Summary of 2bp: 2-stage Backpropagation, by Christopher Rae et al.
2BP: 2-Stage Backpropagation
by Christopher Rae, Joseph K. L. Lee, James Richings
First submitted to arxiv on: 28 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC)
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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 2-stage backpropagation (2BP) approach improves the efficiency of pipeline parallelism for training large Deep Neural Networks (DNNs). By splitting the backward propagation step into two stages, 2BP reduces idle compute time and increases throughput. The method is tested on various model architectures and pipelining schedules, resulting in a significant increase in throughput compared to traditional methods. For example, training a LLaMa-like transformer with 7 billion parameters across 4 GPUs achieves a 1.70x speedup. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows how to make computers train big artificial intelligence models faster. Big AI models need many calculations and usually use many computers to do these calculations quickly. The problem is that the special software used for training these models can slow things down. To fix this, researchers came up with a new way of doing the math called 2-stage backpropagation (2BP). This makes the calculations go faster because it reduces time spent waiting for results. This means bigger AI models can be trained even quicker. |
Keywords
» Artificial intelligence » Backpropagation » Llama » Transformer
