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Status: Unchecked ×Keyword: training efficiency ×Clear all

3 claims from 1 paper

1. Computer Science › Topic Modeling

   Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism

   Shoeybi, Patwary, Puri, LeGresley, Casper and Catanzaro · arXiv (Cornell University) · 2019

   The authors present a simple intra-layer model parallel method in PyTorch for training transformer language models with billions of parameters, and use it to train models reaching state-of-the-art results.

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   1. UncheckedTraining an 8.3-billion-parameter model on 512 GPUs sustained 15.1 PetaFLOPs, 76% scaling efficiency against a 39 TeraFLOP single-GPU baseline.“We sustain 15.1 PetaFLOPs across the entire application with 76% scaling efficiency when compared to a strong single GPU baseline that sustains 39 TeraFLOPs, which is 30% of peak FLOPs.”
   2. Unchecked“We show that careful attention to the placement of layer normalization in BERT-like models is critical to achieving increased performance as the model size grows.”
   3. Unchecked“Our BERT model achieves SOTA results on the RACE dataset (90.9% compared to SOTA accuracy of 89.4%).”