  The recent progress of AI can be largely attributed to large language models
(LLMs). However, their escalating memory requirements introduce challenges for
machine learning (ML) researchers and engineers. Addressing this requires
developers to partition a large model to distribute it across multiple GPUs or
TPUs. This necessitates considerable coding and intricate configuration efforts
with existing model parallel tools, such as Megatron-LM, DeepSpeed, and Alpa.
These tools require users' expertise in machine learning systems (MLSys),
creating a bottleneck in LLM development, particularly for developers without
MLSys background. In this work, we present RedCoast (Redco), a lightweight and
user-friendly tool crafted to automate distributed training and inference for
LLMs, as well as to simplify ML pipeline development. The design of Redco
emphasizes two key aspects. Firstly, to automate model parallelism, our study
identifies two straightforward rules to generate tensor parallel strategies for
any given LLM. Integrating these rules into Redco facilitates effortless
distributed LLM training and inference, eliminating the need of additional
coding or complex configurations. We demonstrate the effectiveness by applying
Redco on a set of LLM architectures, such as GPT-J, LLaMA, T5, and OPT, up to
the size of 66B. Secondly, we propose a mechanism that allows for the
customization of diverse ML pipelines through the definition of merely three
functions, avoiding redundant and formulaic code like multi-host related
processing. This mechanism proves adaptable across a spectrum of ML algorithms,
from foundational language modeling to complex algorithms like meta-learning
and reinforcement learning. As a result, Redco implementations exhibit
significantly fewer lines of code compared to their official counterparts.
