Unthunk tangents (if any) before returning gradient

Project.toml

@@ -57,7 +57,7 @@ Requires = "1.1"
 SpecialFunctions = "1.6, 2"
 Statistics = "1"
 Tracker = "0.2"
-ZygoteRules = "0.2.5"
+ZygoteRules = "0.2.7"
 julia = "1.6"
 
 [extras]

src/compiler/chainrules.jl

@@ -1,11 +1,10 @@
 # ToDo: Move some of this to ZygoteRules, or move unthunk_tangent for Tuple and NamedTuple from
 # Zygote rules here?
-function unthunk_tangent end
-@inline unthunk_tangent(x::AbstractThunk) = wrap_chainrules_output(unthunk(x))
-@inline unthunk_tangent(x::NTuple{N,<:Number}) where N = x
-@inline unthunk_tangent(x::AbstractArray{<:Number,N}) where N = x
-@inline unthunk_tangent(x::AbstractArray) = map(unthunk_tangent, x)
-unthunk_tangent(d::IdDict) = IdDict([unthunk_tangent(k) => unthunk_tangent(v) for (k, v) in d])
+@inline ZygoteRules.unthunk_tangent(x::AbstractThunk) = wrap_chainrules_output(unthunk(x))
+@inline ZygoteRules.unthunk_tangent(x::NTuple{N,<:Number}) where N = x
+@inline ZygoteRules.unthunk_tangent(x::AbstractArray{<:Number,N}) where N = x
+@inline ZygoteRules.unthunk_tangent(x::AbstractArray) = map(unthunk_tangent, x)
+ZygoteRules.unthunk_tangent(d::IdDict) = IdDict([unthunk_tangent(k) => unthunk_tangent(v) for (k, v) in d])
 @non_differentiable unthunk_tangent(::IdDict)
 
 

src/compiler/interface.jl

@@ -152,7 +152,7 @@ julia> gradient([7, 11], 0, 1) do x, y, d
 function gradient(f, args...)
   y, back = pullback(f, args...)
   grad = back(sensitivity(y))
-  return _project_all(args, grad)
+  return _project_all(args, unthunk_tangent(grad))
 end
 
 # Base.adjoint(f::Function) = x -> gradient(f, x)[1]  # piracy!
@@ -218,7 +218,7 @@ function withgradient(f, args...)
   else
     back(sensitivity(y))
   end
-  results = _project_all(args, grad)
+  results = _project_all(args, unthunk_tangent(grad))
   (val=y, grad=results)
 end
 

src/lib/lib.jl

@@ -40,6 +40,9 @@ end
 accum(x::NamedTuple, y::ChainRulesCore.Tangent) = accum(x, wrap_chainrules_output(y))
 accum(x::ChainRulesCore.Tangent, y::NamedTuple) = accum(wrap_chainrules_output(x), y)
 
+accum(x::Nothing, y::AbstractThunk) = y
+accum(x::AbstractThunk, y::Nothing) = x
+
 accum(x, y::AbstractThunk) = @thunk(accum(x, unthunk(y)))
 accum(x::AbstractThunk, y) = @thunk(accum(unthunk(x), y))
 accum(x::AbstractThunk, y::AbstractThunk) = @thunk(accum(unthunk(x), unthunk(y)))

test/chainrules.jl

@@ -428,3 +428,31 @@ end
     @test Zygote.wrap_chainrules_input([[2.0; 4.0], [1.0; 3.0]]) == [[2.0; 4.0], [1.0; 3.0]]
     @test Zygote.wrap_chainrules_input([nothing; 4.0]) == [0.0; 4.0] # ChainRules uses the numeric zero where possible
 end
+
+@testset "Lazy" begin
+    custom_add(x, y) = x + y
+    function ChainRulesCore.rrule(::typeof(custom_add), x, y)
+        function pullback(Δ)
+            return NoTangent(), unthunk(Δ), @thunk(error("Should not compute."))
+        end
+        custom_add(x, y), pullback
+    end
+
+    x, y = 1f0, 1f0
+    Zygote.gradient(x) do x
+        sum(custom_add(x, y))
+    end
+end
+
+@testset "No thunks in the gradient" begin
+    struct CustomDense
+        w::Matrix{Float32}
+    end
+    (d::CustomDense)(x) = d.w * x
+
+    layers = [CustomDense(rand(Float32, 3, 3))]
+    x = ones(Float32, 3)
+    g = gradient(layers -> sum(layers[1](x)), layers)[1]
+    @test g[1] isa NamedTuple
+    @test g[1].w isa Array
+end