Re-evaluating the Memory-balanced Pipeline Parallelism: BPipe
Mincong Huang Chao Wang Chi Ma Yineng Zhang Peng Zhang Lei Yu
Pipeline parallelism is an essential technique in the training of large-scale
Transformer models. However, it suffers from imbalanced memory consumption,
leading to insufficient memory utilization. The BPipe technique was proposed to
address this issue and has proven effective in the GPT-3 model. Nevertheless,
our experiments have not yielded similar benefits for LLaMA training.
Additionally, BPipe only yields negligible benefits for GPT-3 training when
applying flash attention. We analyze the underlying causes of the divergent
performance of BPipe on GPT-3 and LLaMA. Furthermore, we introduce a novel
method to estimate the performance of BPipe.