internal rename

docs/src/gpu.md

@@ -25,16 +25,16 @@ loss(x, y) # ~ 3
 
 Note that we convert both the parameters (`W`, `b`) and the data set (`x`, `y`) to cuda arrays. Taking derivatives and training works exactly as before.
 
-If you define a structured model, like a `Dense` layer or `Chain`, you just need to convert the internal parameters. Flux provides `mapleaves`, which allows you to alter all parameters of a model at once.
+If you define a structured model, like a `Dense` layer or `Chain`, you just need to convert the internal parameters. Flux provides `fmap`, which allows you to alter all parameters of a model at once.
 
 ```julia
 d = Dense(10, 5, σ)
-d = mapleaves(cu, d)
+d = fmap(cu, d)
 d.W # Tracked CuArray
 d(cu(rand(10))) # CuArray output
 
 m = Chain(Dense(10, 5, σ), Dense(5, 2), softmax)
-m = mapleaves(cu, m)
+m = fmap(cu, m)
 d(cu(rand(10)))
 ```
 

docs/src/models/basics.md

@@ -215,7 +215,7 @@ m(5) # => 26
 Flux provides a set of helpers for custom layers, which you can enable by calling
 
 ```julia
-Flux.@treelike Affine
+Flux.@functor Affine
 ```
 
 This enables a useful extra set of functionality for our `Affine` layer, such as [collecting its parameters](../training/optimisers.md) or [moving it to the GPU](../gpu.md).

src/Flux.jl

@@ -11,7 +11,7 @@ export gradient
 
 export Chain, Dense, Maxout, RNN, LSTM, GRU, Conv, CrossCor, ConvTranspose, MaxPool, MeanPool,
        DepthwiseConv, Dropout, AlphaDropout, LayerNorm, BatchNorm, InstanceNorm, GroupNorm,
-       SkipConnection, params, mapleaves, cpu, gpu, f32, f64
+       SkipConnection, params, fmap, cpu, gpu, f32, f64
 
 include("optimise/Optimise.jl")
 using .Optimise

src/functor.jl

@@ -16,7 +16,7 @@ function makefunctor(m::Module, T, fs = fieldnames(T))
 end
 
 function functorm(T, fs = nothing)
-  fs == nothing || isexpr(fs, :tuple) || error("@treelike T (a, b)")
+  fs == nothing || isexpr(fs, :tuple) || error("@functor T (a, b)")
   fs = fs == nothing ? [] : [:($(map(QuoteNode, fs.args)...),)]
   :(makefunctor(@__MODULE__, $(esc(T)), $(fs...)))
 end
@@ -61,8 +61,6 @@ macro treelike(args...)
 end
 mapleaves(f, x) = fmap(f, x)
 
-# function params
-
 function loadparams!(m, xs)
   for (p, x) in zip(params(m), xs)
     size(p) == size(x) ||
@@ -73,7 +71,7 @@ end
 
 # CPU/GPU movement conveniences
 
-cpu(m) = mapleaves(x -> adapt(Array, x), m)
+cpu(m) = fmap(x -> adapt(Array, x), m)
 
 const gpu_adaptor = if has_cuarrays()
   CuArrays.cu
@@ -81,13 +79,13 @@ else
   identity
 end
 
-gpu(x) = mapleaves(gpu_adaptor, x)
+gpu(x) = fmap(gpu_adaptor, x)
 
 # Precision
 
 adapt_storage(T::Type{<:Real}, xs::AbstractArray{<:Real}) = convert.(T, xs)
 
-paramtype(T::Type{<:Real}, m) = mapleaves(x -> adapt(T, x), m)
+paramtype(T::Type{<:Real}, m) = fmap(x -> adapt(T, x), m)
 
 f32(m) = paramtype(Float32, m)
 f64(m) = paramtype(Float64, m)

src/layers/basic.jl

@@ -91,7 +91,7 @@ function Dense(in::Integer, out::Integer, σ = identity;
   return Dense(initW(out, in), initb(out), σ)
 end
 
-@treelike Dense
+@functor Dense
 
 function (a::Dense)(x::AbstractArray)
   W, b, σ = a.W, a.b, a.σ
@@ -130,7 +130,7 @@ end
 Diagonal(in::Integer; initα = ones, initβ = zeros) =
   Diagonal(initα(in), initβ(in))
 
-@treelike Diagonal
+@functor Diagonal
 
 function (a::Diagonal)(x)
   α, β = a.α, a.β
@@ -183,7 +183,7 @@ function Maxout(f, n_alts)
   return Maxout(over)
 end
 
-@treelike Maxout
+@functor Maxout
 
 function (mo::Maxout)(input::AbstractArray)
     mapreduce(f -> f(input), (acc, out) -> max.(acc, out), mo.over)
@@ -208,7 +208,7 @@ struct SkipConnection
   connection  #user can pass arbitrary connections here, such as (a,b) -> a + b
 end
 
-@treelike SkipConnection
+@functor SkipConnection
 
 function (skip::SkipConnection)(input)
   #We apply the layers to the input and return the result of the application of the layers and the original input

src/layers/conv.jl

@@ -45,7 +45,7 @@ Conv(k::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer}, σ = identity;
   Conv(init(k..., ch...), zeros(ch[2]), σ,
        stride = stride, pad = pad, dilation = dilation)
 
-@treelike Conv
+@functor Conv
 
 function (c::Conv)(x::AbstractArray)
   # TODO: breaks gpu broadcast :(
@@ -102,7 +102,7 @@ ConvTranspose(k::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer}, σ = identity
 ConvTranspose(init(k..., reverse(ch)...), zeros(ch[2]), σ,
               stride = stride, pad = pad, dilation = dilation)
 
-@treelike ConvTranspose
+@functor ConvTranspose
 
 function conv_transpose_dims(c::ConvTranspose, x::AbstractArray)
     # Calculate size of "input", from ∇conv_data()'s perspective...
@@ -180,7 +180,7 @@ function DepthwiseConv(k::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer}, σ =
   )
 end
 
-@treelike DepthwiseConv
+@functor DepthwiseConv
 
 function (c::DepthwiseConv)(x)
   σ, b = c.σ, reshape(c.bias, map(_->1, c.stride)..., :, 1)
@@ -244,7 +244,7 @@ CrossCor(k::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer}, σ = identity;
   CrossCor(init(k..., ch...), zeros(ch[2]), σ,
        stride = stride, pad = pad, dilation = dilation)
 
-@treelike CrossCor
+@functor CrossCor
 
 function crosscor(x, w, ddims::DenseConvDims)
   ddims = DenseConvDims(ddims, F=true)

src/layers/normalise.jl

@@ -82,7 +82,7 @@ end
 LayerNorm(h::Integer) =
   LayerNorm(Diagonal(h))
 
-@treelike LayerNorm
+@functor LayerNorm
 
 (a::LayerNorm)(x) = a.diag(normalise(x))
 

src/layers/recurrent.jl

@@ -38,7 +38,7 @@ function (m::Recur)(xs...)
   return y
 end
 
-@treelike Recur cell, init
+@functor Recur cell, init
 
 Base.show(io::IO, m::Recur) = print(io, "Recur(", m.cell, ")")
 
@@ -80,7 +80,7 @@ end
 
 hidden(m::RNNCell) = m.h
 
-@treelike RNNCell
+@functor RNNCell
 
 function Base.show(io::IO, l::RNNCell)
   print(io, "RNNCell(", size(l.Wi, 2), ", ", size(l.Wi, 1))
@@ -128,7 +128,7 @@ end
 
 hidden(m::LSTMCell) = (m.h, m.c)
 
-@treelike LSTMCell
+@functor LSTMCell
 
 Base.show(io::IO, l::LSTMCell) =
   print(io, "LSTMCell(", size(l.Wi, 2), ", ", size(l.Wi, 1)÷4, ")")
@@ -169,7 +169,7 @@ end
 
 hidden(m::GRUCell) = m.h
 
-@treelike GRUCell
+@functor GRUCell
 
 Base.show(io::IO, l::GRUCell) =
   print(io, "GRUCell(", size(l.Wi, 2), ", ", size(l.Wi, 1)÷3, ")")

test/cuda/curnn.jl

@@ -13,7 +13,7 @@ end
 @testset "RNN" begin
   @testset for R in [RNN, GRU, LSTM], batch_size in (1, 5)
     rnn = R(10, 5)
-    curnn = mapleaves(gpu, rnn)
+    curnn = fmap(gpu, rnn)
 
     Flux.reset!(rnn)
     Flux.reset!(curnn)