FlashAttention can be rewritten exactly so each softmax weight is a sigmoid of a neighboring score difference plus a log-weight term, removing max subtraction and simplifying hardware.
A3: Accelerating attention mechanisms in neural networks with approximation,
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FLASH-D: FlashAttention with Hidden Softmax Division
FlashAttention can be rewritten exactly so each softmax weight is a sigmoid of a neighboring score difference plus a log-weight term, removing max subtraction and simplifying hardware.