Breaking the Bubble: Asynchronous Pipeline Parallel Training with Bounded Weight Inconsistency

• URL: http://arxiv.org/abs/2606.07881v1
• Date: Fri, 05 Jun 2026 22:33:57 GMT
• Title: Breaking the Bubble: Asynchronous Pipeline Parallel Training with Bounded Weight Inconsistency
• Abstract summary: PACI is a bubble-free pipeline method that bounds forward/backward version drift without weight stashing, prediction, additional parameter copies, or global synchronization.<n>In GPT-style language-model pretraining, PACI matches the stability and final perplexity of synchronous 1F1B-flush, retains the same peak memory footprint, and achieves fully utilized pipeline throughput.<n>These results show that forward/backward inconsistency need not be eliminated: when explicitly bounded, it can be safely traded for substantial efficiency gains.
• Score: 5.093144724934585
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Pipeline parallelism is essential for training large neural networks, but existing schedules trade off throughput, memory, and optimization consistency. Synchronous pipelines preserve forward/backward weight consistency but suffer from bubbles; asynchronous pipelines remove bubbles but introduce weight-version mismatch, typically requiring weight 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 without weight stashing, prediction, additional parameter copies, or global synchronization. The key idea is to use local gradient accumulation as a version-control mechanism: by slowing parameter-version evolution relative to pipeline delay, PACI limits the number of optimizer updates crossed by any micro-batch while preserving steady-state utilization. In GPT-style language-model pretraining, PACI matches the stability and final perplexity of synchronous 1F1B-flush, retains the same peak memory footprint, achieves fully utilized pipeline throughput, and improves training time-to-accuracy by up to $1.69\times$ 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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