fix CI on 1.6 and MacOS

Author: ToucheSir

src/normalization.jl

@@ -54,8 +54,8 @@ _apply_scale_bias(x, scale, bias) = x .* scale .+ bias
 
 Shared code path for all built-in norm functions.
 
-`μ` and `σ²` should be calculated on the fly using [`NNlib.norm_stats`](@ref),
-or extracted from an existing collection such as [`NNlib.RunningStats`](@ref).
+`μ` and `σ²` should be calculated on the fly using [`norm_stats`](@ref),
+or extracted from an existing collection such as [`RunningStats`](@ref).
 `bias` and `scale` are consistent with cuDNN and Flux.Scale.
 We opt for `scale` over `weight` to avoid confusion with dense layers.
 If the size of the statistics and affine parameters differ,
@@ -79,7 +79,7 @@ Contains running mean and variance estimates for stateful norm functions.
 If the parameters are mutable, they will be updated in-place.
 Otherwise, they will be replaced wholesale.
 
-See also [`NNlib.update_running_stats!`](@ref).
+See also [`update_running_stats!`](@ref).
 """
 mutable struct RunningStats{M <: AbstractArray, V <: AbstractArray, MT <: Real}
     mean::M
@@ -129,10 +129,10 @@ end
                           reduce_dims) where {N}
 
 Performs a moving average update for layers with tracked statistics.
-`μ` and `σ²` are the sample mean and variance, most likely from [`NNlib.norm_stats`](@ref).
-`reduce_dims` should also match the `dims` argument of [`NNlib.norm_stats`](@ref).
+`μ` and `σ²` are the sample mean and variance, most likely from [`norm_stats`](@ref).
+`reduce_dims` should also match the `dims` argument of [`norm_stats`](@ref).
 
-See also [`NNlib.RunningStats`](@ref).
+See also [`RunningStats`](@ref).
 """
 function update_running_stats!(stats::RunningStats, x, μ, σ², reduce_dims::Dims)
     V = eltype(σ²)
@@ -168,7 +168,7 @@ Normalizes `x` along the first `S` dimensions.
 
 For an additional learned affine transform, provide a `S`-dimensional `scale` and `bias`.
 
-See also [`NNlib.batchnorm`](@ref), [`NNlib.instancenorm`](@ref), and [`NNlib.groupnorm`](@ref).
+See also [`batchnorm`](@ref), [`instancenorm`](@ref), and [`groupnorm`](@ref).
 
 # Examples
 
@@ -205,14 +205,14 @@ Functional [Batch Normalization](https://arxiv.org/abs/1502.03167) operation.
 Normalizes `x` along each ``D_1×...×D_{N-2}×1×D_N`` input slice,
 where `N-1` is the "channel" (or "feature", for 2D inputs) dimension.
 
-Provide a [`NNlib.RunningStats`](@ref) to fix a estimated mean and variance.
+Provide a [`RunningStats`](@ref) to fix a estimated mean and variance.
 `batchnorm` will renormalize the input using these statistics during inference,
 and update them using batch-level statistics when training.
 To override this behaviour, manually set a value for `training`.
 
 If specified, `scale` and `bias` will be applied as an additional learned affine transform.
 
-See also [`NNlib.layernorm`](@ref), [`NNlib.instancenorm`](@ref), and [`NNlib.groupnorm`](@ref).
+See also [`layernorm`](@ref), [`instancenorm`](@ref), and [`groupnorm`](@ref).
 """
 function batchnorm(x::AbstractArray{<:Any, N},
                    running_stats::Union{RunningStats, Nothing} = nothing,
@@ -247,7 +247,7 @@ To override this behaviour, manually set a value for `training`.
 
 If specified, `scale` and `bias` will be applied as an additional learned affine transform.
 
-See also [`NNlib.layernorm`](@ref), [`NNlib.batchnorm`](@ref), and [`NNlib.groupnorm`](@ref).
+See also [`layernorm`](@ref), [`batchnorm`](@ref), and [`groupnorm`](@ref).
 """
 function instancenorm(x::AbstractArray{<:Any, N},
                       running_stats::Union{RunningStats, Nothing} = nothing,
@@ -281,7 +281,7 @@ The number of channels must be an integer multiple of the number of groups.
 
 If specified, `scale` and `bias` will be applied as an additional learned affine transform.
 
-See also [`NNlib.layernorm`](@ref), [`NNlib.batchnorm`](@ref), and [`NNlib.instancenorm`](@ref).
+See also [`layernorm`](@ref), [`batchnorm`](@ref), and [`instancenorm`](@ref).
 
 # Examples
 

test/normalization.jl

@@ -35,8 +35,10 @@ end
 
     # Group/InstanceNorm dimensions
     let W = 128, C = 2, N = 2, shape = (W, W, 1, 1)
-        x = [randn_sample(shape, 1, 1);;; randn_sample(shape, 2, 2);;;;
-             randn_sample(shape, 3, 3);;; randn_sample(shape, 4, 4)]
+        # Tile to W x W x 2 x 2
+        x = cat(cat(randn_sample(shape, 1, 1), randn_sample(shape, 2, 2); dims = 3),
+                cat(randn_sample(shape, 3, 3), randn_sample(shape, 4, 4); dims = 3);
+                dims = 4)
         μ, σ² = NNlib.norm_stats(x, (1, 2))
         @test vec(μ)≈1:(C * N) rtol=0.05
         @test vec(σ²)≈abs2.(1:(C * N)) rtol=0.05
@@ -60,7 +62,9 @@ end
             (running_stats, true, y_ns, y_ns, dx_ns),
             (running_stats, false, meanvar, meanvar, NoTangent()),
         ]
-            @test NNlib.maybe_norm_stats(stats, x, dims, !training) == y
+            ŷ = NNlib.maybe_norm_stats(stats, x, dims, !training)
+            @test ŷ[1]≈y[1] rtol=1e-5
+            @test ŷ[2]≈y[2] rtol=1e-5
             ŷ, back = rrule(NNlib.maybe_norm_stats, stats, x, dims, !training)
             @test ŷ == y_ad
             @test back(meanvar) == (NoTangent(), NoTangent(), dx, NoTangent(), NoTangent())
@@ -170,8 +174,7 @@ end
         @testset for use_stats in (true, false)
             stats = use_stats ? NNlib.RunningStats(zeros(2), ones(2), 0.1) : nothing
             y, back = Zygote.pullback(NNlib.instancenorm, x, stats, scale, bias, 1e-5)
-            @test y≈[-1.22474 -1.22474; 0.0 0.0; 1.22474 1.22474;;;
-                     -1.22474 -1.22474; 0.0 0.0; 1.22474 1.22474] rtol=1e-5
+            @test y≈repeat([-1.22474, 0.0, 1.22474], 1, 2, 2) rtol=1e-5
 
             expected_mean, expected_var = [0.5, 0.8], [1.0, 1.0]
             if use_stats
@@ -197,8 +200,7 @@ end
             end
 
             dx, dstats, dscale, dbias, _ = back(fill!(similar(y), 1))
-            @test dx≈[3.6742 3.6742; 1.22474 1.22474; -1.22474 -1.22474;;;
-                      3.6742 3.6742; 1.22474 1.22474; -1.22474 -1.22474] rtol=1e-5
+            @test dx≈repeat([3.6742, 1.22474, -1.22474], 1, 2, 2) rtol=1e-5
             @test dscale == zeros(2)
             @test dbias == fill(6.0, 2)
             @test dstats === nothing