Remove Zygote

docs/src/api.md

@@ -88,7 +88,6 @@ See the [AD guide](https://turinglang.org/docs/tutorials/docs-10-using-turing-au
 |:----------------- |:------------------------------------ |:---------------------- |
 | `AutoForwardDiff` | [`ADTypes.AutoForwardDiff`](@extref) | ForwardDiff.jl backend |
 | `AutoReverseDiff` | [`ADTypes.AutoReverseDiff`](@extref) | ReverseDiff.jl backend |
-| `AutoZygote`      | [`ADTypes.AutoZygote`](@extref)      | Zygote.jl backend      |
 | `AutoMooncake`    | [`ADTypes.AutoMooncake`](@extref)    | Mooncake.jl backend    |
 
 ### Debugging

src/Turing.jl

@@ -106,7 +106,6 @@ export @model,                 # modelling
     externalsampler,
     AutoForwardDiff,        # ADTypes
     AutoReverseDiff,
-    AutoZygote,
     AutoMooncake,
     setprogress!,           # debugging
     Flat,

src/essential/Essential.jl

@@ -11,7 +11,7 @@ using Bijectors: PDMatDistribution
 using AdvancedVI
 using StatsFuns: logsumexp, softmax
 @reexport using DynamicPPL
-using ADTypes: ADTypes, AutoForwardDiff, AutoReverseDiff, AutoZygote, AutoMooncake
+using ADTypes: ADTypes, AutoForwardDiff, AutoReverseDiff, AutoMooncake
 
 using AdvancedPS: AdvancedPS
 
@@ -20,7 +20,6 @@ include("container.jl")
 export @model,
     @varname,
     AutoForwardDiff,
-    AutoZygote,
     AutoReverseDiff,
     AutoMooncake,
     @logprob_str,

test/Project.toml

@@ -36,7 +36,6 @@ StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 StatsFuns = "4c63d2b9-4356-54db-8cca-17b64c39e42c"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 TimerOutputs = "a759f4b9-e2f1-59dc-863e-4aeb61b1ea8f"
-Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [compat]
 AbstractMCMC = "5"
@@ -75,5 +74,4 @@ StableRNGs = "1"
 StatsBase = "0.33, 0.34"
 StatsFuns = "0.9.5, 1"
 TimerOutputs = "0.5"
-Zygote = "0.5.4, 0.6"
 julia = "1.10"

test/essential/ad.jl

@@ -11,7 +11,6 @@ using ReverseDiff
 using Test: @test, @testset
 using Turing
 using Turing: SampleFromPrior
-using Zygote
 
 function test_model_ad(model, f, syms::Vector{Symbol})
     # Set up VI.
@@ -87,20 +86,6 @@ end
             vi, ad_test_f, SampleFromPrior(), DynamicPPL.DefaultContext()
         )
         x = map(x -> Float64(x), vi[SampleFromPrior()])
-
-        zygoteℓ = LogDensityProblemsAD.ADgradient(Turing.AutoZygote(), ℓ)
-        if isdefined(Base, :get_extension)
-            @test zygoteℓ isa
-                Base.get_extension(
-                LogDensityProblemsAD, :LogDensityProblemsADZygoteExt
-            ).ZygoteGradientLogDensity
-        else
-            @test zygoteℓ isa
-                LogDensityProblemsAD.LogDensityProblemsADZygoteExt.ZygoteGradientLogDensity
-        end
-        @test zygoteℓ.ℓ === ℓ
-        ∇E2 = LogDensityProblems.logdensity_and_gradient(zygoteℓ, x)[2]
-        @test sort(∇E2) ≈ grad_FWAD atol = 1e-9
     end
 
     @testset "general AD tests" begin
@@ -135,11 +120,10 @@ end
 
         test_model_ad(wishart_ad(), logp3, [:v])
     end
-    @testset "Simplex Zygote and ReverseDiff (with and without caching) AD" begin
+    @testset "Simplex ReverseDiff (with and without caching) AD" begin
         @model function dir()
             return theta ~ Dirichlet(1 ./ fill(4, 4))
         end
-        sample(dir(), HMC(0.01, 1; adtype=AutoZygote()), 1000)
         sample(dir(), HMC(0.01, 1; adtype=AutoReverseDiff(; compile=false)), 1000)
         sample(dir(), HMC(0.01, 1; adtype=AutoReverseDiff(; compile=true)), 1000)
     end
@@ -149,14 +133,12 @@ end
         end
 
         sample(wishart(), HMC(0.01, 1; adtype=AutoReverseDiff(; compile=false)), 1000)
-        sample(wishart(), HMC(0.01, 1; adtype=AutoZygote()), 1000)
 
         @model function invwishart()
             return theta ~ InverseWishart(4, Matrix{Float64}(I, 4, 4))
         end
 
         sample(invwishart(), HMC(0.01, 1; adtype=AutoReverseDiff(; compile=false)), 1000)
-        sample(invwishart(), HMC(0.01, 1; adtype=AutoZygote()), 1000)
     end
     @testset "Hessian test" begin
         @model function tst(x, ::Type{TV}=Vector{Float64}) where {TV}

test/test_utils/ad_utils.jl

@@ -8,7 +8,6 @@ using Mooncake: Mooncake
 using Test: Test
 using Turing: Turing
 using Turing: DynamicPPL
-using Zygote: Zygote
 
 export ADTypeCheckContext, adbackends
 
@@ -31,9 +30,6 @@ const eltypes_by_adtype = Dict(
         ReverseDiff.TrackedVector,
     ),
     Turing.AutoMooncake => (Mooncake.CoDual,),
-    # Zygote.Dual is actually the same as ForwardDiff.Dual, so can't distinguish between the
-    # two by element type. However, we have other checks for Zygote, see check_adtype.
-    Turing.AutoZygote => (Zygote.Dual,),
 )
 
 """
@@ -90,7 +86,6 @@ For instance, evaluating a model with
 would throw an error if within the model a type associated with e.g. ReverseDiff was
 encountered.
 
-As a current short-coming, this context can not distinguish between ForwardDiff and Zygote.
 """
 struct ADTypeCheckContext{ADType,ChildContext<:DynamicPPL.AbstractContext} <:
        DynamicPPL.AbstractContext
@@ -134,20 +129,9 @@ end
 
 Check that the element types in `vi` are compatible with the ADType of `context`.
 
-When Zygote is being used, we also more explicitly check that `adtype(context)` is
-`AutoZygote`. This is because Zygote uses the same element type as ForwardDiff, so we can't
-discriminate between the two based on element type alone. This function will still fail to
-catch cases where Zygote is supposed to be used, but ForwardDiff is used instead.
-
-Throw an `IncompatibleADTypeError` if an incompatible element type is encountered, or
-`WrongADBackendError` if Zygote is used unexpectedly.
+Throw an `IncompatibleADTypeError` if an incompatible element type is encountered.
 """
 function check_adtype(context::ADTypeCheckContext, vi::DynamicPPL.AbstractVarInfo)
-    Zygote.hook(vi) do _
-        if !(adtype(context) <: Turing.AutoZygote)
-            throw(WrongADBackendError(Turing.AutoZygote, adtype(context)))
-        end
-    end
 
     valids = valid_eltypes(context)
     for val in vi[:]

test/test_utils/test_utils.jl

@@ -7,7 +7,6 @@ using ReverseDiff: ReverseDiff
 using Test: @test, @testset, @test_throws
 using Turing: Turing
 using Turing: DynamicPPL
-using Zygote: Zygote
 
 # Check that the ADTypeCheckContext works as expected.
 @testset "ADTypeCheckContext" begin
@@ -16,20 +15,12 @@ using Zygote: Zygote
     adtypes = (
         Turing.AutoForwardDiff(),
         Turing.AutoReverseDiff(),
-        Turing.AutoZygote(),
         # TODO: Mooncake
         # Turing.AutoMooncake(config=nothing),
     )
     for actual_adtype in adtypes
         sampler = Turing.HMC(0.1, 5; adtype=actual_adtype)
         for expected_adtype in adtypes
-            if (
-                actual_adtype == Turing.AutoForwardDiff() &&
-                expected_adtype == Turing.AutoZygote()
-            )
-                # TODO(mhauru) We are currently unable to check this case.
-                continue
-            end
             contextualised_tm = DynamicPPL.contextualize(
                 tm, ADTypeCheckContext(expected_adtype, tm.context)
             )