Backward Automatic Differentiation Explained


Today I will try to explain how the forward and backward mode in automatic differentiation work. I will only cover the principle, not actual algorithms and the optimizations they apply. While the so called forward mode is quite intuitive, it is not so easy to wrap your head around the backward mode. I will try to go through all steps and not leave out anything seemingly trivial.

We consider the computation of a function f: \mathbb{R}^n \rightarrow \mathbb{R}^m with independent variables x_1, \dots , x_n and dependent variables y_1, \dots , y_m. The ultimate goal is to compute the Jacobian

J = \begin{pmatrix}  \frac{\partial y_1}{\partial x_1} &  & \dots &  & \frac{\partial y_1}{\partial x_n} \\   & \ddots & & & \\  \vdots &  & \frac{\partial y_i}{\partial x_j} &  & \vdots \\   &  &                                   & \ddots & \\  \frac{\partial y_m}{\partial x_1} & & \dots & & \frac{\partial y_m}{\partial x_n}  \end{pmatrix}

We view the function f as a composite of elementary operations

u_k = \Phi_k( \{u_\kappa\}_{(\kappa,k) \in \Lambda})

for k > n where we set u_k = x_k for k=1,\dots,n (i.e. we reserve these indices for the start values of the computation) and u_k = y_k for k=K-m+1, \dots, K (i.e. these are the final results of the computation). The notation should suggest that u_k depends on prior results u_\kappa with (\kappa,k) in some index set \Lambda. Note that if (k,l)\in\Lambda this refers to a direct dependency of u_l on u_k, i.e. if u_k depends on u_j, but u_j does not enter the calculation of u_k directly then (j,l) \notin \Lambda.

As an example consider the function

f(x_1, x_2) = x_1 + x_2^2

for which we would have u_1 = x_1, u_2 = x_2, u_3 = u_2^2, u_4 = y_1 = u_1+u_3. The direct dependencies are (1,4), (2,3) and (3,4), but not (2,4), because x_2=u_2 does not enter the expression for u_4 directly.

We can view the computation chain as a directed graph with vertices u_k and edges (k,l) if (k,l)\in\Lambda. There are no circles allowed in this graph (it is a acyclic graph) and it consists of K vertices.

We write |i,j| for the length of the longest path from u_i to u_j and call that number the distance from i to j. Note that this is not the usual definition of distance normally being the length of the shortest path.

If u_j is not reachable from u_i we set |i,j| = \infty. If u_j is reachable from u_i the distance is finite, since the graph is acyclic.

We can compute a partial derivative \partial u_m / \partial u_k using the chain rule

\frac{\partial u_m}{\partial u_k} = \sum_{l|(l,m)\in\Lambda} \frac{\partial u_m}{\partial u_l} \frac{\partial u_l}{\partial u_k}

This suggest a forward propagation scheme: We start at the initial nodes u_1, ... , u_n. For all nodes u_l with maximum distance 1 from all of these nodes we compute

c_l = \sum_{i=1,\dots,n} \frac{\partial u_l}{\partial u_i} c_{i}

where we can choose c_i for i=1,\dots,n freely at this stage. This assigns the dot product of the gradient of u_l w.r.t. x_1, \dots, x_n and (c_1,\dots,c_n) to the node u_l.

If we choose c_k=1 for one specific k\in\{1,\dots,n\} and zero otherwise, we get the partial derivative of u_l by u_k, but we can compute any other directional derivatives using other vectors (c_1,\dots,c_n). (Remember that the directional derivative is the gradient times the direction w.r.t. which the derivative shall be computed.)

Next we consider nodes with maximum distance 2 from all nodes u_1,\dots,u_n. For such a node u_l

c_l = \sum_{i=1,\dots,n} \frac{\partial u_l}{\partial u_i} c_i = \sum_{i=1,\dots,n} \sum_{k|(k,l)\in\Lambda} \frac{\partial u_l}{\partial u_k} \frac{\partial u_k}{\partial u_i} c_i = \sum_{k|(k,l)\in\Lambda} \frac{\partial u_l}{\partial u_k} c_k

where we can assume that the c_k were computed in the previous step, because their maximum distance to all initial nodes u_1,\dots,u_n muss be less than 2, hence 1.

Also note that if k \in \{1,\dots,n\}, which may be the case, \partial u_l / \partial u_k = 1 if k=l and zero otherwise, so \sum_{i=1,\dots,n} \partial u_k / \partial u_i c_i = c_k trivially. Or seemingly trivial.

The same argument can be iterated for nodes with maximum distance 3, 4, \dots until we reach the final nodes u_{K-m+1},\dots,u_K. This way we can work forward through the computational graph and compute the directional derivative we seek.

In the backward mode we do very similar things, but in a dual way: We start at the final nodes and compute for all nodes u_l with maximum distance 1 from all of these nodes

\overline{c_l} = \sum_{i=K-m+1,\dots,K} \frac{\partial u_i}{\partial u_l} \overline{c_i}

Note that we compute a weighted sum in the dependent variables now. By setting a specific c_k to 1 and the rest to zero again we can compute the partial derivatives of a single final variable. Again using the chain rule we can compute

\overline{c_l} = \sum_{i=K-m+1,\dots,K} \frac{\partial u_i}{\partial u_l} \overline{c_i} = \sum_{i=K-m+1,\dots,K}\sum_{k|(l,k)\in\Lambda} \frac{\partial u_i}{\partial u_k}\frac{\partial u_k}{\partial u_l} \overline{c_i} = \sum_{k|(l,k)\in\Lambda} \frac{\partial u_k}{\partial u_l} \overline{c_k}

for all nodes u_l with maximum distance of 2 from all the final nodes.

Note that the chain rule formally requires to include all variables u_k on which u_i depends. Howvever if u_k does not depend on u_l the whole term will effectively be zero, so we can drop these summands from the beginning. Also we may include indices k on which u_i does not depend in the first place, which is not harmful for the same reason.

As above we can assume all \overline{c_k} to be computed in the previous step, so that we can iterate backwards to the inital nodes to get all partial derivatives of the weighted sum of the final nodes w.r.t. the initial nodes.

Backward Automatic Differentiation Explained

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