深入理解 BigBird 的區塊稀疏注意力機制 (Block Sparse Attention)
Original: Understanding BigBird's Block Sparse Attention
Traditional Transformer models (such as BERT) are constrained by the quadratic complexity $O(N^2)$ of their self-attention mechanism, and…
Google 提出的 BigBird 模型透過「區塊稀疏注意力機制」，成功將傳統 Transformer 的二次方複雜度降至線性複雜度。該機制結合了全域標記、滑動窗口與隨機注意力，並以「區塊（Block）」為單位進行運算以優化 GPU/TPU 效能。這使得 BigBird 能處理高達 4096 個標記的長文本，非常適合問答、摘要與長文本分析等任務。
Traditional Transformer models (such as BERT) are constrained by the quadratic complexity $O(N^2)$ of their self-attention mechanism, and are typically limited to processing sequences of up to 512 tokens. Google's BigBird breaks this barrier, handling long texts of up to 4,096 tokens with only linear complexity $O(N)$.
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