Sequence Parallelism

Sequence parallelism (SP) is a technique for training and deploying large language models (LLMs) and vision transformers (ViTs) on extremely long sequences, overcoming memory limitations of single devices. Current research focuses on optimizing SP for various model architectures, including transformers, by developing efficient communication strategies (e.g., minimizing key-value cache migration, employing sparse attention), and hybrid parallelism approaches that combine SP with other techniques like tensor parallelism and pipeline parallelism. These advancements are crucial for improving the performance and scalability of LLMs and ViTs, enabling applications requiring long-context understanding in areas such as long-video analysis and scientific image processing.