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针对该问题,优化attention的方法已提出很多,比较常见的有KV-cache(这个是长、短文本都适用,已成为LLM推理的标配),StreamingLLM,H2O等。 后面两种方法主要是对self-attention进行优化,优化的思路是不要全局的token都参与attention计算,丢掉一部分token,二者不同在于参与计算的token选择方式不一样。但这两个方法有一个...
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