update softmax gradients

.github/workflows/CI.yml

@@ -20,8 +20,9 @@ jobs:
       fail-fast: false
       matrix:
         version:
-          - '1'
           - '1.3'
+          - '1.6' # LTS
+          - '1'
           - 'nightly'
         os:
           - ubuntu-latest

Project.toml

@@ -1,6 +1,6 @@
 name = "Tracker"
 uuid = "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c"
-version = "0.2.19"
+version = "0.2.20"
 
 [deps]
 Adapt = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
@@ -23,7 +23,7 @@ DiffRules = "1.4"
 ForwardDiff = "0.10"
 LogExpFunctions = "0.3"
 MacroTools = "0.5"
-NNlib = "0.6, 0.7, 0.8"
+NNlib = "0.7.18, 0.8"  # 0.7.18 is the last version which supports Julia 1.3
 NaNMath = "0.3, 1"
 Requires = "0.5, 1.0"
 SpecialFunctions = "0.10, 1, 2"

src/lib/array.jl

@@ -471,11 +471,31 @@ import NNlib: DenseConvDims, DepthwiseConvDims, PoolDims
 
 softmax(xs::TrackedArray; dims=1) = track(softmax, xs; dims=dims)
 
-@grad softmax(xs; dims=1) = softmax(data(xs); dims=dims), Δ -> (nobacksies(:softmax, ∇softmax(data(Δ), data(xs); dims=dims)),)
+if isdefined(NNlib, :∇softmax_data)  # use new form to avoid a depwarn, but only possible Julia 1.6+
+  @eval @grad function softmax(xs; dims=1)
+    y = softmax(data(xs); dims=dims)
+    y, Δ -> (nobacksies(:softmax, NNlib.∇softmax_data(data(Δ), data(y); dims=dims)),)
+  end
+else
+  @eval @grad function softmax(xs; dims=1)  # TODO delete this when dropping Julia 1.3 (and increase NNlib bound)
+    y = softmax(data(xs); dims=dims)
+    y, Δ -> (nobacksies(:softmax, ∇softmax(data(Δ), data(xs), data(y); dims=dims)),)
+  end
+end
 
 logsoftmax(xs::TrackedArray; dims=1) = track(logsoftmax, xs; dims=dims)
 
-@grad logsoftmax(xs; dims=1) = logsoftmax(data(xs); dims=dims), Δ -> (nobacksies(:logsoftmax, ∇logsoftmax(data(Δ), data(xs); dims=dims)),)
+if isdefined(NNlib, :∇logsoftmax_data)  # use new form to avoid a depwarn, but only possible Julia 1.6+
+  @eval @grad function logsoftmax(xs; dims=1)
+    y = logsoftmax(data(xs); dims=dims)
+    y, Δ -> (nobacksies(:logsoftmax, NNlib.∇logsoftmax_data(data(Δ), data(y); dims=dims)),)
+  end
+else
+  @eval @grad function logsoftmax(xs; dims=1)
+    y = logsoftmax(data(xs); dims=dims)
+    y, Δ -> (nobacksies(:logsoftmax, ∇logsoftmax(data(Δ), data(xs), data(y); dims=dims)),)
+  end
+end
 
 depthwiseconv(x::TrackedArray, w::TrackedArray, cdims::DepthwiseConvDims; kw...) = track(depthwiseconv, x, w, cdims; kw...)
 depthwiseconv(x::AbstractArray, w::TrackedArray, cdims::DepthwiseConvDims; kw...) = track(depthwiseconv, x, w, cdims; kw...)

test/tracker.jl

@@ -10,7 +10,9 @@ using Random
 
 gradtest(f, xs::AbstractArray...) = gradcheck((xs...) -> sum(sin.(f(xs...))), xs...)
 gradtest(f, dims...) = gradtest(f, rand.(Float64, dims)...)
-@testset "Tracker" begin
+
+@testset "Tracker" begin # overall testset, rest of the file
+
 @test gradtest((x, W, b) -> σ.(W*x .+ b), 5, (2,5), 2)
 @test gradtest((x, W) -> σ.(W*x), 5, (2,5))
 @test gradtest((x, W, b) -> σ.(W*x .+ b), (5,3), (2,5), 2)
@@ -478,4 +480,4 @@ end
     @test size(y) == (5, 3)
 end
 
-end #testset
+end # overall testset