<?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>AMP on 安橙的博客</title><link>https://blog.ans20xx.com/tags/amp/</link><description>Recent content in AMP on 安橙的博客</description><generator>Hugo -- 0.163.3</generator><language>zh</language><lastBuildDate>Tue, 26 May 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://blog.ans20xx.com/tags/amp/index.xml" rel="self" type="application/rss+xml"/><item><title>Day 12 · 混合精度与 AMP</title><link>https://blog.ans20xx.com/posts/ai/day12/</link><pubDate>Tue, 26 May 2026 00:00:00 +0000</pubDate><guid>https://blog.ans20xx.com/posts/ai/day12/</guid><description>拆开混合精度训练:FP32 / TF32 / FP16 / BF16 / FP8 的数值范围、精度与溢出风险;理解 torch.amp.autocast 的算子选择、GradScaler 的动态 loss scaling,并跑通一个可对比的 AMP benchmark。</description></item></channel></rss>