Remove generic rrule for broadcasted

src/chainrules.jl

@@ -20,31 +20,4 @@ function update_chainrules_primitives!()
 end
 
 
-is_chainrules_primitive(sig) = sig in CHAIN_RULE_PRIMITIVES[]
-
-
-################################################################
-
-# from Zygote:
-# https://github.com/FluxML/Zygote.jl/blob/d5be4d5ca80e79278d714eaac15ca71904a262e3/src/lib/array.jl#L177-L185
-struct StaticGetter{i} end
-(::StaticGetter{i})(v) where {i} = v[i]
-
-@generated function _unzip(tuples, ::Val{N}) where {N}
-  Expr(:tuple, (:(map($(StaticGetter{i}()), tuples)) for i ∈ 1:N)...)
-end
-
-function unzip(tuples)
-  N = length(first(tuples))
-  _unzip(tuples, Val(N))
-end
-
-
-function rrule(::typeof(Broadcast.broadcasted), f::F, args...) where F
-    ys, pbs = unzip(rrule.(f, args...))
-    function pullback(Δ)
-        dxs = map((pb, Δ) -> pb(Δ), pbs, Δ)
-        return NoTangent(), unzip(dxs)...
-    end
-    return ys, pullback
-end
+is_chainrules_primitive(sig) = sig in CHAIN_RULE_PRIMITIVES[]

src/grad.jl

@@ -126,7 +126,7 @@ function step_back!(tape::Tape, y::Variable, deriv_todo::Vector{Variable})
     else
         sig_str = join(["::$T" for T in Ghost.call_signature(tape, tape[y]).parameters], ", ")
         error("No deriative rule found for op $(tape[y]), " *
-              "try defining it using ChainRules.rrule($sig_str) = ...")
+              "try defining it using ChainRulesCore.rrule($sig_str) = ...")
     end
     for (i, x) in enumerate(y_fargs)
         if x isa V

src/gradcheck.jl

@@ -6,41 +6,6 @@ function ngradient(f, args...)
 end
 
 
-# # from https://github.com/FluxML/Zygote.jl/blob/master/test/gradcheck.jl
-
-# function ngradient(f, xs::AbstractArray...)
-#   grads = zero.(xs)
-#   for (x, Δ) in zip(xs, grads), i in 1:length(x)
-#     δ = sqrt(eps())
-#     tmp = x[i]
-#     x[i] = tmp - δ/2
-#     y1 = f(xs...)
-#     x[i] = tmp + δ/2
-#     y2 = f(xs...)
-#     x[i] = tmp
-#     Δ[i] = (y2-y1)/δ
-#   end
-#   return grads
-# end
-
-
-# function ngradient2(f, xs, n)
-#     x = xs[n]
-#     Δ = zero(x)
-#     for i in 1:length(x)
-#         δ = sqrt(eps())
-#         tmp = x[i]
-#         x[i] = tmp - δ/2
-#         y1 = f(xs...)
-#         x[i] = tmp + δ/2
-#         y2 = f(xs...)
-#         x[i] = tmp
-#         Δ[i] = (y2-y1)/δ
-#     end
-#     return Δ
-# end
-
-
 function gradcheck(f, args...)
     y_grads = grad(f, args...)[2]
     # don't check gradient w.r.t. function since ngradient can't do it
@@ -51,10 +16,4 @@ function gradcheck(f, args...)
         push!(results, isapprox(y_grads[n], n_grad[n], rtol = 1e-5, atol = 1e-5))
     end
     return all(results)
-end
-
-
-# gradcheck = gradcheck2
-
-# # gradtest(f, xs::AbstractArray...) = gradcheck((xs...) -> sum(sin.(f(xs...))), xs...)
-# # gradtest(f, dims...) = gradtest(f, rand.(Float64, dims)...)
+end

test/test_examples.jl

@@ -1,5 +1,40 @@
 using Statistics
 using Random
+import ChainRulesCore: rrule, NoTangent
+
+
+################################################################
+
+# TODO: migrate to a common broadcasting rrule
+
+# from Zygote:
+# https://github.com/FluxML/Zygote.jl/blob/d5be4d5ca80e79278d714eaac15ca71904a262e3/src/lib/array.jl#L177-L185
+struct StaticGetter{i} end
+(::StaticGetter{i})(v) where {i} = v[i]
+
+@generated function _unzip(tuples, ::Val{N}) where {N}
+  Expr(:tuple, (:(map($(StaticGetter{i}()), tuples)) for i ∈ 1:N)...)
+end
+
+function unzip(tuples)
+  N = length(first(tuples))
+  _unzip(tuples, Val(N))
+end
+
+
+function rrule(::typeof(Broadcast.broadcasted), f::F, args...) where F
+    ys, pbs = unzip(rrule.(f, args...))
+    function pullback(Δ)
+        dxs = map((pb, Δ) -> pb(Δ), pbs, Δ)
+        return NoTangent(), unzip(dxs)...
+    end
+    return ys, pullback
+end
+
+Yota.update_chainrules_primitives!()
+
+################################################################
+
 
 
 obj(Y, X, b) = mean((Y .- X * b) .^ 2.0) # objective to minimize