From Decoupling to Adaptive Transformation: a Wider Optimization Space for PTQ
Post-Training Quantization
Yes
No ethics review needed.
1. This paper only provides the results under weight-activation quantization. How does the AdaQTransform method perform under the weight-only quantization of CNNs, ViTs, LLMs, and image super-resolution networks?

2. What is the training cost of the additional parameters $\xi$ and $\eta$? How does the required number of iterations of the optimization process (to get the parameters $s_x$, $\alpha$, $\xi$ and $\eta$) compared to the existing methods like BRECQ?

3. Can the authors provide more models and quantization methods to compare the inference performance under the same hardware on TVM?
1. The paper is well-written and easy to follow, presenting a new PTQ method that can be integrated with existing methods into a general quantization framework.

2. Comprehensive experiments across various tasks, models, and datasets demonstrate the method's effectiveness.
This paper proposes AdaQTransform, a new PTQ approach which provides adaptive per-channel quantization on the output feature and can reduce the PTQ feature reconstruction error. This method achieves a new PTQ SOTA on many network structures like CNNs, ViTs, LLMs, and image super-resolution networks. Experiments with TVM on hardware also demonstrate that this method does not introduce additional inference cost.
1. This paper does not provide an analysis of the additional training overhead caused by the training learnable parameters $\xi$ and $\eta$ compared to other methods.

2. The hardware experiment only presents the results of the NWQ method on the Mobile-V2 model, which is not sufficient for the practical applications of relevant researchers and practitioners.
Yes
No ethics review needed.
Why has the parameter s_w been fixed during the other quantization parameter learning?
Is the parameter b not updated during PTQ?
Is the parameter scale in Equation (13) learnable?
The proposed method outperforms the compared methods in various backbone networks and tasks (image classification, super-resolution, and large language models).
This paper presents a network quantization method for post-training quantization (PTQ). The proposed method incorporates learning per-channel scaling and translating by decoupling transformation before and after quantization. Experimental results present that the proposed method outperforms the compared methods in image classification and super-resolution.
The presentation of this paper is very confusing and difficult to follow.
For instance, Figure 1 does not describe “case1.1.1” and s_w’ which make it hard to understand at the beginning of this paper.

The proposed method of per-channel transformation is not clearly described and analyzed.
For instance, the manuscript does not define h(\alpha) in Equation (2). Note that the manuscript should describe the exact operation of AdaRound used in the proposed method. Moreover, the experiments have not presented performance improvement through learning per-channel transformation after quantization and ablation studies of each proposed technique component.

The performance comparisons for image super-resolution in Table 6 incorporate wrong numbers.
EDSR of scale 4 in FP32 can not perform 37.99 dB in Set 5, and AdaBM outperforms the reported scores with lower FAB in the paper.
Yes
No ethics review needed.
See Weaknesses.
1. The proposed method technically sounds and produces superior performance.
In this paper, the authors propose an Adaptive Quantization Transformation (AdaQTransform) for post-training quantization (PTQ) reconstruction, which provides adaptive per-channel transformations on the quantized output features. They explore the quantization step for weights across six cases by decoupling and incorporating learnable parameters in PTQ, such as Batch Normalization (BN) and bias. Extensive experiments on both low-level and high-level tasks demonstrate the superiority of the proposed method.
1. Figure 1 should be revised, as the presentation of decoupled PTQ is not sufficiently clear. The authors should also highlight the advantages of the decoupled design in the introduction.
2. The RepQ-ViT model reparameterizes scales for hardware-friendly layer-wise quantization and log2 quantization during inference. In this paper, the authors introduce additional per-channel factors, which may not be hardware-friendly.
3. Channel-wise scale factor can enhance the performance of PTQ, particularly in low-bit settings. Can the authors provide performance metrics for 2-bit PTQ on the ViT for classification tasks?
4. For both ViT and CNN architectures, can optimizing scale factors in Batch Normalization and Layer Normalization during PTQ yield similar performance?
5.  It would be beneficial for the authors to include an evaluation of inference speed on the hardware.
6. The authors should cite recent works in the related literature, especially concerning Quantization-Aware Training (QAT), such as: "Wang, Longguang, et al. 'Learnable Lookup Table for Neural Network Quantization.' Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022."
Yes
No ethics review needed.
1) Did the authors forget to highlight the best-performing results in Table 3? Because they did use bold-type to mark the best results in Tables 4 and 5. Just curious about this apparent inconsistency.
2) What is the model difference between the two settings (Lines 459 and 460)? Since the results show some notable difference, the two models must be different, I think.
3) Any failure cases or the limitations of AdaQTransform? I think an improved understanding needs to cover both positive and negative findings. 
4) I am curious about the relationship between this work and Outlier Suppression+ (OS+) framework (EMNLP'2023). Both papers deal with scaling and translating. OS+ also developed a fast and stable scheme to calculate effective shifting and scaling values. It will be nice if the authors can compare their strategy for determining scaling/translation parameters.
+ Originality: A notable contribution is the unified framework subsuming PTQs and QATs in the literature. Such a unified treatment is beneficial for improving our understanding of the existing works on network compression scattered in the literature. 
+ Quality: the overall quality of this paper is good. In particular, I appreciate the authors' empirical observations in Sec. 3.2, which helps the explanation of decoupling and the motivation behind AdaQTransform.
+ Clarity: the paper is relatively easy to follow in most parts. The reported experimental results are comprehensive and convincing. Most figures and tables are self-contained.
This paper studies the problem of Post-training low-bit quantization (PTQ) and makes an interesting observation that wider optimization space can lead to better PTQ solutions. The key idea is to unify PTQ with Quantization-Aware Training (QAT) by introducing a scaling and a translating factor. The developed Adaptive Quantization Transformation(AdaQTransform) has been tested on several network architectures including CNNs, ViTs, and LLMs. The reported experimental results are unanimously promising and convincing.
- Significance: "AdaQTransform improves the current best PTQ by 5.7% on W2A2-MobileNet-v2." This claim is vague because it does not mention 5.7% corresponds to which aspect of the performance. I fail to identify the corresponding discussion in Sec. 4. Accordingly, it is difficult to assess the significance of this work. 
- Experimental results. The benchmark methods used on MS COSO for object detection in Table 3 are all published before 2023. I doubt that those methods still represent the current SOTA given the rapid advance in the literature of PTQ. More recent benchmark methods (e.g., [1]-[5]) need to be included into the comparison. 

[1] Albert Tseng, Jerry Chee, Qingyao Sun, Volodymyr Kuleshov, and Christopher De Sa. Quip#: Even better
llm quantization with hadamard incoherence and lattice codebooks. arXiv preprint arXiv:2402.04396,
2024.
[2] Haoxuan Wang, Yuzhang Shang, Zhihang Yuan, Junyi Wu, and Yan Yan. Quest: Low-bit diffusion model
quantization via efficient selective finetuning. arXiv preprint arXiv:2402.03666, 2024.
[3] Xiuying Wei, Yunchen Zhang, Yuhang Li, Xiangguo Zhang, Ruihao Gong, Jinyang Guo, and Xianglong
Liu. Outlier suppression+: Accurate quantization of large language models by equivalent and optimal
shifting and scaling. arXiv preprint arXiv:2304.09145, 2023.
[4] Junyi Wu, Haoxuan Wang, Yuzhang Shang, Mubarak Shah, and Yan Yan. Ptq4dit: Post-training quantization
for diffusion transformers. arXiv preprint arXiv:2405.16005, 2024.
[5] Guangxuan Xiao, Ji Lin, Mickael Seznec, Hao Wu, Julien Demouth, and Song Han. Smoothquant:
Accurate and efficient post-training quantization for large language models. In International Conference
on Machine Learning, pages 38087–38099. PMLR, 2023.
@inproceedings{
anonymous2024from,
title={From Decoupling to Adaptive Transformation: a Wider Optimization Space for {PTQ}},
author={Anonymous},
booktitle={Submitted to The Thirteenth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=JElN0LJMKB},
note={under review}
}
Post-training low-bit quantization (PTQ) is useful to accelerate DNNs due to its high efficiency. Currently, finetuning through self-distillation feature reconstruction is one of the most effective PTQ techniques. However, when bitwidth goes to be extremely low, we find that current parameter update settings in PTQ feature reconstruction are sub-optimal. Considering all possible parameters and the ignored fact that integer weight can be obtained early before actual inference, we thoroughly explore 1) the setting of weight’s quantization step into six cases by decoupling; 2) ignored learnable params in PTQ like BN and bias. Based on these explorations, we find there exist a wider optimization space and a better optimum. Considering these, we propose an Adaptive Quantization Transformation(AdaQTransform) for PTQ reconstruction, which provides adaptive per-channel transformation on the quant output feature, making them better fit FP32 counterpart and achieve lower PTQ feature reconstruction error. During inference, the AdaQTransform parameters can be merged without incurring additional inference costs. Based on AdaQTransform, for the first time, we build a general quantization setting paradigm subsuming current PTQs, QATs and other potential approaches. Experiments demonstrate that AdaQTransform expands the optimization space for PTQ and helps current PTQs find a better optimum over CNNs, ViTs, LLMs and low-level vision networks (image super-resolution). Specifically, AdaQTransform improves the current best PTQ by 5.7\% on W2A2-MobileNet-v2. The code will be released.
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Post-Training Quantization
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From Decoupling to Adaptive Transformation: a Wider Optimization Space for PTQ
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