曾炜
1,2, 苏腾
3, 王晖
1, 田永鸿
1,2, 高文
1
（1. 鹏城实验室，中国 深圳 518055；2. 北京大学，中国 北京 100871；3. 华为技术有限公司，中国 杭州 310052）
摘要：在鹏城云脑II上训练了全球首个拥有全开源2 000亿参数的自回归中文预训练语言大模型——鹏程•盘古。鹏程•盘古模型基于1.1 TB高质量中文训练数据，采用全场景人工智能计算框架MindSpore自动并行技术实现了五维并行训练策略，从而可将训练任务高效扩展到4 096个处理器上。对比实验表明，在少样本或零样本情况下，鹏程•盘古模型在多个中文自然语言理解或生成任务上都具有较优的性能。在此基础上，鹏程•盘古模型在大模型压缩、提示微调学习、多任务学习以及持续学习等方面也取得了很好的应用效果。
关键词：大规模预训练语言模型；鹏城云脑Ⅱ；大规模分布式训练；中文理解与生成；提示微调学习
Pengcheng-PanGu: Large-Scale Autoregressive Pre-trained Chinese Language Model with Auto-Parallel Computation and Its Application
ZENG Wei1,2, SU Teng3, WANG Hui1, TIAN Yonghong1,2, GAO Wen1
(1. Pengcheng Laboratory, Shenzhen 518055, China;2. Peking University, Beijing 100871, China;3. Huawei Technologies Co., Ltd., Hangzhou 310052, China)
Abstract: This paper presents the world's first large-scale autoregressive pre-trained Chinese language model named Pengcheng-PanGu with up to 200 billion parameters. Pengcheng-PanGu is developed under the Pengcheng cloud brain Ⅱ. We collect 1.1 TB high-quality Chinese data from a wide range of domains to pre-train the model. The training parallelism strategy is implemented based on all-scenarios artificial intelligence computing framework MindSpore Auto-parallel, which composes five parallelism dimensions to scale the training task to 4 096 processors efficiently. The experimental results demonstrate the superior capabilities of Pengcheng-PanGu in performing various natural language understanding and natural language generation tasks under few-shot or zero-shot settings. On this basis, Pengcheng-PanGu model has also achieved better application results in large model compression, prompt fine-tuning, multi-task, and continuous learning.
Keywords: large-scale pre-trained language models; Pengcheng cloud brain Ⅱ; large-scale distributed training; Chinese language understanding and generation; tip fine-tuning learning
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