Alright, this looks neat, but I'm having a terrible time figuring out what's going on with the benchmark. Typically for AD, it's easiest to see four things: nu

Alright, this looks neat, but I'm having a terrible time figuring out what's going on with the benchmark. Typically for AD, it's easiest to see four things: number of variables, time to compute function directly with no AD, time to compute function with AD enabled and calculate the gradient, ratio between these two numbers. Then, we run the same example with a varying number of variables to see how things scale. The advantage of reverse mode is that, theoretically, this ratio is fixed and bounded at 4 or 5 regardless of the number of variables. Realistically, this is much higher, but I think it's fine if it's somewhat between 10-40 times function evaluation as long as it scales. I can't figure out what their ratio is or whether or not it's appropriately scaling. Can anyone else?