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English(EN) One-Step Gradient Delay is Not a Barrier for Large-Scale Asynchronous Pipeline Parallel LLM Pretraining

新研究表明单步梯度延迟并非LLM预训练的障碍

一篇新的研究论文探讨了大规模LLM预训练的异步流水线并行方法,挑战了梯度延迟是不可逾越障碍的观点。研究表明,优化器的选择在单步梯度延迟下对性能有显著影响,像Muon这样的较新方法比AdamW等传统优化器更具鲁棒性。研究人员还引入了一种受误差反馈启发的校正方法,以进一步减轻延迟效应,在参数量高达100亿的模型上实现了与同步训练相当的性能。 AI

影响 这项研究通过克服当前并行化技术的局限性,有望实现更高效、可扩展的大型语言模型预训练。

排序理由 在arXiv上发表的研究论文,详细介绍了一种新颖的LLM预训练方法。

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 4 个来源。 我们如何撰写摘要 →

新研究表明单步梯度延迟并非LLM预训练的障碍

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在arXiv上发表的研究论文,详细介绍了一种新颖的LLM预训练方法。
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报道来源 [4]

  1. arXiv cs.LG TIER_1 English(EN) · Philip Zmushko, Egor Petrov, Nursultan Abdullaev, Mikhail Khrushchev, Samuel Horv\'ath ·

    一步梯度延迟并非大规模异步流水线并行LLM预训练的障碍

    arXiv:2606.30634v1 Announce Type: new Abstract: Modern large-scale LLM pretraining benefits from utilizing Pipeline Parallelism; however, synchronous implementations leave GPUs idle during pipeline bubbles, wasting computational resources. Asynchronous Pipeline Parallelism elimin…

  2. arXiv cs.LG TIER_1 English(EN) · Samuel Horváth ·

    一步梯度延迟并非大规模异步流水线并行LLM预训练的障碍

    Modern large-scale LLM pretraining benefits from utilizing Pipeline Parallelism; however, synchronous implementations leave GPUs idle during pipeline bubbles, wasting computational resources. Asynchronous Pipeline Parallelism eliminates these bubbles, maximizing throughput at the…

  3. Hugging Face Daily Papers TIER_1 English(EN) ·

    一步梯度延迟并非大规模异步流水线并行LLM预训练的障碍

    Modern large-scale LLM pretraining benefits from utilizing Pipeline Parallelism; however, synchronous implementations leave GPUs idle during pipeline bubbles, wasting computational resources. Asynchronous Pipeline Parallelism eliminates these bubbles, maximizing throughput at the…

  4. Hugging Face Daily Papers TIER_1 English(EN) ·

    一步梯度延迟并非大规模异步流水线并行LLM预训练的障碍

    Asynchronous pipeline parallelism with PipeDream-2BW can achieve near-synchronous performance through optimizer selection and error feedback correction, overcoming traditional stability concerns.