tensoria

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WGPU-accelerate ndarray, now in rust

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---

<p align="center">
An ergonomic tensor manipulation library running on GPU, self-contained, in pure rust
</p>

> 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](https://burn.dev/) for a more complete one or even [rust binding for PyTorch](https://github.com/LaurentMazare/tch-rs).

## 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

```rust
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(())
}
```

tensoria

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