explicit train, take 2

NEWS.md

@@ -2,6 +2,7 @@
 
 ## v0.13.7
 * Added [`@autosize` macro](https://github.com/FluxML/Flux.jl/pull/2078)
+* New method of `train!` using Zygote's "explicit" mode, allows changing AD back-end.
 
 ## v0.13.4
 * Added [`PairwiseFusion` layer](https://github.com/FluxML/Flux.jl/pull/1983)

Project.toml

@@ -36,11 +36,13 @@ MacroTools = "0.5"
 NNlib = "0.8.9"
 NNlibCUDA = "0.2.4"
 OneHotArrays = "0.1"
-Optimisers = "0.2.1"
+Optimisers = "0.2.10"
 ProgressLogging = "0.1"
 Reexport = "0.2, 1.0"
 SpecialFunctions = "1.8.2, 2.1.2"
 StatsBase = "0.33"
+Tracker = "0.2.22"
+Yota = "0.8.1"
 Zygote = "0.6.34"
 julia = "1.6"
 
@@ -50,7 +52,9 @@ Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 FillArrays = "1a297f60-69ca-5386-bcde-b61e274b549b"
 IterTools = "c8e1da08-722c-5040-9ed9-7db0dc04731e"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
+Tracker = "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c"
+Yota = "cd998857-8626-517d-b929-70ad188a48f0"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["Test", "Documenter", "IterTools", "LinearAlgebra", "FillArrays", "ComponentArrays"]
+test = ["Test", "Documenter", "IterTools", "LinearAlgebra", "FillArrays", "ComponentArrays", "Tracker", "Yota"]

docs/src/models/overview.md

@@ -77,9 +77,9 @@ julia> predict(x_train)
 In order to make better predictions, you'll need to provide a *loss function* to tell Flux how to objectively *evaluate* the quality of a prediction. Loss functions compute the cumulative distance between actual values and predictions. 
 
 ```jldoctest overview; filter = r"[+-]?([0-9]*[.])?[0-9]+(f[+-]*[0-9])?"
-julia> loss(x, y) = Flux.Losses.mse(predict(x), y);
+julia> loss(model, x, y) = mean(abs2.(model(x) .- y));
 
-julia> loss(x_train, y_train)
+julia> loss(predict, x_train, y_train)
 122.64734f0
 ```
 
@@ -131,7 +131,7 @@ The first parameter is the weight and the second is the bias. Flux will adjust p
 This optimiser implements the classic gradient descent strategy. Now improve the parameters of the model with a call to [`Flux.train!`](@ref) like this:
 
 ```jldoctest overview
-julia> train!(loss, parameters, data, opt)
+julia> train!(loss, predict, data, opt)
 ```
 
 And check the loss:
@@ -156,10 +156,10 @@ In the previous section, we made a single call to `train!` which iterates over t
 
 ```jldoctest overview; filter = r"[+-]?([0-9]*[.])?[0-9]+(f[+-]*[0-9])?"
 julia> for epoch in 1:200
-         train!(loss, parameters, data, opt)
+         train!(loss, predict, data, opt)
        end
 
-julia> loss(x_train, y_train)
+julia> loss(predict, x_train, y_train)
 0.00339581f0
 
 julia> parameters
@@ -188,7 +188,7 @@ First, we gathered real-world data into the variables `x_train`, `y_train`, `x_t
 
 Then, we built a single input, single output predictive model, `predict = Dense(1 => 1)`. The initial predictions weren't accurate, because we had not trained the model yet.
 
-After building the model, we trained it with `train!(loss, parameters, data, opt)`. The loss function is first, followed by the `parameters` holding the weights and biases of the model, the training data, and the `Descent` optimizer provided by Flux. We ran the training step once, and observed that the parameters changed and the loss went down. Then, we ran the `train!` many times to finish the training process.
+After building the model, we trained it with `train!(loss, predict, data, opt)`. The loss function is first, followed by the model itself, the training data, and the `Descent` optimizer provided by Flux. We ran the training step once, and observed that the parameters changed and the loss went down. Then, we ran the `train!` many times to finish the training process.
 
 After we trained the model, we verified it with the test data to verify the results. 
 

src/Flux.jl

@@ -34,6 +34,10 @@ export Descent, Adam, Momentum, Nesterov, RMSProp,
   AdamW, RAdam, AdaBelief, InvDecay, ExpDecay,
   WeightDecay, ClipValue, ClipNorm
 
+include("train.jl")
+using .Train
+# using .Train: setup, @train_autodiff
+
 using CUDA
 const use_cuda = Ref{Union{Nothing,Bool}}(nothing)
 

src/deprecations.jl

@@ -82,3 +82,65 @@ Base.@deprecate_binding ADAGrad AdaGrad
 Base.@deprecate_binding ADADelta AdaDelta
 
 @deprecate rng_from_array() default_rng_value()
+
+#=
+  # Valid method in Optimise, old implicit style, is:
+  train!(loss, ps::Params, data, opt::AbstractOptimiser; cb = () -> ())
+  # Valid methods in Train, new explict style, are:
+  train!(loss, model, data, opt)
+  train!(loss, model, data, opt::Optimisers.AbstractRule)
+  # ... and 3-arg:
+  train!(loss, model, opt)
+  train!(loss, model, opt::Optimisers.AbstractRule)
+  # Provide friendly errors for what happens if you mix these up:
+=#
+import .Optimise: train!
+train!(loss, ps::Params, data, opt) = error("can't mix implict Params with explict state")
+train!(loss, ps::Params, opt) = error("can't mix implict Params with explict state")
+
+train!(loss, ps::Params, data, opt::Optimisers.AbstractRule) = error("can't mix implict Params with explict rule")
+train!(loss, ps::Params, opt::Optimisers.AbstractRule) = error("can't mix implict Params with explict rule")
+
+train!(loss, model, data, opt::Optimise.AbstractOptimiser) = train!(loss, model, data, _old_to_new(opt))
+train!(loss, model, opt::Optimise.AbstractOptimiser) = train!(loss, model, _old_to_new(opt))
+
+train!(loss, ps::Params, opt::Optimise.AbstractOptimiser; cb=0) = error("3-arg train does not exist for implicit mode")
+
+# train!(loss::Function, ps::Zygote.Params, data, opt) = throw(ArgumentError(
+#   """On Flux 0.14, `train!` no longer accepts implicit `Zygote.Params`.
+#   Instead of `train!(loss_xy, Flux.params(model), data, Adam())`
+#   it now needs `opt = Flux.setup(Adam(), model); train!(loss_mxy, model, data, opt)`
+#   where `loss_mxy` accepts the model as its first argument.
+#   """
+# ))
+
+# Next, to use the new `setup` with the still-exported old-style Adam etc:
+import .Train: setup
+setup(rule::Optimise.AbstractOptimiser, model) = setup(_old_to_new(rule), model)
+
+for T in [:Descent, :Adam, :Momentum, :Nesterov, :RMSProp,
+   	      :AdaGrad, :AdaMax, :AdaDelta, :AMSGrad, :NAdam, :RAdam, :OAdam, :AdaBelief,
+   	      # :InvDecay, :ExpDecay, 
+          ]
+  @eval function _old_to_new(rule::$T)
+    args = map(f -> getfield(rule, f), fieldnames(Optimisers.$T))
+    Optimisers.$T(args...)
+  end
+end
+_old_to_new(rule::Optimiser) = Optimisers.OptimiserChain(map(_old_to_new, rule.os)...)
+const OptimiserChain = Optimise.Optimiser  # lets you use new name with implicit params too.
+_old_to_new(rule::WeightDecay) = Optimisers.WeightDecay(rule.wd)  # called gamma now
+_old_to_new(rule::ClipNorm) = Optimisers.ClipNorm(rule.thesh)  # called omega, and there are more fields 
+_old_to_new(rule::ClipValue) = Optimisers.ClipGrad(rule.thesh)  # called delta now, and struct name differs
+const ClipGrad = Optimise.ClipValue
+_old_to_new(rule::RMSProp) = Optimisers.RMSProp(rule.eta, rule.rho, rule.epsilon)  # RMSProp has no field centred
+
+_old_to_new(rule) = error("Flux.setup does not know how to translate this old-style implicit rule to a new-style Optimisers.jl explicit rule")
+
+Optimisers.setup(rule::Optimise.AbstractOptimiser, model) = error("please use Flux.setup not Optimisers.setup, it may be able to translate this rule")
+
+# v0.14 deprecations
+
+# Enable these when 0.14 is released, and delete const ClipGrad = Optimise.ClipValue etc: 
+# Base.@deprecate_binding Optimiser OptimiserChain
+# Base.@deprecate_binding ClipValue ClipGrad

src/optimise/train.jl

@@ -1,16 +1,23 @@
 using ProgressLogging: @progress, @withprogress, @logprogress
 import Zygote: Params, gradient
 
+# Add methods to Optimisers.jl's function, so that there is just one Flux.update!
+# for both explicit and implicit parameters.
+import Optimisers.update!
 
 """
     update!(opt, p, g)
     update!(opt, ps::Params, gs)
 
 Perform an update step of the parameters `ps` (or the single parameter `p`)
-according to optimizer `opt`  and the gradients `gs` (the gradient `g`).
+according to optimizer `opt::AbstractOptimiser`  and the gradients `gs` (the gradient `g`).
 
 As a result, the parameters are mutated and the optimizer's internal state may change.
 The gradient could be mutated as well.
+
+!!! note
+    This method for implicit `Params` (and `AbstractOptimiser`) will be removed from Flux 0.14.
+    The explicit method `update!(opt, model, grad)` from Optimisers.jl will remain.
 """
 function update!(opt::AbstractOptimiser, x, x̄)
   x̄r = ArrayInterface.restructure(x, x̄) # address some cases where Zygote's
@@ -88,6 +95,10 @@ batchmemaybe(x::Tuple) = x
 Uses a `loss` function and training `data` to improve the 
 model's parameters according to a particular optimisation rule `opt`.
 
+!!! note
+    This method with implicit `Params` will be removed from Flux 0.14.
+    It should be replaced with the explicit method `train!(loss, model, data, opt)`.
+
 For each `d in data`, first the gradient of the `loss` is computed like this:
 ```
     gradient(() -> loss(d...), pars)  # if d isa Tuple