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```julia
using Enzyme, Reactant
f(x) = sum(abs2, x .* x)
function enzyme_split_mode(x)
dx = Enzyme.make_zero(x)
forward, reverse = autodiff_thunk(
ReverseSplitWithPrimal, Const{typeo…
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Hi Jax team,
We want to calculate hessians of a likelihood function involving an ode integration so that we can do variational inference. We are running into an issue with `custom_vjp`, which we do…
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Is chumpy using Forward mode or Reverse mode automatic differentiation? A quick browse seems like forward mode...
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Automatic differentiation would be useful so we don't have to implement linearizations or Jacobians of complicated analytic functions. Examples where autodiff would help:
- Jacobians and Hessians o…
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In this issue, we would like to share a draft implementation plan for the forward mode autodiff.
# Background
In general, there are two modes for autodiff: reverse mode and forward mode. The t…
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Hi!
As you know, @ExpandingMan and I are looking to optimize performance for StaticArrays. Forward mode works splendidly, but reverse mode still makes one allocation during the `gradient` call:
```jul…
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An implementation of forward-over-reverse, i.e. applying forward mode autodiff to the result of reverse autodiff, can be a way to exploit AD for the computation of Hessians. It might make sense to dir…
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In the notebooks on the documentation here:
https://jax.readthedocs.io/en/latest/notebooks/autodiff_cookbook.html
The author describes topics that he would like to showcase in a future Autodiff co…
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The following code sample results in this error:
`(0): error 99999: Slang compilation aborted due to an exception of N5Slang13InternalErrorE: assert failure: Unhandled instruction
`
slang shader:
…
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Currently in my experiments with `differential(at: p, in: f)` the following error is thrown:
```
Fatal error: JVP does not exist. Differential-first differentiation APIs are experimental and should …