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用NVIDIA Apex和torch.amp加速Transformer训练教程

How to Speed Up Transformer Training Using NVIDIA Apex (FusedAdam, FusedLayerNorm) and Native torch.amp

Primary sourcemarktechpost@Sana Hassan
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Why we picked it

Transformer训练慢是很多开发者的痛点，这篇教程直接给出了用Apex和torch.amp加速的具体步骤和基准测试结果，做NLP或大模型训练的团队可以照着优化自己的代码。

本文介绍了如何通过NVIDIA Apex库中的FusedAdam优化器和FusedLayerNorm层，结合PyTorch原生的torch.

Image · marktechpost
Body

amp混合精度训练，来加速Transformer模型的训练。作者从源码编译Apex，检测融合内核是否可用，并进行了基准测试。

实验表明，这些优化可以显著提升训练速度，同时保持模型精度。对于需要高效训练Transformer的开发者，这是一份实用的性能优化指南。

Original · marktechpost

How to Speed Up Transformer Training Using NVIDIA Apex (FusedAdam, FusedLayerNorm) and Native torch.amp

We build NVIDIA Apex from source, detect fused kernels, and benchmark FusedAdam, FusedLayerNorm, and torch.amp in Transformer training. The post How to Speed Up Transformer Training Using NVIDIA Apex (FusedAdam, FusedLayerNorm) and Native torch.amp appeared first on MarkTechPost .

Industry Coordinates & Source Verification

This report is categorized under AITOP's "How-to" desk, focusing on US frontier AI labs and Silicon Valley technology leaders. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows. Key entities and technical topics under continuous tracking include Transformer, NVIDIA Apex, 混合精度训练, 性能优化.

Primary source verification: published directly by marktechpost with first-party authority.

#Transformer#NVIDIA Apex#混合精度训练#性能优化#PyTorch

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