Prompt learning has emerged as a new paradigm for leveraging pre-trained language models (PLMs) and has shown promising results in downstream tasks with only a slight increase in parameters. However, the current usage of fixed prompts, whether discrete or continuous, assumes that all samples within a task share the same prompt. This assumption may not hold for tasks with diverse samples that require different prompt information. To address this issue, we propose an instance-aware prompt learning method that learns a different prompt for each instance. Specifically, we suppose that each learnable prompt token has a different contribution to different instances, and we learn the contribution by calculating the relevance score between an instance and each prompt token. The contribution weighted prompt would be instance aware. We apply our method to both unidirectional and bidirectional PLMs on both language understanding and generation tasks. Extensive experiments demonstrate that our method achieves comparable results using as few as 1.5\% of the parameters of PLMs tuned and obtains considerable improvements compared to strong baselines. In particular, our method achieves state-of-the-art results using ALBERT-xxlarge-v2 on the SuperGLUE few-shot learning benchmark.