Ulysses Sequence Parallelism: Training with Million-Token Contexts

Training large language models on million-token contexts presents significant memory challenges due to the attention mechanism's quadratic scaling with sequence length. Ulysses Sequence Parallelism offers a solution by distributing the computation across multiple GPUs. The method works by splitting the input sequence and also partitioning the attention heads, using all-to-all communication to allow each GPU to compute attention for its assigned subset of heads. This technique, integrated into the Hugging Face ecosystem via Accelerate and DeepSpeed, enables efficient long-sequence training with lower communication overhead compared to alternatives like Ring Attention. The content details its implementation, configuration, and best practices for frameworks like the Transformers Trainer and TRL.