Differentiable Floating-point Operations

⚠ This crate is very much in progress and should not be used as if!

If you want to contribute, or ask a question, please contact me through GitHub issues.

The DFO crate aims at making your already existing code differentiable, leveraging automatic differentiation.

Crate Structure

As for automatic differentiation, this crate is made of two separate parts:

• the forward module;
• and the backward module.

As their name implies, the former implements forward-mode automatic differentiation, while the latter implements backward-mode.

NOTE: if you are new to automatic differentation, I recommend you to read this excellent article from Max Slater's blog.

Next, each of those modules is split in (at least) two submodules: primitive and generic.

The primitive module implements a given differentiation mode on primitive types, e.g., [f32] with forward::DFloat32.

Example - Forward auto. diff. on primitive types

use dfo::forward::primitive::*;

let f = |x| { x * x + 1.0 - x };
let df = |x| { 2.0 * x - 1.0 };
let x = DFloat32::var(4.0);

assert_eq!(f(x).deriv(), df(x).value());

The generic module implements a given differentiation mode on generic types. For the latter, we recommend using the num_traits::Float trait, for example. Primitive types are usually better optimized (no useless copy, faster compilation time), and recommended unless you want to work with external crates, as ndarray.

Crate Feature Flags

The following crate feature flags are available. They are configured in your Cargo.toml.

• libm: enables num-traits/libm feature
• num-traits: supports for (most) traits defined in num-traits (enabled by default)
• std: Rust standard library-using functionality (enabled by default)
• serde: serialization support for serde 1.x

Crate Goals

DFO has two main objectives. It has to be:

1. fast, meaning reducing computations and copies when possible, intensive use of code inlining, and others;
2. and easy-to-use, leveraging generatic types, and requiring very few modifications to compute derivation.

Crate Status

[TL;DR] Is this create stable? It it production-ready?

No, and maybe not until a long time.

Currently, the following modules are implemented:

• forward
  • primitive
  • generic
• backward
  • primitive
  • generic

Here is a small TO-DO list of things that I would like to work on:

• Create features for primitive, forward and backward
• Create examples for multiple inputs, multiple outputs, ndarrays, and more
• Go for backward autodiff

Similar crates:

• autodiff