JuliaDiff / JuliaDiff/ChainRules.jl

Rule for `vect` unthunks many times

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Dominant language
Julia
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Forks
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Description

This:

using ChainRulesCore
myplus(x,y) = x + y
function ChainRulesCore.rrule(::typeof(myplus), x, y)
  println("myplus rrule forward")
  x+y, dz -> begin
    println("myplus rrule reverse, dz isa ", typeof(dz))
    NoTangent(), @thunk(unthunk(dz)), @thunk @show unthunk(dz)
  end
end

using Yota
grad([1,2,3]) do x
  prod(myplus(x, [4,5,6]))
end

prints this:

myplus rrule forward
myplus rrule reverse, dz isa InplaceableThunk{Thunk{ChainRules.var"#1675#1678"{Float64, Colon, Vector{Float64}, ProjectTo{AbstractArray, NamedTuple{(:element, :axes), Tuple{ProjectTo{Float64, NamedTuple{(), Tuple{}}}, Tuple{Base.OneTo{Int64}}}}}, Float64}}, ChainRules.var"#1674#1677"{Float64, Colon, Vector{Float64}, Float64}}
unthunk(dz) = [63.0, 45.0, 35.0]
unthunk(dz) = [63.0, 45.0, 35.0]  # from @show dz inside the @thunk, runs 3 times
unthunk(dz) = [63.0, 45.0, 35.0]
(315.0, (ZeroTangent(), [63.0, 45.0, 35.0]))

Almost all rules should call unthunk exactly once on their input, maybe others make the same mistake, e.g. hvcat?

Contributor guide

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First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Start by reproducing the ChainRulesCore and Yota example from the issue, then inspect the rules involved in the shown stack and suspected cases such as hvcat for repeated unthunk calls. Done means affected rules unthunk their inputs only once and the reproduction no longer prints repeated evaluations.

Written by the indexing model from the issue text.

Assessment

Tech stack
julia
Domain
machine-learning
Issue type
Bug
Difficulty
3/5
Estimated time
1-2 days
Activity status
Stale
Clarity
Mostly clear
Newbie friendliness
35/100

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