Add adtype to DynamicPPL.Model

HISTORY.md

@@ -128,6 +128,22 @@ This release removes the feature of `VarInfo` where it kept track of which varia
 
 **Other changes**
 
+### Models now store AD backend types
+
+In `DynamicPPL.Model`, an extra field `adtype::Union{Nothing,ADTypes.AbstractADType}` has been added. This field is used to store the AD backend which should be used when calculating gradients of the log density.
+
+The field can be set by passing an extra argument to the `Model` constructor, but more realistically, it is likely that users will want to manually set the `adtype` field on an existing model:
+
+```julia
+@model f() = ...
+model = f()
+model_with_adtype = setadtype(model, AutoForwardDiff())
+```
+
+As far as `DynamicPPL.Model` is concerned, this field does not actually have any effect.
+However, when a `LogDensityFunction` is constructed from said model, it will inherit the `adtype` field from the model.
+See below for more information on `LogDensityFunction`.
+
 ### `LogDensityProblems` interface
 
 LogDensityProblemsAD is now removed as a dependency.
@@ -136,7 +152,8 @@ Instead of constructing a `LogDensityProblemAD.ADgradient` object, we now direct
 Note that if you wish, you can still construct an `ADgradient` out of a `LogDensityFunction` object (there is nothing preventing this).
 
 However, in this version, `LogDensityFunction` now takes an extra AD type argument.
-If this argument is not provided, the behaviour is exactly the same as before, i.e. you can calculate `logdensity` but not its gradient.
+By default, this AD type is inherited from the model that the `LogDensityFunction` is constructed from.
+If the model does not have an AD type, or if the argument is explicitly set to `nothing`, the behaviour is exactly the same as before, i.e. you can calculate `logdensity` but not its gradient.
 However, if you do pass an AD type, that will allow you to calculate the gradient as well.
 You may thus find that it is easier to instead do this:
 

src/DynamicPPL.jl

@@ -84,6 +84,7 @@ export AbstractVarInfo,
     getargnames,
     extract_priors,
     values_as_in_model,
+    setadtype,
     # Samplers
     Sampler,
     SampleFromPrior,

src/logdensityfunction.jl

@@ -111,7 +111,7 @@ struct LogDensityFunction{
         model::Model,
         varinfo::AbstractVarInfo=VarInfo(model),
         context::AbstractContext=leafcontext(model.context);
-        adtype::Union{ADTypes.AbstractADType,Nothing}=nothing,
+        adtype::Union{ADTypes.AbstractADType,Nothing}=model.adtype,
     )
         if adtype === nothing
             prep = nothing

src/model.jl

@@ -1,17 +1,23 @@
 """
-    struct Model{F,argnames,defaultnames,missings,Targs,Tdefaults,Ctx<:AbstractContext}
+struct Model{F,argnames,defaultnames,missings,Targs,Tdefaults,Ctx<:AbstractContext,TAD<:Union{Nothing,ADTypes.AbstractADType}}
         f::F
         args::NamedTuple{argnames,Targs}
         defaults::NamedTuple{defaultnames,Tdefaults}
         context::Ctx=DefaultContext()
+        adtype::TAD=nothing
     end
 
-A `Model` struct with model evaluation function of type `F`, arguments of names `argnames`
-types `Targs`, default arguments of names `defaultnames` with types `Tdefaults`, missing
-arguments `missings`, and evaluation context of type `Ctx`.
+A `Model` struct contains the following fields:
+- `f`, a model evaluation function of type `F`
+- `args`, arguments of names `argnames` with types `Targs`
+- `defaults`, default arguments of names `defaultnames` with types `Tdefaults`
+- `context`, an evaluation context of type `Ctx`
+- `adtype`, which can be nothing, or an automatic differentiation backend of type `TAD`
 
+Its missing arguments are also stored as a type parameter `missings`.
 Here `argnames`, `defaultargnames`, and `missings` are tuples of symbols, e.g. `(:a, :b)`.
-`context` is by default `DefaultContext()`.
+
+`context` is by default `DefaultContext()`, and `adtype` is by default `nothing`.
 
 An argument with a type of `Missing` will be in `missings` by default. However, in
 non-traditional use-cases `missings` can be defined differently. All variables in `missings`
@@ -33,12 +39,21 @@ julia> Model{(:y,)}(f, (x = 1.0, y = 2.0), (x = 42,)) # with special definition
 Model{typeof(f),(:x, :y),(:x,),(:y,),Tuple{Float64,Float64},Tuple{Int64}}(f, (x = 1.0, y = 2.0), (x = 42,))
 ```
 """
-struct Model{F,argnames,defaultnames,missings,Targs,Tdefaults,Ctx<:AbstractContext} <:
-       AbstractProbabilisticProgram
+struct Model{
+    F,
+    argnames,
+    defaultnames,
+    missings,
+    Targs,
+    Tdefaults,
+    Ctx<:AbstractContext,
+    TAD<:Union{Nothing,ADTypes.AbstractADType},
+} <: AbstractProbabilisticProgram
     f::F
     args::NamedTuple{argnames,Targs}
     defaults::NamedTuple{defaultnames,Tdefaults}
     context::Ctx
+    adtype::TAD
 
     @doc """
         Model{missings}(f, args::NamedTuple, defaults::NamedTuple)
@@ -51,9 +66,10 @@ struct Model{F,argnames,defaultnames,missings,Targs,Tdefaults,Ctx<:AbstractConte
         args::NamedTuple{argnames,Targs},
         defaults::NamedTuple{defaultnames,Tdefaults},
         context::Ctx=DefaultContext(),
-    ) where {missings,F,argnames,Targs,defaultnames,Tdefaults,Ctx}
-        return new{F,argnames,defaultnames,missings,Targs,Tdefaults,Ctx}(
-            f, args, defaults, context
+        adtype::TAD=nothing,
+    ) where {missings,F,argnames,Targs,defaultnames,Tdefaults,Ctx,TAD}
+        return new{F,argnames,defaultnames,missings,Targs,Tdefaults,Ctx,TAD}(
+            f, args, defaults, context, adtype
         )
     end
 end
@@ -71,22 +87,40 @@ model with different arguments.
     args::NamedTuple{argnames,Targs},
     defaults::NamedTuple{kwargnames,Tkwargs},
     context::AbstractContext=DefaultContext(),
-) where {F,argnames,Targs,kwargnames,Tkwargs}
+    adtype::TAD=nothing,
+) where {F,argnames,Targs,kwargnames,Tkwargs,TAD}
     missing_args = Tuple(
         name for (name, typ) in zip(argnames, Targs.types) if typ <: Missing
     )
     missing_kwargs = Tuple(
         name for (name, typ) in zip(kwargnames, Tkwargs.types) if typ <: Missing
     )
-    return :(Model{$(missing_args..., missing_kwargs...)}(f, args, defaults, context))
+    return :(Model{$(missing_args..., missing_kwargs...)}(
+        f, args, defaults, context, adtype
+    ))
 end
 
 function Model(f, args::NamedTuple, context::AbstractContext=DefaultContext(); kwargs...)
-    return Model(f, args, NamedTuple(kwargs), context)
+    return Model(f, args, NamedTuple(kwargs), context, nothing)
 end
 
+"""
+    Model(model::Model, adtype::Union{Nothing,ADTypes.AbstractADType})
+
+Create a new model with the same evaluation function and arguments as `model`, but with
+automatic differentiation backend `adtype`.
+"""
+function Model(model::Model, adtype::Union{Nothing,ADTypes.AbstractADType})
+    return Model(model.f, model.args, model.defaults, model.context, adtype)
+end
+
+"""
+    contextualize(model::Model, context::AbstractContext)
+
+Set the context of `model` to `context`.
+"""
 function contextualize(model::Model, context::AbstractContext)
-    return Model(model.f, model.args, model.defaults, context)
+    return Model(model.f, model.args, model.defaults, context, model.adtype)
 end
 
 """

test/logdensityfunction.jl

@@ -9,6 +9,22 @@ using Test, DynamicPPL, ADTypes, LogDensityProblems, ForwardDiff
     end
 end
 
+@testset "AD type forwarding from model" begin
+    @model demo_simple() = x ~ Normal()
+    model = Model(demo_simple(), AutoForwardDiff())
+    ldf = DynamicPPL.LogDensityFunction(model)
+    # Check that the model's AD type is forwarded to the LDF
+    # Note: can't check ldf.adtype == AutoForwardDiff() because `tweak_adtype`
+    # modifies the underlying parameters a bit, so just check that it is still
+    # the correct backend package.
+    @test ldf.adtype isa AutoForwardDiff
+    # Check that the gradient can be evaluated on the resulting LDF
+    @test LogDensityProblems.capabilities(typeof(ldf)) ==
+        LogDensityProblems.LogDensityOrder{1}()
+    @test LogDensityProblems.logdensity(ldf, [1.0]) isa Any
+    @test LogDensityProblems.logdensity_and_gradient(ldf, [1.0]) isa Any
+end
+
 @testset "LogDensityFunction" begin
     @testset "$(nameof(model))" for model in DynamicPPL.TestUtils.DEMO_MODELS
         example_values = DynamicPPL.TestUtils.rand_prior_true(model)

test/model.jl

@@ -100,6 +100,16 @@ const GDEMO_DEFAULT = DynamicPPL.TestUtils.demo_assume_observe_literal()
         end
     end
 
+    @testset "model adtype" begin
+        # Check that adtype can be set and unset
+        @model demo_adtype() = x ~ Normal()
+        adtype = AutoForwardDiff()
+        model = Model(demo_adtype(), adtype)
+        @test model.adtype == adtype
+        model = Model(model, nothing)
+        @test model.adtype === nothing
+    end
+
     @testset "model de/conditioning" begin
         @model function demo_condition()
             x ~ Normal()