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The scaling of large language models has greatly improved natural languageunderstanding, generation, and reasoning. In this work, we develop a systemthat trained a trillion-parameter language model on a cluster of Ascend 910 AIprocessors and MindSpore framework, and present the language model with 1.085Tparameters named PanGu-{\Sigma}. With parameter inherent from PanGu-{\alpha},we extend the dense Transformer model to sparse one with Random Routed Experts(RRE), and efficiently train the model over 329B tokens by using ExpertComputation and Storage Separation(ECSS). This resulted in a 6.3x increase intraining throughput through heterogeneous computing. Our experimental findingsshow that PanGu-{\Sigma} provides state-of-the-art performance in zero-shotlearning of various Chinese NLP downstream tasks. Moreover, it demonstratesstrong abilities when fine-tuned in application data of open-domain dialogue,question answering, machine translation and code generation.

PanGu-{\Sigma}: Towards Trillion Parameter Language Model with Sparse Heterogeneous Computing