Merge branch 'main' into tor/benchmark-update

Author: shravanngoswamii

test/Project.toml

@@ -26,7 +26,6 @@ ReverseDiff = "37e2e3b7-166d-5795-8a7a-e32c996b4267"
 Serialization = "9e88b42a-f829-5b0c-bbe9-9e923198166b"
 StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
-Tracker = "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [compat]
@@ -51,6 +50,5 @@ Mooncake = "0.4.95"
 OrderedCollections = "1"
 ReverseDiff = "1"
 StableRNGs = "1"
-Tracker = "0.2.23"
-Zygote = "0.6"
+Zygote = "0.6, 0.7"
 julia = "1.10"

test/runtests.jl

@@ -14,7 +14,6 @@ using MacroTools
 using MCMCChains
 using Mooncake: Mooncake
 using StableRNGs
-using Tracker
 using ReverseDiff
 using Zygote
 using Compat

test/test_util.jl

@@ -23,24 +23,8 @@ function test_model_ad(model, logp_manual)
     # Gradients based on the manual implementation.
     grad = ForwardDiff.gradient(logp_manual, x)
 
-    y, back = Tracker.forward(logp_manual, x)
-    @test Tracker.data(y) ≈ lp
-    @test Tracker.data(back(1)[1]) ≈ grad
-
-    y, back = Zygote.pullback(logp_manual, x)
-    @test y ≈ lp
-    @test back(1)[1] ≈ grad
-
     # Gradients based on the model.
     @test ForwardDiff.gradient(logp_model, x) ≈ grad
-
-    y, back = Tracker.forward(logp_model, x)
-    @test Tracker.data(y) ≈ lp
-    @test Tracker.data(back(1)[1]) ≈ grad
-
-    y, back = Zygote.pullback(logp_model, x)
-    @test y ≈ lp
-    @test back(1)[1] ≈ grad
 end
 
 """