Summary of Ptq4vm: Post-training Quantization For Visual Mamba, by Younghyun Cho et al.
PTQ4VM: Post-Training Quantization for Visual Mamba
by Younghyun Cho, Changhun Lee, Seonggon Kim, Eunhyeok Park
First submitted to arxiv on: 29 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 Visual Mamba, an extension of the selective space state model Mamba, is applied to vision tasks. It processes image tokens sequentially in a fixed order, accumulating information to generate outputs. The approach has gained popularity for delivering high-quality outputs at a low computational cost across various tasks, but it is highly susceptible to quantization, which makes further performance improvements challenging. Our analysis reveals that the fixed token access order introduces unique quantization challenges, categorized into three main issues: token-wise variance, channel-wise outliers, and a long tail of activations. To address these challenges, we propose Post-Training Quantization for Visual Mamba (PTQ4VM), which includes Per-Token Static (PTS) quantization and Joint Learning of Smoothing Scale and Step Size (JLSS). This is the first quantization study on Visual Mamba, and PTQ4VM can be applied to various backbones, converting the model to a quantized format in under 15 minutes without notable quality degradation. Extensive experiments demonstrate its effectiveness, achieving up to 1.83x speedup on GPUs with negligible accuracy loss compared to FP16. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Visual Mamba is a new approach for processing images and videos. It works by looking at each part of the image in a specific order and building up information to make predictions. This method is good because it uses less computer power than other methods, but it has some problems when you try to use it on lower-power devices like phones. The researchers found that there are three main issues with using this method: some parts of the image are more important than others, some images have weird patterns, and some parts of the image have a lot of detail. To fix these problems, they developed a new way to make the model work on lower-power devices called Post-Training Quantization for Visual Mamba (PTQ4VM). This method can be used with different models, and it makes the model faster without losing much accuracy. |
Keywords
» Artificial intelligence » Quantization » Token
