Recurrence layer

.github/workflows/ci.yml

@@ -75,4 +75,4 @@ jobs:
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
           DOCUMENTER_KEY: ${{ secrets.DOCUMENTER_KEY }}
-          DATADEPS_ALWAYS_ACCEPT: true
+

docs/make.jl

@@ -2,6 +2,7 @@ using Documenter, Flux, NNlib, Functors, MLUtils, BSON, Optimisers,
       OneHotArrays, Zygote, ChainRulesCore, Plots, MLDatasets, Statistics, 
       DataFrames, JLD2, MLDataDevices
 
+ENV["DATADEPS_ALWAYS_ACCEPT"] = true
 
 DocMeta.setdocmeta!(Flux, :DocTestSetup, :(using Flux); recursive = true)
 

docs/src/guide/models/basics.md

@@ -185,7 +185,7 @@ These matching nested structures are at the core of how Flux works.
 <h3><img src="../../../assets/zygote-crop.png" width="40px"/>&nbsp;<a href="https://github.com/FluxML/Zygote.jl">Zygote.jl</a></h3>
 ```
 
-Flux's [`gradient`](@ref) function by default calls a companion packages called [Zygote](https://github.com/FluxML/Zygote.jl).
+Flux's [`gradient`](@ref Flux.gradient) function by default calls a companion packages called [Zygote](https://github.com/FluxML/Zygote.jl).
 Zygote performs source-to-source automatic differentiation, meaning that `gradient(f, x)`
 hooks into Julia's compiler to find out what operations `f` contains, and transforms this
 to produce code for computing `∂f/∂x`.
@@ -372,7 +372,7 @@ How does this `model3` differ from the `model1` we had before?
   Its contents is stored in a tuple, thus `model3.layers[1].weight` is an array.
 * Flux's layer [`Dense`](@ref Flux.Dense) has only minor differences from our `struct Layer`:
   - Like `struct Poly3{T}` above, it has type parameters for its fields -- the compiler does not know exactly what type `layer3s.W` will be, which costs speed.
-  - Its initialisation uses not `randn` (normal distribution) but [`glorot_uniform`](@ref) by default.
+  - Its initialisation uses not `randn` (normal distribution) but [`glorot_uniform`](@ref  Flux.glorot_uniform) by default.
   - It reshapes some inputs (to allow several batch dimensions), and produces more friendly errors on wrong-size input.
   - And it has some performance tricks: making sure element types match, and re-using some memory.
 * The function [`σ`](@ref NNlib.sigmoid) is calculated in a slightly better way,

docs/src/guide/models/recurrence.md

@@ -166,3 +166,50 @@ opt_state = Flux.setup(AdamW(1e-3), model)
 g = gradient(m -> Flux.mse(m(x), y), model)[1]
 Flux.update!(opt_state, model, g)
 ```
+
+Finally, the [`Recurrence`](@ref) layer can be used wrap any recurrent cell to process the entire sequence at once. For instance, a type behaving the same as the `LSTM` layer can be defined as follows:
+
+```julia
+rnn = Recurrence(LSTMCell(2 => 3))   # similar to LSTM(2 => 3)
+x = rand(Float32, 2, 4, 3)
+y = rnn(x)
+```
+
+## Stacking recurrent layers
+
+Recurrent layers can be stacked to form a deeper model by simply chaining them together using the [`Chain`](@ref) layer. The output of a layer is fed as input to the next layer in the chain.
+For instance, a model with two LSTM layers can be defined as follows:
+
+```julia
+stacked_rnn = Chain(LSTM(3 => 5), Dropout(0.5), LSTM(5 => 5))
+x = rand(Float32, 3, 4)
+y = stacked_rnn(x)
+```
+
+If more fine grained control is needed, for instance to have a trainable initial hidden state, one can define a custom model as follows: 
+
+```julia
+struct StackedRNN{L,S}
+    layers::L
+    states0::S
+end
+
+Flux.@layer StackedRNN
+
+function StackedRNN(d::Int; num_layers::Int)
+    layers = [LSTM(d => d) for _ in 1:num_layers]
+    states0 = [Flux.initialstates(l) for l in layers]
+    return StackedRNN(layers, states0)
+end
+
+function (m::StackedRNN)(x)
+   for (layer, state0) in zip(rnn.layers, rnn.states0)
+       x = layer(x, state0) 
+   end
+   return x
+end
+
+rnn = StackedRNN(3; num_layers=2)
+x = rand(Float32, 3, 10)
+y = rnn(x)
+```

docs/src/reference/models/layers.md

@@ -104,6 +104,7 @@ PairwiseFusion
 Much like the core layers above, but can be used to process sequence data (as well as other kinds of structured data).
 
 ```@docs
+Recurrence
 RNNCell
 RNN
 LSTMCell

src/Flux.jl

@@ -38,7 +38,7 @@ using EnzymeCore: EnzymeCore
 export Chain, Dense, Embedding, EmbeddingBag,
        Maxout, SkipConnection, Parallel, PairwiseFusion,
        RNNCell, LSTMCell, GRUCell, GRUv3Cell,
-       RNN, LSTM, GRU, GRUv3,
+       RNN, LSTM, GRU, GRUv3, Recurrence,
        SamePad, Conv, CrossCor, ConvTranspose, DepthwiseConv,
        AdaptiveMaxPool, AdaptiveMeanPool, GlobalMaxPool, GlobalMeanPool, MaxPool, MeanPool,
        Dropout, AlphaDropout,

src/layers/recurrent.jl

@@ -1,8 +1,7 @@
 out_from_state(state) = state
 out_from_state(state::Tuple) = state[1]
 
-function scan(cell, x, state0)
-  state = state0
+function scan(cell, x, state)
   y = []
   for x_t in eachslice(x, dims = 2)
     state = cell(x_t, state)
@@ -12,7 +11,67 @@
   return stack(y, dims = 2)
 end
 
+"""
+    Recurrence(cell)
+
+Create a recurrent layer that processes entire sequences out
+of a recurrent `cell`, such as an [`RNNCell`](@ref), [`LSTMCell`](@ref), or [`GRUCell`](@ref),
+similarly to how [`RNN`](@ref), [`LSTM`](@ref), and [`GRU`](@ref) process sequences.
+
+The `cell` should be a callable object that takes an input `x` and a hidden state `state` and returns
+a new hidden state `state'`. The `cell` should also implement the `initialstates` method that returns
+the initial hidden state. The output of the `cell` is considered to be:
+1. The first element of the `state` tuple if `state` is a tuple (e.g. `(h, c)` for LSTM).
+2. The `state` itself if `state` is not a tuple, e.g. an array `h` for RNN and GRU.
+
+# Forward
+
+    rnn(x, [state])
+
+The input `x` should be an array of size `in x len` or `in x len x batch_size`, 
+where `in` is the input dimension of the cell, `len` is the sequence length, and `batch_size` is the batch size.
+The `state` should be a valid state for the recurrent cell. If not provided, it obtained by calling
+`Flux.initialstates(cell)`.
+
+The output is an array of size `out x len x batch_size`, where `out` is the output dimension of the cell.
+
+The operation performed is semantically equivalent to the following code:
+```julia
+out_from_state(state) = state
+out_from_state(state::Tuple) = state[1]
 
+state = Flux.initialstates(cell)
+out = []
+for x_t in eachslice(x, dims = 2)
+  state = cell(x_t, state)
+  out = [out..., out_from_state(state)]
+end
+stack(out, dims = 2)
+```
+
+# Examples
+
+```jldoctest
+julia> rnn = Recurrence(RNNCell(2 => 3))
+Recurrence(
+  RNNCell(2 => 3, tanh),                # 18 parameters
+)                   # Total: 3 arrays, 18 parameters, 232 bytes.
+
+julia> x = rand(Float32, 2, 3, 4); # in x len x batch_size
+
+julia> y = rnn(x); # out x len x batch_size
+```
+"""
+struct Recurrence{M}
+  cell::M
+end
+
+@layer Recurrence
+
+initialstates(rnn::Recurrence) = initialstates(rnn.cell)
+
+(rnn::Recurrence)(x::AbstractArray) = rnn(x, initialstates(rnn))
+(rnn::Recurrence)(x::AbstractArray, state) = scan(rnn.cell, x, state)
 
 # Vanilla RNN
 @doc raw"""
@@ -184,9 +243,7 @@
 julia> h = zeros(Float32, (d_out, batch_size));
 
 julia> rnn = RNN(d_in => d_out)
-RNN(
-  RNNCell(4 => 6, tanh),                # 66 parameters
-)                   # Total: 3 arrays, 66 parameters, 424 bytes.
+RNN(4 => 6, tanh)   # 66 parameters
 
 julia> y = rnn(x, h);   # [y] = [d_out, len, batch_size]
 ```
@@ -211,7 +268,7 @@
   cell::M
 end
 
-@layer RNN
+@layer :noexpand RNN
 
 initialstates(rnn::RNN) = initialstates(rnn.cell)
 
@@ -232,6 +289,12 @@
   return scan(m.cell, x, h)
 end
 
+function Base.show(io::IO, m::RNN)
+  print(io, "RNN(", size(m.cell.Wi, 2), " => ", size(m.cell.Wi, 1))
+  print(io, ", ", m.cell.σ)
+  print(io, ")")
+end
+
 
 # LSTM
 @doc raw"""
@@ -400,7 +463,7 @@
   cell::M
 end
 
-@layer LSTM
+@layer :noexpand LSTM
 
 initialstates(lstm::LSTM) = initialstates(lstm.cell)
 
@@ -416,6 +479,10 @@
   return scan(m.cell, x, state0)
 end
 
+function Base.show(io::IO, m::LSTM)
+  print(io, "LSTM(", size(m.cell.Wi, 2), " => ", size(m.cell.Wi, 1) ÷ 4, ")")
+end
+
 # GRU
 
 @doc raw"""
@@ -568,7 +635,7 @@
   cell::M
 end
 
-@layer GRU
+@layer :noexpand GRU
 
 initialstates(gru::GRU) = initialstates(gru.cell)
 
@@ -584,6 +651,10 @@
   return scan(m.cell, x, h)
 end
 
+function Base.show(io::IO, m::GRU)
+  print(io, "GRU(", size(m.cell.Wi, 2), " => ", size(m.cell.Wi, 1) ÷ 3, ")")
+end
+
 # GRU v3
 @doc raw"""
     GRUv3Cell(in => out; init_kernel = glorot_uniform,
@@ -728,7 +799,7 @@
   cell::M
 end
 
-@layer GRUv3
+@layer :noexpand GRUv3
 
 initialstates(gru::GRUv3) = initialstates(gru.cell)
 
@@ -743,3 +814,7 @@
   @assert ndims(x) == 2 || ndims(x) == 3
   return scan(m.cell, x, h)
 end
+
+function Base.show(io::IO, m::GRUv3)
+  print(io, "GRUv3(", size(m.cell.Wi, 2), " => ", size(m.cell.Wi, 1) ÷ 3, ")")
+end

test/layers/recurrent.jl

@@ -305,3 +305,12 @@ end
     # no initial state same as zero initial state
     @test gru(x) ≈ gru(x, zeros(Float32, 4))
 end
+
+@testset "Recurrence" begin
+    x = rand(Float32, 2, 3, 4)
+    for rnn in [RNN(2 => 3), LSTM(2 => 3), GRU(2 => 3)]
+        cell = rnn.cell
+        rec = Recurrence(cell)
+        @test rec(x) ≈ rnn(x)
+    end
+end