tidy, tests

src/deprecations.jl

@@ -34,11 +34,6 @@ struct Zeros
 end
 Zeros(args...) = Zeros()  # was used both Dense(10, 2, initb = Zeros) and Dense(rand(2,10), Zeros())
 
-# function Optimise.update!(x::AbstractArray, x̄)
-#   Base.depwarn("`Flux.Optimise.update!(x, x̄)` was not used internally and has been removed. Please write `x .-= x̄` instead.", :update!)
-#   x .-= x̄
-# end
-
 function Diagonal(size::Integer...; kw...)
   Base.depwarn("Flux.Diagonal is now Flux.Scale, and also allows an activation function.", :Diagonal)
   Scale(size...; kw...)

src/train/Train.jl

@@ -4,7 +4,7 @@ using LinearAlgebra
 using Optimisers: Optimisers
 using Functors: fmap
 
-export train!, update!, adjust!, FluxState, @epochs,
+export train!, update!, adjust!, FluxState,
 	Descent, Adam, Momentum, Nesterov, RMSProp,
 	AdaGrad, AdaMax, AdaDelta, AMSGrad, NAdam, AdamW, RAdam, OAdam, AdaBelief #,
   # InvDecay, ExpDecay, WeightDecay, stop, skip, Optimiser,
@@ -15,7 +15,7 @@ export train!, update!, adjust!, FluxState, @epochs,
 
 """
     FluxState(rule, state=missing)
-    
+
 This is an interface between the all-mutable world Flux.jl likes,
 and the could-be-immutable world that Optimisers.jl inhabits.
 
@@ -56,34 +56,14 @@ end
 
 ### Two styles of gradient, and their `train!` functions
 
-using ProgressLogging: @progress, @withprogress, @logprogress
+using ProgressLogging: @progress, @withprogress, @logprogress  # TODO add progress logging again
 using Zygote: Zygote, Params
 
-include("explicit_train.jl.jl")  # new!
-include("implicit_train.jl.jl")  # Params etc, Zygote only
+include("explicit_train.jl")  # new!
+include("implicit_train.jl")  # Params etc, Zygote only
 
 explicit_withgradient(f, args...) = Zygote.withgradient(f, args...)  # can overload this to use e.g. Yota / Diffractor
 
-# using Requires  # Flux doesn't use this right now
-# @init @require Diffractor="9f5e2b26-1114-432f-b630-d3fe2085c51c" begin
-#   @eval function explicit_withgradient(f, args...)
-#     y, back = Diffractor.∂⃖¹(f, args...)
-#     _, grads... = back(Zygote.sensitivity(y))
-#     return (; value = y, gradient = grads)
-#   end
-# end
-
-#=
-
-using Diffractor
-function Flux.Train.explicit_withgradient(f, args...)
-  y, back = Diffractor.∂⃖¹(f, args...)
-  _, grads... = back(one(y))
-  return (; value = y, gradient = grads)
-end
-
-=#
-
 ### Misc. related utilities
 
 """
@@ -107,94 +87,4 @@ function adjust!(opt::FluxState, eta::Real)
   return opt
 end
 
-"""
-    @epochs N body
-
-Run `body` expression `N` times. Mainly useful for quickly doing
-multiple epochs of training in a REPL.
-
-Functionally equivalent to this loop:
-```
-for _ in 1:N   
-    body
-end
-```
-... but adds progress logging and `@info` messages,
-and returns the result of the last iteration.
-
-# Examples
-```jldoctest
-julia> Flux.@epochs 2 println("hello")
-[ Info: Epoch 1
-hello
-[ Info: Epoch 2
-hello
-```
-"""
-macro epochs(n, ex)
-  @gensym val
-  body = :(for i in 1:$(esc(n))
-    @info "Epoch $i"
-    $(esc(val)) = $(esc(ex))
-  end)
-  loop = Expr(:macrocall, Symbol("@progress"), __source__, body)
-  Expr(:block, :($(esc(val)) = nothing), loop, :($(esc(val))))
-  # TODO make this actualy return the value? Names aren't right.
-#
-#     $loop
-#     # @progress for i in 1:$(esc(n))
-# #         @info "Epoch $i"
-# #         $(esc(val)) = $(esc(ex))
-# #     end
-#     $val  # DOESN"T WORK! Expr(:macrocall, ...) ?
-#   end
-end
-
-end
-
-
-#=
-
-using Flux, Random
-data = [(rand(3,2).*[i,1,20/i], [i i]) for i in 1:50] |> shuffle!;
-
-# This exact code works on [email protected]. There, train! returns nothing:
-model2 = Chain(Dense(3 => 7, relu), Dense(7 => 1))
-opt2 = Flux.Adam()
-Flux.train!(Flux.params(model2), data, opt2) do x, y
-  Flux.mse(model2(x), y)
-end
-opt2  # contains an IdDict
-
-# This is the new "explicit" method of Train
-model1 = Chain(Dense(3 => 7, relu), Dense(7 => 1))
-opt1 = Flux.Adam()
-Flux.train!(model1, data, opt1) do m, x, y
-  Flux.mse(m(x), y)
-end |> sum
-opt1  # contains state tree
-
-# This is new 3-arg train!, one step not an iteration over data:
-x1, y1 = data[1]
-Flux.train!(model1, opt1) do m
-  Flux.mse(m(x1), y1)
-end
-
-
-
-
-
-julia> using ProgressLogging
-julia> @macroexpand1 @loop N body
-begin
-  x = nothing
-  @progress for i in 1:N
-    @info "step $i"
-    x = body
-  end
-  x
-end
-
-
-
-=#
+end # module

src/train/explicit_train.jl

@@ -52,26 +52,28 @@ function train!(loss::Function, model, data, opt::FluxState)
   _initialise!(opt, model)
   losses = Float32[]
   s = opt.state
-  s isa IdDict && error("can't mix explicit & implicit!")
+  s isa IdDict && error("""Can't mix explicit & implicit modes!
+                           Once `FluxState` is initialised by `train!` in one mode, it cannot be used in the other.""")
   for d in data
-    l, (g, _...) = Zygote.withgradient(loss, model, train_ok(d)...)
+    l, (g, _...) = explicit_withgradient(loss, model, data_splat(d)...)
     s, model = Optimisers.update!(s, model, g)
     push!(losses, l)
     opt.state = s
   end
-  return losses
+  return losses  # Not entirely sure returning losses is a good idea. Flux 0.13 returns `nothing`.
 end
 
-train_ok(x::T) where T =  error("""train! expects every d in data be a Tuple or a NamedTuple, got $T
-                                   To allow this type, define `Flux.Optimise.train_ok(x::$T) = (x,)`""")
-train_ok(x::Tuple) = x
-train_ok(x::NamedTuple) = x
+data_splat(x::T) where T =  error("""train! expects every d in data be a Tuple or a NamedTuple, got $T
+                                   To allow this type, define `Flux.Train.data_splat(x::$T) = (x,)`""")
+data_splat(x::Tuple) = x
+data_splat(x::NamedTuple) = x
 
 function _initialise!(opt::FluxState, model)
   if opt.state isa Missing
     opt.state = Optimisers.setup(opt.rule, model)
     fmap(model, exclude = Optimisers.isnumeric) do x
-      Optimisers.maywrite(x) || error("model must be fully mutable for train! to work, got $(typeof(x))")
+      Optimisers.maywrite(x) || error("""model must be fully mutable for train! to work, got x::$(typeof(x))
+                                         If `x .+= dx` is in fact ok, define `Optimisers.maywrite(::$(typeof(x))) = true`""")
     end
   end
   opt
@@ -107,12 +109,12 @@ function train!(loss::Function, model, opt::FluxState)
   l
 end
 
+# This method lets you use Optimisers.Descent() instead of Flux.Descent(), when there is no state
 function train!(loss::Function, model, data, opt::Optimisers.AbstractRule)
   _initialise!(opt, model)
-  # fmap(opt.state) do x
-  #   x isa Union{Number, AbstractArray{<:Number}} && @warn "optimiser state will be lost!"
-  #   x
-  # end  # won't work as you need to look inside Leaf for non-nothings.
-  @warn "optimiser state will be lost!"
+  fmap(opt.state, exclude = x -> x isa Optimsers.Leaf) do leaf
+    leaf.state isa Nothing ||  @warn "Optimiser state will be lost! Please wrap optimisation rule in `FluxState`, e.g. by using `Flux.Adam()`" leaf
+    leaf
+  end
   train!(loss, model, data, FluxState(opt))
 end

src/train/implicit_train.jl

@@ -29,7 +29,7 @@ function train!(loss::Function, pars::Params, data, opt::FluxState)
   losses = Float32[]
   for d in data
     l, grads = Zygote.withgradient(() -> loss(batchmemaybe(d)...), pars)
-    update!(opt, pars, grads)
+    _update!(opt, pars, grads)
     push!(losses, l)
   end
   return losses
@@ -49,7 +49,7 @@ function train!(loss::Function, pars::Params, opt::FluxState)
                   Explicit parameters are now preferred, see `train!(loss, model, data, opt)`""", :train!, force=true)
   _initialise!(opt, pars)
   l, grads = Zygote.withgradient(() -> loss(), pars)
-  update!(opt, pars, grads)
+  _update!(opt, pars, grads)
   return l
 end
 
@@ -68,6 +68,12 @@ Legacy method, mimicking the behaviour of Flux <= 0.13.
 """
 function update!(opt::FluxState, xs::Params, gs)
   Base.depwarn("Flux.update! is a legacy function", :update!)
+  _initialise!(opt, xs)
+  _update!(opt, xs, gs)
+end
+# This _update! exists only so that train! above gives one depwarn, not two!
+# ... and also to call _initialise!
+function _update!(opt::FluxState, xs::Params, gs)
   for x in xs
     isnothing(gs[x]) && continue
     update!(opt, x, gs[x])

test/layers/conv.jl

@@ -55,13 +55,13 @@ end
   bias = Conv((2, 2), 1=>3, bias = false);
   ip = zeros(Float32, 28,28,1,1)
   op = zeros(Float32, 27,27,3,1) .+ 2.f0
-  opt = Descent()
+  opt = Flux.Descent()
 
   for _ = 1:10^3
     gs = gradient(Flux.params(bias)) do
       Flux.Losses.mse(bias(ip), op)
     end
-    Flux.Optimise.update!(opt, params(bias), gs)
+    Flux.Optimise.update!(opt, Flux.params(bias), gs)
   end
 
   @test Flux.Losses.mse(bias(ip), op) ≈ 4.f0
@@ -168,7 +168,7 @@ end
 
   x = zeros(Float32, 5, 5, 2, 4)
   m = ConvTranspose((3,3), 2=>3)
-  @test gradient(()->sum(m(x)), params(m)) isa Flux.Zygote.Grads
+  @test gradient(()->sum(m(x)), Flux.params(m)) isa Flux.Zygote.Grads
 
   # test ConvTranspose supports groups argument
   x = randn(Float32, 10, 10, 2, 3)
@@ -178,7 +178,7 @@ end
   m2 = ConvTranspose((3,3), 2=>4, groups=2, pad=SamePad())
   @test size(m2.weight) == (3,3,2,2)
   @test size(m1(x)) == size(m2(x))
-  @test gradient(()->sum(m2(x)), params(m2)) isa Flux.Zygote.Grads
+  @test gradient(()->sum(m2(x)), Flux.params(m2)) isa Flux.Zygote.Grads
 
   x = randn(Float32, 10, 2,1)
   m = ConvTranspose((3,), 2=>4, pad=SamePad(), groups=2)