做LLM压缩和部署的团队值得关注——EinSort用排序技巧解决了张量化的核心痛点，直接提升压缩效率，建议在模型优化流程中试试。
张量网络能高效压缩大型神经网络，但现有方法难以在大模型中识别低秩结构。EinSort提出一种自适应张量化方法，通过索引排序发现目标张量中的内在低秩结构。在权重和KV缓存压缩实验中，该方法相比基线显著提升了重建质量。这为LLM的存储和计算优化提供了新思路，尤其适合资源受限场景。
EinSort: Sorting is All We Need for Tensorizing LLM
Tensor networks provide efficient representations for compressing large neural networks. By carefully designing shapes and topologies, they can significantly reduce memory and computational costs. However, identifying implicit low-rank structures in large foundation models remains challenging due to their enormous scale and un-structured weight distributions. We propose an adaptive tensorization method that discovers inherent low-rank structure in a target tensor by index ordering. Experiments on weight and KV-cache compression demonstrate improved reconstruction quality compared to baselines.