Merge pull request #584 from LuxDL/ap/promote_compact

Improvement to the `@compact` API

.github/workflows/SpellCheck.yml

@@ -0,0 +1,13 @@
+name: Spell Check
+
+on: [pull_request]
+
+jobs:
+ typos-check:
+ name: Spell Check with Typos
+ runs-on: ubuntu-latest
+ steps:
+ - name: Checkout Actions Repository
+ uses: actions/checkout@v4
+ - name: Check spelling
+ uses: crate-ci/typos@v1.18.0

.typos.toml

@@ -0,0 +1,2 @@
+[default.extend-words]
+numer = "numer"

Project.toml

@@ -1,7 +1,7 @@
 name = "Lux"
 uuid = "b2108857-7c20-44ae-9111-449ecde12c47"
 authors = ["Avik Pal <avikpal@mit.edu> and contributors"]
-version = "0.5.34"
+version = "0.5.35"
 
 [deps]
 ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"

docs/src/api/Lux/contrib.md

@@ -6,8 +6,8 @@ CurrentModule = Lux
 
 All features listed on this page are **experimental** which means:
 
-1. No SemVer Guarantees. We use code here to iterate fast and most users should wait for
- these features to be marked non-experimental.
+1. No SemVer Guarantees. We use code here to iterate fast. That said, historically we have
+ never broken any code in this module and have always provided a deprecation period.
 2. Expect edge-cases and report them. It will help us move these features out of
  experimental sooner.
 3. None of the features are exported.
@@ -74,8 +74,14 @@ Lux.Experimental.DebugLayer
 Lux.Experimental.share_parameters
 ```
 
+## StatefulLuxLayer
+
+[`Lux.StatefulLuxLayer`](@ref) used to be part of experimental features, but has been
+promoted to stable API. It is now available via `Lux.StatefulLuxLayer`. Change all uses of
+`Lux.Experimental.StatefulLuxLayer` to `Lux.StatefulLuxLayer`.
+
 ## Compact Layer API
 
-```@docs
-Lux.Experimental.@compact
-```
+[`Lux.@compact`](@ref) used to be part of experimental features, but has been promoted to
+stable API. It is now available via `Lux.@compact`. Change all uses of
+`Lux.Experimental.@compact` to `Lux.@compact`.

docs/src/api/Lux/utilities.md

@@ -54,6 +54,11 @@ Lux.f64
 StatefulLuxLayer
 ```
 
+## Compact Layer
+
+```@docs
+@compact
+```
 
 ## Truncated Stacktraces
 

docs/src/introduction/index.md

@@ -48,21 +48,21 @@ standard AD and Optimisers API.
 
 ```@example quickstart
 # Get the device determined by Lux
-device = gpu_device()
+dev = gpu_device()
 
 # Parameter and State Variables
-ps, st = Lux.setup(rng, model) .|> device
+ps, st = Lux.setup(rng, model) .|> dev
 
 # Dummy Input
-x = rand(rng, Float32, 128, 2) |> device
+x = rand(rng, Float32, 128, 2) |> dev
 
 # Run the model
 y, st = Lux.apply(model, x, ps, st)
 
 # Gradients
 ## Pullback API to capture change in state
-(l, st_), pb = pullback(p -> Lux.apply(model, x, p, st), ps)
-gs = pb((one.(l), nothing))[1]
+(l, st_), pb = pullback(Lux.apply, model, x, ps, st)
+gs = pb((one.(l), nothing))[3]
 
 # Optimization
 st_opt = Optimisers.setup(Adam(0.0001f0), ps)
@@ -74,7 +74,6 @@ st_opt, ps = Optimisers.update(st_opt, ps, gs)
 ```@example custom_compact
 using Lux, Random, Optimisers, Zygote
 # using LuxCUDA, LuxAMDGPU, Metal # Optional packages for GPU support
-import Lux.Experimental: @compact
 using Printf # For pretty printing
 ```
 

docs/src/manual/interface.md

@@ -20,6 +20,13 @@ First let's set the expectations straight.
  functionality in the core library (and officially supported ones) **must** adhere to
  the interface
 
+!!! tip
+
+ While writing out a custom struct and defining dispatches manually is a good way to
+ understand the interface, it is not the most concise way. We recommend using the
+ [`Lux.@compact`](@ref) macro to define layers which makes handling the states and
+ parameters downright trivial.
+
 ## Layer Interface
 
 ### Singular Layer
@@ -35,8 +42,8 @@ architecture cannot change.
 
 !!! tip
 
- For people coming from Flux.jl background this might be weird. We recommend checking out
- [the Flux to Lux migration guide](@ref migrate-from-flux) first before proceeding.
+ For people coming from Flux.jl background, this might be weird. We recommend checking
+ out [the Flux to Lux migration guide](@ref migrate-from-flux) first before proceeding.
 
 ```@example layer_interface
 using Lux, Random
@@ -80,7 +87,7 @@ reconstruction of the parameters and states.
 println("Parameter Length: ", Lux.parameterlength(l), "; State Length: ",
  Lux.statelength(l))
 
-# But still recommened to define these
+# But still recommended to define these
 Lux.parameterlength(l::Linear) = l.out_dims * l.in_dims + l.out_dims
 
 Lux.statelength(::Linear) = 0

docs/src/manual/migrate_from_flux.md

@@ -99,7 +99,7 @@ end
 # `A` is not trainable
 Optimisers.trainable(f::FluxLinear) = (B=f.B,)
 
-# Needed so that both `A` and `B` can be transfered between devices
+# Needed so that both `A` and `B` can be transferred between devices
 Flux.@functor FluxLinear
 
 (l::FluxLinear)(x) = l.A * l.B * x

docs/src/tutorials/index.md

@@ -59,7 +59,7 @@ const advanced = [
  }
 ];
 
-const thrid_party = [
+const third_party = [
  {
  href: "https://docs.sciml.ai/Overview/stable/showcase/pinngpu/",
  src: "../pinn.gif",
@@ -114,7 +114,7 @@ of them are non-functional and we will try to get them updated.
 
 :::
 
-<Gallery :images="thrid_party" />
+<Gallery :images="third_party" />
 
 
 ::: tip

examples/DDIM/README.md

@@ -11,7 +11,7 @@ The model generates images from Gaussian noises by <em>denoising</em> iterativel
 # Usage
 Install Julia and instantiate `Project.toml`.
 
-Follwoing scripts are tested on a single NVIDIA Tesla T4 instance.
+Following scripts are tested on a single NVIDIA Tesla T4 instance.
 ## Dataset
 Download and extract `Dataset images` from [102 Category Flower Dataset](https://www.robots.ox.ac.uk/~vgg/data/flowers/102/).
 

examples/GravitationalWaveForm/main.jl

@@ -163,7 +163,7 @@ function compute_waveform(dt::T, soln, mass_ratio, model_params=nothing) where {
  m₁ = mass_ratio * m₂
 
  orbit₁, orbit₂ = one2two(orbit, m₁, m₂)
- waveform = h_22_strain_two_body(dt, orbit1, mass1, orbit2, mass2)
+ waveform = h_22_strain_two_body(dt, orbit₁, m₁, orbit₂, m₂)
  else
  waveform = h_22_strain_one_body(dt, orbit)
  end

examples/HyperNet/main.jl

@@ -31,33 +31,25 @@ function load_datasets(n_train=1024, n_eval=32, batchsize=256)
 end
 
 # ## Implement a HyperNet Layer
-struct HyperNet{W <: Lux.AbstractExplicitLayer, C <: Lux.AbstractExplicitLayer, A} <:
- Lux.AbstractExplicitContainerLayer{(:weight_generator, :core_network)}
- weight_generator::W
- core_network::C
- ca_axes::A
-end
-
-function HyperNet(w::Lux.AbstractExplicitLayer, c::Lux.AbstractExplicitLayer)
- ca_axes = Lux.initialparameters(Random.default_rng(), c) |> ComponentArray |> getaxes
- return HyperNet(w, c, ca_axes)
-end
-
-function Lux.initialparameters(rng::AbstractRNG, h::HyperNet)
- return (weight_generator=Lux.initialparameters(rng, h.weight_generator),)
+function HyperNet(weight_generator::Lux.AbstractExplicitLayer,
+ core_network::Lux.AbstractExplicitLayer)
+ ca_axes = Lux.initialparameters(Random.default_rng(), core_network) |>
+ ComponentArray |>
+ getaxes
+ return @compact(; ca_axes, weight_generator, core_network, dispatch=:HyperNet) do (x, y)
+ ## Generate the weights
+ ps_new = ComponentArray(vec(weight_generator(x)), ca_axes)
+ return core_network(y, ps_new)
+ end
 end
 
-function (hn::HyperNet)(x, ps, st::NamedTuple)
- ps_new, st_ = hn.weight_generator(x, ps.weight_generator, st.weight_generator)
- @set! st.weight_generator = st_
- return ComponentArray(vec(ps_new), hn.ca_axes), st
-end
+# Defining functions on the CompactLuxLayer requires some understanding of how the layer
+# is structured, as such we don't recommend doing it unless you are familiar with the
+# internals. In this case, we simply write it to ignore the initialization of the 
+# `core_network` parameters.
 
-function (hn::HyperNet)((x, y)::T, ps, st::NamedTuple) where {T <: Tuple}
- ps_ca, st = hn(x, ps, st)
- pred, st_ = hn.core_network(y, ps_ca, st.core_network)
- @set! st.core_network = st_
- return pred, st
+function Lux.initialparameters(rng::AbstractRNG, hn::CompactLuxLayer{:HyperNet})
+ return (; weight_generator=Lux.initialparameters(rng, hn.layers.weight_generator),)
 end
 
 # ## Create and Initialize the HyperNet

examples/NeuralODE/main.jl

@@ -42,7 +42,23 @@ function loadmnist(batchsize, train_split)
 end
 
 # ## Define the Neural ODE Layer
-#
+# 
+# First we will use the [`@compact`](@ref) macro to define the Neural ODE Layer.
+
+function NeuralODECompact(
+ model::Lux.AbstractExplicitLayer; solver=Tsit5(), tspan=(0.0f0, 1.0f0), kwargs...)
+ return @compact(; model, solver, tspan, kwargs...) do x, p
+ dudt(u, p, t) = vec(model(reshape(u, size(x)), p))
+ ## Note the `p.model` here
+ prob = ODEProblem(ODEFunction{false}(dudt), vec(x), tspan, p.model)
+ return solve(prob, solver; kwargs...)
+ end
+end
+
+# We recommend using the compact macro for creating custom layers. The below implementation
+# exists mostly for historical reasons when `@compact` was not part of the stable API. Also,
+# it helps users understand how the layer interface of Lux works.
+
 # The NeuralODE is a ContainerLayer, which stores a `model`. The parameters and states of
 # the NeuralODE are same as those of the underlying model.
 struct NeuralODE{M <: Lux.AbstractExplicitLayer, So, T, K} <:
@@ -154,6 +170,8 @@ function train(model_function; cpu::Bool=false, kwargs...)
  end
 end
 
+train(NeuralODECompact)
+
 train(NeuralODE)
 
 # We can also change the sensealg and train the model! `GaussAdjoint` allows you to use
@@ -173,8 +191,9 @@ train(NeuralODE; sensealg=ReverseDiffAdjoint(), cpu=true)
 
 # ## Alternate Implementation using Stateful Layer
 
-# Starting `v0.5.5`, Lux provides a `Lux.Experimental.StatefulLuxLayer` which can be used
-# to avoid the [`Box`ing of `st`](https://github.com/JuliaLang/julia/issues/15276).
+# Starting `v0.5.5`, Lux provides a [`StatefulLuxLayer`](@ref) which can be used
+# to avoid the [`Box`ing of `st`](https://github.com/JuliaLang/julia/issues/15276). Using
+# the `@compact` API avoids this problem entirely.
 struct StatefulNeuralODE{M <: Lux.AbstractExplicitLayer, So, T, K} <:
  Lux.AbstractExplicitContainerLayer{(:model,)}
  model::M
@@ -189,7 +208,7 @@ function StatefulNeuralODE(
 end
 
 function (n::StatefulNeuralODE)(x, ps, st)
- st_model = Lux.StatefulLuxLayer(n.model, ps, st)
+ st_model = StatefulLuxLayer(n.model, ps, st)
  dudt(u, p, t) = st_model(u, p)
  prob = ODEProblem{false}(ODEFunction{false}(dudt), x, n.tspan, ps)
  return solve(prob, n.solver; n.kwargs...), st_model.st
@@ -219,3 +238,9 @@ x = gpu_device()(ones(Float32, 28, 28, 1, 3));
 
 # Note, that we still recommend using this layer internally and not exposing this as the
 # default API to the users.
+
+# Finally checking the compact model
+
+model_compact, ps_compact, st_compact = create_model(NeuralODECompact)
+
+@code_warntype model_compact(x, ps_compact, st_compact)

examples/SimpleRNN/main.jl

@@ -99,6 +99,25 @@ function (s::SpiralClassifier)(
  return vec(y), st
 end
 
+# ## Using the `@compact` API
+
+# We can also define the model using the [`Lux.@compact`](@ref) API, which is a more concise
+# way of defining models. This macro automatically handles the boilerplate code for you and
+# as such we recommend this way of defining custom layers
+
+function SpiralClassifierCompact(in_dims, hidden_dims, out_dims)
+ lstm_cell = LSTMCell(in_dims => hidden_dims)
+ classifier = Dense(hidden_dims => out_dims, sigmoid)
+ return @compact(; lstm_cell, classifier) do x::AbstractArray{T, 3} where {T}
+ x_init, x_rest = Iterators.peel(Lux._eachslice(x, Val(2)))
+ y, carry = lstm_cell(x_init)
+ for x in x_rest
+ y, carry = lstm_cell((x, carry))
+ end
+ return vec(classifier(y))
+ end
+end
+
 # ## Defining Accuracy, Loss and Optimiser
 
 # Now let's define the binarycrossentropy loss. Typically it is recommended to use
@@ -125,12 +144,12 @@ accuracy(y_pred, y_true) = matches(y_pred, y_true) / length(y_pred)
 
 # ## Training the Model
 
-function main()
+function main(model_type)
  ## Get the dataloaders
  (train_loader, val_loader) = get_dataloaders()
 
  ## Create the model
- model = SpiralClassifier(2, 8, 1)
+ model = model_type(2, 8, 1)
  rng = Xoshiro(0)
 
  dev = gpu_device()
@@ -164,7 +183,12 @@ function main()
  return (train_state.parameters, train_state.states) |> cpu_device()
 end
 
-ps_trained, st_trained = main()
+ps_trained, st_trained = main(SpiralClassifier)
+nothing #hide
+
+# We can also train the compact model with the exact same code!
+
+ps_trained2, st_trained2 = main(SpiralClassifierCompact)
 nothing #hide
 
 # ## Saving the Model

src/Lux.jl

@@ -26,6 +26,10 @@ using PrecompileTools: @recompile_invalidations
  inputsize, outputsize, update_state, trainmode, testmode, setup, apply,
  display_name, replicate
  using LuxDeviceUtils: get_device
+
+ # @compact specific
+ using MacroTools: block, combinedef, splitdef
+ using ConstructionBase: ConstructionBase
 end
 
 @reexport using LuxCore, LuxLib, LuxDeviceUtils, WeightInitializers
@@ -56,6 +60,7 @@ include("contrib/contrib.jl")
 
 # Helpful Functionalities
 include("helpers/stateful.jl")
+include("helpers/compact.jl")
 
 # Transform to and from other frameworks
 include("transform/types.jl")
@@ -70,7 +75,8 @@ include("distributed/public_api.jl")
 include("deprecated.jl")
 
 # Layers
-export cpu, gpu
+export cpu, gpu # deprecated
+
 export Chain, Parallel, SkipConnection, PairwiseFusion, BranchLayer, Maxout, RepeatedLayer
 export Bilinear, Dense, Embedding, Scale
 export Conv, ConvTranspose, CrossCor, MaxPool, MeanPool, GlobalMaxPool, GlobalMeanPool,
@@ -83,6 +89,7 @@ export RNNCell, LSTMCell, GRUCell, Recurrence, StatefulRecurrentCell
 export SamePad, TimeLastIndex, BatchLastIndex
 
 export StatefulLuxLayer
+export @compact, CompactLuxLayer
 
 export f16, f32, f64