Correct Way to Compose ReverseDiff and ForwardDiff

[jd-foster]

Related thread:

Mixed-mode automatic differentiation using ForwardDiff and ReverseDiff

I need to take the gradient of a function. I can use ForwardDiff without any issues, but for part of my code I have found ReverseDiff to run much faster. The issue is that the other part of my code errors with ReverseDiff. The basic structure of my code looks something like function take_my_gradient(x) tmp = errors_with_reversediff(x) faster_with_reversediff(tmp) end Is it possible to differentiate my function using ForwardDiff on the part that doesn’t work with ReverseDiff and using…