The Tensor

The Tensor is the primary user-facing type in zyx. It is designed to be a lightweight handle — only 4 bytes.

pub struct Tensor {
    pub(super) id: TensorId,  // u32 index into the graph slab
}

Design Choices

Why 4 Bytes?

Most ML frameworks have heavyweight tensor objects. PyTorch’s Tensor is a TensorImpl* with shape, stride, dtype, device, storage, and autograd metadata — easily 100+ bytes. In zyx, all metadata lives in the graph, not the tensor handle. The tensor is just an index.

Reference Counting

Tensors are reference-counted via the global RT (a Mutex<Runtime>):

impl Clone for Tensor {
    fn clone(&self) -> Self {
        RT.lock().retain(self.id);
        Tensor { id: self.id }
    }
}

If we used Arc instead, we would still need Mutex for the Runtime — Tensor(id, Arc<Mutex<Runtime>>). The current approach avoids the Arc overhead and keeps Tensor at 4 bytes. Since every tensor operation already locks the runtime to append a graph node, there’s no additional lock contention from reference counting.

Lazy Evaluation

Tensor operations don’t compute anything. They build graph nodes:

extern crate zyx;
use zyx::{DType, Tensor, ZyxError};
fn main() -> Result<(), ZyxError> {
let x = Tensor::randn([1024, 1024], DType::F32)?;
let y = x.relu();
let z = y.tanh();

// This triggers the whole pipeline:
Tensor::realize(vec![&z])?;
Ok(())
}

The key insight: since operations just append to the graph, repeated graph patterns are automatically recognized and optimized. A training loop that builds the same graph structure every iteration gets the benefit of caching without explicit compilation steps.

Construction Methods

Tensors can be created from:

extern crate zyx;
use zyx::{DType, Tensor, ZyxError};
fn main() -> Result<(), ZyxError> {
let t = Tensor::from([1.0f32, 2.0, 3.0]);
let t = Tensor::randn([1024, 1024], DType::F32)?;
let t = Tensor::uniform([1024, 1024], -1.0f32..1.0)?;
let ones = Tensor::ones([3, 3], DType::F32);
let zeros = Tensor::zeros([3, 3], DType::F32);
Ok(())
}

This also works from files on disk (lazy loading).

The Immutability Rule

Tensors are immutable — there is no in-place mutation:

extern crate zyx;
use zyx::{DType, Tensor, ZyxError};
fn main() -> Result<(), ZyxError> {
let x = Tensor::randn([3, 3], DType::F32)?;
let x_plus_one = &x + 1.0;  // new tensor, no mutation
Ok(())
}

This makes autograd simpler (no mutation to track) and eliminates backpropagation errors from in-place modifications.