Rework normalization layers

Manifest.toml

@@ -14,9 +14,9 @@ version = "0.3.3"
 
 [[Adapt]]
 deps = ["LinearAlgebra"]
-git-tree-sha1 = "87491f7d03ae1b423a353aff99cf61a45e3c993a"
+git-tree-sha1 = "ffcfa2d345aaee0ef3d8346a073d5dd03c983ebe"
 uuid = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
-version = "3.1.0"
+version = "3.2.0"
 
 [[Artifacts]]
 deps = ["Pkg"]
@@ -93,9 +93,9 @@ uuid = "e66e0078-7015-5450-92f7-15fbd957f2ae"
 version = "0.3.4+0"
 
 [[DataAPI]]
-git-tree-sha1 = "ad84f52c0b8f05aa20839484dbaf01690b41ff84"
+git-tree-sha1 = "6d64b28d291cb94a0d84e6e41081fb081e7f717f"
 uuid = "9a962f9c-6df0-11e9-0e5d-c546b8b5ee8a"
-version = "1.4.0"
+version = "1.5.0"
 
 [[DataStructures]]
 deps = ["Compat", "InteractiveUtils", "OrderedCollections"]
@@ -134,9 +134,9 @@ version = "0.1.3"
 
 [[FillArrays]]
 deps = ["LinearAlgebra", "Random", "SparseArrays"]
-git-tree-sha1 = "8bd8e47ff5d34b20f0aa9641988eb660590008bc"
+git-tree-sha1 = "50eabdace27aa27b143f65b65e762bb0112a7708"
 uuid = "1a297f60-69ca-5386-bcde-b61e274b549b"
-version = "0.11.0"
+version = "0.11.1"
 
 [[FixedPointNumbers]]
 deps = ["Statistics"]
@@ -146,9 +146,9 @@ version = "0.8.4"
 
 [[ForwardDiff]]
 deps = ["CommonSubexpressions", "DiffResults", "DiffRules", "NaNMath", "Random", "SpecialFunctions", "StaticArrays"]
-git-tree-sha1 = "c26b56e9b9f0687f7ca887f6b6ded03d269e0e35"
+git-tree-sha1 = "d48a40c0f54f29a5c8748cfb3225719accc72b77"
 uuid = "f6369f11-7733-5829-9624-2563aa707210"
-version = "0.10.15"
+version = "0.10.16"
 
 [[Functors]]
 deps = ["MacroTools"]
@@ -227,9 +227,9 @@ version = "0.5.0"
 
 [[Missings]]
 deps = ["DataAPI"]
-git-tree-sha1 = "ed61674a0864832495ffe0a7e889c0da76b0f4c8"
+git-tree-sha1 = "f8c673ccc215eb50fcadb285f522420e29e69e1c"
 uuid = "e1d29d7a-bbdc-5cf2-9ac0-f12de2c33e28"
-version = "0.4.4"
+version = "0.4.5"
 
 [[Mmap]]
 uuid = "a63ad114-7e13-5084-954f-fe012c677804"
@@ -252,9 +252,9 @@ uuid = "efe28fd5-8261-553b-a9e1-b2916fc3738e"
 version = "0.5.3+4"
 
 [[OrderedCollections]]
-git-tree-sha1 = "cf59cfed2e2c12e8a2ff0a4f1e9b2cd8650da6db"
+git-tree-sha1 = "d45739abcfc03b51f6a42712894a593f74c80a23"
 uuid = "bac558e1-5e72-5ebc-8fee-abe8a469f55d"
-version = "1.3.2"
+version = "1.3.3"
 
 [[Pkg]]
 deps = ["Dates", "LibGit2", "Libdl", "Logging", "Markdown", "Printf", "REPL", "Random", "SHA", "UUIDs"]
@@ -375,9 +375,9 @@ version = "1.2.11+18"
 
 [[Zygote]]
 deps = ["AbstractFFTs", "ChainRules", "DiffRules", "Distributed", "FillArrays", "ForwardDiff", "IRTools", "InteractiveUtils", "LinearAlgebra", "MacroTools", "NaNMath", "Random", "Requires", "SpecialFunctions", "Statistics", "ZygoteRules"]
-git-tree-sha1 = "746c9de7fb87a341c809437007cbd172c4d494b4"
+git-tree-sha1 = "52835a83f7c899cfcb95f796d584201812887ea8"
 uuid = "e88e6eb3-aa80-5325-afca-941959d7151f"
-version = "0.6.2"
+version = "0.6.3"
 
 [[ZygoteRules]]
 deps = ["MacroTools"]

NEWS.md

@@ -11,7 +11,7 @@
 * Add [sparse initialization](https://github.com/FluxML/Flux.jl/pull/1454) as described in [Deep learning via Hessian-free optimization](https://dl.acm.org/doi/abs/10.5555/3104322.3104416).
 * Moved GPU CI to use buildkite instead of GitLab
 * New [`Parallel` layer](https://github.com/FluxML/Flux.jl/pull/1462) adds inception module-like building blocks.
-
+* Feature additions and bug fixes for BatchNorm, LayerNorm, InstanceNorm, and GroupNorm [normalization layers](https://github.com/FluxML/Flux.jl/pull/1397)
 
 ## v0.11.2
 

docs/src/models/basics.md

@@ -218,4 +218,4 @@ Flux.@functor Affine
 
 This enables a useful extra set of functionality for our `Affine` layer, such as [collecting its parameters](../training/optimisers.md) or [moving it to the GPU](../gpu.md).
 
-For some more helpful tricks, including parameter freezing, please checkout the [advanced usage guide](advanced.md).
+For some more helpful tricks, including parameter freezing, please checkout the [advanced usage guide](advanced.md).

src/cuda/cudnn.jl

@@ -1,7 +1,20 @@
 import CUDA.CUDNN: batchnorm, ∇batchnorm
 
-(BN::Flux.BatchNorm)(x::Union{CuArray{T,2},CuArray{T,4},CuArray{T,5}}, cache = nothing) where T<:Union{Float32, Float64} =
-  BN.λ.(batchnorm(BN.γ, BN.β, x, BN.μ, BN.σ², BN.momentum; cache = cache, alpha = 1, beta = 0, eps = BN.ϵ, training = Flux.istraining()))
+function (BN::Flux.BatchNorm)(x::Union{CuArray{T,2},CuArray{T,4},CuArray{T,5}},
+                              cache=nothing) where T<:Union{Float32, Float64}
+
+  @assert BN.affine "BatchNorm: only affine=true supported on gpu"
+  @assert BN.track_stats "BatchNorm: only track_stats=true supported on gpu"
+  @assert length(BN.β) == size(x, ndims(x)-1) "BatchNorm: input has wronng number of channels"
+  return BN.λ.(batchnorm(BN.γ, BN.β, x, BN.μ, BN.σ², BN.momentum; 
+                  cache=cache, alpha=1, beta=0, eps=BN.ϵ, 
+                  training=Flux._isactive(BN)))
+end
 
-@adjoint batchnorm(g, b, x, running_mean, running_var, momentum; kw...) =
-  batchnorm(g, b, x, running_mean, running_var, momentum; kw...), Δ -> (∇batchnorm(g, b, x, Δ, running_mean, running_var, momentum; kw...)..., nothing, nothing, nothing)
+@adjoint function batchnorm(g, b, x, running_mean, running_var, momentum; kw...)
+  y = batchnorm(g, b, x, running_mean, running_var, momentum; kw...) 
+  function batchnorm_pullback(Δ)
+    ∇batchnorm(g, b, x, Δ, running_mean, running_var, momentum; kw...)..., nothing, nothing, nothing
+  end
+  y, batchnorm_pullback
+end

src/deprecations.jl

@@ -1,7 +1,7 @@
 # v0.12 deprecations
 @deprecate Dropout(p, dims) Dropout(p; dims=dims)
-@deprecate InstanceNorm(λ, β, γ, μ, σ², ϵ, momentum) InstanceNorm(λ, β, γ, μ, σ², ϵ, momentum, nothing)
-@deprecate BatchNorm(λ, β, γ, μ, σ², ϵ, momentum) BatchNorm(λ, β, γ, μ, σ², ϵ, momentum, nothing)
+@deprecate InstanceNorm(λ, β, γ, μ, σ², ϵ, momentum) InstanceNorm(λ, β, γ, μ, σ², ϵ, momentum, true, true, nothing)
+@deprecate BatchNorm(λ, β, γ, μ, σ², ϵ, momentum) BatchNorm(λ, β, γ, μ, σ², ϵ, momentum, true, true, nothing)
 @deprecate GroupNorm(G, λ, β, γ, μ, σ², ϵ, momentum) GroupNorm(G, λ, β, γ, μ, σ², ϵ, momentum, nothing)
 @deprecate outdims(f, inputsize) outputsize(f, inputsize)
 @deprecate Conv(; weight,  bias, activation=identity, kws...) Conv(weight, bias, activation; kws...) 

src/layers/basic.jl

@@ -134,32 +134,35 @@ function Base.show(io::IO, l::Dense)
 end
 
 """
-    Diagonal(in::Integer)
+    Diagonal(α, β)
+    Diagonal(sz::Integer...; initα=ones, initβ=zeros)
 
-Create an element-wise linear transformation layer with learnable
-vectors `α` and `β`:
+Create an element-wise linear layer with learnable
+arrays `α` and `β` of size `sz`. The layer performs
 
     y = α .* x .+ β
 
-The input `x` must be a array where `size(x, 1) == in`.
+The input `x` must have size broadcast-compatible with `α` and `β`.
+The parameters will be created with the calls 
+`α = initα(sz)` and `β = initβ(sz)`.
 """
 struct Diagonal{T}
   α::T
   β::T
 end
 
-Diagonal(in::Integer; initα = ones, initβ = zeros) =
-  Diagonal(initα(in), initβ(in))
+function Diagonal(sz::Integer...; 
+      initα = i -> ones(Float32, i), 
+      initβ = i -> zeros(Float32, i))
+  Diagonal(initα(sz), initβ(sz))
+end
 
 @functor Diagonal
 
-function (a::Diagonal)(x)
-  α, β = a.α, a.β
-  α.*x .+ β
-end
+(a::Diagonal)(x) = a.α .* x .+ a.β
 
 function Base.show(io::IO, l::Diagonal)
-  print(io, "Diagonal(", length(l.α), ")")
+  print(io, "Diagonal(", size(l.α), ")")
 end
 
 """