  Large language models (LLMs) with hundreds of billions or trillions of
parameters, represented by chatGPT, have achieved profound impact on various
fields. However, training LLMs with super-large-scale parameters requires large
high-performance GPU clusters and long training periods lasting for months. Due
to the inevitable hardware and software failures in large-scale clusters,
maintaining uninterrupted and long-duration training is extremely challenging.
As a result, A substantial amount of training time is devoted to task
checkpoint saving and loading, task rescheduling and restart, and task manual
anomaly checks, which greatly harms the overall training efficiency. To address
these issues, we propose TRANSOM, a novel fault-tolerant LLM training system.
In this work, we design three key subsystems: the training pipeline automatic
fault tolerance and recovery mechanism named Transom Operator and Launcher
(TOL), the training task multi-dimensional metric automatic anomaly detection
system named Transom Eagle Eye (TEE), and the training checkpoint asynchronous
access automatic fault tolerance and recovery technology named Transom
Checkpoint Engine (TCE). Here, TOL manages the lifecycle of training tasks,
while TEE is responsible for task monitoring and anomaly reporting. TEE detects
training anomalies and reports them to TOL, who automatically enters the fault
tolerance strategy to eliminate abnormal nodes and restart the training task.
And the asynchronous checkpoint saving and loading functionality provided by
TCE greatly shorten the fault tolerance overhead. The experimental results
indicate that TRANSOM significantly enhances the efficiency of large-scale LLM
training on clusters. Specifically, the pre-training time for GPT3-175B has
been reduced by 28%, while checkpoint saving and loading performance have
improved by a factor of 20.

Distributed, Parallel, and Cluster Computing;Artificial Intelligence