# External Functions¶

Often parts of the algorithm to be differentiated are not available in source code. For example, a routine from an external math library may be called. Reimplementing it may not be desirable (for performance or development effort reasons), in which case the derivatives of this function need to be implemented manually in some form.

This can be achieved by either:

• Applying finite differences to the library function (bumping),
• Implementing the adjoint code of the function by hand, or
• Computing the derivatives analytically, possibly using other library functions.

In these cases, the external function interface of XAD can be used to integrate the manual derivatives, which is described below. With the same technique, performance- or memory-critical parts of the application may be hand-tuned.

## Example Algorithm¶

We pick an example algorithm which computes the length of a multi dimensional vector. This is defined as:

$y = \sqrt{\sum_0^{N-1} x_i^2}$

The goal is to compute the derivatives of $$y$$ with respect to all input vector elements using adjoint mode.

The algorithm can be implemented in C++ code as:

std::vector<double> xsqr(n);
for (int i = 0; i < n; ++i)
xsqr[i] = x[i] * x[i];
double y = sqrt(sum_elements(x, n));


For this example, we assume that the sum_elements is an external function implemented in a library that we do not have source code of. It has the prototype:

double sum_elements(const double* x, int n);


## External Function For Adjoint Mode¶

To use the external function, we follow this procedure:

1. At the point of the call, convert the values of the input active variables to the underlying plain data type (double)
2. Call the external function passively
3. Assign the result values to active output variables so the tape recording can continue
4. Store the tape slots of the inputs and outputs with a checkpoint callback object and register it with the tape.
5. When computing adjoints, this callback needs to load the adjoint of the outputs, propagate to them to the inputs manually, and increment the input adjoints by these values.

We put all the functionality into a callback object. We derive from the CheckpointCallback base class and implement at least the virtual method CheckpointCallback::computeAdjoint. This method gets called during tape rollback. We also place the forward computation within the same object (this could also be done outside of the callback class). The declaration of our callback class looks like this:

template <class Tape>
{
public:
typedef typename Tape::slot_type   slot_type;   // type for slot in the tape
typedef typename Tape::value_type  value_type;  // double
typedef typename Tape::active_type active_type; // AReal<double>

active_type computeExternal(const active_type* x, int n); // forward compute

private:
std::vector<slot_type> inputSlots_;             // slots of inputs in tape
slot_type outputSlot_;                          // slot of output in tape
};


We declare it as a template for arbitrary tape types, which is good practice as it allows to reuse this implementation with higher order derivatives too.

### computeExternal Method¶

Within the computeExternal method, we first store the slots in the tape for the input variables, as we will need them during adjoint computation to increment the corresponding adjoints. We use the inputSlots_ member vector to keep this information:

for (int i = 0; i < n; ++i)
inputSlots_.push_back(x[i].getSlot());


Then we create a copy of the active inputs and store them in a vector of passive values, with which we can call the external function:

std::vector<value_type> x_p(n);
for (int i = 0; i < n; ++i)
x_p[i] = value(x[i]);

value_type y = sum_elements(&x_p[0], n);


We now need to store this result in an active variable, register it as an output of the external function (to allow the tape to continue recording dependent variables), and keep its slot in the tape for the later adjoint computation:

active_type ret = y;
Tape::getActive()->registerOutput(ret);
outputSlot_ = ret.getSlot();


Finally we need to insert the callback into the tape, hence requesting it to be called during adjoint rollback of the tape, and return:

Tape::getActive()->insertCallback(this);
return ret;


### computeAdjoint Method¶

The computeAdjoint method gets called by XAD during tape rollback. We need to override this method and implement the manual adjoint code. For a simple sum operation, this is straightforward: all input adjoints are equal to the output adjoint since all partial derivatives are 1. Thus we need to obtain the output adjoint and increment all input adjoints by this value:

value_type output_adj = tape->getAndResetOutputAdjoint(outputSlot_);
for (int i = 0; i < inputSlots_.size(); ++i)


The function Tape::getAndResetOutputAdjoint obtains the adjoint value corresponding to the given slot and resets it to zero. This reset is necessary in general as the output variable may have been overwriting other values in the forward computation. The Tape::incrementAdjoint function simply increments the adjoint with the given slot by the given value.

### Wrapper Function¶

With the checkpointing callback class in place, we can implement a sum_elements overload for AReal that wraps the creation of this callback::

template <class T>
{
tape_type* tape = tape_type::getActive();
ExternalSumElementsCallback<tape_type>* ckp =
new ExternalSumElementsCallback<tape_type>;
tape->pushCallback(ckp);

return ckp->computeExternal(x, n);
}


This function dynamically allocates the checkpoint callback object and lets the tape manage its destruction via the Tape::pushCallback function. This call simply ensures that the callback object is destroyed when the tape is destroyed, making sure that no memory is leaked. If the callback object was managed elsewhere, this call would not be necessary. It then redirects the computation to the computeExternal function of the checkpoint callback class. Using this wrapper class, the sum_elements function can be used for active types in the same fashion as the original external function sum_elements for double. Defining it as a template allows us to re-use this function for higher-order derivatives, should we need them in future.

### Call-Site¶

The call site then can be implemented as (assuming that x_ad is the vector holding the independent variables, already registered on tape):

tape.newRecording();

for (int i = 0; i < n; ++i)
AD y = sqrt(sum_elements(xsqr.data(), n)); // calls external function wrapper

tape.registerOutput(y);
derivative(y) = 1.0;

std::cout << "y = " << value(y) << "\n";
for (int i = 0; i < n; ++i)
std::cout << "dy/dx" << i << " = " << derivative(x[i]) << "\n";


This follows exactly the same procedure as given in Basic Usage.

This example is included with XAD (external_function).

## External Function For Forward Mode¶

Since forward mode involves no tape, a manual implementation of the derivative computation needs to be implemented together with computing the value. The manual derivatives can be updated directly in the output values using the derivative function.

In our example, we can implement the external function in forward mode as:

template <class T>
{

std::vector<T> x_p(n);
for (int i = 0; i < n; ++i)
x_p[i] = value(x[i]);

T y_p = sum_elements(&x_p[0], n);

active_type y = y_p;

for (int i = 0; i < n; ++i)
derivative(y) += derivative(x[i]);

return y;
}


We first extract the passive values from the x vector and call the external library function to get the passive output value y_p. This value is then assigned to the active output variable y, which also initializes its derivative to 0.

As we have a simple sum in this example, the derivative of the output is a sum of the derivatives of the inputs, which is computed by the loop in the end.

This example is included with XAD (external_function).