Commonsense　generation　aims　to　generate　a　plausible　sentence　containing　all　given　unordered　concept　words.　Previous　methods　focusing　on　this　task　usually　directly　concatenate　these　words　as　the　input　of　a　pre-trained　language　model　(PLM).　However,　in　PLMs＇　pre-training　process,　the　inputs　are　often　corrupted　sentences　with　correct　word　order.　This　input　distribution　discrepancy　between　pre-training　and　fine-tuning　makes　the　model　difficult　to　fully　utilize　the　knowledge　of　PLMs.　In　this　paper,　we　propose　a　two-stage　framework　to　alleviate　this　issue.　Firstly,　in　pre-training　stage,　we　design　a　new　format　of　input　to　endow　PLMs　the　ability　to　deal　with　masked　sentences　with　incorrect　word　order.　Secondly,　during　fine-tuning,　we　insert　the　special　token　[MASK]　between　two　consecutive　concept　words　to　make　the　input　distribution　more　similar　to　the　input　distribution　in　pre-training.　We　conduct　extensive　experiments　and　provide　a　thorough　analysis　to　demonstrate　the　effectiveness　of　our　proposed　method.　The　code　is　available　at　https://github.com/LHRYANG/CommonGen.