-
Notifications
You must be signed in to change notification settings - Fork 13
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Issues & PRs in other repos #99
Labels
backend
Related to one or more autodiff backends
Comments
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
As part of our unification efforts, we contributed to quite a lot of issues in other repos, sometimes with PRs to fix them. Let's try to keep track.
AbstractDifferentiation
ADTypes
AutoSparseFiniteDiff
the same type parameters asAutoFiniteDiff
SciML/ADTypes.jl#33ComponentArrays
DiffResults
Diffractor
eachindex
JuliaDiff/Diffractor.jl#280Enzyme
Duplicated(x, dx)
assumex
anddx
have the same type? EnzymeAD/Enzyme.jl#1329autodiff_thunk
useful in forward mode? EnzymeAD/Enzyme.jl#1335make_zero
is type-unstable EnzymeAD/Enzyme.jl#1359jacobian
is wrong for matrix input EnzymeAD/Enzyme.jl#1391FastDifferentiation
make_function
returns wrong result with sparse Jacobian for very simple functions only brianguenter/FastDifferentiation.jl#67FiniteDiff
HessianCache
for another value ofx
JuliaDiff/FiniteDiff.jl#185FiniteDifferences
ForwardDiff
gradient!
allocates for matrices but not for vectors JuliaDiff/ForwardDiff.jl#698MathOptSymbolicAD
ReverseDiff
SparseDiffTools
Tapir
rrule!!
compintell/Mooncake.jl#94Tracker
withgradient
andgradient
are incoherent for scalar input FluxML/Tracker.jl#165Zygote
withjacobian
flattens the output when it is a matrix FluxML/Zygote.jl#1506The text was updated successfully, but these errors were encountered: