  Pipeline parallelism enables efficient training of Large Language Models
(LLMs) on large-scale distributed accelerator clusters. Yet, pipeline bubbles
during startup and tear-down reduce the utilization of accelerators. Although
efficient pipeline schemes with micro-batching and bidirectional pipelines have
been proposed to maximize utilization, a significant number of bubbles cannot
be filled using synchronous forward and backward passes. To address this
problem, we suggest that extra work be assigned to the bubbles to gain
auxiliary benefits in LLM training. As an example in this direction, we propose
PipeFisher, which assigns the work of K-FAC, a second-order optimization method
based on the Fisher information matrix, to the bubbles to accelerate
convergence. In Phase 1 pretraining of BERT-Base and -Large models, PipeFisher
reduces the (simulated) training time to 50-75% compared to training with a
first-order optimizer by greatly improving the accelerator utilization and
benefiting from the improved convergence by K-FAC.
