#automatic #differentiation #forward #dual #ad


Forward auto-differentiation, allowing its user to manage memory location and minimize copying

2 unstable releases

0.2.0 May 18, 2020
0.1.0 Apr 29, 2020

#72 in Math

MIT license


Fwd:AD a crate for Forward Auto-Differentiation

This crate allows you to easily write operations on dual numbers and do forward automatic differentiation. It empowers its user to write auto-differentiation code with minimal allocations.

Key selling-points

  1. Clone-free by default. Fwd:AD will never clone memory in its functions (except to_owning()) and std::ops implementations, leveraging Rust's ownership system to ensure correctness memory-wise, and leaving it up to the user to be explicit as to when cloning should happen.
  2. Automatic cloning on demand. If passed the implicit-clone feature, Fwd:AD will implicitly clone Duals when needed. Deciding whether to clone or not is entirely done via the type-system, and hence at compile time.
  3. Generic in memory location: Fwd:AD's structs are generic over a container type, allowing them to be backed by any container of your choice: Vec to rely on the heap, arrays if you're more of a stack-person, or other. For example, it can be used with &mut [f64] to allow an FFI API that won't need to copy memory at its frontier.


Detailled examples are available in the examples/ directory, but some snippets are reproduced below.

Rosenbrock function minimization

extern crate fwd_ad;
use fwd_ad::*;

// The factor by which we will descend along the gradient.
// Rosenbrock function is pretty steep so its quite small.
const ALPHA : f64 = 1e-3;

fn main() {
    // Create two duals with two derivatives each, as well as
    // closures getdx and getdy to get their corresponding derivative
        x = 0.; @ getdx
        y = 0.; @ getdy
    for _ in 0..10000 {
        let xval : f64 = x.val();
        let yval : f64 = y.val();

        let res = (x.clone() - 1.).powf(2.) + 100.*(y-x.powf(2.)).powf(2.);
        println!("At x={}, y={}, the rosenbrock function is {}",xval, yval, res.val());

            newx = xval - ALPHA*getdx(res.view());
            newy = yval - ALPHA*getdy(res.view());
        x = newx;
        y = newy;

Short tutorial

Fdw:AD's main type is the Dual<Container, OM, F> struct. This struct is parametrized by three types, which are:

  1. Container a type indicating what "container" is used to store the struct content. Typical examples include Vec<F>, [F; n], &mut [F], or &[F].
  2. OM an "owning mode" which is one of two possibilities: RW for "read-write", indicating that the content of the Dual is write-able and hence can be reused during computations and RO indicating that it is read-only.
  3. F is the scalar type, typically f32 or f64, but you chan choose to use something different.

A Dual wraps its container, which must be "read-able as an [F]". The first item of this slice of scalars is the dual's actual value and the next ones are the derivative with respect to the successive variables.

To alleviate the burden of writting out long type names, canonical pairs of owning/view duals are defined in the instanciations module.

Fwd:AD Traits

Fwd:AD relies on several traits to be generic enough. Traits a user may need to implement are located in the traits module.

  • ROAble (resp. RWAble) are traits that should be implemented by containers which are able to read (resp. write) their content. All container types must implement ROAble. These traits are similar to AsRef/AsMut from core and a blanket implementation is provided.
  • ToView and ToOwning are traits that are used to defined correspondances of canonical "owning" (which can be RW) and "view" (which only have RO capacity) containers.
  • Scalar is the trait representing scalar numbers, it is merely a supertrait for various traits of num_traits, so these are what you should seek to implement.

Caveat: because you can't implement external traits on external types you may find yourself limited in using duals with an uncommon container or scalar type. If so, please contact the maintainer of this crate.

Comparision with other (forward) AD rust libraries

The last-update column represent the last time the corresponding crate was checked. Crates may have evolved since.

crate version multi-variate higher-order last update
Fwd:AD 0.1.0 ✔️ 2020-04-29
ad 0.1.0 2020-01-01
autodiff 0.1.9 2019-11-07
descent¹ 0.3 ✔️ (2nd order?) 2018-12-10
dual 0.2.0 2015-12-25
dual_num 0.2.7 2019-04-03
hyperdual² 0.3.4 ✔️ 2020-02-08
peroxide 0.21.7 (2nd order) 2020-04-21
  1. descent Automatic differentiation seems promising but isn't very documented and is mixed-up with the IP-OPT interface
  2. hyperdual has similar properties to Fwd:AD, except that all operations will allocate when Fwd:AD tries to reuse existing memory


~49K SLoC