Understanding Flash Attention: Writing the Algorithm from Scratch in Triton

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Flash Attention is a revolutionary technique that dramatically accelerates the attention mechanism in transformer-based models, delivering processing speeds many times faster than naive methods. By cleverly tiling data and minimizing memory transfers, it tackles the notorious GPU memory bottleneck that large language models often struggle with.

In this post, we'll dive into how Flash Attention leverages efficient I/O-awareness to reduce overhead, then take it a step further by crafting a block-sparse attention kernel in Triton.

Tags: Artificial Intelligence Deep Learning Machine Learning Pytorch Transformers

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