As the integrated circuit (IC) supply chain globalizes, fabrication, testing and packaging are outsourced to third-party entities, and intellectual property (IP) is widely used. As a result, new hardware security threats, including IP piracy, have emerged. Graph similarity learning is a promising technique for estimating the similarity between two input graphs and can be used to detect IP piracy after modeling input hardware designs as graphs. In this work, we propose a hardware similarity learning framework, HWSim, for gate-level IP piracy detection. We transform the gate-level netlists to directed graphs and encode graphs using the graph neural network (GNN) model which is trained based on metric learning. We optimize the training process and use a negative mining strategy to improve training efficiency. HWSim can be trained in a much shorter time compared to the baseline method and achieves an AUC score of 0.9963 on our dataset collected from open source benchmarks.