Summary of Usp: a Unified Sequence Parallelism Approach For Long Context Generative Ai, by Jiarui Fang and Shangchun Zhao
USP: A Unified Sequence Parallelism Approach for Long Context Generative AI
by Jiarui Fang, Shangchun Zhao
First submitted to arxiv on: 13 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper investigates the state-of-the-art sequence parallelism (SP) approaches for generative AI models, proposing a unified SP approach that is more robust to transformer model architectures and network hardware topology. The authors compare the communication and memory cost of SP with existing parallelism methods, including data/tensor/zero/pipeline parallelism, and discuss best practices for designing hybrid 4D parallelism involving SP. This research demonstrates the effectiveness of SP in training large-scale generative models, achieving a 47% speedup on two 8xA800 nodes using the LLAMA3-8B model with sequence length 208K. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding ways to make computers work faster when they’re trying to learn from very long sequences of information. It looks at different techniques for doing this and suggests a new way that works well no matter what kind of computer or model you’re using. The researchers tested their idea on a really big language model and were able to make it run much faster than before. |
Keywords
» Artificial intelligence » Language model » Transformer
