In recent years, pre-trained models that take code as input have achieved significant performance gains in various critical code-based tasks. However, these models remain susceptible to adversarial attacks implemented through semantic-preserving code transformations, which can severely compromise model robustness and pose serious security issues. Although adversarial training, leveraging adversarial examples as augmented data, has been employed to enhance robustness, its effectiveness and efficiency often fall short when facing unseen attacks with varying granularities and strategies. To address these limitations, a novel adversarial defense technique based on code normalization, named CoDefense, is proposed. This method integrates a multi-granularity code normalization approach as a preprocessing module, which normalizes both the original training data during training and the inputcode during inference. By doing so, the proposed method mitigates the impact of potential adversarial examples and effectively defends against attacks of diverse types and granularities. To evaluate the effectiveness and efficiency of CoDefense, a comprehensive experimental study is constructed, encompassing 27 scenarios across three representative adversarial attack methods, three widely-used pre-trained code models, and three code-based classification and generation tasks. Experimental results demonstrate that CoDefense significantly outperforms state-of-the-art adversarial training methods in both robustness and efficiency. Specifically, it achieves an average defense success rate of 95.33% against adversarial attacks and improves time efficiency by an average of 85.86%.