Fast Log Density Function

HISTORY.md

@@ -1,5 +1,15 @@
 # DynamicPPL Changelog
 
+## 0.38.10
+
+Added `DynamicPPL.Experimental.FastLDF`, a version of `LogDensityFunction` that provides performance improvements on the order of 2–10× for both model evaluation as well as automatic differentiation.
+Exact speedups depend on the model size: larger models have less significant speedups because the bulk of the work is done in calls to `logpdf`.
+
+Please note that `FastLDF` is currently considered internal and its API may change without warning.
+We intend to replace `LogDensityFunction` with `FastLDF` in a release in the near future, but until then we recommend not using it.
+
+For more information about `FastLDF`, please see https://github.com/TuringLang/DynamicPPL.jl/pull/1113 as well as the `src/fasteval.jl` file, which contains extensive comments.
+
 ## 0.38.9
 
 Remove warning when using Enzyme as the AD backend.

Project.toml

@@ -1,6 +1,6 @@
 name = "DynamicPPL"
 uuid = "366bfd00-2699-11ea-058f-f148b4cae6d8"
-version = "0.38.9"
+version = "0.38.10"
 
 [deps]
 ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"

ext/DynamicPPLEnzymeCoreExt.jl

@@ -1,16 +1,15 @@
 module DynamicPPLEnzymeCoreExt
 
-if isdefined(Base, :get_extension)
-    using DynamicPPL: DynamicPPL
-    using EnzymeCore
-else
-    using ..DynamicPPL: DynamicPPL
-    using ..EnzymeCore
-end
+using DynamicPPL: DynamicPPL
+using EnzymeCore
 
 # Mark is_transformed as having 0 derivative. The `nothing` return value is not significant, Enzyme
 # only checks whether such a method exists, and never runs it.
 @inline EnzymeCore.EnzymeRules.inactive(::typeof(DynamicPPL.is_transformed), args...) =
     nothing
+# Likewise for get_range_and_linked.
+@inline EnzymeCore.EnzymeRules.inactive(
+    ::typeof(DynamicPPL.Experimental.get_range_and_linked), args...
+) = nothing
 
 end

ext/DynamicPPLMooncakeExt.jl

@@ -5,5 +5,8 @@ using Mooncake: Mooncake
 
 # This is purely an optimisation.
 Mooncake.@zero_derivative Mooncake.DefaultCtx Tuple{typeof(is_transformed),Vararg}
+Mooncake.@zero_derivative Mooncake.DefaultCtx Tuple{
+    typeof(DynamicPPL.Experimental.get_range_and_linked),Vararg
+}
 
 end # module

src/compiler.jl

@@ -718,14 +718,15 @@ end
 # TODO(mhauru) matchingvalue has methods that can accept both types and values. Why?
 # TODO(mhauru) This function needs a more comprehensive docstring.
 """
-    matchingvalue(vi, value)
+    matchingvalue(param_eltype, value)
 
-Convert the `value` to the correct type for the `vi` object.
+Convert the `value` to the correct type, given the element type of the parameters
+being used to evaluate the model.
 """
-function matchingvalue(vi, value)
+function matchingvalue(param_eltype, value)
     T = typeof(value)
     if hasmissing(T)
-        _value = convert(get_matching_type(vi, T), value)
+        _value = convert(get_matching_type(param_eltype, T), value)
         # TODO(mhauru) Why do we make a deepcopy, even though in the !hasmissing branch we
         # are happy to return `value` as-is?
         if _value === value
@@ -738,29 +739,30 @@ function matchingvalue(vi, value)
     end
 end
 
-function matchingvalue(vi, value::FloatOrArrayType)
-    return get_matching_type(vi, value)
+function matchingvalue(param_eltype, value::FloatOrArrayType)
+    return get_matching_type(param_eltype, value)
 end
-function matchingvalue(vi, ::TypeWrap{T}) where {T}
-    return TypeWrap{get_matching_type(vi, T)}()
+function matchingvalue(param_eltype, ::TypeWrap{T}) where {T}
+    return TypeWrap{get_matching_type(param_eltype, T)}()
 end
 
 # TODO(mhauru) This function needs a more comprehensive docstring. What is it for?
 """
-    get_matching_type(vi, ::TypeWrap{T}) where {T}
+    get_matching_type(param_eltype, ::TypeWrap{T}) where {T}
 
-Get the specialized version of type `T` for `vi`.
+Get the specialized version of type `T`, given an element type of the parameters
+being used to evaluate the model.
 """
 get_matching_type(_, ::Type{T}) where {T} = T
-function get_matching_type(vi, ::Type{<:Union{Missing,AbstractFloat}})
-    return Union{Missing,float_type_with_fallback(eltype(vi))}
+function get_matching_type(param_eltype, ::Type{<:Union{Missing,AbstractFloat}})
+    return Union{Missing,float_type_with_fallback(param_eltype)}
 end
-function get_matching_type(vi, ::Type{<:AbstractFloat})
-    return float_type_with_fallback(eltype(vi))
+function get_matching_type(param_eltype, ::Type{<:AbstractFloat})
+    return float_type_with_fallback(param_eltype)
 end
-function get_matching_type(vi, ::Type{<:Array{T,N}}) where {T,N}
-    return Array{get_matching_type(vi, T),N}
+function get_matching_type(param_eltype, ::Type{<:Array{T,N}}) where {T,N}
+    return Array{get_matching_type(param_eltype, T),N}
 end
-function get_matching_type(vi, ::Type{<:Array{T}}) where {T}
-    return Array{get_matching_type(vi, T)}
+function get_matching_type(param_eltype, ::Type{<:Array{T}}) where {T}
+    return Array{get_matching_type(param_eltype, T)}
 end

src/contexts/init.jl

@@ -102,7 +102,7 @@ struct InitFromParams{P,S<:Union{AbstractInitStrategy,Nothing}} <: AbstractInitS
     function InitFromParams(
         params::NamedTuple, fallback::Union{AbstractInitStrategy,Nothing}=InitFromPrior()
     )
-        return InitFromParams(to_varname_dict(params), fallback)
+        return new{typeof(params),typeof(fallback)}(params, fallback)
     end
 end
 function init(rng::Random.AbstractRNG, vn::VarName, dist::Distribution, p::InitFromParams)

src/experimental.jl

@@ -2,6 +2,8 @@ module Experimental
 
 using DynamicPPL: DynamicPPL
 
+include("fasteval.jl")
+
 # This file only defines the names of the functions, and their docstrings. The actual implementations are in `ext/DynamicPPLJETExt.jl`, since we don't want to depend on JET.jl other than as a weak dependency.
 """
     is_suitable_varinfo(model::Model, varinfo::AbstractVarInfo; kwargs...)