This text discusses the advancements in language modeling through the use of large language models (LLMs) and the challenges faced in optimizing these models for distributed training. It introduces an innovative asynchronous method that combines delayed Nesterov momentum updates and dynamic local updates, showcasing significant improvements in training efficiency for language models.
Advancements in Language Modeling and Distributed Optimization
Language modeling, a crucial aspect of natural language processing, has seen significant progress with the emergence of large language models (LLMs). However, optimizing these models efficiently poses challenges, especially in distributed training with multiple devices.
Challenges in Distributed Optimization
Traditional methods like Local Stochastic Gradient Descent (Local-SGD) face issues such as communication latency and inefficiency due to varying computational capabilities and geographical dispersion of devices.
Innovative Approach to Asynchronous Local-SGD
DeepMind’s research introduces an innovative method to enhance asynchronous Local-SGD for language modeling. This approach updates global parameters asynchronously as workers complete their Stochastic Gradient Descent (SGD) steps, addressing the limitations of synchronous Local-SGD.
Effective Methodology and Results
The proposed approach incorporates a delayed Nesterov momentum update and dynamic local updates, demonstrating improved training efficiency and scalability. It matches the performance of synchronous optimization in terms of perplexity per update step and outperforms it in wall clock time.
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