Don't load Yota at all (#166)

* Update runtests.jl

* Update runtests.jl

* Update Project.toml

* Update runtests.jl

* Update rules.jl

* Update destructure.jl

* test is no longer broken

* Update index.md

Project.toml

@@ -14,15 +14,13 @@ Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 ChainRulesCore = "1"
 Functors = "0.4"
 Statistics = "1"
-Yota = "0.8.2"
 Zygote = "0.6.40"
 julia = "1.6"
 
 [extras]
 StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
-Yota = "cd998857-8626-517d-b929-70ad188a48f0"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [targets]
-test = ["Test", "StaticArrays", "Yota", "Zygote"]
+test = ["Test", "StaticArrays", "Zygote"]

docs/src/index.md

@@ -80,25 +80,6 @@ Zygote's "implicit" mode `gradient(() -> loss(...), Flux.params(model))` -- see
 [Zygote's documentation](https://fluxml.ai/Zygote.jl/dev/#Explicit-and-Implicit-Parameters-1) for more about this difference.
 
 
-## Usage with [Yota.jl](https://github.com/dfdx/Yota.jl)
-
-Yota is another modern automatic differentiation package, an alternative to Zygote.
-
-Its main function is `Yota.grad`, which returns the loss as well as the gradient (like `Zygote.withgradient`)
-but also returns a gradient component for the loss function.
-To extract what Optimisers.jl needs, you can write (for the Flux model above):
-
-```julia
-using Yota
-
-loss, (∇function, ∇model, ∇image) = Yota.grad(model, image) do m, x
-  sum(m(x)
-end;
-
-# Or else, this may save computing ∇image:
-loss, (_, ∇model) = grad(m -> sum(m(image)), model);
-```
-
 ## Usage with [Lux.jl](https://github.com/avik-pal/Lux.jl)
 
 The main design difference of Lux from Flux is that the tree of parameters is separate from

test/destructure.jl

@@ -98,14 +98,14 @@ end
       sum(gradient(m -> sum(destructure(m)[1])^3, (v, [4,5,6.0]))[1][1])
     end[1] == [378, 378, 378]
 
-    @test_broken gradient([1,2,3.0]) do v
+    VERSION >= v"1.10" && @test gradient([1,2,3.0]) do v
       sum(abs2, gradient(m -> sum(abs2, destructure(m)[1]), (x = v, y = sin, z = [4,5,6.0]))[1][1])
     end[1] ≈ [8,16,24]
     # Zygote error in (::typeof(∂(canonicalize)))(Δ::NamedTuple{(:backing,), Tuple{NamedTuple{(:x, :y, :z)
     # Diffractor error in perform_optic_transform
   end
 
-  VERSION < v"1.9-" && @testset "using Yota" begin
+  false && @testset "using Yota" begin
     @test Yota_gradient(m -> destructure(m)[1][1], m1)[1] == [1,0,0]
     @test Yota_gradient(m -> destructure(m)[1][2], m2)[1] == ([0,1,0], [0,0,0])
     @test Yota_gradient(m -> destructure(m)[1][3], (m1, m1))[1] == ([0,0,1], nothing)
@@ -175,7 +175,7 @@ end
     # Zygote.@adjoint Tangent{T,B}(x::NamedTuple) where {T,B<:NamedTuple} = Tangent{T,B}(x), dx -> (dx,)
   end
 
-  VERSION < v"1.9-" && @testset "using Yota" begin
+  false && @testset "using Yota" begin
     re1 = destructure(m1)[2]
     @test Yota_gradient(x -> re1(x)[1], rand(3))[1] == [1,0,0]
     re2 = destructure(m2)[2]

test/rules.jl

@@ -230,7 +230,7 @@ end
   end
 end
 
-VERSION < v"1.9-" && @testset "using Yota" begin
+false && @testset "using Yota" begin
   @testset "$(name(o))" for o in RULES
     w′ = (abc = (α = rand(3, 3), β = rand(3, 3), γ = rand(3)), d = (δ = rand(3), ε = eps))
     w = (abc = (α = 5rand(3, 3), β = rand(3, 3), γ = rand(3)), d = (δ = rand(3), ε = eps))
@@ -266,4 +266,4 @@ end
 
   tree, x4 = Optimisers.update(tree, x3, g4)
   @test x4 ≈ x3
-end
+end

test/runtests.jl

@@ -1,5 +1,5 @@
 using Optimisers
-using ChainRulesCore, Functors, StaticArrays, Zygote, Yota
+using ChainRulesCore, Functors, StaticArrays, Zygote
 using LinearAlgebra, Statistics, Test, Random
 using Optimisers: @.., @lazy
 using Base.Broadcast: broadcasted, instantiate, Broadcasted
@@ -38,12 +38,15 @@ function Optimisers.apply!(o::BiRule, state, x, dx, dx2)
   return state, dx
 end
 
-# Make Yota's output look like Zygote's:
+# if VERSION < v"1.9-"
+#   using Yota
+# end
+# # Make Yota's output look like Zygote's:
 
-Yota_gradient(f, xs...) = map(y2z, Base.tail(Yota.grad(f, xs...)[2]))
-y2z(::AbstractZero) = nothing  # we don't care about different flavours of zero
-y2z(t::Tangent) = map(y2z, ChainRulesCore.backing(canonicalize(t)))  # namedtuples!
-y2z(x) = x
+# Yota_gradient(f, xs...) = map(y2z, Base.tail(Yota.grad(f, xs...)[2]))
+# y2z(::AbstractZero) = nothing  # we don't care about different flavours of zero
+# y2z(t::Tangent) = map(y2z, ChainRulesCore.backing(canonicalize(t)))  # namedtuples!
+# y2z(x) = x
 
 @testset verbose=true "Optimisers.jl" begin
   @testset verbose=true "Features" begin