Scaling Performance of Large Language Model Pretraining
This addresses the challenge for AI researchers and companies in efficiently scaling LLM training, though it is incremental as it focuses on practical tuning rather than introducing new methods.
The paper tackles the problem of limited public information on scaling performance and training considerations for large language model pretraining, providing practical recommendations for distributed training, managing large datasets, and scaling data parallelism to fully utilize GPU compute capacity.
Large language models (LLMs) show best-in-class performance across a wide range of natural language processing applications. Training these models is an extremely computationally expensive task; frontier Artificial Intelligence (AI) research companies are investing billions of dollars into supercomputing infrastructure to train progressively larger models on increasingly massive datasets. Unfortunately, very little information about the scaling performance and training considerations of these large training pipelines is released publicly. Working with very large datasets and models can be complex and practical recommendations are scarce in the public literature for tuning training performance when scaling up large language models. In this paper, we aim to demystify the large language model pretraining pipeline somewhat - in particular with respect to distributed training, managing large datasets across hundreds of nodes, and scaling up data parallelism with an emphasis on fully leveraging available GPU compute capacity.