Part 8
Completed
Sliding-Window Attention
Implemented sliding-window attention with fixed-size local windows and global tokens. Evaluated on long-document understanding, code analysis, and book summarization tasks. Compared window sizes and global token strategies.
What I Built
Implemented sliding-window attention with fixed-size local windows and global tokens. Evaluated on long-document understanding, code analysis, and book summarization tasks. Compared window sizes and global token strategies.
Key Concepts
Sliding WindowLocal AttentionGlobal TokensLong ContextSparse AttentionWindow Size
Architecture
1
Window Attention Kernel2
Global Token Manager3
Sparse Mask Generator4
Long-Context EvaluatorResults
Handles 128k context with O(n) memory. Maintains 95% of full-attention performance on local tasks. Global tokens critical for cross-window reasoning.
Key Learnings
- Local attention captures most linguistic dependencies
- Global tokens are essential for long-range coherence
- Window size of 4096 balances locality and coverage
Challenges
- Information flow across window boundaries
- Choosing optimal global token placement
- Handling extremely long sequences (>100k tokens)