Summary of A Multi-level Framework For Accelerating Training Transformer Models, by Longwei Zou et al.
A Multi-Level Framework for Accelerating Training Transformer Models
by Longwei Zou, Han Zhang, Yangdong Deng
First submitted to arxiv on: 7 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed multi-level framework for training acceleration revolutionizes the landscape of NLP, CV, and other domains by reducing the computational cost of large-scale deep learning models like Bert, GPT, and ViT. This is crucial as the exponential increase in energy consumption and carbon emissions poses a significant challenge to the adoption of these models. The framework consists of three basic operators: Coalescing, De-coalescing, and Interpolation, which enable a V-cycle training process that progressively down- and up-scales model size, projecting parameters between adjacent levels via coalescing and de-coalescing. This approach leverages smaller models for fast convergence, using trained parameters as high-quality intermediate solutions for the next level larger network. The interpolation operator breaks neuron symmetry incurred by de-coalescing for better convergence performance. Experimental results on transformer-based language models (e.g., Bert, GPT) and a vision model (e.g., DeiT) demonstrate that the proposed framework reduces computational cost by approximately 20% for training BERT/GPT-Base models and up to 51.6% for training the BERT-Large model while preserving performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large-scale deep learning models are changing the game in NLP, CV, and beyond! But training these powerful models requires an enormous amount of computing power, which is bad news for our planet’s energy consumption and carbon emissions. To solve this problem, researchers have developed a new framework that makes training faster and more efficient. The key idea is to use smaller models as “building blocks” to train larger models, kind of like how you might use smaller Legos to build a bigger castle. This approach can reduce the amount of computing power needed by up to 51.6%! And the best part? It works just as well on language models (like Bert and GPT) as it does on vision models (like DeiT). |
Keywords
» Artificial intelligence » Bert » Deep learning » Gpt » Nlp » Transformer » Vit
