README.md

T E N S O R I A

ᕕ(⌐■_■)ᕗ ♪♬

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An ergonomic tensor manipulation library running on GPU, self-contained, in pure rust

At this moment, this library is meant to be the fundamental for one of my research works. There is only very limited set of supported operations. You may consider using burn-rs for a more complete one or even rust binding for PyTorch.

Features

• Supports GPU with CPU fallback.
• Provides automatic gradient computation (autograd).
• Allows creation of tensors with arbitrary dimensions at runtime.
• Offers an ergonomic API.

Note

• As a trade-off for easy API, tensor operations' shape checking occurs at runtime, potentially causing panics due to shape incompatibilities.
• The internal implementation is not thread-safe yet, so please refrain from using this in multithreaded programs. Consequently, when running cargo test, you need to specify -- --test-threads=1.

Example

use std::error::Error;

fn main() -> Result<(), Box<dyn Error>> {
    let x = Tensor::new([1, 2], vec![1., 2.])?;
    let y = Tensor::new([1, 2], vec![3., 4.])?;
    let res = &x + &y;
    assert_eq!(res.data(), vec![4., 6.]);

    // Or use GPU (via WGPU) if you wish by calling `.to_gpu()`.
    // The tensor will now operate on GPU array, while maintaining
    // the same user-facing API.
    let x = Tensor::new([1, 2], vec![1., 2.])?.to_gpu()?;
    let y = Tensor::new([1, 2], vec![3., 4.])?.to_gpu()?;
    let res = &x + &y;
    assert_eq!(res.data(), vec![4., 6.]);

    // Autograd...
    let mut x = Tensor::new([2, 2], vec![1, 2, 3, 4])?.to_gpu()?;
    x.set_requires_grad(true);

    let res = x.mul(&x).mul(&x);
    res.backward()?;
    assert_eq!(x.grad().unwrap(), vec![3, 12, 27, 48]);

    Ok(())
}