Summary of Repquant: Towards Accurate Post-training Quantization Of Large Transformer Models Via Scale Reparameterization, by Zhikai Li et al.
RepQuant: Towards Accurate Post-Training Quantization of Large Transformer Models via Scale Reparameterization
by Zhikai Li, Xuewen Liu, Jing Zhang, Qingyi Gu
First submitted to arxiv on: 8 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 presents a novel post-training quantization (PTQ) framework called RepQuant that tackles the performance bottleneck in existing PTQ methods. By decoupling quantization from inference, RepQuant employs complex quantizers for calibration and simplified quantizers for efficient inference. This approach ensures accurate quantization and efficient inference, making it suitable for large-scale transformer models. The framework is particularly effective for handling extreme distributions in LayerNorm and Softmax activations. Through a combination of channel-wise quantization, log2 quantization, and learnable per-channel dual clipping scheme, RepQuant demonstrates significant performance advantages on various large-scale transformer variants across multiple tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper shows how to make big AI models smaller and work better. Right now, people are using something called post-training quantization (PTQ) to do this. But existing PTQ methods aren’t very good because they focus too much on making sure the model works on different devices, which means they have to use simple ways of reducing data sizes. This makes the models less accurate. The new method, RepQuant, solves this problem by separating the process into two parts: one for calibration and one for inference. It uses more complex methods for calibration and simpler ones for inference. This helps make sure the model is both accurate and efficient. The paper also explains how it handles special cases where data values are very extreme. |
Keywords
* Artificial intelligence * Inference * Quantization * Softmax * Transformer
