Use Zygote.jacobian etc.

Author: devmotion

ext/AbstractDifferentiationZygoteExt.jl

@@ -8,11 +8,27 @@ else
     using ..Zygote: Zygote
 end
 
-AD.ZygoteBackend() = AD.ReverseRuleConfigBackend(Zygote.ZygoteRuleConfig())
-
 # Context should not persist between different AD calls: fixes #69
 function AD.ruleconfig(::AD.ReverseRuleConfigBackend{<:Zygote.ZygoteRuleConfig})
     return Zygote.ZygoteRuleConfig()
 end
 
+function AD.value_and_pullback_function(::AD.ZygoteBackend, f, args...)
+    return Zygote.pullback(f, args...)
+end
+
+AD.gradient(::AD.ZygoteBackend, f, args...) = Zygote.gradient(f, args...)
+function AD.value_and_gradient(::AD.ZygoteBackend, f, args...)
+    res = Zygote.withgradient(f, args...)
+    return res.val, res.grad
+end
+
+AD.jacobian(::AD.ZygoteBackend, f, args...) = Zygote.jacobian(f, args...)
+function AD.value_and_jacobian(::AD.ZygoteBackend, f, args...)
+    res = Zygote.withjacobian(f, args...)
+    return res.val, res.grad
+end
+
+AD.hessian(::AD.ZygoteBackend, f, arg) = Zygote.hessian(f, arg)
+
 end # module

src/backends.jl

@@ -71,13 +71,14 @@ end
 ruleconfig(ba::ReverseRuleConfigBackend) = ba.ruleconfig
 
 """
-    ZygoteBackend()
+    ZygoteBackend
 
 Create an AD backend that uses reverse mode with [Zygote.jl](https://github.com/FluxML/Zygote.jl).
 
-It is a special case of [`ReverseRuleConfigBackend`](@ref).
+Alternatively, you can perform AD with Zygote using a special [`ReverseRuleConfigBackend`](@ref), namely `ReverseRuleConfigBackend(Zygote.ZygoteRuleConfig())`.
+Note, however, that the behaviour of this backend is not equivalent to `ZygoteBackend()` since the former uses a generic implementation of jacobian etc. for ChainRules-compatible AD backends whereas `ZygoteBackend` uses implementations in Zygote.jl.
 
 !!! note
     To be able to use this backend, you have to load Zygote.
 """
-function ZygoteBackend end
+struct ZygoteBackend <: AbstractReverseMode end

test/ruleconfig.jl

@@ -4,7 +4,10 @@ using Test
 using Zygote
 
 @testset "ReverseRuleConfigBackend(ZygoteRuleConfig())" begin
-    backends = [@inferred(AD.ZygoteBackend())]
+    backends = [
+        @inferred(AD.ZygoteBackend()),
+        @inferred(AD.ReverseRuleConfig(Zygote.ZygoteRuleConfig()))
+    ]
     @testset for backend in backends
         @testset "Derivative" begin
             test_derivatives(backend)
@@ -34,7 +37,7 @@ using Zygote
 
     # issue #69
     @testset "Zygote context" begin
-        ad = AD.ZygoteBackend()
+        ad = AD.ReverseRuleConfig(Zygote.ZygoteRuleConfig())
 
         # example in #69: context is not mutated
         @test ad.ruleconfig.context.cache === nothing
@@ -53,6 +56,13 @@ using Zygote
         end
         @test AD.jacobian(ad, f, [1, 2, 3], 3) ==
             ([6.0 0.0 0.0; 0.0 6.0 0.0; 0.0 0.0 6.0], [2.0, 4.0, 6.0])
+
+        # With `AD.ZygoteBackend`:
+        ad = AD.ZygoteBackend()
+        @test AD.derivative(ad, exp, 1.0) === (exp(1.0),)
+        @test AD.derivative(ad, exp, 1.0) === (exp(1.0),)
+        @test AD.jacobian(ad, f, [1, 2, 3], 3) ==
+            ([6.0 0.0 0.0; 0.0 6.0 0.0; 0.0 0.0 6.0], [2.0, 4.0, 6.0])
     end
 
     # issue #57
@@ -65,5 +75,15 @@ using Zygote
 
         @test_logs Zygote.gradient(myfunc, 1) # nothing is logged
         @test_logs AD.derivative(AD.ZygoteBackend(), myfunc, 1) # nothing is logged
+        @test_logs AD.derivative(AD.ReverseRuleConfig(Zygote.ZygoteRuleConfig()), myfunc, 1) # nothing is logged
+    end
+
+    # issue #54
+    @testset "allocations of jacobian" begin
+        f(x) = x .^ 2
+        x = rand(100)
+        ad = AD.ZygoteBackend()
+        @test AD.jacobian(ad, f, x) == Zygote.jacobian(f, x)
+        @test @allocated(AD.jacobian(ad, f, x)) == @allocated(Zygote.jacobian(f, x))
     end
 end