Fuse the activation and bias

Project.toml

@@ -1,7 +1,7 @@
 name = "Lux"
 uuid = "b2108857-7c20-44ae-9111-449ecde12c47"
 authors = ["Avik Pal <avikpal@mit.edu> and contributors"]
-version = "0.5.30"
+version = "0.5.31"
 
 [deps]
 ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"

src/layers/basic.jl

@@ -211,29 +211,29 @@ statelength(d::Dense) = 0
 outputsize(d::Dense) = (d.out_dims,)
 
 @inline function (d::Dense{false})(x::AbstractVecOrMat, ps, st::NamedTuple)
- return __apply_activation(d.activation, ps.weight * x), st
+ return apply_activation(d.activation, ps.weight * x), st
 end
 
 @inline function (d::Dense{false})(x::AbstractArray, ps, st::NamedTuple)
  x_reshaped = reshape(x, size(x, 1), :)
  return (
- reshape(__apply_activation(d.activation, ps.weight * x_reshaped),
+ reshape(apply_activation(d.activation, ps.weight * x_reshaped),
  d.out_dims, size(x)[2:end]...),
  st)
 end
 
 @inline function (d::Dense{true})(x::AbstractVector, ps, st::NamedTuple)
- return __apply_activation(d.activation, ps.weight * x .+ vec(ps.bias)), st
+ return apply_bias_activation(d.activation, ps.weight * x, vec(ps.bias)), st
 end
 
 @inline function (d::Dense{true})(x::AbstractMatrix, ps, st::NamedTuple)
- return __apply_activation(d.activation, ps.weight * x .+ ps.bias), st
+ return apply_bias_activation(d.activation, ps.weight * x, ps.bias), st
 end
 
 @inline function (d::Dense{true})(x::AbstractArray, ps, st::NamedTuple)
  x_reshaped = reshape(x, size(x, 1), :)
  return (
- reshape(__apply_activation(d.activation, ps.weight * x_reshaped .+ ps.bias),
+ reshape(apply_bias_activation(d.activation, ps.weight * x_reshaped, ps.bias),
  d.out_dims, size(x)[2:end]...),
  st)
 end
@@ -315,11 +315,11 @@ statelength(d::Scale) = 0
 outputsize(d::Scale) = d.dims
 
 function (d::Scale{true})(x::AbstractArray, ps, st::NamedTuple)
- return __apply_activation(d.activation, ps.weight .* x .+ ps.bias), st
+ return apply_bias_activation(d.activation, ps.weight .* x, ps.bias), st
 end
 
 function (d::Scale{false})(x::AbstractArray, ps, st::NamedTuple)
- return __apply_activation(d.activation, ps.weight .* x), st
+ return apply_activation(d.activation, ps.weight .* x), st
 end
 
 """
@@ -431,9 +431,9 @@ function (b::Bilinear{use_bias})((x, y)::Tuple{<:AbstractVecOrMat, <:AbstractVec
  Wyx = reshape(batched_mul(Wy, reshape(x, (d_x, 1, :))), (d_z, :))
 
  if use_bias
- return __apply_activation(b.activation, Wyx .+ ps.bias), st
+ return apply_bias_activation(b.activation, Wyx, ps.bias), st
  else
- return __apply_activation(b.activation, Wyx), st
+ return apply_activation(b.activation, Wyx), st
  end
 end
 

src/layers/conv.jl

@@ -117,13 +117,13 @@
 @inline function (c::Conv{N, false})(x::AbstractArray, ps, st::NamedTuple) where {N}
  cdims = DenseConvDims(
  x, ps.weight; stride=c.stride, padding=c.pad, dilation=c.dilation, groups=c.groups)
- return __apply_activation(c.activation, _conv(x, ps.weight, cdims)), st
+ return apply_activation(c.activation, _conv(x, ps.weight, cdims)), st
 end
 
 @inline function (c::Conv{N, true})(x::AbstractArray, ps, st::NamedTuple) where {N}
  cdims = DenseConvDims(
  x, ps.weight; stride=c.stride, padding=c.pad, dilation=c.dilation, groups=c.groups)
- return __apply_activation(c.activation, _conv(x, ps.weight, cdims) .+ ps.bias), st
+ return apply_bias_activation(c.activation, _conv(x, ps.weight, cdims), ps.bias), st
 end
 
 function Base.show(io::IO, l::Conv)
@@ -620,13 +620,13 @@
 @inline function (c::CrossCor{N, false})(x::AbstractArray, ps, st::NamedTuple) where {N}
  cdims = DenseConvDims(
  DenseConvDims(x, ps.weight; c.stride, padding=c.pad, c.dilation); F=true)
- return __apply_activation(c.activation, _conv(x, ps.weight, cdims)), st
+ return apply_activation(c.activation, _conv(x, ps.weight, cdims)), st
 end
 
 @inline function (c::CrossCor{N, true})(x::AbstractArray, ps, st::NamedTuple) where {N}
  cdims = DenseConvDims(
  DenseConvDims(x, ps.weight; c.stride, padding=c.pad, c.dilation); F=true)
- return __apply_activation(c.activation, _conv(x, ps.weight, cdims) .+ ps.bias), st
+ return apply_bias_activation(c.activation, _conv(x, ps.weight, cdims), ps.bias), st
 end
 
 function Base.show(io::IO, l::CrossCor)
@@ -752,14 +752,14 @@
  x::AbstractArray, ps, st::NamedTuple) where {N}
  cdims = _conv_transpose_dims(
  x, ps.weight; c.stride, padding=c.pad, c.dilation, c.groups)
- return __apply_activation(c.activation, _conv_transpose(x, ps.weight, cdims)), st
+ return apply_activation(c.activation, _conv_transpose(x, ps.weight, cdims)), st
 end
 
 @inline function (c::ConvTranspose{N, true})(x::AbstractArray, ps, st::NamedTuple) where {N}
  cdims = _conv_transpose_dims(
  x, ps.weight; c.stride, padding=c.pad, c.dilation, c.groups)
  return (
- __apply_activation(c.activation, _conv_transpose(x, ps.weight, cdims) .+ ps.bias),
+ apply_bias_activation(c.activation, _conv_transpose(x, ps.weight, cdims), ps.bias),
  st)
 end
 

src/layers/normalize.jl

@@ -130,7 +130,7 @@ function (BN::BatchNorm)(x::AbstractArray, ps, st::NamedTuple)
  @set! st.running_var = stats.running_var
  end
 
- return __apply_activation(BN.activation, y), st
+ return apply_activation(BN.activation, y), st
 end
 
 function Base.show(io::IO, l::BatchNorm)
@@ -229,7 +229,7 @@ parameterlength(l::GroupNorm) = _affine(l) ? (l.chs * 2) : 0
 function (GN::GroupNorm)(x::AbstractArray, ps, st::NamedTuple)
  y = groupnorm(x, _getproperty(ps, Val(:scale)),
  _getproperty(ps, Val(:bias)); GN.groups, GN.epsilon)
- return __apply_activation(GN.activation, y), st
+ return apply_activation(GN.activation, y), st
 end
 
 function Base.show(io::IO, l::GroupNorm)
@@ -335,7 +335,7 @@ parameterlength(l::InstanceNorm) = _affine(l) ? (l.chs * 2) : 0
 function (IN::InstanceNorm)(x::AbstractArray, ps, st::NamedTuple)
  y, stats = instancenorm(x, _getproperty(ps, Val(:scale)),
  _getproperty(ps, Val(:bias)); IN.epsilon, st.training)
- return __apply_activation(IN.activation, y), st
+ return apply_activation(IN.activation, y), st
 end
 
 function Base.show(io::IO, l::InstanceNorm)
@@ -554,7 +554,7 @@ end
 function (l::LayerNorm)(x::AbstractArray, ps, st::NamedTuple)
  y = layernorm(
  x, _getproperty(ps, Val(:scale)), _getproperty(ps, Val(:bias)); l.dims, l.epsilon)
- return __apply_activation(l.activation, y), st
+ return apply_activation(l.activation, y), st
 end
 
 function Base.show(io::IO, l::LayerNorm)

src/layers/recurrent.jl

@@ -287,13 +287,13 @@ const _RNNCellInputType = Tuple{<:AbstractMatrix, Tuple{<:AbstractMatrix}}
 
 function (rnn::RNNCell{true})((x, (hidden_state,))::_RNNCellInputType, ps, st::NamedTuple)
  h_new = ps.weight_ih * x .+ ps.weight_hh * hidden_state .+ ps.bias
- h_new = __apply_activation(rnn.activation, h_new)
+ h_new = apply_activation(rnn.activation, h_new)
  return (h_new, (h_new,)), st
 end
 
 function (rnn::RNNCell{false})((x, (hidden_state,))::_RNNCellInputType, ps, st::NamedTuple)
  h_new = ps.weight_ih * x .+ ps.weight_hh * hidden_state
- h_new = __apply_activation(rnn.activation, h_new)
+ h_new = apply_activation(rnn.activation, h_new)
  return (h_new, (h_new,)), st
 end
 

src/utils.jl

@@ -118,10 +118,6 @@ function ∇_eachslice(Δ_raw, x::AbstractArray, ::Val{dims}) where {dims}
  return CRC.ProjectTo(x)(Δ)
 end
 
-# Activation Function
-@inline __apply_activation(::typeof(identity), x) = x
-@inline __apply_activation(f, x) = f.(x)
-
 # Backend Integration
 ## Convolution
 @inline _conv(x, weight, cdims) = conv(x, weight, cdims)
@@ -252,3 +248,11 @@ __named_tuple(nt::NamedTuple) = nt
 
 # Nondifferentiable hasmethod. Avoiding type-piracy
 @inline _hasmethod(f::F, args...) where {F} = hasmethod(f, args...)
+
+# Helpers for bias and activation functions
+## Just Activation Function
+@inline apply_activation(::typeof(identity), x) = x
+@inline apply_activation(f, x) = f.(x)
+
+@inline apply_bias_activation(::typeof(identity), x, b) = x .+ b
+@inline apply_bias_activation(f::F, x, b) where {F} = @. f(x + b)