How to Parallelize a Transformer for Training (ezyang.github.io)

A new section on parallelizing Transformers for training has been released, emphasizing the crucial concept of "model scaling." This process aims to enhance the throughput of AI models by using multiple chips effectively, ensuring that throughput increases linearly with the addition of chips—a principle known as strong scaling. One challenge highlighted is managing inter-chip communication, which can become a bottleneck as more chips are added. The section discusses five common parallelism schemes, including data parallelism and fully-sharded data parallelism (FSDP), each with its own communication costs and implications for performance. This exploration of parallelism is significant for the AI/ML community as it provides insights into optimizing the efficiency of large-scale models, particularly when managing the complex trade-offs between communication overhead and computational capability. The section presents important technical details about how each scheme manages data distribution and communication, illustrating the impact of various architectures on training performance. Ultimately, understanding these methodologies can lead to improved performance of AI models and more effective utilization of hardware resources, paving the way for more powerful and efficient AI systems.