Genie: Show Me the Data for Quantization

Zero-shot quantization is a promising approach for developing lightweight deep neural networks when data is inaccessible owing to various reasons, including cost and issues related to privacy. By utilizing the learned parameters (statistics) of FP32-pre-trained models, zero-shot quantization schemes focus on generating synthetic data by minimizing the distance between the learned parameters ($\mu$ and $\sigma$) and distributions of intermediate activations. Subsequently, they distill knowledge from the pre-trained model (\textit{teacher}) to the quantized model (\textit{student}) such that the quantized model can be optimized with the synthetic dataset. In general, zero-shot quantization comprises two major elements: synthesizing datasets and quantizing models. However, thus far, zero-shot quantization has primarily been discussed in the context of quantization-aware training methods, which require task-specific losses and long-term optimization as much as retraining. We thus introduce a post-training quantization scheme for zero-shot quantization that produces high-quality quantized networks within a few hours on even half an hour. Furthermore, we propose a framework called \genie~that generates data suited for post-training quantization. With the data synthesized by \genie, we can produce high-quality quantized models without real datasets, which is comparable to few-shot quantization. We also propose a post-training quantization algorithm to enhance the performance of quantized models. By combining them, we can bridge the gap between zero-shot and few-shot quantization while significantly improving the quantization performance compared to that of existing approaches. In other words, we can obtain a unique state-of-the-art zero-shot quantization approach.