Table of Links Abstract and 1. Introduction Abstract and 1. Introduction Abstract and 1. Introduction Related work


Method
3.1. Uniform quantizer
3.2. IGQ-ViT
3.3. Group size allocation


Experiments
4.1. Implementation details and 4.2. Results
4.3. Discussion


Conclusion, Acknowledgements, and References Related work Related work Related work Related work Method
3.1. Uniform quantizer
3.2. IGQ-ViT
3.3. Group size allocation Method Method 3.1. Uniform quantizer 3.1. Uniform quantizer 3.2. IGQ-ViT 3.2. IGQ-ViT 3.3. Group size allocation 3.3. Group size allocation Experiments
4.1. Implementation details and 4.2. Results
4.3. Discussion Experiments Experiments 4.1. Implementation details and 4.2. Results 4.1. Implementation details and 4.2. Results 4.3. Discussion 4.3. Discussion Conclusion, Acknowledgements, and References Conclusion, Acknowledgements, and References Conclusion, Acknowledgements, and References Conclusion, Acknowledgements, and References Supplementary Material Supplementary Material A. More implementation details A. More implementation details B. Compatibility with existing hardwares B. Compatibility with existing hardwares C. Latency on practical devices C. Latency on practical devices D. Application to DETR D. Application to DETR 4.3. Discussion Comparison with different quantizers. We compare in Table 4 the results of the variants of our method adopting different types of quantizers on input activations of FC layers and softmax attentions. From the first four rows, we can see that our approach outperforms layer-wise quantization by a large margin, both for linear operations and softmax attentions. This indicates that adopting a single quantization parameter for all channels and rows without considering Comparison with different quantizers. their individual distributions can severely limit the quantization performance. The last three rows compare the results of our approach with channel/row-wise quantization. We observe that the difference in performance between our approach and channel/row-wise quantization is less than 1.8% for three different models. With a small group size, our framework can achieve comparable performance to the upper bound, while maintaining efficiency. Table 5 shows the results of quantizing ViT architectures using various group quantization techniques, including [4, 7, 32], and ours. While the works of [7, 32] divide consecutive channels uniformly into a number of groups, the method of [4] first sorts channels w.r.t. the dynamic ranges before partitioning them into groups. In contrast, we dynamically assign channels to groups according to the statistical properties of the channels. We find that our approach outperforms other methods by a large margin, indicating that fixing the channels assigned to each group can degrade the quantization performance significantly. We also observe that sorting the channels w.r.t. their dynamic ranges during calibration does not boost the quantization performance for DeiT-B [34] and Swin-T [25], suggesting that the dynamic range of each channel vary drastically across different input instances. Analysis on group size. We show in Fig. 5 the results of IGQ-ViT according to the group size for linear operations (left) and softmax attentions (right). We can see that the quantization performance improves as the group size increases, for both linear operations and softmax attentions, demonstrating that using more groups better addresses the scale variation problem for channels and tokens. We also observe that the performance of our approach reaches near the upper bound with a small group size. This suggests that IGQ-ViT can effectively address the variations with a small amount of additional computations. Analysis on group size. Convergence analysis. We compare in Fig. 6(top) distances between channels of activation and quantizers in Eq. (4) (rows of softmax attention and quantizers in Eq. (8)) over optimization steps. It shows that our algorithm converges quickly within a small number of optimization steps. We show in Fig. 6(bottom) the dynamic ranges of activations and attentions in a particular layer, along with their assigned groups after convergence. We can see that activations/attentions in each group share similar statistical properties, demonstrating that they can be effectively quantized with a single quantization parameter. Convergence analysis. Group size allocation. We compare in Table 6 the results of our approach with/without the group size allocation technique. We can see that the group size allocation improves the quantization performance consistently, suggesting that assigning the same group size for all layers is suboptimal. Group size allocation. 5. Conclusion We have observed that activations and softmax attentions in ViTs have significant scale variations for individual channels and tokens, respectively, across different input instances. Based on this, we have introduced a instance-aware group quantization framework for ViTs, IGQ-ViT, that alleviates the scale variation problem across channels and tokens. Specifically, our approach splits the activations and softmax attentions dynamically into multiple groups along the channels and tokens, such that each group shares similar statistical properties. It then applies separate quantizers for individual groups. Additionally, we have presented a simple yet effective method to assign a group size for each layer adaptively. We have shown that IGQ-ViT outperforms the state of the art, using a small number of groups, with various ViT-based architectures. We have also demonstrated the effectiveness of IGQ-ViT compared with its variants, including layer-wise quantizers, channel/row-wise quantizers, and state-of-the-art group quantizers, with a detailed analysis. Acknowledgements. This work was supported in part by the NRF and IITP grants funded by the Korea government (MSIT) (No.2023R1A2C2004306, No.RS-2022-00143524, Development of Fundamental Technology and Integrated Solution for Next-Generation Automatic Artificial Intelligence System, and No.2021-0-02068, Artificial Intelligence Innovation Hub). Acknowledgements References [1] Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton. Layer normalization. arXiv, 2016. 3 [2] Ron Banner, Yury Nahshan, and Daniel Soudry. Post training 4-bit quantization of convolutional networks for rapiddeployment. In NeurIPS, 2019. 2, 3 [3] Yoshua Bengio, Nicholas Leonard, and Aaron Courville. Es- ´ timating or propagating gradients through stochastic neurons for conditional computation. arXiv, 2013. 2 [4] Yelysei Bondarenko, Markus Nagel, and Tijmen Blankevoort. Understanding and overcoming the challenges of efficient transformer quantization. EMNLP, 2021. 3, 4, 7, 12 [5] Zhaowei Cai and Nuno Vasconcelos. 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(1) Jaehyeon Moon, Yonsei University and Articron;
(2) Dohyung Kim, Yonsei University;
(3) Junyong Cheon, Yonsei University;
(4) Bumsub Ham, a Corresponding Author from Yonsei University. Authors: Authors: (1) Jaehyeon Moon, Yonsei University and Articron; (2) Dohyung Kim, Yonsei University; (3) Junyong Cheon, Yonsei University; (4) Bumsub Ham, a Corresponding Author from Yonsei University. This paper is available on arxiv under CC BY-NC-ND 4.0 Deed (Attribution-Noncommercial-Noderivs 4.0 International) license This paper is available on arxiv under CC BY-NC-ND 4.0 Deed (Attribution-Noncommercial-Noderivs 4.0 International) license available on arxiv available on arxiv

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Why Dynamic Grouping Beats Traditional Quantizers for Vision Transformers

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