Part 3
Completed
Multi-Head Attention
Extended single-head attention to multi-head attention with parallel Q/K/V projections, concatenation, and final linear projection. Analyzed how different heads specialize across syntactic, semantic, and positional tasks. Implemented head pruning to study redundancy.
What I Built
Extended single-head attention to multi-head attention with parallel Q/K/V projections, concatenation, and final linear projection. Analyzed how different heads specialize across syntactic, semantic, and positional tasks. Implemented head pruning to study redundancy.
Key Concepts
Parallel Attention HeadsConcatenationHead SpecializationRedundancyHead PruningEnsemble Learning
Architecture
1
Multi-Head Projection2
Parallel Attention Engines3
Concatenation Layer4
Output ProjectionResults
8-head model outperforms single-head by 23% on downstream tasks. Pruning analysis shows 30% of heads can be removed with <2% performance loss.
Key Learnings
- Multiple heads enable attending to different representation subspaces
- Heads develop distinct specializations during training
- There is significant redundancy in multi-head attention
Challenges
- Efficient parallelization of multiple heads
- Understanding why specific heads become redundant
- Balancing head count vs. computational cost