Bump versions and add Mooncake forward

Project.toml

@@ -17,4 +17,5 @@ Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [compat]
-DynamicPPL = "0.36"
+DynamicPPL = "0.37"
+Turing = "0.40"

main.jl

@@ -1,5 +1,5 @@
 using DynamicPPL: DynamicPPL, VarInfo
-using DynamicPPL.TestUtils.AD: run_ad, ADResult, ADIncorrectException
+using DynamicPPL.TestUtils.AD: run_ad, ADResult, ADIncorrectException, WithBackend
 using ADTypes
 using Random: Xoshiro
 
@@ -12,13 +12,14 @@ import Zygote
 
 # AD backends to test.
 ADTYPES = Dict(
-    "FiniteDifferences" => AutoFiniteDifferences(; fdm = central_fdm(5, 1)),
+    "FiniteDifferences" => AutoFiniteDifferences(; fdm=central_fdm(5, 1)),
     "ForwardDiff" => AutoForwardDiff(),
-    "ReverseDiff" => AutoReverseDiff(; compile = false),
-    "ReverseDiffCompiled" => AutoReverseDiff(; compile = true),
-    "Mooncake" => AutoMooncake(; config = nothing),
-    "EnzymeForward" => AutoEnzyme(; mode = set_runtime_activity(Forward, true)),
-    "EnzymeReverse" => AutoEnzyme(; mode = set_runtime_activity(Reverse, true)),
+    "ReverseDiff" => AutoReverseDiff(; compile=false),
+    "ReverseDiffCompiled" => AutoReverseDiff(; compile=true),
+    "MooncakeReverse" => AutoMooncake(),
+    "MooncakeForward" => AutoMooncakeForward(),
+    "EnzymeForward" => AutoEnzyme(; mode=set_runtime_activity(Forward, true)),
+    "EnzymeReverse" => AutoEnzyme(; mode=set_runtime_activity(Reverse, true)),
     "Zygote" => AutoZygote(),
 )
 
@@ -132,21 +133,18 @@ elseif length(ARGS) == 3 && ARGS[1] == "--run"
             # https://github.com/TuringLang/ADTests/issues/4
             vi = DynamicPPL.unflatten(VarInfo(model), [0.5, -0.5])
             params = [-0.5, 0.5]
-            result = run_ad(model, adtype; varinfo = vi, params = params, benchmark = true)
+            result = run_ad(model, adtype; varinfo=vi, params=params, test=WithBackend(ADTYPES["FiniteDifferences"]), benchmark=true)
         else
-            vi = VarInfo(Xoshiro(468), model)
-            linked_vi = DynamicPPL.link!!(vi, model)
-            params = linked_vi[:]
             result = run_ad(
                 model,
                 adtype;
-                params = params,
-                reference_adtype = ADTYPES["FiniteDifferences"],
-                benchmark = true,
+                rng=Xoshiro(468),
+                test=WithBackend(ADTYPES["FiniteDifferences"]),
+                benchmark=true,
             )
         end
         # If reached here - nothing went wrong
-        println(result.time_vs_primal)
+        println(result.grad_time / result.primal_time)
     catch e
         @show e
         if e isa ADIncorrectException

web/src/App.svelte

@@ -102,6 +102,17 @@
                 >Download the raw data (JSON)</a
             >
         </p>
+
+        <p>
+            <b>Note about Enzyme:</b> Enzyme does not work with DynamicPPL 0.37
+            / Turing 0.40 because of
+            <a
+                href="https://github.com/EnzymeAD/Enzyme.jl/issues/2429"
+                target="_blank">this issue</a
+            >. If you want to use Enzyme with Turing, please use an older
+            version of Turing / DynamicPPL.
+        </p>
+
         {#each categorisedData.entries() as [category, modelData]}
             <h3>{category}</h3>
             <ResultsTable data={modelData} {modelDefinitions} />