Kvax: Fast and easy-to-use FlashAttention implementation for JAX

Kvax is a high-performance attention mechanism designed for the JAX framework, aimed at optimizing the training of large language models (LLMs) on long sequences. It addresses challenges in training LLMs with long contexts by implementing a custom Flash Attention component, which significantly reduces computational waste and enhances GPU memory efficiency. This is achieved through techniques like dense packing and blockwise attention computation, which minimize the use of padding tokens and improve the utilization of computational resources. Kvax incorporates advanced parallelism strategies, including tensor and context parallelism, which distribute workloads across multiple GPUs to improve performance and reduce memory consumption. The implementation also features specialized masks—causal and document masks—to ensure efficient attention computation without unnecessary cross-sequence interference. Kvax builds upon the Triton kernel language and integrates enhancements from Flash Attention versions, adapting to various parallelism methods and supporting large-scale LLM training with improved flexibility and efficiency. Available under the Apache 2.0 license on GitHub, Kvax aims to streamline and accelerate LLM training processes.

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