Our HGAR model provides a new semi-template molecular retrosynthesis method that jointly leverages atom-level and motif-level that add atoms rather than motifs, HGAR requires much fewer autoregressive steps to complete synthons and ensures the generated reactants are chemically feasible. To our knowledge, our work is the first semi-template method that applies the pretraining strategy, which mitigates the data insufficiency issue in molecular retrosynthesis. We conduct comprehensive experiments on the USPTO dataset , demonstrating the superiority of our method to state-ofthe-art methods.