Improve type stability of LayerNorm and Dropout

src/Flux.jl

@@ -9,7 +9,9 @@ using MacroTools: @forward
 using MLUtils
 import Optimisers: Optimisers, trainable, destructure  # before v0.13, Flux owned these functions
 
-using Zygote, ChainRulesCore
+using ChainRulesCore
+
+using Zygote
 using Zygote: Params, @adjoint, gradient, pullback, @nograd
 export gradient
 

src/layers/normalise.jl

@@ -2,7 +2,9 @@ istraining() = false
 
 ChainRulesCore.rrule(::typeof(istraining)) = true, _ -> (NoTangent(),)
 
-_isactive(m) = isnothing(m.active) ? istraining() : m.active
+_isactive(m) = isnothing(m.active) ? istraining() : Bool(m.active)
+
+ChainRulesCore.@non_differentiable _isactive(::Any)
 
 _dropout_shape(s, ::Colon) = size(s)
 _dropout_shape(s, dims) = tuple((i ∉ dims ? 1 : si for (i, si) ∈ enumerate(size(s)))...)
@@ -31,26 +33,51 @@ automatically managed using the [`Dropout`](@ref) layer instead of the
 
 The [`Dropout`](@ref) layer is what you should use in most scenarios.
 """
-function dropout(rng, x, p; dims=:, active::Bool=true)
-  active || return x
-  y = dropout_mask(rng, x, p, dims=dims)
-  return x .* y
-end
+dropout(rng, x, p; dims=:, active::Bool=true) = _dropout(rng, x, p, dims, active)
 dropout(x, p; kwargs...) = dropout(rng_from_array(x), x, p; kwargs...)
 
-dropout_mask(rng::CUDA.RNG, x::CuArray, p; kwargs...) = _dropout_mask(rng, x, p; kwargs...)
-dropout_mask(rng, x::CuArray, p; kwargs...) =
-  throw(ArgumentError("x isa CuArray, but rng isa $(typeof(rng)). dropout_mask only support CUDA.RNG for CuArrays."))
-dropout_mask(rng, x, p; kwargs...) = _dropout_mask(rng, x, p; kwargs...)
-function _dropout_mask(rng, x, p; dims=:)
+# Internal function without kwargs to keep Zygote generated code type stable
+function _dropout(rng, x, p, dims, active)
+  mask = active ? dropout_mask(rng, x, p, dims) : nothing
+  return _apply_mask(x, mask)
+end
+
+function ChainRulesCore.rrule(::typeof(_dropout), rng, x, p, dims, active)
+  mask = active ? dropout_mask(rng, x, p, dims) : nothing
+  # Required because we don't always call dropout_mask
+  MT = Core.Compiler.return_type(dropout_mask, Tuple{typeof(rng),typeof(x),typeof(p),typeof(dims)})
+  project_x = ProjectTo(x)
+  return _apply_mask(x, mask), DropoutPullback{MT,typeof(project_x)}(mask, project_x)
+end
+
+# Also needed for type stability. Otherwise inference lifts the Union into a
+# Union{pullback{Nothing}, pullback{AbstractArray}}
+struct DropoutPullback{M<:AbstractArray,P<:ProjectTo{AbstractArray}}
+  mask::Union{Nothing,M}
+  project::P
+end
+
+function (pb::DropoutPullback)(dy)
+  dx = pb.project(_apply_mask(dy, pb.mask))
+  return (NoTangent(), NoTangent(), dx, NoTangent())
+end
+
+_apply_mask(x, ::Nothing) = x
+_apply_mask(x, mask) = x .* mask
+
+dropout_mask(rng::CUDA.RNG, x::CuArray, p, dims) = _dropout_mask(rng, x, p, dims)
+dropout_mask(rng, x::CuArray, p, dims) =
+  throw(ArgumentError("x isa CuArray, but rng isa $(typeof(rng)). dropout_mask only supports CUDA.RNG for CuArrays."))
+dropout_mask(rng, x, p, dims) = _dropout_mask(rng, x, p, dims)
+function _dropout_mask(rng, x, p, dims)
   realfptype = float(real(eltype(x)))
   y = rand!(rng, similar(x, realfptype, _dropout_shape(x, dims)))
   y .= _dropout_kernel.(y, p, 1 - p)
   return y
 end
 
 # TODO move this to NNlib
-ChainRulesCore.@non_differentiable dropout_mask(::Any, ::Any, ::Any)
+ChainRulesCore.@non_differentiable dropout_mask(::Any, ::Any, ::Any, ::Any)
 
 """
     Dropout(p; dims=:, rng = rng_from_array())
@@ -82,10 +109,7 @@ end
 @functor Dropout
 trainable(a::Dropout) = (;)
 
-function (a::Dropout)(x)
-  _isactive(a) || return x
-  return dropout(a.rng, x, a.p; dims=a.dims, active=true)
-end
+(a::Dropout)(x) = _dropout(a.rng, x, a.p, a.dims, _isactive(a))
 
 testmode!(m::Dropout, mode=true) =
   (m.active = (isnothing(mode) || mode == :auto) ? nothing : !mode; m)
@@ -172,7 +196,7 @@ LayerNorm(size_act...; kw...) = LayerNorm(Int.(size_act[1:end-1]), size_act[end]
 
 @functor LayerNorm
 
-(a::LayerNorm)(x) = a.diag(normalise(x, dims=1:length(a.size), ϵ=a.ϵ))
+(a::LayerNorm)(x) = a.diag(_normalize(x, 1:length(a.size), a.ϵ))
 
 function Base.show(io::IO, l::LayerNorm)
   print(io, "LayerNorm(", join(l.size, ", "))

src/layers/stateless.jl

@@ -26,15 +26,41 @@ function flatten(x::AbstractArray)
   return reshape(x, :, size(x)[end])
 end
 
+# Utils for LayerNorm internals.
+# Most of these are required for better performance and type stability under AD.
+# In an ideal world, we'd just have normalise.
+
+function _mean_std(x::AbstractArray, dims)
+  μ = mean(x, dims=dims)
+  σ = std(x, dims=dims, mean=μ, corrected=false)
+  return μ, σ
+end
+
+function ChainRulesCore.rrule(::typeof(_mean_std), x::AbstractArray, dims)
+  μ, mean_pullback = ChainRulesCore.rrule(mean, x, dims=dims)
+  σ, std_pullback = ChainRulesCore.rrule(std, x, dims=dims, mean=μ, corrected=false)
+  function _mean_std_pullback((dμ, dσ))
+    dx = ChainRulesCore.add!!(std_pullback(dσ)[2], mean_pullback(dμ)[2])
+    return (NoTangent(), dx, NoTangent())
+  end
+
+  return (μ, σ), _mean_std_pullback
+end
+
+_zscore(x, μ, σ, ϵ) = (x - μ) / (σ + ϵ)
+
+# We don't define a rrule for the whole function because we want
+# AD to figure out the _zscore broadcast for us.
+function _normalize(x::AbstractArray, dims, ϵ)
+  μ, σ = _mean_std(x, dims)
+  return _zscore.(x, μ, σ, ϵ)
+end
+
 """
     normalise(x; dims=ndims(x), ϵ=1e-5)
 
 Normalise `x` to mean 0 and standard deviation 1 across the dimension(s) given by `dims`.
-Per default, `dims` is the last dimension. 
+Per default, `dims` is the last dimension.
 `ϵ` is a small additive factor added to the denominator for numerical stability.
 """
-@inline function normalise(x::AbstractArray; dims=ndims(x), ϵ=ofeltype(x, 1e-5))
-  μ = mean(x, dims=dims)
-  σ = std(x, dims=dims, mean=μ, corrected=false)
-  return @. (x - μ) / (σ + ϵ)
-end
+@inline normalise(x::AbstractArray; dims=ndims(x), ϵ=ofeltype(x, 1e-5)) = _normalize(x, dims, ϵ)

test/layers/normalisation.jl

@@ -73,6 +73,12 @@ evalwgrad(f, x...) = pullback(f, x...)[1]
       @test cpu(m).rng === only(values(rng_kwargs))
     end
   end
+
+  for active in (true, false)
+    m = Dropout(0.5, :, active)
+    @inferred _, back = pullback(m, rand(10)) # _, DropoutPullback{Array{Float64}}
+    @inferred back(ones(10)) # Array{Float64}
+  end
 end
 
 @testset "AlphaDropout" begin
@@ -343,8 +349,13 @@ end
   @test LayerNorm(2)(x) ≈ Flux.normalise(x, dims=1)
   x = rand(2,3,4,5)
   @test LayerNorm(2)(x) ≈ Flux.normalise(x, dims=1)
+
   x = rand(2)
-  @test LayerNorm(2, tanh)(x) ≈ tanh.(Flux.normalise(x, dims=1))
+  m = LayerNorm(2, tanh)
+  @test m(x) ≈ tanh.(Flux.normalise(x, dims=1))
+  @inferred _, back = pullback(sum∘m, x)
+  @inferred back(1.0)
+
 
   x = rand(2,3,4,5)
   @test LayerNorm((2,3))(x) ≈ Flux.normalise(x, dims=(1,2))