Automatic Differentiation

 Version on this page: 4.2.4 LTS Haskell 21.13: 4.5.4 Stackage Nightly 2023-09-24: 4.5.4 Latest on Hackage: 4.5.4

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A package that provides an intuitive API for Automatic Differentiation (AD) in Haskell. Automatic differentiation provides a means to calculate the derivatives of a function while evaluating it. Unlike numerical methods based on running the program with multiple inputs or symbolic approaches, automatic differentiation typically only decreases performance by a small multiplier.

AD employs the fact that any program `y = F(x)` that computes one or more value does so by composing multiple primitive operations. If the (partial) derivatives of each of those operations is known, then they can be composed to derive the answer for the derivative of the entire program at a point.

This library contains at its core a single implementation that describes how to compute the partial derivatives of a wide array of primitive operations. It then exposes an API that enables a user to safely combine them using standard higher-order functions, just as you would with any other Haskell numerical type.

There are several ways to compose these individual Jacobian matrices. We hide the choice used by the API behind an explicit “Mode” type-class and universal quantification. This prevents users from confusing infinitesimals. If you want to risk infinitesimal confusion in order to get greater flexibility in how you curry, flip and generally combine the differential operators, then the `Rank1.*` modules are probably your cup of tea.

## Features

• Provides forward- and reverse- mode AD combinators with a common API.
• Optional type-level “branding” is available to prevent the end user from confusing infinitesimals
• Each mode has a separate module full of combinators, with a consistent look and feel.

## Examples

You can compute derivatives of functions

``````Prelude Numeric.AD> diff sin 0 {- cos 0 -}
1.0
``````

Or both the answer and the derivative of a function:

``````Prelude Numeric.AD> diff' (exp . log) 2
(2.0,1.0)
``````

You can compute the derivative of a function with a constant parameter using `auto` from Numeric.AD.Types:

``````Prelude Numeric.AD Numeric.AD.Types> let t = 2.0 :: Double
Prelude Numeric.AD Numeric.AD.Types> diff (\ x -> auto t * sin x) 0
2.0
``````

You can use a symbolic numeric type, like the one from `simple-reflect` to obtain symbolic derivatives:

``````Prelude Debug.SimpleReflect Numeric.AD> diff atanh x
recip (1 - x * x) * 1
``````

You can compute gradients for functions that take non-scalar values in the form of a Traversable functor full of AD variables.

``````Prelude Numeric.AD Debug.SimpleReflect> grad (\[x,y,z] -> x * sin (x + log y)) [x,y,z]
[ 0 + (0 + sin (x + log y) * 1 + 1 * (0 + cos (x + log y) * (0 + x * 1)))
, 0 + (0 + recip y * (0 + 1 * (0 + cos (x + log y) * (0 + x * 1))))
, 0
]
``````

which one can simplify to:

``````[ sin (x + log y) + cos (x + log y) * x, recip y * cos (x + log y) * x, 0 ]
``````

If you need multiple derivatives you can calculate them with `diffs`:

``````Prelude Numeric.AD> take 10 \$ diffs sin 1
[0.8414709848078965,0.5403023058681398,-0.8414709848078965,-0.5403023058681398,0.8414709848078965,0.5403023058681398,-0.8414709848078965,-0.5403023058681398,0.8414709848078965,0.5403023058681398]
``````

or if your function takes multiple inputs, you can use grads, which returns an ‘f-branching stream’ of derivatives, that you can inspect lazily. Somewhat more intuitive answers can be obtained by converting the stream into the polymorphically recursive `Jet` data type. With that we can look at a single “layer” of the answer at a time:

``````Prelude Numeric.AD Numeric.AD.Types> headJet \$ jet \$  grads (\[x,y] -> exp (x * y)) [1,2]
7.38905609893065
``````

``````Prelude Numeric.AD Numeric.AD.Types> headJet \$ tailJet \$ jet \$  grads (\[x,y] -> exp (x * y)) [1,2]
[14.7781121978613,7.38905609893065]
``````

The hessian (n * n matrix of 2nd derivatives)

``````Prelude Numeric.AD Numeric.AD.Types> headJet \$ tailJet \$ tailJet \$ jet \$  grads (\[x,y] -> exp (x * y)) [1,2]
[[29.5562243957226,22.16716829679195],[22.16716829679195,7.38905609893065]]
``````

Or even higher order tensors of derivatives as a jet.

``````Prelude Numeric.AD Numeric.AD.Types> headJet \$ tailJet \$ tailJet \$ tailJet \$ jet \$  grads (\[x,y] -> exp (x * y)) [1,2]
[[[59.1124487914452,44.3343365935839],[44.3343365935839,14.7781121978613]],[[44.3343365935839,14.7781121978613],[14.7781121978613,7.38905609893065]]]
``````

Note the redundant values caused by the various symmetries in the tensors. The `ad` library is careful to compute each distinct derivative only once, lazily and to share the resulting computation.

## Overview

### Modules

• `Numeric.AD` computes using whichever mode or combination thereof is suitable to each individual combinator. This mode is the default, re-exported by `Numeric.AD`
• `Numeric.AD.Mode.Forward` provides basic forward-mode AD. It is good for computing simple derivatives.
• `Numeric.AD.Mode.Sparse` computes a sparse forward-mode AD tower. It is good for higher derivatives or large numbers of outputs.
• `Numeric.AD.Mode.Kahn` computes with reverse-mode AD. It is good for computing a few outputs given many inputs.
• `Numeric.AD.Mode.Reverse` computes with reverse-mode AD. It is good for computing a few outputs given many inputs, when not using sparks heavily.
• `Numeric.AD.Mode.Tower` computes a dense forward-mode AD tower useful for higher derivatives of single input functions.
• `Numeric.AD.Newton` provides a number of combinators for root finding using Newton’s method with quadratic convergence.
• `Numeric.AD.Halley` provides a number of combinators for root finding using Halley’s method with cubic convergence.
• `Numeric.AD.Rank1.*` provides combinators for AD that are strictly rank-1. This makes it easier to flip and contort them with higher order functions at the expense of type safety when it comes to infinitsimal confusion.

### Combinators

While not every mode can provide all operations, the following basic operations are supported, modified as appropriate by the suffixes below:

• `grad` computes the gradient (vector of partial derivatives at a given point) of a function.
• `jacobian` computes the Jacobian matrix of a function at a point.
• `diff` computes the derivative of a function at a point.
• `du` computes a directional derivative of a function at a point.
• `hessian` computes the Hessian matrix (matrix of second partial derivatives) of a function at a point.

### Combinator Suffixes

The following suffixes alter the meanings of the functions above as follows:

• `'` also return the answer
• `With` lets the user supply a function to blend the input with the output
• `F` is a version of the base function lifted to return a `Traversable` (or `Functor`) result
• `s` means the function returns all higher derivatives in a list or f-branching `Stream`
• `T` means the result is transposed with respect to the traditional formulation (usually to avoid paying for transposing back)
• `0` means that the resulting derivative list is padded with 0s at the end.

## Contact Information

Contributions and bug reports are welcome!

Please feel free to contact me through github or on the #haskell IRC channel on irc.freenode.net.

-Edward Kmett

## 4.2.4

• Added `Newton.Double` modules for performance.

## 4.2.3

• `reflection` 2 support

## 4.2.2

• Major bug fix for `grads`, `jacobians`, and anything that uses `Sparse` mode in `Numeric.AD`. Derivatives after the first two were previously incorrect.

## 4.2.1.1

• Support `nats` version 1

## 4.2.1

• Added `stochasticGradientDescent`.

## 4.2

• Removed broken `Directed` mode.
• Added `Numeric.AD.Rank1` combinators and moved most infinitesimal handling back out of the modes and into an `AD` wrapper.

## 4.1

• Fixed a bug in the type of `conjugateGradientAscent` and `conjugateGradientDescent` that prevent users from being able to ever call it.

## 4.0.0.1

• Added the missing `instances.h` header file to `extra-source-files`.

## 4.0

• An overhaul permitting monomorphic modes was completed by @alang9.
• Add a `ForwardDouble` monomorphic mode

## 3.4

• Added support for `erf` and `inverf`, etc. from `Data.Number.Erf`.
• Split the infinitesimal and mode into two separate parameters to facilitate inlining and easier extension of the API.

## 3.3.1

• Build system improvements
• Removed unused LANGUAGE pragmas
• We now use exactly the same versions of the packages used to build `ad` when running the doctests.

## 3.3

• Renamed `Reverse` to `Kahn` and `Wengert` to `Reverse`. We use Arthur Kahn’s topological sorting algorithm to sort the tape after the fact in Kahn mode, while the stock Reverse mode builds a Wengert list as it goes, which is more efficient in practice.

## 3.2.2

• Export of the `conjugateGradientDescent` and `gradientDescent` from `Numeric.AD`

## 3.2.1

• `conjugateGradientDescent` now stops before it starts returning NaN results.

## 3.2

• Renamed `Chain` to `Wengert` to reflect its use of Wengert lists for reverse mode.
• Renamed `lift` to `auto` to avoid conflict with the more prevalent `transformers` library.
• Fixed a bug in `Numeric.AD.Forward.gradWith'`, which caused it to return the wrong value for the primal.

## 3.1.4

• Added a better “convergence” test for `findZero`
• Compute `tan` and `tanh` derivatives directly.

## 3.1.3

• Added `conjugateGradientDescent` and `conjugateGradientAscent` to `Numeric.AD.Newton`.

## 3.1.2

• Dependency bump

## 3.1

• Added `Chain` mode, which is `Reverse` using a linear tape that doesn’t need to be sorted.
• Added a suite of doctests.
• Bug fix in `Forward` mode. It was previously yielding incorrect results for anything that used `bind` or `bind'` internally.

## 3.0

• Moved the contents of `Numeric.AD.Mode.Mixed` into `Numeric.AD`
• Split off `Numeric.AD.Variadic` for the variadic combinators
• Removed the `UU`, `FU`, `UF`, and `FF` type aliases.
• Stopped exporting the types for `Mode` and `AD` from almost every module. Import `Numeric.AD.Types` if necessary.
• Renamed `Tensors` to `Jet`
• Dependency bump to be compatible with ghc 7.4.1 and mtl 2.1
• More aggressive zero tracking.
• `diff (**n) 0` for constant n and `diff (0**)` both now yield the correct answer for all modes.