Narrower version of @non_differentiable params

Project.toml

@@ -38,7 +38,7 @@ ProgressLogging = "0.1"
 Reexport = "0.2, 1.0"
 SpecialFunctions = "1.8.2, 2.1.2"
 StatsBase = "0.33"
-Zygote = "0.6.34"
+Zygote = "0.6.49"
 julia = "1.6"
 
 [extras]

src/functor.jl

@@ -89,7 +89,10 @@ function params(m...)
 end
 
 # Allows caching of the parameters when params is called within gradient() to fix #2040.
-@non_differentiable params(m...)
+# @non_differentiable params(m...)  # https://github.com/FluxML/Flux.jl/pull/2054
+# That speeds up implicit use, and silently breaks explicit use. 
+# From @macroexpand Zygote.@nograd params(m...) and https://github.com/FluxML/Zygote.jl/pull/1248
+Zygote._pullback(::Zygote.Context{true}, ::typeof(params), m...) = params(m), _ -> nothing
 
 struct FluxCUDAAdaptor end
 adapt_storage(to::FluxCUDAAdaptor, x) = CUDA.cu(x)

test/utils.jl

@@ -270,6 +270,20 @@ end
   @test size.(Flux.params(m)) == [(2,), (1, 2)]
 end
 
+@testset "params gradient" begin
+  m = (x=[1,2.0], y=[3.0]);
+
+  # Explicit -- was broken by #2054
+  gnew = gradient(m -> (sum(norm, Flux.params(m))), m)[1]
+  @test gnew.x ≈ [0.4472135954999579, 0.8944271909999159]
+  @test gnew.y ≈ [1.0]
+
+  # Implicit
+  gold = gradient(() -> (sum(norm, Flux.params(m))), Flux.params(m))
+  @test gold[m.x] ≈ [0.4472135954999579, 0.8944271909999159]
+  @test gold[m.y] ≈ [1.0]
+end
+
 @testset "Precision" begin
   m = Chain(Dense(10, 5, relu), Dense(5, 2))
   x64 = rand(Float64, 10)