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RFC: In-memory sparse array interchange

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#840 コメント 32 件 リアクション 0 件 担当者 0 名 GitHub で見る

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評価

難易度
5/5
見積もり時間
1週間以上
初心者へのやさしさ
25/100
issue の種類
機能追加
明瞭さ
説明が足りない
活発さ
停滞
技術スタック
python, pytorch, scikit-learn, tensorflow
領域
backend-api-design, data

調査の方向性

issue #840 の RFC 提案を読み、その後、参照されている sparse PR #764 と SciPy PR #22553 を確認して、並列実装を理解してください。descriptor、constituent arrays、from_binsparse interface について合意があるかを議論で確認してください。完了とは、合意された仕様と、参加するライブラリのための明確な進め方がある状態を指します。

索引モデルが issue の本文から書いたものです。

説明

API extension Needs Discussion RFC topic: DLPack

Motivation

The sparse (also called PyData/Sparse) development team have been working on integration efforts with the ecosystem, most notably with SciPy, scikit-learn and others, with CuPy, PyTorch, JAX and TensorFlow also on the radar. One of the challenges we were facing was the lack of (possibly zero-copy) interchange between the different sparse array implementations. We believe this may be a pain point for many sparse array implementations moving forward.

This mirrors an issue seen for dense arrays previously, where the DLPack protocol was the one of the first things to be standardised. We're hoping to achieve community consensus for a similar problem.

Luckily, all sparse array formats (with the possible exception of DOK) are usually collections of dense arrays underneath. In addition, this problem has been solved for on-disk arrays before by the binsparse specification. @willow-ahrens is a co-author of that spec, and is also a collaborator for the sparse work.

Proposal

We propose introducing two new methods to the array-API compliant sparse array objects (such as those in sparse), which are described below.

__binsparse_descriptor__

The returned item is a dict equivalent to a parsed JSON binsparse descriptor of an array.

__binsparse__

The second item is a dict[str, Array] of __dlpack__ compatible arrays, which are the constituent arrays of the sparse array. The key represents the equivalent key in the descriptor.

Introduction of from_binsparse function.

If a library supports sparse arrays, its from_binsparse method should support accepting (when possible, zero-copy) versions of objects that follow this __binsparse__ protocol, and have an equivalent sparse format within the library.

Psuedocode implementation

Here's a psuedocode example using two libraries, xp1 and xp2, both supporting sparse arrays:

# In library code:
xp2_sparray = xp2.from_binsparse(xp1_sparray, ...)

# This psuedocode impl is common between `xp1` and `xp2`
def from_binsparse(x: object, /, *, device: device | None = None, copy: bool | None = None) -> array:
    binsparse_descr = getattr(x, "__binsparse_descriptor__", None)
    binsparse_impl = getattr(x, "__binsparse__", None)
    if binsparse_impl is None or binsparse_descr is None:
        raise TypeError(...)
    
    binsparse_descriptor = binsparse_descr()
    # Will raise an error if the format/descriptor is unsupported.
    sparse_type = _type_from_binsparse_descriptor(binsparse_descriptor)
    constituent_arrays = binsparse_impl()
    my_constituent_arrays = {
        k: from_dlpack(arr, device=device, copy=copy) for k, arr in constituent_arrays.items()
    }
    return sparse_type.from_strided_arrays(my_constituent_arrays, shape=...)

Parallel implementation in sparse: https://github.com/pydata/sparse/pull/764
Parallel implementation in SciPy: https://github.com/scipy/scipy/pull/22553

Alternative solutions

There are formats for on-disk sparse-array interchange [1] [2]; but none for in-memory interchange. binsparse is the one that comes closest to offering in-memory interchange.

Pinging possibly interested parties:

  • @mtsokol @ivirshup (for scipy.sparse)
  • @willow-ahrens @mtsokol (from binsparse and finch-tensor/sparse)
  • @leofang (for cupyx.sparse)
  • @pearu (for torch.sparse)
  • @jakevdp (for JAX/TensorFlow)

Updated on 2024.10.09 as agreed in https://github.com/data-apis/array-api/issues/840#issuecomment-2337333242.

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Python
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281
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