mlx-sparse 0.0.1b0

Sparse array containers and primitives for MLX

mlx-sparse

Warning: beta release This is an early beta. APIs may change, bugs are expected, and some features are still incomplete. Feedback and issue reports are very welcome!

mlx-sparse is an attempt at an MLX-native sparse array package. The public API is Python, while performance-critical operations are implemented as MLX primitives in C++ with CPU backends and Metal kernels for fixed-shape sparse operations.

The supported format surface is COO and CSR for 2D sparse arrays. Current functionality includes construction, validation, COO to CSR, CSR to dense, CSR canonicalization, CSR matrix-vector multiply, CSR matrix-matrix multiply, batched dense RHS products, CSR sparse-sparse products, transpose, Hermitian transpose, and autodiff through sparse values and dense RHS operands, including complex64.

Supported value dtypes are float32, float16, bfloat16, and complex64. Supported index dtypes are int32 and int64 on CPU and GPU.

Quick Start

Install from PyPI:

python -m pip install mlx-sparse

import mlx.core as mx
import numpy as np

import mlx_sparse as ms

ms.use_gpu()

data = mx.array(np.array([2.0, -1.0, 4.0], dtype=np.float32))
row = mx.array(np.array([0, 0, 1], dtype=np.int32))
col = mx.array(np.array([0, 2, 1], dtype=np.int32))

a = ms.coo_array((data, (row, col)), shape=(2, 3)).tocsr(canonical=True)
x = mx.array(np.array([3.0, 10.0, 7.0], dtype=np.float32))

y = a @ x
dense = a.todense()
at = a.T

The package build compiles src/sparse/*.metal into mlx_sparse/mlx_sparse.metallib when the macOS Metal toolchain is available, and the wheel ships that metallib beside the Python package.

Development

For contributors, use an editable install from the repository root. This builds the native extension and installs the development tooling.

python -m pip install -e ".[dev]"

License

This package is licensed under the Apache License 2.0.