port rule definitions to ChainRulesCore

Project.toml

@@ -1,24 +1,30 @@
 name = "NNlib"
 uuid = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
-version = "0.7.9"
+version = "0.8.0"
 
 [deps]
+ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
 Compat = "34da2185-b29b-5c13-b0c7-acf172513d20"
 Libdl = "8f399da3-3557-5675-b5ff-fb832c97cbdb"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
 Requires = "ae029012-a4dd-5104-9daa-d747884805df"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
+ZygoteRules = "700de1a5-db45-46bc-99cf-38207098b444"
 
 [compat]
+ChainRulesCore = "0.9"
 Compat = "3.14"
 Requires = "0.5, 1.0"
+ZygoteRules = "0.2"
 julia = "1.3"
 
 [extras]
+FiniteDifferences = "26cc04aa-876d-5657-8c51-4c34ba976000"
+Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [targets]
-test = ["Test", "Statistics", "Zygote"]
+test = ["FiniteDifferences", "Random", "Statistics", "Test", "Zygote"]

src/NNlib.jl

@@ -6,7 +6,7 @@ using Requires
 include("dim_helpers.jl")
 
 is_nnpack_available() = false
-  
+
 @init @require NNPACK_jll="a6bfbf70-4841-5cb9-aa18-3a8ad3c413ee"  begin
   if isdefined(NNPACK_jll, :libnnpack)
     include("nnpack/NNPACK.jl")
@@ -39,4 +39,8 @@ include("impl/depthwiseconv_im2col.jl")
 # Direct implementations of pooling
 include("impl/pooling_direct.jl")
 
+# differentiation rules
+include("chainrulescore.jl")
+include("zygoterules.jl")
+
 end # module NNlib

src/chainrulescore.jl

@@ -0,0 +1,90 @@
+using ChainRulesCore
+
+const Numeric = Union{AbstractArray{<:T}, T} where {T<:Number}
+
+@scalar_rule(selu(x), dselu(x))
+@scalar_rule(elu(x, α), (delu(x, α), DoesNotExist()))
+@scalar_rule(σ(x::Real), Ω * (1 - Ω))
+
+function dselu(x)
+    λ = oftype(x/1, 1.0507009873554804934193349852946)
+    α = oftype(x/1, 1.6732632423543772848170429916717)
+    return λ * ifelse(x > 0, one(x), α * exp(x))
+end
+delu(x, α) = ifelse(x ≥ 0, one(x), α * exp(x))
+
+for softmax in [:softmax, :logsoftmax]
+    local ∇softmax = Symbol(:∇, softmax)
+    pullback = Symbol(softmax, :_pullback)
+
+    @eval function ChainRulesCore.rrule(::typeof($softmax), xs; dims=1)
+        $pullback(Δ) = (NO_FIELDS, @thunk($∇softmax(Δ, xs, dims=dims)))
+        return $softmax(xs; dims=dims), $pullback
+    end
+end
+
+for Dims in [:DenseConvDims, :DepthwiseConvDims, :PoolDims]
+    pullback = Symbol(Dims, :_pullback)
+    @eval function ChainRulesCore.rrule(::Type{$Dims}, args...; kwargs...)
+        $pullback(Δ) = (NO_FIELDS, ntuple(_ -> DoesNotExist(), length(args))...)
+        return $Dims(args...; kwargs...), $pullback
+    end
+end
+
+colmajor(x) = (is_strided(x) && Base.stride(x, 1) == 1) ? x : collect(x)
+
+for conv in [:conv, :depthwiseconv]
+    local ∇conv_data, ∇conv_filter = Symbol.(:∇, conv, [:_data, :_filter])
+    conv_pullback, ∇conv_data_pullback = Symbol.([conv, ∇conv_data], :_pullback)
+
+    @eval function ChainRulesCore.rrule(::typeof($conv), x, w, cdims; kw...)
+        function $conv_pullback(Δ)
+            Δ = colmajor(Δ)
+            return (
+                NO_FIELDS,
+                @thunk($∇conv_data(Δ, w, cdims, kw...)),
+                @thunk($∇conv_filter(x, Δ, cdims, kw...)),
+                DoesNotExist(),
+            )
+        end
+        return $conv(x, w, cdims; kw...), $conv_pullback
+    end
+
+    @eval function ChainRulesCore.rrule(::typeof($∇conv_data), x, w, cdims; kw...)
+        function $∇conv_data_pullback(Δ)
+            Δ = colmajor(Δ)
+            return (
+                NO_FIELDS,
+                @thunk($conv(Δ, w, cdims, kw...)),
+                @thunk($∇conv_filter(Δ, x, cdims, kw...)),
+                DoesNotExist(),
+            )
+        end
+        return $∇conv_data(x, w, cdims; kw...), $∇conv_data_pullback
+    end
+end
+
+for pool in [:maxpool, :meanpool]
+    ∇pool = Symbol(:∇, pool)
+    pullback = Symbol(pool, :_pullback)
+    @eval function ChainRulesCore.rrule(::typeof($pool), x, pdims::PoolDims; kw...)
+        Ω = $pool(x, pdims; kw...)
+        $pullback(Δ) = (NO_FIELDS, @thunk($∇pool(Δ, Ω, x, pdims; kw...)), DoesNotExist())
+        return Ω, $pullback
+    end
+end
+
+function ChainRulesCore.rrule(
+    ::typeof(batched_mul),
+    A::AbstractArray{S,3},
+    B::AbstractArray{T,3},
+) where {S,T}
+    function batched_mul_pullback(Δ)
+        return (
+            NO_FIELDS,
+            @thunk(batched_mul(Δ, batched_adjoint(B))),
+            @thunk(batched_mul(batched_adjoint(A), Δ)),
+        )
+    end
+    batched_mul(A, B), batched_mul_pullback
+end

src/zygoterules.jl

@@ -0,0 +1,17 @@
+using ZygoteRules
+
+# This is a performance hack specifically for Zygote, because it doesn't handle fused
+# broadcasts well
+for (f, df) in [
+    (:relu, :(x .> 0)),
+    (:selu, :(dselu.(x))),
+    (:elu, :(delu.(x))),
+    (:σ, :(conj.(Ω .* (1 .- Ω)))),
+]
+    pullback = Symbol(:broadcasted_, f, :_pullback)
+    @eval @adjoint function Base.Broadcast.broadcasted(::typeof($f), x::Numeric)
+        Ω = $f.(x)
+        $pullback(Δ) = (nothing, Δ .* $df)
+        return Ω, $pullback
+    end
+end

test/runtests.jl

@@ -19,3 +19,6 @@ end
 @testset "Softmax" begin
     include("softmax.jl")
 end
+@testset "Zygote" begin
+    include("zygote.jl")
+end

test/zygote.jl

@@ -0,0 +1,107 @@
+using Zygote, NNlib
+using Random
+using LinearAlgebra
+using NNlib: conv, ∇conv_data, depthwiseconv, batched_mul
+using FiniteDifferences: grad, central_fdm
+
+function gradcheck(f, xs...; rtol = 1e-5, atol = 1e-5)
+  grad_zygote = gradient(f, xs...)
+  grad_finite_difference = grad(central_fdm(5, 1), f, xs...)
+  #return all(isapprox.(grad_zygote, grad_finite_difference; rtol = rtol, atol = atol))
+  for (grad_zygote, grad_finite_difference) in zip(grad_zygote, grad_finite_difference)
+    @test isapprox(grad_zygote, grad_finite_difference; rtol = rtol, atol = atol)
+    if !isapprox(grad_zygote, grad_finite_difference; rtol = rtol, atol = atol)
+      display(grad_zygote - grad_finite_difference)
+      @show maximum(grad_zygote - grad_finite_difference)
+      @show norm(grad_zygote) norm(grad_finite_difference)
+      println()
+    end
+  end
+end
+
+gradtest(f, xs::AbstractArray...; kw...) = gradcheck((xs...) -> sum(sin.(f(xs...))), xs...; kw...)
+gradtest(f, dims...; kw...) = gradtest(f, rand.(Float64, dims)...; kw...)
+
+gradtest((x, W, b) -> relu.(W*x .+ b), 5, (2,5), 2)
+gradtest((x, W, b) -> relu.(W*x .+ b), (5,3), (2,5), 2)
+gradtest((x, W, b) -> selu.(W*x .+ b), 5, (2,5), 2)
+gradtest((x, W, b) -> selu.(W*x .+ b), (5,3), (2,5), 2)
+gradtest((x, W, b) -> elu.(W*x .+ b, 2), 5, (2,5), 2)
+gradtest((x, W, b) -> elu.(W*x .+ b, 2), (5,3), (2,5), 2)
+
+# tests for https://github.com/FluxML/Zygote.jl/issues/758
+@test gradient(xs -> sum(selu.(xs)), [1_000, 10_000])[1] ≈ [1.0507009873554805, 1.0507009873554805] rtol=1e-8
+@test gradient(x -> selu(x), 1_000) == (1.0507009873554805,)
+@test gradient(xs -> sum(elu.(xs, 2)), [1_000, 10_000]) == ([1., 1.],)
+@test gradient(x -> elu(x, 2), 1_000) == (1.,)
+@test gradient(x -> elu(x, 2), -1) == (2*exp(-1),)
+gradcheck(x->sum(selu.(x)),[100., 1_000.])
+gradcheck(x->sum(elu.(x, 3.5)),[100., 1_000.])
+gradcheck(x->sum(elu.(x, 3.5)),[1_000., 10_000.]) # for elu the tests are passing but for selu not, interesting
+# numerical instability even for the linear part of such function, see:
+# julia> ngradient(x->sum(selu.(x)),[1_000., 10_000.])
+# ([1.0506591796875, 1.0506591796875],)
+# julia> gradient(x->sum(selu.(x)),[1_000., 10_000.])
+# ([1.0507009873554805, 1.0507009873554805],)
+@test_broken gradcheck(x->sum(selu.(x)),[1_000., 10_000.])
+
+gradtest((x, W, b) -> σ.(W*x .+ b), 5, (2,5), 2)
+gradtest((x, W, b) -> σ.(W*x .+ b), (5,3), (2,5), 2)
+gradtest((x, W, b) -> logσ.(W*x .+ b), 5, (2,5), 2)
+gradtest((x, W, b) -> logσ.(W*x .+ b), (5,3), (2,5), 2)
+
+gradtest(x -> softmax(x).*(1:3), 3)
+gradtest(x -> softmax(x).*(1:3), (3,5))
+gradtest(x -> softmax(x, dims=2).*(1:3), (3,5))
+gradtest(x -> logsoftmax(x).*(1:3), 3)
+gradtest(x -> logsoftmax(x).*(1:3), (3,5))
+gradtest(x -> logsoftmax(x, dims=2).*(1:3), (3,5))
+
+@testset "conv: spatial_rank=$spatial_rank" for spatial_rank in (1, 2, 3)
+  x = rand(repeat([5], spatial_rank)..., 3, 2)
+  w = rand(repeat([3], spatial_rank)..., 3, 3)
+  cdims = DenseConvDims(x, w)
+  gradtest((x, w) -> conv(x, w, cdims), x, w)
+  gradtest((x, w) -> sum(conv(x, w, cdims)), x, w)  # https://github.com/FluxML/Flux.jl/issues/1055
+
+  y = conv(x, w, cdims)
+  gradtest((y, w) -> ∇conv_data(y, w, cdims), y, w)
+  if spatial_rank == 3
+    @test_broken gradtest((y, w) -> sum(∇conv_data(y, w, cdims)), y, w)
+  else
+    gradtest((y, w) -> sum(∇conv_data(y, w, cdims)), y, w)
+  end
+
+  dcdims = DepthwiseConvDims(x, w)
+  gradtest((x, w) -> depthwiseconv(x, w, dcdims), x, w)
+
+  y = depthwiseconv(x, w, dcdims)
+  gradtest((y, w) -> ∇depthwiseconv_data(y, w, dcdims), y, w)
+  if spatial_rank == 3
+    @test_broken gradtest((y, w) -> sum(∇depthwiseconv_data(y, w, dcdims)), y, w)
+  else
+    gradtest((y, w) -> sum(∇depthwiseconv_data(y, w, dcdims)), y, w)
+  end
+end
+
+@testset "pooling: spatial_rank=$spatial_rank" for spatial_rank in (1, 2)
+  x = rand(repeat([10], spatial_rank)..., 3, 2)
+  pdims = PoolDims(x, 2)
+  gradtest(x -> maxpool(x, pdims), x)
+  gradtest(x -> meanpool(x, pdims), x)
+  gradtest(x -> sum(maxpool(x, pdims)), x)
+  gradtest(x -> sum(meanpool(x, pdims)), x)
+
+  #https://github.com/FluxML/NNlib.jl/issues/188
+  k = ntuple(_ -> 2, spatial_rank)  # Kernel size of pool in ntuple format
+  gradtest(x -> maxpool(x, k), x)
+  gradtest(x -> meanpool(x, k), x)
+  gradtest(x -> sum(maxpool(x, k)), x)
+  gradtest(x -> sum(meanpool(x, k)), x)
+end
+
+@testset "batched matrix multiplication" begin
+  rng, M, P, Q = MersenneTwister(123456), 13, 7, 11
+  B = 3
+  gradtest(batched_mul, randn(rng, M, P, B), randn(rng, P, Q, B))
+end