<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>FlashAttention on 安橙的博客</title><link>https://blog.ans20xx.com/tags/flashattention/</link><description>Recent content in FlashAttention on 安橙的博客</description><generator>Hugo -- 0.163.3</generator><language>zh</language><lastBuildDate>Sat, 20 Jun 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://blog.ans20xx.com/tags/flashattention/index.xml" rel="self" type="application/rss+xml"/><item><title>Day 14 · 周复盘 + 算子融合</title><link>https://blog.ans20xx.com/posts/ai/day14/</link><pubDate>Sat, 20 Jun 2026 00:00:00 +0000</pubDate><guid>https://blog.ans20xx.com/posts/ai/day14/</guid><description>Phase 1 收官:复盘 PyTorch 核心抽象、Autograd、算子后端、显存管理、AMP 与 torch.compile,再用 FlashAttention 拆开算子融合与 IO-aware kernel 的本质。</description></item><item><title>Day 26 · 算子层加速</title><link>https://blog.ans20xx.com/posts/ai/day26/</link><pubDate>Sat, 20 Jun 2026 00:00:00 +0000</pubDate><guid>https://blog.ans20xx.com/posts/ai/day26/</guid><description>进入 Transformer 算子层加速:理解 FlashAttention v2/v3、PyTorch SDPA、xFormers 与 Apex Fused Kernels 的适用边界,动手替换 attention,并用 TFLOPS / MFU 判断优化是否真的生效。</description></item></channel></rss>