set expand option as default for @layer

README.md

@@ -27,9 +27,9 @@ data = [([x], 2x-x^3) for x in -2:0.1f0:2]
 
 model = Chain(Dense(1 => 23, tanh), Dense(23 => 1, bias=false), only)
 
-optim = Flux.setup(Adam(), model)
+opt_state = Flux.setup(Adam(), model)
 for epoch in 1:1000
-  Flux.train!((m,x,y) -> (m(x) - y)^2, model, data, optim)
+  Flux.train!((m,x,y) -> (m(x) - y)^2, model, data, opt_state)
 end
 
 plot(x -> 2x-x^3, -2, 2, legend=false)

docs/src/guide/saving.md

@@ -21,7 +21,12 @@ julia> Flux.@layer MyModel
 julia> MyModel() = MyModel(Chain(Dense(10 => 5, relu), Dense(5 => 2)));
 
 julia> model = MyModel()
-MyModel(Chain(Dense(10 => 5, relu), Dense(5 => 2)))  # 67 parameters
+MyModel(
+  Chain(
+    Dense(10 => 5, relu),               # 55 parameters
+    Dense(5 => 2),                      # 12 parameters
+  ),
+)                   # Total: 4 arrays, 67 parameters, 484 bytes.
 
 julia> model_state = Flux.state(model);
 

src/Flux.jl

@@ -8,6 +8,7 @@ using MacroTools, Reexport, ProgressLogging, SpecialFunctions
 using MacroTools: @forward
 
 @reexport using NNlib
+using NNlib: conv, ∇conv_data, depthwiseconv, output_size
 using MLUtils
 
 using Optimisers: Optimisers, destructure, freeze!, thaw!, adjust!, trainables, update!
@@ -27,7 +28,7 @@ export gradient
                     CUDADevice, AMDGPUDevice, MetalDevice, oneAPIDevice,
                     XLADevice,
                     # get_device, # we define get_device here for retrocompatibility
-                    # gpu_backend!, # have to define here due to https://github.com/JuliaPackaging/Preferences.jl/issues/39
+                    gpu_backend!,
                     get_device_type,
                     DeviceIterator
 
@@ -118,7 +119,7 @@ include("losses/Losses.jl")
 using .Losses
 
 include("devices.jl")
-export get_device, gpu_backend!
+export get_device
 
 # Distributed Training
 include("distributed/backend.jl")

src/functor.jl

@@ -102,18 +102,6 @@ julia> m.bias
 """
 cpu(x) = cpu_device()(x)
 
-# TODO remove after https://github.com/LuxDL/Lux.jl/pull/1089
-ChainRulesCore.@non_differentiable cpu_device()
-
-
-# Remove when 
-# https://github.com/JuliaPackaging/Preferences.jl/issues/39
-# is resolved
-function gpu_backend!(backend::String)
-    @set_preferences!("gpu_backend" => backend)
-    MLDataDevices.gpu_backend!(backend)
-end
-
 """
     gpu(m)
 

src/layers/attention.jl

@@ -74,7 +74,7 @@ struct MultiHeadAttention{P1, D, P2}
   out_proj::P2
 end
 
-@layer MultiHeadAttention
+@layer :noexpand MultiHeadAttention
 
 function MultiHeadAttention(dims; 
                      nheads::Int = 8,

src/layers/basic.jl

@@ -60,7 +60,7 @@ end
 @forward Chain.layers Base.getindex, Base.length, Base.first, Base.last,
   Base.iterate, Base.lastindex, Base.keys, Base.firstindex
 
-@layer :expand Chain  # the option :expand opts-in to container-style pretty-printing
+@layer Chain
 
 (c::Chain)(x) = _applychain(c.layers, x)
 (c::Chain)(x, ys...) = _applychain(c.layers, (x, ys...))
@@ -334,7 +334,7 @@ end
 Maxout(layers...) = Maxout(layers)
 Maxout(f::Function, n_alts::Integer) = Maxout((f() for _ in 1:n_alts)...)
 
-@layer :expand Maxout
+@layer Maxout
 
 function (mo::Maxout)(input::AbstractArray)
   # Perhaps surprisingly, pairwise max broadcast is often faster,
@@ -381,7 +381,7 @@ struct SkipConnection{T,F}
   connection::F  #user can pass arbitrary connections here, such as (a,b) -> a + b
 end
 
-@layer :expand SkipConnection
+@layer SkipConnection
 
 function (skip::SkipConnection)(input)
   skip.connection(skip.layers(input), input)
@@ -575,7 +575,7 @@ end
 Parallel(connection, layers::Union{Tuple{}, @NamedTuple{}}) =
     throw(ArgumentError("cannot construct a Parallel layer with no sub-layers"))
 
-@layer :expand Parallel
+@layer Parallel
 
 (m::Parallel)(x) = m.connection(map(f -> f(x), Tuple(m.layers))...)  # one argument
 
@@ -705,7 +705,7 @@ end
 end
 applypairwisefusion(layers::NamedTuple, connection, x) = applypairwisefusion(Tuple(layers), connection, x)
 
-@layer :expand PairwiseFusion
+@layer PairwiseFusion
 
 Base.getindex(m::PairwiseFusion, i) = m.layers[i]
 Base.getindex(m::PairwiseFusion, i::AbstractVector) = PairwiseFusion(m.connection, m.layers[i])

src/layers/conv.jl

@@ -1,4 +1,3 @@
-using NNlib: conv, ∇conv_data, depthwiseconv, output_size
 
 # pad dims of x with dims of y until ndims(x) == ndims(y)
 _paddims(x::Tuple, y::Tuple) = (x..., y[(end - (length(y) - length(x) - 1)):end]...)

src/layers/macro.jl

@@ -1,21 +1,24 @@
 
 """
-    @layer Dense
-    @layer :expand Chain
-    @layer BatchNorm trainable=(β,γ)
-
+    @layer [showtype] MyModel [trainable=(field1,...)]
+
 This macro adds convenience functionality to a custom type to serve 
-as a neural network layer, module, or entire model.
+as a neural network layer, as a module, or as an entire model.
 
-The keyword `trainable` allows you to limit this exploration, instead of visiting all `fieldnames(T)`.
+The optional keyword `trainable` allows you to specify which fields of your model can be trained, 
+instead of assuming all `fieldnames(MyModel)` to trainable. 
 Note that it is never necessary to tell Flux to ignore non-array objects such as functions or sizes.
+This can be also be done by defining [`trainable(::MyModel)`](@ref Optimisers.trainable) for your type.
+
+The macro also handles overloads of the 3-arg `show(::IO, ::MIME"text/plain", ::MyModel)` for pretty printing. 
+The optional argument `showtype` can take any of the following values:
 
-The macro also handles overloads of `show` for pretty printing.
-* By default, it adds methods to 3-arg `Base.show` to treat your layer much like `Dense` or `Conv`.
-* If your layer is a container, more like `Chain` or `Parallel`, then `:expand` makes `show` unfold its contents.
-* To disable all `show` overloads, there is an `:ignore` option too.
+- `:expand` (default): This will expand the representation of container types like `Chain`, 
+   while maintaining a compat representation of types like `Dense` containing only arrays.
+- `:noexpand`: This is to be used in case your type contains other layers but you want to keep the representation simple.
+- `:ignore`: To opt out of the pretty printing.
 
-(You probably still want to define 2-arg `show(io::IO, x::Layer)`, the macro does not touch this.)
+You probably still want to define 2-arg `show(::IO, ::MyModel)`, the macro does not touch this.
 
 Note that re-running the macro with different options may not remove all methods, you will need to restart.
 
@@ -26,16 +29,22 @@ julia> struct Trio; a; b; c end
 julia> tri = Trio(Dense([1.1 2.2], [0.0], tanh), Dense(hcat(3.3), false), Dropout(0.4))
 Trio(Dense(2 => 1, tanh), Dense(1 => 1; bias=false), Dropout(0.4))
 
-julia> Flux.@layer :expand Trio
+julia> Flux.@layer Trio
 
 julia> tri  # now the layer is printed like Chain
 Trio(
   Dense(2 => 1, tanh),                  # 3 parameters
   Dense(1 => 1; bias=false),            # 1 parameters
   Dropout(0.4),
 )                   # Total: 3 arrays, 4 parameters, 240 bytes.
-```
 
+julia> Flux.@layer :noexpand Trio trainable=(a,b)
+
+julia> tri  # now the layer is printed compactly
+Trio(Dense(2 => 1, tanh), Dense(1 => 1; bias=false), Dropout(0.4))  # 4 parameters
+
+julia> opt_state = Flux.setup(Adam(), tri); # `c` is not in the optimizer state
+```
 """
 macro layer(exs...)
   _layer_macro(exs...)
@@ -46,14 +55,17 @@ function _layer_macro(exs...)
 
   # These functions are defined in show.jl, and each return an expression overloading Base.show
   type, rest... = if exs[1] == QuoteNode(:expand)
-    push!(out.args, _macro_big_show(esc(exs[2])))  
+    push!(out.args, _macro_big_show(esc(exs[2])))
+    exs[2:end]
+  elseif exs[1] == QuoteNode(:noexpand)
+    push!(out.args, _macro_layer_show(esc(exs[2])))
     exs[2:end]
   elseif exs[1] == QuoteNode(:ignore)
     exs[2:end]
   elseif exs[1] isa QuoteNode
-    error("`@layer` accepts only two options before the layer type, `:expand` and `:ignore` (to control `show`)")
+    error("`@layer` accepts only the options `:ignore`, `:noexpand`, and `:expand` before the layer type (to control `show`).")
   else
-    push!(out.args, _macro_layer_show(esc(exs[1])))
+    push!(out.args, _macro_big_show(esc(exs[1])))
     exs
   end
 

src/layers/normalise.jl

@@ -198,7 +198,7 @@ end
 LayerNorm(size::Integer...; kw...) = LayerNorm(Int.(size); kw...)
 LayerNorm(size_act...; kw...) = LayerNorm(Int.(size_act[1:end-1]), size_act[end]; kw...)
 
-@layer LayerNorm
+@layer :noexpand LayerNorm
 
 function (a::LayerNorm)(x::AbstractArray)
   ChainRulesCore.@ignore_derivatives if a.diag isa Scale

src/layers/recurrent.jl

@@ -158,7 +158,7 @@ struct Model
   h0::AbstractVector
 end
 
-Flux.@layer :expand Model
+Flux.@layer Model
 
 (m::Model)(x) = m.rnn(x, m.h0)
 
@@ -169,7 +169,7 @@ struct RNN{M}
   cell::M
 end
 
-@layer :expand RNN
+@layer RNN
 
 function RNN((in, out)::Pair, σ = tanh; cell_kwargs...)
   cell = RNNCell(in => out, σ; cell_kwargs...)
@@ -344,7 +344,7 @@ struct Model
   c0::AbstractVector
 end
 
-Flux.@layer :expand Model
+Flux.@layer Model
 
 (m::Model)(x) = m.lstm(x, (m.h0, m.c0))
 
@@ -359,7 +359,7 @@ struct LSTM{M}
   cell::M
 end
 
-@layer :expand LSTM
+@layer LSTM
 
 function LSTM((in, out)::Pair; cell_kwargs...)
   cell = LSTMCell(in => out; cell_kwargs...)
@@ -531,7 +531,7 @@ struct GRU{M}
   cell::M
 end
 
-@layer :expand GRU
+@layer GRU
 
 function GRU((in, out)::Pair; cell_kwargs...)
   cell = GRUCell(in => out; cell_kwargs...)
@@ -669,7 +669,7 @@ struct GRUv3{M}
   cell::M
 end
 
-@layer :expand GRUv3
+@layer GRUv3
 
 function GRUv3((in, out)::Pair; cell_kwargs...)
   cell = GRUv3Cell(in => out; cell_kwargs...)

src/layers/show.jl

@@ -1,6 +1,6 @@
 @nospecialize  # just for this file, for startup time
 
-# This is called by @layer :expand, on layers which should be treated like Chain, and returns an expression:
+# This is called by @layer and returns an expression:
 function _macro_big_show(ex)
   quote
     # Entry point:
@@ -83,7 +83,7 @@ function _flat_children(x)
     gamma = ((beta...)...,)
 end
 
-# This is called by @layer, on layers which should be treated like Dense, and returns an expression:
+# This is called by @layer :noexpand, on layers which should be treated like Dense, and returns an expression:
 function _macro_layer_show(ex)
   quote
     # Entry point:
@@ -176,40 +176,53 @@ _all(f, xs) = !_any(!f, xs)
 
 #=
 
-julia> struct Tmp2; x; y; end; Flux.@functor Tmp2
+julia> struct Tmp2; x; y; end;
 
-# Before, notice Array(), NamedTuple(), and values
+julia> t = Tmp2([Dense(2,3), randn(3,4)'], (x=1:4, y=Dense(3,4), z=rand(3)))
+Tmp2(Any[Dense(2 => 3), [-0.559390071462934 -0.6357914190386781 -0.8516823037180543; -2.187495592853204 -0.6807254521505784 -1.2334639710489697; -0.12790952072543338 -1.4672700459421741 1.3687526519721238; 0.5232171922680576 -1.012045481192333 1.4953790632112915]], (x = 1:4, y = Dense(3 => 4), z = [0.29222096031585143, 0.6562195256556428, 0.9741896713499167]))
 
-julia> Chain(Tmp2([Dense(2,3), randn(3,4)'], (x=1:3, y=Dense(3,4), z=rand(3))))
+julia> Chain(t)
 Chain(
   Tmp2(
-    Array(
+    [
       Dense(2 => 3),                    # 9 parameters
-      [0.351978391016603 0.6408681372462821 -1.326533184688648; 0.09481930831795712 1.430103476272605 0.7250467613675332; 2.03372151428719 -0.015879812799495713 1.9499692162118236; -1.6346846180722918 -0.8364610153059454 -1.2907265737483433],  # 12 parameters
-    ),
-    NamedTuple(
-      1:3,                              # 3 parameters
-      Dense(3 => 4),                    # 16 parameters
-      [0.9666158193429335, 0.01613900990539574, 0.0205920186127464],  # 3 parameters
+      4×3 Adjoint{Float64,...},         # 12 parameters
+    ],
+    (;
+      x = 4-element UnitRange{Int64},
+      y = Dense(3 => 4),                # 16 parameters
+      z = 3-element Vector{Float64},    # 3 parameters
     ),
   ),
-)                   # Total: 7 arrays, 43 parameters, 644 bytes.
+)         # Total: 6 trainable arrays, 40 parameters,
+          # plus 1 non-trainable, 4 parameters, summarysize 620 bytes.
+
 
-# After, (; x=, y=, z=) and "3-element Array"
+julia> Flux.@layer Tmp2
 
-julia> Chain(Tmp2([Dense(2,3), randn(3,4)'], (x=1:3, y=Dense(3,4), z=rand(3))))
+julia> t
+Tmp2(
+  [
+    Dense(2 => 3),                      # 9 parameters
+    4×3 Adjoint{Float64,...},           # 12 parameters
+  ],
+  4-element UnitRange{Int64},
+  Dense(3 => 4),                        # 16 parameters
+  3-element Vector{Float64},            # 3 parameters
+)         # Total: 6 trainable arrays, 40 parameters,
+          # plus 1 non-trainable, 4 parameters, summarysize 620 bytes.
+
+julia> Chain(t)
 Chain(
   Tmp2(
     [
       Dense(2 => 3),                    # 9 parameters
-      4×3 Adjoint,                      # 12 parameters
+      4×3 Adjoint{Float64,...},         # 12 parameters
     ],
-    (;
-      x = 3-element UnitRange,          # 3 parameters
-      y = Dense(3 => 4),                # 16 parameters
-      z = 3-element Array,              # 3 parameters
-    ),
+    4-element UnitRange{Int64},
+    Dense(3 => 4),                      # 16 parameters
+    3-element Vector{Float64},          # 3 parameters
   ),
-)                   # Total: 7 arrays, 43 parameters, 644 bytes.
-
+)         # Total: 6 trainable arrays, 40 parameters,
+          # plus 1 non-trainable, 4 parameters, summarysize 620 bytes.
 =#