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I am on Julia 1.8.5, Enzyme main (4c30ed8b693a7a1d41f03bb4ef295201e26435bb) and LoopVectorization 0.12.157. This works:
```julia
using Enzyme, LoopVectorization
function f1(a, b)
s = 0.0
…
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A MWE of the issue I mentioned in the zoom call today:
```
a = [1.0]
da = [0.0]
function f(x); y = copy(x); return sum(y); end
function g(x); y = deepcopy(x); return sum(y); end
Enzyme.autodif…
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While writing #739, I ran into some difficulties defining rules for functions with complex inputs and outputs. Here's a simple example:
```julia
foo(x::Complex) = 2x
function EnzymeRules.augmen…
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I have come across an issue that I think is almost identical to #347, except with arctan2. Here's a repro:
```
def test(a):
b = np.arctan2(a,a)
print(b)
temp = np.array([0.,1.,0.])
…
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Hello there 👋
I am wondering if it would make sense to allow `ForwardDiff.Dual` numbers through the `schur` factorization in this package? Currently, many methods are restricted to
```julia
Stri…
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This works on Julia 1.9 and Enzyme main (b35703b):
```julia
using Enzyme
f(x) = hypot(x, 2x)
autodiff(Reverse, f, Active, Active(2.0))[1][1] # 2.23606797749979
autodiff(Forward, f, Duplicated(2.0…
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I am on Julia 1.9.0, Enzyme 9487eb8349fd7d907403533b53c23822313897bb and StaticArrays v1.5.25. Apologies for the slightly strange example. This errors:
```julia
using Enzyme, StaticArrays
vector_…
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Boiling https://docs.sciml.ai/Overview/stable/showcase/missing_physics/ down to the Enzyme part, start with:
```julia
# SciML Tools
using OrdinaryDiffEq, SciMLSensitivity
using Optimization, Op…
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Hi,
I'm trying to differentiate a function with millions of parameters with only a few parameters requiring grads. However, Enzyme would compute the gradients for all of them. I wondered whether it…
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edit: I was doing something silly, but didn't realise, so this is more of a user-friendliness issue than anything else I guess.
Enzyme v0.11.0
```julia
julia> versioninfo()
Julia Version 1.9.0-r…