Breaking the Bubble: Asynchronous Pipeline Parallel Training with Bounded Weight Inconsistency
Summary
Introduces PACI, a bubble-free asynchronous pipeline parallel training method that bounds forward/backward weight inconsistency using local gradient accumulation, achieving higher throughput and faster time-to-accuracy without sacrificing stability or memory usage.
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Paper page - Breaking the Bubble: Asynchronous Pipeline Parallel Training with Bounded Weight Inconsistency
Source: https://huggingface.co/papers/2606.07881
Abstract
PACI enables efficient asynchronous pipeline training by controlling forward/backward weight inconsistency through local gradient accumulation, achieving higher throughput and faster training time-to-accuracy without sacrificing stability or memory usage.
Pipeline parallelismis essential for training large neural networks, but existing schedules trade off throughput, memory, and optimization consistency.Synchronous pipelinespreserve forward/backward weight consistency but suffer from bubbles;asynchronous pipelinesremove bubbles but introduce weight-version mismatch, typically requiringweight stashing, prediction, or correction mechanisms. We introduce PACI (Pipeline Asynchronous training with Controlled Inconsistency), a bubble-free asynchronous pipeline method that bounds forward/backward version drift withoutweight stashing, prediction, additional parameter copies, or global synchronization. The key idea is to use localgradient accumulationas a version-control mechanism: by slowing parameter-version evolution relative to pipeline delay, PACI limits the number ofoptimizer updatescrossed by any micro-batch while preservingsteady-state utilization. InGPT-style language-model pretraining, PACI matches the stability and final perplexity of synchronous1F1B-flush, retains the samepeak memory footprint, achieves fully utilized pipeline throughput, and improves training time-to-accuracy by up to 1.69times over the fastest flush baseline. These results show that forward/backward inconsistency need not be eliminated: when explicitly bounded, it can be safely traded for substantial efficiency gains.
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