PanGu-$\alpha$: Large-scale Autoregressive Pretrained Chinese Language Models with Auto-parallel Computation

Abstract

Large-scale Pretrained Language Models (PLMs) have become the new paradigm for Natural Language Processing (NLP). PLMs with hundreds of billions parameters such as GPT-3 have demonstrated strong performances on natural language understanding and generation with \textit{few-shot in-context} learning. In this work, we present our practice on training large-scale autoregressive language models named PanGu-$\alpha$, with up to 200 billion parameters. PanGu-$\alpha$ is developed under the MindSpore and trained on a cluster of 2048 Ascend 910 AI processors. The training parallelism strategy is implemented based on MindSpore Auto-parallel, which composes five parallelism dimensions to scale the training task to 2048 processors efficiently, including data parallelism, op-level model parallelism, pipeline model parallelism, optimizer model parallelism and rematerialization. To enhance the generalization ability of PanGu-$\alpha$, we collect 1.1TB high-quality Chinese data from a wide range of domains to pretrain the model. We empirically test the generation ability of PanGu-$\alpha$ in various scenarios including text summarization, question answering, dialogue generation, etc. Moreover, we investigate the effect of model scales on the few-shot performances across a broad range of Chinese NLP tasks. The experimental results demonstrate the superior capabilities of PanGu-$\alpha$ in performing various tasks under few-shot or zero-shot settings.

The technique report for PanGu-$\alpha$

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Cited by in corpus (9)

• ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation
• AMMUS : A Survey of Transformer-based Pretrained Models in Natural Language Processing
• Mengzi: Towards Lightweight yet Ingenious Pre-trained Models for Chinese
• Yuan 1.0: Large-Scale Pre-trained Language Model in Zero-Shot and Few-Shot Learning
• Towards Efficient Post-training Quantization of Pre-trained Language Models
• M6-10T: A Sharing-Delinking Paradigm for Efficient Multi-Trillion Parameter Pretraining
• Pre-Trained Models: Past, Present and Future
• Pro-KD: Progressive Distillation by Following the Footsteps of the Teacher
• Is the Number of Trainable Parameters All That Actually Matters?