JuliaDiff / JuliaDiff/ChainRules.jl
cholesky decomposition
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Description
I'm getting incorrect results when working with the rrule for cholesky where A <: LinearAlgebra.HermOrSym
Passing the input matrix through Matrix fixes the issue. The mul! fix relates to this issue.
using Zygote, ChainRules,LinearAlgebra
# Example matrix
A = [2. -1. 0.0; -1. 2. -1.; 0. -1. 2. ]
import LinearAlgebra.mul!
LinearAlgebra.mul!(C, ::ChainRulesCore.ZeroTangent, ::Any, ::Any, b) = C *=b
# produces zeros
Zygote.jacobian(a -> cholesky(Hermitian(a)).L , A)[1]
# both of the following produce expected result
Zygote.jacobian(a -> cholesky(Matrix(Hermitian(a))).L , A)[1]
Zygote.jacobian(a -> cholesky(a).L , A)[1]
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First steps
- Read the whole issue, then the project's contributing guide.
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Research direction
Start with the rrule for cholesky where A <: LinearAlgebra.HermOrSym in src/rulesets/LinearAlgebra/factorization.jl around line 452, then reproduce the Zygote.jacobian examples from the issue. Compare the Hermitian path with the Matrix and plain cholesky paths, including the linked ChainRulesCore mul! issue. Done means the Hermitian cholesky Jacobian no longer produces zeros.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- julia
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100