flash-linear-attention and Flash-Sparse-Attention
Linear attention and sparse attention are complementary techniques for reducing transformer computational complexity—linear attention approximates full attention in O(n) time via state-space models, while sparse attention maintains exact attention but only between selected token pairs—so these implementations target different efficiency trade-offs and could be used for different use cases rather than as direct alternatives.
About flash-linear-attention
fla-org/flash-linear-attention
🚀 Efficient implementations of state-of-the-art linear attention models
This project offers highly optimized building blocks for developing next-generation AI models that can process very long sequences of information efficiently. It provides ready-to-use implementations of advanced 'linear attention' and 'state space' model architectures. AI researchers and machine learning engineers can use these components to create more powerful and scalable models for tasks like natural language understanding or time-series prediction.
About Flash-Sparse-Attention
Relaxed-System-Lab/Flash-Sparse-Attention
🚀🚀 Efficient implementations of Native Sparse Attention
This project offers an optimized way to train and run large language models (LLMs) more efficiently. It takes in standard LLM input data and processes it using a more performant attention mechanism, leading to faster computations and reduced memory use. Developers and AI engineers working on LLM training and deployment, especially those dealing with models requiring sparse attention, would find this useful.
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