Stacking Your Transformers: A Closer Look at Model Growth for Efficient
  LLM Pre-Training
Authors: name: Wenyu Du, name: Tongxu Luo, name: Zihan Qiu, name: Zeyu Huang, name: Yikang Shen, name: Reynold Cheng, name: Yike Guo, name: Jie Fu

LLMs are computationally expensive to pre-train due to their large scale.
Model growth emerges as a promising approach by leveraging smaller models to
accelerate the training of larger ones. However, the viability of these model
growth methods in efficient LLM pre-training remains underexplored. This work
identifies three critical textit{O}bstacles: (O1)
lack of comprehensive evaluation, (O2) untested viability for
scaling, and (O3) lack of empirical guidelines. To tackle
O1, we summarize existing approaches into four atomic growth
operators and systematically evaluate them in a standardized LLM pre-training
setting. Our findings reveal that a depthwise stacking operator, called
G_{stack}, exhibits remarkable acceleration in training, leading to
decreased loss and improved overall performance on eight standard NLP
benchmarks compared to strong baselines. Motivated by these promising results,
we conduct extensive experiments to delve deeper into G_{stack} to
address O2 and O3. For O2 (untested
scalability), our study shows that G_{stack} is scalable and
consistently performs well, with experiments up to 7B LLMs after growth and
pre-training LLMs with 750B tokens. For example, compared to a conventionally
trained 7B model using 300B tokens, our G_{stack} model converges to
the same loss with 194B tokens, resulting in a 54.6\% speedup. We further
address O3 (lack of empirical guidelines) by formalizing guidelines
to determine growth timing and growth factor for G_{stack}, making it
practical in general LLM pre-training. We also provide in-depth discussions and
comprehensive ablation studies of G_{stack}. Our code and pre-trained
model are available at
https://llm-stacking.github.io/{https://llm-stacking.github.io/}.