KV 缓存详解：优化 Transformer 推理效率
原标题：KV Caching Explained: Optimizing Transformer Inference Efficiency
AI 摘要
Hugging Face 博客发布了一篇关于 KV 缓存的教程，解释了该技术如何通过存储注意力层的键值对来避免重复计算，从而加速 Transformer 模型的文本生成。文章提供了 PyTorch 伪代码和 transformers 库的使用示例，并展示了在 T4 GPU 上使用 KV 缓存可获得约 5.21 倍的加速。该技术对于长文本生成尤为重要，能显著提升推理效率。
正文节选
When AI models generate text, they often repeat many of the same calculations, which can slow things down. Key-Value caching is a technique that helps speed up this process by remembering important information from previous steps. Instead of recomputing everything from scratch, the model reuses what it has already calculated, making text generation much faster and more efficient. In this blogpost, we’ll break down KV caching in an easy-to-understand way, explain why it’s useful, and show how it