oxicuda-seq

Sequence models and structured prediction -- HMMs, CRFs, Kalman filters, alignment, beam search, and stochastic decoders in pure Rust.

Part of the OxiCUDA project.

Overview

oxicuda-seq is the sequence-modelling volume of the OxiCUDA stack. It collects the canonical algorithms for discrete-state sequence models (HMMs, CRFs, MEMMs, structured SVMs), continuous-state filtering (Kalman / EKF / UKF, particle filters, RTS smoothing), pairwise / grid MRFs, classical alignment, and the decoding strategies (Viterbi, beam search, top-k / nucleus / typical sampling) that sit on top of them.

All algorithms are implemented in pure Rust with no external linear-algebra dependencies. GPU PTX kernel generators in ptx_kernels provide kernel strings for the operations whose inner loops are amenable to direct kernel mapping, parameterised on SM compute capability.

The numerical kernels in this crate prefer straight integer-indexed loops over iterator chains because most bodies touch multiple parallel arrays indexed by the same (state, observation, time) triplets, which is why clippy::needless_range_loop is allowed crate-wide.

Modules

Method Coverage

Hidden Markov Models (hmm)

• Discrete and Gaussian emissions
• Forward-backward with scaling for numerical stability
• Viterbi decoding
• Baum-Welch (EM) parameter learning
• Variational Bayes EM with Dirichlet priors
• Hidden semi-Markov extension

Conditional Random Fields (crf)

• Linear-chain CRF: forward-backward in score space, Viterbi decoding
• L-BFGS-B training
• Skip-chain extension for long-range dependencies

State-space filters (kalman)

• Linear Kalman filter and RTS smoother
• Extended Kalman Filter (EKF)
• Unscented Kalman Filter (UKF)
• Particle filter
• EM parameter learning for linear-Gaussian models

Sequence alignment (alignment)

• Needleman-Wunsch (global)
• Smith-Waterman (local)
• Gotoh (affine-gap penalty)
• Hirschberg (linear-space)

Decoding (beam + decoders)

• Beam search with length normalisation and diverse-beam penalty
• Top-k sampling, nucleus / top-p sampling, typical sampling

Graphical models (grid_crf + mrf)

• Pairwise 2D CRF with mean-field variational inference
• General MRF + Ising model
• Gibbs sampler and loopy belief propagation

Quick Start

use oxicuda_seq::hmm::hmm::HmmDiscrete;
use oxicuda_seq::hmm::viterbi::viterbi;
use oxicuda_seq::SeqResult;

fn main() -> SeqResult<()> {
    // Build a discrete-emission HMM.
    let n_states = 2;
    let n_obs = 3;
    let pi: Vec<f64> = unimplemented!(); // length = n_states, sums to 1
    let a: Vec<f64> = unimplemented!();  // length = n_states^2, row-stochastic
    let b: Vec<f64> = unimplemented!();  // length = n_states * n_obs, row-stochastic
    let hmm = HmmDiscrete::new(n_states, n_obs, pi, a, b)?;

    // Observation sequence (indices into [0, n_obs)).
    let obs: Vec<usize> = vec![0, 1, 2, 1, 0];

    // Decode the most-likely state path.
    let _result = viterbi(&hmm, &obs)?;
    Ok(())
}

Status

Alpha -- 20,887 SLoC, 706 passing tests. API may evolve before v1.0.

License

Apache-2.0 -- (C) 2026 COOLJAPAN OU (Team KitaSan)