研究人员推出了一种名为HSAP（Hierarchical Sequence-aware Parallelism）的框架，旨在提高大型语言模型训练的效率。这种新方法解决了处理混合上下文序列和因果注意力计算中的挑战，这些在预训练和微调中常用的打包序列中很常见。HSAP通过优化跨多个设备组的张量传输和注意力计算，并利用JIT编译进行通信策略，来克服现有序列并行方法的局限性。 AI
影响 该框架可能导致更高效的大型语言模型训练，从而降低计算成本并加速开发。
排序理由 该集群包含一篇学术论文，详细介绍了一种用于训练生成模型的新技术框架。
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
arXiv:2606.30460v1 Announce Type: new Abstract: In this paper, we aim to combine the advantages of existing sequence parallelism paradigms and overcomes their drawbacks, the most serious of which is the incapability to correctly compute causal attention on the hybrid-context pack…
In this paper, we aim to combine the advantages of existing sequence parallelism paradigms and overcomes their drawbacks, the most serious of which is the incapability to correctly compute causal attention on the hybrid-context packed sequences, in a stronger sequence parallelism…