大语言模型的推进显著提升了自然语言理解、生成、推理等多项NLP研究。在本文中，来自华为诺亚试验和和分布式软件实验室的研究人员提出了名为PanGu-Σ的万亿级别模型。该模型的训练基于分布式Ascend 910AI计算芯片和MindSpore代码框架，总参数量高达1.085T。
基于这一模型，作者进一步提出了Random Routed Experts（RRE）和Expert Computation and Storage Separation策略以实现参数稀疏化。结果表明上述算法能够提升6.3倍训练速度，并允许3290亿Token作为输入。实验表明，PanGu-Σ能够在多项中文NLP研究中取得一流性能，并可在问答、翻译等多项应用上进行迁移和优化。
论文地址：https://arxiv.org/pdf/2303.10845.pdf
整体框架
Random Routed Experts
Expert Computation and Storage Separation
实验
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