  Quantization is a promising approach for reducing memory overhead and
accelerating inference, especially in large pre-trained language model (PLM)
scenarios. While having no access to original training data due to security and
privacy concerns has emerged the demand for zero-shot quantization. Most of the
cutting-edge zero-shot quantization methods primarily 1) apply to computer
vision tasks, and 2) neglect of overfitting problem in the generative
adversarial learning process, leading to sub-optimal performance. Motivated by
this, we propose a novel zero-shot sharpness-aware quantization (ZSAQ)
framework for the zero-shot quantization of various PLMs. The key algorithm in
solving ZSAQ is the SAM-SGA optimization, which aims to improve the
quantization accuracy and model generalization via optimizing a minimax
problem. We theoretically prove the convergence rate for the minimax
optimization problem and this result can be applied to other nonconvex-PL
minimax optimization frameworks. Extensive experiments on 11 tasks demonstrate
that our method brings consistent and significant performance gains on both
discriminative and generative PLMs, i.e., up to +6.98 average score.
Furthermore, we empirically validate that our method can effectively improve
the model generalization.
