FlashAttention is a novel algorithm that addresses the efficiency of Transformer models by improving speed and memory efficiency through IO-awareness. It reduces the number of memory accesses by dividing data into smaller blocks and loading them into fast memory, achieving practical speedups and enabling training on longer sequences. The algorithm also incorporates recomputation during the backward pass to minimize memory usage, delivering significant improvements in training large models like BERT and GPT-2.
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- Alex Askwell: Our curious and knowledgeable moderator, always ready with the right questions to guide our exploration.
- Dr. Paige Turner: Our lead researcher and paper expert, diving deep into the methods and results.
- Prof. Wyd Spectrum: Our field expert, providing broader context and critical insights.
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