diff --git a/test/rnn-demo.jl b/test/rnn-demo.jl
deleted file mode 100644
index 791d6e0b67..0000000000
--- a/test/rnn-demo.jl
+++ /dev/null
@@ -1,285 +0,0 @@
-using Revise
-using Flux
-using Flux: @functor
-import Flux: trainable
-using Statistics: mean
-using Random: seed!
-
-
-mutable struct Recur2{T,S}
-    cell::T
-    state::S
-end
-
-# original definition
-# function (m::Recur2)(xs...)
-#     m.state, y = m.cell(m.state, xs...)
-#     return y
-# end
-
-# new def
-function (m::Recur2)(xs...)
-    m.state, y = m.cell(m.state, xs...)
-    return y
-end
-
-@functor Recur2
-trainable(a::Recur2) = (a.cell,)
-
-#####################################
-# Basic test
-#####################################
-seed!(123)
-feat = 3
-h_size = 5
-seq_len = 7
-batch_size = 4
-
-X = [rand(Float32, feat, batch_size) for i in 1:seq_len]
-Y = rand(Float32, batch_size, seq_len) ./ 10
-
-cell = Flux.RNNCell(feat, h_size)
-rnn = Recur2(cell, cell.state0)
-
-rnn(X[1])
-rnn.state
-rnn(X[1])
-
-rnn.(X)
-
-function fold_test_1(x, m)
-    foldl((a, b) -> m(b), x)
-end
-fold_test_1(X, rnn)
-
-rnn.(X)
-
-function rnn2(x)
-    # println((x))
-    println("state: ", rnn.state)
-    rnn(x)
-end
-function fold_test_2(x)
-    foldl((a, b) -> rnn(b), x, init=x[1])
-end
-fold_test_2(X)
-rnn.state
-
-function fold_cell_1(x, c)
-    foldl((a, b) -> cell(a, b)[1], x, init=cell.state0)
-end
-fold_cell_1(X, cell)
-rnn.state
-
-
-f1(x) = begin
-    println(x)
-    x^2
-end
-
-function fold_test_2(x)
-    foldl((a, b) -> f1(b), x, init=5)
-end
-x1 = fold_test_2([2,3])
-
-# rnn = Chain(
-#     RNN(feat, h_size),
-#     Dense(h_size, 1, σ),
-#     x -> reshape(x, :))
-
-
-#### transfer to gpu ####
-rnn_gpu = rnn |> gpu
-X_gpu = gpu(X)
-Y_gpu = gpu(Y)
-
-θ = Flux.params(rnn)
-θ_gpu = Flux.params(rnn_gpu)
-length(θ)
-length(θ_gpu)
-function loss(x, y)
-    Flux.reset!(rnn)
-    l = mean((Flux.stack(map(rnn, x), 2) .- y).^2)
-    return l
-end
-function loss_gpu(x, y)
-    Flux.reset!(rnn_gpu)
-    l = mean((Flux.stack(map(rnn_gpu, x), 2) .- y).^2)
-    return l
-end
-
-opt = ADAM(1e-3)
-opt_gpu = ADAM(1e-3)
-for i in 1:5
-    println("iter: ", i)
-    Flux.train!(loss, θ, [(X, Y)], opt)
-    Flux.train!(loss_gpu, θ_gpu, [(X_gpu, Y_gpu)], opt_gpu)
-    println("loss_cpu: ", loss(X, Y))
-    println("loss_gpu: ", loss_gpu(X_gpu, Y_gpu))
-    # println("θ[3][1:2]: ", θ[3][1:2])
-    # println("θ_gpu[3][1:2]: ", θ_gpu[3][1:2])
-    # println("θ[4][1:2]: ", θ[4][1:2])
-    # println("θ_gpu[4][1:2]: ", θ_gpu[4][1:2])
-    # println("rnn.layers[1].state[1:2]: ", rnn.layers[1].state[1:2])
-    # println("rnn_gpu.layers[1].state[1:2]: ", rnn_gpu.layers[1].state[1:2])
-end
-
-@code_warntype rnn(X[1])
-
-function speed_cpu(n=10)
-    for i in 1:n
-        Flux.train!(loss, θ, [(X, Y)], opt)
-    end
-    return loss(X, Y)
-end
-
-function speed_gpu(n=10)
-    for i in 1:n
-        Flux.train!(loss_gpu, θ_gpu, [(X_gpu, Y_gpu)], opt_gpu)
-    end
-    return loss_gpu(X_gpu, Y_gpu)
-end
-
-@time speed_cpu(100)
-@time speed_gpu(100)
-
-
-#####################################
-# RNN vanilla
-#####################################
-seed!(123)
-feat = 32
-h_size = 64
-seq_len = 50
-batch_size = 256
-
-rnn = Chain(
-    RNN(feat, h_size),
-    Dense(h_size, 1, σ),
-    x -> reshape(x, :))
-
-X = [rand(Float32, feat, batch_size) for i in 1:seq_len]
-Y = rand(Float32, batch_size, seq_len) ./ 10
-
-#### transfer to gpu ####
-rnn_gpu = rnn |> gpu
-X_gpu = gpu(X)
-Y_gpu = gpu(Y)
-
-θ = Flux.params(rnn)
-θ_gpu = Flux.params(rnn_gpu)
-length(θ)
-length(θ_gpu)
-function loss(x, y)
-    Flux.reset!(rnn)
-    l = mean((Flux.stack(map(rnn, x), 2) .- y).^2)
-    return l
-end
-function loss_gpu(x, y)
-    Flux.reset!(rnn_gpu)
-    l = mean((Flux.stack(map(rnn_gpu, x), 2) .- y).^2)
-    return l
-end
-
-opt = ADAM(1e-3)
-opt_gpu = ADAM(1e-3)
-for i in 1:5
-    println("iter: ", i)
-    Flux.train!(loss, θ, [(X, Y)], opt)
-    Flux.train!(loss_gpu, θ_gpu, [(X_gpu, Y_gpu)], opt_gpu)
-    println("loss_cpu: ", loss(X, Y))
-    println("loss_gpu: ", loss_gpu(X_gpu, Y_gpu))
-    # println("θ[3][1:2]: ", θ[3][1:2])
-    # println("θ_gpu[3][1:2]: ", θ_gpu[3][1:2])
-    # println("θ[4][1:2]: ", θ[4][1:2])
-    # println("θ_gpu[4][1:2]: ", θ_gpu[4][1:2])
-    # println("rnn.layers[1].state[1:2]: ", rnn.layers[1].state[1:2])
-    # println("rnn_gpu.layers[1].state[1:2]: ", rnn_gpu.layers[1].state[1:2])
-end
-
-@code_warntype rnn(X[1])
-
-function speed_cpu(n=10)
-    for i in 1:n
-        Flux.train!(loss, θ, [(X, Y)], opt)
-    end
-    return loss(X, Y)
-end
-
-function speed_gpu(n=10)
-    for i in 1:n
-        Flux.train!(loss_gpu, θ_gpu, [(X_gpu, Y_gpu)], opt_gpu)
-    end
-    return loss_gpu(X_gpu, Y_gpu)
-end
-
-@time speed_cpu(100)
-@time speed_gpu(100)
-
-#####################################
-# LSTM
-#####################################
-feat = 32
-h_size = 64
-seq_len = 50
-batch_size = 256
-
-rnn = Chain(LSTM(feat, h_size),
-    LSTM(h_size, h_size),
-    LSTM(h_size, h_size),
-    Dense(h_size, 1, σ),
-    x -> reshape(x, :))
-
-X = [rand(Float32, feat, batch_size) for i in 1:seq_len]
-Y = rand(Float32, batch_size, seq_len) ./ 10
-
-#### transfer to gpu ####
-rnn_gpu = rnn |> gpu
-X_gpu = gpu(X)
-Y_gpu = gpu(Y)
-
-θ = Flux.params(rnn)
-θ_gpu = Flux.params(rnn_gpu)
-function loss(x, y)
-    Flux.reset!(rnn)
-    l = mean((Flux.stack(map(rnn, x), 2) .- y).^2)
-    return l
-end
-function loss_gpu(x, y)
-    Flux.reset!(rnn_gpu)
-    l = mean((Flux.stack(map(rnn_gpu, x), 2) .- y).^2)
-    return l
-end
-
-opt = ADAM(1e-3)
-opt_gpu = ADAM(1e-3)
-
-for i in 1:5
-    println("iter: ", i)
-    Flux.train!(loss, θ, [(X, Y)], opt)
-    Flux.train!(loss_gpu, θ_gpu, [(X_gpu, Y_gpu)], opt_gpu)
-    println("loss_cpu: ", loss(X, Y))
-    println("loss_gpu: ", loss_gpu(X_gpu, Y_gpu))
-end
-
-
-function speed_cpu(n=10)
-    for i in 1:n
-        Flux.train!(loss, θ, [(X, Y)], opt)
-    end
-    return loss(X, Y)
-end
-
-function speed_gpu(n=10)
-    for i in 1:n
-        Flux.train!(loss_gpu, θ_gpu, [(X_gpu, Y_gpu)], opt_gpu)
-    end
-    return loss_gpu(X_gpu, Y_gpu)
-end
-
-@code_warntype rnn(X[1])
-
-using BenchmarkTools
-@time speed_cpu(100)
-@btime speed_gpu(100)
-