One-Step Gradient Delay is Not a Barrier for Large-Scale Asynchronous Pipeline Parallel LLM Pretraining
Summary
This paper challenges the assumption that one-step gradient delay in asynchronous pipeline parallelism is inherently unstable, showing that degradation depends on optimizer choice. It demonstrates that optimizers like Muon are robust to one-step delay and introduces an error-feedback correction to further mitigate staleness, achieving near-synchronous performance in LLM pretraining up to 10B parameters.
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Paper page - One-Step Gradient Delay is Not a Barrier for Large-Scale Asynchronous Pipeline Parallel LLM Pretraining
Source: https://huggingface.co/papers/2606.30634
Abstract
Asynchronous pipeline parallelism with PipeDream-2BW can achieve near-synchronous performance through optimizer selection and error feedback correction, overcoming traditional stability concerns.
Modern large-scale LLM pretraining benefits from utilizingPipeline Parallelism; however, synchronous implementations leave GPUs idle during pipeline bubbles, wasting computational resources. AsynchronousPipeline Parallelismeliminates these bubbles, maximizing throughput at the cost ofgradient staleness. Among asynchronous schedules,PipeDream-2BWis particularly appealing: unlike the original PipeDream schedule, it ensures a constant one-step gradient delay regardless of pipeline depth. However, its adoption remains limited due to the common belief that optimizing under staleness is fundamentally unstable. In this work, we challenge this assumption, demonstrating that degradation under one-step delay depends strongly on optimizer choice rather than being an intrinsic limitation. We provide the first comprehensive empirical analysis showing that whileAdamW, the predominant optimizer at the time whenPipeDream-2BWwas introduced, indeed suffers from severe degradation, recent methods likeMuonexhibit strong robustness under a one-step delay. We introduce an optimizer-agnosticError Feedback-inspired correction to further mitigate delay effects. We provide supporting theoretical analysis demonstrating convergence forMuonwith and without this correction. Extensive evaluation on models up to 10B parameters confirms that our strategies bridge the performance gap with synchronous training, highlighting the practical potential of asynchronouspipeline parallelismat scale.
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