Unsupervised rationale extraction aims to identify concise text snippets supporting model predictions without annotated rationales. However, existing two-phase approaches often suffer from interlocking (where predictors overfit to poorly generated rationales) and spurious correlations (where models capture non-causal relationships between text and labels). To address these challenges, we propose a novel single-phase framework called You Only Forward Once (YOFO) that simultaneously explains and predicts. YOFO utilizes a Pre-trained Language Model (PLM) to gradually remove unimportant tokens, with the remaining tokens considered as rationales. We introduce a length configuration list to flexibly control the proportion of remaining tokens in each layer. Importantly, we replace the traditional zero-out operation for token deletion with Attention Mask Deletion (AMD), which significantly improves model performance. Experiments on the BeerAdvocate and Hotel Review datasets demonstrate that YOFO achieves state-of-the-art rationalization performance, improving token-level F1 by up to 14.5% compared to previous methods. Our approach effectively mitigates the interlocking and spurious correlation issues prevalent in two-phase models by eliminating the need for separate generation and prediction phases, offering a relatively robust and efficient approach for explaining Artificial Intelligence (AI) model predictions.