Towards Next-Level Post-Training Quantization of Hyper-Scale Transformers
This work addresses the bottleneck of time and resource consumption in deploying large AI models on edge devices, offering an incremental improvement over existing learning-free quantization methods.
The paper tackles the problem of inefficient post-training quantization for hyper-scale Transformers by proposing aespa, a novel algorithm that balances accuracy and efficiency, achieving competitive results in quantizing various language models.
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 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 hyperparameter tunings are required. As a cost-effective alternative, learning-free PTQ schemes have been proposed. However, the performance is somewhat limited because they cannot consider the inter-layer dependency within the attention module, which is a significant 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 targeting attention-wise reconstruction to consider the cross-layer dependency. Through extensive experiments on various language models and complexity analysis, we demonstrate that aespa is accurate and efficient in quantizing Transformer models.