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English(EN) I built a PyTorch optimizer for large GPU workloads — looking for feedback[P]

开发者发布了用于大型GPU工作负载的ApexW PyTorch优化器

一位开发者创建了一个名为ApexW的新PyTorch优化器,旨在减少大型GPU工作负载的内存使用并加快优化器步骤。早期基准测试显示,ApexW的速度最高可达PyTorch的融合AdamW的1.5倍,并将优化器状态内存使用量减少一半,尤其适用于具有大型张量的模型。然而,由于Python循环开销,它在涉及许多小型张量的工作负载方面面临挑战,并且在端到端训练速度方面尚未比PyTorch的集成C++实现有显著提升。 AI

影响 通过减少优化器内存占用,可能在现有硬件上实现更大的模型或批次大小。

排序理由 开发者创建的工具发布,非来自主要实验室。

在 r/MachineLearning 阅读 →

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开发者发布了用于大型GPU工作负载的ApexW PyTorch优化器

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开发者创建的工具发布,非来自主要实验室。
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报道来源 [1]

  1. r/MachineLearning TIER_1 English(EN) · /u/Ready_Parking_5435 ·

    I built a PyTorch optimizer for large GPU workloads — looking for feedback[P]

    <!-- SC_OFF --><div class="md"><p>Hey, I've been working on a PyTorch optimizer called ApexW and figured I'd share it here to get some feedback from people who know more about this stuff than I do.</p> <p>The main goal is to reduce optimizer-state memory usage and see how much fa…