add tests

finish RNNCell

RNN rework

LSTMCell

LSTM

more work

gru

extended testing

reset! deprecation

fix test

unbreak l2 test

fix tests

fixes

perf/recurrent.jl

@@ -7,12 +7,10 @@ Flux.@functor RNNWrapper
 
 # Need to specialize for RNNWrapper.
 fw(r::RNNWrapper, X::Vector{<:AbstractArray}) = begin
-  Flux.reset!(r.rnn)
   [r.rnn(x) for x in X]
 end
 
 fw(r::RNNWrapper, X) = begin
-  Flux.reset!(r.rnn)
   r.rnn(X)
 end
 

src/deprecations.jl

@@ -167,3 +167,8 @@ end
 #   where `loss_mxy` accepts the model as its first argument.
 #   """
 # ))
+
+function reset!(x)
+  Base.depwarn("reset!(m) is deprecated. You can remove this call as it is no more needed.", :reset!)
+  return x
+end

test/ext_amdgpu/runtests.jl

@@ -11,6 +11,6 @@ end
 end
 
 @testset "Recurrent" begin
-  BROKEN_TESTS = []
+  global BROKEN_TESTS = []
   include("../ext_common/recurrent_gpu_ad.jl")
 end

test/ext_cuda/runtests.jl

@@ -23,7 +23,7 @@ end
   include("cudnn.jl")
 end
 @testset "Recurrent" begin
-  BROKEN_TESTS = []
+  global BROKEN_TESTS = []
   include("../ext_common/recurrent_gpu_ad.jl")
 end
 @testset "ctc" begin

test/ext_enzyme/enzyme.jl

@@ -93,7 +93,6 @@ end
 
 @testset "Models" begin
     function loss(model, x)
-        Flux.reset!(model)
         sum(model(x))
     end
 
@@ -126,7 +125,6 @@ end
 
 @testset "Recurrence Tests" begin
     function loss(model, x)
-        Flux.reset!(model)
         for i in 1:3
             x = model(x)
         end

test/ext_metal/runtests.jl

@@ -33,7 +33,7 @@ end
 end
 
 @testset "Recurrent" begin
-    BROKEN_TESTS = [:lstm, :gru, :gruv3]
+    global BROKEN_TESTS = [:lstm, :gru, :gruv3]
     include("../ext_common/recurrent_gpu_ad.jl")
 end
 

test/utils.jl

@@ -251,19 +251,19 @@ end
 end
 
 @testset "Params" begin
-  m = Dense(10, 5)
+  m = Dense(10 => 5)
   @test size.(params(m)) == [(5, 10), (5,)]
-  m = RNN(10, 5)
-  @test size.(params(m)) == [(5, 10), (5, 5), (5,), (5, 1)]
+  m = RNN(10 => 5)
+  @test size.(params(m)) == [(5, 10), (5, 5), (5,)]
 
   # Layer duplicated in same chain, params just once pls.
   c = Chain(m, m)
-  @test size.(params(c)) == [(5, 10), (5, 5), (5,), (5, 1)]
+  @test size.(params(c)) == [(5, 10), (5, 5), (5,)]
 
   # Self-referential array. Just want params, no stack overflow pls.
   r = Any[nothing,m]
   r[1] = r
-  @test size.(params(r)) == [(5, 10), (5, 5), (5,), (5, 1)]
+  @test size.(params(r)) == [(5, 10), (5, 5), (5,)]
 
   # Ensure functor explores inside Transpose but not SubArray
   m = (x = view([1,2,3]pi, 1:2), y = transpose([4 5]pi))
@@ -273,7 +273,7 @@ end
 @testset "params gradient" begin
   m = (x=[1,2.0], y=[3.0]);
 
-  # Explicit -- was broken by #2054 / then fixed / now broken again on julia v1.11
+  # Explicit -- was broken by #2054
   gnew = gradient(m -> (sum(norm, Flux.params(m))), m)[1]
   @test gnew.x ≈ [0.4472135954999579, 0.8944271909999159]
   @test gnew.y ≈ [1.0]
@@ -286,7 +286,7 @@ end
 end
 
 @testset "Precision" begin
-  m = Chain(Dense(10, 5, relu; bias=false), Dense(5, 2))
+  m = Chain(Dense(10 => 5, relu; bias=false), Dense(5 => 2))
   x64 = rand(Float64, 10)
   x32 = rand(Float32, 10)
   i64 = rand(Int64, 10)
@@ -467,10 +467,10 @@ end
   @test modules[5] === m2
   @test modules[6] === m3
 
-  mod_par = Flux.modules(Parallel(Flux.Bilinear(2,2,2,cbrt), Dense(2,2,abs), Dense(2,2,abs2)))
+  mod_par = Flux.modules(Parallel(Flux.Bilinear(2,2,2,cbrt), Dense(2=>2,abs), Dense(2=>2,abs2)))
   @test length(mod_par) == 5
 
-  mod_rnn = Flux.modules(Chain(Dense(2,3), BatchNorm(3), LSTM(3,4)))
+  mod_rnn = Flux.modules(Chain(Dense(2=>3), BatchNorm(3), LSTM(3=>4)))
   @test length(mod_rnn) == 6
   @test mod_rnn[end] isa Flux.LSTMCell