Fix type-stability for normalization layers

Author: Anton Smirnov

Project.toml

@@ -38,8 +38,8 @@ Colors = "0.12"
 Functors = "0.2.1"
 Juno = "0.8"
 MacroTools = "0.5"
-NNlib = "0.7.24"
-NNlibCUDA = "0.1.7"
+NNlib = "0.8.0"
+NNlibCUDA = "0.2.0"
 Reexport = "0.2, 1.0"
 StatsBase = "0.33"
 ZipFile = "0.9"

src/layers/conv.jl

@@ -162,7 +162,9 @@ end
 
 function (c::Conv)(x::AbstractArray)
   σ, b = c.σ, reshape(c.bias, ntuple(_ -> 1, length(c.stride))..., :, 1)
-  cdims = DenseConvDims(x, c.weight; stride = c.stride, padding = c.pad, dilation = c.dilation, groups = c.groups)
+  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
 
@@ -656,19 +658,23 @@ julia> lay(rand(Float32, 100, 7, 50)) |> size
 (34, 7, 50)
 ```
 """
-struct MaxPool{N,M}
+struct MaxPool{N, M}
   k::NTuple{N,Int}
   pad::NTuple{M,Int}
   stride::NTuple{N,Int}
 end
 
-function MaxPool(k::NTuple{N,Integer}; pad = 0, stride = k) where N
+function MaxPool(k::NTuple{N, Integer}; pad = 0, stride = k) where N
   stride = expand(Val(N), stride)
-  pad = calc_padding(MaxPool ,pad, k, 1, stride)
+  pad = calc_padding(MaxPool, pad, k, 1, stride)
   return MaxPool(k, pad, stride)
 end
 
 function (m::MaxPool)(x)
+  # size_x = size(x)
+  # kernel, stride, padding, dilation = NNlib.prepare_pooldims(
+  #   Val(N), size_x, m.k; padding=m.pad, stride=m.stride)
+  # pdims = PoolDims{kernel, stride, padding, dilation}(size_x)
   pdims = PoolDims(x, m.k; padding=m.pad, stride=m.stride)
   return maxpool(x, pdims)
 end

src/layers/normalise.jl

@@ -122,13 +122,13 @@ testmode!(m::AlphaDropout, mode=true) =
     LayerNorm(sz, λ=identity; affine=true, ϵ=1fe-5)
 
 A [normalisation layer](https://arxiv.org/abs/1607.06450) designed to be
-used with recurrent hidden states. 
-The argument `sz` should be an integer or a tuple of integers. 
-In the forward pass, the layer normalises the mean and standard 
+used with recurrent hidden states.
+The argument `sz` should be an integer or a tuple of integers.
+In the forward pass, the layer normalises the mean and standard
 deviation of the input, the applied the elementwise activation `λ`.
 The input is normalised along the first `length(sz)` dimensions
 for tuple `sz`, along the first dimension for integer `sz`.
-The input  is expected to have first dimensions' size equal to `sz`. 
+The input  is expected to have first dimensions' size equal to `sz`.
 
 If `affine=true` also applies a learnable shift and rescaling
 as in the [`Diagonal`](@ref) layer.
@@ -164,38 +164,70 @@ function Base.show(io::IO, l::LayerNorm)
   print(io, ")")
 end
 
+_maybe_promote_type(::Type{T1}, ::Type{T2}) where {T1, T2} = promote_type(T1, T2)
+_maybe_promote_type(::Type{Nothing}, ::Type{T2}) where T2 = T2
+_maybe_promote_type(::Type{T1}, ::Type{Nothing}) where T1 = T1
+
+_maybe_eltype(::Type{T}) where T <: AbstractArray = eltype(T)
+_maybe_eltype(::Type{Nothing}) = Nothing
+
+abstract type Normalization{F, V, N, W} end
+
+function _promote_to_output(
+  ::Normalization{F, V, N, W}, x::AbstractArray{T},
+) where {F, V, N, W, T}
+  Vel = _maybe_eltype(V)
+  Wel = _maybe_eltype(W)
+  _maybe_promote_type(_maybe_promote_type(
+    _maybe_promote_type(T, Vel), N), Wel)
+end
+
 # For InstanceNorm, GroupNorm, and BatchNorm.
 # Compute the statistics on the slices specified by reduce_dims.
 # reduce_dims=[1,...,N-2,N] for BatchNorm
 # reduce_dims=[1,...,N-2] for InstanceNorm and GroupNorm
-function _norm_layer_forward(l, x::AbstractArray{T,N}; reduce_dims, affine_shape) where {T, N}
+_norm_layer_forward(l, x; reduce_dims, affine_shape) =
+    _norm_layer_forward(l, x, _promote_to_output(l, x); reduce_dims, affine_shape)
+
+function _norm_layer_forward(
+  l, x::Array{T, N}, ::Type{O}; reduce_dims, affine_shape,
+) where {T, N, O}
   if !_isactive(l) && l.track_stats # testmode with tracked stats
     stats_shape = ntuple(i -> i == N-1 ? size(x, N-1) : 1, N)
     μ = reshape(l.μ, stats_shape)
     σ² = reshape(l.σ², stats_shape)
-  else  # trainmode or testmode without tracked stats
+  else # trainmode or testmode without tracked stats
     μ = mean(x; dims=reduce_dims)
     σ² = mean((x .- μ).^2; dims=reduce_dims)
     if l.track_stats
-      ## update moving mean/std
-      Zygote.ignore() do
-        mtm = l.momentum
-        m = prod(size(x, i) for i in reduce_dims)  # needed for computing corrected var
-        μnew = vec(N ∈ reduce_dims ? μ : mean(μ, dims=N))
-        σ²new = vec(N ∈ reduce_dims ? σ² : mean(σ², dims=N))
-        l.μ = (1-mtm) .* l.μ .+ mtm .* μnew
-        l.σ² = (1-mtm) .* l.σ² .+ mtm .* (m / (m - one(eltype(l.σ²)))) .* σ²new
-      end
+      _track_stats!(l, x, μ, σ², reduce_dims) # update moving mean/std
     end
   end
-  if hasaffine(l)
-    γ = reshape(l.γ, affine_shape)
-    β = reshape(l.β, affine_shape)
-    return l.λ.(γ .* (x .- μ) ./ sqrt.(σ² .+ l.ϵ) .+ β)
-  else
-    return l.λ.((x .- μ) ./ sqrt.(σ² .+ l.ϵ))
-  end
+
+  o::Array{O, N} = ((x .- μ) ./ sqrt.(σ² .+ l.ϵ))
+  hasaffine(l) || return l.λ.(o)
+
+  γ = reshape(l.γ, affine_shape)
+  β = reshape(l.β, affine_shape)
+  return l.λ.(γ .* o .+ β)
+end
+
+function _track_stats!(
+  bn, x::AbstractArray{T, N}, μ, σ², reduce_dims,
+) where {T, N}
+  V = eltype(bn.σ²)
+  mtm = bn.momentum
+  res_mtm = one(V) - mtm
+  m = prod(size(x, i) for i in reduce_dims)
+
+  μnew = vec(N ∈ reduce_dims ? μ : mean(μ, dims=N))
+  σ²new = vec(N ∈ reduce_dims ? σ² : mean(σ², dims=N))
+
+  bn.μ = res_mtm .* bn.μ .+ mtm .* μnew
+  bn.σ² = res_mtm .* bn.σ² .+ mtm .* (m / (m - one(V))) .* σ²new
+  nothing
 end
+Zygote.@nograd _track_stats!
 
 """
     BatchNorm(channels::Integer, λ=identity;
@@ -210,15 +242,15 @@ Given an array with `N` dimensions, call the `N-1`th the channel dimension. For
 a batch of feature vectors this is just the data dimension, for `WHCN` images
 it's the usual channel dimension.
 
-`BatchNorm` computes the mean and variance for each `D_1×...×D_{N-2}×1×D_N` 
+`BatchNorm` computes the mean and variance for each `D_1×...×D_{N-2}×1×D_N`
 input slice and normalises the input accordingly.
 
-If `affine=true`, it also applies  a shift and a rescale to the input 
+If `affine=true`, it also applies  a shift and a rescale to the input
 through to learnable per-channel bias β and scale γ parameters.
 
-After normalisation, elementwise activation `λ` is applied.  
+After normalisation, elementwise activation `λ` is applied.
 
-If `track_stats=true`, accumulates mean and var statistics in training phase 
+If `track_stats=true`, accumulates mean and var statistics in training phase
 that will be used to renormalize the input in test phase.
 
 Use [`testmode!`](@ref) during inference.
@@ -233,7 +265,7 @@ m = Chain(
   softmax)
 ```
 """
-mutable struct BatchNorm{F,V,N,W}
+mutable struct BatchNorm{F,V,N,W} <: Normalization{F, V, N, W}
   λ::F  # activation function
   β::V  # bias
   γ::V  # scale
@@ -248,7 +280,7 @@ mutable struct BatchNorm{F,V,N,W}
 end
 
 function BatchNorm(chs::Int, λ=identity;
-          initβ=zeros32, initγ=ones32, 
+          initβ=zeros32, initγ=ones32,
           affine=true, track_stats=true,
           ϵ=1f-5, momentum=0.1f0)
 
@@ -258,8 +290,8 @@ function BatchNorm(chs::Int, λ=identity;
   σ² = track_stats ? ones32(chs) : nothing
 
   return BatchNorm(λ, β, γ,
-            μ, σ², ϵ, momentum, 
-            affine, track_stats, 
+            μ, σ², ϵ, momentum,
+            affine, track_stats,
             nothing, chs)
 end
 
@@ -294,22 +326,22 @@ end
 [Instance Normalization](https://arxiv.org/abs/1607.08022) layer.
 `channels` should be the size of the channel dimension in your data (see below).
 
-Given an array with `N > 2` dimensions, call the `N-1`th the channel dimension. 
+Given an array with `N > 2` dimensions, call the `N-1`th the channel dimension.
 For `WHCN` images it's the usual channel dimension.
 
-`InstanceNorm` computes the mean and variance for each `D_1×...×D_{N-2}×1×1` 
+`InstanceNorm` computes the mean and variance for each `D_1×...×D_{N-2}×1×1`
 input slice and normalises the input accordingly.
 
-If `affine=true`, it also applies  a shift and a rescale to the input 
+If `affine=true`, it also applies  a shift and a rescale to the input
 through to learnable per-channel bias `β` and scale `γ` parameters.
 
-If `track_stats=true`, accumulates mean and var statistics in training phase 
+If `track_stats=true`, accumulates mean and var statistics in training phase
 that will be used to renormalize the input in test phase.
 
-**Warning**: the defaults for `affine` and `track_stats` used to be `true` 
+**Warning**: the defaults for `affine` and `track_stats` used to be `true`
 in previous Flux versions (< v0.12).
 """
-mutable struct InstanceNorm{F,V,N,W}
+mutable struct InstanceNorm{F,V,N,W} <: Normalization{F, V, N, W}
   λ::F  # activation function
   β::V  # bias
   γ::V  # scale
@@ -334,7 +366,7 @@ function InstanceNorm(chs::Int, λ=identity;
   σ² = track_stats ? ones32(chs) : nothing
 
   return InstanceNorm(λ, β, γ,
-            μ, σ², ϵ, momentum, 
+            μ, σ², ϵ, momentum,
             affine, track_stats,
             nothing, chs)
 end
@@ -377,16 +409,16 @@ The number of channels must be an integer multiple of the number of groups.
 
 `channels` should be the size of the channel dimension in your data (see below).
 
-Given an array with `N > 2` dimensions, call the `N-1`th the channel dimension. 
+Given an array with `N > 2` dimensions, call the `N-1`th the channel dimension.
 For `WHCN` images it's the usual channel dimension.
 
-If `affine=true`, it also applies  a shift and a rescale to the input 
+If `affine=true`, it also applies  a shift and a rescale to the input
 through to learnable per-channel bias `β` and scale `γ` parameters.
 
-If `track_stats=true`, accumulates mean and var statistics in training phase 
+If `track_stats=true`, accumulates mean and var statistics in training phase
 that will be used to renormalize the input in test phase.
 """
-mutable struct GroupNorm{F,V,N,W}
+mutable struct GroupNorm{F,V,N,W} <: Normalization{F, V, N, W}
   G::Int  # number of groups
   λ::F  # activation function
   β::V  # bias
@@ -405,7 +437,7 @@ end
 trainable(gn::GroupNorm) = hasaffine(gn) ? (gn.β, gn.γ) : ()
 
 function GroupNorm(chs::Int, G::Int, λ=identity;
-              initβ=zeros32, initγ=ones32, 
+              initβ=zeros32, initγ=ones32,
               affine=true, track_stats=false,
               ϵ=1f-5, momentum=0.1f0)
 
@@ -416,11 +448,11 @@ function GroupNorm(chs::Int, G::Int, λ=identity;
   μ = track_stats ? zeros32(G) : nothing
   σ² = track_stats ? ones32(G) : nothing
 
-  return GroupNorm(G, λ, 
+  return GroupNorm(G, λ,
             β, γ,
-            μ, σ², 
-            ϵ, momentum, 
-            affine, track_stats, 
+            μ, σ²,
+            ϵ, momentum,
+            affine, track_stats,
             nothing, chs)
 end
 
@@ -451,7 +483,7 @@ end
 """
   hasaffine(l)
 
-Return `true` if a normalisation layer has trainable shift and 
+Return `true` if a normalisation layer has trainable shift and
 scale parameters, `false` otherwise.
 
 See [`BatchNorm`](@ref), [`InstanceNorm`](@ref), [`GroupNorm`](@ref), and [`LayerNorm`](@ref).