⚠️ Not for real use — this package is a validation instrument.

RSTSR is a Rust library for n-dimensional tensor computation.

We use this wheel to validate that RSTSR's rust functionality is compatible with the array API standard, so that using RSTSR in rust is similar to using NumPy in Python.

This is a thin Python binding of rstsr's faer device, exposing the namespace rstsr_faer.api so that the Python array API standard conformance suite can grade the Rust library.

• Do not depend on it. Its surface tracks what the conformance suite grades, not what a Python tensor library should offer; names and behaviour may change at any time, without a deprecation cycle.
• Python users: use NumPy, PyTorch, JAX, CuPy or another array library instead.
• Rust users: use rstsr directly. This crate adds nothing for Rust, and it is not published to crates.io.

RSTSR: An n-Dimensional Rust Tensor Toolkit

Resources	Badges
User Document
API Document
Crate

Welcome to RSTSR, an n-dimensional tensor toolkit library, in native rust.

This crate will be a building block for scientific computation in native Rust, similar to NumPy of Python.

For users comes from NumPy, the NumPy-RSTSR Cheatsheet (简体中文) will let you quickly be familiar with RSTSR.

This project is still in early stage, and radical code factorization could occur; dev-documentation can still be greatly improved.

Features

• Simple syntex (looks like NumPy, and some core concepts from rust crate ndarray).
• % (remainder) as matrix multiplication (you can &a % &b to perform a.matmul(&b)).
• Allow different devices in framework.
  • We will try to support CUDA and HIP in near future.
• Full support of n-dimensional, broadcasting, basic slicing, reshape.
• Fast on multi-threading CPU.
  • Matmul is provided by backends (such as faer or OpenBLAS).
  • Other cases (summation, element-wise operations) are on-par or even much faster than NumPy (by fast layout iterators and rayon threading).
• Either row-major or column-major is supported (controled by cargo feature). Dynamic row/col-major control can also be performed by device setting.

Illustrative Example

To start with, you may try to run the following code:

use rstsr::prelude::*;

// 3x2 matrix with c-contiguous memory layout
let a = rt::asarray((vec![6., 2., 7., 4., 8., 5.], [3, 2].c()));

// 2x4x3 matrix by arange and reshaping
let b = rt::arange(24.);
let b = b.reshape((-1, 4, 3));
// in one line, you can also
// let b = rt::arange(24.).into_shape((-1, 4, 3));

// broadcasted matrix multiplication
let c = &b % &a;

// print the result
println!("{:6.1}", c);
// output:
// [[[   23.0   14.0]
//   [   86.0   47.0]
//   [  149.0   80.0]
//   [  212.0  113.0]]
//
//  [[  275.0  146.0]
//   [  338.0  179.0]
//   [  401.0  212.0]
//   [  464.0  245.0]]]

// print layout of the result
println!("{:?}", c.layout());
// output:
// 3-Dim (dyn), contiguous: Cc
// shape: [2, 4, 2], stride: [8, 2, 1], offset: 0

Short FAQs

Why RSTSR? There seems many numeric and machine-learning libraries in rust already.

We need a numeric library that supports

• a data structure that supports arbitary types (including complex, half, and arbitary-precision)
• a framework that supports different backends
• fast, at least efficient on server CPU
• supports parallel by threading (specifically rayon)
• large dynamic dimension tensor and its reshape
• functionality can be extended by other crates

And further more,

• the framework may not overwhelm chemist scientists

Many crates in native rust done well in some aspects but not all.

This crate gets inspire from NumPy, Array API standard, ndarray, candle, Burn.

What is supposed to be supported in near future?

• Plugins: Einstein summation, FFT
• GPU devices: CUDA and HIP device support
• rstsr-sci-traits: Optimization, ODE, special functions
• rstsr-core: Full support of Python array API standard (in native rust instead of python binding)
  • searching functions
  • manipulation functions (tile, roll, moveaxis)

What's RSTSR meaning?

RSTSR either refers to

• its relationship with REST: REST (rust-based electronic structure toolkit) Subproject for Tensor support, or
• the functionality itself: an n-dimensional Rust Tensor toolkit.

This crate was originally trying to developed a more dev-friendly experience for chemist programmer from numpy/scipy/pytorch. However, scientists and engineers from any discipline are welcomed to try this tensor toolkit.

Is there an illustrative project for using RSTSR in real-world project?

We refer a project that developed before rstsr v0.1: showcase of RI-CCSD. File riccsd.rs is a demonstration of code style to use RSTSR.

如果您能阅读简体中文，REST Workshop：Rust 计算化学程序开发演示 是更加完整的实战项目。它表明 RSTSR 在开发电子结构程序时，兼备开发效率与运行效率。该项目也可以作为 RSTSR 的代码风格展示。

What features will not be implemented?

We do not support autodiff and lazy-evaluation in far future. In this mean time, we are not very concern on machine-learning applications, but focus more on traditional scientific computing, especially applications in electronic structure.

Acknowledge

This is a subproject of REST. REST is supported by

• NSFC (National Natural Science Foundation of China): 22125301, 22393911, 22393912, 22321003, 22233002;
• Innovation Program for Quantum Science and Technology: 2021ZD0303305;
• robotic AI-Scientist platform of Chinese Academy of Science.

Additionally, RSTSR is also funded by

• NSFC (National Natural Science Foundation of China): 22503020.

Miscellaneous

Current MSRV (minimal supported rust version) is

• 1.84.1: with crate faer built;
• 1.82.0: other cases (due to crate half and rust language usage of unsafe extern "C").

You are welcomed to raise problems or suggestions in github repo issues or discussions.