From the viewpoint of machine learning, molecular retrosynthesis can be formulated as a conditional generation problem, in which the reactants are generated according to the given products. Following this strategy, many learning-based retrosynthesis methods have been proposed and achieved encouraging performance in recent years. In the aspect of modeling methodology, these methods mainly leverage atom-level information of molecular products (e.g., SMILES strings or topological graphs), either predicting reactants directly or adding atoms to synthons 1 autoregressively . However, predicting reactants in a single step is challenging especially when the corresponding chemical reactions are complicated, while adding atoms one-by-one often suffers from the error propagation issue. As a result, both these two paradigms introduce too much uncertainty in the generative process, and thus lead to sub-optimal performance in general. Recently, some attempts have been made to predict reactants autoregressively in the motif level, e.g., those template-based methods . However, the template-based methods cannot predict the reactants not appearing before, whose generalization power is limited. To our surprise, although both atom-level and motif-level information are proven to be useful for reactant prediction, none of the above methods consider leveraging them jointly.