Skip to content

SeqPacker

High-performance sequence packing for LLM training, written in Rust with Python bindings.

Training LLMs on variable-length sequences? Naive padding wastes 20-40% of GPU compute. SeqPacker packs sequences into fixed-size bins, achieving 95-99% utilization with 11 bin-packing algorithms — from O(n) streaming to near-optimal offline.

Key Features

  • 11 algorithms — NF, FF, BF, WF, FFD, BFD, FFS, MFFD, OBFD, OBFDP, HK
  • Streaming API — bounded-space packing with incremental output
  • HuggingFace integration — one-call pack_dataset for SFTTrainer / TRL
  • PyTorch integration — GPU-ready tensors out of the box
  • NumPy zero-copy — pass arrays directly, no conversion overhead
  • Cross-platform — Linux, macOS, Windows; Python 3.9-3.13

Quick Example

from seqpacker import pack_sequences

lengths = [1000, 800, 600, 500, 400, 300, 200, 100]
result = pack_sequences(lengths, capacity=1024)

print(result.bins)        # [[0], [1, 7], [2, 4], [3, 5, 6]]
print(result.efficiency)  # 0.952...

use seqpacker::{Packer, PackStrategy};

let packer = Packer::new(1024)
    .with_strategy(PackStrategy::OptimizedBestFitDecreasing);

let result = packer.pack_lengths(&[1000, 800, 600, 500, 400, 300, 200, 100]).unwrap();
println!("Efficiency: {:.2}%", result.metrics.efficiency * 100.0);

Next Steps