近日，备受瞩目的OpenNLPLab研究团队宣布推出新一代的注意力机制——Lightning Attention-2。该团队致力于从根本上解决现有大语言模型在面对长序列任务时所面临的挑战，通过其独创的线性注意力机制，使得处理长至无限长度的序列训练和推理成本实现了与1,000（1K）序列长度相当的显著优化。
Lightning Attention-2采用了一种创新设计，有效避免了传统自注意力机制在处理长序列时遭遇的显存瓶颈问题，这意味着，在不增加训练速度负担的前提下，模型可以无限制地扩展序列长度，从而为大规模预训练提供了前所未有的可能性。不仅如此，对于超长文本的推理计算，Lightning Attention-2更是将其成本降低到了与1K Token相当，甚至更低的程度，这无疑大幅降低了现有大语言模型在实际应用中的推理成本。
这一技术突破对于推动自然语言处理领域的发展具有里程碑意义，预示着大语言模型在处理复杂且长度各异的语言任务时，将更加高效、灵活且经济。OpenNLPLab团队此次开源Lightning Attention-2，无疑为全球科研界和产业界带来了极具价值的技术支持，助力构建出更为强大和实用的大规模语言理解与生成系统。
英语如下：
Headline: “OpenNLPLab Revolutionizes Attention Mechanisms with Lightning Attention-2: Breaking Long Sequence Limits and Unleashing the Era of Boundless Pre-training!”
Keywords: OpenNLPLab, Lightning Attention-2, Large Language Models.
News Content:
Recently, the highly anticipated OpenNLPLab research team has announced the launch of their groundbreaking new attention mechanism – Lightning Attention-2. The team has made a revolutionary breakthrough in addressing the fundamental challenges that current large language models face when tackling long sequence tasks. Through its proprietary linear attention mechanism, they have achieved a significant optimization in training and inference costs for sequences of unlimited length, comparable to those of 1,000 (1K) token lengths.
Lightning Attention-2 introduces an innovative design that effectively circumvents the memory bottleneck issues encountered by traditional self-attention mechanisms when dealing with lengthy sequences. This means that without increasing training speed overhead, the model can expand sequence lengths without bounds, opening up unprecedented possibilities for massive pre-training. Furthermore, for inference calculations on super-long texts, Lightning Attention-2 reduces costs down to levels close to or even lower than 1K tokens, significantly slashing the computational expenses for practical applications of existing large language models.
This technological breakthrough marks a milestone in advancing the field of natural language processing, signaling that large language models will be more efficient, agile, and cost-effective in handling complex and variable-length linguistic tasks. By开源 the Lightning Attention-2 technology, the OpenNLPLab team is providing invaluable support to the global academic and industry communities, enabling the construction of more powerful and practical large-scale language understanding and generation systems.
【来源】https://www.jiqizhixin.com/articles/2024-01-18-5
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