Add tests for vararg Parallel and updated docs

Author: darsnack

docs/src/models/advanced.md

@@ -79,7 +79,7 @@ Naively, we could have a struct that stores the weights of along each path and i
 
 ### Multiple inputs: a custom `Join` layer
 
-Our custom `Join` layer will accept multiple inputs at once, pass each input through a separate path, then combine the results together.
+Our custom `Join` layer will accept multiple inputs at once, pass each input through a separate path, then combine the results together. Note that this layer can already be constructed using [`Parallel`](@ref), but we will first walk through how do this manually.
 
 We start by defining a new struct, `Join`, that stores the different paths and a combine operation as its fields.
 ```julia
@@ -128,6 +128,32 @@ model(xs)
 # returns a single float vector with one value
 ```
 
+#### Using `Parallel`
+
+Flux already provides [`Parallel`](@ref) that can offer the same functionality. In this case, `Join` is going to just be syntactic sugar for `Parallel`.
+```julia
+Join(combine, paths) = Parallel(combine, paths)
+Join(combine, paths...) = Join(combine, paths)
+
+# use vararg/tuple version of Parallel forward pass
+model = Chain(
+          Join(vcat,
+            Chain(
+              Dense(1, 5),
+              Dense(5, 1)
+            ),
+            Dense(1, 2),
+            Dense(1, 1),
+          ),
+          Dense(4, 1)
+        ) |> gpu
+
+xs = map(gpu, (rand(1), rand(1), rand(1)))
+
+model(xs)
+# returns a single float vector with one value
+```
+
 ### Multiple outputs: a custom `Split` layer
 
 Our custom `Split` layer will accept a single input, then pass the input through a separate path to produce multiple outputs.

src/layers/basic.jl

@@ -291,6 +291,7 @@ Parallel(connection, layers...) = Parallel(connection, layers)
 
 (m::Parallel)(x::AbstractArray) = mapreduce(f -> f(x), m.connection, m.layers)
 (m::Parallel)(xs::Vararg{<:AbstractArray}) = mapreduce((f, x) -> f(x), m.connection, m.layers, xs)
+(m::Parallel)(xs::Tuple) = m(xs...)
 
 Base.getindex(m::Parallel, i::Integer) = m.layers[i]
 Base.getindex(m::Parallel, i::AbstractVector) = Parallel(m.connection, m.layers[i]...)

test/layers/basic.jl

@@ -117,5 +117,10 @@ import Flux: activations
       input = randn(10, 2)
       @test size(Parallel((a, b) -> cat(a, b; dims=2), Dense(10, 10), identity)(input)) == (10, 4)
     end
+
+    @testset "vararg input" begin
+      inputs = randn(10), randn(5), randn(4)
+      @test size(Parallel(+, Dense(10, 2), Dense(5, 2), Dense(4, 2))(inputs)) == (2,)
+    end
   end
 end