Core Concepts
Self-attention complexity shifted to linear efficiency with TaylorShift.
Abstract
TaylorShift introduces a novel reformulation of the Taylor softmax, enabling full token-to-token interactions in linear time and space. It enhances memory efficiency for sequences as short as 800 tokens and accelerates inference for inputs of approximately 1700 tokens and beyond. The paper explores the transition points where TaylorShift becomes more efficient than traditional attention, aligning closely with empirical measurements. By leveraging insights from diverse applications of Taylor series, TaylorShift efficiently computes token-to-token interactions while preserving individual interactions.
Stats
Sequences as short as 800 tokens show enhanced memory efficiency.
Accelerates inference for inputs of approximately 1700 tokens and beyond.
Quotes
"TaylorShift enhances memory efficiency for sequences as short as 800 tokens."
"TaylorShift accelerates inference for inputs of approximately 1700 tokens and beyond."