Summary of The Solution For the Aigc Inference Performance Optimization Competition, by Sishun Pan et al.
The Solution for the AIGC Inference Performance Optimization Competition
by Sishun Pan, Haonan Xu, Zhonghua Wan, Yang Yang
First submitted to arxiv on: 6 Jul 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 This paper explores the optimization of high-performance inference for Ernie models, a type of large-scale pre-trained language model based on transformer architectures. The researchers focus on GPU acceleration and leverage the Paddle inference framework to achieve efficient processing. Techniques employed include Faster Transformer, embedding layer pruning, and FP16 half-precision inference. To further minimize latency, the approach integrates multi-process parallel processing for efficient data handling. Experimental results show a significant improvement in inference speed of up to 8.96x compared to standard methods while maintaining competitive performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it faster and more efficient to use AI-powered language models like Ernie Wenxin. They’re trying to make these models work better on computers by using special tricks like making the model process data in half-precision numbers, or getting rid of some parts that aren’t as important. They also want to make it so that lots of computers can work together at the same time to process the data. When they tested their new way of doing things, they found out it made a big difference – it was up to 8.96 times faster! |
Keywords
» Artificial intelligence » Embedding » Inference » Language model » Optimization » Precision » Pruning » Transformer
