Diagnosing NaNs from ForwardDiff

I am getting a lot of NaNs from ForwardDiff.gradient on a fairly complicated function. I have an analytical function to compute the gradient that works fine (doesn’t give NaN), and coincides with ForwardDiff’s result when it is not NaN.

I already enabled the NANSAFE_MODE_ENABLED setting, but it is not helping. Is there a way to find the point in my code which makes ForwardDiff return a NaN?


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Do you have calls do divrem (or similar?). That’s the only place I’ve seen ForwardDiff produce NaNs so far (and only at isolated points where the remainder is zero)

For any computed real, you can check for NaN partials, eg

noNaNs(x::Real) = true
noNaNs(x::ForwardDiff.Dual) = !any(isnan, ForwardDiff.partials(x))


@assert noNaNs(some_value)

in your code.

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No, I’m not using divrem. But the function I’m trying to differentiate goes through finding a root of a nonlinear equation.

Ah, this seems like a nice and dirty idea. I will springle noNaNs and see where it fails first! Thanks.