diff --git a/src/rulesets/Base/indexing.jl b/src/rulesets/Base/indexing.jl index 61216bda2..c6623822e 100644 --- a/src/rulesets/Base/indexing.jl +++ b/src/rulesets/Base/indexing.jl @@ -262,20 +262,22 @@ end # Using Val(dim) here is worth a factor of 2 in this, on Julia 1.8- # @btime rrule(eachcol, $([1 2; 3 4]))[2]($([[10, 20], [30, 40]])) function ∇eachslice(dys_raw, x::AbstractArray, vd::Val{dim}) where {dim} - dys = unthunk(dys_raw) + dys = unthunk.(unthunk(dys_raw)) i1 = findfirst(dy -> dy isa AbstractArray, dys) if i1 === nothing # all slices are Zero! return _zero_fill!(similar(x, float(eltype(x)), axes(x))) end + T = Base.promote_eltype(dys...) # The whole point of this gradient is that we can allocate one `dx` array: dx = similar(x, T, axes(x)) for i in axes(x, dim) slice = selectdim(dx, dim, i) - if dys[i] isa AbstractZero + dy = dys[i] + if dy isa AbstractZero _zero_fill!(slice) # Avoids this: copyto!([1,2,3], ZeroTangent()) == [0,2,3] else - copyto!(slice, dys[i]) + copyto!(slice, dy) end end return ProjectTo(x)(dx)