Scaling Up Models and Data with $\texttt{t5x}$ and $\texttt{seqio}$
This work addresses scalability issues for researchers and practitioners in machine learning, though it is incremental as it builds on existing methods with new tools.
The authors tackled the challenges of scaling up neural network-based language models by developing two software libraries, t5x and seqio, which simplify building and training large models and creating reproducible data pipelines, enabling training of models with hundreds of billions of parameters on terabytes of data.
Recent neural network-based language models have benefited greatly from scaling up the size of training datasets and the number of parameters in the models themselves. Scaling can be complicated due to various factors including the need to distribute computation on supercomputer clusters (e.g., TPUs), prevent bottlenecks when infeeding data, and ensure reproducible results. In this work, we present two software libraries that ease these issues: $\texttt{t5x}$ simplifies the process of building and training large language models at scale while maintaining ease of use, and $\texttt{seqio}$ provides a task-based API for simple creation of fast and reproducible training data and evaluation pipelines. These open-source libraries have been used to train models with hundreds of billions of parameters on datasets with multiple terabytes of training data. Along with the libraries, we release configurations and instructions for T5-like encoder-decoder models as well as GPT-like decoder-only architectures. $\texttt{t5x}$ and $\texttt{seqio}$ are open source and available at https://github.com/google-research/t5x and https://github.com/google/seqio, respectively.