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 calledaespais 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 thataespais accurate and efficient in quantizing Transformer models.