harpie: Haskell array programming.

[ bsd3, library, math ] [ Propose Tags ]

This package provides Haskell array programming, interface and environment.

Module names clash with each other and with the Prelude.

Usage

>>> import Harpie.Fixed qualified as F
>>> import Harpie.Shape qualified as S
>>> import Harpie.Array qualified as A
>>> a = F.range @[2,3,4]
>>> F.shape a
[2,3,4]
>>> pretty a
[[[0,1,2,3],
  [4,5,6,7],
  [8,9,10,11]],
 [[12,13,14,15],
  [16,17,18,19],
  [20,21,22,23]]]
>>> a = A.range [2,3,4]
>>> F.shape a
[2,3,4]
>>> pretty a
[[[0,1,2,3],
  [4,5,6,7],
  [8,9,10,11]],
 [[12,13,14,15],
  [16,17,18,19],
  [20,21,22,23]]]

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Versions [RSS] 0.1.0.0, 0.1.0.1, 0.1.1.0
Change log ChangeLog.md
Dependencies adjunctions (>=4.0 && <5), base (>=4.14 && <5), distributive (>=0.4 && <0.7), first-class-families (>=0.8.1 && <0.9), prettyprinter (>=1.7 && <1.8), QuickCheck (>=2.14 && <2.16), quickcheck-instances (>=0.3.31 && <0.4), random (>=1.2 && <1.3), vector (>=0.12.3 && <0.14), vector-algorithms (>=0.9.0 && <0.10) [details]
License BSD-3-Clause
Copyright Tony Day (c) 2016-2024
Author Tony Day
Maintainer tonyday567@gmail.com
Category math
Home page https://github.com/tonyday567/harpie#readme
Bug tracker https://github.com/tonyday567/harpie/issues
Source repo head: git clone https://github.com/tonyday567/harpie
Uploaded by tonyday567 at 2024-10-14T01:59:50Z
Distributions Stackage:0.1.1.0
Reverse Dependencies 3 direct, 7 indirect [details]
Downloads 54 total (54 in the last 30 days)
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Status Docs available [build log]
Last success reported on 2024-10-20 [all 1 reports]

Readme for harpie-0.1.1.0

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harpie

Hackage Build Status harpie

Haskell array programming, interface and environment (harpie).

harpie is an array programming library written in Haskell. Features include:

  • Rank polymorphism: vectors, matrices, tensors and scalars all use the same API and functions and combine naturally, as the math gods intend.

  • Both value- and type- level shapes: consistent functionality for accessing and transforming shape is provided at both value-level and type-level.

  • Dimensional agnosticism: the granularity of an array is flexible with the same function able to be applied over rows, columns, over multiple dimensions at once, or at element-level.

  • Pure Haskell: promoting a higher-kinded, ergonomic style of array programming, in idiomatic Haskell style.

The library is experimental and educational (at least for the authors) and likely to remain so. Collaboration is most welcome.

Usage

Naming conventions clash with the prelude and with each other, so importing should be qualified.

import qualified Harpie.Array as A
import qualified Harpie.Shape as S
import qualified Harpie.Fixed as F

-- >>> a = F.range @[2,3,4]
-- >>> F.shape a
-- [2,3,4]
-- >>> pretty a
-- [[[0,1,2,3],
--   [4,5,6,7],
--   [8,9,10,11]],
--  [[12,13,14,15],
--   [16,17,18,19],
--   [20,21,22,23]]]

-- >>> a = A.range [2,3,4]
-- >>> F.shape a
-- [2,3,4]
-- >>> pretty a
-- [[[0,1,2,3],
--   [4,5,6,7],
--   [8,9,10,11]],
--  [[12,13,14,15],
--   [16,17,18,19],
--   [20,21,22,23]]]

Design notes

Haskell utility

The library attempts to be idiomatic Haskell and otherwise fit in with the language ecosystem. In particular, boxed Vectors are used as the array container to enforce immutability, permit lazy expression, and allow arbitrary element types.

Consistency of type- and value- level list algorithms.

The library is an attempt to provide a consistent approach to array programming whether or not array shape is held and computed at value-level or at type-level.

The Harpie.Shape module contains common list algorithms for value-level shapes (ie Int list operatorions) and type-level shapes (a type-level Nat list operations) that is as close to the same as possible. They cannot be identical because type and value programming in Haskell are very different languages. The first-class-families library was used to achieve this.

Is it safe?

Harpie.Fixed arrays sit at around level 4.5 in Justin Le's type safety heirarchy. They are designed with static type-safety in mind; a run-time error in shape or index computation is a bug. Typed shape information, however, is modelled on GHC.TypeNats with Nat and thus [Nat] being opaque types rather than inductively-structured. This makes compiler KnownNat and KnownNats discovery and proof witnessing problematic.

Instead of dependently-typed approaches, the library leans into switching from fixed to untyped shape representation if shape is not known at runtime.

Is it fast?

Maybe. Does vector streaming lead to efficient code? If it does then harpie should be able to access this efficiency.

If you have a bunch of continguous data in need of strictly simple array processing then there are plenty of faster array programming options out there, in Haskell and elsewhere. If you need close-to-the-metal performance, try:

  • hmatrix provides bindings to BLAS and 45 years of Fortran tweaking.
  • hasktorch or tensorflow bindings to the might of the Python machince learning community.
  • massive or accelerate for homegrown Haskell speed.
  • repa for archeology.
  • orthotope for a more APL treatment of the problem domain, and for unboxed vector usage.

backpermute

As computational complexity increases, either in array programmimg or in connectivity to other problem domains, harpie performance is (one-day) expected to come into her own. Backpermute fusion is the secret sauce.

A canonical backpermute function is detailed in Regular, Shape-polymorphic, Parallel Arrays in Haskell and would be implemented in harpie as:

repa_backpermute f a = tabulate (f (shape a)) (index a . f)

The harpie backpermute splits the function application into two parts, the macro-shape change and the pre-indexing operation:

harpie_backpermute f g a = tabulate (f (shape a)) (index a . g)

This, more general specification, should (one-day) allow more opportunities for the fusion rule to kick in:

forall f f' g g' (a :: forall a. Array a)). backpermute f g (backpermute f' g' a) == backpermute (f . f') (g . g') a

Type Ugliness

Harpies are monstrous beasties, fierce but not conventional or conventionally attractive:

  • type errors and information quickly explode in complexity and types can run to hundreds of lines. It's quite ugly.

  • Constraints cannot be encapsulated in the function they originate and thus have to be propogated in line with usage. Type-level programming in Haskell is a good idea in search of some composition.