QTIP: Quantization with Trellises and Incoherence Processing
<|reference_start|>QTIP: Quantization with Trellises and Incoherence Processing: Post-training quantization (PTQ) reduces the memory footprint of LLMs by quantizing weights to low-precision datatypes. Since LLM inference is usually memory-bound, PTQ methods can improve inference throughput. Recent state-of-the-art PTQ approaches have converged on using vector quantization (VQ) to quantize multiple weights at once, which improves information utilization through better shaping. However, VQ requires a codebook with size exponential in the dimension. This limits current VQ-based PTQ works to low VQ dimensions ($\le 8$) that in turn limit quantization quality. Here, we introduce QTIP, which instead uses trellis coded quantization (TCQ) to achieve ultra-high-dimensional quantization. TCQ uses a stateful decoder that separates the codebook size from the bitrate and effective dimension. QTIP introduces a spectrum of lookup-only to computed lookup-free trellis codes designed for a hardware-efficient "bitshift" trellis structure; these codes achieve state-of-the-art results in both quantization quality and inference speed.<|reference_end|>
@article{tseng2024qtip:,
    title={QTIP: Quantization with Trellises and Incoherence Processing},
    author={Albert Tseng, Qingyao Sun, David Hou, Christopher De Sa},
    journal={arXiv preprint arXiv:2406.11235},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.11235},
    primaryClass={cs.LG}
}