mirror of
https://github.com/MLSysBook/TinyTorch.git
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6491a7512e
- Removed temporary test files and audit reports - Deleted backup and temp_holding directories - Reorganized module structure (07->09 spatial, 09->07 dataloader) - Added new modules: 11-14 (tokenization, embeddings, attention, transformers) - Updated examples with historical ML milestones - Cleaned up documentation structure
33 lines
1.2 KiB
YAML
33 lines
1.2 KiB
YAML
name: "Attention"
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number: 13
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description: "Scaled dot-product and multi-head attention mechanisms that enable transformer architectures"
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learning_objectives:
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- "Implement scaled dot-product attention with proper masking and numerical stability"
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- "Build multi-head attention with parallel head processing and output projection"
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- "Design KV-cache systems for efficient autoregressive generation"
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- "Understand attention's O(N²) scaling and memory optimization techniques"
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- "Analyze attention performance bottlenecks and production optimization strategies"
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prerequisites:
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- "02_tensor"
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- "12_embeddings"
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exports:
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- "ScaledDotProductAttention"
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- "MultiHeadAttention"
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- "KVCache"
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- "AttentionProfiler"
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systems_concepts:
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- "Quadratic memory scaling O(N²) with sequence length"
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- "Memory-bandwidth bound attention computation"
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- "KV-cache optimization for autoregressive generation"
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- "Multi-head parallelization and hardware optimization"
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- "Attention masking patterns and causal dependencies"
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ml_systems_focus: "Attention memory scaling, generation efficiency optimization, sequence length limitations"
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estimated_time: "5-6 hours"
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next_modules:
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- "14_transformers" |