09"
+Mooncake = "da2b9cff-9c12-43a0-ae48-6db2b0edb7d6"
 Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 ReverseDiff = "37e2e3b7-166d-5795-8a7a-e32c996b4267"
@@ -43,6 +45,7 @@ LogDensityProblems = "2"
 LogDensityProblemsAD = "1.7.0"
 MCMCChains = "6.0.4"
 MacroTools = "0.5.6"
+Mooncake = "0.4.50"
 ReverseDiff = "1"
 StableRNGs = "1"
 Tracker = "0.2.23"

test/ad.jl

@@ -1,4 +1,4 @@
-@testset "AD: ForwardDiff and ReverseDiff" begin
+@testset "AD: ForwardDiff, ReverseDiff, and Mooncake" begin
     @testset "$(m.f)" for m in DynamicPPL.TestUtils.DEMO_MODELS
         f = DynamicPPL.LogDensityFunction(m)
         rand_param_values = DynamicPPL.TestUtils.rand_prior_true(m)
@@ -17,11 +17,20 @@
             θ = convert(Vector{Float64}, varinfo[:])
             logp, ref_grad = LogDensityProblems.logdensity_and_gradient(ad_forwarddiff_f, θ)
 
-            @testset "ReverseDiff with compile=$compile" for compile in (false, true)
-                adtype = ADTypes.AutoReverseDiff(; compile=compile)
-                ad_f = LogDensityProblemsAD.ADgradient(adtype, f)
-                _, grad = LogDensityProblems.logdensity_and_gradient(ad_f, θ)
-                @test grad ≈ ref_grad
+            @testset "$adtype" for adtype in [
+                ADTypes.AutoReverseDiff(; compile=false),
+                ADTypes.AutoReverseDiff(; compile=true),
+                ADTypes.AutoMooncake(; config=nothing),
+            ]
+                # Mooncake can't currently handle something that is going on in
+                # SimpleVarInfo{<:VarNamedVector}. Disable all SimpleVarInfo tests for now.
+                if adtype isa ADTypes.AutoMooncake && varinfo isa DynamicPPL.SimpleVarInfo
+                    @test_broken 1 == 0
+                else
+                    ad_f = LogDensityProblemsAD.ADgradient(adtype, f)
+                    _, grad = LogDensityProblems.logdensity_and_gradient(ad_f, θ)
+                    @test grad ≈ ref_grad
+                end
             end
         end
     end

test/runtests.jl

@@ -4,13 +4,15 @@ using DynamicPPL
 using AbstractMCMC
 using AbstractPPL
 using Bijectors
+using DifferentiationInterface
 using Distributions
 using DistributionsAD
 using Documenter
 using ForwardDiff
 using LogDensityProblems, LogDensityProblemsAD
 using MacroTools
 using MCMCChains
+using Mooncake: Mooncake
 using Tracker
 using ReverseDiff
 using Zygote