Use faster activation functions (#1837)

* fast activation functions

* Update src/layers/basic.jl

Co-authored-by: Brian Chen <ToucheSir@users.noreply.github.com>

* Update src/layers/conv.jl

Co-authored-by: Brian Chen <ToucheSir@users.noreply.github.com>

Co-authored-by: Brian Chen <ToucheSir@users.noreply.github.com>

Author: mcabbott

src/layers/basic.jl

@@ -154,7 +154,8 @@ end
 @functor Dense
 
 function (a::Dense)(x::AbstractVecOrMat)
-  W, b, σ = a.weight, a.bias, a.σ
+  W, b = a.weight, a.bias
+  σ = NNlib.fast_act(a.σ, x)  # replaces tanh => tanh_fast, etc
   return σ.(W*x .+ b)
 end
 

src/layers/conv.jl

@@ -161,7 +161,8 @@ end
 @functor Conv
 
 function (c::Conv)(x::AbstractArray)
-  σ, b = c.σ, reshape(c.bias, ntuple(_ -> 1, length(c.stride))..., :, 1)
+  b = reshape(c.bias, map(_->1, c.stride)..., :, 1)
+  σ = NNlib.fast_act(c.σ, x)
   cdims = DenseConvDims(x, c.weight; stride = c.stride, padding = c.pad, dilation = c.dilation, groups = c.groups)
   σ.(conv(x, c.weight, cdims) .+ b)
 end
@@ -278,7 +279,8 @@ end
 @nograd conv_transpose_dims
 
 function (c::ConvTranspose)(x::AbstractArray)
-  σ, b = c.σ, reshape(c.bias, map(_->1, c.stride)..., :, 1)
+  b = reshape(c.bias, map(_->1, c.stride)..., :, 1)
+  σ = NNlib.fast_act(c.σ, x)
   cdims = conv_transpose_dims(c, x)
   σ.(∇conv_data(x, c.weight, cdims) .+ b)
 end
@@ -371,7 +373,8 @@ depthwiseconvfilter(filter::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer};
                     init = glorot_uniform) where N = init(filter..., div(ch[2], ch[1]), ch[1])
 
 function (c::DepthwiseConv)(x)
-  σ, b = c.σ, reshape(c.bias, map(_->1, c.stride)..., :, 1)
+  b = reshape(c.bias, map(_->1, c.stride)..., :, 1)
+  σ = NNlib.fast_act(c.σ, x)
   cdims = DepthwiseConvDims(x, c.weight; stride=c.stride, padding=c.pad, dilation=c.dilation)
   σ.(depthwiseconv(x, c.weight, cdims) .+ b)
 end
@@ -450,7 +453,8 @@ function crosscor(x, w, ddims::DenseConvDims)
 end
 
 function (c::CrossCor)(x::AbstractArray)
-  σ, b = c.σ, reshape(c.bias, map(_->1, c.stride)..., :, 1)
+  b = reshape(c.bias, map(_->1, c.stride)..., :, 1)
+  σ = NNlib.fast_act(c.σ, x)
   cdims = DenseConvDims(x, c.weight; stride=c.stride, padding=c.pad, dilation=c.dilation)
   σ.(crosscor(x, c.weight, cdims) .+ b)
 end

src/layers/recurrent.jl

@@ -117,7 +117,8 @@ RNNCell(in::Integer, out::Integer, σ=tanh; init=Flux.glorot_uniform, initb=zero
   RNNCell(σ, init(out, in), init(out, out), initb(out), init_state(out,1))
 
 function (m::RNNCell{F,A,V,<:AbstractMatrix{T}})(h, x::Union{AbstractVecOrMat{T},OneHotArray}) where {F,A,V,T}
-  σ, Wi, Wh, b = m.σ, m.Wi, m.Wh, m.b
+  Wi, Wh, b = m.Wi, m.Wh, m.b
+  σ = NNlib.fast_act(m.σ, x)
   h = σ.(Wi*x .+ Wh*h .+ b)
   return h, reshape_cell_output(h, x)
 end
@@ -224,8 +225,8 @@ function (m::LSTMCell{A,V,<:NTuple{2,AbstractMatrix{T}}})((h, c), x::Union{Abstr
   b, o = m.b, size(h, 1)
   g = m.Wi*x .+ m.Wh*h .+ b
   input, forget, cell, output = multigate(g, o, Val(4))
-  c′ = @. σ(forget) * c + σ(input) * tanh(cell)
-  h′ = @. σ(output) * tanh(c′)
+  c′ = @. sigmoid_fast(forget) * c + sigmoid_fast(input) * tanh_fast(cell)
+  h′ = @. sigmoid_fast(output) * tanh_fast(c′)
   return (h′, c′), reshape_cell_output(h′, x)
 end
 
@@ -309,7 +310,7 @@ function (m::GRUCell{A,V,<:AbstractMatrix{T}})(h, x::Union{AbstractVecOrMat{T},O
   Wi, Wh, b, o = m.Wi, m.Wh, m.b, size(h, 1)
   gxs, ghs, bs = multigate(Wi*x, o, Val(3)), multigate(Wh*h, o, Val(3)), multigate(b, o, Val(3))
   r, z = _gru_output(gxs, ghs, bs)
-  h̃ = @. tanh(gxs[3] + r * ghs[3] + bs[3])
+  h̃ = @. tanh_fast(gxs[3] + r * ghs[3] + bs[3])
   h′ = @. (1 - z) * h̃ + z * h
   return h′, reshape_cell_output(h′, x)
 end
@@ -387,7 +388,7 @@ function (m::GRUv3Cell{A,V,<:AbstractMatrix{T}})(h, x::Union{AbstractVecOrMat{T}
   Wi, Wh, b, Wh_h̃, o = m.Wi, m.Wh, m.b, m.Wh_h̃, size(h, 1)
   gxs, ghs, bs = multigate(Wi*x, o, Val(3)), multigate(Wh*h, o, Val(2)), multigate(b, o, Val(3))
   r, z = _gru_output(gxs, ghs, bs)
-  h̃ = tanh.(gxs[3] .+ (Wh_h̃ * (r .* h)) .+ bs[3])
+  h̃ = tanh_fast.(gxs[3] .+ (Wh_h̃ * (r .* h)) .+ bs[3])
   h′ = @. (1 - z) * h̃ + z * h
   return h′, reshape_cell_output(h′, x)
 end