In this study, we represent each molecule as a graph G := (A, B), where A = {ai} N i=1 represents the N atoms of the molecule and B = {(ai, aj)} contains the chemical bonds connecting the atoms. Here, we denote the chemical feature of the atom ai, including the atom type and the formal charge, as ai. Similarly, the chemical feature of the bond (ai, aj) is denoted as bij. We would like to build a graph-based molecular retrosynthesis model, inferring reactants based on a product, i.e., M : Gp → {Gr i } N i=1 . To achieve this aim, we build and train a hierarchical graph autoregressive (HGAR) model to i) generate synthons (denoted as {Gs i } N i=1 ) based on the product Gp and ii) complete the synthons autoregressively to infer reactants {Gr i } N i=1 . Different from the models used in existing semi-template methods , our HGAR model constructs a hierarchical graph by connecting the junction tree of motifs to the corresponding molecular graph. As shown in Figure , each motif in the junction tree is connected to the corresponding atoms. We apply coupled Graphormers to embed this hierarchical graph and jointly apply the atom-level and motif-level information to predict reactants.