# A Quick Introduction to AD for Scientists

##### January 18th, 2020

There is a whole lot that can be said about "the right way" to compute the derivative of a program's output with respect to its input. If you don't believe me, go see the 18.337 Lecture Notes!. However, in some sense that can be information overload for practitioners: there's a lot of interesting mathematical and computational questions library developers are still discussing, but as user, what do you actually need to know in order to effectively use automatic differentiation? That is the point of this set of notes.

## Forward-Mode AD As A Direct Alternative to Numerical Differentiation

Let's say we wanted to just calculate a derivative.

$\frac{f(x+\epsilon)-f(x)}{\epsilon}$

with a small enough $\epsilon$. What's a small enough $\epsilon$? That's actually a very hard question, since if $\epsilon$ is small you introduction floating point error, but if $\epsilon$ is large you introduce discretization error. It turns out the somewhat optimal value is around 1e-8 for Float64 and 1e-4 for Float32. With Float64 you generally do not get better than 1e-6 error on your derivative, and no better than 1e-3 for Float32. This is not only an issue for accuracy, but also an issue for performance. If you were using this derivative to do optimization, like gradient descent, being incorrect in the third decimal will mean that you will need more steps to convergence, and ultimately more calculations. Also, this calculation takes two calls to $f$. Can we do better?

Well, if $f$ was $\sin$, then we would know that $f'$ is $\cos$. So if we saw in our program that $f$ was $\sin$, would could in theory automatically replace it with the program for $\cos$. Now what if we had $f(x) = \exp(\sin(x))$? Then by the chain rule we can know program to automatically replace with this as well! Automatic differentiation is the process of building a program for the derivative and using that to calculate the derivative. If you do this, your derivatives are "exact". Also, there are many optimizations that can occur if you know the program for the value and its derivative and run them simultaniously.

Forward-mode automatic differentiation is the analogue to numerical differentiation but doing this program transformation approach. What you do is you define a new number type, the Dual number, $x + D v$ where $D$ is a dimensional construct, like $i$ for imaginary numbers. The rule for a Dual number is simplyD $f(x + D v) = f(x) + f'(x)v$, i.e. the first time carries forward the value of the program while the second term pushes forward the derivative by the chain rule. $f'$ is then an automatically generated program.

## The Issue of Jacobians: Columns for Forward-Mode and Rows for Reverse-Mode

Now let $f$ be a vector-valued function, i.e. $x$ is a vector and $f(x)$ is a vector. If that's the case, $f'(x)$, is the total derivative, i.e. the Jacobian and $f'(x) = [\frac{\partial f_i}{\partial x_j}]$. In this case, numerical differentiation of your program, i.e.

$\frac{f(x+\epsilon e_i)-f(x)}{\epsilon}$

calculates the $i$th column of the Jacobian where $e_i$ is the vector of all zeros but 1 at the $i$th point (the $i$th basis vector). Forward-mode automatic differentiation is then exactly the same, where $f(x + D v) = f(x) + f'(x)v$ is always pushing forward Jacobians, and then forward-mode AD calculates columns of Jacobians at a time. But now, which column is computed is dependent on the choice of $v$. $f(x + De_i)$ will calulate the $i$th column. So we cna now create a larger dual number with more dimensions, i.e. $f(x + D_1 e_1 + \ldots + D_n e_n) = f(x) + D_1 f'(x)e_1 + \ldots + D_n f'(x)e_n$ will compuate the whole Jacobian in one call to $f$ if we can generate a new version of our program that works on these large dual numbers / dual vectors!

However, since $f'(x) = [\frac{\partial f_i}{\partial x_j}]$, one might notice that the rows of this Jacobian are the gradients. So if you wanted to do gradient descent, then you want to calculate rows of Jacobians. Calculating the full Jacobian column-by-column just to get a row isn't a good idea, right? Especialy if it's a single row Jacobian, which is the case for an $R^n \rightarrow R$ function, i.e. a cost function. Thus in this case, what you can do is understand globally the whole program, for example building a tape of how it runs forwards, and reverse the chain rule calculation. It turns out that if you do this you arrive at reverse-mode AD which calculates Jacobians 1 row at a time. It has a lot more overhead, but if what you needed was a row of a Jacobian (a gradient), then it can be much quicker.

## A Summary of AD For Scientists

The summary is then as follows:

• Numerical differentiation always works, but has overhead and issues with accuracy

• Automatic differentiation requires doing a program transformation in order to work, but if it can be done it'll be quicker and more accurate. This means it usually has some restrictions, like everything has to be in one language or you have to hardcode some derivatives for it to work. If AD fails for you, that means it probably couldn't do the program transformation and you should figure out why (i.e., maybe it doesn't know how to transform your call to C!)

• Forward-mode automatic differentiation has little to no overhead (when implemented properly), while reverse-mode automatic differentiation always has a bit more overhead. However, forward-mode computes columns of Jacobians at a time, while reverse-mode computes rows (gradients) in a single go. So if you have an $R^n \rightarrow R^m$ function and want the whole Jacobian, forward mode is faster unless $n$ is much larger than $m$ (generally we see it should be 100x as larger).

• Reverse-mode AD is then quicker for forward-mode AD for gradients, and that's a special case of the above where a gradient is really just a Jacobian of an $R^n \rightarrow R$ function!