Merge branch 'breaking' into mhauru/vnt-for-fastldf

Author: mhauru

HISTORY.md

@@ -1,5 +1,11 @@
 # DynamicPPL Changelog
 
+## 0.40
+
+## 0.39.1
+
+`LogDensityFunction` now allows you to call `logdensity_and_gradient(ldf, x)` with `AbstractVector`s `x` that are not plain Vectors (they will be converted internally before calculating the gradient).
+
 ## 0.39.0
 
 ### Breaking changes

Project.toml

@@ -1,6 +1,6 @@
 name = "DynamicPPL"
 uuid = "366bfd00-2699-11ea-058f-f148b4cae6d8"
-version = "0.39.0"
+version = "0.40"
 
 [deps]
 ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"

benchmarks/Project.toml

@@ -24,7 +24,7 @@ DynamicPPL = {path = "../"}
 ADTypes = "1.14.0"
 Chairmarks = "1.3.1"
 Distributions = "0.25.117"
-DynamicPPL = "0.39"
+DynamicPPL = "0.40"
 Enzyme = "0.13"
 ForwardDiff = "1"
 JSON = "1.3.0"

docs/Project.toml

@@ -21,7 +21,7 @@ Accessors = "0.1"
 Distributions = "0.25"
 Documenter = "1"
 DocumenterMermaid = "0.1, 0.2"
-DynamicPPL = "0.39"
+DynamicPPL = "0.40"
 FillArrays = "0.13, 1"
 ForwardDiff = "0.10, 1"
 JET = "0.9, 0.10, 0.11"

src/logdensityfunction.jl

@@ -144,6 +144,8 @@ struct LogDensityFunction{
     F<:Function,
     VNT<:VarNamedTuple,
     ADP<:Union{Nothing,DI.GradientPrep},
+    # type of the vector passed to logdensity functions
+    X<:AbstractVector,
 }
     model::M
     adtype::AD
@@ -192,12 +194,17 @@ struct LogDensityFunction{
             typeof(getlogdensity),
             typeof(all_ranges),
             typeof(prep),
+            typeof(x),
         }(
             model, adtype, getlogdensity, all_ranges, prep, dim
         )
     end
 end
 
+function _get_input_vector_type(::LogDensityFunction{T,M,A,G,I,P,X}) where {T,M,A,G,I,P,X}
+    return X
+end
+
 ###################################
 # LogDensityProblems.jl interface #
 ###################################
@@ -245,6 +252,7 @@ end
 function LogDensityProblems.logdensity_and_gradient(
     ldf::LogDensityFunction{Tlink}, params::AbstractVector{<:Real}
 ) where {Tlink}
+    params = convert(_get_input_vector_type(ldf), params)
     return DI.value_and_gradient(
         LogDensityAt{Tlink}(ldf.model, ldf._getlogdensity, ldf._varname_ranges),
         ldf._adprep,

test/Project.toml

@@ -39,6 +39,7 @@ Accessors = "0.1"
 Aqua = "0.8"
 BangBang = "0.4"
 Bijectors = "0.15.1"
+Chairmarks = "1"
 Combinatorics = "1"
 DifferentiationInterface = "0.6.41, 0.7"
 Distributions = "0.25"

test/logdensityfunction.jl

@@ -182,6 +182,25 @@ end
         end
     end
 
+    @testset "logdensity_and_gradient with views" begin
+        # This test ensures that you can call `logdensity_and_gradient` with an array 
+        # type that isn't the same as the one used in the gradient preparation.
+        @model function f()
+            x ~ Normal()
+            return y ~ Normal()
+        end
+        @testset "$adtype" for adtype in test_adtypes
+            x = randn(2)
+            ldf = LogDensityFunction(f(); adtype)
+            logp, grad = LogDensityProblems.logdensity_and_gradient(ldf, x)
+            logp_view, grad_view = LogDensityProblems.logdensity_and_gradient(
+                ldf, (@view x[:])
+            )
+            @test logp == logp_view
+            @test grad == grad_view
+        end
+    end
+
     # Test that various different ways of specifying array types as arguments work with all
     # ADTypes.
     @testset "Array argument types" begin