Axis

Axis is an experimental Rust machine-learning library built on NVIDIA cuTile Rust. Tensor dimensions have identities, not positions: a batch axis cannot silently become a class axis because both happen to have the same extent.

The crate currently provides:

• named-axis tensor algebra and reverse-mode differentiation, including deterministic finite minimum reductions;
• Linear, Conv2d, and Conv3d with stride, symmetric padding, and grouped/depthwise channels, named-axis LayerNorm, RmsNorm, GroupNorm, and stateless InstanceNorm, explicit-state LstmCell/Lstm, activations, attention primitives, and sequential composition;
• device-resident SGD, Adam, AdamW, and explicitly oriented rank-2 Muon with an exact AdamW remainder;
• generated and finite data loaders with executable single-pass, finite-pass, and IDR assertions;
• versioned semantic identities with exact retained-population and bounded-memory streaming disjointness;
• exact, batch-composable categorical accuracy counts;
• train-fitted standardization with an explicit variance correction; and
• empirical monotonicity checks over explicitly ordered input pairs, with receipts that distinguish sampled evidence from a global guarantee; and
• empirical learning-progress checks that bind a metric and evaluation population to ordered budget observations without claiming convergence.

use axis::prelude::*;

fn main() -> Result<()> {
    let device = Device::cuda(0)?;
    let batch = Axis::new("batch");
    let input = Axis::new("input");
    let hidden = Axis::new("hidden");
    let class = Axis::new("class");

    let mut model = Sequential::new((
        Linear::new(input, hidden.of(32)),
        GELU,
        Linear::new(hidden, class.of(10)),
    ));
    model.build(&Shape::new([batch.of(64), input.of(49)])?, &device, 42)?;
    Ok(())
}

Axis requires Linux, Rust 1.89 or newer, an NVIDIA GPU supported by cuTile, libclang, and CUDA 13.2 or newer. It is early research software: APIs may change as real training programs expose better defaults and abstractions.

On the development branch, Tensor::gelu_exact() provides erf-form GELU for pretrained-model parity; Tensor::gelu() and GELU retain their tanh formulation. The exact-form operation uses FP32 normal-CDF evaluation and its analytic derivative, not bitwise libm equivalence. This addition is not in release 0.7.0.

The Muon implementation's pinned upstream revision and MIT attribution are in THIRD_PARTY.md, which is included in every published crate.

Conv2d::new and Conv3d::new default to stride one, no padding, and one group. Configure a depthwise layer by setting groups to the input channel count; both input and output channel extents must be divisible by that value. Kernel, stride, and padding entries correspond positionally to the supplied named spatial axes. Padding is symmetric per axis. Dilation and asymmetric padding are not implemented. Both paths materialize FP32 patches before contraction; 3D kernel volumes can make that intermediate large, while later generic layout and broadcast operations retain their index-plan limits.

Lstm names its time, input, and hidden axes and preserves every unrelated stream axis. run starts from device-resident zero state; run_from accepts an explicit LstmState and returns both the complete sequence and terminal hidden and cell state. Returned states stay connected to reverse mode until the caller uses LstmState::detach. The current implementation projects the full input sequence once, then submits one eager recurrent transition per time coordinate and retains its activations. It is a numerical-correctness path, not a fused scan or a sequence-throughput claim.

cargo add axis@0.8.0

The repository contains complete MLP, CNN, attention, generated-data, paired Muon, MNIST, and research-script migrations with independent numerical oracles. Contributions from humans and agents are both welcome under the repository's contribution contract. The project name and branding are covered by its trademark policy.