Generative recommendation (GR) represents items as discrete semantic identifiers (SIDs) and performs next-item prediction via identifier sequence generation. Most existing methods perform prefix-constrained decoding over a trie, where hard structural constraints lead to locally greedy decisions. An early suboptimal prefix irrevocably restricts subsequent decoding, causing error propagation and suboptimal retrieval. In this work, we propose OneGR, a novel generative recommendation framework that formulates SID prediction as a one-pass, structure-constrained decoding problem. OneGR computes globally consistent, position-wise SID scores in a single neural forward pass, eliminating iterative hypothesis expansion and repeated inference. To address the extreme sparsity of valid SIDs, we encode the space of valid SIDs as a deterministic weighted finite-state transducer and perform A* search with the one-pass scores as additive path costs. This structured decoding strategy explores only reachable valid prefixes while guaranteeing the optimal SID prediction. Experiments on multiple benchmarks show that OneGR consistently outperforms strong baselines.