Scaling Up Models and Data with $\texttt{t5x}$ and $\texttt{seqio}$ (2203.17189v1)

Abstract: Recent neural network-based LLMs 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 LLMs 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.

• The paper introduces t5x and seqio, significantly simplifying the training and evaluation workflows of massive neural language models.
• The methodology leverages JAX and the XLA GSPMD backend to enable efficient partitioning of models, data, and activations across TPU clusters.
• The libraries promote modular configurability and reproducible, task-based data processing, democratizing access to large-scale AI research.

Scaling Up Models and Data with t5x and seqio

The paper "Scaling Up Models and Data with t5x and seqio" addresses the challenges associated with building and training large-scale neural network-based LLMs by introducing two open-source software libraries: t5x and seqio. These libraries are designed to streamline the process of scaling neural models, particularly focusing on simplifying the training and evaluation workflows of models with hundreds of billions of parameters using massively parallel computing clusters. Both libraries are robustly integrated with the JAX ecosystem, which is well-suited for efficient parallel computation and model scaling through its array-based programming capabilities and the use of modern compiler technologies.

t5x: A Library for Model Training

t5x emerges as a solution to the complexities involved in training large Transformer-based models, such as T5-like encoder-decoder architectures and GPT-like decoder-only architectures. Built atop JAX, it leverages the XLA GSPMD partitioning back-end to facilitate efficient parameter, activation, and data partitioning across a multitude of hardware devices, optimizing the usage of TPU clusters. The library supports both GPU and CPU acceleration, though it is optimized for TPU environments.

The modularity of t5x uses a high-level API derived from JAX's pjit interface, simplifying model parallelism and reducing configuration overhead for researchers. This approach allows the execution of model parallelism concurrently with data parallelism over a multi-dimensional submesh of TPU devices, enhancing scalability. Acknowledging various user needs, t5x provides a flexible configuration through dependency injection using Gin, enabling not only the customization of hyperparameters and model components but also the substitution of entire modules within the user's custom training regime.

seqio: Task-Based Data Processing

Complementing t5x, seqio offers a task-based API for data wrangling, where tasks are defined by associating data sources with preprocessing operations and shared evaluation metrics. This enables consistent benchmarking and the reuse of a task definition across multiple models. The library utilizes the tensorflow.data API to create these data pipelines, yet provides compatibility across different machine learning frameworks, such as JAX and PyTorch, ensuring flexibility in its application.

Seqio introduces deterministic pipelines, which bring significant benefits for reproducibility, recoverability, sharding, and global data shuffling. These features are particularly advantageous for large-scale training, improving throughput and providing researchers with advanced control over the dataset handling process.

Implications and Future Directions

The introduction of these libraries implies advancements in the infrastructure for training large-scale LLMs, facilitating more extensive experimentation and potentially accelerating research progress. The integration with JAX points towards more seamless incorporations of cutting-edge compiler technology into the machine learning pipeline, enabling deeper insights into model performance optimizations.

Theoretically, such tools could pave the way for exploring new approaches to model scaling and architecture design. As the barriers for scaling models diminish, researchers might focus on domain-specific applications, having the freedom to manipulate and explore complex data relationships without extensive infrastructural constraints.

Practically, greater accessibility to scalable model training and data management could democratize resources traditionally reserved for extensive research facilities, which may instigate a surge in contributions from varied research institutions and spur innovations. The ongoing development of t5x and seqio continues to cater to the evolving requirements of AI researchers, ensuring tools keep pace with the expanding boundaries of AI capabilities.