Implementer guide

Work in progress

Come back later!

The macro @primitive

To implement the AbstractDifferentiation interface for your backend, you only need to provide a "primitive" from which the rest of the functions can be deduced. However, for performance reasons, you can implement more of the interface to make certain calls faster.

At the moment, the only primitives supported are AD.pushforward_function and AD.value_and_pullback_function. The AD.@primitive macro uses the provided function to implement AD.jacobian, and all the other functions follow.

AD.@primitive function AD.myprimitive(ab::MyBackend, f, xs...)
    # write your code here

See the backend-specific extensions in the ext/ folder of the repository for example implementations.

Function dependency graph

These details are not part of the public API and are expected to change. They are just listed here to help readers figure out the code structure:

  • jacobian has no default implementation
  • derivative calls jacobian
  • gradient calls jacobian
  • hessian calls jacobian and gradient
  • second_derivative calls derivative
  • value_and_jacobian calls jacobian
  • value_and_derivative calls value_and_jacobian
  • value_and_gradient calls value_and_jacobian
  • value_and_hessian calls jacobian and gradient
  • value_and_second_derivative calls second_derivative
  • value_gradient_and_hessian calls value_and_jacobian and gradient
  • value_derivative_and_second_derivative calls value_and_derivative and second_derivative
  • pushforward_function calls jacobian
  • value_and_pushforward_function calls pushforward_function
  • pullback_function calls value_and_pullback_function
  • value_and_pullback_function calls gradient