With the increasing complexity of generative AI models, post-training
quantization (PTQ) has emerged as a promising solution for deploying
hyper-scale models on edge devices such as mobile devices and TVs. Existing PTQ
schemes, however, consume considerable time and resources, which could be a
bottleneck in real situations where frequent model updates and multiple
hyper-parameter tunings are required. As a cost-effective alternative, one-shot
PTQ schemes have been proposed. Still, the performance is somewhat limited
because they cannot consider the inter-layer dependency within the attention
module, which is a very important feature of Transformers. In this paper, we
thus propose a novel PTQ algorithm that balances accuracy and efficiency. The
key idea of the proposed algorithm called aespa is to perform quantization
layer-wise for efficiency while considering cross-layer dependency to preserve
the attention score. Through extensive experiments on various language models
and complexity analysis, we demonstrate that aespa is accurate and efficient in
quantizing Transformer models.

本文提出了一种新颖的 PTQ 算法 aespa，通过逐层量化实现高效性，同时考虑跨层依赖以保留注意力分数，通过对多种语言模型的广泛实验和复杂度分析，证明了 aespa 在量化 Transformer 模型时具备准确性和高效性。