jaxkd 0.1.1

Minimal JAX implementation of k-nearest neighbors using a k-d tree.

JAX k-D

Find k-nearest neighbors using a k-d tree in JAX!

This is an implementation of two GPU-friendly tree algorithms [1, 2] using only JAX primitives. The core build_tree, query_neighbors, and count_neighbors operations are compatible with JIT and automatic differentiation. They are reasonably fast when vectorized on GPU/TPU, but will be slower than SciPy's KDTree on CPU. For small problems where a pairwise distance matrix fits in memory, check whether brute force is faster (see jaxkd.extras).

If neighbor search is the performance bottleneck and you only use Nvidia GPUs, there is a CUDA extension available that can be installed as an optional dependency (see below). The intention is to match the behavior of the pure-JAX version and integrate seamlessly with the cuda=True argument. Building the extension will require CMake and NVCC installed on your system. There may be some rough edges and the internal workings may change.

For even more power and flexibility, consider binding the original cudaKDTree library to JAX. Functionality will be different as described in the jaxkd-cuda repository, where example bindings can also be found. Be warned that they will not spark joy. The advantage of the pure-JAX version is that it is portable and easy to use, with the ability to scale up to larger problems without the complexity of integrating non-JAX libraries. Try it out!

Usage

import jax
import jaxkd as jk

kp, kq = jax.random.split(jax.random.key(83))
points = jax.random.normal(kp, shape=(100_000, 3))
queries = jax.random.normal(kq, shape=(10_000, 3))

tree = jk.build_tree(points)
counts = jk.count_neighbors(tree, queries, r=0.1)
neighbors, distances = jk.query_neighbors(tree, queries, k=10)

There is also a one-step build_and_query for convenience, and all these functions accept cuda=True to use the CUDA extension if it is installed.

Additional helpful functionality can be found in jaxkd.extras.

• query_neighbors_pairwise and count_neighbors_pairwise for brute-force neighbor searches
• k_means for clustering using k-means++ initialization, thanks to @NeilGirdhar for contributions

Suggestions and contributions for other extras are always welcome!

Installation

To install, use pip. The only dependency is jax.

python -m pip install jaxkd

Or with the CUDA extension.

python -m pip install jaxkd[cuda]

Or just grab tree.py.