PTMQ: Post-training Multi-Bit Quantization of Neural Networks
Authors: Ke Xu, Zhongcheng Li, Shanshan Wang, Xingyi Zhang
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that PTMQ achieves comparable performance to existing state-of-the-art post-training quantization methods, while optimizing it speeds up by 100 compared to recent multi-bit quantization works. Code can be available at https://github.com/xuke225/PTMQ. |
| Researcher Affiliation | Academia | Ke Xu1,2, Zhongcheng Li2, Shanshan Wang1*, Xingyi Zhang1,3* 1Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University 2School of Artificial Intelligence, Anhui University, Hefei, China 3School of Computer Science and Technology, Anhui University, Hefei, China {xuke,wang.shanshan}@ahu.edu.cn lizhongcheng@stu.ahu.edu.cn xyzhanghust@gmail.com |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | Code can be available at https://github.com/xuke225/PTMQ. |
| Open Datasets | Yes | We assess the performance of the proposed PTMQ scheme on various CNN-based architectures (Res Net (He et al. 2016), Mobile Net V2 (Sandler et al. 2018), Reg Net (Radosavovic et al. 2020)) and transformer-based architectures (Vi T (Dosovitskiy et al. 2021), Dei T (Touvron et al. 2021)) on Image Net (Russakovsky et al. 2014) dataset. |
| Dataset Splits | No | The paper mentions using 'calibration data' but does not specify explicit training/validation/test splits (e.g., percentages or sample counts) or reference predefined standard splits for reproducibility. |
| Hardware Specification | Yes | The time measurement is carried out with NVIDIA 3090. |
| Software Dependencies | No | The paper mentions using other methods like QDrop and PTQ4ViT, but it does not provide specific version numbers for its own software dependencies such as Python, PyTorch, TensorFlow, or CUDA. |
| Experiment Setup | No | The paper describes the overall optimization process and components like MFM and GD-Loss, but it does not provide specific hyperparameters (e.g., learning rate, batch size, number of epochs) or detailed training configurations in the main text. |