JuliaDiff / JuliaDiff/AbstractDifferentiation.jl
Jacobians for functions beyond vector-to-vector?
Open
Nobody has claimed this yet.
bug
- Dominant language
- Julia
- Stars
- 138
- Forks
- 18
- PR merge metrics
- No merged PRs in 30d
Description
julia> using AbstractDifferentiation, Zygote
julia> function ft1(x)
y1 = (x[1]*x[2])^x[3]
y2 = (x[2]*x[3])^x[1]
y3 = (x[3]*x[1])^x[2]
[y1, y2, y3]
end
ft1 (generic function with 1 method)
julia> function ft2(xs)
ys = ft1.(eachcol(xs))
hcat(ys...)
end
ft2 (generic function with 1 method)
julia> r3 = rand(3, 8)
3×8 Matrix{Float64}:
0.0354617 0.444021 0.161892 0.56656 0.92774 0.260982 0.839223 0.175217
0.020074 0.185554 0.747159 0.850257 0.930541 0.451429 0.978923 0.937234
0.213358 0.838412 0.562181 0.256845 0.743921 0.777094 0.207115 0.791544
julia> only(Zygote.jacobian(ft2, r3))
24×24 Matrix{Float64}:
1.28169 2.26417 -1.54394 0.0 0.0 0.0 0.0 0.0 … 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
-4.4943 1.45594 0.136984 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.51321 -4.42796 0.0852995 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.232868 0.557241 -0.307858 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 -0.814452 1.04745 0.231817 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.347887 -0.822595 0.18424 0.0 0.0 … 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 1.05908 0.229477 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 -0.753767 0.188289 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.769966 -0.39986 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 … 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 … 0.326677 -0.405857 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.439836 0.255509 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -0.776414 0.282692 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.236948 0.203133 -0.188738 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -0.418176 0.224654 1.06182 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 … 0.0 0.0 0.210367 -0.315561 0.852398 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.08112 0.202117 -0.432339
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -0.283372 0.177422 0.210078
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.839794 -0.310155 0.185898
julia> only(AD.jacobian(AD.ZygoteBackend(), ft2, r3))
ERROR: "The function `identity_matrix_like` is not defined for the type Matrix{Float64}."
Stacktrace:
[1] identity_matrix_like(x::Matrix{Float64})
@ AbstractDifferentiation C:\Users\Hossein Pourbozorg\.julia\packages\AbstractDifferentiation\o62DE\src\AbstractDifferentiation.jl:612
[2] jacobian(ab::AbstractDifferentiation.ReverseRuleConfigBackend{Zygote.ZygoteRuleConfig{Zygote.Context}}, f::Function, xs::Matrix{Float64})
@ AbstractDifferentiation C:\Users\Hossein Pourbozorg\.julia\packages\AbstractDifferentiation\o62DE\src\AbstractDifferentiation.jl:570
[3] top-level scope
@ REPL[22]:1
[4] top-level scope
@ C:\Users\Hossein Pourbozorg\.julia\packages\CUDA\tTK8Y\src\initialization.jl:52
Contributor guide
No contributing guide indexed for this repository
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reproducing the matrix-input example with AD.jacobian(AD.ZygoteBackend(), ft2, r3). Inspect src/AbstractDifferentiation.jl around identity_matrix_like and the jacobian method at the reported stack-trace lines. Done means Jacobians for the shown non-vector-to-vector case work without the current Matrix{Float64} error.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- julia
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100