seqpacker 0.1.2

High-performance sequence packing library for LLM training with Rust core and Python bindings

SeqPacker

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

Documentation  |  Rust API  |  Benchmarks  |  Contributing

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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.

• 11 algorithms — NF, FF, BF, WF, FFD, BFD, FFS, MFFD, OBFD, OBFDP, HK
• Streaming API — bounded-space packing with incremental output
• 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

Installation

# Python (pip)
pip install seqpacker

# Python (uv)
uv add seqpacker

# Rust
cargo add seqpacker

Quick Start

Python

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...

Rust

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);

Algorithms

11 bin-packing algorithms from O(n) online to optimal offline:

Algorithm	Short	Time	Approx. Ratio	Best For
NextFit	nf	O(n)	2.0	Memory-constrained streaming
FirstFit	ff	O(n log B)	1.7	Online baseline
BestFit	bf	O(n log B)	1.7	Tighter online packing
WorstFit	wf	O(n log B)	2.0	Even distribution
FirstFitDecreasing	ffd	O(n log n)	1.22	Good offline default
BestFitDecreasing	bfd	O(n log n)	1.22	Tighter offline packing
FirstFitShuffle	ffs	O(n log n)	~1.3	Training randomness
ModifiedFFD	mffd	O(n log n)	1.18	Mixed-size distributions
OptimizedBFD	obfd	O(n log n)	1.22	Default (recommended)
ParallelOBFD	obfdp	O(n log n)	1.22	Large datasets (multi-threaded)
Harmonic-K	hk	O(n)	~1.69	Bounded-space online

from seqpacker import Packer

# Use any algorithm by short name (default: obfd)
packer = Packer(capacity=2048, strategy="obfd")
result = packer.pack([500, 600, 400, 1000])

# List all available strategies
print(Packer.strategies())

Usage Modes

Batch Packing

Pack all sequences at once. Best for offline dataset preprocessing. All 11 algorithms available.

from seqpacker import Packer

packer = Packer(capacity=2048, strategy="obfd")
result = packer.pack(sequence_lengths)

for pack in result.packs:
    print(pack.sequence_ids, pack.lengths, pack.used)

print(f"Efficiency: {result.efficiency:.2%}")
print(f"Packs: {result.num_bins}")

Streaming

Feed sequences one at a time. Completed packs are emitted incrementally. Only bounded-space algorithms supported: NextFit (nf) and Harmonic-K (hk).

from seqpacker import StreamPacker

sp = StreamPacker(capacity=2048, strategy="nf")

for length in dataset_lengths:
    for pack in sp.add(length):
        process(pack)  # completed packs emitted as they fill

for pack in sp.finish():
    process(pack)      # flush remaining

Buffer + Batch

Accumulate sequences into a buffer and pack periodically. Requires no special library support -- just call pack() on each buffer. All algorithms available.

from seqpacker import Packer

packer = Packer(capacity=2048, strategy="obfd")
buffer = []

for sample in dataset_stream:
    buffer.append(len(sample["input_ids"]))
    if len(buffer) >= 10_000:
        result = packer.pack(buffer)
        for pack in result.packs:
            yield pack
        buffer.clear()

if buffer:
    result = packer.pack(buffer)
    for pack in result.packs:
        yield pack

PyTorch Integration

seqpacker.torch_utils provides helpers for converting pack results into GPU-ready tensors. Torch is not a dependency -- import only when you need it.

from seqpacker.torch_utils import packed_collate_fn
from torch.utils.data import DataLoader

collate = packed_collate_fn(capacity=2048, strategy="obfd")
loader = DataLoader(dataset, collate_fn=collate, batch_size=256)

for batch in loader:
    outputs = model(
        input_ids=batch.input_ids,
        position_ids=batch.position_ids,
        labels=batch.labels,
    )

Or convert a PackResult directly:

from seqpacker import pack_sequences
from seqpacker.torch_utils import pack_result_to_tensors

result = pack_sequences(lengths, capacity=2048)
batch = pack_result_to_tensors(result=result, token_ids=token_ids)
# batch.input_ids, batch.cu_seqlens, batch.position_ids, batch.labels, batch.attention_mask

NumPy Support

Both list and NumPy array inputs are supported with zero-copy for NumPy:

import numpy as np
from seqpacker import Packer

packer = Packer(capacity=2048)
lengths = np.array([500, 600, 400, 1000], dtype=np.int64)
result = packer.pack(lengths)

# Flat NumPy output for maximum performance
items_flat, bin_offsets = packer.pack_flat(lengths)
bins = np.split(items_flat, bin_offsets)

Performance

SeqPacker achieves equal packing efficiency to competitors while being significantly faster:

Comparison	Speedup	Efficiency
vs LightBinPack (C++)	~1.2-1.5x faster	Equal (98.76%)
vs greedy_ffd (Python)	~400x faster	Equal
vs binpacking (Python)	~1,700x faster	Equal
vs prtpy (Python)	~1,900x faster	Equal

Benchmarked on 10,000 sequences across real-world datasets (Alpaca, UltraChat, C4). See the interactive benchmark dashboard for detailed results.

Contributing

See CONTRIBUTING.md for setup instructions and development workflow.

make install       # Install dependencies
make build-dev     # Build the Rust extension
make test          # Run all tests (400 Rust + 249 Python)
make help          # See all commands

License

MIT