# Memory Optimization

FlexHeadFA is an improved model based on FlashAttention, focusing on providing a fast and memory-efficient accurate attention mechanism. It supports flexible head dimension configuration, significantly enhancing the performance and efficiency of large language models. Key advantages include efficient GPU resource utilization, support for various head dimension configurations, and compatibility with FlashAttention-2 and FlashAttention-3. It is suitable for deep learning scenarios requiring efficient computation and memory optimization, especially excelling in handling long sequences.

FlashMLA is a high-efficiency MLA decoding kernel optimized for Hopper GPUs, specifically designed for variable-length sequence services. Developed using CUDA 12.3 and above, it supports PyTorch 2.0 and above. FlashMLA's primary advantages lie in its efficient memory access and computational performance, achieving up to 3000 GB/s memory bandwidth and 580 TFLOPS computational performance on H800 SXM5. This technology is significant for deep learning tasks requiring large-scale parallel computing and efficient memory management, especially in natural language processing and computer vision. Inspired by FlashAttention 2&3 and the cutlass project, FlashMLA aims to provide researchers and developers with a highly efficient computational tool.

The Gemma 2B - 10M Context is a large-scale language model that, through innovative attention mechanism optimization, can process sequences up to 10M long with memory usage less than 32GB. The model employs recurrent localized attention technology, inspired by the Transformer-XL paper, making it a powerful tool for handling large-scale language tasks.

EasyContext is an open-source project aimed at enabling the training of language models with a 1 million-word context length using ordinary hardware. It primarily utilizes techniques such as sequence parallelism, DeepSpeed Zero3 offloading, Flash Attention, and activation checkpointing. Rather than proposing novel innovations, the project showcases how to combine existing tools to achieve this goal. It has successfully trained two models, Llama-2-7B and Llama-2-13B, achieving 700K and 1M word context lengths respectively on 8 A100 and 16 A100 GPUs.