Tenso-rs

The tenso-rs crate provides functionality to work with N dimensional tensors, similar to NumPy, PyTorch, TensorFlow, the ndarray crate, etc.

This is a toy project, for me to explore how these amazing libraries work under the hood. This is by no means ready for use aside from exploration or being a laughing stock.

Highlights

• N dimensional Tensors with support for custom types (0 != N for now, 0 dimensional tensor coming soon)
• Common tensor creation methods like zeros, linespace, arange, etc.
• Tensor Views
• Basic Tensor manipulation methods like cat, reshape, permute, transpose, etc.
• Views of Tensors; Tensor Slicing; Tensor Iterators
• Common math operations like cos, arctanh, sqrt, clamp
  • Few "Advanced" functions like erfc, sinc, log_gamma

TODOs

Stuff I want to do soon:

• "Good" Macro for tensor creation with user data
• Zero Dimensional Tensors
• Inplace Tensor operations
• Broadcasting
• More Tensor manipulation methods (from numpy API docs)
• More math operations, (from scipy special functions)
• Save and Load using serde
• Optimize this slow code (source code not even bench-marked)
• Linear Algebra and Tensor multiplication
• Integration with BLAS and matrixmultiply crate / custom code
• And more.

Examples

You can create new tensors from the (few) creations methods like so:

// The following represents the tensor: [1, 2, 3, 4, 5, 6, 7, 8, 9]
let t1 = Tensor::<u128>::arange(1, 10, 1).unwrap();

// The following represents the tensor: [-10.0, -5.0, 0.0, 5.0, 10.0]
let t2 = Tensor::<f64>::linespace(-10.0, 10.0, 5).unwrap();

// The following represents the tensor: [[1, 0, 0], [0, 1, 0], [0, 0, 0]]
let t3 = Tensor::<i8>::eye(2, 3).unwrap();

// The following represents the tensor: [[1, 2, 3], [4, 5, 6]].
// As the TODOS mention, a nice macro for tensor from user data does not exist right now.
let t4 = Tensor::from_slice_and_dims(&[1, 2, 3, 4, 5, 6], &[2, 3]).unwrap();

// There are (a few) more!

Modification of tensors:

let t1 = Tensor::from_slice_and_dims(&[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], &[3, 4]).unwrap();
let t2 = Tensor::from_slice_and_dims(&[13, 14, 15, 16, 17, 18], &[3, 2]).unwrap();
let res = t1.cat(&t2, 1).unwrap();
// res represents [[1, 2, 3, 4, 13, 14], [5, 6, 7, 8, 15, 16], [9, 10, 11, 12, 17, 18]]

let t = Tensor::<f64>::arange(0, 9, 1).unwrap();
let res = t.reshape(&[3, 3]).unwrap();
// res represents [[0, 1, 2], [3, 4, 5], [6, 7, 8]]

let t = Tensor::arange(0, 24, 1).unwrap().reshape(&[2, 3, 4]).unwrap();
let res = t.permute(&[2, 0, 1]).unwrap();
// res represents [[[0, 4, 8], [12, 16, 20]], [[1, 5, 9], [13, 17, 21]], [[2, 6, 10], [14, 18, 22]], [[3, 7, 11], [15, 19, 23]]]

// There are (a few) more!

Math Operations:

let t = Tensor::logspace(f64::consts::E, 0.0, 5.0, 6).unwrap();
let res = t.cos();
// res represents [cos(0), cos(e), cos(e^2), cos(e^3), cos(e^4), cos(e^5)]

let t = Tensor::from_slice_and_dims(&[1.0, 4.0, 9.0, 16.0, 25.0], &[5]).unwrap();
let res = t.rsqrt();
// res represents [1, 1 / 2, 1 / 3, 1 / 4, 1 / 5]


let t = Tensor::arange(1.0, 6.0, 1.0).unwrap();
let res = t.gammaf();
// res represents [1.0, 1.0, 2.0, 6.0, 24.0, 120.0]

// There are (a few) more!

Notes

• Untested: Arbitrary Precision is possible using num_bigint
  • Note: "Advanced" Math operations like erf and gamma are not arbitrary precision yet.
• New issues, pull requests, or ideas are more than welcome!