Bump DifferentiationInterface to 0.7

HISTORY.md

@@ -1,5 +1,9 @@
 # DynamicPPL Changelog
 
+## 0.36.4
+
+Added compatibility with DifferentiationInterface.jl 0.7.
+
 ## 0.36.3
 
 Moved the `bijector(model)`, where `model` is a `DynamicPPL.Model`, function from the Turing main repo.

Project.toml

@@ -1,6 +1,6 @@
 name = "DynamicPPL"
 uuid = "366bfd00-2699-11ea-058f-f148b4cae6d8"
-version = "0.36.3"
+version = "0.36.4"
 
 [deps]
 ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"
@@ -54,7 +54,7 @@ ChainRulesCore = "1"
 Chairmarks = "1.3.1"
 Compat = "4"
 ConstructionBase = "1.5.4"
-DifferentiationInterface = "0.6.41"
+DifferentiationInterface = "0.6.41, 0.7"
 Distributions = "0.25"
 DocStringExtensions = "0.9"
 EnzymeCore = "0.6 - 0.8"

src/logdensityfunction.jl

@@ -124,9 +124,7 @@
             # Get a set of dummy params to use for prep
             x = map(identity, varinfo[:])
             if use_closure(adtype)
-                prep = DI.prepare_gradient(
-                    x -> logdensity_at(x, model, varinfo, context), adtype, x
-                )
+                prep = DI.prepare_gradient(LogDensityAt(model, varinfo, context), adtype, x)
             else
                 prep = DI.prepare_gradient(
                     logdensity_at,
@@ -184,6 +182,27 @@
     return getlogp(last(evaluate!!(model, varinfo_new, context)))
 end
 
+"""
+    LogDensityAt(
+        x::AbstractVector,
+        model::Model,
+        varinfo::AbstractVarInfo,
+        context::AbstractContext
+    )
+
+A callable struct that serves the same purpose as `x -> logdensity_at(x, model,
+varinfo, context)`.
+"""
+struct LogDensityAt
+    model::Model
+    varinfo::AbstractVarInfo
+    context::AbstractContext
+end
+function (ld::LogDensityAt)(x::AbstractVector)
+    varinfo_new = unflatten(ld.varinfo, x)
+    return getlogp(last(evaluate!!(ld.model, varinfo_new, ld.context)))
+end
+
 ### LogDensityProblems interface
 
 function LogDensityProblems.capabilities(
@@ -209,7 +228,7 @@
     # branches happen to return different types)
     return if use_closure(f.adtype)
         DI.value_and_gradient(
-            x -> logdensity_at(x, f.model, f.varinfo, f.context), f.prep, f.adtype, x
+            LogDensityAt(f.model, f.varinfo, f.context), f.prep, f.adtype, x
         )
     else
         DI.value_and_gradient(

test/Project.toml

@@ -38,7 +38,7 @@ Aqua = "0.8"
 Bijectors = "0.15.1"
 Combinatorics = "1"
 Compat = "4.3.0"
-DifferentiationInterface = "0.6.41"
+DifferentiationInterface = "0.6.41, 0.7"
 Distributions = "0.25"
 DistributionsAD = "0.6.3"
 Documenter = "1"