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通过投机解码加速基因组和蛋白质组基础模型的推断
bioRxiv : the preprint server for biology
|February 6, 2026
概括
投机解码加速了基因组和蛋白质组序列的生成,通过使用模型草稿来提出用于更大的模型验证的代币. 这种方法显著加快了DNA和蛋白质基础模型的大规模序列生成,而不会影响预测准确度.
科学领域:
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
- 机器学习 机器学习
背景情况:
- 基因组和蛋白质基础模型 (GFMs和PFMs) 在学习生物序列语言方面表现出色.
- 这些模型中的自回归解码速度很慢,由于高延迟,限制了大规模的序列生成.
- 产生长DNA和蛋白质序列的计算成本是一个重要的瓶.
研究的目的:
- 适应和评估投机解码,以加速GFMs和PFMs.
- 评估投机解码对序列生成速度和预测质量的影响.
- 为了调查概率推测解码方法的有效性.
主要方法:
- 针对GFMs (DNAGPT) 和PFMs (ProGen2,ProtGPT2) 进行了推测解码的概率变体的调整.
- 采用轻量级草稿模型提出代币跨度,并用更大的目标模型并行验证.
- 系统地研究了投机窗口长度,温度,草稿架构和提示长度的影响.
- 在多个配置中以每秒令牌的基准性能.
主要成果:
- 投机解码实现了比标准解码方法更稳定的加速度.
- 最大加速度达到100%,平均收益在20%至40% (1.2x-1.4x) 之间.
- 该方法保留了目标模型的采样分布,确保保持预测质量.
结论:
- 投机解码是一种实用且不依赖模型的策略,用于加速生物序列生成.
- 这种方法有效地减少了GFMs和PFMs的延迟,而不会牺牲预测性能.
- 能够更高效地大规模生成基因组和蛋白质组序列.
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