The article further explains how FlashAttention uses attention parallelism to optimize for long sequences, and provides benchmarks comparing its performance with other implementations. It also discusses the benefits of training models with longer context, showing that models with longer context outperform those with shorter context in both pretraining metrics and downstream evaluation. The article concludes by highlighting the potential future applications of long sequence lengths in machine learning models, particularly in the context of personalized and multi-modal models.
Key takeaways:
- FlashAttention, an algorithm that speeds up training and inference, has been improved to be faster for long sequences, enabling training of large language models with longer context. It is now up to 2.7x faster than a standard Pytorch implementation and 2.2x faster than the optimized implementation from Megatron-LM.
- The improved FlashAttention algorithm now parallelizes over the sequence length dimension in addition to batch size and number of heads, resulting in significant speedup for long sequences.
- Training GPT3 models with longer context (8k) using FlashAttention outperforms models with shorter context (2k) in both pretraining metrics and downstream evaluation.
- FlashAttention is a step towards equipping models with long context, which is crucial for future AI agents that need to remember past actions and user feedback, and for multi-modal ML models that need to understand books, high resolution images, and videos.