diff --git a/src/destructure.jl b/src/destructure.jl
index d000ff75..49f73ed1 100644
--- a/src/destructure.jl
+++ b/src/destructure.jl
@@ -53,16 +53,20 @@ end
Base.show(io::IO, re::Restructure{T}) where T = print(io, "Restructure(", T.name.name, ", ..., ", re.length, ")")
Base.length(re::Restructure) = re.length
+struct Offset
+ i::Int
+end
+
# This flattens a model, and returns a web of offsets for later use:
function _flatten(x)
- isnumeric(x) && return vcat(_vec(x)), 0, length(x) # trivial case
+ isnumeric(x) && return vcat(_vec(x)), Offset(0), length(x) # trivial case
arrays = AbstractVector[]
len = Ref(0)
off = fmap(x; exclude = isnumeric, walk = (f, z) -> map(f, _trainable(z))) do y
push!(arrays, _vec(y))
o = len[]
len[] = o + length(y)
- o
+ Offset(o)
end
reduce(vcat, arrays), off, len[]
end
@@ -85,13 +89,13 @@ function _rebuild(x, off, flat::AbstractVector, len = length(flat); walk = _trai
end
end
-_getat(y::Number, o::Int, flat::AbstractVector) = ProjectTo(y)(flat[o + 1])
-_getat(y::AbstractArray, o::Int, flat::AbstractVector) =
- ProjectTo(y)(reshape(flat[o .+ (1:length(y))], axes(y))) # ProjectTo is just correcting eltypes
+_getat(y::Number, off::Offset, flat::AbstractVector) = ProjectTo(y)(flat[off.i + 1])
+_getat(y::AbstractArray, off::Offset, flat::AbstractVector) =
+ ProjectTo(y)(reshape(flat[off.i .+ (1:length(y))], axes(y))) # ProjectTo is just correcting eltypes
function _trainable_biwalk(f, x, aux)
ch, re = functor(typeof(x), x)
- au, _ = functor(typeof(x), aux)
+ au, _ = functor(aux)
_trainmap(f, ch, _trainable(x), au) |> re
end
@@ -103,13 +107,20 @@ end
function _Tangent_biwalk(f, x, aux) # use with prune = NoT
ch, re = functor(typeof(x), x)
- au, _ = functor(typeof(x), aux)
- y = _trainmap(f, ch, _trainable(x), au)
+ au, _ = functor(aux)
+ y = map(ch, _trainable(x), au) do c, t, a # isnothing(t) indicates non-trainable field, safe given isnumeric(c)
+ isnothing(t) ? NoT : f(t, a)
+ end
y isa Tuple{} && return NoT
p = ProjectTo(x)
if p isa ProjectTo # e.g. Array, NamedTuple
p(y)
else # p === identity for unknown structs
+ y = map(backing(x), backing(re(y))) do c, t
+ # backing(re(y)) extracts NamedTuple backing from re(y); required if x has children which aren't its own fields
+ # however, re(y) will repopulate primal field values from x which weren't functor-ed; these gradients should be NoT
+ c === t ? NoT : t
+ end
Tangent{typeof(x), typeof(y)}(y)
end
end
@@ -126,23 +137,23 @@ ChainRulesCore.@non_differentiable _zero(x)
function _grad!(x, dx, off, flat::AbstractVector)
x′, _ = functor(typeof(x), x)
dx′, _ = functor(typeof(x), base(dx))
- off′, _ = functor(typeof(x), off)
+ off′, _ = functor(off)
for (xᵢ, dxᵢ, oᵢ) in zip(x′, dx′, off′)
flat = _grad!(xᵢ, dxᵢ, oᵢ, flat)
end
flat
end
-function _grad!(x, dx, off::Integer, flat::AbstractVector{T}) where T
+function _grad!(x, dx, off::Offset, flat::AbstractVector{T}) where T
dx_un = unthunk(dx)
T2 = promote_type(T, eltype(dx_un))
if T != T2 # then we must widen the type
flat = copyto!(similar(flat, T2), flat)
end
- @views flat[off .+ (1:length(x))] .+= vec(dx_un) # must visit all tied nodes
+ @views flat[off.i .+ (1:length(x))] .+= vec(dx_un) # must visit all tied nodes
flat
end
_grad!(x, dx::Zero, off, flat::AbstractVector) = flat
-_grad!(x, dx::Zero, off::Integer, flat::AbstractVector) = flat # ambiguity
+_grad!(x, dx::Zero, off::Offset, flat::AbstractVector) = flat # ambiguity
# These are only needed for 2nd derivatives:
function ChainRulesCore.rrule(::typeof(_grad!), x, dx, off, flat)
diff --git a/test/destructure.jl b/test/destructure.jl
index d20f4f30..fce4ceeb 100644
--- a/test/destructure.jl
+++ b/test/destructure.jl
@@ -181,6 +181,40 @@ end
end == ([17,17,4,4],) # Flux gave ([4.0, 4.0, 13.0, 13.0],)
end
+@testset "issue 62" begin
+ # Flux.Chain used to have children which aren't its own fields, which Skip immitates.
+
+ sk = Skip([1.0, 2.0], (x=3, y=[4.0, 5.0]))
+ @test fmap(identity, sk) == sk
+
+ gk = gradient(x -> sum(x[2].y), sk)[1]
+ @test fmap(Zygote.accum, sk, gk) isa Skip # this relies on functor(typeof(x), dx)
+
+ st = fmapstructure(identity, sk)
+ @test st isa Tuple{Vector, NamedTuple}
+ @test_throws Exception fmap(+, sk, st) # this fails because of functor(typeof(x), dx)
+
+ v, re = destructure(sk)
+ @test v == [1,2,4,5]
+ @test re(10v) isa Skip
+ @test re(10v)[1] == [10, 20]
+
+ @test gradient(zero(v)) do w
+ re(w)[2].y[1]
+ end == ([0,0,1,0],)
+
+ @test gradient(sk) do x
+ w, _ = destructure(x)
+ w[1]^2 + w[4]^2
+ end == ((layers = ([2.0, 0.0], (x = nothing, y = [0.0, 10.0])),),)
+
+ ac = TwoThirds([1.0, 2.0], [3.0], [4.0, 5.0]) # a,c are functor-ed, and only a is trainable
+ @test gradient(ac) do x
+ w2, _ = destructure(x)
+ w2[2]^2
+ end == ((a = [0.0, 4.0], b = nothing, c = nothing),)
+end
+
@testset "DiffEqFlux issue 699" begin
# The gradient of `re` is a vector into which we accumulate contributions, and the issue
# is that one contribution may have a wider type than `v`, especially for `Dual` numbers.
diff --git a/test/runtests.jl b/test/runtests.jl
index d47bce08..1a54c5e4 100644
--- a/test/runtests.jl
+++ b/test/runtests.jl
@@ -13,6 +13,13 @@ struct TwoThirds a; b; c; end
Functors.@functor TwoThirds (a, c)
Optimisers.trainable(x::TwoThirds) = (a = x.a,)
+struct Skip{T} # like Flux 0.12's Chain
+ layers::T
+ Skip(ls...) = new{typeof(ls)}(ls)
+end
+Base.getindex(x::Skip, i::Integer) = x.layers[i]
+Functors.functor(::Type{<:Skip}, x) = x.layers, ls -> Skip(ls...)
+
@testset verbose=true "Optimisers.jl" begin
@testset verbose=true "Features" begin
@@ -165,6 +172,16 @@ Optimisers.trainable(x::TwoThirds) = (a = x.a,)
@test_throws ArgumentError Optimisers.setup(ADAMW(), m2)
end
+ @testset "issue 62" begin
+ m62 = (s = Skip([1.0, 2.0], Foo([3.0], false)), t = [4.0, 5.0])
+ s62 = Optimisers.setup(Descent(), m62)
+ g62 = gradient(m -> m.s[2].x[1] + 3 * m.t[2], m62)
+ s, m = Optimisers.update(s62, m62, g62...)
+ @test m.s isa Skip
+ @test m.s[2].x ≈ [2.9]
+ @test m.t ≈ [4, 4.7]
+ end
+
end
@testset verbose=true "Destructure" begin
include("destructure.jl")